R/reportEDGETransport.R
In pik-piam/remind: The REMIND R Package
Defines functions
reportEDGETransport
Documented in
reportEDGETransport
#' Reporting for the coupled EDGE-T Transport Sector Model (REMIND Module edge_esm)
#'
#' Data is loaded from the EDGE-T subfolder in the output folder.
#' The input files can be (re-) generated calling
#' `Rscript EDGETransport.R --reporting`
#' from the output folder.
#'
#' *Warning* The function modifies the "REMIND_generic_<scenario>.mif" file by appending the
#' additional reporting variables and replaces the "_withoutPlus" version.
#'
#' Region subsets are obtained from fulldata.gdx
#'
#' @param output_folder path to the output folder, default is current folder.
#' @param remind_root path to the REMIND root directory, defaults to two levels up from output_folder.
#' @author Alois Dirnaichner Marianna Rottoli
#'
#' @importFrom rmndt approx_dt
#' @importFrom gdxdt readgdx
#' @importFrom data.table fread fwrite rbindlist
#' @export
reportEDGETransport <- function(output_folder=".",
remind_root=NULL) {
unit <- NULL
if(is.null(remind_root)){
remind_root <- file.path(output_folder, "../..")
}
gdx <- file.path(output_folder, "fulldata.gdx")
regionSubsetList <- toolRegionSubsets(gdx)
sub_folder = "EDGE-T/"
## NULL Definitons for codeCheck compliance
RegionCode <- CountryCode <- cfg <- `.` <- sector <- subsector_L3 <- region <- year <- NULL
subsector_L2 <- subsector_L1 <- aggr_mode <- vehicle_type <- det_veh <- aggr_nonmot <- NULL
demand_F <- demand_EJ <- remind_rep <- V25 <- aggr_veh <- technology <- NULL
variable <- value <- demand_VKM <- loadFactor <- NULL
all_enty <- ef <- variable_agg <- model <- scenario <- period <- NULL
Region <- Variable <- co2 <- co2val <- elh2 <- fe <- NULL
int <- se <- sec <- sharesec <- te <- tech <- val <- share <- NULL
eff <- sharebio <- sharesyn <- totseliq <- type <- NULL
load(file.path(output_folder, "config.Rdata"))
datapath <- function(fname){
file.path(output_folder, sub_folder, fname)
}
## load input data from last EDGE run
demand_km <- readRDS(datapath(fname = "demandF_plot_pkm.RDS"))[
, demand_F := demand_F * 1e-3] ## million -> billion pkm
load_factor <- readRDS(datapath(fname = "loadFactor.RDS"))
demand_vkm <- merge(demand_km, load_factor, by=c("year", "region", "vehicle_type"))
demand_vkm[, demand_VKM := demand_F/loadFactor] ## billion vkm
demand_ej <- readRDS(datapath(fname = "demandF_plot_EJ.RDS")) ## detailed final energy demand, EJ
name_mif = list.files(output_folder, pattern = "REMIND_generic", full.names = F)
name_mif = file.path(output_folder, name_mif[!grepl("withoutPlu", name_mif)])
stopifnot(typeof(name_mif) == "character")
miffile <- fread(name_mif, sep=";", header=T)
## ES and FE Demand
reportingESandFE <- function(datatable, mode){
datatable[, sector := ifelse(sector %in% c("trn_pass", "trn_aviation_intl"), "Pass", "Freight")]
## attribute aggregated mode and vehicle names for plotting purposes, and aggregate
## Large Categories (as used by REMIND)
datatable[subsector_L2 == "trn_pass_road_LDV",
aggr_mode := "Pass|Road|LDV"]
datatable[subsector_L2 != "trn_pass_road_LDV" & sector == "Pass",
aggr_mode := "Pass|non-LDV"]
## Freight is already in the "normal" reporting
## datatable[is.na(aggr_mode), aggr_mode := "Freight"]
## A little more detail: Vehicle Aggregates
datatable[grepl("^Truck", vehicle_type), aggr_veh := "Freight|Road"]
datatable["Freight Rail_tmp_vehicletype" == vehicle_type, aggr_veh := "Freight|Rail"]
