refactor and clean up FE demand calculations

.buildlibrary

@@ -1,4 +1,4 @@
-ValidationKey: '34746870'
+ValidationKey: '347699371768'
 AcceptedWarnings:
 - 'Warning: package ''.*'' was built under R version'
 - 'Warning: namespace ''.*'' is not available and has been replaced'
@@ -11,3 +11,4 @@ AcceptedNotes:
 - Imports includes .* non-default packages.
 AutocreateReadme: yes
 allowLinterWarnings: yes
+enforceVersionUpdate: yes

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: 0.175.8
-date-released: '2024-02-12'
+version: 0.175.8.9001
+date-released: '2024-02-15'
 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: 0.175.8
-Date: 2024-02-12
+Version: 0.175.8.9001
+Date: 2024-02-15
 Authors@R: c(
     person("Lavinia", "Baumstark", , "[email protected]", role = c("aut", "cre")),
     person("Renato", "Rodrigues", role = "aut"),

R/calcCapacityFactor.R

@@ -4,135 +4,122 @@
 #' @author Renato Rodrigues, Stephen Bi
 #' @importFrom plyr round_any
 #' @examples
-#'
 #' \dontrun{
 #' calcOutput("CapacityFactor")
 #' }
 #'
-
-calcCapacityFactor <- function(){
-
-
-
-
+calcCapacityFactor <- function() {
   ### calculation of coal power capacity factor
   GWh_2_EJ <- 3.6e-6
 
   # Read capacity factor inputs
-  global <- readSource("REMIND_11Regi", subtype="capacityFactorGlobal", convert = FALSE)
+  global <- readSource("REMIND_11Regi", subtype = "capacityFactorGlobal", convert = FALSE)
+
+  # remove no longer used items
+  notUsed <- c("apcarelt", "aptrnelt", "apcarh2t", "apcarpet", "apcardit", "apcardiefft", "apcardieffH2t")
+  global <- global[, , notUsed, invert = TRUE]
+
   # Set coal plant capacity factor long-term assumption to 50% (down from 60%)
-  global[,,"pc"] <- 0.5
+  global[, , "pc"] <- 0.5
   # Read capacity factor rules
-  rules <- readSource("REMIND_11Regi", subtype="capacityFactorRules")
+  rules <- readSource("REMIND_11Regi", subtype = "capacityFactorRules")
 
   #   Creating new MAgPIE object to store the final capacity values
-  output <- new.magpie(getRegions(rules),seq(2005,2150,5),getNames(global))
-
+  output <- new.magpie(getRegions(rules), seq(2005, 2150, 5), getNames(global))
 
   # Merging global and rules values
   # Filling MagPIE object with global values
-  output[,,getNames(global)] <- global[,,getNames(global)]
-
+  output[, , getNames(global)] <- global[, , getNames(global)]
 
   ### Global Coal Plant Tracker Calcs ###
   # Read coal capacity and generation data to derive historical capacity factor rules
   # Read generation data from Energy Balances
-  coalgen_c <- calcOutput("IO",subtype="output",aggregate = F)[,,"pecoal.seel"]
-  coalgen_c <- dimSums(coalgen_c,dim=3)
-  map <- toolGetMapping(getConfig("regionmapping"),type="regional", where = "mappingfolder")
-  coalgen_R <- toolAggregate(coalgen_c,map,weight=NULL)
+  coalgen_c <- calcOutput("IO", subtype = "output", aggregate = FALSE)[, , "pecoal.seel"]
+  coalgen_c <- dimSums(coalgen_c, dim = 3)
+  map <- toolGetMapping(getConfig("regionmapping"), type = "regional", where = "mappingfolder")
+  coalgen_R <- toolAggregate(coalgen_c, map, weight = NULL)
   getNames(coalgen_c) <- "pc"
   getNames(coalgen_R) <- "pc"
 
