Skip to content
Snippets Groups Projects
Commit 245b2e05 authored by Marianna Rottoli's avatar Marianna Rottoli
Browse files

New reporting for EDGE single run.

parent b306dcea
No related branches found
No related tags found
1 merge request!79EDGE-T Validation Output - Fix
This commit is part of merge request !79. Comments created here will be created in the context of that merge request.
Hide whitespace changes
Inline Side-by-side
scripts/output/single/notebook_templates/EDGETransportReport.Rmd
+ 447
− 546
View file @ 245b2e05
......@@ -14,20 +14,24 @@ require(remind)
require(gdxdt)
require(gdx)
require(rmndt)
require(data.table)
require(magclass)
require(quitte)
require(ggpubr)
require(gridExtra)
require(edgeTrpLib)
```
```{r, echo=FALSE, warning=FALSE}
iso_plot = "DEU"
output_folder = "EDGE-T/"
dem_shares <- list()
intensity <- list()
demand_km <- list()
demand_ej <- list()
sw_tech <- list()
prices_FV <- list()
cols <- c("NG" = "#d11141",
"Liquids" = "#8c8c8c",
"Hybrid Liquids" = "#ffc425",
"Hybrid Electric" = "#f37735",
"BEV" = "#00b159",
"FCEV" = "#00aedb")
datapath <- function(fname){
file.path("./input_EDGE/", fname)
......@@ -37,6 +41,7 @@ mapspath <- function(fname, scenariopath=""){
file.path("../../modules/35_transport/edge_esm/input", fname)
}
## Load mappings
EDGE2CESmap <- fread(mapspath("mapping_CESnodes_EDGE.csv"))
......@@ -45,614 +50,510 @@ REMIND2ISO_MAPPING <- fread("../../config/regionmappingH12.csv")[, .(iso = Count
EDGE2teESmap <- fread(mapspath("mapping_EDGE_REMIND_transport_categories.csv"))
years <- c(1990,
REMINDyears <- c(1990,
seq(2005, 2060, by = 5),
seq(2070, 2110, by = 10),
2130, 2150)
REMINDyears <- c(1990,
years <- c(1990,
seq(2005, 2060, by = 5),
seq(2070, 2110, by = 10),
2130, 2150)
## include the paths to the scenarios you want to compare
output_folders <- "./"
for(output_folder in output_folders){
## load gdx for fuel prices and demand
gdx = file.path(output_folder, "fulldata.gdx")
## load policy scenario
load(file.path(output_folder, "config.Rdata"))
REMIND_scenario <- cfg$gms$cm_GDPscen
EDGE_scenario <- cfg$gms$cm_EDGEtr_scen
policy_scenario <- cfg$gms$c_expname
scen <- paste0(REMIND_scenario, "-", EDGE_scenario, "-", policy_scenario)
## load demand
ES_demand = readREMINDdemand(gdx, REMIND2ISO_MAPPING, EDGE2teESmap, years)
## load input data
int_dat <- readRDS(datapath("harmonized_intensities.RDS"))
nonfuel_costs <- readRDS(datapath("UCD_NEC_iso.RDS"))
sw_data <- readRDS(datapath("SW.RDS"))
vot_data <- readRDS(datapath("VOT_iso.RDS"))
logit_params <- readRDS(datapath("logit_exp.RDS"))
price_nonmot <- readRDS(datapath("price_nonmot.RDS"))
## FIXME: hotfix to make the (empty) vot_data$value_time_VS1 with the right column types. Probably there is another way to do that, did not look for it.
vot_data$value_time_VS1$iso = as.character(vot_data$value_time_VS1$iso)
vot_data$value_time_VS1$subsector_L1 = as.character(vot_data$value_time_VS1$subsector_L1)
vot_data$value_time_VS1$vehicle_type = as.character(vot_data$value_time_VS1$vehicle_type)
vot_data$value_time_VS1$year = as.numeric(vot_data$value_time_VS1$year)
vot_data$value_time_VS1$time_price = as.numeric(vot_data$value_time_VS1$time_price)
## calculate prices
REMIND_prices <- merge_prices(
gdx = gdx,
REMINDmapping = REMIND2ISO_MAPPING,
REMINDyears = REMINDyears,
intensity_data = int_dat,
nonfuel_costs = nonfuel_costs)
## calculates logit
logit_data <- calculate_logit(
REMIND_prices[tot_price > 0],
REMIND2ISO_MAPPING,
vot_data = vot_data,
sw_data = sw_data,
logit_params = logit_params,
intensity_data = int_dat,
price_nonmot = price_nonmot)
shares <- logit_data[["share_list"]] ## shares of alternatives for each level of the logit function
mj_km_data <- logit_data[["mj_km_data"]] ## energy intensity at a technology level
prices_FV[[scen]] <- REMIND_prices[, EDGE_scenario := scen] ## prices at each level of the logit function, 1990USD/pkm
## calculate energy intensity and FE demand at a REMIND-region level for the desired level of aggregation
res <- shares_intensity_and_demand(
logit_shares=shares,
MJ_km_base=mj_km_data,
REMIND2ISO_MAPPING=REMIND2ISO_MAPPING,
EDGE2CESmap=EDGE2CESmap,
REMINDyears=REMINDyears,
demand_input=ES_demand)
dem_shares[[scen]] <- res$demand[, EDGE_scenario := scen]
intensity[[scen]] <- res$demandI[, EDGE_scenario := scen]
demand_km[[scen]] <- res$demandF_plot_pkm[, EDGE_scenario := scen]
demand_ej[[scen]] <- res$demandF_plot_EJ[, EDGE_scenario := scen]
sw_tech[[scen]] <- sw_data$FV_final_SW[, EDGE_scenario := scen]
}
load("config.Rdata")
dem_shares <- rbindlist(dem_shares)
intensity <- rbindlist(intensity)
demand_km <- rbindlist(demand_km)
demand_ej <- rbindlist(demand_ej)
sw_tech <- rbindlist(sw_tech)
prices_FV <- rbindlist(prices_FV)
```
EDGE_scenario <- cfg$gms$cm_EDGEtr_scen
## load EDGE settings and apply them
settingsEDGE = readRDS(paste0(output_folder, "settingsEDGE.RDS"))
selfmarket_taxes <<- as.logical(settingsEDGE[settings == "selfmarket_taxes", value])
selfmarket_policypush <<- as.logical(settingsEDGE[settings == "selfmarket_policypush", value])
selfmarket_acceptancy <<- as.logical(settingsEDGE[settings == "selfmarket_acceptancy", value])
techswitch <<- settingsEDGE[settings == "techswitch", value]
enhancedtech <<- as.logical(settingsEDGE[settings == "enhancedtech", value])
rebates_febates <<- as.logical(settingsEDGE[settings == "rebates_febates", value])
```{r, echo=FALSE}
## plot settings
years_plot = c(2010,2015,2020,2025,2030,2040,2050) ## in bar charts, these are the time steps that are represented
year_single = 2050
region_plot = "NEU" ## in case is a region specific plot, this region is represented
sector_plot ="trn_pass" ## in case is a sector specific plot, this sector is represented
