R/zchunk_L232.industry.R
In rohmin9122/gcam-korea-release: Build the GCAM-Korea Input Datasets
Defines functions
module_energy_L232.industry
Documented in
module_energy_L232.industry
#' module_energy_L232.industry
#'
#' Compute a variety of final energy keyword, sector, share weight, and technology information for industry-related GCAM inputs.
#'
#' @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{L232.SectorLogitTables[[ curr_table ]]$data},
#' \code{L232.Supplysector_ind}, \code{L232.SubsectorLogitTables[[ curr_table ]]$data},
#' \code{L232.SubsectorLogit_ind}, \code{L232.FinalEnergyKeyword_ind},
#' \code{L232.SubsectorShrwtFllt_ind}, \code{L232.SubsectorInterp_ind},
#' \code{L232.StubTech_ind}, \code{L232.GlobalTechShrwt_ind}, \code{L232.StubTechInterp_ind},
#' \code{L232.GlobalTechEff_ind}, \code{L232.GlobalTechCoef_ind}, \code{L232.GlobalTechCost_ind},
#' \code{L232.GlobalTechSecOut_ind}, \code{L232.GlobalTechCSeq_ind},
#' \code{L232.StubTechCalInput_indenergy}, \code{L232.StubTechCalInput_indfeed},
#' \code{L232.StubTechProd_industry}, \code{L232.StubTechCoef_industry},
#' \code{L232.FuelPrefElast_indenergy}, \code{L232.PerCapitaBased_ind},
#' \code{L232.PriceElasticity_ind}, \code{L232.BaseService_ind},
#' \code{L232.IncomeElasticity_ind_gcam3}, \code{L232.IncomeElasticity_ind_gssp1},
#' \code{L232.IncomeElasticity_ind_gssp2}, \code{L232.IncomeElasticity_ind_gssp3},
#' \code{L232.IncomeElasticity_ind_gssp4}, \code{L232.IncomeElasticity_ind_gssp5},
#' \code{L232.IncomeElasticity_ind_ssp1}, \code{L232.IncomeElasticity_ind_ssp2},
#' \code{L232.IncomeElasticity_ind_ssp3}, \code{L232.IncomeElasticity_ind_ssp4},
#' \code{L232.IncomeElasticity_ind_ssp5}, \code{object}. The corresponding file in the
#' original data system was \code{L232.industry.R} (energy level2).
#' @details The chunk provides final energy keyword, supplysector/subsector information, supplysector/subsector interpolation information, supplysector/subsector share weights, global technology share weight, global technology efficiency, global technology coefficients, global technology cost, price elasticity, stub technology information, stub technology interpolation information, stub technology calibrated inputs, and etc.
#' @importFrom assertthat assert_that
#' @importFrom dplyr filter mutate select
#' @importFrom tidyr gather spread
#' @author LF October 2017
module_energy_L232.industry <- function(command, ...) {
INCOME_ELASTICITY_OUTPUTS <- c("GCAM3",
paste0("gSSP", 1:5),
paste0("SSP", 1:5))
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "common/GCAM_region_names",
FILE = "energy/calibrated_techs",
FILE = "energy/A_regions",
FILE = "energy/A23.chp_elecratio",
FILE = "energy/A32.sector",
FILE = "energy/A32.subsector_interp",
FILE = "energy/A32.subsector_logit",
FILE = "energy/A32.subsector_shrwt",
FILE = "energy/A32.globaltech_coef",
FILE = "energy/A32.globaltech_cost",
FILE = "energy/A32.globaltech_eff",
FILE = "energy/A32.globaltech_shrwt",
FILE = "energy/A32.globaltech_interp",
FILE = "energy/A32.nonenergy_Cseq",
FILE = "energy/A32.fuelprefElasticity",
FILE = "energy/A32.demand",
"L123.in_EJ_R_indchp_F_Yh",
"L1322.in_EJ_R_indenergy_F_Yh",
"L1322.in_EJ_R_indfeed_F_Yh",
FILE = "socioeconomics/A32.inc_elas_output",
"L101.Pop_thous_GCAM3_R_Y",
"L102.pcgdp_thous90USD_GCAM3_R_Y",
"L102.pcgdp_thous90USD_Scen_R_Y"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L232.Supplysector_ind",
"L232.SubsectorLogit_ind",
"L232.FinalEnergyKeyword_ind",
"L232.SubsectorShrwtFllt_ind",
"L232.SubsectorInterp_ind",
"L232.StubTech_ind",
"L232.GlobalTechShrwt_ind",
"L232.StubTechInterp_ind",
"L232.GlobalTechEff_ind",
"L232.GlobalTechCoef_ind",
"L232.GlobalTechCost_ind",
"L232.GlobalTechSecOut_ind",
"L232.GlobalTechCSeq_ind",
"L232.StubTechCalInput_indenergy",
"L232.StubTechCalInput_indfeed",
"L232.StubTechProd_industry",
"L232.StubTechCoef_industry",
"L232.FuelPrefElast_indenergy",
"L232.PerCapitaBased_ind",
"L232.PriceElasticity_ind",
"L232.BaseService_ind",
paste("L232.IncomeElasticity_ind", tolower(INCOME_ELASTICITY_OUTPUTS), sep = "_")))
} else if(command == driver.MAKE) {
all_data <- list(...)[[1]]
# Load required inputs
GCAM_region_names <- get_data(all_data, "common/GCAM_region_names")
calibrated_techs <- get_data(all_data, "energy/calibrated_techs")
A_regions <- get_data(all_data, "energy/A_regions")
A23.chp_elecratio <- get_data(all_data, "energy/A23.chp_elecratio")
A32.sector <- get_data(all_data, "energy/A32.sector")
A32.subsector_interp <- get_data(all_data, "energy/A32.subsector_interp")
A32.subsector_logit <- get_data(all_data, "energy/A32.subsector_logit")
A32.subsector_shrwt <- get_data(all_data, "energy/A32.subsector_shrwt")
A32.globaltech_coef <- get_data(all_data, "energy/A32.globaltech_coef")
A32.globaltech_cost <- get_data(all_data, "energy/A32.globaltech_cost")
A32.globaltech_eff <- get_data(all_data, "energy/A32.globaltech_eff")
A32.globaltech_shrwt <- get_data(all_data, "energy/A32.globaltech_shrwt")
A32.globaltech_interp <- get_data(all_data, "energy/A32.globaltech_interp")
A32.nonenergy_Cseq <- get_data(all_data, "energy/A32.nonenergy_Cseq")
A32.fuelprefElasticity <- get_data(all_data, "energy/A32.fuelprefElasticity")
A32.demand <- get_data(all_data, "energy/A32.demand")
L123.in_EJ_R_indchp_F_Yh <- get_data(all_data, "L123.in_EJ_R_indchp_F_Yh")
L1322.in_EJ_R_indenergy_F_Yh <- get_data(all_data, "L1322.in_EJ_R_indenergy_F_Yh")
L1322.in_EJ_R_indfeed_F_Yh <- get_data(all_data, "L1322.in_EJ_R_indfeed_F_Yh")
A32.inc_elas_output <- get_data(all_data, "socioeconomics/A32.inc_elas_output")
L101.Pop_thous_GCAM3_R_Y <- get_data(all_data, "L101.Pop_thous_GCAM3_R_Y")
L102.pcgdp_thous90USD_GCAM3_R_Y <- get_data(all_data, "L102.pcgdp_thous90USD_GCAM3_R_Y")
L102.pcgdp_thous90USD_Scen_R_Y <- get_data(all_data, "L102.pcgdp_thous90USD_Scen_R_Y")
# ===================================================
# 0. Give binding for variable names used in pipeline
has_district_heat <- sector <- fuel <- supplysector <- subsector <-
technology <- year.fillout <- to.value <- year <- share.weight <-
efficiency <- minicam.energy.input <- secondary.output <- coefficient <-
elec_ratio <- output.ratio <- . <- year.x <- output.ratio.x <- output.ratio.y <-
input.cost <- minicam.non.energy.input <- GCAM_region_ID <- value <-
calibrated.value <- sector.name <- subsector.name <- region <-
calOutputValue <- subs.share.weight <- calOutputValue.x <- calOutputValue.y <-
output_tot <- value.x <- value.y <- total <- fuelprefElasticity <-
terminal_coef <- criteria <- scenario <- temp_lag <- base.service <- energy.final.demand <-
parameter <- income.elasticity <- L232.IncomeElasticity_ind_gcam3 <-
L232.IncomeElasticity_ind_gssp1 <- L232.IncomeElasticity_ind_gssp2 <-
L232.IncomeElasticity_ind_gssp3 <- L232.IncomeElasticity_ind_gssp4 <-
L232.IncomeElasticity_ind_gssp5 <- L232.IncomeElasticity_ind_ssp1 <-
L232.IncomeElasticity_ind_ssp2 <- L232.IncomeElasticity_ind_ssp3 <-
L232.IncomeElasticity_ind_ssp4 <- L232.IncomeElasticity_ind_ssp5 <-
market.name <- stub.technology <- NULL
# ===================================================
# 1. Perform computations
# Create tables to delete technologies and subsectors in regions where heat is not modeled as a fuel
has_not_heat <- filter(A_regions, has_district_heat == 0) # intermediate tibble
calibrated_techs %>%
filter(grepl("industry", sector) & fuel == "heat") %>%
select(supplysector, subsector, technology) %>%
repeat_add_columns(tibble(GCAM_region_ID = has_not_heat[["GCAM_region_ID"]])) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") ->
L232.rm_heat_techs_R # intermediate tibble
# 1a. Supplysector information
# L232.Supplysector_ind: Supply sector information for industry sector
A32.sector %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["Supplysector"]], LOGIT_TYPE_COLNAME), GCAM_region_names) ->
L232.Supplysector_ind
# L232.FinalEnergyKeyword_ind: Supply sector keywords for industry sector
A32.sector %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["FinalEnergyKeyword"]], GCAM_region_names) %>%
na.omit ->
L232.FinalEnergyKeyword_ind
# 1b. Subsector information
# L232.SubsectorLogit_ind: Subsector logit exponents of industry sector
A32.subsector_logit %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["SubsectorLogit"]], LOGIT_TYPE_COLNAME), GCAM_region_names) %>%
anti_join(L232.rm_heat_techs_R, by = c("region", "subsector")) -> # Remove non-existent heat subsectors from each region
L232.SubsectorLogit_ind
# L232.SubsectorShrwtFllt_ind: Subsector shareweights of industry sector
A32.subsector_shrwt %>%
filter(!is.na(year.fillout)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["SubsectorShrwtFllt"]], GCAM_region_names) %>%
anti_join(L232.rm_heat_techs_R, by = c("region", "subsector")) -> # Remove non-existent heat technologies from each region
L232.SubsectorShrwtFllt_ind
# L232.SubsectorInterp_ind: Subsector shareweight interpolation of industry sector
A32.subsector_interp %>%
filter(is.na(to.value)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["SubsectorInterp"]], GCAM_region_names) %>%
anti_join(L232.rm_heat_techs_R, by = c("region", "subsector")) -> # Remove non-existent heat technologies from each region
L232.SubsectorInterp_ind
# 1c. Technology information
# L232.StubTech_ind: Identification of stub technologies of industrial sector
# Note: assuming that technology list in the shareweight table includes the full set (any others would default to a 0 shareweight)
A32.globaltech_shrwt %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["Tech"]], GCAM_region_names) %>%
anti_join(L232.rm_heat_techs_R, by = c("region", "technology")) %>% # Remove non-existent heat technologies from each region
rename(stub.technology = technology) ->
L232.StubTech_ind
# L232.GlobalTechShrwt_ind: Shareweights of global industrial sector technologies
A32.globaltech_shrwt %>%
gather_years(value_col = "share.weight") %>%
complete(nesting(supplysector, subsector, technology), year = c(year, MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)) %>%