## there seem to be no passenger ships in EDGE-T!
datatable[subsector_L3 == "International Ship", aggr_veh := "Freight|International Shipping"]
datatable[subsector_L3 == "Domestic Ship", aggr_veh := "Freight|Navigation"]
datatable[grepl("Bus", vehicle_type), aggr_veh := "Pass|Road|Bus"]
if(mode == "ES")
datatable[grepl("Cycle|Walk", subsector_L3), aggr_nonmot := "Pass|Road|Non-Motorized"]
## Rail & Aviation
datatable[grepl("Passenger Rail|HSR", vehicle_type), aggr_veh := "Pass|Rail"]
datatable[subsector_L3 == "Domestic Aviation", aggr_veh := "Pass|Aviation|Domestic"]
datatable[subsector_L3 == "International Aviation", aggr_veh := "Pass|Aviation|International"]
## High Detail: Ecoinvent-Compatible Output
datatable[grepl("Subcompact", vehicle_type),
det_veh := "Pass|Road|LDV|Small"]
datatable[grepl("Mini", vehicle_type),
det_veh := "Pass|Road|LDV|Mini"]
datatable[vehicle_type == "Compact", det_veh := "Pass|Road|LDV|Medium"]
datatable[vehicle_type == "Large Car|Midsize Car", det_veh := "Pass|Road|LDV|Large"]
datatable[grepl("SUV", vehicle_type),
det_veh := "Pass|Road|LDV|SUV"]
datatable[grepl("Van|Multipurpose", vehicle_type),
det_veh := "Pass|Road|LDV|Van"]
datatable[grepl("Motorcycle|Scooter|Moped", vehicle_type),
det_veh := "Pass|Road|LDV|Two-Wheelers"]
prepare4MIF <- function(dt, remind_unit, valcol, varcol){
## REMIND years, loading from MIF File would take too long
yrs <- c(seq(2005, 2060, 5), seq(2070, 2110, 10), 2130, 2150)
remind_scenario <- cfg$title
prefix <- switch(mode,
"FE" = "FE|Transport|",
"ES" = "ES|Transport|",
"VKM" = "ES|Transport|VKM|")
## we only care for non-NA variables (NA is basically *all others*)
toadd = dt[!is.na(get(varcol)), .(model="REMIND", scenario=remind_scenario, region,
variable=paste0(prefix, get(varcol)),
unit=remind_unit, period=year,
value=get(valcol))]
toadd <- approx_dt(toadd, yrs, xcol="period", ycol = "value",
idxcols=colnames(toadd)[1:5],
extrapolate=T)
return(toadd)
}
if(mode == "ES"){
report <- rbindlist(list(
prepare4MIF(
datatable[sector == "Pass", sum(demand_F, na.rm=T),
by = c("region", "year", "aggr_mode")],
"bn pkm/yr", "V1", "aggr_mode"),
prepare4MIF(
datatable[sector == "Freight" & !is.na(aggr_veh), sum(demand_F, na.rm=T),
by = c("region", "year", "aggr_veh")],
"bn tkm/yr", "V1", "aggr_veh"),
prepare4MIF(
datatable[sector == "Pass" & !is.na(aggr_veh), sum(demand_F, na.rm=T),
by = c("region", "year", "aggr_veh")],
"bn pkm/yr", "V1", "aggr_veh"),
prepare4MIF(
datatable[!is.na(aggr_nonmot), sum(demand_F, na.rm=T),
by = c("region", "year", "aggr_nonmot")],
"bn pkm/yr", "V1", "aggr_nonmot"),
prepare4MIF(
datatable[!is.na(det_veh), sum(demand_F, na.rm=T),
by = c("region", "year", "det_veh")], "bn pkm/yr", "V1", "det_veh")))
}else if(mode == "VKM"){
report <- rbindlist(list(
prepare4MIF(
datatable[sector == "Pass", sum(demand_VKM, na.rm=T),
by = c("region", "year", "aggr_mode")],
"bn vkm/yr", "V1", "aggr_mode"),
prepare4MIF(
datatable[sector == "Freight" & !is.na(aggr_veh), sum(demand_VKM, na.rm=T),
by = c("region", "year", "aggr_veh")],
"bn vkm/yr", "V1", "aggr_veh"),
prepare4MIF(
datatable[sector == "Pass" & !is.na(aggr_veh), sum(demand_VKM, na.rm=T),
by = c("region", "year", "aggr_veh")],
"bn vkm/yr", "V1", "aggr_veh"),
prepare4MIF(
datatable[!is.na(det_veh), sum(demand_VKM, na.rm=T),
by = c("region", "year", "det_veh")], "bn vkm/yr", "V1", "det_veh")))
}else{
report <- rbindlist(list(
prepare4MIF(
datatable[!is.na(aggr_mode), sum(demand_EJ, na.rm=T),
by = c("region", "year", "aggr_mode")],
"EJ/yr", "V1", "aggr_mode"),
prepare4MIF(
datatable[!is.na(aggr_veh), sum(demand_EJ, na.rm=T),
by = c("region", "year", "aggr_veh")],
"EJ/yr", "V1", "aggr_veh"),
prepare4MIF(
datatable[!is.na(det_veh), sum(demand_EJ, na.rm=T),
by = c("region", "year", "det_veh")],
"EJ/yr", "V1", "det_veh")))
}
## with energy carrier
## remove cycling and walking placeholder techs for ESs
techmap <- data.table(technology=unique(datatable$technology),
key="technology")[!c("Cycle_tmp_technology", "Walk_tmp_technology")]
if(mode == "ES"){
## for energy services, it is better to refer to the actual technologies
## and not the fuel types (-> LCA)
techmap[, remind_rep := technology]
techmap["NG", remind_rep := "Gases"]
datatable <- datatable[techmap, on="technology"]
report_tech <- rbindlist(list(
prepare4MIF(
datatable[sector == "Pass", sum(demand_F, na.rm=T),
by = c("region", "year", "aggr_mode", "remind_rep")
][, aggr_mode := paste0(aggr_mode, "|", remind_rep)],
"bn pkm/yr", "V1", "aggr_mode"),
prepare4MIF(
datatable[sector == "Freight" & !is.na(aggr_veh), sum(demand_F, na.rm=T),
by = c("region", "year", "aggr_veh", "remind_rep")
][, aggr_veh := paste0(aggr_veh, "|", remind_rep)],
"bn tkm/yr", "V1", "aggr_veh"),
prepare4MIF(
datatable[sector == "Pass" & !is.na(aggr_veh), sum(demand_F, na.rm=T),
by = c("region", "year", "aggr_veh", "remind_rep")