## models of ICE are available to consumers?
endogeff <<-TRUE
## save intermediate input for plotting purposes
savetmpinput <<- TRUE
##conversion rate 2005->1990 USD
CONV_2005USD_1990USD=0.67
## is learning applied?
setlearning <<- TRUE
# print(paste0("Scenario: ", REMIND_scenario))
print(paste0("Regional plots are about ",region_plot))
print(paste0("Sectoral plots are about ",sector_plot))
## load input data from REMIND
gdx = paste0("fulldata.gdx") ## gdx file
name_mif = list.files(pattern = "REMIND_generic", full.names = F)
name_mif = name_mif[!grepl("withoutPlu", name_mif)]
miffile <- as.data.table(read.quitte(name_mif))
## maps
cesmap <- data.table(CES_parent=c("_p_sm", "_p_lo", "_f_lo", "_f_sm"),
CES_label=c("Passenger, Short-to-Medium Distances",
"Passenger, Long Distances",
"Freight, Long Distances",
"Freight, Short-to-Medium Distances"))
## load input data from EDGE
input_path = paste0("../../modules/35_transport/edge_esm/input/")
EDGE_sectormap <- data.table(sector=c("trn_pass", "trn_freight", "trn_aviation_intl", "trn_shipping_intl"),
CES_label=c("Passenger, Short-to-Medium Distances",
"Passenger, Long Distances",
"Freight, Long Distances",
"Freight, Short-to-Medium Distances"))
inputdata = createRDS(input_path, SSP_scenario = scenario, EDGE_scenario = EDGE_scenario)
vot_data = inputdata$vot_data
sw_data = inputdata$sw_data
inco_data = inputdata$inco_data
logit_params = inputdata$logit_params
int_dat = inputdata$int_dat
nonfuel_costs = inputdata$nonfuel_costs
price_nonmot = inputdata$price_nonmot
```
## load total energy services demand
ES_demand = readREMINDdemand(gdx, REMIND2ISO_MAPPING, EDGE2teESmap, REMINDyears)
if (setlearning) {
## load non fuel costs based on learning
nonfuel_costs = readRDS(paste0("nonfuel_costs_learning.RDS"))
}
```{r, echo=FALSE}
## aggregate demands to REMIND regions
demandF_plot_EJ <- demand_ej[,c("EDGE_scenario", "sector","subsector_L3","subsector_L2",
"subsector_L1","vehicle_type","technology", "iso","year","demand_EJ")]
demandF_plot_pkm <- demand_km[,c("EDGE_scenario", "sector","subsector_L3","subsector_L2",
"subsector_L1","vehicle_type","technology","iso","year","demand_F")]
demandF_plot_EJ=aggregate_dt(demandF_plot_EJ,REMIND2ISO_MAPPING,
datacols = c("EDGE_scenario", "sector", "subsector_L3", "subsector_L2", "subsector_L1",
"vehicle_type", "technology"),
valuecol = "demand_EJ")
demandF_plot_pkm=aggregate_dt(demandF_plot_pkm,REMIND2ISO_MAPPING,
datacol = c("EDGE_scenario", "sector","subsector_L3","subsector_L2",
"subsector_L1","vehicle_type","technology"),
valuecol = "demand_F")
## add GLO
glo <- demandF_plot_pkm[,.(region="GLO", demand_F=sum(demand_F)),
by=eval(names(demandF_plot_pkm)[2:9])]
demandF_plot_pkm <- rbind(demandF_plot_pkm, glo)
glo <- demandF_plot_EJ[,.(region="GLO", demand_EJ=sum(demand_EJ)),
by=eval(names(demandF_plot_EJ)[2:9])]
demandF_plot_EJ <- rbind(demandF_plot_EJ, glo)
## calculate prices
REMIND_prices <- merge_prices(
gdx = gdx,
REMINDmapping = REMIND2ISO_MAPPING,
REMINDyears = REMINDyears,
intensity_data = int_dat,
nonfuel_costs = nonfuel_costs)
## calculate logit
logit_data <- calculate_logit_inconv_endog(
prices= REMIND_prices[tot_price > 0],
vot_data = vot_data,
inco_data = inco_data,
logit_params = logit_params,
intensity_data = int_dat,
price_nonmot = price_nonmot)
shares <- logit_data[["share_list"]] ## shares of alternatives for each level of the logit function
mj_km_data <- logit_data[["mj_km_data"]] ## energy intensity at a technology level
prices <- logit_data[["prices_list"]] ## prices at each level of the logit function, 1990USD/pkm
sales_LDV <- logit_data[["annual_sales"]] ## annual sales composition of LDVs, %
inco_tech <- logit_data$inconv_cost ## inconvenience cost, 1990USD/pkm
if(savetmpinput){
saveRDS(logit_data$share_list, file = paste0(output_folder, "/share_newvehicles.RDS"))
saveRDS(logit_data$EF_shares, file = paste0(output_folder, "EF_shares.RDS"))
saveRDS(logit_data$mj_km_data, file= paste0(output_folder, "mj_km_data.RDS"))
saveRDS(nonfuel_costs, file=paste0(output_folder, "nonfuel_costs.RDS"))
saveRDS(inco_tech, file=paste0(output_folder, "inco_costs.RDS"))
saveRDS(REMIND_prices, file=paste0(output_folder, "fuel_prices.RDS"))
}
## calculate vintages (new shares, prices, intensity)
vintages = calcVint(shares = shares,
totdem_regr = ES_demand[sector == "trn_pass"],
prices = prices,
mj_km_data = mj_km_data,
years = years)
shares$FV_shares = vintages[["shares"]]$FV_shares ## the shares need to be updated with the vintages calculations
prices = vintages[["prices"]] ## prices as well
mj_km_data = vintages[["mj_km_data"]] ## ... and energy intensity as well
vintcomp = vintages[["vintcomp"]] ## composition of vintages
newcomp = vintages[["newcomp"]] ## composition of new additions
```
## ES
```{r, echo=FALSE}
##chunk of code that plots the ES
ES_modes_bar1=function(demandpkm){
#group by subsector_L3 and summarise the demand
df=demandpkm[, .(demand_F=sum(demand_F)
), by = c("EDGE_scenario", "region", "year","sector","subsector_L1")]
df[,demand_F:=demand_F ## in millionkm
*1e-6 ## in trillion km
]
df=df[order(year)]
# #filter only 2020, 2050 and 2100
df=df[year %in% years_plot,]
#separate into passenger and freight categories
pass=c("trn_pass","trn_aviation_intl")
freight=c("trn_freight","trn_shipping_intl")
## give proper names to the categories
df=merge(df, edgeTrpLib::L1mapping, all.x=TRUE, by="subsector_L1")
#plot
plot_p=ggplot()+
geom_bar(data=df%>%filter(sector %in% pass, year %in% years_plot, region == region_plot),
aes(x=year,y=demand_F,group=mode,fill=mode),position=position_stack(),stat="identity")+
facet_wrap(~EDGE_scenario)+
ggtitle("Energy Services Demand - Passenger Transport Modes")+
theme_light()+
