first commit for release

01_Main.R

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+##### main file for analysis
+##### 
+#####
+
+#required packages
+# general data structures
+library(tidyverse)
+library(data.table)
+#reading in excel data
+library(readxl)
+#smoothing time series data
+library(zoo)
+
+#switch to select either full analysis (T) or simple analysis (F, default)
+#Full analysis reproduces all paper figures, but requires data from ENTSO-E which is very large, can take a few days to obtain (see ENTSO-E_data/README.txt for details), and leads to longer code run times
+#The default simple analysis setting only requires easily available data sources (see other_data/README.txt for how to obtain and prepare them), and runs relatively quickly, and reproduces Figure 1 and Extended Data Figures 1, and 3
+full_analysis <- F
+
+# read in IMF and BP data
+source("02_read_in_imf_bp.R")
+
+# extrapolate generation 2020-2024 
+source("03_extrapolate_bp.R")
+
+#read in H1 2020 data from ember for comparison
+source("04_ember_data.R")
+
+# plot panels for Figure 1a and b, and Extended Data 1
+source("05_plot_power_sector.R")
+
+# plot Figure 1c and Extended Data Figure 3
+source("06_emissions_2020-24.R")
+
+# display of numbers cited in the paper:
+# power sector 2020 emission reduction based on projected low-carbon and total generation
+traj %>% filter(region=="World",period=="2020",scen %in%c("high","med","low","veryhigh","verylow")) %>% select(value)/traj[traj$region=="World"&traj$period=="2019" & traj$scen=="med",]$value-1
+
+#China power sector emissions share over time
+traj %>% filter(period %in% seq(2017,2020),region %in% c("China","World"),scen=="med")%>% group_by(period) %>% spread(region,value) %>%
+  mutate(share=`China`/`World`) 
+
+
+if(isTRUE(full_analysis)){
+#read in recent/historical power sector data (generation and emissions) from EU (ENTSO-E), US(EIA), and India (carbontracker.in, manually copied)
+ source("07_power_data.R") #plot Figure2
+## Figure on coal and gas in EU and US (involves manual run with updated data, some numbers hard-coded)
+ source("08_coal_gas_figure.R") #plot Extended Data Figure 2
+#print relative reductions:
+ arrow %>% select(region,fuel,red)
+ arrow2%>% select(region,fuel,red)
+# plot SI Figure S3 with GDP and demand growth, using world bank data
+ source("09_GDP_demand.R")
+#data work for daily model from Le Quere et al 2020, ploting SI Figures S1 and S2
+ source("10_daily_model.R")
+# display of numbers cited in the paper, from changed and original replicated Le Quere model:
+
+# total 2020 emission reduction based on daily model - Best Guess with our assumptions
+dt_all %>% filter(variable=="TOTAL_CO2" & REGION_CODE == "GLOBAL" & date<"2021-01-01" & scenario %in% c("emi_b2_low",
+                                                                                                        "emi_b2_med","emi_b2_high","constant")) %>% 
+  group_by(scenario) %>% summarise(value=sum(value)/36604-1) 
+
+# power sector 2020 emission reduction based on daily model - Best Guess with our assumptions
+dt_all %>% filter(variable=="POWER" & REGION_CODE == "GLOBAL" & date<"2021-01-01" & scenario %in% c("emi_b2_low",
+                                                                                                    "emi_b2_med","emi_b2_high","constant")) %>% 
+  group_by(scenario) %>% summarise(value=sum(value)/16238-1) 
+
