R/zchunk_L2323.iron_steel_Inc_Elas_scenarios.R

In JGCRI/gcamdata: Build the GCAM Input Datasets

# Copyright 2019 Battelle Memorial Institute; see the LICENSE file.

#' module_socioeconomics_L2323.iron_steel_Inc_Elas_scenarios
#'
#' Calculates iron and steel income elasticity for each GCAM region by linear interpolation of assumption data
#'
#' @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{L2323.iron_steel_incelas_gcam3}, \code{object}. The corresponding file in the
#' original data system was \code{L2323.iron_steel_Inc_Elas_scenarios.R} (socioeconomics level2).
#' @details Takes per-capita GDP from ssp scenarios in each region.
#' Then calculates iron_steel income elasticity for each region by linear interpolation of assumption data.
#' @importFrom assertthat assert_that
#' @importFrom dplyr arrange filter left_join mutate select transmute
#' @importFrom tidyr gather spread
#' @importFrom stats approx
#' @author RH April 2017
module_socioeconomics_L2323.iron_steel_Inc_Elas_scenarios <- function(command, ...) {
  if(command == driver.DECLARE_INPUTS) {
    return(c(FILE = "common/GCAM_region_names",
             FILE = "socioeconomics/A323.inc_elas",
             FILE = "socioeconomics/A323.inc_elas_parameter",
             "L102.pcgdp_thous90USD_Scen_R_Y",
             "L102.pcgdp_thous90USD_GCAM3_R_Y",
             "L101.Pop_thous_Scen_R_Yfut",
             "L101.Pop_thous_R_Yh",
             "L101.Pop_thous_GCAM3_R_Y",
             "L102.gdp_mil90usd_GCAM3_R_Y",
             "L1323.out_Mt_R_iron_steel_Yh"))
  } else if(command == driver.DECLARE_OUTPUTS) {
    return(c("L2323.iron_steel_incelas_gssp1",
             "L2323.iron_steel_incelas_gssp2",
             "L2323.iron_steel_incelas_gssp3",
             "L2323.iron_steel_incelas_gssp4",
             "L2323.iron_steel_incelas_gssp5",
             "L2323.iron_steel_incelas_ssp1",
             "L2323.iron_steel_incelas_ssp2",
             "L2323.iron_steel_incelas_ssp3",
             "L2323.iron_steel_incelas_ssp4",
             "L2323.iron_steel_incelas_ssp5",
             "L2323.iron_steel_incelas_gcam3"))
  } else if(command == driver.MAKE) {

    GCAM_region_ID <- value <- year <- pcgdp_90thousUSD <- scenario <-
        region <- energy.final.demand <- income.elasticity <- . <-
      value.x <- value.y <- pcgdp_90thousUSD_2015 <- a <- b <- m <-
      per_capita_steel <- population <- steel_pro <- pcgdp_90thousUSD_before <-
      steel_pro_before <- steel_hist <- inc_elas <- NULL # silence package check.

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

    # Load required inputs
    COV_1990USD_2005USD = 1.383

    GCAM_region_names <- get_data(all_data, "common/GCAM_region_names")
    A323.inc_elas <- get_data(all_data, "socioeconomics/A323.inc_elas", strip_attributes = TRUE)
    A323.inc_elas_parameter  <- get_data(all_data, "socioeconomics/A323.inc_elas_parameter", strip_attributes = TRUE)
    L102.pcgdp_thous90USD_Scen_R_Y <- get_data(all_data, "L102.pcgdp_thous90USD_Scen_R_Y", strip_attributes = TRUE) %>%
      ungroup() %>%
      rename(pcgdp_90thousUSD = value) %>%
      mutate(year = as.integer(year)) %>%
      left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID")

    L102.pcgdp_thous90USD_GCAM3_R_Y <- get_data(all_data, "L102.pcgdp_thous90USD_GCAM3_R_Y", strip_attributes = TRUE) %>%
      ungroup() %>%
      rename(pcgdp_90thousUSD = value) %>%
      mutate(year = as.integer(year)) %>%
      left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID")