-  #Read coal capacity data from GCPT
-    hist_cap_coal_c <- readSource("GCPT",subtype="historical",convert=F)
-  hist_cap_coal_R <- toolAggregate(hist_cap_coal_c,rel=map,weight=NULL)
-
-  #Calculate historical 5-year average capacity factors by country
-  coal_factor_c <- new.magpie(getRegions(hist_cap_coal_c),years = seq(2005, round_any(max(getYears(coalgen_c, as.integer = TRUE)), 5, f = floor)),names="pc",fill=0)
-  coal_factor_R <- new.magpie(getRegions(hist_cap_coal_R),years = getYears(coal_factor_c),names="pc",fill=0)
-  for (i in getYears(coal_factor_c,as.integer=TRUE)) {
-    if ((i+2) %in% getYears(coalgen_c,as.integer=TRUE)) {
-      coal_factor_c[,i,] <- dimSums(coalgen_c[,(i-2):(i+2),],dim=2)/(dimSums(hist_cap_coal_c[,(i-2):(i+2),],dim=2)*365*24 * GWh_2_EJ)
-      coal_factor_R[,i,] <- dimSums(coalgen_R[,(i-2):(i+2),],dim=2)/(dimSums(hist_cap_coal_R[,(i-2):(i+2),],dim=2)*365*24 * GWh_2_EJ)
-      #coal_factor_c[,i,][which(dimSums(hist_cap_coal_c[,(i-2):(i+2),],dim=2)==0)] <- 0
-    # If latest available data falls in year 4 of a 5-year timestep
-    }else if ((i+1) %in% getYears(coalgen_c,as.integer=TRUE)) {
+  # Read coal capacity data from GCPT
+  hist_cap_coal_c <- readSource("GCPT", subtype = "historical", convert = FALSE)
+  hist_cap_coal_R <- toolAggregate(hist_cap_coal_c, rel = map, weight = NULL)
+
+  # Calculate historical 5-year average capacity factors by country
+  coal_factor_c <- new.magpie(getRegions(hist_cap_coal_c), years = seq(2005, round_any(max(getYears(coalgen_c, as.integer = TRUE)), 5, f = floor)), names = "pc", fill = 0)
+  coal_factor_R <- new.magpie(getRegions(hist_cap_coal_R), years = getYears(coal_factor_c), names = "pc", fill = 0)
+  for (i in getYears(coal_factor_c, as.integer = TRUE)) {
+    if ((i + 2) %in% getYears(coalgen_c, as.integer = TRUE)) {
+      coal_factor_c[, i, ] <- dimSums(coalgen_c[, (i - 2):(i + 2), ], dim = 2) / (dimSums(hist_cap_coal_c[, (i - 2):(i + 2), ], dim = 2) * 365 * 24 * GWh_2_EJ)
+      coal_factor_R[, i, ] <- dimSums(coalgen_R[, (i - 2):(i + 2), ], dim = 2) / (dimSums(hist_cap_coal_R[, (i - 2):(i + 2), ], dim = 2) * 365 * 24 * GWh_2_EJ)
+      # If latest available data falls in year 4 of a 5-year timestep
+    } else if ((i + 1) %in% getYears(coalgen_c, as.integer = TRUE)) {
       # Special treatment of 2020 due to COVID-19: half-weight 2020
-      if (i==2020) {
-        coal_factor_c[,i,] <- (dimSums(coalgen_c[,(i-2):(i+1),],dim=2) - 0.5 * dimSums(coalgen_c[,(i),],dim=2)) /
-          ((dimSums(hist_cap_coal_c[,(i-2):(i+1),],dim=2) + dimSums(hist_cap_coal_c[,(i+1),],dim=2))*365*24 * GWh_2_EJ)
-        coal_factor_R[,i,] <- (dimSums(coalgen_R[,(i-2):(i+1),],dim=2) + dimSums(coalgen_R[,(i+1),],dim=2)) /
-          ((dimSums(hist_cap_coal_R[,(i-2):(i+1),],dim=2) - 0.5 * dimSums(hist_cap_coal_R[,(i),],dim=2))*365*24 * GWh_2_EJ)
-      # For other timesteps, just use a 4-year average
-      }else {
-        coal_factor_c[,i,] <- dimSums(coalgen_c[,(i-2):(i+1),],dim=2) / (dimSums(hist_cap_coal_c[,(i-2):(i+1),],dim=2)*365*24 * GWh_2_EJ)
-        coal_factor_R[,i,] <- dimSums(coalgen_R[,(i-2):(i+1),],dim=2) / (dimSums(hist_cap_coal_R[,(i-2):(i+1),],dim=2)*365*24 * GWh_2_EJ)
+      if (i == 2020) {
+        coal_factor_c[, i, ] <- (dimSums(coalgen_c[, (i - 2):(i + 1), ], dim = 2) - 0.5 * dimSums(coalgen_c[, (i), ], dim = 2)) /
+          ((dimSums(hist_cap_coal_c[, (i - 2):(i + 1), ], dim = 2) + dimSums(hist_cap_coal_c[, (i + 1), ], dim = 2)) * 365 * 24 * GWh_2_EJ)
+        coal_factor_R[, i, ] <- (dimSums(coalgen_R[, (i - 2):(i + 1), ], dim = 2) + dimSums(coalgen_R[, (i + 1), ], dim = 2)) /
+          ((dimSums(hist_cap_coal_R[, (i - 2):(i + 1), ], dim = 2) - 0.5 * dimSums(hist_cap_coal_R[, (i), ], dim = 2)) * 365 * 24 * GWh_2_EJ)
+        # For other timesteps, just use a 4-year average
