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Rewrite calcIOEdgeBuildings and consider unit shift in biomass split
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| Package: mrcommons | ||
| Type: Package | ||
| Title: MadRat commons Input Data Library | ||
| Version: 1.46.8 | ||
| Date: 2024-11-22 | ||
| Version: 1.46.9 | ||
| Date: 2024-11-27 | ||
| Authors@R: c(person("Benjamin Leon", "Bodirsky", email = "[email protected]", role = "aut"), | ||
| person("Kristine", "Karstens", role = "aut"), | ||
| person("Lavinia", "Baumstark", role = "aut"), | ||
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| @@ -1,116 +1,89 @@ | ||
| #' calcIOEdgeBuildings | ||
| #' | ||
| #' Calculates buildings-related energy flows from the IEA energy balances. 'output_EDGE_buildings' is a key input | ||
| #' to EDGE-Buildings providing the historic final energy demand from buildings. 'output_EDGE' does the same for | ||
| #' Calculates buildings-related energy flows from the IEA energy balances. | ||
| #' 'output_EDGE_buildings' is a key input to EDGE-Buildings providing the | ||
| #' historic final energy demand from buildings. 'output_EDGE' does the same for | ||
| #' buildings and industry together. | ||
| #' | ||
| #' @param subtype Data subtype. See default argument for possible values. | ||
| #' @param ieaVersion Release version of IEA data, either 'default' | ||
| #' (vetted and used in REMIND) or 'latest'. | ||
| #' @return IEA data as MAgPIE object aggregated to country level | ||
| #' @returns IEA data as MAgPIE object aggregated to country level | ||
| #' | ||
| #' @author Pascal Sauer, Anastasis Giannousakis, Robin Hasse | ||
| #' | ||
| #' @author Pascal Sauer, Anastasis Giannousakis | ||
| #' @examples | ||
| #' \dontrun{ | ||
| #' a <- calcOutput("IOEdgeBuildings", subtype = "output_EDGE_buildings") | ||
| #' } | ||
| #' | ||
| #' @seealso \code{\link{calcOutput}} | ||
| #' @importFrom dplyr %>% all_of | ||
| #' | ||
| #' @importFrom dplyr %>% .data all_of filter select | ||
| #' @importFrom tidyr unite | ||
| calcIOEdgeBuildings <- function(subtype = c("output_EDGE", "output_EDGE_buildings"), ieaVersion = "default") { | ||
| #' @importFrom madrat readSource toolGetMapping toolAggregate calcOutput | ||
| #' @importFrom utils read.csv2 | ||
| #' @importFrom magclass as.magpie getNames mselect | ||
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| calcIOEdgeBuildings <- function(subtype = c("output_EDGE", "output_EDGE_buildings"), | ||
| ieaVersion = c("default", "latest")) { | ||
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| subtype <- match.arg(subtype) | ||
| switch(subtype, | ||
| output_EDGE = { | ||
| mapping <- toolGetMapping(type = "sectoral", | ||
| name = "structuremappingIO_outputs.csv", | ||
| where = "mrcommons", returnPathOnly = TRUE) | ||
| target <- "EDGEitems" | ||
| }, | ||
| output_EDGE_buildings = { | ||
| mapping <- toolGetMapping(type = "sectoral", | ||
| name = "structuremappingIO_outputs.csv", | ||
| where = "mrcommons", returnPathOnly = TRUE) | ||
| target <- "EDGE_buildings" | ||
| }) | ||
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| if (!(ieaVersion %in% c("default", "latest"))) { | ||
| stop("Invalid parameter `ieaVersion`. Must be either 'default' or 'latest'") | ||
| } | ||
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| ieaSubtype <- if (ieaVersion == "default") "EnergyBalances" else "EnergyBalances-latest" | ||
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| # read in data and convert from ktoe to EJ | ||
| data <- readSource("IEA", subtype = ieaSubtype) * 4.1868e-5 | ||
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| ieamatch <- read.csv2(mapping, stringsAsFactors = FALSE, na.strings = "") | ||
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| # delete NAs rows | ||
| ieamatch <- ieamatch[c("iea_product", "iea_flows", target, "Weight")] %>% | ||
| ieaVersion <- match.arg(ieaVersion) | ||
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| # READ ----------------------------------------------------------------------- | ||
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| # convert from ktoe to EJ | ||
