R/zchunk_L2321.cement.R
In JGCRI/gcamdata: Build the GCAM Input Datasets
Defines functions
module_energy_L2321.cement
Documented in
module_energy_L2321.cement
# Copyright 2019 Battelle Memorial Institute; see the LICENSE file.
#' module_energy_L2321.cement
#'
#' Compute a variety of final energy keyword, sector, share weight, and technology information for cement-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{L2321.SectorLogitTables[[ curr_table ]]$data}, \code{L2321.Supplysector_cement}, \code{L2321.FinalEnergyKeyword_cement},
#' \code{L2321.SubsectorLogitTables[[ curr_table ]]$data}, \code{L2321.SubsectorLogit_cement}, \code{L2321.SubsectorShrwtFllt_cement},
#' \code{L2321.SubsectorInterp_cement}, \code{L2321.StubTech_cement}, \code{L2321.GlobalTechShrwt_cement}, \code{L2321.GlobalTechCoef_cement},
#' \code{L2321.GlobalTechCost_cement}, \code{L2321.GlobalTechCapture_cement}, \code{L2321.StubTechProd_cement}, \code{L2321.StubTechCalInput_cement_heat},
#' \code{L2321.StubTechCoef_cement}, \code{L2321.PerCapitaBased_cement}, \code{L2321.BaseService_cement}, \code{L2321.PriceElasticity_cement},
#' \code{L2321.IncomeElasticity_cement_gcam3}, \code{L2321.IncomeElasticity_cement_gssp1}, \code{L2321.IncomeElasticity_cement_gssp2},
#' \code{L2321.IncomeElasticity_cement_gssp3}, \code{L2321.IncomeElasticity_cement_gssp4}, \code{L2321.IncomeElasticity_cement_gssp5},
#' \code{L2321.IncomeElasticity_cement_ssp1}, \code{L2321.IncomeElasticity_cement_ssp2}, \code{L2321.IncomeElasticity_cement_ssp3},
#' \code{L2321.IncomeElasticity_cement_ssp4}, \code{L2321.IncomeElasticity_cement_ssp5}, \code{object}. The corresponding file in the
#' original data system was \code{L2321.cement.R} (energy level2).
#' @details The chunk provides final energy keyword, supplysector/subsector information, supplysector/subsector interpolation information, 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 for cement sector.
#' @importFrom assertthat assert_that
#' @importFrom dplyr arrange bind_rows distinct filter if_else group_by lag left_join mutate pull select
#' @importFrom tidyr complete nesting
#' @author LF October 2017
module_energy_L2321.cement <- 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/A321.sector",
FILE = "emissions/A_PrimaryFuelCCoef",
FILE = "energy/A321.sector",
FILE = "energy/A321.subsector_interp",
FILE = "energy/A321.subsector_logit",
FILE = "energy/A321.subsector_shrwt",
FILE = "energy/A321.globaltech_coef",
FILE = "energy/A321.globaltech_cost",
FILE = "energy/A321.globaltech_shrwt",
FILE = "energy/A321.globaltech_co2capture",
FILE = "energy/A321.demand",
FILE = "energy/A321.globaltech_retirement",
FILE = "socioeconomics/A321.inc_elas_output",
"L1321.out_Mt_R_cement_Yh",
"L1321.IO_GJkg_R_cement_F_Yh",
"L1321.in_EJ_R_cement_F_Y",
"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("L2321.Supplysector_cement",
"L2321.FinalEnergyKeyword_cement",
"L2321.SubsectorLogit_cement",
"L2321.SubsectorShrwtFllt_cement",
"L2321.SubsectorInterp_cement",
"L2321.StubTech_cement",
"L2321.GlobalTechShrwt_cement",
"L2321.GlobalTechCoef_cement",
"L2321.GlobalTechCost_cement",
"L2321.GlobalTechCapture_cement",
"L2321.GlobalTechShutdown_en",
"L2321.GlobalTechSCurve_en",
"L2321.GlobalTechLifetime_en",
"L2321.GlobalTechProfitShutdown_en",
"L2321.StubTechProd_cement",
"L2321.StubTechCalInput_cement_heat",
"L2321.StubTechCoef_cement",
"L2321.PerCapitaBased_cement",
"L2321.BaseService_cement",
"L2321.PriceElasticity_cement",
paste("L2321.IncomeElasticity_cement", 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")
A321.sector <- get_data(all_data, "energy/A321.sector", strip_attributes = TRUE)
A_PrimaryFuelCCoef <- get_data(all_data, "emissions/A_PrimaryFuelCCoef")
A321.subsector_interp <- get_data(all_data, "energy/A321.subsector_interp", strip_attributes = TRUE)
A321.subsector_logit <- get_data(all_data, "energy/A321.subsector_logit", strip_attributes = TRUE)
A321.subsector_shrwt <- get_data(all_data, "energy/A321.subsector_shrwt", strip_attributes = TRUE)
A321.globaltech_coef <- get_data(all_data, "energy/A321.globaltech_coef")
A321.globaltech_cost <- get_data(all_data, "energy/A321.globaltech_cost")
A321.globaltech_shrwt <- get_data(all_data, "energy/A321.globaltech_shrwt", strip_attributes = TRUE)
A321.globaltech_co2capture <- get_data(all_data, "energy/A321.globaltech_co2capture")
A321.globaltech_retirement <- get_data(all_data, "energy/A321.globaltech_retirement", strip_attributes = TRUE)
A321.demand <- get_data(all_data, "energy/A321.demand", strip_attributes = TRUE)
L1321.out_Mt_R_cement_Yh <- get_data(all_data, "L1321.out_Mt_R_cement_Yh", strip_attributes = TRUE)
L1321.IO_GJkg_R_cement_F_Yh <- get_data(all_data, "L1321.IO_GJkg_R_cement_F_Yh", strip_attributes = TRUE)
L1321.in_EJ_R_cement_F_Y <- get_data(all_data, "L1321.in_EJ_R_cement_F_Y", strip_attributes = TRUE)
A321.inc_elas_output <- get_data(all_data, "socioeconomics/A321.inc_elas_output", strip_attributes = TRUE)
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
year <- value <- GCAM_region_ID <- sector <- fuel <- year.fillout <- to.value <-
technology <- supplysector <- subsector <- minicam.energy.input <- coefficient <-
remove.fraction <- minicam.non.energy.input <- input.cost <- PrimaryFuelCO2Coef.name <-
PrimaryFuelCO2Coef <- calibration <- calOutputValue <- subs.share.weight <- region <-
calibrated.value <- . <- scenario <- temp_lag <- base.service <- energy.final.demand <-
value.x <- value.y <- parameter <- L2321.IncomeElasticity_cement_gcam3 <-
L2321.IncomeElasticity_cement_gssp1 <- L2321.IncomeElasticity_cement_gssp2 <-
L2321.IncomeElasticity_cement_gssp3 <- L2321.IncomeElasticity_cement_gssp4 <-
L2321.IncomeElasticity_cement_gssp5 <- L2321.IncomeElasticity_cement_ssp1 <-
L2321.IncomeElasticity_cement_ssp2 <- L2321.IncomeElasticity_cement_ssp3 <-
L2321.IncomeElasticity_cement_ssp4 <- L2321.IncomeElasticity_cement_ssp5 <- year.x <- year.y <- NULL
# ===================================================
# 1. Perform computations
# Create tables to delete technologies and subsectors in regions where heat is not modeled as a fuel
# 1a. Supplysector information
# L2321.Supplysector_cement: Supply sector information for cement sector
A321.sector %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["Supplysector"]], LOGIT_TYPE_COLNAME), GCAM_region_names) ->
L2321.Supplysector_cement
# L2321.FinalEnergyKeyword_cement: Supply sector keywords for cement sector
A321.sector %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["FinalEnergyKeyword"]], GCAM_region_names) %>%
na.omit ->
L2321.FinalEnergyKeyword_cement
# 1b. Subsector information
# L2321.SubsectorLogit_cement: Subsector logit exponents of cement sector
A321.subsector_logit %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["SubsectorLogit"]], LOGIT_TYPE_COLNAME), GCAM_region_names) ->
L2321.SubsectorLogit_cement
# and L2321.SubsectorShrwtFllt_cement: Subsector shareweights of cement sector
A321.subsector_shrwt %>%
filter(!is.na(year.fillout)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["SubsectorShrwtFllt"]], GCAM_region_names) ->
L2321.SubsectorShrwtFllt_cement
# L2321.SubsectorInterp_cement: Subsector shareweight interpolation of cement sector
A321.subsector_interp %>%
filter(is.na(to.value)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["SubsectorInterp"]], GCAM_region_names) ->
L2321.SubsectorInterp_cement
# 1c. Technology information
# L2321.StubTech_cement: Identification of stub technologies of cement
# Note: assuming that technology list in the shareweight table includes the full set (any others would default to a 0 shareweight)
A321.globaltech_shrwt %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["Tech"]], GCAM_region_names) %>%
rename(stub.technology = technology) ->
