Shift to US$2017 for currency units

.buildlibrary

@@ -1,4 +1,4 @@
-ValidationKey: '38191335'
+ValidationKey: '38213232'
 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: 0.191.1
-date-released: '2024-09-19'
+version: 0.191.2
+date-released: '2024-09-20'
 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: 0.191.1
-Date: 2024-09-19
+Version: 0.191.2
+Date: 2024-09-20
 Authors@R: c(
     person("Lavinia", "Baumstark", , "[email protected]", role = c("aut", "cre")),
     person("Renato", "Rodrigues", role = "aut"),
@@ -52,6 +52,7 @@ Imports:
     data.table,
     dplyr,
     ggplot2,
+    GDPuc (>= 1.0.5),
     Hmisc,
     luscale,
     magrittr,

R/EDGE-Industry.R

@@ -2808,7 +2808,7 @@ calcIndustry_Value_Added <- function(subtype = 'physical',
   return(list(x = x %>%
                 as.magpie(spatial = 2, temporal = 4, data = 5),
               weight = NULL,
-              unit = '$tn/year',
+              unit = 'trillion US$2017/year',
               description = 'chemicals and other industry value added'))
 
 }

R/UNIDO.R

@@ -258,11 +258,16 @@ convertUNIDO <- function(x, subtype = 'INDSTAT2')
                 # the maximum
                 summarise(value = max(.data$value), .groups = 'drop')
 
-            # return ----
+            # convert from $2005 to $2017 and return ----
             x %>%
-                as.magpie(spatial = 1, temporal = 3, data = ncol(.)) %>%
-                toolCountryFill(verbosity = 2) %>%
-                return()
+              as.magpie(spatial = 1, temporal = 3, data = ncol(.)) %>%
+              toolCountryFill(verbosity = 2) %>%
+              GDPuc::convertGDP(
+                unit_in = "constant 2005 US$MER",
+                unit_out = mrdrivers::toolGetUnitDollar(),
+                replace_NAs = "with_USA"
+              ) %>%
+              return()
         }
     )
 
@@ -307,7 +312,7 @@ calcUNIDO <- function(subtype = 'INDSTAT2')
 
             return(list(x = x,
                         weight = NULL,
-                        unit = 'billion US$2005/yr',
+                        unit = 'billion US$2017/yr',
                         description = 'industry subsector value added'))
         }
     )

R/calcBP.R

@@ -113,9 +113,9 @@ calcBP <- function() {
   map <- toolGetMapping("Mapping_BP.csv", type = "reportingVariables", where = "mrremind") %>%
     filter(!is.na(.data$REMIND), .data$REMIND != "") %>%
     mutate(
-      "from" := paste0(trimws(.data$variable), " (", .data$unit, ")"),
-      "to" := paste0(trimws(.data$REMIND), " (", .data$Unit_REMIND, ")"),
-      "conversion" := as.numeric(.data$Factor)
+      "from" = paste0(trimws(.data$variable), " (", .data$unit, ")"),
+      "to" = paste0(trimws(.data$REMIND), " (", .data$Unit_REMIND, ")"),
+      "conversion" = as.numeric(.data$Factor)
     ) %>%
     select("from", "to", "conversion")
 
@@ -139,15 +139,46 @@ calcBP <- function() {
     to = "to", verbosity = 2
   )
 
+  # convert price units ----
+
+  # should be from US$2023, but 'convertGDP' currently returns NA
+  poil <- GDPuc::convertGDP(
+    gdp = x[, , "Price|Primary Energy|Oil (US$2023/GJ)"],
+    unit_in = "constant 2022 US$MER",
+    unit_out = mrdrivers::toolGetUnitDollar(),
+    replace_NAs = "with_USA"
+  )
+
+  getNames(poil) <- gsub("\\$2023", "\\$2017", getNames(poil))
+
+  # assume these units are in current MER
+  pcoalgas <- GDPuc::convertGDP(
+    gdp = x[, , c("Price|Primary Energy|Gas (US$/GJ)", "Price|Primary Energy|Coal (US$/GJ)")],
+    unit_in = "current US$MER",
+    unit_out = mrdrivers::toolGetUnitDollar(),
+    replace_NAs =  "with_USA"
+  )
+
+  getNames(pcoalgas) <- gsub("\\$", "\\$2017", getNames(pcoalgas))
+
+
+  x <- x[, , c("Price|Primary Energy|Oil (US$2023/GJ)",
+               "Price|Primary Energy|Gas (US$/GJ)",
+               "Price|Primary Energy|Coal (US$/GJ)"), invert = TRUE]
+
+  x <- mbind(x, poil, pcoalgas)
+
+  # set weights ----
+
   weights <- x
   weights[, , ] <- NA
-  weights[, , "US$2005/GJ", pmatch = TRUE] <- 1
+  weights[, , "US$2017/GJ", pmatch = TRUE] <- 1
 
