From 629211475a6dd29e4291718765a0a7a36e0c62a5 Mon Sep 17 00:00:00 2001
From: adrivinca <adrivinca@yahoo.it>
Date: Thu, 23 Feb 2023 16:45:37 +0100
Subject: [PATCH 1/4] Adding message-ix-models code as in PR #91 including the
 water module

---
 nest/.gitattributes                           |    3 +
 nest/.github/codecov.yml                      |    3 +
 nest/.github/pull_request_template.md         |   53 +
 nest/.github/workflows/lint.yaml              |   64 +
 nest/.github/workflows/publish.yaml           |   47 +
 nest/.github/workflows/pytest.yaml            |  112 +
 nest/.gitignore                               |  137 +
 nest/.readthedocs.yml                         |   16 +
 nest/LICENSE                                  |  201 +
 nest/README.rst                               |   30 +
 nest/conftest.py                              |    2 +
 nest/doc/Makefile                             |   20 +
 nest/doc/_static/combined-logo-white.png      |  Bin 0 -> 56233 bytes
 nest/doc/_static/combined-logo-white.svg      |  376 ++
 nest/doc/_static/custom.css                   |   77 +
 nest/doc/_static/favicon.svg                  |   65 +
 nest/doc/_static/logo-white.png               |  Bin 0 -> 22635 bytes
 .../_static/message-ix-models-logo-white.svg  |   85 +
 nest/doc/_template/autosummary-class.rst      |   34 +
 nest/doc/_template/autosummary-module.rst     |   68 +
 nest/doc/api/disutility.rst                   |  126 +
 nest/doc/api/model-bare.rst                   |   50 +
 nest/doc/api/model-build.rst                  |   46 +
 nest/doc/api/model-emissions.rst              |   15 +
 nest/doc/api/model.rst                        |   83 +
 nest/doc/api/project.rst                      |    7 +
 nest/doc/api/testing.rst                      |   33 +
 nest/doc/api/tools.rst                        |   50 +
 nest/doc/api/util.rst                         |  178 +
 nest/doc/api/workflow.rst                     |  165 +
 nest/doc/cli.rst                              |  177 +
 nest/doc/conf.py                              |  107 +
 nest/doc/data.rst                             |  262 ++
 nest/doc/develop.rst                          |   47 +
 nest/doc/distrib.rst                          |   41 +
 nest/doc/index.rst                            |   65 +
 nest/doc/install.rst                          |   20 +
 nest/doc/make.bat                             |   35 +
 nest/doc/migrate.rst                          |   48 +
 nest/doc/pkg-data/codelists.rst               |   55 +
 nest/doc/pkg-data/iiasa-se.rst                |   33 +
 nest/doc/pkg-data/node.rst                    |   87 +
 nest/doc/pkg-data/relation.rst                |   43 +
 nest/doc/pkg-data/year.rst                    |   36 +
 nest/doc/releasing.rst                        |   50 +
 nest/doc/repro.rst                            |   79 +
 nest/doc/whatsnew.rst                         |  166 +
 nest/message_ix_models/__init__.py            |   34 +
 nest/message_ix_models/cli.py                 |  138 +
 .../message_ix_models/data/cd_links/unit.yaml |  106 +
 nest/message_ix_models/data/commodity.yaml    |  311 ++
 .../data/edits/messageix-transport-core.yaml  |   70 +
 .../data/edits/messageix-transport-input.yaml |  128 +
 .../data/iiasa-se/def-regions.yaml            |   13 +
 .../data/iiasa-se/mappings-R12.yaml           |   67 +
 nest/message_ix_models/data/level.yaml        |   41 +
 nest/message_ix_models/data/node/ADVANCE.yaml |   35 +
 nest/message_ix_models/data/node/ISR.yaml     |   10 +
 nest/message_ix_models/data/node/R11.yaml     |   77 +
 nest/message_ix_models/data/node/R12.yaml     |   82 +
 nest/message_ix_models/data/node/R14.yaml     |   89 +
 nest/message_ix_models/data/node/R32.yaml     |  196 +
 nest/message_ix_models/data/node/RCP.yaml     |   50 +
 nest/message_ix_models/data/node/ZMB.yaml     |   10 +
 nest/message_ix_models/data/relation/A.yaml   |  154 +
 nest/message_ix_models/data/relation/B.yaml   |  324 ++
 .../data/relation/CD-LINKS.yaml               | 1939 ++++++++++
 nest/message_ix_models/data/technology.yaml   | 3020 +++++++++++++++
 .../advance_compare_20171018-134445.csv.zip   |    3 +
 nest/message_ix_models/data/unit.yaml         |    2 +
 .../data/water/.gitattributes                 |    4 +
 nest/message_ix_models/data/water/README.txt  |    9 +
 .../water/availability/e-flow_2p6_R11.csv     |    3 +
 .../water/availability/e-flow_2p6_ZMB.csv     |    3 +
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 .../availability/e-flow_5y_m_7p0_ZMB.csv      |    3 +
 .../e-flow_5y_m_no_climate_ZMB.csv            |    3 +
 .../water/availability/e-flow_6p0_R11.csv     |    3 +
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 .../availability/e-flow_no_climate_R11.csv    |    3 +
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 .../gw_energy_intensity_depth_R11.csv         |    3 +
 .../gw_energy_intensity_depth_ZMB.csv         |    3 +
 .../historical_new_cap_gw_sw_km3_year_R11.csv |    3 +
 .../historical_new_cap_gw_sw_km3_year_ZMB.csv |    3 +
 .../water/availability/qr_5y_2p6_high_R11.csv |    3 +
 .../water/availability/qr_5y_2p6_high_ZMB.csv |    3 +
 .../water/availability/qr_5y_2p6_low_R11.csv  |    3 +
 .../water/availability/qr_5y_2p6_low_ZMB.csv  |    3 +
 .../water/availability/qr_5y_2p6_med_R11.csv  |    3 +
 .../water/availability/qr_5y_2p6_med_ZMB.csv  |    3 +
 .../water/availability/qr_5y_6p0_high_R11.csv |    3 +
 .../water/availability/qr_5y_6p0_low_R11.csv  |    3 +
 .../water/availability/qr_5y_6p0_med_R11.csv  |    3 +
 .../water/availability/qr_5y_7p0_high_ZMB.csv |    3 +
 .../water/availability/qr_5y_7p0_low_ZMB.csv  |    3 +
 .../water/availability/qr_5y_7p0_med_ZMB.csv  |    3 +
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 .../availability/qr_5y_no_climate_low_R11.csv |    3 +
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 .../availability/qr_5y_no_climate_med_ZMB.csv |    3 +
 .../availability/qtot_5y_2p6_high_R11.csv     |    3 +
 .../availability/qtot_5y_2p6_high_ZMB.csv     |    3 +
 .../availability/qtot_5y_2p6_low_R11.csv      |    3 +
 .../availability/qtot_5y_2p6_low_ZMB.csv      |    3 +
 .../availability/qtot_5y_2p6_med_R11.csv      |    3 +
 .../availability/qtot_5y_2p6_med_ZMB.csv      |    3 +
 .../availability/qtot_5y_6p0_high_R11.csv     |    3 +
 .../availability/qtot_5y_6p0_low_R11.csv      |    3 +
 .../availability/qtot_5y_6p0_med_R11.csv      |    3 +
 .../availability/qtot_5y_7p0_high_ZMB.csv     |    3 +
 .../availability/qtot_5y_7p0_low_ZMB.csv      |    3 +
 .../availability/qtot_5y_7p0_med_ZMB.csv      |    3 +
 .../availability/qtot_5y_m_2p6_low_ZMB.csv    |    3 +
 .../availability/qtot_5y_m_7p0_low_ZMB.csv    |    3 +
 .../qtot_5y_m_no_climate_low_ZMB.csv          |    3 +
 .../qtot_5y_no_climate_high_R11.csv           |    3 +
 .../qtot_5y_no_climate_high_ZMB.csv           |    3 +
 .../qtot_5y_no_climate_low_R11.csv            |    3 +
 .../qtot_5y_no_climate_low_ZMB.csv            |    3 +
 .../qtot_5y_no_climate_med_R11.csv            |    3 +
 .../qtot_5y_no_climate_med_ZMB.csv            |    3 +
 nest/message_ix_models/data/water/config.yaml |   10 +
 .../basins_by_region_simpl_R11.csv            |    3 +
 .../basins_by_region_simpl_R12.csv            |    3 +
 .../basins_by_region_simpl_ZMB.csv            |    3 +
 .../water/delineation/basins_country_R11.csv  |    3 +
 .../water/delineation/basins_country_ZMB.csv  |    3 +
 .../data/water/demands/.gitattributes         |    4 +
 .../water/demands/country_region_map_key.csv  |    3 +
 .../demands/harmonized/R11/all_rates_SSP2.csv |    3 +
 ...regional_manufacturing_return_baseline.csv |    3 +
 ...onal_manufacturing_withdrawal_baseline.csv |    3 +
 ...egional_rural_connection_rate_baseline.csv |    3 +
 .../ssp2_regional_rural_return_baseline.csv   |    3 +
 ...regional_rural_treatment_rate_baseline.csv |    3 +
 ...sp2_regional_rural_withdrawal_baseline.csv |    3 +
 ...egional_urban_connection_rate_baseline.csv |    3 +
 ...regional_urban_recycling_rate_baseline.csv |    3 +
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 ...regional_urban_treatment_rate_baseline.csv |    3 +
 ...p2_regional_urban_withdrawal2_baseline.csv |    3 +
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 ...regional_manufacturing_return_baseline.csv |    3 +
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 ...egional_rural_connection_rate_baseline.csv |    3 +
 .../ssp2_regional_rural_return_baseline.csv   |    3 +
 ...regional_rural_treatment_rate_baseline.csv |    3 +
 ...sp2_regional_rural_withdrawal_baseline.csv |    3 +
 ...egional_urban_connection_rate_baseline.csv |    3 +
 ...regional_urban_recycling_rate_baseline.csv |    3 +
 .../ssp2_regional_urban_return2_baseline.csv  |    3 +
 .../ssp2_regional_urban_return_baseline.csv   |    3 +
 ...regional_urban_treatment_rate_baseline.csv |    3 +
 ...p2_regional_urban_withdrawal2_baseline.csv |    3 +
 ...sp2_regional_urban_withdrawal_baseline.csv |    3 +
 .../data/water/demands/harmonized/Thumbs.db   |  Bin 0 -> 72704 bytes
 .../demands/harmonized/ZMB/all_rates_SSP2.csv |    3 +
 .../harmonized/ZMB/ssp2_m_water_demands.csv   |    3 +
 ...regional_manufacturing_return_baseline.csv |    3 +
 ...onal_manufacturing_withdrawal_baseline.csv |    3 +
 ...egional_rural_connection_rate_baseline.csv |    3 +
 .../ssp2_regional_rural_return_baseline.csv   |    3 +
 ...regional_rural_treatment_rate_baseline.csv |    3 +
 ...sp2_regional_rural_withdrawal_baseline.csv |    3 +
 ...egional_urban_connection_rate_baseline.csv |    3 +
 ...regional_urban_recycling_rate_baseline.csv |    3 +
 .../ssp2_regional_urban_return2_baseline.csv  |    3 +
 .../ssp2_regional_urban_return_baseline.csv   |    3 +
 ...regional_urban_treatment_rate_baseline.csv |    3 +
 ...p2_regional_urban_withdrawal2_baseline.csv |    3 +
 ...sp2_regional_urban_withdrawal_baseline.csv |    3 +
 ...p2_regional_irrigation_withdrawal_base.csv |    3 +
 ...sp2_regional_manufacturing_return_base.csv |    3 +
 ...regional_manufacturing_return_baseline.csv |    3 +
 ...sp2_regional_manufacturing_return_sdg6.csv |    3 +
 ...regional_manufacturing_withdrawal_base.csv |    3 +
 ...onal_manufacturing_withdrawal_baseline.csv |    3 +
 ...regional_manufacturing_withdrawal_sdg6.csv |    3 +
 .../ssp2_regional_pop_scarcity_baseline.csv   |    3 +
 ...egional_rural_connection_rate_baseline.csv |    3 +
 ...p2_regional_rural_connection_rate_sdg6.csv |    3 +
 .../ssp2_regional_rural_return_base.csv       |    3 +
 .../ssp2_regional_rural_return_baseline.csv   |    3 +
 .../ssp2_regional_rural_return_sdg6.csv       |    3 +
 ...regional_rural_treatment_rate_baseline.csv |    3 +
 ...sp2_regional_rural_treatment_rate_sdg6.csv |    3 +
 .../ssp2_regional_rural_withdrawal_base.csv   |    3 +
 ...regional_rural_withdrawal_baseline_old.csv |    3 +
 .../ssp2_regional_rural_withdrawal_sdg6.csv   |    3 +
 ...egional_urban_connection_rate_baseline.csv |    3 +
 ...p2_regional_urban_connection_rate_sdg6.csv |    3 +
 ...ional_urban_desalination_rate_baseline.csv |    3 +
 ...regional_urban_recycling_rate_baseline.csv |    3 +
 ...sp2_regional_urban_recycling_rate_sdg6.csv |    3 +
 .../ssp2_regional_urban_return2_base.csv      |    3 +
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 .../ssp2_regional_urban_return_sdg6.csv       |    3 +
 ...regional_urban_treatment_rate_baseline.csv |    3 +
 .../ssp2_regional_urban_withdrawal2_base.csv  |    3 +
 ...p2_regional_urban_withdrawal2_baseline.csv |    3 +
 .../ssp2_regional_urban_withdrawal2_sdg6.csv  |    3 +
 .../ssp2_regional_urban_withdrawal_base.csv   |    3 +
 ...sp2_regional_urban_withdrawal_baseline.csv |    3 +
 .../ssp2_regional_urban_withdrawal_sdg6.csv   |    3 +
 .../water/infrastructure/desalination.xlsx    |  Bin 0 -> 14799 bytes
 .../infrastructure/extraction_techs.xlsx      |  Bin 0 -> 13915 bytes
 ...cal_capacity_desalination_km3_year_R11.csv |    3 +
 ...cal_capacity_desalination_km3_year_ZMB.csv |    3 +
 ...ed_desalination_potential_km3_year_R11.csv |    3 +
 ...ed_desalination_potential_km3_year_ZMB.csv |    3 +
 .../infrastructure/water_distribution.xlsx    |  Bin 0 -> 15469 bytes
 .../POWER_PLANTS_2016_Raptis.csv              |    3 +
 .../cool_techs_country_share.csv              |    3 +
 .../cool_techs_region_share.csv               |    3 +
 .../cool_techs_region_share_R11.csv           |    3 +
 .../cool_techs_region_share_R12.csv           |    3 +
 .../cooltech_cost_and_shares_country.csv      |    3 +
 .../cooltech_cost_and_shares_ssp_msg.csv      |    3 +
 .../cooltech_cost_and_shares_ssp_msg_R11.csv  |    3 +
 .../cooltech_cost_and_shares_ssp_msg_R12.csv  |    3 +
 .../enhanced_fsu_cool_techs_share.csv         |    3 +
 .../power_plant_cooling_impact_MESSAGE.xlsx   |  Bin 0 -> 24537 bytes
 .../raptis_types_map_cooling_type.csv         |    3 +
 .../raptis_types_map_unit_type.csv            |    3 +
 .../ppl_cooling_tech/tech_names_ssp_msg.csv   |    3 +
 .../tech_water_performance_ssp_msg.csv        |    3 +
 nest/message_ix_models/data/water/set.yaml    | 1025 ++++++
 .../data/water/technology.yaml                | 1326 +++++++
 nest/message_ix_models/data/year/A.yaml       |   41 +
 nest/message_ix_models/data/year/B.yaml       |   64 +
 nest/message_ix_models/model/__init__.py      |    4 +
 nest/message_ix_models/model/bare.py          |  170 +
 nest/message_ix_models/model/build.py         |  169 +
 nest/message_ix_models/model/cli.py           |   22 +
 nest/message_ix_models/model/config.py        |   41 +
 nest/message_ix_models/model/data.py          |   12 +
 nest/message_ix_models/model/disutility.py    |  236 ++
 nest/message_ix_models/model/emissions.py     |   78 +
 nest/message_ix_models/model/structure.py     |  330 ++
 .../message_ix_models/model/water/__init__.py |    3 +
 nest/message_ix_models/model/water/build.py   |  271 ++
 nest/message_ix_models/model/water/cli.py     |  272 ++
 .../model/water/data/__init__.py              |   62 +
 .../model/water/data/demands.py               |  922 +++++
 .../model/water/data/deprecated/Figures.R     |  213 ++
 .../deprecated/add_water_infrastructure.R     | 3229 +++++++++++++++++
 .../water/data/deprecated/cooling_tech_av.R   |  905 +++++
 .../model/water/data/infrastructure.py        |  886 +++++
 .../model/water/data/irrigation.py            |  212 ++
 .../calculate_ppl_cooling_technology_shares.r |  229 ++
 .../water/data/pre-processing/desalination.R  |  945 +++++
 .../model/water/data/pre-processing/e-flow.py |  629 ++++
 .../generate_water_constraints.R              |  697 ++++
 .../generate_water_constraints_monthly.r      |  251 ++
 .../pre-processing/groundwater_harmonize.r    |  236 ++
 .../water/data/pre-processing/hydro_agg_3.py  |  247 ++
 .../data/pre-processing/hydro_agg_spatial.R   |   49 +
 .../data/pre-processing/hydro_agg_temp.py     |  335 ++
 .../data/pre-processing/hydro_agg_temp1.py    |  485 +++
 .../data/pre-processing/hydro_agg_temp2.py    |  362 ++
 .../data/pre-processing/shp_to_raster.py      |  121 +
 .../model/water/data/water_for_ppl.py         |  819 +++++
 .../model/water/data/water_supply.py          |  812 +++++
 .../model/water/doc/files.rst                 |   23 +
 .../model/water/doc/index.rst                 |  167 +
 .../model/water/reporting.py                  | 1174 ++++++
 nest/message_ix_models/model/water/utils.py   |  158 +
 nest/message_ix_models/project/__init__.py    |    0
 nest/message_ix_models/py.typed               |    0
 nest/message_ix_models/testing.py             |  399 ++
 nest/message_ix_models/tests/__init__.py      |    1 +
 .../message_ix_models/tests/model/__init__.py |    1 +
 .../tests/model/test_bare.py                  |   69 +
 .../tests/model/test_build.py                 |  103 +
 .../message_ix_models/tests/model/test_cli.py |    5 +
 .../tests/model/test_config.py                |   27 +
 .../tests/model/test_disutility.py            |  275 ++
 .../tests/model/test_emissions.py             |   54 +
 .../tests/model/test_structure.py             |  259 ++
 nest/message_ix_models/tests/test_cli.py      |   58 +
 nest/message_ix_models/tests/test_import.py   |    3 +
 nest/message_ix_models/tests/test_testing.py  |   36 +
 nest/message_ix_models/tests/test_util.py     |  373 ++
 nest/message_ix_models/tests/test_workflow.py |  144 +
 .../message_ix_models/tests/tools/__init__.py |    0
 .../tests/tools/test_advance.py               |   49 +
 nest/message_ix_models/tests/util/__init__.py |    1 +
 .../tests/util/test_cache.py                  |  109 +
 .../tests/util/test_click.py                  |   60 +
 .../tests/util/test_config.py                 |  133 +
 .../tests/util/test_context.py                |  251 ++
 .../tests/util/test_logging.py                |   40 +
 .../message_ix_models/tests/util/test_node.py |  152 +
 .../tests/util/test_scenarioinfo.py           |  212 ++
 .../message_ix_models/tests/util/test_sdmx.py |   25 +
 nest/message_ix_models/tools/__init__.py      |    0
 nest/message_ix_models/tools/advance.py       |  171 +
 nest/message_ix_models/util/__init__.py       |  577 +++
 nest/message_ix_models/util/_convert_units.py |   76 +
 nest/message_ix_models/util/_logging.py       |  191 +
 nest/message_ix_models/util/cache.py          |   95 +
 nest/message_ix_models/util/click.py          |  193 +
 nest/message_ix_models/util/common.py         |  271 ++
 nest/message_ix_models/util/config.py         |  156 +
 nest/message_ix_models/util/context.py        |  394 ++
 nest/message_ix_models/util/importlib.py      |   43 +
 nest/message_ix_models/util/node.py           |  133 +
 nest/message_ix_models/util/scenarioinfo.py   |  332 ++
 nest/message_ix_models/util/sdmx.py           |   99 +
 nest/message_ix_models/workflow.py            |  244 ++
 nest/pyproject.toml                           |   37 +
 nest/setup.cfg                                |   49 +
 nest/setup.py                                 |    3 +
 323 files changed, 35029 insertions(+)
 create mode 100644 nest/.gitattributes
 create mode 100644 nest/.github/codecov.yml
 create mode 100644 nest/.github/pull_request_template.md
 create mode 100644 nest/.github/workflows/lint.yaml
 create mode 100644 nest/.github/workflows/publish.yaml
 create mode 100644 nest/.github/workflows/pytest.yaml
 create mode 100644 nest/.gitignore
 create mode 100644 nest/.readthedocs.yml
 create mode 100644 nest/LICENSE
 create mode 100644 nest/README.rst
 create mode 100644 nest/conftest.py
 create mode 100644 nest/doc/Makefile
 create mode 100644 nest/doc/_static/combined-logo-white.png
 create mode 100644 nest/doc/_static/combined-logo-white.svg
 create mode 100644 nest/doc/_static/custom.css
 create mode 100644 nest/doc/_static/favicon.svg
 create mode 100644 nest/doc/_static/logo-white.png
 create mode 100644 nest/doc/_static/message-ix-models-logo-white.svg
 create mode 100644 nest/doc/_template/autosummary-class.rst
 create mode 100644 nest/doc/_template/autosummary-module.rst
 create mode 100644 nest/doc/api/disutility.rst
 create mode 100644 nest/doc/api/model-bare.rst
 create mode 100644 nest/doc/api/model-build.rst
 create mode 100644 nest/doc/api/model-emissions.rst
 create mode 100644 nest/doc/api/model.rst
 create mode 100644 nest/doc/api/project.rst
 create mode 100644 nest/doc/api/testing.rst
 create mode 100644 nest/doc/api/tools.rst
 create mode 100644 nest/doc/api/util.rst
 create mode 100644 nest/doc/api/workflow.rst
 create mode 100644 nest/doc/cli.rst
 create mode 100644 nest/doc/conf.py
 create mode 100644 nest/doc/data.rst
 create mode 100644 nest/doc/develop.rst
 create mode 100644 nest/doc/distrib.rst
 create mode 100644 nest/doc/index.rst
 create mode 100644 nest/doc/install.rst
 create mode 100644 nest/doc/make.bat
 create mode 100644 nest/doc/migrate.rst
 create mode 100644 nest/doc/pkg-data/codelists.rst
 create mode 100644 nest/doc/pkg-data/iiasa-se.rst
 create mode 100644 nest/doc/pkg-data/node.rst
 create mode 100644 nest/doc/pkg-data/relation.rst
 create mode 100644 nest/doc/pkg-data/year.rst
 create mode 100644 nest/doc/releasing.rst
 create mode 100644 nest/doc/repro.rst
 create mode 100644 nest/doc/whatsnew.rst
 create mode 100644 nest/message_ix_models/__init__.py
 create mode 100644 nest/message_ix_models/cli.py
 create mode 100644 nest/message_ix_models/data/cd_links/unit.yaml
 create mode 100644 nest/message_ix_models/data/commodity.yaml
 create mode 100644 nest/message_ix_models/data/edits/messageix-transport-core.yaml
 create mode 100644 nest/message_ix_models/data/edits/messageix-transport-input.yaml
 create mode 100644 nest/message_ix_models/data/iiasa-se/def-regions.yaml
 create mode 100644 nest/message_ix_models/data/iiasa-se/mappings-R12.yaml
 create mode 100644 nest/message_ix_models/data/level.yaml
 create mode 100644 nest/message_ix_models/data/node/ADVANCE.yaml
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 create mode 100644 nest/message_ix_models/tests/model/test_emissions.py
 create mode 100644 nest/message_ix_models/tests/model/test_structure.py
 create mode 100644 nest/message_ix_models/tests/test_cli.py
 create mode 100644 nest/message_ix_models/tests/test_import.py
 create mode 100644 nest/message_ix_models/tests/test_testing.py
 create mode 100644 nest/message_ix_models/tests/test_util.py
 create mode 100644 nest/message_ix_models/tests/test_workflow.py
 create mode 100644 nest/message_ix_models/tests/tools/__init__.py
 create mode 100644 nest/message_ix_models/tests/tools/test_advance.py
 create mode 100644 nest/message_ix_models/tests/util/__init__.py
 create mode 100644 nest/message_ix_models/tests/util/test_cache.py
 create mode 100644 nest/message_ix_models/tests/util/test_click.py
 create mode 100644 nest/message_ix_models/tests/util/test_config.py
 create mode 100644 nest/message_ix_models/tests/util/test_context.py
 create mode 100644 nest/message_ix_models/tests/util/test_logging.py
 create mode 100644 nest/message_ix_models/tests/util/test_node.py
 create mode 100644 nest/message_ix_models/tests/util/test_scenarioinfo.py
 create mode 100644 nest/message_ix_models/tests/util/test_sdmx.py
 create mode 100644 nest/message_ix_models/tools/__init__.py
 create mode 100644 nest/message_ix_models/tools/advance.py
 create mode 100644 nest/message_ix_models/util/__init__.py
 create mode 100644 nest/message_ix_models/util/_convert_units.py
 create mode 100644 nest/message_ix_models/util/_logging.py
 create mode 100644 nest/message_ix_models/util/cache.py
 create mode 100644 nest/message_ix_models/util/click.py
 create mode 100644 nest/message_ix_models/util/common.py
 create mode 100644 nest/message_ix_models/util/config.py
 create mode 100644 nest/message_ix_models/util/context.py
 create mode 100644 nest/message_ix_models/util/importlib.py
 create mode 100644 nest/message_ix_models/util/node.py
 create mode 100644 nest/message_ix_models/util/scenarioinfo.py
 create mode 100644 nest/message_ix_models/util/sdmx.py
 create mode 100644 nest/message_ix_models/workflow.py
 create mode 100644 nest/pyproject.toml
 create mode 100644 nest/setup.cfg
 create mode 100644 nest/setup.py

