R/zchunk_L244.building_korea.R

In rohmin9122/gcam-korea-release: Build the GCAM-Korea Input Datasets

#' module_gcam.korea_L244.building_korea
#'
#' Creates gcam-korea building output files for writing to xml.
#'
#' @param command API command to execute
#' @param ... other optional parameters, depending on command
#' @return Depends on \code{command}: either a vector of required inputs,
#' a vector of output names, or (if \code{command} is "MAKE") all
#' the generated outputs: \code{L244.DeleteConsumer_koreaBld}, \code{L244.DeleteSupplysector_koreaBld}, \code{L244.SubregionalShares_gcamKorea},
#' \code{L244.PriceExp_IntGains_gcamKorea}, \code{L244.Floorspace_gcamKorea}, \code{L244.DemandFunction_serv_gcamKorea}, \code{L244.DemandFunction_flsp_gcamKorea},
#' \code{L244.Satiation_flsp_gcamKorea}, \code{L244.SatiationAdder_gcamKorea}, \code{L244.ThermalBaseService_gcamKorea}, \code{L244.GenericBaseService_gcamKorea},
#' \code{L244.ThermalServiceSatiation_gcamKorea}, \code{L244.GenericServiceSatiation_gcamKorea}, \code{L244.Intgains_scalar_gcamKorea},
#' \code{L244.ShellConductance_bld_gcamKorea}, \code{L244.Supplysector_bld_gcamKorea}, \code{L244.FinalEnergyKeyword_bld_gcamKorea}, \code{L244.SubsectorShrwt_bld_gcamKorea},
#' \code{L244.SubsectorShrwtFllt_bld_gcamKorea}, \code{L244.SubsectorInterp_bld_gcamKorea}, \code{L244.SubsectorInterpTo_bld_gcamKorea},
#' \code{L244.SubsectorLogit_bld_gcamKorea}, \code{L244.StubTech_bld_gcamKorea}, \code{L244.StubTechCalInput_bld_gcamKorea}, \code{L244.StubTechMarket_bld_korea},
#' \code{L244.GlobalTechIntGainOutputRatio_korea}, \code{L244.GlobalTechInterpTo_bld_korea}, \code{L244.GlobalTechEff_bld_korea},
#' \code{L244.GlobalTechShrwt_bld_gcamKorea}, \code{L244.GlobalTechCost_bld_gcamKorea}, \code{L244.GlobalTechSCurve_bld_korea}, \code{L244.HDDCDD_A2_GFDL_korea}.
#' The corresponding file in the original data system was \code{L244.building.R} (gcam-korea level2).
#' @details Creates gcam-korea building output files for writing to xml.
#' @importFrom assertthat assert_that
#' @importFrom dplyr filter mutate select
#' @importFrom tidyr gather spread
#' @author M. Roh

module_gcam.korea_L244.building_korea <- function(command, ...) {
  if(command == driver.DECLARE_INPUTS) {
    return(c(FILE = "energy/A44.gcam_consumer",
             FILE = "energy/A44.sector",
             FILE = "gcam-korea/calibrated_techs_bld_korea",
             FILE = "gcam-korea/states_subregions",
             #FILE = "gcam-korea/A44.bld_shell_conductance",-> L144.shell_eff_R_Y
             FILE = "gcam-korea/A44.demandFn_flsp",
             FILE = "gcam-korea/A44.demandFn_serv",
             FILE = "gcam-korea/A44.gcam_consumer",
             FILE = "gcam-korea/A44.satiation_flsp_korea", #gcam default, SSP2 region.class=B
             FILE = "gcam-korea/A44.sector",
             FILE = "gcam-korea/A44.subsector_interp",
             FILE = "gcam-korea/A44.subsector_logit",
             FILE = "gcam-korea/A44.subsector_shrwt",
             FILE = "gcam-korea/A44.globaltech_cost",
             FILE = "gcam-korea/A44.globaltech_eff",
             #FILE = "gcam-korea/A44.globaltech_eff_avg",
             FILE = "gcam-korea/A44.globaltech_shares",
             FILE = "gcam-korea/A44.globaltech_intgains",
             FILE = "gcam-korea/A44.globaltech_retirement",
             FILE = "gcam-korea/A44.globaltech_shrwt",
             #FILE = "gcam-korea/A44.globaltech_interp",
             FILE = "gcam-korea/A44.demand_satiation_mult",
             "L144.shell_eff_R_Y",
             "L144.flsp_bm2_korea_resid",
             "L144.flsp_bm2_korea_comm",
             "L144.in_EJ_korea_comm",
             "L144.in_EJ_korea_resid",
             "L143.HDDCDD_scen_korea",
             "L100.Pop_thous_korea",
             "L100.pcGDP_thous90usd_korea"))
  } else if(command == driver.DECLARE_OUTPUTS) {
    return(c("L244.DeleteConsumer_koreaBld",
             "L244.DeleteSupplysector_koreaBld",
             "L244.SubregionalShares_gcamKorea",
             "L244.PriceExp_IntGains_gcamKorea",
             "L244.Floorspace_gcamKorea",
             "L244.DemandFunction_serv_gcamKorea",
             "L244.DemandFunction_flsp_gcamKorea",
             "L244.Satiation_flsp_gcamKorea",
             "L244.SatiationAdder_gcamKorea",
             "L244.ThermalBaseService_gcamKorea",
             "L244.GenericBaseService_gcamKorea",
             "L244.ThermalServiceSatiation_gcamKorea",
             "L244.GenericServiceSatiation_gcamKorea",
             "L244.Intgains_scalar_gcamKorea",
             "L244.ShellConductance_bld_gcamKorea",
             "L244.Supplysector_bld_gcamKorea",
             "L244.FinalEnergyKeyword_bld_gcamKorea",
             "L244.SubsectorShrwt_bld_gcamKorea",
             "L244.SubsectorShrwtFllt_bld_gcamKorea",
             "L244.SubsectorInterp_bld_gcamKorea",
             "L244.SubsectorInterpTo_bld_gcamKorea",
             "L244.SubsectorLogit_bld_gcamKorea",
             "L244.StubTech_bld_gcamKorea",
             "L244.StubTechCalInput_bld_gcamKorea",
             "L244.StubTechMarket_bld_korea",
             "L244.GlobalTechIntGainOutputRatio_korea",
             "L244.GlobalTechEff_bld_korea",
             "L244.GlobalTechShrwt_bld_gcamKorea",
             "L244.GlobalTechCost_bld_gcamKorea",
             "L244.GlobalTechSCurve_bld_korea",
             "L244.HDDCDD_A2_GFDL_korea"))
  } else if(command == driver.MAKE) {

    # Silence package checks
    GCM <- Scen <- base.building.size <- base.service <- calibrated.value <- comm <-
      degree.days <- efficiency <- efficiency_tech1 <- efficiency_tech2 <- fuel <-
      gcam.consumer <- grid_region <- half_life_new <- half_life_stock <- input.cost <-
      input.ratio <- internal.gains.market.name <- internal.gains.output.ratio <-
      internal.gains.scalar <- market.name <- minicam.energy.input <- multiplier <-
      object <- pcFlsp_mm2 <- pcGDP <- pcflsp_mm2cap <- pop <- region <- resid <-
      satiation.adder <- satiation.level <- sector <- sector.name <- service <- share <-
      share.weight <- share_tech1 <- share_tech2 <- share_type <- state <- steepness_new <-
      steepness_stock <- stockavg <- subsector <- subsector.name <- supplysector <-
      tech_type <- technology <- technology1 <- technology2 <-
      thermal.building.service.input <- to.value <- value <- year <- year.fillout <- . <- NULL

    all_data <- list(...)[[1]]