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
scale_x_continuous(breaks=years_plot)+
xlab("Year")+
ylab("Energy Services Demand (trillion pkm)")+
guides(fill=guide_legend(title="Transport mode"))+
theme(axis.text.x = element_text(angle = 90, hjust = 1),
axis.text = element_text(size=13),
title = element_text(size=13),
legend.text = element_text(size=13))
plot_f=ggplot()+
geom_bar(data=df%>%filter(sector %in% freight,year %in% years_plot, region == region_plot),
aes(x=year,y=demand_F,group=mode,fill=mode),position=position_stack(),stat="identity")+
facet_wrap(~EDGE_scenario)+
ggtitle("Energy Services Demand - Freight Transport Modes")+
theme_light()+
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
scale_x_continuous(breaks=years_plot)+
xlab("Year")+
ylab("Energy Services Demand (trillion tkm)")+
guides(fill=guide_legend(title="Transport mode"))+
theme(axis.text.x = element_text(angle = 90, hjust = 1),
axis.text = element_text(size=13),
title = element_text(size=13),
legend.text = element_text(size=13))
plot=list(plot_p,plot_f)
return(plot)
if (savetmpinput) {
saveRDS(vintages, file=paste0(output_folder, fname = "vintages.RDS"))
}
p=ES_modes_bar1(demandpkm=demandF_plot_pkm)
p[[1]]
p[[2]]
## calculate energy intensity and FE demand at a REMIND-region level for the desired level of aggregation
res <- shares_intensity_and_demand(
logit_shares=shares,
MJ_km_base=mj_km_data,
REMIND2ISO_MAPPING=REMIND2ISO_MAPPING,
EDGE2CESmap=EDGE2CESmap,
REMINDyears=REMINDyears,
demand_input = ES_demand)
```
```{r, echo=FALSE}
##chunk of code that plots the ES
ES_modes_bar=function(demandpkm){
demandpkm[technology == "LA-BEV", technology := "BEV"]
## use proper non-fuel mode names
demandpkm[technology %in% c("Cycle_tmp_technology", "Walk_tmp_technology"), technology := "Human Powered"]
#group by subsector_L3 and summarise the demand
df=demandpkm[, .(demand_F=sum(demand_F)),
by = c("EDGE_scenario", "region", "year","sector","subsector_L1", "technology")]
df[,demand_F:=demand_F ## in millionkm
*1e-6 ## in trillion km
]
df=df[order(year)]
#separate into passenger and freight categories
pass=c("trn_pass","trn_aviation_intl")
freight=c("trn_freight","trn_shipping_intl")
## give proper names to the categories
df=merge(df, edgeTrpLib::L1mapping, all.x=TRUE, by="subsector_L1")
#plot
plot_p=ggplot()+
geom_bar(data=df%>%filter(sector %in% pass,year %in% years_plot,region==region_plot),
aes(x=year,y=demand_F,group=technology,fill=technology),
position=position_stack(),stat="identity")+
ggtitle("Energy Services Demand by Technology, Passenger Transport, EUR")+
theme_light()+
facet_wrap(~EDGE_scenario)+
scale_x_continuous(breaks=years_plot)+
xlab("Year")+
ylab("Energy Services Demand (trillion pkm)")+
guides(fill=guide_legend(title="Technology"))+
theme(axis.text.x = element_text(angle = 90, hjust = 1),
axis.text = element_text(size=13),
title = element_text(size=13),
legend.text = element_text(size=13))
plot_f=ggplot()+
geom_bar(data=df%>%filter(sector %in% freight,year %in% years_plot, region==region_plot),
aes(x=year,y=demand_F, group=technology, fill=technology),
position=position_stack(),stat="identity")+
ggtitle(paste0("Energy Services Demand by Technology, Freight Transport, ", region_plot))+
facet_wrap(~EDGE_scenario) +
theme_light()+
scale_x_continuous(breaks=years_plot)+
xlab("Year")+
ylab("Energy Services Demand (trllion tkm)")+
guides(fill=guide_legend(title="Technology"))+
theme(axis.text.x = element_text(angle = 90, hjust = 1),
axis.text = element_text(size=13),
title = element_text(size=13),
legend.text = element_text(size=13))
plot=list(plot_p,plot_f)
return(plot)
}
if(savetmpinput){
saveRDS(res$demandF_plot_EJ, file=paste0(output_folder, "demandF_plot_EJ.RDS"))
saveRDS(res$demandF_plot_pkm, file=paste0(output_folder, "demandF_plot_pkm.RDS"))
}
p=ES_modes_bar(demandpkm=demandF_plot_pkm)
p[[1]]
p[[2]]
demand_km <- res$demandF_plot_pkm ## detailed energy services demand, million km
demand_ej <- res$demandF_plot_EJ ## detailed final energy demand, EJ
sharesVS1 <- shares$VS1_shares ## shares at vehicle type level
sharesFV <- shares$FV_shares ## shares at fuel type level
```
```{r, echo=FALSE}
## plot ES for LDVs only divided by fuel
ES_modes_LDV_bar=function(demandpkm){
demandpkm[technology == "LA-BEV", technology := "BEV"]
## use proper non-fuel mode names
demandpkm[technology %in% c("Cycle_tmp_technology", "Walk_tmp_technology"), technology := "Human Powered"]
#group by subsector_L3 and summarise the demand
df=demandpkm[, .(demand_F=sum(demand_F)),
by = c("EDGE_scenario", "region", "year","sector","subsector_L1", "technology")]
df[,demand_F:=demand_F ## in millionkm
*1e-6 ## in trillion km
]
df=df[order(year)]
## give proper names to the categories
df=merge(df, edgeTrpLib::L1mapping, all.x=TRUE, by="subsector_L1")
## select order of facets
df$technology = factor(df$technology, levels=c("Liquids","Hybrid Liquids","NG","BEV","FCEV"))
#plot
plot_LDV=ggplot()+
geom_bar(data=df%>%filter(year %in% years_plot,region==region_plot, mode %in% c("4W","2W")),
aes(x=year,y=demand_F,group=technology,fill=technology), alpha = 0.9,
position=position_stack(),stat="identity")+
ggtitle(paste0("Energy Services Demand by Technology, LDVs", region_plot))+
theme_light()+
facet_wrap(~EDGE_scenario)+
theme(axis.text.x = element_text(angle = 90, hjust = 1))+
xlab("Year")+
ylab("Energy Services Demand (trillion pkm)")+
guides(fill=guide_legend(title="Technology"))+
theme(axis.text.x = element_text(angle = 90, hjust = 1),
axis.text = element_text(size=13),
title = element_text(size=13),
legend.text = element_text(size=13),
strip.text.x = element_text(size = 13, color = "black"),
strip.background=element_rect(fill="white"))+
scale_x_continuous(breaks=years_plot)+
