R/readGCPT.R
In pik-piam/mrremind: MadRat REMIND Input Data Package
Defines functions
readGCPT
Documented in
readGCPT
#' Data from the Global Coal Plant Tracker January 2021 release by Global Energy Monitor (formerly EndCoal/CoalSwarm)
#' @description Historical data of operating, under-construction, planned and announced Coal Plants by country (in MW)
#' from the Global Energy Monitor's Global Coal Plant Tracker, and extrapolations for 2025 capacity scenarios
#' @param subtype Options are status, historical, future, lifespans, comp_rates and emissions
#' @author Stephen Bi
#' @importFrom readxl read_excel
#' @importFrom dplyr %>% filter select mutate summarize group_by left_join across everything starts_with
#' @importFrom magclass replace_non_finite
#' @importFrom quitte removeColNa
#' @aliases readEndCoal
readGCPT <- function(subtype) {
map <- toolGetMapping(getConfig("regionmapping"), type="regional", where = "mappingfolder")
year <- NULL
if (!(subtype %in% c("early_retire","historical","status","future","lifespans","emissions","comp_rates","reg_comp_rates","glo_comp_rates","meanAge","ppca_emi","G20_FinEx",
"PEGASOS","PEGASOS_pubfinex","PEGASOS_prefinex","REM_PEGASOS_pubfinex","REM_PEGASOS_pubfinex_REsub"))) {
stop("Invalid subtype!")
}
# Data files to be read in
status_changes_encoding <- 'UTF-8'
if (is.null(year)) {
summary_data <- "GCPT_data_Jan2023.xlsx"
status_changes <-
"Jan 2023 GCPT Status Changes - 2014 - 2022 (final).csv"
status_changes_encoding <- 'ANSI_X3.4-1986'
plant_data <- "Global-Coal-Plant-Tracker-January-2023.xlsx"
lastCol <- "X"
sep = ";"
latest_col <- 3
} else if (grepl('2021', year)) {
summary_data <- "GCPT_data_Jan2021.xlsx"
status_changes <- "GCPT Status Changes_1Feb2021.csv"
plant_data <- "January 2021 Global Coal Plant Tracker.xlsx"
lastCol <- "V"
sep = ","
latest_col <- 3
} else if (grepl('2022', year)) {
summary_data <- "GCPT_data_Jan2022.xlsx"
status_changes <-
"Jan 2022 GCPT Status Changes - 2014 - 2021 (b).csv"
status_changes_encoding <- 'ANSI_X3.4-1986'
plant_data <- "Global-Coal-Plant-Tracker-Jan-2022.xlsx"
lastCol <- "W"
sep = ";"
latest_col <- 3
} else if (grepl('2020', year)) {
summary_data <- "GCPT_data_Jul2020.xlsx"
status_changes <- "GCPT Status Changes H1 2015 to H1 2020.xlsx"
plant_data <- "July 2020 Global Coal Plant Tracker.xlsx"
lastCol <- "V"
sep = ","
plant_status <-
read_excel("GCPT Status Changes H1 2015 to H1 2020.xlsx", sheet = 2)
plant_status <-
plant_status %>% select(Country, MW, Jul20, Jan20, Jan19, Jan18, Jan17, Jan16, Jan15)
colnames(plant_status)[3:ncol(plant_status)] <- c(2020:2014)
latest_col <- 3
}
plant_status <- read.csv(status_changes, sep = sep,
stringsAsFactors = FALSE)
plant_status <- plant_status %>%
select(Country, MW, starts_with("H2")) %>%
# mutate(across(where(is.character), ~iconv(., from = status_changes_encoding, to = "UTF-8"))) %>%
mutate(Country = ifelse(grepl("rkiye", Country), "Turkey", Country),
Country = ifelse(grepl("Ivoire", Country), "Cote d'Ivoire", Country))
colnames(plant_status)[3:ncol(plant_status)] <- as.numeric(gsub("H2.","",colnames(plant_status)[3:ncol(plant_status)]))
present_year <- as.numeric(substr(summary_data,nchar(summary_data)-8,nchar(summary_data)-5))
`Announced + Pre-permit + Permitted` <- NULL
. <- NULL
H2.2014 <- NULL
H2.2015 <- NULL
H2.2016 <- NULL
H2.2017 <- NULL
H2.2018 <- NULL
H2.2019 <- NULL
H2.2020 <- NULL
H2.2021 <- NULL
Jan15 <- NULL
Jan16 <- NULL
Jan17 <- NULL
Jan18 <- NULL
Jan19 <- NULL
Jan20 <- NULL