    # Load required inputs
    A44.gcam_consumer_en <- get_data(all_data, "energy/A44.gcam_consumer")
    A44.sector_en <- get_data(all_data, "energy/A44.sector")
    calibrated_techs_bld_korea <- get_data(all_data, "gcam-korea/calibrated_techs_bld_korea")
    states_subregions <- get_data(all_data, "gcam-korea/states_subregions")
    #A44.bld_shell_conductance <- get_data(all_data, "gcam-korea/A44.bld_shell_conductance")
    A44.demandFn_flsp <- get_data(all_data, "gcam-korea/A44.demandFn_flsp")
    A44.demandFn_serv <- get_data(all_data, "gcam-korea/A44.demandFn_serv")
    A44.gcam_consumer <- get_data(all_data, "gcam-korea/A44.gcam_consumer")
    A44.satiation_flsp_korea <- get_data(all_data, "gcam-korea/A44.satiation_flsp_korea")
    A44.sector <- get_data(all_data, "gcam-korea/A44.sector")
    A44.subsector_interp <- get_data(all_data, "gcam-korea/A44.subsector_interp")
    A44.subsector_logit <- get_data(all_data, "gcam-korea/A44.subsector_logit")
    A44.subsector_shrwt <- get_data(all_data, "gcam-korea/A44.subsector_shrwt")
    A44.globaltech_cost <- get_data(all_data, "gcam-korea/A44.globaltech_cost")
    A44.globaltech_eff <- get_data(all_data, "gcam-korea/A44.globaltech_eff") %>%
      gather_years()
    #A44.globaltech_eff_avg <- get_data(all_data, "gcam-korea/A44.globaltech_eff_avg")
    A44.globaltech_shares <- get_data(all_data, "gcam-korea/A44.globaltech_shares")
    A44.globaltech_intgains <- get_data(all_data, "gcam-korea/A44.globaltech_intgains")
    A44.globaltech_retirement <- get_data(all_data, "gcam-korea/A44.globaltech_retirement")
    A44.globaltech_shrwt <- get_data(all_data, "gcam-korea/A44.globaltech_shrwt")
    #A44.globaltech_interp <- get_data(all_data, "gcam-korea/A44.globaltech_interp")
    A44.demand_satiation_mult <- get_data(all_data, "gcam-korea/A44.demand_satiation_mult")
    L144.flsp_bm2_korea_resid <- get_data(all_data, "L144.flsp_bm2_korea_resid")
    L144.flsp_bm2_korea_comm <- get_data(all_data, "L144.flsp_bm2_korea_comm")
    L144.in_EJ_korea_comm <- get_data(all_data, "L144.in_EJ_korea_comm")
    L144.in_EJ_korea_resid <- get_data(all_data, "L144.in_EJ_korea_resid")
    L144.shell_eff_R_Y <- get_data(all_data, "L144.shell_eff_R_Y")
    L143.HDDCDD_scen_korea <- get_data(all_data, "L143.HDDCDD_scen_korea")
    L100.Pop_thous_korea <- get_data(all_data, "L100.Pop_thous_korea")
    L100.pcGDP_thous90usd_korea <- get_data(all_data, "L100.pcGDP_thous90usd_korea")
    # ===================================================
    # Note: Building energy demands and floorspace are calculated endogenously - these are undergoing review
    # per-capita demand = (satiation.level - satiation.adder) * (1 - exp( -log2 / satiation.impedance * Demand.Driver)) + satiation.adder)
    #
    # floorspace = (satiation.level - satiation.adder) *
    # [1 - exp{(-ln(2) * per-capita-GDP/satiation.impedance) * (energy_cost/base_energy_cost)^price_effect_exponent}] + satiation.adder
    #
    # satiation.level: maximum per-capita demand that can be achieved
    # satiation.adder: value that allow the starting position of any region to be set along the demand function
    # satiation.impedance: shape parameter


    # Need to delete the buildings sector in the USA region (gcam.consumers and supplysectors)
    L244.DeleteConsumer_koreaBld <- tibble(region = gcamkorea.REGION, gcam.consumer = A44.gcam_consumer_en$gcam.consumer)
    L244.DeleteSupplysector_koreaBld <- tibble(region = gcamkorea.REGION, supplysector = A44.sector_en$supplysector)

    # L244.SubregionalShares_gcamKorea: subregional population and income shares (not currently used)
    L244.SubregionalShares_gcamKorea <- write_to_all_states(A44.gcam_consumer, c("region", "gcam.consumer"), region_states=gcamkorea.STATES) %>%
      mutate(pop.year.fillout = min(MODEL_BASE_YEARS),
             inc.year.fillout = min(MODEL_BASE_YEARS),
             subregional.population.share = 1,
             subregional.income.share = 1)

    # L244.PriceExp_IntGains_gcamKorea: price exponent on floorspace and naming of internal gains trial markets
    L244.PriceExp_IntGains_gcamKorea <- write_to_all_states(A44.gcam_consumer, LEVEL2_DATA_NAMES[["PriceExp_IntGains"]], region_states=gcamkorea.STATES)

    # L244.Floorspace_gcamKorea: base year floorspace
    # Keep all historical years for now - these are needed in calculating satiation adders later on

    # Residential floorspace
    L244.Floorspace_resid <- L144.flsp_bm2_korea_resid %>%
      rename(base.building.size = value,
             region = state,
             gcam.consumer = sector) %>%
      mutate(base.building.size = round(base.building.size, energy.DIGITS_FLOORSPACE)) %>%
      left_join_error_no_match(A44.gcam_consumer, by = "gcam.consumer") %>%
      select(LEVEL2_DATA_NAMES[["Floorspace"]])

    # Commercial floorspace
    L244.Floorspace_comm <- L144.flsp_bm2_korea_comm %>%
      rename(base.building.size = value,
             region = state,
             gcam.consumer = sector) %>%
      mutate(base.building.size = round(base.building.size, energy.DIGITS_FLOORSPACE)) %>%
      left_join_error_no_match(A44.gcam_consumer, by = "gcam.consumer") %>%
      select(LEVEL2_DATA_NAMES[["Floorspace"]])

    L244.Floorspace_full <- bind_rows(L244.Floorspace_resid, L244.Floorspace_comm)

    # Final output only has base years
    L244.Floorspace_gcamKorea <- filter(L244.Floorspace_full, year %in% MODEL_BASE_YEARS)

    # L244.DemandFunction_serv_gcamKorea and L244.DemandFunction_flsp_gcamKorea: demand function types
    L244.DemandFunction_serv_gcamKorea <- write_to_all_states(A44.demandFn_serv, LEVEL2_DATA_NAMES[["DemandFunction_serv"]], region_states=gcamkorea.STATES)
    L244.DemandFunction_flsp_gcamKorea <- write_to_all_states(A44.demandFn_flsp, LEVEL2_DATA_NAMES[["DemandFunction_flsp"]], region_states=gcamkorea.STATES)

