R/zchunk_L254.transportation_UCD.R
In bpbond/gcamdata: Build the GCAM Input Datasets
Defines functions
module_energy_L254.transportation_UCD
Documented in
module_energy_L254.transportation_UCD
# Copyright 2019 Battelle Memorial Institute; see the LICENSE file.
#' module_energy_L254.transportation_UCD
#'
#' Calculate transportation data using information from the global UCD transportation technology database.
#'
#' @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.Supplysector_trn}, \code{L254.FinalEnergyKeyword_trn}, \code{L254.tranSubsectorLogit},
#' \code{L254.tranSubsectorShrwt}, \code{L254.tranSubsectorShrwtFllt}, \code{L254.tranSubsectorInterp},
#' \code{L254.tranSubsectorInterpTo}, \code{L254.tranSubsectorSpeed}, \code{L254.tranSubsectorSpeed_passthru},
#' \code{L254.tranSubsectorSpeed_noVOTT}, \code{L254.tranSubsectorSpeed_nonmotor}, \code{L254.tranSubsectorVOTT},
#' \code{L254.tranSubsectorFuelPref}, \code{L254.StubTranTech}, \code{L254.StubTech_passthru}, \code{L254.StubTech_nonmotor},
#' \code{L254.GlobalTechShrwt_passthru}, \code{L254.GlobalTechShrwt_nonmotor}, \code{L254.GlobalTechCoef_passthru},
#' \code{L254.GlobalRenewTech_nonmotor}, \code{L254.GlobalTranTechInterp}, \code{L254.GlobalTranTechShrwt},
#' \code{L254.GlobalTranTechSCurve}, \code{L254.StubTranTechCalInput}, \code{L254.StubTranTechLoadFactor},
#' \code{L254.StubTranTechCost}, \code{L254.StubTranTechCoef}, \code{L254.StubTechCalInput_passthru},
#' \code{L254.StubTechProd_nonmotor}, \code{L254.PerCapitaBased_trn}, \code{L254.PriceElasticity_trn},
#' \code{L254.IncomeElasticity_trn}, \code{L254.BaseService_trn}. The corresponding file in the
#' original data system was \code{L254.transportation_UCD.R} (energy level2).
#' @details Due to the asymmetrical nature of the transportation sectors in the various regions, we can't simply write
#' generic information to all regions. Instead, technology information is read from the global UCD transportation
#' technology database, and supplysector and subsector attributes are matched in from lookup tables.
#' @importFrom assertthat assert_that
#' @importFrom dplyr anti_join arrange bind_rows filter if_else group_by left_join mutate one_of pull select semi_join summarise contains desc
#' @importFrom tidyr complete nesting
#' @author AJS September 2017
module_energy_L254.transportation_UCD <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "common/GCAM_region_names",
FILE = "energy/mappings/UCD_techs",
FILE = "energy/mappings/UCD_techs_revised",
FILE = "energy/A54.demand",
FILE = "energy/A54.demand_ssp1",
FILE = "energy/A54.sector",
FILE = "energy/A54.tranSubsector_logit",
FILE = "energy/A54.tranSubsector_shrwt",
FILE = "energy/A54.tranSubsector_interp",
FILE = "energy/A54.tranSubsector_VOTT",
FILE = "energy/A54.tranSubsector_VOTT_ssp1",
FILE= "energy/mappings/UCD_size_class_revisions",
FILE = "energy/A54.tranSubsector_VOTT_ssp1_revised",
FILE = "energy/A54.tranSubsector_VOTT_revised",
FILE = "energy/A54.tranSubsector_interp_revised",
FILE = "energy/A54.tranSubsector_shrwt_revised",
FILE = "energy/A54.tranSubsector_logit_revised",
FILE = "energy/A54.globaltranTech_retire_revised",
FILE = "energy/A54.globaltranTech_shrwt_revised",
FILE= "energy/A54.globaltranTech_interp_revised",
FILE = "energy/A54.globaltech_passthru",
FILE = "energy/A54.globaltech_passthru_revised",
FILE = "energy/A54.globaltech_nonmotor",
FILE = "energy/A54.globaltranTech_shrwt",
FILE = "energy/A54.globaltranTech_interp",
FILE = "energy/A54.globaltranTech_retire",
"L154.in_EJ_R_trn_m_sz_tech_F_Yh",
"L154.cost_usdvkm_R_trn_m_sz_tech_F_Y",
"L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y",
"L154.loadfactor_R_trn_m_sz_tech_F_Y",
"L154.speed_kmhr_R_trn_m_sz_tech_F_Y",
"L154.out_mpkm_R_trn_nonmotor_Yh"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L254.Supplysector_trn",
"L254.FinalEnergyKeyword_trn",
"L254.tranSubsectorLogit",
"L254.tranSubsectorShrwt",
"L254.tranSubsectorShrwtFllt",
"L254.tranSubsectorInterp",
"L254.tranSubsectorInterpTo",
"L254.tranSubsectorSpeed",
"L254.tranSubsectorSpeed_passthru",
"L254.tranSubsectorSpeed_noVOTT",
"L254.tranSubsectorSpeed_nonmotor",
"L254.tranSubsectorVOTT",
"L254.tranSubsectorFuelPref",
"L254.StubTranTech",
"L254.StubTech_passthru",
"L254.StubTech_nonmotor",
"L254.GlobalTechShrwt_passthru",
"L254.GlobalTechShrwt_nonmotor",
"L254.GlobalTechCoef_passthru",
"L254.GlobalRenewTech_nonmotor",
"L254.GlobalTranTechInterp",
"L254.GlobalTranTechShrwt",
"L254.GlobalTranTechSCurve",
"L254.GlobalTranTechProfitShutdown",
"L254.StubTranTechCalInput",
"L254.StubTranTechLoadFactor",
"L254.StubTranTechCost",
"L254.StubTranTechCoef",
"L254.StubTechCalInput_passthru",
"L254.StubTechProd_nonmotor",
"L254.PerCapitaBased_trn",
"L254.PriceElasticity_trn",
"L254.IncomeElasticity_trn",
"L254.BaseService_trn"))
} else if(command == driver.MAKE) {
all_data <- list(...)[[1]]
# Silence package notes
GCAM_region_ID <- tranTechnology <- region <- supplysector <- . <- technology <- minicam.energy.input <- r_mei <-
year <- year.fillout <- to.value <- value <- speed.source <- tranSubsector.x <- addTimeValue <- time.value.multiplier <-
fuelprefElasticity <- tranSubsector <- share.weight <- calibrated.value <- subs.share.weight <- loadFactor <-
coefficient <- stub.technology <- output <- output_agg <- output_cum <- share.weight.year <- tech.share.weight <-
calOutputValue <- energy.final.demand <- base.service <- object <- r_ss <- UCD_region <- size.class <- sce <-
steepness <- profit.shutdown.steepness <- NULL
# Load required inputs
GCAM_region_names <- get_data(all_data, "common/GCAM_region_names",strip_attributes = TRUE)
UCD_techs <- get_data(all_data, "energy/mappings/UCD_techs",strip_attributes = TRUE)
A54.demand <- get_data(all_data, "energy/A54.demand",strip_attributes = TRUE) %>% mutate(sce=paste0("CORE"))
# This is a special case for SSP1, and the way this is executed will likely change in the future.
# Note that the variable is defined in constants.R
A54.demand_SSP1 <- get_data(all_data, "energy/A54.demand_ssp1",strip_attributes = TRUE)%>% mutate(sce=paste0("SSP1"))
A54.demand<-bind_rows(A54.demand,A54.demand_SSP1)
A54.sector <- get_data(all_data, "energy/A54.sector",strip_attributes = TRUE)
#kbn 2019-10-11 Insert code to use revised versions for subsectors below
Size_class_New<- get_data(all_data, "energy/mappings/UCD_size_class_revisions",strip_attributes = TRUE) %>%
select(-UCD_region) %>%
distinct()
#kbn 2020-03-26 If the user selects the revised modes and size classes, use the revised mapping files.
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'
}
if (toString(energy.TRAN_UCD_MODE)=='rev.mode'){
A54.tranSubsector_logit <- get_data(all_data, "energy/A54.tranSubsector_logit_revised",strip_attributes = TRUE)
A54.tranSubsector_shrwt <- get_data(all_data, "energy/A54.tranSubsector_shrwt_revised",strip_attributes = TRUE)
A54.tranSubsector_interp <- get_data(all_data, "energy/A54.tranSubsector_interp_revised",strip_attributes = TRUE)
A54.tranSubsector_VOTT <- get_data(all_data, "energy/A54.tranSubsector_VOTT_revised",strip_attributes = TRUE) %>% mutate(sce=paste0("CORE"))
A54.tranSubsector_VOTT_SSP1 <- get_data(all_data, "energy/A54.tranSubsector_VOTT_ssp1_revised",strip_attributes = TRUE) %>% mutate(sce=paste0("SSP1"))
A54.tranSubsector_VOTT<- bind_rows(A54.tranSubsector_VOTT,A54.tranSubsector_VOTT_SSP1)
A54.globaltranTech_retire <- get_data(all_data, "energy/A54.globaltranTech_retire_revised",strip_attributes = TRUE)
A54.globaltranTech_shrwt <- get_data(all_data, "energy/A54.globaltranTech_shrwt_revised",strip_attributes = TRUE)
A54.globaltranTech_interp <- get_data(all_data, "energy/A54.globaltranTech_interp_revised",strip_attributes = TRUE)
A54.globaltech_passthru <- get_data(all_data, "energy/A54.globaltech_passthru_revised",strip_attributes = TRUE)
}
else {A54.tranSubsector_logit <- get_data(all_data, "energy/A54.tranSubsector_logit",strip_attributes = TRUE)
A54.tranSubsector_shrwt <- get_data(all_data, "energy/A54.tranSubsector_shrwt",strip_attributes = TRUE)
A54.tranSubsector_interp <- get_data(all_data, "energy/A54.tranSubsector_interp",strip_attributes = TRUE)
A54.tranSubsector_VOTT <- get_data(all_data, "energy/A54.tranSubsector_VOTT",strip_attributes = TRUE)
A54.tranSubsector_VOTT <- get_data(all_data, "energy/A54.tranSubsector_VOTT",strip_attributes = TRUE)
A54.tranSubsector_VOTT_SSP1 <- get_data(all_data, "energy/A54.tranSubsector_VOTT_ssp1",strip_attributes = TRUE) %>% mutate(sce=paste0("SSP1"))
A54.tranSubsector_VOTT<- bind_rows(A54.tranSubsector_VOTT,A54.tranSubsector_VOTT_SSP1)
A54.globaltranTech_retire <- get_data(all_data, "energy/A54.globaltranTech_retire",strip_attributes = TRUE)
A54.globaltranTech_shrwt <- get_data(all_data, "energy/A54.globaltranTech_shrwt",strip_attributes = TRUE)
A54.globaltranTech_interp <- get_data(all_data, "energy/A54.globaltranTech_interp",strip_attributes = TRUE)
A54.globaltech_passthru <- get_data(all_data, "energy/A54.globaltech_passthru",strip_attributes = TRUE)
}
A54.globaltech_nonmotor <- get_data(all_data, "energy/A54.globaltech_nonmotor",strip_attributes = TRUE)
L154.in_EJ_R_trn_m_sz_tech_F_Yh <- get_data(all_data, "L154.in_EJ_R_trn_m_sz_tech_F_Yh",strip_attributes = TRUE)
L154.cost_usdvkm_R_trn_m_sz_tech_F_Y <- get_data(all_data, "L154.cost_usdvkm_R_trn_m_sz_tech_F_Y",strip_attributes = TRUE)
L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y <- get_data(all_data, "L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y",strip_attributes = TRUE)
L154.loadfactor_R_trn_m_sz_tech_F_Y <- get_data(all_data, "L154.loadfactor_R_trn_m_sz_tech_F_Y",strip_attributes = TRUE)
L154.speed_kmhr_R_trn_m_sz_tech_F_Y <- get_data(all_data, "L154.speed_kmhr_R_trn_m_sz_tech_F_Y",strip_attributes = TRUE)
L154.out_mpkm_R_trn_nonmotor_Yh <- get_data(all_data, "L154.out_mpkm_R_trn_nonmotor_Yh",strip_attributes = TRUE)
# ===================================================
# PART A: BUILDING TRANSPORTATION SECTORS FROM THE TECHNOLOGY LEVEL UP
# L254.StubTranTech: Transportation stub technologies (built from technologies with coefficients in the UCD database)
#kbn 2020-06-02 Making changes to generate xmls for SSPs flexibly- We are now calculating values for the transportation variables for all SSPs in LA154.
# We will also calculate calibrated values, co-efficients by SSPs. Everywhere, we will split out results from the core and the SSPs and calculate values for
# all scenarios separately. See search string kbn 2020-02-06 for changes. We are calculating these separate values for sectors and subsectors (Part A, Part B),
# andcalibration and region specific data (Part E).
#kbn 2019-10-14 Switching to left_join_keep_first, since we have fewer mode categories now.
#kbn 2020-06-02 Adding sce below (See description of changes using search string kbn 2020-06-02 Making changes to generate xmls for SSPs flexibly)
L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y %>%
select(GCAM_region_ID, one_of(c("UCD_sector", "mode", "size.class", "UCD_technology", "UCD_fuel","sce"))) %>%
# Match in region names and UCD techs
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_keep_first_only(UCD_techs, by = c("UCD_sector", "mode", "size.class", "UCD_technology", "UCD_fuel")) %>%
unique() %>%
rename(stub.technology = tranTechnology) %>%
select(LEVEL2_DATA_NAMES[["StubTranTech"]],sce) ->
L254.StubTranTech # OUTPUT
# Write the pass-through technologies to all regions
# First, create two lists to filter technologies later
L254.StubTranTech %>%
mutate(r_ss = paste(region, supplysector)) %>%
pull(r_ss) %>%
unique() ->
LIST_r_ss
# A54.globaltech_passthru reports transportation technology defaults (all parameters; pass-through technologies only)
A54.globaltech_passthru %>%
rename(stub.technology = technology) %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["StubTranTech"]], "minicam.energy.input"), GCAM_region_names = GCAM_region_names) %>%