Jul20 <- NULL
Country <- NULL
CountryCode <- NULL
MW <- NULL
`2015` <- NULL
`2016` <- NULL
`2017` <- NULL
`2018` <- NULL
`2019` <- NULL
`2020` <- NULL
`Plant Age` <- NULL
`Capacity (MW)` <- NULL
Status <- NULL
RETIRED <- NULL
`Planned Retire` <- NULL
Avg_Ret_Age <- NULL
`Heat rate` <- NULL
`Emission factor` <- NULL
`Combustion technology` <- NULL
Year <- NULL
Period <- NULL
Cap_Factor <- NULL
green_5yr_emi <- NULL
bau_5yr_emi <- NULL
brown_5yr_emi <- NULL
norm_5yr_emi <- NULL
BAU_emi <- NULL
All_total_emi <- NULL
Value <- NULL
BAU_total_emi <- NULL
green_total_emi <- NULL
brown_total_emi <- NULL
norm_total_emi <- NULL
brown_emi <- NULL
green_emi <- NULL
norm_emi <- NULL
# Capacity by country as of January 2022, with select manual updates according to the Global Coal Finance Tracker and news media
cap_sum <- read_excel(summary_data,sheet="Summary",range="A4:K112") %>%
filter(!grepl("Total",Country) & !grepl("World",Country))
# cap_sum <- ifelse(grepl("2020",subtype) | grepl("2021",subtype),
# read_excel(summary_data,sheet="Summary",range="A4:K112"),
# read_excel(summary_data,sheet="Summary"))
cap_sum <- cap_sum %>% select(-`Announced + Pre-permit + Permitted`)
cap_sum <- as.magpie(cap_sum,spatial=1)
cap_sum <- toolCountryFill(setItems(dim = 1, x = cap_sum,
value = toolCountry2isocode(mapping = c("DR Congo" = "COD"),
country = getItems(dim = 1, x = cap_sum))),
fill=0,
no_remove_warning = "KOS",
verbosity=2)
cap_sum_oper <- cap_sum[,,"Operating"]
###################################################
### Historical capacity additions & retirements ###
###################################################
additions <- read_excel(summary_data,sheet="Additions",range = c(paste0("A4:",lastCol,"112"))) %>%
filter(!grepl("Total",Country) & !grepl("World",Country))
# colnames(additions)[length(colnames(additions))]="2020"
retirements <- read_excel(summary_data,sheet="Retirements",range = c(paste0("A4:",lastCol,"112"))) %>%
filter(!grepl("Total",Country) & !grepl("World",Country))
add_startyr <- as.numeric(colnames(additions)[2])
add_endyr <- as.numeric(colnames(additions)[length(additions)])
status_startyr <- as.numeric(colnames(plant_status)[ncol(plant_status)])
status_endyr <- as.numeric(colnames(plant_status)[3])
# Filter for all plants that were mothballed during this period
mothballed <- plant_status %>%
filter(rowSums(across(everything(),~grepl("Moth",.)))>0) %>%
filter(rowSums(across(everything(),~grepl("Oper",.)))>0)
# Calculate capacity that was mothballed or restarted each year since 2014 (to be fed into back-calculation of annual capacity below)
cap_moth <- new.magpie(getItems(dim = 1, x = cap_sum),years=as.numeric(colnames(additions)[2:length(additions)]),names=c("Operating","pre_status"),fill=0)
for (j in 4:ncol(mothballed)) {
# Mothballed plants in 2020 which were operating earlier
oper_moth <- mothballed %>%
filter(mothballed[,j]=="Operating" & mothballed[,3]=="Mothballed") %>%
group_by(Country) %>%
summarize(sum=sum(MW))
oper_moth$Country <- toolCountry2isocode(mapping = c("DR Congo" = "COD"), country = oper_moth$Country)
oper_moth <- suppressWarnings(
toolCountryFill(as.magpie(oper_moth,spatial=1,temporal=as.numeric(colnames(mothballed)[j]),datacol=2),verbosity=2,fill=0,no_remove_warning = "KOS")
)
# Retired plants in 2020 which were mothballed earlier
moth_ret <- mothballed %>%
filter(mothballed[,j]=="Mothballed" & mothballed[,3]=="Retired") %>%
group_by(Country) %>%
summarize(sum=sum(MW))