L2321.StubTech_cement
# L2321.GlobalTechShrwt_cement: Shareweights of global cement technologies
A321.globaltech_shrwt %>%
gather_years %>%
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, value, 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") ->
L2321.GlobalTechShrwt_cement
# L2321.GlobalTechCoef_cement: Energy inputs and coefficients of cement technologies
A321.globaltech_coef %>%
gather_years %>%
complete(nesting(supplysector, subsector, technology, minicam.energy.input), year = c(year, MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)) %>%
arrange(supplysector, subsector, technology, minicam.energy.input, year) %>%
group_by(supplysector, subsector, technology, minicam.energy.input) %>%
mutate(coefficient = approx_fun(year, value, rule = 1),
coefficient = round(coefficient, energy.DIGITS_COEFFICIENT)) %>%
ungroup %>%
filter(year %in% c(MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)) %>%
rename(sector.name = supplysector,
subsector.name = subsector) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechCoef"]]) ->
L2321.GlobalTechCoef_cement
# Carbon capture rates from technologies with CCS
# L2321.GlobalTechCapture_cement: CO2 capture fractions from global cement production technologies with CCS
# No need to consider historical periods or intermittent technologies here
A321.globaltech_co2capture %>%
gather_years %>%
complete(nesting(supplysector, subsector, technology), year = c(year, MODEL_FUTURE_YEARS)) %>%
arrange(supplysector, subsector, technology, year) %>%
group_by(supplysector, subsector, technology) %>%
mutate(remove.fraction = approx_fun(year, value, rule = 1),
remove.fraction = round(remove.fraction, energy.DIGITS_REMOVE.FRACTION)) %>%
ungroup %>%
filter(year %in% MODEL_FUTURE_YEARS) %>%
rename(sector.name = supplysector,
subsector.name = subsector) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechYr"]], "remove.fraction") %>%
mutate(storage.market = energy.CO2.STORAGE.MARKET) ->
L2321.GlobalTechCapture_cement
# L2321.GlobalTechCost_cement: Non-energy costs of global cement manufacturing technologies
A321.globaltech_cost %>%
gather_years %>%
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, value, rule = 1),
input.cost = round(input.cost, energy.DIGITS_COST)) %>%
ungroup %>%
filter(year %in% c(MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)) %>%
rename(sector.name = supplysector,
subsector.name = subsector) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechCost"]]) ->
L2321.GlobalTechCost_cement # intermediate tibble
# Note: adjusting non-energy costs of technologies with CCS to include CO2 capture costs
# The additional CCS-related non-energy costs are not included in the global technology assessment.
# Calculate here in two steps:
# (1) calculate the additional CCS costs per unit of carbon produced in 1975$
# (2) calculate the quantity of CO2 produced per unit of cement produced (in kgC per kg cement)
cement_CCS_cost_total_1975USDtC <- energy.CEMENT_CCS_COST_2000USDTCO2 * gdp_deflator(1975, base_year = 2000) * emissions.CONV_C_CO2
CO2_storage_cost_1975USDtC <- energy.CO2_STORAGE_COST_1990_USDTC * gdp_deflator(1975, base_year = 1990)
cement_CCS_cost_1975USDtC <- cement_CCS_cost_total_1975USDtC - CO2_storage_cost_1975USDtC
L2321.GlobalTechCapture_cement %>%
pull(remove.fraction) %>%
mean -> cement_CO2_capture_frac
L2321.GlobalTechCoef_cement %>%
filter(minicam.energy.input == "limestone") %>%
pull(coefficient) %>%
mean ->
coef_mean # temporary value
A_PrimaryFuelCCoef %>%
filter(PrimaryFuelCO2Coef.name == "limestone") %>%
pull(PrimaryFuelCO2Coef) %>%
mean ->
PrimaryFuelCO2Coef_mean # temporary value
CO2_IO_kgCkgcement <- coef_mean * PrimaryFuelCO2Coef_mean
CO2stored_IO_kgCkgcement <- CO2_IO_kgCkgcement * cement_CO2_capture_frac
cement_CCS_cost_75USD_tcement <- cement_CCS_cost_1975USDtC * CO2stored_IO_kgCkgcement / CONV_T_KG
# Adjust the non-energy costs in the table for model input
L2321.GlobalTechCost_cement %>%
filter(technology %in% L2321.GlobalTechCapture_cement[["technology"]]) %>%
mutate(input.cost = input.cost + cement_CCS_cost_75USD_tcement) %>%
bind_rows(filter(L2321.GlobalTechCost_cement, !(technology %in% L2321.GlobalTechCapture_cement[["technology"]]))) %>%
mutate(input.cost = round(input.cost, energy.DIGITS_COST)) ->
L2321.GlobalTechCost_cement
# Calibration and region-specific data
# L2321.StubTechProd_cement: calibrated cement production
calibrated_techs %>%
filter(calibration == "output") %>% # Only take the tech IDs where the calibration is identified as output
select(sector, supplysector, subsector, technology) %>%
distinct ->
calibrated_techs_export # temporary tibble
L1321.out_Mt_R_cement_Yh %>%
filter(year %in% MODEL_BASE_YEARS) %>%
mutate(calOutputValue = round(value, energy.DIGITS_CALOUTPUT)) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_error_no_match(calibrated_techs_export, by = "sector") %>%
mutate(stub.technology = 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"]]) ->
L2321.StubTechProd_cement
# L2321.StubTechCoef_cement: region-specific coefficients of cement production technologies
# Take this as a given in all years for which data is available
calibrated_techs %>%
select(sector, fuel, supplysector, subsector, technology, minicam.energy.input) %>%
distinct ->
calibrated_techs_export # temporary tibble
L1321.IO_GJkg_R_cement_F_Yh %>%
filter(year %in% HISTORICAL_YEARS[HISTORICAL_YEARS %in% c(MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)]) %>%
mutate(coefficient = round(value, energy.DIGITS_COEFFICIENT)) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_error_no_match(calibrated_techs_export, by = c("sector", "fuel")) %>%
mutate(stub.technology = technology,
market.name = region) %>%
select(LEVEL2_DATA_NAMES[["StubTechCoef"]]) ->
L2321.StubTechCoef_cement
# L2321.StubTechCalInput_cement_heat: calibrated cement production
calibrated_techs %>%
select(sector, fuel, supplysector, subsector, technology, minicam.energy.input) %>%
distinct ->
calibrated_techs_export # temporary tibble
L1321.in_EJ_R_cement_F_Y %>%
filter(year %in% MODEL_BASE_YEARS) %>%
mutate(calibrated.value = round(value, energy.DIGITS_CALOUTPUT)) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_error_no_match(calibrated_techs_export, by = c("sector", "fuel")) %>%
# This table should only be the technologies for producing heat - drop the electricity inputs to the cement production technology
filter(!(supplysector %in% L2321.StubTechCoef_cement[["supplysector"]])) %>%
mutate(stub.technology = technology,
share.weight.year = year,
subs.share.weight = if_else(calibrated.value > 0, 1, 0),
tech.share.weight = subs.share.weight) %>%
select(LEVEL2_DATA_NAMES[["StubTechCalInput"]]) ->
L2321.StubTechCalInput_cement_heat
# L2321.PerCapitaBased_cement: per-capita based flag for cement exports final demand
A321.demand %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["PerCapitaBased"]], GCAM_region_names) ->
L2321.PerCapitaBased_cement
# L2321.BaseService_cement: base-year service output of cement
L2321.StubTechProd_cement %>%
select(region, year, base.service = calOutputValue) %>%
mutate(energy.final.demand = A321.demand[["energy.final.demand"]]) ->
L2321.BaseService_cement
# Retirement information
A321.globaltech_retirement %>%
set_years() %>%
mutate(year = as.integer(year)) %>%
rename(sector.name = supplysector, subsector.name = subsector) ->
L2321.globaltech_retirement_base
# Copies first future year retirment information into all future years and appends back onto base year
L2321.globaltech_retirement_base %>%
mutate(year = as.integer(year)) %>%
filter(year == min(MODEL_FUTURE_YEARS)) %>%
repeat_add_columns(tibble(year = MODEL_FUTURE_YEARS)) %>%
select(-year.x) %>%
rename(year = year.y) ->
