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EDGE-T Validation Output - Fix

Merged EDGE-T Validation Output - Fix
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Merged Jerome Hilaire requested to merge github/fork/MariannaR/reporting_fix into develop
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scripts/output/single/notebook_templates/EDGETransportReport.Rmd
+ 345
− 564
@@ -14,645 +14,426 @@ require(remind)
@@ -14,645 +14,426 @@ require(remind)
require(gdxdt)
require(gdxdt)
require(gdx)
require(gdx)
require(rmndt)
require(rmndt)
require(data.table)
require(magclass)
require(quitte)
 
require(ggpubr)
 
require(gridExtra)
require(edgeTrpLib)
require(edgeTrpLib)
```
```
```{r, echo=FALSE, warning=FALSE}
```{r, echo=FALSE, warning=FALSE}
 
iso_plot = "DEU"
 
output_folder = "EDGE-T/"
dem_shares <- list()
cols <- c("NG" = "#d11141",
intensity <- list()
"Liquids" = "#8c8c8c",
demand_km <- list()
"Hybrid Liquids" = "#ffc425",
demand_ej <- list()
"Hybrid Electric" = "#f37735",
sw_tech <- list()
"BEV" = "#00b159",
prices_FV <- list()
"FCEV" = "#00aedb")
datapath <- function(fname){
datapath <- function(fname){
file.path("./input_EDGE/", fname)
file.path("./EDGE-T/", fname)
}
}
mapspath <- function(fname, scenariopath=""){
mapspath <- function(fname, scenariopath=""){
file.path("../../modules/35_transport/edge_esm/input", fname)
file.path("../../modules/35_transport/edge_esm/input", fname)
}
}
 
## Load mappings
## Load mappings
EDGE2CESmap <- fread(mapspath("mapping_CESnodes_EDGE.csv"))
REMIND2ISO_MAPPING <- fread("../../config/regionmappingH12.csv")[, .(iso = CountryCode, region = RegionCode)]
 
EDGE2teESmap <- fread(mapspath("mapping_EDGE_REMIND_transport_categories.csv"))
REMIND2ISO_MAPPING <- fread("../../config/regionmappingH12.csv")[, .(iso = CountryCode,
## load input data from last EDGE run
region = RegionCode)]
demand_km <- readRDS(datapath(fname = "demandF_plot_pkm.RDS")) ## detailed energy services demand, million km
 
demand_ej <- readRDS(datapath(fname = "demandF_plot_EJ.RDS")) ## detailed final energy demand, EJ
 
vintcomp <- readRDS(datapath(fname = "vintcomp.RDS"))
 
newcomp <- readRDS(datapath(fname = "newcomp.RDS"))
 
shares <- readRDS(datapath(fname = "shares.RDS"))
 
inco_tech <- readRDS(datapath(fname = "inco_costs.RDS"))
 
EF_shares <- readRDS(datapath(fname = "EF_shares.RDS"))
 
annual_sales <- readRDS(datapath(fname = "annual_sales.RDS"))
 
mj_km_data <- readRDS(datapath(fname = "mj_km_data.RDS"))
 
 
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))
 
```
EDGE2teESmap <- fread(mapspath("mapping_EDGE_REMIND_transport_categories.csv"))
# LDVs vintages
years <- c(1990,
```{r, echo=FALSE, warning=FALSE}
seq(2005, 2060, by = 5),
seq(2070, 2110, by = 10),
2130, 2150)
REMINDyears <- 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]
}
dem_shares <- rbindlist(dem_shares)
plotVint = function(vintcomp, newcomp, sharesVS1){
intensity <- rbindlist(intensity)
vintcomp = vintcomp[,.(totdem, iso, subsector_L1, year, technology,vehicle_type, sector, sharetech_vint)]
demand_km <- rbindlist(demand_km)
newcomp = newcomp[,.(iso, subsector_L1, year, technology,vehicle_type, sector, sharetech_new)]
demand_ej <- rbindlist(demand_ej)
sw_tech <- rbindlist(sw_tech)
allfleet = merge(newcomp, vintcomp, all =TRUE, by = c("iso", "sector", "subsector_L1", "vehicle_type", "technology", "year"))
prices_FV <- rbindlist(prices_FV)
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"), 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)
 