+# power sector 2020 emission reduction based on daily model - replicated Le Quere et al specification
+dt_all %>% filter(variable=="POWER" & REGION_CODE == "GLOBAL" & date<"2021-01-01" & scenario %in% c("emi_s3_low",
+                                                                                                    "emi_s3_med","emi_s3_high","constant")) %>% 
+  group_by(scenario) %>% summarise(value=sum(value)/16238-1) 
+
+}
+

02_read_in_imf_bp.R

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+### file to read in IMF data (lines 5-20)
+
+### and read-in and prepare BP power generation and capacity data (lines 23-120) 
+
+
+## read in June update of IMF
+imf <- read.csv2("other_data/WEO-Oct2020update.csv",stringsAsFactors = F)
+imf$X <- NULL # remove unwanted columns
+imf$X.1 <- NULL
+imf$X.2 <- NULL
+imf[imf$Country=="Russia",]$Country <- "Russian Federation"
+imf[imf$Country=="United States",]$Country <- "US"
+imf[imf$Country=="Korea",]$Country <- "South Korea"
+imf[imf$Country=="World",]$Country <- "Total World"
+imf[imf$Country=="Euro Area",]$Country <- "European Union"
+imf[imf$Country=="Advanced Economies",]$Country <- "of which: OECD"
+imf$Country <- as.factor(imf$Country)
+imf <- imf %>% gather(period,value,-Country)
+imf$value <- as.numeric(imf$value)
+
+## read in BP data
+filename <- "other_data/bp-stats-review-2020-all-data.xlsx"
+sheets <- excel_sheets(filename)
+
+# ct <- read_excel(filename, sheet ="Contents")
+# def <- read_excel(filename, sheet ="Definitions")
+# meth <- read_excel(filename, sheet ="Methodology")
+
+eg <- read_excel(filename, sheet ="Electricity Generation " ,skip = 2)
+pv <- read_excel(filename, sheet = "Solar Capacity",skip=3)
+wi <- read_excel(filename, sheet = "Wind Capacity",skip=3)
+co <- read_excel(filename, sheet = "Elec Gen from Coal",skip=2)
+ga <- read_excel(filename, sheet = "Elec Gen from Gas",skip=2)
+oi <- read_excel(filename, sheet = "Elec Gen from Oil",skip=2)
+co %>% filter(`Terawatt-hours`=="Total World") %>% select(`Terawatt-hours`,as.character(c(2011:2018)),`2019...36`)
+ga %>% filter(`Terawatt-hours`=="Total World") %>% select(`Terawatt-hours`,as.character(c(2011:2018)),`2019...36`)
+oi %>% filter(`Terawatt-hours`=="Total World") %>% select(`Terawatt-hours`,as.character(c(2011:2018)),`2019...36`)
+
+enu <- read_excel(filename, sheet = "Nuclear Generation - TWh",skip=2,col_types = c("text",rep("numeric",58)))
+ehy <- read_excel(filename, sheet = "Hydro Generation - TWh",skip=2,col_types = c("text",rep("numeric",58)))
+epv <- read_excel(filename, sheet = "Solar Generation - TWh",skip=2,col_types = c("text",rep("numeric",58)))
+ewi <- read_excel(filename, sheet = "Wind Generation -TWh",skip=2,col_types = c("text",rep("numeric",58)))
+gbo <- read_excel(filename, sheet = "Geo Biomass Other - TWh",skip=2,col_types = c("text",rep("numeric",58)))
+
+
+eg <- eg %>% select(-'2019...37',-'2008-18',-'2019...39') %>% rename('2019'=`2019...36`) %>% 
+  mutate(add19 =`2019`-`2018`,add18 =`2018`-`2017`,add17 =`2017`-`2016`,add16 =`2016`-`2015`,add15 =`2015`-`2014`,add14 =`2014`-`2013`,add13 =`2013`-`2012`,add12 =`2012`-`2011`,add11 =`2011`-`2010`,
+         r19 =(`2019`/`2018`)-1,r18 =(`2018`/`2017`)-1,r17 =(`2017`/`2016`)-1,r16 =(`2016`/`2015`)-1,r15 =(`2015`/`2014`)-1,r14 =(`2014`/`2013`)-1,r13 =(`2013`/`2012`)-1,r12 =(`2012`/`2011`)-1,r11 =(`2011`/`2010`)-1) %>% 
+  rename(region='Terawatt-hours') %>% gather(period,value,-region) %>% mutate(variable="SE|Electricity",value=as.numeric(value))