L2321.globaltech_retirement_future
# filters base years from original and then appends future years
L2321.globaltech_retirement_base %>%
mutate(year = as.integer(year)) %>%
filter(year == max(MODEL_BASE_YEARS)) %>%
bind_rows(L2321.globaltech_retirement_future) ->
L2321.globaltech_retirement
# Retirement may consist of any of three types of retirement function (phased, s-curve, or none)
# This section checks L2321.globaltech_retirement for each of these functions and creates a separate level 2 file for each
# All of these options have different headers, and all are allowed
if(any(!is.na(L2321.globaltech_retirement$shutdown.rate))) {
L2321.globaltech_retirement %>%
filter(!is.na(L2321.globaltech_retirement$shutdown.rate)) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechYr"]], "lifetime", "shutdown.rate") ->
L2321.GlobalTechShutdown_en
}
if(any(!is.na(L2321.globaltech_retirement$half.life))) {
L2321.globaltech_retirement %>%
filter(!is.na(L2321.globaltech_retirement$half.life)) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechYr"]], "lifetime", "steepness", "half.life") ->
L2321.GlobalTechSCurve_en
}
# L2321.GlobalTechLifetime_en: Global tech lifetime
if(any(is.na(L2321.globaltech_retirement$shutdown.rate) & is.na(L2321.globaltech_retirement$half.life))) {
L2321.globaltech_retirement %>%
filter(is.na(L2321.globaltech_retirement$shutdown.rate) & is.na(L2321.globaltech_retirement$half.life)) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechYr"]], "lifetime") ->
L2321.GlobalTechLifetime_en
}
# L2321.GlobalTechProfitShutdown_en: Global tech profit shutdown decider and parameters
if(any(!is.na(L2321.globaltech_retirement$median.shutdown.point))) {
L2321.globaltech_retirement %>%
filter(!is.na(L2321.globaltech_retirement$median.shutdown.point)) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechYr"]], "median.shutdown.point", "profit.shutdown.steepness") ->
L2321.GlobalTechProfitShutdown_en
}
# L2321.PriceElasticity_cement: price elasticity
A321.demand %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["PriceElasticity"]][LEVEL2_DATA_NAMES[["PriceElasticity"]] != "year"], GCAM_region_names) %>%
repeat_add_columns(tibble(year = MODEL_FUTURE_YEARS)) %>%
select(LEVEL2_DATA_NAMES[["PriceElasticity"]]) ->
L2321.PriceElasticity_cement
# L2321.IncomeElasticity_cement_scen: income elasticity of cement (scenario-specific)
# First, calculate the per-capita GDP pathways of every GDP scenario and combine
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) ->
L2321.pcgdpRatio_ALL_R_Y # intermediate tibble
# Calculate the cement output as the base-year cement output times the GDP ratio raised to the income elasticity
# The income elasticity is looked up based on the prior year's output
L2321.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(L2321.BaseService_cement, 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_MIL_THOUS / value) %>%
select(-base.service, -energy.final.demand) ->
L2321.Output_cement # intermediate tibble
# 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
# production/consumption.
elast_years <- c(max(MODEL_BASE_YEARS), MODEL_FUTURE_YEARS)
for(i in seq_along(elast_years)[-1]) {
L2321.Output_cement %>%
filter(year == elast_years[i - 1]) %>%
# strick left join fails timeshift test due to NAs in L102.pcgdp_thous90USD_Scen_R_Y under timeshift mode
left_join(filter(L2321.pcgdpRatio_ALL_R_Y, year == elast_years[i]), by = c("GCAM_region_ID", "scenario")) ->
intermediate
intermediate %>%
mutate(parameter = approx(x = A321.inc_elas_output[["pc.output_t"]],
y = A321.inc_elas_output[["inc_elas"]],
xout = intermediate[["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(L2321.Output_cement) ->
L2321.Output_cement
}
# Now that we have cement output, we can back out the appropriate income elasticities
L2321.Output_cement %>%
filter(year %in% MODEL_FUTURE_YEARS) %>%
mutate(value = approx( x = A321.inc_elas_output[["pc.output_t"]],
y = A321.inc_elas_output[["inc_elas"]],
xout = value, rule = 2)[["y"]],
value = round(value, energy.DIGITS_INCELAS_IND)) %>%
rename(income.elasticity = value) %>%
mutate(energy.final.demand = A321.demand[["energy.final.demand"]]) ->
L2321.IncomeElasticity_cement # intermediate tibble
# ===================================================
# Produce outputs
# Extract GCAM3, SSP, and gSSP data and assign to separate tables
for(ieo in INCOME_ELASTICITY_OUTPUTS) {
L2321.IncomeElasticity_cement %>%
filter(scenario == ieo) %>%
select(LEVEL2_DATA_NAMES[["IncomeElasticity"]]) %>%
add_title(paste("Income elasticity of cement -", ieo)) %>%
add_units("Unitless") %>%
add_comments("First calculate cement output as the base-year cement output times the GDP ratio raised to the income elasticity") %>%
add_comments("Then back out the appropriate income elasticities from cement output") %>%
add_legacy_name(paste0("L2321.IncomeElasticity_cement_", tolower(ieo))) %>%
add_precursors("L102.pcgdp_thous90USD_GCAM3_R_Y", "L102.pcgdp_thous90USD_Scen_R_Y", "common/GCAM_region_names", "energy/A321.demand", "energy/calibrated_techs",
"L1321.out_Mt_R_cement_Yh", "L101.Pop_thous_GCAM3_R_Y", "socioeconomics/A321.inc_elas_output") ->
x
assign(paste0("L2321.IncomeElasticity_cement_", tolower(ieo)), x)
}
L2321.Supplysector_cement %>%
add_title("Supply sector information for cement sector") %>%
add_units("NA") %>%
add_comments("For cement sector, the supply sector information (output.unit, input.unit, price.unit, logit.year.fillout, logit.exponent) from A321.sector is expended into all GCAM regions") %>%
add_legacy_name("L2321.Supplysector_cement") %>%
add_precursors("energy/A321.sector", "common/GCAM_region_names") ->
L2321.Supplysector_cement
L2321.FinalEnergyKeyword_cement %>%
add_title("Supply sector keywords for cement sector") %>%
add_units("NA") %>%
add_comments("For cement sector, the supply sector final energy keywords from A321.sector are expended into all GCAM regions") %>%
add_legacy_name("L2321.FinalEnergyKeyword_cement") %>%
add_precursors("energy/A321.sector", "common/GCAM_region_names") ->
L2321.FinalEnergyKeyword_cement
L2321.SubsectorLogit_cement %>%
add_title("Subsector logit exponents of cement sector") %>%
add_units("Unitless") %>%
add_comments("For cement sector, the subsector logit exponents from A321.subsector_logit are expanded into all GCAM regions") %>%
add_legacy_name("L2321.SubsectorLogit_cement") %>%
add_precursors("energy/A321.subsector_logit", "common/GCAM_region_names") ->
L2321.SubsectorLogit_cement
L2321.SubsectorShrwtFllt_cement %>%
add_title("Subsector shareweights of cement sector") %>%
add_units("unitless") %>%
add_comments("For cement sector, the subsector shareweights from A321.subsector_shrwt are expanded into all GCAM regions") %>%
add_legacy_name("L2321.SubsectorShrwtFllt_cement") %>%
add_precursors("energy/A321.subsector_shrwt", "common/GCAM_region_names") ->
L2321.SubsectorShrwtFllt_cement
L2321.SubsectorInterp_cement %>%
add_title("Subsector shareweight interpolation of cement sector") %>%
add_units("NA") %>%
add_comments("For cement sector, the subsector shareweight interpolation function infromation from A321.subsector_interp is expanded into all GCAM regions") %>%
add_legacy_name("L2321.SubsectorInterp_cement") %>%
add_precursors("energy/A321.subsector_interp", "common/GCAM_region_names") ->
L2321.SubsectorInterp_cement
L2321.StubTech_cement %>%
add_title("Identification of stub technologies of cement") %>%
add_units("NA") %>%
add_comments("For cement sector, the stub technologies from A321.globaltech_shrwt are expanded into all GCAM regions") %>%
add_legacy_name("L2321.StubTech_cement") %>%
add_precursors("energy/A321.globaltech_shrwt", "common/GCAM_region_names") ->
L2321.StubTech_cement
L2321.GlobalTechShrwt_cement %>%
add_title("Shareweights of global cement technologies") %>%