+      } else {
+        coal_factor_c[, i, ] <- dimSums(coalgen_c[, (i - 2):(i + 1), ], dim = 2) / (dimSums(hist_cap_coal_c[, (i - 2):(i + 1), ], dim = 2) * 365 * 24 * GWh_2_EJ)
+        coal_factor_R[, i, ] <- dimSums(coalgen_R[, (i - 2):(i + 1), ], dim = 2) / (dimSums(hist_cap_coal_R[, (i - 2):(i + 1), ], dim = 2) * 365 * 24 * GWh_2_EJ)
       }
-    # If latest available data falls in the middle of a timestep
-    }else {
+      # If latest available data falls in the middle of a timestep
+    } else {
       # if only data until 2020 is available, assign a double-weight to 2018 and 2019
-      if (i==2020) {
-        coal_factor_c[,i,] <- (dimSums(coalgen_c[,(i-2):i,],dim=2) + dimSums(coalgen_c[,(i-2):(i-1),],dim=2)) /
-          ((dimSums(hist_cap_coal_c[,(i-2):i,],dim=2) + dimSums(hist_cap_coal_c[,(i-2):(i-1),],dim=2))*365*24 * GWh_2_EJ)
-        coal_factor_R[,i,] <- (dimSums(coalgen_R[,(i-2):i,],dim=2) + dimSums(coalgen_R[,(i-2):(i-1),],dim=2)) /
-          ((dimSums(hist_cap_coal_R[,(i-2):i,],dim=2) + dimSums(hist_cap_coal_R[,(i-2):(i-1),],dim=2))*365*24 * GWh_2_EJ)
-      # For other timesteps, assign triple-weight to final year
-      # Valid assumption for 2015 capacity factors
-      }else {
-        coal_factor_c[,i,] <- (dimSums(coalgen_c[,(i-2):i,],dim=2) + 2*coalgen_c[,i,]) /
-                                 ((dimSums(hist_cap_coal_c[,(i-2):i,],dim=2) + 2*hist_cap_coal_c[,i,])*365*24 * GWh_2_EJ)
-        coal_factor_R[,i,] <- (dimSums(coalgen_R[,(i-2):i,],dim=2) + 2*coalgen_R[,i,]) /
-          ((dimSums(hist_cap_coal_R[,(i-2):i,],dim=2) + 2*hist_cap_coal_R[,i,])*365*24 * GWh_2_EJ)
+      if (i == 2020) {
+        coal_factor_c[, i, ] <- (dimSums(coalgen_c[, (i - 2):i, ], dim = 2) + dimSums(coalgen_c[, (i - 2):(i - 1), ], dim = 2)) /
+          ((dimSums(hist_cap_coal_c[, (i - 2):i, ], dim = 2) + dimSums(hist_cap_coal_c[, (i - 2):(i - 1), ], dim = 2)) * 365 * 24 * GWh_2_EJ)
+        coal_factor_R[, i, ] <- (dimSums(coalgen_R[, (i - 2):i, ], dim = 2) + dimSums(coalgen_R[, (i - 2):(i - 1), ], dim = 2)) /
+          ((dimSums(hist_cap_coal_R[, (i - 2):i, ], dim = 2) + dimSums(hist_cap_coal_R[, (i - 2):(i - 1), ], dim = 2)) * 365 * 24 * GWh_2_EJ)
+        # For other timesteps, assign triple-weight to final year
+        # Valid assumption for 2015 capacity factors
+      } else {
+        coal_factor_c[, i, ] <- (dimSums(coalgen_c[, (i - 2):i, ], dim = 2) + 2 * coalgen_c[, i, ]) /
+          ((dimSums(hist_cap_coal_c[, (i - 2):i, ], dim = 2) + 2 * hist_cap_coal_c[, i, ]) * 365 * 24 * GWh_2_EJ)
+        coal_factor_R[, i, ] <- (dimSums(coalgen_R[, (i - 2):i, ], dim = 2) + 2 * coalgen_R[, i, ]) /
+          ((dimSums(hist_cap_coal_R[, (i - 2):i, ], dim = 2) + 2 * hist_cap_coal_R[, i, ]) * 365 * 24 * GWh_2_EJ)
       }
-      #coal_factor_c[,i,][which(hist_cap_coal_c[,i,]==0)] <- 0
     }
-    #Replace countries without coal power with regional capacity factor.
-    coal_factor_c[,i,][which(!is.finite(coal_factor_c[,i,]))] <-
-      coal_factor_R[map$RegionCode[which(map$CountryCode %in% getRegions(coal_factor_c)[which(!is.finite(coal_factor_c[,i,]))])],i,]
+    # Replace countries without coal power with regional capacity factor.
+    coal_factor_c[, i, ][which(!is.finite(coal_factor_c[, i, ]))] <-
+      coal_factor_R[map$RegionCode[which(map$CountryCode %in% getRegions(coal_factor_c)[which(!is.finite(coal_factor_c[, i, ]))])], i, ]
     # The derived coal capacity factors for Bosnia, Ireland and Myanmar are > 1 in some years for as yet unclear reasons.
-    coal_factor_c[,i,][which(coal_factor_c[,i,]>1)] <- max(coal_factor_c[,i,][which(coal_factor_c[,i,]<1)])
+    coal_factor_c[, i, ][which(coal_factor_c[, i, ] > 1)] <- max(coal_factor_c[, i, ][which(coal_factor_c[, i, ] < 1)])
   }
 