arrange(supplysector, subsector, technology, year) %>%
group_by(supplysector, subsector, technology) %>%
mutate(share.weight = approx_fun(year, share.weight, rule = 1)) %>%
ungroup %>%
filter(year %in% c(MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)) %>%
rename(sector.name = supplysector,
subsector.name = subsector) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechYr"]], "share.weight") ->
L232.GlobalTechShrwt_ind
# L232.StubTechInterp_ind: Shareweight interpolation of global industrial sector technologies
A32.globaltech_interp %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["TechInterp"]], GCAM_region_names) %>%
rename(stub.technology = technology) ->
L232.StubTechInterp_ind
# L232.GlobalTechEff_ind: Energy inputs and efficiency of global industrial energy use and feedstocks technologies
A32.globaltech_eff %>%
gather_years(value_col = "efficiency") %>%
complete(nesting(supplysector, subsector, technology, minicam.energy.input, secondary.output),
year = c(year, MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)) %>%
arrange(supplysector, subsector, technology, minicam.energy.input, secondary.output, year) %>%
group_by(supplysector, subsector, technology, minicam.energy.input, secondary.output) %>%
mutate(efficiency = approx_fun(year, efficiency, rule = 1),
efficiency = round(efficiency, energy.DIGITS_EFFICIENCY)) %>%
ungroup %>%
filter(year %in% c(MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)) %>%
# Assign the columns "sector.name" and "subsector.name", consistent with the location info of a global technology
rename(sector.name = supplysector,
subsector.name = subsector) ->
L232.globaltech_eff.long # intermediate tibble
L232.globaltech_eff.long %>%
select(LEVEL2_DATA_NAMES[["GlobalTechEff"]]) ->
L232.GlobalTechEff_ind
# Coefficients on global industry sector technologies (not energy-use or feedstocks)
# L232.GlobalTechCoef_ind: Energy inputs and coefficients of global industry technologies
A32.globaltech_coef %>%
rename(coefficient = "terminal_coef") %>%
repeat_add_columns(tibble(year = c(year, MODEL_BASE_YEARS, MODEL_FUTURE_YEARS))) %>%
rename(sector.name = supplysector,
subsector.name = subsector) %>% # Assign the columns "sector.name" and "subsector.name", consistent with the location info of a global technology
select(LEVEL2_DATA_NAMES[["GlobalTechCoef"]]) ->
L232.GlobalTechCoef_ind
# Secondary outputs of cogen technologies: these are input as a ratio
# L232.GlobalTechSecOut_ind: Secondary output ratios of industrial cogeneration technologies
A32.globaltech_eff %>%
gather_years(value_col = "efficiency") %>%
complete(nesting(supplysector, subsector, technology, minicam.energy.input, secondary.output),
year = c(year, MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)) %>%
arrange(supplysector, subsector, technology, minicam.energy.input, secondary.output, year) %>%
group_by(supplysector, subsector, technology, minicam.energy.input, secondary.output) %>%
mutate(efficiency = approx_fun(year, efficiency, rule = 1),
efficiency = round(efficiency, energy.DIGITS_EFFICIENCY)) %>%
filter(year %in% c(MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)) %>%
filter(!is.na(secondary.output)) %>%
left_join_error_no_match(A23.chp_elecratio, by = c("subsector" = "fuel")) %>%
mutate(output.ratio = elec_ratio / efficiency,
output.ratio = round(output.ratio, energy.DIGITS_EFFICIENCY)) %>%
# NOTE: holding the output ratio constant over time in future periods
left_join_error_no_match(select(filter(., year == max(MODEL_BASE_YEARS)), -efficiency, -elec_ratio),
by = c("supplysector", "subsector", "technology", "minicam.energy.input", "secondary.output")) %>%
mutate(output.ratio = if_else(year.x %in% MODEL_BASE_YEARS, output.ratio.x, output.ratio.y)) %>%
ungroup %>%
rename(year = year.x,
sector.name = supplysector,
subsector.name = subsector) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechSecOut"]]) ->
L232.GlobalTechSecOut_ind
# Costs of global technologies
# L232.GlobalTechCost_ind: Capital costs of global industrial technologies
A32.globaltech_cost %>%
gather_years(value_col = "input.cost") %>%
complete(nesting(supplysector, subsector, technology, minicam.non.energy.input),
year = c(year, MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)) %>%
arrange(supplysector, subsector, technology, minicam.non.energy.input, year) %>%
group_by(supplysector, subsector, technology, minicam.non.energy.input) %>%
mutate(input.cost = approx_fun(year, input.cost, rule = 1)) %>%
ungroup %>%
filter(year %in% c(MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)) %>%
rename(sector.name = supplysector,
subsector.name = subsector) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechCost"]]) ->
L232.GlobalTechCost_ind
# Carbon capture from feedstock carbon sequestration
# L232.GlobalTechCSeq_ind: CO2 capture fractions from global electricity generation technologies
## No need to consider historical periods or intermittent technologies here
A32.nonenergy_Cseq %>%
repeat_add_columns(tibble(year = c(MODEL_BASE_YEARS, MODEL_FUTURE_YEARS))) %>%
rename(sector.name = supplysector,
subsector.name = subsector) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechCSeq"]]) ->
L232.GlobalTechCSeq_ind
# Calibration and region-specific data
# L232.StubTechCalInput_indenergy: calibrated input of industrial energy use technologies (including cogen)
L1322.in_EJ_R_indenergy_F_Yh %>%
bind_rows(L123.in_EJ_R_indchp_F_Yh) %>%
complete(nesting(GCAM_region_ID, sector, fuel), year = c(year, MODEL_BASE_YEARS)) %>%
arrange(GCAM_region_ID, sector, fuel, year) %>%
group_by(GCAM_region_ID, sector, fuel) %>%
mutate(value = approx_fun(year, value, rule = 1)) %>%
ungroup %>%
filter(year %in% MODEL_BASE_YEARS) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join(select(calibrated_techs, sector, fuel, supplysector, subsector, technology), by = c("sector", "fuel")) %>%
rename(stub.technology = technology) ->
L232.in_EJ_R_indenergy_F_Yh # intermediate tibble
L232.in_EJ_R_indenergy_F_Yh %>%
left_join_error_no_match(distinct(select(A32.globaltech_eff, subsector, technology, minicam.energy.input)),
by = c("subsector", "stub.technology" = "technology")) %>%
mutate(calibrated.value = round(value, energy.DIGITS_CALOUTPUT),
share.weight.year = year) %>%
rename(calOutputValue = calibrated.value) %>% # temporary column name change to accommodate function set_subsector_shrwt
set_subsector_shrwt %>%
rename(calibrated.value = calOutputValue) %>% # temporary column name change to accommodate function set_subsector_shrwt
mutate(tech.share.weight = if_else(calibrated.value > 0, 1, 0)) %>%
select(LEVEL2_DATA_NAMES[["StubTechCalInput"]]) ->
L232.StubTechCalInput_indenergy
# L232.StubTechCalInput_indfeed: calibrated input of industrial feedstock technologies
L1322.in_EJ_R_indfeed_F_Yh %>%
filter(year %in% MODEL_BASE_YEARS) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_error_no_match(distinct(select(calibrated_techs, sector, fuel, supplysector, subsector, technology)),
by = c("sector", "fuel")) %>%
rename(stub.technology = technology) ->
L232.in_EJ_R_indfeed_F_Yh # intermediate tibble
L232.in_EJ_R_indfeed_F_Yh %>%
select(LEVEL2_DATA_NAMES[["StubTechYr"]], "value") %>%
left_join(distinct(select(A32.globaltech_eff, subsector, technology, minicam.energy.input)),
by = c("subsector", "stub.technology" = "technology")) %>%
mutate(calibrated.value = round(value, energy.DIGITS_CALOUTPUT)) %>%
select(-value) %>%
mutate(share.weight.year = year) %>%
rename(calOutputValue = calibrated.value) %>% # temporary column name change to accommodate function set_subsector_shrwt
set_subsector_shrwt %>%
rename(calibrated.value = calOutputValue) %>% # temporary column name change to accommodate function set_subsector_shrwt
mutate(tech.share.weight = if_else(calibrated.value > 0, 1, 0)) ->
L232.StubTechCalInput_indfeed
# L232.StubTechProd_industry: calibrated output of industrial sector
# First, calculate service output by technology, for energy-use and feedstocks
L232.in_EJ_R_indenergy_F_Yh %>%
bind_rows(L232.in_EJ_R_indfeed_F_Yh) %>%
left_join_error_no_match(select(L232.globaltech_eff.long, sector.name, subsector.name, technology, year,efficiency),
by = c("supplysector" = "sector.name", "subsector" = "subsector.name",
"stub.technology" = "technology", "year")) %>%
mutate(calOutputValue = round(value * efficiency, energy.DIGITS_CALOUTPUT)) ->
L232.out_EJ_R_ind_serv_F_Yh # intermediate tibble
# intermediate tibble to extract industry names
A32.globaltech_shrwt %>%
filter(supplysector == "industry") %>%
select(supplysector, subsector, technology) ->
L232.industry_names
# Aggregate service output by region. This is the output of the industrial sector in each region.
L232.out_EJ_R_ind_serv_F_Yh %>%
group_by(region, GCAM_region_ID, year) %>%
summarise(calOutputValue = sum(calOutputValue)) %>%
ungroup %>%
mutate(supplysector = L232.industry_names[["supplysector"]],
subsector = L232.industry_names[["subsector"]],
stub.technology = L232.industry_names[["technology"]],
share.weight.year = year,
subs.share.weight = if_else(calOutputValue > 0, 1, 0),
tech.share.weight = subs.share.weight) %>%
select(LEVEL2_DATA_NAMES[["StubTechProd"]]) ->
L232.StubTechProd_industry
# L232.StubTechCoef_industry: calibrated output of industrial sector
# Next, aggregate service output by sector to calculate the portion of each input
L232.out_EJ_R_ind_serv_F_Yh %>%
group_by(region, GCAM_region_ID, supplysector, year) %>%
summarise(calOutputValue = sum(calOutputValue)) %>%
ungroup %>%
left_join_error_no_match(select(L232.StubTechProd_industry, calOutputValue, region, year),
by = c("region", "year")) %>%
rename(calOutputValue = calOutputValue.x,
output_tot = calOutputValue.y) %>%
mutate(coefficient = calOutputValue / output_tot) %>%
rename(minicam.energy.input = supplysector) %>%
mutate(supplysector = L232.industry_names[["supplysector"]],
subsector = L232.industry_names[["subsector"]],
stub.technology = L232.industry_names[["technology"]],
market.name = region) %>%
select(LEVEL2_DATA_NAMES[["StubTechCoef"]]) ->
L232.StubTechCoef_industry_base # intermediate tibble?
