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remove subtype CapitalUnit from calcCapital
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,163 +1,121 @@ | ||
| calcCapital <- function(subtype = "Capital") { | ||
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| calcCapital <- function() { | ||
| #--- Parameters --- | ||
| #***Reproduce this in the aggregation of Capital in convertEDGE | ||
| corres_ener_cap = c(kapal = "fealelb", | ||
| kapsc = "fescelb", | ||
| kaphc = "ueswb") | ||
| #--- | ||
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| if (subtype == "Capital") { | ||
| # get capital stocks for EDGE sectors | ||
| cap_macro <- readSource("EDGE",subtype = subtype) | ||
| millionDol2trillionDol <- 1e-6 | ||
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| additional_years <- seq(2105, 2150, 5) | ||
| cap_macro <- time_interpolate(cap_macro, | ||
| additional_years, | ||
| integrate_interpolated_years = TRUE, | ||
| extrapolation_type = "constant") | ||
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| # compute macroeconomic capital stock based on capital intensities from PWT and ssp scenarios | ||
| # t.b.d.: correct for capital stock part that enters energy sectors | ||
| capital <- readSource("PWT")[, , "rkna"] | ||
| getNames(capital) <- "kap" | ||
| capital[is.na(capital)] <- 0 | ||
| gdpppp_hist <- calcOutput("GDPPast", GDPPast = "PWT", aggregate = FALSE) | ||
| #pop = calcOutput("Population", aggregate = F) | ||
| cap_intensity <- capital / setNames(gdpppp_hist, NULL) | ||
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| #use initial gdp as in REMIND which differs from PWT | ||
| gdpppp_hist = calcOutput("GDPPast", aggregate = FALSE) | ||
| gdpppp <- calcOutput("GDP", aggregate = FALSE, years = seq(2005, 2150, 5)) | ||
| #getNames(gdpppp) <- sub("gdp_","",getNames(gdpppp)) | ||
| my_scen <- c("gdp_SSP1", "gdp_SSP2", "gdp_SSP3", "gdp_SSP4", "gdp_SSP5", "gdp_SSP2EU", | ||
| "gdp_SDP", "gdp_SDP_EI", "gdp_SDP_RC", "gdp_SDP_MC") | ||
| gdpppp <- mselect(gdpppp, variable = my_scen) | ||
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| p41 <- setYears(cap_intensity[,rep(1,32),],seq(1995,2150,5)) | ||
| p41 <- add_dimension(p41, dim=3.1, add="ssp",nm=my_scen) | ||
| cap_intensity_future <- p41 | ||
| convtime <- p41 | ||
| gdp_weight <- p41 | ||
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| #ssp variation | ||
| convtime[,,"gdp_SSP1"] = 150 | ||
| convtime[,,"gdp_SSP2"] = 250 | ||
| convtime[,,"gdp_SSP3"] = 500 | ||
| convtime[,,"gdp_SSP4"] = 300 | ||
| convtime[,,"gdp_SSP5"] = 150 | ||
| convtime[,,"gdp_SDP"] = 150 | ||
| convtime[,,"gdp_SDP_EI"] = 150 | ||
| convtime[,,"gdp_SDP_RC"] = 150 | ||
| convtime[,,"gdp_SDP_MC"] = 150 | ||
| convtime[,,"gdp_SSP2EU"] = 250 | ||
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| for (t in c("y1995","y2000","y2005")){ | ||
| cap_intensity_future[,t,] <- cap_intensity[,t,] | ||
| gdp_weight[,t,] <- gdpppp_hist[,t,] | ||
| } | ||
| cap_intensity_ref = cap_intensity["JPN","y2010"] | ||
| getRegions(cap_intensity_ref) <- "GLO" | ||
| lambda=0 | ||
| for(t in getYears(gdpppp)) { | ||
| cap_intensity_future[,t,] = ((convtime[,t,]-lambda) * collapseNames(setYears(cap_intensity[,"y2010",])) + + | ||