-  #Derive coal capacity factor rule assumptions for 2020 - 2035 (linear convergence of historical trends to global default)
+  # Derive coal capacity factor rule assumptions for 2020 - 2035 (linear convergence of historical trends to global default)
   hist_yr <- "y2015"
   start_yr <- "y2020"
   end_yr <- "y2030"
   conv_yr <- "y2035"
 
   coal_factor_c_years <- intersect(getYears(coal_factor_c), getYears(output))
   coal_factor_c_years <- coal_factor_c_years[coal_factor_c_years < start_yr]
-  output[,getYears(output)[getYears(output) < start_yr],"pc"] <-
-    coal_factor_c[,coal_factor_c_years,]
+  output[, getYears(output)[getYears(output) < start_yr], "pc"] <-
+    coal_factor_c[, coal_factor_c_years, ]
 
-  output[,getYears(output)[which(getYears(output)>=conv_yr)],"pc"] <- global[,,"pc"]
-  slope <- (output[,conv_yr,'pc'] - output[,hist_yr,'pc']) / (as.numeric(gsub("y","",conv_yr)) - as.numeric(gsub("y","",hist_yr)))
-  for (t in getYears(output)[which(getYears(output)>=start_yr & getYears(output)<=end_yr)]) {
-    output[,t,"pc"] <- output[,hist_yr,"pc"] + slope * (as.numeric(gsub("y","",t)) - as.numeric(gsub("y","",hist_yr)))
+  output[, getYears(output)[which(getYears(output) >= conv_yr)], "pc"] <- global[, , "pc"]
+  slope <- (output[, conv_yr, "pc"] - output[, hist_yr, "pc"]) / (as.numeric(gsub("y", "", conv_yr)) - as.numeric(gsub("y", "", hist_yr)))
+  for (t in getYears(output)[which(getYears(output) >= start_yr & getYears(output) <= end_yr)]) {
+    output[, t, "pc"] <- output[, hist_yr, "pc"] + slope * (as.numeric(gsub("y", "", t)) - as.numeric(gsub("y", "", hist_yr)))
   }
 
