Refactor calcCapital

.buildlibrary

@@ -1,4 +1,4 @@
-ValidationKey: '40962870'
+ValidationKey: '41196800'
 AcceptedWarnings:
 - Invalid URL: .*
 - 'Warning: package ''.*'' was built under R version'
@@ -10,3 +10,4 @@ AutocreateReadme: yes
 allowLinterWarnings: yes
 enforceVersionUpdate: yes
 skipCoverage: yes
+AutocreateCITATION: 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: 0.204.1
-date-released: '2024-12-13'
+version: 0.205.0
+date-released: '2025-01-08'
 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: 0.204.1
-Date: 2024-12-13
+Version: 0.205.0
+Date: 2025-01-08
 Authors@R: c(
     person("Lavinia", "Baumstark", , "[email protected]", role = c("aut", "cre")),
     person("Renato", "Rodrigues", role = "aut"),

NAMESPACE

@@ -3,6 +3,7 @@
 export(calcAGEB)
 export(calcBP)
 export(calcCCScapacity)
+export(calcCapital)
 export(calcEDGAR7Fgases)
 export(calcEEAGHGProjections)
 export(calcEmber)

R/calcCapital.R

@@ -1,87 +1,71 @@
+#' Calculate macroeconomic capital stock
+#'
+#' Compute macroeconomic capital stock based on capital intensities from PWT and GDP scenarios from [mrdrivers].
+#' The PWT capital intensities are used up until 2010. After that, the capital intensities converge towards
+#' 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].
+#'
+#' @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() {
 
-  # compute macroeconomic capital stock based on capital intensities from PWT and ssp scenarios
-  # t.b.d.: correct for capital stock part that enters energy sectors
-  capital <- readSource("PWT")[, , "rkna"]
-  gdppwt <- readSource("PWT")[, , "rgdpna"]
-  getNames(capital) <- "kap"
-  cap_intensity <- capital / setNames(gdppwt, NULL)
+  # Get capital intensities (capital over GDP) from PWT
+  kPWT <- readSource("PWT")[, , "rkna"]
+  gdpPWT <- readSource("PWT")[, , "rgdpna"]
+  kIntPWT <- kPWT / setNames(gdpPWT, NULL)
 
-  # use initial gdp as in REMIND which differs from PWT
-  gdpppp_hist <- calcOutput("GDP", aggregate = FALSE, years = c("y1995", "y2000", "y2005"))
-  gdpppp <- calcOutput("GDP", aggregate = FALSE, years = seq(2005, 2150, 5))
-  my_scen <- c("gdp_SSP1", "gdp_SSP2", "gdp_SSP3", "gdp_SSP4", "gdp_SSP5", "gdp_SSP2EU",
-               "gdp_SDP", "gdp_SDP_EI", "gdp_SDP_RC", "gdp_SDP_MC")
-  gdpppp <- mselect(gdpppp, variable = my_scen)
+  # Get GDP from mrdrivers (which differs from GDP in PWT)
+  gdp <- calcOutput("GDP", naming = "scenario", aggregate = FALSE, years = seq(1995, 2150, 5))
 
-  p41 <- setYears(cap_intensity[, rep(1, 32), ], seq(1995, 2150, 5))
-  p41 <- add_dimension(p41, dim = 3.1, add = "ssp", nm = my_scen)
-  cap_intensity_future <- p41
-  convtime <- p41
-  gdp_weight <- p41
+  # 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" = 500, "SSP4" = 300, "SSP5" = 150,
+                "SDP" = 150, "SDP_EI" = 150, "SDP_RC" = 150, "SDP_MC" = 150, "SSP2EU" = 250)
 