moth_ret$Country <- toolCountry2isocode(mapping = c("DR Congo" = "COD"), country = moth_ret$Country)
moth_ret <- suppressWarnings(
toolCountryFill(as.magpie(moth_ret,spatial=1,temporal=as.numeric(colnames(mothballed)[j]),datacol=2),verbosity=2,fill=0,no_remove_warning = "KOS")
)
# Operating plants in 2020 which were mothballed earlier
moth_oper <- mothballed %>%
filter(mothballed[,j]=="Mothballed" & mothballed[,3]=="Operating") %>%
group_by(Country) %>%
summarize(sum=sum(MW))
moth_oper$Country <- toolCountry2isocode(mapping = c("DR Congo" = "COD"), country = moth_oper$Country)
moth_oper <- suppressWarnings(
toolCountryFill(as.magpie(moth_oper,spatial=1,temporal=as.numeric(colnames(mothballed)[j]),datacol=2),verbosity=2,fill=0,no_remove_warning = "KOS")
)
constr_moth <- mothballed %>%
filter(mothballed[,3]=="Mothballed" & mothballed[,j]!="Mothballed" & mothballed[,j]!="Operating") %>%
group_by(Country) %>%
summarize(sum=sum(MW))
constr_moth$Country <- toolCountry2isocode(mapping = c("DR Congo" = "COD"), country = constr_moth$Country)
constr_moth <- suppressWarnings(
toolCountryFill(as.magpie(constr_moth,spatial=1,temporal=as.numeric(colnames(mothballed)[j]),datacol=2),verbosity=2,fill=0,no_remove_warning = "KOS")
)
# Annual flux of mothballing/restarting
cap_moth[,as.numeric(colnames(mothballed)[j]),"Operating"] <- oper_moth + constr_moth - moth_oper - moth_ret
}
# Capacity with mothballed status in the first and last year shall be added as operating capacity in years prior to status tracking
moth_moth <- mothballed %>%
filter(mothballed[,3]=="Mothballed" & mothballed[,j]=="Mothballed") %>%
group_by(Country) %>%
summarize(sum=sum(MW))
moth_moth$Country <- toolCountry2isocode(mapping = c("DR Congo" = "COD"), country = moth_moth$Country)
moth_moth <- suppressWarnings(
toolCountryFill(as.magpie(moth_moth,spatial=1,temporal=as.numeric(colnames(mothballed)[j]),datacol=2),verbosity=2,fill=0,no_remove_warning = "KOS")
)
cap_moth[,add_startyr:(as.numeric(colnames(mothballed)[j])-1),"pre_status"] <- oper_moth + constr_moth + moth_moth
# Back-calculate annual capacity by adding retired and mothballed plants and subtracting added plants each year
cap <- new.magpie(getItems(dim = 1, x = cap_sum),years=as.numeric(colnames(additions)[2:length(additions)]),fill = 0)
# j <- ncol(mothballed)
for (i in add_startyr:(add_endyr-1)) {
cap_add <- additions %>% select(Country,as.character(i+1):as.character(add_endyr)) %>%
mutate(Operating=rowSums(.[-1])) %>%
mutate(Country=toolCountry2isocode(mapping = c("DR Congo" = "COD"), country = Country))
cap_add <- as.magpie(cap_add[,c("Country","Operating")])
cap_add <- toolCountryFill(cap_add,verbosity=2,fill=0,no_remove_warning = "KOS")
getItems(cap_add,dim=2) <- i
cap_ret <- retirements %>% select(Country,as.character(i+1):as.character(add_endyr)) %>%
mutate(Operating=rowSums(.[-1])) %>%
mutate(Country=toolCountry2isocode(mapping = c("DR Congo" = "COD"), country = Country))
cap_ret <- as.magpie(cap_ret[,c("Country","Operating")])
cap_ret <- toolCountryFill(cap_ret,verbosity=2,fill=0,no_remove_warning = "KOS")
getItems(cap_ret,dim=2) <- i
cap[,i,] <- cap_sum[,,"Operating"] - cap_add[,,"Operating"] + cap_ret[,,"Operating"] + cap_moth[,i,"Operating"] + cap_moth[,i,"pre_status"]
}
cap[,add_endyr,] <- cap_sum_oper
if (grepl("historical",subtype)) {
return(cap/1000)
}else if (grepl("status",subtype)) {
return(cap_sum/1000)
}
#Plant-level data detailing capacities, commissioning and retirement dates, location and more.