    # L244.Satiation_flsp_gcamKorea: Satiation levels assumed for floorspace
    L244.Satiation_flsp_gcamKorea <- A44.satiation_flsp_korea %>%
      gather(gcam.consumer, value, resid, comm) %>%
      rename(region = state) %>%
      # Need to make sure that the satiation level is greater than the floorspace in the final base year
      left_join_error_no_match(L244.Floorspace_gcamKorea %>%
                                 filter(year == max(MODEL_BASE_YEARS)), by = c("region", "gcam.consumer")) %>%
      left_join_error_no_match(L100.Pop_thous_korea %>% rename(pop = value), by = c("region" = "state", "year")) %>%
      mutate(year = as.integer(year),
             # value.y = population
             pcflsp_mm2cap = base.building.size / pop,
             # Satiation level = must be greater than the observed value in the final calibration year, so if observed value is
             # greater than calculated, multiply observed by 1.001
             satiation.level = round(pmax(value * CONV_THOUS_BIL, pcflsp_mm2cap * 1.001), energy.DIGITS_SATIATION_ADDER)) %>%
      left_join_error_no_match(A44.gcam_consumer, by = c("gcam.consumer", "nodeInput", "building.node.input")) %>%
      select(LEVEL2_DATA_NAMES[["BldNodes"]], "satiation.level")

    #write.csv(L244.Satiation_flsp_gcamKorea,"./ouputs/Satiation_flsp_gcamKorea.csv")

    # L244.SatiationAdder_gcamKorea: Satiation adders in floorspace demand function
    # Required for shaping the future floorspace growth trajectories in each region
    # Match in the per-capita GDP, total floorspace, and population (for calculating per-capita floorspace)

    # We will filter GDP to energy.SATIATION_YEAR, but this may be greater than the historical years present
    # under timeshift conditions. So we adjust energy.SATIATION_YEAR
    energy.SATIATION_YEAR <- min(max(MODEL_BASE_YEARS), energy.SATIATION_YEAR)

    # calc median satiation
    # KOREA_MID_SATIATION <- L244.Satiation_flsp_gcamKorea %>%
    #   group_by(gcam.consumer) %>%
    #   mutate(mid.satiation = median(satiation.level)*1000000) %>%
    #   ungroup()
    # replace energy.GDP_MID_SATIATION to KOREA_MID_SATIATION$mid.satiation
    # energy.GDP_MID_SATIATION is a fixed value, so it dosen't match the korean value

    # change energy.GDP_MID_SATIATION to 50(assumption)
    L244.SatiationAdder_gcamKorea <- L244.Satiation_flsp_gcamKorea %>%
      # Add per capita GDP
      left_join_error_no_match(L100.pcGDP_thous90usd_korea %>%
                                 filter(year == energy.SATIATION_YEAR), by = c("region" = "state")) %>%
      rename(pcGDP = value) %>%
      # Add floorspace
      left_join_error_no_match(L244.Floorspace_full, by = c("region", "gcam.consumer", "year", "nodeInput", "building.node.input")) %>%
      # Add population
      left_join_error_no_match(L100.Pop_thous_korea, by = c("region" = "state", "year")) %>%
      rename(pop = value) %>%
      # Calculate per capita floorspace
      mutate(pcFlsp_mm2 = base.building.size / pop,
             # Calculate the satiation adders
             satiation.adder = round(satiation.level - (
               exp(log(2) * pcGDP / energy.GDP_MID_SATIATION) * (satiation.level - pcFlsp_mm2)),
               energy.DIGITS_SATIATION_ADDER),
             # The satiation adder (million square meters of floorspace per person) needs to be less than the per-capita demand in the final calibration year
             satiation.adder = if_else(satiation.adder > pcFlsp_mm2, pcFlsp_mm2 * 0.999, satiation.adder)) %>%
      select(LEVEL2_DATA_NAMES[["SatiationAdder"]])

    # Heating and cooling degree days (thermal services only)
    # First, separate the thermal from the generic services. Generic services will be assumed to produce
    # internal gain energy, so anything in the internal gains assumptions table will be assumed generic
    generic_services <- unique(A44.globaltech_intgains$supplysector)
    thermal_services <- setdiff(unique(A44.sector$supplysector), generic_services)

    # L244.HDDCDD: Heating and cooling degree days by scenario
    L244.HDDCDD_scen_state <- L143.HDDCDD_scen_korea %>%
      rename(region = state, degree.days = value)

    # Let's make a climate normal (historical average) for each region, using a selected interval of years
    # Don't want to just set one year, because we want average values for all regions
    L244.HDDCDD_normal_state <- L244.HDDCDD_scen_state %>%
      filter(year %in% seq(1981, 2000)) %>%
      group_by(region, variable) %>%
      summarise(degree.days = mean(degree.days)) %>%
      ungroup()

    # Subset the heating and cooling services, separately
    heating_services <- thermal_services[grepl("heating", thermal_services)]
    cooling_services <- thermal_services[grepl("cooling", thermal_services)]

    L244.HDDCDD_A2_GFDL_korea <- tidyr::crossing(region = gcamkorea.STATES, thermal.building.service.input = thermal_services) %>%
      # Add in gcam.consumer
      left_join_error_no_match(calibrated_techs_bld_korea %>%
                                 select(service, gcam.consumer = sector) %>%
                                 distinct(), by = c("thermal.building.service.input" = "service")) %>%
      # Add in nodeInput and building.node.input
      left_join_error_no_match(A44.gcam_consumer, by = "gcam.consumer") %>%
      select(LEVEL2_DATA_NAMES[["BldNodes"]], thermal.building.service.input) %>%
      # Add in model years
      repeat_add_columns(tibble(year = MODEL_YEARS)) %>%
      # Add HDD/CDD so that we can join with L244.HDDCDD_scen_state, remove at end
      mutate(variable = if_else(thermal.building.service.input %in% heating_services, "HDD", "CDD")) %>%
      # Add in degree days
      left_join_error_no_match(L244.HDDCDD_scen_state, by = c("region", "variable", "year")) %>%
      mutate(degree.days = round(degree.days, energy.DIGITS_HDDCDD)) %>%
      # Don't need to keep Scen and GCM identifiers because only one is used
      select(-Scen, -GCM, -variable)

    # L244.ShellConductance_bld_gcamKorea: Shell conductance (inverse of shell efficiency)
    # L144.shell_eff_R_Y is used instead of gcam-korea/A44.bld_shell_conductance
    L244.ShellConductance_bld_gcamKorea <- L144.shell_eff_R_Y %>%
      filter(GCAM_region_ID == gcamkorea.RegionNum, year %in% MODEL_YEARS) %>%
      rename(gcam.consumer = supplysector) %>%
      # Repeat for all states
      write_to_all_states(names = c(names(.), "region"), region_states=gcamkorea.STATES) %>%
      # Add nodeInput and building.node.input
      left_join_error_no_match(A44.gcam_consumer, by = "gcam.consumer") %>%
      mutate(floor.to.surface.ratio = energy.FLOOR_TO_SURFACE_RATIO,
             shell.year = year) %>%
      # Rename columns
      rename(shell.conductance = value) %>%
      select(LEVEL2_DATA_NAMES[["ShellConductance"]])

    # The remainder of the building-level parameters require information about the output of each service, which we do not have yet

    # L244.Supplysector_bld: Supplysector info for buildings
    L244.Supplysector_bld_gcamKorea <- write_to_all_states(A44.sector, c(LEVEL2_DATA_NAMES[["Supplysector"]], LOGIT_TYPE_COLNAME), region_states=gcamkorea.STATES)

    # L244.FinalEnergyKeyword_bld: Supply sector keywords for detailed building sector
    L244.FinalEnergyKeyword_bld_gcamKorea <- write_to_all_states(A44.sector, LEVEL2_DATA_NAMES[["FinalEnergyKeyword"]], region_states=gcamkorea.STATES)

