Merge pull request #120 from pik-piam/revert-119-gdp_update

Revert "Gdp update"
pik-piam · Sep 3, 2024 · b23d349 · b23d349
2 parents 89ad7e7 + bf853ba
commit b23d349
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Showing 9 changed files with 75 additions and 128 deletions.
diff --git a/R/calcGTAPTotalTransportCosts.R b/R/calcGTAPTotalTransportCosts.R
@@ -47,11 +47,11 @@ calcGTAPTotalTransportCosts <- function(costType = "transport", version = "9") {
   totalTcosts <- (tcostInputs + tcostToSecondary) / 2
 
   totalTcosts <-  GDPuc::convertGDP(totalTcosts, unit_in = "current US$MER",
-                                    unit_out = "constant 2017 US$MER",
+                                    unit_out = "constant 2005 US$MER",
                                     replace_NAs = "no_conversion")
 
   return(list(x = totalTcosts,
               weight = NULL,
-              unit = "million constant 2017 US$MER/yr",
+              unit = "million constant 2005 US$MER/yr",
               description = "Costs for GTAP commodities (half of input to market and market to value"))
 }
diff --git a/R/calcIniFoodPrice.R b/R/calcIniFoodPrice.R
@@ -9,7 +9,7 @@
 #' @param year Specifies the year for the initialization of prices in MAgPIE. Default is `y2005`.
 #' @param products subselection of products to be returned
 #'
-#' @return List with a magpie object with commodity prices on global level in $17/tDM.
+#' @return List with a magpie object with commodity prices on global level in $05/tDM.
 #' @author Mishko Stevanovic, Benjamin Leon Bodirsky
 #' @seealso
 #' [readIMPACT3.2.2World_Price()]
@@ -27,7 +27,7 @@ calcIniFoodPrice <- function(datasource = "FAO", year = "y2005", products = "kfo
   dm <- collapseNames(dm)
 
   if (datasource == "IMPACT3.2.2World_Price") {
-    out <- readSource("IMPACT3.2.2World_Price") #in 2017 prices now
+    out <- readSource("IMPACT3.2.2World_Price")
     # select scenario and initial year
     out <- collapseNames(out[, year, "SSP2-NoCC-NoCC-379"])
     out <- setYears(out, NULL)
@@ -68,18 +68,6 @@ calcIniFoodPrice <- function(datasource = "FAO", year = "y2005", products = "kfo
     out[, , "res_nonfibrous"]   <- 50 # quick google search
     out[, , "pasture"]          <- 50 # quick google search
     out[, , "scp"]              <- 1500 # using upper end prices similar to fishmeal
-
-   #[DC] At some point probably better to do another 'quick google search',
-   #for now convert these to 2017 values using US inflation, also for consistency 
-    conv <- c("sugr_cane", "sugr_beet", "alcohol", "fish", "oilpalm", "oilcakes", "cottn_pro", "foddr",
-              "brans", "distillers_grain", "ethanol", "fibres", "molasses", "wood", "woodfuel", "begr",
-              "betr", "res_cereals", "res_fibrous", "pasture", "scp")
-    getItems(out, dim = 1) <- "USA"
-    out[, , conv] <- GDPuc::convertGDP(out[, , conv],
-                            unit_in = "constant 2005 US$MER",
-                            unit_out = "constant 2017 US$MER",
-                            replace_NAs = "no_conversion")
-    getItems(out, dim = 1) <- "GLO"
 
     # correct the prices for dry matter values
     out <- out[, , ] / dm[, , ]
@@ -118,17 +106,6 @@ calcIniFoodPrice <- function(datasource = "FAO", year = "y2005", products = "kfo
     out[, , "foddr"]            <- 50 # quick google search
     out[, , "sugar"]            <- 250 # quick google search
 
-   conv <- c("fish", "oilcakes", "brans", "distillers_grain", "ethanol", "alcohol", 
-              "molasses", "wood", "woodfuel", "begr",
-              "betr", "res_cereals", "res_fibrous", "pasture", "scp",
-              "foddr", "sugar")
-    getItems(out, dim = 1) <- "USA"
-    out[, , conv] <- GDPuc::convertGDP(out[, , conv],
-                            unit_in = "constant 2005 US$MER",
-                            unit_out = "constant 2017 US$MER",
-                            replace_NAs = "no_conversion")
-    getItems(out, dim = 1) <- "GLO"
-
     # correct the prices from online sources for dry matter values
     out[, , missingCommodities] <- out[, , missingCommodities] / dm[, , missingCommodities]
 
