Merge branch 'master' of https://github.com/pik-piam/mrremind into struct

Author: fbenke-pik

.buildlibrary

@@ -1,4 +1,4 @@
-ValidationKey: '55213360'
+ValidationKey: '55242005'
 AcceptedWarnings:
 - Invalid URL: .*
 - 'Warning: package ''.*'' was built under R version'

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: 0.268.0
-date-released: '2026-05-29'
+version: 0.268.1
+date-released: '2026-06-01'
 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: 0.268.0
-Date: 2026-05-29
+Version: 0.268.1
+Date: 2026-06-01
 Authors@R: c(
     person("Lavinia", "Baumstark", , "lavinia@pik-potsdam.de", role = c("aut", "cre")),
     person("Renato", "Rodrigues", role = "aut"),

R/calcFEShares.R

@@ -1,40 +1,44 @@
 #' FE Share parameters used in REMIND
 #'
-#' @param subtype 'ind_coal' for the share of coal used in industry. 'ind_bio' for the share of biomass used in industry
+#' @param subtype 'ind_coal' for the share of coal used in industry.
+#' 'ind_bio' for the share of biomass used in industry
 #' @param scenario Vector of strings. Used here only to optimize madrat cache usage,
-#' as in the end only the 2005 FEdemand value is actually used - which is equal across scenarios.
+#' as in the end only the 2005 FE demand value is actually used - which is equal across scenarios.
 #' @author Antoine Levesque
 #'
 calcFEShares <- function(subtype, scenario) {
-  if (! subtype %in% c("ind_coal", "ind_bio", "ind_liq")) {
+
+  if (!subtype %in% c("ind_coal", "ind_bio", "ind_liq")) {
     stop("Unknown subtype.")
   }
 
-  edge_buildings <- calcOutput("IOEdgeBuildings", subtype = "output_EDGE_buildings", aggregate = FALSE)
+  edge_buildings <- calcOutput("IOEdgeBuildings", subtype = "output_EDGE_buildings",
+                               aggregate = FALSE)
   output <- calcOutput("IO", subtype = "output", corrected = TRUE, aggregate = FALSE)
-  # Get FEdemand data for 2005 (equal across scenarios, so just pick SSP2).
-  ## Keep scenario selection as is (this optimizes madrat cache usage).
-  fe_demand <- calcOutput("FEdemand", scenario = scenario, aggregate = FALSE)[, 2005, "SSP2"] %>% collapseNames()
 
   if (subtype == "ind_coal") {
     share <- 1 - dimSums(edge_buildings[, 2005, "coal"]) / output[, 2005, "pecoal.sesofos.coaltr"]
     weight <- output[, 2005, "pecoal.sesofos.coaltr"]
     descr <- glue::glue("share of coal used in industry (computed by retrieving buildings uses, i.e., considering \\
                         transport and ONONSPEC are null)")
-  }
-
-  if (subtype == "ind_bio") {
+  } else if (subtype == "ind_bio") {
     share <- 1 - dimSums(edge_buildings[, 2005, c("biomod", "biotrad")]) /
       dimSums(output[, 2005, c("pebiolc.sesobio.biotr", "pebiolc.sesobio.biotrmod")], dim = 3)
     weight <- dimSums(output[, 2005, c("pebiolc.sesobio.biotr", "pebiolc.sesobio.biotrmod")], dim = 3)
     descr <- glue::glue("share of biomass used in industry (computed by retrieving buildings uses, i.e., considering \\
                         transport and ONONSPEC are null)")
-  }
+  } else if (subtype == "ind_liq") {
+
+    # Get FE demand data for 2005 (equal across scenarios, so just pick SSP2).
+    ## Keep scenario selection as is (this optimizes madrat cache usage).
+
+    fe_dem_build <- calcOutput("FeDemandBuildings", subtype = "FE", scenario = scenario, aggregate = FALSE)
+    fe_dem_ind <- calcOutput("FeDemandIndustry",  scenarios = scenario, aggregate = FALSE)[, , "SSP2"]
+
+    fehoi <- dimSums(fe_dem_ind[, 2005, c("feli_cement", "feli_chemicals", "feli_steel", "feli_otherInd")], dim = 3)
 