][, aggr_veh := paste0(aggr_veh, "|", remind_rep)],
"bn pkm/yr", "V1", "aggr_veh"),
prepare4MIF(datatable[!is.na(det_veh), sum(demand_F, na.rm=T),
by = c("region", "year", "det_veh", "remind_rep")
][, det_veh := paste0(det_veh, "|", remind_rep)],
"bn pkm/yr", "V1", "det_veh")))
}else if(mode == "VKM"){
## for energy services, it is better to refer to the actual technologies
## and not the fuel types (-> LCA)
techmap[, remind_rep := technology]
techmap["LA-BEV", remind_rep := "BEV"]
techmap["NG", remind_rep := "Gases"]
datatable <- datatable[techmap, on="technology"]
report_tech <- rbindlist(list(
prepare4MIF(
datatable[sector == "Pass", sum(demand_VKM, na.rm=T),
by = c("region", "year", "aggr_mode", "remind_rep")
][, aggr_mode := paste0(aggr_mode, "|", remind_rep)],
"bn vkm/yr", "V1", "aggr_mode"),
prepare4MIF(
datatable[sector == "Freight" & !is.na(aggr_veh), sum(demand_VKM, na.rm=T),
by = c("region", "year", "aggr_veh", "remind_rep")
][, aggr_veh := paste0(aggr_veh, "|", remind_rep)],
"bn vkm/yr", "V1", "aggr_veh"),
prepare4MIF(
datatable[sector == "Pass" & !is.na(aggr_veh), sum(demand_VKM, na.rm=T),
by = c("region", "year", "aggr_veh", "remind_rep")
][, aggr_veh := paste0(aggr_veh, "|", remind_rep)],
"bn vkm/yr", "V1", "aggr_veh"),
prepare4MIF(datatable[!is.na(det_veh), sum(demand_VKM, na.rm=T),
by = c("region", "year", "det_veh", "remind_rep")
][, det_veh := paste0(det_veh, "|", remind_rep)],
"bn vkm/yr", "V1", "det_veh")))
}else{
techmap["BEV", remind_rep := "Electricity"]
techmap["Electric", remind_rep := "Electricity"]
techmap["FCEV", remind_rep := "Hydrogen"]
techmap["Hydrogen", remind_rep := "Hydrogen"]
techmap["Liquids", remind_rep := "Liquids"]
techmap["NG", remind_rep := "Gases"]
datatable <- datatable[techmap, on="technology"]
report_tech <- rbindlist(list(
prepare4MIF(
datatable[!is.na(aggr_mode), sum(demand_EJ, na.rm=T),
by = c("region", "year", "aggr_mode", "remind_rep")
][, aggr_mode := paste0(aggr_mode, "|", remind_rep)],
"EJ/yr", "V1", "aggr_mode"),
prepare4MIF(
datatable[!is.na(aggr_veh), sum(demand_EJ, na.rm=T),
by = c("region", "year", "aggr_veh", "remind_rep")
][, aggr_veh := paste0(aggr_veh, "|", remind_rep)],
"EJ/yr", "V1", "aggr_veh"),
prepare4MIF(
datatable[!is.na(det_veh), sum(demand_EJ, na.rm=T),
by = c("region", "year", "det_veh", "remind_rep")
][, det_veh := paste0(det_veh, "|", remind_rep)],
"EJ/yr", "V1", "det_veh")))
}
## add World
report_w = report[,.(value = sum(value), region = "World"), by = .(model, scenario, variable, unit, period)]
report = rbind(report, report_w)
report_tech_w = report_tech[,.(value = sum(value), region = "World"), by = .(model, scenario, variable, unit, period)]
report_tech = rbind(report_tech, report_tech_w)
return(rbindlist(list(report, report_tech)))
}
## Demand emissions
reportingEmi <- function(repFE, gdx, miffile){
## load emission factors for fossil fuels
p_ef_dem <- fread(text='
"fedie", 69.3
"fepet", 68.5
"fegat", 50.3
', col.names=c("all_enty", "ef"))
## attribute explicitly fuel used to the FE values
emidem = repFE[grepl("Liquids|Gases|Hydrogen|Electricity", variable) & region != "World"] ## EJ
emidem[grepl("LDV", variable), all_enty := ifelse(grepl("Liquids", variable), "fepet", NA)]
emidem[!grepl("LDV", variable) | grepl("non-LDV", variable), all_enty := ifelse(grepl("Liquids", variable), "fedie", all_enty)]
emidem[, all_enty := ifelse(grepl("Gases", variable), "fegat", all_enty)]
emidem[, all_enty := ifelse(grepl("Electricity", variable), "feelt", all_enty)]
emidem[, all_enty := ifelse(grepl("Hydrogen", variable), "feh2t", all_enty)]
## merge with emission factors
emidem = emidem[p_ef_dem, on = "all_enty"]
## calculate emissions and attribute variable and unit names
emidem[, value := value*ef][, c("variable", "unit") := list(gsub("FE", "Emi\\|CO2", variable), "Mt CO2/yr")]
## create the "tailpipe" and the "demand" emissions starting from the same values
emidem = rbind(emidem[region %in% unique(emidem$region)][,type := "tailpipe"], emidem[region %in% unique(emidem$region)][,type := "demand"])
## load the composition of liquids for the whole LDV sector->fepet and the HDV sector->fedie
liq_comp = miffile[Variable %in% c("FE|Transport|Liquids|LDV|Synthetic|New Reporting", "FE|Transport|Liquids|LDV|Biomass|New Reporting", "FE|Transport|Liquids|LDV|Fossil|New Reporting",
"FE|Transport|Liquids|HDV|Synthetic|New Reporting", "FE|Transport|Liquids|HDV|Biomass|New Reporting", "FE|Transport|Liquids|HDV|Fossil|New Reporting")]
## re-arrange liq_comp
liq_comp[, c("V25", "Model", "Scenario", "Unit"):=NULL]
liq_comp = data.table::melt(liq_comp, id.vars = c("Region", "Variable"))
## change col names and type of numeric cols
setnames(liq_comp, old =c("variable", "Region"), new = c("period", "region"))
liq_comp[, value:= as.numeric(value)]
liq_comp[, period := as.numeric(as.character(period))]