diff --git a/nest/.gitattributes b/nest/.gitattributes
new file mode 100644
index 0000000..da70138
--- /dev/null
+++ b/nest/.gitattributes
@@ -0,0 +1,3 @@
+# Reduce the number of merge conflicts
+doc/whatsnew.rst merge=union
+*.zip filter=lfs diff=lfs merge=lfs -text
diff --git a/nest/.github/codecov.yml b/nest/.github/codecov.yml
new file mode 100644
index 0000000..01fa01a
--- /dev/null
+++ b/nest/.github/codecov.yml
@@ -0,0 +1,3 @@
+comment:
+  layout: "diff, files"
+  behavior: once
diff --git a/nest/.github/pull_request_template.md b/nest/.github/pull_request_template.md
new file mode 100644
index 0000000..c6d9465
--- /dev/null
+++ b/nest/.github/pull_request_template.md
@@ -0,0 +1,53 @@
+<!--
+
+Delete each of these instruction comments as you complete it.
+
+Title: use a short, declarative statement similar to a commit message,
+e.g. “Change [thing X] to [fix solve bug|enable feature Y]”
+
+-->
+
+**Required:** write a single sentence that describes the changes made by this PR.
+
+<!-- Optional: write a longer description to help a reviewer understand the PR in ~3 minutes. -->
+
+## How to review
+
+**Required:** describe specific things that reviewer(s) must do, in order to ensure that the PR achieves its goal.
+If no review is required, write “No review:” and describe why.
+
+<!--
+For example, one or more of:
+
+- Read the diff and note that the CI checks all pass.
+- Run a specific code snippet or command and check the output.
+- Build the documentation and look at a certain page.
+- Ensure that changes/additions are self-documenting, i.e. that another
+  developer (someone like the reviewer) will be able to understand what the code
+  does in the future.
+-->
+
+## PR checklist
+
+<!-- This item is always required. -->
+- [ ] Continuous integration checks all ✅
+  <!--
+  The following items are all *required* if the PR results in changes to user-
+  facing behaviour, e.g. new features or fixes to existing behaviour. They are
+  *optional* if the changes are solely to documentation, CI configuration, etc.
+
+  In ambiguous cases, strike them out and add a short explanation, e.g.
+
+  - ~Add or expand tests.~ No change in behaviour, simply refactoring.
+  -->
+- [ ] Add or expand tests; coverage checks both ✅
+- [ ] Add, expand, or update documentation.
+- [ ] Update doc/whatsnew.
+  <!--
+  To do this, add a single line at the TOP of the “Next release” section of
+  doc/whatsnew.rst, where '999' is the GitHub pull request number:
+
+  - Title or single-sentence description from above (:pull:`999`:).
+
+  Commit with a message like “Add #999 to doc/whatsnew”
+  -->
diff --git a/nest/.github/workflows/lint.yaml b/nest/.github/workflows/lint.yaml
new file mode 100644
index 0000000..45308a4
--- /dev/null
+++ b/nest/.github/workflows/lint.yaml
@@ -0,0 +1,64 @@
+name: Lint
+
+on:
+  push:
+    branches: [ main ]
+  pull_request:
+    branches: [ main, "migrate-*"]
+
+env:
+  # Maximum McCabe complexity for code
+  max-complexity: 15
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+
+    continue-on-error: true
+
+    steps:
+    - uses: actions/checkout@v3
+
+    - uses: actions/setup-python@v4
+      with:
+        # If the "Latest version testable on GitHub Actions" in pytest.yaml
+        # is not the latest 3.x stable version, adjust here to match:
+        # python-version: "3.x"
+        python-version: "3.10"
+        cache: pip
+        cache-dependency-path: "**/setup.cfg"
+
+    - name: Upgrade pip and install requirements
+      run: |
+        python -m pip install --upgrade pip
+        pip install black flake8 isort mypy setuptools-scm twine wheel
+
+    - name: Check "black" code style
+      run: black --check .
+
+    - name: Lint with flake8 & isort
+      run: |
+        flake8 --count --max-complexity=${{ env.max-complexity }} --show-source --statistics
+        isort --check-only .
+
+    - name: Check out ixmp
+      uses: actions/checkout@v3
+      with:
+        repository: iiasa/ixmp
+        path: ixmp
+
+    - name: Check out message-ix
+      uses: actions/checkout@v3
+      with:
+        repository: iiasa/message_ix
+        path: message-ix
+
+    - name: Check typing with mypy
+      # Give the paths to ixmp and message_ix sources. These need not be
+      # installed for static type checking
+      env:
+        MYPYPATH: "./ixmp/:./message-ix/"
+      # Also install stub packages and others that provide type hints
+      run: |
+        pip install genno iam-units pytest sdmx1 types-PyYAML types-setuptools
+        mypy ./message_ix_models
diff --git a/nest/.github/workflows/publish.yaml b/nest/.github/workflows/publish.yaml
new file mode 100644
index 0000000..12dfc37
--- /dev/null
+++ b/nest/.github/workflows/publish.yaml
@@ -0,0 +1,47 @@
+name: Publish
+
+on:
+  push:
+    branches: [ main ]
+    tags: [ "v*" ]
+  release:
+    types: [ published ]
+
+jobs:
+  publish:
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v3
+
+    - uses: actions/setup-python@v4
+      with:
+        # If the "Latest version testable on GitHub Actions" in pytest.yaml
+        # is not the latest 3.x stable version, adjust here to match:
+        # python-version: "3.x"
+        python-version: "3.10"
+        cache: pip
+        cache-dependency-path: "**/setup.cfg"
+
+    - name: Upgrade pip, setuptools-scm, twine, wheel
+      run: python -m pip install --upgrade pip setuptools-scm twine wheel
+
+    - name: Build package
+      run: |
+        python3 setup.py bdist_wheel sdist
+        twine check dist/*
+
+    - name: Publish to TestPyPI
+      uses: pypa/gh-action-pypi-publish@v1.5.0
+      if: github.event_name == 'push' && startsWith(github.ref, 'refs/tags')
+      with:
+        user: __token__
+        password: ${{ secrets.TESTPYPI_TOKEN }}
+        repository_url: https://test.pypi.org/legacy/
+
+    - name: Publish to PyPI
+      uses: pypa/gh-action-pypi-publish@v1.5.0
+      if: github.event_name == 'release'
+      with:
+        user: __token__
+        password: ${{ secrets.PYPI_TOKEN }}
diff --git a/nest/.github/workflows/pytest.yaml b/nest/.github/workflows/pytest.yaml
new file mode 100644
index 0000000..713f3e6
--- /dev/null
+++ b/nest/.github/workflows/pytest.yaml
@@ -0,0 +1,112 @@
+name: Test
+
+on:
+  push:
+    branches: [ main ]
+  pull_request:
+    branches: [ main , "migrate-*"]
+  schedule:
+  # 05:00 UTC = 06:00 CET = 07:00 CEST
+  - cron: "0 5 * * *"
+
+env:
+  # For setuptools-scm. With fetch --tags below, this ensures that enough
+  # history is fetched to contain the latest tag, so that setuptools-scm can
+  # generate the version number. Update:
+  # - See https://github.com/iiasa/message-ix-models/releases, at "NN commits to
+  #   main since this release". The value should be at least equal to NN + the
+  #   number of commits on any PR branch.
+  # - Reset to a lower value, e.g. 100, after a new release.
+  depth: 100
+
+jobs:
+  pytest:
+    strategy:
+      matrix:
+        # One job per OS; latest python version testable on GitHub actions.
+        # These should match the versions used in the "pytest" workflows of both
+        # ixmp and message_ix.
+        version:
+        - { os: macos-latest, python: "3.10" }
+        - { os: ubuntu-latest, python: "3.10" }
+        - { os: windows-latest, python: "3.10" }
+        # Versions of both ixmp and message_ix to use
+        upstream-version:
+        - v3.4.0  # Minimum version given in setup.cfg
+        - v3.5.0
+        - v3.6.0  # Latest released version
+        - main    # Development version
+
+      fail-fast: false
+
+    runs-on: ${{ matrix.version.os }}
+    name: ${{ matrix.version.os }}-py${{ matrix.version.python }}-upstream-${{ matrix.upstream-version }}
+
+    steps:
+    - name: Cancel previous runs that have not completed
+      uses: styfle/cancel-workflow-action@0.11.0
+
+    - name: Check out ixmp
+      uses: actions/checkout@v3
+      with:
+        repository: iiasa/ixmp
+        ref: ${{ matrix.upstream-version }}
+        path: ixmp
+        fetch-depth: ${{ env.depth }}
+
+    - name: Check out message-ix
+      uses: actions/checkout@v3
+      with:
+        repository: iiasa/message_ix
+        ref: ${{ matrix.upstream-version }}
+        path: message-ix
+        fetch-depth: ${{ env.depth }}
+
+    - name: Check out message-ix-models
+      uses: actions/checkout@v3
+      with:
+        lfs: true
+        path: message-ix-models
+        fetch-depth: ${{ env.depth }}
+
+    - name: Fetch tags (for setuptools-scm)
+      run: |
+        (cd ixmp; git fetch --tags --depth=${{ env.depth }})
+        (cd message-ix; git fetch --tags --depth=${{ env.depth }})
+        (cd message-ix-models; git fetch --tags --depth=${{ env.depth }})
+      shell: bash
+
+    - uses: actions/setup-python@v4
+      with:
+        python-version: ${{ matrix.version.python }}
+        cache: pip
+        cache-dependency-path: "**/setup.cfg"
+
+    - name: Upgrade pip
+      run: python -m pip install --upgrade pip
+
+    - uses: iiasa/actions/setup-gams@main
+      with:
+        version: 25.1.1
+        license: ${{ secrets.GAMS_LICENSE }}
+
+    - name: Install packages and dependencies
+      run: |
+        pip install ./ixmp
+        pip install ./message-ix[tests]
+        pip install ./message-ix-models[tests]
+
+    - name: Run test suite using pytest
+      working-directory: message-ix-models
+      run: pytest message_ix_models -rA --verbose --color=yes --cov-report=xml --cov-report=term-missing
+
+    - name: Test documentation build using Sphinx
+      if: startsWith(matrix.os, 'ubuntu')
+      env:
+        RTD_TOKEN_MESSAGE_DATA: ${{ secrets.RTD_TOKEN_MESSAGE_DATA }}
+      run: make --directory=message-ix-models/doc html
+
+    - name: Upload test coverage to Codecov.io
+      uses: codecov/codecov-action@v3
+      with:
+        root_dir: message-ix-models
diff --git a/nest/.gitignore b/nest/.gitignore
new file mode 100644
index 0000000..3adc2ad
--- /dev/null
+++ b/nest/.gitignore
@@ -0,0 +1,137 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+doc/_autosummary/
+doc/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# PyCharm
+/**/.idea
+
+# Generated and temporary data files
+debug/
+cache/
diff --git a/nest/.readthedocs.yml b/nest/.readthedocs.yml
new file mode 100644
index 0000000..e7c5532
--- /dev/null
+++ b/nest/.readthedocs.yml
@@ -0,0 +1,16 @@
+version: 2
+
+# Also build PDF format docs
+formats:
+- pdf
+
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.10"
+
+python:
+  install:
+  - method: pip
+    path: .
+    extra_requirements: [docs]
diff --git a/nest/LICENSE b/nest/LICENSE
new file mode 100644
index 0000000..261eeb9
--- /dev/null
+++ b/nest/LICENSE
@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
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+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
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+      of this License, Derivative Works shall not include works that remain
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+      "Contribution" shall mean any work of authorship, including
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+   2. Grant of Copyright License. Subject to the terms and conditions of
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+   3. Grant of Patent License. Subject to the terms and conditions of
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+      or contributory patent infringement, then any patent licenses
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+   4. Redistribution. You may reproduce and distribute copies of the
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+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
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+   5. Submission of Contributions. Unless You explicitly state otherwise,
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+   6. Trademarks. This License does not grant permission to use the trade
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+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright [yyyy] [name of copyright owner]
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.
diff --git a/nest/README.rst b/nest/README.rst
new file mode 100644
index 0000000..9f35d9a
--- /dev/null
+++ b/nest/README.rst
@@ -0,0 +1,30 @@
+Tools for MESSAGEix-GLOBIOM models
+**********************************
+
+.. image:: https://img.shields.io/pypi/v/message-ix-models.svg
+   :alt: PyPI version
+   :target: https://pypi.python.org/pypi/message-ix-models/
+
+.. image:: https://readthedocs.com/projects/iiasa-energy-program-message-ix-models/badge/?version=latest&token=e26aafa64d5e0792f1198aa1cc328a453f7bc63360efbe1031a13f13c7ba286f
+   :alt: Documentation status
+   :target: https://docs.messageix.org/projects/models/en/latest/?badge=latest
+
+.. image:: https://github.com/iiasa/message-ix-models/actions/workflows/pytest.yaml/badge.svg
+   :alt: Test status
+   :target: https://github.com/iiasa/message-ix-models/actions/workflows/pytest.yaml
+
+.. image:: https://codecov.io/gh/iiasa/message-ix-models/branch/main/graph/badge.svg
+   :alt: Test coverage
+   :target: https://codecov.io/gh/iiasa/message-ix-models
+
+``message_ix_models`` provides tools for research using the MESSAGEix-GLOBIOM family of models.
+These models are built in the `MESSAGEix framework <https://docs.messageix.org>`_ and on the `ix modeling platform (ixmp) <https://docs.messageix.org/ixmp/>`_.
+
+See the `online documentation <https://docs.messageix.org/projects/models/>`_ for further information.
+
+License
+=======
+
+Copyright © 2020–2023 IIASA Energy, Climate, and Environment (ECE) program
+
+Licensed under the Apache License, version 2.0.
diff --git a/nest/conftest.py b/nest/conftest.py
new file mode 100644
index 0000000..5c73f57
--- /dev/null
+++ b/nest/conftest.py
@@ -0,0 +1,2 @@
+# Modules containing fixtures to use in testing
+pytest_plugins = ["ixmp.testing", "message_ix_models.testing"]
diff --git a/nest/doc/Makefile b/nest/doc/Makefile
new file mode 100644
index 0000000..d4bb2cb
--- /dev/null
+++ b/nest/doc/Makefile
@@ -0,0 +1,20 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = .
+BUILDDIR      = _build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
diff --git a/nest/doc/_static/combined-logo-white.png b/nest/doc/_static/combined-logo-white.png
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diff --git a/nest/doc/_static/custom.css b/nest/doc/_static/custom.css
new file mode 100644
index 0000000..097100d
--- /dev/null
+++ b/nest/doc/_static/custom.css
@@ -0,0 +1,77 @@
+/*
+
+IIASA blue + three-stage gradient to white
+- #00589d / #3f81b5 / #7fabce / #bfd5e6
+
+IIASA secondary colours + two-stage gradient to white
+- #62c5c0 / #96d8d5 / #caebea
+- #247f6e
+- #fdbb40
+- #ee6a6b
+- #6a4c93
+
+*/
+
+/* Overall backgrounds */
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+  background: #00589d;
+}
+
+/* White line between search & menu panels */
+.wy-side-nav-search {
+  border-bottom: 1px solid #ffffff;
+}
+
+/* Text colours of menu items: general */
+.wy-menu-vertical a {
+  color: #ffffff;
+}
+
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+.wy-menu-vertical li.on a,
+.wy-menu-vertical li.current > a,
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+}
+
+/* …: current item level 4 */
+.wy-menu-vertical li.toctree-l3 > a {
+  color: #000000;
+}
+
+/* Borders around current items */
+.wy-menu-vertical li.current a {
+  border-right: 1px solid #00589d;
+}
+
+.wy-menu-vertical li.toctree-l1.current > a {
+  border-bottom: 1px solid #00589d;
+  border-right: none;
+}
+
+/* Gradient colouring of sub-menus */
+.wy-menu-vertical li.current,
+.wy-menu-vertical li.toctree-l2.current > a {
+  background: #bfd5e6; /* blue */
+}
+
+
+.wy-menu-vertical li.toctree-l2.current li.toctree-l3 > a {
+  background: #7fabce; /* blue */
+}
+
+.wy-menu-vertical li.toctree-l3.current li.toctree-l4 > a {
+  background: #3f81b5; /* blue */
+  color: #ffffff;
+}
+
+/* Hovered links; all the same colour */
+.wy-menu-vertical li > a:hover,
+.wy-menu-vertical li.current > a:hover,
+.wy-menu-vertical li.current li.toctree-l2 > a:hover,
+.wy-menu-vertical li.toctree-l2.current li.toctree-l3 > a:hover,
+.wy-menu-vertical li.toctree-l3.current li.toctree-l4 > a:hover {
+  /* white on purple */
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+}
diff --git a/nest/doc/_static/favicon.svg b/nest/doc/_static/favicon.svg
new file mode 100644
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+  </g>
+</svg>
diff --git a/nest/doc/_template/autosummary-class.rst b/nest/doc/_template/autosummary-class.rst
new file mode 100644
index 0000000..ec37d3c
--- /dev/null
+++ b/nest/doc/_template/autosummary-class.rst
@@ -0,0 +1,34 @@
+.. From https://stackoverflow.com/a/62613202/2362198
+
+{{ fullname | escape | underline}}
+
+.. currentmodule:: {{ module }}
+
+.. autoclass:: {{ objname }}
+   :members:
+   :show-inheritance:
+   :inherited-members:
+
+   {% block methods %}
+   .. automethod:: __init__
+
+   {% if methods %}
+   .. rubric:: {{ _('Methods') }}
+
+   .. autosummary::
+   {% for item in methods %}
+      ~{{ name }}.{{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+   {% block attributes %}
+   {% if attributes %}
+   .. rubric:: {{ _('Attributes') }}
+
+   .. autosummary::
+   {% for item in attributes %}
+      ~{{ name }}.{{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
diff --git a/nest/doc/_template/autosummary-module.rst b/nest/doc/_template/autosummary-module.rst
new file mode 100644
index 0000000..68d7c0f
--- /dev/null
+++ b/nest/doc/_template/autosummary-module.rst
@@ -0,0 +1,68 @@
+.. From https://stackoverflow.com/a/62613202/2362198
+
+{{ fullname | escape | underline}}
+
+.. automodule:: {{ fullname }}
+
+   {% block attributes %}
+   {% if attributes %}
+   .. rubric:: Module Attributes
+
+   .. autosummary::
+      :toctree:
+   {% for item in attributes %}
+      {{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+   {% block functions %}
+   {% if functions %}
+   .. rubric:: {{ _('Functions') }}
+
+   .. autosummary::
+      :toctree:
+   {% for item in functions %}
+      {{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+   {% block classes %}
+   {% if classes %}
+   .. rubric:: {{ _('Classes') }}
+
+   .. autosummary::
+      :toctree:
+      :template: autosummary-class.rst
+   {% for item in classes %}
+      {{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+   {% block exceptions %}
+   {% if exceptions %}
+   .. rubric:: {{ _('Exceptions') }}
+
+   .. autosummary::
+      :toctree:
+   {% for item in exceptions %}
+      {{ item }}
+   {%- endfor %}
+   {% endif %}
+   {% endblock %}
+
+{% block modules %}
+{% if modules %}
+.. rubric:: Modules
+
+.. autosummary::
+   :toctree:
+   :template: autosummary-module.rst
+   :recursive:
+{% for item in modules %}
+   {{ item }}
+{%- endfor %}
+{% endif %}
+{% endblock %}
diff --git a/nest/doc/api/disutility.rst b/nest/doc/api/disutility.rst
new file mode 100644
index 0000000..088e821
--- /dev/null
+++ b/nest/doc/api/disutility.rst
@@ -0,0 +1,126 @@
+.. currentmodule:: message_ix_models.model.disutility
+
+Consumer disutility
+*******************
+
+This module provides a generalized consumer disutility formulation, currently used by :mod:`message_data.model.transport`.
+The formulation rests on the concept of “consumer groups”; each consumer group may have a distinct disutility associated with using the outputs of each technology.
+A set of ‘pseudo-’/‘virtual’/non-physical “usage technologies” converts the outputs of the actual technologies into the commodities demanded by each group, while also requiring input of a costly “disutility” commodity.
+
+
+Method & usage
+==============
+
+Use this code by calling :func:`add`, which takes arguments that describe the concrete usage:
+
+Consumer groups
+   This is a list of :class:`.Code` objects describing the consumer groups.
+   The list must be 1-dimensional, but can be composed (as in :mod:`message_data.model.transport`) from multiple dimensions.
+
+Technologies
+   This is a list of :class:`.Code` objects describing the technologies for which the consumers in the different groups experience disutility.
+   Each object must be have 'input' and 'output' annotations (:attr:`.Code.anno`); each of these is a :class:`dict` with the keys 'commodity', 'input', and 'unit', describing the source or sink for the technology.
+
+Template
+   This is also a :class:`.Code` object, similar to those in ``technologies``; see below.
+
+The code creates a source technology for the “disutility” commodity.
+The code does *not* perform the following step(s) needed to completely parametrize the formulation:
+
+- Set consumer group-specific ``demand`` parameter values for new commodities.
+- Set the amounts of “disutility” commodities used as ``input`` to the new usage technologies.
+
+These must be parametrized based on the particular application.
+
+Detailed example
+================
+
+This example is similar to the one used in :func:`.test_disutility.test_minimal`:
+
+.. code-block:: python
+
+    # Two consumer groups
+    groups = [Code(id="g0"), Code(id="g1")]
+
+    # Two technologies, for which groups may have different disutilities.
+    techs = [Code(id="t0"), Code(id="t1")]
+
+    # Add generalized disutility formulation to some technologies
+    disutility.add(
+        scenario,
+        groups=groups,
+        technologies=techs,
+
+        template=Code(
+            # Template for IDs of conversion technologies
+            id="usage of {technology} by {group}",
+
+            # Templates for inputs of conversion technologies
+            input=dict(
+                # Technology-specific output commodity
+                commodity="output of {technology}",
+                level="useful",
+                unit="kg",
+            ),
+
+            # Templates for outputs of conversion technologies
+            output=dict(
+                # Consumer-group–specific demand commodity
+                commodity="demand of group {group}",
+                level="useful",
+                unit="kg",
+            ),
+        ),
+        **options,
+    )
+
+
+:func:`add` uses :func:`get_spec` to generate a specification that adds the following:
+
+- For the set ``commodity``:
+
+  - The single element “disutility”.
+  - One element per `technologies`, using the `template` “input” annotation, e.g. “output of t0” generated from ``output of {technology}`` and the id “t0”.
+    These **may** already be present in the `scenario`; if not, the spec causes them to be added.
+  - One elements per `groups`, using the `template` “output” annotation, e.g. “demand of group g1” generated from ``demand of group {group}`` and the id “g1”.
+    These **may** already be present in the `scenario`; if not, the spec causes them to be added.
+
+- For the set ``technology``:
+
+  - The single element “disutility source”.
+  - One element per each combination of disutility-affected technology (`technologies`) and consumer group (`groups`).
+    For example, “usage of t0 by g1” generated from ``usage of {technology} by {group}``, and the ids “t0” and “g1”.
+
+The spec is applied to the target scenario using :func:`.model.build.apply_spec`.
+If the arguments produce a spec that is inconsistent with the target scenario, an exception will by raised at this point.
+
+
+Next, :func:`add` uses :func:`data_conversion` and :func:`data_source` to generate:
+
+- ``output`` and ``var_cost`` parameter data for “disutility source”.
+  This technology outputs the unitless commodity “disutility” at a cost of 1.0 per unit.
+
+- ``input`` and ``output`` parameter data for the new usage technologies.
+  For example, the new technology “usage of t0 by g1”…
+
+  - …takes input from the *technology-specific* commodity “output of t0”.
+  - …takes input from the common commodity “disutility”, in an amount specific to group “g1”.
+  - …outputs to a *group-specific* commodity “demand of group g1”.
+
+Note that the `technologies` towards which the groups have disutility are assumed to already be configured to ``output`` to the corresponding commodities.
+For example, the technology “t0” outputs to the commodity “output of t0”; the ``output`` values for this technology are **not** added/introduced by :func:`add`.
+
+.. _disutility-units:
+
+(Dis)utility is generally dimensionless.
+In :mod:`pint` and thus also :mod:`message_ix_models`, this should be represented by ``""``.
+However, to work around `iiasa/ixmp#425 <https://github.com/iiasa/ixmp/issues/425>`__, :func:`data_conversion` and :func:`data_source` return data with ``"-"`` as units.
+See :issue:`45` for more information.
+
+Code reference
+==============
+
+See also :mod:`message_ix_models.tests.model.test_disutility`.
+
+.. automodule:: message_ix_models.model.disutility
+   :members:
diff --git a/nest/doc/api/model-bare.rst b/nest/doc/api/model-bare.rst
new file mode 100644
index 0000000..5ba5903
--- /dev/null
+++ b/nest/doc/api/model-bare.rst
@@ -0,0 +1,50 @@
+Reproduce the RES (:mod:`.model.bare`)
+**************************************
+
+In contrast to :mod:`.model.create`, this module creates the RES 'from scratch'.
+:func:`.create_res` begins by creating a new, totally empty :class:`.Scenario` and adding data to it (instead of cloning and modifying an existing scenario).
+
+.. note:: Currently, the Scenario returned by :func:`.create_res`…
+
+   - is not complete, nor the official/preferred version of MESSAGEix-GLOBIOM, and as such **must not** be used for actual research,
+   - however, it **should** be used for creating unit tests of other code that is designed to operate on MESSAGEix-GLOBIOM scenarios; code that works against the bare RES should also work against MESSAGEix-GLOBIOM scenarios.
+
+:func:`.bare.get_spec` can also be used directly, to get a *description* of the RES based on certain settings/options, but without any need to connect to a database, load an existing Scenario, or call :func:`.bare.create_res`.
+This can be useful in code that processes data into a form compatible with MESSAGEix-GLOBIOM.
+
+Configuration
+=============
+
+The code obeys the settings on the :class:`.model.Config` instance stored at ``context.model``.
+
+
+Code reference
+==============
+
+.. currentmodule:: message_ix_models.model.bare
+
+.. automodule:: message_ix_models.model.bare
+   :members:
+   :exclude-members: get_spec
+
+.. automethod:: message_ix_models.model.bare.get_spec
+
+   Since the RES is the base for all variants of MESSAGEix-GLOBIOM, the 'require' and 'remove' portions of the spec are empty.
+
+   For the 'add' section, :func:`message_ix_models.model.structure.get_codes` is used to retrieve data from the YAML files in :mod:`message_ix_models`.
+
+   Settings are retrieved from `context`, as above.
+
+
+.. currentmodule:: message_ix_models.model.data
+
+.. automodule:: message_ix_models.model.data
+   :members:
+
+
+.. Roadmap
+.. =======
+
+.. todo:: With `ixmp#212 <https://github.com/iiasa/ixmp/pull/212>`_ merged,
+   some :mod:`.model.bare` code could be moved to a new class and method like
+   :meth:`.MESSAGE_GLOBIOM.initialize`.
diff --git a/nest/doc/api/model-build.rst b/nest/doc/api/model-build.rst
new file mode 100644
index 0000000..378465c
--- /dev/null
+++ b/nest/doc/api/model-build.rst
@@ -0,0 +1,46 @@
+Building models (:mod:`.model.build`)
+*************************************
+
+:func:`.apply_spec` can be used to be build (compose, assemble, construct, …) models given three pieces of information:
+
+- A :class:`.message_ix.Scenario` to be used as a base.
+  This scenario may be empty.
+- A specification, or :class:`.Spec`, which is trio of :class:`.ScenarioInfo` objects; see below.
+- An optional function that adds or produces `data` to add to the `scenario`.
+
+The spec is applied as follows:
+
+1. For each :mod:`ixmp` set that exists in `scenario`:
+
+   a. **Required** elements from :attr:`.Spec.require`, if any, are checked.
+
+      If they are missing, :func:`apply_spec` raises :class:`ValueError`.
+      This indicates that `spec` is not compatible with the given `scenario`.
+
+   b. Elements from :attr:`.Spec.remove`, if any, are **removed**.
+
+      Any parameter values which reference these set elements are also removed, using :func:`.strip_par_data`.
+
+   c. New set elements from :attr:`.Spec.add`, if any, are **added**.
+
+2. Elements in ``spec.add.set["unit"]`` are added to the Platform on which
+   `scenario` is stored.
+
+3. The `data` argument, a function, is called with `scenario` as the first argument, and a keyword argument `dry_run` from :func:`.apply_spec`.
+   `data` may either add to `scenario` directly (by calling :meth:`.Scenario.add_par` and similar methods); or it can return a :class:`dict` that can be passed to :func:`.add_par_data`.
+
+
+The following modules use this workflow and can be examples for developing similar code:
+
+- :mod:`.model.bare`
+- :mod:`.model.disutility`
+- :mod:`message_data.model.transport`
+
+
+Code reference
+==============
+
+.. currentmodule:: message_ix_models.model.build
+
+.. automodule:: message_ix_models.model.build
+   :members:
diff --git a/nest/doc/api/model-emissions.rst b/nest/doc/api/model-emissions.rst
new file mode 100644
index 0000000..8e2e81d
--- /dev/null
+++ b/nest/doc/api/model-emissions.rst
@@ -0,0 +1,15 @@
+Emissions data (:mod:`.model.emissions`)
+========================================
+
+.. currentmodule:: message_ix_models.model.emissions
+
+:mod:`.model.emissions` contains codes for working with emissions data, including policies on emissions.
+
+In general, models created with :mod:`message_ix_models`:
+
+- Use tonnes of carbon equivalent ("t C") as units for mass of emissions.
+- Use "USD / t C" as units for price of emissions.
+  Because (as of 2022-07-20) :mod:`iam_units` treats "USD" as an alias for "USD_2005", this is implicitly USD_2005 / t C.
+
+.. automodule:: message_ix_models.model.emissions
+   :members:
diff --git a/nest/doc/api/model.rst b/nest/doc/api/model.rst
new file mode 100644
index 0000000..28f6465
--- /dev/null
+++ b/nest/doc/api/model.rst
@@ -0,0 +1,83 @@
+Models and variants (:mod:`~message_ix_models.model`)
+*****************************************************
+
+.. currentmodule:: message_ix_models.model
+
+.. automodule:: message_ix_models.model
+   :members:
+
+:mod:`.model.structure`: Model structure information
+====================================================
+
+.. currentmodule:: message_ix_models.model.structure
+
+.. automodule:: message_ix_models.model.structure
+   :members:
+   :exclude-members: get_codes
+
+   .. autosummary::
+
+      codelists
+      generate_product
+      generate_set_elements
+      get_codes
+      get_region_codes
+      process_units_anno
+      process_commodity_codes
+      process_technology_codes
+
+.. autofunction:: get_codes
+
+   The available code lists are reproduced as part of this documentation.
+   The returned code objects have annotations that vary by set.
+   See:
+
+   - :doc:`/pkg-data/codelists`
+   - :doc:`/pkg-data/node`
+
+   Also available is :file:`cd_links/unit.yaml`.
+   This is a project-specific list of units appearing in CD-LINKS scenarios.
+
+   **Example:**
+
+   .. code-block:: python
+
+      >>> from message_ix_models.model.structure import get_codes
+      >>> codes = get_codes("node/R14")
+
+      # Show the codes
+      >>> codes
+      [<Code ABW: Aruba>,
+       <Code AFG: Afghanistan>,
+       <Code AGO: Angola>,
+       ...
+       <Code ZWE: Zimbabwe>,
+       <Code World: World>,
+       ...
+       <Code R11_PAS: Other Pacific Asia>,
+       <Code R11_SAS: South Asia>,
+       <Code R11_WEU: Western Europe>]
+
+      # Retrieve one code matching a certain ID
+      >>> world = codes[codes.index("World")]
+
+      # Get its children's IDs strings, e.g. for a "node" dimension
+      >>> [str(c) for c in world.child]
+      ['R11_AFR',
+       'R11_CPA',
+       'R11_EEU',
+       'R11_FSU',
+       'R11_LAM',
+       'R11_MEA',
+       'R11_NAM',
+       'R11_PAO',
+       'R11_PAS',
+       'R11_SAS',
+       'R11_WEU']
+
+      # Navigate from one ISO 3166-3 country code to its parent
+      >>> AUT = codes[codes.index("AUT")]
+      >>> AUT.parent
+      <Code R11_WEU: Western Europe>
+
+   .. seealso:: :func:`.adapt_R11_R14`
diff --git a/nest/doc/api/project.rst b/nest/doc/api/project.rst
new file mode 100644
index 0000000..71ce7d4
--- /dev/null
+++ b/nest/doc/api/project.rst
@@ -0,0 +1,7 @@
+Specific research projects (:mod:`.project`)
+********************************************
+
+.. currentmodule:: message_ix_models.project
+
+.. automodule:: message_ix_models.project
+   :members:
diff --git a/nest/doc/api/testing.rst b/nest/doc/api/testing.rst
new file mode 100644
index 0000000..7d55232
--- /dev/null
+++ b/nest/doc/api/testing.rst
@@ -0,0 +1,33 @@
+Test utilities and fixtures (:mod:`.testing`)
+*********************************************
+
+.. currentmodule:: message_ix_models.testing
+
+:doc:`Fixtures <pytest:explanation/fixtures>`:
+
+.. autosummary::
+
+   mix_models_cli
+   session_context
+   test_context
+   user_context
+
+:doc:`Marks <pytest:how-to/mark>`:
+
+.. autosummary::
+
+   NIE
+   not_ci
+
+Others:
+
+.. autosummary::
+
+   EXPORT_OMIT
+   CliRunner
+   bare_res
+   export_test_data
+   pytest_addoption
+
+.. automodule:: message_ix_models.testing
+   :members:
diff --git a/nest/doc/api/tools.rst b/nest/doc/api/tools.rst
new file mode 100644
index 0000000..2057c07
--- /dev/null
+++ b/nest/doc/api/tools.rst
@@ -0,0 +1,50 @@
+General purpose modeling tools
+******************************
+
+“Tools” can include, *inter alia*:
+
+- Codes for retrieving data from specific data sources and adapting it for use with :mod:`message_ix_models`.
+- Codes for modifying scenarios; although tools for building models should go in :mod:`message_ix_models.model`.
+
+On this page:
+
+.. contents::
+   :local:
+   :backlinks: none
+
+.. currentmodule:: message_ix_models.tools
+
+.. automodule:: message_ix_models.tools
+   :members:
+
+
+ADVANCE data
+============
+
+.. currentmodule:: message_ix_models.tools.advance
+
+.. autosummary::
+   get_advance_data
+   advance_data
+
+.. autodata:: LOCATION
+
+This is a location relative to a parent directory.
+The specific parent directory depends on whether :mod:`message_data` is available:
+
+Without :mod:`message_data`:
+   The code finds the data within :ref:`local-data` (see discussion there for how to configure this location).
+   Users should:
+
+   1. Visit https://tntcat.iiasa.ac.at/ADVANCEWP2DB/dsd?Action=htmlpage&page=about and register for access to the data.
+   2. Log in.
+   3. Download the snapshot with the file name given in :data:`LOCATION` to a subdirectory :file:`advance/` within their local data directory.
+
+With :mod:`message_data`:
+   The code finds the data within :ref:`private-data`.
+   The snapshot is stored directly in the repository using Git LFS.
+
+.. automodule:: message_ix_models.tools.advance
+   :members:
+   :exclude-members: LOCATION
+   :private-members:
diff --git a/nest/doc/api/util.rst b/nest/doc/api/util.rst
new file mode 100644
index 0000000..bfab7c2
--- /dev/null
+++ b/nest/doc/api/util.rst
@@ -0,0 +1,178 @@
+Low-level utilities (:mod:`~message_ix_models.util`)
+****************************************************
+
+
+.. currentmodule:: message_ix_models.util
+
+Submodules:
+
+.. autosummary::
+
+   click
+   context
+   importlib
+   _logging
+   scenarioinfo
+
+Commonly used:
+
+.. autosummary::
+
+   ~config.Config
+   ~config.ConfigHelper
+   ~context.Context
+   ~scenarioinfo.ScenarioInfo
+   ~scenarioinfo.Spec
+   adapt_R11_R12
+   adapt_R11_R14
+   as_codes
+   broadcast
+   cached
+   check_support
+   convert_units
+   copy_column
+   ffill
+   identify_nodes
+   load_package_data
+   load_private_data
+   local_data_path
+   make_io
+   make_matched_dfs
+   make_source_tech
+   maybe_query
+   merge_data
+   ~node.nodes_ex_world
+   package_data_path
+   private_data_path
+   same_node
+   same_time
+   series_of_pint_quantity
+
+.. automodule:: message_ix_models.util
+   :members:
+
+.. autodata:: message_ix_models.util.cache.SKIP_CACHE
+
+
+:mod:`.util.click`
+==================
+
+.. currentmodule:: message_ix_models.util.click
+
+.. automodule:: message_ix_models.util.click
+   :members:
+
+:mod:`.util.config`
+===================
+
+.. currentmodule:: message_ix_models.util.config
+
+.. automodule:: message_ix_models.util.config
+   :members:
+   :exclude-members: Config
+
+:mod:`.util.context`
+====================
+
+.. currentmodule:: message_ix_models.util.context
+
+.. automodule:: message_ix_models.util.context
+   :members:
+   :exclude-members: Context
+
+.. autoclass:: Context
+   :members:
+   :exclude-members: clone_to_dest
+
+   Context is a subclass of :class:`dict`, so common methods like :meth:`~dict.copy` and :meth:`~dict.setdefault` may be used to handle settings.
+   To be forgiving, it also provides attribute access; ``context.foo`` is equivalent to ``context["foo"]``.
+
+   A Context instance always has the following members:
+
+   - ``core``: an instance of :class:`message_ix_models.Config`.
+   - ``model``: an instance of :class:`message_ix_models.model.Config`.
+
+   Attributes of these classes may be accessed by shorthand, e.g. ``context.regions`` is shorthand for ``context.model.regions``.
+
+   Context provides additional methods to do common tasks that depend on configurable settings:
+
+   .. autosummary::
+      clone_to_dest
+      close_db
+      delete
+      get_cache_path
+      get_local_path
+      get_platform
+      get_scenario
+      handle_cli_args
+      only
+      use_defaults
+
+   .. The following Context methods and attribute are **deprecated**:
+
+      .. autosummary::
+         (currently none)
+
+   .. automethod:: clone_to_dest
+
+      To use this method, either decorate a command with :func:`common_params`:
+
+      .. code-block:: python
+
+         from message_data.tools.cli import common_params
+
+         @click.command()
+         @common_params("dest")
+         @click.pass_obj
+         def foo(context, dest):
+             scenario, mp = context.clone_to_dest()
+
+      or, store the settings :attr:`.Config.dest_scenario` and optionally :attr:`.Config.dest_platform` on `context`:
+
+      .. code-block:: python
+
+         c = Context.get_instance()
+
+         c.dest_scenario = dict(model="foo model", scenario="foo scenario")
+         scenario_mp = context.clone_to_dest()
+
+      The resulting scenario has the indicated model- and scenario names.
+
+      If ``--url`` (or ``--platform``, ``--model``, ``--scenario``, and optionally ``--version``) are given, the identified scenario is used as a 'base' scenario, and is cloned.
+      If ``--url``/``--platform`` and ``--dest`` refer to different Platform instances, then this is a two-platform clone.
+
+      If no base scenario can be loaded, :func:`.bare.create_res` is called to generate a base scenario.
+
+
+:mod:`.util.importlib`
+======================
+
+.. currentmodule:: message_ix_models.util.importlib
+
+.. automodule:: message_ix_models.util.importlib
+   :members:
+
+:mod:`.util._logging`
+=====================
+
+.. currentmodule:: message_ix_models.util._logging
+
+.. automodule:: message_ix_models.util._logging
+   :members:
+
+:mod:`.util.node`
+==================
+
+.. currentmodule:: message_ix_models.util.node
+
+.. automodule:: message_ix_models.util.node
+   :members:
+   :exclude-members: identify_nodes
+
+:mod:`.util.scenarioinfo`
+=========================
+
+.. currentmodule:: message_ix_models.util.scenarioinfo
+
+.. automodule:: message_ix_models.util.scenarioinfo
+   :members:
diff --git a/nest/doc/api/workflow.rst b/nest/doc/api/workflow.rst
new file mode 100644
index 0000000..2e2ee18
--- /dev/null
+++ b/nest/doc/api/workflow.rst
@@ -0,0 +1,165 @@
+Multi-scenario workflows (:mod:`.workflow`)
+*******************************************
+
+.. contents::
+   :local:
+   :backlinks: none
+
+Concept & design
+================
+
+Research with MESSAGEix models often involves multiple scenarios that are related to one another or derived from one another by certain modifications.
+Together, the solutions/reported information from these scenarios provide the output data used in research products, e.g. a plot comparing total emissions in a policy scenario to a reference scenario.
+
+:mod:`.model.build` provides tools to build models or scenarios based on (possibly empty) base scenarios; and :mod:`~message_ix_models.tools` provides tools for manipulating scenarios or model input data (parameters).
+The :class:`.Workflow` API provided in this module allows researchers to use these pieces and atomic, reusable functions to define arbitrarily complex workflows involving many, related scenarios; and then to solve, report, or otherwise operate on those scenarios.
+
+The generic pattern for workflows is:
+
+- Each scenario has zero or 1 (or more?) base/precursor/antecedent scenarios.
+  These must exist before the target scenario can be created.
+- A workflow ‘step’ includes:
+
+  1. A precursor scenario is obtained.
+
+     It may be returned by a prior workflow step, or loaded from a :class:`Platform`.
+  2. (Optional) The precursor scenario is cloned to a target model name and scenario name.
+  3. A function is called to operate on the scenario.
+     This function may do zero or more of:
+
+     - Apply structure or data modifications, for example:
+
+       - Set up a model variant, e.g. adding the MESSAGEix-Materials structure to a base MESSAGEix-GLOBIOM model.
+       - Change policy variables via constraint parameters.
+       - Any other possible modification.
+
+     - Solve the target scenario.
+     - Invoke reporting.
+  4. The resulting function is passed to the next workflow step.
+
+- A workflow can consist of any number of scenarios and steps.
+- The same precursor scenario can be used as the basis for multiple target scenarios.
+- A workflow is :meth:`.Workflow.run` starting with the earliest precursor scenario, ending with 1 or many target scenarios.
+
+The implementation is based on the observation that these form a graph (specifically, a directed, acyclic graph, or DAG) of nodes (= scenarios) and edges (= steps), in the same way that :mod:`message_ix.reporting` calculations do; and so the :mod:`dask` DAG features (via :mod:`genno`) can be used to organize the workflow.
+
+Usage
+=====
+
+General
+-------
+
+Define a workflow using ordinary Python functions, each handling the modifications/manipulations in an atomic workflow step.
+These functions **must**:
+
+- Accept at least 2 arguments:
+
+  1. A :class:`Context` instance.
+  2. The precursor scenario.
+  3. Optionally additional, keyword-only arguments.
+
+- Return either:
+
+  - a :class:`.Scenario` object, that can be the same object provided as an argument, or a different scenario, e.g. a clone or a different scenario, even from a different platform.
+  - :class:`None`.
+    In this case, any modifications implemented by the step should be reflected in the :class:`.Scenario` given as an argument.
+
+The functions **may**:
+
+- call any other code, and
+- be as short (one line) or long (many lines) as desired;
+
+and they **should**:
+
+- respond in documented, simple ways to settings on the Context argument and/or their keyword argument(s), if any.
+
+.. code-block:: python
+
+    def changes_a(s: Scenario) -> None:
+        """Change a scenario by modifying structure data, but not data."""
+        with s.transact():
+            s.add_set("technology", "test_tech")
+
+        # Here, invoke other code to further modify `s`
+
+    def changes_b(s: Scenario, value=100.0) -> None:
+        """Change a scenario by modifying parameter data, but not structure.
+
+        This function takes an extra argument, `values`, so functools.partial()
+        can be used to supply different values when it is used in different
+        workflow steps. See below.
+        """
+        with s.transact():
+            s.add_par(
+                "technical_lifetime",
+                make_df(
+                    "technical_lifetime",
+                    node_loc=s.set("node")[0],
+                    year_vtg=s.set("year")[0],
+                    technology="test_tech",
+                    value=100.0,
+                    unit="y",
+                ),
+            )
+
+        # Here, invoke other code to further modify `s`
+
+With the steps defined, the workflow is composed using a :class:`.Workflow` instance.
+Call :meth:`.Workflow.add_step` to define each target model with its precursor and the function that will create the former from the latter:
+
+.. code-block:: python
+
+    from message_ix_models import Context, Workflow
+
+    # Create the workflow
+    ctx = Context.get_instance()
+    wf = Workflow(ctx)
+
+    # "Model name/base" is loaded from an existing platform
+    wf.add_step(
+        "base",
+        None,
+        target="ixmp://example-platform/Model name/base#123",
+    )
+
+    # "Model/A" is created from "Model/base" by calling changes_a()
+    wf.add_step("A", "base", changes_a, target="Model/A")
+
+    # "Model/B1" is created from "Model/A" by calling changes_b() with the
+    # default value
+    wf.add_step("B1", "A", changes_b, target="Model/B1")
+
+    # "Model/B2" is similar, but uses a different value
+    wf.add_step("B2", "A", partial(changes_b, value=200.0), target="model/B2")
+
+Finally, the workflow is triggered using :meth:`.Workflow.run`, giving either one step name or a list of names.
+The indicated scenarios are created (and solved, if the workflow steps involve solving); if this requires any precursor scenarios, those are first created and solved, etc. as required.
+Other, unrelated scenarios/steps are not created.
+
+.. code-block:: python
+
+    s1, s2 = wf.run(["B1", "B2"])
+
+Usage examples
+--------------
+
+- :mod:`message_data.projects.navigate.workflow`
+
+.. todo::
+
+   Expand with discussion of workflow patterns common in research projects using MESSAGEix, e.g.:
+
+   - Run the same scenario with multiple emissions budgets.
+
+API reference
+=============
+
+.. currentmodule:: message_ix_models.workflow
+
+.. automodule:: message_ix_models.workflow
+   :members:
+   :exclude-members: WorkflowStep
+
+.. autoclass:: WorkflowStep
+   :members:
+   :special-members: __call__
diff --git a/nest/doc/cli.rst b/nest/doc/cli.rst
new file mode 100644
index 0000000..ed7a2ff
--- /dev/null
+++ b/nest/doc/cli.rst
@@ -0,0 +1,177 @@
+Command-line interface
+**********************
+
+This page describes how to use the :program:`mix-models` command-line interface (CLI) to perform common tasks.
+:program:`mix-models` is organized into **commands** and **subcommands**, sometimes in multiple levels.
+Our goal is that the *semantics* of all commands are similar, so that interacting with each command feels similar.
+
+.. contents::
+   :local:
+
+
+Controlling CLI behaviour
+=========================
+
+To support a variety of complex use-cases, the MESSAGEix stack takes configuration and inputs from several places:
+
+:mod:`ixmp` configuration file: :file:`config.json`
+---------------------------------------------------
+:mod:`ixmp` keeps track of named Platforms and their associated databases, and stores information in its :file:`config.json` file.
+See :mod:`ixmp.config`.
+List existing platforms::
+
+    $ ixmp platform list
+
+To add a specific database, you can use the ixmp CLI [1]_::
+
+    $ ixmp platform add [PLATFORMNAME] jdbc oracle [COMPUTER]:[PORT]/[SERVICENAME] [USERNAME] [PASSWORD]
+
+You may also want to make this the *default* platform.
+Unless told otherwise, :mod:`message_ix_models` creates :class:`~.Platform` objects without any arguments (``mp = ixmp.Platform()``); this loads the default platform.
+Set the default::
+
+    $ ixmp platform add default [PLATFORMNAME]
+
+:mod:`message_ix` stores only one configuration value in :file:`config.json`: ``'message model dir'``, the path to the GAMS model files.
+MESSAGEix-GLOBIOM uses the GAMS model files from the current :mod:`message_ix` ``master`` branch, so you should not set this, or unset it when using :mod:`message_ix_models`.
+
+:mod:`message_ix_models` will use the :file:`config.json` value ``"message_local_data"`` for local data, if it is set and not overridden.
+
+.. [1] ``[COMPUTER]`` is in this case either the hostname or the IP address.
+
+Environment variables
+---------------------
+Some code responds to environment variables.
+For example, ixmp responds to ``IXMP_DATA``, which tells it where to find the file :file:`config.json`.
+
+:mod:`message_ix_models` responds to ``MESSAGE_LOCAL_DATA``; see :ref:`the discussion of local data <local-data>`.
+
+
+CLI parameters (arguments and options)
+--------------------------------------
+
+Each command has zero or more arguments and options.
+**Arguments** are mandatory and follow the command name in a certain order.
+**Options**, as the name implies, are not required.
+If an option is omitted, a default value is used; the code and ``--help`` text make clear what the default behaviour is.
+
+Arguments and options are **hierarchical**.
+Consider the following examples::
+
+    $ mix-data --opt0=foo cmd1 --opt1=bar arg1 cmd2 --opt2=baz arg2
+    $ mix-data --opt0=foo cmd1            arg1 cmd3 --opt3=baz arg3a arg3b
+
+In these examples:
+
+- ``--opt0`` is an option that (potentially) affects **any** command, including the subcommands ``cmd2`` or ``cmd3``.
+- ``--opt1`` and ``arg1`` are an option and mandatory argument to the command ``cmd1``.
+  They might not have any relevance to other ``mix-data`` commands.
+- ``cmd2`` and ``cmd3`` are distinct subcommands of ``cmd1``.
+
+  - They *may* respond to ``--opt1`` and ``arg1``, and to ``--opt0``; at least, they *must* not contradict them.
+  - They each may have their own options and arguments, which can be distinct.
+
+.. tip:: Use ``--help`` for any (sub)command to read about its behaviour.
+   If the help text does not make the behaviour clear, `file an issue <https://github.com/iiasa/message-ix-models/issues/new>`_.
+
+Configuration files and metadata
+--------------------------------
+For some features of the code, the default behaviour is very elaborate and serves for most uses; but we also provide the option to override it.
+This default behaviour or optional behaviour is defined by reading an input file.
+These are stored in the :ref:`package data <package-data>` directory.
+
+For example, ``mix-models report`` loads reporting configuration from :file:`message_ix_models/data/report/global.yaml`, a YAML file with hundreds of lines.
+Optionally, a different file can be used::
+
+    $ mix-models report --config other
+
+…looks for a file :file:`other.yaml` in the :ref:`local data <local-data>` directory or current working directory. Or::
+
+    $ mix-models report --config /path/to/another/file.yaml
+
+…can be used to point to a file in a different directory.
+
+
+Important CLI options and commands
+==================================
+
+.. _cli-help:
+
+Top-level options and commands
+------------------------------
+``mix-models --help`` describes these::
+
+    $ mix-models --help
+    Usage: mix-models [OPTIONS] COMMAND [ARGS]...
+
+      Command-line interface for MESSAGEix-GLOBIOM model tools.
+
+      Every tool and script in this repository is accessible through this CLI.
+      Scripts are grouped into commands and sub-commands. For help on specific
+      (sub)commands, use --help, e.g.:
+
+              mix-models cd-links --help
+              mix-models cd-links run --help
+
+      The top-level options --platform, --model, and --scenario are used by
+      commands that access specific message_ix scenarios; these can also be
+      specified with --url.
+
+      For more information, see
+      https://docs.messageix.org/projects/models2/en/latest/cli.html
+
+    Options:
+      --url ixmp://PLATFORM/MODEL/SCENARIO[#VERSION]
+                                      Scenario URL.
+      --platform PLATFORM             Configured platform name.
+      --model MODEL                   Model name for some commands.
+      --scenario SCENARIO             Scenario name for some commands.
+      --version INTEGER               Scenario version.
+      --local-data PATH               Base path for local data.
+      -v, --verbose                   Print DEBUG-level log messages.
+      --help                          Show this message and exit.
+
+    Commands:
+      cd-links         CD-LINKS project.
+      dl               Retrieve data from primary sources.
+      engage           ENGAGE project.
+      iiasapp          Import power plant capacity.
+      material         Model with materials accounting.
+      prep-submission  Prepare scenarios for submission to database.
+      report           Postprocess results.
+      res              MESSAGE-GLOBIOM reference energy system (RES).
+      techs            Export data from data/technology.yaml to CSV.
+      transport        MESSAGEix-Transport variant.
+
+To explain further:
+
+``--platform PLATFORM`` or ``--url``
+   By default, message_data connects to the default ixmp Platform.
+   These options direct it to work with a different Platform.
+
+``--model MODEL --scenario SCENARIO`` or ``--url``
+    Many commands use an *existing* :class:`~.Scenario` as a starting point, and begin by cloning that Scenario to a new (model name, scenario name).
+    For any such command, these top-level options define the starting point/initial Scenario to clone/‘baseline’.
+
+    In contrast, see ``--output-model``, below.
+
+
+Common options
+--------------
+Since :mod:`message_ix_models.model` and :mod:`message_ix_models.project` codes often perform similar tasks, their CLI options and arguments are provided in :mod:`.util.click` for easy re-use.
+These include:
+
+``ssp`` argument
+   This takes one of the values 'SSP1', 'SSP2', or 'SSP3'.
+
+   Commands that will not work for one or more of the SSPs should check the argument value given by the user and raise :class:`NotImplementedError`.
+
+``--output-model NAME`` option
+   This option is a counterpart to the top-level ``--url/--model/--scenario`` options.
+   A command that starts from one Scenario, and builds one or more Scenarios from it will clone *to* a new (model name, scenario name);
+   ``--output-model`` gives the model name.
+
+   Current code generates a variety of fixed (non-configurable) scenario names; use ``--help`` for each command to see which.
+
+
+To employ these in new code, refer to the example of existing code.
diff --git a/nest/doc/conf.py b/nest/doc/conf.py
new file mode 100644
index 0000000..9dc3491
--- /dev/null
+++ b/nest/doc/conf.py
@@ -0,0 +1,107 @@
+# Configuration file for the Sphinx documentation builder.
+#
+# This file only contains a selection of the most common options. For a full list see
+# the documentation: https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+import os
+from typing import TYPE_CHECKING
+
+if TYPE_CHECKING:
+    import sphinx.application
+
+# -- Project information ---------------------------------------------------------------
+
+project = "message-ix-models"
+copyright = "2020–2023, IIASA Energy, Climate, and Environment (ECE) Program"
+author = "IIASA Energy, Climate, and Environment (ECE) Program"
+
+
+# -- General configuration -------------------------------------------------------------
+
+# Add any Sphinx extension module names here, as strings. They can be extensions coming
+# with Sphinx (named 'sphinx.ext.*') or your custom ones.
+extensions = [
+    "sphinx.ext.autodoc",
+    "sphinx.ext.autosummary",
+    "sphinx.ext.extlinks",
+    "sphinx.ext.intersphinx",
+    "sphinx.ext.napoleon",
+    "sphinx.ext.todo",
+    "sphinx.ext.viewcode",
+]
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ["_template"]
+
+# List of patterns, relative to source directory, that match files and directories to
+# ignore when looking for source files. This pattern also affects html_static_path and
+# html_extra_path.
+exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
+
+
+def setup(app: "sphinx.application.Sphinx") -> None:
+    """Copied from pytest's conf.py to enable intersphinx references to these."""
+    app.add_crossref_type(
+        "fixture",
+        "fixture",
+        objname="built-in fixture",
+        indextemplate="pair: %s; fixture",
+    )
+
+
+# -- Options for HTML output -----------------------------------------------------------
+
+# A list of CSS files.
+html_css_files = ["custom.css"]
+
+html_favicon = "_static/favicon.svg"
+
+# The name of an image file (relative to this directory) to place at the top of the
+# sidebar.
+html_logo = "_static/combined-logo-white.png"
+
+# Add any paths that contain custom static files (such as style sheets) here, relative
+# to this directory. They are copied after the builtin static files, so a file named
+# "default.css" will overwrite the builtin "default.css".
+html_static_path = ["_static"]
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for a list of
+# builtin themes.
+html_theme = "sphinx_rtd_theme"
+
+# -- Options for sphinx.ext.autosummary ------------------------------------------------
+
+autosummary_generate = True
+
+
+# -- Options for sphinx.ext.extlinks ---------------------------------------------------
+
+extlinks = {
+    "issue": ("https://github.com/iiasa/message-ix-models/issue/%s", "GH #%s"),
+    "pull": ("https://github.com/iiasa/message-ix-models/pull/%s", "PR #%s"),
+}
+
+
+# -- Options for sphinx.ext.intersphinx ------------------------------------------------
+
+# For message-data, see: https://docs.readthedocs.io/en/stable/guides
+# /intersphinx.html#intersphinx-with-private-projects
+_token = os.environ.get("RTD_TOKEN_MESSAGE_DATA", "")
+
+intersphinx_mapping = {
+    "genno": ("https://genno.readthedocs.io/en/stable", None),
+    "ixmp": ("https://docs.messageix.org/projects/ixmp/en/latest/", None),
+    "message-ix": ("https://docs.messageix.org/en/latest/", None),
+    "m-data": (
+        f"https://{_token}:@docs.messageix.org/projects/models-internal/en/latest/",
+        None,
+    ),
+    "pandas": ("https://pandas.pydata.org/pandas-docs/stable/", None),
+    "pytest": ("https://docs.pytest.org/en/stable/", None),
+    "python": ("https://docs.python.org/3/", None),
+    "sdmx": ("https://sdmx1.readthedocs.io/en/stable/", None),
+}
+
+# -- Options for sphinx.ext.todo -------------------------------------------------------
+
+todo_include_todos = True
diff --git a/nest/doc/data.rst b/nest/doc/data.rst
new file mode 100644
index 0000000..a2642a7
--- /dev/null
+++ b/nest/doc/data.rst
@@ -0,0 +1,262 @@
+Data, metadata, and configuration
+*********************************
+
+Many, varied kinds of data are used to prepare and modify MESSAGEix-GLOBIOM scenarios.
+Other data are produced by code as incidental or final output.
+These can be categorized in several ways.
+One is by the purpose they serve:
+
+- **data**—actual numerical values—used or produced by code,
+- **metadata**, information describing where data is, how to manipulate it, how it is structured, etc.;
+- **configuration** that otherwise affects how code works.
+
+Another is whether they are **input** or **output** data.
+
+This page describes how to store and handle such files in both :mod:`message_ix_models` and :mod:`message_data`.
+
+.. contents::
+   :local:
+
+
+.. _data-goes-where:
+
+Choose locations for data
+=========================
+
+These are listed in order of preference.
+
+.. _package-data:
+
+(1) :file:`message_ix_models/data/`
+-----------------------------------
+
+- Files in this directory are **public**, and are packaged, published, and installable from PyPI with :mod:`message_ix_models`; in standard Python terms, these are “package data”.
+- This is the preferred location for:
+
+  - General-purpose metadata.
+  - Basic configuration, e.g. for reporting, not specific to any model variant or project.
+  - Data for publicized model variants and completed/published projects.
+
+- Data here can be loaded with :func:`.load_package_data` or other, more specialized code.
+- Documentation files like :file:`doc/pkg-data/*.rst` describe the contents of these files.
+  For example: :doc:`pkg-data/node`.
+
+.. _private-data:
+
+(2) :file:`data/` directory in the :mod:`message_data` repo
+-----------------------------------------------------------
+
+- Files in this directory are **private** and not installable from PyPI (because :mod:`message_data` is not installable).
+- This is the preferred location for:
+
+  - Data for model variants and projects under current development.
+  - Specific data files that cannot be made public, e.g. due to licensing issues.
+
+- Data here can be loaded with :func:`.load_private_data` or other, more specialized code.
+
+.. _local-data:
+
+(3) Other, system-specific (“local”) directories
+------------------------------------------------
+
+- These are the preferred location for:
+
+  1. Caches i.e. temporary data files used to speed up other code.
+  2. Output e.g. data or figure files generated by reporting.
+  3. Data files not distributed with :mod:`message_ix_models`, e.g. those with access conditions (registration, payment, etc.).
+
+- These kinds of data **must not** be committed to :mod:`message_ix_models`.
+  Caches and output **should not** be committed to :mod:`message_data`.
+- Each user **may** configure a location for these data, appropriate to their system.
+
+  This setting can be made in multiple ways.
+  In order of ascending precedence:
+
+  1. The default location is the *current working directory*, i.e. whichever directory the :doc:`cli` is invoked in, or in which Python code is run that imports and uses :mod:`message_ix_models`.
+  2. The :mod:`ixmp` configuration file setting ``message local data``.
+  3. The ``MESSAGE_LOCAL_DATA`` environment variable.
+  4. The ``--local-data`` CLI option and related options such as ``--cache-path`` or the ``--output`` option to the ``report`` command.
+  5. Code that directly modifies the ``local_data`` setting on :class:`.Context`.
+
+  - This location **should** be outside the Git-controlled directories for :mod:`message_ix_models` or :mod:`message_data`.
+    If not, use :file:`.gitignore` files to hide these from Git.
+
+- Use :meth:`.Context.get_local_path` and :func:`.local_data_path` to construct paths under this directory.
+
+
+General guidelines
+==================
+
+Always consider: “Will this code work on another researcher's computer?”
+
+Prefer text formats
+   …such as e.g. CSV and YAML.
+   CSV files up to several thousand lines are compressed by Git automatically, and Git can handle diffs to these files easily.
+
+*Do not* hard-code paths
+   Data stored with (1) or (2) above can be retrieved with the utility funtions mentioned, instead of hard-coded paths.
+
+   For system-specific paths (3) only, get a :obj:`.Context` object and use it to get an appropriate :class:`.Path` object pointing to a file
+
+   .. code-block:: python
+
+       # Store a base path
+       project_path = context.get_local_path("myproject", "output")
+
+       # Use the Path object to generate a subpath
+       run_id = "foo"
+       output_file = project_path.joinpath("reporting", run_id, "all.xlsx")
+
+Keep input and output data separate
+   Where possible, use (1) or (2) above for the former, and (3) for the latter.
+
+Use a consistent scheme for data locations
+   For a submodule for a specific model variant or project named, e.g. ``message_ix_models.model.[name]`` or ``message_ix_models.project.[name]``, keep input data in a well-organized directory under:
+
+   - :file:`[base]/[name]/` —preferred, flatter,
+   - :file:`[base]/model/[name]/`,
+   - :file:`[base]/project/[name]/`,
+   - or similar,
+
+   where ``[base]`` is (1) or (2), above.
+
+   Keep *project-specific configuration files* in the same locations, or (less preferable) alongside Python code files:
+
+   .. code-block:: python
+
+      # Located in `message_ix_models/data/`:
+      config = load_package_data("myproject", "config.yaml")
+
+      # Located in `data/` in the message_data repo:
+      config = load_private_data("myproject", "config.yaml")
+
+      # Located in the same directory as the code
+      config = yaml.safe_load(open(Path(__file__).with_name("config.yaml")))
+
+   Use a similar scheme for output data, except under (3).
+
+Re-use configuration
+   Configuration to run a set of scenarios or to prepare reported submissions **should** re-use or extend existing, general-purpose code.
+   Do not duplicate code or configuration.
+   Instead, adjust or selectively overwrite its behaviour via project-specific configuration read from a file.
+
+
+.. _binary-input-data:
+
+Large/binary input data
+=======================
+
+These data, such as Microsoft Excel spreadsheets, **must not** be committed as ordinary Git objects.
+This is because the entire file is re-added to the Git history for even small modifications, making it very large (see `issue #37 <https://github.com/iiasa/message_data/issues/37>`_).
+
+Instead, use one of the following patterns, in order of preference.
+Whichever pattern is used, code for handling large input data must be in :mod:`message_ix_models`, even if the data itself is private, e.g. in :mod:`message_data` or another location.
+
+Fetch from a remote source
+--------------------------
+
+Use a configuration file in :mod:`message_ix_models` to store metadata, i.e. the Internet location and other information needed to retrieve the data.
+Then, write code that retrieves the data and caches it locally:
+
+.. code-block:: python
+
+    import requests
+
+    # Load some configuration
+    config = yaml.safe_load(load_package_data("big-data-source", "config.yaml"))
+
+    # Local paths for the cached raw files and extracted file(s)
+    cache_path = context.get_cache_path("big-data-source")
+    downloaded = cache_path / "downloaded_file.zip"
+    extracted = cache_path / "extracted_file.csv"
+
+    with open(downloaded) as f:
+        remote_data = requests.get(config["url"])
+        # Handle the data, writing to `f`
+
+    # Extract the data from `downloaded` to `extracted`
+
+This pattern is preferred because it can be replicated by anyone, and the reference data is public.
+
+Use Git Large File Storage (LFS)
+--------------------------------
+
+`Git LFS <https://git-lfs.github.com/>`_ is a Git extension that allows for storing large, binary files without bloating the commit history.
+Essentially, Git stores a one-line text file with a hash of the full file, and the full file is stored separately.
+The IIASA GitHub account has up to 300 GB of space for LFS objects.
+
+To use this pattern, simply ``git add ...`` and ``git commit`` files in an appropriate location (above).
+New or unusual binary file extensions may require a ``git lfs`` command or modification to :file:`.gitattributes` to ensure they are tracked by LFS and not by ordinary Git history.
+See the Git LFS documentation at the link above for more detail.
+
+
+Retrieve data from existing databases
+-------------------------------------
+
+These include the same IIASA ENE ixmp databases that are used to store scenarios.
+Documentation **must** be provided that ensures this data is reproducible, i.e. any original source and code to create the database used by :mod:`message_data`.
+
+
+Other patterns
+--------------
+
+Some other patterns exist, but should not be repeated in new code, and should be migrated to one of the above patterns.
+
+- SQL queries against a Oracle/JDBC database. See :ref:`message_data:data-iea` (in :mod:`message_data`) and `issue #53 <https://github.com/iiasa/message_data/issues/53#issuecomment-669117393>`_ for a description of how to replace/simplify this code.
+
+
+Configuration
+=============
+
+:class:`.Context` objects are used to carry configuration, environment information, and other data between parts of the code.
+Scripts and user code can also store values in a Context object.
+
+.. code-block:: python
+
+    # Get an existing instance of Context. There is always at
+    # least 1 instance available
+    c = Context.get_instance()
+
+    # Store a value using attribute syntax
+    c.foo = 42
+
+    # Store a value with spaces in the name using item syntax
+    c["PROJECT data source"] = "Source A"
+
+    # my_function() responds to 'foo' or 'PROJECT data source'
+    my_function(c)
+
+    # Store a sub-dictionary of values
+    c["PROJECT2"] = {"setting A": 123, "setting B": 456}
+
+    # Create a subcontext with all the settings of `c`
+    c2 = deepcopy(c)
+
+    # Modify one setting
+    c2.foo = 43
+
+    # Run code with this alternate setting
+    my_function(c2)
+
+
+For the CLI, every command decorated with ``@click.pass_obj`` gets a first positional argument ``context``, which is an instance of this class.
+The settings are populated based on the command-line parameters given to ``mix-models`` or (sub)commands.
+
+.. _context:
+
+Top-level settings
+------------------
+
+These are defined by :class:`message_ix_models.Config`.
+
+Specific modules for model variants, projects, etc. **should**:
+
+- Define a single :mod:`dataclass <dataclasses>` to express the configuration options they understand.
+  See for example :class:`.model.Config` (for constructing new models), and :class:`message_data.model.buildings.Config` (for the MESSAGEix-Buildings model variant / linkage).
+- Store this on the :class:`Context` at a simple key.
+  For example :class:`.model.Config` is stored at ``context.model`` or ``context["model"]``.
+- Retrieve and respect configuration from existing objects, i.e. only duplicate settings with the same meaning when strictly necessary.
+- Communicate to other modules by setting the appropriate configuration values.
+
+.. autoclass:: message_ix_models.Config
+   :members:
diff --git a/nest/doc/develop.rst b/nest/doc/develop.rst
new file mode 100644
index 0000000..99cabf1
--- /dev/null
+++ b/nest/doc/develop.rst
@@ -0,0 +1,47 @@
+Development practices
+*********************
+
+This page describes development practices for :mod:`message_ix_models` and :mod:`message_data` intended to help reproducibility, interoperability, and reusability.
+
+In the following, the bold-face words **required**, **optional**, etc. have specific meanings as described in `IETF RFC 2119 <https://tools.ietf.org/html/rfc2119>`_.
+
+On other pages:
+
+- :doc:`message-ix:contributing` in the MESSAGEix docs.
+  *All* of these apply to contributions to :mod:`message_ix_models` and :mod:`message_data`, including the :ref:`message-ix:code-style`.
+- :doc:`data`, for how to add and handle these.
+
+On this page:
+
+.. contents::
+   :local:
+   :backlinks: none
+
+.. _check-support:
+
+Advertise and check compatibility
+=================================
+
+There are multiple choices of the base structure for a model in the MESSAGEix-GLOBIOM family, e.g. different :doc:`pkg-data/node` and :doc:`pkg-data/year`.
+
+Code that will only work with certain structures…
+
+- **must** be documented, and include in its documentation any such limitation, e.g. “:func:`example_func` only produces data for R11 and year list B.”
+- **should** use :func:`.check_support` in individual pieces of code to pre-emptively check and raise an exception.
+  This prevents inadvertent use of the code where its data will be invalid:
+
+.. code-block:: python
+
+    def myfunc(context, *args):
+        """A function that only works on R11 and years ‘B’."""
+
+        check_support(
+            context,
+            dict(regions=["R11"], years=["B"]),
+            "Example data produced"
+        )
+
+        # … function code to execute if the check passes
+
+Code **may** also check a :class:`.Context` instance and automatically adapt data from certain structures to others, e.g. by interpolating data for certain periods or areas.
+To help with validation, code that does this **should** log on the :data:`logging.INFO` level to advertise these steps.
diff --git a/nest/doc/distrib.rst b/nest/doc/distrib.rst
new file mode 100644
index 0000000..9dbae15
--- /dev/null
+++ b/nest/doc/distrib.rst
@@ -0,0 +1,41 @@
+Distributed computing
+*********************
+
+This page introduces considerations, tools, and features for using **distributed** or **high-throughput computing** with MESSAGEix-GLOBIOM.
+
+.. contents::
+   :local:
+   :backlinks: none
+
+Overview
+========
+
+Scenarios in the MESSAGEix-GLOBIOM global model family are characterized by:
+
+- ca. 100 MB of data, depending on storage format (e.g. in a :class:`.JDBCBackend` local, HyperSQL database, or :ref:`ixmp:excel-data-format` in Excel files).
+- :meth:`.solve` times of between 10 and 60 minutes, depending on hardware and configuration, plus similar amounts of time to run the legacy reporting in :mod:`message_data`.
+- Memory usage of ~10 GB or more using :class:`.JDBCBackend`, currently the only supported backend.
+
+These resource needs can be a bottleneck in applications, for example:
+
+- where many/related scenarios must be solved.
+- when iteration (repeatedly solving 1 or more scenarios) is a key approach in developing code that sets up scenarios.
+
+To improve research productivity, researchers may choose to run scenarios or ‘workflows’ (a combination of solving scenarios and pre- and post-processing steps or codes) through **distributed computing**, i.e. not on their local machine.
+Hardware and software environments for distributed computing can vary widely, and can be categorized in multiple ways, such as:
+
+1. More powerful single-CPU systems, accessed remotely.
+2. Cloud services, e.g. Google Compute Engine; Amazon AWS; Github Actions; etc. providing access to one or more machines.
+3. Dedicated cluster systems (sometimes labelled **high-throughput computing**, HTC, or **high-performance computing**, HPC, systems) for scientific computing, operated by a variety of parties.
+
+Tooling
+=======
+
+- :mod:`message_ix_models` and related packages (:mod:`ixmp`, :mod:`message_ix`, :mod:`message_data`) aim to provide *simple, general-purpose features* that allow working with a variety of distributed systems.
+- Specific configuration necessarily depends on the specific system(s) in use and the researcher's application.
+- These features should not duplicate features of existing tools such as
+  `Slurm <https://slurm.schedmd.com>`_,
+  `HTCondor <https://htcondor.readthedocs.io/en/latest/>`_, etc., but rather allow :mod:`message_ix` & co. to be used with/through those.
+- The individual features, tools, utilities, etc. should each be simple, i.e. `do one thing, and do it well <https://en.wikipedia.org/wiki/Unix_philosophy#Do_One_Thing_and_Do_It_Well>`__.
+
+.. todo:: Extend.
diff --git a/nest/doc/index.rst b/nest/doc/index.rst
new file mode 100644
index 0000000..08a5e5f
--- /dev/null
+++ b/nest/doc/index.rst
@@ -0,0 +1,65 @@
+Tools for MESSAGEix-GLOBIOM models
+**********************************
+
+:mod:`message_ix_models` provides tools for research using the **MESSAGEix-GLOBIOM family of models** developed by the IIASA Energy, Climate, and Environment (ECE) Program and its collaborators.
+This ‘family’ includes single-country and other models derived from the main, global model; all built in the `MESSAGEix framework <https://docs.messageix.org>`_ and on the `ix modeling platform (ixmp) <https://docs.messageix.org/ixmp/>`_.
+
+Among other tasks, the tools allow modelers to:
+
+- retrieve input data from various upstream sources,
+- process/transform upstream data into model input parameters,
+- create, populate, modify, and parametrize scenarios,
+- conduct model runs,
+- set up model *variants* with additional details or features, and
+- report quantities computed from model outputs.
+
+.. toctree::
+   :maxdepth: 1
+   :caption: User guide
+
+   install
+   data
+   cli
+   repro
+   distrib
+
+.. toctree::
+   :maxdepth: 2
+   :caption: API reference
+
+   api/model
+   api/model-bare
+   api/model-build
+   api/model-emissions
+   api/disutility
+   api/project
+   api/tools
+   api/util
+   api/testing
+   api/workflow
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Package data
+
+   pkg-data/node
+   pkg-data/relation
+   pkg-data/year
+   pkg-data/codelists
+   pkg-data/iiasa-se
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Development
+
+   develop
+   whatsnew
+   migrate
+   releasing
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`
diff --git a/nest/doc/install.rst b/nest/doc/install.rst
new file mode 100644
index 0000000..9b57bd8
--- /dev/null
+++ b/nest/doc/install.rst
@@ -0,0 +1,20 @@
+Installation
+************
+
+From PyPI
+=========
+
+1. Run::
+
+    $ pip install message-ix-models
+
+
+From source
+===========
+
+1. Clone the code.
+2. Run [1]_::
+
+    $ pip install --no-use-pep517 --editable .
+
+.. [1] See `pypa/pip#7953 <https://github.com/pypa/pip/issues/7953#issuecomment-676600130>`_.
diff --git a/nest/doc/make.bat b/nest/doc/make.bat
new file mode 100644
index 0000000..922152e
--- /dev/null
+++ b/nest/doc/make.bat
@@ -0,0 +1,35 @@
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=.
+set BUILDDIR=_build
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.may add the Sphinx directory to PATH.
+	echo.
+	echo.If you don't have Sphinx installed, grab it from
+	echo.http://sphinx-doc.org/
+	exit /b 1
+)
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+
+:end
+popd
diff --git a/nest/doc/migrate.rst b/nest/doc/migrate.rst
new file mode 100644
index 0000000..11eb3a4
--- /dev/null
+++ b/nest/doc/migrate.rst
@@ -0,0 +1,48 @@
+Migrating from :mod:`message_data`
+**********************************
+
+:mod:`message_ix_models` coexists with the private repository/package currently named :mod:`message_data`.
+The latter is the location for code related to new research that has not yet been completed and published, data that must remain closed-source permanently, etc.
+
+Over time:
+
+- All other code will be migrated from :mod:`message_data` to :mod:`message_ix_models`.
+- Code and data for individual projects will be moved from :mod:`message_data` to :mod:`message_ix_models` at a suitable point during the process of publication.
+  (This point may vary from project to project.)
+- :mod:`message_data` may be renamed.
+
+This page gives some practices and tips for using the two packages together.
+
+Always import via :mod:`message_ix_models`
+   The package installs :class:`.MessageDataFinder` into Python's import system (`importlib <https://docs.python.org/3/library/importlib.html>`_), which changes its default behaviour as follows: if
+
+   1. A module :samp:`message_ix_models.model.{model_name}` or :samp:`message_ix_models.project.{project_name}` is imported, and
+   2. This module does not actually exist in :mod:`message_ix_models`,
+   3. Then the code will instead file the respective modules :samp:`message_data.model.{model_name}` or :samp:`message_data.project.{project_name}`.
+
+   Even when using code that currently or temporarily lives in :mod:`message_data`, access it like this:
+
+   .. code-block:: python
+
+      # Code in message_data/model/mymodelvariant.py
+      from message_ix_models.model import mymodelvariant
+
+      mymodelvariant.build(...)
+
+   This code is *future-proof*: it will not need adjustment if/when “mymodelvariant” is eventually moved from :mod:`message_data` to :mod:`message_ix_models`.
+
+Use the :program:`mix-models` command-line interface (CLI)
+   All CLI commands and subcommands defined in :mod:`message_data` are also made available through the :mod:`message_ix_models` CLI, the executable :program:`mix-models`.
+
+   Use this program in documentation examples and in scripts.
+   In a similar manner to the point above, these documents and scripts will remain correct if/when code is moved.
+
+Don't import from :mod:`message_data` in :mod:`message_ix_models`
+   The open-source code should not depend on any private code.
+   If this appears necessary, the code in :mod:`message_data` can probably be moved to :mod:`message_ix_models`.
+
+Use :mod:`message_ix_models.tools` and :mod:`~message_ix_models.util` in :mod:`message_data`
+   The former have stricter quality standards and are more transparent, which is better for reproducibility.
+
+   At some points, similar code may appear in both packages as it is being migrated.
+   In such cases, always import and use the code in :mod:`message_ix_models`, making any adjustments that are necessary.
diff --git a/nest/doc/pkg-data/codelists.rst b/nest/doc/pkg-data/codelists.rst
new file mode 100644
index 0000000..29e5e6b
--- /dev/null
+++ b/nest/doc/pkg-data/codelists.rst
@@ -0,0 +1,55 @@
+Other code lists
+****************
+
+.. contents::
+   :local:
+
+.. _commodity-yaml:
+
+Commodities (``commodity.yaml``)
+================================
+
+Each of these codes has the following annotations:
+
+``level``
+   Level where this commodity typically (not exclusively) occurs.
+``unit``
+   Units typically associated with this commodity.
+
+.. literalinclude:: ../../message_ix_models/data/commodity.yaml
+   :language: yaml
+
+.. _level-yaml:
+
+Levels (``level.yaml``)
+=======================
+
+This code list has no annotations and no hierarchy.
+
+.. literalinclude:: ../../message_ix_models/data/level.yaml
+   :language: yaml
+
+
+.. _technology-yaml:
+
+Technologies (``technology.yaml``)
+==================================
+
+.. warning:: This list is *only for reference*; particular MESSAGE-GLOBIOM scenarios may not contain all these technologies, or may contain other technologies not listed.
+
+Each of these codes has the following annotations:
+
+``sector``
+   A categorization of the technology.
+``input``
+   (``commodity``, ``level``) for input to the technology.
+``output``
+   (``commodity``, ``level``) for output from the technology.
+``vintaged``
+   :obj:`True` if the technology is subject to vintaging.
+``type``
+   Same as ``output[1]``.
+
+
+.. literalinclude:: ../../message_ix_models/data/technology.yaml
+   :language: yaml
diff --git a/nest/doc/pkg-data/iiasa-se.rst b/nest/doc/pkg-data/iiasa-se.rst
new file mode 100644
index 0000000..3c95b51
--- /dev/null
+++ b/nest/doc/pkg-data/iiasa-se.rst
@@ -0,0 +1,33 @@
+IIASA Scenario Explorer metadata
+********************************
+
+Each instance of the IIASA Scenario Explorer (e.g. https://data.ene.iiasa.ac.at/ar6/) requires a distinct collection of information about the structure of model output data to be accepted.
+The process of providing this structure information is termed **“model registration,”** and is partly documented at https://nomenclature-iamc.readthedocs.io, including the expected `directory and file layout <https://nomenclature-iamc.readthedocs.io/en/stable/user_guide/directory-structure.html>`_.
+The process must be repeated for additional Scenario Explorer instances.
+
+:mod:`message_ix_models` includes, in :file:`data/iiasa-se/`, files that can be used for such registration.
+As of 2022-08-15, these are exactly the files use in the NAVIGATE project instance of the Scenario Explorer.
+These files **should**:
+
+- …match the actual structure and metadata of models/scenarios generated by :mod:`message_ix_models`,
+- …be used as the basis for, and/or be updated to match, files used for registration in other Scenario Explorer instances.
+
+.. contents::
+   :local:
+   :backlinks: none
+
+:file:`mappings-R12.yaml`
+=========================
+
+This file would be placed at e.g. :file:`mappings/MESSAGEix-GLOBIOM_1.1-R12.yaml` in a registration repository.
+
+.. literalinclude:: ../../message_ix_models/data/iiasa-se/mappings-R12.yaml
+   :language: yaml
+
+:file:`def-regions.yaml`
+========================
+
+This file would be placed at e.g. :file:`definitions/region/MESSAGEix-GLOBIOM_1.1-R12.yaml` in a registration repository.
+
+.. literalinclude:: ../../message_ix_models/data/iiasa-se/def-regions.yaml
+   :language: yaml
diff --git a/nest/doc/pkg-data/node.rst b/nest/doc/pkg-data/node.rst
new file mode 100644
index 0000000..bdf3c4a
--- /dev/null
+++ b/nest/doc/pkg-data/node.rst
@@ -0,0 +1,87 @@
+.. _node-yaml:
+
+Node code lists
+***************
+
+The codes in these lists denote **regions** and **countries**.
+
+When loaded using :func:`.get_codes`, the :attr:`.Code.child` attribute is a list of child codes.
+See the function documentation for how to retrieve these.
+
+.. seealso:: :func:`.adapt_R11_R12`, :func:`.adapt_R11_R14`, :func:`.identify_nodes`.
+
+.. contents::
+   :local:
+
+Models with global scope
+========================
+
+.. _R32:
+
+32-region aggregation (``R32``)
+-------------------------------
+
+.. literalinclude:: ../../message_ix_models/data/node/R32.yaml
+   :language: yaml
+
+.. _R14:
+
+14-region aggregation (``R14``)
+-------------------------------
+
+.. literalinclude:: ../../message_ix_models/data/node/R14.yaml
+   :language: yaml
+
+.. _R11:
+
+11-region aggregation (``R11``)
+-------------------------------
+
+.. literalinclude:: ../../message_ix_models/data/node/R11.yaml
+   :language: yaml
+
+.. _R12:
+
+12-region aggregation (``R12``)
+-------------------------------
+
+.. literalinclude:: ../../message_ix_models/data/node/R12.yaml
+   :language: yaml
+
+.. _RCP:
+
+5-region aggregation (``RCP``)
+-------------------------------
+
+.. literalinclude:: ../../message_ix_models/data/node/RCP.yaml
+   :language: yaml
+
+
+Others
+======
+
+These include models scoped to a single country or region, or a subset of all countries or regions, as well as code lists used in specific data sets from which :mod:`message_ix_models` handles data.
+
+.. _ADVANCE-nodes:
+
+ADVANCE project (``ADVANCE``)
+-----------------------------
+
+.. literalinclude:: ../../message_ix_models/data/node/ADVANCE.yaml
+   :language: yaml
+
+.. _ISR:
+
+Israel (``ISR``)
+----------------
+
+.. literalinclude:: ../../message_ix_models/data/node/ISR.yaml
+   :language: yaml
+
+.. _ZMB:
+
+Zambia (``ZMB``)
+----------------
+
+.. literalinclude:: ../../message_ix_models/data/node/ZMB.yaml
+   :language: yaml
\ No newline at end of file
diff --git a/nest/doc/pkg-data/relation.rst b/nest/doc/pkg-data/relation.rst
new file mode 100644
index 0000000..bcd3db2
--- /dev/null
+++ b/nest/doc/pkg-data/relation.rst
@@ -0,0 +1,43 @@
+.. _relation-yaml:
+
+Relations (:file:`relation/*.yaml`)
+***********************************
+
+These lists provide codes (ID, optional name and description) for the ``relation`` :ref:`MESSAGEix set <message-ix:section_set_def>`.
+See the :doc:`MESSAGEix documentation <message-ix:index>` sections on:
+
+- :ref:`message-ix:section_of_generic_relations`.
+- :ref:`message-ix:section_parameter_generic_relations`.
+
+The codes in these lists have the following annotations:
+
+``group``
+   1 or more strings identifying (a) group(s) to which the relations belong(s).
+
+``parameters``
+   1 or more strings indicating relation parameters for which there should be entries corresponding to this relation ID.
+   For example, "activity, lower" indicates there should be ``relation_activity`` and ``relation_lower`` values for this relation ID.
+
+``technology-entry``
+   .. todo:: Describe the meaning of this annotation.
+
+.. contents::
+   :local:
+
+List ``A``
+----------
+
+.. literalinclude:: ../../message_ix_models/data/relation/A.yaml
+   :language: yaml
+
+List ``B``
+----------
+
+.. literalinclude:: ../../message_ix_models/data/relation/B.yaml
+   :language: yaml
+
+List ``CD-LINKS``
+-----------------
+
+.. literalinclude:: ../../message_ix_models/data/relation/CD-LINKS.yaml
+   :language: yaml
diff --git a/nest/doc/pkg-data/year.rst b/nest/doc/pkg-data/year.rst
new file mode 100644
index 0000000..ba35810
--- /dev/null
+++ b/nest/doc/pkg-data/year.rst
@@ -0,0 +1,36 @@
+.. _year-yaml:
+
+Years or time periods (:file:`year/*.yaml`)
+*******************************************
+
+- See also:
+
+  - The discussion of :doc:`message-ix:time` in the :mod:`message_ix` documentation, which explains the standard sense of time periods used across the MESSAGEix framework and specific models based on it.
+  - :meth:`.ScenarioInfo.year_from_codes`
+
+- These are not the only possible meanings of these codes; others may be used in data from other sources.
+
+  For instance, the ID ``2020`` could be used to represent the period from 2017-07-01 to 2022-06-30.
+  These lists alone cannot resolve these differences; they exist only to provide clarity about the sense used in :mod:`message_ix_models`.
+
+- When working with data from other sources, :mod:`message_ix_models` code **must**:
+
+  - Explicitly note (e.g. in comments or docstrings) any differing time discretization used in the other data.
+  - Perform appropriate conversion *or* record a decision to use the data directly, without conversion.
+
+- It is **optional** for code to fill Scenario parameters for the full set of historical years.
+
+  For instance, when working with list ``B``, code for a model variant or project could only populate parameter values for the historical periods ``2010`` and ``2015``, but not ``2005`` and earlier.
+  This **should** be described and documented on at the scope (function or module) where such subsets are selected from the full codelist.
+
+List ``A``
+----------
+
+.. literalinclude:: ../../message_ix_models/data/year/A.yaml
+   :language: yaml
+
+List ``B``
+----------
+
+.. literalinclude:: ../../message_ix_models/data/year/B.yaml
+   :language: yaml
diff --git a/nest/doc/releasing.rst b/nest/doc/releasing.rst
new file mode 100644
index 0000000..9409dd6
--- /dev/null
+++ b/nest/doc/releasing.rst
@@ -0,0 +1,50 @@
+Releasing
+*********
+
+Version numbers
+===============
+
+:mod:`message_ix_models` uses date-based version numbers like ``Y.M.D``.
+Thus version ``2021.2.23`` is released on 23 February 2021.
+This is to establish a more direct correspondence between outputs of the code and the version(s) used to produce it.
+
+Procedure
+=========
+
+Before releasing, check:
+
+- https://github.com/iiasa/message-ix-models/actions?query=branch:main to ensure that the push and scheduled builds are passing.
+- https://readthedocs.com/projects/iiasa-energy-program-message-ix-models/builds/ to ensure that the docs build is passing.
+
+Address any failures before releasing.
+
+1. Edit :file:`doc/whatsnew.rst`.
+   Comment the heading "Next release", then insert another heading below it, at the same level, with the version number and date.
+   Make a commit with a message like "Mark vX.Y.Z in doc/whatsnew".
+
+2. Tag the release candidate version, i.e. with a ``rcN`` suffix, and push::
+
+    $ git tag v1.2.3rc1
+    $ git push --tags origin main
+
+3. Check:
+
+   - at https://github.com/iiasa/message-ix-models/actions?query=workflow:publish that the workflow completes: the package builds successfully and is published to TestPyPI.
+   - at https://test.pypi.org/project/message-ix-models/ that:
+
+      - The package can be downloaded, installed and run.
+      - The README is rendered correctly.
+
+   Address any warnings or errors that appear.
+   If needed, make a new commit and go back to step (2), incrementing the rc number.
+
+4. (optional) Tag the release itself and push::
+
+    $ git tag v1.2.3
+    $ git push --tags origin main
+
+   This step (but *not* step (2)) can also be performed directly on GitHub; see (5), next.
+
+5. Visit https://github.com/iiasa/message-ix-models/releases and mark the new release: either using the pushed tag from (4), or by creating the tag and release simultaneously.
+
+6. Check at https://github.com/iiasa/message-ix-models/actions?query=workflow:publish and https://pypi.org/project/message-ix-models/ that the distributions are published.
diff --git a/nest/doc/repro.rst b/nest/doc/repro.rst
new file mode 100644
index 0000000..366c646
--- /dev/null
+++ b/nest/doc/repro.rst
@@ -0,0 +1,79 @@
+Reproducibility
+***************
+
+On this page:
+
+.. contents::
+   :local:
+   :backlinks: none
+
+Elsewhere:
+
+- A `high-level introduction <https://paul.kishimoto.name/2021/06/issst/>`_, to how testing supports validity, reproducibility, interoperability, and reusability, in :mod:`message_ix_models` and related packages.
+- :doc:`api/testing` (:mod:`message_ix_models.testing`), on a separate page.
+
+Strategy
+========
+
+The code in :mod:`.model.bare` generates a “bare” reference energy system.
+This is a Scenario that has the same *structure* (ixmp 'sets') as actual instances of the MESSAGEix-GLOBIOM global model, but contains no *data* (ixmp 'parameter' values).
+Code that operates on the global model can be tested on the bare RES; if it works on that scenario, this is one indication (necessary, but not always sufficient) that it should work on fully-populated scenarios.
+Such tests are faster and lighter than testing on fully-populated scenarios, and make it easier to isolate errors in the code that is being tested.
+
+Test suite (:mod:`message_ix_models.tests`)
+===========================================
+
+:mod:`message_ix_models.tests` contains a suite of tests written using `Pytest <https://docs.pytest.org/>`_.
+
+The following is automatically generated documentation of all modules, test classes, functions, and fixtures in the test suite.
+Each test **should** have a docstring explaining what it checks.
+
+.. currentmodule:: message_ix_models
+
+.. autosummary::
+   :toctree: _autosummary
+   :template: autosummary-module.rst
+   :recursive:
+
+   tests
+
+Running the test suite
+======================
+
+Some notes for running the test suite.
+
+:func:`.cached` is used to cache the data resulting from slow operations, like parsing large input files.
+Data are stored in a location described by the :class:`.Context` setting ``cache_path``.
+The test suite interacts with caches in two ways:
+
+- ``--local-cache``: Giving this option causes pytest to use whatever cache directory is configured for normal runs/usage of :mod:`message_ix_models` or :command:`mix-models`.
+- By default (without ``--local-cache``), the test suite uses :ref:`pytest's built-in cache fixture <pytest:cache>`.
+  This creates and uses a temporary directory, usually :file:`.pytest_cache/d/cache/` within the repository root.
+  This location is used *only* by tests, and not by normal runs/usage of the code.
+
+In either case:
+
+- The tests use existing cached data in these locations and skip over code that generates this data.
+  If the generating code is changed, the cached data **must** be deleted in order to actually check that the code runs properly.
+- Running the test suite with ``--local-cache`` causes the local cache to be populated, and this will affect subsequent runs.
+- The continuous integration (below) services don't preserve caches, so code always runs.
+
+Continuous testing
+==================
+
+The test suite (:mod:`message_ix_models.tests`) is run using GitHub Actions for new commits on the ``main`` branch, or on any branch associated with a pull request.
+
+Because it is closed-source and requires access to internal IIASA resources, including databases, continuous integration for :mod:`message_data` is handled by an internal server running `TeamCity <https://www.jetbrains.com/teamcity/>`_: https://ene-builds.iiasa.ac.at/project/message (requires authorization)
+
+
+.. _export-test-data:
+
+Prepare data for testing
+========================
+
+Use the ``export-test-data`` CLI command::
+
+  mix-models --url="ixmp://ixmp-dev/ENGAGE_SSP2_v4.1.7/baseline" export-test-data
+
+See also the documentation for :func:`export_test_data`.
+Use the :command:`--exclude`, :command:`--nodes`, and :command:`--techs` options to control the content of the resulting file.
diff --git a/nest/doc/whatsnew.rst b/nest/doc/whatsnew.rst
new file mode 100644
index 0000000..ac14e2c
--- /dev/null
+++ b/nest/doc/whatsnew.rst
@@ -0,0 +1,166 @@
+What's new
+**********
+
+.. Next release
+.. ============
+
+2023.2.8
+========
+
+- Codelists for the ``relation`` :ref:`MESSAGEix set <message-ix:section_set_def>` (:pull:`85`):
+
+  - Add :ref:`three relation codelists <relation-yaml>`.
+  - The :doc:`“bare” reference energy system <api/model-bare>` now includes relations from the codelist indicated by :attr:`.model.Config.relations`; default "A".
+
+- :ref:`commodity-yaml` (:pull:`85`):
+
+  - Add "biomass", "non-comm", "rc_spec", and "rc_therm".
+  - Add "report" annotations for some items.
+    These include string fragments to be used in variable names when reporting data in the IAMC data structure.
+
+- :func:`.generate_product` (and :func:`.generate_set_elements`) can handle a :doc:`regular expression <python:library/re>` to select a subset of codes for the Cartesian product (:pull:`85`).
+- New utility method :meth:`.Context.write_debug_archive` writes a ZIP archive containing files listed by :attr:`.Config.debug_paths` (:pull:`85`).
+- :class:`.WorkflowStep` can store and apply keyword options for the optional :meth:`~.message_ix.Scenario.clone` step at the start of the step execution (:pull:`85`).
+- Bugfix: :meth:`.WorkflowStep.__call__` ensures that :attr:`.Config.scenario_info` on the :class:`.Context` instance passed to its callback matches the target scenario (:pull:`85`).
+
+2022.11.7
+=========
+
+- Add the :ref:`ZMB` node code list (:pull:`83`).
+- Add the utility :func:`same_time`, to copy the set time in paramenters (:pull:`83`).
+- New :class:`~message_ix_models.Config` and :class:`.model.Config` :py:mod:`dataclasses` for clearer description/handling of recognized settings stored on :class:`.Context` (:pull:`82`).
+  :class:`.ConfigHelper` for convenience/utility functionality in :mod:`message_ix_models`-based code.
+- New functions :func:`.generate_product`, :func:`.generate_set_elements`, :func:`.get_region_codes` in :mod:`.model.structure` (:pull:`82`).
+- Revise and improve the :doc:`Workflow API </api/workflow>` (:pull:`82`).
+- Adjust for pandas 1.5.0 (:pull:`81`).
+
+2022.8.17
+=========
+
+- Add :func:`.nodes_ex_world` and use this in :func:`.disutility.data_conversion` instead of expected a "World" node ID to be the first element in :attr:`.ScenarioInfo.N` (:pull:`78`).
+- Add example files and documentation for :doc:`pkg-data/iiasa-se` (:pull:`78`).
+- Expand :file:`~` (i.e. ``$HOME``) in the ``"message local data"`` :ref:`configuration setting <local-data>` (:pull:`78`).
+
+2022.7.25
+=========
+
+- Add :func:`get_advance_data`, and related tools for data from the ADVANCE project, including the :ref:`node codelist <ADVANCE-nodes>` for the data (:pull:`76`).
+- Add unit annotations to :ref:`commodity-yaml` (:pull:`76`).
+- New utility methods :meth:`.ScenarioInfo.io_units` to derive units for ``input`` and ``output`` parameters from :meth:`.units_for` commodity stocks and technology activities (:pull:`76`).
+- Transfer :func:`.add_tax_emission` from :mod:`message_data`, improve, and add tests (:pull:`76`).
+- Unit annotations on commodity and technology codes are copied to child codes using :func:`.process_units_anno` (:pull:`76`).
+- :func:`.make_matched_dfs` accepts :class:`pint.Quantity` to set both magnitude and units in generated data (:pull:`76`).
+- :func:`.strip_par_data` also removes the set element for which data is being stripped (:pull:`76`).
+- The common CLI options :program:`--verbose` and :program:`--dry-run` are stored on :class:`.Context` automatically (:pull:`76`).
+- New utility method :meth:`.Context.set_scenario` (:pull:`76`).
+- :data:`iam_units.registry` is the default unit registry even when :mod:`message_data` is not installed (:pull:`76`).
+- Expand :func:`.broadcast` to allow :class:`~.pandas.DataFrame` with multiple dimensions as input (:pull:`74`).
+
+2022.5.6
+========
+
+- Bump minimum required version of :mod:`message_ix` to v3.4.0 from v3.2.0 (:pull:`71`).
+- Add a documentation page on :doc:`distrib` (:pull:`59`).
+- Add :func:`.testing.not_ci` for marking tests not to be run on continuous integration services; improve :func:`~.testing.session_context` (:pull:`62`).
+- :func:`.apply_spec` also adds elements of the "node" set using :meth:`.ixmp.Platform.add_region` (:pull:`62`).
+- Add new logo the documentation (:pull:`68`).
+- Add :class:`.Workflow`; see :doc:`api/workflow` (:pull:`60`).
+
+2022.3.30
+=========
+
+- Add :func:`adapt_R11_R12`, a function for adapting data from the :ref:`R11` to the :ref:`R12` node lists (:pull:`56`).
+- Work around `iiasa/ixmp#425 <https://github.com/iiasa/ixmp/issues/425>`__ in :func:`.disutility.data_conversion` (:ref:`docs <disutility-units>`, :pull:`55`).
+
+2022.3.3
+========
+
+- Change the node name in R12.yaml from R12_CPA to R12_RCPA (:pull:`49`).
+- Register “message local data” ixmp configuration file setting and use to set the :attr:`.Context.local_path` when provided.
+  See :ref:`local-data` (:pull:`47`)
+
+2022.1.26
+=========
+
+- New :class:`.Spec` class for easier handling of specifications of model (or model variant) structure (:pull:`39`)
+- New utility function :func:`.util.local_data_path` (:pull:`39`).
+- :func:`.repr` of :class:`.Context` no longer prints a (potentially very long) list of all keys and settings (:pull:`39`).
+- :func:`.as_codes` accepts a :class:`.dict` with :class:`.Code` values (:pull:`39`).
+
+Earlier releases
+================
+
+2021.11.24
+----------
+
+- Add :command:`--years` and :command:`--nodes` to :func:`.common_params` (:pull:`35`).
+- New utility function :func:`.structure.codelists` (:pull:`35`).
+
+2021.7.27
+---------
+
+- Improve caching using  mod:`genno` v1.8.0 (:pull:`29`).
+
+2021.7.22
+---------
+
+- Migrate utilities :func:`cached`, :func:`.check_support`, :func:`.convert_units`, :func:`.maybe_query`, :func:`.series_of_pint_quantity` (:pull:`27`)
+- Add :data:`.testing.NIE`.
+- Add the ``--jvmargs`` option to :command:`pytest` (see :func:`.pytest_addoption`).
+- Remove :meth:`.Context.get_config_file`, :meth:`.get_path`, :meth:`.load_config`, and :meth:`.units`, all deprecated since 2021-02-28.
+
+2021.7.6
+--------
+
+- Add :func:`identify_nodes`, a function for identifying a :doc:`pkg-data/node` based on a :class:`.Scenario` (:pull:`24`).
+- Add :func:`adapt_R11_R14`, a function for adapting data from the :ref:`R11` to the :ref:`R14` node lists (:pull:`24`).
+- Add :func:`.export_test_data` and :command:`mix-models export-test-data` command (:pull:`16`).
+  See :ref:`export-test-data`.
+- Allow use of pytest's persistent cache across test sessions (:pull:`23`).
+  See :doc:`repro`.
+- Add the :ref:`R12` node code list (:pull:`14`).
+
+2021.4.7
+--------
+
+- Add :mod:`.model.disutility`, code for setting up structure and data for generalized consumer disutility (:pull:`13`)
+
+2021.3.24
+---------
+
+- Add :doc:`pkg-data/year`, YAML data files, :meth:`.ScenarioInfo.year_from_codes` and associated tests (:issue:`11`, :pull:`12`)
+
+2021.3.22
+---------
+
+- Migrate :mod:`.model.bare`, :mod:`.model.build`, :mod:`.model.cli`, and associated documentation (:pull:`9`)
+- Migrate utilities: :class:`.ScenarioInfo`, :func:`.add_par_data`, :func:`.eval_anno`, :func:`.iter_parameters`, and :func:`.strip_par_data`.
+
+2021.3.3
+--------
+
+- Migrate :mod:`.util.click`, :mod:`.util.logging`; expand documentation (:pull:`8`:).
+- :meth:`.Context.clone_to_dest` method replaces :func:`clone_to_dest` function.
+- Build PDF documentation on ReadTheDocs.
+- Allow CLI commands from both :mod:`message_ix_models` and :mod:`message_data` via :program:`mix-models`.
+- Migrate :program:`mix-models techs` CLI command.
+
+2021.2.28
+---------
+
+- Migrate :class:`.Context` class and :mod:`.testing` module from :mod:`message_data` (:pull:`5`:).
+- Add :func:`.load_private_data`, :func:`.package_data_path`, :func:`.private_data_path`.
+- Document: :doc:`data` and :doc:`cli`.
+- Update :doc:`node codelists <pkg-data/node>` to ensure they contain both current and former ISO 3166 codes for countries that have changed status (:pull:`6`:).
+  For instance, ANT dissolved into BES, CUW, and SXM in 2010; all four are included in R11_LAM so this list can be used to handle data from either before or after 2010.
+
+2021.2.26
+---------
+
+- Add :func:`.get_codes` and related code lists (:pull:`2`:).
+- Add :class:`.MessageDataFinder` and document :doc:`migrate` (:pull:`3`:).
+
+2021.2.23
+---------
+
+Initial release.
diff --git a/nest/message_ix_models/__init__.py b/nest/message_ix_models/__init__.py
new file mode 100644
index 0000000..1cf94ca
--- /dev/null
+++ b/nest/message_ix_models/__init__.py
@@ -0,0 +1,34 @@
+import sys
+
+import pint
+from iam_units import registry
+from pkg_resources import DistributionNotFound, get_distribution
+
+from message_ix_models.util._logging import setup as setup_logging
+from message_ix_models.util.config import Config
+from message_ix_models.util.context import Context
+from message_ix_models.util.importlib import MessageDataFinder
+from message_ix_models.util.scenarioinfo import ScenarioInfo, Spec
+from message_ix_models.workflow import Workflow
+
+# Expose utility classes
+__all__ = ["Config", "Context", "ScenarioInfo", "Spec", "Workflow"]
+
+try:
+    # Version string for reference in other code
+    __version__ = get_distribution(__name__).version
+except DistributionNotFound:  # pragma: no cover
+    # Package is not installed
+    __version__ = "999"
+
+# No logging to stdout (console) by default
+setup_logging(console=False)
+
+# Use iam_units.registry as the default pint.UnitsRegistry
+pint.set_application_registry(registry)
+
+# Ensure at least one Context instance is created
+Context()
+
+# Use this finder only if others fail
+sys.meta_path.append(MessageDataFinder())
diff --git a/nest/message_ix_models/cli.py b/nest/message_ix_models/cli.py
new file mode 100644
index 0000000..4df15ab
--- /dev/null
+++ b/nest/message_ix_models/cli.py
@@ -0,0 +1,138 @@
+"""Command-line interface for MESSAGEix-GLOBIOM model tools.
+
+Every tool and script in this repository is accessible through this CLI. Scripts are
+grouped into commands and sub-commands. For help on specific (sub)commands, use --help,
+e.g.:
+
+    \b
+    mix-models cd-links --help
+    mix-models cd-links run --help
+
+The top-level options --platform, --model, and --scenario are used by commands that
+access specific message_ix scenarios; these can also be specified with --url.
+
+For more information, see https://docs.messageix.org/projects/models2/en/latest/cli.html
+"""
+import logging
+import sys
+from pathlib import Path
+
+import click
+
+from message_ix_models.util._logging import mark_time
+from message_ix_models.util._logging import setup as setup_logging
+from message_ix_models.util.click import common_params
+from message_ix_models.util.context import Context
+
+log = logging.getLogger(__name__)
+
+
+# Main command group. The code in this function is ALWAYS executed, so it should only
+# include tasks that are common to all CLI commands.
+@click.group(help=__doc__)
+@click.option(
+    "--url", metavar="ixmp://PLATFORM/MODEL/SCENARIO[#VERSION]", help="Scenario URL."
+)
+@click.option("--platform", metavar="PLATFORM", help="Configured platform name.")
+@click.option(
+    "--model", "model_name", metavar="MODEL", help="Model name for some commands."
+)
+@click.option(
+    "--scenario",
+    "scenario_name",
+    metavar="SCENARIO",
+    help="Scenario name for some commands.",
+)
+@click.option("--version", type=int, help="Scenario version.")
+@click.option("--local-data", type=Path, help="Base path for local data.")
+@common_params("verbose")
+@click.pass_context
+def main(click_ctx, **kwargs):
+    # Start timer
+    mark_time(quiet=True)
+
+    # Log to console
+    setup_logging(level="DEBUG" if kwargs.pop("verbose") else "INFO", console=True)
+
+    # Store the most recently created instance of message_ix_models.Context. click
+    # carries this object to any subcommand decorated with @click.pass_obj.
+    # NB this can't be Context.only(). When click.testing.CliRunner is used, there may
+    #    already be ≥2 Context instances created elsewhere in the test session before
+    #    this function is called to run CLI commands within the test session.
+    click_ctx.obj = Context.get_instance(-1)
+
+    # Handle command-line parameters
+    click_ctx.obj.handle_cli_args(**kwargs)
+
+    # Close any database connections when the CLI exits
+    click_ctx.call_on_close(click_ctx.obj.close_db)
+
+
+@main.command("export-test-data")
+@click.option("--exclude", default="", help="Sheets to exclude.")
+@click.option("--nodes", default="R11_AFR,R11_CPA", help="Nodes to include.")
+@click.option("--techs", default="coal_ppl", help="Technologies to include.")
+@click.pass_obj
+def export_test_data_cmd(ctx, exclude, nodes, techs):
+    """Prepare data for testing.
+
+    Option values for --exclude, --nodes, and --techs must be comma-separated lists.
+    """
+    from message_ix_models.testing import export_test_data
+
+    # Store CLI options on the Context
+    ctx.export_exclude = list(filter(None, exclude.split(",")))  # Exclude empty string
+    ctx.export_nodes = nodes.split(",")
+    ctx.export_techs = techs.split(",")
+
+    mark_time()
+
+    # Export
+    export_test_data(ctx)
+
+    mark_time()
+
+
+@main.command(hidden=True)
+@click.pass_obj
+def debug(ctx):
+    """Hidden command for debugging."""
+    # Print the local data path
+    log.debug(ctx.local_data)
+
+
+#: List of submodules providing CLI (sub)commands accessible through `mix-models`.
+#: Each of these should contain a function named ``cli`` decorated with @click.command
+#: or @click.group.
+submodules = [
+    "message_ix_models.model.cli",
+    "message_ix_models.model.structure",
+    "message_ix_models.model.water.cli",
+]
+
+try:
+    import message_data.cli
+except ImportError:
+    # message_data is not installed or contains some ImportError of its own
+    from traceback import format_exception
+
+    # Display information for debugging
+    etype, value, tb = sys.exc_info()
+    print("", *format_exception(etype, value, tb, limit=-1, chain=False)[1:], sep="\n")
+else:  # pragma: no cover  (needs message_data)
+    # Also add message_data submodules
+    submodules.extend(
+        f"message_data.{name}" for name in message_data.cli.MODULES_WITH_CLI
+    )
+
+for name in submodules:
+    # Import the module and retrieve the click.Command object
+    __import__(name)
+    cmd = getattr(sys.modules[name], "cli")
+
+    # Avoid replacing message-ix-models CLI with message_data CLI
+    if cmd.name in main.commands:  # pragma: no cover  (needs message_data)
+        log.warning(f"Skip {repr(cmd.name)} CLI from {repr(name)}; already defined")
+        continue
+
+    main.add_command(cmd)
diff --git a/nest/message_ix_models/data/cd_links/unit.yaml b/nest/message_ix_models/data/cd_links/unit.yaml
new file mode 100644
index 0000000..d747a0f
--- /dev/null
+++ b/nest/message_ix_models/data/cd_links/unit.yaml
@@ -0,0 +1,106 @@
+# Units appearing in CD-LINKS scenarios
+#
+# This list should ONLY be used when trying to load such scenarios on to a
+# Platform without such units defined.
+
+- "-"
+- "???"
+- "%"
+- "billion US$2010/yr"
+- "cases"
+- "EJ_final"
+- "EJ_primary"
+- "EJ/yr"
+- "G$"
+- "GW"
+- "GW/a"
+- "GWa"
+- "GWa/a"
+- "Index (2005 = 1)"
+- "kcal/cap/day"
+- "kg"
+- "kg/kWa"
+- "km"
+- "km3"
+- "km3/yr"
+- "kt BC/yr"
+- "kt CF4/yr"
+- "kt CH4/yr"
+- "kt CO/yr"
+- "kt HFC125/yr"
+- "kt HFC134a-equiv/yr"
+- "kt HFC134a/yr"
+- "kt HFC143a/yr"
+- "kt HFC227ea/yr"
+- "kt HFC23/yr"
+- "kt HFC245fa/yr"
+- "kt HFC32/yr"
+- "kt HFC43-10/yr"
+- "kt N2O/yr"
+- "kt NH3/yr"
+- "kt NOx/yr"
+- "kt OC/yr"
+- "kt SF6/yr"
+- "kt SO2/yr"
+- "kt VOC/yr"
+- "Mha"
+- "million"
+- "million ha"
+- "million m3/yr"
+- "million t DM/yr"
+- "Mm3"
+- "Mt"
+- "Mt BC/yr"
+- "Mt C-eq/yr"
+- "Mt C/yr"
+- "Mt CH4/yr"
+- "Mt CO/yr"
+- "Mt CO2-equiv/yr"
+- "Mt CO2/yr"
+- "Mt CO2eq/yr"
+- "Mt NH3/yr"
+- "Mt NOx/yr"
+- "Mt OC/yr"
+- "Mt SO2/yr"
+- "Mt VOC/yr"
+- "MtCO2"
+- "MtCO2eq/year"
+- "MUSD/a"
+- "MW"
+- "MWa"
+- "price_MESSAGE"
+- "t"
+- "t DM/ha/yr"
+- "T$"
+- "tC"
+- "tCO2"
+- "tCO2/ha"
+- "tCO2/kWa"
+- "Tg N/yr"
+- "Tg P/yr"
+- "TUSD"
+- "US$2005/GJ"
+- "US$2005/t CO2"
+- "US$2010/GJ or local currency/GJ"
+- "US$2010/kW"
+- "US$2010/kW/yr"
+- "US$2010/kWh"
+- "US$2010/t CO2 or local currency/t CO2"
+- "USD"
+- "USD/(tC/yr)"
+- "USD/(tC/yr)/yr"
+- "USD/GWa"
+- "USD/kg"
+- "USD/km"
+- "USD/kW"
+- "USD/kW/a"
+- "USD/kWa"
+- "USD/tC"
+- "USD/tCE/yr"
+- "USD/tCO2"
+- "USD2000_GJprim"
+- "USD2000_tCO2eq"
+- "y"
+- "year"
+- "years"
+- "ZJ"
diff --git a/nest/message_ix_models/data/commodity.yaml b/nest/message_ix_models/data/commodity.yaml
new file mode 100644
index 0000000..e02911e
--- /dev/null
+++ b/nest/message_ix_models/data/commodity.yaml
@@ -0,0 +1,311 @@
+biomass:
+  units: GWa
+  report: Solids|Biomass
+
+coal:
+  name: Coal
+  units: GWa
+  report: Solids|Fossil
+
+crudeoil:
+  name: Crude oil
+  description: >-
+    For secondary energy, use 'fueloil', 'lightoil', etc.
+  level: primary
+  units: GWa
+  report: Oil
+
+d_heat:
+  name: (?) District heat
+  description: >-
+    FIXME provide an unambiguous description of what this commodity represents.
+  report: Heat
+
+electr:
+  name: Electricity
+  units: GWa
+  report: Electricity
+
+ethanol:
+  name: Ethanol
+  units: GWa
+  report: Liquids|Biomass
+
+freshwater_supply:
+  name: (?) Fresh water
+  description: >-
+    FIXME provide an unambiguous description of what this commodity represents.
+
+fueloil:
+  name: Fuel oil
+  description: Heavy fuel oil.
+  level: secondary
+  units: GWa
+
+gas:
+  name: Natural Gas
+  units: GWa
+  report: Gases
+
+hydrogen:
+  name: Gaseous hydrogen
+  units: GWa
+
+lh2:
+  name: Liquid hydrogen
+  units: GWa
+
+lightoil:
+  name: Light oil
+  description: Includes gasoline, diesel oil.
+  # level: secondary
+  units: GWa
+  report: Liquids|Oil
+
+methanol:
+  name: Methanol
+  units: GWa
+  report: Liquids|Coal
+
+non-comm:
+  name: Non-commercial biomass
+  units: GWa
+  report: Solids|Biomass|Traditional
+
+rc_spec:
+  name: Residential and commercial non-substitutable fuels
+
+rc_therm:
+  name: Residential and commercial thermal
+
+transport:
+  name: Transportation
+  description: >-
+    For MESSAGEix-Transport, this commodity is not used; it is replaced by a
+    disaggregated set of transport service demands (representing e.g. light-
+    duty vehicles, civil aviation, freight transport, etc.)
+  level: useful
+  units: GWa
+
+# The following codes also appear in a recent (2020-02-28) SSP2 scenario, but
+# are not currently used by model.bare.create_res.
+#
+# Aff_CO2_G4M
+# Agri_CH4
+# Agri_N2O
+# Agri_N2O_calc
+# Agricultural Demand
+# Agricultural Demand|Bioenergy
+# Agricultural Demand|Bioenergy|1st generation
+# Agricultural Demand|Bioenergy|2nd generation
+# Agricultural Demand|Feed
+# Agricultural Demand|Feed|Crops
+# Agricultural Demand|Food
+# Agricultural Demand|Food|Crops
+# Agricultural Demand|Food|Livestock
+# Agricultural Demand|Non-Food
+# Agricultural Demand|Non-Food|Crops
+# Agricultural Demand|Non-Food|Livestock
+# Agricultural Production
+# Agricultural Production|Energy Crops
+# Agricultural Production|Livestock
+# Agricultural Production|Non-Energy Crops
+# Agricultural Production|Non-Energy Crops|Cereals
+# BCA_LandUseChangeEM
+# BCA_SavanBurnEM
+# Biodiesel_G1
+# bioenergy
+# Bioethanol_G1
+# CalAnim
+# CalCrop
+# CalTot
+# CH4_LandUseChangeEM
+# CH4_SavanBurnEM
+# CO_LandUseChangeEM
+# CO_SavanBurnEM
+# CO2_oil
+# CO2_rem
+# cooling__bio_hpl
+# cooling__bio_istig
+# cooling__bio_istig_ccs
+# cooling__bio_ppl
+# cooling__coal_adv
+# cooling__coal_adv_ccs
+# cooling__coal_ppl
+# cooling__coal_ppl_u
+# cooling__foil_hpl
+# cooling__foil_ppl
+# cooling__gas_cc
+# cooling__gas_cc_ccs
+# cooling__gas_hpl
+# cooling__gas_ppl
+# cooling__geo_hpl
+# cooling__geo_ppl
+# cooling__igcc
+# cooling__igcc_ccs
+# cooling__loil_cc
+# cooling__loil_ppl
+# cooling__nuc_hc
+# cooling__nuc_lc
+# cooling__solar_th_ppl
+# CrpLnd
+# crude_1
+# crude_2
+# crude_3
+# crude_4
+# crude_5
+# crude_6
+# crude_7
+# crude_8
+# Def_CO2_G4M
+# Def_CO2_GLO
+# dumagr
+# dumfert
+# Emissions|CH4|Land Use
+# Emissions|CH4|Land Use|Agricultural Waste Burning
+# Emissions|CH4|Land Use|Agriculture
+# Emissions|CH4|Land Use|Agriculture|AWM
+# Emissions|CH4|Land Use|Agriculture|Enteric Fermentation
+# Emissions|CH4|Land Use|Agriculture|Rice
+# Emissions|CH4|Land Use|Savannah Burning
+# Emissions|CO2|Land Use
+# Emissions|CO2|Land Use|Negative
+# Emissions|CO2|Land Use|Positive
+# Emissions|N2O|Land Use
+# Emissions|N2O|Land Use|Agricultural Waste Burning
+# Emissions|N2O|Land Use|Agriculture
+# Emissions|N2O|Land Use|Agriculture|AWM
+# Emissions|N2O|Land Use|Agriculture|Cropland Soils
+# Emissions|N2O|Land Use|Agriculture|Pasture
+# Emissions|N2O|Land Use|Savannah Burning
+# EnergyRoundwood
+# exports
+# Fertilizer Use|Nitrogen
+# Fertilizer Use|Phosphorus
+# Fmg_CO2_G4M
+# Food Demand
+# Food Demand|Crops
+# Food Demand|Livestock
+# Food Energy Demand
+# Food Energy Demand|Livestock
+# ForestBiomass
+# ForestBiomass_G4M
+# ForestHarvestDF_G4M
+# ForestHarvestFM_G4M
+# ForestHarvestTot_G4M
+# ForestHarvestTot_GLO
+# Forestry Demand|Roundwood
+# Forestry Demand|Roundwood|Industrial Roundwood
+# Forestry Demand|Roundwood|Wood Fuel
+# Forestry Production|Forest Residues
+# Forestry Production|Roundwood
+# Forestry Production|Roundwood|Industrial Roundwood
+# Forestry Production|Roundwood|Wood Fuel
+# freshwater_instream
+# FuelWood
+# FuelWood_G4M
+# gas
+# gas_1
+# gas_2
+# gas_3
+# gas_4
+# gas_5
+# gas_6
+# gas_7
+# gas_8
+# gas_afr
+# gas_cpa
+# gas_eeu
+# gas_nam
+# gas_pao
+# gas_pas
+# gas_sas
+# gas_weu
+# GrsLnd
+# i_feed
+# i_spec
+# i_therm
+# IrriWithdrawal
+# Land Cover
+# Land Cover|Cropland
+# Land Cover|Cropland|Cereals
+# Land Cover|Cropland|Energy Crops
+# Land Cover|Cropland|Irrigated
+# Land Cover|Forest
+# Land Cover|Forest|Afforestation and Reforestation
+# Land Cover|Forest|Forestry
+# Land Cover|Forest|Managed
+# Land Cover|Forest|Natural Forest
+# Land Cover|Other Natural Land
+# Land Cover|Pasture
+# lh2
+# lignite
+# LiquidTotal
+# LNG
+# LoggingResidues
+# LU_CO2
+# LU_GHG
+# LucGrs_CO2
+# LucOth_CO2
+# NewFor_G4M
+# NH3_LandUseChangeEM
+# NH3_ManureEM
+# NH3_RiceEM
+# NH3_SavanBurnEM
+# NH3_SoilEM
+# NOx_LandUseChangeEM
+# NOx_SavanBurnEM
+# NOx_SoilEM
+# nucfuel
+# OCA_LandUseChangeEM
+# OCA_SavanBurnEM
+# oil_st
+# Olc_CO2_GLO
+# Olc_CO2_GLO_neg
+# Olc_CO2_GLO_pos
+# OldFor_G4M
+# OtherLnd
+# OthSolidNonComm
+# PlantationBiomass
+# PlantationHarvest_GLO
+# PltArt
+# PltFor
+# PltFor_gr
+# Price_BIO
+# Price_CO2
+# Price|Agriculture|Non-Energy Crops and Livestock|Index
+# Price|Agriculture|Non-Energy Crops|Index
+# Price|Primary Energy|Biomass
+# pu
+# puq
+# puq2
+# saline_supply
+# SawmillResidues
+# SawmillResidues_G4M
+# shipping
+# SO2_LandUseChangeEM
+# SO2_SavanBurnEM
+# SolidExogenous
+# SolidExogenous_G4M
+# SolidTotal
+# SolidTotal_G4M
+# TCE
+# TimberIndust
+# TimberIndust_G4M
+# Tot_CO2_G4M
+# total_cost
+# TotalLnd
+# TotFor_G4M
+# u5
+# u5q
+# u5t
+# upstream_landuse
+# uq
+# uranium
+# VOC_LandUseChangeEM
+# VOC_SavanBurnEM
+# water_constraint
+# Water|Withdrawal|Irrigation
+# Yield|Cereal
+# Yield|Oilcrops
+# Yield|Sugarcrops
diff --git a/nest/message_ix_models/data/edits/messageix-transport-core.yaml b/nest/message_ix_models/data/edits/messageix-transport-core.yaml
new file mode 100644
index 0000000..7b5951a
--- /dev/null
+++ b/nest/message_ix_models/data/edits/messageix-transport-core.yaml
@@ -0,0 +1,70 @@
+format: 1
+
+title: MESSAGEix-Transport model core
+description: |-
+  These are the data represented in the model core of MESSAGEix-Transport. This
+  model uses the same, generic MESSAGE mathematical formulation as the
+  MESSAGEix-GLOBIOM global model (without transport detail), and so this data
+  description also applies to other instances ("scenarios") of that model.
+
+  All of the core data are available directly as output, as well as aggregated
+  and derived quantities.
+
+  [Choose one: “For derived output quantities, see the separate `....yaml`.”
+  OR “In this file, derived output quantities are denoted with [...].”]
+
+provider:
+  organisation: International Institute for Applied Systems Analysis (IIASA)
+  contact: kishimot@iiasa.ac.at
+
+classifiers:
+- "Kind :: Model core"
+
+dimension:
+  time:
+    scope: 2015 to 2110
+    resolution: 5- or 10-year periods
+    description: Periods are labelled with their final year.
+  space:
+    scope: global
+    resolution: R11 regions
+    description: |-
+      See https://docs.messageix.org/projects/models2/en/latest/pkg-data/node.html
+      or https://github.com/iiasa/message-ix-models/blob/main/message_ix_models/data/node/R11.yaml
+  # TODO add mode, technology: same as in messageix-transport-input.yaml
+
+measure:
+  stock:
+    description: Quantity of transport vehicles.
+
+quantity:
+- measure: activity
+  kind: core
+  dimensions: [time, space, mode, technology]
+
+- measure: freight activity
+  kind: core
+  dimensions: [time, space, mode, technology]
+  description: >-
+    For freight truck `technology` only.
+
+- measure: passenger activity
+  kind: core
+  dimensions: [time, space, mode, technology]
+  description: >-
+    For non-LDV `mode` only. For these modes & technologies, there is no
+    distinction by `consumer_group`.
+
+- measure: passenger activity
+  kind: core
+  dimensions: [time, space, technology, consumer_group]
+  description: >-
+    For LDV `technology` only. The model core optimizes the activity of each
+    `consumer_group` by each `LDV` technology.
+
+- measure: stock
+  kind: derived
+  dimensions: [time, space, mode, technology]
+  units: 10^3 vehicles
+  description: >-
+    Excluding aviation `mode` and associated technologies.
diff --git a/nest/message_ix_models/data/edits/messageix-transport-input.yaml b/nest/message_ix_models/data/edits/messageix-transport-input.yaml
new file mode 100644
index 0000000..0dbad13
--- /dev/null
+++ b/nest/message_ix_models/data/edits/messageix-transport-input.yaml
@@ -0,0 +1,128 @@
+format: 1
+
+title: MESSAGEix-Transport inputs
+description: |-
+  These data are ingested by the setup/calibration code for MESSAGEix-Transport.
+  This code takes an instance ("scenario") of the MESSAGEix-GLOBIOM global model
+  and alters it to add the detailed transport structure.
+
+  See the description `messageix-transport-core.yaml` for the data in the core
+  model formulation. Data that exactly matches the core formulation can also be
+  used as input; this file describes non-core data that can be transformed/used
+  with existing code & methods.
+
+  This setup code was originally developed for use with data from the US-TIMES
+  and MA³T models.
+
+provider:
+  organisation: International Institute for Applied Systems Analysis (IIASA)
+  contact: kishimot@iiasa.ac.at
+
+classifiers:
+- "Kind :: Model input"
+
+dimension:
+  time:
+    scope: historical (1990-2020) to 2100
+    resolution: 5- or 10-year periods, or annual
+    description: Data at <5 year resolution can be aggregated.
+  space:
+    scope: global
+    resolution: country or R11 regions
+    description: Data at the country resolution can be aggregated.
+  census_division:
+    scope: Continental United States
+    resolution: 9 census regions, or total
+    description: >-
+      Census divisons of the United States. This is used to map data from
+      US-TIMES and MA³T.
+  area_type:
+    scope: all areas within a given spatial unit
+    resolution: >-
+      3-point scale: urban, suburban, or rural.
+    description: Urbanization or urban form of a sub-national area.
+  attitude:
+    scope: all people in a given (sub-)population
+    resolution: >-
+      3-point scale: early adopter, early majority, late majority
+    description: >-
+      New technology adoption attitude or propensity, in particular towards new
+      transport technologies, modes, options, etc.
+  driver_type:
+    scope: all vehicle drivers in a given (sub-)population
+    resolution: >-
+      3-point scale: moderate, average, frequent
+    description: >-
+      The amount of annual driving done by a person, relative to others in the
+      population.
+  consumer_group:
+    scope: all people in a given population
+    resolution: 27-point scale; see description
+    description: |-
+      Group of people within a population that have similar transport demand/
+      activity, behaviour, etc. characteristics. In MESSAGEix-Transport, this is
+      operationalized as the Cartesian product of the `area_type`, `attitude`,
+      and `driver_type` concepts/dimensions.
+
+      E.g. the label "RUEMF" means the subset of people who live in rural
+      areas ("RU"), have early majority ("EM") technology attitudes, and are
+      frequent drivers ("F").
+  mode:
+    scope: the points listed below; excluding e.g. pipelines
+    resolution: >-
+      5-point scale: air, bus, rail, LDV, 2-3 wheelers
+    description: |-
+      Mode or medium of passenger transport. In the MESSAGEix-Transport setup
+      code, this is operationalized in a way that includes both different
+      vehicle types (e.g. bus, LDV, 2W+3W) and the `mode` concept in the iTEM
+      sense (e.g. "Road" for all 3 of these).
+  technology:
+    scope: All transport technologies
+    description: The powertrain technology of a transport vehicle.
+    resolution: |-
+      43 distinct labels. Some of these are only valid in combination with
+      certain other values for the `mode` dimension. In particular, the number
+      of labels is:
+
+      - Rail: 6
+      - 2-3 wheelers: 2
+      - LDV: 12
+      - Aviation: 4
+      - Bus: 10
+
+      As well, for freight road (not included in the `mode` dimension, above),
+      there are 9 `technology` labels.
+
+measure:
+  population:
+    description: Count of people in a given area or category.
+  disutility cost:
+    description: >-
+      Monetary equivalent to the intangible/non-monetary disutility
+      (‘inconvenience’, etc.) that a person has towards using a specific
+      technology; separate from the real, monetary costs of obtaining or using
+      that technology.
+  travel speed:
+    description: Average travel speed by a particular means of transport.
+
+quantity:
+- measure: population
+  dimensions: [time, space, consumer_group]
+  units: persons
+  description: >-
+    With these dimensions, this data captures the "shares" or division of the
+    total population across the consumer groups.
+
+- measure: disutility cost
+  dimensions: [time, space, technology, consumer_group]
+  units: USD_2015
+  description: >-
+    These data only cover the 12 `technology` labels associated with the LDV
+    `mode`.
+
+- measure: travel speed
+  dimensions: [time, space, mode]
+  units: km/h
+  description: >-
+    The code uses the assumption that travel speeds are the same for every
+    `technology` with the same `mode`.
diff --git a/nest/message_ix_models/data/iiasa-se/def-regions.yaml b/nest/message_ix_models/data/iiasa-se/def-regions.yaml
new file mode 100644
index 0000000..df13608
--- /dev/null
+++ b/nest/message_ix_models/data/iiasa-se/def-regions.yaml
@@ -0,0 +1,13 @@
+- MESSAGEix-GLOBIOM 1.1-R12:
+  - MESSAGEix-GLOBIOM 1.1-R12|Sub-saharan Africa
+  - MESSAGEix-GLOBIOM 1.1-R12|China
+  - MESSAGEix-GLOBIOM 1.1-R12|Rest Centrally Planned Asia
+  - MESSAGEix-GLOBIOM 1.1-R12|Eastern Europe
+  - MESSAGEix-GLOBIOM 1.1-R12|Former Soviet Union
+  - MESSAGEix-GLOBIOM 1.1-R12|Latin America and the Caribbean
+  - MESSAGEix-GLOBIOM 1.1-R12|Middle East and North Africa
+  - MESSAGEix-GLOBIOM 1.1-R12|North America
+  - MESSAGEix-GLOBIOM 1.1-R12|Pacific OECD
+  - MESSAGEix-GLOBIOM 1.1-R12|Other Pacific Asia
+  - MESSAGEix-GLOBIOM 1.1-R12|South Asia
+  - MESSAGEix-GLOBIOM 1.1-R12|Western Europe
diff --git a/nest/message_ix_models/data/iiasa-se/mappings-R12.yaml b/nest/message_ix_models/data/iiasa-se/mappings-R12.yaml
new file mode 100644
index 0000000..0131a9e
--- /dev/null
+++ b/nest/message_ix_models/data/iiasa-se/mappings-R12.yaml
@@ -0,0 +1,67 @@
+# Model 'registration' file for use with `nomenclature` package/IIASA Scenario Explorer
+# workflows
+#
+# TODO generate programmatically from node codelists
+
+model:
+- MESSAGEix-GLOBIOM 1.1-BM-R12
+- MESSAGEix-GLOBIOM 1.1-BMT-R12
+- MESSAGEix-GLOBIOM 1.1-M-R12
+- MESSAGEix-GLOBIOM 1.1-MT-R12
+
+native_regions:
+- R12_AFR: MESSAGEix-GLOBIOM 1.1-R12|Sub-saharan Africa
+- R12_CHN: MESSAGEix-GLOBIOM 1.1-R12|China
+- R12_EEU: MESSAGEix-GLOBIOM 1.1-R12|Eastern Europe
+- R12_FSU: MESSAGEix-GLOBIOM 1.1-R12|Former Soviet Union
+- R12_LAM: MESSAGEix-GLOBIOM 1.1-R12|Latin America and the Caribbean
+- R12_MEA: MESSAGEix-GLOBIOM 1.1-R12|Middle East and North Africa
+- R12_NAM: MESSAGEix-GLOBIOM 1.1-R12|North America
+- R12_PAO: MESSAGEix-GLOBIOM 1.1-R12|Pacific OECD
+- R12_PAS: MESSAGEix-GLOBIOM 1.1-R12|Other Pacific Asia
+- R12_RCPA: MESSAGEix-GLOBIOM 1.1-R12|Rest Centrally Planned Asia
+- R12_SAS: MESSAGEix-GLOBIOM 1.1-R12|South Asia
+- R12_WEU: MESSAGEix-GLOBIOM 1.1-R12|Western Europe
+
+common_regions:
+- World:
+  - R12_AFR
+  - R12_CHN
+  - R12_EEU
+  - R12_FSU
+  - R12_LAM
+  - R12_MEA
+  - R12_NAM
+  - R12_PAO
+  - R12_PAS
+  - R12_RCPA
+  - R12_SAS
+  - R12_WEU
+# R5 regions
+- R5ASIA:
+  - R12_CHN
+  - R12_PAS
+  - R12_RCPA
+  - R12_SAS
+- R5LAM:
+  - R12_LAM
+- R5MAF:
+  - R12_AFR
+  - R12_MEA
+- R5OECD90+EU:
+  - R12_EEU
+  - R12_NAM
+  - R12_PAO
+  - R12_WEU
+- R5REF:
+  - R12_FSU
+# Individual countries
+- CHN:
+  - R12_CHN
+- EU:
+  - R12_EEU
+  - R12_WEU
+- IND:
+  - R12_SAS
+- USA:
+  - R12_NAM
diff --git a/nest/message_ix_models/data/level.yaml b/nest/message_ix_models/data/level.yaml
new file mode 100644
index 0000000..4257921
--- /dev/null
+++ b/nest/message_ix_models/data/level.yaml
@@ -0,0 +1,41 @@
+primary:
+  name: Primary Energy
+  description: >-
+    A form found in nature that has not been subjected to any human engineered
+    conversion process.
+
+secondary:
+  name: Secondary Energy
+  description: Forms which have been transformed from primary energy.
+
+final:
+  name: Final Energy
+  description: >-
+    Represents end-use demands of the end-use sectors (e.g. industry, transport,
+    residential, commercial and agriculture).
+
+import:
+  name: Imports
+
+useful:
+  name: Useful Energy
+  description: >-
+    Represents energy-service demands (or activity levels) of the end-use
+    sectors in non-energy units.
+
+water_supply:
+  name: Water Supply
+  description: >-
+    FIXME provide an unambiguous description of what this level represents.
+
+# The following codes also appear in a recent (2020-02-28) SSP2 scenario, but
+# are not currently used by model.bare.create_res.
+#
+# cooling
+# export
+# land_use_reporting
+# land_use
+# piped-gas
+# resource
+# stocks
+# water_supply_constraint
diff --git a/nest/message_ix_models/data/node/ADVANCE.yaml b/nest/message_ix_models/data/node/ADVANCE.yaml
new file mode 100644
index 0000000..d11440c
--- /dev/null
+++ b/nest/message_ix_models/data/node/ADVANCE.yaml
@@ -0,0 +1,35 @@
+# Node code list for the ADVANCE project
+#
+# Source: https://db1.ene.iiasa.ac.at/ADVANCEDB/dsd?Action=htmlpage&page=10
+# Transcribed 2022-07-22 by P.N. Kishimoto
+#
+
+World:
+  # Countries represented individually
+  child: [BRA, CHN, IND, JPN, RUS, USA]
+
+EU:
+  description: >-
+    European Union (28 member states from the accession of HRV in 2013 to the
+    withdrawal of GBR in 2020).
+  child: [AUT, BEL, BGR, CYP, CZE, DEU, DNK, ESP, EST, FIN, FRA, GBR, GRC, HRV, HUN, IRL, ITA, LTU, LUX, LVA, MLT, NLD, POL, PRT, ROU, SVK, SVN, SWE]
+
+OECD90+EU:
+  description: Includes the OECD 1990 countries as well as EU members and candidates.
+  child: [ALB, AUS, AUT, BEL, BGR, BIH, CAN, CHE, CYP, CZE, DEU, DNK, ESP, EST, FIN, FJI, FRA, GBR, GRC, GUM, HRV, HUN, IRL, ISL, ITA, JPN, LTU, LUX, LVA, MKD, MLT, MNE, NCL, NLD, NOR, NZL, POL, PRT, PYF, ROU, SLB, SRB, SVK, SVN, SWE, TUR, USA, VUT, WSM]
+
+REF:
+  description: Countries from the Reforming Ecomonies of the Former Soviet Union.
+  child: [ARM, AZE, BLR, GEO, KAZ, KGZ, MDA, RUS, TJK, TKM, UKR, UZB]
+
+ASIA:
+  description: The region includes most Asian countries with the exception of the Middle East, Japan and Former Soviet Union states.
+  child: [AFG, BGD, BRN, BTN, CHN, HKG, IDN, IND, KHM, KOR, LAO, LKA, MAC, MDV, MMR, MNG, MYS, NPL, PAK, PHL, PNG, PRK, SGP, THA, TMP, TWN, VNM]
+
+MAF:
+  description: This region includes the countries of the Middle East and Africa.
+  child: [DZA, AGO, ARE, BDA, BEN, BFA, BHR, BWA, CAF, CIV, CMR, COD, COG, COM, CPV, DJI, EGY, ERI, ESH, ETH, GAB, GHA, GIN, GMB, GNB, GNQ, IRN, IRQ, ISR, JOR, KEN, KWT, LBN, LBR, LBY, LSO, MAR, MDG, MLI, MOZ, MRT, MUS, MWI, NAM, NER, NGA, OMN, QAT, REU, RQA, SAU, SDN, SEN, SLE, SOM, SWZ, SYR, TCD, TGO, TUN, TZA, UGA, YEM, ZAF, ZMB, ZWE]
+
+LAM:
+  description: This region includes the countries of Latin America and the Caribbean.
+  child: [ARG, ANT, BHS, BLZ, BOL, BRA, BRB, CHL, COL, CRA, CUB, DOM, EDU, GLP, GTM, GUY, HND, HTI, JAM, MEX, MTQ, NIC, PAN, PER, PRI, PRY, SLV, SUR, TTO, URY, VEN]
diff --git a/nest/message_ix_models/data/node/ISR.yaml b/nest/message_ix_models/data/node/ISR.yaml
new file mode 100644
index 0000000..8fc7f42
--- /dev/null
+++ b/nest/message_ix_models/data/node/ISR.yaml
@@ -0,0 +1,10 @@
+# Codes for the "node" dimension of the MESSAGE-IL model
+
+World:
+  name: World
+  description: MESSAGE-IL regions
+
+Israel:
+  name: Israel
+  parent: World
+  child: [ISR]
diff --git a/nest/message_ix_models/data/node/R11.yaml b/nest/message_ix_models/data/node/R11.yaml
new file mode 100644
index 0000000..924bb55
--- /dev/null
+++ b/nest/message_ix_models/data/node/R11.yaml
@@ -0,0 +1,77 @@
+# Region code list
+#
+# - See message_data.tools.regions.
+# - The ISO 3166-1 alpha-3 codes are not defined in this file, but loaded from
+#   a copy of the ISO database, e.g. in pycountry.
+# - Among others, there are no assignments for:
+#   - ATA Antarctica
+#   - IOT British Indian Ocean Territory
+#   - SGS South Georgia
+
+World:
+  name: World
+  description: R11 regions
+
+R11_AFR:
+  parent: World
+  name: Sub-Saharan Africa
+  child: [AGO, BDI, BEN, BFA, BWA, CAF, CIV, CMR, COD, COG, COM, CPV, DJI, ERI, ETH, GAB, GHA, GIN, GMB, GNB, GNQ, KEN, LBR, LSO, MDG, MLI, MOZ, MRT, MUS, MWI, MYT, NAM, NER, NGA, REU, RWA, SEN, SHN, SLE, SOM, STP, SWZ, SYC, TCD, TGO, TZA, UGA, ZAF, ZMB, ZWE]
+
+R11_CPA:
+  parent: World
+  name: Centrally Planned Asia
+  child: [CHN, HKG, KHM, LAO, MNG, PRK, VNM]
+
+R11_EEU:
+  parent: World
+  name: Central and Eastern Europe
+  description: >-
+    Serbia and Montenegro (SCG) and Yugoslavia (YUG) still included in this list,
+    even though their ISO 3166-1 codes were deleted in 2006 and 2003, respectively.
+  child: [ALB, BGR, BIH, CZE, EST, HRV, HUN, LTU, LVA, MKD, MNE, POL, ROU, SCG, SRB, SVK, SVN, YUG]
+
+R11_FSU:
+  parent: World
+  name: Former Soviet Union
+  child: [ARM, AZE, BLR, GEO, KAZ, KGZ, MDA, RUS, TJK, TKM, UKR, UZB]
+
+R11_LAM:
+  parent: World
+  name: Latin America and The Caribbean
+  description: >-
+    The source includes “Netherlands Antilles” which has a provisional ISO 3166-2 alpha-3 code (ANT),
+    but is not a country. It was dissolved in 2010 into BES, CUW and SXM, also included.
+  child: [ABW, AIA, ANT, ARG, ATG, BES, BHS, BLZ, BMU, BOL, BRA, BRB, CHL, COL, CRI, CUB, CUW, CYM, DMA, DOM, ECU, FLK, GLP, GRD, GTM, GUF, GUY, HND, HTI, JAM, KNA, LCA, MEX, MSR, MTQ, NIC, PAN, PER, PRY, SLV, SUR, SXM, TCA, TTO, URY, VCT, VEN, VGB]
+
+R11_MEA:
+  parent: World
+  name: Middle East and North Africa
+  child: [ARE, BHR, DZA, EGY, ESH, IRN, IRQ, ISR, JOR, KWT, LBN, LBY, MAR, OMN, PSE, QAT, SAU, SDN, SSD, SYR, TUN, YEM]
+
+R11_NAM:
+  parent: World
+  name: North America
+  child: [CAN, GUM, PRI, SPM, USA, VIR]
+
+R11_PAO:
+  parent: World
+  name: Pacific OECD
+  child: [AUS, JPN, NZL]
+
+R11_PAS:
+  parent: World
+  name: Other Pacific Asia
+  description: >-
+    Trust Territory of the Pacific Islands (PCI) still included in this list,
+    but it was dissolved into MHL, FSM, MNP and PLW in 1986.
+  child: [ASM, BRN, CCK, COK, CXR, FJI, FSM, IDN, KIR, KOR, MAC, MHL, MMR, MNP, MYS, NCL, NFK, NIU, NRU, PCI, PCN, PHL, PLW, PNG, PYF, SGP, SLB, THA, TKL, TLS, TON, TUV, TWN, VUT, WLF, WSM]
+
+R11_SAS:
+  parent: World
+  name: South Asia
+  child: [AFG, BGD, BTN, IND, LKA, MDV, NPL, PAK]
+
+R11_WEU:
+  parent: World
+  name: Western Europe
+  child: [AND, AUT, BEL, CHE, CYP, DEU, DNK, ESP, FIN, FRA, FRO, GBR, GIB, GRC, GRL, IMN, IRL, ISL, ITA, LIE, LUX, MCO, MLT, NLD, NOR, PRT, SJM, SMR, SWE, TUR, VAT]
diff --git a/nest/message_ix_models/data/node/R12.yaml b/nest/message_ix_models/data/node/R12.yaml
new file mode 100644
index 0000000..09df520
--- /dev/null
+++ b/nest/message_ix_models/data/node/R12.yaml
@@ -0,0 +1,82 @@
+# Region code list
+#
+# - See message_data.tools.regions.
+# - The ISO 3166-1 alpha-3 codes are not defined in this file, but loaded from
+#   a copy of the ISO database, e.g. in pycountry.
+# - Among others, there are no assignments for:
+#   - ATA Antarctica
+#   - IOT British Indian Ocean Territory
+#   - SGS South Georgia
+
+World:
+  name: World
+  description: R12 regions
+
+R12_AFR:
+  parent: World
+  name: Sub-Saharan Africa
+  child: [AGO, BDI, BEN, BFA, BWA, CAF, CIV, CMR, COD, COG, COM, CPV, DJI, ERI, ETH, GAB, GHA, GIN, GMB, GNB, GNQ, KEN, LBR, LSO, MDG, MLI, MOZ, MRT, MUS, MWI, MYT, NAM, NER, NGA, REU, RWA, SEN, SHN, SLE, SOM, STP, SWZ, SYC, TCD, TGO, TZA, UGA, ZAF, ZMB, ZWE]
+
+R12_RCPA:
+  parent: World
+  name: Rest Centrally Planned Asia
+  child: [KHM, LAO, MNG, PRK, VNM]
+  
+R12_CHN:
+  parent: World
+  name: China
+  child: [CHN, HKG]  
+  
+R12_EEU:
+  parent: World
+  name: Central and Eastern Europe
+  description: >-
+    Serbia and Montenegro (SCG) and Yugoslavia (YUG) still included in this list,
+    even though their ISO 3166-1 codes were deleted in 2006 and 2003, respectively.
+  child: [ALB, BGR, BIH, CZE, EST, HRV, HUN, LTU, LVA, MKD, MNE, POL, ROU, SCG, SRB, SVK, SVN, YUG]
+
+R12_FSU:
+  parent: World
+  name: Former Soviet Union
+  child: [ARM, AZE, BLR, GEO, KAZ, KGZ, MDA, RUS, TJK, TKM, UKR, UZB]
+
+R12_LAM:
+  parent: World
+  name: Latin America and The Caribbean
+  description: >-
+    The source includes “Netherlands Antilles” which has a provisional ISO 3166-2 alpha-3 code (ANT),
+    but is not a country. It was dissolved in 2010 into BES, CUW and SXM, also included.
+  child: [ABW, AIA, ANT, ARG, ATG, BES, BHS, BLZ, BMU, BOL, BRA, BRB, CHL, COL, CRI, CUB, CUW, CYM, DMA, DOM, ECU, FLK, GLP, GRD, GTM, GUF, GUY, HND, HTI, JAM, KNA, LCA, MEX, MSR, MTQ, NIC, PAN, PER, PRY, SLV, SUR, SXM, TCA, TTO, URY, VCT, VEN, VGB]
+
+R12_MEA:
+  parent: World
+  name: Middle East and North Africa
+  child: [ARE, BHR, DZA, EGY, ESH, IRN, IRQ, ISR, JOR, KWT, LBN, LBY, MAR, OMN, PSE, QAT, SAU, SDN, SSD, SYR, TUN, YEM]
+
+R12_NAM:
+  parent: World
+  name: North America
+  child: [CAN, GUM, PRI, SPM, USA, VIR]
+
+R12_PAO:
+  parent: World
+  name: Pacific OECD
+  child: [AUS, JPN, NZL]
+
+R12_PAS:
+  parent: World
+  name: Other Pacific Asia
+  description: >-
+    Trust Territory of the Pacific Islands (PCI) still included in this list,
+    but it was dissolved into MHL, FSM, MNP and PLW in 1986.
+  child: [ASM, BRN, CCK, COK, CXR, FJI, FSM, IDN, KIR, KOR, MAC, MHL, MMR, MNP, MYS, NCL, NFK, NIU, NRU, PCI, PCN, PHL, PLW, PNG, PYF, SGP, SLB, THA, TKL, TLS, TON, TUV, TWN, VUT, WLF, WSM]
+
+R12_SAS:
+  parent: World
+  name: South Asia
+  child: [AFG, BGD, BTN, IND, LKA, MDV, NPL, PAK]
+
+R12_WEU:
+  parent: World
+  name: Western Europe
+  child: [AND, AUT, BEL, CHE, CYP, DEU, DNK, ESP, FIN, FRA, FRO, GBR, GIB, GRC, GRL, IMN, IRL, ISL, ITA, LIE, LUX, MCO, MLT, NLD, NOR, PRT, SJM, SMR, SWE, TUR, VAT]
diff --git a/nest/message_ix_models/data/node/R14.yaml b/nest/message_ix_models/data/node/R14.yaml
new file mode 100644
index 0000000..197c4b4
--- /dev/null
+++ b/nest/message_ix_models/data/node/R14.yaml
@@ -0,0 +1,89 @@
+# Region code list
+#
+# - See message_data.tools.regions.
+# - The ISO 3166-1 alpha-3 codes are not defined in this file, but loaded from
+#   a copy of the ISO database, e.g. in pycountry.
+# - Among others, there are no assignments for:
+#   - ATA Antarctica
+#   - IOT British Indian Ocean Territory
+#   - SGS South Georgia
+
+World:
+  name: World
+  description: R14 regions
+
+R14_AFR:
+  parent: World
+  name: Sub-Saharan Africa
+  child: [AGO, BDI, BEN, BFA, BWA, CAF, CIV, CMR, COD, COG, COM, CPV, DJI, ERI, ETH, GAB, GHA, GIN, GMB, GNB, GNQ, KEN, LBR, LSO, MDG, MLI, MOZ, MRT, MUS, MWI, MYT, NAM, NER, NGA, REU, RWA, SDN, SEN, SHN, SLE, SOM, STP, SWZ, SYC, TCD, TGO, TZA, UGA, ZAF, ZMB, ZWE]
+
+R14_CAS:
+  parent: World
+  name: Central Asia
+  child: [KAZ, KGZ, TJK, TKM, UZB]
+
+R14_CPA:
+  parent: World
+  name: Centrally Planned Asia
+  child: [CHN, KHM, LAO, MAC, MNG, PRK, TWN, VNM]
+
+R14_EEU:
+  parent: World
+  name: Central and Eastern Europe
+  description: >-
+    Serbia and Montenegro (SCG) and Yugoslavia (YUG) still included in this list,
+    even though their ISO 3166-1 codes were deleted in 2006 and 2003, respectively.
+  child: [ALB, BGR, BIH, CZE, EST, HRV, HUN, LTU, LVA, MKD, MNE, POL, ROU, SCG, SRB, SVK, SVN, YUG]
+
+R14_LAM:
+  parent: World
+  name: Latin America and The Caribbean
+  description: >-
+    The source includes “Netherlands Antilles” which has a provisional ISO 3166-2 alpha-3 code (ANT),
+    but is not a country. It was dissolved in 2010 into BES, CUW and SXM, also included.
+  child: [ABW, AIA, ANT, ARG, ATG, BES, BHS, BLZ, BMU, BOL, BRA, BRB, CHL, COL, CRI, CUB, CUW, CYM, DMA, DOM, ECU, FLK, GLP, GRD, GTM, GUF, GUY, HND, HTI, JAM, LCA, MEX, MSR, MTQ, NIC, PAN, PER, PRI, PRY, SLV, SUR, SXM, TCA, TTO, URY, VCT, VEN, VGB]
+
+R14_MEA:
+  parent: World
+  name: Middle East and North Africa
+  child: [ARE, BHR, DZA, EGY, ESH, IRN, IRQ, ISR, JOR, KWT, LBN, LBY, MAR, OMN, PSE, QAT, SAU, SDN, SSD, SYR, TUN, YEM]
+
+R14_NAM:
+  parent: World
+  name: North America
+  child: [CAN, GUM, SPM, USA]
+
+R14_PAO:
+  parent: World
+  name: Pacific OECD
+  child: [AUS, JPN, NZL]
+
+R14_PAS:
+  parent: World
+  name: Other Pacific Asia
+  child: [ASM, BRN, CCK, COK, CXR, FJI, FSM, IDN, KIR, KOR, MHL, MMR, MNP, MYS, NCL, NFK, NIU, NRU, PCN, PHL, PLW, PNG, PYF, SGP, SLB, THA, TKL, TLS, TON, TUV, VUT, WLF, WSM]
+
+R14_RUS:
+  parent: World
+  name: Russia
+  child: [RUS]
+
+R14_SAS:
+  parent: World
+  name: South Asia
+  child: [AFG, BGD, BTN, IND, LKA, MDV, NPL, PAK]
+
+R14_SCS:
+  parent: World
+  name: Caspian States
+  child: [ARM, AZE, GEO]
+
+R14_UBM:
+  parent: World
+  name: Ukraine, Belarus, and Moldova
+  child: [BLR, MDA, UKR]
+
+R14_WEU:
+  parent: World
+  name: Western Europe
+  child: [AND, AUT, BEL, CHE, CYP, DEU, DNK, ESP, FIN, FRA, FRO, GBR, GIB, GRC, GRL, IMN, IRL, ISL, ITA, LIE, LUX, MCO, MLT, NLD, NOR, PRT, SJM, SMR, SWE, TUR, VAT]
diff --git a/nest/message_ix_models/data/node/R32.yaml b/nest/message_ix_models/data/node/R32.yaml
new file mode 100644
index 0000000..bb5976a
--- /dev/null
+++ b/nest/message_ix_models/data/node/R32.yaml
@@ -0,0 +1,196 @@
+World:
+  name: World
+  description: |-
+    Region code list for the SSP 32-region aggregation.
+
+    Source: https://tntcat.iiasa.ac.at/SspDb/dsd?Action=htmlpage&page=about
+
+R32ANUZ:
+  parent: World
+  name: Australia & New Zealand
+  description: This region includes Australia and New Zealand.
+  child: [AUS, NZL]
+
+R32BRA:
+  parent: World
+  name: Brazil
+  child: [BRA]
+
+R32CAN:
+  parent: World
+  name: Canada
+  child: [CAN]
+
+R32CAS:
+  parent: World
+  name: Central Asia
+  description: This region includes the countries of Central Asia.
+  child: [ARM, AZE, GEO, KAZ, KGZ, TJK, TKM, UZB]
+
+R32CHN:
+  parent: World
+  name: China excl. Taiwan
+  description: China (Mainland, Hongkong, Macao; excl. Taiwan).
+  child: [CHN, HKG, MAC]
+
+R32EEU:
+  parent: World
+  name: Eastern Europe
+  description: |-
+    Eastern Europe (excl. former Soviet Union and EU member states).
+
+    The source page describes “the former Yugoslav Republic of Macedonia”; this entity was renamed in 2018 to “North Macedonia”.
+  child: [ALB, BIH, HRV, MKD, MNE, SRB]
+
+R32EEU-FSU:
+  parent: World
+  name: Former Soviet Union in Eastern Europe
+  description: Eastern Europe, former Soviet Union (excl. Russia and EU members).
+  child: [BLR, MDA, UKR]
+
+R32EFTA:
+  parent: World
+  name: European Free Trade Association
+  description: |-
+    This region includes Iceland, Norway, Switzerland.
+
+    The source omits Liechtenstein, but it is included as a child.
+  child: [ISL, LIE, NOR, CHE]
+
+R32EU12-H:
+  parent: World
+  name: EU member states new in 2004, high income
+  description: New EU member states that joined as of 2004 - high income.
+  child: [CYP, CZE, EST, HUN, MLT, POL, SVK, SVN]
+
+R32EU12-M:
+  parent: World
+  name: EU member states new in 2004, middle income
+  description: New EU member states that joined as of 2004 - medium income.
+  child: [BGR, LTU, LVA, ROU]
+
+R32EU15:
+  parent: World
+  name: EU member states pre-2004
+  description: This region includes European Union member states that joined prior to 2004.
+  child: [AUT, BEL, DEU, DNK, ESP, FIN, FRA, GBR, GRC, IRL, ITA, LUX, NLD, PRT, SWE]
+
+R32IDN:
+  parent: World
+  name: Indonesia
+  child: [IDN]
+
+R32IND:
+  parent: World
+  name: India
+  child: [IND]
+
+R32JPN:
+  parent: World
+  name: Japan
+  child: [JPN]
+
+R32KOR:
+  parent: World
+  name: Republic of Korea
+  child: [KOR]
+
+R32LAM-L:
+  parent: World
+  name: Latin America, low income
+  description: This region includes the countries of Latin America (excl. Brazil, Mexico) - low income.
+  child: [BLZ, GTM, HND, HTI, NIC]
+
+R32LAM-M:
+  parent: World
+  name: Latin America, middle & high income
+  description: |-
+    This region includes the countries of Latin America (excl. Brazil, Mexico) - medium and high income.
+
+    The source includes “Netherlands Antilles” which has a provisional ISO 3166-2 alpha-3 code (ANT), but is not a country. It was dissolved in 2010 into BES, CUW and SXM, also included.
+  child: [ABW, AIA, ANT, BES, BHS, BMU, BOL, BRB, CHL, COL, CRI, CUB, CUW, DMA, DOM, ECU, GLP, GRD, GUF, GUY, JAM, KNA, LCA, PAN, PER, PRY, MTQ, SLV, SUR, SXM, TTO, URY, VCT, VEN]
+
+R32MEA-H:
+  parent: World
+  name: Middle East & Asia, high income
+  description: This region includes the countries of Middle East Asia - high income.
+  child: [ARE, BHR, ISR, KWT, OMN, QAT, SAU]
+
+R32MEA-M:
+  parent: World
+  name: Middle East & Asia, low & middle income
+  description: This region includes the countries of Middle East Asia - low and medium income.
+  child: [IRN, IRQ, JOR, LBN, PSE, SYR, YEM]
+
+R32MEX:
+  parent: World
+  name: Mexico
+  child: [MEX]
+
+R32NAF:
+  parent: World
+  name: North Africa
+  description: This region includes the countries of North Africa.
+  child: [DZA, EGY, ESH, LBY, MAR, TUN]
+
+R32OAS-CPA:
+  parent: World
+  name: Other Asia
+  description: This region includes the countries of Other Asia - former Centrally Planned Asia.
+  child: [KHM, LAO, MNG, VNM]
+
+R32OAS-L:
+  parent: World
+  name: Other Asia, low income
+  description: This region includes the countries of Other Asia - low income.
+  child: [BGD, FJI, FSM, MMR, NPL, PHL, PNG, PRK, SLB, TLS, TON, VUT, WSM]
+
+R32OAS-M:
+  parent: World
+  name: Other Asia, middle & high income
+  description: This region includes the countries of Other Asia - medium and high income.
+  child: [BRN, BTN, GUM, LKA, MDV, MYS, NCL, PYF, SGP, THA]
+
+R32PAK:
+  parent: World
+  name: Pakistan & Afghanistan
+  description: This region includes Pakistan and Afghanistan.
+  child: [AFG, PAK]
+
+R32RUS:
+  parent: World
+  name: Russian Federation
+  child: [RUS]
+
+R32SAF:
+  parent: World
+  name: South Africa
+  child: [ZAF]
+
+R32SSA-L:
+  parent: World
+  name: Sub-Saharan Africa, low income
+  description: This region includes the countries of Subsahara Africa (excl. South Africa) - low income.
+  child: [BDI, BEN, BFA, CAF, CIV, CMR, COD, COG, COM, CPV, DJI, ERI, ETH, GHA, GIN, GMB, GNB, KEN, LBR, LSO, MDG, MLI, MOZ, MRT, MWI, NER, NGA, RWA, SDN, SEN, SLE, SOM, SSD, STP, SWZ, TCD, TGO, TZA, UGA, ZMB, ZWE]
+
+R32SSA-M:
+  parent: World
+  name: Sub-Saharan Africa, middle & high income
+  description: This region includes the countries of Subsahara Africa (excl. South Africa) - medium and high income.
+  child: [AGO, BWA, GAB, GNQ, MUS, MYT, NAM, REU, SYC]
+
+R32TUR:
+  parent: World
+  name: Turkey
+  child: [TUR]
+
+R32TWN:
+  parent: World
+  name: Taiwan
+  child: [TWN]
+
+R32USA:
+  parent: World
+  name: United States of America
+  description: United States of America.
+  child: [PRI, USA, VIR]
diff --git a/nest/message_ix_models/data/node/RCP.yaml b/nest/message_ix_models/data/node/RCP.yaml
new file mode 100644
index 0000000..e5a391d
--- /dev/null
+++ b/nest/message_ix_models/data/node/RCP.yaml
@@ -0,0 +1,50 @@
+# Codes for the "node" dimension of the Representative Concentration Pathways
+#
+# - See message_data.tools.regions.
+# - Since ixmp does not support the "." character in IDs, the names "R5.2ASIA"
+#   are transformed to "R5_ASIA" etc. The original code is left in a
+#   description.
+# - The ISO 3166-1 alpha-3 codes are not defined in this file, but loaded from
+#   a copy of the ISO database, e.g. in pycountry.
+# - Among others, there are no assignments for:
+#   - ATA Antarctica
+#   - IOT British Indian Ocean Territory
+#   - SGS South Georgia
+
+World:
+  name: World
+  description: RCP regions
+
+R5_ASIA:
+  parent: World
+  description: |-
+    Officially "R5.2ASIA".
+
+    Trust Territory of the Pacific Islands (PCI) still included in this list, but it was dissolved into MHL, FSM, MNP and PLW in 1986.
+  child: [AFG, ASM, BGD, BRN, BTN, CCK, CHN, COK, CXR, FJI, FSM, GUM, HKG, IDN, IND, KHM, KIR, KOR, LAO, LKA, MAC, MDV, MHL, MMR, MNG, MNP, MYS, MYT, NCL, NFK, NIU, NPL, NRU, PAK, PCI, PCN, PHL, PLW, PNG, PRK, PYF, SGP, SLB, SYC, THA, TKL, TLS, TON, TUV, TWN, VNM, VUT, WSM]
+
+R5_LAM:
+  parent: World
+  description: |-
+    Officially "R5.2LAM".
+
+    The source includes “Netherlands Antilles” which has a provisional ISO 3166-2 alpha-3 code (ANT), but is not a country. It was dissolved in 2010 into BES, CUW and SXM, also included.
+  child: [ABW, AIA, ANT, ARG, ATG, BES, BHS, BLZ, BMU, BOL, BRA, BRB, CHL, COL, CRI, CUB, CUW, CYM, DMA, DOM, ECU, GLP, GRD, GTM, GUF, GUY, HND, HTI, JAM, KNA, LCA, MEX, MSR, MTQ, NIC, PAN, PER, PRY, SLV, SUR, SXM, TTO, URY, VCT, VEN]
+
+R5_MAF:
+  parent: World
+  description: Officially "R5.2MAF".
+  child: [AGO, ARE, BDI, BEN, BFA, BHR, BWA, CAF, CIV, CMR, COD, COG, COM, CPV, DJI, DZA, EGY, ERI, ESH, ETH, GAB, GHA, GIN, GMB, GNB, GNQ, IRN, IRQ, ISR, JOR, KEN, KWT, LBN, LBR, LBY, LSO, MAR, MDG, MLI, MOZ, MRT, MUS, MWI, NAM, NER, NGA, OMN, PSE, QAT, REU, RWA, SAU, SDN, SEN, SLE, SOM, SSD, STP, SWZ, SYR, TCD, TGO, TUN, TZA, UGA, YEM, ZAF, ZMB, ZWE]
+
+R5_OECD:
+  parent: World
+  description: |-
+    Officially "R5.2OECD".
+
+    Serbia and Montenegro (SCG) and Yugoslavia (YUG) still included in this list, even though their ISO 3166-1 codes were deleted in 2006 and 2003, respectively.
+  child: [ALB, AND, AUS, AUT, BEL, BGR, BIH, CAN, CHE, CYP, CZE, DEU, DNK, ESP, EST, FIN, FLK, FRA, FRO, GBR, GIB, GRC, GRL, HRV, HUN, IMN, IRL, ISL, ITA, JPN, LIE, LTU, LUX, LVA, MCO, MKD, MLT, MNE, NLD, NOR, NZL, POL, PRI, PRT, ROU, SCG, SHN, SJM, SMR, SPM, SRB, SVK, SVN, SWE, TCA, TUR, USA, VAT, VGB, VIR, WLF, YUG]
+
+R5_REF:
+  parent: World
+  description: Officially "R5.2REF".
+  child: [ARM, AZE, BLR, GEO, KAZ, KGZ, MDA, RUS, TJK, TKM, UKR, UZB]
diff --git a/nest/message_ix_models/data/node/ZMB.yaml b/nest/message_ix_models/data/node/ZMB.yaml
new file mode 100644
index 0000000..3c50a10
--- /dev/null
+++ b/nest/message_ix_models/data/node/ZMB.yaml
@@ -0,0 +1,10 @@
+# Codes for the "node" dimension of the MESSAGE-ZM model
+
+World:
+  name: World
+  description: MESSAGE-ZM regions
+
+Zambia:
+  name: Zambia
+  parent: World
+  child: [ZMB]
diff --git a/nest/message_ix_models/data/relation/A.yaml b/nest/message_ix_models/data/relation/A.yaml
new file mode 100644
index 0000000..e2501df
--- /dev/null
+++ b/nest/message_ix_models/data/relation/A.yaml
@@ -0,0 +1,154 @@
+# Relation codes
+#
+# This set includes all those expected by code in message_data as of 2023-02-04.
+# It is the union of entries from:
+#
+# - data/buildings/set.yaml
+# - data/transport/set.yaml
+#
+# The descriptions are derived from CD-LINKS.yaml
+
+BCA_Emission:
+  description: >-
+    Emissions of BCA.
+
+    Weight as BCA, kg BCA per kW·a.
+  group: emission_factor
+  parameters: activity, lower
+  technology-entry: 1
+
+CH4_Emission:
+  description: >-
+    Emissions of CH4
+
+    Weight as CH4, kg CH4 per kW·a.
+  group: emission_factor
+  parameters: activity, lower, upper
+  technology-entry: 1
+
+CO_Emission:
+  description: >-
+    Emissions of CO (weight as CO, kg CO per kWyr)
+  group: emission_factor
+  parameters: activity, lower
+  technology-entry: 1
+
+CO2_r_c:
+  description: CO₂ emissions of residential and commercial buildings sector.
+
+CO2_trp:
+  description: CO₂ emissions of transport sector.
+
+HFC_Emission:
+  description: >-
+    Emissions of HFC (weight as HFC, kg HFC-134a-equivalent per kWyr)
+  group: emission_factor
+  parameters: activity, lower, upper
+  technology-entry: 1
+
+HFC_foam_red:
+  description: >-
+    Constraint to link HFC emissions from insulation foams to reduction
+    efficiency of abatement technologies.
+  group: emission_factor
+  parameters: activity, lower
+  technology-entry: 1
+
+HFC_mobile_red:
+  description: >-
+    Constraint to link HFC emissions from mobile air conditioning to reduction
+    efficiency of abatement technologies.
+  group: emission_factor
+  parameters: activity, lower
+  technology-entry: 1
+
+HFC_rescom_red:
+  description: >-
+    Constraint to link HFC emissions from residential & commercial refrigeration
+    & AC to reduction efficiency of abatement technologies.
+  group: emission_factor
+  parameters: activity, lower
+  technology-entry: 1
+
+N2O_Emission:
+  description: >-
+    Emissions of N₂O
+
+    Weight as N₂O, kg N₂O per kW·a.
+  group: emission_factor
+  parameters: activity, lower, upper
+  technology-entry: 1
+
+NH3_Emission:
+  description: >-
+    Emissions of NH₃
+
+    Weight as NH₃, kg NH₃ per kW·a.
+  group: emission_factor
+  parameters: activity, lower
+  technology-entry: 1
+
+NOx_Emission:
+  description: >-
+    Emissions of nitrous oxides (NOx)
+
+    Weight as NOx, kg NOx per kW·a.
+  group: emission_factor
+  parameters: activity, lower
+  technology-entry: 1
+
+OCA_Emission:
+  description: >-
+    Emissions of OCA
+
+    Weight as OCA, kg OCA per kW·a.
+  group: emission_factor
+  parameters: activity, lower
+  technology-entry: 1
+
+oper_res:
+  description: Operating reserve equation for electricity systems integration.
+  technology-entry: 1
+  parameters: activity, lower
+  group: renewable formulation
+
+SF6_Emission:
+  description: >-
+    Emissions of SF₆
+
+    Weight as SF₆, kg SF₆ per kW·a.
+  group: emission_factor
+  parameters: activity, lower, upper
+  technology-entry: 1
+
+SF6_mag_red:
+  description: >-
+    Constraint to link SF₆ emissions from electrical equipment to reduction
+    efficiency of abatement technologies.
+  group: emission_factor
+  parameters: activity, lower
+  technology-entry: 1
+
+SO2_Emission:
+  description: >-
+    Emissions of SO₂
+
+    Weight as SO₂, kg SO₂ per kW·a.
+  group: emission_factor
+  parameters: activity, lower
+  technology-entry: 1
+
+solar_th_pot:
+  description: Solar thermal potential
+  technology-entry: 1
+  parameters: activity, upper
+  group: renewable formulation
+
+VOC_Emission:
+  description: >-
+    Emissions of volatile organic compounds (VOCs)
+
+    Weight as VOC, kg VOC per kW·a.
+  group: emission_factor
+  parameters: activity, lower
+  technology-entry: 1
diff --git a/nest/message_ix_models/data/relation/B.yaml b/nest/message_ix_models/data/relation/B.yaml
new file mode 100644
index 0000000..8294c49
--- /dev/null
+++ b/nest/message_ix_models/data/relation/B.yaml
@@ -0,0 +1,324 @@
+# Relation codes
+#
+# This set includes all values appearing in:
+# ixmp://ixmp-dev/MESSAGEix-GLOBIOM 1.1-BMT-R12 (NAVIGATE)/NPi-ref#2
+
+- BCA_Emission
+- BCA_Emission_bunkers
+- CF4_Emission
+- CF4_alm_red
+- CH4_Emission
+- CH4_Emission_bunkers
+- CH4_RemainingLink
+- CH4_new_Emission
+- CH4_nonenergy
+- CH4n_landfills_red
+- CO2_Emission
+- CO2_Emission_Global_Total
+- CO2_cc
+- CO2_feedstocks
+- CO2_ind
+- CO2_r_c
+- CO2_shipping
+- CO2_trade
+- CO2_trp
+- CO_Emission
+- CO_Emission_bunkers
+- CO_nonenergy
+- FE_coal
+- FE_electricity+dh
+- FE_feedstock
+- FE_final_energy
+- FE_gaseous
+- FE_industry                                                                                                 [266/1626]
+- FE_liquids
+- FE_new_biomass
+- FE_old_biomass
+- FE_resid_comm
+- FE_transport
+- HFC_Emission
+- HFC_foam_red
+- HFC_mobile_red
+- HFC_rescom_red
+- HFCequivOther
+- IndGDPAdLink
+- IndGDPNiLink
+- IndThermDemLink
+- N2O_Emission
+- N2O_Emission_bunkers
+- N2O_nonenergy
+- N2On_adipic_red
+- N2On_nitric_red
+- N2Oother
+- NH3_Emission
+- NOx_Emission
+- NOx_Emission_bunkers
+- NOx_nonenergy
+- OCA_Emission
+- OCA_Emission_bunkers                                                                                        [241/1626]
+- OilPrices
+- PE_BACKSTOPS
+- PE_coal
+- PE_domestic_total
+- PE_export_total
+- PE_gas
+- PE_hydro
+- PE_import_share
+- PE_import_total
+- PE_new_renewables
+- PE_nuclear
+- PE_oil
+- PE_old_renewables
+- PE_synliquids
+- PE_total_direct
+- PE_total_engineering
+- PE_total_traditional
+- PM2_Emission
+- POPtoAeroNonMDILink
+- POPtoAerosolMDILink
+- POPtoDomWasteWaLink
+- POPtoFireExtLink
+- POPtoHuSewageLink
+- POPtoSolvent
+- RES_variable_limt
+- SF6_Emission
+- SF6_elec_red                                                                                                [214/1626]
+- SF6_mag_red
+- SO2_Emission
+- SO2_Emission_bunkers
+- SO2_elec
+- SO2_import
+- SO2_ind
+- SO2_r_c
+- SO2_red_ref
+- SO2_red_synf
+- SolWaPOPLink
+- TCE_Emission
+- TCE_Emission_AFR_CPA_LAC_MEA_PAS_SAS
+- TCE_Emission_FSU_MEA
+- TCE_Emission_NAM_PAO_EEU_WEU
+- TCE_Emission_NAM_PAO_EEU_WEU_FSU
+- TCE_regional
+- TCE_rel2020
+- TCE_trade
+- TCEl
+- TCF4_Emission
+- TCH4_Emission
+- TCO2_Emission
+- THFC_Emission
+- TN2O_Emission
+- TSF6_Emission
+- UE_feedstock
+- UE_feedstock_coal                                                                                           [187/1626]
+- UE_feedstock_foil
+- UE_feedstock_gas
+- UE_feedstock_liquid
+- UE_industry_sp
+- UE_industry_sp_fc
+- UE_industry_sp_liquid
+- UE_industry_sp_solar
+- UE_industry_th
+- UE_industry_th_electric
+- UE_industry_th_foil
+- UE_industry_th_gas
+- UE_industry_th_hydrogen
+- UE_industry_th_liquid
+- UE_industry_th_low_temp_heat
+- UE_industry_th_solar
+- UE_industry_th_solid
+- UE_res_comm_sp
+- UE_res_comm_sp_fc
+- UE_res_comm_sp_solar
+- UE_res_comm_th
+- UE_res_comm_th_biomass
+- UE_res_comm_th_electric
+- UE_res_comm_th_foil
+- UE_res_comm_th_gas
+- UE_res_comm_th_heat
+- UE_res_comm_th_hp
+- UE_res_comm_th_hydrogen                                                                                     [160/1626]
+- UE_res_comm_th_liquid
+- UE_res_comm_th_solar
+- UE_res_comm_th_solids
+- UE_transport
+- UE_transport_electric
+- UE_transport_electric_Minimum
+- UE_transport_fc
+- UE_transport_foil
+- UE_transport_gas
+- UE_transport_liquid
+- VOC_Emission
+- VOC_Emission_bunkers
+- VOC_IndThermLink
+- VOC_nonenergy
+- WasteGenToBCALink
+- WasteGenToCH4Link
+- WasteGenToCOLink
+- WasteGenToNOxLink
+- WasteGenToOCALink
+- WasteGenToPM2Link
+- WasteGenToSO2Link
+- WasteGenToVOCLink
+- bco2_trans_disp
+- bio_extr_mpen_c                                                                                             [136/1626]
+- bio_scrub_lim
+- co2_trans_disp
+- co_gen_limit
+- coal-exp
+- coal_extr
+- coal_extr_mpen_c
+- coal_scrub_lim
+- csp_japan_limit
+- demF_limit
+- demI_limit
+- demR_limit
+- demb_limit
+- demp_limit
+- demt_limit
+- domestic_coal
+- domestic_gas
+- ele_nuc
+- elec_coal
+- elec_gas
+- elec_hydro
+- elec_nuclear
+- elec_relations2_coal
+- elec_relations2_gas
+- elec_relations2_hydro
+- elec_relations2_nuclear
+- elec_relations2_oil
+- elec_relations2_renewable                                                                                   [109/1626]
+- extraction_coal
+- extraction_gas
+- extraction_oil
+- foil_prod
+- gas_export
+- gas_extr_mpen_c
+- gas_mix_lim
+- gas_prod
+- gas_scrub_lim
+- gas_util
+- global_GCO2
+- global_GSO2
+- h2_exp_limit
+- h2_scrub_limit
+- h2mix_direct
+- hydro_min
+- hydro_pot
+- import_dependence
+- lfil_limit
+- lim_exp_afr_LNG
+- lim_exp_afr_coal
+- lim_exp_afr_eth
+- lim_exp_afr_meth
+- lim_exp_cpa_oil
+- lim_exp_eeu_eth
+- lim_exp_eeu_meth
+- lim_exp_fsu_LNG                                                                                              [82/1626]
+- lim_exp_fsu_coal
+- lim_exp_fsu_eth
+- lim_exp_fsu_meth
+- lim_exp_fsu_oil
+- lim_exp_lam_LNG
+- lim_exp_lam_oil
+- lim_exp_mea_LNG
+- lim_exp_mea_oil
+- lim_exp_nam_LNG
+- lim_exp_nam_coal
+- lim_exp_pao_coal
+- lim_exp_pao_eth
+- lim_exp_pao_meth
+- lim_exp_pas_LNG
+- lim_exp_weu_eth
+- lim_exp_weu_meth
+- limit_tot_BECCS
+- max_coal_ind
+- meth_exp_limit
+- min-liquids_industry
+- min-liquids_res-com
+- minimum_recycling_aluminum
+- minimum_recycling_steel
+- nica_limit
+- nuc_lc_in_el
+- oil_based_elec_gen
+- oil_extr_mpen_c                                                                                              [55/1626]
+- oil_imp_c
+- oil_prod
+- oil_trd
+- oper_res
+- pass_out_trb
+- prod_low_lim_BIEL
+- prod_low_lim_BIFU
+- prod_low_lim_BIHE
+- prod_low_lim_COEL
+- prod_low_lim_GAEL
+- prod_low_lim_GEEL
+- prod_low_lim_HYEL
+- prod_low_lim_NUEL
+- prod_low_lim_OIEL
+- prod_low_lim_SOEL
+- prod_low_lim_WIEL
+- res_japan_limit
+- res_marg
+- share_low_lim_NFEG
+- share_low_lim_NFPE
+- share_low_lim_PEGas
+- share_low_lim_REEG
+- share_low_lim_REFE
+- share_low_lim_REPE
+- sm1_ppl_res
+- sm3_ppl_res
+- solar_csp_pot
+- solar_csp_pot1                                                                                               [27/1626]
+- solar_csp_pot2
+- solar_csp_pot3
+- solar_csp_pot4
+- solar_csp_pot5
+- solar_csp_pot6
+- solar_csp_pot7
+- solar_curtailment_1
+- solar_curtailment_2
+- solar_curtailment_3
+- solar_pot
+- solar_pot2
+- solar_pot3
+- solar_pot4
+- solar_pot5
+- solar_pot6
+- solar_pot7
+- solar_pot8
+- solar_pot_hist_2010
+- solar_pot_hist_2015
+- solar_pot_hist_2020
+- solar_ppl_res
+- solar_res_cv
+- solar_step
+- solar_step2
+- solar_step3
+- solar_th_pot'
+- syn_liq_exp
+- total_TCO2
+- trp_energy
+- unabated_coal_lim
+- wind_curtailment_1
+- wind_curtailment_2
+- wind_curtailment_3
+- wind_pof
+- wind_pof2
+- wind_pof3
+- wind_pof4
+- wind_pof5
+- wind_pot
+- wind_pot2
+- wind_pot3
+- wind_pot4
+- wind_pot_hist_2005
+- wind_pot_hist_2010
+- wind_pot_hist_2015
+- wind_pot_hist_2020
+- wind_ppl_offshore_res
+- wind_ppl_res
+- wind_res_cv
+- wind_step
+- wind_step2
+- wind_step3
diff --git a/nest/message_ix_models/data/relation/CD-LINKS.yaml b/nest/message_ix_models/data/relation/CD-LINKS.yaml
new file mode 100644
index 0000000..453170a
--- /dev/null
+++ b/nest/message_ix_models/data/relation/CD-LINKS.yaml
@@ -0,0 +1,1939 @@
+# Relation codes
+#
+# The IDs, descriptions, and other annotations are derived from a spreadsheet
+# appearing in IIASA ECE SharePoint, related to the CD-LINKS project.
+#
+# The entries appear in the same order as in the file.
+
+BCA_Emission_bunkers:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    BC emission from international shipping (bunker fuel)
+  group: removed
+CO_Emission_bunkers:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    CO emission from international shipping (bunker fuel)
+  group: removed
+CO2_cc:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    Emissions of CO2 in central conversion and extraction
+  group: removed
+CO2_ind:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    CO2 emissions of industry sector
+  group: removed
+CO2_r_c:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    CO2 emissions of residential and commercial sector
+  # NB despite this annotation, the relation still has entries in current
+  #    scenarios; see A.yaml and B.yaml
+  group: removed
+CO2_trp:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    CO2 esmissions of transport sector
+  # NB despite this annotation, the relation still has entries in current
+  #    scenarios; see A.yaml and B.yaml
+  group: removed
+coal_extr:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    In R11 GLB only - used for accounting or limiting global coal extraction
+    (currently inactive as only positive entries)
+  group: removed
+elec_relations2_coal:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    "(?) Counter - electricty from coal (inactive as no -1)
+  group: removed
+elec_relations2_gas:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    "(?) Counter - electricty from gas (inactive as no -1)
+  group: removed
+elec_relations2_hydro:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    "(?) Counter - electricty from hydro (inactive as no -1)
+  group: removed
+elec_relations2_nuclear:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    "(?) Counter - electricty from nuclear (inactive as no -1)
+  group: removed
+elec_relations2_oil:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    "(?) Counter - electricty from oil (inactive as no -1)
+  group: removed
+elec_relations2_renewable:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    "(?) Counter - electricty from renewable (inactive as no -1)
+  group: removed
+extraction_coal:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    Constrains/calibrates historical coal extraction (inactive as no -1)
+  group: removed
+extraction_gas:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    Constrains/calibrates historical gas extraction (inactive as no -1)
+  group: removed
+extraction_oil:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    Constrains/calibrates historical oil extraction (inactive as no -1)
+  group: removed
+FE_coal:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    final energy accounting equation
+  group: removed
+FE_electricity:
+  kind:
+  technology-entry: 1
+  parameters2: upper
+  description: >-
+    final energy accounting equation
+  group: removed
+FE_feedstock:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    final energy accounting equation
+  group: removed
+FE_final_energy:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    final energy accounting equation
+  group: removed
+FE_gaseous:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    final energy accounting equation
+  group: removed
+FE_industry:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    final energy accounting equation
+  group: removed
+FE_liquids:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    final energy accounting equation
+  group: removed
+FE_new_biomass:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    final energy accounting equation
+  group: removed
+FE_old_biomass:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    final energy accounting equation
+  group: removed
+FE_resid_comm:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    final energy accounting equation
+  group: removed
+FE_transport:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    final energy use in transport sector
+  group: removed
+global_GCO2:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    R11-GLB only - Total CO2 emissions
+  group: removed
+global_GSO2:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    R11-GLB only - Total SO2 Emissions
+  group: removed
+import_dependence:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    maximum import dependence
+  group: removed
+NOx_Emission_bunkers:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    NOx emission from international shipping (bunker fuel)
+  group: removed
+nuc_lc_in_el:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    Constrains nuc_lc contribution to electricity production
+  group: removed
+OCA_Emission_bunkers:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    OC emission from international shipping (bunker fuel)
+  group: removed
+oil_prod:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    R11-NAM only - Limits import dependence of crude oil to max 60% (inactive
+    as there are no negative entries and no limits)
+  group: removed
+oil_trd:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    In R11 GLB only - penalty for international trade introduced to reflect
+    costs for importing region. In SOME other regions - links/limits net oil
+    product imports to fraction of net crude imports.
+  group: removed
+OilPrices:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    crude oil import prices
+  group: removed
+PE_BACKSTOPS:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    primary energy accounting equation
+  group: removed
+PE_coal:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    primary energy accounting equation
+  group: removed
+PE_gas:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    primary energy accounting equation
+  group: removed
+PE_hydro:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    primary energy accounting equation
+  group: removed
+PE_new_renewables:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    primary energy accounting equation
+  group: removed
+PE_nuclear:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    primary energy accounting equation
+  group: removed
+PE_oil:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    primary energy accounting equation
+  group: removed
+PE_old_renewables:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    primary energy accounting equation
+  group: removed
+PE_synliquids:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    primary energy accounting equation
+  group: removed
+PE_total_direct:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    primary energy accounting equation
+  group: removed
+PE_total_engineering:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    primary energy accounting equation
+  group: removed
+PE_total_traditional:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    primary energy accounting equation
+  group: removed
+prod_low_lim_BIEL:
+  technology-entry: 0
+  parameters: ""
+  description: >-
+    INDC Policy constraint
+  group: removed
+prod_low_lim_BIFU:
+  technology-entry: 0
+  parameters: ""
+  description: >-
+    INDC Policy constraint
+  group: removed
+prod_low_lim_BIHE:
+  technology-entry: 0
+  parameters: ""
+  description: >-
+    INDC Policy constraint
+  group: removed
+prod_low_lim_COEL:
+  technology-entry: 0
+  parameters: ""
+  description: >-
+    INDC Policy constraint
+  group: removed
+prod_low_lim_GAEL:
+  technology-entry: 0
+  parameters: ""
+  description: >-
+    INDC Policy constraint
+  group: removed
+prod_low_lim_GEEL:
+  technology-entry: 0
+  parameters: ""
+  description: >-
+    INDC Policy constraint
+  group: removed
+prod_low_lim_HYEL:
+  technology-entry: 0
+  parameters: ""
+  description: >-
+    INDC Policy constraint
+  group: removed
+prod_low_lim_NUEL:
+  technology-entry: 0
+  parameters: ""
+  description: >-
+    INDC Policy constraint
+  group: removed
+prod_low_lim_OIEL:
+  technology-entry: 0
+  parameters: ""
+  description: >-
+    INDC Policy constraint
+  group: removed
+prod_low_lim_SOEL:
+  technology-entry: 0
+  parameters: ""
+  description: >-
+    INDC Policy constraint
+  group: removed
+prod_low_lim_WIEL:
+  technology-entry: 0
+  parameters: ""
+  description: >-
+    INDC Policy constraint
+  group: removed
+share_low_lim_NFEG:
+  technology-entry: 0
+  parameters: ""
+  description: >-
+    removed
+share_low_lim_NFPE:
+  technology-entry: 0
+  parameters: ""
+  description: >-
+    removed
+share_low_lim_PEGas:
+  technology-entry: 0
+  parameters: ""
+  description: >-
+    removed
+share_low_lim_REEG:
+  technology-entry: 0
+  parameters: ""
+  group: removed
+share_low_lim_REFE:
+  technology-entry: 0
+  parameters: ""
+  group: removed
+share_low_lim_REPE:
+  technology-entry: 0
+  parameters: ""
+  group: removed
+SO2_Emission_bunkers:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    SO2 emission from international shipping (bunker fuel)
+  group: removed
+TCE_Emission_AFR_CPA_LAC_MEA_PAS_SAS:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    total carbon-equivalent emissions for set of regions
+  group: removed
+TCE_Emission_FSU_MEA:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    total carbon-equivalent emissions for set of regions
+  group: removed
+TCE_Emission_NAM_PAO_EEU_WEU:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    total carbon-equivalent emissions for set of regions
+  group: removed
+TCE_Emission_NAM_PAO_EEU_WEU_FSU:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    total carbon-equivalent emissions for set of regions
+  group: removed
+TCE_regional:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    total carbon-equivalent emission equation (regional)
+  group: removed
+TCE_rel2020:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    "(?) Counter - TCE in 2020
+  group: removed
+TCE_trade:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    trade in carbon-equivalent emissions
+  group: removed
+TCF4_Emission:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    R11-GLB only - Total Emissions of CF4 (weight as CF4, kg CF4 per kWyr)
+  group: removed
+TCH4_Emission:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    total CH4 emission equation
+  group: removed
+TCO2_Emission:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    total CO2 emission equation
+  group: removed
+THFC_Emission:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    R11-GLB only - Counter for HFC emissions
+  group: removed
+TN2O_Emission:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    total N2O emission equation
+  group: removed
+total_TCO2:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    Total CO2 emissions including feedstocks
+  group: removed
+trp_energy:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    limits total final energy in transport, if necessary (inactive as set to
+    free)
+  group: removed
+TSF6_Emission:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    R11-GLB only - Total Emissions of SF6 (weight as SF6, kg SF6 per kWyr)
+  group: removed
+VOC_Emission_bunkers:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    VOC emission from international shipping (bunker fuel)
+  group: removed
+VOC_IndThermLink:
+  technology-entry: 1
+  parameters: activity
+  description: >-
+    Constraint links VOC solvent emissions to its driver: the industrial themal
+    demand.
+  group: removed
+BCA_Emission:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    Emissions of BCA (weight as BCA, kg BCA per kWyr)
+  group: emission_factor
+bco2_trans_disp:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    CO2 transport and injection from BECCS
+bio_extr_mpen_c:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    link equation for joint diffusion constraint of biomass extraction technologies (deprecated?)
+bio_scrub_lim:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    limit on CO2 scrubber application (CCS add-on) for biomass power plants
+cement_pro:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    relation for cement production linked to ind thermal
+cement_scrub_lim:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    Constraint limiting cement scrubber activity to cement CO2 production
+CF4_alm_red:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    Constraint to link CF4 emissions from aluminum to reduction efficiency of
+    abatement technologies.
+  group: add-on
+CF4_Emission:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    Emissions of CF4 (weight as CF4, kg CF4 per kWyr)
+  group: emission_factor
+CH4_Emission:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    Emissions of CH4 (weight as CH4, kg CH4 per kWyr)
+  group: emission_factor
+CH4_Emission_bunkers:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    Counter - R11-GLB only - CH4 emissions of international shipping
+  group: emission_factor
+CH4_new_Emission:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    Counter for CH4 emissions from newly added sources
+  group: emission_factor
+CH4_nonenergy:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    Emissions of CH4 from nonenergy sources (weight as CH4, kg CH4 per kWyr)
+  group: emission_factor
+CH4_RemainingLink:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    Constraint links the remaining CH4 emissions to oil extraction
+  group: emission_factor
+CH4n_landfills_red:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    Constraint to link landfill emissions to reduction efficiency of CH4
+    abatement technologies.
+  group: add-on
+CO_Emission:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    Emissions of CO (weight as CO, kg CO per kWyr)
+  group: emission_factor
+co_gen_limit:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    Limits cogeneration as share of total central heat production
+  group: share-constraint formulation
+CO_nonenergy:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    CO emission from non-energy sectors
+  group: emission_factor
+CO2_Emission:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    Emissions of CO2 (weight as carbon, ton C/kWyr)
+  group: emission_factor
+CO2_feedstocks:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    CO2 emissions of feedstock use (non-energy use)
+  group: emission_factor
+CO2_shipping:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    Counter - R11-GLB only - CO2 emissions of international shipping
+  group: emission_factor
+CO2_trade:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    Counter - R11-GLB only - CO2 emissions of energy trade
+  group: emission_factor
+co2_trans_disp:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    Constraint to connect captured carbon to transportation and disposal
+  group: add-on
+coal_extr_mpen_c:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    link equation for joint diffusion constraint of coal extraction technologies
+  group: fossil resources
+coal_scrub_lim:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    limit on CO2 scrubber application (CCS add-on) for coal power plants
+  group: add-on
+coal-exp:
+  parameters: activity, upper
+  technology-entry: 1
+  description: >-
+    Restricts coal exports to hard coal only  by limiting lignite to power
+    plants and other central conversion.
+  group: share-constraint formulation
+csp_japan_limit:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    limit on renewable resource use of Japan in PAO region
+  group: renewable formulation
+demb_limit:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    Constraint linking mpa for additional VOM costs for breaching share
+    constraints related to Industrial Thermal sector to growth rate
+  group: share-constraint formulation
+demF_limit:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    Constraint linking mpa for additional VOM costs for breaching share
+    constraints related to Feedstock sector to growth rate
+  group: share-constraint formulation
+demI_limit:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    Constraint linking mpa for additional VOM costs for breaching share
+    constraints related to Industrial Specific sector to growth rate (currently
+    inactive as all technologies have bda up 0)
+  group: share-constraint formulation
+demp_limit:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    Constraint linking mpa for additional VOM costs for breaching share
+    constraints related to Transport sector to growth rate
+  group: share-constraint formulation
+demR_limit:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    Constraint linking mpa for additional VOM costs for breaching share
+    constraints related to Residential Commercial Specific sector to growth rate
+  group: share-constraint formulation
+demt_limit:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    Constraint linking mpa for additional VOM costs for breaching share
+    constraints related to Residential Commercial Thermal sector to growth rate
+  group: share-constraint formulation
+domestic_coal:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    R11-PAO only - Constraint - limits domestic coal use to x% of total primary
+    energy.
+  group: share-constraint formulation
+domestic_gas:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    R11-PAO only - Constraint - limits domestic coal use to x% of total primary
+    energy.
+  group: share-constraint formulation
+ele_nuc:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    Constrains/calibrates historical nuclear
+  group: hisc
+elec_coal:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    Constrains/calibrates historical electricity production from coal
+  group: hisc
+elec_gas:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    Constrains/calibrates historical electricity production from natural gas
+  group: hisc
+elec_hydro:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    Constrains/calibrates historical electricity production from hydro
+  group: hisc
+elec_nuclear:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    Constrains/calibrates historical electricity production from nuclear
+  group: hisc
+foil_prod:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    Constrains/calibrates historical refinery output
+  group: hisc
+gas_export:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    Constrains/calibrates historical gas exports
+  group: hisc
+gas_extr_mpen_c:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    link equation for joint diffusion constraint of gas extraction technologies
+  group: fossil resources
+gas_mix_lim:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    R11-AFR only - Constrains hydrogen contribution to final gas usage
+  group: share-constraint formulation
+gas_prod:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    links and stabilizes gas production (currently inactive - no positive entry)
+  group: obsolete (?)
+gas_scrub_lim:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    limit on CO2 scrubber application (CCS add-on) for gas power plants
+  group: add-on
+gas_util:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    Links gas infrastructure growth between utilities versus residential and
+    industry (inactive as not negative entry).
+  group: obsolete (?)
+h2_exp_limit:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    limits hydrogen exports as share of total hydrogen production
+  group: share-constraint formulation
+h2_scrub_limit:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    scrubbing limit of CO2 from natural gas due to hydrogen blending
+  group: add-on
+h2mix_direct:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    blending of hydrogen into gas grid
+  group: RES
+HFC_Emission:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    Emissions of HFC (weight as HFC, kg HFC-134a-equivalent per kWyr)
+  group: emission_factor
+HFC_foam_red:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    Constraint to link HFC emissions from insulation foams to reduction
+    efficiency of abatement technologies.
+  group: emission_factor
+HFC_mobile_red:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    Constraint to link HFC emissions from mobile airconditioning to reduction
+    efficiency of abatement technologies.
+  group: emission_factor
+HFC_rescom_red:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    Constraint to link HFC emissions from residential & commercial refrigeration
+    & AC to reduction efficiency of abatement technologies.
+  group: emission_factor
+HFCequivOther:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    Counter for the HFC-134 equiv. emissions from solvents, fire extinguishers,
+    aerosols MDI, aerosols non-MDI.
+  group: emission_factor
+hydro_min:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    Constraint limiting minimum hydro production
+  group: hydro resource
+hydro_pot:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    Constraint limiting maximum hydro potential
+  group: hydro resource
+IndGDPAdLink:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    link of GDP to adipic acid
+  group: emission_factor
+IndGDPNiLink:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    link of GDP to nitric acid
+  group: emission_factor
+IndThermDemLink:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    Constraint links industry waste water CH4 emissions to its driver: the
+    industrial themal demand.
+  group: emission_factor
+lfil_limit:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    Constraint limiting gr of landfill mitigaiton technology activty (mpa)
+  group: emission_factor
+lim_exp_afr_coal:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    limit on coal exports
+  group: share-constraint formulation
+lim_exp_afr_eth:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    limit on hydrogen exports
+  group: share-constraint formulation
+lim_exp_afr_LNG:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    R11_GLB only - limits LNG exports from afr
+  group: share-constraint formulation
+lim_exp_afr_meth:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    limit on methanol (synthetic fossil fuel) exports
+  group: share-constraint formulation
+lim_exp_cpa_oil:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    limit on oil exports
+  group: share-constraint formulation
+lim_exp_eeu_eth:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    limit on ethanol (biofuel) exports
+  group: share-constraint formulation
+lim_exp_eeu_meth:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    limit on methanol (synthetic fossil fuel) exports
+  group: share-constraint formulation
+lim_exp_fsu_coal:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    limit on coal exports
+  group: share-constraint formulation
+lim_exp_fsu_eth:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    limit on ethanol (biofuel) exports
+  group: share-constraint formulation
+lim_exp_fsu_LNG:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    R11_GLB only - limits LNG exports from fsu
+  group: share-constraint formulation
+lim_exp_fsu_meth:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    limit on methanol (synthetic fossil fuel) exports
+  group: share-constraint formulation
+lim_exp_fsu_oil:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    limit on oil exports
+  group: share-constraint formulation
+lim_exp_lam_LNG:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    R11_GLB only - limits LNG exports from lam
+  group: share-constraint formulation
+lim_exp_lam_oil:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    limit on oil exports
+  group: share-constraint formulation
+lim_exp_mea_LNG:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    R11_GLB only - limits LNG exports from mea
+  group: share-constraint formulation
+lim_exp_mea_oil:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    limit on oil exports
+  group: share-constraint formulation
+lim_exp_nam_coal:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    limit on coal exports
+  group: share-constraint formulation
+lim_exp_nam_LNG:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    R11_GLB only - limits LNG exports from nam
+  group: share-constraint formulation
+lim_exp_pao_coal:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    limit on coal exports
+  group: share-constraint formulation
+lim_exp_pao_eth:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    limit on ethanol (biofuel) exports
+  group: share-constraint formulation
+lim_exp_pao_meth:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    limit on methanol (synthetic fossil fuel) exports
+  group: share-constraint formulation
+lim_exp_pas_LNG:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    R11_GLB only - limits LNG exports from pas
+  group: share-constraint formulation
+lim_exp_weu_eth:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    limit on ethanol (biofuel) exports
+  group: share-constraint formulation
+lim_exp_weu_meth:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    limit on methanol (synthetic fossil fuel) exports
+  group: share-constraint formulation
+limit_tot_BECCS:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    limit on BECCS application
+  group: share-constraint formulation
+max_coal_ind:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    R11-NAM only - limits coal to max of 50% of gas in industries
+  group: share-constraint formulation
+meth_exp_limit:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    limits methanol exports as share of total methanol production
+  group: share-constraint formulation
+min-liquids_industry:
+  technology-entry: 1
+  parameters:  activity, lower
+  group: share-constraint formulation
+min-liquids_res_com:
+  technology-entry: 1
+  parameters: activity, lower
+  group: share-constraint formulation
+N2O_Emission:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    Emissions of N2O (weight as N2O, kg N2O per kWyr)
+  group: emission_factor
+N2O_Emission_bunkers:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    Counter - R11-GLB only - N2O emissions of international shipping
+  group: emission_factor
+N2O_nonenergy:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    Emissions of N2O from nonenergy sources (weight as N2O, kg N2O per kWyr)
+  group: emission_factor
+N2On_adipic_red:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    Constraint to link adipic acid emissions to reduction efficiency of N2O
+    abatement technologies.
+  group: emission_factor
+N2On_nitric_red:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    Constraint to link nitric acid emissions to reduction efficiency of N2O
+    abatement technologies.
+  group: emission_factor
+N2Oother:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    Counter for the N2O emissions from Manure Management, Human Sewage, Other
+    Agricultural sources, Other Non Ag Sources.
+  group: emission_factor
+NH3_Emission:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    Emissions of NH3 (weight as NH3, kg NH3 per kWyr)
+  group: emission_factor
+nica_limit:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    Constraint limiting gr of catalytic converter (mitigaiton) technology
+    activty (mpa).
+  group: emission_factor
+NOx_Emission:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    Emissions of NOx (weight as NOx, kg NOx per kWyr)
+  group: emission_factor
+NOx_nonenergy:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    NOx emission from non-energy sectors
+  group: emission_factor
+OCA_Emission:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    Emissions of OCA (weight as OCA, kg OCA per kWyr)
+  group: emission_factor
+oil_based_elec_gen:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    Constrains/calibrates historical minimum share of oil for electricity
+    generation.
+  group: hisc
+oil_extr_mpen_c:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    link equation for joint diffusion constraint of oil extraction technologies
+  group: fossil resources
+oil_imp_c:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    R11-NAM only - Limits import dependence of crude oil in relation to oil
+    extraction.
+  group: share-constraint formulation
+oper_res:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    operating reserve equation for electricity systems integration
+  group: renewable formulation
+pass_out_trb:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    link to use of passout turbine from thermal generators
+  group: add-on
+PE_domestic_total:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    primary energy accounting equation
+  group: obsolete (?)
+PE_export_total:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    primary energy accounting equation
+  group: obsolete (?)
+PE_import_share:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    primary energy accounting equation
+  group: obsolete (?)
+PE_import_total:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    primary energy accounting equation
+  group: obsolete (?)
+PM2_Emission:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    PM2.5 emission
+  group: emission_factor
+POPtoAeroNonMDILink:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    link from population to aerosol non-medical use (driver for F gas emission)
+  group: emission_factor
+POPtoAerosolMDILink:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    link from population to aerosol medical use (driver for F gas emission)
+  group: emission_factor
+POPtoDomWasteWaLink:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    link from population to domestic waste water
+  group: emission_factor
+POPtoFireExtLink:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    link from population to fire extiguisher (driver for F gas emission)
+  group: emission_factor
+POPtoHuSewageLink:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    link from population to human sewage
+  group: emission_factor
+POPtoSolvent:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    link from population to solvent use
+  group: emission_factor
+res_japan_limit:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    limit on renewable resource use of Japan in PAO region
+  group: fossil resources
+res_marg:
+  parameters: activity, new_capacity, total_capacity
+  technology-entry: 3
+  parameters2: lower
+  description: >-
+    electricity systems integration - reserve margin
+  group: renewable formulation
+RES_variable_limt:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    variable renewable energy limit
+  group: "share-constraint formulation, renewable formulation"
+SF6_elec_red:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    Constraint to link SF6 emissions from electrical equipment to reduction
+    efficiency of abatement technologies.
+  group: emission_factor
+SF6_Emission:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    Emissions of SF6 (weight as SF6, kg SF6 per kWyr)
+  group: emission_factor
+SF6_mag_red:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    Constraint to link SF6 emissions from electrical equipment to reduction
+    efficiency of abatement technologies.
+  group: emission_factor
+sm1_ppl_res:
+  technology-entry: 1
+  parameters: total_capacity, lower, upper
+  description: >-
+    renewable formulation
+sm3_ppl_res:
+  technology-entry: 1
+  parameters: total_capacity, lower, upper
+  description: >-
+    renewable formulation
+SO2_elec:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    Emissions of SO2 in ppl, counter for DESOX (formerly SO2_emi_el)
+  group: emission_factor
+SO2_Emission:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    Emissions of SO2 (weight as SO2, kg SO2 per kWyr)
+  group: emission_factor
+SO2_import:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    Import of of low sulfur coal (0.6% S)
+  group: emission_factor
+SO2_ind:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    Counter - Emissions of CO2 in industrial sector
+  group: obsolete (?)
+SO2_r_c:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    Counter - Emissions of CO2 in residential/commercial sector
+  group: obsolete (?)
+SO2_red_ref:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    Limit on SO2 Emission reduction in refinery products
+  group: emission_factor
+SO2_red_synf:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    Limit on SO2 Emission reduction in synfuel production
+  group: emission_factor
+solar_csp_pot:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    CSP resource potential category
+  group: renewable formulation
+solar_csp_pot1:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    CSP resource potential category
+  group: renewable formulation
+solar_csp_pot2:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    CSP resource potential category
+  group: renewable formulation
+solar_csp_pot3:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    CSP resource potential category
+  group: renewable formulation
+solar_csp_pot4:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    CSP resource potential category
+  group: renewable formulation
+solar_csp_pot5:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    CSP resource potential category
+  group: renewable formulation
+solar_csp_pot6:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    CSP resource potential category
+  group: renewable formulation
+solar_csp_pot7:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    CSP resource potential category
+  group: renewable formulation
+solar_curtailment_1:
+  parameters: activity, total_capacity
+  technology-entry: 2
+  parameters2: upper
+  description: >-
+    electricity systems integration constraint (curtailment)
+  group: renewable formulation
+solar_curtailment_2:
+  parameters: activity, total_capacity
+  technology-entry: 2
+  parameters2: upper
+  description: >-
+    electricity systems integration constraint (curtailment)
+  group: renewable formulation
+solar_curtailment_3:
+  parameters: activity, total_capacity
+  technology-entry: 2
+  parameters2: upper
+  description: >-
+    electricity systems integration constraint (curtailment)
+  group: renewable formulation
+solar_pot:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    solar PV resource potential
+  group: renewable formulation
+solar_pot2:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    solar PV resource potential
+  group: renewable formulation
+solar_pot3:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    solar PV resource potential
+  group: renewable formulation
+solar_pot4:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    solar PV resource potential
+  group: renewable formulation
+solar_pot5:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    solar PV resource potential
+  group: renewable formulation
+solar_pot6:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    solar PV resource potential
+  group: renewable formulation
+solar_pot7:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    solar PV resource potential
+  group: renewable formulation
+solar_pot8:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    solar PV resource potential
+  group: renewable formulation
+solar_ppl_res:
+  technology-entry: 1
+  parameters: total_capacity, lower, upper
+  group: renewable formulation
+solar_res_cv:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    balances the solar_res and solar_cv technologies
+  group: renewable formulation
+solar_step:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    first step of solar PV generation is limited to a certain fraction of load
+    (higher steps have worse oper and resm).
+  group: renewable formulation
+solar_step2:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    second step of solar PV generation is limited to a certain fraction of load
+    (higher steps have worse oper and resm).
+  group: renewable formulation
+solar_step3:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    third step of solar PV generation is limited to a certain fraction of load
+    (higher steps have worse oper and resm).
+  group: renewable formulation
+solar_th_pot:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    solar thermal potential
+  group: renewable formulation
+SolWaPOPLink:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    link from population to solid waste
+  group: emission_factor
+syn_liq_exp:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    R11-FSU only - constraints the export of synthetic liquids to x% of
+    indigenous oil use.
+  group: share-constraint formulation
+TCE_Emission:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    R11-only - total carbon-equivalent emission equation
+  group: emission_factor
+TCEl:
+  technology-entry: 0
+  parameters: lower
+  group: obsolete (?)
+UE_feedstock:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    Feedstock share constraint - connects all relevant technologies
+  group: share-constraint formulation
+UE_feedstock_coal:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    Feedstock share constraint - restrict contribution of coal to x % of
+    feedstock demand.
+  group: share-constraint formulation
+UE_feedstock_foil:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    Feedstock share constraint - restrict contribution of liquid fuels to x % of
+    feedstock demand.
+  group: share-constraint formulation
+UE_feedstock_gas:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    Feedstock share constraint - restrict contribution of heavy fuel oil to x %
+    of feedstock demand.
+  group: share-constraint formulation
+UE_feedstock_liquid:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    Feedstock share constraint - restrict contribution of gaseous fuels to x %
+    of feedstock demand.
+  group: share-constraint formulation
+UE_industry_sp:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    share constraint for useful energy industry specific
+  group: share-constraint formulation
+UE_industry_sp_fc:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    share constraint for fuel cell (onsite FC) useful energy industry specific
+  group: share-constraint formulation
+UE_industry_sp_liquid:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    share constraint for liquids (onsite IC generator) useful energy industry
+    specific.
+  group: share-constraint formulation
+UE_industry_sp_solar:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    Industry Specific sahre constraint - restrict contribution of solar
+    technologies to x % of industry thermal energy demand.
+  group: share-constraint formulation
+UE_industry_th:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    share constraint for useful energy industry thermal
+  group: share-constraint formulation
+UE_industry_th_electric:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    share constraint for electric useful energy industry thermal
+  group: share-constraint formulation
+UE_industry_th_foil:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    share constraint for fuel oil useful energy industry thermal
+  group: share-constraint formulation
+UE_industry_th_gas:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    share constraint for gas useful energy industry thermal
+  group: share-constraint formulation
+UE_industry_th_hydrogen:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    share constraint for hydrogen useful energy industry thermal
+  group: share-constraint formulation
+UE_industry_th_liquid:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    share constraint for liquids useful energy industry thermal
+  group: share-constraint formulation
+UE_industry_th_low_temp_heat:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    share constraint for low temperature heat useful energy industry thermal
+  group: share-constraint formulation
+UE_industry_th_solar:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    share constraint for solar thermal useful energy industry thermal
+  group: share-constraint formulation
+UE_industry_th_solid:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    share constraint for solids useful energy industry thermal
+  group: share-constraint formulation
+UE_res_comm_sp:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    Residential&Commercial Specific share constraint  - connects all relevant
+    technologies.
+  group: share-constraint formulation
+UE_res_comm_sp_fc:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    Residential&Commercial Specific share constraint - restrict contribution of
+    fuel cell technologies to x % of residential specific energy demand.
+  group: share-constraint formulation
+UE_res_comm_sp_solar:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    Residential&Commercial Specific share constraint - restrict contribution of
+    solar technologies to x % of residential specific energy demand.
+  group: share-constraint formulation
+UE_res_comm_th:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    Residential&Commercial Thermal share constraint  - connects all relevant
+    technologies.
+  group: share-constraint formulation
+UE_res_comm_th_biomass:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    Residential&Commercial Thermal share constraint  - restrict contribution of
+    biomass technologies to x % of residential/commercial thermal energy demand.
+  group: share-constraint formulation
+UE_res_comm_th_electric:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    Residential&Commercial Thermal share constraint  - restrict contribution of
+    electricity to x % of residential/commercial thermal energy demand.
+  group: share-constraint formulation
+UE_res_comm_th_foil:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    Residential&Commercial Thermal share constraint  - restrict contribution of
+    heavy fuel oil to x % of residential/commercial thermal energy demand.
+  group: share-constraint formulation
+UE_res_comm_th_gas:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    Residential&Commercial Thermal share constraint  - restrict contribution of
+    gaseous fuels to x % of residential/commercial thermal energy demand.
+  group: share-constraint formulation
+UE_res_comm_th_heat:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    Residential&Commercial Thermal share constraint  - restrict contribution of
+    district heat to x % of residential/commercial thermal energy demand.
+  group: share-constraint formulation
+UE_res_comm_th_hp:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    Residential&Commercial Thermal share constraint  - restrict contribution of
+    heat pumps to x % of residential/commercial thermal energy demand.
+  group: share-constraint formulation
+UE_res_comm_th_hydrogen:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    Residential&Commercial Thermal share constraint  - restrict contribution of
+    hydrogen to x % of residential/commercial thermal energy demand.
+  group: share-constraint formulation
+UE_res_comm_th_liquid:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    Residential&Commercial Thermal share constraint  - restrict contribution of
+    liquid fuels to x % of residential/commercial thermal energy demand.
+  group: share-constraint formulation
+UE_res_comm_th_solar:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    Residential&Commercial Thermal share constraint  - restrict contribution of
+    solar technologies to x % of residential/commercial thermal energy demand.
+  group: share-constraint formulation
+UE_res_comm_th_solids:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    Residential&Commercial Thermal share constraint  - restrict contribution of
+    solid fuels to x % of residential/commercial thermal energy demand.
+  group: share-constraint formulation
+UE_transport:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    Transport share constraint  - connects all relevant technologies
+  group: share-constraint formulation
+UE_transport_electric:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    Transport share constraint  - restrict contribution of electric technologies
+    to x % of transport energy demand.
+  group: share-constraint formulation
+UE_transport_electric_Minimum:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    Transport share constraint  - restrict LOWER contribution of electric
+    technologies to x % of transport energy demand.
+  group: share-constraint formulation
+UE_transport_fc:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    Transport share constraint  - restrict contribution of fuel cell
+    technologies to x % of transport energy demand.
+  group: share-constraint formulation
+UE_transport_foil:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    Transport share constraint  - restrict contribution of heavy fuel oil to x %
+    of transport energy demand.
+  group: share-constraint formulation
+UE_transport_gas:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    Transport share constraint  - restrict contribution of gaseous fuels to x %
+    of transport energy demand.
+  group: share-constraint formulation
+UE_transport_liquid:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    Transport share constraint  - restrict contribution of liquid fuels to x %
+    of transport energy demand.
+  group: share-constraint formulation
+unabated_coal_lim:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    R11-PAO, R11-NAM, R11-WEU only - Limits unabated coal power plants to
+    present share.
+  group: share-constraint formulation
+VOC_Emission:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    Emissions of VOC (weight as VOC, kg VOC per kWyr)
+  group: emission_factor
+VOC_nonenergy:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    VOC emission from non-energy sectors
+  group: emission_factor
+WasteGenToBCALink:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    link from waste generation to BC
+  group: emission_factor
+WasteGenToCH4Link:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    link from waste generation to CH4
+  group: emission_factor
+WasteGenToCOLink:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    link from waste generation to CO
+  group: emission_factor
+WasteGenToNOxLink:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    link from waste generation to Nox
+  group: emission_factor
+WasteGenToOCALink:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    link from waste generation to OC
+  group: emission_factor
+WasteGenToPM2Link:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    link from waste generation to PM2.5
+  group: emission_factor
+WasteGenToSO2Link:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    link from waste generation to SO2
+  group: emission_factor
+WasteGenToVOCLink:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    link from waste generation to VOC
+  group: emission_factor
+weight_ind:
+  technology-entry: 1
+  parameters: activity, cost
+  description: >-
+    Weight factor representing costs, inconvenience, etc  in lieu of end-use
+    technology costs in industries.
+weight_rc:
+  technology-entry: 1
+  parameters: activity, cost
+  description: >-
+    inconvenience costs for residential and commercial sector
+weight_trp:
+  technology-entry: 1
+  parameters: activity, cost
+  description: >-
+    inconvenience costs for transport sector
+wind_curtailment_1:
+  parameters: activity, total_capacity
+  technology-entry: 2
+  parameters2: upper
+  description: >-
+    electricity systems integration constraint (curtailment)
+  group: renewable formulation
+wind_curtailment_2:
+  parameters: activity, total_capacity
+  technology-entry: 2
+  parameters2: upper
+  description: >-
+    electricity systems integration constraint (curtailment)
+  group: renewable formulation
+wind_curtailment_3:
+  parameters: activity, total_capacity
+  technology-entry: 2
+  parameters2: upper
+  description: >-
+    electricity systems integration constraint (curtailment)
+  group: renewable formulation
+wind_pof:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    offshore wind resource potential
+  group: renewable formulation
+wind_pof2:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    offshore wind resource potential
+  group: renewable formulation
+wind_pof3:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    offshore wind resource potential
+  group: renewable formulation
+wind_pof4:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    offshore wind resource potential
+  group: renewable formulation
+wind_pof5:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    offshore wind resource potential
+  group: renewable formulation
+wind_pot:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    onshore wind resource potential
+  group: renewable formulation
+wind_pot2:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    onshore wind resource potential
+  group: renewable formulation
+wind_pot3:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    onshore wind resource potential
+  group: renewable formulation
+wind_pot4:
+  technology-entry: 1
+  parameters: activity, upper
+  description: >-
+    onshore wind resource potential
+  group: renewable formulation
+wind_ppl_offshore_res:
+  technology-entry: 1
+  parameters: total_capacity, lower, upper
+  group: renewable formulation
+wind_ppl_res:
+  technology-entry: 1
+  parameters: total_capacity, lower, upper
+  group: renewable formulation
+wind_res_cv:
+  technology-entry: 1
+  parameters: activity, lower, upper
+  description: >-
+    balances the wind_res and wind_cv technologies
+  group: renewable formulation
+wind_step:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    first step of wind generation is limited to a certain fraction of load
+    (higher steps have worse oper and resm).
+  group: renewable formulation
+wind_step2:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    second step of wind generation is limited to a certain fraction of load
+    (higher steps have worse oper and resm).
+  group: renewable formulation
+wind_step3:
+  technology-entry: 1
+  parameters: activity, lower
+  description: >-
+    third step of wind generation is limited to a certain fraction of load
+    (higher steps have worse oper and resm).
+  group: renewable formulation
diff --git a/nest/message_ix_models/data/technology.yaml b/nest/message_ix_models/data/technology.yaml
new file mode 100644
index 0000000..78ee17c
--- /dev/null
+++ b/nest/message_ix_models/data/technology.yaml
@@ -0,0 +1,3020 @@
+# This file describes a possible set of base technologies to be used in the
+# global model. It will be usable by both model creation and reporting code.
+#
+# Each entry includes the following fields:
+# - name, description: required.
+# - sector: a label to group multiple technologies to a notional sector.
+#   Required.
+# - output (required) and input (optional): either
+#   - a list of [commodity, level] giving the output generated or input used by
+#     the technology; or,
+#   - a list of 2 or more such lists, if the technology has multiple inputs or
+#     outputs.
+# - vintaged: True if the technology's properties vary by year_vintage.
+#   Optional; False if omitted.
+# - type: In the Excel file used to create this YAML file (see
+#   https://github.com/iiasa/message_data/issues/74), 'type' appears to be
+#   always the same as 'output'/level; *unless* the 'output'/commodity is a
+#   dummy commodity, in which case it is 'dummy'.
+#
+#   TODO if this is the case, remove 'type' from this file, and generate 'type'
+#        in tools.technologies.get_info.
+
+CF4_TCE:
+  name: CF4_TCE
+  description: Tetrafluoromethane (CF4) Total Carbon Emissions
+  type: primary
+  sector: dummy
+  output: [dummy, primary]
+
+CH4_TCE:
+  name: CH4_TCE
+  description: Methane total carbon equivalent emissions
+  type: dummy
+  sector: dummy
+  output: [dummy, primary]
+
+CH4g_TCE:
+  name: CH4g_TCE
+  description: CH4 emissions from animals directly in Total Carbon Equivalent emissions
+  type: dummy
+  sector: dummy
+  output: [dummy agriculture, primary]
+
+CH4n_TCE:
+  name: CH4n_TCE
+  description: CH4 emissions from anaerobic waste decomposition in Total Carbon Equivalent emissions
+  type: dummy
+  sector: dummy
+  output: [dummy, primary]
+
+CH4o_TCE:
+  name: CH4o_TCE
+  description: Dummy technology converting CH4 emissions from industrial and domestic wastewater, non energy biomass burning and other CH4 emissions, to total carbon equivalent emissions (TCE)
+  type: dummy
+  sector: dummy
+  output: [dummy, primary]
+
+CO2_TCE:
+  name: CO2_TCE
+  description: CO2 total carbon equivalent emissions
+  type: dummy
+  sector: dummy
+  output: [dummy, primary]
+
+dom_total:
+  name: dom_total
+  description: Used in balance equation for domestic energy supply and in the equation for constraining net imports
+  type: secondary
+  sector: dummy
+  output: [exports, secondary]
+
+dummy_producer:
+  name: dummy_producer
+  description: Technology added to produce dummy energy to avoid infeasibility if needed (e.g. when CO2_TCE needs to go negative)
+  type: primary
+  sector: dummy
+  output: [dummy, primary]
+
+exp_total:
+  name: exp_total
+  description: Used in balance equation for exported energy supply and in the equation for constraining net imports
+  type: secondary
+  sector: dummy
+  output: [exports, secondary]
+
+HFC_TCE:
+  name: HFC_TCE
+  description: HFC total carbon equivalent emissions
+  type: primary
+  sector: dummy
+  output: [dummy, primary]
+
+HFCo_TCE:
+  name: HFCo_TCE
+  description: Dummy technology converting HFC equiv emissions from solvents, fire extinguishers, aerosols MDI, aerosols non-MDI to total carbon equivalent emissions (TCE)
+  type: primary
+  sector: dummy
+  output: [dummy, primary]
+
+imp_total:
+  name: imp_total
+  description: Used in balance equation for imported energy supply
+  type: exports
+  sector: dummy
+  output: [exports, secondary]
+
+N2O_TCE:
+  name: N2O_TCE
+  description: N2O total carbon equivalent emissions
+  type: dummy
+  sector: dummy
+  output: [dummy]
+
+N2OG_TCE:
+  name: N2OG_TCE
+  description: N2O soil emissions total carbon equivalent emissions
+  type: dummy
+  sector: dummy
+  output: [dummy]
+
+N2On_TCE:
+  name: N2On_TCE
+  description: N2O adipic acid total carbon equivalent emissions
+  type: dummy
+  sector: dummy
+  output: [dummy]
+
+N2Oo_TCE:
+  name: N2Oo_TCE
+  description: Dummy technology converting N2O emissions from Manure Management, Human Sewage, Other Agricultural sources, Other Non Ag Sources to total carbon equivalent emissions (TCE)
+  type: dummy
+  sector: dummy
+  output: [dummy]
+
+nica_con:
+  name: nica_con
+  type: dummy
+  sector: dummy
+  output: [dummy]
+
+nitric_catalytic1:
+  name: nitric_catalytic1
+  description: Mitigation technology (catalytic converter) category 2 for N2O emissions
+  type: dummy
+  sector: dummy
+  output: [dummy, primary]
+
+nitric_catalytic2:
+  name: nitric_catalytic2
+  description: Mitigation technology (catalytic converter) category 1 for N2O emissions
+  type: dummy
+  sector: dummy
+  output: [dummy, primary]
+
+nitric_catalytic3:
+  name: nitric_catalytic3
+  description: Mitigation technology (catalytic converter) category 3 for N2O emissions
+  type: dummy
+  sector: dummy
+  output: [dummy, primary]
+
+nitric_catalytic4:
+  name: nitric_catalytic4
+  description: Mitigation technology (catalytic converter) category 4 for N2O emissions
+  type: dummy
+  sector: dummy
+  output: [dummy, primary]
+
+nitric_catalytic5:
+  name: nitric_catalytic5
+  description: Mitigation technology (catalytic converter) category 5 for N2O emissions
+  type: dummy
+  sector: dummy
+  output: [dummy, primary]
+
+nitric_catalytic6:
+  name: nitric_catalytic6
+  description: Mitigation technology (catalytic converter) category 6 for N2O emissions
+  type: dummy
+  sector: dummy
+  output: [dummy, primary]
+
+nitric_catalytic7:
+  name: nitric_catalytic7
+  description: Mitigation technology (catalytic converter) category 7 for N2O emissions
+  type: dummy
+  sector: dummy
+  output: [dummy, primary]
+
+SF6_TCE:
+  name: SF6_TCE
+  description: SF6 total carbon equivalent emissions
+  type: primary
+  sector: dummy
+  output: [dummy, primary]
+
+useful_feedstock:
+  name: useful_feedstock
+  description: Share constraint for industry feedstocks
+  type: dummy
+  sector: dummy
+  output: [dummy_useful, primary]
+
+useful_industry_sp:
+  name: useful_industry_sp
+  description: Share constraint for Industry Specific
+  type: dummy
+  sector: dummy
+  output: [dummy_useful, primary]
+
+useful_industry_th:
+  name: useful_industry_th
+  description: Share constraint for Industry Thermal
+  type: dummy
+  sector: dummy
+  output: [dummy_useful, primary]
+
+useful_res/comm_sp:
+  name: useful_res/comm_sp
+  description: Share constraint for Residential and Commercial Specific
+  type: dummy
+  sector: dummy
+  output: [dummy_useful, primary]
+
+useful_res/comm_th:
+  name: useful_res/comm_th
+  description: Share constraint for Residential and Commercial Thermal
+  type: dummy
+  sector: dummy
+  output: [dummy_useful, primary]
+
+useful_transport:
+  name: useful_transport
+  description: Share constraint for Transport
+  type: dummy
+  sector: dummy
+  output: [dummy_useful, primary]
+
+bio_istig:
+  name: bio_istig
+  description: Advanced biomass power plant- gasified biomass is burned in gas turbine plant - modes with and without net carbon release
+  type: secondary
+  vintaged: TRUE
+  sector: electricity
+  input: [biomass, primary]
+  output: [electr, secondary]
+
+bio_istig_ccs:
+  name: bio_istig_ccs
+  description: Advanced biomass power plant with carbon capture and storage- gasified biomass is burned in gas turbine plant - modes with and without net carbon release
+  type: secondary
+  vintaged: TRUE
+  sector: electricity
+  input: [biomass, primary]
+  output: [electr, secondary]
+
+bio_ppl:
+  name: bio_ppl
+  description: Bio powerplant
+  type: secondary
+  vintaged: TRUE
+  sector: electricity
+  input:
+    - [biomass, primary]
+    - [cooling__bio_ppl, cooling]
+    - [freshwater_supply, water_supply]
+  output: [electr, secondary]
+
+coal_adv:
+  name: coal_adv
+  description: Advanced coal power plant
+  type: secondary
+  vintaged: TRUE
+  sector: electricity
+  input: [coal, secondary]
+  output: [electr, secondary]
+
+coal_adv_ccs:
+  name: coal_adv_ccs
+  description: Advanced coal power plant with carbon capture and storage
+  type: secondary
+  vintaged: TRUE
+  sector: electricity
+  input: [coal, secondary]
+  output: [electr, secondary]
+
+coal_ppl:
+  name: coal_ppl
+  description: Coal power-plant
+  type: secondary
+  vintaged: TRUE
+  sector: electricity
+  input: [coal, secondary]
+  output: [electr, secondary]
+
+coal_ppl_u:
+  name: coal_ppl_u
+  description: Coal power plant without abatement measures
+  type: secondary
+  vintaged: TRUE
+  sector: electricity
+  input: [coal, secondary]
+  output: [electr, secondary]
+
+elec_exp:
+  name: elec_exp
+  description: Net export of electricity
+  type: exports
+  sector: electricity
+  input: [electr, secondary]
+  output: [electr, exports]
+
+elec_imp:
+  name: elec_imp
+  description: Net import of electricity
+  type: secondary
+  sector: electricity
+  input: [electr, imports]
+  output: [electr, secondary]
+
+elec_t/d:
+  name: elec_t/d
+  description: Grid technology cost converted to 2005$
+  type: final
+  vintaged: TRUE
+  sector: electricity
+  input: [electr, secondary]
+  output: [electr, final]
+
+foil_ppl:
+  name: foil_ppl
+  description: New standard oil power plant, Rankine cycle
+  type: secondary
+  vintaged: TRUE
+  sector: electricity
+  input: [fueloil, secondary]
+  output: [electr, secondary]
+
+gas_cc:
+  name: gas_cc
+  description: Gas combined cycle power-plant
+  type: secondary
+  vintaged: TRUE
+  sector: electricity
+  input: [gas, secondary]
+  output: [electr, secondary]
+
+gas_cc_ccs:
+  name: gas_cc_ccs
+  description: Gas combined cycle power-plant with carbon capture and storage
+  type: secondary
+  vintaged: TRUE
+  sector: electricity
+  input: [gas, secondary]
+  output: [electr, secondary]
+
+gas_ct:
+  name: gas_ct
+  description: Gas combustion-turbine power plant
+  type: secondary
+  vintaged: TRUE
+  sector: electricity
+  input: [gas, secondary]
+  output: [electr, secondary]
+
+gas_htfc:
+  name: gas_htfc
+  description: High temperature fuel cell powered with natural gas
+  type: secondary
+  vintaged: TRUE
+  sector: electricity
+  input: [gas, secondary]
+  output: [electr, secondary]
+
+gas_ppl:
+  name: gas_ppl
+  description: Gas power plant, Rankine cycle
+  type: secondary
+  vintaged: TRUE
+  sector: electricity
+  input: [gas, secondary]
+  output: [electr, secondary]
+
+geo_ppl:
+  name: geo_ppl
+  description: Geothermal power plant
+  type: secondary
+  vintaged: TRUE
+  sector: electricity
+  output: [electr, secondary]
+
+glb_elec_exp:
+  name: glb_elec_exp
+  description: Global net export of electricity
+  type: imports
+  sector: electricity
+  output: [electr, imports]
+
+glb_elec_imp:
+  name: glb_elec_imp
+  description: Global net import of electricity
+  type: exports
+  sector: electricity
+  input: [electr, exports]
+  output: [exports]
+
+hydro_hc:
+  name: hydro_hc
+  description: High cost hydro power plant
+  type: secondary
+  vintaged: TRUE
+  sector: electricity
+  output: [electr, secondary]
+
+hydro_lc:
+  name: hydro_lc
+  description: Low cost hydro power plant
+  type: secondary
+  vintaged: TRUE
+  sector: electricity
+  output: [electr, secondary]
+
+igcc:
+  name: igcc
+  description: Integrated gasification combined cycle (IGCC) power plant
+  type: secondary
+  vintaged: TRUE
+  sector: electricity
+  input: [coal, secondary]
+  output: [electr, secondary]
+
+igcc_ccs:
+  name: igcc_ccs
+  description: Integrated gasification combined cycle (IGCC) power plant with carbon capture and storage
+  type: secondary
+  vintaged: TRUE
+  sector: electricity
+  input: [coal, secondary]
+  output: [electr, secondary]
+
+igcc_co2scr:
+  name: igcc_co2scr
+  description: New coal scrubber for igcc plants
+  type: exports
+  vintaged: TRUE
+  sector: electricity
+  output: [exports, secondary]
+
+loil_cc:
+  name: loil_cc
+  description: Light oil combined cycle
+  type: secondary
+  vintaged: TRUE
+  sector: electricity
+  input: [lightoil, secondary]
+  output: [electr, secondary]
+
+loil_ppl:
+  name: loil_ppl
+  description: Existing light oil power-plant
+  type: secondary
+  vintaged: TRUE
+  sector: electricity
+  input: [lightoil, secondary]
+  output: [electr, secondary]
+
+nuc_hc:
+  name: nuc_hc
+  description: Nuclear power plant (~GEN III+), high cost
+  type: secondary
+  vintaged: TRUE
+  sector: electricity
+  input: [uranium, stocks]
+  output: [electr, secondary]
+
+nuc_lc:
+  name: nuc_lc
+  description: Nuclear power plant (~GEN II), low cost
+  type: secondary
+  vintaged: TRUE
+  sector: electricity
+  input: [uranium, stocks]
+  output: [electr, secondary]
+
+solar_curtailment1:
+  name: solar_curtailment1
+  description: Solar PV curtailment steps
+  type: dummy
+  sector: electricity
+  input: [electr, secondary]
+  output: [dummy renewable, secondary]
+
+solar_curtailment2:
+  name: solar_curtailment2
+  description: Solar PV curtailment steps
+  type: dummy
+  sector: electricity
+  input: [electr, secondary]
+  output: [dummy renewable, secondary]
+
+solar_curtailment3:
+  name: solar_curtailment3
+  description: Solar PV curtailment steps
+  type: dummy
+  sector: electricity
+  input: [electr, secondary]
+  output: [dummy renewable, secondary]
+
+solar_cv1:
+  name: solar_cv1
+  description: Quadratic systems integration costs added to solar PV
+  type: dummy
+  sector: electricity
+  output: [dummy renewable, secondary]
+
+solar_cv2:
+  name: solar_cv2
+  description: Quadratic systems integration costs added to solar PV
+  type: dummy
+  sector: electricity
+  output: [dummy renewable, secondary]
+
+solar_cv3:
+  name: solar_cv3
+  description: Quadratic systems integration costs added to solar PV
+  type: dummy
+  sector: electricity
+  output: [dummy renewable, secondary]
+
+solar_cv4:
+  name: solar_cv4
+  description: Quadratic systems integration costs added to solar PV
+  type: dummy
+  sector: electricity
+  output: [dummy renewable, secondary]
+
+solar_pv_ppl:
+  name: solar_pv_ppl
+  description: Solar photovoltaic power plant (no storage)
+  type: dummy
+  vintaged: TRUE
+  sector: electricity
+  output: [dummy renewable, secondary]
+
+solar_res1:
+  name: solar_res1
+  description: Maximum solar electricity potential 1
+  type: secondary
+  sector: electricity
+  output: [electr, secondary]
+
+solar_res2:
+  name: solar_res2
+  description: Maximum solar electricity potential 2
+  type: secondary
+  sector: electricity
+  output: [electr, secondary]
+
+solar_res3:
+  name: solar_res3
+  description: Maximum solar electricity potential 3
+  type: secondary
+  sector: electricity
+  output: [electr, secondary]
+
+solar_res4:
+  name: solar_res4
+  description: Maximum solar electricity potential 4
+  type: secondary
+  sector: electricity
+  output: [electr, secondary]
+
+solar_res5:
+  name: solar_res5
+  description: Maximum solar electricity potential 5
+  type: secondary
+  sector: electricity
+  output: [electr, secondary]
+
+solar_res6:
+  name: solar_res6
+  description: Maximum solar electricity potential 6
+  type: secondary
+  sector: electricity
+  output: [electr, secondary]
+
+solar_res7:
+  name: solar_res7
+  description: Maximum solar electricity potential 7
+  type: secondary
+  sector: electricity
+  output: [electr, secondary]
+
+solar_th_ppl:
+  name: solar_th_ppl
+  description: Solar thermal power plant with storage
+  type: secondary
+  vintaged: TRUE
+  sector: electricity
+  output: [electr, secondary]
+
+stor_ppl:
+  name: stor_ppl
+  description: Generic electric storage
+  type: secondary
+  vintaged: TRUE
+  sector: electricity
+  input: [electr, secondary]
+  output: [exports, secondary]
+
+wind_curtailment1:
+  name: wind_curtailment1
+  description: Wind curtailment steps
+  type: dummy
+  sector: electricity
+  input: [secondary, electricity]
+  output: [dummy renewable, secondary]
+
+wind_curtailment2:
+  name: wind_curtailment2
+  description: Wind curtailment steps
+  type: dummy
+  sector: electricity
+  input: [secondary, electricity]
+  output: [dummy renewable, secondary]
+
+wind_curtailment3:
+  name: wind_curtailment3
+  description: Wind curtailment steps
+  type: dummy
+  sector: electricity
+  input: [secondary, electricity]
+  output: [dummy renewable, secondary]
+
+wind_cv1:
+  name: wind_cv1
+  description: Wind flexibility requirement and firm capacity contribution, quadratic systems integration costs added to wind
+  type: dummy
+  sector: electricity
+  output: [dummy renewable, secondary]
+
+wind_cv2:
+  name: wind_cv2
+  description: Wind flexibility requirement and firm capacity contribution, quadratic systems integration costs added to wind
+  type: dummy
+  sector: electricity
+  output: [dummy renewable, secondary]
+
+wind_cv3:
+  name: wind_cv3
+  description: Wind flexibility requirement and firm capacity contribution, quadratic systems integration costs added to wind
+  type: dummy
+  sector: electricity
+  output: [dummy renewable, secondary]
+
+wind_cv4:
+  name: wind_cv4
+  description: Wind flexibility requirement and firm capacity contribution, quadratic systems integration costs added to wind
+  type: dummy
+  sector: electricity
+  output: [dummy renewable, secondary]
+
+wind_ppl:
+  name: wind_ppl
+  description: Wind power plant onshore (provides capacity only)
+  type: dummy
+  vintaged: TRUE
+  sector: electricity
+  output: [dummy renewable, secondary]
+
+wind_res1:
+  name: wind_res1
+  description: Wind onshore potential and generation 1
+  type: secondary
+  sector: electricity
+  output: [electr, secondary]
+
+wind_res2:
+  name: wind_res2
+  description: Wind onshore potential and generation 2
+  type: secondary
+  sector: electricity
+  output: [electr, secondary]
+
+wind_res3:
+  name: wind_res3
+  description: Wind onshore potential and generation 3
+  type: secondary
+  sector: electricity
+  output: [electr, secondary]
+
+wind_res4:
+  name: wind_res4
+  description: Wind onshore potential and generation 4
+  type: secondary
+  sector: electricity
+  output: [electr, secondary]
+
+wind_ppf:
+  name: wind_ppf
+  description: Wind power plant offshore (provides capacity only)
+  type: secondary
+  vintaged: TRUE
+  sector: electricity
+  output: [dummy renewable, secondary]
+
+wind_ref1:
+  name: wind_ref1
+  description: Wind offshore potential and generation 1
+  type: secondary
+  sector: electricity
+  output: [electr, secondary]
+
+wind_ref2:
+  name: wind_ref2
+  description: Wind offshore potential and generation 2
+  type: secondary
+  sector: electricity
+  output: [electr, secondary]
+
+wind_ref3:
+  name: wind_ref3
+  description: Wind offshore potential and generation 3
+  type: secondary
+  sector: electricity
+  output: [electr, secondary]
+
+wind_ref4:
+  name: wind_ref4
+  description: Wind offshore potential and generation 4
+  type: secondary
+  sector: electricity
+  output: [electr, secondary]
+
+wind_ref5:
+  name: wind_ref5
+  description: Wind offshore potential and generation 5
+  type: secondary
+  sector: electricity
+  output: [electr, secondary]
+
+csp_sm3_res:
+  name: csp_sm3_res
+  description: Concentrating solar power (CSP) with solar multiple of 3 potential and generation 1
+  type: secondary
+  sector: electricity
+  output: [electr, secondary]
+
+csp_sm3_res1:
+  name: csp_sm3_res1
+  description: Concentrating solar power (CSP) with solar multiple of 3 potential and generation 2
+  type: secondary
+  sector: electricity
+  output: [electr, secondary]
+
+csp_sm3_res2:
+  name: csp_sm3_res2
+  description: Concentrating solar power (CSP) with solar multiple of 3 potential and generation 3
+  type: secondary
+  sector: electricity
+  output: [electr, secondary]
+
+csp_sm3_res3:
+  name: csp_sm3_res3
+  description: Concentrating solar power (CSP) with solar multiple of 3 potential and generation 4
+  type: secondary
+  sector: electricity
+  output: [electr, secondary]
+
+csp_sm3_res4:
+  name: csp_sm3_res4
+  description: Concentrating solar power (CSP) with solar multiple of 3 potential and generation 5
+  type: secondary
+  sector: electricity
+  output: [electr, secondary]
+
+csp_sm3_res5:
+  name: csp_sm3_res5
+  description: Concentrating solar power (CSP) with solar multiple of 3 potential and generation 6
+  type: secondary
+  sector: electricity
+  output: [electr, secondary]
+
+csp_sm3_res6:
+  name: csp_sm3_res6
+  description: Concentrating solar power (CSP) with solar multiple of 3 potential and generation 7
+  type: secondary
+  sector: electricity
+  output: [electr, secondary]
+
+csp_sm3_res7:
+  name: csp_sm3_res7
+  description: Concentrating solar power (CSP) with solar multiple of 3 potential and generation 8
+  type: secondary
+  sector: electricity
+  output: [electr, secondary]
+
+csp_sm3_ppl:
+  name: csp_sm3_ppl
+  description: Concentrating solar power (CSP) with solar multiple of 3 (provides capacity only)
+  type: secondary
+  vintaged: TRUE
+  sector: electricity
+  output: [dummy renewable, secondary]
+
+csp_sm1_res:
+  name: csp_sm1_res
+  description: Concentrating solar power (CSP) with solar multiple of 1 potential and generation 1
+  type: secondary
+  sector: electricity
+  output: [electr, secondary]
+
+csp_sm1_res1:
+  name: csp_sm1_res1
+  description: Concentrating solar power (CSP) with solar multiple of 1 potential and generation 2
+  type: secondary
+  sector: electricity
+  output: [electr, secondary]
+
+csp_sm1_res2:
+  name: csp_sm1_res2
+  description: Concentrating solar power (CSP) with solar multiple of 1 potential and generation 3
+  type: secondary
+  sector: electricity
+  output: [electr, secondary]
+
+csp_sm1_res3:
+  name: csp_sm1_res3
+  description: Concentrating solar power (CSP) with solar multiple of 1 potential and generation 4
+  type: secondary
+  sector: electricity
+  output: [electr, secondary]
+
+csp_sm1_res4:
+  name: csp_sm1_res4
+  description: Concentrating solar power (CSP) with solar multiple of 1 potential and generation 5
+  type: secondary
+  sector: electricity
+  output: [electr, secondary]
+
+csp_sm1_res5:
+  name: csp_sm1_res5
+  description: Concentrating solar power (CSP) with solar multiple of 1 potential and generation 6
+  type: secondary
+  sector: electricity
+  output: [electr, secondary]
+
+csp_sm1_res6:
+  name: csp_sm1_res6
+  description: Concentrating solar power (CSP) with solar multiple of 1 potential and generation 7
+  type: secondary
+  sector: electricity
+  output: [electr, secondary]
+
+csp_sm1_res7:
+  name: csp_sm1_res7
+  description: Concentrating solar power (CSP) with solar multiple of 1 potential and generation 8
+  type: secondary
+  sector: electricity
+  output: [electr, secondary]
+
+csp_sm1_ppl:
+  name: csp_sm1_ppl
+  description: Concentrating solar power (CSP) with solar multiple of 1 (provides capacity only)
+  type: secondary
+  vintaged: TRUE
+  sector: electricity
+  output: [dummy renewable, secondary]
+
+bio_extr_1:
+  name: bio_extr_1
+  description: Biomass Extraction
+  type: primary
+  sector: extraction
+  output: [biomass, primary]
+
+bio_extr_2:
+  name: bio_extr_2
+  description: Biomass Extraction
+  type: primary
+  sector: extraction
+  output: [biomass, primary]
+
+bio_extr_3:
+  name: bio_extr_3
+  description: Biomass Extraction
+  type: primary
+  sector: extraction
+  output: [biomass, primary]
+
+bio_extr_4:
+  name: bio_extr_4
+  description: Biomass Extraction
+  type: primary
+  sector: extraction
+  output: [biomass, primary]
+
+bio_extr_5:
+  name: bio_extr_5
+  description: Biomass Extraction
+  type: primary
+  sector: extraction
+  output: [biomass, primary]
+
+bio_extr_6:
+  name: bio_extr_6
+  description: Biomass Extraction
+  type: primary
+  sector: extraction
+  output: [biomass, primary]
+
+coal_extr:
+  name: coal_extr
+  description: Hard coal extraction, world average grade A
+  type: primary
+  sector: extraction
+  input: [coal, resource]
+  output: [coal, primary]
+
+coal_extr_ch4:
+  name: coal_extr_ch4
+  description: Describes efforts in CH4-reduction from coal mining
+  type: primary
+  vintaged: TRUE
+  sector: extraction
+  input: [coal, resource]
+  output: [coal, primary]
+
+flaring_CO2:
+  name: flaring_CO2
+  description: Co2 emissions from gas flaring
+  type: exports
+  sector: extraction
+  output: [exports, exports]
+
+gas_extr_1:
+  name: gas_extr_1
+  description: Natural gas extraction, Cat I = Master et al.14.WPC "Identified Reserves"
+  type: primary
+  sector: extraction
+  input: [resource]
+  output: [primary]
+
+gas_extr_2:
+  name: gas_extr_2
+  description: Natural gas extraction, Cat II = Master et al.14.WPC "Mode" undiscovered natural gas
+  type: primary
+  sector: extraction
+  input: [resource]
+  output: [primary]
+
+gas_extr_3:
+  name: gas_extr_3
+  description: Natural gas extraction, Cat III = Masters  et al.14.WPC Difference between "Mode and 5%"  undiscovered natural gas
+  type: primary
+  sector: extraction
+  input: [resource]
+  output: [primary]
+
+gas_extr_4:
+  name: gas_extr_4
+  description: Natural gas extraction, Cat IV = Estimated enhanced Recovery (30% of Resources I+II+III)  plus 15% of historical production
+  type: primary
+  sector: extraction
+  input: [resource]
+  output: [primary]
+
+gas_extr_5:
+  name: gas_extr_5
+  description: Natural gas extraction, Cat V = Non-conventional reserves (20% of Coal bed; 15% of fractured Shale; 15% of Tight formation)
+  type: primary
+  sector: extraction
+  input: [resource]
+  output: [primary]
+
+gas_extr_6:
+  name: gas_extr_6
+  description: Natural gas extraction, Cat VI -VII= Non-conventional resources. Rest of Coal bed (80%), fractured Shale (85%) and Tight formation (85%) were aggregated and then distributed to VI (40%) nd VII (60%)
+  type: primary
+  sector: extraction
+  input: [resource]
+  output: [primary]
+
+gas_extr_mpen:
+  name: gas_extr_mpen
+  description: Common Market penetration for all gas extraction technologies
+  type: secondary
+  sector: extraction
+  output: [exports, secondary]
+
+lignite_extr:
+  name: lignite_extr
+  description: Lignite extraction, world average grade A
+  type: primary
+  sector: extraction
+  input: [lignite, resource]
+  output: [coal, primary]
+
+oil_extr_1:
+  name: oil_extr_1
+  description: Crude oil extraction, Cat I = Masters 14 WPC conv. oil reserves
+  type: primary
+  sector: extraction
+  input: [crude 1 resource, primary]
+  output: [crude oil, primary]
+
+oil_extr_1_ch4:
+  name: oil_extr_1_ch4
+  description: Describes efforts in CH4-reduction from oil extraction of Cat I
+  type: primary
+  sector: extraction
+  input: [crude 1 resource, primary]
+  output: [crude oil, primary]
+
+oil_extr_2:
+  name: oil_extr_2
+  description: Crude oil extraction, Cat II = Masters mode undiscovered conv. oil (incl. NGL)
+  type: primary
+  sector: extraction
+  input: [crude 2 resource, primary]
+  output: [crude oil, primary]
+
+oil_extr_2_ch4:
+  name: oil_extr_2_ch4
+  description: Describes efforts in CH4-reduction from oil extraction of Cat II
+  type: primary
+  sector: extraction
+  input: [crude 2 resource, primary]
+  output: [crude oil, primary]
+
+oil_extr_3:
+  name: oil_extr_3
+  description: Crude oil extraction, Cat III = Masters 5% - Masters 50%
+  type: primary
+  sector: extraction
+  input: [crude 3 resource, primary]
+  output: [crude oil, primary]
+
+oil_extr_3_ch4:
+  name: oil_extr_3_ch4
+  description: Describes efforts in CH4-reduction from oil extraction of Cat III
+  type: primary
+  sector: extraction
+  input: [crude 3 resource, primary]
+  output: [crude oil, primary]
+
+oil_extr_4:
+  name: oil_extr_4
+  description: Crude oil extraction, Cat IV = Recoverable "reserves" non-conventional oil
+  type: primary
+  sector: extraction
+  input: [crude 4 resource, primary]
+  output: [crude oil, primary]
+
+oil_extr_4_ch4:
+  name: oil_extr_4_ch4
+  description: Describes efforts in CH4-reduction from oil extraction of Cat IV
+  type: primary
+  sector: extraction
+  input: [crude 4 resource, primary]
+  output: [crude oil, primary]
+
+oil_extr_5:
+  name: oil_extr_5
+  description: Crude oil extraction, Cat V = Recoverable reserves of nonconventional oil = Shale, tarsands/bitumen and heavy oils
+  type: primary
+  sector: extraction
+  input: [crude 5 resource, primary]
+  output: [crude oil, primary]
+
+oil_extr_6:
+  name: oil_extr_6
+  description: Crude oil extraction, Cat VI = 20% of estimated occurrences (-reserves) of shale, heavy oils, tarsands/bitumen
+  type: primary
+  sector: extraction
+  input: [crude 6 resource, primary]
+  output: [crude oil, primary]
+
+oil_extr_mpen:
+  name: oil_extr_mpen
+  description: Common Market penetration for all oil extraction technologies
+  type: secondary
+  sector: extraction
+  output: [exports, secondary]
+
+Feeds_1:
+  name: Feeds_1
+  description: Conservation cost curve step for industry feedstock demand
+  type: useful
+  sector: feedstock
+  output: [i_feed, useful]
+
+Feeds_2:
+  name: Feeds_2
+  description: Conservation cost curve step for industry feedstock demand
+  type: useful
+  sector: feedstock
+  output: [i_feed, useful]
+
+Feeds_3:
+  name: Feeds_3
+  description: Conservation cost curve step for industry feedstock demand
+  type: useful
+  sector: feedstock
+  output: [i_feed, useful]
+
+Feeds_4:
+  name: Feeds_4
+  description: Conservation cost curve step for industry feedstock demand
+  type: useful
+  sector: feedstock
+  output: [i_feed, useful]
+
+Feeds_5:
+  name: Feeds_5
+  description: Conservation cost curve step for industry feedstock demand
+  type: useful
+  sector: feedstock
+  output: [i_feed, useful]
+
+Feeds_con:
+  name: Feeds_con
+  description: Joint diffusion constraint for feedstock conservation cost curve steps
+  type: primary
+  sector: feedstock
+  output: [dummy agriculture, primary]
+
+coal_fs:
+  name: coal_fs
+  description: Coal as industry feedstock
+  type: useful
+  sector: feedstock
+  input: [coal, final]
+  output: [i_feed, useful]
+
+ethanol_fs:
+  name: ethanol_fs
+  description: Ethanol as industry feedstock
+  type: useful
+  sector: feedstock
+  input: [ethanol, final]
+  output: [i_feed, useful]
+
+foil_fs:
+  name: foil_fs
+  description: Fuel oil as industry feedstock
+  type: useful
+  sector: feedstock
+  input: [fueloil, final]
+  output: [i_feed, useful]
+
+gas_fs:
+  name: gas_fs
+  description: Gas as industry feedstock
+  type: useful
+  sector: feedstock
+  input: [gas, final]
+  output: [i_feed, useful]
+
+loil_fs:
+  name: loil_fs
+  description: Lightoil as industry feedstock
+  type: useful
+  sector: feedstock
+  input: [lightoil, final]
+  output: [i_feed, useful]
+
+methanol_fs:
+  name: methanol_fs
+  description: Methanol as industry feedstock
+  type: useful
+  sector: feedstock
+  input: [methanol, final]
+  output: [i_feed, useful]
+
+coal_gas:
+  name: coal_gas
+  description: Hard coal gasification
+  type: secondary
+  vintaged: TRUE
+  sector: gas
+  input:
+    - [coal, secondary]
+    - [freshwater_supply, water_supply]
+  output: [gas, secondary]
+
+g_ppl_co2scr:
+  name: g_ppl_co2scr
+  description: CO2 scrubber for natural gas power plant
+  type: secondary
+  vintaged: TRUE
+  sector: gas
+  output: [exports, secondary]
+
+gas_bal:
+  name: gas_bal
+  description: Link technology to stabilize gas production
+  type: secondary
+  sector: gas
+  input: [gas, primary]
+  output: [gas, secondary]
+
+gas_bio:
+  name: gas_bio
+  description: Synthesis gas production from biomass
+  type: secondary
+  vintaged: TRUE
+  sector: gas
+  input:
+    - [biomass, primary]
+    - [electr, secondary]
+    - [freshwater_supply, water_supply]
+  output: [gas, secondary]
+
+gas_imp:
+  name: gas_imp
+  description: Piped Gas imports
+  type: secondary
+  sector: gas
+  input: [gas, exports]
+  output: [gas, secondary]
+
+gas_rc:
+  name: gas_rc
+  description: Gas heating in residential/commercial sector
+  type: final
+  sector: gas
+  input: [gas, secondary]
+  output: [gas, final]
+
+gas_t/d:
+  name: gas_t/d
+  description: Transmission/Distribution of gas
+  type: final
+  vintaged: TRUE
+  sector: gas
+  input: [gas, secondary]
+  output: [gas, final]
+
+gas_t/d_ch4:
+  name: gas_t/d_ch4
+  description: Transmission/Distribution of gas with CH4 mitigation
+  type: final
+  vintaged: TRUE
+  sector: gas
+  input: [gas, secondary]
+  output: [gas, final]
+
+gfc_co2scr:
+  name: gfc_co2scr
+  description: New co2 scrubber for gas fuel cells
+  type: secondary
+  vintaged: TRUE
+  sector: gas
+  output: [exports, secondary]
+
+glb_gas_exp:
+  name: glb_gas_exp
+  description: Global net export of gas
+  type: exports
+  sector: gas
+  output: [gas, exports]
+
+glb_LNG_exp:
+  name: glb_LNG_exp
+  description: Global net export of liquified natural gas
+  type: imports
+  sector: gas
+  output: [LNG, imports]
+
+h2_mix:
+  name: h2_mix
+  description: Hydrogen injection into the natural gas system
+  type: secondary
+  sector: gas
+  input: [hydrogen, secondary]
+  output: [gas, secondary]
+
+LNG_bal:
+  name: LNG_bal
+  description: Link technology to stabilize liquified natural gas production
+  type: secondary
+  sector: gas
+  input: [LNG, primary]
+  output: [LNG, secondary]
+
+LNG_imp:
+  name: LNG_imp
+  description: LNG Imports
+  type: imports
+  sector: gas
+  input: [LNG, imports]
+  output: [LNG, secondary]
+
+LNG_regas:
+  name: LNG_regas
+  description: LNG regasification (just link; losses are in trade)
+  type: secondary
+  vintaged: TRUE
+  sector: gas
+  input: [LNG, secondary]
+  output: [gas, secondary]
+
+bio_hpl:
+  name: bio_hpl
+  description: Biomass heating plant
+  type: secondary
+  vintaged: TRUE
+  sector: heat
+  input:
+    - [biomass, primary]
+    - [cooling__bio_hpl, cooling]
+    - [freshwater_supply, water_supply]
+  output: [d_heat, secondary]
+
+coal_hpl:
+  name: coal_hpl
+  description: Coal heating plant
+  type: secondary
+  vintaged: TRUE
+  sector: heat
+  input: [coal, secondary]
+  output: [d_heat, secondary]
+
+foil_hpl:
+  name: foil_hpl
+  description: Fuel oil heating plant
+  type: secondary
+  vintaged: TRUE
+  sector: heat
+  input: [fueloil, secondary]
+  output: [d_heat, secondary]
+
+gas_hpl:
+  name: gas_hpl
+  description: Natural gas heating plant
+  type: secondary
+  vintaged: TRUE
+  sector: heat
+  input: [gas, secondary]
+  output: [d_heat, secondary]
+
+geo_hpl:
+  name: geo_hpl
+  description: Geothermal heat plant
+  type: secondary
+  vintaged: TRUE
+  sector: heat
+  output: [d_heat, secondary]
+
+heat_t/d:
+  name: heat_t/d
+  description: Transmission/Distribution of district heat
+  type: final
+  vintaged: TRUE
+  sector: heat
+  input: [d_heat, secondary]
+  output: [d_heat, final]
+
+po_turbine:
+  name: po_turbine
+  description: Pass out turbine
+  type: secondary
+  vintaged: TRUE
+  sector: heat
+  input: [electr, secondary]
+  output: [d_heat, secondary]
+
+glb_lh2_imp:
+  name: glb_lh2_imp
+  description: Global net import of liquid hydrogen
+  type: exports
+  sector: hydrogen
+  input: [liquid hydrogen, exports]
+  output: [exports]
+
+h2_bio:
+  name: h2_bio
+  description: Hydrogen production from biomass with C (via gasification)
+  type: secondary
+  vintaged: TRUE
+  sector: hydrogen
+  input:
+    - [biomass, primary]
+    - [freshwater_supply, water_supply]
+  output:
+    - [hydrogen, secondary]
+    - [electr, secondary]
+
+h2_bio_ccs:
+  name: h2_bio_ccs
+  description: Hydrogen production from biomass with C (via gasification) with carbon capture and storage
+  type: secondary
+  vintaged: TRUE
+  sector: hydrogen
+  input:
+    - [biomass, primary]
+    - [freshwater_supply, water_supply]
+  output:
+    - [hydrogen, secondary]
+    - [electr, secondary]
+
+h2_co2_scrub:
+  name: h2_co2_scrub
+  description: CO2 scrubber for h2 production from coal and gas
+  type: exports
+  vintaged: TRUE
+  sector: hydrogen
+  input: [electr, secondary]
+  output: [exports, secondary]
+
+h2_coal:
+  name: h2_coal
+  description: Hydrogen production via coal gasification
+  type: secondary
+  vintaged: TRUE
+  sector: hydrogen
+  input:
+    - [coal, secondary]
+    - [freshwater_supply, water_supply]
+  output:
+    - [hydrogen, secondary]
+    - [electr, secondary]
+
+h2_coal_ccs:
+  name: h2_coal_ccs
+  description: Hydrogen production via coal gasification with carbon capture and storage
+  type: secondary
+  vintaged: TRUE
+  sector: hydrogen
+  input:
+    - [coal, secondary]
+    - [freshwater_supply, water_supply]
+  output:
+    - [hydrogen, secondary]
+    - [electr, secondary]
+
+h2_elec:
+  name: h2_elec
+  description: Hydrogen production via electrolysis
+  type: secondary
+  vintaged: TRUE
+  sector: hydrogen
+  input: [electr, secondary]
+  output: [hydrogen, secondary]
+
+h2_liq:
+  name: h2_liq
+  description: Hydrogen liquefaction
+  type: primary
+  vintaged: TRUE
+  sector: hydrogen
+  input: [hydrogen, primary]
+  output: [liquid hydrogen, primary]
+
+h2_smr:
+  name: h2_smr
+  description: Hydrogen production via steam-methane reforming of natural gas
+  type: secondary
+  vintaged: TRUE
+  sector: hydrogen
+  input:
+    - [gas, secondary]
+    - [freshwater_supply, water_supply]
+  output:
+    - [hydrogen, secondary]
+    - [electr, secondary]
+
+h2_smr_ccs:
+  name: h2_smr_ccs
+  description: Hydrogen production via steam-methane reforming of natural gas with carbon capture and storage
+  type: secondary
+  vintaged: TRUE
+  sector: hydrogen
+  input:
+    - [gas, secondary]
+    - [freshwater_supply, water_supply]
+  output:
+    - [hydrogen, secondary]
+    - [electr, secondary]
+
+h2_t/d:
+  name: h2_t/d
+  description: Transmission/Distribution of gaseous hydrogen (just linking technology)
+  type: final
+  vintaged: TRUE
+  sector: hydrogen
+  input: [hydrogen, secondary]
+  output: [hydrogen, final]
+
+h2b_co2_scrub:
+  name: h2b_co2_scrub
+  description: CO2 scrubber for h2 production from biomass
+  type: secondary
+  vintaged: TRUE
+  sector: hydrogen
+  input: [electr, secondary]
+  output: [exports, secondary]
+
+lh2_bal:
+  name: lh2_bal
+  description: Link technology to stabilize liquid hydrogen production
+  type: secondary
+  sector: hydrogen
+  input: [liquid hydrogen, primary]
+  output: [liquid hydrogen, secondary]
+
+lh2_exp:
+  name: lh2_exp
+  description: Exports of liquid hydrogen
+  type: exports
+  sector: hydrogen
+  input: [liquid hydrogen, primary]
+  output: [liquid hydrogen, exports]
+
+lh2_imp:
+  name: lh2_imp
+  description: Imports of liquid hydrogen
+  type: secondary
+  sector: hydrogen
+  input: [liquid hydrogen, exports]
+  output: [liquid hydrogen, secondary]
+
+lh2_regas:
+  name: lh2_regas
+  description: Regasification of liquid hydrogen
+  type: secondary
+  vintaged: TRUE
+  sector: hydrogen
+  input: [liquid hydrogen, secondary]
+  output: [hydrogen, secondary]
+
+lh2_t/d:
+  name: lh2_t/d
+  description: Transmission/Distribution of liquid hydrogen
+  type: final
+  vintaged: TRUE
+  sector: hydrogen
+  input: [liquid hydrogen, secondary]
+  output: [liquid hydrogen, final]
+
+back_bio_ind:
+  name: back_bio_ind
+  description: Backstop for diagnosing model infeasibility
+  type: final
+  sector: industry
+  output: [biomass, useful]
+
+back_fs:
+  name: back_fs
+  description: Backstop for diagnosing model infeasibility
+  type: useful
+  sector: industry
+  output: [i_feed, useful]
+
+back_I:
+  name: back_I
+  description: Backstop for diagnosing model infeasibility
+  type: useful
+  sector: industry
+  output: [i_spec, useful]
+
+cement_CO2:
+  name: cement_CO2
+  description: Co2 emissions from cement production
+  type: secondary
+  sector: industry
+  output: [exports, secondary]
+
+cement_co2scr:
+  name: cement_co2scr
+  description: Cement CO2 scrubber (CCS)
+  type: secondary
+  vintaged: TRUE
+  sector: industry
+  output: [exports, secondary]
+
+coal_i:
+  name: coal_i
+  description: Coal in industry thermal
+  type: useful
+  vintaged: TRUE
+  sector: industry
+  input: [coal, final]
+  output: [i_therm, useful]
+
+elec_i:
+  name: elec_i
+  description: Electricity in industry thermal
+  type: useful
+  vintaged: TRUE
+  sector: industry
+  input: [electr, final]
+  output: [i_therm, useful]
+
+eth_i:
+  name: eth_i
+  description: Ethanol (without C) replacement for use as liquid fuel in industry thermal
+  type: useful
+  vintaged: TRUE
+  sector: industry
+  input: [ethanol, final]
+  output: [i_therm, useful]
+
+foil_i:
+  name: foil_i
+  description: Fuel oil for thermal uses in industry thermal
+  type: useful
+  vintaged: TRUE
+  sector: industry
+  input: [fueloil, final]
+  output: [i_therm, useful]
+
+gas_i:
+  name: gas_i
+  description: Gas for thermal uses in industry thermal
+  type: useful
+  vintaged: TRUE
+  sector: industry
+  input: [gas, final]
+  output: [i_therm, useful]
+
+h2_i:
+  name: h2_i
+  description: Gaseous hydrogen in industry thermal
+  type: useful
+  vintaged: TRUE
+  sector: industry
+  input: [hydrogen, final]
+  output: [i_therm, useful]
+
+heat_i:
+  name: heat_i
+  description: District heating for thermal uses in industry thermal
+  type: useful
+  vintaged: TRUE
+  sector: industry
+  input: [d_heat, final]
+  output: [i_therm, useful]
+
+hp_el_i:
+  name: hp_el_i
+  description: Electric heat pump in industry thermal
+  type: useful
+  vintaged: TRUE
+  sector: industry
+  input: [electr, final]
+  output: [i_therm, useful]
+
+hp_gas_i:
+  name: hp_gas_i
+  description: Natural gas heat pump in industry thermal
+  type: useful
+  vintaged: TRUE
+  sector: industry
+  input: [gas, final]
+  output: [i_therm, useful]
+
+loil_i:
+  name: loil_i
+  description: Lightoil for thermal uses in industry thermal
+  type: useful
+  vintaged: TRUE
+  sector: industry
+  input: [lightoil, final]
+  output: [i_therm, useful]
+
+meth_i:
+  name: meth_i
+  description: Methanol (with C) replacement for use as liquid fuel in industry thermal
+  type: useful
+  vintaged: TRUE
+  sector: industry
+  input: [methanol, final]
+  output: [i_therm, useful]
+
+solar_i:
+  name: solar_i
+  description: Solar thermal in industry thermal sector
+  type: useful
+  vintaged: TRUE
+  sector: industry
+  output: [i_therm, useful]
+
+Ispec_1:
+  name: Ispec_1
+  description: Conservation cost curve step for industry specific demand
+  type: useful
+  sector: industry
+  output: [i_spec, useful]
+
+Ispec_2:
+  name: Ispec_2
+  description: Conservation cost curve step for industry specific demand
+  type: useful
+  sector: industry
+  output: [i_spec, useful]
+
+Ispec_3:
+  name: Ispec_3
+  description: Conservation cost curve step for industry specific demand
+  type: useful
+  sector: industry
+  output: [i_spec, useful]
+
+Ispec_4:
+  name: Ispec_4
+  description: Conservation cost curve step for industry specific demand
+  type: useful
+  sector: industry
+  output: [i_spec, useful]
+
+Ispec_5:
+  name: Ispec_5
+  description: Conservation cost curve step for industry specific demand
+  type: useful
+  sector: industry
+  output: [i_spec, useful]
+
+Ispec_con:
+  name: Ispec_con
+  description: Joint diffusion constraint for industry specific conservation cost curve steps
+  type: primary
+  sector: industry
+  output: [dummy agriculture, primary]
+
+Itherm_1:
+  name: Itherm_1
+  description: Conservation cost curve step for industry thermal demand
+  type: useful
+  sector: industry
+  output: [i_therm, useful]
+
+Itherm_2:
+  name: Itherm_2
+  description: Conservation cost curve step for industry thermal demand
+  type: useful
+  sector: industry
+  output: [i_therm, useful]
+
+Itherm_3:
+  name: Itherm_3
+  description: Conservation cost curve step for industry thermal demand
+  type: useful
+  sector: industry
+  output: [i_therm, useful]
+
+Itherm_4:
+  name: Itherm_4
+  description: Conservation cost curve step for industry thermal demand
+  type: useful
+  sector: industry
+  output: [i_therm, useful]
+
+Itherm_5:
+  name: Itherm_5
+  description: Conservation cost curve step for industry thermal demand
+  type: useful
+  sector: industry
+  output: [i_therm, useful]
+
+Itherm_con:
+  name: Itherm_con
+  description: Joint diffusion constraint for industry thermal conservation cost curve steps
+  type: dummy
+  sector: industry
+  output: [dummy agriculture, primary]
+
+solar_pv_I:
+  name: solar_pv_I
+  description: On-site solar photovoltaic power plant (no storage) in industry specific
+  type: useful
+  vintaged: TRUE
+  sector: industry
+  output: [i_spec, useful]
+
+h2_fc_I:
+  name: h2_fc_I
+  description: Hydrogen fuel cell cogeneration system for industry specific
+  type: useful
+  vintaged: TRUE
+  sector: industry
+  input: [hydrogen, final]
+  output: [i_spec, useful]
+
+sp_coal_I:
+  name: sp_coal_I
+  description: Specific use of coal in industry
+  type: useful
+  sector: industry
+  input: [coal, final]
+  output: [i_spec, useful]
+
+sp_el_I:
+  name: sp_el_I
+  description: Specific use of electricity in industry
+  type: useful
+  sector: industry
+  input: [electr, final]
+  output: [i_spec, useful]
+
+sp_eth_I:
+  name: sp_eth_I
+  description: Ethanol (without net C) replacement for specific use of light oil in industry
+  type: useful
+  sector: industry
+  input: [ethanol, final]
+  output: [i_spec, useful]
+
+sp_liq_I:
+  name: sp_liq_I
+  description: Specific use of light oil in industry
+  type: useful
+  sector: industry
+  input: [lightoil, final]
+  output: [i_spec, useful]
+
+sp_meth_I:
+  name: sp_meth_I
+  description: Methanol (with C) replacement for  specific use of light oil in industry
+  type: useful
+  sector: industry
+  input: [methanol, final]
+  output: [i_spec, useful]
+
+bio_extr_mpen:
+  name: bio_extr_mpen
+  description: Slack primary biomass created with new implementation of non commercial biomass
+  type: secondary
+  sector: land
+  output: [exports, secondary]
+
+forest_CO2:
+  name: forest_CO2
+  description: Co2 emissions from forests
+  type: secondary
+  sector: land
+  output: [exports, secondary]
+
+sinks_1:
+  name: sinks_1
+  description: Potential for sinks (50. US$)
+  type: secondary
+  sector: land
+  output: [exports, secondary]
+
+sinks_2:
+  name: sinks_2
+  description: Potential for sinks (100. US$)
+  type: secondary
+  sector: land
+  output: [exports, secondary]
+
+sinks_3:
+  name: sinks_3
+  description: Potential for sinks (200. US$)
+  type: secondary
+  sector: land
+  output: [exports, secondary]
+
+sinks_4:
+  name: sinks_4
+  description: Potential for sinks (300. US$)
+  type: secondary
+  sector: land
+  output: [exports, secondary]
+
+eth_bal:
+  name: eth_bal
+  description: Link technology to stabilize ethanol production
+  type: secondary
+  sector: liquids
+  input: [ethanol, primary]
+  output: [ethanol, secondary]
+
+eth_bio:
+  name: eth_bio
+  description: Ethanol synthesis via biomass gasification
+  type: primary
+  vintaged: TRUE
+  sector: liquids
+  input:
+    - [biomass, primary]
+    - [freshwater_supply, water_supply]
+  output:
+    - [ethanol, primary]
+    - [electr, secondary]
+
+eth_bio_ccs:
+  name: eth_bio_ccs
+  description: Ethanol synthesis via biomass gasification with carbon capture and storage
+  type: primary
+  vintaged: TRUE
+  sector: liquids
+  input:
+    - [biomass, primary]
+    - [freshwater_supply, water_supply]
+  output:
+    - [ethanol, primary]
+    - [electr, secondary]
+
+eth_exp:
+  name: eth_exp
+  description: Exports of ethanol (no accounting of CO2 transferred)
+  type: exports
+  sector: liquids
+  input: [ethanol, primary]
+  output: [ethanol, exports]
+
+eth_imp:
+  name: eth_imp
+  description: Imports of ethanol (no accounting of CO2 transferred)
+  type: secondary
+  sector: liquids
+  input: [ethanol, exports]
+  output: [ethanol, secondary]
+
+eth_t/d:
+  name: eth_t/d
+  description: Transmission/Distribution of methanol without net C (just linking technology)
+  type: final
+  sector: liquids
+  input: [ethanol, secondary]
+  output: [ethanol, final]
+
+foil_exp:
+  name: foil_exp
+  description: Net exports of crude oil at 95% of oil price
+  type: exports
+  sector: liquids
+  input: [fueloil, secondary]
+  output: [fueloil, exports]
+
+foil_imp:
+  name: foil_imp
+  description: Net imports of residual oil at 95% of oil price
+  type: secondary
+  sector: liquids
+  input: [fueloil, imports]
+  output: [fueloil, secondary]
+
+foil_t/d:
+  name: foil_t/d
+  description: Transmission/Distribution of fueloil  (just linking technology)
+  type: final
+  sector: liquids
+  input: [fueloil, secondary]
+  output: [fueloil, final]
+
+glb_eth_imp:
+  name: glb_eth_imp
+  description: Global net import of ethanol
+  type: exports
+  sector: liquids
+  input: [ethanol, exports]
+  output: [exports]
+
+glb_foil_exp:
+  name: glb_foil_exp
+  description: Global net export of fuel oil
+  type: imports
+  sector: liquids
+  output: [fueloil, imports]
+
+glb_foil_imp:
+  name: glb_foil_imp
+  description: Global net import of fuel oil
+  type: exports
+  sector: liquids
+  input: [fueloil, exports]
+  output: [exports]
+
+glb_loil_exp:
+  name: glb_loil_exp
+  description: Global net export of light oil
+  type: imports
+  sector: liquids
+  output: [loil, imports]
+
+glb_loil_imp:
+  name: glb_loil_imp
+  description: Global net import of light oil
+  type: exports
+  sector: liquids
+  input: [loil, exports]
+  output: [exports]
+
+glb_meth_imp:
+  name: glb_meth_imp
+  description: Global net import of methanol
+  type: exports
+  sector: liquids
+  input: [methanol, exports]
+  output: [exports]
+
+glb_oil_exp:
+  name: glb_oil_exp
+  description: Global net export of crude oil
+  type: imports
+  sector: liquids
+  output: [oil, imports]
+
+glb_oil_imp:
+  name: glb_oil_imp
+  description: Global net import of crude oil
+  type: exports
+  sector: liquids
+  input: [oil, exports]
+  output: [exports]
+
+liq_bio:
+  name: liq_bio
+  description: Second Generation Ethanol Production based on Biomass to FTL
+  type: primary
+  vintaged: TRUE
+  sector: liquids
+  input:
+    - [biomass, primary]
+    - [freshwater_supply, water_supply]
+  output:
+    - [ethanol, primary]
+    - [electr, secondary]
+
+liq_bio_ccs:
+  name: liq_bio_ccs
+  description: Second Generation Ethanol Production with carbon capture and storage based on Biomass to FTL
+  type: primary
+  vintaged: TRUE
+  sector: liquids
+  input:
+    - [biomass, primary]
+    - [freshwater_supply, water_supply]
+  output:
+    - [ethanol, primary]
+    - [electr, secondary]
+
+loil_exp:
+  name: loil_exp
+  description: Net exports of crude oil at 15% above oil price
+  type: exports
+  sector: liquids
+  input: [lightoil, secondary]
+  output: [lightoil, exports]
+
+loil_imp:
+  name: loil_imp
+  description: Net imports of light oil at 15% above crude price
+  type: secondary
+  sector: liquids
+  input: [lightoil, imports]
+  output: [lightoil, secondary]
+
+loil_std:
+  name: loil_std
+  description: Standard light oil power-plant
+  type: secondary
+  vintaged: TRUE
+  sector: liquids
+  output: [lightoil, secondary]
+
+loil_t/d:
+  name: loil_t/d
+  description: Transmission/Distribution of light oil (just linking technology)
+  type: final
+  sector: liquids
+  input: [lightoil, secondary]
+  output: [lightoil, final]
+
+meth_coal:
+  name: meth_coal
+  description: Methanol synthesis via coal gasification
+  type: primary
+  vintaged: TRUE
+  sector: liquids
+  input:
+    - [coal, secondary]
+    - [freshwater_supply, water_supply]
+  output:
+    - [methanol, primary]
+    - [electr, secondary]
+
+meth_coal_ccs:
+  name: meth_coal_ccs
+  description: Methanol synthesis via coal gasification with carbon capture and storage
+  type: primary
+  vintaged: TRUE
+  sector: liquids
+  input:
+    - [coal, secondary]
+    - [freshwater_supply, water_supply]
+  output:
+    - [methanol, primary]
+    - [electr, secondary]
+
+meth_exp:
+  name: meth_exp
+  description: Exports of methanol (no accounting of CO2 transferred)
+  type: exports
+  sector: liquids
+  input: [methanol, primary]
+  output: [methanol, exports]
+
+meth_imp:
+  name: meth_imp
+  description: Imports of methanol (no accounting of CO2 transferred)
+  type: secondary
+  sector: liquids
+  input: [methanol, exports]
+  output: [methanol, secondary]
+
+meth_ng:
+  name: meth_ng
+  description: Methanol synthesis via natural gas
+  type: primary
+  vintaged: TRUE
+  sector: liquids
+  input:
+    - [gas, secondary]
+    - [freshwater_supply, water_supply]
+  output: [methanol, primary]
+
+meth_ng_ccs:
+  name: meth_ng_ccs
+  description: Methanol synthesis via natural gas with carbon capture and storage
+  type: primary
+  vintaged: TRUE
+  sector: liquids
+  input:
+    - [gas, secondary]
+    - [freshwater_supply, water_supply]
+  output: [methanol, primary]
+
+meth_t/d:
+  name: meth_t/d
+  description: Transmission/Distribution of methanol with C
+  type: final
+  sector: liquids
+  input: [methanol, secondary]
+  output: [methanol, final]
+
+oil_bal:
+  name: oil_bal
+  description: Link technology to stabilize crude oil production
+  type: secondary
+  sector: liquids
+  input: [crude oil, primary]
+  output: [crude oil, secondary]
+
+oil_exp:
+  name: oil_exp
+  description: Net exports of crude oil at 100% of oil price
+  type: exports
+  sector: liquids
+  input: [crude oil, primary]
+  output: [oil, exports]
+
+oil_imp:
+  name: oil_imp
+  description: Net imports of oil
+  type: secondary
+  sector: liquids
+  input: [oil, imports]
+  output: [crude oil, secondary]
+
+plutonium_prod:
+  name: plutonium_prod
+  description: Plutonium production
+  type: stocks
+  sector: liquids
+  input: [plutonium, stocks]
+  output: [plutonium, stocks]
+
+ref_hil:
+  name: ref_hil
+  description: New deeply upgraded refineries
+  type: secondary
+  vintaged: TRUE
+  sector: liquids
+  input: [crude oil, secondary]
+  output: [fueloil, secondary]
+
+ref_lol:
+  name: ref_lol
+  description: Existing refineries (low yield)
+  type: secondary
+  vintaged: TRUE
+  sector: liquids
+  input: [crude oil, secondary]
+  output: [fueloil, secondary]
+
+SO2_scrub_ref:
+  name: SO2_scrub_ref
+  description: SO2 scrubber for refineries
+  type: secondary
+  vintaged: TRUE
+  sector: liquids
+  output: [exports, secondary]
+
+syn_liq:
+  name: syn_liq
+  description: Coal liquefaction and light oil synthesis
+  type: secondary
+  vintaged: TRUE
+  sector: liquids
+  input:
+    - [coal, secondary]
+    - [freshwater_supply, water_supply]
+  output:
+    - [lightoil, secondary]
+    - [electr, secondary]
+
+syn_liq_ccs:
+  name: syn_liq_ccs
+  description: Coal liquefaction and light oil synthesis with carbon capture and storage
+  type: secondary
+  vintaged: TRUE
+  sector: liquids
+  input:
+    - [coal, secondary]
+    - [freshwater_supply, water_supply]
+  output:
+    - [lightoil, secondary]
+    - [electr, secondary]
+
+adipic_thermal:
+  name: adipic_thermal
+  description: Thermal destruction tech for adipic acid sector
+  type: dummy
+  sector: non-co2
+  output: [dummy, primary]
+
+ammonia_secloop:
+  name: ammonia_secloop
+  description: Ammonia Secondary Loop Systems
+  type: dummy
+  sector: non-co2
+  output: [dummy, primary]
+
+enre_con:
+  name: enre_con
+  description: Joint diffusion constraint for enteric fermentation mitigation technologies
+  type: dummy
+  sector: non-co2
+  output: [dummy agriculture, primary]
+
+ent_red1:
+  name: ent_red1
+  description: Mitigation for CH4 emissions from animals directly
+  type: dummy
+  sector: non-co2
+  output: [dummy agriculture, primary]
+
+ent_red2:
+  name: ent_red2
+  description: Mitigation for CH4 emissions from animals directly
+  type: dummy
+  sector: non-co2
+  output: [dummy agriculture, primary]
+
+ent_red3:
+  name: ent_red3
+  description: Mitigation for CH4 emissions from animals directly
+  type: dummy
+  sector: non-co2
+  output: [dummy agriculture, primary]
+
+landfill_compost1:
+  name: landfill_compost1
+  description: Landfill mitigation technology (composting) technology 1 for CH4 mitigation
+  type: dummy
+  vintaged: TRUE
+  sector: non-co2
+  output: [dummy, primary]
+
+landfill_compost2:
+  name: landfill_compost2
+  description: Landfill mitigation technology (composting) technology 2 for CH4 mitigation
+  type: dummy
+  vintaged: TRUE
+  sector: non-co2
+  output: [dummy, primary]
+
+landfill_direct1:
+  name: landfill_direct1
+  description: Landfill mitigation technology (direct) 1 for CH4 mitigation
+  type: dummy
+  vintaged: TRUE
+  sector: non-co2
+  output: [dummy, primary]
+
+landfill_direct2:
+  name: landfill_direct2
+  description: Landfill mitigation technology (direct) 1 for CH4 mitigation
+  type: dummy
+  vintaged: TRUE
+  sector: non-co2
+  output: [dummy, primary]
+
+landfill_ele:
+  name: landfill_ele
+  description: Landfill mitigation technology (ele) 1 for CH4 mitigation
+  type: dummy
+  vintaged: TRUE
+  sector: non-co2
+  output: [dummy, primary]
+
+landfill_flaring:
+  name: landfill_flaring
+  description: Landfill mitigation technology (flaring) 1 for CH4 mitigation
+  type: dummy
+  vintaged: TRUE
+  sector: non-co2
+  output: [dummy, primary]
+
+landfill_heatprdn:
+  name: landfill_heatprdn
+  description: Landfill mitigation technology (heat production) 1 for CH4 mitigation
+  type: dummy
+  vintaged: TRUE
+  sector: non-co2
+  output: [dummy, primary]
+
+landfill_oxdn:
+  name: landfill_oxdn
+  description: Landfill mitigation technology (oxidation) 1 for CH4 mitigation
+  type: dummy
+  vintaged: TRUE
+  sector: non-co2
+  output: [dummy, primary]
+
+leak_repair:
+  name: leak_repair
+  description: Leak-repairs, HFC-134a from refrigeration & AC(SR)
+  type: dummy
+  sector: non-co2
+  output: [dummy, primary]
+
+leak_repairsf6:
+  name: leak_repairsf6
+  description: Recycling gas carts for SF6 recovery during assembly of gas insulated equipment
+  type: dummy
+  sector: non-co2
+  output: [dummy, primary]
+
+lfil_con:
+  name: lfil_con
+  description: Joint diffusion constraint for landfill mitigation technologies
+  type: dummy
+  sector: non-co2
+  output: [dummy agriculture, primary]
+
+manu_con:
+  name: manu_con
+  description: Joint diffusion constraint for manure management+B249 mitigation technologies
+  type: primary
+  sector: non-co2
+  output: [dummy agriculture, primary]
+
+meth_bal:
+  name: meth_bal
+  description: Link technology to stabilize methanol production
+  type: primary
+  sector: non-co2
+  input: [methanol, primary]
+  output: [methanol, secondary]
+
+mvac_co2:
+  name: mvac_co2
+  description: Transcritical vapor cycle CO2 systems for mobile vehicle air conditioners
+  type: dummy
+  sector: non-co2
+  output: [dummy]
+
+recycling_gas1:
+  name: recycling_gas1
+  description: Recycling gas carts for SF6 recovery during maintenance of gas insulated equipment(SR)
+  type: dummy
+  sector: non-co2
+  output: [dummy, primary]
+
+refrigerant_recover:
+  name: refrigerant_recover
+  description: Recovery of refrigerant, HFC-134a from refrigeration and AC (SR)
+  type: primary
+  sector: non-co2
+  output: [dummy, primary]
+
+repl_hc:
+  name: repl_hc
+  description: Replacement with HC for foams
+  type: primary
+  sector: non-co2
+  output: [dummy, primary]
+
+replacement_so2:
+  name: replacement_so2
+  description: Replacing SF6 by SO2(SR)
+  type: primary
+  sector: non-co2
+  output: [dummy, primary]
+
+rice_red1:
+  name: rice_red1
+  description: Mitigation for CH4 emissions from rice
+  type: primary
+  sector: non-co2
+  output: [dummy agriculture, primary]
+
+rice_red2:
+  name: rice_red2
+  description: Mitigation for CH4 emissions from rice
+  type: primary
+  sector: non-co2
+  output: [dummy agriculture, primary]
+
+rice_red3:
+  name: rice_red3
+  description: Mitigation for CH4 emissions from rice
+  type: primary
+  sector: non-co2
+  output: [dummy agriculture, primary]
+
+rire_con:
+  name: rire_con
+  description: Joint diffusion constraint for rice cultivation mitigation technologies
+  type: primary
+  sector: non-co2
+  output: [dummy  agriculture, primary]
+
+soil_red1:
+  name: soil_red1
+  description: Mitigation for N2Oemissions from soil
+  type: primary
+  sector: non-co2
+  output: [dummy agriculture, primary]
+
+soil_red2:
+  name: soil_red2
+  description: Mitigation for N2Oemissions from soil
+  type: primary
+  sector: non-co2
+  output: [dummy agriculture, primary]
+
+soil_red3:
+  name: soil_red3
+  description: Mitigation for N2Oemissions from soil
+  type: primary
+  sector: non-co2
+  output: [dummy agriculture, primary]
+
+vertical_stud:
+  name: vertical_stud
+  description: Soderberg process for CF4 from aluminum (SR)
+  type: dummy
+  sector: non-co2
+  output: [dummy, primary]
+
+u5-reproc:
+  name: u5-reproc
+  description: Uranium reprocessing
+  type: stocks
+  vintaged: TRUE
+  sector: nuclear
+  input: [uranium, stocks]
+  output: [uranium, stocks]
+
+Uran_extr:
+  name: Uran_extr
+  description: Uranium extraction, milling for FBR blanket
+  type: stocks
+  sector: nuclear
+  input: [uranium, resource]
+  output: [uranium, stocks]
+
+uran2u5:
+  name: uran2u5
+  description: Uranium extraction, milling, fluorination and enrichment per t U5
+  type: stocks
+  sector: nuclear
+  input: [uranium, resource]
+  output: [uranium, stocks]
+
+bco2_tr_dis:
+  name: bco2_tr_dis
+  description: Technology for co2 transportation and disposal from biomass technologies
+  type: secondary
+  sector: other conversion
+  output: [exports, secondary]
+
+co2_tr_dis:
+  name: co2_tr_dis
+  description: Technology for co2 transportation and disposal
+  type: secondary
+  sector: other conversion
+  output: [exports, secondary]
+
+back_rc:
+  name: back_RC
+  description: Backstop for diagnosing model infeasibility
+  type: useful
+  sector: residential/commercial
+  output: [rc_spec, useful]
+
+solar_rc:
+  name: solar_rc
+  description: Solar thermal in residential/commercial sector
+  type: useful
+  sector: residential/commercial
+  output: [rc_therm, useful]
+
+biomass_rc:
+  name: biomass_rc
+  description: Biomass with C for heating in residential/commercial sector
+  type: useful
+  sector: residential/commercial
+  input: [biomass, final]
+  output: [rc_therm, useful]
+
+coal_rc:
+  name: coal_rc
+  description: Coal heating in residential/commercial sector
+  type: useful
+  sector: residential/commercial
+  input: [coal, final]
+  output: [rc_therm, useful]
+
+elec_rc:
+  name: elec_rc
+  description: Electricity heating in residential/commercial sector
+  type: useful
+  sector: residential/commercial
+  input: [electr, final]
+  output: [rc_therm, useful]
+
+eth_rc:
+  name: eth_rc
+  description: Ethanol (without C) replacement for use as liquid fuel in residential/commercial
+  type: useful
+  sector: residential/commercial
+  input: [ethanol, final]
+  output: [rc_therm, useful]
+
+foil_rc:
+  name: foil_rc
+  description: Fuel oil heating in residential/commercial sector
+  type: useful
+  sector: residential/commercial
+  input: [fueloil, final]
+  output: [rc_therm, useful]
+
+h2_rc:
+  name: h2_rc
+  description: Hydrogen (gaseous) catalytic heating in the residential/commercial sector
+  type: secondary
+  sector: residential/commercial
+  input: [hydrogen, final]
+  output: [rc_therm, useful]
+
+heat_rc:
+  name: heat_rc
+  description: District heating in residential/commercial sector
+  type: useful
+  sector: residential/commercial
+  input: [d_heat, final]
+  output: [rc_therm, useful]
+
+hp_el_rc:
+  name: hp_el_rc
+  description: Electric heat pump in residential/commercial sector
+  type: useful
+  sector: residential/commercial
+  input: [electr, final]
+  output: [rc_therm, useful]
+
+hp_gas_rc:
+  name: hp_gas_rc
+  description: Natural gas heat pump in residential/commercial sector
+  type: useful
+  sector: residential/commercial
+  input: [gas, final]
+  output: [rc_therm, useful]
+
+loil_rc:
+  name: loil_rc
+  description: Lightoil heating in residential/commercial sector
+  type: useful
+  sector: residential/commercial
+  input: [lightoil, final]
+  output: [rc_therm, useful]
+
+meth_rc:
+  name: meth_rc
+  description: Methanol (with C) replacement for use as liquid fuel in residential/commercial
+  type: useful
+  sector: residential/commercial
+  input: [methanol, final]
+  output: [rc_therm, useful]
+
+RCspec_1:
+  name: RCspec_1
+  description: Conservation cost curve step for residential/commercial specific demand
+  type: useful
+  sector: residential/commercial
+  output: [useful]
+
+RCspec_2:
+  name: RCspec_2
+  description: Conservation cost curve step for residential/commercial specific demand
+  type: useful
+  sector: residential/commercial
+  output: [useful]
+
+RCspec_3:
+  name: RCspec_3
+  description: Conservation cost curve step for residential/commercial specific demand
+  type: useful
+  sector: residential/commercial
+  output: [useful]
+
+RCspec_4:
+  name: RCspec_4
+  description: Conservation cost curve step for residential/commercial specific demand
+  type: useful
+  sector: residential/commercial
+  output: [useful]
+
+RCspec_5:
+  name: RCspec_5
+  description: Conservation cost curve step for residential/commercial specific demand
+  type: useful
+  sector: residential/commercial
+  output: [useful]
+
+RCspec_con:
+  name: RCspec_con
+  description: Joint diffusion constraint for residential/commercial specific conservation cost curve steps
+  type: dummy
+  sector: residential/commercial
+  output: [dummy agriculture, primary]
+
+RCtherm_1:
+  name: RCtherm_1
+  description: Conservation cost curve step for residential/commercial thermal demand
+  type: useful
+  sector: residential/commercial
+  output: [rc_therm, useful]
+
+RCtherm_2:
+  name: RCtherm_2
+  description: Conservation cost curve step for residential/commercial thermal demand
+  type: useful
+  sector: residential/commercial
+  output: [rc_therm, useful]
+
+RCtherm_3:
+  name: RCtherm_3
+  description: Conservation cost curve step for residential/commercial thermal demand
+  type: useful
+  sector: residential/commercial
+  output: [rc_therm, useful]
+
+RCtherm_4:
+  name: RCtherm_4
+  description: Conservation cost curve step for residential/commercial thermal demand
+  type: useful
+  sector: residential/commercial
+  output: [rc_therm, useful]
+
+RCtherm_5:
+  name: RCtherm_5
+  description: Conservation cost curve step for residential/commercial thermal demand
+  type: useful
+  sector: residential/commercial
+  output: [rc_therm, useful]
+
+RCtherm_con:
+  name: RCtherm_con
+  description: Joint diffusion constraint for residential/commercial thermal conservation cost curve steps
+  type: dummy
+  sector: residential/commercial
+  output: [dummy agriculture, primary]
+
+solar_pv_RC:
+  name: solar_pv_RC
+  description: Specific use of on-site solar photovoltaic power plant (no storage) in residential/commercial
+  type: useful
+  vintaged: TRUE
+  sector: residential/commercial
+  output: [rc_spec, useful]
+
+sp_el_RC:
+  name: sp_el_RC
+  description: Specific use of electricity in residential/commercial
+  type: useful
+  sector: residential/commercial
+  input: [electr, final]
+  output: [rc_spec, useful]
+
+h2_fc_RC:
+  name: h2_fc_RC
+  description: Specific use of hydrogen fuel cell cogeneration system in res/comm
+  type: useful
+  vintaged: TRUE
+  sector: residential/commercial
+  input: [hydrogen, final]
+  output: [rc_spec, useful]
+
+bio_extr_chp:
+  name: bio_extr_chp
+  description: Supply of biomass without net C emissions;  defined here as agricultural wastes, and other crops with cycle of 1 year or less
+  type: primary
+  sector: solids
+  output: [biomass, primary]
+
+bio_ppl_co2scr:
+  name: bio_ppl_co2scr
+  description: New coal scrubber for power plants
+  type: secondary
+  vintaged: TRUE
+  sector: solids
+  output: [exports, secondary]
+
+biomass_i:
+  name: biomass_i
+  description: Biomass with C in industry thermal
+  type: useful
+  vintaged: TRUE
+  sector: solids
+  input: [biomass, final]
+  output: [i_therm, useful]
+
+biomass_nc:
+  name: biomass_nc
+  description: Non-commercial biomass with C
+  type: useful
+  vintaged: TRUE
+  sector: solids
+  input: [biomass, primary]
+  output: [non-comm, useful]
+
+biomass_t/d:
+  name: biomass_t/d
+  description: Transmission/Distribution of biomass with C
+  type: useful
+  sector: solids
+  input: [biomass, primary]
+  output: [biomass, final]
+
+c_ppl_co2scr:
+  name: c_ppl_co2scr
+  description: New coal scrubber for coal power plants
+  type: secondary
+  vintaged: TRUE
+  sector: solids
+  output: [exports, secondary]
+
+cfc_co2scr:
+  name: cfc_co2scr
+  description: Co2 scrubber for coal fuel cells (CCS)
+  type: secondary
+  vintaged: TRUE
+  sector: solids
+  output: [exports, secondary]
+
+coal_bal:
+  name: coal_bal
+  description: Link technology to stabilize coal production
+  type: secondary
+  sector: solids
+  input: [coal, primary]
+  output: [coal, secondary]
+
+coal_exp:
+  name: coal_exp
+  description: Net exports of coal at fixed price of about US$ 42/tce
+  type: exports
+  sector: solids
+  input: [coal, primary]
+  output: [coal, exports]
+
+coal_imp:
+  name: coal_imp
+  description: Net imports of coal
+  type: secondary
+  sector: solids
+  input: [coal, imports]
+  output: [coal, secondary]
+
+coal_t/d:
+  name: coal_t/d
+  description: Transmission/Distribution of coal
+  type: final
+  sector: solids
+  input: [coal, secondary]
+  output: [coal, final]
+
+coal_t/d-in-06%:
+  name: coal_t/d-in-06%
+  description: Transmission/Distribution of coal industry like coal_t/d but imported coal  with 0.6% S content
+  type: final
+  sector: solids
+  input: [coal, secondary]
+  output: [coal, final]
+
+coal_t/d-in-SO2:
+  name: coal_t/d-in-SO2
+  description: Transmission/Distribution of coal industry like coal_t/d but processed coal
+  type: final
+  sector: solids
+  input: [coal, secondary]
+  output: [coal, final]
+
+coal_t/d-rc-06%:
+  name: coal_t/d-rc-06%
+  description: Transmission/Distribution of coal residential/commercial like coal_t/d but imported coal with 0.6% S content
+  type: final
+  sector: solids
+  input: [coal, secondary]
+  output: [coal, final]
+
+coal_t/d-rc-SO2:
+  name: coal_t/d-rc-SO2
+  description: Transmission/Distribution of coal like coal_t/d but processed coal
+  type: final
+  sector: solids
+  input: [coal, secondary]
+  output: [coal, final]
+
+glb_coal_exp:
+  name: glb_coal_exp
+  description: Global net export of coal
+  type: imports
+  sector: solids
+  output: [coal, imports]
+
+glb_coal_imp:
+  name: glb_coal_imp
+  description: Global net import of coal
+  type: exports
+  sector: solids
+  input: [electr, exports]
+  output: [exports]
+
+back_trp:
+  name: back_trp
+  description: Backstop for diagnosing model infeasibility
+  type: useful
+  sector: transport
+  output: [transport, useful]
+
+coal_trp:
+  name: coal_trp
+  description: Coal-based transport
+  type: useful
+  sector: transport
+  input: [coal, final]
+  output: [transport, useful]
+
+elec_trp:
+  name: elec_trp
+  description: Electricity-based transport
+  type: useful
+  sector: transport
+  input: [electr, final]
+  output: [transport, useful]
+
+eth_fc_trp:
+  name: eth_fc_trp
+  description: Ethanol (without net C) fuel cell-based transport
+  type: useful
+  sector: transport
+  input: [ethanol, final]
+  output: [transport, useful]
+
+eth_ic_trp:
+  name: eth_ic_trp
+  description: Ethanol (without net C) ic-engine-based transport
+  type: useful
+  sector: transport
+  input: [ethanol, final]
+  output: [transport, useful]
+
+foil_trp:
+  name: foil_trp
+  description: Fueloil-based transport
+  type: useful
+  sector: transport
+  input: [fueloil, final]
+  output: [transport, useful]
+
+gas_trp:
+  name: gas_trp
+  description: Gas-based transport
+  type: useful
+  sector: transport
+  input: [gas, final]
+  output: [transport, useful]
+
+h2_fc_trp:
+  name: h2_fc_trp
+  description: Hydrogen fuel cell-based transport (plus off-hours electricity generation)
+  type: useful
+  vintaged: TRUE
+  sector: transport
+  input: [liquid hydrogen, final]
+  output: [transport, useful]
+
+loil_trp:
+  name: loil_trp
+  description: Lightoil-based transport
+  type: useful
+  sector: transport
+  input: [lightoil, final]
+  output: [transport, useful]
+
+meth_fc_trp:
+  name: meth_fc_trp
+  description: Methanol (with C) fuel cell-based transport
+  type: useful
+  sector: transport
+  input: [methanol, final]
+  output: [transport, useful]
+
+meth_ic_trp:
+  name: meth_ic_trp
+  description: Methanol (with C) ic-engine-based transport
+  type: useful
+  sector: transport
+  input: [methanol, final]
+  output: [transport, useful]
+
+Trans_1:
+  name: Trans_1
+  description: Conservation cost curve step for transport demand
+  type: useful
+  sector: transport
+  output: [transport, useful]
+
+Trans_2:
+  name: Trans_2
+  description: Conservation cost curve step for transport demand
+  type: useful
+  sector: transport
+  output: [transport, useful]
+
+Trans_3:
+  name: Trans_3
+  description: Conservation cost curve step for transport demand
+  type: useful
+  sector: transport
+  output: [transport, useful]
+
+Trans_4:
+  name: Trans_4
+  description: Conservation cost curve step for transport demand
+  type: useful
+  sector: transport
+  output: [transport, useful]
+
+Trans_5:
+  name: Trans_5
+  description: Conservation cost curve step for transport demand
+  type: useful
+  sector: transport
+  output: [transport, useful]
+
+Trans_con:
+  name: Trans_con
+  description: Joint diffusion constraint for transport conservation cost curve steps
+  type: primary
+  sector: transport
+  output: [dummy agriculture, primary]
+
+foil_bunker:
+  name: foil_bunker
+  description: Fuel oil demand for international shipping bunkers (global trade)
+  type: useful
+  sector: shipping
+  input: [fueloil, final]
+  output: [shipping, useful]
+
+loil_bunker:
+  name: loil_bunker
+  description: Light oil demand for international shipping bunkers (global trade)
+  type: useful
+  sector: shipping
+  input: [lightoil, final]
+  output: [shipping, useful]
+
+meth_bunker:
+  name: meth_bunker
+  description: Methanol demand for international shipping bunkers (global trade)
+  type: useful
+  sector: shipping
+  input: [methanol, final]
+  output: [shipping, useful]
+
+eth_bunker:
+  name: eth_bunker
+  description: Ethanol demand for international shipping bunkers (global trade)
+  type: useful
+  sector: shipping
+  input: [ethanol, final]
+  output: [shipping, useful]
+
+LNG_bunker:
+  name: LNG_bunker
+  description: Liquified natural gas demand for international shipping bunkers (global trade)
+  type: useful
+  sector: shipping
+  input: [LNG, final]
+  output: [shipping, useful]
+
+LH2_bunker:
+  name: LH2_bunker
+  description: Liquified hydrogen  demand for international shipping bunkers (global trade)
+  type: useful
+  sector: shipping
+  input: [lh2, final]
+  output: [shipping, useful]
diff --git a/nest/message_ix_models/data/test/advance/advance_compare_20171018-134445.csv.zip b/nest/message_ix_models/data/test/advance/advance_compare_20171018-134445.csv.zip
new file mode 100644
index 0000000..7be054b
--- /dev/null
+++ b/nest/message_ix_models/data/test/advance/advance_compare_20171018-134445.csv.zip
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 3122
diff --git a/nest/message_ix_models/data/unit.yaml b/nest/message_ix_models/data/unit.yaml
new file mode 100644
index 0000000..ec45d8d
--- /dev/null
+++ b/nest/message_ix_models/data/unit.yaml
@@ -0,0 +1,2 @@
+"":
+  name: dimensionless
diff --git a/nest/message_ix_models/data/water/.gitattributes b/nest/message_ix_models/data/water/.gitattributes
new file mode 100644
index 0000000..b67374a
--- /dev/null
+++ b/nest/message_ix_models/data/water/.gitattributes
@@ -0,0 +1,4 @@
+*.shp filter=lfs diff=lfs merge=lfs -text
+*.Rda filter=lfs diff=lfs merge=lfs -text
+*.csv filter=lfs diff=lfs merge=lfs -text
+ppl_cooling_tech/*.csv filter=lfs diff=lfs merge=lfs -text
\ No newline at end of file
diff --git a/nest/message_ix_models/data/water/README.txt b/nest/message_ix_models/data/water/README.txt
new file mode 100644
index 0000000..8126cdd
--- /dev/null
+++ b/nest/message_ix_models/data/water/README.txt
@@ -0,0 +1,9 @@
+The data and scripts used to generate the shapefiles are located in the interla IIASA drives
+
+
+
+
+
+For cooling technologies, the database with all power plants used in, called bz the scripts message_data.model.water.data.calculate_ppl_cooling_technology_shares.r
+https://github.com/OFR-IIASA/message_data/blob/RES_add_5_year_timesteps2/data/model/platts_historical_capacity/merge_WEPP_Raptis_CARMA.py and located in
+P:\ene.model\NEST\cooling technologies\test.xslx
\ No newline at end of file
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\ No newline at end of file
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diff --git a/nest/message_ix_models/data/water/ppl_cooling_tech/raptis_types_map_cooling_type.csv b/nest/message_ix_models/data/water/ppl_cooling_tech/raptis_types_map_cooling_type.csv
new file mode 100644
index 0000000..9c47ea5
--- /dev/null
+++ b/nest/message_ix_models/data/water/ppl_cooling_tech/raptis_types_map_cooling_type.csv
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7a6f722103b60992412aba1f51cf3bbaad06d585de044b7c6b0824a5855fc19d
+size 401
diff --git a/nest/message_ix_models/data/water/ppl_cooling_tech/raptis_types_map_unit_type.csv b/nest/message_ix_models/data/water/ppl_cooling_tech/raptis_types_map_unit_type.csv
new file mode 100644
index 0000000..6c3238d
--- /dev/null
+++ b/nest/message_ix_models/data/water/ppl_cooling_tech/raptis_types_map_unit_type.csv
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1370ec1ee0a88142c4aed8724b5fa8b0a0e94b9a7c35d5cc81e0475c9e9c7236
+size 1062
diff --git a/nest/message_ix_models/data/water/ppl_cooling_tech/tech_names_ssp_msg.csv b/nest/message_ix_models/data/water/ppl_cooling_tech/tech_names_ssp_msg.csv
new file mode 100644
index 0000000..b9296a1
--- /dev/null
+++ b/nest/message_ix_models/data/water/ppl_cooling_tech/tech_names_ssp_msg.csv
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6a7dca2e09101660bd1f6ece6bc00fe82e4db1c055dd30208fe3716aef453f5b
+size 15268
diff --git a/nest/message_ix_models/data/water/ppl_cooling_tech/tech_water_performance_ssp_msg.csv b/nest/message_ix_models/data/water/ppl_cooling_tech/tech_water_performance_ssp_msg.csv
new file mode 100644
index 0000000..ea5d7e0
--- /dev/null
+++ b/nest/message_ix_models/data/water/ppl_cooling_tech/tech_water_performance_ssp_msg.csv
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5da3683b8d9f939d0d3277f8d97244d6c55a651228d5e13bd4e191a9f92ecb78
+size 17428
diff --git a/nest/message_ix_models/data/water/set.yaml b/nest/message_ix_models/data/water/set.yaml
new file mode 100644
index 0000000..3d5254d
--- /dev/null
+++ b/nest/message_ix_models/data/water/set.yaml
@@ -0,0 +1,1025 @@
+#Set configuration for the MESSAGE-Water model
+
+# For each set in the MESSAGEix framework, the group contains:
+# - 'require': elements that must be present for the model to be set up.
+# - 'remove': elements to remove.
+# - 'add': elements to add. This is a mapping from element short names to a
+#   longer description.
+
+nexus:
+  commodity:
+    require:
+    - electr
+
+    remove:
+    # removing all technologies which are related to water in the previous model
+    # Re added in the newer implementation with lesser commodities
+    - saline_supply
+    #- freshwater_instream
+    - freshwater_supply
+    - desalinated_water
+    - saline_supply_ppl
+    - urban_collected_wst
+    - urban_uncollected_wst
+    - yield_freshwater_suply
+    - rural_collected_wst
+    - rural_uncollected_wst
+    - urban_mw
+    - urban_dis
+    - rural_mw
+    - rural_dis
+    - cooling__bio_hpl
+    - cooling__bio_ppl
+    - cooling__bio_istig
+    - cooling__bio_ppl
+    - cooling__coal_adv
+    - cooling__coal_adv_ccs
+    - cooling__coal_ppl
+    - cooling__coal_ppl_u
+    - cooling__foil_hpl
+    - cooling__foil_ppl
+    - cooling__gas_cc
+    - cooling__gas_cc_ccs
+    - cooling__gas_hpl
+    - cooling__gas_ppl
+    - cooling__geo_hpl
+    - cooling__geo_ppl
+    - cooling__igcc
+    - cooling__igcc_ccs
+    - cooling__loil_cc
+    - cooling__loil_ppl
+    - cooling__nuc_hc
+    - cooling__solar_th_ppl
+
+    add:
+    - electr
+    - salinewater
+    - freshwater_instream
+    - freshwater
+    - freshwater_basin
+    - surfacewater_basin
+    - groundwater_basin
+    - salinewater_basin
+    - desalinated_water
+    - saline_ppl
+    - urban_collected_wst
+    - urban_uncollected_wst
+    - yield_freshwater_suply
+    - rural_collected_wst
+    - rural_uncollected_wst
+    - urban_mw
+    - industry_mw
+    - industry_uncollected_wst
+    - urban_disconnected
+    - rural_mw
+    - rural_disconnected
+
+
+  level:
+    require:
+    - secondary
+
+    remove:
+    - water_supply
+    - cooling
+    - desalination_supply
+    - water_treat
+    add:
+    - water_supply
+    - water_supply_basin
+    - saline_supply_basin
+    - water_treat
+    - water_avail_basin
+    - saline_supply
+    - irr_cereal
+    - irr_sugarcrops
+    - irr_oilcrops
+
+  mode:
+    require:
+    - all
+    add:
+    - Mf # efficient mode
+
+  technology:
+    remove:
+    - rural_treatment
+    - urban_treatment
+    - extract__saline_supply
+    - bio_hpl__ot_fresh
+    - bio_hpl__cl_fresh
+    - bio_hpl__cl_fresh
+    - bio_hpl__ot_saline
+    - bio_hpl__air
+    - bio_istig__ot_fresh
+    - bio_istig__cl_fresh
+    - bio_istig__cl_fresh
+    - bio_istig__ot_saline
+    - bio_istig__air
+    - bio_istig_ccs__ot_fresh
+    - bio_istig_ccs__cl_fresh
+    - bio_istig_ccs__cl_fresh
+    - bio_istig_ccs__ot_saline
+    - bio_istig_ccs__air
+    - bio_ppl__ot_fresh
+    - bio_ppl__cl_fresh
+    - bio_ppl__cl_fresh
+    - bio_ppl__ot_saline
+    - bio_ppl__air
+    - coal_adv__ot_fresh
+    - coal_adv__cl_fresh
+    - coal_adv__cl_fresh
+    - coal_adv__ot_saline
+    - coal_adv__air
+    - coal_adv_ccs__ot_fresh
+    - coal_adv_ccs__cl_fresh
+    - coal_adv_ccs__cl_fresh
+    - coal_adv_ccs__ot_saline
+    - coal_ppl__ot_fresh
+    - coal_ppl__cl_fresh
+    - coal_ppl__cl_fresh
+    - coal_ppl__ot_saline
+    - coal_ppl__air
+    - coal_ppl_u__ot_fresh
+    - coal_ppl_u__cl_fresh
+    - coal_ppl_u__cl_fresh
+    - coal_ppl_u__ot_saline
+    - coal_ppl_u__air
+    - foil_hpl__ot_fresh
+    - foil_hpl__cl_fresh
+    - foil_hpl__cl_fresh
+    - foil_hpl__ot_saline
+    - foil_hpl__air
+    - foil_ppl__ot_fresh
+    - foil_ppl__cl_fresh
+    - foil_ppl__cl_fresh
+    - foil_ppl__ot_saline
+    - foil_ppl__air
+    - gas_cc__ot_fresh
+    - gas_cc__cl_fresh
+    - gas_cc__cl_fresh
+    - gas_cc__ot_saline
+    - gas_cc__air
+    - gas_cc_ccs__ot_fresh
+    - gas_cc_ccs__cl_fresh
+    - gas_cc_ccs__cl_fresh
+    - gas_cc_ccs__ot_saline
+    - gas_hpl__ot_fresh
+    - gas_hpl__cl_fresh
+    - gas_hpl__cl_fresh
+    - gas_hpl__ot_saline
+    - gas_hpl__air
+    - gas_ppl__ot_fresh
+    - gas_ppl__cl_fresh
+    - gas_ppl__cl_fresh
+    - gas_ppl__ot_saline
+    - gas_ppl__air
+    - geo_hpl__ot_fresh
+    - geo_hpl__cl_fresh
+    - geo_hpl__cl_fresh
+    - geo_hpl__ot_saline
+    - geo_hpl__air
+    - geo_ppl__ot_fresh
+    - geo_ppl__cl_fresh
+    - geo_ppl__cl_fresh
+    - geo_ppl__ot_saline
+    - geo_ppl__air
+    - igcc__ot_fresh
+    - igcc__cl_fresh
+    - igcc__cl_fresh
+    - igcc__ot_saline
+    - igcc__air
+    - igcc_ccs__ot_fresh
+    - igcc_ccs__cl_fresh
+    - igcc_ccs__cl_fresh
+    - igcc_ccs__ot_saline
+    - loil_cc__ot_fresh
+    - loil_cc__cl_fresh
+    - loil_cc__cl_fresh
+    - loil_cc__ot_saline
+    - loil_cc__air
+    - loil_ppl__ot_fresh
+    - loil_ppl__cl_fresh
+    - loil_ppl__cl_fresh
+    - loil_ppl__ot_saline
+    - loil_ppl__air
+    - nuc_hc__ot_fresh
+    - nuc_hc__cl_fresh
+    - nuc_hc__cl_fresh
+    - nuc_hc__ot_saline
+    - solar_th_ppl__ot_fresh
+    - solar_th_ppl__cl_fresh
+    - solar_th_ppl__cl_fresh
+    - solar_th_ppl__ot_saline
+    - solar_th_ppl__air
+    - extract__freshwater_supply
+    - extract__freshwater_instream
+    - extract__saline_supply
+    #add:
+    # Adds all technologies from technologies.yaml file again to reconfigure
+
+  time:
+    # NB cannot handle models with sub-annual time resolution
+    require:
+    - year
+
+  # Addon techs include cooling technologies for now
+
+  addon:
+    add:
+    - bio_hpl__ot_fresh
+    - bio_hpl__cl_fresh
+    - bio_hpl__ot_saline
+    - bio_hpl__air
+    - bio_istig__ot_fresh
+    - bio_istig__cl_fresh
+    - bio_istig__ot_saline
+    - bio_istig__air
+    - bio_istig_ccs__ot_fresh
+    - bio_istig_ccs__cl_fresh
+    - bio_istig_ccs__ot_saline
+    - bio_istig_ccs__air
+    - bio_ppl__ot_fresh
+    - bio_ppl__cl_fresh
+    - bio_ppl__ot_saline
+    - bio_ppl__air
+    - coal_adv__ot_fresh
+    - coal_adv__cl_fresh
+    - coal_adv__ot_saline
+    - coal_adv__air
+    - coal_adv_ccs__ot_fresh
+    - coal_adv_ccs__cl_fresh
+    - coal_adv_ccs__ot_saline
+    - coal_adv_ccs__air
+    - coal_ppl__ot_fresh
+    - coal_ppl__cl_fresh
+    - coal_ppl__ot_saline
+    - coal_ppl__air
+    - coal_ppl_u__ot_fresh
+    - coal_ppl_u__cl_fresh
+    - coal_ppl_u__ot_saline
+    - coal_ppl_u__air
+    - foil_ppl__ot_fresh
+    - foil_ppl__cl_fresh
+    - foil_ppl__ot_saline
+    - foil_ppl__air
+    - foil_hpl__ot_fresh
+    - foil_hpl__cl_fresh
+    - foil_hpl__ot_saline
+    - foil_hpl__air
+    - gas_cc__ot_fresh
+    - gas_cc__cl_fresh
+    - gas_cc__ot_saline
+    - gas_cc__air
+    - gas_cc_ccs__ot_fresh
+    - gas_cc_ccs__cl_fresh
+    - gas_cc_ccs__ot_saline
+    - gas_cc_ccs__air
+    - gas_hpl__ot_fresh
+    - gas_hpl__cl_fresh
+    - gas_hpl__ot_saline
+    - gas_hpl__air
+    - gas_ppl__ot_fresh
+    - gas_ppl__cl_fresh
+    - gas_ppl__ot_saline
+    - gas_ppl__air
+    - geo_hpl__ot_fresh
+    - geo_hpl__cl_fresh
+    - geo_hpl__cl_fresh
+    - geo_hpl__ot_saline
+    - geo_hpl__air
+    - geo_ppl__ot_fresh
+    - geo_ppl__cl_fresh
+    - geo_ppl__cl_fresh
+    - geo_ppl__ot_saline
+    - geo_ppl__air
+    - igcc__ot_fresh
+    - igcc__cl_fresh
+    - igcc__ot_saline
+    - igcc__air
+    - igcc_ccs__ot_fresh
+    - igcc_ccs__cl_fresh
+    - igcc_ccs__ot_saline
+    - igcc_ccs__air
+    - loil_cc__ot_fresh
+    - loil_cc__cl_fresh
+    - loil_cc__ot_saline
+    - loil_cc__air
+    - loil_ppl__ot_fresh
+    - loil_ppl__cl_fresh
+    - loil_ppl__ot_saline
+    - loil_ppl__air
+    - nuc_hc__ot_fresh
+    - nuc_hc__cl_fresh
+    - nuc_hc__ot_saline
+    - nuc_hc__air
+    - solar_th_ppl__ot_fresh
+    - solar_th_ppl__cl_fresh
+    - solar_th_ppl__ot_saline
+    - solar_th_ppl__air
+
+  # cat_addon includes cooling technology addons for parent technologies
+  type_addon:
+    add:
+    - cooling__bio_hpl
+    - cooling__bio_istig
+    - cooling__bio_istig_ccs
+    - cooling__bio_ppl
+    - cooling__coal_adv
+    - cooling__coal_adv_ccs
+    - cooling__coal_ppl
+    - cooling__coal_ppl_u
+    - cooling__foil_hpl
+    - cooling__foil_ppl
+    - cooling__gas_cc
+    - cooling__gas_cc_ccs
+    - cooling__gas_hpl
+    - cooling__gas_ppl
+    - cooling__geo_hpl
+    - cooling__geo_ppl
+    - cooling__igcc
+    - cooling__igcc_ccs
+    - cooling__loil_cc
+    - cooling__loil_ppl
+    - cooling__nuc_hc
+    - cooling__nuc_lc
+    - cooling__solar_th_ppl
+
+  map_tec_addon:
+    add:
+    - [bio_hpl,cooling__bio_hpl]
+    - [bio_istig, cooling__bio_istig]
+    - [bio_istig_ccs, cooling__bio_istig_ccs]
+    - [bio_ppl,cooling__bio_ppl]
+    - [coal_adv, cooling__coal_adv]
+    - [coal_adv_ccs, cooling__coal_adv_ccs]
+    - [coal_ppl, cooling__coal_ppl]
+    - [coal_ppl_u, cooling__coal_ppl_u]
+    - [foil_hpl, cooling__foil_hpl]
+    - [foil_ppl, cooling__foil_ppl]
+    - [gas_cc, cooling__gas_cc]
+    - [gas_cc_ccs, cooling__gas_cc_ccs]
+    - [gas_hpl, cooling__gas_hpl]
+    - [gas_ppl, cooling__gas_ppl]
+    - [geo_hpl, cooling__geo_hpl]
+    - [geo_ppl, cooling__geo_ppl]
+    - [igcc, cooling__igcc]
+    - [igcc_ccs, cooling__igcc_ccs]
+    - [loil_cc, cooling__loil_cc]
+    - [loil_ppl, cooling__loil_ppl]
+    - [nuc_hc, cooling__nuc_hc]
+    - [nuc_lc, cooling__nuc_lc]
+    - [solar_th_ppl, cooling__solar_th_ppl]
+
+
+  cat_addon:
+  #   #TODO check whether these needs to be added ?
+    add:
+    - [cooling__bio_hpl, bio_hpl__ot_fresh]
+    - [cooling__bio_hpl, bio_hpl__cl_fresh]
+    - [cooling__bio_hpl, bio_hpl__ot_saline]
+    - [cooling__bio_hpl, bio_hpl__air]
+    - [cooling__bio_istig, bio_istig__ot_fresh]
+    - [cooling__bio_istig, bio_istig__cl_fresh]
+    - [cooling__bio_istig, bio_istig__ot_saline]
+    - [cooling__bio_istig, bio_istig__air]
+    - [cooling__bio_istig_ccs, bio_istig_ccs__ot_fresh]
+    - [cooling__bio_istig_ccs, bio_istig_ccs__cl_fresh]
+    - [cooling__bio_istig_ccs, bio_istig_ccs__ot_saline]
+    - [cooling__bio_istig_ccs, bio_istig_ccs__air]
+    - [cooling__coal_adv, coal_adv__ot_fresh]
+    - [cooling__coal_adv, coal_adv__cl_fresh]
+    - [cooling__coal_adv, coal_adv__ot_saline]
+    - [cooling__coal_adv, coal_adv__air]
+    - [cooling__coal_adv_ccs, coal_adv_ccs__ot_fresh]
+    - [cooling__coal_adv_ccs, coal_adv_ccs__cl_fresh]
+    - [cooling__coal_adv_ccs, coal_adv_ccs__ot_saline]
+    - [cooling__coal_adv_ccs, coal_adv_ccs__air]
+    - [cooling__bio_ppl, bio_ppl__ot_fresh]
+    - [cooling__bio_ppl, bio_ppl__cl_fresh]
+    - [cooling__bio_ppl, bio_ppl__ot_saline]
+    - [cooling__bio_ppl, bio_ppl__air]
+    - [cooling__coal_ppl, coal_ppl__ot_fresh]
+    - [cooling__coal_ppl, coal_ppl__cl_fresh]
+    - [cooling__coal_ppl, coal_ppl__ot_saline]
+    - [cooling__coal_ppl, coal_ppl__air]
+    - [cooling__coal_ppl_u, coal_ppl_u__ot_fresh]
+    - [cooling__coal_ppl_u, coal_ppl_u__cl_fresh]
+    - [cooling__coal_ppl_u, coal_ppl_u__ot_saline]
+    - [cooling__coal_ppl_u, coal_ppl_u__air]
+    - [cooling__foil_hpl, foil_hpl__ot_fresh]
+    - [cooling__foil_hpl, foil_hpl__cl_fresh]
+    - [cooling__foil_hpl, foil_hpl__ot_saline]
+    - [cooling__foil_hpl, foil_hpl__air]
+    - [cooling__foil_ppl, foil_ppl__ot_fresh]
+    - [cooling__foil_ppl, foil_ppl__cl_fresh]
+    - [cooling__foil_ppl, foil_ppl__ot_saline]
+    - [cooling__foil_ppl, foil_ppl__air]
+    - [cooling__gas_cc, gas_cc__ot_fresh]
+    - [cooling__gas_cc, gas_cc__cl_fresh]
+    - [cooling__gas_cc, gas_cc__ot_saline]
+    - [cooling__gas_cc, gas_cc__air]
+    - [cooling__gas_cc_ccs, gas_cc_ccs__ot_fresh]
+    - [cooling__gas_cc_ccs, gas_cc_ccs__cl_fresh]
+    - [cooling__gas_cc_ccs, gas_cc_ccs__ot_saline]
+    - [cooling__gas_cc_ccs, gas_cc_ccs__air]
+    - [cooling__gas_hpl, gas_hpl__ot_fresh]
+    - [cooling__gas_hpl, gas_hpl__cl_fresh]
+    - [cooling__gas_hpl, gas_hpl__ot_saline]
+    - [cooling__gas_hpl, gas_hpl__air]
+    - [cooling__gas_ppl, gas_ppl__ot_fresh]
+    - [cooling__gas_ppl, gas_ppl__cl_fresh]
+    - [cooling__gas_ppl, gas_ppl__ot_saline]
+    - [cooling__gas_ppl, gas_ppl__air]
+    - [cooling__geo_hpl, gas_hpl__ot_fresh]
+    - [cooling__geo_hpl, gas_hpl__cl_fresh]
+    - [cooling__geo_hpl, gas_hpl__ot_saline]
+    - [cooling__geo_hpl, gas_hpl__air]
+    - [cooling__geo_ppl, geo_ppl__ot_fresh]
+    - [cooling__geo_ppl, geo_ppl__cl_fresh]
+    - [cooling__geo_ppl, geo_ppl__ot_saline]
+    - [cooling__geo_ppl, geo_ppl__air]
+    - [cooling__igcc, igcc__ot_fresh]
+    - [cooling__igcc, igcc__cl_fresh]
+    - [cooling__igcc, igcc__ot_saline]
+    - [cooling__igcc, igcc__air]
+    - [cooling__igcc_ccs, igcc_ccs__ot_fresh]
+    - [cooling__igcc_ccs, igcc_ccs__cl_fresh]
+    - [cooling__igcc_ccs, igcc_ccs__ot_saline]
+    - [cooling__igcc_ccs, igcc_ccs__air]
+    - [cooling__loil_cc, loil_cc__ot_fresh]
+    - [cooling__loil_cc, loil_cc__cl_fresh]
+    - [cooling__loil_cc, loil_cc__ot_saline]
+    - [cooling__loil_cc, loil_cc__air]
+    - [cooling__loil_ppl, loil_ppl__ot_fresh]
+    - [cooling__loil_ppl, loil_ppl__cl_fresh]
+    - [cooling__loil_ppl, loil_ppl__ot_saline]
+    - [cooling__loil_ppl, loil_ppl__air]
+    - [cooling__nuc_hc, nuc_hc__ot_fresh]
+    - [cooling__nuc_hc, nuc_hc__cl_fresh]
+    - [cooling__nuc_hc, nuc_hc__ot_saline]
+    - [cooling__nuc_hc, nuc_hc__air]
+    - [cooling__nuc_lc, nuc_lc__ot_fresh]
+    - [cooling__nuc_lc, nuc_lc__cl_fresh]
+    - [cooling__nuc_lc, nuc_lc__ot_saline]
+    - [cooling__nuc_lc, nuc_lc__air]
+    - [cooling__solar_th_ppl, solar_th_ppl__ot_fresh]
+    - [cooling__solar_th_ppl, solar_th_ppl__cl_fresh]
+    - [cooling__solar_th_ppl, solar_th_ppl__ot_saline]
+    - [cooling__solar_th_ppl, solar_th_ppl__air]
+
+  type_tec:
+    add:
+    - water_distribution
+    - water_efficiency
+    - wastewater_treatment
+    - desalination
+    - share_low_lim_GWat_total
+    - share_low_lim_GWat_share
+    - share_wat_recycle_total
+    - share_wat_recycle_share
+
+  cat_tec:
+    add:
+    - [share_low_lim_GWat_total, extract_surfacewater]
+    - [share_low_lim_GWat_total, extract_groundwater]
+    - [share_low_lim_GWat_share, extract_groundwater]
+    - [share_wat_recycle_total,urban_recycle]
+    - [share_wat_recycle_total,urban_discharge]
+    - [share_wat_recycle_share,urban_recycle]
+    - [water_distribution, urban_t_d]
+    - [water_distribution, rural_t_d]
+    - [water_distribution, urban_unconnected]
+    - [water_distribution, rural_unconnected]
+    - [water_distribution, urban_sewerage]
+    - [wastewater_treatment, urban_sewerage]
+    - [wastewater_treatment, urban_untreated]
+    - [wastewater_treatment, urban_recycle]
+    - [wastewater_treatment, rural_sewerage]
+    - [wastewater_treatment, rural_untreated]
+    - [water_efficiency, ueff1]
+    - [water_efficiency, ueff2]
+    - [water_efficiency, ueff3]
+    - [water_efficiency, reff1]
+    - [water_efficiency, reff2]
+    - [water_efficiency, reff3]
+    - [water_efficiency, ieff1]
+    - [water_efficiency, ieff2]
+    - [water_efficiency, ieff3]
+    #- [water_resource_extraction, extract_upstream_landuse]
+    - [water_resource_extraction, extract_surfacewater]
+    #- [water_resource_extraction, extract_freshwater_instream]
+    - [water_resource_extraction, extract_salinewater]
+    - [water_resource_extraction, extract_groundwater]
+    - [water_resource_extraction, extract_gw_fossil]
+    - [desalination, membrane]
+    - [desalination, distillation]
+
+  balance_equality:
+    add:
+    - [freshwater, water_supply]
+    - [freshwater_basin, water_supply_basin]
+    - [surfacewater_basin, water_avail_basin]
+    - [groundwater_basin, water_avail_basin]
+    - [salinewater_basin,water_avail_basin]
+    - [urban_collected_wst, final]
+    - [urban_collected_wst, water_treat]
+    - [urban_uncollected_wst, final]
+    - [industry_uncollected_wst,final]
+    - [rural_collected_wst, final]
+    - [rural_collected_wst, water_treat]
+    - [rural_uncollected_wst, final]
+    - [urban_mw, final]
+    - [industry_mw,final]
+    - [urban_disconnected, final]
+    - [rural_mw, final]
+    - [rural_disconnected, final]
+
+
+  shares:
+    add:
+    - share_basin
+    - share_low_lim_GWat
+    - share_wat_recycle
+
+  # relation:
+  #   add:
+    # - gw_share
+
+  unit:
+    add:
+    - km3/GWa
+    - km3
+    - km3/year
+    - USD/km3
+
+  emission:
+    add:
+    - fresh_return
+
+  type_emission:
+    add:
+    - water_consumption
+
+  cat_emission:
+    add:
+    - [water_consumption, fresh_return]
+
+cooling:
+  commodity:
+    require:
+    - electr
+
+    remove:
+    # removing all technologies which are related to water in the previous model
+    # Re added in the newer implementation with lesser commodities
+    - saline_supply
+    #- freshwater_instream
+    - freshwater_supply
+    - desalinated_water
+    - saline_supply_ppl
+    - urban_collected_wst
+    - urban_uncollected_wst
+    - yield_freshwater_suply
+    - rural_collected_wst
+    - rual_uncollected_wst
+    - urban_mw
+    - urban_dis
+    - rural_mw
+    - rural_dis
+    - cooling__bio_hpl
+    - cooling__bio_ppl
+    - cooling__bio_istig
+    - cooling__bio_ppl
+    - cooling__coal_adv
+    - cooling__coal_adv_ccs
+    - cooling__coal_ppl
+    - cooling__coal_ppl_u
+    - cooling__foil_hpl
+    - cooling__foil_ppl
+    - cooling__gas_cc
+    - cooling__gas_cc_ccs
+    - cooling__gas_hpl
+    - cooling__gas_ppl
+    - cooling__geo_hpl
+    - cooling__geo_ppl
+    - cooling__igcc
+    - cooling__igcc_ccs
+    - cooling__loil_cc
+    - cooling__loil_ppl
+    - cooling__nuc_hc
+    - cooling__solar_th_ppl
+
+    add:
+    - electr
+    - salinewater
+    - freshwater_instream
+    - freshwater
+    - freshwater_basin
+    - surfacewater_basin
+    - groundwater_basin
+    - desalinated_water
+    - saline_ppl
+
+
+  level:
+    require:
+    - secondary
+
+    remove:
+    - water_supply
+    - cooling
+    - desalination_supply
+    - water_treat
+    add:
+    - water_supply
+    - saline_supply
+
+  mode:
+    require:
+    - all
+
+  technology:
+    remove:
+    - extract__saline_supply
+    - bio_hpl__ot_fresh
+    - bio_hpl__cl_fresh
+    - bio_hpl__cl_fresh
+    - bio_hpl__ot_saline
+    - bio_hpl__air
+    - bio_istig__ot_fresh
+    - bio_istig__cl_fresh
+    - bio_istig__cl_fresh
+    - bio_istig__ot_saline
+    - bio_istig__air
+    - bio_istig_ccs__ot_fresh
+    - bio_istig_ccs__cl_fresh
+    - bio_istig_ccs__cl_fresh
+    - bio_istig_ccs__ot_saline
+    - bio_istig_ccs__air
+    - bio_ppl__ot_fresh
+    - bio_ppl__cl_fresh
+    - bio_ppl__cl_fresh
+    - bio_ppl__ot_saline
+    - bio_ppl__air
+    - coal_adv__ot_fresh
+    - coal_adv__cl_fresh
+    - coal_adv__cl_fresh
+    - coal_adv__ot_saline
+    - coal_adv__air
+    - coal_adv_ccs__ot_fresh
+    - coal_adv_ccs__cl_fresh
+    - coal_adv_ccs__cl_fresh
+    - coal_adv_ccs__ot_saline
+    - coal_ppl__ot_fresh
+    - coal_ppl__cl_fresh
+    - coal_ppl__cl_fresh
+    - coal_ppl__ot_saline
+    - coal_ppl__air
+    - coal_ppl_u__ot_fresh
+    - coal_ppl_u__cl_fresh
+    - coal_ppl_u__cl_fresh
+    - coal_ppl_u__ot_saline
+    - coal_ppl_u__air
+    - foil_hpl__ot_fresh
+    - foil_hpl__cl_fresh
+    - foil_hpl__cl_fresh
+    - foil_hpl__ot_saline
+    - foil_hpl__air
+    - foil_ppl__ot_fresh
+    - foil_ppl__cl_fresh
+    - foil_ppl__cl_fresh
+    - foil_ppl__ot_saline
+    - foil_ppl__air
+    - gas_cc__ot_fresh
+    - gas_cc__cl_fresh
+    - gas_cc__cl_fresh
+    - gas_cc__ot_saline
+    - gas_cc__air
+    - gas_cc_ccs__ot_fresh
+    - gas_cc_ccs__cl_fresh
+    - gas_cc_ccs__cl_fresh
+    - gas_cc_ccs__ot_saline
+    - gas_hpl__ot_fresh
+    - gas_hpl__cl_fresh
+    - gas_hpl__cl_fresh
+    - gas_hpl__ot_saline
+    - gas_hpl__air
+    - gas_ppl__ot_fresh
+    - gas_ppl__cl_fresh
+    - gas_ppl__cl_fresh
+    - gas_ppl__ot_saline
+    - gas_ppl__air
+    - geo_hpl__ot_fresh
+    - geo_hpl__cl_fresh
+    - geo_hpl__cl_fresh
+    - geo_hpl__ot_saline
+    - geo_hpl__air
+    - geo_ppl__ot_fresh
+    - geo_ppl__cl_fresh
+    - geo_ppl__cl_fresh
+    - geo_ppl__ot_saline
+    - geo_ppl__air
+    - igcc__ot_fresh
+    - igcc__cl_fresh
+    - igcc__cl_fresh
+    - igcc__ot_saline
+    - igcc__air
+    - igcc_ccs__ot_fresh
+    - igcc_ccs__cl_fresh
+    - igcc_ccs__cl_fresh
+    - igcc_ccs__ot_saline
+    - loil_cc__ot_fresh
+    - loil_cc__cl_fresh
+    - loil_cc__cl_fresh
+    - loil_cc__ot_saline
+    - loil_cc__air
+    - loil_ppl__ot_fresh
+    - loil_ppl__cl_fresh
+    - loil_ppl__cl_fresh
+    - loil_ppl__ot_saline
+    - loil_ppl__air
+    - nuc_hc__ot_fresh
+    - nuc_hc__cl_fresh
+    - nuc_hc__cl_fresh
+    - nuc_hc__ot_saline
+    - solar_th_ppl__ot_fresh
+    - solar_th_ppl__cl_fresh
+    - solar_th_ppl__cl_fresh
+    - solar_th_ppl__ot_saline
+    - solar_th_ppl__air
+    - extract__upstream_landuse
+    - extract__saline_supply
+    - extract__freshwater_supply
+    - extract__freshwater_instream
+
+    #add:
+    # Adds all technologies from technologies.yaml file again to reconfigure
+
+  time:
+    # NB cannot handle models with sub-annual time resolution
+    require:
+    - year
+
+  # Addon techs include cooling technologies for now
+  addon:
+    add:
+    - bio_hpl__ot_fresh
+    - bio_hpl__cl_fresh
+    - bio_hpl__ot_saline
+    - bio_hpl__air
+    - bio_istig__ot_fresh
+    - bio_istig__cl_fresh
+    - bio_istig__ot_saline
+    - bio_istig__air
+    - bio_istig_ccs__ot_fresh
+    - bio_istig_ccs__cl_fresh
+    - bio_istig_ccs__ot_saline
+    - bio_istig_ccs__air
+    - bio_ppl__ot_fresh
+    - bio_ppl__cl_fresh
+    - bio_ppl__ot_saline
+    - bio_ppl__air
+    - coal_adv__ot_fresh
+    - coal_adv__cl_fresh
+    - coal_adv__ot_saline
+    - coal_adv__air
+    - coal_adv_ccs__ot_fresh
+    - coal_adv_ccs__cl_fresh
+    - coal_adv_ccs__ot_saline
+    - coal_adv_ccs__air
+    - coal_ppl__ot_fresh
+    - coal_ppl__cl_fresh
+    - coal_ppl__ot_saline
+    - coal_ppl__air
+    - coal_ppl_u__ot_fresh
+    - coal_ppl_u__cl_fresh
+    - coal_ppl_u__ot_saline
+    - coal_ppl_u__air
+    - foil_ppl__ot_fresh
+    - foil_ppl__cl_fresh
+    - foil_ppl__ot_saline
+    - foil_ppl__air
+    - foil_hpl__ot_fresh
+    - foil_hpl__cl_fresh
+    - foil_hpl__ot_saline
+    - foil_hpl__air
+    - gas_cc__ot_fresh
+    - gas_cc__cl_fresh
+    - gas_cc__ot_saline
+    - gas_cc__air
+    - gas_cc_ccs__ot_fresh
+    - gas_cc_ccs__cl_fresh
+    - gas_cc_ccs__ot_saline
+    - gas_cc_ccs__air
+    - gas_hpl__ot_fresh
+    - gas_hpl__cl_fresh
+    - gas_hpl__ot_saline
+    - gas_hpl__air
+    - gas_ppl__ot_fresh
+    - gas_ppl__cl_fresh
+    - gas_ppl__ot_saline
+    - gas_ppl__air
+    - geo_hpl__ot_fresh
+    - geo_hpl__cl_fresh
+    - geo_hpl__cl_fresh
+    - geo_hpl__ot_saline
+    - geo_hpl__air
+    - geo_ppl__ot_fresh
+    - geo_ppl__cl_fresh
+    - geo_ppl__cl_fresh
+    - geo_ppl__ot_saline
+    - geo_ppl__air
+    - igcc__ot_fresh
+    - igcc__cl_fresh
+    - igcc__ot_saline
+    - igcc__air
+    - igcc_ccs__ot_fresh
+    - igcc_ccs__cl_fresh
+    - igcc_ccs__ot_saline
+    - igcc_ccs__air
+    - loil_cc__ot_fresh
+    - loil_cc__cl_fresh
+    - loil_cc__ot_saline
+    - loil_cc__air
+    - loil_ppl__ot_fresh
+    - loil_ppl__cl_fresh
+    - loil_ppl__ot_saline
+    - loil_ppl__air
+    - nuc_hc__ot_fresh
+    - nuc_hc__cl_fresh
+    - nuc_hc__ot_saline
+    - nuc_hc__air
+    - solar_th_ppl__ot_fresh
+    - solar_th_ppl__cl_fresh
+    - solar_th_ppl__ot_saline
+    - solar_th_ppl__air
+
+  # cat_addon includes cooling technology addons for parent technologies
+  type_addon:
+    add:
+    - cooling__bio_hpl
+    - cooling__bio_istig
+    - cooling__bio_istig_ccs
+    - cooling__bio_ppl
+    - cooling__coal_adv
+    - cooling__coal_adv_ccs
+    - cooling__coal_ppl
+    - cooling__coal_ppl_u
+    - cooling__foil_hpl
+    - cooling__foil_ppl
+    - cooling__gas_cc
+    - cooling__gas_cc_ccs
+    - cooling__gas_hpl
+    - cooling__gas_ppl
+    - cooling__geo_hpl
+    - cooling__geo_ppl
+    - cooling__igcc
+    - cooling__igcc_ccs
+    - cooling__loil_cc
+    - cooling__loil_ppl
+    - cooling__nuc_hc
+    - cooling__nuc_lc
+    - cooling__solar_th_ppl
+
+  map_tec_addon:
+    add:
+    - [bio_hpl,cooling__bio_hpl]
+    - [bio_istig, cooling__bio_istig]
+    - [bio_istig_ccs, cooling__bio_istig_ccs]
+    - [bio_ppl,cooling__bio_ppl]
+    - [coal_adv, cooling__coal_adv]
+    - [coal_adv_ccs, cooling__coal_adv_ccs]
+    - [coal_ppl, cooling__coal_ppl]
+    - [coal_ppl_u, cooling__coal_ppl_u]
+    - [foil_hpl, cooling__foil_hpl]
+    - [foil_ppl, cooling__foil_ppl]
+    - [gas_cc, cooling__gas_cc]
+    - [gas_cc_ccs, cooling__gas_cc_ccs]
+    - [gas_hpl, cooling__gas_hpl]
+    - [gas_ppl, cooling__gas_ppl]
+    - [geo_hpl, cooling__geo_hpl]
+    - [geo_ppl, cooling__geo_ppl]
+    - [igcc, cooling__igcc]
+    - [igcc_ccs, cooling__igcc_ccs]
+    - [loil_cc, cooling__loil_cc]
+    - [loil_ppl, cooling__loil_ppl]
+    - [nuc_hc, cooling__nuc_hc]
+    - [nuc_lc, cooling__nuc_lc]
+    - [solar_th_ppl, cooling__solar_th_ppl]
+
+
+  cat_addon:
+  #   #TODO check whether these needs to be added ?
+    add:
+    - [cooling__bio_hpl, bio_hpl__ot_fresh]
+    - [cooling__bio_hpl, bio_hpl__cl_fresh]
+    - [cooling__bio_hpl, bio_hpl__ot_saline]
+    - [cooling__bio_hpl, bio_hpl__air]
+    - [cooling__bio_istig, bio_istig__ot_fresh]
+    - [cooling__bio_istig, bio_istig__cl_fresh]
+    - [cooling__bio_istig, bio_istig__ot_saline]
+    - [cooling__bio_istig, bio_istig__air]
+    - [cooling__bio_istig_ccs, bio_istig_ccs__ot_fresh]
+    - [cooling__bio_istig_ccs, bio_istig_ccs__cl_fresh]
+    - [cooling__bio_istig_ccs, bio_istig_ccs__ot_saline]
+    - [cooling__bio_istig_ccs, bio_istig_ccs__air]
+    - [cooling__coal_adv, coal_adv__ot_fresh]
+    - [cooling__coal_adv, coal_adv__cl_fresh]
+    - [cooling__coal_adv, coal_adv__ot_saline]
+    - [cooling__coal_adv, coal_adv__air]
+    - [cooling__coal_adv_ccs, coal_adv_ccs__ot_fresh]
+    - [cooling__coal_adv_ccs, coal_adv_ccs__cl_fresh]
+    - [cooling__coal_adv_ccs, coal_adv_ccs__ot_saline]
+    - [cooling__coal_adv_ccs, coal_adv_ccs__air]
+    - [cooling__bio_ppl, bio_ppl__ot_fresh]
+    - [cooling__bio_ppl, bio_ppl__cl_fresh]
+    - [cooling__bio_ppl, bio_ppl__ot_saline]
+    - [cooling__bio_ppl, bio_ppl__air]
+    - [cooling__coal_ppl, coal_ppl__ot_fresh]
+    - [cooling__coal_ppl, coal_ppl__cl_fresh]
+    - [cooling__coal_ppl, coal_ppl__ot_saline]
+    - [cooling__coal_ppl, coal_ppl__air]
+    - [cooling__coal_ppl_u, coal_ppl_u__ot_fresh]
+    - [cooling__coal_ppl_u, coal_ppl_u__cl_fresh]
+    - [cooling__coal_ppl_u, coal_ppl_u__ot_saline]
+    - [cooling__coal_ppl_u, coal_ppl_u__air]
+    - [cooling__foil_hpl, foil_hpl__ot_fresh]
+    - [cooling__foil_hpl, foil_hpl__cl_fresh]
+    - [cooling__foil_hpl, foil_hpl__ot_saline]
+    - [cooling__foil_hpl, foil_hpl__air]
+    - [cooling__foil_ppl, foil_ppl__ot_fresh]
+    - [cooling__foil_ppl, foil_ppl__cl_fresh]
+    - [cooling__foil_ppl, foil_ppl__ot_saline]
+    - [cooling__foil_ppl, foil_ppl__air]
+    - [cooling__gas_cc, gas_cc__ot_fresh]
+    - [cooling__gas_cc, gas_cc__cl_fresh]
+    - [cooling__gas_cc, gas_cc__ot_saline]
+    - [cooling__gas_cc, gas_cc__air]
+    - [cooling__gas_cc_ccs, gas_cc_ccs__ot_fresh]
+    - [cooling__gas_cc_ccs, gas_cc_ccs__cl_fresh]
+    - [cooling__gas_cc_ccs, gas_cc_ccs__ot_saline]
+    - [cooling__gas_cc_ccs, gas_cc_ccs__air]
+    - [cooling__gas_hpl, gas_hpl__ot_fresh]
+    - [cooling__gas_hpl, gas_hpl__cl_fresh]
+    - [cooling__gas_hpl, gas_hpl__ot_saline]
+    - [cooling__gas_hpl, gas_hpl__air]
+    - [cooling__gas_ppl, gas_ppl__ot_fresh]
+    - [cooling__gas_ppl, gas_ppl__cl_fresh]
+    - [cooling__gas_ppl, gas_ppl__ot_saline]
+    - [cooling__gas_ppl, gas_ppl__air]
+    - [cooling__geo_hpl, gas_hpl__ot_fresh]
+    - [cooling__geo_hpl, gas_hpl__cl_fresh]
+    - [cooling__geo_hpl, gas_hpl__ot_saline]
+    - [cooling__geo_hpl, gas_hpl__air]
+    - [cooling__geo_ppl, geo_ppl__ot_fresh]
+    - [cooling__geo_ppl, geo_ppl__cl_fresh]
+    - [cooling__geo_ppl, geo_ppl__ot_saline]
+    - [cooling__geo_ppl, geo_ppl__air]
+    - [cooling__igcc, igcc__ot_fresh]
+    - [cooling__igcc, igcc__cl_fresh]
+    - [cooling__igcc, igcc__ot_saline]
+    - [cooling__igcc, igcc__air]
+    - [cooling__igcc_ccs, igcc_ccs__ot_fresh]
+    - [cooling__igcc_ccs, igcc_ccs__cl_fresh]
+    - [cooling__igcc_ccs, igcc_ccs__ot_saline]
+    - [cooling__igcc_ccs, igcc_ccs__air]
+    - [cooling__loil_cc, loil_cc__ot_fresh]
+    - [cooling__loil_cc, loil_cc__cl_fresh]
+    - [cooling__loil_cc, loil_cc__ot_saline]
+    - [cooling__loil_cc, loil_cc__air]
+    - [cooling__loil_ppl, loil_ppl__ot_fresh]
+    - [cooling__loil_ppl, loil_ppl__cl_fresh]
+    - [cooling__loil_ppl, loil_ppl__ot_saline]
+    - [cooling__loil_ppl, loil_ppl__air]
+    - [cooling__nuc_hc, nuc_hc__ot_fresh]
+    - [cooling__nuc_hc, nuc_hc__cl_fresh]
+    - [cooling__nuc_hc, nuc_hc__ot_saline]
+    - [cooling__nuc_hc, nuc_hc__air]
+    - [cooling__nuc_lc, nuc_lc__ot_fresh]
+    - [cooling__nuc_lc, nuc_lc__cl_fresh]
+    - [cooling__nuc_lc, nuc_lc__ot_saline]
+    - [cooling__nuc_lc, nuc_lc__air]
+    - [cooling__solar_th_ppl, solar_th_ppl__ot_fresh]
+    - [cooling__solar_th_ppl, solar_th_ppl__cl_fresh]
+    - [cooling__solar_th_ppl, solar_th_ppl__ot_saline]
+    - [cooling__solar_th_ppl, solar_th_ppl__air]
+
+
+  unit:
+    add:
+    - km3/GWa
+    - km3
+    - km3/year
+
+  emission:
+    add:
+    - fresh_return
+
+  type_emission:
+    add:
+    - water_consumption
+
+  cat_emission:
+    add:
+    - [water_consumption, fresh_return]
diff --git a/nest/message_ix_models/data/water/technology.yaml b/nest/message_ix_models/data/water/technology.yaml
new file mode 100644
index 0000000..03af31c
--- /dev/null
+++ b/nest/message_ix_models/data/water/technology.yaml
@@ -0,0 +1,1326 @@
+#  Water technologies
+nexus:
+  extract_surfacewater:
+    description: >-
+      Freshwater extraction technology
+    input: {commodity: freshwater_supply}
+
+  extract_salinewater:
+    description: >-
+      Sea water extraction used for cooling technologies requiring seawater
+    input: {commodity: saline_supply}
+
+  extract_groundwater:
+    description: >-
+      Groundwater extraction technology
+    input: {commodity: freshwater_supply}
+
+  extract_gw_fossil:
+    description: >-
+      Groundwater extraction from fossil reservoirs (unlimited used as slack)
+
+  extract_salinewater_basin:
+    description: >-
+      Sea water extraction used for desalination and providing freshwater
+    input: {commodity: saline_supply_basin}
+
+  bio_hpl__ot_fresh:
+    description: >-
+      Biomass heating power plant cooling by once through cooling technology
+      using freshwater
+    input: {commodity: freshwater_supply}
+
+  bio_hpl__cl_fresh:
+    description: >-
+      Biomass heating power plant cooling by closed loop cooling technology
+      using freshwater  & freshwater supply
+    input: {commodity: freshwater_supply, electr}
+
+  bio_hpl__ot_saline:
+    description: >-
+      Biomass heating power plant cooling by once through cooling technology
+      using saline
+    input: {commodity: saline_supply_ppl}
+
+  bio_hpl__air:
+    description: >-
+      Biomass heating power plant cooling by air cooled technology
+      using freshwater supply
+    input: {commodity: electr}
+
+  bio_istig__ot_fresh:
+    description: >-
+      # TODO fill in description of bio_istig cooling by once through cooling
+      # technology using freshwater
+    input: {commodity: freshwater_supply}
+
+  bio_istig__cl_fresh:
+    description: >-
+    # TODO fill in description of bio_istig cooling by closed loop  cooling
+    # technology using freshwater
+    input: {commodity: freshwater_supply,electr}
+
+  bio_istig__ot_saline:
+    description: >-
+    #   TODO fill in description of bio_istig through once through cooling
+    #   technology using saline water
+    input: {commodity: saline_supply_ppl}
+
+  bio_istig__air:
+    description: >-
+    #   TODO fill in description of bio_istig through air cooled
+    #   technology using freshwater supply
+    input: {commodity: electr}
+
+
+  bio_istig_ccs__ot_fresh:
+    description: >-
+    # TODO fill in description of bio_istig cooling by once through cooling
+    # technology using freshwater
+    input: {commodity: freshwater_supply}
+
+  bio_istig_ccs__cl_fresh:
+    description: >-
+    # TODO fill in description of bio_istig cooling by closed loop  cooling
+    # technology using freshwater
+    input: {commodity: freshwater_supply,electr}
+
+  bio_istig_ccs__ot_saline:
+    description: >-
+    #   TODO fill in description of bio_istig through once through cooling
+    #   technology using saline water
+    input: {commodity: saline_supply_ppl}
+
+  bio_istig_ccs__air:
+    description: >-
+    #   TODO fill in description of bio_istig through air cooled
+    #   technology using freshwater supply
+    input: {commodity: electr}
+
+  bio_ppl__ot_fresh:
+    description: >-
+      Bio power plant cooling by once through cooling technology using freshwater
+    input: {commodity: freshwater_supply}
+
+  bio_ppl__cl_fresh:
+    description: >-
+      Bio power plant cooling by closed loop cooling technology using
+      freshwater & freshwater supply
+    input: {commodity: freshwater_supply, electr}
+
+  bio_ppl__ot_saline:
+    description: >-
+      Bio power plant cooling by once through cooling technology
+      using saline water
+    input: {commodity: saline_supply_ppl}
+
+  bio_ppl__air:
+    description: >-
+      Biopower plant cooling by air cooled technology
+      using freshwater supply
+    input: {commodity: electr}
+
+  coal_adv__ot_fresh:
+    description: >-
+      Advanced coal power plant cooling by once through cooling technology using
+      freshwater
+    input: {commodity: freshwater_supply}
+
+  coal_adv__cl_fresh:
+    description: >-
+      Advanced coal power plant cooling by closed loop cooling technology using
+      freshwater & freshwater supply
+    input: {commodity: freshwater_supply,electr}
+
+  coal_adv__ot_saline:
+    description: >-
+      Advanced coal power plant cooling by once through cooling technology using
+      saline water
+    input: {commodity: saline_supply_ppl}
+
+  coal_adv__air:
+    description: >-
+      Advanced coal power plant cooling by air cooled cooling technology using
+      freshwater supply
+    input: {commodity: electr}
+
+  coal_adv_ccs__ot_fresh:
+    description: >-
+      Advanced coal power plant with carbon capture and storage cooling by once
+      through cooling technology using freshwater
+    input: {commodity: freshwater_supply}
+
+  coal_adv_ccs__cl_fresh:
+    description: >-
+      Advanced coal power plant with carbon capture and storage by closed loop
+      cooling technology using freshwater
+    input: {commodity: freshwater_supply, electr}
+
+  coal_adv_ccs__ot_saline:
+    description: >-
+      Advanced coal power plant with carbon capture and storage by once through
+      cooling technology using freshwater
+    input: {commodity: saline_supply_ppl}
+
+  coal_adv_ccs__air:
+    description: >-
+      Advanced coal power plant with carbon capture and storage by air cooled
+      cooling technology using freshwater supply
+    input: {commodity: electr}
+
+  coal_ppl__ot_fresh:
+    description: >-
+      Coal power plant cooling by once through cooling technology using freshwater
+    input: {commodity: freshwater_supply}
+
+  coal_ppl__cl_fresh:
+    description: >-
+      Coal power plant cooling by closed loop cooling technology using freshwater
+      & freshwater supply
+    input: {commodity: freshwater_supply,electr}
+
+  coal_ppl__ot_saline:
+    description: >-
+      Coal power plant cooling by once through cooling technology using freshwater
+    input: {commodity: saline_supply_ppl}
+
+  coal_ppl__air:
+    description: >-
+      Coal power plant cooling by air cooled cooling technology using parasitic
+      electricity
+    input: {commodity: electr}
+
+  coal_ppl_u__ot_fresh:
+    description: >-
+      Coal power plant without abatement measures cooling by once through
+      cooling technology using freshwater
+    input: {commodity: freshwater_supply}
+
+  coal_ppl_u__cl_fresh:
+    description: >-
+      Coal power plant without abatement measures cooling by closed loop
+      cooling technology using freshwater & freshwater supply
+    input: {commodity: freshwater_supply,electr}
+
+  coal_ppl_u__ot_saline:
+    description: >-
+      Coal power plant without abatement measures cooling by once through
+      cooling technology using saline water
+    input: {commodity: saline_supply_ppl}
+
+  coal_ppl_u__air:
+    description: >-
+      Coal power plant without abatement measures cooling by air cooled
+      cooling technology using freshwater supply
+    input: {commodity: electr}
+
+  foil_ppl__ot_fresh:
+    description: >-
+      New standard oil power plant (Rankine cycle), cooling by once through
+      cooling technology using freshwater
+      using freshwater
+    input: {commodity: freshwater_supply}
+
+  foil_ppl__cl_fresh:
+    description: >-
+      New standard oil power plant (Rankine cycle),cooling by closed loop
+      cooling technology using freshwater & freshwater supply
+    input: {commodity: freshwater_supply, electr}
+
+  foil_ppl__ot_saline:
+    description: >-
+      New standard oil power plant (Rankine cycle),cooling by once through
+      cooling technology using saline water
+    input: {commodity: saline_supply_ppl}
+
+  foil_ppl__air:
+    description: >-
+      New standard oil power plant (Rankine cycle),cooling by air cooled
+      cooling technology using parasitic electricity
+    input: {commodity: electr}
+
+  foil_hpl__ot_fresh:
+    description: >-
+      Fuel oil heating plant cooling by once through cooling technology using
+      freshwater
+    input: {commodity: freshwater_supply}
+
+  foil_hpl__cl_fresh:
+    description: >-
+      Fuel oil heating plant cooling by closed loop cooling technology using
+      freshwater & freshwater supply
+    input: {commodity: freshwater_supply,electr}
+
+  foil_hpl__ot_saline:
+    description: >-
+      Fuel oil heating plant cooling by once through cooling technology using
+      saline water
+    input: {commodity: saline_supply_ppl}
+
+  foil_hpl__air:
+    description: >-
+      Fuel oil heating plant cooling by cooling by air cooled cooling technology
+      using parasitic electricity
+    input: {commodity: electr}
+
+  gas_cc__ot_fresh:
+    description: >-
+      Gas combined cycle power plant cooling by once through cooling technology
+      using freshwater
+    input: {commodity: freshwater_supply}
+
+  gas_cc__cl_fresh:
+    description: >-
+      Gas combined cycle power plant cooling by closed loop cooling technology
+      using freshwater & freshwater supply
+    input: {commodity: freshwater_supply,electr}
+
+  gas_cc__ot_saline:
+    description: >-
+      Gas combined cycle power plant cooling by once through cooling technology
+      using saline water
+    input: {commodity: saline_supply_ppl}
+
+  gas_cc__air:
+    description: >-
+      Gas combined cycle power plant cooling by air cooled cooling technology
+      using parasitic electricity
+    input: {commodity: electr}
+
+  gas_cc_ccs__ot_fresh:
+    description: >-
+      Gas combined cycle power-plant with carbon capture and storage cooling by
+      once through cooling technology using freshwater
+    input: {commodity: freshwater_supply}
+
+  gas_cc_ccs__cl_fresh:
+    description: >-
+      Gas combined cycle power-plant with carbon capture and storage cooling by
+      closed loop cooling technology using freshwater & freshwater supply
+    input: {commodity: freshwater_supply,electr}
+
+  gas_cc_ccs__ot_saline:
+    description: >-
+      Gas combined cycle power-plant with carbon capture and storage cooling by
+      once through cooling technology using saline water
+    input: {commodity: saline_supply_ppl}
+
+  gas_cc_ccs__air:
+    description: >-
+      Gas combined cycle power-plant with carbon capture and storage cooling by
+      air cooled cooling technology using parasitic electricity
+    input: {commodity: electr}
+
+  gas_hpl__ot_fresh:
+    description: >-
+      Natural gas heating plant plant cooling by once through cooling technology
+      using freshwater
+    input: {commodity: freshwater_supply}
+
+  gas_hpl__cl_fresh:
+    description: >-
+      Natural gas heating plant cooling by closed loop cooling technology using
+      freshwater & freshwater supply
+    input: {commodity: freshwater_supply,electr}
+
+  gas_hpl__ot_saline:
+    description: >-
+      Natural gas heating plant cooling by once through cooling technology using
+      saline water
+    input: {commodity: saline_supply_ppl}
+
+  gas_hpl__air:
+    description: >-
+      Natural gas heating plant cooling by once through cooling technology by
+      air cooled cooling technology using parasitic electricity
+    input: {commodity: electr}
+
+  gas_ppl__ot_fresh:
+    description: >-
+      Gas power plant (Rankine cycle) cooling by once through cooling technology
+      using freshwater
+    input: {commodity: freshwater_supply}
+
+  gas_ppl__cl_fresh:
+    description: >-
+      Gas power plant (Rankine cycle) cooling by closed loop cooling technology
+      using freshwater & freshwater supply
+    input: {commodity: freshwater_supply,electr}
+
+  gas_ppl__ot_saline:
+    description: >-
+      Biomass heating power plant cooling by once through cooling technology using
+      freshwater
+    input: {commodity: saline_supply_ppl}
+
+  gas_ppl__air:
+    description: >-
+      Biomass heating power plant cooling by air cooled cooling technology using
+      freshwater supply
+    input: {commodity: electr}
+
+  geo_hpl__ot_fresh:
+    description: >-
+      Geothermal heat plant cooling by once through cooling technology using
+      freshwater
+    input: {commodity: freshwater_supply}
+
+  geo_hpl__cl_fresh:
+    description: >-
+      Geothermal heat plant cooling by closed loop cooling technology using
+      freshwater & freshwater supply
+    input: {commodity: freshwater_supply,electr}
+
+  geo_hpl__ot_saline:
+    description: >-
+      Geothermal heat plant cooling by once through cooling technology using
+      saline water
+    input: {commodity: saline_supply_ppl}
+
+  geo_hpl__air:
+    description: >-
+      Geothermal heat plant cooling by air cooled cooling technology using
+      freshwater supply
+    input: {commodity: electr}
+
+  geo_ppl__ot_fresh:
+    description: >-
+      Geothermal power plant cooling by once through cooling technology using
+      freshwater
+    input: {commodity: freshwater_supply}
+
+  geo_ppl__cl_fresh:
+    description: >-
+      Geothermal power plant cooling by closed loop cooling technology using
+      freshwater & freshwater supply
+    input: {commodity: freshwater_supply,electr}
+
+  geo_ppl__ot_saline:
+    description: >-
+      Geothermal power plant cooling by once through cooling technology using
+      saline water
+    input: {commodity: saline_supply_ppl}
+
+  geo_ppl__air:
+    description: >-
+      Geothermal power plant cooling by closed loop cooling technology using
+      freshwater supply
+    input: {commodity: electr}
+
+  igcc__ot_fresh:
+    description: >-
+      Integrated gasification combined cycle (IGCC) power plant cooling by once
+      through cooling technology using freshwater
+    input: {commodity: freshwater_supply}
+
+  igcc__cl_fresh:
+    description: >-
+      Integrated gasification combined cycle (IGCC) power plant cooling by closed
+      loop cooling technology using freshwater & freshwater supply
+    input: {commodity: freshwater_supply,electr}
+
+  igcc__ot_saline:
+    description: >-
+      Integrated gasification combined cycle (IGCC) power plant cooling by once
+      through cooling technology using saline water
+    input: {commodity: saline_supply_ppl}
+
+  igcc__air:
+    description: >-
+      Integrated gasification combined cycle (IGCC) power plant cooling by air cooled
+      cooling technology using freshwater supply
+    input: {commodity: electr}
+
+  igcc_ccs__ot_fresh:
+    description: >-
+      Integrated gasification combined cycle (IGCC) power plant with carbon capture
+      and storage cooling by once through cooling technology using freshwater
+    input: {commodity: freshwater_supply}
+
+  igcc_ccs__cl_fresh:
+    description: >-
+      Integrated gasification combined cycle (IGCC) power plant with carbon capture
+      and storage cooling by closed loop cooling technology using freshwater &
+      freshwater supply
+    input: {commodity: freshwater_supply,electr}
+
+  igcc_ccs__ot_saline:
+    description: >-
+      Integrated gasification combined cycle (IGCC) power plant with carbon capture
+      and storage cooling by once through cooling technology using saline water
+    input: {commodity: saline_supply_ppl}
+
+  igcc_ccs__air:
+    description: >-
+      Integrated gasification combined cycle (IGCC) power plant with carbon capture
+      and storage cooling by air cooled cooling technology using freshwater supply
+    input: {commodity: electr}
+
+  loil_cc__ot_fresh:
+    description: >-
+      Light oil combined cycle cooling by once through cooling technology using
+      freshwater
+    input: {commodity: freshwater_supply}
+
+  loil_cc__cl_fresh:
+    description: >-
+      Light oil combined cycle cooling by closed loop cooling technology using
+      freshwater & freshwater supply
+    input: {commodity: freshwater_supply,electr}
+
+  loil_cc__ot_saline:
+    description: >-
+      Light oil combined cycle cooling by once through cooling technology using
+      saline supply
+    input: {commodity: saline_supply_ppl}
+
+  loil_cc__air:
+    description: >-
+      Light oil combined cycle cooling by air cooled cooling technology using
+      freshwater supply
+    input: {commodity: electr}
+
+  loil_ppl__ot_fresh:
+    description: >-
+      Existing light oil power plant cooling by once through cooling technology
+      using extract_freshwater_supply
+    input: {commodity: freshwater_supply}
+
+  loil_ppl__cl_fresh:
+    description: >-
+      Existing light oil power plant cooling by once through cooling technology using
+      freshwater supply
+    input: {commodity: freshwater_supply,electr}
+
+  loil_ppl__ot_saline:
+    description: >-
+      Existing light oil power plant cooling by once through cooling technology
+      using freshwater supply
+    input: {commodity: electr}
+
+  loil_ppl__air:
+    description: >-
+      Existing light oil power plant cooling by air cooled cooling technology
+      using parasitic electricity
+    input: {commodity: electr}
+
+  nuc_hc__ot_fresh:
+    description: >-
+      Nuclear power plant (~GEN III+) (high cost) cooling by once through cooling
+      technology using freshwater supply
+    input: {commodity: freshwater_supply}
+
+  nuc_hc__cl_fresh:
+    description: >-
+      Nuclear power plant (~GEN III+) (high cost) cooling by closed loop cooling
+      technology using freshwater supply & parasitic
+    input: {commodity: freshwater_supply,electr}
+
+  nuc_hc__ot_saline:
+    description: >-
+      Nuclear power plant (~GEN III+) (high cost) cooling by once through cooling
+      technology using saline water supply
+    input: {commodity: saline_supply_ppl}
+
+  nuc_hc__air:
+    description: >-
+      Nuclear power plant (~GEN III+) (high cost) cooling by once through cooling
+      technology using parasitic electricity
+    input: {commodity: electr}
+
+  nuc_lc__ot_fresh:
+    description: >-
+      Nuclear power plant (~GEN III+) (low cost) cooling by once through cooling
+      technology using freshwater supply
+    input: {commodity: freshwater_supply}
+
+  nuc_lc__cl_fresh:
+    description: >-
+      Nuclear power plant (~GEN III+) (low cost) cooling by closed loop cooling
+      technology using freshwater supply & parasitic
+    input: {commodity: freshwater_supply,electr}
+
+  nuc_lc__ot_saline:
+    description: >-
+      Nuclear power plant (~GEN III+) (low cost) cooling by once through cooling
+      technology using saline water supply
+    input: {commodity: saline_supply_ppl}
+
+  nuc_lc__air:
+    description: >-
+      Nuclear power plant (~GEN III+) (low cost) cooling by once through cooling
+      technology using parasitic electricity
+    input: {commodity: electr}
+
+  solar_th_ppl__ot_fresh:
+    description: >-
+      Solar thermal power plant with storage cooling by once through cooling
+      technology using freshwater supply
+    input: {commodity: freshwater_supply}
+
+  solar_th_ppl__cl_fresh:
+    description: >-
+      Solar thermal power plant with storage cooling by closed loop cooling
+      technology using freshwater supply & parasitic electricity
+    input: {commodity: freshwater_supply,electr}
+
+  solar_th_ppl__ot_saline:
+    description: >-
+      Solar thermal power plant with storage cooling by once through cooling
+      technology using freshwater supply
+    input: {commodity: saline_supply_ppl}
+
+  solar_th_ppl__air:
+    description: >-
+      Solar thermal power plant with storage cooling by once through cooling
+      technology using parasitic electricity
+    input: {commodity: electr}
+
+  urban_t_d:
+    description: >-
+      Urban water transmission & distribution
+    input: {commodity: freshwater_supply}
+
+  rural_t_d:
+    description: >-
+      Rural water transmission & distribution
+    input: {commodity: freshwater_supply}
+
+  industry_unconnected:
+    description: >-
+      unconnected industry water not connected to system
+    input: {commodity: freshwater_supply}
+
+  industry_untreated:
+    description: >-
+      untreated industry water return flows
+    input: {commodity: urban_uncollected_wst}
+
+  urban_unconnected:
+    description: >-
+      untreated urban water not connected to system
+    input: {commodity: freshwater_supply}
+
+  rural_unconnected:
+    description: >-
+      untreated rural water not connected to system
+    input: {commodity: freshwater_supply}
+
+  urban_sewerage:
+    description: >-
+      urban wastewater
+    input: {commodity: freshwater_supply}
+
+  urban_untreated:
+    description: >-
+      untreated urban water return flows
+    input: {commodity: urban_uncollected_wst}
+
+  urban_discharge:
+    description: >-
+      treated urban water which is discharged
+    input: {commodity: electr, urban_collected_wst}
+
+  urban_recycle:
+    description: >-
+      treated wastewater used as available water
+    input: {commodity: electr, urban_collected_wst}
+
+  rural_discharge:
+    description: >-
+      treated rural water which is discharged
+    input: {commodity: rural_collected_wst}
+
+  rural_untreated:
+    description: >-
+      untreated rural water return flows
+    input: {commodity: rural_collected_wst}
+
+  membrane:
+    description: >-
+      desalination technology using membrane i.e water and salts is separated
+      through a semipermeable membrane
+    input: {commodity: electr,saline_supply}
+
+  distillation:
+    description: >-
+      traditional desalination technology i.e boiling and recondensation of
+      seawater to leave salt  and impurities behin
+    input: {commodity: electr,saline_supply,d_heat}
+
+  desal_t_d:
+    description: >-
+      Desalinated water transmission and distribution
+    input: {commodity: desalinated_water}
+
+  saline_ppl_t_d:
+    description: >-
+      Saline power plant transmission & distribution
+      # TODO verify description
+    input: {commodity: saline_supply}
+
+  basin_to_reg:
+    description: >-
+      dummy technology to map basin technologies water supply to energy technologies
+
+    input: {commodity: freshwater_supply}
+
+  ueff1:
+    description: >-
+      low urban efficiency
+
+    input: {commodity: urban_mw, urban_collected_wst}
+
+  ueff2:
+    description: >-
+      low urban efficiency
+
+    input: {commodity: urban_mw, urban_collected_wst}
+
+  ueff3:
+    description: >-
+      low urban efficiency
+
+    input: {commodity: urban_mw, urban_collected_wst}
+
+  reff1:
+    description: >-
+      low rural efficiency
+
+    input: {commodity: rural_mw, rural_collected_wst}
+
+  reff2:
+    description: >-
+      mid rural efficiency
+
+    input: {commodity: rural_mw, rural_collected_wst}
+
+  reff3:
+    description: >-
+      high rural efficiency
+
+    input: {commodity: rural_mw, rural_collected_wst}
+
+  ieff1:
+    description: >-
+      low irrigation efficiency
+
+    input: {commodity: freshwater_supply}
+
+  ieff2:
+    description: >-
+      mid irrigation efficiency
+
+    input: {commodity: freshwater_supply}
+
+  ieff3:
+    description: >-
+      high irrigation efficiency
+
+    input: {commodity: freshwater_supply}
+
+  return_flow:
+    description: >-
+      remaining surface water left after consumption
+
+  gw_recharge:
+    description: >-
+      remaining groundwater left after consumption
+
+  rural_recycle:
+    description: >-
+      recycled water after rural treatment
+
+  rural_sewerage:
+    description: >-
+      treated rural water
+
+  irrigation_oilcrops:
+    description: >-
+      Irrigation technology connecting irrigation withdrawals with the demands
+    input: {commodity: freshwater}
+
+  irrigation_sugarcrops:
+    description: >-
+      Irrigation technology connecting irrigation withdrawals with the demands
+    input: {commodity: freshwater}
+
+  irrigation_cereal:
+    description: >-
+      Irrigation technology connecting irrigation withdrawals with the demands
+    input: {commodity: freshwater}
+
+  salinewater_return:
+    description: >-
+      remaining salinewater left after consumption
+
+
+cooling:
+  #  Water technologies
+  extract_surfacewater:
+    description: >-
+      Freshwater extraction technology
+    input: {commodity: freshwater_supply}
+
+  extract_salinewater:
+    description: >-
+      Sea water extraction used for cooling technologies requiring seawater
+    input: {commodity: saline_supply_ppl}
+
+  extract_groundwater:
+    description: >-
+      Groundwater extraction technology
+    input: {commodity: freshwater_supply}
+
+  bio_hpl__ot_fresh:
+    description: >-
+      Biomass heating power plant cooling by once through cooling technology
+      using freshwater
+    input: {commodity: freshwater_supply}
+
+  bio_hpl__cl_fresh:
+    description: >-
+      Biomass heating power plant cooling by closed loop cooling technology
+      using freshwater  & freshwater supply
+    input: {commodity: freshwater_supply, electr}
+
+  bio_hpl__ot_saline:
+    description: >-
+      Biomass heating power plant cooling by once through cooling technology
+      using saline
+    input: {commodity: saline_supply_ppl}
+
+  bio_hpl__air:
+    description: >-
+      Biomass heating power plant cooling by air cooled technology
+      using freshwater supply
+    input: {commodity: electr}
+
+  bio_istig__ot_fresh:
+    description: >-
+    # TODO fill in description of bio_istig cooling by once through cooling
+    # technology using freshwater
+    input: {commodity: freshwater_supply}
+
+  bio_istig__cl_fresh:
+    description: >-
+    # TODO fill in description of bio_istig cooling by closed loop  cooling
+    # technology using freshwater
+    input: {commodity: freshwater_supply,electr}
+
+  bio_istig__ot_saline:
+    description: >-
+    #   TODO fill in description of bio_istig through once through cooling
+    #   technology using saline water
+    input: {commodity: saline_supply_ppl}
+
+  bio_istig__air:
+    description: >-
+    #   TODO fill in description of bio_istig through air cooled
+    #   technology using freshwater supply
+    input: {commodity: electr}
+
+
+  bio_istig_ccs__ot_fresh:
+    description: >-
+    # TODO fill in description of bio_istig cooling by once through cooling
+    # technology using freshwater
+    input: {commodity: freshwater_supply}
+
+  bio_istig_ccs__cl_fresh:
+    description: >-
+    # TODO fill in description of bio_istig cooling by closed loop  cooling
+    # technology using freshwater
+    input: {commodity: freshwater_supply,electr}
+
+  bio_istig_ccs__ot_saline:
+    description: >-
+    #   TODO fill in description of bio_istig through once through cooling
+    #   technology using saline water
+    input: {commodity: saline_supply_ppl}
+
+  bio_istig_ccs__air:
+    description: >-
+    #   TODO fill in description of bio_istig through air cooled
+    #   technology using freshwater supply
+    input: {commodity: electr}
+
+  bio_ppl__ot_fresh:
+    description: >-
+      Bio power plant cooling by once through cooling technology using freshwater
+    input: {commodity: freshwater_supply}
+
+  bio_ppl__cl_fresh:
+    description: >-
+      Bio power plant cooling by closed loop cooling technology using
+      freshwater & freshwater supply
+    input: {commodity: freshwater_supply, electr}
+
+  bio_ppl__ot_saline:
+    description: >-
+      Bio power plant cooling by once through cooling technology
+      using saline water
+    input: {commodity: saline_supply_ppl}
+
+  bio_ppl__air:
+    description: >-
+      Biopower plant cooling by air cooled technology
+      using freshwater supply
+    input: {commodity: electr}
+
+  coal_adv__ot_fresh:
+    description: >-
+      Advanced coal power plant cooling by once through cooling technology using
+      freshwater
+    input: {commodity: freshwater_supply}
+
+  coal_adv__cl_fresh:
+    description: >-
+      Advanced coal power plant cooling by closed loop cooling technology using
+      freshwater & freshwater supply
+    input: {commodity: freshwater_supply,electr}
+
+  coal_adv__ot_saline:
+    description: >-
+      Advanced coal power plant cooling by once through cooling technology using
+      saline water
+    input: {commodity: saline_supply_ppl}
+
+  coal_adv__air:
+    description: >-
+      Advanced coal power plant cooling by air cooled cooling technology using
+      freshwater supply
+    input: {commodity: electr}
+
+  coal_adv_ccs__ot_fresh:
+    description: >-
+      Advanced coal power plant with carbon capture and storage cooling by once
+      through cooling technology using freshwater
+    input: {commodity: freshwater_supply}
+
+  coal_adv_ccs__cl_fresh:
+    description: >-
+      Advanced coal power plant with carbon capture and storage by closed loop
+      cooling technology using freshwater
+    input: {commodity: freshwater_supply, electr}
+
+  coal_adv_ccs__ot_saline:
+    description: >-
+      Advanced coal power plant with carbon capture and storage by once through
+      cooling technology using freshwater
+    input: {commodity: saline_supply_ppl}
+
+  coal_adv_ccs__air:
+    description: >-
+      Advanced coal power plant with carbon capture and storage by air cooled
+      cooling technology using freshwater supply
+    input: {commodity: electr}
+
+  coal_ppl__ot_fresh:
+    description: >-
+      Coal power plant cooling by once through cooling technology using freshwater
+    input: {commodity: freshwater_supply}
+
+  coal_ppl__cl_fresh:
+    description: >-
+      Coal power plant cooling by closed loop cooling technology using freshwater
+      & freshwater supply
+    input: {commodity: freshwater_supply,electr}
+
+  coal_ppl__ot_saline:
+    description: >-
+      Coal power plant cooling by once through cooling technology using freshwater
+    input: {commodity: saline_supply_ppl}
+
+  coal_ppl__air:
+    description: >-
+      Coal power plant cooling by air cooled cooling technology using parasitic
+      electricity
+    input: {commodity: electr}
+
+  coal_ppl_u__ot_fresh:
+    description: >-
+      Coal power plant without abatement measures cooling by once through
+      cooling technology using freshwater
+    input: {commodity: freshwater_supply}
+
+  coal_ppl_u__cl_fresh:
+    description: >-
+      Coal power plant without abatement measures cooling by closed loop
+      cooling technology using freshwater & freshwater supply
+    input: {commodity: freshwater_supply,electr}
+
+  coal_ppl_u__ot_saline:
+    description: >-
+      Coal power plant without abatement measures cooling by once through
+      cooling technology using saline water
+    input: {commodity: saline_supply_ppl}
+
+  coal_ppl_u__air:
+    description: >-
+      Coal power plant without abatement measures cooling by air cooled
+      cooling technology using freshwater supply
+    input: {commodity: electr}
+
+  foil_ppl__ot_fresh:
+    description: >-
+      New standard oil power plant (Rankine cycle), cooling by once through
+      cooling technology using freshwater
+      using freshwater
+    input: {commodity: freshwater_supply}
+
+  foil_ppl__cl_fresh:
+    description: >-
+      New standard oil power plant (Rankine cycle),cooling by closed loop
+      cooling technology using freshwater & freshwater supply
+    input: {commodity: freshwater_supply, electr}
+
+  foil_ppl__ot_saline:
+    description: >-
+      New standard oil power plant (Rankine cycle),cooling by once through
+      cooling technology using saline water
+    input: {commodity: saline_supply_ppl}
+
+  foil_ppl__air:
+    description: >-
+      New standard oil power plant (Rankine cycle),cooling by air cooled
+      cooling technology using parasitic electricity
+    input: {commodity: electr}
+
+  foil_hpl__ot_fresh:
+    description: >-
+      Fuel oil heating plant cooling by once through cooling technology using
+      freshwater
+    input: {commodity: freshwater_supply}
+
+  foil_hpl__cl_fresh:
+    description: >-
+      Fuel oil heating plant cooling by closed loop cooling technology using
+      freshwater & freshwater supply
+    input: {commodity: freshwater_supply,electr}
+
+  foil_hpl__ot_saline:
+    description: >-
+      Fuel oil heating plant cooling by once through cooling technology using
+      saline water
+    input: {commodity: saline_supply_ppl}
+
+  foil_hpl__air:
+    description: >-
+      Fuel oil heating plant cooling by cooling by air cooled cooling technology
+      using parasitic electricity
+    input: {commodity: electr}
+
+  gas_cc__ot_fresh:
+    description: >-
+      Gas combined cycle power plant cooling by once through cooling technology
+      using freshwater
+    input: {commodity: freshwater_supply}
+
+  gas_cc__cl_fresh:
+    description: >-
+      Gas combined cycle power plant cooling by closed loop cooling technology
+      using freshwater & freshwater supply
+    input: {commodity: freshwater_supply,electr}
+
+  gas_cc__ot_saline:
+    description: >-
+      Gas combined cycle power plant cooling by once through cooling technology
+      using saline water
+    input: {commodity: saline_supply_ppl}
+
+  gas_cc__air:
+    description: >-
+      Gas combined cycle power plant cooling by air cooled cooling technology
+      using parasitic electricity
+    input: {commodity: electr}
+
+  gas_cc_ccs__ot_fresh:
+    description: >-
+      Gas combined cycle power-plant with carbon capture and storage cooling by
+      once through cooling technology using freshwater
+    input: {commodity: freshwater_supply}
+
+  gas_cc_ccs__cl_fresh:
+    description: >-
+      Gas combined cycle power-plant with carbon capture and storage cooling by
+      closed loop cooling technology using freshwater & freshwater supply
+    input: {commodity: freshwater_supply,electr}
+
+  gas_cc_ccs__ot_saline:
+    description: >-
+      Gas combined cycle power-plant with carbon capture and storage cooling by
+      once through cooling technology using saline water
+    input: {commodity: saline_supply_ppl}
+
+  gas_cc_ccs__air:
+    description: >-
+      Gas combined cycle power-plant with carbon capture and storage cooling by
+      air cooled cooling technology using parasitic electricity
+    input: {commodity: electr}
+
+  gas_hpl__ot_fresh:
+    description: >-
+      Natural gas heating plant plant cooling by once through cooling technology
+      using freshwater
+    input: {commodity: freshwater_supply}
+
+  gas_hpl__cl_fresh:
+    description: >-
+      Natural gas heating plant cooling by closed loop cooling technology using
+      freshwater & freshwater supply
+    input: {commodity: freshwater_supply,electr}
+
+  gas_hpl__ot_saline:
+    description: >-
+      Natural gas heating plant cooling by once through cooling technology using
+      saline water
+    input: {commodity: saline_supply_ppl}
+
+  gas_hpl__air:
+    description: >-
+      Natural gas heating plant cooling by once through cooling technology by
+      air cooled cooling technology using parasitic electricity
+    input: {commodity: electr}
+
+  gas_ppl__ot_fresh:
+    description: >-
+      Gas power plant (Rankine cycle) cooling by once through cooling technology
+      using freshwater
+    input: {commodity: freshwater_supply}
+
+  gas_ppl__cl_fresh:
+    description: >-
+      Gas power plant (Rankine cycle) cooling by closed loop cooling technology
+      using freshwater & freshwater supply
+    input: {commodity: freshwater_supply,electr}
+
+  gas_ppl__ot_saline:
+    description: >-
+      Biomass heating power plant cooling by once through cooling technology using
+      freshwater
+    input: {commodity: saline_supply_ppl}
+
+  gas_ppl__air:
+    description: >-
+      Biomass heating power plant cooling by air cooled cooling technology using
+      freshwater supply
+    input: {commodity: electr}
+
+  geo_hpl__ot_fresh:
+    description: >-
+      Geothermal heat plant cooling by once through cooling technology using
+      freshwater
+    input: {commodity: freshwater_supply}
+
+  geo_hpl__cl_fresh:
+    description: >-
+      Geothermal heat plant cooling by closed loop cooling technology using
+      freshwater & freshwater supply
+    input: {commodity: freshwater_supply,electr}
+
+  geo_hpl__ot_saline:
+    description: >-
+      Geothermal heat plant cooling by once through cooling technology using
+      saline water
+    input: {commodity: saline_supply_ppl}
+
+  geo_hpl__air:
+    description: >-
+      Geothermal heat plant cooling by air cooled cooling technology using
+      freshwater supply
+    input: {commodity: electr}
+
+  geo_ppl__ot_fresh:
+    description: >-
+      Geothermal power plant cooling by once through cooling technology using
+      freshwater
+    input: {commodity: freshwater_supply}
+
+  geo_ppl__cl_fresh:
+    description: >-
+      Geothermal power plant cooling by closed loop cooling technology using
+      freshwater & freshwater supply
+    input: {commodity: freshwater_supply,electr}
+
+  geo_ppl__ot_saline:
+    description: >-
+      Geothermal power plant cooling by once through cooling technology using
+      saline water
+    input: {commodity: saline_supply_ppl}
+
+  geo_ppl__air:
+    description: >-
+      Geothermal power plant cooling by closed loop cooling technology using
+      freshwater supply
+    input: {commodity: electr}
+
+  igcc__ot_fresh:
+    description: >-
+      Integrated gasification combined cycle (IGCC) power plant cooling by once
+      through cooling technology using freshwater
+    input: {commodity: freshwater_supply}
+
+  igcc__cl_fresh:
+    description: >-
+      Integrated gasification combined cycle (IGCC) power plant cooling by closed
+      loop cooling technology using freshwater & freshwater supply
+    input: {commodity: freshwater_supply,electr}
+
+  igcc__ot_saline:
+    description: >-
+      Integrated gasification combined cycle (IGCC) power plant cooling by once
+      through cooling technology using saline water
+    input: {commodity: saline_supply_ppl}
+
+  igcc__air:
+    description: >-
+      Integrated gasification combined cycle (IGCC) power plant cooling by air cooled
+      cooling technology using freshwater supply
+    input: {commodity: electr}
+
+  igcc_ccs__ot_fresh:
+    description: >-
+      Integrated gasification combined cycle (IGCC) power plant with carbon capture
+      and storage cooling by once through cooling technology using freshwater
+    input: {commodity: freshwater_supply}
+
+  igcc_ccs__cl_fresh:
+    description: >-
+      Integrated gasification combined cycle (IGCC) power plant with carbon capture
+      and storage cooling by closed loop cooling technology using freshwater &
+      freshwater supply
+    input: {commodity: freshwater_supply,electr}
+
+  igcc_ccs__ot_saline:
+    description: >-
+      Integrated gasification combined cycle (IGCC) power plant with carbon capture
+      and storage cooling by once through cooling technology using saline water
+    input: {commodity: saline_supply_ppl}
+
+  igcc_ccs__air:
+    description: >-
+      Integrated gasification combined cycle (IGCC) power plant with carbon capture
+      and storage cooling by air cooled cooling technology using freshwater supply
+    input: {commodity: electr}
+
+  loil_cc__ot_fresh:
+    description: >-
+      Light oil combined cycle cooling by once through cooling technology using
+      freshwater
+    input: {commodity: freshwater_supply}
+
+  loil_cc__cl_fresh:
+    description: >-
+      Light oil combined cycle cooling by closed loop cooling technology using
+      freshwater & freshwater supply
+    input: {commodity: freshwater_supply,electr}
+
+  loil_cc__ot_saline:
+    description: >-
+      Light oil combined cycle cooling by once through cooling technology using
+      saline supply
+    input: {commodity: saline_supply_ppl}
+
+  loil_cc__air:
+    description: >-
+      Light oil combined cycle cooling by air cooled cooling technology using
+      freshwater supply
+    input: {commodity: electr}
+
+  loil_ppl__ot_fresh:
+    description: >-
+      Existing light oil power plant cooling by once through cooling technology
+      using extract_freshwater_supply
+    input: {commodity: freshwater_supply}
+
+  loil_ppl__cl_fresh:
+    description: >-
+      Existing light oil power plant cooling by once through cooling technology using
+      freshwater supply
+    input: {commodity: freshwater_supply,electr}
+
+  loil_ppl__ot_saline:
+    description: >-
+      Existing light oil power plant cooling by once through cooling technology
+      using freshwater supply
+    input: {commodity: electr}
+
+  loil_ppl__air:
+    description: >-
+      Existing light oil power plant cooling by air cooled cooling technology
+      using parasitic electricity
+    input: {commodity: electr}
+
+  nuc_hc__ot_fresh:
+    description: >-
+      Nuclear power plant (~GEN III+) (high cost) cooling by once through cooling
+      technology using freshwater supply
+    input: {commodity: freshwater_supply}
+
+  nuc_hc__cl_fresh:
+    description: >-
+      Nuclear power plant (~GEN III+) (high cost) cooling by closed loop cooling
+      technology using freshwater supply & parasitic
+    input: {commodity: freshwater_supply,electr}
+
+  nuc_hc__ot_saline:
+    description: >-
+      Nuclear power plant (~GEN III+) (high cost) cooling by once through cooling
+      technology using saline water supply
+    input: {commodity: saline_supply_ppl}
+
+  nuc_hc__air:
+    description: >-
+      Nuclear power plant (~GEN III+) (high cost) cooling by once through cooling
+      technology using parasitic electricity
+    input: {commodity: electr}
+
+  nuc_lc__ot_fresh:
+    description: >-
+      Nuclear power plant (~GEN III+) (low cost) cooling by once through cooling
+      technology using freshwater supply
+    input: {commodity: freshwater_supply}
+
+  nuc_lc__cl_fresh:
+    description: >-
+      Nuclear power plant (~GEN III+) (low cost) cooling by closed loop cooling
+      technology using freshwater supply & parasitic
+    input: {commodity: freshwater_supply,electr}
+
+  nuc_lc__ot_saline:
+    description: >-
+      Nuclear power plant (~GEN III+) (low cost) cooling by once through cooling
+      technology using saline water supply
+    input: {commodity: saline_supply_ppl}
+
+  nuc_lc__air:
+    description: >-
+      Nuclear power plant (~GEN III+) (low cost) cooling by once through cooling
+      technology using parasitic electricity
+    input: {commodity: electr}
+
+  solar_th_ppl__ot_fresh:
+    description: >-
+      Solar thermal power plant with storage cooling by once through cooling
+      technology using freshwater supply
+    input: {commodity: freshwater_supply}
+
+  solar_th_ppl__cl_fresh:
+    description: >-
+      Solar thermal power plant with storage cooling by closed loop cooling
+      technology using freshwater supply & parasitic electricity
+    input: {commodity: freshwater_supply,electr}
+
+  solar_th_ppl__ot_saline:
+    description: >-
+      Solar thermal power plant with storage cooling by once through cooling
+      technology using freshwater supply
+    input: {commodity: saline_supply_ppl}
+
+  solar_th_ppl__air:
+    description: >-
+      Solar thermal power plant with storage cooling by once through cooling
+      technology using parasitic electricity
+    input: {commodity: electr}
+
diff --git a/nest/message_ix_models/data/year/A.yaml b/nest/message_ix_models/data/year/A.yaml
new file mode 100644
index 0000000..585ad69
--- /dev/null
+++ b/nest/message_ix_models/data/year/A.yaml
@@ -0,0 +1,41 @@
+# Time periods used in CD-LINKS, inter alia
+
+1960:
+  description: Period from 1951-01-01 to 1960-12-31.
+  # Durations of subsequent periods are implied by the prior period
+  duration_period: 10
+
+1970:
+  description: Period from 1961-01-01 to 1970-12-31.
+1980:
+  description: Period from 1971-01-01 to 1980-12-31.
+1990:
+  description: Period from 1981-01-01 to 1990-12-31.
+2000:
+  description: Period from 1991-01-01 to 2000-12-31.
+2010:
+  description: Period from 2001-01-01 to 2010-12-31.
+
+2020:
+  description: Period from 2011-01-01 to 2020-12-31.
+  # Periods before this are historical
+  firstmodelyear: true
+
+2030:
+  description: Period from 2021-01-01 to 2030-12-31.
+2040:
+  description: Period from 2031-01-01 to 2040-12-31.
+2050:
+  description: Period from 2041-01-01 to 2050-12-31.
+2060:
+  description: Period from 2051-01-01 to 2060-12-31.
+2070:
+  description: Period from 2061-01-01 to 2070-12-31.
+2080:
+  description: Period from 2071-01-01 to 2080-12-31.
+2090:
+  description: Period from 2081-01-01 to 2090-12-31.
+2100:
+  description: Period from 2091-01-01 to 2100-12-31.
+2110:
+  description: Period from 2101-01-01 to 2110-12-31.
diff --git a/nest/message_ix_models/data/year/B.yaml b/nest/message_ix_models/data/year/B.yaml
new file mode 100644
index 0000000..dc5af38
--- /dev/null
+++ b/nest/message_ix_models/data/year/B.yaml
@@ -0,0 +1,64 @@
+# Time periods used in ENGAGE, inter alia
+
+1950:
+  description: Period from 1946-01-01 to 1950-12-31.
+  # Durations of subsequent periods are implied by the prior period
+  duration_period: 5
+1955:
+  description: Period from 1951-01-01 to 1955-12-31.
+1960:
+  description: Period from 1956-01-01 to 1960-12-31.
+1965:
+  description: Period from 1961-01-01 to 1965-12-31.
+1970:
+  description: Period from 1966-01-01 to 1970-12-31.
+1975:
+  description: Period from 1971-01-01 to 1975-12-31.
+1980:
+  description: Period from 1976-01-01 to 1980-12-31.
+1985:
+  description: Period from 1981-01-01 to 1985-12-31.
+1990:
+  description: Period from 1986-01-01 to 1990-12-31.
+1995:
+  description: Period from 1991-01-01 to 1995-12-31.
+2000:
+  description: Period from 1996-01-01 to 2000-12-31.
+2005:
+  description: Period from 2001-01-01 to 2005-12-31.
+2010:
+  description: Period from 2006-01-01 to 2010-12-31.
+2015:
+  description: Period from 2011-01-01 to 2015-12-31.
+
+2020:
+  description: Period from 2016-01-01 to 2020-12-31.
+  # Periods before this are historical
+  firstmodelyear: true
+
+2025:
+  description: Period from 2021-01-01 to 2025-12-31.
+2030:
+  description: Period from 2026-01-01 to 2030-12-31.
+2035:
+  description: Period from 2031-01-01 to 2035-12-31.
+2040:
+  description: Period from 2036-01-01 to 2040-12-31.
+2045:
+  description: Period from 2041-01-01 to 2045-12-31.
+2050:
+  description: Period from 2046-01-01 to 2050-12-31.
+2055:
+  description: Period from 2046-01-01 to 2055-12-31.
+2060:
+  description: Period from 2056-01-01 to 2060-12-31.
+2070:
+  description: Period from 2061-01-01 to 2070-12-31.
+2080:
+  description: Period from 2071-01-01 to 2080-12-31.
+2090:
+  description: Period from 2081-01-01 to 2090-12-31.
+2100:
+  description: Period from 2091-01-01 to 2100-12-31.
+2110:
+  description: Period from 2101-01-01 to 2110-12-31.
diff --git a/nest/message_ix_models/model/__init__.py b/nest/message_ix_models/model/__init__.py
new file mode 100644
index 0000000..a968090
--- /dev/null
+++ b/nest/message_ix_models/model/__init__.py
@@ -0,0 +1,4 @@
+"""Code for constructing models/scenarios in the MESSAGEix-GLOBIOM model family."""
+from .config import Config
+
+__all__ = ["Config"]
diff --git a/nest/message_ix_models/model/bare.py b/nest/message_ix_models/model/bare.py
new file mode 100644
index 0000000..82fa673
--- /dev/null
+++ b/nest/message_ix_models/model/bare.py
@@ -0,0 +1,170 @@
+import logging
+from copy import copy
+from functools import partial
+from urllib.parse import urlunsplit
+
+import message_ix
+from sdmx.model import Code
+
+import message_ix_models
+from message_ix_models import ScenarioInfo, Spec
+from message_ix_models.util import eval_anno
+
+from .build import apply_spec
+from .config import Config
+from .data import get_data
+from .structure import codelists, get_codes
+
+log = logging.getLogger(__name__)
+
+
+def _default_first(kind, default):
+    return [default] + list(filter(lambda id: id != default, codelists(kind)))
+
+
+#: Deprecated; use :class:`.model.Config` instead.
+SETTINGS = dict(
+    # Place the default value first
+    regions=_default_first("node", "R14"),
+    years=_default_first("year", "B"),
+    res_with_dummies=[False, True],
+)
+
+
+def create_res(context, quiet=True):
+    """Create a 'bare' MESSAGEix-GLOBIOM reference energy system (RES).
+
+    Parameters
+    ----------
+    context : .Context
+        :attr:`.Context.scenario_info` determines the model name and scenario name of
+        the created Scenario. If not provided, the defaults are:
+
+        - Model name generated by :func:`name`.
+        - Scenario name "baseline".
+    quiet : bool, optional
+        Passed to `quiet` argument of :func:`.build.apply_spec`.
+
+    Returns
+    -------
+    message_ix.Scenario
+        A scenario as described by :func:`.bare.get_spec`, prepared using
+        :func:`.apply_spec`.
+    """
+    mp = context.get_platform()
+
+    # Retrieve the spec; this also sets defaults expected by name()
+    spec = get_spec(context)
+
+    # Model and scenario name for the RES
+    args = dict(
+        mp=mp,
+        model=context.scenario_info.get("model", name(context)),
+        scenario=context.scenario_info.get("scenario", "baseline"),
+        version="new",
+    )
+
+    # TODO move this to ixmp as a method similar to ixmp.util.parse_url()
+    url = urlunsplit(
+        ("ixmp", mp.name, args["model"] + "/" + args["scenario"], "", args["version"])
+    )
+    log.info(f"Create {repr(url)}")
+
+    # Create the Scenario
+    scenario = message_ix.Scenario(**args)
+
+    # TODO move to message_ix
+    scenario.init_par("MERtoPPP", ["node", "year"])
+
+    # Apply the spec
+    apply_spec(
+        scenario,
+        spec,
+        data=partial(get_data, context=context, spec=spec),
+        quiet=quiet,
+        message=f"Create using message-ix-models {message_ix_models.__version__}",
+    )
+
+    return scenario
+
+
+def get_spec(context) -> Spec:
+    """Return the spec for the MESSAGE-GLOBIOM global model RES.
+
+    If :attr:`.Config.res_with_dummies` is set, additional elements are added:
+
+    - ``commodity``: "dummy"
+    - ``technology``: "dummy", "dummy supply"
+
+    These **may** be used for testing purposes, but **should not** be used in production
+    models.
+
+    Returns
+    -------
+    :class:`dict` of :class:`.ScenarioInfo` objects
+    """
+    context.setdefault("model", Config())
+
+    add = ScenarioInfo()
+
+    # Add technologies
+    add.set["technology"] = copy(get_codes("technology"))
+
+    # Add regions
+
+    # Load configuration for the specified region mapping
+    nodes = get_codes(f"node/{context.model.regions}")
+
+    # Top-level "World" node
+    # FIXME typing ignored temporarily for PR#9
+    world = nodes[nodes.index("World")]  # type: ignore [arg-type]
+
+    # Set elements: World, followed by the direct children of World
+    add.set["node"] = [world] + world.child
+
+    # Add relations
+    add.set["relation"] = get_codes(f"relation/{context.model.relations}")
+
+    # Initialize time periods
+    add.year_from_codes(get_codes(f"year/{context.model.years}"))
+
+    # Add levels
+    add.set["level"] = get_codes("level")
+
+    # Add commodities
+    add.set["commodity"] = get_codes("commodity")
+
+    # Add units, associated with commodities
+    units = set(eval_anno(commodity, "unit") for commodity in add.set["commodity"])
+    # Deduplicate by converting to a set and then back; not strictly necessary,
+    # but reduces duplicate log entries
+    add.set["unit"] = sorted(filter(None, units))
+
+    if context.model.res_with_dummies:
+        # Add dummy technologies
+        add.set["technology"].extend([Code(id="dummy"), Code(id="dummy source")])
+        # Add a dummy commodity
+        add.set["commodity"].append(Code(id="dummy"))
+
+    # The RES is the base, so does not require or remove any elements
+    return Spec(add=add)
+
+
+def name(context):
+    """Generate a candidate name for a model given `context`.
+
+    The name has a form like::
+
+        MESSAGEix-GLOBIOM R99 YA +D
+
+    where:
+
+    - "R99" is the node list/regional aggregation.
+    - "YA" indicates the year codelist (:doc:`/pkg-data/year`).
+    - "+D" appears if :attr:`.Config.res_with_dummies` is true.
+
+    """
+    cfg = context.model
+    return f"MESSAGEix-GLOBIOM {cfg.regions} Y{cfg.years}" + (
+        " +D" if cfg.res_with_dummies else ""
+    )
diff --git a/nest/message_ix_models/model/build.py b/nest/message_ix_models/model/build.py
new file mode 100644
index 0000000..e406d2e
--- /dev/null
+++ b/nest/message_ix_models/model/build.py
@@ -0,0 +1,169 @@
+import logging
+from typing import Callable, Dict, List, Mapping, Optional, Union
+
+import ixmp
+import pandas as pd
+from ixmp.utils import maybe_check_out, maybe_commit
+from message_ix import Scenario
+from sdmx.model import Code
+
+from message_ix_models.util import add_par_data, strip_par_data
+from message_ix_models.util.scenarioinfo import ScenarioInfo, Spec
+
+log = logging.getLogger(__name__)
+
+
+def _add_unit(mp: ixmp.Platform, unit: str, comment: str) -> None:
+    """Handle exceptions in :meth:`.Platform.add_unit`."""
+    # TODO move upstream to ixmp.JDBCBackend
+    log.info(f"Add unit {repr(unit)}")
+    try:
+        mp.add_unit(unit, comment)
+    except Exception as e:  # pragma: no cover
+        if "Error assigning an unit-key-id mapping" in str(e) and "" == str(unit):
+            log.warning(f"…skip {repr(unit)} (ixmp.JDBCBackend with Oracle database)")
+        else:
+            raise
+
+
+def apply_spec(
+    scenario: Scenario,
+    spec: Union[Spec, Mapping[str, ScenarioInfo]],
+    data: Optional[Callable] = None,
+    **options,
+):
+    """Apply `spec` to `scenario`.
+
+    Parameters
+    ----------
+    spec : .Spec
+        Specification of changes to make to `scenario`.
+    data : callable, optional
+        Function to add data to `scenario`. `data` can either manipulate the scenario
+        directly, or return a :class:`dict` compatible with :func:`.add_par_data`.
+
+    Other parameters
+    ----------------
+    dry_run : bool
+        Don't modify `scenario`; only show what would be done. Default :obj:`False`.
+        Exceptions will still be raised if the elements from ``spec['required']`` are
+        missing; this serves as a check that the scenario has the required features for
+        applying the spec.
+    fast : bool
+        Do not remove existing parameter data; increases speed on large scenarios.
+    quiet : bool
+        Only show log messages at level ``ERROR`` and higher. If :obj:`False` (default),
+        show log messages at level ``DEBUG`` and higher.
+    message : str
+        Commit message.
+
+    See also
+    --------
+    .add_par_data
+    .strip_par_data
+    .Code
+    .ScenarioInfo
+    """
+    dry_run = options.get("dry_run", False)
+    fast = options.get("fast", False)
+
+    log.setLevel(logging.ERROR if options.get("quiet", False) else logging.DEBUG)
+
+    if not dry_run:
+        try:
+            scenario.remove_solution()
+        except ValueError:
+            pass
+        maybe_check_out(scenario)
+
+    dump: Dict[str, pd.DataFrame] = {}  # Removed data
+
+    for set_name in scenario.set_list():
+        # Check whether this set is mentioned at all in the spec
+        if 0 == sum(map(lambda info: len(info.set[set_name]), spec.values())):
+            # Not mentioned; don't do anything
+            continue
+
+        log.info(f"Set {repr(set_name)}")
+
+        # Base contents of the set
+        base_set = scenario.set(set_name)
+        # Unpack a multi-dimensional/indexed set to a list of tuples
+        base = (
+            list(base_set.itertuples(index=False))
+            if isinstance(base_set, pd.DataFrame)
+            else base_set.tolist()
+        )
+
+        log.info(f"  {len(base)} elements")
+        # log.debug(', '.join(map(repr, base)))  # All elements; verbose
+
+        # Check for required elements
+        require = spec["require"].set[set_name]
+        log.info(f"  Check {len(require)} required elements")
+
+        # Raise an exception about the first missing element
+        missing = list(filter(lambda e: e not in base, require))
+        if missing:
+            log.error(f"  {len(missing)} elements not found: {missing!r}")
+            raise ValueError
+
+        # Remove elements and associated parameter values
+        for element in spec["remove"].set[set_name]:
+            strip_par_data(
+                scenario,
+                set_name,
+                element,
+                dry_run=dry_run,
+                dump=None if fast else dump,
+            )
+
+        # Add elements
+        add = [] if dry_run else spec["add"].set[set_name]
+        for element in add:
+            name = element.id if isinstance(element, Code) else element
+            scenario.add_set(set_name, name)
+            if set_name == "node":
+                scenario.platform.add_region(name, "region")
+
+        if len(add):
+            log.info(f"  Add {len(add)} element(s)")
+            log.debug("  " + ellipsize(add))
+
+        log.info("  ---")
+
+    if not fast:
+        N_removed = sum(len(d) for d in dump.values())
+        log.info(f"{N_removed} total rows removed")
+
+    # Add units to the Platform before adding data
+    for unit in spec["add"].set["unit"]:
+        unit = unit if isinstance(unit, Code) else Code(id=unit, name=unit)
+        _add_unit(scenario.platform, unit.id, str(unit.name))
+
+    # Add data
+    if callable(data):
+        result = data(scenario, dry_run=dry_run)
+        if result:
+            # `data` function returned some data; use add_par_data()
+            add_par_data(scenario, result, dry_run=dry_run)
+
+    # Finalize
+    log.info("Commit results.")
+    maybe_commit(
+        scenario,
+        condition=not dry_run,
+        message=options.get("message", f"{__name__}.apply_spec()"),
+    )
+
+
+def ellipsize(elements: List) -> str:
+    """Generate a short string representation of `elements`.
+
+    If the list has more than 5 elements, only the first two and last two are shown,
+    with "..." between.
+    """
+    if len(elements) > 5:
+        return ", ".join(map(str, elements[:2] + ["..."] + elements[-2:]))
+    else:
+        return ", ".join(map(str, elements))
diff --git a/nest/message_ix_models/model/cli.py b/nest/message_ix_models/model/cli.py
new file mode 100644
index 0000000..ce83fc9
--- /dev/null
+++ b/nest/message_ix_models/model/cli.py
@@ -0,0 +1,22 @@
+import click
+
+from message_ix_models.util.click import common_params
+
+
+@click.group("res")
+@click.pass_obj
+def cli(context):
+    """MESSAGEix-GLOBIOM reference energy system (RES)."""
+
+
+@cli.command("create-bare")
+@common_params("nodes")
+@click.pass_obj
+def create_bare(context, nodes):
+    """Create the RES from scratch."""
+    from .bare import create_res
+
+    if nodes:
+        context.regions = nodes
+
+    create_res(context)
diff --git a/nest/message_ix_models/model/config.py b/nest/message_ix_models/model/config.py
new file mode 100644
index 0000000..a5318ec
--- /dev/null
+++ b/nest/message_ix_models/model/config.py
@@ -0,0 +1,41 @@
+from dataclasses import dataclass, fields
+
+from message_ix_models.model.structure import codelists
+from message_ix_models.util.context import _ALIAS
+
+
+@dataclass
+class Config:
+    """Settings and valid values for :mod:`message_ix_models.model` and submodules."""
+
+    #: The 'node' codelist (regional aggregation) to use. Must be one of the lists of
+    #: nodes described at :doc:`/pkg-data/node`.
+    regions: str = "R14"
+
+    #: The 'relations' codelist to use. Must be one of the lists of relations described
+    #: at :doc:`/pkg-data/relation`.
+    relations: str = "A"
+
+    #: The 'year' codelist (time periods) to use, Must be one of the lists of periods
+    #: described at :doc:`/pkg-data/year`.
+    years: str = "B"
+
+    #: Create the reference energy system with dummy commodities and technologies. See
+    #: :func:`.bare.get_spec`.
+    res_with_dummies: bool = False
+
+    def check(self):
+        """Check the validity of :attr:`regions`, :attr:`relations`, :attr:`years`."""
+        for attr, name in [
+            ("regions", "node"),
+            ("relations", "relation"),
+            ("years", "year"),
+        ]:
+            if getattr(self, attr) not in codelists(name):
+                raise ValueError(
+                    f"{attr}={getattr(self, attr)!r} not among {codelists(name)}"
+                )
+
+
+# Extend the list of settings that can be set directly on a Context instance.
+_ALIAS.update({f.name: "model" for f in fields(Config)})
diff --git a/nest/message_ix_models/model/data.py b/nest/message_ix_models/model/data.py
new file mode 100644
index 0000000..409ee65
--- /dev/null
+++ b/nest/message_ix_models/model/data.py
@@ -0,0 +1,12 @@
+import logging
+
+log = logging.getLogger(__name__)
+
+
+def get_data(scenario, context, spec, **options):
+    """Data for the bare RES."""
+    if context.model.res_with_dummies:
+        log.warning("get_dummy_data() not migrated")
+        # return get_dummy_data(scenario, spec)
+    else:
+        return dict()
diff --git a/nest/message_ix_models/model/disutility.py b/nest/message_ix_models/model/disutility.py
new file mode 100644
index 0000000..ba0baa0
--- /dev/null
+++ b/nest/message_ix_models/model/disutility.py
@@ -0,0 +1,236 @@
+import logging
+from collections import defaultdict
+from functools import partial
+from itertools import product
+from typing import List, Mapping, Sequence, Union
+
+import message_ix
+import pandas as pd
+from sdmx.model import Annotation, Code
+
+from message_ix_models import ScenarioInfo, Spec
+from message_ix_models.model.build import apply_spec
+from message_ix_models.util import (
+    broadcast,
+    eval_anno,
+    make_io,
+    make_matched_dfs,
+    make_source_tech,
+    merge_data,
+    nodes_ex_world,
+    same_node,
+)
+
+log = logging.getLogger(__name__)
+
+CodeLike = Union[str, Code]
+
+
+def add(
+    scenario: message_ix.Scenario,
+    groups: Sequence[Code],
+    technologies: Sequence[Code],
+    template: Code,
+    **options,
+) -> Spec:
+    """Add disutility formulation to `scenario`."""
+    # Generate the spec given the configuration options
+    spec = get_spec(groups, technologies, template)
+
+    # Apply spec and add data
+    apply_spec(scenario, spec, partial(get_data, spec=spec), **options)
+
+    return spec
+
+
+def get_spec(
+    groups: Sequence[Code], technologies: Sequence[Code], template: Code
+) -> Spec:
+    """Get a spec for a disutility formulation.
+
+    Parameters
+    ----------
+    groups : list of Code
+        Identities of the consumer groups with distinct disutilities.
+    technologies : list of Code
+        The technologies to which the disutilities are applied.
+    template : .Code
+
+    """
+    s = Spec()
+
+    s.require.set["technology"].extend(technologies)
+
+    # Disutility commodity and source
+    s.add.set["commodity"] = [Code(id="disutility")]
+    s.add.set["technology"] = [Code(id="disutility source")]
+
+    # Disutility is unitless
+    # NB this value is currently ignored by .build.apply_spec(). See #45.
+    s.add.set["unit"].append("")
+
+    # Add conversion technologies
+    for t, g in product(technologies, groups):
+        # String formatting arguments
+        fmt = dict(technology=t, group=g)
+
+        # Format each field in the "input" and "output" annotations
+        input = {k: v.format(**fmt) for k, v in eval_anno(template, id="input").items()}
+        output = {
+            k: v.format(**fmt) for k, v in eval_anno(template, id="output").items()
+        }
+
+        # - Format the ID string from the template
+        # - Copy the "output" annotation without modification
+        t_code = Code(
+            id=template.id.format(**fmt),
+            annotations=[
+                Annotation(id="input", text=repr(input)),
+                Annotation(id="output", text=repr(output)),
+            ],
+        )
+
+        # "commodity" set elements to add
+        s.add.set["commodity"].extend([input["commodity"], output["commodity"]])
+
+        # "technology" set elements to add
+        t_code.annotations.append(Annotation(id="input", text=repr(input)))
+        s.add.set["technology"].append(t_code)
+
+    # Deduplicate "commodity" set elements
+    s.add.set["commodity"] = sorted(map(str, set(s.add.set["commodity"])))
+
+    return s
+
+
+def get_data(scenario, spec, **kwargs) -> Mapping[str, pd.DataFrame]:
+    """Get data for disutility formulation.
+
+    Calls :meth:`data_conversion` and :meth:`data_source`.
+
+    Parameters
+    ----------
+    spec : dict
+        The output of :meth:`get_spec`.
+    """
+    if len(kwargs):
+        log.warning(f"Ignore {repr(kwargs)}")
+
+    info = ScenarioInfo(scenario)
+
+    # Get conversion technology data
+    data = data_conversion(info, spec)
+
+    # Get and append source data
+    merge_data(data, data_source(info, spec))
+
+    return data
+
+
+def dp_for(col_name: str, info: ScenarioInfo) -> pd.Series:  # pragma: no cover
+    """:meth:`pandas.DataFrame.assign` helper for ``duration_period``.
+
+    Returns a callable to be passed to :meth:`pandas.DataFrame.assign`. The callable
+    takes a data frame as the first argument, and returns a :class:`pandas.Series`
+    based on the ``duration_period`` parameter in `info`, aligned to `col_name` in the
+    data frame.
+
+    Currently (2021-04-07) unused.
+    """
+
+    def func(df):
+        return df.merge(info.par["duration_period"], left_on=col_name, right_on="year")[
+            "value_y"
+        ]
+
+    return func
+
+
+def data_conversion(info, spec) -> Mapping[str, pd.DataFrame]:
+    """Generate input and output data for disutility conversion technologies."""
+    common = dict(
+        mode="all",
+        year_vtg=info.Y,
+        year_act=info.Y,
+        # No subannual detail
+        time="year",
+        time_origin="year",
+        time_dest="year",
+    )
+
+    # Use the spec to retrieve information
+    technology = spec["add"].set["technology"]
+
+    # Data to return
+    data0: Mapping[str, List[pd.DataFrame]] = defaultdict(list)
+
+    # Loop over conversion technologies
+    for t in technology:
+        # Use the annotations on the technology Code to get information about the
+        # commodity, level, and unit
+        input = eval_anno(t, "input")
+        output = eval_anno(t, "output")
+        if None in (input, output):
+            if t.id == "disutility source":
+                continue  # Data for this tech is from data_source()
+            else:  # pragma: no cover
+                raise ValueError(t)  # Error in user input
+
+        # Make input and output data frames
+        i_o = make_io(
+            (input["commodity"], input["level"], input["unit"]),
+            (output["commodity"], output["level"], output["unit"]),
+            1.0,
+            on="output",
+            technology=t.id,
+            **common,
+        )
+        for par, df in i_o.items():
+            # Broadcast across nodes
+            df = df.pipe(broadcast, node_loc=nodes_ex_world(info.N)).pipe(same_node)
+
+            if par == "input":
+                # Add input of disutility
+                df = pd.concat(
+                    [df, df.assign(commodity="disutility", unit="-")], ignore_index=True
+                )
+
+            data0[par].append(df)
+
+    # Concatenate to a single data frame per parameter
+    data = {par: pd.concat(dfs, ignore_index=True) for par, dfs in data0.items()}
+
+    # Create data for capacity_factor
+    data.update(make_matched_dfs(base=data["input"], capacity_factor=1.0))
+
+    return data
+
+
+def data_source(info, spec) -> Mapping[str, pd.DataFrame]:
+    """Generate data for a technology that emits the “disutility” commodity."""
+    # List of input levels where disutility commodity must exist
+    levels = set()
+    for t in spec["add"].set["technology"]:
+        input = eval_anno(t, "input")
+        if input:
+            levels.add(input["level"])
+
+    log.info(f"Generate disutility on level(s): {repr(levels)}")
+
+    # Use default capacity_factor = 1.0
+    result = make_source_tech(
+        info,
+        common=dict(
+            commodity="disutility",
+            mode="all",
+            technology="disutility source",
+            time="year",
+            time_dest="year",
+            unit="-",
+        ),
+        output=1.0,
+        var_cost=1.0,
+    )
+    result["output"] = result["output"].pipe(broadcast, level=sorted(levels))
+
+    return result
diff --git a/nest/message_ix_models/model/emissions.py b/nest/message_ix_models/model/emissions.py
new file mode 100644
index 0000000..a357d3a
--- /dev/null
+++ b/nest/message_ix_models/model/emissions.py
@@ -0,0 +1,78 @@
+import logging
+from typing import Optional
+
+import pandas as pd
+from iam_units import convert_gwp
+from message_ix import Scenario, make_df
+
+from message_ix_models import ScenarioInfo
+
+log = logging.getLogger(__name__)
+
+
+def add_tax_emission(
+    scen: Scenario,
+    price: float,
+    conversion_factor: Optional[float] = None,
+    drate_parameter="drate",
+) -> None:
+    """Add a global CO₂ price to `scen`.
+
+    A carbon price is implemented on node=“World” by populating the
+    :ref:`MESSAGEix parameter <message-ix:section_parameter_emissions>`
+    ``tax_emission``, starting from the first model year and covering the entire model
+    horizon. The tax has an annual growth rate equal to the discount rate.
+
+    The other dimensions of ``tax_emission`` are filled with type_emission=“TCE” and
+    type_tec=“all”.
+
+    Parameters
+    ----------
+    scen : :class:`message_ix.Scenario`
+    price : float
+        Price in the first model year, in USD / tonne CO₂.
+    conversion_factor : float, optional
+        Factor for converting `price` into the model's internal emissions units,
+        currently USD / tonne carbon. Optional: a default value is retrieved from
+        :mod:`iam_units`.
+    drate_parameter : str; one of "drate" or "interestrate"
+        Name of the parameter to use for the growth rate of the carbon price.
+    """
+    years = ScenarioInfo(scen).Y
+    filters = dict(year=years)
+    # Default: since the mass of the species is in the denominator, take the inverse
+    conversion_factor = conversion_factor or 1.0 / convert_gwp(
+        "AR5GWP100", "1 t", "CO2", "C"
+    )
+
+    # Duration of periods
+    dp = scen.par("duration_period", filters=filters).set_index("year")["value"]
+
+    # Retrieve the discount rate
+    if drate_parameter == "interestrate":
+        # MESSAGE parameter with "year" dimension
+        r = scen.par(drate_parameter, filters=filters).set_index("year")["value"]
+    else:
+        # MACRO parameter with "node" dimension
+        drates = scen.par(drate_parameter).value.unique()
+        if len(drates) > 1:
+            log.warning(f"Using the first of multiple discount rates: drate={drates}")
+        r = pd.Series([drates[0]] * len(years), index=pd.Index(years, name="year"))
+
+    # Compute cumulative growth versus the first period
+    r_cumulative = (r + 1).pow(dp.shift(-1)).cumprod().shift(1, fill_value=1.0)
+
+    # Assemble the parameter data
+    name = "tax_emission"
+    data = make_df(
+        name,
+        value=(price * conversion_factor * r_cumulative),
+        type_year=r_cumulative.index,
+        node="World",
+        type_emission="TCE",
+        type_tec="all",
+        unit="USD/tC",
+    )
+
+    with scen.transact("Added carbon price"):
+        scen.add_par(name, data)
diff --git a/nest/message_ix_models/model/structure.py b/nest/message_ix_models/model/structure.py
new file mode 100644
index 0000000..fc37a77
--- /dev/null
+++ b/nest/message_ix_models/model/structure.py
@@ -0,0 +1,330 @@
+import logging
+import re
+from collections import ChainMap
+from functools import lru_cache
+from itertools import product
+from typing import Dict, List, Mapping, MutableMapping, Tuple
+
+import click
+import pandas as pd
+import pycountry
+import xarray as xr
+from iam_units import registry
+from sdmx.model import Annotation, Code
+
+from message_ix_models.util import eval_anno, load_package_data, package_data_path
+from message_ix_models.util.sdmx import as_codes
+
+log = logging.getLogger(__name__)
+
+
+@lru_cache()
+def codelists(kind: str) -> List[str]:
+    """Return a valid IDs for code lists of `kind`.
+
+    Parameters
+    ----------
+    kind : str
+        "node" or "year".
+    """
+    return sorted(path.stem for path in package_data_path(kind).glob("*.yaml"))
+
+
+@lru_cache()
+def get_codes(name: str) -> List[Code]:
+    """Return codes for the dimension/set `name` in MESSAGE-GLOBIOM scenarios.
+
+    The information is read from :file:`data/{name}.yaml`, e.g.
+    :file:`data/technology.yaml`.
+
+    When `name` includes "node", then child codes are automatically populated from the
+    ISO 3166 database via :mod:`pycountry`. For instance:
+
+    .. code-block:: yaml
+
+       myregion:
+         name: Custom region
+         child: [AUT, SCG]
+
+    …results in a region with child codes for Austria (a current country) and the
+    formerly-existing country Serbia and Montenegro.
+
+    Parameters
+    ----------
+    name : :class:`str`
+        Any :file:`.yaml` file in the folder :file:`message_ix_models/data/`.
+
+    Returns
+    -------
+    list of :class:`~sdmx.model.Code`
+        Every Code has :attr:`id`, :attr:`name`, :attr:`description`, and
+        :attr:`annotations` attributes. Calling :func:`str` on a code returns its
+        :attr:`id`.
+    """
+    # Raw contents of the config file
+    config = load_package_data(name)
+
+    if "node" in name:
+        # Automatically add information for countries within regions in the node
+        # codelists. Use a ChainMap to combine a the `config` loaded from file and then
+        # fall back to contents of the pycountry databases.
+        config = ChainMap(
+            config,
+            # Create codes using the ISO database via pycountry
+            {c.alpha_3: dict(id=c.alpha_3, name=c.name) for c in pycountry.countries},
+            # Also include historic countries
+            {
+                c.alpha_3: dict(id=c.alpha_3, name=c.name)
+                for c in pycountry.historic_countries
+            },
+        )
+
+    # Convert to codes
+    data = as_codes(config)
+
+    # Fill in additional data, defaults, etc.
+    if name == "commodity":
+        process_commodity_codes(data)
+    elif name == "technology":
+        process_technology_codes(data)
+
+    return data
+
+
+@lru_cache()
+def get_region_codes(codelist: str) -> List[Code]:
+    """Return the codes that are children of "World" in the specified `codelist`."""
+    nodes = get_codes(f"node/{codelist}")
+    return nodes[nodes.index(Code(id="World"))].child
+
+
+def generate_product(
+    data: Mapping, name: str, template: Code
+) -> Tuple[List[Code], Dict[str, xr.DataArray]]:
+    """Generates codes using a `template` by Cartesian product along ≥1 dimensions.
+
+    :func:`generate_set_elements` is called for each of the `dims`, and these values
+    are used to format `base`.
+
+    Parameters
+    ----------
+    data
+        Mapping from dimension IDs to lists of codes.
+    name : str
+        Name of the set.
+    template : .Code
+        Must have Python format strings for its its :attr:`id` and :attr:`name`
+        attributes.
+    """
+    # eval() and remove the original annotation
+    dims = eval_anno(template, "_generate")
+    template.pop_annotation(id="_generate")
+
+    def _base(dim, match):
+        """Return codes along dimension `dim`.
+
+        If `match` is given, only children matching an expression."""
+        dim_codes = data[dim]["add"]
+
+        try:
+            i = dim_codes.index(match)
+        except ValueError:
+            if isinstance(match, str):
+                expr = re.compile(match)
+                dim_codes = list(filter(lambda c: expr.match(c.id), dim_codes))
+        else:
+            dim_codes = dim_codes[i].child
+
+        return dim_codes
+
+    codes = []  # Accumulate codes and indices
+    indices = []
+
+    # Iterate over the product of filtered codes for each dimension in
+    for item in product(*[_base(*dm) for dm in dims.items()]):
+        result = template.copy()  # Duplicate the template
+
+        fmt = dict(zip(dims.keys(), item))  # Format the ID and name
+        result.id = result.id.format(**fmt)
+        result.name = str(result.name).format(**fmt)  # type: ignore [assignment]
+
+        codes.append(result)  # Store code and indices
+        indices.append(item)
+
+    # - Convert length-N sequence of D-tuples to D iterables each of length N.
+    # - Convert to D × 1-dimensional xr.DataArrays, each of length N.
+    tmp = zip(*indices)
+    indexers = {d: xr.DataArray(list(i), dims=name) for d, i in zip(dims.keys(), tmp)}
+    # Corresponding indexer with the full code IDs
+    indexers[name] = xr.DataArray([c.id for c in codes], dims=name)
+
+    return codes, indexers
+
+
+def generate_set_elements(data: MutableMapping, name) -> None:
+    """Generate elements for set `name`.
+
+    This function converts lists of codes in `data`, calling :func:`generate_product`
+    and :func:`process_units_anno` as appropriate.
+
+    Parameters
+    ----------
+    data
+        Mapping from dimension IDs to lists of codes.
+    name : str
+        Name of the set for which to generate elements e.g. "commodity" or "technology".
+    """
+    hierarchical = name in {"technology"}
+
+    codes = []  # Accumulate codes
+    deferred = []
+    for code in as_codes(data[name].get("add", [])):
+        if name in {"commodity", "technology"}:
+            process_units_anno(name, code, quiet=True)
+
+        if eval_anno(code, "_generate"):
+            # Requires a call to generate_product(); do these last
+            deferred.append(code)
+            continue
+
+        codes.append(code)
+
+        if hierarchical:
+            # Store the children of `code`
+            codes.extend(filter(lambda c: c not in codes, code.child))
+
+    # Store codes processed so far, in case used recursively by generate_product()
+    data[name]["add"] = codes
+
+    # Use generate_product() to generate codes and indexers based on other sets
+    for code in deferred:
+        generated, indexers = generate_product(data, name, code)
+
+        # Store
+        data[name]["add"].extend(generated)
+
+        # NB if there are >=2 generated groups, only indexers for the last are kept
+        data[name]["indexers"] = indexers
+
+
+def process_units_anno(set_name: str, code: Code, quiet: bool = False) -> None:
+    """Process an annotation on `code` with id="units".
+
+    The annotation text is wrapped as ``'registry.Unit("{text}")'``, such that it can
+    be retrieved with :func:`.eval_anno` or :meth:`.ScenarioInfo.units_for`. If `code`
+    has direct children, the annotation is also copied to those codes.
+
+    Parameters
+    ----------
+    set_name : str
+        Used in logged messages when `quiet` is :data:`False`.
+    quiet : bool, optional
+        If :data:`False` (the default), log on level :ref:`WARNING <python:levels>` if:
+
+        - the annotation is missing, or
+        - its text is not parseable with the :mod:`pint` application registry, i.e.
+          :data:`iam_units.registry`.
+
+        Otherwise, log on :ref:`DEBUG <python:levels>`.
+
+    """
+    level = logging.DEBUG if quiet else logging.WARNING
+    # Convert a "units" annotation to a code snippet that will return a pint.Unit
+    # via eval_anno()
+    try:
+        units_anno = code.get_annotation(id="units")
+    except KeyError:
+        log.log(level, f"{set_name.title()} {code} lacks defined units")
+        return
+
+    # First try the expression as-is, in case already processed
+    expr = None
+    for candidate in (str(units_anno.text), f'registry.Unit("{units_anno.text}")'):
+        # Check that the unit can be parsed by the pint.UnitRegistry
+        try:
+            result = eval(candidate)
+        except Exception:
+            continue
+        else:
+            if isinstance(result, registry.Unit):
+                expr = candidate
+                break
+
+    if not expr:  # pragma: no cover
+        # No coverage: code that triggers this exception should never be committed
+        log.log(
+            level,
+            f"Unit '{units_anno.text}' for {set_name} {code} not pint compatible",
+        )
+    else:
+        # Modify the annotation so eval_anno() can be used
+        units_anno.text = expr
+
+    # Also annotate child codes
+    for c in code.child:
+        c.annotations.append(units_anno.copy())
+
+
+def process_commodity_codes(codes):
+    """Process a list of codes for ``commodity``.
+
+    The function warns for commodities missing units or with non-:mod:`pint`-compatible
+    units.
+    """
+    for code in codes:
+        # FIXME remove quiet=True; instead improve commodity.yaml with units
+        process_units_anno("commodity", code, quiet=True)
+
+
+def process_technology_codes(codes):
+    """Process a list of codes for ``technology``.
+
+    This function ensures every code has an annotation with id "vintaged", default
+    :obj:`False`.
+    """
+    for code in codes:
+        # FIXME remove quiet=True; instead improve technology.yaml with units
+        process_units_anno("technology", code, quiet=True)
+
+        try:
+            anno = code.pop_annotation(id="vintaged")
+        except KeyError:
+            # Default value for 'vintaged'
+            anno = Annotation(id="vintaged", text=repr(False))
+
+        code.annotations.append(anno)
+
+
+@click.command(name="techs")
+@click.pass_obj
+def cli(ctx):
+    """Export metadata to technology.csv.
+
+    This command transforms the technology metadata from the YAML file to CSV format.
+    """
+    # Convert each code to a pd.Series
+    data = []
+    for code in get_codes("technology"):
+        # Base attributes
+        d = dict(id=code.id, name=str(code.name), description=str(code.description))
+
+        # Annotations
+        for anno in ("type", "vintaged", "sector", "input", "output"):
+            try:
+                d[anno] = str(code.get_annotation(id=anno).text)
+            except KeyError:
+                pass
+
+        data.append(pd.Series(d))
+
+    # Combine series to a data frame
+    techs = pd.DataFrame(data)
+
+    # Write to file
+    dest = ctx.get_local_path("technology.csv")
+    print(f"Write to {dest}")
+
+    techs.to_csv(dest, index=None, header=True)
+
+    # Print the first few items of the data frame
+    print(techs.head())
diff --git a/nest/message_ix_models/model/water/__init__.py b/nest/message_ix_models/model/water/__init__.py
new file mode 100644
index 0000000..c68e125
--- /dev/null
+++ b/nest/message_ix_models/model/water/__init__.py
@@ -0,0 +1,3 @@
+from .utils import read_config
+
+__all__ = ["read_config"]
diff --git a/nest/message_ix_models/model/water/build.py b/nest/message_ix_models/model/water/build.py
new file mode 100644
index 0000000..064eda8
--- /dev/null
+++ b/nest/message_ix_models/model/water/build.py
@@ -0,0 +1,271 @@
+import logging
+from functools import lru_cache, partial
+from typing import Mapping
+
+import pandas as pd
+
+from message_ix_models import ScenarioInfo
+from message_ix_models.model import build
+from message_ix_models.model.structure import get_codes
+from message_ix_models.util import private_data_path
+
+from .utils import read_config
+
+log = logging.getLogger(__name__)
+
+
+def get_spec(context) -> Mapping[str, ScenarioInfo]:
+    """Return the specification for nexus implementation
+
+    Parameters
+    ----------
+    context : .Context
+        The key ``regions`` determines the regional aggregation used.
+    """
+
+    context = read_config()
+
+    require = ScenarioInfo()
+    remove = ScenarioInfo()
+    add = ScenarioInfo()
+
+    if context.nexus_set == "nexus":
+        # Merge technology.yaml with set.yaml
+        context["water set"]["nexus"]["technology"]["add"] = context[
+            "water technology"
+        ]["nexus"]
+        # Update the ScenarioInfo objects with required and new set elements
+        for set_name, config in context["water set"]["nexus"].items():
+            # Required elements
+            require.set[set_name].extend(config.get("require", []))
+
+            # Elements to remove
+            remove.set[set_name].extend(config.get("remove", []))
+
+            # Elements to add
+            add.set[set_name].extend(config.get("add", []))
+
+        # The set of required nodes varies according to context.regions
+        nodes = get_codes(f"node/{context.regions}")
+        nodes = list(map(str, nodes[nodes.index("World")].child))
+        require.set["node"].extend(nodes)
+
+        # Share commodity for groundwater
+        results = {}
+        df_node = context.all_nodes
+        n = len(df_node.values)
+
+        d = {
+            "shares": ["share_low_lim_GWat"] * n,
+            "node_share": df_node,
+            "node": df_node,
+            "type_tec": ["share_low_lim_GWat_share"] * n,
+            "mode": ["M1"] * n,
+            "commodity": ["groundwater_basin"] * n,
+            "level": ["water_avail_basin"] * n,
+        }
+
+        df_share = pd.DataFrame(data=d)
+        df_list = df_share.values.tolist()
+        results["map_shares_commodity_share"] = df_list
+
+        d = {
+            "shares": ["share_low_lim_GWat"] * n,
+            "node_share": df_node,
+            "node": df_node,
+            "type_tec": ["share_low_lim_GWat_total"] * n,
+            "mode": ["M1"] * n,
+            "commodity": ["surfacewater_basin"] * n,
+            "level": ["water_avail_basin"] * n,
+        }
+
+        df_share = pd.DataFrame(data=d)
+
+        d2 = {
+            "shares": ["share_low_lim_GWat"] * n,
+            "node_share": df_node,
+            "node": df_node,
+            "type_tec": ["share_low_lim_GWat_total"] * n,
+            "mode": ["M1"] * n,
+            "commodity": ["groundwater_basin"] * n,
+            "level": ["water_avail_basin"] * n,
+        }
+
+        df_share2 = pd.DataFrame(data=d2)
+
+        df_share = df_share.append(df_share2)
+        df_list = df_share.values.tolist()
+
+        results["map_shares_commodity_total"] = df_list
+
+        for set_name, config in results.items():
+            # Sets  to add
+            add.set[set_name].extend(config)
+
+        results = {}
+
+        # Share commodity for urban water recycling
+        d = {
+            "shares": ["share_wat_recycle"] * n,
+            "node_share": df_node,
+            "node": df_node,
+            "type_tec": ["share_wat_recycle_share"] * n,
+            "mode": ["M1"] * n,
+            "commodity": ["urban_collected_wst"] * n,
+            "level": ["water_treat"] * n,
+        }
+
+        df_share = pd.DataFrame(data=d)
+        df_list = df_share.values.tolist()
+        results["map_shares_commodity_share"] = df_list
+
+        d = {
+            "shares": ["share_wat_recycle"] * n,
+            "node_share": df_node,
+            "node": df_node,
+            "type_tec": ["share_wat_recycle_total"] * n,
+            "mode": ["M1"] * n,
+            "commodity": ["urban_collected_wst"] * n,
+            "level": ["water_treat"] * n,
+        }
+
+        df_share = pd.DataFrame(data=d)
+
+        d2 = {
+            "shares": ["share_wat_recycle"] * n,
+            "node_share": df_node,
+            "node": df_node,
+            "type_tec": ["share_wat_recycle_total"] * n,
+            "mode": ["M1"] * n,
+            "commodity": ["urban_collected_wst"] * n,
+            "level": ["water_treat"] * n,
+        }
+
+        df_share2 = pd.DataFrame(data=d2)
+
+        df_share = df_share.append(df_share2)
+        df_list = df_share.values.tolist()
+
+        results["map_shares_commodity_total"] = df_list
+
+        for set_name, config in results.items():
+            # Sets  to add
+            add.set[set_name].extend(config)
+
+    elif context.nexus_set == "cooling":
+        # Merge technology.yaml with set.yaml
+        context["water set"]["cooling"]["technology"]["add"] = context[
+            "water technology"
+        ]["cooling"]
+        # Update the ScenarioInfo objects with required and new set elements
+        for set_name, config in context["water set"]["cooling"].items():
+            # Required elements
+            require.set[set_name].extend(config.get("require", []))
+
+            # Elements to remove
+            remove.set[set_name].extend(config.get("remove", []))
+
+            # Elements to add
+            add.set[set_name].extend(config.get("add", []))
+
+    return dict(require=require, remove=remove, add=add)
+
+    return dict(require=require, remove=remove, add=add)
+
+
+@lru_cache()
+def generate_set_elements(set_name, match=None):
+    codes = read_config()["water set"][set_name].get("add", [])
+
+    hierarchical = set_name in {"technology"}
+
+    results = []
+    for code in codes:
+        if match and code.id != match:
+            continue
+        elif hierarchical:
+            results.extend(code)
+
+    return results
+
+
+def map_basin(context) -> Mapping[str, ScenarioInfo]:
+    """Return specification for mapping basins to regions
+
+    The basins are spatially consolidated from HydroSHEDS basins delineation
+    database.This delineation is then intersected with MESSAGE regions to form new
+    water sector regions for the nexus module.
+    The nomenclature for basin names is <basin_id>|<MESSAGEregion> such as R1|AFR
+    """
+    context = read_config()
+
+    add = ScenarioInfo()
+
+    require = ScenarioInfo()
+
+    remove = ScenarioInfo()
+
+    # define an empty dictionary
+    results = {}
+    # read csv file for basin names and region mapping
+    # reading basin_delineation
+    FILE = f"basins_by_region_simpl_{context.regions}.csv"
+    PATH = private_data_path("water", "delineation", FILE)
+
+    df = pd.read_csv(PATH)
+    # Assigning proper nomenclature
+    df["node"] = "B" + df["BCU_name"].astype(str)
+    df["mode"] = "M" + df["BCU_name"].astype(str)
+    if context.type_reg == "country":
+        df["region"] = context.map_ISO_c[context.regions]
+    else:
+        df["region"] = f"{context.regions}_" + df["REGION"].astype(str)
+
+    results["node"] = df["node"]
+    results["mode"] = df["mode"]
+    # map nodes as per dimensions
+    df1 = pd.DataFrame({"node_parent": df["region"], "node": df["node"]})
+    df2 = pd.DataFrame({"node_parent": df["node"], "node": df["node"]})
+    frame = [df1, df2]
+    df_node = pd.concat(frame)
+    nodes = df_node.values.tolist()
+
+    results["map_node"] = nodes
+
+    context.all_nodes = df["node"]
+
+    for set_name, config in results.items():
+        # Sets  to add
+        add.set[set_name].extend(config)
+
+    return dict(require=require, remove=remove, add=add)
+
+
+def main(context, scenario, **options):
+    """Set up MESSAGEix-Nexus on `scenario`.
+
+    See also
+    --------
+    add_data
+    apply_spec
+    get_spec
+    """
+    from .data import add_data
+
+    log.info("Set up MESSAGEix-Nexus")
+
+    if context.nexus_set == "nexus":
+        # Add water balance
+        spec = map_basin(context)
+
+        # Apply the structural changes AND add the data
+        build.apply_spec(scenario, spec, **options)
+
+    # Core water structure
+    spec1 = get_spec(context)
+
+    # Apply the structural changes AND add the data
+    build.apply_spec(scenario, spec1, partial(add_data, context=context), **options)
+
+    # Uncomment to dump for debugging
+    # scenario.to_excel('debug.xlsx')
diff --git a/nest/message_ix_models/model/water/cli.py b/nest/message_ix_models/model/water/cli.py
new file mode 100644
index 0000000..3057fb7
--- /dev/null
+++ b/nest/message_ix_models/model/water/cli.py
@@ -0,0 +1,272 @@
+import logging
+
+import click
+
+from message_ix_models.model.structure import get_codes
+from message_ix_models.util.click import common_params
+
+log = logging.getLogger(__name__)
+
+
+# allows to activate water module
+@click.group("water-ix")
+@common_params("regions")
+@click.option("--time", help="Manually defined time")
+@click.pass_obj
+def cli(context, regions, time):
+    """MESSAGEix-Water and Nexus variant."""
+    water_ini(context, regions, time)
+
+
+def water_ini(context, regions, time):
+    """Add components of the MESSAGEix-Nexus module
+
+    This function modifies model name & scenario name
+    and verifies the region setup
+    Parameters
+    ----------
+    context : `class`:message.Context
+        Information about target Scenario.
+    regions : str (if not defined already in context.regions)
+        Specifies what region definition is used ['R11','R12','ISO3']
+    """
+
+    from .utils import read_config
+
+    # Ensure water model configuration is loaded
+    read_config(context)
+    if not context.scenario_info:
+        context.scenario_info.update(
+            dict(model="ENGAGE_SSP2_v4.1.7", scenario="baseline_clone_test")
+        )
+    context.output_scenario = context.scenario_info["scenario"]
+    context.output_model = context.scenario_info["model"]
+
+    # Handle --regions; use a sensible default for MESSAGEix-Nexus
+    if regions:
+        log.info(f"Regions choice {regions}")
+        if regions in ["R14", "R32", "RCP"]:
+            log.warning(
+                "the MESSAGEix-Nexus module might not be compatible"
+                "with your 'regions' choice"
+            )
+    else:
+        log.info("Use default --regions=R11")
+        regions = "R11"
+    # add an attribute to distinguish country models
+    if regions in ["R11", "R12", "R14", "R32", "RCP"]:
+        context.type_reg = "global"
+    else:
+        context.type_reg = "country"
+    context.regions = regions
+
+    # create a mapping ISO code :
+    # region name, for other scripts
+    # only needed for 1-country models
+    nodes = get_codes(f"node/{context.regions}")
+    nodes = list(map(str, nodes[nodes.index("World")].child))
+    if context.type_reg == "country":
+        map_ISO_c = {context.regions: nodes[0]}
+        context.map_ISO_c = map_ISO_c
+        log.info(f"mapping {context.map_ISO_c[context.regions]}")
+
+    # deinfe the timestep
+    if not time:
+        sc_ref = context.get_scenario()
+        time = sc_ref.set("time")
+        sub_time = list(time[time != "year"])
+        if len(sub_time) == 0:
+            context.time = ["year"]
+
+        else:
+            context.time = sub_time
+    else:
+        context.time = list(time)
+    log.info(f"Using the following time-step for the water module: {context.time}")
+
+    # setting the time information in context
+
+
+_RCPS = ["no_climate", "6p0", "2p6", "7p0"]
+_REL = ["low", "med", "high"]
+
+
+@cli.command("nexus")
+@click.pass_obj
+@click.option("--rcps", default="6p0", type=click.Choice(_RCPS))
+@click.option("--rels", default="low", type=click.Choice(_REL))
+@click.option(
+    "--sdgs",
+    is_flag=True,
+    help="Defines whether water SDG measures are activated or not",
+)
+@click.option(
+    "--macro",
+    is_flag=True,
+    help="Defines whether the model solves with macro",
+)
+@common_params("regions")
+def nexus_cli(context, regions, rcps, sdgs, rels, macro=False):
+    """
+    Add basin structure connected to the energy sector and
+    water balance linking different water demands to supply.
+    """
+
+    nexus(context, regions, rcps, sdgs, rels, macro)
+
+
+def nexus(context, regions, rcps, sdgs, rels, macro=False):
+    """Add basin structure connected to the energy sector and
+    water balance linking different water demands to supply.
+
+    Use the --url option to specify the base scenario.
+
+    Parameters
+    ----------
+    context : `class`:message.Context
+        Information about target Scenario.
+    regions : str (if not defined already in context.regions)
+        Specifies what region definition is used ['R11','R12','ISO3']
+    RCP : str
+        Specifies the climate scenario used ['no_climate','6p0','2p6']
+    SDG : True/False
+        Defines whether water SDG measures are activated or not
+    REL: str
+        Specifies the reliability of hydrological data ['low','mid','high']
+    """
+    # add input information to the class context
+    context.nexus_set = "nexus"
+    if not context.regions:
+        context.regions = regions
+
+    context.RCP = rcps
+    context.SDG = sdgs
+    context.REL = rels
+
+    log.info(f"RCP assumption is {context.RCP}. SDG is {context.SDG}")
+
+    from .build import main as build
+
+    # Determine the output scenario name based on the --url CLI option. If the
+    # user did not give a recognized value, this raises an error
+    output_scenario_name = context.output_scenario + "_nexus"
+    output_model_name = context.output_model
+
+    # Clone and build
+    sc_ref = context.get_scenario()
+    scen = sc_ref.clone(
+        model=output_model_name, scenario=output_scenario_name, keep_solution=False
+    )
+    log.info(
+        f" clone from {sc_ref.model}.{sc_ref.scenario} to {scen.model}.{scen.scenario}"
+    )
+    # Exporting the built model (Scenario) to GAMS with an optional case name
+    caseName = scen.model + "__" + scen.scenario + "__v" + str(scen.version)
+
+    # Build
+    build(context, scen)
+
+    # Set scenario as default
+    scen.set_as_default()
+
+    # Solve
+    if macro:
+        scen.solve(
+            model="MESSAGE-MACRO",
+            solve_options={"lpmethod": "4", "scaind": "1"},
+            case=caseName,
+        )
+    else:
+        scen.solve(solve_options={"lpmethod": "4"}, case=caseName)
+
+    # if options["report"]:
+    #     # Also output diagnostic reports
+    #     from message_data.model.water import report, run_old_reporting
+    #
+    #     old reporting
+    #     run_old_reporting(scen)
+
+    #     log.info(f"Report plots to {rep.graph['config']['output_dir']}")
+
+    #     mark_time()
+
+
+@cli.command("cooling")
+@common_params("regions")
+@click.option("--rcps", type=click.Choice(_RCPS))
+@click.option("--rels", type=click.Choice(_REL))
+@click.pass_obj
+def cooling_cli(context, regions, rcps, rels):
+    """Build and solve model with new cooling technologies."""
+    cooling(context, regions, rcps, rels)
+
+
+def cooling(context, regions, rcps, rels):
+    """Build and solve model with new cooling technologies.
+
+    Use the --url option to specify the base scenario.
+
+    Parameters
+    ----------
+    context : `class`:message.Context
+        Information about target Scenario.
+    regions : str (if not defined already in context.regions)
+        Specifies what region definition is used ['R11','R12','ISO3']
+    RCP : str
+        Specifies the climate scenario used ['no_climate','6p0','2p6']
+
+    """
+    context.nexus_set = "cooling"
+    context.RCP = rcps
+    context.REL = rels
+
+    from .build import main as build
+
+    # Determine the output scenario name based on the --url CLI option. If the
+    # user did not give a recognized value, this raises an error.
+
+    output_scenario_name = context.output_scenario + "_cooling"
+    output_model_name = context.output_model
+
+    # Clone and build
+    scen = context.get_scenario().clone(
+        model=output_model_name, scenario=output_scenario_name, keep_solution=False
+    )
+
+    print(scen.model)
+    print(scen.scenario)
+
+    # Exporting the built model (Scenario) to GAMS with an optional case name
+    caseName = scen.model + "__" + scen.scenario + "__v" + str(scen.version)
+
+    # Build
+    build(context, scen)
+
+    # Solve
+    scen.solve(solve_options={"lpmethod": "4"}, case=caseName)
+
+
+@cli.command("report")
+@click.pass_obj
+@click.option(
+    "--sdgs",
+    is_flag=True,
+    help="Defines whether water SDG measures are activated or not",
+)
+@common_params("output_model")
+def report_cli(context, output_model, sdgs):
+    """function to run the water report_full from cli to the
+    scenario defined by the user with --url
+
+    Parameters
+    ----------
+    context : `class`:message.Context
+        Information about target Scenario.
+    output_model : str (optional, otherwise default args used)
+        Specifies the model name of the scenarios which are run.
+    """
+
+    from message_ix_models.model.water.reporting import report_full
+
+    sc = context.get_scenario()
+    report_full(sc, sdgs)
diff --git a/nest/message_ix_models/model/water/data/__init__.py b/nest/message_ix_models/model/water/data/__init__.py
new file mode 100644
index 0000000..931af07
--- /dev/null
+++ b/nest/message_ix_models/model/water/data/__init__.py
@@ -0,0 +1,62 @@
+"""Generate input data."""
+
+import logging
+
+from message_ix_models import ScenarioInfo
+from message_ix_models.util import add_par_data
+
+from .demands import add_irrigation_demand, add_sectoral_demands, add_water_availability
+from .infrastructure import add_desalination, add_infrastructure_techs
+from .irrigation import add_irr_structure
+from .water_for_ppl import cool_tech, non_cooling_tec
+from .water_supply import add_e_flow, add_water_supply
+
+log = logging.getLogger(__name__)
+
+DATA_FUNCTIONS = [
+    add_water_supply,
+    cool_tech,  # Water & parasitic_electricity requirements for cooling technologies
+    non_cooling_tec,
+    add_sectoral_demands,
+    add_water_availability,
+    add_irrigation_demand,
+    add_infrastructure_techs,
+    add_desalination,
+    add_e_flow,
+    add_irr_structure,
+]
+
+DATA_FUNCTIONS_COUNTRY = [
+    add_water_supply,
+    cool_tech,  # Water & parasitic_electricity requirements for cooling technologies
+    non_cooling_tec,
+    add_sectoral_demands,
+    add_water_availability,
+    # add_irrigation_demand, # not used and coming from GLOBIOM for the global region
+    add_infrastructure_techs,
+    add_desalination,
+    add_e_flow,
+    # add if statement: if irrigation: land component from external model
+]
+
+
+def add_data(scenario, context, dry_run=False):
+    """Populate `scenario` with MESSAGEix-Nexus data."""
+
+    info = ScenarioInfo(scenario)
+    context["water build info"] = info
+
+    data_funcs = (
+        [add_water_supply, cool_tech, non_cooling_tec]
+        if context.nexus_set == "cooling"
+        else DATA_FUNCTIONS
+        if context.type_reg == "global"
+        else DATA_FUNCTIONS_COUNTRY
+    )
+
+    for func in data_funcs:
+        # Generate or load the data; add to the Scenario
+        log.info(f"from {func.__name__}()")
+        add_par_data(scenario, func(context), dry_run=dry_run)
+
+    log.info("done")
diff --git a/nest/message_ix_models/model/water/data/demands.py b/nest/message_ix_models/model/water/data/demands.py
new file mode 100644
index 0000000..56e231f
--- /dev/null
+++ b/nest/message_ix_models/model/water/data/demands.py
@@ -0,0 +1,922 @@
+"""Prepare data for adding demands"""
+
+import os
+
+import numpy as np
+import pandas as pd
+import xarray as xr
+from message_ix import make_df
+
+from message_ix_models.util import broadcast, private_data_path
+
+
+def target_rate(df, basin, val):
+    """
+    Sets target connection and sanitation rates for SDG scenario
+    It filters out the basins as developing and developed baed on the countries
+    overlapping basins. If the numbers of developing countries in the basins are
+    more than basin is cateogirzez as developing and vice versa.
+    If the number of developing and developed countries are equal in a basin, then
+    the basin is assumed developing.
+    For developed basins target is set at 2030.
+    For developing basins, the access target is set at 2040 and 2035 target is the average of
+    2030 original rate and 2040 target.
+    Returns
+    -------
+    data : dict of (str -> pandas.DataFrame)
+    """
+    value = []
+    for i in df.node.unique():
+        temp = basin[basin["BCU_name"] == i]
+
+        sizes = temp.pivot_table(index=["STATUS"], aggfunc="size")
+
+        if len(sizes) > 1:
+            if sizes["DEV"] > sizes["IND"] or sizes["DEV"] == sizes["IND"]:
+                # for j in urban[urban["node"] == i][urban[urban["node"] == i]["year"] == 2030].index:
+                #      if urban[urban["node"] == i][urban[urban["node"] == i]["year"] == 2030].at[j, "value"] < np.float64(0.75):
+                #          value.append([j, np.float64(0.75)])
+
+                for ind, j in enumerate(
+                    df[df["node"] == i][df[df["node"] == i]["year"] == 2030].index
+                ):
+                    jj = df[df["node"] == i][df[df["node"] == i]["year"] == 2035].index
+                    temp = (
+                        df[df["node"] == i][df[df["node"] == i]["year"] == 2030].at[
+                            j, "value"
+                        ]
+                        + val
+                    ) / 2
+                    value.append([jj[ind], np.float64(temp)])
+
+                for j in df[df["node"] == i][df[df["node"] == i]["year"] >= 2040].index:
+                    if df[df["node"] == i][df[df["node"] == i]["year"] >= 2040].at[
+                        j, "value"
+                    ] < np.float64(val):
+                        value.append([j, np.float64(val)])
+            else:
+                for j in df[df["node"] == i][df[df["node"] == i]["year"] >= 2030].index:
+                    if df[df["node"] == i][df[df["node"] == i]["year"] >= 2030].at[
+                        j, "value"
+                    ] < np.float64(val):
+                        value.append([j, np.float64(val)])
+        else:
+            if sizes.index[0] == "DEV":
+                # for j in urban[urban["node"] == i][urban[urban["node"] == i]["year"] == 2030].index:
+                #       if urban[urban["node"] == i][urban[urban["node"] == i]["year"] == 2030].at[j, "value"] < np.float64(0.75):