    # L244.SubsectorLogit_bld: Subsector logit exponents of building sector
    L244.SubsectorLogit_bld_gcamKorea <- write_to_all_states(A44.subsector_logit, c(LEVEL2_DATA_NAMES[["SubsectorLogit"]], LOGIT_TYPE_COLNAME), region_states=gcamkorea.STATES)

    # L244.SubsectorShrwt_bld and L244.SubsectorShrwtFllt_bld: Subsector shareweights of building sector
    if(any(!is.na(A44.subsector_shrwt$year))) {
      L244.SubsectorShrwt_bld_gcamKorea <- write_to_all_states(A44.subsector_shrwt %>%
                                                               filter(!is.na(year)), LEVEL2_DATA_NAMES[["SubsectorShrwt"]], region_states=gcamkorea.STATES)
    }
    if(any(!is.na(A44.subsector_shrwt$year.fillout))) {
      L244.SubsectorShrwtFllt_bld_gcamKorea <- write_to_all_states(A44.subsector_shrwt %>%
                                                                   filter(!is.na(year.fillout)), LEVEL2_DATA_NAMES[["SubsectorShrwtFllt"]], region_states=gcamkorea.STATES)
    }

    # L244.SubsectorInterp_bld and L244.SubsectorInterpTo_bld: Subsector shareweight interpolation of building sector
    if(any(is.na(A44.subsector_interp$to.value))) {
      L244.SubsectorInterp_bld_gcamKorea <- write_to_all_states(A44.subsector_interp %>%
                                                                filter(is.na(to.value)), LEVEL2_DATA_NAMES[["SubsectorInterp"]], region_states=gcamkorea.STATES)
    }
    if(any(!is.na(A44.subsector_interp$to.value))) {
      L244.SubsectorInterpTo_bld_gcamKorea <- write_to_all_states(A44.subsector_interp %>%
                                                                  filter(!is.na(to.value)), LEVEL2_DATA_NAMES[["SubsectorInterpTo"]], region_states=gcamkorea.STATES)
    }

    # L244.StubTech_bld_gcamKorea: Identification of stub technologies for buildings
    L244.StubTech_bld_gcamKorea <- A44.globaltech_eff %>%
      select(supplysector, subsector, technology) %>%
      distinct() %>%
      write_to_all_states(LEVEL2_DATA_NAMES[["Tech"]], region_states=gcamkorea.STATES) %>%
      rename(stub.technology = technology)

    # L244.GlobalTechEff_bld_korea: Assumed efficiencies (all years) of buildings technologies
    L244.end_use_eff <- A44.globaltech_eff %>%
      complete(nesting(supplysector, subsector, technology, minicam.energy.input), year = c(year, MODEL_YEARS)) %>%
      group_by(supplysector, subsector, technology, minicam.energy.input) %>%
      mutate(value = approx_fun(year, value)) %>%
      ungroup() %>%
      filter(year %in% MODEL_YEARS) %>%
      mutate(value = round(value, energy.DIGITS_CALOUTPUT)) %>%
      rename(efficiency = value)

    L244.GlobalTechEff_bld_korea <- L244.end_use_eff %>%
      rename(sector.name = supplysector,
             subsector.name = subsector) %>%
      select(LEVEL2_DATA_NAMES[["GlobalTechEff"]])

    # L244.StubTechMarket_bld_korea: Specify market names for fuel inputs to all technologies in each state
    L244.StubTechMarket_bld_korea <- L244.end_use_eff %>%
      mutate(market.name = gcamkorea.REGION) %>%
      rename(stub.technology = technology) %>%
      write_to_all_states(LEVEL2_DATA_NAMES[["StubTechMarket"]], region_states=gcamkorea.STATES) %>%
      # Electricity is consumed from state markets, so change market.name to states for electricity
      mutate(market.name = if_else(minicam.energy.input %in% gcamusa.ELECT_TD_SECTORS, region, market.name))

    # If true, then we change market.name for selected fuels to state markets, rather than USA
    if(gcamusa.USE_REGIONAL_FUEL_MARKETS) {
      L244.StubTechMarket_bld_korea <- L244.StubTechMarket_bld_korea %>%
        left_join_error_no_match(states_subregions, by = c("region" = "state")) %>%
        mutate(market.name = if_else(minicam.energy.input %in% gcamkorea.REGIONAL_FUEL_MARKETS,
                                     grid_region, market.name)) %>%
        select(LEVEL2_DATA_NAMES[["StubTechMarket"]])
    }

    # L244.StubTechCalInput_bld: Calibrated energy consumption by buildings technologies
    # Combine residential and commercial energy data
    L244.in_EJ_R_bld_serv_F_Yh <- bind_rows(L144.in_EJ_korea_resid, L144.in_EJ_korea_comm) %>%
      filter(year %in% MODEL_YEARS) %>%
      mutate(calibrated.value = round(value, energy.DIGITS_CALOUTPUT)) %>%
      rename(supplysector = service) %>%
      # Add subsector and energy.input
      left_join(calibrated_techs_bld_korea %>%
                  select(sector, supplysector, fuel, subsector, minicam.energy.input) %>%
                  distinct(), by = c("sector", "supplysector", "fuel")) %>%
      select(region = state, supplysector, subsector, minicam.energy.input, year, calibrated.value) %>%
      drop_na() #filter(!is.na(a))


    # Shares allocated to partitioned technologies need to be computed first using efficiencies
    # L244.globaltech_eff_prt <- A44.globaltech_eff %>%
    #   semi_join(A44.globaltech_eff_avg, by = c("supplysector", "subsector")) %>%
    #   filter(year == gcamkorea.EFFICIENCY_PARTITION_YEAR) %>%
    #   select(supplysector, subsector, technology, efficiency = value)

    # Calculate technology shares using efficiency values
    # L244.globaltech_shares <- A44.globaltech_eff_avg %>%
    #   # Adding specific technology efficiency to stock average efficiency
    #   left_join_error_no_match(L244.globaltech_eff_prt, by = c("supplysector", "subsector", "technology1" = "technology")) %>%
    #   rename(efficiency_tech1 = efficiency) %>%
    #   left_join_error_no_match(L244.globaltech_eff_prt, by = c("supplysector", "subsector", "technology2" = "technology")) %>%
    #   rename(efficiency_tech2 = efficiency) %>%
    #   # Calculate technology shares using stock average efficiency and individual technology efficiencies
    #   # Equation can be derived by solving following system of equations:
    #   # stockavg = efficiency_tech1 * share_tech1 + efficiency_tech2 * share_tech2
    #   # share_tech1 + share_tech2 = 1
    #   mutate(share_tech1 = (stockavg - efficiency_tech2) / (efficiency_tech1 - efficiency_tech2),
    #          share_tech2 = 1 - share_tech1) %>%
    #   # Keep only same names as A44.globaltech_shares and bind with A44.globaltech_shares
    #   select(names(A44.globaltech_shares)) %>%
    #   bind_rows(A44.globaltech_shares) %>%
    #   # Clunky, but we want only one technology and share value, currently have technology1, technology2, share1, share2
    #   gather(share_type, share, share_tech1, share_tech2)%>%
    #   gather(tech_type, technology, technology1, technology2) %>%
    #   # Filter for same technology and share number, then remove tech_type and share_type columns
    #   filter(substr(tech_type, nchar(tech_type), nchar(tech_type)) == substr(share_type, nchar(share_type), nchar(share_type))) %>%
    #   select(-tech_type, -share_type)