## attribute fepet if LDV, fedie otherwise
liq_comp[, all_enty := ifelse(grepl("LDV", Variable), "fepet", "fedie")]
## calculate shares of the liquid fuels
liq_comp = liq_comp[, share := value/sum(value), by = c("region", "period", "all_enty")]
## remove value col
liq_comp[, value := NULL]
## decrease the "demand" entries by multiplying times the share of conventional liquids
emidem = merge(emidem, liq_comp[grepl("Fossil", Variable)], by = c("region", "period", "all_enty"))
emidem[type == "demand" & all_enty %in% c("fedie", "fepet"), value := value*share]
## the taipipe emissions are to be labeled as "Tailpipe"
emidem[type == "tailpipe", variable := paste0(variable, "|Tailpipe")]
## the demand emissions are to be labeled as "Demand"
emidem[type == "demand", variable := paste0(variable, "|Demand")]
emidem[, c("share", "type", "ef", "V3", "V2", "all_enty") := NULL]
## aggregate removing the fuel dependency
emidem[, variable_agg := gsub("\\|Liquids|\\|Electricity|\\|Hydrogen|\\|Gases", "", variable)]
emidem = emidem[, .(value = sum(value)), by = c("model", "scenario", "region", "unit", "period", "variable_agg")]
setnames(emidem, old = "variable_agg", new = "variable")
emidem = emidem[, .(model, scenario, region, variable, unit, period, value)]
return(emidem)
}
repFE <- reportingESandFE(
demand_ej,
mode ="FE")
toMIF <- rbindlist(list(
repFE,
reportingESandFE(
datatable=demand_km,
mode="ES"),
reportingESandFE(
datatable=demand_vkm,
mode="VKM"),
reportingEmi(repFE = repFE, gdx = gdx, miffile = miffile)
))
## add Road Totals
toMIF <- rbindlist(list(
toMIF,
toMIF[grep("ES\\|Transport\\|Pass\\|Road\\|[A-Za-z-]+$", variable),
.(variable="ES|Transport|Pass|Road",
unit="bn pkm/yr", value=sum(value)),
by=c("model", "scenario", "region", "period")],
toMIF[grep("Emi\\|CO2\\|Transport\\|Pass\\|Road\\|[A-Za-z-]+\\|Tailpipe$", variable),
.(variable="Emi|CO2|Transport|Pass|Road|Tailpipe",
unit="Mt CO2/yr", value=sum(value)),
by=c("model", "scenario", "region", "period")],
toMIF[grep("Emi\\|CO2\\|Transport\\|Pass\\|Road\\|[A-Za-z-]+\\|Demand$", variable),
.(variable="Emi|CO2|Transport|Pass|Road|Demand",
unit="Mt CO2/yr", value=sum(value)),
by=c("model", "scenario", "region", "period")],
toMIF[grep("ES\\|Transport\\|VKM\\|Pass\\|Road\\|[A-Za-z-]+$", variable),
.(variable="ES|Transport|VKM|Pass|Road",
unit="bn vkm/yr", value=sum(value)),
by=c("model", "scenario", "region", "period")],
toMIF[grep("FE\\|Transport\\|Pass\\|Road\\|[A-Za-z-]+$", variable),
.(variable="FE|Transport|Pass|Road",
unit="EJ/yr", value=sum(value)),
by=c("model", "scenario", "region", "period")]), use.names = TRUE)
## VKM totals, Road and Rail
toMIF <- rbindlist(list(
toMIF,
toMIF[grep("ES\\|Transport\\|VKM\\|(Pass|Freight)\\|Road$", variable),
.(variable="ES|Transport|VKM|Road",
unit="bn vkm/yr", value=sum(value)),
by=c("model", "scenario", "region", "period")],
toMIF[grep("ES\\|Transport\\|VKM\\|(Pass|Freight)\\|Rail$", variable),
.(variable="ES|Transport|VKM|Rail",
unit="bn vkm/yr", value=sum(value)),
by=c("model", "scenario", "region", "period")]
), use.names = TRUE)
toMIF <- rbindlist(list(
toMIF,
toMIF[grep("FE\\|Transport\\|(Pass|Freight)\\|Road$", variable),
.(variable="FE|Transport|Road",
unit="EJ/yr", value=sum(value)),
by=c("model", "scenario", "region", "period")],
toMIF[grep("Emi\\|CO2\\|Transport\\|(Pass|Freight)\\|Road\\|Tailpipe$", variable),
.(variable="Emi|CO2|Transport|Road|Tailpipe",
unit="Mt CO2/yr", value=sum(value)),
by=c("model", "scenario", "region", "period")],
toMIF[grep("Emi\\|CO2\\|Transport\\|(Pass|Freight)\\|Rail\\|Tailpipe$", variable),
.(variable="Emi|CO2|Transport|Rail|Tailpipe",
unit="Mt CO2/yr", value=sum(value)),
by=c("model", "scenario", "region", "period")],
toMIF[grep("Emi\\|CO2\\|Transport\\|(Pass|Freight)\\|Road\\|Demand$", variable),
.(variable="Emi|CO2|Transport|Road|Demand",
unit="Mt CO2/yr", value=sum(value)),
by=c("model", "scenario", "region", "period")],
toMIF[grep("Emi\\|CO2\\|Transport\\|(Pass|Freight)\\|Rail\\|Demand$", variable),
.(variable="Emi|CO2|Transport|Rail|Demand",
unit="Mt CO2/yr", value=sum(value)),
by=c("model", "scenario", "region", "period")],
toMIF[grep("FE\\|Transport\\|(Pass|Freight)\\|Rail$", variable),
.(variable="FE|Transport|Rail",
unit="EJ/yr", value=sum(value)),
by=c("model", "scenario", "region", "period")]), use.names = TRUE)
if(!is.null(regionSubsetList)){
toMIF <- toMIF[region %in% regionSubsetList]
}
## Make sure there are no duplicates!
idx <- anyDuplicated(toMIF, by = c("region", "variable", "period"))
if(idx){
warning(paste0("Duplicates found in EDGE-T reporting output:",
capture.output(toMIF[idx]), collapse="\n"))
}
toMIF <- data.table::dcast(toMIF, ... ~ period, value.var="value")
EOL <- if (.Platform$OS.type=="windows") ";\r\n" else ";\n"
fwrite(toMIF, name_mif, append=T, sep=";", eol=EOL)
deletePlus(name_mif, writemif=T)
}
pik-piam/remind documentation built on Sept. 9, 2021, 1:09 p.m.