   return(list(
     x = x,
     weight = weights,
     mixed_aggregation = TRUE,
-    unit = c("Mt CO2/yr", "GW", "EJ/yr", "US$2005/GJ"),
+    unit = c("Mt CO2/yr", "GW", "EJ/yr", "US$2017/GJ"),
     description = "Historical World Energy Statistics values as REMIND variables"
   ))
 }

R/calcBiomassPrices.R

@@ -1,37 +1,48 @@
 #' read biomass supply curves from Magpie emulator
 #' @return Magpie object with two parameters determining linear biomass supply curve
-#' @import mrcommons
 
-calcBiomassPrices <- function(){
-  
+calcBiomassPrices <- function() {
+
   x <- readSource("MAgPIE", subtype = "supplyCurve_magpie_40")
 
   # rename the rcp-scenarios to cm_rcp_scen switches used in REMIND
-  getNames(x) <- gsub("NDC-nocc_hist-PkBudg650",  "rcp20", getNames(x)) 
+  getNames(x) <- gsub("NDC-nocc_hist-PkBudg650",  "rcp20", getNames(x))
   getNames(x) <- gsub("NDC-nocc_hist-PkBudg1050", "rcp26", getNames(x))
   getNames(x) <- gsub("NDC-nocc_hist-NDC",        "rcp45", getNames(x))
-  getNames(x) <- gsub("NPI-nocc_hist-Base",       "none" , getNames(x))
-  
+  getNames(x) <- gsub("NPI-nocc_hist-Base",       "none", getNames(x))
+
   # rename SSP-scenarios to cm_LU_emi_scen switches used in REMIND
   getNames(x) <- gsub("SSP2EU", "SSP2", getNames(x))
   getNames(x) <- gsub("SDP-MC", "SDP",  getNames(x))
-  
+
   # Introduce new SSP/SDP dimension by replacing "-" with "."
-  getNames(x) <- gsub("(SSP[0-9]|SDP)-","\\1.",getNames(x))
-
+  getNames(x) <- gsub("(SSP[0-9]|SDP)-", "\\1.", getNames(x))
+  getSets(x)["d3.1"] <- "scen1"
+  getSets(x)["d3.2"] <- "scen2"
+  getSets(x)["d3.3"] <- "char"
+
   # add supply curves for SSP3 using the curves from SSP2
-  tmp <- x[,,"SSP2"]
+  tmp <- x[, , "SSP2"]
   getNames(tmp) <- gsub("SSP2", "SSP3", getNames(tmp))
   x <- mbind(x, tmp)
-  
+
   # if fit coefficients of a country are NA for all years (there is no supplycurve at all for this country)
   # generate artificial supplycurve with VERY high prices
-  x[,,"a"][is.na(x[,,"a"])] <- 1
-  x[,,"b"][is.na(x[,,"b"])] <- 0.1
+  x[, , "a"][is.na(x[, , "a"])] <- 1
+  x[, , "b"][is.na(x[, , "b"])] <- 0.1
+
+  # apply conversion from US$2005 to US$2017 to "a" and "b", so that the price
+  # calculated with these coefficients will be in US$2017
+  x <- GDPuc::convertGDP(
+    gdp = x,
+    unit_in = "constant 2005 US$MER",
+    unit_out = mrdrivers::toolGetUnitDollar(),
+    replace_NAs = "with_USA"
+  )
 
   return(list(x           = x,
-              weight      = calcOutput("FAOLand", aggregate = F)[,,"6610",pmatch=TRUE][,"y2010",],
+              weight      = calcOutput("FAOLand", aggregate = FALSE)[, , "6610", pmatch = TRUE][, "y2010", ],
               unit        = "none",
               description = "coefficients for the bioenergy supplycurve",
-              aggregationFunction=toolBiomassSupplyAggregate))
+              aggregationFunction = toolBiomassSupplyAggregate))
 }