    L244.globaltech_shares <- A44.globaltech_shares %>%
      # Clunky, but we want only one technology and share value, currently have technology1, technology2, share1, share2
      gather(share_type, share, share_tech1, share_tech2)%>%
      gather(tech_type, technology, technology1, technology2) %>%
      # Filter for same technology and share number, then remove tech_type and share_type columns
      filter(substr(tech_type, nchar(tech_type), nchar(tech_type)) == substr(share_type, nchar(share_type), nchar(share_type))) %>%
      select(-tech_type, -share_type)

    # For calibration table, start with global tech efficiency table, repeat by states, and match in tech shares.
    L244.StubTechCalInput_bld_gcamKorea <- L244.GlobalTechEff_bld_korea %>%
      filter(year %in% MODEL_BASE_YEARS) %>%
      write_to_all_states(names = c(names(.), "region"), region_states=gcamkorea.STATES) %>%
      rename(supplysector = sector.name, subsector = subsector.name, stub.technology = technology) %>%
      # Using left_join because we don't have shares for all technologies, NAs will be set to 1
      left_join(L244.globaltech_shares, by = c("supplysector", "subsector", "stub.technology" = "technology")) %>%
      replace_na(list(share = 1)) %>%
      # Add energy by state/service/fuel
      left_join_error_no_match(L244.in_EJ_R_bld_serv_F_Yh, by = c("region", "supplysector", "subsector", "minicam.energy.input", "year")) %>%
      # calibrated.value = energy * share
      mutate(calibrated.value = round(share * calibrated.value, energy.DIGITS_CALOUTPUT),
             share.weight.year = year,
             calOutputValue = calibrated.value) %>%
      # Set subsector and technology shareweights
      set_subsector_shrwt() %>%
      mutate(tech.share.weight =  if_else(calibrated.value > 0, 1, 0)) %>%
      select(LEVEL2_DATA_NAMES[["StubTechCalInput"]])

    # L244.GlobalTechShrwt_bld_gcamKorea: Default shareweights for global building technologies
    L244.GlobalTechShrwt_bld_gcamKorea <- A44.globaltech_shrwt %>%
      gather_years(value_col = "share.weight") %>%
      complete(nesting(supplysector, subsector, technology), year = c(year, MODEL_YEARS)) %>%
      group_by(supplysector, subsector, technology) %>%
      mutate(share.weight = approx_fun(year, share.weight)) %>%
      ungroup() %>%
      filter(year %in% MODEL_YEARS) %>%
      rename(sector.name = supplysector,
             subsector.name = subsector) %>%
      select(LEVEL2_DATA_NAMES[["GlobalTechYr"]], share.weight)

    # Korea does not use new tech.
    # The block is for new tech such as appliances
    # L244.GlobalTechInterpTo_bld_korea: Technology shareweight interpolation (selected techs only)
    #L244.GlobalTechInterpTo_bld_korea <- A44.globaltech_interp %>%
    #  mutate(sector.name = supplysector,
    #         subsector.name = subsector) %>%
    #  select(LEVEL2_DATA_NAMES[["GlobalTechInterpTo"]])

    # L244.GlobalTechCost_bld: Non-fuel costs of global building technologies
    L244.GlobalTechCost_bld_gcamKorea <- A44.globaltech_cost %>%
      gather_years(value_col = "input.cost") %>%
      complete(nesting(supplysector, subsector, technology), year = c(year, MODEL_YEARS)) %>%
      group_by(supplysector, subsector, technology) %>%
      mutate(input.cost = approx_fun(year, input.cost)) %>%
      ungroup() %>%
      filter(year %in% MODEL_YEARS) %>%
      rename(sector.name = supplysector, subsector.name = subsector) %>%
      mutate(minicam.non.energy.input = "non-energy") %>%
      select(LEVEL2_DATA_NAMES[["GlobalTechCost"]])

    # L244.GlobalTechSCurve_bld_korea: Retirement rates for building technologies
    L244.GlobalTechSCurve_bld_korea <- L244.GlobalTechCost_bld_gcamKorea %>%
      filter(year %in% c(max(MODEL_BASE_YEARS), MODEL_FUTURE_YEARS),
             sector.name %in% A44.globaltech_retirement$supplysector) %>%
      # Add lifetimes and steepness
      left_join_error_no_match(A44.globaltech_retirement, by = c("sector.name" = "supplysector")) %>%
      # Set steepness/halflife values to stock for base years, new for future years
      mutate(steepness = if_else(year == max(MODEL_BASE_YEARS), steepness_stock, steepness_new),
             half.life = if_else(year == max(MODEL_BASE_YEARS), half_life_stock, half_life_new)) %>%
      select(LEVEL2_DATA_NAMES[["GlobalTechSCurve"]])

    # L244.GlobalTechIntGainOutputRatio_korea: Output ratios of internal gain energy from non-thermal building services
    calibrated_techs_bld_korea_consumer <- calibrated_techs_bld_korea %>%
      select(gcam.consumer = sector, supplysector) %>%
      distinct()

    L244.GlobalTechIntGainOutputRatio_korea <- A44.globaltech_intgains %>%
      repeat_add_columns(tibble(year = MODEL_YEARS))%>%
      # Add gcam.consumer (sector)
      left_join_error_no_match(calibrated_techs_bld_korea_consumer, by = "supplysector") %>%
      rename(sector.name = supplysector,
             subsector.name = subsector) %>%
      # Add internal.gains.market.name
      left_join_error_no_match(A44.gcam_consumer, by = "gcam.consumer") %>%
      # Add efficiency
      left_join_error_no_match(L244.GlobalTechEff_bld_korea,
                               by = c("sector.name", "subsector.name", "technology", "year")) %>%
      mutate(internal.gains.output.ratio = round(input.ratio / efficiency, energy.DIGITS_EFFICIENCY)) %>%
      select(LEVEL2_DATA_NAMES[["GlobalTechYr"]], internal.gains.output.ratio, internal.gains.market.name)

    # L244.GenericBaseService and L244.ThermalBaseService: Base year output of buildings services (per unit floorspace)
    # Base-service: Multiply energy consumption by efficiency for each technology, and aggregate by service
    L244.base_service <- L244.StubTechCalInput_bld_gcamKorea %>%
      # Add in efficiency by technology
      left_join_error_no_match(L244.GlobalTechEff_bld_korea,
                               by = c("supplysector" = "sector.name", "subsector" = "subsector.name",
                                      "stub.technology" = "technology", "year", "minicam.energy.input")) %>%
      # Calculate base.service = calibrated.value(energy) * efficiency
      mutate(base.service = round(calibrated.value * efficiency, energy.DIGITS_CALOUTPUT)) %>%
      # Aggregate base service by service (supplysector)
      group_by(region, supplysector, year) %>%
      summarise(base.service = sum(base.service)) %>%
      ungroup() %>%
      # Add gcam.consumer (sector)
      left_join_error_no_match(calibrated_techs_bld_korea_consumer, by = "supplysector") %>%
      # Add nodeInput and building.node.input
      left_join_error_no_match(A44.gcam_consumer, by = "gcam.consumer")

    # Separate thermal and generic services into separate tables with different ID strings
    L244.GenericBaseService_gcamKorea <- L244.base_service %>%
      filter(supplysector %in% generic_services) %>%
      rename(building.service.input = supplysector) %>%
      select(LEVEL2_DATA_NAMES[["GenericBaseService"]])

    L244.ThermalBaseService_gcamKorea <- L244.base_service %>%
      filter(supplysector %in% thermal_services) %>%
      rename(thermal.building.service.input = supplysector) %>%
      select(LEVEL2_DATA_NAMES[["ThermalBaseService"]])

