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Marianna Rottoli authoredMarianna Rottoli authored
EDGETransportComparison.Rmd 37.48 KiB
title: "Compare scenarios Transport"
output:
pdf_document: default
html_document:
df_print: paged
classoption: landscapeknitr::opts_chunk$set(dev = 'pdf')require(ggplot2)
require(moinput)
require(rmndt)
require(quitte)
library(lucode)
library(magpie)
library(quitte)
library(cowplot)# Set RDS files path
EJmode_all = readRDS("EJmode_all.RDS")
EJroad_all = readRDS("EJroad_all.RDS")
fleet_all = readRDS("fleet_all.RDS")
salescomp_all = readRDS("salescomp_all.RDS")
ESmodecap_all = readRDS("ESmodecap_all.RDS")
CO2km_int_newsales_all = readRDS("CO2km_int_newsales_all.RDS")
EJpass_all = readRDS("EJfuelsPass_all.RDS")
EJfrgt_all = readRDS("EJfuelsFrgt_all.RDS")
emidem_all = readRDS("emidem_all.RDS")
costs_all = readRDS("costs_all.RDS")
pref_FV_all = readRDS("pref_FV_all.RDS")
dempkm_cap_all = readRDS("demgdpcap_all.RDS")
setConfig(forcecache=T)
cols <- c("NG" = "#d11141",
"Liquids" = "#8c8c8c",
"Hybrid Liquids" = "#ffc425",
"Hybrid Electric" = "#f37735",
"BEV" = "#00b159",
"Electricity" = "#00b159",
"Electric" = "#00b159",
"FCEV" = "#00aedb",
"Hydrogen" = "#00aedb",
"Biodiesel" = "#66a182",
"Synfuel" = "orchid",
"Oil" = "#2e4057",
"Operating and maintenance" = "#edae49",
"Range anxiety" = "#e43f5a",
"Refuel availability" = "#f79071",
"Purchase" = "#d1495b",
"Model availability" = "#58b4ae",
"Inconvenience cost" = "#58b4ae",
"Charging" = "#007892",
"Policy induced inconvenience" = "#2e4057",
"Risk" = "#feb72b",
"Fuel" = "#035aa6",
"International Aviation" = "#9acd32",
"Domestic Aviation" = "#7cfc00",
"Bus" = "#32cd32",
"Passenger Rail" = "#2e8b57",
"Freight Rail" = "#ee4000",
"Trucks" = "#ff6a6a",
"International Shipping" = "#cd2626",
"Domestic Shipping" = "#ff4040",
"Shipping" = "#ff4040",
"Truck" = "#ff7f50",
"Trucks (<3.5t)" = "#ff7f50",
"Trucks (3.5t-16)" = "#8b0000",
"Trucks (>16)" = "#fa8072",
"Motorbikes" = "#1874cd", #"dodgerblue3",
"Small Cars" = "#87cefa",
"Large Cars" = "#6495ed",
"Van" = " #40e0d0",
"LDV" = "#00bfff",
"Non motorized" = "#da70d6",
"Freight"="#ff0000",
"Freight (Inland)" = "#cd5555",
"Pass non LDV" = "#6b8e23",
"Pass" = "#66cdaa",
"Pass non LDV (Domestic)" = "#54ff9f",
"refined liquids enduse" = "#8c8c8c",
"FE|Transport|Hydrogen" = "#00aedb",
"FE|Transport|NG" = "#d11141",
"FE|Transport|Liquids" = "#8c8c8c",
"FE|Transport|Electricity" = "#00b159",
"FE|Transport" = "#1e90ff",
"FE|Buildings" = "#d2b48c",
"FE|Industry" = "#919191",
"Electricity_push" = "#00b159",
"ElecEra" = "#00b159",
"ElecEraWise" = "#68c6a4",
"HydrHype" = "#00aedb",
"HydrHypeWise" = "#o3878f",
"Hydrogen_push" = "#00aedb",
"Smart_lifestyles_Electricity_push" = "#68c6a4",
# "Smart_lyfestiles_Electricity_push" = "#03878f", ##maybe "#o3878f"
"Conservative_liquids" = "#113245",
"ConvCase" = "#113245",
"ConvCaseWise" = "#d11141",
"Emi|CO2|Transport|Demand" = "#113245",
"Emi|CO2|Industry|Gross" = "#919191",
"Emi|CO2|Buildings|Direct" = "#d2b48c",
"Emi|CO2|Energy|Supply|Gross" = "#f2b531",
"Emi|CO2|CDR|BECCS" = "#ed5958",
"Emi|CO2|Land-Use Change" = "#66a182",
"Cons. + Synfuels" = "orchid",
"Ctax_Conservative" = "#d11141")
legend_ord_modes <- c("Freight Rail", "Truck", "Shipping", "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)", "Non motorized")
legend_ord_fuels <- c("BEV", "Electricity", "Hybrid Electric", "FCEV", "Hydrogen", "Hybrid Liquids", "Liquids", "Oil", "Biodiesel", "Synfuel", "NG")
legend_ord_costs <- c("Inconvenience cost", "Risk", "Charging", "Model availability", "Range anxiety", "Refuel availability", "Policy induced inconvenience","Fuel", "Purchase", "Operating and maintenance")
legend_ord_emissions <- c("Emi|CO2|Industry|Gross", "Emi|CO2|Buildings|Direct", "Emi|CO2|Transport|Demand", "Emi|CO2|Energy|Supply|Gross", "Emi|CO2|Land-Use Change","Emi|CO2|CDR|BECCS")
legend_ord = c(legend_ord_modes, legend_ord_fuels, legend_ord_costs)
