R/zchunk_L2322.Fert.R
In bpbond/gcamdata: Build the GCAM Input Datasets
Defines functions
module_energy_L2322.Fert
Documented in
module_energy_L2322.Fert
# Copyright 2019 Battelle Memorial Institute; see the LICENSE file.
#' module_energy_L2322.Fert
#'
#' Provide supply sector information/keywords, subsector shareweights, global technology lifetime,
#' energy inputs and coefficients, global fertilizer manufacturing technologies, etc. for the fertilizer sector.
#'
#' @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{L2322.Supplysector_Fert}, \code{L2322.FinalEnergyKeyword_Fert}, \code{L2322.SubsectorLogit_Fert}, \code{L2322.SubsectorShrwtFllt_Fert}, \code{L2322.SubsectorInterp_Fert}, \code{L2322.StubTech_Fert}, \code{L2322.GlobalTechShrwt_Fert}, \code{L2322.GlobalTechCoef_Fert}, \code{L2322.GlobalTechCost_Fert}, \code{L2322.GlobalTechCapture_Fert}, \code{L2322.GlobalTechSCurve_Fert}, \code{L2322.GlobalTechProfitShutdown_Fert}, \code{L2322.StubTechProd_Fert}, \code{L2322.StubTechCoef_Fert}, \code{L2322.StubTechFixOut_Fert_imp}, \code{L2322.StubTechFixOut_Fert_exp}, \code{L2322.PerCapitaBased_Fert}, \code{L2322.BaseService_Fert}. The corresponding file in the
#' original data system was \code{L2322.Fert.R} (energy level2).
#' @details This chunk provides supply sector information/keywords, subsector shareweights, global technology lifetime,
#' energy inputs and coefficients, global fertilizer manufacturing technologies, etc. for the fertilizer sector.
#' @importFrom assertthat assert_that
#' @importFrom dplyr arrange bind_rows filter if_else group_by left_join mutate select
#' @importFrom tidyr complete nesting
#' @author LF September 2017
module_energy_L2322.Fert <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "common/GCAM_region_names",
FILE = "energy/calibrated_techs",
FILE = "energy/A322.sector",
FILE = "energy/A322.subsector_interp",
FILE = "energy/A322.subsector_logit",
FILE = "energy/A322.subsector_shrwt",
FILE = "energy/A322.globaltech_coef",
FILE = "energy/A322.globaltech_shrwt",
FILE = "energy/A322.globaltech_co2capture",
FILE = "energy/A322.globaltech_renew",
FILE = "energy/A322.globaltech_retirement",
"L1322.Fert_Prod_MtN_R_F_Y",
"L1322.IO_R_Fert_F_Yh",
"L1322.Fert_NEcost_75USDkgN_F",
"L142.ag_Fert_NetExp_MtN_R_Y"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L2322.Supplysector_Fert",
"L2322.FinalEnergyKeyword_Fert",
"L2322.SubsectorLogit_Fert",
"L2322.SubsectorShrwtFllt_Fert",
"L2322.SubsectorInterp_Fert",
"L2322.StubTech_Fert",
"L2322.GlobalTechShrwt_Fert",
"L2322.GlobalTechCoef_Fert",
"L2322.GlobalTechCost_Fert",
"L2322.GlobalTechCapture_Fert",
"L2322.GlobalTechSCurve_Fert",
"L2322.GlobalTechProfitShutdown_Fert",
"L2322.StubTechProd_Fert",
"L2322.StubTechCoef_Fert",
"L2322.StubTechFixOut_Fert_imp",
"L2322.StubTechFixOut_Fert_exp",
"L2322.PerCapitaBased_Fert",
"L2322.BaseService_Fert"))
} 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")
A322.sector <- get_data(all_data, "energy/A322.sector", strip_attributes = TRUE)
A322.subsector_interp <- get_data(all_data, "energy/A322.subsector_interp", strip_attributes = TRUE)
A322.subsector_logit <- get_data(all_data, "energy/A322.subsector_logit", strip_attributes = TRUE)
A322.subsector_shrwt <- get_data(all_data, "energy/A322.subsector_shrwt", strip_attributes = TRUE)
A322.globaltech_coef <- get_data(all_data, "energy/A322.globaltech_coef")
A322.globaltech_shrwt <- get_data(all_data, "energy/A322.globaltech_shrwt", strip_attributes = TRUE)
A322.globaltech_co2capture <- get_data(all_data, "energy/A322.globaltech_co2capture")
A322.globaltech_renew <- get_data(all_data, "energy/A322.globaltech_renew")
A322.globaltech_retirement <- get_data(all_data, "energy/A322.globaltech_retirement", strip_attributes = TRUE)
L1322.Fert_Prod_MtN_R_F_Y <- get_data(all_data, "L1322.Fert_Prod_MtN_R_F_Y", strip_attributes = TRUE)
L1322.IO_R_Fert_F_Yh <- get_data(all_data, "L1322.IO_R_Fert_F_Yh", strip_attributes = TRUE)
L1322.Fert_NEcost_75USDkgN_F <- get_data(all_data, "L1322.Fert_NEcost_75USDkgN_F")
L142.ag_Fert_NetExp_MtN_R_Y <- get_data(all_data, "L142.ag_Fert_NetExp_MtN_R_Y", strip_attributes = TRUE)
# ===================================================
# 0. Give binding for variable names used in pipeline
year.fillout <- to.value <- technology <- year <-
share.weight <- supplysector <- subsector <- coefficient <- minicam.energy.input <-
NEcost_75USDkgN <- input.cost <- remove.fraction <- half.life <- median.shutdown.point <-
value <- calOutputValue <- sector <- fuel <- subs.share.weight <- region <- fixedOutput <- . <- NULL
# ===================================================
# 1. Perform computations
# Create tables to delete technologies and subsectors in regions where heat is not modeled as a fuel
# 1a. Supplysector information
# L2322.Supplysector_Fert: Supply sector information for fertilizer sector
A322.sector %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["Supplysector"]], LOGIT_TYPE_COLNAME), GCAM_region_names) ->
L2322.Supplysector_Fert
# L2322.FinalEnergyKeyword_Fert: Supply sector keywords for fertilizer sector
A322.sector %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["FinalEnergyKeyword"]], GCAM_region_names) %>%
na.omit ->
L2322.FinalEnergyKeyword_Fert
# 2b. Subsector information
# L2322.SubsectorLogit_Fert: Subsector logit exponents of fertilizer sector
A322.subsector_logit %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["SubsectorLogit"]], LOGIT_TYPE_COLNAME), GCAM_region_names) ->
