R/zchunk_L254.transportation_USA.R
In JGCRI/gcamdata: Build the GCAM Input Datasets
Defines functions
module_gcamusa_L254.transportation_USA
Documented in
module_gcamusa_L254.transportation_USA
# Copyright 2019 Battelle Memorial Institute; see the LICENSE file.
#' module_gcamusa_L254.transportation_USA
#'
#' Generates GCAM-USA model inputs for transportation sector by states.
#'
#' @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{L254.DeleteSupplysector_USAtrn}, \code{L254.DeleteFinalDemand_USAtrn},
#' \code{L254.Supplysector_trn_USA}, \code{L254.FinalEnergyKeyword_trn_USA}, \code{L254.tranSubsectorLogit_USA},
#' \code{L254.tranSubsectorShrwtFllt_USA}, \code{L254.tranSubsectorInterp_USA}, \code{L254.tranSubsectorSpeed_USA},
#' \code{L254.tranSubsectorSpeed_passthru_USA}, \code{L254.tranSubsectorSpeed_noVOTT_USA},
#' \code{L254.tranSubsectorSpeed_nonmotor_USA}, \code{L254.tranSubsectorVOTT_USA}, \code{L254.tranSubsectorFuelPref_USA},
#' \code{L254.StubTranTech_USA}, \code{L254.StubTranTech_passthru_USA}, \code{L254.StubTranTech_nonmotor_USA},
#' \code{L254.StubTranTechLoadFactor_USA}, \code{L254.StubTranTechCost_USA}, \code{L254.StubTranTechCoef_USA},
#' \code{L254.PerCapitaBased_trn_USA}, \code{L254.PriceElasticity_trn_USA}, \code{L254.IncomeElasticity_trn_USA},
#' \code{L254.StubTranTechCalInput_USA}, \code{L254.StubTranTechProd_nonmotor_USA}, \code{L254.StubTranTechCalInput_passthru_USA},
#' \code{L254.BaseService_trn_USA}.
#' The corresponding file in the original data system was \code{L254.transportation_USA.R} (gcam-usa level2).
#' @details This chunk generates input files for transportation sector with generic information for supplysector,
#' subsector and technologies, as well as calibrated inputs and outputs by the US states.
#' @note The transportation structure is heavily nested. The GCAM structure of sector/subsector/technology only
#' allows two levels of nesting within any sector, but a technology of one sector (e.g., trn_pass) can consume the
#' output of another "sector" (e.g., trn_pass_road) that is really just used to represent lower nesting levels of
#' that first, or parent, sector. In the transportation sector, each lower-level nesting "sector" is named by
#' appending a string to the parent sector. So, \code{trn_pass} contains \code{trn_pass_road} which has
#' \code{trn_pass_road_LDV} which has \code{trn_pass_road_LDV_4W}. Each of the links between any two of those sectors
#' is done with a pass-through technology within the parent sector that consumes the output of the child sector.
#' The technology is called a "pass-through" because it (generally) only consumes the output of the child "sector"
#' without making any changes to it. There's an additional complication in the transportation sector, that the
#' pass-through technologies are normal, standard GCAM technologies, not "tranTechnologies" which have different
#' parameters read in, and perform a bunch of hard-wired unit conversions between inputs and outputs.
#' @importFrom assertthat assert_that
#' @importFrom dplyr arrange bind_rows filter if_else group_by left_join mutate select semi_join summarise
#' @importFrom tidyr replace_na
#' @author RC Oct 2017
module_gcamusa_L254.transportation_USA <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "energy/mappings/UCD_techs",
FILE = "energy/A54.globaltech_nonmotor",
FILE = "energy/A54.globaltech_passthru",
FILE = "energy/A54.sector",
FILE= "energy/mappings/UCD_size_class_revisions",
FILE= "energy/mappings/UCD_techs_revised",
FILE = "gcam-usa/states_subregions",
"L254.Supplysector_trn",
"L254.FinalEnergyKeyword_trn",
"L254.tranSubsectorLogit",
"L254.tranSubsectorShrwtFllt",
"L254.tranSubsectorInterp",
"L254.tranSubsectorSpeed",
"L254.tranSubsectorSpeed_passthru",
"L254.tranSubsectorSpeed_noVOTT",
"L254.tranSubsectorSpeed_nonmotor",
"L254.tranSubsectorVOTT",
"L254.tranSubsectorFuelPref",
"L254.StubTranTech",
"L254.StubTech_passthru",
"L254.StubTech_nonmotor",
"L254.StubTranTechLoadFactor",
"L254.StubTranTechCost",
"L254.StubTranTechCoef",
"L254.PerCapitaBased_trn",
"L254.PriceElasticity_trn",
"L254.IncomeElasticity_trn",
"L154.in_EJ_state_trn_m_sz_tech_F",
"L154.out_mpkm_state_trn_nonmotor_Yh"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L254.DeleteSupplysector_USAtrn",
"L254.DeleteFinalDemand_USAtrn",
"L254.Supplysector_trn_USA",
"L254.FinalEnergyKeyword_trn_USA",
"L254.tranSubsectorLogit_USA",
"L254.tranSubsectorShrwtFllt_USA",
"L254.tranSubsectorInterp_USA",
"L254.tranSubsectorSpeed_USA",
"L254.tranSubsectorSpeed_passthru_USA",
"L254.tranSubsectorSpeed_noVOTT_USA",
"L254.tranSubsectorSpeed_nonmotor_USA",
"L254.tranSubsectorVOTT_USA",
"L254.tranSubsectorFuelPref_USA",
"L254.StubTranTech_USA",
"L254.StubTranTech_passthru_USA",
"L254.StubTranTech_nonmotor_USA",
"L254.StubTranTechLoadFactor_USA",
"L254.StubTranTechCost_USA",
"L254.StubTranTechCoef_USA",
"L254.PerCapitaBased_trn_USA",
"L254.PriceElasticity_trn_USA",
"L254.IncomeElasticity_trn_USA",
"L254.StubTranTechCalInput_USA",
"L254.StubTranTechProd_nonmotor_USA",
"L254.StubTranTechCalInput_passthru_USA",
"L254.BaseService_trn_USA",
"L254.StubTranTechOutput_USA"))
} else if(command == driver.MAKE) {
all_data <- list(...)[[1]]
# Silence package notes
region <- supplysector <- technology <- minicam.energy.input <- year <- value <- coefficient <-
UCD_fuel <- UCD_sector <- UCD_technology <- calibrated.value <- coefficient <- loadFactor <-
size.class <- state <- tranSubsector <- tranTechnology <- calibrated.value <- stub.technology <-
output <- output_agg <- output_cum <- share.weight <- subs.share.weight <- share.weight.year <-
tech.share.weight <- calOutputValue <- energy.final.demand <- base.service <- year.fillout <-
fuelprefElasticity <- income.elasticity <- UCD_region <- sce <- . <- NULL
# Load required inputs
#kbn 2019-10-14 Making same changes here for UCD techs that we made in L254:
Size_class_New<- get_data(all_data, "energy/mappings/UCD_size_class_revisions",strip_attributes = TRUE) %>%
select(-UCD_region) %>%
distinct()
UCD_techs <- get_data(all_data, "energy/mappings/UCD_techs",strip_attributes = TRUE) # Mapping file of transportation technology from the UC Davis report (Mishra et al. 2013)
if (toString(energy.TRAN_UCD_MODE)=='rev.mode'){
UCD_techs <- get_data(all_data, "energy/mappings/UCD_techs_revised")
UCD_techs<-UCD_techs %>%
inner_join(Size_class_New, by=c("mode","size.class"))%>%
select(-mode,-size.class)%>%
distinct()
colnames(UCD_techs)[colnames(UCD_techs)=='rev_size.class']<-'size.class'
colnames(UCD_techs)[colnames(UCD_techs)=='rev.mode']<-'mode'
}
A54.globaltech_nonmotor <- get_data(all_data, "energy/A54.globaltech_nonmotor",strip_attributes = TRUE)
A54.globaltech_passthru <- get_data(all_data, "energy/A54.globaltech_passthru",strip_attributes = TRUE)
A54.sector <- get_data(all_data, "energy/A54.sector",strip_attributes = TRUE)
states_subregions <- get_data(all_data, "gcam-usa/states_subregions",strip_attributes = TRUE)
#kbn 2020-02-27 Making changes to select the CORE scenario for transportation in GCAM USA
L254.Supplysector_trn <- get_data(all_data, "L254.Supplysector_trn",strip_attributes = TRUE) %>% filter(sce %in% c("CORE"))
L254.FinalEnergyKeyword_trn <- get_data(all_data, "L254.FinalEnergyKeyword_trn",strip_attributes = TRUE)%>% filter(sce %in% c("CORE"))
L254.tranSubsectorLogit <- get_data(all_data, "L254.tranSubsectorLogit",strip_attributes = TRUE)%>% filter(sce %in% c("CORE"))
L254.tranSubsectorShrwtFllt <- get_data(all_data, "L254.tranSubsectorShrwtFllt",strip_attributes = TRUE)%>% filter(sce %in% c("CORE"))
L254.tranSubsectorInterp <- get_data(all_data, "L254.tranSubsectorInterp",strip_attributes = TRUE)%>% filter(sce %in% c("CORE"))
L254.tranSubsectorSpeed <- get_data(all_data, "L254.tranSubsectorSpeed",strip_attributes = TRUE)%>% filter(sce %in% c("CORE"))
L254.tranSubsectorSpeed_passthru <- get_data(all_data, "L254.tranSubsectorSpeed_passthru",strip_attributes = TRUE)%>% filter(sce %in% c("CORE"))
L254.tranSubsectorSpeed_noVOTT <- get_data(all_data, "L254.tranSubsectorSpeed_noVOTT",strip_attributes = TRUE)%>% filter(sce %in% c("CORE"))
L254.tranSubsectorSpeed_nonmotor <- get_data(all_data, "L254.tranSubsectorSpeed_nonmotor",strip_attributes = TRUE)%>% filter(sce %in% c("CORE"))
L254.tranSubsectorVOTT <- get_data(all_data, "L254.tranSubsectorVOTT",strip_attributes = TRUE)%>% filter(sce %in% c("CORE"))
L254.tranSubsectorFuelPref <- get_data(all_data, "L254.tranSubsectorFuelPref",strip_attributes = TRUE)%>% filter(sce %in% c("CORE"))
L254.StubTranTech <- get_data(all_data, "L254.StubTranTech",strip_attributes = TRUE)%>% filter(sce %in% c("CORE"))
L254.StubTech_passthru <- get_data(all_data, "L254.StubTech_passthru",strip_attributes = TRUE)%>% filter(sce %in% c("CORE"))
L254.StubTech_nonmotor <- get_data(all_data, "L254.StubTech_nonmotor",strip_attributes = TRUE)%>% filter(sce %in% c("CORE"))
L254.StubTranTechLoadFactor <- get_data(all_data, "L254.StubTranTechLoadFactor",strip_attributes = TRUE)%>% filter(sce %in% c("CORE"))
L254.StubTranTechCost <- get_data(all_data, "L254.StubTranTechCost",strip_attributes = TRUE)%>% filter(sce %in% c("CORE"))
L254.StubTranTechCoef <- get_data(all_data, "L254.StubTranTechCoef",strip_attributes = TRUE)%>% filter(sce %in% c("CORE"))
L254.PerCapitaBased_trn <- get_data(all_data, "L254.PerCapitaBased_trn",strip_attributes = TRUE)%>% filter(sce %in% c("CORE"))
L254.PriceElasticity_trn <- get_data(all_data, "L254.PriceElasticity_trn",strip_attributes = TRUE)%>% filter(sce %in% c("CORE"))
L254.IncomeElasticity_trn <- get_data(all_data, "L254.IncomeElasticity_trn",strip_attributes = TRUE)%>% filter(sce %in% c("CORE"))
L154.in_EJ_state_trn_m_sz_tech_F <- get_data(all_data, "L154.in_EJ_state_trn_m_sz_tech_F",strip_attributes = TRUE)
L154.out_mpkm_state_trn_nonmotor_Yh <- get_data(all_data, "L154.out_mpkm_state_trn_nonmotor_Yh",strip_attributes = TRUE)
# Need to delete the transportation sector in the USA region (energy-final-demands and supplysectors)
# L254.DeleteSupplysector_USAtrn: Delete transportation supplysectors of the USA region
L254.Supplysector_trn %>%
mutate(region = region) %>% # strip off attributes like title, etc.