# Of the pass-throughs, subset only the ones whose "input" actually exists in the given region OR
# ones whose input is in the list of pass-through technologies.
mutate(r_mei = paste(region, minicam.energy.input)) %>%
filter((r_mei %in% LIST_r_ss) |
(minicam.energy.input %in% A54.globaltech_passthru$supplysector)) %>%
select(LEVEL2_DATA_NAMES[["StubTranTech"]]) %>%
mutate(sce = paste0("CORE"))->
L254.StubTech_passthru # OUTPUT
# Write the non-motorized technologies to all regions
A54.globaltech_nonmotor %>%
rename(stub.technology = technology) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["StubTranTech"]], GCAM_region_names = GCAM_region_names) %>%
mutate(sce = paste0("CORE"))->
L254.StubTech_nonmotor # OUTPUT
# L254.Supplysector_trn: Supply sector information for transportation sector
# Writing the generic supplysector table to all regions may generate combinations that don't apply
# Use this for filtering below
r_ss_all <- bind_rows(L254.StubTranTech, L254.StubTech_passthru, L254.StubTech_nonmotor)
A54.sector %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["Supplysector"]], LOGIT_TYPE_COLNAME), GCAM_region_names = GCAM_region_names) %>%
# Subset only the combinations of region and supplysector that are available in the stub technology table
mutate(sce = paste0("CORE")) %>%
semi_join(r_ss_all, by = c("region", "supplysector","sce")) %>%
select(c(LEVEL2_DATA_NAMES[["Supplysector"]], LOGIT_TYPE_COLNAME,sce)) ->
L254.Supplysector_trn # OUTPUT
# L254.FinalEnergyKeyword_trn: Supply sector keywords for transportation sector
L254.Supplysector_trn %>%
left_join_keep_first_only(A54.sector, by = "supplysector") %>%
select(LEVEL2_DATA_NAMES[["FinalEnergyKeyword"]],sce) %>%
mutate(sce=paste0("CORE"))->
L254.FinalEnergyKeyword_trn # OUTPUT
# PART B: SUBSECTOR INFORMATION
# L254.tranSubsectorLogit: Subsector logit exponents of transportation sector
LEVEL2_DATA_NAMES[["tranSubsector"]] <- c("region", "supplysector", "tranSubsector")
# Use this for filtering datasets below
r_ss_ts_all <- bind_rows(L254.StubTranTech, L254.StubTech_passthru, L254.StubTech_nonmotor)
# A54.tranSubsector_logit reports transportation default subsector logit exponents
A54.tranSubsector_logit %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["tranSubsectorLogit"]], LOGIT_TYPE_COLNAME), GCAM_region_names = GCAM_region_names) %>%
mutate(sce = paste0("CORE") ) %>%
# Subset only the combinations of region, supplysector, and tranSubsector that are available
semi_join(r_ss_ts_all, by = c("region", "supplysector", "tranSubsector","sce")) %>%
select(c(LEVEL2_DATA_NAMES[["tranSubsectorLogit"]], LOGIT_TYPE_COLNAME),sce) ->
L254.tranSubsectorLogit # OUTPUT
# L254.tranSubsectorShrwt and L254.tranSubsectorShrwtFllt: Subsector shareweights of transportation sector
if(any(!is.na(A54.tranSubsector_shrwt$year))) {
A54.tranSubsector_shrwt %>%
filter(!is.na(year)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["tranSubsectorShrwt"]], GCAM_region_names = GCAM_region_names) %>%
mutate(sce = paste0("CORE"),year = as.integer(year)) %>%
# Subset only the combinations of region, supplysector, and tranSubsector that are available
semi_join(r_ss_ts_all, by = c("region", "supplysector", "tranSubsector","sce")) %>%
select(LEVEL2_DATA_NAMES[["tranSubsectorShrwt"]],sce) ->
L254.tranSubsectorShrwt # OUTPUT
}
if(any(!is.na(A54.tranSubsector_shrwt$year.fillout))) {
A54.tranSubsector_shrwt %>%
filter(!is.na(year.fillout)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["tranSubsectorShrwtFllt"]], GCAM_region_names = GCAM_region_names) %>%
mutate(sce = paste0("CORE"),year.fillout = as.integer(year.fillout)) %>%
# Subset only the combinations of region, supplysector, and tranSubsector that are available
semi_join(r_ss_ts_all, by = c("region", "supplysector", "tranSubsector","sce")) %>%
select(LEVEL2_DATA_NAMES[["tranSubsectorShrwtFllt"]],sce) ->
L254.tranSubsectorShrwtFllt # OUTPUT
}
# L254.tranSubsectorInterp and L254.tranSubsectorInterpTo: Subsector shareweight interpolation of transportation sector
if(any(is.na(A54.tranSubsector_interp$to.value))) {
A54.tranSubsector_interp %>%
filter(is.na(to.value)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["tranSubsectorInterp"]], GCAM_region_names = GCAM_region_names) %>%
mutate(sce = paste0("CORE")) %>%
# Subset only the combinations of region, supplysector, and tranSubsector that are available
semi_join(r_ss_ts_all, by = c("region", "supplysector", "tranSubsector","sce")) %>%
select(LEVEL2_DATA_NAMES[["tranSubsectorInterp"]],sce) ->
L254.tranSubsectorInterp # OUTPUT
}
if(any(!is.na(A54.tranSubsector_interp$to.value))) {
A54.tranSubsector_interp %>%
filter(!is.na(to.value)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["tranSubsectorInterpTo"]], GCAM_region_names = GCAM_region_names) %>%
# Subset only the combinations of region, supplysector, and tranSubsector that are available
semi_join(r_ss_ts_all, by = c("region", "supplysector", "tranSubsector","sce")) %>%
select(LEVEL2_DATA_NAMES[["tranSubsectorInterpTo"]],sce) ->
L254.tranSubsectorInterp # OUTPUT
}
# L254.tranSubsectorSpeed: Speeds of transportation modes (not including pass-through sectors)
#kbn 2019-10-14 Switching to left_join_keep_first, since we have fewer mode categories now.
#kbn 2020-06-02 Updating with sce below (See description of changes using search string kbn 2020-06-02 Making changes to generate xmls for SSPs flexibly)
L154.speed_kmhr_R_trn_m_sz_tech_F_Y %>%
filter(year %in% MODEL_YEARS) %>%
# Match in GCAM region names and UCD technologies
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_keep_first_only(UCD_techs, by = c("UCD_sector", "mode", "size.class", "UCD_technology", "UCD_fuel")) %>%
mutate(speed = round(value, energy.DIGITS_SPEED)) %>%
select(LEVEL2_DATA_NAMES[["tranSubsectorSpeed"]],sce) %>%
distinct()->
L254.tranSubsectorSpeed # OUTPUT
# This does not include the pass-through tranSectors
# Pass-through tranSubsectors for which time value is added are assigned a sector from which to get their speed.
# L254.tranSubsectorSpeed_passthru: speeds of pass-through transportation subsectors
# A54.tranSubsector_VOTT reports transportation default subsector value of time in transit multipliers (VOTT = wage rate * this factor)
A54.tranSubsector_VOTT %>%
filter(!is.na(speed.source)) %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["tranSubsector"]], "speed.source","sce"), GCAM_region_names = GCAM_region_names) %>%
repeat_add_columns(tibble(year = MODEL_YEARS)) %>%
#mutate(sce= paste0("CORE")) %>%
# Match in speed
left_join_keep_first_only(L254.tranSubsectorSpeed, by = c("region", "speed.source" = "supplysector", "year","sce")) %>%
rename(tranSubsector = tranSubsector.x) %>%
select(LEVEL2_DATA_NAMES[["tranSubsector"]], "year", "speed","sce") %>%
#We won't have historical data on speed for the SSPs
na.omit()->
L254.tranSubsectorSpeed_passthru
# L254.tranSubsectorSpeed_noVOTT: Speeds of transportation subsectors whose time value is not considered
# NOTE: This step should be unnecessary. Currently there is no model default value for speed, and a subsector
# with no speed read in will cause a model crash, even for modes such as freight where time value is not modeled
# Start with all observed subsectors in the transportation module
# Use this for filtering below
r_ss_ts_speed_all <- bind_rows(L254.tranSubsectorSpeed, L254.tranSubsectorSpeed_passthru)
#kbn 2020-02-06 Adding sce below (See description of changes using search string kbn 2020-06-02 Making changes to generate xmls for SSPs flexibly)
L254.StubTranTech %>%
select(LEVEL2_DATA_NAMES[["tranSubsector"]],sce) %>%
# First subset table
bind_rows(select(L254.StubTech_passthru , one_of(LEVEL2_DATA_NAMES[["tranSubsector"]]),sce)) %>%
unique() %>%
# Subset only those whose speeds have NOT already been specified
anti_join(r_ss_ts_speed_all, by = c("region", "supplysector", "tranSubsector","sce")) %>%
# Repeat by the number of model time periods
repeat_add_columns(tibble(year = MODEL_YEARS)) %>%
# Write in a default value for speed
mutate(speed = 1) ->
L254.tranSubsectorSpeed_noVOTT
# L254.tranSubsectorSpeed_nonmotor: Speeds of non-motorized transportation subsectors
A54.globaltech_nonmotor %>%
repeat_add_columns(tibble(year = MODEL_YEARS)) %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["tranSubsector"]], "year", "speed"), GCAM_region_names = GCAM_region_names) %>%
mutate(sce= paste0("CORE"))->
L254.tranSubsectorSpeed_nonmotor
# L254.tranSubsectorVOTT: Value of time in transit parameterization
# NOTE: These are currently considered time- and region-independent characteristics
A54.tranSubsector_VOTT %>%
filter(addTimeValue == 1) %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["tranSubsector"]], "addTimeValue", "time.value.multiplier","sce"),
GCAM_region_names = GCAM_region_names) %>%
mutate(year.fillout = min(MODEL_YEARS),
sce= if_else(is.na(sce),"CORE",sce)) %>%
# Subset only the combinations of region, supplysector, and tranSubsector
semi_join(r_ss_ts_all, by = c("region", "supplysector", "tranSubsector","sce")) %>%
select(LEVEL2_DATA_NAMES[["tranSubsector"]], year.fillout, addTimeValue, time.value.multiplier,sce) %>%
na.omit()->
L254.tranSubsectorVOTT
# L254.tranSubsectorFuelPref: Subsector preferences that are tied to GDP (unrelated to time value)
A54.tranSubsector_VOTT %>%
filter(fuelprefElasticity != 0) %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["tranSubsector"]], "fuelprefElasticity","sce"),
GCAM_region_names = GCAM_region_names) %>%
mutate(year.fillout = min(MODEL_YEARS)) %>%
#mutate(sce = paste0("CORE")) %>%
# Subset only the combinations of region, supplysector, and tranSubsector that are available
semi_join(r_ss_ts_all, by = c("region", "supplysector", "tranSubsector")) %>%
select(LEVEL2_DATA_NAMES[["tranSubsector"]], year.fillout, fuelprefElasticity,sce) %>% na.omit() ->
L254.tranSubsectorFuelPref # OUTPUT
# PART C: TECHNOLOGY INFORMATION: GLOBAL TECHNOLOGIES (i.e., not tranTechnologies)
# L254.GlobalTechShrwt_passthru: Shareweights of global transportation sector technologies (not tranTechnologies)
A54.globaltech_passthru %>%
repeat_add_columns(tibble(year = MODEL_YEARS)) %>%
mutate(sce= paste0("CORE")) %>%
rename(sector.name = supplysector, subsector.name = tranSubsector) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechYr"]], share.weight) ->
L254.GlobalTechShrwt_passthru
# L254.GlobalTechShrwt_nonmotor: Shareweights of non-motorized global transportation sector technologies (not tranTechnologies)
A54.globaltech_nonmotor %>%
repeat_add_columns(tibble(year = MODEL_YEARS)) %>%
mutate(sce =paste0("CORE")) %>%
rename(sector.name = supplysector, subsector.name = tranSubsector) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechYr"]], share.weight) ->
L254.GlobalTechShrwt_nonmotor
# L254.GlobalTechCoef_passthru: Coefficients of global transportation sector technologies (not tranTechnologies)
A54.globaltech_passthru %>%
repeat_add_columns(tibble(year = MODEL_YEARS)) %>%
mutate(sce =paste0("CORE")) %>%
rename(sector.name = supplysector, subsector.name = tranSubsector) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechCoef"]]) ->
L254.GlobalTechCoef_passthru
# L254.GlobalRenewTech_nonmotor: Renewable inputs to non-motorized transportation technologies
A54.globaltech_nonmotor %>%
repeat_add_columns(tibble(year = MODEL_YEARS)) %>%
mutate(sce =paste0("CORE")) %>%
rename(sector.name = supplysector, subsector.name = tranSubsector) %>%
select(LEVEL2_DATA_NAMES[["GlobalRenewTech"]]) ->
L254.GlobalRenewTech_nonmotor
# PART D: TECHNOLOGY INFORMATION - GLOBAL TRANTECHNOLOGIES
# L254.GlobalTranTechInterp: Shareweight interpolation of global tranTechnologies
A54.globaltranTech_interp %>%
mutate(supplysector = supplysector) %>% # create new tibble, stripping attributes
set_years() %>%
rename(sector.name = supplysector, subsector.name = tranSubsector) %>%
select(LEVEL2_DATA_NAMES[["GlobalTranTechInterp"]],"sce") ->
L254.GlobalTranTechInterp
# L254.GlobalTranTechShrwt: Shareweights of global tranTechnologies
A54.globaltranTech_shrwt %>%
filter(sce=="CORE") %>%
select(-sce) %>%
gather_years %>%
# Expand table to include all model years
complete(year = c(year, MODEL_YEARS), nesting(supplysector, tranSubsector, tranTechnology)) %>%
# 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, tranSubsector, tranTechnology) %>%
mutate(share.weight = approx_fun(year, value, rule = 2),
share.weight = round(share.weight, energy.DIGITS_SHRWT)) %>%
ungroup() %>%
filter(year %in% MODEL_YEARS) %>%
mutate(sce= paste0("CORE")) %>%
rename(sector.name = supplysector, subsector.name = tranSubsector) %>%
select(LEVEL2_DATA_NAMES[["GlobalTranTechShrwt"]],sce) ->
L254.GlobalTranTechShrwt_CORE # OUTPUT
# A54.globaltranTech_shrwt %>%
# filter(sce=="highEV") %>%
# select(-sce) %>%
# gather_years %>%
# # Expand table to include all model years
# complete(year = c(year, MODEL_YEARS), nesting(supplysector, tranSubsector, tranTechnology)) %>%
# # 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, tranSubsector, tranTechnology) %>%
# mutate(share.weight = approx_fun(year, value, rule = 2),
# share.weight = round(share.weight, energy.DIGITS_SHRWT)) %>%
# ungroup() %>%
# filter(year %in% MODEL_YEARS) %>%
# mutate(sce= paste0("highEV")) %>%
# rename(sector.name = supplysector, subsector.name = tranSubsector) %>%
# select(LEVEL2_DATA_NAMES[["GlobalTranTechShrwt"]],sce) ->
# L254.GlobalTranTechShrwt_highEV # OUTPUT
#L254.GlobalTranTechShrwt <- bind_rows(L254.GlobalTranTechShrwt_highEV,L254.GlobalTranTechShrwt_CORE)
L254.GlobalTranTechShrwt <- L254.GlobalTranTechShrwt_CORE
# L254.GlobalTranTechSCurve and L254.GlobalTranTechProfitShutdown: Retirement of global tranTechnologies
# A54.globaltranTech_retire reports transportation technology retirement parameters. Only applies to vintaged technologies
A54.globaltranTech_retire %>%
set_years() %>%
filter(year < max(year)) %>%
mutate(year = as.numeric(year)) ->
L254.GlobalTranTechSCurve_1
# Copy the final year forward to all future time periods
L254.GlobalTranTechSCurve_MAX_YEAR <- max(L254.GlobalTranTechSCurve_1$year)
A54.globaltranTech_retire %>%
set_years() %>%
filter(year == max(year)) %>%
select(-year) %>%
repeat_add_columns(tibble(year = MODEL_YEARS)) %>%
filter(year > L254.GlobalTranTechSCurve_MAX_YEAR) %>%
bind_rows(L254.GlobalTranTechSCurve_1) %>%
rename(sector.name = supplysector, subsector.name = tranSubsector) ->
A54.globaltranTech_retire
A54.globaltranTech_retire %>%
select(LEVEL2_DATA_NAMES[["GlobalTranTechSCurve"]]) ->
L254.GlobalTranTechSCurve # OUTPUT
A54.globaltranTech_retire %>%
select(-steepness) %>%
rename(steepness = profit.shutdown.steepness) %>%
select(LEVEL2_DATA_NAMES[["GlobalTranTechProfitShutdown"]]) ->
L254.GlobalTranTechProfitShutdown
# PART E: CALIBRATION AND REGION-SPECIFIC DATA
# L254.StubTranTechCalInput: calibrated input of tranTechnologies
# L154.in_EJ_R_trn_m_sz_tech_F_Yh reports transportation energy consumption by GCAM region / mode / size class / technology / fuel / historical year
#kbn 2019-10-14 Switching to left_join_keep_first, since we have fewer mode categories now.
L154.in_EJ_R_trn_m_sz_tech_F_Yh %>%
filter(year %in% MODEL_BASE_YEARS) %>%
mutate(calibrated.value = round(value, energy.DIGITS_CALOUTPUT)) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_keep_first_only(UCD_techs, by = c("UCD_sector", "mode", "size.class",
"UCD_technology", "UCD_fuel")) %>%
select(region, supplysector, tranSubsector, stub.technology = tranTechnology,
year, minicam.energy.input, calibrated.value) ->
L254.StubTranTechCalInput_basetable
#kbn 2020-02-06 Energy intensity are not separated by SSPs. So, just copying information from CORE to all SSPs.