L2322.SubsectorLogit_Fert
# L2322.SubsectorShrwtFllt_Fert: Subsector shareweights of fertilizer sector
A322.subsector_shrwt %>%
filter(!is.na(year.fillout)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["SubsectorShrwtFllt"]], GCAM_region_names) ->
L2322.SubsectorShrwtFllt_Fert
# L2322.SubsectorInterp_Fert: Subsector shareweight interpolation of fertilizer sector
A322.subsector_interp %>%
filter(is.na(to.value)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["SubsectorInterp"]], GCAM_region_names) ->
L2322.SubsectorInterp_Fert
# 2c. Technology information
# L2322.StubTech_Fert: Identification of stub technologies of fertilizer sector
# Note: assuming that technology list in the shareweight table includes the full set (any others would default to a 0 shareweight)
A322.globaltech_shrwt %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["Tech"]], GCAM_region_names) %>%
rename(stub.technology = technology) ->
L2322.StubTech_Fert
# L2322.GlobalTechShrwt_Fert: Shareweights of global fertilizer sector technologies
A322.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) ->
L2322.GlobalTechShrwt_Fert
# L2322.GlobalTechCoef_Fert: Energy inputs and coefficients of global fertilizer energy use and feedstocks technologies
A322.globaltech_coef %>%
gather_years(value_col = "coefficient") %>%
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, coefficient, 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) ->
L2322.GlobalTechCoef_Fert
# Costs of global technologies
# L2322.GlobalTechCost_Fert: Non-energy costs of global fertilizer manufacturing technologies
L2322.GlobalTechCoef_Fert %>%
select(LEVEL2_DATA_NAMES[["GlobalTechYr"]]) %>%
mutate(minicam.non.energy.input = "non-energy") %>%
left_join(L1322.Fert_NEcost_75USDkgN_F, by = c('technology' = 'fuel') ) %>% # expecting NAs in the joined tibble
rename(input.cost = NEcost_75USDkgN) %>%
mutate(input.cost = round(input.cost, energy.DIGITS_COST)) %>%
na.omit -> # Export technologies have no cost assigned. Just drop the object
L2322.GlobalTechCost_Fert
# Carbon capture rates from technologies with CCS
# L2322.GlobalTechCapture_Fert: CO2 capture fractions from global fertilizer production technologies with CCS
## No need to consider historical periods or intermittent technologies here
A322.globaltech_co2capture %>%
gather_years(value_col = "remove.fraction") %>%
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, remove.fraction, 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) %>%
mutate(storage.market = "carbon-storage") ->
L2322.GlobalTechCapture_Fert
# Retirement information
A322.globaltech_retirement %>%
set_years %>%
mutate(year = as.integer(year)) %>%
rename(sector.name = supplysector, subsector.name = subsector) ->
A322.globaltech_retirement_with_years
# Copy the data in the last base year period through to the end year
A322.globaltech_retirement_with_years %>%
filter(year == max(MODEL_BASE_YEARS)) ->
A322.globaltech_retirement_max_baseyear
A322.globaltech_retirement_with_years %>%
filter(year == min(MODEL_FUTURE_YEARS)) %>%
select(-year) %>%
repeat_add_columns(tibble(year = MODEL_FUTURE_YEARS)) %>%
bind_rows(A322.globaltech_retirement_max_baseyear) ->
L2322.globaltech_retirement
# Retirement may consist of any of three types of retirement function (phased, s-curve, or none)
# All of these options have different headers, and all are allowed
# L2322.GlobalTechSCurve_Fert: Global tech lifetime and s-curve retirement function
L2322.globaltech_retirement %>%
filter(!is.na(half.life)) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechYr"]], "lifetime", "steepness", "half.life") ->
L2322.GlobalTechSCurve_Fert
# L2322.GlobalTechProfitShutdown_Fert: Global tech profit shutdown decider.
L2322.globaltech_retirement %>%
filter(!is.na(median.shutdown.point)) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechYr"]], "median.shutdown.point", "profit.shutdown.steepness") ->
L2322.GlobalTechProfitShutdown_Fert
# Calibration and region-specific data
# L2322.StubTechProd_Fert: calibrated output of fertilizer technologies
L1322.Fert_Prod_MtN_R_F_Y %>%
filter(year %in% MODEL_BASE_YEARS) %>%
rename(calOutputValue = value) %>%
mutate(calOutputValue = round(calOutputValue, energy.DIGITS_CALOUTPUT)) %>%
left_join_error_no_match(GCAM_region_names, by = 'GCAM_region_ID') %>%
left_join_error_no_match(select(calibrated_techs, sector, fuel, supplysector, subsector, technology), by = c("sector", "fuel") ) %>%
rename(stub.technology = technology) %>%
mutate(share.weight.year = year,
subs.share.weight = if_else(calOutputValue > 0, 1, 0),
tech.share.weight = subs.share.weight) %>%
select(LEVEL2_DATA_NAMES[["StubTechProd"]]) ->
L2322.StubTechProd_Fert
# L2322.StubTechCoef_Fert: calibrated base-year coefficients of fertilizer production technologies
L1322.IO_R_Fert_F_Yh %>%
filter(year %in% MODEL_BASE_YEARS) %>%
rename(coefficient = value) %>%
mutate(coefficient = round(coefficient, energy.DIGITS_COEFFICIENT)) %>%
filter(coefficient != 0) %>% # Where 0, drop from this table (to revert to assumed defaults)
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_error_no_match(select(calibrated_techs, sector, fuel, supplysector, subsector, technology, minicam.energy.input), by = c("sector", "fuel")) %>%
mutate(stub.technology = technology, market.name = region) %>%
select(LEVEL2_DATA_NAMES[["StubTechCoef"]]) ->
L2322.StubTechCoef_Fert