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Defines functions reportEDGETransport
Documented in reportEDGETransport
#' Reporting for the coupled EDGE-T Transport Sector Model (REMIND Module edge_esm)
#'
#' Data is loaded from the EDGE-T subfolder in the output folder.
#' The input files can be (re-) generated calling
#' `Rscript EDGETransport.R --reporting`
#' from the output folder.
#'
#' *Warning* The function modifies the "REMIND_generic_<scenario>.mif" file by appending the
#' additional reporting variables and replaces the "_withoutPlus" version.
#'
#' Region subsets are obtained from fulldata.gdx
#'
#' @param output_folder path to the output folder, default is current folder.
#' @param remind_root path to the REMIND root directory, defaults to two levels up from output_folder.
#' @author Alois Dirnaichner Marianna Rottoli
#'
#' @importFrom rmndt approx_dt
#' @importFrom gdxdt readgdx
#' @importFrom data.table fread fwrite rbindlist
#' @export
reportEDGETransport <- function(output_folder=".",
remind_root=NULL) {
unit <- NULL
if(is.null(remind_root)){
remind_root <- file.path(output_folder, "../..")
}
gdx <- file.path(output_folder, "fulldata.gdx")
regionSubsetList <- toolRegionSubsets(gdx)
sub_folder = "EDGE-T/"
## NULL Definitons for codeCheck compliance
RegionCode <- CountryCode <- cfg <- `.` <- sector <- subsector_L3 <- region <- year <- NULL
subsector_L2 <- subsector_L1 <- aggr_mode <- vehicle_type <- det_veh <- aggr_nonmot <- NULL
demand_F <- demand_EJ <- remind_rep <- V25 <- aggr_veh <- technology <- NULL
variable <- value <- demand_VKM <- loadFactor <- NULL
all_enty <- ef <- variable_agg <- model <- scenario <- period <- NULL
Region <- Variable <- co2 <- co2val <- elh2 <- fe <- NULL
int <- se <- sec <- sharesec <- te <- tech <- val <- share <- NULL
eff <- sharebio <- sharesyn <- totseliq <- type <- NULL
load(file.path(output_folder, "config.Rdata"))
datapath <- function(fname){
file.path(output_folder, sub_folder, fname)
}
## load input data from last EDGE run
demand_km <- readRDS(datapath(fname = "demandF_plot_pkm.RDS"))[
, demand_F := demand_F * 1e-3] ## million -> billion pkm
load_factor <- readRDS(datapath(fname = "loadFactor.RDS"))
demand_vkm <- merge(demand_km, load_factor, by=c("year", "region", "vehicle_type"))
demand_vkm[, demand_VKM := demand_F/loadFactor] ## billion vkm
demand_ej <- readRDS(datapath(fname = "demandF_plot_EJ.RDS")) ## detailed final energy demand, EJ
name_mif = list.files(output_folder, pattern = "REMIND_generic", full.names = F)
name_mif = file.path(output_folder, name_mif[!grepl("withoutPlu", name_mif)])
stopifnot(typeof(name_mif) == "character")
miffile <- fread(name_mif, sep=";", header=T)
## ES and FE Demand
reportingESandFE <- function(datatable, mode){
datatable[, sector := ifelse(sector %in% c("trn_pass", "trn_aviation_intl"), "Pass", "Freight")]
## attribute aggregated mode and vehicle names for plotting purposes, and aggregate
## Large Categories (as used by REMIND)
datatable[subsector_L2 == "trn_pass_road_LDV",
aggr_mode := "Pass|Road|LDV"]
datatable[subsector_L2 != "trn_pass_road_LDV" & sector == "Pass",
aggr_mode := "Pass|non-LDV"]
## Freight is already in the "normal" reporting
## datatable[is.na(aggr_mode), aggr_mode := "Freight"]
## A little more detail: Vehicle Aggregates
datatable[grepl("^Truck", vehicle_type), aggr_veh := "Freight|Road"]
datatable["Freight Rail_tmp_vehicletype" == vehicle_type, aggr_veh := "Freight|Rail"]