## mapping for scenario names
mapping_scens = data.table(scenario = c("Budg1100_ConvCase", "Budg1100_ElecEra", "Budg1100_HydrHype", "Budg1100_SynSurge", "NDC_ConvCase", "Budg1100_ConvCaseWise", "Budg1100_ElecEraWise", "Budg1100_HydrHypeWise", "Budg1100_SynSurgeWise", "NDC_ConvCaseWise"), scen_name = c("ConvCase", "ElecEra", "HydrHype", "SynSurge", "Baseline", "ConvCaseWise", "ElecEraWise", "HydrHypeWise", "SynSurgeWise", "BaselineWise"))
regionplot = "EUR"## Vintages
vintcomparisonpf = function(dt){
dt = dt[year %in% c(2015, 2050, 2100)]
plot = ggplot()+
geom_bar(data = dt,
aes(x=scenario, y=value, group=interaction(variable, technology),
fill = technology, width=.75), alpha = 0.5, position="stack", stat = "identity", width = 0.5)+
geom_bar(data = dt,
aes(x=scenario, y=value, group=interaction(variable, technology),
fill = technology, alpha = factor(alphaval), width=.75), position="stack", stat = "identity", width = 0.5, color = "black", size=0.05)+
guides(fill = guide_legend(reverse=TRUE))+
theme_minimal()+
facet_grid(year~region)+
theme(axis.text.x = element_text(angle = 90, size=14, vjust=0.5, hjust=1),
axis.text.y = element_text(size=14),
axis.title.y = element_text(size=14),
title = element_text(size=14),
axis.line = element_line(size = 0.5, colour = "grey"),
legend.text = element_text(size=14),
strip.text = element_text(size=14),
strip.background = element_rect(color = "grey"))+
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 = "[million Veh]", x="", title = "LDV fleet")
return(plot)
}
vintcomparisonpf(fleet_all)
Sales composition
salescompf = function(dt){
plot = ggplot()+
geom_bar(data = dt, aes(x=as.numeric(as.character(year)),y=shareFS1, group = technology, fill = technology), position = position_stack(), stat = "identity")+
theme_minimal()+
facet_grid(region ~ scenario)+
scale_fill_manual("Technology", values = cols)+
expand_limits(y = c(0,1))+
scale_x_continuous(breaks = c(2015,2030,2050, 2100))+
theme(axis.text.x = element_text(angle = 90, vjust=0.5, hjust=1, size = 14),
axis.text.y = element_text(size = 14),
axis.line = element_line(size = 0.5, colour = "grey"),
axis.title = element_text(size = 14),
title = element_text(size = 14),
legend.text = element_text(size = 14),
legend.title = element_text(size = 14),
strip.text = element_text(size = 14),
strip.background = element_rect(color = "grey"))+
labs(x = "", y = "[%]", title = "Market share of new LDV sales")
return(plot)
}
salescompf(salescomp_all)
EJroadpf = function(dt){
dt[, technology := factor(technology, levels = legend_ord)]
dt = dt[year >= 2020]
plotLDV = ggplot()+
geom_area(data = dt[subsector_L1 == "trn_pass_road_LDV_4W"], aes(x=year, y=demand_EJ, group = technology, fill = technology), color = "black", size=0.05, position= position_stack())+
labs(x = "", y = "[EJ]", title = "LDV Final Energy demand")+
theme_minimal()+
facet_grid(scenario~region)+
scale_fill_manual("Technology", values = cols, breaks=legend_ord)+
expand_limits(y = c(0,1))+
scale_x_continuous(breaks = c(2020, 2030,2050, 2100))+
theme(axis.text.x = element_text(angle = 90, size = 14, vjust=0.5, hjust=1),
axis.text.y = element_text(size = 14),
axis.title = element_text(size = 14),
axis.line = element_line(size = 0.5, colour = "grey"),
title = element_text(size = 14),
legend.text = element_text(size = 14),
legend.title = element_text(size = 14),
strip.text = element_text(size = 14),
strip.background = element_rect(color = "grey"))
plotBus = ggplot()+
geom_area(data = dt[subsector_L1 %in% c("trn_pass_road_bus_tmp_subsector_L1", "Bus_tmp_subsector_L1")], aes(x=year, y=demand_EJ, group = technology, fill = technology), color = "black", size=0.05, position= position_stack())+
labs(x = "", y = "[EJ]", title = "Buses Final Energy demand")+
theme_minimal()+
facet_grid(scenario~region)+
scale_fill_manual("Technology", values = cols, breaks=legend_ord)+
expand_limits(y = c(0,1))+
scale_x_continuous(breaks = c(2020, 2030,2050, 2100))+
theme(axis.text.x = element_text(angle = 90, size = 14, vjust=0.5, hjust=1),
axis.text.y = element_text(size = 14),
axis.title = element_text(size = 14),
axis.line = element_line(size = 0.5, colour = "grey"),