add_units("Unitless") %>%
add_comments("For cement sector, the share weights from A321.globaltech_shrwt are interpolated into all base years and future years") %>%
add_legacy_name("L2321.GlobalTechShrwt_cement") %>%
add_precursors("energy/A321.globaltech_shrwt") ->
L2321.GlobalTechShrwt_cement
L2321.GlobalTechCoef_cement %>%
add_title("Energy inputs and coefficients of cement technologies") %>%
add_units("limestone input is unitless (Mt limestone per Mt cement); all others are GJ per kg (EJ of energy per Mt of cement)") %>%
add_comments("For cement sector, the energy use coefficients from A321.globaltech_coef are interpolated into all model years") %>%
add_legacy_name("L2321.GlobalTechCoef_cement") %>%
add_precursors("energy/A321.globaltech_coef") ->
L2321.GlobalTechCoef_cement
L2321.GlobalTechCost_cement %>%
add_title("Non-energy costs of global cement manufacturing technologies") %>%
add_units("1975$/kg for supplysector cement; 1975$/GJ for supplysector process heat cement") %>%
add_comments("For cement sector, the Non-energy costs of global cement manufacturing technologies are calculated then adjusted with CCS to include CO2 capture costs") %>%
add_legacy_name("L2321.GlobalTechCost_cement") %>%
add_precursors("energy/A321.globaltech_cost", "energy/A321.globaltech_co2capture", "energy/A321.globaltech_coef", "emissions/A_PrimaryFuelCCoef") ->
L2321.GlobalTechCost_cement
L2321.GlobalTechCapture_cement %>%
add_title("CO2 capture fractions from global cement production technologies with CCS") %>%
add_units("Unitless") %>%
add_comments("For cement sector, the remove fractions from A321.globaltech_co2capture are interpolated into all model years") %>%
add_legacy_name("L2321.GlobalTechCapture_cement") %>%
add_precursors("energy/A321.globaltech_co2capture") ->
L2321.GlobalTechCapture_cement
L2321.StubTechProd_cement %>%
add_title("calibrated cement production") %>%
add_units("Mt") %>%
add_comments("Values are calculated using L1321.out_Mt_R_cement_Yh then added GCAM region information and supplysector, subsector, and technology information") %>%
add_legacy_name("L2321.StubTechProd_cement") %>%
add_precursors("energy/calibrated_techs", "L1321.out_Mt_R_cement_Yh", "common/GCAM_region_names") ->
L2321.StubTechProd_cement
L2321.StubTechCalInput_cement_heat %>%
add_title("calibrated cement production") %>%
add_units("EJ") %>%
add_comments("Values are calculated using L1321.in_EJ_R_cement_F_Y then added GCAM region information and supplysector, subsector, technology, and input information") %>%
add_legacy_name("L2321.StubTechCalInput_cement_heat") %>%
add_precursors("energy/calibrated_techs", "L1321.in_EJ_R_cement_F_Y", "common/GCAM_region_names") ->
L2321.StubTechCalInput_cement_heat
L2321.StubTechCoef_cement %>%
add_title("region-specific coefficients of cement production technologies") %>%
add_units("limestone input is unitless (Mt limestone per Mt cement); all others are GJ per kg (EJ of energy per Mt of cement)") %>%
add_comments("Coefficients are calculated using L1321.IO_GJkg_R_cement_F_Yh") %>%
add_legacy_name("L2321.StubTechCoef_cement") %>%
add_precursors("energy/calibrated_techs", "L1321.IO_GJkg_R_cement_F_Yh", "common/GCAM_region_names") ->
L2321.StubTechCoef_cement
L2321.PerCapitaBased_cement %>%
add_title("per-capita based flag for cement exports final demand") %>%
add_units("NA") %>%
add_comments("Per-capita based flags for cement from A321.demand are expanded into all GCAM regions") %>%
add_legacy_name("L2321.PerCapitaBased_cement") %>%
add_precursors("energy/A321.demand", "common/GCAM_region_names") ->
L2321.PerCapitaBased_cement
if(exists("L2321.GlobalTechShutdown_en")) {
L2321.GlobalTechShutdown_en %>%
add_title("Global tech lifetime for techs with shutdown rate") %>%
add_units("Lifetime in years") %>%
add_comments("Filters for any technology that uses a phased retirement function") %>%
add_legacy_name("L2321.GlobalTechShutdown_en") %>%
add_precursors("energy/A321.globaltech_retirement") ->
L2321.GlobalTechShutdown_en
} else {
missing_data() %>%
add_legacy_name("energy/L2321.GlobalTechShutdown_en") ->
L2321.GlobalTechShutdown_en
}
if(exists("L2321.GlobalTechSCurve_en")) {
L2321.GlobalTechSCurve_en %>%
add_title("Global tech lifetime for techs with s-curve retirement function") %>%
add_units("Lifetime in years, half-life in years") %>%
add_comments("Filters for any technology that uses an S-curve retirement function") %>%
add_legacy_name("L2321.GlobalTechSCurve_en") %>%
add_precursors("energy/A321.globaltech_retirement") ->
L2321.GlobalTechSCurve_en
} else {
missing_data() %>%
add_legacy_name("energy/L2321.GlobalTechSCurve_en") ->
L2321.GlobalTechSCurve_en
}
if(exists("L2321.GlobalTechLifetime_en")) {
L2321.GlobalTechLifetime_en %>%
add_title("Global tech lifetime for any technology with no retirement function") %>%
add_units("Lifetime in years") %>%
add_comments("Filters for any technology that has no phased or S-curve retirement function, empty by default.") %>%
add_legacy_name("L2321.GlobalTechLifetime_en") %>%
add_precursors("energy/A321.globaltech_retirement") ->
L2321.GlobalTechLifetime_en
} else {
missing_data() %>%
add_legacy_name("energy/L2321.GlobalTechLifetime_en") ->
L2321.GlobalTechLifetime_en
}
if(exists("L2321.GlobalTechProfitShutdown_en")) {
L2321.GlobalTechProfitShutdown_en %>%
add_title("Global tech profit shutdown decider and parameters") %>%
add_units("Unitless, used to determine shape of the function defining the relationship between shutdown rate and profitability") %>%
add_comments("Filters for any technologies that use a profit-based shutdown parameter") %>%
add_legacy_name("L2321.GlobalTechProfitShutdown_en") %>%
add_precursors("energy/A321.globaltech_retirement") ->
L2321.GlobalTechProfitShutdown_en
} else {
missing_data() %>%
add_legacy_name("energy/L2321.GlobalTechProfitShutdown_en") ->
L2321.GlobalTechProfitShutdown_en
}
L2321.BaseService_cement %>%
add_title("base-year service output of cement") %>%
add_units("Mt") %>%
add_comments("Transformed from L2321.StubTechProd_cement by adding energy.final.demand from A321.demand") %>%
add_legacy_name("L2321.BaseService_cement") %>%
add_precursors("energy/A321.demand", "energy/calibrated_techs", "L1321.out_Mt_R_cement_Yh", "common/GCAM_region_names") ->
L2321.BaseService_cement
L2321.PriceElasticity_cement %>%
add_title("price elasticity for cement") %>%
add_units("Unitless") %>%
add_comments("The elasticity values from A321.demand are expanded into all GCAM_regions") %>%
add_legacy_name("L2321.PriceElasticity_cement") %>%
add_precursors("energy/A321.demand", "common/GCAM_region_names") ->
L2321.PriceElasticity_cement
return_data(L2321.Supplysector_cement, L2321.FinalEnergyKeyword_cement, L2321.SubsectorLogit_cement,
L2321.SubsectorShrwtFllt_cement, L2321.SubsectorInterp_cement,
L2321.StubTech_cement, L2321.GlobalTechShrwt_cement,
L2321.GlobalTechCoef_cement, L2321.GlobalTechCost_cement, L2321.GlobalTechCapture_cement,
L2321.StubTechProd_cement, L2321.StubTechCalInput_cement_heat, L2321.StubTechCoef_cement,
L2321.PerCapitaBased_cement, L2321.BaseService_cement,L2321.GlobalTechShutdown_en,
L2321.GlobalTechSCurve_en, L2321.GlobalTechLifetime_en, L2321.GlobalTechProfitShutdown_en,
L2321.PriceElasticity_cement, L2321.IncomeElasticity_cement_gcam3,
L2321.IncomeElasticity_cement_gssp1, L2321.IncomeElasticity_cement_gssp2,
L2321.IncomeElasticity_cement_gssp3, L2321.IncomeElasticity_cement_gssp4,
L2321.IncomeElasticity_cement_gssp5, L2321.IncomeElasticity_cement_ssp1,
L2321.IncomeElasticity_cement_ssp2, L2321.IncomeElasticity_cement_ssp3,
L2321.IncomeElasticity_cement_ssp4, L2321.IncomeElasticity_cement_ssp5)
} else {
stop("Unknown command")
}
}
JGCRI/gcamdata documentation built on March 21, 2023, 2:19 a.m.