+                #           value.append([j, np.float64(0.75)])
+
+                for ind, j in enumerate(
+                    df[df["node"] == i][df[df["node"] == i]["year"] == 2030].index
+                ):
+                    jj = df[df["node"] == i][df[df["node"] == i]["year"] == 2035].index
+                    temp = (
+                        df[df["node"] == i][df[df["node"] == i]["year"] == 2030].at[
+                            j, "value"
+                        ]
+                        + val
+                    ) / 2
+                    value.append([jj[ind], np.float64(temp)])
+
+                for j in df[df["node"] == i][df[df["node"] == i]["year"] >= 2040].index:
+                    if df[df["node"] == i][df[df["node"] == i]["year"] >= 2040].at[
+                        j, "value"
+                    ] < np.float64(val):
+                        value.append([j, np.float64(val)])
+            else:
+                for j in df[df["node"] == i][df[df["node"] == i]["year"] >= 2030].index:
+                    if df[df["node"] == i][df[df["node"] == i]["year"] >= 2030].at[
+                        j, "value"
+                    ] < np.float64(val):
+                        value.append([j, np.float64(val)])
+    valuetest = pd.DataFrame(data=value, columns=["Index", "Value"])
+
+    for i in range(len(valuetest["Index"])):
+        df.at[valuetest["Index"][i], "Value"] = valuetest["Value"][i]
+
+    real_value = df["Value"].combine_first(df["value"])
+
+    df.drop(["value", "Value"], axis=1, inplace=True)
+    df["value"] = real_value
+
+    return df
+
+
+def target_rate_trt(df, basin):
+    """
+    Sets target treatment rates for SDG scenario. The target value for
+    developed and developing region is making sure that the amount of untreated
+    wastewater is halved beyond 2030 & 2040 respectively.
+    Returns
+    -------
+    data : dict of (str -> pandas.DataFrame)
+    """
+
+    value = []
+    for i in df.node.unique():
+        temp = basin[basin["BCU_name"] == i]
+
+        sizes = temp.pivot_table(index=["STATUS"], aggfunc="size")
+
+        if len(sizes) > 1:
+            if sizes["DEV"] > sizes["IND"] or sizes["DEV"] == sizes["IND"]:
+                for j in df[df["node"] == i][df[df["node"] == i]["year"] >= 2040].index:
+                    temp = df[df["node"] == i][df[df["node"] == i]["year"] >= 2040].at[
+                        j, "value"
+                    ]
+                    temp = temp + (1 - temp) / 2
+                    value.append([j, np.float64(temp)])
+            else:
+                for j in df[df["node"] == i][df[df["node"] == i]["year"] >= 2030].index:
+                    temp = df[df["node"] == i][df[df["node"] == i]["year"] >= 2030].at[
+                        j, "value"
+                    ]
+                    temp = temp + (1 - temp) / 2
+                    value.append([j, np.float64(temp)])
+        else:
+            if sizes.index[0] == "DEV":
+                for j in df[df["node"] == i][df[df["node"] == i]["year"] >= 2040].index:
+                    temp = df[df["node"] == i][df[df["node"] == i]["year"] >= 2040].at[
+                        j, "value"
+                    ]
+                    temp = temp + (1 - temp) / 2
+                    value.append([j, np.float64(temp)])
+            else:
+                for j in df[df["node"] == i][df[df["node"] == i]["year"] >= 2030].index:
+                    temp = df[df["node"] == i][df[df["node"] == i]["year"] >= 2030].at[
+                        j, "value"
+                    ]
+                    temp = temp + (1 - temp) / 2
+                    value.append([j, np.float64(temp)])
+
+    valuetest = pd.DataFrame(data=value, columns=["Index", "Value"])
+
+    for i in range(len(valuetest["Index"])):
+        df.at[valuetest["Index"][i], "Value"] = valuetest["Value"][i]
+
+    real_value = df["Value"].combine_first(df["value"])
+
+    df.drop(["value", "Value"], axis=1, inplace=True)
+
+    df["value"] = real_value
+    return df
+
+
+def add_sectoral_demands(context):
+    """
+    Adds water sectoral demands
+    Parameters
+    ----------
+    context : .Context
+    Returns
+    -------
+    data : dict of (str -> pandas.DataFrame)
+        Keys are MESSAGE parameter names such as 'input', 'fix_cost'. Values
+        are data frames ready for :meth:`~.Scenario.add_par`.
+    """
+
+    # define an empty dictionary
+    results = {}
+
+    # Reference to the water configuration
+    info = context["water build info"]
+
+    # defines path to read in demand data
+    region = f"{context.regions}"
+    sub_time = context.time
+    path = private_data_path("water", "demands", "harmonized", region, ".")
+    # make sure all of the csvs have format, otherwise it might not work
+    list_of_csvs = list(path.glob("*_baseline.csv"))
+    # define names for variables
+    fns = [os.path.splitext(os.path.basename(x))[0] for x in list_of_csvs]
+    fns = " ".join(fns).replace("ssp2_regional_", "").split()
+    # dictionary for reading csv files
+    d = {}
+
+    for i in range(len(fns)):
+        d[fns[i]] = pd.read_csv(list_of_csvs[i])
+
+    # d is a dictionary that have ist of dataframes read in this folder
+    dfs = {}
+    for key, df in d.items():
+        df.rename(columns={"Unnamed: 0": "year"}, inplace=True)
+        df.index = df["year"]
+        df = df.drop(columns=["year"])
+        dfs[key] = df
+
+    # convert the dictionary of dataframes to xarray
+    df_x = xr.Dataset(dfs).to_array()
+    df_x_interp = df_x.interp(year=[2015, 2025, 2035, 2045, 2055])
+    df_x_c = df_x.combine_first(df_x_interp)
+    # Unstack xarray back to pandas dataframe
+    df_f = df_x_c.to_dataframe("").unstack()
+
+    # Format the dataframe to be compatible with message format
+    df_dmds = df_f.stack().reset_index(level=0).reset_index()
+    df_dmds.columns = ["year", "node", "variable", "value"]
+    df_dmds.sort_values(["year", "node", "variable", "value"], inplace=True)
+
+    df_dmds["time"] = "year"
+
+    # Write final interpolated values as csv
+    # df2_f.to_csv('final_interpolated_values.csv')
+
+    # if we are using sub-annual timesteps we replace the rural and municipal
+    # withdrawals and return flows with monthly data and also add industrial
+    if "year" not in context.time:
+        PATH = private_data_path(
+            "water", "demands", "harmonized", region, "ssp2_m_water_demands.csv"
+        )
+        df_m = pd.read_csv(PATH)
+        df_m.value *= 30  # from mcm/day to mcm/month
+        df_m["sector"][df_m["sector"] == "industry"] = "manufacturing"
+        df_m["variable"] = df_m["sector"] + "_" + df_m["type"] + "_baseline"
+        df_m["variable"].replace(
+            "urban_withdrawal_baseline", "urban_withdrawal2_baseline", inplace=True
+        )
+        df_m["variable"].replace(
+            "urban_return_baseline", "urban_return2_baseline", inplace=True
+        )
+        df_m = df_m[["year", "pid", "variable", "value", "month"]]
+        df_m.columns = ["year", "node", "variable", "value", "time"]
+
+        # remove yearly parts from df_dms
+        df_dmds = df_dmds[
+            ~df_dmds["variable"].isin(
+                [
+                    "urban_withdrawal2_baseline",
+                    "rural_withdrawal_baseline",
+                    "manufacturing_withdrawal_baseline",
+                    "manufacturing_return_baseline",
+                    "urban_return2_baseline",
+                    "rural_return_baseline",
+                ]
+            )
+        ]
+        # attach the monthly demand
+        df_dmds = pd.concat([df_dmds, df_m])
+
+    urban_withdrawal_df = df_dmds[df_dmds["variable"] == "urban_withdrawal2_baseline"]
+    rual_withdrawal_df = df_dmds[df_dmds["variable"] == "rural_withdrawal_baseline"]
+    industrial_withdrawals_df = df_dmds[
+        df_dmds["variable"] == "manufacturing_withdrawal_baseline"
+    ]
+    industrial_return_df = df_dmds[
+        df_dmds["variable"] == "manufacturing_return_baseline"
+    ]
+    urban_return_df = df_dmds[df_dmds["variable"] == "urban_return2_baseline"]
+    urban_return_df.reset_index(drop=True, inplace=True)
+    rural_return_df = df_dmds[df_dmds["variable"] == "rural_return_baseline"]
+    rural_return_df.reset_index(drop=True, inplace=True)
+    urban_connection_rate_df = df_dmds[
+        df_dmds["variable"] == "urban_connection_rate_baseline"
+    ]
+    urban_connection_rate_df.reset_index(drop=True, inplace=True)
+    rural_connection_rate_df = df_dmds[
+        df_dmds["variable"] == "rural_connection_rate_baseline"
+    ]
+    rural_connection_rate_df.reset_index(drop=True, inplace=True)
+
+    urban_treatment_rate_df = df_dmds[
+        df_dmds["variable"] == "urban_treatment_rate_baseline"
+    ]
+    urban_treatment_rate_df.reset_index(drop=True, inplace=True)
+
+    rural_treatment_rate_df = df_dmds[
+        df_dmds["variable"] == "rural_treatment_rate_baseline"
+    ]
+    rural_treatment_rate_df.reset_index(drop=True, inplace=True)
+
+    df_recycling = df_dmds[df_dmds["variable"] == "urban_recycling_rate_baseline"]
+    df_recycling.reset_index(drop=True, inplace=True)
+
+    all_rates_base = pd.concat(
+        [
+            urban_connection_rate_df,
+            rural_connection_rate_df,
+            urban_treatment_rate_df,
+            rural_treatment_rate_df,
+            df_recycling,
+        ]
+    )
+
+    if context.SDG:
+        # reading basin mapping to countries
+        FILE2 = f"basins_country_{context.regions}.csv"
+        PATH = private_data_path("water", "delineation", FILE2)
+
+        df_basin = pd.read_csv(PATH)
+
+        # Applying 80% sanitation rate for rural sanitation
+        rural_treatment_rate_df = rural_treatment_rate_df_sdg = target_rate(
+            rural_treatment_rate_df, df_basin, 0.8
+        )
+        # Applying 95% sanitation rate for urban sanitation
+        urban_treatment_rate_df = urban_treatment_rate_df_sdg = target_rate(
+            urban_treatment_rate_df, df_basin, 0.95
+        )
+        # Applying 99% connection rate for urban infrastructure
+        urban_connection_rate_df = urban_connection_rate_df_sdg = target_rate(
+            urban_connection_rate_df, df_basin, 0.99
+        )
+        # Applying 80% connection rate for rural infrastructure
+        rural_connection_rate_df = rural_connection_rate_df_sdg = target_rate(
+            rural_connection_rate_df, df_basin, 0.8
+        )
+        # Applying sdg6 waste water treatment target
+        df_recycling = df_recycling_sdg = target_rate_trt(df_recycling, df_basin)
+
+        all_rates_sdg = pd.concat(
+            [
+                urban_connection_rate_df_sdg,
+                rural_connection_rate_df_sdg,
+                urban_treatment_rate_df_sdg,
+                rural_treatment_rate_df_sdg,
+                df_recycling_sdg,
+            ]
+        )
+        all_rates_sdg["variable"] = [
+            x.replace("baseline", "sdg") for x in all_rates_sdg["variable"]
+        ]
+        all_rates = pd.concat([all_rates_base, all_rates_sdg])
+        save_path = private_data_path("water", "demands", "harmonized", context.regions)
+        # save all the rates for reporting purposes
+        all_rates.to_csv(save_path / "all_rates_SSP2.csv", index=False)
+
+    # urban water demand and return. 1e-3 from mcm to km3
+    urban_mw = urban_withdrawal_df.reset_index(drop=True)
+    urban_mw = urban_mw.merge(
+        urban_connection_rate_df.drop(columns=["variable", "time"]).rename(
+            columns={"value": "rate"}
+        )
+    )
+    urban_mw["value"] = (1e-3 * urban_mw["value"]) * urban_mw["rate"]
+
+    dmd_df = make_df(
+        "demand",
+        node="B" + urban_mw["node"],
+        commodity="urban_mw",
+        level="final",
+        year=urban_mw["year"],
+        time=urban_mw["time"],
+        value=urban_mw["value"],
+        unit="km3/year",
+    )
+    urban_dis = urban_withdrawal_df.reset_index(drop=True)
+    urban_dis = urban_dis.merge(
+        urban_connection_rate_df.drop(columns=["variable", "time"]).rename(
+            columns={"value": "rate"}
+        )
+    )
+    urban_dis["value"] = (1e-3 * urban_dis["value"]) * (1 - urban_dis["rate"])
+
+    dmd_df = dmd_df.append(
+        make_df(
+            "demand",
+            node="B" + urban_dis["node"],
+            commodity="urban_disconnected",
+            level="final",
+            year=urban_dis["year"],
+            time=urban_dis["time"],
+            value=urban_dis["value"],
+            unit="km3/year",
+        )
+    )
+    # rural water demand and return
+    rural_mw = rual_withdrawal_df.reset_index(drop=True)
+    rural_mw = rural_mw.merge(
+        rural_connection_rate_df.drop(columns=["variable", "time"]).rename(
+            columns={"value": "rate"}
+        )
+    )
+    rural_mw["value"] = (1e-3 * rural_mw["value"]) * rural_mw["rate"]
+
+    dmd_df = dmd_df.append(
+        make_df(
+            "demand",
+            node="B" + rural_mw["node"],
+            commodity="rural_mw",
+            level="final",
+            year=rural_mw["year"],
+            time=rural_mw["time"],
+            value=rural_mw["value"],
+            unit="km3/year",
+        )
+    )
+
+    rural_dis = rual_withdrawal_df.reset_index(drop=True)
+    rural_dis = rural_dis.merge(
+        rural_connection_rate_df.drop(columns=["variable", "time"]).rename(
+            columns={"value": "rate"}
+        )
+    )
+    rural_dis["value"] = (1e-3 * rural_dis["value"]) * (1 - rural_dis["rate"])
+
+    dmd_df = dmd_df.append(
+        make_df(
+            "demand",
+            node="B" + rural_dis["node"],
+            commodity="rural_disconnected",
+            level="final",
+            year=rural_dis["year"],
+            time=rural_dis["time"],
+            value=rural_dis["value"],
+            unit="km3/year",
+        )
+    )
+
+    # manufactury/ industry water demand and return
+    manuf_mw = industrial_withdrawals_df.reset_index(drop=True)
+    manuf_mw["value"] = 1e-3 * manuf_mw["value"]
+
+    dmd_df = dmd_df.append(
+        make_df(
+            "demand",
+            node="B" + manuf_mw["node"],
+            commodity="industry_mw",
+            level="final",
+            year=manuf_mw["year"],
+            time=manuf_mw["time"],
+            value=manuf_mw["value"],
+            unit="km3/year",
+        )
+    )
+
+    manuf_uncollected_wst = industrial_return_df.reset_index(drop=True)
+    manuf_uncollected_wst["value"] = 1e-3 * manuf_uncollected_wst["value"]
+
+    dmd_df = dmd_df.append(
+        make_df(
+            "demand",
+            node="B" + manuf_uncollected_wst["node"],
+            commodity="industry_uncollected_wst",
+            level="final",
+            year=manuf_uncollected_wst["year"],
+            time=manuf_uncollected_wst["time"],
+            value=-manuf_uncollected_wst["value"],
+            unit="km3/year",
+        )
+    )
+
+    urban_collected_wst = urban_return_df.reset_index(drop=True)
+    urban_collected_wst = urban_collected_wst.merge(
+        urban_treatment_rate_df.drop(columns=["variable", "time"]).rename(
+            columns={"value": "rate"}
+        )
+    )
+    urban_collected_wst["value"] = (
+        1e-3 * urban_collected_wst["value"]
+    ) * urban_collected_wst["rate"]
+
+    dmd_df = dmd_df.append(
+        make_df(
+            "demand",
+            node="B" + urban_collected_wst["node"],
+            commodity="urban_collected_wst",
+            level="final",
+            year=urban_collected_wst["year"],
+            time=urban_collected_wst["time"],
+            value=-urban_collected_wst["value"],
+            unit="km3/year",
+        )
+    )
+
+    rural_collected_wst = rural_return_df.reset_index(drop=True)
+    rural_collected_wst = rural_collected_wst.merge(
+        rural_treatment_rate_df.drop(columns=["variable", "time"]).rename(
+            columns={"value": "rate"}
+        )
+    )
+    rural_collected_wst["value"] = (
+        1e-3 * rural_collected_wst["value"]
+    ) * rural_collected_wst["rate"]
+
+    dmd_df = dmd_df.append(
+        make_df(
+            "demand",
+            node="B" + rural_collected_wst["node"],
+            commodity="rural_collected_wst",
+            level="final",
+            year=rural_collected_wst["year"],
+            time=rural_collected_wst["time"],
+            value=-rural_collected_wst["value"],
+            unit="km3/year",
+        )
+    )
+    urban_uncollected_wst = urban_return_df.reset_index(drop=True)
+    urban_uncollected_wst = urban_uncollected_wst.merge(
+        urban_treatment_rate_df.drop(columns=["variable", "time"]).rename(
+            columns={"value": "rate"}
+        )
+    )
+    urban_uncollected_wst["value"] = (1e-3 * urban_uncollected_wst["value"]) * (
+        1 - urban_uncollected_wst["rate"]
+    )
+
+    dmd_df = dmd_df.append(
+        make_df(
+            "demand",
+            node="B" + urban_uncollected_wst["node"],
+            commodity="urban_uncollected_wst",
+            level="final",
+            year=urban_uncollected_wst["year"],
+            time=urban_uncollected_wst["time"],
+            value=-urban_uncollected_wst["value"],
+            unit="km3/year",
+        )
+    )
+
+    rural_uncollected_wst = rural_return_df.reset_index(drop=True)
+    rural_uncollected_wst = rural_uncollected_wst.merge(
+        rural_treatment_rate_df.drop(columns=["variable", "time"]).rename(
+            columns={"value": "rate"}
+        )
+    )
+    rural_uncollected_wst["value"] = (1e-3 * rural_uncollected_wst["value"]) * (
+        1 - rural_uncollected_wst["rate"]
+    )
+
+    dmd_df = dmd_df.append(
+        make_df(
+            "demand",
+            node="B" + rural_uncollected_wst["node"],
+            commodity="rural_uncollected_wst",
+            level="final",
+            year=rural_uncollected_wst["year"],
+            time=rural_uncollected_wst["time"],
+            value=-rural_uncollected_wst["value"],
+            unit="km3/year",
+        )
+    )
+    # Add 2010 & 2015 values as historical activities to corresponding technologies
+    h_act = dmd_df[dmd_df["year"].isin([2010, 2015])]
+
+    dmd_df = dmd_df[dmd_df["year"].isin(info.Y)]
+    results["demand"] = dmd_df
+
+    # create a list of our conditions
+    conditions = [
+        (h_act["commodity"] == "urban_mw"),
+        (h_act["commodity"] == "industry_mw"),
+        (h_act["commodity"] == "rural_mw"),
+        (h_act["commodity"] == "urban_disconnected"),
+        (h_act["commodity"] == "rural_disconnected"),
+        (h_act["commodity"] == "urban_collected_wst"),
+        (h_act["commodity"] == "rural_collected_wst"),
+        (h_act["commodity"] == "urban_uncollected_wst"),
+        (h_act["commodity"] == "industry_uncollected_wst"),
+        (h_act["commodity"] == "rural_uncollected_wst"),
+    ]
+
+    # create a list of the values we want to assign for each condition
+    values = [
+        "urban_t_d",
+        "industry_unconnected",
+        "rural_t_d",
+        "urban_unconnected",
+        "rural_unconnected",
+        "urban_sewerage",
+        "rural_sewerage",
+        "urban_untreated",
+        "industry_untreated",
+        "rural_untreated",
+    ]
+    # create a new column and use np.select to assign values to it using our lists as arguments
+    h_act["commodity"] = np.select(conditions, values)
+    h_act["value"] = h_act["value"].abs()
+
+    hist_act = make_df(
+        "historical_activity",
+        node_loc=h_act["node"],
+        technology=h_act["commodity"],
+        year_act=h_act["year"],
+        mode="M1",
+        time=h_act["time"],
+        value=h_act["value"],
+        unit="km3/year",
+    )
+    results["historical_activity"] = hist_act
+
+    h_cap = h_act[h_act["year"] >= 2015]
+    h_cap = (
+        h_cap.groupby(["node", "commodity", "level", "year", "unit"])["value"]
+        .sum()
+        .reset_index()
+    )
+
+    hist_cap = make_df(
+        "historical_new_capacity",
+        node_loc=h_cap["node"],
+        technology=h_cap["commodity"],
+        year_vtg=h_cap["year"],
+        value=h_cap["value"] / 5,
+        unit="km3/year",
+    )
+
+    results["historical_new_capacity"] = hist_cap
+
+    # share constraint lower bound on urban_Water recycling
+    df_share_wat = make_df(
+        "share_commodity_lo",
+        shares="share_wat_recycle",
+        node_share="B" + df_recycling["node"],
+        year_act=df_recycling["year"],
+        value=df_recycling["value"],
+        unit="-",
+    ).pipe(
+        broadcast,
+        time=sub_time,
+    )
+
+    df_share_wat = df_share_wat[df_share_wat["year_act"].isin(info.Y)]
+    results["share_commodity_lo"] = df_share_wat
+
+    # rel = make_df(
+    #     "relation_activity",
+    #     relation="recycle_rel",
+    #     node_rel="B" + df_recycling["node"],
+    #     year_rel=df_recycling["year"],
+    #     node_loc="B" + df_recycling["node"],
+    #     technology="urban_recycle",
+    #     year_act=df_recycling["year"],
+    #     mode="M1",
+    #     value=-df_recycling["value"],
+    #     unit="-",
+    # )
+
+    # rel = rel.append(
+    #     make_df(
+    #         "relation_activity",
+    #         relation="recycle_rel",
+    #         node_rel="B" + df_recycling["node"],
+    #         year_rel=df_recycling["year"],
+    #         node_loc="B" + df_recycling["node"],
+    #         technology="urban_sewerage",
+    #         year_act=df_recycling["year"],
+    #         mode="M1",
+    #         value=1,
+    #         unit="-",
+    #     )
+    # )
+
+    # results["relation_activity"] = rel
+
+    # rel_lo = make_df(
+    #     "relation_lower",
+    #     relation="recycle_rel",
+    #     node_rel="B" + df_recycling["node"],
+    #     value=0,
+    #     unit="-",
+    # ).pipe(broadcast, year_rel=info.Y)
+
+    # results["relation_lower"] = rel_lo
+
+    # rel_up = make_df(
+    #     "relation_upper",
+    #     relation="recycle_rel",
+    #     node_rel="B" + df_recycling["node"],
+    #     value=0,
+    #     unit="-",
+    # ).pipe(broadcast, year_rel=info.Y)
+
+    # results["relation_upper"] = rel_up
+
+    return results
+
+
+def read_water_availability(context):
+    """
+    Reads water availability data and bias correct it for the historical years and no climate
+    scenario assumptions.
+
+
+    Parameters
+    ----------
+    context : .Context
+    Returns
+    -------
+    data : dict of (str -> pandas.DataFrame)
+        Keys are MESSAGE parameter names such as 'input', 'fix_cost'. Values
+        are data frames ready for :meth:`~.Scenario.add_par`.
+    """
+
+    # Reference to the water configuration
+    info = context["water build info"]
+    # reading sample for assiging basins
+    # info = context["water build info"]
+
+    PATH = private_data_path(
+        "water", "delineation", f"basins_by_region_simpl_{context.regions}.csv"
+    )
+    df_x = pd.read_csv(PATH)
+
+    if "year" in context.time:
+        # path for reading basin delineation file
+        PATH = private_data_path(
+            "water", "delineation", f"basins_by_region_simpl_{context.regions}.csv"
+        )
+        df_x = pd.read_csv(PATH)
+        # Adding freshwater supply constraints
+        # Reading data, the data is spatially and temprally aggregated from GHMs
+        path1 = private_data_path(
+            "water",
+            "availability",
+            f"qtot_5y_{context.RCP}_{context.REL}_{context.regions}.csv",
+        )
+        # Read rcp 2.6 data
+        df_sw = pd.read_csv(path1)
+        df_sw.drop(["Unnamed: 0"], axis=1, inplace=True)
+
+        df_sw.index = df_x["BCU_name"]
+        df_sw = df_sw.stack().reset_index()
+        df_sw.columns = ["Region", "years", "value"]
+        df_sw.fillna(0, inplace=True)
+        df_sw.reset_index(drop=True, inplace=True)
+        df_sw["year"] = pd.DatetimeIndex(df_sw["years"]).year
+        df_sw["time"] = "year"
+        df_sw2210 = df_sw[df_sw["year"] == 2100]
+        df_sw2210["year"] = 2110
+        df_sw = pd.concat([df_sw, df_sw2210])
+        df_sw = df_sw[df_sw["year"].isin(info.Y)]
+
+        # Adding groundwater supply constraints
+        # Reading data, the data is spatially and temprally aggregated from GHMs
+        path1 = private_data_path(
+            "water",
+            "availability",
+            f"qr_5y_{context.RCP}_{context.REL}_{context.regions}.csv",
+        )
+
+        # Read groundwater data
+        df_gw = pd.read_csv(path1)
+        df_gw.drop(["Unnamed: 0"], axis=1, inplace=True)
+        df_gw.index = df_x["BCU_name"]
+        df_gw = df_gw.stack().reset_index()
+        df_gw.columns = ["Region", "years", "value"]
+        df_gw.fillna(0, inplace=True)
+        df_gw.reset_index(drop=True, inplace=True)
+        df_gw["year"] = pd.DatetimeIndex(df_gw["years"]).year
+        df_gw["time"] = "year"
+        df_gw2210 = df_gw[df_gw["year"] == 2100]
+        df_gw2210["year"] = 2110
+        df_gw = pd.concat([df_gw, df_gw2210])
+        df_gw = df_gw[df_gw["year"].isin(info.Y)]
+
+    else:
+        # Adding freshwater supply constraints
+        # Reading data, the data is spatially and temprally aggregated from GHMs
+        path1 = private_data_path(
+            "water",
+            "availability",
+            f"qtot_5y_m_{context.RCP}_{context.REL}_{context.regions}.csv",
+        )
+        df_sw = pd.read_csv(path1)
+        df_sw.drop(["Unnamed: 0"], axis=1, inplace=True)
+
+        df_sw.index = df_x["BCU_name"]
+        df_sw = df_sw.stack().reset_index()
+        df_sw.columns = ["Region", "years", "value"]
+        df_sw.sort_values(["Region", "years", "value"], inplace=True)
+        df_sw.fillna(0, inplace=True)
+        df_sw.reset_index(drop=True, inplace=True)
+        df_sw["year"] = pd.DatetimeIndex(df_sw["years"]).year
+        df_sw["time"] = pd.DatetimeIndex(df_sw["years"]).month
+        df_sw2210 = df_sw[df_sw["year"] == 2100]
+        df_sw2210["year"] = 2110
+        df_sw = pd.concat([df_sw, df_sw2210])
+        df_sw = df_sw[df_sw["year"].isin(info.Y)]
+
+        # Reading data, the data is spatially and temporally aggregated from GHMs
+        path1 = private_data_path(
+            "water",
+            "availability",
+            f"qr_5y_m_{context.RCP}_{context.REL}_{context.regions}.csv",
+        )
+        df_gw = pd.read_csv(path1)
+        df_gw.drop(["Unnamed: 0"], axis=1, inplace=True)
+
+        df_gw.index = df_x["BCU_name"]
+        df_gw = df_gw.stack().reset_index()
+        df_gw.columns = ["Region", "years", "value"]
+        df_gw.sort_values(["Region", "years", "value"], inplace=True)
+        df_gw.fillna(0, inplace=True)
+        df_gw.reset_index(drop=True, inplace=True)
+        df_gw["year"] = pd.DatetimeIndex(df_gw["years"]).year
+        df_gw["time"] = pd.DatetimeIndex(df_gw["years"]).month
+        df_gw2210 = df_gw[df_gw["year"] == 2100]
+        df_gw2210["year"] = 2110
+        df_gw = pd.concat([df_gw, df_sw2210])
+        df_gw = df_gw[df_gw["year"].isin(info.Y)]
+
+    return df_sw, df_gw
+
+
+def add_water_availability(context):
+    """
+    Adds water supply constraints
+    Parameters
+    ----------
+    context : .Context
+    Returns
+    -------
+    data : dict of (str -> pandas.DataFrame)
+        Keys are MESSAGE parameter names such as 'input', 'fix_cost'. Values
+        are data frames ready for :meth:`~.Scenario.add_par`.
+    """
+    # Reference to the water configuration
+    info = context["water build info"]
+
+    # define an empty dictionary
+    results = {}
+    # Adding freshwater supply constraints
+    # Reading data, the data is spatially and temprally aggregated from GHMs
+
+    df_sw, df_gw = read_water_availability(context)
+
+    dmd_df = make_df(
+        "demand",
+        node="B" + df_sw["Region"].astype(str),
+        commodity="surfacewater_basin",
+        level="water_avail_basin",
+        year=df_sw["year"],
+        time=df_sw["time"],
+        value=-df_sw["value"],
+        unit="km3/year",
+    )
+
+    dmd_df = dmd_df.append(
+        make_df(
+            "demand",
+            node="B" + df_gw["Region"].astype(str),
+            commodity="groundwater_basin",
+            level="water_avail_basin",
+            year=df_gw["year"],
+            time=df_gw["time"],
+            value=-df_gw["value"],
+            unit="km3/year",
+        )
+    )
+
+    dmd_df["value"] = dmd_df["value"].apply(lambda x: x if x <= 0 else 0)
+
+    results["demand"] = dmd_df
+
+    # share constraint lower bound on groundwater
+    df_share = make_df(
+        "share_commodity_lo",
+        shares="share_low_lim_GWat",
+        node_share="B" + df_gw["Region"],
+        year_act=df_gw["year"],
+        time=df_gw["time"],
+        value=df_gw["value"]
+        / (df_sw["value"] + df_gw["value"])
+        * 0.95,  # 0.95 buffer factor to avoid numerical error
+        unit="-",
+    )
+
+    df_share["value"] = df_share["value"].fillna(0)
+
+    results["share_commodity_lo"] = df_share
+
+    return results
+
+
+def add_irrigation_demand(context):
+    """
+    Adds endogenous irrigation water demands from GLOBIOM emulator
+    Parameters
+    ----------
+    context : .Context
+    Returns
+    -------
+    data : dict of (str -> pandas.DataFrame)
+        Keys are MESSAGE parameter names such as 'input', 'fix_cost'. Values
+        are data frames ready for :meth:`~.Scenario.add_par`.
+    """
+    # define an empty dictionary
+    results = {}
+
+    scen = context.get_scenario()
+    # add water for irrigation from globiom
+    land_out_1 = scen.par(
+        "land_output", {"commodity": "Water|Withdrawal|Irrigation|Cereals"}
+    )
+    land_out_1["level"] = "irr_cereal"
+    land_out_2 = scen.par(
+        "land_output", {"commodity": "Water|Withdrawal|Irrigation|Oilcrops"}
+    )
+    land_out_2["level"] = "irr_oilcrops"
+    land_out_3 = scen.par(
+        "land_output", {"commodity": "Water|Withdrawal|Irrigation|Sugarcrops"}
+    )
+    land_out_3["level"] = "irr_sugarcrops"
+
+    land_out = pd.concat([land_out_1, land_out_2, land_out_3])
+    land_out["commodity"] = "freshwater"
+
+    land_out["value"] = 1e-3 * land_out["value"]
+
+    # take land_out edited and add as a demand in  land_input
+    results["land_input"] = land_out
+
+    return results
diff --git a/nest/message_ix_models/model/water/data/deprecated/Figures.R b/nest/message_ix_models/model/water/data/deprecated/Figures.R
new file mode 100644
index 0000000..5e49c40
--- /dev/null
+++ b/nest/message_ix_models/model/water/data/deprecated/Figures.R
@@ -0,0 +1,213 @@
+# Figures
+# This s script produces plots fo governance and water stress data 
+
+# after loading dat.df, water scarcity data and governance
+
+wsi_stat = dat.df %>% select(urban_pop.2010,urban_pop.2020,urban_pop.2030, urban_pop.2040,urban_pop.2050,
+                             rural_pop.2020,rural_pop.2030,rural_pop.2040,rural_pop.2050,urban_pop.2060,rural_pop.2060,
+                             urban_gdp.2020,  urban_gdp.2030, urban_gdp.2040,
+                             urban_gdp.2050,rural_gdp.2020,rural_gdp.2020,rural_gdp.2030,rural_gdp.2030,
+                             rural_gdp.2040,rural_gdp.2040,rural_gdp.2050,urban_gdp.2060,rural_gdp.2060,
+                             WSI.2020,WSI.2030,WSI.2040,WSI.2050,WSI.2010,WSI.2060,) %>%
+  mutate(urban_gdp_pc.2010 = urban_gdp.2020/urban_pop.2010) %>%
+  mutate(urban_gdp_pc.2020 = urban_gdp.2020/urban_pop.2020) %>%
+  mutate(urban_gdp_pc.2030 = urban_gdp.2020/urban_pop.2030) %>%
+  mutate(urban_gdp_pc.2040 = urban_gdp.2020/urban_pop.2040) %>%
+  mutate(urban_gdp_pc.2050 = urban_gdp.2020/urban_pop.2050) %>%
+  mutate(urban_gdp_pc.2060 = urban_gdp.2060/urban_pop.2060) %>%
+  mutate(rural_gdp_pc.2010 = rural_gdp.2020/rural_pop.2010) %>%
+  mutate(rural_gdp_pc.2020 = rural_gdp.2020/rural_pop.2020) %>%
+  mutate(rural_gdp_pc.2030 = rural_gdp.2020/rural_pop.2030) %>%
+  mutate(rural_gdp_pc.2040 = rural_gdp.2020/rural_pop.2040) %>%
+  mutate(rural_gdp_pc.2050 = rural_gdp.2020/rural_pop.2050) %>%
+  mutate(rural_gdp_pc.2060 = rural_gdp.2060/rural_pop.2060) %>%
+  select(-urban_gdp.2020,-rural_gdp.2020,-urban_gdp.2060,-rural_gdp.2060)
+
+wsi_stat[is.na(wsi_stat)] = 0
+
+# N of people in water stress areas in 2020
+nppl = wsi_stat %>% filter(WSI.2020 %in% c('High Stress','Extreme Stress')) %>%
+  #million people
+  summarise(urban_pop.2020 = sum(urban_pop.2020)*1e-6, rural_pop.2020 = sum(rural_pop.2020)*1e-6) %>%
+  mutate(year = 2020) %>%
+  gather(key = 'urb_rur', value = 'value',1:2) %>% bind_rows(
+    wsi_stat %>% filter(WSI.2060 %in% c('High Stress','Extreme Stress')) %>%
+      #million people
+      summarise(urban_pop.2060 = sum(urban_pop.2060)*1e-6, rural_pop.2060 = sum(rural_pop.2060)*1e-6) %>%
+      mutate(year = 2060) %>%
+      gather(key = 'urb_rur', value = 'value',1:2)
+  ) %>%
+  mutate(urb_rur = gsub('\\..*','',urb_rur) )
+
+# GDP per capita of people highly exposed
+gdppc =  wsi_stat %>% filter(WSI.2020 %in% c('High Stress','Extreme Stress')) %>%
+  #million people
+  summarise(urban_gdp_pc.2020 = mean(urban_pop.2020), rural_gdp_pc.2020 = mean(rural_gdp_pc.2020)) %>%
+  mutate(year = 2020) %>%
+  gather(key = 'urb_rur', value = 'value',1:2) %>% bind_rows(
+    wsi_stat %>% filter(WSI.2060 %in% c('High Stress','Extreme Stress')) %>%
+      #million people
+      summarise(urban_gdp_pc.2060 = mean(urban_gdp_pc.2060), rural_gdp_pc.2060 = mean(rural_gdp_pc.2060)) %>%
+      mutate(year = 2060) %>%
+      gather(key = 'urb_rur', value = 'value',1:2)
+  ) %>%
+  mutate(urb_rur = gsub('\\..*','',urb_rur) )
+
+library(ggplot2)
+ggplot(nppl)+
+  geom_bar(aes(x = year, y = value, fill = urb_rur),stat = "identity", position = "stack")+
+  theme_bw()+ggtitle('Population with high/extreme water stress')+
+  xlab('Million people')
+
+ggplot(gdppc)+
+  geom_bar(aes(x = year, y = value, fill = urb_rur),stat = "identity", position = "dodge")+
+  theme_bw()+ggtitle('GDP per capita of people with high/extreme water stress')+
+  xlab('Thousands $')
+
+
+# MAps  of global population-WSI-governance-GDP
+dat_2050.df = dat.df %>%
+  group_by(country) %>%
+  summarise(population = sum(rural_pop.2050 + urban_pop.2050, na.rm = T),
+            GDP = sum(rural_gdp.2050 + urban_gdp.2050, na.rm = T),
+            WSI = mean(WSI.2050, na.rm = T),
+            gov = mean(gov.2050, na.rm = T)) %>%
+  mutate(GDPpc = GDP/population/1000) # 1000 $ per capita
+
+#### from marina ####
+library(ggplot2)
+library(countrycode)
+library(viridis)
+library(ggthemes)
+library(maps)
+library(rworldmap)
+library(mapproj)
+
+# Merge the country data with maps
+
+map <- joinCountryData2Map(dat_2050.df, joinCode = "ISO3", nameJoinColumn = "country")
+map_poly <-  fortify(map) %>%
+  merge(map@data, by.x="id", by.y="ADMIN", all.x=T) %>%
+  arrange(id, order)
+
+
+# Governance
+ggplot(map_poly, aes( x = long, y = lat, group = group )) +
+  coord_map(projection = 'mollweide', xlim = c(-180, 180), ylim = c(-60, 75))  + # Remove antarctica
+  geom_polygon(aes(fill = gov)) +
+  scale_fill_viridis() +
+  labs(fill = 'Value'
+       ,title = 'Governance in 2050 - SSP2'   # Change the title of the map
+       ,x = NULL
+       ,y = NULL) +
+  theme(text = element_text(family = 'Helvetica', color = 'gray40')
+        ,plot.title = element_text(size = 18)
+        ,axis.ticks = element_blank()
+        ,axis.text = element_blank()
+        ,axis.line = element_blank()
+        ,panel.grid = element_blank()
+        ,panel.background = element_rect(fill = 'white')
+        ,plot.background = element_rect(fill = 'white')
+        ,legend.position = c(.08,.26)
+        ,legend.background = element_blank()
+        ,legend.key = element_blank()
+  ) +
+  annotate(geom = 'text'
+           ,label = ''
+           ,x = 18, y = -55
+           ,size = 3
+           ,family = 'Helvetica'
+           ,color = 'gray50'
+           ,hjust = 'left'
+  )
+
+# population
+ggplot(map_poly, aes( x = long, y = lat, group = group )) +
+  coord_map(projection = 'mollweide', xlim = c(-180, 180), ylim = c(-60, 75))  + # Remove antarctica
+  geom_polygon(aes(fill = population/1e6)) +
+  scale_fill_viridis() +
+  labs(fill = 'Million people'
+       ,title = 'Population in 2050 - SSP2'   # Change the title of the map
+       ,x = NULL
+       ,y = NULL) +
+  theme(text = element_text(family = 'Helvetica', color = 'gray40')
+        ,plot.title = element_text(size = 18)
+        ,axis.ticks = element_blank()
+        ,axis.text = element_blank()
+        ,axis.line = element_blank()
+        ,panel.grid = element_blank()
+        ,panel.background = element_rect(fill = 'white')
+        ,plot.background = element_rect(fill = 'white')
+        ,legend.position = c(.08,.26)
+        ,legend.background = element_blank()
+        ,legend.key = element_blank()
+  ) +
+  annotate(geom = 'text'
+           ,label = ''
+           ,x = 18, y = -55
+           ,size = 3
+           ,family = 'Helvetica'
+           ,color = 'gray50'
+           ,hjust = 'left'
+  )
+
+# GDP per capita
+ggplot(map_poly, aes( x = long, y = lat, group = group )) +
+  coord_map(projection = 'mollweide', xlim = c(-180, 180), ylim = c(-60, 75))  + # Remove antarctica
+  geom_polygon(aes(fill = GDPpc)) +
+  scale_fill_viridis() +
+  labs(fill = 'Thousands $ pc'
+       ,title = 'GDP per capita in 2050 - SSP2'   # Change the title of the map
+       ,x = NULL
+       ,y = NULL) +
+  theme(text = element_text(family = 'Helvetica', color = 'gray40')
+        ,plot.title = element_text(size = 18)
+        ,axis.ticks = element_blank()
+        ,axis.text = element_blank()
+        ,axis.line = element_blank()
+        ,panel.grid = element_blank()
+        ,panel.background = element_rect(fill = 'white')
+        ,plot.background = element_rect(fill = 'white')
+        ,legend.position = c(.08,.26)
+        ,legend.background = element_blank()
+        ,legend.key = element_blank()
+  ) +
+  annotate(geom = 'text'
+           ,label = ''
+           ,x = 18, y = -55
+           ,size = 3
+           ,family = 'Helvetica'
+           ,color = 'gray50'
+           ,hjust = 'left'
+  )
+
+# WSI
+
+ggplot(map_poly, aes( x = long, y = lat, group = group )) +
+  coord_map(projection = 'mollweide', xlim = c(-180, 180), ylim = c(-60, 75))  + # Remove antarctica
+  geom_polygon(aes(fill = WSI)) +
+  scale_fill_viridis() +
+  labs(fill = 'Value'
+       ,title = 'WSI in 2050 - SSP2'   # Change the title of the map
+       ,x = NULL
+       ,y = NULL) +
+  theme(text = element_text(family = 'Helvetica', color = 'gray40')
+        ,plot.title = element_text(size = 18)
+        ,axis.ticks = element_blank()
+        ,axis.text = element_blank()
+        ,axis.line = element_blank()
+        ,panel.grid = element_blank()
+        ,panel.background = element_rect(fill = 'white')
+        ,plot.background = element_rect(fill = 'white')
+        ,legend.position = c(.08,.26)
+        ,legend.background = element_blank()
+        ,legend.key = element_blank()
+  ) +
+  annotate(geom = 'text'
+           ,label = ''
+           ,x = 18, y = -55
+           ,size = 3
+           ,family = 'Helvetica'
+           ,color = 'gray50'
+           ,hjust = 'left'
+  )
diff --git a/nest/message_ix_models/model/water/data/deprecated/add_water_infrastructure.R b/nest/message_ix_models/model/water/data/deprecated/add_water_infrastructure.R
new file mode 100644
index 0000000..797c8e1
--- /dev/null
+++ b/nest/message_ix_models/model/water/data/deprecated/add_water_infrastructure.R
@@ -0,0 +1,3229 @@
+#Creates 4 new IAM scenarios including a representation of
+# Solves the models on the local machine
+# Uploads the solutions to the ix DB
+
+# NOTE: Requires core model configured with full commodity balance
+
+rm(list=ls())
+graphics.off()
+
+options(java.parameters = "-Xmx16g")
+
+library("rixmp")
+library("rmessageix")
+
+require(rgdal)
+require(raster)
+require(rgeos)
+require(countrycode)
+require(RCurl)
+require(xlsx)
+require(tidyr)
+require(dplyr)
+
+## Set path ixmp folder in message_ix working copy
+message_ix_path = Sys.getenv("MESSAGE_IX_PATH")
+
+## Set path data folder in message_ix working copy
+data_path = Sys.getenv("IIASA_DATA_PATH")
+
+# Country region mapping key
+country_region_map_key.df = data.frame( read.csv( paste( data_path, 'Water/water_demands/country_region_map_key.csv',  sep = '/' ), stringsAsFactors=FALSE) )
+
+# SSP scenario - currently only configured for SSP2
+ssss = 'SSP2'
+
+# Rounding parameter for decimals
+rnd = 6
+
+# list of model-scenario combinations
+model_scenarios = data.frame( 	model = c( "MESSAGE-GLOBIOM CD-LINKS R2.3.1", "MESSAGE-GLOBIOM CD-LINKS R2.3.1" , "MESSAGE-GLOBIOM CD-LINKS R2.3.1" , "MESSAGE-GLOBIOM CD-LINKS R2.3.1" ,"MESSAGE-GLOBIOM CD-LINKS R2.3.1" ),
+               scenario = c( "baseline", "baseline", "baseline", "baseline", "baseline" ),
+               newscenarioName = c( "baseline_globiom_base_watbaseline", "baseline_globiom_SDG_sdg6eff", "baseline_globiom_SDG_sdg6supp", "baseline_globiom_SDG_sdg6supp2", "baseline_globiom_SDG_sdg6led" )
+               )
+
+# launch the IX modeling platform using the default central ORCALE database
+ix_platform = ixmp.Platform( dbprops = 'ixmp.properties' )
+
+for( sc in 1:nrow(model_scenarios) )
+ {
+
+ gc()
+
+ # existing model and scenario names to start with
+ modelName = as.character( unlist( model_scenarios$model[sc] ) )
+ scenarioName = as.character( unlist( model_scenarios$scenario[sc] ) )
+
+ # new model and scenario name for ix platform
+ newmodelName = as.character( unlist( model_scenarios$model[sc] ) )
+ newscenarioName = as.character( unlist( model_scenarios$newscenarioName[sc] ) )
+
+ # Comment
+ comment = paste("water infrastructure included", sep = '')
+
+ # check if basis for scenarip exists
+ scen_exists = tryCatch( ix_platform$Scenario( model=modelName, scen=paste(scenarioName,'watbasis',sep='_') ), error = function(e){} )
+ if( is.null(scen_exists) )
+   {
+
+   print('no basis  - creating from existing scenario')
+
+   ixDStemp = ix_platform$Scenario( model=modelName, scen=scenarioName )
+
+   # clone data structure with new scenario name
+   ixDS0 = ixDStemp$clone( new_model = modelName, new_scen = paste(scenarioName,'watbasis',sep='_'), annotation = comment, keep_sol = FALSE )
+
+   # check out and remove original datastructure
+   ixDS0$check_out()
+
+   # Remove some of the old water implementation - this takes about an hour
+   chk = tryCatch( ixDS0$par('bound_activity_up', list(technology = 'extract__saline_supply')), error = function(e){} )
+   if( !is.null(chk) ){ ret = lapply( 1:nrow(chk), function(r){ ixDS0$remove_par( 'bound_activity_up', paste( as.character( as.matrix(unlist(chk[r, 1:(which(names(chk)=='value')-1)])) ), collapse = '.' ) ) } ) }
+   chk = tryCatch( ixDS0$par('input', list(commodity = 'saline_supply')), error = function(e){} )
+   if( !is.null(chk) ){ ret = lapply( 1:nrow(chk), function(r){ ixDS0$remove_par( 'input', paste( as.character( as.matrix(unlist(chk[r, 1:(which(names(chk)=='value')-1)])) ), collapse = '.' ) ) } ) }
+   chk = tryCatch( ixDS0$par('emission_factor', list(emission = c('fresh_wastewater','saline_wastewater'))), error = function(e){} )
+   if( !is.null(chk) ){ ret = lapply( 1:nrow(chk), function(r){ ixDS0$remove_par( 'emission_factor', paste( as.character( as.matrix(unlist(chk[r, 1:(which(names(chk)=='value')-1)])) ), collapse = '.' ) ) } ) }
+
+   ixDS0$commit(paste(comment,'basis for water policies',sep=' '))
+   ixDS0$set_as_default()
+
+   rm(ixDStemp)
+   rm(ixDS0)
+
+   }
+
+ ixDSoriginal = ix_platform$Scenario( model=modelName, scen=paste(scenarioName,'watbasis',sep='_') )
+
+ print('basis loaded successfully')
+
+ # Branching points for energy efficiency and cost of water technologies
+ parameter_levels = 'mid'
+ if( model_scenarios$newscenarioName[sc] %in% c("baseline_globiom_SDG_sdg6supp","baseline_globiom_SDG_sdg6supp2","baseline_globiom_SDG_sdg6eff","baseline_globiom_SDG_sdg6led") ){ water_policies = 'SDG6' }else{ water_policies = 'NoWatPol' }
+ if( model_scenarios$newscenarioName[sc] %in% c("baseline_globiom_SDG_sdg6eff","baseline_globiom_SDG_sdg6led") ){ parameter_levels2 = 'high' }else{ parameter_levels2 = 'mid' }
+
+ #-------------------------------------------------------------------------------
+ # Load the MESSAGE-GLOBIOM SSP baseline scenario
+ #-------------------------------------------------------------------------------
+
+ # model and scenario names for database
+ comment = paste( "Water infrastructure and policies", newscenarioName, sep = '' )
+
+ # clone data structure with new scenario name
+ ixDS = ixDSoriginal$clone( new_model = modelName, new_scen = newscenarioName, annotation = comment, keep_sol = FALSE )
+
+ # check out
+ ixDS$check_out()
+
+ #-------------------------------------------------------------------------------------------------------
+ # Compare the data in the database to the data in the csv files to determine the cooling technologies to include
+ #-------------------------------------------------------------------------------------------------------
+
+ # Import raw cooling water data:
+ # Input data file 1: CSV containing the water use coefficients (incl. parasitic electricity) for each MESSAGE technology
+ # Source for power plant cooling water use: Meldrum et al. 2013
+ # Source for hydropower water use: Taken as an average across Grubert et al 2016 and Scherer and Pfister 2016.
+ # Parasitic electricity requirements estimated from Loew et al. 2016.
+ # All other water coefficients come from Fricko et al 2016.
+ # To compile the data, a complete list of technologies from MESSAGE was initially output to CSV.
+ # The water parameters were then checked and entered manually based on data reported in the sources above.
+ tech_water_performance_ssp_msg_raw.df = data.frame(read.csv(paste(data_path,'water/water_parameters_message_ix','tech_water_performance_ssp_msg.csv',sep='/'),stringsAsFactors=FALSE))
+
+ # Input data file 2: CSV containing the regional shares of each cooling technology and the investment costs
+ # The regional shares are estimated using the dataset from Raptis and Pfister 2016 and country boundaries from the GADM dataset
+ # Each plant type in the Raptis and Pfister dataset is mapped to the message technologies. The fraction is calculated using
+ # the total capacity identified for each cooling technology in each country.  These results are aggregated into the message 11 regions.
+ # The costs are estimated from Loew et al. 2016.
+ cooltech_cost_and_shares_ssp_msg_raw.df = data.frame(read.csv(paste(data_path,'water/water_parameters_message_ix','cooltech_cost_and_shares_ssp_msg.csv',sep='/'),stringsAsFactors=FALSE))
+
+ # Define alternate id for cooling technologies from csv file
+ cooltech_cost_and_shares_ssp_msg_raw.df$alt_id = apply( cbind(cooltech_cost_and_shares_ssp_msg_raw.df[,1:2]), 1, paste, collapse='__' )
+
+ # Get the names of the technologies in the MESSAGE IAM from the database
+ technologies_in_message = ixDS$set( 'technology' )
+
+ # Get the name of the regions
+ region = as.character( ixDS$set('cat_node')$node )
+
+ # Get the name of the commodities
+ commodity = ixDS$set( 'commodity' )
+
+ # Technology categories
+ cat_tec = ixDS$set('cat_tec')
+
+ # Whether or not to use initial shares from Davies et al 2013 - alternatively uses shares estimated with the dataset from Raptis et al 2016.
+ use_davies_shares = FALSE
+
+ # Fraction of desal demand
+ desal_fraction = 1
+
+ # Freshwater extraction average electricity input
+ fw_electricity_input = round( data.frame( low = 0.1 * ( 1e3 / (24 * 365) ) , mid = 0.2 * ( 1e3 / (24 * 365) ), high = 0.3 * ( 1e3 / (24 * 365) ) ), digits = 4 )
+
+ # Model years
+ model_years = ixDS$set('year')
+ dmd_yrs = model_years[ which( model_years >= min(as.numeric(as.character(ixDS$par('demand',list(node = 'R11_NAM'))[,'year']))) & model_years <= max(as.numeric(as.character(ixDS$par('demand',list(node = 'R11_NAM'))[,'year']))) ) ]
+ firstmodelyear = as.numeric( as.character( ixDS$set('cat_year')[which(as.character(unlist(ixDS$set('cat_year')[,'type_year']))=='firstmodelyear'),'year'] ) )
+ lastmodelyear = as.numeric( as.character( ixDS$set('cat_year')[which(as.character(unlist(ixDS$set('cat_year')[,'type_year']))=='lastmodelyear'),'year'] ) )
+
+ # Model timeslices
+ model_time = ixDS$set('time')
+
+ # Which technologies in MESSAGE are also included in csv files?
+ message_technologies_with_water_data = as.character(tech_water_performance_ssp_msg_raw.df$technology_name)[ which( as.character(tech_water_performance_ssp_msg_raw.df$technology_name) %in% technologies_in_message )]
+ message_technologies_without_water_data = as.character(tech_water_performance_ssp_msg_raw.df$technology_name)[ which( !(as.character(tech_water_performance_ssp_msg_raw.df$technology_name) %in% technologies_in_message ) )]
+ message_technologies_with_water_data_alt = as.character(technologies_in_message)[ which(  technologies_in_message %in% as.character(tech_water_performance_ssp_msg_raw.df$technology_name) )]
+ message_technologies_without_water_data_alt = as.character(technologies_in_message)[ which(  !(technologies_in_message %in% as.character(tech_water_performance_ssp_msg_raw.df$technology_name) )) ]
+
+ # Define a common mode using the existing DB settings
+ mode_common = ixDS$set('mode')[1] # Common mode name from set list
+
+ # Define hydropower technologies
+ hydro_techs = c('hydro_hc','hydro_lc')
+
+ #-------------------------------------------------------------------
+ # Add LED demands from Gruebler et al. 2018 - only if LED scenario
+ #-------------------------------------------------------------------
+
+ if( as.character( unlist( model_scenarios$newscenarioName[sc] ) ) == "baseline_globiom_SDG_sdg6led" )
+   {
+
+   # read demands from xlsx file
+
+   path.data = paste( data_path, '/water/water_demands/ALPS_1.5C_demand/demands_inp_files', sep = '' )
+   setwd(path.data)
+   data.demand <- read.xlsx("MESSAGE_demand_v3_20170913.xlsx", sheetName = "data")
+
+   names(data.demand) = c("MODEL", "SCENARIO", "REGION", "VARIABLE", "UNIT", "2000", "2005", "2010", "2020", "2030", "2040", "2050", "2060", "2070", "2080", "2090", "2100", "2110")
+
+   demand.mapping = data.frame(VARIABLE = c("Demand|Feedstocks", "Demand|Industrial Specific", "Demand|Industrial Thermal", "Demand|Non-Commercial", "Demand|Residential and Commercial Specific", "Demand|Residential and Commercial Thermal", "Demand|Transportation", "Demand|Shipping"),
+                 DEMAND = c("i_feed","i_spec", "i_therm","non-comm", "rc_spec", "rc_therm", "transport", "shipping"))
+
+
+   # turn demand table into long table format (relational database like) and convert from EJ to GWa as used in MESSAGE
+   data.table = inner_join(data.demand, demand.mapping) %>% select(one_of("DEMAND", "REGION"), starts_with("2")) %>% gather(key = "YEAR", value = "VALUE", 3:15) %>% mutate(VALUE = VALUE * 1000 / (8.76*3.6))
+
+   # nodes, periods and demand commodities to add to the model
+   node_list = levels(data.table$REGION)
+   node_list = node_list[node_list != "R11_GLB"]  ## remove the "R11_GLB" region from the list, since this is only for the shipping demand commodity and we want to treat this separately in the code below
+   ##period_list = seq(2020, 2110, 10)  ## use this command if we want to change model parameters from 2020 onward
+   period_list = seq(2030, 2110, 10)  ## use this command if we want to change model parameters from 2030 onward (i.e., if a slice was used up to 2020 in the scenario that this one is cloned from)
+   commodity_list = levels(data.table$DEMAND)
+   commodity_list = commodity_list[commodity_list != "shipping"]  ## remove the "shipping" commodity from the list, since this is only for the "R11_GLB" region and we want to treat this separately in the code below
+
+   #-------------------------------------------------------------------------------
+
+   # read technology investment costs from xlsx file
+   path.data.invcosts = paste( data_path, '/water/water_demands/ALPS_1.5C_demand/technology_inp_files', sep='' )
+   setwd(path.data.invcosts)
+
+   data.invcosts <- read.xlsx("granular-techs_cost_comparison_20170831_revAG_SDS.XLSX", sheetName = "NewCosts_fixed")
+
+   names(data.invcosts) = c("TECHNOLOGY", "REGION", "2005", "2010", "2020", "2030", "2040", "2050", "2060", "2070", "2080", "2090", "2100", "2110")
+
+   # turn demand table into long table format (relational database like)
+   data.table.invcosts = data.invcosts %>% select(one_of("TECHNOLOGY", "REGION"), starts_with("2")) %>% gather(key = "YEAR", value = "VALUE", 3:14)
+
+   # No need to update the nodes or periods because they are defined already above. However, the technology list is something new that needs to be read in here.
+   ##node_list = levels(data.invcosts$REGION)
+   ##period_list = seq(2030, 2110, 10)
+   technology_list = levels(data.invcosts$TECHNOLOGY)
+
+   #-------------------------------------------------------------------------------
+
+   # read technology fixed O&M costs from xlsx file
+   path.data.fomcosts = paste( data_path, '/water/water_demands/ALPS_1.5C_demand/technology_inp_files', sep = '' )
+   setwd(path.data.fomcosts)
+
+   data.fomcosts <- read.xlsx("granular-techs_cost_comparison_20170831_revAG_SDS.XLSX", sheetName = "NewFOMCosts_fixed")
+
+   names(data.fomcosts) = c("TECHNOLOGY", "REGION", "2005", "2010", "2020", "2030", "2040", "2050", "2060", "2070", "2080", "2090", "2100", "2110")
+
+   # turn demand table into long table format (relational database like)
+   data.table.fomcosts = data.fomcosts %>% select(one_of("TECHNOLOGY", "REGION"), starts_with("2")) %>% gather(key = "YEAR", value = "VALUE", 3:14)
+
+   # No need to update the nodes or periods because they are defined already above. However, the technology list is something new that needs to be read in here.
+   ##node_list = levels(data.fomcosts$REGION)
+   ##period_list = seq(2030, 2110, 10)
+   technology_list6 = levels(data.fomcosts$TECHNOLOGY)
+
+   #-------------------------------------------------------------------------------
+
+   # read technology variable O&M costs from xlsx file
+   path.data.vomcosts = paste( data_path, '/water/water_demands/ALPS_1.5C_demand/technology_inp_files', sep = '' )
+   setwd(path.data.vomcosts)
+
+   data.vomcosts <- read.xlsx("granular-techs_cost_comparison_20170831_revAG_SDS.XLSX", sheetName = "NewVOMCosts_fixed")
+
+   names(data.vomcosts) = c("TECHNOLOGY", "REGION", "2005", "2010", "2020", "2030", "2040", "2050", "2060", "2070", "2080", "2090", "2100", "2110")
+
+   # turn demand table into long table format (relational database like)
+   data.table.vomcosts = data.vomcosts %>% select(one_of("TECHNOLOGY", "REGION"), starts_with("2")) %>% gather(key = "YEAR", value = "VALUE", 3:14)
+
+   # No need to update the nodes or periods because they are defined already above. However, the technology list is something new that needs to be read in here.
+   ##node_list = levels(data.vomcosts$REGION)
+   ##period_list = seq(2030, 2110, 10)
+   technology_list7 = levels(data.vomcosts$TECHNOLOGY)
+
+   #-------------------------------------------------------------------------------
+
+   # read solar and wind intermittency assumptions from xlsx file
+   path.data.steps = paste( data_path, '/water/water_demands/ALPS_1.5C_demand/technology_inp_files', sep='' )
+   setwd(path.data.steps)
+
+   data.steps <- read.xlsx("solar_wind_intermittency_20170831.XLSX", sheetName = "steps_NEW")
+
+   names(data.steps) = c("TECHNOLOGY", "RELATION", "REGION", "2005", "2010", "2020", "2030", "2040", "2050", "2060", "2070", "2080", "2090", "2100", "2110")
+
+   # turn demand table into long table format (relational database like)
+   data.table.steps = data.steps %>% select(one_of("TECHNOLOGY", "RELATION", "REGION"), starts_with("2")) %>% gather(key = "YEAR", value = "VALUE", 4:15)
+
+   # No need to update the nodes or periods because they are defined already above. However, the technology list is something new that needs to be read in here.
+   ##node_list = levels(data.steps$REGION)
+   ##period_list = seq(2030, 2110, 10)
+   technology_list2 = levels(data.steps$TECHNOLOGY)
+   relation_list2 = levels(data.steps$RELATION)
+
+   #-------------------------------------------------------------------------------
+
+   # read solar and wind intermittency assumptions from xlsx file
+   path.data.oper = paste( data_path, '/water/water_demands/ALPS_1.5C_demand/technology_inp_files', sep = '' )
+   setwd(path.data.oper)
+
+   data.oper <- read.xlsx("solar_wind_intermittency_20170831.XLSX", sheetName = "oper_NEW")
+
+   names(data.oper) = c("TECHNOLOGY", "RELATION", "REGION", "2005", "2010", "2020", "2030", "2040", "2050", "2060", "2070", "2080", "2090", "2100", "2110")
+
+   # turn demand table into long table format (relational database like)
+   data.table.oper = data.oper %>% select(one_of("TECHNOLOGY", "RELATION", "REGION"), starts_with("2")) %>% gather(key = "YEAR", value = "VALUE", 4:15)
+
+   # No need to update the nodes or periods because they are defined already above. However, the technology list is something new that needs to be read in here.
+   ##node_list = levels(data.oper$REGION)
+   ##period_list = seq(2030, 2110, 10)
+   technology_list3 = levels(data.oper$TECHNOLOGY)
+   relation_list3 = levels(data.oper$RELATION)
+
+   #-------------------------------------------------------------------------------
+
+   # read solar and wind intermittency assumptions from xlsx file
+   path.data.resm = paste( data_path, '/water/water_demands/ALPS_1.5C_demand/technology_inp_files', sep = '' )
+   setwd(path.data.resm)
+
+   data.resm <- read.xlsx("solar_wind_intermittency_20170831.XLSX", sheetName = "resm_NEW")
+
+   names(data.resm) = c("TECHNOLOGY", "RELATION", "REGION", "2005", "2010", "2020", "2030", "2040", "2050", "2060", "2070", "2080", "2090", "2100", "2110")
+
+   # turn demand table into long table format (relational database like)
+   data.table.resm = data.resm %>% select(one_of("TECHNOLOGY", "RELATION", "REGION"), starts_with("2")) %>% gather(key = "YEAR", value = "VALUE", 4:15)
+
+   # No need to update the nodes or periods because they are defined already above. However, the technology list is something new that needs to be read in here.
+   ##node_list = levels(data.resm$REGION)
+   ##period_list = seq(2030, 2110, 10)
+   technology_list4 = levels(data.resm$TECHNOLOGY)
+   relation_list4 = levels(data.resm$RELATION)
+
+   #-------------------------------------------------------------------------------
+
+   # read useful level fuel potential contribution assumptions from xlsx file
+   path.data.uefuel = paste( data_path, '/water/water_demands/ALPS_1.5C_demand/technology_inp_files', sep = '' )
+   setwd(path.data.uefuel)
+
+   data.uefuel <- read.xlsx("useful_level_fuel_potential_contribution_20170907.XLSX", sheetName = "UE_constraints_NEW")
+
+   names(data.uefuel) = c("TECHNOLOGY", "RELATION", "REGION", "2005", "2010", "2020", "2030", "2040", "2050", "2060", "2070", "2080", "2090", "2100", "2110")
+
+   # turn demand table into long table format (relational database like)
+   data.table.uefuel = data.uefuel %>% select(one_of("TECHNOLOGY", "RELATION", "REGION"), starts_with("2")) %>% gather(key = "YEAR", value = "VALUE", 4:15)
+
+   # No need to update the nodes or periods because they are defined already above. However, the technology list is something new that needs to be read in here.
+   ##node_list = levels(data.uefuel$REGION)
+   ##period_list = seq(2030, 2110, 10)
+   technology_list5 = levels(data.uefuel$TECHNOLOGY)
+   relation_list5 = levels(data.uefuel$RELATION)
+
+   #-------------------------------------------------------------------------------
+
+   # insert new demands into the model - 7 standard demands in 11 standard regions
+   for (node in node_list) {
+     for (commodity in commodity_list) {
+     for (year in period_list) {
+       ixDS$add_par("demand",paste(node,commodity,"useful",as.character(year),"year",sep='.'),filter(data.table, DEMAND == commodity & REGION == node & YEAR == as.character(year))$VALUE,"GWa")
+     }
+     }
+   }
+
+
+   # insert new demands into the model - shipping demands in GLB region only
+   for (year in period_list) {
+     ixDS$add_par("demand",paste("R11_GLB","shipping","useful",as.character(year),"year",sep='.'),filter(data.table, DEMAND == "shipping" & REGION == "R11_GLB" & YEAR == as.character(year))$VALUE,"GWa")
+   }
+
+
+   # insert emission constraint leading to different carbon budgets (2011-2100)
+   #
+   # But actually in MESSAGEix, the budget is defined from the starting year up to 2110, and in terms of annual average emissions over that timeframe
+   # 400 GtCO2 (1.5 deg C): 1800 MtCeq is for 2011-2110
+   # 400 GtCO2 (1.5 deg C): 433 MtCeq is for 2021-2110
+   #
+   #ixDS$add_par("bound_emission",paste("World", "TCE", "all", "cumulative", sep='.'), 1150, "tC")
+   #ixDS$add_par("bound_emission",paste("World", "TCE", "all", "cumulative", sep='.'), 450, "tC")  ## This budget (450 MtC; for 2021-2110) produced an infeasible solution on 2017-08-09 using Simon's initial demands from 2017-08-06.
+   #ixDS$add_par("bound_emission",paste("World", "TCE", "all", "cumulative", sep='.'), 1800, "tC")  ## This budget (for 2021-2110) was run in scenario '2C_NPi2020_noBECCS_V1' to test that a more relaxed budget would be feasible in the current set-up (low demands from Simon on 2017-08-06 and also no BECCS).
+   #ixDS$add_par("bound_emission",paste("World", "TCE", "all", "cumulative", sep='.'), 1200, "tC")  ## This budget (for 2021-2110) was run in scenario 'b2C_NPi2020_noBECCS_V1' to test that a more relaxed budget would be feasible in the current set-up (low demands from Simon on 2017-08-06 and also no BECCS).
+   ##ixDS$add_par("bound_emission",paste("World", "TCE", "all", "cumulative", sep='.'), 800, "tC")  ## Budget of 800 MtC annual average per year (for 2021-2110) was found to be too tight in an earlier run of scenario 'b2C_NPi2020_noBECCS_V2'. Infeasible solution; model did not solve.
+   #ixDS$add_par("bound_emission",paste("World", "TCE", "all", "cumulative", sep='.'), 1000, "tC")  ## This budget (for 2021-2110) was run in scenarios 'b2C_NPi2020_noBECCS_V2' and similar up to 'V6' to get something around 1.5C. (The same budget was attempted for the initial V7 run, but the removal of Fossil CCS made it infeasible.) Median temperature is around 1.57C in 2050 and 1.31C in 2100. Median radiative forcing (total) is 1.81 W/m2 in 2100. (low demands from Simon on 2017-08-06 and also no BECCS).
+   #ixDS$add_par("bound_emission",paste("World", "TCE", "all", "cumulative", sep='.'), 1200, "tC")  ## This budget (for 2021-2110) was run in scenarios 'b2C_NPi2020_noBECCS_V7' to get something around 1.5C. (A budget of 1000 was initially attempted for V7, but the lack of Fossil CCS made it infeasible.) Median temperature is around 1.60C in 2050 and 1.34 in 2100. Median radiative forcing (total) is 1.89 W/m2 in 2100. (low demands from Simon on 2017-08-31 and also no BECCS and no Fossil CCS).
+   #ixDS$add_par("bound_emission",paste("World", "TCE", "all", "cumulative", sep='.'), 1400, "tC")  ## This budget (for 2021-2110) was run in scenarios 'b2C_NPi2020_noBECCS_V7a' to 'V8' to get something around 1.5C. (A budget of 1200 was initially used for V7, but the carbon prices were ridiculously higher.) Median temperature is around 1.61C in 2050 and 1.37 in 2100. Median radiative forcing (total) is 1.94 W/m2 in 2100. (low demands from Simon on 2017-09-08 and also no BECCS and no Fossil CCS).
+   #ixDS$add_par("bound_emission",paste("World", "TCE", "all", "cumulative", sep='.'), 2500, "tC")  ## This budget (for 2021-2110) was run in scenarios '2C_NPi2020_noBECCS_V2' to try and get something nearer to 2C by end of century. Median temperature is around 1.66C in 2050 and 1.53C in 2100. Median radiative forcing (total) is 2.22 W/m2 in 2100. (low demands from Simon on 2017-08-06 and also no BECCS).
+   #ixDS$add_par("bound_emission",paste("World", "TCE", "all", "cumulative", sep='.'), 3500, "tC")  ## This budget (for 2021-2110) was run in scenarios '2C_NPi2020_noBECCS_V3' to try and get something nearer to 2C by end of century. Median temperature is around 1.71C in 2050 and 1.67C in 2100. Median radiative forcing (total) is 2.48 W/m2 in 2100. (low demands from Simon on 2017-08-06 and also no BECCS).
+   #ixDS$add_par("bound_emission",paste("World", "TCE", "all", "cumulative", sep='.'), 4000, "tC")  ## This budget (for 2021-2110) was run in scenarios '2C_NPi2020_noBECCS_V4' to try and get something nearer to 2C by end of century. Median temperature is around 1.73C in 2050, peaking at 1.79C in 2070/2080, and then coming back down to 1.74C in 2100. Median radiative forcing (total) is 2.6 W/m2 in 2100. (low demands from Simon on 2017-08-06 and also no BECCS).
+
+   # exclude BECCS by constrainig activity of 'bco2_tr_dis' technology to 0 in all years
+   for (node in node_list) {
+     for (year in period_list) {
+     ixDS$add_par("bound_activity_up",paste(node, "bco2_tr_dis", as.character(year), "M1", "year", sep='.'), 0, "GWa")
+     }
+   }
+
+   # exclude Fossil CCS by constrainig activity of 'co2_tr_dis' technology to 0 in all years
+   for (node in node_list) {
+     for (year in period_list) {
+     ixDS$add_par("bound_activity_up",paste(node, "co2_tr_dis", as.character(year), "M1", "year", sep='.'), 0, "GWa")
+     }
+   }
+
+   # adjust limit to electrify transport to XX% of useful energy (negative number, i.e. -0.9 refers to 90%)
+   for (node in node_list) {
+     for (year in period_list) {
+     #ixDS$add_par("relation_activity",paste("UE_transport_electric", node, as.character(year), node, "useful_transport", as.character(year), "M1", sep='.'), -0.9, "-")
+     ixDS$add_par("relation_activity",paste("UE_transport_electric", node, as.character(year), node, "useful_transport", as.character(year), "M1", sep='.'), -0.8, "-")
+     }
+   }
+
+   # adjust limit for hydrogen in transport to 100% of useful energy (negative number, i.e. -1.0 refers to 100%)
+   for (node in node_list) {
+     for (year in period_list) {
+     ixDS$add_par("relation_activity",paste("UE_transport_fc", node, as.character(year), node, "useful_transport", as.character(year), "M1", sep='.'), -1.0, "-")
+     }
+   }
+
+   # adjust efficiency of the hydrogen transport technology to make it more efficient; a value of 0.45 means that the hydrogen transport technology is 2.22 times more efficient than the liquid fuel combustion technology (0.45 = 1/2.22)
+   # but only change parameters for vintage-activity year combinations that are currently specified for this technology in the model
+   technology = "h2_fc_trp"
+   for (node in node_list) {
+    for (year_vtg in period_list) {
+     if (any(class(tryCatch((ixDS$years_active(node, technology, year_vtg)), error = function(e) e)) == "error")) {
+       print(paste(node, technology, as.character(year_vtg), sep='.'))
+       print('Error: technology does not exist in this region for this vintage year')
+     } else {
+       years_tec_active = ixDS$years_active(node, technology, year_vtg)
+       for (year_act in years_tec_active) {
+       #print(ixDS$add_par("input",paste(node, technology, as.character(year_vtg), as.character(year_act), "M1", node, "lh2", "final", "year", "year", sep='.'), 0.45, "GWa"))
+       ixDS$add_par("input",paste(node, technology, as.character(year_vtg), as.character(year_act), "M1", node, "lh2", "final", "year", "year", sep='.'), 0.45, "GWa")
+       }
+     }
+    }
+   }
+
+   # # adjust investment cost of the hydrogen transport technology to make it less expensive; 'h2_fc_trp' is the only end-use transport technology with an explicit 'inv_cost' on it; all others have an indirect cost that comes in through the 'weight_trp' relation
+   # # but only change parameters for vintage years that are currently specified for this technology in the model
+   # technology = "h2_fc_trp"
+   # for (node in node_list) {
+   #   for (year_vtg in period_list) {
+   #     if (any(class(tryCatch((ixDS$years_active(node, technology, year_vtg)), error = function(e) e)) == "error")) {
+   #       print(paste(node, technology, as.character(year_vtg), sep='.'))
+   #       print('Error: technology does not exist in this region for this vintage year')
+   #     } else {
+   #       ixDS$add_par("inv_cost",paste(node, technology, as.character(year_vtg), sep='.'), 1.0, "USD/GWa")  # specify units in 'USD/GWa', even though they are actually $/kW within the model
+   #     }
+   #   }
+   # }
+
+   # adjust investment costs of a set of technologies that are defined in another XLS file
+   # but only change parameters for vintage years that are currently specified for this technology in the model
+   #technology = "h2_fc_trp"
+   for (node in node_list) {
+     for (year_vtg in period_list) {
+     for (technology in technology_list) {
+       if (any(class(tryCatch((ixDS$years_active(node, technology, year_vtg)), error = function(e) e)) == "error")) {
+       print(paste(node, technology, as.character(year_vtg), sep='.'))
+       print('Error: technology does not exist in this region for this vintage year')
+       } else {
+       ixDS$add_par("inv_cost",paste(node, technology, as.character(year_vtg), sep='.'), (filter(data.table.invcosts, REGION == node & TECHNOLOGY == technology & YEAR == as.character(year_vtg))$VALUE), "USD/GWa")  # specify units in 'USD/GWa', even though they are actually $/kW within the model
+       #print(paste(node, technology, as.character(year_vtg), sep='.'),(filter(data.table.invcosts, REGION == node & TECHNOLOGY == technology & YEAR == as.character(year_vtg))$VALUE), digits=4, quote = TRUE)
+       }
+     }
+     }
+   }
+
+   # adjust fixed O&M costs of a set of technologies that are defined in another XLS file
+   # but only change parameters for vintage years that are currently specified for this technology in the model
+   for (node in node_list) {
+     for (year_vtg in period_list) {
+     for (technology in technology_list6) {
+       if (any(class(tryCatch((ixDS$years_active(node, technology, year_vtg)), error = function(e) e)) == "error")) {
+       print(paste(node, technology, as.character(year_vtg), sep='.'))
+       print('Error: technology does not exist in this region for this vintage-activity year combination')
+       } else {
+       #ixDS$add_par("fix_cost",paste(node, technology, as.character(year_vtg), as.character(year_act), sep='.'), (filter(data.table.fomcosts, REGION == node & TECHNOLOGY == technology & YEAR == as.character(year_act))$VALUE), "USD/GWa")  # specify units in 'USD/GWa', even though they are actually $/kWyr/yr within the model
+       years_tec_active = ixDS$years_active(node, technology, year_vtg)
+       for (year_act in years_tec_active) {
+         #print(ixDS$add_par("fix_cost",paste(node, technology, as.character(year_vtg), as.character(year_act), sep='.'), (filter(data.table.fomcosts, REGION == node & TECHNOLOGY == technology & YEAR == as.character(year_act))$VALUE), "USD/GWa"))
+         ixDS$add_par("fix_cost",paste(node, technology, as.character(year_vtg), as.character(year_act), sep='.'), (filter(data.table.fomcosts, REGION == node & TECHNOLOGY == technology & YEAR == as.character(year_act))$VALUE), "USD/GWa")
+       }
+       }
+     }
+     }
+   }
+
+   # adjust variable O&M costs of a set of technologies that are defined in another XLS file
+   # but only change parameters for vintage years that are currently specified for this technology in the model
+   for (node in node_list) {
+     for (year_vtg in period_list) {
+     for (technology in technology_list7) {
+       if (any(class(tryCatch((ixDS$years_active(node, technology, year_vtg)), error = function(e) e)) == "error")) {
+       print(paste(node, technology, as.character(year_vtg), sep='.'))
+       print('Error: technology does not exist in this region for this vintage-activity year combination')
+       } else {
+       #ixDS$add_par("var_cost",paste(node, technology, as.character(year_vtg), as.character(year_act), "M1", "year", sep='.'), (filter(data.table.fomcosts, REGION == node & TECHNOLOGY == technology & YEAR == as.character(year_act))$VALUE), "USD/GWa")  # specify units in 'USD/GWa', even though they are actually $/kWyr/yr within the model
+       years_tec_active = ixDS$years_active(node, technology, year_vtg)
+       for (year_act in years_tec_active) {
+         #print(ixDS$add_par("var_cost",paste(node, technology, as.character(year_vtg), as.character(year_act), "M1", "year", sep='.'), (filter(data.table.fomcosts, REGION == node & TECHNOLOGY == technology & YEAR == as.character(year_act))$VALUE), "USD/GWa"))
+         ixDS$add_par("var_cost",paste(node, technology, as.character(year_vtg), as.character(year_act), "M1", "year", sep='.'), (filter(data.table.fomcosts, REGION == node & TECHNOLOGY == technology & YEAR == as.character(year_act))$VALUE), "USD/GWa")
+       }
+       }
+     }
+     }
+   }
+
+   # adjust wind and solar PV resource steps (contribution to total electricity generation) for a set of technologies that are defined in another XLS file
+   # these changes allow for greater contribution of intermittent solar and wind to total electricity generation
+   for (node in node_list) {
+     for (year_act in period_list) {
+     for (relation in relation_list2) {
+       for (technology in technology_list2) {
+       ixDS$add_par("relation_activity",paste(relation, node, as.character(year_act), node, technology, as.character(year_act), "M1", sep='.'), (filter(data.table.steps, REGION == node & TECHNOLOGY == technology & RELATION == relation & YEAR == as.character(year_act))$VALUE), "-")
+       #print(paste(node, technology, relation, as.character(year_act), sep='.'),(filter(data.table.steps, REGION == node & TECHNOLOGY == technology & RELATION == relation & YEAR == as.character(year_act))$VALUE))
+
+       # if (any(class(tryCatch((ixDS$years_active(node, technology, year_act)), error = function(e) e)) == "error")) {
+       #   print(paste(node, technology, as.character(year_act), sep='.'))
+       #   print('Error: technology does not exist in this region for this activity year')
+       # } else {
+       #   ixDS$add_par("relation_activity",paste(relation, node, as.character(year_act), node, technology, as.character(year_act), "M1", sep='.'), (filter(data.table.steps, REGION == node & TECHNOLOGY == technology & RELATION == relation & YEAR == as.character(year_act))$VALUE), "-")
+       #   #print(paste(node, technology, relation, as.character(year_act), sep='.'),(filter(data.table.steps, REGION == node & TECHNOLOGY == technology & RELATION == relation & YEAR == as.character(year_act))$VALUE))
+       # }
+
+       }
+     }
+     }
+   }
+
+   # adjust wind and solar PV operating reserve requirements (amount of flexible generation that needs to be run for every unit of intermittent solar and wind => variable renewables <0, non-dispatchable thermal 0, flexible >0); done for a set of technologies that are defined in another XLS file
+   # also adjust the contribution of electric transport technologies to the operating reserves, increasing the amount they can contribute (vehicle-to-grid)
+   # these changes reduce the effective cost of building and running intermittent solar and wind plants, since the amount of back-up capacity built is less than before
+   for (node in node_list) {
+     for (year_act in period_list) {
+     for (relation in relation_list3) {
+       for (technology in technology_list3) {
+       ixDS$add_par("relation_activity",paste(relation, node, as.character(year_act), node, technology, as.character(year_act), "M1", sep='.'), (filter(data.table.oper, REGION == node & TECHNOLOGY == technology & RELATION == relation & YEAR == as.character(year_act))$VALUE), "-")
+       #print(paste(node, technology, relation, as.character(year_act), sep='.'),(filter(data.table.oper, REGION == node & TECHNOLOGY == technology & RELATION == relation & YEAR == as.character(year_act))$VALUE))
+
+       # if (any(class(tryCatch((ixDS$years_active(node, technology, year_act)), error = function(e) e)) == "error")) {
+       #   print(paste(node, technology, as.character(year_act), sep='.'))
+       #   print('Error: technology does not exist in this region for this activity year')
+       # } else {
+       #   #ixDS$add_par("relation_activity",paste(relation, node, as.character(year_act), node, technology, as.character(year_act), "M1", sep='.'), (filter(data.table.oper, REGION == node & TECHNOLOGY == technology & RELATION == relation & YEAR == as.character(year_act))$VALUE), "-")
+       #   print(paste(node, technology, relation, as.character(year_act), sep='.'),(filter(data.table.oper, REGION == node & TECHNOLOGY == technology & RELATION == relation & YEAR == as.character(year_act))$VALUE))
+       # }
+
+       }
+     }
+     }
+   }
+
+   # adjust wind and solar PV reserve margin requirements (amount of firm capacity that needs to be run to meet peak load and contingencies; intermittent solar and wind do not contribute a full 100% to the reserve margin); done for a set of technologies that are defined in another XLS file
+   # these changes allow for greater contribution of intermittent solar and wind to total electricity generation
+   for (node in node_list) {
+     for (year_act in period_list) {
+     for (relation in relation_list4) {
+       for (technology in technology_list4) {
+       ixDS$add_par("relation_activity",paste(relation, node, as.character(year_act), node, technology, as.character(year_act), "M1", sep='.'), (filter(data.table.resm, REGION == node & TECHNOLOGY == technology & RELATION == relation & YEAR == as.character(year_act))$VALUE), "-")
+       #print(paste(node, technology, relation, as.character(year_act), sep='.'),(filter(data.table.resm, REGION == node & TECHNOLOGY == technology & RELATION == relation & YEAR == as.character(year_act))$VALUE))
+       }
+     }
+     }
+   }
+
+   # adjust limits to potential fuel-specific contributions at useful energy level (in each end-use sector separately); done for a set of technologies and relations that are defined in another XLS file
+   for (node in node_list) {
+     for (year_act in period_list) {
+     for (relation in relation_list5) {
+       for (technology in technology_list5) {
+       #ixDS$add_par("relation_activity",paste(relation, node, as.character(year_act), node, technology, as.character(year_act), "M1", sep='.'), (filter(data.table.uefuel, REGION == node & TECHNOLOGY == technology & RELATION == relation & YEAR == as.character(year_act))$VALUE), "-")
+       #print(paste(node, technology, relation, as.character(year_act), sep='.'),(filter(data.table.uefuel, REGION == node & TECHNOLOGY == technology & RELATION == relation & YEAR == as.character(year_act))$VALUE))
+       #print(paste(node, technology, relation, as.character(year_act), sep='.'))
+
+       if (any(class(tryCatch(ixDS$add_par("relation_activity",paste(relation, node, as.character(year_act), node, technology, as.character(year_act), "M1", sep='.'), (filter(data.table.uefuel, REGION == node & TECHNOLOGY == technology & RELATION == relation & YEAR == as.character(year_act))$VALUE), "-"), error = function(e) e)) == "error")) {
+         #print(paste(node, technology, relation, as.character(year_act), sep='.'))
+         #print('Error: technology either does not exist in this region for this activity year or it does not write into this relation')
+       } else {
+         ixDS$add_par("relation_activity",paste(relation, node, as.character(year_act), node, technology, as.character(year_act), "M1", sep='.'), (filter(data.table.uefuel, REGION == node & TECHNOLOGY == technology & RELATION == relation & YEAR == as.character(year_act))$VALUE), "-")
+       }
+
+       }
+     }
+     }
+   }
+
+   ### Remove any "bound_activity_up" values that might exist for the technology 'h2_fc_trp'; it seems that some are floating around in the base CD-LINKS scenario that we use as a starting point for our work.
+   # To get this working properly and without errors, I add an upper bound on the technology in all regions and years, and then remove it immediately afterwards (in all regions and years)
+   technology = "h2_fc_trp"
+   for (node in node_list) {
+     for (year in period_list) {
+     #print(paste(node, technology, as.character(year), "M1", "year", sep='.'))
+     ixDS$add_par("bound_activity_up",paste(node, technology, as.character(year), "M1", "year", sep='.'), 0, "GWa")
+     ixDS$remove_par("bound_activity_up",paste(node, technology, as.character(year), "M1", "year", sep='.'))
+     }
+   }
+   ### This block of code should have worked in the for loop above for removing parameters, but I couldn't get it working.
+     # if (any(class(tryCatch((ixDS$remove_par("bound_activity_up",paste(node, technology, as.character(year), "M1", "year", sep='.'))), error = function(e) print('...'))) == "error")) {
+     #   print(paste(node, technology, as.character(year), "M1", "year", sep='.'))
+     #   print('Error: technology does not have an upper bound in this region for this vintage year, thus none added')
+     # } else {
+     #   ixDS$remove_par("bound_activity_up",paste(node, technology, as.character(year), "M1", "year", sep='.'))
+     # }
+
+   ### Some commands for testing. Can be deleted.
+   #ixDS$remove_par("bound_activity_up",paste(node, "h2_fc_trp", as.character(year), "M1", "year", sep='.'))
+   #ixDS$remove_par("bound_activity_up",paste("R11_AFR", "h2_fc_trp", as.character(2060), "M1", "year", sep='.'))
+   #any(class(tryCatch((ixDS$remove_par("bound_activity_up",paste("R11_AFR", technology, 2030, "M1", "year", sep='.'))), error = function(e) print('...'))) == "error")
+
+   # Exclude ethanol fuel cell transport technology by constraining activity to 0 in all years
+   # Need to do this even though the Web UI does not show that there are upper bounds on this technology in any years.
+   for (node in node_list) {
+     for (year in period_list) {
+     ixDS$add_par("bound_activity_up",paste(node, "eth_fc_trp", as.character(year), "M1", "year", sep='.'), 0, "GWa")
+     }
+   }
+
+   # Exclude methanol fuel cell transport technology by constraining activity to 0 in all years
+   # Need to do this even though the Web UI does not show that there are upper bounds on this technology in any years.
+   for (node in node_list) {
+     for (year in period_list) {
+     ixDS$add_par("bound_activity_up",paste(node, "meth_fc_trp", as.character(year), "M1", "year", sep='.'), 0, "GWa")
+     }
+   }
+
+
+   # Increase the initial starting point value for activity growth bounds on the electric transport technology
+   years_subset = c(2030)
+   for (node in node_list) {
+     for (year in years_subset) {
+     ixDS$add_par("initial_activity_up",paste(node, "elec_trp", as.character(year), "year", sep='.'), 90, "GWa")
+     }
+   }
+
+   # Increase the initial starting point value for activity growth bounds on the hydrogen fuel cell technology in transport
+   years_subset = c(2030, 2040, 2050)
+   technology = "h2_fc_trp"
+   for (node in node_list) {
+     for (year in years_subset) {
+     #ixDS$add_par("initial_activity_up",paste(node, technology, as.character(year), "year", sep='.'), 90, "GWa")
+
+     #if (any(class(tryCatch(ixDS$add_par("initial_activity_up",paste(node, technology, as.character(year), "year", sep='.'), 90, "GWa"), error = function(e) e)) == "error")) {
+     if (any(class(tryCatch((ixDS$years_active(node, technology, year)), error = function(e) e)) == "error")) {
+       print(paste(node, technology, as.character(year), sep='.'))
+       print('Error: technology does not exist in this region for this activity year')
+     } else {
+       ixDS$add_par("initial_activity_up",paste(node, technology, as.character(year), "year", sep='.'), 90, "GWa")
+     }
+
+     }
+   }
+
+   # Increase the initial starting point value for activity growth bounds on the hydrogen fuel cell technology in industry
+   years_subset = c(2030, 2040, 2050)
+   technology = "h2_fc_I"
+   for (node in node_list) {
+     for (year in years_subset) {
+     #ixDS$add_par("initial_activity_up",paste(node, technology, as.character(year), "year", sep='.'), 90, "GWa")
+
+     #if (any(class(tryCatch(ixDS$add_par("initial_activity_up",paste(node, technology, as.character(year), "year", sep='.'), 90, "GWa"), error = function(e) e)) == "error")) {
+     if (any(class(tryCatch((ixDS$years_active(node, technology, year)), error = function(e) e)) == "error")) {
+       print(paste(node, technology, as.character(year), sep='.'))
+       print('Error: technology does not exist in this region for this activity year')
+     } else {
+       ixDS$add_par("initial_activity_up",paste(node, technology, as.character(year), "year", sep='.'), 90, "GWa")
+     }
+
+     }
+   }
+
+   # Increase the initial starting point value for activity growth bounds on the hydrogen fuel cell technology in industry
+   years_subset = c(2030, 2040, 2050)
+   technology = "h2_fc_RC"
+   for (node in node_list) {
+     for (year in years_subset) {
+     #ixDS$add_par("initial_activity_up",paste(node, technology, as.character(year), "year", sep='.'), 90, "GWa")
+
+     #if (any(class(tryCatch(ixDS$add_par("initial_activity_up",paste(node, technology, as.character(year), "year", sep='.'), 90, "GWa"), error = function(e) e)) == "error")) {
+     if (any(class(tryCatch((ixDS$years_active(node, technology, year)), error = function(e) e)) == "error")) {
+       print(paste(node, technology, as.character(year), sep='.'))
+       print('Error: technology does not exist in this region for this activity year')
+     } else {
+       ixDS$add_par("initial_activity_up",paste(node, technology, as.character(year), "year", sep='.'), 90, "GWa")
+     }
+
+     }
+   }
+
+   # adjust efficiency of the hydrogen fuel cell co-generation technology (electricity + heat) in the RES-COM sector (satisfying both RC-specific and RC-thermal useful demands), in order to make it more efficient; a value of 2.1 means the technology has a combined efficiency (electricity + heat) of 0.86 (2.1 units of hydrogen input produces 1.0 units of electricity and 0.80 units of heat => thus 1.8/2.1 = 0.86)
+   # but only change parameters for vintage-activity year combinations that are currently specified for this technology in the model
+   technology = "h2_fc_RC"
+   for (node in node_list) {
+     for (year_vtg in period_list) {
+     if (any(class(tryCatch((ixDS$years_active(node, technology, year_vtg)), error = function(e) e)) == "error")) {
+       print(paste(node, technology, as.character(year_vtg), sep='.'))
+       print('Error: technology does not exist in this region for this vintage year')
+     } else {
+       years_tec_active = ixDS$years_active(node, technology, year_vtg)
+       for (year_act in years_tec_active) {
+       #print(ixDS$add_par("input",paste(node, technology, as.character(year_vtg), as.character(year_act), "M1", node, "hydrogen", "final", "year", "year", sep='.'), 2.1, "GWa"))
+       ixDS$add_par("input",paste(node, technology, as.character(year_vtg), as.character(year_act), "M1", node, "hydrogen", "final", "year", "year", sep='.'), 2.1, "GWa")
+       }
+     }
+     }
+   }
+
+   # adjust efficiency of the hydrogen fuel cell co-generation technology (electricity + heat) in the IND sector (satisfying both IND-specific and IND-thermal useful demands), in order to make it more efficient; a value of 2.0 means the technology has a combined efficiency (electricity + heat) of 0.86 (2.0 units of hydrogen input produces 1.0 units of electricity and 0.71 units of heat => thus 1.71/2.0 = 0.86)
+   # but only change parameters for vintage-activity year combinations that are currently specified for this technology in the model
+   technology = "h2_fc_I"
+   for (node in node_list) {
+     for (year_vtg in period_list) {
+     if (any(class(tryCatch((ixDS$years_active(node, technology, year_vtg)), error = function(e) e)) == "error")) {
+       print(paste(node, technology, as.character(year_vtg), sep='.'))
+       print('Error: technology does not exist in this region for this vintage year')
+     } else {
+       years_tec_active = ixDS$years_active(node, technology, year_vtg)
+       for (year_act in years_tec_active) {
+       #print(ixDS$add_par("input",paste(node, technology, as.character(year_vtg), as.character(year_act), "M1", node, "hydrogen", "final", "year", "year", sep='.'), 2.1, "GWa"))
+       ixDS$add_par("input",paste(node, technology, as.character(year_vtg), as.character(year_act), "M1", node, "hydrogen", "final", "year", "year", sep='.'), 2.1, "GWa")
+       }
+     }
+     }
+   }
+
+
+   # Increase the initial starting point value for activity growth bounds on the solar PV technology (centralized generation)
+   # Only do this for a subset of the regions for which there are currently "bound_activity_up" (formerly "mpa up") values defined. We don't want to specify an "initial_activity_up" for a technology that does not have a "bound_activity_up".
+   years_subset = c(2030, 2040, 2050)
+   node_subset = c("R11_CPA","R11_FSU","R11_LAM","R11_MEA","R11_NAM","R11_PAS")
+   technology = "solar_pv_ppl"
+   for (node in node_subset) {
+     for (year in years_subset) {
+     #ixDS$add_par("initial_activity_up",paste(node, technology, as.character(year), "year", sep='.'), 90, "GWa")
+
+     #if (any(class(tryCatch(ixDS$add_par("initial_activity_up",paste(node, technology, as.character(year), "year", sep='.'), 90, "GWa"), error = function(e) e)) == "error")) {
+     if (any(class(tryCatch((ixDS$years_active(node, technology, year)), error = function(e) e)) == "error")) {
+       print(paste(node, technology, as.character(year), sep='.'))
+       print('Error: technology does not exist in this region for this activity year')
+     } else {
+       #print(paste(node, technology, as.character(year), sep='.'))
+       ixDS$add_par("initial_activity_up",paste(node, technology, as.character(year), "year", sep='.'), 90, "GWa")
+     }
+
+     }
+   }
+
+
+   # Increase the initial starting point value for capacity growth bounds on the solar PV technology (centralized generation)
+   years_subset = c(2030, 2040, 2050)
+   technology = "solar_pv_ppl"
+   for (node in node_list) {
+     for (year in years_subset) {
+     #ixDS$add_par("initial_new_capacity_up",paste(node, technology, as.character(year), sep='.'), 10, "GW")
+
+     #if (any(class(tryCatch(ixDS$add_par("initial_new_capacity_up",paste(node, technology, as.character(year), sep='.'), 10, "GW"), error = function(e) e)) == "error")) {
+     if (any(class(tryCatch((ixDS$years_active(node, technology, year)), error = function(e) e)) == "error")) {
+       print(paste(node, technology, as.character(year), sep='.'))
+       print('Error: technology does not exist in this region for this vintage year')
+     } else {
+       #print(paste(node, technology, as.character(year), sep='.'))
+       ixDS$add_par("initial_new_capacity_up",paste(node, technology, as.character(year), sep='.'), 10, "GW")
+     }
+
+     }
+   }
+
+   # read demands from xlsx file
+
+   path.data = paste( data_path, '/water/water_demands/ALPS_1.5C_demand/demands_inp_files', sep='' )
+   wds = getwd()
+   setwd(path.data)
+   data.demand <- read.xlsx("MESSAGE_demand_v3_20170913.xlsx", sheetName = "data")
+
+   names(data.demand) = c("MODEL", "SCENARIO", "REGION", "VARIABLE", "UNIT", "2000", "2005", "2010", "2020", "2030", "2040", "2050", "2060", "2070", "2080", "2090", "2100", "2110")
+
+   demand.mapping = data.frame(VARIABLE = c("Demand|Feedstocks", "Demand|Industrial Specific", "Demand|Industrial Thermal", "Demand|Non-Commercial", "Demand|Residential and Commercial Specific", "Demand|Residential and Commercial Thermal", "Demand|Transportation", "Demand|Shipping"),
+                 DEMAND = c("i_feed","i_spec", "i_therm","non-comm", "rc_spec", "rc_therm", "transport", "shipping"))
+
+
+   # turn demand table into long table format (relational database like) and convert from EJ to GWa as used in MESSAGE
+   data.table = inner_join(data.demand, demand.mapping) %>% select(one_of("DEMAND", "REGION"), starts_with("2")) %>% gather(key = "YEAR", value = "VALUE", 3:15) %>% mutate(VALUE = VALUE * 1000 / (8.76*3.6))
+
+   # nodes, periods and demand commodities to add to the model
+   node_list = levels(data.table$REGION)
+   node_list = node_list[node_list != "R11_GLB"]  ## remove the "R11_GLB" region from the list, since this is only for the shipping demand commodity and we want to treat this separately in the code below
+   ##period_list = seq(2020, 2110, 10)  ## use this command if we want to change model parameters from 2020 onward
+   period_list = seq(2030, 2110, 10)  ## use this command if we want to change model parameters from 2030 onward (i.e., if a slice was used up to 2020 in the scenario that this one is cloned from)
+   commodity_list = levels(data.table$DEMAND)
+   commodity_list = commodity_list[commodity_list != "shipping"]  ## remove the "shipping" commodity from the list, since this is only for the "R11_GLB" region and we want to treat this separately in the code below
+
+   demand_factor = 1
+
+   # insert new demands into the model - 7 standard demands in 11 standard regions
+   res = lapply( node_list, function(node){
+
+     lapply( commodity_list, function(commodity){
+
+       lapply( period_list, function( year ){
+
+         ixDS$add_par("demand",paste(node,commodity,"useful",as.character(year),"year",sep='.'),filter(data.table, DEMAND == commodity & REGION == node & YEAR == as.character(year))$VALUE * demand_factor,"GWa")
+
+         } )
+
+       } )
+
+     } )
+
+   setwd(wds)
+
+   }
+
+ #-------------------------------------------------------------------------------------------------------
+ # Add water technologies using historical ppl data and pre-defined regional shares
+ #-------------------------------------------------------------------------------------------------------
+
+ print('Initializing water technologies')
+
+ # Cooling technologies:
+ # Once through (fresh and saline), closed-loop (fresh) and air cooled options considered
+ # No air cooling options for nuclear and CCS
+ # Only consider the cooling technologies that correspond to power plant technologies in the MESSAGE model.
+ cooling_technologies_to_consider = c( apply( cooltech_cost_and_shares_ssp_msg_raw.df[ which( as.character( cooltech_cost_and_shares_ssp_msg_raw.df$utype ) %in% as.character( message_technologies_with_water_data )  ), c('utype','cooling') ], 1, paste, collapse='__') )
+
+ # Cooling commodities by power plant type - output commodity for cooling technology
+ cooling_commodities = unlist( lapply( 1:length( unique( cooltech_cost_and_shares_ssp_msg_raw.df$utype[ as.numeric( names(cooling_technologies_to_consider) )  ] ) ), function(x){ paste( 'cooling', unique( cooltech_cost_and_shares_ssp_msg_raw.df$utype[ as.numeric(names(cooling_technologies_to_consider)) ] )[x], sep='__' ) } ) )
+ ret = lapply( 1:length(cooling_commodities), function(x){ ixDS$add_set( 'commodity', as.character( cooling_commodities[x] ) ) } ) # Add commodity to ix DB
+
+ # Water sources - input commodity for cooling technology
+ water_supply_type = unique(tech_water_performance_ssp_msg_raw.df$water_supply_type[ which( as.character(tech_water_performance_ssp_msg_raw.df$technology_name) %in% message_technologies_with_water_data | as.character(tech_water_performance_ssp_msg_raw.df$technology_group) == 'cooling' ) ] )
+ water_supply_type = c( water_supply_type[-1*which(is.na(water_supply_type))] )
+ ret = lapply( c( as.character( water_supply_type ), 'saline_supply_ppl' ), function(x){ ixDS$add_set( 'commodity', x ) } ) # Add commodity to ix DB
+
+ # Water supply and cooling as a new level
+ water_supply_level = 'water_supply'
+ water_treat_level = 'water_treat'
+ ret = ixDS$add_set( 'level', water_supply_level ) # Add level to ix DB
+ ret = ixDS$add_set( 'level', water_treat_level ) # Add level to ix DB
+ cooling_level = 'cooling'
+ ret = ixDS$add_set( 'level', cooling_level ) # Add level to ix DB
+
+ # Wastewater as an emission - could alternatively be included as commodity balance
+ ret = ixDS$add_set( 'emission', 'fresh_wastewater' )
+ ret = ixDS$add_set( 'emission', 'unfresh_wastewater' )
+ ret = ixDS$add_set( 'emission', 'saline_wastewater' ) # some of these should alternatively be defined in the input files
+
+ # Thermal pollution as an emission - consider oceans and rivers
+ ret = ixDS$add_set( 'emission', 'fresh_thermal_pollution' )
+ ret = ixDS$add_set( 'emission', 'saline_thermal_pollution' )
+
+ # Initialize urban, rural and irrigation water demands
+ water_end_use_commodities = c( 'urban_mw', 'urban_dis', 'rural_mw', 'rural_dis' )
+ ret = lapply( water_end_use_commodities, function(x){ ixDS$add_set( 'commodity', x ) } ) # Add commodity to ix DB
+
+ # Initialize wastewater commodities
+ wastewater_commodities = c( 'urban_collected_wst', 'rural_collected_wst', 'urban_uncollected_wst', 'rural_uncollected_wst' )
+ ret = lapply( wastewater_commodities, function(x){ ixDS$add_set( 'commodity', x ) } ) # Add commodity to ix DB
+
+ # Water source extraction technologies - add to technology and type sets
+ # Currently distinguishes: freshwater_instream (hydropower), freshwater_supply (all techs using freshwater), saline_supply (ocean and brackish resources) and upstream_landuse (globiom accounting)
+ water_source_extraction_techs =  unlist( lapply( 1:length(water_supply_type), function(x){ paste( 'extract', as.character( water_supply_type[x] ), sep='__' ) } ) )
+ ret = lapply( 1:length(water_supply_type), function(x){ ixDS$add_set( 'technology', water_source_extraction_techs[x] ) } )
+ water_resource_extraction_tech_type = 'water_resource_extraction'
+ ret = ixDS$add_set( 'type_tec', water_resource_extraction_tech_type ) # Add to technology types
+ ret = lapply( 1:length(water_supply_type), function(x){ ixDS$add_set( 'cat_tec', paste( water_resource_extraction_tech_type , water_source_extraction_techs[x], sep='.') ) } )
+ ret = lapply( 1:length(water_supply_type), function(x){ ixDS$add_set( 'cat_tec', paste( 'investment_other' , water_source_extraction_techs[x], sep='.') ) } )
+
+ # Power plant cooling technologies as technology types - will set technology names later
+ power_plant_cooling_tech_type = 'power_plant_cooling'
+ ixDS$add_set( 'type_tec', power_plant_cooling_tech_type )
+
+ # Create data.frame that stores the relevant cost data from the csv files
+ cooling_technology_costs = data.frame( 	inv_costs = unlist( lapply( cooling_technologies_to_consider, function(techs2check){ as.numeric( cooltech_cost_and_shares_ssp_msg_raw.df$investment_million_USD_per_MW_mid[ which( as.character( cooltech_cost_and_shares_ssp_msg_raw.df$alt_id ) == as.character( techs2check ) ) ] ) } ) ),
+                     fixed_costs = 0,#unlist( lapply( cooling_technologies_to_consider, function(techs2check){ as.numeric( cooltech_cost_and_shares_ssp_msg_raw.df$fixed_million_USD_per_MW[ which( as.character( cooltech_cost_and_shares_ssp_msg_raw.df$alt_id ) == as.character( techs2check ) ) ] ) } ) ),
+                     var_costs = 0,#unlist( lapply( cooling_technologies_to_consider, function(techs2check){ as.numeric( cooltech_cost_and_shares_ssp_msg_raw.df$variable_million_USD_per_MW[ which( as.character( cooltech_cost_and_shares_ssp_msg_raw.df$alt_id ) == as.character( techs2check ) ) ] ) } ) ),
+                     row.names = cooling_technologies_to_consider )
+
+ # Recover the names of the cooled technologies in MESSAGE (i.e., the thermal power plants)
+ cooled_technologies_in_message = unique( as.character( unlist(data.frame(strsplit( cooling_technologies_to_consider, '__'))[1,]) ) )
+
+ # Manually set efficiencies - data not readily extracted from the DB - currently manually read from .inp file :(
+ manually_set_efficiencies = list( 	geo_hpl = 0.850,
+                   geo_ppl = 0.385,
+                   nuc_hc = 0.326, # couldn't find these numbers in the .inp file so using average heat rate of 3.065 kWh heat per kWh electricity from EIA
+                   nuc_lc = 0.326, # couldn't find these numbers in the .inp file so using average heat rate of 3.065 kWh heat per kWh electricity from EIA
+                   solar_th_ppl = 0.385 )
+
+ #----------------------------------------------------------------------------------------------------------------------
+ # Initialize the full commodity balance set and commodities, as well as the associated equation added to the core model
+ #----------------------------------------------------------------------------------------------------------------------
+
+ ixDS$init_set( "full_balance", c( "commodity" ) )
+ res = lapply( wastewater_commodities, function( www ){ ixDS$add_set( "full_balance", www ) } )
+ ixDS$init_equ( "COMMODITY_BALANCE_FULL", c( "node", "commodity", "level", "year", "time" ) )
+
+ #-------------------------------------------------------------------------------------------------------
+ # Add urban and rural demands / return flows as exogenous inputs
+ #-------------------------------------------------------------------------------------------------------
+
+ # Import urban and rural demands from csv - convert mcm/year to km3/year
+ if( as.character( model_scenarios$newscenarioName[sc] ) == 'baseline_globiom_SDG_sdg6supp' ){ pth = 'sdg6_supp'; ipth = 'baseline' }
+ if( as.character( model_scenarios$newscenarioName[sc] ) == 'baseline_globiom_SDG_sdg6eff' ){ pth = 'sdg6_eff'; ipth = 'sdg6_eff' }
+ if( as.character( model_scenarios$newscenarioName[sc] ) == 'baseline_globiom_base_watbaseline' ){ pth = 'baseline'; ipth = 'baseline' }
+ if( as.character( model_scenarios$newscenarioName[sc] ) == "baseline_globiom_SDG_sdg6led" ){ pth = 'sdg6_eff'; ipth = 'sdg6_eff' }
+ if( as.character( model_scenarios$newscenarioName[sc] ) == 'baseline_globiom_SDG_sdg6supp2' ){ pth = 'sdg6_supp'; ipth = 'sdg6_eff' }
+ urban_withdrawal.df = 1e-3 * data.frame( read.csv( paste( data_path, '/water/water_demands/harmonized/ssp', unlist(strsplit(ssss,'P'))[2], '_regional_urban_withdrawal', pth, '.csv', sep = '' ), stringsAsFactors=FALSE, row.names = 1 ) )
+ rural_withdrawal.df = 1e-3 * data.frame( read.csv( paste( data_path, '/water/water_demands/harmonized/ssp', unlist(strsplit(ssss,'P'))[2], '_regional_rural_withdrawal', pth, '.csv', sep = '' ), stringsAsFactors=FALSE, row.names = 1 ) )
+ urban_return.df = 1e-3 * data.frame( read.csv( paste( data_path, '/water/water_demands/harmonized/ssp', unlist(strsplit(ssss,'P'))[2], '_regional_urban_return', pth, '.csv', sep = '' ), stringsAsFactors=FALSE, row.names = 1 ) )
+ rural_return.df = 1e-3 * data.frame( read.csv( paste( data_path, '/water/water_demands/harmonized/ssp', unlist(strsplit(ssss,'P'))[2], '_regional_rural_return', pth, '.csv', sep = '' ), stringsAsFactors=FALSE, row.names = 1 ) )
+ urban_connected_fraction.df = data.frame( read.csv( paste( data_path, '/water/water_demands/harmonized/ssp', unlist(strsplit(ssss,'P'))[2], '_regional_urban_connection_rate', pth, '.csv', sep = '' ), stringsAsFactors=FALSE, row.names = 1 ) )
+ rural_connected_fraction.df = data.frame( read.csv( paste( data_path, '/water/water_demands/harmonized/ssp', unlist(strsplit(ssss,'P'))[2], '_regional_rural_connection_rate', pth, '.csv', sep = '' ), stringsAsFactors=FALSE, row.names = 1 ) )
+ urban_treated_fraction.df = data.frame( read.csv( paste( data_path, '/water/water_demands/harmonized/ssp', unlist(strsplit(ssss,'P'))[2], '_regional_urban_treatment_rate', pth, '.csv', sep = '' ), stringsAsFactors=FALSE, row.names = 1 ) )
+ rural_treated_fraction.df = data.frame( read.csv( paste( data_path, '/water/water_demands/harmonized/ssp', unlist(strsplit(ssss,'P'))[2], '_regional_rural_treatment_rate', pth, '.csv', sep = '' ), stringsAsFactors=FALSE, row.names = 1 ) )
+ urban_desal_fraction.df = data.frame( read.csv( paste( data_path, '/water/water_demands/harmonized/ssp', unlist(strsplit(ssss,'P'))[2], '_regional_urban_desalination_rate', pth, '.csv', sep = '' ), stringsAsFactors=FALSE, row.names = 1 ) )
+ urban_reuse_fraction.df = data.frame( read.csv( paste( data_path, '/water/water_demands/harmonized/ssp', unlist(strsplit(ssss,'P'))[2], '_regional_urban_recycling_rate', pth, '.csv', sep = '' ), stringsAsFactors=FALSE, row.names = 1 ) )
+ irrigation_withdrawal.df = 1e-3 * data.frame( read.csv( paste( data_path, '/water/water_demands/harmonized/ssp', unlist(strsplit(ssss,'P'))[2], '_regional_irrigation_withdrawal', ipth, '.csv', sep = '' ), stringsAsFactors=FALSE, row.names = 1 ) )
+
+ # # Import irrigation demands from GLOBIOM and harmonize
+ # irrig = ixDSoriginal$par('land_output',list(commodity='Water|Withdrawal|Irrigation'))
+ # land = ixDSoriginal$var('LAND')
+ # irrigation_withdrawal.df = 1e-3 * data.frame( do.call( cbind, lapply( names( urban_reuse_fraction.df ), function(rr){ sapply( c( row.names( urban_reuse_fraction.df ), '2100' ), function(yy){
+   # out = irrig[ which( grepl( rr, irrig$node ) & grepl( as.numeric( yy ), irrig$year ) ) ,  ]
+   # lnd = land[ which( grepl( rr, land$node ) & grepl( as.numeric( yy ), land$year ) ) , ]
+   # cmb = merge( out, lnd, by = 'land_scenario')
+   # res = max( sum( cmb$value * cmb$level.y ), 0, na.rm=TRUE )
+   # return(res)
+   # } ) } ) ) )
+ # names( irrigation_withdrawal.df ) = names( urban_reuse_fraction.df )
+
+ # Import and harmonize historical municipal and manufacturing demands from Floerke et al. 'Domestic and industrial water uses as a mirror for socioeconomic development'
+ watergap_mw_hist.df = data.frame( read.csv( paste( data_path, '/water/water_demands/watergap_historical_water_use_mw.csv', sep=''), stringsAsFactors = FALSE ) )
+ watergap_mw_hist.df$country = countrycode(watergap_mw_hist.df$iso, 'iso3n', 'iso3c')
+ watergap_mw_hist.df$region = sapply( watergap_mw_hist.df$country, function(x){ return( country_region_map_key.df$Eleven_region[ which( country_region_map_key.df$Region == x ) ] ) } )
+ watergap_mf_hist.df = data.frame( read.csv( paste( data_path, '/water/water_demands/watergap_historical_water_use_mf.csv', sep=''), stringsAsFactors = FALSE ) )
+ watergap_mf_hist.df$country = countrycode(watergap_mf_hist.df$iso, 'iso3n', 'iso3c')
+ watergap_mf_hist.df$region = sapply( watergap_mf_hist.df$country, function(x){ return( country_region_map_key.df$Eleven_region[ which( country_region_map_key.df$Region == x ) ] ) } )
+
+ # Add exogenous water demands to the db
+ ret = lapply( region, function(rr){ lapply( dmd_yrs, function(yy){
+
+   ## Add urban and rural demands for all historical and future years to db
+   if( yy %in% as.numeric( row.names( urban_withdrawal.df ) ) )
+     {
+
+     ixDS$add_par( 	'demand', # parameter name
+             paste( rr, 'urban_mw', 'final', yy, model_time, sep = '.' ), # set key
+             round( c( urban_withdrawal.df[ as.character( yy ), unlist( strsplit( rr, '_') )[2] ] * urban_connected_fraction.df[ as.character( yy ), unlist( strsplit( rr, '_') )[2] ] ), digits = 2),
+             '-' )
+
+     ixDS$add_par( 	'demand', # parameter name
+             paste( rr, 'urban_dis', 'final', yy, model_time, sep = '.' ), # set key
+             round( c( urban_withdrawal.df[ as.character( yy ), unlist( strsplit( rr, '_') )[2] ] * (1-urban_connected_fraction.df[ as.character( yy ), unlist( strsplit( rr, '_') )[2] ]) ), digits = 2),
+             '-' )
+
+     ixDS$add_par( 	'demand', # parameter name
+             paste( rr, 'rural_mw', 'final', yy, model_time, sep = '.' ), # set key
+             round( c( rural_withdrawal.df[ as.character( yy ), unlist( strsplit( rr, '_') )[2] ] * rural_connected_fraction.df[ as.character( yy ), unlist( strsplit( rr, '_') )[2] ] ), digits = 2), # value
+             '-' )	# unit
+
+     ixDS$add_par( 	'demand', # parameter name
+             paste( rr, 'rural_dis', 'final', yy, model_time, sep = '.' ), # set key
+             round( c( rural_withdrawal.df[ as.character( yy ), unlist( strsplit( rr, '_') )[2] ] * (1-rural_connected_fraction.df[ as.character( yy ), unlist( strsplit( rr, '_') )[2] ]) ), digits = 2), # value
+             '-' )	# unit
+
+     ixDS$add_par( 	'demand', # parameter name
+             paste( rr, 'urban_collected_wst', 'final', yy, model_time, sep = '.' ), # set key
+             -1 * round( c( urban_return.df[ as.character( yy ), unlist( strsplit( rr, '_') )[2] ] * urban_treated_fraction.df[ as.character( yy ), unlist( strsplit( rr, '_') )[2] ] ), digits = 2),
+             '-' )
+
+     ixDS$add_par( 	'demand', # parameter name
+             paste( rr, 'rural_collected_wst', 'final', yy, model_time, sep = '.' ), # set key
+             -1 * round( c( rural_return.df[ as.character( yy ), unlist( strsplit( rr, '_') )[2] ] * rural_treated_fraction.df[ as.character( yy ), unlist( strsplit( rr, '_') )[2] ] ), digits = 2), # value
+             '-' )	# unit
+
+     ixDS$add_par( 	'demand', # parameter name
+             paste( rr, 'urban_uncollected_wst', 'final', yy, model_time, sep = '.' ), # set key
+             -1 * round( c( urban_return.df[ as.character( yy ), unlist( strsplit( rr, '_') )[2] ] * (1-urban_treated_fraction.df[ as.character( yy ), unlist( strsplit( rr, '_') )[2] ]) ), digits = 2),
+             '-' )
+
+     ixDS$add_par( 	'demand', # parameter name par[a,s,d,f,g]
+             paste( rr, 'rural_uncollected_wst', 'final', yy, model_time, sep = '.' ), # set key
+             -1 * round( c( rural_return.df[ as.character( yy ), unlist( strsplit( rr, '_') )[2] ] * (1-rural_treated_fraction.df[ as.character( yy ), unlist( strsplit( rr, '_') )[2] ]) ), digits = 2), # value
+             '-' )	# unit
+
+     }else # year outside range of projections either historical or beyond proejections - use average growth rates to estimate
+     {
+
+     if ( yy < min( as.numeric( row.names( urban_withdrawal.df ) ) ) )
+       {
+
+       dmd2010 = round( c( urban_withdrawal.df[ as.character( 2010 ), unlist( strsplit( rr, '_') )[2] ] * urban_connected_fraction.df[ as.character( 2010 ), unlist( strsplit( rr, '_') )[2] ] ), digits = 2)
+       dmd2020 = round( c( urban_withdrawal.df[ as.character( 2020 ), unlist( strsplit( rr, '_') )[2] ] * urban_connected_fraction.df[ as.character( 2020 ), unlist( strsplit( rr, '_') )[2] ] ), digits = 2)
+       delta = ( dmd2020/dmd2010 )^(1/10) - 1
+       dmdhist = round( dmd2010*( (1 + delta)^(-1*(2010 - as.numeric(yy))) ), digits = 2)
+       ixDS$add_par( 	'demand', # parameter name
+             paste( rr, 'urban_mw', 'final', yy, model_time, sep = '.' ), # set key
+             dmdhist,
+             '-' )
+
+       dmd2010 = round( c( urban_withdrawal.df[ as.character( 2010 ), unlist( strsplit( rr, '_') )[2] ] * (1-urban_connected_fraction.df[ as.character( 2010 ), unlist( strsplit( rr, '_') )[2] ]) ), digits = 2)
+       dmd2020 = round( c( urban_withdrawal.df[ as.character( 2020 ), unlist( strsplit( rr, '_') )[2] ] * (1-urban_connected_fraction.df[ as.character( 2020 ), unlist( strsplit( rr, '_') )[2] ]) ), digits = 2)
+       delta = ( dmd2020/dmd2010 )^(1/10) - 1
+       dmdhist =  round( dmd2010*( (1 + delta)^(-1*(2010 - as.numeric(yy))) ), digits = 2)
+       ixDS$add_par( 	'demand', # parameter name
+               paste( rr, 'urban_dis', 'final', yy, model_time, sep = '.' ), # set key
+               dmdhist,
+               '-' )
+
+       dmd2010 = round( c( rural_withdrawal.df[ as.character( 2010 ), unlist( strsplit( rr, '_') )[2] ] * rural_connected_fraction.df[ as.character( 2010 ), unlist( strsplit( rr, '_') )[2] ] ), digits = 2)
+       dmd2020 = round( c( rural_withdrawal.df[ as.character( 2020 ), unlist( strsplit( rr, '_') )[2] ] * rural_connected_fraction.df[ as.character( 2020 ), unlist( strsplit( rr, '_') )[2] ] ), digits = 2)
+       delta = ( dmd2020/dmd2010 )^(1/10) - 1
+       dmdhist =  round( dmd2010*( (1 + delta)^(-1*(2010 - as.numeric(yy))) ), digits = 2)
+       ixDS$add_par( 	'demand', # parameter name
+             paste( rr, 'rural_mw', 'final', yy, model_time, sep = '.' ), # set key
+             dmdhist, # value
+             '-' )	# unit
+
+       dmd2010 = round( c( rural_withdrawal.df[ as.character( 2010 ), unlist( strsplit( rr, '_') )[2] ] * (1-rural_connected_fraction.df[ as.character( 2010 ), unlist( strsplit( rr, '_') )[2] ]) ), digits = 2)
+       dmd2020 = round( c( rural_withdrawal.df[ as.character( 2020 ), unlist( strsplit( rr, '_') )[2] ] * (1-rural_connected_fraction.df[ as.character( 2020 ), unlist( strsplit( rr, '_') )[2] ]) ), digits = 2)
+       delta = ( dmd2020/dmd2010 )^(1/10) - 1
+       dmdhist =  round( dmd2010*( (1 + delta)^(-1*(2010 - as.numeric(yy))) ), digits = 2)
+       ixDS$add_par( 'demand', # parameter name
+             paste( rr, 'rural_dis', 'final', yy, model_time, sep = '.' ), # set key
+             dmdhist,
+             '-' )
+
+       dmd2010 = -1 * round( c( urban_return.df[ as.character( 2010 ), unlist( strsplit( rr, '_') )[2] ] * urban_treated_fraction.df[ as.character( 2010 ), unlist( strsplit( rr, '_') )[2] ] ), digits = 2)
+       dmd2020 = -1 * round( c( urban_return.df[ as.character( 2020 ), unlist( strsplit( rr, '_') )[2] ] * urban_treated_fraction.df[ as.character( 2020 ), unlist( strsplit( rr, '_') )[2] ] ), digits = 2)
+       delta = ( dmd2020/dmd2010 )^(1/10) - 1
+       dmdhist =  round( dmd2010*( (1 + delta)^(-1*(2010 - as.numeric(yy))) ), digits = 2)
+       ixDS$add_par( 'demand', # parameter name
+               paste( rr, 'urban_collected_wst', 'final', yy, model_time, sep = '.' ), # set key
+               dmdhist,
+               '-' )
+
+       dmd2010 = -1 * round( c( urban_return.df[ as.character( 2010 ), unlist( strsplit( rr, '_') )[2] ] * (1-urban_treated_fraction.df[ as.character( 2010 ), unlist( strsplit( rr, '_') )[2] ]) ), digits = 2 )
+       dmd2020 = -1 * round( c( urban_return.df[ as.character( 2020 ), unlist( strsplit( rr, '_') )[2] ] * (1-urban_treated_fraction.df[ as.character( 2020 ), unlist( strsplit( rr, '_') )[2] ]) ), digits = 2 )
+       delta = ( dmd2020/dmd2010 )^(1/10) - 1
+       dmdhist =  round( dmd2010*( (1 + delta)^(-1*(2010 - as.numeric(yy))) ), digits = 2)
+       ixDS$add_par( 'demand', # parameter name
+               paste( rr, 'urban_uncollected_wst', 'final', yy, model_time, sep = '.' ), # set key
+               dmdhist,
+               '-' )
+
+       dmd2010 = -1 * round( c( rural_return.df[ as.character( 2010 ), unlist( strsplit( rr, '_') )[2] ] * rural_treated_fraction.df[ as.character( 2010 ), unlist( strsplit( rr, '_') )[2] ] ), digits = 2)
+       dmd2020 = -1 * round( c( rural_return.df[ as.character( 2020 ), unlist( strsplit( rr, '_') )[2] ] * rural_treated_fraction.df[ as.character( 2020 ), unlist( strsplit( rr, '_') )[2] ] ), digits = 2)
+       delta = ( dmd2020/dmd2010 )^(1/10) - 1
+       dmdhist =  round( dmd2010*( (1 + delta)^(-1*(2010 - as.numeric(yy))) ), digits = 2)
+       ixDS$add_par( 'demand', # parameter name
+               paste( rr, 'rural_collected_wst', 'final', yy, model_time, sep = '.' ), # set key
+               dmdhist,
+               '-' )
+
+       dmd2010 = -1 * round( c( rural_return.df[ as.character( 2010 ), unlist( strsplit( rr, '_') )[2] ] * (1-rural_treated_fraction.df[ as.character( 2010 ), unlist( strsplit( rr, '_') )[2] ]) ), digits = 2)
+       dmd2020 = -1 * round( c( rural_return.df[ as.character( 2020 ), unlist( strsplit( rr, '_') )[2] ] * (1-rural_treated_fraction.df[ as.character( 2020 ), unlist( strsplit( rr, '_') )[2] ]) ), digits = 2)
+       delta = ( dmd2020/dmd2010 )^(1/10) - 1
+       dmdhist =  round( dmd2010*( (1 + delta)^(-1*(2010 - as.numeric(yy))) ), digits = 2)
+       ixDS$add_par( 'demand', # parameter name
+               paste( rr, 'rural_uncollected_wst', 'final', yy, model_time, sep = '.' ), # set key
+               dmdhist,
+               '-' )
+
+       }else # beyond projections - assume previous decadal growth / decay rate
+       {
+
+       dmd2090 = round( c( urban_withdrawal.df[ as.character( 2090 ), unlist( strsplit( rr, '_') )[2] ] * urban_connected_fraction.df[ as.character( 2090 ), unlist( strsplit( rr, '_') )[2] ] ), digits = 2 )
+       dmd2080 = round( c( urban_withdrawal.df[ as.character( 2080 ), unlist( strsplit( rr, '_') )[2] ] * urban_connected_fraction.df[ as.character( 2080 ), unlist( strsplit( rr, '_') )[2] ] ), digits = 2 )
+       delta = ( dmd2090/dmd2080 )^(1/10) - 1
+       if(is.nan(delta)){delta=0}
+       dmdfut = round( dmd2090*( (1 + delta)^((as.numeric(yy) - 2090)) ), digits = 2 )
+       ixDS$add_par( 'demand', # parameter name
+               paste( rr, 'urban_mw', 'final', yy, model_time, sep = '.' ), # set key
+               dmdfut,
+               '-' )
+
+       dmd2090 = round( c( urban_withdrawal.df[ as.character( 2090 ), unlist( strsplit( rr, '_') )[2] ] * (1-urban_connected_fraction.df[ as.character( 2090 ), unlist( strsplit( rr, '_') )[2] ]) ), digits = 2 )
+       dmd2080 = round( c( urban_withdrawal.df[ as.character( 2080 ), unlist( strsplit( rr, '_') )[2] ] * (1-urban_connected_fraction.df[ as.character( 2080 ), unlist( strsplit( rr, '_') )[2] ]) ), digits = 2 )
+       delta = ( dmd2090/dmd2080 )^(1/10) - 1
+       if(is.nan(delta)){delta=0}
+       dmdfut = round( dmd2090*( (1 + delta)^((as.numeric(yy) - 2090)) ), digits = 2 )
+       ixDS$add_par( 'demand', # parameter name
+               paste( rr, 'urban_dis', 'final', yy, model_time, sep = '.' ), # set key
+               dmdfut,
+               '-' )
+
+       dmd2090 = round( c( rural_withdrawal.df[ as.character( 2090 ), unlist( strsplit( rr, '_') )[2] ] * rural_connected_fraction.df[ as.character( 2090 ), unlist( strsplit( rr, '_') )[2] ] ), digits = 2 )
+       dmd2080 = round( c( rural_withdrawal.df[ as.character( 2080 ), unlist( strsplit( rr, '_') )[2] ] * rural_connected_fraction.df[ as.character( 2080 ), unlist( strsplit( rr, '_') )[2] ] ), digits = 2 )
+       delta = ( dmd2090/dmd2080 )^(1/10) - 1
+       if(is.nan(delta)){delta=0}
+       dmdfut = round( dmd2090*( (1 + delta)^((as.numeric(yy) - 2090)) ), digits = 2 )
+       ixDS$add_par( 'demand', # parameter name
+               paste( rr, 'rural_mw', 'final', yy, model_time, sep = '.' ), # set key
+               dmdfut,
+               '-' )
+
+       dmd2090 = round( c( rural_withdrawal.df[ as.character( 2090 ), unlist( strsplit( rr, '_') )[2] ] * rural_connected_fraction.df[ as.character( 2090 ), unlist( strsplit( rr, '_') )[2] ] ), digits = 2 )
+       dmd2080 = round( c( rural_withdrawal.df[ as.character( 2080 ), unlist( strsplit( rr, '_') )[2] ] * rural_connected_fraction.df[ as.character( 2080 ), unlist( strsplit( rr, '_') )[2] ] ), digits = 2 )
+       delta = ( dmd2090/dmd2080 )^(1/10) - 1
+       if(is.nan(delta)){delta=0}
+       dmdfut = round( dmd2090*( (1 + delta)^((as.numeric(yy) - 2090)) ), digits = 2 )
+       ixDS$add_par( 'demand', # parameter name
+               paste( rr, 'rural_dis', 'final', yy, model_time, sep = '.' ), # set key
+               dmdfut,
+               '-' )
+
+       dmd2090 = -1 * round( c( urban_return.df[ as.character( 2090 ), unlist( strsplit( rr, '_') )[2] ] * urban_treated_fraction.df[ as.character( 2090 ), unlist( strsplit( rr, '_') )[2] ] ), digits = 2)
+       dmd2080 = -1 * round( c( urban_return.df[ as.character( 2080 ), unlist( strsplit( rr, '_') )[2] ] * urban_treated_fraction.df[ as.character( 2080 ), unlist( strsplit( rr, '_') )[2] ] ), digits = 2)
+       delta = ( dmd2090/dmd2080 )^(1/10) - 1
+       if(is.nan(delta)){delta=0}
+       dmdfut = round( dmd2090*( (1 + delta)^((as.numeric(yy) - 2090)) ), digits = 2 )
+       ixDS$add_par( 'demand', # parameter name
+               paste( rr, 'urban_collected_wst', 'final', yy, model_time, sep = '.' ), # set key
+               dmdfut,
+               '-' )
+
+       dmd2090 = -1 * round( c( urban_return.df[ as.character( 2090 ), unlist( strsplit( rr, '_') )[2] ] * (1-urban_treated_fraction.df[ as.character( 2090 ), unlist( strsplit( rr, '_') )[2] ]) ), digits = 2)
+       dmd2080 = -1 * round( c( urban_return.df[ as.character( 2080 ), unlist( strsplit( rr, '_') )[2] ] * (1-urban_treated_fraction.df[ as.character( 2080 ), unlist( strsplit( rr, '_') )[2] ]) ), digits = 2)
+       delta = ( dmd2090/dmd2080 )^(1/10) - 1
+       if(is.nan(delta)){delta=0}
+       dmdfut = round( dmd2090*( (1 + delta)^((as.numeric(yy) - 2090)) ), digits = 2 )
+       ixDS$add_par( 'demand', # parameter name
+               paste( rr, 'urban_uncollected_wst', 'final', yy, model_time, sep = '.' ), # set key
+               dmdfut,
+               '-' )
+
+       dmd2090 = -1 * round( c( rural_return.df[ as.character( 2090 ), unlist( strsplit( rr, '_') )[2] ] * rural_treated_fraction.df[ as.character( 2090 ), unlist( strsplit( rr, '_') )[2] ] ), digits = 2)
+       dmd2080 = -1 * round( c( rural_return.df[ as.character( 2080 ), unlist( strsplit( rr, '_') )[2] ] * rural_treated_fraction.df[ as.character( 2080 ), unlist( strsplit( rr, '_') )[2] ] ), digits = 2)
+       delta = ( dmd2090/dmd2080 )^(1/10) - 1
+       if(is.nan(delta)){delta=0}
+       dmdfut = round( dmd2090*( (1 + delta)^((as.numeric(yy) - 2090)) ), digits = 2 )
+       ixDS$add_par( 'demand', # parameter name
+               paste( rr, 'rural_collected_wst', 'final', yy, model_time, sep = '.' ), # set key
+               dmdfut,
+               '-' )
+
+       dmd2090 = -1 * round( c( rural_return.df[ as.character( 2090 ), unlist( strsplit( rr, '_') )[2] ] * (1-rural_treated_fraction.df[ as.character( 2090 ), unlist( strsplit( rr, '_') )[2] ]) ), digits = 2)
+       dmd2080 = -1 * round( c( rural_return.df[ as.character( 2080 ), unlist( strsplit( rr, '_') )[2] ] * (1-rural_treated_fraction.df[ as.character( 2080 ), unlist( strsplit( rr, '_') )[2] ]) ), digits = 2)
+       delta = ( dmd2090/dmd2080 )^(1/10) - 1
+       if(is.nan(delta)){delta=0}
+       dmdfut = round( dmd2090*( (1 + delta)^((as.numeric(yy) - 2090)) ), digits = 2 )
+       ixDS$add_par( 'demand', # parameter name
+               paste( rr, 'rural_uncollected_wst', 'final', yy, model_time, sep = '.' ), # set key
+               dmdfut,
+               '-' )
+       }
+     }
+
+   # Add irrigation demands for all historical and future years to db - writes directly into the freshwater supply level
+   if( yy %in% as.numeric( row.names( irrigation_withdrawal.df ) ) )
+     {
+
+     ixDS$add_par( 	'demand', # parameter name
+             paste( rr, 'freshwater_supply', water_supply_level, yy, model_time, sep = '.' ), # set key
+             round( irrigation_withdrawal.df[ as.character( yy ), unlist( strsplit( rr, '_') )[2] ], digits = 2),
+             '-' )
+
+     }else # year outside range of projections either historical or beyond proejections
+     {
+
+     if ( yy < min( as.numeric( row.names( urban_withdrawal.df ) ) ) )
+       {
+       ixDS$add_par( 	'demand', # parameter name
+               paste( rr, 'freshwater_supply', water_supply_level, yy, model_time, sep = '.' ), # set key
+               round( irrigation_withdrawal.df[ as.character( min( as.numeric( row.names( irrigation_withdrawal.df ) ) ) ), unlist( strsplit( rr, '_') )[2] ], digits = 2),
+               '-' )
+       }else
+       {
+
+       di = irrigation_withdrawal.df[ as.character( max( as.numeric( row.names( irrigation_withdrawal.df ) ) ) ), unlist( strsplit( rr, '_') )[2] ] / irrigation_withdrawal.df[ ( which.max( as.numeric( row.names( irrigation_withdrawal.df ) ) ) - 1 ), unlist( strsplit( rr, '_') )[2] ]
+
+       ixDS$add_par( 'demand', # parameter name
+               paste( rr, 'freshwater_supply', water_supply_level, yy, model_time, sep = '.' ), # set key
+               round( ( irrigation_withdrawal.df[ as.character( max( as.numeric( row.names( irrigation_withdrawal.df ) ) ) ), unlist( strsplit( rr, '_') )[2] ] * ( di^( which(dmd_yrs == yy) - which( dmd_yrs == as.character( max( as.numeric( row.names( irrigation_withdrawal.df ) ) ) ) ) ) ) ), digits = 2),
+               '-' )
+       }
+
+     }
+
+   } ) } )
+
+ # Base year urban water demands for senstivity
+ # ssdm = do.call( cbind, lapply( region, function(rr){ data.frame( sapply( dmd_yrs, function(yy){ unlist( ixDS$par( 'demand', list( node = rr, commodity = 'urban_mw', level = 'final', year = yy, time  = model_time) )['value'] ) } ) ) } ) )
+ # row.names(ssdm) = dmd_yrs
+ # names(ssdm) = region
+ # ssdw = do.call( cbind, lapply( region, function(rr){ data.frame( sapply( dmd_yrs, function(yy){ unlist( ixDS$par( 'demand', list( node = rr, commodity = 'urban_collected_wst', level = 'final', year = yy, time = model_time) )['value'] ) } ) ) } ) )
+ # row.names(ssdw) = dmd_yrs
+ # names(ssdw) = region
+ # ssdi = do.call( cbind, lapply( region, function(rr){ data.frame( sapply( dmd_yrs, function(yy){ unlist( ixDS$par( 'demand', list( node = rr, commodity = 'freshwater_supply', level = 'water_supply', year = yy, time  = model_time) )['value'] ) } ) ) } ) )
+ # row.names(ssdi) = dmd_yrs
+ # names(ssdi) = region
+ # print(ssdm)
+ # print(ssdw)
+ # print(ssdi)
+
+ # ------------------------------
+ # Add technologies that distribute water to end use - allows tracking (estimating) investments in distribution infrastructure
+ # ------------------------------
+
+ ret = ixDS$add_set( 'type_tec', 'water_distribution' )
+ d_tech = data.frame( urban_mw = 'urban_t_d', urban_dis = 'urban_unconnected', rural_mw = 'rural_t_d', rural_dis = 'rural_unconnected', urban_collected_wst = 'urban_sewerage' )
+
+ # distribution cost parameters
+ # Estimated by taking the per capita connection costs from Table 4 in 'Evaluation of the Costs and Benefits of Water and Sanitation Improvements at the Global Level'
+ # Component for piping and pretreatment estimated from breakdown across cost components presented in 'The Economic Costs and Benefits of Investments in Municipal Water and Sanitation Infrastructure: A Global Perspective'
+ # The per capita level is converted to per m3 considering a 'decent living' water requirement of 50 liters per day per person (18.25 m3 per year) from Gleick 1996
+ # 'household connection' to represent the urban distribution and 'borehole' to represent the rural distribution
+ # Irrigation infrastructure optimized in GLOBIOM - not considered in this implementation but irrigation demands accounted for in the freshwater balance
+ urban_mw_par.list = list( 	investment = round( 0.45 * 1.1 * 1e3 * data.frame( low = ( 55 / 18.25 ), mid = ( 102 / 18.25 ), high = ( 144 / 18.25 ) ) ),
+               fixed_cost = round( 0.25 * 0.45 * 1.1 * 1e3 * data.frame( low = ( 55 / 18.25 ), mid = ( 102 / 18.25 ), high = ( 144 / 18.25 ) ) ),
+               var_cost = data.frame( low = 0, mid = 0, high = 0 ),
+               lifetime = data.frame( low = 50, mid = 40, high = 30 ) )
+ urban_dis_par.list = list( 	investment = data.frame( low = 0, mid = 0, high = 0 ),
+               fixed_cost = data.frame( low = 0, mid = 0, high = 0 ),
+               var_cost = data.frame( low = 0, mid = 0, high = 0 ),
+               lifetime = data.frame( low = 1, mid = 1, high = 1 ) )
+ rural_mw_par.list = list( 	investment = round( 0.65 * 1.1 * 1e3 * data.frame( low = ( 17 / 18.25 ), mid = ( 23 / 18.25 ), high = ( 55 / 18.25 ) ) ),
+               fixed_cost = round( 0.05 * 0.65 * 1.1 * 1e3 * data.frame( low = ( 17 / 18.25 ), mid = ( 23 / 18.25 ), high = ( 55 / 18.25 ) ) ),
+               var_cost = data.frame( low = 0, mid = 0, high = 0 ),
+               lifetime = data.frame( low = 30, mid = 20, high = 10 ) )
+ rural_dis_par.list = list( 	investment = data.frame( low = 0, mid = 0, high = 0 ),
+               fixed_cost = data.frame( low = 0, mid = 0, high = 0 ),
+               var_cost = data.frame( low = 0, mid = 0, high = 0 ),
+               lifetime = data.frame( low = 1, mid = 1, high = 1 ) )
+ urban_collected_wst_par.list = list( 	investment = round( 0.35 * 1.1 * 1e3 * data.frame( low = ( 55 / 18.25 ), mid = ( 102 / 18.25 ), high = ( 144 / 18.25 ) ) ),
+                     fixed_cost = round( 0.25 * 0.35 * 1.1 * 1e3 * data.frame( low = ( 55 / 18.25 ), mid = ( 102 / 18.25 ), high = ( 144 / 18.25 ) ) ),
+                     var_cost = data.frame( low = 0, mid = 0, high = 0 ),
+                     lifetime = data.frame( low = 50, mid = 40, high = 30 ) )
+
+ ret = lapply( names( d_tech ), function(tt){
+
+   ixDS$add_set( 'technology', as.character( d_tech[[ tt ]] ) )
+   ixDS$add_set( 'cat_tec', paste( 'investment_other' , as.character( d_tech[[ tt ]] ), sep='.' ) ) # Add technology to list of water system investments
+   ixDS$add_set( 'cat_tec', paste( 'water_distribution', as.character( d_tech[[ tt ]] ), sep='.' ) ) # Add technology to list of desal techs
+
+   par.list = get( paste( tt, 'par.list', sep= '_') )
+
+   lapply( region, function(rr){
+
+     tst = as.numeric( as.character( unlist(ixDS$par('demand',list( node = rr, commodity = tt ))['year']) ) )
+
+     lapply( tst , function(yy){
+
+       # Add the investment cost
+       ixDS$add_par( 'inv_cost', # parameter name
+           paste(rr, as.character( d_tech[[ tt ]] ), yy, sep = '.' ), # set key
+           round( unlist( par.list[[ 'investment' ]][ parameter_levels ] ), digits = 3 ),
+           '-' )
+
+       # Add the technical lifetime
+       ixDS$add_par( 'technical_lifetime', # parameter name
+             paste(rr, as.character( d_tech[[ tt ]] ), yy, sep = '.' ), # set key
+             unlist( par.list[[ 'lifetime' ]][ parameter_levels ] ),
+             'y' )
+
+       # Add the construction time
+       ixDS$add_par( 'construction_time', # parameter name
+             paste( rr, as.character( d_tech[[ tt ]] ), yy, sep = '.' ), # set key
+             1,
+             'y' )
+
+       # Add the historical capacity and activity
+       if( yy <= 2010 )
+         {
+
+         ixDS$add_par( 'historical_activity', paste(  rr, as.character( d_tech[[ tt ]] ), yy, mode_common , model_time, sep = '.' ), abs(unlist(ixDS$par('demand',list( node = rr, commodity = tt, year = yy )))['value']), '-' )
+
+         if( yy == min( tst ) )
+           {
+           ixDS$add_par( 	'historical_new_capacity', # parameter name
+                   paste(  rr, as.character( d_tech[[ tt ]] ), yy, sep = '.' ), # set key
+                   round(1/10*abs(unlist(ixDS$par('demand',list( node = rr, commodity = tt, year = yy )))['value'])/3, digits=rnd),
+                   '-' )
+           }else
+           {
+           ixDS$add_par( 	'historical_new_capacity', # parameter name
+                   paste(  rr, as.character( d_tech[[ tt ]] ), yy, sep = '.' ), # set key
+                   round(max( 0, 1/5*c( abs(unlist(ixDS$par('demand',list( node = rr, commodity = tt, year = yy )))['value']) - abs(unlist( ixDS$par( 'demand',list( node = rr, commodity = tt, year = tst[ ( which( tst == yy ) - 1 ) ] ) ) )['value'] ) ) ),digits=rnd),
+                   '-' )
+           }
+
+         }
+
+       # Add the historical capacity and activity for locked-in future years in the mitigation and no policy scenarios (e.g., 2030, 2040)
+       if( yy > 2010 & yy < firstmodelyear )
+         {
+         ixDS$add_par( 'historical_activity', paste(  rr, as.character( d_tech[[ tt ]] ), yy, mode_common , model_time, sep = '.' ), max(0, base_hist_act[ which( as.character( unlist( base_hist_act$tec ) ) == as.character( d_tech[[ tt ]] ) & as.numeric( unlist( base_hist_act$year_all ) ) == yy & as.character( unlist( base_hist_act$node ) ) == rr ), 'val' ], na.rm=TRUE), '-' )
+         ixDS$add_par( 'historical_new_capacity', paste(  rr, as.character( d_tech[[ tt ]] ), yy, sep = '.' ), max(0, base_hist_ncap[ which( as.character( unlist( base_hist_ncap$tec ) ) == as.character( d_tech[[ tt ]] ) & as.numeric( unlist( base_hist_ncap$year_all ) ) == yy & as.character( unlist( base_hist_ncap$node ) ) == rr ), 'val' ], na.rm=TRUE), '-' )
+         }
+
+       # Add i-o parameters
+       lapply( model_years[ which( model_years >= as.numeric(yy) & model_years <= ( as.numeric(yy) + unlist( par.list[['lifetime']][parameter_levels] ) ) ) ], function(aa){
+
+         # Add the input efficiency ratio
+         if(tt != 'urban_collected_wst')
+           {
+           incmd = 'freshwater_supply'
+           inlvl = water_supply_level
+           outlvl = 'final'
+           }else
+           {
+           incmd = tt
+           inlvl = 'final'
+           outlvl = water_treat_level
+           }
+
+         # Add the output efficiency ratio
+         ixDS$add_par( 'output', # parameter name
+           paste( rr, as.character( d_tech[[ tt ]] ), yy, aa, mode_common, rr, tt, outlvl, model_time, model_time, sep = '.' ), # set key
+           1, # parameter value
+           '-' )
+
+
+         ixDS$add_par( 'input', # parameter name
+           paste( rr, as.character( d_tech[[ tt ]] ), yy, aa, mode_common, rr, incmd, inlvl, model_time, model_time, sep = '.' ), # set key
+           1,
+           '-' )
+
+         # Add the variable cost
+         ixDS$add_par( 'var_cost', # parameter name
+           paste( rr, as.character( d_tech[[ tt ]] ), yy, aa, mode_common, model_time, sep = '.' ), # set key
+           unlist( par.list[['var_cost']][parameter_levels] ),
+           '-' ) # units
+
+         # Add fixed costs
+         ixDS$add_par( 'fix_cost', # parameter name
+           paste( rr, as.character( d_tech[[ tt ]] ), yy, aa, sep = '.' ), # set key
+           unlist( par.list[['fixed_cost']][parameter_levels] ),
+           '-' )
+
+         # Add the capacity factor
+         ixDS$add_par( 'capacity_factor', # parameter name
+           paste( rr, as.character( d_tech[[ tt ]] ), yy, aa, model_time, sep = '.' ), # set key
+           1,
+           '-' )
+
+         } )
+
+       } )
+
+     } )
+
+   } )
+
+ # -------------------------------------------------------------------
+ # Add simplified conservation curves as technologies representing step functions
+ # -------------------------------------------------------------------
+
+ if( as.character( model_scenarios$newscenarioName[sc] ) == 'baseline_globiom_SDG_sdg6supp' ){ flg = 1 }
+ if( as.character( model_scenarios$newscenarioName[sc] ) == 'baseline_globiom_SDG_sdg6eff' ){ flg = 1 }
+ if( as.character( model_scenarios$newscenarioName[sc] ) == 'baseline_globiom_base_watbaseline' ){ flg = 0 }
+ if( as.character( model_scenarios$newscenarioName[sc] ) == "baseline_globiom_SDG_sdg6led" ){ flg = 1 }
+ if( as.character( model_scenarios$newscenarioName[sc] ) == 'baseline_globiom_SDG_sdg6supp2' ){ flg = 1 }
+
+
+ if( water_policies == 'SDG6' ){ scld = 0.05 }else{ scld = 0 } # Assume 10% more efficiency for SDG6 scenario
+ ret = ixDS$add_set( 'type_tec', 'water_efficiency' )
+ d_tech = data.frame( 	urban_low = 'ueff1', urban_mid = 'ueff2', urban_high = 'ueff3',
+             rural_low = 'reff1', rural_mid = 'reff2', rural_high = 'reff3',
+             irrig_low = 'ieff1', irrig_mid = 'ieff2', irrig_high = 'ieff3' )
+
+ # Backstop costs - variable cost associated with backstop supply technology to set the threshold for investment into efficiency / price responsive demand
+ # Mid-range case from Ward et al. 2010, Partial costs of global climate change adaptation for water supply... using 0.45 to achieve average (mid-point) costs of 0.3 USD per m3
+ # Lower from A.A. Keller "Water Scarcity and the Role of Storage in Development", backstop technology ranges estimated from Table 5. In USD per m3
+ # Upper from, Ouda (2013) "Review of Saudi Arabia Municipal Water Tariff", report a production cost for desalination of 1.09 USD per m3.
+ cbu = 1e3 * data.frame( low = 0.2, mid = 0.45, high = 1 ) # units are converted to million USD / km3 / yr
+ cbr = 1e3 * data.frame( low = 0.2, mid = 0.45, high = 1 )
+ cbi = 1e3 * data.frame( low = 0.2, mid = 0.45, high = 1 )
+
+ # Water conservation cost parameters
+ urban_low_par.list = list( 	investment = data.frame( low = 0, mid = 0, high = 0 ) ,
+               fixed_cost  = data.frame( low = 0, mid = 0, high = 0 ) ,
+               var_cost = data.frame( low = (1/3) * cbu['low'], mid = (1/3) * cbu['mid'], high = (1/3) * cbu['high'] ),
+               max_response = data.frame( low = (1/3) * ( scld + 0.35 ), mid = (1/3) * ( scld + 0.25 ), high = (1/3) * ( scld + 0.15 ) ) )
+ urban_mid_par.list = list( 	investment = data.frame( low = 0, mid = 0, high = 0 ) ,
+               fixed_cost  = data.frame( low = 0, mid = 0, high = 0 ) ,
+               var_cost = data.frame( low = (2/3) * cbu['low'], mid = (2/3) * cbu['mid'], high = (2/3) * cbu['high'] ),
+               max_response = data.frame( low = (1/3) * ( scld + 0.35 ), mid = (1/3) * ( scld + 0.25 ), high = (1/3) *( scld + 0.15 ) ) )
+ urban_high_par.list = list( investment = data.frame( low = 0, mid = 0, high = 0 ) ,
+               fixed_cost  = data.frame( low = 0, mid = 0, high = 0 ) ,
+               var_cost = data.frame( low = cbu['low'], mid = cbu['mid'], high = cbu['high'] ) ,
+               max_response = data.frame( low = (1/3) * ( scld + 0.35 ), mid = (1/3) * ( scld + 0.25 ), high = (1/3) * ( scld + 0.15 ) ) )
+ rural_low_par.list = list( 	investment = data.frame( low = 0, mid = 0, high = 0 ) ,
+               fixed_cost  = data.frame( low = 0, mid = 0, high = 0 ) ,
+               var_cost = data.frame( low = (1/3) * cbr['low'], mid = (1/3) * cbr['mid'], high = (1/3) * cbr['high'] ) ,
+               max_response = data.frame( low = (1/3) * ( scld + 0.35 ), mid = (1/3) * ( scld + 0.25 ), high = (1/3) * ( scld + 0.15 ) ) )
+ rural_mid_par.list = list( 	investment = data.frame( low = 0, mid = 0, high = 0 ) ,
+               fixed_cost  = data.frame( low = 0, mid = 0, high = 0 ) ,
+               var_cost = data.frame( low = (2/3) * cbr['low'], mid = (2/3) * cbr['mid'], high = (2/3) * cbr['high'] ) ,
+               max_response = data.frame( low = (1/3) * ( scld + 0.35 ), mid = (1/3) * ( scld + 0.25 ), high = (1/3) * ( scld + 0.15 ) ) )
+ rural_high_par.list = list( investment = data.frame( low = 0, mid = 0, high = 0 ) ,
+               fixed_cost  = data.frame( low = 0, mid = 0, high = 0 ) ,
+               var_cost = data.frame( low = cbr['low'], mid = cbr['mid'], high = cbr['high'] ) ,
+               max_response = data.frame( low = (1/3) * ( scld + 0.35 ), mid = (1/3) * ( scld + 0.25 ), high = (1/3) * ( scld + 0.15 ) ) )
+ irrig_low_par.list = list( 	investment = data.frame( low = 0, mid = 0, high = 0 ) ,
+               fixed_cost  = data.frame( low = 0, mid = 0, high = 0 ) ,
+               var_cost = data.frame( low = (1/3) * cbi['low'], mid = (1/3) * cbi['mid'], high = (1/3)* cbi['high'] ) ,
+               max_response = flg * data.frame( low = (1/3) * 0.35, mid = (1/3) * 0.25, high = (1/3) * 0.25 ) )
+ irrig_mid_par.list = list( 	investment = data.frame( low = 0, mid = 0, high = 0 ) ,
+               fixed_cost  = data.frame( low = 0, mid = 0, high = 0 ) ,
+               var_cost = data.frame( low = (2/3) * cbi['low'], mid = (2/3) * cbi['mid'], high = (2/3) * cbi['high'] ) ,
+               max_response = flg * data.frame( low = (1/3) * 0.35, mid = (1/3) * 0.25, high = (1/3) * 0.25 ) )
+ irrig_high_par.list = list( investment = data.frame( low = 0, mid = 0, high = 0 ) ,
+               fixed_cost  = data.frame( low = 0, mid = 0, high = 0 ) ,
+               var_cost = data.frame( low = cbi['low'], mid = cbi['mid'], high = cbi['high'] ) ,
+               max_response = flg * data.frame( low = (1/3) * 0.35, mid = (1/3) * 0.25, high = (1/3) * 0.25 ) )
+
+ ret = lapply( names( d_tech ), function(tt){
+
+   ixDS$add_set( 'technology', as.character( d_tech[[ tt ]] ) )
+   ixDS$add_set( 'cat_tec', paste( 'investment_other' , as.character( d_tech[[ tt ]] ), sep='.' ) ) # Add technology to list of water system investments
+   ixDS$add_set( 'cat_tec', paste( 'water_efficiency', as.character( d_tech[[ tt ]] ), sep='.' ) ) # Add technology to list of efficiency techs
+
+   par.list = get( paste( tt, 'par.list', sep= '_') )
+
+   if( unlist(strsplit(tt,'_'))[1] == 'urban' ){cmdtys=c('urban_mw','urban_collected_wst')}else if(unlist(strsplit(tt,'_'))[1] == 'rural'){cmdtys=c('rural_mw','rural_collected_wst')}else{cmdtys=c('freshwater_supply',NA)}
+   if( unlist(strsplit(tt,'_'))[1] == 'urban' ){lvl=c('final')}else if(unlist(strsplit(tt,'_'))[1] == 'rural'){lvl=c('final')}else{lvl=water_supply_level}
+
+   lapply( region, function(rr){
+
+     tst = as.numeric( as.character( unlist(ixDS$par('demand',list( node = rr, commodity = cmdtys[1] ))['year']) ) )
+     tst = tst[tst>2010]
+
+     lapply( tst , function(yy){
+
+       # Add the investment cost
+       ixDS$add_par( 'inv_cost', # parameter name
+           paste(rr, as.character( d_tech[[ tt ]] ), yy, sep = '.' ), # set key
+           0,
+           '-' )
+
+       # Add the technical lifetime
+       ixDS$add_par( 'technical_lifetime', # parameter name
+             paste(rr, as.character( d_tech[[ tt ]] ), yy, sep = '.' ), # set key
+             10,
+             'y' )
+
+       # Add the construction time
+       ixDS$add_par( 'construction_time', # parameter name
+             paste( rr, as.character( d_tech[[ tt ]] ), yy, sep = '.' ), # set key
+             0,
+             'y' )
+
+       # Add i-o parameters
+       aa = yy
+
+       # Add the output efficiency ratio
+       ixDS$add_par( 	'output', # parameter name
+               paste( rr, as.character( d_tech[[ tt ]] ), yy, aa, mode_common, rr, cmdtys[1], lvl, model_time, model_time, sep = '.' ), # set key
+               1,
+               '-' )
+
+       if( !is.na(cmdtys[2]) )
+         {
+         # Add the output efficiency ratio for wastewater - it is assumed efficiency measures reduce both withdrawals and return flows symmetrically.
+         ixDS$add_par( 	'input', # parameter name
+                 paste( rr, as.character( d_tech[[ tt ]] ), yy, aa, mode_common, rr, cmdtys[2], lvl, model_time, model_time, sep = '.' ), # set key
+                 min( abs( round( unlist( ixDS$par('demand',list(node=rr,commodity=cmdtys[2],year=aa))['value'] ) / unlist( ixDS$par('demand',list(node=rr,commodity=cmdtys[1],year=aa))['value'] ), digits = rnd ) ), 0, na.rm=TRUE ),
+                 '-' )
+         }
+
+       # Add the variable cost
+       ixDS$add_par( 'var_cost', # parameter name
+         paste( rr, as.character( d_tech[[ tt ]] ), yy, aa, mode_common, model_time, sep = '.' ), # set key
+         unlist( par.list[['var_cost']][parameter_levels] ),
+         '-' ) # units
+
+       # Add fixed costs
+       ixDS$add_par( 'fix_cost', # parameter name
+         paste( rr, as.character( d_tech[[ tt ]] ), yy, aa, sep = '.' ), # set key
+         unlist( par.list[['fixed_cost']][parameter_levels] ),
+         '-' )
+
+       # Add the capacity factor
+       ixDS$add_par( 'capacity_factor', # parameter name
+         paste( rr, as.character( d_tech[[ tt ]] ), yy, aa, model_time, sep = '.' ), # set key
+         1,
+         '-' )
+
+       # Add upper bounds
+       ixDS$add_par( 'bound_activity_up', # parameter name
+         paste( rr, as.character( d_tech[[ tt ]] ), yy, mode_common, model_time, sep = '.' ), # set key
+         round( c( unlist( par.list[['max_response']][parameter_levels] ) * unlist( ixDS$par('demand',list(node=rr,commodity=cmdtys[1],year=aa))['value'] ) ) , digits = 3 ),
+         '-' )
+
+       } )
+
+     } )
+
+   } )
+
+ #-------------------------------------------------------------------------------------------------------
+ # Add wastewater treatment technologies
+ #-------------------------------------------------------------------------------------------------------
+
+ ret = ixDS$add_set( 'type_tec', 'wastewater_treatment' )
+ wwt_techs = c( 'urban_treatment', 'urban_untreated', 'urban_recycle', 'rural_treatment','rural_untreated' )
+
+ wwt_techs_wst = list( urban_treatment = 'urban_collected_wst', urban_untreated = 'urban_uncollected_wst', urban_recycle = 'urban_collected_wst', rural_treatment = 'rural_collected_wst', rural_untreated = 'rural_uncollected_wst' )
+ wwt_techs_wout = list( urban_treatment = NA, urban_untreated = NA, urban_recycle = 'freshwater_supply', rural_treatment = NA, rural_untreated = NA )
+ wwt_techs_wems = list( urban_treatment = 'fresh_wastewater', urban_untreated = 'unfresh_wastewater', urban_recycle = NA, rural_treatment = 'fresh_wastewater', rural_untreated = 'unfresh_wastewater' )
+
+ wwt_tech_par.list = list(
+
+   # Investment costs taken from Gonzalez-Serrano et al. 2005 'Cost of reclaimed municipal wastewater for applications in seasonally stressed semi-arid regions'
+   urban_untreated = data.frame(	recovery_rate = data.frame( low = 1, mid = 1, high = 1 ),
+                   electricity_input = data.frame( low = 0 , mid = 0, high = 0 ),
+                   investment = data.frame( low = 0, mid = 0, high = 0 ),
+                   fixed_cost = data.frame( low = 0, mid = 0, high = 0 ),
+                   var_cost = data.frame( low = 0, mid = 0, high = 0 ),
+                   capacity_factor = data.frame( low = 1, mid = 1, high = 1 ),
+                   lifetime = data.frame( low = 1, mid = 1, high = 1 ) ),
+
+   # Using the 'T1' configuration option from Gonzalez-Serrano et al. 2005 in table 4 which represents discharge to surface or sea. Converted to USD using 1.27 USD / euro exchange rate
+   # Electricity intensity follows Kajenthira et al. 2014 Table 1 Appendix A: 'A New Case for Wastewater Reuse in Saudi Arabia'
+   urban_treatment = data.frame(	recovery_rate = data.frame( low = 0.95, mid = 0.9, high = 0.85 ),
+                   electricity_input = round( data.frame( low = 0.13 * ( 1e3 / (24 * 365) ) , mid = 0.38 * ( 1e3 / (24 * 365) ), high = 0.64 * ( 1e3 / (24 * 365) ) ), digits = 4 ),
+                   investment = round(data.frame( low = 303*(1/0.365), mid = 429*(1/0.365), high = 627*(1/0.365) )), # converted usd/m3/day -> million usd/km3/yr
+                   fixed_cost = round(data.frame( low = 21*(1/0.365), mid = 35*(1/0.365), high = 67*(1/0.365) )),
+                   var_cost = data.frame( low = 0, mid = 0, high = 0 ),
+                   capacity_factor = data.frame( low = 0.95, mid = 0.9, high = 0.85 ),
+                   lifetime = data.frame( low = 40, mid = 30, high = 25 ) ) ,
+
+   # Using the 'T4' configuration option from Gonzalez-Serrano et al. 2005 which is for direct injection into aquifers for domestic uses
+   # Electricity intensity follows Kajenthira et al. 2014 Table 1 Appendix A: 'A New Case for Wastewater Reuse in Saudi Arabia'
+   urban_recycle = data.frame(		recovery_rate = data.frame( low = 0.9, mid = 0.8, high = 0.7 ),
+                   electricity_input = round( data.frame( low = 0.8 * ( 1e3 / (24 * 365) ) , mid = 1 * ( 1e3 / (24 * 365) ), high = 1.5 * ( 1e3 / (24 * 365) ) ), digits = 4),  # converted kWh / m3 -> GW-yr/km3
+                   investment = round(data.frame( low = 1008*(1/0.365), mid = 1352*(1/0.365), high = 2010*(1/0.365) )), # Converted from $/m3/day to million $ / km3 / yr
+                   fixed_cost = round(data.frame( low = 63*(1/0.365), mid = 98*(1/0.365), high = 175*(1/0.365) )),
+                   var_cost = data.frame( low = 0, mid = 0, high = 0 ),
+                   capacity_factor = data.frame( low = 0.95, mid = 0.9, high = 0.85 ),
+                   lifetime = data.frame( low = 40, mid = 30, high = 25 ) ) ,
+
+   # Rural treatment: Using Septic system costs stated in per capita  from The Costs of Meeting the 2030 Sustainable Development Goal Targets on Drinking Water, Sanitation, and Hygiene
+   # The per capita level is converted to per m3 considering an average per capita wastewater requirement of 50 cubic meters of return flow per year
+   rural_treatment = data.frame(	recovery_rate = data.frame( low = 0.97, mid = 0.95, high = 0.9 ),
+                   electricity_input = data.frame( low = 0 , mid = 0, high = 0 ),
+                   investment = round( data.frame( low = ( 33 / ( 50 * 0.001 ) ) , mid = ( 104 / ( 50 * 0.001 ) ), high = ( 178 / ( 50 * 0.001 ) ) )),
+                   fixed_cost = round(data.frame( low = 0.1 * ( 33 / ( 50 * 0.001 ) ) , mid = 0.1 * ( 104 / ( 50 * 0.001 ) ), high = 0.1 * ( 178 / ( 50 * 0.001 ) ) )),
+                   var_cost = data.frame( low = 0, mid = 0, high = 0 ),
+                   capacity_factor = data.frame( low = 0.98, mid = 0.96, high = 0.94 ),
+                   lifetime = data.frame( low = 30, mid = 25, high = 20 ) ) ,
+
+   rural_untreated = data.frame(	recovery_rate = data.frame( low = 1, mid = 1, high = 1 ),
+                   electricity_input = data.frame( low = 0 , mid = 0, high = 0 ),
+                   investment = data.frame( low = 0, mid = 0, high = 0 ),
+                   fixed_cost = data.frame( low = 0, mid = 0, high = 0 ),
+                   var_cost = data.frame( low = 0, mid = 0, high = 0 ),
+                   capacity_factor = data.frame( low = 1, mid = 1, high = 1 ),
+                   lifetime = data.frame( low = 1, mid = 1, high = 1 ) )
+
+   )
+
+ inv_cost_recycle = round( data.frame( urban_recycle = (c(1301,1293,1244)/1301)* unlist( wwt_tech_par.list[['urban_recycle']][paste('investment',parameter_levels2,sep='.')] ) ), digits = 3 )
+ row.names(inv_cost_recycle) = c( 2020, 2030, 2050 )
+
+ ret = lapply( names( wwt_tech_par.list ), function(tt){
+
+   # Add to set list
+   ixDS$add_set( 'technology', tt )
+   ixDS$add_set( 'cat_tec', paste( 'investment_other' , tt, sep='.') ) # Add technology to list of water system investments
+   ixDS$add_set( 'cat_tec', paste( 'wastewater_treatment', tt, sep='.' ) ) # Add technology to list of desal techs
+
+   # Performance parameters - across all regions, and historical and future years
+   lapply( region, function(rr){
+
+     tst = as.numeric( as.character( unlist(ixDS$par('demand',list( node = rr, commodity = wwt_techs_wst[[ tt ]] ))['year']) ) )
+
+     lapply( tst, function(yy){
+
+       if( tt == 'urban_recycle' ){ cst = inv_cost_recycle[ which.min( (as.numeric(yy) - as.numeric(row.names(inv_cost_recycle)))^2 ),tt ] }else{ cst = round( unlist( wwt_tech_par.list[[tt]][paste('investment',parameter_levels2,sep='.')] ), digits = 3 ) }
+       # Add the investment cost
+       ixDS$add_par( 'inv_cost', # parameter name
+           paste(rr, tt, yy, sep = '.' ), # set key
+           cst,
+           '-' )
+
+       # Add the technical lifetime
+       ixDS$add_par( 'technical_lifetime', # parameter name
+             paste(rr, tt, yy, sep = '.' ), # set key
+             round( unlist( wwt_tech_par.list[[tt]][paste('lifetime',parameter_levels,sep='.')] ), digits = 3 ),
+             'y' )
+
+       # Add the construction time
+       ixDS$add_par( 'construction_time', # parameter name
+             paste(rr, tt, yy, sep = '.' ), # set key
+             3,
+             'y' )
+
+       # Add the historical capacity and activity
+       if( yy <= 2010 )
+         {
+
+         if( tt == 'urban_recycle' | tt == 'urban_treatment' )
+           {
+
+           if( tt == 'urban_recycle' )
+             {
+             ixDS$add_par( 	'historical_activity', # parameter name
+                     paste(  rr, tt, yy, mode_common , model_time, sep = '.' ), # set key
+                     round( abs( unlist(ixDS$par('demand',list( node = rr, commodity = 'urban_collected_wst', year = yy )))['value'] * urban_connected_fraction.df[ '2010', unlist( strsplit( rr, '_') )[2] ] * urban_reuse_fraction.df[ '2010', unlist( strsplit( rr, '_') )[2] ] ), digits = 2),
+                     '-' )
+             if( yy == min( tst ) )
+               {
+               ixDS$add_par( 	'historical_new_capacity', # parameter name
+                       paste(  rr, tt, yy, sep = '.' ), # set key
+                       round( 1/10*abs( unlist(ixDS$par('demand',list( node = rr, commodity = 'urban_collected_wst', year = yy )))['value'] * urban_connected_fraction.df[ '2010', unlist( strsplit( rr, '_') )[2] ] * urban_reuse_fraction.df[ '2010', unlist( strsplit( rr, '_') )[2] ] ), digits = 2),
+                       '-' )
+               }else
+               {
+               ixDS$add_par( 	'historical_new_capacity', # parameter name
+                       paste(  rr, tt, yy, sep = '.' ), # set key
+                       max( 0, 1/5*c( round( abs( unlist(ixDS$par('demand',list( node = rr, commodity = 'urban_collected_wst', year = yy )))['value'] * urban_connected_fraction.df[ '2010', unlist( strsplit( rr, '_') )[2] ] * urban_reuse_fraction.df[ '2010', unlist( strsplit( rr, '_') )[2] ] ), digits = 2) - round( abs( unlist(ixDS$par('demand',list( node = rr, commodity = 'urban_collected_wst', year = tst[ ( which( tst == yy ) - 1 ) ] )))['value'] * urban_connected_fraction.df[ '2010', unlist( strsplit( rr, '_') )[2] ] * urban_reuse_fraction.df[ '2010', unlist( strsplit( rr, '_') )[2] ] ), digits = 2) ) ),
+                       '-' )
+               }
+             }else # urban_treatment - need to subtract recycled shared
+             {
+             ixDS$add_par( 	'historical_activity', # parameter name
+                     paste(  rr, tt, yy, mode_common , model_time, sep = '.' ), # set key
+                     round( abs( unlist(ixDS$par('demand',list( node = rr, commodity = wwt_techs_wst[[ tt ]], year = yy )))['value'] - round( c( unlist(ixDS$par('demand',list( node = rr, commodity = 'urban_collected_wst', year = yy )))['value'] * urban_connected_fraction.df[ '2010', unlist( strsplit( rr, '_') )[2] ] * urban_reuse_fraction.df[ '2010', unlist( strsplit( rr, '_') )[2] ] ), digits = 2) ), digits = 2),
+                     '-' )
+             if( yy == min( tst ) )
+               {
+               ixDS$add_par( 	'historical_new_capacity', # parameter name
+                       paste(  rr, tt, yy, sep = '.' ), # set key
+                       round( 1/10 * abs( unlist(ixDS$par('demand',list( node = rr, commodity = wwt_techs_wst[[ tt ]], year = yy )))['value'] - round( c( unlist(ixDS$par('demand',list( node = rr, commodity = 'urban_collected_wst', year = yy )))['value'] * urban_connected_fraction.df[ '2010', unlist( strsplit( rr, '_') )[2] ] * urban_reuse_fraction.df[ '2010', unlist( strsplit( rr, '_') )[2] ] ), digits = 2) ), digits = 2),
+                       '-' )
+               }else
+               {
+               ixDS$add_par( 	'historical_new_capacity', # parameter name
+                       paste(  rr, tt, yy, sep = '.' ), # set key
+                       round( 1/5 * abs( max( 0, abs( unlist(ixDS$par('demand',list( node = rr, commodity = wwt_techs_wst[[ tt ]], year = yy )))['value'] - unlist( ixDS$par( 'demand',list( node = rr, commodity = wwt_techs_wst[[ tt ]], year = tst[ ( which( tst == yy ) - 1 ) ] ) ) )['value'] ) ) - max( 0, abs( round( c( unlist(ixDS$par('demand',list( node = rr, commodity = 'urban_collected_wst', year = yy )))['value'] * urban_connected_fraction.df[ '2010', unlist( strsplit( rr, '_') )[2] ] * urban_reuse_fraction.df[ '2010', unlist( strsplit( rr, '_') )[2] ] ), digits = 2) - round( abs( unlist(ixDS$par('demand',list( node = rr, commodity = 'urban_collected_wst', year = tst[ ( which( tst == yy ) - 1 ) ] )))['value'] * urban_connected_fraction.df[ '2010', unlist( strsplit( rr, '_') )[2] ] * urban_reuse_fraction.df[ '2010', unlist( strsplit( rr, '_') )[2] ] ), digits = 2) ) ) ), digits = 2),
+                       '-' )
+               }
+             }
+
+           }else
+           {
+
+           ixDS$add_par( 	'historical_activity', # parameter name
+                   paste(  rr, tt, yy, mode_common , model_time, sep = '.' ), # set key
+                   abs(unlist(ixDS$par('demand',list( node = rr, commodity = wwt_techs_wst[[ tt ]], year = yy )))['value']),
+                   '-' )
+
+           if( yy == min( tst ) )
+             {
+             ixDS$add_par( 	'historical_new_capacity', # parameter name
+                     paste(  rr, tt, yy, sep = '.' ), # set key
+                     round( 1/10 * abs(unlist(ixDS$par('demand',list( node = rr, commodity = wwt_techs_wst[[ tt ]], year = yy )))['value']), digits = rnd ),
+                     '-' )
+             }else
+             {
+             ixDS$add_par( 	'historical_new_capacity', # parameter name
+                     paste(  rr, tt, yy, sep = '.' ), # set key
+                     round( 1/5 * max( 0, c( abs(unlist(ixDS$par('demand',list( node = rr, commodity = wwt_techs_wst[[ tt ]], year = yy )))['value']) - abs(unlist( ixDS$par( 'demand',list( node = rr, commodity = wwt_techs_wst[[ tt ]], year = tst[ ( which( tst == yy ) - 1 ) ] ) ) )['value']) ) ), digits = rnd ),
+                     '-' )
+             }
+
+           }
+
+         }
+
+       # Add the historical capacity and activity for locked-in future years in the mitigation and no policy scenarios (e.g., 2030, 2040)
+       if( yy > 2010 & yy < firstmodelyear )
+         {
+         ixDS$add_par( 'historical_activity', paste(  rr, as.character( tt ), yy, mode_common , model_time, sep = '.' ), max(0, base_hist_act[ which( as.character( unlist( base_hist_act$tec ) ) == as.character( d_tech[[ tt ]] ) & as.numeric( unlist( base_hist_act$year_all ) ) == yy & as.character( unlist( base_hist_act$node ) ) == rr ), 'val' ], na.rm=TRUE), '-' )
+         ixDS$add_par( 'historical_new_capacity', paste(  rr, as.character( tt ), yy, sep = '.' ), max(0, base_hist_ncap[ which( as.character( unlist( base_hist_ncap$tec ) ) == as.character( d_tech[[ tt ]] ) & as.numeric( unlist( base_hist_ncap$year_all ) ) == yy & as.character( unlist( base_hist_ncap$node ) ) == rr ), 'val' ], na.rm=TRUE), '-' )
+         }
+
+       # # Add upper bounds to reflect availability of return flows
+       # if( tt %in% c('urban_recycle') & yy >= 2010 )
+         # {
+         # mxp = 0.6
+         # mx = mxp * abs( unlist(ixDS$par('demand',list( node = rr, commodity = wwt_techs_wst[[ tt ]], year = yy ))['value']) )
+         # ixDS$add_par( 'bound_activity_up', # parameter name
+                 # paste(  rr, tt, yy, mode_common , model_time, sep = '.' ), # set key
+                 # round( mx, digits = 2 ),
+                 # '-' )
+         # }
+
+       # Add activity bounds for urban recycling technologies to reflect demand in water stressed regions
+       if( yy > 2010 & tt == 'urban_recycle' )
+         {
+         if( yy <  max( as.numeric( row.names( urban_withdrawal.df ) ) ) )
+           {
+           ixDS$add_par( 'bound_activity_lo', # parameter name
+                 paste(  rr, tt, yy, mode_common , model_time, sep = '.' ), # set key
+                 round( c( urban_return.df[ as.character( yy ), unlist( strsplit( rr, '_') )[2] ] * urban_treated_fraction.df[ as.character( yy ), unlist( strsplit( rr, '_') )[2] ] * urban_reuse_fraction.df[ as.character( yy ), unlist( strsplit( rr, '_') )[2] ] ), digits = 2),
+                 '-' )
+           }else
+           {
+           ixDS$add_par( 'bound_activity_lo', # parameter name
+                 paste(  rr, tt, yy, mode_common , model_time, sep = '.' ), # set key
+                 round( c( urban_return.df[ as.character( max( as.numeric( row.names( urban_withdrawal.df ) ) ) ), unlist( strsplit( rr, '_') )[2] ] * urban_treated_fraction.df[ as.character( max( as.numeric( row.names( urban_withdrawal.df ) ) ) ), unlist( strsplit( rr, '_') )[2] ] * urban_reuse_fraction.df[ as.character( max( as.numeric( row.names( urban_withdrawal.df ) ) ) ), unlist( strsplit( rr, '_') )[2] ] ), digits = 2),
+                 '-' )
+           }
+         }
+       # if( yy > 2010 & tt == 'urban_treatment' )
+         # {
+         # if( yy <  max( as.numeric( row.names( urban_withdrawal.df ) ) ) )
+           # {
+           # ixDS$add_par( 'bound_activity_lo', # parameter name
+                 # paste(  rr, tt, yy, mode_common , model_time, sep = '.' ), # set key
+                 # round( c( urban_return.df[ as.character( yy ), unlist( strsplit( rr, '_') )[2] ] * urban_treated_fraction.df[ as.character( yy ), unlist( strsplit( rr, '_') )[2] ] * (1-urban_reuse_fraction.df[ as.character( yy ), unlist( strsplit( rr, '_') )[2] ]) ), digits = 2),
+                 # '-' )
+           # }else
+           # {
+           # ixDS$add_par( 'bound_activity_lo', # parameter name
+                 # paste(  rr, tt, yy, mode_common , model_time, sep = '.' ), # set key
+                 # round( c( urban_return.df[ as.character( max( as.numeric( row.names( urban_withdrawal.df ) ) ) ), unlist( strsplit( rr, '_') )[2] ] * urban_treated_fraction.df[ as.character( max( as.numeric( row.names( urban_withdrawal.df ) ) ) ), unlist( strsplit( rr, '_') )[2] ] * (1-urban_reuse_fraction.df[ as.character( max( as.numeric( row.names( urban_withdrawal.df ) ) ) ), unlist( strsplit( rr, '_') )[2] ]) ), digits = 2),
+                 # '-' )
+           # }
+         # }
+
+       # Add i-o parameters
+       ret = lapply( tst[ which( tst >= as.numeric(yy) & tst <= ( as.numeric(yy) + unlist( wwt_tech_par.list[[tt]][paste('lifetime',parameter_levels,sep='.')]  ) ) ) ], function(aa){
+
+
+         if( tt %in% c('urban_treatment','urban_recycle')){inlvl = water_treat_level}else{inlvl = 'final'}
+
+         # Add the input water efficiency ratio
+         ixDS$add_par( 'input', # parameter name
+           paste( rr, tt, yy, aa, mode_common, rr, wwt_techs_wst[[ tt ]], inlvl, model_time, model_time, sep = '.' ), # set key
+           1, # parameter value
+           '-' )
+
+
+         # Add the input efficiency ratio - electricity
+         ixDS$add_par( 'input', # parameter name
+           paste( rr, tt, yy, aa, mode_common, rr, 'electr', 'final', model_time, model_time, sep = '.' ), # set key
+           round( unlist( wwt_tech_par.list[[tt]][paste('electricity_input',parameter_levels2,sep='.')] ), digits = 5 ), # parameter value
+           '-' )
+
+         # Add the output efficiency ratio - recycled water, and bound the activity based on the available return flow
+         if( !is.na( wwt_techs_wout[[ tt ]] ) )
+           {
+           ixDS$add_par( 'output', # parameter name
+             paste( rr, tt, yy, aa, mode_common, rr, wwt_techs_wout[[ tt ]], water_supply_level, model_time, model_time, sep = '.' ), # set key
+             round( unlist( wwt_tech_par.list[[tt]][paste('recovery_rate',parameter_levels2,sep='.')] ), digits = 3 ), # parameter value
+             '-' )
+           }
+
+         if( !is.na( wwt_techs_wems[[ tt ]] ) )
+           {
+
+           # Add treated wastewater as emission
+           ixDS$add_par( 'emission_factor', # parameter name
+               paste(  rr, tt, yy, aa, mode_common, wwt_techs_wems[[ tt ]], sep = '.' ), # set key
+               round( unlist( wwt_tech_par.list[[tt]][paste('recovery_rate',parameter_levels2,sep='.')] ), digits = 3 ) ,
+               '-' )
+           }
+
+         # Add the variable cost
+         ixDS$add_par( 'var_cost', # parameter name
+           paste( rr, tt, yy, aa, mode_common, model_time, sep = '.' ), # set key
+           unlist( wwt_tech_par.list[[tt]][paste('var_cost',parameter_levels2,sep='.')] ),
+           '-' ) # units
+
+         # Add fixed costs
+         ixDS$add_par( 'fix_cost', # parameter name
+           paste( rr, tt, yy, aa, sep = '.' ), # set key
+           unlist( wwt_tech_par.list[[tt]][paste('fixed_cost',parameter_levels2,sep='.')] ),
+           '-' )
+
+         # Add the capacity factor
+         ixDS$add_par( 'capacity_factor', # parameter name
+           paste( rr, tt, yy, aa, model_time, sep = '.' ), # set key
+           unlist( wwt_tech_par.list[[tt]][paste('capacity_factor',parameter_levels,sep='.')] ),
+           '-' )
+
+         } )
+
+       } )
+
+     } )
+
+   } )
+
+ #-------------------------------------------------------------------------------------------------------
+ # Add stand-alone desalination technologies and desalination demand
+ #-------------------------------------------------------------------------------------------------------
+
+ # Initialize general technology and commodity parameters for desalination
+ desalination_level = 'desalination_supply'
+ ret = ixDS$add_set( 'level', desalination_level ) # Add level to ix DB
+ desalination_tech_type = 'desalination'
+ ret = ixDS$add_set( 'type_tec', desalination_tech_type )
+ desalination_commodity = 'desalinated_water'
+ ret = lapply( desalination_commodity, function(x){ ixDS$add_set( 'commodity', as.character( x ) ) } ) # Add commodity to ix DB
+
+ # Stand-alone desalination technologies to be added
+ desalination_technologies = c( 'membrane_desal',
+                  'distillation_desal' )
+
+ # Define performance range - assuming freshwater output in km3 for desal tech is 1 to 1 with the activity level
+ # Energy intensities from Ghaffour et al (2013) 'Technical review and evaluation of the economics of water desalination'
+ # Thermal balances from 'Desalination and advanced wastewater treatment: Economics and Financing' by Corrado Sommariva
+ desal_par.list = list(
+
+   distillation = data.frame(	recovery_rate = data.frame( low = 0.4, mid = 0.35, high = 0.3 ),
+                 cooling_water_withdrawal = data.frame( low = 2.4, mid = 5.2, high = 7.1 ), # Estimated from Table 6.2 in 'Desalination and advanced wastewater treatment: Economics and Financing' by Corrado Sommariva
+                 cooling_water_return = data.frame( low = 2.3, mid = 5.1, high = 7.0 ), # from
+                 electricity_input = data.frame( low = 0.171 , mid = 0.342, high = 0.456 ), # Converted from kWh/m3 to GW-yr/km3
+                 heat_input = data.frame( low = 0.456, mid = 1.026, high = 1.368 ), # Converted from kWh/m3 to GW-yr/km3
+                 thermal_pollution = data.frame( low = 0.360, mid = 0.952, high = 1.081 ), # Estimated from Figure 6.1 in 'Desalination and advanced wastewater treatment: Economics and Financing' by Corrado Sommariva
+                 investment = round(data.frame( low = 1750*(1/0.365), mid = 1900*(1/0.365), high = 2200*(1/0.365) )),
+                 fixed_cost = round(data.frame( low = 0.1*1350*(1/0.365), mid = 0.1*1550*(1/0.365), high = 0.1*2000*(1/0.365) )),
+                 var_cost = data.frame( low = 0, mid = 0, high = 0 ),
+                 capacity_factor = data.frame( low = 0.9, mid = 0.85, high = 0.8 ),
+                 lifetime = data.frame( low = 50, mid = 30, high = 25 ) ) ,
+
+   membrane = data.frame(	recovery_rate = data.frame( low = 0.45, mid = 0.4, high = 0.35 ), # inverse of recovery rate in km3/km3
+               cooling_water_withdrawal = data.frame( low = 0, mid = 0, high = 0 ), #
+               cooling_water_return = data.frame( low = 0, mid = 0, high = 0 ), #
+               electricity_input = data.frame( low = 0.342, mid = 0.456, high = 0.571 ), # Converted from kWh/m3 to GW-yr/km3
+               heat_input = data.frame( low = 0, mid = 0, high = 0 ), #
+               thermal_pollution = data.frame( low = 0, mid = 0, high = 0 ), #
+               investment = round( data.frame( low = 1300*(1/0.365), mid = 1550*(1/0.365), high = 1800*(1/0.365) ) ), # Converted from $/m3/day to million $ / km3 / yr
+               fixed_cost = round( 0.1 * data.frame( low = 1300*(1/0.365), mid = 1650*(1/0.365), high = 1800*(1/0.365) ) ),
+               var_cost = data.frame( low = 0, mid = 0, high = 0 ),
+               capacity_factor = data.frame( low = 0.9, mid = 0.85, high = 0.8 ),
+               lifetime = data.frame( low = 50, mid = 30, high = 25) )
+
+   )
+
+ inv_cost_desal = round( data.frame( membrane = ( (c(1301,1293,1244)/1301)* unlist( desal_par.list[['membrane']][paste('investment',parameter_levels2,sep='.')] ) ), distillation = ( (c(1301,1293,1244)/1301)* unlist( desal_par.list[['distillation']][paste('investment',parameter_levels2,sep='.')] ) ) ), digits = 3 )
+ row.names(inv_cost_desal) = c(2020,2030,2050)
+
+ # Load in the cleaned desal database from Hanasaki et al 2016 and add MESSAGE regions
+ reg.spdf = readOGR('P:/ene.model/data/desalination','REGION_dissolved',verbose=FALSE)
+ global_desal.spdf = spTransform( readOGR('P:/ene.model/data/desalination','global_desalination_plants'), crs(reg.spdf) )
+ global_desal.spdf@data$region = over(global_desal.spdf,reg.spdf[,which(names(reg.spdf) == 'REGION')])
+ global_desal.spdf = global_desal.spdf[ -1*which( is.na(global_desal.spdf@data$region) ), ]
+ global_desal.spdf@data$msg_vintage = sapply( global_desal.spdf@data$online, function(x){ as.numeric( model_years[ which.min( ( as.numeric( model_years ) - x )^2 ) ] ) } )
+ global_desal.spdf@data$technology_2 = sapply( global_desal.spdf@data$technology, function(x){ if( grepl('MSF',x)|grepl('MED',x) ){ return('distillation') }else{ return('membrane') }} )
+
+ # From : Chart 1.1 in 'Executive Summary Desalination Technology Markets Global Demand Drivers, Technology Issues, Competitive Landscape, and Market Forecasts'
+ # Global desalination capacity in 2010 was approx. 24 km3 / year
+ global_desal.spdf@data$m3_per_day = global_desal.spdf@data$m3_per_day  * ( 24  / ( sum( global_desal.spdf@data$m3_per_day ) * 365 / 1e9 ) )
+
+ # Match to message vintaging and regions
+ historical_desal_capacity.list = lapply( c('membrane','distillation'), function(tt){
+   temp = data.frame( do.call(cbind, lapply( region, function(reg){ sapply( unique(global_desal.spdf@data$msg_vintage)[order(unique(global_desal.spdf@data$msg_vintage))], function(y){ (365/1e9) * max( 0,  sum( global_desal.spdf@data$m3_per_day[ which( global_desal.spdf@data$msg_vintage == y & global_desal.spdf@data$region == unlist(strsplit(reg,'_'))[2] & global_desal.spdf@data$technology_2 == tt ) ] , na.rm=TRUE ), na.rm=TRUE ) } ) } ) ) )
+   names(temp) = region
+   row.names(temp) = unique(global_desal.spdf@data$msg_vintage)[order(unique(global_desal.spdf@data$msg_vintage))]
+   return(temp)
+   } )
+ names(historical_desal_capacity.list) = c('membrane','distillation')
+
+ # Add existing infrastructure and performance parameters for stand alone desal techs
+ ret = lapply( names(historical_desal_capacity.list), function(tt){
+
+   # Add to set list
+   ixDS$add_set( 'technology', tt )
+   ixDS$add_set( 'cat_tec', paste( 'investment_other' , tt, sep='.') ) # Add technology to list of water system investments
+   ixDS$add_set( 'cat_tec', paste( desalination_tech_type, tt, sep='.') ) # Add technology to list of desal techs
+
+   # Performance parameters - across all historical and future years
+   lapply( region, function(rr){ lapply( model_years[ which( model_years >= min( as.numeric( row.names( historical_desal_capacity.list[[tt]] ) ) ) ) ], function(yy){
+
+     cst = inv_cost_desal[ which.min( (as.numeric(yy) - as.numeric(row.names(inv_cost_desal)))^2 ),tt ]
+     # Add the investment cost
+     ixDS$add_par( 'inv_cost', # parameter name
+         paste(rr, tt, yy, sep = '.' ), # set key
+         unlist( cst ),
+         '-' )
+
+     # Add the technical lifetime
+     ixDS$add_par( 'technical_lifetime', # parameter name
+           paste(rr, tt, yy, sep = '.' ), # set key
+           unlist( desal_par.list[[tt]][paste('lifetime',parameter_levels2,sep='.')] ),
+           'y' )
+
+     # Add the construction time
+     ixDS$add_par( 'construction_time', # parameter name
+           paste(rr, tt, yy, sep = '.' ), # set key
+           3,
+           'y' )
+
+     # Add the historical capacity and activity for locked-in future years in the mitigation and no policy scenarios (e.g., 2030, 2040)
+     if( yy > 2010 & yy < firstmodelyear )
+       {
+       ixDS$add_par( 'historical_activity', paste(  rr, as.character( tt ), yy, mode_common , model_time, sep = '.' ), max(0, base_hist_act[ which( as.character( unlist( base_hist_act$tec ) ) == as.character( d_tech[[ tt ]] ) & as.numeric( unlist( base_hist_act$year_all ) ) == yy & as.character( unlist( base_hist_act$node ) ) == rr ), 'val' ], na.rm=TRUE), '-' )
+       ixDS$add_par( 'historical_new_capacity', paste(  rr, as.character( tt ), yy, sep = '.' ), max(0, base_hist_ncap[ which( as.character( unlist( base_hist_ncap$tec ) ) == as.character( d_tech[[ tt ]] ) & as.numeric( unlist( base_hist_ncap$year_all ) ) == yy & as.character( unlist( base_hist_ncap$node ) ) == rr ), 'val' ], na.rm=TRUE), '-' )
+       }
+
+     # Add i-o parameters
+     lapply( model_years[ which( model_years >= as.numeric(yy) & model_years <= ( as.numeric(yy) + unlist( desal_par.list[[tt]][paste('lifetime',parameter_levels,sep='.')]  ) ) ) ], function(aa){
+
+       # Add the output efficiency ratio
+       ixDS$add_par( 'output', # parameter name
+         paste( rr, tt, yy, aa, mode_common, rr, 'desalinated_water', desalination_level, model_time, model_time, sep = '.' ), # set key
+         1, # parameter value
+         '-' )
+
+       # Add the input efficiency ratio - seawater
+       ixDS$add_par( 'input', # parameter name
+         paste( rr, tt, yy, aa, mode_common, rr, 'saline_supply', water_supply_level, model_time, model_time, sep = '.' ), # set key
+         round( c( unlist( desal_par.list[[tt]][ paste('cooling_water_withdrawal',parameter_levels2,sep='.') ] ) + unlist( desal_par.list[[tt]][paste('recovery_rate',parameter_levels,sep='.')] )^-1 ), digits = 5), # parameter value
+         '-' )
+
+       # Add the input efficiency ratio - electricity
+       ixDS$add_par( 'input', # parameter name
+         paste( rr, tt, yy, aa, mode_common, rr, 'electr', 'final', model_time, model_time, sep = '.' ), # set key
+         round( unlist( desal_par.list[[tt]][paste('electricity_input',parameter_levels2,sep='.')] ), digits = 5 ), # parameter value
+         '-' )
+
+       # Add the input efficiency ratio - heat
+       ixDS$add_par( 'input', # parameter name
+         paste( rr, tt, yy, aa, mode_common, rr, 'd_heat', 'final', model_time, model_time, sep = '.' ), # set key
+         round( unlist( desal_par.list[[tt]][paste('heat_input',parameter_levels2,sep='.')] ), digits = 5 ), # parameter value
+         '-' )
+
+       # Add thermal pollution emission
+       ixDS$add_par( 'emission_factor', # parameter name
+           paste(  rr, tt, yy, aa, mode_common, 'saline_thermal_pollution', sep = '.' ), # set key
+           round( unlist( desal_par.list[[tt]][paste('thermal_pollution',parameter_levels2,sep='.')] ), digits = 5 ),
+           '-' )
+
+       # Add wastewater (brine)
+       ixDS$add_par( 'emission_factor', # parameter name
+           paste(  rr, tt, yy, aa, mode_common, 'saline_wastewater', sep = '.' ), # set key
+           round( c( unlist( desal_par.list[[tt]][paste('cooling_water_return',parameter_levels2,sep='.')] + (1 - desal_par.list[[tt]][paste('recovery_rate',parameter_levels,sep='.')] ) ) ), digits = 5) ,
+           '-' )
+
+       # Add the variable cost
+       ixDS$add_par( 'var_cost', # parameter name
+         paste( rr, tt, yy, aa, mode_common, model_time, sep = '.' ), # set key
+         unlist( desal_par.list[[tt]][paste('var_cost',parameter_levels2,sep='.')] ),
+         '-' ) # units
+
+       # Add fixed costs
+       ixDS$add_par( 'fix_cost', # parameter name
+         paste( rr, tt, yy, aa, sep = '.' ), # set key
+         unlist( desal_par.list[[tt]][paste('fixed_cost',parameter_levels2,sep='.')] ),
+         '-' )
+
+       # Add the capacity factor
+       ixDS$add_par( 'capacity_factor', # parameter name
+         paste( rr, tt, yy, aa, model_time, sep = '.' ), # set key
+         unlist( desal_par.list[[tt]][paste('capacity_factor',parameter_levels2,sep='.')] ),
+         '-' )
+
+       } )
+
+     } ) } )
+
+   # Add existing infrastructure and calibrate demands
+   lapply( region, function(rr){ lapply( row.names( historical_desal_capacity.list[[tt]] ), function(yy){
+
+     # Add the historical capacity
+     ixDS$add_par( 'historical_new_capacity',
+             paste( rr, tt, yy, sep = '.' ),
+             round( 1/5*unlist( historical_desal_capacity.list[[tt]][as.character(yy),rr] ), digits = 5),
+             '-' )
+
+     # Add the historical activity, calibrate the demands and bound lower activities of historical capacity to ensure utilization in future years
+     lapply( model_years[ which( model_years >= as.numeric(yy) & model_years <= ( as.numeric(yy)+ 5 + unlist( desal_par.list[[tt]][paste('lifetime',parameter_levels,sep='.')]  ) ) ) ], function(aa){
+
+       # Add the historical activity and demands
+       if( as.numeric( aa ) <= 2010 )
+         {
+         chk =  tryCatch( ixDS$par( 'historical_activity', list( node_loc = rr, technology = tt, year_act = aa, mode = mode_common, time = model_time ) ), error = function(e){} )
+         if( is.null( chk ) )
+           {
+           ixDS$add_par( 'historical_activity', # parameter name
+             paste(  rr, tt, aa, mode_common , model_time, sep = '.' ), # set key
+             round( unlist( desal_par.list[[tt]][paste('capacity_factor',parameter_levels,sep='.')] ) * unlist( historical_desal_capacity.list[[tt]][as.character(yy),rr] ), digits = 5 ), # parameter value
+             '-' )
+           }else
+           {
+           ixDS$add_par( 'historical_activity', # parameter name
+             paste(  rr, tt, aa, mode_common , model_time, sep = '.' ), # set key
+             round( unlist( chk['value'] ) + unlist( desal_par.list[[tt]][paste('capacity_factor',parameter_levels,sep='.')] ) * unlist( historical_desal_capacity.list[[tt]][as.character(yy),rr] ), digits = 5 ), # parameter value
+             '-' )
+           }
+         }else
+         {
+         # Add lower activity bounds in future years to calibrate to make sure capacity is utilized - added cumulatively to account for vintaging
+         chk =  tryCatch( ixDS$par( 'bound_activity_lo', list( node_loc = rr, technology = tt, year_act = aa, mode = mode_common, time = model_time ) ), error = function(e){} )
+         if( is.null( chk ) )
+           {
+           ixDS$add_par( 'bound_activity_lo', # parameter name
+             paste(  rr, tt, aa, mode_common , model_time, sep = '.' ), # set key
+             round( unlist( desal_par.list[[tt]][paste('capacity_factor',parameter_levels,sep='.')] ) * unlist( historical_desal_capacity.list[[tt]][as.character(yy),rr] ), digits = 5 ), # parameter value
+             '-' )
+           }else
+           {
+           ixDS$add_par( 'bound_activity_lo', # parameter name
+             paste(  rr, tt, aa, mode_common , model_time, sep = '.' ), # set key
+             round( unlist( chk['value'] ) + unlist( desal_par.list[[tt]][paste('capacity_factor',parameter_levels,sep='.')] ) * unlist( historical_desal_capacity.list[[tt]][as.character(yy),rr] ), digits = 5 ), # parameter value
+             '-' )
+           }
+         }
+
+       # calibrate energy demands
+       chk =  tryCatch( ixDS$par( 'demand', list( node = rr, commodity = 'i_spec', level = 'useful', year = aa, time = model_time ) ), error = function(e){} )
+       if(!is.null( chk ) )
+         {
+         ixDS$add_par( 'demand', # parameter name
+           paste( rr, 'i_spec', 'useful', aa, model_time, sep = '.' ), # set key
+           round( c( max( 0,  unlist( chk['value'] ) - unlist( desal_par.list[[tt]][paste('capacity_factor',parameter_levels,sep='.')] ) * unlist( historical_desal_capacity.list[[tt]][as.character(yy),rr] ) * unlist( desal_par.list[[tt]][paste('electricity_input',parameter_levels,sep='.')] ) ) ),  digits = 5),
+           'GWa' )
+         }
+
+       chk =  tryCatch( ixDS$par( 'demand', list( node = rr, commodity = 'i_therm', level = 'useful', year = aa, time = model_time ) ), error = function(e){} )
+       if(!is.null( chk ) )
+         {
+         ixDS$add_par( 'demand', # parameter name
+           paste( rr, 'i_therm', 'useful', aa, model_time, sep = '.' ), # set key
+           round( c( max( 0,  ( unlist( chk['value'] ) - unlist( desal_par.list[[tt]][paste('capacity_factor',parameter_levels,sep='.')] ) * unlist( historical_desal_capacity.list[[tt]][as.character(yy),rr] ) * unlist( desal_par.list[[tt]][paste('heat_input',parameter_levels,sep='.')] ) ) ) ), digits = 5),
+           'GWa' )
+         }
+
+       } )
+
+     } ) } )
+
+   } )
+
+ #### Aggregate the desalination production to allow for activity constraints covering all types of this technology group
+
+ # Add to set list
+ tt = 'desal_t_d'
+ ixDS$add_set( 'technology', tt )
+ ixDS$add_set( 'cat_tec', paste( 'investment_other' , tt, sep='.') ) # Add technology to list of water system investments
+ ixDS$add_set( 'cat_tec', paste( desalination_tech_type, tt, sep='.') ) # Add technology to list of desal techs
+
+ # Performance parameters - across all historical and future years
+ ret = lapply( region, function(rr){ lapply( model_years[ which( model_years >= min( as.numeric( row.names( historical_desal_capacity.list[[1]] ) ) ) ) ], function(yy){
+
+   # Add the investment cost
+   ixDS$add_par( 'inv_cost', # parameter name
+       paste(rr, tt, yy, sep = '.' ), # set key
+       0,
+       '-' )
+
+   # Add the technical lifetime
+   ixDS$add_par( 'technical_lifetime', # parameter name
+         paste(rr, tt, yy, sep = '.' ), # set key
+         1,
+         'y' )
+
+   # Add the construction time
+   ixDS$add_par( 'construction_time', # parameter name
+         paste(rr, tt, yy, sep = '.' ), # set key
+         1,
+         'y' )
+
+   # Add i-o parameters
+   lapply( model_years[ which( model_years >= as.numeric(yy) & model_years <= ( as.numeric(yy) + unlist( desal_par.list[[1]][paste('lifetime',parameter_levels,sep='.')]  ) ) ) ], function(aa){
+
+     # Add the output efficiency ratio
+     ixDS$add_par( 'output', # parameter name
+       paste( rr, tt, yy, aa, mode_common, rr, 'freshwater_supply', water_supply_level, model_time, model_time, sep = '.' ), # set key
+       1, # parameter value
+       '-' )
+
+     # Add the input efficiency ratio
+     ixDS$add_par( 'input', # parameter name
+       paste( rr, tt, yy, aa, mode_common, rr, 'desalinated_water', desalination_level, model_time, model_time, sep = '.' ), # set key
+       1,
+       '-' )
+
+     # Add the variable cost
+     ixDS$add_par( 'var_cost', # parameter name
+       paste( rr, tt, yy, aa, mode_common, model_time, sep = '.' ), # set key
+       0,
+       '-' ) # units
+
+     # Add fixed costs
+     ixDS$add_par( 'fix_cost', # parameter name
+       paste( rr, tt, yy, aa, sep = '.' ), # set key
+       0,
+       '-' )
+
+     # Add the capacity factor
+     ixDS$add_par( 'capacity_factor', # parameter name
+       paste( rr, tt, yy, aa, model_time, sep = '.' ), # set key
+       1,
+       '-' )
+
+     } )
+
+   } ) } )
+
+ # Add existing infrastructure
+ ret = lapply( names(historical_desal_capacity.list), function(dd){ lapply( region, function(rr){ lapply( row.names( historical_desal_capacity.list[[dd]] ), function(yy){
+
+   # Add the historical capacity
+   chk =  tryCatch( ixDS$par( 'historical_new_capacity', list( node_loc = rr, technology = tt, year = yy ) ), error = function(e){} )
+     if( is.null( chk ) )
+       {
+       ixDS$add_par( 'historical_new_capacity',
+           paste( rr, tt, yy, sep = '.' ),
+           round( 1/5 * unlist( historical_desal_capacity.list[[dd]][as.character(yy),rr] ), digits = 5),
+           '-' )
+       }else
+       {
+       ixDS$add_par( 'historical_new_capacity',
+           paste( rr, tt, yy, sep = '.' ),
+           round( unlist( chk['value'] ) + 1/5 * unlist( historical_desal_capacity.list[[dd]][as.character(yy),rr] ), digits = 5),
+           '-' )
+       }
+
+   # Add the historical activity
+   tstp = model_years[ which( model_years >= as.numeric(yy) & model_years <= 2010 ) ]
+   if( length(tstp) > 0 )
+     {
+     lapply( tstp, function(aa){
+
+       chk =  tryCatch( ixDS$par( 'historical_activity', list( node_loc = rr, technology = tt, year_act = aa, mode = mode_common, time = model_time ) ), error = function(e){} )
+
+       if( is.null( chk ) )
+         {
+         ixDS$add_par( 'historical_activity', # parameter name
+           paste(  rr, tt, aa, mode_common , model_time, sep = '.' ), # set key
+           round( unlist( desal_par.list[[dd]][paste('capacity_factor',parameter_levels,sep='.')] ) * unlist( historical_desal_capacity.list[[dd]][as.character(yy),rr] ), digits = 5 ), # parameter value
+           '-' )
+         }else
+         {
+         ixDS$add_par( 'historical_activity', # parameter name
+           paste(  rr, tt, aa, mode_common , model_time, sep = '.' ), # set key
+           round( unlist( chk['value'] ) + unlist( desal_par.list[[dd]][paste('capacity_factor',parameter_levels,sep='.')] ) * unlist( historical_desal_capacity.list[[dd]][as.character(yy),rr] ), digits = 5 ), # parameter value
+           '-' )
+         }
+
+       } )
+
+     }
+
+   } ) } ) } )
+
+ # # Add future desalination demands based on projections developed previously  - set as activity bound on aggregate desal tech
+ ret = lapply( region, function(rr){ lapply( model_years[ which( model_years > 2010 ) ], function(yy){
+   if( as.numeric(yy) < 2100 )
+     {
+     ixDS$add_par( 'bound_activity_lo', # parameter name
+         paste(  rr, tt, yy, mode_common , model_time, sep = '.' ), # set key
+         round( c( urban_desal_fraction.df[ as.character( yy ), unlist( strsplit( rr, '_') )[2] ] * urban_withdrawal.df[ as.character( yy ), unlist( strsplit( rr, '_') )[2] ] ), digits = 2), # parameter value
+         '-' )
+     }else
+     {
+     ixDS$add_par( 'bound_activity_lo', # parameter name
+         paste(  rr, tt, yy, mode_common , model_time, sep = '.' ), # set key
+         round( c( urban_desal_fraction.df[ as.character( 2090 ), unlist( strsplit( rr, '_') )[2] ] * urban_withdrawal.df[ as.character( 2090 ), unlist( strsplit( rr, '_') )[2] ] ), digits = 2), # parameter value
+         '-' )
+     }
+   } ) } )
+
+ #-------------------------------------------------------------------------------------------------------
+ # Add water withdrawal and return flow for thermal ppl cooling technologies
+ #-------------------------------------------------------------------------------------------------------
+
+ # Technologies with historical capacity and activity
+ all_historical_new_capacity = ixDS$par( 'historical_new_capacity' )
+ all_historical_activity = ixDS$par( 'historical_activity' )
+
+ # Add the cooling technology data to the db
+ skipped_tech_reg = NULL
+ ret = lapply( cooled_technologies_in_message, function(tech){
+
+   # Status update
+   print(paste( round( 100 * ( ( which( cooled_technologies_in_message == tech ) - 1 ) / ( length(cooled_technologies_in_message) ) ) ), ' % complete', sep=''))
+
+   # Need to grab the entire data series for these variables to check whether they exist in all regions: there is probably a more efficient way to do this.
+   all_output = ixDS$par( 'output', list(technology = tech) )
+   all_technical_lifetime = ixDS$par( 'technical_lifetime', list(technology = tech) )
+   all_construction_time = ixDS$par( 'construction_time', list(technology = tech) )
+   all_fix_cost = ixDS$par( 'fix_cost', list(technology = tech) )
+
+   all_inv_cost = ixDS$par( 'inv_cost', list(technology = tech) )
+
+   lapply( region, function(reg){ # go through each region
+
+     # Retrieve the output for this particular cooled technology in MESSAGE
+     if(length(which(as.character(all_output$node_loc) == reg))>0)
+       {
+
+       # Grab the output activity ratio
+       output = ixDS$par( 'output', list( node_loc = reg, technology = tech ) )
+
+       # Check if multiple modes - only need one
+       if( length(unique(output$mode)) > 1 ){ output = output[ which( output$mode == unique(output$mode)[1] ) , ] }
+
+       # Check if multiple commodities - only need one
+       if( length(unique(output$commodity)) > 1 ){ output = output[ which( output$commodity == unique(output$commodity)[1] ) , ] }
+
+       # Define the vintaging and time slicing parameters for the cooling technologies to match the cooled MESSAGE techs in the db
+       ind = data.frame( 	year_vtg = as.character( output$year_vtg ),
+                 year_act = as.character( output$year_act ),
+                 mode = as.character( output$mode ),
+                 time = as.character( output$time ) 	)
+
+       # Retrieve the input for this particular cooled technology in MESSAGE
+       if( !( tech %in% names( manually_set_efficiencies ) ) )
+         {
+
+         # Grab the input activity ratio
+         input = ixDS$par( 'input', list( node_loc = reg, technology = tech ) )
+
+         # Check if multiple modes
+         if( length(unique(input$mode)) > 1 ){ input = input[ which( input$mode == unique(input$mode)[1] ) , ] }
+
+         # Check if multiple commodities
+         if( length(unique(input$commodity)) > 1 ){ input = input[ which( input$commodity == unique(input$commodity)[1] ) , ] }
+
+         # Check if output and input matrices of different length
+         if( nrow(ind) == nrow(input) ){ input_vec = input$value }else{ print('output and input different lengths') }
+
+         }else{ input_vec = rep( (1/manually_set_efficiencies[[ tech ]]), nrow(ind) ) } # Use the manually set values where applicable
+
+       # Retrieve the historical capacity for this particular cooled technology in MESSAGE
+       if(length(which( as.character(all_historical_new_capacity$node_loc) == reg & as.character(all_historical_new_capacity$technology) == tech ))>0)
+         {
+         historical_new_capacity = ixDS$par( 'historical_new_capacity', list( node_loc = reg, technology = tech ) )
+         }else
+         {
+         historical_new_capacity = NULL
+         }
+
+       # Retrieve the historical activity for this particular cooled technology in MESSAGE
+       if(length(which( as.character(all_historical_activity$node_loc) == reg & as.character(all_historical_activity$technology) == tech ))>0)
+         {
+         historical_activity = ixDS$par( 'historical_activity', list( node_loc = reg, technology = tech ) )
+         }else
+         {
+         historical_activity = NULL
+         }
+
+       # Technical lifetime
+       if(length(which(as.character(all_technical_lifetime$node_loc) == reg))>0){ technical_lifetime = ixDS$par( 'technical_lifetime', list( node_loc = reg, technology = tech ) ) }
+
+       # Investment costs
+       if(length(which(as.character(all_inv_cost$node_loc) == reg))>0){ inv_cost = ixDS$par( 'inv_cost', list( node_loc = reg, technology = tech ) ) }
+
+       # Fixed costs
+       if(length(which(as.character(all_fix_cost$node_loc) == reg))>0){ fix_cost = ixDS$par( 'fix_cost', list( node_loc = reg, technology = tech ) ) }
+
+       # Construction time
+       if(length(which(as.character(all_construction_time$node_loc) == reg))>0){ construction_time = ixDS$par( 'construction_time', list( node_loc = reg, technology = tech ) ) }
+
+       # Get the name of the cooling technologies for this particular cooled MESSAGE technology
+       techs_to_update = cooling_technologies_to_consider[ which( as.character( unlist(data.frame(strsplit( cooling_technologies_to_consider, '__'))[1,]) )  == as.character(tech) ) ]
+       id2 = apply(cbind(as.character( unlist(data.frame(strsplit( techs_to_update, '__'))[2,]) ),as.character( unlist(data.frame(strsplit( techs_to_update, '__'))[1,]) )),1,paste,collapse='_') # alternate ID in csv file
+
+       # Go through each vintage and cooling option and add the corresponding data to the DB
+       ret = lapply( 1:length(ind$year_vtg), function(v){ lapply( 1:length(techs_to_update), function(ttt){
+
+         # Add the technology to the set list - only need to for one region
+         if(reg == region[1])
+           {
+           ixDS$add_set( 'technology', as.character( techs_to_update[ ttt ] ) )
+           ixDS$add_set( 'cat_tec', paste( power_plant_cooling_tech_type , as.character( techs_to_update[ ttt ] ), sep='.') ) # Add technology to list of cooling technology types
+           ixDS$add_set( 'cat_tec', paste( 'investment_electricity' , as.character( techs_to_update[ ttt ] ), sep='.') ) # Add technology to list of electricity system investments
+           }
+
+         # Set the input commodity using the name from the csv file, define output commodity using names generated and initialized previously
+         cmdty_in = as.character( tech_water_performance_ssp_msg_raw.df$water_supply_type[ which( as.character(tech_water_performance_ssp_msg_raw.df$technology_name) == id2[ ttt ] ) ] )
+         if( !is.na(cmdty_in) ){ if( cmdty_in == 'saline_supply' ){ cmdty_in = 'saline_supply_ppl' } }
+         cmdty_out = cooling_commodities[ which( unlist( strsplit( cooling_commodities, '__') )[seq(2, length(unlist( strsplit( cooling_commodities, '__') )), by=2)] == tech ) ]
+
+         ## Set the water withdrawal, return flow, thermal pollution and parasitic electricity use for this cooling technology (as intensities)
+
+           # Get the heat lost to emissions and electricity production and use to compute cooling fraction
+           emissions_heat_fraction = as.numeric(tech_water_performance_ssp_msg_raw.df$emissions_heat_fraction[ which( as.character(tech_water_performance_ssp_msg_raw.df$technology_name) == tech  ) ]) # fraction of heat lost through emissions
+           cooling_fraction = input_vec * ( 1 - emissions_heat_fraction / max(input_vec) ) - 1  # scale heat lost to emission proportionally to the changes in the cooling fraction
+
+           # Scale historical withdrawal to follow heat rate improvements
+           water_withdrawal = round( ( 60 * 60 * 24 * 365 ) * ( 1e-9 ) * ( cooling_fraction / max(cooling_fraction) ) * as.numeric( tech_water_performance_ssp_msg_raw.df[ which( as.character(tech_water_performance_ssp_msg_raw.df$technology_name) == id2[ttt]  ), which(as.character(names(tech_water_performance_ssp_msg_raw.df)) == paste('water_withdrawal_',parameter_levels,'_m3_per_output',sep='') ) ] ), digits = rnd )
+
+           # Return flow using consumption intensity
+           return_flow = round( water_withdrawal * ( 1 - as.numeric( tech_water_performance_ssp_msg_raw.df[ which( as.character(tech_water_performance_ssp_msg_raw.df$technology_name) == id2[ ttt ]  ), which(as.character(names(tech_water_performance_ssp_msg_raw.df)) == paste('water_consumption_',parameter_levels,'_m3_per_output',sep='') ) ] ) / as.numeric( tech_water_performance_ssp_msg_raw.df[ which( as.character(tech_water_performance_ssp_msg_raw.df$technology_name) == id2[ttt]  ), which(as.character(names(tech_water_performance_ssp_msg_raw.df)) == paste('water_withdrawal_',parameter_levels,'_m3_per_output',sep='') ) ] ) ), digits = rnd )
+
+           # Parasitic electricity consumption
+           parasitic_electricity = as.numeric( tech_water_performance_ssp_msg_raw.df[ which( as.character(tech_water_performance_ssp_msg_raw.df$technology_name) == id2[ ttt ]  ), 'parasitic_electricity_demand_fraction' ] )
+
+         ##
+         # Add the variable cost
+         ixDS$add_par( 'var_cost', # parameter name
+           paste( reg, techs_to_update[ ttt ], as.character( ind$year_vtg[ v ] ), as.character( ind$year_act[ v ] ), as.character( ind$mode[ v ] ), as.character( ind$time[ v ] ), sep = '.' ), # set key
+           round( ( 1e3 ) * cooling_technology_costs$var_costs[ which(as.character(row.names(cooling_technology_costs)) == as.character(techs_to_update[ ttt ]) ) ],digits = rnd), # parameter value
+           'USD/GWa' ) # units
+
+         # Add the capacity factor
+         ixDS$add_par( 'capacity_factor', # parameter name
+           paste( reg, techs_to_update[ ttt ], as.character( ind$year_vtg[ v ] ), as.character( ind$year_act[ v ] ),  as.character( ind$time[ v ] ), sep = '.' ), # set key
+           1, # Assume for now that cooling technologies are always available
+           '-' ) # units
+
+         # Add the output efficiency ratio
+         ixDS$add_par( 'output', # parameter name
+           paste( reg, techs_to_update[ ttt ], as.character( ind$year_vtg[ v ] ), as.character( ind$year_act[ v ] ), as.character( ind$mode[ v ] ), reg, cmdty_out, cooling_level, as.character( ind$time[ v ] ), as.character( ind$time[ v ] ), sep = '.' ), # set key
+           1, # parameter value
+           '-' )
+
+         if( unlist(strsplit(id2[ttt],'_'))[1] != 'air' ) # Don't need to add water use inputs for air cooling technologies
+           {
+
+           # Add the input efficiency ratio - water withdrawal
+           ixDS$add_par( 'input', # parameter name
+             paste( reg, techs_to_update[ ttt ], as.character( ind$year_vtg[ v ] ), as.character( ind$year_act[ v ] ), as.character( ind$mode[ v ] ), reg, cmdty_in, water_supply_level, as.character( ind$time[ v ] ), as.character( ind$time[ v ] ), sep = '.' ), # set key
+             water_withdrawal[ v ], # parameter value
+             '-' )
+
+           # Add the thermal pollution emission factor
+           if( unlist(strsplit(id2[ttt],'_'))[1] == 'ot' ) # only for once through cooling technologies
+             {
+             if( unlist(strsplit(id2[ttt],'_'))[2] == 'fresh' ){ emis = 'fresh_thermal_pollution' }else{ emis = 'saline_thermal_pollution' }
+             ixDS$add_par( 'emission_factor', # parameter name
+             paste( reg, techs_to_update[ ttt ], as.character( ind$year_vtg[ v ] ), as.character( ind$year_act[ v ] ), as.character( ind$mode[ v ] ), emis, sep = '.' ), # set key
+             round( cooling_fraction[ v ], digits = rnd ), # parameter value
+             '-' )
+             }
+
+           # Add the wastewater emission factor
+           if( unlist(strsplit(id2[ttt],'_'))[2] == 'fresh' ){ emis = 'fresh_wastewater' }else{ emis = 'saline_wastewater' }
+           ixDS$add_par( 'emission_factor', # parameter name
+           paste( reg, techs_to_update[ ttt ], as.character( ind$year_vtg[ v ] ), as.character( ind$year_act[ v ] ), as.character( ind$mode[ v ] ), emis, sep = '.' ), # set key
+           return_flow[ v ], # parameter value
+           '-' )
+
+           }
+
+         # Add the input efficiency ratio - parasitic electricity consumption
+         if( parasitic_electricity > 0 ) # only for some cooling technologies
+           {
+           ixDS$add_par( 'input', # parameter name
+           paste( reg, techs_to_update[ ttt ], as.character( ind$year_vtg[ v ] ), as.character( ind$year_act[ v ] ), as.character( ind$mode[ v ] ), reg, 'electr', 'secondary', as.character( ind$time[ v ] ), as.character( ind$time[ v ] ), sep = '.' ), # set key
+           parasitic_electricity, # parameter value
+           '-' )
+           }
+
+         # Add the cooling commodity to the cooled message technology input list
+         if( ttt == 1 ) # only need to do once
+           {
+           ixDS$add_par( 'input', # parameter name
+             paste( reg, tech, as.character( ind$year_vtg[ v ] ), as.character( ind$year_act[ v ] ), as.character( ind$mode[ v ] ), reg, cmdty_out, cooling_level, as.character( ind$time[ v ] ), as.character( ind$time[ v ] ), sep = '.' ), # set key
+             1, # parameter value
+             '-' )
+           }
+
+         } ) } )
+
+       # Go through each vintage and cooling option and add the corresponding data to the DB for investement costs
+       ret = lapply( 1:length(inv_cost$year_vtg), function(v){ lapply( 1:length(techs_to_update), function(ttt){
+         # Add the investment cost
+         ixDS$add_par( 'inv_cost', # parameter name
+             paste( reg, techs_to_update[ ttt ], as.character( inv_cost$year_vtg[ v ] ), sep = '.' ), # set key
+             round( ( 1e3 ) * cooling_technology_costs$inv_costs[ which(as.character(row.names(cooling_technology_costs)) == as.character(techs_to_update[ ttt ]) ) ],digits = rnd), # parameter value (convert from mill USD/MW to USD/GWa)
+             'USD/GWa' )
+         } ) } )
+
+       # Go through each vintage and cooling option and add the corresponding data to the DB for investement costs
+       ret = lapply( 1:length(fix_cost$year_vtg), function(v){ lapply( 1:length(techs_to_update), function(ttt){
+         # Add the fixed cost
+         ixDS$add_par( 'fix_cost', # parameter name
+           paste( reg, techs_to_update[ ttt ], as.character( ind$year_vtg[ v ] ), as.character( ind$year_act[ v ] ), sep = '.' ), # set key
+           round( ( 1e3 ) * cooling_technology_costs$fixed_costs[ which(as.character(row.names(cooling_technology_costs)) == as.character(techs_to_update[ ttt ]) ) ],digits = rnd), # parameter value
+           'USD/GWa' )
+         } ) } )
+
+       # Go through each vintage and cooling option and add the corresponding data to the DB for historical capacity
+       ret = lapply( 1:length(historical_new_capacity$year_vtg), function(v){ lapply( 1:length(techs_to_update), function(ttt){
+
+         # Add the historical capacity
+         if( !is.null(historical_new_capacity) ) # Check if historical capacity exists
+           {
+           # The output of the power plant cooling technologies are defined in terms of the electric power output supported
+           # Use the historical capacity for each cooled power plant type and the share of each cooling technology to estimate the historical capacity
+           if(use_davies_shares)
+             {
+             shr = cooltech_cost_and_shares_ssp_msg_raw.df[ which( ( as.character(cooltech_cost_and_shares_ssp_msg_raw.df$utype) == unlist(strsplit(techs_to_update[ ttt ],'__'))[1] ) & ( as.character(cooltech_cost_and_shares_ssp_msg_raw.df$cooling) == unlist(strsplit(techs_to_update[ ttt ],'__'))[2] ) ), paste('mix',unlist(strsplit(reg,'_'))[2],'Davies_2013',sep='_') ]
+             }else
+             {
+             shr = cooltech_cost_and_shares_ssp_msg_raw.df[ which( ( as.character(cooltech_cost_and_shares_ssp_msg_raw.df$utype) == unlist(strsplit(techs_to_update[ ttt ],'__'))[1] ) & ( as.character(cooltech_cost_and_shares_ssp_msg_raw.df$cooling) == unlist(strsplit(techs_to_update[ ttt ],'__'))[2] ) ), paste('mix',unlist(strsplit(reg,'_'))[2],sep='_') ]
+             }
+           cap = round( shr * historical_new_capacity[ which( as.character( historical_new_capacity$year_vtg ) == as.character( historical_new_capacity$year_vtg[v] )  ) ,  'value' ], digits = rnd )
+
+           # Add the ix db
+           ixDS$add_par( 'historical_new_capacity',
+             paste( reg, techs_to_update[ ttt ], as.character( historical_new_capacity$year_vtg[ v ] ), sep = '.' ),
+             cap,
+             as.character( historical_new_capacity$unit[1] ) )
+           }
+
+         } ) } )
+
+       # Go through each year and cooling option and add the corresponding data to the DB for historical activity
+       ret = lapply( 1:length(historical_activity$year_act), function(v){ lapply( 1:length(techs_to_update), function(ttt){
+
+         # Add the historical_activity
+         if( !is.null(historical_activity) ) # Check if historical_activity exists
+           {
+
+           # The output of the power plant cooling technologies are defined in terms of the electric power output supported
+           # Use the historical_activity for each cooled power plant type and the share of each cooling technology to estimate the historical activity
+           if(use_davies_shares) # whether or not to use the share from Davies or Raptis
+             {
+             shr = cooltech_cost_and_shares_ssp_msg_raw.df[ which( ( as.character(cooltech_cost_and_shares_ssp_msg_raw.df$utype) == unlist(strsplit(techs_to_update[ ttt ],'__'))[1] ) & ( as.character(cooltech_cost_and_shares_ssp_msg_raw.df$cooling) == unlist(strsplit(techs_to_update[ ttt ],'__'))[2] ) ), paste('mix',unlist(strsplit(reg,'_'))[2],'Davies_2013',sep='_') ]
+             }else
+             {
+             shr = cooltech_cost_and_shares_ssp_msg_raw.df[ which( ( as.character(cooltech_cost_and_shares_ssp_msg_raw.df$utype) == unlist(strsplit(techs_to_update[ ttt ],'__'))[1] ) & ( as.character(cooltech_cost_and_shares_ssp_msg_raw.df$cooling) == unlist(strsplit(techs_to_update[ ttt ],'__'))[2] ) ), paste('mix',unlist(strsplit(reg,'_'))[2],sep='_') ]
+             }
+           act = round( shr * historical_activity[ which( as.character( historical_activity$year_act ) == as.character( historical_activity$year_act[v] )  ) ,  'value' ], digits = rnd )
+
+           # Add the ix db
+           ixDS$add_par( 'historical_activity',
+             paste( reg, techs_to_update[ ttt ], as.character( historical_activity$year_act[ v ] ), as.character( historical_activity$mode[ v ] ) , as.character( historical_activity$time[ v ] ), sep = '.' ),
+             act,
+             as.character( historical_activity$unit[1] ) )
+
+           }
+         } ) } )
+
+       ret = lapply( 1:length(technical_lifetime$year_vtg), function(v){ lapply( 1:length(techs_to_update), function(ttt){
+         # Add the technical lifetime
+         ixDS$add_par( 'technical_lifetime', # parameter name
+             paste( reg, techs_to_update[ ttt ], as.character( technical_lifetime$year_vtg[ v ] ), sep = '.' ), # set key
+             technical_lifetime[ v ,  'value' ],
+             'y' )
+         } ) } )
+
+       ret = lapply( 1:length(construction_time$year_vtg), function(v){ lapply( 1:length(techs_to_update), function(ttt){
+         # Add the construction time
+         ixDS$add_par( 'construction_time', # parameter name
+             paste( reg, techs_to_update[ ttt ], as.character( construction_time$year_vtg[ v ] ), sep = '.' ), # set key
+             construction_time[ v ,  'value' ],
+             as.character( construction_time[ v ,  'unit' ] ) )
+         } ) } )
+
+       }else{ skipped_tech_reg = rbind( skipped_tech_reg, data.frame( technology = tech, region = reg, comb = paste(tech,reg,sep='_') ) ) } # add to list of skipped techs
+     } )
+   } )
+
+ #-------------------------------------------------------------------------------------------------------
+ # Add water withdrawal and return flow for non-cooling technologies
+ #-------------------------------------------------------------------------------------------------------
+
+ print('Adding water coefficients for non-cooling technologies')
+
+ # Could speed this up a bit by allocating the ppl water use during the previous step.
+
+ if(ssss == 'SSP3')
+   {
+   skipped_techs = c('igcc_co2scr','gfc_co2scr','cfc_co2scr','h2_co2_scrub','h2b_co2_scrub','gas_htfc','h2_bio','h2_bio_ccs','h2_smr','h2_smr_ccs','h2_coal','h2_coal_ccs','h2_elec','solar_pv_ppl','wind_ppl')
+   }else
+   {
+   skipped_techs = c('igcc_co2scr','gfc_co2scr','cfc_co2scr','h2_co2_scrub','h2b_co2_scrub','gas_htfc','solar_pv_ppl','wind_ppl')
+   }
+ message_technologies_with_water_data2 = message_technologies_with_water_data[which(!(message_technologies_with_water_data %in% skipped_techs))]
+ ret = lapply( message_technologies_with_water_data2, function(tech){
+
+   # Status update
+   print(paste( round( 100 * ( ( which( message_technologies_with_water_data2 == tech ) - 1 ) / ( length(message_technologies_with_water_data2) ) ) ), ' % complete', sep=''))
+
+   all_output = ixDS$par( 'output', list(technology = tech) )
+
+   lapply( region, function(reg){
+
+     if( length(which(as.character(all_output$node_loc) == reg))>0 )
+       {
+
+       output = ixDS$par( 'output', list( node_loc = reg, technology = tech ) )
+
+       # Check if multiple modes - only need one
+       if( length(unique(output$mode)) > 1 ){ output = output[ which( output$mode == unique(output$mode)[1] ) , ] }
+
+       # Check if multiple commodities - only need one
+       if( length(unique(output$commodity)) > 1 ){ output = output[ which( output$commodity == unique(output$commodity)[1] ) , ] }
+
+       # Define the vintaging and time slicing parameters
+       ind = data.frame( 	year_vtg = as.character( output$year_vtg ),
+                 year_act = as.character( output$year_act ),
+                 mode = as.character( output$mode ),
+                 time = as.character( output$time ) 	)
+
+       # Input commodity
+       cmdty_in = as.character( tech_water_performance_ssp_msg_raw.df$water_supply_type[ which( as.character(tech_water_performance_ssp_msg_raw.df$technology_name) == tech ) ] )
+       if( !is.na(cmdty_in) ){ if( cmdty_in == 'saline_supply' ){ cmdty_in = 'saline_supply_ppl' } }
+
+       # Add the water coefficients for each vintage and time slice
+       ret = lapply( 1:nrow( ind ), function(v){
+
+         # Using data from input csv files and converted from m3 / GJ to km3 / GWa
+         water_withdrawal = round( ( 60 * 60 * 24 * 365 ) * ( 1e-9 ) * as.numeric( tech_water_performance_ssp_msg_raw.df[ which( as.character(tech_water_performance_ssp_msg_raw.df$technology_name) == tech  ), which(as.character(names(tech_water_performance_ssp_msg_raw.df)) == paste('water_withdrawal_',parameter_levels,'_m3_per_output',sep='') ) ] ), digits = rnd )
+         return_flow = round( water_withdrawal - ( 60 * 60 * 24 * 365 ) * ( 1e-9 ) * as.numeric( tech_water_performance_ssp_msg_raw.df[ which( as.character(tech_water_performance_ssp_msg_raw.df$technology_name) == tech  ), which(as.character(names(tech_water_performance_ssp_msg_raw.df)) == paste('water_consumption_',parameter_levels,'_m3_per_output',sep='') ) ] ), digits = rnd )
+
+         # Add the withdrawal to db as an input
+         ixDS$add_par( 'input', # parameter name
+           paste( reg, tech, as.character( ind$year_vtg[ v ] ), as.character( ind$year_act[ v ] ), as.character( ind$mode[ v ] ), reg, cmdty_in, water_supply_level, as.character( ind$time[ v ] ), as.character( ind$time[ v ] ), sep = '.' ), # set key
+           water_withdrawal,
+           '-' )
+
+         # Add the return flow to the emission factors
+         if( !( tech %in% hydro_techs ) ) # no wastewater for hydropower / instream technologies sads
+           {
+           ixDS$add_par( 'emission_factor', # parameter name
+             paste( reg, tech, as.character( ind$year_vtg[ v ] ), as.character( ind$year_act[ v ] ), as.character( ind$mode[ v ] ), 'fresh_wastewater', sep = '.' ),
+             return_flow,
+             '-' )
+           }
+         } )
+       }
+     } )
+   } )
+
+ #-------------------------------------------------------------------------------------------------------
+ # Add water resource extraction technologies
+ #-------------------------------------------------------------------------------------------------------
+
+ print('Adding water source extraction technologies')
+
+ # List of technologies with historical water use
+ hist_techs = list( 	freshwater_supply = c( as.character( message_technologies_with_water_data2 )[which( as.character( message_technologies_with_water_data2  ) %in% as.character( unique( all_historical_activity$technology ) ) & !( as.character( message_technologies_with_water_data2 )  %in% c( tech_water_performance_ssp_msg_raw.df$technology_name[which(tech_water_performance_ssp_msg_raw.df$water_supply_type %in% c('freshwater_instream','upstream_landuse'))]  ) ) )], cooling_technologies_to_consider[ which( unlist(lapply(cooling_technologies_to_consider,function(zzz){unlist(strsplit( zzz, '__' ))[1]})) %in% unique( all_historical_activity$technology ) &  unlist(lapply( unlist(lapply(cooling_technologies_to_consider,function(zzz){unlist(strsplit( zzz, '__' ))[2]})), function(xxx){ unlist(strsplit( xxx, '_' ))[2] }  ) ) == 'fresh' ) ] ),
+           saline_supply = cooling_technologies_to_consider[ which( c( unlist(lapply( unlist(lapply(cooling_technologies_to_consider,function(zzz){unlist(strsplit( zzz, '__' ))[2]})), function(xxx){ unlist(strsplit( xxx, '_' ))[2] }  ) ) == 'saline' ) ) ],
+           freshwater_instream = c( tech_water_performance_ssp_msg_raw.df$technology_name[which(tech_water_performance_ssp_msg_raw.df$water_supply_type == 'freshwater_instream')]  ),
+           upstream_landuse = c( tech_water_performance_ssp_msg_raw.df$technology_name[which(tech_water_performance_ssp_msg_raw.df$water_supply_type == 'upstream_landuse')] ) )
+
+ # Technologies with historical capacity and activity - update
+ all_historical_new_capacity = ixDS$par( 'historical_new_capacity' )
+ all_historical_activity = ixDS$par( 'historical_activity' )
+
+ ret = lapply( water_source_extraction_techs, function(tech){
+
+   # Status update
+   print( paste( round( 100 * ( ( which( water_source_extraction_techs == tech ) - 1 ) / ( length(water_source_extraction_techs) ) ) ), ' % complete', sep='') )
+
+   lapply( region, function(reg){ # across each region
+
+     # Get the commodity for the source type (freshwater or saline or instream)
+     cmdty_out = as.character( unlist(strsplit(tech,'__'))[2] )
+
+     lapply( model_years, function( yy ){ # go through the years
+
+       # Add the output for each timeslice
+       ret = lapply( model_time, function(tm){
+
+         ixDS$add_par( 'output', # parameter name
+           paste( reg, tech, as.character( yy ), as.character( yy ), mode_common, reg, cmdty_out, water_supply_level, as.character( tm ), as.character( tm ), sep = '.' ), # set key
+           1, # parameter value
+           '-' )
+
+         } )
+
+       # Add the electricity usage for freshwater supply
+       if( tech == 'extract__freshwater_supply' )
+         {
+         ret = lapply( model_time, function(tm){
+             ixDS$add_par( 'input', # parameter name
+             paste( reg, tech, as.character( yy ), as.character( yy ), mode_common, reg, 'electr', 'final', as.character( tm ), as.character( tm ), sep = '.' ), # set key
+             unlist( fw_electricity_input[ parameter_levels ] ), # parameter value
+             '-' )
+           } )
+         }
+
+       # Add the investment cost
+       ixDS$add_par( 'inv_cost', # parameter name
+         paste( reg, tech, as.character( yy ), sep = '.' ), # set key
+         0,
+         '-' )
+
+       # Add the fixed cost
+       # print(ixDS$get_par_set('fix_cost'))
+       ixDS$add_par( 'fix_cost', # parameter name
+         paste( reg, tech, as.character( yy ), as.character( yy ), sep = '.' ), # set key
+         0,
+         '-' )
+
+       # Average water supply expansion costs
+       # Medium-scale storage costs from A.A. Keller et al. assuming long-term storage costs - converted from USD / m3 to million USD / km3
+       #fwc = 1e3 * data.frame(low = 0.01, mid = 0.02, high = 0.1)
+       fwc = 1e3 * data.frame(low = 0, mid = 0, high = 0)
+       if( tech == 'extract__freshwater_supply' ){ vc = unlist(fwc[ parameter_levels ]) }else{ vc = 0 }
+       # Add the variable costs
+       # print(ixDS$get_par_set('var_cost'))
+       ret = lapply( model_time, function(tm){
+         ixDS$add_par( 'var_cost', # parameter name
+           paste( reg, tech, as.character( yy ), as.character( yy ), as.character( mode_common ), as.character( tm ), sep = '.' ), # set key
+           vc, # default for now
+           '-' )
+         } )
+
+       # Add the capacity factor
+       # print(ixDS$get_par_set('capacity_factor'))
+       ret = lapply( model_time, function(tm){
+         ixDS$add_par( 'capacity_factor', # parameter name
+           paste( reg, tech, as.character( yy ), as.character( yy ), as.character( tm ), sep = '.' ), # set key
+           1,
+           '-' )
+         } )
+
+       # Add the technical lifetime
+       if(yy < model_years[length(model_years)]){tl = as.numeric(model_years[which(model_years==yy)+1]) - as.numeric(yy)}else{tl = as.numeric(model_years[length(model_years)]) - as.numeric(model_years[length(model_years)-1])}
+       ixDS$add_par( 'technical_lifetime', # parameter name
+         paste( reg, tech, as.character( yy ), sep = '.' ), # set key
+         tl,
+         'y' )
+
+       # Add the contruction time
+       ixDS$add_par( 'construction_time', # parameter name
+         paste( reg, tech, as.character( yy ), sep = '.' ), # set key
+         0,
+         'y' )
+
+       # Add capacity in locked in years for mitigation and no policy scenarios
+       if( yy > 2010 & yy < firstmodelyear )
+         {
+         ixDS$add_par( 'historical_activity', paste(  reg, as.character( tt ), yy, mode_common , model_time, sep = '.' ), max(0, base_hist_act[ which( as.character( unlist( base_hist_act$tec ) ) == as.character( d_tech[[ tt ]] ) & as.numeric( unlist( base_hist_act$year_all ) ) == yy & as.character( unlist( base_hist_act$node ) ) == reg ), 'val' ], na.rm=TRUE), '-' )
+         ixDS$add_par( 'historical_new_capacity', paste(  reg, as.character( tt ), yy, sep = '.' ), max(0, base_hist_ncap[ which( as.character( unlist( base_hist_ncap$tec ) ) == as.character( d_tech[[ tt ]] ) & as.numeric( unlist( base_hist_ncap$year_all ) ) == yy & as.character( unlist( base_hist_ncap$node ) ) == reg ), 'val' ], na.rm=TRUE), '-' )
+         }
+
+       } )
+
+     # Historical activity of extraction techs determined from
+     # the historical activity of the MESSAGE techs
+     techs_hist = unlist( hist_techs[ cmdty_out ] )
+     temp = lapply(techs_hist, function(ttt){
+       hist_act = all_historical_activity[which( as.character(all_historical_activity$technology) == ttt  & as.character(all_historical_activity$node_loc) == reg ),]
+       hist_act_yy = unique(hist_act$year_act)
+       if( length(hist_act_yy) > 0)
+         {
+         if( cmdty_out == 'saline_supply' ){ cmdty_out2 = 'saline_supply_ppl' }else{ cmdty_out2 = cmdty_out }
+         ret1 = data.frame( do.call(rbind, lapply( hist_act_yy, function(yyy){
+           yr = as.character(yyy)
+           inp = mean( ixDS$par( 'input', list( node_loc = reg, technology = ttt, year_act = as.character(hist_act$year_act[which(hist_act$year_act == yyy)]), commodity = cmdty_out2 ) )[,'value'], na.rm=TRUE )
+           act = hist_act$value[which(hist_act$year_act == yyy)]
+           return( c(yr, inp * sum(act)) ) # multiply input by activity to estimate historical demands
+           } ) ) )
+         }else{ ret1 = NULL }
+       return(ret1)
+       })
+     names(temp) = techs_hist
+
+     # total historical is summed across the demands from all technologies
+     hist_years = unique(unlist(lapply( 1:length(techs_hist), function(xxx){ if(!is.null(temp[[xxx]])){ unlist( as.character( temp[[xxx]][,1] ) )}})))
+     hist_tot_act = unlist( lapply( 1:length(hist_years), function(yyy){ sum( unlist( lapply( 1:length(techs_hist), function(xxx){
+       temp2 = temp[[xxx]]
+       if( hist_years[yyy] %in% as.character( temp2[,1] ) )
+         {
+         return( as.numeric( as.character( temp2[ which( as.character( temp2[,1] ) == as.character( hist_years[yyy] ) ), 2 ] ) ) )
+         }else{ return(NULL) }
+       } ) ), na.rm = TRUE ) } ) )
+     names( hist_tot_act ) = hist_years
+
+     # total historical demand from desalination
+     if( cmdty_out == 'saline_supply' )
+       {
+       techs_hist = c('membrane','distillation')
+       temp = lapply(techs_hist, function(ttt){
+         hist_act = all_historical_activity[which( as.character(all_historical_activity$technology) == ttt  & as.character(all_historical_activity$node_loc) == reg ),]
+         hist_act_yy = unique(hist_act$year_act)
+         if( length(hist_act_yy) > 0)
+           {
+           ret1 = data.frame( do.call(rbind, lapply( hist_act_yy, function(yyy){
+             yr = as.character(yyy)
+             inp = mean( ixDS$par( 'input', list( node_loc = reg, technology = ttt, year_act = as.character(hist_act$year_act[which(hist_act$year_act == yyy)]), commodity = cmdty_out ) )[,'value'], na.rm=TRUE )
+             act = hist_act$value[which(hist_act$year_act == yyy)]
+             return( c(yr, inp * sum(act)) ) # multiply input by activity to estimate historical demands
+             } ) ) )
+           }else{ ret1 = NULL }
+         return(ret1)
+         })
+       names(temp) = techs_hist
+
+       # total historical is summed across the demands from all technologies
+       hist_years = unique(unlist(lapply( 1:length(techs_hist), function(xxx){ if(!is.null(temp[[xxx]])){ unlist( as.character( temp[[xxx]][,1] ) )}})))
+       hist_tot_act_ds = unlist( lapply( 1:length(hist_years), function(yyy){ sum( unlist( lapply( 1:length(techs_hist), function(xxx){
+         temp2 = temp[[xxx]]
+         if( hist_years[yyy] %in% as.character( temp2[,1] ) )
+           {
+           return( as.numeric( as.character( temp2[ which( as.character( temp2[,1] ) == as.character( hist_years[yyy] ) ), 2 ] ) ) )
+           }else{ return(NULL) }
+         } ) ), na.rm = TRUE ) } ) )
+       names( hist_tot_act_ds ) = hist_years
+       hist_tot_act = sapply( unique( names( hist_tot_act_ds ), ( hist_tot_act ) ), function(xxx){ return( max( 0, hist_tot_act_ds[ xxx ], na.rm = TRUE ) + max( 0, hist_tot_act[ xxx ], na.rm = TRUE ) ) } )
+       names( hist_tot_act ) = unique( names( hist_tot_act_ds ), ( hist_tot_act ) )
+       }
+
+     # Add the ix db
+     ret = lapply( names( hist_tot_act ), function(yyy){
+       wtdm = hist_tot_act[ yyy ] + max( 0, tryCatch( sum( ixDS$par( 'demand', list( node = reg, commodity = c('freshwater_supply','urban_mw','urban_dis','rural_mw','rural_dis'), year = yyy ) )[ 'value' ], na.rm=TRUE ), error = function(e){} ), na.rm = TRUE )
+       ixDS$add_par( 'historical_activity',
+         paste( reg, tech, yyy, as.character( mode_common ) , model_time, sep = '.' ),
+         round(wtdm, digits=rnd),
+         '-' )
+       } )
+
+     # Calibrate the electricity demands	for freshwater supply
+     if( tech == 'extract__freshwater_supply' )
+       {
+       hist_elec_2010.df = data.frame( hist_elec_2010 = unlist(ixDS$par( 'historical_activity', list( technology = tech, node_loc=reg ) )['value']) * unlist( fw_electricity_input[ parameter_levels ] ) )
+       row.names( hist_elec_2010.df ) = unlist(ixDS$par( 'historical_activity', list( technology = tech, node_loc=reg ) )['year_act'])
+       ret = lapply( row.names(hist_elec_2010.df), function(aaa){
+         chk =  tryCatch( ixDS$par( 'demand', list( node = reg, commodity = 'i_spec', level = 'useful', year = aaa, time = model_time ) ), error = function(e){} )
+         if(!is.null( chk ) )
+           {
+           ixDS$add_par( 'demand', # parameter name
+             paste( reg, 'i_spec', 'useful', aaa, model_time, sep = '.' ), # set key
+             round( c( max( 0,  c( unlist( chk['value'] ) - unlist( hist_elec_2010.df[ aaa , ] ) ) ) ),  digits = 5),
+             'GWa' )
+           }
+         } )
+       ret = lapply( as.character( model_years[ which(model_years > 2010) ] ), function(aaa){
+         chk =  tryCatch( ixDS$par( 'demand', list( node = reg, commodity = 'i_spec', level = 'useful', year = aaa, time = model_time ) ), error = function(e){} )
+         if(!is.null( chk ) )
+           {
+           ixDS$add_par( 'demand', # parameter name
+             paste( reg, 'i_spec', 'useful', aaa, model_time, sep = '.' ), # set key
+             round( c( max( 0,  c( unlist( chk['value'] ) - unlist( hist_elec_2010.df[ '2010' , ] ) ) ) ),  digits = 5),
+             'GWa' )
+           }
+         } )
+       }
+     } )
+   } )
+
+ #-------------------------------------------------------------------------------------------------------
+ # Add reservoir technologies
+ #-------------------------------------------------------------------------------------------------------
+
+ print('Adding reservoir technologies')
+
+ ret = ixDS$add_set( 'commodity', 'yield_freshwater_supply' )
+ ret = ixDS$add_set( 'technology', 'storage__freshwater_supply' )
+ ret = ixDS$add_set( 'type_tec', 'water_resource_storage' ) # Add to technology types
+ ret = ixDS$add_set( 'cat_tec', paste( 'water_resource_storage' , 'storage__freshwater_supply', sep='.') )
+ ret = ixDS$add_set( 'cat_tec', paste( 'investment_other', 'storage__freshwater_supply', sep='.') )
+
+ # Medium-scale storage costs from Table 5 in A.A. Keller et al. assuming long-term storage costs - converted from USD / m3 to million USD / km3
+ storage_yield_efficiency = list( low = 0.7, mid = 1, high = 1.3 )
+ inv_storage__freshwater_supply = list( low = 170, mid = 420, high = 2140 )
+ fix_storage__freshwater_supply = list( low = 0.02*170, mid = 0.02*420, high = 0.02*2140 )
+ lifetime_storage__freshwater_supply = list( low = 90, mid = 80, high = 50 )
+ constime_storage__freshwater_supply = list( low = 1, mid = 1, high = 1 )
+
+ ret = lapply( region, function(reg){ # across each region
+
+   # Get the commodity for the source type (freshwater or saline or instream)
+   cmdty_out = 'yield_freshwater_supply'
+   tech = 'storage__freshwater_supply'
+
+   lapply( model_years, function( yy ){ # go through the years
+
+     # Add the output for storage each timeslice
+     ret = lapply( model_time, function(tm){
+
+       ixDS$add_par( 'output', # parameter name
+         paste( reg, tech, as.character( yy ), as.character( yy ), mode_common, reg, cmdty_out, water_supply_level, as.character( tm ), as.character( tm ), sep = '.' ), # set key
+         1, # parameter value
+         '-' )
+
+       } )
+
+     # Add the input for extraction technologies each timeslice
+     ret = lapply( model_time, function(tm){
+
+       ixDS$add_par( 'input', # parameter name
+         paste( reg, 'extract__freshwater_supply', as.character( yy ), as.character( yy ), mode_common, reg, cmdty_out, water_supply_level, as.character( tm ), as.character( tm ), sep = '.' ), # set key
+         storage_yield_efficiency[[ parameter_levels ]], # parameter value
+         '-' )
+
+       } )
+
+     # Add the investment cost
+     ixDS$add_par( 'inv_cost', # parameter name
+       paste( reg, tech, as.character( yy ), sep = '.' ), # set key
+       inv_storage__freshwater_supply[[ parameter_levels ]],
+       '-' )
+
+     # Add the fixed cost
+     # print(ixDS$get_par_set('fix_cost'))
+     ixDS$add_par( 'fix_cost', # parameter name
+       paste( reg, tech, as.character( yy ), as.character( yy ), sep = '.' ), # set key
+       fix_storage__freshwater_supply[[ parameter_levels ]],
+       '-' )
+
+     # Add the technical lifetime
+     ixDS$add_par( 'technical_lifetime', # parameter name
+       paste( reg, tech, as.character( yy ), sep = '.' ), # set key
+       lifetime_storage__freshwater_supply[[ parameter_levels ]],
+       'y' )
+
+     # Add the contruction time
+     ixDS$add_par( 'construction_time', # parameter name
+       paste( reg, tech, as.character( yy ), sep = '.' ), # set key
+       constime_storage__freshwater_supply[[ parameter_levels ]],
+       'y' )
+
+     } )
+
+   # Historical activity of extraction techs determined from
+   # the historical activity of the MESSAGE techs
+   cmdty_out = 'freshwater_supply'
+   techs_hist = unlist( hist_techs[ cmdty_out ] )
+   temp = lapply(techs_hist, function(ttt){
+     hist_act = all_historical_activity[which( as.character(all_historical_activity$technology) == ttt  & as.character(all_historical_activity$node_loc) == reg ),]
+     hist_act_yy = unique(hist_act$year_act)
+     if( length(hist_act_yy) > 0)
+       {
+       if( cmdty_out == 'saline_supply' ){ cmdty_out2 = 'saline_supply_ppl' }else{ cmdty_out2 = cmdty_out }
+       ret1 = data.frame( do.call(rbind, lapply( hist_act_yy, function(yyy){
+         yr = as.character(yyy)
+         inp = mean( ixDS$par( 'input', list( node_loc = reg, technology = ttt, year_act = as.character(hist_act$year_act[which(hist_act$year_act == yyy)]), commodity = cmdty_out2 ) )[,'value'], na.rm=TRUE )
+         act = hist_act$value[which(hist_act$year_act == yyy)]
+         return( c(yr, inp * sum(act)) ) # multiply input by activity to estimate historical demands
+         } ) ) )
+       }else{ ret1 = NULL }
+     return(ret1)
+     })
+   names(temp) = techs_hist
+
+   # total historical is summed across the demands from all technologies
+   hist_years = unique(unlist(lapply( 1:length(techs_hist), function(xxx){ if(!is.null(temp[[xxx]])){ unlist( as.character( temp[[xxx]][,1] ) )}})))
+   hist_tot_act = unlist( lapply( 1:length(hist_years), function(yyy){ sum( unlist( lapply( 1:length(techs_hist), function(xxx){
+     temp2 = temp[[xxx]]
+     if( hist_years[yyy] %in% as.character( temp2[,1] ) )
+       {
+       return( as.numeric( as.character( temp2[ which( as.character( temp2[,1] ) == as.character( hist_years[yyy] ) ), 2 ] ) ) )
+       }else{ return(NULL) }
+     } ) ), na.rm = TRUE ) } ) )
+   names( hist_tot_act ) = hist_years
+
+
+   # Add the ix db
+   ret = lapply( names( hist_tot_act ), function(yyy){
+     wtdm = hist_tot_act[ yyy ] + max( 0, tryCatch( sum( ixDS$par( 'demand', list( node = reg, commodity = c('freshwater_supply','urban_mw','urban_dis','rural_mw','rural_dis'), year = yyy ) )[ 'value' ], na.rm=TRUE ), error = function(e){} ), na.rm = TRUE )
+     ixDS$add_par( 'historical_activity',
+       paste( reg, tech, yyy, as.character( mode_common ) , model_time, sep = '.' ),
+       round(wtdm, digits=rnd),
+       '-' )
+     } )
+
+   } )
+
+ #---------------------------------------------------
+ # Add consumption as emission from the energy sector
+ #---------------------------------------------------
+
+ ## Add consumption as an emission
+ ret = ixDS$add_set( "emission", "fresh_consumption" )
+ ret = ixDS$add_set( "emission", "saline_consumption" )
+ ret = ixDS$add_set( "emission", "instream_consumption" )
+ ret = ixDS$add_set( 'type_emission', 'water_consumption' ) # Add to technology types
+ ret = ixDS$add_set( 'cat_emission', paste( 'water_consumption', 'fresh_consumption', sep='.' ) )
+ consumption_type = data.frame( freshwater_supply = "fresh_consumption", saline_supply = "saline_consumption", saline_supply_ppl = "saline_consumption" )
+ res = lapply( c( ixDS$set('node') ), function(nn){
+
+   withdrawal_intensities.df = data.frame( tryCatch( ixDS$par( 'input', list(commodity=c('freshwater_supply','saline_supply_ppl'),level=water_supply_level,node_loc=nn) ), error = function(e){} ) )
+   t2r = c('urban_t_d','urban_unconnected','rural_t_d','rural_unconnected')
+   withdrawal_intensities.df = withdrawal_intensities.df[ -1*( which( as.character( withdrawal_intensities.df$technology ) %in% t2r ) ), ]
+
+   if(length(withdrawal_intensities.df)>0)
+     {
+     return_intensities.df = data.frame( tryCatch( ixDS$par( 'emission_factor', list( emission=c('fresh_wastewater','saline_wastewater'), node_loc=nn, technology=unique( as.character(withdrawal_intensities.df$technology)) ) ), error = function(e){} ) )
+     res1 = lapply( 1:nrow(withdrawal_intensities.df), function(i){
+       ixDS$add_par( 	"emission_factor",
+               paste( nn, withdrawal_intensities.df$technology[ i ], as.character( withdrawal_intensities.df$year_vtg[ i ] ), as.character( withdrawal_intensities.df$year_act[ i ] ), as.character( withdrawal_intensities.df$mode[ i ] ), as.character( unlist( consumption_type[ as.character( withdrawal_intensities.df$commodity[ i ] ) ] ) ), sep = '.' ), # set key
+               round( c( unlist(withdrawal_intensities.df$value[ i ]) - unlist(return_intensities.df$value[ which( as.character( unlist( return_intensities.df$technology ) ) == as.character( unlist( withdrawal_intensities.df$technology[ i ] ) ) & as.character( unlist( return_intensities.df$year_vtg ) ) == as.character( unlist( withdrawal_intensities.df$year_vtg[ i ] ) ) & as.character( unlist( return_intensities.df$year_act ) ) == as.character( unlist( withdrawal_intensities.df$year_act[ i ] ) ) & as.character( unlist( return_intensities.df$mode ) ) == as.character( unlist( withdrawal_intensities.df$mode[ i ] )) ) ] ) ), digits = 6 ), # parameter value
+               '-' )
+       } )
+     }
+   } )
+
+ # Hydropower consumption
+ withdrawal_intensities.df = data.frame( tryCatch( ixDS$par( 'input', list(commodity=c('freshwater_instream')) ), error = function(e){} ) )
+ res = lapply( 1:nrow(withdrawal_intensities.df), function(i){
+   ixDS$add_par( 	"emission_factor",
+           paste( as.character( withdrawal_intensities.df$node_loc[ i ] ), withdrawal_intensities.df$technology[ i ], as.character( withdrawal_intensities.df$year_vtg[ i ] ), as.character( withdrawal_intensities.df$year_act[ i ] ), as.character( withdrawal_intensities.df$mode[ i ] ), "instream_consumption", sep = '.' ), # set key
+           round( ( withdrawal_intensities.df$value[ i ] ), digits = 6 ), # parameter value
+           '-' )
+   } )
+
+
+ #-------------------------------------------------------------------------------------------------------
+ # Add baseline cooling technology policy - No new once-through freshwater cooling capacity
+ #-------------------------------------------------------------------------------------------------------
+ print('Adding once through freshwater baseline cooling policy')
+
+ # Once through freshwater cooled techs and seawater for SDG6 scenarios
+ if( model_scenarios$newscenarioName[sc] %in% c("baseline_globiom_SDG_sdg6eff","baseline_globiom_SDG_sdg6led","baseline_globiom_SDG_sdg6supp","baseline_globiom_SDG_sdg6supp2") )
+   {
+   otc = unlist(lapply( ixDS$set('technology'), function(x) if( length(unlist(strsplit(x,'__'))==2) ){ if( unlist(strsplit(x,'__'))[2] %in% c('ot_fresh','ot_saline') ){ return( x ) } } ))
+   }else{
+   otc = unlist(lapply( ixDS$set('technology'), function(x) if( length(unlist(strsplit(x,'__'))==2) ){ if( unlist(strsplit(x,'__'))[2] %in% c('ot_fresh') ){ return( x ) } } ))
+   }
+
+ # Loop through and bound new capacity additions to zero for otc techs
+ ret = lapply( otc, function(tech){
+
+   # status
+   print(paste( round( 100 * ( ( which( otc == tech ) - 1 ) / ( length(otc) ) ) ), ' % complete', sep=''))
+
+   # Across all regions
+   lapply( region, function(reg){
+     if( !( paste(tech,reg,sep='_') %in% skipped_tech_reg$comb ) )
+       {
+       if( !( unlist(strsplit(tech,'__'))[1] == 'nuc_lc' & reg == 'R11_MEA') )
+         {
+         vtg = unique( ixDS$par( 'output', list( node_loc = reg, technology = tech ) )$year_vtg )
+         lapply( vtg, function(vv){
+           ixDS$add_par( 'bound_new_capacity_up',
+             paste( reg, tech, vv, sep = '.' ),
+             0,
+             '-' )
+           } )
+         }
+       }
+     } )
+   } )
+
+ #-------------------------------------------------------------------------------------------------------
+ # Add baseline cooling technology policy - No new once-through seawater cooling capacity beyond existing levels
+ #-------------------------------------------------------------------------------------------------------
+
+ print('Adding once through saline water baseline cooling policy')
+
+ #### Aggregate saline water for ppl cooling to allow for activity constraints covering all types of this technology group
+
+ # Add to set list
+ tt = 'saline_ppl_t_d'
+ ixDS$add_set( 'technology', tt )
+ ixDS$add_set( 'cat_tec', paste( 'investment_other' , tt, sep='.') ) # Add technology to list of water system investments
+
+ # List of techs with saline once through
+ otc = unlist(lapply( ixDS$set('technology'), function(x) if( length(unlist(strsplit(x,'__'))==2) ){ if( unlist(strsplit(x,'__'))[2] %in% c('ot_saline') ){ return( x ) } } ))
+ yb = 2010 # base year
+
+ # Getting the historical activity of all seawater cooling technologies - will use to constrain future years
+ nms_hist = sapply(1:nrow( all_historical_activity ),function(fff){ paste(all_historical_activity$technology[fff],all_historical_activity$node_loc[fff],sep='_') })
+ ret = do.call( cbind, lapply( otc, function(tech){
+   print(paste( round( 100 * ( ( which( otc == tech ) - 1 ) / ( length(otc) ) ) ), ' % complete', sep=''))
+   dfs = data.frame( do.call(rbind, lapply( region, function(reg){
+     if( !( paste(tech,reg,sep='_') %in% skipped_tech_reg$comb ) & paste(tech,reg,sep='_') %in% nms_hist )
+       {
+       # Total historical activity of cooled powered plant and share for ocean water cooling
+       chk =  tryCatch( ixDS$par( 'historical_activity', list( node_loc = reg, technology = unlist(strsplit(tech,'__'))[1], year_act= 2010  ) ), error = function(e){} )
+       if(!is.null(chk))
+         {
+         hist_act = sum( chk[,'value'] )
+         if(use_davies_shares)
+           {
+           hist_shr = cooltech_cost_and_shares_ssp_msg_raw.df[ which( ( as.character(cooltech_cost_and_shares_ssp_msg_raw.df$utype) == unlist(strsplit(tech,'__'))[1] ) & ( as.character(cooltech_cost_and_shares_ssp_msg_raw.df$cooling) == unlist(strsplit(tech,'__'))[2] ) ), paste('mix',unlist(strsplit(reg,'_'))[2],'Davies_2013',sep='_') ]
+           }else
+           {
+           hist_shr = cooltech_cost_and_shares_ssp_msg_raw.df[ which( ( as.character(cooltech_cost_and_shares_ssp_msg_raw.df$utype) == unlist(strsplit(tech,'__'))[1] ) & ( as.character(cooltech_cost_and_shares_ssp_msg_raw.df$cooling) == unlist(strsplit(tech,'__'))[2] ) ), paste('mix',unlist(strsplit(reg,'_'))[2],sep='_') ]
+           }
+
+           #return(as.matrix(max(0,hist_act*hist_shr,na.rm=TRUE))) # multiply the activity by the share to estimate the total historical output
+
+         }else
+         {
+         return(as.matrix(c(0)))
+         }
+       }else
+       {
+       return(as.matrix(c(0)))
+       }
+     } ) ) )
+   row.names(dfs) = region
+   return(dfs) } ) )
+ names(ret) = otc
+ ww1 = rowSums(ret)
+
+ # Performance parameters - across all historical and future years
+ ret = lapply( region, function(rr){ lapply( model_years, function(yy){
+
+   # Add the investment cost
+   ixDS$add_par( 'inv_cost', # parameter name
+       paste(rr, tt, yy, sep = '.' ), # set key
+       0,
+       '-' )
+
+   # Add the technical lifetime
+   ixDS$add_par( 'technical_lifetime', # parameter name
+         paste(rr, tt, yy, sep = '.' ), # set key
+         1,
+         'y' )
+
+   # Add the construction time
+   ixDS$add_par( 'construction_time', # parameter name
+         paste(rr, tt, yy, sep = '.' ), # set key
+         1,
+         'y' )
+
+   # Add i-o parameters
+   lapply( model_years[ which( model_years >= as.numeric(yy) & model_years <= ( as.numeric(yy) + unlist( desal_par.list[[1]][paste('lifetime',parameter_levels,sep='.')]  ) ) ) ], function(aa){
+
+     # Add the output efficiency ratio
+     ixDS$add_par( 'output', # parameter name
+       paste( rr, tt, yy, aa, mode_common, rr, 'saline_supply_ppl', water_supply_level, model_time, model_time, sep = '.' ), # set key
+       1, # parameter value
+       '-' )
+
+     # Add the input efficiency ratio
+     ixDS$add_par( 'input', # parameter name
+       paste( rr, tt, yy, aa, mode_common, rr, 'saline_supply', water_supply_level, model_time, model_time, sep = '.' ), # set key
+       1,
+       '-' )
+
+     # Add the variable cost
+     ixDS$add_par( 'var_cost', # parameter name
+       paste( rr, tt, yy, aa, mode_common, model_time, sep = '.' ), # set key
+       0,
+       '-' ) # units
+
+     # Add fixed costs
+     ixDS$add_par( 'fix_cost', # parameter name
+       paste( rr, tt, yy, aa, sep = '.' ), # set key
+       0,
+       '-' )
+
+     # Add the capacity factor
+     ixDS$add_par( 'capacity_factor', # parameter name
+       paste( rr, tt, yy, aa, model_time, sep = '.' ), # set key
+       1,
+       '-' )
+
+     } )
+
+   } ) } )
+
+ # Add existing infrastructure
+ ret = lapply( region, function(rr){ lapply( model_years, function(yy){
+
+   ixDS$add_par( 'historical_new_capacity',
+           paste( rr, tt, yy, sep = '.' ),
+           round( ww1[ rr ]/5 , digits = 2 ),
+           '-' )
+
+   ixDS$add_par( 'historical_activity',
+           paste( rr, tt, yy, mode_common, model_time, sep = '.' ),
+           round( ww1[ rr ] , digits = 2 ),
+           '-' )
+
+   } ) } )
+
+ # # Add future constraints  - no expansion of seawater cooling beyond current capacity
+ ret = lapply( region, function(rr){ lapply( model_years[ which( model_years > 2010 ) ], function(yy){
+   ixDS$add_par( 'bound_activity_up', # parameter name
+         paste(  rr, tt, yy, mode_common , model_time, sep = '.' ), # set key
+         round( ww1[ rr ] , digits = 2 ), # parameter value
+         '-' )
+   } ) } )
+
+ # Include set of all tecs in the type tec category
+ ret = ixDS$add_set( 'type_tec', 'all_tec' ) # Add to technology types
+ ret = lapply( unique(ixDSoriginal$set( 'technology' )), function(x){ ixDS$add_set( 'cat_tec', paste( 'all_tec' , x, sep='.' ) ) } )
+
+ #---------------------------------------------------------------------
+ # Fix the land-use scenarios that can be used for mitigation
+ #---------------------------------------------------------------------
+
+ lsn = ixDS$set('land_scenario')
+ lsn = lsn[ which( as.numeric( unlist( strsplit( lsn, 'GHG' ) )[ seq( 2, 2*length(lsn), by = 2 ) ] ) >= 200 ) ]
+ if ( length(lsn) > 0 )
+   {
+
+   ret = lapply( region, function( rr ){ lapply( model_years[ which( model_years > 2010 ) ], function( yy ){ lapply( lsn, function( ll ){
+
+     ixDS$add_par( 'fixed_land', # parameter name
+           paste(  rr, ll, yy, sep = '.' ), # set key
+           0, # parameter value
+           '%' )
+
+     # ixDS$remove_par( 'fixed_land', paste(  rr, ll, yy, sep = '.' ) )
+
+     } ) } ) } )
+
+   }
+
+ #--------------------------------------------------
+ # Commit to DB and set to default
+ #--------------------------------------------------
+
+ # Commit the current editions to the db and set as default
+ ixDS$commit(paste(comment,water_policies,sep=' '))
+ ixDS$set_as_default()
+ rm(ixDS)
+ gc()
+
+ #----------------------------------------------------
+ # Generate water and emission constrained scenarios
+ #----------------------------------------------------
+
+ # options(java.parameters = "-Xmx16g")
+
+ # library("rixmp")
+ # library("rmessageix")
+
+ # ## Set path ixmp folder in message_ix working copy
+ # message_ix_path = Sys.getenv("MESSAGE_IX_PATH")
+
+ # # Country region mapping key
+ # country_region_map_key.df = data.frame( read.csv('P:/ene.model/data/Water/demands/country_region_map_key.csv', stringsAsFactors=FALSE) )
+
+ # # SSP scenario
+ # ssss = 'SSP2'
+
+ # # Rounding parameter for decimals
+ # rnd = 6
+
+ # # list of model-scenario combinations
+ # model_scenarios = data.frame( 	model = c( "MESSAGE-GLOBIOM CD-LINKS R2.3.1", "MESSAGE-GLOBIOM CD-LINKS R2.3.1" , "MESSAGE-GLOBIOM CD-LINKS R2.3.1","MESSAGE-GLOBIOM CD-LINKS R2.3.1" ),
+                 # scenario = c( "baseline", "baseline", "baseline", "baseline" ),
+                 # newscenarioName = c( "baseline_globiom_base_watbaseline", "baseline_globiom_SDG_sdg6supp", "baseline_globiom_SDG_sdg6eff", "baseline_globiom_SDG_sdg6led" ) 	)
+
+ # # launch the IX modeling platform using the default central ORCALE database
+ # ix_platform = ixmp.Platform( dbprops = 'ixmp.properties' )
+
+ # sc = 2
+ # newscenarioName = as.character( unlist( model_scenarios$newscenarioName[sc] ) )
+
+ # if( model_scenarios$newscenarioName[sc] %in% c("baseline_globiom_SDG_sdg6eff","baseline_globiom_SDG_sdg6led") ){ water_policies = 'SDG6' }else{ water_policies = 'NoWatPol' }
+
+ ixDS = ix_platform$Scenario( model=as.character( unlist( model_scenarios$model[sc] ) ), scen=as.character( unlist( model_scenarios$newscenarioName[sc] ) ) )
+
+ #solve for policy scenarios - make sure to remember that the water constraints depend on baseline (i.e., c0 and w0)
+ for( climpol in c( 'c0', '1p5', '2p0' ) )
+   {
+
+   # Set water constraint level for energy sector
+   if( as.character( model_scenarios$newscenarioName[sc] ) == "baseline_globiom_base_watbaseline" ){ watcon = 'w0' }
+   if( as.character( model_scenarios$newscenarioName[sc] ) == "baseline_globiom_SDG_sdg6supp" ){ watcon = 'w5' }
+   if( as.character( model_scenarios$newscenarioName[sc] ) == "baseline_globiom_SDG_sdg6supp2" ){ watcon = 'w5' }
+   if( as.character( model_scenarios$newscenarioName[sc] ) == "baseline_globiom_SDG_sdg6eff" ){ watcon = 'w10' }
+   if( as.character( model_scenarios$newscenarioName[sc] ) == "baseline_globiom_SDG_sdg6led" ){ watcon = 'w10' }
+
+   # Sensitivity cases for alternative regional freshwater constraints
+   for( watcon2 in c('w0','w10','w20','w30') )
+     {
+
+     # status
+     print( paste( 'Working on: ',as.character( model_scenarios$newscenarioName[sc] ),' - ', climpol, ' - ', watcon2, sep = '' ) )
+
+     # Clone basis
+     scname = paste(newscenarioName,watcon2,climpol,sep='_')
+     ixDS0 = ixDS$clone( new_model = as.character( unlist( model_scenarios$model[sc] ) ), new_scen = scname, annotation = 'constraints sensitivity', keep_sol = FALSE )
+     ixDS0$check_out()
+
+     # Constrain withdrawals to fraction of basis
+     wcons = as.numeric( strsplit( watcon, 'w' )[[1]][2] ) / 100
+     wcons2 = as.numeric( strsplit( watcon2, 'w' )[[1]][2] ) / 100
+
+     if( wcons > 0 )
+       {
+
+       require(gdxrrw)
+       igdx( 'C:/GAMS/win64/24.8' )
+       upath = paste(message_ix_path,'/model/output/',sep='')
+       scen = paste('MSGoutput',"baseline_globiom_base_watbaseline_w0_c0",sep='_')
+       res.list = lapply( scen, function(fpath){
+         vars = c( 'ACT' )
+         gdx_res = lapply( vars, function( vv ){
+           tmp = rgdx( paste( upath, fpath, sep = '' ), list( name = vv, form = "sparse" ) )
+           names(tmp$uels) = tmp$domains
+           rs = data.frame( tmp$val )
+           names(rs) = c( unlist( tmp$domains ), 'val' )
+           rs[ , which( names(rs) != 'val' ) ] = do.call( cbind, lapply( names( rs )[ which( names(rs) != 'val' ) ], function( cc ){ sapply( unlist( rs[ , cc ] ) , function(ii){ return( tmp$uels[[ cc ]][ ii ] ) } ) } ) )
+           return(rs)
+           } )
+         names(gdx_res) = vars
+         return(gdx_res)
+         } )
+       names(res.list) = scen
+       res.df = data.frame( res.list[[1]][[1]] )
+       res.df = res.df[ which( as.character( unlist( res.df$tec ) ) == "extract__freshwater_supply" & as.numeric( unlist( res.df$year_all ) ) >= 2030 ), ]
+
+       # Upper activity bound to reflect water conservation constraint based on baseline trajectory
+       ret = lapply( 1:nrow(res.df), function(ii){ ixDS0$add_par( 'bound_activity_up', paste( res.df[ii,'node'], res.df[ii,'tec'], res.df[ii,'year_all'], ixDS0$set('mode')[1], ixDS0$set('time'), sep = '.' ), round( c( res.df[ii,'val'] * ( 1 - wcons2 ) ) , digits = 3 ), '-' ) } )
+
+       }
+
+     if( wcons2 > 0 )
+       {
+
+       require(gdxrrw)
+       igdx( 'C:/GAMS/win64/24.8' )
+       upath = paste(message_ix_path,'/model/output/',sep='')
+       scen = paste('MSGoutput',"baseline_globiom_base_watbaseline_w0_c0",sep='_')
+
+       # Get the consumption trajectory for the baseline scenario
+       res.list = lapply( scen, function(fpath){
+         vars = c( 'EMISS' )
+         gdx_res = lapply( vars, function( vv ){
+           tmp = rgdx( paste( upath, fpath, sep = '' ), list( name = vv, form = "sparse" ) )
+           names(tmp$uels) = tmp$domains
+           rs = data.frame( tmp$val )
+           names(rs) = c( unlist( tmp$domains ), 'val' )
+           rs[ , which( names(rs) != 'val' ) ] = do.call( cbind, lapply( names( rs )[ which( names(rs) != 'val' ) ], function( cc ){ sapply( unlist( rs[ , cc ] ) , function(ii){ return( tmp$uels[[ cc ]][ ii ] ) } ) } ) )
+           return(rs)
+           } )
+         names(gdx_res) = vars
+         return(gdx_res)
+         } )
+       names( res.list ) = scen
+       res.df = data.frame( res.list[[1]][[1]] )
+       res.df = res.df[ which( as.character( unlist( res.df$emission ) ) == "fresh_consumption" & as.character( unlist( res.df$node ) ) != "World" & as.character( unlist( res.df$type_tec ) ) == "all_tec" & as.numeric( unlist( res.df$year_all ) ) >= 2030  ), ]
+
+       # Upper activity bound to reflect water conservation constraint based on baseline trajectory
+       ret = lapply( 1:nrow(res.df), function(ii){ ixDS0$add_par( 'bound_emission', paste(res.df[ii,'node'],'water_consumption','all',res.df[ii,'year_all'],sep='.'), round( c( res.df[ii,'val'] * ( 1 - wcons ) ) , digits = 3 ) , '-' ) } )
+
+       }
+
+     # Set cumulative emissions bound if 1p5 degree scenario - using emissions bound from Gruebler et al. 2018
+     if( climpol == '1p5' )
+       {
+       ixDS0$add_par("bound_emission",paste("World", "TCE", "all", "cumulative", sep='.'), 1400, "tC")  ## This budget (for 2021-2110) was run in scenarios 'b2C_NPi2020_noBECCS_V7a' to 'V8' to get something around 1.5C. (A budget of 1200 was initially used for V7, but the carbon prices were ridiculously higher.) Median temperature is around 1.61C in 2050 and 1.37 in 2100. Median radiative forcing (total) is 1.94 W/m2 in 2100.
+       }
+
+     if( climpol == '2p0' )
+       {
+       ixDS0$add_par("bound_emission",paste("World", "TCE", "all", "cumulative", sep='.'), 2500, "tC")  #
+       }
+
+     if( as.character( model_scenarios$newscenarioName[sc] ) == "baseline_globiom_SDG_sdg6led" ){ runm = 'MESSAGE' }else{ runm = 'MESSAGE-MACRO' }
+
+     # Commit, solve and upload solution
+     ixDS0$commit(paste(water_policies,sep=' '))
+     ixDS0$to_gdx( paste(message_ix_path,'/model/data',sep=''), paste( 'MSGdata_', scname, '.gdx',sep='') )
+     current_working_drive = getwd()
+     setwd(paste(message_ix_path,'/model',sep='')) # set based on local machine
+     cmd = paste( "gams ",runm,"_run.gms --in=data\\MSGdata_",scname,".gdx --out=output\\MSGoutput_",scname,".gdx", sep='' )
+     res = system(cmd)
+     setwd(current_working_drive)
+     if( res == 0 )
+       {
+       ixDS0$read_sol_from_gdx( 	paste(message_ix_path,'/model/output',sep=''),
+                     paste( 'MSGoutput_', scname, '.gdx',sep=''),
+                     var_list=NULL,
+                     equ_list="COMMODITY_BALANCE_FULL",
+                     check_sol=TRUE	)
+       }
+     ixDS0$set_as_default()
+     rm(ixDS0)
+     gc()
+
+     }
+
+   }
+
+ }
diff --git a/nest/message_ix_models/model/water/data/deprecated/cooling_tech_av.R b/nest/message_ix_models/model/water/data/deprecated/cooling_tech_av.R
new file mode 100644
index 0000000..c6e3895
--- /dev/null
+++ b/nest/message_ix_models/model/water/data/deprecated/cooling_tech_av.R
@@ -0,0 +1,905 @@
+
+# call using e.g.,: source('C:/Users/parkinso/git/message_ix/workflow/water/ssp_water_implement.r')
+options( java.parameters = "-Xmx16g" )
+rm(list=ls())
+graphics.off()
+
+library(reticulate)
+path_to_python <- "/anaconda/bin/python"
+use_python(path_to_python)
+#-------------------------------------------------------------------------------
+# load ixToolbox auxiliary functions and get the gateway to the ixToolbox JVM
+#-------------------------------------------------------------------------------
+print('Initializing functions and parameters')
+
+# launch the IX modeling platform using the default central ORCALE database
+import('ixmp')
+ixmp <- import('ixmp')
+message_ix <- import('message_ix')
+#source(file.path(Sys.getenv("IXMP_R_PATH"),"ixmp.r"))
+#ix_platform = ixPlatform()
+mp = ixmp$Platform(name='ixmp_dev')
+scen_list = mp$scenario_list(default = F)
+
+# Set local message_ix and GAMS directories 
+wkd_message_ix = "C:/Users/parkinso/git/" # !!!!! Set based on local machine
+wkd_GAMS = paste(unlist(strsplit( unlist(strsplit(Sys.getenv("PATH"),';'))[which(
+  grepl('GAMS', unlist(strsplit(Sys.getenv("PATH"),';')) ))[1]], '[\\]' )),collapse='/')
+
+SSP_scenarios = c('SSP1') # go through and create a baseline for each scenario
+
+for( ssss in SSP_scenarios ) # this should be parrellized
+{
+  
+  #-------------------------------------------------------------------------------
+  # Load the MESSAGE-GLOBIOM SSP baseline scenario 
+  # UPDATE: MESSAGE_GLOBIOM scenario give error with the current version of message+ix
+  # because the set 'rating' and probably other are missing
+  # I will use an CD-Links scenario, which has all SSPs
+  #-------------------------------------------------------------------------------
+  
+  modelName = paste( "CD_Links", ssss, sep = "_" )
+  scenarioName = "baseline"
+  newscenarioName = "baseline_with_water_2020"
+  comment = "Adding water for energy structure and basic data for SSP to existing MESSAGE run"
+  ixDSoriginal = message_ix$Scenario(mp, model = modelName, scenario = scenarioName)
+  ixDS = ixDSoriginal$clone(modelName, newscenarioName, keep_solution = F)
+  ixDS$check_out()
+  
+  #-------------------------------------------------------------------------------
+  # Set some global parameters
+  #-------------------------------------------------------------------------------
+  
+  # Rounding parameter for decimals
+  rnd = 6
+  
+  # low, mid or high parameter settings for water techs
+  parameter_levels = 'mid'
+  
+  # Whether or not to use initial shares from Davies et al 2013 - alternatively uses shares estimated with the dataset from Raptis et al 2016.
+  use_davies_shares = TRUE
+  
+  #-------------------------------------------------------------------------------------------------------
+  # Compare the data in the database to the data in the csv files to determine the technologies to include
+  #-------------------------------------------------------------------------------------------------------
+  
+  # Import raw cooling water data: 
+  # Input data file 1: CSV containing the water use coefficients (incl. parasitic electricity) for each MESSAGE technology 
+  # Source for power plant cooling water use: Meldrum et al. 2013
+  # Source for hydropower water use: Taken as an average across Grubert et al 2016 and Scherer and Pfister 2016.
+  # Parasitic electricity requirements estimated from Loew et al. 2016.
+  # All other water coefficients come from Fricko et al 2016.
+  # To compile the data, a complete list of technologies from MESSAGE was initially output to CSV. 
+  # The water parameters were then checked and entered manually based on data reported in the sources above.
+  
+  tech_water_performance_ssp_msg_raw.df = data.frame(read.csv(paste('~/Reps/message_data/data/water/tech_water_performance_ssp_msg.csv' )),stringsAsFactors=FALSE)
+  # Input data file 2: CSV containing the regional shares of each cooling technology and the investment costs
+  # The regional shares are estimated using the dataset from Raptis and Pfister 2016 and country boundaries from the GADM dataset
+  # Each plant type in the Raptis and Pfister dataset is mapped to the message technologies. The fraction is calculated using 
+  # the total capacity identified for each cooling technology in each country.  These results are aggregated into the message 11 regions.
+  # The costs are estimated from Loew et al. 2016.  
+  cooltech_cost_and_shares_ssp_msg_raw.df = data.frame(read.csv(paste(
+    '~/Reps/message_data/data/water/cooltech_cost_and_shares_ssp_msg.csv'),stringsAsFactors=FALSE))
+  
+  # Define alternate id for cooling technologies from csv file 
+  cooltech_cost_and_shares_ssp_msg_raw.df$alt_id = apply( 
+    cbind(cooltech_cost_and_shares_ssp_msg_raw.df[,1:2]), 1, paste, collapse="__") 
+  
+  # Get the names of the technologies in the MESSAGE IAM from the database
+  technologies_in_message = ixDS$set('technology')
+  
+  # Get the name of the regions 
+  region = as.character( ixDS$set('cat_node')$node )
+  
+  # Get the name of the commodities
+  commodity = ixDS$set('commodity')
+  
+  # Model years
+  model_years = ixDS$set('year')
+  
+  # Model timeslices
+  model_time = ixDS$set('time')
+  
+  # Which technologies in MESSAGE are also included in csv files?
+  message_technologies_with_water_data = as.character(tech_water_performance_ssp_msg_raw.df$technology_name)[ 
+    which( as.character(tech_water_performance_ssp_msg_raw.df$technology_name) %in% technologies_in_message )]
+  
+  message_technologies_without_water_data = as.character(tech_water_performance_ssp_msg_raw.df$technology_name)[ 
+    which( !(as.character(tech_water_performance_ssp_msg_raw.df$technology_name) %in% technologies_in_message ) )]
+  
+  message_technologies_with_water_data_alt = as.character(technologies_in_message)[ 
+    which(  technologies_in_message %in% as.character(tech_water_performance_ssp_msg_raw.df$technology_name) )]
+  
+  message_technologies_without_water_data_alt = as.character(technologies_in_message)[ 
+    which(  !(technologies_in_message %in% as.character(tech_water_performance_ssp_msg_raw.df$technology_name) )) ]
+  
+  # Define a common mode using the existing DB settings
+  mode_common = ixDS$set('mode')[1] # Common mode name from set list
+  
+  # Define hydropower technologies
+  hydro_techs = c('hydro_hc','hydro_lc')
+  
+  #-------------------------------------------------------------------------------------------------------
+  # Add cooling technologies using historical ppl data and pre-defined regional shares
+  #-------------------------------------------------------------------------------------------------------
+  print('Adding cooling technologies')
+  
+  # Cooling technologies:
+  # Once through (fresh and saline), closed-loop (fresh) and air cooled options considered
+  # No air cooling options for nuclear and CCS 
+  # Only consider the cooling technologies that correspond to power plant technologies in the MESSAGE model. 
+  cooling_technologies_to_consider = c( apply( cooltech_cost_and_shares_ssp_msg_raw.df[ which( as.character( cooltech_cost_and_shares_ssp_msg_raw.df$utype ) %in% as.character( message_technologies_with_water_data )  ), c("utype","cooling") ], 1, paste, collapse="__") )
+  
+  # Cooling commodities by power plant type - output commodity for cooling technology
+  cooling_commodities = unlist( lapply( 1:length( unique( cooltech_cost_and_shares_ssp_msg_raw.df$utype[ as.numeric( names(cooling_technologies_to_consider) )  ] ) ), function(x){ paste( 'cooling', unique( cooltech_cost_and_shares_ssp_msg_raw.df$utype[ as.numeric(names(cooling_technologies_to_consider)) ] )[x], sep='__' ) } ) )
+  ret = lapply( 1:length(cooling_commodities), function(x){ ixDS$add_set( "commodity", as.character( cooling_commodities[x] ) ) } ) # Add commodity to ix DB
+  
+  # Water sources - input commodity for cooling technology
+  water_supply_type = unique(tech_water_performance_ssp_msg_raw.df$water_supply_type[ which( as.character(tech_water_performance_ssp_msg_raw.df$technology_name) %in% message_technologies_with_water_data | as.character(tech_water_performance_ssp_msg_raw.df$technology_group) == 'cooling' ) ] )
+  water_supply_type = water_supply_type[-1*which(is.na(water_supply_type))]
+  ret = lapply( 1:length(water_supply_type), function(x){ ixDS$add_set( "commodity", as.character( water_supply_type[x] ) ) } ) # Add commodity to ix DB
+  
+  # Water supply and cooling as a new level 
+  water_supply_level = 'water_supply'
+  ret = ixDS$add_set( "level", water_supply_level ) # Add level to ix DB
+  cooling_level = 'cooling'
+  ret = ixDS$add_set( "level", cooling_level ) # Add level to ix DB
+  
+  # Wastewater as an emission - could alternatively be included as commodity balance
+  ret = ixDS$add_set( "emission", "fresh_wastewater" )
+  ret = ixDS$add_set( "emission", "saline_wastewater" ) # some of these should alternatively be defined in the input files
+  
+  # Thermal pollution as an emission - consider oceans and rivers
+  ret = ixDS$add_set( "emission", "fresh_thermal_pollution" )
+  ret = ixDS$add_set( "emission", "saline_thermal_pollution" )
+  
+  # Water source extraction technologies - add to technology and type sets
+  # Currently distinguishes: freshwater_instream (hydropower), freshwater_supply (all techs using freshwater), saline_supply (ocean and brackish resources) and upstream_landuse (globiom accounting)
+  water_source_extraction_techs =  unlist( lapply( 1:length(water_supply_type), 
+                                                   function(x){ paste( 'extract', as.character( water_supply_type[x] ), sep='__' ) } ) )
+  ret = lapply( 1:length(water_supply_type), function(x){ ixDS$add_set( "technology", water_source_extraction_techs[x] ) } )
+  water_resource_extraction_tech_type = 'water_resource_extraction'
+  ret = ixDS$add_set( "type_tec", water_resource_extraction_tech_type ) # Add to technology types
+  
+  # the way mapping sets are defined is different, remove paste and add c('type_tec','tec')
+  ret = lapply( 1:length(water_supply_type), function(x){ ixDS$add_set( "cat_tec", c( water_resource_extraction_tech_type , water_source_extraction_techs[x]) ) } ) 
+  ret = lapply( 1:length(water_supply_type), function(x){ ixDS$add_set( "cat_tec", c( 'investment_other' , water_source_extraction_techs[x]) ) } ) 
+  
+  # Power plant cooling technologies as technology types - will set technology names later
+  power_plant_cooling_tech_type = 'power_plant_cooling'
+  ixDS$add_set( "type_tec", power_plant_cooling_tech_type )
+  
+  # Create data.frame that stores the relevant cost data from the csv files
+  cooling_technology_costs = data.frame( 	inv_costs = unlist( lapply( cooling_technologies_to_consider, function(techs2check){ as.numeric( cooltech_cost_and_shares_ssp_msg_raw.df$investment_million_USD_per_MW_mid[ which( as.character( cooltech_cost_and_shares_ssp_msg_raw.df$alt_id ) == as.character( techs2check ) ) ] ) } ) ),
+                                          fixed_costs = 0,#unlist( lapply( cooling_technologies_to_consider, function(techs2check){ as.numeric( cooltech_cost_and_shares_ssp_msg_raw.df$fixed_million_USD_per_MW[ which( as.character( cooltech_cost_and_shares_ssp_msg_raw.df$alt_id ) == as.character( techs2check ) ) ] ) } ) ),
+                                          var_costs = 0,#unlist( lapply( cooling_technologies_to_consider, function(techs2check){ as.numeric( cooltech_cost_and_shares_ssp_msg_raw.df$variable_million_USD_per_MW[ which( as.character( cooltech_cost_and_shares_ssp_msg_raw.df$alt_id ) == as.character( techs2check ) ) ] ) } ) ),										
+                                          row.names = cooling_technologies_to_consider )
+  
+  # Recover the names of the cooled technologies in MESSAGE (i.e., the thermal power plants)
+  cooled_technologies_in_message = unique( as.character( unlist(data.frame(strsplit( cooling_technologies_to_consider, '__'))[1,]) ) )
+  
+  # Manually set efficiencies - data not readily extracted from the DB - currently manually read from .inp file :(
+  manually_set_efficiencies = list( 	geo_hpl = 0.850,
+                                     geo_ppl = 0.385,
+                                     nuc_hc = 0.326, # couldn't find these numbers in the .inp file so using average heat rate of 3.065 kWh heat per kWh electricity from EIA
+                                     nuc_lc = 0.326, # couldn't find these numbers in the .inp file so using average heat rate of 3.065 kWh heat per kWh electricity from EIA
+                                     solar_th_ppl = 0.385	)
+  
+  # Technologies with historical capacity and activity
+  all_historical_new_capacity = ixDS$par( 'historical_new_capacity' )
+  all_historical_activity = ixDS$par( 'historical_activity' )
+  
+  # Add the cooling technology data to the db
+  skipped_tech_reg = NULL 
+  ret = lapply( cooled_technologies_in_message, function(tech){ 
+    
+    # Status update
+    print(paste( round( 100 * ( ( which( cooled_technologies_in_message == tech ) - 1 ) / ( length(cooled_technologies_in_message) ) ) ), ' % complete', sep=''))
+    
+    # Need to grab the entire data series for these variables to check whether they exist in all regions: there is probably a more efficient way to do this.
+    all_output = ixDS$par( 'output', list(technology = tech) )
+    all_technical_lifetime = ixDS$par( 'technical_lifetime', list(technology = tech) )
+    all_construction_time = ixDS$par( 'construction_time', list(technology = tech) )
+    all_fix_cost = ixDS$par( 'fix_cost', list(technology = tech) )
+    all_inv_cost = ixDS$par( 'inv_cost', list(technology = tech) )
+    
+    lapply( region, function(reg){ # go through each region
+      
+      # Retrieve the output for this particular cooled technology in MESSAGE	
+      if(length(which(as.character(all_output$node_loc) == reg))>0)
+      { 
+        
+        # Grab the output activity ratio
+        output = ixDS$par( 'output', list( node_loc = reg, technology = tech ) ) 
+        
+        # Check if multiple modes - only need one
+        if( length(unique(output$mode)) > 1 ){ output = output[ which( output$mode == unique(output$mode)[1] ) , ] }
+        
+        # Check if multiple commodities - only need one
+        if( length(unique(output$commodity)) > 1 ){ output = output[ which( output$commodity == unique(output$commodity)[1] ) , ] }	
+        
+        # Define the vintaging and time slicing parameters for the cooling technologies to match the cooled MESSAGE techs in the db
+        ind = data.frame( 	year_vtg = as.character( output$year_vtg ), 
+                           year_act = as.character( output$year_act ), 
+                           mode = as.character( output$mode ), 
+                           time = as.character( output$time ) 	)
+        
+        # Retrieve the input for this particular cooled technology in MESSAGE
+        if( !( tech %in% names( manually_set_efficiencies ) ) )	
+        {
+          
+          # Grab the input activity ratio
+          input = ixDS$par( 'input', list( node_loc = reg, technology = tech ) )
+          
+          # Check if multiple modes
+          if( length(unique(input$mode)) > 1 ){ input = input[ which( input$mode == unique(input$mode)[1] ) , ] }
+          
+          # Check if multiple commodities
+          if( length(unique(input$commodity)) > 1 ){ input = input[ which( input$commodity == unique(input$commodity)[1] ) , ] }		
+          
+          # Check if output and input matrices of different length
+          if( nrow(ind) == nrow(input) ){ input_vec = input$value }else{ print('output and input different lengths') }
+          
+        }else{ input_vec = rep( (1/manually_set_efficiencies[[ tech ]]), nrow(ind) ) } # Use the manually set values where applicable
+        
+        # Retrieve the historical capacity for this particular cooled technology in MESSAGE
+        if(length(which( as.character(all_historical_new_capacity$node_loc) == reg & 
+                         as.character(all_historical_new_capacity$technology) == tech ))>0)
+        { 
+          historical_new_capacity = ixDS$par( 'historical_new_capacity', list( node_loc = reg, technology = tech ) ) 
+        }else
+        { 
+          historical_new_capacity = NULL
+        }
+        
+        # Retrieve the historical activity for this particular cooled technology in MESSAGE
+        if(length(which( as.character(all_historical_activity$node_loc) == reg & 
+                         as.character(all_historical_activity$technology) == tech ))>0)
+        { 
+          historical_activity = ixDS$par( 'historical_activity', list( node_loc = reg, technology = tech ) ) 
+        }else
+        { 
+          historical_activity = NULL
+        }	
+        
+        # Technical lifetime
+        if(length(which(as.character(all_technical_lifetime$node_loc) == reg))>0){ 
+          technical_lifetime = ixDS$par( 'technical_lifetime', list( node_loc = reg, technology = tech ) ) }	
+        
+        # Investment costs
+        if(length(which(as.character(all_inv_cost$node_loc) == reg))>0){ 
+          inv_cost = ixDS$par( 'inv_cost', list( node_loc = reg, technology = tech ) ) }	
+        
+        # Fixed costs
+        if(length(which(as.character(all_fix_cost$node_loc) == reg))>0){ 
+          fix_cost = ixDS$par( 'fix_cost', list( node_loc = reg, technology = tech ) ) }
+        
+        # Construction time
+        if(length(which(as.character(all_construction_time$node_loc) == reg))>0){ 
+          construction_time = ixDS$par( 'construction_time', list( node_loc = reg, technology = tech ) ) }
+        
+        # Get the name of the cooling technologies for this particular cooled MESSAGE technology
+        techs_to_update = cooling_technologies_to_consider[ which( 
+          as.character( unlist(data.frame(strsplit( cooling_technologies_to_consider, '__'))[1,]) )  == as.character(tech) ) ]	
+        id2 = apply(cbind(as.character( unlist(data.frame(strsplit( 
+          techs_to_update, '__'))[2,]) ),as.character( unlist(data.frame(strsplit( 
+            techs_to_update, '__'))[1,]) )),1,paste,collapse='_') # alternate ID in csv file
+        
+        # Go through each vintage and cooling option and add the corresponding data to the DB
+        ret = lapply( 1:length(ind$year_vtg), function(v){ lapply( 1:length(techs_to_update), function(ttt){
+          
+          # Add the technology to the set list - only need to for one region
+          if(reg == region[1])
+          {
+            ixDS$add_set( "technology", as.character( techs_to_update[ ttt ] ) )
+            ixDS$add_set( "cat_tec", c( power_plant_cooling_tech_type , as.character( techs_to_update[ ttt ] )) ) # Add technology to list of cooling technology types
+            ixDS$add_set( "cat_tec", c( 'investment_electricity' , as.character( techs_to_update[ ttt ] )) ) # Add technology to list of electricity system investments
+          }
+          
+          # Set the input commodity using the name from the csv file, define output commodity using names generated and initialized previously
+          cmdty_in = as.character( tech_water_performance_ssp_msg_raw.df$water_supply_type[ which( as.character(tech_water_performance_ssp_msg_raw.df$technology_name) == id2[ ttt ] ) ] )
+          cmdty_out = cooling_commodities[ which( unlist( strsplit( cooling_commodities, '__') )[seq(2, length(unlist( strsplit( cooling_commodities, '__') )), by=2)] == tech ) ]
+          
+          ## Set the water withdrawal, return flow, thermal pollution and parasitic electricity use for this cooling technology (as intensities)
+          
+          # Get the heat lost to emissions and electricity production and use to compute cooling fraction
+          emissions_heat_fraction = as.numeric(tech_water_performance_ssp_msg_raw.df$emissions_heat_fraction[ which( as.character(tech_water_performance_ssp_msg_raw.df$technology_name) == tech  ) ]) # fraction of heat lost through emissions
+          cooling_fraction = input_vec * ( 1 - emissions_heat_fraction / max(input_vec) ) - 1  # scale heat lost to emission proportionally to the changes in the cooling fraction
+          
+          # Scale historical withdrawal to follow heat rate improvements
+          water_withdrawal = round( ( 60 * 60 * 24 * 365 ) * ( 1e-9 ) * ( cooling_fraction / max(cooling_fraction) ) * as.numeric( tech_water_performance_ssp_msg_raw.df[ which( as.character(tech_water_performance_ssp_msg_raw.df$technology_name) == id2[ttt]  ), which(as.character(names(tech_water_performance_ssp_msg_raw.df)) == paste('water_withdrawal_',parameter_levels,'_m3_per_output',sep='') ) ] ), digits = rnd )
+          
+          # Return flow using consumption intensity
+          return_flow = round( water_withdrawal * ( 1 - as.numeric( tech_water_performance_ssp_msg_raw.df[ which( as.character(tech_water_performance_ssp_msg_raw.df$technology_name) == id2[ ttt ]  ), which(as.character(names(tech_water_performance_ssp_msg_raw.df)) == paste('water_consumption_',parameter_levels,'_m3_per_output',sep='') ) ] ) / as.numeric( tech_water_performance_ssp_msg_raw.df[ which( as.character(tech_water_performance_ssp_msg_raw.df$technology_name) == id2[ttt]  ), which(as.character(names(tech_water_performance_ssp_msg_raw.df)) == paste('water_withdrawal_',parameter_levels,'_m3_per_output',sep='') ) ] ) ), digits = rnd )
+          
+          # Parasitic electricity consumption
+          parasitic_electricity = as.numeric( tech_water_performance_ssp_msg_raw.df[ which( as.character(tech_water_performance_ssp_msg_raw.df$technology_name) == id2[ ttt ]  ), "parasitic_electricity_demand_fraction" ] )
+          
+          ##
+          # Add the variable cost
+          ixDS$add_par( "var_cost", # parameter name
+                        c( reg, techs_to_update[ ttt ], as.character( ind$year_vtg[ v ] ), as.character( ind$year_act[ v ] ), 
+                           as.character( ind$mode[ v ] ), as.character( ind$time[ v ] ) ), # set key
+                        round( ( 1e9/(60*60*24*365) ) * cooling_technology_costs$var_costs[ which(
+                          as.character(row.names(cooling_technology_costs)) == as.character(techs_to_update[ ttt ]) ) ],digits = rnd), # parameter value
+                        'USD/GWa' ) # units
+          
+          # Add the capacity factor
+          ixDS$add_par( "capacity_factor", # parameter name
+                        c( reg, techs_to_update[ ttt ], as.character( ind$year_vtg[ v ] ), as.character( ind$year_act[ v ] ),  
+                           as.character( ind$time[ v ] )), # set key
+                        1, # Assume for now that cooling technologies are always available
+                        '-' ) # units
+          
+          # Add the output efficiency ratio 
+          ixDS$add_par( "output", # parameter name
+                        c( reg, techs_to_update[ ttt ], as.character( ind$year_vtg[ v ] ), as.character( ind$year_act[ v ] ), as.character( ind$mode[ v ] ), reg, cmdty_out, cooling_level, as.character( ind$time[ v ] ), as.character( ind$time[ v ] ) ), # set key
+                        1, # parameter value
+                        '-' )
+          
+          if( unlist(strsplit(id2[ttt],'_'))[1] != 'air' ) # Don't need to add water use inputs for air cooling technologies
+          {
+            
+            # Add the input efficiency ratio - water withdrawal 
+            ixDS$add_par( "input", # parameter name
+                          c( reg, techs_to_update[ ttt ], as.character( ind$year_vtg[ v ] ), 
+                             as.character( ind$year_act[ v ] ), as.character( ind$mode[ v ] ), 
+                             reg, cmdty_in, water_supply_level, as.character( ind$time[ v ] ), as.character( ind$time[ v ] )), # set key
+                          water_withdrawal[ v ], # parameter value
+                          '-' )
+            
+            # Add the thermal pollution emission factor
+            if( unlist(strsplit(id2[ttt],'_'))[1] == 'ot' ) # only for once through cooling technologies
+            {
+              if( unlist(strsplit(id2[ttt],'_'))[2] == 'fresh' ){ emis = 'fresh_thermal_pollution' }else{ emis = 'saline_thermal_pollution' }
+              ixDS$add_par( "emission_factor", # parameter name
+                            c( reg, techs_to_update[ ttt ], as.character( ind$year_vtg[ v ] ), 
+                               as.character( ind$year_act[ v ] ), as.character( ind$mode[ v ] ), emis  ), # set key
+                            round( cooling_fraction[ v ], digits = rnd ), # parameter value
+                            '-' )		
+            }
+            
+            # Add the wastewater emission factor	
+            if( unlist(strsplit(id2[ttt],'_'))[2] == 'fresh' ){ emis = 'fresh_wastewater' }else{ emis = 'saline_wastewater' }
+            ixDS$add_par( "emission_factor", # parameter name
+                          c( reg, techs_to_update[ ttt ], as.character( ind$year_vtg[ v ] ), 
+                             as.character( ind$year_act[ v ] ), as.character( ind$mode[ v ] ), emis  ), # set key
+                          return_flow[ v ], # parameter value
+                          '-' )		
+            
+          }
+          
+          # Add the input efficiency ratio - parasitic electricity consumption
+          if( parasitic_electricity > 0 ) # only for some cooling technologies
+          {
+            ixDS$add_par( "input", # parameter name
+                          c( reg, techs_to_update[ ttt ], as.character( ind$year_vtg[ v ] ), as.character( ind$year_act[ v ] ), 
+                             as.character( ind$mode[ v ] ), reg, 'electr', 'secondary', as.character( ind$time[ v ] ), 
+                             as.character( ind$time[ v ] )  ), # set key
+                          parasitic_electricity, # parameter value
+                          '-' )		
+          }	
+          
+          # Add the cooling commodity to the cooled message technology input list
+          if( ttt == 1 ) # only need to do once
+          {
+            ixDS$add_par( "input", # parameter name
+                          c( reg, tech, as.character( ind$year_vtg[ v ] ), as.character( ind$year_act[ v ] ), 
+                             as.character( ind$mode[ v ] ), reg, cmdty_out, cooling_level, as.character( ind$time[ v ] ), 
+                             as.character( ind$time[ v ] )  ), # set key
+                          1, # parameter value
+                          '-' )
+          }
+          
+        } ) } )
+        
+        # Go through each vintage and cooling option and add the corresponding data to the DB for investement costs
+        ret = lapply( 1:length(inv_cost$year_vtg), function(v){ lapply( 1:length(techs_to_update), function(ttt){
+          # Add the investment cost
+          ixDS$add_par( "inv_cost", # parameter name
+                        c( reg, techs_to_update[ ttt ], as.character( inv_cost$year_vtg[ v ] )  ), # set key
+                        round( ( 1e9/(60*60*24*365) ) * cooling_technology_costs$inv_costs[ which(as.character(row.names(cooling_technology_costs)) == as.character(techs_to_update[ ttt ]) ) ],digits = rnd), # parameter value (convert from mill USD/MW to USD/GWa)
+                        'USD/GWa' )
+        } ) } )	
+        
+        # Go through each vintage and cooling option and add the corresponding data to the DB for investement costs
+        ret = lapply( 1:length(fix_cost$year_vtg), function(v){ lapply( 1:length(techs_to_update), function(ttt){
+          # Add the fixed cost
+          ixDS$add_par( "fix_cost", # parameter name
+                        c( reg, techs_to_update[ ttt ], as.character( fix_cost$year_vtg[ v ] ), as.character( fix_cost$year_act[ v ] )  ), # set key
+                        round( ( 1e9/(60*60*24*365) ) * cooling_technology_costs$fixed_costs[ which(as.character(row.names(cooling_technology_costs)) == as.character(techs_to_update[ ttt ]) ) ],digits = rnd), # parameter value
+                        'USD/GWa' )
+        } ) } )		
+        
+        # Go through each vintage and cooling option and add the corresponding data to the DB for historical capacity
+        ret = lapply( 1:length(historical_new_capacity$year_vtg), function(v){ lapply( 1:length(techs_to_update), function(ttt){	
+          
+          # Add the historical capacity
+          if( !is.null(historical_new_capacity) ) # Check if historical capacity exists
+          {
+            # The output of the power plant cooling technologies are defined in terms of the electric power output supported
+            # Use the historical capacity for each cooled power plant type and the share of each cooling technology to estimate the historical capacity		
+            if(use_davies_shares)
+            {
+              shr = cooltech_cost_and_shares_ssp_msg_raw.df[ 
+                which( ( as.character(cooltech_cost_and_shares_ssp_msg_raw.df$utype) == 
+                           unlist(strsplit(techs_to_update[ ttt ],'__'))[1] ) & ( 
+                             as.character(cooltech_cost_and_shares_ssp_msg_raw.df$cooling) == 
+                               unlist(strsplit(techs_to_update[ ttt ],'__'))[2] ) ), 
+                paste("mix",unlist(strsplit(reg,'_'))[2],'Davies_2013',sep="_") ]
+            }else
+            {
+              shr = cooltech_cost_and_shares_ssp_msg_raw.df[ 
+                which( ( as.character(cooltech_cost_and_shares_ssp_msg_raw.df$utype) == 
+                           unlist(strsplit(techs_to_update[ ttt ],'__'))[1] ) & ( 
+                             as.character(cooltech_cost_and_shares_ssp_msg_raw.df$cooling) == 
+                               unlist(strsplit(techs_to_update[ ttt ],'__'))[2] ) ), 
+                paste("mix",unlist(strsplit(reg,'_'))[2],sep="_") ]
+            }
+            cap = round( shr * historical_new_capacity[ 
+              which( as.character( historical_new_capacity$year_vtg ) == 
+                       as.character( historical_new_capacity$year_vtg[v] )  ) ,  'value' ], digits = rnd )
+            
+            # Add the ix db
+            ixDS$add_par( "historical_new_capacity", 
+                          c( reg, techs_to_update[ ttt ], as.character( historical_new_capacity$year_vtg[ v ] )  ), 
+                          cap, 
+                          as.character( historical_new_capacity$unit[1] ) )
+          }	
+          
+        } ) } )
+        
+        # Go through each year and cooling option and add the corresponding data to the DB for historical activity
+        ret = lapply( 1:length(historical_activity$year_act), function(v){ lapply( 1:length(techs_to_update), function(ttt){	
+          
+          # Add the historical_activity
+          if( !is.null(historical_activity) ) # Check if historical_activity exists
+          {
+            
+            # The output of the power plant cooling technologies are defined in terms of the electric power output supported
+            # Use the historical_activity for each cooled power plant type and the share of each cooling technology to estimate the historical activity	
+            if(use_davies_shares) # whether or not to use the share from Davies or Raptis
+            {
+              shr = cooltech_cost_and_shares_ssp_msg_raw.df[ 
+                which( ( as.character(cooltech_cost_and_shares_ssp_msg_raw.df$utype) == 
+                           unlist(strsplit(techs_to_update[ ttt ],'__'))[1] ) & 
+                         ( as.character(cooltech_cost_and_shares_ssp_msg_raw.df$cooling) == 
+                             unlist(strsplit(techs_to_update[ ttt ],'__'))[2] ) ), 
+                paste("mix",unlist(strsplit(reg,'_'))[2],'Davies_2013',sep="_") ]
+            }else
+            {
+              shr = cooltech_cost_and_shares_ssp_msg_raw.df[ 
+                which( ( as.character(cooltech_cost_and_shares_ssp_msg_raw.df$utype) == 
+                           unlist(strsplit(techs_to_update[ ttt ],'__'))[1] ) & 
+                         ( as.character(cooltech_cost_and_shares_ssp_msg_raw.df$cooling) == 
+                             unlist(strsplit(techs_to_update[ ttt ],'__'))[2] ) ), 
+                paste("mix",unlist(strsplit(reg,'_'))[2],sep="_") ]
+            }
+            act = round( shr * historical_activity[ 
+              which( as.character( historical_activity$year_act ) == 
+                       as.character( historical_activity$year_act[v] )  ) ,  'value' ], digits = rnd )
+            
+            # Add the ix db
+            ixDS$add_par( "historical_activity", 
+                          c( reg, techs_to_update[ ttt ], as.character( historical_activity$year_act[ v ] ), 
+                             as.character( historical_activity$mode[ v ] ) , as.character( historical_activity$time[ v ] )  ), 
+                          act, 
+                          as.character( historical_activity$unit[1] ) )
+            
+          }	
+        } ) } )	
+        
+        ret = lapply( 1:length(technical_lifetime$year_vtg), function(v){ lapply( 1:length(techs_to_update), function(ttt){		
+          # Add the technical lifetime
+          ixDS$add_par( "technical_lifetime", # parameter name
+                        c( reg, techs_to_update[ ttt ], as.character( technical_lifetime$year_vtg[ v ] )  ), # set key
+                        technical_lifetime[ v ,  'value' ],
+                        'y' )
+        } ) } )
+        
+        ret = lapply( 1:length(construction_time$year_vtg), function(v){ lapply( 1:length(techs_to_update), function(ttt){		
+          # Add the construction time
+          ixDS$add_par( "construction_time", # parameter name
+                        c( reg, techs_to_update[ ttt ], as.character( construction_time$year_vtg[ v ] )  ), # set key
+                        construction_time[ v ,  'value' ],
+                        as.character( construction_time[ v ,  'unit' ] ) )
+        } ) } )		
+        
+      }else{ skipped_tech_reg = rbind( skipped_tech_reg, data.frame( 
+        technology = tech, region = reg, comb = paste(tech,reg,sep='_') ) ) } # add to list of skipped techs
+    } ) 
+  } )	 
+  
+  #-------------------------------------------------------------------------------------------------------
+  # Add water withdrawal and return flow for non-cooling technologies
+  #-------------------------------------------------------------------------------------------------------
+  
+  print('Adding water coefficients for non-cooling technologies')
+  
+  # Could speed this up a bit by allocating the ppl water use during the previous step.
+  
+  if(ssss == 'SSP3')
+  {
+    skipped_techs = c("igcc_co2scr","gfc_co2scr","cfc_co2scr","h2_co2_scrub","h2b_co2_scrub","gas_htfc","h2_bio","h2_bio_ccs","h2_smr","h2_smr_ccs","h2_coal","h2_coal_ccs","h2_elec","solar_pv_ppl","wind_ppl")
+  }else
+  {
+    skipped_techs = c("igcc_co2scr","gfc_co2scr","cfc_co2scr","h2_co2_scrub","h2b_co2_scrub","gas_htfc","solar_pv_ppl","wind_ppl")
+  }
+  message_technologies_with_water_data2 = message_technologies_with_water_data[which(!(message_technologies_with_water_data %in% skipped_techs))]
+  ret = lapply( message_technologies_with_water_data2, function(tech){ 
+    
+    # Status update
+    print(paste( round( 100 * ( ( which( message_technologies_with_water_data2 == tech ) - 1 ) / ( length(message_technologies_with_water_data2) ) ) ), ' % complete', sep=''))
+    
+    all_output = ixDS$par( 'output', list(technology = tech) )
+    
+    lapply( region, function(reg){
+      
+      if( length(which(as.character(all_output$node_loc) == reg))>0 )
+      {
+        
+        output = ixDS$par( 'output', list( node_loc = reg, technology = tech ) ) 
+        
+        # Check if multiple modes - only need one
+        if( length(unique(output$mode)) > 1 ){ output = output[ which( output$mode == unique(output$mode)[1] ) , ] }
+        
+        # Check if multiple commodities - only need one
+        if( length(unique(output$commodity)) > 1 ){ output = output[ which( output$commodity == unique(output$commodity)[1] ) , ] }	
+        
+        # Define the vintaging and time slicing parameters 
+        ind = data.frame( 	year_vtg = as.character( output$year_vtg ), 
+                           year_act = as.character( output$year_act ), 
+                           mode = as.character( output$mode ), 
+                           time = as.character( output$time ) 	)
+        
+        # Input commodity
+        cmdty_in = as.character( tech_water_performance_ssp_msg_raw.df$water_supply_type[ which( as.character(tech_water_performance_ssp_msg_raw.df$technology_name) == tech ) ] )
+        
+        # Add the water coefficients for each vintage and time slice
+        ret = lapply( 1:nrow( ind ), function(v){  
+          
+          # Using data from input csv files and converted from m3 / GJ to km3 / GWa
+          water_withdrawal = round( ( 60 * 60 * 24 * 365 ) * ( 1e-9 ) * as.numeric( tech_water_performance_ssp_msg_raw.df[ which( as.character(tech_water_performance_ssp_msg_raw.df$technology_name) == tech  ), which(as.character(names(tech_water_performance_ssp_msg_raw.df)) == paste('water_withdrawal_',parameter_levels,'_m3_per_output',sep='') ) ] ), digits = rnd )
+          return_flow = round( water_withdrawal - ( 60 * 60 * 24 * 365 ) * ( 1e-9 ) * as.numeric( tech_water_performance_ssp_msg_raw.df[ which( as.character(tech_water_performance_ssp_msg_raw.df$technology_name) == tech  ), which(as.character(names(tech_water_performance_ssp_msg_raw.df)) == paste('water_consumption_',parameter_levels,'_m3_per_output',sep='') ) ] ), digits = rnd )
+          
+          # Add the withdrawal to db as an input
+          ixDS$add_par( "input", # parameter name
+                        c( reg, tech, as.character( ind$year_vtg[ v ] ), as.character( ind$year_act[ v ] ), 
+                           as.character( ind$mode[ v ] ), reg, cmdty_in, water_supply_level, as.character( ind$time[ v ] ), 
+                           as.character( ind$time[ v ] )  ), # set key
+                        water_withdrawal,
+                        '-' )
+          
+          # Add the return flow to the emission factors	
+          if( !( tech %in% hydro_techs ) ) # no wastewater for hydropower / instream technologies sads
+          {
+            ixDS$add_par( "emission_factor", # parameter name
+                          c( reg, tech, 
+                             as.character( ind$year_vtg[ v ] ), as.character( ind$year_act[ v ] ), 
+                             as.character( ind$mode[ v ] ), 'fresh_wastewater'  ),
+                          return_flow,
+                          '-' )
+          }		
+        } ) 
+        
+      }
+    } )
+  } )
+  
+  #-------------------------------------------------------------------------------------------------------
+  # Add water resource extraction technologies
+  #-------------------------------------------------------------------------------------------------------
+  
+  print('Adding water source extraction technologies')
+  
+  # List of technologies with historical water use
+  hist_techs = list( 	freshwater_supply = c( as.character( message_technologies_with_water_data2 )[which( as.character( message_technologies_with_water_data2  ) %in% as.character( unique( all_historical_activity$technology ) ) & !( as.character( message_technologies_with_water_data2 )  %in% c( tech_water_performance_ssp_msg_raw.df$technology_name[which(tech_water_performance_ssp_msg_raw.df$water_supply_type %in% c('freshwater_instream','upstream_landuse'))]  ) ) )], cooling_technologies_to_consider[ which( unlist(lapply(cooling_technologies_to_consider,function(zzz){unlist(strsplit( zzz, '__' ))[1]})) %in% unique( all_historical_activity$technology ) &  unlist(lapply( unlist(lapply(cooling_technologies_to_consider,function(zzz){unlist(strsplit( zzz, '__' ))[2]})), function(xxx){ unlist(strsplit( xxx, '_' ))[2] }  ) ) == 'fresh' ) ] ),
+                      saline_supply = cooling_technologies_to_consider[ which( c( unlist(lapply( unlist(lapply(cooling_technologies_to_consider,function(zzz){unlist(strsplit( zzz, '__' ))[2]})), function(xxx){ unlist(strsplit( xxx, '_' ))[2] }  ) ) == 'saline' ) ) ],
+                      freshwater_instream = c( tech_water_performance_ssp_msg_raw.df$technology_name[which(tech_water_performance_ssp_msg_raw.df$water_supply_type == 'freshwater_instream')]  ),
+                      upstream_landuse = c( tech_water_performance_ssp_msg_raw.df$technology_name[which(tech_water_performance_ssp_msg_raw.df$water_supply_type == 'upstream_landuse')] ) )
+  
+  # Technologies with historical capacity and activity - update
+  all_historical_new_capacity = ixDS$par( 'historical_new_capacity' )
+  all_historical_activity = ixDS$par( 'historical_activity' )
+  
+  ret = lapply( water_source_extraction_techs, function(tech){ 
+    
+    # Status update
+    print(paste( round( 100 * ( ( which( water_source_extraction_techs == tech ) - 1 ) / ( length(water_source_extraction_techs) ) ) ), ' % complete', sep=''))
+    
+    lapply( region, function(reg){ # across each region
+      
+      # Get the commodity for the source type (freshwater or saline or instream)
+      cmdty_out = as.character( unlist(strsplit(tech,'__'))[2] )
+      
+      lapply( model_years, function( yy ){ # go through the years
+        
+        
+        # Add the output for each timeslice
+        ret = lapply( model_time, function(tm){ 
+          
+          ixDS$add_par( "output", # parameter name
+                        c( reg, tech, as.character( yy ), 
+                           as.character( yy ), mode_common, reg, cmdty_out, water_supply_level, 
+                           as.character( tm ), as.character( tm )  ), # set key
+                        1, # parameter value
+                        '-' )
+          
+        } )
+        
+        # Add the investment cost
+        ixDS$add_par( "inv_cost", # parameter name
+                      c( reg, tech, as.character( yy )  ), # set key
+                      0, 
+                      '-' )
+        
+        # Add the fixed cost
+        # print(ixDS$get_par_set('fix_cost'))
+        ixDS$add_par( "fix_cost", # parameter name
+                      c( reg, tech, as.character( yy ), as.character( yy )  ), # set key
+                      0,
+                      '-' )
+        
+        # Add the variable cost
+        # print(ixDS$get_par_set('var_cost'))
+        ret = lapply( model_time, function(tm){ 
+          ixDS$add_par( "var_cost", # parameter name
+                        c( reg, tech, as.character( yy ), as.character( yy ), as.character( mode_common ), as.character( tm )  ), # set key
+                        0.0001, # default for now 
+                        '-' )
+        } )	
+        
+        # Add the capacity factor
+        # print(ixDS$get_par_set('capacity_factor'))
+        ret = lapply( model_time, function(tm){ 
+          ixDS$add_par( "capacity_factor", # parameter name
+                        c( reg, tech, as.character( yy ), as.character( yy ), as.character( tm )  ), # set key
+                        1,  
+                        '-' )
+        } )
+        
+        # Add the technical lifetime
+        if(yy < model_years[length(model_years)]){tl = as.numeric(model_years[which(model_years==yy)+1]) - as.numeric(yy)}else{tl = as.numeric(model_years[length(model_years)]) - as.numeric(model_years[length(model_years)-1])}
+        ixDS$add_par( "technical_lifetime", # parameter name
+                      c( reg, tech, as.character( yy )  ), # set key
+                      tl,
+                      'y' )
+        
+        # Add the contruction time
+        ixDS$add_par( "construction_time", # parameter name
+                      c( reg, tech, as.character( yy )  ), # set key
+                      0,
+                      'y' )	
+        
+      } )
+      
+      # Historical activity of extraction techs determined from 
+      # the historical activity of the MESSAGE techs
+      techs_hist = unlist( hist_techs[ cmdty_out ] ) 
+      temp = lapply(techs_hist, function(ttt){
+        hist_act = all_historical_activity[which( as.character(all_historical_activity$technology) == ttt  & as.character(all_historical_activity$node_loc) == reg ),]
+        hist_act_yy = unique(hist_act$year_act)
+        if( length(hist_act_yy) > 0)
+        {
+          ret1 = data.frame( do.call(rbind, lapply( hist_act_yy, function(yyy){ 
+            yr = as.character(yyy)
+            inp = mean( ixDS$par( 'input', list( node_loc = reg, technology = ttt, year_act = as.character(hist_act$year_act[which(hist_act$year_act == yyy)]), commodity = cmdty_out ) )[,'value'], na.rm=TRUE )
+            act = hist_act$value[which(hist_act$year_act == yyy)]
+            return( c(yr, inp * sum(act)) ) # multiply input by activity to estimate historical demands
+          } ) ) )
+        }else{ ret1 = NULL }
+        return(ret1)	
+      })	
+      names(temp) = techs_hist	
+      
+      # total historical is summed across the demands from all technologies 
+      hist_years = unique(unlist(lapply( 1:length(techs_hist), function(xxx){ if(!is.null(temp[[xxx]])){ unlist( as.character( temp[[xxx]][,1] ) )}})))
+      hist_tot_act = unlist( lapply( 1:length(hist_years), function(yyy){ sum( unlist( lapply( 1:length(techs_hist), function(xxx){    
+        temp2 = temp[[xxx]]
+        if( hist_years[yyy] %in% as.character( temp2[,1] ) )
+        {
+          return( as.numeric( as.character( temp2[ which( as.character( temp2[,1] ) == as.character( hist_years[yyy] ) ), 2 ] ) ) )
+        }else{ return(NULL) }
+      } ) ), na.rm = TRUE ) } ) )
+      
+      # Add the ix db
+      ret = lapply( 1:length(hist_years), function(yyy){
+        ixDS$add_par( "historical_activity", 
+                      c( reg, tech, as.character( hist_years[ yyy ] ), as.character( mode_common ) , model_time  ), 
+                      hist_tot_act[ yyy ], 
+                      '-' )
+      } )
+    } ) 
+  } )			
+  
+  #-------------------------------------------------------------------------------------------------------
+  # Add baseline cooling technology policy - No new once-through freshwater cooling capacity
+  #-------------------------------------------------------------------------------------------------------
+  print('Adding once through freshwater baseline cooling policy')
+  
+  # Once through freshwater cooled techs
+  otc = unlist(lapply( ixDS$set('technology'), function(x) if( length(unlist(strsplit(x,'__'))==2) ){ if( unlist(strsplit(x,'__'))[2] %in% c('ot_fresh') ){ return( x ) } } ))
+  
+  # Loop through and bound new capacity additions to zero for otc techs
+  ret = lapply( otc, function(tech){ 
+    
+    # status
+    print(paste( round( 100 * ( ( which( otc == tech ) - 1 ) / ( length(otc) ) ) ), ' % complete', sep=''))
+    
+    # Across all regions
+    lapply( region, function(reg){
+      if( !( paste(tech,reg,sep='_') %in% skipped_tech_reg$comb ) )
+      {
+        if( !( unlist(strsplit(tech,'__'))[1] == 'nuc_lc' & reg == 'R11_MEA') )
+        {
+          vtg = unique( ixDS$par( 'output', list( node_loc = reg, technology = tech ) )$year_vtg ) 
+          lapply( vtg, function(vv){
+            ixDS$add_par( 'bound_new_capacity_up', 
+                          c( reg, tech, vv  ), 
+                          0, 
+                          '-' )
+          } )
+        }	
+      }		
+    } ) 
+  } )	
+  
+  #-------------------------------------------------------------------------------------------------------
+  # Add baseline cooling technology policy - No new once-through seawater cooling capacity beyond existing levels 
+  #-------------------------------------------------------------------------------------------------------
+  
+  print('Adding once through saline water baseline cooling policy')
+  
+  # List of techs with saline once through 
+  otc = unlist(lapply( ixDS$set('technology'), function(x) if( length(unlist(strsplit(x,'__'))==2) ){ if( unlist(strsplit(x,'__'))[2] %in% c('ot_saline') ){ return( x ) } } ))
+  yb = 2010 # base year
+  
+  # Getting the historical activity of all seawater cooling technologies - will use to constrain future years
+  nms_hist = sapply(1:nrow( all_historical_activity ),function(fff){ 
+    paste(all_historical_activity$technology[fff],all_historical_activity$node_loc[fff],sep='_') })
+  
+  ret = do.call(cbind, lapply( otc, function(tech){ 
+    print(paste( round( 100 * ( ( which( otc == tech ) - 1 ) / ( length(otc) ) ) ), ' % complete', sep=''))
+    dfs = data.frame( do.call(rbind, lapply( region, function(reg){
+      if( !( paste(tech,reg,sep='_') %in% skipped_tech_reg$comb ) & paste(tech,reg,sep='_') %in% nms_hist )
+      {
+        # Total historical activity of cooled powered plant and share for ocean water cooling
+        hist_act = sum( ixDS$par( 'historical_activity', list( node_loc = reg, technology = unlist(strsplit(tech,'__'))[1], year_act= 2010  ) )[,'value'] )
+        if(use_davies_shares)
+        {
+          hist_shr = cooltech_cost_and_shares_ssp_msg_raw.df[ which( ( as.character(cooltech_cost_and_shares_ssp_msg_raw.df$utype) == unlist(strsplit(tech,'__'))[1] ) & ( as.character(cooltech_cost_and_shares_ssp_msg_raw.df$cooling) == unlist(strsplit(tech,'__'))[2] ) ), paste("mix",unlist(strsplit(reg,'_'))[2],'Davies_2013',sep="_") ]
+        }else
+        {
+          hist_shr = cooltech_cost_and_shares_ssp_msg_raw.df[ which( ( as.character(cooltech_cost_and_shares_ssp_msg_raw.df$utype) == unlist(strsplit(tech,'__'))[1] ) & ( as.character(cooltech_cost_and_shares_ssp_msg_raw.df$cooling) == unlist(strsplit(tech,'__'))[2] ) ), paste("mix",unlist(strsplit(reg,'_'))[2],sep="_") ]
+        }
+        return(as.matrix(max(0,hist_act*hist_shr,na.rm=TRUE))) # multiply the activity by the share to estimate the total historical output
+      }else{ return(as.matrix(c(0))) }
+    } ) ) )
+    row.names(dfs) = region
+    return(dfs) } ) )
+  names(ret) = otc	
+  ww1 = rowSums(ret)	
+  
+  # Add the constraint to the db by bounding the activity in each year of the regional extraction technology
+  ret = lapply( c('extract__saline_supply'), function(tech){ lapply( region, function(reg){
+    year_all = seq(2020,2100,by=10)
+    lapply( as.character(year_all), function(vv){
+      ixDS$add_par( 'bound_activity_up', 
+                    c( reg, tech, vv, mode_common, model_time  ), 
+                    ww1[reg], 
+                    '-' )
+    } )
+  } ) 
+  } )
+  
+  #-------------------------------------------------------------------------------------------------------
+  # Initialize water constraint criteria - an extra technology that outputs into water extraction techs
+  #-------------------------------------------------------------------------------------------------------
+  
+  # Water constraints are implemented using an extra technology (extract__water_constraint) that outputs into water extraction techs
+  # This choice enables modeling policies that cover multiple water commodities. E.g., simultaneous reduction targets for saline and freshwater, etc.
+  # The water resources to be constrained can be modified by selecting the techs that take in the output commodity from extract__water_constraint
+  # Note that the constraint is left disconnected in the baseline script and that the following merely initializes the general structure.
+  
+  print('Initializing water constraints')
+  
+  # Add the water constraint technology to the set list
+  tech = "extract__water_constraint"
+  cmdty_out = "water_constraint"
+  water_supply_level_constraint = paste(water_supply_level,'constraint',sep='_')
+  ret = ixDS$add_set( "technology", tech )
+  ret = ixDS$add_set( "commodity", cmdty_out )
+  ret = ixDS$add_set( "level", water_supply_level_constraint ) # Add level to ix DB
+  
+  # Go through each region and add the technology data
+  res = lapply( region, function(reg){ 
+    
+    # Add the output for each timeslice
+    ret = lapply( model_years, function(yy){ 
+      
+      ixDS$add_par( "output", # parameter name
+                    c( reg, tech, as.character( yy ), as.character( yy ), mode_common, reg, cmdty_out, 
+                       water_supply_level_constraint, model_time, model_time  ), # set key
+                    1, # parameter value
+                    '-' )
+      
+      # Add the investment cost
+      ixDS$add_par( "inv_cost", # parameter name
+                    c( reg, tech, as.character( yy )  ), # set key
+                    0, 
+                    '-' )
+      
+      # Add the fixed cost
+      # print(ixDS$get_par_set('fix_cost'))
+      ixDS$add_par( "fix_cost", # parameter name
+                    c( reg, tech, as.character( yy ), as.character( yy )  ), # set key
+                    0,
+                    '-' )
+      
+      # Add the variable cost
+      # print(ixDS$get_par_set('var_cost'))
+      ret = lapply( model_time, function(tm){ 
+        ixDS$add_par( "var_cost", # parameter name
+                      c( reg, tech, as.character( yy ), as.character( yy ), as.character( mode_common ), model_time  ), # set key
+                      0,  
+                      '-' )
+      } )	
+      
+      # Add the capacity factor
+      # print(ixDS$get_par_set('capacity_factor'))
+      ret = lapply( model_time, function(tm){ 
+        ixDS$add_par( "capacity_factor", # parameter name
+                      c( reg, tech, as.character( yy ), as.character( yy ), model_time  ), # set key
+                      1,  
+                      '-' )
+      } )
+      
+      # Add the technical lifetime
+      if(yy < model_years[length(model_years)]){tl = as.numeric(model_years[which(model_years==yy)+1]) - as.numeric(yy)}else{tl = as.numeric(model_years[length(model_years)]) - as.numeric(model_years[length(model_years)-1])}
+      ixDS$add_par( "technical_lifetime", # parameter name
+                    c( reg, tech, as.character( yy )  ), # set key
+                    tl,
+                    'y' )
+      
+      # Add the contruction time
+      ixDS$add_par( "construction_time", # parameter name
+                    c( reg, tech, as.character( yy )  ), # set key
+                    0,
+                    'y' )	
+      
+    } )
+  } )
+  
+  #-------------------------------------------------------------------------------------------------------
+  # Commit to DB, solve, read GDX and set to default scenario
+  #-------------------------------------------------------------------------------------------------------
+  # REPLACE SIMPLY with SOLVE
+  # 
+  # 	# Need to use this library for calling GAMS from R
+  # 	require(gdxrrw)
+  # 
+  # 	# Commit to DB
+  ixDS$commit(comment)
+  ixDS$set_as_default()
+  ixDS$solve()
+  # 	fname = paste(ssss, newscenarioName, sep = '_')
+  # 	
+  # 	# Set the GAMS path and file information
+  # 	gdx_path_data = paste(wkd_message_ix,"message_ix/model/data",sep='') # set based on local machine
+  # 	gdx_name = paste( 'MSGdata_', fname, ".gdx",sep="")
+  # 
+  # 	# Output to gdx
+  # 	ixDS$to_gdx(gdx_path_data, gdx_name)	
+  # 
+  # 	# Solve the model using the gdxrrw utilities
+  # 	current_working_drive = getwd()
+  # 	setwd(paste(wkd_message_ix,'message_ix/model',sep='')) # set based on local machine
+  # 	igdx(wkd_GAMS) # set based on local machine
+  # 	ret = gams(paste("MESSAGE_master.gms --fname=",fname,sep=""))
+  # 	setwd(current_working_drive)		
+  # 
+  # 	# Upload the gdx to the db and set as default if solved correctly
+  # 	ixDS$read_sol_from_gdx(paste(wkd_message_ix,"message_ix/model/output",sep=""),paste("MSGoutput_",fname,".gdx",sep=''))
+  # 	ixDS$set_as_default()
+  
+} # Next SSP	
\ No newline at end of file
diff --git a/nest/message_ix_models/model/water/data/infrastructure.py b/nest/message_ix_models/model/water/data/infrastructure.py
new file mode 100644
index 0000000..3b539d6
--- /dev/null
+++ b/nest/message_ix_models/model/water/data/infrastructure.py
@@ -0,0 +1,886 @@
+"""Prepare data for adding techs related to water distribution,
+ treatment in urban & rural"""
+
+from collections import defaultdict
+
+import pandas as pd
+from message_ix import make_df
+
+from message_ix_models.model.water.utils import map_yv_ya_lt
+from message_ix_models.util import (
+    broadcast,
+    make_matched_dfs,
+    private_data_path,
+    same_node,
+    same_time,
+)
+
+
+def add_infrastructure_techs(context):
+    """Process water distribution data for a scenario instance.
+    Parameters
+    ----------
+    context : .Context
+    Returns
+    -------
+    data : dict of (str -> pandas.DataFrame)
+        Keys are MESSAGE parameter names such as 'input', 'fix_cost'.
+        Values are data frames ready for :meth:`~.Scenario.add_par`.
+        Years in the data include the model horizon indicated by
+        ``context["water build info"]``, plus the additional year 2010.
+    """
+
+    # Reference to the water configuration
+    info = context["water build info"]
+
+    # define an empty dictionary
+    results = {}
+    sub_time = context.time
+    # load the scenario from context
+    scen = context.get_scenario()
+
+    year_wat = [2010, 2015]
+    year_wat.extend(info.Y)
+
+    # first activity year for all water technologies is 2020
+    first_year = scen.firstmodelyear
+
+    # reading basin_delineation
+    FILE2 = f"basins_by_region_simpl_{context.regions}.csv"
+    PATH = private_data_path("water", "delineation", FILE2)
+
+    df_node = pd.read_csv(PATH)
+    # Assigning proper nomenclature
+    df_node["node"] = "B" + df_node["BCU_name"].astype(str)
+    df_node["mode"] = "M" + df_node["BCU_name"].astype(str)
+    if context.type_reg == "country":
+        df_node["region"] = context.map_ISO_c[context.regions]
+    else:
+        df_node["region"] = f"{context.regions}_" + df_node["REGION"].astype(str)
+
+    # Reading water distribution mapping from csv
+    path = private_data_path("water", "infrastructure", "water_distribution.xlsx")
+    df = pd.read_excel(path)
+
+    techs = [
+        "urban_t_d",
+        "urban_unconnected",
+        "industry_unconnected",
+        "rural_t_d",
+        "rural_unconnected",
+    ]
+
+    df_non_elec = df[df["incmd"] != "electr"].reset_index()
+    df_dist = df_non_elec[df_non_elec["tec"].isin(techs)]
+    df_non_elec = df_non_elec[~df_non_elec["tec"].isin(techs)]
+    df_elec = df[df["incmd"] == "electr"].reset_index()
+
+    inp_df = pd.DataFrame([])
+
+    # Input Dataframe for non elec commodities
+    for index, rows in df_non_elec.iterrows():
+        inp_df = pd.concat(
+            [
+                inp_df,
+                (
+                    make_df(
+                        "input",
+                        technology=rows["tec"],
+                        value=rows["value_mid"],
+                        unit="-",
+                        level=rows["inlvl"],
+                        commodity=rows["incmd"],
+                        mode="M1",
+                        node_loc=df_node["node"],
+                    )
+                    .pipe(
+                        broadcast,
+                        map_yv_ya_lt(
+                            year_wat, rows["technical_lifetime_mid"], first_year
+                        ),
+                        time=sub_time,
+                    )
+                    .pipe(same_node)
+                    .pipe(same_time)
+                ),
+            ]
+        )
+
+    if context.SDG:
+        for index, rows in df_dist.iterrows():
+            inp_df = pd.concat(
+                [
+                    inp_df,
+                    (
+                        make_df(
+                            "input",
+                            technology=rows["tec"],
+                            value=rows["value_high"],
+                            unit="-",
+                            level=rows["inlvl"],
+                            commodity=rows["incmd"],
+                            mode="Mf",
+                        )
+                        .pipe(
+                            broadcast,
+                            map_yv_ya_lt(
+                                year_wat, rows["technical_lifetime_mid"], first_year
+                            ),
+                            node_loc=df_node["node"],
+                            time=sub_time,
+                        )
+                        .pipe(same_node)
+                        .pipe(same_time)
+                    ),
+                ]
+            )
+    else:
+        for index, rows in df_dist.iterrows():
+            inp_df = pd.concat(
+                [
+                    inp_df,
+                    (
+                        make_df(
+                            "input",
+                            technology=rows["tec"],
+                            value=rows["value_mid"],
+                            unit="-",
+                            level=rows["inlvl"],
+                            commodity=rows["incmd"],
+                            mode="M1",
+                        )
+                        .pipe(
+                            broadcast,
+                            map_yv_ya_lt(
+                                year_wat, rows["technical_lifetime_mid"], first_year
+                            ),
+                            node_loc=df_node["node"],
+                            time=sub_time,
+                        )
+                        .pipe(same_node)
+                        .pipe(same_time)
+                    ),
+                ]
+            )
+
+            inp_df = inp_df.append(
+                (
+                    make_df(
+                        "input",
+                        technology=rows["tec"],
+                        value=rows["value_high"],
+                        unit="-",
+                        level=rows["inlvl"],
+                        commodity=rows["incmd"],
+                        mode="Mf",
+                    )
+                    .pipe(
+                        broadcast,
+                        map_yv_ya_lt(
+                            year_wat, rows["technical_lifetime_mid"], first_year
+                        ),
+                        node_loc=df_node["node"],
+                        time=sub_time,
+                    )
+                    .pipe(same_node)
+                    .pipe(same_time)
+                )
+            )
+    result_dc = defaultdict(list)
+
+    for index, rows in df_elec.iterrows():
+        if rows["tec"] in techs:
+            if context.SDG:
+                inp = make_df(
+                    "input",
+                    technology=rows["tec"],
+                    value=rows["value_high"],
+                    unit="-",
+                    level="final",
+                    commodity="electr",
+                    mode="Mf",
+                    time_origin="year",
+                    node_loc=df_node["node"],
+                    node_origin=df_node["region"],
+                ).pipe(
+                    broadcast,
+                    map_yv_ya_lt(
+                        year_wat,
+                        # 1 because elec commodities don't have technical lifetime
+                        1,
+                        first_year,
+                    ),
+                    time=sub_time,
+                )
+
+                result_dc["input"].append(inp)
+            else:
+                inp = make_df(
+                    "input",
+                    technology=rows["tec"],
+                    value=rows["value_high"],
+                    unit="-",
+                    level="final",
+                    commodity="electr",
+                    mode="Mf",
+                    time_origin="year",
+                    node_loc=df_node["node"],
+                    node_origin=df_node["region"],
+                ).pipe(
+                    broadcast,
+                    map_yv_ya_lt(
+                        year_wat,
+                        # 1 because elec commodities don't have technical lifetime
+                        1,
+                        first_year,
+                    ),
+                    time=sub_time,
+                )
+
+                inp = inp.append(
+                    make_df(
+                        "input",
+                        technology=rows["tec"],
+                        value=rows["value_mid"],
+                        unit="-",
+                        level="final",
+                        commodity="electr",
+                        mode="M1",
+                        time_origin="year",
+                        node_loc=df_node["node"],
+                        node_origin=df_node["region"],
+                    ).pipe(
+                        broadcast,
+                        # 1 because elec commodities don't have technical lifetime
+                        map_yv_ya_lt(year_wat, 1, first_year),
+                        time=sub_time,
+                    )
+                )
+
+                result_dc["input"].append(inp)
+        else:
+            inp = make_df(
+                "input",
+                technology=rows["tec"],
+                value=rows["value_mid"],
+                unit="-",
+                level="final",
+                commodity="electr",
+                mode="M1",
+                time_origin="year",
+                node_loc=df_node["node"],
+                node_origin=df_node["region"],
+            ).pipe(
+                broadcast,
+                map_yv_ya_lt(year_wat, 1, first_year),
+                time=sub_time,
+            )
+
+            result_dc["input"].append(inp)
+
+    results_new = {par_name: pd.concat(dfs) for par_name, dfs in result_dc.items()}
+
+    inp_df = inp_df.append(results_new["input"])
+    # inp_df.dropna(inplace = True)
+    results["input"] = inp_df
+
+    # add output dataframe
+    df_out = df[~df["outcmd"].isna()]
+    df_out_dist = df_out[df_out["tec"].isin(techs)]
+    df_out = df_out[~df_out["tec"].isin(techs)]
+
+    out_df = pd.DataFrame([])
+    for index, rows in df_out.iterrows():
+        out_df = out_df.append(
+            (
+                make_df(
+                    "output",
+                    technology=rows["tec"],
+                    value=rows["out_value_mid"],
+                    unit="-",
+                    level=rows["outlvl"],
+                    commodity=rows["outcmd"],
+                    mode="M1",
+                )
+                .pipe(
+                    broadcast,
+                    map_yv_ya_lt(year_wat, rows["technical_lifetime_mid"], first_year),
+                    node_loc=df_node["node"],
+                    time=sub_time,
+                )
+                .pipe(same_node)
+                .pipe(same_time)
+            )
+        )
+
+    if context.SDG:
+        out_df = out_df.append(
+            make_df(
+                "output",
+                technology=df_out_dist["tec"],
+                value=df_out_dist["out_value_mid"],
+                unit="-",
+                level=df_out_dist["outlvl"],
+                commodity=df_out_dist["outcmd"],
+                mode="Mf",
+            )
+            .pipe(
+                broadcast,
+                map_yv_ya_lt(year_wat, rows["technical_lifetime_mid"], first_year),
+                node_loc=df_node["node"],
+                time=sub_time,
+            )
+            .pipe(same_node)
+            .pipe(same_time)
+        )
+    else:
+        out_df = out_df.append(
+            make_df(
+                "output",
+                technology=df_out_dist["tec"],
+                value=df_out_dist["out_value_mid"],
+                unit="-",
+                level=df_out_dist["outlvl"],
+                commodity=df_out_dist["outcmd"],
+                mode="M1",
+            )
+            .pipe(
+                broadcast,
+                map_yv_ya_lt(year_wat, rows["technical_lifetime_mid"], first_year),
+                node_loc=df_node["node"],
+                time=sub_time,
+            )
+            .pipe(same_node)
+            .pipe(same_time)
+        )
+        out_df = out_df.append(
+            make_df(
+                "output",
+                technology=df_out_dist["tec"],
+                value=df_out_dist["out_value_mid"],
+                unit="-",
+                level=df_out_dist["outlvl"],
+                commodity=df_out_dist["outcmd"],
+                mode="Mf",
+            )
+            .pipe(
+                broadcast,
+                map_yv_ya_lt(year_wat, rows["technical_lifetime_mid"], first_year),
+                node_loc=df_node["node"],
+                time=sub_time,
+            )
+            .pipe(same_node)
+            .pipe(same_time)
+        )
+
+    results["output"] = out_df
+
+    # Filtering df for capacity factors
+    df_cap = df.dropna(subset=["capacity_factor_mid"])
+    cap_df = pd.DataFrame([])
+    # Adding capacity factor dataframe
+    for index, rows in df_cap.iterrows():
+        cap_df = cap_df.append(
+            make_df(
+                "capacity_factor",
+                technology=rows["tec"],
+                value=rows["capacity_factor_mid"],
+                unit="%",
+            )
+            .pipe(
+                broadcast,
+                map_yv_ya_lt(year_wat, rows["technical_lifetime_mid"], first_year),
+                node_loc=df_node["node"],
+                time=sub_time,
+            )
+            .pipe(same_node)
+        )
+
+    results["capacity_factor"] = cap_df
+
+    # Filtering df for capacity factors
+    df_tl = df.dropna(subset=["technical_lifetime_mid"])
+
+    tl = (
+        make_df(
+            "technical_lifetime",
+            technology=df_tl["tec"],
+            value=df_tl["technical_lifetime_mid"],
+            unit="y",
+        )
+        .pipe(broadcast, year_vtg=year_wat, node_loc=df_node["node"])
+        .pipe(same_node)
+    )
+
+    results["technical_lifetime"] = tl
+
+    cons_time = make_matched_dfs(tl, construction_time=1)
+    results["construction_time"] = cons_time["construction_time"]
+
+    # Investment costs
+    df_inv = df.dropna(subset=["investment_mid"])
+
+    # Prepare dataframe for investments
+    inv_cost = make_df(
+        "inv_cost",
+        technology=df_inv["tec"],
+        value=df_inv["investment_mid"],
+        unit="USD/km3",
+    ).pipe(broadcast, year_vtg=year_wat, node_loc=df_node["node"])
+    inv_cost = inv_cost[~inv_cost["technology"].isin(techs)]
+    results["inv_cost"] = inv_cost
+
+    # Fixed costs
+    # Prepare data frame for fix_cost
+    fix_cost = pd.DataFrame([])
+    var_cost = pd.DataFrame([])
+
+    for index, rows in df_inv.iterrows():
+        fix_cost = fix_cost.append(
+            make_df(
+                "fix_cost",
+                technology=df_inv["tec"],
+                value=df_inv["fix_cost_mid"],
+                unit="USD/km3",
+            ).pipe(
+                broadcast,
+                map_yv_ya_lt(year_wat, rows["technical_lifetime_mid"], first_year),
+                node_loc=df_node["node"],
+            )
+        )
+
+        fix_cost = fix_cost[~fix_cost["technology"].isin(techs)]
+
+        results["fix_cost"] = fix_cost
+
+    df_var = df_inv[~df_inv["tec"].isin(techs)]
+    df_var_dist = df_inv[df_inv["tec"].isin(techs)]
+
+    df_var = df_inv[~df_inv["tec"].isin(techs)]
+    df_var_dist = df_inv[df_inv["tec"].isin(techs)]
+
+    if context.SDG:
+        for index, rows in df_var.iterrows():
+            # Variable cost
+            var_cost = var_cost.append(
+                make_df(
+                    "var_cost",
+                    technology=rows["tec"],
+                    value=rows["var_cost_mid"],
+                    unit="USD/km3",
+                    mode="M1",
+                ).pipe(
+                    broadcast,
+                    map_yv_ya_lt(year_wat, rows["technical_lifetime_mid"], first_year),
+                    node_loc=df_node["node"],
+                    time=sub_time,
+                )
+            )
+
+        # Variable cost for distribution technologies
+        for index, rows in df_var_dist.iterrows():
+            var_cost = var_cost.append(
+                make_df(
+                    "var_cost",
+                    technology=rows["tec"],
+                    value=rows["var_cost_high"],
+                    unit="USD/km3",
+                    mode="Mf",
+                ).pipe(
+                    broadcast,
+                    map_yv_ya_lt(year_wat, rows["technical_lifetime_mid"], first_year),
+                    node_loc=df_node["node"],
+                    time=sub_time,
+                )
+            )
+        results["var_cost"] = var_cost
+    else:
+        # Variable cost
+        for index, rows in df_var.iterrows():
+            var_cost = var_cost.append(
+                make_df(
+                    "var_cost",
+                    technology=rows["tec"],
+                    value=df_var["var_cost_mid"],
+                    unit="USD/km3",
+                    mode="M1",
+                ).pipe(
+                    broadcast,
+                    map_yv_ya_lt(year_wat, rows["technical_lifetime_mid"], first_year),
+                    node_loc=df_node["node"],
+                    time=sub_time,
+                )
+            )
+
+        for index, rows in df_var_dist.iterrows():
+            var_cost = var_cost.append(
+                make_df(
+                    "var_cost",
+                    technology=rows["tec"],
+                    value=rows["var_cost_mid"],
+                    unit="USD/km3",
+                    mode="M1",
+                ).pipe(
+                    broadcast,
+                    map_yv_ya_lt(year_wat, rows["technical_lifetime_mid"], first_year),
+                    node_loc=df_node["node"],
+                    time=sub_time,
+                )
+            )
+
+            var_cost = var_cost.append(
+                make_df(
+                    "var_cost",
+                    technology=rows["tec"],
+                    value=rows["var_cost_high"],
+                    unit="USD/km3",
+                    mode="Mf",
+                ).pipe(
+                    broadcast,
+                    map_yv_ya_lt(year_wat, rows["technical_lifetime_mid"], first_year),
+                    node_loc=df_node["node"],
+                    time=sub_time,
+                )
+            )
+        results["var_cost"] = var_cost
+
+    return results
+
+
+def add_desalination(context):
+    """Add desalination infrastructure
+    Two types of desalination are considered;
+    1. Membrane
+    2. Distillation
+    Parameters
+    ----------
+    context : .Context
+    Returns
+    -------
+    data : dict of (str -> pandas.DataFrame)
+        Keys are MESSAGE parameter names such as 'input', 'fix_cost'.
+        Values are data frames ready for :meth:`~.Scenario.add_par`.
+        Years in the data include the model horizon indicated by
+        ``context["water build info"]``, plus the additional year 2010.
+    """
+    # define an empty dictionary
+    results = {}
+    sub_time = context.time
+    # Reference to the water configuration
+    info = context["water build info"]
+
+    # load the scenario from context
+    scen = context.get_scenario()
+
+    year_wat = [2010, 2015]
+    year_wat.extend(info.Y)
+
+    # first activity year for all water technologies is 2020
+    first_year = scen.firstmodelyear
+
+    # Reading water distribution mapping from csv
+    path = private_data_path("water", "infrastructure", "desalination.xlsx")
+    path2 = private_data_path(
+        "water",
+        "infrastructure",
+        f"historical_capacity_desalination_km3_year_{context.regions}.csv",
+    )
+    path3 = private_data_path(
+        "water",
+        "infrastructure",
+        f"projected_desalination_potential_km3_year_{context.regions}.csv",
+    )
+    # Reading dataframes
+    df_desal = pd.read_excel(path)
+    df_hist = pd.read_csv(path2)
+    df_proj = pd.read_csv(path3)
+    df_proj = df_proj[df_proj["rcp"] == f"{context.RCP}"]
+    df_proj = df_proj[~(df_proj["year"] == 2065) & ~(df_proj["year"] == 2075)]
+    df_proj.reset_index(inplace=True, drop=True)
+    df_proj = df_proj[df_proj["year"].isin(info.Y)]
+
+    # reading basin_delineation
+    FILE2 = f"basins_by_region_simpl_{context.regions}.csv"
+    PATH = private_data_path("water", "delineation", FILE2)
+
+    df_node = pd.read_csv(PATH)
+    # Assigning proper nomenclature
+    df_node["node"] = "B" + df_node["BCU_name"].astype(str)
+    df_node["mode"] = "M" + df_node["BCU_name"].astype(str)
+    if context.type_reg == "country":
+        df_node["region"] = context.map_ISO_c[context.regions]
+    else:
+        df_node["region"] = f"{context.regions}_" + df_node["REGION"].astype(str)
+    # output dataframe linking to desal tech types
+    out_df = (
+        make_df(
+            "output",
+            technology="extract_salinewater_basin",
+            value=1,
+            unit="km3/year",
+            level="water_avail_basin",
+            commodity="salinewater_basin",
+            mode="M1",
+        )
+        .pipe(
+            broadcast,
+            map_yv_ya_lt(year_wat, 20, first_year),
+            node_loc=df_node["node"],
+            time=sub_time,
+        )
+        .pipe(same_node)
+        .pipe(same_time)
+    )
+
+    tl = (
+        make_df(
+            "technical_lifetime",
+            technology="extract_salinewater_basin",
+            value=20,
+            unit="y",
+        )
+        .pipe(broadcast, year_vtg=year_wat, node_loc=df_node["node"])
+        .pipe(same_node)
+    )
+
+    # Historical capacity of desalination technologies
+    df_hist_cap = make_df(
+        "historical_new_capacity",
+        node_loc="B" + df_hist["BCU_name"],
+        technology=df_hist["tec_type"],
+        year_vtg=df_hist["year"],
+        value=df_hist["cap_km3_year"],
+        unit="km3/year",
+    )
+    # Divide the historical capacity by 5 since the existing data is summed over
+    # 5 years and model needs per year
+    df_hist_cap["value"] = df_hist_cap["value"] / 5
+
+    results["historical_new_capacity"] = df_hist_cap
+
+    # Desalination potentials are added as an upper bound
+    # to limit the salinewater extraction
+    bound_up = make_df(
+        "bound_total_capacity_up",
+        node_loc="B" + df_proj["BCU_name"],
+        technology="extract_salinewater_basin",
+        year_act=df_proj["year"],
+        value=df_proj["cap_km3_year"],
+        unit="km3/year",
+    )
+    # Making negative values zero
+    bound_up["value"].clip(lower=0, inplace=True)
+    # Bound should start from 2025
+    bound_up = bound_up[bound_up["year_act"] > 2020]
+
+    results["bound_total_capacity_up"] = bound_up
+    # Investment costs
+    inv_cost = make_df(
+        "inv_cost",
+        technology=df_desal["tec"],
+        value=df_desal["inv_cost_mid"],
+        unit="USD/km3",
+    ).pipe(broadcast, year_vtg=year_wat, node_loc=df_node["node"])
+
+    results["inv_cost"] = inv_cost
+
+    fix_cost = pd.DataFrame([])
+    var_cost = pd.DataFrame([])
+    for index, rows in df_desal.iterrows():
+        # Fixed costs
+        # Prepare dataframe for fix_cost
+        fix_cost = fix_cost.append(
+            make_df(
+                "fix_cost",
+                technology=rows["tec"],
+                value=rows["fix_cost_mid"],
+                unit="USD/km3",
+            ).pipe(
+                broadcast,
+                map_yv_ya_lt(year_wat, rows["lifetime_mid"], first_year),
+                node_loc=df_node["node"],
+            )
+        )
+
+        results["fix_cost"] = fix_cost
+
+        # Variable cost
+        var_cost = var_cost.append(
+            make_df(
+                "var_cost",
+                technology=rows["tec"],
+                value=rows["var_cost_mid"],
+                unit="USD/km3",
+                mode="M1",
+            ).pipe(
+                broadcast,
+                map_yv_ya_lt(year_wat, rows["lifetime_mid"], first_year),
+                node_loc=df_node["node"],
+                time=sub_time,
+            )
+        )
+
+    # Dummy  Variable cost for salinewater extrqction
+    # var_cost = var_cost.append(
+    #     make_df(
+    #     "var_cost",
+    #     technology='extract_salinewater_basin',
+    #     value= 100,
+    #     unit="USD/km3",
+    #     mode="M1",
+    #     time="year",
+    # ).pipe(broadcast, year_vtg=year_wat, year_act=year_wat, node_loc=df_node["node"])
+    # )
+
+    results["var_cost"] = var_cost
+
+    tl = tl.append(
+        (
+            make_df(
+                "technical_lifetime",
+                technology=df_desal["tec"],
+                value=df_desal["lifetime_mid"],
+                unit="y",
+            )
+            .pipe(broadcast, year_vtg=year_wat, node_loc=df_node["node"])
+            .pipe(same_node)
+        )
+    )
+
+    results["technical_lifetime"] = tl
+
+    cons_time = make_matched_dfs(tl, construction_time=3)
+    results["construction_time"] = cons_time["construction_time"]
+
+    from collections import defaultdict
+
+    result_dc = defaultdict(list)
+
+    for index, rows in df_desal.iterrows():
+        inp = make_df(
+            "input",
+            technology=rows["tec"],
+            value=rows["electricity_input_mid"],
+            unit="-",
+            level="final",
+            commodity="electr",
+            mode="M1",
+            time_origin="year",
+            node_loc=df_node["node"],
+            node_origin=df_node["region"],
+        ).pipe(
+            broadcast,
+            map_yv_ya_lt(year_wat, rows["lifetime_mid"], first_year),
+            time=sub_time,
+        )
+
+        result_dc["input"].append(inp)
+
+    results_new = {par_name: pd.concat(dfs) for par_name, dfs in result_dc.items()}
+
+    inp_df = results_new["input"]
+
+    # Adding input dataframe
+    df_heat = df_desal[df_desal["heat_input_mid"] > 0]
+
+    result_dc = defaultdict(list)
+
+    for index, rows in df_heat.iterrows():
+        inp = make_df(
+            "input",
+            technology=rows["tec"],
+            value=rows["heat_input_mid"],
+            unit="-",
+            level="final",
+            commodity="d_heat",
+            mode="M1",
+            time_origin="year",
+            node_loc=df_node["node"],
+            node_origin=df_node["region"],
+        ).pipe(
+            broadcast,
+            map_yv_ya_lt(year_wat, rows["lifetime_mid"], first_year),
+            time=sub_time,
+        )
+
+        result_dc["input"].append(inp)
+
+    results_new = {par_name: pd.concat(dfs) for par_name, dfs in result_dc.items()}
+
+    inp_df = inp_df.append(results_new["input"])
+
+    # Adding input dataframe
+    for index, rows in df_desal.iterrows():
+        inp_df = inp_df.append(
+            (
+                make_df(
+                    "input",
+                    technology=rows["tec"],
+                    value=1,
+                    unit="-",
+                    level=rows["inlvl"],
+                    commodity=rows["incmd"],
+                    mode="M1",
+                )
+                .pipe(
+                    broadcast,
+                    map_yv_ya_lt(year_wat, rows["lifetime_mid"], first_year),
+                    node_loc=df_node["node"],
+                    time=sub_time,
+                )
+                .pipe(same_node)
+                .pipe(same_time)
+            )
+        )
+
+        inp_df.dropna(inplace=True)
+
+        results["input"] = inp_df
+
+        out_df = out_df.append(
+            (
+                make_df(
+                    "output",
+                    technology=rows["tec"],
+                    value=1,
+                    unit="-",
+                    level=rows["outlvl"],
+                    commodity=rows["outcmd"],
+                    mode="M1",
+                )
+                .pipe(
+                    broadcast,
+                    map_yv_ya_lt(year_wat, rows["lifetime_mid"], first_year),
+                    node_loc=df_node["node"],
+                    time=sub_time,
+                )
+                .pipe(same_node)
+                .pipe(same_time)
+            )
+        )
+
+        results["output"] = out_df
+
+    # putting a lower bound on desalination tecs based on hist capacities
+    df_bound = df_hist[df_hist["year"] == 2015]
+    bound_lo = make_df(
+        "bound_activity_lo",
+        node_loc="B" + df_bound["BCU_name"],
+        technology=df_bound["tec_type"],
+        mode="M1",
+        value=df_bound["cap_km3_year"],
+        unit="km3/year",
+    ).pipe(
+        broadcast,
+        year_act=year_wat,
+        time=sub_time,
+    )
+
+    bound_lo = bound_lo[bound_lo["year_act"] <= 2030]
+    # Divide the histroical capacity by 5 since the existing data is summed over
+    # 5 years and model needs per year
+    bound_lo["value"] = bound_lo["value"] / 5
+
+    results["bound_activity_lo"] = bound_lo
+
+    return results
diff --git a/nest/message_ix_models/model/water/data/irrigation.py b/nest/message_ix_models/model/water/data/irrigation.py
new file mode 100644
index 0000000..e694793
--- /dev/null
+++ b/nest/message_ix_models/model/water/data/irrigation.py
@@ -0,0 +1,212 @@
+"""Prepare data for water use for cooling & energy technologies."""
+
+import pandas as pd
+from message_ix import make_df
+
+from message_ix_models.util import broadcast, private_data_path
+
+
+# water & electricity for irrigation
+def add_irr_structure(context):
+    """Add irrigation withdrawal infrastructure
+    The irrigation demands are added in
+    Parameters
+    ----------
+    context : .Context
+    Returns
+    -------
+    data : dict of (str -> pandas.DataFrame)
+        Keys are MESSAGE parameter names such as 'input', 'fix_cost'.
+        Values are data frames ready for :meth:`~.Scenario.add_par`.
+        Years in the data include the model horizon indicated by
+        ``context["water build info"]``, plus the additional year 2010.
+    """
+
+    # define an empty dictionary
+    results = {}
+
+    # reading basin_delineation
+    FILE2 = f"basins_by_region_simpl_{context.regions}.csv"
+    PATH = private_data_path("water", "delineation", FILE2)
+    df_node = pd.read_csv(PATH)
+    # Assigning proper nomenclature
+    df_node["node"] = "B" + df_node["BCU_name"].astype(str)
+    df_node["mode"] = "M" + df_node["BCU_name"].astype(str)
+    if context.type_reg == "country":
+        df_node["region"] = context.map_ISO_c[context.regions]
+    else:
+        df_node["region"] = f"{context.regions}_" + df_node["REGION"].astype(str)
+
+    # Reference to the water configuration
+    info = context["water build info"]
+
+    # probably can be removed
+    year_wat = [2010, 2015]
+    year_wat.extend(info.Y)
+
+    inp = make_df(
+        "input",
+        technology="irrigation_cereal",
+        value=1,
+        unit="-",
+        level="water_supply",
+        commodity="freshwater",
+        mode="M1",
+        time="year",
+        time_origin="year",
+        node_origin=df_node["region"],
+        node_loc=df_node["region"],
+    ).pipe(broadcast, year_vtg=info.Y)
+
+    inp = inp.append(
+        make_df(
+            "input",
+            technology="irrigation_oilcrops",
+            value=1,
+            unit="-",
+            level="water_supply",
+            commodity="freshwater",
+            mode="M1",
+            time="year",
+            time_origin="year",
+            node_origin=df_node["region"],
+            node_loc=df_node["region"],
+        ).pipe(broadcast, year_vtg=info.Y)
+    )
+
+    inp = inp.append(
+        make_df(
+            "input",
+            technology="irrigation_sugarcrops",
+            value=1,
+            unit="-",
+            level="water_supply",
+            commodity="freshwater",
+            mode="M1",
+            time="year",
+            time_origin="year",
+            node_origin=df_node["region"],
+            node_loc=df_node["region"],
+        ).pipe(broadcast, year_vtg=info.Y)
+    )
+    # year_act = year_vts for tecs with 1 time-step lifetime
+    inp["year_act"] = inp["year_vtg"]
+
+    # Electricity values per unit of irrigation water supply
+    # Reference: Evaluation of Water and Energy Use in Pressurized Irrigation Networks in Southern Spain
+    # Diaz et al. 2011 https://ascelibrary.org/doi/10.1061/%28ASCE%29IR.1943-4774.0000338
+    # Low Value :0.04690743
+    # Average Value :0.101598174
+    # High Value : 0.017123288
+
+    # inp = inp.append(
+    #     make_df(
+    #         "input",
+    #         technology="irrigation_sugarcrops",
+    #         value=0.04690743,
+    #         unit="-",
+    #         level="final",
+    #         commodity="electr",
+    #         mode="M1",
+    #         time="year",
+    #         time_origin="year",
+    #         node_origin=df_node["region"],
+    #         node_loc=df_node["region"],
+    #     ).pipe(broadcast, year_vtg=year_wat, year_act=year_wat)
+    # )
+    #
+    # inp = inp.append(
+    #     make_df(
+    #         "input",
+    #         technology="irrigation_oilcrops",
+    #         value=0.04690743,
+    #         unit="-",
+    #         level="final",
+    #         commodity="electr",
+    #         mode="M1",
+    #         time="year",
+    #         time_origin="year",
+    #         node_origin=df_node["region"],
+    #         node_loc=df_node["region"],
+    #     ).pipe(broadcast, year_vtg=year_wat, year_act=year_wat)
+    # )
+    #
+    # inp = inp.append(
+    #     make_df(
+    #         "input",
+    #         technology="irrigation_cereal",
+    #         value=0.04690743,
+    #         unit="-",
+    #         level="final",
+    #         commodity="electr",
+    #         mode="M1",
+    #         time="year",
+    #         time_origin="year",
+    #         node_origin=df_node["region"],
+    #         node_loc=df_node["region"],
+    #     ).pipe(broadcast, year_vtg=year_wat, year_act=year_wat)
+    # )
+    # inp.loc[(inp['node_loc'] == 'R11_SAS') &
+    #         (inp['commodity'] == 'electr'),
+    #         "value",
+    # ] *= 0.00004690743
+
+    results["input"] = inp
+
+    irr_out = make_df(
+        "output",
+        technology="irrigation_cereal",
+        value=1,
+        unit="km3/year",
+        level="irr_cereal",
+        commodity="freshwater",
+        mode="M1",
+        time="year",
+        time_dest="year",
+        node_loc=df_node["region"],
+        node_dest=df_node["region"],
+    ).pipe(broadcast, year_vtg=info.Y)
+
+    irr_out = irr_out.append(
+        make_df(
+            "output",
+            technology="irrigation_sugarcrops",
+            value=1,
+            unit="km3/year",
+            level="irr_sugarcrops",
+            commodity="freshwater",
+            mode="M1",
+            time="year",
+            time_dest="year",
+            node_loc=df_node["region"],
+            node_dest=df_node["region"],
+        ).pipe(
+            broadcast,
+            year_vtg=info.Y,
+        )
+    )
+
+    irr_out = irr_out.append(
+        make_df(
+            "output",
+            technology="irrigation_oilcrops",
+            value=1,
+            unit="km3/year",
+            level="irr_oilcrops",
+            commodity="freshwater",
+            mode="M1",
+            time="year",
+            time_dest="year",
+            node_loc=df_node["region"],
+            node_dest=df_node["region"],
+        ).pipe(
+            broadcast,
+            year_vtg=info.Y,
+        )
+    )
+
+    irr_out["year_act"] = irr_out["year_vtg"]
+
+    results["output"] = irr_out
+
+    return results
diff --git a/nest/message_ix_models/model/water/data/pre-processing/calculate_ppl_cooling_technology_shares.r b/nest/message_ix_models/model/water/data/pre-processing/calculate_ppl_cooling_technology_shares.r
new file mode 100644
index 0000000..020c48d
--- /dev/null
+++ b/nest/message_ix_models/model/water/data/pre-processing/calculate_ppl_cooling_technology_shares.r
@@ -0,0 +1,229 @@
+
+rm(list=ls())
+graphics.off()
+library(raster)
+library(sp)
+library(ncdf4)
+library(rgdal)
+library(rgeos)
+library(maptools)
+library(geosphere)
+library(countrycode)
+library(ggmap)
+library(tidyverse)
+library(yaml)
+library("readxl")
+#library(reticulate)
+
+# Increase memory size
+memory.limit(size = 1e6)
+
+#this path need tobe automatize, maybe loading with retisulate a python ojects that contains the rith path.
+#otherwise environment path an be used
+msg_data = Sys.getenv("MESSAGE_DATA_PATH")
+# message-ix-models path
+msg_ix_model = Sys.getenv("MESSAGE-IX-MODELS")
+data_subf = path.expand(paste0(msg_data,'\\data\\water\\ppl_cooling_tech'))
+
+all_units = read_excel('P:/ene.model/NEST/ppl_cooling_tech/PLATTS_3.7.xlsx')
+
+all_units.df = all_units %>% select(UNIT,STATUS,ISO,msgregion, msg_combo2,cool_group_msg,MW_x,WW,WC,lat,long)
+# here we need to adapt for a different regional conigurations of the GLOBAL model
+
+for (reg in c('R11','R12')){
+# for global R11 model
+  cooling_plants = all_units.df %>% filter(STATUS == 'OPR',
+                                                !cool_group_msg %in% c('CHP','NCN'),
+                                           !is.na(cool_group_msg),
+                                           !is.na(msgregion)) %>% 
+    rename(utype = msg_combo2, cooling = cool_group_msg) %>% ungroup()
+  
+  # get mapping from yaml file in message-ix-models
+  file = paste0(msg_ix_model,'/message_ix_models/data/node/',reg,'.yaml')
+  from_yaml = read_yaml(file, fileEncoding = "UTF-8" )
+  names_loop = names(from_yaml)[names(from_yaml) != 'World']
+  reg_map.df = data.frame()
+  for (rr in names_loop){
+    reg_map.df = rbind(reg_map.df,data.frame(msgregion = rr, ISO = from_yaml[[rr]]$child))
+  }
+  
+  # add R## to the msgregions, to go the in any column
+  cooling_plants = cooling_plants %>% select(-msgregion) %>% 
+    left_join(reg_map.df)
+  
+  # add the data to the cooltech_cost_and_shares_ssp_msg.csv file
+  cooltech_cost_shares = read.csv(paste0(data_subf,'/cooltech_cost_and_shares_ssp_msg.csv'),stringsAsFactors=FALSE)
+  
+  #### shares by message REGION #### needed to establish initial mapping
+  shars_cooling_MSG_global = cooling_plants %>% group_by(utype,cooling,msgregion) %>% #change to COUNTRY
+    summarise(MW_x = sum(MW_x)) %>% ungroup() %>% 
+    group_by(utype,msgregion) %>% #change to COUNTRY
+    mutate(cap_reg_unit = sum(MW_x)) %>% ungroup() %>% 
+    mutate(shares = MW_x / cap_reg_unit) %>% 
+    select(utype,cooling,msgregion,shares) %>% 
+    spread(msgregion, shares)
+  
+  shars_cooling_MSG_global[is.na(shars_cooling_MSG_global)] = 0
+  
+  # mapping in order to add missing technologies
+  platts_types = shars_cooling_MSG_global %>% select(utype) %>% rename(utype_pl = utype) %>% 
+    mutate(match = gsub('_.*','',utype_pl)) %>% group_by(match) %>% 
+    summarise(utype_pl = first(utype_pl), match = first(match))
+  map_all_types = cooltech_cost_shares %>% select(utype,cooling) %>% 
+    mutate(match = gsub('_.*','',utype)) %>% left_join(platts_types) %>% select(-match) %>% 
+    filter(!is.na(utype_pl)) %>% distinct()
+  
+  # This will be the file
+  write.csv(shars_cooling_MSG_global,paste0(data_subf,'/cool_techs_region_share_',reg,'.csv'),row.names = FALSE)
+  
+  # back to shares, change names
+  new_names = paste0('mix_', names(shars_cooling_MSG_global)[!names(shars_cooling_MSG_global) %in% c('utype','cooling')] )
+  names(shars_cooling_MSG_global)[!names(shars_cooling_MSG_global) %in% c('utype','cooling')] = new_names
+  shars_cooling_MSG_global = shars_cooling_MSG_global %>% mutate(utype_pl = utype)
+  
+  # missing shares
+  all_shares = map_all_types %>% 
+    filter(!utype %in% unique(shars_cooling_MSG_global$utype) ) %>% 
+    left_join(shars_cooling_MSG_global %>% select(-utype), by = c('utype_pl','cooling') ) %>% 
+    select(-utype_pl) %>% bind_rows(shars_cooling_MSG_global)
+  
+  cooltech_cost_shares = cooltech_cost_shares %>%
+    select(utype,	cooling,investment_million_USD_per_MW_low,	investment_million_USD_per_MW_mid,	investment_million_USD_per_MW_high) %>% 
+    left_join(all_shares)
+  
+  cooltech_cost_shares[is.na(cooltech_cost_shares)] = 0
+  # write new file
+  write.csv(cooltech_cost_shares,paste0(data_subf,'/cooltech_cost_and_shares_ssp_msg_',reg,'.csv'),row.names = FALSE)
+}
+
+#### SHARES by COUNTRY ####
+#shares by message REGION
+shars_cooling_country = cooling_plants %>% group_by(utype,cooling,ISO) %>% #change to COUNTRY
+  summarise(MW_x = sum(MW_x)) %>% ungroup() %>% 
+  group_by(utype,ISO) %>% #change to COUNTRY
+  mutate(cap_reg_unit = sum(MW_x)) %>% ungroup() %>% 
+  mutate(shares = MW_x / cap_reg_unit) %>% 
+  select(utype,cooling,ISO,shares) %>% 
+  spread(ISO, shares)
+
+shars_cooling_country[is.na(shars_cooling_country)] = 0
+
+# This will be the file
+write.csv(shars_cooling_country,paste0(data_subf,'/cool_techs_country_share.csv'),row.names = FALSE)
+
+# back to shares, change names
+new_names = paste0('mix_', names(shars_cooling_country)[!names(shars_cooling_country) %in% c('utype','cooling')] )
+names(shars_cooling_country)[!names(shars_cooling_country) %in% c('utype','cooling')] = new_names
+shars_cooling_country = shars_cooling_country %>% mutate(utype_pl = utype)
+
+# missing shares
+all_shares_c = map_all_types %>% 
+  filter(!utype %in% unique(shars_cooling_country$utype) ) %>% 
+  left_join(shars_cooling_country %>% select(-utype), by = c('utype_pl','cooling') ) %>% 
+  select(-utype_pl) %>% bind_rows(shars_cooling_country)
+
+cooltech_cost_shares_c = cooltech_cost_shares %>%
+  select(utype,	cooling,investment_million_USD_per_MW_low,	investment_million_USD_per_MW_mid,	investment_million_USD_per_MW_high) %>% 
+  left_join(all_shares_c)
+
+cooltech_cost_shares_c[is.na(cooltech_cost_shares_c)] = 0
+# write new file
+write.csv(cooltech_cost_shares_c,paste0(data_subf,'/cooltech_cost_and_shares_country.csv'),row.names = FALSE)
+
+
+# OLD DATABASE, with the new one, most of this processing is done already by the script
+# https://github.com/OFR-IIASA/message_data/blob/RES_add_5_year_timesteps2/data/model/platts_historical_capacity/merge_WEPP_Raptis_CARMA.py
+
+# # loading
+# temp = readOGR(path.expand(paste0(data_subf,'\\delineation')),'MSG11_reg_simpl',verbose=FALSE)
+# 
+# # the input shape file should come externally, in some model setup
+# # usually it has two columns, the first should be called 'PID', the second 'REGION'
+# # only the second column matter in terms of name, and should have the region names used in the model
+# 
+# region_units.spdf = spTransform(temp, CRS("+proj=longlat"))
+# rm(temp)
+
+# # Read in Raptis et al data
+# ppl.df = data.frame(read.csv(paste0(data_subf,'/ppl_cooling_tech/POWER_PLANTS_2016_Raptis.csv'),stringsAsFactors=FALSE))
+# raptis_types_map_cooling.df = data.frame(read.csv(paste0(data_subf,'/ppl_cooling_tech/raptis_types_map_cooling_type.csv'),stringsAsFactors=FALSE))
+# raptis_types_map_unit.df = data.frame(read.csv(paste0(data_subf,'/ppl_cooling_tech/raptis_types_map_unit_type.csv'),stringsAsFactors=FALSE))
+# 
+# # MESSAGE technologies
+# msg_types.df = data.frame(read.csv(paste0(data_subf,'/ppl_cooling_tech/tech_names_ssp_msg.csv'),stringsAsFactors=FALSE))
+# msg_ppl = unique(c(raptis_types_map_unit.df$msgssp_type,raptis_types_map_unit.df$alt1,raptis_types_map_unit.df$alt2,raptis_types_map_unit.df$alt3))
+# msg_ppl = msg_ppl[-1*(which(msg_ppl==''))]
+# msg_cool = unique(raptis_types_map_cooling.df$msgssp_type)
+# 
+# fuel_group = lapply(1:length(msg_ppl),function(x){ 
+# 	ind1 = which( as.character(raptis_types_map_unit.df$msgssp_type) == as.character(msg_ppl[x]) | as.character(raptis_types_map_unit.df$alt1) == as.character(msg_ppl[x]) | as.character(raptis_types_map_unit.df$alt2) == as.character(msg_ppl[x]) | as.character(raptis_types_map_unit.df$alt3) == as.character(msg_ppl[x]) )
+# 	return(unique(unlist(strsplit(  as.character(raptis_types_map_unit.df$raptis_type[ind1]),'[.]' ))[seq(1,length(unlist(strsplit(  as.character(raptis_types_map_unit.df$raptis_type[ind1]),'[.]' ))),b=2)]))   
+# 	})
+# 
+# utype = lapply(1:length(msg_ppl),function(x){ 
+# 	ind1 = which( as.character(raptis_types_map_unit.df$msgssp_type) == as.character(msg_ppl[x]) | as.character(raptis_types_map_unit.df$alt1) == as.character(msg_ppl[x]) | as.character(raptis_types_map_unit.df$alt2) == as.character(msg_ppl[x]) | as.character(raptis_types_map_unit.df$alt3) == as.character(msg_ppl[x]) )
+# 	return(unique(unlist(strsplit(  as.character(raptis_types_map_unit.df$raptis_type[ind1]),'[.]' ))[seq(2,length(unlist(strsplit(  as.character(raptis_types_map_unit.df$raptis_type[ind1]),'[.]' ))),b=2)]))   
+# 	}	)
+# 
+# cool = lapply(1:length(msg_cool),function(x){ as.character(raptis_types_map_cooling.df$raptis_type[which( as.character(raptis_types_map_cooling.df$msgssp_type) == as.character(msg_cool[x]) )])})
+# 	
+# # Pull out data for this specific technology for each region
+# cool_techs = unlist(lapply(1:length(msg_cool), function(x){as.matrix(unlist(lapply(1:length(msg_ppl),function(y){paste(msg_ppl[y],msg_cool[x],sep='.')})))}))
+# xs = rep(seq(1,length(msg_ppl),by=1),length(msg_cool))
+# ys = unlist(lapply(1:length(msg_cool),function(dd){ rep(dd,length(msg_ppl)) })) 
+# 
+# # Cooling techs by pid
+# cool_techs_by_pid = data.frame(do.call(rbind, lapply(1:length(cool_techs), function(z){
+# 	xx = xs[z]
+# 	yy = ys[z]
+# 	temp = ppl.df[which( as.character(ppl.df$Fuel_group) %in% as.character(fuel_group[[xx]]) &
+# 	                       as.character(ppl.df$UTYPE) %in% as.character(utype[[xx]]) & 
+# 	                       as.character(ppl.df$COOL_complete) %in% as.character(cool[[yy]]) ),]
+# 	if(nrow(temp)>0)
+# 		{
+# 		temp$x = temp$long
+# 		temp$y = temp$lat
+# 		coordinates(temp) = ~ x + y
+# 		proj4string(temp) = proj4string(region_units.spdf)
+# 		temp$pid = unlist(over(temp,region_units.spdf[,which(names(region_units.spdf) == 'PID')]))
+# 		temp = temp[-1*(which(is.na(temp$pid) | is.na(temp$MW))),]
+# 		mw_by_pid = unlist(lapply(1:length(region_units.spdf@data$PID), function(bb){ max(0, sum( temp$MW[ which( as.character(temp$pid) == as.character(region_units.spdf@data$PID[bb]) ) ], na.rm=TRUE ), na.rm=TRUE) } ) )
+# 		return(mw_by_pid)
+# 		}else
+# 		{
+# 		mw_by_pid  = rep(0,length(region_units.spdf@data$REGION))
+# 		return(mw_by_pid)
+# 		}	
+# 	})))
+# row.names(cool_techs_by_pid) = cool_techs
+# 	
+# type_general = unlist(strsplit( cool_techs, '[.]' ))[seq(1,length(unlist(strsplit( cool_techs, '[.]' ))),by=2)]
+# cool_general = unlist(strsplit( cool_techs, '[.]' ))[seq(2,length(unlist(strsplit( cool_techs, '[.]' ))),by=2)]
+# cool_techs_shares_by_pid = data.frame( do.call( cbind, lapply(1:length(region_units.spdf@data$REGION), function(r){ as.matrix( unlist( lapply( 1:length(cool_techs), function(z){
+# 	share =  cool_techs_by_pid[z,r] / sum( c( cool_techs_by_pid[ which( as.character(type_general) == as.character(type_general[z]) ), r] ), na.rm = TRUE)
+# 	if( is.na(share) | length(share) == 0 ){share =  round( sum( c( cool_techs_by_pid[ which( as.character(cool_general) == as.character(cool_general[z]) ), r] ), na.rm = TRUE) / sum( c( cool_techs_by_pid[ , r] ), na.rm = TRUE), digits = 3)}
+# 	return(round(share, digits = 3)) } ) ) ) } ) ) )	
+# row.names(cool_techs_shares_by_pid) = cool_techs		
+# names(cool_techs_shares_by_pid) = 	as.character(region_units.spdf@data$REGION)	
+# cool_techs_shares_by_pid$cooling_utype = as.character(cool_techs)
+# cool_techs_shares_by_pid$cooling = as.character(cool_general)
+# cool_techs_shares_by_pid$utype = as.character(type_general)
+# cool_techs_shares_by_pid = cool_techs_shares_by_pid[,c('utype','cooling',as.character(region_units.spdf@data$REGION))]
+# cool_techs_shares_by_pid = cool_techs_shares_by_pid[ order( cool_techs_shares_by_pid$utype ), ]
+# 
+# # This will be the file
+# write.csv(cool_techs_shares_by_pid,paste0(data_subf,'/cool_techs_region_share.csv'),row.names = FALSE)
+# 
+# #add 'mix_' to the columns names for regions
+# cool_techs_shares_by_pid2 = cool_techs_shares_by_pid
+# new_names = paste0('mix_', names(cool_techs_shares_by_pid2)[!names(cool_techs_shares_by_pid2) %in% c('utype','cooling')] )
+# names(cool_techs_shares_by_pid2)[!names(cool_techs_shares_by_pid2) %in% c('utype','cooling')] = new_names
+# 
+# # add the data to the cooltech_cost_and_shares_ssp_msg.csv file
+# cooltech_cost_shares = read.csv(paste0(data_subf,'/cooltech_cost_and_shares_ssp_msg.csv'),stringsAsFactors=FALSE)
+# cooltech_cost_shares = cooltech_cost_shares %>% 
+#   select(utype,	cooling,investment_million_USD_per_MW_low,	investment_million_USD_per_MW_mid,	investment_million_USD_per_MW_high) %>% 
+#   left_join(cool_techs_shares_by_pid2)
+# 
+# # write new file
+# write.csv(cool_techs_shares_by_pid,paste0(data_subf,'/cooltech_cost_and_shares_ssp_msg.csv'),row.names = FALSE)
diff --git a/nest/message_ix_models/model/water/data/pre-processing/desalination.R b/nest/message_ix_models/model/water/data/pre-processing/desalination.R
new file mode 100644
index 0000000..2b74aa5
--- /dev/null
+++ b/nest/message_ix_models/model/water/data/pre-processing/desalination.R
@@ -0,0 +1,945 @@
+# Desalination
+rm(list = ls())
+require(reshape)
+require(rgdal)
+require(raster)
+require(rgeos)
+require(countrycode)
+require(RCurl)
+require(xlsx)
+
+require(maptools)
+require(ncdf4)
+
+library(broom)
+library(readstata13)
+library(sandwich)
+library(lmtest)
+library(zoo)
+library(readxl)
+library(corrplot)
+library(plm)
+library(car)
+# library(gghighlight)
+
+library(tidyverse)
+# library(caret)
+
+# here we put together the data processing scripts that
+# 1. determine historical capacity (from oder add_Water_infractructure.R from Simon)
+# 2. set future base desalination trends
+# 3. set upperbound for desalination expansion/speed of growth
+
+model_years = c(1990,1995,2000,2005,2010,2015)
+#define region
+
+#### not needed####
+# Load in the cleaned desal database from Hanasaki et al 2016 and add MESSAGE regions
+reg.spdf = readOGR('P:/ene.model/data/desalination','REGION_dissolved',verbose=FALSE) # old
+global_desal.spdf = spTransform( readOGR('P:/ene.model/NEST/desalination/Hanasaki_et_al_2015','global_desalination_plants'), crs(reg.spdf) )
+global_desal.spdf@data$region = over(global_desal.spdf,reg.spdf[,which(names(reg.spdf) == 'REGION')])$REGION
+global_desal.spdf = global_desal.spdf[ -1*which( is.na(global_desal.spdf@data$region) ), ]
+global_desal.spdf@data$msg_vintage = sapply( global_desal.spdf@data$online, function(x){ as.numeric( model_years[ which.min( ( as.numeric( model_years ) - x )^2 ) ] ) } )
+global_desal.spdf@data$technology_2 = sapply( global_desal.spdf@data$technology, function(x){ if( grepl('MSF',x)|grepl('MED',x) ){ return('distillation') }else{ return('membrane') }} )
+
+# From : Chart 1.1 in 'Executive Summary Desalination Technology Markets Global Demand Drivers, Technology Issues, Competitive Landscape, and Market Forecasts'
+# Global desalination capacity in 2010 was approx. 24 km3 / year
+global_desal.spdf@data$m3_per_day = global_desal.spdf@data$m3_per_day  * ( 24  / ( sum( global_desal.spdf@data$m3_per_day ) * 365 / 1e9 ) )
+
+region = unique(global_desal.spdf$region)
+# Match to message vintaging and regions, km3/years
+historical_desal_capacity.list = lapply( c('membrane','distillation'), function(tt){
+  temp = data.frame( do.call(cbind, lapply( region, function(reg){ sapply( unique(global_desal.spdf@data$msg_vintage)[
+    order(unique(global_desal.spdf@data$msg_vintage))], function(y){
+      (365/1e9) * max( 0,  sum( global_desal.spdf@data$m3_per_day[
+        which( global_desal.spdf@data$msg_vintage == y & global_desal.spdf@data$region == reg &
+                 global_desal.spdf@data$technology_2 == tt ) ] , na.rm=TRUE ), na.rm=TRUE ) } ) } ) ) )
+  names(temp) = region
+  row.names(temp) = unique(global_desal.spdf@data$msg_vintage)[order(unique(global_desal.spdf@data$msg_vintage))]
+  return(temp)
+} )
+names(historical_desal_capacity.list) = c('membrane','distillation')
+
+#### from here ####
+
+#summarise for regression
+global_desal.csv = read.csv('P:/ene.model/NEST/desalination/Hanasaki_et_al_2015/desal_data_Hanasaki_et_al_2015.csv')
+
+desal_data.df = global_desal.csv
+# clean up names
+names(desal_data.df) = c("country","location","m3_per_day","technology","water_type","online","user","price","lat","lon")
+desal_data.df$country = as.character(desal_data.df$country)
+desal_data.df$location = as.character(desal_data.df$location)
+desal_data.df$technology = as.character(desal_data.df$technology)
+desal_data.df$water_type = as.character(desal_data.df$water_type)
+desal_data.df$user = as.character(desal_data.df$user)
+desal_data.df$price = as.character(desal_data.df$price)
+desal_data.df$m3_per_day = gsub(",", "", desal_data.df$m3_per_day)
+desal_data.df$m3_per_day = as.numeric(desal_data.df$m3_per_day)
+desal_data.df$online = as.numeric(desal_data.df$online)
+desal_data.df$lat = as.numeric(desal_data.df$lat)
+desal_data.df$lon = as.numeric(desal_data.df$lon)
+temp1 = unique(desal_data.df$user)
+temp2 = c('municipal','industry','power','tourism','irrigation','military')
+for (i in 1:length(temp1))
+{
+  desal_data.df$user[desal_data.df$user == temp1[i]] = temp2[i]
+}
+temp1 = unique(desal_data.df$water_type)
+temp2 = c('seawater','brine')
+for (i in 1:length(temp1))
+{
+  desal_data.df$water_type[desal_data.df$water_type == temp1[i]] = temp2[i]
+}
+head(desal_data.df)
+
+map_iso3 = data.frame(country = unique(desal_data.df$country),
+                      iso3 = countrycode(unique(desal_data.df$country),
+                                         origin = 'country.name',
+                                         destination = 'iso3c' ))
+desal_data.df = desal_data.df %>% left_join(map_iso3)
+
+#### DESALDATA direclty form database and see if there is more data than from Hanasaki et al 2016 ####
+desaldata.csv = read.csv('P:/ene.model/NEST/desalination/DESALCAPACITYDATA/DesalData-2016-06-15.csv')
+
+desaldata.df = desaldata.csv %>%
+  select(Country,Location,Capacity..m3.d.,Technology,Feedwater,Online.date,Customer.type,EPC.price,Plant.status,Latitude,Longitude)
+
+# clean up names
+names(desaldata.df) = c("country","location","m3_per_day","technology","water_type","online","user","price","status","lat","lon")
+desaldata.df$country = as.character(desaldata.df$country)
+desaldata.df$location = as.character(desaldata.df$location)
+desaldata.df$technology = as.character(desaldata.df$technology)
+desaldata.df$water_type = as.character(desaldata.df$water_type)
+desaldata.df$user = as.character(desaldata.df$user)
+desaldata.df$price = as.character(desaldata.df$price)
+desaldata.df$m3_per_day = gsub(",", "", desaldata.df$m3_per_day)
+desaldata.df$m3_per_day = as.numeric(desaldata.df$m3_per_day)
+desaldata.df$online = as.numeric(desaldata.df$online)
+desaldata.df$status = as.character(desaldata.df$status)
+desaldata.df$lat = as.numeric(desaldata.df$lat)
+desaldata.df$lon = as.numeric(desaldata.df$lon)
+temp1 = unique(desaldata.df$user)
+temp2 = c('municipal','industry','power','tourism','irrigation','military')
+for (i in 1:length(temp1))
+{
+  desaldata.df$user[desaldata.df$user == temp1[i]] = temp2[i]
+}
+temp1 = unique(desaldata.df$water_type)
+temp2 = c('seawater','brine')
+for (i in 1:length(temp1))
+{
+  desaldata.df$water_type[desaldata.df$water_type == temp1[i]] = temp2[i]
+}
+# status online, or ofline
+desaldata.df = desaldata.df %>% mutate(status = if_else(grepl('Offline|Cancelled',status), 'offline','online') )
+head(desaldata.df)
+write.csv(desaldata.df,'P:/ene.model/NEST/desalination/DESALCAPACITYDATA2016_clean.csv')
+# online plants for historical capacity
+desaldata_online.df = desaldata.df %>% filter(status == 'online')
+write.csv(desaldata_online.df,'P:/ene.model/NEST/desalination/DESALCAPACITYDATA2016_current_online.csv')
+
+desaldata.df = desaldata.df %>% select(-status)
+map_iso3 = data.frame(country = unique(desaldata.df$country),
+                      iso3 = countrycode(unique(desaldata.df$country),
+                                         origin = 'country.name',
+                                         destination = 'iso3c' ))
+desaldata.df = desaldata.df %>% left_join(map_iso3)
+
+# m3_day yearly new capacity
+desal_new_cap.df = desal_data.df %>%
+  rename(year = online) %>%
+  group_by(iso3,year) %>%
+  summarise(desal_cap = sum(m3_per_day)) %>% ungroup() %>%
+  group_by(iso3) %>%
+  mutate(cum_desal = cumsum(desal_cap)) %>% ungroup()
+
+#from desalCAPACITYDATA database
+desal_new_cap2.df = desaldata.df %>%
+  rename(year = online) %>% filter(!is.na(year),!is.na(m3_per_day)) %>%
+  group_by(iso3,year) %>%
+  summarise(desal_cap = sum(m3_per_day)) %>% ungroup() %>%
+  group_by(iso3) %>%
+  mutate(cum_desal = cumsum(desal_cap)) %>% ungroup()
+
+desal_new_cap_glb.df = desal_new_cap2.df %>% group_by(year) %>% 
+  summarise(desal_cap = sum(desal_cap),
+            cum_desal = sum(cum_desal)) %>% ungroup() %>% 
+  mutate(cum_desal2 = cumsum(desal_cap)) %>% 
+  gather(key = "cum_type",value = "value",cum_desal,cum_desal2)
+
+ggplot(desal_new_cap_glb.df)+
+  geom_line(aes(x = year,y = value, color = cum_type))
+
+#comparison
+desal_comp = desal_new_cap.df %>%
+  left_join(desal_new_cap2.df  %>%
+              rename(desal2 = desal_cap,
+                     cumsum2 = cum_desal)) %>%
+  select(iso3,year,desal_cap,desal2,cum_desal,cumsum2) %>%
+  group_by(iso3) %>%
+  mutate(max_cumacp1 = max(cum_desal),
+         max_cumcap2 = max(cumsum2)) %>% ungroup() %>%
+  filter( (cum_desal == max_cumacp1),
+          cum_desal > 1000000)
+
+# it seems that the NA in the DESALCAPACITYDATA are included in other years in Hanasaki et al 2016
+# moreover DESALCAPACITYDATA has longer historical than Hanasaki et al 2016
+# Hanasaki et al 2016 summarizes some histircal data in later starting years
+desal_new_cap3.df = desal_new_cap2.df %>% ungroup()
+
+write.csv(desal_new_cap3.df,'P:/ene.model/NEST/desalination/DESALCAPACITYDATA2016_aggregated.csv')
+# highest
+quantiles = desal_new_cap3.df %>% group_by(iso3) %>%
+  filter(cum_desal == max(cum_desal))
+quantiles = quantiles %>%
+  mutate(quant = if_else(cum_desal <= quantile(quantiles$cum_desal, 0.33), 'q3_3',
+                         if_else(quantile(quantiles$cum_desal, 0.33) < cum_desal & cum_desal <= quantile(quantiles$cum_desal, 0.66), 'q2_3',
+                                 'q1_3'))) %>%
+  select(iso3,quant) %>% drop_na()
+
+# plot to see if lines can be approssimates with linear models
+ggplot(desal_new_cap3.df %>% left_join(quantiles) %>% filter(quant == 'q1_3'))+
+  geom_line(aes(x = year,y = cum_desal))+
+  facet_wrap(~iso3)+theme_bw()
+ggplot(desal_new_cap3.df %>% left_join(quantiles) %>% filter(quant == 'q2_3'))+
+  geom_line(aes(x = year,y = cum_desal))+
+  facet_wrap(~iso3)+theme_bw()
+ggplot(desal_new_cap3.df %>% left_join(quantiles) %>% filter(quant == 'q3_3'))+
+  geom_line(aes(x = year,y = cum_desal))+
+  facet_wrap(~iso3)+theme_bw()
+# veryfy normal distribution assumption
+hist(desal_new_cap3.df$desal_cap)
+hist(desal_new_cap3.df$cum_desal)
+# log
+hist(log(desal_new_cap3.df$desal_cap) )
+#it does not look very normal, it is right-skewed
+hist(log(desal_new_cap3.df$cum_desal) )
+# using cumulative capacity it has a normal shape
+
+# load other variables for regression model
+# GDP WB 2021 data , it has long history wrt to what Marina used
+# change to NAVIGATE updates
+gdp = read.csv('P:/ene.model/NEST/GDP/WB_GDP_2021/GDP_country_cleaned.csv') %>%
+  gather(key = 'year',value = 'gdp',5:65) %>% select(iso3,year,gdp) %>%
+  mutate(year = as.numeric(gsub('X','',year)))
+
+gdp_historical<-read.csv("P:/ene.model/NEST/governance/governance_2021/input/navigate_ssp.csv") %>%
+  filter(scenario=="WDI_2021", variable=="gdppc") %>%
+  rename(iso3=region) %>%
+  select(-c(scenario, variable)) %>%
+  pivot_longer(!iso3, names_to = "Year", values_to = "gdp") %>%
+  mutate(year = as.numeric(gsub('X','',Year))) %>%
+  filter(year<2020) %>%
+  select(iso3,year,gdp)
+
+gdp_new_hist_proj = read.csv('P:/ene.model/NEST/governance/governance_2021/input/gdp_full_WB_NAVIGATE.csv')
+gdp_new = gdp_new_hist_proj %>% select(iso3c,year,gdppc) %>%
+  rename(iso3 = iso3c, gdp = gdppc) %>% drop_na()
+
+hist(gdp$gdp)
+hist(gdp_historical$gdp)
+hist(log(gdp$gdp) )
+hist(log(gdp_historical$gdp) )
+hist(gdp_new$gdp)
+hist(log(gdp_new$gdp))
+# log has a better distribution
+
+# Worldwide Governance Indicators - updates Kaufmann, Kraay & Mastruzzi (2021)
+# The values of indicators span a range from -2.5 to 2.5, here they were
+# transformed to fall in the 0 to 1 range
+
+# Function to standardize the values from 0 to 1
+range01 <- function(x){(x - min(x, na.rm = T))/(max(x, na.rm = T) - min(x, na.rm = T))}
+
+wgi <- read.dta13('P:/ene.model/NEST/governance/wgidataset.dta') %>%
+  select(code, countryname, year, contains('e'), -ges, -gen, -ger, -gel, -geu) %>%
+  rename(voic.ac = vae,
+         pol.stab = pve,
+         gov.eff = gee,
+         reg.qual = rqe,
+         ru.law = rle,
+         corr.cont = cce,
+         countrycode = code) %>%
+  gather(var, value, -countrycode, -countryname, -year) %>%
+  group_by(var) %>%
+  mutate(value = range01(value)) %>%
+  ungroup() %>%
+  mutate(year = as.integer(year)) %>%
+  spread(var, value) %>%
+  mutate(governance = rowMeans(select(., voic.ac,
+                                      pol.stab, gov.eff, reg.qual, ru.law, corr.cont))) %>%  # # Governance variable as the arithmetic average of the six components of WGI) %>%
+  select(-countryname)
+
+gov = wgi %>% select(countrycode,year,governance)
+names(gov) = c('iso3','year','gov')
+names(wgi) = c('iso3','year',"corr.cont","gov.eff","pol.stab","reg.qual","ru.law","voic.ac",'gov')
+
+# other from Elina
+gov_eff<- read.csv("P:/ene.model/NEST/governance/governance_2021/input/governance_2021.csv") %>%
+  mutate(gov_norm=range01(gee)) %>%
+  select(countryname, year, gov_norm) %>%
+  arrange(countryname, year) %>%
+  mutate(iso3=countrycode(countryname, "country.name", "iso3c")) %>%
+  select(iso3,year,gov_norm)
+
+gov_eff<- read.csv("P:/ene.model/NEST/governance/governance_2021/input/qog_2021.csv") %>%
+  mutate(gov_norm=range01(icrg_qog)) %>%
+  select(ccodealp, year, gov_norm) %>%
+  arrange(ccodealp, year) %>%
+  mutate(iso3=ccodealp) %>%
+  select(iso3,year,gov_norm) %>% drop_na()
+
+hist(gov$gov)
+hist(gov_eff$gov_norm)
+# without log, it has a normal distribution
+
+# WSI from Ed
+wsi_hist = read.xlsx('P:/ene.model/data/Water/water_stress_cc/ISO_water_stress/wsi_tables_hist_new.xlsx',
+                     sheetIndex = 1)
+
+wsi.df = wsi_hist %>% gather(key = year, value = wsi , 3:length(wsi_hist)) %>%
+  filter(!grepl('_bin',year)) %>%
+  mutate(year = as.numeric(gsub('X','',year)),
+         wsi = round(as.numeric(wsi),digits = 3)) %>%
+  rename(iso3 = ISO) %>%
+  select(iso3,year,wsi)
+#very high values
+
+hist(wsi.df$wsi)
+hist(log(wsi.df$wsi))
+#use log
+library(WDI)
+wsi_wb <- WDI(country = "all",
+                     indicator = c('water_stress'="ER.H2O.FWST.ZS"),
+                     start = 1960,end = 2020,extra = TRUE) %>%
+  as_tibble() %>%
+  filter(region!="Aggregates")
+wsi_wb = wsi_wb %>% drop_na()
+# probably notusing it, weird values
+
+# presence of coast # of gridcels
+# it would be nice to use historical trends of population living close to the coast
+coast = read.csv('P:/ene.model/NEST/desalination/coast.csv')
+names(coast) = c('iso3','coast')
+
+hist(coast$coast)
+# skewed, take log
+hist(log(coast$coast))
+
+# use all log variables, except from gov
+# what about NA, how to handle?
+master = desal_new_cap3.df %>% select(-cum_desal) %>%
+  full_join(gdp_historical) %>%  # add gdp
+  mutate(desal_cap = if_else(is.na(desal_cap), 0 ,desal_cap)) %>%
+  # calculate cum capacity with in na after merging
+  #re-aggregate desalination capacity
+  arrange(year, .by_group = T) %>% ungroup() %>% group_by(iso3) %>%
+  mutate(cum_desal = cumsum(desal_cap)) %>% ungroup() %>%
+  # remove gdp na
+  filter(!is.na(gdp)) %>%
+  #retail only countries with desaliantion
+  filter(iso3 %in% unique(desal_new_cap3.df$iso3)) %>%
+  left_join(gov_eff) %>%
+  left_join(wgi ) %>%
+  left_join(coast) %>% filter(!is.na(coast)) %>%
+  left_join(wsi.df) %>%
+  ungroup() %>% group_by(iso3) %>%
+  mutate(wsi = as.numeric(if_else(is.na(wsi), stats::quantile(wsi,0.75,na.rm = T), wsi)),
+         gov_norm = as.numeric(if_else(year < 1984, stats::quantile(gov_norm,0.25,na.rm = T), gov_norm)),
+         gov = as.numeric(if_else(year < 2004, stats::quantile(gov,0.25,na.rm = T), gov)),
+         # corr.cont = as.numeric(if_else(year < 1996, stats::quantile(corr.cont,0.25,na.rm = T), corr.cont)),
+         # gov.eff = as.numeric(if_else(year < 1996, stats::quantile(gov.eff,0.25,na.rm = T), gov.eff)),
+         # pol.stab = as.numeric(if_else(year < 1996, stats::quantile(pol.stab,0.25,na.rm = T), pol.stab)),
+         # ru.law = as.numeric(if_else(year < 1996, stats::quantile(ru.law,0.25,na.rm = T), ru.law)),
+         # voic.ac = as.numeric(if_else(year < 1996, stats::quantile(voic.ac,0.25,na.rm = T), voic.ac))
+         ) %>%
+  ungroup() %>%
+  mutate(log_desal = log(cum_desal),
+         log_gdp = log(gdp),
+         log_coast = log(coast),
+         log_wsi = log(wsi)) %>%
+  # filter out -inf, all val before first desal plant
+  filter(!is.infinite(log_desal),!is.infinite(log_wsi)) %>%
+  # select(iso3,year,log_desal,log_gdp,gov.eff,pol.stab,reg.qual,ru.law,voic.ac,gov_norm,log_wsi,log_coast)
+  select(iso3,year,log_desal,log_gdp,gov_norm,log_wsi,log_coast)
+
+master = master %>% rename(gov = gov_norm)
+#### ANALYSIS ###
+# Explore correlations between the variables in the dataset
+cor.exp <- master %>%
+  select(log_desal, log_gdp, gov,log_wsi, log_coast) %>%
+  # select(log_desal, log_gdp, gov.eff, pol.stab, reg.qual, ru.law, voic.ac, gov_norm,log_wsi, log_coast) %>%
+  drop_na() %>%
+  cor()
+
+corrplot(cor.exp, method = 'number',type = 'upper')
+
+# Split the data into training and test set
+data(mtcars)
+
+## 75% of the sample size
+smp_size <- floor(0.75 * nrow(master))
+
+## set the seed to make your partition reproducible
+set.seed(123)
+train_ind <- sample(seq_len(nrow(master)), size = smp_size)
+
+train.data <- master[train_ind, ]
+test.data <- master[-train_ind, ]
+
+#### simple model #### checking multicollinearity
+# lm1 <- lm(log_desal ~ log_gdp + pol.stab + gov.eff + log_wsi + log_coast, data = train.data)
+lm1 <- lm(log_desal ~ log_gdp + gov + log_wsi + log_coast, data = master)
+summary(lm1)
+# multicolinearity
+# The smallest possible value of VIF is one (absence of multicollinearity).
+# As a rule of thumb, a VIF value that exceeds 5 or 10 indicates a problematic amount of collinearity (James et al. 2014).
+vif(lm1)
+# remove gov.eff,ru.law,reg.qual  highest value
+
+predictions = lm1 %>% predict(test.data)
+RMSE(predictions, test.data$log_desal)
+R2(predictions, test.data$log_desal)
+# plot prediction
+pred1 = test.data %>% bind_cols(prediction = predictions)
+
+ggplot(pred1)+
+  geom_point(aes(x = log_desal,y = prediction))+
+  geom_abline()+
+  scale_x_continuous(limits = c(0,20))+
+  scale_y_continuous(limits = c(0,20))+
+  xlab('log desal cap')+ylab('predictions')
+
+#### fixed effect test ####
+# 1) way
+# lm2 <- lm(log_desal ~ log_gdp + pol.stab + gov.eff + log_wsi + log_coast + factor(iso3) -1, data = master)
+# lm2 <- lm(log_desal ~ log_gdp + gov + log_wsi + log_coast + factor(iso3) -1, data = master)
+# summary(lm2)
+# 
+# predictions2 = lm2 %>% predict()
+# # remove countries that do not have enough data
+# # BHS, HKG, MLT, PSE
+# test.data2 = test.data %>% filter(!iso3 %in% c('BHS','HKG','MLT','PSE' ))
+# predictions2 = lm2 %>% predict(test.data2)
+# pred2 = test.data2 %>% bind_cols(prediction = predictions2)
+# 
+# ggplot(pred2)+
+#   geom_point(aes(x = log_desal,y = prediction, color = iso3))+
+#   geom_abline()+
+#   scale_x_continuous(limits = c(0,20))+
+#   scale_y_continuous(limits = c(0,20))+
+#   xlab('log desal cap')+ylab('predictions')
+
+# 2) way, with plm
+lm3 <- plm(log_desal ~ log_gdp + gov + log_wsi + log_coast,
+           data = master,
+           index = c("iso3"),
+           model = "within",
+           effect = 'individual'
+           )
+summary(lm3)
+summary(fixef(lm3))
+
+pred3 = master %>%
+  group_by(iso3) %>%
+  mutate(y_mean = mean(log_desal)) %>% ungroup() %>%
+  mutate(pred = predict(lm3)  + y_mean) # this predict only work on the train.data, canno apply to other data
+
+ggplot(pred3)+
+  geom_point(aes(x = log_desal, y = pred, color = iso3))+
+  geom_abline()+
+  scale_x_continuous(limits = c(0,20))+
+  scale_y_continuous(limits = c(0,20))+
+  xlab('log desal cap')+ylab('predictions')+theme_bw()
+
+# Ftest. The null is that no time-fixed effects are needed
+# maybe not needed, it was for testing time effect
+pFtest(lm3, lm1)
+plmtest(lm3, c("time"), type=("bp"))
+
+# store models
+# manually select model names
+model_names = c("lm1",'lm2',"lm3")
+
+# create a list based on models names provided
+list_models = lapply(model_names, get)
+names(list_models) = model_names
+summary(list_models[['lm2']])
+
+library(rlist)
+list.save(list_models, 'P:/ene.model/NEST/desalination/regression_models_lm_plm.rdata')
+# test load
+tes = list.load(file="P:/ene.model/NEST/desalination/regression_models_lm_plm.RData")
+
+# print summary using robust standard errors
+coeftest(lm3, vcov. = vcovHC, type = "HC1")
+
+#### TEST and COMPARE ####
+
+get_estimate = function(es,var){
+  c = es %>%
+    filter(term == var) %>%
+    select(estimate)
+  return(c)
+}
+
+adjust_fe = function(fe){
+
+  years <- c(year = seq(1995, 2100, 1))
+
+  cntry.fe <-fe %>%
+    merge(years, all.y = T) %>%
+    dplyr::rename(year = y) %>% drop_na()
+  # mutate(fe=abs(fe))
+
+  # Country fixed effects: they are expected to converge to the 75th percentile of the present-day distribution,
+  # in years in the future changing by the SSPs. We follow the approach of Crespo Cuaresma (2017),
+  # and let the fixed effects converge for SSP1: 2130, SSP2:2250, SSP3: No convergence, SSP4: 2250, SSP5: 2180
+  #adjust for the year 2020 as the base
+
+  #The convergence rates for SSP2 and SSP4 are the same
+
+  conv.rates <- data.frame(years = c(110, 230, 3000, 230, 160),
+                           scenario = c('SSP1', 'SSP2', 'SSP3', 'SSP4', 'SSP5'))
+  target <- quantile(cntry.fe$fe, 0.50)
+  if (target > 0){
+    max.fe <- max(cntry.fe$fe)
+    } else {
+    max.fe <- min(cntry.fe$fe)
+    }
+
+  #Calculated decreasing rates for each SSP
+
+  ssps<-conv.rates %>%
+    mutate(r=-log(target/max.fe)/years) %>%
+    select(-years)
+
+  ssps<- merge(ssps, years, all.y = T) %>%
+    dplyr::rename(year=y)
+
+  fe_adjusted<-merge(cntry.fe, ssps, by=c("year"))
+
+  fe_adjusted<-fe_adjusted %>%
+    filter(year>2019) %>%
+    group_by(iso3, scenario) %>%
+    mutate(fe_adj=fe) %>%
+    mutate(fe_adj=ifelse(abs(fe)>abs(target), fe*(1+r)^(2021-year), fe_adj)) %>%
+    # mutate(fe_adj=-fe_adj) %>%
+    # rename(Year=year)
+  return(fe_adjusted)
+}
+
+#lm2
+lm2_es <- tidy(lm2)
+
+c1l2 <- get_estimate(lm2_es,'log_gdp')$estimate
+# c2l2  <- get_estimate(lm2_es,'pol.stab')$estimate
+c3l2 <- get_estimate(lm2_es,'gov')$estimate
+c4l2 <- get_estimate(lm2_es,'log_wsi')$estimate
+c5l2 <- get_estimate(lm2_es,'log_coast')$estimate
+
+cntry.fe2 <- lm2_es %>%
+  filter(grepl('factor',term)) %>%
+  dplyr::rename(iso3 = term,fe = estimate) %>%
+  mutate(iso3 = gsub('[factor(iso3)]','',iso3)) %>%
+  select(iso3,fe)
+
+fe2 = adjust_fe(cntry.fe2)
+
+#lm3
+lm3_es <- tidy(lm3)
+
+c1l3 <- get_estimate(lm3_es,'log_gdp')$estimate
+# c2l3  <- get_estimate(lm3_es,'pol.stab')$estimate
+c3l3 <- get_estimate(lm3_es,'gov')$estimate
+c4l3 <- get_estimate(lm3_es,'log_wsi')$estimate
+# c5l3 <- get_estimate(lm3_es,'log_coast')$estimate
+
+#Calculation country fixed effects
+
+cntry.fe3 <- fixef(lm3) %>%
+  as.data.frame() %>%
+  rownames_to_column("iso3") %>%
+  dplyr::rename(fe = ".")
+
+fe3 = adjust_fe(cntry.fe3)
+
+# plot
+fe2 %>%
+  filter(iso3 == "CHN") %>%
+  filter(scenario!="SSP4") %>%
+  ggplot(aes(x = year, y = fe_adj, color = scenario)) +
+  geom_line(size = 1) +
+  labs(y = "Fixed effect", x = "China")
+
+# tests
+test.res = master %>%
+  left_join(cntry.fe2 %>% dplyr::rename(fe2 = fe)) %>%
+  left_join(cntry.fe3 %>% dplyr::rename(fe3 = fe))
+
+projected<-test.res %>%
+  mutate(log_desal2 = log_gdp*(c1l2) + gov*(c3l2) + log_wsi*(c4l2) + log_coast*(c5l2) + fe2,
+         log_desal3 = log_gdp*(c1l3) + gov*(c3l3) + log_wsi*(c4l3) + fe3)
+
+# plot
+ggplot(projected %>% filter( iso3 %in% c('USA','ARE','SAU','ESP','KWT','QAT')) )+
+  geom_line(aes(x = year,y = exp(log_desal) ), color = 'black')+
+  geom_line(aes(x = year,y = exp(log_desal2) ), color = 'blue')+
+  geom_line(aes(x = year,y = exp(log_desal3) ), color = 'red')+
+  facet_wrap(~iso3)+theme_bw()
+
+# check master, which variables are driving drop in projections for ARE, SAU, KWT
+ggplot( )+
+  geom_line(data = projected %>% filter( iso3 %in% c('ARE','SAU','KWT','QAT')),aes(x = year,y = log_desal2 ), color = 'grey',size = 2)+
+  geom_line(data = master %>% filter( iso3 %in% c('ARE','SAU','KWT','QAT')),aes(x = year,y = log_desal ), color = 'black')+
+  geom_line(data = master %>% filter( iso3 %in% c('ARE','SAU','KWT','QAT')),aes(x = year,y = log_gdp ), color = 'blue')+
+  geom_line(data = master %>% filter( iso3 %in% c('ARE','SAU','KWT','QAT')),aes(x = year,y = log_wsi ), color = 'red')+
+  geom_line(data = master %>% filter( iso3 %in% c('ARE','SAU','KWT','QAT')),aes(x = year,y = gov ), color = 'green')+
+  facet_wrap(~iso3)+theme_bw()
+
+ggplot(projected %>% filter( iso3 %in%  quantiles[quantiles$quant == 'q1_3',]$iso3 ,
+                             !iso3 %in% c('USA','ARE','SAU','ESP','KWT','QAT') ) )+
+  geom_line(aes(x = year,y = exp(log_desal) ), color = 'black')+
+  geom_line(aes(x = year,y = exp(log_desal2) ), color = 'blue')+
+  geom_line(aes(x = year,y = exp(log_desal3) ), color = 'red')+
+  facet_wrap(~iso3)+theme_bw()
+
+ggplot(projected %>% filter( iso3 %in%  quantiles[quantiles$quant == 'q2_3',]$iso3 ,
+                             !iso3 %in% c('USA','ARE','SAU','ESP','KWT','QAT') ) )+
+  geom_line(aes(x = year,y = exp(log_desal) ), color = 'black')+
+  geom_line(aes(x = year,y = exp(log_desal2) ), color = 'blue')+
+  geom_line(aes(x = year,y = exp(log_desal3) ), color = 'red')+
+  facet_wrap(~iso3)+theme_bw()
+
+ggplot(projected %>% filter( iso3 %in%  quantiles[quantiles$quant == 'q3_3',]$iso3 ) )+
+  geom_line(aes(x = year,y = exp(log_desal) ), color = 'black')+
+  geom_line(aes(x = year,y = exp(log_desal2) ), color = 'blue')+
+  geom_line(aes(x = year,y = exp(log_desal3) ), color = 'red')+
+  facet_wrap(~iso3)+theme_bw()
+
+# lm2 and lm3 equal now
+###########################
+##### PROJECTIONS #########
+###########################
+
+#### TAKE MESSAGE REGION-BASIN structure ####
+reg = 'R11' # or R12 or a ISO3
+scen = 'SSP2'
+if (!reg %in% master$iso3){
+  print(paste0("ATTENTION ",reg," is not in the dataset, continue only if its is a non-country (e.g. R11)"))
+}
+basin_by_region.spdf = readOGR('P:/ene.model/NEST/delineation/data/delineated_basins_new',
+                               paste0('basins_by_region_simpl_',reg), verbose=FALSE)
+row.names(basin_by_region.spdf@data) = 1:length(basin_by_region.spdf$BASIN)
+
+# Gadm country deineation
+countries.spdf = readOGR('P:/ene.general/Water/global_basin_modeling/basin_delineation/data/country_delineation/gadm/output_data', 'gadm_country_boundaries', verbose=FALSE)
+countries.spdf@data$ID = as.numeric(countrycode(countries.spdf@data$ISO, "iso3c", "iso3n"))
+countries.spdf = countries.spdf[,c('ID','ISO','NAME','REGION','STATUS','CONTINENT')]
+
+#read in raster from ED. ATTENTION 2100 and 2110 missing
+## Set path data folder in message_ix working copy
+msg_data = paste0('P:/ene.model/NEST')
+data_path = path.expand(msg_data)
+RCPs = c('no_climate','2p6','6p0')
+
+nc = nc_open( paste0(data_path,'/water_scarcity/wsi_memean_ssp2_rcp6p0.nc'), verbose=FALSE)
+watstress.brick = brick( paste0(data_path,'/water_scarcity/wsi_memean_ssp2_rcp6p0.nc') )
+
+ras1 = watstress.brick$X2010s
+plot(ras1)
+start.spdf = rasterToPoints(ras1, spatial = TRUE)
+
+writeOGR(
+  start.spdf,
+  'P:/ene.model/NEST/desalination',
+  paste0('WSI_check'),
+  driver="ESRI Shapefile",
+  overwrite_layer=TRUE
+)
+start.spdf$X2010s = NULL
+
+basin_info = over(start.spdf,basin_by_region.spdf)
+country_info = over(start.spdf,countries.spdf)
+
+for (rcp in RCPs) {
+  print(paste0("RCP: ",rcp))
+  if (rcp == '2p6'){
+    nc = nc_open( paste0(data_path,'/water_scarcity/wsi_memean_ssp1_rcp4p5.nc'), verbose=FALSE)
+    watstress.brick = brick( paste0(data_path,'/water_scarcity/wsi_memean_ssp1_rcp4p5.nc') )
+
+  } else{
+    nc = nc_open( paste0(data_path,'/water_scarcity/wsi_memean_ssp2_rcp6p0.nc'), verbose=FALSE)
+    watstress.brick = brick( paste0(data_path,'/water_scarcity/wsi_memean_ssp2_rcp6p0.nc') )
+  }
+
+  ras1 = watstress.brick$X2010s
+  plot(ras1)
+  start.spdf = rasterToPoints(ras1, spatial = TRUE)
+  start.spdf$X2010s = NULL
+  initial_years <- seq(2010, 2090, 5)
+
+  for (i in seq(1:nlayers(watstress.brick)) ){
+    r <- raster(watstress.brick, layer=i)
+    proj4string(r) = proj4string(watstress.brick)
+    yr = as.numeric(gsub('X','',gsub('s','',names(r)) ))
+    if (yr %in% initial_years){
+      column_name = paste0('WSI.', yr )
+      start.spdf@data[,column_name] = raster::extract(r,start.spdf)
+      # there are 876 Na values, we set them = 0
+      start.spdf@data[column_name][is.na(start.spdf@data[column_name])] = 0
+    } else {}
+
+  }
+
+  data.spdf = start.spdf
+  data.spdf@data = data.spdf@data %>%
+    bind_cols(basin_info %>% select(BCU_name),
+              country_info %>% select(ISO) %>% dplyr::rename(iso3 = ISO))
+
+  map_years = rbind(data.frame(my = initial_years) %>% mutate(sy = my -2),
+                    data.frame(my = initial_years) %>% mutate(sy = my -1),
+                    data.frame(my = initial_years) %>% mutate(sy = my),
+                    data.frame(my = initial_years) %>% mutate(sy = my +1),
+                    data.frame(my = initial_years) %>% mutate(sy = my +2)) %>%
+    arrange(my)
+
+  # - makje a df with coordinates and years as columns
+  data.df = cbind(data.spdf@coords, data.spdf@data) %>% select(BCU_name,iso3,everything()) %>%
+    gather(key = year, value = wsi, 5:19) %>%
+    mutate(year = as.numeric( gsub('WSI.','',year))) %>% drop_na()
+  # - get national GDP, national governance indicator. point wsi and national coast values (as in regression)
+  # - mask out points not in coast area
+  # - project and make basin average
+
+  # GDP
+  gdp_proj<-read.csv("P:/ene.model/NEST/governance/governance_2021/input/navigate_ssp.csv") %>%
+    filter(scenario=="SSP2", variable=="gdppc") %>%
+    rename(iso3=region) %>%
+    select(-c(scenario, variable)) %>%
+    pivot_longer(!iso3, names_to = "Year", values_to = "gdp") %>%
+    mutate(year = as.numeric(gsub('X','',Year))) %>%
+    filter(year>=2020) %>%
+    select(iso3,year,gdp)
+
+  # make 5y averages
+  gdp_proj = gdp_proj %>% filter(year <= 2092) %>% drop_na() %>%
+    rename(sy = year) %>%
+    left_join(map_years) %>% group_by(iso3,my) %>%
+    summarise(gdp = mean(gdp)) %>% ungroup() %>%
+    rename(year = my) %>%
+    select(iso3, year, gdp)
+
+  # Governance projections
+  gov_proj<- read.csv("P:/ene.model/NEST/governance/governance_obs_project.csv") %>%
+    mutate(gov=range01(governance)) %>%
+    arrange(countrycode, year) %>%
+    dplyr::rename(iso3 = countrycode) %>%
+    filter(year >= 2018,year < 2092,
+           scenario == scen) %>%
+    select(iso3, year, gov)
+
+  # make 5y averages
+  gov_proj = gov_proj %>% filter(year <= 2092) %>% drop_na() %>%
+    rename(sy = year) %>%
+    left_join(map_years) %>% group_by(iso3,my) %>%
+    summarise(gov = mean(gov)) %>% ungroup() %>%
+    rename(year = my) %>%
+    select(iso3, year, gov)
+
+  # Governance from Elina
+  read.csv("P:/ene.model/NEST/governance/governance_2021/output/projections_country_ssp2.csv")
+
+  #values in lat historical period, LOG
+  hist_cum_cap = master %>% select(iso3,year,log_desal) %>% group_by(iso3) %>%
+    mutate(hist_cap = max(log_desal)) %>% ungroup() %>%
+    select(iso3,hist_cap) %>% distinct()
+
+  future_proj = data.df %>% left_join(gdp_proj) %>% left_join(gov_proj) %>%
+    left_join(hist_cum_cap) %>% drop_na()
+
+  if (rcp == 'no_climate'){
+    future_proj = future_proj %>% group_by(iso3,x,y) %>%
+    mutate(wsi = first(wsi)) %>% ungroup()
+    }
+
+  future_proj = future_proj %>% mutate(log_gdp = log(gdp),
+                                       log_wsi = log(wsi)) %>%
+    left_join(fe3 %>% ungroup() %>%
+                select(iso3,year,fe_adj)) %>%
+    mutate(log_desal3 = log_gdp*(c1l3) + gov*(c3l3) + log_wsi*(c4l3) + fe_adj) %>%
+    filter(!is.infinite(log_wsi)) %>% drop_na(fe_adj)
+
+  #scale based on last historical year
+  future_plot = future_proj %>% group_by(iso3,year) %>%
+    mutate(desal_cap = exp(log_desal3) ) %>% 
+    summarise(desal_cap = mean(desal_cap)) %>%
+    # adjust to hit_Cap
+    group_by(iso3) %>% mutate(min_cap = min(desal_cap)) %>%
+    left_join(hist_cum_cap) %>%
+    # move to absolute values
+    mutate(hist_cap = exp(hist_cap) ) %>% 
+    # diff_hist is in ABSOLUTE value
+    mutate(diff_hist =  hist_cap - min_cap,
+           desal_cap = desal_cap + diff_hist) %>%
+    ungroup()
+
+  #apply diff_hist to all countries in the point database
+  diff_hist = future_plot %>% select(iso3,diff_hist) %>% distinct()
+  ## check if initial projected values is higher/equal to historical data
+
+  future_proj_out= future_proj %>% left_join(diff_hist) %>%
+    mutate(desal_cap = exp(log_desal3),
+           desal_cap = desal_cap + diff_hist)
+
+  ggplot( )+
+    # geom_line(data = projected %>% filter( iso3 %in% c('ARE','USA','ESP','TKM','SAU','KWT','QAT')),aes(x = year,y = log_desal3 ), color = 'grey',size = 2)+
+    geom_line(data = master %>% filter( iso3 %in% c('ARE','USA','ESP','TKM','SAU','KWT','QAT')),aes(x = year,y = exp(log_desal) ), color = 'black')+
+    # geom_line(data = gdp_proj %>% filter( iso3 %in% c('ARE','USA','ESP','TKM','SAU','KWT','QAT')),aes(x = year,y = log(gdp) ), color = 'green')+
+    geom_line(data = future_plot %>% filter( iso3 %in% c('ARE','USA','ESP','TKM','SAU','KWT','QAT')),aes(x = year,y = desal_cap ), color = 'red')+
+    facet_wrap(~iso3)+theme_bw()+ggtitle('SECOND case, abs val')
+
+  ggplot ()+
+    # geom_line(data = projected %>% filter( iso3 %in%  quantiles[quantiles$quant == 'q1_3',]$iso3 ,
+    #                                        !iso3 %in% c('USA','ARE','SAU','ESP','KWT','QAT','TKM') ),
+    #           aes(x = year,y = exp(log_desal3) ), color = 'grey', size = 2)+
+    geom_line(data = master %>% filter( iso3 %in%  quantiles[quantiles$quant == 'q1_3',]$iso3 ,
+                                           !iso3 %in% c('USA','ARE','SAU','ESP','KWT','QAT','TKM') ),
+              aes(x = year,y = exp(log_desal) ), color = 'black')+
+    geom_line(data = future_plot %>% filter( iso3 %in%  quantiles[quantiles$quant == 'q1_3',]$iso3 ,
+                                           !iso3 %in% c('USA','ARE','SAU','ESP','KWT','QAT','TKM') ),
+              aes(x = year,y = desal_cap ), color = 'red')+
+    facet_wrap(~iso3)+theme_bw()
+
+  # TKM weird GDP trend
+  # basin average without trimming out location far from coasts
+  fut_proj_basin = future_proj_out %>% group_by(BCU_name, year) %>%
+    summarise(cap_m3_day = mean(desal_cap)) %>% ungroup() %>%
+    mutate(cap_km3_year = cap_m3_day *1e-9 * 365)
+
+  assign(paste0('basin_desal_',rcp),fut_proj_basin)
+}
+
+# compare
+comp = basin_desal_2p6 %>% mutate(rcp = '2p6') %>% bind_rows(
+  basin_desal_6p0 %>% mutate(rcp = '6p0'),
+  basin_desal_no_climate %>% mutate(rcp = 'no_climate')
+)
+# global capacity
+comp_glob = comp %>% group_by(rcp,year) %>%
+  summarise(cap_km3_year = sum(cap_km3_year)) %>% ungroup()
+
+ggplot(comp_glob)+
+  geom_line(aes(x = year,y = cap_km3_year, color = rcp),size = 1)+
+  theme_bw()+
+  ggtitle("Desalination potential projections, global, SSP2")+
+  ylab("km3/year")+
+  theme(axis.title.x = element_blank(),
+        legend.position = 'bottom')
+
+# Main basins
+ggplot(comp %>% filter(BCU_name %in% c("120|MEA","66|SAS","33|NAM","4|WEU","158|AFR","7|AFR")) )+
+  geom_line(aes(x = year,y = cap_km3_year, color = rcp),
+            size = 1)+
+  facet_wrap(~BCU_name)+theme_bw()+
+  ggtitle("Desalination potential projections, basins, SSP2")+
+  ylab("km3/year")+
+  theme(axis.title.x = element_blank(),
+        legend.position = 'bottom')
+
+# set negative values to 0
+comp = comp %>% 
+  mutate(cap_km3_year = if_else(cap_km3_year < 0, 0, cap_km3_year))
+
+# output
+write.csv(comp %>% select(BCU_name,rcp,year,cap_km3_year),
+          paste0('P:/ene.model/NEST/desalination/projected_desalination_potential_km3_year_',reg,'.csv'),
+          row.names = F)
+
+
+#### Historical capacity by basin ####
+# to set existing capacity in historical time periods
+hist_cap.df = read.csv('P:/ene.model/NEST/desalination/DESALCAPACITYDATA2016_current_online.csv') %>%
+  mutate(tec_type = if_else(technology %in% c( "MSF (Multi-stage Flash)","MED (Multi-effect Distillation)",
+                                               "MD (Membrane Distillation)","VC (Vapour Compression)","Other / Unknown"),
+                            'distillation','membrane')) %>%
+  rename(year = online) %>%
+  group_by(lat,lon,year,tec_type) %>%
+  summarise(cap_km3_year = sum(m3_per_day) * 1e-9*365) %>% ungroup() %>%
+  drop_na()
+
+map_hist_years = rbind(data.frame(my = seq(1995,2015,5)) %>% mutate(sy = my -2),
+                  data.frame(my = seq(1995,2015,5)) %>% mutate(sy = my -1),
+                  data.frame(my = seq(1995,2015,5)) %>% mutate(sy = my),
+                  data.frame(my = seq(1995,2015,5)) %>% mutate(sy = my +1),
+                  data.frame(my = seq(1995,2015,5)) %>% mutate(sy = my +2)) %>%
+  arrange(my)
+
+hist_cap.df = hist_cap.df %>% left_join(map_hist_years %>% rename(year = sy)) %>%
+  mutate(my = if_else(is.na(my) & year < 1995, 1995,
+                      if_else(is.na(my) & year > 2015,2015,my))) %>%
+  filter(!year > 2020, !year < 1990) %>%
+  group_by(lat,lon,my,tec_type) %>%
+  summarise(cap_km3_year = sum(cap_km3_year) ) %>% ungroup() %>%
+  rename(year = my)
+
+hist_cap.spdf = hist_cap.df
+coordinates(hist_cap.spdf) = ~ lon + lat
+proj4string( hist_cap.spdf) = crs(basin_by_region.spdf)
+basin_info_desal = over(hist_cap.spdf,basin_by_region.spdf)
+
+hist_cap.out = hist_cap.df %>% bind_cols(basin_info_desal) %>%
+  group_by(BCU_name,year,tec_type) %>%
+  summarise(cap_km3_year = sum(cap_km3_year)) %>% ungroup() %>%
+  #there are soem NA
+  drop_na()
+
+#technical lifetime of desal tech = 30 y
+
+# compare historical with first year of projections
+hist_comp = hist_cap.out %>% ungroup() %>% group_by(BCU_name, year) %>% 
+  summarise(hist_cap = sum(cap_km3_year)) %>% 
+  summarise(year = year,
+            hist_cap = hist_cap,
+            hist_cap_cum = cumsum(hist_cap)) %>% ungroup() %>% 
+  left_join(comp %>% select(BCU_name,rcp,year,cap_km3_year) %>% 
+  filter(year == 2020) %>% select(-year) ) %>% 
+  # any cases of hist 2015 higher than 2020
+  mutate(diff2020 = cap_km3_year - hist_cap_cum)
+
+# for cases of mismatch, cum 2015 and projections
+high2020 = hist_comp %>% filter(year == 2015, diff2020 < 0) %>% 
+  mutate(Xhist = hist_cap_cum/ cap_km3_year,
+         fianl_hist_cum = hist_cap_cum / (Xhist * 1.05)) %>% 
+  filter(rcp == 'no_climate') %>% 
+  select(BCU_name, Xhist)
+
+# adjust hist cap
+hist_cap.out = hist_cap.out %>% left_join(high2020) %>% 
+  mutate(cap_km3_year = if_else(!is.na(Xhist),cap_km3_year / (Xhist * 1.05),cap_km3_year )) %>% 
+  select(-Xhist)
+
+write.csv(hist_cap.out,
+          paste0('P:/ene.model/NEST/desalination/historical_capacity_desalination_km3_year_',reg,'.csv'),
+          row.names = F)
+
+# for countries with no desalination"))
+hist_cap.out2 = data.frame(BCU_name = basin_by_region.spdf$BCU_name) %>% 
+  crossing(year = c(2010,2015)) %>% 
+  crossing(tec_type = c("membrane","distillation")) %>% 
+  mutate(cap_km3_year = 0)
+
+write.csv(hist_cap.out,
+          paste0('P:/ene.model/NEST/desalination/historical_capacity_desalination_km3_year_',reg,'.csv'),
+          row.names = F)
+
+# projection
+comp2 = data.frame(BCU_name = basin_by_region.spdf$BCU_name) %>% 
+  crossing(rcp = RCPs) %>% 
+  crossing(year = seq(2020, 2090, 5)) %>% 
+  mutate(cap_km3_year = 0.001)
+
+write.csv(comp2 %>% select(BCU_name,rcp,year,cap_km3_year),
+          paste0('P:/ene.model/NEST/desalination/projected_desalination_potential_km3_year_',reg,'.csv'),
+          row.names = F)
diff --git a/nest/message_ix_models/model/water/data/pre-processing/e-flow.py b/nest/message_ix_models/model/water/data/pre-processing/e-flow.py
new file mode 100644
index 0000000..d3a9432
--- /dev/null
+++ b/nest/message_ix_models/model/water/data/pre-processing/e-flow.py
@@ -0,0 +1,629 @@
+"""
+This script calculates envrionmental flows using monthly runoff (qtot from
+                                                                 ISIMIP)
+It calculates environmental flows using Variable Monthly Flow method
+
+"""
+
+
+import numpy as np
+import pandas as pd
+
+wd = r"P:\ene.model\NEST\hydrology\post-processed"
+
+# climate forcing
+scenarios = ["rcp26", "rcp60"]
+
+scen = "rcp26"
+# variable, for detailed symbols, refer to ISIMIP2b documentation
+variables = [
+    "qtot",  # total runoff
+    "dis",  # discharge
+    "qg",  # groundwater runoff
+    "qr",  # groundwater recharge
+]
+var = "qr"
+# climate model
+climmodels = ["gfdl-esm2m", "hadgem2-es", "ipsl-cm5a-lr", "miroc5"]
+climmodel = "miroc5"
+
+GCM = "mmean"  # 'dry'choose driest GCM else 'mmean' for multi model mean
+# else mmmean
+extreme_scen = "extreme"  # 'extreme' q90 else 'normal' q50
+
+# select interpolation method
+intp = "linear"
+
+env_flow = True
+
+
+dfs_dry = {}
+df_mmmean = {}
+dfs_wd = {}
+dfs_ann = {}
+df_env = {}
+eflow = {}
+eflow1 = {}
+eflow2 = {}
+dffinal = {}
+
+
+# %%
+def temporal_agg(data, extreme_scen):
+    # data = data.set_axis(pd.date_range(
+    #     '2006-1-1', '2099-12-31', freq = 'M'), axis=1)
+
+    if extreme_scen == "extreme":
+        data = data.rolling(240, min_periods=1, axis=1).quantile(
+            0.1, interpolation=intp
+        )
+        # data = data.rolling(
+        #     240, min_periods = 1, axis = 1).mean()
+    elif extreme_scen == "normal":
+        data = data.rolling(240, min_periods=1, axis=1).quantile(
+            0.5, interpolation=intp
+        )
+        # data = data.rolling(
+        #     240, min_periods = 1, axis = 1).mean()
+
+    for i in range(1, round(len(data.transpose()) / 60 + 1)):
+        start = 0 if i == 1 else end
+        end = i * 60
+
+        temp = data.transpose().iloc[start:end]
+
+        name = str(temp.index[-1].year)
+        temp = temp.groupby(temp.index.month).mean()
+        col_name = [name + "," + str(i) for i in range(1, 13)]
+
+        if i == 1:
+            final_data = temp.transpose()
+            final_data = final_data.set_axis(col_name, axis=1)
+        else:
+            temp_data = temp.transpose()
+            temp_data = temp_data.set_axis(col_name, axis=1)
+            final_data = pd.concat((final_data, temp_data), axis=1)
+
+    return final_data
+
+
+# %%
+for scen in scenarios:
+    # Reading data files from four GCMs
+    df = (
+        pd.read_csv(wd + f"\{var}_monthly_{climmodels[0]}_{scen}.csv")
+        .iloc[:, 8:]
+        .set_axis(pd.date_range("2006-1-1", "2099-12-31", freq="M"), axis=1)
+    )
+
+    df1 = (
+        pd.read_csv(wd + f"\{var}_monthly_{climmodels[0]}_{scen}.csv")
+        .iloc[:, 8:]
+        .set_axis(pd.date_range("2006-1-1", "2099-12-31", freq="M"), axis=1)
+    )
+
+    df2 = (
+        pd.read_csv(wd + f"\{var}_monthly_{climmodels[1]}_{scen}.csv")
+        .iloc[:, 8:]
+        .set_axis(pd.date_range("2006-1-1", "2099-12-31", freq="M"), axis=1)
+    )
+
+    df3 = (
+        pd.read_csv(wd + f"\{var}_monthly_{climmodels[2]}_{scen}.csv")
+        .iloc[:, 8:]
+        .set_axis(pd.date_range("2006-1-1", "2099-12-31", freq="M"), axis=1)
+    )
+
+    # df4 = pd.read_csv(
+    #     wd + f"\{var}_monthly_{climmodels[3]}_{scen}.csv")
+
+    #
+    # Final data frame after choosing multi model mean
+    df_mmmean[scen] = pd.concat([df1, df2, df3]).groupby(level=0).mean()
+
+    dffinal[scen] = temporal_agg(df_mmmean[scen], extreme_scen)
+    df1test = temporal_agg(df1, extreme_scen)
+    # data_1_mean = temporal_agg(df2,extreme_scen)
+    # data_2_mean = temporal_agg(df3,extreme_scen)
+    # data_3_mean = temporal_agg(df4,extreme_scen)
+
+    # new_cols = pd.to_datetime(data_0_mean.columns, format='%Y,%m')
+    # data_0_mean.columns = new_cols
+    # data_1_mean.columns = new_cols
+    # data_2_mean.columns = new_cols
+    # data_3_mean.columns = new_cols
+
+    # Choose driest value from each cell from four dataframes
+    # final_array = np.amin(np.vstack((np.array(data_0_mean).flatten(),
+    #                                  np.array(data_1_mean).flatten(),
+    #                                  np.array(data_2_mean).flatten(),
+    #                                  np.array(data_3_mean).flatten())),
+    #                       axis=0).reshape(210,228)
+
+    # df_drymodel = pd.DataFrame(final_array, columns = data_0_mean.columns)
+
+    # Choose direst GCM per basin
+
+    mean_values = np.stack(
+        (
+            data_0_mean.mean(axis=1).values,
+            data_1_mean.mean(axis=1).values,
+            data_2_mean.mean(axis=1).values,
+        )
+    )
+    # data_3_mean.mean(axis = 1).values))
+
+    final_array = []
+    for i, min_col in enumerate(mean_values.argmin(axis=0)):
+        if min_col == 0:
+            final_array.append(data_0_mean.iloc[i].values)
+        elif min_col == 1:
+            final_array.append(data_1_mean.iloc[i].values)
+        elif min_col == 2:
+            final_array.append(data_2_mean.iloc[i].values)
+        # else:
+        #     final_array.append(data_3_mean.iloc[i].values)
+    if GCM == "dry":
+        # Final data frame after choosing driest GCM
+        dfs_dry[scen] = pd.DataFrame(final_array, columns=data_0_mean.columns)
+
+        dfs_dry[scen].to_csv(
+            wd + f"\{var}_5ymonthly_{GCM}_{extreme_scen}_{scen}_{intp}.csv"
+        )
+        print("5 year Monthly values and quantiles processed")
+        # Store for next processing
+        df = dfs_dry[scen]
+        # Convert to 5 year annual values
+        new_cols = pd.to_datetime(dfs_dry[scen].columns, format="%Y,%m")
+        dfs_dry[scen].columns = new_cols
+        dfs_ann[scen] = dfs_dry[scen].resample("5Y", axis=1).mean()
+
+        dfs_ann[scen].to_csv(wd + f"\{var}_5y_{GCM}_{extreme_scen}_{scen}_{intp}.csv")
+
+        print("5 year annual values processed")
+
+    elif GCM == "mmean":
+        # Final data frame after choosing multi model mean
+        # df_mmmean[scen] = pd.concat(
+        #     [data_0_mean, data_1_mean, data_2_mean]).groupby(
+        #         level=0).mean()
+        dffinal[scen].to_csv(
+            wd + f"\{var}_5ymonthly_{GCM}_{extreme_scen}_{scen}_{intp}.csv"
+        )
+        print("5 year Monthly values and quantiles processed")
+        # Store for next processing
+        df = dffinal[scen]
+        # Convert to 5 year annual values
+        new_cols = pd.to_datetime(dffinal[scen].columns, format="%Y,%m")
+        dffinal[scen].columns = new_cols
+
+        dfs_ann[scen] = dffinal[scen].resample("5Y", axis=1).mean()
+
+        dfs_ann[scen].to_csv(wd + f"\{var}_5y_{GCM}_{extreme_scen}_{scen}_{intp}.csv")
+
+        print("5 year annual values processed")
+
+    # # Convert monthly values to wet and dry
+    # for z in range(len(df.columns) // 12):
+
+    #     col_start = 0 if z == 0 else col_end  # start col number
+    #     col_end = (z + 1) * 12  # ending col number
+    #     data = df.iloc[:, col_start:col_end]  # assigning relevant data
+    #     maxindex = data.idxmax(axis=1)  # getting max indexes
+
+    #     wet_name = "mean." + maxindex[0][:4] + "_wet"
+    #     dry_name = "mean." + maxindex[0][:4] + "_dry"
+
+    #     year_data = pd.DataFrame(
+    #         columns=[wet_name, dry_name]
+    #     )  # creaing and empty data frame
+
+    #     for i in range(len(maxindex)):
+    #         col_names = data.columns  # getting column names
+    #         get_col_index = col_names == maxindex[i]  # getting match column index
+    #         wet_start = (
+    #             0
+    #             if np.where(get_col_index)[0] - 3 < 0
+    #             else np.where(get_col_index)[0][0] - 3
+    #         )  # condition for negative col
+    #         wet_add = np.where(get_col_index)[0] - 3  # getting negative columns
+    #         wet_end = np.where(get_col_index)[0] + 2
+    #         wet_col = col_names[wet_start : wet_end[0] + 1].tolist()
+
+    #         # for adding negative columns
+    #         if np.where(get_col_index)[0] - 3 >= 0:
+    #             wet_col
+    #         else:
+    #             wet_col.extend(col_names[wet_add[0] :].tolist())
+
+    #         # for adding positive columns
+    #         if len(wet_col) < 6:
+    #             col_add = 6 - len(wet_col)
+    #             wet_col.extend(col_names[:col_add])
+
+    #         start_row = 0 if i == 0 else end_row
+    #         end_row = i + 1
+
+    #         wet_mean = data.iloc[start_row:end_row, :][wet_col].mean(axis=1)
+    #         dry_mean = (
+    #             data.iloc[start_row:end_row, :]
+    #             .loc[:, ~data.iloc[start_row:end_row, :].columns.isin(wet_col)]
+    #             .mean(axis=1)
+    #         )
+
+    #         row_array = np.array([wet_mean[i], dry_mean[i]])  # making an array
+
+    #         year_data.loc[len(year_data)] = row_array  # appending an array
+
+    #     if z == 0:
+    #         dfs_wd[scen] = year_data.copy()
+    #     else:
+    #         dfs_wd[scen] = pd.concat([dfs_wd[scen], year_data], axis=1)
+
+    # dfs_wd[scen].to_csv(wd + f"\{var}_wetdry_{GCM}_unprocessed_{scen}.csv")
+
+    # print("Wet Dry values processed")
+
+    # # Converting to 5 year annual
+    # for i in range(len(dfs_wd[scen].columns) // 2):
+    #     col_start = 0 if i == 0 else col_end
+    #     col_end = (i + 1) * 2
+    #     data = dfs_wd[scen].iloc[:, col_start:col_end]
+
+    #     name = data.columns[0][5:9]
+
+    #     wet_mul = data.iloc[:, 0]
+    #     dry_mul = data.iloc[:, 1]
+
+    #     mul_data = (wet_mul + dry_mul) / 2
+
+    #     if i == 0:
+    #         dfs_ann[scen] = pd.DataFrame(mul_data, columns=[name])
+    #     else:
+    #         tmp_data = pd.DataFrame(mul_data, columns=[name])
+    #         dfs_ann[scen] = pd.concat([dfs_ann[scen], tmp_data], axis=1)
+
+
+# %%
+var = "qtot"
+
+if env_flow:
+    for scen in scenarios:
+        # Reading data files from four GCMs
+        df = pd.read_csv(wd + f"\{var}_monthly_{climmodels[0]}_{scen}.csv")
+
+        df1 = pd.read_csv(wd + f"\{var}_monthly_{climmodels[0]}_{scen}.csv")
+
+        df2 = pd.read_csv(wd + f"\{var}_monthly_{climmodels[1]}_{scen}.csv")
+
+        df3 = pd.read_csv(wd + f"\{var}_monthly_{climmodels[2]}_{scen}.csv")
+
+        # Final data frame after choosing multi model mean
+        df_mmmean[scen] = pd.concat([df1, df2, df3]).groupby(level=0).mean()
+
+        df_env[scen] = df_mmmean[scen].iloc[:, 4:]
+        eflow[scen] = []
+
+        # df = data.iloc[:,5:]
+        for z in range(len(df_env[scen].columns) // 12):
+            col_start = 0 if z == 0 else col_end  # start col number
+            col_end = (z + 1) * 12  # ending col number
+            temp = df_env[scen].iloc[:, col_start:col_end]  # assigning relevant data
+
+            col_names = temp.columns  # getting column names
+            MAF = temp.mean(axis=1)  # take mean acorss
+
+            for j in range(len(temp.columns)):
+                temp.iloc[:, j] = np.where(
+                    temp.iloc[:, j] > 0.8 * MAF,
+                    temp.iloc[:, j] * 0.2,
+                    np.where(
+                        (temp.iloc[:, j] > 0.4 * MAF) & (temp.iloc[:, j] <= 0.8 * MAF),
+                        temp.iloc[:, j] * 0.45,
+                        temp.iloc[:, j] * 0.6,
+                    ),
+                )
+
+            if z == 0:
+                eflow[scen] = temp
+            else:
+                eflow[scen] = pd.concat((eflow[scen], temp), axis=1)
+
+        # Convert to 5 year annual values
+        new_cols = pd.to_datetime(eflow[scen].columns, format="sum.X%Y.%m.%d")
+        eflow[scen].columns = new_cols
+
+        df = eflow[scen]
+
+        for i in range(1, round(len(df.transpose()) / 60 + 1)):
+            start = 0 if i == 1 else end
+            end = i * 60
+
+            temp = df.transpose().iloc[start:end]
+
+            name = str(temp.index[-1].year)
+            temp = temp.groupby(temp.index.month).mean()
+            col_name = [name + "," + str(i) for i in range(1, 13)]
+
+            if i == 1:
+                eflow2[scen] = temp.transpose()
+                eflow2[scen] = eflow2[scen].set_axis(col_name, axis=1)
+            else:
+                eflow1[scen] = temp.transpose()
+                eflow1[scen] = eflow1[scen].set_axis(col_name, axis=1)
+                eflow2[scen] = pd.concat((eflow2[scen], eflow1[scen]), axis=1)
+
+        new_cols = pd.to_datetime(eflow2[scen].columns, format="%Y,%m")
+        eflow2[scen].columns = new_cols
+        eflow2[scen] = eflow2[scen].resample("5Y", axis=1).mean()
+
+        eflow2[scen].to_csv(wd + f"\e-flow_{var}_{scen}.csv")
+
+    df1 = pd.read_csv(wd + f"\e-flow_{variables[3]}_{scen}.csv")
+    df2 = pd.read_csv(wd + f"\e-flow_{variables[0]}_{scen}.csv")
+
+    df2.subtract(df1, axis=1).drop(columns="Unnamed: 0").to_csv(
+        wd + f"\e-flow__{scen}.csv"
+    )
+
+    print("Environmental Flow Values processed")
+# %%
+## Unporcessed monthly data dry and extreme for comparison
+
+
+for scen in scenarios:
+    # Reading data files from four GCMs
+    df = pd.read_csv(wd + f"\{var}_monthly_{climmodels[0]}_{scen}.csv")
+
+    df1 = pd.read_csv(wd + f"\{var}_monthly_{climmodels[0]}_{scen}.csv")
+
+    df2 = pd.read_csv(wd + f"\{var}_monthly_{climmodels[1]}_{scen}.csv")
+
+    df3 = pd.read_csv(wd + f"\{var}_monthly_{climmodels[2]}_{scen}.csv")
+
+    # def temporal_agg(data,extreme_scen):
+
+    #     data = data.iloc[:,8:].set_axis(pd.date_range(
+    #         '2006-1-1', '2099-12-31', freq = 'M'), axis=1)
+
+    #     for i in range(1, round(len(data.transpose())/60+1)):
+    #         start = 0 if i == 1 else end
+    #         end = i*60
+
+    #         temp = data.transpose().iloc[start:end]
+
+    #         name = str(temp.index[-1].year)
+    #         temp = temp.groupby(temp.index.month).mean()
+    #         col_name = [name + "," + str(i) for i in range(1, 13)]
+
+    #         if i == 1:
+    #             final_data = temp.transpose()
+    #             final_data = final_data.set_axis(col_name, axis=1)
+    #         else:
+    #             temp_data = temp.transpose()
+    #             temp_data = temp_data.set_axis(col_name, axis=1)
+    #             final_data = pd.concat((final_data, temp_data), axis = 1)
+
+    #     return final_data
+
+    # data_0_mean = temporal_agg(df1,extreme_scen)
+    # data_1_mean = temporal_agg(df2,extreme_scen)
+    # data_2_mean = temporal_agg(df3,extreme_scen)
+
+    data_0_mean = df1
+    data_1_mean = df2
+    data_2_mean = df3
+
+    # Choose direst GCM per basin
+
+    mean_values = np.stack(
+        (
+            data_0_mean.mean(axis=1).values,
+            data_1_mean.mean(axis=1).values,
+            data_2_mean.mean(axis=1).values,
+        )
+    )
+    # data_3_mean.mean(axis = 1).values))
+
+    final_array = []
+    for i, min_col in enumerate(mean_values.argmin(axis=0)):
+        if min_col == 0:
+            final_array.append(data_0_mean.iloc[i].values)
+        elif min_col == 1:
+            final_array.append(data_1_mean.iloc[i].values)
+        elif min_col == 2:
+            final_array.append(data_2_mean.iloc[i].values)
+        # else:
+        #     final_array.append(data_3_mean.iloc[i].values)
+    if GCM == "dry":
+        # Final data frame after choosing driest GCM
+        dfs_dry[scen] = pd.DataFrame(final_array, columns=data_0_mean.columns)
+
+        dfs_dry[scen].to_csv(wd + f"\{var}_1ymonthly_{GCM}_unprocessed_{scen}.csv")
+        # Store for next processing
+        df = dfs_dry[scen]
+
+    elif GCM == "mmean":
+        # Final data frame after choosing multi model mean
+        df_mmmean[scen] = (
+            pd.concat([data_0_mean, data_1_mean, data_2_mean]).groupby(level=0).mean()
+        )
+        df_mmmean[scen].to_csv(wd + f"\{var}_1ymonthly_{GCM}_unprocessed_{scen}.csv")
+        # Store for next processing
+
+        df = df_mmmean[scen]
+        d
+        df1.iloc[:, 8:]
+        # Convert to 5 year annual values
+        new_cols = pd.to_datetime(df_mmmean[scen].columns, format="%Y,%m")
+        df_mmmean[scen].columns = new_cols
+
+        dfs_ann[scen] = df_mmmean[scen].resample("5Y", axis=1).mean()
+
+        dfs_ann[scen].to_csv(wd + f"\{var}_5y_{GCM}_{extreme_scen}_{scen}_{intp}.csv")
+
+        df = df1.iloc[:, 8:]
+        df = df3.iloc[:, 8:]
+        new_cols = pd.to_datetime(df.columns, format="sum.X%Y.%m.%d")
+        df.columns = new_cols
+        df = df.resample("5Y", axis=1).mean()
+
+        df.to_csv(wd + f"\{var}_5y_{climmodels[2]}_{scen}.csv")
+
+    print("5 year Monthly values and quantiles processed")
+
+    # # Convert monthly values to wet and dry
+    # for z in range(len(df.columns) // 12):
+
+    #     col_start = 0 if z == 0 else col_end  # start col number
+    #     col_end = (z + 1) * 12  # ending col number
+    #     data = df.iloc[:, col_start:col_end]  # assigning relevant data
+    #     maxindex = data.idxmax(axis=1)  # getting max indexes
+
+    #     wet_name = "mean." + maxindex[0][:4] + "_wet"
+    #     dry_name = "mean." + maxindex[0][:4] + "_dry"
+
+    #     year_data = pd.DataFrame(
+    #         columns=[wet_name, dry_name]
+    #     )  # creaing and empty data frame
+
+    #     for i in range(len(maxindex)):
+    #         col_names = data.columns  # getting column names
+    #         get_col_index = col_names == maxindex[i]  # getting match column index
+    #         wet_start = (
+    #             0
+    #             if np.where(get_col_index)[0] - 3 < 0
+    #             else np.where(get_col_index)[0][0] - 3
+    #         )  # condition for negative col
+    #         wet_add = np.where(get_col_index)[0] - 3  # getting negative columns
+    #         wet_end = np.where(get_col_index)[0] + 2
+    #         wet_col = col_names[wet_start : wet_end[0] + 1].tolist()
+
+    #         # for adding negative columns
+    #         if np.where(get_col_index)[0] - 3 >= 0:
+    #             wet_col
+    #         else:
+    #             wet_col.extend(col_names[wet_add[0] :].tolist())
+
+    #         # for adding positive columns
+    #         if len(wet_col) < 6:
+    #             col_add = 6 - len(wet_col)
+    #             wet_col.extend(col_names[:col_add])
+
+    #         start_row = 0 if i == 0 else end_row
+    #         end_row = i + 1
+
+    #         wet_mean = data.iloc[start_row:end_row, :][wet_col].mean(axis=1)
+    #         dry_mean = (
+    #             data.iloc[start_row:end_row, :]
+    #             .loc[:, ~data.iloc[start_row:end_row, :].columns.isin(wet_col)]
+    #             .mean(axis=1)
+    #         )
+
+    #         row_array = np.array([wet_mean[i], dry_mean[i]])  # making an array
+
+    #         year_data.loc[len(year_data)] = row_array  # appending an array
+
+    #     if z == 0:
+    #         dfs_wd[scen] = year_data.copy()
+    #     else:
+    #         dfs_wd[scen] = pd.concat([dfs_wd[scen], year_data], axis=1)
+
+    # dfs_wd[scen].to_csv(wd + f"\{var}_wetdry_{GCM}_unprocessed_{scen}.csv")
+
+    # print("Wet Dry values processed")
+
+    # # Calculating environmental flows using wet and dry seasonal calculations
+    # for i in range(len(dfs_wd[scen].columns) // 2):
+    #     col_start = 0 if i == 0 else col_end
+    #     col_end = (i + 1) * 2
+    #     data = dfs_wd[scen].iloc[:, col_start:col_end]
+
+    #     name = data.columns[0][5:9]
+
+    #     wet_mul = data.iloc[:, 0]
+    #     dry_mul = data.iloc[:, 1]
+
+    #     mul_data = (wet_mul + dry_mul) / 2
+
+    #     if i == 0:
+    #         dfs_ann[scen] = pd.DataFrame(mul_data, columns=[name])
+    #     else:
+    #         tmp_data = pd.DataFrame(mul_data, columns=[name])
+    #         dfs_ann[scen] = pd.concat([dfs_ann[scen], tmp_data], axis=1)
+
+    # dfs_ann[scen].to_csv(wd + f"\{var}_5yann_{GCM}_unprocessed_{scen}.csv")
+
+    # print("5 year annual values processed")
+
+
+# %%
+# def env_flow(df, context, wetvaldev, dryvaldev, wetvalind, dryvalind):
+
+#     # reading basin mapping to countries
+#     FILE = f"basins_country_{context.regions}.csv"
+#     PATH = private_data_path("water", "delineation", FILE)
+
+#     basin = pd.read_csv(PATH)
+#     basin["BCU_name"] = "B" + basin["BCU_name"]
+
+#     bcu_names = df.basin
+
+#     for i in range(len(df.columns) // 2):
+
+#         col_start = 0 if i == 0 else col_end
+#         col_end = (i + 1) * 2
+
+#         data = df.iloc[:, col_start:col_end]
+#         data["BCU_name"] = bcu_names
+
+#         name = data.columns[0][5:9]
+
+#         value = []
+#         for j in df.basin.unique():
+#             temp = basin[basin["BCU_name"] == j]
+#             sizes = temp.pivot_table(index=["STATUS"], aggfunc="size")
+#             # if size of countries in basins is > 1
+#             if len(sizes) > 1:
+#                 # Developing Basins
+#                 if sizes["DEV"] > sizes["IND"] or sizes["DEV"] == sizes["IND"]:
+#                     temp2 = data[data["BCU_name"] == j]
+#                     wet_mul = temp2.iloc[:, 0] * wetvaldev
+#                     dry_mul = temp2.iloc[:, 1] * dryvaldev
+
+#                     mul_data = (wet_mul + dry_mul) / 2
+#                     value.append(mul_data.values)
+#                 # Developed basins
+#                 else:
+#                     temp2 = data[data["BCU_name"] == j]
+#                     wet_mul = temp2.iloc[:, 0] * wetvalind
+#                     dry_mul = temp2.iloc[:, 1] * dryvalind
+
+#                     mul_data = (wet_mul + dry_mul) / 2
+#                     value.append(mul_data.values)
+#             # no. of basin in country = 1
+#             else:
+#                 # Developing Basins
+#                 if sizes.index[0] == "DEV":
+#                     temp2 = data[data["BCU_name"] == j]
+#                     wet_mul = temp2.iloc[:, 0] * wetvaldev
+#                     dry_mul = temp2.iloc[:, 1] * dryvaldev
+
+#                     mul_data = (wet_mul + dry_mul) / 2
+#                     value.append(mul_data.values)
+#                 # Developed basins
+#                 else:
+#                     temp2 = data[data["BCU_name"] == j]
+#                     wet_mul = temp2.iloc[:, 0] * wetvalind
+#                     dry_mul = temp2.iloc[:, 1] * dryvalind
+
+#                     mul_data = (wet_mul + dry_mul) / 2
+#                     value.append(mul_data.values)
+
+#         if i == 0:
+#             PCT_data = pd.DataFrame(data=value, columns=[name])
+#         else:
+#             tmp_data = pd.DataFrame(data=value, columns=[name])
+#             PCT_data = pd.concat([PCT_data, tmp_data], axis=1)
+
+#     return PCT_data
diff --git a/nest/message_ix_models/model/water/data/pre-processing/generate_water_constraints.R b/nest/message_ix_models/model/water/data/pre-processing/generate_water_constraints.R
new file mode 100644
index 0000000..f1f4504
--- /dev/null
+++ b/nest/message_ix_models/model/water/data/pre-processing/generate_water_constraints.R
@@ -0,0 +1,697 @@
+# Clear memory and close all windows
+rm(list = ls())
+graphics.off()
+
+require(reshape)
+require(maptools)
+require(countrycode)
+require(raster)
+require(rgeos)
+require(rgdal)
+require(ncdf4)
+require(tidyverse)
+
+memory.limit(size=1e9)
+
+# message-ix-models path
+msg_ix_model = Sys.getenv("MESSAGE-IX-MODELS")
+## Set path data folder in message_ix working copy
+msg_data = paste0('P:/ene.model/NEST')
+data_path = path.expand(msg_data)
+
+crs = "+proj=longlat +datum=WGS84 +no_defs"
+
+# Country region mapping key
+country_region_map_key.df = data.frame( read.csv( paste( data_path, '/water_demands/country_region_map_key.csv',  sep = '/' ), stringsAsFactors=FALSE) )
+
+# ssp
+ssp = 2
+
+# Load municipal water demands and socioeconomic parameters from local drive
+dat.df = merge_recurse( lapply( seq(2010,2090,by=10), function(y){
+
+  dat.df = data.frame( readRDS( paste(data_path,'/harmonized_rcp_ssp_data/water_use_ssp2_rcp2_',y,'_data.Rda',sep='') ) )
+  dat.df = cbind( dat.df[ ,c('country_id', paste('xloc',y,sep= '.'), paste('yloc', y, sep= '.'), paste('urban_pop', y, sep= '.'), paste('rural_pop', y, sep= '.'), paste('urban_gdp', y, sep= '.'), paste('rural_gdp', y, sep= '.') ) ],
+                  rowSums( do.call( cbind, lapply( 1:12, function(m){ dat.df[ ,paste('urban_withdrawal',y,m,sep= '.') ] } ) ) ),
+                  rowSums( do.call( cbind, lapply( 1:12, function(m){ dat.df[ ,paste('rural_withdrawal',y,m,sep= '.') ] } ) ) ),
+                  rowSums( do.call( cbind, lapply( 1:12, function(m){ dat.df[ ,paste('urban_return',y,m,sep= '.') ] } ) ) ),
+                  rowSums( do.call( cbind, lapply( 1:12, function(m){ dat.df[ ,paste('rural_return',y,m,sep= '.') ] } ) ) ),
+                  rowSums( do.call( cbind, lapply( 1:12, function(m){ dat.df[ ,paste('urban_withdrawal',y,m,sep= '.') ] } ) ) ) / dat.df[, paste('urban_pop', y, sep= '.') ],
+                  rowSums( do.call( cbind, lapply( 1:12, function(m){ dat.df[ ,paste('rural_withdrawal',y,m,sep= '.') ] } ) ) ) / dat.df[, paste('rural_pop', y, sep= '.') ]	)
+  names(dat.df) = c('country_id', 'xloc', 'yloc', paste('urban_pop',y,sep= '.'), paste('rural_pop', y, sep= '.'), paste('urban_gdp', y, sep= '.'), paste('rural_gdp', y, sep= '.'), paste('urban_withdrawal', y, sep= '.'), paste('rural_withdrawal', y, sep= '.'), paste('urban_return', y, sep= '.'), paste('rural_return', y, sep= '.'), paste('urban_per_capita_withdrawal', y, sep= '.'), paste('rural_per_capita_withdrawal', y, sep= '.') )
+
+  # Make sure return flows don't exceed withdrawals
+  inds = which( unlist( dat.df[, paste('urban_return',y,sep= '.') ] ) > 0.92 * unlist(dat.df[, paste('urban_withdrawal', y, sep= '.') ] ) )
+  if( length(inds)>0 ){ dat.df[ inds, paste('urban_return',y,sep= '.') ] = dat.df[inds, paste('urban_withdrawal', y, sep= '.') ] * 0.92 }
+  inds = which( unlist( dat.df[, paste('rural_return',y,sep= '.') ] ) > 0.92 * unlist(dat.df[, paste('rural_withdrawal', y, sep= '.') ] ) )
+  if( length(inds)>0 ){ dat.df[ inds, paste('rural_return',y,sep= '.') ] = dat.df[inds, paste('rural_withdrawal', y, sep= '.') ] * 0.92 }
+
+  # Set NA to 0
+  dat.df[which(is.nan(dat.df[,12])),12] = 0
+  dat.df[which(is.nan(dat.df[,13])),13] = 0
+  dat.df[which(is.na(dat.df[,12])),12] = 0
+  dat.df[which(is.na(dat.df[,13])),13] = 0
+
+  return(dat.df)
+
+} ), by = c('xloc','yloc','country_id') )
+
+dat.df[is.na(dat.df)] = 0
+
+# year interpolation
+var_2_intpl = unique(gsub('\\..*','',names(dat.df)))
+var_2_intpl = var_2_intpl[!var_2_intpl %in% c('xloc','yloc','country_id')]
+initial_years = unique(gsub('.*\\.','',names(dat.df)))
+initial_years = as.numeric(initial_years[!initial_years %in% c('xloc','yloc','country_id')])
+yrs = initial_years
+
+yrs = seq(2010,2090,by=10)
+
+# Countries, regions and basins
+# Here we can add specification in case of a country model
+dat.df$country = sapply( dat.df$country_id, function(cc){ if( cc %in% country_region_map_key.df$UN_Code )
+{ return( country_region_map_key.df$ISO3[ which(country_region_map_key.df$UN_Code == cc) ] ) 
+}else{ return(NA) } } )
+dat.df$R11 = sapply( dat.df$country_id, function(cc){ if( cc %in% country_region_map_key.df$UN_Code )
+{ return( country_region_map_key.df$R11[ which(country_region_map_key.df$UN_Code == cc) ] ) 
+}else{ return(NA) } } )
+dat.df$R12 = sapply( dat.df$country_id, function(cc){ if( cc %in% country_region_map_key.df$UN_Code )
+{ return( country_region_map_key.df$R12[ which(country_region_map_key.df$UN_Code == cc) ] ) 
+}else{ return(NA) } } )
+# dat.df = dat.df[which(!is.na(dat.df$R11)),]
+
+#### Make spatial####
+dat.spdf = dat.df
+coordinates(dat.spdf) = ~ xloc + yloc
+
+# Add distance to coastline
+coast = readShapeLines('P:/ene.model/data/Water/ne_10m_coastline/ne_10m_coastline.shp')
+#distance2coast = sapply( 1:nrow(dat.spdf), function(x){  gDistance( dat.spdf[x,], coast ) } )
+#write.csv( distance2coast, 'C:/Users/parkinso/Documents/distance2coastssp2.csv', row.names=FALSE )
+distance2coast = data.frame( read.csv( 'P:/ene.model/data/Water/distance2coastssp2.csv', stringsAsFactors = FALSE ) )
+dat.spdf$distance2coast = distance2coast$x  # in degrees
+
+# number of grid cells close to the coast
+presence_of_coast = dat.spdf@data %>% filter(distance2coast <= 1.5) %>% 
+  mutate(num = as.numeric(1) ) %>% select(country,distance2coast,num) %>% 
+  group_by(country) %>% 
+  summarise(num = sum(num))
+write.csv(presence_of_coast,'P:/ene.model/NEST/desalination/coast.csv',row.names = F)
+  
+# some northern countries seem to not be included TOCHECK
+# a = dat.spdf[,'distance2coast']
+# a$distance2coast = as.numeric(a$distance2coast)/0.125
+# gridded(a) = TRUE
+# windows()
+# plot(a )
+# plot( coast, add=TRUE)
+# rm(a)
+
+#read in raster from ED. ATTENTION 2100 and 2110 missing
+nc = nc_open( paste0(data_path,'/water_scarcity/wsi_memean_ssp2_rcp6p0.nc'), verbose=FALSE)
+watstress.brick = brick( paste0(data_path,'/water_scarcity/wsi_memean_ssp2_rcp6p0.nc') )
+proj4string(dat.spdf) = proj4string(watstress.brick)
+for (i in seq(1:nlayers(watstress.brick)) ){
+    r <- raster(watstress.brick, layer=i)
+    proj4string(r) = proj4string(dat.spdf)
+    yr = as.numeric(gsub('X','',gsub('s','',names(r)) ))
+    if (yr %in% initial_years){
+      column_name = paste0('WSI.', yr )
+      dat.spdf@data[,column_name] = raster::extract(r,dat.spdf)
+      # there are 876 Na values, we set them = 0
+      dat.spdf@data[column_name][is.na(dat.spdf@data[column_name])] = 0
+      } else {}
+
+}
+
+dat.df = bind_cols(as.data.frame(dat.spdf@coords),dat.spdf@data)
+
+## END TESTING
+
+# OLD PART TO REMOVE
+# Add water stress level - map to gridded withdrawals
+
+# temp = readOGR('P:/ene.model/data/Water','water_scarcity',verbose=FALSE)
+# temp = SpatialPolygonsDataFrame(as(temp,'SpatialPolygons'), data.frame(temp), match.ID = TRUE)
+# all_bas = temp
+# all_bas@data$PID = as.character( all_bas$ECOREGION )
+# temp = temp[ which(as.character( temp@data$wtr_stress ) %in% c( 'Stress','High stress','Low stress') ), ]
+# temp@data$ID = 1
+# temp@data$ID2 = sapply( as.character( temp@data$wtr_stress ), function(st){ if(st == 'Stress'){return(0.75)}; if(st == 'High stress'){return(1)}; if(st == 'Low stress'){return(0.6)} } )
+# temp = temp[ ,c('ID2') ]
+# temp@data$ID = as.numeric( temp@data$ID2 )
+# temp@data$PID = row.names( temp )
+# proj4string(dat.spdf) = proj4string(temp)
+# dat.df = cbind( data.frame(dat.spdf), over( dat.spdf,temp ) )
+# rm( dat.spdf )
+# dat.df$ID[ which( is.na( dat.df$ID ) ) ] = 0
+
+## END PARt TO REMOVE
+
+#### GOVERNANCE ####
+length(unique(dat.df$country))
+gov.df = read.csv(paste0(data_path,'/governance/governance_obs_project.csv')) %>%
+  filter(!is.na(governance)) %>%
+  filter(scenario == paste0('SSP',ssp), year %in% initial_years ) %>%
+  dplyr::select(countrycode, year, governance)
+names(gov.df)
+mean_gov = mean(gov.df$governance)
+# missing countries, just add mean_gov
+to_add = data.frame(countrycode = c('AFG','AGO', 'ALB', 'ARE','MNE', 'MMR', 'PSE', 'QAT', 'TLS','TWN'), governance = mean_gov) %>%
+  crossing(year = unique(gov.df$year)) %>%
+  dplyr::select(countrycode,year,governance)
+
+gov.df = gov.df %>% bind_rows(to_add) %>%
+  dplyr::rename(gov = year, country = countrycode) %>%
+  spread(gov,governance, sep = '.')
+
+dat.df = dat.df %>% left_join(gov.df)
+#countries that have no governance values
+dat.df %>% filter(is.na(gov.2020)) %>%
+  distinct(country) # not anymore
+
+#re-add to dat.spdf
+dat.spdf = dat.df
+coordinates(dat.spdf) = ~ xloc + yloc
+
+#### Existing urban wastewater treatment ####
+# WARNING: loading this file or the 'over' function MIGHT TAKE HOURS
+# WRI aquedoct. 
+# https://www.wri.org/data/aqueduct-global-maps-30-data
+# wri_map.spdf = readShapeLines('P:/ene.model/NEST/water_access/Y2019M07D12_Aqueduct30_V01/wri.shp',
+#                               delete_null_obj=TRUE)
+wri_map.spdf = readOGR('P:/ene.model/NEST/water_access/Y2019M07D12_Aqueduct30_V01','wri',verbose=FALSE)
+
+wri_map.spdf = wri_map.spdf[c("udw_raw","usa_raw")]
+
+dat_simpl.spdf = dat.spdf[c('urban_gdp.2020','rural_gdp.2020')]
+dat_simpl.spdf$xcord = dat_simpl.spdf$xloc
+dat_simpl.spdf$ycord = dat_simpl.spdf$yloc
+
+# proj4string(dat_simpl.spdf) = proj4string(wri_map.spdf)
+new_proj = CRS("+proj=longlat +datum=WGS84 +no_defs")
+proj4string(dat_simpl.spdf) = new_proj
+proj4string(wri_map.spdf) = new_proj
+# dat_proj.spdf = spTransform(dat_simpl.spdf, new_proj)
+
+wri_over = over(dat_simpl.spdf,wri_map.spdf)
+identicalCRS(dat_simpl.spdf, wri_map.spdf)
+
+dat.spdf = cbind(dat.spdf , wri_over)
+
+vec = names(dat.spdf)[grepl('urban_gdp|rural_gdp|urban_pop|rural_pop',names(dat.spdf))]
+
+# with new data
+ww.df2 = dat.spdf@data %>%  
+  select(country_id,all_of(vec), udw_raw, usa_raw) %>% 
+  bind_cols(xcord = dat.spdf$xloc,ycord = dat.spdf$yloc) %>% 
+  mutate(income.2020 = (urban_gdp.2020 + rural_gdp.2020)/(urban_pop.2020 + rural_pop.2020)) %>%
+  # mutate(urb_income.2020 = (urban_gdp.2020)/(urban_pop.2020)) %>% 
+  # mutate(rur_income.2020 = (rural_gdp.2020)/(rural_pop.2020)) %>% 
+  filter(income.2020 > 0, !is.na(udw_raw), !is.na(usa_raw)) %>% 
+  mutate(udw_raw = (1 - udw_raw) * 100,
+         usa_raw = (1 - usa_raw) * 100) 
+
+ww.df2_plot = ww.df2 %>% group_by(country_id) %>% 
+  summarise(income.2020 = mean(income.2020),
+            udw_raw = mean(udw_raw),
+            usa_raw = mean(usa_raw)) %>% ungroup() %>% 
+  gather(udw_raw,usa_raw, key = 'type',value = 'value')
+
+# new
+x = c(33,ww.df2$income.2020)
+y = c(0.0001,ww.df2$udw_raw)
+r = nls(y ~ SSlogis(x,a,m,s))
+cp.a = 1
+cp.m = coef(r)[2]
+cp.s = coef(r)[3]
+# new
+y = c(0.0001,ww.df2$usa_raw)
+r = nls(y ~ SSlogis(x,a,m,s))
+tp.a = 1
+tp.m = coef(r)[2]
+tp.s = coef(r)[3]
+
+# Plot historical connection levels and model
+cpm = data.frame( x = seq( 1, 56000, by = 1000 ), y = SSlogis( seq( 1, 56000, by = 1000 ), 0.99, cp.m, cp.s ) )
+tpm = data.frame( x = seq( 1, 56000, by = 1000 ), y = SSlogis( seq( 1, 56000, by = 1000 ), 0.99, tp.m, tp.s ) )
+
+cpm$type = 'udw_raw'
+tpm$type = 'usa_raw'
+cpm = cpm %>% bind_rows(tpm)
+
+type_names <- list(
+  'udw_raw'="Water access",
+  'usa_raw'="Sanitation"
+)
+type_labeller <- function(variable,value){
+  return(type_names[value])
+}
+
+ggplot()+
+  geom_line(data = cpm,aes(x = x, y = 100*y) )+
+  geom_point(data = ww.df2_plot, aes(x = income.2020, y= value))+
+  ggtitle('Model vs country averages')+
+  facet_wrap(~type, labeller = type_labeller) +
+  xlab('Per capita Income in 2020 [USD/pc]')+
+  ylab('share of population with access [%]')+
+  theme_bw()+
+  theme(panel.grid.minor = element_blank())
+
+# piped water access / sewerage connection
+
+UR = c('urban','rural')
+ww_proj = function(ww.df2,UR,yy){ 