# L2322.StubTechFixOut_Fert_imp: fixed output of import technology (fixed imports)
# Imports are negative net exports
L142.ag_Fert_NetExp_MtN_R_Y %>%
filter(year %in% MODEL_BASE_YEARS) %>%
rename(fixedOutput = value) %>%
left_join_error_no_match(GCAM_region_names, by = 'GCAM_region_ID') %>%
mutate(supplysector = A322.globaltech_renew[["supplysector"]],
subsector = A322.globaltech_renew[["subsector"]],
stub.technology = A322.globaltech_renew[["technology"]],
fixedOutput = round(pmax(0, -1 * fixedOutput), energy.DIGITS_CALOUTPUT),
share.weight.year = year, subs.share.weight = 0, tech.share.weight = 0) %>%
bind_rows(repeat_add_columns(select(filter(., year == max(MODEL_BASE_YEARS)), -year), tibble(year = MODEL_FUTURE_YEARS))) %>%
select(LEVEL2_DATA_NAMES[["StubTechFixOut"]]) -> # Repeat final year to all future years and rbind
L2322.StubTechFixOut_Fert_imp
# L2322.StubTechFixOut_Fert_exp: fixed output of import technology (fixed imports)
# Exports are positive net exports
# Need to do this initial filter and extract outside of main pipeline (at least for dplyr 0.5)
exports <- filter(A322.globaltech_shrwt, grepl("Exports", supplysector))
sups <- exports[["supplysector"]]
subs <- exports[["subsector"]]
techs <- exports[["technology"]]
L142.ag_Fert_NetExp_MtN_R_Y %>%
filter(year %in% MODEL_BASE_YEARS) %>%
rename(fixedOutput = value) %>%
mutate(fixedOutput = round(fixedOutput, energy.DIGITS_CALOUTPUT)) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
mutate(supplysector = sups,
subsector = subs,
stub.technology = techs,
fixedOutput = pmax(0, fixedOutput),
share.weight.year = year,
subs.share.weight = 0,
tech.share.weight = 0) ->
L2322.StubTechFixOut_Fert_exp_base
L2322.StubTechFixOut_Fert_exp_base %>%
bind_rows(repeat_add_columns(select(filter(., year == max(MODEL_BASE_YEARS)), -year), tibble(year = MODEL_FUTURE_YEARS))) %>% # Repeat final year to all future years and rbind
select(LEVEL2_DATA_NAMES[["StubTechFixOut"]]) ->
L2322.StubTechFixOut_Fert_exp
# L2322.PerCapitaBased_Fert: per-capita based flag for fertilizer exports final demand
tibble(region = GCAM_region_names[["region"]]) %>%
mutate(energy.final.demand = sups,
perCapitaBased = 0) ->
L2322.PerCapitaBased_Fert
# L2322.BaseService_Fert: base-year service output of fertilizer exports final demand
# Base service is equal to the output of the exports supplysector
tibble(region = L2322.StubTechFixOut_Fert_exp_base[["region"]]) %>%
mutate(energy.final.demand = L2322.StubTechFixOut_Fert_exp_base[["supplysector"]],
year = L2322.StubTechFixOut_Fert_exp_base[["year"]],
base.service = L2322.StubTechFixOut_Fert_exp_base[["fixedOutput"]]) ->
L2322.BaseService_Fert
# ===================================================
# Produce outputs
L2322.Supplysector_Fert %>%
add_title("Supply sector information for fertilizer sector") %>%
add_units("NA") %>%
add_comments("For fertilizer sector, the supply sector information (output.unit, input.unit, price.unit, logit.year.fillout, logit.exponent) from A322.sector is expended into all GCAM regions") %>%
add_legacy_name("L2322.Supplysector_Fert") %>%
add_precursors("common/GCAM_region_names", "energy/A322.sector") ->
L2322.Supplysector_Fert
L2322.FinalEnergyKeyword_Fert %>%
add_title("Supply sector keywords for fertilizer sector") %>%
add_units("NA") %>%
add_comments("For fertilizer sector, the supply sector final energy keywords from A322.sector are expended into all GCAM regions") %>%
add_legacy_name("L2322.FinalEnergyKeyword_Fert") %>%
add_precursors("common/GCAM_region_names", "energy/A322.sector") ->
L2322.FinalEnergyKeyword_Fert
L2322.SubsectorLogit_Fert %>%
add_title("Subsector logit exponents of fertilizer sector") %>%
add_units("NA") %>%
add_comments("For fertilizer sector, the subsector logit exponents from A322.subsector_logit are expanded into all GCAM regions") %>%
add_legacy_name("L2322.SubsectorLogit_Fert") %>%
add_precursors("energy/A322.subsector_logit", "common/GCAM_region_names") ->
L2322.SubsectorLogit_Fert
L2322.SubsectorShrwtFllt_Fert %>%
add_title("Subsector shareweights of fertilizer") %>%
add_units("Unitless") %>%
add_comments("For fertilizer sector, the subsector shareweights from A322.subsector_shrwt are expanded into all GCAM regions") %>%
add_legacy_name("L2322.SubsectorShrwtFllt_Fert") %>%
add_precursors("energy/A322.subsector_shrwt", "common/GCAM_region_names") ->
L2322.SubsectorShrwtFllt_Fert
L2322.SubsectorInterp_Fert %>%
add_title("Subsector shareweight interpolation of fertilizer sector") %>%
add_units("NA") %>%
add_comments("For fertilizer sector, the subsector shareweight interpolation function infromation from A322.subsector_interp is expanded into all GCAM regions") %>%
add_legacy_name("L2322.SubsectorInterp_Fert") %>%
add_precursors("energy/A322.subsector_interp", "common/GCAM_region_names") ->
L2322.SubsectorInterp_Fert
L2322.StubTech_Fert %>%
add_title("Stub-technology (coal, coal CCS, and etc.) for fertilizer sector") %>%
add_units("NA") %>%
add_comments("For fertilizer sector, the stub technologies from A322.globaltech_shrwt are expanded into all GCAM regions") %>%
add_legacy_name("L2322.StubTech_Fert") %>%
add_precursors("energy/A322.globaltech_shrwt", "common/GCAM_region_names") ->
L2322.StubTech_Fert
L2322.GlobalTechShrwt_Fert %>%
add_title("Shareweights of global fertilizer sector technologies") %>%
add_units("Unitless") %>%
add_comments("For fertilizer sector, the share weights from A322.globaltech_shrwt are interpolated into all base years and future years") %>%