filter(region == gcam.USA_REGION) %>%
select(region, supplysector) ->
L254.DeleteSupplysector_USAtrn
# L254.DeleteFinalDemand_USAtrn: Delete energy final demand sectors of the USA region
L254.PerCapitaBased_trn %>%
mutate(region = region) %>% # strip off attributes like title, etc.
filter(region == gcam.USA_REGION) %>%
select(LEVEL2_DATA_NAMES[["EnergyFinalDemand"]],sce) ->
L254.DeleteFinalDemand_USAtrn
# Process tables at the USA region level to the states level.
# All tables for which processing is identical are done by a function.
# This applies to the supplysectors, subsectors, and stub tech characteristics of the states.
process_USA_to_states <- function(data) {
state <- region <- grid_region <- subsector <- market.name <-
minicam.energy.input <- NULL # silence package check notes
data_new <- data %>%
filter(region == gcam.USA_REGION) %>%
write_to_all_states(names = c(names(data), "region"))
# Re-set markets from USA to grid region, if the minicam.energy.input is considered a regional fuel market
if("market.name" %in% names(data_new)) {
data_new <- data_new %>%
left_join_error_no_match(select(states_subregions, state, grid_region), by = c("region" = "state")) %>%
mutate(market.name = replace(market.name, minicam.energy.input %in% gcamusa.REGIONAL_FUEL_MARKETS,
grid_region[minicam.energy.input %in% gcamusa.REGIONAL_FUEL_MARKETS])) %>%
select(-grid_region)
}
# For fuels consumed from state markets, the market.name is the region
if("market.name" %in% names(data_new)) {
data_new <- data_new %>%
mutate(market.name = replace(market.name, minicam.energy.input %in% gcamusa.STATE_FUEL_MARKETS,
region[minicam.energy.input %in% gcamusa.STATE_FUEL_MARKETS]))
}
data_new
}
process_USA_to_states(L254.Supplysector_trn) -> L254.Supplysector_trn_USA
process_USA_to_states(L254.FinalEnergyKeyword_trn) -> L254.FinalEnergyKeyword_trn_USA
process_USA_to_states(L254.tranSubsectorLogit) -> L254.tranSubsectorLogit_USA
process_USA_to_states(L254.tranSubsectorShrwtFllt) -> L254.tranSubsectorShrwtFllt_USA
process_USA_to_states(L254.tranSubsectorInterp) -> L254.tranSubsectorInterp_USA
process_USA_to_states(L254.tranSubsectorSpeed) %>%
# parent file (L254.tranSubsectorSpeed) contains duplicate values... remove these
distinct()-> L254.tranSubsectorSpeed_USA
process_USA_to_states(L254.tranSubsectorSpeed_passthru) -> L254.tranSubsectorSpeed_passthru_USA
process_USA_to_states(L254.tranSubsectorSpeed_noVOTT) -> L254.tranSubsectorSpeed_noVOTT_USA
process_USA_to_states(L254.tranSubsectorSpeed_nonmotor) -> L254.tranSubsectorSpeed_nonmotor_USA
process_USA_to_states(L254.tranSubsectorVOTT) -> L254.tranSubsectorVOTT_USA
process_USA_to_states(L254.tranSubsectorFuelPref) -> L254.tranSubsectorFuelPref_USA
process_USA_to_states(L254.StubTranTech) -> L254.StubTranTech_USA
process_USA_to_states(L254.StubTech_passthru) -> L254.StubTranTech_passthru_USA
process_USA_to_states(L254.StubTech_nonmotor) -> L254.StubTranTech_nonmotor_USA
process_USA_to_states(L254.StubTranTechLoadFactor) -> L254.StubTranTechLoadFactor_USA
process_USA_to_states(L254.StubTranTechCost) -> L254.StubTranTechCost_USA
L254.StubTranTechCoef %>%
mutate(coefficient = round(coefficient, digits = gcamusa.DIGITS_TRNUSA_DEFAULT)) %>%
process_USA_to_states ->
L254.StubTranTechCoef_USA
process_USA_to_states(L254.PerCapitaBased_trn) -> L254.PerCapitaBased_trn_USA
process_USA_to_states(L254.PriceElasticity_trn) -> L254.PriceElasticity_trn_USA
process_USA_to_states(L254.IncomeElasticity_trn) -> L254.IncomeElasticity_trn_USA
# Calibration
# L254.StubTranTechCalInput_USA: calibrated energy consumption by all technologies
#kbn 2019-10-14 Switching to left_join_keep_first
L154.in_EJ_state_trn_m_sz_tech_F %>%
mutate(sce=paste0("CORE")) %>%
filter(year %in% MODEL_BASE_YEARS) %>%
mutate(calibrated.value = round(value, digits = energy.DIGITS_CALOUTPUT),
region = state) %>%
left_join_keep_first_only(select(UCD_techs, UCD_sector, mode, size.class, UCD_technology, UCD_fuel,
supplysector, tranSubsector, stub.technology = tranTechnology, minicam.energy.input),
by = c("UCD_sector", "mode", "size.class", "UCD_technology", "UCD_fuel")) %>%
select(LEVEL2_DATA_NAMES[["StubTranTech"]], year, minicam.energy.input, calibrated.value,sce) ->
L254.StubTranTechCalInput_USA
# NOTE: NEED TO WRITE THIS OUT FOR ALL TECHNOLOGIES, NOT JUST THOSE THAT EXIST IN SOME BASE YEARS.
# Model may make up calibration values otherwise.
L254.StubTranTechCoef_USA %>%
filter(sce=="CORE") %>%
filter(year %in% MODEL_BASE_YEARS) %>%
select(c(names(.)[names(.) %in% LEVEL2_DATA_NAMES[["StubTranTechCalInput"]]],sce)) %>%
left_join(L254.StubTranTechCalInput_USA,
by = c("region", "supplysector", "tranSubsector", "stub.technology", "year", "minicam.energy.input","sce")) %>%