title = element_text(size = 14),
legend.text = element_text(size = 14),
legend.title = element_text(size = 14),
strip.text = element_text(size = 14),
strip.background = element_rect(color = "grey"))
plotTruck = ggplot()+
geom_area(data = dt[subsector_L1 %in% c("trn_freight_road_tmp_subsector_L1")], aes(x=year, y=demand_EJ, group = technology, fill = technology), color = "black", size=0.05, position= position_stack())+
labs(x = "", y = "[EJ]", title = "Trucks Final Energy demand")+
theme_minimal()+
facet_grid(scenario~region)+
scale_fill_manual("Technology", values = cols, breaks=legend_ord)+
expand_limits(y = c(0,1))+
scale_x_continuous(breaks = c(2020, 2030,2050, 2100))+
theme(axis.text.x = element_text(angle = 90, size = 14, vjust=0.5, hjust=1),
axis.text.y = element_text(size = 14),
axis.title = element_text(size = 14),
axis.line = element_line(size = 0.5, colour = "grey"),
title = element_text(size = 14),
legend.text = element_text(size = 14),
legend.title = element_text(size = 14),
strip.text = element_text(size = 14),
strip.background = element_rect(color = "grey"))
return(plotlist = list(plotLDV = plotLDV, plotBus = plotBus, plotTruck = plotTruck))
}
EJroadpf(EJroad_all)
EJmodepf = function(dt){
dt = dt[year >= 2020]
plot = ggplot()+
geom_area(data = dt, aes(x=year, y=demand_EJ, group = interaction(vehicle_type_plot,aggr_mode), fill = vehicle_type_plot), color = "black", size=0.05, position= position_stack())+
labs(x = "", y = "[EJ]", title = "Total transport final energy demand")+
theme_minimal()+
facet_grid(scenario~region)+
scale_fill_manual("Vehicle Type",values = cols, breaks=legend_ord)+
expand_limits(y = c(0,1))+
scale_x_continuous(breaks = c(2020,2030,2050, 2100))+
theme(axis.text.x = element_text(angle = 90, size = 14, vjust=0.5, hjust=1),
axis.text.y = element_text(size = 14),
axis.title = element_text(size = 14),
title = element_text(size = 14),
legend.text = element_text(size = 14),
legend.title = element_text(size = 14),
strip.text = element_text(size = 14),
axis.line = element_line(size = 0.5, colour = "grey"),
strip.background = element_rect(color = "grey"))
return(plot)
}
EJmodepf(EJmode_all)
ESmodecappf = function(dt){
dt[, vehicle_type_plot := factor(vehicle_type_plot, levels = legend_ord)]
plot_frgt = ggplot()+
geom_area(data = dt[mode == "freight" & year >= 2020], aes(x=year, y=cap_dem, group = vehicle_type_plot, fill = vehicle_type_plot), color="black", size=0.05, position= position_stack())+
labs(x = "", y = "Energy Services demand [tkm/cap]")+
theme_minimal()+
facet_grid(scenario~region)+
scale_fill_manual("Vehicle Type",values = cols, breaks=legend_ord)+
expand_limits(y = c(0,1))+
scale_x_continuous(breaks = c(2020,2030,2050, 2100))+
theme(axis.text.x = element_text(angle = 90, size = 14, vjust=0.5, hjust=1),
axis.text.y = element_text(size = 14),
axis.title = element_text(size = 14),
title = element_text(size = 14),
legend.text = element_text(size = 14),
legend.title = element_text(size = 14),
strip.text = element_text(size = 14),
strip.background = element_rect(color = "grey"),
axis.line = element_line(size = 0.5, colour = "grey"))
plot_pass = ggplot()+
geom_area(data = dt[mode == "pass" & year >= 2020], aes(x=year, y=cap_dem, group = vehicle_type_plot, fill = vehicle_type_plot), color="black", size=0.05, position= position_stack())+
labs(x = "", y = "Energy Services demand [pkm/cap]")+
theme_minimal()+
facet_grid(scenario~region)+
scale_fill_manual("Vehicle Type",values = cols, breaks=legend_ord)+
expand_limits(y = c(0,1))+
scale_x_continuous(breaks = c(2020,2030,2050, 2100))+
theme(axis.text.x = element_text(angle = 90, size = 14, vjust=0.5, hjust=1),
axis.text.y = element_text(size = 14),
axis.title = element_text(size = 14),
title = element_text(size = 14),
legend.text = element_text(size = 14),
legend.title = element_text(size = 14),
strip.text = element_text(size = 14),
strip.background = element_rect(color = "grey"),
axis.line = element_line(size = 0.5, colour = "grey"))
return(list(plot_pass = plot_pass, plot_frgt = plot_frgt))
}
ESmodecappf(ESmodecap_all)