## there seem to be no passenger ships in EDGE-T!
datatable[subsector_L3 == "International Ship", aggr_veh := "Freight|International Shipping"]
datatable[subsector_L3 == "Domestic Ship", aggr_veh := "Freight|Navigation"]
datatable[grepl("Bus", vehicle_type), aggr_veh := "Pass|Road|Bus"]
if(mode == "ES")
datatable[grepl("Cycle|Walk", subsector_L3), aggr_nonmot := "Pass|Road|Non-Motorized"]
## Rail & Aviation
datatable[grepl("Passenger Rail|HSR", vehicle_type), aggr_veh := "Pass|Rail"]
datatable[subsector_L3 == "Domestic Aviation", aggr_veh := "Pass|Aviation|Domestic"]
datatable[subsector_L3 == "International Aviation", aggr_veh := "Pass|Aviation|International"]
## High Detail: Ecoinvent-Compatible Output
datatable[grepl("Subcompact", vehicle_type),
det_veh := "Pass|Road|LDV|Small"]
datatable[grepl("Mini", vehicle_type),
det_veh := "Pass|Road|LDV|Mini"]
datatable[vehicle_type == "Compact", det_veh := "Pass|Road|LDV|Medium"]
datatable[vehicle_type == "Large Car|Midsize Car", det_veh := "Pass|Road|LDV|Large"]
datatable[grepl("SUV", vehicle_type),
det_veh := "Pass|Road|LDV|SUV"]
datatable[grepl("Van|Multipurpose", vehicle_type),
det_veh := "Pass|Road|LDV|Van"]
datatable[grepl("Motorcycle|Scooter|Moped", vehicle_type),
det_veh := "Pass|Road|LDV|Two-Wheelers"]
prepare4MIF <- function(dt, remind_unit, valcol, varcol){
## REMIND years, loading from MIF File would take too long
yrs <- c(seq(2005, 2060, 5), seq(2070, 2110, 10), 2130, 2150)
remind_scenario <- cfg$title
prefix <- switch(mode,
"FE" = "FE|Transport|",
"ES" = "ES|Transport|",
"VKM" = "ES|Transport|VKM|")
## we only care for non-NA variables (NA is basically *all others*)
toadd = dt[!is.na(get(varcol)), .(model="REMIND", scenario=remind_scenario, region,
variable=paste0(prefix, get(varcol)),
unit=remind_unit, period=year,
value=get(valcol))]
toadd <- approx_dt(toadd, yrs, xcol="period", ycol = "value",
idxcols=colnames(toadd)[1:5],
extrapolate=T)
return(toadd)
}
if(mode == "ES"){
report <- rbindlist(list(
prepare4MIF(
datatable[sector == "Pass", sum(demand_F, na.rm=T),
by = c("region", "year", "aggr_mode")],
"bn pkm/yr", "V1", "aggr_mode"),
prepare4MIF(
datatable[sector == "Freight" & !is.na(aggr_veh), sum(demand_F, na.rm=T),
by = c("region", "year", "aggr_veh")],
"bn tkm/yr", "V1", "aggr_veh"),
prepare4MIF(
datatable[sector == "Pass" & !is.na(aggr_veh), sum(demand_F, na.rm=T),
by = c("region", "year", "aggr_veh")],
"bn pkm/yr", "V1", "aggr_veh"),
prepare4MIF(
datatable[!is.na(aggr_nonmot), sum(demand_F, na.rm=T),
by = c("region", "year", "aggr_nonmot")],
"bn pkm/yr", "V1", "aggr_nonmot"),
prepare4MIF(
datatable[!is.na(det_veh), sum(demand_F, na.rm=T),
by = c("region", "year", "det_veh")], "bn pkm/yr", "V1", "det_veh")))
}else if(mode == "VKM"){
report <- rbindlist(list(
prepare4MIF(
datatable[sector == "Pass", sum(demand_VKM, na.rm=T),
by = c("region", "year", "aggr_mode")],
"bn vkm/yr", "V1", "aggr_mode"),
prepare4MIF(
datatable[sector == "Freight" & !is.na(aggr_veh), sum(demand_VKM, na.rm=T),
by = c("region", "year", "aggr_veh")],
"bn vkm/yr", "V1", "aggr_veh"),
prepare4MIF(
datatable[sector == "Pass" & !is.na(aggr_veh), sum(demand_VKM, na.rm=T),
by = c("region", "year", "aggr_veh")],
"bn vkm/yr", "V1", "aggr_veh"),
prepare4MIF(
datatable[!is.na(det_veh), sum(demand_VKM, na.rm=T),
by = c("region", "year", "det_veh")], "bn vkm/yr", "V1", "det_veh")))
}else{
report <- rbindlist(list(
prepare4MIF(
datatable[!is.na(aggr_mode), sum(demand_EJ, na.rm=T),
by = c("region", "year", "aggr_mode")],
"EJ/yr", "V1", "aggr_mode"),
prepare4MIF(
datatable[!is.na(aggr_veh), sum(demand_EJ, na.rm=T),
by = c("region", "year", "aggr_veh")],
"EJ/yr", "V1", "aggr_veh"),
prepare4MIF(
datatable[!is.na(det_veh), sum(demand_EJ, na.rm=T),
by = c("region", "year", "det_veh")],
"EJ/yr", "V1", "det_veh")))
}
## with energy carrier
## remove cycling and walking placeholder techs for ESs
techmap <- data.table(technology=unique(datatable$technology),
key="technology")[!c("Cycle_tmp_technology", "Walk_tmp_technology")]
if(mode == "ES"){
## for energy services, it is better to refer to the actual technologies
## and not the fuel types (-> LCA)
techmap[, remind_rep := technology]
techmap["NG", remind_rep := "Gases"]
datatable <- datatable[techmap, on="technology"]
report_tech <- rbindlist(list(
prepare4MIF(
datatable[sector == "Pass", sum(demand_F, na.rm=T),
by = c("region", "year", "aggr_mode", "remind_rep")
][, aggr_mode := paste0(aggr_mode, "|", remind_rep)],
"bn pkm/yr", "V1", "aggr_mode"),
prepare4MIF(
datatable[sector == "Freight" & !is.na(aggr_veh), sum(demand_F, na.rm=T),
by = c("region", "year", "aggr_veh", "remind_rep")
][, aggr_veh := paste0(aggr_veh, "|", remind_rep)],
"bn tkm/yr", "V1", "aggr_veh"),
prepare4MIF(
datatable[sector == "Pass" & !is.na(aggr_veh), sum(demand_F, na.rm=T),
by = c("region", "year", "aggr_veh", "remind_rep")