+  
+  t1 = rlang::sym(paste0(UR,'_gdp.',yy))
+  t2 = rlang::sym(paste0(UR,'_pop.',yy) )
+  inc_f = rlang::sym(paste0(UR,'_income.',yy) )
+  varc = rlang::sym(paste0(UR,'_connection_rate.',yy) )
+  vart = rlang::sym(paste0(UR,'_treatment_rate.',yy) )
+  decay = max( 0, 1 / length(yrs) * log( 1 / 0.01 ), na.rm=TRUE )
+  
+  ww.df2 %>% 
+    mutate("{UR}_income.{yy}" := pmax(0, !!t1 / !!t2 ,na.rm = T) ,
+           year = yy) %>% 
+    mutate(c0 = udw_raw / 100,
+           t0 = usa_raw / 100) %>% 
+    mutate(c_mod = c( SSlogis( !!inc_f, cp.a, cp.m, cp.s ) ),
+           t_mod = c( SSlogis( !!inc_f, tp.a, tp.m, tp.s ) )) %>% 
+    # 2020 is base year, 2010 need smaller values, 2010 need to changed at the end or not calculated
+    mutate(!!varc := pmin(0.999, if_else(year == 2010, 0.95*c0,
+                                         ( 1 +  ( c0 / c_mod - 1 ) * 
+                                             exp( -1 * decay * ( which(yrs == yy) - 2 ) ) ) * c_mod ) ),
+           !!vart := pmin(0.999, if_else(year == 2010, 0.95*t0, #assumption in 2010
+                                         ( 1 +  ( t0 / t_mod - 1 ) * 
+                                             exp( -1 * decay * ( which(yrs == yy) - 2 ) ) ) * t_mod ) ) ) %>% 
+    mutate(!!varc := if_else(!!inc_f == 0, 0, !!varc),
+           !!vart := if_else(!!inc_f == 0, 0, !!vart) )  %>% 
+    select(-year, -c0, -t0, -c_mod,-t_mod)
+}
+
+# recycling rate, 0.8 of treatment
+add_recycl = function(df,yy){
+  varr = rlang::sym(paste0('urban_treatment_rate.',yy) )
+  df %>%
+    mutate("recycling_rate.{yy}" := pmin(0.8, 0.8 * !!varr ))
+}
+
+#adding all estimation for future years
+ww_proj.df = ww.df2
+for (yy in yrs) {
+  for (uu in UR) {
+    ww_proj.df = ww_proj.df %>% left_join(
+      ww_proj(ww.df2,uu,yy) )
+  }
+  ww_proj.df = ww_proj.df %>% left_join(
+    add_recycl(ww_proj.df,yy) )
+}
+
+dat.df = dat.spdf@data %>% 
+  mutate(xcord = dat_simpl.spdf$xloc,
+         ycord = dat_simpl.spdf$yloc) %>% 
+  left_join(ww_proj.df %>% 
+              select(-all_of(vec),-country_id,-udw_raw, -usa_raw))
+
+dat.df[is.na(dat.df)] = 0
+
+#sanitation curve more on the right, but also urban wrt rural, not clear
+# for sanitation it makes a difference if I take country means of single point
+# for water access the difference is minimal
+
+#Plotting # of people without access: Urban and rural in 2020
+#re-add to dat.spdf
+dat.spdf = dat.df
+coordinates(dat.spdf) = ~ xcord + ycord
+
+ww_plot = dat.spdf[c("country",'urban_pop.2020','rural_pop.2020',"urban_connection_rate.2020",
+                     "rural_connection_rate.2020","urban_treatment_rate.2020",
+                     "rural_treatment_rate.2020")]
+ww_plot@data$urban_no_wa.2020 = ww_plot$urban_pop.2020 * (1 - ww_plot$urban_connection_rate.2020) *1e-6
+ww_plot@data$rural_no_wa.2020 = ww_plot$rural_pop.2020 * (1 - ww_plot$rural_connection_rate.2020) *1e-6
+ww_plot@data$urban_no_san.2020 = ww_plot$urban_pop.2020 * (1 - ww_plot$urban_treatment_rate.2020) *1e-6
+ww_plot@data$rural_no_san.2020 = ww_plot$rural_pop.2020 * (1 - ww_plot$rural_treatment_rate.2020) *1e-6
+
+totals_ww = ww_plot@data %>% summarise(urban_no_wa.2020 = sum(urban_no_wa.2020),
+                                       rural_no_wa.2020 = sum(rural_no_wa.2020),
+                                       urban_no_san.2020 = sum(urban_no_san.2020),
+                                       rural_no_san.2020 = sum(rural_no_san.2020))
+
+extent(ww_plot)
+proj4string(ww_plot) = c("+proj=longlat +datum=WGS84 +no_defs")
+empty.rs = extent(-180, 180, -90, 90)
+empty.rs <- raster(empty.rs)
+res(empty.rs)<- 0.25 # area is about 27*27 km
+projection(empty.rs)<-CRS(proj4string(ww_plot))
+
+rw.rs = rasterize(ww_plot,empty.rs,'rural_no_wa.2020',fun = sum)
+names(rw.rs) = 'value'
+# names(rw.rs) = 'Rural pop. w/o clean water access.2020 [millions]'
+uw.rs = rasterize(ww_plot,empty.rs,'urban_no_wa.2020',fun = sum)
+names(uw.rs) = 'value'
+# names(uw.rs) = 'Urban pop. w/o clean water access.2020 [millions]'
+tot_cw.rs = rw.rs + uw.rs
+
+rs.rs = rasterize(ww_plot,empty.rs,'rural_no_san.2020',fun = sum)
+names(rs.rs) = 'value'
+#names(rs.rs) = 'Rural pop. w/o clean sanitation [millions]'
+us.rs = rasterize(ww_plot,empty.rs,'urban_no_san.2020',fun = sum)
+names(us.rs) = 'value'
+#names(us.rs) = 'Urban pop. w/o clean sanitation.2020 [millions]'
+tot_s.rs = rs.rs + us.rs
+
+ww.br = brick(rw.rs,uw.rs,rs.rs,us.rs)
+plot(tot_cw.rs)
+
+# store for later
+bbbb = dat.df
+
+#rst = lapply( 1:2, function( add_SDG_constrain ){
+#for( add_SDG_constrain in 1:3 )
+
+dat.df = bbbb
+
+scn = 'baseline'
+# Minimum level of daily water demand for decent living - SSP1 manufacturing demands to incorporate expected water efficiency measures in SDG6 scenarios
+if( scn %in% c('sdg6') ){
+
+   fsc = SSP
+
+  # Add manufacturing demands - simple approach where national values generated previously are downscaled to urban areas based on population
+  mf_withdrawal.df = data.frame( read.csv( paste0(data_path,'/manufacturing_water_demand_results/national/IIASA_water_withdrawal_manufacturing_Static.csv'), stringsAsFactors = FALSE ) )
+  mf_return.df = data.frame( read.csv( paste0(data_path,'/manufacturing_water_demand_results/national/IIASA_water_return_manufacturing_Static.csv'), stringsAsFactors = FALSE ) )
+  mf_withdrawal.df = mf_withdrawal.df[which( as.character(mf_withdrawal.df$Scenario) == fsc & !is.na(mf_withdrawal.df$UN_Code)),]
+  mf_return.df = mf_return.df[which( as.character(mf_return.df$Scenario) == fsc & !is.na(mf_return.df$UN_Code)),]
+
+  # Downscale based on country
+  dat.df = cbind( dat.df, do.call( cbind, lapply( yrs, function(yy){
+    cnt = unique( as.character( dat.df$country ) )[ which( unique( as.character( dat.df$country ) ) %in% unique( as.character( mf_withdrawal.df$Country_Code ) ) ) ]
+    mfpc = unlist( sapply( cnt, function(cc){ return( mf_withdrawal.df[ which( as.character( mf_withdrawal.df$Scenario ) == fsc & as.character( mf_withdrawal.df$Country_Code ) == cc ), paste('X',yy,sep='') ] / sum(dat.df[ which( dat.df$country == cc ), paste('urban_pop',yy,sep= '.') ], na.rm = TRUE ) ) } ) )
+    tmp.df = data.frame( country = dat.df$country )
+    tmp.df$res = NA
+    for( x in cnt ){ tmp.df$res[ as.character( tmp.df$country ) == x ] = dat.df[ as.character( dat.df$country ) == x, paste('urban_pop',yy,sep= '.') ] * mfpc[x ] }
+    res = data.frame( tmp.df$res )
+    names(res) = paste('mf_withdrawal',yy,sep= '.')
+    row.names(res) = row.names(dat.df)
+    return(res) } ) ) )
+  dat.df = cbind( dat.df, do.call( cbind, lapply( yrs, function(yy){
+    cnt = unique( as.character( dat.df$country ) )[ which( unique( as.character( dat.df$country ) ) %in% unique( as.character( mf_return.df$Country_Code ) ) ) ]
+    mfpc = unlist( sapply( cnt, function(cc){ return( mf_return.df[ which( as.character( mf_return.df$Scenario ) == fsc & as.character( mf_return.df$Country_Code ) == cc ), paste('X',yy,sep='') ] / sum(dat.df[ which( dat.df$country == cc ), paste('urban_pop',yy,sep= '.') ], na.rm = TRUE ) ) } ) )
+    tmp.df = data.frame( country = dat.df$country )
+    tmp.df$res = NA
+    for( x in cnt ){ tmp.df$res[ as.character( tmp.df$country ) == x ] = dat.df[ as.character( dat.df$country ) == x, paste('urban_pop',yy,sep= '.') ] * mfpc[x ] }
+    res = data.frame( tmp.df$res )
+    names(res) = paste('mf_return',yy,sep= '.')
+    row.names(res) = row.names(dat.df)
+    return(res) } ) ) )
+
+  ch = scn
+
+  # Adjust demands in urban grid cells with less than 100 L per day and for rural cells with less than 50 L per day - only for post-2030
+  urb_min_sdg = 100 / 0.8
+  rur_min_sdg = 50 / 0.8
+  for( yyy in yrs[ yrs >= 2030 ] )
+  {
+
+    upo = dat.df[,paste('urban_pop',yyy,sep= '.')]
+    rpo = dat.df[,paste('rural_pop',yyy,sep= '.')]
+
+    upc = dat.df[,paste('urban_per_capita_withdrawal',yyy,sep= '.')] * 1e9 / 365 # convert from mcm per year to liters per day
+    rpc = dat.df[,paste('rural_per_capita_withdrawal',yyy,sep= '.')] * 1e9 / 365 # convert from mcm per year to liters per day
+    upc[is.na(upc)]=0
+    rpc[is.na(rpc)]=0
+
+    udxi = density( upc[ which( upc > 0 ) ], weights = upo[ which( upc > 0 ) ] / sum( upo[ which( upc > 0 ) ] ) )
+    rdxi = density( rpc[ which( rpc > 0 ) ], weights = rpo[ which( rpc > 0 ) ] / sum( rpo[ which( rpc > 0 ) ] ) )
+
+    upc[ which( upc < urb_min_sdg & upc > 0 ) ] = urb_min_sdg
+    rpc[ which( rpc < rur_min_sdg & rpc > 0 ) ] = rur_min_sdg
+
+    udxf = density( upc[ which( upc > 0 ) ], weights = upo[ which( upc > 0 ) ] / sum( upo[ which( upc > 0 ) ] ) )
+    rdxf = density( rpc[ which( rpc > 0 ) ], weights = rpo[ which( rpc > 0 ) ] / sum( rpo[ which( rpc > 0 ) ] ) )
+
+    # pdf(paste('C:/Users/parkinso/Documents/mwdens',yyy,'.pdf',sep=''))
+    # p1 = layout(matrix(c(1,2),1,2,byrow=TRUE),widths=c(0.45,0.45),heights=c(0.9,0.1))
+    # plot( udxi$x * 0.8 , cumsum(udxi$y)/max(cumsum(udxi$y)), type='l', col = 'black', xlab = 'Liters per day', ylab = 'Cumulative Population Distribution', main = paste( 'Urban',yyy, sep=' - ') )
+    # lines( udxf$x * 0.8, cumsum(udxf$y)/max(cumsum(udxf$y)), type='l', col = 'red' )
+    # plot( rdxi$x * 0.8, cumsum(rdxi$y)/max(cumsum(rdxi$y)), type='l', col = 'black', xlab = 'Liters per day', ylab = 'Cumulative Population Distribution', main = paste( 'Rural',yyy, sep=' - ') )
+    # lines( rdxf$x * 0.8, cumsum(rdxf$y)/max(cumsum(rdxf$y)), type='l', col = 'red' )
+    # legend( 'bottomright', bty = 'n', legend = c('Baseline', 'SDG6'), lty = 1, col = c('black','red') )
+    # dev.off()
+
+    # Convert units to km3 per year
+    dat.df[,paste('urban_per_capita_withdrawal',yyy,sep= '.')] = upc * 1e-9 * 365
+    dat.df[,paste('rural_per_capita_withdrawal',yyy,sep= '.')] = rpc * 1e-9 * 365
+
+    dat.df[,paste('urban_withdrawal',yyy,sep= '.')] = dat.df[, paste('urban_per_capita_withdrawal', yyy, sep= '.')] * dat.df[, paste('urban_pop', yyy, sep= '.')]
+    dat.df[,paste('rural_withdrawal',yyy,sep= '.')] = dat.df[, paste('rural_per_capita_withdrawal', yyy, sep= '.')] * dat.df[, paste('rural_pop', yyy, sep= '.')]
+
+      # previously sdg6_eff
+      # Further end-use conservation assumed combining a 10% reduction in withdrawals due to
+      # behavioral changes and 10% recycling
+      fct = 0.9 * 0.9
+
+      dat.df[,paste('urban_withdrawal',yyy,sep= '.')] = fct * dat.df[, paste('urban_withdrawal', yyy, sep= '.')]
+      dat.df[,paste('urban_return',yyy,sep= '.')] = fct * dat.df[, paste('urban_return', yyy, sep= '.')]
+      dat.df[,paste('rural_withdrawal',yyy,sep= '.')] = fct * dat.df[, paste('rural_withdrawal', yyy, sep= '.')]
+      dat.df[,paste('rural_return',yyy,sep= '.')] = fct * dat.df[, paste('rural_return', yyy, sep= '.')]
+      dat.df[,paste('mf_withdrawal',yyy,sep= '.')] = fct * dat.df[, paste('mf_withdrawal', yyy, sep= '.')]
+      dat.df[,paste('mf_return',yyy,sep= '.')] = fct * dat.df[, paste('mf_return', yyy, sep= '.')]
+
+
+    inds = which( unlist( dat.df[, paste('urban_return',yyy,sep= '.') ] ) > 0.92 * unlist(dat.df[, paste('urban_withdrawal', yyy, sep= '.') ] ) )
+    if( length(inds)>0 ){ dat.df[ inds, paste('urban_return',yyy,sep= '.') ] = dat.df[inds, paste('urban_withdrawal', yyy, sep= '.') ] * 0.92 }
+    inds = which( unlist( dat.df[, paste('rural_return',yyy,sep= '.') ] ) > 0.92 * unlist(dat.df[, paste('rural_withdrawal', yyy, sep= '.') ] ) )
+    if( length(inds)>0 ){ dat.df[ inds, paste('rural_return',yyy,sep= '.') ] = dat.df[inds, paste('rural_withdrawal', yyy, sep= '.') ] * 0.92 }
+    inds = which( unlist( dat.df[, paste('mf_return',yyy,sep= '.') ] ) > 0.92 * unlist(dat.df[, paste('mf_withdrawal', yyy, sep= '.') ] ) )
+    if( length(inds)>0 ){ dat.df[ inds, paste('mf_return',yyy,sep= '.') ] = dat.df[inds, paste('mf_withdrawal', yyy, sep= '.') ] * 0.92 }
+
+  }
+
+
+}else{ # baseline
+
+
+  # Add manufacturing demands - simple approach where national values generated previously are downscaled to urban areas based on population
+  mf_withdrawal.df = data.frame( read.csv( paste0(data_path,'/manufacturing_water_demand_results/national/IIASA_water_withdrawal_manufacturing_Static.csv'), stringsAsFactors = FALSE ) )
+  mf_return.df = data.frame( read.csv( paste0(data_path,'/manufacturing_water_demand_results/national/IIASA_water_return_manufacturing_Static.csv'), stringsAsFactors = FALSE ) )
+  mf_withdrawal.df = mf_withdrawal.df[which( as.character(mf_withdrawal.df$Scenario) == 'SSP2' & !is.na(mf_withdrawal.df$UN_Code)),]
+  mf_return.df = mf_return.df[which( as.character(mf_return.df$Scenario) == 'SSP2' & !is.na(mf_return.df$UN_Code)),]
+
+  dat.df = cbind( dat.df, do.call( cbind, lapply( yrs, function(yy){
+    cnt = unique( as.character( dat.df$country ) )[ which( unique( as.character( dat.df$country ) ) %in% unique( as.character( mf_withdrawal.df$Country_Code ) ) ) ]
+    mfpc = unlist( sapply( cnt, function(cc){ return( mf_withdrawal.df[ which( as.character( mf_withdrawal.df$Scenario ) == 'SSP2' & as.character( mf_withdrawal.df$Country_Code ) == cc ), paste('X',yy,sep='') ] / sum(dat.df[ which( dat.df$country == cc ), paste('urban_pop',yy,sep= '.') ], na.rm = TRUE ) ) } ) )
+    tmp.df = data.frame( country = dat.df$country )
+    tmp.df$res = NA
+    for( x in cnt ){ tmp.df$res[ as.character( tmp.df$country ) == x ] = dat.df[ as.character( dat.df$country ) == x, paste('urban_pop',yy,sep= '.') ] * mfpc[x ] }
+    res = data.frame( tmp.df$res )
+    names(res) = paste('mf_withdrawal',yy,sep= '.')
+    row.names(res) = row.names(dat.df)
+    return(res) } ) ) )
+  dat.df = cbind( dat.df, do.call( cbind, lapply( yrs, function(yy){
+    cnt = unique( as.character( dat.df$country ) )[ which( unique( as.character( dat.df$country ) ) %in% unique( as.character( mf_return.df$Country_Code ) ) ) ]
+    mfpc = unlist( sapply( cnt, function(cc){ return( mf_return.df[ which( as.character( mf_return.df$Scenario ) == 'SSP2' & as.character( mf_return.df$Country_Code ) == cc ), paste('X',yy,sep='') ] / sum(dat.df[ which( dat.df$country == cc ), paste('urban_pop',yy,sep= '.') ], na.rm = TRUE ) ) } ) )
+    tmp.df = data.frame( country = dat.df$country )
+    tmp.df$res = NA
+    for( x in cnt ){ tmp.df$res[ as.character( tmp.df$country ) == x ] = dat.df[ as.character( dat.df$country ) == x, paste('urban_pop',yy,sep= '.') ] * mfpc[x ] }
+    res = data.frame( tmp.df$res )
+    names(res) = paste('mf_return',yy,sep= '.')
+    row.names(res) = row.names(dat.df)
+    return(res) } ) ) )
+
+  ch = scn
+
+} # end baseline
+# CHECK TILL HERE
+
+#### format OUTPUT ####
+cccc = dat.df
+#one can run also simply for a country
+# reg = 'ZMB'
+for(reg in c('R11','R12')){
+  #### Make spatial####
+  dat.spdf = cccc
+  coordinates(dat.spdf) = ~ xcord + ycord
+  #load shapefile
+  basin_by_region.spdf = readOGR('P:/ene.model/NEST/delineation/data/delineated_basins_new',
+                                paste0('basins_by_region_simpl_',reg), verbose=FALSE)
+  basin_by_region.spdf = gBuffer(basin_by_region.spdf, byid=TRUE, width=0.2)
+  
+  proj4string(dat.spdf) = basin_by_region.spdf@proj4string
+  
+  empty_points = SpatialPoints(coords = coordinates(dat.spdf))
+  proj4string(empty_points) = proj4string(dat.spdf)
+  
+  inters = over(empty_points,basin_by_region.spdf)
+  
+  inters_cols = inters %>% select(NAME,REGION,BCU_name)
+  #two alternatives
+  dat.spdf@data = dat.spdf@data %>% bind_cols(inters_cols)
+  print(paste0(sum(is.na(inters$BCU_name)),' points have no basin infomration, are removed'))
+  #
+  dat.df = dat.spdf@data
+  dat.df = dat.df[!is.na(dat.df$BCU_name),]
+  dat.df$region = dat.df$BCU_name
+  
+  ysr_full = c(yrs)
+  
+  #including manufacturing
+  regional_urban_withdrawal.df = data.frame( do.call( cbind, lapply( unique( dat.df$region ), function(rr){ sapply( ysr_full, function(yy){  sum(unlist( dat.df[ which( dat.df$region == rr ) , c( paste( 'urban_withdrawal', yy, sep= '.') ) ] ) , na.rm=TRUE ) + sum(unlist(dat.df[which(dat.df$region == rr ) , c(paste('mf_withdrawal', yy, sep= '.') ) ] ) , na.rm=TRUE ) } ) } ) ) )
+  names(regional_urban_withdrawal.df) = unique( dat.df$region )
+  row.names(regional_urban_withdrawal.df) = ysr_full
+  regional_urban_withdrawal.df = round( regional_urban_withdrawal.df, digits = 3 )
+  
+  #initialize var_list
+  var_list = list('regional_urban_withdrawal' = regional_urban_withdrawal.df)
+
+  # urban return flows
+  regional_urban_return.df = data.frame( do.call( cbind, lapply( unique( dat.df$region ), function(rr){ sapply( ysr_full, function(yy){  sum(unlist( dat.df[ which( dat.df$region == rr ) , c( paste( 'urban_return', yy, sep= '.') ) ] ) , na.rm=TRUE ) + sum(unlist(dat.df[which(dat.df$region == rr ) , c(paste('mf_return', yy, sep= '.') ) ] ) , na.rm=TRUE ) } ) } ) ) )
+  names(regional_urban_return.df) = unique( dat.df$region )
+  row.names(regional_urban_return.df) = ysr_full
+  regional_urban_return.df = round( regional_urban_return.df, digits = 3 )
+  var_list = append(var_list,list('regional_urban_return' = regional_urban_return.df) )
+
+  # without manufacturing
+  regional_urban_withdrawal2.df = data.frame( do.call( cbind, lapply( unique( dat.df$region ), function(rr){ sapply( ysr_full, function(yy){  sum(unlist( dat.df[ which( dat.df$region == rr ) , c( paste( 'urban_withdrawal', yy, sep= '.') ) ] ) , na.rm=TRUE ) } ) } ) ) )
+  names(regional_urban_withdrawal2.df) = unique( dat.df$region )
+  row.names(regional_urban_withdrawal2.df) = ysr_full
+  regional_urban_withdrawal2.df = round( regional_urban_withdrawal2.df, digits = 3 )
+  var_list = append(var_list,list('regional_urban_withdrawal2' = regional_urban_withdrawal2.df))
+
+  regional_urban_return2.df = data.frame( do.call( cbind, lapply( unique( dat.df$region ), function(rr){ sapply( ysr_full, function(yy){  sum(unlist( dat.df[ which( dat.df$region == rr ) , c( paste( 'urban_return', yy, sep= '.') ) ] ) , na.rm=TRUE ) } ) } ) ) )
+  names(regional_urban_return2.df) = unique( dat.df$region )
+  row.names(regional_urban_return2.df) = ysr_full
+  regional_urban_return2.df = round( regional_urban_return2.df, digits = 3 )
+  var_list = append(var_list,list('regional_urban_return2' = regional_urban_return2.df))
+
+  # manufacturing withdrawals
+  regional_manufacturing_withdrawal.df = data.frame( do.call( cbind, lapply( unique( dat.df$region ), function(rr){ sapply( ysr_full, function(yy){  sum(unlist( dat.df[ which( dat.df$region == rr ) , c( paste( 'mf_withdrawal', yy, sep= '.') ) ] ) , na.rm=TRUE ) } ) } ) ) )
+  names(regional_manufacturing_withdrawal.df) = unique( dat.df$region )
+  row.names(regional_manufacturing_withdrawal.df) = ysr_full
+  regional_manufacturing_withdrawal.df = round( regional_manufacturing_withdrawal.df, digits = 3 )
+  var_list = append(var_list,list('regional_manufacturing_withdrawal' = regional_manufacturing_withdrawal.df))
+
+  # manufacturing return flows
+  regional_manufacturing_return.df = data.frame( do.call( cbind, lapply( unique( dat.df$region ), function(rr){ sapply( ysr_full, function(yy){ sum(unlist( dat.df[ which( dat.df$region == rr ) , c( paste( 'mf_return', yy, sep= '.') ) ] ) , na.rm=TRUE ) } ) } ) ) )
+  names(regional_manufacturing_return.df) = unique( dat.df$region )
+  row.names(regional_manufacturing_return.df) = ysr_full
+  regional_manufacturing_return.df = round( regional_manufacturing_return.df, digits = 3 )
+  var_list = append(var_list,list('regional_manufacturing_return' = regional_manufacturing_return.df))
+
+  # urban connection rate
+  regional_urban_connection_rate.df = data.frame( do.call( cbind, lapply( unique( dat.df$region ), function(rr){ sapply( ysr_full, function(yy){ sum(c( unlist( dat.df[ which( dat.df$region == rr ) , c( paste( 'urban_pop', yy, sep= '.') ) ] )  * unlist(dat.df[which(dat.df$region == rr ) , c(paste('urban_connection_rate', yy, sep= '.') ) ] ) ) , na.rm=TRUE ) / sum(c(unlist(dat.df[which(dat.df$region == rr ) , c(paste('urban_pop', yy, sep= '.') ) ] ) ) , na.rm = TRUE ) } ) } ) ) )
+  names(regional_urban_connection_rate.df) = unique( dat.df$region )
+  row.names(regional_urban_connection_rate.df) = ysr_full
+  regional_urban_connection_rate.df = round( regional_urban_connection_rate.df, digits = 3 )
+  var_list = append(var_list,list('regional_urban_connection_rate' = regional_urban_connection_rate.df))
+
+  #urban treatment rate
+  regional_urban_treatment_rate.df = data.frame( do.call( cbind, lapply( unique( dat.df$region ), function(rr){ sapply( ysr_full, function(yy){  sum( c( unlist( dat.df[ which( dat.df$region == rr ) , c( paste( 'urban_pop', yy, sep='.' ) ) ] )  * unlist( dat.df[ which( dat.df$region == rr ) , c( paste( 'urban_treatment_rate', yy, sep='.' ) ) ] ) ) , na.rm=TRUE ) / sum( c( unlist( dat.df[ which( dat.df$region == rr ) , c( paste( 'urban_pop', yy, sep='.' ) ) ] ) ) , na.rm = TRUE ) } ) } ) ) )
+  names(regional_urban_treatment_rate.df ) = unique( dat.df$region )
+  row.names(regional_urban_treatment_rate.df) = ysr_full
+  regional_urban_treatment_rate.df = round( regional_urban_treatment_rate.df, digits = 3 )
+  var_list = append(var_list,list('regional_urban_treatment_rate' = regional_urban_treatment_rate.df))
+  
+  ## COMMENTED FOR NOW
+  # regional_urban_desalination_rate.df =  data.frame( round(  do.call( cbind, lapply( unique( dat.df$region ), function(rr){ sapply( ysr_full, function(yy){ sum( unlist( dat.df[ which( dat.df$region == rr ) , c( paste( 'desalinated', yy, sep='.' ) ) ] ) , na.rm=TRUE ) / sum( unlist( dat.df[ which( dat.df$region == rr ) , c( paste( 'urban_withdrawal', yy, sep='.' ) ) ] ) + unlist( dat.df[ which( dat.df$region == rr ) , c( paste( 'mf_withdrawal', yy, sep='.' ) ) ] ) , na.rm=TRUE ) } ) } ) ), digits = 3 ) )
+  # names(regional_urban_desalination_rate.df) = unique( dat.df$region )
+  # row.names(regional_urban_desalination_rate.df) = ysr_full
+  # regional_urban_desalination_rate.df = round( regional_urban_desalination_rate.df, digits = 3 )
+  # regional_urban_desalination_rate.df[ regional_urban_desalination_rate.df < 0.005 ] = 0.005
+  # var_list = append(var_list,list('regional_urban_desalination_rate' = regional_urban_desalination_rate.df))
+  # # END COMMENTED PART
+
+  # urban recycling rate
+  regional_urban_recycling_rate.df = data.frame( round(do.call( cbind, lapply( unique( dat.df$region ), function(rr){ 
+    sapply( ysr_full, function(yy){ 
+      sum( (unlist(dat.df[which(dat.df$region == rr ) , c(paste('urban_withdrawal', yy, sep= '.') ) ] ) + 
+              unlist(dat.df[which(dat.df$region == rr ) , c(paste('mf_withdrawal', yy, sep= '.') ) ] )) * 
+             unlist( dat.df[ which( dat.df$region == rr ) , c( paste( 'recycling_rate', yy, sep= '.') ) ] ) , na.rm=TRUE ) / 
+        sum(unlist(dat.df[which(dat.df$region == rr ) , c(paste('urban_withdrawal', yy, sep= '.') ) ] ) + 
+              unlist(dat.df[which(dat.df$region == rr ) , c(paste('mf_withdrawal', yy, sep= '.') ) ] ) , na.rm=TRUE )
+      } ) } ) ), digits = 3 ) )
+  names(regional_urban_recycling_rate.df) = unique( dat.df$region )
+  row.names(regional_urban_recycling_rate.df) = ysr_full
+  regional_urban_recycling_rate.df = round( regional_urban_recycling_rate.df, digits = 3 )
+  regional_urban_recycling_rate.df[ regional_urban_recycling_rate.df < 0.005 ] = 0.005
+  var_list = append(var_list,list( 'regional_urban_recycling_rate' = regional_urban_recycling_rate.df))
+
+  # rural connection rate
+  regional_rural_connection_rate.df = data.frame( do.call( cbind, lapply( unique( dat.df$region ), function(rr){ sapply( ysr_full, function(yy){  sum(c( unlist( dat.df[ which( dat.df$region == rr ) , c( paste( 'rural_pop', yy, sep= '.') ) ] )  * unlist(dat.df[which(dat.df$region == rr ) , c(paste('rural_connection_rate', yy, sep= '.') ) ] ) ) , na.rm=TRUE ) / sum(c(unlist(dat.df[which(dat.df$region == rr ) , c(paste('rural_pop', yy, sep= '.') ) ] ) ) , na.rm = TRUE ) } ) } ) ) )
+  names(regional_rural_connection_rate.df) = unique( dat.df$region )
+  row.names(regional_rural_connection_rate.df) = ysr_full
+  regional_rural_connection_rate.df = round( regional_rural_connection_rate.df, digits = 3 )
+  var_list = append(var_list,list('regional_rural_connection_rate' = regional_rural_connection_rate.df))
+
+  # rural treatment rates
+  regional_rural_treatment_rate.df = data.frame( do.call( cbind, lapply( unique( dat.df$region ), function(rr){ sapply( ysr_full, function(yy){  sum(c( unlist( dat.df[ which( dat.df$region == rr ) , c( paste( 'rural_pop', yy, sep= '.') ) ] )  * unlist(dat.df[which(dat.df$region == rr ) , c(paste('rural_treatment_rate', yy, sep= '.') ) ] ) ) , na.rm=TRUE ) / sum(c(unlist(dat.df[which(dat.df$region == rr ) , c(paste('rural_pop', yy, sep= '.') ) ] ) ) , na.rm = TRUE ) } ) } ) ) )
+  names(regional_rural_treatment_rate.df) = unique( dat.df$region )
+  row.names(regional_rural_treatment_rate.df) = ysr_full
+  regional_rural_treatment_rate.df = round( regional_rural_treatment_rate.df, digits = 3 )
+  var_list = append(var_list,list('regional_rural_treatment_rate' = regional_rural_treatment_rate.df))
+
+  # rural withdrawals
+  regional_rural_withdrawal.df = data.frame( do.call( cbind, lapply( unique( dat.df$region ), function(rr){ sapply( ysr_full, function(yy){  sum(unlist( dat.df[ which( dat.df$region == rr ) , c( paste( 'rural_withdrawal', yy, sep= '.') ) ] ) , na.rm=TRUE ) } ) } ) ) )
+  names(regional_rural_withdrawal.df) = unique( dat.df$region )
+  row.names(regional_rural_withdrawal.df) = ysr_full
+  regional_rural_withdrawal.df = round( regional_rural_withdrawal.df, digits = 3 )
+  var_list = append(var_list,list('regional_rural_withdrawal' = regional_rural_withdrawal.df))
+
+  # return flows, only SDG6
+  regional_rural_return.df = data.frame(
+    do.call( cbind, lapply( unique( dat.df$region ), function(rr){
+      sapply( ysr_full, function(yy){
+        sum(unlist( dat.df[ which( dat.df$region == rr ) ,
+                             c( paste( 'rural_return', yy, sep= '.') ) ] ) , na.rm=TRUE ) } ) } ) ) )
+  names(regional_rural_return.df) = unique( dat.df$region )
+  row.names(regional_rural_return.df) = ysr_full
+  regional_rural_return.df = round( regional_rural_return.df, digits = 3 )
+  var_list = append(var_list,list('regional_rural_return' = regional_rural_return.df))
+
+  # ADDING FINAL TIME STEPS AND SAVE csv #
+  rates = names(var_list)[grepl('_rate',names(var_list))]
+  for (v in names(var_list)){
+    df = as.data.frame(var_list[v][[1]])
+    df[is.na(df)] = 0
+    n=ncol(df)+1
+    
+    for_lm = df %>%
+      mutate(year = row.names(.)) %>%
+      select(year,everything()) %>%
+      filter(year >= 2060) %>%
+      gather(key = 'region',value = 'value',2:n) %>%
+      group_by(region) %>%
+      mutate(int = lm(value ~ year)$coefficients[1],
+             coef = lm(value ~ year)$coefficients[2]) %>%
+      ungroup()
+    # add 2100, 2110
+    new_vals = for_lm %>% select(-year,-value) %>%
+      distinct() %>%
+      mutate(`2100` := int + (2100-2060)/10 * coef ,
+             `2110` := int + (2110-2060)/10 * coef  ) %>%
+      mutate(`2100` =  pmax(0,`2100` ),
+             `2110` = pmax(0,`2110`) ) 
+    
+    if (v %in% rates) {
+      new_vals = new_vals %>% 
+        mutate(`2100` =  pmin(0.99,`2100` ),
+               `2110` = pmin(0.99,`2110`)) %>% 
+        gather(key = year, value = 'value',`2100`,`2110`)
+    }else{ new_vals = new_vals %>% 
+      gather(key = year, value = 'value',`2100`,`2110`)}
+    
+    # add back to df
+    final_df = df %>% bind_rows(
+      new_vals %>% select(year,region,value) %>%
+        spread(region,value) %>%
+        tibble::column_to_rownames(var = 'year')
+    )
+  # Write in forlders according to region definition
+    write.csv( final_df,
+               paste0( data_path, '/water_demands/harmonized/',reg,'/ssp',ssp,'_',
+                      v,'_', ch, '.csv' ), row.names = TRUE )
+
+  }
+
+} # for R11,R12
+
diff --git a/nest/message_ix_models/model/water/data/pre-processing/generate_water_constraints_monthly.r b/nest/message_ix_models/model/water/data/pre-processing/generate_water_constraints_monthly.r
new file mode 100644
index 0000000..87b8aa3
--- /dev/null
+++ b/nest/message_ix_models/model/water/data/pre-processing/generate_water_constraints_monthly.r
@@ -0,0 +1,251 @@
+# generate water demands monthly - from NEST script demands.r
+require(tidyverse)
+require(ncdf4)
+require(rgdal)
+require(raster)
+require(rgeos)
+require(ggmap)
+require(rasterVis)
+require(countrycode)
+memory.limit(size=1e9)
+
+ssps= c( 'SSP1', 'SSP2', 'SSP3', 'SSP4', 'SSP5' )
+n_days = c(31,28,31,30,31,30,31,31,30,31,30,31)
+
+msg_data = paste0('P:/ene.model/NEST')
+data_path = path.expand(msg_data)
+
+crs = "+proj=longlat +datum=WGS84 +no_defs"
+
+# ssp, we can also run 1 and 5, but need to retreive the data from older work
+ssp = c(2)
+
+# Grab the basin boundaries
+reg = 'ZMB'
+basin.spdf = readOGR('P:/ene.model/NEST/delineation/data/delineated_basins_new',
+                               paste0('basins_by_region_simpl_',reg), verbose=FALSE)
+basin.spdf2 = readOGR( paste( getwd(), 'input', sep = '/' ), 'Indus_bcu', verbose = FALSE )
+
+basin.spdf = spTransform(basin.spdf, CRS("+proj=longlat"))
+basin.spdf@data$PID = as.character( basin.spdf@data$BCU_name )
+basin.spdf@data$CNTRY_ID = unlist( strsplit( basin.spdf@data$PID, '_' ) )[ seq(1,2*length(basin.spdf),by=2) ]
+basin.sp = gUnaryUnion( basin.spdf )
+basin.sp = SpatialPolygons(list(Polygons(Filter(function(f){f@ringDir==1},basin.sp@polygons[[1]]@Polygons),ID=1)))
+buff.sp = gBuffer( basin.sp, width = 0.1 ) 
+buff2.sp = gBuffer( basin.sp, width = 10 ) 
+proj4string(basin.sp) = proj4string(basin.spdf)
+proj4string(buff.sp) = proj4string(basin.spdf)
+proj4string(buff2.sp) = proj4string(basin.spdf)
+
+### Water demands from gridded indicators - using harmonized spatial datasets from Parkinson et al. 2016 A spatially explicit ...	
+# Simulation horizon
+yy = c(2015,seq(2020,2060,by=10))
+
+# import national manufacturing demand projections 
+national_manufacturing_withdrawal.df = data.frame(read.csv(paste0(data_path,"/manufacturing_water_demand_results/national/IIASA_water_withdrawal_manufacturing_Static.csv"), header=TRUE, sep=",", stringsAsFactors=F, as.is=T))
+national_manufacturing_return.df = data.frame(read.csv(paste0(data_path,"/manufacturing_water_demand_results/national/IIASA_water_return_manufacturing_Static.csv"), header=TRUE, sep=",", stringsAsFactors=F, as.is=T))
+
+# National GDP projections for scaling the manufacturing demands
+national_gdp.df = data.frame(read.csv(paste0(data_path,"/GDP/OECD_SSP_GDP_PPP.csv"), header=TRUE, sep=",", stringsAsFactors=F, as.is=T)) %>% # in billions
+  filter( Region %in% c(reg) ) %>%
+  dplyr::select( Scenario, Region, paste('X',seq(2015,2060,by=5),sep='') )
+
+# hours in each month
+hr = c(31,28.25,31,30,31,30,31,31,30,31,30,31) * 24 
+
+# go through each ssp and year and create flat dataframe containing the demand parameters
+demands.df = bind_rows( lapply( ssp, function(ss){ 
+  
+  bind_rows( lapply( 1:length(yy), function(y){
+    
+    # haven't generated the data for each SSP - this is the mapping to the RCPs - could be updated to reflect different RCPs
+    if(ss %in% c( 1,3,4 )){rr=1}
+    if(ss==2){ rr=2 }
+    if(ss==5){ rr=4 }
+    
+    # 2015 not included in the data so using 2010
+    if( yy[y]==2015 ){ yy2=2010 }else{ yy2=yy[y] }
+    
+    # import the harmonzied gridded data and rename to make generic headings, add PID by overlaying the polygons, and then sum to PID-level
+    dat.df = data.frame( readRDS( paste0(data_path,'/harmonized_rcp_ssp_data/water_use_ssp',ss,'_rcp',rr,'_',yy2,'_data.Rda') ) )
+    cols = c( 'xloc', 'yloc', 'urban_pop', 'rural_pop', 'urban_gdp', 'rural_gdp', 'mean_tas', 'urban_withdrawal', 'rural_withdrawal', 'urban_return', 'rural_return' )
+    for( nm in cols ){ 
+      ind = which( grepl( nm, names( dat.df ) ) )
+      if( length( ind ) > 1 ){ nm2 = paste( nm, 1:12,sep='.' )}else{ nm2 = nm }
+      names(dat.df)[ ind ] = nm2 }
+    dat.df = dat.df[,grepl( paste(cols,collapse='|'), names(dat.df) ) ]
+    dat.df = dat.df %>%
+      'coordinates<-'(~xloc+yloc) %>%
+      'gridded<-'(TRUE) %>%
+      'proj4string<-'( proj4string(basin.spdf) )
+    dat.df$PID = unlist( over( dat.df, basin.spdf[,which(names(basin.spdf) == 'PID')] ) )
+    dat.df = data.frame( dat.df ) %>% filter( !is.na( PID ) ) %>% dplyr::select( -xloc, -yloc ) 
+    
+    # elec.df = bind_rows( lapply( 1:12, function( mm ){
+    #   left_join( 	dat.df %>% dplyr::select( PID, paste( 'mean_tas', mm, sep = '.' ) ),
+    #               dat.df %>% dplyr::select( PID, urban_pop, rural_pop )  ) %>%
+    #     dplyr::rename(tas = paste( 'mean_tas', mm, sep = '.' ) ) %>%
+    #     group_by( PID ) %>%
+    #     summarise( 	turb = abs( -18 -273 + sum( tas * urban_pop ) / sum( urban_pop ) ), 	# population weighted temperature
+    #                 trur = abs( -18 -273 + sum( tas * rural_pop ) / sum( rural_pop ) ) ) %>%
+    #     as.data.frame( ) %>% 
+    #     mutate( turb = ifelse( is.nan( turb ), 0, turb ), trur = ifelse( is.nan( trur ), 0, trur ) ) %>%
+    #     mutate( time = mm ) %>%
+    #     dplyr::select( PID, time, turb, trur ) } ) ) %>%
+    #   left_join( ., data.frame( time = 1:12, hr = hr ) ) %>% # add the hours in each month
+    #   group_by( PID ) %>%
+    #   mutate( turb =  0.4 * turb / sum( turb ) + 0.6 * hr / 8760, # downscaling factor assuming part of load insensitive to temperature
+    #           trur =  0.4 * trur / sum( trur ) + 0.6 * hr / 8760 ) %>%	
+    #   as.data.frame( ) %>%
+    #   mutate( turb = ifelse( is.nan( turb ), 0, turb ), trur = ifelse( is.nan( trur ), 0, trur ) ) %>%		
+    #   left_join( 	., dat.df %>% # adding gdp and pop for projecting demands based on intensities above
+    #                 dplyr::select( PID, urban_gdp, urban_pop, rural_gdp, rural_pop ) %>%
+    #                 group_by( PID ) %>% summarise_each( funs( sum ) ) %>% # summarise by PID
+    #                 as.data.frame( ) %>%
+    #                 mutate( urban_inc = urban_gdp / urban_pop, rural_inc = rural_gdp / rural_pop )	%>% # don't actually use the per capita income buts it's there in case used as alternative indicator
+    #                 mutate( urban_inc = ifelse( is.nan( urban_inc ), 0, round( urban_inc ) ), 
+    #                         rural_inc = ifelse( is.nan( rural_inc ), 0, round( rural_inc ) ) ) ) %>%
+    #   left_join( ., data.frame( PID =  basin.spdf@data$PID, country = unlist( strsplit( basin.spdf@data$PID, '_' ) )[ seq( 1, 2*length(basin.spdf@data$PID), by=2 ) ] ) ) %>%	# add countries	
+    #   left_join( . ,	as.data.frame( national.list )[ paste0( 'SSP', ss ),  ] %>% 
+    #                gather( parameter, value ) %>% # flatten
+    #                mutate( country = unlist( strsplit( parameter, '[.]' ) )[ seq( 1, 4*length( parameter ), by = 4 ) ],
+    #                        var = unlist( strsplit( parameter, '[.]' ) )[ seq( 2, 4*length( parameter ), by = 4 ) ],
+    #                        year = unlist( strsplit( parameter, '[.]' ) )[ seq( 4, 4*length( parameter ), by = 4 ) ] ) %>%
+    #                filter( year == yy[y] ) %>% dplyr::select( country, var, value ) %>%
+    #                spread( var, value ) ) %>%
+    #   group_by( PID, time ) %>% # now project the demands using the gdp - might also instead use the per capita demand model
+    #   summarise( 	urban_municipal_mw = round( 1e-6 * urban_gdp * ( national_residential_kwh_per_usd + national_commercial_kwh_per_usd ) * turb / hr / 1e-3 ),
+    #               rural_municipal_mw = round( 1e-6 * rural_gdp * ( national_residential_kwh_per_usd + national_commercial_kwh_per_usd ) * trur / hr / 1e-3 ),
+    #               industry_mw = round( 1e-6 * ( urban_gdp + rural_gdp ) * national_industry_kwh_per_usd / 8760 / 1e-3 ) ) %>%
+    #   as.data.frame( ) %>% mutate( year = yy[y], scenario = paste0( 'SSP', ss ) ) %>%
+    #   dplyr::select( scenario, PID, year, time, urban_municipal_mw, rural_municipal_mw, industry_mw )	%>%
+    #   gather( type, value, urban_municipal_mw, rural_municipal_mw, industry_mw ) %>% 
+    #   mutate( units = 'MW',  
+    #           type = ifelse( grepl( 'urban', type ), 'urban_final', ifelse( grepl( 'rural', type ), 'rural_final', 'industry_final' ) ) ) %>%
+    #   dplyr::select( scenario, PID, type, year, time, value )		
+    
+    # format for water demands
+    dat.df = dat.df[ , ! grepl('mean_tas',names( dat.df )) ] # remove temperature
+    dat.df = dat.df %>% group_by( PID ) %>% 
+      summarise_each( funs( sum ) ) %>%
+      as.data.frame( ) %>%
+      mutate( urban_inc = urban_gdp / urban_pop, rural_inc = rural_gdp / rural_pop )	%>%
+      mutate( urban_inc = ifelse( is.nan( urban_inc ), 0, round( urban_inc ) ), rural_inc = ifelse( is.nan( rural_inc ), 0, round( rural_inc ) ) )
+    
+    # Add manufactruring demands
+    mf.df = rbind( 
+      national_manufacturing_withdrawal.df %>% 
+        filter( Country_Code %in% reg ) %>%
+        filter( Scenario == paste0( 'SSP', ss ) ) %>%
+        dplyr::rename( scenario = Scenario, country = Country_Code ) %>%
+        mutate( type = 'withdrawal' ) %>%
+        dplyr::select( scenario, country, type, paste0( 'X', yy2  ) ),
+      national_manufacturing_return.df %>% 
+        filter( Country_Code %in% reg ) %>%
+        filter( Scenario == paste0( 'SSP', ss ) ) %>%
+        dplyr::rename( scenario = Scenario, country = Country_Code ) %>%
+        mutate( type = 'return' ) %>%
+        dplyr::select( scenario, country, type, paste0( 'X', yy2  ) ) ) 
+    names( mf.df )[ ncol( mf.df ) ] = 'value'
+    mf.df$value = round( mf.df$value / 365.25, digits = 2 ) # convert to mcm_per_day
+    mf.df = mf.df %>% spread( type, value )
+    
+    # national gdp for scaling mf demands
+    gdp.df = national_gdp.df %>% 
+      dplyr::rename( scenario = Scenario, country = Region ) %>% 
+      filter(grepl(paste0("SSP",ssp),scenario)) %>% 
+      mutate( scenario = unlist( strsplit( scenario, '_' ) )[1] ) %>%
+      dplyr::select( scenario, country, paste0( 'X', yy[y]  ) )
+    names( gdp.df )[ ncol( gdp.df ) ] = 'gdp_national'	
+    
+    # now scale
+    dat.df = dat.df %>% 
+      mutate( country = reg) %>%
+      left_join( ., left_join( mf.df, gdp.df ), by = c( 'country' ) ) %>%
+      mutate( manufacturing_withdrawal = round( withdrawal * ( urban_gdp + rural_gdp ) / ( 1e9 * gdp_national ), digits = 3 ) )  %>%
+      mutate( manufacturing_return = round( return * ( urban_gdp + rural_gdp ) / ( 1e9 * gdp_national ), digits = 3 ) ) %>%
+      dplyr::select(  -urban_pop, -urban_gdp, -rural_pop, -rural_gdp, -urban_inc, -rural_inc, -country, -scenario, -return, -withdrawal, -gdp_national )
+    
+    
+    # expand manufacturing withdrawals and retunr flows to months - assuming constant instensity across the year
+    mfw = bind_cols( lapply( 1:12, function( mmm ){ dat.df$manufacturing_withdrawal } ) )	%>% as.data.frame() %>% 'names<-'(paste('manufacturing_withdrawal',1:12,sep='.'))
+    mfr = bind_cols( lapply( 1:12, function( mmm ){ dat.df$manufacturing_return } ) )	%>% as.data.frame() %>% 'names<-'(paste('manufacturing_return',1:12,sep='.'))
+    dat.df = dat.df %>% 
+      dplyr::select( -manufacturing_withdrawal ) %>% cbind( ., mfw ) %>%
+      dplyr::select( -manufacturing_return ) %>% cbind( ., mfr )
+    
+    # flatten the df
+    dat.df = bind_rows( lapply( c( 'urban_withdrawal', 'urban_return', 'rural_withdrawal', 'rural_return', 'manufacturing_withdrawal', 'manufacturing_return'  ), function( tp ){
+      dat.df %>% 
+        dplyr::select( PID, paste( tp, 1:12, sep = '.' ) ) %>%  
+        gather( 'month','value',-PID) %>% 
+        mutate( time = unlist( strsplit( month, '[.]' ) )[ seq(2,2*length(month),by=2) ], type = tp, scenario = paste0( 'SSP', ss ), year = yy[y], value = round( value, digits = 3 ) ) %>%
+        dplyr::select( scenario, PID, type, year, time, value ) 
+    } ) ) 
+    
+    # But the urban and rural withdrawal / return flow data are in million cubic meters per month not per day
+    type2check = c( 'urban_withdrawal', 'urban_return', 'rural_withdrawal', 'rural_return' )
+    dat.df = rbind( dat.df %>% filter( ! type %in% type2check ),
+                    dat.df %>% filter( type %in% type2check ) %>% 
+                      left_join( ., data.frame( time = as.character( 1:12 ), days = c(31, 28.25, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31) ) ) %>%
+                      mutate( value = round( value / days, digits = 3 ) ) %>% dplyr::select( -days ) )
+    
+    # add units
+    dat.df$units = 'mcm_per_day'
+    # elec.df$units = 'MW'
+    
+    
+    dat.df = rbind( dat.df) #, elec.df )
+    
+    return( dat.df )
+    
+  } ) )
+  
+} ) )
+
+# Harmonize with naming convention used previously - might update later
+demands.df = demands.df %>% 
+  mutate( sector = unlist( strsplit( type, '_') )[seq(1,2*length(type),by=2)] ) %>%
+  mutate( sector = ifelse( sector == 'manufacturing', 'industry', sector ) ) %>%
+  mutate( type = unlist( strsplit( type, '_') )[seq(2,2*length(type),by=2)] ) %>%
+  mutate( type = ifelse( type == 'final', 'electricity', type ) ) %>%
+  dplyr::rename( pid = PID, month = time ) %>%
+  dplyr::select( scenario, sector,  type,  pid,  year,  month, value, units )
+
+write.csv( demands.df, paste0(data_path,"/water_demands/harmonized/reg/",
+                              "_ssp",ssp,"_m_water_demands.csv"), row.names=FALSE )
+
+demand.df = read.csv( 'input/indus_demands_new.csv', stringsAsFactors=FALSE )
+
+# Plot the results
+
+windows()
+p1 = layout( matrix( c(5,5,1,2,3,4),3,2,byrow=TRUE ), widths = c(0.3,0.3), heights= c(0.05,0.3,0.3) )
+hh = 1
+for( tp in c('withdrawal') )
+{
+  for(s in c('urban','rural','industry'))
+  {
+    
+    tmp = data.frame( do.call( cbind, lapply( unique(demand.df$scenario), function(pp){
+      res = do.call( rbind, lapply( unique(demand.df$year), function(y){ 
+        res2 = sum( sapply( unique(demand.df$month), function(m){ return( demand.df[ which( demand.df$sector == s & demand.df$type == tp & demand.df$year == y & demand.df$month == m & demand.df$scenario == pp ), 'value' ] ) } ) )
+        return(res2)
+      } ) )
+      return(res)
+    } ) ) )
+    names(tmp) = unique(demand.df$scenario)		
+    row.names(tmp) = unique(demand.df$year)	
+    
+    if( tp == 'withdrawal'){ yl = c('million m3 per day'); fc = 1 }
+    if( tp == 'electricity'){ yl = c('Gigawatts'); fc = 1e3 }
+    
+    matplot( as.numeric( row.names(tmp) ), tmp/fc, type = 'l', col = c('green','blue','orange'), xlab = 'year', lty = 1, main = paste( s, tp, sep = ' - ' ), ylab = yl )
+    
+  }
+  
+}
+
+par(mar=c(0,0,0,0))	
+plot.new()
+legend('bottom',legend=c('SSP1','SSP2','SSP5'),col = c('green','blue','orange'),lty=1,bty='n',ncol=3)
diff --git a/nest/message_ix_models/model/water/data/pre-processing/groundwater_harmonize.r b/nest/message_ix_models/model/water/data/pre-processing/groundwater_harmonize.r
new file mode 100644
index 0000000..69e1f9a
--- /dev/null
+++ b/nest/message_ix_models/model/water/data/pre-processing/groundwater_harmonize.r
@@ -0,0 +1,236 @@
+
+#source('P:/ene.model/NEST/groundwater/groundwater_harmonize.r')
+
+# Clear memory 
+rm(list = ls()) 
+
+# close all plot windows
+graphics.off() 
+
+# libraries
+library(raster)
+library(rgdal)
+require(rgeos)
+library(ncdf4)
+library(tidyverse)
+
+#### REGION ####
+# define what region you work on
+# R11, R12 or a country
+reg = 'ZMB'
+
+# Define global raster at 1/8th degree
+base_raster = raster()
+res(base_raster) = 1/8
+base_raster = crop(base_raster,extent(-180,180,-60,85))
+base_raster[] = 0
+
+# Continental Tiles
+tile_names = c('Africa','Australia','Eurasia','N_America','S_America')
+for (i in 1:length(tile_names))
+	{
+	
+	# Import data from netcdf and convert to raster aligned with base raster
+  # unit meters m
+	nc_filename = paste(tile_names[i],'_model_wtd_v2.nc',sep='')
+	folder = c('P:/ene.model/NEST/groundwater/table_depth/')
+	raster_shape = raster(paste(folder,nc_filename,sep=''), varname='WTD') # doesn't seem to work for this type of ncdf file but provides shape
+	nc = nc_open(paste(folder,nc_filename,sep=''))
+	wtd = t(ncvar_get(nc, 'WTD')) # transpose and flip
+	wtd = wtd[nrow(wtd):1,]
+	wtd.raster = raster(wtd)
+	extent(wtd.raster) = extent(raster_shape)
+	proj4string(wtd.raster) = proj4string(raster_shape)
+	wtd.raster = projectRaster( from = wtd.raster , to = base_raster , method = "bilinear" )
+	
+	# Add to global raster
+	if(i == 1)
+		{
+		global_water_table_depth_0125 = wtd.raster
+		}else
+		{
+		global_water_table_depth_0125 = do.call(merge, list(global_water_table_depth_0125, wtd.raster))
+		}
+	}
+
+# Write to geotiff
+temp = writeRaster(global_water_table_depth_0125, paste(folder,'global_water_table_depth_0125',sep=''), format = 'GTiff', overwrite = TRUE)
+
+#### TAKE MESSAGE REGION-BASIN structure ####
+basin_by_region.spdf = readOGR('P:/ene.model/NEST/delineation/data/delineated_basins_new',
+                               paste0('basins_by_region_simpl_',reg), verbose=FALSE)
+row.names(basin_by_region.spdf@data) = 1:length(basin_by_region.spdf$BASIN)
+# basin_by_region.spdf2 = gBuffer(basin_by_region.spdf, byid=TRUE, width=-0.2)
+
+proj4string(global_water_table_depth_0125) = basin_by_region.spdf@proj4string
+identicalCRS(global_water_table_depth_0125, basin_by_region.spdf)
+
+table_depth.df  = raster::extract(global_water_table_depth_0125, basin_by_region.spdf,fun=mean,df=TRUE,na.rm=TRUE)
+names(table_depth.df) = c('id','table_depth_m')
+table_depth.spdf = cbind(basin_by_region.spdf, table_depth.df)
+library(broom)
+table_depth.tidy <- broom::tidy(table_depth.spdf)
+table_depth.tidy2 <- dplyr::left_join(table_depth.tidy %>% mutate(id = as.integer(id)),
+                                table_depth.spdf@data, by='id')
+# table_depth.tidy2 = table_depth.tidy2[table_depth.tidy2$BASIN_ID %in% c(64), ]
+# plot mismatch regions...
+ggplot() + 
+  geom_polygon( data=table_depth.tidy2, aes(x=long, y=lat, group = group, fill = table_depth_m),
+                color="black" )
+
+#some regions are not plotted correctly but the data is there
+table_depth.out = table_depth.spdf@data %>% 
+  mutate(table_depth_m = if_else(is.na(table_depth_m), 
+                                 mean(table_depth_m,na.rm = T) , table_depth_m),
+         # Energy use in GW / MCM/day
+         GW_per_MCM_per_day = round( 0.85 * 9.81 / 86400 * table_depth_m , digits = 5 ),
+         # in GW/km3/year 
+         GW_per_km3_per_year = GW_per_MCM_per_day *1000/365)
+# to be changed to the unit we need
+
+write.csv( table_depth.out, paste0('gw_energy_intensity_depth_',reg,'.csv'), row.names = FALSE)	
+
+
+#### GW abstraction, historical capaicty ####
+# 1- get historical and projections for abstraction (only use hist for now)
+# 2- from Wada et al, also import different water demands
+# 3- gw_fraction = tot_demand / gw abstraction
+# 4- use gw_fraction on our actual demands in the model -> historical capacity
+setwd('P:/ene.model/NEST/groundwater/')
+# Groundwater abstraction fro Wada et al
+
+nc = nc_open('Wada_groundwater_abstraction/waterdemand_30min_groundwaterabstraction_million_m3_month.nc', verbose=FALSE)
+gwabstract.stack = stack( 'Wada_groundwater_abstraction/waterdemand_30min_groundwaterabstraction_million_m3_month.nc' )
+extent(gwabstract.stack) = extent( min( ncvar_get(nc, "longitude") ), max( ncvar_get(nc, "longitude") ), min( ncvar_get(nc, "latitude") ), max( ncvar_get(nc, "latitude") ) )
+proj4string( gwabstract.stack ) = proj4string( basin_by_region.spdf )
+#gwabstract.stack = crop( gwabstract.stack, extent(basin_by_region.spdf) )
+names(gwabstract.stack) = c( sapply( 1:(nlayers(gwabstract.stack)/12), function(yy){ return( paste( ( as.numeric( unlist( strsplit( as.character( as.Date("1901-01-01") + min( ncvar_get(nc, "time") ) ), '-' ) )[1] ) + yy - 1 ), seq(1,12,by=1), sep='.') ) } ) ) 
+gwabstract.stack = gwabstract.stack[[ c( which(grepl( 'X2010',names(gwabstract.stack) )) ) ]] # keep 2010
+
+# Irrigation
+nc = nc_open('Wada_groundwater_abstraction/pcrglobwb_WFDEI_historical_PIrrWW_monthly_1960_2010.nc4', verbose=FALSE)
+irrigation.stack = stack( 'Wada_groundwater_abstraction/pcrglobwb_WFDEI_historical_PIrrWW_monthly_1960_2010.nc4' )
+extent(irrigation.stack) = extent( min( ncvar_get(nc, "longitude") ), max( ncvar_get(nc, "longitude") ), min( ncvar_get(nc, "latitude") ), max( ncvar_get(nc, "latitude") ) )
+proj4string( irrigation.stack ) = proj4string( basin_by_region.spdf )
+# irrigation.stack = crop( irrigation.stack, extent(buff.sp) )
+names(irrigation.stack) = c( sapply( 1:(nlayers(irrigation.stack)/12), function(yy){ return( paste( ( as.numeric( unlist( strsplit( as.character( as.Date("1901-01-01") + min( ncvar_get(nc, "time") ) ), '-' ) )[1] ) + yy - 1 ), seq(1,12,by=1), sep='.') ) } ) ) 
+irrigation.stack = irrigation.stack[[ c( which(grepl( 'X2010',names(irrigation.stack) )) ) ]] # keep 2010
+
+year_irr = sum(irrigation.stack)
+tot_km3 = sum(year_irr@data@values,na.rm = T)*0.001
+# Industrial
+nc = nc_open('Wada_groundwater_abstraction/pcrglobwb_WFDEI_historical_PIndWW_monthly_1960_2010.nc4', verbose=FALSE)
+industrial.stack = stack( 'Wada_groundwater_abstraction/pcrglobwb_WFDEI_historical_PIndWW_monthly_1960_2010.nc4' )
+extent(industrial.stack) = extent( min( ncvar_get(nc, "longitude") ), max( ncvar_get(nc, "longitude") ), min( ncvar_get(nc, "latitude") ), max( ncvar_get(nc, "latitude") ) )
+proj4string( industrial.stack ) = proj4string( basin_by_region.spdf )
+# industrial.stack = crop( industrial.stack, extent(buff.sp) )
+names(industrial.stack) = c( sapply( 1:(nlayers(industrial.stack)/12), function(yy){ return( paste( ( as.numeric( unlist( strsplit( as.character( as.Date("1901-01-01") + min( ncvar_get(nc, "time") ) ), '-' ) )[1] ) + yy - 1 ), seq(1,12,by=1), sep='.') ) } ) ) 
+industrial.stack = industrial.stack[[ c( which(grepl( 'X2010',names(industrial.stack) )) ) ]] # keep 2010
+
+# Domestic
+nc = nc_open('Wada_groundwater_abstraction/pcrglobwb_WFDEI_historical_PDomWW_monthly_1960_2010.nc4', verbose=FALSE)
+domestic.stack = stack( 'Wada_groundwater_abstraction/pcrglobwb_WFDEI_historical_PDomWW_monthly_1960_2010.nc4' )
+extent(domestic.stack) = extent( min( ncvar_get(nc, "longitude") ), max( ncvar_get(nc, "longitude") ), min( ncvar_get(nc, "latitude") ), max( ncvar_get(nc, "latitude") ) )
+proj4string( domestic.stack ) = proj4string( basin_by_region.spdf )
+# domestic.stack = crop( domestic.stack, extent(buff.sp) )
+names(domestic.stack) = c( sapply( 1:(nlayers(domestic.stack)/12), function(yy){ return( paste( ( as.numeric( unlist( strsplit( as.character( as.Date("1901-01-01") + min( ncvar_get(nc, "time") ) ), '-' ) )[1] ) + yy - 1 ), seq(1,12,by=1), sep='.') ) } ) ) 
+domestic.stack = domestic.stack[[ c( which(grepl( 'X2010',names(domestic.stack) )) ) ]] # keep 2010
+
+# Existing groundwater capacity from initial extraction levels
+total.stack = domestic.stack + industrial.stack + irrigation.stack
+total.stack  =  sum(stack(total.stack))
+total_gwabstract.stack = sum(stack(gwabstract.stack))
+gwfraction.stack = ( total_gwabstract.stack / total.stack )
+# areas with 0 demand nad maybe little vals of gw abstraction
+# otherwise results in 100% where actually gw is minimal, no demand
+gwfraction.stack[gwfraction.stack == Inf] = 0
+gwfraction.stack[gwfraction.stack[]>1]=1
+gwfraction.stack[is.na(gwfraction.stack)] = 0
+
+
+frac = c( unlist( lapply( 1:length(basin_by_region.spdf), function(x){ 
+  
+  gw = sum( data.frame( raster::extract( total_gwabstract.stack, as(basin_by_region.spdf[x,],'SpatialPolygons'), na.rm=TRUE, cellnumbers = TRUE )[[1]])['value'] )
+  
+  ww = sum( data.frame( raster::extract( total.stack, as(basin_by_region.spdf[x,],'SpatialPolygons'), na.rm=TRUE, cellnumbers = TRUE )[[1]])['value'] )
+  
+  return( round( min( 1, max( 0, gw/ww, na.rm=TRUE ) ), digits = 3 ) ) 
+  
+} ) ) )
+
+# Data frame output by PID
+groundwater_fraction.df = data.frame( gw_fraction = frac ) %>% 
+  mutate(id = row_number(),
+         # many values close to 0 are bo due to lack of data
+         gw_fraction = if_else(gw_fraction <= 0.02,mean(gw_fraction),gw_fraction))
+groundwater_fraction.spdf = cbind(basin_by_region.spdf,groundwater_fraction.df) 
+
+writeOGR(
+  groundwater_fraction.spdf, 
+  'P:/ene.model/NEST/groundwater', 
+  paste0('gw_fraction_',reg),
+  driver="ESRI Shapefile", 
+  overwrite_layer=TRUE
+)
+
+gw_fraction.tidy <- broom::tidy(groundwater_fraction.spdf)
+gw_fraction.tidy2 <- dplyr::left_join(gw_fraction.tidy %>% mutate(id = as.integer(id)),
+                                      groundwater_fraction.spdf@data, by='id')
+gw_fraction.tidy2 = gw_fraction.tidy2[gw_fraction.tidy2$BASIN_ID %in% c(64), ]
+# this plot is completely wrong!! use ArcGIS instead
+ggplot() + 
+  geom_polygon( data=gw_fraction.tidy2, aes(x=long, y=lat, group = group, fill = gw_fraction),
+                color="black" )
+
+# Now use the groundwater fraction to set the historical capacities based on the historical demand level
+# load all demands
+## CAREFUL! irr_dem needs to be manually defined
+# for R11 it is from GLOBIOM, in MCM
+irr_dem = read.csv(paste0('P:/ene.model/NEST/groundwater/hist_irrigation_withdrawals_',reg,'.csv'),
+                   check.names = F) %>% 
+  gather(key = 'year',value = 'irr_dem',-node) %>% rename(REGION = node) %>% 
+  mutate(year = as.numeric(year))
+# urban + manufacturing and rural demand depend on the region, in MCM!!
+urban_man_dem = read.csv(paste0('P:/ene.model/NEST/water_demands/harmonized/',
+                                reg,'/ssp2_regional_urban_withdrawal_baseline.csv'),
+                         check.names = F) %>% rename(year = '') %>% 
+  gather(key = 'BCU_name',value = 'urb_dem',-year) %>% 
+  mutate(urb_dem = urb_dem/1000) # km3
+
+rural_dem = read.csv(paste0('P:/ene.model/NEST/water_demands/harmonized/',
+                            reg,'/ssp2_regional_rural_withdrawal_baseline.csv'),
+                     check.names = F) %>% rename(year = '') %>% 
+  gather(key = 'BCU_name',value = 'rur_dem',-year) %>% 
+  mutate(rur_dem = rur_dem/1000) # km3
+
+#specific region name for R11 or R12
+if (reg %in% c(" R11"," R12")){
+  reg_add = paste0(reg,"_")
+} else{
+  reg_add = ""
+}
+tot_demand = urban_man_dem %>% left_join(rural_dem) %>% 
+  left_join(basin_by_region.spdf@data %>% select(REGION,BCU_name) %>% 
+              mutate(REGION = paste0(reg_add,REGION))) %>% 
+  filter(year == 2010) %>% 
+  left_join(irr_dem) %>% 
+  group_by(REGION) %>% 
+  mutate(tot_urb_reg_wtr = sum(urb_dem),
+         region_water_share = urb_dem  / tot_urb_reg_wtr) %>% ungroup() %>% 
+  #use this share to allocate irrigation withdrawals to basins
+  mutate(irr_dem_bas = irr_dem * region_water_share,
+         tot_dem = irr_dem_bas + urb_dem + rur_dem)
+
+hist_cap_gw_sw = tot_demand %>% select(BCU_name,tot_dem) %>% 
+  left_join(groundwater_fraction.spdf@data %>% select(BCU_name,gw_fraction)) %>% 
+  mutate(hist_cap_gw_km3_year = round( tot_dem * gw_fraction,3 ),
+         hist_cap_sw_km3_year = round( tot_dem * (1- gw_fraction) ,3) ) %>% 
+  select(-tot_dem,-gw_fraction)
+
+### Append historical capacity csv to include freshwater extration capacities
+
+# historical capacity csv
+write.csv( 	hist_cap_gw_sw, 
+            paste0("historical_new_cap_gw_sw_km3_year_",reg,".csv"), 
+            row.names = FALSE )
+
diff --git a/nest/message_ix_models/model/water/data/pre-processing/hydro_agg_3.py b/nest/message_ix_models/model/water/data/pre-processing/hydro_agg_3.py
new file mode 100644
index 0000000..7451f30
--- /dev/null
+++ b/nest/message_ix_models/model/water/data/pre-processing/hydro_agg_3.py
@@ -0,0 +1,247 @@
+"""
+This script calculates envrionmental flows using monthly runoff (qtot from
+                                                                 ISIMIP)
+It calculates environmental flows using Variable Monthly Flow method
+
+"""
+
+
+import os
+from sqlite3 import TimeFromTicks
+
+import numpy as np
+import pandas as pd
+
+type_reg = "global"  # else 'global'
+country = "ZMB"
+global_reg = "R11"
+type_reg = "ZMB"
+
+if type_reg == "global":
+    wd = os.path.join(
+        "p:",
+        "ene.model",
+        "NEST",
+        "hydrology",
+        "post-processed_qs",
+        f"global_{global_reg}",
+    )
+    # upload_files = glob.glob(os.path.join(upload_dir, "MESSAGEix*.xlsx"))
+elif type_reg == "country":
+    # This is for Zambia
+    wd = os.path.join("p:", "ene.model", "NEST", country, "hydrology", "post-processed")
+
+
+# climate forcing
+scenarios = ["rcp26", "rcp60"]
+
+scen = "rcp60"
+# variable, for detailed symbols, refer to ISIMIP2b documentation
+variables = [
+    "qtot",  # total runoff
+    "dis",  # discharge
+    "qg",  # groundwater runoff
+    "qr",  # groundwater recharge
+]
+var = "qr"
+timestep = "5y"
+
+# read quantiles
+df_26_q50 = pd.read_csv(wd + f"\{var}_{timestep}_mmean_{scenarios[0]}_q50.csv")
+df_26_q70 = pd.read_csv(wd + f"\{var}_{timestep}_mmean_{scenarios[0]}_q70.csv")
+df_26_q90 = pd.read_csv(wd + f"\{var}_{timestep}_mmean_{scenarios[0]}_q90.csv")
+
+df_60_q50 = pd.read_csv(wd + f"\{var}_{timestep}_mmean_{scenarios[1]}_q50.csv")
+df_60_q70 = pd.read_csv(wd + f"\{var}_{timestep}_mmean_{scenarios[1]}_q70.csv")
+df_60_q90 = pd.read_csv(wd + f"\{var}_{timestep}_mmean_{scenarios[1]}_q90.csv")
+
+# Bias Correction
+# Q50
+
+df_26_q50.drop(["Unnamed: 0"], axis=1, inplace=True)
+df_60_q50.drop(["Unnamed: 0"], axis=1, inplace=True)
+
+# Bias adjustment in the data
+val2020_q50 = (
+    df_60_q50.iloc[:, 1:5].mean(axis=1) + df_26_q50.iloc[:, :5].mean(axis=1)
+) / 2
+delta60 = df_60_q50.iloc[:, 1:5].mean(axis=1) - val2020_q50
+delta26 = df_26_q50.iloc[:, 1:5].mean(axis=1) - val2020_q50
+
+# df_sw26_adjusted_q50 = df_26_q50.apply(lambda x:x - delta26)
+# df_sw60_adjusted_q50 = df_60_q50.apply(lambda x:x - delta60)
+
+df_26_q50["2020-12-31"] = val2020_q50
+df_26_q50["2025-12-31"] = df_26_q50["2025-12-31"] - delta26
+df_26_q50["2030-12-31"] = df_26_q50["2030-12-31"] - (0.8 * delta26)
+df_26_q50["2035-12-31"] = df_26_q50["2035-12-31"] - (0.6 * delta26)
+df_26_q50["2040-12-31"] = df_26_q50["2040-12-31"] - (0.4 * delta26)
+df_26_q50["2045-12-31"] = df_26_q50["2045-12-31"] - (0.2 * delta26)
+
+df_60_q50["2020-12-31"] = val2020_q50
+df_60_q50["2025-12-31"] = df_60_q50["2025-12-31"] - delta60
+df_60_q50["2030-12-31"] = df_60_q50["2030-12-31"] - (0.8 * delta60)
+df_60_q50["2035-12-31"] = df_60_q50["2035-12-31"] - (0.6 * delta60)
+df_60_q50["2040-12-31"] = df_60_q50["2040-12-31"] - (0.4 * delta60)
+df_60_q50["2045-12-31"] = df_60_q50["2045-12-31"] - (0.2 * delta60)
+
+df_60_q50.to_csv(wd + f"\{var}_{timestep}_mmean_{scenarios[0]}_q50_ba.csv")
+df_26_q50.to_csv(wd + f"\{var}_{timestep}_mmean_{scenarios[1]}_q50_ba.csv")
+
+
+# Q70
+# Bias Correction
+df_26_q70.drop(["Unnamed: 0"], axis=1, inplace=True)
+df_60_q70.drop(["Unnamed: 0"], axis=1, inplace=True)
+
+
+# Bias adjustment in the data
+val2020_q70 = (
+    df_60_q70.iloc[:, 1:5].mean(axis=1) + df_26_q70.iloc[:, :5].mean(axis=1)
+) / 2
+delta60 = df_60_q70.iloc[:, 1:5].mean(axis=1) - val2020_q70
+delta26 = df_26_q70.iloc[:, 1:5].mean(axis=1) - val2020_q70
+
+df_26_q70["2020-12-31"] = val2020_q70
+df_26_q70["2025-12-31"] = df_26_q70["2025-12-31"] - delta26
+df_26_q70["2030-12-31"] = df_26_q70["2030-12-31"] - (0.8 * delta26)
+df_26_q70["2035-12-31"] = df_26_q70["2035-12-31"] - (0.6 * delta26)
+df_26_q70["2040-12-31"] = df_26_q70["2040-12-31"] - (0.4 * delta26)
+df_26_q70["2045-12-31"] = df_26_q70["2045-12-31"] - (0.2 * delta26)
+
+df_60_q70["2020-12-31"] = val2020_q70
+df_60_q70["2025-12-31"] = df_60_q70["2025-12-31"] - delta60
+df_60_q70["2030-12-31"] = df_60_q70["2030-12-31"] - (0.8 * delta60)
+df_60_q70["2035-12-31"] = df_60_q70["2035-12-31"] - (0.6 * delta60)
+df_60_q70["2040-12-31"] = df_60_q70["2040-12-31"] - (0.4 * delta60)
+df_60_q70["2045-12-31"] = df_60_q70["2045-12-31"] - (0.2 * delta60)
+
+# df_sw26_adjusted_q70 = df_26_q70.apply(lambda x:x - delta26)
+# df_sw60_adjusted_q70 = df_60_q70.apply(lambda x:x - delta60)
+
+df_26_q70.to_csv(wd + f"\{var}_{timestep}_mmean_{scenarios[0]}_q70_ba.csv")
+df_60_q70.to_csv(wd + f"\{var}_{timestep}_mmean_{scenarios[1]}_q70_ba.csv")
+
+
+# Q90
+# Bias Correction
+df_26_q90.drop(["Unnamed: 0"], axis=1, inplace=True)
+df_60_q90.drop(["Unnamed: 0"], axis=1, inplace=True)
+
+
+# Bias adjustment in the data
+val2020_q90 = (
+    df_60_q90.iloc[:, 1:5].mean(axis=1) + df_26_q90.iloc[:, :5].mean(axis=1)
+) / 2
+delta60 = df_60_q90.iloc[:, 1:5].mean(axis=1) - val2020_q90
+delta26 = df_26_q90.iloc[:, 1:5].mean(axis=1) - val2020_q90
+
+# df_sw26_adjusted_q90 = df_26_q90.apply(lambda x:x - delta26)
+# df_sw60_adjusted_q90 = df_60_q90.apply(lambda x:x - delta60)
+
+
+df_26_q90["2020-12-31"] = val2020_q90
+df_26_q90["2025-12-31"] = df_26_q90["2025-12-31"] - delta26
+df_26_q90["2030-12-31"] = df_26_q90["2030-12-31"] - (0.8 * delta26)
+df_26_q90["2035-12-31"] = df_26_q90["2035-12-31"] - (0.6 * delta26)
+df_26_q90["2040-12-31"] = df_26_q90["2040-12-31"] - (0.4 * delta26)
+df_26_q90["2045-12-31"] = df_26_q90["2045-12-31"] - (0.2 * delta26)
+
+df_60_q90["2020-12-31"] = val2020_q90
+df_60_q90["2025-12-31"] = df_60_q90["2025-12-31"] - delta60
+df_60_q90["2030-12-31"] = df_60_q90["2030-12-31"] - (0.8 * delta60)
+df_60_q90["2035-12-31"] = df_60_q90["2035-12-31"] - (0.6 * delta60)
+df_60_q90["2040-12-31"] = df_60_q90["2040-12-31"] - (0.4 * delta60)
+df_60_q90["2045-12-31"] = df_60_q90["2045-12-31"] - (0.2 * delta60)
+
+df_26_q90.to_csv(wd + f"\{var}_{timestep}_mmean_{scenarios[0]}_q90_ba.csv")
+df_60_q90.to_csv(wd + f"\{var}_{timestep}_mmean_{scenarios[1]}_q90_ba.csv")
+df_noclim_q90 = df_60_q90.apply(lambda x: val2020_q90)
+df_noclim_q90.to_csv(wd + f"\{var}_{timestep}_mmean_no_climate_q90_ba.csv")
+
+# Reliability Scenarios
+# Low Reliability = q50
+df_60_q50.to_csv(wd + f"\{var}_{timestep}_2p6_low_{type_reg}.csv")
+df_26_q50.to_csv(wd + f"\{var}_{timestep}_6p0_low_{type_reg}.csv")
+
+df_sw_noclimate = df_60_q50.apply(lambda x: val2020_q50)
+df_sw_noclimate.to_csv(wd + f"\{var}_{timestep}_no_climate_low_{type_reg}.csv")
+
+# Medium Reliability = q70
+df_med_26_1 = df_26_q50.iloc[:, :3]
+df_med_26_2 = df_26_q70.iloc[:, 3:]
+df_med_26 = pd.concat([df_med_26_1, df_med_26_2], axis=1)
+df_med_26.to_csv(wd + f"\{var}_{timestep}_2p6_med_{type_reg}.csv")
+
+df_med_60_1 = df_60_q50.iloc[:, :3]
+df_med_60_2 = df_60_q70.iloc[:, 3:]
+df_med_60 = pd.concat([df_med_60_1, df_med_60_2], axis=1)
+df_med_60.to_csv(wd + f"\{var}_{timestep}_6p0_med_{type_reg}.csv")
+
+df_med_noclimate_1 = df_med_60.iloc[:, :3].apply(lambda x: val2020_q50)
+df_med_noclimate_2 = df_med_60.iloc[:, 4:].apply(lambda x: val2020_q70)
+df_med_noclimate = pd.concat([df_med_noclimate_1, df_med_noclimate_2], axis=1)
+df_med_noclimate.to_csv(wd + f"\{var}_{timestep}_no_climate_med_{type_reg}.csv")
+
+df_med_noclimate.iloc[:, :3] = df_sw_noclimate.iloc[:, :3]
+
+# High Reliability - 2020,q50 . 2025:2030, q70 , 2035:2100, q90
+df_high_26_1 = df_26_q50.iloc[:, :3]
+df_high_26_2 = df_26_q70.iloc[:, 3:6]
+df_high_26_3 = df_26_q90.iloc[:, 6:]
+df_high_26 = pd.concat([df_high_26_1, df_high_26_2, df_high_26_3], axis=1)
+df_high_26.to_csv(wd + f"\{var}_{timestep}_2p6_high_{type_reg}.csv")
+
+df_high_60_1 = df_60_q50.iloc[:, :3]
+df_high_60_2 = df_60_q70.iloc[:, 3:6]
+df_high_60_3 = df_60_q90.iloc[:, 6:]
+df_high_60 = pd.concat([df_high_60_1, df_high_60_2, df_high_60_3], axis=1)
+df_high_60.to_csv(wd + f"\{var}_{timestep}_6p0_high_{type_reg}.csv")
+
+df_high_noclimate_1 = df_high_60.iloc[:, :3].apply(lambda x: val2020_q50)
+df_high_noclimate_2 = df_high_60.iloc[:, 3:6].apply(lambda x: val2020_q70)
+df_high_noclimate_3 = df_high_60.iloc[:, 6:].apply(lambda x: val2020_q90)
+df_high_noclimate = pd.concat(
+    [df_high_noclimate_1, df_high_noclimate_2, df_high_noclimate_3], axis=1
+)
+df_high_noclimate.to_csv(wd + f"\{var}_{timestep}_no_climate_high_{type_reg}.csv")
+
+
+# E-Flow bias correction
+
+df_60 = pd.read_csv(wd + f"\e-flow_rcp60.csv")
+df_26 = pd.read_csv(wd + f"\e-flow_rcp26.csv")
+
+# Bias Correction
+# Q50
+
+df_60.drop(["Unnamed: 0"], axis=1, inplace=True)
+df_26.drop(["Unnamed: 0"], axis=1, inplace=True)
+
+# Bias adjustment in the data
+val2020 = (df_60.iloc[:, 1:5].mean(axis=1) + df_26.iloc[:, :5].mean(axis=1)) / 2
+delta60 = df_60.iloc[:, 1:5].mean(axis=1) - val2020
+delta26 = df_26.iloc[:, 1:5].mean(axis=1) - val2020
+
+# df_sw26_adjusted_q50 = df_26_q50.apply(lambda x:x - delta26)
+# df_sw60_adjusted_q50 = df_60_q50.apply(lambda x:x - delta60)
+
+df_26["2020-12-31"] = val2020
+df_26["2025-12-31"] = df_26["2025-12-31"] - delta26
+df_26["2030-12-31"] = df_26["2030-12-31"] - (0.8 * delta26)
+df_26["2035-12-31"] = df_26["2035-12-31"] - (0.6 * delta26)
+df_26["2040-12-31"] = df_26["2040-12-31"] - (0.4 * delta26)
+df_26["2045-12-31"] = df_26["2045-12-31"] - (0.2 * delta26)
+
+df_60["2020-12-31"] = val2020
+df_60["2025-12-31"] = df_60["2025-12-31"] - delta60
+df_60["2030-12-31"] = df_60["2030-12-31"] - (0.8 * delta60)
+df_60["2035-12-31"] = df_60["2035-12-31"] - (0.6 * delta60)
+df_60["2040-12-31"] = df_60["2040-12-31"] - (0.4 * delta60)
+df_60["2045-12-31"] = df_60["2045-12-31"] - (0.2 * delta60)
+
+df_60.to_csv(wd + f"\e-flow_2p6_{type_reg}.csv")
+df_26.to_csv(wd + f"\e-flow_6p0_R11.csv")
+
+df_noclim = df_60.apply(lambda x: val2020)
+df_noclim.to_csv(wd + f"\e-flow_no_climate_{type_reg}.csv")
diff --git a/nest/message_ix_models/model/water/data/pre-processing/hydro_agg_spatial.R b/nest/message_ix_models/model/water/data/pre-processing/hydro_agg_spatial.R
new file mode 100644
index 0000000..5682c9e
--- /dev/null
+++ b/nest/message_ix_models/model/water/data/pre-processing/hydro_agg_spatial.R
@@ -0,0 +1,49 @@
+require(reshape)
+require(rgdal)
+require(raster)
+require(rgeos)
+require(tidyr)
+require(dplyr)
+require(maptools)
+require(ncdf4)
+library(sp)
+library(countrycode)
+library(ggmap)
+library(sf)
+library(exactextractr)
+
+nc = nc_open(
+    paste0(
+        "C:/Users/awais/Documents/GitHub/agg_data/output/dis_5y__hadgem2-es_rcp60_temp_agg.nc"
+    ),
+    verbose=FALSE,
+)
+nc.brick = brick(
+    "C:/Users/awais/Documents/GitHub/agg_data/output/qtot_5y__gfdl-esm2m_rcp60_temp_agg.nc"
+)
+
+
+basin < -read.csv(
+    "C:/Users/awais/Documents/GitHub/agg_data/output/basins_by_region_simpl_R11.csv"
+)
+
+# load shapefile
+basin_by_region.spdf = read_sf(
+    "P:/ene.model/NEST/delineation/data/delineated_basins_new",
+    paste0("basins_by_region_simpl_R11"),
+)
+
+
+agg < -exact_extract(nc.brick, basin_by_region.spdf, "sum")
+
+
+output < -cbind(basin, agg)
+
+write.csv(
+    output,
+    "C:/Users/awais/Documents/GitHub/agg_data/output/qtot_5y__gfdl-esm2m_rcp60_temp_agg.nc",
+)
+
+basin < -read.csv(
+    "C:/Users/awais/Documents/GitHub/agg_data/output/basins_by_region_simpl_R11.csv"
+)
diff --git a/nest/message_ix_models/model/water/data/pre-processing/hydro_agg_temp.py b/nest/message_ix_models/model/water/data/pre-processing/hydro_agg_temp.py
new file mode 100644
index 0000000..9f145f2
--- /dev/null
+++ b/nest/message_ix_models/model/water/data/pre-processing/hydro_agg_temp.py
@@ -0,0 +1,335 @@
+import matplotlib.pylab as plt
+import seaborn as sns
+
+sns.set()
+import numpy as np
+import pandas as pd
+import seaborn as sns
+
+sns.set_style("whitegrid")
+import os
+
+# variable, for detailed symbols, refer to ISIMIP2b documentation
+variables = ["qtot", "dis", "qr"]  # total runoff  # discharge  # groundwater run
+
+var = "qr"
+
+
+GCMs = ["mmean", "dry"]
+GCM = "mmean"  # 'dry'choose driest GCM else 'mmean' for multi model mean
+# else mmmean
+iso3 = "ZMB"
+isimip = "3b"
+
+# climate model
+if isimip == "2b":
+    climmodels = ["gfdl-esm2m", "hadgem2-es", "ipsl-cm5a-lr", "miroc5"]
+    climmodel = "gfdl-esm2m"
+    # climate forcing
+    scenarios = ["rcp26", "rcp60"]
+    scen = "rcp26"
+    wd1 = os.path.join("p:", "ene.model", "NEST", "hydrological_data_agg")
+    wd = os.path.join("p:", "watxene", "ISIMIP", "ISIMIP2b", "output", "LPJmL")
+    wd2 = os.path.join("p:", "ene.model", "NEST", "hydrology", "processed_nc4")
+else:
+    climmodels = [
+        "gfdl-esm4",
+        "ipsl-cm6a-lr",
+        "mpi-esm1-2-hr",
+        "mri-esm2-0",
+        "ukesm1-0-ll",
+    ]
+    cl = "gfdl-esm4"
+    scenarios = ["ssp126", "ssp370", "ssp585"]
+    scen = "ssp126"
+    wd1 = os.path.join("p:", "ene.model", "NEST", "hydrological_data_agg")
+    wd11 = os.path.join("p:", "ene.model", "NEST", iso3, "hydrology", "isimip3B_CWatM")
+
+
+#
+qtot_7p0_gfdl = pd.read_csv(wd11 + f"\{var}_monthly_gfdl-esm4_ssp370_future.csv").drop(
+    [
+        "Unnamed: 0",
+        "NAME",
+        "BASIN",
+        "BASIN_ID",
+        "area",
+        "area_km2",
+        "X",
+        "REGION",
+        "BCU_name",
+    ],
+    axis=1,
+)
+
+new_cols = pd.date_range("2015-01-01", periods=1032, freq="M")
+qtot_7p0_gfdl.columns = new_cols
+
+
+qtot_2p6_gfdl = pd.read_csv(wd11 + f"\{var}_monthly_gfdl-esm4_ssp126_future.csv").drop(
+    [
+        "Unnamed: 0",
+        "NAME",
+        "BASIN",
+        "BASIN_ID",
+        "area",
+        "area_km2",
+        "X",
+        "REGION",
+        "BCU_name",
+    ],
+    axis=1,
+)
+
+new_cols = pd.date_range("2015-01-01", periods=1032, freq="M")
+qtot_2p6_gfdl.columns = new_cols
+
+
+qtot_2p6_gfdl_avg = qtot_2p6_gfdl[pd.date_range("2015-01-01", periods=192, freq="M")]
+qtot_7p0_gfdl_avg = qtot_7p0_gfdl[pd.date_range("2015-01-01", periods=192, freq="M")]
+
+val_2020 = (
+    qtot_7p0_gfdl_avg.groupby(qtot_7p0_gfdl_avg.columns.month, axis=1).mean()
+    + qtot_2p6_gfdl_avg.groupby(qtot_2p6_gfdl_avg.columns.month, axis=1).mean()
+) / 2
+val_2020_annual = val_2020.mean(axis=1)
+
+delta60 = (
+    qtot_7p0_gfdl_avg.groupby(qtot_7p0_gfdl_avg.columns.month, axis=1).mean() - val_2020
+)
+delta26 = (
+    qtot_2p6_gfdl_avg.groupby(qtot_2p6_gfdl_avg.columns.month, axis=1).mean() - val_2020
+)
+
+
+def bias_correction(df):
+    """ias corrects the data such that 2020 value is same
+    for both scenario and then apply
+    Input
+    ----------
+    df : raw input monthly data
+    Returns
+    -------
+    df : bias corrected monthly data with also replacing 5 year timestep average
+    df_monthly: monthly bias corrected data
+    df_5y_m: bias corrected 5 y monthly
+    df_5y: bias corrected 5 y average
+    """
+    # Starting value of delat is 1 and then it will reduce to zero with each 5 year timestep
+    # till 2045 with a difference of 0.2. This means the bias correction will fade away till 2045
+    delta_multiply = 1
+
+    for year in np.arange(2020, 2105, 5):
+        # for 2020 delta all scenario data frame will be same
+        if year == 2020:
+            df[pd.date_range("2020-01-01", periods=12, freq="M")] = val_2020
+
+        else:
+            # for delta years after 2020
+            if delta_multiply > 0.1:
+                for i in np.arange(4, -1, -1):
+                    if i == 4:
+                        delta60.columns = pd.date_range(
+                            str(year - i) + "-01-01", periods=12, freq="M"
+                        )
+                        final_temp = df[
+                            pd.date_range(
+                                str(year - i) + "-01-01", periods=12, freq="M"
+                            )
+                        ] - (delta_multiply * delta60)
+
+                    else:
+                        delta60.columns = pd.date_range(
+                            str(year - i) + "-01-01", periods=12, freq="M"
+                        )
+                        temp = df[
+                            pd.date_range(
+                                str(year - i) + "-01-01", periods=12, freq="M"
+                            )
+                        ] - (delta_multiply * delta60)
+
+                        final_temp = pd.concat((final_temp, temp), axis=1)
+
+                df_monthly = final_temp
+                df[
+                    pd.date_range(str(year) + "-01-01", periods=12, freq="M")
+                ] = final_temp.groupby(final_temp.columns.month, axis=1).mean()
+                # 5 year monthly data
+                df_5y_m = df[df.columns[df.columns.year.isin(years)]]
+                # 5 year annual
+                # 50th quantile - q50
+                df_q50 = (
+                    df_monthly.rolling(240, min_periods=1, axis=1)
+                    .quantile(0.5, interpolation="linear")
+                    .resample("5Y", axis=1)
+                    .mean()
+                )
+                df_q50["2020-12-31"] = val_2020_annual
+                # 70th quantile - q70
+                df_q70 = (
+                    df_monthly.rolling(240, min_periods=1, axis=1)
+                    .quantile(0.3, interpolation="linear")
+                    .resample("5Y", axis=1)
+                    .mean()
+                )
+                df_q70["2020-12-31"] = val_2020_annual
+                # 90th quantile - q90
+                df_q90 = (
+                    df_monthly.rolling(240, min_periods=1, axis=1)
+                    .quantile(0.1, interpolation="linear")
+                    .resample("5Y", axis=1)
+                    .mean()
+                )
+
+                df_q90["2020-12-31"] = val_2020_annual
+
+                delta_multiply -= 0.2
+
+            else:
+                # after detlta years
+                temp_daterange = pd.date_range(
+                    str(year - 4) + "-01-01", periods=60, freq="M"
+                )
+                # Monthly Bias corrected data
+                df_monthly = df
+
+                df[pd.date_range(str(year) + "-01-01", periods=12, freq="M")] = (
+                    df[temp_daterange]
+                    .groupby(df[temp_daterange].columns.month, axis=1)
+                    .mean()
+                )
+                # 5 year monthly
+                df_5y_m = df[df.columns[df.columns.year.isin(years)]]
+                # 5 year annual
+                # 50th quantile - q50
+                df_q50 = (
+                    df_monthly.rolling(240, min_periods=1, axis=1)
+                    .quantile(0.5, interpolation="linear")
+                    .resample("5Y", axis=1)
+                    .mean()
+                )
+                df_q50["2020-12-31"] = val_2020_annual
+                # 70th quantile - q70
+                df_q70 = (
+                    df_monthly.rolling(240, min_periods=1, axis=1)
+                    .quantile(0.3, interpolation="linear")
+                    .resample("5Y", axis=1)
+                    .mean()
+                )
+                df_q70["2020-12-31"] = val_2020_annual
+                # 90th quantile - q90
+                df_q90 = (
+                    df_monthly.rolling(240, min_periods=1, axis=1)
+                    .quantile(0.1, interpolation="linear")
+                    .resample("5Y", axis=1)
+                    .mean()
+                )
+
+                df_q90["2020-12-31"] = val_2020_annual
+
+    return df, df_monthly, df_5y_m, df_q50, df_q70, df_q90
+
+
+years = np.arange(2015, 2105, 5)
+
+df_26, df_monthly_26, df_5y_m_26, df_q50_26, df_q70_26, df_q90_26 = bias_correction(
+    qtot_2p6_gfdl
+)
+
+df_70, df_monthly_70, df_5y_m_70, df_q50_70, df_q70_70, df_q90_70 = bias_correction(
+    qtot_7p0_gfdl
+)
+
+
+df_monthly_26.to_csv(wd11 + f"/{var}_monthly_2p6_{iso3}.csv")
+df_5y_m_26.to_csv(wd11 + f"/{var}_5y_m_2p6_low_{iso3}.csv")
+df_q50_26.to_csv(wd11 + f"/{var}_5y_2p6_low_{iso3}.csv")
+df_q70_26.to_csv(wd11 + f"/{var}_5y_2p6_med_{iso3}.csv")
+df_q90_26.to_csv(wd11 + f"/{var}_5y_2p6_high_{iso3}.csv")
+
+df_monthly_70.to_csv(wd11 + f"/{var}_monthly_7p0_{iso3}.csv")
+df_5y_m_70.to_csv(wd11 + f"/{var}_5y_m_7p0_low_{iso3}.csv")
+df_q50_70.to_csv(wd11 + f"/{var}_5y_7p0_low_{iso3}.csv")
+df_q70_70.to_csv(wd11 + f"/{var}_5y_7p0_med_{iso3}.csv")
+df_q90_70.to_csv(wd11 + f"/{var}_5y_7p0_high_{iso3}.csv")
+
+# No climate scenarios
+df_q50_70.apply(lambda x: val_2020_annual)
+for y in years:
+    df_5y_m_70[pd.date_range(f"{y}-01-01", periods=12, freq="M")] = val_2020
+df_5y_m_70.to_csv(wd11 + f"/{var}_5y_m_no_climate_low_{iso3}.csv")
+df_q50_70.apply(lambda x: val_2020_annual).to_csv(
+    wd11 + f"/{var}_5y_no_climate_low_{iso3}.csv"
+)
+df_q70_70.apply(lambda x: val_2020_annual).to_csv(
+    wd11 + f"/{var}_5y_no_climate_med_{iso3}.csv"
+)
+df_q90_70.apply(lambda x: val_2020_annual).to_csv(
+    wd11 + f"/{var}_5y_no_climate_high_{iso3}.csv"
+)
+
+
+## Environmental Flow
+
+df_env = df_monthly_70
+
+# df = data.iloc[:,5:]
+for z in range(len(df_env.columns) // 12):
+    col_start = 0 if z == 0 else col_end  # start col number
+    col_end = (z + 1) * 12  # ending col number
+    temp = df_env.iloc[:, col_start:col_end]  # assigning relevant data
+
+    col_names = temp.columns  # getting column names
+    MAF = temp.mean(axis=1)  # take mean acorss
+
+    for j in range(len(temp.columns)):
+        temp.iloc[:, j] = np.where(
+            temp.iloc[:, j] > 0.8 * MAF,
+            temp.iloc[:, j] * 0.2,
+            np.where(
+                (temp.iloc[:, j] > 0.4 * MAF) & (temp.iloc[:, j] <= 0.8 * MAF),
+                temp.iloc[:, j] * 0.45,
+                temp.iloc[:, j] * 0.6,
+            ),
+        )
+
+    if z == 0:
+        eflow = temp
+    else:
+        eflow = pd.concat((eflow, temp), axis=1)
+
+    eflow = eflow.abs()
+
+# Convert to 5 year annual values
+eflow_5y = eflow.resample("5Y", axis=1).mean()
+eflow_5y.to_csv(wd11 + f"/e-flow_7p0_ZMB.csv")
+
+for year in np.arange(2020, 2105, 5):
+    for i in np.arange(4, -1, -1):
+        if i == 4:
+            final_temp = eflow[
+                pd.date_range(str(year - i) + "-01-01", periods=12, freq="M")
+            ]
+
+        else:
+            final_temp = eflow[
+                pd.date_range(str(year - i) + "-01-01", periods=12, freq="M")
+            ]
+
+    df_monthly = final_temp
+    eflow[
+        pd.date_range(str(year) + "-01-01", periods=12, freq="M")
+    ] = final_temp.groupby(final_temp.columns.month, axis=1).mean()
+
+    eflow_5y_m = eflow[eflow.columns[eflow.columns.year.isin(years)]]
+
+    eflow_5y_m.to_csv(wd11 + f"/e-flow_5y_m_7p0_ZMB.csv")
+
+val_2020_eflow = eflow_5y_m[pd.date_range(f"2020-01-01", periods=12, freq="M")].values()
+val_2020_eflowy = eflow_5y["2020-12-31"]
+eflow_5y.apply(lambda x: val_2020_eflowy).to_csv(wd11 + f"/e-flow_no_climate_ZMB.csv")
+
+for y in years:
+    eflow_5y_m[pd.date_range(f"{y}-01-01", periods=12, freq="M")] = val_2020_eflow
+
+eflow_5y_m.to_csv(wd11 + f"/e-flow_5y_m_no_climate_ZMB.csv")
diff --git a/nest/message_ix_models/model/water/data/pre-processing/hydro_agg_temp1.py b/nest/message_ix_models/model/water/data/pre-processing/hydro_agg_temp1.py
new file mode 100644
index 0000000..d1c5f3a
--- /dev/null
+++ b/nest/message_ix_models/model/water/data/pre-processing/hydro_agg_temp1.py
@@ -0,0 +1,485 @@
+"""
+This script aggregates the global gridded data to any scale. The following
+script specifically aggregates global gridded hydrological data onto the basin
+ mapping used in the nexus module.
+"""
+import os
+import sys
+
+print(sys.executable)
+# import salem
+import glob
+
+#  Import packages
+from datetime import datetime as dt
+
+import dask
+import numpy as np
+import pandas as pd
+import xarray as xr
+
+start = dt.now()
+from dask.diagnostics import ProgressBar
+
+# from salem import open_wrf_dataset, get_demo_file
+
+
+# variable, for detailed symbols, refer to ISIMIP2b documentation
+variables = [
+    "qtot",  # total runoff
+    "dis",  # discharge
+    "qg",  # groundwater runoff
+    "qr",
+]  # groudnwater recharge
+var = "qr"
+
+isimip = "3b"
+
+data = "future"  # else future
+
+# %%
+# if multimodelensemble:
+#     # iterate through scenarios (3)
+#     for model in climmodels:
+#         hydro_data = wd + f"\hydrology\{model}*{scen}*{var}*.nc4"
+#         files = glob.glob(hydro_data)
+#         ds = xr.Dataset()
+#         # Iterate through 5 GCMs for each scenario
+#         for file in files:
+#             model = file[57:-57]
+#             da = xr.open_dataarray(file, decode_times=False)  # open
+#             # da['time'] = np.arange(2005,2100, dtype=int) #fix time
+#             ds[model] = da  # add to main dataset
+#         # collapse the 4 GCMs to one dataarray and take mean
+#         ds["multimodelmean"] = ds.to_array(dim="memean").mean(axis=0)
+#         # Assign attributes
+#         ds.attrs["scenario"] = scen
+#         ds.to_netcdf(wd + f"{var}_{scen}_non_agg_combined.nc")
+
+# else:
+# wd = "P:\watxene\ISIMIP\ISIMIP3b\CWatM_results\gfdl-esm4\future"
+
+for cl in climmodels:
+    # climate model
+    if isimip == "2b":
+        climmodels = ["gfdl-esm2m", "hadgem2-es", "ipsl-cm5a-lr", "miroc5"]
+        climmodel = "gfdl-esm2m"
+        # climate forcing
+        scenarios = ["rcp26", "rcp60"]
+        scen = "rcp26"
+        wd1 = os.path.join("p:", "ene.model", "NEST", "hydrological_data_agg")
+        wd = os.path.join("p:", "watxene", "ISIMIP", "ISIMIP2b", "output", "LPJmL")
+        wd2 = os.path.join("p:", "ene.model", "NEST", "hydrology", "processed_nc4")
+    else:
+        climmodels = [
+            "gfdl-esm4",
+            "ipsl-cm6a-lr",
+            "mpi-esm1-2-hr",
+            "mri-esm2-0",
+            "ukesm1-0-ll",
+        ]
+        scenarios = ["ssp126", "ssp370", "ssp585"]
+        scen = "ssp126"
+        wd1 = os.path.join("p:", "ene.model", "NEST", "hydrological_data_agg")
+        wd = os.path.join(
+            "p:", "watxene", "ISIMIP", "ISIMIP3b", "CWatM_results", f"{cl}", f"{data}"
+        )
+        wd2 = os.path.join("p:", "ene.model", "NEST", "hydrology", "processed_nc4")
+
+    # Define if monthly aggregation is required
+    monthlyscale = True
+    # define qauantile for statistical aggregation
+    quant = 0.1
+    # define if multi climate models mean
+    multimodelensemble = True
+    # deinfe lat and long chunk for reducing computational load
+    latchunk = 120
+    lonchunk = 640
+    # Define if use all touched raster
+    all_touched = True
+
+    if var == "dis":
+        # define a spatial method to aggregate
+        spatialmethod = "meansd"
+    else:
+        # define a spatial method to aggregate
+        spatialmethod = "sum"
+
+    """
+    The hydrological data can be accessed in watxene p drive. For accessing
+    particular drive, seek permission from Edward Byers (byers@iiasa.ac.at)
+    The files should be copied on to local drive
+    """
+
+    # Open raster area file
+    # The file landareamaskmap0.nc can be found under
+    # P:\ene.model\NEST\delineation\data\delineated_basins_new
+
+    # t = np.arange(datetime(2006,1,1), datetime(2099,1,1), timedelta(month =1)).astype(datetime)
+    # t = np.arange(np.datetime64('2006-01-01'), np.datetime64('2099-01-08'))
+    area = xr.open_dataarray(wd1 + "\landareamaskmap0.nc")
+
+    # TO AVOID ERROR WHEN OPENING AND SLICING INPUT DATA - CHECK!
+    dask.config.set({"array.slicing.split-large-chunks": False})
+
+    if data == "historical":
+        hydro_data = wd + f"\*{cl}*{var}*monthly*.nc"
+    elif data == "future":
+        hydro_data = wd + f"\*{cl}*{scen}*{var}*monthly*.nc"
+
+    files = glob.glob(hydro_data)
+    if var != "qr":
+        # Open hydrological data as a combined dataset
+        da = xr.open_mfdataset(files)
+    else:
+        # Open hydrological data as a combined dataset
+        da = xr.open_mfdataset(files)
+        # da["time"] = pd.date_range(start="1/1/2015", end="31/12/2100", freq="M")
+
+        # da.to_netcdf(wd+f'\{var}_memean_{scen}.nc')
+
+    if monthlyscale:
+        # da = da.sel(time=slice('2010-01-01', '2025-12-31'))
+        # years = pd.DatetimeIndex(da["time"].values)
+        years = np.arange(2010, 2105, 5)
+    else:
+        years = np.arange(2010, 2105, 5)
+
+    # ds = xr.open_dataset(wd+f'{var}_memean_{scen}.nc')
+    # Resample daily data to monthly (by summing daily values)
+
+    # if var != "qr":
+    #     da = da.resample(time="M").mean()
+    #     # Calculate roolling average of 3 months to make the data consistent
+    #     da = da.rolling(time=3, min_periods=1).mean()
+
+    # da = da.fillna(0)
+
+    # %%
+    if monthlyscale:
+        # dpm = {
+        #     "noleap": [0, 31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31],
+        #     "365_day": [0, 31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31],
+        #     "standard": [0, 31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31],
+        #     "gregorian": [0, 31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31],
+        #     "proleptic_gregorian": [0, 31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31],
+        #     "all_leap": [0, 31, 29, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31],
+        #     "366_day": [0, 31, 29, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31],
+        #     "360_day": [0, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30],
+        # }
+
+        # def leap_year(year, calendar="standard"):
+        #     """Determine if year is a leap year"""
+        #     leap = False
+        #     if (
+        #         calendar in ["standard", "gregorian", "proleptic_gregorian", "julian"]
+        #     ) and (year % 4 == 0):
+        #         leap = True
+        #         if (
+        #             (calendar == "proleptic_gregorian")
+        #             and (year % 100 == 0)
+        #             and (year % 400 != 0)
+        #         ):
+        #             leap = False
+        #         elif (
+        #             (calendar in ["standard", "gregorian"])
+        #             and (year % 100 == 0)
+        #             and (year % 400 != 0)
+        #             and (year < 1583)
+        #         ):
+        #             leap = False
+        #     return leap
+
+        # def get_dpm(time, calendar="standard"):
+        #     """
+        #     return a array of days per month corresponding to the months provided
+        #     in `months`
+        #     """
+        #     month_length = np.zeros(len(time), dtype=np.int)
+        #     cal_days = dpm[calendar]
+        #     for i, (month, year) in enumerate(zip(time.month, time.year)):
+        #         month_length[i] = cal_days[month]
+        #         if leap_year(year, calendar=calendar):
+        #             month_length[i] += 1
+        #     return month_length
+
+        # month_length = xr.DataArray(
+        #     get_dpm(da.time.to_index(), calendar="standard"),
+        #     coords=[da.time],
+        #     name="month_length",
+        # )
+
+        # da = (da * month_length).resample(time="QS").sum() / month_length.resample(
+        #     time="QS"
+        # ).sum()
+
+        if var == "dis":
+            # # Converts the discharge  into km3/year
+            da = da * 0.031556952
+            da["dis"] = da.dis.chunk(
+                {"lat": latchunk, "lon": lonchunk, "time": len(da.dis.time)}
+            )
+            # slice is required to reduce the dataset.
+            # da = da.sel(time=slice("2010-01-01", "2050-12-31"))
+
+            da.dis.attrs["unit"] = "km3/year"
+            # chunking reduces computational burden
+            da["dis"] = da.dis.chunk(
+                {"lat": latchunk, "lon": lonchunk, "time": len(da.dis.time)}
+            )
+            # saving the file here and reloading reduces disk space
+            da.to_netcdf(wd2 + f"{var}_monthly__{cl}_{scen}.nc")
+        elif var == "qtot":
+            # da["qtot"] = da.qtot.chunk(
+            #     {"lat": latchunk, "lon": lonchunk, "time": len(da.qtot.time)}
+            # )
+            # Filter 5 year interval monthly time step
+            # da = da.sel(time=da.time.dt.year.isin(years))
+            # 1kg/m2/sec = 86400 mm/day
+            # 86400 mm/day X  Area (mm2) = 86400 mm3/day
+            # 86400 mm3/day = 86400 X  1000000 3.65 e-16 km3/year
+            da = da * 86400 * area * 3.65e-16 * 1000000
+
+            # Resample daily data to monthly (by summing daily values)
+            # da = da.resample(time="M").mean()
+
+            # da.rolling(time=12, center=True).construct(
+            #     'tmp').quantile(.1, dim='tmp', skipna=False).dropna(
+            #         'time')
+
+            # da = da.rolling(time=20, min_periods=1).mean()
+            # da["qtot"] = da.qtot.chunk(
+            #     {"lat": latchunk, "lon": lonchunk, "time": len(da.qtot.time)}
+            # )
+
+            da.to_netcdf(wd2 + f"\{var}_monthly_{cl}_{scen}_{data}.nc")
+        elif var == "qr":
+            # Filter 5 year interval monthly time step
+            # da = da.sel(time=da.time.dt.year.isin(years))
+            # 1kg/m2/sec = 86400 mm/day
+            # 86400 mm/day X  Area (mm2) = 86400 mm3/day
+            # 86400 mm3/day = 86400 X  1000000 3.65 e-16 km3/year
+            da = da * 86400 * area * 3.65e-16 * 1000000
+            da.qr.attrs["unit"] = "km3/year"
+            # da["qr"] = da.qr.chunk(
+            #     {"lat": latchunk, "lon": lonchunk, "time": len(da.qr.time)}
+            # )
+
+            da.to_netcdf(wd2 + f"\{var}_monthly_{cl}_{scen}_{data}.nc")
+
+    else:
+        if var == "dis":
+            da["dis"] = da.dis.chunk(
+                {"lat": latchunk, "lon": lonchunk, "time": len(da.dis.time)}
+            )
+            # Resample daily data to monthly (by summing daily values)
+            da = da.resample(time="Y").mean()
+
+            da["dis"] = da.dis.chunk(
+                {"lat": latchunk, "lon": lonchunk, "time": len(da.dis.time)}
+            )
+            # Take 20 year rolling average to make the time scale cosnistent
+            da = da.rolling(time=20).mean()
+
+            # Converts the discharge  into km3/year
+            da = da * 0.031556952
+            da.dis.attrs["unit"] = "km3/year"
+            # Long term Mean annual  discharge
+            davg = da.resample(time="30Y", loffset="4Y").mean()
+            davg["dis"] = davg.dis.chunk(
+                {"lat": latchunk, "lon": lonchunk, "time": len(davg.dis.time)}
+            )
+            davg.to_netcdf(
+                wd2 + f"\output\{var}_90Y_avg_5y__{climmodel}_{scen}_temp_agg.nc"
+            )
+
+            # Now resample to an average value for each 5-year block, and
+            # offset by 2 years so that the value is centred to start 2101
+            da = da.resample(time="5Y", loffset="4Y").mean()
+
+            da.to_netcdf(wd2 + f"\output\{var}_5y__{climmodel}_{scen}_temp_agg.nc")
+            # read in saved data again to further process
+            da = xr.open_dataset(
+                wd2 + f"\output\{var}_5y__{climmodel}_{scen}_temp_agg.nc"
+            )
+
+        elif var == "qtot":
+            da["qtot"] = da.qtot.chunk(
+                {"lat": latchunk, "lon": lonchunk, "time": len(da.qtot.time)}
+            )
+            # Resample daily data to monthly (by summing daily values)
+            da = da.resample(time="Y").mean()
+            da["qtot"] = da.qtot.chunk(
+                {"lat": latchunk, "lon": lonchunk, "time": len(da.qtot.time)}
+            )
+            da = da.rolling(time=20, min_periods=1).mean()
+            da["qtot"] = da.qtot.chunk(
+                {"lat": latchunk, "lon": lonchunk, "time": len(da.qtot.time)}
+            )
+            # Converts to total runoff per gs into km3/year
+            da = da * 86400 * area * 3.65e-16 * 1000000
+            da.qtot.attrs["unit"] = "km3/year"
+            da["qtot"] = da.qtot.chunk(
+                {"lat": latchunk, "lon": lonchunk, "time": len(da.qtot.time)}
+            )
+
+            # Calculate roolling average of 20 years to make the data consistent
+            # da = da.rolling(time = 20).mean()
+            # Now resample to an average value for each 5-year block
+            da = da.resample(time="5Y", loffset="4Y").mean()
+            da.to_netcdf(wd2 + f"\output\{var}_monthly__{climmodel}_{scen}_temp_agg.nc")
+            # read in saved data again to further process
+            # da = xr.\
+            #    open_dataset( wd2+f'\output\{var}_5y__{climmodel}_{scen}_temp_agg.nc')
+            # da['qtot'] = da.qtot.chunk({'lat': latchunk, 'lon': lonchunk,\
+            #                                    'time': len(da.qtot.time)})
+
+        elif var == "qr":
+            da["qr"] = da.qr.chunk(
+                {"lat": latchunk, "lon": lonchunk, "time": len(da.qr.time)}
+            )
+            # Resample daily data to monthly (by summing daily values)
+            da = da.resample(time="Y").mean()
+            da["qr"] = da.qr.chunk(
+                {"lat": latchunk, "lon": lonchunk, "time": len(da.qr.time)}
+            )
+            da = da.rolling(time=20, min_periods=1).mean()
+            da["qr"] = da.qr.chunk(
+                {"lat": latchunk, "lon": lonchunk, "time": len(da.qr.time)}
+            )
+            # TODO check unit
+            # 1kg/m2/sec = 86400 mm/day
+            # 86400 mm/day X  Area (mm2) = 86400 mm3/day
+            # 86400 mm3/day = 86400 X  1000000 3.65 e-16 km3/year
+            da = da * 86400 * area * 3.65e-16 * 1000000
+            da.qr.attrs["unit"] = "km3/year"
+            da["qr"] = da.qr.chunk(
+                {"lat": latchunk, "lon": lonchunk, "time": len(da.qr.time)}
+            )
+
+            # Calculate roolling average of 20 years to make the data consistent
+            # da = da.rolling(time = 3).mean()
+            # Now resample to an average value for each 5-year block
+            da = da.resample(time="5Y", loffset="4Y").mean()
+
+            da.to_netcdf(wd2 + f"\output\{var}_5y__{climmodel}_{scen}.nc")
+            # read in saved data again to further process
+            # da = xr.\
+        #     open_dataset( wd+f'\output\{var}_5y__{climmodel}_{scen}_temp_agg.nc')
+# %%
+# if all_touched:
+#     # open raster fle in wdhsapes directory, if not there copy from p drive
+#     basinraster = xr.open_dataarray(
+#         wd + "\delineation\\basins_by_region_simpl_R11_at.nc"
+#     )
+# else:
+#     # open raster fle in wdhsapes directory, if not there copy from p drive
+#     basinraster = xr.open_dataarray(
+#         wd + "\delineation\\basins_by_region_simpl_R11_nt.nc"
+#     )
+# # helps to load the dataset into memory
+# with ProgressBar():
+#     da.compute()
+# #%% also
+# # read shapefile of basins
+# shp_fn = wd + "\delineation\\basins_by_region_simpl_R11.shp"
+# shapes = salem.read_shapefile(shp_fn, cached=True)
+
+# df = shapes[["BASIN_ID", "BCU_name"]]
+# df["Model"] = f"LPJML"
+# df["Scenario"] = f"{climmodel}_{scen}"
+# df["Variable"] = f"{var}"
+# df["Region"] = df["BCU_name"]
+
+# if var == "dis":
+#     df["Unit"] = "km3/year"
+# else:
+#     df["Unit"] = "km3/year"
+
+
+# for row in df.itertuples():
+#     for year in years:
+#         basinid = row.Index  # BASIN_ID
+#         print(row.Index)
+
+#         if monthlyscale:
+#             da_t = da.sel(time=da.time.isin(years))
+#         else:
+#             da_t = da.sel(time=da.time.dt.year.isin(years))
+#         # for year in years:
+#         yr = f"{year}"
+#         if var == "dis":
+#             val = da_t.dis.where(basinraster == basinid)
+#         elif var == "qtot":
+#             val = da_t.qtot.where(basinraster == basinid)
+#         elif var == "qr":
+#             val = da_t.qr.where(basinraster == basinid)
+
+#         std = val.squeeze().std().values
+
+#         # Try dim dimension and provide exact dimension
+#         # Handle non-nans
+#         if spatialmethod == "sum":
+#             val = val.sum()
+#         elif spatialmethod == "max":
+#             val = val.max()
+#         elif spatialmethod == "mean":
+#             val = val.mean()
+#         elif spatialmethod == "quantile":
+#             val = val.quantile(quant)
+#         elif spatialmethod == "meansd":
+#             val = std + val.mean()
+
+#         df.loc[row.Index, year] = val.values
+#         print(dt.now() - start)
+
+# #%%
+# # val.to_netcdf(wd+ f'\output\{var}_5y__{climmodel}_{scen}_aggregated.nc')
+
+# df.to_csv(
+#     wd + "\output" + f"\LPJML_{var}_{climmodel}_{scen}_{spatialmethod}_5y_rasternew.csv"
+# )
+
+
+# if all_touched:
+
+#     df.to_csv(
+#         wd + "\output" + f"\LPJML_{var}_{climmodel}_{scen}_{spatialmethod}_5y_at.csv"
+#     )
+# else:
+#     df.to_csv(
+#         wd + "\output" + f"\LPJML_{var}_{climmodel}_{scen}_{spatialmethod}_5y_nt.csv"
+#     )
+
+
+# #%%
+# air = ds.air.isel(time=0)
+
+# df, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(8, 6))
+
+# # The first plot (in kelvins) chooses "viridis" and uses the data's min/max
+# air.plot(ax=ax1, cbar_kwargs={"label": "K"})
+# ax1.set_title("Kelvins: default")
+# ax2.set_xlabel("")
+
+# # The second plot (in celsius) now chooses "BuRd" and centers min/max around 0
+# airc = air - 273.15
+# airc.plot(ax=ax2, cbar_kwargs={"label": "°C"})
+# ax2.set_title("Celsius: default")
+# ax2.set_xlabel("")
+# ax2.set_ylabel("")
+
+# # The center doesn't have to be 0
+# air.plot(ax=ax3, center=273.15, cbar_kwargs={"label": "K"})
+# ax3.set_title("Kelvins: center=273.15")
+
+# # Or it can be ignored
+# airc.plot(ax=ax4, center=False, cbar_kwargs={"label": "°C"})
+# ax4.set_title("Celsius: center=False")
+# ax4.set_ylabel("")
+
+# # Make it nice
+# plt.tight_layout()
+
+# da = da.qtot.isel(time=da.time.isin(year)).salem.quick_map()
+# da.salem.quick_map()
diff --git a/nest/message_ix_models/model/water/data/pre-processing/hydro_agg_temp2.py b/nest/message_ix_models/model/water/data/pre-processing/hydro_agg_temp2.py
new file mode 100644
index 0000000..cdc87b9
--- /dev/null
+++ b/nest/message_ix_models/model/water/data/pre-processing/hydro_agg_temp2.py
@@ -0,0 +1,362 @@
+"""
+This script calculates envrionmental flows using monthly runoff (qtot from
+                                                                 ISIMIP)
+It calculates environmental flows using Variable Monthly Flow method
+
+"""
+
+import os
+from sqlite3 import TimeFromTicks
+
+import numpy as np
+import pandas as pd
+
+type_reg = "global"  # else 'global'
+country = "ZMB"
+global_reg = "R11"
+isimip = "3b"
+
+if type_reg == "global":
+    wd = os.path.join("p:", "ene.model", "NEST", "hydrological_data_agg")
+    # upload_files = glob.glob(os.path.join(upload_dir, "MESSAGEix*.xlsx"))
+    wd_out = os.path.join(
+        "p:",
+        "ene.model",
+        "NEST",
+        "hydrology",
+        "post-processed_qs",
+        f"global_{global_reg}",
+    )
+
+
+elif type_reg == "country":
+    # This is for Zambia
+    wd = os.path.join("p:", "ene.model", "NEST", country, "hydrology")
+    wd_out = os.path.join(
+        "p:", "ene.model", "NEST", country, "hydrology", "post-processed"
+    )
+
+
+# climate model
+if isimip == "2b":
+    climmodels = ["gfdl-esm2m", "hadgem2-es", "ipsl-cm5a-lr", "miroc5"]
+    climmodel = "gfdl-esm2m"
+    # climate forcing
+    scenarios = ["rcp26", "rcp60"]
+    scen = "rcp26"
+    wd1 = os.path.join("p:", "ene.model", "NEST", "hydrological_data_agg")
+    wd = os.path.join("p:", "watxene", "ISIMIP", "ISIMIP2b", "output", "LPJmL")
+    wd2 = os.path.join("p:", "ene.model", "NEST", "hydrology", "processed_nc4")
+else:
+    climmodels = [
+        "gfdl-esm4",
+        "ipsl-cm6a-lr",
+        "mpi-esm1-2-hr",
+        "mri-esm2-0",
+        "ukesm1-0-ll",
+    ]
+    scenarios = ["ssp126", "ssp370", "ssp585"]
+    scen = "ssp126"
+    wd1 = os.path.join("p:", "ene.model", "NEST", "hydrological_data_agg")
+    wd = os.path.join(
+        "p:", "watxene", "ISIMIP", "ISIMIP3b", "CWatM_results", f"{cl}", f"{data}"
+    )
+    wd2 = os.path.join("p:", "ene.model", "NEST", "hydrology", "processed_nc4")
+
+# Define the scenario 'extreme' or 'normal'
+extreme_scen = "high"
+
+
+# climate forcing
+scenarios = ["rcp26", "rcp60"]
+
+scen = "rcp60"
+# variable, for detailed symbols, refer to ISIMIP2b documentation
+variables = [
+    "qtot",  # total runoff
+    "dis",  # discharge
+    "qg",  # groundwater runoff
+    "qr",  # groundwater recharge
+]
+var = "qr"
+# climate model
+climmodels = ["gfdl-esm2m", "hadgem2-es", "ipsl-cm5a-lr", "miroc5"]
+climmodel = "miroc5"
+timestep = "monthly"
+
+
+# select interpolation method
+intp = "linear"
+quant = 0.5
+env_flow = True
+
+# Read sample file to keep all columns
+df_chk = pd.read_csv(wd + f"\{var}_monthly_miroc5_rcp26.csv")
+
+# Reading files and taking multimodel ensemble
+# df1 = pd.read_csv(wd + f"\{var}_{timestep}_miroc5_{scen}.csv").iloc[:, 8:]
+df2 = pd.read_csv(wd + f"\{var}_{timestep}_gfdl-esm2m_{scen}.csv").iloc[:, 8:]
+df3 = pd.read_csv(wd + f"\{var}_{timestep}_hadgem2-es_{scen}.csv").iloc[:, 8:]
+df4 = pd.read_csv(wd + f"\{var}_{timestep}_ipsl-cm5a-lr_{scen}.csv").iloc[:, 8:]
+
+
+data = pd.concat([df2, df3, df4]).groupby(level=0).mean()
+# data["basin"] = "B" + df_chk["BCU_name"]
+
+# data.to_csv(wd + f"\{var}_{timestep}_{scen}_{extreme_scen}_{country}.csv")
+
+if extreme_scen == "high":
+    data = data.rolling(240, min_periods=1, axis=1).quantile(quant, interpolation=intp)
+    # data = data.rolling(
+    #     240, min_periods = 1, axis = 1).mean()
+elif extreme_scen == "med":
+    data = data.rolling(240, min_periods=1, axis=1).quantile(0.5, interpolation=intp)
+    # data = data.rolling(
+    #     240, min_periods = 1, axis = 1).mean()
+
+new_cols = pd.to_datetime(data.columns, format="sum.X%Y.%m.%d")
+data.columns = new_cols
+data = data.iloc[:, 48:]
+# data = data.resample('5Y', axis = 1).mean()
+
+data.to_csv(wd_out + f"\{var}_monthly_mmean_{scen}.csv")
+
+# df_6p0_temp = df_6p0.iloc[:, :216]
+
+# df = df_6p0_temp.copy()
+
+# # Convert monthly values to wet and dry
+# for z in range(len(df.columns) // 12):
+#     col_end = (z + 1) * 12  # ending col number
+#     col_start = 0 if z == 0 else col_end  # start col number
+
+#     data = df.iloc[:, col_start:col_end]  # assigning relevant data
+#     maxindex = data.idxmax(axis=1)  # getting max indexes
+
+#     wet_name = "mean." + maxindex[0][4:9] + "_wet"
+#     dry_name = "mean." + maxindex[0][4:9] + "_dry"
+
+#     year_data = pd.DataFrame(
+#         columns=[wet_name, dry_name]
+#     )  # creaing and empty data frame
+
+#     for i in range(len(maxindex)):
+#         col_names = data.columns  # getting column names
+#         get_col_index = col_names == maxindex[i]  # getting match column index
+#         wet_start = (
+#             0
+#             if np.where(get_col_index)[0] - 3 < 0
+#             else np.where(get_col_index)[0][0] - 3
+#         )  # condition for negative col
+#         wet_add = np.where(get_col_index)[0] - 3  # getting negative columns
+#         wet_end = np.where(get_col_index)[0] + 2
+#         wet_col = col_names[wet_start : wet_end[0] + 1].tolist()
+
+#         # for adding negative columns
+#         if np.where(get_col_index)[0] - 3 >= 0:
+#             wet_col
+#         else:
+#             wet_col.extend(col_names[wet_add[0] :].tolist())
+
+#         # for adding positive columns
+#         if len(wet_col) < 6:
+#             col_add = 6 - len(wet_col)
+#             wet_col.extend(col_names[:col_add])
+
+#         start_row = 0 if i == 0 else end_row
+#         end_row = i + 1
+
+#         wet_mean = data.iloc[start_row:end_row, :][wet_col].mean(axis=1)
+#         dry_mean = (
+#             data.iloc[start_row:end_row, :]
+#             .loc[:, ~data.iloc[start_row:end_row, :].columns.isin(wet_col)]
+#             .mean(axis=1)
+#         )
+
+#         row_array = np.array([wet_mean[i], dry_mean[i]])  # making an array
+
+#         year_data.loc[len(year_data)] = row_array  # appending an array
+
+
+#     #
+#     # Final data frame after choosing multi model mean
+#     df_mmmean[scen] = pd.concat(
+#         [df1, df2, df3]).groupby(
+#             level=0).mean()
+
+#     dffinal[scen] = temporal_agg(df_mmmean[scen],extreme_scen)
+
+#     # data_1_mean = temporal_agg(df2,extreme_scen)
+#     # data_2_mean = temporal_agg(df3,extreme_scen)
+#     # data_3_mean = temporal_agg(df4,extreme_scen)
+
+# print("Done")
+
+# df_2p6_temp1 = final_data.copy()
+# df_2p6_temp1.to_csv(wd + r"\qtot_wetdry_multimodelmean_rcp2p6.csv")
+
+# # df_2p6_temp1 = pd.read_csv(wd + r"\qtot_wetdry_multimodelmean_rcp2p6.csv")
+
+# final_data = df_2p6_temp1.copy()
+
+# df_6p0_temp1 = final_data.copy()
+# df_6p0_temp1.to_csv(wd + r"\qtot_wetdry_multimodelmean_rcp6p0.csv")
+
+# df_6p0_temp1 = pd.read_csv(wd + r"\qtot_wetdry_multimodelmean_rcp6p0.csv")
+
+# mean_values = np.stack((data_0_mean.mean(axis = 1).values,
+#                         data_1_mean.mean(axis = 1).values,
+#                         data_2_mean.mean(axis = 1).values))
+#                         # data_3_mean.mean(axis = 1).values))
+
+# final_array = []
+# for i, min_col in enumerate(mean_values.argmin(axis = 0)):
+#     if min_col == 0:
+#         final_array.append(data_0_mean.iloc[i].values)
+#     elif min_col == 1:
+#         final_array.append(data_1_mean.iloc[i].values)
+#     elif min_col == 2:
+#         final_array.append(data_2_mean.iloc[i].values)
+# else:
+#     final_array.append(data_3_mean.iloc[i].values)
+if GCM == "dry":
+    # Final data frame after choosing driest GCM
+    dfs_dry[scen] = pd.DataFrame(final_array, columns=data_0_mean.columns)
+
+# Calculating environmental flows using wet and dry seasonal calculations
+for i in range(len(final_data.columns) // 2):
+    col_start = 0 if i == 0 else col_end
+    col_end = (i + 1) * 2
+    data = final_data.iloc[:, col_start:col_end]
+
+    name = data.columns[0][6:10]
+
+    wet_mul = data.iloc[:, 0]  # * 0.6
+    dry_mul = data.iloc[:, 1]  # * 0.3
+
+    mul_data = (wet_mul + dry_mul) / 2
+
+    if i == 0:
+        PCT_data = pd.DataFrame(mul_data, columns=[name])
+    else:
+        tmp_data = pd.DataFrame(mul_data, columns=[name])
+        PCT_data = pd.concat([PCT_data, tmp_data], axis=1)
+
+print("Done")
+
+#                     mul_data = (wet_mul + dry_mul) / 2
+#                     value.append(mul_data.values)
+#                 # Developed basins
+#                 else:
+#                     temp2 = data[data["BCU_name"] == j]
+#                     wet_mul = temp2.iloc[:, 0] * wetvalind
+#                     dry_mul = temp2.iloc[:, 1] * dryvalind
+
+#                     mul_data = (wet_mul + dry_mul) / 2
+#                     value.append(mul_data.values)
+
+#         if i == 0:
+#             PCT_data = pd.DataFrame(data=value, columns=[name])
+#         else:
+#             tmp_data = pd.DataFrame(data=value, columns=[name])
+#             PCT_data = pd.concat([PCT_data, tmp_data], axis=1)
+
+#     return PCT_data
+
+dfs_dry = {}
+df_mmmean = {}
+dfs_wd = {}
+dfs_ann = {}
+df_env = {}
+eflow = {}
+eflow1 = {}
+eflow2 = {}
+dffinal = {}
+var = "qtot"
+env_flow = True
+
+for scen in scenarios:
+    # Reading data files from four GCMs
+    df = pd.read_csv(wd + f"\{var}_monthly_{climmodels[0]}_{scen}.csv")
+
+    df1 = pd.read_csv(wd + f"\{var}_monthly_{climmodels[1]}_{scen}.csv")
+
+    df2 = pd.read_csv(wd + f"\{var}_monthly_{climmodels[2]}_{scen}.csv")
+
+    # df3 = pd.read_csv(
+    #     wd + f"\{var}_monthly_{climmodels[3]}_{scen}.csv")
+
+    # Final data frame after choosing multi model mean
+    df_mmmean[scen] = pd.concat([df1, df2, df3]).groupby(level=0).mean()
+
+    df_env[scen] = df_mmmean[scen].iloc[:, 5:]
+
+    eflow[scen] = []
+
+    # df = data.iloc[:,5:]
+    for z in range(len(df_env[scen].columns) // 12):
+        col_start = 0 if z == 0 else col_end  # start col number
+        col_end = (z + 1) * 12  # ending col number
+        temp = df_env[scen].iloc[:, col_start:col_end]  # assigning relevant data
+
+        col_names = temp.columns  # getting column names
+        MAF = temp.mean(axis=1)  # take mean acorss
+
+        for j in range(len(temp.columns)):
+            temp.iloc[:, j] = np.where(
+                temp.iloc[:, j] > 0.8 * MAF,
+                temp.iloc[:, j] * 0.2,
+                np.where(
+                    (temp.iloc[:, j] > 0.4 * MAF) & (temp.iloc[:, j] <= 0.8 * MAF),
+                    temp.iloc[:, j] * 0.45,
+                    temp.iloc[:, j] * 0.6,
+                ),
+            )
+
+        if z == 0:
+            eflow[scen] = temp
+        else:
+            eflow[scen] = pd.concat((eflow[scen], temp), axis=1)
+
+    # Convert to 5 year annual values
+    new_cols = pd.to_datetime(eflow[scen].columns, format="sum.X%Y.%m.%d")
+    eflow[scen].columns = new_cols
+    eflow[scen] = eflow[scen].iloc[:, 48:]
+    eflow[scen] = eflow[scen].resample("5Y", axis=1).mean()
+    eflow[scen].to_csv(wd_out + f"\e-flow_{scen}.csv")
+
+    # df = eflow[scen]
+
+    # for i in range(1, round(len(df.transpose())/60+1)):
+    #     start = 0 if i == 1 else end
+    #     end = i*60
+
+    #     temp = df.transpose().iloc[start:end]
+
+    #     name = str(temp.index[-1].year)
+    #     temp = temp.groupby(temp.index.month).mean()
+    #     col_name = [name + "," + str(i) for i in range(1, 13)]
+
+    #     if i == 1:
+    #         eflow2[scen] = temp.transpose()
+    #         eflow2[scen] = eflow2[scen].set_axis(col_name, axis=1)
+    #     else:
+    #         eflow1[scen] = temp.transpose()
+    #         eflow1[scen] = eflow1[scen].set_axis(col_name, axis=1)
+    #         eflow2[scen] = pd.concat((eflow2[scen], eflow1[scen]), axis = 1)
+
+    # new_cols = pd.to_datetime(eflow2[scen].columns, format='%Y,%m')
+    # eflow2[scen].columns = new_cols
+    # eflow2[scen] = eflow2[scen].resample('5Y',axis = 1).mean()
+
+    # eflow2[scen].to_csv(wd + f"\e-flow_{var}_{scen}.csv")
+
+    # df1 = pd.read_csv(wd + f"\e-flow_{variables[3]}_{scen}.csv")
+    # df2 = pd.read_csv(wd + f"\e-flow_{variables[0]}_{scen}.csv")
+
+    # df2.subtract(df1,axis = 1).drop(columns = 'Unnamed: 0').to_csv(wd + f"\e-flow__{scen}.csv")
+
+    print("Environmental Flow Values processed")
+
+
+## Adjusting bias for the data in qs and return
diff --git a/nest/message_ix_models/model/water/data/pre-processing/shp_to_raster.py b/nest/message_ix_models/model/water/data/pre-processing/shp_to_raster.py
new file mode 100644
index 0000000..e840e79
--- /dev/null
+++ b/nest/message_ix_models/model/water/data/pre-processing/shp_to_raster.py
@@ -0,0 +1,121 @@
+"""Converts a shapefile to a raster"""
+
+
+# Import packages
+from datetime import datetime as dt
+
+import numpy as np
+import salem
+import xarray as xr
+
+from message_ix_models.util import private_data_path
+
+# NB The code runs only when the data is in local folder because of salem package dependency
+# wdshapes =
+wdshapes = r"C:\Users\awais\Documents\GitHub\agg_data\delineation"
+
+
+manual_edit = False
+rasterize = True
+all_touched = True
+nx = 720
+ny = 360
+# Define grid (must match template below)
+grid = salem.Grid(proj=salem.wgs84, x0y0=(-180, -90), nxny=(nx, ny), dxdy=(0.5, 0.5))
+# temporary netcdf that gives the dimensions of output file,should match the salem.Grid defined below
+# should be copied from P:\ene.model\NEST\delineation\data\delineated_basins
+tempgrid = wdshapes + "\\template360x720_05.nc"
+
+
+# Define custom fucntions
+def create_raster(
+    shp_fn, namestr, grid, tempgrid, melt=False, all_touched=False, epsg=None
+):
+    # Read in shapes, then look into table to see the columns names, e.g. shapes.columns
+    shapes = salem.read_shapefile(shp_fn, cached=True)
+    if epsg:
+        shapes = shapes.to_crs(epsg=epsg)
+    print(shapes.columns)
+    print("Start " + shp_fn)
+    print(dt.now())
+    # e.g. states, countries, basins
+    import os
+
+    tempshape = (
+        wdshapes + "\\temp.shp"
+    )  # this is the filename of a temporary shapefile written in and out. No need to change
+
+    out_fn = shp_fn[:-3]  # output filename
+
+    # Open grid that is same size, e.g. from template (or create empty xarray with desired dimensions)
+    template = xr.open_dataarray(tempgrid)
+    nd = xr.full_like(template, np.nan, dtype=float)
+    nd.attrs = {}
+    td = nd.copy(deep=True)
+
+    idxs = {}
+
+    if melt:
+        mask_default = grid.region_of_interest(shape=tempshape, all_touched=all_touched)
+        mask_default = mask_default.astype(float)
+        # mask_default[mask_default==1] = g
+        mask_default[mask_default == 0] = np.nan
+        td.values = np.flipud(mask_default)
+        nd = nd.combine_first(td)
+        # idxs[str(g)] = str(shapes[namestr].iloc[0])
+    else:
+        # Run loop through geometries
+        for g, geo in enumerate(shapes.geometry):
+            print(g)
+            region = shapes.iloc[[g]]
+            region.set_crs(epsg=4326)
+            region.to_file(tempshape)
+            mask_default = grid.region_of_interest(
+                shape=tempshape, all_touched=all_touched
+            )
+            mask_default = mask_default.astype(float)
+            mask_default[mask_default == 1] = g + 1
+            mask_default[mask_default == 0] = np.nan
+            td.values = np.flipud(mask_default)
+            nd = nd.combine_first(td)
+            idxs[str(g + 1)] = str(region[namestr].iloc[0])
+    nd.attrs = idxs
+    # nd.attrs['index'] = idxs
+    # nd.attrs['meta'] = {'file': shp_fn,
+    #                     'date': str(dt.now()),
+    #                     }
+    # File saves out using same name as the input shapefile.
+    nd.name = namestr
+    fname = shp_fn[:-4]
+    if all_touched:
+        fname = fname
+    nd.to_netcdf(fname + "0.05" ".nc")
+
+    # Downsized
+    xmin = shapes.min_x[0]
+    xmax = shapes.max_x[0]
+    ymin = shapes.min_y[0]
+    ymax = shapes.max_y[0]
+
+    das = nd.sel(lat=slice(ymax, ymin), lon=slice(xmin, xmax))
+    das.to_netcdf(fname + "_crop.nc")
+    das.close()
+    # plt.figure()
+    # plt.savefig(fname+'.png', dpi=400, bbox_inches='tight')
+
+    os.remove(tempshape)
+    return nd
+
+
+# NB The shapefiles are located in P:\ene.model\NEST\delineation\data\delineated_basins_new and should be copied in
+# wdhsapes folder. Below is an example for R11
+shp_fn = wdshapes + "\\basins_by_region_simpl_R11.shp"
+
+create_raster(
+    shp_fn,
+    namestr="BCU_name",
+    grid=grid,
+    tempgrid=tempgrid,
+    melt=False,
+    all_touched=all_touched,
+)
diff --git a/nest/message_ix_models/model/water/data/water_for_ppl.py b/nest/message_ix_models/model/water/data/water_for_ppl.py
new file mode 100644
index 0000000..162a109
--- /dev/null
+++ b/nest/message_ix_models/model/water/data/water_for_ppl.py
@@ -0,0 +1,819 @@
+"""Prepare data for water use for cooling & energy technologies."""
+
+import numpy as np
+import pandas as pd
+from message_ix import make_df
+
+from message_ix_models.model.water.data.water_supply import map_basin_region_wat
+from message_ix_models.util import (
+    broadcast,
+    make_matched_dfs,
+    private_data_path,
+    same_node,
+)
+
+
+# water & electricity for cooling technologies
+def cool_tech(context):
+    """Process cooling technology data for a scenario instance.
+    The input values of parent technologies are read in from a scenario instance and
+    then cooling fractions are calculated by using the data from
+    ``tech_water_performance_ssp_msg.csv``.
+    It adds cooling  technologies as addons to the parent technologies.The
+    nomenclature for cooling technology is <parenttechnologyname>__<coolingtype>.
+    E.g: `coal_ppl__ot_fresh`
+    Parameters
+    ----------
+    context : .Context
+    Returns
+    -------
+    data : dict of (str -> pandas.DataFrame)
+        Keys are MESSAGE parameter names such as 'input', 'fix_cost'.
+        Values are data frames ready for :meth:`~.Scenario.add_par`.
+        Years in the data include the model horizon indicated by
+        ``context["water build info"]``, plus the additional year 2010.
+    """
+    #: Name of the input file.
+    # The input file mentions water withdrawals and emission heating fractions for
+    # cooling technologies alongwith parent technologies:
+    FILE = "tech_water_performance_ssp_msg.csv"
+    # Investment costs & regional shares of hist. activities of cooling
+    # technologies
+    FILE1 = (
+        "cooltech_cost_and_shares_"
+        + (f"ssp_msg_{context.regions}" if context.type_reg == "global" else "country")
+        + ".csv"
+    )
+
+    # define an empty dictionary
+    results = {}
+
+    # Reference to the water configuration
+    info = context["water build info"]
+    sub_time = context.time
+
+    # reading basin_delineation
+    FILE2 = f"basins_by_region_simpl_{context.regions}.csv"
+    PATH = private_data_path("water", "delineation", FILE2)
+
+    df_node = pd.read_csv(PATH)
+    # Assigning proper nomenclature
+    df_node["node"] = "B" + df_node["BCU_name"].astype(str)
+    df_node["mode"] = "M" + df_node["BCU_name"].astype(str)
+    if context.type_reg == "country":
+        df_node["region"] = context.map_ISO_c[context.regions]
+    else:
+        df_node["region"] = f"{context.regions}_" + df_node["REGION"].astype(str)
+
+    node_region = df_node["region"].unique()
+    # reading ppl cooling tech dataframe
+    path = private_data_path("water", "ppl_cooling_tech", FILE)
+    df = pd.read_csv(path)
+    cooling_df = df.loc[df["technology_group"] == "cooling"]
+    # Separate a column for parent technologies of respective cooling
+    # techs
+    cooling_df["parent_tech"] = (
+        cooling_df["technology_name"]
+        .apply(lambda x: pd.Series(str(x).split("__")))
+        .drop(columns=1)
+    )
+
+    scen = context.get_scenario()
+
+    # Extracting input database from scenario for parent technologies
+    # Extracting input values from scenario
+    ref_input = scen.par("input", {"technology": cooling_df["parent_tech"]})
+    # Extracting historical activity from scenario
+    ref_hist_act = scen.par(
+        "historical_activity", {"technology": cooling_df["parent_tech"]}
+    )
+    # Extracting historical capacity from scenario
+    ref_hist_cap = scen.par(
+        "historical_new_capacity", {"technology": cooling_df["parent_tech"]}
+    )
+    # cooling fraction = H_cool = Hi - 1 - Hi*(h_fg)
+    # where h_fg (flue gasses losses) = 0.1
+    ref_input["cooling_fraction"] = ref_input["value"] * 0.9 - 1
+
+    def missing_tech(x):
+        """Assign values to missing data.
+        It goes through the input data frame and extract the technologies which
+        don't have input values and then assign manual  values to those technologies
+        along with assigning them an arbitrary level i.e dummy supply
+        """
+        data_dic = {
+            "geo_hpl": 1 / 0.850,
+            "geo_ppl": 1 / 0.385,
+            "nuc_hc": 1 / 0.326,
+            "nuc_lc": 1 / 0.326,
+            "solar_th_ppl": 1 / 0.385,
+        }
+
+        if data_dic.get(x["technology"]):
+            if x["level"] == "cooling":
+                return pd.Series((data_dic.get(x["technology"]), "dummy_supply"))
+            else:
+                return pd.Series((data_dic.get(x["technology"]), x["level"]))
+        else:
+            return pd.Series((x["value"], x["level"]))
+
+    ref_input[["value", "level"]] = ref_input[["technology", "value", "level"]].apply(
+        missing_tech, axis=1
+    )
+
+    # Combines the input df of parent_tech with water withdrawal data
+    input_cool = (
+        cooling_df.set_index("parent_tech")
+        .combine_first(ref_input.set_index("technology"))
+        .reset_index()
+    )
+
+    # Drops NA values from the value column
+    input_cool = input_cool.dropna(subset=["value"])
+
+    # Convert year values into integers to be compatibel for model
+    input_cool.year_vtg = input_cool.year_vtg.astype(int)
+    input_cool.year_act = input_cool.year_act.astype(int)
+    # Drops extra technologies from the data
+    input_cool = input_cool[
+        (input_cool["level"] != "water_supply") & (input_cool["level"] != "cooling")
+    ]
+
+    input_cool = input_cool[
+        ~input_cool["technology_name"].str.contains("hpl", na=False)
+    ]
+    input_cool = input_cool[
+        (input_cool["node_loc"] != f"{context.regions}_GLB")
+        & (input_cool["node_origin"] != f"{context.regions}_GLB")
+    ]
+
+    def cooling_fr(x):
+        """Calculate cooling fraction
+        Returns the calculated cooling fraction after for two categories;
+        1. Technologies that produce heat as an output
+            cooling_fraction(h_cool) = input value(hi) - 1
+        Simply subtract 1 from the heating value since the rest of the part is already
+        accounted in the heating value
+        2. Rest of technologies
+            h_cool  =  hi -Hi* h_fg - 1,
+            where:
+                h_fg (flue gasses losses) = 0.1 (10% assumed losses)
+        """
+        if "hpl" in x["index"]:
+            return x["value"] - 1
+        else:
+            return x["value"] - (x["value"] * 0.1) - 1
+
+    input_cool["cooling_fraction"] = input_cool.apply(cooling_fr, axis=1)
+
+    # Converting water withdrawal units to Km3/GWa
+    # this refers to activity per cooling requirement (heat)
+    input_cool["value_cool"] = (
+        input_cool["water_withdrawal_mid_m3_per_output"]
+        * 60
+        * 60
+        * 24
+        * 365
+        * 1e-9
+        / input_cool["cooling_fraction"]
+    )
+
+    input_cool["return_rate"] = 1 - (
+        input_cool["water_consumption_mid_m3_per_output"]
+        / input_cool["water_withdrawal_mid_m3_per_output"]
+    )
+    # consumption to be saved in emissions rates for reporting purposes
+    input_cool["consumption_rate"] = (
+        input_cool["water_consumption_mid_m3_per_output"]
+        / input_cool["water_withdrawal_mid_m3_per_output"]
+    )
+
+    input_cool["value_return"] = input_cool["return_rate"] * input_cool["value_cool"]
+
+    # only for reporting purposes
+    input_cool["value_consumption"] = (
+        input_cool["consumption_rate"] * input_cool["value_cool"]
+    )
+
+    # def foo3(x):
+    #     """
+    #     This function is similar to foo2, but it returns electricity values
+    #     per unit of cooling for techs that require parasitic electricity demand
+    #     """
+    #     if "hpl" in x['index']:
+    #         return x['parasitic_electricity_demand_fraction']
+    #
+    #     elif x['parasitic_electricity_demand_fraction'] > 0.0:
+    #         return x['parasitic_electricity_demand_fraction'] / x['cooling_fraction']
+
+    # Filter out technologies that requires parasitic electricity
+    electr = input_cool[input_cool["parasitic_electricity_demand_fraction"] > 0.0]
+
+    # Make a new column 'value_cool' for calculating values against technologies
+    electr["value_cool"] = (
+        electr["parasitic_electricity_demand_fraction"] / electr["cooling_fraction"]
+    )
+    # Filters out technologies requiring saline water supply
+    saline_df = input_cool[
+        input_cool["technology_name"].str.endswith("ot_saline", na=False)
+    ]
+
+    # input_cool_minus_saline_elec_df
+    con1 = input_cool["technology_name"].str.endswith("ot_saline", na=False)
+    con2 = input_cool["technology_name"].str.endswith("air", na=False)
+    icmse_df = input_cool[(~con1) & (~con2)]
+
+    inp = make_df(
+        "input",
+        node_loc=electr["node_loc"],
+        technology=electr["technology_name"],
+        year_vtg=electr["year_vtg"],
+        year_act=electr["year_act"],
+        mode=electr["mode"],
+        node_origin=electr["node_origin"],
+        commodity="electr",
+        level="secondary",
+        time="year",
+        time_origin="year",
+        value=electr["value_cool"],
+        unit="GWa",
+    )
+    # once through and closed loop freshwater
+    inp = inp.append(
+        make_df(
+            "input",
+            node_loc=icmse_df["node_loc"],
+            technology=icmse_df["technology_name"],
+            year_vtg=icmse_df["year_vtg"],
+            year_act=icmse_df["year_act"],
+            mode=icmse_df["mode"],
+            node_origin=icmse_df["node_origin"],
+            commodity="freshwater",
+            level="water_supply",
+            time="year",
+            time_origin="year",
+            value=icmse_df["value_cool"],
+            unit="km3/GWa",
+        )
+    )
+    # saline cooling technologies
+    inp = inp.append(
+        make_df(
+            "input",
+            node_loc=saline_df["node_loc"],
+            technology=saline_df["technology_name"],
+            year_vtg=saline_df["year_vtg"],
+            year_act=saline_df["year_act"],
+            mode=saline_df["mode"],
+            node_origin=saline_df["node_origin"],
+            commodity="saline_ppl",
+            level="saline_supply",
+            time="year",
+            time_origin="year",
+            value=saline_df["value_cool"],
+            unit="km3/GWa",
+        )
+    )
+
+    # Drops NA values from the value column
+    inp = inp.dropna(subset=["value"])
+
+    # append the input data to results
+    results["input"] = inp
+
+    # add water consumption as emission factor, also for saline tecs
+    emiss_df = input_cool[(~con2)]
+    emi = make_df(
+        "emission_factor",
+        node_loc=emiss_df["node_loc"],
+        technology=emiss_df["technology_name"],
+        year_vtg=emiss_df["year_vtg"],
+        year_act=emiss_df["year_act"],
+        mode=emiss_df["mode"],
+        emission="fresh_return",
+        value=emiss_df["value_return"],
+        unit="km3/yr",
+    )
+
+    results["emission_factor"] = emi
+
+    # add water return flows for cooling tecs
+    # Use share of basin availability to distribute the return flow from
+    df_sw = map_basin_region_wat(context)
+    df_sw.drop(columns={"mode", "date", "MSGREG"}, inplace=True)
+    df_sw.rename(
+        columns={"region": "node_dest", "time": "time_dest", "year": "year_act"},
+        inplace=True,
+    )
+    df_sw["time_dest"] = df_sw["time_dest"].astype(str)
+    if context.nexus_set == "nexus":
+        out = pd.DataFrame()
+        for nn in icmse_df.node_loc.unique():
+            # input cooling fresh basin
+            icfb_df = icmse_df[icmse_df["node_loc"] == nn]
+            bs = list(df_node[df_node["region"] == nn]["node"])
+
+            out_t = (
+                make_df(
+                    "output",
+                    node_loc=icfb_df["node_loc"],
+                    technology=icfb_df["technology_name"],
+                    year_vtg=icfb_df["year_vtg"],
+                    year_act=icfb_df["year_act"],
+                    mode=icfb_df["mode"],
+                    # node_origin=icmse_df["node_origin"],
+                    commodity="surfacewater_basin",
+                    level="water_avail_basin",
+                    time="year",
+                    value=icfb_df["value_return"],
+                    unit="km3/GWa",
+                )
+                .pipe(broadcast, node_dest=bs, time_dest=sub_time)
+                .merge(df_sw, how="left")
+            )
+            # multiply by basin water availability share
+            out_t["value"] = out_t["value"] * out_t["share"]
+            out_t.drop(columns={"share"}, inplace=True)
+            out = out.append(out_t)
+
+        out = out.dropna(subset=["value"])
+        out.reset_index(drop=True, inplace=True)
+        results["output"] = out
+
+    # costs and historical parameters
+    path1 = private_data_path("water", "ppl_cooling_tech", FILE1)
+    cost = pd.read_csv(path1)
+    # Combine technology name to get full cooling tech names
+    cost["technology"] = cost["utype"] + "__" + cost["cooling"]
+    # Filtering out 2010 data to use for historical values
+    input_cool_2010 = input_cool[
+        (input_cool["year_act"] == 2010) & (input_cool["year_vtg"] == 2010)
+    ]
+    # Filter out columns that contain 'mix' in column name
+    columns = [col for col in cost.columns if "mix_" in col]
+    # Rename column names to R11 to match with the previous df
+
+    cost.rename(columns=lambda name: name.replace("mix_", ""), inplace=True)
+    search_cols = [
+        col for col in cost.columns if context.regions in col or "technology" in col
+    ]
+    hold_df = input_cool_2010[
+        ["node_loc", "technology_name", "cooling_fraction"]
+    ].drop_duplicates()
+    search_cols_cooling_fraction = [col for col in search_cols if col != "technology"]
+
+    def shares(x, context):
+        """Process share and cooling fraction.
+        Returns
+        -------
+        Product of value of shares of cooling technology types of regions with
+        corresponding cooling fraction
+        """
+        for col in search_cols_cooling_fraction:
+            # MAPPING ISOCODE to region name, assume one country only
+            col2 = context.map_ISO_c[col] if context.type_reg == "country" else col
+            cooling_fraction = hold_df[
+                (hold_df["node_loc"] == col2)
+                & (hold_df["technology_name"] == x["technology"])
+            ]["cooling_fraction"]
+            x[col] = x[col] * cooling_fraction
+
+        results = []
+        for i in x:
+            if isinstance(i, str):
+                results.append(i)
+            else:
+                if not len(i):
+                    return pd.Series(
+                        [i for i in range(len(search_cols) - 1)] + ["delme"],
+                        index=search_cols,
+                    )
+                else:
+                    results.append(float(i))
+        return pd.Series(results, index=search_cols)
+
+    # Apply function to the
+    hold_cost = cost[search_cols].apply(shares, axis=1, context=context)
+    hold_cost = hold_cost[hold_cost["technology"] != "delme"]
+
+    def hist_act(x, context):
+        """Calculate historical activity of cooling technology.
+        The data for shares is read from ``cooltech_cost_and_shares_ssp_msg.csv``
+        Returns
+        -------
+        hist_activity(cooling_tech) = hist_activitiy(parent_technology) * share
+        *cooling_fraction
+        """
+        tech_df = hold_cost[
+            hold_cost["technology"].str.startswith(x.technology)
+        ]  # [x.node_loc]
+
+        node_search = (
+            context.regions if context.type_reg == "country" else x["node_loc"]
+        )
+
+        node_loc = x["node_loc"]
+        technology = x["technology"]
+        cooling_technologies = list(tech_df["technology"])
+        new_values = tech_df[node_search] * x.value
+
+        return [
+            [
+                node_loc,
+                technology,
+                cooling_technology,
+                x.year_act,
+                x.value,
+                new_value,
+                x.unit,
+            ]
+            for new_value, cooling_technology in zip(new_values, cooling_technologies)
+        ]
+
+    changed_value_series = ref_hist_act.apply(hist_act, axis=1, context=context)
+    changed_value_series_flat = [
+        row for series in changed_value_series for row in series
+    ]
+    columns = [
+        "node_loc",
+        "technology",
+        "cooling_technology",
+        "year_act",
+        "value",
+        "new_value",
+        "unit",
+    ]
+    # dataframe for historical activities of cooling techs
+    act_value_df = pd.DataFrame(changed_value_series_flat, columns=columns)
+
+    def hist_cap(x, context):
+        """Calculate historical capacity of cooling technology.
+        The data for shares is read from ``cooltech_cost_and_shares_ssp_msg.csv``
+        Returns
+        -------
+        hist_new_capacity(cooling_tech) = historical_new_capacity(parent_technology)*
+        share * cooling_fraction
+        """
+        tech_df = hold_cost[
+            hold_cost["technology"].str.startswith(x.technology)
+        ]  # [x.node_loc]
+        if context.type_reg == "country":
+            node_search = context.regions
+        else:
+            node_search = x["node_loc"]  # R11_EEU
+        node_loc = x["node_loc"]
+        technology = x["technology"]
+        cooling_technologies = list(tech_df["technology"])
+        new_values = tech_df[node_search] * x.value
+
+        return [
+            [
+                node_loc,
+                technology,
+                cooling_technology,
+                x.year_vtg,
+                x.value,
+                new_value,
+                x.unit,
+            ]
+            for new_value, cooling_technology in zip(new_values, cooling_technologies)
+        ]
+
+    changed_value_series = ref_hist_cap.apply(hist_cap, axis=1, context=context)
+    changed_value_series_flat = [
+        row for series in changed_value_series for row in series
+    ]
+    columns = [
+        "node_loc",
+        "technology",
+        "cooling_technology",
+        "year_vtg",
+        "value",
+        "new_value",
+        "unit",
+    ]
+    cap_value_df = pd.DataFrame(changed_value_series_flat, columns=columns)
+
+    # Make model compatible df for historical activitiy
+    h_act = make_df(
+        "historical_activity",
+        node_loc=act_value_df["node_loc"],
+        technology=act_value_df["cooling_technology"],
+        year_act=act_value_df["year_act"],
+        mode="M1",
+        time="year",
+        value=act_value_df["new_value"],
+        # TODO finalize units
+        unit="GWa",
+    )
+
+    results["historical_activity"] = h_act
+    # Make model compatible df for histroical new capacity
+    h_cap = make_df(
+        "historical_new_capacity",
+        node_loc=cap_value_df["node_loc"],
+        technology=cap_value_df["cooling_technology"],
+        year_vtg=cap_value_df["year_vtg"],
+        value=cap_value_df["new_value"],
+        unit="GWa",
+    )
+
+    results["historical_new_capacity"] = h_cap
+
+    # Add upper bound for seawater cooling
+    # sums up all the historical activities of seawater cooling technologies
+    # h_act_saline = h_act[h_act["technology"].str.endswith("saline")]
+    # h_act_saline = h_act_saline[h_act_saline["year_act"] == 2015]
+    # h_act_saline.drop(columns=["year_act", "mode", "time", "unit"], inplace=True)
+    # h_act_saline = h_act_saline.groupby(["node_loc"]).sum()
+
+    # inp_saline = inp[inp["technology"].str.endswith("ot_saline")]
+    # inp_saline = inp_saline[
+    #     (inp_saline["year_vtg"] == 2015) & (inp_saline["year_act"] == 2015)
+    # ]
+    # inp_saline.drop(
+    #     columns=[
+    #         "year_vtg",
+    #         "commodity",
+    #         "year_act",
+    #         "mode",
+    #         "level",
+    #         "time",
+    #         "time_origin",
+    #         "unit",
+    #         "node_origin",
+    #     ],
+    #     inplace=True,
+    # )
+    # water_fr = inp_saline.groupby(["node_loc"]).mean()
+    # # multiplying input values of water withdrawal with
+    # bound_saline = water_fr.mul(h_act_saline)
+
+    # bound_up = make_df(
+    #     "bound_activity_up",
+    #     node_loc=bound_saline.index,
+    #     technology="extract_salinewater",
+    #     mode="M1",
+    #     time="year",
+    #     value=bound_saline["value"].values,
+    #     unit="km3/year",
+    # ).pipe(broadcast, year_act=info.Y)
+
+    # results["bound_activity_up"] = bound_up
+
+    # Filter out just cl_fresh & air technologies for adding inv_cost in model,
+    # The rest of technologies are assumed to have costs included in parent technologies
+    # con3 = cost['technology'].str.endswith("cl_fresh")
+    # con4 = cost['technology'].str.endswith("air")
+    # con5 = cost.technology.isin(input_cool['technology_name'])
+    # inv_cost = cost[(con3) | (con4)]
+    inv_cost = cost.copy()
+    # Manually removing extra technologies not required
+    # TODO make it automatic to not include the names manually
+    techs_to_remove = [
+        "mw_ppl__ot_fresh",
+        "mw_ppl__ot_saline",
+        "mw_ppl__cl_fresh",
+        "mw_ppl__air",
+        "nuc_fbr__ot_fresh",
+        "nuc_fbr__ot_saline",
+        "nuc_fbr__cl_fresh",
+        "nuc_fbr__air",
+        "nuc_htemp__ot_fresh",
+        "nuc_htemp__ot_saline",
+        "nuc_htemp__cl_fresh",
+        "nuc_htemp__air",
+    ]
+    inv_cost = inv_cost[~inv_cost["technology"].isin(techs_to_remove)]
+    # Converting the cost to USD/GW
+    inv_cost["investment_USD_per_GW_mid"] = (
+        inv_cost["investment_million_USD_per_MW_mid"] * 1e3
+    )
+
+    inv_cost = (
+        make_df(
+            "inv_cost",
+            technology=inv_cost["technology"],
+            value=inv_cost["investment_USD_per_GW_mid"],
+            unit="USD/GWa",
+        )
+        .pipe(same_node)
+        .pipe(broadcast, node_loc=node_region, year_vtg=info.Y)
+    )
+
+    results["inv_cost"] = inv_cost
+
+    # Addon conversion
+    adon_df = input_cool.copy()
+    # Add 'cooling_' before name of parent technologies that are type_addon
+    # nomenclature
+    adon_df["tech"] = "cooling__" + adon_df["index"].astype(str)
+    # technology : 'parent technology' and type_addon is type of addons such
+    # as 'cooling__bio_hpl'
+    addon_df = make_df(
+        "addon_conversion",
+        node=adon_df["node_loc"],
+        technology=adon_df["index"],
+        year_vtg=adon_df["year_vtg"],
+        year_act=adon_df["year_act"],
+        mode=adon_df["mode"],
+        time="year",
+        type_addon=adon_df["tech"],
+        value=adon_df["cooling_fraction"],
+        unit="km3/GWa",
+    )
+
+    results["addon_conversion"] = addon_df
+
+    # Addon_lo will remain 1 for all cooling techs so it allows 100% activity of
+    # parent technologies
+    addon_lo = make_matched_dfs(addon_df, addon_lo=1)
+    results["addon_lo"] = addon_lo["addon_lo"]
+
+    # technical lifetime
+    # make_matched_dfs didn't map all technologies
+    # tl = make_matched_dfs(inv_cost,
+    #                       technical_lifetime = 30)
+    year = info.Y
+    if 2010 in year:
+        pass
+    else:
+        year.insert(0, 2010)
+
+    tl = (
+        make_df(
+            "technical_lifetime",
+            technology=inp["technology"].drop_duplicates(),
+            value=30,
+            unit="year",
+        )
+        .pipe(broadcast, year_vtg=year, node_loc=node_region)
+        .pipe(same_node)
+    )
+
+    results["technical_lifetime"] = tl
+
+    cap_fact = make_matched_dfs(inp, capacity_factor=1)
+    # Climate Impacts on freshwater cooling capacity
+    # Taken from https://www.sciencedirect.com/science/article/pii/S0959378016301236?via%3Dihub#sec0080
+    if context.RCP == "no_climate":
+        df = cap_fact["capacity_factor"]
+    else:
+        df = cap_fact["capacity_factor"]
+        # reading ppl cooling impact dataframe
+        path = private_data_path(
+            "water", "ppl_cooling_tech", "power_plant_cooling_impact_MESSAGE.xlsx"
+        )
+        df_impact = pd.read_excel(path, sheet_name=f"{context.regions}_{context.RCP}")
+
+        for n in df_impact["node"]:
+            conditions = [
+                df["technology"].str.contains("fresh")
+                & (df["year_act"] >= 2025)
+                & (df["year_act"] < 2050)
+                & (df["node_loc"] == n),
+                df["technology"].str.contains("fresh")
+                & (df["year_act"] >= 2050)
+                & (df["year_act"] < 2070)
+                & (df["node_loc"] == n),
+                df["technology"].str.contains("fresh")
+                & (df["year_act"] >= 2070)
+                & (df["node_loc"] == n),
+            ]
+
+            choices = [
+                df_impact[(df_impact["node"] == n)]["2025s"],
+                df_impact[(df_impact["node"] == n)]["2050s"],
+                df_impact[(df_impact["node"] == n)]["2070s"],
+            ]
+
+            df["value"] = np.select(conditions, choices, default=df["value"])
+
+    results["capacity_factor"] = df
+    # results = {par_name: pd.concat(dfs) for par_name, dfs in results.items()}
+
+    # growth activity low to allow the cooling techs to be operational
+    g_lo = make_df(
+        "growth_activity_lo",
+        technology=inp["technology"].drop_duplicates(),
+        value=-0.05,
+        unit="%",
+        time="year",
+    ).pipe(broadcast, year_act=info.Y, node_loc=node_region)
+    # Alligining certain technologies with growth constriants
+    g_lo.loc[g_lo["technology"].str.contains("bio_ppl|loil_ppl"), "value"] = -0.5
+    g_lo.loc[g_lo["technology"].str.contains("coal_ppl_u|coal_ppl"), "value"] = -0.5
+    g_lo.loc[
+        (g_lo["technology"].str.contains("coal_ppl_u|coal_ppl"))
+        & (g_lo["node_loc"].str.contains("CPA|PAS")),
+        "value",
+    ] = -1
+    results["growth_activity_lo"] = g_lo
+
+    # growth activity up on saline water
+    inp_saline = inp[inp["technology"].str.endswith("ot_saline")]
+
+    g_up = make_df(
+        "growth_activity_up",
+        technology=inp_saline["technology"].drop_duplicates(),
+        value=0.05,
+        unit="%",
+        time="year",
+    ).pipe(broadcast, year_act=info.Y, node_loc=node_region)
+    results["growth_activity_up"] = g_up
+
+    # # adding initial activity
+    # in_lo = h_act.copy()
+    # in_lo.drop(columns='mode', inplace=True)
+    # in_lo = in_lo[in_lo['year_act'] == 2015]
+    # in_lo_1 = make_df('initial_activity_lo',
+    #                   node_loc=in_lo['node_loc'],
+    #                   technology=in_lo['technology'],
+    #                   time='year',
+    #                   value=in_lo['value'],
+    #                   unit='GWa').pipe(broadcast, year_act=[2015, 2020])
+    # results['initial_activity_lo'] = in_lo_1
+
+    return results
+
+
+# Water use & electricity for non-cooling technologies
+def non_cooling_tec(context):
+    """Process data for water usage of power plants (non-cooling technology related).
+    Water withdrawal values for power plants are read in from
+    ``tech_water_performance_ssp_msg.csv``
+    Parameters
+    ----------
+    context : .Context
+    Returns
+    -------
+    data : dict of (str -> pandas.DataFrame)
+        Keys are MESSAGE parameter names such as 'input', 'fix_cost'.
+        Values are data frames ready for :meth:`~.Scenario.add_par`.
+        Years in the data include the model horizon indicated by
+        ``context["transport build info"]``, plus the additional year 2010.
+    """
+    results = {}
+
+    FILE = "tech_water_performance_ssp_msg.csv"
+    path = private_data_path("water", "ppl_cooling_tech", FILE)
+    df = pd.read_csv(path)
+    cooling_df = df.loc[df["technology_group"] == "cooling"]
+    # Separate a column for parent technologies of respective cooling
+    # techs
+    cooling_df["parent_tech"] = (
+        cooling_df["technology_name"]
+        .apply(lambda x: pd.Series(str(x).split("__")))
+        .drop(columns=1)
+    )
+    non_cool_df = df[
+        (df["technology_group"] != "cooling")
+        & (df["water_supply_type"] == "freshwater_supply")
+    ]
+
+    scen = context.get_scenario()
+    tec_lt = scen.par("technical_lifetime")
+    all_tech = list(tec_lt["technology"].unique())
+    # all_tech = list(scen.set("technology"))
+    tech_non_cool_csv = list(non_cool_df["technology_name"])
+    techs_to_remove = [tec for tec in tech_non_cool_csv if tec not in all_tech]
+
+    non_cool_df = non_cool_df[~non_cool_df["technology_name"].isin(techs_to_remove)]
+    non_cool_df = non_cool_df.rename(columns={"technology_name": "technology"})
+
+    non_cool_df["value"] = (
+        non_cool_df["water_withdrawal_mid_m3_per_output"] * 60 * 60 * 24 * 365 * (1e-9)
+    )
+
+    non_cool_tech = list(non_cool_df["technology"].unique())
+
+    n_cool_df = scen.par("output", {"technology": non_cool_tech})
+    n_cool_df = n_cool_df[
+        (n_cool_df["node_loc"] != "R11_GLB") & (n_cool_df["node_dest"] != "R11_GLB")
+    ]
+    n_cool_df_merge = pd.merge(n_cool_df, non_cool_df, on="technology", how="right")
+    n_cool_df_merge.dropna(inplace=True)
+
+    # Input dataframe for non cooling technologies
+    # only water withdrawals are being taken
+    # Only freshwater supply is assumed for simplicity
+    inp_n_cool = make_df(
+        "input",
+        technology=n_cool_df_merge["technology"],
+        value=n_cool_df_merge["value_y"],
+        unit="km3/GWa",
+        level="water_supply",
+        commodity="freshwater",
+        time_origin="year",
+        mode="M1",
+        time="year",
+        year_vtg=n_cool_df_merge["year_vtg"].astype(int),
+        year_act=n_cool_df_merge["year_act"].astype(int),
+        node_loc=n_cool_df_merge["node_loc"],
+        node_origin=n_cool_df_merge["node_dest"],
+    )
+
+    # append the input data to results
+    results["input"] = inp_n_cool
+
+    return results
diff --git a/nest/message_ix_models/model/water/data/water_supply.py b/nest/message_ix_models/model/water/data/water_supply.py
new file mode 100644
index 0000000..01e8775
--- /dev/null
+++ b/nest/message_ix_models/model/water/data/water_supply.py
@@ -0,0 +1,812 @@
+"""Prepare data for water use for cooling & energy technologies."""
+
+import numpy as np
+import pandas as pd
+from message_ix import make_df
+
+from message_ix_models.model.water.data.demands import read_water_availability
+from message_ix_models.model.water.utils import map_yv_ya_lt
+from message_ix_models.util import broadcast, private_data_path, same_node, same_time
+
+
+def map_basin_region_wat(context):
+    """
+    Calculate share of water avaialbility of basins per each parent region.
+
+    The parent region could be global message regions or country
+
+    Parameters
+    ----------
+    context : .Context
+    Returns
+    -------
+    data : dict of (str -> pandas.DataFrame)
+        Keys are MESSAGE parameter names such as 'input', 'fix_cost'. Values
+        are data frames ready for :meth:`~.Scenario.add_par`.
+    """
+    info = context["water build info"]
+
+    if "year" in context.time:
+        PATH = private_data_path(
+            "water", "delineation", f"basins_by_region_simpl_{context.regions}.csv"
+        )
+        df_x = pd.read_csv(PATH)
+        # Adding freshwater supply constraints
+        # Reading data, the data is spatially and temprally aggregated from GHMs
+        path1 = private_data_path(
+            "water",
+            "availability",
+            f"qtot_5y_{context.RCP}_{context.REL}_{context.regions}.csv",
+        )
+
+        df_sw = pd.read_csv(path1)
+        df_sw.drop(["Unnamed: 0"], axis=1, inplace=True)
+
+        # Reading data, the data is spatially and temporally aggregated from GHMs
+        df_sw["BCU_name"] = df_x["BCU_name"]
+
+        if context.type_reg == "country":
+            df_sw["MSGREG"] = context.map_ISO_c[context.regions]
+        else:
+            df_sw["MSGREG"] = f"{context.regions}_" + df_sw["BCU_name"].str[-3:]
+
+        df_sw = df_sw.set_index(["MSGREG", "BCU_name"])
+
+        # Calculating ratio of water availability in basin by region
+        df_sw = df_sw.groupby(["MSGREG"]).apply(lambda x: x / x.sum())
+        df_sw.reset_index(inplace=True)
+        df_sw["Region"] = "B" + df_sw["BCU_name"].astype(str)
+        df_sw["Mode"] = df_sw["Region"].replace(regex=["^B"], value="M")
+        df_sw.drop(columns=["BCU_name"], inplace=True)
+        df_sw.set_index(["MSGREG", "Region", "Mode"], inplace=True)
+        df_sw = df_sw.stack().reset_index(level=0).reset_index()
+        df_sw.columns = ["region", "mode", "date", "MSGREG", "share"]
+        df_sw.sort_values(["region", "date", "MSGREG", "share"], inplace=True)
+        df_sw["year"] = pd.DatetimeIndex(df_sw["date"]).year
+        df_sw["time"] = "year"
+        df_sw = df_sw[df_sw["year"].isin(info.Y)]
+        df_sw.reset_index(drop=True, inplace=True)
+
+    else:
+        # add water return flows for cooling tecs
+        # Use share of basin availability to distribute the return flow from
+        path3 = private_data_path(
+            "water",
+            "availability",
+            f"qtot_5y_m_{context.RCP}_{context.REL}_{context.regions}.csv",
+        )
+        df_sw = pd.read_csv(path3)
+
+        # reading sample for assiging basins
+        PATH = private_data_path(
+            "water", "delineation", f"basins_by_region_simpl_{context.regions}.csv"
+        )
+        df_x = pd.read_csv(PATH)
+
+        # Reading data, the data is spatially and temporally aggregated from GHMs
+        df_sw["BCU_name"] = df_x["BCU_name"]
+
+        if context.type_reg == "country":
+            df_sw["MSGREG"] = context.map_ISO_c[context.regions]
+        else:
+            df_sw["MSGREG"] = f"{context.regions}_" + df_sw["BCU_name"].str[-3:]
+
+        df_sw = df_sw.set_index(["MSGREG", "BCU_name"])
+        df_sw.drop(columns="Unnamed: 0", inplace=True)
+
+        # Calculating ratio of water availability in basin by region
+        df_sw = df_sw.groupby(["MSGREG"]).apply(lambda x: x / x.sum())
+        df_sw.reset_index(inplace=True)
+        df_sw["Region"] = "B" + df_sw["BCU_name"].astype(str)
+        df_sw["Mode"] = df_sw["Region"].replace(regex=["^B"], value="M")
+        df_sw.drop(columns=["BCU_name"], inplace=True)
+        df_sw.set_index(["MSGREG", "Region", "Mode"], inplace=True)
+        df_sw = df_sw.stack().reset_index(level=0).reset_index()
+        df_sw.columns = ["node", "mode", "date", "MSGREG", "share"]
+        df_sw.sort_values(["node", "date", "MSGREG", "share"], inplace=True)
+        df_sw["year"] = pd.DatetimeIndex(df_sw["date"]).year
+        df_sw["time"] = pd.DatetimeIndex(df_sw["date"]).month
+        df_sw = df_sw[df_sw["year"].isin(info.Y)]
+        df_sw.reset_index(drop=True, inplace=True)
+
+    return df_sw
+
+
+def add_water_supply(context):
+    """Add Water supply infrastructure
+    This function links the water supply based on different settings and options.
+    It defines the supply linkages for freshwater, groundwater and salinewater.
+    Parameters
+    ----------
+    context : .Context
+    Returns
+    -------
+    data : dict of (str -> pandas.DataFrame)
+        Keys are MESSAGE parameter names such as 'input', 'fix_cost'.
+        Values are data frames ready for :meth:`~.Scenario.add_par`.
+        Years in the data include the model horizon indicated by
+        ``context["water build info"]``, plus the additional year 2010.
+    """
+    # define an empty dictionary
+    results = {}
+
+    # Reference to the water configuration
+    info = context["water build info"]
+    # load the scenario from context
+    scen = context.get_scenario()
+
+    year_wat = [2010, 2015]
+    fut_year = info.Y
+    year_wat.extend(info.Y)
+    sub_time = context.time
+
+    # first activity year for all water technologies is 2020
+    first_year = scen.firstmodelyear
+
+    print(" future year = ", fut_year)
+    print(" year_wat = ", year_wat)
+
+    # reading basin_delineation
+    FILE = f"basins_by_region_simpl_{context.regions}.csv"
+    PATH = private_data_path("water", "delineation", FILE)
+
+    df_node = pd.read_csv(PATH)
+    # Assigning proper nomenclature
+    df_node["node"] = "B" + df_node["BCU_name"].astype(str)
+    df_node["mode"] = "M" + df_node["BCU_name"].astype(str)
+    if context.type_reg == "country":
+        df_node["region"] = context.map_ISO_c[context.regions]
+    else:
+        df_node["region"] = f"{context.regions}_" + df_node["REGION"].astype(str)
+
+    # Storing the energy MESSAGE region names
+    node_region = df_node["region"].unique()
+
+    # reading groundwater energy intensity data
+    FILE1 = f"gw_energy_intensity_depth_{context.regions}.csv"
+    PATH1 = private_data_path("water", "availability", FILE1)
+    df_gwt = pd.read_csv(PATH1)
+    if context.type_reg == "country":
+        df_gwt["region"] = context.map_ISO_c[context.regions]
+    else:
+        df_gwt["REGION"] = f"{context.regions}_" + df_gwt["REGION"].astype(str)
+
+    # reading groundwater energy intensity data
+    FILE2 = f"historical_new_cap_gw_sw_km3_year_{context.regions}.csv"
+    PATH2 = private_data_path("water", "availability", FILE2)
+    df_hist = pd.read_csv(PATH2)
+    df_hist["BCU_name"] = "B" + df_hist["BCU_name"].astype(str)
+
+    if context.nexus_set == "cooling":
+        # Add output df  for surfacewater supply for regions
+        output_df = (
+            make_df(
+                "output",
+                technology="extract_surfacewater",
+                value=1,
+                unit="km3",
+                year_vtg=year_wat,
+                year_act=year_wat,
+                level="water_supply",
+                commodity="freshwater",
+                mode="M1",
+                time="year",
+                time_dest="year",
+                time_origin="year",
+            )
+            .pipe(broadcast, node_loc=node_region)
+            .pipe(same_node)
+        )
+
+        # Add output df  for groundwater supply for regions
+        output_df = output_df.append(
+            make_df(
+                "output",
+                technology="extract_groundwater",
+                value=1,
+                unit="km3",
+                year_vtg=year_wat,
+                year_act=year_wat,
+                level="water_supply",
+                commodity="freshwater",
+                mode="M1",
+                time="year",
+                time_dest="year",
+                time_origin="year",
+            )
+            .pipe(broadcast, node_loc=node_region)
+            .pipe(same_node)
+        )
+        # Add output of saline water supply for regions
+        output_df = output_df.append(
+            make_df(
+                "output",
+                technology="extract_salinewater",
+                value=1,
+                unit="km3",
+                year_vtg=year_wat,
+                year_act=year_wat,
+                level="water_supply",
+                commodity="saline_ppl",
+                mode="M1",
+                time="year",
+                time_dest="year",
+                time_origin="year",
+            )
+            .pipe(broadcast, node_loc=node_region)
+            .pipe(same_node)
+        )
+        results["output"] = output_df
+
+    elif context.nexus_set == "nexus":
+        # input data frame  for slack technology balancing equality with demands
+        inp = (
+            make_df(
+                "input",
+                technology="return_flow",
+                value=1,
+                unit="-",
+                level="water_avail_basin",
+                commodity="surfacewater_basin",
+                mode="M1",
+                year_vtg=year_wat,
+                year_act=year_wat,
+            )
+            .pipe(
+                broadcast,
+                node_loc=df_node["node"],
+                time=sub_time,
+            )
+            .pipe(same_node)
+            .pipe(same_time)
+        )
+
+        # input data frame  for slack technology balancing equality with demands
+        inp = inp.append(
+            make_df(
+                "input",
+                technology="gw_recharge",
+                value=1,
+                unit="-",
+                level="water_avail_basin",
+                commodity="groundwater_basin",
+                mode="M1",
+                year_vtg=year_wat,
+                year_act=year_wat,
+            )
+            .pipe(
+                broadcast,
+                node_loc=df_node["node"],
+                time=sub_time,
+            )
+            .pipe(same_node)
+            .pipe(same_time)
+        )
+
+        # input dataframe  linking water supply to energy dummy technology
+        inp = inp.append(
+            make_df(
+                "input",
+                technology="basin_to_reg",
+                value=1,
+                unit="-",
+                level="water_supply_basin",
+                commodity="freshwater_basin",
+                mode=df_node["mode"],
+                node_origin=df_node["node"],
+                node_loc=df_node["region"],
+            )
+            .pipe(
+                broadcast,
+                year_vtg=year_wat,
+                time=sub_time,
+            )
+            .pipe(same_time)
+        )
+        inp["year_act"] = inp["year_vtg"]
+        # # input data frame  for slack technology balancing equality with demands
+        # inp = inp.append(
+        #     make_df(
+        #         "input",
+        #         technology="salinewater_return",
+        #         value=1,
+        #         unit="-",
+        #         level="water_avail_basin",
+        #         commodity="salinewater_basin",
+        #         mode="M1",
+        #         time="year",
+        #         time_origin="year",
+        #         node_origin=df_node["node"],
+        #         node_loc=df_node["node"],
+        #     ).pipe(broadcast, year_vtg=year_wat, year_act=year_wat)
+        # )
+
+        # input data frame  for freshwater supply
+        yv_ya_sw = map_yv_ya_lt(year_wat, 50, first_year)
+
+        inp = inp.append(
+            make_df(
+                "input",
+                technology="extract_surfacewater",
+                value=1,
+                unit="-",
+                level="water_avail_basin",
+                commodity="surfacewater_basin",
+                mode="M1",
+                node_origin=df_node["node"],
+                node_loc=df_node["node"],
+            )
+            .pipe(
+                broadcast,
+                yv_ya_sw,
+                time=sub_time,
+            )
+            .pipe(same_time)
+        )
+
+        # input dataframe  for groundwater supply
+        yv_ya_gw = map_yv_ya_lt(year_wat, 20, first_year)
+        inp = inp.append(
+            make_df(
+                "input",
+                technology="extract_groundwater",
+                value=1,
+                unit="-",
+                level="water_avail_basin",
+                commodity="groundwater_basin",
+                mode="M1",
+                node_origin=df_node["node"],
+                node_loc=df_node["node"],
+            )
+            .pipe(
+                broadcast,
+                yv_ya_gw,
+                time=sub_time,
+            )
+            .pipe(same_time)
+        )
+
+        # electricity input dataframe  for extract freshwater supply
+        # low: 0.001141553, mid: 0.018835616, high: 0.03652968
+        inp = inp.append(
+            make_df(
+                "input",
+                technology="extract_surfacewater",
+                value=0.018835616,
+                unit="-",
+                level="final",
+                commodity="electr",
+                mode="M1",
+                time_origin="year",
+                node_origin=df_node["region"],
+                node_loc=df_node["node"],
+            ).pipe(
+                broadcast,
+                yv_ya_sw,
+                time=sub_time,
+            )
+        )
+
+        inp = inp.append(
+            make_df(
+                "input",
+                technology="extract_groundwater",
+                value=df_gwt["GW_per_km3_per_year"] + 0.043464579,
+                unit="-",
+                level="final",
+                commodity="electr",
+                mode="M1",
+                time_origin="year",
+                node_origin=df_gwt["REGION"],
+                node_loc=df_node["node"],
+            ).pipe(
+                broadcast,
+                yv_ya_gw,
+                time=sub_time,
+            )
+        )
+
+        inp = inp.append(
+            make_df(
+                "input",
+                technology="extract_gw_fossil",
+                value=(df_gwt["GW_per_km3_per_year"] + 0.043464579)
+                * 2,  # twice as much normal gw
+                unit="-",
+                level="final",
+                commodity="electr",
+                mode="M1",
+                time_origin="year",
+                node_origin=df_gwt["REGION"],
+                node_loc=df_node["node"],
+            ).pipe(
+                broadcast,
+                yv_ya_gw,
+                time=sub_time,
+            )
+        )
+
+        if context.type_reg == "global":
+            inp.loc[
+                (inp["technology"].str.contains("extract_gw_fossil"))
+                & (inp["year_act"] == 2020)
+                & (inp["node_loc"] == "R11_SAS"),
+                "value",
+            ] *= 0.5
+
+        results["input"] = inp
+
+        # Add output df  for freshwater supply for basins
+        output_df = (
+            make_df(
+                "output",
+                technology="extract_surfacewater",
+                value=1,
+                unit="-",
+                level="water_supply_basin",
+                commodity="freshwater_basin",
+                mode="M1",
+                node_loc=df_node["node"],
+                node_dest=df_node["node"],
+            )
+            .pipe(
+                broadcast,
+                yv_ya_sw,
+                time=sub_time,
+            )
+            .pipe(same_time)
+        )
+        # Add output df  for groundwater supply for basins
+        output_df = output_df.append(
+            make_df(
+                "output",
+                technology="extract_groundwater",
+                value=1,
+                unit="-",
+                level="water_supply_basin",
+                commodity="freshwater_basin",
+                mode="M1",
+                node_loc=df_node["node"],
+                node_dest=df_node["node"],
+            )
+            .pipe(
+                broadcast,
+                yv_ya_gw,
+                time=sub_time,
+            )
+            .pipe(same_time)
+        )
+
+        # Add output df  for groundwater supply for basins
+        output_df = output_df.append(
+            make_df(
+                "output",
+                technology="extract_gw_fossil",
+                value=1,
+                unit="-",
+                level="water_supply_basin",
+                commodity="freshwater_basin",
+                mode="M1",
+                node_loc=df_node["node"],
+                node_dest=df_node["node"],
+                time_origin="year",
+            )
+            .pipe(
+                broadcast,
+                yv_ya_gw,
+                time=sub_time,
+            )
+            .pipe(same_time)
+        )
+
+        # Add output of saline water supply for regions
+        output_df = output_df.append(
+            make_df(
+                "output",
+                technology="extract_salinewater",
+                value=1,
+                unit="km3",
+                year_vtg=year_wat,
+                year_act=year_wat,
+                level="saline_supply",
+                commodity="saline_ppl",
+                mode="M1",
+                time="year",
+                time_dest="year",
+                time_origin="year",
+            )
+            .pipe(broadcast, node_loc=node_region)
+            .pipe(same_node)
+        )
+
+        hist_new_cap = make_df(
+            "historical_new_capacity",
+            node_loc=df_hist["BCU_name"],
+            technology="extract_surfacewater",
+            value=df_hist["hist_cap_sw_km3_year"] / 5,  # n period
+            unit="km3/year",
+            year_vtg=2015,
+        )
+
+        hist_new_cap = hist_new_cap.append(
+            make_df(
+                "historical_new_capacity",
+                node_loc=df_hist["BCU_name"],
+                technology="extract_groundwater",
+                value=df_hist["hist_cap_gw_km3_year"] / 5,
+                unit="km3/year",
+                year_vtg=2015,
+            )
+        )
+
+        results["historical_new_capacity"] = hist_new_cap
+
+        # output data frame linking water supply to energy dummy technology
+        output_df = output_df.append(
+            make_df(
+                "output",
+                technology="basin_to_reg",
+                value=1,
+                unit="-",
+                level="water_supply",
+                commodity="freshwater",
+                time_dest="year",
+                node_loc=df_node["region"],
+                node_dest=df_node["region"],
+                mode=df_node["mode"],
+            ).pipe(broadcast, year_vtg=year_wat, time=sub_time)
+        )
+
+        output_df["year_act"] = output_df["year_vtg"]
+
+        results["output"] = output_df
+
+        # dummy variable cost for dummy water to energy technology
+        var = make_df(
+            "var_cost",
+            technology="basin_to_reg",
+            mode=df_node["mode"],
+            node_loc=df_node["region"],
+            value=20,
+            unit="-",
+        ).pipe(broadcast, year_vtg=year_wat, time=sub_time)
+        var["year_act"] = var["year_vtg"]
+        # # Dummy cost for extract surface ewater to prioritize water sources
+        # var = var.append(make_df(
+        #     "var_cost",
+        #     technology='extract_surfacewater',
+        #     value= 0.0001,
+        #     unit="USD/km3",
+        #     mode="M1",
+        #     time="year",
+        #     ).pipe(broadcast, year_vtg=year_wat, year_act=year_wat, node_loc=df_node["node"]
+        #        )
+        #                  )
+        # # Dummy cost for extract groundwater
+        # var = var.append(make_df(
+        #     "var_cost",
+        #     technology='extract_groundwater',
+        #     value= 0.001,
+        #     unit="USD/km3",
+        #     mode="M1",
+        #     time="year",
+        # ).pipe(broadcast, year_vtg=year_wat, year_act=year_wat, node_loc=df_node["node"])
+        #                )
+        results["var_cost"] = var
+
+        # load the share of sw
+        df_sw = map_basin_region_wat(context)
+
+        share = make_df(
+            "share_mode_up",
+            shares="share_basin",
+            technology="basin_to_reg",
+            mode=df_sw["mode"],
+            node_share=df_sw["MSGREG"],
+            time=df_sw["time"],
+            value=df_sw["share"],
+            unit="%",
+            year_act=df_sw["year"],
+        )
+
+        results["share_mode_up"] = share
+
+        tl = (
+            make_df(
+                "technical_lifetime",
+                technology="extract_surfacewater",
+                value=50,
+                unit="y",
+            )
+            .pipe(broadcast, year_vtg=year_wat, node_loc=df_node["node"])
+            .pipe(same_node)
+        )
+
+        tl = tl.append(
+            make_df(
+                "technical_lifetime",
+                technology="extract_groundwater",
+                value=20,
+                unit="y",
+            )
+            .pipe(broadcast, year_vtg=year_wat, node_loc=df_node["node"])
+            .pipe(same_node)
+        )
+
+        tl = tl.append(
+            make_df(
+                "technical_lifetime",
+                technology="extract_gw_fossil",
+                value=20,
+                unit="y",
+            )
+            .pipe(broadcast, year_vtg=year_wat, node_loc=df_node["node"])
+            .pipe(same_node)
+        )
+
+        results["technical_lifetime"] = tl
+
+        # Prepare dataframe for investments
+        inv_cost = make_df(
+            "inv_cost",
+            technology="extract_surfacewater",
+            value=155.57,
+            unit="USD/km3",
+        ).pipe(broadcast, year_vtg=year_wat, node_loc=df_node["node"])
+
+        inv_cost = inv_cost.append(
+            make_df(
+                "inv_cost",
+                technology="extract_groundwater",
+                value=54.52,
+                unit="USD/km3",
+            ).pipe(broadcast, year_vtg=year_wat, node_loc=df_node["node"])
+        )
+
+        inv_cost = inv_cost.append(
+            make_df(
+                "inv_cost",
+                technology="extract_gw_fossil",
+                value=54.52 * 150,  # assume higher as normal GW
+                unit="USD/km3",
+            ).pipe(broadcast, year_vtg=year_wat, node_loc=df_node["node"])
+        )
+
+        results["inv_cost"] = inv_cost
+
+        fix_cost = make_df(
+            "fix_cost",
+            technology="extract_gw_fossil",
+            value=300,  # assumed
+            unit="USD/km3",
+        ).pipe(broadcast, yv_ya_gw, node_loc=df_node["node"])
+
+        results["fix_cost"] = fix_cost
+
+    return results
+
+
+def add_e_flow(context):
+    """Add environmental flows
+    This function bounds the available water and allocates the environmental
+    flows.Environmental flow bounds are calculated using Variable Monthly Flow
+    (VMF) method. The VMF method is applied to wet and dry seasonal runoff
+    values. These wet and dry seasonal values are then aggregated to annual
+    values.Environmental flows in the model will be incorporated as bounds on
+    'return_flow' technology. The lower bound on this technology will ensure
+    that certain amount of water remain
+    Parameters
+    ----------
+    context : .Context
+    Returns
+    -------
+    data : dict of (str -> pandas.DataFrame)
+        Keys are MESSAGE parameter names such as 'input', 'fix_cost'.
+        Values are data frames ready for :meth:`~.Scenario.add_par`.
+        Years in the data include the model horizon indicated by
+        ``context["water build info"]``, plus the additional year 2010.
+    """
+    # define an empty dictionary
+    results = {}
+
+    info = context["water build info"]
+
+    # Adding freshwater supply constraints
+    # Reading data, the data is spatially and temprally aggregated from GHMs
+    df_sw, df_gw = read_water_availability(context)
+
+    # reading sample for assiging basins
+    PATH = private_data_path(
+        "water", "delineation", f"basins_by_region_simpl_{context.regions}.csv"
+    )
+    df_x = pd.read_csv(PATH)
+
+    dmd_df = make_df(
+        "demand",
+        node="B" + df_sw["Region"].astype(str),
+        commodity="surfacewater_basin",
+        level="water_avail_basin",
+        year=df_sw["year"],
+        time=df_sw["time"],
+        value=df_sw["value"],
+        unit="km3/year",
+    )
+    dmd_df = dmd_df[dmd_df["year"] >= 2025].reset_index(drop=True)
+    dmd_df["value"] = dmd_df["value"].apply(lambda x: x if x >= 0 else 0)
+
+    if "year" in context.time:
+        # Reading data, the data is spatially and temporally aggregated from GHMs
+        path1 = private_data_path(
+            "water",
+            "availability",
+            f"e-flow_{context.RCP}_{context.regions}.csv",
+        )
+        df_env = pd.read_csv(path1)
+        df_env.drop(["Unnamed: 0"], axis=1, inplace=True)
+        df_env.index = df_x["BCU_name"]
+        df_env = df_env.stack().reset_index()
+        df_env.columns = ["Region", "years", "value"]
+        df_env.sort_values(["Region", "years", "value"], inplace=True)
+        df_env.fillna(0, inplace=True)
+        df_env.reset_index(drop=True, inplace=True)
+        df_env["year"] = pd.DatetimeIndex(df_env["years"]).year
+        df_env["time"] = "year"
+        df_env2210 = df_env[df_env["year"] == 2100]
+        df_env2210["year"] = 2110
+        df_env = pd.concat([df_env, df_env2210])
+        df_env = df_env[df_env["year"].isin(info.Y)]
+    else:
+        # Reading data, the data is spatially and temporally aggregated from GHMs
+        path1 = private_data_path(
+            "water",
+            "availability",
+            f"e-flow_5y_m_{context.RCP}_{context.regions}.csv",
+        )
+        df_env = pd.read_csv(path1)
+        df_env.drop(["Unnamed: 0"], axis=1, inplace=True)
+        new_cols = pd.to_datetime(df_env.columns, format="%Y/%m/%d")
+        df_env.columns = new_cols
+        df_env.index = df_x["BCU_name"]
+        df_env = df_env.stack().reset_index()
+        df_env.columns = ["Region", "years", "value"]
+        df_env.sort_values(["Region", "years", "value"], inplace=True)
+        df_env.fillna(0, inplace=True)
+        df_env.reset_index(drop=True, inplace=True)
+        df_env["year"] = pd.DatetimeIndex(df_env["years"]).year
+        df_env["time"] = pd.DatetimeIndex(df_env["years"]).month
+        df_env2210 = df_env[df_env["year"] == 2100]
+        df_env2210["year"] = 2110
+        df_env = pd.concat([df_env, df_env2210])
+        df_env = df_env[df_env["year"].isin(info.Y)]
+
+    # Return a processed dataframe for env flow calculations
+    if context.SDG:
+        # dataframe to put constraints on env flows
+        eflow_df = make_df(
+            "bound_activity_lo",
+            node_loc="B" + df_env["Region"],
+            technology="return_flow",
+            year_act=df_env["year"],
+            mode="M1",
+            time=df_env["time"],
+            value=df_env["value"],
+            unit="km3/year",
+        )
+
+        eflow_df["value"] = eflow_df["value"].apply(lambda x: x if x >= 0 else 0)
+        eflow_df = eflow_df[eflow_df["year_act"] >= 2025].reset_index(drop=True)
+
+        dmd_df.sort_values(by=["node", "year"], inplace=True)
+        dmd_df.reset_index(drop=True, inplace=True)
+        eflow_df.sort_values(by=["node_loc", "year_act"], inplace=True)
+        eflow_df.reset_index(drop=True, inplace=True)
+
+        eflow_df["value"] = np.where(
+            eflow_df["value"] >= 0.7 * dmd_df["value"],
+            0.7 * dmd_df["value"],
+            eflow_df["value"],
+        )
+
+        results["bound_activity_lo"] = eflow_df
+
+    return results
diff --git a/nest/message_ix_models/model/water/doc/files.rst b/nest/message_ix_models/model/water/doc/files.rst
new file mode 100644
index 0000000..b4b45de
--- /dev/null
+++ b/nest/message_ix_models/model/water/doc/files.rst
@@ -0,0 +1,23 @@
+Data and Configuration files
+****************************
+
+
+Technology (:file:`water/technology.yaml`)
+==========================================
+
+.. literalinclude:: ../../../../data/water/technology.yaml
+   :language: yaml
+
+
+Other sets (:file:`water/set.yaml`)
+===================================
+
+.. literalinclude:: ../../../../data/water/set.yaml
+   :language: yaml
+
+Configuration (:file:`water/config.yaml`)
+=============================================
+
+.. literalinclude:: ../../../../data/water/config.yaml
+   :language: yaml
+
diff --git a/nest/message_ix_models/model/water/doc/index.rst b/nest/message_ix_models/model/water/doc/index.rst
new file mode 100644
index 0000000..6edd370
--- /dev/null
+++ b/nest/message_ix_models/model/water/doc/index.rst
@@ -0,0 +1,167 @@
+MESSAGEix-Nexus (:mod:`~messag_data.model.water`)
+*************************************************
+
+.. warning::
+
+   MESSAGEix-Nexus is **under development**.
+
+   For details, see the
+   `water <https://github.com/iiasa/message_data/labels/water>`_ label,
+   `project board <https://github.com/iiasa/message_data/projects/7>`_, and
+   `current tracking issue (#246) <https://github.com/iiasa/message_data/issues/246>`_.
+
+
+:mod:`message_data.model.water` adds water usage and demand related representation to the MESSAGEix-GLOBIOM global model.
+The resulting model is referred to as **“MESSAGEix-Nexus”**. This work extends the water sector linkage described by Parkinson et al. (2019) :cite:`parkinson2019`.
+
+.. contents::
+   :local:
+
+CLI usage
+=========
+
+Use the :doc:`CLI <message_ix_models:cli>` command ``mix-data water`` to invoke the commands defined in :mod:`.water.cli`. Example:
+``mix-models --url=ixmp://ixmp_dev/ENGAGE_SSP2_v4.1.7/baseline_clone_test water cooling``
+model and scenario specifications can be either set manually in ``cli.py`` or specificed in the ``--url`` option
+
+.. code::
+
+   Usage: mix-models water [OPTIONS] COMMAND [ARGS]...
+
+   Options:
+   --regions [ISR|R11|R12|R14|R32|RCP|ZMB]
+                                    Code list to use for 'node' dimension.
+   --help                          Show this message and exit.
+
+   Commands:
+   cooling  Build and solve model with new cooling technologies.
+   nexus    Add basin structure connected to the energy sector and water...
+   report   function to run the water report_full from cli to the scenario...
+
+Country vs Global implementation
+--------------------------------
+
+The :mod:`message_data.model.water` is designed to being able to add water components to either a global R11 (or R12) model or any country model designed with `the MESSAGEix single country <https://github.com/iiasa/message_single_country>`_ model prototype.
+For any of the region configuration a shapefile is needed to run the pre-processing part, while, once the data is prepared, only a .csv file similar to those in `message_data.data.water.delineation` is needed.
+
+To work with a country model please ensure that:
+
+1. country model and scenario are specified either in ``--url`` or in the ``cli.py`` script
+2. the option ``--regions`` is used with the ISO3 code of the country (e.g. for Israel ``--regions=ISR``)
+3. Following the Israel example add a 'country'.yaml file in `message_ix_models.data.node` for the specific country
+4. Following the Israel example add the country ISO3 code in the 'regions' options in `message_ix_models.utils.click`
+
+Annual vs Sub-anunal implementation
+-----------------------------------
+
+if a sub-annual timestep is defined (e.g. seasons or months), the water module will automatically generate the water system with seasonality, using the `time` set components that is not `year` (assuming that the data has been prepared accordingly).
+
+In the case you want to add seasonality in the water sector to a model with only annual timesteps, the best way is to pre-define the required sets (e.g. `time` and `map_time`). Then, runing the water nexus module will automatically build the sub-annual water module.
+
+In the case there are already multiple sub-annual time-steps levels already defined and not all are relevant to the water module, the components of the set `time` that are of interest for the water module should be manually added as a cli option (e.g. `--time=year` or `--time=[1,2]`)
+
+Code reference
+==============
+
+.. currentmodule:: message_data.model.water
+
+.. automodule:: message_data.model.water
+   :members:
+
+Build and run
+-------------
+.. automodule:: message_data.model.water.build
+   :members:
+
+
+Data preparation
+----------------
+
+.. automodule:: message_data.model.water.data
+   :members:
+
+.. automodule:: message_data.model.water.data.water_for_ppl
+   :members:
+
+.. automodule:: message_data.model.water.data.demands
+   :members:
+
+.. automodule:: message_data.model.water.data.infrastructure
+   :members:
+
+.. automodule:: message_data.model.water.data.water_supply
+   :members:
+
+.. automodule:: message_data.model.water.data.irrigation
+   :members:
+
+
+Utilities and CLI
+-----------------
+
+.. automodule:: message_data.model.water.utils
+   :members:
+   :exclude-members: read_config
+
+.. automodule:: message_data.model.water.cli
+   :members:
+
+Reporting
+---------
+
+.. warning::
+
+   The current reporting features only work for the global model
+
+.. automodule:: message_data.model.water.reporting
+   :members:
+
+
+
+Data, metadata, and config files
+================================
+
+See also: :doc:`water/files`.
+
+- :file:`data/water/`: contains input data used for building the Nexus module
+
+  - :file:`delineation/`: contains geospatial files for basin mapping and MESSAGE regions. These spatial files are created through intersecting HydroSHEDS basin and the MESSAGE region shapefile. The scripts and processing data at 'P:\ene.model\NEST\delineation'
+  - :file:`ppl_cooling_tech/`: contains cooling technology shares, costs and water intensities for different regional definitions
+  - :file:`water_demands/`: contains water sectoral demands, connection rates for basins
+  - :file:`water_dist/`: contains water infrastructure (distribution, treatment mapping) and historical and projected capacities  of desalination technologies
+  - :file:`technology.yaml`: metadata for the 'technology' dimension.
+  - :file:`set.yaml`: metadata for other sets.
+
+Pre-processing
+==============
+- :file:`data/water/`: contains scripts used in pre-processing source data for the water sector implementation
+
+  
+  - :file:`calculate_ppl_cooling_technology_shares.r`: contains script for processing cooling technology shares at global level for different regional specifications.
+  - :file:`groundwater_harmonize.r`: contains workflow to calculate historical capcity of renewable groundwater, table depth and energy consumption
+  - :file:`generate_water_constraints.r`: contains function to calculate municipal, manufactury, rural water demands, water access and sanitation rates
+  - :file:`desalination.r`: contains script for assessing the historical and possible future desalination capacity of a region or country
+  - :file:`hydro_agg_temp1.py`: contains workflow for processing the hydrological data in NC4 and adjust the unit conversions, daily to monthly aggregation.
+  - :file:`hydro_agg_spatial.R`: contains workflow for spatially aggregating monthly hydrological data onto basin using appropriate raster masking onto shapefiles
+  - :file:`hydro_agg_temp2.py`: contains workflow for aggregating monthly data to 5 yearly averages using appropriate statistical methods (quantiles, averages etc.). It also caculates e flows based on Variable MF method.
+  - :file:`shp_to_raster.py`: converts shapefile to raster
+
+
+
+Deprecated R Code
+=================
+
+- :file:`data/water/deprecated`: contains `R` scripts from the older water sector implementation
+
+  - :file:`Figures.R`: R script for producing figures
+  - :file:`cooling_tech_av.R`: contains similar code as in the above-mentioned scripts, but this was originated from another workstream
+  - :file:`add_water_infrastructure.R`: contains spatially-explicit analysis of gridded demands and socioeconomic indicators to develop pathways for sectoral water withdrawals, return flows and infrastructure penetration rates in each MESSAGE region. The pathways feature branching points reflecting a specific water sector development narrative (e.g., convergence towards achieving specific SDG targets).
+
+
+Reference
+=========
+
+.. toctree::
+   :maxdepth: 2
+
+   water/files
diff --git a/nest/message_ix_models/model/water/reporting.py b/nest/message_ix_models/model/water/reporting.py
new file mode 100644
index 0000000..afe2d80
--- /dev/null
+++ b/nest/message_ix_models/model/water/reporting.py
@@ -0,0 +1,1174 @@
+import logging
+
+import numpy as np
+import pandas as pd
+import pyam
+from message_ix.reporting import Reporter
+
+from message_ix_models.util import private_data_path
+
+try:
+    from message_data.tools.post_processing.iamc_report_hackathon import (
+        report as old_reporting,
+    )
+except ImportError:  # message_data not installed
+    old_reporting = None
+
+log = logging.getLogger(__name__)
+
+
+def run_old_reporting(sc=False):
+    mp2 = sc.platform
+
+    log.info(
+        f" Start reporting of the global energy system (old reporting scheme)"
+        f"for the scenario {sc.model}.{sc.scenario}"
+    )
+    old_reporting(
+        mp=mp2,
+        scen=sc,
+        merge_hist=True,
+        merge_ts=False,
+    )
+
+
+def reg_index(region):
+    temp = []
+    for i, c in enumerate(region):
+        if c == "|":
+            temp.append(i)
+    return temp
+
+
+def remove_duplicate(data):
+    final_list = []
+    indexes = data[data["Variable"].str.contains("basin_to_reg")].index
+    for i in data["Region"].index:
+        strr = data["Region"][i]
+        oprlist = reg_index(strr)
+        if i in indexes:
+            if len(oprlist) > 1:
+                final_list.append(strr[oprlist[0] + 1 :])
+            elif len(oprlist) == 1 and oprlist[0] > 6:
+                final_list.append(strr[: oprlist[0]])
+            else:
+                final_list.append(strr)
+        else:
+            if len(oprlist) > 1:
+                final_list.append(strr[: oprlist[1]])
+            elif len(oprlist) == 1 and oprlist[0] > 6:
+                final_list.append(strr[: oprlist[0]])
+            else:
+                final_list.append(strr)
+    return final_list
+
+
+def report(sc=False, sdgs=False):
+    """Report nexus module results"""
+
+    # Generating reporter
+    rep = Reporter.from_scenario(sc)
+    report = rep.get("message::default")
+    # Create a timeseries dataframe
+    report_df = report.timeseries()
+    report_df.reset_index(inplace=True)
+    report_df.columns = report_df.columns.astype(str)
+    report_df.columns = report_df.columns.str.title()
+
+    # Removing duplicate region names
+    report_df["Region"] = remove_duplicate(report_df)
+
+    # Adding Water availability as resource in demands
+    # This is not automatically reported using message:default
+    rep_dm = rep
+    rep_dm.set_filters(l="water_avail_basin")
+
+    def collapse_callback(df):
+        """Callback function to populate the IAMC 'variable' column."""
+        df["variable"] = "Water Resource|" + df["c"]
+        return df.drop(["c"], axis=1)
+
+    # Mapping from dimension IDs to column names
+    rename = dict(n="region", y="year")
+
+    key = rep_dm.convert_pyam("demand", rename=rename, collapse=collapse_callback)
+    # Making a dataframe for demands
+    df_dmd = rep.get(key).as_pandas()
+    df_dmd["value"] = df_dmd["value"].abs()
+    df_dmd["variable"].replace(
+        "Water Resource|groundwater_basin", "Water Resource|Groundwater", inplace=True
+    )
+    df_dmd["variable"].replace(
+        "Water Resource|surfacewater_basin",
+        "Water Resource|Surface Water",
+        inplace=True,
+    )
+    df_dmd = df_dmd.drop(columns=["exclude"])
+    df_dmd["unit"] = "km3"
+    df_dmd1 = pyam.IamDataFrame(df_dmd).timeseries()
+
+    # Convert to pyam dataframe
+    report_iam = pyam.IamDataFrame(report_df)
+    # Merge both dataframes in pyam
+    report_iam = report_iam.append(df_dmd1)
+
+    # mapping model outputs for aggregation
+    urban_infrastructure = [
+        "CAP_NEW|new capacity|rural_recycle",
+        "CAP_NEW|new capacity|rural_sewerage",
+        "CAP_NEW|new capacity|rural_t_d",
+        "CAP_NEW|new capacity|rural_treatment",
+        "CAP_NEW|new capacity|rural_unconnected",
+        "CAP_NEW|new capacity|rural_untreated",
+    ]
+
+    rural_infrastructure = [
+        "CAP_NEW|new capacity|rural_recycle",
+        "CAP_NEW|new capacity|rural_sewerage",
+        "CAP_NEW|new capacity|rural_t_d",
+        "CAP_NEW|new capacity|rural_treatment",
+        "CAP_NEW|new capacity|rural_unconnected",
+        "CAP_NEW|new capacity|rural_untreated",
+    ]
+
+    urban_treatment_recycling = [
+        "CAP_NEW|new capacity|urban_recycle",
+        "CAP_NEW|new capacity|urban_sewerage",
+        "CAP_NEW|new capacity|urban_treatment",
+    ]
+
+    rural_treatment_recycling = [
+        "CAP_NEW|new capacity|rural_recycle",
+        "CAP_NEW|new capacity|rural_sewerage",
+        "CAP_NEW|new capacity|rural_treatment",
+    ]
+
+    rural_dist = ["CAP_NEW|new capacity|rural_t_d"]
+    urban_dist = ["CAP_NEW|new capacity|urban_t_d"]
+
+    rural_unconnected = [
+        "CAP_NEW|new capacity|rural_unconnected",
+        "CAP_NEW|new capacity|rural_untreated",
+    ]
+
+    urban_unconnected = [
+        "CAP_NEW|new capacity|urban_unconnected",
+        "CAP_NEW|new capacity|urban_untreated",
+    ]
+
+    industry_unconnected = [
+        "CAP_NEW|new capacity|industry_unconnected",
+        "CAP_NEW|new capacity|industry_untreated",
+    ]
+
+    extrt_sw_cap = ["CAP_NEW|new capacity|extract_surfacewater"]
+    extrt_gw_cap = ["CAP_NEW|new capacity|extract_groundwater"]
+    extrt_fgw_cap = ["CAP_NEW|new capacity|extract_gw_fossil"]
+
+    extrt_sw_inv = ["inv cost|extract_surfacewater"]
+    extrt_gw_inv = ["inv cost|extract_groundwater"]
+    # Calculating fossil groundwater invwatments
+    # 163.56 million USD/km3 x 2 times the reneewable gw costs
+
+    report_iam = report_iam.append(
+        report_iam.multiply(
+            "CAP_NEW|new capacity|extract_gw_fossil",
+            163.56,
+            "Fossil GW inv",
+            ignore_units=True,
+        )
+    )
+    extrt_fgw_inv = report_iam.filter(variable="Fossil GW inv").variable
+
+    rural_infrastructure_inv = [
+        "inv cost|rural_recycle",
+        "inv cost|rural_sewerage",
+        "inv cost|rural_t_d",
+        "inv cost|rural_treatment",
+        "inv cost|rural_unconnected",
+        "inv cost|rural_untreated",
+    ]
+
+    urban_infrastructure_inv = [
+        "inv cost|urban_recycle",
+        "inv cost|urban_sewerage",
+        "inv cost|urban_t_d",
+        "inv cost|urban_treatment",
+        "inv cost|urban_unconnected",
+        "inv cost|urban_untreated",
+    ]
+
+    urban_treatment_recycling_inv = [
+        "inv cost|urban_recycle",
+        "inv cost|urban_sewerage",
+        "inv cost|urban_treatment",
+    ]
+
+    rural_treatment_recycling_inv = [
+        "inv cost|rural_recycle",
+        "inv cost|rural_sewerage",
+        "inv cost|rural_treatment",
+    ]
+
+    rural_dist_inv = ["inv cost|rural_t_d"]
+    urban_dist_inv = ["inv cost|urban_t_d"]
+
+    rural_unconnected_inv = [
+        "inv cost|rural_unconnected",
+        "inv cost|rural_untreated",
+    ]
+
+    urban_unconnected_inv = [
+        "inv cost|urban_unconnected",
+        "inv cost|urban_untreated",
+    ]
+
+    industry_unconnected_inv = [
+        "inv cost|industry_unconnected",
+        "inv cost|industry_untreated",
+    ]
+
+    saline_inv = [
+        "inv cost|membrane",
+        "inv cost|distillation",
+    ]
+    saline_totalom = [
+        "total om cost|membrane",
+        "total om cost|distillation",
+    ]
+
+    extrt_fgw_om = ["total om cost|extract_gw_fossil"]
+
+    urban_infrastructure_totalom = [
+        "total om cost|urban_recycle",
+        "total om cost|urban_sewerage",
+        "total om cost|urban_t_d",
+        "total om cost|urban_treatment",
+        "total om cost|urban_unconnected",
+        "total om cost|urban_untreated",
+    ]
+
+    rural_infrastructure_totalom = [
+        "total om cost|rural_recycle",
+        "total om cost|rural_sewerage",
+        "total om cost|rural_t_d",
+        "total om cost|rural_treatment",
+        "total om cost|rural_unconnected",
+        "total om cost|rural_untreated",
+    ]
+
+    rural_treatment_recycling_totalom = [
+        "total om cost|rural_recycle",
+        "total om cost|rural_sewerage",
+        "total om cost|rural_treatment",
+    ]
+
+    urban_treatment_recycling_totalom = [
+        "total om cost|urban_recycle",
+        "total om cost|urban_sewerage",
+        "total om cost|urban_treatment",
+    ]
+
+    rural_dist_totalom = ["total om cost|rural_t_d"]
+    urban_dist_totalom = ["total om cost|urban_t_d"]
+
+    rural_unconnected_totalom = [
+        "total om cost|rural_unconnected",
+        "total om cost|rural_untreated",
+    ]
+
+    urban_unconnected_totalom = [
+        "total om cost|urban_unconnected",
+        "total om cost|urban_untreated",
+    ]
+
+    industry_unconnected_totalom = [
+        "total om cost|industry_unconnected",
+        "total om cost|industry_untreated",
+    ]
+
+    extract_sw = ["in|water_avail_basin|surfacewater_basin|extract_surfacewater|M1"]
+
+    extract_gw = ["in|water_avail_basin|groundwater_basin|extract_groundwater|M1"]
+    extract_fgw = ["out|water_supply_basin|freshwater_basin|extract_gw_fossil|M1"]
+
+    desal_membrane = ["out|water_supply_basin|freshwater_basin|membrane|M1"]
+    desal_distill = ["out|water_supply_basin|freshwater_basin|distillation|M1"]
+    env_flow = ["in|water_avail_basin|surfacewater_basin|return_flow|M1"]
+    gw_recharge = ["in|water_avail_basin|groundwater_basin|gw_recharge|M1"]
+
+    rural_mwdem_unconnected = ["out|final|rural_disconnected|rural_unconnected|M1"]
+    rural_mwdem_unconnected_eff = ["out|final|rural_disconnected|rural_unconnected|Mf"]
+    rural_mwdem_connected = ["out|final|rural_mw|rural_t_d|M1"]
+    rural_mwdem_connected_eff = ["out|final|rural_mw|rural_t_d|Mf"]
+    urban_mwdem_unconnected = ["out|final|urban_disconnected|urban_unconnected|M1"]
+    urban_mwdem_unconnected_eff = ["out|final|urban_disconnected|urban_unconnected|Mf"]
+    urban_mwdem_connected = ["out|final|urban_mw|urban_t_d|M1"]
+    urban_mwdem_connected_eff = ["out|final|urban_mw|urban_t_d|Mf"]
+    industry_mwdem_unconnected = ["out|final|industry_mw|industry_unconnected|M1"]
+
+    electr_gw = ["in|final|electr|extract_groundwater|M1"]
+    electr_fgw = ["in|final|electr|extract_gw_fossil|M1"]
+    electr_sw = ["in|final|electr|extract_surfacewater|M1"]
+    extract_saline_region = ["out|saline_supply|saline_ppl|extract_salinewater|M1"]
+    extract_saline_basin = [
+        "out|water_avail_basin|salinewater_basin|extract_salinewater_basin|M1"
+    ]
+    electr_rural_trt = ["in|final|electr|rural_sewerage|M1"]
+    electr_urban_trt = ["in|final|electr|urban_sewerage|M1"]
+    electr_urban_recycle = ["in|final|electr|urban_recycle|M1"]
+    electr_rural_recycle = ["in|final|electr|rural_recycle|M1"]
+    electr_saline = [
+        "in|final|electr|distillation|M1",
+        "in|final|electr|distillation|M1",
+    ]
+
+    electr_urban_t_d = ["in|final|electr|urban_t_d|M1"]
+    electr_urban_t_d_eff = ["in|final|electr|urban_t_d|Mf"]
+    electr_rural_t_d = ["in|final|electr|rural_t_d|M1"]
+    electr_rural_t_d_eff = ["in|final|electr|rural_t_d|Mf"]
+
+    electr_irr = [
+        "in|final|electr|irrigation_cereal|M1",
+        "in|final|electr|irrigation_oilcrops|M1",
+        "in|final|electr|irrigation_sugarcrops|M1",
+    ]
+
+    urban_collctd_wstwtr = ["in|final|urban_collected_wst|urban_sewerage|M1"]
+    rural_collctd_wstwtr = ["in|final|rural_collected_wst|rural_sewerage|M1"]
+
+    urban_treated_wstwtr = ["in|water_treat|urban_collected_wst|urban_recycle|M1"]
+    rural_treated_wstwtr = ["in|water_treat|rural_collected_wst|rural_recycle|M1"]
+
+    urban_wstwtr_recycle = ["out|water_supply_basin|freshwater_basin|urban_recycle|M1"]
+    rural_wstwtr_recycle = ["out|water_supply_basin|freshwater_basin|rural_recycle|M1"]
+
+    urban_transfer = ["in|water_supply_basin|freshwater_basin|urban_t_d|M1"]
+    urban_transfer_eff = ["in|water_supply_basin|freshwater_basin|urban_t_d|Mf"]
+    rural_transfer = ["in|water_supply_basin|freshwater_basin|rural_t_d|M1"]
+    rural_transfer_eff = ["in|water_supply_basin|freshwater_basin|rural_t_d|Mf"]
+
+    # irr_water = ["out|water_irr|freshwater|irrigation|M1"]
+
+    irr_c = ["in|water_supply|freshwater|irrigation_cereal|M1"]
+    irr_o = ["in|water_supply|freshwater|irrigation_oilcrops|M1"]
+    irr_s = ["in|water_supply|freshwater|irrigation_sugarcrops|M1"]
+
+    region_withdr = report_iam.filter(
+        variable="in|water_supply_basin|freshwater_basin|basin_to_reg|*"
+    ).variable
+
+    cooling_saline_inv = report_iam.filter(variable="inv cost|*saline").variable
+    cooling_air_inv = report_iam.filter(variable="inv cost|*air").variable
+    cooling_ot_fresh = report_iam.filter(variable="inv cost|*ot_fresh").variable
+    cooling_cl_fresh = report_iam.filter(variable="inv cost|*cl_fresh").variable
+
+    elec_hydro_var = report_iam.filter(variable="out|secondary|electr|hydro*").variable
+
+    for var in elec_hydro_var:
+        if "hydro_1" in var or "hydro_hc" in var:
+            report_iam = report_iam.append(
+                # Multiply electricity output of hydro to get withdrawals
+                # this is an ex-post model calculation and the values are taken from
+                # data/water/ppl_cooling_tech/tech_water_performance_ssp_msg.csv
+                # for hydr_n water_withdrawal_mid_m3_per output is converted by
+                # multiplying with   60 * 60* 24 * 365 * 1e-9 to convert it
+                # into km3/output
+                report_iam.multiply(
+                    f"{var}", 0.161, f"Water Withdrawal|Electricity|Hydro|{var[21:28]}"
+                )
+            )
+        else:
+            report_iam = report_iam.append(
+                report_iam.multiply(
+                    f"{var}", 0.323, f"Water Withdrawal|Electricity|Hydro|{var[21:28]}"
+                )
+            )
+
+    water_hydro_var = report_iam.filter(
+        variable="Water Withdrawal|Electricity|Hydro|*"
+    ).variable
+
+    # mapping for aggregation
+    map_agg_pd = pd.DataFrame(
+        [
+            ["Water Extraction", extract_gw + extract_fgw + extract_sw, "km3/yr"],
+            ["Water Extraction|Groundwater", extract_gw, "km3/yr"],
+            ["Water Extraction|Brackish Water", extract_fgw, "km3/yr"],
+            ["Water Extraction|Surface Water", extract_sw, "km3/yr"],
+            [
+                "Water Extraction|Seawater",
+                extract_saline_basin + extract_saline_region,
+                "km3/yr",
+            ],
+            ["Water Extraction|Seawater|Desalination", extract_saline_basin, "km3/yr"],
+            ["Water Extraction|Seawater|Cooling", extract_saline_region, "km3/yr"],
+            ["Water Desalination", desal_membrane + desal_distill, "km3/yr"],
+            ["Water Desalination|Membrane", desal_membrane, "km3/yr"],
+            ["Water Desalination|Distillation", desal_distill, "km3/yr"],
+            [
+                "Water Transfer",
+                urban_transfer
+                + rural_transfer
+                + urban_transfer_eff
+                + rural_transfer_eff,
+                "km3/yr",
+            ],
+            ["Water Transfer|Urban", urban_transfer + urban_transfer_eff, "km3/yr"],
+            ["Water Transfer|Rural", rural_transfer + rural_transfer_eff, "km3/yr"],
+            [
+                "Water Withdrawal",
+                region_withdr
+                + rural_mwdem_unconnected
+                + rural_mwdem_unconnected_eff
+                + rural_mwdem_connected
+                + rural_mwdem_connected_eff
+                + urban_mwdem_connected
+                + urban_mwdem_connected_eff
+                + urban_mwdem_unconnected
+                + urban_mwdem_unconnected_eff
+                + industry_mwdem_unconnected,
+                "km3/yr",
+            ],
+            ["Water Withdrawal|Energy techs & Irrigation", region_withdr, "km3/yr"],
+            # ["Water Withdrawal|Irrigation", irr_c + irr_o + irr_s, "km3/yr"],
+            ["Water Withdrawal|Irrigation|Cereal", irr_c, "km3/yr"],
+            ["Water Withdrawal|Irrigation|Oil Crops", irr_o, "km3/yr"],
+            ["Water Withdrawal|Irrigation|Sugar Crops", irr_s, "km3/yr"],
+            ["Water Withdrawal|Electricity|Hydro", water_hydro_var, "km3/yr"],
+            [
+                "Capacity Additions|Infrastructure|Water",
+                rural_infrastructure
+                + urban_infrastructure
+                + urban_treatment_recycling
+                + rural_treatment_recycling
+                + urban_dist
+                + rural_dist
+                + rural_unconnected
+                + urban_unconnected
+                + industry_unconnected,
+                "km3/yr",
+            ],
+            [
+                "Capacity Additions|Infrastructure|Water|Extraction",
+                extrt_sw_cap + extrt_gw_cap + extrt_fgw_cap,
+                "km3/yr",
+            ],
+            [
+                "Capacity Additions|Infrastructure|Water|Extraction|Surface Water",
+                extrt_sw_cap,
+                "km3/yr",
+            ],
+            [
+                "Capacity Additions|Infrastructure|Water|Extraction|Groundwater",
+                extrt_gw_cap + extrt_fgw_cap,
+                "km3/yr",
+            ],
+            [
+                "Capacity Additions|Infrastructure|Water|Extraction|Groundwater|Renewable",
+                extrt_gw_cap,
+                "km3/yr",
+            ],
+            [
+                "Capacity Additions|Infrastructure|Water|Extraction|Groundwater|Fossil",
+                extrt_fgw_cap,
+                "km3/yr",
+            ],
+            [
+                "Capacity Additions|Infrastructure|Water|Rural",
+                rural_infrastructure,
+                "km3/yr",
+            ],
+            [
+                "Capacity Additions|Infrastructure|Water|Urban",
+                urban_infrastructure,
+                "km3/yr",
+            ],
+            [
+                "Capacity Additions|Infrastructure|Water|Industrial",
+                industry_unconnected,
+                "km3/yr",
+            ],
+            [
+                "Capacity Additions|Infrastructure|Water|Treatment & Recycling|Urban",
+                urban_treatment_recycling,
+                "km3/yr",
+            ],
+            [
+                "Capacity Additions|Infrastructure|Water|Treatment & Recycling|Rural",
+                rural_treatment_recycling,
+                "km3/yr",
+            ],
+            [
+                "Capacity Additions|Infrastructure|Water|Distribution|Rural",
+                rural_dist,
+                "km3/yr",
+            ],
+            [
+                "Capacity Additions|Infrastructure|Water|Distribution|Urban",
+                urban_dist,
+                "km3/yr",
+            ],
+            [
+                "Capacity Additions|Infrastructure|Water|Unconnected|Rural",
+                rural_unconnected,
+                "km3/yr",
+            ],
+            [
+                "Capacity Additions|Infrastructure|Water|Unconnected|Urban",
+                urban_unconnected,
+                "km3/yr",
+            ],
+            ["Freshwater|Environmental Flow", env_flow, "km3/yr"],
+            ["Groundwater Recharge", gw_recharge, "km3/yr"],
+            [
+                "Water Withdrawal|Municipal Water",
+                rural_mwdem_unconnected
+                + rural_mwdem_unconnected_eff
+                + rural_mwdem_connected
+                + rural_mwdem_connected_eff
+                + urban_mwdem_unconnected
+                + urban_mwdem_unconnected_eff
+                + urban_mwdem_connected
+                + urban_mwdem_connected_eff,
+                "km3/yr",
+            ],
+            [
+                "Water Withdrawal|Municipal Water|Unconnected|Rural",
+                rural_mwdem_unconnected,
+                "km3/yr",
+            ],
+            [
+                "Water Withdrawal|Municipal Water|Unconnected|Rural Eff",
+                rural_mwdem_unconnected_eff,
+                "km3/yr",
+            ],
+            [
+                "Water Withdrawal|Municipal Water|Connected|Rural",
+                rural_mwdem_connected,
+                "km3/yr",
+            ],
+            [
+                "Water Withdrawal|Municipal Water|Connected|Rural Eff",
+                rural_mwdem_connected_eff,
+                "km3/yr",
+            ],
+            [
+                "Water Withdrawal|Municipal Water|Unconnected|Urban",
+                urban_mwdem_unconnected,
+                "km3/yr",
+            ],
+            [
+                "Water Withdrawal|Municipal Water|Unconnected|Urban Eff",
+                urban_mwdem_unconnected_eff,
+                "km3/yr",
+            ],
+            [
+                "Water Withdrawal|Municipal Water|Connected|Urban",
+                urban_mwdem_connected,
+                "km3/yr",
+            ],
+            [
+                "Water Withdrawal|Municipal Water|Connected|Urban Eff",
+                urban_mwdem_connected_eff,
+                "km3/yr",
+            ],
+            [
+                "Water Withdrawal|Industrial Water|Unconnected",
+                industry_mwdem_unconnected,
+                "km3/yr",
+            ],
+            # ["Water Withdrawal|Irrigation", irr_water, "km3/yr"],
+            [
+                "Final Energy|Commercial",
+                electr_saline
+                + electr_gw
+                + electr_fgw
+                + electr_sw
+                + electr_rural_trt
+                + electr_urban_trt
+                + electr_urban_recycle
+                + electr_rural_recycle
+                + electr_urban_t_d
+                + electr_urban_t_d_eff
+                + electr_rural_t_d
+                + electr_rural_t_d_eff
+                + electr_irr,
+                "GWa",
+            ],
+            [
+                "Final Energy|Commercial|Water",
+                electr_saline
+                + electr_gw
+                + electr_fgw
+                + electr_sw
+                + electr_rural_trt
+                + electr_urban_trt
+                + electr_urban_recycle
+                + electr_rural_recycle
+                + electr_urban_t_d
+                + electr_urban_t_d_eff
+                + electr_rural_t_d
+                + electr_rural_t_d_eff
+                + electr_irr,
+                "GWa",
+            ],
+            ["Final Energy|Commercial|Water|Desalination", electr_saline, "GWa"],
+            [
+                "Final Energy|Commercial|Water|Groundwater Extraction",
+                electr_gw + electr_fgw,
+                "GWa",
+            ],
+            [
+                "Final Energy|Commercial|Water|Surface Water Extraction",
+                electr_sw,
+                "GWa",
+            ],
+            ["Final Energy|Commercial|Water|Irrigation", electr_irr, "GWa"],
+            ["Final Energy|Commercial|Water|Treatment", electr_rural_trt, "GWa"],
+            [
+                "Final Energy|Commercial|Water|Treatment|Rural",
+                electr_urban_trt + electr_rural_trt,
+                "GWa",
+            ],
+            ["Final Energy|Commercial|Water|Treatment|Urban", electr_urban_trt, "GWa"],
+            ["Final Energy|Commercial|Water|Reuse", electr_urban_recycle, "GWa"],
+            [
+                "Final Energy|Commercial|Water|Transfer",
+                electr_urban_t_d
+                + electr_urban_t_d_eff
+                + electr_rural_t_d
+                + electr_rural_t_d_eff,
+                "GWa",
+            ],
+            [
+                "Final Energy|Commercial|Water|Transfer|Urban",
+                electr_urban_t_d + electr_urban_t_d_eff,
+                "GWa",
+            ],
+            [
+                "Final Energy|Commercial|Water|Transfer|Rural",
+                electr_rural_t_d + electr_urban_t_d_eff,
+                "GWa",
+            ],
+            [
+                "Water Waste|Collected",
+                urban_collctd_wstwtr + rural_collctd_wstwtr,
+                "km3/yr",
+            ],
+            ["Water Waste|Collected|Urban", urban_collctd_wstwtr, "km3/yr"],
+            ["Water Waste|Collected|Rural", rural_collctd_wstwtr, "km3/yr"],
+            [
+                "Water Waste|Treated",
+                urban_treated_wstwtr + rural_treated_wstwtr,
+                "km3/yr",
+            ],
+            ["Water Waste|Treated|Urban", urban_treated_wstwtr, "km3/yr"],
+            ["Water Waste|Treated|Rural", rural_treated_wstwtr, "km3/yr"],
+            [
+                "Water Waste|Reuse",
+                urban_wstwtr_recycle + rural_wstwtr_recycle,
+                "km3/yr",
+            ],
+            ["Water Waste|Reuse|Urban", urban_wstwtr_recycle, "km3/yr"],
+            ["Water Waste|Reuse|Rural", rural_wstwtr_recycle, "km3/yr"],
+            [
+                "Investment|Infrastructure|Water",
+                rural_infrastructure_inv
+                + urban_infrastructure_inv
+                + extrt_sw_inv
+                + extrt_gw_inv
+                + extrt_fgw_inv
+                + saline_inv
+                + cooling_ot_fresh
+                + cooling_cl_fresh
+                + cooling_saline_inv
+                + cooling_air_inv,
+                +industry_unconnected_inv,
+                "million US$2010/yr",
+            ],
+            [
+                "Investment|Infrastructure|Water|Extraction",
+                extrt_sw_inv + extrt_gw_inv + extrt_fgw_inv,
+                "million US$2010/yr",
+            ],
+            [
+                "Investment|Infrastructure|Water|Other",
+                extrt_sw_inv + extrt_gw_inv + extrt_fgw_inv,
+                "million US$2010/yr",
+            ],
+            [
+                "Investment|Infrastructure|Water|Extraction|Surface",
+                extrt_sw_inv,
+                "million US$2010/yr",
+            ],
+            [
+                "Investment|Infrastructure|Water|Extraction|Groundwater",
+                extrt_gw_inv + extrt_fgw_inv,
+                "million US$2010/yr",
+            ],
+            [
+                "Investment|Infrastructure|Water|Extraction|Groundwater|Fossil",
+                extrt_fgw_inv,
+                "million US$2010/yr",
+            ],
+            [
+                "Investment|Infrastructure|Water|Extraction|Groundwater|Renewable",
+                extrt_gw_inv,
+                "million US$2010/yr",
+            ],
+            [
+                "Investment|Infrastructure|Water|Desalination",
+                saline_inv,
+                "million US$2010/yr",
+            ],
+            [
+                "Investment|Infrastructure|Water|Cooling",
+                cooling_ot_fresh
+                + cooling_cl_fresh
+                + cooling_saline_inv
+                + cooling_air_inv,
+                "million US$2010/yr",
+            ],
+            [
+                "Investment|Infrastructure|Water|Cooling|Once through freshwater",
+                cooling_ot_fresh,
+                "million US$2010/yr",
+            ],
+            [
+                "Investment|Infrastructure|Water|Cooling|Closed loop freshwater",
+                cooling_cl_fresh,
+                "million US$2010/yr",
+            ],
+            [
+                "Investment|Infrastructure|Water|Cooling|Once through saline",
+                cooling_saline_inv,
+                "million US$2010/yr",
+            ],
+            [
+                "Investment|Infrastructure|Water|Cooling|Air cooled",
+                cooling_air_inv,
+                "million US$2010/yr",
+            ],
+            # [
+            #     "Investment|Infrastructure|Water",
+            #     rural_infrastructure_inv + urban_infrastructure_inv,
+            #     "million US$2010/yr",
+            # ],
+            [
+                "Investment|Infrastructure|Water|Rural",
+                rural_infrastructure_inv,
+                "million US$2010/yr",
+            ],
+            [
+                "Investment|Infrastructure|Water|Urban",
+                urban_infrastructure_inv,
+                "million US$2010/yr",
+            ],
+            [
+                "Investment|Infrastructure|Water|Industrial",
+                industry_unconnected_inv,
+                "million US$2010/yr",
+            ],
+            [
+                "Investment|Infrastructure|Water|Treatment & Recycling",
+                urban_treatment_recycling_inv + rural_treatment_recycling_inv,
+                "million US$2010/yr",
+            ],
+            [
+                "Investment|Infrastructure|Water|Treatment & Recycling|Urban",
+                urban_treatment_recycling_inv,
+                "million US$2010/yr",
+            ],
+            [
+                "Investment|Infrastructure|Water|Treatment & Recycling|Rural",
+                rural_treatment_recycling_inv,
+                "million US$2010/yr",
+            ],
+            [
+                "Investment|Infrastructure|Water|Distribution",
+                rural_dist_inv + urban_dist_inv,
+                "million US$2010/yr",
+            ],
+            [
+                "Investment|Infrastructure|Water|Distribution|Rural",
+                rural_dist_inv,
+                "million US$2010/yr",
+            ],
+            [
+                "Investment|Infrastructure|Water|Distribution|Urban",
+                urban_dist_inv,
+                "million US$2010/yr",
+            ],
+            [
+                "Investment|Infrastructure|Water|Unconnected",
+                rural_unconnected_inv
+                + urban_unconnected_inv
+                + industry_unconnected_inv,
+                "million US$2010/yr",
+            ],
+            [
+                "Investment|Infrastructure|Water|Unconnected|Rural",
+                rural_unconnected_inv,
+                "million US$2010/yr",
+            ],
+            [
+                "Investment|Infrastructure|Water|Unconnected|Urban",
+                urban_unconnected_inv,
+                "million US$2010/yr",
+            ],
+            [
+                "Total Operation Management Cost|Infrastructure|Water|Desalination",
+                saline_totalom,
+                "million US$2010/yr",
+            ],
+            [
+                "Total Operation Management Cost|Infrastructure|Water|Extraction",
+                extrt_fgw_om,
+                "million US$2010/yr",
+            ],
+            [
+                "Total Operation Management Cost|Infrastructure|Water|Rural",
+                rural_infrastructure_totalom,
+                "million US$2010/yr",
+            ],
+            [
+                "Total Operation Management Cost|Infrastructure|Water|Urban",
+                urban_infrastructure_totalom,
+                "million US$2010/yr",
+            ],
+            [
+                "Total Operation Management Cost|Infrastructure|Water|Treatment & Recycling",
+                urban_treatment_recycling_totalom + rural_treatment_recycling_totalom,
+                "million US$2010/yr",
+            ],
+            [
+                "Total Operation Management Cost|Infrastructure|Water|Treatment & Recycling|Urban",
+                urban_treatment_recycling_totalom,
+                "million US$2010/yr",
+            ],
+            [
+                "Total Operation Management Cost|Infrastructure|Water|Treatment & Recycling|Rural",
+                rural_treatment_recycling_totalom,
+                "million US$2010/yr",
+            ],
+            [
+                "Total Operation Management Cost|Infrastructure|Water| Distribution",
+                rural_dist_totalom + rural_dist_totalom,
+                "million US$2010/yr",
+            ],
+            [
+                "Total Operation Management Cost|Infrastructure|Water|Distribution|Rural",
+                rural_dist_totalom,
+                "million US$2010/yr",
+            ],
+            [
+                "Total Operation Management Cost|Infrastructure|Water|Distribution|Urban",
+                urban_dist_totalom,
+                "million US$2010/yr",
+            ],
+            [
+                "Total Operation Management Cost|Infrastructure|Water|Unconnected",
+                rural_unconnected_totalom
+                + urban_unconnected_totalom
+                + industry_unconnected_totalom,
+                "million US$2010/yr",
+            ],
+            [
+                "Total Operation Management Cost|Infrastructure|Water|Unconnected|Rural",
+                rural_unconnected_totalom,
+                "million US$2010/yr",
+            ],
+            [
+                "Total Operation Management Cost|Infrastructure|Water|Unconnected|Urban",
+                urban_unconnected_totalom,
+                "million US$2010/yr",
+            ],
+            [
+                "Total Operation Management Cost|Infrastructure|Water|Unconnected|Industry",
+                industry_unconnected_totalom,
+                "million US$2010/yr",
+            ],
+        ],
+        columns=["names", "list_cat", "unit"],
+    )
+
+    # add population with sanitation or drinking water access
+    mp2 = sc.platform
+    map_node = sc.set("map_node")
+    # this might not be the best way to get the region, better from context
+    if "R11" in map_node.node.to_list()[1]:
+        reg = "R11"
+    elif "R12" in map_node.node.to_list()[1]:
+        reg = "R12"
+    else:
+        print("Check the region of the model is consistent with R11,R12")
+
+    # load data on water and sanitation access
+    load_path = private_data_path("water", "demands", "harmonized", reg)
+    all_rates = pd.read_csv(load_path / "all_rates_SSP2.csv")
+
+    pop_check = sc.timeseries(variable=("Population"))
+    pop_check = pop_check[pop_check.year >= 2020]
+    if pop_check.empty:
+        print("The Population data does not exist or timeseries() has no future values")
+    else:
+        pop_drink_tot = pd.DataFrame()
+        pop_sani_tot = pd.DataFrame()
+        pop_sdg6 = pd.DataFrame()
+        for ur in ["urban", "rural"]:
+            # CHANGE TO URBAN AND RURAL POP
+            pop_tot = sc.timeseries(variable=("Population|" + ur.capitalize()))
+            pop_tot = pop_tot[-(pop_tot.region == "GLB region (R11)")]
+            pop_reg = np.unique(pop_tot["region"])
+            # need to change names
+            reg_map = mp2.regions()
+            reg_map = reg_map[reg_map.mapped_to.isin(pop_reg)].drop(
+                columns=["parent", "hierarchy"]
+            )
+            reg_map["region"] = [x.split("_")[1] for x in reg_map.region]
+
+            df_rate = all_rates[all_rates.variable.str.contains(ur)]
+
+            df_rate = df_rate[
+                df_rate.variable.str.contains("sdg" if sdgs else "baseline")
+            ]
+
+            df_rate["region"] = [x.split("|")[1] for x in df_rate.node]
+            df_rate = df_rate.drop(columns=["node"])
+            # make region mean (no weighted average)
+            df_rate = (
+                df_rate.groupby(["year", "variable", "region"])["value"]
+                .mean()
+                .reset_index()
+            )
+            # convert region name
+            df_rate = df_rate.merge(reg_map, how="left")
+            df_rate = df_rate.drop(columns=["region"])
+            df_rate = df_rate.rename(
+                columns={"mapped_to": "region", "variable": "new_var", "value": "rate"}
+            )
+
+            # Population|Drinking Water Access
+            df_drink = df_rate[df_rate.new_var.str.contains("connection")]
+            pop_drink = pop_tot.merge(df_drink, how="left")
+            pop_drink["variable"] = (
+                "Population|Drinking Water Access|" + ur.capitalize()
+            )
+            pop_drink["value"] = pop_drink.value * pop_drink.rate
+            cols = pop_tot.columns
+            pop_drink = pop_drink[cols]
+            pop_drink_tot = pop_drink_tot.append(pop_drink)
+            pop_sdg6 = pop_sdg6.append(pop_drink)
+
+            # Population|Sanitation Acces
+            df_sani = df_rate[df_rate.new_var.str.contains("treatment")]
+            pop_sani = pop_tot.merge(df_sani, how="left")
+            pop_sani["variable"] = "Population|Sanitation Access|" + ur.capitalize()
+            pop_sani["value"] = pop_sani.value * pop_sani.rate
+            pop_sani = pop_sani[cols]
+            pop_sani_tot = pop_sani_tot.append(pop_drink)
+            pop_sdg6 = pop_sdg6.append(pop_sani)
+
+        # total values
+        pop_drink_tot = (
+            pop_drink_tot.groupby(["region", "unit", "year", "model", "scenario"])[
+                "value"
+            ]
+            .sum()
+            .reset_index()
+        )
+        pop_drink_tot["variable"] = "Population|Drinking Water Access"
+        pop_drink_tot = pop_drink_tot[cols]
+        pop_sani_tot = (
+            pop_sani_tot.groupby(["region", "unit", "year", "model", "scenario"])[
+                "value"
+            ]
+            .sum()
+            .reset_index()
+        )
+        pop_sani_tot["variable"] = "Population|Sanitation Access"
+        pop_sani_tot = pop_sani_tot[cols]
+        # global values
+        pop_sdg6 = pop_sdg6.append(pop_drink_tot).append(pop_sani_tot)
+        pop_sdg6_glb = (
+            pop_sdg6.groupby(["variable", "unit", "year", "model", "scenario"])["value"]
+            .sum()
+            .reset_index()
+        )
+        pop_sdg6_glb["region"] = "World"
+        pop_sdg6_glb = pop_sdg6_glb[cols]
+
+        pop_sdg6 = pop_sdg6.append(pop_sdg6_glb)
+        print("Population|Drinking Water Access")
+
+    # Add water prices, ad-hoc procedure
+    wp = sc.var(
+        "PRICE_COMMODITY", {"commodity": ["urban_mw", "rural_mw", "freshwater"]}
+    )
+    wp["value"] = wp["lvl"] / 1000
+    wp["unit"] = "US$2010/m3"
+    wp = wp.rename(columns={"node": "region"})
+    # get withdrawals for weighted mean
+    ww = report_iam.as_pandas()
+    ww = ww[
+        ww.variable.isin(
+            ["out|final|rural_mw|rural_t_d|M1", "out|final|urban_mw|urban_t_d|M1"]
+        )
+    ]
+    ww["commodity"] = np.where(
+        ww.variable.str.contains("urban_mw"), "urban_mw", "rural_mw"
+    )
+    ww["wdr"] = ww["value"]
+    ww = ww[["region", "year", "commodity", "wdr"]]
+    # irrigation water, at regional level
+    wp_irr = wp[wp.level == "water_irr"]
+    wp_irr["variable"] = "Price|Irrigation Water"
+    wp_irr = wp_irr.drop(columns={"level", "lvl", "mrg"})
+    # driking water
+    wr_dri = wp[wp.commodity.isin(["urban_mw", "rural_mw"])]
+    wr_dri = wr_dri.drop(columns={"level", "lvl", "mrg"})
+    wr_dri = wr_dri.merge(ww, how="left")
+    wr_dri["variable"] = np.where(
+        wr_dri.commodity == "urban_mw",
+        "Price|Drinking Water|Urban",
+        "Price|Drinking Water|Rural",
+    )
+    wr_dri_m = (
+        wr_dri.groupby(["region", "unit", "year"])
+        .apply(lambda x: np.average(x.value, weights=x.wdr))
+        .reset_index()
+    )
+    wr_dri_m["value"] = wr_dri_m[0]
+    wr_dri_m = wr_dri_m.drop(columns={0})
+    wr_dri_m["variable"] = "Price|Drinking Water"
+
+    wp = wp_irr.append(wr_dri).append(wr_dri_m)
+
+    wp["model"] = sc.model
+    wp["scenario"] = sc.scenario
+    wp = wp[["model", "scenario", "region", "variable", "unit", "year", "value"]]
+
+    wp_iam = pyam.IamDataFrame(wp)
+    # Merge both dataframes in pyam
+    report_iam = report_iam.append(wp_iam)
+
+    # Fetching nodes from the scenario to aggregate to MESSAGE energy region definition
+    map_node = sc.set("map_node")
+    map_node = map_node[map_node["node_parent"] != map_node["node"]]
+    map_node_dict = map_node.groupby("node_parent")["node"].apply(list).to_dict()
+
+    for index, row in map_agg_pd.iterrows():
+        print(row["names"])
+        # Aggregates variables as per standard reporting
+        report_iam.aggregate(row["names"], components=row["list_cat"], append=True)
+
+        if row["names"] in (
+            "Water Extraction|Seawater|Cooling",
+            "Investment|Infrastructure|Water",
+            "Water Extraction|Seawater",
+        ):
+            report_iam.aggregate_region(row["names"], append=True)
+        else:
+            for rr in map_node_dict:
+                report_iam.aggregate_region(
+                    row["names"], region=rr, subregions=map_node_dict[rr], append=True
+                )
+    # Aggregates variables separately that are not included map_agg_pd
+    for rr in map_node_dict:
+        report_iam.aggregate_region(
+            "Water Resource|*", region=rr, subregions=map_node_dict[rr], append=True
+        )
+        report_iam.aggregate_region(
+            "Price|*",
+            method="mean",
+            region=rr,
+            subregions=map_node_dict[rr],
+            append=True,
+        )
+
+    # Remove duplicate variables
+    varsexclude = [
+        "Investment|Infrastructure|Water",
+        "Investment|Infrastructure|Water|Extraction",
+        "Investment|Infrastructure|Water|Other",
+        "Investment|Infrastructure|Water|Extraction|Groundwater",
+    ]
+    report_iam.filter(variable=varsexclude, unit="unknown", keep=False, inplace=True)
+    # prepare data for loading timeserie
+    report_pd = report_iam.as_pandas()
+    report_pd = report_pd.drop(columns=["exclude"])
+    # all initial variables form Reporte will be filtered out
+    d = report_df.Variable.unique()
+    d1 = pd.DataFrame({"variable": d})
+    d1[["to_keep"]] = "No"
+    # filter out initial variables
+    report_pd = report_pd.merge(d1, how="left")
+    report_pd = report_pd[report_pd["to_keep"] != "No"]
+    report_pd = report_pd.drop(columns=["to_keep"])
+
+    # ecluded other intermediate variables added later to report_iam
+    report_pd = report_pd[-report_pd.variable.isin(water_hydro_var)]
+
+    # add water population
+    report_pd = report_pd.append(pop_sdg6)
+    # add units
+    for index, row in map_agg_pd.iterrows():
+        report_pd.loc[(report_pd.variable == row["names"]), "unit"] = row["unit"]
+
+    df_unit = pyam.IamDataFrame(report_pd)
+    df_unit.convert_unit("GWa", to="EJ", inplace=True)
+    df_unit_inv = df_unit.filter(variable="Investment*")
+    df_unit_inv.convert_unit(
+        "million US$2010/yr", to="billion US$2010/yr", factor=0.001, inplace=True
+    )
+
+    df_unit = df_unit.as_pandas()
+    df_unit = df_unit[~df_unit["variable"].str.contains("Investment")]
+    df_unit_inv = df_unit_inv.as_pandas()
+    report_pd = pd.concat([df_unit, df_unit_inv])
+    report_pd = report_pd.drop(columns=["exclude"])
+    report_pd["unit"].replace("EJ", "EJ/yr", inplace=True)
+
+    sc.check_out(timeseries_only=True)
+    print("Starting to upload timeseries")
+    print(report_pd.head())
+    sc.add_timeseries(report_pd)
+    print("Finished uploading timeseries")
+    sc.commit("Reporting uploaded as timeseries")
+
+
+def report_full(sc=False, sdgs=False):
+    """Combine old and new reporting workflows"""
+    a = sc.timeseries()
+    # keep historical part, if present
+    a = a[a.year >= 2020]
+
+    sc.check_out(timeseries_only=True)
+    print("Remove any previous timeseries")
+
+    sc.remove_timeseries(a)
+    print("Finished removing timeseries, now commit..")
+    sc.commit("Remove existing timeseries")
+
+    run_old_reporting(sc)
+    print("First part of reporting completed, now procede with the water variables")
+
+    report(sc, sdgs)
+    print("overall NAVIGATE reporting completed")
+
+    # add ad-hoc caplculated variables with a function
+    ts = sc.timeseries()
+
+    out_path = private_data_path().parents[0] / "reporting_output/NAVIGATE"
+
+    if not out_path.exists():
+        out_path.mkdir()
+
+    out_file = out_path / f"{sc.model}_{sc.scenario}.csv"
+
+    # Convert to pyam dataframe
+    ts_long = pyam.IamDataFrame(ts)
+
+    ts_long.to_csv(out_file)
+    print(f"Saving csv to {out_file}")
diff --git a/nest/message_ix_models/model/water/utils.py b/nest/message_ix_models/model/water/utils.py
new file mode 100644
index 0000000..a72e0ef
--- /dev/null
+++ b/nest/message_ix_models/model/water/utils.py
@@ -0,0 +1,158 @@
+from collections import defaultdict
+from functools import lru_cache
+from itertools import product
+from typing import Tuple
+
+import numpy as np
+import pandas as pd
+import xarray as xr
+from sdmx.model import Code
+
+from message_ix_models import Context
+from message_ix_models.model.structure import get_codes
+from message_ix_models.util import load_private_data
+
+# Configuration files
+METADATA = [
+    # Information about MESSAGE-water
+    ("water", "config"),
+    ("water", "set"),
+    ("water", "technology"),
+]
+
+
+def read_config(context=None):
+    """Read the water model configuration / metadata from file.
+
+    Numerical values are converted to computation-ready data structures.
+
+    Returns
+    -------
+    .Context
+        The current Context, with the loaded configuration.
+    """
+
+    context = context or Context.get_instance(0)
+
+    # if context.nexus_set == 'nexus':
+    if "water set" in context:
+        # Already loaded
+        return context
+
+    # Load water configuration
+    for parts in METADATA:
+        # Key for storing in the context
+        key = " ".join(parts)
+
+        # Actual filename parts; ends with YAML
+        _parts = list(parts)
+        _parts[-1] += ".yaml"
+
+        context[key] = load_private_data(*_parts)
+
+    return context
+
+
+@lru_cache()
+def map_add_on(rtype=Code):
+    """Map addon & type_addon in ``sets.yaml``."""
+    dims = ["add_on", "type_addon"]
+
+    # Retrieve configuration
+    context = read_config()
+
+    # Assemble group information
+    result = defaultdict(list)
+
+    for indices in product(*[context["water set"][d]["add"] for d in dims]):
+        # Create a new code by combining two
+        result["code"].append(
+            Code(
+                id="".join(c.id for c in indices),
+                name=", ".join(c.name for c in indices),
+            )
+        )
+
+        # Tuple of the values along each dimension
+        result["index"].append(tuple(c.id for c in indices))
+
+    if rtype == "indexers":
+        # Three tuples of members along each dimension
+        indexers = zip(*result["index"])
+        indexers = {
+            d: xr.DataArray(list(i), dims="consumer_group")
+            for d, i in zip(dims, indexers)
+        }
+        indexers["consumer_group"] = xr.DataArray(
+            [c.id for c in result["code"]],
+            dims="consumer_group",
+        )
+        return indexers
+    elif rtype is Code:
+        return sorted(result["code"], key=str)
+    else:
+        raise ValueError(rtype)
+
+
+def add_commodity_and_level(df, default_level=None):
+    # Add input commodity and level
+    t_info = Context.get_instance()["water set"]["technology"]["add"]
+    c_info = get_codes("commodity")
+
+    @lru_cache()
+    def t_cl(t):
+        input = t_info[t_info.index(t)].anno["input"]
+        # Commodity must be specified
+        commodity = input["commodity"]
+        # Use the default level for the commodity in the RES (per
+        # commodity.yaml)
+        level = (
+            input.get("level", "water_supply")
+            or c_info[c_info.index(commodity)].anno.get("level", None)
+            or default_level
+        )
+
+        return commodity, level
+
+    def func(row):
+        row[["commodity", "level"]] = t_cl(row["technology"])
+        return row
+
+    return df.apply(func, axis=1)
+
+
+def map_yv_ya_lt(periods: Tuple[int, ...], lt, ya=None) -> pd.DataFrame:
+    """All meaningful combinations of (vintage year, active year) given `periods`.
+
+    Parameters
+    ----------
+    labels : pandas.DataFrame
+        Each column (dimension) corresponds to one in `df`. Each row represents one
+        matched set of labels for those dimensions.
+    lt : int, lifetime
+    """
+    # commented: currently unused, this does the same as the line below, using (start
+    # period, final period, uniform ``duration_period). The intermediate periods are
+    # inferred
+    # _s = slice(periods_or_start, end + 1, dp)
+    # data = np.mgrid[_s, _s]
+
+    if not ya:
+        ya = periods[0]
+        print(("first active year set as"), ya)
+    if not lt:
+        raise ValueError("Add a fixed lifetime parameter 'lt'")
+
+    # Create a mesh grid using numpy built-ins
+    data = np.meshgrid(periods, periods, indexing="ij")
+    # Take the upper-triangular portion (setting the rest to 0), reshape
+    data = np.triu(data).reshape((2, -1))
+    # Filter only non-zero pairs
+    df = pd.DataFrame(
+        filter(sum, zip(data[0, :], data[1, :])),
+        columns=["year_vtg", "year_act"],
+        dtype=np.int64,
+    )
+    df = df[df.year_act >= ya]
+    df = df[(df.year_act - df.year_vtg) <= lt]
+    return df
diff --git a/nest/message_ix_models/project/__init__.py b/nest/message_ix_models/project/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/nest/message_ix_models/py.typed b/nest/message_ix_models/py.typed
new file mode 100644
index 0000000..e69de29
diff --git a/nest/message_ix_models/testing.py b/nest/message_ix_models/testing.py
new file mode 100644
index 0000000..e1f20a4
--- /dev/null
+++ b/nest/message_ix_models/testing.py
@@ -0,0 +1,399 @@
+import logging
+import os
+from copy import deepcopy
+from pathlib import Path
+from tempfile import TemporaryDirectory
+
+import click.testing
+import message_ix
+import pandas as pd
+import pytest
+from ixmp import Platform
+from ixmp import config as ixmp_config
+
+from message_ix_models import cli, util
+from message_ix_models.util._logging import mark_time, preserve_log_level
+from message_ix_models.util.context import Context
+
+log = logging.getLogger(__name__)
+
+# pytest hooks
+
+
+def pytest_addoption(parser):
+    """Add two command-line options to pytest:
+
+    ``--local-cache``
+       Use existing, local cache files in tests. This option can speed up tests that
+       *use* the results of slow data loading/parsing. However, if cached values are not
+       up to date with the current code, unexpected failure may occur.
+
+    ``--jvmargs``
+       Additional arguments to give for the Java Virtual Machine used by :mod:`ixmp`'s
+       :class:`.JDBCBackend`. Used by :func:`session_context`.
+    """
+    parser.addoption(
+        "--local-cache",
+        action="store_true",
+        help="Use existing local cache files in tests",
+    )
+    parser.addoption(
+        "--jvmargs",
+        action="store",
+        default="",
+        help="Arguments for Java VM used by ixmp JDBCBackend",
+    )
+
+
+def pytest_sessionstart():
+    # Quiet logs for some upstream packages
+    for name in ("pycountry.db", "matplotlib.backends", "matplotlib.font_manager"):
+        logging.getLogger(name).setLevel(logging.DEBUG + 1)
+
+
+# Fixtures
+
+
+@pytest.fixture(scope="session")
+def session_context(pytestconfig, tmp_env):
+    """A :class:`.Context` connected to a temporary, in-memory database.
+
+    This Context is suitable for modifying and running test code that does not affect
+    the user/developer's filesystem and configured :mod:`ixmp` databases.
+
+    Uses the :func:`.tmp_env` fixture from ixmp. This fixture also sets:
+
+    - :attr:`.Context.cache_path`, depending on whether the :program:`--local-cache` CLI
+      option was given:
+
+      - If not given: pytest's :doc:`standard cache directory <pytest:how-to/cache>`.
+      - If given: the :file:`/cache/` directory under the user's "message local data"
+        directory.
+
+    - the "message local data" config key to a temporary directory :file:`/data/` under
+      the :ref:`pytest tmp_path directory <pytest:tmp_path>`.
+
+    """
+    ctx = Context.only()
+
+    # Temporary, empty local directory for local data
+    session_tmp_dir = Path(pytestconfig._tmp_path_factory.mktemp("data"))
+
+    # Set the cache path according to whether pytest --local-cache was given. If True,
+    # pick up the existing setting from the user environment. If False, use a pytest-
+    # managed cache directory that persists across test sessions.
+    ctx.cache_path = (
+        ctx.local_data.joinpath("cache")
+        if pytestconfig.option.local_cache
+        # TODO use pytestconfig.cache.mkdir() when pytest >= 6.3 is available
+        else Path(pytestconfig.cache.makedir("cache"))
+    )
+
+    # Other local data in the temporary directory for this session only
+    ctx.local_data = session_tmp_dir
+
+    # Also set the "message local data" key in the ixmp config
+    ixmp_config.set("message local data", session_tmp_dir)
+
+    # If message_data is not installed, use a temporary path for private_data_path()
+    message_data_path = util.MESSAGE_DATA_PATH
+    if util.MESSAGE_DATA_PATH is None:
+        util.MESSAGE_DATA_PATH = session_tmp_dir.joinpath("message_data")
+
+        # Create some subdirectories
+        util.MESSAGE_DATA_PATH.joinpath("data", "tests").mkdir(parents=True)
+
+    # Add a platform connected to an in-memory database
+    platform_name = "message-ix-models"
+    ixmp_config.add_platform(
+        platform_name,
+        "jdbc",
+        "hsqldb",
+        url=f"jdbc:hsqldb:mem://{platform_name}",
+        jvmargs=pytestconfig.option.jvmargs,
+    )
+
+    # Launch Platform and connect to testdb (reconnect if closed)
+    mp = Platform(name=platform_name)
+    mp.open_db()
+
+    ctx.platform_info["name"] = platform_name
+
+    try:
+        yield ctx
+    finally:
+        ctx.close_db()
+        ixmp_config.remove_platform(platform_name)
+
+        # Restore prior value
+        util.MESSAGE_DATA_PATH = message_data_path
+
+
+@pytest.fixture(scope="function")
+def test_context(request, session_context):
+    """A copy of :func:`session_context` scoped to one test function."""
+    ctx = deepcopy(session_context)
+
+    yield ctx
+
+    ctx.delete()
+
+
+@pytest.fixture(scope="function")
+def user_context(request):  # pragma: no cover
+    """Context which can access user's configuration, e.g. platform names."""
+    # Disabled; this is bad practice
+    raise NotImplementedError
+
+
+class CliRunner(click.testing.CliRunner):
+    """Subclass of :class:`click.testing.CliRunner` with extra features."""
+
+    # NB decorator ensures any changes that the CLI makes to the logger level are
+    #    restored
+    @preserve_log_level()
+    def invoke(self, *args, **kwargs):
+        """Invoke the :program:`mix-models` CLI."""
+        result = super().invoke(cli.main, *args, **kwargs)
+
+        # Store the result to be used by assert_exit_0()
+        self.last_result = result
+
+        return result
+
+    def assert_exit_0(self, *args, **kwargs):
+        """Assert a result has exit_code 0, or print its traceback.
+
+        If any `args` or `kwargs` are given, :meth:`.invoke` is first called. Otherwise,
+        the result from the last call of :meth:`.invoke` is used.
+
+        Raises
+        ------
+        AssertionError
+            if the result exit code is not 0. The exception contains the traceback from
+            within the CLI.
+
+        Returns
+        -------
+        click.testing.Result
+        """
+        __tracebackhide__ = True
+
+        if len(args) + len(kwargs):
+            self.invoke(*args, **kwargs)
+
+        if self.last_result.exit_code != 0:
+            # Re-raise the exception triggered within the CLI invocation
+            raise (
+                self.last_result.exc_info[1].__context__ or self.last_result.exc_info[1]
+            )
+
+        return self.last_result
+
+
+@pytest.fixture(scope="session")
+def mix_models_cli(request, session_context, tmp_env):
+    """A :class:`.CliRunner` object that invokes the :program:`mix-models` CLI."""
+    # Require the `session_context` fixture in order to set Context.local_data
+    yield CliRunner(env=tmp_env)
+
+
+@cli.main.group("_test", hidden=True)
+def cli_test_group():
+    """Hidden group of CLI commands.
+
+    Other code which needs to test CLI behaviour **may** attach temporary/throw-away
+    commands to this group and then invoke them using :func:`mix_models_cli`. This
+    avoids the need to expose additional commands for testing purposes only.
+    """
+
+
+# Testing utility functions
+
+
+def bare_res(request, context: Context, solved: bool = False) -> message_ix.Scenario:
+    """Return or create a Scenario containing the bare RES, for use in testing.
+
+    The Scenario has a model name like "MESSAGEix-GLOBIOM [regions]
+    [start]:[duration]:[end]", e.g. "MESSAGEix-GLOBIOM R14 2020:10:2110" (see
+    :func:`.bare.name`) and the scenario name "baseline".
+
+    This function should:
+
+    - only be called from within test code, i.e. in :mod:`message_data.tests`.
+    - be called once for each test function, so that each test receives a fresh copy of
+      the RES scenario.
+
+    Parameters
+    ----------
+    request : .Request or None
+        The pytest :fixture:`pytest:request` fixture. If provided the pytest test node
+        name is used for the scenario name of the returned Scenario.
+    context : .Context
+        Passed to :func:`.testing.bare_res`.
+    solved : bool, optional
+        Return a solved Scenario.
+
+    Returns
+    -------
+    .Scenario
+        The scenario is a fresh clone, so can be modified freely without disturbing
+        other tests.
+    """
+    from message_ix_models.model import bare
+
+    name = bare.name(context)
+    mp = context.get_platform()
+
+    try:
+        base = message_ix.Scenario(mp, name, "baseline")
+    except ValueError:
+        log.info(f"Create '{name}/baseline' for testing")
+        context.scenario_info.update(model=name, scenario="baseline")
+        base = bare.create_res(context)
+
+    if solved and not base.has_solution():
+        log.info("Solve")
+        base.solve(solve_options=dict(lpmethod=4), quiet=True)
+
+    try:
+        new_name = request.node.name
+    except AttributeError:
+        new_name = "baseline"
+
+    log.info(f"Clone to '{name}/{new_name}'")
+    return base.clone(scenario=new_name, keep_solution=solved)
+
+
+#: Items with names that match (partially or fully) these names are omitted by
+#: :func:`export_test_data`.
+EXPORT_OMIT = [
+    "aeei",
+    "cost_MESSAGE",
+    "demand_MESSAGE",
+    "demand",
+    "depr",
+    "esub",
+    "gdp_calibrate",
+    "grow",
+    "historical_gdp",
+    "kgdp",
+    "kpvs",
+    "lakl",
+    "land",
+    "lotol",
+    "mapping_macro_sector",
+    "MERtoPPP",
+    "prfconst",
+    "price_MESSAGE",
+    "ref_",
+    "sector",
+]
+
+
+def export_test_data(context: Context):
+    """Export a subset of data from a scenario, for use in tests.
+
+    The context settings ``export_nodes`` (default: "R11_AFR" and "R11_CPA") and
+    ``export_techs`` (default: "coal_ppl") are used to filter the data exported.
+    In addition, any item (set, parameter, variable, or equation) with a name matching
+    :data:`EXPORT_OMIT` *or* the context setting ``export_exclude`` is discarded.
+
+    The output is stored at :file:`data/tests/{model name}_{scenario name}_{techs}.xlsx`
+    in :mod:`message_data`.
+
+    See also
+    --------
+    :ref:`export-test-data`
+    """
+    from message_ix_models.util import private_data_path
+
+    # Load the scenario to be exported
+    scen = context.get_scenario()
+
+    # Retrieve the context settings giving the nodes and technologies to export
+    nodes = context.get("export_nodes", ["R11_AFR", "R11_CPA"])
+    technology = context.get("export_techs", ["coal_ppl"])
+
+    # Construct the destination file name
+    dest_file = private_data_path(
+        "tests", f"{scen.model}_{scen.scenario}_{'_'.join(technology)}.xlsx"
+    )
+    # Temporary file name
+    td = TemporaryDirectory()
+    tmp_file = Path(td.name).joinpath("export_test_data.xlsx")
+
+    # Ensure the target directory exists
+    dest_file.parent.mkdir(exist_ok=True)
+
+    # Dump data to temporary Excel file
+    log.info(f"Export test data to {dest_file}")
+    scen.to_excel(
+        tmp_file,
+        filters={
+            "technology": technology,
+            "node": nodes,
+            "node_dest": nodes,
+            "node_loc": nodes,
+            "node_origin": nodes,
+            "node_parent": nodes,
+            "node_rel": nodes,
+            "node_share": nodes,
+        },
+    )
+
+    mark_time()
+
+    log.info("Reduce test data")
+
+    # Read from temporary file and write to final file, omitting unnecessary sheets
+    reader = pd.ExcelFile(tmp_file)
+    writer = pd.ExcelWriter(dest_file)
+
+    # Retrieve the type mapping first, to be modified as sheets are discarded
+    ix_type_mapping = reader.parse("ix_type_mapping").set_index("item")
+
+    for name in reader.sheet_names:
+        # Check if this sheet is to be included
+        if name == "ix_type_mapping":
+            # Already handled
+            continue
+        elif any(i in name for i in (EXPORT_OMIT + context.get("export_exclude", []))):
+            log.info(f"Discard sheet '{name}'")
+
+            # Remove from the mapping
+            ix_type_mapping.drop(name, inplace=True)
+
+            continue
+
+        # Copy the sheet from temporary to final file
+        reader.parse(name).to_excel(writer, sheet_name=name, index=False)
+
+    # Close the temporary file
+    reader.close()
+
+    # Write the mapping
+    ix_type_mapping.reset_index().to_excel(
+        writer, sheet_name="ix_type_mapping", index=False
+    )
+
+    # Close the final file
+    writer.close()
+
+    mark_time()
+
+
+#: Shorthand for marking a parametrized test case that is expected to fail because it is
+#: not implemented.
+NIE = pytest.mark.xfail(raises=NotImplementedError)
+
+
+def not_ci(reason=None, action="skip"):
+    """Mark a test as xfail or skipif if on CI infrastructure.
+
+    Checks the ``GITHUB_ACTIONS`` environment variable; returns a pytest mark.
+    """
+    action = "skipif" if action == "skip" else action
+    return getattr(pytest.mark, action)(
+        condition="GITHUB_ACTIONS" in os.environ, reason=reason
+    )
diff --git a/nest/message_ix_models/tests/__init__.py b/nest/message_ix_models/tests/__init__.py
new file mode 100644
index 0000000..9fc108b
--- /dev/null
+++ b/nest/message_ix_models/tests/__init__.py
@@ -0,0 +1 @@
+"""Test suite for :mod:`message_ix_models`."""
diff --git a/nest/message_ix_models/tests/model/__init__.py b/nest/message_ix_models/tests/model/__init__.py
new file mode 100644
index 0000000..49b710c
--- /dev/null
+++ b/nest/message_ix_models/tests/model/__init__.py
@@ -0,0 +1 @@
+"""Tests of :mod:`message_ix_models.model` and submodules."""
diff --git a/nest/message_ix_models/tests/model/test_bare.py b/nest/message_ix_models/tests/model/test_bare.py
new file mode 100644
index 0000000..7e19156
--- /dev/null
+++ b/nest/message_ix_models/tests/model/test_bare.py
@@ -0,0 +1,69 @@
+import message_ix
+import pytest
+
+from message_ix_models import testing
+from message_ix_models.model.bare import Config
+
+#: Number of items in the respective YAML files.
+SET_SIZE = dict(
+    commodity=17,
+    level=6,
+    node=14 + 1,  # R14 is default, and 'World' exists automatically
+    relation=19,
+    technology=377,
+    year=28,  # YB is default: 1950, ..., 2020, 2025, ..., 2055, 2060, ..., 2110
+)
+
+
+@pytest.mark.parametrize(
+    "settings, expected",
+    [
+        # Defaults per bare.SETTINGS
+        (dict(), dict()),
+        #
+        # Different regional aggregations
+        (dict(regions="R11"), dict(node=11 + 1)),
+        (dict(regions="RCP"), dict(node=5 + 1)),
+        # MESSAGE-IL
+        (dict(regions="ISR"), dict(node=1 + 1)),
+        #
+        # Different time periods
+        (dict(years="A"), dict(year=16)),  # ..., 2010, 2020, ..., 2110
+        #
+        # Option to add a dummy technology/commodity so the model solves
+        (
+            dict(res_with_dummies=True),
+            dict(
+                commodity=SET_SIZE["commodity"] + 1,
+                technology=SET_SIZE["technology"] + 2,
+            ),
+        ),
+    ],
+)
+def test_create_res(request, test_context, settings, expected):
+    # Apply settings to the temporary context
+    test_context.model = Config(**settings)
+
+    # Call bare.create_res() via testing.bare_res(). This ensures the slow step of
+    # creating the scenario occurs only once per test session. If it fails, it will
+    # either fail within this test, or in some other test function that calls
+    # testing.bare_res() with the same arguments.
+    scenario = testing.bare_res(request, test_context, solved=False)
+
+    # Returns a Scenario object
+    assert isinstance(scenario, message_ix.Scenario)
+
+    # Sets contain the expected number of elements
+    sets = SET_SIZE.copy()
+    sets.update(expected)
+    for name, size in sets.items():
+        values = scenario.set(name)
+        assert size == len(values), (name, values)
+
+
+class TestConfig:
+    def test_init(self):
+        c = Config()
+        c.regions = "R999"
+
+        # TODO expand
diff --git a/nest/message_ix_models/tests/model/test_build.py b/nest/message_ix_models/tests/model/test_build.py
new file mode 100644
index 0000000..5296364
--- /dev/null
+++ b/nest/message_ix_models/tests/model/test_build.py
@@ -0,0 +1,103 @@
+import logging
+
+import pytest
+from ixmp.testing import assert_logs
+from message_ix import make_df
+from message_ix.testing import make_dantzig
+
+from message_ix_models import ScenarioInfo
+from message_ix_models.model.build import apply_spec
+
+
+@pytest.fixture
+def scenario(test_context):
+    mp = test_context.get_platform()
+    yield make_dantzig(mp)
+
+
+@pytest.fixture(scope="function")
+def spec():
+    yield dict(add=ScenarioInfo(), require=ScenarioInfo(), remove=ScenarioInfo())
+
+
+def test_apply_spec0(caplog, scenario, spec):
+    """Require missing element raises ValueError."""
+    spec["require"].set["node"].append("vienna")
+
+    with pytest.raises(ValueError):
+        apply_spec(scenario, spec)
+
+    assert (
+        "message_ix_models.model.build",
+        logging.ERROR,
+        "  1 elements not found: ['vienna']",
+    ) in caplog.record_tuples
+
+
+def test_apply_spec1(caplog, scenario, spec):
+    """Add data using the data= argument."""
+
+    def add_data_func(scenario, dry_run):
+        return dict(
+            demand=make_df(
+                "demand",
+                commodity="cases",
+                level="consumption",
+                node="chicago",
+                time="year",
+                unit="case",
+                value=301.0,
+                year=1963,
+            )
+        )
+
+    apply_spec(scenario, spec, data=add_data_func, dry_run=True)
+
+    # Messages are logged about additions
+    assert_logs(caplog, "1 rows in 'demand'")
+    # …but no change because `dry_run`
+    assert not any(301.0 == scenario.par("demand")["value"])
+
+    caplog.clear()
+
+    apply_spec(scenario, spec, data=add_data_func)
+
+    # Messages are logged about additions
+    assert_logs(caplog, "1 rows in 'demand'")
+    # Value was actually changed
+    assert 1 == sum(301.0 == scenario.par("demand")["value"])
+
+
+def test_apply_spec2(caplog, scenario, spec):
+    """Remove an element, with fast=True."""
+    spec["remove"].set["node"] = ["new-york", "not-a-node"]
+
+    apply_spec(scenario, spec, fast=True)
+
+    # Messages are logged about removals
+    assert_logs(
+        caplog,
+        (
+            "Remove 'new-york' from set 'node'",
+            "Remove 'not-a-node' from set 'node'",
+            "  …not found",
+        ),
+    )
+
+
+def test_apply_spec3(caplog, scenario, spec):
+    """Actually remove data."""
+    spec["remove"].set["node"] = ["new-york"]
+
+    apply_spec(scenario, spec)
+
+    # Messages are logged about removals
+    assert_logs(
+        caplog,
+        (
+            "Remove data with node='new-york'",
+            "  1 rows in 'demand'",
+            "  2 rows in 'output'",
+            "  3 rows total",
+        ),
+    )
diff --git a/nest/message_ix_models/tests/model/test_cli.py b/nest/message_ix_models/tests/model/test_cli.py
new file mode 100644
index 0000000..20e8554
--- /dev/null
+++ b/nest/message_ix_models/tests/model/test_cli.py
@@ -0,0 +1,5 @@
+def test_create_bare(mix_models_cli):
+    """The ``res create-bare`` CLI command can be invoked."""
+    # "--nodes" is not a required option, but we give it anyway to test the CLI code
+    # that handles it
+    mix_models_cli.assert_exit_0(["res", "create-bare", "--nodes=R11"])
diff --git a/nest/message_ix_models/tests/model/test_config.py b/nest/message_ix_models/tests/model/test_config.py
new file mode 100644
index 0000000..6221af1
--- /dev/null
+++ b/nest/message_ix_models/tests/model/test_config.py
@@ -0,0 +1,27 @@
+import pytest
+
+from message_ix_models.model import Config
+
+
+class TestConfig:
+    @pytest.mark.parametrize(
+        "values",
+        [
+            dict(regions="R11", years="A"),
+            pytest.param(
+                dict(regions="R99", years="A"),
+                marks=pytest.mark.xfail(
+                    raises=ValueError, reason="regions='R99' not among […]"
+                ),
+            ),
+            pytest.param(
+                dict(regions="R11", years="C"),
+                marks=pytest.mark.xfail(
+                    raises=ValueError, reason="regions='C' not among ['A', 'B']"
+                ),
+            ),
+        ],
+    )
+    def test_check(self, values):
+        c = Config(**values)
+        c.check()
diff --git a/nest/message_ix_models/tests/model/test_disutility.py b/nest/message_ix_models/tests/model/test_disutility.py
new file mode 100644
index 0000000..652a1d9
--- /dev/null
+++ b/nest/message_ix_models/tests/model/test_disutility.py
@@ -0,0 +1,275 @@
+"""Tests of :mod:`.model.disutility`."""
+from itertools import product
+
+import pandas as pd
+import pandas.testing as pdt
+import pytest
+from message_ix import make_df
+from sdmx.model import Annotation, Code
+
+from message_ix_models import ScenarioInfo, testing
+from message_ix_models.model import disutility
+from message_ix_models.util import (
+    add_par_data,
+    copy_column,
+    make_source_tech,
+    merge_data,
+)
+
+# Common data and fixtures for test_minimal() and other tests
+
+COMMON = dict(
+    level="useful",
+    mode="all",
+    node_dest="R14_AFR",
+    node_loc="R14_AFR",
+    node_origin="R14_AFR",
+    node="R14_AFR",
+    time_dest="year",
+    time_origin="year",
+    time="year",
+    unit="kg",
+)
+
+
+@pytest.fixture
+def groups():
+    """Fixture: list of 2 consumer groups."""
+    yield [Code(id="g0"), Code(id="g1")]
+
+
+@pytest.fixture
+def techs():
+    """Fixture: list of 2 technologies for which groups can have disutility."""
+    yield [Code(id="t0"), Code(id="t1")]
+
+
+@pytest.fixture
+def template():
+    """Fixture: :class:.`Code` with annotations, for :func:`.disutility.get_spec`."""
+    # Template for inputs of conversion technologies, from a technology-specific
+    # commodity
+    input = dict(commodity="output of {technology}", level="useful", unit="kg")
+
+    # Template for outputs of conversion technologies, to a group–specific demand
+    # commodity
+    output = dict(commodity="demand of group {group}", level="useful", unit="kg")
+
+    # Code's ID is itself a template for IDs of conversion technologies
+    yield Code(
+        id="usage of {technology} by {group}",
+        annotations=[
+            Annotation(id="input", text=repr(input)),
+            Annotation(id="output", text=repr(output)),
+        ],
+    )
+
+
+@pytest.fixture
+def spec(groups, techs, template):
+    """Fixture: a prepared spec for the minimal test case."""
+    yield disutility.get_spec(groups, techs, template)
+
+
+@pytest.fixture
+def scenario(request, test_context, techs):
+    """Fixture: a :class:`.Scenario` with technologies given by :func:`techs`."""
+    test_context.regions = "R14"
+    s = testing.bare_res(request, test_context, solved=False)
+    s.check_out()
+
+    s.add_set("technology", ["t0", "t1"])
+
+    s.commit("Test fixture for .model.disutility")
+    yield s
+
+
+def test_add(scenario, groups, techs, template):
+    """:func:`.disutility.add` runs on the bare RES; the result solves."""
+    disutility.add(scenario, groups, techs, template)
+
+    # Scenario solves (no demand)
+    scenario.solve(quiet=True)
+    assert (scenario.var("ACT")["lvl"] == 0).all()
+
+
+def minimal_test_data(scenario):
+    """Generate data for :func:`test_minimal`."""
+    common = COMMON.copy()
+    common.pop("node_loc")
+    common.update(dict(mode="all"))
+
+    data = dict()
+
+    info = ScenarioInfo(scenario)
+    y0 = info.Y[0]
+    y1 = info.Y[1]
+
+    # Output from t0 and t1
+    for t in ("t0", "t1"):
+        common.update(dict(technology=t, commodity=f"output of {t}"))
+        merge_data(data, make_source_tech(info, common, output=1.0, var_cost=1.0))
+
+    # Disutility input for each combination of (tech) × (group) × (2 years)
+    input_data = pd.DataFrame(
+        [
+            ["usage of t0 by g0", y0, 0.1],
+            ["usage of t0 by g0", y1, 0.1],
+            ["usage of t1 by g0", y0, 0.1],
+            ["usage of t1 by g0", y1, 0.1],
+            ["usage of t0 by g1", y0, 0.1],
+            ["usage of t0 by g1", y1, 0.1],
+            ["usage of t1 by g1", y0, 0.1],
+            ["usage of t1 by g1", y1, 0.1],
+        ],
+        columns=["technology", "year_vtg", "value"],
+    )
+    data["input"] = make_df(
+        "input", **input_data, commodity="disutility", **COMMON
+    ).assign(node_origin=copy_column("node_loc"), year_act=copy_column("year_vtg"))
+
+    # Demand
+    c, y = zip(*product(["demand of group g0", "demand of group g1"], [y0, y1]))
+    data["demand"] = make_df("demand", commodity=c, year=y, value=1.0, **COMMON)
+
+    # Constraint on activity in the first period
+    t = sorted(input_data["technology"].unique())
+    for bound in ("lo", "up"):
+        par = f"bound_activity_{bound}"
+        data[par] = make_df(par, value=0.5, technology=t, year_act=y0, **COMMON)
+
+    # Constraint on activity growth
+    annual = (1.1 ** (1.0 / 5.0)) - 1.0
+    for bound, factor in (("lo", -1.0), ("up", 1.0)):
+        par = f"growth_activity_{bound}"
+        data[par] = make_df(
+            par, value=factor * annual, technology=t, year_act=y1, **COMMON
+        )
+
+    return data, y0, y1
+
+
+def test_minimal(scenario, groups, techs, template):
+    """Expected results are generated from a minimal test case."""
+    # Set up structure
+    disutility.add(scenario, groups, techs, template)
+
+    # Add test-specific data
+    data, y0, y1 = minimal_test_data(scenario)
+
+    scenario.check_out()
+    add_par_data(scenario, data)
+    scenario.commit("Disutility test 1")
+
+    # commented: pre-solve debugging output
+    # for par in ("input", "output", "technical_lifetime", "var_cost"):
+    #     scenario.par(par).to_csv(f"debug-{par}.csv")
+
+    scenario.solve(quiet=True)
+
+    # Helper function to retrieve ACT data and condense for inspection
+    def get_act(s):
+        result = (
+            scenario.var("ACT")
+            .query("lvl > 0")
+            .drop(columns=["node_loc", "mode", "time", "mrg"])
+            .sort_values(["year_vtg", "technology"])
+            .reset_index(drop=True)
+        )
+        # No "stray" activity of technologies beyond the vintage periods
+        pdt.assert_series_equal(
+            result["year_act"], result["year_vtg"], check_names=False
+        )
+        result = result.drop(columns=["year_vtg"]).set_index(["technology", "year_act"])
+        # Return the activity and its inter-period delta
+        return result, (
+            result.xs(y1, level="year_act") - result.xs(y0, level="year_act")
+        )
+
+    # Post-solve debugging output TODO comment before merging
+    ACT1, ACT1_delta = get_act(scenario)
+
+    # Increase the disutility of for t0 for g0 in period y1
+    data["input"].loc[1, "value"] = 0.2
+
+    # Re-solve
+    scenario.remove_solution()
+    scenario.check_out()
+    scenario.add_par("input", data["input"])
+    scenario.commit("Disutility test 2")
+    scenario.solve(quiet=True)
+
+    # Compare activity
+    ACT2, ACT2_delta = get_act(scenario)
+
+    merged = ACT1.merge(ACT2, left_index=True, right_index=True)
+    merged["lvl_diff"] = merged["lvl_y"] - merged["lvl_x"]
+
+    merged_delta = ACT1_delta.merge(ACT2_delta, left_index=True, right_index=True)
+
+    # commented: for debugging
+    # print(merged, merged_delta)
+
+    # Group g0 decreases usage of t0, and increases usage of t1, in period y1 vs. y0
+    assert merged_delta.loc["usage of t0 by g0", "lvl_y"] < 0
+    assert merged_delta.loc["usage of t1 by g0", "lvl_y"] > 0
+
+    # Group g0 usage of t0 is lower when the disutility is higher
+    assert merged.loc[("usage of t0 by g0", y1), "lvl_diff"] < 0
+    # Group g0 usage of t1 is correspondingly higher
+    assert merged.loc[("usage of t1 by g0", y1), "lvl_diff"] > 0
+
+
+def test_data_conversion(scenario, spec):
+    """:func:`~.disutility.data_conversion` runs."""
+    info = ScenarioInfo(scenario)
+    disutility.data_conversion(info, spec)
+
+
+def test_data_source(scenario, spec):
+    """:func:`~.disutility.data_source` runs."""
+    info = ScenarioInfo(scenario)
+    disutility.data_source(info, spec)
+
+
+def test_get_data(scenario, spec):
+    """:func:`~.disutility.get_data` runs."""
+    data = disutility.get_data(scenario, spec)
+
+    # Test that the code will not encounter #45 / iiasa/ixmp#425
+    for name, df in data.items():
+        assert (
+            "" not in df["unit"].unique()
+        ), f"{repr(name)} data has dimensionless units"
+
+
+def test_get_spec(groups, techs, template):
+    """:func:`~.disutility.get_spec` runs and produces expected output."""
+    spec = disutility.get_spec(groups, techs, template)
+
+    # Spec requires the existence of the base technologies
+    assert {"technology"} == set(spec["require"].set.keys())
+    assert techs == spec["require"].set["technology"]
+
+    # Spec removes nothing
+    assert set() == set(spec["remove"].set.keys())
+
+    # Spec adds the "disutility" commodity; and adds (if not existing) the output
+    # commodities for t[01] and demand commodities for g[01]
+    assert {
+        "disutility",
+        "output of t0",
+        "output of t1",
+        "demand of group g0",
+        "demand of group g1",
+    } == set(map(str, spec["add"].set["commodity"]))
+
+    # Spec adds the "distuility source" technology, and "usage of {tech} by {group}"
+    # for each tech × group, per the template
+    assert {
+        "disutility source",
+        "usage of t0 by g0",
+        "usage of t0 by g1",
+        "usage of t1 by g0",
+        "usage of t1 by g1",
+    } == set(map(str, spec["add"].set["technology"]))
diff --git a/nest/message_ix_models/tests/model/test_emissions.py b/nest/message_ix_models/tests/model/test_emissions.py
new file mode 100644
index 0000000..4a1dbaa
--- /dev/null
+++ b/nest/message_ix_models/tests/model/test_emissions.py
@@ -0,0 +1,54 @@
+import numpy as np
+from message_ix import make_df
+from message_ix.models import MACRO
+
+from message_ix_models import testing
+from message_ix_models.model.emissions import add_tax_emission
+
+
+def add_test_data(scenario):
+    scenario.platform.add_unit("")
+    with scenario.transact():
+        scenario.add_set("node", "foo")
+        scenario.add_set("type_emission", "TCE")
+
+        name = "interestrate"
+        df = make_df(name, year=scenario.set("year"), value=0.05, unit="")
+        scenario.add_par(name, df)
+
+        # Initialize drate
+        MACRO.initialize(scenario)
+        name = "drate"
+        df = scenario.add_par(
+            name, make_df(name, node=["World", "foo"], value=[0.05, 0.03], unit="")
+        )
+
+
+def test_add_tax_emission(request, caplog, test_context):
+    test_context.regions = "R12"
+    s = testing.bare_res(request, test_context, solved=False)
+    add_test_data(s)
+
+    value = 1.1
+
+    add_tax_emission(s, value, drate_parameter="interestrate")
+
+    # Retrieve the added data
+    data = s.par("tax_emission").set_index("type_year")
+    y0 = min(data.index)
+    y_max = max(data.index)
+
+    # First model period value is converted from [money] / t CO₂ to [money] / t C
+    v0 = value * 44.0 / 12
+    assert np.isclose(v0, data.loc[y0, "value"])
+
+    # Final period value is the same, inflated by the number of intervening years
+    assert np.isclose(v0 * 1.05 ** (int(y_max) - int(y0)), data.loc[y_max, "value"])
+
+    # Same using drate
+    add_tax_emission(s, value)
+    # Warning is logged about multiple drates
+    assert (
+        "Using the first of multiple discount rates: drate=[0.05 0.03]"
+        == caplog.messages[-1]
+    )
diff --git a/nest/message_ix_models/tests/model/test_structure.py b/nest/message_ix_models/tests/model/test_structure.py
new file mode 100644
index 0000000..5f2d126
--- /dev/null
+++ b/nest/message_ix_models/tests/model/test_structure.py
@@ -0,0 +1,259 @@
+import re
+from collections import defaultdict
+from copy import deepcopy
+from pathlib import Path
+
+import pytest
+from iam_units import registry
+from sdmx.model import Annotation, Code
+
+from message_ix_models.model.structure import (
+    codelists,
+    generate_set_elements,
+    get_codes,
+    get_region_codes,
+    process_commodity_codes,
+    process_units_anno,
+)
+from message_ix_models.util import as_codes, eval_anno
+
+
+@pytest.mark.parametrize(
+    "kind, exp",
+    [
+        ("node", ["ADVANCE", "ISR", "R11", "R12", "R14", "R32", "RCP", "ZMB"]),
+        ("relation", ["A", "B", "CD-LINKS"]),
+        ("year", ["A", "B"]),
+    ],
+)
+def test_codelists(kind, exp):
+    """:func:`codelists` returns the expected IDs."""
+    assert exp == codelists(kind)
+
+
+class TestGetCodes:
+    """Test :func:`get_codes` for different code lists."""
+
+    @pytest.mark.parametrize(
+        "name",
+        (
+            "cd_links/unit",
+            "commodity",
+            "level",
+            "node/ISR",
+            "node/R11",
+            "node/R14",
+            "node/R32",
+            "node/RCP",
+            "node/ZMB",
+            "technology",
+            "relation/A",
+            "relation/B",
+            "relation/CD-LINKS",
+            "year/A",
+            "year/B",
+        ),
+    )
+    def test_get_codes(self, name):
+        """The included code lists can be loaded."""
+        get_codes(name)
+
+    def test_hierarchy(self):
+        """get_codes() returns objects with the expected hierarchical relationship."""
+        codes = get_codes("node/R11")
+
+        AUT = codes[codes.index("AUT")]
+        R11_WEU = codes[codes.index("R11_WEU")]
+        World = codes[codes.index("World")]
+
+        assert R11_WEU is AUT.parent
+        assert AUT in R11_WEU.child
+
+        assert World is R11_WEU.parent
+        assert R11_WEU in World.child
+
+    def test_commodities(self):
+        data = get_codes("commodity")
+
+        # Some expected commodities are present
+        for check in "coal", "electr":
+            assert check in data
+
+        # Units for one commodity can be retrieved and parsed
+        coal = data[data.index("coal")]
+        assert isinstance(eval_anno(coal, "units"), registry.Unit)
+
+        # Descriptions are parsed without new lines
+        crudeoil = data[data.index("crudeoil")]
+        assert "\n" not in str(crudeoil.description)
+
+        # Processing a second time does not double-wrap the unit expressions
+        process_commodity_codes(data)
+        coal = data[data.index("coal")]
+        assert isinstance(eval_anno(coal, "units"), registry.Unit)
+
+    def test_levels(self):
+        data = get_codes("level")
+
+        # Some expected commodities are present
+        for check in "primary", "useful":
+            assert check in data
+
+        # Descriptions are parsed without new lines
+        assert "\n" not in str(data[data.index("primary")].description)
+
+    @pytest.mark.parametrize(
+        "codelist, to_check, length, member",
+        [
+            ("RCP", "R5_MAF", 69, "CIV"),
+            ("R11", "R11_AFR", 50, "CIV"),
+            ("R14", "R14_AFR", 51, "CIV"),
+            ("R32", "R32SSA-L", 41, "CIV"),
+        ],
+    )
+    def test_nodes(self, codelist, to_check, length, member):
+        """Tests of node codelists."""
+        # Node codelist can be loaded
+        data = get_codes(f"node/{codelist}")
+
+        # List contains a particular region
+        assert to_check in data
+
+        # Region contains the correct number of countries
+        code = data[data.index(to_check)]
+        assert len(code.child) == length
+
+        # A specific country is present in the region
+        assert member in code.child
+
+        # get_region_codes() also works
+        region_codes = get_region_codes(codelist)
+        assert to_check in region_codes
+        # Does not include codes that are not children of "World"
+        assert member not in region_codes
+
+    def test_node_historic_country(self):
+        """get_codes() handles ISO 3166 alpha-3 codes for historic countries."""
+        assert "SCG" in get_codes("node/R11")
+
+    @pytest.mark.parametrize(
+        "codelist, length, expected",
+        [
+            ("A", 19, {"emission_factor": 15, "renewable formulation": 2}),
+            ("B", 319, {}),
+            ("CD-LINKS", 314, {"removed": 80}),
+        ],
+    )
+    def test_relation(self, codelist, length, expected):
+        # Codelist can be loaded
+        data = get_codes(f"relation/{codelist}")
+
+        # Number of codes is as expected
+        assert length == len(data)
+
+        # Count codes by "group" annotation
+        group_count = defaultdict(lambda: 0)
+        for code in data:
+            try:
+                group = str(code.get_annotation(id="group").text)
+            except KeyError:
+                continue
+            group_count[group] += 1
+
+        # print(groups)
+
+        # Each group has the expected number of members
+        for group, N in expected.items():
+            assert N == group_count[group]
+
+    def test_technologies(self):
+        # Retrieve the tech info without calling technologies.cli
+        data = get_codes("technology")
+
+        # Check the length of the returned dataframe
+        assert len(data) == 377
+
+        # Get info on a certain technology
+        h2_fc_trp = data[data.index("h2_fc_trp")]
+        assert ["transport", "useful"] == eval(
+            str(h2_fc_trp.get_annotation(id="output").text)
+        )
+
+        # Check that the default value for 'vintaged' is False when omitted from the
+        # YAML file
+        elec_exp = data[data.index("elec_exp")]
+        assert False is eval(str(elec_exp.get_annotation(id="vintaged").text))
+
+    @pytest.mark.parametrize("codelist, length", [("A", 16), ("B", 28)])
+    def test_year(self, codelist, length):
+        """Year code lists can be loaded and contain the correct number of codes.
+
+        :seealso: :meth:`.TestScenarioInfo.test_year_from_codes`.
+        """
+        # Year codelist can be loaded
+        data = get_codes(f"year/{codelist}")
+
+        # List contains the expected number of codes
+        assert len(data) == length
+
+
+def test_cli_techs(session_context, mix_models_cli):
+    """Test the `techs` CLI command."""
+    # Command runs without error
+    result = mix_models_cli.assert_exit_0(["techs"])
+
+    # Result test
+    assert result.output.endswith("[5 rows x 8 columns]\n")
+
+    # Path to the temporary file written by the command
+    path = Path(re.search("Write to (.*.csv)", result.output)[1])
+
+    # File was written in the local data directory
+    assert Path("technology.csv") == path.relative_to(session_context.local_data)
+
+    # File was written with the expected contents
+    assert path.read_text().startswith(
+        "id,name,description,type,vintaged,sector,output,input\n"
+        "CF4_TCE,CF4_TCE,Tetrafluoromethane (CF4) Total Carbon Emissions,"
+        "primary,False,dummy,\"['dummy', 'primary']\",\n"
+    )
+
+
+GSE_DATA = {
+    "colour": {"add": as_codes(["blue", "green", "red"])},
+    # This is configuration to generate codes based on the colour set
+    "technology": {"add": [Code(id="foo-{colour.id}"), Code(id="bar")]},
+}
+
+
+@pytest.mark.parametrize(
+    "colour_expr, expected",
+    [
+        (None, {"foo-blue", "foo-green", "foo-red", "bar"}),
+        ("red", {"bar"}),  # "red" has no .child codes
+        ("^.*(?<!red)$", {"foo-blue", "foo-green", "bar"}),
+    ],
+)
+def test_generate_set_elements(colour_expr, expected):
+    data = deepcopy(GSE_DATA)
+
+    # Add a _generate annotation
+    data["technology"]["add"][0].annotations = [
+        Annotation(id="_generate", text=repr(dict(colour=colour_expr)))
+    ]
+
+    # Code runs
+    generate_set_elements(data, "technology")
+
+    # Codes are generated according to the contents of the _generate annotation
+    assert expected == set(map(str, data["technology"]["add"]))
+
+
+def test_process_units_anno():
+    # Prepare 2 codes: the parent has a units annotation, the child has none
+    codes = as_codes({"foo": {"units": "kg"}, "bar": {"parent": "foo"}})
+
+    process_units_anno("", codes[0])
+
+    # Parents' units are propagated to the child
+    assert registry.Unit("kg") == eval_anno(codes[1], "units")
diff --git a/nest/message_ix_models/tests/test_cli.py b/nest/message_ix_models/tests/test_cli.py
new file mode 100644
index 0000000..360bd5f
--- /dev/null
+++ b/nest/message_ix_models/tests/test_cli.py
@@ -0,0 +1,58 @@
+"""Basic tests of the command line."""
+import ixmp
+import pytest
+from message_ix.testing import make_dantzig
+
+from message_ix_models import util
+
+SUBCOMMANDS = [
+    tuple(),
+    ("debug",),
+]
+
+
+def _cli_help_id(argvalue):
+    return f"mix-models {' '.join(argvalue)} --help"
+
+
+@pytest.mark.parametrize("subcommand", SUBCOMMANDS, ids=_cli_help_id)
+def test_cli_help(mix_models_cli, subcommand):
+    """--help works for every CLI command."""
+    mix_models_cli.assert_exit_0(list(subcommand) + ["--help"])
+
+
+def test_cli_debug(mix_models_cli):
+    """The 'debug' CLI command can be invoked."""
+    mix_models_cli.assert_exit_0(["debug"])
+
+
+def test_cli_export_test_data(monkeypatch, session_context, mix_models_cli, tmp_path):
+    """The :command:`export-test-data` command can be invoked."""
+    # Create an empty scenario in the temporary local file database
+    platform = "local"
+    mp = ixmp.Platform(platform)
+    scen = make_dantzig(mp)
+
+    # URL
+    url = f"ixmp://{platform}/{scen.model}/{scen.scenario}#{scen.version}"
+
+    # Monkeypatch MESSAGE_DATA_PATH in case tests are being performed on a system
+    # without message_data installed
+    monkeypatch.setattr(util.common, "MESSAGE_DATA_PATH", tmp_path)
+    tmp_path.joinpath("data", "tests").mkdir(exist_ok=True, parents=True)
+
+    # File that will be created
+    technology = ["coal_ppl"]
+    dest_file = util.private_data_path(
+        "tests", f"{scen.model}_{scen.scenario}_{'_'.join(technology)}.xlsx"
+    )
+
+    # Release the database lock
+    mp.close_db()
+
+    # Export works
+    result = mix_models_cli.assert_exit_0([f"--url={url}", "export-test-data"])
+
+    # The file is created in the expected location
+    assert str(dest_file) in result.output
+    assert dest_file.exists()
diff --git a/nest/message_ix_models/tests/test_import.py b/nest/message_ix_models/tests/test_import.py
new file mode 100644
index 0000000..1f0815d
--- /dev/null
+++ b/nest/message_ix_models/tests/test_import.py
@@ -0,0 +1,3 @@
+def test_import():
+    """Test that the package can be imported."""
+    import message_ix_models  # noqa: F401
diff --git a/nest/message_ix_models/tests/test_testing.py b/nest/message_ix_models/tests/test_testing.py
new file mode 100644
index 0000000..eb14361
--- /dev/null
+++ b/nest/message_ix_models/tests/test_testing.py
@@ -0,0 +1,36 @@
+import os
+
+import click
+import pytest
+
+from message_ix_models.testing import bare_res, not_ci
+
+
+def test_bare_res_no_request(test_context):
+    """:func:`bare_res` works with `request` = :obj:`None`."""
+    bare_res(None, test_context, solved=False)
+
+
+def test_bare_res_solved(request, test_context):
+    """:func:`bare_res` works with `solve` = :obj:`True`.
+
+    This test can be removed once this feature of the test function is used by another
+    test.
+    """
+    bare_res(request, test_context, solved=True)
+
+
+def test_cli_runner(mix_models_cli):
+    with pytest.raises(click.exceptions.UsageError, match="No such command 'foo'"):
+        mix_models_cli.assert_exit_0(["foo", "bar"])
+
+
+@not_ci(reason="foo", action="skip")
+def test_not_ci_skip():
+    """Test not_ci(action="skip")."""
+
+
+@not_ci(reason="foo", action="xfail")
+def test_not_ci_xfail():
+    """Test not_ci(action="skip")."""
+    assert "GITHUB_ACTIONS" not in os.environ
diff --git a/nest/message_ix_models/tests/test_util.py b/nest/message_ix_models/tests/test_util.py
new file mode 100644
index 0000000..4066597
--- /dev/null
+++ b/nest/message_ix_models/tests/test_util.py
@@ -0,0 +1,373 @@
+"""Tests of :mod:`message_ix_models.util`."""
+import logging
+import re
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import pytest
+from iam_units import registry
+from ixmp.testing import assert_logs
+from message_ix import Scenario, make_df
+from message_ix.testing import make_dantzig
+from pandas.testing import assert_series_equal
+
+from message_ix_models import ScenarioInfo
+from message_ix_models.util import (
+    MESSAGE_DATA_PATH,
+    MESSAGE_MODELS_PATH,
+    as_codes,
+    broadcast,
+    check_support,
+    convert_units,
+    copy_column,
+    ffill,
+    iter_parameters,
+    load_package_data,
+    load_private_data,
+    local_data_path,
+    make_source_tech,
+    maybe_query,
+    package_data_path,
+    private_data_path,
+    same_node,
+    same_time,
+    series_of_pint_quantity,
+    strip_par_data,
+)
+
+_actual_package_data = Path(__file__).parents[1].joinpath("data")
+
+
+def test_as_codes():
+    """Forward reference to a child is silently dropped."""
+    data = dict(
+        foo=dict(child=["bar"]),
+        bar=dict(name="Bar!"),
+    )
+    result = as_codes(data)
+    assert result[1] not in result[0].child
+
+    # With Codes already, the function is a pass-through
+    assert result == as_codes(result)
+
+
+def test_broadcast(caplog):
+    # Base data frame to be broadcast, with 2 rows and dimensions:
+    # - a: length 2
+    # - b, c, d: missing
+    N_a = 2
+    base = pd.DataFrame([["a0", 1.2], ["a1", 3.4]], columns=["a", "value"]).assign(
+        b=None, c=None, d=None
+    )
+
+    # broadcast works with DataFrame.pipe(), using keyword arguments
+    result = base.pipe(
+        broadcast, b="b0 b1 b2".split(), c="c0 c1 c2 c3".split(), d=["d0"]
+    )
+    # Results have the expected length: original × cartesian product of 3, 4, and 1
+    assert N_a * 3 * 4 * 1 == len(result)
+    # Resulting array is completely full, no missing labels
+    assert not result.isna().any(axis=None)
+
+    # Length zero labels for one dimension—debug message is logged
+    with caplog.at_level(logging.DEBUG, logger="message_ix_models"):
+        result = base.pipe(broadcast, b="b0 b1".split(), c="c0 c1".split(), d=[])
+
+    # Debug message is logged
+    assert "Don't broadcast over 'd'; labels [] have length 0" in caplog.messages
+    caplog.clear()
+    assert N_a * 2 * 2 * 1 == len(result)  # Expected length
+    assert result["d"].isna().all()  # Dimension d remains empty
+    assert not result.drop("d", axis=1).isna().any(axis=None)  # Others completely full
+
+    # Using a DataFrame as the first/only positional argument, plus keyword arguments
+    labels = pd.DataFrame(dict(b="b0 b1 b2".split(), c="c0 c1 c2".split()))
+
+    result = base.pipe(broadcast, labels, d="d0 d1".split())
+    assert N_a * 3 * 2 == len(result)  # (b, c) dimensions linked with 3 pairs of labels
+    assert not result.isna().any(axis=None)  # Completely full
+
+    # Using a positional argument with only 1 column
+    result = base.pipe(broadcast, labels[["b"]], c="c0 c1 c2 c3".split(), d=["d0"])
+    assert N_a * 3 * 4 * 1 == len(result)  # Expected length
+    assert not result.isna().any(axis=None)  # Completely full
+
+    # Overlap between columns in the positional argument and keywords
+    with pytest.raises(ValueError):
+        result = base.pipe(broadcast, labels, c="c0 c1 c2 c3".split(), d=["d0"])
+
+    # Extra, invalid dimensions result in ValueError
+    with pytest.raises(ValueError):
+        base.pipe(broadcast, b="b0 b1 b2".split(), c="c0 c1 c2 c3".split(), e=["e0"])
+
+    labels["e"] = "e0 e1 e2".split()
+
+    with pytest.raises(ValueError):
+        base.pipe(broadcast, labels, d=["d0"])
+
+
+@pytest.mark.parametrize(
+    "data",
+    (
+        set(),
+        # dict() with a value that is not a str or a further dict()
+        dict(foo="foo", bar=[1, 2, 3]),
+    ),
+)
+def test_as_codes_invalid(data):
+    """as_codes() rejects invalid data."""
+    with pytest.raises(TypeError):
+        as_codes(data)
+
+
+def test_check_support(test_context):
+    """:func:`.check_support` raises an exception for missing/non-matching values."""
+    args = [test_context, dict(regions=["R11", "R14"]), "Test data available"]
+
+    # Setting not set → KeyError
+    with pytest.raises(KeyError, match="baz"):
+        check_support(test_context, dict(baz=["baz"]), "Baz is not set")
+
+    # Accepted value
+    test_context.regions = "R11"
+    check_support(*args)
+
+    # Wrong setting
+    test_context.regions = "FOO"
+    with pytest.raises(
+        NotImplementedError,
+        match=re.escape("Test data available for ['R11', 'R14']; got 'FOO'"),
+    ):
+        check_support(*args)
+
+
+def test_convert_units(recwarn):
+    """:func:`.convert_units` and :func:`.series_of_pint_quantity` work."""
+    # Common arguments
+    args = [pd.Series([1.1, 10.2, 100.3], name="bar"), dict(bar=(10.0, "lb", "kg"))]
+
+    exp = series_of_pint_quantity(
+        [registry("4.9895 kg"), registry("46.2664 kg"), registry("454.9531 kg")],
+    )
+
+    # With store="quantity", a series of pint.Quantity is returned
+    result = convert_units(*args, store="quantity")
+    assert all(np.isclose(a, b, atol=1e-4) for a, b in zip(exp.values, result.values))
+
+    # With store="magnitude", a series of floats
+    exp = pd.Series([q.magnitude for q in exp.values], name="bar")
+    assert_series_equal(exp, convert_units(*args, store="magnitude"), check_dtype=False)
+
+    # Other values for store= are errors
+    with pytest.raises(ValueError, match="store = 'foo'"):
+        convert_units(*args, store="foo")
+
+    # series_of_pint_quantity() successfully caught warnings
+    assert 0 == len(recwarn)
+
+
+def test_copy_column():
+    df = pd.DataFrame([[0, 1], [2, 3]], columns=["a", "b"])
+    df = df.assign(c=copy_column("a"), d=4)
+    assert all(df["c"] == [0, 2])
+    assert all(df["d"] == 4)
+
+
+def test_ffill():
+    years = list(range(6))
+
+    df = (
+        make_df(
+            "fix_cost",
+            year_act=[0, 2, 4],
+            year_vtg=[0, 2, 4],
+            technology=["foo", "bar", "baz"],
+            unit="USD",
+        )
+        .pipe(broadcast, node_loc=["A", "B", "C"])
+        .assign(value=list(map(float, range(9))))
+    )
+
+    # Function completes
+    result = ffill(df, "year_vtg", years, "year_act = year_vtg")
+
+    assert 2 * len(df) == len(result)
+    assert years == sorted(result["year_vtg"].unique())
+
+    # Cannot ffill on "value" and "unit" dimensions
+    with pytest.raises(ValueError, match="value"):
+        ffill(df, "value", [])
+
+    # TODO test some specific values
+
+
+def test_iter_parameters(test_context):
+    """Parameters indexed by set 'node' can be retrieved."""
+    result = list(iter_parameters("node"))
+    assert result[0] == "abs_cost_activity_soft_lo"
+    assert result[-1] == "var_cost"
+    # The length of this list depends on message_ix. Changes in message_ix may increase
+    # the number of parameters, so use <= to future-proof. See the method comments.
+    assert 99 <= len(result)
+
+
+@pytest.mark.parametrize("path", _actual_package_data.rglob("*.yaml"))
+def test_load_package_data(path):
+    """Existing package data can be loaded."""
+    load_package_data(*path.relative_to(_actual_package_data).parts)
+
+
+def test_load_package_data_twice(caplog):
+    """Loading the same data twice logs a message."""
+    caplog.set_level(logging.DEBUG, logger="message_ix_models")
+    load_package_data("node", "R11")
+    load_package_data("node", "R11")
+    assert "'node R11' already loaded; skip" in caplog.messages
+
+
+def test_load_package_data_invalid():
+    """load_package_data() raises an exception for an unsupported file type."""
+    with pytest.raises(ValueError):
+        load_package_data("test.xml")
+
+
+@pytest.mark.xfail(
+    condition=MESSAGE_DATA_PATH is None, reason="Requires message_data to be installed."
+)
+def test_load_private_data(*parts, suffix=None):
+    load_private_data("sources.yaml")
+
+
+_MST_COMMON = dict(
+    commodity="commodity",
+    level="level",
+    mode="mode",
+    technology="technology",
+    time="time",
+    time_dest="time",
+    unit="unit",
+)
+_MST_VALUES = dict(
+    capacity_factor=1.0,
+    output=2.0,
+    var_cost=3.0,
+    technical_lifetime=4.0,
+)
+
+
+def test_make_source_tech0():
+    info = ScenarioInfo()
+    info.set["node"] = ["World", "node0", "node1"]
+    info.set["year"] = [1, 2, 3]
+
+    values = _MST_VALUES.copy()
+
+    # Code runs
+    result = make_source_tech(info, _MST_COMMON, **values)
+
+    # Result is dictionary with the expected keys
+    assert isinstance(result, dict)
+    assert set(result.keys()) == set(values.keys())
+
+    # "World" node does not appear in results
+    assert set(result["output"]["node_loc"].unique()) == set(info.N[1:])
+
+    for df in result.values():
+        # Results have 2 nodes × 3 years
+        assert len(df) == 2 * 3
+        # No empty values
+        assert not df.isna().any(axis=None)
+
+    del values["var_cost"]
+    with pytest.raises(ValueError, match=re.escape("needs values for {'var_cost'}")):
+        make_source_tech(info, _MST_COMMON, **values)
+
+
+def test_make_source_tech1(test_mp):
+    """Test make_source_tech() with a Scenario object as input."""
+    s = Scenario(test_mp, model="model", scenario="scenario", version="new")
+    s.add_set("node", ["World", "node0", "node1"])
+    s.add_set("technology", ["t"])
+    s.add_horizon([1, 2, 3])
+    s.commit("")
+
+    make_source_tech(s, _MST_COMMON, **_MST_VALUES)
+
+
+def test_maybe_query():
+    """:func:`.maybe_query` works as intended."""
+    s = pd.Series(
+        [0, 1, 2, 3],
+        index=pd.MultiIndex.from_product(
+            [["a", "b"], ["c", "d"]], names=["foo", "bar"]
+        ),
+    )
+
+    # No-op
+    assert_series_equal(s, maybe_query(s, None))
+
+    # Select a few rows
+    assert 2 == len(maybe_query(s, "bar == 'c'"))
+
+
+def test_local_data_path(tmp_path_factory, session_context):
+    assert tmp_path_factory.getbasetemp().joinpath(
+        "data0", "foo", "bar"
+    ) == local_data_path("foo", "bar")
+
+
+def test_package_data_path():
+    assert MESSAGE_MODELS_PATH.joinpath("data", "foo", "bar") == package_data_path(
+        "foo", "bar"
+    )
+
+
+@pytest.mark.xfail(
+    condition=MESSAGE_DATA_PATH is None, reason="Requires message_data to be installed."
+)
+def test_private_data_path():
+    assert MESSAGE_DATA_PATH.joinpath("data", "foo", "bar") == private_data_path(
+        "foo", "bar"
+    )
+
+
+@pytest.mark.parametrize(
+    "name, func, col", [("node", same_node, "node_loc"), ("time", same_time, "time")]
+)
+def test_same(name, func, col):
+    """Test both :func:`.same_node` and :func:`.same_time`."""
+    df_in = pd.DataFrame(
+        {
+            col: ["foo", "bar", "baz"],
+            f"{name}_dest": None,
+            f"{name}_origin": None,
+            "value": [1.1, 2.2, 3.3],
+        }
+    )
+
+    df_out = func(df_in)
+
+    assert not df_out.isna().any(axis=None)
+    assert_series_equal(df_out[f"{name}_dest"], df_in[col], check_names=False)
+    assert_series_equal(df_out[f"{name}_origin"], df_in[col], check_names=False)
+
+
+def test_strip_par_data(caplog, test_context):
+    """Test the "dry run" feature of :func:`.strip_par_data`."""
+    s = make_dantzig(test_context.get_platform())
+
+    N = len(s.par("output"))
+    strip_par_data(s, "technology", "canning_plant", dry_run=True, dump=dict())
+
+    assert_logs(
+        caplog,
+        [
+            "Remove data with technology='canning_plant' (DRY RUN)",
+            "2 rows in 'output'",
+            "with commodity=['cases']",
+            "with level=['supply']",
+        ],
+    )
+    # Nothing was actually removed
+    assert N == len(s.par("output"))
diff --git a/nest/message_ix_models/tests/test_workflow.py b/nest/message_ix_models/tests/test_workflow.py
new file mode 100644
index 0000000..a8be6c3
--- /dev/null
+++ b/nest/message_ix_models/tests/test_workflow.py
@@ -0,0 +1,144 @@
+import re
+
+import ixmp
+import pytest
+from genno import KeyExistsError
+from message_ix import make_df
+
+from message_ix_models import Workflow, testing
+from message_ix_models.workflow import WorkflowStep, solve
+
+
+def changes_a(c, s):
+    """Change a scenario by modifying structure data, but not data."""
+    with s.transact():
+        s.add_set("technology", "test_tech")
+
+
+def changes_b(c, s, value=None):
+    """Change a scenario by modifying parameter data, but not structure."""
+    with s.transact():
+        s.add_par(
+            "technical_lifetime",
+            make_df(
+                "technical_lifetime",
+                node_loc=s.set("node")[0],
+                year_vtg=s.set("year")[0],
+                technology="test_tech",
+                value=value,
+                unit="y",
+            ),
+        )
+
+
+class TestWorkflowStep:
+    def test_call(self, test_context):
+        def action(c, s):
+            pass  # pragma: no cover
+
+        ws = WorkflowStep(action=action)
+
+        with pytest.raises(RuntimeError):
+            ws(test_context, None)
+
+    def test_repr(self):
+        assert "<Step load>" == repr(WorkflowStep(None))
+
+
+@pytest.mark.skipif(
+    condition=ixmp.__version__ < "3.5",
+    reason="ixmp.TimeSeries.url not available prior to ixmp 3.5.0",
+)
+def test_workflow(caplog, request, test_context):
+    # FIXME disentangle this to fewer tests of atomic behaviour
+    base_scenario = testing.bare_res(request, test_context, solved=False)
+    base_url = base_scenario.url
+    base_platform = base_scenario.platform.name
+    del base_scenario
+
+    caplog.clear()
+
+    # Create the workflow
+    wf = Workflow(test_context)
+
+    # Model/base is created from nothing by calling base_scenario
+    wf.add_step("base", None, target=base_url)
+    # Model/A is created from Model/base by calling changes_a
+    wf.add_step("A", "base", changes_a)
+    # Model/B is created from Model/A by calling changes_b
+    wf.add_step("B", "A", changes_b, value=100.0)
+
+    # "B solved" is created from "Model/B" by clone and running solve()
+    # clone=True without target= raises an exception
+    with pytest.raises(TypeError, match="target= must be supplied"):
+        wf.add_step("B solved", "B", solve, clone=True)
+
+    wf.add_step("B solved", "B", solve, clone=True, target="foo/bar")
+
+    # Trigger the creation and solve of Model/B and all required precursor scenarios
+    s = wf.run("B solved")
+
+    # Scenario contains changes from the first and second step
+    assert "test_tech" in set(s.set("technology"))
+    assert 1 == len(s.par("technical_lifetime"))
+    # Scenario was solved
+    assert s.has_solution()
+
+    # Log messages reflect workflow steps executed
+    start_index = 1 if caplog.messages[0].startswith("Cull") else 0
+    mp = base_platform
+    m = "MESSAGEix-GLOBIOM R14 YB"
+    messages = [
+        f"Loaded ixmp://{mp}/{m}/test_workflow#1",
+        f"Step runs on ixmp://{mp}/{m}/test_workflow#1",
+        "Execute <function changes_a at [^>]*>",
+        f"…nothing returned, workflow will continue with {m}/test_workflow#1",
+        f"Step runs on ixmp://{mp}/{m}/test_workflow#1",
+        "Execute <function changes_b at [^>]*>",
+        f"…nothing returned, workflow will continue with {m}/test_workflow#1",
+        f"Step runs on ixmp://{mp}/{m}/test_workflow#1",
+        "  with context.dest_scenario={'model': 'foo', 'scenario': 'bar'}",
+        "Clone to foo/bar",
+        "Execute <function solve at [^>]*>",
+    ]
+    for expr, message in zip(messages, caplog.messages[start_index:]):
+        assert re.match(expr, message)
+
+    assert re.match(
+        r"""'B':
+- <Step changes_b\(\)>
+- 'context':
+  - <Context object at \w+ with \d+ keys>
+- 'A':
+  - <Step changes_a\(\)>
+  - 'context' \(above\)
+  - 'base':
+    - <Step load -> MESSAGEix-GLOBIOM R14 YB/test_workflow>
+    - 'context' \(above\)
+    - None""",
+        wf.describe("B"),
+    )
+
+    # Now truncate the workflow at "Model/A"
+    with pytest.raises(RuntimeError, match="Unable to locate platform info for"):
+        wf.truncate("A")
+
+    # Add a full URL including platform info
+    with pytest.raises(KeyExistsError):
+        wf.add_step("base", None, target=f"ixmp://{base_platform}/{base_url}")
+
+    wf.add_step("base", None, target=f"ixmp://{base_platform}/{base_url}", replace=True)
+    wf.truncate("A")
+
+    # Description reflects that changes_a() will no longer be called
+    assert re.match(
+        r"""'B':
+- <Step changes_b\(\)>
+- 'context':
+  - <Context object at \w+ with \d+ keys>
+- 'A':
+  - <Step load -> MESSAGEix-GLOBIOM R14 YB/test_workflow>
+  - 'context' \(above\)
+  - None""",
+        wf.describe("B"),
+    )
diff --git a/nest/message_ix_models/tests/tools/__init__.py b/nest/message_ix_models/tests/tools/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/nest/message_ix_models/tests/tools/test_advance.py b/nest/message_ix_models/tests/tools/test_advance.py
new file mode 100644
index 0000000..b75d215
--- /dev/null
+++ b/nest/message_ix_models/tests/tools/test_advance.py
@@ -0,0 +1,49 @@
+from shutil import copyfile
+
+import pytest
+
+from message_ix_models.tools.advance import (
+    DIMS,
+    LOCATION,
+    _fuzz_data,
+    advance_data,
+    get_advance_data,
+)
+from message_ix_models.util import package_data_path
+
+
+@pytest.fixture(scope="module")
+def advance_test_data(session_context):
+    # Copy test data from the package directory into the local data directory for
+    # `test_context`. get_advance_data() only uses this file if :mod:`message_data` is
+    # NOT installed.
+    target = session_context.get_local_path(*LOCATION)
+    target.parent.mkdir(parents=True)
+    copyfile(package_data_path("test", *LOCATION), target)
+
+
+pytestmark = pytest.mark.usefixtures("advance_test_data")
+
+
+def test_get_advance_data(session_context):
+    """Test :func:`.get_advance_data`."""
+    # Returns a pd.Series with the expected index levels
+    result = get_advance_data()
+    assert DIMS == result.index.names
+
+    # Returns a genno.Quantity with the expected units
+    result = advance_data("Transport|Service demand|Road|Freight")
+    assert {"[length]": 1, "[mass]": 1, "[time]": -1} == result.units.dimensionality
+
+
+def test_fuzz_data(session_context):
+    """Test that :func:`_fuzz_data` runs successfully.
+
+    NB this only produces a file in the :mod:`pytest` temporary directory. To update
+    the test specimen in the package directory that is used by
+    :func:`test_get_advance_data`, see the body of _fuzz_data.
+    """
+    # size argument should be a fraction (~= 0.1) of the size of the test specimen
+    _fuzz_data(
+        size=50, include=[("Transport|Service demand|Road|Freight", "billion tkm/yr")]
+    )
diff --git a/nest/message_ix_models/tests/util/__init__.py b/nest/message_ix_models/tests/util/__init__.py
new file mode 100644
index 0000000..354166e
--- /dev/null
+++ b/nest/message_ix_models/tests/util/__init__.py
@@ -0,0 +1 @@
+"""Tests of submodules of :mod:`message_ix_models.util`."""
diff --git a/nest/message_ix_models/tests/util/test_cache.py b/nest/message_ix_models/tests/util/test_cache.py
new file mode 100644
index 0000000..4247511
--- /dev/null
+++ b/nest/message_ix_models/tests/util/test_cache.py
@@ -0,0 +1,109 @@
+import logging
+from copy import deepcopy
+
+import pytest
+import sdmx.model
+import xarray as xr
+from genno.caching import hash_args
+from ixmp.testing import assert_logs
+
+import message_ix_models.util.cache
+from message_ix_models import ScenarioInfo
+from message_ix_models.util import cached
+
+log = logging.getLogger(__name__)
+
+
+class TestEncoder:
+    def test_sdmx(self):
+        """:mod:`message_ix_models` configures :class:`.Encoder` for :class:`.Code`."""
+        codes0 = [sdmx.model.Code(id=f"FOO{i}", name="foo") for i in range(5)]
+        codes1 = [f"FOO{i}" for i in range(5)]
+
+        # List of codes hashes the same as a list of their string IDs
+        expected = "40a0735385448dcbe745904ebfec7255995ca451"
+        assert expected == hash_args(codes0, bar="baz") == hash_args(codes1, bar="baz")
+
+
+def test_cached(caplog, test_context, tmp_path):
+    """:func:`.cached` works as expected.
+
+    .. todo:: test behaviour when :data:`.SKIP_CACHE` is :obj:`True`
+    """
+    # Clear seen paths, so that log message below is guaranteed to occur
+    message_ix_models.util.cache.PATHS_SEEN.clear()
+
+    # Store in the temporary directory for this session, to avoid collisions across
+    # sessions
+    test_context.cache_path = tmp_path.joinpath("cache")
+
+    # A dummy path to be hashed as an argument
+    path_foo = tmp_path.joinpath("foo", "bar")
+
+    with caplog.at_level(logging.DEBUG, logger="message_ix_models"):
+
+        @cached
+        def func0(ctx, a, path, b=3):
+            """A test function."""
+            log.info("func0 runs")
+            return f"{id(ctx)}, {a + b}"
+
+    # Docstring is modified
+    assert "Data returned by this function is cached" in func0.__doc__
+    # Message is logged
+    assert f"func0() will cache in {tmp_path.joinpath('cache')}" in caplog.messages
+
+    @cached
+    def func1(x=1, y=2, **kwargs):
+        # Function with defaults for all arguments
+        log.info("func1 runs")
+        return x + y
+
+    caplog.clear()
+
+    # pathlib.Path argument is serialized to JSON as part of the argument hash;
+    # function runs, messages logged
+    with assert_logs(caplog, "func0 runs"):
+        result0 = func0(test_context, 1, path_foo)
+
+    caplog.clear()
+    result1 = func0(test_context, 1, path_foo)
+    # Function does not run
+    assert "func0 runs" not in caplog.messages
+    assert caplog.messages[0].startswith("Cache hit for func0")
+    # Results identical
+    assert result0 == result1
+
+    # Different context object with identical contents hashes equal
+    ctx2 = deepcopy(test_context)
+    assert id(test_context) != id(ctx2)
+
+    result2 = func0(ctx2, 1, path_foo)
+    # Function does not run
+    assert "func0 runs" not in caplog.messages
+    # Results are identical, i.e. including the old ID
+    assert result0 == result2
+
+    ctx2.delete()
+    caplog.clear()
+
+    # Hash of no arguments is the same, function only runs once
+    assert 3 == func1() == func1()
+    assert 1 == sum(m == "func1 runs" for m in caplog.messages)
+
+    # Warnings logged for unhashables; ScenarioInfo is hashed as dict
+    caplog.clear()
+    with assert_logs(
+        caplog,
+        [
+            "ignores <class 'xarray.core.dataset.Dataset'>",
+            "ignores <class 'ixmp.core.platform.Platform'>",
+        ],
+    ):
+        func1(ds=xr.Dataset(), mp=test_context.get_platform(), si=ScenarioInfo())
+
+    # Unserializable type raises an exception
+    with pytest.raises(
+        TypeError, match="Object of type slice is not JSON serializable"
+    ):
+        func1(arg=slice(None))
diff --git a/nest/message_ix_models/tests/util/test_click.py b/nest/message_ix_models/tests/util/test_click.py
new file mode 100644
index 0000000..801371b
--- /dev/null
+++ b/nest/message_ix_models/tests/util/test_click.py
@@ -0,0 +1,60 @@
+"""Basic tests of the command line."""
+from contextlib import contextmanager
+
+import click
+
+from message_ix_models.cli import main
+from message_ix_models.util.click import common_params
+
+
+@contextmanager
+def temporary_command(group, func):
+    """Context manager that temporarily attaches command `func` to `group`."""
+    try:
+        group.add_command(func)
+        yield
+    finally:
+        group.commands.pop(func.name)
+
+
+def test_default_path_cb(session_context, mix_models_cli):
+    """Test :func:`.default_path_cb`."""
+
+    # Create a hidden command and attach it to the CLI
+    @click.command(name="_test_default_path_cb", hidden=True)
+    @common_params("rep_out_path")
+    @click.pass_obj
+    def func(ctx, rep_out_path):
+        print(ctx["rep_out_path"])  # Print the value stored on the Context object
+
+    # Command parameters: --local-data gives the local data path, but the --rep-out-path
+    # option is *not* given
+    cmd = [f"--local-data={session_context.local_data}", func.name]
+
+    # …so default_path_cb() should supply "{local_data}/reporting_output".
+    expected = session_context.local_data / "reporting_output"
+
+    # Run the command
+    with temporary_command(main, func):
+        result = mix_models_cli.assert_exit_0(cmd)
+
+    # The value was stored on, and retrieved from, `ctx`
+    assert f"{expected}\n" == result.output
+
+
+def test_store_context(mix_models_cli):
+    """Test :func:`.store_context`."""
+
+    # Create a hidden command and attach it to the CLI
+    @click.command(name="_test_store_context", hidden=True)
+    @common_params("ssp")
+    @click.pass_obj
+    def func(ctx, ssp):
+        print(ctx["ssp"])  # Print the value stored on the Context object
+
+    # Run the command with a valid value
+    with temporary_command(main, func):
+        result = mix_models_cli.assert_exit_0([func.name, "SSP2"])
+
+    # The value was stored on, and retrieved from, `ctx`
+    assert "SSP2\n" == result.output
diff --git a/nest/message_ix_models/tests/util/test_config.py b/nest/message_ix_models/tests/util/test_config.py
new file mode 100644
index 0000000..0962a81
--- /dev/null
+++ b/nest/message_ix_models/tests/util/test_config.py
@@ -0,0 +1,133 @@
+from dataclasses import dataclass, field
+from typing import Type
+
+import pytest
+
+from message_ix_models.util.config import ConfigHelper
+
+
+class TestConfigHelper:
+    @pytest.fixture
+    def cls(self) -> Type:
+        """A class which inherits from ConfigHelper."""
+
+        @dataclass
+        class Config(ConfigHelper):
+            foo_1: int = 1
+            foo_2: str = ""
+            foo_3: bool = True
+
+        return Config
+
+    @pytest.fixture
+    def cls2(self, cls) -> Type:
+        """A class with an attribute."""
+
+        @dataclass
+        class Config3:
+            """NOT a ConfigHelper subclass."""
+
+            baz_1: int = 1
+            baz_2: int = 2
+
+        @dataclass
+        class Config2(ConfigHelper):
+            bar_1: float = 0.01
+            subconfig_a: cls = field(default_factory=cls)  # type: ignore [valid-type]
+            subconfig_b: Config3 = field(default_factory=Config3)
+
+        return Config2
+
+    @pytest.fixture
+    def c(self, cls):
+        return cls(foo_1=99, foo_2="bar", foo_3=False)
+
+    @pytest.fixture
+    def c2(self, cls, cls2):
+        result = cls2(bar_1=3.14, subconfig_a=cls(foo_1=99, foo_2="bar", foo_3=False))
+        result.subconfig_b.baz_1 = 2
+        result.subconfig_b.baz_2 = 1
+        return result
+
+    def test_canonical_name(self, cls):
+        assert "foo_1" == cls._canonical_name("foo 1")
+        assert "foo_2" == cls._canonical_name("foo-2")
+        assert "foo_3" == cls._canonical_name("foo_3")
+        assert None is cls._canonical_name("foo 4")
+
+    def test_from_dict(self, cls, c):
+        values = {"foo 1": 99, "foo-2": "bar", "foo_3": False}
+        assert c == cls.from_dict({"foo 1": 99, "foo-2": "bar", "foo_3": False})
+
+        values.update(foo_4=3.14)
+        with pytest.raises(ValueError):
+            cls.from_dict(values)
+
+    def test_read_file(self, caplog, tmp_path, cls, cls2, c, c2):
+        # Write a YAML snippet to file
+        yaml_path = tmp_path.joinpath("config.yaml")
+        yaml_path.write_text(
+            """
+foo 1: 99
+
+foo-2: bar
+
+foo_3: false
+
+foo_4: 3.14
+            """
+        )
+
+        obj1 = cls()
+        # Method runs
+        obj1.read_file(yaml_path, fail=False)
+        # Values are read
+        assert c == obj1, obj1
+        # Messages are logged
+        assert [
+            "Config has no attribute for file section 'foo_4'; ignored"
+        ] == caplog.messages
+        caplog.clear()
+
+        yaml_path.write_text(
+            """
+bar_1: 3.14
+subconfig a:
+  foo 1: 99
+  foo-2: bar
+  foo_3: false
+subconfig-b:  # No name manipulation for subkeys here
+  baz_1: 2
+  baz_2: 1
+            """
+        )
+
+        obj2 = cls2()
+        # Method runs
+        obj2.read_file(yaml_path, fail=False)
+        # Values are read
+        assert c2 == obj2, obj2
+
+        json_path = tmp_path.joinpath("config.json")
+        json_path.write_text(
+            """{
+"foo 1": 99,
+"foo-2": "bar",
+"foo_3": false
+}           """
+        )
+
+        obj3 = cls()
+        # Method runs
+        obj3.read_file(json_path)
+        # Values are read
+        assert c == obj3, obj3
+
+        obj4 = cls()
+        with pytest.raises(NotImplementedError):
+            obj4.read_file(yaml_path.with_suffix(".xlsx"))
+
+    def test_replace(self, c):
+        result = c.replace(foo_2="baz")
+        assert result is not c
+        assert "baz" == result.foo_2
diff --git a/nest/message_ix_models/tests/util/test_context.py b/nest/message_ix_models/tests/util/test_context.py
new file mode 100644
index 0000000..4958da1
--- /dev/null
+++ b/nest/message_ix_models/tests/util/test_context.py
@@ -0,0 +1,251 @@
+import logging
+import re
+from copy import deepcopy
+from pathlib import Path
+
+import click
+import ixmp
+import pytest
+from message_ix import Scenario
+
+from message_ix_models import Context
+
+
+class TestContext:
+    def test_get_instance(self, session_context):
+        c = Context()
+        assert c is Context.get_instance(-1)
+        c.delete()
+
+    def test_only(self):
+        # Ensure at least 2 instances exist
+        c2 = Context()
+
+        with pytest.raises(IndexError, match=r"ambiguous: \d+ Context instances"):
+            Context.only()
+
+        c2.delete()
+
+    def test_clone_to_dest(self, caplog, test_context):
+        ctx = test_context
+
+        platform_name = ctx.platform_info["name"]
+        model_name = "foo model"
+        scenario_name = "bar scenario"
+
+        # Works with direct settings, no URL
+        c = deepcopy(ctx)
+
+        # Force the base scenario info to be empty
+        c.scenario_info.clear()
+        c.dest_scenario = dict(model=model_name, scenario=scenario_name)
+
+        # Fails with create=False
+        with pytest.raises(
+            TypeError, match="missing 1 required positional argument: 'model'"
+        ):
+            c.clone_to_dest(create=False)
+
+        # Succeeds with default create=True
+        s = c.clone_to_dest()
+
+        # Base scenario was created
+        assert "Base scenario not given or found" in caplog.messages
+
+        # Works with a URL to parse and no base scenario
+        url = f"ixmp://{platform_name}/{model_name}/{scenario_name}"
+
+        c2 = deepcopy(ctx)
+        c2.scenario_info.clear()
+        c2.dest = url
+        s = c2.clone_to_dest()
+        assert model_name == s.model and scenario_name == s.scenario
+
+        del s
+
+        # Works with a base scenario
+        c2.handle_cli_args(url=url)
+        c2.dest_scenario = dict(model="baz model", scenario="baz scenario")
+        c2.dest_platform.clear()
+        s = c2.clone_to_dest()
+
+        assert s.model.startswith("baz") and s.scenario.startswith("baz")
+
+    def test_dealias(self, caplog):
+        """Aliasing works with :meth:`Context.__init__`, :meth:`Context.update`."""
+        c = Context()
+        c.update(regions="R99")
+        assert [] == caplog.messages  # No log warnings for core Config, .model.Config
+        assert "R99" == c.model.regions
+
+        c = Context(regions="R98")
+        assert [
+            "Create a Config instance instead of passing ['regions'] to Context()"
+        ] == caplog.messages
+        caplog.clear()
+        assert "R98" == c.model.regions == c.regions
+        assert [] == caplog.messages  # No log warnings for access
+
+    def test_default_value(self, test_context):
+        # Setting is missing
+        with pytest.raises(AttributeError):
+            test_context.foo
+
+        # setdefault() returns the new value
+        assert test_context.setdefault("foo", 23) == 23
+
+        # setdefault() returns the existing value
+        assert test_context.setdefault("foo", 45) == 23
+
+        # Attribute access works
+        assert test_context.foo == 23
+
+    def test_deepcopy(self, session_context):
+        """Paths are preserved through deepcopy()."""
+        ld = session_context.local_data
+
+        c = deepcopy(session_context)
+
+        assert ld == c.local_data
+
+    def test_get_cache_path(self, pytestconfig, test_context):
+        """cache_path() returns the expected output."""
+        # One of two values depending on whether the user has given --local-cache
+        assert test_context.get_cache_path("pytest", "bar.pkl") in (
+            test_context.local_data.joinpath("cache", "pytest", "bar.pkl"),
+            Path(pytestconfig.cache.makedir("cache")).joinpath("pytest", "bar.pkl"),
+        )
+
+    def test_get_local_path(self, tmp_path_factory, session_context):
+        assert str(tmp_path_factory.mktemp("data").joinpath("foo", "bar")).replace(
+            "data1", "data0"
+        ) == str(session_context.get_local_path("foo", "bar"))
+
+    def test_get_platform(self, session_context):
+        assert isinstance(session_context.get_platform(), ixmp.Platform)
+        assert isinstance(session_context.get_platform(reload=True), ixmp.Platform)
+
+    def test_get_scenario(self, test_context):
+        test_context.scenario_info = dict(model="model name", scenario="scenario name")
+        with pytest.raises(ValueError):
+            test_context.get_scenario()
+
+    def test_set_scenario(self, test_context):
+        mp = test_context.get_platform()
+        s = Scenario(mp, "foo", "bar", version="new")
+
+        # set_scenario() updates Context.scenario_info
+        test_context.scenario_info = dict()
+        test_context.set_scenario(s)
+        assert (
+            dict(model="foo", scenario="bar", version=0) == test_context.scenario_info
+        )
+
+    def test_write_debug_archive(self, mix_models_cli):
+        """:meth:`.write_debug_archive` works."""
+        # Create a CLI command attached to the hidden "_test" group
+
+        from message_ix_models.testing import cli_test_group
+
+        @cli_test_group.command("write-debug-archive")
+        @click.pass_obj
+        def _(context):
+            # Register one file to be archived
+            p = context.core.local_data.joinpath("foo.txt")
+            context.core.debug_paths.append(p)
+
+            # Write some text to this file
+            p.write_text("Here is some debug output in a file.")
+
+            # Register a non-existent path
+            context.core.debug_paths.append(p.with_name("bar.txt"))
+
+            # Write the archive
+            context.write_debug_archive()
+
+        # Invoke the command; I/O occurs in a temporary directory
+        result = mix_models_cli.invoke(["_test", "write-debug-archive"])
+
+        # Output path is constructed as expected; file exists
+        match = re.search(
+            r"Write to: (.*main-_test-write-debug-archive-[\dabcdefT\-]+.zip)",
+            result.output,
+        )
+        assert Path(match.group(1)).exists()
+
+        # Log output is generated for the non-existent path in Context.debug_paths
+        assert re.search(r"Not found: .*bar.txt", result.output)
+
+    def test_handle_cli_args(self):
+        p = "platform name"
+        m = "model name"
+        s = "scenario name"
+        v = "42"
+
+        args1 = dict(
+            local_data=Path("foo", "bar"),
+            platform=p,
+            model_name=m,
+            scenario_name=s,
+            version=v,
+        )
+
+        expected = dict(
+            local_data=args1["local_data"],
+            platform_info=dict(name=p),
+            scenario_info=dict(model=m, scenario=s, version=v),
+        )
+
+        ctx = Context()
+        ctx.handle_cli_args(**args1)
+        assert all(ctx[k] == v for k, v in expected.items())
+
+        url = f"ixmp://{p}/{m}/{s}#{v}"
+        args2 = args1.copy()
+        args2["url"] = url
+
+        with pytest.raises(click.BadOptionUsage, match="redundant with --url"):
+            ctx.handle_cli_args(**args2)
+
+        # New instance
+        ctx.delete()
+        ctx = Context()
+
+        # Platform and scenario info are empty
+        assert 0 == len(ctx["platform_info"]) == len(ctx["scenario_info"])
+
+        ctx.handle_cli_args(url=url, local_data=args1["local_data"])
+
+        # ixmp parse_url() converts the version number to an int
+        expected["scenario_info"]["version"] = int(v)
+        # url is also stored
+        expected["url"] = url
+
+        assert all(ctx[k] == v for k, v in expected.items()), ctx
+
+        ctx.delete()
+
+    def test_repr(self):
+        c = Context()
+        assert re.fullmatch("<Context object at [^ ]+ with 2 keys>", repr(c))
+
+    def test_use_defaults(self, caplog):
+        caplog.set_level(logging.INFO)
+
+        c = Context()
+
+        defaults = dict(foo=["foo2", "foo1", "foo3"], bar=["bar1", "bar3"])
+
+        c.foo = "foo1"
+
+        c.use_defaults(defaults)
+        assert ["Use default bar=bar1"] == caplog.messages
+
+        c.bar = "bar2"
+
+        with pytest.raises(
+            ValueError, match=re.escape("bar must be in ['bar1', 'bar3']; got bar2")
+        ):
+            c.use_defaults(defaults)
+
+        c.delete()
diff --git a/nest/message_ix_models/tests/util/test_logging.py b/nest/message_ix_models/tests/util/test_logging.py
new file mode 100644
index 0000000..c9d7e1c
--- /dev/null
+++ b/nest/message_ix_models/tests/util/test_logging.py
@@ -0,0 +1,40 @@
+import logging
+import re
+
+from message_ix_models.util._logging import mark_time, silence_log
+
+
+def test_mark_time(caplog):
+    # Call 3 times
+    mark_time()  # Will only log if already called during the course of another test
+    mark_time()
+    mark_time()
+
+    # Either 2 or 3 records
+    assert len(caplog.records) in (2, 3)
+
+    # Each message matches the expected format
+    assert all(re.match(r" \+\d+\.\d = \d+\.\d seconds", m) for m in caplog.messages)
+
+
+def test_silence_log(caplog):
+    # An example logger
+    log = logging.getLogger("message_ix_models.model")
+
+    msg = "Here's a warning!"
+
+    # pytest caplog fixture picks up warning messages
+    log.warning(msg)
+    assert [msg] == caplog.messages
+
+    caplog.clear()
+
+    # silence_log() hides the messages
+    with silence_log():
+        log.warning(msg)
+
+    assert [] == caplog.messages
+
+    # After the "with" block, logging is restored
+    log.warning(msg)
+    assert [msg] == caplog.messages
diff --git a/nest/message_ix_models/tests/util/test_node.py b/nest/message_ix_models/tests/util/test_node.py
new file mode 100644
index 0000000..d859ed7
--- /dev/null
+++ b/nest/message_ix_models/tests/util/test_node.py
@@ -0,0 +1,152 @@
+"""Tests of :mod:`message_ix_models.util.node`."""
+import re
+
+import pandas as pd
+import pytest
+from genno import Quantity
+from message_ix import Scenario, make_df
+
+from message_ix_models.model.bare import create_res
+from message_ix_models.model.structure import get_codes
+from message_ix_models.util import (
+    adapt_R11_R12,
+    adapt_R11_R14,
+    broadcast,
+    identify_nodes,
+)
+from message_ix_models.util.node import MappingAdapter
+
+
+def test_mapping_adapter():
+    """Generic test of MappingAdapter."""
+    a = MappingAdapter({"foo": [("a", "x"), ("a", "y"), ("b", "z")]})
+
+    columns = ["foo", "bar", "value"]
+
+    df = pd.DataFrame([["a", "m", 1], ["b", "n", 2]], columns=columns)
+
+    result = a(df)
+
+    assert all(columns + ["unit"] == result.columns)
+
+    with pytest.raises(TypeError):
+        a(1.2)
+
+
+PAR = "technical_lifetime"
+VALUE = [0.1, 0.2]
+
+
+@pytest.fixture(scope="function")
+def input():
+    """Fixture: test data for :func:`.adapt_R11_R14`."""
+    R11_all = get_codes("node/R11")
+    R11_reg = R11_all[R11_all.index("World")].child
+    df = make_df(
+        PAR, technology="coal_ppl", year_vtg=[2021, 2022], value=[1.2, 3.4], unit="year"
+    ).pipe(broadcast, node_loc=R11_reg)
+
+    # Set a specific value for the regions to be broadcast
+    df["value"] = df["value"].where(df["node_loc"] != "R11_CPA", VALUE[0])
+    df["value"] = df["value"].where(df["node_loc"] != "R11_FSU", VALUE[1])
+
+    return {PAR: df}
+
+
+@pytest.mark.parametrize(
+    "func,N,expected,target_nodes",
+    [
+        (adapt_R11_R12, 12, VALUE[0], ("R12_CHN", "R12_RCPA")),
+        (adapt_R11_R14, 14, VALUE[1], ("R14_CAS", "R14_RUS", "R14_SCS", "R14_UBM")),
+    ],
+)
+def test_adapt_df(input, func, N, expected, target_nodes):
+    """:func:`.adapt_R11_R14` handles :class:`pandas.DataFrame`."""
+    # Function runs
+    output = func(input)
+
+    # Output is a dict containing 1 entry
+    df_out = output.pop(PAR)
+    assert 0 == len(output)
+
+    # Output covers all R14 regions
+    all_nodes = get_codes(f"node/R{N}")
+    regions = all_nodes[all_nodes.index("World")].child
+    assert set(regions) == set(df_out["node_loc"])
+
+    # Output has expected length
+    assert N * 2 == len(df_out)
+
+    # Output values for new regions match input value for the base region
+    assert (expected == df_out[df_out["node_loc"].isin(target_nodes)]["value"]).all()
+
+
+@pytest.mark.parametrize(
+    "func,expected,node_loc",
+    [(adapt_R11_R12, VALUE[0], "R12_CHN"), (adapt_R11_R14, VALUE[1], "R14_CAS")],
+)
+def test_adapt_qty(input, func, expected, node_loc):
+    """:func:`.adapt_R11_R14` handles :class:`genno.Quantity`."""
+    # Convert to genno.Quantity
+    df = input[PAR]
+    input[PAR] = Quantity.from_series(df.set_index(df.columns[:-2].tolist())["value"])
+    input[PAR].attrs["_unit"] = df["unit"].unique()[0]
+
+    # Function runs
+    output = func(input)
+
+    # Output is a dict containing 1 entry
+    qty_out = output.pop(PAR)
+    assert 0 == len(output)
+
+    assert isinstance(qty_out, Quantity)
+    assert "year" == qty_out.attrs["_unit"]
+
+    # Output values for new regions match input value for the base region
+    assert (
+        expected == qty_out.sel(node_loc=node_loc, technology="coal_ppl", year=2022)
+    ).all()
+
+
+@pytest.mark.parametrize("regions", ["R11", "R12", "R14"])
+def test_identify_nodes(caplog, test_context, regions):
+    ctx = test_context
+    ctx.regions = regions
+    scenario = create_res(ctx)
+
+    # The ID of the node codelist can be recovered from scenario contents
+    assert regions == identify_nodes(scenario)
+
+    # A node like "R11_GLB" is ignored
+    with scenario.transact():
+        scenario.add_set("node", f"{regions}_GLB")
+    assert regions == identify_nodes(scenario)
+
+    # Remove one element from the node set
+    with scenario.transact():
+        scenario.remove_set("node", scenario.set("node").tolist()[0])
+
+    # No longer any match
+    with pytest.raises(
+        ValueError, match=f"IDs suggest codelist {repr(regions)}, values do not match"
+    ):
+        identify_nodes(scenario)
+
+
+def test_identify_nodes1(test_context):
+    mp = test_context.get_platform()
+    scenario = Scenario(
+        mp, model="identify_nodes", scenario="identify_nodes", version="new"
+    )
+    scenario.add_set("technology", "t")
+    scenario.add_set("year", 0)
+    scenario.commit("")
+
+    with scenario.transact():
+        scenario.add_set("node", "R99_ZZZ")
+
+    with pytest.raises(
+        ValueError,
+        match=re.escape("Couldn't identify node codelist from ['R99_ZZZ', 'World']"),
+    ):
+        identify_nodes(scenario)
diff --git a/nest/message_ix_models/tests/util/test_scenarioinfo.py b/nest/message_ix_models/tests/util/test_scenarioinfo.py
new file mode 100644
index 0000000..3b051f9
--- /dev/null
+++ b/nest/message_ix_models/tests/util/test_scenarioinfo.py
@@ -0,0 +1,212 @@
+import logging
+
+import pandas as pd
+import pytest
+from message_ix import make_df
+from message_ix.testing import make_dantzig
+from pandas.testing import assert_frame_equal
+from sdmx.model import Code
+
+from message_ix_models import ScenarioInfo, Spec
+from message_ix_models.model.structure import get_codes, process_technology_codes
+from message_ix_models.util import as_codes
+
+
+class TestScenarioInfo:
+    def test_empty(self):
+        """ScenarioInfo created from scratch."""
+        info = ScenarioInfo()
+
+        # Set values directly
+        info.set["node"] = [Code(id="AT", name="Austria")]
+        info.set["year"] = [1000, 1010, 1020, 1030]
+        info.y0 = 1010
+
+        # Shorthand properties
+
+        # `yv_ya` is generated
+        assert_frame_equal(
+            pd.DataFrame(
+                [
+                    [1010, 1010],
+                    [1010, 1020],
+                    [1010, 1030],
+                    [1020, 1020],
+                    [1020, 1030],
+                    [1030, 1030],
+                ],
+                columns=["year_vtg", "year_act"],
+            ),
+            info.yv_ya,
+        )
+
+        # List of Codes is converted to list of strings
+        assert ["AT"] == info.N
+
+        # Only years >= y0
+        assert [1010, 1020, 1030] == info.Y
+
+    def test_units(self, caplog):
+        """Test both :meth:`.io_units` and :meth:`.units_for`."""
+        # Prepare ScenarioInfo with some commodities and technologies
+        info = ScenarioInfo()
+        info.set["commodity"] = get_codes("commodity")
+        # NB create a technology with units annotation, since technology.yaml lacks
+        #    units as of 2022-07-20
+        t = as_codes({"example tech": {"units": "coulomb"}})
+        process_technology_codes(t)
+        info.set["technology"].extend(t)
+
+        # units_for() runs, produces energy units
+        c_units = info.units_for("commodity", "electr")
+        assert {"[length]": 2, "[mass]": 1, "[time]": -2} == c_units.dimensionality
+
+        # units_for() runs, produces expected units
+        t_units = info.units_for("technology", "example tech")
+        assert {"[current]": 1, "[time]": 1} == t_units.dimensionality
+
+        # ValueError is raised for invalid input
+        with pytest.raises(ValueError):
+            info.units_for("commodity", "not a commodity")
+
+        # io_units() runs, produces a ratio of commodity / technology units
+        with caplog.at_level(logging.DEBUG, "message_ix_models"):
+            assert (c_units / t_units) == info.io_units(
+                "example tech", "electr", level="useful"
+            )
+        # level= keyword argument → logged warning
+        assert "level = 'useful' ignored" == caplog.messages[-1]
+
+    def test_from_scenario(self, test_context):
+        """ScenarioInfo initialized from an existing Scenario."""
+        mp = test_context.get_platform()
+        scenario = make_dantzig(mp, multi_year=True)
+
+        # ScenarioInfo can be initialized from the scenario
+        info = ScenarioInfo(scenario)
+
+        # Shorthand properties
+        assert_frame_equal(
+            pd.DataFrame(
+                [
+                    [1962, 1963],
+                    [1962, 1964],
+                    [1962, 1965],
+                    [1963, 1963],
+                    [1963, 1964],
+                    [1963, 1965],
+                    [1964, 1964],
+                    [1964, 1965],
+                    [1965, 1965],
+                ],
+                columns=["year_vtg", "year_act"],
+            ),
+            info.yv_ya,
+        )
+        assert [
+            "World",
+            "seattle",
+            "san-diego",
+            "new-york",
+            "chicago",
+            "topeka",
+        ] == info.N
+        assert 1963 == info.y0
+        assert [1963, 1964, 1965] == info.Y
+
+    def test_repr(self):
+        si = ScenarioInfo()
+        si.set["foo"] = [1, 2, 3]
+        assert "<ScenarioInfo: 3 code(s) in 1 set(s)>" == repr(si)
+
+    def test_update(self):
+        si = ScenarioInfo()
+        si.par["demand"] = make_df("demand")
+
+        # update() fails
+        with pytest.raises(NotImplementedError):
+            ScenarioInfo().update(si)
+
+    @pytest.mark.parametrize(
+        "codelist, y0, N_all, N_Y, y_m1, dp_checks",
+        [
+            (
+                "A",
+                2020,
+                16,
+                10,
+                2110,
+                ((1990, 10), (2010, 10), (2020, 10), (2050, 10), (2110, 10)),
+            ),
+            (
+                "B",
+                2020,
+                28,
+                14,
+                2110,
+                ((1990, 5), (2010, 5), (2020, 5), (2055, 5), (2110, 10)),
+            ),
+        ],
+    )
+    def test_year_from_codes(self, caplog, codelist, y0, N_all, N_Y, y_m1, dp_checks):
+        caplog.set_level(logging.DEBUG, logger="message_ix_models")
+
+        info = ScenarioInfo()
+        codes = get_codes(f"year/{codelist}")
+        info.year_from_codes(codes)
+
+        # First model period
+        assert y0 == info.y0
+        assert ("firstmodelyear", y0) in info.set["cat_year"]
+
+        # Total number of periods
+        assert N_all == len(info.set["year"])
+
+        # Number of model periods
+        assert N_Y == len(info.Y)
+
+        # Final period
+        assert y_m1 == info.Y[-1]
+
+        # Convert the data frame to a series
+        dp = info.par["duration_period"].set_index("year")["value"]
+
+        # duration_period entries are as expected
+        for key, expected in dp_checks:
+            assert expected == dp[key]
+
+        # Test logging
+        assert 0 == len(caplog.messages)
+
+        info.year_from_codes(codes)
+
+        assert 3 == len(caplog.messages)
+        assert all(msg.startswith("Discard existing") for msg in caplog.messages)
+
+
+class TestSpec:
+    def test_getitem(self):
+        s = Spec()
+        with pytest.raises(KeyError):
+            s["foo"]
+
+    def test_setitem(self):
+        s = Spec()
+
+        # Setting a valid key via attribute access syntax works
+        s.add = ScenarioInfo()
+        # Setting a valid key via item access syntax works
+        s["add"] = ScenarioInfo()
+
+        # Setting an invalid key raises KeyError
+        with pytest.raises(KeyError):
+            s["foo"] = ScenarioInfo()
+
+    def test_merge(self):
+        s1 = Spec()
+        s1.add.set["technology"] = ["t1", "t3", "t5"]
+        s2 = Spec()
+        s2.add.set["technology"] = ["t2", "t4", "t6"]
+
+        s3 = Spec.merge(s1, s2)
+        assert 6 == len(s3.add.set["technology"])
diff --git a/nest/message_ix_models/tests/util/test_sdmx.py b/nest/message_ix_models/tests/util/test_sdmx.py
new file mode 100644
index 0000000..f39ed33
--- /dev/null
+++ b/nest/message_ix_models/tests/util/test_sdmx.py
@@ -0,0 +1,25 @@
+import logging
+import re
+
+from sdmx.model import Annotation, Code
+
+from message_ix_models.util.sdmx import eval_anno
+
+
+def test_eval_anno(caplog):
+    c = Code()
+
+    assert None is eval_anno(c, "foo")
+
+    c.annotations.append(Annotation(id="foo", text="bar baz"))
+
+    with caplog.at_level(logging.DEBUG, logger="message_ix_models"):
+        assert "bar baz" == eval_anno(c, "foo")
+
+    assert re.fullmatch(
+        r"Could not eval\('bar baz'\): .* \(<string>, line 1\)", caplog.messages[0]
+    )
+
+    c.annotations.append(Annotation(id="qux", text="3 + 4"))
+
+    assert 7 == eval_anno(c, id="qux")
diff --git a/nest/message_ix_models/tools/__init__.py b/nest/message_ix_models/tools/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/nest/message_ix_models/tools/advance.py b/nest/message_ix_models/tools/advance.py
new file mode 100644
index 0000000..3dc2adf
--- /dev/null
+++ b/nest/message_ix_models/tools/advance.py
@@ -0,0 +1,171 @@
+"""Handle data from the ADVANCE project."""
+import logging
+from pathlib import Path
+from typing import List, Optional, Tuple
+from zipfile import ZIP_DEFLATED, ZipFile
+
+import numpy as np
+import pandas as pd
+import pint
+from genno import Quantity
+
+from message_ix_models.util import (
+    cached,
+    local_data_path,
+    maybe_query,
+    private_data_path,
+)
+
+log = logging.getLogger(__name__)
+
+#: Expected location of the ADVANCE WP2 data snapshot.
+LOCATION = "advance", "advance_compare_20171018-134445.csv.zip"
+
+#: Name of the data file within the archive.
+NAME = "advance_compare_20171018-134445.csv"
+
+#: Standard dimensions for data produced as snapshots from the IIASA ENE Program
+#: “WorkDB”.
+#:
+#: .. todo:: Move to a common location for use with other snapshots in the same format.
+DIMS = ["model", "scenario", "region", "variable", "unit", "year"]
+
+
+@cached
+def get_advance_data(query: Optional[str] = None) -> pd.Series:
+    """Return data from the ADVANCE Work Package 2 data snapshot at :data:`LOCATION`.
+
+    Parameters
+    ----------
+    query : str, optional
+        Passed to :meth:`pandas.DataFrame.query` to limit the returned values.
+
+    Returns
+    -------
+    pandas.Series
+        with a :class:`pandas.MultiIndex` having the levels :data:`.DIMS`.
+    """
+    try:
+        path = private_data_path(*LOCATION)
+    except TypeError:
+        path = local_data_path(*LOCATION)
+
+    return _read_workdb_snapshot(path, NAME).pipe(maybe_query, query)
+
+
+def advance_data(variable: str, query: Optional[str] = None) -> Quantity:
+    """Return a single ADVANCE data `variable` as a :class:`genno.Quantity`.
+
+    Parameters
+    ----------
+    query : str, optional
+        Passed to :func:`get_advance_data`.
+
+    Returns
+    -------
+    genno.Quantity
+        with the dimensions :data:`.DIMS` and name `variable`. If the units of the data
+        for `variable` are consistent and parseable by :mod:`pint`, the returned
+        Quantity has these units; otherwise units are discarded and the returned
+        Quantity is dimensionless.
+    """
+    data = (
+        get_advance_data(query)
+        .rename("value")
+        .xs(variable, level="variable")
+        .reset_index("unit")
+    )
+    if len(data.unit.unique()) > 1:  # pragma: no cover
+        log.info(f"Non-unique units for {variable!r}; discarded")
+        units = ""
+    else:
+        units = data.unit.iloc[0]
+
+    result = Quantity(data["value"], name=variable)
+
+    try:
+        result.units = units
+    except pint.errors.PintError as e:  # pragma: no cover
+        log.info(f'"{e}" when parsing {units!r}; discarded')
+
+    return result
+
+
+@cached
+def _read_workdb_snapshot(path: Path, name: str) -> pd.Series:
+    """Read the data file.
+
+    The expected format is a ZIP archive at `path` containing a member at `name` in CSV
+    format, with columns corresponding to :data:`DIMS`, except for “year”, which is
+    stored as column headers (‘wide’ format). (This corresponds to an older version of
+    the “IAMC format,” without more recent additions intended to represent sub-annual
+    time resolution using a separate column.)
+
+    .. todo:: Move to a general location for use with other files in the same format.
+    """
+    with ZipFile(path) as zf:  # Open the ZIP archive
+        with zf.open(name) as f:  # Open a particular member
+            # - Read data using upper case column names, then convert to lower-case.
+            # - Drop null rows.
+            # - Stack the “year” dimension (‘long’ format), creating a pd.Series.
+            # - Apply the index names.
+            return (
+                pd.read_csv(f, index_col=list(map(str.upper, DIMS[:-1])))
+                .rename(columns=lambda c: int(c))
+                .dropna(how="all")
+                .stack()
+                .rename_axis(DIMS)
+            )
+
+
+def _fuzz_data(size=1e2, include: List[Tuple[str, str]] = []):
+    """Select a subset of the data for use in testing.
+
+    Parameters
+    ----------
+    size : numeric
+        Number of rows to include.
+    include : sequence of 2-tuple (str, str)
+        (variable name, unit) to include. The data will be partly duplicated to ensure
+        the given variable name(s) are included.
+    """
+    size = int(size)
+    rng = np.random.default_rng()
+
+    # - Select `size` rows at random from the full data set.
+    # - Use their index for a new pd.Series with random data.
+    # - Convert to pd.DataFrame with upper-case column names
+    # - Drop duplicated indices
+    # - Return to original wide format.
+    columns = list(map(str.upper, DIMS))
+    dfs = [
+        pd.Series(
+            rng.random(size), index=get_advance_data().sample(size).index, name="value"
+        )
+        .rename_axis(columns)
+        .reset_index()
+        .drop_duplicates(subset=columns)
+        .pivot(index=columns[:-1], columns="YEAR", values="value")
+    ]
+
+    # Duplicate data for (variable, unit) pairs required per `include`
+    for variable, unit in include:
+        dfs.append(
+            dfs[0]
+            .query(f"VARIABLE != {variable!r}")
+            .assign(VARIABLE=variable, UNIT=unit)
+        )
+
+    # Path for output archive
+
+    # For ordinary testing, output to a temporary directory
+    target = local_data_path("test", *LOCATION)
+    # To update/overwrite the data file in the repo, uncomment this line
+    # target = package_data_path("test", *LOCATION)
+
+    target.parent.mkdir(exist_ok=True, parents=True)
+
+    # Concatenate data, write to the target member of the target
+    with ZipFile(target, "w", ZIP_DEFLATED) as zf:
+        with zf.open(NAME, "w") as f:
+            pd.concat(dfs).to_csv(f, index=False)
diff --git a/nest/message_ix_models/util/__init__.py b/nest/message_ix_models/util/__init__.py
new file mode 100644
index 0000000..e061b65
--- /dev/null
+++ b/nest/message_ix_models/util/__init__.py
@@ -0,0 +1,577 @@
+import logging
+from collections import ChainMap, defaultdict
+from typing import Dict, Mapping, MutableMapping, Optional, Sequence, Union
+
+import message_ix
+import pandas as pd
+import pint
+from message_ix.models import MESSAGE_ITEMS
+
+from ._convert_units import convert_units, series_of_pint_quantity
+from .cache import cached
+from .common import (
+    MESSAGE_DATA_PATH,
+    MESSAGE_MODELS_PATH,
+    load_package_data,
+    load_private_data,
+    local_data_path,
+    package_data_path,
+    private_data_path,
+)
+from .node import adapt_R11_R12, adapt_R11_R14, identify_nodes, nodes_ex_world
+from .scenarioinfo import ScenarioInfo
+from .sdmx import CodeLike, as_codes, eval_anno
+
+__all__ = [
+    "MESSAGE_DATA_PATH",
+    "MESSAGE_MODELS_PATH",
+    "adapt_R11_R12",
+    "adapt_R11_R14",
+    "as_codes",
+    "broadcast",
+    "cached",
+    "check_support",
+    "convert_units",
+    "eval_anno",
+    "identify_nodes",
+    "load_package_data",
+    "load_private_data",
+    "local_data_path",
+    "make_io",
+    "maybe_query",
+    "package_data_path",
+    "private_data_path",
+    "series_of_pint_quantity",
+]
+
+log = logging.getLogger(__name__)
+
+
+def add_par_data(
+    scenario: message_ix.Scenario,
+    data: Mapping[str, pd.DataFrame],
+    dry_run: bool = False,
+):
+    """Add `data` to `scenario`.
+
+    Parameters
+    ----------
+    data
+        Dict with keys that are parameter names, and values are pd.DataFrame or other
+        arguments
+    dry_run : optional
+        Only show what would be done.
+
+    See also
+    --------
+    strip_par_data
+    """
+    # TODO optionally add units automatically
+
+    total = 0
+
+    for par_name, values in data.items():
+        N = values.shape[0]
+        log.info(f"{N} rows in {repr(par_name)}")
+        log.debug("\n" + values.to_string(max_rows=5))
+
+        total += N
+
+        if dry_run:
+            continue
+
+        # Work around iiasa/ixmp#425
+        values["unit"] = values["unit"].str.replace("^$", "-", regex=True)
+
+        try:
+            scenario.add_par(par_name, values)
+        except Exception:  # pragma: no cover
+            print(values.head())
+            raise
+
+    return total
+
+
+def aggregate_codes(df: pd.DataFrame, dim: str, codes):  # pragma: no cover
+    """Aggregate `df` along dimension `dim` according to `codes`."""
+    raise NotImplementedError
+
+    # Construct an inverse mapping
+    mapping = {}
+    for code in codes:
+        mapping.update({child.id: code.id for child in code.child})
+
+    for key, group_series in df.groupby(dim):
+        print(key, group_series.replace({dim: mapping}))
+
+
+def broadcast(
+    df: pd.DataFrame, labels: Optional[pd.DataFrame] = None, **kwargs
+) -> pd.DataFrame:
+    """Fill missing data in `df` by broadcasting.
+
+    :func:`broadcast` is suitable for use with partly-filled data frames returned by
+    :func:`.message_ix.util.make_df`, with 1 column per dimension, plus a "value"
+    column. It is also usable with :meth:`pandas.DataFrame.pipe` for chained operations.
+
+    `labels` (if any) are handled first: one copy or duplicate of `df` is produced for
+    each row (set of labels) in this argument. Then, `kwargs` are handled;
+    :func:`broadcast` returns one copy for each element in the cartesian product of the
+    dimension labels given by `kwargs`.
+
+    Parameters
+    ----------
+    labels : pandas.DataFrame
+        Each column (dimension) corresponds to one in `df`. Each row represents one
+        matched set of labels for those dimensions.
+    kwargs
+        Keys are dimensions. Values are labels along that dimension to fill.
+
+    Returns
+    -------
+    pandas.DataFrame
+        The length is either 1 or an integer multiple of the length of `df`.
+
+    Raises
+    ------
+    ValueError
+        if any of the columns in `labels` or `kwargs` are not present in `df`, or if
+        those columns are present but not empty.
+
+    Examples
+    --------
+    >>> from message_ix import make_df
+    >>> from message_ix_models.util import broadcast
+    # Create a base data frame with some empty columns
+    >>> base = make_df("input", technology="t", value=[1.1, 2.2])
+    # Broadcast (duplicate) the data across 2 dimensions
+    >>> df = base.pipe(broadcast, node_loc=["node A", "node B"], mode=["m0", "m1"])
+    # Show part of the result
+    >>> df.dropna(axis=1)
+      mode node_loc technology  value
+    0   m0   node A          t    1.1
+    1   m0   node A          t    2.2
+    2   m0   node B          t    1.1
+    3   m0   node B          t    2.2
+    4   m1   node A          t    1.1
+    5   m1   node A          t    2.2
+    6   m1   node B          t    1.1
+    7   m1   node B          t    2.2
+    """
+
+    def _check_dim(d):
+        try:
+            if not df[d].isna().all():
+                raise ValueError(f"Dimension {d} was not empty\n\n{df.head()}")
+        except KeyError:
+            raise ValueError(f"Dimension {d} not among {list(df.columns)}")
+
+    # Broadcast using matched labels for 1+ dimensions from a data frame
+    if labels is not None:
+        # Check the dimensions
+        for dim in labels.columns:
+            _check_dim(dim)
+        # Concatenate 1 copy of `df` for each row in `labels`
+        df = pd.concat(
+            [df.assign(**row) for _, row in labels.iterrows()], ignore_index=True
+        )
+
+    # Next, broadcast other dimensions given as keyword arguments
+    for dim, levels in kwargs.items():
+        _check_dim(dim)
+        if len(levels) == 0:
+            log.debug(
+                f"Don't broadcast over {repr(dim)}; labels {levels} have length 0"
+            )
+            continue
+
+        # - Duplicate the data
+        # - Drop the existing column named 'dim'
+        # - Re-add the column from the constructed MultiIndex
+        # - Reindex for sequential row numbers
+        df = (
+            pd.concat([df] * len(levels), keys=levels, names=[dim])
+            .drop(dim, axis=1)
+            .reset_index(dim)
+            .reset_index(drop=True)
+        )
+    return df
+
+
+def check_support(context, settings=dict(), desc: str = "") -> None:
+    """Check whether a Context is compatible with certain `settings`.
+
+    Raises
+    ------
+    :class:`NotImplementedError`
+        if any `context` value for a key of `settings` is not among the values in
+        `settings`.
+    :class:`KeyError`
+        if the key is not set on `context` at all.
+
+    See also
+    --------
+    :ref:`check-support`
+
+    """
+    __tracebackhide__ = True
+    for key, values in settings.items():
+        if context[key] not in values:
+            raise NotImplementedError(
+                f"{desc} for {repr(values)}; got {repr(context[key])}"
+            )
+
+
+def copy_column(column_name):
+    """For use with :meth:`pandas.DataFrame.assign`.
+
+    Examples
+    --------
+    Modify `df` by filling the column 'baz' with the value ``3``, and copying the column
+    'bar' into column 'foo'.
+
+    >>> df.assign(foo=copy_column('bar'), baz=3)
+
+    Note that a similar assignment can be achieved with :meth:`~pandas.DataFrame.eval`:
+
+    >>> df.eval("foo = bar")
+
+    :func:`copy_column` is useful in the context of more complicated calls to
+    :meth:`~pandas.DataFrame.assign`.
+    """
+    return lambda df: df[column_name]
+
+
+def ffill(
+    df: pd.DataFrame, dim: str, values: Sequence[CodeLike], expr: Optional[str] = None
+) -> pd.DataFrame:
+    """Forward-fill `df` on `dim` to cover `values`.
+
+    Parameters
+    ----------
+    df : .DataFrame
+        Data to fill forwards.
+    dim : str
+        Dimension to fill along. Must be a column in `df`.
+    values : list of str
+        Labels along `dim` that must be present in the returned data frame.
+    expr : str, optional
+        If provided, :meth:`.DataFrame.eval` is called. This can be used to assign one
+        column to another. For instance, if `dim` == "year_vtg" and `expr` is "year_act
+        = year_vtg", then forward filling is performed along the "year_vtg" dimension/
+        column, and then the filled values are copied to the "year_act" column.
+    """
+    if dim in ("value", "unit"):
+        raise ValueError(dim)
+
+    # Mapping from (values existing in `df`) -> equal or greater members of `values`
+    mapping = defaultdict(set)
+    last_seen = None
+    for v in sorted(set(values) | set(df[dim].unique())):
+        if v in df[dim].unique():
+            last_seen = v
+        mapping[last_seen].add(v)
+
+    def _maybe_eval(df):
+        return df.eval(expr) if expr is not None else df
+
+    dfs = [df]
+    for key, group_df in df.groupby(dim):
+        for new_label in sorted(mapping[key])[1:]:
+            # Duplicate the data; assign the new_label to `dim`
+            dfs.append(group_df.assign(**{dim: new_label}).pipe(_maybe_eval))
+
+    return pd.concat(dfs, ignore_index=True)
+
+
+def iter_parameters(set_name):
+    """Iterate over MESSAGEix parameters with *set_name* as a dimension.
+
+    Parameters
+    ----------
+    set_name : str
+        Name of a set.
+
+    Yields
+    ------
+    str
+        Names of parameters that have `set_name` indexing ≥1 dimension.
+    """
+    # TODO move upstream. See iiasa/ixmp#402 and iiasa/message_ix#444
+    for name, info in MESSAGE_ITEMS.items():
+        if info["ix_type"] == "par" and set_name in info["idx_sets"]:
+            yield name
+
+
+def make_io(src, dest, efficiency, on="input", **kwargs):
+    """Return input and output data frames for a 1-to-1 technology.
+
+    Parameters
+    ----------
+    src : tuple (str, str, str)
+        Input commodity, level, unit.
+    dest : tuple (str, str, str)
+        Output commodity, level, unit.
+    efficiency : float
+        Conversion efficiency.
+    on : 'input' or 'output'
+        If 'input', `efficiency` applies to the input, and the output, thus the activity
+        level of the technology, is in dest[2] units. If 'output', the opposite.
+    kwargs
+        Passed to :func:`make_df`.
+
+    Returns
+    -------
+    dict (str -> pd.DataFrame)
+        Keys are 'input' and 'output'; values are data frames.
+    """
+    return dict(
+        input=message_ix.make_df(
+            "input",
+            commodity=src[0],
+            level=src[1],
+            unit=src[2],
+            value=efficiency if on == "input" else 1.0,
+            **kwargs,
+        ),
+        output=message_ix.make_df(
+            "output",
+            commodity=dest[0],
+            level=dest[1],
+            unit=dest[2],
+            value=1.0 if on == "input" else efficiency,
+            **kwargs,
+        ),
+    )
+
+
+def make_matched_dfs(
+    base: MutableMapping, **par_value: Union[float, pint.Quantity]
+) -> Dict[str, pd.DataFrame]:
+    """Return data frames derived from `base` for multiple parameters.
+
+    Creates one data frame per keyword argument.
+
+    Parameters
+    ----------
+    base : pd.DataFrame, dict, etc.
+        Used to populate other columns of each data frame.
+        Duplicates—which occur when the target parameter has fewer dimensions than
+        `base`—are dropped.
+    par_values :
+        Argument names (e.g. ‘fix_cost’) are passed to :func:`.make_df`.
+        If the value is :class:`float`, it overwrites the "value" column; if
+        :class:`pint.Quantity`, its magnitude overwrites "value" and its units the
+        "units" column, as a formatted string.
+
+    Returns
+    -------
+    :class:`dict` of :class:`pandas.DataFrame`
+        one for each parameter in `par_values`.
+
+    Examples
+    --------
+    >>> input = make_df("input", ...)
+    >>> cf_tl = make_matched_dfs(
+    >>>     input,
+    >>>     capacity_factor=1,
+    >>>     technical_lifetime=pint.Quantity(8, "year"),
+    >>> )
+    """
+    data = ChainMap(dict(), base)
+    result = {}
+    for par, value in par_value.items():
+        data.maps[0] = (
+            dict(value=value.magnitude, unit=f"{value.units:~}")
+            if isinstance(value, pint.Quantity)
+            else dict(value=value)
+        )
+        result[par] = (
+            message_ix.make_df(par, **data).drop_duplicates().reset_index(drop=True)
+        )
+    return result
+
+
+def make_source_tech(
+    info: Union[message_ix.Scenario, ScenarioInfo], common, **values
+) -> Dict[str, pd.DataFrame]:
+    """Return parameter data for a ‘source’ technology.
+
+    The technology has no inputs; its output commodity and/or level are determined by
+    `common`; either single values, or :obj:`None` if the result will be
+    :meth:`~DataFrame.pipe`'d through :func:`broadcast`.
+
+    Parameters
+    ----------
+    info : .Scenario or .ScenarioInfo
+    common : dict
+        Passed to :func:`make_df`.
+    **values
+        Values for 'capacity_factor' (optional; default 1.0), 'output', 'var_cost', and
+        optionally 'technical_lifetime'.
+
+    Returns
+    -------
+    dict
+        Suitable for :func:`add_par_data`.
+    """
+    # Check arguments
+    if isinstance(info, message_ix.Scenario):
+        info = ScenarioInfo(info)
+
+    values.setdefault("capacity_factor", 1.0)
+    missing = {"capacity_factor", "output", "var_cost"} - set(values.keys())
+    if len(missing):
+        raise ValueError(f"make_source_tech() needs values for {repr(missing)}")
+    elif "technical_lifetime" not in values:
+        log.debug("No technical_lifetime for source technology")
+
+    # Create data for "output"
+    result = dict(
+        output=message_ix.make_df(
+            "output",
+            value=values.pop("output"),
+            year_act=info.Y,
+            year_vtg=info.Y,
+            **common,
+        )
+        .pipe(broadcast, node_loc=nodes_ex_world(info.N))
+        .pipe(same_node)
+    )
+
+    # Add data for other parameters
+    result.update(make_matched_dfs(base=result["output"], **values))
+
+    return result
+
+
+def maybe_query(series: pd.Series, query: Optional[str]) -> pd.Series:
+    """Apply :meth:`pandas.Series.query` if the `query` arg is not :obj:`None`.
+
+    :meth:`~pandas.Series.query` is not chainable (`pandas-dev/pandas#37941
+    <https://github.com/pandas-dev/pandas/issues/37941>`_). Use this function with
+    :func:`pandas.Series.pipe`, passing an argument that may be :obj:`None`, to have a
+    chainable query operation that can be a no-op.
+    """
+    # Convert Series to DataFrame, query(), then retrieve the single column
+    return series if query is None else series.to_frame().query(query)[0]
+
+
+def merge_data(base, *others):
+    """Merge dictionaries of DataFrames together into `base`."""
+    for other in others:
+        for par, df in other.items():
+            base[par] = pd.concat([base.get(par, None), df])
+
+
+def same_node(df: pd.DataFrame) -> pd.DataFrame:
+    """Fill 'node_origin'/'node_dest' in `df` from 'node_loc'."""
+    cols = list(set(df.columns) & {"node_origin", "node_dest"})
+    return df.assign(**{c: copy_column("node_loc") for c in cols})
+
+
+def same_time(df: pd.DataFrame) -> pd.DataFrame:
+    """Fill 'time_origin'/'time_dest' in `df` from 'time'."""
+    cols = list(set(df.columns) & {"time_origin", "time_dest"})
+    return df.assign(**{c: copy_column("time") for c in cols})
+
+
+def strip_par_data(
+    scenario: message_ix.Scenario,
+    set_name: str,
+    element: str,
+    dry_run: bool = False,
+    dump: Optional[Dict[str, pd.DataFrame]] = None,
+):
+    """Remove `element` from `set_name` in scenario, optionally dumping to `dump`.
+
+    Parameters
+    ----------
+    dry_run : bool, optional
+        If :data:`True`, only show what would be done.
+    dump : dict, optional
+        If provided, stripped data are stored in this dictionary. Otherwise, they are
+        discarded.
+
+    Returns
+    -------
+    Total number of rows removed across all parameters.
+
+    See also
+    --------
+    add_par_data
+    """
+    par_list = scenario.par_list()
+    no_data = set()  # Names of parameters with no data being stripped
+    total = 0  # Total observations stripped
+
+    if dump is None:
+        pars = []  # Don't iterate over parameters unless dumping
+    else:
+        log.info(
+            f"Remove data with {set_name}={element!r}"
+            + (" (DRY RUN)" if dry_run else "")
+        )
+        # Iterate over parameters with ≥1 dimensions indexed by `set_name`
+        pars = iter_parameters(set_name)
+
+    for par_name in pars:
+        if par_name not in par_list:  # pragma: no cover
+            log.warning(
+                f"  MESSAGEix parameter {par_name!r} missing in Scenario {scenario.url}"
+            )
+            continue
+
+        # Iterate over dimensions indexed by `set_name`
+        for dim, _ in filter(
+            lambda item: item[1] == set_name,
+            zip(scenario.idx_names(par_name), scenario.idx_sets(par_name)),
+        ):
+            # Check for contents of par_name that include `element`
+            par_data = scenario.par(par_name, filters={dim: element})
+            N = len(par_data)
+            total += N
+
+            if N == 0:
+                # No data; no need to do anything further
+                no_data.add(par_name)
+                continue
+            elif dump is not None:
+                dump[par_name] = pd.concat(
+                    [dump.get(par_name, pd.DataFrame()), par_data]
+                )
+
+            log.info(f"  {N} rows in {par_name!r}")
+
+            # Show some debug info
+            for col in filter(
+                lambda c: c != set_name and c in par_data.columns,
+                ("commodity", "level", "technology"),
+            ):
+                log.info(f"  with {col}={sorted(par_data[col].unique())}")
+
+            if dry_run:
+                continue
+
+            # Actually remove the data
+            scenario.remove_par(par_name, key=par_data)
+
+            # NB would prefer to do the following, but raises an exception:
+            # scenario.remove_par(par_name, key={set_name: [value]})
+
+    if not dry_run and dump is not None:
+        log.info(f"  {total} rows total")
+    if no_data:
+        log.debug(f"No data removed from {len(no_data)} other parameters")
+
+    if not dry_run:
+        log.info(f"Remove {element!r} from set {set_name!r}")
+        try:
+            scenario.remove_set(set_name, element)
+        except Exception as e:
+            if "does not have an element" in str(e):
+                log.info("  …not found")
+            else:  # pragma: no cover
+                raise
+
+    return total
diff --git a/nest/message_ix_models/util/_convert_units.py b/nest/message_ix_models/util/_convert_units.py
new file mode 100644
index 0000000..c35d5b2
--- /dev/null
+++ b/nest/message_ix_models/util/_convert_units.py
@@ -0,0 +1,76 @@
+from typing import Mapping, Optional, Tuple
+from warnings import catch_warnings, filterwarnings
+
+import pandas as pd
+from iam_units import registry
+
+
+def series_of_pint_quantity(*args, **kwargs) -> pd.Series:
+    """Suppress a spurious warning.
+
+    Creating a :class:`pandas.Series` with a list of :class:`pint.Quantity` triggers a
+    warning “The unit of the quantity is stripped when downcasting to ndarray,” even
+    though the entire object is being stored and the unit is **not** stripped. This
+    function suppresses this warning.
+    """
+    with catch_warnings():
+        filterwarnings(
+            "ignore",
+            message="The unit of the quantity is stripped when downcasting to ndarray",
+            module="pandas.core.dtypes.cast",
+        )
+
+        return pd.Series(*args, **kwargs)
+
+
+def convert_units(
+    s: pd.Series,
+    unit_info: Mapping[str, Tuple[float, str, Optional[str]]],
+    store="magnitude",
+) -> pd.Series:
+    """Convert units of `s`, for use with :meth:`~pandas.DataFrame.apply`.
+
+    ``s.name`` is used to retrieve a tuple of (`factor`, `input_unit`, `output_unit`)
+    from `unit_info`. The (:class:`float`) values of `s` are converted to
+    :class:`pint.Quantity` with the `input_unit` and factor; then cast to `output_unit`,
+    if provided.
+
+    Parameters
+    ----------
+    s : pandas.Series
+    unit_info : dict (str -> tuple)
+        Mapping from quantity name (matched to ``s.name``) to 3-tuples of (`factor`,
+        `input_unit`, `output_unit`). `output_unit` may be :obj:`None`. For example,
+        see :data:`.ikarus.UNITS`.
+    store : "magnitude" or "quantity"
+        If "magnitude", the values of the returned series are the magnitudes of the
+        results, with no output units. If "quantity", the values are scalar
+        :class:`~pint.Quantity` objects.
+
+    Returns
+    -------
+    pandas.Series
+        Same shape, index, and values as `s`, with output units.
+    """
+    if store not in "magnitude quantity":
+        raise ValueError(f"{store = }")
+
+    # Retrieve the information from `unit_info`
+    factor, unit_in, unit_out = unit_info[s.name]
+
+    # Default: `unit_out` is the same as `unit_in`
+    unit_out = unit_out or unit_in
+
+    # - Convert the values to a pint.Quantity(array) with the input units
+    # - Convert to output units
+    # - According to `store`, either extract just the magnitude, or store scalar
+    #   pint.Quantity objects.
+    # - Reassemble into a series with index matching `s`
+    result = registry.Quantity(factor * s.values, unit_in).to(unit_out)
+
+    return series_of_pint_quantity(
+        result.magnitude if store == "magnitude" else result.tolist(),
+        index=s.index,
+        dtype=object,
+        name=s.name,
+    )
diff --git a/nest/message_ix_models/util/_logging.py b/nest/message_ix_models/util/_logging.py
new file mode 100644
index 0000000..65d5d22
--- /dev/null
+++ b/nest/message_ix_models/util/_logging.py
@@ -0,0 +1,191 @@
+"""Logging utilities."""
+
+import logging
+import logging.config
+from contextlib import contextmanager
+from copy import deepcopy
+from time import process_time
+
+__all__ = [
+    "Formatter",
+    "make_formatter",
+    "setup",
+    "silence_log",
+]
+
+
+@contextmanager
+def silence_log():
+    """Context manager to temporarily silence log output.
+
+    Examples
+    --------
+    >>> with silence_log():
+    >>>     log.warning("This message is not recorded.")
+    """
+    # Restore the log level at the end of the context
+    with preserve_log_level():
+        # Set the level to a very high value
+        logging.getLogger(__name__.split(".")[0]).setLevel(100)
+        yield
+
+
+@contextmanager
+def preserve_log_level():
+    """Context manager to preserve the level of the ``message_ix_models`` logger."""
+    # Get the top-level logger for the package containing this file
+    main_log = logging.getLogger(__name__.split(".")[0])
+
+    try:
+        # Store the current level
+        level = main_log.getEffectiveLevel()
+        yield
+    finally:
+        # Restore the level
+        main_log.setLevel(level)
+
+
+class Formatter(logging.Formatter):
+    """Formatter for log records.
+
+    Parameters
+    ----------
+    colorama : module
+        If provided, :mod:`colorama` is used to colour log messages printed to stdout.
+    """
+
+    CYAN = ""
+    DIM = ""
+    RESET_ALL = ""
+
+    _short_name = None
+
+    def __init__(self, colorama):
+        super().__init__()
+        if colorama:
+            self.CYAN = colorama.Fore.CYAN
+            self.DIM = colorama.Style.DIM
+            self.RESET_ALL = colorama.Style.RESET_ALL
+
+    def format(self, record):
+        """Format `record`.
+
+        Records are formatted like::
+
+            model.transport.data.add_par_data  220 rows in 'input'
+            ...add_par_data:  further messages
+
+        …with the calling function name (e.g. 'add_par_data') coloured for legibility
+        on first occurrence, then dimmed when repeated.
+        """
+        # Remove the leading 'message_data.' from the module name
+        name_parts = record.name.split(".")
+        if name_parts[0] in ("message_ix_models", "message_data"):
+            short_name = ".".join(["—"] + name_parts[1:])
+        else:
+            short_name = record.name
+
+        if self._short_name != short_name:
+            self._short_name = short_name
+            prefix = f"{self.CYAN}{short_name}."
+        else:
+            prefix = f"{self.DIM}..."
+
+        return f"{prefix}{record.funcName}{self.RESET_ALL}  {record.getMessage()}"
+
+
+def make_formatter():
+    """Return a :class:`Formatter` instance for the ``message_ix_models`` logger.
+
+    See also
+    --------
+    setup
+    """
+    try:
+        # Initialize colorama
+        import colorama
+
+        colorama.init()
+    except ImportError:  # pragma: no cover
+        # Colorama not installed
+        colorama = None
+
+    return Formatter(colorama)
+
+
+_TIMES = []
+
+
+def mark_time(quiet=False):
+    """Record and log (if `quiet` is :obj:`True`) a time mark."""
+    _TIMES.append(process_time())
+    if not quiet and len(_TIMES) > 1:
+        logging.getLogger(__name__).info(
+            f" +{_TIMES[-1] - _TIMES[-2]:.1f} = {_TIMES[-1]:.1f} seconds"
+        )
+
+
+CONFIG = dict(
+    version=1,
+    disable_existing_loggers=False,
+    formatters=dict(simple={"()": "message_ix_models.util._logging.make_formatter"}),
+    handlers=dict(
+        console={
+            "class": "logging.StreamHandler",
+            "level": "NOTSET",
+            "formatter": "simple",
+            "stream": "ext://sys.stdout",
+        },
+        # commented: needs code in setup() to choose an appropriate file path
+        # file_handler={
+        #    "class": "logging.handlers.RotatingFileHandler",
+        #    "level": "DEBUG",
+        #    "formatter": "simple",
+        #    "backupCount": "100",
+        #    "delay": True,
+        # },
+    ),
+    loggers=dict(
+        message_ix_models=dict(
+            level="NOTSET",
+            # propagate=False,
+            # handlers=[],
+        ),
+        message_data=dict(
+            level="NOTSET",
+            # propagate=False,
+            # handlers=[],
+        ),
+    ),
+    root=dict(
+        handlers=[],
+    ),
+)
+
+
+def setup(
+    level="NOTSET",
+    console=True,
+    # file=False,
+):
+    """Initialize logging.
+
+    Parameters
+    ----------
+    level : str, optional
+        Log level for :mod:`message_ix_models` and :mod:`message_data`.
+    console : bool, optional
+        If :obj:`True`, print all messages to console using a :class:`Formatter`.
+    """
+    # Copy to avoid modifying with the operations below
+    config = deepcopy(CONFIG)
+
+    config["root"].setdefault("level", level)
+
+    if console:
+        config["root"]["handlers"].append("console")
+    # if file:
+    #     config["loggers"]["message_data"]["handlers"].append("file")
+
+    # Apply the configuration
+    logging.config.dictConfig(config)
diff --git a/nest/message_ix_models/util/cache.py b/nest/message_ix_models/util/cache.py
new file mode 100644
index 0000000..9a4e0f6
--- /dev/null
+++ b/nest/message_ix_models/util/cache.py
@@ -0,0 +1,95 @@
+"""Cache data for expensive operations.
+
+This module extends :class:`genno.caching.Encoder` to handle classes common in
+:mod:`message_ix_models`, so these can be used as arguments to cached functions and
+included in the computed cache key:
+
+- :class:`sdmx.model.IdentifiableArtefact`, including :class:`.Code`: hashed as
+  their string representation / ID.
+- :class:`ixmp.Platform`, :class:`xarray.Dataset`: ignored, with a warning logged.
+- :class:`ScenarioInfo`: only the :attr:`~ScenarioInfo.set` entries are hashed.
+
+"""
+import json
+import logging
+from dataclasses import asdict, is_dataclass
+from typing import Callable
+
+import genno.caching
+import ixmp
+import sdmx.model
+import xarray as xr
+
+from .context import Context
+from .scenarioinfo import ScenarioInfo
+
+log = logging.getLogger(__name__)
+
+
+#: Controls whether cached data is returned for functions decorated with
+#: :func:`.cached`. Set to :obj:`True` to force reload.
+SKIP_CACHE = False
+
+# Paths already logged, to decrease verbosity
+PATHS_SEEN = set()
+
+
+# Show genno how to hash function arguments seen in message_ix_models
+
+
+@genno.caching.Encoder.register
+def _sdmx_identifiable(o: sdmx.model.IdentifiableArtefact):
+    return str(o)
+
+
+@genno.caching.Encoder.register
+def _dataclass(o: object):
+    return asdict(o) if is_dataclass(o) else json.JSONEncoder().default(o)
+
+
+@genno.caching.Encoder.register
+def _si(o: ScenarioInfo):
+    return dict(o.set)
+
+
+genno.caching.Encoder.ignore(xr.Dataset, ixmp.Platform)
+
+
+def cached(func: Callable) -> Callable:
+    """Decorator to cache the return value of a function `func`.
+
+    On a first call, the data requested is returned and also cached under
+    :meth:`.Context.get_cache_path`. On subsequent calls, if the cache exists, it is
+    used instead of calling the (possibly slow) `func`.
+
+    When :data:`.SKIP_CACHE` is true, `func` is always called.
+
+    See also
+    --------
+    :doc:`genno:cache` in the :mod:`genno` documentation
+    """
+    # Determine and create the cache path
+    cache_path = Context.get_instance(-1).get_cache_path()
+    cache_path.mkdir(exist_ok=True, parents=True)
+
+    if cache_path not in PATHS_SEEN:
+        log.debug(f"{func.__name__}() will cache in {cache_path}")
+        PATHS_SEEN.add(cache_path)
+
+    # Use the genno internals to wrap the function.
+    cached_load = genno.caching.decorate(
+        func, cache_path=cache_path, cache_skip=SKIP_CACHE
+    )
+
+    if cached_load.__doc__ is not None:
+        # Determine the indent
+        line = cached_load.__doc__.split("\n")[-1]
+        indent = len(line) - len(line.lstrip(" "))
+
+        # Add a note that the results are cached
+        cached_load.__doc__ += (
+            f"\n\n{' ' * indent}Data returned by this function is cached using "
+            ":func:`.cached`; see also :data:`.SKIP_CACHE`."
+        )
+
+    return cached_load
diff --git a/nest/message_ix_models/util/click.py b/nest/message_ix_models/util/click.py
new file mode 100644
index 0000000..3adaa99
--- /dev/null
+++ b/nest/message_ix_models/util/click.py
@@ -0,0 +1,193 @@
+"""Command-line utilities.
+
+These are used for building CLIs using :mod:`click`.
+"""
+import logging
+from datetime import datetime
+from typing import Optional, Union
+
+import click
+from click import Argument, Choice, Option
+
+from message_ix_models import Context
+from message_ix_models.model.structure import codelists
+
+log = logging.getLogger(__name__)
+
+
+def common_params(param_names: str):
+    """Decorate a click.command with common parameters `param_names`.
+
+    `param_names` must be a space-separated string of names appearing in :data:`PARAMS`,
+    e.g. ``"ssp force output_model"``. The decorated function receives keyword
+    arguments with these names::
+
+        @click.command()
+        @common_params("ssp force output_model")
+        def mycmd(ssp, force, output_model)
+            # ...
+    """
+
+    # Simplified from click.decorators._param_memo
+    def decorator(f):
+        if not hasattr(f, "__click_params__"):
+            f.__click_params__ = []
+        f.__click_params__.extend(
+            PARAMS[name] for name in reversed(param_names.split())
+        )
+        return f
+
+    return decorator
+
+
+def default_path_cb(*default_parts):
+    """Return a callback function for click.Option handling.
+
+    If no option value is given, the callback uses :meth:`Context.get_local_path` and
+    `default_parts` to provide a path that is relative to local data directory, e.g.
+    the current working directory (see :doc:`/data`).
+    """
+
+    def _callback(context, param, value):
+        value = value or context.obj.get_local_path(*default_parts)
+        setattr(context.obj, param.name, value)
+        return value
+
+    return _callback
+
+
+def format_sys_argv() -> str:
+    """Format :data:`sys.argv` in a readable manner."""
+    import sys
+
+    lines = ["Invoked:"]
+    indent = ""
+    for item in sys.argv:
+        lines.append(f"{indent}{item} \\")
+        indent = "  "
+
+    return "\n".join(lines)[:-2]
+
+
+def store_context(context: Union[click.Context, Context], param, value):
+    """Callback that simply stores a value on the :class:`Context` object.
+
+    Use this for parameters that are not used directly in a @click.command() function,
+    but need to be carried by the Context for later use.
+    """
+    setattr(
+        context.obj if isinstance(context, click.Context) else context,
+        param.name,
+        value,
+    )
+    return value
+
+
+def unique_id() -> str:
+    """Return a unique ID for a CLI invocation.
+
+    The return value resembles "mix-models-debug-3332d415ef65bf2a-2023-02-02T162931" and
+    contains:
+
+    - The CLI name and (sub)commands.
+    - A hash of all the CLI parameters (options and arguments).
+    - The current date and time, in ISO format with Windows-incompatible ":" removed.
+
+    """
+    click_context = click.get_current_context()
+
+    # Collapse CLI (sub)commands and their arguments to a hashable data structure
+    # This also includes CLI options *not* given
+    c: Optional[click.Context] = click_context
+    data = []
+    while c is not None:
+        data.append((c.command.name, tuple(sorted(c.params.items()))))
+        c = c.parent
+
+    # Assemble parts
+    return "-".join(
+        [
+            click_context.command_path.replace(" ", "-"),
+            f"{hash(tuple(reversed(data))):x}",
+            datetime.now().isoformat(timespec="seconds").replace(":", ""),
+        ]
+    )
+
+
+#: Common command-line parameters (arguments and options). See :func:`common_params`.
+PARAMS = {
+    "dest": Option(
+        ["--dest"],
+        callback=store_context,
+        help="Destination URL for created scenario(s).",
+    ),
+    "dry_run": Option(
+        ["--dry-run"],
+        is_flag=True,
+        callback=store_context,
+        help="Only show what would be done.",
+    ),
+    "force": Option(
+        ["--force"],
+        is_flag=True,
+        callback=store_context,
+        help="Overwrite or modify existing model/scenario.",
+    ),
+    "nodes": Option(
+        ["--nodes"],
+        help="Code list to use for 'node' dimension.",
+        type=Choice(codelists("node")),
+    ),
+    "output_model": Option(
+        ["--output-model"], help="Model name under which scenarios should be generated."
+    ),
+    "policy_path": Option(
+        ["--policy-path"],
+        callback=default_path_cb("scenario_generation", "policies"),
+        help="Path to policy scripts.",
+    ),
+    "platform_dest": Option(["--platform-dest"], help="Name of destination Platform."),
+    "quiet": Option(
+        ["--quiet"],
+        is_flag=True,
+        help="Show less or no output.",
+    ),
+    "regions": Option(
+        ["--regions"],
+        help="Code list to use for 'node' dimension.",
+        type=Choice(codelists("node")),
+    ),
+    "rep_out_path": Option(
+        ["--rep-out-path"],
+        callback=default_path_cb("reporting_output"),
+        help="Path for reporting output.",
+    ),
+    "rep_template": Option(
+        ["--rep-template"],
+        callback=default_path_cb(
+            "message_data", "tools", "post_processing", "MESSAGEix_WorkDB_Template.xlsx"
+        ),
+        help="Path incl. filename and extension to reporting template.",
+    ),
+    "run_reporting_only": Option(
+        ["--run-reporting-only"],
+        is_flag=True,
+        callback=store_context,
+        help="Run only reporting.",
+    ),
+    "ssp": Argument(
+        ["ssp"], callback=store_context, type=Choice(["SSP1", "SSP2", "SSP3"])
+    ),
+    "verbose": Option(
+        # NB cannot use store_callback here; this is processed in the top-level CLI
+        #    before the message_ix_models.Context() object is set up
+        ["--verbose", "-v"],
+        is_flag=True,
+        help="Print DEBUG-level log messages.",
+    ),
+    "years": Option(
+        ["--years"],
+        help="Code list to use for the 'year' dimension.",
+        type=Choice(codelists("year")),
+    ),
+}
diff --git a/nest/message_ix_models/util/common.py b/nest/message_ix_models/util/common.py
new file mode 100644
index 0000000..26515a1
--- /dev/null
+++ b/nest/message_ix_models/util/common.py
@@ -0,0 +1,271 @@
+import logging
+from abc import abstractmethod
+from pathlib import Path
+from typing import Any, Dict, Mapping, Optional, Sequence, Tuple, cast
+
+import pandas as pd
+from genno.computations import concat
+from message_ix.reporting import Quantity
+
+log = logging.getLogger(__name__)
+
+try:
+    import message_data
+except ImportError:
+    log.warning("message_data is not installed or cannot be imported")
+    MESSAGE_DATA_PATH: Optional[Path] = None
+else:  # pragma: no cover  (needs message_data)
+    # Root directory of the message_data repository.
+    MESSAGE_DATA_PATH = Path(message_data.__file__).parents[1]
+
+# Directory containing message_ix_models.__init__
+MESSAGE_MODELS_PATH = Path(__file__).parents[1]
+
+#: Package data already loaded with :func:`load_package_data`.
+PACKAGE_DATA: Dict[str, Any] = dict()
+
+#: Data already loaded with :func:`load_private_data`.
+PRIVATE_DATA: Dict[str, Any] = dict()
+
+
+class Adapter:
+    """Adapt `data`.
+
+    Adapter is an abstract base class for tools that adapt data in any way, e.g.
+    between different code lists for certain dimensions. An instance of an Adapter can
+    be called with any of the following as `data`:
+
+    - :class:`genno.Quantity`,
+    - :class:`pandas.DataFrame`, or
+    - :class:`dict` mapping :class:`str` parameter names to values (either of the above
+      types).
+
+    …and will return data of the same type.
+
+    Subclasses can implement different adapter logic by overriding the abstract
+    :meth:`adapt` method.
+    """
+
+    def __call__(self, data):
+        if isinstance(data, Quantity):
+            return self.adapt(data)
+        elif isinstance(data, pd.DataFrame):
+            # Convert to Quantity
+            idx_cols = list(filter(lambda c: c not in ("value", "unit"), data.columns))
+            qty = Quantity.from_series(data.set_index(idx_cols)["value"])
+
+            # Store units
+            if "unit" in data.columns:
+                units = data["unit"].unique()
+                assert 1 == len(units), f"Non-unique units {units}"
+                unit = units[0]
+            else:
+                unit = ""  # dimensionless
+
+            # Adapt, convert back to pd.DataFrame, return
+            return self.adapt(qty).to_dataframe().assign(unit=unit).reset_index()
+        elif isinstance(data, Mapping):
+            return {par: self(value) for par, value in data.items()}
+        else:
+            raise TypeError(type(data))
+
+    @abstractmethod
+    def adapt(self, qty: Quantity) -> Quantity:
+        """Adapt data."""
+
+
+class MappingAdapter(Adapter):
+    """Adapt data using mappings for 1 or more dimension(s).
+
+    Parameters
+    ----------
+    maps : dict of sequence of (str, str)
+        Keys are names of dimensions. Values are sequences of 2-tuples; each tuple
+        consists of an original label and a target label.
+
+    Examples
+    --------
+    >>> a = MappingAdapter({"foo": [("a", "x"), ("a", "y"), ("b", "z")]})
+    >>> df = pd.DataFrame(
+    ...     [["a", "m", 1], ["b", "n", 2]], columns=["foo", "bar", "value"]
+    ... )
+    >>> a(df)
+      foo  bar  value
+    0   x    m      1
+    1   y    m      1
+    2   z    n      2
+    """
+
+    maps: Mapping
+
+    def __init__(self, maps: Mapping[str, Sequence[Tuple[str, str]]]):
+        self.maps = maps
+
+    def adapt(self, qty: Quantity) -> Quantity:
+        result = qty
+
+        for dim, labels in self.maps.items():
+            if dim not in qty.dims:  # type: ignore [attr-defined]
+                continue
+            result = concat(
+                *[
+                    qty.sel(
+                        {dim: label[0]}, drop=True
+                    ).expand_dims(  # type: ignore [attr-defined]
+                        {dim: [label[1]]}
+                    )
+                    for label in labels
+                ]
+            )
+
+        return result
+
+
+def _load(
+    var: Dict, base_path: Path, *parts: str, default_suffix: Optional[str] = None
+) -> Any:
+    """Helper for :func:`.load_package_data` and :func:`.load_private_data`."""
+    key = " ".join(parts)
+    if key in var:
+        log.debug(f"{repr(key)} already loaded; skip")
+        return var[key]
+
+    path = _make_path(base_path, *parts, default_suffix=default_suffix)
+
+    if path.suffix == ".yaml":
+        import yaml
+
+        with open(path, encoding="utf-8") as f:
+            var[key] = yaml.safe_load(f)
+    else:
+        raise ValueError(path.suffix)
+
+    return var[key]
+
+
+def _make_path(
+    base_path: Path, *parts: str, default_suffix: Optional[str] = None
+) -> Path:
+    p = base_path.joinpath(*parts)
+    return p.with_suffix(p.suffix or default_suffix) if default_suffix else p
+
+
+def load_package_data(*parts: str, suffix: Optional[str] = ".yaml") -> Any:
+    """Load a :mod:`message_ix_models` package data file and return its contents.
+
+    Data is re-used if already loaded.
+
+    Example
+    -------
+
+    The single call:
+
+    >>> info = load_package_data("node", "R11")
+
+    1. loads the metadata file :file:`data/node/R11.yaml`, parsing its contents,
+    2. stores those values at ``PACKAGE_DATA["node R11"]`` for use by other code, and
+    3. returns the loaded values.
+
+    Parameters
+    ----------
+    parts : iterable of str
+        Used to construct a path under :file:`message_ix_models/data/`.
+    suffix : str, optional
+        File name suffix, including, the ".", e.g. :file:`.yaml`.
+
+    Returns
+    -------
+    dict
+        Configuration values that were loaded.
+    """
+    return _load(
+        PACKAGE_DATA,
+        MESSAGE_MODELS_PATH / "data",
+        *parts,
+        default_suffix=suffix,
+    )
+
+
+def load_private_data(*parts: str) -> Mapping:  # pragma: no cover (needs message_data)
+    """Load a private data file from :mod:`message_data` and return its contents.
+
+    Analogous to :mod:`load_package_data`, but for non-public data.
+
+    Parameters
+    ----------
+    parts : iterable of str
+        Used to construct a path under :file:`data/` in the :mod:`message_data`
+        repository.
+
+    Returns
+    -------
+    dict
+        Configuration values that were loaded.
+
+    Raises
+    ------
+    RuntimeError
+        if :mod:`message_data` is not installed.
+    """
+    if MESSAGE_DATA_PATH is None:
+        raise RuntimeError("message_data is not installed")
+
+    return _load(PRIVATE_DATA, MESSAGE_DATA_PATH / "data", *parts)
+
+
+def local_data_path(*parts) -> Path:
+    """Construct a path for local data.
+
+    The setting ``message local data`` in the user's :ref:`ixmp configuration file
+    <ixmp:configuration>` is used as a base path. If this is not configured, the
+    current working directory is used.
+
+    Parameters
+    ----------
+    parts : sequence of str or Path
+        Joined to the base path using :meth:`.Path.joinpath`.
+
+    See also
+    --------
+    :ref:`Choose locations for data <local-data>`
+    """
+    import ixmp
+
+    # The default value, Path.cwd(), is set in context.py
+    return _make_path(Path(ixmp.config.get("message local data")), *parts)
+
+
+def package_data_path(*parts) -> Path:
+    """Construct a path to a file under :file:`message_ix_models/data/`.
+
+    Use this function to access data packaged and installed with
+    :mod:`message_ix_models`.
+
+    Parameters
+    ----------
+    parts : sequence of str or Path
+        Joined to the base path using :meth:`.Path.joinpath`.
+
+    See also
+    --------
+    :ref:`Choose locations for data <package-data>`
+    """
+    return _make_path(MESSAGE_MODELS_PATH / "data", *parts)
+
+
+def private_data_path(*parts) -> Path:  # pragma: no cover (needs message_data)
+    """Construct a path to a file under :file:`data/` in :mod:`message_data`.
+
+    Use this function to access non-public (e.g. embargoed or proprietary) data stored
+    in the :mod:`message_data` repository.
+
+    Parameters
+    ----------
+    parts : sequence of str or Path
+        Joined to the base path using :meth:`.Path.joinpath`.
+
+    See also
+    --------
+    :ref:`Choose locations for data <private-data>`
+    """
+    return _make_path(cast(Path, MESSAGE_DATA_PATH) / "data", *parts)
diff --git a/nest/message_ix_models/util/config.py b/nest/message_ix_models/util/config.py
new file mode 100644
index 0000000..3a2e150
--- /dev/null
+++ b/nest/message_ix_models/util/config.py
@@ -0,0 +1,156 @@
+import logging
+import os
+from dataclasses import dataclass, field, fields, is_dataclass, replace
+from pathlib import Path
+from typing import Any, Hashable, Mapping, MutableMapping, Optional, Sequence, Set
+
+import ixmp
+
+log = logging.getLogger(__name__)
+
+ixmp.config.register("message local data", Path, Path.cwd())
+
+
+def _local_data_factory():
+    """Default values for :attr:`.Config.local_data."""
+    return (
+        Path(
+            os.environ.get("MESSAGE_LOCAL_DATA", None)
+            or ixmp.config.get("message local data")
+        )
+        .expanduser()
+        .resolve()
+    )
+
+
+class ConfigHelper:
+    """Mix-in for :class:`dataclass`-based configuration classes.
+
+    This provides 3 methods—:meth:`read_file`, :meth:`replace`, and :meth:`from_dict`—
+    that help to use :class:`dataclass` classes for handling :mod:`message_ix_models`
+    configuration.
+
+    All 3 methods take advantage of name manipulations: the characters "-" and " " are
+    replaced with underscores ("_"). This allows to write the names of attributes in
+    legible ways—e.g. "attribute name" or “attribute-name” instead of "attribute_name"—
+    in configuration files and/or code.
+    """
+
+    @classmethod
+    def _fields(cls) -> Set[str]:
+        """Names of fields in `cls`."""
+        return set(map(lambda f: f.name, fields(cls))) | set(dir(cls))
+
+    @classmethod
+    def _canonical_name(cls, name: Hashable) -> Optional[str]:
+        """Canonicalize a name into a valid Python attribute name."""
+        _name = str(name).replace(" ", "_").replace("-", "_")
+        return _name if _name in cls._fields() else None
+
+    @classmethod
+    def _munge_dict(cls, data: Mapping[Hashable, Any], fail: str, kind: str):
+        for key, value in data.items():
+            name = cls._canonical_name(key)
+
+            if name:
+                yield name, value
+            else:
+                msg = f"{cls.__name__} has no attribute for {kind} {key!r}"
+                if fail == "raise":
+                    raise ValueError(msg)
+                else:
+                    log.info(f"{msg}; ignored")
+
+    def read_file(self, path: Path, fail="raise") -> None:
+        """Update configuration from file.
+
+        Parameters
+        ----------
+        path
+            to a :file:`.yaml` file containing a top-level mapping.
+        fail : str
+            if "raise" (the default), any names in `path` which do not match attributes
+            of the dataclass raise a ValueError. Ottherwise, a message is logged.
+        """
+        if path.suffix == ".yaml":
+            import yaml
+
+            with open(path, encoding="utf-8") as f:
+                data = yaml.safe_load(f)
+        elif path.suffix == ".json":
+            import json
+
+            with open(path) as f:
+                data = json.load(f)
+        else:
+            raise NotImplementedError(f"Read from {path.suffix}")
+
+        for key, value in self._munge_dict(data, fail, "file section"):
+            existing = getattr(self, key, None)
+            if is_dataclass(existing):
+                # Attribute value is also a dataclass; update it recursively
+                if isinstance(existing, ConfigHelper):
+                    # Use name manipulation on the attribute value also
+                    value = existing.replace(**value)
+                else:
+                    value = replace(existing, **value)
+            setattr(self, key, value)
+
+    def replace(self, **kwargs):
+        """Like :func:`dataclasses.replace` with name manipulation."""
+        return replace(
+            self,
+            **{k: v for k, v in self._munge_dict(kwargs, "raise", "keyword argument")},
+        )
+
+    @classmethod
+    def from_dict(cls, data: Mapping):
+        """Construct an instance from `data` with name manipulation."""
+        return cls(**{k: v for k, v in cls._munge_dict(data, "raise", "mapping key")})
+
+
+@dataclass
+class Config:
+    """Top-level configuration for :mod:`message_ix_models` and :mod:`message_data`."""
+
+    #: Base path for :ref:`system-specific data <local-data>`, i.e. as given by the
+    #: :program:`--local-data` CLI option or `message local data` key in the ixmp
+    #: configuration file.
+    local_data: Path = field(default_factory=_local_data_factory)
+
+    #: Keyword arguments—especially `name`—for the :class:`ixmp.Platform` constructor,
+    #: from the :program:`--platform` or :program:`--url` CLI option.
+    platform_info: MutableMapping[str, str] = field(default_factory=dict)
+
+    #: Keyword arguments—`model`, `scenario`, and optionally `version`—for the
+    #: :class:`ixmp.Scenario` constructor, as given by the :program:`--model`/
+    #: :program:`--scenario` or :program:`--url` CLI options.
+    scenario_info: MutableMapping[str, str] = field(default_factory=dict)
+
+    #: Like :attr:`platform_info`, used by e.g. :meth:`clone_to_dest`.
+    dest_platform: MutableMapping[str, str] = field(default_factory=dict)
+
+    #: Like :attr:`scenario_info`, used by e.g. :meth:`clone_to_dest`.
+    dest_scenario: MutableMapping[str, str] = field(default_factory=dict)
+
+    #: A scenario URL, e.g. as given by the :program:`--url` CLI option.
+    url: Optional[str] = None
+
+    #: Like :attr:`url`, used by e.g. :meth:`clone_to_dest`.
+    dest: Optional[str] = None
+
+    #: Base path for cached data, e.g. as given by the :program:`--cache-path` CLI
+    #: option. Default: :file:`{local_data}/cache/`.
+    cache_path: Optional[str] = None
+
+    #: Paths of files containing debug outputs. See :meth:`Context.write_debug_archive`.
+    debug_paths: Sequence[str] = field(default_factory=list)
+
+    #: Whether an operation should be carried out, or only previewed. Different modules
+    #: will respect :attr:`dry_run` in distinct ways, if at all, and **should** document
+    #: behaviour.
+    dry_run: bool = False
+
+    #: Flag for causing verbose output to logs or stdout. Different modules will respect
+    #: :attr:`verbose` in distinct ways.
+    verbose: bool = False
diff --git a/nest/message_ix_models/util/context.py b/nest/message_ix_models/util/context.py
new file mode 100644
index 0000000..cdda226
--- /dev/null
+++ b/nest/message_ix_models/util/context.py
@@ -0,0 +1,394 @@
+"""Context and settings for :mod:`message_ix_models` code."""
+import logging
+from copy import deepcopy
+from dataclasses import fields
+from pathlib import Path
+from typing import List
+
+import ixmp
+import message_ix
+from click import BadOptionUsage
+
+from .config import Config
+
+log = logging.getLogger(__name__)
+
+#: List of Context instances, from first created to last.
+_CONTEXTS: List["Context"] = []
+
+
+# Configuration keys which can be accessed directly on Context.
+_ALIAS = dict()
+_ALIAS.update({f.name: "core" for f in fields(Config)})
+
+
+def _dealiased(base: str, data: dict) -> dict:
+    """Separate values from `data` which belong on `base` according to `_ALIAS`."""
+    result = {}
+    for name, path in filter(lambda ap: ap[1] == base, _ALIAS.items()):
+        try:
+            result[name] = data.pop(name)
+        except KeyError:
+            pass
+
+    if len(result):
+        log.warning(
+            f"Create a Config instance instead of passing {list(result.keys())} to"
+            " Context()"
+        )
+
+    return result
+
+
+class Context(dict):
+    """Context and settings for :mod:`message_ix_models` code."""
+
+    # NB the docs contain a table of settings
+
+    @classmethod
+    def get_instance(cls, index=0) -> "Context":
+        """Return a Context instance; by default, the first created.
+
+        Parameters
+        ----------
+        index : int, optional
+            Index of the Context instance to return, e.g. ``-1`` for the most recently
+            created.
+        """
+        return _CONTEXTS[index]
+
+    @classmethod
+    def only(cls) -> "Context":
+        """Return the only :class:`.Context` instance.
+
+        Raises
+        ------
+        IndexError
+            If there is more than one instance.
+        """
+        if len(_CONTEXTS) > 1:
+            raise IndexError(f"ambiguous: {len(_CONTEXTS)} Context instances")
+        return _CONTEXTS[0]
+
+    def __init__(self, *args, **kwargs):
+        from message_ix_models.model import Config as ModelConfig
+
+        if len(_CONTEXTS) == 0:
+            log.info("Create root Context")
+
+        # Handle keyword arguments going to known config dataclasses
+        kwargs["core"] = Config(**_dealiased("core", kwargs))
+        kwargs["model"] = ModelConfig(**_dealiased("model", kwargs))
+
+        # Store any keyword arguments
+        super().__init__(*args, **kwargs)
+
+        # Store a reference for get_instance()
+        _CONTEXTS.append(self)
+
+    def _dealias(self, name):
+        base = _ALIAS[name]
+
+        # Warn about direct reference to aliased attributes
+        if base not in {"core", "model"}:  # pragma: no cover
+            log.warnings(f"Use Context.{base}.{name} instead of Context.{name}")
+
+        return getattr(self, base), name
+
+    # Item access
+    def __getitem__(self, name):
+        try:
+            return getattr(*self._dealias(name))
+        except KeyError:
+            return super().__getitem__(name)
+
+    def __setitem__(self, name, value):
+        try:
+            return setattr(*self._dealias(name), value)
+        except KeyError:
+            super().__setitem__(name, value)
+
+    def update(self, arg=None, **kwargs):
+        # Force update() to use the __setitem__ above
+        for k, v in dict(*filter(None, [arg]), **kwargs).items():
+            self.__setitem__(k, v)
+
+    # Attribute access
+    def __setattr__(self, name, value):
+        self[name] = value
+
+    def __getattr__(self, name):
+        try:
+            return self[name]
+        except KeyError:
+            raise AttributeError(name) from None
+
+    def __deepcopy__(self, memo):
+        mp = self.pop("_mp", None)
+
+        result = deepcopy(super(), memo)
+
+        if mp is not None:
+            self._mp = mp
+
+        _CONTEXTS.append(result)
+
+        return result
+
+    def __repr__(self):
+        return f"<{self.__class__.__name__} object at {id(self)} with {len(self)} keys>"
+
+    def delete(self):
+        """Hide the current Context from future :meth:`.get_instance` calls."""
+        # Index of the *last* matching instance
+        index = len(_CONTEXTS) - 1 - list(reversed(_CONTEXTS)).index(self)
+
+        if index > 0:
+            _CONTEXTS.pop(index)
+        else:  # pragma: no cover
+            # The `session_context` fixture means this won't occur during tests
+            log.warning("Won't delete the only Context instance")
+
+        self.close_db()
+
+    def write_debug_archive(self) -> None:
+        """Write an archive containing the files listed in :attr:`.debug_paths`.
+
+        The archive file name is constructed using :func:`.unique_id` and appears in a
+        :file:`debug` subdirectory under the :ref:`local data path <local-data>`.
+
+        The archive also contains a file :file:`command.txt` that gives the full
+        command-line used to invoke :program:`mix-models`.
+        """
+        from zipfile import ZIP_DEFLATED, ZipFile
+
+        from .click import format_sys_argv, unique_id
+
+        # Output file
+        target = self.core.local_data.joinpath("debug", f"{unique_id()}.zip")
+        log.info(f"Write to: {target}")
+
+        target.parent.mkdir(parents=True, exist_ok=True)
+
+        with ZipFile(target, mode="w", compression=ZIP_DEFLATED) as zf:
+            # Write a file that contains the CLI invocation
+            zf.writestr("command.txt", format_sys_argv())
+
+            # Write the identified files
+            for dp in self.core.debug_paths:
+                if not dp.exists():
+                    log.info(f"Not found: {dp}")
+                    continue
+
+                zf.write(dp, arcname=dp.relative_to(self.core.local_data))
+                # log.info(debug_path)
+
+    def clone_to_dest(self, create=True) -> message_ix.Scenario:
+        """Return a scenario based on the ``--dest`` command-line option.
+
+        Parameters
+        ----------
+        create : bool, optional
+            If :obj:`True` (the default) and the base scenario does not exist, a bare
+            RES scenario is created. Otherwise, an exception is raised.
+
+        Returns
+        -------
+        Scenario
+            To prevent the scenario from being garbage collected, keep a reference to
+            its Platform:
+
+            .. code-block: python
+
+               s = context.clone_to_dest()
+               mp = s.platform
+
+        See also
+        --------
+        create_res
+        """
+        cfg = self.core
+        if not cfg.dest_scenario:
+            # No information on the destination; try to parse a URL, storing the keys
+            # dest_platform and dest_scenario.
+            self.handle_cli_args(
+                url=cfg.dest, _store_as=("dest_platform", "dest_scenario")
+            )
+
+        try:
+            # Get the base scenario, e.g. from the --url CLI argument
+            scenario_base = self.get_scenario()
+
+            # By default, clone to the same platform
+            mp_dest = scenario_base.platform
+
+            try:
+                if cfg.dest_platform["name"] != mp_dest.name:
+                    # Different platform
+                    # Not tested; current test fixtures make it difficult to create
+                    # *two* temporary platforms simultaneously
+                    mp_dest = ixmp.Platform(**cfg.dest_platform)  # pragma: no cover
+            except KeyError:
+                pass
+        except Exception as e:
+            log.info("Base scenario not given or found")
+            log.debug(f"{type(e).__name__}: {e}")
+
+            if not create:
+                log.error("and create=False")
+                raise
+
+            # Create a bare RES to be the base scenario
+
+            from message_ix_models.model.bare import create_res
+
+            # Create on the destination platform
+            ctx = deepcopy(self)
+            ctx.core.platform_info.update(cfg.dest_platform)
+            scenario_base = create_res(ctx)
+
+            # Clone to the same platform
+            mp_dest = scenario_base.platform
+
+        # Clone
+        log.info(f"Clone to {repr(cfg.dest_scenario)}")
+        return scenario_base.clone(platform=mp_dest, **cfg.dest_scenario)
+
+    def close_db(self):
+        try:
+            mp = self.pop("_mp")
+            mp.close_db()
+        except KeyError:
+            pass
+
+    def get_cache_path(self, *parts) -> Path:
+        """Return a path to a local cache file, i.e. within :attr:`.Config.cache_path`.
+
+        The directory containing the resulting path is created if it does not already
+        exist.
+        """
+        # Construct relative to local_data if cache_path is not defined
+        base = self.core.cache_path or self.core.local_data.joinpath("cache")
+
+        result = base.joinpath(*parts)
+
+        # Ensure the directory exists
+        result.parent.mkdir(parents=True, exist_ok=True)
+
+        return result
+
+    def get_local_path(self, *parts: str, suffix=None) -> Path:
+        """Return a path under :attr:`.Config.local_data`.
+
+        Parameters
+        ==========
+        parts :
+            Path fragments, e.g. directories, passed to :meth:`Path.joinpath`.
+        suffix :
+            File name suffix including a ".", e.g. ".csv", passed to
+            :meth:`Path.with_suffix`.
+        """
+        result = self.core.local_data.joinpath(*parts)
+        return result.with_suffix(suffix) if suffix else result
+
+    def get_platform(self, reload=False) -> ixmp.Platform:
+        """Return a :class:`ixmp.Platform` from :attr:`platform_info`.
+
+        When used through the CLI, :attr:`platform_info` is a 'base' platform as
+        indicated by the --url or --platform  options.
+
+        If a Platform has previously been instantiated with :meth:`get_platform`, the
+        same object is returned unless `reload=True`.
+        """
+        if not reload:
+            # Return an existing Platform, if any
+            try:
+                return self["_mp"]
+            except KeyError:
+                pass
+
+        # Close any existing Platform, e.g. to reload it
+        try:
+            self["_mp"].close_db()
+            del self["_mp"]
+        except KeyError:
+            pass
+
+        # Create a Platform
+        self["_mp"] = ixmp.Platform(**self.core.platform_info)
+        return self["_mp"]
+
+    def get_scenario(self) -> message_ix.Scenario:
+        """Return a :class:`message_ix.Scenario` from :attr:`scenario_info`.
+
+        When used through the CLI, :attr:`scenario_info` is a ‘base’ scenario for an
+        operation, indicated by the ``--url`` or ``--platform/--model/--scenario``
+        options.
+        """
+        return message_ix.Scenario(self.get_platform(), **self.core.scenario_info)
+
+    def set_scenario(self, scenario: message_ix.Scenario) -> None:
+        """Update :attr:`scenario_info` to match an existing `scenario`."""
+        self.core.scenario_info.update(
+            model=scenario.model, scenario=scenario.scenario, version=scenario.version
+        )
+
+    def handle_cli_args(
+        self,
+        url=None,
+        platform=None,
+        model_name=None,
+        scenario_name=None,
+        version=None,
+        local_data=None,
+        verbose=False,
+        _store_as=("platform_info", "scenario_info"),
+    ):
+        """Handle command-line arguments.
+
+        May update the :attr:`data_path`, :attr:`platform_info`, :attr:`scenario_info`,
+        and/or :attr:`url` settings.
+        """
+        self.core.verbose = verbose
+
+        # Store the path to command-specific data and metadata
+        if local_data:
+            self.core.local_data = local_data
+
+        # References to the Context settings to be updated
+        platform_info = getattr(self.core, _store_as[0])
+        scenario_info = getattr(self.core, _store_as[1])
+
+        # Store information for the target Platform
+        if url:
+            if platform or model_name or scenario_name or version:
+                raise BadOptionUsage(
+                    "--platform --model --scenario and/or --version",
+                    " redundant with --url",
+                )
+
+            self.core.url = url
+            urlinfo = ixmp.utils.parse_url(url)
+            platform_info.update(urlinfo[0])
+            scenario_info.update(urlinfo[1])
+        elif platform:
+            platform_info["name"] = platform
+
+        # Store information about the target Scenario
+        if model_name:
+            scenario_info["model"] = model_name
+        if scenario_name:
+            scenario_info["scenario"] = scenario_name
+        if version:
+            scenario_info["version"] = version
+
+    def use_defaults(self, settings):
+        """Update from `settings`."""
+        for setting, info in settings.items():
+            if setting not in self:
+                log.info(f"Use default {setting}={info[0]}")
+
+            value = self.setdefault(setting, info[0])
+
+            if value not in info:
+                raise ValueError(f"{setting} must be in {info}; got {value}")
diff --git a/nest/message_ix_models/util/importlib.py b/nest/message_ix_models/util/importlib.py
new file mode 100644
index 0000000..ef93dd7
--- /dev/null
+++ b/nest/message_ix_models/util/importlib.py
@@ -0,0 +1,43 @@
+"""Load model and project code from :mod:`message_data`."""
+
+import re
+from importlib import util
+from importlib.abc import MetaPathFinder
+from importlib.machinery import ModuleSpec, SourceFileLoader
+
+
+class MessageDataFinder(MetaPathFinder):
+    """Load model and project code from :mod:`message_data`."""
+
+    # Expression for supported module names
+    expr = re.compile(r"message_ix_models\.(?P<name>(model|project)\..*)")
+
+    # NB coverage is excluded, because the message-ix-models test suite does not
+    #    install/use message-data.
+    @classmethod
+    def find_spec(cls, name, path, target=None):  # pragma: no cover
+        match = cls.expr.match(name)
+        try:
+            # Construct the name for the actual module to load
+            new_name = f"message_data.{match.group('name')}"
+        except AttributeError:
+            # `match` was None; unsupported. Let the importlib defaults take over.
+            return None
+
+        # Get an import spec for the message_data submodule
+        spec = util.find_spec(new_name)
+        if not spec:
+            return None
+
+        # Create a new spec that loads message_data.model.foo as if it were
+        # message_ix_models.model.foo
+        new_spec = ModuleSpec(
+            name=name,
+            # Create a new loader that loads from the actual file with the desired name
+            loader=SourceFileLoader(fullname=name, path=spec.origin),
+            origin=spec.origin,
+        )
+        # These can't be passed through the constructor
+        new_spec.submodule_search_locations = spec.submodule_search_locations
+
+        return new_spec
diff --git a/nest/message_ix_models/util/node.py b/nest/message_ix_models/util/node.py
new file mode 100644
index 0000000..0d406e2
--- /dev/null
+++ b/nest/message_ix_models/util/node.py
@@ -0,0 +1,133 @@
+"""Utilities for nodes."""
+import logging
+from typing import List, Sequence, Union
+
+from message_ix import Scenario
+from sdmx.model import Code
+
+from .common import Adapter, MappingAdapter  # noqa: F401
+
+log = logging.getLogger(__name__)
+
+#: Names of dimensions indexed by 'node'.
+#:
+#: .. todo:: to be robust to changes in :mod:`message_ix`, read these names from that
+#:    package.
+NODE_DIMS = [
+    "n",
+    "node",
+    "node_loc",
+    "node_origin",
+    "node_dest",
+    "node_rel",
+    "node_share",
+]
+
+#: Mapping from R11 to R12 node IDs.
+R11_R12 = (
+    ("R11_AFR", "R12_AFR"),
+    ("R11_CPA", "R12_CHN"),
+    ("R11_EEU", "R12_EEU"),
+    ("R11_FSU", "R12_FSU"),
+    ("R11_LAM", "R12_LAM"),
+    ("R11_MEA", "R12_MEA"),
+    ("R11_NAM", "R12_NAM"),
+    ("R11_PAO", "R12_PAO"),
+    ("R11_PAS", "R12_PAS"),
+    ("R11_CPA", "R12_RCPA"),
+    ("R11_SAS", "R12_SAS"),
+    ("R11_WEU", "R12_WEU"),
+)
+
+#: Mapping from R11 to R14 node IDs.
+R11_R14 = (
+    ("R11_AFR", "R14_AFR"),
+    ("R11_FSU", "R14_CAS"),
+    ("R11_CPA", "R14_CPA"),
+    ("R11_EEU", "R14_EEU"),
+    ("R11_LAM", "R14_LAM"),
+    ("R11_MEA", "R14_MEA"),
+    ("R11_NAM", "R14_NAM"),
+    ("R11_PAO", "R14_PAO"),
+    ("R11_PAS", "R14_PAS"),
+    ("R11_FSU", "R14_RUS"),
+    ("R11_SAS", "R14_SAS"),
+    ("R11_FSU", "R14_SCS"),
+    ("R11_FSU", "R14_UBM"),
+    ("R11_WEU", "R14_WEU"),
+)
+
+#: Adapt data from the R11 to the R14 node list.
+#:
+#: The data is adapted using the mappings in :data:`R11_R12` for each of the dimensions
+#: in :data:`NODE_DIMS`.
+adapt_R11_R12 = MappingAdapter({d: R11_R12 for d in NODE_DIMS})
+
+#: Adapt data from the R11 to the R14 node list.
+#:
+#: The data is adapted using the mappings in :data:`R11_R14` for each of the dimensions
+#: in :data:`NODE_DIMS`.
+adapt_R11_R14 = MappingAdapter({d: R11_R14 for d in NODE_DIMS})
+
+
+def identify_nodes(scenario: Scenario) -> str:
+    """Return the ID of a node codelist given the contents of `scenario`.
+
+    Returns
+    -------
+    str
+        The ID of the :doc:`/pkg-data/node` containing the regions of `scenario`.
+
+    Raises
+    ------
+    ValueError
+        if no codelist can be identified, or the nodes in the scenario do not match the
+        children of the “World” node in the codelist.
+    """
+    from message_ix_models.model.structure import get_codes
+
+    nodes = sorted(scenario.set("node"))
+
+    # Candidate ID: split e.g. "R14_AFR" to "R14"
+    id = nodes[0].split("_")[0]
+
+    try:
+        # Get the corresponding codelist
+        codes = get_codes(f"node/{id}")
+    except FileNotFoundError:
+        raise ValueError(f"Couldn't identify node codelist from {repr(nodes)}")
+
+    glb_node = [n.endswith("_GLB") for n in nodes]
+    if any(glb_node):
+        omit = nodes.pop(glb_node.index(True))
+        log.info(f"Omit known, non-standard node '{omit}' from set to match")
+
+    # Expected list of nodes
+    world = codes[codes.index("World")]  # type: ignore [arg-type]
+    codes = [world] + world.child
+
+    try:
+        assert set(nodes) == set(map(str, codes))
+    except AssertionError:
+        raise ValueError(
+            "\n".join(
+                [
+                    f"Node IDs suggest codelist {repr(id)}, values do not match:",
+                    repr(nodes),
+                    repr(codes),
+                ]
+            )
+        )
+    else:
+        log.info(f"Identified node codelist {repr(id)}")
+        return id
+
+
+def nodes_ex_world(nodes: Sequence[Union[str, Code]]) -> List[Union[str, Code]]:
+    """Exclude "World" and anything containing "GLB" from `nodes`.
+
+    May also be used as a reporting computation.
+
+    .. todo:: Make available from :mod:`message_ix_models.report`.
+    """
+    return list(filter(lambda n_: "GLB" not in n_ and n_ != "World", nodes))
diff --git a/nest/message_ix_models/util/scenarioinfo.py b/nest/message_ix_models/util/scenarioinfo.py
new file mode 100644
index 0000000..d622eab
--- /dev/null
+++ b/nest/message_ix_models/util/scenarioinfo.py
@@ -0,0 +1,332 @@
+""":class:`ScenarioInfo` class."""
+import logging
+from collections import defaultdict
+from dataclasses import dataclass, field
+from itertools import product
+from typing import Dict, List, Optional
+
+import pandas as pd
+import pint
+import sdmx.model
+
+from .sdmx import eval_anno
+
+log = logging.getLogger(__name__)
+
+
+class ScenarioInfo:
+    """Information about a :class:`~message_ix.Scenario` object.
+
+    Code that prepares data for a target Scenario can accept a ScenarioInfo instance.
+    This avoids the need to load a Scenario, which can be slow under some conditions.
+
+    ScenarioInfo objects can also be used (e.g. by :func:`.apply_spec`) to describe the
+    contents of a Scenario *before* it is created.
+
+    ScenarioInfo objects have the following convenience attributes:
+
+    .. autosummary::
+       set
+       io_units
+       is_message_macro
+       N
+       units_for
+       Y
+       y0
+       yv_ya
+
+    Parameters
+    ----------
+    scenario : message_ix.Scenario
+        If given, :attr:`.set` is initialized from this existing scenario.
+
+    See also
+    --------
+    .Spec
+    """
+
+    #: Elements of :mod:`ixmp`/:mod:`message_ix` sets.
+    set: Dict[str, List] = {}
+
+    #: Elements of :mod:`ixmp`/:mod:`message_ix` parameters.
+    par: Dict[str, pd.DataFrame] = {}
+
+    #: First model year, if set, else ``Y[0]``.
+    y0: int = -1
+
+    #: :obj:`True` if a MESSAGE-MACRO scenario.
+    is_message_macro: bool = False
+
+    _yv_ya: pd.DataFrame = None
+
+    def __init__(self, scenario=None):
+        self.set = defaultdict(list)
+        self.par = dict()
+
+        if not scenario:
+            return
+
+        for name in scenario.set_list():
+            try:
+                self.set[name] = scenario.set(name).tolist()
+            except AttributeError:
+                continue  # pd.DataFrame for ≥2-D set; don't convert
+
+        for name in ("duration_period",):
+            self.par[name] = scenario.par(name)
+
+        self.is_message_macro = "PRICE_COMMODITY" in scenario.par_list()
+
+        # Computed once
+        fmy = scenario.cat("year", "firstmodelyear")
+        self.y0 = int(fmy[0]) if len(fmy) else self.set["year"][0]
+
+        self._yv_ya = scenario.vintage_and_active_years()
+
+    @property
+    def yv_ya(self):
+        """:class:`pandas.DataFrame` with valid ``year_vtg``, ``year_act`` pairs."""
+        if self._yv_ya is None:
+            first = self.y0
+
+            # Product of all years
+            yv = ya = self.set["year"]
+
+            # Predicate for filtering years
+            def _valid(elem):
+                yv, ya = elem
+                return first <= yv <= ya
+
+            # - Cartesian product of all yv and ya.
+            # - Filter only valid years.
+            # - Convert to data frame.
+            self._yv_ya = pd.DataFrame(
+                filter(_valid, product(yv, ya)), columns=["year_vtg", "year_act"]
+            )
+
+        return self._yv_ya
+
+    @property
+    def N(self):
+        """Elements of the set 'node'.
+
+        See also
+        --------
+        .nodes_ex_world
+        """
+        return list(map(str, self.set["node"]))
+
+    @property
+    def Y(self):
+        """Elements of the set 'year' that are >= the first model year."""
+        return list(filter(lambda y: y >= self.y0, self.set["year"]))
+
+    def update(self, other: "ScenarioInfo"):
+        """Update with the set elements of `other`."""
+        for name, data in other.set.items():
+            self.set[name].extend(filter(lambda id: id not in self.set[name], data))
+
+        for name, data in other.par.items():
+            raise NotImplementedError("Merging parameter data")
+
+    def __repr__(self):
+        return (
+            f"<ScenarioInfo: {sum(len(v) for v in self.set.values())} code(s) in "
+            f"{len(self.set)} set(s)>"
+        )
+
+    def units_for(self, set_name: str, id: str) -> pint.Unit:
+        """Return the units associated with code `id` in MESSAGE set `set_name`.
+
+        :mod:`ixmp` (or the sole :class:`.JDBCBackend`, as of v3.5.0) does not handle
+        unit information for variables and equations (unlike parameter values), such as
+        MESSAGE decision variables ``ACT``, ``CAP``, etc. In :mod:`message_ix_models`
+        and :mod:`message_data`, the following conventions are (generally) followed:
+
+        - The units of ``ACT`` and others are consistent for each ``technology``.
+        - The units of ``COMMODITY_BALANCE``, ``STOCK``, ``commodity_stock``, etc. are
+          consistent for each ``commodity``.
+
+        Thus, codes for elements of these sets (e.g. :ref:`commodity-yaml`) can be used
+        to carry the standard units for the corresponding quantities. :func:`units_for`
+        retrieves these units, for use in model-building and reporting.
+
+        .. todo:: Expand this discussion and transfer to the :mod:`message_ix` docs.
+
+        See also
+        --------
+        io_units
+        """
+        try:
+            idx = self.set[set_name].index(id)
+        except ValueError:
+            print(self.set[set_name])
+            raise
+
+        return eval_anno(self.set[set_name][idx], "units")
+
+    def io_units(
+        self, technology: str, commodity: str, level: Optional[str] = None
+    ) -> pint.Unit:
+        """Return units for the MESSAGE ``input`` or ``output`` parameter.
+
+        These are implicitly determined as the ratio of:
+
+        - The units for the origin (for ``input``) or destination `commodity`, per
+          :meth:`.units_for`.
+        - The units of activity for the `technology`.
+
+        Parameters
+        ----------
+        level : str
+            Placeholder for future functionality, i.e. to use different units per
+            (commodity, level). Currently ignored. If given, a debug message is logged.
+        """
+        if level is not None:
+            log.debug(f"{level = } ignored")
+        return self.units_for("commodity", commodity) / self.units_for(
+            "technology", technology
+        )
+
+    def year_from_codes(self, codes: List[sdmx.model.Code]):
+        """Update using a list of `codes`.
+
+        The following are updated:
+
+        - :attr:`.set` ``year``
+        - :attr:`.set` ``cat_year``, with the first model year.
+        - :attr:`.par` ``duration_period``
+
+        Any existing values are discarded.
+
+        After this, the attributes :attr:`.y0` and :attr:`.Y` give the first model year
+        and model years, respectively.
+
+        Examples
+        --------
+        Get a particular code list, create a ScenarioInfo instance, and update using
+        the codes:
+
+        >>> years = get_codes("year/A")
+        >>> info = ScenarioInfo()
+        >>> info.year_from_codes(years)
+
+        Use populated values:
+
+        >>> info.y0
+        2020
+        >>> info.Y[:3]
+        [2020, 2030, 2040]
+        >>> info.Y[-3:]
+        [2090, 2100, 2110]
+
+        """
+        from message_ix_models.util import eval_anno
+
+        # Clear existing values
+        if len(self.set["year"]):
+            log.debug(f"Discard existing 'year' elements: {repr(self.set['year'])}")
+            self.set["year"] = []
+        if len(self.set["cat_year"]):
+            log.debug(
+                f"Discard existing 'cat_year' elements: {repr(self.set['cat_year'])}"
+            )
+            self.set["cat_year"] = []
+        if "duration_period" in self.par:
+            log.debug("Discard existing 'duration_period' elements")
+
+        fmy_set = False
+        duration_period: List[Dict] = []
+
+        # TODO use sorted() here once supported by sdmx
+        for code in codes:
+            year = int(code.id)
+            # Store the year
+            self.set["year"].append(year)
+
+            # Check for an annotation 'firstmodelyear: true'
+            if eval_anno(code, "firstmodelyear"):
+                if fmy_set:
+                    # No coverage: data that triggers this should not be committed
+                    raise ValueError(  # pragma: no cover
+                        "≥2 periods are annotated firstmodelyear: true"
+                    )
+
+                self.y0 = year
+                self.set["cat_year"].append(("firstmodelyear", year))
+                fmy_set = True
+
+            # Store the duration_period: either from an annotation, or computed vs. the
+            # prior period
+            duration_period.append(
+                dict(
+                    year=year,
+                    value=eval_anno(code, "duration_period")
+                    or (year - duration_period[-1]["year"]),
+                    unit="y",
+                )
+            )
+
+        # Store
+        self.par["duration_period"] = pd.DataFrame(duration_period)
+
+
+@dataclass
+class Spec:
+    """A specification for the structure of a model or variant.
+
+    A Spec collects 3 :class:`.ScenarioInfo` instances at the attributes :attr:`.add`,
+    :attr:`.remove`, and :attr:`.require`. This is the type that is accepted by
+    :func:`.apply_spec`; :doc:`model-build` describes how a Spec is used to modify a
+    :class:`Scenario`. A Spec may also be used to express information about the target
+    structure of data to be prepared; like :class:`.ScenarioInfo`, this can happen
+    before the target :class:`.Scenario` exists.
+
+    Spec also provides:
+
+    - Dictionary-style access, e.g. ``s["add"]`` is equivalent to ``s.add.``.
+    - Error checking; setting keys other than add/remove/require results in an error.
+    - :meth:`.merge`, a helper method.
+    """
+
+    #: Structure to be added to a base scenario.
+    add: ScenarioInfo = field(default_factory=ScenarioInfo)
+    #: Structure to be removed from a base scenario.
+    remove: ScenarioInfo = field(default_factory=ScenarioInfo)
+    #: Structure that must be present in a base scenario.
+    require: ScenarioInfo = field(default_factory=ScenarioInfo)
+
+    # Dict-like features
+
+    def __getitem__(self, key):
+        try:
+            return getattr(self, key)
+        except AttributeError:
+            raise KeyError(key)
+
+    def __setitem__(self, key, value: ScenarioInfo):
+        if not hasattr(self, key):
+            raise KeyError(key)
+        setattr(self, key, value)
+
+    def values(self):
+        yield self.add
+        yield self.remove
+        yield self.require
+
+    # Static methods
+
+    @staticmethod
+    def merge(a: "Spec", b: "Spec") -> "Spec":
+        """Merge Specs `a` and `b` together.
+
+        Returns a new Spec where each member is a union of the respective members of
+        `a` and `b`.
+        """
+        result = Spec()
+
+        for key in {"add", "remove", "require"}:
+            result[key].update(a[key])
+            result[key].update(b[key])
+
+        return result
diff --git a/nest/message_ix_models/util/sdmx.py b/nest/message_ix_models/util/sdmx.py
new file mode 100644
index 0000000..5553c73
--- /dev/null
+++ b/nest/message_ix_models/util/sdmx.py
@@ -0,0 +1,99 @@
+"""Utilities for handling objects from :mod:`sdmx`."""
+import logging
+from copy import copy
+from typing import Dict, List, Mapping, Union
+
+from iam_units import registry
+from sdmx.model import AnnotableArtefact, Annotation, Code, InternationalString
+
+log = logging.getLogger(__name__)
+
+CodeLike = Union[str, Code]
+
+
+def as_codes(data: Union[List[str], Dict[str, CodeLike]]) -> List[Code]:
+    """Convert *data* to a :class:`list` of :class:`.Code` objects.
+
+    Various inputs are accepted:
+
+    - :class:`list` of :class:`str`.
+    - :class:`dict`, in which keys are :attr:`.Code.id` and values are further
+      :class:`dict` with keys matching other :class:`.Code` attributes.
+    """
+    # Assemble results as a dictionary
+    result: Dict[str, Code] = {}
+
+    if isinstance(data, list):
+        # FIXME typing ignored temporarily for PR#9
+        data = dict(zip(data, data))  # type: ignore [arg-type]
+    elif not isinstance(data, Mapping):
+        raise TypeError(data)
+
+    for id, info in data.items():
+        # Pass through Code; convert other types to dict()
+        if isinstance(info, Code):
+            result[info.id] = info
+            continue
+        elif isinstance(info, str):
+            _info = dict(name=info)
+        elif isinstance(info, Mapping):
+            _info = copy(info)
+        else:
+            raise TypeError(info)
+
+        # Create a Code object
+        code = Code(
+            id=str(id),
+            name=_info.pop("name", str(id).title()),
+        )
+
+        # Store the description, if any
+        try:
+            code.description = InternationalString(value=_info.pop("description"))
+        except KeyError:
+            pass
+
+        # Associate with a parent
+        try:
+            parent_id = _info.pop("parent")
+        except KeyError:
+            pass  # No parent
+        else:
+            result[parent_id].append_child(code)
+
+        # Associate with any children
+        for id in _info.pop("child", []):
+            try:
+                code.append_child(result[id])
+            except KeyError:
+                pass  # Not parsed yet
+
+        # Convert other dictionary (key, value) pairs to annotations
+        for id, value in _info.items():
+            code.annotations.append(
+                Annotation(id=id, text=value if isinstance(value, str) else repr(value))
+            )
+
+        result[code.id] = code
+
+    return list(result.values())
+
+
+def eval_anno(obj: AnnotableArtefact, id: str):
+    """Retrieve the annotation `id` from `obj`, run :func:`eval` on its contents.
+
+    This can be used for unpacking Python values (e.g. :class:`dict`) stored as an
+    annotation on a :class:`~sdmx.model.Code`.
+
+    Returns :obj:`None` if no attribute exists with the given `id`.
+    """
+    try:
+        value = str(obj.get_annotation(id=id).text)
+    except KeyError:  # No such attribute
+        return None
+
+    try:
+        return eval(value, {"registry": registry})
+    except Exception as e:  # Something that can't be eval()'d, e.g. a plain string
+        log.debug(f"Could not eval({value!r}): {e}")
+        return value
diff --git a/nest/message_ix_models/workflow.py b/nest/message_ix_models/workflow.py
new file mode 100644
index 0000000..296f568
--- /dev/null
+++ b/nest/message_ix_models/workflow.py
@@ -0,0 +1,244 @@
+"""Tools for modeling workflows."""
+import logging
+from typing import Callable, List, Optional, Union
+
+from genno import Computer
+from ixmp.utils import parse_url
+from message_ix import Scenario
+
+from message_ix_models.util.context import Context
+
+log = logging.getLogger(__name__)
+
+# commented: this conflicts with option keyword arguments to workflow step functions
+# CallbackType = Callable[[Context, Scenario], Scenario]
+CallbackType = Callable
+
+
+class WorkflowStep:
+    """Single step in a multi-scenario workflow.
+
+    Nothing occurs when the WorkflowStep is instantiated.
+
+    Parameters
+    ----------
+    name : str
+        ``"model name/scenario name"`` for the :class:`.Scenario` produced by the step.
+    action : CallbackType, optional
+        Function to be executed to modify the base into the target Scenario.
+    clone : bool, optional
+        :obj:`True` to clone the base scenario the target.
+    target : str, optional
+        URL for the scenario produced by the workflow step. Parsed to
+        :attr:`scenario_info` and :attr:`platform_info`.
+    kwargs
+        Keyword arguments for `action`.
+    """
+
+    #: Function to be executed on the subject scenario. If :obj:`None`, the target
+    #: scenario is loaded via :meth:`Context.get_scenario`.
+    action: Optional[CallbackType] = None
+
+    #: :obj:`True` or a :class:`dict` with keyword arguments to clone before
+    #: :attr:`action` is executed. Default: :obj:`False`, do not clone.
+    clone: Union[bool, dict] = False
+
+    #: Keyword arguments passed to :attr:`action`.
+    kwargs: dict
+
+    #: Target platform name and additional options.
+    platform_info: dict
+
+    #: Target model name, scenario name, and optional version.
+    scenario_info: dict
+
+    def __init__(
+        self, action: Optional[CallbackType], target=None, clone=False, **kwargs
+    ):
+        try:
+            # Store platform and scenario info by parsing the `target` URL
+            self.platform_info, self.scenario_info = parse_url(target)
+        except (AttributeError, ValueError):
+            if clone is not False:
+                raise TypeError("target= must be supplied for clone=True")
+            self.platform_info = self.scenario_info = dict()
+
+        # Store the callback and options
+        self.action = action
+        self.clone = clone
+        self.kwargs = kwargs
+
+    def __call__(
+        self, context: Context, scenario: Optional[Scenario] = None
+    ) -> Scenario:
+        """Execute the workflow step."""
+        if scenario is None:
+            # No base scenario
+            if self.action:
+                raise RuntimeError(
+                    f"Step with action {self.action!r} requires a base scenario"
+                )
+            # Use Context to retrieve the identified scenario
+            context.platform_info.update(self.platform_info)
+            context.scenario_info.update(self.scenario_info)
+            s = context.get_scenario()
+            log.info(f"Loaded ixmp://{s.platform.name}/{s.url}")
+        else:
+            # Modify the context to identify destination scenario; possibly nothing
+            context.dest_scenario.update(self.scenario_info)
+            s = scenario
+            log.info(f"Step runs on ixmp://{s.platform.name}/{s.url}")
+
+        if context.dest_scenario:
+            log.info(f"  with context.dest_scenario={context.dest_scenario}")
+
+        if self.clone is not False:
+            # Clone to target model/scenario name
+            log.info("Clone to {model}/{scenario}".format(**self.scenario_info))
+            kw = self.scenario_info.copy()
+            # If clone contains keyword arguments, e.g. shift_first_model_year, use them
+            # NB user code should give clone = dict(keep_solution=True) if desired
+            kw.update(
+                self.clone
+                if isinstance(self.clone, dict)
+                else dict(keep_solution=False)
+            )
+            s = s.clone(**kw)
+
+        if not self.action:
+            return s
+
+        log.info(f"Execute {self.action!r}")
+
+        # Modify context to identify the target scenario
+        context.set_scenario(s)
+
+        try:
+            # Invoke the callback
+            result = self.action(context, s, **self.kwargs)
+        except Exception:  # pragma: no cover
+            s.platform.close_db()  # Avoid locking the scenario
+            raise
+
+        if result is None:
+            log.info(f"…nothing returned, workflow will continue with {s.url}")
+            result = s
+
+        return result
+
+    def __repr__(self):
+        action = f"{self.action.__name__}()" if self.action else "load"
+        dest = ""
+        if self.scenario_info:
+            dest = " -> {model}/{scenario}".format(**self.scenario_info)
+        return f"<Step {action}{dest}>"
+
+
+class Workflow(Computer):
+    """Workflow for operations on multiple :class:`Scenarios <.Scenario>`.
+
+    Parameters
+    ----------
+    context : .Context
+        Context object with settings common to the entire workflow.
+    """
+
+    def __init__(self, context: Context):
+        super().__init__()
+        self.add_single("context", context)
+
+    def add_step(
+        self,
+        name: str,
+        base: Optional[str] = None,
+        action: Optional[CallbackType] = None,
+        replace=False,
+        **kwargs,
+    ) -> WorkflowStep:
+        """Add a :class:`WorkflowStep` to the workflow.
+
+        Parameters
+        ----------
+        name : str
+            Name for the new step.
+        base : str or None
+            Previous step that produces the a pre-requisite scenario for this step.
+        action : CallbackType
+            Function to be executed to modify the base into the target Scenario.
+        replace : bool
+            :data:`True` to replace an existing step.
+        kwargs
+            Keyword arguments for `action`; passed to and stored on the
+            :class:`WorkflowStep` until used.
+
+        Returns
+        -------
+        WorkflowStep
+            a reference to the added step, for optional further modification.
+
+        Raises
+        ------
+        genno.KeyExistsError
+            if the step `name` already exists. Use `replace` to force overwriting an
+            existing step.
+        """
+        # Create the workflow step
+        step = WorkflowStep(action, **kwargs)
+
+        if replace:
+            # Remove any existing step
+            self.graph.pop(name, None)
+
+        # Add to the Computer
+        self.add_single(name, step, "context", base, strict=True)
+
+        return step
+
+    def run(self, name_or_names: Union[str, List[str]]):
+        """Run all workflow steps necessary to produce `name_or_names`.
+
+        Parameters
+        ----------
+        name_or_names: str or list of str
+            Identifier(s) of steps to run.
+        """
+        return self.get(name_or_names)
+
+    def truncate(self, name: str):
+        """Truncate the workflow at the step `name`.
+
+        The step `name` is replaced with a new :class:`WorkflowStep` that simply loads
+        the target :class:`Scenario` that would be produced by the original step.
+
+        Raises
+        ------
+        KeyError
+            if step `name` does not exist.
+        """
+
+        def _recurse_info(kind: str, step_name: str):
+            """Traverse the graph looking for non-empty platform_info/scenario_info."""
+            task = self.graph[step_name]
+            return getattr(task[0], f"{kind}_info") or _recurse_info(kind, task[2])
+
+        # Generate a new step that merely loads the scenario identified by `name` or
+        # its base
+        step = WorkflowStep(None)
+        step.scenario_info = _recurse_info("scenario", name)
+        try:
+            step.platform_info = _recurse_info("platform", name)
+        except KeyError as e:
+            if e.args[0] is None:
+                raise RuntimeError(
+                    f"Unable to locate platform info for {step.scenario_info}"
+                )
+            else:  # pragma: no cover
+                raise  # Something else
+
+        # Replace the existing step
+        self.add_single(name, step, "context", None)
+
+
+def solve(context, scenario, **kwargs):
+    scenario.solve(**kwargs)
+    return scenario
diff --git a/nest/pyproject.toml b/nest/pyproject.toml
new file mode 100644
index 0000000..1aed65f
--- /dev/null
+++ b/nest/pyproject.toml
@@ -0,0 +1,37 @@
+[build-system]
+requires = ["setuptools", "setuptools-scm"]
+
+[tool.pytest.ini_options]
+# Disable faulthandler plugin on Windows to prevent spurious console noise
+addopts = "-p no:faulthandler --cov=message_ix_models --cov-report="
+filterwarnings = "ignore:distutils Version classes.*:DeprecationWarning"
+
+[tool.isort]
+profile = "black"
+
+[[tool.mypy.overrides]]
+module = [
+  "dask",
+  "colorama",
+  "jpype",
+  "matplotlib.*",
+  "message_data.*",
+  "pandas.*",
+  "pint._vendor",
+  "pyam",
+  "pycountry",
+  "setuptools",
+  # For ixmp.testing
+  "nbclient",
+  "nbformat",
+  "memory_profiler",
+]
+ignore_missing_imports = true
+
+
+[[tool.mypy.overrides]]
+# Work around iiasa/ixmp#465 and similar
+module = [
+  "ixmp.*",
+]
+no_implicit_optional = false
diff --git a/nest/setup.cfg b/nest/setup.cfg
new file mode 100644
index 0000000..3f4a669
--- /dev/null
+++ b/nest/setup.cfg
@@ -0,0 +1,49 @@
+[metadata]
+name = message-ix-models
+author = IIASA Energy, Climate, and Environment (ECE) Program
+author_email = message_ix@iiasa.ac.at
+license = Apache 2.0
+description = Tools for the MESSAGEix-GLOBIOM family of models
+long_description = file: README.rst
+long_description_content_type = text/x-rst
+url = https://github.com/iiasa/message-ix-models
+
+[options]
+packages = message_ix_models
+python_requires = >=3.7
+include_package_data = True
+zip_safe = True
+install_requires =
+    click
+    colorama
+    # When the minimum is greater than the minimum via message_ix; e.g.
+    # message_ix >= 3.4.0 → ixmp >= 3.4.0 → genno >= 1.6.0
+    genno >= 1.8.0
+    iam_units
+    message_ix >= 3.4.0
+    pyam-iamc >= 0.6
+    pycountry
+    PyYAML
+    sdmx1 >= 2.2.0
+    setuptools >= 41
+
+[options.extras_require]
+tests =
+    # For nbclient, thus nbformat
+    ixmp[tests]
+    pytest
+    pytest-cov
+docs =
+    # sphinx.ext.autosummary covers the test suite, so all test requirements
+    %(tests)s
+    # Temporary: should be covered via message_ix[report]
+    genno[compat]
+    sphinx >= 4
+    sphinx_rtd_theme
+
+[options.entry_points]
+console_scripts =
+    mix-models = message_ix_models.cli:main
+
+[flake8]
+max-line-length = 88
diff --git a/nest/setup.py b/nest/setup.py
new file mode 100644
index 0000000..e02756e
--- /dev/null
+++ b/nest/setup.py
@@ -0,0 +1,3 @@
+import setuptools
+
+setuptools.setup(use_scm_version=True)