# This set of coefficients covers only the base years; the first "future" period will default to the global tech coefficient
# Instead, interpolate the coefficients to these global default values in a specified period
L232.StubTechCoef_industry_base %>%
complete(nesting(region, supplysector, subsector, stub.technology, minicam.energy.input, market.name),
year = unique(c(MODEL_YEARS, energy.INDCOEF_CONVERGENCE_YR))) %>%
left_join(select(A32.globaltech_coef, supplysector, subsector, technology, minicam.energy.input, terminal_coef),
by = c("supplysector", "subsector", stub.technology = "technology", "minicam.energy.input")) %>%
mutate(coefficient = if_else(year == energy.INDCOEF_CONVERGENCE_YR, terminal_coef, coefficient)) %>%
select(-terminal_coef) %>%
group_by(region, supplysector, subsector, stub.technology, minicam.energy.input) %>%
mutate(coefficient = round(approx_fun(year, coefficient), energy.DIGITS_COEFFICIENT)) %>%
ungroup() %>%
filter(year %in% MODEL_YEARS) -> # drop the terminal coef year if it's outside of the model years
L232.StubTechCoef_industry
# L232.FuelPrefElast_indenergy: fuel preference elasticities of industrial energy use
# First, calculate the fuel shares allocated to each fuel
L232.in_EJ_R_indenergy_F_Yh %>%
group_by(region, GCAM_region_ID, supplysector, subsector, year) %>%
summarise(value = sum(value)) %>%
ungroup ->
L232.indenergy_fuel_shares # intermediate tibble
L232.indenergy_fuel_shares %>%
group_by(region, GCAM_region_ID, supplysector, year) %>%
summarise(value = sum(value)) %>%
ungroup %>%
right_join(L232.indenergy_fuel_shares, by = c("region", "GCAM_region_ID", "supplysector", "year")) %>%
rename(total = value.x,
value = value.y) %>%
mutate(share = value / total) %>%
filter(year == max(MODEL_BASE_YEARS)) %>%
mutate(fuelprefElasticity = 0) ->
L232.indenergy_fuel_shares
# Set fuel preference elasticities as indicated by exogenous rules
# For each row of L232.indenergy_fuel_shares, we check for any entries in A32.fuelprefElasticity
# that have the same `subsector` entry, and apply the rule specified (the share in a 'greater than'
# entry is only used if the share value is larger; similar logic applies to 'lesser than' entries).
# This is an inefficient way to do this, but the datasets involved are small
A32.fuelprefElasticity.gt <- filter(A32.fuelprefElasticity, criteria == "greater than")
for(j in seq_len(nrow(L232.indenergy_fuel_shares))) {
for(i in seq_len(nrow(A32.fuelprefElasticity.gt))) {
L232.indenergy_fuel_shares$fuelprefElasticity[j][
L232.indenergy_fuel_shares$subsector[j] == A32.fuelprefElasticity.gt$subsector[i] &
L232.indenergy_fuel_shares$share[j] > A32.fuelprefElasticity.gt$share[i] ] <-
A32.fuelprefElasticity.gt$fuelprefElasticity[i]
}
}
A32.fuelprefElasticity.lt <- filter(A32.fuelprefElasticity, criteria == "lesser than")
for(j in seq_len(nrow(L232.indenergy_fuel_shares))) {
for(i in seq_len(nrow(A32.fuelprefElasticity.lt))) {
L232.indenergy_fuel_shares$fuelprefElasticity[j][
L232.indenergy_fuel_shares$subsector[j] == A32.fuelprefElasticity.lt$subsector[i] &
L232.indenergy_fuel_shares$share[j] < A32.fuelprefElasticity.lt$share[i] ] <-
A32.fuelprefElasticity.lt$fuelprefElasticity[i]
}
}
# The fuel preference elasticities only matter in future periods. Fill out from the first future model time period
L232.indenergy_fuel_shares %>%
mutate(year.fillout = min(MODEL_FUTURE_YEARS)) %>%
filter(fuelprefElasticity != 0) %>%
select(LEVEL2_DATA_NAMES[["FuelPrefElast"]]) ->
L232.FuelPrefElast_indenergy
# L232.PerCapitaBased_ind: per-capita based flag for industry final demand
tibble(region = GCAM_region_names[["region"]]) %>%
mutate(energy.final.demand = A32.demand[["energy.final.demand"]],
perCapitaBased = A32.demand[["perCapitaBased"]]) ->
L232.PerCapitaBased_ind
# L232.PriceElasticity_ind: price elasticity of industry final demand
# Price elasticities are only applied to future periods. Application in base years will cause solution failure
tibble(region = rep(GCAM_region_names[["region"]], times = length(MODEL_FUTURE_YEARS))) %>%
mutate(energy.final.demand = A32.demand[["energy.final.demand"]],
year = sort(rep(MODEL_FUTURE_YEARS, times = nrow(GCAM_region_names))),
price.elasticity = A32.demand[["price.elasticity"]]) ->
L232.PriceElasticity_ind
# L232.BaseService_ind: base-year service output of industry final demand
# Base service is equal to the output of the industry supplysector
L232.StubTechProd_industry %>%
select(region, year, calOutputValue) %>%
rename(base.service = calOutputValue) %>%
mutate(energy.final.demand = A32.demand[["energy.final.demand"]]) ->
L232.BaseService_ind
# L232.IncomeElasticity_ind_scen: income elasticity of industry (scenario-specific)
L102.pcgdp_thous90USD_GCAM3_R_Y %>%
# Combine GCAM 3.0 with the SSPs, and subset only the relevant years
mutate(scenario = 'GCAM3') %>%
bind_rows(L102.pcgdp_thous90USD_Scen_R_Y) %>%
filter(year %in% c(max(MODEL_BASE_YEARS), MODEL_FUTURE_YEARS)) %>%
# Per-capita GDP ratios, which are used in the equation for demand growth
group_by(GCAM_region_ID, scenario) %>%
mutate(temp_lag = lag(value, 1),
value = value / temp_lag) %>%
ungroup %>%
select(-temp_lag) %>%
filter(year %in% MODEL_FUTURE_YEARS) ->
L232.pcgdpRatio_ALL_R_Y # intermediate tibble
# Calculate the industrial output as the base-year industrial output times the GDP ratio raised to the income elasticity
# The income elasticity is looked up based on the prior year's output
L232.pcgdpRatio_ALL_R_Y %>%
select(GCAM_region_ID, scenario) %>%
distinct %>%
left_join_error_no_match(GCAM_region_names, by = 'GCAM_region_ID') %>%
mutate(year = max(MODEL_BASE_YEARS)) %>%
left_join_error_no_match(L232.BaseService_ind, by = c("year", "region")) %>%
left_join_error_no_match(L101.Pop_thous_GCAM3_R_Y, by = c("year", "GCAM_region_ID")) %>%
mutate(value = base.service * CONV_BIL_THOUS / value) %>%
select(-base.service, -energy.final.demand) ->
L232.Output_ind
# At each time, the output is equal to the prior period's output times the GDP ratio, raised to the elasticity
# that corresponds to the output that was observed in the prior time period. This method prevents (ideally) runaway
# industrial production.
elast_years <- c(max(MODEL_BASE_YEARS), MODEL_FUTURE_YEARS)
for(i in seq_along(elast_years)[-1]) {
L232.Output_ind %>%
filter(year == elast_years[i - 1]) %>%
left_join(filter(L232.pcgdpRatio_ALL_R_Y, year == elast_years[i]), by = c("GCAM_region_ID", "scenario")) %>% # strick left join fails timeshift test due to NAs in L102.pcgdp_thous90USD_Scen_R_Y under timeshift mode