    L101.Pop_thous_R_Yh <- get_data(all_data, "L101.Pop_thous_R_Yh", strip_attributes = TRUE) %>%
      rename(population = value) %>%
      left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID")

    L101.Pop_thous_Scen_R_Yfut <- get_data(all_data, "L101.Pop_thous_Scen_R_Yfut", strip_attributes = TRUE) %>%
      ungroup() %>%
      rename(population = value) %>%
      mutate(year = as.integer(year)) %>%
      left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID")

    L101.Pop_thous_GCAM3_R_Y <- get_data(all_data, "L101.Pop_thous_GCAM3_R_Y", strip_attributes = TRUE) %>%
      ungroup() %>%
      rename(population = value) %>%
      mutate(year = as.integer(year)) %>%
      left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID")

    L102.gdp_mil90usd_GCAM3_R_Y <- get_data(all_data, "L102.gdp_mil90usd_GCAM3_R_Y", strip_attributes = TRUE) %>%
      ungroup() %>%
      rename(pcgdp_90thousUSD = value) %>%
      mutate(year = as.integer(year)) %>%
      left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID")

    steel_pro_2015 <- get_data(all_data, "L1323.out_Mt_R_iron_steel_Yh") %>%
      group_by(GCAM_region_ID,year) %>%
      summarise(steel_hist = sum(value))%>%
      ungroup() %>%
      filter(year == MODEL_FINAL_BASE_YEAR) %>%
      mutate(year = MODEL_FINAL_BASE_YEAR + 5)

    pcgdp_2015 <- L102.pcgdp_thous90USD_Scen_R_Y %>%
      filter(year == MODEL_FINAL_BASE_YEAR) %>%
      mutate(pcgdp_90thousUSD_2015 = pcgdp_90thousUSD,year = MODEL_FINAL_BASE_YEAR + 5 ) %>%
      select(scenario, GCAM_region_ID, year,pcgdp_90thousUSD_2015)

    pcgdp_2015_GCAM3 <- L102.pcgdp_thous90USD_GCAM3_R_Y %>%
      filter(year == MODEL_FINAL_BASE_YEAR) %>%
      mutate(pcgdp_90thousUSD_2015 = pcgdp_90thousUSD,year = MODEL_FINAL_BASE_YEAR + 5 ) %>%
      select( GCAM_region_ID, year,pcgdp_90thousUSD_2015)



    # ===================================================
    # Create one population dataset to pass timeshift tests
    # This is required because L101.Pop_thous_Scen_R_Yfut uses FUTURE_YEARS,
    # but here we want to create a dataset from MODEL_FUTURE_YEARS, which may start before FUTURE_YEARS
    L101_Pop_hist_and_fut <- L101.Pop_thous_R_Yh %>%
      repeat_add_columns(distinct(L101.Pop_thous_Scen_R_Yfut, scenario)) %>%
      bind_rows(L101.Pop_thous_Scen_R_Yfut)

    #First calculate the per capita steel consumption
    L2323.pcgdp_thous90USD_Scen_R_Y  <- L102.pcgdp_thous90USD_Scen_R_Y %>%
      filter(year %in%  MODEL_FUTURE_YEARS) %>%
      left_join_error_no_match(L101_Pop_hist_and_fut, by = c("scenario", "GCAM_region_ID", "year", "region")) %>%
      left_join_error_no_match(A323.inc_elas_parameter, by = c( "region")) %>%
      mutate(per_capita_steel = a * exp(b/(pcgdp_90thousUSD * 1000 * COV_1990USD_2005USD)) * (1-m) ^ (year- MODEL_FINAL_BASE_YEAR),
             steel_pro = per_capita_steel * population*0.000001)


  #Rebuild a new tibble save the previous year value
    L2323.pcgdp_thous90USD_Scen_R_Y_5_before <- L2323.pcgdp_thous90USD_Scen_R_Y %>%
      mutate(year= year+5,pcgdp_90thousUSD_before = pcgdp_90thousUSD ,steel_pro_before = steel_pro) %>%
      select(scenario,GCAM_region_ID,region, year,pcgdp_90thousUSD_before,steel_pro_before )

