R/zchunk_L252.transportation.R
In rohmin9122/gcam-korea-release: Build the GCAM-Korea Input Datasets
Defines functions
module_energy_L252.transportation
Documented in
module_energy_L252.transportation
#' module_energy_L252.transportation
#'
#' Calculate supply sector, subsector, and technology information for the transportation 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{L252.SectorLogitTables[[ curr_table ]]$data}, \code{L252.Supplysector_trn}, \code{L252.FinalEnergyKeyword_trn}, \code{L252.SubsectorLogitTables[[ curr_table ]]$data}, \code{L252.SubsectorLogit_trn}, \code{L252.SubsectorShrwt_trn}, \code{L252.SubsectorShrwtFllt_trn}, \code{L252.SubsectorInterp_trn}, \code{L252.SubsectorInterpTo_trn}, \code{L252.StubTech_trn}, \code{L252.GlobalTechShrwt_trn}, \code{L252.GlobalTechEff_trn}, \code{L252.GlobalTechCost_trn}, \code{L252.StubTechCalInput_trn}, \code{L252.PerCapitaBased_trn}, \code{L252.PriceElasticity_trn}, \code{L252.BaseService_trn}. The corresponding file in the
#' original data system was \code{L252.transportation.R} (energy level2).
#' @details Calculate shareweights, cost, price elasticity, calibrated, and other data for the transportation sector
#' @importFrom assertthat assert_that
#' @importFrom dplyr filter mutate select
#' @importFrom tidyr gather spread
#' @author AJS August 2017
module_energy_L252.transportation <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "common/GCAM_region_names",
FILE = "energy/mappings/calibrated_techs_trn_agg",
FILE = "energy/A52.sector",
FILE = "energy/A52.subsector_interp",
FILE = "energy/A52.subsector_logit",
FILE = "energy/A52.subsector_shrwt",
FILE = "energy/A52.globaltech_cost",
FILE = "energy/A52.globaltech_eff",
FILE = "energy/A52.globaltech_shrwt",
FILE = "energy/A52.demand",
"L152.in_EJ_R_trn_F_Yh"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L252.Supplysector_trn",
"L252.FinalEnergyKeyword_trn",
"L252.SubsectorLogit_trn",
"L252.SubsectorShrwt_trn",
"L252.SubsectorShrwtFllt_trn",
"L252.SubsectorInterp_trn",
"L252.SubsectorInterpTo_trn",
"L252.StubTech_trn",
"L252.GlobalTechShrwt_trn",
"L252.GlobalTechEff_trn",
"L252.GlobalTechCost_trn",
"L252.StubTechCalInput_trn",
"L252.PerCapitaBased_trn",
"L252.PriceElasticity_trn",
"L252.BaseService_trn"))
} 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_trn_agg <- get_data(all_data, "energy/mappings/calibrated_techs_trn_agg")
A52.sector <- get_data(all_data, "energy/A52.sector")
A52.subsector_interp <- get_data(all_data, "energy/A52.subsector_interp")
A52.subsector_logit <- get_data(all_data, "energy/A52.subsector_logit")
A52.subsector_shrwt <- get_data(all_data, "energy/A52.subsector_shrwt")
A52.globaltech_cost <- get_data(all_data, "energy/A52.globaltech_cost")
A52.globaltech_eff <- get_data(all_data, "energy/A52.globaltech_eff")
A52.globaltech_shrwt <- get_data(all_data, "energy/A52.globaltech_shrwt")
A52.demand <- get_data(all_data, "energy/A52.demand")
L152.in_EJ_R_trn_F_Yh <- get_data(all_data, "L152.in_EJ_R_trn_F_Yh")
# ===================================================
# Silence Package Notes
region <- supplysector <- output.unit <- input.unit <- price.unit <- logit.year.fillout <-
logit.exponent <- final.energy <- subsector <- logit.type <- logit.exponent <- year <-
share.weight <- year.fillout <- apply.to <- from.year <- to.year <- interpolation.function <-
to.value <- technology <- stub.technology <- sector.name <- subsector.name <- minicam.energy.input <-
efficiency <- minicam.non.energy.input <- input.cost <- calibrated.value <- share.weight.year <-
subs.share.weight <- tech.share.weight <- energy.final.demand <- perCapitaBased <-
price.elasticity <- GAM_region_ID <- base.serive <- coefficient <- curr_table <- value <-
. <- output <- base.service <- NULL
# PART A: SUPPLYSECTOR INFORMATION
# Write supply sector information for transportation sector for all regions
A52.sector %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["Supplysector"]], LOGIT_TYPE_COLNAME),
GCAM_region_names = GCAM_region_names) ->
L252.Supplysector_trn # OUTPUT
# Write supply sector keywords for transportation sector for all regions
A52.sector %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["FinalEnergyKeyword"]],
GCAM_region_names = GCAM_region_names) ->
L252.FinalEnergyKeyword_trn # OUTPUT
# PART B: SUBSECTOR INFORMATION
# Write subsector logit exponents of transportation sector for all regions
A52.subsector_logit %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["SubsectorLogit"]], LOGIT_TYPE_COLNAME),
GCAM_region_names = GCAM_region_names) ->
L252.SubsectorLogit_trn # OUTPUT
# Expand subsector shareweights of transportation sector across all regions
if(any(!is.na(A52.subsector_shrwt$year))) {
A52.subsector_shrwt %>%
filter(!is.na(year)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["SubsectorShrwt"]],
GCAM_region_names = GCAM_region_names) ->
L252.SubsectorShrwt_trn # OUTPUT
}
if(any(!is.na(A52.subsector_shrwt$year.fillout))) {
A52.subsector_shrwt %>%
filter(!is.na(year.fillout)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["SubsectorShrwtFllt"]],
GCAM_region_names = GCAM_region_names) ->
L252.SubsectorShrwtFllt_trn # OUTPUT
}
# Write subsector shareweight interpolation of transportation sector for all regions
if(any(is.na(A52.subsector_interp$to.value))) {
A52.subsector_interp %>%
filter(is.na(to.value)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["SubsectorInterp"]],
GCAM_region_names = GCAM_region_names) ->
L252.SubsectorInterp_trn # OUTPUT
}
if(any(!is.na(A52.subsector_interp$to.value))) {
A52.subsector_interp %>%
filter(is.na(!to.value)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["SubsectorInterpTo"]],
GCAM_region_names = GCAM_region_names) ->
L252.SubsectorInterpTo_trn # OUTPUT (not generated at this time)