CO2km_int_newsalespf = function(dt){
dt = dt[!is.na(gCO2_km_ave)]
plot = ggplot()+
geom_line(data = dt[year >= 2020], aes(x = year, y = gCO2_km_ave, group = scenario, color = scenario))+
labs(title = expression(paste(CO["2"], " intensity of LDVs new additions")), y = expression(paste("[", gCO["2"], "/km]")), x = "")+
expand_limits(y = c(0,1))+
scale_x_continuous(breaks = c(2020, 2030, 2050, 2100))+
theme_minimal()+
facet_grid(~region)+
theme(axis.text.x = element_text(angle = 90, size = 14, vjust=0.5, hjust=1),
axis.text.y = element_text(size = 14),
axis.title = element_text(size = 14),
title = element_text(size = 14),
legend.text = element_text(size = 14),
legend.title = element_text(size = 14),
strip.text = element_text(size = 14),
strip.background = element_rect(color = "grey"),
axis.line = element_line(size = 0.5, colour = "grey"))+
guides(linetype = FALSE)
return(plot)
}
CO2km_int_newsalespf(CO2km_int_newsales_all)## passenger by fuel
EJfuels_pf = function(dt_p, dt_f){
dt_p = dt_p[year >= 2020]
dt_p = dt_p[, .(demand_EJ = sum(demand_EJ)), by = c("subtech", "year", "region", "scenario")]
plotp = ggplot()+
geom_area(data = dt_p, aes(x=year, y=demand_EJ, group = subtech, fill = subtech), color="black", size=0.05, position= position_stack())+
labs(x = "", y = "[EJ]", title = "Passenger transport FE demand by fuel")+
theme_minimal()+
facet_grid(scenario~region)+
scale_fill_manual("Technology",values = cols, breaks=legend_ord)+
expand_limits(y = c(0,1))+
scale_x_continuous(breaks = c(2020, 2030,2050, 2100))+
theme(axis.text.x = element_text(angle = 90, size = 14, vjust=0.5, hjust=1),
axis.text.y = element_text(size = 14),
axis.title = element_text(size = 14),
title = element_text(size = 14),
legend.text = element_text(size = 14),
legend.title = element_text(size = 14),
strip.text = element_text(size = 14),
strip.background = element_rect(color = "grey"),
axis.line = element_line(size = 0.5, colour = "grey"))
dt_f = dt_f[year >= 2020]
plotf_lo = ggplot()+
geom_area(data = dt_f[sector == "trn_shipping_intl"], aes(x=year, y=demand_EJ, group = subtech, fill = subtech), color="black", size=0.05, position= position_stack())+
labs(x = "", y = "[EJ]", title = "International freight FE demand by fuel")+
theme_minimal()+
facet_grid(scenario~region)+
scale_fill_manual("Technology",values = cols, breaks=legend_ord)+
expand_limits(y = c(0,1))+
scale_x_continuous(breaks = c(2020, 2030,2050, 2100))+
theme(axis.text.x = element_text(angle = 90, size = 14, vjust=0.5, hjust=1),
axis.text.y = element_text(size = 14),
axis.title = element_text(size = 14),
title = element_text(size = 14),
legend.text = element_text(size = 14),
legend.title = element_text(size = 14),
strip.text = element_text(size = 14),
strip.background = element_rect(color = "grey"),
axis.line = element_line(size = 0.5, colour = "grey"))
plotf_sm = ggplot()+
geom_area(data = dt_f[sector == "trn_freight"], aes(x=year, y=demand_EJ, group = subtech, fill = subtech), color="black", size=0.05, position= position_stack())+
labs(x = "", y = "[EJ]", title = "Short-medium freight FE demand by fuel")+
theme_minimal()+
facet_grid(scenario~region)+
scale_fill_manual("Technology",values = cols, breaks=legend_ord)+
expand_limits(y = c(0,1))+
scale_x_continuous(breaks = c(2020, 2030,2050, 2100))+
theme(axis.text.x = element_text(angle = 90, size = 14, vjust=0.5, hjust=1),
axis.text.y = element_text(size = 14),
axis.title = element_text(size = 14),
title = element_text(size = 14),
legend.text = element_text(size = 14),
legend.title = element_text(size = 14),
strip.text = element_text(size = 14),
strip.background = element_rect(color = "grey"),
axis.line = element_line(size = 0.5, colour = "grey"))
plot = list(plotf_lo = plotf_lo, plotf_sm = plotf_sm, plotp = plotp)
return(plot)
}
EJfuels_pf(dt_p = EJpass_all, dt_f = EJfrgt_all)emidem_pf = function(dt){
dt[, scenario := as.character(scenario)]
plot = ggplot()+
geom_line(data = dt, aes(x = year, y = value, group = scenario, color = scenario))+
labs(x = "", y = "CO2 emissions [Mt/CO2]", title = "Emissions from transport demand")+