-  # ssp variation
-  convtime[, , "gdp_SSP1"]   <- 150
-  convtime[, , "gdp_SSP2"]   <- 250
-  convtime[, , "gdp_SSP3"]   <- 500
-  convtime[, , "gdp_SSP4"]   <- 300
-  convtime[, , "gdp_SSP5"]   <- 150
-  convtime[, , "gdp_SDP"]    <- 150
-  convtime[, , "gdp_SDP_EI"] <- 150
-  convtime[, , "gdp_SDP_RC"] <- 150
-  convtime[, , "gdp_SDP_MC"] <- 150
-  convtime[, , "gdp_SSP2EU"]   <- 250
-
-  for (t in c("y1995", "y2000", "y2005")) {
-    cap_intensity_future[, t, ] <- cap_intensity[, t, ]
-    gdp_weight[, t, ] <- gdpppp_hist[, t, ]
-  }
-  cap_intensity_ref <- cap_intensity["JPN", "y2010"]
-  getRegions(cap_intensity_ref) <- "GLO"
-  lambda <- 0
-  for (t in getYears(gdpppp)) {
-      cap_intensity_future[, t, ] <- ((convtime[, t, ] - lambda) * collapseNames(setYears(cap_intensity[, "y2010", ])) +
-                                        lambda * setNames(setYears(cap_intensity_ref, NULL), NULL)) / convtime[, t, ]
-      lambda <- lambda + 5
-      gdp_weight[, t, ] <- gdpppp[, t, ]
-  }
-  cap_intensity_future[is.na(cap_intensity_future)] <- 0
-  cap_int_new <- cap_intensity_future
-  for (t in getYears(cap_intensity_future)) {
-    for (r in getRegions(cap_intensity_future)) {
-      if (cap_intensity_future[r, t, "gdp_SSP2"] == 0) {
-        # get current mapping
-        map <- toolGetMapping(type = "regional", name = getConfig("regionmapping"), where = "mappingfolder")
-        # get list of countries that belong to the same region as r
-        regi   <- map$RegionCode[map$CountryCode == r]
-        c_regi <- map$CountryCode[map$RegionCode == regi]
-        # filter out the regions that are 0
-        c_regi <- c_regi[!cap_intensity_future[c_regi, t, "gdp_SSP2"] == 0]
-        # generate mapping for the aggregation
-        mapping            <- map[which(map$CountryCode %in% c_regi), ]
-        mapping$RegionCode <- r
-        # store calculated data in separate file
-        cap_int_new[r, t, ] <- toolAggregate(cap_intensity_future[c_regi, t, ],
-                                             mapping,
-                                             weight = gdp_weight[c_regi, t, ])
-      }
+  # Create kInt magpie object with the same dimension as gdp, and assign the PWT capital intensities for the
+  # historic years until 2010.
+  kInt <- gdp * NaN
+  hy <- c(1995, 2000, 2005, 2010)
+  kInt[, hy, ] <- kIntPWT[, hy, ]
+  # For future years (after 2010), linearly converge towards kIntRef
+  fy <- setdiff(getYears(gdp, as.integer = TRUE), hy)
+  for (t in fy) {
+    for (s in getNames(kInt)) {
+      kInt[, t, s] <-  kInt[, 2010, s] + (kIntRef - kInt[, 2010, s]) * (t - 2010) / convTime[s]
     }
   }
 
-  cap_future <- cap_int_new * gdp_weight * 1e-6
+  # 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.
+  nmc <- getItems(kInt, dim = 1)[!is.nan(kInt[, 2010, "SSP2"])]
+  map <- toolGetMapping("regionmappingH12.csv")
+  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)
+  kInt[mc, , ] <- kIntReg[mc, , ]
 
-  # add industry subsectors energy efficiency capital stocks ----
+  # Compute capital stock
+  k <- kInt * gdp * 1e-6
 
-  kap <- cap_future %>%
-    `[`(, 2015, "gdp_SSP2EU.kap") %>%
-    quitte::magclass_to_tibble(c("iso3c", NA, NA, NA, "kap")) %>%
-    dplyr::select("iso3c", "kap")
+  # 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, supplementary = FALSE, aggregate = FALSE, years = getYears(cap_future))
+  # Modify names to match "all_demScen" in remind ("gdp_" prefix), and differentiate the macroeconomic capital "kap"
+  # from EEK capital stocks.
+  getNames(k) <- paste0("gdp_", getNames(k), ".kap")
+  getSets(k) <- c("iso3c", "year", "ssp", "variable")
+  x <- mbind(k, EEK)
 
-  # tie outputs together ----
-  list(x = mbind(cap_future, EEK),
+  list(x = x,
        weight = NULL,
        unit = "trillion US$2017",
-       description = "Capital stock, computed using the capital to GDP ratio from PWT, and GDP scenarios from mrdrivers.")
+       description = "Capital stock computed using the capital/GDP ratio from PWT, and GDP scenarios from mrdrivers.")
 }