retire <- suppressWarnings(read_excel(plant_data,sheet=2))
retire$`Capacity (MW)` <- as.numeric(retire$`Capacity (MW)`)
retire$`Planned Retire` <- suppressWarnings(as.numeric(retire$`Planned Retire`))
retire$Country <- toolCountry2isocode(mapping = c("DR Congo" = "COD"), country = retire$Country)
retire <- retire[-which(retire$Country=="KOS"),]
if (present_year!=2020) {
retire <- retire %>% mutate(`Plant Age` =
ifelse(is.na(Year),
NA,
ifelse(grepl("retire",Status,ignore.case=T),
ifelse(is.na(RETIRED),
NA,
RETIRED - Year),
present_year - Year)))
}
retAge <- retire %>% select(Country,`Plant Age`,`Capacity (MW)`,Status) %>%
filter(Status %in% c("Retired","retired")) %>% filter(!is.na(`Plant Age`)) %>% filter(`Plant Age` > 0) %>% filter(!is.na(`Capacity (MW)`))
avgRetAge <- retAge %>% group_by(Country) %>% summarise(Avg_Ret_Age=weighted.mean(`Plant Age`,`Capacity (MW)`))
retCap <- retAge %>% group_by(Country) %>% summarise(Retired_Cap=sum(`Capacity (MW)`))
avgRetAge <- avgRetAge[do.call(order,avgRetAge),]
retCap <- retCap[do.call(order,retCap),]
mavgRetAge <- as.magpie(avgRetAge,spatial=1)
mavgRetAge <- toolCountryFill(mavgRetAge,fill=0,no_remove_warning = "KOS",verbosity=2)
retCap <- as.magpie(retCap,spatial=1)
retCap <- toolCountryFill(retCap,fill=0,no_remove_warning = "KOS",verbosity=2)
regAvgRetAge <- toolAggregate(mavgRetAge,map,weight=retCap)
regAvgRetAge[which(regAvgRetAge==0)] <- mean(regAvgRetAge)
mavgRetAge[getItems(dim = 1, x = mavgRetAge)[mavgRetAge == 0],,] <- as.vector(regAvgRetAge[map$RegionCode[which(map$CountryCode %in% getItems(dim = 1, x = mavgRetAge)[mavgRetAge == 0])],,])
if (grepl("lifespans",subtype)) {
return(mavgRetAge)
}else if (grepl("meanAge",subtype)) {
meanAge <- retire %>% select(Country,`Plant Age`,`Capacity (MW)`,Status) %>%
filter(Status %in% c("Operating","operating") & !is.na(`Capacity (MW)`) & !is.na(`Plant Age`))
meanAge_c <- meanAge %>% group_by(Country) %>% summarise(meanAge=weighted.mean(`Plant Age`,`Capacity (MW)`))
meanAge_c <- toolCountryFill(as.magpie(meanAge_c,spatial=1),fill=0, verbosity = 2)
capWeight <- meanAge %>% group_by(Country) %>% summarise(Capacity=sum(`Capacity (MW)`))
capWeight <- toolCountryFill(as.magpie(capWeight,spatial=1),fill=0, verbosity = 2)
return(toolAggregate(meanAge_c,map,capWeight))
}
# Return national average lifespans to calculate early retirement adjustment factors
if (grepl("early_retire",subtype)) {
retired <- retire %>%
select(Country,`Plant Age`,`Capacity (MW)`,RETIRED) %>%
left_join(avgRetAge,by="Country")
#Sum up capacity retired in each country in each year 2000-2020
early_ret <- retired %>%
filter(!is.na(RETIRED) & !is.na(`Plant Age`)) %>%
filter(`Plant Age` < Avg_Ret_Age) %>%
group_by(Country,.add=TRUE) %>%
group_by(RETIRED,.add=TRUE) %>%
summarise(Retired_cap=sum(`Capacity (MW)`))
early_ret <- as.magpie(early_ret,spatial=1,temporal=2)
early_ret <- toolCountryFill(early_ret,fill=0,no_remove_warning = "KOS",verbosity = 0)
ret_rate <- new.magpie(getItems(dim = 1, x = mavgRetAge),seq(2000,2015,5),names="Early_Retirement",fill=0)
for (t in seq(2000,2015,5)) {
ret_rate[,t,] <- dimSums(early_ret[,t:(t+5),],dim=2)/cap[,t,]
}
ret_rate <- replace_non_finite(ret_rate)
max_ret <- new.magpie(getItems(dim = 1, x = ret_rate),NULL,names="Max_Early_Retirement",fill=0)
for (reg in getItems(dim = 1, x = ret_rate)) {
max_ret[reg,,] <- max(ret_rate[reg,,])
}
return(max_ret)
}
#Use the national lifespans to derive scenarios of 2025 coal capacity
avgRetAge <- data.frame(Country=getItems(dim = 1, x = mavgRetAge),Avg_Ret_Age=array(mavgRetAge))
retire <- left_join(retire,avgRetAge,by="Country")
#Neutral COVID recovery scenario: Countries maintain their historical average lifespans
retireBAU <- retire %>% select(Country,`Planned Retire`,`Plant Age`,`Capacity (MW)`,Status,Avg_Ret_Age) %>%