add_legacy_name("L2322.GlobalTechShrwt_Fert") %>%
add_precursors("energy/A322.globaltech_shrwt") ->
L2322.GlobalTechShrwt_Fert
L2322.GlobalTechCoef_Fert %>%
add_title("Energy inputs and coefficients of global fertilizer energy use and feedstocks technologies") %>%
add_units("GJ/kg N for supplysector N fertilizer; Unitless for supplysector Exports_fertilizer") %>%
add_comments("For fertilizer sector, the energy use coefficients from A322.globaltech_coef are interpolated into all model years") %>%
add_legacy_name("energy/A322.globaltech_coef") %>%
add_precursors("energy/A322.globaltech_coef") ->
L2322.GlobalTechCoef_Fert
L2322.GlobalTechCost_Fert %>%
add_title("Non-energy costs of global fertilizer manufacturing technologies") %>%
add_units("1975 USD/kg N") %>%
add_comments("For fertilizer sector, the non-energy costs of global fertilizer manufacturing technologies are calculated using values from L1322.Fert_NEcost_75USDkgN_F") %>%
add_legacy_name("L2322.GlobalTechCost_Fert") %>%
add_precursors("L1322.Fert_NEcost_75USDkgN_F") ->
L2322.GlobalTechCost_Fert
L2322.GlobalTechCapture_Fert %>%
add_title("CO2 capture fractions from global fertilizer production technologies with CCS") %>%
add_units("Unitless") %>%
add_comments("For fertilizer sector, the remove fractions from A322.globaltech_co2capture are interpolated into all model years") %>%
add_legacy_name("L2322.GlobalTechCapture_Fert") %>%
add_precursors("energy/A322.globaltech_co2capture") ->
L2322.GlobalTechCapture_Fert
L2322.GlobalTechSCurve_Fert %>%
add_title("Global tech lifetime and s-curve retirement function") %>%
add_units("year for lifetime and halflife; Unitless for steepness") %>%
add_comments("The values are extracted from L2322.globaltech_retirement for entries that half life value is not NA") %>%
add_legacy_name("L2322.GlobalTechSCurve_Fert") %>%
add_precursors("energy/A322.globaltech_retirement") ->
L2322.GlobalTechSCurve_Fert
L2322.GlobalTechProfitShutdown_Fert %>%
add_title("Global tech profit shutdown decider") %>%
add_units("Unitless") %>%
add_comments("The values are extracted from L2322.globaltech_retirement for entries that median shutdown point is not NA") %>%
add_legacy_name("L2322.GlobalTechProfitShutdown_Fert") %>%
add_precursors("energy/A322.globaltech_retirement") ->
L2322.GlobalTechProfitShutdown_Fert
L2322.StubTechProd_Fert %>%
add_title("calibrated output of fertilizer technologies") %>%
add_units("Mt N") %>%
add_comments("Values are calculated using L1322.Fert_Prod_MtN_R_F_Y then added GCAM region information") %>%
add_legacy_name("L2322.StubTechProd_Fert") %>%
add_precursors("L1322.Fert_Prod_MtN_R_F_Y", "common/GCAM_region_names", "energy/calibrated_techs") ->
L2322.StubTechProd_Fert
L2322.StubTechCoef_Fert %>%
add_title("calibrated base-year coefficients of fertilizer production technologies") %>%
add_units("Unitless") %>%
add_comments("Coefficients are calculated using L1322.IO_R_Fert_F_Yh") %>%
add_legacy_name("L2322.StubTechCoef_Fert") %>%
add_precursors("L1322.IO_R_Fert_F_Yh", "common/GCAM_region_names", "energy/calibrated_techs") ->
L2322.StubTechCoef_Fert
L2322.StubTechFixOut_Fert_imp %>%
add_title("fixed output of import technology (fixed imports)") %>%
add_units("Mt N") %>%
add_comments("The fixed import values are calculated using L142.ag_Fert_NetExp_MtN_R_Y then added GCAM region information") %>%
add_legacy_name("L2322.StubTechFixOut_Fert_imp") %>%
add_precursors("L142.ag_Fert_NetExp_MtN_R_Y", "common/GCAM_region_names", "energy/A322.globaltech_renew") ->
L2322.StubTechFixOut_Fert_imp
L2322.StubTechFixOut_Fert_exp %>%
add_title("fixed output of import technology (fixed imports)") %>%
add_units("Mt N") %>%
add_comments("The fixed export values are alculated using L142.ag_Fert_NetExp_MtN_R_Y") %>%
add_legacy_name("L2322.StubTechFixOut_Fert_exp") %>%
add_precursors("L142.ag_Fert_NetExp_MtN_R_Y", "common/GCAM_region_names", "energy/A322.globaltech_shrwt") ->
L2322.StubTechFixOut_Fert_exp
L2322.PerCapitaBased_Fert %>%
add_title("per-capita based flag for fertilizer exports final demand") %>%
add_units("Unitless") %>%
add_comments("Per Capita Based value are assigned as 0 for all GCAM regions for Exports_fertilizer of energy final demand") %>%
add_legacy_name("L2322.PerCapitaBased_Fert") %>%
add_precursors("common/GCAM_region_names", "energy/A322.globaltech_shrwt") ->
L2322.PerCapitaBased_Fert
L2322.BaseService_Fert %>%
add_title("base-year service output of fertilizer exports final demand") %>%
add_units("Mt N") %>%
add_comments("Base service values are extracted from L2322.StubTechFixOut_Fert_exp_base") %>%
add_legacy_name("L2322.BaseService_Fert") %>%
add_precursors("L142.ag_Fert_NetExp_MtN_R_Y", "common/GCAM_region_names", "energy/A322.globaltech_shrwt") ->
L2322.BaseService_Fert
return_data(L2322.Supplysector_Fert, L2322.FinalEnergyKeyword_Fert, L2322.SubsectorLogit_Fert,
L2322.SubsectorShrwtFllt_Fert, L2322.SubsectorInterp_Fert,
L2322.StubTech_Fert, L2322.GlobalTechShrwt_Fert,
L2322.GlobalTechCoef_Fert, L2322.GlobalTechCost_Fert, L2322.GlobalTechCapture_Fert,
L2322.GlobalTechSCurve_Fert,
L2322.GlobalTechProfitShutdown_Fert, L2322.StubTechProd_Fert, L2322.StubTechCoef_Fert,
L2322.StubTechFixOut_Fert_imp, L2322.StubTechFixOut_Fert_exp, L2322.PerCapitaBased_Fert,
L2322.BaseService_Fert)
} else {
stop("Unknown command")
}
}
bpbond/gcamdata documentation built on March 22, 2023, 4:52 a.m.