-  if (subtype == "ind_liq") {
-    fehoi <- dimSums(fe_demand[, 2005, c("feli_cement", "feli_chemicals", "feli_steel", "feli_otherInd")])
-    share <- fehoi / (fehoi + fe_demand[, 2005, c("fehob")])
-    weight <- (fehoi + fe_demand[, 2005, c("fehob")])
+    share <- fehoi / (fehoi + fe_dem_build[, 2005, "SSP2.none.fehob"])
+    weight <- (fehoi + fe_dem_build[, 2005, "SSP2.none.fehob"])
     descr <- "share of stationary heating oil used in industry"
   }
 

R/calcFEdemand.R

@@ -1,6 +1,6 @@
 #' Returns the EDGE-Buildings data as REMIND variables
 #'
-#' @param subtype either "FE", "FE_buildings", or "UE_buildings"
+#' @param subtype either "FE", or "UE"
 #' @param scenario A string (or vector of strings) designating the scenario(s) to be returned.
 #'
 #' @author Robin Hasse
@@ -12,33 +12,26 @@ calcFeDemandBuildings <- function(subtype, scenario) {
   # end of history
   eoh <- 2025
 
-  if (!subtype %in% c("FE", "FE_buildings", "UE_buildings")) {
+  if (!subtype %in% c("FE", "UE")) {
     stop(paste0("Unsupported subtype: ", subtype))
   }
 
   ## Replace any calls to scenario groups such as "SSPs" and "SSP2IndiaDEAs", to calls of the individual scenarios.
   scenario <- mrdrivers::toolReplaceShortcuts(scenario) %>% unique()
 
   # Data Processing ----
-  ononspec <- calcOutput("FeDemandONONSPEC", scenario = scenario, eoh = eoh,
-                         aggregate = FALSE)
+  ononspec <- calcOutput("FeDemandONONSPEC", scenario = scenario, eoh = eoh, aggregate = FALSE)
   buildings  <- readSource("EdgeBuildings", subtype = "FE", subset = scenario)
 
   # Aggregate to 5-year averages to suppress volatility
   buildings <- toolAggregateTimeSteps(buildings)
   ononspec <- toolAggregateTimeSteps(ononspec)
 
-  if (subtype == "FE") {
-    # Drop RCP dimension (use fixed RCP)
-    buildings <- mselect(buildings, rcp = "none") %>% collapseDim(dim = "rcp")
-
-  } else {
-    # For each scenario add the rcp scenarios present in buildings to stationary
-    ononspec <- do.call(mbind, lapply(scenario, function(scen) {
-      rcps <- getItems(mselect(buildings, scenario = scen), dim = "rcp")
-      toolAddDimensions(mselect(ononspec, scenario = scen), dimVals = rcps, dimName = "rcp", dimCode = 3.2)
-    }))
-  }
+  # For each scenario add the rcp scenarios present in buildings to stationary
+  ononspec <- do.call(mbind, lapply(scenario, function(scen) {
+    rcps <- getItems(mselect(buildings, scenario = scen), dim = "rcp")
+    toolAddDimensions(mselect(ononspec, scenario = scen), dimVals = rcps, dimName = "rcp", dimCode = 3.2)
+  }))
 
   # Extrapolate years missing in buildings, but existing in stationary
   buildings <- time_interpolate(buildings,
@@ -52,29 +45,21 @@ calcFeDemandBuildings <- function(subtype, scenario) {
                             name = "mappingEDGEBuildingsToREMIND.csv",
                             where = "mrremind")
 
