diff --git a/scripts/output/comparison/coupling-summary.R b/scripts/output/comparison/coupling-summary.R deleted file mode 100644 index 166688215..000000000 --- a/scripts/output/comparison/coupling-summary.R +++ /dev/null @@ -1,940 +0,0 @@ -# | (C) 2006-2023 Potsdam Institute for Climate Impact Research (PIK) -# | authors, and contributors see CITATION.cff file. This file is part -# | of REMIND and licensed under AGPL-3.0-or-later. Under Section 7 of -# | AGPL-3.0, you are granted additional permissions described in the -# | REMIND License Exception, version 1.0 (see LICENSE file). -# | Contact: remind@pik-potsdam.de -# Coupling summary - -library(magclass) -library(mip) -library(ggplot2) -library(lusweave) - -# ---- Settings ---- - -setwd("~/Documents/1_Projekte/Kopplung/2019_Validation-R2M4") - -fileName <- "coupling-summary.pdf" -y_bar <- c(2010,2030,2050,2100) -y_bar_near_term <- c(2010,2030,2050) -hist <- "historical.mif" -y_hist <- c(seq(1960,2015,1)) - -scenarios <- c("C_Base","Base","C_NPi","NPi","C_NDC","NDC","C_Budg600","Budg600") - -# /p/projects/remind/runs/r8375-C/output/all-r8375.mif -#data_all <- read.report("all-r8423-8438.mif",as.list = FALSE) - -# Remove -mag-* and -rem-* from scenario names to make them the same -getNames(data_all,dim=1) <- gsub("-(rem|mag)-[0-9]{1,2}","",getNames(data_all,dim=1)) - -data <- data_all - -getNames(data,dim=1) <- gsub("r8423-","",getNames(data,dim=1)) -getNames(data,dim=1) <- gsub("r8438-","",getNames(data,dim=1)) - -# Pick scenarios in the order defined above (for a proper order in the plots) -data <- data[,getYears(data)<="y2100",scenarios] - -data_near_term <- data[,getYears(data)<="y2050",] - -# Read historical data -hist <- read.report(hist,as.list=FALSE) -if(all(getRegions(data) %in% getRegions(hist))) { - hist = hist[getRegions(data),,] - if ( any(grepl("EDGE_SSP2",getNames(hist)))){ - hist = hist[,,"EDGE_SSP2", invert = T] - } - hist <- hist[,y_hist,] -} - -# ---- Open output-pdf ---- - -template <- c("\\documentclass[a4paper,landscape,twocolumn]{article}", - "\\setlength{\\oddsidemargin}{-0.8in}", - "\\setlength{\\evensidemargin}{-0.5in}", - "\\setlength{\\topmargin}{-0.8in}", - "\\setlength{\\parindent}{0in}", - "\\setlength{\\headheight}{0in}", - "\\setlength{\\topskip}{0in}", - "\\setlength{\\headsep}{0in}", - "\\setlength{\\footskip}{0.2in}", - "\\setlength\\textheight{0.95\\paperheight}", - "\\setlength\\textwidth{0.95\\paperwidth}", - "\\setlength{\\parindent}{0in}", - "\\usepackage{float}", - "\\usepackage[bookmarksopenlevel=section,colorlinks=true,linkbordercolor={0.9882353 0.8352941 0.7098039}]{hyperref}", - "\\hypersetup{bookmarks=true,pdfauthor={GES group, PIK}}", - "\\usepackage{graphicx}", - "\\usepackage[strings]{underscore}", - "\\usepackage{Sweave}", - "\\begin{document}", - "<>=", - "options(width=110)", - "@") - -sw <- swopen(fileName,template = template) -swlatex(sw,"\\tableofcontents\\cleardoublepage") - -# ---- ++++ EMISSIONS ++++ ---- - -swlatex(sw,"\\section{Emissions}") - -# ---- CO2eq by source ---- - -swlatex(sw,"\\subsection{CO2eq by source}") -GWP <- c("CO2"=1,"CH4"=28,"N2O"=265) -var <- NULL -var <- mbind(var,data[,,"Emi|CO2|Land-Use Change (Mt CO2/yr)"] *GWP["CO2"]) -var <- mbind(var,data[,,"Emi|CO2|Gross Fossil Fuels and Industry (Mt CO2/yr)"] *GWP["CO2"]) -var <- mbind(var,data[,,"Emi|CO2|Carbon Capture and Storage|Biomass (Mt CO2/yr)"]*-GWP["CO2"]) -var <- mbind(var,data[,,"Emi|CH4|Energy Supply and Demand (Mt CH4/yr)"] *GWP["CH4"]) -var <- mbind(var,data[,,"Emi|CH4|Land Use (Mt CH4/yr)"] *GWP["CH4"]) -var <- mbind(var,data[,,"Emi|CH4|Other (Mt CH4/yr)"] *GWP["CH4"]) -var <- mbind(var,data[,,"Emi|CH4|Waste (Mt CH4/yr)"] *GWP["CH4"]) -var <- mbind(var,data[,,"Emi|N2O|Land Use (kt N2O/yr)"] *GWP["N2O"]/1000) -var <- mbind(var,data[,,"Emi|N2O|Energy Supply and Demand (kt N2O/yr)"] *GWP["N2O"]/1000) -var <- mbind(var,data[,,"Emi|N2O|Waste (kt N2O/yr)"] *GWP["N2O"]/1000) -var <- mbind(var,data[,,"Emi|N2O|Industry (kt N2O/yr)"] *GWP["N2O"]/1000) -var <- setNames(var,gsub(" \\(.*\\)"," (Mt CO2eq/yr)",getNames(var))) - -p <- mipArea(var["GLO",,],scales="free_y") -p <- p + theme(legend.position="none") -swfigure(sw,print,p,sw_option="height=3.5,width=7") - -p <- mipBarYearData(var["GLO",y_bar,]) -p <- p + theme(legend.position="none") -swfigure(sw,print,p,sw_option="height=4.5,width=7") - -p <- mipBarYearData(var[,y_bar,]["GLO",,,invert=TRUE]) -swfigure(sw,print,p,sw_option="height=9,width=8") - -swlatex(sw,"\\onecolumn") -p <- mipArea(var["GLO",,,invert=TRUE],scales="free_y") -swfigure(sw,print,p,sw_option="height=8,width=16") -swlatex(sw,"\\twocolumn") - -# ---- CO2 by sector ---- - -swlatex(sw,"\\subsection{CO2 by sector}") - -tot <-"Emi|CO2 (Mt CO2/yr)" -items <- c("Emi|CO2|Land-Use Change (Mt CO2/yr)", - "Emi|CO2|Energy|Supply|Non-Elec (Mt CO2/yr)", - "Emi|CO2|Energy|Supply|Electricity|Gross (Mt CO2/yr)", - "Emi|CO2|Energy|Demand|Industry|Gross (Mt