| data <- switch(ieaVersion, | ||
| default = readSource("IEA", subtype = "EnergyBalances"), | ||
| latest = readSource("IEA", subtype = "EnergyBalances-latest") | ||
| ) * 4.1868e-5 | ||
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| # AGGREGATE ------------------------------------------------------------------ | ||
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| target <- switch(subtype, | ||
| output_EDGE = "EDGEitems", | ||
| output_EDGE_buildings = "EDGE_buildings" | ||
| ) | ||
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| mapping <- toolGetMapping(type = "sectoral", | ||
| name = "structuremappingIO_outputs.csv", | ||
| where = "mrcommons", returnPathOnly = TRUE) %>% | ||
| read.csv2(stringsAsFactors = FALSE, na.strings = "") %>% | ||
| select(all_of(c("iea_product", "iea_flows", target, "Weight"))) %>% | ||
| na.omit() %>% | ||
| unite("target", all_of(target), sep = ".") %>% | ||
| unite("product.flow", c("iea_product", "iea_flows"), sep = ".", remove = FALSE) %>% | ||
| filter(!!sym("product.flow") %in% getNames(data)) | ||
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| magpieNames <- ieamatch[["target"]] %>% unique() | ||
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| # in case we include IEA categories in the output, iea categories in `ieamatch` got renamed | ||
| ieapname <- "iea_product" | ||
| ieafname <- "iea_flows" | ||
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| reminditems <- do.call(mbind, | ||
| lapply(magpieNames, function(item) { | ||
| testdf <- ieamatch[ieamatch$target == item, c(ieapname, ieafname, "Weight")] | ||
| prfl <- paste(testdf[, ieapname], testdf[, ieafname], sep = ".") | ||
| vec <- as.numeric(ieamatch[rownames(testdf), "Weight"]) | ||
| names(vec) <- prfl | ||
| tmp <- data[, , prfl] * as.magpie(vec) | ||
| tmp <- dimSums(tmp, dim = 3, na.rm = TRUE) | ||
| getNames(tmp) <- item | ||
| return(tmp) | ||
| })) | ||
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| # Split residential Biomass into traditional and modern biomass depending upon the income per capita | ||
| if (subtype == "output_EDGE") { | ||
| nBiotrad <- "feresbiotrad" | ||
| nBiomod <- "feresbiomod" | ||
| nBioshare <- "feresbioshare" | ||
| } else if (subtype == "output_EDGE_buildings") { | ||
| nBiotrad <- "biotrad" | ||
| nBiomod <- "biomod" | ||
| nBioshare <- "bioshare" | ||
| } | ||
| # Read-in data to compute income per capita | ||
| gdp <- calcOutput("GDPPast", aggregate = FALSE) | ||
| pop <- calcOutput("PopulationPast", aggregate = FALSE) | ||
| gdppop <- gdp[, intersect(getYears(gdp), getYears(pop)), ] / pop[, intersect(getYears(gdp), getYears(pop)), ] | ||
| # Create a lambda which is 1 for income per capita <= 1.23E4, and 0 above 1.85E4 | ||
| # the multiplication by gdppop was necessary to avoid error from vector length. | ||
| lambda <- pmin(gdppop * 0 + 1, pmax(0 * gdppop, (15000 - gdppop) / (15000 - 10000))) | ||
| lambda <- time_interpolate(lambda, getYears(reminditems), extrapolation_type = "constant") | ||
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| # Split Bioshare (residential PRIMSBIO) between traditional and modern biomass according to lambda | ||
| bioshareTrad <- setNames(reminditems[, , nBioshare] * lambda, nBiotrad) | ||
| bioshareMod <- setNames(reminditems[, , nBioshare] - bioshareTrad, nBiomod) | ||
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| # In case biomod and biotrad do not exist yet in the data set, create dummy items | ||
| if (!any(nBiomod %in% getNames(reminditems))) { | ||
| reminditems <- mbind(reminditems, | ||
| setNames(reminditems[, , nBioshare] * 0, nBiomod)) | ||
| } | ||
| if (!any(nBiotrad %in% getNames(reminditems))) { | ||
| reminditems <- mbind(reminditems, | ||
| setNames(reminditems[, , nBioshare] * 0, nBiotrad)) | ||
| } | ||
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| # Add the values from bioshare to the other modern and traditional biomass | ||
| reminditems[, , nBiotrad] <- reminditems[, , nBiotrad] + bioshareTrad | ||
| reminditems[, , nBiomod] <- reminditems[, , nBiomod] + bioshareMod | ||