+countries <- unique(eg$region)[seq(2,103)]
+eg <- eg %>% filter(region %in% countries,!is.na(region))
+eg[eg$region=="European Union #",]$region <- "European Union"
+
+countries <- gsub(countries[!is.na(countries)],pattern = " #",replacement = "")
+
+eg %>% filter(region == "Total World",period==2019)
+eg %>% filter(region %in% c("Total World","European Union","India","US"),period==2019) %>% select(-period) %>% 
+  spread(region,value) %>%  mutate(share=((US+India+`European Union`)/`Total World`))
+
+pv <- pv %>% select(-'2019...26',-'2008-18',-'2019...28') %>% rename('2019'=`2019...25`) %>% 
+  mutate(add19 =`2019`-`2018`,add18 =`2018`-`2017`,add17 =`2017`-`2016`,add16 =`2016`-`2015`,add15 =`2015`-`2014`,add14 =`2014`-`2013`,add13 =`2013`-`2012`,add12 =`2012`-`2011`,add11 =`2011`-`2010`,
+         r19 =(`2019`/`2018`)-1,r18 =(`2018`/`2017`)-1,r17 =(`2017`/`2016`)-1,r16 =(`2016`/`2015`)-1,r15 =(`2015`/`2014`)-1,r14 =(`2014`/`2013`)-1,r13 =(`2013`/`2012`)-1,r12 =(`2012`/`2011`)-1,r11 =(`2011`/`2010`)-1) %>% 
+  rename(region=Megawatts) %>% gather(period,value,-region) %>% mutate(variable="Capacity|PV")
+pv <- pv %>% filter(region %in% countries,!is.na(region))
+pv %>% filter(period=="add19") %>% arrange(desc(value))
+pv %>% filter(period=="add18") %>% arrange(desc(value))
+
+wi <- wi %>% select(-'2019...27',-'2008-2018',-'2019...29') %>% rename('2019'=`2019...26`) %>% 
+  mutate(add19 =`2019`-`2018`,add18 =`2018`-`2017`,add17 =`2017`-`2016`,add16 =`2016`-`2015`,add15 =`2015`-`2014`,add14 =`2014`-`2013`,add13 =`2013`-`2012`,add12 =`2012`-`2011`,add11 =`2011`-`2010`,
+         r19 =(`2019`/`2018`)-1,r18 =(`2018`/`2017`)-1,r17 =(`2017`/`2016`)-1,r16 =(`2016`/`2015`)-1,r15 =(`2015`/`2014`)-1,r14 =(`2014`/`2013`)-1,r13 =(`2013`/`2012`)-1,r12 =(`2012`/`2011`)-1,r11 =(`2011`/`2010`)-1) %>% 
+  rename(region=Megawatts) %>% gather(period,value,-region) %>% mutate(variable="Capacity|Wind")
+wi <- wi %>% filter(region %in% countries,!is.na(region))
+wi %>% filter(period=="add19") %>% arrange(desc(value))
+wi %>% filter(period=="add18") %>% arrange(desc(value))
+
+epv <- epv %>% select(-'2019...57',-'2008-18',-'2019...59') %>% rename('2019'=`2019...56`) %>% 
+  mutate(add19 =`2019`-`2018`,add18 =`2018`-`2017`,add17 =`2017`-`2016`,add16 =`2016`-`2015`,add15 =`2015`-`2014`,add14 =`2014`-`2013`,add13 =`2013`-`2012`,add12 =`2012`-`2011`,add11 =`2011`-`2010`,
+         r19 =(`2019`/`2018`)-1,r18 =(`2018`/`2017`)-1,r17 =(`2017`/`2016`)-1,r16 =(`2016`/`2015`)-1,r15 =(`2015`/`2014`)-1,r14 =(`2014`/`2013`)-1,r13 =(`2013`/`2012`)-1,r12 =(`2012`/`2011`)-1,r11 =(`2011`/`2010`)-1) %>% 
+  rename(region='Terawatt-hours') %>% gather(period,value,-region) %>% mutate(variable="Generation|PV")
+epv[!is.na(epv$region) & epv$region=="European Union #",]$region <- "European Union"
+epv <- epv %>% filter(region %in% countries,!is.na(region))
+epv %>% filter(period=="add19") %>% arrange(desc(value))
+
+ewi <- ewi %>% select(-'2019...57',-'2008-18',-'2019...59') %>% rename('2019'=`2019...56`) %>% 
+  mutate(add19 =`2019`-`2018`,add18 =`2018`-`2017`,add17 =`2017`-`2016`,add16 =`2016`-`2015`,add15 =`2015`-`2014`,add14 =`2014`-`2013`,add13 =`2013`-`2012`,add12 =`2012`-`2011`,add11 =`2011`-`2010`,
+         r19 =(`2019`/`2018`)-1,r18 =(`2018`/`2017`)-1,r17 =(`2017`/`2016`)-1,r16 =(`2016`/`2015`)-1,r15 =(`2015`/`2014`)-1,r14 =(`2014`/`2013`)-1,r13 =(`2013`/`2012`)-1,r12 =(`2012`/`2011`)-1,r11 =(`2011`/`2010`)-1) %>% 
