Implement explicit scenario choice across package.

.buildlibrary

@@ -1,4 +1,4 @@
-ValidationKey: '43925340'
+ValidationKey: '44106600'
 AcceptedWarnings:
 - Invalid URL: .*
 - 'Warning: package ''.*'' was built under R version'

CITATION.cff

@@ -2,7 +2,7 @@ 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: 0.218.1
+version: 0.219.0
 date-released: '2025-02-21'
 abstract: The mrremind packages contains data preprocessing for the REMIND model.
 authors:

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: mrremind
 Title: MadRat REMIND Input Data Package
-Version: 0.218.1
+Version: 0.219.0
 Date: 2025-02-21
 Authors@R: c(
     person("Lavinia", "Baumstark", , "[email protected]", role = c("aut", "cre")),

R/calcCO2Prices.R

@@ -1,10 +1,7 @@
 calcCO2Prices <- function() {
-
-  # read data
   x <- readSource("ExpertGuess", subtype = "co2prices")
 
-  # convert from $2005 to $2017
-
+  # Convert from $2005 to $2017
   x <- GDPuc::toolConvertGDP(
     gdp = x,
     unit_in = "constant 2005 US$MER",
@@ -14,17 +11,15 @@ calcCO2Prices <- function() {
 
   getNames(x) <- NULL
 
-  # read data used for weight
+  # Read data used for weight
   ceds <- calcOutput("Emissions", datasource = "CEDS2024", aggregate = FALSE)
   ceds <- ceds[, , "Emi|CO2|Energy and Industrial Processes (Mt CO2/yr)"]
-  # for years in the future, use last year available from CEDS
+  # For years in the future, use last year available from CEDS
   ceds <- ceds[, pmin(getYears(x), max(getYears(ceds))), ]
   getYears(ceds) <- getYears(x)
 
-  return(list(
-    x = x,
-    weight = ceds,
-    unit = "US$2017/t CO2",
-    description = "CO2 prices in 2010, 2015 and 2020"
-  ))
+  list(x = x,
+       weight = ceds,
+       unit = "US$2017/t CO2",
+       description = "CO2 prices in 2010, 2015 and 2020")
 }

R/calcCapTarget.R

@@ -1,104 +1,87 @@
 #' Capacity targets from two sources
+#'
 #' @description The capacity targets (GW)  at regional level are produced from two different databases-
-#' UNFCCC_NDC database, an update of the Rogelj 2017 paper (see readme in inputdata), and REN21 Global Renewables report
-#' The UNFCCC_NDC capacity targets are further broken down to conditional and unconditional targets.
-#' @author Aman Malik, Oliver Richters
+#' UNFCCC_NDC database, an update of the Rogelj 2017 paper (see readme in inputdata), and REN21 Global Renewables
+#' report. The UNFCCC_NDC capacity targets are further broken down to conditional and unconditional targets.
+#'
 #' @param sources Database source
-#' @importFrom dplyr filter
-
-
+#'
 calcCapTarget <- function(sources) {
 
-  convertNAto0 <- function(x) {
-    x[is.na(x)] <- 0
-    return(x)
+  if (! sources %in% c("REN21", "UNFCCC_NDC", "UNFCCC_NDC+REN21+CHN_NUC", "NewClimate")) {
+    stop("Unknown 'sources' argument.")
   }
 
-  REN21data <- readSource("REN21", subtype = "Capacity")
-
-  if (sources == "REN21") { # only REN21
-
-
-    return(list(x = REN21data,
+  if (sources == "REN21") {
+    return(list(x = readSource("REN21", subtype = "Capacity"),
                 weight = NULL,
                 unit = "GW",
-                description = "Capacity targets from REN 21(2017) database")
-    )
-
-  } else if (sources == "NewClimate") {
+                description = "Capacity targets from REN 21(2017) database"))
+  }
 