theme_minimal()+
facet_grid(~region)+
theme(axis.text.x = element_text(angle = 90, size = 14, vjust=0.5, hjust=1),
axis.text.y = element_text(size = 14),
axis.title = element_text(size = 14),
title = element_text(size = 14),
legend.text = element_text(size = 14),
legend.title = element_text(size = 14),
strip.text = element_text(size = 14),
strip.background = element_rect(color = "grey"),
axis.line = element_line(size = 0.5, colour = "grey"))
return(plot)
}
emidem_pf(emidem_all)demand per capita VS gdp per capita
demcapgdpcap_pf = function(dt) {
dt = dt[year >= 2005]
dt = dt[, year := as.character(year)]
ppass_sm = ggplot()+
geom_line(data = dt[sector == "trn_pass"], aes(x = GDP_cap, y = demcap, color = region, group = region))+
geom_point(data = dt[sector == "trn_pass" & year %in% c("2020", "2030", "2050", "2070", "2100")], aes(x = GDP_cap, y = demcap, shape = year, group = region, color = region))+
theme_minimal()+
facet_grid(scenario~sector)+
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))+
labs(y = "Per capita demand [km/capita]", x="GDP per capita [$/person]", title = "Passenger short-medium")
pfreigt_sm = ggplot()+
geom_line(data = dt[sector == "trn_freight"], aes(x = GDP_cap, y = demcap, color = region, group = region))+
geom_point(data = dt[sector == "trn_freight" & year %in% c("2020", "2030", "2050", "2070", "2100")], aes(x = GDP_cap, y = demcap, shape = year, group = region, color = region))+
theme_minimal()+
facet_grid(scenario~sector)+
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))+
labs(y = "Per capita demand [km/capita]", x="GDP per capita [$/person]", title = "Freight short-medium")
ppass_lo = ggplot()+
geom_line(data = dt[sector == "trn_aviation_intl"], aes(x = GDP_cap, y = demcap, color = region, group = region))+
geom_point(data = dt[sector == "trn_aviation_intl" & year %in% c("2020", "2030", "2050", "2070", "2100")], aes(x = GDP_cap, y = demcap, shape = year, group = region, color = region))+
theme_minimal()+
facet_grid(scenario~sector)+
theme(axis.text.x = element_text(angle = 90, hjust = 1),
axis.text = element_text(size=7),
title = element_text(size=8),
legend.text = element_text(size=8))+
labs(y = "Per capita demand [km/capita]", x="GDP per capita [$/person]", title = "International aviation")
pfreigt_lo = ggplot()+
geom_line(data = dt[sector == "trn_shipping_intl"], aes(x = GDP_cap, y = demcap, color = region, group = region))+
geom_point(data = dt[sector == "trn_shipping_intl" & year %in% c("2020", "2030", "2050", "2070", "2100")], aes(x = GDP_cap, y = demcap, shape = year, group = region, color = region))+
theme_minimal()+
facet_grid(scenario~sector)+
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))+
labs(y = "Per capita demand [km/capita]", x="GDP per capita [$/person]", title = "International shipping")
p = list(pfreigt_lo = pfreigt_lo, ppass_lo = ppass_lo, pfreigt_sm = pfreigt_sm, ppass_sm = ppass_sm)
return(p)
}
demcapgdpcap_pf(dempkm_cap_all)Focus on slected region
vintages
vintcomparison_regi_pf = function(dt, mapping_scens, rp){
dt = dt[year %in% c(2020, 2030, 2050) & region == rp]
## apply more esthetic scenario names
dt = merge(dt, mapping_scens, by = "scenario")
## if scenario name not available in the mapping, apply scenario extended name (e.g. NPi, Budget950... are not recorded in the mapping)
dt[is.na(scen_name), scen_name := scenario]
## define the maximum on the y-axis depending on the maximum value across scenarios
dt[, sum := sum(value), by = c("year", "scenario")]
dt[, maxval := max(sum)]
## select a random scenario for 2020 and attribute the label "Historical"
p1 = ggplot()+
geom_bar(data = dt[year == 2020 & scenario == unique(dt$scenario)[1]][, scen_name := "Historical"],
aes(x=scen_name, y=value, group= technology,
fill = technology, width=.75), position="stack", stat = "identity", width = 0.5, alpha = 0.9)+
theme_minimal()+
ylim(0,unique(dt[, maxval]))+
facet_wrap(~ year, nrow = 1)+
theme(axis.text.x = element_text(angle = 90, size=14, vjust=0.5, hjust=1),