| lambda* setNames(setYears(cap_intensity_ref,NULL),NULL))/convtime[,t,] | ||
| lambda =lambda+5 | ||
| gdp_weight[,t,] <- gdpppp[,t,] | ||
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| # get capital stocks for EDGE sectors | ||
| cap_macro <- readSource("EDGE", subtype = "Capital") | ||
| millionDol2trillionDol <- 1e-6 | ||
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| additional_years <- seq(2105, 2150, 5) | ||
| cap_macro <- time_interpolate(cap_macro, | ||
| additional_years, | ||
| integrate_interpolated_years = TRUE, | ||
| extrapolation_type = "constant") | ||
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| # compute macroeconomic capital stock based on capital intensities from PWT and ssp scenarios | ||
| # t.b.d.: correct for capital stock part that enters energy sectors | ||
| capital <- readSource("PWT")[, , "rkna"] | ||
| getNames(capital) <- "kap" | ||
| capital[is.na(capital)] <- 0 | ||
| gdpppp_hist <- calcOutput("GDPPast", GDPPast = "PWT", aggregate = FALSE) | ||
| cap_intensity <- capital / setNames(gdpppp_hist, NULL) | ||
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| # use initial gdp as in REMIND which differs from PWT | ||
| gdpppp_hist <- calcOutput("GDPPast", aggregate = FALSE) | ||
| gdpppp <- calcOutput("GDP", aggregate = FALSE, years = seq(2005, 2150, 5)) | ||
| my_scen <- c("gdp_SSP1", "gdp_SSP2", "gdp_SSP3", "gdp_SSP4", "gdp_SSP5", "gdp_SSP2EU", | ||
| "gdp_SDP", "gdp_SDP_EI", "gdp_SDP_RC", "gdp_SDP_MC") | ||
| gdpppp <- mselect(gdpppp, variable = my_scen) | ||
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| p41 <- setYears(cap_intensity[, rep(1, 32), ], seq(1995, 2150, 5)) | ||
| p41 <- add_dimension(p41, dim = 3.1, add = "ssp", nm = my_scen) | ||
| cap_intensity_future <- p41 | ||
| convtime <- p41 | ||
| gdp_weight <- p41 | ||
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| # ssp variation | ||
| convtime[, , "gdp_SSP1"] <- 150 | ||
| convtime[, , "gdp_SSP2"] <- 250 | ||
| convtime[, , "gdp_SSP3"] <- 500 | ||
| convtime[, , "gdp_SSP4"] <- 300 | ||
| convtime[, , "gdp_SSP5"] <- 150 | ||
| convtime[, , "gdp_SDP"] <- 150 | ||
| convtime[, , "gdp_SDP_EI"] <- 150 | ||
| convtime[, , "gdp_SDP_RC"] <- 150 | ||
| convtime[, , "gdp_SDP_MC"] <- 150 | ||
| convtime[, , "gdp_SSP2EU"] <- 250 | ||
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| for (t in c("y1995", "y2000", "y2005")) { | ||
| cap_intensity_future[, t, ] <- cap_intensity[, t, ] | ||
| gdp_weight[, t, ] <- gdpppp_hist[, t, ] | ||
| } | ||
| cap_intensity_ref <- cap_intensity["JPN", "y2010"] | ||
| getRegions(cap_intensity_ref) <- "GLO" | ||
| lambda <- 0 | ||
| for (t in getYears(gdpppp)) { | ||
| cap_intensity_future[, t, ] <- ((convtime[, t, ] - lambda) * collapseNames(setYears(cap_intensity[, "y2010", ])) + + | ||
| lambda * setNames(setYears(cap_intensity_ref, NULL), NULL)) / convtime[, t, ] | ||
| lambda <- lambda + 5 | ||
| gdp_weight[, t, ] <- gdpppp[, t, ] | ||
| } | ||
| cap_intensity_future[is.na(cap_intensity_future)] <- 0 | ||
| cap_int_new <- cap_intensity_future | ||
| for (t in getYears(cap_intensity_future)) { | ||
| for (r in getRegions(cap_intensity_future)) { | ||