+  if (sources == "NewClimate") {
     capCond <- readSource("NewClimate", subtype = "Capacity_2025_cond")
     capUncond <- readSource("NewClimate", subtype = "Capacity_2025_uncond")
-
     x <- mbind(capCond, capUncond)
-
     return(list(x = x,
                 weight = NULL,
                 unit = "GW",
                 description = "Capacity targets combined from NewClimate Database for Current Policy Scenarios")
     )
-
-
-  } else { # import NDC capacity target
-    listCapacitiesNDC <- list(
-      "2018_cond"   = readSource("UNFCCC_NDC", subtype = "Capacity_2018_cond"),
-      "2018_uncond" = readSource("UNFCCC_NDC", subtype = "Capacity_2018_uncond"),
-      "2021_cond"   = readSource("UNFCCC_NDC", subtype = "Capacity_2021_cond"),
-      "2021_uncond" = readSource("UNFCCC_NDC", subtype = "Capacity_2021_uncond"),
-      "2022_cond"   = readSource("UNFCCC_NDC", subtype = "Capacity_2022_cond"),
-      "2022_uncond" = readSource("UNFCCC_NDC", subtype = "Capacity_2022_uncond"),
-      "2023_cond"   = readSource("UNFCCC_NDC", subtype = "Capacity_2023_cond"),
-      "2023_uncond" = readSource("UNFCCC_NDC", subtype = "Capacity_2023_uncond"),
-      "2024_cond"   = readSource("UNFCCC_NDC", subtype = "Capacity_2024_cond"),
-      "2024_uncond" = readSource("UNFCCC_NDC", subtype = "Capacity_2024_uncond")
-    )
-
-    listYears   <- lapply(listCapacitiesNDC, getItems, dim = "year") %>% unlist() %>% unique() %>% sort()
-    listRegions <- lapply(listCapacitiesNDC, getItems, dim = "region") %>% unlist() %>% unique() %>% sort()
-
-    # expand all magpies to listYears
-    expandMagpieYears <- function(x) {
-      y <- new.magpie(cells_and_regions = listRegions, years = listYears, names = getNames(x))
-      for (year in getItems(x, dim = "year")) {
-        y[, year, ] <- x[, year, ]
-      }
-      return(y)
-    }
-
-    lapply(listCapacitiesNDC, expandMagpieYears) %>% mbind() %>% convertNAto0() -> NDC
   }
 
-  if (sources == "UNFCCC_NDC") { # if no other source
-    description <- "Capacity targets from Nationally Determined Contributions (NDC)"
-    return(list(x = NDC, weight = NULL, unit = "GW", description = description))
+  listCapacitiesNDC <- list(
+    "2018_cond"   = readSource("UNFCCC_NDC", subtype = "Capacity_2018_cond"),
+    "2018_uncond" = readSource("UNFCCC_NDC", subtype = "Capacity_2018_uncond"),
+    "2021_cond"   = readSource("UNFCCC_NDC", subtype = "Capacity_2021_cond"),
+    "2021_uncond" = readSource("UNFCCC_NDC", subtype = "Capacity_2021_uncond"),
+    "2022_cond"   = readSource("UNFCCC_NDC", subtype = "Capacity_2022_cond"),
+    "2022_uncond" = readSource("UNFCCC_NDC", subtype = "Capacity_2022_uncond"),
+    "2023_cond"   = readSource("UNFCCC_NDC", subtype = "Capacity_2023_cond"),
+    "2023_uncond" = readSource("UNFCCC_NDC", subtype = "Capacity_2023_uncond"),
+    "2024_cond"   = readSource("UNFCCC_NDC", subtype = "Capacity_2024_cond"),
+    "2024_uncond" = readSource("UNFCCC_NDC", subtype = "Capacity_2024_uncond")
+  )
+  listYears <- lapply(listCapacitiesNDC, getItems, dim = "year") %>% unlist() %>% unique() %>% sort()
+  NDC <- purrr::map(listCapacitiesNDC,
+                    ~ add_columns(.x, listYears[!listYears %in% getItems(.x, dim = "year")], 2)) %>%
+    mbind()
+  NDC <- NDC[, sort(getYears(NDC)), ]
+  NDC[is.na(NDC)] <- 0
+
+  if (sources == "UNFCCC_NDC") {
+    return(list(x = NDC,
+                weight = NULL,
+                unit = "GW",
+                description = "Capacity targets from Nationally Determined Contributions (NDC)"))
   }
 
-  if (sources == "UNFCCC_NDC+REN21+CHN_NUC") { # Additional CHN nuclear target and EV target
-
+  if (sources == "UNFCCC_NDC+REN21+CHN_NUC") {
     # used to extend non NDC-data to data structure of NDC targets
     extend2Dim <- function(x, dimNames) {
       listx <- rep(list(x), length(dimNames))
-      for (i in seq(length(dimNames))) {
+      for (i in seq_along(dimNames)) {
         getNames(listx[[i]]) <- paste(dimNames[[i]], getNames(x), sep = ".")
       }
       return(mbind(listx))
     }
 
+    REN21data <- readSource("REN21", subtype = "Capacity")
     REN21 <- extend2Dim(REN21data, names(listCapacitiesNDC))
 