    # L244.GenericServiceSatiation_gcamKorea: Satiation levels assumed for non-thermal building services
    # Just multiply the base-service by an exogenous multiplier
    L244.GenericServiceSatiation_gcamKorea <- L244.GenericBaseService_gcamKorea %>%
      filter(year == max(MODEL_BASE_YEARS)) %>%
      # Add floorspace
      left_join_error_no_match(L244.Floorspace_gcamKorea, by = c(LEVEL2_DATA_NAMES[["BldNodes"]], "year")) %>%
      # Add multiplier
      left_join_error_no_match(A44.demand_satiation_mult, by = c("building.service.input" = "supplysector")) %>%
      # Satiation level = service per floorspace * multiplier
      mutate(satiation.level = round(base.service / base.building.size * multiplier, energy.DIGITS_COEFFICIENT)) %>%
      select(LEVEL2_DATA_NAMES[["GenericServiceSatiation"]])


    # L244.ThermalServiceSatiation: Satiation levels assumed for thermal building services
    L244.ThermalServiceSatiation_gcamKorea <- L244.ThermalBaseService_gcamKorea %>%
      filter(year == max(MODEL_BASE_YEARS)) %>%
      # Add floorspace
      left_join_error_no_match(L244.Floorspace_gcamKorea, by = c(LEVEL2_DATA_NAMES[["BldNodes"]], "year")) %>%
      # Add multiplier
      left_join_error_no_match(A44.demand_satiation_mult, by = c("thermal.building.service.input" = "supplysector")) %>%
      # Satiation level = service per floorspace * multiplier
      mutate(satiation.level = round(base.service / base.building.size * multiplier, energy.DIGITS_COEFFICIENT)) %>%
      select(LEVEL2_DATA_NAMES[["ThermalServiceSatiation"]])

    # L244.Intgains_scalar: Scalers relating internal gain energy to increased/reduced cooling/heating demands
    variable <- c("HDD", "CDD")
    scalar <- c(gcamkorea.INTERNAL_GAINS_SCALAR_H, gcamkorea.INTERNAL_GAINS_SCALAR_C)
    DDnorm <- c(gcamkorea.BASE_HDD, gcamkorea.BASE_CDD)
    kor.base.scalar <- tibble(variable, scalar, DDnorm)
    threshold_HDD <- 500

    L244.Intgains_scalar_gcamKorea <- L244.ThermalServiceSatiation_gcamKorea %>%
      # Assign HDD or CDD
      mutate(variable = if_else(thermal.building.service.input %in% heating_services, "HDD", "CDD")) %>%
      # Add DDnorm & scalar
      left_join_error_no_match(kor.base.scalar, by = "variable") %>%
      # Add degree days
      left_join_error_no_match(L244.HDDCDD_normal_state, by = c("region", "variable")) %>%
      mutate(internal.gains.scalar = round(scalar * degree.days / DDnorm, energy.DIGITS_HDDCDD),
             # Prevent very warm places from having negative heating demands, using exogenous threshold
             internal.gains.scalar = if_else(variable == "HDD" & degree.days < threshold_HDD, 0, internal.gains.scalar)) %>%
      select(LEVEL2_DATA_NAMES[["Intgains_scalar"]])



    # ===================================================
    # Produce outputs
    L244.DeleteConsumer_koreaBld %>%
      add_title("Deletes building sector in USA region to rewrite with gcam-korea data") %>%
      add_units("NA") %>%
      add_comments("gcam.consumer column from A44.gcam_consumer") %>%
      add_legacy_name("L244.DeleteConsumer_koreaBld") %>%
      add_precursors("energy/A44.gcam_consumer") ->
      L244.DeleteConsumer_koreaBld

    L244.DeleteSupplysector_koreaBld %>%
      add_title("Deletes building sector in USA region to rewrite with gcam-korea data") %>%
      add_units("NA") %>%
      add_comments("supplysector column from A44.sector") %>%
      add_legacy_name("L244.DeleteSupplysector_koreaBld") %>%
      add_precursors("energy/A44.sector") ->
      L244.DeleteSupplysector_koreaBld

    L244.SubregionalShares_gcamKorea %>%
      add_title("Subregional population and income shares") %>%
      add_units("Unitless") %>%
      add_comments("Default values used for years and shares") %>%
      add_legacy_name("L244.SubregionalShares") %>%
      add_precursors("gcam-korea/A44.gcam_consumer") ->
      L244.SubregionalShares_gcamKorea

    L244.PriceExp_IntGains_gcamKorea %>%
      add_title("Price exponent on floorspace and naming of internal gains trial markets") %>%
      add_units("Unitless") %>%
      add_comments("A44.gcam_consumer written to all states") %>%
      add_legacy_name("L244.PriceExp_IntGains") %>%
      add_precursors("gcam-korea/A44.gcam_consumer") ->
      L244.PriceExp_IntGains_gcamKorea

    L244.Floorspace_gcamKorea %>%
      add_title("base year floorspace") %>%
      add_units("billion m2") %>%
      add_comments("Data from L144.flsp_bm2_korea_resid and L144.flsp_bm2_korea_comm") %>%
      add_legacy_name("L244.Floorspace") %>%
      add_precursors("L144.flsp_bm2_korea_resid", "L144.flsp_bm2_korea_comm", "gcam-korea/A44.gcam_consumer") ->
      L244.Floorspace_gcamKorea

    L244.DemandFunction_serv_gcamKorea %>%
      add_title("Service demand function types") %>%
      add_units("NA") %>%
      add_comments("A44.demandFn_serv written to all states") %>%
      add_legacy_name("L244.DemandFunction_serv") %>%
      add_precursors("gcam-korea/A44.demandFn_serv") ->
      L244.DemandFunction_serv_gcamKorea

    L244.DemandFunction_flsp_gcamKorea %>%
      add_title("Floorspace demand function types") %>%
      add_units("NA") %>%
      add_comments("A44.demandFn_flsp written to all states") %>%
      add_legacy_name("L244.DemandFunction_flsp") %>%
      add_precursors("gcam-korea/A44.demandFn_flsp") ->
      L244.DemandFunction_flsp_gcamKorea

    L244.Satiation_flsp_gcamKorea %>%
      add_title("Satiation levels assumed for floorspace") %>%
      add_units("million m2 / person") %>%
      add_comments("Values from A44.satiation_flsp_korea or L244.Floorspace_gcamKorea/L100.Pop_thous_korea") %>%
      add_comments("Whichever is larger") %>%
      add_legacy_name("L244.Satiation_flsp") %>%
      add_precursors("gcam-korea/A44.satiation_flsp_korea", "gcam-korea/A44.gcam_consumer", "L100.Pop_thous_korea",
                     "L144.flsp_bm2_korea_resid", "L144.flsp_bm2_korea_comm") ->
      L244.Satiation_flsp_gcamKorea

    L244.SatiationAdder_gcamKorea %>%
      add_title("Satiation adders in floorspace demand function") %>%
      add_units("million m2 / person") %>%
      add_comments("Calculated with function dependent on satiation leve, per capita floorspace, and per capita GDP") %>%
      add_legacy_name("L244.SatiationAdder") %>%
      add_precursors("gcam-korea/A44.satiation_flsp_korea", "gcam-korea/A44.gcam_consumer", "L100.Pop_thous_korea",
                     "L144.flsp_bm2_korea_resid", "L144.flsp_bm2_korea_comm", "L100.pcGDP_thous90usd_korea") ->
      L244.SatiationAdder_gcamKorea

