update IEA WEO reference data to 2023 data

.buildlibrary

@@ -1,4 +1,4 @@
-ValidationKey: '37105140'
+ValidationKey: '37153004'
 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: 0.186.0
-date-released: '2024-08-14'
+version: 0.186.1
+date-released: '2024-08-29'
 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: 0.186.0
-Date: 2024-08-14
+Version: 0.186.1
+Date: 2024-08-29
 Authors@R: c(
     person("Lavinia", "Baumstark", , "[email protected]", role = c("aut", "cre")),
     person("Renato", "Rodrigues", role = "aut"),

R/calcFeDemandIndustry.R

@@ -950,53 +950,8 @@ calcFeDemandIndustry <- function(use_ODYM_RECC = FALSE) {
   industry_subsectors_en <- industry_subsectors_en %>%
     mutate(pf = sub("^feelwlth_", "feel_", .data$pf))
 
-  ### extend to 2020 using WEO 2021 data ----
-  IEA_WEO_2021_ref_year <- 2020L
-  last_industry_subsectors_en_year <- max(industry_subsectors_en$year)
-  if (IEA_WEO_2021_ref_year > last_industry_subsectors_en_year) {
-    industry_subsectors_en <- bind_rows(
-      industry_subsectors_en,
 
-      inner_join(
-        industry_subsectors_en %>%
-          filter(2010 == .data$year),
-
-        readSource(type = "IEA_WorldEnergyOutlook", subtype = "2021-region",
-                   convert = TRUE) %>%
-          magclass_to_tibble(c("iso3c", "year", "scenario", "variable", "industry.FE")) %>%
-          filter(.data$year %in% c(2010, IEA_WEO_2021_ref_year),
-                 "Stated Policies Scenario" == .data$scenario,
-                 "Energy-Total-Industry (EJ)" == .data$variable) %>%
-          left_join(region_mapping_21, "iso3c") %>%
-          group_by(!!!syms(c("region", "year"))) %>%
-          summarise(industry.FE = sum(.data$industry.FE, na.rm = TRUE),
-                    .groups = "drop_last") %>%
-          summarise(change = last(.data$industry.FE, order_by = .data$year)
-                    / first(.data$industry.FE, order_by = .data$year),
-                    .groups = "drop"),
-
-        "region"
-      ) %>%
-        mutate(value = .data$value * .data$change,
-               year  = IEA_WEO_2021_ref_year) %>%
-        select(-"change")
-    ) %>%
-      pivot_wider(names_from = "year") %>%
-      mutate(`2015` = (!!sym("2010")
-                        + !!sym("2015")
-                        + !!sym(as.character(IEA_WEO_2021_ref_year))
-      )
-      / 3) %>%
-      pivot_longer(matches("^[0-9]*$"), names_to = "year",
-                   names_transform = list(year = as.integer)) %>%
-      interpolate_missing_periods_(periods = list(year = seq_range(range(.$year)))) %>%
-      assert(not_na, everything()) %>%
-      verify(is.finite(.data$value))
-
-    FE_alpha_mod <- 0.9   # found by fiddling around
-  } else {
-    FE_alpha_mod <- 1
-  }
+  FE_alpha_mod <- 1
 
   ### calculate 1993-2020 industry subsector FE shares ----
   industry_subsectors_en_shares <- industry_subsectors_en %>%

R/calcIEA_WorldEnergyOutlook.R

@@ -1,61 +1,46 @@
 #' Calculate REMIND variables from IEA World Energy Outlook data.
 #'
-#' @param version of the WEO data, either 'default' (full paid version) or
-#' 'latest' (free, restricted, up-to-date dataset)
 #' @author Falk Benke
 #' @export
 
-calcIEA_WorldEnergyOutlook <- function(version = "latest") { # nolint
+calcIEA_WorldEnergyOutlook <- function() { # nolint
 