# Set calibration values as zero for technolgies that do not exist in some base years
replace_na(list(calibrated.value = 0)) %>%
mutate(share.weight.year = year,
# Create the needed variables to use the function set_subsector_shrwt
subsector = tranSubsector, calOutputValue = calibrated.value) %>%
set_subsector_shrwt() %>%
mutate(tech.share.weight = if_else(calibrated.value > 0, 1, 0)) %>%
select(LEVEL2_DATA_NAMES[["StubTranTechCalInput"]],sce) ->
L254.StubTranTechCalInput_USA
# Non-motorized technologies
# L254.StubTranTechProd_nonmotor_USA: service output of non-motorized transportation technologies
L154.out_mpkm_state_trn_nonmotor_Yh %>%
filter(year %in% MODEL_BASE_YEARS) %>%
mutate(calOutputValue = round(value, digits = energy.DIGITS_MPKM),
region = state, tranSubsector = mode) %>%
left_join_error_no_match(A54.globaltech_nonmotor, by = "tranSubsector") %>%
mutate(stub.technology = technology) %>%
# There is no need to match shareweights to the calOutputValue because no region should ever have a 0 here
select(LEVEL2_DATA_NAMES[["StubTranTech"]], year, calOutputValue) ->
L254.StubTranTechProd_nonmotor_USA
# L254.StubTranTechCalInput_passthru_USA: calibrated input of passthrough technologies
# trn_pass, trn_pass_road, trn_pass_road_LDV, trn_freight
# The transportation structure is heavily nested.
# The GCAM structure of sector/subsector/technology only allows two levels of nesting within any sector,
# but a technology of one sector (e.g., trn_pass) can consume the output of another "sector" (e.g., trn_pass_road)
# that is really just used to represent lower nesting levels of that first, or parent, sector. In the
# transportation sector, each lower-level nesting "sector" is named by appending a string to the parent sector.
# So, trn_pass contains trn_pass_road which has trn_pass_road_LDV which has trn_pass_road_LDV_4W. Each of the links
# between any two of those sectors is done with a pass-through technology within the parent sector that consumes
# the output of the child sector. The technology is called a "pass-through" because it (generally) only consumes
# the output of the child "sector" without making any changes to it. There's an additional complication in the
# transportation sector: the pass-through technologies are normal, standard GCAM technologies, not "tranTechnologies"
# which have different parameters read in, and perform a bunch of hard-wired unit conversions between inputs and outputs.
# First, need to calculate the service output for all tranTechnologies
# calInput * loadFactor * unit_conversion / (coef * unit conversion)
L254.StubTranTechCalInput_USA %>%
left_join_error_no_match(L254.StubTranTechLoadFactor_USA %>% filter(sce=="CORE"),
by = c("region", "supplysector", "tranSubsector", "stub.technology", "year","sce")) %>%
left_join_error_no_match(L254.StubTranTechCoef_USA%>% filter(sce=="CORE"),
by = c("region", "supplysector", "tranSubsector", "stub.technology", "year", "minicam.energy.input","sce")) %>%
mutate(output = round(calibrated.value * loadFactor * CONV_EJ_GJ / (coefficient * CONV_BTU_KJ),
digits = gcamusa.DIGITS_TRNUSA_DEFAULT)) ->
L254.StubTranTechOutput_USA
# The next step is to calculate the aggregated outputs by supplysector
# Outputs of certain supplysectors are inputs for the passthrough technologies
L254.StubTranTechOutput_USA %>%
filter(sce=="CORE") %>%
group_by(region, year, supplysector) %>%
summarise(output_agg = sum(output)) %>%
ungroup() ->
L254.StubTranTechOutput_USA_agg
# Write all possible pass-through technologies to all regions
A54.globaltech_passthru %>%
repeat_add_columns(tibble(year = MODEL_BASE_YEARS)) %>%
write_to_all_states(names = c(names(.), "region")) %>%
select(region, supplysector, tranSubsector, stub.technology = technology, year, minicam.energy.input) %>%
# Subset only the passthrough technologies that are applicable in each region
semi_join(L254.StubTranTech_passthru_USA,
by = c("region", "supplysector", "tranSubsector", "stub.technology")) %>%
# Match in outputs of supplysectors that are inputs for the passthrough technologies
left_join(L254.StubTranTechOutput_USA_agg,
by = c("region", "year", "minicam.energy.input" = "supplysector")) %>%
# Some of the technologies are sub-totals, assign zero value now, will be calculated below
replace_na(list(output_agg = 0)) %>%
# Arrange input sectors so that sub-total sector is behind the subsectors
arrange(dplyr::desc(minicam.energy.input)) %>%
group_by(region, year) %>%
# Calculate the cumulative for sub-total sector
mutate(output_cum = cumsum(output_agg)) %>%
ungroup() ->
L254.StubTranTechCalInput_passthru_USA_cum
# Prepare a list of the supplysector in the passthrough input table to filter the sub-total sectors
LIST_supplysector <- unique(L254.StubTranTechCalInput_passthru_USA_cum$supplysector)
L254.StubTranTechCalInput_passthru_USA_cum %>%
# Use the cumulative value for sub-total sectors
mutate(calibrated.value = if_else(minicam.energy.input %in% LIST_supplysector,
output_cum, output_agg)) %>%
mutate(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[["StubTranTechCalInput"]]) ->
L254.StubTranTechCalInput_passthru_USA
# L254.BaseService_trn_USA: base-year service output of transportation final demand
L254.StubTranTechOutput_USA %>%
select(LEVEL2_DATA_NAMES[["StubTranTech"]], year, base.service = output) %>%
bind_rows(L254.StubTranTechProd_nonmotor_USA %>%
select(LEVEL2_DATA_NAMES[["StubTranTech"]], year, base.service = calOutputValue)) %>%
left_join_error_no_match(select(A54.sector, supplysector, energy.final.demand), by = "supplysector") %>%
group_by(region, energy.final.demand, year) %>%
summarise(base.service = sum(base.service)) %>%
ungroup ->
L254.BaseService_trn_USA
# Produce outputs
L254.DeleteSupplysector_USAtrn %>%
add_title("Delect transportation supply sectors of the full USA region") %>%
add_units("NA") %>%
add_comments("Delect transportation supply sectors of the full USA region") %>%
add_legacy_name("L254.DeleteSupplysector_USAtrn") %>%
add_precursors("L254.Supplysector_trn") ->
L254.DeleteSupplysector_USAtrn
L254.DeleteFinalDemand_USAtrn %>%
add_title("Delete energy final demand sectors of the full USA region") %>%
add_units("NA") %>%
add_comments("Delete energy final demand sectors of the full USA region") %>%
add_legacy_name("L254.DeleteFinalDemand_USAtrn") %>%
add_precursors("L254.PerCapitaBased_trn") ->
L254.DeleteFinalDemand_USAtrn
L254.Supplysector_trn_USA %>%
add_title("Supply sector information for transportation sector in the US states") %>%
add_units("Unitless") %>%
add_comments("The same USA region values are repeated for each state") %>%
add_legacy_name("L254.Supplysector_trn_USA") %>%
add_precursors("gcam-usa/states_subregions",
"L254.Supplysector_trn") ->
L254.Supplysector_trn_USA
L254.FinalEnergyKeyword_trn_USA %>%
add_title("Supply sector final energy keywords for transportation sector in the US states") %>%
add_units("NA") %>%
add_comments("The same USA region values are repeated for each state") %>%
add_legacy_name("L254.FinalEnergyKeyword_trn_USA") %>%
add_precursors("gcam-usa/states_subregions",
"L254.FinalEnergyKeyword_trn") ->
L254.FinalEnergyKeyword_trn_USA
L254.tranSubsectorLogit_USA %>%
add_title("Subsector logit exponents of transportation sector in the US states") %>%
add_units("Unitless") %>%
add_comments("The same USA region values are repeated for each state") %>%
add_legacy_name("L254.tranSubsectorLogit_USA") %>%
add_precursors("gcam-usa/states_subregions",
"L254.tranSubsectorLogit") ->
L254.tranSubsectorLogit_USA
L254.tranSubsectorShrwtFllt_USA %>%
add_title("Subsector shareweights of transportation sector in the US states") %>%
add_units("Unitless") %>%
add_comments("The same USA region values are repeated for each state") %>%
add_legacy_name("L254.tranSubsectorShrwtFllt_USA") %>%
add_precursors("gcam-usa/states_subregions",
"L254.tranSubsectorShrwtFllt") ->
L254.tranSubsectorShrwtFllt_USA
L254.tranSubsectorInterp_USA %>%
add_title("Temporal subsector shareweight interpolation of transportation sector in the US states") %>%
add_units("Unitless") %>%
add_comments("The same USA region values are repeated for each state") %>%
add_legacy_name("L254.tranSubsectorInterp_USA") %>%
add_precursors("gcam-usa/states_subregions",
"L254.tranSubsectorInterp") ->
L254.tranSubsectorInterp_USA
L254.tranSubsectorSpeed_USA %>%
add_title("Speeds of transportation modes (not including pass-through sectors) in the US states") %>%
add_units("km / hr") %>%
add_comments("The same USA region values are repeated for each state") %>%
add_legacy_name("L254.tranSubsectorSpeed_USA") %>%
add_precursors("gcam-usa/states_subregions",
"L254.tranSubsectorSpeed") ->
L254.tranSubsectorSpeed_USA
L254.tranSubsectorSpeed_passthru_USA %>%
add_title("Speeds of pass-through transportation subsectors in the US states") %>%
add_units("km / hr") %>%
add_comments("The same USA region values are repeated for each state") %>%
add_legacy_name("L254.tranSubsectorSpeed_passthru_USA") %>%
add_precursors("gcam-usa/states_subregions",
"L254.tranSubsectorSpeed_passthru") ->