][, aggr_veh := paste0(aggr_veh, "|", remind_rep)],
"bn pkm/yr", "V1", "aggr_veh"),
prepare4MIF(datatable[!is.na(det_veh), sum(demand_F, na.rm=T),
by = c("region", "year", "det_veh", "remind_rep")
][, det_veh := paste0(det_veh, "|", remind_rep)],
"bn pkm/yr", "V1", "det_veh")))
}else if(mode == "VKM"){
## for energy services, it is better to refer to the actual technologies
## and not the fuel types (-> LCA)
techmap[, remind_rep := technology]
techmap["LA-BEV", remind_rep := "BEV"]
techmap["NG", remind_rep := "Gases"]
datatable <- datatable[techmap, on="technology"]
report_tech <- rbindlist(list(
prepare4MIF(
datatable[sector == "Pass", sum(demand_VKM, na.rm=T),
by = c("region", "year", "aggr_mode", "remind_rep")
][, aggr_mode := paste0(aggr_mode, "|", remind_rep)],
"bn vkm/yr", "V1", "aggr_mode"),
prepare4MIF(
datatable[sector == "Freight" & !is.na(aggr_veh), sum(demand_VKM, na.rm=T),
by = c("region", "year", "aggr_veh", "remind_rep")
][, aggr_veh := paste0(aggr_veh, "|", remind_rep)],
"bn vkm/yr", "V1", "aggr_veh"),
prepare4MIF(
datatable[sector == "Pass" & !is.na(aggr_veh), sum(demand_VKM, na.rm=T),
by = c("region", "year", "aggr_veh", "remind_rep")
][, aggr_veh := paste0(aggr_veh, "|", remind_rep)],
"bn vkm/yr", "V1", "aggr_veh"),
prepare4MIF(datatable[!is.na(det_veh), sum(demand_VKM, na.rm=T),
by = c("region", "year", "det_veh", "remind_rep")
][, det_veh := paste0(det_veh, "|", remind_rep)],
"bn vkm/yr", "V1", "det_veh")))
}else{
techmap["BEV", remind_rep := "Electricity"]
techmap["Electric", remind_rep := "Electricity"]
techmap["FCEV", remind_rep := "Hydrogen"]
techmap["Hydrogen", remind_rep := "Hydrogen"]
techmap["Liquids", remind_rep := "Liquids"]
techmap["NG", remind_rep := "Gases"]
datatable <- datatable[techmap, on="technology"]
report_tech <- rbindlist(list(
prepare4MIF(
datatable[!is.na(aggr_mode), sum(demand_EJ, na.rm=T),
by = c("region", "year", "aggr_mode", "remind_rep")
][, aggr_mode := paste0(aggr_mode, "|", remind_rep)],
"EJ/yr", "V1", "aggr_mode"),
prepare4MIF(
datatable[!is.na(aggr_veh), sum(demand_EJ, na.rm=T),
by = c("region", "year", "aggr_veh", "remind_rep")
][, aggr_veh := paste0(aggr_veh, "|", remind_rep)],
"EJ/yr", "V1", "aggr_veh"),
prepare4MIF(
datatable[!is.na(det_veh), sum(demand_EJ, na.rm=T),
by = c("region", "year", "det_veh", "remind_rep")
][, det_veh := paste0(det_veh, "|", remind_rep)],
"EJ/yr", "V1", "det_veh")))
}
## add World
report_w = report[,.(value = sum(value), region = "World"), by = .(model, scenario, variable, unit, period)]
report = rbind(report, report_w)
report_tech_w = report_tech[,.(value = sum(value), region = "World"), by = .(model, scenario, variable, unit, period)]
report_tech = rbind(report_tech, report_tech_w)
return(rbindlist(list(report, report_tech)))
}
## Demand emissions
reportingEmi <- function(repFE, gdx, miffile){
## load emission factors for fossil fuels
p_ef_dem <- fread(text='
"fedie", 69.3
"fepet", 68.5
"fegat", 50.3
', col.names=c("all_enty", "ef"))
## attribute explicitly fuel used to the FE values
emidem = repFE[grepl("Liquids|Gases|Hydrogen|Electricity", variable) & region != "World"] ## EJ
emidem[grepl("LDV", variable), all_enty := ifelse(grepl("Liquids", variable), "fepet", NA)]
emidem[!grepl("LDV", variable) | grepl("non-LDV", variable), all_enty := ifelse(grepl("Liquids", variable), "fedie", all_enty)]
emidem[, all_enty := ifelse(grepl("Gases", variable), "fegat", all_enty)]
emidem[, all_enty := ifelse(grepl("Electricity", variable), "feelt", all_enty)]
emidem[, all_enty := ifelse(grepl("Hydrogen", variable), "feh2t", all_enty)]
## merge with emission factors
emidem = emidem[p_ef_dem, on = "all_enty"]
## calculate emissions and attribute variable and unit names
emidem[, value := value*ef][, c("variable", "unit") := list(gsub("FE", "Emi\\|CO2", variable), "Mt CO2/yr")]
## create the "tailpipe" and the "demand" emissions starting from the same values
emidem = rbind(emidem[region %in% unique(emidem$region)][,type := "tailpipe"], emidem[region %in% unique(emidem$region)][,type := "demand"])
## load the composition of liquids for the whole LDV sector->fepet and the HDV sector->fedie
liq_comp = miffile[Variable %in% c("FE|Transport|Liquids|LDV|Synthetic|New Reporting", "FE|Transport|Liquids|LDV|Biomass|New Reporting", "FE|Transport|Liquids|LDV|Fossil|New Reporting",
"FE|Transport|Liquids|HDV|Synthetic|New Reporting", "FE|Transport|Liquids|HDV|Biomass|New Reporting", "FE|Transport|Liquids|HDV|Fossil|New Reporting")]
## re-arrange liq_comp
liq_comp[, c("V25", "Model", "Scenario", "Unit"):=NULL]
liq_comp = data.table::melt(liq_comp, id.vars = c("Region", "Variable"))
## change col names and type of numeric cols
setnames(liq_comp, old =c("variable", "Region"), new = c("period", "region"))
liq_comp[, value:= as.numeric(value)]
liq_comp[, period := as.numeric(as.character(period))]