}
# PART C: TECHNOLOGY INFORMATION
# Write stub technologies of the transportation sector for all regions.
# Note: assuming that technology list in the shareweight table includes the full set (any others would default to a 0 shareweight)
A52.globaltech_shrwt %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["Tech"]],
GCAM_region_names = GCAM_region_names) %>%
select(region, supplysector, subsector, stub.technology = technology) ->
L252.StubTech_trn # OUTPUT
# Interpolate shareweights of the global transportation sector technologies across model years
A52.globaltech_shrwt %>%
gather_years(value_col = "share.weight") %>%
# Expand table to include all model base and future years
complete(year = c(year, MODEL_YEARS), nesting(supplysector, subsector, technology)) %>%
# Extrapolate to fill out values for all years
# Rule 2 is used so years that may be outside of min-max range are assigned values from closest data, as opposed to NAs
group_by(supplysector, subsector, technology) %>%
mutate(share.weight = approx_fun(year, share.weight, rule = 2)) %>%
ungroup() %>%
filter(year %in% MODEL_YEARS) %>% # This will drop 1971
# Assign the columns "sector.name" and "subsector.name", consistent with the location info of a global technology
select(sector.name = supplysector, subsector.name = subsector, technology, year, share.weight) ->
L252.GlobalTechShrwt_trn # OUTPUT
# Calculate energy inputs and coefficients of global transportation energy use and feedstocks technologies
# for all model years
DIGITS_EFFICIENCY <- 3
A52.globaltech_eff %>%
gather_years(value_col = "efficiency") %>%
# Expand table to include all model years
complete(year = c(year, MODEL_YEARS), nesting(supplysector, subsector, technology, minicam.energy.input)) %>%
# Extrapolate to fill out values for all years
# Rule 2 is used so years that may be outside of min-max range are assigned values from closest data, as opposed to NAs
group_by(supplysector, subsector, technology, minicam.energy.input) %>%
mutate(efficiency = approx_fun(year, efficiency, rule = 2)) %>%
ungroup() %>%
filter(year %in% MODEL_YEARS) %>% # This will drop 1971
mutate(efficiency = round(efficiency, digits = DIGITS_EFFICIENCY)) %>%
# Assign the columns "sector.name" and "subsector.name", consistent with the location info of a global technology
select(sector.name = supplysector, subsector.name = subsector, technology, minicam.energy.input, year, efficiency) ->
L252.GlobalTechEff_trn # OUTPUT
# Costs of global technologies
# Capital costs of global transportation technologies were interpolated across model years
A52.globaltech_cost %>%
gather_years(value_col = "input.cost") %>%
# Expand table to include all model base and future years
complete(year = c(year, MODEL_YEARS), nesting(supplysector, subsector, technology, minicam.non.energy.input)) %>%
# Extrapolate to fill out values for all years
# Rule 2 is used so years that may be outside of min-max range are assigned values from closest data, as opposed to NAs
group_by(supplysector, subsector, technology, minicam.non.energy.input) %>%
mutate(input.cost = approx_fun(year, input.cost, rule = 2)) %>%
ungroup() %>%
filter(year %in% MODEL_YEARS) %>% # This will drop 1971
# Assign the columns "sector.name" and "subsector.name", consistent with the location info of a global technology
select(sector.name = supplysector, subsector.name = subsector, technology, minicam.non.energy.input, year, input.cost) ->
L252.GlobalTechCost_trn # OUTPUT
# Calibration and region-specific data
# L252.StubTechCalInput_trn: calibrated input of transportation energy use technologies (including cogen)
L152.in_EJ_R_trn_F_Yh %>%
# Expand table to include all model base years
filter(year %in% MODEL_BASE_YEARS) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
# Match in supplysector, subsector, technology
left_join_error_no_match(calibrated_techs_trn_agg, by = c("sector", "fuel")) %>%
# Aggregate as indicated in the supplysector/subsector/technology mapping (dropping fuel)
group_by(region, supplysector, subsector, stub.technology = technology, year) %>%
summarise(value = sum(value)) %>%
ungroup() ->
L252.in_EJ_R_trn_F_Yh
DIGITS_CALOUTPUT <- 7
L252.in_EJ_R_trn_F_Yh %>%
select(LEVEL2_DATA_NAMES[["StubTechYr"]], "value") %>%
# Match in minicam.energy.input
left_join_error_no_match(A52.globaltech_eff, by = c("supplysector", "subsector", "stub.technology" = "technology")) %>%
mutate(calibrated.value = round(value, digits = DIGITS_CALOUTPUT),
share.weight.year = year,
subs.share.weight = if_else(calibrated.value > 0, 1, 0),
tech.share.weight = if_else(calibrated.value > 0, 1, 0)) %>%
select(LEVEL2_DATA_NAMES[["StubTechCalInput"]]) ->
L252.StubTechCalInput_trn # OUTPUT
# Write per-capita based flag for transportation final demand for all regions
A52.demand %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["PerCapitaBased"]],