axis.text.y = element_text(size=14),
axis.title.y = element_text(size=14),
title = element_text(size=14),
axis.line = element_line(size = 0.5, colour = "grey"),
legend.text = element_text(size=14),
strip.text = element_text(size=14),
strip.background = element_rect(color = "grey"),
legend.position = "none")+
scale_fill_manual(values = cols)+
labs(y = "[million Veh]", x="")
p2 = ggplot()+
geom_bar(data = dt[year != 2020],
aes(x=scen_name, y=value, group=interaction(variable, technology),
fill = technology, width=.75), position="stack", stat = "identity", width = 0.5, alpha = 0.9)+
theme_minimal()+
ylim(0,unique(dt[, maxval]))+
facet_wrap(~ year, nrow = 1)+
theme(axis.text.x = element_text(angle = 90, size=14, vjust=0.5, hjust=1),
axis.text.y = element_blank(),
axis.title.y = element_text(size=14),
title = element_text(size=14),
axis.line = element_line(size = 0.5, colour = "grey"),
legend.text = element_text(size=14),
strip.text = element_text(size=14),
strip.background = element_rect(color = "grey"))+
guides(fill=guide_legend(title="Powertrain"))+
scale_fill_manual(values = cols)+
labs(y = "", x="")
plot = plot_grid(p1, p2, align = "h", ncol = 2, rel_widths = c(0.15,0.85))
return(plot)
}
p = vintcomparison_regi_pf(fleet_all, mapping_scens, rp = regionplot)
p
aspect_ratio <- 1.5
height <- 6
ggsave("pvint.png", p, dpi=500, height = height , width = height * aspect_ratio)Costs of LDVs by technology
costspf = function(dt, rp){
dt = dt[region == rp]
map = data.table(name = c("Risk", "Charging", "Model availability", "Range anxiety", "Refuel availability", "Fuel", "Policy induced inconvenience", "Purchase", "Operating and maintenance"),
logit_type = c("prisk", "pchar", "pmod_av", "prange", "pref", "fuel_price", "pinco_tot", "purchase", "other"),
type = c("inc", "inc", "inc", "inc", "inc", "real", "inc", "real", "real"))
dt = merge(dt, map, by = "logit_type")
dt_inc = dt[type == "inc"]
dt_inc = dt_inc[,.(cost = sum(cost)), by = c("region", "year", "type", "technology", "scenario")]
dt_tot = rbind(dt_inc[, c("name", "logit_type") := list("Inconvenience cost", "pinc")], dt[type == "real"])
dt[, name := factor(name, levels = legend_ord)]
dt_tot[, name := factor(name, levels = legend_ord)]
dt_tot[, alph := ifelse(logit_type=="pinc", 0.6, 1)]
plot1 = ggplot()+
geom_bar(data = dt[type == "inc"], aes(x = year, y = cost, group = name, fill = name), position = "stack", stat = "identity")+
theme_minimal()+
facet_grid(scenario~technology)+
theme(axis.text.x = element_text(angle = 90, size=14, vjust=0.5, hjust=1),
axis.text.y = element_text(size=14),
axis.title.y = element_text(size=14),
title = element_text(size=14),
axis.line = element_line(size = 0.5, colour = "grey"),
legend.text = element_text(size=14),
strip.text = element_text(size=12),
strip.background = element_rect(color = "grey"))+
guides(fill=guide_legend(title="Cost component"))+
scale_fill_manual(values = cols)+
labs(y = "Costs [$/pkm]", x="")
plot2 = ggplot()+
geom_bar(data = dt_tot, aes(x = year, y = cost, group = name, fill = name, alpha = alph), position = "stack", stat = "identity")+
theme_minimal()+
facet_grid(scenario~technology)+
theme(axis.text.x = element_text(angle = 90, size=14, vjust=0.5, hjust=1),
axis.text.y = element_text(size=14),
axis.title.y = element_text(size=14),
title = element_text(size=14),
axis.line = element_line(size = 0.5, colour = "grey"),
legend.text = element_text(size=14),
strip.text = element_text(size=12),
strip.background = element_rect(color = "grey"))+
scale_fill_manual(values = cols)+
labs(y = "Costs [$/pkm]", x="")+
scale_alpha(range=c(0.4,1)) +
guides(alpha=FALSE, linetype=FALSE,
fill=guide_legend(reverse=TRUE, title="Cost component"))
plot = list(plot1 = plot1, plot2 = plot2)
return(plot)
}
p = costspf(costs_all, rp = regionplot)
p
aspect_ratio <- 1.5
height <- 8
ggsave("LDVinccost.png", p$plot1, dpi=500, height = height , width = height * aspect_ratio)
ggsave("LDVtotcost.png", p$plot2, dpi=500, height = height , width = height * aspect_ratio)
Sales composition
salescom_regi_pf = function(dt, rp){