    L2323.pcgdp_thous90USD_Scen_R_Y <- L2323.pcgdp_thous90USD_Scen_R_Y %>%
      left_join(L2323.pcgdp_thous90USD_Scen_R_Y_5_before, by = c("scenario", "GCAM_region_ID", "year","region"))%>%
      #Add 2015 data
      left_join(steel_pro_2015, by = c("GCAM_region_ID", "year")) %>%
      left_join(pcgdp_2015, by = c("scenario", "GCAM_region_ID", "year")) %>%
      mutate(pcgdp_90thousUSD_before = replace_na(pcgdp_90thousUSD_before,0),steel_pro_before  = replace_na(steel_pro_before ,0),
             steel_hist  = replace_na(steel_hist,0),pcgdp_90thousUSD_2015 = replace_na(pcgdp_90thousUSD_2015,0),
             pcgdp_90thousUSD_before = pcgdp_90thousUSD_before + pcgdp_90thousUSD_2015, steel_pro_before = steel_pro_before + steel_hist,
             #cal
             inc_elas = log(steel_pro / steel_pro_before)/log(pcgdp_90thousUSD/pcgdp_90thousUSD_before),
             income.elasticity = inc_elas,energy.final.demand = "iron and steel") %>%
      select(scenario, region, energy.final.demand, year, income.elasticity) %>%
      arrange(year) %>%
      #replace those huge number
      mutate(income.elasticity = replace(income.elasticity,income.elasticity > 10 , 10),
             income.elasticity = replace(income.elasticity,income.elasticity < -10,-10))


    # Split by scenario and remove scenario column from each tibble
    L2323.pcgdp_thous90USD_Scen_R_Y <- L2323.pcgdp_thous90USD_Scen_R_Y %>%
      split(.$scenario) %>%
      lapply(function(df) {select(df, -scenario) %>%
          add_units("Unitless (% change in service demand / % change in income)") %>%
          add_comments("Uses previously calculated per-capita GDP assumptions for all ssp scenarios") %>%
          add_comments("iron_steel income elasticity for each GCAM region generated by linear interpolation of assumption data") %>%
          add_precursors("common/GCAM_region_names", "socioeconomics/A323.inc_elas","socioeconomics/A323.inc_elas_parameter",
                         "L102.pcgdp_thous90USD_Scen_R_Y") })

    ####Cal gcam3
    #First calculate the per capita steel consumption
    L2323.pcgdp_thous90USD_GCAM3_R_Y  <- L102.gdp_mil90usd_GCAM3_R_Y%>%
      filter(year %in%  MODEL_FUTURE_YEARS) %>%
      left_join_error_no_match(L101.Pop_thous_GCAM3_R_Y, by = c("GCAM_region_ID", "year", "region")) %>%
      left_join_error_no_match(A323.inc_elas_parameter, by = c( "region")) %>%
      mutate(per_capita_steel = a * exp(b/(pcgdp_90thousUSD * 1000 * COV_1990USD_2005USD)) * (1-m) ^ (year- 2015),
             steel_pro = per_capita_steel * population*0.000001)


    #Rebuild a new tibble save the previous year value
    L2323.pcgdp_thous90USD_GCAM3_R_Y_5_before <- L2323.pcgdp_thous90USD_GCAM3_R_Y %>%
      mutate(year= year+5,pcgdp_90thousUSD_before = pcgdp_90thousUSD ,steel_pro_before = steel_pro) %>%
      select(scenario,GCAM_region_ID,region, year,pcgdp_90thousUSD_before,steel_pro_before )