From efbe3738f05e37e25e248d3b1116610d56789db5 Mon Sep 17 00:00:00 2001
From: adrivinca <adrivinca@yahoo.it>
Date: Thu, 23 Feb 2023 16:49:32 +0100
Subject: [PATCH 2/4] Adding Leap-re project code and processed data, requires
 path adjustment

---
 .../leap_re_nest/OnSSET_cost_paramters.csv    |   71 +
 .../leap_re_nest/OnSSET_cost_paramters.xlsx   |  Bin 0 -> 14735 bytes
 .../delineation/Zambia_NEST_delineation.cpg   |    1 +
 .../delineation/Zambia_NEST_delineation.dbf   |  Bin 0 -> 3234 bytes
 .../delineation/Zambia_NEST_delineation.prj   |    1 +
 .../delineation/Zambia_NEST_delineation.sbn   |  Bin 0 -> 348 bytes
 .../delineation/Zambia_NEST_delineation.sbx   |  Bin 0 -> 132 bytes
 .../delineation/Zambia_NEST_delineation.shp   |  Bin 0 -> 1103580 bytes
 .../Zambia_NEST_delineation.shp.xml           |    2 +
 .../delineation/Zambia_NEST_delineation.shx   |  Bin 0 -> 292 bytes
 .../basins_by_region_simpl_ZMB.csv            |   25 +
 .../electricity_demand_MLED_NEST_GWh_mth.csv  | 8641 +++++++++++++++++
 .../data/leap_re_nest/energy_total_all.csv    |  121 +
 .../data/leap_re_nest/grid_cost_bcu.csv       |   49 +
 .../data/leap_re_nest/input_data_12_ZMB.xlsx  |  Bin 0 -> 72804 bytes
 .../input_data_48_CHN - Copy.xlsx             |  Bin 0 -> 78004 bytes
 .../data/leap_re_nest/ref_scen.xlsx           |  Bin 0 -> 2255630 bytes
 .../project/leap_re_nest/build.py             |  120 +
 .../project/leap_re_nest/doc.rst              |   84 +
 .../leap_re_nest/script/add_MLED_demand.py    |   82 +
 .../script/add_grid_shares_OnSSET.py          |  406 +
 .../leap_re_nest/script/add_timeslice.py      | 1273 +++
 .../project/leap_re_nest/utils.py             |   47 +
 23 files changed, 10923 insertions(+)
 create mode 100644 nest/message_ix_models/data/leap_re_nest/OnSSET_cost_paramters.csv
 create mode 100644 nest/message_ix_models/data/leap_re_nest/OnSSET_cost_paramters.xlsx
 create mode 100644 nest/message_ix_models/data/leap_re_nest/delineation/Zambia_NEST_delineation.cpg
 create mode 100644 nest/message_ix_models/data/leap_re_nest/delineation/Zambia_NEST_delineation.dbf
 create mode 100644 nest/message_ix_models/data/leap_re_nest/delineation/Zambia_NEST_delineation.prj
 create mode 100644 nest/message_ix_models/data/leap_re_nest/delineation/Zambia_NEST_delineation.sbn
 create mode 100644 nest/message_ix_models/data/leap_re_nest/delineation/Zambia_NEST_delineation.sbx
 create mode 100644 nest/message_ix_models/data/leap_re_nest/delineation/Zambia_NEST_delineation.shp
 create mode 100644 nest/message_ix_models/data/leap_re_nest/delineation/Zambia_NEST_delineation.shp.xml
 create mode 100644 nest/message_ix_models/data/leap_re_nest/delineation/Zambia_NEST_delineation.shx
 create mode 100644 nest/message_ix_models/data/leap_re_nest/delineation/basins_by_region_simpl_ZMB.csv
 create mode 100644 nest/message_ix_models/data/leap_re_nest/electricity_demand_MLED_NEST_GWh_mth.csv
 create mode 100644 nest/message_ix_models/data/leap_re_nest/energy_total_all.csv
 create mode 100644 nest/message_ix_models/data/leap_re_nest/grid_cost_bcu.csv
 create mode 100644 nest/message_ix_models/data/leap_re_nest/input_data_12_ZMB.xlsx
 create mode 100644 nest/message_ix_models/data/leap_re_nest/input_data_48_CHN - Copy.xlsx
 create mode 100644 nest/message_ix_models/data/leap_re_nest/ref_scen.xlsx
 create mode 100644 nest/message_ix_models/project/leap_re_nest/build.py
 create mode 100644 nest/message_ix_models/project/leap_re_nest/doc.rst
 create mode 100644 nest/message_ix_models/project/leap_re_nest/script/add_MLED_demand.py
 create mode 100644 nest/message_ix_models/project/leap_re_nest/script/add_grid_shares_OnSSET.py
 create mode 100644 nest/message_ix_models/project/leap_re_nest/script/add_timeslice.py
 create mode 100644 nest/message_ix_models/project/leap_re_nest/utils.py