@@ -143,7 +120,7 @@ calcIniFoodPrice <- function(datasource = "FAO", year = "y2005", products = "kfo
 
   return(list(x = out,
               weight = NULL,
-              unit = "US$2017/tDM",
+              unit = "US$05/tDM",
               description = description,
               isocountries = FALSE))
 }
diff --git a/R/calcMAgPIEReport.R b/R/calcMAgPIEReport.R
@@ -4,15 +4,15 @@ calcMAgPIEReport <- function(subtype) {
   x <- readSource("MAgPIE", subtype = "MAgPIEReport_extensive")
 
   if (subtype == "CostTotal") {
-    # with transformation factor from 10E6 US$2017 to 10E12 US$2017
-    x <- x[, , "Costs Without Incentives (million US$17/yr)"] / 1000 / 1000
+    # with transformation factor from 10E6 US$2005 to 10E12 US$2005
+    x <- x[, , "Costs Without Incentives (million US$05/yr)"] / 1000 / 1000
     d <- "Total Landuse Costs from MAgPIE excluding emission costs"
-    u <- "T$2017/yr"
+    u <- "T$2005/yr"
   } else if (subtype == "CostMAC") {
-    # with transformation factor from 10E6 US$2017 to 10E12 US$2017
-    x <- x[, , "Costs Accounting|+|MACCS (million US$17/yr)"] / 1000 / 1000
+    # with transformation factor from 10E6 US$2005 to 10E12 US$2005
+    x <- x[, , "Costs Accounting|+|MACCS (million US$05/yr)"] / 1000 / 1000
     d <- "MAC Costs for LU emissions from MAgPIE"
-    u <- "T$2017/yr"
+    u <- "T$2005/yr"
   } else if (subtype == "ProductionBiomass") {
     x <- x[, , "Demand|Bioenergy|2nd generation|++|Bioenergy crops (EJ/yr)"] / 31.536 # EJ to TWa
     d <- "Production of ligno-cellulosic purpose grown biomass in MAgPIE"

diff --git a/R/calcPriceAgriculture.R b/R/calcPriceAgriculture.R
@@ -37,7 +37,7 @@
 #' @importFrom reshape2 melt acast
 #'
 
-calcPriceAgriculture <- function(datasource = "IMPACT3.2.2World_Price", unit = "US$17/tDM") {
+calcPriceAgriculture <- function(datasource = "IMPACT3.2.2World_Price", unit = "US$05/tDM") {
 
   if (datasource == "IMPACT3.2.2World_Price") {
 
@@ -70,7 +70,7 @@ calcPriceAgriculture <- function(datasource = "IMPACT3.2.2World_Price", unit = "
 
   } else if (datasource == "WBGEM") {
 
-    # Prices in US$17/tDM
+    # Prices in US$05/tDM
     x <- calcOutput("WBGEM", aggregate = FALSE)
 
     # sectoral mappings
@@ -138,7 +138,7 @@ calcPriceAgriculture <- function(datasource = "IMPACT3.2.2World_Price", unit = "
 
   } else if (datasource == "FAO") {
 
-    # Annual producer prices in US$17/tDM
+    # Annual producer prices in US$05/tDM
     out         <- readSource("FAO_online", subtype = "PricesProducerAnnual", convert = TRUE)
     aggregation <- toolGetMapping("FAOitems_online.csv",
                                   type = "sectoral", where = "mappingfolder")
@@ -184,10 +184,10 @@ calcPriceAgriculture <- function(datasource = "IMPACT3.2.2World_Price", unit = "
     isocountries <- TRUE
   }
 
-  if (unit != "US$17/tDM") {
+  if (unit != "US$05/tDM") {
     # Transform to selected currency unit
     out <- GDPuc::convertGDP(out,
-                             unit_in = "constant 2017 US$MER",
+                             unit_in = "constant 2005 US$MER",
                              unit_out =  unit,
                              replace_NAs = "no_conversion")
   }

diff --git a/R/calcWBGEM.R b/R/calcWBGEM.R
@@ -44,17 +44,9 @@ calcWBGEM <- function() {
   out <- mbind(.rename(tmp1), .rename(tmp2), .rename(tmp3))
   out <- out * adjustFactor2005
   out[is.na(out)] <- 0
-
-  #inflate to 2017 using US inflation for global value
-  getItems(out, dim = 1) <- "USA"
-  out <- GDPuc::convertGDP(out,
-                            unit_in = "constant 2005 US$MER",
-                            unit_out = "constant 2017 US$MER",
-                            replace_NAs = "no_conversion")
-  getItems(out, dim = 1) <- "GLO"
 
   return(list(x = out,
-              unit = "real2017 USD per ton",
+              unit = "real2005 USD per ton",
               weight = NULL,
               description = "WBGEM global price of commodity"))
 }
diff --git a/R/convertGTAPv8v9.R b/R/convertGTAPv8v9.R
@@ -66,10 +66,9 @@ convertGTAPv8v9 <- function(x, subtype) {
 