L254.tranSubsectorSpeed_passthru_USA
L254.tranSubsectorSpeed_noVOTT_USA %>%
add_title("Speeds of transportation subsectors whose time value is not considered in the US states") %>%
add_units("km / hr") %>%
add_comments("The same USA region values are repeated for each state") %>%
add_legacy_name("L254.tranSubsectorSpeed_noVOTT_USA") %>%
add_precursors("gcam-usa/states_subregions",
"L254.tranSubsectorSpeed_noVOTT") ->
L254.tranSubsectorSpeed_noVOTT_USA
L254.tranSubsectorSpeed_nonmotor_USA %>%
add_title("Speeds of non-motorized transportation subsectors in the US states") %>%
add_units("km / hr") %>%
add_comments("The same USA region values are repeated for each state") %>%
add_legacy_name("L254.tranSubsectorSpeed_nonmotor_USA") %>%
add_precursors("gcam-usa/states_subregions",
"L254.tranSubsectorSpeed_nonmotor") ->
L254.tranSubsectorSpeed_nonmotor_USA
L254.tranSubsectorVOTT_USA %>%
add_title("descriptive title of data") %>%
add_units("units") %>%
add_comments("The same USA region values are repeated for each state") %>%
add_legacy_name("L254.tranSubsectorVOTT_USA") %>%
add_precursors("gcam-usa/states_subregions",
"L254.tranSubsectorVOTT") ->
L254.tranSubsectorVOTT_USA
L254.tranSubsectorFuelPref_USA %>%
add_title("Subsector (fuel) preferences elasticity that are tied to GDP in the US states") %>%
add_units("Unitless") %>%
add_comments("The same USA region values are repeated for each state") %>%
add_comments("Fuel preferences are unrelated to time value") %>%
add_legacy_name("L254.tranSubsectorFuelPref_USA") %>%
add_precursors("gcam-usa/states_subregions",
"L254.tranSubsectorFuelPref") ->
L254.tranSubsectorFuelPref_USA
L254.StubTranTech_USA %>%
add_title("Transportation stub technologies in the US states") %>%
add_units("NA") %>%
add_comments("The same USA region values are repeated for each state") %>%
add_legacy_name("L254.StubTranTech_USA") %>%
add_precursors("gcam-usa/states_subregions",
"L254.StubTranTech") ->
L254.StubTranTech_USA
L254.StubTranTech_passthru_USA %>%
add_title("Transportation stub technologies for passthrough sectors in the US states") %>%
add_units("NA") %>%
add_comments("The same USA region values are repeated for each state") %>%
add_legacy_name(" L254.StubTranTech_passthru_USA") %>%
add_precursors("gcam-usa/states_subregions",
"L254.StubTech_passthru") ->
L254.StubTranTech_passthru_USA
L254.StubTranTech_nonmotor_USA %>%
add_title("Transportation stub technologies for non-motorized subsectors in the US states") %>%
add_units("NA") %>%
add_comments("The same USA region values are repeated for each state") %>%
add_legacy_name("L254.StubTranTech_nonmotor_USA") %>%
add_precursors("gcam-usa/states_subregions",
"L254.StubTech_nonmotor") ->
L254.StubTranTech_nonmotor_USA
L254.StubTranTechLoadFactor_USA %>%
add_title("Load factors of transportation stub technologies in the US states") %>%
add_units("person/vehicle and tonnes/vehicle") %>%
add_comments("The same USA region values are repeated for each state") %>%
add_legacy_name("L254.StubTranTechLoadFactor_USA") %>%
add_precursors("gcam-usa/states_subregions",
"L254.StubTranTechLoadFactor") ->
L254.StubTranTechLoadFactor_USA
L254.StubTranTechCost_USA %>%
add_title("Costs of transportation stub technologies in the US states") %>%
add_units("$1990USD / vkm") %>%
add_comments("The same USA region values are repeated for each state") %>%
add_legacy_name("L254.StubTranTechCost_USA") %>%
add_precursors("gcam-usa/states_subregions",
"L254.StubTranTechCost") ->
L254.StubTranTechCost_USA
L254.StubTranTechCoef_USA %>%
add_title("Coefficients of transportation stub technologies in the US states") %>%
add_units("BTU / vkm") %>%
add_comments("The same USA region values are repeated for each state") %>%
add_comments("Re-set electricity consumed at the state markets") %>%
add_legacy_name("L254.StubTranTechCoef_USA") %>%
add_precursors("gcam-usa/states_subregions",
"L254.StubTranTechCoef") ->
L254.StubTranTechCoef_USA
L254.PerCapitaBased_trn_USA %>%
add_title("Per-capita based flag for transportation final demand in the US states") %>%
add_units("NA") %>%
add_comments("The same USA region values are repeated for each state") %>%
add_legacy_name("L254.PerCapitaBased_trn_USA") %>%
add_precursors("gcam-usa/states_subregions",
"L254.PerCapitaBased_trn") ->
L254.PerCapitaBased_trn_USA
L254.PriceElasticity_trn_USA %>%
add_title("Price elasticity of transportation final demand in the US states") %>%
add_units("Unitless") %>%
add_comments("The same USA region values are repeated for each state") %>%
add_legacy_name("L254.PriceElasticity_trn_USA") %>%
add_precursors("gcam-usa/states_subregions",
"L254.PriceElasticity_trn") ->
L254.PriceElasticity_trn_USA
L254.IncomeElasticity_trn_USA %>%
add_title("Income elasticity of transportation final demand in the US states") %>%
add_units("Unitless") %>%
add_comments("The same USA region values are repeated for each state") %>%
add_legacy_name("L254.IncomeElasticity_trn_USA") %>%
add_precursors("gcam-usa/states_subregions",
"L254.IncomeElasticity_trn") ->
L254.IncomeElasticity_trn_USA
L254.StubTranTechCalInput_USA %>%
add_title("Calibrated energy consumption by all transportation stub technologies in the US states") %>%
add_units("EJ") %>%
add_comments("Set calibration values for those technologies that do not exist in some base years as zero") %>%
add_legacy_name("L254.StubTranTechCalInput_USA") %>%
same_precursors_as("L254.StubTranTechCoef_USA") %>%
add_precursors("L154.in_EJ_state_trn_m_sz_tech_F",
"energy/mappings/UCD_techs",
"energy/mappings/UCD_techs_revised") ->
L254.StubTranTechCalInput_USA
L254.StubTranTechProd_nonmotor_USA %>%
add_title("Calibrated service output of non-motorized transportation technologies in the US states") %>%
add_units("Million pass-km") %>%
add_comments("Not match shareweights to the calOutputValue because no region should ever have a zero here") %>%
add_legacy_name("L254.StubTranTechProd_nonmotor_USA") %>%
add_precursors("L154.out_mpkm_state_trn_nonmotor_Yh",
"energy/A54.globaltech_nonmotor") ->
L254.StubTranTechProd_nonmotor_USA
L254.StubTranTechCalInput_passthru_USA %>%
add_title("Calibrated energy consumption of transportation passthrough technologies in the US states") %>%
add_units("EJ") %>%
add_comments("Use outputs of the supplysectors that are inputs for passthrough technologies") %>%
add_comments("Outputs of all motorized technologies are calculated as calInput * loadFactor / coefficient") %>%
add_legacy_name("L254.StubTranTechCalInput_passthru_USA") %>%
same_precursors_as("L254.StubTranTechCalInput_USA") %>%
same_precursors_as("L254.StubTranTechLoadFactor_USA") %>%
same_precursors_as("L254.StubTranTechCoef_USA") %>%
same_precursors_as("L254.StubTranTech_passthru_USA") %>%
add_precursors("energy/A54.globaltech_passthru") ->
L254.StubTranTechCalInput_passthru_USA
L254.BaseService_trn_USA %>%
add_title("Base-year service output of transportation final demand") %>%
add_units("Million pass-km and million ton-km") %>%
add_comments("Service outputs of all motorized technologies are calculated as calInput * loadFactor / coefficient") %>%
add_comments("Combine with service output of non-motorized transportation technologies") %>%
add_legacy_name("L254.BaseService_trn_USA") %>%
same_precursors_as("L254.StubTranTechCalInput_USA") %>%
same_precursors_as("L254.StubTranTechLoadFactor_USA") %>%
same_precursors_as("L254.StubTranTechCoef_USA") %>%
same_precursors_as("L254.StubTranTechProd_nonmotor_USA") %>%
add_precursors("energy/A54.sector",
"energy/mappings/UCD_size_class_revisions") ->
L254.BaseService_trn_USA
L254.StubTranTechOutput_USA %>%
add_title("service output for all tranTechnologies") %>%
add_units("Million pass-km and million ton-km") %>%
add_comments("Service outputs of all motorized technologies are calculated as calInput * loadFactor / coefficient") %>%
add_legacy_name("L254.StubTranTechOutput_USA") %>%
same_precursors_as("L254.StubTranTechCalInput_USA") %>%
same_precursors_as("L254.StubTranTechLoadFactor_USA") %>%
same_precursors_as("L254.StubTranTechCoef_USA") ->
L254.StubTranTechOutput_USA
return_data(L254.DeleteSupplysector_USAtrn, L254.DeleteFinalDemand_USAtrn,
L254.Supplysector_trn_USA,
L254.FinalEnergyKeyword_trn_USA,
L254.tranSubsectorLogit_USA,
L254.tranSubsectorShrwtFllt_USA,
L254.tranSubsectorInterp_USA,
L254.tranSubsectorSpeed_USA,
L254.tranSubsectorSpeed_passthru_USA,
L254.tranSubsectorSpeed_noVOTT_USA,
L254.tranSubsectorSpeed_nonmotor_USA,
L254.tranSubsectorVOTT_USA,
L254.tranSubsectorFuelPref_USA,
L254.StubTranTech_USA,
L254.StubTranTech_passthru_USA,
L254.StubTranTech_nonmotor_USA,
L254.StubTranTechLoadFactor_USA,
L254.StubTranTechCost_USA,
L254.StubTranTechCoef_USA,
L254.PerCapitaBased_trn_USA,
L254.PriceElasticity_trn_USA,
L254.IncomeElasticity_trn_USA,
L254.StubTranTechCalInput_USA, L254.StubTranTechProd_nonmotor_USA,
L254.StubTranTechCalInput_passthru_USA, L254.BaseService_trn_USA,
L254.StubTranTechOutput_USA)
} else {
stop("Unknown command")
}
}
JGCRI/gcamdata documentation built on March 21, 2023, 2:19 a.m.