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Defines functions module_energy_L2321.cement
Documented in module_energy_L2321.cement
# Copyright 2019 Battelle Memorial Institute; see the LICENSE file.
#' module_energy_L2321.cement
#'
#' Compute a variety of final energy keyword, sector, share weight, and technology information for cement-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{L2321.SectorLogitTables[[ curr_table ]]$data}, \code{L2321.Supplysector_cement}, \code{L2321.FinalEnergyKeyword_cement},
#' \code{L2321.SubsectorLogitTables[[ curr_table ]]$data}, \code{L2321.SubsectorLogit_cement}, \code{L2321.SubsectorShrwtFllt_cement},
#' \code{L2321.SubsectorInterp_cement}, \code{L2321.StubTech_cement}, \code{L2321.GlobalTechShrwt_cement}, \code{L2321.GlobalTechCoef_cement},
#' \code{L2321.GlobalTechCost_cement}, \code{L2321.GlobalTechCapture_cement}, \code{L2321.StubTechProd_cement}, \code{L2321.StubTechCalInput_cement_heat},
#' \code{L2321.StubTechCoef_cement}, \code{L2321.PerCapitaBased_cement}, \code{L2321.BaseService_cement}, \code{L2321.PriceElasticity_cement},
#' \code{L2321.IncomeElasticity_cement_gcam3}, \code{L2321.IncomeElasticity_cement_gssp1}, \code{L2321.IncomeElasticity_cement_gssp2},
#' \code{L2321.IncomeElasticity_cement_gssp3}, \code{L2321.IncomeElasticity_cement_gssp4}, \code{L2321.IncomeElasticity_cement_gssp5},
#' \code{L2321.IncomeElasticity_cement_ssp1}, \code{L2321.IncomeElasticity_cement_ssp2}, \code{L2321.IncomeElasticity_cement_ssp3},
#' \code{L2321.IncomeElasticity_cement_ssp4}, \code{L2321.IncomeElasticity_cement_ssp5}, \code{object}. The corresponding file in the
#' original data system was \code{L2321.cement.R} (energy level2).
#' @details The chunk provides final energy keyword, supplysector/subsector information, supplysector/subsector interpolation information, 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 for cement sector.
#' @importFrom assertthat assert_that
#' @importFrom dplyr arrange bind_rows distinct filter if_else group_by lag left_join mutate pull select
#' @importFrom tidyr complete nesting
#' @author LF October 2017
module_energy_L2321.cement <- 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/A321.sector",
FILE = "emissions/A_PrimaryFuelCCoef",
FILE = "energy/A321.sector",
FILE = "energy/A321.subsector_interp",
FILE = "energy/A321.subsector_logit",
FILE = "energy/A321.subsector_shrwt",
FILE = "energy/A321.globaltech_coef",
FILE = "energy/A321.globaltech_cost",
FILE = "energy/A321.globaltech_shrwt",
FILE = "energy/A321.globaltech_co2capture",
FILE = "energy/A321.demand",
FILE = "energy/A321.globaltech_retirement",
FILE = "socioeconomics/A321.inc_elas_output",
"L1321.out_Mt_R_cement_Yh",
"L1321.IO_GJkg_R_cement_F_Yh",
"L1321.in_EJ_R_cement_F_Y",
"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("L2321.Supplysector_cement",
"L2321.FinalEnergyKeyword_cement",
"L2321.SubsectorLogit_cement",
"L2321.SubsectorShrwtFllt_cement",
"L2321.SubsectorInterp_cement",
"L2321.StubTech_cement",
"L2321.GlobalTechShrwt_cement",
"L2321.GlobalTechCoef_cement",
"L2321.GlobalTechCost_cement",
"L2321.GlobalTechCapture_cement",
"L2321.GlobalTechShutdown_en",
"L2321.GlobalTechSCurve_en",
"L2321.GlobalTechLifetime_en",
"L2321.GlobalTechProfitShutdown_en",
"L2321.StubTechProd_cement",
"L2321.StubTechCalInput_cement_heat",
"L2321.StubTechCoef_cement",
"L2321.PerCapitaBased_cement",
"L2321.BaseService_cement",
"L2321.PriceElasticity_cement",
paste("L2321.IncomeElasticity_cement", 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")
A321.sector <- get_data(all_data, "energy/A321.sector", strip_attributes = TRUE)
A_PrimaryFuelCCoef <- get_data(all_data, "emissions/A_PrimaryFuelCCoef")
A321.subsector_interp <- get_data(all_data, "energy/A321.subsector_interp", strip_attributes = TRUE)
A321.subsector_logit <- get_data(all_data, "energy/A321.subsector_logit", strip_attributes = TRUE)
A321.subsector_shrwt <- get_data(all_data, "energy/A321.subsector_shrwt", strip_attributes = TRUE)
A321.globaltech_coef <- get_data(all_data, "energy/A321.globaltech_coef")
A321.globaltech_cost <- get_data(all_data, "energy/A321.globaltech_cost")
A321.globaltech_shrwt <- get_data(all_data, "energy/A321.globaltech_shrwt", strip_attributes = TRUE)
A321.globaltech_co2capture <- get_data(all_data, "energy/A321.globaltech_co2capture")
A321.globaltech_retirement <- get_data(all_data, "energy/A321.globaltech_retirement", strip_attributes = TRUE)
A321.demand <- get_data(all_data, "energy/A321.demand", strip_attributes = TRUE)
L1321.out_Mt_R_cement_Yh <- get_data(all_data, "L1321.out_Mt_R_cement_Yh", strip_attributes = TRUE)
L1321.IO_GJkg_R_cement_F_Yh <- get_data(all_data, "L1321.IO_GJkg_R_cement_F_Yh", strip_attributes = TRUE)
L1321.in_EJ_R_cement_F_Y <- get_data(all_data, "L1321.in_EJ_R_cement_F_Y", strip_attributes = TRUE)
A321.inc_elas_output <- get_data(all_data, "socioeconomics/A321.inc_elas_output", strip_attributes = TRUE)
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
year <- value <- GCAM_region_ID <- sector <- fuel <- year.fillout <- to.value <-
technology <- supplysector <- subsector <- minicam.energy.input <- coefficient <-
remove.fraction <- minicam.non.energy.input <- input.cost <- PrimaryFuelCO2Coef.name <-
PrimaryFuelCO2Coef <- calibration <- calOutputValue <- subs.share.weight <- region <-
calibrated.value <- . <- scenario <- temp_lag <- base.service <- energy.final.demand <-
value.x <- value.y <- parameter <- L2321.IncomeElasticity_cement_gcam3 <-
L2321.IncomeElasticity_cement_gssp1 <- L2321.IncomeElasticity_cement_gssp2 <-
L2321.IncomeElasticity_cement_gssp3 <- L2321.IncomeElasticity_cement_gssp4 <-
L2321.IncomeElasticity_cement_gssp5 <- L2321.IncomeElasticity_cement_ssp1 <-
L2321.IncomeElasticity_cement_ssp2 <- L2321.IncomeElasticity_cement_ssp3 <-
L2321.IncomeElasticity_cement_ssp4 <- L2321.IncomeElasticity_cement_ssp5 <- year.x <- year.y <- NULL
# ===================================================
# 1. Perform computations
# Create tables to delete technologies and subsectors in regions where heat is not modeled as a fuel
# 1a. Supplysector information
# L2321.Supplysector_cement: Supply sector information for cement sector
A321.sector %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["Supplysector"]], LOGIT_TYPE_COLNAME), GCAM_region_names) ->
L2321.Supplysector_cement
# L2321.FinalEnergyKeyword_cement: Supply sector keywords for cement sector
A321.sector %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["FinalEnergyKeyword"]], GCAM_region_names) %>%
na.omit ->
L2321.FinalEnergyKeyword_cement
# 1b. Subsector information
# L2321.SubsectorLogit_cement: Subsector logit exponents of cement sector
A321.subsector_logit %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["SubsectorLogit"]], LOGIT_TYPE_COLNAME), GCAM_region_names) ->
L2321.SubsectorLogit_cement
# and L2321.SubsectorShrwtFllt_cement: Subsector shareweights of cement sector
A321.subsector_shrwt %>%
filter(!is.na(year.fillout)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["SubsectorShrwtFllt"]], GCAM_region_names) ->
L2321.SubsectorShrwtFllt_cement
# L2321.SubsectorInterp_cement: Subsector shareweight interpolation of cement sector
A321.subsector_interp %>%
filter(is.na(to.value)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["SubsectorInterp"]], GCAM_region_names) ->
L2321.SubsectorInterp_cement
# 1c. Technology information
# L2321.StubTech_cement: Identification of stub technologies of cement
# Note: assuming that technology list in the shareweight table includes the full set (any others would default to a 0 shareweight)
A321.globaltech_shrwt %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["Tech"]], GCAM_region_names) %>%
rename(stub.technology = technology) ->