| if (cap_intensity_future[r, t, "gdp_SSP2"] == 0) { | ||
| # get current mapping | ||
| map <- toolGetMapping(type = "regional", name = getConfig("regionmapping"), where = "mappingfolder") | ||
| # get list of countries that belong to the same region as r | ||
| regi <- map$RegionCode[map$CountryCode == r] | ||
| c_regi <- map$CountryCode[map$RegionCode == regi] | ||
| # filter out the regions that are 0 | ||
| c_regi <- c_regi[!cap_intensity_future[c_regi, t, "gdp_SSP2"] == 0] | ||
| # generate mapping for the aggregation | ||
| mapping <- map[which(map$CountryCode %in% c_regi), ] | ||
| mapping$RegionCode <- r | ||
| # store calculated data in separate file | ||
| cap_int_new[r, t, ] <- toolAggregate(cap_intensity_future[c_regi, t, ], mapping, weight = gdp_weight[c_regi, t, ]) | ||
| } | ||
| cap_intensity_future[is.na(cap_intensity_future)] <- 0 | ||
| cap_int_new <- cap_intensity_future | ||
| for(t in getYears(cap_intensity_future)) { | ||
| for(r in getRegions(cap_intensity_future)) { | ||
| if(cap_intensity_future[r,t,"gdp_SSP2"]==0) { | ||
| # get current mapping | ||
| map <- toolGetMapping(type = "regional", name = getConfig("regionmapping"), where = "mappingfolder") | ||
| # get list of countries that belong to the same region as r | ||
| regi <- map$RegionCode[map$CountryCode==r] | ||
| c_regi <- map$CountryCode[map$RegionCode==regi] | ||
| # filter out the regions that are 0 | ||
| c_regi <- c_regi[!cap_intensity_future[c_regi,t,"gdp_SSP2"]==0] | ||
| # generate mapping for the aggregation | ||
| mapping <- map[which(map$CountryCode %in% c_regi),] | ||
| mapping$RegionCode <- r | ||
| # store calculated data in separate file | ||
| cap_int_new[r,t,] <- toolAggregate(cap_intensity_future[c_regi,t,],mapping,weight=gdp_weight[c_regi,t,]) | ||
| } | ||
| } | ||
| } | ||
| } | ||
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| cap_future <- cap_int_new * gdp_weight | ||
| y = intersect(getYears(cap_future), getYears(cap_macro)) | ||
| cap_future <- cap_int_new * gdp_weight | ||
| y <- intersect(getYears(cap_future), getYears(cap_macro)) | ||
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| # Add SSP2EU and SDP scenarios | ||
| cap_macro_SSP2A <- cap_macro[,,"gdp_SSP2"] | ||
| getNames(cap_macro_SSP2A) <- gsub("SSP2", "SSP2EU", getNames(cap_macro_SSP2A)) | ||
| cap_macro <- mbind(cap_macro, cap_macro_SSP2A) | ||
| # Add SSP2EU and SDP scenarios | ||
| cap_macro_SSP2A <- cap_macro[, , "gdp_SSP2"] | ||
| getNames(cap_macro_SSP2A) <- gsub("SSP2", "SSP2EU", getNames(cap_macro_SSP2A)) | ||
| cap_macro <- mbind(cap_macro, cap_macro_SSP2A) | ||
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| cap_macro_SDP <- cap_macro[,,"gdp_SSP1"] | ||
| for (i in c("SDP", "SDP_EI", "SDP_RC", "SDP_MC")) { | ||
| getNames(cap_macro_SDP) <- gsub("SSP1", i, getNames(cap_macro[,,"gdp_SSP1"])) | ||
| cap_macro <- mbind(cap_macro, cap_macro_SDP) | ||
| } | ||
| cap_macro_SDP <- cap_macro[, , "gdp_SSP1"] | ||
| for (i in c("SDP", "SDP_EI", "SDP_RC", "SDP_MC")) { | ||
| getNames(cap_macro_SDP) <- gsub("SSP1", i, getNames(cap_macro[, , "gdp_SSP1"])) | ||
| cap_macro <- mbind(cap_macro, cap_macro_SDP) | ||
| } | ||