## attribute fepet if LDV, fedie otherwise
liq_comp[, all_enty := ifelse(grepl("LDV", Variable), "fepet", "fedie")]
## calculate shares of the liquid fuels
liq_comp = liq_comp[, share := value/sum(value), by = c("region", "period", "all_enty")]
## remove value col
liq_comp[, value := NULL]
## decrease the "demand" entries by multiplying times the share of conventional liquids
emidem = merge(emidem, liq_comp[grepl("Fossil", Variable)], by = c("region", "period", "all_enty"))
emidem[type == "demand" & all_enty %in% c("fedie", "fepet"), value := value*share]
## the taipipe emissions are to be labeled as "Tailpipe"
emidem[type == "tailpipe", variable := paste0(variable, "|Tailpipe")]
## the demand emissions are to be labeled as "Demand"
emidem[type == "demand", variable := paste0(variable, "|Demand")]
emidem[, c("share", "type", "ef", "V3", "V2", "all_enty") := NULL]
## aggregate removing the fuel dependency
emidem[, variable_agg := gsub("\\|Liquids|\\|Electricity|\\|Hydrogen|\\|Gases", "", variable)]
emidem = emidem[, .(value = sum(value)), by = c("model", "scenario", "region", "unit", "period", "variable_agg")]
setnames(emidem, old = "variable_agg", new = "variable")
emidem = emidem[, .(model, scenario, region, variable, unit, period, value)]
return(emidem)
}
repFE <- reportingESandFE(
demand_ej,
mode ="FE")
toMIF <- rbindlist(list(
repFE,
reportingESandFE(
datatable=demand_km,
mode="ES"),
reportingESandFE(
datatable=demand_vkm,
mode="VKM"),
reportingEmi(repFE = repFE, gdx = gdx, miffile = miffile)
))
## add Road Totals
toMIF <- rbindlist(list(
toMIF,
toMIF[grep("ES\\|Transport\\|Pass\\|Road\\|[A-Za-z-]+$", variable),
.(variable="ES|Transport|Pass|Road",
unit="bn pkm/yr", value=sum(value)),
by=c("model", "scenario", "region", "period")],
toMIF[grep("Emi\\|CO2\\|Transport\\|Pass\\|Road\\|[A-Za-z-]+\\|Tailpipe$", variable),
.(variable="Emi|CO2|Transport|Pass|Road|Tailpipe",
unit="Mt CO2/yr", value=sum(value)),
by=c("model", "scenario", "region", "period")],
toMIF[grep("Emi\\|CO2\\|Transport\\|Pass\\|Road\\|[A-Za-z-]+\\|Demand$", variable),
.(variable="Emi|CO2|Transport|Pass|Road|Demand",
unit="Mt CO2/yr", value=sum(value)),
by=c("model", "scenario", "region", "period")],
toMIF[grep("ES\\|Transport\\|VKM\\|Pass\\|Road\\|[A-Za-z-]+$", variable),
.(variable="ES|Transport|VKM|Pass|Road",
unit="bn vkm/yr", value=sum(value)),
by=c("model", "scenario", "region", "period")],
toMIF[grep("FE\\|Transport\\|Pass\\|Road\\|[A-Za-z-]+$", variable),
.(variable="FE|Transport|Pass|Road",
unit="EJ/yr", value=sum(value)),
by=c("model", "scenario", "region", "period")]), use.names = TRUE)
## VKM totals, Road and Rail
toMIF <- rbindlist(list(
toMIF,
toMIF[grep("ES\\|Transport\\|VKM\\|(Pass|Freight)\\|Road$", variable),
.(variable="ES|Transport|VKM|Road",
unit="bn vkm/yr", value=sum(value)),
by=c("model", "scenario", "region", "period")],
toMIF[grep("ES\\|Transport\\|VKM\\|(Pass|Freight)\\|Rail$", variable),
.(variable="ES|Transport|VKM|Rail",
unit="bn vkm/yr", value=sum(value)),
by=c("model", "scenario", "region", "period")]
), use.names = TRUE)
toMIF <- rbindlist(list(
toMIF,
toMIF[grep("FE\\|Transport\\|(Pass|Freight)\\|Road$", variable),
.(variable="FE|Transport|Road",
unit="EJ/yr", value=sum(value)),
by=c("model", "scenario", "region", "period")],
toMIF[grep("Emi\\|CO2\\|Transport\\|(Pass|Freight)\\|Road\\|Tailpipe$", variable),
.(variable="Emi|CO2|Transport|Road|Tailpipe",
unit="Mt CO2/yr", value=sum(value)),
by=c("model", "scenario", "region", "period")],
toMIF[grep("Emi\\|CO2\\|Transport\\|(Pass|Freight)\\|Rail\\|Tailpipe$", variable),
.(variable="Emi|CO2|Transport|Rail|Tailpipe",
unit="Mt CO2/yr", value=sum(value)),
by=c("model", "scenario", "region", "period")],
toMIF[grep("Emi\\|CO2\\|Transport\\|(Pass|Freight)\\|Road\\|Demand$", variable),
.(variable="Emi|CO2|Transport|Road|Demand",
unit="Mt CO2/yr", value=sum(value)),
by=c("model", "scenario", "region", "period")],
toMIF[grep("Emi\\|CO2\\|Transport\\|(Pass|Freight)\\|Rail\\|Demand$", variable),
.(variable="Emi|CO2|Transport|Rail|Demand",
unit="Mt CO2/yr", value=sum(value)),
by=c("model", "scenario", "region", "period")],
toMIF[grep("FE\\|Transport\\|(Pass|Freight)\\|Rail$", variable),
.(variable="FE|Transport|Rail",
unit="EJ/yr", value=sum(value)),
by=c("model", "scenario", "region", "period")]), use.names = TRUE)
if(!is.null(regionSubsetList)){
toMIF <- toMIF[region %in% regionSubsetList]
}
## Make sure there are no duplicates!
idx <- anyDuplicated(toMIF, by = c("region", "variable", "period"))
if(idx){
warning(paste0("Duplicates found in EDGE-T reporting output:",
capture.output(toMIF[idx]), collapse="\n"))
}
toMIF <- data.table::dcast(toMIF, ... ~ period, value.var="value")
EOL <- if (.Platform$OS.type=="windows") ";\r\n" else ";\n"
fwrite(toMIF, name_mif, append=T, sep=";", eol=EOL)
deletePlus(name_mif, writemif=T)
}
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