-  if (!(version %in% c("default", "latest"))) {
-    stop("Invalid parameter `version`. Must be either 'default' or 'latest'")
-  }
-
-  refYear <- if (version == "default") 2021 else 2023
-
-  dataGlo <- readSource("IEA_WorldEnergyOutlook", subtype = paste0(refYear, "-global"))
-  dataReg <- readSource("IEA_WorldEnergyOutlook", subtype = paste0(refYear, "-region"))
+  dataGlo <- readSource("IEA_WorldEnergyOutlook", convert = FALSE)["World", , ]
+  magclass::getItems(dataGlo, dim = 1) <- "GLO"
+  dataReg <- readSource("IEA_WorldEnergyOutlook", convert = TRUE)
 
   .mapToRemind <- function(data) {
-    if (refYear == 2021) {
-      # copy over Stated Policies Scenario for 2010 - 2020 to other scenarios
-      for (s in getNames(data, dim = 1)) {
-        data[, c("y2010", "y2019", "y2020"), s] <-
-          data[, c("y2010", "y2019", "y2020"), "Stated Policies Scenario"][, , getNames(data[, , s], dim = 2)]
-      }
+
+    # copy over Stated Policies Scenario for 2010 - 2022 to other scenarios
+    for (s in magclass::getNames(data, dim = 1)) {
+      data[, c("y2010", "y2015", "y2021", "y2022"), s] <-
+        data[, c("y2010", "y2015", "y2021", "y2022"), "Stated Policies Scenario"][, , magclass::getNames(data[, , s], dim = 2)]
     }
 
+    # rename scenarios
     scens <- c(
       "Stated Policies Scenario" = "SPS",
       "Announced pledges scenario" = "APS",
       "Announced Pledges Scenario" = "APS",
-      "Sustainable Development Scenario" = "SDS",
       "Net Zero Emissions by 2050 Scenario" = "Net2050"
     )
 
-    getNames(data, dim = 1) <- paste0("IEA WEO ", refYear, " ", scens[getNames(data, dim = 1)])
-    getSets(data)[3] <- "model"
+    magclass::getNames(data, dim = 1) <- paste0("IEA WEO 2023 ", scens[magclass::getNames(data, dim = 1)])
+    magclass::getSets(data)[3] <- "model"
 
-    map <- toolGetMapping(
-      "Mapping_IEA_WEO_2021_complete.csv",
-      type = "reportingVariables", where = "mrremind"
-    ) %>%
+    map <- toolGetMapping("Mapping_IEA_WEO_complete.csv", type = "reportingVariables", where = "mrremind") %>%
       dplyr::filter(!is.na(.data$REMIND), .data$REMIND != "") %>%
-      dplyr::mutate(
-        "from" = paste0(trimws(.data$WEO), " (", .data$Unit_WEO, ")"),
-        "to" = paste0(trimws(.data$REMIND), " (", .data$Unit_REMIND, ")"),
-        "conversion" = as.numeric(.data$Conversion)
-      ) %>%
-      dplyr::select("from", "to", "conversion")
+      dplyr::mutate("conversion" = as.numeric(.data$Conversion)) %>%
+      dplyr::select("from" = "Variable", "to" = "REMIND", "conversion")
 
     for (var in intersect(getNames(data, dim = 2), unique(map$from))) {
       conv <- map[map$from == var, "conversion"]
 
       # if there is more than one conversion factor, it means that one source variable
-      # is converted two more than one target variable using a different conversion
+      # is converted two more than one target variable using a different conversion factor
       if (length(unique(conv)) > 1) {
 
-        # create unique "from" variables for each mapping entry
+        # create unique "from" variables for each mapping entry by appending numbers
         map[map$from == var, "from"] <- paste0(map[map$from == var, "from"], " ", seq(1, nrow(map[map$from == var, ])))
 
         # duplicate "from" data with for each mapping entry
@@ -81,67 +66,62 @@ calcIEA_WorldEnergyOutlook <- function(version = "latest") { # nolint
   dataGlo <- .mapToRemind(dataGlo)
   dataReg <- .mapToRemind(dataReg)
 