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Defines functions module_gcamusa_L254.transportation_USA
Documented in module_gcamusa_L254.transportation_USA
# Copyright 2019 Battelle Memorial Institute; see the LICENSE file.
#' module_gcamusa_L254.transportation_USA
#'
#' Generates GCAM-USA model inputs for transportation sector by states.
#'
#' @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{L254.DeleteSupplysector_USAtrn}, \code{L254.DeleteFinalDemand_USAtrn},
#' \code{L254.Supplysector_trn_USA}, \code{L254.FinalEnergyKeyword_trn_USA}, \code{L254.tranSubsectorLogit_USA},
#' \code{L254.tranSubsectorShrwtFllt_USA}, \code{L254.tranSubsectorInterp_USA}, \code{L254.tranSubsectorSpeed_USA},
#' \code{L254.tranSubsectorSpeed_passthru_USA}, \code{L254.tranSubsectorSpeed_noVOTT_USA},
#' \code{L254.tranSubsectorSpeed_nonmotor_USA}, \code{L254.tranSubsectorVOTT_USA}, \code{L254.tranSubsectorFuelPref_USA},
#' \code{L254.StubTranTech_USA}, \code{L254.StubTranTech_passthru_USA}, \code{L254.StubTranTech_nonmotor_USA},
#' \code{L254.StubTranTechLoadFactor_USA}, \code{L254.StubTranTechCost_USA}, \code{L254.StubTranTechCoef_USA},
#' \code{L254.PerCapitaBased_trn_USA}, \code{L254.PriceElasticity_trn_USA}, \code{L254.IncomeElasticity_trn_USA},
#' \code{L254.StubTranTechCalInput_USA}, \code{L254.StubTranTechProd_nonmotor_USA}, \code{L254.StubTranTechCalInput_passthru_USA},
#' \code{L254.BaseService_trn_USA}.
#' The corresponding file in the original data system was \code{L254.transportation_USA.R} (gcam-usa level2).
#' @details This chunk generates input files for transportation sector with generic information for supplysector,
#' subsector and technologies, as well as calibrated inputs and outputs by the US states.
#' @note The transportation structure is heavily nested. The GCAM structure of sector/subsector/technology only
#' allows two levels of nesting within any sector, but a technology of one sector (e.g., trn_pass) can consume the
#' output of another "sector" (e.g., trn_pass_road) that is really just used to represent lower nesting levels of
#' that first, or parent, sector. In the transportation sector, each lower-level nesting "sector" is named by
#' appending a string to the parent sector. So, \code{trn_pass} contains \code{trn_pass_road} which has
#' \code{trn_pass_road_LDV} which has \code{trn_pass_road_LDV_4W}. Each of the links between any two of those sectors
#' is done with a pass-through technology within the parent sector that consumes the output of the child sector.
#' The technology is called a "pass-through" because it (generally) only consumes the output of the child "sector"
#' without making any changes to it. There's an additional complication in the transportation sector, that the
#' pass-through technologies are normal, standard GCAM technologies, not "tranTechnologies" which have different
#' parameters read in, and perform a bunch of hard-wired unit conversions between inputs and outputs.
#' @importFrom assertthat assert_that
#' @importFrom dplyr arrange bind_rows filter if_else group_by left_join mutate select semi_join summarise
#' @importFrom tidyr replace_na
#' @author RC Oct 2017
module_gcamusa_L254.transportation_USA <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "energy/mappings/UCD_techs",
FILE = "energy/A54.globaltech_nonmotor",
FILE = "energy/A54.globaltech_passthru",
FILE = "energy/A54.sector",
FILE= "energy/mappings/UCD_size_class_revisions",
FILE= "energy/mappings/UCD_techs_revised",
FILE = "gcam-usa/states_subregions",
"L254.Supplysector_trn",
"L254.FinalEnergyKeyword_trn",
"L254.tranSubsectorLogit",
"L254.tranSubsectorShrwtFllt",
"L254.tranSubsectorInterp",
"L254.tranSubsectorSpeed",
"L254.tranSubsectorSpeed_passthru",
"L254.tranSubsectorSpeed_noVOTT",
"L254.tranSubsectorSpeed_nonmotor",
"L254.tranSubsectorVOTT",
"L254.tranSubsectorFuelPref",
"L254.StubTranTech",
"L254.StubTech_passthru",
"L254.StubTech_nonmotor",
"L254.StubTranTechLoadFactor",
"L254.StubTranTechCost",
"L254.StubTranTechCoef",
"L254.PerCapitaBased_trn",
"L254.PriceElasticity_trn",
"L254.IncomeElasticity_trn",
"L154.in_EJ_state_trn_m_sz_tech_F",
"L154.out_mpkm_state_trn_nonmotor_Yh"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L254.DeleteSupplysector_USAtrn",
"L254.DeleteFinalDemand_USAtrn",
"L254.Supplysector_trn_USA",
"L254.FinalEnergyKeyword_trn_USA",
"L254.tranSubsectorLogit_USA",
"L254.tranSubsectorShrwtFllt_USA",
"L254.tranSubsectorInterp_USA",
"L254.tranSubsectorSpeed_USA",
"L254.tranSubsectorSpeed_passthru_USA",
"L254.tranSubsectorSpeed_noVOTT_USA",
"L254.tranSubsectorSpeed_nonmotor_USA",
"L254.tranSubsectorVOTT_USA",
"L254.tranSubsectorFuelPref_USA",
"L254.StubTranTech_USA",
"L254.StubTranTech_passthru_USA",
"L254.StubTranTech_nonmotor_USA",
"L254.StubTranTechLoadFactor_USA",
"L254.StubTranTechCost_USA",
"L254.StubTranTechCoef_USA",
"L254.PerCapitaBased_trn_USA",
"L254.PriceElasticity_trn_USA",
"L254.IncomeElasticity_trn_USA",
"L254.StubTranTechCalInput_USA",
"L254.StubTranTechProd_nonmotor_USA",
"L254.StubTranTechCalInput_passthru_USA",
"L254.BaseService_trn_USA",
"L254.StubTranTechOutput_USA"))
} else if(command == driver.MAKE) {
all_data <- list(...)[[1]]
# Silence package notes
region <- supplysector <- technology <- minicam.energy.input <- year <- value <- coefficient <-
UCD_fuel <- UCD_sector <- UCD_technology <- calibrated.value <- coefficient <- loadFactor <-
size.class <- state <- tranSubsector <- tranTechnology <- calibrated.value <- stub.technology <-
output <- output_agg <- output_cum <- share.weight <- subs.share.weight <- share.weight.year <-
tech.share.weight <- calOutputValue <- energy.final.demand <- base.service <- year.fillout <-
fuelprefElasticity <- income.elasticity <- UCD_region <- sce <- . <- NULL
# Load required inputs
#kbn 2019-10-14 Making same changes here for UCD techs that we made in L254:
Size_class_New<- get_data(all_data, "energy/mappings/UCD_size_class_revisions",strip_attributes = TRUE) %>%
select(-UCD_region) %>%
distinct()
UCD_techs <- get_data(all_data, "energy/mappings/UCD_techs",strip_attributes = TRUE) # Mapping file of transportation technology from the UC Davis report (Mishra et al. 2013)
if (toString(energy.TRAN_UCD_MODE)=='rev.mode'){
UCD_techs <- get_data(all_data, "energy/mappings/UCD_techs_revised")
UCD_techs<-UCD_techs %>%
inner_join(Size_class_New, by=c("mode","size.class"))%>%
select(-mode,-size.class)%>%
distinct()
colnames(UCD_techs)[colnames(UCD_techs)=='rev_size.class']<-'size.class'
colnames(UCD_techs)[colnames(UCD_techs)=='rev.mode']<-'mode'
}
A54.globaltech_nonmotor <- get_data(all_data, "energy/A54.globaltech_nonmotor",strip_attributes = TRUE)
A54.globaltech_passthru <- get_data(all_data, "energy/A54.globaltech_passthru",strip_attributes = TRUE)
A54.sector <- get_data(all_data, "energy/A54.sector",strip_attributes = TRUE)
states_subregions <- get_data(all_data, "gcam-usa/states_subregions",strip_attributes = TRUE)
#kbn 2020-02-27 Making changes to select the CORE scenario for transportation in GCAM USA
L254.Supplysector_trn <- get_data(all_data, "L254.Supplysector_trn",strip_attributes = TRUE) %>% filter(sce %in% c("CORE"))
L254.FinalEnergyKeyword_trn <- get_data(all_data, "L254.FinalEnergyKeyword_trn",strip_attributes = TRUE)%>% filter(sce %in% c("CORE"))
L254.tranSubsectorLogit <- get_data(all_data, "L254.tranSubsectorLogit",strip_attributes = TRUE)%>% filter(sce %in% c("CORE"))
L254.tranSubsectorShrwtFllt <- get_data(all_data, "L254.tranSubsectorShrwtFllt",strip_attributes = TRUE)%>% filter(sce %in% c("CORE"))
L254.tranSubsectorInterp <- get_data(all_data, "L254.tranSubsectorInterp",strip_attributes = TRUE)%>% filter(sce %in% c("CORE"))
L254.tranSubsectorSpeed <- get_data(all_data, "L254.tranSubsectorSpeed",strip_attributes = TRUE)%>% filter(sce %in% c("CORE"))
L254.tranSubsectorSpeed_passthru <- get_data(all_data, "L254.tranSubsectorSpeed_passthru",strip_attributes = TRUE)%>% filter(sce %in% c("CORE"))
L254.tranSubsectorSpeed_noVOTT <- get_data(all_data, "L254.tranSubsectorSpeed_noVOTT",strip_attributes = TRUE)%>% filter(sce %in% c("CORE"))
L254.tranSubsectorSpeed_nonmotor <- get_data(all_data, "L254.tranSubsectorSpeed_nonmotor",strip_attributes = TRUE)%>% filter(sce %in% c("CORE"))
L254.tranSubsectorVOTT <- get_data(all_data, "L254.tranSubsectorVOTT",strip_attributes = TRUE)%>% filter(sce %in% c("CORE"))
L254.tranSubsectorFuelPref <- get_data(all_data, "L254.tranSubsectorFuelPref",strip_attributes = TRUE)%>% filter(sce %in% c("CORE"))
L254.StubTranTech <- get_data(all_data, "L254.StubTranTech",strip_attributes = TRUE)%>% filter(sce %in% c("CORE"))
L254.StubTech_passthru <- get_data(all_data, "L254.StubTech_passthru",strip_attributes = TRUE)%>% filter(sce %in% c("CORE"))
L254.StubTech_nonmotor <- get_data(all_data, "L254.StubTech_nonmotor",strip_attributes = TRUE)%>% filter(sce %in% c("CORE"))
L254.StubTranTechLoadFactor <- get_data(all_data, "L254.StubTranTechLoadFactor",strip_attributes = TRUE)%>% filter(sce %in% c("CORE"))
L254.StubTranTechCost <- get_data(all_data, "L254.StubTranTechCost",strip_attributes = TRUE)%>% filter(sce %in% c("CORE"))
L254.StubTranTechCoef <- get_data(all_data, "L254.StubTranTechCoef",strip_attributes = TRUE)%>% filter(sce %in% c("CORE"))
L254.PerCapitaBased_trn <- get_data(all_data, "L254.PerCapitaBased_trn",strip_attributes = TRUE)%>% filter(sce %in% c("CORE"))
L254.PriceElasticity_trn <- get_data(all_data, "L254.PriceElasticity_trn",strip_attributes = TRUE)%>% filter(sce %in% c("CORE"))
L254.IncomeElasticity_trn <- get_data(all_data, "L254.IncomeElasticity_trn",strip_attributes = TRUE)%>% filter(sce %in% c("CORE"))
L154.in_EJ_state_trn_m_sz_tech_F <- get_data(all_data, "L154.in_EJ_state_trn_m_sz_tech_F",strip_attributes = TRUE)
L154.out_mpkm_state_trn_nonmotor_Yh <- get_data(all_data, "L154.out_mpkm_state_trn_nonmotor_Yh",strip_attributes = TRUE)
# Need to delete the transportation sector in the USA region (energy-final-demands and supplysectors)
# L254.DeleteSupplysector_USAtrn: Delete transportation supplysectors of the USA region
L254.Supplysector_trn %>%
mutate(region = region) %>% # strip off attributes like title, etc.