mutate(parameter = approx(x = A32.inc_elas_output[["pc.output_GJ"]],
y = A32.inc_elas_output[["inc_elas"]],
xout = value.x,
rule = 2)[['y']],
value = value.x * value.y ^ parameter,
year = elast_years[i]) %>%
select(GCAM_region_ID, scenario, region, year, value) %>%
bind_rows(L232.Output_ind) ->
L232.Output_ind
}
# Now that we have industrial output, we can back out the appropriate income elasticities
L232.Output_ind %>%
filter(year %in% MODEL_FUTURE_YEARS) %>%
mutate(value = approx( x = A32.inc_elas_output[["pc.output_GJ"]],
y = A32.inc_elas_output[["inc_elas"]],
xout = value,
rule = 2)[["y"]]) %>%
mutate(value = round(value, energy.DIGITS_INCELAS_IND)) %>%
rename(income.elasticity = value) %>%
mutate(energy.final.demand = A32.demand[["energy.final.demand"]]) ->
L232.IncomeElasticity_ind # intermediate tibble
# KVC: SSP1 needs lower income elasticities. Storyline has limited growth in energy-related industries
# because of warm fuzzy feelings about environment. We are hard-coding this for a while.
L232.IncomeElasticity_ind %>%
filter(scenario == "SSP1") %>%
mutate(income.elasticity = income.elasticity * 0.75) %>%
bind_rows(filter(L232.IncomeElasticity_ind, scenario != "SSP1")) ->
L232.IncomeElasticity_ind
# ===================================================
# Produce outputs
# Extract GCAM3, SSP, and gSSP data and assign to separate tables
for(ieo in INCOME_ELASTICITY_OUTPUTS) {
L232.IncomeElasticity_ind %>%
filter(scenario == ieo) %>%
select(LEVEL2_DATA_NAMES[["IncomeElasticity"]]) %>%
add_title(paste("Income elasticity of industry -", ieo)) %>%
add_units("Unitless") %>%
add_comments("First calculate industrial output as the base-year industrial output times the GDP ratio raised to the income elasticity") %>%
add_comments("Then back out the appropriate income elasticities from industrial output") %>%
add_comments("Note lower income elasticities for SSP1 are hard-coded.") %>%
add_legacy_name(paste0("L232.IncomeElasticity_ind_", tolower(ieo))) %>%
add_precursors("L102.pcgdp_thous90USD_GCAM3_R_Y", "L102.pcgdp_thous90USD_Scen_R_Y", "common/GCAM_region_names", "L1322.in_EJ_R_indenergy_F_Yh", "L123.in_EJ_R_indchp_F_Yh", "energy/calibrated_techs", "L1322.in_EJ_R_indfeed_F_Yh", "energy/A32.globaltech_eff", "energy/A32.globaltech_shrwt", "energy/A32.demand", "L101.Pop_thous_GCAM3_R_Y", "socioeconomics/A32.inc_elas_output") ->
x
assign(paste0("L232.IncomeElasticity_ind_", tolower(ieo)), x)
}
L232.Supplysector_ind %>%
add_title("Supply sector information for industry sector") %>%
add_units("NA") %>%
add_comments("For industry sector, the supply sector information (output.unit, input.unit, price.unit, logit.year.fillout, logit.exponent) from A32.sector is expended into all GCAM regions") %>%
add_legacy_name("L232.Supplysector_ind") %>%
add_precursors("common/GCAM_region_names", "energy/A32.sector") ->
L232.Supplysector_ind
L232.SubsectorLogit_ind %>%
add_title("Subsector logit exponents of industry sector") %>%
add_units("Unitless") %>%
add_comments("For industry sector, the subsector logit exponents from A32.subsector_logit are expanded into all GCAM regions with non-existent heat subsectors removed") %>%
add_legacy_name("L232.SubsectorLogit_ind") %>%
add_precursors("energy/A32.subsector_logit", "common/GCAM_region_names", "energy/A_regions", "energy/calibrated_techs") ->
L232.SubsectorLogit_ind
L232.FinalEnergyKeyword_ind %>%
add_title("Supply sector keywords for industry sector") %>%
add_units("NA") %>%
add_comments("Set supply sector keywords for industry sector for all GCAM regions") %>%
add_legacy_name("L232.FinalEnergyKeyword_ind") %>%
add_precursors("energy/A32.sector", "common/GCAM_region_names") ->
L232.FinalEnergyKeyword_ind
L232.SubsectorShrwtFllt_ind %>%
add_title("Subsector shareweights of industry sector") %>%
add_units("Unitless") %>%
add_comments("For industry sector, the subsector shareweights from A32.subsector_shrwt are expanded into all GCAM regions with non-existent heat technologies") %>%
add_legacy_name("L232.SubsectorShrwtFllt_ind") %>%
add_precursors("energy/A32.subsector_shrwt", "common/GCAM_region_names", "energy/A_regions", "energy/calibrated_techs") ->
L232.SubsectorShrwtFllt_ind
L232.SubsectorInterp_ind %>%
add_title("Subsector shareweight interpolation of industry sector") %>%
add_units("NA") %>%
add_comments("For industry sector, the subsector shareweight interpolation function infromation from A32.subsector_interp is expanded into all GCAM regions with non-existent heat technologies removed") %>%
add_legacy_name("L232.SubsectorInterp_ind") %>%
add_precursors("energy/A32.subsector_interp", "common/GCAM_region_names", "energy/A_regions", "energy/calibrated_techs") ->
L232.SubsectorInterp_ind
L232.StubTech_ind %>%
add_title("Identification of stub technologies of industrial sector") %>%
add_units("NA") %>%
add_comments("For industry sector, the stub technologies from A32.globaltech_shrwt are expanded into all GCAM regions with non-existent heat technologies removed") %>%
add_legacy_name("L232.StubTech_ind") %>%
add_precursors("energy/A32.globaltech_shrwt", "common/GCAM_region_names", "energy/A_regions", "energy/calibrated_techs") ->
L232.StubTech_ind
L232.GlobalTechShrwt_ind %>%
add_title("Shareweights of global industrial sector technologies") %>%
add_units("Unitless") %>%
add_comments("For industry sector, the share weights from A32.globaltech_shrwt are interpolated into all base years and future years") %>%
add_legacy_name("L232.GlobalTechShrwt_ind") %>%
add_precursors("energy/A32.globaltech_shrwt") ->
L232.GlobalTechShrwt_ind
L232.StubTechInterp_ind %>%
add_title("Shareweight interpolation of global industrial sector technologies") %>%
add_units("NA") %>%
add_comments("For industry sector, the interpolation function from A32.globaltech_interp are expanded into all GCAM regions") %>%
add_legacy_name("L232.StubTechInterp_ind") %>%
add_precursors("energy/A32.globaltech_interp", "common/GCAM_region_names") ->
L232.StubTechInterp_ind
L232.GlobalTechEff_ind %>%
add_title("Energy inputs and efficiency of global industrial energy use and feedstocks technologies") %>%
add_units("Unitless") %>%
add_comments("For industry sector, the efficiency values from A32.globaltech_eff are interpolated into all base years and future years") %>%
add_legacy_name("L232.GlobalTechEff_ind") %>%
add_precursors("energy/A32.globaltech_eff") ->
L232.GlobalTechEff_ind
L232.GlobalTechCoef_ind %>%
add_title("Energy inputs and coefficients of global industry technologies") %>%
add_units("Unitless") %>%
add_comments("For industry sector, the coefficients from A32.globaltech_coef are interpolated into all base years and future years") %>%
add_legacy_name("L232.GlobalTechCoef_ind") %>%
add_precursors("energy/A32.globaltech_coef") ->
L232.GlobalTechCoef_ind
L232.GlobalTechCost_ind %>%
add_title("Capital costs of global industrial technologies") %>%
add_units("1975$/GJ") %>%
add_comments("For industry sector, the capital costs from A32.globaltech_cost are interpolated into all base years and future years") %>%
add_legacy_name("L232.GlobalTechCost_ind") %>%
add_precursors("energy/A32.globaltech_cost") ->
L232.GlobalTechCost_ind
L232.GlobalTechSecOut_ind %>%
add_title("Secondary output ratios of industrial cogeneration technologies") %>%
add_units("Unitless") %>%
add_comments("Secondary output ratios are calculated as electricity ratio (Assumed CHP electricity output per unit fuel input) over efficiency") %>%
add_legacy_name("L232.GlobalTechSecOut_ind") %>%
add_precursors("energy/A23.chp_elecratio", "energy/A32.globaltech_eff") ->
L232.GlobalTechSecOut_ind
L232.GlobalTechCSeq_ind %>%
add_title("CO2 capture fractions from global electricity generation technologies") %>%
add_units("Unitless") %>%
add_comments("Remove fractions from A32.nonenergy_Cseq are expanded into all model years") %>%
add_legacy_name("L232.GlobalTechCSeq_ind") %>%
add_precursors("energy/A32.nonenergy_Cseq") ->
L232.GlobalTechCSeq_ind
L232.StubTechCalInput_indenergy %>%
add_title("Calibrated input of industrial energy use technologies (including cogen)") %>%
add_units("EJ") %>%
add_comments("Calibrated input of industrial energy use values are calculated using L1322.in_EJ_R_indenergy_F_Yh then added information such as subsector, technology, minicam.energy.input, calibration, tech.share.weight, and etc.") %>%
add_legacy_name("L232.StubTechCalInput_indenergy") %>%
add_precursors("L1322.in_EJ_R_indenergy_F_Yh", "energy/calibrated_techs", "energy/A32.globaltech_eff") ->
L232.StubTechCalInput_indenergy
L232.StubTechCalInput_indfeed %>%
add_title("Calibrated input of industrial feedstock technologies") %>%
add_units("EJ") %>%
add_comments("calibrated input of industrial feedstock technologies values are calculated using L1322.in_EJ_R_indfeed_F_Yh then added information such as subsector, technology, minicam.energy.input, calibration, tech.share.weight, and etc.") %>%
add_legacy_name("L232.StubTechCalInput_indfeed") %>%
add_precursors("L1322.in_EJ_R_indfeed_F_Yh", "common/GCAM_region_names", "energy/calibrated_techs", "energy/A32.globaltech_eff") ->
L232.StubTechCalInput_indfeed
L232.StubTechProd_industry %>%
add_title("Calibrated output of industrial sector") %>%
add_units("EJ") %>%
add_comments("Service output values are calculated by technology, for energy-use and feedstocks then aggregated by region") %>%
add_legacy_name("L232.StubTechProd_industry") %>%
add_precursors("L1322.in_EJ_R_indenergy_F_Yh", "L123.in_EJ_R_indchp_F_Yh", "common/GCAM_region_names", "energy/calibrated_techs", "L1322.in_EJ_R_indfeed_F_Yh", "energy/A32.globaltech_eff", "energy/A32.globaltech_shrwt") ->
L232.StubTechProd_industry
L232.StubTechCoef_industry %>%
add_title("Calibrated output of industrial sector") %>%
add_units("Unitless") %>%
add_comments("Service output values were first aggregated by sector to calculate the portion of each input as coefficients, then the coefficients were interpolated to cover last base year, future years and industry coefficient convergence year") %>%
add_legacy_name("L232.StubTechCoef_industry") %>%
add_precursors("L1322.in_EJ_R_indenergy_F_Yh", "L123.in_EJ_R_indchp_F_Yh", "common/GCAM_region_names", "energy/calibrated_techs", "L1322.in_EJ_R_indfeed_F_Yh", "energy/A32.globaltech_eff", "energy/A32.globaltech_shrwt") ->
L232.StubTechCoef_industry
L232.FuelPrefElast_indenergy %>%
add_title("Fuel preference elasticities of industrial energy use") %>%
add_units("Unitless") %>%
add_comments("First, calculate the fuel shares allocated to each fuel, then set fuel preference elasticities as indicated by exogenous rules(A32.fuelprefElasticity), lastly fill out elasticities from the first future model time period") %>%
add_legacy_name("L232.FuelPrefElast_indenergy") %>%
add_precursors("L1322.in_EJ_R_indenergy_F_Yh", "L123.in_EJ_R_indchp_F_Yh", "common/GCAM_region_names", "energy/calibrated_techs", "energy/A32.fuelprefElasticity") ->
L232.FuelPrefElast_indenergy
L232.PerCapitaBased_ind %>%
add_title("Per-capita based flag for industry final demand") %>%
add_units("NA") %>%
add_comments("Extracted per-capita based flag for industry final demand from A32.demand") %>%
add_legacy_name("L232.PerCapitaBased_ind") %>%
add_precursors("common/GCAM_region_names", "energy/A32.demand") ->
L232.PerCapitaBased_ind
L232.PriceElasticity_ind %>%
add_title("Price elasticity of industry final demand") %>%
add_units("Unitless") %>%
add_comments("Extracted price elasticity of industry final demand from A32.demand") %>%
add_comments("Price elasticities are only applied to future periods. Application in base years will cause solution failure") %>%
add_legacy_name("L232.PriceElasticity_ind") %>%
add_precursors("common/GCAM_region_names", "energy/A32.demand") ->
L232.PriceElasticity_ind
L232.BaseService_ind %>%
add_title("Base-year service output of industry final demand") %>%
add_units("EJ") %>%
add_comments("Extracted base-year service output of industry final demand from L232.StubTechProd_industry") %>%
add_legacy_name("L232.BaseService_ind") %>%
add_precursors("L1322.in_EJ_R_indenergy_F_Yh", "L123.in_EJ_R_indchp_F_Yh", "common/GCAM_region_names", "energy/calibrated_techs", "L1322.in_EJ_R_indfeed_F_Yh", "energy/A32.globaltech_eff", "energy/A32.globaltech_shrwt", "energy/A32.demand") ->
L232.BaseService_ind
return_data(L232.Supplysector_ind, L232.SubsectorLogit_ind, L232.FinalEnergyKeyword_ind,
L232.SubsectorShrwtFllt_ind, L232.SubsectorInterp_ind,
L232.StubTech_ind, L232.GlobalTechShrwt_ind,
L232.StubTechInterp_ind, L232.GlobalTechEff_ind, L232.GlobalTechCoef_ind,
L232.GlobalTechCost_ind, L232.GlobalTechSecOut_ind, L232.GlobalTechCSeq_ind,
L232.StubTechCalInput_indenergy, L232.StubTechCalInput_indfeed, L232.StubTechProd_industry,
L232.StubTechCoef_industry, L232.FuelPrefElast_indenergy, L232.PerCapitaBased_ind,
L232.PriceElasticity_ind, L232.BaseService_ind,
L232.IncomeElasticity_ind_gcam3, L232.IncomeElasticity_ind_gssp1,
L232.IncomeElasticity_ind_gssp2, L232.IncomeElasticity_ind_gssp3,
L232.IncomeElasticity_ind_gssp4, L232.IncomeElasticity_ind_gssp5,
L232.IncomeElasticity_ind_ssp1, L232.IncomeElasticity_ind_ssp2,
L232.IncomeElasticity_ind_ssp3, L232.IncomeElasticity_ind_ssp4,
L232.IncomeElasticity_ind_ssp5)
} else {
stop("Unknown command")
}
}
rohmin9122/gcam-korea-release documentation built on Nov. 26, 2020, 8:11 a.m.