GCAM_region_names = GCAM_region_names) ->
L252.PerCapitaBased_trn # OUTPUT
# Expand price elasticity of transportation final demand across all regions and future years.
# Price elasticities are only applied to future periods. Application in base years will cause solution failure
A52.demand %>%
repeat_add_columns(tibble(MODEL_FUTURE_YEARS)) %>%
rename(year = MODEL_FUTURE_YEARS) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["PriceElasticity"]],
GCAM_region_names = GCAM_region_names) ->
L252.PriceElasticity_trn # OUTPUT
# Base-year service output of transportation final demand
# Base service is equal to the output of the transportation supplysector
L252.StubTechCalInput_trn %>%
left_join_error_no_match(L252.GlobalTechEff_trn, by = c("supplysector" = "sector.name", "subsector" = "subsector.name",
"stub.technology" = "technology", "minicam.energy.input",
"share.weight.year" = "year")) %>%
mutate(output = calibrated.value * efficiency) %>%
group_by(region, supplysector, year) %>%
summarise(output = sum(output)) %>%
ungroup() %>%
mutate(base.service = round(output, digits = DIGITS_CALOUTPUT)) %>%
select(region, energy.final.demand = supplysector, year, base.service) ->
L252.BaseService_trn # OUTPUT
## NOTE: income elasticities are GDP-dependent and are set in the socioeconomics module
# ===================================================
L252.Supplysector_trn %>%
add_title("Supply sector information for transportation sector") %>%
add_units("Unitless") %>%
add_comments("Supply sector information for transportation sector was expanded across all regions") %>%
add_legacy_name("L252.Supplysector_trn") %>%
add_precursors("common/GCAM_region_names", "energy/A52.sector") ->
L252.Supplysector_trn
L252.FinalEnergyKeyword_trn %>%
add_title("Supply sector keywords for transportation sector") %>%
add_units("NA") %>%
add_comments("Supply sector keywords for transportation sector were written for all regions") %>%
add_legacy_name("L252.FinalEnergyKeyword_trn") %>%
add_precursors("common/GCAM_region_names", "energy/A52.sector") ->
L252.FinalEnergyKeyword_trn
L252.SubsectorLogit_trn %>%
add_title("Subsector logit exponents of transportation sector") %>%
add_units("Unitless") %>%
add_comments("Subsector logit exponents of transportation sector were written for all regions") %>%
add_legacy_name("L252.SubsectorLogit_trn") %>%
add_precursors("common/GCAM_region_names", "energy/A52.subsector_logit") ->
L252.SubsectorLogit_trn
if(exists("L252.SubsectorShrwt_trn")) {
L252.SubsectorShrwt_trn %>%
add_title("Subsector shareweights of transportation sector") %>%
add_units("Unitless") %>%
add_comments("Subsector shareweights of transportation sector were written for all regions") %>%
add_comments("Only rows with an entry for year were selected") %>%
add_legacy_name("L252.SubsectorShrwt_trn") %>%
add_precursors("common/GCAM_region_names", "energy/A52.subsector_shrwt") ->
L252.SubsectorShrwt_trn
} else {
missing_data() %>%
add_legacy_name("L252.SubsectorShrwt_trn") ->
L252.SubsectorShrwt_trn
}
if(exists("L252.SubsectorShrwtFllt_trn")) {
L252.SubsectorShrwtFllt_trn %>%
add_title("Subsector shareweights of transportation sector") %>%
add_units("Unitless") %>%
add_comments("Subsector shareweights of transportation sector were written for all regions") %>%
add_comments("Only rows with an entry for year.fillout were selected") %>%
add_legacy_name("L252.SubsectorShrwtFllt_trn") %>%
add_precursors("common/GCAM_region_names", "energy/A52.subsector_shrwt") ->
L252.SubsectorShrwtFllt_trn
} else {
missing_data() %>%
add_legacy_name("L252.SubsectorShrwtFllt_trn") ->
L252.SubsectorShrwtFllt_trn
}
if(exists("L252.SubsectorInterp_trn")) {
L252.SubsectorInterp_trn %>%
add_title("Subsector shareweight interpolation data of transportation sector") %>%
add_units("NA") %>%
add_comments("Subsector shareweight interpolation data of transportation sector were written for all regions") %>%
add_comments("Only rows without an entry for to.value were selected") %>%
add_legacy_name("L252.SubsectorInterp_trn") %>%
add_precursors("common/GCAM_region_names", "energy/A52.subsector_interp") ->
L252.SubsectorInterp_trn
} else {
missing_data() %>%
add_legacy_name("L252.SubsectorInterp_trn") ->