filter(region == gcam.USA_REGION) %>%
select(region, supplysector) ->
L254.DeleteSupplysector_USAtrn
# L254.DeleteFinalDemand_USAtrn: Delete energy final demand sectors of the USA region
L254.PerCapitaBased_trn %>%
mutate(region = region) %>% # strip off attributes like title, etc.
filter(region == gcam.USA_REGION) %>%
select(LEVEL2_DATA_NAMES[["EnergyFinalDemand"]],sce) ->
L254.DeleteFinalDemand_USAtrn
# Process tables at the USA region level to the states level.
# All tables for which processing is identical are done by a function.
# This applies to the supplysectors, subsectors, and stub tech characteristics of the states.
process_USA_to_states <- function(data) {
state <- region <- grid_region <- subsector <- market.name <-
minicam.energy.input <- NULL # silence package check notes
data_new <- data %>%
filter(region == gcam.USA_REGION) %>%
write_to_all_states(names = c(names(data), "region"))
# Re-set markets from USA to grid region, if the minicam.energy.input is considered a regional fuel market
if("market.name" %in% names(data_new)) {
data_new <- data_new %>%
left_join_error_no_match(select(states_subregions, state, grid_region), by = c("region" = "state")) %>%
mutate(market.name = replace(market.name, minicam.energy.input %in% gcamusa.REGIONAL_FUEL_MARKETS,
grid_region[minicam.energy.input %in% gcamusa.REGIONAL_FUEL_MARKETS])) %>%
select(-grid_region)
}
# For fuels consumed from state markets, the market.name is the region
if("market.name" %in% names(data_new)) {
data_new <- data_new %>%
mutate(market.name = replace(market.name, minicam.energy.input %in% gcamusa.STATE_FUEL_MARKETS,
region[minicam.energy.input %in% gcamusa.STATE_FUEL_MARKETS]))
}
data_new
}
process_USA_to_states(L254.Supplysector_trn) -> L254.Supplysector_trn_USA
process_USA_to_states(L254.FinalEnergyKeyword_trn) -> L254.FinalEnergyKeyword_trn_USA
process_USA_to_states(L254.tranSubsectorLogit) -> L254.tranSubsectorLogit_USA
process_USA_to_states(L254.tranSubsectorShrwtFllt) -> L254.tranSubsectorShrwtFllt_USA
process_USA_to_states(L254.tranSubsectorInterp) -> L254.tranSubsectorInterp_USA
process_USA_to_states(L254.tranSubsectorSpeed) %>%
# parent file (L254.tranSubsectorSpeed) contains duplicate values... remove these
distinct()-> L254.tranSubsectorSpeed_USA
process_USA_to_states(L254.tranSubsectorSpeed_passthru) -> L254.tranSubsectorSpeed_passthru_USA
process_USA_to_states(L254.tranSubsectorSpeed_noVOTT) -> L254.tranSubsectorSpeed_noVOTT_USA
process_USA_to_states(L254.tranSubsectorSpeed_nonmotor) -> L254.tranSubsectorSpeed_nonmotor_USA
process_USA_to_states(L254.tranSubsectorVOTT) -> L254.tranSubsectorVOTT_USA
process_USA_to_states(L254.tranSubsectorFuelPref) -> L254.tranSubsectorFuelPref_USA
process_USA_to_states(L254.StubTranTech) -> L254.StubTranTech_USA
process_USA_to_states(L254.StubTech_passthru) -> L254.StubTranTech_passthru_USA
process_USA_to_states(L254.StubTech_nonmotor) -> L254.StubTranTech_nonmotor_USA
process_USA_to_states(L254.StubTranTechLoadFactor) -> L254.StubTranTechLoadFactor_USA
process_USA_to_states(L254.StubTranTechCost) -> L254.StubTranTechCost_USA
L254.StubTranTechCoef %>%
mutate(coefficient = round(coefficient, digits = gcamusa.DIGITS_TRNUSA_DEFAULT)) %>%
process_USA_to_states ->
L254.StubTranTechCoef_USA
process_USA_to_states(L254.PerCapitaBased_trn) -> L254.PerCapitaBased_trn_USA
process_USA_to_states(L254.PriceElasticity_trn) -> L254.PriceElasticity_trn_USA
process_USA_to_states(L254.IncomeElasticity_trn) -> L254.IncomeElasticity_trn_USA
# Calibration
# L254.StubTranTechCalInput_USA: calibrated energy consumption by all technologies
#kbn 2019-10-14 Switching to left_join_keep_first
L154.in_EJ_state_trn_m_sz_tech_F %>%
mutate(sce=paste0("CORE")) %>%
filter(year %in% MODEL_BASE_YEARS) %>%
mutate(calibrated.value = round(value, digits = energy.DIGITS_CALOUTPUT),
region = state) %>%
left_join_keep_first_only(select(UCD_techs, UCD_sector, mode, size.class, UCD_technology, UCD_fuel,
supplysector, tranSubsector, stub.technology = tranTechnology, minicam.energy.input),
by = c("UCD_sector", "mode", "size.class", "UCD_technology", "UCD_fuel")) %>%
select(LEVEL2_DATA_NAMES[["StubTranTech"]], year, minicam.energy.input, calibrated.value,sce) ->
L254.StubTranTechCalInput_USA
# NOTE: NEED TO WRITE THIS OUT FOR ALL TECHNOLOGIES, NOT JUST THOSE THAT EXIST IN SOME BASE YEARS.
# Model may make up calibration values otherwise.
L254.StubTranTechCoef_USA %>%
filter(sce=="CORE") %>%
filter(year %in% MODEL_BASE_YEARS) %>%
select(c(names(.)[names(.) %in% LEVEL2_DATA_NAMES[["StubTranTechCalInput"]]],sce)) %>%
left_join(L254.StubTranTechCalInput_USA,
by = c("region", "supplysector", "tranSubsector", "stub.technology", "year", "minicam.energy.input","sce")) %>%