    L244.HDDCDD_A2_GFDL_korea %>%
      add_title("Heating and Cooling Degree Days by State for GFDL A2") %>%
      add_units("Fahrenheit Degree Days") %>%
      add_comments("L143.HDDCDD_scen_korea assigned to GCAM subsectors") %>%
      add_legacy_name("L244.HDDCDD_A2_GFDL_korea") %>%
      add_precursors("L143.HDDCDD_scen_korea", "gcam-korea/A44.sector",
                     "gcam-korea/calibrated_techs_bld_korea", "gcam-korea/A44.gcam_consumer") ->
      L244.HDDCDD_A2_GFDL_korea

    L244.ThermalBaseService_gcamKorea %>%
      add_title("Base year output of thermal buildings services") %>%
      add_units("EJ per unit floorspace") %>%
      add_comments("Multiplied energy consumption by efficiency for each technology, then aggregated by service") %>%
      add_legacy_name("L244.ThermalBaseService") %>%
      add_precursors("L144.in_EJ_korea_resid", "L144.in_EJ_korea_comm", "gcam-korea/calibrated_techs_bld_korea",
                     "gcam-korea/A44.globaltech_eff", "gcam-korea/A44.globaltech_shares",
                     "gcam-korea/A44.gcam_consumer") ->
      L244.ThermalBaseService_gcamKorea

    L244.GenericBaseService_gcamKorea %>%
      add_title("Base year output of generic buildings services") %>%
      add_units("EJ per unit floorspace") %>%
      add_comments("Multiplied energy consumption by efficiency for each technology, then aggregated by service") %>%
      add_legacy_name("L244.GenericBaseService") %>%
      add_precursors("L144.in_EJ_korea_resid", "L144.in_EJ_korea_comm", "gcam-korea/calibrated_techs_bld_korea",
                     "gcam-korea/A44.globaltech_eff", "gcam-korea/A44.globaltech_shares",
                     "gcam-korea/A44.gcam_consumer") ->
      L244.GenericBaseService_gcamKorea

    L244.GenericServiceSatiation_gcamKorea %>%
      add_title("Satiation levels assumed for non-thermal building services") %>%
      add_units("EJ/billion m2 floorspace") %>%
      add_comments("Satiation level = base service / floorspace * exogenous multiplier") %>%
      add_legacy_name("L244.GenericServiceSatiation") %>%
      add_precursors("L144.in_EJ_korea_resid", "L144.in_EJ_korea_comm", "gcam-korea/calibrated_techs_bld_korea",
                     "gcam-korea/A44.globaltech_eff", "gcam-korea/A44.globaltech_shares",
                     "gcam-korea/A44.gcam_consumer", "L144.flsp_bm2_korea_resid", "L144.flsp_bm2_korea_comm",
                     "gcam-korea/A44.demand_satiation_mult") ->
      L244.GenericServiceSatiation_gcamKorea

    L244.ThermalServiceSatiation_gcamKorea %>%
      add_title("Satiation levels assumed for thermal building services") %>%
      add_units("EJ/billion m2 floorspace") %>%
      add_comments("Satiation level = base service / floorspace * exogenous multiplier") %>%
      add_legacy_name("L244.ThermalServiceSatiation") %>%
      add_precursors("L144.in_EJ_korea_resid", "L144.in_EJ_korea_comm", "gcam-korea/calibrated_techs_bld_korea",
                     "gcam-korea/A44.globaltech_eff",  "gcam-korea/A44.globaltech_shares",
                     "gcam-korea/A44.gcam_consumer", "L144.flsp_bm2_korea_resid", "L144.flsp_bm2_korea_comm",
                     "gcam-korea/A44.demand_satiation_mult") ->
      L244.ThermalServiceSatiation_gcamKorea

    L244.Intgains_scalar_gcamKorea %>%
      add_title("Scalers relating internal gain energy to increased/reduced cooling/heating demands") %>%
      add_units("Unitless") %>%
      add_comments("internal.gains.scalar = exogenous scalar * degree.days / exogenous degree day norm") %>%
      add_legacy_name("L244.Intgains_scalar") %>%
      add_precursors("L144.in_EJ_korea_resid", "L144.in_EJ_korea_comm", "gcam-korea/calibrated_techs_bld_korea",
                     "gcam-korea/A44.globaltech_eff", "gcam-korea/A44.globaltech_shares",
                     "gcam-korea/A44.gcam_consumer", "L144.flsp_bm2_korea_resid", "L144.flsp_bm2_korea_comm",
                     "gcam-korea/A44.demand_satiation_mult", "L143.HDDCDD_scen_korea") ->
      L244.Intgains_scalar_gcamKorea

    L244.ShellConductance_bld_gcamKorea %>%
      add_title("Shell conductance (inverse of shell efficiency) by state") %>%
      add_units("Unitless") %>%
      add_comments("values from L144.shell_eff_R_Y") %>%
      add_legacy_name("L244.ShellConductance_bld") %>%
      add_precursors("L144.shell_eff_R_Y", "gcam-korea/A44.gcam_consumer") ->
      L244.ShellConductance_bld_gcamKorea

    L244.Supplysector_bld_gcamKorea %>%
      add_title("Supplysector info for buildings") %>%
      add_units("Unitless") %>%
      add_comments("A44.sector written to all states") %>%
      add_legacy_name("L244.Supplysector_bld") %>%
      add_precursors("gcam-korea/A44.sector") ->
      L244.Supplysector_bld_gcamKorea

    L244.FinalEnergyKeyword_bld_gcamKorea %>%
      add_title("Supply sector keywords for detailed building sector") %>%
      add_units("NA") %>%
      add_comments("A44.sector written to all states") %>%
      add_legacy_name("L244.FinalEnergyKeyword_bld") %>%
      add_precursors("gcam-korea/A44.sector") ->
      L244.FinalEnergyKeyword_bld_gcamKorea

    if(exists("L244.SubsectorShrwt_bld")) {
      L244.SubsectorShrwt_bld_gcamKorea %>%
        add_title("Subsector shareweights of building sector") %>%
        add_units("Unitless") %>%
        add_comments("A44.subsector_shrwt written to all regions") %>%
        add_legacy_name("L244.SubsectorShrwt_bld") %>%
        add_precursors("gcam-korea/A44.subsector_shrwt") ->
        L244.SubsectorShrwt_bld_gcamKorea
    } else {
      missing_data() %>%
        add_legacy_name("L244.SubsectorShrwt_bld") ->
        L244.SubsectorShrwt_bld_gcamKorea
    }

    if(exists("L244.SubsectorShrwtFllt_bld_gcamKorea")) {
      L244.SubsectorShrwtFllt_bld_gcamKorea %>%
        add_title("Subsector shareweights of building sector") %>%
        add_units("Unitless") %>%
        add_comments("A44.subsector_shrwt written to all regions") %>%
        add_legacy_name("L244.SubsectorShrwtFllt_bld") %>%
        add_precursors("gcam-korea/A44.subsector_shrwt") ->
        L244.SubsectorShrwtFllt_bld_gcamKorea
    } else {
      missing_data() %>%
        add_legacy_name("L244.SubsectorShrwtFllt_bld") ->
        L244.SubsectorShrwtFllt_bld_gcamKorea
    }


    if(exists("L244.SubsectorInterp_bld_gcamKorea")) {
      L244.SubsectorInterp_bld_gcamKorea %>%
        add_title("Subsector shareweight interpolation of building sector") %>%
        add_units("NA") %>%
        add_comments("A44.subsector_interp written to all regions") %>%
        add_legacy_name("L244.SubsectorInterp_bld") %>%
        add_precursors("gcam-korea/A44.subsector_interp") ->
        L244.SubsectorInterp_bld_gcamKorea
    } else {
      missing_data() %>%
        add_legacy_name("L244.SubsectorInterp_bld") ->
        L244.SubsectorInterp_bld_gcamKorea
    }

    if(exists("L244.SubsectorInterpTo_bld_gcamKorea")) {
      L244.SubsectorInterpTo_bld_gcamKorea %>%
        add_title("Subsector shareweight interpolation of building sector") %>%
        add_units("NA") %>%
        add_comments("A44.subsector_interp written to all regions") %>%
        add_legacy_name("L244.SubsectorInterpTo_bld") %>%
        add_precursors("gcam-korea/A44.subsector_interp") ->
        L244.SubsectorInterpTo_bld_gcamKorea
    } else {
      missing_data() %>%
        add_legacy_name("L244.SubsectorInterpTo_bld") ->
        L244.SubsectorInterpTo_bld_gcamKorea
    }

