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---
title: "Compare scenarios Transport"
output:
pdf_document: default
html_document:
df_print: paged
classoption: landscape
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(dev = 'pdf')
```
```{r, echo=FALSE, message=FALSE, warning=FALSE}
require(ggplot2)
require(moinput)
require(rmndt)
require(quitte)
library(lucode)
library(magpie)
library(quitte)
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library(cowplot)
```
```{r, echo=FALSE, message=FALSE, warning=FALSE}
# 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")
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EJpass_all = readRDS("EJfuelsPass_all.RDS")
EJfrgt_all = readRDS("EJfuelsFrgt_all.RDS")
emidem_all = readRDS("emidem_all.RDS")
setConfig(forcecache=T)
cols <- c("NG" = "#d11141",
"Liquids" = "#8c8c8c",
"Hybrid Liquids" = "#ffc425",
"Hybrid Electric" = "#f37735",
"BEV" = "#00b159",
"Electricity" = "#00b159",
"FCEV" = "#00aedb",
"Hydrogen" = "#00aedb",
"Biodiesel" = "#66a182",
"Synfuel" = "orchid",
"Oil" = "#2e4057",
"fuel price pkm" = "#edae49",
"Operating costs registration and insurance" = "#8d96a3",
"Operating costs maintenance" = "#00798c",
"Capital cost" = "#d1495b",
"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("fuel price pkm", "Operating costs registration and insurance", "Operating costs maintenance", "Capital cost")
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)
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regionplot = "EUR"
```
```{r, echo=FALSE, message=FALSE, warning=FALSE}
```
```{r, echo=FALSE, message=FALSE, warning=FALSE, fig.width=14, fig.height=12}
## 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)
```
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## Sales composition
```{r, echo=FALSE, message=FALSE, warning=FALSE, fig.width=14, fig.height=12}
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)
```
```{r, echo=FALSE, message=FALSE, warning=FALSE, fig.width=14, fig.height=12}
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"))
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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"))
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return(plotlist = list(plotLDV = plotLDV, plotBus = plotBus, plotTruck = plotTruck))
}
```
```{r, echo=FALSE, message=FALSE, warning=FALSE, fig.width=14, fig.height=12}
EJmodepf = function(dt){
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)
```
```{r, echo=FALSE, message=FALSE, warning=FALSE, fig.width=14, fig.height=12}
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)
```
```{r, echo=FALSE, message=FALSE, warning=FALSE, fig.width=14, fig.height=12}
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)
```
```{r, echo=FALSE, message=FALSE, warning=FALSE, fig.width=14, fig.height=12}
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## 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"))
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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)
}
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EJfuels_pf(dt_p = EJpass_all, dt_f = EJfrgt_all)
```
```{r, echo=FALSE, message=FALSE, warning=FALSE, fig.width=14, fig.height=12}
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)
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## Focus on slected region
## vintages
```{r, echo=FALSE, message=FALSE, warning=FALSE, fig.width=14, fig.height=12}
vintcomparison_regi_pf = function(dt, rp){
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dt = dt[year %in% c(2020, 2030, 2050) & region == rp]
p1 = ggplot()+
geom_bar(data = dt[year == 2020 & scenario == unique(dt$scenario)[1]][, scenario := "Historical"],
aes(x=scenario, y=value, group= technology,
fill = technology, width=.75), position="stack", stat = "identity", width = 0.5)+
theme_minimal()+
ylim(0,500)+
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],
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aes(x=scenario, y=value, group=interaction(variable, technology),
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fill = technology, width=.75), position="stack", stat = "identity", width = 0.5)+
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theme_minimal()+
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ylim(0,500)+
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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"))+
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guides(fill=guide_legend(title="Transport mode"))+
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scale_fill_manual(values = cols)+
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labs(y = "", x="")
plot = plot_grid(p1, p2, align = "h", ncol = 2, rel_widths = c(0.15,0.85))
return(plot)
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}
p = vintcomparison_regi_pf(fleet_all, rp = regionplot)
p
aspect_ratio <- 1.5
height <- 6
ggsave("pvint.png", p, dpi=500, height = height , width = height * aspect_ratio)
```
## Sales composition
```{r, echo=FALSE, message=FALSE, warning=FALSE, fig.width=14, fig.height=12}
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)
```
```{r, echo=FALSE, message=FALSE, warning=FALSE, fig.width=14, fig.height=12}
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)
```
```{r, echo=FALSE, message=FALSE, warning=FALSE, fig.width=14, fig.height=12}
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)
```