L252.SubsectorInterp_trn
}
if(exists("L252.SubsectorInterpTo_trn")) {
L252.SubsectorInterpTo_trn %>%
add_title("Subsector shareweight interpolation data of transportation sector") %>%
add_units("NA") %>%
add_comments("Subsector shareweight interpolation data of transportation sector were written for all regions") %>%
add_comments("Only rows with an entry for to.value were selected") %>%
add_legacy_name("L252.SubsectorInterpTo_trn") %>%
add_precursors("common/GCAM_region_names", "energy/A52.subsector_interp") ->
L252.SubsectorInterpTo_trn
} else {
missing_data() %>%
add_legacy_name("L252.SubsectorInterpTo_trn") ->
L252.SubsectorInterpTo_trn
}
L252.StubTech_trn %>%
add_title("Identification of stub technologies of transportation sector") %>%
add_units("NA") %>%
add_comments("Identification of stub technologies of transportation sector were written across all regions") %>%
add_comments("Region/fuel combinations where heat and traditional biomass are not modeled as separate fuels were removed") %>%
add_legacy_name("L252.StubTech_trn") %>%
add_precursors("common/GCAM_region_names", "energy/A52.globaltech_shrwt") ->
L252.StubTech_trn
L252.GlobalTechShrwt_trn %>%
add_title("Shareweights of global transportation sector technologies") %>%
add_units("Unitless") %>%
add_comments("Interpolated shareweight data across model years") %>%
add_legacy_name("L252.GlobalTechShrwt_trn") %>%
add_precursors("energy/A52.globaltech_shrwt") ->
L252.GlobalTechShrwt_trn
L252.GlobalTechEff_trn %>%
add_title("Energy inputs and coefficients of global transportation energy use and feedstocks technologies") %>%
add_units("Unitless") %>%
add_comments("Interpolated data across model years") %>%
add_legacy_name("L252.GlobalTechEff_trn") %>%
add_precursors("energy/A52.globaltech_eff") ->
L252.GlobalTechEff_trn
L252.GlobalTechCost_trn %>%
add_title("Capital costs of global transportation technologies") %>%
add_units("Unitless") %>%
add_comments("Capital costs were interpolated across model years") %>%
add_legacy_name("L252.GlobalTechCost_trn") %>%
add_precursors("energy/A52.globaltech_cost") ->
L252.GlobalTechCost_trn
L252.StubTechCalInput_trn %>%
add_title("Calibrated input of transportation energy use technologies (including cogen)") %>%
add_units("EJ") %>%
add_comments("Data was aggregated (dropping fuel) and shareweights were determined from the calibrated value") %>%
add_legacy_name("L252.StubTechCalInput_trn") %>%
add_precursors("common/GCAM_region_names", "energy/mappings/calibrated_techs_trn_agg",
"energy/A52.globaltech_eff", "L152.in_EJ_R_trn_F_Yh") ->
L252.StubTechCalInput_trn
L252.PerCapitaBased_trn %>%
add_title("Per-capita based flag for transportation final demand") %>%
add_units("Unitless") %>%
add_comments("Data was expanded across all regions") %>%
add_legacy_name("L252.PerCapitaBased_trn") %>%
add_precursors("common/GCAM_region_names", "energy/A52.demand") ->
L252.PerCapitaBased_trn
L252.PriceElasticity_trn %>%
add_title("Price elasticity of transportation final demand") %>%
add_units("Unitless") %>%
add_comments("Price elasticity data were written for all regions and only applied to future model years") %>%
add_legacy_name("L252.PriceElasticity_trn") %>%
add_precursors("common/GCAM_region_names", "energy/A52.demand") ->
L252.PriceElasticity_trn
L252.BaseService_trn %>%
add_title("Base-year service output of transportation final demand") %>%
add_units("EJ") %>%
add_comments("Base service is equal to the output of the transportation supplysector") %>%
add_legacy_name("L252.BaseService_trn") %>%
add_precursors("common/GCAM_region_names", "energy/mappings/calibrated_techs_trn_agg",
"energy/A52.globaltech_eff", "L152.in_EJ_R_trn_F_Yh") ->
L252.BaseService_trn
return_data(L252.Supplysector_trn, L252.FinalEnergyKeyword_trn, L252.SubsectorLogit_trn, L252.SubsectorShrwt_trn, L252.SubsectorShrwtFllt_trn, L252.SubsectorInterp_trn, L252.SubsectorInterpTo_trn, L252.StubTech_trn, L252.GlobalTechShrwt_trn, L252.GlobalTechEff_trn, L252.GlobalTechCost_trn, L252.StubTechCalInput_trn, L252.PerCapitaBased_trn, L252.PriceElasticity_trn, L252.BaseService_trn)
} else {
stop("Unknown command")
}
}
rohmin9122/gcam-korea-release documentation built on Nov. 26, 2020, 8:11 a.m.