L2321.StubTech_cement
# L2321.GlobalTechShrwt_cement: Shareweights of global cement technologies
A321.globaltech_shrwt %>%
gather_years %>%
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, value, 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") ->
L2321.GlobalTechShrwt_cement
# L2321.GlobalTechCoef_cement: Energy inputs and coefficients of cement technologies
A321.globaltech_coef %>%
gather_years %>%
complete(nesting(supplysector, subsector, technology, minicam.energy.input), year = c(year, MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)) %>%
arrange(supplysector, subsector, technology, minicam.energy.input, year) %>%
group_by(supplysector, subsector, technology, minicam.energy.input) %>%
mutate(coefficient = approx_fun(year, value, rule = 1),
coefficient = round(coefficient, energy.DIGITS_COEFFICIENT)) %>%
ungroup %>%
filter(year %in% c(MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)) %>%
rename(sector.name = supplysector,
subsector.name = subsector) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechCoef"]]) ->
L2321.GlobalTechCoef_cement
# Carbon capture rates from technologies with CCS
# L2321.GlobalTechCapture_cement: CO2 capture fractions from global cement production technologies with CCS
# No need to consider historical periods or intermittent technologies here
A321.globaltech_co2capture %>%
gather_years %>%
complete(nesting(supplysector, subsector, technology), year = c(year, MODEL_FUTURE_YEARS)) %>%
arrange(supplysector, subsector, technology, year) %>%
group_by(supplysector, subsector, technology) %>%
mutate(remove.fraction = approx_fun(year, value, rule = 1),
remove.fraction = round(remove.fraction, energy.DIGITS_REMOVE.FRACTION)) %>%
ungroup %>%
filter(year %in% MODEL_FUTURE_YEARS) %>%
rename(sector.name = supplysector,
subsector.name = subsector) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechYr"]], "remove.fraction") %>%
mutate(storage.market = energy.CO2.STORAGE.MARKET) ->
L2321.GlobalTechCapture_cement
# L2321.GlobalTechCost_cement: Non-energy costs of global cement manufacturing technologies
A321.globaltech_cost %>%
gather_years %>%
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, value, rule = 1),
input.cost = round(input.cost, energy.DIGITS_COST)) %>%
ungroup %>%
filter(year %in% c(MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)) %>%
rename(sector.name = supplysector,
subsector.name = subsector) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechCost"]]) ->
L2321.GlobalTechCost_cement # intermediate tibble
# Note: adjusting non-energy costs of technologies with CCS to include CO2 capture costs
# The additional CCS-related non-energy costs are not included in the global technology assessment.
# Calculate here in two steps:
# (1) calculate the additional CCS costs per unit of carbon produced in 1975$
# (2) calculate the quantity of CO2 produced per unit of cement produced (in kgC per kg cement)
cement_CCS_cost_total_1975USDtC <- energy.CEMENT_CCS_COST_2000USDTCO2 * gdp_deflator(1975, base_year = 2000) * emissions.CONV_C_CO2
CO2_storage_cost_1975USDtC <- energy.CO2_STORAGE_COST_1990_USDTC * gdp_deflator(1975, base_year = 1990)
cement_CCS_cost_1975USDtC <- cement_CCS_cost_total_1975USDtC - CO2_storage_cost_1975USDtC
L2321.GlobalTechCapture_cement %>%
pull(remove.fraction) %>%
mean -> cement_CO2_capture_frac
L2321.GlobalTechCoef_cement %>%
filter(minicam.energy.input == "limestone") %>%
pull(coefficient) %>%
mean ->
coef_mean # temporary value
A_PrimaryFuelCCoef %>%
filter(PrimaryFuelCO2Coef.name == "limestone") %>%
pull(PrimaryFuelCO2Coef) %>%
mean ->
PrimaryFuelCO2Coef_mean # temporary value
CO2_IO_kgCkgcement <- coef_mean * PrimaryFuelCO2Coef_mean
CO2stored_IO_kgCkgcement <- CO2_IO_kgCkgcement * cement_CO2_capture_frac
cement_CCS_cost_75USD_tcement <- cement_CCS_cost_1975USDtC * CO2stored_IO_kgCkgcement / CONV_T_KG
# Adjust the non-energy costs in the table for model input
L2321.GlobalTechCost_cement %>%
filter(technology %in% L2321.GlobalTechCapture_cement[["technology"]]) %>%
mutate(input.cost = input.cost + cement_CCS_cost_75USD_tcement) %>%
bind_rows(filter(L2321.GlobalTechCost_cement, !(technology %in% L2321.GlobalTechCapture_cement[["technology"]]))) %>%
mutate(input.cost = round(input.cost, energy.DIGITS_COST)) ->
L2321.GlobalTechCost_cement
# Calibration and region-specific data
# L2321.StubTechProd_cement: calibrated cement production
calibrated_techs %>%
filter(calibration == "output") %>% # Only take the tech IDs where the calibration is identified as output
select(sector, supplysector, subsector, technology) %>%
distinct ->
calibrated_techs_export # temporary tibble
L1321.out_Mt_R_cement_Yh %>%
filter(year %in% MODEL_BASE_YEARS) %>%
mutate(calOutputValue = round(value, energy.DIGITS_CALOUTPUT)) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_error_no_match(calibrated_techs_export, by = "sector") %>%
mutate(stub.technology = 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"]]) ->
L2321.StubTechProd_cement
# L2321.StubTechCoef_cement: region-specific coefficients of cement production technologies
# Take this as a given in all years for which data is available
calibrated_techs %>%
select(sector, fuel, supplysector, subsector, technology, minicam.energy.input) %>%
distinct ->
calibrated_techs_export # temporary tibble
L1321.IO_GJkg_R_cement_F_Yh %>%
filter(year %in% HISTORICAL_YEARS[HISTORICAL_YEARS %in% c(MODEL_BASE_YEARS, MODEL_FUTURE_YEARS)]) %>%
mutate(coefficient = round(value, energy.DIGITS_COEFFICIENT)) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_error_no_match(calibrated_techs_export, by = c("sector", "fuel")) %>%
mutate(stub.technology = technology,
market.name = region) %>%
select(LEVEL2_DATA_NAMES[["StubTechCoef"]]) ->
L2321.StubTechCoef_cement
# L2321.StubTechCalInput_cement_heat: calibrated cement production
calibrated_techs %>%
select(sector, fuel, supplysector, subsector, technology, minicam.energy.input) %>%
distinct ->
calibrated_techs_export # temporary tibble
L1321.in_EJ_R_cement_F_Y %>%
filter(year %in% MODEL_BASE_YEARS) %>%
mutate(calibrated.value = round(value, energy.DIGITS_CALOUTPUT)) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_error_no_match(calibrated_techs_export, by = c("sector", "fuel")) %>%
# This table should only be the technologies for producing heat - drop the electricity inputs to the cement production technology
filter(!(supplysector %in% L2321.StubTechCoef_cement[["supplysector"]])) %>%
mutate(stub.technology = technology,
share.weight.year = year,
subs.share.weight = if_else(calibrated.value > 0, 1, 0),
tech.share.weight = subs.share.weight) %>%
select(LEVEL2_DATA_NAMES[["StubTechCalInput"]]) ->
L2321.StubTechCalInput_cement_heat
# L2321.PerCapitaBased_cement: per-capita based flag for cement exports final demand
A321.demand %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["PerCapitaBased"]], GCAM_region_names) ->
L2321.PerCapitaBased_cement
# L2321.BaseService_cement: base-year service output of cement
L2321.StubTechProd_cement %>%
select(region, year, base.service = calOutputValue) %>%
mutate(energy.final.demand = A321.demand[["energy.final.demand"]]) ->
L2321.BaseService_cement
# Retirement information
A321.globaltech_retirement %>%
set_years() %>%
mutate(year = as.integer(year)) %>%
rename(sector.name = supplysector, subsector.name = subsector) ->
L2321.globaltech_retirement_base
# Copies first future year retirment information into all future years and appends back onto base year
L2321.globaltech_retirement_base %>%
mutate(year = as.integer(year)) %>%
filter(year == min(MODEL_FUTURE_YEARS)) %>%
repeat_add_columns(tibble(year = MODEL_FUTURE_YEARS)) %>%
select(-year.x) %>%
rename(year = year.y) ->
L2321.globaltech_retirement_future