# L254.StubTranTechCalInput_basetable<- bind_rows(L254.StubTranTechCalInput_basetable %>% mutate(sce= paste0("CORE")),
# L254.StubTranTechCalInput_basetable %>% mutate(sce= paste0("SSP1")),
# L254.StubTranTechCalInput_basetable %>% mutate(sce= paste0("SSP3")),
# L254.StubTranTechCalInput_basetable %>% mutate(sce= paste0("SSP5")),
# L254.StubTranTechCalInput_basetable %>% mutate(sce= paste0("highEV")))
L254.StubTranTechCalInput_basetable<- bind_rows(L254.StubTranTechCalInput_basetable %>% mutate(sce= paste0("CORE")),
L254.StubTranTechCalInput_basetable %>% mutate(sce= paste0("SSP1")),
L254.StubTranTechCalInput_basetable %>% mutate(sce= paste0("SSP3")),
L254.StubTranTechCalInput_basetable %>% mutate(sce= paste0("SSP5")))
# Aggregate to set subsector share weights according to region, supplysector, tranSubsector, year combination
# kbn 2020-02-06 Add sce below (See description of changes using search string kbn 2020-06-02 Making changes to generate xmls for SSPs flexibly)
L254.StubTranTechCalInput_basetable %>%
group_by(region, supplysector, tranSubsector, year,sce) %>%
summarise(subs.share.weight = sum(calibrated.value)) %>%
ungroup() %>%
mutate(subs.share.weight = if_else(subs.share.weight > 0, 1, 0))->L254.StubTranTechCalInput_Shareweight
L254.StubTranTechCalInput_basetable %>%
# Match in subsector share weights
#Add sce below kbn 2020-06-02 (See description of changes using search string kbn 2020-06-02 Making changes to generate xmls for SSPs flexibly)
left_join_error_no_match(L254.StubTranTechCalInput_Shareweight, by = c("region", "supplysector",
"tranSubsector", "year","sce")) %>%
mutate(share.weight.year = year,
tech.share.weight = if_else(calibrated.value > 0, 1, 0)) %>%
#Add sce below kbn 2020-06-02 (See description of changes using search string kbn 2020-06-02 Making changes to generate xmls for SSPs flexibly)
select(LEVEL2_DATA_NAMES[["StubTranTechCalInput"]],sce) ->
L254.StubTranTechCalInput # OUTPUT
# L254.StubTranTechLoadFactor: tranTechnology load factors (all periods)
# L154.loadfactor_R_trn_m_sz_tech_F_Y reports load factors by GCAM region / mode / size class / technology / fuel / year
#kbn 2019-10-14 Switching to left_join_keep_first, since we have fewer mode categories now.
#kbn 2020-06-02 Adding sce below (See description of changes using search string kbn 2020-06-02 Making changes to generate xmls for SSPs flexibly)
L154.loadfactor_R_trn_m_sz_tech_F_Y %>%
filter(year %in% MODEL_YEARS) %>%
mutate(loadFactor = round(value, energy.DIGITS_LOADFACTOR)) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_keep_first_only(UCD_techs, by = c("UCD_sector", "mode", "size.class", "UCD_technology", "UCD_fuel")) %>%
rename(stub.technology = tranTechnology) %>%
select(LEVEL2_DATA_NAMES[["StubTranTechLoadFactor"]],sce) ->
L254.StubTranTechLoadFactor # OUTPUT
# L254.StubTranTechCost: tranTechnology costs (all periods)
# L154.cost_usdvkm_R_trn_m_sz_tech_F_Y reports non-fuel cost by GCAM region / mode / size class / technology / fuel / year "
##kbn 2019-10-14 Switching to left_join_keep_first, since we have fewer mode categories now.
#kbn 2020-06-02 Updating with sce below (See description of changes using search string kbn 2020-06-02 Making changes to generate xmls for SSPs flexibly)
L154.cost_usdvkm_R_trn_m_sz_tech_F_Y %>%
filter(year %in% MODEL_YEARS) %>%
mutate(input.cost = round((value / gdp_deflator(2005, 1990)), energy.DIGITS_COST)) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_keep_first_only(UCD_techs, by = c("UCD_sector", "mode", "size.class", "UCD_technology", "UCD_fuel")) %>%
rename(stub.technology = tranTechnology) %>%
mutate(minicam.non.energy.input = "non-energy") %>%
select(LEVEL2_DATA_NAMES[["StubTranTechCost"]],sce) ->
L254.StubTranTechCost # OUTPUT
# L254.StubTranTechCoef: tranTechnology coefficients (intensities; all periods)
# L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y reports vehicle energy intensity by GCAM region / mode / size class / technology / fuel / year
#kbn 2019-10-14 Switching to left_join_keep_first, since we have fewer mode categories now.
L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y %>%
filter(year %in% MODEL_YEARS) %>%
mutate(coefficient = round((value * CONV_MJ_BTU), energy.DIGITS_COEFFICIENT)) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_keep_first_only(UCD_techs, by = c("UCD_sector", "mode", "size.class", "UCD_technology", "UCD_fuel")) %>%
rename(stub.technology = tranTechnology) %>%
# Currently, the market names for the fuels will be the same as the region
mutate(market.name = region) %>%
#kbn 2020-06-02 adding sce here (See description of changes using search string kbn 2020-06-02 Making changes to generate xmls for SSPs flexibly)
select(LEVEL2_DATA_NAMES[["StubTranTechCoef"]],sce) ->
L254.StubTranTechCoef # OUTPUT
# L254.StubTechCalInput_passthru: calibrated input of passthrough technologies
# First, need to calculate the service output for all tranTechnologies (= calInput * loadFactor * unit_conversion / (coef * unit conversion))
#Switching to normal left_joins below since we have more
#2020
L254.StubTranTechCalInput %>%
select(-contains("share")) %>%
left_join(L254.StubTranTechLoadFactor, by = c("region", "supplysector", "tranSubsector",
"stub.technology", "year", "sce")) %>%
left_join(L254.StubTranTechCoef, by = c("region", "supplysector", "tranSubsector",
"stub.technology", "minicam.energy.input", "year","sce")) %>%
mutate(loadFactor=if_else(is.na(loadFactor),0,loadFactor),
coefficient=if_else(is.na(coefficient),0,coefficient),
output = calibrated.value * loadFactor * CONV_EJ_GJ / (coefficient * CONV_BTU_KJ),
output = if_else(is.na(output),0,output)) %>%
select(region, supplysector, tranSubsector, stub.technology, year, minicam.energy.input,
calibrated.value, loadFactor, coefficient, output,sce) ->
L254.StubTranTechOutput
# The next step is to bind rows with all pass-through technologies on to this table
A54.globaltech_passthru %>%
repeat_add_columns(tibble(year = MODEL_BASE_YEARS)) %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["tranSubsector"]], "technology", "year", "minicam.energy.input"),
GCAM_region_names = GCAM_region_names) %>%
rename(stub.technology = technology) %>%
mutate(sce =paste0("CORE")) %>%
# Subset only the passthrough technologies that are applicable in each region
semi_join(L254.StubTech_passthru, by = c("region", "supplysector", "tranSubsector", "stub.technology","sce")) %>%
# Start with a 0 value for output, and bind this to the table of output by tranTechnology (using only columns whose names match)
mutate(output = 0) %>%
bind_rows(
select(L254.StubTranTechOutput, one_of(c(LEVEL2_DATA_NAMES[["tranSubsector"]]),
"stub.technology", "year", "output", "minicam.energy.input","sce"))) ->
L254.StubTechCalInput_passthru_all_rows
#kbn 2020-06-02 Adding sce below (See description of changes using search string kbn 2020-06-02 Making changes to generate xmls for SSPs flexibly)
L254.StubTechCalInput_passthru_all_rows %>%
group_by(region, year, supplysector,sce) %>%
summarise(output_agg = sum(output)) %>%
ungroup() ->
L254.StubTechCalInput_passthru_agg
L254.StubTechCalInput_passthru_all_rows %>%
left_join(L254.StubTechCalInput_passthru_agg, by = c("region", "year", "minicam.energy.input" = "supplysector","sce")) %>%
# remove the technologies that are not pass-through sectors
semi_join(L254.StubTech_passthru, by = c("region", "supplysector", "tranSubsector", "stub.technology","sce")) %>%
# compute cumulative sum for use below
arrange(desc(minicam.energy.input)) %>%
group_by(region, year) %>%
mutate(output_cum = cumsum(output_agg)) %>%
ungroup() ->
L254.StubTechCalInput_passthru_cum
LIST_supplysector <- unique(L254.StubTechCalInput_passthru_cum$supplysector)
L254.StubTechCalInput_passthru_cum %>%
mutate(calibrated.value = if_else(minicam.energy.input %in% LIST_supplysector,
output_cum, output_agg),
share.weight.year = year,
subs.share.weight = if_else(calibrated.value > 0, 1, 0),
tech.share.weight = subs.share.weight) %>%
select(region, supplysector, tranSubsector, stub.technology, year,
minicam.energy.input, calibrated.value, share.weight.year,
subs.share.weight, tech.share.weight,sce) ->
L254.StubTechCalInput_passthru # OUTPUT
# PART F: NON-MOTORIZED TRANSPORTATION - SERVICE OUTPUT
# L254.StubTechProd_nonmotor: service output of non-motorized transportation technologies
# L154.out_mpkm_R_trn_nonmotor_Yh reports service output by GCAM region / non-motorized transport mode / year
L154.out_mpkm_R_trn_nonmotor_Yh %>%
mutate(sce= paste0("CORE")) %>%
filter(year %in% MODEL_BASE_YEARS) %>%
mutate(calOutputValue = round(value, energy.DIGITS_MPKM)) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_error_no_match(A54.globaltech_nonmotor, by = c("mode" = "tranSubsector")) %>%
rename(stub.technology = technology, tranSubsector = mode) %>%
# 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,sce) ->
L254.StubTechProd_nonmotor
# L254.PerCapitaBased_trn: per-capita based flag for transportation final demand
A54.demand %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["PerCapitaBased"]],"sce"), GCAM_region_names = GCAM_region_names) %>% na.omit() ->
L254.PerCapitaBased_trn # OUTPUT
# L254.PriceElasticity_trn: price elasticity of transportation final demand")
# Price elasticities are only applied to future periods. Application in base years will cause solution failure
A54.demand %>%
repeat_add_columns(tibble(year = MODEL_FUTURE_YEARS)) %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["PriceElasticity"]],"sce"), GCAM_region_names = GCAM_region_names) %>% na.omit() ->
L254.PriceElasticity_trn # OUTPUT
# L254.IncomeElasticity_trn: Income elasticity of transportation final demand
# Income elasticities are only applied to future periods
A54.demand %>%
repeat_add_columns(tibble(year = MODEL_FUTURE_YEARS)) %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["IncomeElasticity"]],"sce"), GCAM_region_names = GCAM_region_names) %>% na.omit() ->
L254.IncomeElasticity_trn # OUTPUT
# L254.BaseService_trn: Base-year service output of transportation final demand
L254.StubTranTechOutput %>%
select(LEVEL2_DATA_NAMES[["StubTranTech"]], year, output,sce) %>%
bind_rows(
select(L254.StubTechProd_nonmotor, one_of(LEVEL2_DATA_NAMES[["StubTranTech"]]), year, calOutputValue,sce)) %>%
mutate(base.service = if_else(!is.na(output), output, calOutputValue)) %>%
# Match in energy.final.demand from transportation supplysector information
# NAs will be introduced, so use left-join
left_join(A54.sector, by = "supplysector") %>%
# Aggregate base-year service output to region, energy.final.demand, and year
group_by(region, energy.final.demand, year,sce) %>%
summarise(base.service = sum(base.service)) %>%
ungroup() %>%
filter(sce=="CORE") ->
#kbn 2020-06-02 Base service values only needed for CORE.
L254.BaseService_trn # OUTPUT
# ===================================================
L254.Supplysector_trn %>%
add_title("Supply sector information for transportation sector") %>%
add_units("Unitless") %>%
add_comments("Supply sector information was written for all GCAM regions and subset for the combinations of region and supplysector that are available in the stub technology table") %>%
add_legacy_name("L254.Supplysector_trn") %>%
add_precursors("common/GCAM_region_names", "energy/mappings/UCD_techs", "energy/mappings/UCD_techs_revised", "energy/mappings/UCD_size_class_revisions", "L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y",
"energy/A54.sector", "energy/A54.globaltech_nonmotor", "energy/A54.globaltech_passthru", "energy/A54.globaltech_passthru_revised") ->
L254.Supplysector_trn
L254.FinalEnergyKeyword_trn %>%
add_title("Supply sector keywords for transportation sector") %>%
add_units("NA") %>%
add_comments("Final energy names were matched to supply sector information") %>%
add_legacy_name("L254.FinalEnergyKeyword_trn") %>%
add_precursors("common/GCAM_region_names", "energy/mappings/UCD_techs", "energy/mappings/UCD_techs_revised", "energy/mappings/UCD_size_class_revisions", "L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y",
"energy/A54.sector", "energy/A54.globaltech_nonmotor") ->
L254.FinalEnergyKeyword_trn
L254.tranSubsectorLogit %>%
add_title("Subsector logit exponents of transportation sector") %>%
add_units("Unitless") %>%
add_comments("Transportation default subsector logit exponents were written for all regions") %>%
add_legacy_name("L254.tranSubsectorLogit") %>%
add_precursors("common/GCAM_region_names", "energy/mappings/UCD_techs", "energy/mappings/UCD_techs_revised", "energy/mappings/UCD_size_class_revisions", "L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y",
"energy/A54.tranSubsector_logit", "energy/A54.tranSubsector_logit_revised", "energy/A54.globaltech_nonmotor") ->
L254.tranSubsectorLogit
if(exists("L254.tranSubsectorShrwt")) {
L254.tranSubsectorShrwt %>%
add_title("Subsector shareweights of transportation sector") %>%
add_units("Unitless") %>%
add_comments("Subsector shareweights of transportation sector were written for all regions and subset for existing combinations of region, supplysector, and tranSubsector") %>%
add_comments("Only rows with an entry for year were selected") %>%
add_legacy_name("L254.tranSubsectorShrwt") %>%
add_precursors("common/GCAM_region_names", "energy/mappings/UCD_techs", "energy/mappings/UCD_techs_revised", "energy/mappings/UCD_size_class_revisions", "L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y",
"energy/A54.tranSubsector_shrwt", "energy/A54.tranSubsector_shrwt_revised", "energy/A54.globaltech_nonmotor") ->
L254.tranSubsectorShrwt
} else {
missing_data() %>%
add_legacy_name("L254.tranSubsectorShrwt") ->
L254.tranSubsectorShrwt
}
if(exists("L254.tranSubsectorShrwtFllt")) {
L254.tranSubsectorShrwtFllt %>%
add_title("Subsector shareweights of transportation sector") %>%
add_units("Unitless") %>%
add_comments("Subsector shareweights of transportation sector were written for all regions and subset for existing combinations of region, supplysector, and tranSubsector") %>%
add_comments("Only rows with an entry for year.fillout were selected") %>%
add_legacy_name("L254.tranSubsectorShrwtFllt") %>%
add_precursors("common/GCAM_region_names", "energy/mappings/UCD_techs", "energy/mappings/UCD_techs_revised", "energy/mappings/UCD_size_class_revisions", "L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y",
"energy/A54.tranSubsector_shrwt", "energy/A54.globaltech_nonmotor") ->
L254.tranSubsectorShrwtFllt
} else {
missing_data() %>%
add_legacy_name("L254.tranSubsectorShrwtFllt") ->
L254.tranSubsectorShrwtFllt
}
if(exists("L254.tranSubsectorInterp")) {
L254.tranSubsectorInterp %>%
add_title("Subsector shareweight interpolation of transportation sector") %>%
add_units("Unitless") %>%
add_comments("Subsector shareweight interpoloation data of transportation sector were written for all regions and subset for existing combinations of region, supplysector, and tranSubsector") %>%
add_comments("Only rows without an entry for to.value were selected") %>%
add_legacy_name("L254.tranSubsectorInterp") %>%
add_precursors("common/GCAM_region_names", "energy/mappings/UCD_techs", "energy/mappings/UCD_techs_revised", "energy/mappings/UCD_size_class_revisions", "L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y",