CO2/yr)", - "Emi|CO2|FFaI|Industry|Process (Mt CO2/yr)", - # "Emi|CO2|Industrial Processes (Mt CO2/yr)", - "Emi|CO2|Buildings|Direct (Mt CO2/yr)", - "Emi|CO2|Transport|Demand (Mt CO2/yr)", - "Emi|CO2|Carbon Capture and Storage|Biomass|Neg (Mt CO2/yr)", - "Emi|CO2|CDR|DAC (Mt CO2/yr)", - "Emi|CO2|CDR|EW (Mt CO2/yr)") -var <- data[,,intersect(items,getNames(data,dim=3))] - -p <- mipArea(var["GLO",,],total=data["GLO",,tot],scales="free_y") -p <- p + theme(legend.position="none") -swfigure(sw,print,p,sw_option="height=3.5,width=7") - -p <- mipBarYearData(var["GLO",y_bar,]) -p <- p + theme(legend.position="none") -swfigure(sw,print,p,sw_option="height=4.5,width=7") - -p <- mipBarYearData(var[,y_bar,]["GLO",,,invert=TRUE]) -swfigure(sw,print,p,sw_option="height=9,width=8") - -swlatex(sw,"\\onecolumn") -p <- mipArea(var["GLO",,,invert=TRUE],total=data[,,tot]["GLO",,,invert=TRUE],scales="free_y") -swfigure(sw,print,p,sw_option="height=8,width=16") -swlatex(sw,"\\twocolumn") - -# ---- CO2 land-use ---- - -swlatex(sw,"\\subsection{CO2 land-use}") -p <- mipLineHistorical(data["GLO",,"Emi|CO2|Land-Use Change (Mt CO2/yr)"],#x_hist=hist["GLO",,"Emi|CO2|Land Use (Mt CO2/yr)"], - ylab='Emi|CO2|Land-Use Change [Mt CO2/yr]',scales="free_y",plot.priority=c("x_hist","x","x_proj")) -swfigure(sw,print,p,sw_option="height=8,width=8") -p <- mipLineHistorical(data[,,"Emi|CO2|Land-Use Change (Mt CO2/yr)"]["GLO",,,invert=TRUE],#x_hist=hist[,,"Emi|CO2|Land Use (Mt CO2/yr)"]["GLO",,,invert=TRUE], - ylab='Emi|CO2|Land-Use Change [Mt CO2/yr]',scales="free_y",plot.priority=c("x_hist","x","x_proj"),facet.ncol=3) -swfigure(sw,print,p,sw_option="height=9,width=8") - -# ---- CH4 land-use ---- - -swlatex(sw,"\\subsection{CH4 land-use}") -p <- mipLineHistorical(data["GLO",,"Emi|CH4|Land Use (Mt CH4/yr)"],#x_hist=hist["GLO",,"Emi|CH4|Land Use (Mt CH4/yr)"], - ylab='Emi|CH4|Land Use [Mt CH4/yr]',scales="free_y",plot.priority=c("x_hist","x","x_proj")) -swfigure(sw,print,p,sw_option="height=8,width=8") -p <- mipLineHistorical(data[,,"Emi|CH4|Land Use (Mt CH4/yr)"]["GLO",,,invert=TRUE],#x_hist=hist[,,"Emi|CH4|Land Use (Mt CH4/yr)"]["GLO",,,invert=TRUE], - ylab='Emi|CH4|Land Use [Mt CH4/yr]',scales="free_y",plot.priority=c("x_hist","x","x_proj"),facet.ncol=3) -swfigure(sw,print,p,sw_option="height=9,width=8") - -# ---- N2O land-use ---- - -swlatex(sw,"\\subsection{N2O}") -p <- mipLineHistorical(data["GLO",,"Emi|N2O|Land Use (kt N2O/yr)"],#x_hist=hist["GLO",,"Emi|N2O|Land Use (kt N2O/yr)"], - ylab='Emi|N2O|Land Use [kt N2O/yr]',scales="free_y",plot.priority=c("x_hist","x","x_proj")) -swfigure(sw,print,p,sw_option="height=8,width=8") -p <- mipLineHistorical(data[,,"Emi|N2O|Land Use (kt N2O/yr)"]["GLO",,,invert=TRUE],#x_hist=hist[,,"Emi|N2O|Land Use (kt N2O/yr)"]["GLO",,,invert=TRUE], - ylab='Emi|N2O|Land Use [kt N2O/yr]',scales="free_y",plot.priority=c("x_hist","x","x_proj"),facet.ncol=3) -swfigure(sw,print,p,sw_option="height=9,width=8") - -# ---- CDR by sector ---- - -swlatex(sw,"\\subsection{CDR by sector}") -tot <-"Emi|CO2 (Mt CO2/yr)" -items <- c("Emi|CO2|Gross Fossil Fuels and Industry (Mt CO2/yr)", - "Emi|CO2|Carbon Capture and Storage|Fossil (Mt CO2/yr)", - #"Emissions|CO2|Land|Land-use Change|+|Positive (Mt CO2/yr)", - #"Emissions|CO2|Land|Land-use Change|+|Negative (Mt CO2/yr)", - "Emi|CO2|Land-Use Change (Mt CO2/yr)", - "Emi|CO2|CDR|BECCS (Mt CO2/yr)", - "Emi|CO2|CDR|DAC (Mt CO2/yr)", - "Emi|CO2|CDR|EW (Mt CO2/yr)") -var <- data[,,intersect(items,getNames(data,dim=3))] - -# remove model dimension, because it has remind AND magpie, but mipBarYearData can't handle that -var <- collapseNames(var,collapsedim = 2) - -p <- mipArea(var["GLO",,],total=data["GLO",,tot],scales="free_y") -p <- p + theme(legend.position="none") -swfigure(sw,print,p,sw_option="height=3.5,width=7") - -p <- mipBarYearData(var["GLO",y_bar,]) -p <- p + theme(legend.position="none") -swfigure(sw,print,p,sw_option="height=4.5,width=7") - -p <- mipBarYearData(var[,y_bar,]["GLO",,,invert=TRUE]) -swfigure(sw,print,p,sw_option="height=9,width=8") - -swlatex(sw,"\\onecolumn") -p <- mipArea(var["GLO",,,invert=TRUE],total=data[,,tot]["GLO",,,invert=TRUE],scales="free_y") -swfigure(sw,print,p,sw_option="height=8,width=16") -swlatex(sw,"\\twocolumn") - -# ---- ++++ EMISSIONS - near-term ++++ ---- - -swlatex(sw,"\\section{Emissions - near-term}") - -# ---- CO2eq by source - near-term---- - -swlatex(sw,"\\subsection{CO2eq by source - near-term}") -GWP <- c("CO2"=1,"CH4"=28,"N2O"=265) -var <- NULL -var <- mbind(var,data_near_term[,,"Emi|CO2|Land-Use Change (Mt CO2/yr)"] *GWP["CO2"]) -var <- mbind(var,data_near_term[,,"Emi|CO2|Gross Fossil Fuels and Industry (Mt CO2/yr)"] *GWP["CO2"]) -var <- mbind(var,data_near_term[,,"Emi|CO2|Carbon Capture and Storage|Biomass (Mt CO2/yr)"]*-GWP["CO2"]) -var <- mbind(var,data_near_term[,,"Emi|CH4|Energy Supply and Demand (Mt CH4/yr)"] *GWP["CH4"]) -var <- mbind(var,data_near_term[,,"Emi|CH4|Land Use (Mt CH4/yr)"] *GWP["CH4"]) -var <- mbind(var,data_near_term[,,"Emi|CH4|Other (Mt CH4/yr)"] *GWP["CH4"]) -var <- mbind(var,data_near_term[,,"Emi|CH4|Waste (Mt CH4/yr)"] *GWP["CH4"]) -var <- mbind(var,data_near_term[,,"Emi|N2O|Land Use (kt N2O/yr)"] *GWP["N2O"]/1000) -var <- mbind(var,data_near_term[,,"Emi|N2O|Energy Supply and Demand (kt N2O/yr)"] *GWP["N2O"]/1000) -var <- mbind(var,data_near_term[,,"Emi|N2O|Waste (kt N2O/yr)"] *GWP["N2O"]/1000) -var <- mbind(var,data_near_term[,,"Emi|N2O|Industry (kt N2O/yr)"] *GWP["N2O"]/1000) -var <- setNames(var,gsub(" \\(.