     # names of all technologies in REN21 and NDC database + apCarElT
-    c(getNames(NDC), getNames(REN21), paste(names(listCapacitiesNDC), "apCarElT", sep = ".")) %>%
-      unique() %>% sort() -> techNames
+    techNames <- c(getNames(NDC), getNames(REN21), paste(names(listCapacitiesNDC), "apCarElT", sep = ".")) %>%
+      unique() %>%
+      sort()
 
     x <- new.magpie(getItems(REN21, dim = "region"), getYears(REN21), techNames)
     # China's nuclear target
-    common_tech <- intersect(
-      getNames(REN21) %>% unlist() %>% unique(),
-      getNames(NDC)   %>% unlist() %>% unique())
+    common_tech <- intersect(getNames(REN21) %>% unlist() %>% unique(),
+                             getNames(NDC)   %>% unlist() %>% unique())
     # for common technologies, take bigger value
     x[, listYears, common_tech] <- pmax(REN21[, listYears, common_tech], NDC[, , common_tech])
     # for tech. in REN21 but not in NDC, take REN21 values
-    x[, , setdiff(getNames(REN21), common_tech)]           <- REN21[, , setdiff(getNames(REN21), common_tech)]
+    x[, , setdiff(getNames(REN21), common_tech)] <- REN21[, , setdiff(getNames(REN21), common_tech)]
     # for tech. in NDC but not in REN21, take NDC values
     x[, getYears(NDC), setdiff(getNames(NDC), common_tech)] <- NDC[, , setdiff(getNames(NDC), common_tech)]
     # additional nuclear policy for CHN. The target is actually 2020 in 58 GW in 2020, but setting this leads to an
@@ -119,8 +102,7 @@ calcCapTarget <- function(sources) {
     # hydrogen capacity targets from national/EU hydrogen strategies
 
     # Region targets
-    reg.map <- toolGetMapping("regionmappingH12.csv", type = "regional",
-                              where = "mappingfolder") # get H12 regionmapping
+    reg.map <- toolGetMapping("regionmappingH12.csv", type = "regional", where = "mappingfolder")
     H2Target.reg <- new.magpie(unique(reg.map$RegionCode), getYears(x), "elh2", fill = 0)
     # Electrolyzer capacity target from the EU Hydrogen Strategy
     # https://ec.europa.eu/energy/sites/ener/files/hydrogen_strategy.pdf
@@ -140,28 +122,28 @@ calcCapTarget <- function(sources) {
     H2Target <- new.magpie(unique(reg.map$RegionCode), getYears(x), "elh2", fill = 0)
     H2Target["EUR", , "elh2"] <- H2Target.reg["EUR", , "elh2"] - H2Target.CountryAgg["EUR", , "elh2"]
 
-    # # SE VRE Production in 2015 to be used as weight for disaggregation EU target to iso countries
-    # SEHistVRE <- dimSums(calcOutput("FE", aggregate = FALSE)[,"y2015",c("SE|Electricity|Solar (EJ/yr)",
-    #                                                                 "SE|Electricity|Wind (EJ/yr)")],
-    #                      dim = 3)
-
     # GDP 2015 to be used as weight for disaggregation of EU target to iso coutries
     GDP2015 <- calcOutput("GDPPast", aggregate = FALSE)[, "y2015", ]
 
     # regionmapping without countries that already have a country target
-    CountryCode <- NULL
-    reg.map.reduced <- reg.map %>%
-      filter(!CountryCode %in% country.target.regs)
+    reg.map.reduced <- reg.map %>% dplyr::filter(!.data$CountryCode %in% country.target.regs)
     # disaggregate EU target to iso-countries
-    H2Target.disagg <- toolAggregate(H2Target, reg.map.reduced,
-                                     from = "RegionCode", to = "CountryCode", dim = 1,
+    H2Target.disagg <- toolAggregate(H2Target,
+                                     reg.map.reduced,
+                                     from = "RegionCode",
+                                     to = "CountryCode",
+                                     dim = 1,
                                      weight = GDP2015[country.target.regs, , , invert = TRUE])
     # bind country target together with disaggregation of EU targets to other countries
     H2Target.out <- magpiesort(mbind(H2Target.country[country.target.regs, , ], H2Target.disagg))
     x <- mbind(x, extend2Dim(H2Target.out, names(listCapacitiesNDC)))
-    ### Hydrogen Target End
 
-    description <- "Capacity targets combined from REN 21(2017), NDC database, special case for China nuclear and EV"
-    return(list(x = convertNAto0(x), weight = NULL, unit = "GW", description = description))
-  } # end sources = UNFCCC_NDC+REN21+CHN_NUC
+    x[is.na(x)] <- 0
+
+    return(list(x = x,
+                weight = NULL,
+                unit = "GW",
+                description = glue::glue("Capacity targets combined from REN 21(2017), NDC database, special case \\
+                                         for China nuclear and EV")))
+  }
 }