"energy/A54.tranSubsector_interp", "energy/A54.tranSubsector_interp_revised", "energy/A54.globaltech_nonmotor") ->
L254.tranSubsectorInterp
} else {
missing_data() %>%
add_legacy_name("L254.tranSubsectorInterp") ->
L254.tranSubsectorInterp
}
if(exists("L254.tranSubsectorInterpTo")) {
L254.tranSubsectorInterpTo %>%
add_title("Subsector shareweight interpolation of transportation sector") %>%
add_units("Unitless") %>%
add_comments("Subsector shareweight interpoloation data of transportation sector were written for all regions and subset for existing combinations of region, supplysector, and tranSubsector") %>%
add_comments("Only rows with an entry for to.value were selected") %>%
add_legacy_name("L254.tranSubsectorInterpTo") %>%
add_precursors("common/GCAM_region_names", "energy/mappings/UCD_techs", "energy/mappings/UCD_techs_revised", "energy/mappings/UCD_size_class_revisions", "L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y",
"energy/A54.tranSubsector_interp", "energy/A54.globaltech_nonmotor") ->
L254.tranSubsectorInterpTo
} else {
missing_data() %>%
add_legacy_name("L254.tranSubsectorInterpTo") ->
L254.tranSubsectorInterpTo
}
L254.tranSubsectorSpeed %>%
add_title("Speeds of transportation modes (not including pass-through sectors)") %>%
add_units("km / hr") %>%
add_comments("Speed information was written for all regions and mapped from UCD technology to GCAM technology") %>%
add_legacy_name("L254.tranSubsectorSpeed") %>%
add_precursors("common/GCAM_region_names", "energy/mappings/UCD_techs", "energy/mappings/UCD_techs_revised", "energy/mappings/UCD_size_class_revisions", "L154.speed_kmhr_R_trn_m_sz_tech_F_Y") ->
L254.tranSubsectorSpeed
L254.tranSubsectorSpeed_passthru %>%
add_title("Speeds of pass-through transportation subsectors") %>%
add_units("km / hr") %>%
add_comments("Transportation default subsector value of time in transit (VOTT) multipliers were written for all regions and model years") %>%
add_comments("Speeds were matched in by region, supplysector, and year") %>%
add_legacy_name("L254.tranSubsectorSpeed_passthru") %>%
add_precursors("common/GCAM_region_names", "energy/A54.tranSubsector_VOTT", "energy/A54.tranSubsector_VOTT_revised",
"energy/mappings/UCD_techs", "energy/mappings/UCD_techs_revised", "energy/mappings/UCD_size_class_revisions", "L154.speed_kmhr_R_trn_m_sz_tech_F_Y") ->
L254.tranSubsectorSpeed_passthru
L254.tranSubsectorSpeed_noVOTT %>%
add_title("Speeds of transportation subsectors whose time value is not considered") %>%
add_units("km / hr") %>%
add_comments("Sector data was subsetted for only those whose speeds have not already been specified") %>%
add_legacy_name("L254.tranSubsectorSpeed_noVOTT") %>%
add_precursors("common/GCAM_region_names", "energy/mappings/UCD_techs", "energy/mappings/UCD_techs_revised", "energy/mappings/UCD_size_class_revisions", "L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y",
"energy/A54.globaltech_passthru", "energy/A54.globaltech_passthru_revised") ->
L254.tranSubsectorSpeed_noVOTT
L254.tranSubsectorSpeed_nonmotor %>%
add_title("Speeds of non-motorized transportation subsectors") %>%
add_units("km / hr") %>%
add_comments("Data was written for all model years and regions") %>%
add_legacy_name("L254.tranSubsectorSpeed_nonmotor") %>%
add_precursors("energy/A54.globaltech_nonmotor") ->
L254.tranSubsectorSpeed_nonmotor
L254.tranSubsectorVOTT %>%
add_title("Value of time in transit parameterization") %>%
add_units("Unitless") %>%
add_comments("Data was written for all regions") %>%
add_comments("Year.fillout was populated with minimum model year") %>%
add_legacy_name("L254.tranSubsectorVOTT") %>%
add_precursors("common/GCAM_region_names", "energy/A54.tranSubsector_VOTT", "energy/A54.tranSubsector_VOTT_revised",
"energy/mappings/UCD_techs", "energy/mappings/UCD_techs_revised", "energy/mappings/UCD_size_class_revisions", "L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y",
"energy/A54.globaltech_passthru", "energy/A54.globaltech_passthru_revised", "energy/A54.globaltech_nonmotor") ->
L254.tranSubsectorVOTT
L254.tranSubsectorFuelPref %>%
add_title("Subsector preferences that are tied to GDP (unrelated to time value)") %>%
add_units("Unitless") %>%
add_comments("Data was written for all regions") %>%
add_comments("Year.fillout was populated with minimum model year") %>%
add_legacy_name("L254.tranSubsectorFuelPref") %>%
add_precursors("common/GCAM_region_names",
"energy/A54.tranSubsector_VOTT", "energy/A54.tranSubsector_VOTT_revised", "energy/A54.tranSubsector_VOTT_ssp1", "energy/A54.tranSubsector_VOTT_ssp1_revised",
"energy/mappings/UCD_techs", "energy/mappings/UCD_techs_revised", "energy/mappings/UCD_size_class_revisions", "L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y",
"energy/A54.globaltech_passthru", "energy/A54.globaltech_passthru_revised", "energy/A54.globaltech_nonmotor") ->
L254.tranSubsectorFuelPref
L254.StubTranTech %>%
add_title("Transportation stub technologies (built from technologies with coefficients in the UCD database)") %>%
add_units("NA") %>%
add_comments("Data was written for all regions and mapped from UCD technology to GCAM technology") %>%
add_legacy_name("L254.StubTranTech") %>%
add_precursors("common/GCAM_region_names", "energy/mappings/UCD_techs", "energy/mappings/UCD_techs_revised", "energy/mappings/UCD_size_class_revisions", "L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y") ->
L254.StubTranTech
L254.StubTech_passthru %>%
add_title("Transportation stub technologies (passthru)") %>%
add_units("NA") %>%
add_comments("Data was written for all regions and subsetted for only the ones whose input actually exists in the given region or ones whose input is in the list of pass-through technologies") %>%
add_legacy_name("L254.StubTech_passthru") %>%
add_precursors("common/GCAM_region_names", "energy/mappings/UCD_techs", "energy/mappings/UCD_techs_revised", "energy/mappings/UCD_size_class_revisions", "L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y",
"energy/A54.globaltech_passthru", "energy/A54.globaltech_passthru_revised") ->
L254.StubTech_passthru
L254.StubTech_nonmotor %>%
add_title("Non-motorized transportation stub technologies") %>%
add_units("NA") %>%
add_comments("Data was written for all regions") %>%
add_legacy_name("L254.StubTech_nonmotor") %>%
add_precursors("common/GCAM_region_names", "energy/A54.globaltech_nonmotor") ->
L254.StubTech_nonmotor
L254.GlobalTechShrwt_passthru %>%
add_title("Shareweights of global transportation sector technologies (not tranTechnologies)") %>%
add_units("Unitless") %>%
add_comments("Data was written for all model years") %>%
add_legacy_name("L254.GlobalTechShrwt_passthru") %>%
add_precursors("energy/A54.globaltech_passthru", "energy/A54.globaltech_passthru_revised") ->
L254.GlobalTechShrwt_passthru
L254.GlobalTechShrwt_nonmotor %>%
add_title("Shareweights of non-motorized global transportation sector technologies (not tranTechnologies)") %>%
add_units("Unitless") %>%
add_comments("Data was written for all model years") %>%
add_legacy_name("L254.GlobalTechShrwt_nonmotor") %>%
add_precursors("energy/A54.globaltech_nonmotor") ->
L254.GlobalTechShrwt_nonmotor
L254.GlobalTechCoef_passthru %>%
add_title("Coefficients of global transportation sector technologies (not tranTechnologies)") %>%
add_units("Unitless") %>%
add_comments("Coefficients were written for all model years") %>%
add_legacy_name("L254.GlobalTechCoef_passthru") %>%
add_precursors("energy/A54.globaltech_passthru", "energy/A54.globaltech_passthru_revised") ->
L254.GlobalTechCoef_passthru
L254.GlobalRenewTech_nonmotor %>%
add_title("Renewable inputs to non-motorized transportation technologies") %>%
add_units("NA") %>%
add_comments("Renewable inputs were written for all model years") %>%
add_legacy_name("L254.GlobalRenewTech_nonmotor") %>%
add_precursors("energy/A54.globaltech_nonmotor") ->
L254.GlobalRenewTech_nonmotor
L254.GlobalTranTechInterp %>%
add_title("Shareweight interpolation of global tranTechnologies") %>%
add_units("NA") %>%
add_comments("Populated placeholders for final calibration year and end year") %>%
add_legacy_name("L254.GlobalTranTechInterp") %>%
add_precursors("energy/A54.globaltranTech_interp", "energy/A54.globaltranTech_interp_revised") ->
L254.GlobalTranTechInterp
L254.GlobalTranTechShrwt %>%
add_title("Shareweights of global tranTechnologies") %>%
add_units("Unitless") %>%
add_comments("Data was subsetted for model years") %>%
add_legacy_name("L254.GlobalTranTechShrwt") %>%
add_precursors("energy/A54.globaltranTech_shrwt", "energy/A54.globaltranTech_shrwt_revised") ->
L254.GlobalTranTechShrwt
L254.GlobalTranTechSCurve %>%
add_title("Retirement of global tranTechnologies") %>%
add_units("lifetime and half-life in years") %>%
add_comments("Retirement parameters in the final year of the base data were carried forward to all future time periods") %>%
add_legacy_name("L254.GlobalTranTechSCurve") %>%
add_precursors("energy/A54.globaltranTech_retire", "energy/A54.globaltranTech_retire_revised") ->
L254.GlobalTranTechSCurve
L254.GlobalTranTechProfitShutdown %>%
add_title("Profit shutdown parameters of global tranTechnologies") %>%
add_units("unitless function parameters") %>%
add_comments("Profit shutdown parameters in the final year of the base data were carried forward to all future time periods") %>%
same_precursors_as(L254.GlobalTranTechSCurve) ->
L254.GlobalTranTechProfitShutdown
L254.StubTranTechCalInput %>%
add_title("Calibrated input of tranTechnologies") %>%
add_units("Unitless") %>%
add_comments("Share weights were calculate by aggregating energy consumption to the region, supplysector, tranSubsector, year level") %>%
add_legacy_name("L254.StubTranTechCalInput") %>%
add_precursors("common/GCAM_region_names", "energy/mappings/UCD_techs", "energy/mappings/UCD_techs_revised", "energy/mappings/UCD_size_class_revisions", "L154.in_EJ_R_trn_m_sz_tech_F_Yh") ->
L254.StubTranTechCalInput
L254.StubTranTechLoadFactor %>%
add_title("TranTechnology load factors (all periods)") %>%
add_units("person/vehicle and tonnes/vehicle") %>%
add_comments("Data was subsetted to model years and mapped from UCD technology to GCAM technology") %>%
add_legacy_name("L254.StubTranTechLoadFactor") %>%
add_precursors("common/GCAM_region_names", "energy/mappings/UCD_techs", "energy/mappings/UCD_techs_revised", "energy/mappings/UCD_size_class_revisions", "L154.loadfactor_R_trn_m_sz_tech_F_Y") ->
L254.StubTranTechLoadFactor
L254.StubTranTechCost %>%
add_title("TranTechnology costs (all periods)") %>%
add_units("$1990USD / vkm") %>%
add_comments("Non-fuel cost was adjusted to 1990") %>%
add_comments("Transportation cost table was mapped from UCD technology to GCAM technology") %>%
add_legacy_name("L254.StubTranTechCost") %>%
add_precursors("common/GCAM_region_names", "energy/mappings/UCD_techs", "energy/mappings/UCD_techs_revised", "energy/mappings/UCD_size_class_revisions", "L154.cost_usdvkm_R_trn_m_sz_tech_F_Y") ->
L254.StubTranTechCost
L254.StubTranTechCoef %>%
add_title("TranTechnology coefficients (intensities; all periods)") %>%
add_units("BTU / vkm") %>%
add_comments("MJ was converted to BTU") %>%
add_comments("Vehicle energy intensity information was mapped from UCD technology to GCAM technology") %>%
add_legacy_name("L254.StubTranTechCoef") %>%
add_precursors("common/GCAM_region_names", "energy/mappings/UCD_techs", "energy/mappings/UCD_techs_revised", "energy/mappings/UCD_size_class_revisions", "L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y") ->
L254.StubTranTechCoef
L254.StubTechCalInput_passthru %>%
add_title("Calibrated input of passthrough technologies") %>%
add_units("Unitless") %>%
add_comments("Pass-through transportationtechnologies were written to all regions") %>%
add_comments("Share weights were calculated from calibrated output values") %>%
add_legacy_name("L254.StubTechCalInput_passthru") %>%
add_precursors("common/GCAM_region_names", "energy/mappings/UCD_techs", "energy/mappings/UCD_techs_revised", "energy/mappings/UCD_size_class_revisions",
"L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y", "energy/A54.globaltech_passthru", "energy/A54.globaltech_passthru_revised") ->
L254.StubTechCalInput_passthru
L254.StubTechProd_nonmotor %>%
add_title("Service output of non-motorized transportation technologies") %>%
add_units("Million pass-km") %>%
add_comments("Supply sector and stub.technology information was added to non-motorized service output information") %>%
add_legacy_name("L254.StubTechProd_nonmotor") %>%
add_precursors("common/GCAM_region_names", "energy/A54.sector", "L154.out_mpkm_R_trn_nonmotor_Yh") ->
L254.StubTechProd_nonmotor
L254.PerCapitaBased_trn %>%
add_title("Per-capita based flag for transportation final demand") %>%
add_units("NA") %>%
add_comments("Per-capita based flag information written for all GCAM regions") %>%
add_legacy_name("L254.PerCapitaBased_trn") %>%
add_precursors("common/GCAM_region_names", "energy/A54.demand", "energy/A54.demand_ssp1") ->
L254.PerCapitaBased_trn
L254.PriceElasticity_trn %>%
add_title("Price elasticity of transportation final demand") %>%
add_units("Unitless") %>%
add_comments("Price elasticity information written for all GCAM regions and model future years") %>%
add_legacy_name("L254.PriceElasticity_trn") %>%
add_precursors("common/GCAM_region_names", "energy/A54.demand") ->
L254.PriceElasticity_trn
L254.IncomeElasticity_trn %>%
add_title("Income elasticity of transportation final demand") %>%
add_units("Unitless") %>%
add_comments("Income elasticity information written for all GCAM regions and model future years") %>%
add_legacy_name("L254.IncomeElasticity_trn") %>%
add_precursors("common/GCAM_region_names", "energy/A54.demand") ->
L254.IncomeElasticity_trn
L254.BaseService_trn %>%
add_title("Base-year service output of transportation final demand") %>%
add_units("units") %>%
add_comments("comments describing how data generated") %>%
add_legacy_name("L254.BaseService_trn") %>%
add_precursors("common/GCAM_region_names", "energy/A54.sector", "energy/mappings/UCD_techs", "energy/mappings/UCD_techs_revised", "energy/mappings/UCD_size_class_revisions",
"L154.out_mpkm_R_trn_nonmotor_Yh", "L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y",
"L154.loadfactor_R_trn_m_sz_tech_F_Y", "L154.in_EJ_R_trn_m_sz_tech_F_Yh") ->
L254.BaseService_trn
return_data(L254.Supplysector_trn, L254.FinalEnergyKeyword_trn, L254.tranSubsectorLogit,
L254.tranSubsectorShrwt, L254.tranSubsectorShrwtFllt, L254.tranSubsectorInterp,
L254.tranSubsectorInterpTo, L254.tranSubsectorSpeed, L254.tranSubsectorSpeed_passthru,
L254.tranSubsectorSpeed_noVOTT, L254.tranSubsectorSpeed_nonmotor, L254.tranSubsectorVOTT,
L254.tranSubsectorFuelPref, L254.StubTranTech, L254.StubTech_passthru, L254.StubTech_nonmotor,
L254.GlobalTechShrwt_passthru, L254.GlobalTechShrwt_nonmotor, L254.GlobalTechCoef_passthru,
L254.GlobalRenewTech_nonmotor, L254.GlobalTranTechInterp, L254.GlobalTranTechShrwt,
L254.GlobalTranTechSCurve, L254.GlobalTranTechProfitShutdown, L254.StubTranTechCalInput, L254.StubTranTechLoadFactor,
L254.StubTranTechCost, L254.StubTranTechCoef, L254.StubTechCalInput_passthru,
L254.StubTechProd_nonmotor, L254.PerCapitaBased_trn, L254.PriceElasticity_trn,
L254.IncomeElasticity_trn, L254.BaseService_trn)
} else {
stop("Unknown command")
}
}
bpbond/gcamdata documentation built on March 22, 2023, 4:52 a.m.