diff --git a/nest/message_ix_models/data/leap_re_nest/OnSSET_cost_paramters.csv b/nest/message_ix_models/data/leap_re_nest/OnSSET_cost_paramters.csv
new file mode 100644
index 0000000..e90f98b
--- /dev/null
+++ b/nest/message_ix_models/data/leap_re_nest/OnSSET_cost_paramters.csv
@@ -0,0 +1,71 @@
+Variable,Value,Source,Comments,Description
+Start_year,2020,,,
+End_year,2030,,,
+End year electrification rate target,1,,,
+Intermediate target year,2025,,,
+Intermediate electrification rate target,0.6375,,,
+PV cost adjustment factor,1,,,
+Urban target tier,5,,,
+Rural target tier,3,,,
+Prioritization,5,,,"5 = least cost, 4 = forced grid within buffer only, 6 = forced grid within buffer & least cost out of the buffer zone"
+Auto intensification distance,5,,,Buffer distance (km) for automatic intensification if choosing prioritization 1
+discount_rate,0.08,,,
+pop_threshold,0,,,
+pop_start_year,18384000,,,the population in the base year (e.g. 2018)
+pop_end_year,24859380,,,the projected population in the end year (e.g. 2030)
+urban_ratio_start_year,0.446,,,the urban population population ratio in the base year (e.g. 2018)
+urban_ratio_end_year,0.505,,,the urban population population ratio in the end year (e.g. 2030)
+num_people_per_hh_urban,4.5,,,the number of people per household expected in the end year (e.g. 2030)
+num_people_per_hh_rural,5.3,,,the number of people per household expected in the end year (e.g. 2030)
+elec_ratio_start_year,0.398,,,the electrification rate in the base year (e.g. 2018)
+urban_elec_ratio,0.772,,,urban electrification rate in the base year (e.g. 2018)
+rural_elec_ratio,0.11,,,rural electrification rate in the base year (e.g. 2018)
+grid_generation_cost,0.024,,,This is the grid cost electricity USD/kWh as expected in the end year of the analysis
+grid_power_plants_capital_cost,1441,,,The cost in USD/kW to for capacity upgrades of the grid-connected power plants
+grid_losses,0.183,,,The fraction of electricity lost in transmission and distribution (percentage)
+base_to_peak,0.8,,,The ratio of base grid demand to peak demand (percentage)
+existing_grid_cost_ratio,0.1,,,The additional cost per round of electrification (percentage)
+diesel_price,1.16,,,This is the diesel price in USD/liter as expected in the end year of the analysis
+sa_diesel_capital_cost,938,,,Stand-alone Diesel capital cost (USD/kW) as expected in the years of the analysis
+mg_diesel_capital_cost,721,,,Mini-grid Diesel capital cost (USD/kW) as expected in the years of the analysis
+mg_pv_capital_cost,2950,,,Mini-grid PV capital cost total system (USD/kW) as expected in the years of the analysis
+mg_wind_capital_cost,3750,,,Mini-grid Wind capital cost (USD/kW) as expected in the years of the analysis
+mg_hydro_capital_cost,3000,,,Mini-grid Hydro capital cost (USD/kW) as expected in the years of the analysis
+mg_hybrid_solar_panels,503,,,USD/kW
+mg_hybrid_batteries,139,,,USD/kWh (battery capacity - inverter sized seperately)
+mg_hybrid_inverter,80,,,USD/kW
+mg_hybrid_charge_controller,142,,,USD/kW
+mg_hybrid_diesel_generator,261,,,USD/kW
+mg_hybrid_wind_turbine,2800,,,USD/kW
+sa_pv_capital_cost_1,9620,,,Stand-alone PV capital cost (USD/kW) for household systems under 20 W
+sa_pv_capital_cost_2,8780,,,Stand-alone PV capital cost (USD/kW) for household systems between 21-50 W
+sa_pv_capital_cost_3,6380,,,Stand-alone PV capital cost (USD/kW) for household systems between 51-100 W
+sa_pv_capital_cost_4,4470,,,Stand-alone PV capital cost (USD/kW) for household systems between 101-200 W
+sa_pv_capital_cost_5,6950,,,Stand-alone PV capital cost (USD/kW) for household systems over 200 W
+mv_line_cost,7000,,,Cost of MV lines in USD/km
+lv_line_cost,4250,,,Cost of LV lines in USD/km
+mv_line_capacity,33,,,Capacity of MV lines in kW/line
+lv_line_capacity,0.24,,,Capacity of LV lines in kW/line
+lv_line_max_length,0.5,,,Maximum length of LV lines (km)
+hv_line_cost,53000,,,Cost of HV lines in USD/km
+mv_line_max_length,50,,,Maximum length of MV lines (km)
+hv_lv_transformer_cost,25000,,,Cost of HV/MV transformer - 1000 kVa (USD/unit)
+mv_increase_rate,0.1,,,percentage
+max_grid_extension_dist,50,,,Maximum distance that the grid may be extended by means of MV lines
+annual_new_grid_connections_limit_intermediate,999999999,,,This is the maximum amount of new households that can be connected to the grid in one year (thousands)
+annual_new_grid_connections_limit_end,999999999,,,This is the maximum amount of new households that can be connected to the grid in one year (thousands)
+grid_capacity_limit_end,999999999,,,This is the maximum generation capacity that can be added to the grid in one year (MW)
+grid_capacity_limit_intermediate,,,,This is the maximum generation capacity that can be added to the grid in one year (MW)
+GIS data: Administrative boundaries,,,,Delineates the boundaries of the analysis.
+GIS data: DEM,,,,"Filled DEM (elevation) maps are use in a number of processes in the analysis (Energy potentials, restriction zones, grid extension suitability map etc.)."
+GIS data: Hydropower,,,,Points showing potential mini/small hydropower potential.  Provides power availability in each identified point.
+GIS data: Land Cover,,,,"Land cover maps are use in a number of processes in the analysis (Energy potentials, restriction zones, grid extension suitability map etc.)."
+GIS data: Night-time Lights,,,,"Dataset used to,identify and spatially calibrate the currently electrified/non-electrified population."
+GIS data: Population,,,,Spatial identification and quantification of the current (base year) population. This dataset sets the basis of the ONSSET analysis as it is directly connected with the electricity demand and the assignment of energy access goals
+GIS data: Roads,,,,Current road infrastructure is used in order to specify grid extension suitability.
+GIS data: Solar GHI,,,,Provide information about the Global Horizontal Irradiation (kWh/m2/year) over an area. This is later used to identify the availability/suitability of Photovoltaic systems.
+GIS data: Substations,,,,Current Substation infrastructure is used in order to specify grid extension suitability.
+GIS data: Existing grid,,,,Current grid network
+GIS data: Planned grid,,,,Planned/committed grid network extensions
+GIS data: Travel-time,,,,"Visualizes spatially the travel time required to reach from any individual cell to the closest town with population more than 50,000 people."
+GIS data: Wind velocity,,,,Provide information about the wind velocity (m/sec) over an area. This is later used to identify the availability/suitability of wind power (using Capacity factors).
diff --git a/nest/message_ix_models/data/leap_re_nest/OnSSET_cost_paramters.xlsx b/nest/message_ix_models/data/leap_re_nest/OnSSET_cost_paramters.xlsx
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HcmV?d00001