-  # do additional calculations on full dataset for 2021 (won't work for incomplete 2023 data)
-  if (refYear == 2021) {
-    .calcAdditionalVars <- function(x) {
-      # correct PE|Nuclear and PE
-      # PE Nuclear is usually reported in direct equivalents, values from IEA are
-      # roughly 3 times higher than the REMIND ones
-      x[, , "PE (EJ/yr)"] <- x[, , "PE (EJ/yr)"] - x[, , "PE|Nuclear (EJ/yr)"]
-      x[, , "PE|Nuclear (EJ/yr)"] <- x[, , "PE|Nuclear (EJ/yr)"] / 3
-      x[, , "PE (EJ/yr)"] <- x[, , "PE (EJ/yr)"] + x[, , "PE|Nuclear (EJ/yr)"]
+  .calcAdditionalVars <- function(x) {
+    # correct PE|Nuclear and PE
+    # PE Nuclear is usually reported in direct equivalents, values from IEA are
+    # roughly 3 times higher than the REMIND ones
+    x[, , "PE (EJ/yr)"] <- x[, , "PE (EJ/yr)"] - x[, , "PE|Nuclear (EJ/yr)"]
+    x[, , "PE|Nuclear (EJ/yr)"] <- x[, , "PE|Nuclear (EJ/yr)"] / 3
+    x[, , "PE (EJ/yr)"] <- x[, , "PE (EJ/yr)"] + x[, , "PE|Nuclear (EJ/yr)"]
 
-      return(x)
-    }
+    return(x)
+  }
 
-    dataGlo <- .calcAdditionalVars(dataGlo)
-    dataReg <- .calcAdditionalVars(dataReg)
+  dataGlo <- .calcAdditionalVars(dataGlo)
+  dataReg <- .calcAdditionalVars(dataReg)
 
-    dataGlo <- add_columns(dataGlo, "Cap|Electricity|Biomass|w/o CC (GW)", dim = 3.2)
-    dataGlo[, , "Cap|Electricity|Biomass|w/o CC (GW)"] <-
-      dataGlo[, , "Cap|Electricity|Biomass (GW)"] - dataGlo[, , "Cap|Electricity|Biomass|w/ CC (GW)"]
+  dataGlo <- magclass::add_columns(dataGlo, "Cap|Electricity|Biomass|w/o CC (GW)", dim = 3.2)
+  dataGlo[, , "Cap|Electricity|Biomass|w/o CC (GW)"] <-
+    dataGlo[, , "Cap|Electricity|Biomass (GW)"] - dataGlo[, , "Cap|Electricity|Biomass|w/ CC (GW)"]
 
-    dataGlo <- add_columns(dataGlo, "Cap|Electricity|Coal (GW)", dim = 3.2)
-    dataGlo[, , "Cap|Electricity|Coal (GW)"] <-
-      dataGlo[, , "Cap|Electricity|Coal|w/o CC (GW)"] + dataGlo[, , "Cap|Electricity|Coal|w/ CC (GW)"]
+  dataGlo <- magclass::add_columns(dataGlo, "Cap|Electricity|Coal (GW)", dim = 3.2)
+  dataGlo[, , "Cap|Electricity|Coal (GW)"] <-
+    dataGlo[, , "Cap|Electricity|Coal|w/o CC (GW)"] + dataGlo[, , "Cap|Electricity|Coal|w/ CC (GW)"]
 
-    dataGlo <- add_columns(dataGlo, "Cap|Electricity|Solar (GW)", dim = 3.2)
-    dataGlo[, , "Cap|Electricity|Solar (GW)"] <-
-      dataGlo[, , "Cap|Electricity|Solar|CSP (GW)"] + dataGlo[, , "Cap|Electricity|Solar|PV (GW)"]
+  dataGlo <- magclass::add_columns(dataGlo, "Cap|Electricity|Solar (GW)", dim = 3.2)
+  dataGlo[, , "Cap|Electricity|Solar (GW)"] <-
+    dataGlo[, , "Cap|Electricity|Solar|CSP (GW)"] + dataGlo[, , "Cap|Electricity|Solar|PV (GW)"]
 