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Defines functions module_energy_L2322.Fert
Documented in module_energy_L2322.Fert
# Copyright 2019 Battelle Memorial Institute; see the LICENSE file.
#' module_energy_L2322.Fert
#'
#' Provide supply sector information/keywords, subsector shareweights, global technology lifetime,
#' energy inputs and coefficients, global fertilizer manufacturing technologies, etc. for the fertilizer sector.
#'
#' @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{L2322.Supplysector_Fert}, \code{L2322.FinalEnergyKeyword_Fert}, \code{L2322.SubsectorLogit_Fert}, \code{L2322.SubsectorShrwtFllt_Fert}, \code{L2322.SubsectorInterp_Fert}, \code{L2322.StubTech_Fert}, \code{L2322.GlobalTechShrwt_Fert}, \code{L2322.GlobalTechCoef_Fert}, \code{L2322.GlobalTechCost_Fert}, \code{L2322.GlobalTechCapture_Fert}, \code{L2322.GlobalTechSCurve_Fert}, \code{L2322.GlobalTechProfitShutdown_Fert}, \code{L2322.StubTechProd_Fert}, \code{L2322.StubTechCoef_Fert}, \code{L2322.StubTechFixOut_Fert_imp}, \code{L2322.StubTechFixOut_Fert_exp}, \code{L2322.PerCapitaBased_Fert}, \code{L2322.BaseService_Fert}. The corresponding file in the
#' original data system was \code{L2322.Fert.R} (energy level2).
#' @details This chunk provides supply sector information/keywords, subsector shareweights, global technology lifetime,
#' energy inputs and coefficients, global fertilizer manufacturing technologies, etc. for the fertilizer sector.
#' @importFrom assertthat assert_that
#' @importFrom dplyr arrange bind_rows filter if_else group_by left_join mutate select
#' @importFrom tidyr complete nesting
#' @author LF September 2017
module_energy_L2322.Fert <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "common/GCAM_region_names",
FILE = "energy/calibrated_techs",
FILE = "energy/A322.sector",
FILE = "energy/A322.subsector_interp",
FILE = "energy/A322.subsector_logit",
FILE = "energy/A322.subsector_shrwt",
FILE = "energy/A322.globaltech_coef",
FILE = "energy/A322.globaltech_shrwt",
FILE = "energy/A322.globaltech_co2capture",
FILE = "energy/A322.globaltech_renew",
FILE = "energy/A322.globaltech_retirement",
"L1322.Fert_Prod_MtN_R_F_Y",
"L1322.IO_R_Fert_F_Yh",
"L1322.Fert_NEcost_75USDkgN_F",
"L142.ag_Fert_NetExp_MtN_R_Y"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L2322.Supplysector_Fert",
"L2322.FinalEnergyKeyword_Fert",
"L2322.SubsectorLogit_Fert",
"L2322.SubsectorShrwtFllt_Fert",
"L2322.SubsectorInterp_Fert",
"L2322.StubTech_Fert",
"L2322.GlobalTechShrwt_Fert",
"L2322.GlobalTechCoef_Fert",
"L2322.GlobalTechCost_Fert",
"L2322.GlobalTechCapture_Fert",
"L2322.GlobalTechSCurve_Fert",
"L2322.GlobalTechProfitShutdown_Fert",
"L2322.StubTechProd_Fert",
"L2322.StubTechCoef_Fert",
"L2322.StubTechFixOut_Fert_imp",
"L2322.StubTechFixOut_Fert_exp",
"L2322.PerCapitaBased_Fert",
"L2322.BaseService_Fert"))
} 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")
A322.sector <- get_data(all_data, "energy/A322.sector", strip_attributes = TRUE)
A322.subsector_interp <- get_data(all_data, "energy/A322.subsector_interp", strip_attributes = TRUE)
A322.subsector_logit <- get_data(all_data, "energy/A322.subsector_logit", strip_attributes = TRUE)
A322.subsector_shrwt <- get_data(all_data, "energy/A322.subsector_shrwt", strip_attributes = TRUE)
A322.globaltech_coef <- get_data(all_data, "energy/A322.globaltech_coef")
A322.globaltech_shrwt <- get_data(all_data, "energy/A322.globaltech_shrwt", strip_attributes = TRUE)
A322.globaltech_co2capture <- get_data(all_data, "energy/A322.globaltech_co2capture")
A322.globaltech_renew <- get_data(all_data, "energy/A322.globaltech_renew")
A322.globaltech_retirement <- get_data(all_data, "energy/A322.globaltech_retirement", strip_attributes = TRUE)
L1322.Fert_Prod_MtN_R_F_Y <- get_data(all_data, "L1322.Fert_Prod_MtN_R_F_Y", strip_attributes = TRUE)
L1322.IO_R_Fert_F_Yh <- get_data(all_data, "L1322.IO_R_Fert_F_Yh", strip_attributes = TRUE)
L1322.Fert_NEcost_75USDkgN_F <- get_data(all_data, "L1322.Fert_NEcost_75USDkgN_F")
L142.ag_Fert_NetExp_MtN_R_Y <- get_data(all_data, "L142.ag_Fert_NetExp_MtN_R_Y", strip_attributes = TRUE)
# ===================================================
# 0. Give binding for variable names used in pipeline
year.fillout <- to.value <- technology <- year <-
share.weight <- supplysector <- subsector <- coefficient <- minicam.energy.input <-
NEcost_75USDkgN <- input.cost <- remove.fraction <- half.life <- median.shutdown.point <-
value <- calOutputValue <- sector <- fuel <- subs.share.weight <- region <- fixedOutput <- . <- NULL
# ===================================================
# 1. Perform computations
# Create tables to delete technologies and subsectors in regions where heat is not modeled as a fuel
# 1a. Supplysector information
# L2322.Supplysector_Fert: Supply sector information for fertilizer sector
A322.sector %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["Supplysector"]], LOGIT_TYPE_COLNAME), GCAM_region_names) ->
L2322.Supplysector_Fert
# L2322.FinalEnergyKeyword_Fert: Supply sector keywords for fertilizer sector
A322.sector %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["FinalEnergyKeyword"]], GCAM_region_names) %>%
na.omit ->
L2322.FinalEnergyKeyword_Fert
# 2b. Subsector information
# L2322.SubsectorLogit_Fert: Subsector logit exponents of fertilizer sector
A322.subsector_logit %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["SubsectorLogit"]], LOGIT_TYPE_COLNAME), GCAM_region_names) ->