   # Overwriting MAgPie object with rules values
-  output[getRegions(rules),getYears(rules),getNames(rules)] <- ifelse(rules[getRegions(rules),getYears(rules),getNames(rules)]!=0, rules[getRegions(rules),getYears(rules),getNames(rules)], output[getRegions(rules),getYears(rules),getNames(rules)])
-
-  # Convergence and lin.convergence functions are not working...
-  # output[,getYears(output)[which(getYears(output)>=hist_yr & getYears(output)<=conv_yr)],"pc"] <-
-  #   convergence(origin=output[,getYears(output)[which(getYears(output)>=hist_yr & getYears(output)<=conv_yr)],"pc"],
-  #               aim=as.numeric(global[,,"pc"]),start_year=start_yr,end_year=conv_yr)
-  # Define weight aggregation for capacity factors
-  # using final energy as a proxy for the existent capacity factor to weight the capacity factor aggregation (it should be changed if the information about the existent capacity factor become available in the future)
-
+  output[getRegions(rules), getYears(rules), getNames(rules)] <- ifelse(rules[getRegions(rules), getYears(rules), getNames(rules)] != 0, rules[getRegions(rules), getYears(rules), getNames(rules)], output[getRegions(rules), getYears(rules), getNames(rules)])
 
   # change coal capacity factor in Germany to reflect observed decrease in coal electricity in recent years, note: to be checked whether necessary for other regions as well
   # https://static.agora-energiewende.de/fileadmin/Projekte/2021/2020_01_Jahresauswertung_2020/200_A-EW_Jahresauswertung_2020_WEB.pdf
-  output["DEU",c("y2020","y2025"),"pc"] = 0.43
-  output["DEU",c("y2030"),"pc"] = 0.4
+  output["DEU", c("y2020", "y2025"), "pc"] <- 0.43
+  output["DEU", c("y2030"), "pc"] <- 0.4
 
-  weight <- calcOutput("FE",source="IEA",aggregate=FALSE)[,2015,"FE (EJ/yr)"]
+  weight <- calcOutput("FE", source = "IEA", aggregate = FALSE)[, 2015, "FE (EJ/yr)"]
 
   # Return regions aggregation weighted by final energy
-  return(list(x=output, weight=weight,
-               unit="% of capacity",
-               description="Installed capacity availability - capacity factor (fraction of the year that a plant is running)"
+  return(list(
+    x = output, weight = weight,
+    unit = "% of capacity",
+    description = "Installed capacity availability - capacity factor (fraction of the year that a plant is running)"
   ))
-
 }

R/calcEDGETransport.R

@@ -124,11 +124,6 @@ calcEDGETransport <- function(subtype = "logit_exponent") {
            weight = NULL
            unit = "EJ"
            description = "UE demand divided by technologies for different ES on the CES level."
-         },
-         "f35_bunkers_fe" = {
-           weight = NULL
-           unit = "EJ"
-           description = "Bunkers FE trajectory from edge-t."
          })
 
   return(list(x           = data,

R/calcFE.R

@@ -27,7 +27,8 @@ calcFE <- function(source = "IEA", scenario_proj = "SSP2", ieaVersion = "default
     colnames(map) <- gsub("io", "names_in", colnames(map))
 
     # Give description
-    descript <- "IEA Final Energy Data based on 2022 version of IEA Energy Balances"
+    ieaYear <- if (ieaVersion == "default") 2022 else 2023
+    descript <- paste0("IEA Final Energy Data based on ", ieaYear, " version of IEA Energy Balances")
 
     #------ PROCESS DATA ------------------------------------------
     # select data that have names

R/calcFEdemand.R

@@ -6,6 +6,19 @@ calcFEdemand <- function() {
   feIndustry <- calcOutput("FeDemandIndustry", warnNA = FALSE, aggregate = FALSE)
   feTransport <- calcOutput("FeDemandTransport", warnNA = FALSE, aggregate = FALSE)
 