scale_fill_brewer(palette = "Set1")
return(plot_LDV)
}
# LDVs vintages
p=ES_modes_LDV_bar(demandpkm=demandF_plot_pkm)
```{r, echo=FALSE, warning=FALSE}
p
plotVint = function(vintcomp, newcomp, sharesVS1){
vintcomp = vintcomp[,.(totdem, iso, subsector_L1, year, technology,vehicle_type, sector, sharetech_vint, EDGE_scenario)]
newcomp = newcomp[,.(iso, subsector_L1, year, technology,vehicle_type, sector, sharetech_new, EDGE_scenario)]
allfleet = merge(newcomp, vintcomp, all =TRUE, by = c("iso", "sector", "subsector_L1", "vehicle_type", "technology", "year", "EDGE_scenario"))
allfleet = merge(allfleet, sharesVS1[,.(shareVS1 = share, iso, year, vehicle_type, subsector_L1)], all.x=TRUE, by = c("iso", "year", "vehicle_type", "subsector_L1"))
allfleet[,vintdem:=totdem*sharetech_vint*shareVS1]
allfleet[,newdem:=totdem*sharetech_new*shareVS1]
allfleet=melt(allfleet, id.vars = c("iso", "sector", "subsector_L1", "vehicle_type", "technology",
"year", "EDGE_scenario"), measure.vars = c("vintdem", "newdem"))
allfleet[,alpha:=ifelse(variable == "vintdem", 0, 1)]
load_factor = 2
annual_mileage = 15000
allfleet = allfleet[,.(value = sum(value/load_factor/annual_mileage)), by = c("iso", "technology", "variable", "year")]
allfleet = merge(allfleet, REMIND2ISO_MAPPING, by = "iso")
allfleet = allfleet[,.(value = sum(value)), by = c("region", "technology", "variable", "year")]
allfleet[,alphaval := ifelse(variable =="vintdem", 1,0)]
p = ggplot()+
geom_bar(data = allfleet[year %in% c(2015,2030,2050)],
aes(x=as.character(year),y=value, group=interaction(variable, technology),
fill = technology), alpha = 0.5, position="stack", stat = "identity", width = 0.5)+
geom_bar(data = allfleet[year %in% c(2015,2030,2050)],
aes(x=as.character(year),y=value, group=interaction(variable, technology),
fill = technology, alpha = factor(alphaval)), position="stack", stat = "identity", width = 0.5, color = "black")+
guides(fill = guide_legend(reverse=TRUE))+
theme_minimal()+
facet_wrap(~region, nrow = 4)+
theme(axis.text.x = element_text(angle = 90, hjust = 1),
axis.text = element_text(size=7),
title = element_text(size=8),
legend.text = element_text(size=8))+
scale_x_discrete(breaks = c(2015,2030,2050))+
scale_alpha_discrete(breaks = c(1,0), name = "Status", labels = c("Vintages","New additions")) +
guides(linetype=FALSE,
fill=guide_legend(reverse=FALSE, title="Transport mode"))+
scale_fill_manual(values = cols)+
labs(y = "LDV fleet [million Veh]", x="")
```
return(p)
}
## FE
```{r, echo=FALSE}
FE_modes_bar=function(demandEJ){
#group by subsector_L1 and summarise the demand
df=demandEJ[, .(demand_EJ=sum(demand_EJ)),
by = c("EDGE_scenario", "region", "year","subsector_L1","subsector_L3","sector")]
df=df[order(year)]
df=df[year %in% years_plot,]
#separate into passenger and freight categories
pass=c("trn_pass","trn_aviation_intl")
freight=c("trn_freight","trn_shipping_intl")
## give proper names to the categories
df <- merge(df, edgeTrpLib::L1mapping, all.x=TRUE, by="subsector_L1")
#plot
plot_p=ggplot()+
geom_bar(data=df%>%filter(sector %in% pass, region==region_plot),
aes(x=year,y=demand_EJ,group=mode,fill=mode),position=position_stack(),stat="identity",color="black")+
facet_wrap(~EDGE_scenario)+
ylab("Energy (EJ)") +
ggtitle(paste0("Final Energy Demand by Mode, Passenger, ", region_plot))+
theme(axis.text.x = element_text(angle = 90))+
scale_x_continuous(breaks=years_plot)
plot_f=ggplot()+
geom_bar(data=df%>%filter(sector %in% freight,region==region_plot),
aes(x=year,y=demand_EJ,group=mode,fill=mode),position=position_stack(),stat="identity",color="black")+
facet_wrap(~EDGE_scenario) +
ggtitle(paste0("Final Energy Demand by Mode, Freight, ", region_plot))+
ylab("Energy (EJ)") +
theme(axis.text.x = element_text(angle = 90))+
scale_x_continuous(breaks=years_plot)
plot=list(plot_p,plot_f)
return(plot)
}
p=FE_modes_bar(demandEJ = demandF_plot_EJ)
p[[1]]
p[[2]]
p = plotVint(vintcomp, newcomp, sharesVS1)
p
```
```{r, echo=FALSE}
## function that calculates FE split and splits out the liquids by source
FE_modes_bar_oilcomponent=function(demandEJ, msect="trn_pass", region_plot){
if(msect == "trn_pass")
sector_display = "Passenger"
if(msect == "trn_freight")
sector_display = "Freight"
#group by subsector_L1 and summarise the demand
df=demandEJ[, .(demand_EJ=sum(demand_EJ)),
by = c("EDGE_scenario", "region", "year","subsector_L1","subsector_L3","sector","technology")]
df=df[order(year)]
df=df[year %in% years_plot,]
## give proper names to the categories
df=merge(df, edgeTrpLib::L1mapping, all.x=TRUE, by="subsector_L1")
df[,technology := ifelse(technology == "LA-BEV", "BEV", technology)]
df[,technology := ifelse(technology == "Electric", "El. Trains", technology)]
## select order of facerts
df$technology = factor(df$technology, levels=c("BEV","FCEV","Hybrid Liquids", "El. Trains", "NG","Liquids", "Coal"))
#plot
plot_psm = ggplot()+
geom_bar(data=df%>%filter(sector == msect, region == region_plot),
aes(x=year,y=demand_EJ,group=technology,fill=technology),
position=position_stack(),stat="identity", alpha = 0.9)+
theme_light()+
ggtitle(paste0("Final Energy Demand by Tech, ", sector_display, ", ", region_plot))+
theme(axis.text.x = element_text(angle = 90),
strip.text.x = element_text(size = 13, color = "black"))+
scale_x_continuous(breaks=years_plot)+
scale_fill_brewer(palette = "Set1")+
xlab("Year")+
ylab("Final energy demand [EJ]")+
facet_wrap(~EDGE_scenario) +
guides(fill=guide_legend(title="Technology"))
plot_psm_LDV = ggplot()+
geom_bar(data=df%>%filter(sector == msect, region == region_plot, mode == "4W"),
aes(x=year,y=demand_EJ,group=technology,fill=technology),
position=position_stack(),stat="identity", alpha = 0.9)+
theme_light()+