# filters base years from original and then appends future years
L2321.globaltech_retirement_base %>%
mutate(year = as.integer(year)) %>%
filter(year == max(MODEL_BASE_YEARS)) %>%
bind_rows(L2321.globaltech_retirement_future) ->
L2321.globaltech_retirement
# Retirement may consist of any of three types of retirement function (phased, s-curve, or none)
# This section checks L2321.globaltech_retirement for each of these functions and creates a separate level 2 file for each
# All of these options have different headers, and all are allowed
if(any(!is.na(L2321.globaltech_retirement$shutdown.rate))) {
L2321.globaltech_retirement %>%
filter(!is.na(L2321.globaltech_retirement$shutdown.rate)) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechYr"]], "lifetime", "shutdown.rate") ->
L2321.GlobalTechShutdown_en
}
if(any(!is.na(L2321.globaltech_retirement$half.life))) {
L2321.globaltech_retirement %>%
filter(!is.na(L2321.globaltech_retirement$half.life)) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechYr"]], "lifetime", "steepness", "half.life") ->
L2321.GlobalTechSCurve_en
}
# L2321.GlobalTechLifetime_en: Global tech lifetime
if(any(is.na(L2321.globaltech_retirement$shutdown.rate) & is.na(L2321.globaltech_retirement$half.life))) {
L2321.globaltech_retirement %>%
filter(is.na(L2321.globaltech_retirement$shutdown.rate) & is.na(L2321.globaltech_retirement$half.life)) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechYr"]], "lifetime") ->
L2321.GlobalTechLifetime_en
}
# L2321.GlobalTechProfitShutdown_en: Global tech profit shutdown decider and parameters
if(any(!is.na(L2321.globaltech_retirement$median.shutdown.point))) {
L2321.globaltech_retirement %>%
filter(!is.na(L2321.globaltech_retirement$median.shutdown.point)) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechYr"]], "median.shutdown.point", "profit.shutdown.steepness") ->
L2321.GlobalTechProfitShutdown_en
}
# L2321.PriceElasticity_cement: price elasticity
A321.demand %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["PriceElasticity"]][LEVEL2_DATA_NAMES[["PriceElasticity"]] != "year"], GCAM_region_names) %>%
repeat_add_columns(tibble(year = MODEL_FUTURE_YEARS)) %>%
select(LEVEL2_DATA_NAMES[["PriceElasticity"]]) ->
L2321.PriceElasticity_cement
# L2321.IncomeElasticity_cement_scen: income elasticity of cement (scenario-specific)
# First, calculate the per-capita GDP pathways of every GDP scenario and combine
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) ->
L2321.pcgdpRatio_ALL_R_Y # intermediate tibble
# Calculate the cement output as the base-year cement output times the GDP ratio raised to the income elasticity
# The income elasticity is looked up based on the prior year's output
L2321.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(L2321.BaseService_cement, 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_MIL_THOUS / value) %>%
select(-base.service, -energy.final.demand) ->
L2321.Output_cement # intermediate tibble
# 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
# production/consumption.
elast_years <- c(max(MODEL_BASE_YEARS), MODEL_FUTURE_YEARS)
for(i in seq_along(elast_years)[-1]) {
L2321.Output_cement %>%
filter(year == elast_years[i - 1]) %>%
# strick left join fails timeshift test due to NAs in L102.pcgdp_thous90USD_Scen_R_Y under timeshift mode
left_join(filter(L2321.pcgdpRatio_ALL_R_Y, year == elast_years[i]), by = c("GCAM_region_ID", "scenario")) ->
intermediate
intermediate %>%
mutate(parameter = approx(x = A321.inc_elas_output[["pc.output_t"]],
y = A321.inc_elas_output[["inc_elas"]],
xout = intermediate[["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(L2321.Output_cement) ->
L2321.Output_cement
}
# Now that we have cement output, we can back out the appropriate income elasticities
L2321.Output_cement %>%
filter(year %in% MODEL_FUTURE_YEARS) %>%
mutate(value = approx( x = A321.inc_elas_output[["pc.output_t"]],
y = A321.inc_elas_output[["inc_elas"]],
xout = value, rule = 2)[["y"]],
value = round(value, energy.DIGITS_INCELAS_IND)) %>%
rename(income.elasticity = value) %>%
mutate(energy.final.demand = A321.demand[["energy.final.demand"]]) ->
L2321.IncomeElasticity_cement # intermediate tibble
# ===================================================
# Produce outputs
# Extract GCAM3, SSP, and gSSP data and assign to separate tables
for(ieo in INCOME_ELASTICITY_OUTPUTS) {
L2321.IncomeElasticity_cement %>%
filter(scenario == ieo) %>%
select(LEVEL2_DATA_NAMES[["IncomeElasticity"]]) %>%
add_title(paste("Income elasticity of cement -", ieo)) %>%
add_units("Unitless") %>%
add_comments("First calculate cement output as the base-year cement output times the GDP ratio raised to the income elasticity") %>%
add_comments("Then back out the appropriate income elasticities from cement output") %>%
add_legacy_name(paste0("L2321.IncomeElasticity_cement_", tolower(ieo))) %>%
add_precursors("L102.pcgdp_thous90USD_GCAM3_R_Y", "L102.pcgdp_thous90USD_Scen_R_Y", "common/GCAM_region_names", "energy/A321.demand", "energy/calibrated_techs",
"L1321.out_Mt_R_cement_Yh", "L101.Pop_thous_GCAM3_R_Y", "socioeconomics/A321.inc_elas_output") ->
x
assign(paste0("L2321.IncomeElasticity_cement_", tolower(ieo)), x)
}
L2321.Supplysector_cement %>%
add_title("Supply sector information for cement sector") %>%
add_units("NA") %>%
add_comments("For cement sector, the supply sector information (output.unit, input.unit, price.unit, logit.year.fillout, logit.exponent) from A321.sector is expended into all GCAM regions") %>%
add_legacy_name("L2321.Supplysector_cement") %>%
add_precursors("energy/A321.sector", "common/GCAM_region_names") ->
L2321.Supplysector_cement
L2321.FinalEnergyKeyword_cement %>%
add_title("Supply sector keywords for cement sector") %>%
add_units("NA") %>%
add_comments("For cement sector, the supply sector final energy keywords from A321.sector are expended into all GCAM regions") %>%
add_legacy_name("L2321.FinalEnergyKeyword_cement") %>%
add_precursors("energy/A321.sector", "common/GCAM_region_names") ->
L2321.FinalEnergyKeyword_cement
L2321.SubsectorLogit_cement %>%
add_title("Subsector logit exponents of cement sector") %>%
add_units("Unitless") %>%
add_comments("For cement sector, the subsector logit exponents from A321.subsector_logit are expanded into all GCAM regions") %>%
add_legacy_name("L2321.SubsectorLogit_cement") %>%
add_precursors("energy/A321.subsector_logit", "common/GCAM_region_names") ->
L2321.SubsectorLogit_cement
L2321.SubsectorShrwtFllt_cement %>%
add_title("Subsector shareweights of cement sector") %>%
add_units("unitless") %>%
add_comments("For cement sector, the subsector shareweights from A321.subsector_shrwt are expanded into all GCAM regions") %>%
add_legacy_name("L2321.SubsectorShrwtFllt_cement") %>%
add_precursors("energy/A321.subsector_shrwt", "common/GCAM_region_names") ->
L2321.SubsectorShrwtFllt_cement
L2321.SubsectorInterp_cement %>%
add_title("Subsector shareweight interpolation of cement sector") %>%
add_units("NA") %>%
add_comments("For cement sector, the subsector shareweight interpolation function infromation from A321.subsector_interp is expanded into all GCAM regions") %>%
add_legacy_name("L2321.SubsectorInterp_cement") %>%
add_precursors("energy/A321.subsector_interp", "common/GCAM_region_names") ->
L2321.SubsectorInterp_cement
L2321.StubTech_cement %>%
add_title("Identification of stub technologies of cement") %>%
add_units("NA") %>%
add_comments("For cement sector, the stub technologies from A321.globaltech_shrwt are expanded into all GCAM regions") %>%
add_legacy_name("L2321.StubTech_cement") %>%
add_precursors("energy/A321.globaltech_shrwt", "common/GCAM_region_names") ->
L2321.StubTech_cement
L2321.GlobalTechShrwt_cement %>%
add_title("Shareweights of global cement technologies") %>%