+  # duplicate scenarios ----
+
+  # add Navigate and Campaigners scenarios to industry and transport to match buildings scenarios by duplication
+  duplicateScens <- paste0("gdp_SSP2EU_", c("NAV_act", "NAV_ele", "NAV_tec", "NAV_lce", "NAV_all", "CAMP_weak", "CAMP_strong"))
+
+  feTransport <- mbind(feTransport, do.call(mbind, lapply(duplicateScens, function(to) {
+    setItems(feTransport[, , "gdp_SSP2EU"], 3.1, to)
+  })))
+
+  feIndustry <- mbind(feIndustry, do.call(mbind, lapply(duplicateScens, function(to) {
+    setItems(feIndustry[, , "gdp_SSP2EU"], 3.1, to)
+  })))
+
   # add up industry and buildings contributions to stationary
   stationaryItems <- c("fehes", "feh2s")
   feStationary <- feIndustry[, , stationaryItems] + feBuildings[, , stationaryItems]

R/calcFeDemandBuildings.R

@@ -22,17 +22,27 @@ calcFeDemandBuildings <- function(subtype) {
   buildings <- toolAggregateTimeSteps(buildings)
   stationary <- toolAggregateTimeSteps(stationary)
 
+  # add scenarios to stationary to match buildings scenarios by duplication
+  duplicateScens <- paste0("SSP2EU_", c("NAV_act", "NAV_ele", "NAV_tec", "NAV_lce", "NAV_all", "CAMP_weak", "CAMP_strong"))
+  stationary <- mbind(stationary, do.call(mbind, lapply(duplicateScens, function(to) {
+    setItems(stationary[, , "SSP2EU"], 3.1, to)
+  })))
+
   if (subtype == "FE") {
+
     # drop RCP dimension (use fixed RCP)
     buildings <- mselect(buildings, rcp = "fixed", collapseNames = TRUE)
+
   } else {
+
     # rename RCP scenarios in buildings
     rcps <- paste0("rcp", gsub("p", "", getItems(buildings, "rcp")))
     rcps <- gsub("rcpfixed", "none", rcps)
     getItems(buildings, "rcp") <- rcps
 
     # expand stationary to all RCP scenarios
     stationary <- toolAddDimensions(x = stationary, dimVals = rcps, dimName = "rcp", dimCode = 3.2)
+
   }
 
   # extrapolate years missing in buildings, but existing in stationary
@@ -122,8 +132,7 @@ calcFeDemandBuildings <- function(subtype) {
 
   # remove missing NAVIGATE scenarios
   if (subtype %in% c("FE_buildings", "UE_buildings")) {
-    remind <- remind[, , grep("SSP2EU_(NAV|CAMP)_[a-z]*\\.rcp", getItems(remind, 3), value = TRUE),
-                     invert = TRUE]
+    remind <- remind[, , grep("SSP2EU_(NAV|CAMP)_[a-z]*\\.rcp", getItems(remind, 3), value = TRUE), invert = TRUE]
   }
 
   # change the scenario names for consistency with REMIND sets

R/calcFeDemandIndustry.R

@@ -1605,27 +1605,8 @@ calcFeDemandIndustry <- function(use_ODYM_RECC = FALSE) {
 
   remind <- mbind(remind, industry_subsectors_en, industry_subsectors_ue)
 
-  # ---- _ duplicate SSP2EU scenarios of industry for Navigate and Campaigners scenarios ----
-
-  industryItems <- grep("(.*i$)|chemicals|steel|otherInd|cement",
-                        getItems(remind, 3.2), value = TRUE)
-  nonIndustryItems <- setdiff(getItems(remind, 3.2), industryItems)
-  duplScenarios <- grep("SSP2EU_(NAV|CAMP)_", getItems(remind, 3.1), value = TRUE)
-  nonDuplScenarios <- setdiff(getItems(remind, 3.1), duplScenarios)
-  remind <- mbind(
-    mselect(remind, scenario = nonDuplScenarios),
-    mselect(remind, scenario = duplScenarios, item = nonIndustryItems),
-    toolAddDimensions(x = mselect(remind, scenario = "gdp_SSP2EU", item = industryItems,
-                                  collapseNames = TRUE),
-                      dimVals = c(paste0("gdp_SSP2EU_NAV_", c("act", "tec", "ele", "lce", "all")),
-                                  paste0("gdp_SSP2EU_CAMP_", c("weak", "strong"))),
-                      dimName = "scenario",
-                      dimCode = 3.1)
-  )
-
   # ---- _ prepare output ----
 
-
   return(list(
     x = remind,
     weight = NULL,