ggtitle(paste0("Final Energy Demand by Tech, LDVs, ", region_plot))+
theme(axis.text.x = element_text(angle = 90),
strip.text.x = element_text(size = 13, color = "black"),
strip.background=element_rect(fill="white"),
axis.text = element_text(size=13),
title = element_text(size=13),
legend.text = element_text(size=13))+
scale_x_continuous(breaks=years_plot)+
scale_fill_brewer(palette = "Set1")+
xlab("Year")+
ylab("Final energy demand [EJ]")+
facet_wrap(~EDGE_scenario) +
guides(fill=guide_legend(title="Technology"))
plot_list = list(plot_psm, plot_psm_LDV)
return(plot_list)
}
# Inconvenience cost trend
```{r, echo=FALSE, warning=FALSE}
p=ggplot()+
geom_bar(data = inco_tech[iso == iso_plot & subsector_L1 == "trn_pass_road_LDV_4W" & vehicle_type == "Large Car and SUV" & year<=2100 & year>=2010], aes(x = as.character(year), y = pinco, group = technology, fill = technology), position = position_stack(), stat = "identity")+
facet_grid(~technology)+
theme_minimal()+
scale_fill_manual(values = cols)+
expand_limits(y = c(0,0.8))+
scale_x_discrete(breaks = c(2015,2050,2100))+
theme(axis.text.x = element_text(angle = 90, vjust = +0.1),
legend.position = "none",
strip.background = element_rect(color = "grey"))+
labs(x = "", y = "Inconvenience cost [$/pkm]", title = paste0("Example of ", iso_plot))
FE_modes_bar_oilcomponent(demandEJ = demandF_plot_EJ, msect="trn_pass", region=region_plot)
FE_modes_bar_oilcomponent(demandEJ = demandF_plot_EJ, msect="trn_pass", region="GLO")
p
```
# Endogenous intensity for Liquids
```{r, echo=FALSE, message=FALSE, warning=FALSE}
## Choice of the energy intensity (of the new sales)
intcompplotf = function(EF_shares, FV_shares, VS1_shares){
EF_shares = EF_shares[,c("iso", "year", "technology", "vehicle_type", "subsector_L1", "subsector_L2", "subsector_L3", "sector", "share","type")]
setnames(EF_shares, old="share", new = "shareINT")
FV_shares = FV_shares[iso == iso_plot & subsector_L1 == "trn_pass_road_LDV_4W" & technology == "Liquids"]
setnames(FV_shares, old="share", new = "shareF")
VS1_shares = VS1_shares[iso == iso_plot & subsector_L1 == "trn_pass_road_LDV_4W"]
shares_LDV = merge(FV_shares, EF_shares, all = FALSE, by = c("iso", "year", "technology", "vehicle_type", "subsector_L1"))
shares_LDV[, shareIF := shareF*shareINT]
shares_LDV <- shares_LDV[,.(shareIF=sum(shareIF)),by=c("iso","technology","type","vehicle_type","subsector_L1", "year")]
shares_LDV = merge(shares_LDV, VS1_shares, all = TRUE, by = c("iso", "year", "vehicle_type", "subsector_L1"))
shares_LDV[, shareIS1 := shareIF*share]
shares_LDV <- shares_LDV[,.(shareIS1=sum(shareIS1)),by=c("iso","type", "technology","subsector_L1","year")]
p = ggplot()+
geom_bar(data = shares_LDV[year<=2100 & year>=2025], aes(x=year,y=shareIS1, group = technology, fill = technology), alpha = 0.5, position = position_fill(), stat = "identity")+
geom_bar(data = shares_LDV[year<=2100 & year>=2025], aes(x=year,y=shareIS1, group = technology, fill = technology, alpha = type), position = position_fill(), stat = "identity")+
facet_wrap("technology")+
theme_minimal()+
expand_limits(y = c(0,1))+
scale_fill_manual("technology", values = cols)+
scale_alpha_discrete("Type")+
labs(y = "Share [%]", title = paste0("Energy intensity new sales of Liquids, example for ", iso_plot))
return(p)
```{r, echo=FALSE}
FE_modes_bar1=function(demandEJ, msect="trn_pass", region_plot){
if(msect == "trn_pass")
sector_display = "Passenger"
if(msect == "trn_freight")
sector_display = "Freight"
## group by subsector_L1 and summarise the demand
df=demandEJ[, .(demand_EJ=sum(demand_EJ)),
by = c("EDGE_scenario", "region", "year","subsector_L1","sector")]
df=df[order(year) & year %in% years_plot]
## give proper names to the categories
df=merge(df, edgeTrpLib::L1mapping, all.x=TRUE, by="subsector_L1")
#plot
plot_p=ggplot()+
geom_bar(data=df%>%filter(sector == msect, region == region_plot),
aes(x=year,y=demand_EJ,group=mode,fill=mode),
position=position_stack(),stat="identity",color="black")+
facet_wrap(~EDGE_scenario) +
ylab("Energy (EJ)") +
ggtitle(paste0("Final Energy Demand by Mode, ", sector_display, ", ", region_plot))+
theme(axis.text.x = element_text(angle = 90))+
scale_x_continuous(breaks=years_plot)
return(plot_p)
}
FE_modes_bar1(demandEJ = demandF_plot_EJ, region_plot = region_plot)
intcompplotf(logit_data$EF_shares, sharesFV, sharesVS1)
```
# Sales of LDVs
```{r, echo=FALSE, warning=FALSE}
salesplot = function(sales_LDV){
sales_LDV = unique(sales_LDV[,c("iso","year", "technology", "shareFS1")])
sales_LDV <- sales_LDV[,.(shareFS1=sum(shareFS1)),by=c("iso","technology","year")]
p = ggplot()+
geom_bar(data = sales_LDV[year<=2050 & year>=2015 & iso == iso_plot], aes(x=as.numeric(as.character(year)),y=shareFS1, group = technology, fill = technology), position = position_stack(), stat = "identity")+
theme_minimal()+
scale_fill_manual("Technology", values = cols)+
expand_limits(y = c(0,1))+
scale_x_continuous(breaks = c(2015,2030,2050))+
theme(axis.text.x = element_text(angle = 90, vjust = +0.1),
strip.background = element_rect(color = "grey"),
legend.position = "none")+
labs(x = "", y = "Market share on LDVs [%]", title = paste0("Sales composition, example of ", iso_plot))
```{r, echo=FALSE}
FE_modes_bar_oilVSelec=function(demandEJ, region_plot){
sector_display = "Total transport"
## group by subsector_L1 and summarise the demand
df=demandEJ[, .(demand_EJ=sum(demand_EJ)),
by = c("EDGE_scenario", "region", "year","subsector_L1","sector","technology")]
df[, tech_plot := ifelse(technology %in% c("BEV","Electric"), "Electriciy", NA)]
df[, tech_plot := ifelse(technology %in% c("Liquids", "Hybrid Liquids"), "Liquids", tech_plot)]
df=df[!is.na(tech_plot),] ## only liquids and electric driven entries interesting