-    dataGlo <- add_columns(dataGlo, "Cap|Electricity|Fossil (GW)", dim = 3.2)
-    dataGlo[, , "Cap|Electricity|Fossil (GW)"] <-
-      dataGlo[, , "Cap|Electricity|Fossil|w/o CC (GW)"] + dataGlo[, , "Cap|Electricity|Fossil|w/ CC (GW)"]
+  dataGlo <- magclass::add_columns(dataGlo, "Cap|Electricity|Fossil (GW)", dim = 3.2)
+  dataGlo[, , "Cap|Electricity|Fossil (GW)"] <-
+    dataGlo[, , "Cap|Electricity|Fossil|w/o CC (GW)"] + dataGlo[, , "Cap|Electricity|Fossil|w/ CC (GW)"]
 
-    dataGlo <- add_columns(dataGlo, "Cap|Electricity|Gas (GW)", dim = 3.2)
-    dataGlo[, , "Cap|Electricity|Gas (GW)"] <-
-      dataGlo[, , "Cap|Electricity|Gas|w/o CC (GW)"] + dataGlo[, , "Cap|Electricity|Gas|w/ CC (GW)"]
+  dataGlo <- magclass::add_columns(dataGlo, "Cap|Electricity|Gas (GW)", dim = 3.2)
+  dataGlo[, , "Cap|Electricity|Gas (GW)"] <-
+    dataGlo[, , "Cap|Electricity|Gas|w/o CC (GW)"] + dataGlo[, , "Cap|Electricity|Gas|w/ CC (GW)"]
 
-    dataGlo <- add_columns(dataGlo, "SE|Electricity|Solar (EJ/yr)", dim = 3.2)
-    dataGlo[, , "SE|Electricity|Solar (EJ/yr)"] <-
-      dataGlo[, , "SE|Electricity|Solar|PV (EJ/yr)"] + dataGlo[, , "SE|Electricity|Solar|CSP (EJ/yr)"]
-  }
+  dataGlo <- magclass::add_columns(dataGlo, "SE|Electricity|Solar (EJ/yr)", dim = 3.2)
+  dataGlo[, , "SE|Electricity|Solar (EJ/yr)"] <-
+    dataGlo[, , "SE|Electricity|Solar|PV (EJ/yr)"] + dataGlo[, , "SE|Electricity|Solar|CSP (EJ/yr)"]
 
   # includes values from the original source for global region instead of calculating
   # it as the sum of all countries (as countries are incomplete)
   .customAggregate <- function(x, rel, to = NULL, glo) {
     x <- toolAggregate(x, rel = rel, to = to)
 
-    if ("GLO" %in% getItems(x, dim = 1)) {
+    if ("GLO" %in% magclass::getItems(x, dim = 1)) {
       x <- x["GLO", , , invert = TRUE]
 
-      glo <- dimSums(glo, dim = 1, na.rm = FALSE)
-
-      out <- new.magpie(
-        cells_and_regions = union(getItems(x, dim = 1), "GLO"),
-        years = union(getYears(x), getYears(glo)),
-        names = union(getNames(x), getNames(glo)),
+      out <- magclass::new.magpie(
+        cells_and_regions = union(magclass::getItems(x, dim = 1), "GLO"),
+        years = union(magclass::getYears(x), magclass::getYears(glo)),
+        names = union(magclass::getNames(x), magclass::getNames(glo)),
         fill = NA,
         sets = names(dimnames(x))
       )
 
-      out[getItems(x, dim = 1), getYears(x), getNames(x)] <- x
-      out["GLO", getYears(glo), getNames(glo)] <- glo
+      out[magclass::getItems(x, dim = 1), magclass::getYears(x), magclass::getNames(x)] <- x
+      out["GLO", magclass::getYears(glo), magclass::getNames(glo)] <- glo
 
       return(out)
     } else {
@@ -155,6 +135,6 @@ calcIEA_WorldEnergyOutlook <- function(version = "latest") { # nolint
     unit = c("GW", "EJ/yr", "Mt CO2/yr"),
     aggregationFunction = .customAggregate,
     aggregationArguments = list(glo = dataGlo),
-    description = "IEA World Energy Outlook values as REMIND variables"
+    description = "IEA World Energy Outlook 2023 values as REMIND variables"
   ))
 }