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| cap_macro = mbind(cap_macro[,y,], cap_future[,y,]) | ||
| cap_macro <- mbind(cap_macro[, y, ], cap_future[, y, ]) | ||
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| cap_macro <- cap_macro * millionDol2trillionDol | ||
| cap_macro <- cap_macro * millionDol2trillionDol | ||
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| # add industry subsectors energy efficiency capital stocks ---- | ||
| # add industry subsectors energy efficiency capital stocks ---- | ||
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| kap <- cap_macro %>% | ||
| `[`(,2015,'gdp_SSP2EU.kap') %>% | ||
| quitte::magclass_to_tibble(c('iso3c', NA, NA, NA, 'kap')) %>% | ||
| dplyr::select('iso3c', 'kap') | ||
| kap <- cap_macro %>% | ||
| `[`(, 2015, "gdp_SSP2EU.kap") %>% | ||
| quitte::magclass_to_tibble(c("iso3c", NA, NA, NA, "kap")) %>% | ||
| dplyr::select("iso3c", "kap") | ||
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| EEK <- calcOutput('Industry_EEK', kap = kap, supplementary = FALSE, | ||
| aggregate = FALSE, years = getYears(cap_macro)) | ||
| EEK <- calcOutput("Industry_EEK", kap = kap, supplementary = FALSE, | ||
| aggregate = FALSE, years = getYears(cap_macro)) | ||
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| # tie outputs together ---- | ||
| output <- list( | ||
| x = mbind(cap_macro, EEK), | ||
| weight = NULL, | ||
| unit = "trillion 2005US$", | ||
| description = "Capital stock at constant 2005 national prices") | ||
| } | ||
| else if( subtype == "CapitalUnit") { | ||
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| data = readSource("EDGE", subtype = subtype) | ||
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| wfe <- calcOutput("FEdemand", subtype = "FE", aggregate = F)[,2015,"gdp_SSP2"] | ||
| wfe = collapseNames(wfe) | ||
| getSets(wfe) = gsub("item","variable", getSets(wfe)) | ||
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| wfe_kap = do.call(mbind, | ||
| lapply(names(corres_ener_cap), function(kap_nm){ | ||
| ener_nm = corres_ener_cap[kap_nm] | ||
| tmp = wfe[,,ener_nm] | ||
| getNames(tmp) = gsub(ener_nm,kap_nm,getNames(tmp)) | ||
| return(tmp) | ||
| }) | ||
| ) | ||
| wg_prep = mbind(wfe[,,corres_ener_cap], | ||
| wfe_kap) | ||
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| # Apply the structure of data to the weights to avoid issues with multiplication | ||
| weights = data | ||
| weights[] <- NA | ||
| for (item in getNames(wg_prep)){ | ||
| for (type in getNames(weights[,,item], dim = "type")){ | ||
| weights[,,item][,,type] <- wg_prep[,,item] | ||
| } | ||
| } | ||
| # tie outputs together ---- | ||
| output <- list( | ||
| x = mbind(cap_macro, EEK), | ||
| weight = NULL, | ||
| unit = "trillion 2005US$", | ||
| description = "Capital stock at constant 2005 national prices") | ||
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| output = list(x=data,weight=weights, | ||
| unit = "kWh for energy, $ for capital", | ||
| description = "Technology units with capital and energy properties") | ||
| } | ||
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| return(output) | ||
| } |
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