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Defines functions module_energy_L232.industry
Documented in module_energy_L232.industry
#' module_energy_L232.industry
#'
#' Compute a variety of final energy keyword, sector, share weight, and technology information for industry-related GCAM inputs.
#'
#' @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{L232.SectorLogitTables[[ curr_table ]]$data},
#' \code{L232.Supplysector_ind}, \code{L232.SubsectorLogitTables[[ curr_table ]]$data},
#' \code{L232.SubsectorLogit_ind}, \code{L232.FinalEnergyKeyword_ind},
#' \code{L232.SubsectorShrwtFllt_ind}, \code{L232.SubsectorInterp_ind},
#' \code{L232.StubTech_ind}, \code{L232.GlobalTechShrwt_ind}, \code{L232.StubTechInterp_ind},
#' \code{L232.GlobalTechEff_ind}, \code{L232.GlobalTechCoef_ind}, \code{L232.GlobalTechCost_ind},
#' \code{L232.GlobalTechSecOut_ind}, \code{L232.GlobalTechCSeq_ind},
#' \code{L232.StubTechCalInput_indenergy}, \code{L232.StubTechCalInput_indfeed},
#' \code{L232.StubTechProd_industry}, \code{L232.StubTechCoef_industry},
#' \code{L232.FuelPrefElast_indenergy}, \code{L232.PerCapitaBased_ind},
#' \code{L232.PriceElasticity_ind}, \code{L232.BaseService_ind},
#' \code{L232.IncomeElasticity_ind_gcam3}, \code{L232.IncomeElasticity_ind_gssp1},
#' \code{L232.IncomeElasticity_ind_gssp2}, \code{L232.IncomeElasticity_ind_gssp3},
#' \code{L232.IncomeElasticity_ind_gssp4}, \code{L232.IncomeElasticity_ind_gssp5},
#' \code{L232.IncomeElasticity_ind_ssp1}, \code{L232.IncomeElasticity_ind_ssp2},
#' \code{L232.IncomeElasticity_ind_ssp3}, \code{L232.IncomeElasticity_ind_ssp4},
#' \code{L232.IncomeElasticity_ind_ssp5}, \code{object}. The corresponding file in the
#' original data system was \code{L232.industry.R} (energy level2).
#' @details The chunk provides final energy keyword, supplysector/subsector information, supplysector/subsector interpolation information, supplysector/subsector share weights, global technology share weight, global technology efficiency, global technology coefficients, global technology cost, price elasticity, stub technology information, stub technology interpolation information, stub technology calibrated inputs, and etc.
#' @importFrom assertthat assert_that
#' @importFrom dplyr filter mutate select
#' @importFrom tidyr gather spread
#' @author LF October 2017
module_energy_L232.industry <- function(command, ...) {
INCOME_ELASTICITY_OUTPUTS <- c("GCAM3",
paste0("gSSP", 1:5),
paste0("SSP", 1:5))
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "common/GCAM_region_names",
FILE = "energy/calibrated_techs",
FILE = "energy/A_regions",
FILE = "energy/A23.chp_elecratio",
FILE = "energy/A32.sector",
FILE = "energy/A32.subsector_interp",
FILE = "energy/A32.subsector_logit",
FILE = "energy/A32.subsector_shrwt",
FILE = "energy/A32.globaltech_coef",
FILE = "energy/A32.globaltech_cost",
FILE = "energy/A32.globaltech_eff",
FILE = "energy/A32.globaltech_shrwt",
FILE = "energy/A32.globaltech_interp",
FILE = "energy/A32.nonenergy_Cseq",
FILE = "energy/A32.fuelprefElasticity",
FILE = "energy/A32.demand",
"L123.in_EJ_R_indchp_F_Yh",
"L1322.in_EJ_R_indenergy_F_Yh",
"L1322.in_EJ_R_indfeed_F_Yh",
FILE = "socioeconomics/A32.inc_elas_output",
"L101.Pop_thous_GCAM3_R_Y",
"L102.pcgdp_thous90USD_GCAM3_R_Y",
"L102.pcgdp_thous90USD_Scen_R_Y"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L232.Supplysector_ind",
"L232.SubsectorLogit_ind",
"L232.FinalEnergyKeyword_ind",
"L232.SubsectorShrwtFllt_ind",
"L232.SubsectorInterp_ind",
"L232.StubTech_ind",
"L232.GlobalTechShrwt_ind",
"L232.StubTechInterp_ind",
"L232.GlobalTechEff_ind",
"L232.GlobalTechCoef_ind",
"L232.GlobalTechCost_ind",
"L232.GlobalTechSecOut_ind",
"L232.GlobalTechCSeq_ind",
"L232.StubTechCalInput_indenergy",
"L232.StubTechCalInput_indfeed",
"L232.StubTechProd_industry",
"L232.StubTechCoef_industry",
"L232.FuelPrefElast_indenergy",
"L232.PerCapitaBased_ind",
"L232.PriceElasticity_ind",
"L232.BaseService_ind",
paste("L232.IncomeElasticity_ind", tolower(INCOME_ELASTICITY_OUTPUTS), sep = "_")))
} else if(command == driver.MAKE) {
all_data <- list(...)[[1]]
# Load required inputs
GCAM_region_names <- get_data(all_data, "common/GCAM_region_names")
calibrated_techs <- get_data(all_data, "energy/calibrated_techs")
A_regions <- get_data(all_data, "energy/A_regions")
A23.chp_elecratio <- get_data(all_data, "energy/A23.chp_elecratio")
A32.sector <- get_data(all_data, "energy/A32.sector")
A32.subsector_interp <- get_data(all_data, "energy/A32.subsector_interp")
A32.subsector_logit <- get_data(all_data, "energy/A32.subsector_logit")
A32.subsector_shrwt <- get_data(all_data, "energy/A32.subsector_shrwt")
A32.globaltech_coef <- get_data(all_data, "energy/A32.globaltech_coef")
A32.globaltech_cost <- get_data(all_data, "energy/A32.globaltech_cost")
A32.globaltech_eff <- get_data(all_data, "energy/A32.globaltech_eff")
A32.globaltech_shrwt <- get_data(all_data, "energy/A32.globaltech_shrwt")
A32.globaltech_interp <- get_data(all_data, "energy/A32.globaltech_interp")
A32.nonenergy_Cseq <- get_data(all_data, "energy/A32.nonenergy_Cseq")
A32.fuelprefElasticity <- get_data(all_data, "energy/A32.fuelprefElasticity")
A32.demand <- get_data(all_data, "energy/A32.demand")
L123.in_EJ_R_indchp_F_Yh <- get_data(all_data, "L123.in_EJ_R_indchp_F_Yh")
L1322.in_EJ_R_indenergy_F_Yh <- get_data(all_data, "L1322.in_EJ_R_indenergy_F_Yh")
L1322.in_EJ_R_indfeed_F_Yh <- get_data(all_data, "L1322.in_EJ_R_indfeed_F_Yh")
A32.inc_elas_output <- get_data(all_data, "socioeconomics/A32.inc_elas_output")
L101.Pop_thous_GCAM3_R_Y <- get_data(all_data, "L101.Pop_thous_GCAM3_R_Y")
L102.pcgdp_thous90USD_GCAM3_R_Y <- get_data(all_data, "L102.pcgdp_thous90USD_GCAM3_R_Y")
L102.pcgdp_thous90USD_Scen_R_Y <- get_data(all_data, "L102.pcgdp_thous90USD_Scen_R_Y")
# ===================================================
# 0. Give binding for variable names used in pipeline
has_district_heat <- sector <- fuel <- supplysector <- subsector <-
technology <- year.fillout <- to.value <- year <- share.weight <-
efficiency <- minicam.energy.input <- secondary.output <- coefficient <-
elec_ratio <- output.ratio <- . <- year.x <- output.ratio.x <- output.ratio.y <-
input.cost <- minicam.non.energy.input <- GCAM_region_ID <- value <-
calibrated.value <- sector.name <- subsector.name <- region <-
calOutputValue <- subs.share.weight <- calOutputValue.x <- calOutputValue.y <-
output_tot <- value.x <- value.y <- total <- fuelprefElasticity <-
terminal_coef <- criteria <- scenario <- temp_lag <- base.service <- energy.final.demand <-
parameter <- income.elasticity <- L232.IncomeElasticity_ind_gcam3 <-
L232.IncomeElasticity_ind_gssp1 <- L232.IncomeElasticity_ind_gssp2 <-
L232.IncomeElasticity_ind_gssp3 <- L232.IncomeElasticity_ind_gssp4 <-
L232.IncomeElasticity_ind_gssp5 <- L232.IncomeElasticity_ind_ssp1 <-
L232.IncomeElasticity_ind_ssp2 <- L232.IncomeElasticity_ind_ssp3 <-
L232.IncomeElasticity_ind_ssp4 <- L232.IncomeElasticity_ind_ssp5 <-
market.name <- stub.technology <- NULL
# ===================================================
# 1. Perform computations
# Create tables to delete technologies and subsectors in regions where heat is not modeled as a fuel
has_not_heat <- filter(A_regions, has_district_heat == 0) # intermediate tibble
calibrated_techs %>%
filter(grepl("industry", sector) & fuel == "heat") %>%
select(supplysector, subsector, technology) %>%
repeat_add_columns(tibble(GCAM_region_ID = has_not_heat[["GCAM_region_ID"]])) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") ->
L232.rm_heat_techs_R # intermediate tibble
# 1a. Supplysector information
# L232.Supplysector_ind: Supply sector information for industry sector
A32.sector %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["Supplysector"]], LOGIT_TYPE_COLNAME), GCAM_region_names) ->
L232.Supplysector_ind
# L232.FinalEnergyKeyword_ind: Supply sector keywords for industry sector
A32.sector %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["FinalEnergyKeyword"]], GCAM_region_names) %>%
na.omit ->
L232.FinalEnergyKeyword_ind
# 1b. Subsector information
# L232.SubsectorLogit_ind: Subsector logit exponents of industry sector
A32.subsector_logit %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["SubsectorLogit"]], LOGIT_TYPE_COLNAME), GCAM_region_names) %>%
anti_join(L232.rm_heat_techs_R, by = c("region", "subsector")) -> # Remove non-existent heat subsectors from each region
L232.SubsectorLogit_ind
# L232.SubsectorShrwtFllt_ind: Subsector shareweights of industry sector
A32.subsector_shrwt %>%
filter(!is.na(year.fillout)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["SubsectorShrwtFllt"]], GCAM_region_names) %>%
anti_join(L232.rm_heat_techs_R, by = c("region", "subsector")) -> # Remove non-existent heat technologies from each region
L232.SubsectorShrwtFllt_ind
# L232.SubsectorInterp_ind: Subsector shareweight interpolation of industry sector
A32.subsector_interp %>%
filter(is.na(to.value)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["SubsectorInterp"]], GCAM_region_names) %>%
anti_join(L232.rm_heat_techs_R, by = c("region", "subsector")) -> # Remove non-existent heat technologies from each region
L232.SubsectorInterp_ind
# 1c. Technology information
# L232.StubTech_ind: Identification of stub technologies of industrial sector
# Note: assuming that technology list in the shareweight table includes the full set (any others would default to a 0 shareweight)
A32.globaltech_shrwt %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["Tech"]], GCAM_region_names) %>%
anti_join(L232.rm_heat_techs_R, by = c("region", "technology")) %>% # Remove non-existent heat technologies from each region
rename(stub.technology = technology) ->
L232.StubTech_ind
# L232.GlobalTechShrwt_ind: Shareweights of global industrial sector technologies
A32.globaltech_shrwt %>%
gather_years(value_col = "share.weight") %>%
complete(nesting(supplysector, subsector, technology), year = c(year, MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)) %>%
arrange(supplysector, subsector, technology, year) %>%