# Set calibration values as zero for technolgies that do not exist in some base years
replace_na(list(calibrated.value = 0)) %>%
mutate(share.weight.year = year,
# Create the needed variables to use the function set_subsector_shrwt
subsector = tranSubsector, calOutputValue = calibrated.value) %>%
set_subsector_shrwt() %>%
mutate(tech.share.weight = if_else(calibrated.value > 0, 1, 0)) %>%
select(LEVEL2_DATA_NAMES[["StubTranTechCalInput"]],sce) ->
L254.StubTranTechCalInput_USA
# Non-motorized technologies
# L254.StubTranTechProd_nonmotor_USA: service output of non-motorized transportation technologies
L154.out_mpkm_state_trn_nonmotor_Yh %>%
filter(year %in% MODEL_BASE_YEARS) %>%
mutate(calOutputValue = round(value, digits = energy.DIGITS_MPKM),
region = state, tranSubsector = mode) %>%
left_join_error_no_match(A54.globaltech_nonmotor, by = "tranSubsector") %>%
mutate(stub.technology = technology) %>%
# There is no need to match shareweights to the calOutputValue because no region should ever have a 0 here
select(LEVEL2_DATA_NAMES[["StubTranTech"]], year, calOutputValue) ->
L254.StubTranTechProd_nonmotor_USA
# L254.StubTranTechCalInput_passthru_USA: calibrated input of passthrough technologies
# trn_pass, trn_pass_road, trn_pass_road_LDV, trn_freight
# The transportation structure is heavily nested.
# The GCAM structure of sector/subsector/technology only allows two levels of nesting within any sector,
# but a technology of one sector (e.g., trn_pass) can consume the output of another "sector" (e.g., trn_pass_road)
# that is really just used to represent lower nesting levels of that first, or parent, sector. In the
# transportation sector, each lower-level nesting "sector" is named by appending a string to the parent sector.
# So, trn_pass contains trn_pass_road which has trn_pass_road_LDV which has trn_pass_road_LDV_4W. Each of the links
# between any two of those sectors is done with a pass-through technology within the parent sector that consumes
# the output of the child sector. The technology is called a "pass-through" because it (generally) only consumes
# the output of the child "sector" without making any changes to it. There's an additional complication in the
# transportation sector: the pass-through technologies are normal, standard GCAM technologies, not "tranTechnologies"
# which have different parameters read in, and perform a bunch of hard-wired unit conversions between inputs and outputs.
# First, need to calculate the service output for all tranTechnologies
# calInput * loadFactor * unit_conversion / (coef * unit conversion)
L254.StubTranTechCalInput_USA %>%
left_join_error_no_match(L254.StubTranTechLoadFactor_USA %>% filter(sce=="CORE"),
by = c("region", "supplysector", "tranSubsector", "stub.technology", "year","sce")) %>%
left_join_error_no_match(L254.StubTranTechCoef_USA%>% filter(sce=="CORE"),
by = c("region", "supplysector", "tranSubsector", "stub.technology", "year", "minicam.energy.input","sce")) %>%
mutate(output = round(calibrated.value * loadFactor * CONV_EJ_GJ / (coefficient * CONV_BTU_KJ),
digits = gcamusa.DIGITS_TRNUSA_DEFAULT)) ->
L254.StubTranTechOutput_USA
# The next step is to calculate the aggregated outputs by supplysector
# Outputs of certain supplysectors are inputs for the passthrough technologies
L254.StubTranTechOutput_USA %>%
filter(sce=="CORE") %>%
group_by(region, year, supplysector) %>%
summarise(output_agg = sum(output)) %>%
ungroup() ->
L254.StubTranTechOutput_USA_agg
# Write all possible pass-through technologies to all regions
A54.globaltech_passthru %>%
repeat_add_columns(tibble(year = MODEL_BASE_YEARS)) %>%
write_to_all_states(names = c(names(.), "region")) %>%
select(region, supplysector, tranSubsector, stub.technology = technology, year, minicam.energy.input) %>%
# Subset only the passthrough technologies that are applicable in each region
semi_join(L254.StubTranTech_passthru_USA,
by = c("region", "supplysector", "tranSubsector", "stub.technology")) %>%
# Match in outputs of supplysectors that are inputs for the passthrough technologies
left_join(L254.StubTranTechOutput_USA_agg,
by = c("region", "year", "minicam.energy.input" = "supplysector")) %>%
# Some of the technologies are sub-totals, assign zero value now, will be calculated below
replace_na(list(output_agg = 0)) %>%
# Arrange input sectors so that sub-total sector is behind the subsectors
arrange(dplyr::desc(minicam.energy.input)) %>%
group_by(region, year) %>%
# Calculate the cumulative for sub-total sector
mutate(output_cum = cumsum(output_agg)) %>%
ungroup() ->
L254.StubTranTechCalInput_passthru_USA_cum
# Prepare a list of the supplysector in the passthrough input table to filter the sub-total sectors
LIST_supplysector <- unique(L254.StubTranTechCalInput_passthru_USA_cum$supplysector)
L254.StubTranTechCalInput_passthru_USA_cum %>%
# Use the cumulative value for sub-total sectors
mutate(calibrated.value = if_else(minicam.energy.input %in% LIST_supplysector,
output_cum, output_agg)) %>%
mutate(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[["StubTranTechCalInput"]]) ->
L254.StubTranTechCalInput_passthru_USA
# L254.BaseService_trn_USA: base-year service output of transportation final demand
L254.StubTranTechOutput_USA %>%
select(LEVEL2_DATA_NAMES[["StubTranTech"]], year, base.service = output) %>%
bind_rows(L254.StubTranTechProd_nonmotor_USA %>%
select(LEVEL2_DATA_NAMES[["StubTranTech"]], year, base.service = calOutputValue)) %>%
left_join_error_no_match(select(A54.sector, supplysector, energy.final.demand), by = "supplysector") %>%
group_by(region, energy.final.demand, year) %>%
summarise(base.service = sum(base.service)) %>%
ungroup ->
L254.BaseService_trn_USA
# Produce outputs
L254.DeleteSupplysector_USAtrn %>%
add_title("Delect transportation supply sectors of the full USA region") %>%
add_units("NA") %>%
add_comments("Delect transportation supply sectors of the full USA region") %>%
add_legacy_name("L254.DeleteSupplysector_USAtrn") %>%
add_precursors("L254.Supplysector_trn") ->
L254.DeleteSupplysector_USAtrn
L254.DeleteFinalDemand_USAtrn %>%
add_title("Delete energy final demand sectors of the full USA region") %>%
add_units("NA") %>%
add_comments("Delete energy final demand sectors of the full USA region") %>%
add_legacy_name("L254.DeleteFinalDemand_USAtrn") %>%
add_precursors("L254.PerCapitaBased_trn") ->
L254.DeleteFinalDemand_USAtrn
L254.Supplysector_trn_USA %>%
add_title("Supply sector information for transportation sector in the US states") %>%
add_units("Unitless") %>%
add_comments("The same USA region values are repeated for each state") %>%
add_legacy_name("L254.Supplysector_trn_USA") %>%
add_precursors("gcam-usa/states_subregions",
"L254.Supplysector_trn") ->
L254.Supplysector_trn_USA
L254.FinalEnergyKeyword_trn_USA %>%
add_title("Supply sector final energy keywords for transportation sector in the US states") %>%
add_units("NA") %>%
add_comments("The same USA region values are repeated for each state") %>%
add_legacy_name("L254.FinalEnergyKeyword_trn_USA") %>%
add_precursors("gcam-usa/states_subregions",
"L254.FinalEnergyKeyword_trn") ->
L254.FinalEnergyKeyword_trn_USA
L254.tranSubsectorLogit_USA %>%
add_title("Subsector logit exponents of transportation sector in the US states") %>%
add_units("Unitless") %>%
add_comments("The same USA region values are repeated for each state") %>%
add_legacy_name("L254.tranSubsectorLogit_USA") %>%
add_precursors("gcam-usa/states_subregions",
"L254.tranSubsectorLogit") ->
L254.tranSubsectorLogit_USA
L254.tranSubsectorShrwtFllt_USA %>%
add_title("Subsector shareweights of transportation sector in the US states") %>%
add_units("Unitless") %>%
add_comments("The same USA region values are repeated for each state") %>%
add_legacy_name("L254.tranSubsectorShrwtFllt_USA") %>%
add_precursors("gcam-usa/states_subregions",
"L254.tranSubsectorShrwtFllt") ->
L254.tranSubsectorShrwtFllt_USA
L254.tranSubsectorInterp_USA %>%
add_title("Temporal subsector shareweight interpolation of transportation sector in the US states") %>%
add_units("Unitless") %>%
add_comments("The same USA region values are repeated for each state") %>%
add_legacy_name("L254.tranSubsectorInterp_USA") %>%
add_precursors("gcam-usa/states_subregions",
"L254.tranSubsectorInterp") ->
L254.tranSubsectorInterp_USA
L254.tranSubsectorSpeed_USA %>%
add_title("Speeds of transportation modes (not including pass-through sectors) in the US states") %>%
add_units("km / hr") %>%
add_comments("The same USA region values are repeated for each state") %>%
add_legacy_name("L254.tranSubsectorSpeed_USA") %>%
add_precursors("gcam-usa/states_subregions",
"L254.tranSubsectorSpeed") ->
L254.tranSubsectorSpeed_USA
L254.tranSubsectorSpeed_passthru_USA %>%
add_title("Speeds of pass-through transportation subsectors in the US states") %>%
add_units("km / hr") %>%
add_comments("The same USA region values are repeated for each state") %>%
add_legacy_name("L254.tranSubsectorSpeed_passthru_USA") %>%
add_precursors("gcam-usa/states_subregions",
"L254.tranSubsectorSpeed_passthru") ->