add_units("Unitless") %>%
add_comments("For cement sector, the share weights from A321.globaltech_shrwt are interpolated into all base years and future years") %>%
add_legacy_name("L2321.GlobalTechShrwt_cement") %>%
add_precursors("energy/A321.globaltech_shrwt") ->
L2321.GlobalTechShrwt_cement
L2321.GlobalTechCoef_cement %>%
add_title("Energy inputs and coefficients of cement technologies") %>%
add_units("limestone input is unitless (Mt limestone per Mt cement); all others are GJ per kg (EJ of energy per Mt of cement)") %>%
add_comments("For cement sector, the energy use coefficients from A321.globaltech_coef are interpolated into all model years") %>%
add_legacy_name("L2321.GlobalTechCoef_cement") %>%
add_precursors("energy/A321.globaltech_coef") ->
L2321.GlobalTechCoef_cement
L2321.GlobalTechCost_cement %>%
add_title("Non-energy costs of global cement manufacturing technologies") %>%
add_units("1975$/kg for supplysector cement; 1975$/GJ for supplysector process heat cement") %>%
add_comments("For cement sector, the Non-energy costs of global cement manufacturing technologies are calculated then adjusted with CCS to include CO2 capture costs") %>%
add_legacy_name("L2321.GlobalTechCost_cement") %>%
add_precursors("energy/A321.globaltech_cost", "energy/A321.globaltech_co2capture", "energy/A321.globaltech_coef", "emissions/A_PrimaryFuelCCoef") ->
L2321.GlobalTechCost_cement
L2321.GlobalTechCapture_cement %>%
add_title("CO2 capture fractions from global cement production technologies with CCS") %>%
add_units("Unitless") %>%
add_comments("For cement sector, the remove fractions from A321.globaltech_co2capture are interpolated into all model years") %>%
add_legacy_name("L2321.GlobalTechCapture_cement") %>%
add_precursors("energy/A321.globaltech_co2capture") ->
L2321.GlobalTechCapture_cement
L2321.StubTechProd_cement %>%
add_title("calibrated cement production") %>%
add_units("Mt") %>%
add_comments("Values are calculated using L1321.out_Mt_R_cement_Yh then added GCAM region information and supplysector, subsector, and technology information") %>%
add_legacy_name("L2321.StubTechProd_cement") %>%
add_precursors("energy/calibrated_techs", "L1321.out_Mt_R_cement_Yh", "common/GCAM_region_names") ->
L2321.StubTechProd_cement
L2321.StubTechCalInput_cement_heat %>%
add_title("calibrated cement production") %>%
add_units("EJ") %>%
add_comments("Values are calculated using L1321.in_EJ_R_cement_F_Y then added GCAM region information and supplysector, subsector, technology, and input information") %>%
add_legacy_name("L2321.StubTechCalInput_cement_heat") %>%
add_precursors("energy/calibrated_techs", "L1321.in_EJ_R_cement_F_Y", "common/GCAM_region_names") ->
L2321.StubTechCalInput_cement_heat
L2321.StubTechCoef_cement %>%
add_title("region-specific coefficients of cement production technologies") %>%
add_units("limestone input is unitless (Mt limestone per Mt cement); all others are GJ per kg (EJ of energy per Mt of cement)") %>%
add_comments("Coefficients are calculated using L1321.IO_GJkg_R_cement_F_Yh") %>%
add_legacy_name("L2321.StubTechCoef_cement") %>%
add_precursors("energy/calibrated_techs", "L1321.IO_GJkg_R_cement_F_Yh", "common/GCAM_region_names") ->
L2321.StubTechCoef_cement
L2321.PerCapitaBased_cement %>%
add_title("per-capita based flag for cement exports final demand") %>%
add_units("NA") %>%
add_comments("Per-capita based flags for cement from A321.demand are expanded into all GCAM regions") %>%
add_legacy_name("L2321.PerCapitaBased_cement") %>%
add_precursors("energy/A321.demand", "common/GCAM_region_names") ->
L2321.PerCapitaBased_cement
if(exists("L2321.GlobalTechShutdown_en")) {
L2321.GlobalTechShutdown_en %>%
add_title("Global tech lifetime for techs with shutdown rate") %>%
add_units("Lifetime in years") %>%
add_comments("Filters for any technology that uses a phased retirement function") %>%
add_legacy_name("L2321.GlobalTechShutdown_en") %>%
add_precursors("energy/A321.globaltech_retirement") ->
L2321.GlobalTechShutdown_en
} else {
missing_data() %>%
add_legacy_name("energy/L2321.GlobalTechShutdown_en") ->
L2321.GlobalTechShutdown_en
}
if(exists("L2321.GlobalTechSCurve_en")) {
L2321.GlobalTechSCurve_en %>%
add_title("Global tech lifetime for techs with s-curve retirement function") %>%
add_units("Lifetime in years, half-life in years") %>%
add_comments("Filters for any technology that uses an S-curve retirement function") %>%
add_legacy_name("L2321.GlobalTechSCurve_en") %>%
add_precursors("energy/A321.globaltech_retirement") ->
L2321.GlobalTechSCurve_en
} else {
missing_data() %>%
add_legacy_name("energy/L2321.GlobalTechSCurve_en") ->
L2321.GlobalTechSCurve_en
}
if(exists("L2321.GlobalTechLifetime_en")) {
L2321.GlobalTechLifetime_en %>%
add_title("Global tech lifetime for any technology with no retirement function") %>%
add_units("Lifetime in years") %>%
add_comments("Filters for any technology that has no phased or S-curve retirement function, empty by default.") %>%
add_legacy_name("L2321.GlobalTechLifetime_en") %>%
add_precursors("energy/A321.globaltech_retirement") ->
L2321.GlobalTechLifetime_en
} else {
missing_data() %>%
add_legacy_name("energy/L2321.GlobalTechLifetime_en") ->
L2321.GlobalTechLifetime_en
}
if(exists("L2321.GlobalTechProfitShutdown_en")) {
L2321.GlobalTechProfitShutdown_en %>%
add_title("Global tech profit shutdown decider and parameters") %>%
add_units("Unitless, used to determine shape of the function defining the relationship between shutdown rate and profitability") %>%
add_comments("Filters for any technologies that use a profit-based shutdown parameter") %>%
add_legacy_name("L2321.GlobalTechProfitShutdown_en") %>%
add_precursors("energy/A321.globaltech_retirement") ->
L2321.GlobalTechProfitShutdown_en
} else {
missing_data() %>%
add_legacy_name("energy/L2321.GlobalTechProfitShutdown_en") ->
L2321.GlobalTechProfitShutdown_en
}
L2321.BaseService_cement %>%
add_title("base-year service output of cement") %>%
add_units("Mt") %>%
add_comments("Transformed from L2321.StubTechProd_cement by adding energy.final.demand from A321.demand") %>%
add_legacy_name("L2321.BaseService_cement") %>%
add_precursors("energy/A321.demand", "energy/calibrated_techs", "L1321.out_Mt_R_cement_Yh", "common/GCAM_region_names") ->
L2321.BaseService_cement
L2321.PriceElasticity_cement %>%
add_title("price elasticity for cement") %>%
add_units("Unitless") %>%
add_comments("The elasticity values from A321.demand are expanded into all GCAM_regions") %>%
add_legacy_name("L2321.PriceElasticity_cement") %>%
add_precursors("energy/A321.demand", "common/GCAM_region_names") ->
L2321.PriceElasticity_cement
return_data(L2321.Supplysector_cement, L2321.FinalEnergyKeyword_cement, L2321.SubsectorLogit_cement,
L2321.SubsectorShrwtFllt_cement, L2321.SubsectorInterp_cement,
L2321.StubTech_cement, L2321.GlobalTechShrwt_cement,
L2321.GlobalTechCoef_cement, L2321.GlobalTechCost_cement, L2321.GlobalTechCapture_cement,
L2321.StubTechProd_cement, L2321.StubTechCalInput_cement_heat, L2321.StubTechCoef_cement,
L2321.PerCapitaBased_cement, L2321.BaseService_cement,L2321.GlobalTechShutdown_en,
L2321.GlobalTechSCurve_en, L2321.GlobalTechLifetime_en, L2321.GlobalTechProfitShutdown_en,
L2321.PriceElasticity_cement, L2321.IncomeElasticity_cement_gcam3,
L2321.IncomeElasticity_cement_gssp1, L2321.IncomeElasticity_cement_gssp2,
L2321.IncomeElasticity_cement_gssp3, L2321.IncomeElasticity_cement_gssp4,
L2321.IncomeElasticity_cement_gssp5, L2321.IncomeElasticity_cement_ssp1,
L2321.IncomeElasticity_cement_ssp2, L2321.IncomeElasticity_cement_ssp3,
L2321.IncomeElasticity_cement_ssp4, L2321.IncomeElasticity_cement_ssp5)
} else {
stop("Unknown command")
}
}
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