}
```{r, echo=FALSE}
p = plotVint(vintcomp, newcomp, shares$VS1_shares)
## 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
 
p
##conversion rate 2005->1990 USD
```
CONV_2005USD_1990USD=0.67
# print(paste0("Scenario: ", REMIND_scenario))
# Inconvenience cost trend
print(paste0("Regional plots are about ",region_plot))
print(paste0("Sectoral plots are about ",sector_plot))
## maps
```{r, echo=FALSE, warning=FALSE}
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"))
EDGE_sectormap <- data.table(sector=c("trn_pass", "trn_freight", "trn_aviation_intl", "trn_shipping_intl"),
p=ggplot()+
CES_label=c("Passenger, Short-to-Medium Distances",
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")+
"Passenger, Long Distances",
facet_grid(~technology)+
"Freight, Long Distances",
theme_minimal()+
"Freight, Short-to-Medium Distances"))
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))
 
 
p
```
```
 
# Endogenous intensity for Liquids
```{r, echo=FALSE}
```{r, echo=FALSE, message=FALSE, warning=FALSE}
## aggregate demands to REMIND regions
## Choice of the energy intensity (of the new sales)
demandF_plot_EJ <- demand_ej[,c("EDGE_scenario", "sector","subsector_L3","subsector_L2",
intcompplotf = function(EF_shares, FV_shares, VS1_shares){
"subsector_L1","vehicle_type","technology", "iso","year","demand_EJ")]
EF_shares = EF_shares[,c("iso", "year", "technology", "vehicle_type", "subsector_L1", "subsector_L2", "subsector_L3", "sector", "share","type")]
demandF_plot_pkm <- demand_km[,c("EDGE_scenario", "sector","subsector_L3","subsector_L2",
setnames(EF_shares, old="share", new = "shareINT")
"subsector_L1","vehicle_type","technology","iso","year","demand_F")]
FV_shares = FV_shares[iso == iso_plot & subsector_L1 == "trn_pass_road_LDV_4W" & technology == "Liquids"]
setnames(FV_shares, old="share", new = "shareF")
demandF_plot_EJ=aggregate_dt(demandF_plot_EJ,REMIND2ISO_MAPPING,
VS1_shares = VS1_shares[iso == iso_plot & subsector_L1 == "trn_pass_road_LDV_4W"]
datacols = c("EDGE_scenario", "sector", "subsector_L3", "subsector_L2", "subsector_L1",
shares_LDV = merge(FV_shares, EF_shares, all = FALSE, by = c("iso", "year", "technology", "vehicle_type", "subsector_L1"))
"vehicle_type", "technology"),
valuecol = "demand_EJ")
shares_LDV[, shareIF := shareF*shareINT]
shares_LDV <- shares_LDV[,.(shareIF=sum(shareIF)),by=c("iso","technology","type","vehicle_type","subsector_L1", "year")]
demandF_plot_pkm=aggregate_dt(demandF_plot_pkm,REMIND2ISO_MAPPING,
datacol = c("EDGE_scenario", "sector","subsector_L3","subsector_L2",
shares_LDV = merge(shares_LDV, VS1_shares, all = TRUE, by = c("iso", "year", "vehicle_type", "subsector_L1"))
"subsector_L1","vehicle_type","technology"),
shares_LDV[, shareIS1 := shareIF*share]
valuecol = "demand_F")
shares_LDV <- shares_LDV[,.(shareIS1=sum(shareIS1)),by=c("iso","type", "technology","subsector_L1","year")]
## add GLO
p = ggplot()+
glo <- demandF_plot_pkm[,.(region="GLO", demand_F=sum(demand_F)),
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")+
by=eval(names(demandF_plot_pkm)[2:9])]
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")+
demandF_plot_pkm <- rbind(demandF_plot_pkm, glo)
facet_wrap("technology")+
glo <- demandF_plot_EJ[,.(region="GLO", demand_EJ=sum(demand_EJ)),
theme_minimal()+
by=eval(names(demandF_plot_EJ)[2:9])]
expand_limits(y = c(0,1))+
demandF_plot_EJ <- rbind(demandF_plot_EJ, glo)
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)
## 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)
}
}
p=ES_modes_bar1(demandpkm=demandF_plot_pkm)
intcompplotf(EF_shares, shares$FV_shares, shares$VS1_shares)
p[[1]]
p[[2]]
```
```
```{r, echo=FALSE}
# Sales of LDVs
##chunk of code that plots the ES
ES_modes_bar=function(demandpkm){
```{r, echo=FALSE, warning=FALSE}
demandpkm[technology == "LA-BEV", technology := "BEV"]
salesplot = function(annual_sales){
## use proper non-fuel mode names
annual_sales = unique(annual_sales[,c("iso","year", "technology", "shareFS1")])
demandpkm[technology %in% c("Cycle_tmp_technology", "Walk_tmp_technology"), technology := "Human Powered"]
annual_sales <- annual_sales[,.(shareFS1=sum(shareFS1)),by=c("iso","technology","year")]
#group by subsector_L3 and summarise the demand
df=demandpkm[, .(demand_F=sum(demand_F)),
p = ggplot()+
by = c("EDGE_scenario", "region", "year","sector","subsector_L1", "technology")]
geom_bar(data = annual_sales[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")+
df[,demand_F:=demand_F ## in millionkm
theme_minimal()+
*1e-6 ## in trillion km
scale_fill_manual("Technology", values = cols)+
]
expand_limits(y = c(0,1))+
df=df[order(year)]
scale_x_continuous(breaks = c(2015,2030,2050))+
#separate into passenger and freight categories
theme(axis.text.x = element_text(angle = 90, vjust = +0.1),
pass=c("trn_pass","trn_aviation_intl")
strip.background = element_rect(color = "grey"),
freight=c("trn_freight","trn_shipping_intl")
legend.position = "none")+
## give proper names to the categories
labs(x = "", y = "Market share on LDVs [%]", title = paste0("Sales composition, example of ", iso_plot))
df=merge(df, edgeTrpLib::L1mapping, all.x=TRUE, by="subsector_L1")
#plot
return(p)
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)
}
}
p=ES_modes_bar(demandpkm=demandF_plot_pkm)
p[[1]]
p[[2]]
 
salesplot(annual_sales)
```
```
```{r, echo=FALSE}
# Final energy demand
## 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)
}
p=ES_modes_LDV_bar(demandpkm=demandF_plot_pkm)
```{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
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))
```
## FE
return(p)
```{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)
## Final Energy demand
p[[1]]
demandEJplotf(demand_ej)
p[[2]]
```
```
```{r, echo=FALSE}
# LDVs final energy demand
## 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)
}
 
```{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)
 
}
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")
 
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))
 
 
 
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)
## energy services demand
 
demandpkmplotf(demand_km)
```
```
 
# CO2 intensity of new sales
 