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Defines functions module_energy_L254.transportation_UCD
Documented in module_energy_L254.transportation_UCD
# Copyright 2019 Battelle Memorial Institute; see the LICENSE file.
#' module_energy_L254.transportation_UCD
#'
#' Calculate transportation data using information from the global UCD transportation technology database.
#'
#' @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.Supplysector_trn}, \code{L254.FinalEnergyKeyword_trn}, \code{L254.tranSubsectorLogit},
#' \code{L254.tranSubsectorShrwt}, \code{L254.tranSubsectorShrwtFllt}, \code{L254.tranSubsectorInterp},
#' \code{L254.tranSubsectorInterpTo}, \code{L254.tranSubsectorSpeed}, \code{L254.tranSubsectorSpeed_passthru},
#' \code{L254.tranSubsectorSpeed_noVOTT}, \code{L254.tranSubsectorSpeed_nonmotor}, \code{L254.tranSubsectorVOTT},
#' \code{L254.tranSubsectorFuelPref}, \code{L254.StubTranTech}, \code{L254.StubTech_passthru}, \code{L254.StubTech_nonmotor},
#' \code{L254.GlobalTechShrwt_passthru}, \code{L254.GlobalTechShrwt_nonmotor}, \code{L254.GlobalTechCoef_passthru},
#' \code{L254.GlobalRenewTech_nonmotor}, \code{L254.GlobalTranTechInterp}, \code{L254.GlobalTranTechShrwt},
#' \code{L254.GlobalTranTechSCurve}, \code{L254.StubTranTechCalInput}, \code{L254.StubTranTechLoadFactor},
#' \code{L254.StubTranTechCost}, \code{L254.StubTranTechCoef}, \code{L254.StubTechCalInput_passthru},
#' \code{L254.StubTechProd_nonmotor}, \code{L254.PerCapitaBased_trn}, \code{L254.PriceElasticity_trn},
#' \code{L254.IncomeElasticity_trn}, \code{L254.BaseService_trn}. The corresponding file in the
#' original data system was \code{L254.transportation_UCD.R} (energy level2).
#' @details Due to the asymmetrical nature of the transportation sectors in the various regions, we can't simply write
#' generic information to all regions. Instead, technology information is read from the global UCD transportation
#' technology database, and supplysector and subsector attributes are matched in from lookup tables.
#' @importFrom assertthat assert_that
#' @importFrom dplyr anti_join arrange bind_rows filter if_else group_by left_join mutate one_of pull select semi_join summarise contains desc
#' @importFrom tidyr complete nesting
#' @author AJS September 2017
module_energy_L254.transportation_UCD <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "common/GCAM_region_names",
FILE = "energy/mappings/UCD_techs",
FILE = "energy/mappings/UCD_techs_revised",
FILE = "energy/A54.demand",
FILE = "energy/A54.demand_ssp1",
FILE = "energy/A54.sector",
FILE = "energy/A54.tranSubsector_logit",
FILE = "energy/A54.tranSubsector_shrwt",
FILE = "energy/A54.tranSubsector_interp",
FILE = "energy/A54.tranSubsector_VOTT",
FILE = "energy/A54.tranSubsector_VOTT_ssp1",
FILE= "energy/mappings/UCD_size_class_revisions",
FILE = "energy/A54.tranSubsector_VOTT_ssp1_revised",
FILE = "energy/A54.tranSubsector_VOTT_revised",
FILE = "energy/A54.tranSubsector_interp_revised",
FILE = "energy/A54.tranSubsector_shrwt_revised",
FILE = "energy/A54.tranSubsector_logit_revised",
FILE = "energy/A54.globaltranTech_retire_revised",
FILE = "energy/A54.globaltranTech_shrwt_revised",
FILE= "energy/A54.globaltranTech_interp_revised",
FILE = "energy/A54.globaltech_passthru",
FILE = "energy/A54.globaltech_passthru_revised",
FILE = "energy/A54.globaltech_nonmotor",
FILE = "energy/A54.globaltranTech_shrwt",
FILE = "energy/A54.globaltranTech_interp",
FILE = "energy/A54.globaltranTech_retire",
"L154.in_EJ_R_trn_m_sz_tech_F_Yh",
"L154.cost_usdvkm_R_trn_m_sz_tech_F_Y",
"L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y",
"L154.loadfactor_R_trn_m_sz_tech_F_Y",
"L154.speed_kmhr_R_trn_m_sz_tech_F_Y",
"L154.out_mpkm_R_trn_nonmotor_Yh"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L254.Supplysector_trn",
"L254.FinalEnergyKeyword_trn",
"L254.tranSubsectorLogit",
"L254.tranSubsectorShrwt",
"L254.tranSubsectorShrwtFllt",
"L254.tranSubsectorInterp",
"L254.tranSubsectorInterpTo",
"L254.tranSubsectorSpeed",
"L254.tranSubsectorSpeed_passthru",
"L254.tranSubsectorSpeed_noVOTT",
"L254.tranSubsectorSpeed_nonmotor",
"L254.tranSubsectorVOTT",
"L254.tranSubsectorFuelPref",
"L254.StubTranTech",
"L254.StubTech_passthru",
"L254.StubTech_nonmotor",
"L254.GlobalTechShrwt_passthru",
"L254.GlobalTechShrwt_nonmotor",
"L254.GlobalTechCoef_passthru",
"L254.GlobalRenewTech_nonmotor",
"L254.GlobalTranTechInterp",
"L254.GlobalTranTechShrwt",
"L254.GlobalTranTechSCurve",
"L254.GlobalTranTechProfitShutdown",
"L254.StubTranTechCalInput",
"L254.StubTranTechLoadFactor",
"L254.StubTranTechCost",
"L254.StubTranTechCoef",
"L254.StubTechCalInput_passthru",
"L254.StubTechProd_nonmotor",
"L254.PerCapitaBased_trn",
"L254.PriceElasticity_trn",
"L254.IncomeElasticity_trn",
"L254.BaseService_trn"))
} else if(command == driver.MAKE) {
all_data <- list(...)[[1]]
# Silence package notes
GCAM_region_ID <- tranTechnology <- region <- supplysector <- . <- technology <- minicam.energy.input <- r_mei <-
year <- year.fillout <- to.value <- value <- speed.source <- tranSubsector.x <- addTimeValue <- time.value.multiplier <-
fuelprefElasticity <- tranSubsector <- share.weight <- calibrated.value <- subs.share.weight <- loadFactor <-
coefficient <- stub.technology <- output <- output_agg <- output_cum <- share.weight.year <- tech.share.weight <-
calOutputValue <- energy.final.demand <- base.service <- object <- r_ss <- UCD_region <- size.class <- sce <-
steepness <- profit.shutdown.steepness <- NULL
# Load required inputs
GCAM_region_names <- get_data(all_data, "common/GCAM_region_names",strip_attributes = TRUE)
UCD_techs <- get_data(all_data, "energy/mappings/UCD_techs",strip_attributes = TRUE)
A54.demand <- get_data(all_data, "energy/A54.demand",strip_attributes = TRUE) %>% mutate(sce=paste0("CORE"))
# This is a special case for SSP1, and the way this is executed will likely change in the future.
# Note that the variable is defined in constants.R
A54.demand_SSP1 <- get_data(all_data, "energy/A54.demand_ssp1",strip_attributes = TRUE)%>% mutate(sce=paste0("SSP1"))
A54.demand<-bind_rows(A54.demand,A54.demand_SSP1)
A54.sector <- get_data(all_data, "energy/A54.sector",strip_attributes = TRUE)
#kbn 2019-10-11 Insert code to use revised versions for subsectors below
Size_class_New<- get_data(all_data, "energy/mappings/UCD_size_class_revisions",strip_attributes = TRUE) %>%
select(-UCD_region) %>%
distinct()
#kbn 2020-03-26 If the user selects the revised modes and size classes, use the revised mapping files.
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'
}
if (toString(energy.TRAN_UCD_MODE)=='rev.mode'){
A54.tranSubsector_logit <- get_data(all_data, "energy/A54.tranSubsector_logit_revised",strip_attributes = TRUE)
A54.tranSubsector_shrwt <- get_data(all_data, "energy/A54.tranSubsector_shrwt_revised",strip_attributes = TRUE)
A54.tranSubsector_interp <- get_data(all_data, "energy/A54.tranSubsector_interp_revised",strip_attributes = TRUE)
A54.tranSubsector_VOTT <- get_data(all_data, "energy/A54.tranSubsector_VOTT_revised",strip_attributes = TRUE) %>% mutate(sce=paste0("CORE"))
A54.tranSubsector_VOTT_SSP1 <- get_data(all_data, "energy/A54.tranSubsector_VOTT_ssp1_revised",strip_attributes = TRUE) %>% mutate(sce=paste0("SSP1"))
A54.tranSubsector_VOTT<- bind_rows(A54.tranSubsector_VOTT,A54.tranSubsector_VOTT_SSP1)
A54.globaltranTech_retire <- get_data(all_data, "energy/A54.globaltranTech_retire_revised",strip_attributes = TRUE)
A54.globaltranTech_shrwt <- get_data(all_data, "energy/A54.globaltranTech_shrwt_revised",strip_attributes = TRUE)
A54.globaltranTech_interp <- get_data(all_data, "energy/A54.globaltranTech_interp_revised",strip_attributes = TRUE)
A54.globaltech_passthru <- get_data(all_data, "energy/A54.globaltech_passthru_revised",strip_attributes = TRUE)
}
else {A54.tranSubsector_logit <- get_data(all_data, "energy/A54.tranSubsector_logit",strip_attributes = TRUE)
A54.tranSubsector_shrwt <- get_data(all_data, "energy/A54.tranSubsector_shrwt",strip_attributes = TRUE)
A54.tranSubsector_interp <- get_data(all_data, "energy/A54.tranSubsector_interp",strip_attributes = TRUE)
A54.tranSubsector_VOTT <- get_data(all_data, "energy/A54.tranSubsector_VOTT",strip_attributes = TRUE)
A54.tranSubsector_VOTT <- get_data(all_data, "energy/A54.tranSubsector_VOTT",strip_attributes = TRUE)
A54.tranSubsector_VOTT_SSP1 <- get_data(all_data, "energy/A54.tranSubsector_VOTT_ssp1",strip_attributes = TRUE) %>% mutate(sce=paste0("SSP1"))
A54.tranSubsector_VOTT<- bind_rows(A54.tranSubsector_VOTT,A54.tranSubsector_VOTT_SSP1)
A54.globaltranTech_retire <- get_data(all_data, "energy/A54.globaltranTech_retire",strip_attributes = TRUE)
A54.globaltranTech_shrwt <- get_data(all_data, "energy/A54.globaltranTech_shrwt",strip_attributes = TRUE)
A54.globaltranTech_interp <- get_data(all_data, "energy/A54.globaltranTech_interp",strip_attributes = TRUE)
A54.globaltech_passthru <- get_data(all_data, "energy/A54.globaltech_passthru",strip_attributes = TRUE)
}
A54.globaltech_nonmotor <- get_data(all_data, "energy/A54.globaltech_nonmotor",strip_attributes = TRUE)
L154.in_EJ_R_trn_m_sz_tech_F_Yh <- get_data(all_data, "L154.in_EJ_R_trn_m_sz_tech_F_Yh",strip_attributes = TRUE)
L154.cost_usdvkm_R_trn_m_sz_tech_F_Y <- get_data(all_data, "L154.cost_usdvkm_R_trn_m_sz_tech_F_Y",strip_attributes = TRUE)
L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y <- get_data(all_data, "L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y",strip_attributes = TRUE)
L154.loadfactor_R_trn_m_sz_tech_F_Y <- get_data(all_data, "L154.loadfactor_R_trn_m_sz_tech_F_Y",strip_attributes = TRUE)
L154.speed_kmhr_R_trn_m_sz_tech_F_Y <- get_data(all_data, "L154.speed_kmhr_R_trn_m_sz_tech_F_Y",strip_attributes = TRUE)
L154.out_mpkm_R_trn_nonmotor_Yh <- get_data(all_data, "L154.out_mpkm_R_trn_nonmotor_Yh",strip_attributes = TRUE)
# ===================================================
# PART A: BUILDING TRANSPORTATION SECTORS FROM THE TECHNOLOGY LEVEL UP
# L254.StubTranTech: Transportation stub technologies (built from technologies with coefficients in the UCD database)
#kbn 2020-06-02 Making changes to generate xmls for SSPs flexibly- We are now calculating values for the transportation variables for all SSPs in LA154.
# We will also calculate calibrated values, co-efficients by SSPs. Everywhere, we will split out results from the core and the SSPs and calculate values for
# all scenarios separately. See search string kbn 2020-02-06 for changes. We are calculating these separate values for sectors and subsectors (Part A, Part B),
# andcalibration and region specific data (Part E).
#kbn 2019-10-14 Switching to left_join_keep_first, since we have fewer mode categories now.
#kbn 2020-06-02 Adding sce below (See description of changes using search string kbn 2020-06-02 Making changes to generate xmls for SSPs flexibly)
L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y %>%
select(GCAM_region_ID, one_of(c("UCD_sector", "mode", "size.class", "UCD_technology", "UCD_fuel","sce"))) %>%
# Match in region names and UCD techs
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_keep_first_only(UCD_techs, by = c("UCD_sector", "mode", "size.class", "UCD_technology", "UCD_fuel")) %>%
unique() %>%
rename(stub.technology = tranTechnology) %>%
select(LEVEL2_DATA_NAMES[["StubTranTech"]],sce) ->
L254.StubTranTech # OUTPUT
# Write the pass-through technologies to all regions
# First, create two lists to filter technologies later
L254.StubTranTech %>%
mutate(r_ss = paste(region, supplysector)) %>%
pull(r_ss) %>%
unique() ->
LIST_r_ss
# A54.globaltech_passthru reports transportation technology defaults (all parameters; pass-through technologies only)
A54.globaltech_passthru %>%
rename(stub.technology = technology) %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["StubTranTech"]], "minicam.energy.input"), GCAM_region_names = GCAM_region_names) %>%