*\\)"," (Mt CO2eq/yr)",getNames(var))) - -p <- mipArea(var["GLO",,],scales="free_y") -p <- p + theme(legend.position="none") -swfigure(sw,print,p,sw_option="height=3.5,width=7") - -p <- mipBarYearData(var["GLO",y_bar_near_term,]) -p <- p + theme(legend.position="none") -swfigure(sw,print,p,sw_option="height=4.5,width=7") - -p <- mipBarYearData(var[,y_bar_near_term,]["GLO",,,invert=TRUE]) -swfigure(sw,print,p,sw_option="height=9,width=8") - -swlatex(sw,"\\onecolumn") -p <- mipArea(var["GLO",,,invert=TRUE],scales="free_y") -swfigure(sw,print,p,sw_option="height=8,width=16") -swlatex(sw,"\\twocolumn") - -# ---- CO2 by sector - near-term ---- - -swlatex(sw,"\\subsection{CO2 by sector - near-term}") - -tot <-"Emi|CO2 (Mt CO2/yr)" -items <- c("Emi|CO2|Land-Use Change (Mt CO2/yr)", - "Emi|CO2|Energy|Supply|Non-Elec (Mt CO2/yr)", - "Emi|CO2|Energy|Supply|Electricity|Gross (Mt CO2/yr)", - "Emi|CO2|Energy|Demand|Industry|Gross (Mt CO2/yr)", - "Emi|CO2|FFaI|Industry|Process (Mt CO2/yr)", - # "Emi|CO2|Industrial Processes (Mt CO2/yr)", - "Emi|CO2|Buildings|Direct (Mt CO2/yr)", - "Emi|CO2|Transport|Demand (Mt CO2/yr)", - "Emi|CO2|Carbon Capture and Storage|Biomass|Neg (Mt CO2/yr)", - "Emi|CO2|CDR|DAC (Mt CO2/yr)", - "Emi|CO2|CDR|EW (Mt CO2/yr)") -var <- data_near_term[,,intersect(items,getNames(data_near_term,dim=3))] - -p <- mipArea(var["GLO",,],total=data_near_term["GLO",,tot],scales="free_y") -p <- p + theme(legend.position="none") -swfigure(sw,print,p,sw_option="height=3.5,width=7") - -p <- mipBarYearData(var["GLO",y_bar_near_term,]) -p <- p + theme(legend.position="none") -swfigure(sw,print,p,sw_option="height=4.5,width=7") - -p <- mipBarYearData(var[,y_bar_near_term,]["GLO",,,invert=TRUE]) -swfigure(sw,print,p,sw_option="height=9,width=8") - -swlatex(sw,"\\onecolumn") -p <- mipArea(var["GLO",,,invert=TRUE],total=data_near_term[,,tot]["GLO",,,invert=TRUE],scales="free_y") -swfigure(sw,print,p,sw_option="height=8,width=16") -swlatex(sw,"\\twocolumn") - -# ---- CO2 land-use - near-term ---- - -swlatex(sw,"\\subsection{CO2 land-use - near-term}") -p <- mipLineHistorical(data_near_term["GLO",,"Emi|CO2|Land-Use Change (Mt CO2/yr)"],#x_hist=hist["GLO",,"Emi|CO2|Land Use (Mt CO2/yr)"], - ylab='Emi|CO2|Land-Use Change [Mt CO2/yr]',scales="free_y",plot.priority=c("x_hist","x","x_proj")) -swfigure(sw,print,p,sw_option="height=8,width=8") -p <- mipLineHistorical(data_near_term[,,"Emi|CO2|Land-Use Change (Mt CO2/yr)"]["GLO",,,invert=TRUE],#x_hist=hist[,,"Emi|CO2|Land Use (Mt CO2/yr)"]["GLO",,,invert=TRUE], - ylab='Emi|CO2|Land-Use Change [Mt CO2/yr]',scales="free_y",plot.priority=c("x_hist","x","x_proj"),facet.ncol=3) -swfigure(sw,print,p,sw_option="height=9,width=8") - -# ---- CH4 land-use - near-term ---- - -swlatex(sw,"\\subsection{CH4 land-use - near-term}") -p <- mipLineHistorical(data_near_term["GLO",,"Emi|CH4|Land Use (Mt CH4/yr)"],#x_hist=hist["GLO",,"Emi|CH4|Land Use (Mt CH4/yr)"], - ylab='Emi|CH4|Land Use [Mt CH4/yr]',scales="free_y",plot.priority=c("x_hist","x","x_proj")) -swfigure(sw,print,p,sw_option="height=8,width=8") -p <- mipLineHistorical(data_near_term[,,"Emi|CH4|Land Use (Mt CH4/yr)"]["GLO",,,invert=TRUE],#x_hist=hist[,,"Emi|CH4|Land Use (Mt CH4/yr)"]["GLO",,,invert=TRUE], - ylab='Emi|CH4|Land Use [Mt CH4/yr]',scales="free_y",plot.priority=c("x_hist","x","x_proj"),facet.ncol=3) -swfigure(sw,print,p,sw_option="height=9,width=8") - -# ---- N2O land-use - near-term ---- - -swlatex(sw,"\\subsection{N2O land-use - near-term}") -p <- mipLineHistorical(data_near_term["GLO",,"Emi|N2O|Land Use (kt N2O/yr)"],#x_hist=hist["GLO",,"Emi|N2O|Land Use (kt N2O/yr)"], - ylab='Emi|N2O|Land Use [kt N2O/yr]',scales="free_y",plot.priority=c("x_hist","x","x_proj")) -swfigure(sw,print,p,sw_option="height=8,width=8") -p <- mipLineHistorical(data_near_term[,,"Emi|N2O|Land Use (kt N2O/yr)"]["GLO",,,invert=TRUE],#x_hist=hist[,,"Emi|N2O|Land Use (kt N2O/yr)"]["GLO",,,invert=TRUE], - ylab='Emi|N2O|Land Use [kt N2O/yr]',scales="free_y",plot.priority=c("x_hist","x","x_proj"),facet.ncol=3) -swfigure(sw,print,p,sw_option="height=9,width=8") - -# ---- CDR by sector - near-term ---- - -swlatex(sw,"\\subsection{CDR by sector - near-term}") -tot <-"Emi|CO2 (Mt CO2/yr)" -items <- c("Emi|CO2|Gross Fossil Fuels and Industry (Mt CO2/yr)", - "Emi|CO2|Carbon Capture and Storage|Fossil (Mt CO2/yr)", - #"Emissions|CO2|Land|Land-use Change|+|Positive (Mt