L254.tranSubsectorSpeed_passthru_USA
L254.tranSubsectorSpeed_noVOTT_USA %>%
add_title("Speeds of transportation subsectors whose time value is not considered in the US states") %>%
add_units("km / hr") %>%
add_comments("The same USA region values are repeated for each state") %>%
add_legacy_name("L254.tranSubsectorSpeed_noVOTT_USA") %>%
add_precursors("gcam-usa/states_subregions",
"L254.tranSubsectorSpeed_noVOTT") ->
L254.tranSubsectorSpeed_noVOTT_USA
L254.tranSubsectorSpeed_nonmotor_USA %>%
add_title("Speeds of non-motorized transportation subsectors in the US states") %>%
add_units("km / hr") %>%
add_comments("The same USA region values are repeated for each state") %>%
add_legacy_name("L254.tranSubsectorSpeed_nonmotor_USA") %>%
add_precursors("gcam-usa/states_subregions",
"L254.tranSubsectorSpeed_nonmotor") ->
L254.tranSubsectorSpeed_nonmotor_USA
L254.tranSubsectorVOTT_USA %>%
add_title("descriptive title of data") %>%
add_units("units") %>%
add_comments("The same USA region values are repeated for each state") %>%
add_legacy_name("L254.tranSubsectorVOTT_USA") %>%
add_precursors("gcam-usa/states_subregions",
"L254.tranSubsectorVOTT") ->
L254.tranSubsectorVOTT_USA
L254.tranSubsectorFuelPref_USA %>%
add_title("Subsector (fuel) preferences elasticity that are tied to GDP in the US states") %>%
add_units("Unitless") %>%
add_comments("The same USA region values are repeated for each state") %>%
add_comments("Fuel preferences are unrelated to time value") %>%
add_legacy_name("L254.tranSubsectorFuelPref_USA") %>%
add_precursors("gcam-usa/states_subregions",
"L254.tranSubsectorFuelPref") ->
L254.tranSubsectorFuelPref_USA
L254.StubTranTech_USA %>%
add_title("Transportation stub technologies in the US states") %>%
add_units("NA") %>%
add_comments("The same USA region values are repeated for each state") %>%
add_legacy_name("L254.StubTranTech_USA") %>%
add_precursors("gcam-usa/states_subregions",
"L254.StubTranTech") ->
L254.StubTranTech_USA
L254.StubTranTech_passthru_USA %>%
add_title("Transportation stub technologies for passthrough sectors in the US states") %>%
add_units("NA") %>%
add_comments("The same USA region values are repeated for each state") %>%
add_legacy_name(" L254.StubTranTech_passthru_USA") %>%
add_precursors("gcam-usa/states_subregions",
"L254.StubTech_passthru") ->
L254.StubTranTech_passthru_USA
L254.StubTranTech_nonmotor_USA %>%
add_title("Transportation stub technologies for non-motorized subsectors in the US states") %>%
add_units("NA") %>%
add_comments("The same USA region values are repeated for each state") %>%
add_legacy_name("L254.StubTranTech_nonmotor_USA") %>%
add_precursors("gcam-usa/states_subregions",
"L254.StubTech_nonmotor") ->
L254.StubTranTech_nonmotor_USA
L254.StubTranTechLoadFactor_USA %>%
add_title("Load factors of transportation stub technologies in the US states") %>%
add_units("person/vehicle and tonnes/vehicle") %>%
add_comments("The same USA region values are repeated for each state") %>%
add_legacy_name("L254.StubTranTechLoadFactor_USA") %>%
add_precursors("gcam-usa/states_subregions",
"L254.StubTranTechLoadFactor") ->
L254.StubTranTechLoadFactor_USA
L254.StubTranTechCost_USA %>%
add_title("Costs of transportation stub technologies in the US states") %>%
add_units("$1990USD / vkm") %>%
add_comments("The same USA region values are repeated for each state") %>%
add_legacy_name("L254.StubTranTechCost_USA") %>%
add_precursors("gcam-usa/states_subregions",
"L254.StubTranTechCost") ->
L254.StubTranTechCost_USA
L254.StubTranTechCoef_USA %>%
add_title("Coefficients of transportation stub technologies in the US states") %>%
add_units("BTU / vkm") %>%
add_comments("The same USA region values are repeated for each state") %>%
add_comments("Re-set electricity consumed at the state markets") %>%
add_legacy_name("L254.StubTranTechCoef_USA") %>%
add_precursors("gcam-usa/states_subregions",
"L254.StubTranTechCoef") ->
L254.StubTranTechCoef_USA
L254.PerCapitaBased_trn_USA %>%
add_title("Per-capita based flag for transportation final demand in the US states") %>%
add_units("NA") %>%
add_comments("The same USA region values are repeated for each state") %>%
add_legacy_name("L254.PerCapitaBased_trn_USA") %>%
add_precursors("gcam-usa/states_subregions",
"L254.PerCapitaBased_trn") ->
L254.PerCapitaBased_trn_USA
L254.PriceElasticity_trn_USA %>%
add_title("Price elasticity of transportation final demand in the US states") %>%
add_units("Unitless") %>%
add_comments("The same USA region values are repeated for each state") %>%
add_legacy_name("L254.PriceElasticity_trn_USA") %>%
add_precursors("gcam-usa/states_subregions",
"L254.PriceElasticity_trn") ->
L254.PriceElasticity_trn_USA
L254.IncomeElasticity_trn_USA %>%
add_title("Income elasticity of transportation final demand in the US states") %>%
add_units("Unitless") %>%
add_comments("The same USA region values are repeated for each state") %>%
add_legacy_name("L254.IncomeElasticity_trn_USA") %>%
add_precursors("gcam-usa/states_subregions",
"L254.IncomeElasticity_trn") ->
L254.IncomeElasticity_trn_USA
L254.StubTranTechCalInput_USA %>%
add_title("Calibrated energy consumption by all transportation stub technologies in the US states") %>%
add_units("EJ") %>%
add_comments("Set calibration values for those technologies that do not exist in some base years as zero") %>%
add_legacy_name("L254.StubTranTechCalInput_USA") %>%
same_precursors_as("L254.StubTranTechCoef_USA") %>%
add_precursors("L154.in_EJ_state_trn_m_sz_tech_F",
"energy/mappings/UCD_techs",
"energy/mappings/UCD_techs_revised") ->
L254.StubTranTechCalInput_USA
L254.StubTranTechProd_nonmotor_USA %>%
add_title("Calibrated service output of non-motorized transportation technologies in the US states") %>%
add_units("Million pass-km") %>%
add_comments("Not match shareweights to the calOutputValue because no region should ever have a zero here") %>%
add_legacy_name("L254.StubTranTechProd_nonmotor_USA") %>%
add_precursors("L154.out_mpkm_state_trn_nonmotor_Yh",
"energy/A54.globaltech_nonmotor") ->
L254.StubTranTechProd_nonmotor_USA
L254.StubTranTechCalInput_passthru_USA %>%
add_title("Calibrated energy consumption of transportation passthrough technologies in the US states") %>%
add_units("EJ") %>%
add_comments("Use outputs of the supplysectors that are inputs for passthrough technologies") %>%
add_comments("Outputs of all motorized technologies are calculated as calInput * loadFactor / coefficient") %>%
add_legacy_name("L254.StubTranTechCalInput_passthru_USA") %>%
same_precursors_as("L254.StubTranTechCalInput_USA") %>%
same_precursors_as("L254.StubTranTechLoadFactor_USA") %>%
same_precursors_as("L254.StubTranTechCoef_USA") %>%
same_precursors_as("L254.StubTranTech_passthru_USA") %>%
add_precursors("energy/A54.globaltech_passthru") ->
L254.StubTranTechCalInput_passthru_USA
L254.BaseService_trn_USA %>%
add_title("Base-year service output of transportation final demand") %>%
add_units("Million pass-km and million ton-km") %>%
add_comments("Service outputs of all motorized technologies are calculated as calInput * loadFactor / coefficient") %>%
add_comments("Combine with service output of non-motorized transportation technologies") %>%
add_legacy_name("L254.BaseService_trn_USA") %>%
same_precursors_as("L254.StubTranTechCalInput_USA") %>%
same_precursors_as("L254.StubTranTechLoadFactor_USA") %>%
same_precursors_as("L254.StubTranTechCoef_USA") %>%
same_precursors_as("L254.StubTranTechProd_nonmotor_USA") %>%
add_precursors("energy/A54.sector",
"energy/mappings/UCD_size_class_revisions") ->
L254.BaseService_trn_USA
L254.StubTranTechOutput_USA %>%
add_title("service output for all tranTechnologies") %>%
add_units("Million pass-km and million ton-km") %>%
add_comments("Service outputs of all motorized technologies are calculated as calInput * loadFactor / coefficient") %>%
add_legacy_name("L254.StubTranTechOutput_USA") %>%
same_precursors_as("L254.StubTranTechCalInput_USA") %>%
same_precursors_as("L254.StubTranTechLoadFactor_USA") %>%
same_precursors_as("L254.StubTranTechCoef_USA") ->
L254.StubTranTechOutput_USA
return_data(L254.DeleteSupplysector_USAtrn, L254.DeleteFinalDemand_USAtrn,
L254.Supplysector_trn_USA,
L254.FinalEnergyKeyword_trn_USA,
L254.tranSubsectorLogit_USA,
L254.tranSubsectorShrwtFllt_USA,
L254.tranSubsectorInterp_USA,
L254.tranSubsectorSpeed_USA,
L254.tranSubsectorSpeed_passthru_USA,
L254.tranSubsectorSpeed_noVOTT_USA,
L254.tranSubsectorSpeed_nonmotor_USA,
L254.tranSubsectorVOTT_USA,
L254.tranSubsectorFuelPref_USA,
L254.StubTranTech_USA,
L254.StubTranTech_passthru_USA,
L254.StubTranTech_nonmotor_USA,
L254.StubTranTechLoadFactor_USA,
L254.StubTranTechCost_USA,
L254.StubTranTechCoef_USA,
L254.PerCapitaBased_trn_USA,
L254.PriceElasticity_trn_USA,
L254.IncomeElasticity_trn_USA,
L254.StubTranTechCalInput_USA, L254.StubTranTechProd_nonmotor_USA,
L254.StubTranTechCalInput_passthru_USA, L254.BaseService_trn_USA,
L254.StubTranTechOutput_USA)
} else {
stop("Unknown command")
}
}
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