   } else {
     gdpMer <- calcOutput("GDPPast", GDPPast = "WDI-MI",
-                         unit = "constant 2017 US$MER", aggregate = FALSE)
+                         unit = "constant 2005 US$MER", aggregate = FALSE)
     gdpMer <- gdpMer[, getYears(x), , drop = TRUE]
-    w1 <- GDPuc::convertGDP(gdpMer,
-                            unit_in = "constant 2017 US$MER",
+    w1 <- GDPuc::convertGDP(gdpMer, unit_in = "constant 2005 US$MER",
                             unit_out = "current US$MER",
                             replace_NAs = "no_conversion")
     getNames(w1) <- NULL

diff --git a/R/convertICP2017.R b/R/convertICP2017.R
@@ -1,7 +1,7 @@
 #' @title convertICP2017
 #' @description  converts data of World Bank ICP round, downloaded from
 #' here: https://databank.worldbank.org/source/icp-2017
-#' mainly a currency conversion to MER2017. Assume that in the original dataset,
+#' mainly a currency conversion to MER05. Assume that in the original dataset,
 #' 2011 values are in 2011MER and 2017 in 2017MER, given the ICP rounds
 #'
 #' @param subtype data subtype needed. Either "priceLevel", or "per_cap_exp"
@@ -17,21 +17,17 @@ convertICP2017 <- function(x, subtype = "per_cap_expMER") {
   # currency convert for expenditures
   if (subtype == "per_cap_expMER") {
 
-    x2011 <- GDPuc::convertGDP(x[, 2011, ],
-                               unit_in = "constant 2011 US$MER",
-                               unit_out = "constant 2017 US$MER")
-    x2017 <- x[, 2017, ]
+    x2011 <- GDPuc::convertGDP(x[, 2011, ], unit_in = "constant 2011 US$MER", unit_out = "constant 2005 US$MER")
+    x2017 <- GDPuc::convertGDP(x[, 2017, ], unit_in = "constant 2017 US$MER", unit_out = "constant 2005 US$MER")
     x <- mbind(x2011, x2017)
     x[is.na(x)] <- 0
   }
 
   # currency convert for expenditures
   if (subtype == "per_cap_expPPP") {
 
-    x2011 <- GDPuc::convertGDP(x[, 2011, ],
-                               unit_in = "constant 2011 US$PPP",
-                               unit_out = "constant 2017 US$MER")
-    x2017 <- x[, 2017, ]                               
+    x2011 <- GDPuc::convertGDP(x[, 2011, ], unit_in = "constant 2011 US$PPP", unit_out = "constant 2005 US$MER")
+    x2017 <- GDPuc::convertGDP(x[, 2017, ], unit_in = "constant 2017 US$PPP", unit_out = "constant 2005 US$MER")
     x <- mbind(x2011, x2017)
     x[is.na(x)] <- 0
   }

diff --git a/R/convertSSPResults.R b/R/convertSSPResults.R
@@ -41,14 +41,14 @@ convertSSPResults <- function(x) {
 
   # ---- Land ----
 
-  # nolint start
+  #nolint start
   selection <- c("Land Cover (million ha)", "Land Cover|Built-up Area (million ha)",
                  "Land Cover|Cropland (million ha)", "Land Cover|Cropland|Energy Crops (million ha)",
                  "Land Cover|Forest (million ha)", "Land Cover|Forest|Forestry (million ha)",
                  "Land Cover|Forest|Forestry|Harvested Area (million ha)", "Land Cover|Forest|Natural Forest (million ha)",
                  "Land Cover|Other Arable Land (million ha)", "Land Cover|Other Land (million ha)",
                  "Land Cover|Other Natural Land (million ha)", "Land Cover|Pasture (million ha)")
-  # nolint end
+  #nolint end
 
   # use land area as weight
   weight <- setYears(dimSums(calcOutput("LanduseInitialisation", aggregate = FALSE), dim = 3)[, 2010, ], NULL)
@@ -69,13 +69,13 @@ convertSSPResults <- function(x) {
   # ---- Bioenergy ----
 
   selection <- c("Primary Energy|Biomass|1st Generation (EJ/yr)",
-                 "Primary Energy|Biomass|Energy Crops (EJ/yr)",
-                 "Agricultural Demand|Bioenergy|1st generation (million t DM/yr)",
-                 "Agricultural Demand|Bioenergy|2nd generation (million t DM/yr)")
+               "Primary Energy|Biomass|Energy Crops (EJ/yr)",
+               "Agricultural Demand|Bioenergy|1st generation (million t DM/yr)",
+               "Agricultural Demand|Bioenergy|2nd generation (million t DM/yr)")
 