    L244.SubsectorLogit_bld_gcamKorea %>%
      add_title("Subsector logit exponents of building sector") %>%
      add_units("Unitless") %>%
      add_comments("A44.subsector_logit written to all states") %>%
      add_legacy_name("L244.SubsectorLogit_bld") %>%
      add_precursors("gcam-korea/A44.subsector_logit") ->
      L244.SubsectorLogit_bld_gcamKorea

    L244.StubTech_bld_gcamKorea %>%
      add_title("Identification of stub technologies for buildings") %>%
      add_units("NA") %>%
      add_comments("A44.globaltech_eff written to all states") %>%
      add_legacy_name("L244.StubTech_bld") %>%
      add_precursors("gcam-korea/A44.globaltech_eff") ->
      L244.StubTech_bld_gcamKorea

    L244.StubTechCalInput_bld_gcamKorea %>%
      add_title("Calibrated energy consumption and share weights by buildings technologies") %>%
      add_units("calibrated.value: EJ/yr; shareweights: Unitless") %>%
      add_comments("Energy consumption multiplied by shares to get calibrated energy") %>%
      add_comments("Shares calculated using efficiency averages") %>%
      add_legacy_name("L244.StubTechCalInput_bld") %>%
      add_precursors("L144.in_EJ_korea_resid", "L144.in_EJ_korea_comm", "gcam-korea/calibrated_techs_bld_korea",
                     "gcam-korea/A44.globaltech_eff", "gcam-korea/A44.globaltech_shares") ->
      L244.StubTechCalInput_bld_gcamKorea

    L244.StubTechMarket_bld_korea %>%
      add_title("market names for fuel inputs to all technologies in each state") %>%
      add_units("NA") %>%
      add_comments("Categories from A44.globaltech_eff written to all states") %>%
      add_comments("Market set to states for electricity") %>%
      add_legacy_name("L244.StubTechMarket_bld_korea") %>%
      add_precursors("gcam-korea/states_subregions") ->
      L244.StubTechMarket_bld_korea

    L244.GlobalTechIntGainOutputRatio_korea %>%
      add_title("Output ratios of internal gain energy from non-thermal building services") %>%
      add_units("Unitless") %>%
      add_comments("internal.gains.output.ratio = input.ratio from A44.globaltech_intgains divided by efficiency from L244.GlobalTechEff_bld_korea") %>%
      add_legacy_name("L244.GlobalTechIntGainOutputRatio_korea") %>%
      add_precursors("gcam-korea/A44.globaltech_intgains", "gcam-korea/calibrated_techs_bld_korea",
                     "gcam-korea/A44.gcam_consumer", "gcam-korea/A44.globaltech_eff") ->
      L244.GlobalTechIntGainOutputRatio_korea

    #L244.GlobalTechInterpTo_bld_korea %>%
    #  add_title("Technology shareweight interpolation") %>%
    #  add_units("NA") %>%
    #  add_comments("Directly from A44.globaltech_interp") %>%
    #  add_legacy_name("L244.GlobalTechInterpTo_bld_korea") %>%
    #  add_precursors("gcam-korea/A44.globaltech_interp") ->
    #  L244.GlobalTechInterpTo_bld_korea

    L244.GlobalTechEff_bld_korea %>%
      add_title("Assumed efficiencies (all years) of buildings technologies") %>%
      add_units("Unitless") %>%
      add_comments("Values from A44.globaltech_eff") %>%
      add_legacy_name("L244.GlobalTechEff_bld_korea") %>%
      add_precursors("gcam-korea/A44.globaltech_eff") ->
      L244.GlobalTechEff_bld_korea

    L244.GlobalTechShrwt_bld_gcamKorea %>%
      add_title("Default shareweights for global building technologies") %>%
      add_units("Unitless") %>%
      add_comments("Values interpolated to model years from A44.globaltech_shrwt") %>%
      add_legacy_name("L244.GlobalTechShrwt_bld") %>%
      add_precursors("gcam-korea/A44.globaltech_shrwt") ->
      L244.GlobalTechShrwt_bld_gcamKorea

    L244.GlobalTechCost_bld_gcamKorea %>%
      add_title("Non-fuel costs of global building technologies") %>%
      add_units("1975$/GJ") %>%
      add_comments("Values from A44.globaltech_cost") %>%
      add_legacy_name("L244.GlobalTechCost_bld") %>%
      add_precursors("gcam-korea/A44.globaltech_cost") ->
      L244.GlobalTechCost_bld_gcamKorea

    L244.GlobalTechSCurve_bld_korea %>%
      add_title("Retirement rates for building technologies") %>%
      add_units("lifetime/half.life = years") %>%
      add_comments("Lifetime, steepness, and half.life from A44.globaltech_retirement") %>%
      add_legacy_name("L244.GlobalTechSCurve_bld_korea") %>%
      add_precursors("gcam-korea/A44.globaltech_cost", "gcam-korea/A44.globaltech_retirement") ->
      L244.GlobalTechSCurve_bld_korea

    return_data(L244.DeleteConsumer_koreaBld, L244.DeleteSupplysector_koreaBld, L244.SubregionalShares_gcamKorea,
                L244.PriceExp_IntGains_gcamKorea, L244.Floorspace_gcamKorea, L244.DemandFunction_serv_gcamKorea, L244.DemandFunction_flsp_gcamKorea,
                L244.Satiation_flsp_gcamKorea, L244.SatiationAdder_gcamKorea, L244.ThermalBaseService_gcamKorea, L244.GenericBaseService_gcamKorea,
                L244.ThermalServiceSatiation_gcamKorea, L244.GenericServiceSatiation_gcamKorea, L244.Intgains_scalar_gcamKorea, L244.ShellConductance_bld_gcamKorea,
                L244.Supplysector_bld_gcamKorea, L244.FinalEnergyKeyword_bld_gcamKorea, L244.SubsectorShrwt_bld_gcamKorea, L244.SubsectorShrwtFllt_bld_gcamKorea,
                L244.SubsectorInterp_bld_gcamKorea, L244.SubsectorInterpTo_bld_gcamKorea, L244.SubsectorLogit_bld_gcamKorea, L244.StubTech_bld_gcamKorea,
                L244.StubTechCalInput_bld_gcamKorea, L244.StubTechMarket_bld_korea, L244.GlobalTechIntGainOutputRatio_korea,
                L244.GlobalTechEff_bld_korea, L244.GlobalTechShrwt_bld_gcamKorea, L244.GlobalTechCost_bld_gcamKorea,
                L244.GlobalTechSCurve_bld_korea, L244.HDDCDD_A2_GFDL_korea)
  } else {
    stop("Unknown command")
  }
}