R/readGlobalCCSinstitute.R

@@ -3,8 +3,7 @@
 #' @md
 #' @param subtype Project Database version to read, one of
 #'     - `'08-09-2017'`: Data apparently from June 2017.
-#'     - `'2023-11'`: Data from the
-#'       [Global Status of CCS 2023](zotero://select/items/3_E5GNNPZ8) report.
+#'     - `'2023-11'`: Data from the Global Status of CCS 2023 report.
 #'
 #' @return A [`magpie`][magclass::magclass] object.
 #'

R/readPWT.R

@@ -1,6 +1,6 @@
 #' Read PWT
 #'
-#' Read-in PWT data as magclass object
+#' Read-in PWT data (version 8.0) as magclass object
 #'
 #' @order 1
 #' @inherit madrat::readSource return

README.md

@@ -1,6 +1,6 @@
 # MadRat REMIND Input Data Package
 
-R package **mrremind**, version **0.204.1**
+R package **mrremind**, version **0.205.0**
 
 [![CRAN status](https://www.r-pkg.org/badges/version/mrremind)](https://cran.r-project.org/package=mrremind) [![R build status](https://github.com/pik-piam/mrremind/workflows/check/badge.svg)](https://github.com/pik-piam/mrremind/actions) [![codecov](https://codecov.io/gh/pik-piam/mrremind/branch/master/graph/badge.svg)](https://app.codecov.io/gh/pik-piam/mrremind) [![r-universe](https://pik-piam.r-universe.dev/badges/mrremind)](https://pik-piam.r-universe.dev/builds)
 
@@ -39,16 +39,17 @@ In case of questions / problems please contact Lavinia Baumstark <lavinia@pik-po
 
 To cite package **mrremind** in publications use:
 
-Baumstark L, Rodrigues R, Levesque A, Oeser J, Bertram C, Mouratiadou I, Malik A, Schreyer F, Soergel B, Rottoli M, Mishra A, Dirnaichner A, Pehl M, Giannousakis A, Klein D, Strefler J, Feldhaus L, Brecha R, Rauner S, Dietrich J, Bi S, Benke F, Weigmann P, Richters O, Hasse R, Fuchs S, Mandaroux R, Koch J (2024). _mrremind: MadRat REMIND Input Data Package_. R package version 0.204.1, <https://github.com/pik-piam/mrremind>.
+Baumstark L, Rodrigues R, Levesque A, Oeser J, Bertram C, Mouratiadou I, Malik A, Schreyer F, Soergel B, Rottoli M, Mishra A, Dirnaichner A, Pehl M, Giannousakis A, Klein D, Strefler J, Feldhaus L, Brecha R, Rauner S, Dietrich J, Bi S, Benke F, Weigmann P, Richters O, Hasse R, Fuchs S, Mandaroux R, Koch J (2025). "mrremind: MadRat REMIND Input Data Package." Version: 0.205.0, <https://github.com/pik-piam/mrremind>.
 
 A BibTeX entry for LaTeX users is
 
  ```latex
-@Manual{,
+@Misc{,
   title = {mrremind: MadRat REMIND Input Data Package},
   author = {Lavinia Baumstark and Renato Rodrigues and Antoine Levesque and Julian Oeser and Christoph Bertram and Ioanna Mouratiadou and Aman Malik and Felix Schreyer and Bjoern Soergel and Marianna Rottoli and Abhijeet Mishra and Alois Dirnaichner and Michaja Pehl and Anastasis Giannousakis and David Klein and Jessica Strefler and Lukas Feldhaus and Regina Brecha and Sebastian Rauner and Jan Philipp Dietrich and Stephen Bi and Falk Benke and Pascal Weigmann and Oliver Richters and Robin Hasse and Sophie Fuchs and Rahel Mandaroux and Johannes Koch},
-  year = {2024},
-  note = {R package version 0.204.1},
+  date = {2025-01-08},
+  year = {2025},
   url = {https://github.com/pik-piam/mrremind},
+  note = {Version: 0.205.0},
 }
 ```