   # use harvested area as weight
   weightBio <- setYears(dimSums(calcOutput("Croparea", sectoral = "kcr", physical = TRUE, aggregate = FALSE),
-                                dim = 3)[, 2010, ], NULL)
+                                 dim = 3)[, 2010, ], NULL)
 
   for (sel in selection) {
     print(sel)
@@ -93,10 +93,6 @@ convertSSPResults <- function(x) {
 
   data <- x[, , selection]
   data[is.na(data)] <- 0
-  data <- convertGDP(data, unit_in = "constant 2005 US$MER",
-                     unit_out = "constant 2017 US$MER",
-                     replace_NAs = "no_conversion")
-  getNames(data, dim = 3) <- "Price|Carbon (US$2017/t CO2)"
   aggregatedREG <- toolAggregate(data, rel = mappingFile, weight = NULL, dim = 1, partrel = TRUE,
                                  from = "RegionCode", to = "CountryCode")
   aggregatedREG <- toolCountryFill(aggregatedREG, 000)
@@ -107,68 +103,64 @@ convertSSPResults <- function(x) {
   selection <- c("Price|Primary Energy|Biomass (US$2005/GJ)")
 
   data <- x[, , selection]
-  data <- convertGDP(data, unit_in = "constant 2005 US$MER",
-                     unit_out = "constant 2017 US$MER",
-                     replace_NAs = "no_conversion")
-  getNames(data, dim = 3) <- "Price|Primary Energy|Biomass (US$2017/GJ)"                
   aggregatedREG <- toolAggregate(data, rel = mappingFile, weight = NULL, dim = 1, partrel = TRUE,
                                  from = "RegionCode", to = "CountryCode")
   aggregatedREG <- toolCountryFill(aggregatedREG, 000)
   out <- mbind(out, aggregatedREG)
 
   ### check for NA's
   if (any(is.na(out))) {
-    vcat(verbosity = 1, "SSP3 results of MESSAGE-GLOBIOM contains NAs which where substituted by 0")
-  }
+vcat(verbosity = 1, "SSP3 results of MESSAGE-GLOBIOM contains NAs which where substituted by 0")
+}
   out[, c("y2005", "y2010"), "MESSAGE-GLOBIOM.Land Cover|Cropland|Energy Crops (million ha)"] <- 0
   if (any(is.na(out))) {
-    vcat(verbosity = 1, "NAs in dataset")
-  }
+vcat(verbosity = 1, "NAs in dataset")
+}
 
