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Marianna Rottoli authoredMarianna Rottoli authored
EDGETransportComparison.Rmd 13.71 KiB
title: "Compare scenarios Transport"
output:
pdf_document: default
html_document:
df_print: paged
classoption: landscaperequire(ggplot2)
require(moinput)
require(rmndt)
require(quitte)
library(lucode)
library(magpie)
library(quitte)# Set RDS files path
EJmode_all = readRDS("EJmode_all.RDS")
EJLDV_all = readRDS("EJLDV_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")
EJfuels_all = readRDS("EJfuels_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)
## 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=8, vjust=0.5, hjust=1),
title = element_text(size=8),
axis.line = element_line(size = 0.5, colour = "grey"),
# legend.text = element_text(size=8),
strip.text = element_text(size=8),
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 = 8),
axis.text.y = element_text(size=8),
axis.line = element_line(size = 0.5, colour = "grey"),
axis.title = element_text(size = 8),
title = element_text(size = 8),
# legend.text = element_text(8),
# legend.title = element_text(size = 8),
strip.text = element_text(size=8),
strip.background = element_rect(color = "grey"))+
labs(x = "", y = "[%]", title = "Market share of new LDV sales")
return(plot)
}
salescompf(salescomp_all)
EJLDVpf = function(dt){
dt[, technology := factor(technology, levels = legend_ord)]
plot = ggplot()+
geom_area(data = dt, aes(x=year, y=demand_EJ, group = technology, fill = technology), color="black",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(2015,2030,2050, 2100))+
theme(axis.text.x = element_text(angle = 90, size = 8, vjust=0.5, hjust=1),
axis.text.y = element_text(size = 8),
axis.title = element_text(size = 8),
axis.line = element_line(size = 0.5, colour = "grey"),
title = element_text(size = 8),
# legend.text = element_text(size =8),
# legend.title = element_text(size = 8),
strip.text = element_text(size=8),
strip.background = element_rect(color = "grey"))
return(plot)
}
EJLDVpf(EJLDV_all)
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", 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(2015,2030,2050, 2100))+
theme(axis.text.x = element_text(angle = 90, size = 8, vjust=0.5, hjust=1),
axis.text.y = element_text(size=8),
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),
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"], aes(x=year, y=cap_dem, group = vehicle_type_plot, fill = vehicle_type_plot), color="black",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(2015,2030,2050, 2100))+
theme(axis.text.x = element_text(angle = 90, size = 8, vjust=0.5, hjust=1),
axis.text.y = element_text(size = 8),
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),
strip.background = element_rect(color = "grey"),
axis.line = element_line(size = 0.5, colour = "grey"))
plot_pass = ggplot()+
geom_area(data = dt[mode == "pass"], aes(x=year, y=cap_dem, group = vehicle_type_plot, fill = vehicle_type_plot), color="black",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(2015,2030,2050, 2100))+
theme(axis.text.x = element_text(angle = 90, size = 8, vjust=0.5, hjust=1),
axis.text.y = element_text(size = 8),
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),
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(2015,2030,2050, 2100))+
theme_minimal()+
facet_grid(~region)+
theme(axis.text.x = element_text(angle = 90, size = 8, vjust=0.5, hjust=1),
axis.text.y = element_text(size = 8),
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),
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)## LDV by fuel
EJfuels_pf = function(dt){
plot = ggplot()+
geom_area(data = dt, aes(x=year, y=demand_EJ, group = subtech, fill = subtech), color="black", position= position_stack())+
labs(x = "", y = "[EJ]", title = "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(2015,2030,2050, 2100))+
theme(axis.text.x = element_text(angle = 90, size = 8, vjust=0.5, hjust=1),
axis.text.y = element_text(size = 8),
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),
strip.background = element_rect(color = "grey"),
axis.line = element_line(size = 0.5, colour = "grey"))
return(plot)
}
EJfuels_pf(EJfuels_all)emidem_pf = function(dt){
plot = ggplot()+
geom_line(data = dt, aes(x = year, y = value, group = variable, color = variable))+
labs(x = "", y = "CO2 emissions [Mt/CO2]")+
theme_minimal()+
facet_grid(~region)+
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.position = "none",
strip.text = element_text(size=9))+
scale_color_manual("Scenario", values = cols)
return(plot)
}
emidem_pf(emidem_all)