# Of the pass-throughs, subset only the ones whose "input" actually exists in the given region OR
# ones whose input is in the list of pass-through technologies.
mutate(r_mei = paste(region, minicam.energy.input)) %>%
filter((r_mei %in% LIST_r_ss) |
(minicam.energy.input %in% A54.globaltech_passthru$supplysector)) %>%
select(LEVEL2_DATA_NAMES[["StubTranTech"]]) %>%
mutate(sce = paste0("CORE"))->
L254.StubTech_passthru # OUTPUT
# Write the non-motorized technologies to all regions
A54.globaltech_nonmotor %>%
rename(stub.technology = technology) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["StubTranTech"]], GCAM_region_names = GCAM_region_names) %>%
mutate(sce = paste0("CORE"))->
L254.StubTech_nonmotor # OUTPUT
# L254.Supplysector_trn: Supply sector information for transportation sector
# Writing the generic supplysector table to all regions may generate combinations that don't apply
# Use this for filtering below
r_ss_all <- bind_rows(L254.StubTranTech, L254.StubTech_passthru, L254.StubTech_nonmotor)
A54.sector %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["Supplysector"]], LOGIT_TYPE_COLNAME), GCAM_region_names = GCAM_region_names) %>%
# Subset only the combinations of region and supplysector that are available in the stub technology table
mutate(sce = paste0("CORE")) %>%
semi_join(r_ss_all, by = c("region", "supplysector","sce")) %>%
select(c(LEVEL2_DATA_NAMES[["Supplysector"]], LOGIT_TYPE_COLNAME,sce)) ->
L254.Supplysector_trn # OUTPUT
# L254.FinalEnergyKeyword_trn: Supply sector keywords for transportation sector
L254.Supplysector_trn %>%
left_join_keep_first_only(A54.sector, by = "supplysector") %>%
select(LEVEL2_DATA_NAMES[["FinalEnergyKeyword"]],sce) %>%
mutate(sce=paste0("CORE"))->
L254.FinalEnergyKeyword_trn # OUTPUT
# PART B: SUBSECTOR INFORMATION
# L254.tranSubsectorLogit: Subsector logit exponents of transportation sector
LEVEL2_DATA_NAMES[["tranSubsector"]] <- c("region", "supplysector", "tranSubsector")
# Use this for filtering datasets below
r_ss_ts_all <- bind_rows(L254.StubTranTech, L254.StubTech_passthru, L254.StubTech_nonmotor)
# A54.tranSubsector_logit reports transportation default subsector logit exponents
A54.tranSubsector_logit %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["tranSubsectorLogit"]], LOGIT_TYPE_COLNAME), GCAM_region_names = GCAM_region_names) %>%
mutate(sce = paste0("CORE") ) %>%
# Subset only the combinations of region, supplysector, and tranSubsector that are available
semi_join(r_ss_ts_all, by = c("region", "supplysector", "tranSubsector","sce")) %>%
select(c(LEVEL2_DATA_NAMES[["tranSubsectorLogit"]], LOGIT_TYPE_COLNAME),sce) ->
L254.tranSubsectorLogit # OUTPUT
# L254.tranSubsectorShrwt and L254.tranSubsectorShrwtFllt: Subsector shareweights of transportation sector
if(any(!is.na(A54.tranSubsector_shrwt$year))) {
A54.tranSubsector_shrwt %>%
filter(!is.na(year)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["tranSubsectorShrwt"]], GCAM_region_names = GCAM_region_names) %>%
mutate(sce = paste0("CORE"),year = as.integer(year)) %>%
# Subset only the combinations of region, supplysector, and tranSubsector that are available
semi_join(r_ss_ts_all, by = c("region", "supplysector", "tranSubsector","sce")) %>%
select(LEVEL2_DATA_NAMES[["tranSubsectorShrwt"]],sce) ->
L254.tranSubsectorShrwt # OUTPUT
}
if(any(!is.na(A54.tranSubsector_shrwt$year.fillout))) {
A54.tranSubsector_shrwt %>%
filter(!is.na(year.fillout)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["tranSubsectorShrwtFllt"]], GCAM_region_names = GCAM_region_names) %>%
mutate(sce = paste0("CORE"),year.fillout = as.integer(year.fillout)) %>%
# Subset only the combinations of region, supplysector, and tranSubsector that are available
semi_join(r_ss_ts_all, by = c("region", "supplysector", "tranSubsector","sce")) %>%
select(LEVEL2_DATA_NAMES[["tranSubsectorShrwtFllt"]],sce) ->
L254.tranSubsectorShrwtFllt # OUTPUT
}
# L254.tranSubsectorInterp and L254.tranSubsectorInterpTo: Subsector shareweight interpolation of transportation sector
if(any(is.na(A54.tranSubsector_interp$to.value))) {
A54.tranSubsector_interp %>%
filter(is.na(to.value)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["tranSubsectorInterp"]], GCAM_region_names = GCAM_region_names) %>%
mutate(sce = paste0("CORE")) %>%
# Subset only the combinations of region, supplysector, and tranSubsector that are available
semi_join(r_ss_ts_all, by = c("region", "supplysector", "tranSubsector","sce")) %>%
select(LEVEL2_DATA_NAMES[["tranSubsectorInterp"]],sce) ->
L254.tranSubsectorInterp # OUTPUT
}
if(any(!is.na(A54.tranSubsector_interp$to.value))) {
A54.tranSubsector_interp %>%
filter(!is.na(to.value)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["tranSubsectorInterpTo"]], GCAM_region_names = GCAM_region_names) %>%
# Subset only the combinations of region, supplysector, and tranSubsector that are available
semi_join(r_ss_ts_all, by = c("region", "supplysector", "tranSubsector","sce")) %>%
select(LEVEL2_DATA_NAMES[["tranSubsectorInterpTo"]],sce) ->
L254.tranSubsectorInterp # OUTPUT
}
# L254.tranSubsectorSpeed: Speeds of transportation modes (not including pass-through sectors)
#kbn 2019-10-14 Switching to left_join_keep_first, since we have fewer mode categories now.
#kbn 2020-06-02 Updating with sce below (See description of changes using search string kbn 2020-06-02 Making changes to generate xmls for SSPs flexibly)
L154.speed_kmhr_R_trn_m_sz_tech_F_Y %>%
filter(year %in% MODEL_YEARS) %>%
# Match in GCAM region names and UCD technologies
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_keep_first_only(UCD_techs, by = c("UCD_sector", "mode", "size.class", "UCD_technology", "UCD_fuel")) %>%
mutate(speed = round(value, energy.DIGITS_SPEED)) %>%
select(LEVEL2_DATA_NAMES[["tranSubsectorSpeed"]],sce) %>%
distinct()->
L254.tranSubsectorSpeed # OUTPUT
# This does not include the pass-through tranSectors
# Pass-through tranSubsectors for which time value is added are assigned a sector from which to get their speed.
# L254.tranSubsectorSpeed_passthru: speeds of pass-through transportation subsectors
# A54.tranSubsector_VOTT reports transportation default subsector value of time in transit multipliers (VOTT = wage rate * this factor)
A54.tranSubsector_VOTT %>%
filter(!is.na(speed.source)) %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["tranSubsector"]], "speed.source","sce"), GCAM_region_names = GCAM_region_names) %>%
repeat_add_columns(tibble(year = MODEL_YEARS)) %>%
#mutate(sce= paste0("CORE")) %>%
# Match in speed
left_join_keep_first_only(L254.tranSubsectorSpeed, by = c("region", "speed.source" = "supplysector", "year","sce")) %>%
rename(tranSubsector = tranSubsector.x) %>%
select(LEVEL2_DATA_NAMES[["tranSubsector"]], "year", "speed","sce") %>%
#We won't have historical data on speed for the SSPs
na.omit()->
L254.tranSubsectorSpeed_passthru
# L254.tranSubsectorSpeed_noVOTT: Speeds of transportation subsectors whose time value is not considered
# NOTE: This step should be unnecessary. Currently there is no model default value for speed, and a subsector
# with no speed read in will cause a model crash, even for modes such as freight where time value is not modeled
# Start with all observed subsectors in the transportation module
# Use this for filtering below
r_ss_ts_speed_all <- bind_rows(L254.tranSubsectorSpeed, L254.tranSubsectorSpeed_passthru)
#kbn 2020-02-06 Adding sce below (See description of changes using search string kbn 2020-06-02 Making changes to generate xmls for SSPs flexibly)
L254.StubTranTech %>%
select(LEVEL2_DATA_NAMES[["tranSubsector"]],sce) %>%
# First subset table
bind_rows(select(L254.StubTech_passthru , one_of(LEVEL2_DATA_NAMES[["tranSubsector"]]),sce)) %>%
unique() %>%
# Subset only those whose speeds have NOT already been specified
anti_join(r_ss_ts_speed_all, by = c("region", "supplysector", "tranSubsector","sce")) %>%
# Repeat by the number of model time periods
repeat_add_columns(tibble(year = MODEL_YEARS)) %>%
# Write in a default value for speed
mutate(speed = 1) ->
L254.tranSubsectorSpeed_noVOTT
# L254.tranSubsectorSpeed_nonmotor: Speeds of non-motorized transportation subsectors
A54.globaltech_nonmotor %>%
repeat_add_columns(tibble(year = MODEL_YEARS)) %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["tranSubsector"]], "year", "speed"), GCAM_region_names = GCAM_region_names) %>%
mutate(sce= paste0("CORE"))->
L254.tranSubsectorSpeed_nonmotor
# L254.tranSubsectorVOTT: Value of time in transit parameterization
# NOTE: These are currently considered time- and region-independent characteristics
A54.tranSubsector_VOTT %>%
filter(addTimeValue == 1) %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["tranSubsector"]], "addTimeValue", "time.value.multiplier","sce"),
GCAM_region_names = GCAM_region_names) %>%
mutate(year.fillout = min(MODEL_YEARS),
sce= if_else(is.na(sce),"CORE",sce)) %>%
# Subset only the combinations of region, supplysector, and tranSubsector
semi_join(r_ss_ts_all, by = c("region", "supplysector", "tranSubsector","sce")) %>%
select(LEVEL2_DATA_NAMES[["tranSubsector"]], year.fillout, addTimeValue, time.value.multiplier,sce) %>%
na.omit()->
L254.tranSubsectorVOTT
# L254.tranSubsectorFuelPref: Subsector preferences that are tied to GDP (unrelated to time value)
A54.tranSubsector_VOTT %>%
filter(fuelprefElasticity != 0) %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["tranSubsector"]], "fuelprefElasticity","sce"),
GCAM_region_names = GCAM_region_names) %>%
mutate(year.fillout = min(MODEL_YEARS)) %>%
#mutate(sce = paste0("CORE")) %>%
# Subset only the combinations of region, supplysector, and tranSubsector that are available
semi_join(r_ss_ts_all, by = c("region", "supplysector", "tranSubsector")) %>%
select(LEVEL2_DATA_NAMES[["tranSubsector"]], year.fillout, fuelprefElasticity,sce) %>% na.omit() ->
L254.tranSubsectorFuelPref # OUTPUT
# PART C: TECHNOLOGY INFORMATION: GLOBAL TECHNOLOGIES (i.e., not tranTechnologies)
# L254.GlobalTechShrwt_passthru: Shareweights of global transportation sector technologies (not tranTechnologies)
A54.globaltech_passthru %>%
repeat_add_columns(tibble(year = MODEL_YEARS)) %>%
mutate(sce= paste0("CORE")) %>%
rename(sector.name = supplysector, subsector.name = tranSubsector) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechYr"]], share.weight) ->
L254.GlobalTechShrwt_passthru
# L254.GlobalTechShrwt_nonmotor: Shareweights of non-motorized global transportation sector technologies (not tranTechnologies)
A54.globaltech_nonmotor %>%
repeat_add_columns(tibble(year = MODEL_YEARS)) %>%
mutate(sce =paste0("CORE")) %>%
rename(sector.name = supplysector, subsector.name = tranSubsector) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechYr"]], share.weight) ->
L254.GlobalTechShrwt_nonmotor
# L254.GlobalTechCoef_passthru: Coefficients of global transportation sector technologies (not tranTechnologies)
A54.globaltech_passthru %>%
repeat_add_columns(tibble(year = MODEL_YEARS)) %>%
mutate(sce =paste0("CORE")) %>%
rename(sector.name = supplysector, subsector.name = tranSubsector) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechCoef"]]) ->
L254.GlobalTechCoef_passthru
# L254.GlobalRenewTech_nonmotor: Renewable inputs to non-motorized transportation technologies
A54.globaltech_nonmotor %>%
repeat_add_columns(tibble(year = MODEL_YEARS)) %>%
mutate(sce =paste0("CORE")) %>%
rename(sector.name = supplysector, subsector.name = tranSubsector) %>%
select(LEVEL2_DATA_NAMES[["GlobalRenewTech"]]) ->
L254.GlobalRenewTech_nonmotor
# PART D: TECHNOLOGY INFORMATION - GLOBAL TRANTECHNOLOGIES
# L254.GlobalTranTechInterp: Shareweight interpolation of global tranTechnologies
A54.globaltranTech_interp %>%
mutate(supplysector = supplysector) %>% # create new tibble, stripping attributes
set_years() %>%
rename(sector.name = supplysector, subsector.name = tranSubsector) %>%
select(LEVEL2_DATA_NAMES[["GlobalTranTechInterp"]],"sce") ->
L254.GlobalTranTechInterp
# L254.GlobalTranTechShrwt: Shareweights of global tranTechnologies
A54.globaltranTech_shrwt %>%
filter(sce=="CORE") %>%
select(-sce) %>%
gather_years %>%
# Expand table to include all model years
complete(year = c(year, MODEL_YEARS), nesting(supplysector, tranSubsector, tranTechnology)) %>%
# 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, tranSubsector, tranTechnology) %>%
mutate(share.weight = approx_fun(year, value, rule = 2),
share.weight = round(share.weight, energy.DIGITS_SHRWT)) %>%
ungroup() %>%
filter(year %in% MODEL_YEARS) %>%
mutate(sce= paste0("CORE")) %>%
rename(sector.name = supplysector, subsector.name = tranSubsector) %>%
select(LEVEL2_DATA_NAMES[["GlobalTranTechShrwt"]],sce) ->
L254.GlobalTranTechShrwt_CORE # OUTPUT
# A54.globaltranTech_shrwt %>%
# filter(sce=="highEV") %>%
# select(-sce) %>%
# gather_years %>%
# # Expand table to include all model years
# complete(year = c(year, MODEL_YEARS), nesting(supplysector, tranSubsector, tranTechnology)) %>%
# # 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, tranSubsector, tranTechnology) %>%
# mutate(share.weight = approx_fun(year, value, rule = 2),
# share.weight = round(share.weight, energy.DIGITS_SHRWT)) %>%
# ungroup() %>%
# filter(year %in% MODEL_YEARS) %>%
# mutate(sce= paste0("highEV")) %>%
# rename(sector.name = supplysector, subsector.name = tranSubsector) %>%
# select(LEVEL2_DATA_NAMES[["GlobalTranTechShrwt"]],sce) ->
# L254.GlobalTranTechShrwt_highEV # OUTPUT
#L254.GlobalTranTechShrwt <- bind_rows(L254.GlobalTranTechShrwt_highEV,L254.GlobalTranTechShrwt_CORE)
L254.GlobalTranTechShrwt <- L254.GlobalTranTechShrwt_CORE
# L254.GlobalTranTechSCurve and L254.GlobalTranTechProfitShutdown: Retirement of global tranTechnologies
# A54.globaltranTech_retire reports transportation technology retirement parameters. Only applies to vintaged technologies
A54.globaltranTech_retire %>%
set_years() %>%
filter(year < max(year)) %>%
mutate(year = as.numeric(year)) ->
L254.GlobalTranTechSCurve_1
# Copy the final year forward to all future time periods
L254.GlobalTranTechSCurve_MAX_YEAR <- max(L254.GlobalTranTechSCurve_1$year)
A54.globaltranTech_retire %>%
set_years() %>%
filter(year == max(year)) %>%
select(-year) %>%
repeat_add_columns(tibble(year = MODEL_YEARS)) %>%
filter(year > L254.GlobalTranTechSCurve_MAX_YEAR) %>%
bind_rows(L254.GlobalTranTechSCurve_1) %>%
rename(sector.name = supplysector, subsector.name = tranSubsector) ->
A54.globaltranTech_retire
A54.globaltranTech_retire %>%
select(LEVEL2_DATA_NAMES[["GlobalTranTechSCurve"]]) ->
L254.GlobalTranTechSCurve # OUTPUT
A54.globaltranTech_retire %>%
select(-steepness) %>%
rename(steepness = profit.shutdown.steepness) %>%
select(LEVEL2_DATA_NAMES[["GlobalTranTechProfitShutdown"]]) ->
L254.GlobalTranTechProfitShutdown
# PART E: CALIBRATION AND REGION-SPECIFIC DATA
# L254.StubTranTechCalInput: calibrated input of tranTechnologies
# L154.in_EJ_R_trn_m_sz_tech_F_Yh reports transportation energy consumption by GCAM region / mode / size class / technology / fuel / historical year
#kbn 2019-10-14 Switching to left_join_keep_first, since we have fewer mode categories now.
L154.in_EJ_R_trn_m_sz_tech_F_Yh %>%
filter(year %in% MODEL_BASE_YEARS) %>%
mutate(calibrated.value = round(value, energy.DIGITS_CALOUTPUT)) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_keep_first_only(UCD_techs, by = c("UCD_sector", "mode", "size.class",
"UCD_technology", "UCD_fuel")) %>%
select(region, supplysector, tranSubsector, stub.technology = tranTechnology,
year, minicam.energy.input, calibrated.value) ->
L254.StubTranTechCalInput_basetable
#kbn 2020-02-06 Energy intensity are not separated by SSPs. So, just copying information from CORE to all SSPs.
