📊 Update dataset on national contributions to climate change #2496
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… github.com:owid/etl into update-national-contributions-data
| name: CC BY 4.0 | ||
| url: https://zenodo.org/record/7636699#.ZFCy4exBweZ | ||
| wdir: ../../../data/snapshots/emissions/2023-11-23 | ||
| Jones, M. W. (2024) “National contributions to climate change due to historical emissions of carbon dioxide, methane and nitrous oxide”, Scientific Data. Zenodo. doi: 10.5281/zenodo.10839859. |
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One problem with the automatic extraction of the text is that it uses an APA-like kind of format we are moving away from
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They offer different choices. I changed APA to Chicago style.
| "Ryukyu Islands": "Ryukyu Islands", | ||
| "St. Kitts-Nevis-Anguilla": "St. Kitts-Nevis-Anguilla", | ||
| "LDC": "Least developed countries (Jones et al.)", | ||
| "OECD": "OECD (Jones et al.)" | ||
| } |
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I wonder why Pacific Islands (Palau) is not here, but in the excluded list, when there are several such entities in this file.
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There was overlapping data for "Palau" and for "Pacific Islands (Palau)", so I simply excluded this region instead of trying to figure out a better solution.
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Nit: On line 271, it seems redundant to use the maps_series functions when you run run_sanity_checks_on_inputs just before. I think replace can do the job just as well
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Also, warnings for the harmonizing countries function are True by default, so there's no need to keep the parameter
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If I remember correctly, map_series warns if something can't be mapped, while replace silently creates nan.
| #################################################################################################################### | ||
| for column in columns_that_cannot_be_negative: | ||
| # Ensure all negative values are just numerical noise. | ||
| assert (tb[column].fillna(0) >= -2e-4).all() |
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You can set the tolerance value above and call it here to easily change it if needed
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I preferred to have this value here, close to where it's most relevant, given that it's just a very specific check. That value is not an important global variable that is used elsewhere or that affects the data.
| assert emissions_after_jump / emissions_before_jump > 10, error | ||
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| # Visually inspect the jump. | ||
| # import plotly.express as px |
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Remove this code if you're not using it anymore
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I think it's convenient to have it there, as a quick way to check on next update if the spurious jump is still in the data.
| @@ -39,173 +39,237 @@ tables: | |||
| unit: tonnes | |||
| short_unit: t | |||
| description_short: *measured-in-tonnes | |||
| presentation: | |||
| title_public: Annual methane emissions from fossil fuels and industry | |||
| annual_emissions_ch4_land: | |||
| title: Annual methane emissions from agriculture and land use | |||
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Maybe it could be useful to explain what land use includes in description_short
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I think it would be even better to have a DoD about land use emissions, I'll write that down.
| @@ -214,16 +278,22 @@ tables: | |||
| description_key: | |||
| - This temperature change measures each country's contribution to global mean surface temperature (GMST) rise from its cumulative emissions of carbon dioxide, methane and nitrous oxide. | |||
| - The warming effects of each gas are calculated based on cumulative CO₂-equivalent emissions using the Global Warming Potential (GWP*) approach. | |||
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You could maybe expand on what Global Warming Potential (GWP*) approach is
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We have a DoD for GWP. I suppose it could be better to add a sentence or a paragraph about GWP* there.
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Thank you very much for the review, @paarriagadap! |
pabloarosado
merged commit 11642b0
into
update-national-contributions-data-reference-branch
…e branch (#2501) * Duplicate code from previous version of national contributions to global warming dataset * Update dataset on national contributions to climate change (#2496) * Adapt snapshots, meadow, garden and grapher steps * Fix spurious negative numbers * Improve format * Various small improvements, following Pablo A suggestions
pabloarosado
changed the title
Update dataset on national contributions to global warming (Jones et al.).