R/calcCapital.R

@@ -5,32 +5,36 @@
 #' that of Japan in 2010, at speeds that vary across scenarios. The final capital stocks are the product
 #' of the capital intensities and the gdp scenarios from [mrdrivers].
 #'
+#' @param scenario GDP and pop scenarios. Passed to [mrdrivers::calcGDP()].
 #' @export
 #' @seealso \itemize{
 #'   \item See the vignette \code{vignette("scenarios", "mrdrivers")} for information on the GDP scenarios.
 #'   \item [readPWT()] for information on the PWT version used.
 #' }
 #' @inherit madrat::calcOutput return
-calcCapital <- function() {
-
+calcCapital <- function(scenario) {
   # Get capital intensities (capital over GDP) from PWT
   kPWT <- readSource("PWT")[, , "rkna"]
   gdpPWT <- readSource("PWT")[, , "rgdpna"]
   kIntPWT <- kPWT / setNames(gdpPWT, NULL)
 
-  # Get GDP from mrdrivers (which differs from GDP in PWT)
-  gdp <- calcOutput("GDP",
-                    scenario = c("SSPs", "SDPs"),
-                    naming = "scenario",
-                    aggregate = FALSE,
-                    years = seq(1995, 2150, 5))
+  # Get GDP from mrdrivers (which differs from GDP in PWT). Make sure SSP2 is always returned.
+  gdp <- calcOutput("GDP", scenario = unique(c(scenario, "SSP2")), aggregate = FALSE, years = seq(1995, 2150, 5))
 
   # Define reference capital intensity, and the convergence time in years, of the countries capital intensities towards
   # that reference, for the different GDP scenarios. The convergence assumptions should follow the SSP narratives.
   # Convergence starts after 2010.
   kIntRef <- kIntPWT["JPN", 2010, ] %>% as.numeric()
-  convTime <- c("SSP1" = 150, "SSP2" = 250, "SSP3" = 150, "SSP4" = 300, "SSP5" = 150,
-                "SDP" = 150, "SDP_EI" = 150, "SDP_RC" = 150, "SDP_MC" = 150)
+  convTime <- c("SSP1" = 150, "SSP2" = 250, "SSP3" = 150, "SSP4" = 300, "SSP5" = 150)
+
+  # If required, add any assumption on convergence times for non SSP scenarios. By default use the SSP2 convTime.
+  if (any(! getNames(gdp) %in% names(convTime))) {
+    scens <- getNames(gdp)[! getNames(gdp) %in% names(convTime)]
+    message(glue::glue("Adding {paste(scens, collapse = ', ')} assumptions as copies of SSP2."))
+    addConvTime <- purrr::map(scens, ~`names<-`(convTime["SSP2"], .x)) %>% purrr::list_c()
+    convTime <- c(convTime, addConvTime)
+  }
+
 
   # Create kInt magpie object with the same dimension as gdp, and assign the PWT capital intensities for the
   # historic years until 2010.
@@ -60,9 +64,9 @@ calcCapital <- function() {
 
   # Use regional average for countries missing data
   ## To get regional aggregate values for all countries: first aggregate, using only non-missing countries and
-  ## partrel = TRUE, then disaggregate again.
+  ## partrel = TRUE, then disaggregate again. (Faster than toolFillWithRegionAvg).
   nmc <- getItems(kInt, dim = 1)[!is.nan(kInt[, 2010, "SSP2"])]
-  map <- toolGetMapping("regionmappingH12.csv")
+  map <- toolGetMapping("regionmappingH12.csv", where = "mappingfolder", type = "regional")
   kIntReg <- toolAggregate(kInt[nmc, , ], rel = map, weight = gdp[nmc, , ], partrel = TRUE) %>%
     toolAggregate(rel = map, from = "RegionCode", to = "CountryCode")
   mc <- setdiff(getItems(kInt, dim = 1), nmc)
@@ -73,7 +77,7 @@ calcCapital <- function() {
 
   # Add industry energy efficiency capital stocks (passing 2015 SSP2 capital stock as "kap")
   kap <- k[, 2015, "SSP2"] %>% tibble::as_tibble() %>% dplyr::select("iso3c", "kap" = "value")
-  EEK <- calcOutput("Industry_EEK", kap = kap, aggregate = FALSE, years = getYears(k))
+  EEK <- calcOutput("Industry_EEK", kap = kap, scenarios = scenario, aggregate = FALSE, years = getYears(k))
 
   # Modify names to differentiate the macroeconomic capital "kap" from EEK capital stocks.
   getNames(k) <- paste0(getNames(k), ".kap")