R/convertMAgPIE.R

In pik-piam/mrcommons: MadRat commons Input Data Library

#' Convert MAgPIE data
#'
#' Convert MAgPIE data to country-level.
#'
#' @param x input MAgPIE data on region-level
#' @param subtype Either "EmiAirPoll", "macBase", "co2tax", "supplyCurve_magpie_40", "abatparam_co2"
#' @importFrom madrat regionscode
#' @return magpie object
#' @author David Klein, Felix Schreyer
#' @export
#' @importFrom magclass as.magpie dimReduce
#' @importFrom dplyr %>% mutate select rename left_join
#' @importFrom rlang .data
#' @importFrom quitte as.quitte
#'
convertMAgPIE <- function(x, subtype) {

  if (subtype == "EmiAirPoll") {
    # read CEDS emissions data from sources (only airpollutants)
    bc    <- readSource("CEDS", subtype = "BC")
    ch4   <- readSource("CEDS", subtype = "CH4")
    co    <- readSource("CEDS", subtype = "CO")
    nh3   <- readSource("CEDS", subtype = "NH3")
    nox   <- readSource("CEDS", subtype = "NOx")
    nmvoc <- readSource("CEDS", subtype = "NMVOC")
    oc    <- readSource("CEDS", subtype = "OC")
    so2   <- readSource("CEDS", subtype = "SO2")

    y <- "y2005"
    ap <- mbind(bc[, y, ], ch4[, y, ], co[, y, ], nh3[, y, ], nox[, y, ], nmvoc[, y, ], oc[, y, ], so2[, y, ])

    # convert to sectoral mapping
    map <- toolGetMapping(type = "sectoral", name = "mappingCEDS59toSectors.csv", where = "mappingfolder")

    ap <- toolAggregate(ap, map, dim = 3.1, from = "CEDS59", to = "Sectors")

    weight <- dimSums(ap[, , c("agriculture", "luc")], dim = 3)

    mappingfile <- toolGetMapping(type = "regional", name = "regionmappingMAgPIE.csv",
                                  returnPathOnly = TRUE, where = "mappingfolder")
    mapping <- read.csv2(mappingfile)

  } else if (subtype == "macBase") {

    mappingfile <- toolGetMapping(type = "regional", name = "regionmappingMAgPIE.csv",
                                  returnPathOnly = TRUE, where = "mappingfolder")
    mapping <- read.csv2(mappingfile)
    primap <- readSource("PRIMAPhist", subtype = "hist")
    weight <- dimSums(primap[, "y2005", c("CAT4", "CAT5")][, , c("n2o_n", "ch4")], dim = 3)

  } else if (subtype %in% c("co2tax", "macBaseCO2luc")) {

    mapping <- toolGetMapping(type = "regional", name = "regionmappingH12.csv",
                              returnPathOnly = TRUE, where = "mappingfolder")
    # use total land area as weight
    weight <- calcOutput("LanduseInitialisation", aggregate = FALSE)[, 2005, ]
    # sum over 3.dimension
    weight <- dimSums(weight, dim = 3)
    getYears(weight) <- NULL

  } else if (subtype == "supplyCurve_magpie_40") {

    # FS: Disaggregation of MAgPIE biomass supply curves to iso-countries:
    # We assume that countries that are part of a MAgPIE region have the same fix cost to produce the first unit of
    # biomass (same offset parameter). For the slope parameter, we assume that biomass can be produced at lower cost in
    # countries that currently have a large agricultural area. We therefore divide the slope parameter of the MAgPIE
    # region by the share in agricultural area of the iso country relative to the MAgPIE region to obtain the
    # disaggregated slope parameter of the iso-country. Note: This disaggregation does not work if iso-countries cover
    # more than one MAgPIE region. For this case, this needs to be reworked!

    # regionmapping of input MAgPIE data, needs to be changed if MAgPIE data changes regionmapping
    mapping <- toolGetMapping(type = "regional", name = "regionmappingH12.csv", where = "mappingfolder")

    # aggregate to from regions to iso-countries -> just copies regional values to countries
    y <- toolAggregate(x, mapping, weight = NULL)

    # calculate share in agricultural land area of countries relative to MAgPIE regions
    # get agricultural land for iso-countries in 2010 from FAO
    # 6610 = "6610|Agricultural area.area" or "6610|Agricultural land.Area_(1000_ha)"
    agrLandIso <- calcOutput("FAOLand", aggregate = FALSE)[, "y2010", ][, , "6610", pmatch = TRUE]
    # aggregate agricultural land to regions in regionmapping
    agrLandReg <- toolAggregate(agrLandIso, mapping)

    # calculate share of agricultural land area for each iso-country relative to the MAgPIE region it is in
    agrLandShare <- as.quitte(agrLandIso) %>%
      select(.data$region, .data$value) %>%
      rename(CountryCode = .data$region) %>%
      left_join(mapping, by = "CountryCode") %>%
      left_join((as.quitte(agrLandReg) %>%
                   select(.data$region, .data$value) %>%
                   rename(RegionCode = .data$region, Total = .data$value)), by = "RegionCode") %>%
      mutate(value = .data$value / .data$Total) %>%
      # if no agricultural area at all -> assume very low share of 1e-5
      mutate(value = ifelse(.data$value == 0, 1e-5, .data$value)) %>%
      select("CountryCode", "value") %>%
      as.magpie(spatial = 1, datacol = 2) %>%
      suppressWarnings() %>%
      dimReduce()

    # divide slope parameter b by agricultural land share
    y[, , "b"] <- y[, , "b"] / agrLandShare
    x <- y

  } else if (subtype == "MAgPIEReport_extensive") {
    # remove GLO values because they cannot be handled by the disaggregation to ISO countries below
    x <- x["GLO", , , invert = TRUE]

    # currently the MAgPIE data is only available in H12 resolution
    mapping <- toolGetMapping(type = "regional", name = "regionmappingH12.csv",
                              returnPathOnly = TRUE, where = "mappingfolder")

    # use total land area as weight for regional disaggregation
    weight <- calcOutput("LanduseInitialisation", aggregate = FALSE)[, 2005, ]
    # sum over 3.dimension
    weight <- dimSums(weight, dim = 3)
    getYears(weight) <- NULL

  }

  # supplyCurve_magpie_40 disaggregated above
  if (!(subtype %in% c("abatparam_co2", "supplyCurve_magpie_40"))) {
    y <- toolAggregate(x, mapping, weight = weight)
    y <- toolCountryFill(y, fill = 0)
  } else if (subtype %in% c("abatparam_co2")) {
    # abatparam_co2
    y <- x
  }

  return(y)
}