   # ---- Select scenarios ----
 
   subset <- c("SSP1-19-SPA1-V16.IMAGE",
-              "SSP1-26-SPA1-V15.IMAGE",
-              "SSP1-34-SPA1-V15.IMAGE",
-              "SSP1-45-SPA1-V15.IMAGE",
-              "SSP1-Ref-SPA0-V15.IMAGE",
-
-              "SSP1-19-SPA1-V16.REMIND-MAGPIE",
-              "SSP1-26-SPA1-V15.REMIND-MAGPIE",
-              "SSP1-34-SPA1-V15.REMIND-MAGPIE",
-              "SSP1-45-SPA1-V15.REMIND-MAGPIE",
-              "SSP1-Ref-SPA0-V15.REMIND-MAGPIE",
-
-              "SSP2-19-SPA2-V16.MESSAGE-GLOBIOM",
-              "SSP2-26-SPA2-V16.MESSAGE-GLOBIOM",
-              "SSP2-34-SPA2-V16.MESSAGE-GLOBIOM",
-              "SSP2-45-SPA2-V16.MESSAGE-GLOBIOM",
-              "SSP2-60-SPA2-V16.MESSAGE-GLOBIOM",
-              "SSP2-Ref-SPA0-V16.MESSAGE-GLOBIOM",
-
-              "SSP2-19-SPA2-V16.REMIND-MAGPIE",
-              "SSP2-26-SPA2-V15.REMIND-MAGPIE",
-              "SSP2-34-SPA2-V15.REMIND-MAGPIE",
-              "SSP2-45-SPA2-V15.REMIND-MAGPIE",
-              "SSP2-60-SPA2-V15.REMIND-MAGPIE",
-              "SSP2-Ref-SPA0-V15.REMIND-MAGPIE",
-
-              "SSP3-34-SPA3-V15.AIM/CGE",
-              "SSP3-45-SPA3-V15.AIM/CGE",
-              "SSP3-60-SPA3-V15.AIM/CGE",
-
-              "SSP4-26-SPA4-V16.GCAM4",
-              "SSP4-34-SPA4-V16.GCAM4",
-              "SSP4-45-SPA4-V16.GCAM4",
-              "SSP4-60-SPA4-V16.GCAM4",
-              "SSP4-Ref-SPA0-V16.GCAM4",
-
-              "SSP5-19-SPA5-V16.REMIND-MAGPIE",
-              "SSP5-26-SPA5-V15.REMIND-MAGPIE",
-              "SSP5-34-SPA5-V15.REMIND-MAGPIE",
-              "SSP5-45-SPA5-V15.REMIND-MAGPIE",
-              "SSP5-60-SPA5-V15.REMIND-MAGPIE",
-              "SSP5-Ref-SPA0-V15.REMIND-MAGPIE")
+            "SSP1-26-SPA1-V15.IMAGE",
+            "SSP1-34-SPA1-V15.IMAGE",
+            "SSP1-45-SPA1-V15.IMAGE",
+            "SSP1-Ref-SPA0-V15.IMAGE",
+
+            "SSP1-19-SPA1-V16.REMIND-MAGPIE",
+            "SSP1-26-SPA1-V15.REMIND-MAGPIE",
+            "SSP1-34-SPA1-V15.REMIND-MAGPIE",
+            "SSP1-45-SPA1-V15.REMIND-MAGPIE",
+            "SSP1-Ref-SPA0-V15.REMIND-MAGPIE",
+
+            "SSP2-19-SPA2-V16.MESSAGE-GLOBIOM",
+            "SSP2-26-SPA2-V16.MESSAGE-GLOBIOM",
+            "SSP2-34-SPA2-V16.MESSAGE-GLOBIOM",
+            "SSP2-45-SPA2-V16.MESSAGE-GLOBIOM",
+            "SSP2-60-SPA2-V16.MESSAGE-GLOBIOM",
+            "SSP2-Ref-SPA0-V16.MESSAGE-GLOBIOM",
+
+            "SSP2-19-SPA2-V16.REMIND-MAGPIE",
+            "SSP2-26-SPA2-V15.REMIND-MAGPIE",
+            "SSP2-34-SPA2-V15.REMIND-MAGPIE",
+            "SSP2-45-SPA2-V15.REMIND-MAGPIE",
+            "SSP2-60-SPA2-V15.REMIND-MAGPIE",
+            "SSP2-Ref-SPA0-V15.REMIND-MAGPIE",
+
+            "SSP3-34-SPA3-V15.AIM/CGE",
+            "SSP3-45-SPA3-V15.AIM/CGE",
+            "SSP3-60-SPA3-V15.AIM/CGE",
+
+            "SSP4-26-SPA4-V16.GCAM4",
+            "SSP4-34-SPA4-V16.GCAM4",
+            "SSP4-45-SPA4-V16.GCAM4",
+            "SSP4-60-SPA4-V16.GCAM4",
+            "SSP4-Ref-SPA0-V16.GCAM4",
+
+            "SSP5-19-SPA5-V16.REMIND-MAGPIE",
+            "SSP5-26-SPA5-V15.REMIND-MAGPIE",
+            "SSP5-34-SPA5-V15.REMIND-MAGPIE",
+            "SSP5-45-SPA5-V15.REMIND-MAGPIE",
+            "SSP5-60-SPA5-V15.REMIND-MAGPIE",
+            "SSP5-Ref-SPA0-V15.REMIND-MAGPIE")
 
   out <- out[, , subset]
 

diff --git a/R/readIMPACT3.2.2World_Price.R b/R/readIMPACT3.2.2World_Price.R
@@ -19,14 +19,5 @@ readIMPACT3.2.2World_Price <- function() { # nolint: object_name_linter.
   data <- data[!is.na(data[[1]]), ]
   data$scenario <- sub(".", "p", data$scenario, fixed = TRUE)
   data <- as.magpie(data)
-
-  #inflate to 2017 using US inflation for global value
-  getItems(data, dim = 1) <- "USA"
-  data <- GDPuc::convertGDP(data,
-                            unit_in = "constant 2005 US$MER",
-                            unit_out = "constant 2017 US$MER",
-                            replace_NAs = "no_conversion")
-  getItems(data, dim = 1) <- "GLO"
-
   return(data)
 }