-  if (subtype == "FE") {
-
-    remindVars <- unique(mapping$REMINDitems_out)
-    remindDims <- quitte::cartesian(getNames(data, dim = "scenario"), remindVars)
-
-  } else {
+  remindVars <- filter(mapping, grepl("^fe..b$|^feel..b$|^feelcb$", .data$REMINDitems_out))
+  remindVars <- unique(remindVars$REMINDitems_out)
 
-    remindVars <- filter(mapping, grepl("^fe..b$|^feel..b$|^feelcb$", .data$REMINDitems_out))
-    remindVars <- unique(remindVars$REMINDitems_out)
-
-    # Extend mapping for Useful Energy
-    if (subtype == "UE_buildings") {
-      mapping <- mapping %>%
-        mutate(EDGE_buildings_items = gsub("_fe$", "_ue", .data[["EDGE_buildings_items"]]),
-               REMINDitems_out = gsub("^fe", "ue", .data[["REMINDitems_out"]])) %>%
-        rbind(mapping)
-      remindVars <- gsub("^fe", "ue", remindVars)
-    }
-
-    scenarioRcp <- unique(gsub("^(.*\\..*)\\..*$", "\\1", getItems(data, dim = 3)))
-    remindDims <- quitte::cartesian(scenarioRcp, remindVars)
+  # Extend mapping for Useful Energy
+  if (subtype == "UE") {
+    mapping <- mapping %>%
+      mutate(EDGE_buildings_items = gsub("_fe$", "_ue", .data[["EDGE_buildings_items"]]),
+             REMINDitems_out = gsub("^fe", "ue", .data[["REMINDitems_out"]])) %>%
+      rbind(mapping)
+    remindVars <- gsub("^fe", "ue", remindVars)
   }
 
+  scenarioRcp <- unique(gsub("^(.*\\..*)\\..*$", "\\1", getItems(data, dim = 3)))
+  remindDims <- quitte::cartesian(scenarioRcp, remindVars)
+
   # Apply Mapping
   remind <- new.magpie(cells_and_regions = getItems(data, dim = 1),
                        years = getYears(data),
@@ -93,20 +78,18 @@ calcFeDemandBuildings <- function(subtype, scenario) {
 
   # Prepare Output
   ## Change item names back from UE to FE
-  if (subtype == "UE_buildings") {
+  if (subtype == "UE") {
     getItems(remind, "item") <- gsub("^ue", "fe", getItems(remind, "item"))
   }
 
   description <- switch(subtype,
-    FE = "final energy demand in buildings and industry (stationary)",
-    FE_buildings = "final energy demand in buildings",
-    UE_buildings = "useful energy demand in buildings"
+    FE = "final energy demand in buildings",
+    UE = "useful energy demand in buildings"
   )
 
   outputStructure <- switch(subtype,
-    FE = "^(SSP[1-5].*|SDP.*)\\.(fe|ue)",
-    FE_buildings = "^(SSP[1-5]|SDP).*\\..*\\.fe.*b$",
-    UE_buildings = "^(SSP[1-5]|SDP).*\\..*\\.fe.*b$"
+    FE = "^(SSP[1-5]|SDP).*\\..*\\.fe.*b$",
+    UE = "^(SSP[1-5]|SDP).*\\..*\\.fe.*b$"
   )
 