L2322.SubsectorLogit_Fert
# L2322.SubsectorShrwtFllt_Fert: Subsector shareweights of fertilizer sector
A322.subsector_shrwt %>%
filter(!is.na(year.fillout)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["SubsectorShrwtFllt"]], GCAM_region_names) ->
L2322.SubsectorShrwtFllt_Fert
# L2322.SubsectorInterp_Fert: Subsector shareweight interpolation of fertilizer sector
A322.subsector_interp %>%
filter(is.na(to.value)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["SubsectorInterp"]], GCAM_region_names) ->
L2322.SubsectorInterp_Fert
# 2c. Technology information
# L2322.StubTech_Fert: Identification of stub technologies of fertilizer sector
# Note: assuming that technology list in the shareweight table includes the full set (any others would default to a 0 shareweight)
A322.globaltech_shrwt %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["Tech"]], GCAM_region_names) %>%
rename(stub.technology = technology) ->
L2322.StubTech_Fert
# L2322.GlobalTechShrwt_Fert: Shareweights of global fertilizer sector technologies
A322.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) ->
L2322.GlobalTechShrwt_Fert
# L2322.GlobalTechCoef_Fert: Energy inputs and coefficients of global fertilizer energy use and feedstocks technologies
A322.globaltech_coef %>%
gather_years(value_col = "coefficient") %>%
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, coefficient, 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) ->
L2322.GlobalTechCoef_Fert
# Costs of global technologies
# L2322.GlobalTechCost_Fert: Non-energy costs of global fertilizer manufacturing technologies
L2322.GlobalTechCoef_Fert %>%
select(LEVEL2_DATA_NAMES[["GlobalTechYr"]]) %>%
mutate(minicam.non.energy.input = "non-energy") %>%
left_join(L1322.Fert_NEcost_75USDkgN_F, by = c('technology' = 'fuel') ) %>% # expecting NAs in the joined tibble
rename(input.cost = NEcost_75USDkgN) %>%
mutate(input.cost = round(input.cost, energy.DIGITS_COST)) %>%
na.omit -> # Export technologies have no cost assigned. Just drop the object
L2322.GlobalTechCost_Fert
# Carbon capture rates from technologies with CCS
# L2322.GlobalTechCapture_Fert: CO2 capture fractions from global fertilizer production technologies with CCS
## No need to consider historical periods or intermittent technologies here
A322.globaltech_co2capture %>%
gather_years(value_col = "remove.fraction") %>%
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, remove.fraction, 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) %>%
mutate(storage.market = "carbon-storage") ->
L2322.GlobalTechCapture_Fert
# Retirement information
A322.globaltech_retirement %>%
set_years %>%
mutate(year = as.integer(year)) %>%
rename(sector.name = supplysector, subsector.name = subsector) ->
A322.globaltech_retirement_with_years
# Copy the data in the last base year period through to the end year
A322.globaltech_retirement_with_years %>%
filter(year == max(MODEL_BASE_YEARS)) ->
A322.globaltech_retirement_max_baseyear
A322.globaltech_retirement_with_years %>%
filter(year == min(MODEL_FUTURE_YEARS)) %>%
select(-year) %>%
repeat_add_columns(tibble(year = MODEL_FUTURE_YEARS)) %>%
bind_rows(A322.globaltech_retirement_max_baseyear) ->
L2322.globaltech_retirement
# Retirement may consist of any of three types of retirement function (phased, s-curve, or none)
# All of these options have different headers, and all are allowed
# L2322.GlobalTechSCurve_Fert: Global tech lifetime and s-curve retirement function
L2322.globaltech_retirement %>%
filter(!is.na(half.life)) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechYr"]], "lifetime", "steepness", "half.life") ->
L2322.GlobalTechSCurve_Fert
# L2322.GlobalTechProfitShutdown_Fert: Global tech profit shutdown decider.
L2322.globaltech_retirement %>%
filter(!is.na(median.shutdown.point)) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechYr"]], "median.shutdown.point", "profit.shutdown.steepness") ->
L2322.GlobalTechProfitShutdown_Fert
# Calibration and region-specific data
# L2322.StubTechProd_Fert: calibrated output of fertilizer technologies
L1322.Fert_Prod_MtN_R_F_Y %>%
filter(year %in% MODEL_BASE_YEARS) %>%
rename(calOutputValue = value) %>%
mutate(calOutputValue = round(calOutputValue, energy.DIGITS_CALOUTPUT)) %>%
left_join_error_no_match(GCAM_region_names, by = 'GCAM_region_ID') %>%
left_join_error_no_match(select(calibrated_techs, sector, fuel, supplysector, subsector, technology), by = c("sector", "fuel") ) %>%
rename(stub.technology = technology) %>%
mutate(share.weight.year = year,
subs.share.weight = if_else(calOutputValue > 0, 1, 0),
tech.share.weight = subs.share.weight) %>%
select(LEVEL2_DATA_NAMES[["StubTechProd"]]) ->
L2322.StubTechProd_Fert
# L2322.StubTechCoef_Fert: calibrated base-year coefficients of fertilizer production technologies
L1322.IO_R_Fert_F_Yh %>%
filter(year %in% MODEL_BASE_YEARS) %>%
rename(coefficient = value) %>%
mutate(coefficient = round(coefficient, energy.DIGITS_COEFFICIENT)) %>%
filter(coefficient != 0) %>% # Where 0, drop from this table (to revert to assumed defaults)
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_error_no_match(select(calibrated_techs, sector, fuel, supplysector, subsector, technology, minicam.energy.input), by = c("sector", "fuel")) %>%
mutate(stub.technology = technology, market.name = region) %>%
select(LEVEL2_DATA_NAMES[["StubTechCoef"]]) ->
L2322.StubTechCoef_Fert