df=df[order(year) & year %in% years_plot]
## give proper names to the categories
df=merge(df, edgeTrpLib::L1mapping, all.x=TRUE, by="subsector_L1")
df[,short_names:=ifelse(mode %in% c("Buses","Rail Passenger","High Speed Rail"),"Other Passenger",NA)]
df[,short_names:=ifelse(mode %in% c("2W","4W","Three Wheelers"),"LDV",short_names)]
df[,short_names:=ifelse(mode %in% c("Domestic Aviation","International Aviation"),"Aviation",short_names)]
df[,short_names:=ifelse(mode %in% c("International Shipping","Domestic Shipping"),"Shipping",short_names)]
df[,short_names:=ifelse(mode %in% c("Road Freight","Rail Freight"),"Road and Rail Freight",short_names)]
#plot
plot_p=ggplot()+
geom_bar(data=df%>%filter(region == region_plot, year ==2050),
aes(x=tech_plot,y=demand_EJ,group=short_names,fill=short_names),
position=position_stack(),stat="identity",alpha=0.95)+
facet_wrap(~EDGE_scenario) +
ylab("Energy (EJ)") +
ggtitle(paste0("Final Energy Demand by Mode in 2050, total transport ", region_plot))+
theme_light()+
theme(axis.text.x = element_text(angle = 90),
axis.title.x = element_blank(),
strip.text.x = element_text(size = 13, color = "black"),
strip.background=element_rect(fill="white"),
axis.text = element_text(size=13),
title = element_text(size=13),
legend.text = element_text(size=13))+
scale_fill_brewer(palette = "Set2")+
guides(fill=guide_legend(title="Transport mode"))
return(plot_p)
return(p)
}
FE_modes_bar_oilVSelec(demandEJ = demandF_plot_EJ, region_plot = region_plot)
salesplot(sales_LDV)
```
# Final energy demand
## FE composition
```{r, echo=FALSE}
FE_modeshares_area=function(demandEJ){
#group by subsector_L3 and summarise the demand
df=demandEJ[, .(demand_EJ=sum(demand_EJ)),
by = c("EDGE_scenario", "region", "year","subsector_L3")]
#order by year
df=df[order(year)]
df=df[year>=2005,]
#plot
plot=ggplot()+
geom_area(data=df %>% filter(year <= max(years_plot), region == region_plot),
aes(x=year,y=demand_EJ,group=subsector_L3,fill=subsector_L3),
color="black")+
facet_wrap(~EDGE_scenario)+
ylab("Energy (EJ)") +
ggtitle("Final Energy Demand, Mode composition")+
theme(axis.text.x = element_text(angle = 90, hjust = 1))
return(plot)
```{r, echo=FALSE, warning=FALSE}
demandEJplotf = function(demandEJ){
## EDGE results
demandEJ <- demandEJ[, c("sector", "subsector_L3", "subsector_L2", "subsector_L1", "vehicle_type", "technology", "iso", "year", "demand_EJ")]
## attribute aggregated mode and vehicle names for plotting purposes, and aggregate
demandEJ[, aggr_mode := ifelse(subsector_L2 == "trn_pass_road_LDV", "LDV", NA)]
demandEJ[, aggr_mode := ifelse(subsector_L3 %in% c("Passenger Rail", "HSR", "International Aviation", "Domestic Aviation"), "Pass non LDV", aggr_mode)]
demandEJ[, aggr_mode := ifelse(subsector_L2 %in% c("trn_pass_road_bus", "Bus"), "Pass non LDV", aggr_mode)]
demandEJ[, aggr_mode := ifelse(is.na(aggr_mode), "Freight", aggr_mode)]
demandEJ[, veh := ifelse(vehicle_type %in% c("Truck (0-1t)", "Truck (0-3.5t)", "Truck (0-2.7t)", "Truck (0-2t)"), "Trucks (<3.5t)", NA)]
demandEJ[, veh := ifelse(vehicle_type %in% c("Truck (16-32t)", "Truck (3.5-16t)", "Truck (6-15t)", "Truck (4.5-12t)", "Truck (2.7-4.5t)", "Truck (4.5-15t)"), "Trucks (3.5t-16)", veh)]
demandEJ[, veh := ifelse(vehicle_type %in% c("Truck (>15t)", "Truck (16-32t)", "Truck (>32t)" ), "Trucks (>16)", veh)]
demandEJ[, veh := ifelse(grepl("Large|SUV|Midsize|Multipurpose Vehicle|Van|3W Rural", vehicle_type), "Large Cars", veh)]
demandEJ[, veh := ifelse(grepl("Subcompact|Compact|Mini|Three-Wheeler", vehicle_type), "Small Cars", veh)]
demandEJ[, veh := ifelse(grepl("Motorcycle|Moped|Scooter", vehicle_type), "Motorbikes", veh)]
demandEJ[, veh := ifelse(grepl("bus|Bus", vehicle_type), "Bus", veh)]
demandEJ[, veh := ifelse(grepl("Freight Rail_tmp_vehicletype", vehicle_type), "Freight Rail", veh)]
demandEJ[, veh := ifelse(grepl("Passenger Rail|HSR", vehicle_type), "Passenger Rail", veh)]
demandEJ[, veh := ifelse(subsector_L3 == "Domestic Ship", "Domestic Shipping", veh)]
demandEJ[, veh := ifelse(subsector_L3 == "International Ship", "International Shipping", veh)]
demandEJ[, veh := ifelse(subsector_L3 == "Domestic Aviation", subsector_L3, veh)]
demandEJ[, veh := ifelse(subsector_L3 == "International Aviation", subsector_L3, veh)]
demandEJ[, veh := ifelse(is.na(veh), vehicle_type, veh)]
demandEJ = demandEJ[,.(demand_EJ = sum(demand_EJ)), by = c("iso", "year", "aggr_mode", "veh")]
demandEJ[, vehicle_type_plot := factor(veh, levels = c("LDV","Freight Rail", "Trucks (<3.5t)", "Trucks (3.5t-16)", "Truck (>12t)", "Trucks (>16)", "Trucks","Domestic Shipping", "International Shipping",
"Motorbikes", "Small Cars", "Large Cars", "Van",
"Domestic Aviation", "International Aviation", "Bus", "Passenger Rail",
"Freight", "Freight (Inland)", "Pass non LDV", "Pass non LDV (Domestic)"))]
legend_ord <- c("Freight Rail", "Trucks (<3.5t)", "Trucks (3.5t-16)", "Truck (>12t)", "Trucks (>16)", "International Shipping","Domestic Shipping", "Trucks",
"Motorbikes", "Small Cars", "Large Cars", "Van",
"International Aviation", "Domestic Aviation","Bus", "Passenger Rail",
"Freight", "LDV", "Pass non LDV", "Freight (Inland)", "Pass non LDV (Domestic)")
demandEJ = merge(demandEJ, REMIND2ISO_MAPPING, by = "iso")
demandEJ = demandEJ[,.(demand_EJ= sum(demand_EJ)), by = c("region", "year", "vehicle_type_plot", "aggr_mode")]
p=ggplot()+
geom_area(data = demandEJ[year > 2010], aes(x=year, y=demand_EJ, group = interaction(vehicle_type_plot,aggr_mode), fill = vehicle_type_plot), color = "black", position= position_stack())+
facet_wrap(~region, nrow = 4)
labs(x = "", y = "Final Energy demand [EJ]")+
theme_minimal()+