diff --git a/nest/message_ix_models/data/leap_re_nest/delineation/Zambia_NEST_delineation.shp.xml b/nest/message_ix_models/data/leap_re_nest/delineation/Zambia_NEST_delineation.shp.xml
new file mode 100644
index 0000000..80af345
--- /dev/null
+++ b/nest/message_ix_models/data/leap_re_nest/delineation/Zambia_NEST_delineation.shp.xml
@@ -0,0 +1,2 @@
+<?xml version="1.0" encoding="UTF-8"?>
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diff --git a/nest/message_ix_models/data/leap_re_nest/delineation/Zambia_NEST_delineation.shx b/nest/message_ix_models/data/leap_re_nest/delineation/Zambia_NEST_delineation.shx
new file mode 100644
index 0000000000000000000000000000000000000000..b45023bd4303acbc595c93e24e187a4f4c4dd81f
GIT binary patch
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diff --git a/nest/message_ix_models/data/leap_re_nest/delineation/basins_by_region_simpl_ZMB.csv b/nest/message_ix_models/data/leap_re_nest/delineation/basins_by_region_simpl_ZMB.csv
new file mode 100644
index 0000000..b44e6db
--- /dev/null
+++ b/nest/message_ix_models/data/leap_re_nest/delineation/basins_by_region_simpl_ZMB.csv
@@ -0,0 +1,25 @@
+"","NAME","BASIN","REGION","BASIN_ID","area","area_km2","BCU_name"
+"0","Eastern_1","Eastern","Zambia","31",0.523959234644462,6258.5580698919,"1|Eastern"
+"1","Central_2","Central","Zambia","32",0.0815871847897459,970.510889363209,"2|Central"
+"2","Lusaka_3","Lusaka","Zambia","32",0.799537665854889,9494.09442000237,"3|Lusaka"
+"3","Central_4","Central","Zambia","33",3.60760898438715,43119.7619743396,"4|Central"
+"4","Copperbelt_5","Copperbelt","Zambia","34",2.58304468887982,30985.2974803959,"5|Copperbelt"
+"5","Lusaka_6","Lusaka","Zambia","33",0.66513975736225,7911.78785217012,"6|Lusaka"
+"6","Lusaka_7","Lusaka","Zambia","34",0.56362395281543,6688.54837394216,"7|Lusaka"
+"7","North-Western_8","North-Western","Zambia","34",3.43706970758245,41202.2918277751,"8|North-Western"
+"8","Western_9","Western","Zambia","37",9.51807006516233,113006.817929057,"9|Western"
+"9","Lusaka_10","Lusaka","Zambia","35",0.113152577372315,1338.98003930387,"10|Lusaka"
+"10","Southern_11","Southern","Zambia","35",2.55097021780597,30041.2705948085,"11|Southern"
+"11","Western_12","Western","Zambia","36",1.23996738231784,14617.3659250655,"12|Western"
+"12","North-Western_13","North-Western","Zambia","37",6.97305737476054,83703.5176556987,"13|North-Western"
+"13","Southern_14","Southern","Zambia","37",1.29347616772761,15242.3709165862,"14|Southern"
+"14","Luapula_15","Luapula","Zambia","84",4.12292027912631,49885.3132551532,"15|Luapula"
+"15","Muchinga_16","Muchinga","Zambia","84",3.11665223233439,37657.1546746454,"16|Muchinga"
+"16","Northern_17","Northern","Zambia","84",4.95376630391797,60079.9030720852,"17|Northern"
+"17","Northern_18","Northern","Zambia","85",1.30914888947857,15923.5994075535,"18|Northern"
+"18","Central_19","Central","Zambia","84",0.833206036116116,10010.3989949136,"19|Central"
+"19","Northern_20","Northern","Zambia","208",0.126487398005057,1538.31243855708,"20|Northern"
+"20","Eastern_21","Eastern","Zambia","33",3.72949874341118,44666.8009195124,"21|Eastern"
+"21","Southern_22","Southern","Zambia","34",1.8820233828308,22270.8044712217,"22|Southern"
+"22","Muchinga_23","Muchinga","Zambia","33",4.07901206892867,49221.5274809364,"23|Muchinga"
+"23","Central_24","Central","Zambia","34",4.61958483596155,54967.4760221014,"24|Central"
diff --git a/nest/message_ix_models/data/leap_re_nest/electricity_demand_MLED_NEST_GWh_mth.csv b/nest/message_ix_models/data/leap_re_nest/electricity_demand_MLED_NEST_GWh_mth.csv
new file mode 100644
index 0000000..c256123
--- /dev/null
+++ b/nest/message_ix_models/data/leap_re_nest/electricity_demand_MLED_NEST_GWh_mth.csv
@@ -0,0 +1,8641 @@
+"BCU","isurban","year","month","unit","mess_sect","value"
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+1,0,2020,3,"GWh/mth","agri",0.000264674243168601
+1,0,2020,3,"GWh/mth","ind_man",0.305835879977258
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+1,0,2020,4,"GWh/mth","agri",0.000104278483319652
+1,0,2020,4,"GWh/mth","ind_man",0.299027490766752
+1,0,2020,4,"GWh/mth","res_com",0.78936341093426
+1,0,2020,5,"GWh/mth","agri",6.10568335300012e-05
+1,0,2020,5,"GWh/mth","ind_man",0.290139873983946
+1,0,2020,5,"GWh/mth","res_com",0.767210843819947
+1,0,2020,6,"GWh/mth","agri",4.72671297625167e-05
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+1,0,2020,6,"GWh/mth","res_com",0.748403952848872
+1,0,2020,7,"GWh/mth","agri",6.60696628541223e-05
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+1,0,2020,8,"GWh/mth","agri",0.000726682888471323
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+1,0,2020,9,"GWh/mth","agri",0.00284527401914093
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+1,0,2020,10,"GWh/mth","agri",0.00337935267752316
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+1,0,2020,12,"GWh/mth","agri",0.00069433816189529
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+1,0,2030,1,"GWh/mth","agri",0.00196585719697345
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diff --git a/nest/message_ix_models/data/leap_re_nest/energy_total_all.csv b/nest/message_ix_models/data/leap_re_nest/energy_total_all.csv
new file mode 100644
index 0000000..5306434
--- /dev/null
+++ b/nest/message_ix_models/data/leap_re_nest/energy_total_all.csv
@@ -0,0 +1,121 @@
+BCU,year,urb_rur,tec,unit,value
+1,2025,rural,grid,GWh,1.09
+2,2025,rural,grid,GWh,16.68
+3,2025,rural,grid,GWh,1.93
+4,2025,rural,grid,GWh,27.53
+5,2025,rural,grid,GWh,29.13
+6,2025,rural,grid,GWh,37.47
+7,2025,rural,grid,GWh,90.84
+8,2025,rural,grid,GWh,46.94
+9,2025,rural,grid,GWh,10.07
+10,2025,rural,grid,GWh,5.54
+11,2025,rural,grid,GWh,16.18
+12,2025,rural,grid,GWh,2.38
+13,2025,rural,grid,GWh,20.77
+14,2025,rural,grid,GWh,6.38
+15,2025,rural,grid,GWh,11.67
+16,2025,rural,grid,GWh,29.13
+17,2025,rural,grid,GWh,53.42
+18,2025,rural,grid,GWh,13.83
+19,2025,rural,grid,GWh,6.39
+20,2025,rural,grid,GWh,23.35
+21,2025,rural,grid,GWh,1.14
+22,2025,rural,grid,GWh,16.7
+23,2025,rural,grid,GWh,29.25
+24,2025,rural,grid,GWh,0.39
+1,2025,rural,mg_hydro,GWh,0
+2,2025,rural,mg_hydro,GWh,0
+3,2025,rural,mg_hydro,GWh,0
+4,2025,rural,mg_hydro,GWh,0.08
+5,2025,rural,mg_hydro,GWh,0
+6,2025,rural,mg_hydro,GWh,0
+7,2025,rural,mg_hydro,GWh,0
+8,2025,rural,mg_hydro,GWh,0.42
+9,2025,rural,mg_hydro,GWh,0
+10,2025,rural,mg_hydro,GWh,0
+11,2025,rural,mg_hydro,GWh,0
+12,2025,rural,mg_hydro,GWh,0
+13,2025,rural,mg_hydro,GWh,0
+14,2025,rural,mg_hydro,GWh,0
+15,2025,rural,mg_hydro,GWh,0.04
+16,2025,rural,mg_hydro,GWh,0.31
+17,2025,rural,mg_hydro,GWh,0.42
+18,2025,rural,mg_hydro,GWh,0
+19,2025,rural,mg_hydro,GWh,0
+20,2025,rural,mg_hydro,GWh,0
+21,2025,rural,mg_hydro,GWh,0.04
+22,2025,rural,mg_hydro,GWh,0
+23,2025,rural,mg_hydro,GWh,0.06
+24,2025,rural,mg_hydro,GWh,0
+1,2025,rural,mg_pv,GWh,0.02
+2,2025,rural,mg_pv,GWh,4.3
+3,2025,rural,mg_pv,GWh,3.23
+4,2025,rural,mg_pv,GWh,8.91
+5,2025,rural,mg_pv,GWh,2.53
+6,2025,rural,mg_pv,GWh,0.65
+7,2025,rural,mg_pv,GWh,9.31
+8,2025,rural,mg_pv,GWh,7.76
+9,2025,rural,mg_pv,GWh,0.16
+10,2025,rural,mg_pv,GWh,0.19
+11,2025,rural,mg_pv,GWh,0.36
+12,2025,rural,mg_pv,GWh,0.05
+13,2025,rural,mg_pv,GWh,7.78
+14,2025,rural,mg_pv,GWh,14.34
+15,2025,rural,mg_pv,GWh,3.9
+16,2025,rural,mg_pv,GWh,12.67
+17,2025,rural,mg_pv,GWh,18.08
+18,2025,rural,mg_pv,GWh,3.13
+19,2025,rural,mg_pv,GWh,0.18
+20,2025,rural,mg_pv,GWh,4.99
+21,2025,rural,mg_pv,GWh,1.24
+22,2025,rural,mg_pv,GWh,5.15
+23,2025,rural,mg_pv,GWh,31.46
+24,2025,rural,mg_pv,GWh,3.31
+1,2025,rural,sa_pv,GWh,0
+2,2025,rural,sa_pv,GWh,0.42
+3,2025,rural,sa_pv,GWh,0.45
+4,2025,rural,sa_pv,GWh,12.9
+5,2025,rural,sa_pv,GWh,0
+6,2025,rural,sa_pv,GWh,0
+7,2025,rural,sa_pv,GWh,0
+8,2025,rural,sa_pv,GWh,8.72
+9,2025,rural,sa_pv,GWh,0
+10,2025,rural,sa_pv,GWh,0
+11,2025,rural,sa_pv,GWh,0
+12,2025,rural,sa_pv,GWh,0
+13,2025,rural,sa_pv,GWh,6.24
+14,2025,rural,sa_pv,GWh,2.67
+15,2025,rural,sa_pv,GWh,0.24
+16,2025,rural,sa_pv,GWh,5.07
+17,2025,rural,sa_pv,GWh,5.17
+18,2025,rural,sa_pv,GWh,0.48
+19,2025,rural,sa_pv,GWh,0
+20,2025,rural,sa_pv,GWh,14.41
+21,2025,rural,sa_pv,GWh,6.66
+22,2025,rural,sa_pv,GWh,17.26
+23,2025,rural,sa_pv,GWh,110.61
+24,2025,rural,sa_pv,GWh,13.44
+1,2025,urban,grid,GWh,5.74
+2,2025,urban,grid,GWh,298.68
+3,2025,urban,grid,GWh,16.04
+4,2025,urban,grid,GWh,72.53
+5,2025,urban,grid,GWh,1157.79
+6,2025,urban,grid,GWh,40.57
+7,2025,urban,grid,GWh,242.46
+8,2025,urban,grid,GWh,500.06
+9,2025,urban,grid,GWh,2063.73
+10,2025,urban,grid,GWh,9.83
+11,2025,urban,grid,GWh,197
+12,2025,urban,grid,GWh,32.57
+13,2025,urban,grid,GWh,159.45
+14,2025,urban,grid,GWh,15.64
+15,2025,urban,grid,GWh,201.15
+16,2025,urban,grid,GWh,154.64
+17,2025,urban,grid,GWh,222.21
+18,2025,urban,grid,GWh,66.28
+19,2025,urban,grid,GWh,2.69
+20,2025,urban,grid,GWh,143.7
+21,2025,urban,grid,GWh,0
+22,2025,urban,grid,GWh,162.45
+23,2025,urban,grid,GWh,182.56
+24,2025,urban,grid,GWh,3.07
diff --git a/nest/message_ix_models/data/leap_re_nest/grid_cost_bcu.csv b/nest/message_ix_models/data/leap_re_nest/grid_cost_bcu.csv
new file mode 100644
index 0000000..e3a5be3
--- /dev/null
+++ b/nest/message_ix_models/data/leap_re_nest/grid_cost_bcu.csv
@@ -0,0 +1,49 @@
+BCU,urb_rur,mean_dist,w.mean_dist,cost_usd_kW_km,fix_cost_usd_kW,tot_cost_usd2010_kW
+1,_rur,4.973524731745616,4.621119091694185,212,1770,2291.3977061993064
+1,_urb,2.26654,0.7543092721087301,212,1770,1608.261304739209
+2,_rur,9.55652217415164,9.080941430664478,212,1770,3079.2996527507244
+2,_urb,7.839310626702997,1.8971715162131675,212,1770,1810.1669678643264
+3,_rur,23.3269306125551,24.598716072032936,212,1770,5820.773172725819
+3,_urb,33.63517786561265,23.707226964938602,212,1770,5663.276763805819
+4,_rur,15.38506987340525,14.39688118217184,212,1770,4018.449008850359
+4,_urb,11.793435852372584,8.482887188893741,212,1770,2973.643403371228
+5,_rur,6.718076702982282,6.247321961235886,212,1770,2578.6935464850067
+5,_urb,3.764888307155323,0.4146309254201761,212,1770,1548.2514634908978
+6,_rur,6.107860546194073,5.802888871336607,212,1770,2500.1770339361337
+6,_urb,6.127243515850144,5.836295349755402,212,1770,2506.0788451234544
+7,_rur,11.54530883294023,10.264717898118425,212,1770,3288.433495334255
+7,_urb,8.765904778393352,5.598737002086263,212,1770,2464.110203701907
+8,_rur,12.34072314962563,10.21843988797383,212,1770,3280.2577135420433
+8,_urb,12.304018607856651,5.332356692618429,212,1770,2417.0496823625895
+9,_rur,6.324903563129357,5.05846139712313,212,1770,2368.661513491753
+9,_urb,3.4951810584958216,0.08121347226958453,212,1770,1489.3477134342934
+10,_rur,6.0888821693907875,5.509297819811152,212,1770,2448.3092814999704
+10,_urb,3.669456375838926,1.4173208843062204,212,1770,1725.3933562274324
+11,_rur,4.35968078363584,2.021465067379349,212,1770,1832.1254952370184
+11,_urb,2.6330487804878047,0.9557581383333392,212,1770,1643.85060443889
+12,_rur,6.13566006097561,6.129484541278141,212,1770,2557.8756022924717
+12,_urb,8.135428571428571,1.7175112303139342,212,1770,1778.4269840221282
+13,_rur,16.74176991627553,16.750748626701103,212,1770,4434.2989240505285
+13,_urb,15.037524973432518,7.333615635598758,212,1770,2770.6054289557806
+14,_rur,44.36753633411321,45.22660600382322,25,1770,2417.2209584129837
+14,_urb,47.425075977653634,39.94268374438931,25,1770,2307.1392446747777
+15,_rur,15.996613893151068,14.541192833627534,212,1770,4043.9440672741985
+15,_urb,13.7221861575179,3.4669133750692276,212,1770,2087.4880295955636
+16,_rur,21.94951888514074,17.420920282508103,212,1770,4552.695916576431
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diff --git a/nest/message_ix_models/data/leap_re_nest/input_data_12_ZMB.xlsx b/nest/message_ix_models/data/leap_re_nest/input_data_12_ZMB.xlsx
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diff --git a/nest/message_ix_models/data/leap_re_nest/input_data_48_CHN - Copy.xlsx b/nest/message_ix_models/data/leap_re_nest/input_data_48_CHN - Copy.xlsx
new file mode 100644
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diff --git a/nest/message_ix_models/project/leap_re_nest/build.py b/nest/message_ix_models/project/leap_re_nest/build.py
new file mode 100644
index 0000000..fa79b0b
--- /dev/null
+++ b/nest/message_ix_models/project/leap_re_nest/build.py
@@ -0,0 +1,120 @@
+# LEAP-RE NEST implementation of a national model
+
+import ixmp as ix
+import message_ix
+import pandas as pd
+from message_ix_models.util import private_data_path
+
+from message_data.projects.leap_re_nest.utils import map_basin
+from message_data.projects.leap_re_nest.script.add_timeslice import (
+    xls_to_df,
+    time_setup,
+    duration_time
+    )
+
+from message_data.projects.leap_re_nest.script import add_MLED_demand
+from message_data.projects.leap_re_nest.script import add_grid_shares_OnSSET
+
+# 1) Generate a Country model. See documentation #
+
+# 2) adjust nodes, years and time-steps 
+# 
+# 2.1 add sub-basin nodes
+
+# load a scenario
+# IIASA users
+mp = ix.Platform(name='ixmp_dev' , jvmargs=['-Xmx14G'])
+# external users
+# mp2 = ix.Platform(name='local' , jvmargs=['-Xmx14G'])
+
+modelName = 'MESSAGEix_ZM'
+scenarioName = 'single_node' 
+scen2Name = 'sub-units'
+
+# IIASA users
+sc_ref = message_ix.Scenario(mp, modelName, scenarioName,cache=True)
+# sc_ref.to_excel(private_data_path("projects","leap_re_nest","ref_scen.xlsx") )
+# # external users in local database
+# sc_ref2 = ix.Scenario(mp2, modelName, "test", version='new',annotation="load from excel")
+# # the following units seem to not be present in the platform, need to add them the first time
+# ix.Platform.add_unit(mp2,unit = 'MUSD/a')
+# ix.Platform.add_unit(mp2,unit = 'GWa/a')
+# ix.Platform.add_unit(mp2,unit = 'TUSD')
+
+# sc_ref2.read_excel(private_data_path("projects","leap_re_nest","ref_scen.xlsx"),
+#                    add_units=True,
+#                    init_items=True,
+#                    commit_steps=True)
+# sc_ref2.commit("")
+# sc_ref2.solve(solve_options={"lpmethod": "4"},model="MESSAGE")
+
+# for all
+sc = sc_ref.clone(modelName, scen2Name,keep_solution=False)
+
+sc.check_out()
+# add basins
+map_basin(sc)
+# check
+sc.set('node')
+sc.commit("add nodes")
+
+# 2.2 add sub-annual time steps
+n_time = 12     # number of time slices <= file ID
+file_id = "12"
+model_family = "ZMB"
+set_update = True  # if True, adds time slices and set adjustments
+last_year = 2060  # either int (year) or None (removes extra years)
+node_exlude = ["World"]
+
+xls_file = "input_data_" + file_id + "_" + model_family + ".xlsx"
+path_xls = private_data_path("projects","leap_re_nest",xls_file)
+
+if sc.has_solution():
+        sc.remove_solution()
+
+nodes = [x for x in sc.set("node") if x not in ["World"] + node_exlude]
+
+# 2.2.1) Loading Excel data (time series)
+xls = pd.ExcelFile(path_xls)
+
+# 2.2.1) Updating sets related to time
+# Adding subannual time slices to the relevant sets
+duration, df_time, dict_xls = xls_to_df(xls, n_time, nodes)
+times = df_time["time"].tolist()
+
+if set_update:
+    time_setup(sc, df_time, last_year)
+    duration_time(sc, df_time)
+    if last_year:
+        df = sc.par("bound_activity_up")
+        assert max(set(df["year_act"])) <= last_year
+    
+sc.set('map_time')
+sc.set_as_default()
+
+# scen_list = mp.scenario_list(default=False)
+# scen_list = scen_list[(scen_list['model']==modelName)]
+
+# 3) Demand processing
+# TODO adding the scenario dimension
+# scen = ["baseline", "moderate", "increased"]
+scen3Name = "MLED_demand"
+sc3 = sc.clone(modelName, scen3Name,keep_solution=False)
+
+add_MLED_demand.main(sc3)
+add_grid_shares_OnSSET.main(sc3)
+
+caseName = sc3.model + "__" + sc3.scenario + "__v" + str(sc3.version)
+
+# %%4) add water structure
+
+# when using the CLI it would be something like
+# with the correct scenari name
+# mix-models --url=ixmp://ixmp_dev/MESSAGEix_ZM/MLED_demand water --regions=ZMB nexus --rcps=7p0 --rels=low
+
+# Solving the model
+sc3.solve(solve_options={"lpmethod": "4"},model="MESSAGE", case=caseName)
+sc3.set_as_default()
+
+# ADD GDP and Pop info in the timeseries
+
diff --git a/nest/message_ix_models/project/leap_re_nest/doc.rst b/nest/message_ix_models/project/leap_re_nest/doc.rst
new file mode 100644
index 0000000..9a08f10
--- /dev/null
+++ b/nest/message_ix_models/project/leap_re_nest/doc.rst
@@ -0,0 +1,84 @@
+LEAP-RE NEST implementation of a national model
+***********************************************
+
+Project organization
+====================
+
+- https://www.leap-re.eu/re4afagri/
+- Duration 2021-05 to 2024-03
+- Modeling work is divided into 4 parts across the consortium partners. The ECE Program is involved with the development of the MLED model and NEST, the latter using the MESSAGEix framework.
+- `Comprehensive Project documentation <https://re4afagri-platform-docs.readthedocs.io/en/latest/>`_
+- `p:LEAP_RE label <https://github.com/iiasa/message_data/issues?q=label:p:LEAP_RE>`_ on GitHub.
+
+.. contents::
+   :local:
+
+Task 12.2 Integration of Modelling Infrastructure
+-------------------------------------------------
+
+Project Lead:
+  Giacomo Falchetta
+Lead Modeler:
+  Adriano Vinca
+Technical Advice:
+  Muhammad Awais
+
+- Build a country (Zambia) national model, using the downscaling prototype​
+- Set right setup for parameters that will not be edited later, e.g. resources​
+- Add sub-annual time steps​
+- Energy demand from MLED (will require the basin structure, either add only the nodes, or do after adding the water module). Not all demand will be edited​
+- Rural energy supply options (ONSET connection: solar, small generation, minigrid, connection to main grid​
+- Water module (Need clean PR to be finalized)​
+- Crops, connect to Watercrop
+
+Material and code
+=================
+
+This folder contains scripts and data processing tools for developing national models for climate-energy-land-water analysis. Particulat features are the sub-national spatial disaggregation and the sub-annual time resolution.
+
+.. contents::
+   :local:
+   :backlinks: none
+
+Folders
+=======
+
+:file:`data/projects/leap_re_nest`
+   TODO
+:file:`message_data/projects/leap_re_nest/script/`
+   Contains the script to build scenarios, run the model and post-processing.
+
+Usage
+=====
+
+Make sure *ixmp* amd *message_ix* packages are cloned and *ixmp* is installed.
+
+**Different required steps rely on external repositories (waiting for PRs)**
+
+
+1) Country model prototype
+--------------------------
+
+Clone the repository `Github branch <https://github.com/iiasa/message_single_country/tree/message_zmb>`_ 
+run the script run :file:`interface_standalone.py`.
+
+
+2) Scenario generation and run
+------------------------------
+
+In :file:`/projects/leap_re_nest/script/`:
+
+1. :file:`1_adding_time_steps.py` this file take a scenario from the ENGAGE family, clones it, and calibrates it with MACRO.
+   The output will have the scenario name ``baseline_calibrated``.
+
+   - Make sure the model and scenario names are those desired.
+
+2. :file:`2_fix_units.py` this script takes the baseline calibrated scenario adjust units for few reported variables and add historical data from original ENGAGE scenario.
+
+   - Make sure the ENGAGE scenario for historical data is the same as in step 1.
+
+3. :file:`3_scenario_editor_R11_markers.py` this script generates, calibrates and run the post-processing for four different COVID-19 scenarios with a fixed GDP trajectory.
+
+4. [not necessary for the following steps] In case interested in running scenarios that explore GDP uncertainty, run :file:`4. GDP_sensitivity.py`.
+
+The scripts :file:`scenario_editor_functions.py` and :file:`adjust_growth_rates.py` contain functions used in 1-4, do not need to be run separately.
diff --git a/nest/message_ix_models/project/leap_re_nest/script/add_MLED_demand.py b/nest/message_ix_models/project/leap_re_nest/script/add_MLED_demand.py
new file mode 100644
index 0000000..326bdea
--- /dev/null
+++ b/nest/message_ix_models/project/leap_re_nest/script/add_MLED_demand.py
@@ -0,0 +1,82 @@
+# add MLED demand and create distribution line technologies
+# also using OnSSeT data
+from message_ix_models.util import (broadcast,private_data_path)
+import numpy as np
+import pandas as pd
+
+def dem_from_csv(sc,csv_file):
+    
+    strings = ["B"+ str(x) for x in csv_file['BCU']]
+    csv_file['BCU'] = strings
+    
+    nodes = list(sc.set('node'))
+    nodes_df = pd.DataFrame({
+        "node" : nodes,
+        "BCU" : [x.split("|")[0] for x in nodes]
+        })
+    
+    dem = csv_file.merge(nodes_df,how = 'left')
+    dem = dem[dem['mess_sect'] != "agri"]
+    dem_names = sc.idx_names('demand')
+    dem_ur = dem[dem["isurban"] == 1]
+    dem_ur['mess_sect'] = dem_ur['mess_sect']+ "_urb"
+    dem_rur = dem[dem["isurban"] == 0]
+    dem_rur['mess_sect'] = dem_rur['mess_sect']+ "_rur"
+    
+    dem = pd.concat([dem_ur,dem_rur]) 
+    dem['value'] = dem['value'] / 8760 # from GWh/month to GWa/m
+    dem.drop(columns=["isurban"],inplace = True)
+    dem.columns = ['id', 'year', 'time', 'unit', 'commodity', 'value', 'node']
+    dem['level'] = "useful"
+    dem['unit'] = 'GWa/a' # actual unit is GWa/m
+    dem = dem[[*dem_names,'unit','value']]
+    
+    return dem
+
+
+def main(sc):
+    """ This script removes old electricity demand from MESSAGE and
+    replaces it with estimated demand from MLED, with sub-annual timestep 
+    (by adding the technologies...)
+    """
+
+    #load previous total demand in firstmodelyear
+    firstmy = sc.firstmodelyear
+    dem_el_old = sc.par("demand",{"year" : firstmy,
+                                  "commodity" : ["i_spec","rc_spec"]})
+    tot_dem_el_old = sum(dem_el_old.value) # GWa/a
+    
+    dem_remove = sc.par("demand", {"commodity" : ["i_spec","rc_spec"]})
+    sc.check_out()
+    sc.remove_par("demand",dem_remove)
+    print("Removed old electricity demand: " + str(tot_dem_el_old) + 
+          " GWa/a")
+    
+    file = "electricity_demand_MLED_NEST_GWh_mth.csv"
+    path_csv = private_data_path('projects','leap_re_nest',file)
+    
+    dem_csv = pd.read_csv(path_csv)
+    dem = dem_from_csv(sc,dem_csv)
+    tot_dem_el_new = sum(dem[dem["year"] == firstmy]["value"])
+    
+    #add demand
+    sc.add_set('commodity',["ind_man_urb","ind_man_rur",
+                            "res_com_urb","res_com_rur"])
+    sc.add_par('demand',dem)
+    sc.commit("")
+    print('Electricity demand from MLED added ' + str(tot_dem_el_new) + 
+          " GWa/a")
+    
+
+
+if __name__ == '__main__':
+    import sys
+    # parse sys.argv[1:] using optparse or argparse or what have you
+    main('test')
+    
+    
+    
+    
+    
+    
+    
diff --git a/nest/message_ix_models/project/leap_re_nest/script/add_grid_shares_OnSSET.py b/nest/message_ix_models/project/leap_re_nest/script/add_grid_shares_OnSSET.py
new file mode 100644
index 0000000..2515d16
--- /dev/null
+++ b/nest/message_ix_models/project/leap_re_nest/script/add_grid_shares_OnSSET.py
@@ -0,0 +1,406 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Mon Nov 14 16:38:39 2022
+
+@author: vinca
+"""
+
+# add OnSSET shares of grid vs off grid electricity
+from message_ix_models.util import (broadcast,private_data_path,
+                                    same_node,same_time)
+import numpy as np
+import pandas as pd
+from message_ix import make_df
+from message_data.model.water.utils import map_yv_ya_lt
+
+def add_trsm_dist_basin(sc):
+    
+    # sub-time
+    ti = list(sc.set('time'))
+    ti.remove('year')
+    # sub-nodes, basins
+    ba = np.array(sc.set('node'))
+    ba = ba[['|' in x for x in ba]]
+    # modes for sub-basins
+    mo = np.array(sc.set('mode'))
+    mo = mo[['|' in x for x in mo]]
+    
+    map_mode_node = pd.DataFrame({"mode" : mo,
+                                  "node_loc" : ba})
+    last_vtg_year = 2010
+    file = "OnSSET_cost_paramters.csv"
+    path_csv = private_data_path('projects','leap_re_nest',file)
+    onsset_pars = pd.read_csv(path_csv)
+    file = "grid_cost_bcu.csv"
+    path_csv = private_data_path('projects','leap_re_nest',file)
+    grid_cost = pd.read_csv(path_csv)[["BCU", "urb_rur","tot_cost_usd2010_kW"]]
+    grid_loss = onsset_pars["Value"][onsset_pars["Variable"] == "grid_losses"]
+    sc.check_out()
+    
+    for ur in ["_urb","_rur"]:
+        # load elec_t_d and make a elec_t_basin with outputs to the basin by mode
+        inp_t = sc.par('input',{'technology' : "elec_t_d"})
+        inp_t["mode"] = np.nan
+        inp_t["node_loc"] = np.nan
+        inp_t["time"] = np.nan
+        map_mode_node_t = pd.DataFrame({"mode" : mo,
+                                      "node_loc" : ba})
+        inp_t = inp_t.pipe(broadcast, map_mode_node_t, time = ti)   
+        inp_t['technology'] = 'elec_t_d_basin'+ur
+        
+        sc.add_set('technology', ["elec_t_d_basin"+ur,"sp_el_I_basin"+ur,
+                                  "sp_el_RC_basin"+ur])  
+        sc.add_set('level',["sec_urb","sec_rur",
+                            "final_urb","final_rur"])
+        # no ned for parameter before 2010, save space
+        inp_t = inp_t[inp_t["year_vtg"] >= last_vtg_year]
+        sc.add_par('input',inp_t)
+        
+        # from country to sub-basins
+        out_t = sc.par('output',{'technology' : "elec_t_d"})
+        out_t["time_dest"] = np.nan
+        out_t["time"] = np.nan
+        out_t["mode"] = np.nan
+        out_t["node_dest"] = np.nan
+        out_t["node_loc"] = np.nan
+        map_mode_node_t = pd.DataFrame({"mode" : mo,
+                                      "node_loc" : ba})
+        out_t = out_t.pipe(broadcast, map_mode_node_t,time = ti).pipe(same_time).pipe(same_node)  
+        out_t['technology'] = 'elec_t_d_basin'+ur
+        out_t['level'] = 'sec'+ur
+        # Apply grid losses from OnSET
+        out_t['value'] = out_t['value'] - float(grid_loss)
+        # no ned for parameter before 2010, save space
+        out_t = out_t[out_t["year_vtg"] >= last_vtg_year]
+        sc.add_par('output',out_t)
+        
+        # tec lifetime
+        tecl_t = sc.par('technical_lifetime',{'technology' : "elec_t_d"})
+        tecl_t['technology'] = 'elec_t_d_basin'+ur
+        tecl_t["node_loc"] = np.nan
+        tecl_t = tecl_t.pipe(broadcast, node_loc = ba)
+        tecl_t = tecl_t[tecl_t["year_vtg"] >= last_vtg_year]
+        sc.add_par('technical_lifetime',tecl_t)
+        
+        # add inv cotst
+        grid_cost
+        inv_t = sc.par('inv_cost',{'technology' : "elec_t_d"})
+        inv_t['technology'] = 'elec_t_d_basin'+ur
+        inv_t["node_loc"] = np.nan
+        inv_t = inv_t.pipe(broadcast, node_loc = ba)
+        inv_t["BCU"] = [int(x.split('|')[0][1:]) for x in inv_t.node_loc]
+        inv_t = inv_t.merge(grid_cost[grid_cost["urb_rur"] == ur], 
+                             how = "left")
+        inv_t["value"] = inv_t["tot_cost_usd2010_kW"]
+        inv_t = inv_t[['node_loc', 'technology', 'year_vtg', 'value', 'unit',]]
+        inv_t = inv_t[inv_t["year_vtg"] >= last_vtg_year]
+        
+        sc.add_par('inv_cost',inv_t)
+        # fix cost
+        fix_t = sc.par('fix_cost',{'technology' : "elec_t_d"})
+        fix_t['technology'] = 'elec_t_d_basin'+ur
+        fix_t["node_loc"] = np.nan
+        fix_t = fix_t.pipe(broadcast, node_loc = ba)
+        fix_t = fix_t[fix_t["year_vtg"] >= last_vtg_year]
+        sc.add_par('fix_cost',fix_t)
+        # add var cost
+        var_t = sc.par('var_cost',{'technology' : "elec_t_d"})
+        # add the mode dimension when creating heterogenous cost mapping
+        var_t["mode"] = np.nan
+        var_t["node_loc"] = np.nan
+        var_t["time"] = np.nan
+        var_t = var_t.pipe(broadcast, map_mode_node_t,time = ti)
+        var_t['technology'] = 'elec_t_d_basin'+ur
+        var_t = var_t[var_t["year_vtg"] >= last_vtg_year]
+        sc.add_par('var_cost',var_t)
+            
+        # add sp_el_I and sp_el_RC to link the new transmission technologies to the new demands
+        # between transmission and final user there is a splitter that takes some energy form rural generation
+        
+        inp = sc.par('input',{'technology' : "sp_el_I"})
+        inp["time"] = np.nan
+        inp["node_loc"] = np.nan
+        inp = inp.pipe(broadcast, time = ti,
+                       node_loc = ba)           
+        inp["level"] = 'final'+ur
+        inp["time_origin"] = inp["time"]
+        inp["node_origin"] = inp["node_loc"]
+        
+        inp_I = inp.copy(deep = True)
+        inp_I['technology'] = 'sp_el_I_basin'+ur
+        inp_RC = inp.copy(deep = True)
+        inp_RC['technology'] = 'sp_el_RC_basin'+ur
+        inp_RC = inp_RC[inp_RC["year_vtg"] >= last_vtg_year]
+        sc.add_par('input', pd.concat([inp_I,inp_RC]) )    
+        # output, need to be split into basins
+        out = sc.par('output',{'technology' : "sp_el_I"})
+        out["time"] = np.nan
+        out["node_loc"] = np.nan
+        out = out.pipe(broadcast, time = ti,
+                       node_loc = ba)           
+        out["time_dest"] = out["time"]
+        out["node_dest"] = out["node_loc"]
+        
+        out_I = out.copy(deep = True)
+        out_I['technology'] = 'sp_el_I_basin'+ur
+        out_I['commodity'] = 'ind_man'+ur
+        out_RC = out.copy(deep = True)
+        out_RC['technology'] = 'sp_el_RC_basin'+ur
+        out_RC['commodity'] = 'res_com'+ur
+        
+        #existing capacity should be added
+        # 1.  first save a model output where the model builds needed capacity in 2020
+        # 2. that can be used as calibration and to be compared with OnSSET data
+        # This is done externlly to this function, in calib_energy()
+        out_RC = out_RC[out_RC["year_vtg"] >= last_vtg_year]
+        sc.add_par('output', pd.concat([out_I,out_RC]) ) 
+        
+    sc.commit('new electricity transmission')
+    print('New distribution and linkaged to basin\'s demand added')
+    
+def add_offgrid_gen(sc):
+    
+    # get some info needed
+    first_year = sc.firstmodelyear
+    years = sc.set("year")
+    
+    ti = list(sc.set('time'))
+    ti.remove('year')
+    # lifetime of offgrid technologies
+    lt = 30
+    yv_ya_gr = map_yv_ya_lt(years[years >= first_year-lt], lt, first_year)
+    
+    # add needed sets
+    sc.check_out()
+    sc.add_set("technology",["offgrid_urb","offgrid_rur"])
+    sc.add_set("level",["offgrid_final_urb","offgrid_final_rur"])
+    
+    file = "energy_total_all.csv"
+    path_csv = private_data_path('projects','leap_re_nest',file)
+    onsset_en = pd.read_csv(path_csv)
+    
+    strings = ["B"+ str(x) for x in onsset_en['BCU']]
+    onsset_en['BCU'] = strings
+    
+    nodes = list(sc.set('node'))
+    nodes_df = pd.DataFrame({
+        "node" : nodes,
+        "BCU" : [x.split("|")[0] for x in nodes]
+        })
+    
+    # Calculate the share of grid vs offgrid generation and use it to make a 
+    # share constraints
+    df = onsset_en.merge(nodes_df,how = 'left')
+    df_grid = df.copy()
+    df_grid["tec"][df_grid["tec"] != "grid"] = "offgrid"
+    df_grid = (df_grid
+         .groupby(['node','year','urb_rur',"tec"],as_index=False)
+         .sum("value")
+         )
+    df_grid = (df_grid
+         .groupby(['node','year','urb_rur'])
+         .apply(lambda grp: grp.assign(share = lambda x: x.value / x.value.sum() ) )
+    )
+    df_grid["urb_rur"] = "_" + df_grid["urb_rur"]
+    
+    # also calculate the share of different off-grid technologies within 
+    # the off-grid generation. This is used to assign costs to the single
+    # off-grid technology
+    
+    df_off = df.copy()
+    df_off = df_off[df_off["tec"] != "grid"]
+    df_off = (df_off
+         .groupby(['node','year','urb_rur'])
+         .apply(lambda grp: grp.assign(share = lambda x: x.value / x.value.sum() ) )
+    )
+    
+    # load file with investment cost assumption from OnSSET
+    file = "OnSSET_cost_paramters.xlsx"
+    path_xls = private_data_path('projects','leap_re_nest',file)
+    tec_cost = pd.read_excel(path_xls, sheet_name="for_NEST")
+    
+    df_offc = df_off.merge(tec_cost,how = "left")
+    df_offc['tot_cost'] = df_offc["share"] * df_offc["inv_cost"]
+    df_offc = (df_offc
+         .groupby(['node','year','urb_rur'],as_index=False)
+         .sum("tot_cost")
+         )
+    
+    df_offc["urb_rur"] = "_" + df_offc["urb_rur"]
+    
+    for ur in ["_urb","_rur"]:
+        
+        # only one off-grid technology
+        out_t = (
+            make_df(
+                "output",
+                technology = "offgrid" + ur,
+                commodity = "electr",
+                level  = "offgrid_final"+ur,
+                time = ti,
+                mode = "M1",    
+                unit = "GWa",
+                value = 1,
+            )
+            .pipe(broadcast,yv_ya_gr,
+                  node_loc = df.node.unique(),
+                  )
+            .pipe(same_node)
+            .pipe(same_time)
+        )
+        
+        # lifetime
+        lt_t = (
+            make_df( "technical_lifetime",
+            technology = "offgrid" + ur,
+            year_vtg = out_t.year_vtg.unique(),
+            unit = "year",
+            value = lt
+            )
+        .pipe(broadcast, 
+              node_loc = df.node.unique() )
+        )
+            
+        # capital cost
+        inv_df = df_offc[df_offc['urb_rur'].str.contains(ur) ]
+         
+        inv_t = (
+            make_df( "inv_cost",
+            technology = "offgrid" + ur,
+            # year_vtg = inv_df["year"], # to change once we have values for all years
+            unit = "USD/kW",
+            node_loc = inv_df["node"],
+            value = 3000 if inv_df.empty else  inv_df["tot_cost"]
+            )
+        .pipe(broadcast, 
+              year_vtg = out_t.year_vtg.unique()
+              )
+        )
+        
+        # add paramenters
+        sc.add_par("output",out_t)
+        sc.add_par("technical_lifetime",lt_t)
+        sc.add_par("inv_cost",inv_t)
+        
+        # add technology with share constraint
+        sc.add_set("technology",["elec_fin" + ur])
+        map_lvl_mode = pd.DataFrame({"level" : ["sec" + ur, "offgrid_final" + ur],
+                                     "mode" : ["M1","M2"]})
+        inp_f = (
+            make_df("input",
+                    technology = "elec_fin" + ur,
+                    commodity = "electr",
+                    year_vtg = out_t.year_vtg.unique(),
+                    year_act = out_t.year_vtg.unique(),
+                    unit = "GWa",
+                    value = 1
+                    )
+            .pipe(broadcast, map_lvl_mode,
+                  time = ti,
+                  node_loc = df.node.unique())
+            .pipe(same_node)
+            .pipe(same_time)
+            )
+        
+        out_f = (
+            make_df("output",
+                    technology = "elec_fin" + ur,
+                    commodity = "electr",
+                    level = "final" + ur,
+                    year_vtg = out_t.year_vtg.unique(),
+                    year_act = out_t.year_vtg.unique(),
+                    unit = "GWa",
+                    value = 1
+                    )
+            .pipe(broadcast, mode = ["M1","M2"],
+                  time = ti,
+                  node_loc = df.node.unique())
+            .pipe(same_node)
+            .pipe(same_time)
+            )
+        
+        sc.add_par("input",inp_f)
+        sc.add_par("output",out_f)
+        
+        # add share for the electricity use from df_gridd
+        sc.add_set("shares",["share_grid"])
+        share_df = df_grid[df_grid['urb_rur'].str.contains(ur) ]
+        share_df["share"][share_df["share"].isna() ] = 1
+        share_g = share_df[share_df["tec"] == "grid"]
+        share_og = share_g.copy()
+        share_og["tec"] = "offgrid"
+        share_og["share"] = 1 - share_og["share"]
+        share_df = pd.concat([share_g,share_og])
+        
+        share_df["mode"] = np.where(share_df['tec'] == "grid", 'M1','M2')
+        
+        share_f = (
+            make_df(
+            "share_mode_up",
+            shares="share_grid",
+            technology="elec_fin"+ur,
+            mode=share_df["mode"],
+            node_share=share_df["node"],
+            value=share_df["share"],
+            unit="%",
+            # year_act=df_sw["year"], # to be fixed when we have annual values
+            )
+            .pipe(broadcast,
+                  time = ti,
+                  year_act = out_t.year_vtg.unique() )
+        )
+        
+        sc.add_par("share_mode_up",share_f)
+        sc.add_par("share_mode_lo",share_f)
+        
+    sc.commit("adding off-grid generation and share constraints")
+    print("Adding off-grid generation and share constraints")
+    
+def calib_energy(sc,sc_cali):
+    """ calibrating the historical capacity of different energy technologies
+    including transmission lines
+    """
+    calib_cap = sc_cali.var("CAP_NEW")
+    calib_cap = calib_cap[(calib_cap["year_vtg"] == 2020) & 
+                          (calib_cap["lvl"] > 0)]
+    calib_cap["value"] = calib_cap["lvl"]
+    # if set to 2020 it will be inglored by the model (not consider as historical)
+    calib_cap["year_vtg"] = 2015
+    calib_cap.drop(columns = {"lvl","mrg"}, inplace=True)
+    calib_cap.reset_index(drop = True, inplace=True)
+    calib_cap["unit"] = "GWa/a"
+    
+    # add it as historical new capacity to the main scenario
+    sc.check_out()
+    sc.add_par("historical_new_capacity", calib_cap)
+    sc.commit("calibrating historical new capcity")
+    print("Calibrated historical new caapcity added to the model")
+    
+def main(sc):
+    """ Adding transmission technologies in the sub-nodes and off-grid generation
+    """
+    
+    #load previous total demand in firstmodelyear
+    
+    add_trsm_dist_basin(sc)
+    add_offgrid_gen(sc)
+    
+    # calibration
+    sc_cali = sc.clone(sc.model, sc.scenario + '_cali',keep_solution=False)
+    sc_cali.check_out()
+    ytr = sc.set("year")
+    ytr = ytr[ytr > sc.firstmodelyear]
+    print("Removing future years for calibration")
+    sc_cali.remove_set("year",ytr)
+    sc_cali.commit("removing future years for calibration...")
+    print( "Running the calibration...")
+    sc_cali.solve(solve_options={"lpmethod": "4"},model="MESSAGE")
+    sc_cali.set_as_default()
+    print("calibration solved")
+    
+    calib_energy(sc,sc_cali)
+
+
+if __name__ == '__main__':
+    # parse sys.argv[1:] using optparse or argparse or what have you
+    main('test')
\ No newline at end of file
diff --git a/nest/message_ix_models/project/leap_re_nest/script/add_timeslice.py b/nest/message_ix_models/project/leap_re_nest/script/add_timeslice.py
new file mode 100644
index 0000000..79c434b
--- /dev/null
+++ b/nest/message_ix_models/project/leap_re_nest/script/add_timeslice.py
@@ -0,0 +1,1273 @@
+# -*- coding: utf-8 -*-
+"""
+This script receives a scenario with only 'year' as time slice, and an Excel
+file for the input data for a number of sub-annual time slices.
+Then the script does the following:
+    - it reads data of timeslices and their duration from Excel
+    - it adds timeslices to relevant parameters, e.g., "output", "input",
+    "capacity_factor", "var_cost" etc., for those technologies that their
+    electricity generation must be at the sub-annual level.
+    - adds year-equivalent of all technologies with timeslices, by relations
+    for summing up each technology to 'year'.
+    - modifies parameters as needed
+    Als it can do the following (some are optional):
+    - remove extra vintage years, and exctra technologies
+    - correct capacity factor of zero for VRE
+    - copying historical data to "ref" parameters
+"""
+
+import pandas as pd
+import os
+from itertools import product
+from datetime import datetime
+from timeit import default_timer as timer
+
+# path_files = r"C:\Users\zakeri\Documents\Github\python_scripts"
+# os.chdir(path_files)
+
+# 1.1) A function for reading input data from excel and formatting as needed
+def xls_to_df(xls, n_time, nodes, exclude_sheets=["peak_demand"]):
+    # Converting time series based on the number of time slices
+    df_time = xls.parse("time_steps", converters={"time": str})
+    df_time = df_time.loc[df_time["lvl_temporal"] != "Sum"]
+    n_xls = len(df_time["time"])
+    dur = df_time["duration_time"]
+    length = int(n_xls / n_time)
+    par_list = [x for x in xls.sheet_names if x not in ["time_steps"] + exclude_sheets]
+
+    dict_xls = {}
+    for p in par_list:
+        df_xls = xls.parse(p)
+        idx = df_xls.columns[0]
+        df_xls = df_xls.set_index(idx)
+
+        new = pd.DataFrame(index=range(1, n_time + 1), columns=df_xls.columns)
+        for col, rate in df_xls.loc["rate", :].to_dict().items():
+            df_xls = df_xls.loc[df_xls.index != "rate"]
+            z = 1
+            for i in list(range(0, n_xls, length)):
+                if rate == "Yes":
+                    new.loc[z, col] = float(
+                        df_xls.iloc[i: i + length, :][col].sum())
+                elif rate == "No":
+                    new.loc[z, col] = float(
+                        df_xls.iloc[i: i + length, :][col].mean())
+                z = z + 1
+            new.loc["rate", col] = rate
+
+        # populating "all" nodes
+        for col in new.columns:
+            if col.split(",")[0] == "all":
+                for node in nodes:
+                    c = node + "," + col.split(",")[1]
+                    new[c] = new.loc[:, col]
+                new = new.drop(col, axis=1)
+
+        dict_xls[p] = new
+
+    duration = [round(x, 8) for x in dur.groupby(dur.index // length).sum()]
+    df_time = df_time.loc[0: n_time - 1, :]
+    df_time["duration_time"] = duration
+    return duration, df_time, dict_xls
+
+
+# 1.2) A function for updating sets and duration time after adding time slices
+def time_setup(sc, df, last_year=False, remove_old_time_lvl=False):
+    sc.check_out()
+    # Changing the last year if needed
+    if last_year:
+        print("- Removing extra model years...")
+        for y in [x for x in sc.set("year").tolist() if int(x) > last_year]:
+            sc.remove_set("year", y)
+        # sc.commit('')
+        # sc.check_out()
+        print("- Extra model years bigger than {}" " removed.".format(str(last_year)))
+
+    # Adding sub-annual time slices to the set 'time'
+    for t in df["time"].tolist():
+        sc.add_set("time", t)
+
+    # Adding sub-annual time levels to the set 'lvl_temporal'
+    for l in set(df["lvl_temporal"]):
+        sc.add_set("lvl_temporal", l)
+
+    # Adding temporal hierarchy
+    df_ref = sc.set("map_temporal_hierarchy")
+    d = pd.DataFrame(columns=df_ref.columns, index=[0])
+
+    for i in df.index:
+        d["lvl_temporal"] = df.loc[i, "lvl_temporal"]
+        d["time"] = df.loc[i, "time"]
+        d["time_parent"] = df.loc[i, "parent_time"]
+        # df = df.append(df_new, ignore_index=True)
+        sc.add_set("map_temporal_hierarchy", d)
+
+    if remove_old_time_lvl:
+        sc.remove_set("map_temporal_hierarchy", df_ref)
+    sc.commit("")
+    print("- All sets modified for the new time slices.")
+
+
+# 1.3) A function for modifying duration time
+def duration_time(sc, df):
+    sc.check_out()
+    df_ref = sc.par("duration_time")
+    d = df_ref.copy()
+    sc.remove_par("duration_time", df_ref)
+
+    for i in df.index:
+        d["time"] = df.loc[i, "time"]
+        d["unit"] = "%"
+        df["value"] = df.loc[i, "duration_time"]
+        sc.add_par("duration_time", df)
+
+    check = sc.par("duration_time")
+    check.loc[0, "value"] = check.loc[0, "value"] + 1.00 - check["value"].sum()
+    sc.add_par("duration_time", check.loc[[0]])
+    sc.commit("duration time modified")
+    print('- Parameter "duration_time" updated for new values.')
+
+
+# 1.4) Setting up for parameters
+def par_setup(sc, dict_xls, par_update):
+    sc.check_out()
+    xls_pars = [x for x in dict_xls.keys() if x in sc.par_list()]
+
+    index_cols = {}
+    for parname in set(list(par_update.keys()) + xls_pars):
+        if "technology" in sc.idx_names(parname):
+            index_cols[parname] = "technology"
+        elif "commodity" in sc.idx_names(parname):
+            index_cols[parname] = "commodity"
+        elif "relation" in sc.idx_names(parname):
+            index_cols[parname] = "relation"
+
+        if parname in xls_pars:
+            df_xls = dict_xls[parname]
+
+            for item in df_xls.columns:
+                if df_xls.loc["rate", item] == "No":
+                    if "," in item:
+                        node_col = [x for x in sc.idx_names(parname) if "node" in x]
+                        df_item = sc.par(
+                            parname,
+                            {
+                                index_cols[parname]: [item.split(",")[1]],
+                                node_col[0]: [item.split(",")[0]],
+                            },
+                        )
+                    else:
+                        df_item = sc.par(parname, {index_cols[parname]: [item]})
+                    df_item["value"] = 1
+                    sc.add_par(parname, df_item)
+
+                if parname not in par_update.keys():
+                    par_update[parname] = {}
+
+                par_update[parname][item] = df_xls[item].tolist()
+    sc.commit("")
+    print("- Input data of parameters are set up.")
+    return par_update, index_cols
+
+
+# 1.5) The main function for adding timeslices to a scenario
+def par_time_update(sc, parname, data_dict, index_col, item_list, n1, nn, sc_ref=None):
+    sc.check_out()
+    if not sc_ref:
+        sc_ref = sc
+
+    node_tec = [
+        (k.split(",")[0], k.split(",")[1]) for k in data_dict.keys() if "," in k
+    ]
+    node_col = [
+        x for x in sc.idx_names(parname) if x in ["node", "node_loc", "node_rel"]
+    ][0]
+    for key, ratio in data_dict.items():
+        df = []
+
+        if key == "all":
+            if item_list:
+                df_ref = sc_ref.par(parname, {index_col: item_list, "time": "year"})
+            else:
+                df_ref = sc_ref.par(parname, {"time": "year"})
+
+            # Excluding those technologies that have explicit CF through Excel
+            df_ref = df_ref.loc[~df_ref[index_col].isin(data_dict.keys())]
+
+            # also for "node,technology" pairs
+            for x in node_tec:
+                df_ref = df_ref.loc[
+                    (df_ref[index_col] != x[1]) & (df_ref[node_col] != x[1])
+                ]
+
+        # This part is for those parameters defined per node (key='KAZ,i_spec')
+        elif "," in key:
+
+            df_ref = sc_ref.par(
+                parname,
+                {
+                    index_col: key.split(",")[1],
+                    node_col: key.split(",")[0],
+                    "time": "year",
+                },
+            )
+
+        else:
+            df_ref = sc_ref.par(parname, {index_col: key, "time": "year"})
+
+        if not df_ref.empty:
+            if nn >= n_time:
+                sc.remove_par(parname, df_ref)
+            time_cols = [
+                x
+                for x in sc.idx_names(parname)
+                if x in ["time", "time_dest", "time_origin"]
+            ]
+
+            for ti in df_time["time"][n1:nn].tolist():
+                df_new = df_ref.copy()
+                for col in time_cols:
+                    if col == "time_origin":
+                        df_new.loc[df_new["technology"].isin(tec_inp), col] = ti
+                    elif col == "time_dest":
+                        df_new.loc[~df_new["technology"].isin(tec_only_inp), col] = ti
+                    else:
+                        df_new[col] = ti
+                df_new["value"] *= ratio[df_time["time"].tolist().index(ti)]
+                df.append(df_new)
+
+            df = pd.concat(df, ignore_index=True)
+            sc.add_par(parname, df)
+    sc.commit("")
+    df = df_new = []
+
+
+# 1.6) A function for minor adjustments in some parameters
+def par_adjust(parname, dict_data, multiplier=0, plus=0):
+    df = sc.par(parname, dict_data)
+    df["value"] *= multiplier
+    df["value"] += plus
+    sc.add_par(parname, df)
+
+
+# 1.7) Removing cooling technologies from a list
+def remove_cooling(sc, tec_list):
+    print("- Removing cooling technologies...")
+    # Both commodity and technology must be removed
+    cool_terms = ["__air", "__cl_fresh", "__ot_fresh", "__ot_saline"]
+    tec_cool = [
+        x
+        for x in set(sc.set("technology"))
+        if x in [y + z for (y, z) in product(tec_list, cool_terms)]
+    ]
+    # tec_cool = sc2.set('cat_tec', {'type_tec': 'power_plant_cooling'})
+    com_cool = [
+        x
+        for x in sc.set("commodity").tolist()
+        if any([y in x for y in ["cooling", "freshwater"]])
+    ]
+    lvl_cool = ["cooling", "water_supply"]
+
+    sc.check_out()
+    for x in tec_cool:
+        sc.remove_set("technology", x)
+    for x in com_cool:
+        sc.remove_set("commodity", x)
+    for x in lvl_cool:
+        if x in sc.set("level"):
+            sc.remove_set("level", x)
+    tec_list = [x for x in tec_list if x not in tec_cool]
+    tec_rem = list(set([x.split("__")[0] for x in tec_cool]))
+    print(
+        "- Cooling technologies for {}, and cooling commodities and"
+        " levels were removed.".format(tec_rem)
+    )
+    sc.commit("cooling removed")
+    return sorted(tec_list)
+
+
+# 1.8) Removing data from a parameter if value iz zero
+def remove_data_zero(sc, parname, filter_dict, value=0):
+    sc.check_out()
+    df = sc.par(parname, filter_dict)
+    df = df.loc[df["value"] == value]
+    sc.remove_par(parname, df)
+    sc.commit("zero removed")
+    return df
+
+
+# 1.9) removing some parameters if needed
+def par_remove(sc, par_dict, verb=True):
+    for parname, filters in par_dict.items():
+        df = sc.par(parname, filters)
+        sc.remove_par(parname, df)
+        if verb:
+            if not filters:
+                filters = "All"
+            print('> {} data was removed from parmeter "{}".'.format(filters, parname))
+
+
+# 1.10) Adding year-equivalent of technologies at the timeslice level
+def rel_year_equivalent(
+    sc,
+    tec_list,
+    rel_ref="res_marg",
+    tec_ref="elec_t_d",
+    rel_bound="CH4_Emission",
+    remove_existing=False,
+):
+    """
+    sc: object, MESSAGEix scenario
+    tec_list: list, technologies for which to add a 'year' equivalent relaiton
+    rel_ref: string, a relation to copy data of relation activity from
+    tec_ref: string or list, a technology to copy a relation from, if list
+        the technology and nodes are mapped as ['tec', ['node1', 'node2']]
+    rel_bound: string, a relation to copy data of relation upper and lower from
+    remove_existing: boolean, if True all data for (rel_new) is removed
+
+    """
+    parname = "relation_activity_time"
+    for tec in tec_list:
+        rel_new = tec + "_year"
+
+        # adding sets
+        if remove_existing and rel_new in set(sc.set("relation")):
+            sc.remove_set("relation", rel_new)
+
+        sc.add_set("relation", rel_new)
+
+        # The part for technologies in 'time' slices'
+        df = sc.par(parname, {"relation": rel_ref, "technology": tec_ref})
+        df["relation"] = rel_new
+        df["technology"] = tec
+        df["value"] = -1
+
+        # mapping with technical_lifetime
+        df_lt = sc.par("technical_lifetime", {"technology": tec})
+        df_lt = df_lt.set_index(["node_loc", "technology", "year_vtg"])
+        df = df.set_index(["node_loc", "technology", "year_act"])
+
+        if not df_lt.empty:
+            df = df.loc[df.index.isin(set(df_lt.index))]
+        df = df.reset_index()
+
+        # The part for equivalent technology at 'year' level
+        d = df.loc[df["time"] == df["time"][0]].copy()
+        d["value"] = 1
+        d["time"] = "year"
+
+        df = df.append(d, ignore_index=True)
+        sc.add_par(parname, df)
+
+        # relation upper and lower
+        df = df.set_index(["node_rel", "year_rel"])
+        for par in ["relation_lower_time", "relation_upper_time"]:
+            df_b = sc.par(par, {"relation": rel_bound})
+            df_b = df_b.set_index(["node_rel", "year_rel"])
+            df_b = df_b.loc[df_b.index.isin(set(df.index))]
+            df_b = df_b.reset_index()
+
+            df_b["relation"] = rel_new
+            sc.add_par(par, df_b)
+
+
+# 1.11) Sorting relations based on their applicability to time slices
+def relation_tec(
+    sc,
+    tec_list,
+    times,
+    rel_yr_ti=["res_marg"],
+    rel_remain=["CO2_cc", "CO2_trp", "CO2_ind", "CO2_r_c"],
+):
+
+    sc.check_out()
+    print("- Removing extra relations...")
+    # removing extra relations
+    for r in ["RES_variable_limt", "gas_prod"]:
+        if r in set(sc.set("relation")):
+            sc.remove_set("relation", r)
+            print(r)
+
+    rel_list = [x for x in sc.set("relation").to_list() if x not in rel_remain]
+    for r in rel_list:
+        if (
+            sc.par("relation_lower", {"relation": r}).empty
+            and (sc.par("relation_upper", {"relation": r}).empty)
+            and (sc.par("relation_cost", {"relation": r}).empty)
+        ):
+            sc.remove_set("relation", r)
+            print(r)
+
+    rel_list = sc.set("relation").to_list()
+    tec_in = list(set(sc.par("input")["technology"]))
+    tec_out = list(set(sc.par("output")["technology"]))
+    tec_in_out = set(tec_in + tec_out)
+
+    rel_time = []  # relations at time level (including their lower/upper)
+    account = {}  # most of accounting relations (will be at year level)
+    rel_year = []  # relations must be at year (but time related tecs at time)
+    rel_pot = []  # potential relations (must be at year level)
+    tecs_free = []
+    yr_list = [x for x in sc.set("year") if x >= sc.firstmodelyear]
+
+    for r in rel_list:
+        d = sc.par("relation_activity", {"relation": r, "year_act": yr_list})
+
+        # free technologies (have no input/output)
+        tec_free = [x for x in set(d["technology"]) if x not in tec_in_out]
+        tecs_free = tecs_free + tec_free
+
+        # technologies in this relation that must be at time level
+        tec_time = [x for x in set(d["technology"]) if x in tec_list]
+        tec_time = tec_time + tec_free
+
+        # if all tecs at time slices, then the relation at time level
+        if len(tec_time) == len(set(d["technology"])):
+            rel_time.append(r)
+
+        # if all tecs at time slice but one not, collected as accounting for now
+        elif len(tec_time) + 1 == len(set(d["technology"])) and len(tec_time) > 0:
+            account[r] = [x for x in set(d["technology"]) if x not in tec_time][0]
+
+        # all other relations (mix of time and non-time tecs, at year level)
+        else:
+            rel_year.append(r)
+
+        d2 = sc.par("relation_upper", {"relation": r, "year_rel": yr_list})
+        if not d2.empty:
+            if d2["value"].mode()[0] != 0:
+                rel_pot.append(r)
+        d3 = sc.par("relation_lower", {"relation": r, "year_rel": yr_list})
+        if not d3.empty:
+            if d3["value"].mode()[0] != 0:
+                rel_pot.append(r)
+
+    # potential and accounting relations will be accounted at the year level
+    rel_year2 = [x for x in rel_pot if x not in rel_year]
+    rel_year3 = [x for x in set(account.keys()) if x not in rel_year]
+
+    # here we exclude all relations that can be with time index but not urgent
+    rel_year4 = [x for x in rel_time if x[0].isupper()]
+
+    # min/max and 'res_marg' at the 'year' level
+    rel_year5 = [
+        x
+        for x in rel_time
+        if any(
+            [y in x for y in ["min", "max", "lim", "unabated", "weight", "res_marg"]]
+        )
+    ]
+
+    # final set of year and time relations
+    rel_yr = sorted(rel_year + rel_year2 + rel_year3 + rel_year4 + rel_year5)
+    rel_ti = [x for x in rel_time if x not in rel_yr]
+
+    # 1) adding relation for year level
+    df = sc.par("relation_activity", {"relation": rel_yr, "year_act": yr_list})
+
+    # Not adding 'time' for relations at 'year' (except 'res_marg')
+    df_yr = df.loc[~df["relation"].isin(rel_yr_ti)].copy()
+    df_yr = df_yr.append(
+        df.loc[(df["relation"].isin(rel_yr_ti)) & (~df["technology"].isin(tec_list))],
+        ignore_index=True,
+    )
+    df_yr.loc[:, "time"] = "year"
+    sc.add_par("relation_activity_time", df_yr)
+    print('- Relation activity for "year" related relations added.')
+
+    # adding relation for time-related technologies in year level
+    df_ti = df.loc[
+        (df["relation"].isin(rel_yr_ti)) & (df["technology"].isin(tec_list))
+    ].copy()
+
+    for ti in times:
+        df_ti.loc[:, "time"] = ti
+        sc.add_par("relation_activity_time", df_ti)
+
+    # 2) adding relations for time level
+    df = sc.par("relation_activity", {"relation": rel_ti, "year_act": yr_list})
+    for ti in times:
+        df.loc[:, "time"] = ti
+        sc.add_par("relation_activity_time", df)
+    print("- Relation activity for relations at time slice level added.")
+
+    for relname in ["relation_upper", "relation_lower"]:
+        df = sc.par(relname)
+        sc.remove_par(relname, df)
+        df = df.loc[df["year_rel"].isin(yr_list)]
+        df_yr = df.loc[df["relation"].isin(rel_yr)].copy()
+
+        df_yr.loc[:, "time"] = "year"
+        sc.add_par(relname + "_time", df_yr)
+
+        df_ti = df.loc[df["relation"].isin(rel_ti)].copy()
+        for ti in times:
+            df_ti.loc[:, "time"] = ti
+            sc.add_par(relname + "_time", df_ti)
+
+    print("- Relation upper and lower updated for time slices.")
+
+    # 3) Conversion of rel_total_capcaity to rel_activity for curtailment
+    rel_curt = [x for x in rel_list if "curtailment" in x]
+    df = sc.par("relation_total_capacity", {"relation": rel_curt})
+    if not df.empty:
+        sc.remove_par("relation_total_capacity", df)
+
+        tecs = list(set(df["technology"]))
+        df = df.loc[df["year_rel"].isin(yr_list)]
+        df = df.set_index(["node_rel", "technology", "year_rel"]).sort_index()
+
+        # first, dividing by capacity factor of the respective technologies
+        cf = (
+            sc.par("capacity_factor", {"technology": tecs})
+            .groupby(["node_loc", "technology", "year_act"])
+            .mean()
+            .sort_index()
+        )
+
+        for i in set(df.index):
+            df.loc[i, "value"] /= cf.loc[i, "value"]
+
+        # then, converting to relation_activity
+        df = df.reset_index()
+        df["year_act"] = df["year_rel"]
+        df["node_loc"] = df["node_rel"]
+        df["mode"] = "M1"
+        for ti in times:
+            df.loc[:, "time"] = ti
+            sc.add_par("relation_activity_time", df)
+        print(
+            "- Relation total capacity converted to activity for"
+            " curtailment technologies."
+        )
+
+    # 4) Adding year equivalent relation for technologies at 'time' level
+    print(
+        "- Adding year-equivalent relations for technologies"
+        " active at time slice level..."
+    )
+    rel_year_equivalent(sc, tec_list)
+
+    # 5) Removing relation_activity for all
+    sc.remove_par("relation_activity", sc.par("relation_activity"))
+    sc.commit("relations updated")
+    print("- Relations were successfully set up for time slices.")
+    return rel_yr, rel_ti
+
+
+# 1.12) Adding mapping parameters
+def mapping_sets(sc, par_list):
+    sc.check_out()
+    for parname in par_list:
+        setname = "is_" + parname
+
+        # initiating the sets
+        idx_s = sc.idx_sets(parname)
+        idx_n = sc.idx_names(parname)
+        try:
+            sc.set(setname)
+        except:
+            sc.init_set(setname, idx_sets=idx_s, idx_names=idx_n)
+            print("- Set {} was initiated.".format(setname))
+
+        # emptying old data in sets
+        df = sc.set(setname)
+        sc.remove_set(setname, df)
+
+        # adding data to the mapping sets
+        df = sc.par(parname)
+        if not df.empty:
+            # for i in df.index:
+            #     d = df.loc[i, :].copy().drop(['value', 'unit'])
+            df = df.drop(["value", "unit"], axis=1)
+            sc.add_set(setname, df)
+
+            print('- Mapping sets updated for "{}"'.format(setname))
+    sc.commit("")
+
+
+# 1.13) Initialization of new parameters
+def init_par_time(sc, par_list=[]):
+    sc.check_out()
+    if not par_list:
+        par_list = [
+            "emission_factor",
+            "relation_activity",
+            "relation_upper",
+            "relation_lower",
+        ]
+    for parname in par_list:
+        idx_s = sc.idx_sets(parname) + ["time"]
+        idx_n = sc.idx_names(parname) + ["time"]
+        try:
+            sc.par(parname + "_time")
+        except:
+            sc.init_par(parname + "_time", idx_sets=idx_s, idx_names=idx_n)
+            print("- Parameter {} was initiated.".format(parname + "_time"))
+    for setname in ["is_relation_lower_time", "is_relation_upper_time"]:
+        p = setname.split("is_")[1]
+        idx_s = sc.idx_sets(p)
+        idx_n = sc.idx_names(p)
+        try:
+            sc.set(setname)
+        except:
+            sc.init_set(setname, idx_sets=idx_s, idx_names=idx_n)
+            print("- Set {} was initiated.".format(setname))
+
+    sc.commit("")
+
+
+# 1.14) Updating reserve margin
+def update_res_marg(sc, path_xls, sc_ref=[], # based on a reference scenario
+                    parname='relation_activity_time',
+                    reserve_margin=0.2,  # on top of peak load for reserves
+                    xls_sheet='peak_demand', nodes='all'):
+    # Notice: if based on sc_ref, double check if should reserve_margin = 0
+
+    # 1) Loading Excel data (peak demand per region in GW)
+    xls = pd.ExcelFile(path_xls)
+    df = xls.parse('peak_demand').set_index('year')
+    peak = df.loc['peak', :]
+    peak_growth = df.loc[df.index != 'peak']
+
+
+    resm_old = sc.par(parname, {'relation': 'res_marg',
+                                'technology': 'elec_t_d'})
+    if sc_ref:
+        resm_ref = sc_ref.par('relation_activity', {'relation': 'res_marg',
+                                        'technology': 'elec_t_d'})
+        dem_ref = sc_ref.par('demand', {'commodity': ['i_spec', 'rc_spec']})
+        dem_ref = dem_ref.groupby(['node', 'year', 'time']).sum()
+
+
+    if nodes == 'all':
+        nodes = list(set(resm_old['node_loc']))
+
+    if 'time' not in sc.idx_names(parname):
+        resm_old['time'] = 'year'
+        if sc_ref:
+            resm_ref['time'] = 'year'
+            resm_ref = resm_ref.set_index(['node_loc', 'year_act', 'time']
+                                          ).sort_index()
+
+    resm = resm_old.set_index(['node_loc', 'year_act', 'time']).sort_index()
+
+    # Total electricity demand
+    dur = sc.par('duration_time')
+    dem = sc.par('demand', {'commodity': ['i_spec', 'rc_spec']})
+    dem = dem.groupby(['node', 'year', 'time']).sum().reset_index()
+    d = dem.pivot_table(values='value',
+                           columns='year', index=['node', 'time'])
+    dem_growth = d.copy()
+    yr_first = sc.firstmodelyear
+    dem_growth[yr_first] = 0
+    for yr in [x for x in dem_growth.columns if x > yr_first]:
+        dem_growth[yr] = (d[yr] - d[yr_first]) / d[yr_first]
+
+    # Finding max demand based on duration time
+    dem['actual'] = 0
+    for t in set(sc.set('time')):
+        dem.loc[dem['time'] == t, 'actual'
+                ] = dem.loc[dem['time'] == t, 'value'] / float(
+            dur.loc[dur['time'] == t, 'value'])
+    dem_max = dem.loc[dem.groupby(['node', 'year'])['actual'].idxmax()].set_index(
+        ['node', 'year', 'time'])
+
+    # udating res_marg only for timeslices that maximum demand (GW) happens
+    resm = resm.loc[resm.index.isin(dem_max.index)]
+
+    for i in resm.index:
+        if sc_ref:
+            ref_res = float(resm_ref.loc[(i[0], i[1]), 'value'])
+            ref_dem = float(dem_ref.loc[(i[0], i[1]), 'value'])
+            peak_dem = -ref_res * ref_dem
+        else:
+            growth_demand = 1 + dem_growth.loc[(i[0], i[2]), i[1]]
+            growth_peak = 1 + peak_growth.loc[i[1], i[0]]
+            peak_dem = float(peak[i[0]]) * growth_demand * growth_peak
+
+        # duration = float(dur.loc[dur['time'] == i[2], 'value'])
+        # max_dem = dem.loc[i, 'value'] / duration
+        max_dem = dem_max.loc[i, 'actual']
+
+        if (peak_dem / max_dem) < 1:
+            new_res = - (1. + reserve_margin)
+        else:
+            new_res = - (peak_dem / max_dem) * (1. + reserve_margin)
+
+        resm.loc[i, 'value'] = new_res
+
+    if 'time' not in sc.idx_names(parname):
+        resm = resm.reset_index().drop(['time'], axis=1)
+
+    sc.check_out()
+    sc.remove_par(parname, resm_old)
+    sc.add_par(parname, resm.reset_index())
+    sc.commit('res_marg updated')
+    print('- Reserve margin updated based on the new peak demand data.')
+
+
+# 1.15) Copying historical data to "ref" parameters
+def historical_to_ref(sc, year_list=[], sc_ref=None, regions=all,
+                      remove_ref=True):
+
+    if regions == all:
+        regions = list(sc.set('node'))
+
+    sc.check_out()
+
+    if not year_list:
+        year_list = [x for x in set(sc.set('year')) if x < sc.firstmodelyear]
+
+    par_list = ['historical_activity', 'historical_new_capacity',
+                'historical_extraction']
+    par_list_ref = [x.replace('historical', 'ref') for x in par_list]
+
+    # If no reference scenario is given, uses the same scenario
+    if not sc_ref:
+        sc_ref = sc
+
+    # Remove the existing "ref" data
+    if remove_ref:
+        for parname in par_list_ref:
+            node_col = [x for x in sc.par(parname).columns if 'node' in x][0]
+            year_col = [x for x in sc.par(parname).columns if 'year' in x][0]
+            df_ref = sc.par(parname, {node_col: regions, year_col: year_list})
+            sc.remove_par(parname, df_ref)
+
+    # Add new "ref" data
+    for parname in par_list:
+        node_col = [x for x in sc.par(parname).columns if 'node' in x][0]
+        year_col = [x for x in sc.par(parname).columns if 'year' in x][0]
+        df_par = sc_ref.par(parname, {node_col: regions, year_col: year_list})
+        if not df_par.empty:
+            sc.add_par(par_list_ref[par_list.index(parname)], df_par)
+
+            print('> The content of parameter "{}" was copied to "{}" to the scenario.'.format(parname,
+                  par_list_ref[par_list.index(parname)]))
+
+    sc.commit(' ')
+# %% 2) Input data and specifications
+if __name__ == "__main__":
+    import ixmp as ix
+    import message_ix
+
+    mp = ix.Platform(name="ene_ixmp", jvmargs=["-Xms2g", "-Xmx14g"])
+    start = timer()
+    # model, scenario, version, suffix of Excel file
+    # Country model: 'MESSAGE_China', 'baseline', 7, '48'  (cleaned up, VRE new, unit, 2110)
+    model_ref = "MESSAGEix_China"
+    scenario_ref = "baseline"
+    version_ref = None
+    model_family = "CHN"
+    n_time = 48     # number of time slices <= file ID
+    file_id = "48"
+
+    path_files = r"C:\Users\zakeri\Documents\Github\time_clustering"
+    path_xls = path_files + "\\scenario work\\China\\data"
+    xls_file = "input_data_" + file_id + "_" + model_family + ".xlsx"
+
+    os.chdir(path_files)
+
+    clone = True  # if True, clones a new scenario
+    model_new = model_ref
+    scenario_new = scenario_ref + "_t" + str(n_time)
+    version_new = 1  # if clone False will be used
+
+    interval = 50  # intervals for commiting to the DB (no impact on results)
+    set_update = True  # if True, adds time slices and set adjustments
+    last_year = None  # either int (year) or None (removes extra years)
+    node_exlude = ["World"]  # nodes to be excluded from the timeslicing
+
+    # 2) Adjustment related to general cleanup
+    vre_cleanup = False  # do it if not yet, cleanup VRE and new implementation
+    unit_correct = False  # if True, corrects the units
+    cleaning = False  # cleanup of technologies and relations
+
+    # 3) Adjustment related to cleanup before adding time slices
+    remove_old_time_lvl = True  # if True, removes old temporal mapping
+    remove_cool_tec = True  # if True, removes all cooling technologies
+    remove_extra_vintage = False  # removes vintaging before 2020 (not safe)
+
+    # 3) Adjustment after adding time slices
+    growth_act_adjust = True  # if true, does some adjustments for growth rates
+
+    # a relation to connect collectors and their input tecs (not needed)
+    add_relation_collector = False
+
+    # adding index of 'time' to parameters like rel_activity (removes old data)
+    add_time_index = []  # ['emission_factor']       # a list of parameters or []
+
+    # if True, finds relations for tecs at time slice level and addes times
+    relation_modification = True
+
+    # if True, the content of historical parameters will be copied to ref
+    history_to_ref = False
+
+    # if a list, removes those parameters like relation_activity # can be []
+    par_old_removal = [  # 'relation_activity', 'relation_upper', 'relation_lower',
+        # 'relation_cost',
+    ]
+    # updating reserve margin
+    update_reserve_marg = True  # if True, based on Excel sheet: peak_demand
+    # 2.1) Loading/clonning scenarios
+    print("- Loading reference scenario and cloning...")
+    sc_ref = message_ix.Scenario(
+        mp, model=model_ref, scenario=scenario_ref, version=version_ref
+    )
+    if clone:
+        sc = sc_ref.clone(model=model_new, scenario=scenario_new, keep_solution=False)
+    else:
+        sc = message_ix.Scenario(
+            mp, model=model_new, scenario=scenario_new, version=version_new
+        )
+    # sc_ref = None
+    version_cloned = sc.version
+
+    if sc.has_solution():
+        sc.remove_solution()
+
+    # try:
+    #     sc.par("storage_initial")
+    # except:
+    #     addNewParameter(sc, path_xls, remove_par=True)
+
+    nodes = [x for x in sc.set("node") if x not in ["World"] + node_exlude]
+
+    # 2.2) Loading Excel data (time series)
+    xls = pd.ExcelFile("//".join([path_xls, xls_file]))
+
+    # -----------------------------------------------------------------------------
+    # 3) Updating sets related to time
+    # Adding subannual time slices to the relevant sets
+    duration, df_time, dict_xls = xls_to_df(xls, n_time, nodes)
+    times = df_time["time"].tolist()
+
+    if set_update:
+        time_setup(sc, df_time, last_year)
+        duration_time(sc, df_time)
+        if last_year:
+            df = sc.par("bound_activity_up")
+            assert max(set(df["year_act"])) <= last_year
+
+    # -----------------------------------------------------------------------------
+    # 4) Modifying some parameters before adding sub-annual time slices
+    # List of technologies that will have output to time slices
+    # sectors/commodities represented at time slice level
+    # Representaiton model (input= to, ta ... output: ta, td)
+
+    print("- Identifying technologies to be represented at subannual level...")
+    sectors = ["rc_spec", "i_spec", "i_therm", "rc_therm", "transport"]
+    coms = ["electr", "d_heat"]
+
+    tec_out = list(set(sc.par("output", {"commodity": coms + sectors})["technology"]))
+    extra_com = ["Trans", "Itherm", "RCtherm", "back_"]
+    tec_out = sorted([x for x in tec_out if not any([y in x for y in extra_com])])
+    len(tec_out)
+
+    # List of technologies that must have input from time slices
+    tec_inp = sorted(set(sc.par("input", {"commodity": coms})["technology"]))
+    len(tec_inp)
+
+    # Excluding technologies with zero output from time slices
+    df_rem_out = set(
+        remove_data_zero(sc, "output", {"technology": tec_out})["technology"]
+    )
+    tec_out = sorted([x for x in tec_out if x not in df_rem_out])
+
+    df_rem_in = set(
+        remove_data_zero(sc, "input", {"technology": tec_inp})["technology"]
+    )
+    tec_inp = sorted([x for x in tec_inp if x not in df_rem_in])
+    len(tec_inp)
+
+    # technologies with output to 'useful' level (already timeslices in demand)
+    use_tec = set(
+        sc.par("output", {"commodity": sectors, "level": "useful"})["technology"]
+    )
+
+    # Technologies with only input needed at timeslice (output: 'year'/nothing)
+    # representaiton mode (to='1', ta='1' ... ta='1', td='year')
+    tec_only_inp = [x for x in tec_inp if x not in tec_out and x not in use_tec]
+    len(tec_only_inp)
+
+    # All technologies relevant to time slices
+    tec_list = tec_out + tec_inp
+
+    if remove_cool_tec:
+        tec_list = remove_cooling(sc, tec_list)
+    tec_list = list(set(tec_list))
+
+    if remove_extra_vintage:
+        extra_y = [int(y) for y in sc.set("year") if int(y) < sc.firstmodelyear]
+        for parname in [
+            p
+            for p in sc.par_list()
+            if len([y for y in set(sc.idx_names(p)) if "year" in y]) == 2
+            and "year_act" in set(sc.idx_names(p))
+        ]:
+            df_rem = sc.par(parname, {"year_act": extra_y})
+            sc.remove_par(parname, df_rem)
+
+    # first initialization of parameters with time slice
+    init_par_time(sc)
+
+    # second, converting and updating relation parameters
+    if relation_modification:
+        relation_tec(sc, tec_list, times)
+
+    # finding relevant parameters
+    par_list = [
+        x
+        for x in sc.par_list()
+        if "time" in sc.idx_sets(x) and "technology" in sc.idx_sets(x)
+    ]
+
+    # parameters that should get the same value as 'year'
+    par_equal = [
+        "input",
+        "output",
+        "capacity_factor",
+        "var_cost",
+        # 'growth_activity_up', 'growth_activity_lo',
+        # 'level_cost_activity_soft_up',
+        # 'level_cost_activity_soft_lo',
+        # 'soft_activity_lo', 'soft_activity_up',
+    ]
+
+    # parameters that should divide values of 'year' between time slices
+    par_divide = [  # 'demand'          # demand will be treated from EXcel
+        #  'historical_activity',
+        # 'bound_activity_lo', 'bound_activity_up',
+        # 'initial_activity_up', 'initial_activity_lo',
+        # 'abs_cost_activity_soft_up', 'abs_cost_activity_soft_lo',
+    ]
+
+    # parameters that should be neglected for time slices (removed)
+    par_ignore = []
+
+    sc.check_out()
+    par_update = {}  # a dictionary for updating parameters with time index
+    for parname in par_list:
+        if parname in par_equal:
+            par_update[parname] = {"all": [1] * n_time}
+        elif parname in par_divide:
+            par_update[parname] = {"all": duration}
+        elif parname in par_ignore:
+            par_remove(sc, {parname: {"technology": tec_list}}, verb=False)
+    sc.commit("")
+
+    # Moving parameters with no 'time' index to the new ones with 'time'
+    for parname in add_time_index:
+        df = sc.par(parname)
+        df_new = df.copy()
+        df_new["time"] = "year"
+        sc.remove_par(parname, df)
+        sc.add_par(parname + "_time", df_new)
+        par_update[parname + "_time"] = {"all": [1] * n_time}
+
+    # Setting up the data and values of parameters
+    par_update, index_cols = par_setup(sc, dict_xls, par_update)
+
+    print("- Parameters are being modified for time slices...")
+    for parname, data_dict in par_update.items():
+        index_col = index_cols[parname]
+        if index_col == "technology":
+            item_list = tec_list
+        else:
+            item_list = None
+        n1 = 0
+        while n1 < n_time:
+            nn = min([n_time, n1 + interval - 1])
+            par_time_update(sc, parname, data_dict, index_col, item_list, n1, nn)
+            print("    ", end="\r")
+            print(str(nn + 1), end="\r")
+            n1 = n1 + interval
+        print('- Time slices added to "{}".'.format(parname))
+    print("- All parameters sucessfully modified for time slices.")
+
+    # -------------------------------------------------------------------------
+    # 5) Doing some adjustments and solving
+    sc.check_out()
+
+    # Finding other commodity/levels that must be at the time slice level
+    # 1) Correcting input of tec_inp technologies
+    com_div = []
+    for t in tec_inp:
+        # technologies with electr input that have other input commodities too
+        df = sc.par("input", {"technology": t})
+        df = df.loc[~df["commodity"].isin(coms + sectors)].set_index(
+            ["commodity", "level"]
+        )
+        com_div = com_div + list(set(df.index))
+    com_div = set(com_div)
+    print(
+        "- Divider technologies being added for commodity/levels of"
+        ": {}".format(com_div)
+    )
+
+    for x in com_div:
+        tec = x[0] + "_divider"
+        sc.add_set("technology", tec)
+
+        yr_list = [int(x) for x in sc.set("year") if int(x) >= sc.firstmodelyear]
+        df = sc.par(
+            "input",
+            {
+                "commodity": x[0],
+                "level": x[1],
+                "time": "year",
+                "year_act": yr_list,
+                "year_vtg": yr_list,
+            },
+        )
+        df = df.loc[(df["year_act"] == df["year_vtg"])].copy()
+
+        df["technology"] = tec
+        df = df.drop_duplicates()
+        df["value"] = 1
+
+        for ti in df_time["time"].tolist():
+            df["time"] = ti
+            sc.add_par("input", df)
+
+            df2 = df.copy()
+            df2 = df2.rename(
+                {"time_origin": "time_dest", "node_origin": "node_dest"}, axis=1
+            )
+            df2["time_dest"] = ti
+            sc.add_par("output", df2)
+
+    # 2) Correcting output of tec_out technologies
+    com_coll = []
+    for t in tec_out:
+        df = sc.par("output", {"technology": t})
+        df = df.loc[~df["commodity"].isin(coms + sectors)].set_index(
+            ["commodity", "level"]
+        )
+        com_coll = com_coll + list(set(df.index))
+    com_coll = set(com_coll)
+    print(
+        "- Collector technologies being added for commodity/levels of"
+        ": {}".format(com_coll)
+    )
+
+    for x in com_coll:
+        tec = x[0] + "_collector"
+        sc.add_set("technology", tec)
+
+        df = sc.par(
+            "output",
+            {
+                "commodity": x[0],
+                "level": x[1],
+                "time": times,
+                "year_act": yr_list,
+                "year_vtg": yr_list,
+            },
+        )
+        t_list = list(set(df["technology"]))
+        df = df.loc[(df["year_act"] == df["year_vtg"])].copy()
+
+        df["technology"] = tec
+        df = df.drop_duplicates()
+        df["value"] = 1
+        df["time_dest"] = "year"
+
+        for ti in times:
+            df["time"] = ti
+            sc.add_par("output", df)
+
+            # adding input of collector
+            inp = df.copy()
+            inp = inp.rename(
+                {"time_dest": "time_origin", "node_dest": "node_origin"}, axis=1
+            )
+            inp["node_origin"] = inp["node_loc"]
+            inp["time_origin"] = ti
+            sc.add_par("input", inp)
+
+        # adding a relation for collectors
+        if add_relation_collector:
+            rel = x[0] + "_aggregation"
+            sc.add_set("relation", rel)
+            rac = df.copy()
+            rac = rac.rename(
+                {
+                    "commodity": "relation",
+                    "year_vtg": "year_rel",
+                    "node_dest": "node_rel",
+                },
+                axis=1,
+            ).drop(["time_dest", "level"], axis=1)
+            rac["relation"] = rel
+            rac["value"] = -1
+            sc.add_par("relation_activity_time", rac)
+            dlo = rac.copy().drop(
+                ["node_loc", "year_act", "technology", "mode"], axis=1
+            )
+            dlo["value"] = 0
+            sc.add_par("relation_lower_time", dlo)
+
+            rac["value"] = 1
+            for tec, ti in product(t_list, times):
+                rac["technology"] = tec
+                rac["time"] = ti
+                sc.add_par("relation_activity_time", rac)
+
+    if par_old_removal:
+        for par in par_old_removal:
+            par_remove(sc, {par: {}})
+
+    # Saving tec_list as a tec_type
+    for t in tec_list:
+        sc.add_cat("technology", "time_slice", t)
+    print(
+        "- Technologies active at time slice level saved with a type_tec"
+        ' of "time_slice" in this scenario.'
+    )
+
+    # Adjusting of dynamic act for some technologies
+    # (if growth and initial parameters were at the "time" level)
+    if growth_act_adjust:
+
+        if "R11_WEU" in set(sc.set("node")):  # for t=12
+            par_adjust(
+                "relation_activity_time",
+                {
+                    "node_loc": "R11_WEU",
+                    "relation": "UE_res_comm_th_biomass",
+                    "technology": ["useful_res_comm_th"],
+                },
+                multiplier=0.70,
+            )
+
+        if "R11_CPA" in set(sc.set("node")):  # for t=24
+            par_adjust(
+                "bound_activity_up",
+                {"node_loc": "R11_CPA", "technology": ["hydro_lc"], "year_act": 2020},
+                multiplier=1.05,
+            )
+
+        if "R11_SAS" in set(sc.set("node")):
+            df = sc.par(
+                "bound_new_capacity_up",
+                {
+                    "node_loc": "R11_SAS",
+                    "technology": "hydro_lc",
+                    "year_vtg": [
+                        x for x in set(sc.set("year")) if x >= sc.firstmodelyear
+                    ],
+                },
+            )
+            sc.remove_par("bound_new_capacity_up", df)
+            df["value"] = 41.7
+            df = df.rename({"year_vtg": "year_act"}, axis=1)
+            sc.add_par("bound_total_capacity_up", df)
+
+    # TODO MESSAGE bug: changing capacity factor of zero
+    tecs = ["wind_ppl", "wind_ppf", "solar_pv_ppl", "hydro_lc", "hydro_hc"]
+    df = sc.par("capacity_factor", {"technology": tecs, "time": times})
+    df = df.loc[df["value"] == 0].copy()
+    df["value"] = 0.000001
+    sc.add_par("capacity_factor", df)
+
+    sc.commit("")
+
+    # Adjustment of reserve margins (in relation_activity_time)
+    if update_reserve_marg:
+        update_res_marg(
+            sc, "//".join([path_xls, xls_file]))
+
+    # copying historical activity to ref_activity
+    if history_to_ref:
+        historical_to_ref(sc)
+
+    # At the end, updating mapping sets based on new relation_act_time
+    mapping_sets(sc, par_list=["relation_upper_time", "relation_lower_time"])
+    end = timer()
+
+    print(
+        "Elapsed time for adding time slices and modifying scenario:",
+        int((end - start) / 60),
+        "min and",
+        round((end - start) % 60, 2),
+        "sec.",
+    )
+
+    casename = sc.model + "__" + sc.scenario + "__v" + str(sc.version)
+    print(
+        'Solving scenario "{}", started at {}, please wait...'
+        "!".format(casename, datetime.now().strftime("%H:%M:%S"))
+    )
+    start = timer()
+    sc.solve(case=casename, solve_options={"lpmethod": "4"})
+    end = timer()
+    print(
+        "Elapsed time solving scenario:",
+        int((end - start) / 60),
+        "min and",
+        round((end - start) % 60, 2),
+        "sec.",
+    )
+
+    # %% Some specific adjustments for some scenarios
+    # 1) Correcting historical activity of some technologies from Excel
+    parname = "historical_activity"
+    xls = pd.ExcelFile("//".join([path_xls, xls_file]))
+    df_xls = xls.parse(parname, converters={"time": str})
+    df_xls = df_xls.set_index(df_xls.columns[0])
+
+    sc.check_out()
+    for tec in df_xls.columns:
+        df = sc.par(parname, {"technology": tec})
+        df_yr = df.copy().groupby(["node_loc", "year_act"]).sum()
+        df_yr = df_yr.loc[df_yr["value"] > 0]
+
+        df = df.set_index(["node_loc", "year_act", "time"])
+        for i in df_yr.index:
+            for ti in df_time["time"].tolist():
+                df.loc[(i[0], i[1], ti), "value"] = (
+                    df_xls.loc[ti, tec] * df_yr.loc[i, "value"]
+                )
+        sc.add_par(parname, df.reset_index())
+
+    # 2) Removing initial_act_up and _lo for time slices
+    sc.check_out()
+    par_list = [
+        "initial_activity_up",
+        "initial_activity_lo",
+        "growth_activity_up",
+        "growth_activity_lo",
+    ]
+    for parname in par_list:
+        df = sc.par(parname)
+        df = df.loc[df["time"] != "year"]
+        sc.remove_par(parname, df)
+    sc.commit("")
+
+    # 3) Copying relation parameters back
+    for p in par_old_removal:
+        df = sc_ref.par(p)
+        df = df.loc[df["relation"].isin(set(sc.set("relation")))]
+        if "technology" in df.columns:
+            df = df.loc[df["technology"].isin(set(sc.set("technology")))]
+        sc.add_par(p, df)
+
+    # 4) testing the balance of rc_therm
+    ti = "1"
+    dur = float(sc.par("duration_time", {"time": ti})["value"])
+    for com, lvl in com_coll:
+        tec = com + "_collector"
+        tecs = list(
+            set(sc.par("output", {"commodity": com, "level": lvl})["technology"])
+        )
+        df = sc.var("ACT", {"technology": tecs, "year_act": yr_list})
+        coll = df.loc[df["technology"] == tec].groupby(["year_act"]).sum()
+        if not coll.empty:
+            rest = df.loc[df["technology"] != tec].groupby(["year_act"]).sum()
+            ratio = (rest[rest["lvl"] > 0] / coll[coll["lvl"] > 0])["lvl"]
+            print(
+                "- Results for commodity {} at level {} is {}.".format(com, lvl, ratio)
+            )
+    # df=sc.par('input',{'technology':'rc_therm_collector'})
diff --git a/nest/message_ix_models/project/leap_re_nest/utils.py b/nest/message_ix_models/project/leap_re_nest/utils.py
new file mode 100644
index 0000000..4f58a48
--- /dev/null
+++ b/nest/message_ix_models/project/leap_re_nest/utils.py
@@ -0,0 +1,47 @@
+# utils for NEST model
+import pandas as pd
+from message_ix_models.util import private_data_path
+
+def map_basin(sc):
+    """Return specification for mapping basins to regions
+
+    The basins are spatially consolidated from HydroSHEDS basins delineation
+    database.This delineation is then intersected with MESSAGE regions to form new
+    water sector regions for the nexus module.
+    The nomenclature for basin names is <basin_id>|<MESSAGEregion> such as R1|AFR
+    """
+
+
+    # define an empty dictionary
+    results = {}
+    # read csv file for basin names and region mapping
+    # reading basin_delineation
+    FILE = f"basins_by_region_simpl_ZMB.csv"
+    PATH = private_data_path("projects","leap_re_nest", "delineation", FILE)
+
+    df = pd.read_csv(PATH)
+    # Assigning proper nomenclature
+    df["node"] = "B" + df["BCU_name"].astype(str)
+    df["mode"] = "M" + df["BCU_name"].astype(str)
+    df["region"] = df["REGION"].astype(str)
+    results["node"] = df["node"]
+    results["mode"] = df["mode"]
+    # map nodes as per dimensions
+    df1 = pd.DataFrame({"node_parent": df["region"], "node": df["node"]})
+    df2 = pd.DataFrame({"node_parent": df["node"], "node": df["node"]})
+    frame = [df1, df2]
+    df_node = pd.concat(frame)
+    nodes = df_node.values.tolist()
+
+    results["map_node"] = nodes
+
+    # context.all_nodes = df["node"]
+
+    for set_name, config in results.items():
+        
+        print("Adding set",set_name)
+        # print("config",config)
+        # Sets  to add
+        sc.add_set(set_name,config)
+        
+    print("sets for nodes updated")
\ No newline at end of file