filter(Status %in% c("Operating","operating")) %>%
filter(`Planned Retire`<=2028 | `Plant Age` >= (Avg_Ret_Age-5)) %>%
filter(!is.na(`Capacity (MW)`)) %>%
group_by(Country) %>% summarise(Retiring_Cap=sum(`Capacity (MW)`))
retireBAU$Retiring_Cap[which(is.na(retireBAU$Retiring_Cap))] <- 0
retireBAU <- as.magpie(retireBAU,spatial=1)
retireBAU <- toolCountryFill(retireBAU,fill=0,no_remove_warning = "KOS",verbosity=2)
#Brown COVID recovery scenario: Countries extend their average lifespans by 5 years
retireBrown <- retire %>% select(Country,`Planned Retire`,`Plant Age`,`Capacity (MW)`,Status,Avg_Ret_Age) %>%
filter(Status %in% c("Operating","operating")) %>%
filter(`Planned Retire`<=2028 | `Plant Age` >= (Avg_Ret_Age)) %>%
filter(!is.na(`Capacity (MW)`)) %>%
group_by(Country) %>% summarise(Retiring_Cap=sum(`Capacity (MW)`))
retireBrown$Retiring_Cap[which(is.na(retireBrown$Retiring_Cap))] <- 0
retireBrown <- as.magpie(retireBrown,spatial=1)
retireBrown <- toolCountryFill(retireBrown,fill=0,no_remove_warning = "KOS",verbosity=2)
#Green COVID recovery scenario: Countries reduce historical average lifespans by 5 years
retireGreen <- retire %>% select(Country,`Planned Retire`,`Plant Age`,`Capacity (MW)`,Status,Avg_Ret_Age) %>%
filter(Status %in% c("Operating","operating")) %>%
filter(`Planned Retire`<=2028 | `Plant Age` >= (Avg_Ret_Age-10)) %>%
filter(!is.na(`Capacity (MW)`)) %>%
group_by(Country) %>% summarise(Retiring_Cap=sum(`Capacity (MW)`))
retireGreen$Retiring_Cap[which(is.na(retireGreen$Retiring_Cap))] <- 0
retireGreen <- as.magpie(retireGreen,spatial=1)
retireGreen <- toolCountryFill(retireGreen,fill=0,no_remove_warning = "KOS",verbosity=2)
#Literature norm scenario: Popular choice in literature has been to use a globally uniform 40-yr lifespan
retireNorm <- retire %>% select(Country,`Planned Retire`,`Plant Age`,`Capacity (MW)`,Status,Avg_Ret_Age) %>%
filter(Status %in% c("Operating","operating")) %>%
filter(`Plant Age` >= 35) %>%
filter(!is.na(`Capacity (MW)`)) %>%
group_by(Country) %>% summarise(Retiring_Cap=sum(`Capacity (MW)`))
retireNorm$Retiring_Cap[which(is.na(retireNorm$Retiring_Cap))] <- 0
retireNorm <- as.magpie(retireNorm,spatial=1)
retireNorm <- toolCountryFill(retireNorm,fill=0,no_remove_warning = "KOS",verbosity=2)
#Relevant regional mappings and country classifications
#Current members of PPCA
PPCAmap <- toolGetMapping("regionmappingPPCA.csv",type = "regional")
#Current OECD members
OECDmap <- toolGetMapping("regionmappingOECD.csv",type = "regional")
ppca <- PPCAmap$CountryCode[which(PPCAmap$RegionCode=="PPCA")]
oecd <- OECDmap$CountryCode[which(OECDmap$RegionCode=="OECD")]
EU27 <- map$CountryCode[which(map$RegionCode=="EUR")]
nonoecd <- OECDmap$CountryCode[which(OECDmap$RegionCode=="NON")]
#The PPCA stipulates that OECD and EU members must phase out coal power by 2030
oecdPPCA <- ppca[which(ppca %in% oecd | ppca %in% EU27)]
#While non-OECD members must phase out coal power by 2050
nonOECDppca <- ppca[which(!(ppca %in% oecdPPCA))]
#There are several countries which have explicit plans to abandon coal power in their COVID recovery packages
greenNations <- c(EU27[-which(EU27=="POL")][-which(EU27 %in% ppca)],"EGY","AUS","KOR","CHL")
if (subtype!="PEGASOS") greenNations <- c(greenNations,"BGD","PAK","PHL","VNM")
###########################
### ANNUAL PLANT STATUS ###
###########################
# Remove any plants with unknown (NA) capacity from plant status data frame
plant_status <- plant_status %>% filter(!is.na(MW))
tran <- data.frame(Country=unique(plant_status$Country),shelved=0,she2she=0,she2can=0,she2oper=0,she2ann=0,she2pre=0,pre2she=0,pre2con=0,
she2perm=0,she2con=0,newcon=0,con2she=0,con2she2oper=0,con2she2con=0,con2she2can=0,con2can=0,perm2she=0,perm2con=0,