plot = ggplot()+
geom_area(data = dt[region == rp], aes(x=as.numeric(as.character(year)), y = shareFS1, group = technology, fill = technology), position = position_fill())+
theme_minimal()+
facet_wrap( ~ scenario, nrow = 1)+
scale_fill_manual("Technology", values = cols)+
expand_limits(y = c(0,1))+
scale_x_continuous(breaks = c(2015,2030,2050, 2100))+
scale_y_continuous(labels = scales::percent)+
theme(axis.text.x = element_text(angle = 90, vjust=0.5, hjust=1, size = 14),
axis.text.y = element_text(size = 14),
axis.line = element_line(size = 0.5, colour = "grey"),
axis.title = element_text(size = 14),
title = element_text(size = 14),
legend.text = element_text(size = 14),
legend.title = element_text(size = 14),
strip.text = element_text(size = 14),
strip.background = element_rect(color = "grey"))+
labs(x = "", y = "[%]", title = "Market share of new LDV sales")
return(plot)
}
p = salescom_regi_pf(salescomp_all, rp = regionplot)
p
aspect_ratio <- 2
height <- 5
ggsave("psales.png", p, dpi=500, height = height , width = height * aspect_ratio)
CO2km_int_regi_newsalespf = function(dt, rp){
dt = dt[!is.na(gCO2_km_ave)]
if (rp == "EUR"){
## add historical values
historical_values = data.table(year = c(2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018), emi = c(159, 157, 145, 140, 137, 132, 128, 124, 120, 119, 119, 120))
targets = data.table(name = c("2021 target", "2025 target", "2030 target"), value = c(95, 95*(1-0.15), 95*(1-0.37)))
plot = ggplot()+
geom_line(data = dt[year >= 2020 & region == rp], aes(x = year, y = gCO2_km_ave, group = scenario, color = scenario))+
geom_point(data = historical_values, aes(x = year, y = emi), color = "grey20")+
geom_hline(data = targets, aes(yintercept = value, linetype = name), color = "grey20", size=0.1)+
geom_text(data = targets, aes(y = value+5, x = c(2025, 2030, 2035), label = name), size = 5)+
labs(title = expression(paste(CO["2"], " intensity of LDVs new additions")), y = expression(paste("[", gCO["2"], "/km]")), x = "")+
expand_limits(y = c(0,1))+
scale_x_continuous(breaks = c(2020, 2030, 2050, 2100))+
theme_minimal()+
theme(axis.text.x = element_text(angle = 90, size = 14, vjust=0.5, hjust=1),
axis.text.y = element_text(size = 14),
axis.title = element_text(size = 14),
title = element_text(size = 14),
legend.text = element_text(size = 14),
legend.title = element_text(size = 14),
strip.text = element_text(size = 14),
strip.background = element_rect(color = "grey"),
axis.line = element_line(size = 0.5, colour = "grey"))+
guides(linetype = FALSE)
} else {
## historical values are not available
plot = ggplot()+
geom_line(data = dt[year >= 2020 & region == rp], aes(x = year, y = gCO2_km_ave, group = scenario, color = scenario))+
labs(title = expression(paste(CO["2"], " intensity of LDVs new additions")), y = expression(paste("[", gCO["2"], "/km]")), x = "")+
expand_limits(y = c(0,1))+
scale_x_continuous(breaks = c(2020, 2030, 2050, 2100))+
theme_minimal()+
theme(axis.text.x = element_text(angle = 90, size = 14, vjust=0.5, hjust=1),
axis.text.y = element_text(size = 14),
axis.title = element_text(size = 14),
title = element_text(size = 14),
legend.text = element_text(size = 14),
legend.title = element_text(size = 14),
strip.text = element_text(size = 14),
strip.background = element_rect(color = "grey"),
axis.line = element_line(size = 0.5, colour = "grey"))+
guides(linetype = FALSE)
}
return(plot)
}
p = CO2km_int_regi_newsalespf(CO2km_int_newsales_all, rp = regionplot)
p
aspect_ratio <- 1.5
height <- 6
ggsave("pCO2int.png", p, dpi=500, height = height , width = height * aspect_ratio)EJroad_regi_pf = function(dt, rp){
dt[, technology := factor(technology, levels = legend_ord)]
dt = dt[year >= 2020]
plotLDV = ggplot()+
geom_area(data = dt[subsector_L1 == "trn_pass_road_LDV_4W" & region == rp], aes(x=year, y=demand_EJ, group = technology, fill = technology), color = "black", size=0.05, position= position_stack())+
labs(x = "", y = "[EJ]", title = "LDV Final Energy demand")+
theme_minimal()+
facet_wrap(~scenario, nrow = 1)+