    L2323.iron_steel_incelas_gcam3 <- L2323.pcgdp_thous90USD_GCAM3_R_Y %>%
      left_join(L2323.pcgdp_thous90USD_GCAM3_R_Y_5_before, by = c("GCAM_region_ID", "year","region"))%>%
      #Add 2015 data
      left_join(steel_pro_2015, by = c("GCAM_region_ID", "year")) %>%
      left_join(pcgdp_2015_GCAM3, by = c( "GCAM_region_ID", "year")) %>%
      mutate(pcgdp_90thousUSD_before = replace_na(pcgdp_90thousUSD_before,0),steel_pro_before  = replace_na(steel_pro_before ,0),
             steel_hist  = replace_na(steel_hist,0),pcgdp_90thousUSD_2015 = replace_na(pcgdp_90thousUSD_2015,0),
             pcgdp_90thousUSD_before = pcgdp_90thousUSD_before + pcgdp_90thousUSD_2015, steel_pro_before = steel_pro_before + steel_hist,
             #cal
             inc_elas = log(steel_pro / steel_pro_before)/log(pcgdp_90thousUSD/pcgdp_90thousUSD_before),
             income.elasticity = inc_elas,energy.final.demand = "iron and steel") %>%
      select(region, energy.final.demand, year, income.elasticity) %>%
      arrange(year) %>%
      #replace those huge number
      mutate(income.elasticity = replace(income.elasticity,income.elasticity > 10 , 10),
             income.elasticity = replace(income.elasticity,income.elasticity < -10,-10))


    # ===================================================

    # Produce outputs
    L2323.pcgdp_thous90USD_Scen_R_Y[["gSSP1"]] %>%
      add_title("iron_steel Income Elasticity: gssp1") %>%
      add_legacy_name("L2323.iron_steel_incelas_gssp1")%>%
      add_precursors("L102.pcgdp_thous90USD_Scen_R_Y","L101.Pop_thous_Scen_R_Yfut", "L101.Pop_thous_R_Yh","socioeconomics/A323.inc_elas_parameter") ->
      L2323.iron_steel_incelas_gssp1

    L2323.pcgdp_thous90USD_Scen_R_Y[["gSSP2"]] %>%
      add_title("iron_steel Income Elasticity: gssp2") %>%
      add_legacy_name("L2323.iron_steel_incelas_gssp2")%>%
      add_precursors("L102.pcgdp_thous90USD_Scen_R_Y","L101.Pop_thous_Scen_R_Yfut", "L101.Pop_thous_R_Yh","socioeconomics/A323.inc_elas_parameter") ->
      L2323.iron_steel_incelas_gssp2

    L2323.pcgdp_thous90USD_Scen_R_Y[["gSSP3"]] %>%
      add_title("iron_steel Income Elasticity: gssp3") %>%
      add_legacy_name("L2323.iron_steel_incelas_gssp3")%>%
      add_precursors("L102.pcgdp_thous90USD_Scen_R_Y","L101.Pop_thous_Scen_R_Yfut", "L101.Pop_thous_R_Yh","socioeconomics/A323.inc_elas_parameter") ->
      L2323.iron_steel_incelas_gssp3

    L2323.pcgdp_thous90USD_Scen_R_Y[["gSSP4"]] %>%
      add_title("iron_steel Income Elasticity: gssp4") %>%
      add_legacy_name("L2323.iron_steel_incelas_gssp4")%>%
      add_precursors("L102.pcgdp_thous90USD_Scen_R_Y","L101.Pop_thous_Scen_R_Yfut", "L101.Pop_thous_R_Yh","socioeconomics/A323.inc_elas_parameter") ->
      L2323.iron_steel_incelas_gssp4

    L2323.pcgdp_thous90USD_Scen_R_Y[["gSSP5"]] %>%
      add_title("iron_steel Income Elasticity: gssp5") %>%
      add_legacy_name("L2323.iron_steel_incelas_gssp5")%>%
      add_precursors("L102.pcgdp_thous90USD_Scen_R_Y","L101.Pop_thous_Scen_R_Yfut", "L101.Pop_thous_R_Yh","socioeconomics/A323.inc_elas_parameter") ->
      L2323.iron_steel_incelas_gssp5

