R/calcEDGETransport.R

In pik-piam/mrremind: MadRat REMIND Input Data Package

#' @title Prepare EDGETransport inputs
#'
#' @author Johanna Hoppe
#' @seealso \code{\link{readSource}}
#' @param subtype  REMIND/iterative EDGE-T input data subtypes
#' @return REMIND/iterative EDGE-T input data for all scenario combinations
#' @examples
#' \dontrun{ a <- calcOutput(type = "EDGETransport", subtype = "CAPEXandNonFuelOPEX", aggregate = F)
#' }

calcEDGETransport <- function(subtype) {

  x <- readSource("EDGETransport", subtype)

  switch(subtype,
         "f35_esCapCost" = {
           weight <- readSource("EDGETransport", subtype = "weightESdemand")
           #Rule out numerical errors after disaggregating very small numbers
           weight[weight < 1e-5] <- 0
           #check whether weightsum is zero for some cases
           #if so, the values should just be aggregated equally in order to prevent zeros in the results
           weight <- rmndt::magpie2dt(weight)
           regMap <- toolGetMapping(getConfig("regionmapping"), type = "regional", where = "mappingfolder")
           setnames(regMap, "CountryCode", "all_regi")
           weight <- merge(weight, regMap[, c("all_regi", "RegionCode")], by = "all_regi")
           weight[, weightSum := sum(value), by = c("RegionCode", "GDP_scenario", "DEM_scenario", "EDGE_scenario", "all_teEs", "tall")]
           weight[weightSum == 0, value := 1]
           weight[, c("RegionCode", "weightSum") := NULL]
           weight <- as.magpie(weight)
           unit = "2005US$/(p|t)km"
           description = "Capital cost (purchase) per energy service demand on CES level."
         },
         "f35_fe2es" = {
           weight = readSource("EDGETransport", "f35_demByTech")
           weight[weight < 1e-5] <- 0
           #check whether weightsum is zero for some cases
           #if so, the values should just be aggregated equally in order to prevent zeros in the results
           weight <- rmndt::magpie2dt(weight)
           regMap <- toolGetMapping(getConfig("regionmapping"), type = "regional", where = "mappingfolder")
           setnames(regMap, "CountryCode", "all_regi")
           weight <- merge(weight, regMap[, c("all_regi", "RegionCode")], by = "all_regi")
           weight[, weightSum := sum(value), by = c("RegionCode", "GDP_scenario", "DEM_scenario", "EDGE_scenario", "all_enty", "all_in", "all_teEs", "tall")]
           weight[weightSum == 0, value := 1]
           weight[, c("RegionCode", "weightSum", "all_enty", "all_in") := NULL]
           weight <- as.magpie(weight)
           unit = "trn (p|t)km/Twa"
           description = "Energy efficiency on CES level."
         },
         "f35_demByTech" = {
           weight = NULL
           unit = "TWa"
           description = "Final energy demand on CES level."
         },
         "f29_trpdemand" = {
           weight = NULL
           unit = "trillion pkm/trillion tkm"
           description = "Energy service demand on CES level."
         },
         "CAPEXandNonFuelOPEX" = {
           gdp <- calcOutput("GDP", aggregate = FALSE)[,,"gdp_SSP2"]
           weight = gdp |> time_interpolate(getYears(x))
           unit = "2005US$/(p|t)km"
           description = "Capital cost (purchase) and non-fuel operational costs on technology level."
         },
         "scenSpecPrefTrends" = {
           gdp <- calcOutput("GDP", aggregate = FALSE)[,,"gdp_SSP2"]
           weight = gdp |> time_interpolate(getYears(x))
           unit = "-"
           description = "Scenario specific preference trends on technology level."
         },
         "scenSpecLoadFactor" = {
           gdp <- calcOutput("GDP", aggregate = FALSE)[,,"gdp_SSP2"]
           weight = gdp |> time_interpolate(getYears(x))
           unit = "-"
           description = "Scenario specific load factor on technology level."
         },
         "scenSpecEnIntensity" = {
           gdp <- calcOutput("GDP", aggregate = FALSE)[,,"gdp_SSP2"]
           weight = gdp |> time_interpolate(getYears(x))
           unit = "MJ/vehkm"
           description = "Scenario specific energy intensity on technology level."
         },
         "initialIncoCosts" = {
           gdp <- calcOutput("GDP", aggregate = FALSE)[,,"gdp_SSP2"]
           weight = gdp |> time_interpolate(getYears(x))
           unit = "2005US$/(p|t)km"
           description = "Initial inconvenience cost values."
         },
         "annualMileage" = {
           gdp <- calcOutput("GDP", aggregate = FALSE)[,,"gdp_SSP2"]
           weight = gdp |> time_interpolate(getYears(x))
           unit = "vehkm/yr"
           description = "Annual vehicle km traveled."
         },
         "timeValueCosts" = {
           gdp <- calcOutput("GDP", aggregate = FALSE)[,,"gdp_SSP2"]
           weight = gdp |> time_interpolate(getYears(x))
           unit = "2005US$/(p|t)km"
           description = "Value of time cost equivalent."
         }
       )

  return(list(x           = x,
              weight      = weight,
              unit        = unit,
              description = description))
}