# L254.StubTranTechCalInput_basetable<- bind_rows(L254.StubTranTechCalInput_basetable %>% mutate(sce= paste0("CORE")),
# L254.StubTranTechCalInput_basetable %>% mutate(sce= paste0("SSP1")),
# L254.StubTranTechCalInput_basetable %>% mutate(sce= paste0("SSP3")),
# L254.StubTranTechCalInput_basetable %>% mutate(sce= paste0("SSP5")),
# L254.StubTranTechCalInput_basetable %>% mutate(sce= paste0("highEV")))
L254.StubTranTechCalInput_basetable<- bind_rows(L254.StubTranTechCalInput_basetable %>% mutate(sce= paste0("CORE")),
L254.StubTranTechCalInput_basetable %>% mutate(sce= paste0("SSP1")),
L254.StubTranTechCalInput_basetable %>% mutate(sce= paste0("SSP3")),
L254.StubTranTechCalInput_basetable %>% mutate(sce= paste0("SSP5")))
# Aggregate to set subsector share weights according to region, supplysector, tranSubsector, year combination
# kbn 2020-02-06 Add sce below (See description of changes using search string kbn 2020-06-02 Making changes to generate xmls for SSPs flexibly)
L254.StubTranTechCalInput_basetable %>%
group_by(region, supplysector, tranSubsector, year,sce) %>%
summarise(subs.share.weight = sum(calibrated.value)) %>%
ungroup() %>%
mutate(subs.share.weight = if_else(subs.share.weight > 0, 1, 0))->L254.StubTranTechCalInput_Shareweight
L254.StubTranTechCalInput_basetable %>%
# Match in subsector share weights
#Add sce below kbn 2020-06-02 (See description of changes using search string kbn 2020-06-02 Making changes to generate xmls for SSPs flexibly)
left_join_error_no_match(L254.StubTranTechCalInput_Shareweight, by = c("region", "supplysector",
"tranSubsector", "year","sce")) %>%
mutate(share.weight.year = year,
tech.share.weight = if_else(calibrated.value > 0, 1, 0)) %>%
#Add sce below kbn 2020-06-02 (See description of changes using search string kbn 2020-06-02 Making changes to generate xmls for SSPs flexibly)
select(LEVEL2_DATA_NAMES[["StubTranTechCalInput"]],sce) ->
L254.StubTranTechCalInput # OUTPUT
# L254.StubTranTechLoadFactor: tranTechnology load factors (all periods)
# L154.loadfactor_R_trn_m_sz_tech_F_Y reports load factors by GCAM region / mode / size class / technology / fuel / year
#kbn 2019-10-14 Switching to left_join_keep_first, since we have fewer mode categories now.
#kbn 2020-06-02 Adding sce below (See description of changes using search string kbn 2020-06-02 Making changes to generate xmls for SSPs flexibly)
L154.loadfactor_R_trn_m_sz_tech_F_Y %>%
filter(year %in% MODEL_YEARS) %>%
mutate(loadFactor = round(value, energy.DIGITS_LOADFACTOR)) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_keep_first_only(UCD_techs, by = c("UCD_sector", "mode", "size.class", "UCD_technology", "UCD_fuel")) %>%
rename(stub.technology = tranTechnology) %>%
select(LEVEL2_DATA_NAMES[["StubTranTechLoadFactor"]],sce) ->
L254.StubTranTechLoadFactor # OUTPUT
# L254.StubTranTechCost: tranTechnology costs (all periods)
# L154.cost_usdvkm_R_trn_m_sz_tech_F_Y reports non-fuel cost by GCAM region / mode / size class / technology / fuel / year "
##kbn 2019-10-14 Switching to left_join_keep_first, since we have fewer mode categories now.
#kbn 2020-06-02 Updating with sce below (See description of changes using search string kbn 2020-06-02 Making changes to generate xmls for SSPs flexibly)
L154.cost_usdvkm_R_trn_m_sz_tech_F_Y %>%
filter(year %in% MODEL_YEARS) %>%
mutate(input.cost = round((value / gdp_deflator(2005, 1990)), energy.DIGITS_COST)) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_keep_first_only(UCD_techs, by = c("UCD_sector", "mode", "size.class", "UCD_technology", "UCD_fuel")) %>%
rename(stub.technology = tranTechnology) %>%
mutate(minicam.non.energy.input = "non-energy") %>%
select(LEVEL2_DATA_NAMES[["StubTranTechCost"]],sce) ->
L254.StubTranTechCost # OUTPUT
# L254.StubTranTechCoef: tranTechnology coefficients (intensities; all periods)
# L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y reports vehicle energy intensity by GCAM region / mode / size class / technology / fuel / year
#kbn 2019-10-14 Switching to left_join_keep_first, since we have fewer mode categories now.
L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y %>%
filter(year %in% MODEL_YEARS) %>%
mutate(coefficient = round((value * CONV_MJ_BTU), energy.DIGITS_COEFFICIENT)) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_keep_first_only(UCD_techs, by = c("UCD_sector", "mode", "size.class", "UCD_technology", "UCD_fuel")) %>%
rename(stub.technology = tranTechnology) %>%
# Currently, the market names for the fuels will be the same as the region
mutate(market.name = region) %>%
#kbn 2020-06-02 adding sce here (See description of changes using search string kbn 2020-06-02 Making changes to generate xmls for SSPs flexibly)
select(LEVEL2_DATA_NAMES[["StubTranTechCoef"]],sce) ->
L254.StubTranTechCoef # OUTPUT
# L254.StubTechCalInput_passthru: calibrated input of passthrough technologies
# First, need to calculate the service output for all tranTechnologies (= calInput * loadFactor * unit_conversion / (coef * unit conversion))
#Switching to normal left_joins below since we have more
#2020
L254.StubTranTechCalInput %>%
select(-contains("share")) %>%
left_join(L254.StubTranTechLoadFactor, by = c("region", "supplysector", "tranSubsector",
"stub.technology", "year", "sce")) %>%
left_join(L254.StubTranTechCoef, by = c("region", "supplysector", "tranSubsector",
"stub.technology", "minicam.energy.input", "year","sce")) %>%
mutate(loadFactor=if_else(is.na(loadFactor),0,loadFactor),
coefficient=if_else(is.na(coefficient),0,coefficient),
output = calibrated.value * loadFactor * CONV_EJ_GJ / (coefficient * CONV_BTU_KJ),
output = if_else(is.na(output),0,output)) %>%
select(region, supplysector, tranSubsector, stub.technology, year, minicam.energy.input,
calibrated.value, loadFactor, coefficient, output,sce) ->
L254.StubTranTechOutput
# The next step is to bind rows with all pass-through technologies on to this table
A54.globaltech_passthru %>%
repeat_add_columns(tibble(year = MODEL_BASE_YEARS)) %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["tranSubsector"]], "technology", "year", "minicam.energy.input"),
GCAM_region_names = GCAM_region_names) %>%
rename(stub.technology = technology) %>%
mutate(sce =paste0("CORE")) %>%
# Subset only the passthrough technologies that are applicable in each region
semi_join(L254.StubTech_passthru, by = c("region", "supplysector", "tranSubsector", "stub.technology","sce")) %>%
# Start with a 0 value for output, and bind this to the table of output by tranTechnology (using only columns whose names match)
mutate(output = 0) %>%
bind_rows(
select(L254.StubTranTechOutput, one_of(c(LEVEL2_DATA_NAMES[["tranSubsector"]]),
"stub.technology", "year", "output", "minicam.energy.input","sce"))) ->
L254.StubTechCalInput_passthru_all_rows
#kbn 2020-06-02 Adding sce below (See description of changes using search string kbn 2020-06-02 Making changes to generate xmls for SSPs flexibly)
L254.StubTechCalInput_passthru_all_rows %>%
group_by(region, year, supplysector,sce) %>%
summarise(output_agg = sum(output)) %>%
ungroup() ->
L254.StubTechCalInput_passthru_agg
L254.StubTechCalInput_passthru_all_rows %>%
left_join(L254.StubTechCalInput_passthru_agg, by = c("region", "year", "minicam.energy.input" = "supplysector","sce")) %>%
# remove the technologies that are not pass-through sectors
semi_join(L254.StubTech_passthru, by = c("region", "supplysector", "tranSubsector", "stub.technology","sce")) %>%
# compute cumulative sum for use below
arrange(desc(minicam.energy.input)) %>%
group_by(region, year) %>%
mutate(output_cum = cumsum(output_agg)) %>%
ungroup() ->
L254.StubTechCalInput_passthru_cum
LIST_supplysector <- unique(L254.StubTechCalInput_passthru_cum$supplysector)
L254.StubTechCalInput_passthru_cum %>%
mutate(calibrated.value = if_else(minicam.energy.input %in% LIST_supplysector,
output_cum, output_agg),
share.weight.year = year,
subs.share.weight = if_else(calibrated.value > 0, 1, 0),
tech.share.weight = subs.share.weight) %>%
select(region, supplysector, tranSubsector, stub.technology, year,
minicam.energy.input, calibrated.value, share.weight.year,
subs.share.weight, tech.share.weight,sce) ->
L254.StubTechCalInput_passthru # OUTPUT
# PART F: NON-MOTORIZED TRANSPORTATION - SERVICE OUTPUT
# L254.StubTechProd_nonmotor: service output of non-motorized transportation technologies
# L154.out_mpkm_R_trn_nonmotor_Yh reports service output by GCAM region / non-motorized transport mode / year
L154.out_mpkm_R_trn_nonmotor_Yh %>%
mutate(sce= paste0("CORE")) %>%
filter(year %in% MODEL_BASE_YEARS) %>%
mutate(calOutputValue = round(value, energy.DIGITS_MPKM)) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_error_no_match(A54.globaltech_nonmotor, by = c("mode" = "tranSubsector")) %>%
rename(stub.technology = technology, tranSubsector = mode) %>%
# 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,sce) ->
L254.StubTechProd_nonmotor
# L254.PerCapitaBased_trn: per-capita based flag for transportation final demand
A54.demand %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["PerCapitaBased"]],"sce"), GCAM_region_names = GCAM_region_names) %>% na.omit() ->
L254.PerCapitaBased_trn # OUTPUT
# L254.PriceElasticity_trn: price elasticity of transportation final demand")
# Price elasticities are only applied to future periods. Application in base years will cause solution failure
A54.demand %>%
repeat_add_columns(tibble(year = MODEL_FUTURE_YEARS)) %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["PriceElasticity"]],"sce"), GCAM_region_names = GCAM_region_names) %>% na.omit() ->
L254.PriceElasticity_trn # OUTPUT
# L254.IncomeElasticity_trn: Income elasticity of transportation final demand
# Income elasticities are only applied to future periods
A54.demand %>%
repeat_add_columns(tibble(year = MODEL_FUTURE_YEARS)) %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["IncomeElasticity"]],"sce"), GCAM_region_names = GCAM_region_names) %>% na.omit() ->
L254.IncomeElasticity_trn # OUTPUT
# L254.BaseService_trn: Base-year service output of transportation final demand
L254.StubTranTechOutput %>%
select(LEVEL2_DATA_NAMES[["StubTranTech"]], year, output,sce) %>%
bind_rows(
select(L254.StubTechProd_nonmotor, one_of(LEVEL2_DATA_NAMES[["StubTranTech"]]), year, calOutputValue,sce)) %>%
mutate(base.service = if_else(!is.na(output), output, calOutputValue)) %>%
# Match in energy.final.demand from transportation supplysector information
# NAs will be introduced, so use left-join
left_join(A54.sector, by = "supplysector") %>%
# Aggregate base-year service output to region, energy.final.demand, and year
group_by(region, energy.final.demand, year,sce) %>%
summarise(base.service = sum(base.service)) %>%
ungroup() %>%
filter(sce=="CORE") ->
#kbn 2020-06-02 Base service values only needed for CORE.
L254.BaseService_trn # OUTPUT
# ===================================================
L254.Supplysector_trn %>%
add_title("Supply sector information for transportation sector") %>%
add_units("Unitless") %>%
add_comments("Supply sector information was written for all GCAM regions and subset for the combinations of region and supplysector that are available in the stub technology table") %>%
add_legacy_name("L254.Supplysector_trn") %>%
add_precursors("common/GCAM_region_names", "energy/mappings/UCD_techs", "energy/mappings/UCD_techs_revised", "energy/mappings/UCD_size_class_revisions", "L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y",
"energy/A54.sector", "energy/A54.globaltech_nonmotor", "energy/A54.globaltech_passthru", "energy/A54.globaltech_passthru_revised") ->
L254.Supplysector_trn
L254.FinalEnergyKeyword_trn %>%
add_title("Supply sector keywords for transportation sector") %>%
add_units("NA") %>%
add_comments("Final energy names were matched to supply sector information") %>%
add_legacy_name("L254.FinalEnergyKeyword_trn") %>%
add_precursors("common/GCAM_region_names", "energy/mappings/UCD_techs", "energy/mappings/UCD_techs_revised", "energy/mappings/UCD_size_class_revisions", "L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y",
"energy/A54.sector", "energy/A54.globaltech_nonmotor") ->
L254.FinalEnergyKeyword_trn
L254.tranSubsectorLogit %>%
add_title("Subsector logit exponents of transportation sector") %>%
add_units("Unitless") %>%
add_comments("Transportation default subsector logit exponents were written for all regions") %>%
add_legacy_name("L254.tranSubsectorLogit") %>%
add_precursors("common/GCAM_region_names", "energy/mappings/UCD_techs", "energy/mappings/UCD_techs_revised", "energy/mappings/UCD_size_class_revisions", "L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y",
"energy/A54.tranSubsector_logit", "energy/A54.tranSubsector_logit_revised", "energy/A54.globaltech_nonmotor") ->
L254.tranSubsectorLogit
if(exists("L254.tranSubsectorShrwt")) {
L254.tranSubsectorShrwt %>%
add_title("Subsector shareweights of transportation sector") %>%
add_units("Unitless") %>%
add_comments("Subsector shareweights of transportation sector were written for all regions and subset for existing combinations of region, supplysector, and tranSubsector") %>%
add_comments("Only rows with an entry for year were selected") %>%
add_legacy_name("L254.tranSubsectorShrwt") %>%
add_precursors("common/GCAM_region_names", "energy/mappings/UCD_techs", "energy/mappings/UCD_techs_revised", "energy/mappings/UCD_size_class_revisions", "L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y",
"energy/A54.tranSubsector_shrwt", "energy/A54.tranSubsector_shrwt_revised", "energy/A54.globaltech_nonmotor") ->
L254.tranSubsectorShrwt
} else {
missing_data() %>%
add_legacy_name("L254.tranSubsectorShrwt") ->
L254.tranSubsectorShrwt
}
if(exists("L254.tranSubsectorShrwtFllt")) {
L254.tranSubsectorShrwtFllt %>%
add_title("Subsector shareweights of transportation sector") %>%
add_units("Unitless") %>%
add_comments("Subsector shareweights of transportation sector were written for all regions and subset for existing combinations of region, supplysector, and tranSubsector") %>%
add_comments("Only rows with an entry for year.fillout were selected") %>%
add_legacy_name("L254.tranSubsectorShrwtFllt") %>%
add_precursors("common/GCAM_region_names", "energy/mappings/UCD_techs", "energy/mappings/UCD_techs_revised", "energy/mappings/UCD_size_class_revisions", "L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y",
"energy/A54.tranSubsector_shrwt", "energy/A54.globaltech_nonmotor") ->
L254.tranSubsectorShrwtFllt
} else {
missing_data() %>%
add_legacy_name("L254.tranSubsectorShrwtFllt") ->
L254.tranSubsectorShrwtFllt
}
if(exists("L254.tranSubsectorInterp")) {
L254.tranSubsectorInterp %>%
add_title("Subsector shareweight interpolation of transportation sector") %>%
add_units("Unitless") %>%
add_comments("Subsector shareweight interpoloation data of transportation sector were written for all regions and subset for existing combinations of region, supplysector, and tranSubsector") %>%
add_comments("Only rows without an entry for to.value were selected") %>%
add_legacy_name("L254.tranSubsectorInterp") %>%
add_precursors("common/GCAM_region_names", "energy/mappings/UCD_techs", "energy/mappings/UCD_techs_revised", "energy/mappings/UCD_size_class_revisions", "L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y",