   list(x = remind,

R/calcFeDemandBuildings.R

@@ -53,17 +53,10 @@ fullREMIND <- function() {
     match.steel.historic.values = TRUE,
     match.steel.estimates = "IEA_ETP"
   )
-  calcOutput("FEdemand", scenario = feDemScen, signif = 4, file = "f_fedemand.cs4r")
-  calcOutput("FeDemandBuildings",
-             subtype = "FE_buildings",
-             scenario = feDemScen,
-             round = 8,
-             file = "f_fedemand_build.cs4r")
-  calcOutput("FeDemandBuildings",
-             subtype = "UE_buildings",
-             scenario = feDemScen,
-             round = 8,
-             file = "f36_uedemand_build.cs4r")
+
+  calcOutput("FeDemandIndustry", scenarios = feDemScen, signif = 4, file = "f_fedemandInd.cs4r")
+  calcOutput("FeDemandBuildings", subtype = "FE", scenario = feDemScen, round = 8, file = "f_fedemandBuild.cs4r")
+  calcOutput("FeDemandBuildings", subtype = "UE", scenario = feDemScen, round = 8, file = "f36_uedemand_build.cs4r")
   calcOutput("ChemicalFeedstocksShare",                     round = 2, file = "p37_chemicals_feedstock_share.cs4r")
   calcOutput("Floorspace", scenario = feDemScen, onlyTotal = TRUE, round = 1, file = "p36_floorspace_scen.cs4r")
   calcOutput("Floorspace", scenario = feDemScen,            round = 1, file = "f36_floorspace_scen.cs4r")

R/fullREMIND.R

@@ -5,7 +5,8 @@
 #' @author Felix Schreyer
 #' @param subtype "FE" (Total final energy consumption), "SE|Electricity" (SE electricity generation)
 #' @param subset GDP scenario to use
-#' @param scenario set of GDP scenarios to use for calcFEDemand calculation (trigger standard cache in this function)
+#' @param scenario set of GDP scenarios to use for calcFeDemandBuildings and calcFeDemandIndustry
+#' calculation (trigger standard cache in this function)
 #' @param years target years for projection
 #' @seealso [convertNewClimate()]
 
@@ -32,7 +33,7 @@ toolCalcEnergyProj <- function(subtype, subset, scenario, years = seq(2020, 2050
   # function get historical energy data for 2020
   .getHistData <- function(subtype, AvgSeveralYears) {
     # get historical FE and SE data from IEA energy balances
-    IEA <- calcOutput("FE", ieaVersion = "default", aggregate = F)
+    IEA <- calcOutput("FE", ieaVersion = "default", aggregate = FALSE)
 
     # add items to calculate historical FE demand or historical SE generation
     if (subtype == "FE") {
@@ -62,19 +63,21 @@ toolCalcEnergyProj <- function(subtype, subset, scenario, years = seq(2020, 2050
     FETransport <- calcOutput("EDGETransport", subtype = "f35_demByTech")
     # filter for SSP2 scenario and Mix1 EDGE scenario
     FETransport <- collapseNames(mselect(FETransport, GDP_scenario = subset, DEM_scenario = subset, EDGE_scenario = "Mix1"))
+
     # buildings and industry FE demand from EDGE-B and EDGE-I (in EJ/yr)
-    FEBuildIndustry <- calcOutput("FEdemand", scenario = scenario , signif = 4)
-    # filter for SSP2 scenario
-    FEBuildIndustry <- collapseNames(FEBuildIndustry[, , subset])
+    FEBuild <- calcOutput("FeDemandBuildings", subtype = "FE", scenario = scenario, signif = 4)[, , subset][, , "none"] %>%
+      collapseNames()
+    FEIndustry <- calcOutput("FeDemandIndustry", scenarios = scenario, signif = 4)[, , subset]
+
     # define CES inputs over which to sum for respective FE demand
     if (subtype == "FE") {
       # for FE projection, get FE trend of total FE
       input.fe.build <- c("feelcb", "feelhpb", "feelrhb", "fegab", "fehob", "fesob", "feheb")
       input.fe.indst <- c(
-        grep("fe.*steel", getNames(FEBuildIndustry), value = T),
-        grep("fe.*cement", getNames(FEBuildIndustry), value = T),
-        grep("fe.*chem", getNames(FEBuildIndustry), value = T),
-        grep("fe.*otherInd", getNames(FEBuildIndustry), value = T)
+        grep("fe.*steel", getNames(FEIndustry), value = TRUE),
+        grep("fe.*cement", getNames(FEIndustry), value = TRUE),
+        grep("fe.*chem", getNames(FEIndustry), value = TRUE),
+        grep("fe.*otherInd", getNames(FEIndustry), value = TRUE)
       )
       input.fe.trans <- getNames(FETransport)
     } else if (subtype == "SE|Electricity") {
@@ -89,11 +92,13 @@ toolCalcEnergyProj <- function(subtype, subset, scenario, years = seq(2020, 2050
     }
 