con2oper=0,con2she2she=0,plan2she=0,ann=0,ann2oper=0,ann2can=0,ann2she=0,ann2con=0,pre=0,pre2oper=0,pre2can=0,perm=0,
perm2oper=0,perm2can=0,moth=0,moth2ret=0,moth2oper=0)
for (i in 1:nrow(plant_status)) {
j <- ncol(plant_status)
if (plant_status[i,j]=="XXX") {
plant_status[i,which(plant_status[i,]=="XXX")] <- "Announced"
}
while (j > latest_col) {
if (grepl("She",plant_status[i,j])) {
if (j==ncol(plant_status) || !any(grepl("She",plant_status[i,j:ncol(plant_status)]))) {
tran$shelved[which(tran$Country==plant_status$Country[i])] <- tran$shelved[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
if (grepl("Can",plant_status[i,latest_col])) {
tran$she2can[which(tran$Country==plant_status$Country[i])]<- tran$she2can[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (grepl("She",plant_status[i,latest_col])) {
tran$she2she[which(tran$Country==plant_status$Country[i])]<- tran$she2she[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (grepl("Oper",plant_status[i,latest_col])) {
tran$she2oper[which(tran$Country==plant_status$Country[i])]<- tran$she2oper[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (grepl("Ann",plant_status[i,latest_col])) {
tran$she2ann[which(tran$Country==plant_status$Country[i])]<- tran$she2ann[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (grepl("Pre",plant_status[i,latest_col])) {
tran$she2pre[which(tran$Country==plant_status$Country[i])]<- tran$she2pre[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (grepl("Perm",plant_status[i,latest_col])) {
tran$she2perm[which(tran$Country==plant_status$Country[i])]<- tran$she2perm[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (grepl("Con",plant_status[i,latest_col])) {
tran$she2con[which(tran$Country==plant_status$Country[i])]<- tran$she2con[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}
}
}else if (grepl("Con",plant_status[i,j])) {
if (j==ncol(plant_status) || !any(grepl("Con",plant_status[i,(j+1):ncol(plant_status)]))) {
tran$newcon[which(tran$Country==plant_status$Country[i])] <- tran$newcon[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
if (grepl("Can",plant_status[i,latest_col])) {
tran$con2can[which(tran$Country==plant_status$Country[i])] <- tran$con2can[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (grepl("She",plant_status[i,latest_col])) {
tran$con2she2she[which(tran$Country==plant_status$Country[i])]<- tran$con2she2she[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (grepl("Oper",plant_status[i,latest_col])) {
tran$con2oper[which(tran$Country==plant_status$Country[i])]<- tran$con2oper[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (grepl("Con",plant_status[i,latest_col])) {
tran$con2con[which(tran$Country==plant_status$Country[i])]<- tran$con2con[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}
}
}else if (grepl("Ann",plant_status[i,j])) {
if (j==ncol(plant_status) || !any(grepl("Ann",plant_status[i,(j+1):ncol(plant_status)]))) {
tran$ann[which(tran$Country==plant_status$Country[i])] <- tran$ann[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
if (grepl("Can",plant_status[i,latest_col])) {
tran$ann2can[which(tran$Country==plant_status$Country[i])] <- tran$ann2can[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (grepl("She",plant_status[i,latest_col])) {
tran$ann2she[which(tran$Country==plant_status$Country[i])]<- tran$ann2she[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (grepl("Oper",plant_status[i,latest_col])) {
tran$ann2oper[which(tran$Country==plant_status$Country[i])]<- tran$ann2oper[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (grepl("Con",plant_status[i,latest_col])) {
tran$ann2con[which(tran$Country==plant_status$Country[i])]<- tran$ann2con[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}
}
}else if (grepl("Pre",plant_status[i,j])) {