# scale_fill_manual("Vehicle Type",values = cols, breaks=legend_ord)+
theme(axis.text.x = element_text(size = 8),
axis.text.y = element_text(size=8),
axis.title = element_text(size = 9),
title = element_text(size = 9),
legend.text = element_text(size = 9),
legend.title = element_text(size =9),
strip.text = element_text(size=9))
return(p)
}
p=FE_modeshares_area(demandEJ = demandF_plot_EJ)
p
## ggsave("FE_modeshares.png")
## Final Energy demand
demandEJplotf(demand_ej)
```
# LDVs final energy demand
```{r, echo=FALSE, warning=FALSE}
## demand EJ for LDV, divided by fuel type
demandEJLDVplotf <- function(demandEJ){
demandEJ = demandEJ[subsector_L1 == "trn_pass_road_LDV_4W",]
demandEJ <- demandEJ[, c("sector", "subsector_L3", "subsector_L2", "subsector_L1", "vehicle_type", "technology", "iso", "year", "demand_EJ")]
demandEJ = merge(demandEJ, REMIND2ISO_MAPPING, by = "iso")
demandEJ[technology == "Hybrid Liquids", technology := "Liquids"]
demandEJ[technology == "FCEV", technology := "Hydrogen"]
demandEJ[technology == "BEV", technology := "Electricity"]
demandEJ = demandEJ[, .(demand_EJ = sum(demand_EJ)), by = c("region", "year", "technology")]
p = ggplot()+
geom_area(data = demandEJ[year > 2010], aes(x=year, y=demand_EJ, group = technology, fill = technology), color="black",position= position_stack())+
labs(x = "", y = "Final energy demand for LDVs [EJ]")+
facet_wrap(~region, nrow = 4)
theme_minimal()+
# scale_fill_manual("Vehicle Type",values = cols, breaks=legend_ord)+
theme(axis.text.x = element_text(size = 7),
axis.text.y = element_text(size=7),
axis.title = element_text(size = 8),
title = element_text(size = 8),
legend.text = element_text(size = 8),
legend.title = element_text(size = 8),
strip.text = element_text(size=8))
return(p)
```{r, echo=FALSE}
## fuel use by sector
fuel_shares_area=function(demandEJ, msect="trn_pass", region_plot){
if(msect == "trn_pass")
sector_display = "Passenger"
if(msect == "trn_freight")
sector_display = "Freight"
##group by sector and technology and summarise demand
df=demandEJ[, .(demand_EJ=sum(demand_EJ)),
by = c("EDGE_scenario", "region", "year","technology","sector")]
df=df[order(year) & year>=2005,]
#plot
plot1=ggplot()+
geom_area(data=df%>%filter(sector == msect, year <= max(years_plot), region == region_plot),
aes(x=year,y=demand_EJ,group=technology,fill=technology),position="fill")+
facet_wrap(~EDGE_scenario)+
ggtitle(paste0("Final Energy Demand, ", sector_display, ", Fuel Composition"))+
ylab("Share")
theme(axis.text.x = element_text(angle = 90, hjust = 1))
return(plot1)
}
p=fuel_shares_area(demandEJ = demandF_plot_EJ, region_plot = region_plot)
p
demandEJLDVplotf(demand_ej)
```
# Energy services demand
```{r, echo=FALSE, warning=FALSE}
demandpkmplotf = function(demandpkm){
## REMIND-EDGE results
demandpkm <- demandpkm[,c("sector","subsector_L3","subsector_L2",
"subsector_L1","vehicle_type","technology", "iso","year","demand_F")]
demandpkm[,demand_F:=demand_F ## in millionkm
*1e-6 ## in trillion km
]
## attribute aggregated mode and vehicle names for plotting purposes, and aggregate
demandpkm[, aggr_mode := ifelse(subsector_L1 %in% c("Three-Wheeler", "trn_pass_road_LDV_4W"), "LDV", NA)]
demandpkm[, aggr_mode := ifelse(sector %in% c("trn_freight", "trn_shipping_intl"), "Freight", aggr_mode)]
demandpkm[, aggr_mode := ifelse(sector %in% c("trn_aviation_intl"), "Pass. non LDV", aggr_mode)]
demandpkm[, aggr_mode := ifelse(subsector_L2 %in% c("trn_pass_road_bus", "HSR_tmp_subsector_L2", "Passenger Rail_tmp_subsector_L2", "Cycle_tmp_subsector_L2", "Walk_tmp_subsector_L2", "Domestic Aviation_tmp_subsector_L2", "Bus") | subsector_L1 %in% c("trn_pass_road_LDV_2W"), "Pass. non LDV", aggr_mode)]
demandpkm[, veh := ifelse(vehicle_type %in% c("Truck (0-1t)", "Truck (0-3.5t)"), "Trucks (<3.5t)", "Trucks (<3.5t)")]
demandpkm[, veh := ifelse(vehicle_type %in% c("Truck (16-32t)", "Truck (3.5-16t)", "Truck (6-15t)"), "Trucks (3.5t-16)", veh)]
demandpkm[, veh := ifelse(vehicle_type %in% c("Truck (>15t)", "Truck (16-32t)", "Truck (>32t)" ), "Trucks (>16)", veh)]
demandpkm[, veh := ifelse(grepl("Large|SUV|Midsize|Multipurpose Vehicle|Van|3W Rural", vehicle_type), "Large Cars", veh)]
demandpkm[, veh := ifelse(grepl("Subcompact|Compact|Mini|Three-Wheeler_tmp_vehicletype", vehicle_type), "Small Cars", veh)]
demandpkm[, veh := ifelse(grepl("Motorcycle|Moped|Scooter", vehicle_type), "Motorbikes", veh)]
demandpkm[, veh := ifelse(grepl("bus|Bus", vehicle_type), "Bus", veh)]
demandpkm[, veh := ifelse(subsector_L3 == "Domestic Aviation", "Domestic Aviation", veh)]
demandpkm[, veh := ifelse(subsector_L3 == "International Aviation", "International Aviation", veh)]
demandpkm[, veh := ifelse(grepl("Freight Rail", vehicle_type), "Freight Rail", veh)]
demandpkm[, veh := ifelse(grepl("Passenger Rail|HSR", vehicle_type), "Passenger Rail", veh)]
demandpkm[, veh := ifelse(grepl("Ship", vehicle_type), "Shipping", veh)]
demandpkm[, veh := ifelse(grepl("Cycle|Walk", subsector_L3), "Non motorized", veh)]
demandpkm = demandpkm[,.(demand_F = sum(demand_F)), by = c("iso", "year", "aggr_mode", "veh")]
setnames(demandpkm, old = "veh", new = "vehicle_type")
demandpkm[, vehicle_type_plot := factor(vehicle_type, levels = c("LDV","Freight Rail", "Trucks (<3.5t)", "Trucks (3.5t-16)", "Trucks (>16)", "Trucks",
"Motorbikes", "Small Cars", "Large Cars", "Van",
"Domestic Aviation", "International Aviation","Bus", "Passenger Rail",
"Freight", "Non motorized", "Shipping"))]
demandpkm[, mode := ifelse(vehicle_type %in% c("Freight", "Freight Rail", "Trucks", "Trucks (3.5t-16)", "Trucks (>16)", "Shipping"),"freight", "pass")]
demandpkm = merge(demandpkm, REMIND2ISO_MAPPING, by = "iso")
demandpkm = demandpkm[, .(demand_F = sum(demand_F)), by = c("region", "year", "vehicle_type_plot", "aggr_mode", "mode")]