group_by(supplysector, subsector, technology) %>%
mutate(share.weight = approx_fun(year, share.weight, rule = 1)) %>%
ungroup %>%
filter(year %in% c(MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)) %>%
rename(sector.name = supplysector,
subsector.name = subsector) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechYr"]], "share.weight") ->
L232.GlobalTechShrwt_ind
# L232.StubTechInterp_ind: Shareweight interpolation of global industrial sector technologies
A32.globaltech_interp %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["TechInterp"]], GCAM_region_names) %>%
rename(stub.technology = technology) ->
L232.StubTechInterp_ind
# L232.GlobalTechEff_ind: Energy inputs and efficiency of global industrial energy use and feedstocks technologies
A32.globaltech_eff %>%
gather_years(value_col = "efficiency") %>%
complete(nesting(supplysector, subsector, technology, minicam.energy.input, secondary.output),
year = c(year, MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)) %>%
arrange(supplysector, subsector, technology, minicam.energy.input, secondary.output, year) %>%
group_by(supplysector, subsector, technology, minicam.energy.input, secondary.output) %>%
mutate(efficiency = approx_fun(year, efficiency, rule = 1),
efficiency = round(efficiency, energy.DIGITS_EFFICIENCY)) %>%
ungroup %>%
filter(year %in% c(MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)) %>%
# Assign the columns "sector.name" and "subsector.name", consistent with the location info of a global technology
rename(sector.name = supplysector,
subsector.name = subsector) ->
L232.globaltech_eff.long # intermediate tibble
L232.globaltech_eff.long %>%
select(LEVEL2_DATA_NAMES[["GlobalTechEff"]]) ->
L232.GlobalTechEff_ind
# Coefficients on global industry sector technologies (not energy-use or feedstocks)
# L232.GlobalTechCoef_ind: Energy inputs and coefficients of global industry technologies
A32.globaltech_coef %>%
rename(coefficient = "terminal_coef") %>%
repeat_add_columns(tibble(year = c(year, MODEL_BASE_YEARS, MODEL_FUTURE_YEARS))) %>%
rename(sector.name = supplysector,
subsector.name = subsector) %>% # Assign the columns "sector.name" and "subsector.name", consistent with the location info of a global technology
select(LEVEL2_DATA_NAMES[["GlobalTechCoef"]]) ->
L232.GlobalTechCoef_ind
# Secondary outputs of cogen technologies: these are input as a ratio
# L232.GlobalTechSecOut_ind: Secondary output ratios of industrial cogeneration technologies
A32.globaltech_eff %>%
gather_years(value_col = "efficiency") %>%
complete(nesting(supplysector, subsector, technology, minicam.energy.input, secondary.output),
year = c(year, MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)) %>%
arrange(supplysector, subsector, technology, minicam.energy.input, secondary.output, year) %>%
group_by(supplysector, subsector, technology, minicam.energy.input, secondary.output) %>%
mutate(efficiency = approx_fun(year, efficiency, rule = 1),
efficiency = round(efficiency, energy.DIGITS_EFFICIENCY)) %>%
filter(year %in% c(MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)) %>%
filter(!is.na(secondary.output)) %>%
left_join_error_no_match(A23.chp_elecratio, by = c("subsector" = "fuel")) %>%
mutate(output.ratio = elec_ratio / efficiency,
output.ratio = round(output.ratio, energy.DIGITS_EFFICIENCY)) %>%
# NOTE: holding the output ratio constant over time in future periods
left_join_error_no_match(select(filter(., year == max(MODEL_BASE_YEARS)), -efficiency, -elec_ratio),
by = c("supplysector", "subsector", "technology", "minicam.energy.input", "secondary.output")) %>%
mutate(output.ratio = if_else(year.x %in% MODEL_BASE_YEARS, output.ratio.x, output.ratio.y)) %>%
ungroup %>%
rename(year = year.x,
sector.name = supplysector,
subsector.name = subsector) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechSecOut"]]) ->
L232.GlobalTechSecOut_ind
# Costs of global technologies
# L232.GlobalTechCost_ind: Capital costs of global industrial technologies
A32.globaltech_cost %>%
gather_years(value_col = "input.cost") %>%
complete(nesting(supplysector, subsector, technology, minicam.non.energy.input),
year = c(year, MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)) %>%
arrange(supplysector, subsector, technology, minicam.non.energy.input, year) %>%
group_by(supplysector, subsector, technology, minicam.non.energy.input) %>%
mutate(input.cost = approx_fun(year, input.cost, rule = 1)) %>%
ungroup %>%
filter(year %in% c(MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)) %>%
rename(sector.name = supplysector,
subsector.name = subsector) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechCost"]]) ->
L232.GlobalTechCost_ind
# Carbon capture from feedstock carbon sequestration
# L232.GlobalTechCSeq_ind: CO2 capture fractions from global electricity generation technologies
## No need to consider historical periods or intermittent technologies here
A32.nonenergy_Cseq %>%
repeat_add_columns(tibble(year = c(MODEL_BASE_YEARS, MODEL_FUTURE_YEARS))) %>%
rename(sector.name = supplysector,
subsector.name = subsector) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechCSeq"]]) ->
L232.GlobalTechCSeq_ind
# Calibration and region-specific data
# L232.StubTechCalInput_indenergy: calibrated input of industrial energy use technologies (including cogen)
L1322.in_EJ_R_indenergy_F_Yh %>%
bind_rows(L123.in_EJ_R_indchp_F_Yh) %>%
complete(nesting(GCAM_region_ID, sector, fuel), year = c(year, MODEL_BASE_YEARS)) %>%
arrange(GCAM_region_ID, sector, fuel, year) %>%
group_by(GCAM_region_ID, sector, fuel) %>%
mutate(value = approx_fun(year, value, rule = 1)) %>%
ungroup %>%
filter(year %in% MODEL_BASE_YEARS) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join(select(calibrated_techs, sector, fuel, supplysector, subsector, technology), by = c("sector", "fuel")) %>%
rename(stub.technology = technology) ->
L232.in_EJ_R_indenergy_F_Yh # intermediate tibble
L232.in_EJ_R_indenergy_F_Yh %>%
left_join_error_no_match(distinct(select(A32.globaltech_eff, subsector, technology, minicam.energy.input)),
by = c("subsector", "stub.technology" = "technology")) %>%
mutate(calibrated.value = round(value, energy.DIGITS_CALOUTPUT),
share.weight.year = year) %>%
rename(calOutputValue = calibrated.value) %>% # temporary column name change to accommodate function set_subsector_shrwt
set_subsector_shrwt %>%
rename(calibrated.value = calOutputValue) %>% # temporary column name change to accommodate function set_subsector_shrwt
mutate(tech.share.weight = if_else(calibrated.value > 0, 1, 0)) %>%
select(LEVEL2_DATA_NAMES[["StubTechCalInput"]]) ->
L232.StubTechCalInput_indenergy
# L232.StubTechCalInput_indfeed: calibrated input of industrial feedstock technologies
L1322.in_EJ_R_indfeed_F_Yh %>%
filter(year %in% MODEL_BASE_YEARS) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_error_no_match(distinct(select(calibrated_techs, sector, fuel, supplysector, subsector, technology)),
by = c("sector", "fuel")) %>%
rename(stub.technology = technology) ->
L232.in_EJ_R_indfeed_F_Yh # intermediate tibble
L232.in_EJ_R_indfeed_F_Yh %>%
select(LEVEL2_DATA_NAMES[["StubTechYr"]], "value") %>%
left_join(distinct(select(A32.globaltech_eff, subsector, technology, minicam.energy.input)),
by = c("subsector", "stub.technology" = "technology")) %>%
mutate(calibrated.value = round(value, energy.DIGITS_CALOUTPUT)) %>%
select(-value) %>%
mutate(share.weight.year = year) %>%
rename(calOutputValue = calibrated.value) %>% # temporary column name change to accommodate function set_subsector_shrwt
set_subsector_shrwt %>%
rename(calibrated.value = calOutputValue) %>% # temporary column name change to accommodate function set_subsector_shrwt
mutate(tech.share.weight = if_else(calibrated.value > 0, 1, 0)) ->
L232.StubTechCalInput_indfeed
# L232.StubTechProd_industry: calibrated output of industrial sector
# First, calculate service output by technology, for energy-use and feedstocks
L232.in_EJ_R_indenergy_F_Yh %>%
bind_rows(L232.in_EJ_R_indfeed_F_Yh) %>%
left_join_error_no_match(select(L232.globaltech_eff.long, sector.name, subsector.name, technology, year,efficiency),
by = c("supplysector" = "sector.name", "subsector" = "subsector.name",
"stub.technology" = "technology", "year")) %>%
mutate(calOutputValue = round(value * efficiency, energy.DIGITS_CALOUTPUT)) ->
L232.out_EJ_R_ind_serv_F_Yh # intermediate tibble
# intermediate tibble to extract industry names
A32.globaltech_shrwt %>%
filter(supplysector == "industry") %>%
select(supplysector, subsector, technology) ->
L232.industry_names
# Aggregate service output by region. This is the output of the industrial sector in each region.
L232.out_EJ_R_ind_serv_F_Yh %>%
group_by(region, GCAM_region_ID, year) %>%
summarise(calOutputValue = sum(calOutputValue)) %>%
ungroup %>%
mutate(supplysector = L232.industry_names[["supplysector"]],
subsector = L232.industry_names[["subsector"]],
stub.technology = L232.industry_names[["technology"]],
share.weight.year = year,
subs.share.weight = if_else(calOutputValue > 0, 1, 0),
tech.share.weight = subs.share.weight) %>%
select(LEVEL2_DATA_NAMES[["StubTechProd"]]) ->
L232.StubTechProd_industry
# L232.StubTechCoef_industry: calibrated output of industrial sector
# Next, aggregate service output by sector to calculate the portion of each input
L232.out_EJ_R_ind_serv_F_Yh %>%
group_by(region, GCAM_region_ID, supplysector, year) %>%
summarise(calOutputValue = sum(calOutputValue)) %>%
ungroup %>%
left_join_error_no_match(select(L232.StubTechProd_industry, calOutputValue, region, year),
by = c("region", "year")) %>%
rename(calOutputValue = calOutputValue.x,
output_tot = calOutputValue.y) %>%
mutate(coefficient = calOutputValue / output_tot) %>%
rename(minicam.energy.input = supplysector) %>%
mutate(supplysector = L232.industry_names[["supplysector"]],
subsector = L232.industry_names[["subsector"]],
stub.technology = L232.industry_names[["technology"]],
market.name = region) %>%
select(LEVEL2_DATA_NAMES[["StubTechCoef"]]) ->
L232.StubTechCoef_industry_base # intermediate tibble?