# L2322.StubTechFixOut_Fert_imp: fixed output of import technology (fixed imports)
# Imports are negative net exports
L142.ag_Fert_NetExp_MtN_R_Y %>%
filter(year %in% MODEL_BASE_YEARS) %>%
rename(fixedOutput = value) %>%
left_join_error_no_match(GCAM_region_names, by = 'GCAM_region_ID') %>%
mutate(supplysector = A322.globaltech_renew[["supplysector"]],
subsector = A322.globaltech_renew[["subsector"]],
stub.technology = A322.globaltech_renew[["technology"]],
fixedOutput = round(pmax(0, -1 * fixedOutput), energy.DIGITS_CALOUTPUT),
share.weight.year = year, subs.share.weight = 0, tech.share.weight = 0) %>%
bind_rows(repeat_add_columns(select(filter(., year == max(MODEL_BASE_YEARS)), -year), tibble(year = MODEL_FUTURE_YEARS))) %>%
select(LEVEL2_DATA_NAMES[["StubTechFixOut"]]) -> # Repeat final year to all future years and rbind
L2322.StubTechFixOut_Fert_imp
# L2322.StubTechFixOut_Fert_exp: fixed output of import technology (fixed imports)
# Exports are positive net exports
# Need to do this initial filter and extract outside of main pipeline (at least for dplyr 0.5)
exports <- filter(A322.globaltech_shrwt, grepl("Exports", supplysector))
sups <- exports[["supplysector"]]
subs <- exports[["subsector"]]
techs <- exports[["technology"]]
L142.ag_Fert_NetExp_MtN_R_Y %>%
filter(year %in% MODEL_BASE_YEARS) %>%
rename(fixedOutput = value) %>%
mutate(fixedOutput = round(fixedOutput, energy.DIGITS_CALOUTPUT)) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
mutate(supplysector = sups,
subsector = subs,
stub.technology = techs,
fixedOutput = pmax(0, fixedOutput),
share.weight.year = year,
subs.share.weight = 0,
tech.share.weight = 0) ->
L2322.StubTechFixOut_Fert_exp_base
L2322.StubTechFixOut_Fert_exp_base %>%
bind_rows(repeat_add_columns(select(filter(., year == max(MODEL_BASE_YEARS)), -year), tibble(year = MODEL_FUTURE_YEARS))) %>% # Repeat final year to all future years and rbind
select(LEVEL2_DATA_NAMES[["StubTechFixOut"]]) ->
L2322.StubTechFixOut_Fert_exp
# L2322.PerCapitaBased_Fert: per-capita based flag for fertilizer exports final demand
tibble(region = GCAM_region_names[["region"]]) %>%
mutate(energy.final.demand = sups,
perCapitaBased = 0) ->
L2322.PerCapitaBased_Fert
# L2322.BaseService_Fert: base-year service output of fertilizer exports final demand
# Base service is equal to the output of the exports supplysector
tibble(region = L2322.StubTechFixOut_Fert_exp_base[["region"]]) %>%
mutate(energy.final.demand = L2322.StubTechFixOut_Fert_exp_base[["supplysector"]],
year = L2322.StubTechFixOut_Fert_exp_base[["year"]],
base.service = L2322.StubTechFixOut_Fert_exp_base[["fixedOutput"]]) ->
L2322.BaseService_Fert
# ===================================================
# Produce outputs
L2322.Supplysector_Fert %>%
add_title("Supply sector information for fertilizer sector") %>%
add_units("NA") %>%
add_comments("For fertilizer sector, the supply sector information (output.unit, input.unit, price.unit, logit.year.fillout, logit.exponent) from A322.sector is expended into all GCAM regions") %>%
add_legacy_name("L2322.Supplysector_Fert") %>%
add_precursors("common/GCAM_region_names", "energy/A322.sector") ->
L2322.Supplysector_Fert
L2322.FinalEnergyKeyword_Fert %>%
add_title("Supply sector keywords for fertilizer sector") %>%
add_units("NA") %>%
add_comments("For fertilizer sector, the supply sector final energy keywords from A322.sector are expended into all GCAM regions") %>%
add_legacy_name("L2322.FinalEnergyKeyword_Fert") %>%
add_precursors("common/GCAM_region_names", "energy/A322.sector") ->
L2322.FinalEnergyKeyword_Fert
L2322.SubsectorLogit_Fert %>%
add_title("Subsector logit exponents of fertilizer sector") %>%
add_units("NA") %>%
add_comments("For fertilizer sector, the subsector logit exponents from A322.subsector_logit are expanded into all GCAM regions") %>%
add_legacy_name("L2322.SubsectorLogit_Fert") %>%
add_precursors("energy/A322.subsector_logit", "common/GCAM_region_names") ->
L2322.SubsectorLogit_Fert
L2322.SubsectorShrwtFllt_Fert %>%
add_title("Subsector shareweights of fertilizer") %>%
add_units("Unitless") %>%
add_comments("For fertilizer sector, the subsector shareweights from A322.subsector_shrwt are expanded into all GCAM regions") %>%
add_legacy_name("L2322.SubsectorShrwtFllt_Fert") %>%
add_precursors("energy/A322.subsector_shrwt", "common/GCAM_region_names") ->
L2322.SubsectorShrwtFllt_Fert
L2322.SubsectorInterp_Fert %>%
add_title("Subsector shareweight interpolation of fertilizer sector") %>%
add_units("NA") %>%
add_comments("For fertilizer sector, the subsector shareweight interpolation function infromation from A322.subsector_interp is expanded into all GCAM regions") %>%
add_legacy_name("L2322.SubsectorInterp_Fert") %>%
add_precursors("energy/A322.subsector_interp", "common/GCAM_region_names") ->
L2322.SubsectorInterp_Fert
L2322.StubTech_Fert %>%
add_title("Stub-technology (coal, coal CCS, and etc.) for fertilizer sector") %>%
add_units("NA") %>%
add_comments("For fertilizer sector, the stub technologies from A322.globaltech_shrwt are expanded into all GCAM regions") %>%
add_legacy_name("L2322.StubTech_Fert") %>%
add_precursors("energy/A322.globaltech_shrwt", "common/GCAM_region_names") ->
L2322.StubTech_Fert
L2322.GlobalTechShrwt_Fert %>%
add_title("Shareweights of global fertilizer sector technologies") %>%
add_units("Unitless") %>%
add_comments("For fertilizer sector, the share weights from A322.globaltech_shrwt are interpolated into all base years and future years") %>%