if (j==ncol(plant_status) || !any(grepl("Pre",plant_status[i,(j+1):ncol(plant_status)]))) {
tran$pre[which(tran$Country==plant_status$Country[i])] <- tran$pre[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
if (grepl("Can",plant_status[i,latest_col])) {
tran$pre2can[which(tran$Country==plant_status$Country[i])] <- tran$pre2can[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (grepl("She",plant_status[i,latest_col])) {
tran$pre2she[which(tran$Country==plant_status$Country[i])]<- tran$pre2she[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (grepl("Oper",plant_status[i,latest_col])) {
tran$pre2oper[which(tran$Country==plant_status$Country[i])]<- tran$pre2oper[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (grepl("Con",plant_status[i,latest_col])) {
tran$pre2con[which(tran$Country==plant_status$Country[i])]<- tran$pre2con[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}
}
}else if (grepl("Perm",plant_status[i,j])) {
if (j==ncol(plant_status) || !any(grepl("Perm",plant_status[i,(j+1):ncol(plant_status)]))) {
tran$perm[which(tran$Country==plant_status$Country[i])] <- tran$perm[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
if (grepl("Can",plant_status[i,latest_col])) {
tran$perm2can[which(tran$Country==plant_status$Country[i])] <- tran$perm2can[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (grepl("She",plant_status[i,latest_col])) {
tran$perm2she[which(tran$Country==plant_status$Country[i])]<- tran$perm2she[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (grepl("Oper",plant_status[i,latest_col])) {
tran$perm2oper[which(tran$Country==plant_status$Country[i])]<- tran$perm2oper[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (grepl("Con",plant_status[i,latest_col])) {
tran$perm2con[which(tran$Country==plant_status$Country[i])]<- tran$perm2con[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}
}
}else if (grepl("Moth",plant_status[i,j])) {
if (j==ncol(plant_status) || !any(grepl("Moth",plant_status[i,(j+1):ncol(plant_status)]))) {
tran$moth[which(tran$Country==plant_status$Country[i])] <- tran$moth[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
if (grepl("Ret",plant_status[i,latest_col])) {
tran$moth2ret[which(tran$Country==plant_status$Country[i])] <- tran$moth2ret[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (any(grepl("Oper",plant_status[i,latest_col:(j-1)]))) {
tran$moth2oper[which(tran$Country==plant_status$Country[i])] <- tran$moth2oper[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}
}
}
j <- j-1
if (grepl("She",plant_status[i,j])) {
if (plant_status[i,j+1]=="Con") {
tran$con2she[which(tran$Country==plant_status$Country[i])]<- tran$con2she[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
if (grepl("Oper",plant_status[i,latest_col])) {
tran$con2she2oper[which(tran$Country==plant_status$Country[i])] <- tran$con2she2oper[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (grepl("Con",plant_status[i,latest_col])) {
tran$con2she2con[which(tran$Country==plant_status$Country[i])] <- tran$con2she2con[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (grepl("Can",plant_status[i,latest_col])) {
tran$con2she2can[which(tran$Country==plant_status$Country[i])] <- tran$con2she2can[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}
}else if (grepl("Ann",plant_status[i,j+1]) | grepl("Pre",plant_status[i,j+1]) | grepl("Perm",plant_status[i,j+1])) {
tran$plan2she[which(tran$Country==plant_status$Country[i])] <- tran$plan2she[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}
}
}
}
tran <- tran[-which(tran$Country=="Kosovo"),]
mtran <- as.magpie(tran,spatial=1)
getYears(mtran) <- "y2020"
mtran <- setItems(dim = 1, x = mtran,
value = toolCountry2isocode(mapping = c("DR Congo" = "COD"), country = getItems(dim = 1, x = mtran)))