CO2/yr)", - #"Emissions|CO2|Land|Land-use Change|+|Negative (Mt CO2/yr)", - "Emi|CO2|Land-Use Change (Mt CO2/yr)", - "Emi|CO2|CDR|BECCS (Mt CO2/yr)", - "Emi|CO2|CDR|DAC (Mt CO2/yr)", - "Emi|CO2|CDR|EW (Mt CO2/yr)") -var <- data_near_term[,,intersect(items,getNames(data_near_term,dim=3))] - -# remove model dimension, because it has remind AND magpie, but mipBarYearData can't handle that -var <- collapseNames(var,collapsedim = 2) - -p <- mipArea(var["GLO",,],total=data_near_term["GLO",,tot],scales="free_y") -p <- p + theme(legend.position="none") -swfigure(sw,print,p,sw_option="height=3.5,width=7") - -p <- mipBarYearData(var["GLO",y_bar_near_term,]) -p <- p + theme(legend.position="none") -swfigure(sw,print,p,sw_option="height=4.5,width=7") - -p <- mipBarYearData(var[,y_bar_near_term,]["GLO",,,invert=TRUE]) -swfigure(sw,print,p,sw_option="height=9,width=8") - -swlatex(sw,"\\onecolumn") -p <- mipArea(var["GLO",,,invert=TRUE],total=data_near_term[,,tot]["GLO",,,invert=TRUE],scales="free_y") -swfigure(sw,print,p,sw_option="height=8,width=16") -swlatex(sw,"\\twocolumn") - -# ---- ++++ ENERGY ++++ ---- - -swlatex(sw,"\\section{Energy}") - -# ---- PE by carrier ---- - -swlatex(sw,"\\subsection{PE by carrier}") - -items <-c("PE|+|Coal (EJ/yr)", - "PE|+|Oil (EJ/yr)", - "PE|+|Gas (EJ/yr)", - "PE|+|Biomass (EJ/yr)", - "PE|+|Nuclear (EJ/yr)", - "PE|+|Solar (EJ/yr)", - "PE|+|Wind (EJ/yr)", - "PE|+|Hydro (EJ/yr)", - "PE|+|Geothermal (EJ/yr)") -var <- data[,,intersect(items,getNames(data,dim=3))] - -p <- mipArea(var["GLO",,],scales="free_y") -p <- p + theme(legend.position="none") -swfigure(sw,print,p,sw_option="height=3.5,width=7") - -p <- mipBarYearData(var["GLO",y_bar,]) -p <- p + theme(legend.position="none") -swfigure(sw,print,p,sw_option="height=4.5,width=7") - -p <- mipBarYearData(var[,y_bar,]["GLO",,,invert=TRUE]) -swfigure(sw,print,p,sw_option="height=9,width=8") - -swlatex(sw,"\\onecolumn") -p <- mipArea(var["GLO",,,invert=TRUE],scales="free_y") -swfigure(sw,print,p,sw_option="height=8,width=16") -swlatex(sw,"\\twocolumn") - -# ---- SE Electricity by carrier ---- - -swlatex(sw,"\\subsection{SE Electricity by carrier}") - -items<- c ("SE|Electricity|Coal|w/ CCS (EJ/yr)", - "SE|Electricity|Coal|w/o CCS (EJ/yr)", - "SE|Electricity|Oil (EJ/yr)", - "SE|Electricity|Gas|w/ CCS (EJ/yr)", - "SE|Electricity|Gas|w/o CCS (EJ/yr)", - "SE|Electricity|Geothermal (EJ/yr)", - "SE|Electricity|Hydro (EJ/yr)", - "SE|Electricity|Nuclear (EJ/yr)", - "SE|Electricity|Biomass|w/ CCS (EJ/yr)", - "SE|Electricity|Biomass|w/o CCS (EJ/yr)", - "SE|Electricity|Solar|CSP (EJ/yr)", - "SE|Electricity|Solar|PV (EJ/yr)", - "SE|Electricity|Wind (EJ/yr)", - "SE|Electricity|Hydrogen (EJ/yr)") -var <- data[,,intersect(items,getNames(data,dim=3))] - -p <- mipArea(var["GLO",,],scales="free_y") -p <- p + theme(legend.position="none") -swfigure(sw,print,p,sw_option="height=3.5,width=7") - -p <- mipBarYearData(var["GLO",y_bar,]) -p <- p + theme(legend.position="none") -swfigure(sw,print,p,sw_option="height=4.5,width=7") - -p <- mipBarYearData(var[,y_bar,]["GLO",,,invert=TRUE]) -swfigure(sw,print,p,sw_option="height=9,width=8") - -swlatex(sw,"\\onecolumn") -p <- mipArea(var["GLO",,,invert=TRUE],scales="free_y") -swfigure(sw,print,p,sw_option="height=8,width=16") -swlatex(sw,"\\twocolumn") - -# ---- FE by sector ---- - -swlatex(sw,"\\subsection{FE by sector}") - -items<- c("FE|CDR (EJ/yr)", - "FE|Transport (EJ/yr)", - "FE|Buildings (EJ/yr)", - "FE|Industry (EJ/yr)") -var <- data[,,intersect(items,getNames(data,dim=3))] - -p <- mipArea(var["GLO",,], scales="free_y") -p <- p + theme(legend.position="none") -swfigure(sw,print,p,sw_option="height=3.5,width=7") - -p <- mipBarYearData(var["GLO",y_bar,]) -p <- p + theme(legend.position="none") -swfigure(sw,print,p,sw_option="height=4.5,width=7") - -p <- mipBarYearData(var[,y_bar,]["GLO",,,invert=TRUE]) -swfigure(sw,print,p,sw_option="height=9,width=8") - -swlatex(sw,"\\onecolumn") -p <- mipArea(var["GLO",,,invert=TRUE],scales="free_y") -swfigure(sw,print,p,sw_option="height=8,width=16") -swlatex(sw,"\\twocolumn") - -# ---- FE by carrier ---- - -swlatex(sw,"\\subsection{FE by carrier}") - -items<- c("FE|+|Solids (EJ/yr)", - "FE|+|Liquids (EJ/yr)", - "FE|+|Gases (EJ/yr)", - "FE|+|Heat (EJ/yr)", - "FE|+|Hydrogen (EJ/yr)", - "FE|+|Electricity (EJ/yr)") -var <- data[,,intersect(items,getNames(data,dim=3))] - -p <- mipArea(var["GLO",,],scales="free_y") -p <- p + theme(legend.position="none") -swfigure(sw,print,p,sw_option="height=3.5,width=7") - -p <- mipBarYearData(var["GLO",y_bar,]) -p <- p + theme(legend.position="none") -swfigure(sw,print,p,sw_option="height=4.5,width=7") - -p <- mipBarYearData(var[,y_bar,]["GLO",,,invert=TRUE]) -swfigure(sw,print,p,sw_option="height=9,width=8") - -swlatex(sw,"\\onecolumn") -p <- mipArea(var["GLO",,,invert=TRUE],scales="free_y") -swfigure(sw,print,p,sw_option="height=8,width=16") -swlatex(sw,"\\twocolumn") - -# ---- PE Biomass by soure ---- -swlatex(sw,"\\subsection{Biomass Production and Consumption}") - -# Add inverted trade variable (export are negative and imports are positive) -tmp <- -data[,,"Trade|Biomass (EJ/yr)"] -getNames(tmp,dim=3) <- "Trade|Biomass inverted (EJ/yr)" -data <- mbind(data,tmp) - -items <- c("Trade|Biomass inverted (EJ/yr)", - "Primary Energy Production|Biomass|Energy Crops (EJ/yr)", - "PE|Biomass|Residues (EJ/yr)", - "PE|Biomass|1st Generation (EJ/yr)") - # PE|Biomass|Modern - # PE|Biomass|Traditional - # PE|Biomass|Energy Crops - -var <- data[,,intersect(items,getNames(data,dim=3))] - -p <- mipArea(var["GLO",,],total=data["GLO",,"PE|+|Biomass (EJ/yr)"]) -p <- p + theme(legend.position="none") -swfigure(sw,print,p,sw_option="height=3.5,width=7") - -p <- mipBarYearData(var["GLO",y_bar,]) -p <- p + theme(legend.position="none") -swfigure(sw,print,p,sw_option="height=4.5,width=7") - -p <- mipBarYearData(var[,y_bar,]["GLO",,,invert=TRUE]) -swfigure(sw,print,p,sw_option="height=9,width=8") - -swlatex(sw,"\\onecolumn") -p <- mipArea(var["GLO",,,invert=TRUE],total=data[,,"PE|+|Biomass (EJ/yr)"]["GLO",,,invert=TRUE]) -swfigure(sw,print,p,sw_option="height=8,width=16") -swlatex(sw,"\\twocolumn") - -# ---- PE Biomass by demand ---- - -swlatex(sw,"\\subsection{PE|Biomass Area}") - -items<- c ("PE|Biomass|Solids (EJ/yr)", - "PE|Biomass|Heat (EJ/yr)", - "PE|Biomass|Liquids|w/ CCS (EJ/yr)", - "PE|Biomass|Liquids|w/o CCS (EJ/yr)", - "PE|Biomass|Gases (EJ/yr)", - "PE|Biomass|Electricity|w/ CCS (EJ/yr)", - "PE|Biomass|Electricity|w/o CCS (EJ/yr)", - "PE|Biomass|Hydrogen|w/ CCS (EJ/yr)", - "PE|Biomass|Hydrogen|w/o CCS (EJ/yr)") -var <- data[,,intersect(items,getNames(data,dim=3))] - -p <- mipArea(var["GLO",,],scales="free_y") -p <- p + theme(legend.position="none") -swfigure(sw,print,p,sw_option="height=3.5,width=7") - -p <- mipBarYearData(var["GLO",y_bar,]) -p <- p + theme(legend.position="none") -swfigure(sw,print,p,sw_option="height=4.5,width=7") - -p <- mipBarYearData(var[,y_bar,]["GLO",,,invert=TRUE]) -swfigure(sw,print,p,sw_option="height=9,width=8") - -swlatex(sw,"\\onecolumn") -p <- mipArea(var["GLO",,,invert=TRUE],scales="free_y") -swfigure(sw,print,p,sw_option="height=8,width=16") -swlatex(sw,"\\twocolumn") - -# ---- PE Biomass (line) ---- - -swlatex(sw,"\\subsection{PE|Biomass}") -var <- "PE|+|Biomass (EJ/yr)" -p <- mipLineHistorical(data["GLO",,var],x_hist=hist["GLO",,"PE|Biomass (EJ/yr)"], - ylab=var,scales="free_y",plot.priority=c("x_hist","x","x_proj")) -swfigure(sw,print,p,sw_option="height=8,width=8") -p <- mipLineHistorical(data[,,var]["GLO",,,invert=TRUE],x_hist=hist[,,"PE|Biomass (EJ/yr)"]["GLO",,,invert=TRUE], - ylab=var,scales="free_y",plot.priority=c("x_hist","x","x_proj"),facet.ncol=3) -swfigure(sw,print,p,sw_option="height=9,width=8") - -# ---- PE Biomass purpose grown (line) ---- - -swlatex(sw,"\\subsection{PE|Biomass purpose grown}") -var <- "Primary Energy Production|Biomass|Energy Crops (EJ/yr)" -p <- mipLineHistorical(data["GLO",,var], - ylab=var,scales="free_y",plot.priority=c("x_hist","x","x_proj")) -swfigure(sw,print,p,sw_option="height=8,width=8") -p <- mipLineHistorical(data[,,var]["GLO",,,invert=TRUE], - ylab=var,scales="free_y",plot.priority=c("x_hist","x","x_proj"),facet.ncol=3) -swfigure(sw,print,p,sw_option="height=9,width=8") - -# ---- Trade Biomass ---- - -swlatex(sw,"\\subsection{Trade Biomass}") -var <- "Trade|Biomass (EJ/yr)" -p <- mipArea(data[,,var]) -swfigure(sw,print,p,sw_option="height=8,width=8") -p <- mipLineHistorical(data[,,var]["GLO",,,invert=TRUE],x_hist=NULL, - ylab=var,scales="free_y",plot.priority=c("x_hist","x","x_proj"),facet.ncol=3) -swfigure(sw,print,p,sw_option="height=9,width=8") - -# ---- ++++ LAND COVER ++++ ---- - -# ---- Yields ---- - -swlatex(sw,"\\subsection{Bioenerg yields}") -var <- "Productivity|Yield|+|Bioenergy crops (t DM/ha)" -#p <- mipLineHistorical(data["GLO",,var], -# ylab=var,scales="free_y",plot.priority=c("x_hist","x","x_proj")) -#swfigure(sw,print,p,sw_option="height=8,width=8") -p <- mipLineHistorical(data[,,var]["GLO",,,invert=TRUE], - ylab=var,scales="free_y",plot.priority=c("x_hist","x","x_proj"),facet.ncol=3) -swfigure(sw,print,p,sw_option="height=9,width=8") - -swlatex(sw,"\\subsection{Cereal yields}") -var <- "Productivity|Yield|Crops|+|Cereals (t DM/ha)" -#p <- mipLineHistorical(data["GLO",,var], -# ylab=var,scales="free_y",plot.priority=c("x_hist","x","x_proj")) -#swfigure(sw,print,p,sw_option="height=8,width=8") -p <- mipLineHistorical(data[,,var]["GLO",,,invert=TRUE], - ylab=var,scales="free_y",plot.priority=c("x_hist","x","x_proj"),facet.ncol=3) -swfigure(sw,print,p,sw_option="height=9,width=8") - -# ---- TAU ---- - -swlatex(sw,"\\subsection{TAU}") -var <- "Productivity|Landuse Intensity Indicator Tau (Index)" -p <- mipLineHistorical(data["GLO",,var], - ylab=var,scales="free_y",plot.priority=c("x_hist","x","x_proj")) -swfigure(sw,print,p,sw_option="height=8,width=8") -p <- mipLineHistorical(data[,,var]["GLO",,,invert=TRUE], - ylab=var,scales="free_y",plot.priority=c("x_hist","x","x_proj"),facet.ncol=3) -swfigure(sw,print,p,sw_option="height=9,width=8") - -# ---- Land cover Stacked ---- - -swlatex(sw,"\\section{Land cover}") -# select the variables that have "+" at the 3rd and at the 4th slot -n <- c(grep("Resources\\|Land Cover\\|\\+.