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| # Remove the bioshare item | ||
| reminditems <- reminditems[, , nBioshare, invert = TRUE] | ||
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| return(list(x = reminditems, weight = NULL, unit = "EJ", | ||
| description = paste("Historic final energy demand from buildings (and industry)", | ||
| "based on the 2022 IEA World Energy Balances"))) | ||
| filter(.data[["product.flow"]] %in% getNames(data), | ||
| .data[["Weight"]] != 0) %>% | ||
| mutate(Weight = as.numeric(.data[["Weight"]])) | ||
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| weight <- as.magpie(mapping[, c("iea_product", "iea_flows", "Weight")]) | ||
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| data <- toolAggregate(data[, , mapping[["product.flow"]]] * weight, | ||
| rel = mapping, from = "product.flow", to = "target", dim = 3) | ||
| getSets(data)[3] <- "d3" | ||
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| # SPLIT BIOMASS -------------------------------------------------------------- | ||
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| gdppop <- calcOutput("GDPpc", average2020 = FALSE, aggregate = FALSE) %>% | ||
| mselect(variable = "gdppc_SSP2", collapseNames = TRUE) | ||
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| data <- switch(subtype, | ||
| output_EDGE = toolSplitBiomass(data, gdppop, split = "feresbioshare", | ||
| into = c("feresbiotrad", "feresbiomod")), | ||
| output_EDGE_buildings = toolSplitBiomass(data, gdppop, split = "bioshare") | ||
| ) | ||
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| return(list(x = data, | ||
| weight = NULL, | ||
| unit = "EJ/yr", | ||
| description = paste("Historic FE demand from buildings", | ||
| "(and industry) based on IEA Energy Balances"))) | ||
| } |
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| Original file line number | Diff line number | Diff line change |
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| #' Split Biomass into modern and traditional | ||
| #' | ||
| #' We assume that below a given GDP/cap level, all biomass is traditional and | ||
| #' above a higher limit, all biomass is modern with a linear transition between | ||
| #' the limits. | ||
| #' | ||
| #' @param x MagPIE object including biomass data | ||
| #' @param gdppop MagPIE object with GDP/cap data | ||
| #' @param split character, name of item to split | ||
| #' @param into character vector of length two with the names of the split items | ||
| #' @param dim dimension of \code{x} with the item to split | ||
| #' @param limits numeric vector of length two with the corresponding GDP/cap | ||
| #' limits. The default values used to be 10k and 15k USD/cap converted from | ||
| #' 2005 to 2017 dollars. | ||
| #' @returns MagPIE object including split biomass data | ||
| #' | ||
| #' @author Robin Hasse | ||
| #' | ||
| #' @importFrom dplyr %>% | ||
| #' @importFrom magclass time_interpolate setNames getYears | ||
| #' @export | ||
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| toolSplitBiomass <- function(x, | ||
| gdppop, | ||
| split = "biomass", | ||
| into = c("biotrad", "biomod"), | ||
| dim = 3.1, | ||
| limits = c(1.23E4, 1.85E4)) { | ||
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| .rename <- function(x, to) { | ||
| getItems(x, dim)[getItems(x, dim) == split] <- to | ||
| return(x) | ||
| } | ||
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| r <- getItems(x, 1) | ||
| weight <- ((gdppop[r, , ] - limits[1]) / (limits[2] - limits[1])) %>% | ||
| collapseDim() %>% | ||
| time_interpolate(getYears(x), extrapolation_type = "constant") %>% | ||
| pmax(0) %>% | ||
| pmin(1) | ||
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| items <- setdiff(getItems(x, dim), split) | ||
| xSplit <- lapply(into, function(i) if (i %in% items) x[, , i] else 0) | ||
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| mbind(xSplit[[2]] + .rename(x[, , split] * weight, into[2]), | ||
| xSplit[[1]] + .rename(x[, , split] * (1 - weight), into[1]), | ||
| x[, , setdiff(getItems(x, dim), c(split, into))]) | ||
| } |
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