add_legacy_name("L2322.GlobalTechShrwt_Fert") %>%
add_precursors("energy/A322.globaltech_shrwt") ->
L2322.GlobalTechShrwt_Fert
L2322.GlobalTechCoef_Fert %>%
add_title("Energy inputs and coefficients of global fertilizer energy use and feedstocks technologies") %>%
add_units("GJ/kg N for supplysector N fertilizer; Unitless for supplysector Exports_fertilizer") %>%
add_comments("For fertilizer sector, the energy use coefficients from A322.globaltech_coef are interpolated into all model years") %>%
add_legacy_name("energy/A322.globaltech_coef") %>%
add_precursors("energy/A322.globaltech_coef") ->
L2322.GlobalTechCoef_Fert
L2322.GlobalTechCost_Fert %>%
add_title("Non-energy costs of global fertilizer manufacturing technologies") %>%
add_units("1975 USD/kg N") %>%
add_comments("For fertilizer sector, the non-energy costs of global fertilizer manufacturing technologies are calculated using values from L1322.Fert_NEcost_75USDkgN_F") %>%
add_legacy_name("L2322.GlobalTechCost_Fert") %>%
add_precursors("L1322.Fert_NEcost_75USDkgN_F") ->
L2322.GlobalTechCost_Fert
L2322.GlobalTechCapture_Fert %>%
add_title("CO2 capture fractions from global fertilizer production technologies with CCS") %>%
add_units("Unitless") %>%
add_comments("For fertilizer sector, the remove fractions from A322.globaltech_co2capture are interpolated into all model years") %>%
add_legacy_name("L2322.GlobalTechCapture_Fert") %>%
add_precursors("energy/A322.globaltech_co2capture") ->
L2322.GlobalTechCapture_Fert
L2322.GlobalTechSCurve_Fert %>%
add_title("Global tech lifetime and s-curve retirement function") %>%
add_units("year for lifetime and halflife; Unitless for steepness") %>%
add_comments("The values are extracted from L2322.globaltech_retirement for entries that half life value is not NA") %>%
add_legacy_name("L2322.GlobalTechSCurve_Fert") %>%
add_precursors("energy/A322.globaltech_retirement") ->
L2322.GlobalTechSCurve_Fert
L2322.GlobalTechProfitShutdown_Fert %>%
add_title("Global tech profit shutdown decider") %>%
add_units("Unitless") %>%
add_comments("The values are extracted from L2322.globaltech_retirement for entries that median shutdown point is not NA") %>%
add_legacy_name("L2322.GlobalTechProfitShutdown_Fert") %>%
add_precursors("energy/A322.globaltech_retirement") ->
L2322.GlobalTechProfitShutdown_Fert
L2322.StubTechProd_Fert %>%
add_title("calibrated output of fertilizer technologies") %>%
add_units("Mt N") %>%
add_comments("Values are calculated using L1322.Fert_Prod_MtN_R_F_Y then added GCAM region information") %>%
add_legacy_name("L2322.StubTechProd_Fert") %>%
add_precursors("L1322.Fert_Prod_MtN_R_F_Y", "common/GCAM_region_names", "energy/calibrated_techs") ->
L2322.StubTechProd_Fert
L2322.StubTechCoef_Fert %>%
add_title("calibrated base-year coefficients of fertilizer production technologies") %>%
add_units("Unitless") %>%
add_comments("Coefficients are calculated using L1322.IO_R_Fert_F_Yh") %>%
add_legacy_name("L2322.StubTechCoef_Fert") %>%
add_precursors("L1322.IO_R_Fert_F_Yh", "common/GCAM_region_names", "energy/calibrated_techs") ->
L2322.StubTechCoef_Fert
L2322.StubTechFixOut_Fert_imp %>%
add_title("fixed output of import technology (fixed imports)") %>%
add_units("Mt N") %>%
add_comments("The fixed import values are calculated using L142.ag_Fert_NetExp_MtN_R_Y then added GCAM region information") %>%
add_legacy_name("L2322.StubTechFixOut_Fert_imp") %>%
add_precursors("L142.ag_Fert_NetExp_MtN_R_Y", "common/GCAM_region_names", "energy/A322.globaltech_renew") ->
L2322.StubTechFixOut_Fert_imp
L2322.StubTechFixOut_Fert_exp %>%
add_title("fixed output of import technology (fixed imports)") %>%
add_units("Mt N") %>%
add_comments("The fixed export values are alculated using L142.ag_Fert_NetExp_MtN_R_Y") %>%
add_legacy_name("L2322.StubTechFixOut_Fert_exp") %>%
add_precursors("L142.ag_Fert_NetExp_MtN_R_Y", "common/GCAM_region_names", "energy/A322.globaltech_shrwt") ->
L2322.StubTechFixOut_Fert_exp
L2322.PerCapitaBased_Fert %>%
add_title("per-capita based flag for fertilizer exports final demand") %>%
add_units("Unitless") %>%
add_comments("Per Capita Based value are assigned as 0 for all GCAM regions for Exports_fertilizer of energy final demand") %>%
add_legacy_name("L2322.PerCapitaBased_Fert") %>%
add_precursors("common/GCAM_region_names", "energy/A322.globaltech_shrwt") ->
L2322.PerCapitaBased_Fert
L2322.BaseService_Fert %>%
add_title("base-year service output of fertilizer exports final demand") %>%
add_units("Mt N") %>%
add_comments("Base service values are extracted from L2322.StubTechFixOut_Fert_exp_base") %>%
add_legacy_name("L2322.BaseService_Fert") %>%
add_precursors("L142.ag_Fert_NetExp_MtN_R_Y", "common/GCAM_region_names", "energy/A322.globaltech_shrwt") ->
L2322.BaseService_Fert
return_data(L2322.Supplysector_Fert, L2322.FinalEnergyKeyword_Fert, L2322.SubsectorLogit_Fert,
L2322.SubsectorShrwtFllt_Fert, L2322.SubsectorInterp_Fert,
L2322.StubTech_Fert, L2322.GlobalTechShrwt_Fert,
L2322.GlobalTechCoef_Fert, L2322.GlobalTechCost_Fert, L2322.GlobalTechCapture_Fert,
L2322.GlobalTechSCurve_Fert,
L2322.GlobalTechProfitShutdown_Fert, L2322.StubTechProd_Fert, L2322.StubTechCoef_Fert,
L2322.StubTechFixOut_Fert_imp, L2322.StubTechFixOut_Fert_exp, L2322.PerCapitaBased_Fert,
L2322.BaseService_Fert)
} else {
stop("Unknown command")
}
}
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