# This set of coefficients covers only the base years; the first "future" period will default to the global tech coefficient
# Instead, interpolate the coefficients to these global default values in a specified period
L232.StubTechCoef_industry_base %>%
complete(nesting(region, supplysector, subsector, stub.technology, minicam.energy.input, market.name),
year = unique(c(MODEL_YEARS, energy.INDCOEF_CONVERGENCE_YR))) %>%
left_join(select(A32.globaltech_coef, supplysector, subsector, technology, minicam.energy.input, terminal_coef),
by = c("supplysector", "subsector", stub.technology = "technology", "minicam.energy.input")) %>%
mutate(coefficient = if_else(year == energy.INDCOEF_CONVERGENCE_YR, terminal_coef, coefficient)) %>%
select(-terminal_coef) %>%
group_by(region, supplysector, subsector, stub.technology, minicam.energy.input) %>%
mutate(coefficient = round(approx_fun(year, coefficient), energy.DIGITS_COEFFICIENT)) %>%
ungroup() %>%
filter(year %in% MODEL_YEARS) -> # drop the terminal coef year if it's outside of the model years
L232.StubTechCoef_industry
# L232.FuelPrefElast_indenergy: fuel preference elasticities of industrial energy use
# First, calculate the fuel shares allocated to each fuel
L232.in_EJ_R_indenergy_F_Yh %>%
group_by(region, GCAM_region_ID, supplysector, subsector, year) %>%
summarise(value = sum(value)) %>%
ungroup ->
L232.indenergy_fuel_shares # intermediate tibble
L232.indenergy_fuel_shares %>%
group_by(region, GCAM_region_ID, supplysector, year) %>%
summarise(value = sum(value)) %>%
ungroup %>%
right_join(L232.indenergy_fuel_shares, by = c("region", "GCAM_region_ID", "supplysector", "year")) %>%
rename(total = value.x,
value = value.y) %>%
mutate(share = value / total) %>%
filter(year == max(MODEL_BASE_YEARS)) %>%
mutate(fuelprefElasticity = 0) ->
L232.indenergy_fuel_shares
# Set fuel preference elasticities as indicated by exogenous rules
# For each row of L232.indenergy_fuel_shares, we check for any entries in A32.fuelprefElasticity
# that have the same `subsector` entry, and apply the rule specified (the share in a 'greater than'
# entry is only used if the share value is larger; similar logic applies to 'lesser than' entries).
# This is an inefficient way to do this, but the datasets involved are small
A32.fuelprefElasticity.gt <- filter(A32.fuelprefElasticity, criteria == "greater than")
for(j in seq_len(nrow(L232.indenergy_fuel_shares))) {
for(i in seq_len(nrow(A32.fuelprefElasticity.gt))) {
L232.indenergy_fuel_shares$fuelprefElasticity[j][
L232.indenergy_fuel_shares$subsector[j] == A32.fuelprefElasticity.gt$subsector[i] &
L232.indenergy_fuel_shares$share[j] > A32.fuelprefElasticity.gt$share[i] ] <-
A32.fuelprefElasticity.gt$fuelprefElasticity[i]
}
}
A32.fuelprefElasticity.lt <- filter(A32.fuelprefElasticity, criteria == "lesser than")
for(j in seq_len(nrow(L232.indenergy_fuel_shares))) {
for(i in seq_len(nrow(A32.fuelprefElasticity.lt))) {
L232.indenergy_fuel_shares$fuelprefElasticity[j][
L232.indenergy_fuel_shares$subsector[j] == A32.fuelprefElasticity.lt$subsector[i] &
L232.indenergy_fuel_shares$share[j] < A32.fuelprefElasticity.lt$share[i] ] <-
A32.fuelprefElasticity.lt$fuelprefElasticity[i]
}
}
# The fuel preference elasticities only matter in future periods. Fill out from the first future model time period
L232.indenergy_fuel_shares %>%
mutate(year.fillout = min(MODEL_FUTURE_YEARS)) %>%
filter(fuelprefElasticity != 0) %>%
select(LEVEL2_DATA_NAMES[["FuelPrefElast"]]) ->
L232.FuelPrefElast_indenergy
# L232.PerCapitaBased_ind: per-capita based flag for industry final demand
tibble(region = GCAM_region_names[["region"]]) %>%
mutate(energy.final.demand = A32.demand[["energy.final.demand"]],
perCapitaBased = A32.demand[["perCapitaBased"]]) ->
L232.PerCapitaBased_ind
# L232.PriceElasticity_ind: price elasticity of industry final demand
# Price elasticities are only applied to future periods. Application in base years will cause solution failure
tibble(region = rep(GCAM_region_names[["region"]], times = length(MODEL_FUTURE_YEARS))) %>%
mutate(energy.final.demand = A32.demand[["energy.final.demand"]],
year = sort(rep(MODEL_FUTURE_YEARS, times = nrow(GCAM_region_names))),
price.elasticity = A32.demand[["price.elasticity"]]) ->
L232.PriceElasticity_ind
# L232.BaseService_ind: base-year service output of industry final demand
# Base service is equal to the output of the industry supplysector
L232.StubTechProd_industry %>%
select(region, year, calOutputValue) %>%
rename(base.service = calOutputValue) %>%
mutate(energy.final.demand = A32.demand[["energy.final.demand"]]) ->
L232.BaseService_ind
# L232.IncomeElasticity_ind_scen: income elasticity of industry (scenario-specific)
L102.pcgdp_thous90USD_GCAM3_R_Y %>%
# Combine GCAM 3.0 with the SSPs, and subset only the relevant years
mutate(scenario = 'GCAM3') %>%
bind_rows(L102.pcgdp_thous90USD_Scen_R_Y) %>%
filter(year %in% c(max(MODEL_BASE_YEARS), MODEL_FUTURE_YEARS)) %>%
# Per-capita GDP ratios, which are used in the equation for demand growth
group_by(GCAM_region_ID, scenario) %>%
mutate(temp_lag = lag(value, 1),
value = value / temp_lag) %>%
ungroup %>%
select(-temp_lag) %>%
filter(year %in% MODEL_FUTURE_YEARS) ->
L232.pcgdpRatio_ALL_R_Y # intermediate tibble
# Calculate the industrial output as the base-year industrial output times the GDP ratio raised to the income elasticity
# The income elasticity is looked up based on the prior year's output
L232.pcgdpRatio_ALL_R_Y %>%
select(GCAM_region_ID, scenario) %>%
distinct %>%
left_join_error_no_match(GCAM_region_names, by = 'GCAM_region_ID') %>%
mutate(year = max(MODEL_BASE_YEARS)) %>%
left_join_error_no_match(L232.BaseService_ind, by = c("year", "region")) %>%
left_join_error_no_match(L101.Pop_thous_GCAM3_R_Y, by = c("year", "GCAM_region_ID")) %>%
mutate(value = base.service * CONV_BIL_THOUS / value) %>%
select(-base.service, -energy.final.demand) ->
L232.Output_ind
# At each time, the output is equal to the prior period's output times the GDP ratio, raised to the elasticity
# that corresponds to the output that was observed in the prior time period. This method prevents (ideally) runaway
# industrial production.
elast_years <- c(max(MODEL_BASE_YEARS), MODEL_FUTURE_YEARS)
for(i in seq_along(elast_years)[-1]) {
L232.Output_ind %>%
filter(year == elast_years[i - 1]) %>%
left_join(filter(L232.pcgdpRatio_ALL_R_Y, year == elast_years[i]), by = c("GCAM_region_ID", "scenario")) %>% # strick left join fails timeshift test due to NAs in L102.pcgdp_thous90USD_Scen_R_Y under timeshift mode