+  rename(region='Terawatt-hours') %>% gather(period,value,-region) %>% mutate(variable="Generation|Wind")
+ewi[!is.na(ewi$region) & ewi$region=="European Union #",]$region <- "European Union"
+ewi <- ewi %>% filter(region %in% countries,!is.na(region))
+ewi %>% filter(period=="add19") %>% arrange(desc(value))
+
+enu <- enu %>% select(-'2019...57',-'2008-18',-'2019...59') %>% rename('2019'=`2019...56`) %>% 
+  mutate(add19 =`2019`-`2018`,add18 =`2018`-`2017`,add17 =`2017`-`2016`,add16 =`2016`-`2015`,add15 =`2015`-`2014`,add14 =`2014`-`2013`,add13 =`2013`-`2012`,add12 =`2012`-`2011`,add11 =`2011`-`2010`,
+         r19 =(`2019`/`2018`)-1,r18 =(`2018`/`2017`)-1,r17 =(`2017`/`2016`)-1,r16 =(`2016`/`2015`)-1,r15 =(`2015`/`2014`)-1,r14 =(`2014`/`2013`)-1,r13 =(`2013`/`2012`)-1,r12 =(`2012`/`2011`)-1,r11 =(`2011`/`2010`)-1) %>% 
+  rename(region='Terawatt-hours') %>% gather(period,value,-region) %>% mutate(variable="Generation|Nuclear")
+enu[!is.na(enu$region) & enu$region=="European Union #",]$region <- "European Union"
+enu <- enu %>% filter(region %in% countries,!is.na(region))
+enu %>% filter(period=="add19") %>% arrange(desc(value))
+
+ehy <- ehy %>% select(-'2019...57',-'2008-18',-'2019...59') %>% rename('2019'=`2019...56`) %>% 
+  mutate(add19 =`2019`-`2018`,add18 =`2018`-`2017`,add17 =`2017`-`2016`,add16 =`2016`-`2015`,add15 =`2015`-`2014`,add14 =`2014`-`2013`,add13 =`2013`-`2012`,add12 =`2012`-`2011`,add11 =`2011`-`2010`,
+         r19 =(`2019`/`2018`)-1,r18 =(`2018`/`2017`)-1,r17 =(`2017`/`2016`)-1,r16 =(`2016`/`2015`)-1,r15 =(`2015`/`2014`)-1,r14 =(`2014`/`2013`)-1,r13 =(`2013`/`2012`)-1,r12 =(`2012`/`2011`)-1,r11 =(`2011`/`2010`)-1) %>% 
+  rename(region='Terawatt-hours') %>% gather(period,value,-region) %>% mutate(variable="Generation|Hydro")
+ehy[!is.na(ehy$region) & ehy$region=="European Union #",]$region <- "European Union"
+ehy <- ehy %>% filter(region %in% countries,!is.na(region))
+ehy %>% filter(period=="add19") %>% arrange(desc(value))
+
+gbo <- gbo %>% select(-'2019...57',-'2008-18',-'2019...59') %>% rename('2019'=`2019...56`) %>% 
+  mutate(add19 =`2019`-`2018`,add18 =`2018`-`2017`,add17 =`2017`-`2016`,add16 =`2016`-`2015`,add15 =`2015`-`2014`,add14 =`2014`-`2013`,add13 =`2013`-`2012`,add12 =`2012`-`2011`,add11 =`2011`-`2010`,
+         r19 =(`2019`/`2018`)-1,r18 =(`2018`/`2017`)-1,r17 =(`2017`/`2016`)-1,r16 =(`2016`/`2015`)-1,r15 =(`2015`/`2014`)-1,r14 =(`2014`/`2013`)-1,r13 =(`2013`/`2012`)-1,r12 =(`2012`/`2011`)-1,r11 =(`2011`/`2010`)-1) %>% 
+  rename(region='Terawatt-hours') %>% gather(period,value,-region) %>% mutate(variable="Generation|AllOtherLowC")
+gbo[!is.na(gbo$region) & gbo$region=="European Union #",]$region <- "European Union"
+gbo <- gbo %>% filter(region %in% countries,!is.na(region))
+gbo %>% filter(period=="add19") %>% arrange(desc(value))
+
+
+bp <- rbind(eg,pv,wi,ewi,epv,enu,ehy,gbo)
+
+#add total low carbon generation
+lowc <- bp %>% spread(variable,value) %>% 
+  mutate(`Generation|LowC`=`Generation|Hydro`+`Generation|Nuclear`+`Generation|Wind`+`Generation|PV`+`Generation|AllOtherLowC`)%>% 
+  gather(variable,value,-period,-region) %>% filter(variable=="Generation|LowC")
+
+bp <- rbind(bp,lowc)
+
+fosl <- bp %>% spread(variable,value) %>% 
+  mutate(`Generation_Fossil`=`SE|Electricity`-`Generation|LowC`)%>% 
+  gather(variable,value,-period,-region) %>% filter(variable=="Generation_Fossil")
+
+bp <- rbind(bp,fosl)