R/calcExogDemScen.R

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

#' calculate exogenuous FE and ES demand pathways
#' @description  prepare data for exogenuous FE and ES demand pathways that do not
#' come from EDGE models but from other sources and/or scenario literature.
#' REMIND can be fixed to those demand pathways if the switch cm_exogDem_scen is activated.
#'
#' @return A [`magpie`][magclass::magclass] object.
#' @author Felix Schreyer

calcExogDemScen <- function() {

  # read scenario data from Ariadne IIASA database
  data <- readSource("AriadneDB")

  # which variables, scenarios and models from Ariadne DB to select
  ariadne.vars <- c("Production|Steel|Primary", "Production|Steel|Secondary", "Production|Non-Metallic Minerals|Cement")
  ariadne.scens <- c("KN2045_Bal_v4", "KN2045plus_EasyRide", "KN2045minus_WorstCase")
  ariadne.model <- c("FORECAST v1_0")

  # select only specified variables and scenarios for Germany,
  # convert from Mt production to Gt production
  ariadne.ind.demand <- mselect(data["DEU", , ] * 1e-3,
    variable = ariadne.vars,
    scenario = ariadne.scens,
    model = ariadne.model
  )

  # map Ariadne scenarios and variables to scenario and production factor names used for input data in REMIND
  mapping.scens <- data.frame(
    scen.ariadne = ariadne.scens,
    scen.remind = c(
      "ariadne_bal",
      "ariadne_lowDem",
      "ariadne_highDem"
    )
  )

  mapping.vars <- data.frame(
    vars.ariadne = ariadne.vars,
    vars.remind = c(
      "ue_steel_primary",
      "ue_steel_secondary",
      "ue_cement"
    )
  )

  ariadne.ind.demand <- collapseNames(ariadne.ind.demand)

  ariadne.ind.demand <- toolAggregate(
    x = ariadne.ind.demand,
    rel = mapping.scens,
    dim = 3.1
  )

  ariadne.ind.demand <- toolAggregate(
    x = ariadne.ind.demand,
    rel = mapping.vars,
    dim = 3.2
  )

  # rearrange to output format
  years <- c(
    2005, 2010, 2015, 2020, 2025, 2030, 2035, 2040, 2045,
    2050, 2055, 2060, 2070, 2080, 2090, 2100
  )

  out <- new.magpie(
    cells_and_regions = magclass::getItems(data, dim = 1),
    years = years,
    names = magclass::getNames(ariadne.ind.demand),
    fill = 0
  )

  # take data from Ariadne DB only between 2025 and 2050
  ariadne.period <- seq(2025, 2050, 5)
  out["DEU", paste0("y", ariadne.period), ] <- ariadne.ind.demand["DEU", paste0("y", ariadne.period), ]

  # FORECAST only provides data until 2050. After 2050, assume linear decline until 2080 to
  # levels in line with REMIND runs with endogenous production".

  out["DEU", 2080, "ue_steel_primary"] <- 10 * 1e-3
  out["DEU", 2080, "ue_steel_secondary"] <- 20 * 1e-3
  out["DEU", 2080, "ue_cement"] <- 20 * 1e-3


  out["DEU", c(2050, 2055, 2060, 2070, 2080), ] <- madrat::toolFillYears(
    out["DEU", c(2050, 2080), ], c(2050, 2055, 2060, 2070, 2080)
  )

  return(list(
    x = out,
    weight = NULL,
    unit = c("Gt steel/yr for ue_steel_primary and ue_steel_secondary, Gt cement/yr for ue_cement"),
    description = "Exogenous demand scenarios that REMIND can be fixed to using cm_exogDem_scen"
  ))
}