mutate(parameter = approx(x = A32.inc_elas_output[["pc.output_GJ"]],
y = A32.inc_elas_output[["inc_elas"]],
xout = value.x,
rule = 2)[['y']],
value = value.x * value.y ^ parameter,
year = elast_years[i]) %>%
select(GCAM_region_ID, scenario, region, year, value) %>%
bind_rows(L232.Output_ind) ->
L232.Output_ind
}
# Now that we have industrial output, we can back out the appropriate income elasticities
L232.Output_ind %>%
filter(year %in% MODEL_FUTURE_YEARS) %>%
mutate(value = approx( x = A32.inc_elas_output[["pc.output_GJ"]],
y = A32.inc_elas_output[["inc_elas"]],
xout = value,
rule = 2)[["y"]]) %>%
mutate(value = round(value, energy.DIGITS_INCELAS_IND)) %>%
rename(income.elasticity = value) %>%
mutate(energy.final.demand = A32.demand[["energy.final.demand"]]) ->
L232.IncomeElasticity_ind # intermediate tibble
# KVC: SSP1 needs lower income elasticities. Storyline has limited growth in energy-related industries
# because of warm fuzzy feelings about environment. We are hard-coding this for a while.
L232.IncomeElasticity_ind %>%
filter(scenario == "SSP1") %>%
mutate(income.elasticity = income.elasticity * 0.75) %>%
bind_rows(filter(L232.IncomeElasticity_ind, scenario != "SSP1")) ->
L232.IncomeElasticity_ind
# ===================================================
# Produce outputs
# Extract GCAM3, SSP, and gSSP data and assign to separate tables
for(ieo in INCOME_ELASTICITY_OUTPUTS) {
L232.IncomeElasticity_ind %>%
filter(scenario == ieo) %>%
select(LEVEL2_DATA_NAMES[["IncomeElasticity"]]) %>%
add_title(paste("Income elasticity of industry -", ieo)) %>%
add_units("Unitless") %>%
add_comments("First calculate industrial output as the base-year industrial output times the GDP ratio raised to the income elasticity") %>%
add_comments("Then back out the appropriate income elasticities from industrial output") %>%
add_comments("Note lower income elasticities for SSP1 are hard-coded.") %>%
add_legacy_name(paste0("L232.IncomeElasticity_ind_", tolower(ieo))) %>%
add_precursors("L102.pcgdp_thous90USD_GCAM3_R_Y", "L102.pcgdp_thous90USD_Scen_R_Y", "common/GCAM_region_names", "L1322.in_EJ_R_indenergy_F_Yh", "L123.in_EJ_R_indchp_F_Yh", "energy/calibrated_techs", "L1322.in_EJ_R_indfeed_F_Yh", "energy/A32.globaltech_eff", "energy/A32.globaltech_shrwt", "energy/A32.demand", "L101.Pop_thous_GCAM3_R_Y", "socioeconomics/A32.inc_elas_output") ->
x
assign(paste0("L232.IncomeElasticity_ind_", tolower(ieo)), x)
}
L232.Supplysector_ind %>%
add_title("Supply sector information for industry sector") %>%
add_units("NA") %>%
add_comments("For industry sector, the supply sector information (output.unit, input.unit, price.unit, logit.year.fillout, logit.exponent) from A32.sector is expended into all GCAM regions") %>%
add_legacy_name("L232.Supplysector_ind") %>%
add_precursors("common/GCAM_region_names", "energy/A32.sector") ->
L232.Supplysector_ind
L232.SubsectorLogit_ind %>%
add_title("Subsector logit exponents of industry sector") %>%
add_units("Unitless") %>%
add_comments("For industry sector, the subsector logit exponents from A32.subsector_logit are expanded into all GCAM regions with non-existent heat subsectors removed") %>%
add_legacy_name("L232.SubsectorLogit_ind") %>%
add_precursors("energy/A32.subsector_logit", "common/GCAM_region_names", "energy/A_regions", "energy/calibrated_techs") ->
L232.SubsectorLogit_ind
L232.FinalEnergyKeyword_ind %>%
add_title("Supply sector keywords for industry sector") %>%
add_units("NA") %>%
add_comments("Set supply sector keywords for industry sector for all GCAM regions") %>%
add_legacy_name("L232.FinalEnergyKeyword_ind") %>%
add_precursors("energy/A32.sector", "common/GCAM_region_names") ->
L232.FinalEnergyKeyword_ind
L232.SubsectorShrwtFllt_ind %>%
add_title("Subsector shareweights of industry sector") %>%
add_units("Unitless") %>%
add_comments("For industry sector, the subsector shareweights from A32.subsector_shrwt are expanded into all GCAM regions with non-existent heat technologies") %>%
add_legacy_name("L232.SubsectorShrwtFllt_ind") %>%
add_precursors("energy/A32.subsector_shrwt", "common/GCAM_region_names", "energy/A_regions", "energy/calibrated_techs") ->
L232.SubsectorShrwtFllt_ind
L232.SubsectorInterp_ind %>%
add_title("Subsector shareweight interpolation of industry sector") %>%
add_units("NA") %>%
add_comments("For industry sector, the subsector shareweight interpolation function infromation from A32.subsector_interp is expanded into all GCAM regions with non-existent heat technologies removed") %>%
add_legacy_name("L232.SubsectorInterp_ind") %>%
add_precursors("energy/A32.subsector_interp", "common/GCAM_region_names", "energy/A_regions", "energy/calibrated_techs") ->
L232.SubsectorInterp_ind
L232.StubTech_ind %>%
add_title("Identification of stub technologies of industrial sector") %>%
add_units("NA") %>%
add_comments("For industry sector, the stub technologies from A32.globaltech_shrwt are expanded into all GCAM regions with non-existent heat technologies removed") %>%
add_legacy_name("L232.StubTech_ind") %>%
add_precursors("energy/A32.globaltech_shrwt", "common/GCAM_region_names", "energy/A_regions", "energy/calibrated_techs") ->
L232.StubTech_ind
L232.GlobalTechShrwt_ind %>%
add_title("Shareweights of global industrial sector technologies") %>%
add_units("Unitless") %>%
add_comments("For industry sector, the share weights from A32.globaltech_shrwt are interpolated into all base years and future years") %>%
add_legacy_name("L232.GlobalTechShrwt_ind") %>%
add_precursors("energy/A32.globaltech_shrwt") ->
L232.GlobalTechShrwt_ind
L232.StubTechInterp_ind %>%
add_title("Shareweight interpolation of global industrial sector technologies") %>%
add_units("NA") %>%
add_comments("For industry sector, the interpolation function from A32.globaltech_interp are expanded into all GCAM regions") %>%
add_legacy_name("L232.StubTechInterp_ind") %>%
add_precursors("energy/A32.globaltech_interp", "common/GCAM_region_names") ->
L232.StubTechInterp_ind
L232.GlobalTechEff_ind %>%
add_title("Energy inputs and efficiency of global industrial energy use and feedstocks technologies") %>%
add_units("Unitless") %>%
add_comments("For industry sector, the efficiency values from A32.globaltech_eff are interpolated into all base years and future years") %>%
add_legacy_name("L232.GlobalTechEff_ind") %>%
add_precursors("energy/A32.globaltech_eff") ->
L232.GlobalTechEff_ind
L232.GlobalTechCoef_ind %>%
add_title("Energy inputs and coefficients of global industry technologies") %>%
add_units("Unitless") %>%
add_comments("For industry sector, the coefficients from A32.globaltech_coef are interpolated into all base years and future years") %>%
add_legacy_name("L232.GlobalTechCoef_ind") %>%
add_precursors("energy/A32.globaltech_coef") ->
L232.GlobalTechCoef_ind
L232.GlobalTechCost_ind %>%
add_title("Capital costs of global industrial technologies") %>%
add_units("1975$/GJ") %>%
add_comments("For industry sector, the capital costs from A32.globaltech_cost are interpolated into all base years and future years") %>%
add_legacy_name("L232.GlobalTechCost_ind") %>%
add_precursors("energy/A32.globaltech_cost") ->
L232.GlobalTechCost_ind
L232.GlobalTechSecOut_ind %>%
add_title("Secondary output ratios of industrial cogeneration technologies") %>%
add_units("Unitless") %>%
add_comments("Secondary output ratios are calculated as electricity ratio (Assumed CHP electricity output per unit fuel input) over efficiency") %>%
add_legacy_name("L232.GlobalTechSecOut_ind") %>%
add_precursors("energy/A23.chp_elecratio", "energy/A32.globaltech_eff") ->
L232.GlobalTechSecOut_ind
L232.GlobalTechCSeq_ind %>%
add_title("CO2 capture fractions from global electricity generation technologies") %>%
add_units("Unitless") %>%
add_comments("Remove fractions from A32.nonenergy_Cseq are expanded into all model years") %>%
add_legacy_name("L232.GlobalTechCSeq_ind") %>%
add_precursors("energy/A32.nonenergy_Cseq") ->
L232.GlobalTechCSeq_ind
L232.StubTechCalInput_indenergy %>%
add_title("Calibrated input of industrial energy use technologies (including cogen)") %>%
add_units("EJ") %>%
add_comments("Calibrated input of industrial energy use values are calculated using L1322.in_EJ_R_indenergy_F_Yh then added information such as subsector, technology, minicam.energy.input, calibration, tech.share.weight, and etc.") %>%
add_legacy_name("L232.StubTechCalInput_indenergy") %>%
add_precursors("L1322.in_EJ_R_indenergy_F_Yh", "energy/calibrated_techs", "energy/A32.globaltech_eff") ->
L232.StubTechCalInput_indenergy
L232.StubTechCalInput_indfeed %>%
add_title("Calibrated input of industrial feedstock technologies") %>%
add_units("EJ") %>%
add_comments("calibrated input of industrial feedstock technologies values are calculated using L1322.in_EJ_R_indfeed_F_Yh then added information such as subsector, technology, minicam.energy.input, calibration, tech.share.weight, and etc.") %>%
add_legacy_name("L232.StubTechCalInput_indfeed") %>%
add_precursors("L1322.in_EJ_R_indfeed_F_Yh", "common/GCAM_region_names", "energy/calibrated_techs", "energy/A32.globaltech_eff") ->
L232.StubTechCalInput_indfeed
L232.StubTechProd_industry %>%
add_title("Calibrated output of industrial sector") %>%
add_units("EJ") %>%
add_comments("Service output values are calculated by technology, for energy-use and feedstocks then aggregated by region") %>%
add_legacy_name("L232.StubTechProd_industry") %>%
add_precursors("L1322.in_EJ_R_indenergy_F_Yh", "L123.in_EJ_R_indchp_F_Yh", "common/GCAM_region_names", "energy/calibrated_techs", "L1322.in_EJ_R_indfeed_F_Yh", "energy/A32.globaltech_eff", "energy/A32.globaltech_shrwt") ->
L232.StubTechProd_industry
L232.StubTechCoef_industry %>%
add_title("Calibrated output of industrial sector") %>%
add_units("Unitless") %>%
add_comments("Service output values were first aggregated by sector to calculate the portion of each input as coefficients, then the coefficients were interpolated to cover last base year, future years and industry coefficient convergence year") %>%
add_legacy_name("L232.StubTechCoef_industry") %>%
add_precursors("L1322.in_EJ_R_indenergy_F_Yh", "L123.in_EJ_R_indchp_F_Yh", "common/GCAM_region_names", "energy/calibrated_techs", "L1322.in_EJ_R_indfeed_F_Yh", "energy/A32.globaltech_eff", "energy/A32.globaltech_shrwt") ->
L232.StubTechCoef_industry
L232.FuelPrefElast_indenergy %>%
add_title("Fuel preference elasticities of industrial energy use") %>%
add_units("Unitless") %>%
add_comments("First, calculate the fuel shares allocated to each fuel, then set fuel preference elasticities as indicated by exogenous rules(A32.fuelprefElasticity), lastly fill out elasticities from the first future model time period") %>%
add_legacy_name("L232.FuelPrefElast_indenergy") %>%
add_precursors("L1322.in_EJ_R_indenergy_F_Yh", "L123.in_EJ_R_indchp_F_Yh", "common/GCAM_region_names", "energy/calibrated_techs", "energy/A32.fuelprefElasticity") ->
L232.FuelPrefElast_indenergy
L232.PerCapitaBased_ind %>%
add_title("Per-capita based flag for industry final demand") %>%
add_units("NA") %>%
add_comments("Extracted per-capita based flag for industry final demand from A32.demand") %>%
add_legacy_name("L232.PerCapitaBased_ind") %>%
add_precursors("common/GCAM_region_names", "energy/A32.demand") ->
L232.PerCapitaBased_ind
L232.PriceElasticity_ind %>%
add_title("Price elasticity of industry final demand") %>%
add_units("Unitless") %>%
add_comments("Extracted price elasticity of industry final demand from A32.demand") %>%
add_comments("Price elasticities are only applied to future periods. Application in base years will cause solution failure") %>%
add_legacy_name("L232.PriceElasticity_ind") %>%
add_precursors("common/GCAM_region_names", "energy/A32.demand") ->
L232.PriceElasticity_ind
L232.BaseService_ind %>%
add_title("Base-year service output of industry final demand") %>%
add_units("EJ") %>%
add_comments("Extracted base-year service output of industry final demand from L232.StubTechProd_industry") %>%
add_legacy_name("L232.BaseService_ind") %>%
add_precursors("L1322.in_EJ_R_indenergy_F_Yh", "L123.in_EJ_R_indchp_F_Yh", "common/GCAM_region_names", "energy/calibrated_techs", "L1322.in_EJ_R_indfeed_F_Yh", "energy/A32.globaltech_eff", "energy/A32.globaltech_shrwt", "energy/A32.demand") ->
L232.BaseService_ind
return_data(L232.Supplysector_ind, L232.SubsectorLogit_ind, L232.FinalEnergyKeyword_ind,
L232.SubsectorShrwtFllt_ind, L232.SubsectorInterp_ind,
L232.StubTech_ind, L232.GlobalTechShrwt_ind,
L232.StubTechInterp_ind, L232.GlobalTechEff_ind, L232.GlobalTechCoef_ind,
L232.GlobalTechCost_ind, L232.GlobalTechSecOut_ind, L232.GlobalTechCSeq_ind,
L232.StubTechCalInput_indenergy, L232.StubTechCalInput_indfeed, L232.StubTechProd_industry,
L232.StubTechCoef_industry, L232.FuelPrefElast_indenergy, L232.PerCapitaBased_ind,
L232.PriceElasticity_ind, L232.BaseService_ind,
L232.IncomeElasticity_ind_gcam3, L232.IncomeElasticity_ind_gssp1,
L232.IncomeElasticity_ind_gssp2, L232.IncomeElasticity_ind_gssp3,
L232.IncomeElasticity_ind_gssp4, L232.IncomeElasticity_ind_gssp5,
L232.IncomeElasticity_ind_ssp1, L232.IncomeElasticity_ind_ssp2,
L232.IncomeElasticity_ind_ssp3, L232.IncomeElasticity_ind_ssp4,
L232.IncomeElasticity_ind_ssp5)
} else {
stop("Unknown command")
}
}
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