*",getNames(data,dim=3),value=TRUE), - grep("Resources\\|Land Cover\\|[^\\|]*\\|\\+.*",getNames(data,dim=3),value=TRUE)) - -cat("There are the following land variables potentially relevant. Please manually choose the relevant ones!\n") -cat(sort(n),sep='",\n"') - -var <- c(#"Resources|Land Cover|+|Cropland (million ha)", - #"Resources|Land Cover|+|Forest (million ha)", - "Resources|Land Cover|+|Other Land (million ha)", - "Resources|Land Cover|+|Pastures and Rangelands (million ha)", - "Resources|Land Cover|Cropland|+|Bioenergy crops (million ha)", - "Resources|Land Cover|Cropland|+|Crops (million ha)", - "Resources|Land Cover|Cropland|+|Forage (million ha)", - "Resources|Land Cover|Forest|+|Managed Forest (million ha)", - "Resources|Land Cover|Forest|+|Natural Forest (million ha)", - "Resources|Land Cover|+|Urban Area (million ha)") - -tmp <- data[,,var] - -getNames(tmp,dim=3) <- gsub("\\+\\|","",getNames(tmp,dim=3)) -getNames(tmp,dim=3) <- gsub("Cropland\\|","",getNames(tmp,dim=3)) -getNames(tmp,dim=3) <- gsub("Forest\\|","",getNames(tmp,dim=3)) - -# retrieve colors -land_colors <- plotstyle(shorten_legend(getNames(tmp,dim=3),identical=TRUE)) - -# correct missing colors -land_colors["Bioenergy crops"] <- "goldenrod4" -land_colors["Crops"] <- "#8B4513" -land_colors["Forage"] <- "purple" -land_colors["Managed Forest"] <- "#006400" -land_colors["Natural Forest"] <- "#66A61E" - -p <- mipArea(tmp["GLO",,],total = FALSE) -p <- p + scale_fill_manual(values=land_colors) -swfigure(sw,print,p,sw_option="height=8,width=8") - -p <- mipArea(tmp["GLO",,invert=TRUE],total = FALSE) -p <- p + scale_fill_manual(values=land_colors) -swfigure(sw,print,p,sw_option="height=8,width=8") - -# ---- Land cover Bioenergy cropland ---- - -swlatex(sw,"\\subsection{Land cover Bioenergy Cropland}") - -var <- c("Resources|Land Cover|Cropland|+|Bioenergy crops (million ha)") - -p <- mipLineHistorical(data["GLO",,var], - ylab=var,scales="free_y",plot.priority=c("x_hist","x","x_proj")) -swfigure(sw,print,p,sw_option="height=8,width=8") - -p <- mipLineHistorical(data[,,var]["GLO",,,invert=TRUE], - ylab=var,scales="free_y",plot.priority=c("x_hist","x","x_proj")) -swfigure(sw,print,p,sw_option="height=8,width=8") - -# ---- Land cover change by scenario (line)---- - -var <- c("Resources|Land Cover Change|+|Cropland (million ha wrt 1995)", - "Resources|Land Cover Change|+|Pastures and Rangelands (million ha wrt 1995)", - "Resources|Land Cover Change|+|Forest (million ha wrt 1995)", - "Resources|Land Cover Change|+|Other Land (million ha wrt 1995)") - -for (scen in getNames(data[,,"MAgPIE"],dim=1)) { - swlatex(sw,paste0("\\subsection{Land cover change ",scen,"}")) - - p <- mipLineHistorical(data["GLO",,var][,,scen],color.dim="variable", - ylab=var,scales="free_y",plot.priority=c("x_hist","x","x_proj")) - swfigure(sw,print,p,sw_option="height=8,width=8") - - p <- mipLineHistorical(data[,,var][,,scen]["GLO",,,invert=TRUE],color.dim="variable", - ylab=var,scales="free_y",plot.priority=c("x_hist","x","x_proj")) - swfigure(sw,print,p,sw_option="height=8,width=8") -} - -# ---- Land cover change Cropland ---- - -swlatex(sw,"\\subsection{Land cover change Cropland}") - -var <- c("Resources|Land Cover Change|+|Cropland (million ha wrt 1995)") - -p <- mipLineHistorical(data["GLO",,var], - ylab=var,scales="free_y",plot.priority=c("x_hist","x","x_proj")) -swfigure(sw,print,p,sw_option="height=8,width=8") - -p <- mipLineHistorical(data[,,var]["GLO",,,invert=TRUE], - ylab=var,scales="free_y",plot.priority=c("x_hist","x","x_proj")) -swfigure(sw,print,p,sw_option="height=8,width=8") - -# ---- Land cover change Other Land ---- - -swlatex(sw,"\\subsection{Land cover change Other Land}") - -var <- c("Resources|Land Cover Change|+|Other Land (million ha wrt 1995)") - -p <- mipLineHistorical(data["GLO",,var], - ylab=var,scales="free_y",plot.priority=c("x_hist","x","x_proj")) -swfigure(sw,print,p,sw_option="height=8,width=8") - -p <- mipLineHistorical(data[,,var]["GLO",,,invert=TRUE], - ylab=var,scales="free_y",plot.priority=c("x_hist","x","x_proj")) -swfigure(sw,print,p,sw_option="height=8,width=8") - -# ---- Land cover change Pastures and Rangelands ---- - -swlatex(sw,"\\subsection{Land cover change Pastures and Rangelands}") - -var <- c("Resources|Land Cover Change|+|Pastures and Rangelands (million ha wrt 1995)") - -p <- mipLineHistorical(data["GLO",,var], - ylab=var,scales="free_y",plot.priority=c("x_hist","x","x_proj")) -swfigure(sw,print,p,sw_option="height=8,width=8") - -p <- mipLineHistorical(data[,,var]["GLO",,,invert=TRUE], - ylab=var,scales="free_y",plot.priority=c("x_hist","x","x_proj")) -swfigure(sw,print,p,sw_option="height=8,width=8") - -# ---- Land cover change Managed Forest ---- - -swlatex(sw,"\\subsection{Land cover change Managed Forest}") - -var <- c("Resources|Land Cover Change|Forest|+|Managed Forest (million ha wrt 1995)") - -p <- mipLineHistorical(data["GLO",,var], - ylab=var,scales="free_y",plot.priority=c("x_hist","x","x_proj")) -swfigure(sw,print,p,sw_option="height=8,width=8") - -p <- mipLineHistorical(data[,,var]["GLO",,,invert=TRUE], - ylab=var,scales="free_y",plot.priority=c("x_hist","x","x_proj")) -swfigure(sw,print,p,sw_option="height=8,width=8") - -# ---- Land cover change Natural Forest ---- - -swlatex(sw,"\\subsection{Land cover change Natural Forest}") - -var <- c("Resources|Land Cover Change|Forest|+|Natural Forest (million ha wrt 1995)") - -p <- mipLineHistorical(data["GLO",,var], - ylab=var,scales="free_y",plot.priority=c("x_hist","x","x_proj")) -swfigure(sw,print,p,sw_option="height=8,width=8") - -p <- mipLineHistorical(data[,,var]["GLO",,,invert=TRUE], - ylab=var,scales="free_y",plot.priority=c("x_hist","x","x_proj")) -swfigure(sw,print,p,sw_option="height=8,width=8") - -# ---- ++++ PRICES ++++ ---- - -swlatex(sw,"\\section{Prices}") - -# ---- Food price index ---- - -swlatex(sw,"\\subsection{Food price index}") -var <- "Prices|Food Price Index (Index 2010=100)" -p <- mipLineHistorical(data["GLO",,var], - ylab=var,scales="free_y",plot.priority=c("x_hist","x","x_proj")) -swfigure(sw,print,p,sw_option="height=8,width=8") -p <- mipLineHistorical(data[,,var]["GLO",,,invert=TRUE], - ylab=var,scales="free_y",plot.priority=c("x_hist","x","x_proj"),facet.ncol=3) -swfigure(sw,print,p,sw_option="height=9,width=8") - -# ---- SE prices ---- - -swlatex(sw,"\\subsection{Prices Electricity}") -var <- "Price|Secondary Energy|Electricity (US$2005/GJ)" -p <- mipLineHistorical(data["GLO",,var], - ylab=var,scales="free_y",plot.priority=c("x_hist","x","x_proj")) -swfigure(sw,print,p,sw_option="height=8,width=8") -p <- mipLineHistorical(data[,,var]["GLO",,,invert=TRUE], - ylab=var,scales="free_y",plot.priority=c("x_hist","x","x_proj"),facet.ncol=3) -swfigure(sw,print,p,sw_option="height=9,width=8") - -swlatex(sw,"\\subsection{Prices Liquids}") -var <- "Price|Secondary Energy|Liquids (US$2005/GJ)" -p <- mipLineHistorical(data["GLO",,var], - ylab=var,scales="free_y",plot.priority=c("x_hist","x","x_proj")) -swfigure(sw,print,p,sw_option="height=8,width=8") -p <- mipLineHistorical(data[,,var]["GLO",,,invert=TRUE], - ylab=var,scales="free_y",plot.priority=c("x_hist","x","x_proj"),facet.ncol=3) -swfigure(sw,print,p,sw_option="height=9,width=8") - -# ---- PE prices Bioenergy - MAgPIE ---- - -swlatex(sw,"\\subsection{Prices Bioenergy MAgPIE}") -var <- "Prices|Bioenergy (US$05/GJ)" -p <- mipLineHistorical(data["GLO",,var], - ylab=var,scales="free_y",plot.priority=c("x_hist","x","x_proj")) -swfigure(sw,print,p,sw_option="height=8,width=8") -p <- mipLineHistorical(data[,,var]["GLO",,,invert=TRUE], - ylab=var,scales="free_y",plot.priority=c("x_hist","x","x_proj"),facet.ncol=3) -swfigure(sw,print,p,sw_option="height=9,width=8") - -# ---- PE prices Bioenergy - REMIND ---- - -swlatex(sw,"\\subsection{Prices Bioenergy REMIND}") -var <- "Price|Biomass|Primary Level (US$2005/GJ)" -p <- mipLineHistorical(data["GLO",,var], - ylab=var,scales="free_y",plot.priority=c("x_hist","x","x_proj")) -swfigure(sw,print,p,sw_option="height=8,width=8") -p <- mipLineHistorical(data[,,var]["GLO",,,invert=TRUE], - ylab=var,scales="free_y",plot.priority=c("x_hist","x","x_proj"),facet.ncol=3) -swfigure(sw,print,p,sw_option="height=9,width=8") - -# ---- CO2 Prices ---- - -swlatex(sw,"\\subsection{CO2 Prices}") - -p <- mipLineHistorical(data["GLO",,"Price|Carbon (US$2005/t CO2)"],x_hist=NULL, - ylab='Price|Carbon [US$2005/t CO2]',scales="free_y",plot.priority=c("x_hist","x","x_proj")) -p <- p + theme(legend.position="none") -swfigure(sw,print,p,sw_option="height=3.5,width=7") -p <- mipLineHistorical(data["GLO",,"Price|Carbon (US$2005/t CO2)"],x_hist=NULL, - ylab='Price|Carbon_log [US$2005/t CO2]',ybreaks=c(20,30,40,50,60,75,100,200,500,1000,2000,3000), - ylim=c(20,3000),ylog=TRUE) -swfigure(sw,print,p,sw_option="height=4.5,width=7") -p <- mipLineHistorical(data[,,"Price|Carbon (US$2005/t CO2)"]["GLO",,,invert=TRUE],x_hist=NULL, - ylab='Price|Carbon [US$2005/t CO2]',scales="free_y",plot.priority=c("x_hist","x","x_proj"),facet.ncol=3) -swfigure(sw,print,p,sw_option="height=9,width=8") - -# ---- CO2 prices comparison to MAgPIE ---- - -swlatex(sw,"\\subsection{GHG prices}") -var <- c("Price|Carbon (US$2005/t CO2)", - "Prices|GHG Emission|CO2 (US$2005/tCO2)") - -p <- ggplot(luplot::as.ggplot(data["GLO",,var]), aes_string(x="Year",y="Value")) + - geom_line(aes_string(colour="Data1",linetype="Data3"),size=1) + - geom_point(aes_string(colour="Data1")) + labs(y ="US$2005 / tCO2") + theme(legend.position="none") -swfigure(sw,print,p,sw_option="height=8,width=8") - -p <- ggplot(luplot::as.ggplot(data[,,var]["GLO",,,invert=TRUE]), aes_string(x="Year",y="Value")) + - geom_line(aes_string(colour="Data1",linetype="Data3"),size=1) + - geom_point(aes_string(colour="Data1")) + labs(y ="US$2005 / tCO2") + - facet_wrap(~Region) + theme(legend.position="bottom") + - guides(colour = guide_legend(nrow = 5,title.position = "top"),linetype = guide_legend(nrow = 2,title.position = "top")) - -swfigure(sw,print,p,sw_option="height=8,width=8") - -# "Price|CH4 (US$2005/t CH4)" -# "Prices|GHG Emission|CH4 (US$2005/tCH4)" - -# "Price|N2O (US$2005/t N2O)" -# "Prices|GHG Emission|N2O (US$2005/tN2O)" - -# ---- ++++ COSTS ++++ ---- - -swlatex(sw,"\\section{Costs}") - -# ---- Total ag costs ---- -swlatex(sw,"\\subsection{Total Agricultural Costs}") -var <- "Costs|Land Use (billion US$2005/yr)" -p <- mipLineHistorical(data["GLO",,var], - ylab=var,scales="free_y",plot.priority=c("x_hist","x","x_proj")) -swfigure(sw,print,p,sw_option="height=8,width=8") -p <- mipLineHistorical(data[,,var]["GLO",,,invert=TRUE], - ylab=var,scales="free_y",plot.priority=c("x_hist","x","x_proj"),facet.ncol=3) -swfigure(sw,print,p,sw_option="height=9,width=8") - -swlatex(sw,"\\subsection{Total Agricultural Costs (look-up table or MAgPIE value)}") -var <- "Costs|Land Use with MAC-costs from MAgPIE (billion US$2005/yr)" -p <- mipLineHistorical(data["GLO",,var], - ylab=var,scales="free_y",plot.priority=c("x_hist","x","x_proj")) -swfigure(sw,print,p,sw_option="height=8,width=8") -p <- mipLineHistorical(data[,,var]["GLO",,,invert=TRUE], - ylab=var,scales="free_y",plot.priority=c("x_hist","x","x_proj"),facet.ncol=3) -swfigure(sw,print,p,sw_option="height=9,width=8") - -# ---- MAC costs ---- - -swlatex(sw,"\\subsection{Costs MAC (REMIND endogenous)}") -var <- "Costs|Land Use|MAC-costs (billion US$2005/yr)" -p <- mipLineHistorical(data["GLO",,var], - ylab=var,scales="free_y",plot.priority=c("x_hist","x","x_proj")) -swfigure(sw,print,p,sw_option="height=8,width=8") -p <- mipLineHistorical(data[,,var]["GLO",,,invert=TRUE], - ylab=var,scales="free_y",plot.priority=c("x_hist","x","x_proj"),facet.ncol=3) -swfigure(sw,print,p,sw_option="height=9,width=8") - -# ---- COSTS MAC Line ---- - -swlatex(sw,"\\subsection{Costs MAC Line}") -# add REMIND's MAC costs for CH4 and N2O to make it comparable to MAgPIE's aggregate -var_rem <- c("Costs|Land Use|MAC-costs|N2O (billion US$2005/yr)", - "Costs|Land Use|MAC-costs|CH4 (billion US$2005/yr)") -tmp <- dimSums(data[,,var_rem],dim = 3.3)*1000 -tmp <- add_dimension(tmp,dim = 3.3,add = "variable",nm = "Costs|Land Use|MAC-costs|CH4 N2O (million US$2005/yr)") -data <- mbind(data,tmp) - -# convert lookup values to million -var_rem <- "Costs|Land Use|Mac-costs Lookup (billion US$2005/yr)" -tmp <- data[,,var_rem]*1000 -getNames(tmp,dim=3) <- gsub("bi","mi",getNames(tmp,dim=3)) -data <- mbind(data,tmp) - -var <- c("Costs|MainSolve|MACCS (million US$05/yr)", - "Costs|Land Use|MAC-costs|CH4 N2O (million US$2005/yr)", - "Costs|Land Use|Mac-costs Lookup (million US$2005/yr)") - -p <- ggplot(luplot::as.ggplot(data["GLO",,var]), aes_string(x="Year",y="Value")) + - geom_line(aes_string(colour="Data1",linetype="Data3"),size=1) + - geom_point(aes_string(colour="Data1")) + labs(y ="million US$2005 / tCO2") + theme(legend.position="none") -swfigure(sw,print,p,sw_option="height=8,width=8") - -p <- ggplot(luplot::as.ggplot(data[,,var]["GLO",,,invert=TRUE]), aes_string(x="Year",y="Value")) + - geom_line(aes_string(colour="Data1",linetype="Data3"),size=1) + - geom_point(aes_string(colour="Data1")) + labs(y ="million US$2005 / tCO2") + - facet_wrap(~Region) + theme(legend.position="bottom") + - guides(colour = guide_legend(nrow = 5,title.position = "top"),linetype = guide_legend(nrow = 3,title.position = "top")) -swfigure(sw,print,p,sw_option="height=8,width=8") - -# ---- COSTS MAC Area ---- - -swlatex(sw,"\\subsection{Costs MAC Area}") - -# Make mac costs from lookup table negative since they are substracted from the total costs in REMIND -data[,,"Costs|Land Use|Mac-costs Lookup (billion US$2005/yr)"] <- -data[,,"Costs|Land Use|Mac-costs Lookup (billion US$2005/yr)"] - -var <- c("Costs|Land Use with MAC-costs from MAgPIE (billion US$2005/yr)", - "Costs|Land Use|Mac-costs Lookup (billion US$2005/yr)", - "Costs|Land Use|MAC-costs|CO2 (billion US$2005/yr)", - "Costs|Land Use|MAC-costs|CH4 (billion US$2005/yr)", - "Costs|Land Use|MAC-costs|N2O (billion US$2005/yr)") - -p <- mipArea(data["GLO",,var],total = data["GLO",,"Costs|Land Use (billion US$2005/yr)"]) + theme(legend.position="none") -swfigure(sw,print,p,sw_option="height=8,width=8") - -p <- mipArea(data[,,var]["GLO",,,invert=TRUE],total = data[,,"Costs|Land Use (billion US$2005/yr)"]["GLO",,,invert=TRUE]) + - theme(legend.position="bottom") + - guides(fill = guide_legend(nrow = 6,title.position = "top")) -swfigure(sw,print,p,sw_option="height=12,width=8") - -# ---- END ---- - -swclose(sw)