demandpkm = demandpkm[order(aggr_mode)]
p = ggplot()+
geom_area(data = demandpkm[mode =="pass"& year > 2010], aes(x=year, y=demand_F, group = interaction(vehicle_type_plot,aggr_mode), fill = vehicle_type_plot), color="black",position= position_stack())+
labs(x = "", y = "Energy Services demand [trillion pkm]")+
facet_wrap(~region, nrow = 4)
theme_minimal()+
# scale_fill_manual("Vehicle Type",values = cols, breaks=legend_ord)+
theme(axis.text.x = element_text(size = 7),
axis.text.y = element_text(size=7),
axis.title = element_text(size = 8),
title = element_text(size = 8),
legend.text = element_text(size = 8),
legend.title = element_text(size = 8),
strip.text = element_text(size=8))
```{r, echo=FALSE}
SW_trend_plot = function(FV_SW,sector_plot){
if (sector_plot == "trn_pass") {
FV_SW=FV_SW[iso=="DEU" & vehicle_type =="Large Car and SUV",]
} else if (sector_plot =="trn_freight"){
FV_SW=FV_SW[iso=="DEU" & vehicle_type =="Truck (16-32t)",]
} else if (sector_plot =="trn_aviation_intl"){
FV_SW=FV_SW[iso=="DEU" & subsector_L3 =="International Aviation",]
} else if (sector_plot =="trn_shipping_intl"){
FV_SW=FV_SW[iso=="DEU" & subsector_L3 =="International Ship",]
}
FV_SW[,type:=ifelse(technology=="Liquids", "Conventional ICE (Liquid fuels)",NA)]
FV_SW[,type:=ifelse(technology=="NG", "Natural Gas ICE",type)]
FV_SW[,type:=ifelse(technology=="BEV", "Alternative fuels: BEV",type)]
FV_SW[,type:=ifelse(technology=="FCEV", "Alternative fuels: FCEV",type)]
FV_SW[,type:=ifelse(technology=="Hybrid Liquids", "Unconventional ICE (Hybrid)",type)]
p=ggplot()+
geom_line(data=FV_SW%>%filter(year>= min(years_plot), year<=max(years_plot)),aes(x=year,y=sw,group=type,color=type),alpha = 0.8,size=1.5)+
theme_light()+
facet_wrap(~EDGE_scenario)+
theme(axis.text.x = element_text(angle = 90, hjust = 1),
axis.text = element_text(size=13),
title = element_text(size=14),
legend.text = element_text(size=13))+
scale_x_continuous(breaks=years_plot)+
xlab("Year")+
ylab ("Preference factors tech. types [-]")+
ggtitle(paste0("Preference factors trend for tech. types for ", sector_plot, " [-]"))+
theme(strip.text.x = element_text(size=13,color="black"),
strip.background = element_rect(fill="white",color = "black"))+
scale_color_discrete(name="Technology type")
return(p)
}
p=SW_trend_plot(FV_SW=sw_tech,sector_plot)
p
}
## energy services demand
demandpkmplotf(demand_km)
```
# CO2 intensity of new sales
```{r, echo=FALSE, warning=FALSE}
CO2km_intensity_newsalesplotf = function(shares_LDV, mj_km_data, sharesVS1, shares_source_liquids){
shares_source_liquids[, technology := ifelse(variable %in% c("FE|Transport|Liquids|Oil", "FE|Transport|Liquids|Coal"), "Oil", "Biodiesel")]
shares_source_liquids = shares_source_liquids[,.(value = sum(value)), by = c("model","scenario","region", "period", "unit","technology")]
shares_source_liquids = shares_source_liquids[region != "World"]
shares_source_liquids[, region:=as.character(region)]
shares_source_liquids[, year := period]
shares_source_liquids[, period:=NULL]
gdp <- getRMNDGDP(scenario = "SSP2", usecache = T)
shares_source_liquids <- disaggregate_dt(shares_source_liquids, REMIND2ISO_MAPPING,
valuecol="value",
datacols=c("model","scenario", "unit","technology"),
weights=gdp)
shares_source_liquids[, shareliq := value/sum(value),by=c("iso", "year")]
# ## CO2 content
# CO2_petrol = 3.1 ## gCO2/gFUEL
# CO2_biodiesel = 2.7 ## TODO this number is made up!
# CO2_cng = 2.7 ## gCO2/gFUEL
## TODO of CO2 content of biodiesel is made up! gCO2/gFUEL
emi_fuel = data.table(technology = c("Oil", "Biodiesel", "NG"), ei_gF_MJ = c(20, 20, 20), emi_cGO2_gF = c(3.1, 3.1, 2.7))
emi_liquids = merge(shares_source_liquids, emi_fuel, all.x = TRUE, by = "technology")
emi_liquids = emi_liquids[, .(ei_gF_MJ = sum(shareliq*ei_gF_MJ), emi_cGO2_gF = sum(shareliq*emi_cGO2_gF)), by = c("iso", "year")][, technology := "Liquids"]
emi_NG = cbind(emi_fuel[technology == "NG"], unique(shares_source_liquids[,c("year", "iso")]))
emi_fuel = rbind(emi_NG, emi_liquids)
emi_fuel[, gCO2_MJ := ei_gF_MJ*emi_cGO2_gF]
emi_fuel = merge(mj_km_data[subsector_L1 == "trn_pass_road_LDV_4W"], emi_fuel, all.x = TRUE, by = c("iso", "year", "technology"))
emi_fuel[is.na(gCO2_MJ) & !technology %in% c("Liquids", "NG"), gCO2_MJ := 0]
emi_fuel[, gCO2_km := MJ_km * gCO2_MJ]
totalemi = merge(emi_fuel, shares_LDV, all.y = TRUE, by = c("iso", "year", "technology", "vehicle_type", "subsector_L1"), all.x = TRUE)
totalemi = totalemi[!is.na(share) & !is.na(gCO2_km)]
totalemi[, gCO2_km_ave := gCO2_km*share]
##totalemi = merge(totalemi, demand_ej_plot)
totalemi = totalemi[,.(gCO2_km_ave = sum(gCO2_km_ave)), by = c("year", "iso", "vehicle_type")]
totalemi = merge(totalemi, sharesVS1, all.x = TRUE, by = c("iso", "year", "vehicle_type"))
totalemi = totalemi[,.(gCO2_km_ave = sum(gCO2_km_ave*share)), by = c("iso", "year", "subsector_L1")]
totalemi = merge(totalemi, REMIND2ISO_MAPPING, by="iso")
totalemi = merge(totalemi, gdp, all.x=TRUE, by = c("iso", "year"))
totalemi[, share := weight/sum(weight), by = c("year", "region")]
totalemi = totalemi[,.(gCO2_km_ave = sum(gCO2_km_ave*share)), by = c("year", "region")]
p = ggplot()+
geom_line(data = totalemi, aes(x = year, y = gCO2_km_ave))+
labs(title = "gCO2/km average", y = "Average gCO2/km LDVs new additions")+
facet_wrap(~region, nrow = 4)+
theme_minimal()
return(p)
}
shares_source_liquids = miffile[variable %in% c("FE|Transport|Liquids|Biomass", "FE|Transport|Liquids|Coal", "FE|Transport|Liquids|Oil"),]
CO2km_intensity_newsalesplotf(sales_LDV, mj_km_data, sharesVS1 = vintages$shares$VS1_shares, shares_source_liquids)
```
\ No newline at end of file
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment