refactor calcFEdemand

.buildlibrary

@@ -1,4 +1,4 @@
-ValidationKey: '34630515'
+ValidationKey: '34657286'
 AcceptedWarnings:
 - 'Warning: package ''.*'' was built under R version'
 - 'Warning: namespace ''.*'' is not available and has been replaced'

CITATION.cff

@@ -2,8 +2,8 @@ cff-version: 1.2.0
 message: If you use this software, please cite it using the metadata from this file.
 type: software
 title: 'mrremind: MadRat REMIND Input Data Package'
-version: 0.175.3
-date-released: '2024-02-02'
+version: 0.175.4
+date-released: '2024-02-06'
 abstract: The mrremind packages contains data preprocessing for the REMIND model.
 authors:
 - family-names: Baumstark

DESCRIPTION

@@ -1,8 +1,8 @@
 Type: Package
 Package: mrremind
 Title: MadRat REMIND Input Data Package
-Version: 0.175.3
-Date: 2024-02-02
+Version: 0.175.4
+Date: 2024-02-06
 Authors@R: c(
     person("Lavinia", "Baumstark", , "[email protected]", role = c("aut", "cre")),
     person("Renato", "Rodrigues", role = "aut"),

NAMESPACE

@@ -61,6 +61,7 @@ export(readUSGS)
 export(readindustry_subsectors_specific)
 export(readvanRuijven2016)
 export(readworldsteel)
+export(toolAggregateTimeSteps)
 export(toolBiomassSupplyAggregate)
 export(toolCubicFunctionAggregate)
 export(toolCubicFunctionDisaggregate)
@@ -229,7 +230,6 @@ importFrom(quitte,character.data.frame)
 importFrom(quitte,df_populate_range)
 importFrom(quitte,duplicate)
 importFrom(quitte,duplicate_)
-importFrom(quitte,getVars)
 importFrom(quitte,inline.data.frame)
 importFrom(quitte,interpolate_missing_periods)
 importFrom(quitte,interpolate_missing_periods_)

R/calcCapital.R

@@ -1,163 +1,93 @@
-calcCapital <- function(subtype = "Capital") {
-
-  #--- Parameters ---
-  #***Reproduce this in the aggregation of Capital in convertEDGE
-  corres_ener_cap = c(kapal = "fealelb",
-                      kapsc = "fescelb",
-                      kaphc = "ueswb")
-  #---
-
-  if (subtype == "Capital") {
-    # get capital stocks for EDGE sectors
-    cap_macro <- readSource("EDGE",subtype = subtype)
-    millionDol2trillionDol <- 1e-6
-
-    additional_years <- seq(2105, 2150, 5)
-    cap_macro <- time_interpolate(cap_macro,
-                                  additional_years,
-                                  integrate_interpolated_years = TRUE,
-                                  extrapolation_type = "constant")
-
-    # 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)
-
-
-    #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)
-
-    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
-
-    #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
-
-    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) {
+calcCapital <- function() {
+
+  # 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)
+
+  # 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)
+
+  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
+
+  # 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
+
+  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]
+        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]
+        c_regi <- c_regi[!cap_intensity_future[c_regi, t, "gdp_SSP2"] == 0]
         # generate mapping for the aggregation
-        mapping            <- map[which(map$CountryCode %in% c_regi),]
+        mapping            <- map[which(map$CountryCode %in% c_regi), ]
         mapping$RegionCode <- r
         # store calculated data in separate file
-        cap_int_new[r,t,] <- toolAggregate(cap_intensity_future[c_regi,t,],mapping,weight=gdp_weight[c_regi,t,])
-      }
+        cap_int_new[r, t, ] <- toolAggregate(cap_intensity_future[c_regi, t, ],
+                                             mapping,
+                                             weight = gdp_weight[c_regi, t, ])
       }
     }
-
-    cap_future <- cap_int_new * gdp_weight
-    y = intersect(getYears(cap_future), getYears(cap_macro))
-
-
-    # Add SSP2EU and SDP scenarios
-    cap_macro_SSP2A <- cap_macro[,,"gdp_SSP2"]
-    getNames(cap_macro_SSP2A) <- gsub("SSP2", "SSP2EU", getNames(cap_macro_SSP2A))
-    cap_macro <- mbind(cap_macro, cap_macro_SSP2A)
-
-    cap_macro_SDP <- cap_macro[,,"gdp_SSP1"]
-    for (i in c("SDP", "SDP_EI", "SDP_RC", "SDP_MC")) {
-      getNames(cap_macro_SDP) <- gsub("SSP1", i, getNames(cap_macro[,,"gdp_SSP1"]))
-      cap_macro <- mbind(cap_macro, cap_macro_SDP)
-    }
-
-
-    cap_macro = mbind(cap_macro[,y,], cap_future[,y,])
-
-    cap_macro <- cap_macro * millionDol2trillionDol
-
-    # add industry subsectors energy efficiency capital stocks ----
-
-    kap <- cap_macro %>%
-      `[`(,2015,'gdp_SSP2EU.kap') %>%
-      quitte::magclass_to_tibble(c('iso3c', NA, NA, NA, 'kap')) %>%
-      dplyr::select('iso3c', 'kap')
-
-    EEK <- calcOutput('Industry_EEK', kap = kap, supplementary = FALSE,
-                      aggregate = FALSE, years = getYears(cap_macro))
-
-    # tie outputs together ----
-    output <- list(
-      x = mbind(cap_macro, EEK),
-      weight = NULL,
-      unit = "trillion 2005US$",
-      description = "Capital stock at constant 2005 national prices")
   }
-  else if( subtype == "CapitalUnit") {
 
-    data = readSource("EDGE", subtype = subtype)
+  cap_future <- cap_int_new * gdp_weight * 1e-6
 
-    wfe    <- calcOutput("FEdemand", subtype = "FE", aggregate = F)[,2015,"gdp_SSP2"]
-    wfe  = collapseNames(wfe)
-    getSets(wfe) = gsub("item","variable", getSets(wfe))
+  # add industry subsectors energy efficiency capital stocks ----
 
+  kap <- cap_future %>%
+    `[`(, 2015, "gdp_SSP2EU.kap") %>%
+    quitte::magclass_to_tibble(c("iso3c", NA, NA, NA, "kap")) %>%
+    dplyr::select("iso3c", "kap")
 
-    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)
+  EEK <- calcOutput("Industry_EEK", kap = kap, supplementary = FALSE,
+                    aggregate = FALSE, years = getYears(cap_future))
 
-    # 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]
-      }
-    }
-
-    output = list(x=data,weight=weights,
-                  unit = "kWh for energy, $ for capital",
-                  description = "Technology units with capital and energy properties")
-  }
+  # tie outputs together ----
+  output <- list(
+    x = mbind(cap_future, EEK),
+    weight = NULL,
+    unit = "trillion 2005US$",
+    description = "Capital stock at constant 2005 national prices"
+  )
 
   return(output)
 }