R/calcCO2Prices.R

@@ -1,6 +1,17 @@
 calcCO2Prices <- function() {
+
   # read data
   x <- readSource("ExpertGuess", subtype = "co2prices")
+
+  # convert from $2005 to $2017
+
+  x <- GDPuc::convertGDP(
+    gdp = x,
+    unit_in = "constant 2005 US$MER",
+    unit_out = mrdrivers::toolGetUnitDollar(),
+    replace_NAs = "with_USA"
+  )
+
   getNames(x) <- NULL
 
   # read data used for weight
@@ -12,7 +23,7 @@ calcCO2Prices <- function() {
   return(list(
     x = x,
     weight = ceds,
-    unit = "US$2005/t CO2",
+    unit = "US$2017/t CO2",
     description = "CO2 prices in 2010, 2015 and 2020"
   ))
 }

R/calcCapital.R

@@ -82,6 +82,6 @@ calcCapital <- function() {
   # tie outputs together ----
   list(x = mbind(cap_future, EEK),
        weight = NULL,
-       unit = "trillion 2005US$",
+       unit = "trillion US$2017",
        description = "Capital stock, computed using the capital to GDP ratio from PWT, and GDP scenarios from mrdrivers.")
 }

R/calcCostsTradePeFinancial.R

@@ -1,23 +1,30 @@
 #' Calculate Trade Cost
-#' 
-#' Provides REMIND data for PE trade cost (energy losses on import, export and
-#' use).
-#' 
-#' 
+#'
+#' Provides REMIND data for PE trade cost (energy losses on import, export and use).
+#'
 #' @author Regina Brecha, Lavinia Baumstark
 #' @seealso \code{\link{calcOutput}}, \code{\link{readSource}}
 #' @examples
-#' 
-#' \dontrun{ 
+#' \dontrun{
 #' calcOutput("CostsTradePeFinancial")
 #' }
-#' 
+#'
 calcCostsTradePeFinancial <- function() {
-
-  data <- readSource("ExpertGuess", subtype="costsTradePeFinancial")
-  w    <- calcOutput("GDP",aggregate=FALSE)[,2005,"gdp_SSP2"]
-
-  return(list(x=data,weight=w,
-              unit="TW and ratio",
-              description="PE tradecosts (financial costs on import, export and use)"))
+  data <- readSource("ExpertGuess", subtype = "costsTradePeFinancial")
+
+  data <- GDPuc::convertGDP(
+    gdp = data,
+    unit_in = "constant 2005 US$MER",
+    unit_out = mrdrivers::toolGetUnitDollar(),
+    replace_NAs = "with_USA"
+  )
+
+  w <- calcOutput("GDP", aggregate = FALSE)[, 2005, "gdp_SSP2"]
+
+  return(list(
+    x = data,
+    weight = w,
+    unit = "trillion US$2017/TWa",
+    description = "PE tradecosts (financial costs on import, export and use)"
+  ))
 }

R/calcCostsWeathering.R

@@ -3,21 +3,32 @@
 #' @return transport costs of spreading rock on the fields
 #' @seealso \code{\link{calcOutput}}
 #' @examples
-#'
 #' \dontrun{
 #' calcOutput("CostsWeathering")
 #' }
 #'
-
 calcCostsWeathering <- function() {
 
   costs <- readSource("Strefler", subtype = "weathering_costs")
-  weight <- costs # get the same dimensions of the data
+
+  # convert from $2005 to $2017
+
+  x <- GDPuc::convertGDP(
+    gdp = costs,
+    unit_in = "constant 2005 US$MER",
+    unit_out = mrdrivers::toolGetUnitDollar(),
+    replace_NAs = "with_USA"
+  )
+
+  weight <- x # get the same dimensions of the data
   weight[, , ] <- 1 # this will take the average of the countries to get the regional resolution
 
-  return(list(x = costs,
-              weight = weight,
-              unit = "T$/Gt stone",
-              description = "Transport costs for spreading rocks on the fields. The first rlf corresponds to climate regions (1 - warm, 2 - temperate). The second rlf are the distances of fields from the sources of the material."
+  return(list(
+    x = x,
+    weight = weight,
+    unit = "trillion US$2017/Gt stone",
+    description = "Transport costs for spreading rocks on the fields.
+              The first rlf corresponds to climate regions (1 - warm, 2 - temperate).
+              The second rlf are the distances of fields from the sources of the material."
   ))
 }