"energy/A54.tranSubsector_interp", "energy/A54.tranSubsector_interp_revised", "energy/A54.globaltech_nonmotor") ->
L254.tranSubsectorInterp
} else {
missing_data() %>%
add_legacy_name("L254.tranSubsectorInterp") ->
L254.tranSubsectorInterp
}
if(exists("L254.tranSubsectorInterpTo")) {
L254.tranSubsectorInterpTo %>%
add_title("Subsector shareweight interpolation of transportation sector") %>%
add_units("Unitless") %>%
add_comments("Subsector shareweight interpoloation data of transportation sector were written for all regions and subset for existing combinations of region, supplysector, and tranSubsector") %>%
add_comments("Only rows with an entry for to.value were selected") %>%
add_legacy_name("L254.tranSubsectorInterpTo") %>%
add_precursors("common/GCAM_region_names", "energy/mappings/UCD_techs", "energy/mappings/UCD_techs_revised", "energy/mappings/UCD_size_class_revisions", "L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y",
"energy/A54.tranSubsector_interp", "energy/A54.globaltech_nonmotor") ->
L254.tranSubsectorInterpTo
} else {
missing_data() %>%
add_legacy_name("L254.tranSubsectorInterpTo") ->
L254.tranSubsectorInterpTo
}
L254.tranSubsectorSpeed %>%
add_title("Speeds of transportation modes (not including pass-through sectors)") %>%
add_units("km / hr") %>%
add_comments("Speed information was written for all regions and mapped from UCD technology to GCAM technology") %>%
add_legacy_name("L254.tranSubsectorSpeed") %>%
add_precursors("common/GCAM_region_names", "energy/mappings/UCD_techs", "energy/mappings/UCD_techs_revised", "energy/mappings/UCD_size_class_revisions", "L154.speed_kmhr_R_trn_m_sz_tech_F_Y") ->
L254.tranSubsectorSpeed
L254.tranSubsectorSpeed_passthru %>%
add_title("Speeds of pass-through transportation subsectors") %>%
add_units("km / hr") %>%
add_comments("Transportation default subsector value of time in transit (VOTT) multipliers were written for all regions and model years") %>%
add_comments("Speeds were matched in by region, supplysector, and year") %>%
add_legacy_name("L254.tranSubsectorSpeed_passthru") %>%
add_precursors("common/GCAM_region_names", "energy/A54.tranSubsector_VOTT", "energy/A54.tranSubsector_VOTT_revised",
"energy/mappings/UCD_techs", "energy/mappings/UCD_techs_revised", "energy/mappings/UCD_size_class_revisions", "L154.speed_kmhr_R_trn_m_sz_tech_F_Y") ->
L254.tranSubsectorSpeed_passthru
L254.tranSubsectorSpeed_noVOTT %>%
add_title("Speeds of transportation subsectors whose time value is not considered") %>%
add_units("km / hr") %>%
add_comments("Sector data was subsetted for only those whose speeds have not already been specified") %>%
add_legacy_name("L254.tranSubsectorSpeed_noVOTT") %>%
add_precursors("common/GCAM_region_names", "energy/mappings/UCD_techs", "energy/mappings/UCD_techs_revised", "energy/mappings/UCD_size_class_revisions", "L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y",
"energy/A54.globaltech_passthru", "energy/A54.globaltech_passthru_revised") ->
L254.tranSubsectorSpeed_noVOTT
L254.tranSubsectorSpeed_nonmotor %>%
add_title("Speeds of non-motorized transportation subsectors") %>%
add_units("km / hr") %>%
add_comments("Data was written for all model years and regions") %>%
add_legacy_name("L254.tranSubsectorSpeed_nonmotor") %>%
add_precursors("energy/A54.globaltech_nonmotor") ->
L254.tranSubsectorSpeed_nonmotor
L254.tranSubsectorVOTT %>%
add_title("Value of time in transit parameterization") %>%
add_units("Unitless") %>%
add_comments("Data was written for all regions") %>%
add_comments("Year.fillout was populated with minimum model year") %>%
add_legacy_name("L254.tranSubsectorVOTT") %>%
add_precursors("common/GCAM_region_names", "energy/A54.tranSubsector_VOTT", "energy/A54.tranSubsector_VOTT_revised",
"energy/mappings/UCD_techs", "energy/mappings/UCD_techs_revised", "energy/mappings/UCD_size_class_revisions", "L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y",
"energy/A54.globaltech_passthru", "energy/A54.globaltech_passthru_revised", "energy/A54.globaltech_nonmotor") ->
L254.tranSubsectorVOTT
L254.tranSubsectorFuelPref %>%
add_title("Subsector preferences that are tied to GDP (unrelated to time value)") %>%
add_units("Unitless") %>%
add_comments("Data was written for all regions") %>%
add_comments("Year.fillout was populated with minimum model year") %>%
add_legacy_name("L254.tranSubsectorFuelPref") %>%
add_precursors("common/GCAM_region_names",
"energy/A54.tranSubsector_VOTT", "energy/A54.tranSubsector_VOTT_revised", "energy/A54.tranSubsector_VOTT_ssp1", "energy/A54.tranSubsector_VOTT_ssp1_revised",
"energy/mappings/UCD_techs", "energy/mappings/UCD_techs_revised", "energy/mappings/UCD_size_class_revisions", "L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y",
"energy/A54.globaltech_passthru", "energy/A54.globaltech_passthru_revised", "energy/A54.globaltech_nonmotor") ->
L254.tranSubsectorFuelPref
L254.StubTranTech %>%
add_title("Transportation stub technologies (built from technologies with coefficients in the UCD database)") %>%
add_units("NA") %>%
add_comments("Data was written for all regions and mapped from UCD technology to GCAM technology") %>%
add_legacy_name("L254.StubTranTech") %>%
add_precursors("common/GCAM_region_names", "energy/mappings/UCD_techs", "energy/mappings/UCD_techs_revised", "energy/mappings/UCD_size_class_revisions", "L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y") ->
L254.StubTranTech
L254.StubTech_passthru %>%
add_title("Transportation stub technologies (passthru)") %>%
add_units("NA") %>%
add_comments("Data was written for all regions and subsetted for only the ones whose input actually exists in the given region or ones whose input is in the list of pass-through technologies") %>%
add_legacy_name("L254.StubTech_passthru") %>%
add_precursors("common/GCAM_region_names", "energy/mappings/UCD_techs", "energy/mappings/UCD_techs_revised", "energy/mappings/UCD_size_class_revisions", "L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y",
"energy/A54.globaltech_passthru", "energy/A54.globaltech_passthru_revised") ->
L254.StubTech_passthru
L254.StubTech_nonmotor %>%
add_title("Non-motorized transportation stub technologies") %>%
add_units("NA") %>%
add_comments("Data was written for all regions") %>%
add_legacy_name("L254.StubTech_nonmotor") %>%
add_precursors("common/GCAM_region_names", "energy/A54.globaltech_nonmotor") ->
L254.StubTech_nonmotor
L254.GlobalTechShrwt_passthru %>%
add_title("Shareweights of global transportation sector technologies (not tranTechnologies)") %>%
add_units("Unitless") %>%
add_comments("Data was written for all model years") %>%
add_legacy_name("L254.GlobalTechShrwt_passthru") %>%
add_precursors("energy/A54.globaltech_passthru", "energy/A54.globaltech_passthru_revised") ->
L254.GlobalTechShrwt_passthru
L254.GlobalTechShrwt_nonmotor %>%
add_title("Shareweights of non-motorized global transportation sector technologies (not tranTechnologies)") %>%
add_units("Unitless") %>%
add_comments("Data was written for all model years") %>%
add_legacy_name("L254.GlobalTechShrwt_nonmotor") %>%
add_precursors("energy/A54.globaltech_nonmotor") ->
L254.GlobalTechShrwt_nonmotor
L254.GlobalTechCoef_passthru %>%
add_title("Coefficients of global transportation sector technologies (not tranTechnologies)") %>%
add_units("Unitless") %>%
add_comments("Coefficients were written for all model years") %>%
add_legacy_name("L254.GlobalTechCoef_passthru") %>%
add_precursors("energy/A54.globaltech_passthru", "energy/A54.globaltech_passthru_revised") ->
L254.GlobalTechCoef_passthru
L254.GlobalRenewTech_nonmotor %>%
add_title("Renewable inputs to non-motorized transportation technologies") %>%
add_units("NA") %>%
add_comments("Renewable inputs were written for all model years") %>%
add_legacy_name("L254.GlobalRenewTech_nonmotor") %>%
add_precursors("energy/A54.globaltech_nonmotor") ->
L254.GlobalRenewTech_nonmotor
L254.GlobalTranTechInterp %>%
add_title("Shareweight interpolation of global tranTechnologies") %>%
add_units("NA") %>%
add_comments("Populated placeholders for final calibration year and end year") %>%
add_legacy_name("L254.GlobalTranTechInterp") %>%
add_precursors("energy/A54.globaltranTech_interp", "energy/A54.globaltranTech_interp_revised") ->
L254.GlobalTranTechInterp
L254.GlobalTranTechShrwt %>%
add_title("Shareweights of global tranTechnologies") %>%
add_units("Unitless") %>%
add_comments("Data was subsetted for model years") %>%
add_legacy_name("L254.GlobalTranTechShrwt") %>%
add_precursors("energy/A54.globaltranTech_shrwt", "energy/A54.globaltranTech_shrwt_revised") ->
L254.GlobalTranTechShrwt
L254.GlobalTranTechSCurve %>%
add_title("Retirement of global tranTechnologies") %>%
add_units("lifetime and half-life in years") %>%
add_comments("Retirement parameters in the final year of the base data were carried forward to all future time periods") %>%
add_legacy_name("L254.GlobalTranTechSCurve") %>%
add_precursors("energy/A54.globaltranTech_retire", "energy/A54.globaltranTech_retire_revised") ->
L254.GlobalTranTechSCurve
L254.GlobalTranTechProfitShutdown %>%
add_title("Profit shutdown parameters of global tranTechnologies") %>%
add_units("unitless function parameters") %>%
add_comments("Profit shutdown parameters in the final year of the base data were carried forward to all future time periods") %>%
same_precursors_as(L254.GlobalTranTechSCurve) ->
L254.GlobalTranTechProfitShutdown
L254.StubTranTechCalInput %>%
add_title("Calibrated input of tranTechnologies") %>%
add_units("Unitless") %>%
add_comments("Share weights were calculate by aggregating energy consumption to the region, supplysector, tranSubsector, year level") %>%
add_legacy_name("L254.StubTranTechCalInput") %>%
add_precursors("common/GCAM_region_names", "energy/mappings/UCD_techs", "energy/mappings/UCD_techs_revised", "energy/mappings/UCD_size_class_revisions", "L154.in_EJ_R_trn_m_sz_tech_F_Yh") ->
L254.StubTranTechCalInput
L254.StubTranTechLoadFactor %>%
add_title("TranTechnology load factors (all periods)") %>%
add_units("person/vehicle and tonnes/vehicle") %>%
add_comments("Data was subsetted to model years and mapped from UCD technology to GCAM technology") %>%
add_legacy_name("L254.StubTranTechLoadFactor") %>%
add_precursors("common/GCAM_region_names", "energy/mappings/UCD_techs", "energy/mappings/UCD_techs_revised", "energy/mappings/UCD_size_class_revisions", "L154.loadfactor_R_trn_m_sz_tech_F_Y") ->
L254.StubTranTechLoadFactor
L254.StubTranTechCost %>%
add_title("TranTechnology costs (all periods)") %>%
add_units("$1990USD / vkm") %>%
add_comments("Non-fuel cost was adjusted to 1990") %>%
add_comments("Transportation cost table was mapped from UCD technology to GCAM technology") %>%
add_legacy_name("L254.StubTranTechCost") %>%
add_precursors("common/GCAM_region_names", "energy/mappings/UCD_techs", "energy/mappings/UCD_techs_revised", "energy/mappings/UCD_size_class_revisions", "L154.cost_usdvkm_R_trn_m_sz_tech_F_Y") ->
L254.StubTranTechCost
L254.StubTranTechCoef %>%
add_title("TranTechnology coefficients (intensities; all periods)") %>%
add_units("BTU / vkm") %>%
add_comments("MJ was converted to BTU") %>%
add_comments("Vehicle energy intensity information was mapped from UCD technology to GCAM technology") %>%
add_legacy_name("L254.StubTranTechCoef") %>%
add_precursors("common/GCAM_region_names", "energy/mappings/UCD_techs", "energy/mappings/UCD_techs_revised", "energy/mappings/UCD_size_class_revisions", "L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y") ->
L254.StubTranTechCoef
L254.StubTechCalInput_passthru %>%
add_title("Calibrated input of passthrough technologies") %>%
add_units("Unitless") %>%
add_comments("Pass-through transportationtechnologies were written to all regions") %>%
add_comments("Share weights were calculated from calibrated output values") %>%
add_legacy_name("L254.StubTechCalInput_passthru") %>%
add_precursors("common/GCAM_region_names", "energy/mappings/UCD_techs", "energy/mappings/UCD_techs_revised", "energy/mappings/UCD_size_class_revisions",
"L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y", "energy/A54.globaltech_passthru", "energy/A54.globaltech_passthru_revised") ->
L254.StubTechCalInput_passthru
L254.StubTechProd_nonmotor %>%
add_title("Service output of non-motorized transportation technologies") %>%
add_units("Million pass-km") %>%
add_comments("Supply sector and stub.technology information was added to non-motorized service output information") %>%
add_legacy_name("L254.StubTechProd_nonmotor") %>%
add_precursors("common/GCAM_region_names", "energy/A54.sector", "L154.out_mpkm_R_trn_nonmotor_Yh") ->
L254.StubTechProd_nonmotor
L254.PerCapitaBased_trn %>%
add_title("Per-capita based flag for transportation final demand") %>%
add_units("NA") %>%
add_comments("Per-capita based flag information written for all GCAM regions") %>%
add_legacy_name("L254.PerCapitaBased_trn") %>%
add_precursors("common/GCAM_region_names", "energy/A54.demand", "energy/A54.demand_ssp1") ->
L254.PerCapitaBased_trn
L254.PriceElasticity_trn %>%
add_title("Price elasticity of transportation final demand") %>%
add_units("Unitless") %>%
add_comments("Price elasticity information written for all GCAM regions and model future years") %>%
add_legacy_name("L254.PriceElasticity_trn") %>%
add_precursors("common/GCAM_region_names", "energy/A54.demand") ->
L254.PriceElasticity_trn
L254.IncomeElasticity_trn %>%
add_title("Income elasticity of transportation final demand") %>%
add_units("Unitless") %>%
add_comments("Income elasticity information written for all GCAM regions and model future years") %>%
add_legacy_name("L254.IncomeElasticity_trn") %>%
add_precursors("common/GCAM_region_names", "energy/A54.demand") ->
L254.IncomeElasticity_trn
L254.BaseService_trn %>%
add_title("Base-year service output of transportation final demand") %>%
add_units("units") %>%
add_comments("comments describing how data generated") %>%
add_legacy_name("L254.BaseService_trn") %>%
add_precursors("common/GCAM_region_names", "energy/A54.sector", "energy/mappings/UCD_techs", "energy/mappings/UCD_techs_revised", "energy/mappings/UCD_size_class_revisions",
"L154.out_mpkm_R_trn_nonmotor_Yh", "L154.intensity_MJvkm_R_trn_m_sz_tech_F_Y",
"L154.loadfactor_R_trn_m_sz_tech_F_Y", "L154.in_EJ_R_trn_m_sz_tech_F_Yh") ->
L254.BaseService_trn
return_data(L254.Supplysector_trn, L254.FinalEnergyKeyword_trn, L254.tranSubsectorLogit,
L254.tranSubsectorShrwt, L254.tranSubsectorShrwtFllt, L254.tranSubsectorInterp,
L254.tranSubsectorInterpTo, L254.tranSubsectorSpeed, L254.tranSubsectorSpeed_passthru,
L254.tranSubsectorSpeed_noVOTT, L254.tranSubsectorSpeed_nonmotor, L254.tranSubsectorVOTT,
L254.tranSubsectorFuelPref, L254.StubTranTech, L254.StubTech_passthru, L254.StubTech_nonmotor,
L254.GlobalTechShrwt_passthru, L254.GlobalTechShrwt_nonmotor, L254.GlobalTechCoef_passthru,
L254.GlobalRenewTech_nonmotor, L254.GlobalTranTechInterp, L254.GlobalTranTechShrwt,
L254.GlobalTranTechSCurve, L254.GlobalTranTechProfitShutdown, L254.StubTranTechCalInput, L254.StubTranTechLoadFactor,
L254.StubTranTechCost, L254.StubTranTechCoef, L254.StubTechCalInput_passthru,
L254.StubTechProd_nonmotor, L254.PerCapitaBased_trn, L254.PriceElasticity_trn,
L254.IncomeElasticity_trn, L254.BaseService_trn)
} else {
stop("Unknown command")
}
}
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