     # common years across EDGE outputs
-    common.years <- intersect(getYears(FEBuildIndustry), getYears(FETransport))
+    common.years <- intersect(
+      getYears(FEIndustry), intersect(getYears(FEBuild), getYears(FETransport))
+    )
 
     # sum FE demand across sectors per REMIND region
-    FeTotal <- dimSums(FEBuildIndustry[, common.years, input.fe.build], dim = 3) +
-      dimSums(FEBuildIndustry[, common.years, input.fe.indst], dim = 3) +
+    FeTotal <- dimSums(FEBuild[, common.years, input.fe.build], dim = 3) +
+      dimSums(FEIndustry[, common.years, input.fe.indst], dim = 3) +
       dimSums(FETransport[, common.years, input.fe.trans], dim = 3) * 31.536 # convert TWa/yr to EJ/yr
     # calculate FE trend of target year with respect to 2020
     FeTrend <- collapseDim(FeTotal[, years, ] / FeTotal[, "y2020", ])
@@ -108,7 +113,7 @@ toolCalcEnergyProj <- function(subtype, subset, scenario, years = seq(2020, 2050
   # function to calculate difference in GDP trend between country and REMIND region
   .computeGDPTrendDiff <- function(subset, years, MaxThreshold) {
     # GDP on country-level
-    GDP_country <- calcOutput("GDP", scenario = subset, years = years, aggregate = F)
+    GDP_country <- calcOutput("GDP", scenario = subset, years = years, aggregate = FALSE)
     # GDP on REMIND region-level
     GDP_region <- calcOutput("GDP", scenario = subset, years = years)
     # get regionmapping
@@ -123,14 +128,13 @@ toolCalcEnergyProj <- function(subtype, subset, scenario, years = seq(2020, 2050
     GDPTrendDiff[GDPTrendDiff < -MaxThreshold] <- -MaxThreshold
     GDPTrendDiff[GDPTrendDiff > MaxThreshold] <- MaxThreshold
 
-
     return(GDPTrendDiff)
   }
 
   ### main function ----
 
   # 1. get historical energy data for 2020 ----
-  xHist <- .getHistData(subtype, AvgSeveralYears = T)
+  xHist <- .getHistData(subtype, AvgSeveralYears = TRUE)
   # 2. calculate FE trend on level of REMIND regions based on EDGE data ----
   FeTrend <- .computeFeTrend(subtype, subset, years)
   # 3. calculate difference in GDP trend between country and REMIND region ----

R/toolCalcEnergyProj.R

@@ -1,6 +1,6 @@
 # MadRat REMIND Input Data Package
 
-R package **mrremind**, version **0.268.0**
+R package **mrremind**, version **0.268.1**
 
    [![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)
 
@@ -39,17 +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, Abrahao G, Dorndorf T (2026). "mrremind: MadRat REMIND Input Data Package." Version: 0.268.0, <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, Abrahao G, Dorndorf T (2026). "mrremind: MadRat REMIND Input Data Package." Version: 0.268.1, <https://github.com/pik-piam/mrremind>.
 
 A BibTeX entry for LaTeX users is
 
  ```latex
 @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 and Gabriel Abrahao and Tabea Dorndorf},
-  date = {2026-05-29},
+  date = {2026-06-01},
   year = {2026},
   url = {https://github.com/pik-piam/mrremind},
-  note = {Version: 0.268.0},
+  note = {Version: 0.268.1},
 }
 ```