    L2323.pcgdp_thous90USD_Scen_R_Y[["SSP1"]] %>%
      add_title("iron_steel Income Elasticity: ssp1") %>%
      add_legacy_name("L2323.iron_steel_incelas_ssp1")%>%
      add_precursors("L102.pcgdp_thous90USD_Scen_R_Y","L101.Pop_thous_Scen_R_Yfut", "L101.Pop_thous_R_Yh","socioeconomics/A323.inc_elas_parameter") ->
      L2323.iron_steel_incelas_ssp1

    L2323.pcgdp_thous90USD_Scen_R_Y[["SSP2"]] %>%
      add_title("iron_steel Income Elasticity: ssp2") %>%
      add_legacy_name("L2323.iron_steel_incelas_ssp2")%>%
      add_precursors("L102.pcgdp_thous90USD_Scen_R_Y","L101.Pop_thous_Scen_R_Yfut", "L101.Pop_thous_R_Yh","socioeconomics/A323.inc_elas_parameter") ->
      L2323.iron_steel_incelas_ssp2

    L2323.pcgdp_thous90USD_Scen_R_Y[["SSP3"]] %>%
      add_title("iron_steel Income Elasticity: ssp3") %>%
      add_legacy_name("L2323.iron_steel_incelas_ssp3")%>%
      add_precursors("L102.pcgdp_thous90USD_Scen_R_Y","L101.Pop_thous_Scen_R_Yfut", "L101.Pop_thous_R_Yh","socioeconomics/A323.inc_elas_parameter") ->
      L2323.iron_steel_incelas_ssp3

    L2323.pcgdp_thous90USD_Scen_R_Y[["SSP4"]] %>%
      add_title("iron_steel Income Elasticity: ssp4") %>%
      add_legacy_name("L2323.iron_steel_incelas_ssp4")%>%
      add_precursors("L102.pcgdp_thous90USD_Scen_R_Y","L101.Pop_thous_Scen_R_Yfut", "L101.Pop_thous_R_Yh","socioeconomics/A323.inc_elas_parameter") ->
      L2323.iron_steel_incelas_ssp4

    L2323.pcgdp_thous90USD_Scen_R_Y[["SSP5"]] %>%
      add_title("iron_steel Income Elasticity: ssp5") %>%
      add_legacy_name("L2323.iron_steel_incelas_ssp5")%>%
      add_precursors("L102.pcgdp_thous90USD_Scen_R_Y","L101.Pop_thous_Scen_R_Yfut", "L101.Pop_thous_R_Yh","socioeconomics/A323.inc_elas_parameter") ->
      L2323.iron_steel_incelas_ssp5

    L2323.iron_steel_incelas_gcam3 %>%
      add_title("iron_steel Income Elasticity: gcam3") %>%
      add_units("Unitless (% change in service demand / % change in income)") %>%
      add_comments("Uses previously calculated per-capita GDP assumptions of iron_steel elastciity") %>%
      add_comments("iron_steel income elasticity for each GCAM region generated by linear interpolation of assumption data") %>%
      add_legacy_name("L2323.iron_steel_incelas_gcam3") %>%
      add_precursors("common/GCAM_region_names", "socioeconomics/A323.inc_elas","socioeconomics/A323.inc_elas_parameter",
                     "L101.Pop_thous_GCAM3_R_Y", "L102.gdp_mil90usd_GCAM3_R_Y",
                     "L1323.out_Mt_R_iron_steel_Yh","L102.pcgdp_thous90USD_GCAM3_R_Y") ->
      L2323.iron_steel_incelas_gcam3

    return_data(L2323.iron_steel_incelas_gssp1,
                L2323.iron_steel_incelas_gssp2,
                L2323.iron_steel_incelas_gssp3,
                L2323.iron_steel_incelas_gssp4,
                L2323.iron_steel_incelas_gssp5,
                L2323.iron_steel_incelas_ssp1,
                L2323.iron_steel_incelas_ssp2,
                L2323.iron_steel_incelas_ssp3,
                L2323.iron_steel_incelas_ssp4,
                L2323.iron_steel_incelas_ssp5,
                L2323.iron_steel_incelas_gcam3)
  } else {
    stop("Unknown command")
  }
}