R/calcBiomassPrices.R

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

#' read biomass supply curves from Magpie emulator
#' @return Magpie object with two parameters determining linear biomass supply curve
#' @importFrom magclass dimReduce
#' @importFrom madrat readSource
#' @import mrcommons

calcBiomassPrices <- function(){
  
  x <- readSource("MAgPIE", subtype = "supplyCurve_magpie_40")

  # rename the rcp-scenarios to cm_rcp_scen switches used in REMIND
  getNames(x) <- gsub("NDC-nocc_hist-PkBudg650",  "rcp20", getNames(x)) 
  getNames(x) <- gsub("NDC-nocc_hist-PkBudg1050", "rcp26", getNames(x))
  getNames(x) <- gsub("NDC-nocc_hist-NDC",        "rcp45", getNames(x))
  getNames(x) <- gsub("NPI-nocc_hist-Base",       "none" , getNames(x))
  
  # rename SSP-scenarios to cm_LU_emi_scen switches used in REMIND
  getNames(x) <- gsub("SSP2EU", "SSP2", getNames(x))
  getNames(x) <- gsub("SDP-MC", "SDP",  getNames(x))
  
  # Introduce new SSP/SDP dimension by replacing "-" with "."
  getNames(x) <- gsub("(SSP[0-9]|SDP)-","\\1.",getNames(x))
  
  # if fit coefficients of a country are NA for all years (there is no supplycurve at all for this country)
  # generate artificial supplycurve with VERY high prices
  x[,,"a"][is.na(x[,,"a"])] <- 1
  x[,,"b"][is.na(x[,,"b"])] <- 0.1

  return(list(x           = x,
              weight      = calcOutput("FAOLand", aggregate = F)[,,"6610",pmatch=TRUE][,"y2010",],
              unit        = "none",
              description = "coefficients for the bioenergy supplycurve",
              aggregationFunction=toolBiomassSupplyAggregate))
}