From 0b243fedfe2253f71e5b64b0e8757f2fedd85781 Mon Sep 17 00:00:00 2001
From: adrivinca <adrivinca@yahoo.it>
Date: Tue, 28 Feb 2023 12:31:29 +0100
Subject: [PATCH 3/4] Update readme and build for NEST

---
 .../project/leap_re_nest/build.py             | 32 ++++++++-----------
 1 file changed, 14 insertions(+), 18 deletions(-)

diff --git a/nest/message_ix_models/project/leap_re_nest/build.py b/nest/message_ix_models/project/leap_re_nest/build.py
index fa79b0b..a2cbb2c 100644
--- a/nest/message_ix_models/project/leap_re_nest/build.py
+++ b/nest/message_ix_models/project/leap_re_nest/build.py
@@ -23,33 +23,29 @@
 
 # load a scenario
 # IIASA users
-mp = ix.Platform(name='ixmp_dev' , jvmargs=['-Xmx14G'])
+# mp = ix.Platform(name='ixmp_dev' , jvmargs=['-Xmx14G'])
 # external users
-# mp2 = ix.Platform(name='local' , jvmargs=['-Xmx14G'])
+mp2 = ix.Platform(name='local' , jvmargs=['-Xmx14G'])
 
 modelName = 'MESSAGEix_ZM'
 scenarioName = 'single_node' 
 scen2Name = 'sub-units'
 
 # IIASA users
-sc_ref = message_ix.Scenario(mp, modelName, scenarioName,cache=True)
+# sc_ref = message_ix.Scenario(mp, modelName, scenarioName,cache=True)
 # sc_ref.to_excel(private_data_path("projects","leap_re_nest","ref_scen.xlsx") )
 # # external users in local database
-# sc_ref2 = ix.Scenario(mp2, modelName, "test", version='new',annotation="load from excel")
-# # the following units seem to not be present in the platform, need to add them the first time
-# ix.Platform.add_unit(mp2,unit = 'MUSD/a')
-# ix.Platform.add_unit(mp2,unit = 'GWa/a')
-# ix.Platform.add_unit(mp2,unit = 'TUSD')
-
-# sc_ref2.read_excel(private_data_path("projects","leap_re_nest","ref_scen.xlsx"),
-#                    add_units=True,
-#                    init_items=True,
-#                    commit_steps=True)
+sc_ref2 = message_ix.Scenario(mp2, modelName, "test", version='new',annotation="load from excel")
+
+sc_ref2.read_excel(private_data_path("projects","leap_re_nest","ref_scen.xlsx"),
+                    add_units=True,
+                    init_items=True,
+                    commit_steps=True)
 # sc_ref2.commit("")
 # sc_ref2.solve(solve_options={"lpmethod": "4"},model="MESSAGE")
 
 # for all
-sc = sc_ref.clone(modelName, scen2Name,keep_solution=False)
+sc = sc_ref2.clone(modelName, scen2Name,keep_solution=False)
 
 sc.check_out()
 # add basins
@@ -105,6 +101,9 @@
 add_grid_shares_OnSSET.main(sc3)
 
 caseName = sc3.model + "__" + sc3.scenario + "__v" + str(sc3.version)
+# Solving the model
+sc3.solve(solve_options={"lpmethod": "4"},model="MESSAGE", case=caseName)
+sc3.set_as_default()
 
 # %%4) add water structure
 
@@ -112,9 +111,6 @@
 # with the correct scenari name
 # mix-models --url=ixmp://ixmp_dev/MESSAGEix_ZM/MLED_demand water --regions=ZMB nexus --rcps=7p0 --rels=low
 
-# Solving the model
-sc3.solve(solve_options={"lpmethod": "4"},model="MESSAGE", case=caseName)
-sc3.set_as_default()
+# %% 5) add irrigation and adjust electricity uses in the water
 
 # ADD GDP and Pop info in the timeseries
-

From fe341799ed00f31cfbd8099405dfd998d162a73f Mon Sep 17 00:00:00 2001
From: Adriano Vinca <vinca@iiasa.ac.at>
Date: Tue, 28 Feb 2023 14:57:19 +0100
Subject: [PATCH 4/4] Update README.md

Nest part
---
 README.md | 18 ++++++++++++------
 1 file changed, 12 insertions(+), 6 deletions(-)

diff --git a/README.md b/README.md
index b01b6a7..e4f3445 100644
--- a/README.md
+++ b/README.md
@@ -31,7 +31,7 @@ Once downloaded, the database(s) (a zipped folder for each of the four models) s
 - For WaterCROP:
 - For M-LED: at *line 10* of the `MLED_hourly.R` file, defining the `db_folder` parameter
 - For OnSSET: include the OnSSET replication data folder unzipped in `onsset\onsset_replication` (more details below)
-- For NEST:
+- For NEST: The database only raw data needed in the pre-processing phase. The data needed to run the model is already included in the Github repository
 
 ## Setting up the environment
 
@@ -66,7 +66,11 @@ Each models is developed in a specific programming language and has thus specifi
   - Go to the instructions below to run the different OnSSET parts of the model.
 
 ### For NEST:
-  - XXX
+  - The requirements to run NEST are the same for running MESSAGEix, please check the [MESSAGEix documentation](https://docs.messageix.org/en/stable/install.html)
+  - Python 3.7 or newer
+  - GAMS with an active licence for the solver cplex
+  - R only needed to run pre-processing scripts
+  - once the setup is installed, also install the content of this folder as a `message-ix-models` package from source, following ths instructions in the `message-ix-models` [documentation](https://docs.messageix.org/projects/models/en/latest/install.html)
   
 ## Operating the platform
 
@@ -84,16 +88,18 @@ Each models is developed in a specific programming language and has thus specifi
 - Open the `OnSSET_Scenario_Running.ipynb` Notebook and run all of the cells. This will take a few minutes and will run the different scenarios and calculate the least-cost electrification options for the entire country. It will output it's results into several folders as .CSV files both as full results files for every population cluster in the country as well as summary files (also used later by NEST). 
 
 ### For NEST:
-  - XXX
+  - After having installed the folder by source (see *Setting up the environment*) run the script `nest\message_ix_models\project\leap_re_nest\build.py`
+  - around line 126, at * 4) add water structure* follow the instruction and run the command in the command prompt
 
 ## Soft-linking the models
 
+**RELEASE NOtE:** the current release is still not including all the exchange of data for future scenarios, which will be included in the next release.
+
 ### WaterCrop to M-LED:
   - WaterCrop produces netcdf files of irrigation water requirements and yield growth potential for all African countries. These files are contained (and can be updated) in the `./MLED_database/input_folder/watercrop` folder and corresponding subfolders for each crop. These files are then read in the `scenario_countryname.R` file of M-LED.
  
 ### WaterCROP to NEST:
-  - xxx
-  - 
+  - NEST uses the same input as M-LED
 
 ### M-LED to OnSSET: 
   - The details of transferring the demand data from the MLED modelling into OnSSET compatible files are completed in the `MLED_extraction_to_OnSSET.ipynb` notebook in the onsset root folder
@@ -128,7 +134,7 @@ In particular results can be examined by:
   - Otherwise they can be visusalised in GIS software such as QGIS: https://download.qgis.org/. They can be linked back to the cluster .gpkg shape files using a join on the "id" variable to visualise the shapes in addition to the electrification optimization information. 
   - Go to the RE4AFAGRI visualisation platform (coming soon) to see the existing scenarios. 
 
-### For NEST:
+### For NEST: work in progress
 
 ## Support