```{r, echo=FALSE, warning=FALSE}
```{r, echo=FALSE}
CO2km_intensity_newsalesplotf = function(annual_sales, mj_km_data, sharesVS1, shares_source_liquids){
FE_modes_bar_oilVSelec=function(demandEJ, region_plot){
shares_source_liquids[, technology := ifelse(variable %in% c("FE|Transport|Liquids|Oil", "FE|Transport|Liquids|Coal"), "Oil", "Biodiesel")]
sector_display = "Total transport"
shares_source_liquids = shares_source_liquids[,.(value = sum(value)), by = c("model","scenario","region", "period", "unit","technology")]
## group by subsector_L1 and summarise the demand
shares_source_liquids = shares_source_liquids[region != "World"]
df=demandEJ[, .(demand_EJ=sum(demand_EJ)),
shares_source_liquids[, region:=as.character(region)]
by = c("EDGE_scenario", "region", "year","subsector_L1","sector","technology")]
shares_source_liquids[, year := period]
df[, tech_plot := ifelse(technology %in% c("BEV","Electric"), "Electriciy", NA)]
shares_source_liquids[, period:=NULL]
df[, tech_plot := ifelse(technology %in% c("Liquids", "Hybrid Liquids"), "Liquids", tech_plot)]
gdp <- getRMNDGDP(scenario = "SSP2", usecache = T)
df=df[!is.na(tech_plot),] ## only liquids and electric driven entries interesting
shares_source_liquids <- disaggregate_dt(shares_source_liquids, REMIND2ISO_MAPPING,
df=df[order(year) & year %in% years_plot]
valuecol="value",
## give proper names to the categories
datacols=c("model","scenario", "unit","technology"),
df=merge(df, edgeTrpLib::L1mapping, all.x=TRUE, by="subsector_L1")
weights=gdp)
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)
}
FE_modes_bar_oilVSelec(demandEJ = demandF_plot_EJ, region_plot = region_plot)
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
## FE composition
## 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))
```{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)
}
p=FE_modeshares_area(demandEJ = demandF_plot_EJ)
emi_liquids = merge(shares_source_liquids, emi_fuel, all.x = TRUE, by = "technology")
p
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"]
## ggsave("FE_modeshares.png")
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]
```{r, echo=FALSE}
totalemi = merge(emi_fuel, annual_sales, all.y = TRUE, by = c("iso", "year", "technology", "vehicle_type", "subsector_L1"), all.x = TRUE)
## fuel use by sector
totalemi = totalemi[!is.na(share) & !is.na(gCO2_km)]
fuel_shares_area=function(demandEJ, msect="trn_pass", region_plot){
totalemi[, gCO2_km_ave := gCO2_km*share]
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
```
##totalemi = merge(totalemi, demand_ej_plot)
```{r, echo=FALSE}
totalemi = totalemi[,.(gCO2_km_ave = sum(gCO2_km_ave)), by = c("year", "iso", "vehicle_type")]
SW_trend_plot = function(FV_SW,sector_plot){
totalemi = merge(totalemi, sharesVS1, all.x = TRUE, by = c("iso", "year", "vehicle_type"))
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)
totalemi = totalemi[,.(gCO2_km_ave = sum(gCO2_km_ave*share)), by = c("iso", "year", "subsector_L1")]
p
```
 
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(annual_sales, mj_km_data, sharesVS1 = shares$VS1_shares, shares_source_liquids)
 
```
 
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