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Defines functions module_energy_L252.transportation
Documented in module_energy_L252.transportation
#' module_energy_L252.transportation
#'
#' Calculate supply sector, subsector, and technology information for the transportation 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{L252.SectorLogitTables[[ curr_table ]]$data}, \code{L252.Supplysector_trn}, \code{L252.FinalEnergyKeyword_trn}, \code{L252.SubsectorLogitTables[[ curr_table ]]$data}, \code{L252.SubsectorLogit_trn}, \code{L252.SubsectorShrwt_trn}, \code{L252.SubsectorShrwtFllt_trn}, \code{L252.SubsectorInterp_trn}, \code{L252.SubsectorInterpTo_trn}, \code{L252.StubTech_trn}, \code{L252.GlobalTechShrwt_trn}, \code{L252.GlobalTechEff_trn}, \code{L252.GlobalTechCost_trn}, \code{L252.StubTechCalInput_trn}, \code{L252.PerCapitaBased_trn}, \code{L252.PriceElasticity_trn}, \code{L252.BaseService_trn}. The corresponding file in the
#' original data system was \code{L252.transportation.R} (energy level2).
#' @details Calculate shareweights, cost, price elasticity, calibrated, and other data for the transportation sector
#' @importFrom assertthat assert_that
#' @importFrom dplyr filter mutate select
#' @importFrom tidyr gather spread
#' @author AJS August 2017
module_energy_L252.transportation <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "common/GCAM_region_names",
FILE = "energy/mappings/calibrated_techs_trn_agg",
FILE = "energy/A52.sector",
FILE = "energy/A52.subsector_interp",
FILE = "energy/A52.subsector_logit",
FILE = "energy/A52.subsector_shrwt",
FILE = "energy/A52.globaltech_cost",
FILE = "energy/A52.globaltech_eff",
FILE = "energy/A52.globaltech_shrwt",
FILE = "energy/A52.demand",
"L152.in_EJ_R_trn_F_Yh"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L252.Supplysector_trn",
"L252.FinalEnergyKeyword_trn",
"L252.SubsectorLogit_trn",
"L252.SubsectorShrwt_trn",
"L252.SubsectorShrwtFllt_trn",
"L252.SubsectorInterp_trn",
"L252.SubsectorInterpTo_trn",
"L252.StubTech_trn",
"L252.GlobalTechShrwt_trn",
"L252.GlobalTechEff_trn",
"L252.GlobalTechCost_trn",
"L252.StubTechCalInput_trn",
"L252.PerCapitaBased_trn",
"L252.PriceElasticity_trn",
"L252.BaseService_trn"))
} 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_trn_agg <- get_data(all_data, "energy/mappings/calibrated_techs_trn_agg")
A52.sector <- get_data(all_data, "energy/A52.sector")
A52.subsector_interp <- get_data(all_data, "energy/A52.subsector_interp")
A52.subsector_logit <- get_data(all_data, "energy/A52.subsector_logit")
A52.subsector_shrwt <- get_data(all_data, "energy/A52.subsector_shrwt")
A52.globaltech_cost <- get_data(all_data, "energy/A52.globaltech_cost")
A52.globaltech_eff <- get_data(all_data, "energy/A52.globaltech_eff")
A52.globaltech_shrwt <- get_data(all_data, "energy/A52.globaltech_shrwt")
A52.demand <- get_data(all_data, "energy/A52.demand")
L152.in_EJ_R_trn_F_Yh <- get_data(all_data, "L152.in_EJ_R_trn_F_Yh")
# ===================================================
# Silence Package Notes
region <- supplysector <- output.unit <- input.unit <- price.unit <- logit.year.fillout <-
logit.exponent <- final.energy <- subsector <- logit.type <- logit.exponent <- year <-
share.weight <- year.fillout <- apply.to <- from.year <- to.year <- interpolation.function <-
to.value <- technology <- stub.technology <- sector.name <- subsector.name <- minicam.energy.input <-
efficiency <- minicam.non.energy.input <- input.cost <- calibrated.value <- share.weight.year <-
subs.share.weight <- tech.share.weight <- energy.final.demand <- perCapitaBased <-
price.elasticity <- GAM_region_ID <- base.serive <- coefficient <- curr_table <- value <-
. <- output <- base.service <- NULL
# PART A: SUPPLYSECTOR INFORMATION
# Write supply sector information for transportation sector for all regions
A52.sector %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["Supplysector"]], LOGIT_TYPE_COLNAME),
GCAM_region_names = GCAM_region_names) ->
L252.Supplysector_trn # OUTPUT
# Write supply sector keywords for transportation sector for all regions
A52.sector %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["FinalEnergyKeyword"]],
GCAM_region_names = GCAM_region_names) ->
L252.FinalEnergyKeyword_trn # OUTPUT
# PART B: SUBSECTOR INFORMATION
# Write subsector logit exponents of transportation sector for all regions
A52.subsector_logit %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["SubsectorLogit"]], LOGIT_TYPE_COLNAME),
GCAM_region_names = GCAM_region_names) ->
L252.SubsectorLogit_trn # OUTPUT
# Expand subsector shareweights of transportation sector across all regions
if(any(!is.na(A52.subsector_shrwt$year))) {
A52.subsector_shrwt %>%
filter(!is.na(year)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["SubsectorShrwt"]],
GCAM_region_names = GCAM_region_names) ->
L252.SubsectorShrwt_trn # OUTPUT
}
if(any(!is.na(A52.subsector_shrwt$year.fillout))) {
A52.subsector_shrwt %>%
filter(!is.na(year.fillout)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["SubsectorShrwtFllt"]],
GCAM_region_names = GCAM_region_names) ->
L252.SubsectorShrwtFllt_trn # OUTPUT
}
# Write subsector shareweight interpolation of transportation sector for all regions
if(any(is.na(A52.subsector_interp$to.value))) {
A52.subsector_interp %>%
filter(is.na(to.value)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["SubsectorInterp"]],
GCAM_region_names = GCAM_region_names) ->
L252.SubsectorInterp_trn # OUTPUT
}
if(any(!is.na(A52.subsector_interp$to.value))) {
A52.subsector_interp %>%
filter(is.na(!to.value)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["SubsectorInterpTo"]],
GCAM_region_names = GCAM_region_names) ->
L252.SubsectorInterpTo_trn # OUTPUT (not generated at this time)
}
# PART C: TECHNOLOGY INFORMATION
# Write stub technologies of the transportation sector for all regions.
# Note: assuming that technology list in the shareweight table includes the full set (any others would default to a 0 shareweight)
A52.globaltech_shrwt %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["Tech"]],
GCAM_region_names = GCAM_region_names) %>%
select(region, supplysector, subsector, stub.technology = technology) ->
L252.StubTech_trn # OUTPUT
# Interpolate shareweights of the global transportation sector technologies across model years
A52.globaltech_shrwt %>%
gather_years(value_col = "share.weight") %>%
# Expand table to include all model base and future years
complete(year = c(year, MODEL_YEARS), nesting(supplysector, subsector, technology)) %>%
# Extrapolate to fill out values for all years
# Rule 2 is used so years that may be outside of min-max range are assigned values from closest data, as opposed to NAs
group_by(supplysector, subsector, technology) %>%
mutate(share.weight = approx_fun(year, share.weight, rule = 2)) %>%
ungroup() %>%
filter(year %in% MODEL_YEARS) %>% # This will drop 1971
# Assign the columns "sector.name" and "subsector.name", consistent with the location info of a global technology
select(sector.name = supplysector, subsector.name = subsector, technology, year, share.weight) ->
L252.GlobalTechShrwt_trn # OUTPUT
# Calculate energy inputs and coefficients of global transportation energy use and feedstocks technologies
# for all model years
DIGITS_EFFICIENCY <- 3
A52.globaltech_eff %>%
gather_years(value_col = "efficiency") %>%
# Expand table to include all model years
complete(year = c(year, MODEL_YEARS), nesting(supplysector, subsector, technology, minicam.energy.input)) %>%
# Extrapolate to fill out values for all years
# Rule 2 is used so years that may be outside of min-max range are assigned values from closest data, as opposed to NAs
group_by(supplysector, subsector, technology, minicam.energy.input) %>%
mutate(efficiency = approx_fun(year, efficiency, rule = 2)) %>%
ungroup() %>%
filter(year %in% MODEL_YEARS) %>% # This will drop 1971
mutate(efficiency = round(efficiency, digits = DIGITS_EFFICIENCY)) %>%
# Assign the columns "sector.name" and "subsector.name", consistent with the location info of a global technology
select(sector.name = supplysector, subsector.name = subsector, technology, minicam.energy.input, year, efficiency) ->
L252.GlobalTechEff_trn # OUTPUT
# Costs of global technologies
# Capital costs of global transportation technologies were interpolated across model years
A52.globaltech_cost %>%
gather_years(value_col = "input.cost") %>%
# Expand table to include all model base and future years
complete(year = c(year, MODEL_YEARS), nesting(supplysector, subsector, technology, minicam.non.energy.input)) %>%
# Extrapolate to fill out values for all years
# Rule 2 is used so years that may be outside of min-max range are assigned values from closest data, as opposed to NAs
group_by(supplysector, subsector, technology, minicam.non.energy.input) %>%
mutate(input.cost = approx_fun(year, input.cost, rule = 2)) %>%
ungroup() %>%
filter(year %in% MODEL_YEARS) %>% # This will drop 1971
# Assign the columns "sector.name" and "subsector.name", consistent with the location info of a global technology
select(sector.name = supplysector, subsector.name = subsector, technology, minicam.non.energy.input, year, input.cost) ->
L252.GlobalTechCost_trn # OUTPUT
# Calibration and region-specific data
# L252.StubTechCalInput_trn: calibrated input of transportation energy use technologies (including cogen)
L152.in_EJ_R_trn_F_Yh %>%
# Expand table to include all model base years
filter(year %in% MODEL_BASE_YEARS) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
# Match in supplysector, subsector, technology
left_join_error_no_match(calibrated_techs_trn_agg, by = c("sector", "fuel")) %>%
# Aggregate as indicated in the supplysector/subsector/technology mapping (dropping fuel)
group_by(region, supplysector, subsector, stub.technology = technology, year) %>%
summarise(value = sum(value)) %>%
ungroup() ->
L252.in_EJ_R_trn_F_Yh
DIGITS_CALOUTPUT <- 7
L252.in_EJ_R_trn_F_Yh %>%
select(LEVEL2_DATA_NAMES[["StubTechYr"]], "value") %>%
# Match in minicam.energy.input
left_join_error_no_match(A52.globaltech_eff, by = c("supplysector", "subsector", "stub.technology" = "technology")) %>%
mutate(calibrated.value = round(value, digits = DIGITS_CALOUTPUT),
share.weight.year = year,
subs.share.weight = if_else(calibrated.value > 0, 1, 0),
tech.share.weight = if_else(calibrated.value > 0, 1, 0)) %>%
select(LEVEL2_DATA_NAMES[["StubTechCalInput"]]) ->
L252.StubTechCalInput_trn # OUTPUT
# Write per-capita based flag for transportation final demand for all regions
A52.demand %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["PerCapitaBased"]],
GCAM_region_names = GCAM_region_names) ->
L252.PerCapitaBased_trn # OUTPUT
# Expand price elasticity of transportation final demand across all regions and future years.
# Price elasticities are only applied to future periods. Application in base years will cause solution failure
A52.demand %>%
repeat_add_columns(tibble(MODEL_FUTURE_YEARS)) %>%
rename(year = MODEL_FUTURE_YEARS) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["PriceElasticity"]],
GCAM_region_names = GCAM_region_names) ->
L252.PriceElasticity_trn # OUTPUT
# Base-year service output of transportation final demand
# Base service is equal to the output of the transportation supplysector
L252.StubTechCalInput_trn %>%
left_join_error_no_match(L252.GlobalTechEff_trn, by = c("supplysector" = "sector.name", "subsector" = "subsector.name",
"stub.technology" = "technology", "minicam.energy.input",
"share.weight.year" = "year")) %>%
mutate(output = calibrated.value * efficiency) %>%
group_by(region, supplysector, year) %>%
summarise(output = sum(output)) %>%
ungroup() %>%
mutate(base.service = round(output, digits = DIGITS_CALOUTPUT)) %>%
select(region, energy.final.demand = supplysector, year, base.service) ->
L252.BaseService_trn # OUTPUT
## NOTE: income elasticities are GDP-dependent and are set in the socioeconomics module
# ===================================================
L252.Supplysector_trn %>%
add_title("Supply sector information for transportation sector") %>%
add_units("Unitless") %>%
add_comments("Supply sector information for transportation sector was expanded across all regions") %>%
add_legacy_name("L252.Supplysector_trn") %>%
add_precursors("common/GCAM_region_names", "energy/A52.sector") ->
L252.Supplysector_trn
L252.FinalEnergyKeyword_trn %>%
add_title("Supply sector keywords for transportation sector") %>%
add_units("NA") %>%
add_comments("Supply sector keywords for transportation sector were written for all regions") %>%
add_legacy_name("L252.FinalEnergyKeyword_trn") %>%
add_precursors("common/GCAM_region_names", "energy/A52.sector") ->
L252.FinalEnergyKeyword_trn
L252.SubsectorLogit_trn %>%
add_title("Subsector logit exponents of transportation sector") %>%
add_units("Unitless") %>%
add_comments("Subsector logit exponents of transportation sector were written for all regions") %>%
add_legacy_name("L252.SubsectorLogit_trn") %>%
add_precursors("common/GCAM_region_names", "energy/A52.subsector_logit") ->
L252.SubsectorLogit_trn
if(exists("L252.SubsectorShrwt_trn")) {
L252.SubsectorShrwt_trn %>%
add_title("Subsector shareweights of transportation sector") %>%
add_units("Unitless") %>%
add_comments("Subsector shareweights of transportation sector were written for all regions") %>%
add_comments("Only rows with an entry for year were selected") %>%
add_legacy_name("L252.SubsectorShrwt_trn") %>%
add_precursors("common/GCAM_region_names", "energy/A52.subsector_shrwt") ->
L252.SubsectorShrwt_trn
} else {
missing_data() %>%
add_legacy_name("L252.SubsectorShrwt_trn") ->
L252.SubsectorShrwt_trn
}
if(exists("L252.SubsectorShrwtFllt_trn")) {
L252.SubsectorShrwtFllt_trn %>%
add_title("Subsector shareweights of transportation sector") %>%
add_units("Unitless") %>%
add_comments("Subsector shareweights of transportation sector were written for all regions") %>%
add_comments("Only rows with an entry for year.fillout were selected") %>%
add_legacy_name("L252.SubsectorShrwtFllt_trn") %>%
add_precursors("common/GCAM_region_names", "energy/A52.subsector_shrwt") ->
L252.SubsectorShrwtFllt_trn
} else {
missing_data() %>%
add_legacy_name("L252.SubsectorShrwtFllt_trn") ->
L252.SubsectorShrwtFllt_trn
}
if(exists("L252.SubsectorInterp_trn")) {
L252.SubsectorInterp_trn %>%
add_title("Subsector shareweight interpolation data of transportation sector") %>%
add_units("NA") %>%
add_comments("Subsector shareweight interpolation data of transportation sector were written for all regions") %>%
add_comments("Only rows without an entry for to.value were selected") %>%
add_legacy_name("L252.SubsectorInterp_trn") %>%
add_precursors("common/GCAM_region_names", "energy/A52.subsector_interp") ->
L252.SubsectorInterp_trn
} else {
missing_data() %>%
add_legacy_name("L252.SubsectorInterp_trn") ->
L252.SubsectorInterp_trn
}
if(exists("L252.SubsectorInterpTo_trn")) {
L252.SubsectorInterpTo_trn %>%
add_title("Subsector shareweight interpolation data of transportation sector") %>%
add_units("NA") %>%
add_comments("Subsector shareweight interpolation data of transportation sector were written for all regions") %>%
add_comments("Only rows with an entry for to.value were selected") %>%
add_legacy_name("L252.SubsectorInterpTo_trn") %>%
add_precursors("common/GCAM_region_names", "energy/A52.subsector_interp") ->
L252.SubsectorInterpTo_trn
} else {
missing_data() %>%
add_legacy_name("L252.SubsectorInterpTo_trn") ->
L252.SubsectorInterpTo_trn
}
L252.StubTech_trn %>%
add_title("Identification of stub technologies of transportation sector") %>%
add_units("NA") %>%
add_comments("Identification of stub technologies of transportation sector were written across all regions") %>%
add_comments("Region/fuel combinations where heat and traditional biomass are not modeled as separate fuels were removed") %>%
add_legacy_name("L252.StubTech_trn") %>%
add_precursors("common/GCAM_region_names", "energy/A52.globaltech_shrwt") ->
L252.StubTech_trn
L252.GlobalTechShrwt_trn %>%
add_title("Shareweights of global transportation sector technologies") %>%
add_units("Unitless") %>%
add_comments("Interpolated shareweight data across model years") %>%
add_legacy_name("L252.GlobalTechShrwt_trn") %>%
add_precursors("energy/A52.globaltech_shrwt") ->
L252.GlobalTechShrwt_trn
L252.GlobalTechEff_trn %>%
add_title("Energy inputs and coefficients of global transportation energy use and feedstocks technologies") %>%
add_units("Unitless") %>%
add_comments("Interpolated data across model years") %>%
add_legacy_name("L252.GlobalTechEff_trn") %>%
add_precursors("energy/A52.globaltech_eff") ->
L252.GlobalTechEff_trn
L252.GlobalTechCost_trn %>%
add_title("Capital costs of global transportation technologies") %>%
add_units("Unitless") %>%
add_comments("Capital costs were interpolated across model years") %>%
add_legacy_name("L252.GlobalTechCost_trn") %>%
add_precursors("energy/A52.globaltech_cost") ->
L252.GlobalTechCost_trn
L252.StubTechCalInput_trn %>%
add_title("Calibrated input of transportation energy use technologies (including cogen)") %>%
add_units("EJ") %>%
add_comments("Data was aggregated (dropping fuel) and shareweights were determined from the calibrated value") %>%
add_legacy_name("L252.StubTechCalInput_trn") %>%
add_precursors("common/GCAM_region_names", "energy/mappings/calibrated_techs_trn_agg",
"energy/A52.globaltech_eff", "L152.in_EJ_R_trn_F_Yh") ->
L252.StubTechCalInput_trn
L252.PerCapitaBased_trn %>%
add_title("Per-capita based flag for transportation final demand") %>%
add_units("Unitless") %>%
add_comments("Data was expanded across all regions") %>%
add_legacy_name("L252.PerCapitaBased_trn") %>%
add_precursors("common/GCAM_region_names", "energy/A52.demand") ->
L252.PerCapitaBased_trn
L252.PriceElasticity_trn %>%
add_title("Price elasticity of transportation final demand") %>%
add_units("Unitless") %>%
add_comments("Price elasticity data were written for all regions and only applied to future model years") %>%
add_legacy_name("L252.PriceElasticity_trn") %>%
add_precursors("common/GCAM_region_names", "energy/A52.demand") ->
L252.PriceElasticity_trn
L252.BaseService_trn %>%
add_title("Base-year service output of transportation final demand") %>%
add_units("EJ") %>%
add_comments("Base service is equal to the output of the transportation supplysector") %>%
add_legacy_name("L252.BaseService_trn") %>%
add_precursors("common/GCAM_region_names", "energy/mappings/calibrated_techs_trn_agg",
"energy/A52.globaltech_eff", "L152.in_EJ_R_trn_F_Yh") ->
L252.BaseService_trn
return_data(L252.Supplysector_trn, L252.FinalEnergyKeyword_trn, L252.SubsectorLogit_trn, L252.SubsectorShrwt_trn, L252.SubsectorShrwtFllt_trn, L252.SubsectorInterp_trn, L252.SubsectorInterpTo_trn, L252.StubTech_trn, L252.GlobalTechShrwt_trn, L252.GlobalTechEff_trn, L252.GlobalTechCost_trn, L252.StubTechCalInput_trn, L252.PerCapitaBased_trn, L252.PriceElasticity_trn, L252.BaseService_trn)
} else {
stop("Unknown command")
}
}
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