scale_fill_manual("Technology", values = cols, breaks=legend_ord)+
expand_limits(y = c(0,1))+
scale_x_continuous(breaks = c(2020, 2030,2050, 2100))+
theme(axis.text.x = element_text(angle = 90, size = 14, vjust=0.5, hjust=1),
axis.text.y = element_text(size = 14),
axis.title = element_text(size = 14),
axis.line = element_line(size = 0.5, colour = "grey"),
title = element_text(size = 14),
legend.text = element_text(size = 14),
legend.title = element_text(size = 14),
strip.text = element_text(size = 14),
strip.background = element_rect(color = "grey"))
plotBus = ggplot()+
geom_area(data = dt[subsector_L1 %in% c("trn_pass_road_bus_tmp_subsector_L1", "Bus_tmp_subsector_L1") & region == rp], aes(x=year, y=demand_EJ, group = technology, fill = technology), color = "black", size=0.05, position= position_stack())+
labs(x = "", y = "[EJ]", title = "Buses Final Energy demand")+
theme_minimal()+
facet_wrap(~scenario, nrow = 1)+
scale_fill_manual("Technology", values = cols, breaks=legend_ord)+
expand_limits(y = c(0,1))+
scale_x_continuous(breaks = c(2020, 2030,2050, 2100))+
theme(axis.text.x = element_text(angle = 90, size = 14, vjust=0.5, hjust=1),
axis.text.y = element_text(size = 14),
axis.title = element_text(size = 14),
axis.line = element_line(size = 0.5, colour = "grey"),
title = element_text(size = 14),
legend.text = element_text(size = 14),
legend.title = element_text(size = 14),
strip.text = element_text(size = 14),
strip.background = element_rect(color = "grey"))
plotTruck = ggplot()+
geom_area(data = dt[subsector_L1 %in% c("trn_freight_road_tmp_subsector_L1") & region == rp], aes(x=year, y=demand_EJ, group = technology, fill = technology), color = "black", size=0.05, position= position_stack())+
labs(x = "", y = "[EJ]", title = "Trucks Final Energy demand")+
theme_minimal()+
facet_wrap(~scenario, nrow = 1)+
scale_fill_manual("Technology", values = cols, breaks=legend_ord)+
expand_limits(y = c(0,1))+
scale_x_continuous(breaks = c(2020, 2030,2050, 2100))+
theme(axis.text.x = element_text(angle = 90, size = 14, vjust=0.5, hjust=1),
axis.text.y = element_text(size = 14),
axis.title = element_text(size = 14),
axis.line = element_line(size = 0.5, colour = "grey"),
title = element_text(size = 14),
legend.text = element_text(size = 14),
legend.title = element_text(size = 14),
strip.text = element_text(size = 14),
strip.background = element_rect(color = "grey"))
return(plotlist = list(plotLDV = plotLDV, plotBus = plotBus, plotTruck = plotTruck))
}
plist = EJroad_regi_pf(EJroad_all, rp = regionplot)
plist
pLDV = plist[["plotLDV"]]
pBus = plist[["plotBus"]]
pTruck = plist[["plotTruck"]]
aspect_ratio <- 1.5
height <- 6
ggsave("pLDV.png", pLDV, dpi=500, height = height , width = height * aspect_ratio)
ggsave("pBus.png", pBus, dpi=500, height = height , width = height * aspect_ratio)
ggsave("pTruck.png", pTruck, dpi=500, height = height , width = height * aspect_ratio)
Trend of preference factors for Buses and Trucks
prefBusTrucksplotf = function(pref){
pref = pref[iso=="USA" & technology %in% c("Electric", "FCEV") & vehicle_type %in% c("Bus_tmp_vehicletype", "Truck (0-2.7t)")]
pref = pref[year >= 2020 & year <= 2050]
pref[, vehicle_type := ifelse(vehicle_type == "Bus_tmp_vehicletype", "Large Trucks and Buses", "Small Trucks")]
p = ggplot()+
geom_line(data = pref, aes(x = year, y = value, group = interaction(technology, vehicle_type), color = technology, linetype = vehicle_type))+
facet_wrap(~scenario, ncol = 1)+
theme_minimal()+
theme(axis.text.x = element_text(angle = 90, hjust = 1),
axis.text = element_text(size=7),
title = element_text(size=7),
legend.text = element_text(size=7),
strip.text = element_text(size=7))+
labs(y = "Preference factor [-]", x="")+
scale_color_manual("Technology", values = cols,labels = c("Electric", "FCEV"))
scale_linetype_manual("Vehicle", values = c(1,2),
labels = c("Large Trucks and Buses", "Small Trucks"))
return(p)
}
p = prefBusTrucksplotf(pref_FV_all)
p
aspect_ratio <- 1
height <- 4
ggsave("buses_trucks_SW.png", p, dpi=500, height = height , width = height * aspect_ratio)