mtran <- toolCountryFill(mtran,fill=0,verbosity=2)
# Combined cancellation + shelving rates of each project phase, to be used for completion rate calculation
can_she_rate <- new.magpie(getItems(dim = 1, x = mtran),getYears(mtran),getNames(mtran)[which(!grepl("2",getNames(mtran)) & getNames(mtran)!="moth")],fill=NA)
# Shelving rate of projects in each phase
she_rate <- new.magpie(getItems(dim = 1, x = mtran),getYears(mtran),getNames(can_she_rate),fill=NA)
# Cancellation rate of projects in the shelving phase
can_rate <- new.magpie(getItems(dim = 1, x = mtran),getYears(mtran),"shelved",fill=NA)
reg_can_rate <- new.magpie(getItems(dim = 1, x = regAvgRetAge),getYears(mtran),getNames(can_rate))
reg_can_she_rate <- new.magpie(getItems(dim = 1, x = regAvgRetAge),getYears(mtran),getNames(can_she_rate))
reg_she_rate <- new.magpie(getItems(dim = 1, x = regAvgRetAge),getYears(mtran),getNames(she_rate))
for (phase in getNames(can_she_rate)) {
nonzero_countries <- getItems(dim = 1, x = mtran)[which(mtran[,,phase]!=0)]
zero_countries <- getItems(dim = 1, x = mtran)[which(mtran[,,phase]==0)]
if (phase=="newcon") {
can_she_rate[,,phase] <- (mtran[,,"con2can"] + mtran[,,"con2she2she"])/mtran[,,"newcon"]
she_rate[,,phase] <- mtran[,,"con2she2she"]/mtran[,,"newcon"]
}else if (phase=="shelved") {
can_she_rate[,,phase] <- (mtran[,,"she2can"] + mtran[,,"she2she"])/mtran[,,phase]
she_rate[,,phase] <- mtran[,,"she2she"]/mtran[,,"shelved"]
can_rate[nonzero_countries,,phase] <- mtran[nonzero_countries,,"she2can"]/mtran[nonzero_countries,,"shelved"]
#Calculate global mean rates
glo_can_rate <- weighted.mean(can_rate[nonzero_countries,,phase],mtran[nonzero_countries,,phase])
#Calculate regional rates
tmp <- toolAggregate(can_rate[nonzero_countries,,phase],rel=map[which(map$CountryCode %in% nonzero_countries),],mtran[nonzero_countries,,phase])
#For regions which had zero projects in a certain phase, assign the global weighted mean
reg_can_rate <- suppressWarnings(mbind(tmp,new.magpie(getItems(dim = 1, x = reg_can_rate)[which(!(getItems(dim = 1, x = reg_can_rate)) %in% getItems(dim = 1, x = tmp))],getYears(can_rate),phase,glo_can_rate)))
#Assign regional rates to countries without projects in the present phase
can_rate[zero_countries,,phase] <- as.numeric(reg_can_rate[map$RegionCode[which(map$CountryCode %in% zero_countries)],,phase])
}else {
can_she_rate[,,phase] <- (mtran[,,paste0(phase,"2can")] + mtran[,,paste0(phase,"2she")])/mtran[,,phase]
she_rate[,,phase] <- mtran[,,paste0(phase,"2she")]/mtran[,,phase]
}
#Calculate global mean rates
glo_can_she_rate <- weighted.mean(can_she_rate[nonzero_countries,,phase],mtran[nonzero_countries,,phase])
glo_she_rate <- weighted.mean(she_rate[nonzero_countries,,phase],mtran[nonzero_countries,,phase])
#Calculate regional rates
tmp_can_she <- toolAggregate(can_she_rate[nonzero_countries,,phase],rel=map[which(map$CountryCode %in% nonzero_countries),],mtran[nonzero_countries,,phase])
tmp_she <- toolAggregate(she_rate[nonzero_countries,,phase],rel=map[which(map$CountryCode %in% nonzero_countries),],mtran[nonzero_countries,,phase])
#For regions which had zero projects in a certain phase, assign the global weighted mean
reg_can_she_rate[,,phase] <- suppressWarnings(mbind(tmp_can_she,
new.magpie(getItems(dim = 1, x = reg_can_she_rate)[which(!(getItems(dim = 1, x = reg_can_she_rate)) %in% getItems(dim = 1, x = tmp_can_she))],getYears(mtran),phase,glo_can_she_rate)))
reg_she_rate[,,phase] <- suppressWarnings(mbind(tmp_she,
new.magpie(getItems(dim = 1, x = reg_she_rate)[which(!(getItems(dim = 1, x = reg_she_rate)) %in% getItems(dim = 1, x = tmp_she))],getYears(mtran),phase,glo_she_rate)))
#Assign regional rates to countries without projects in the present phase
can_she_rate[zero_countries,,phase] <- as.numeric(reg_can_she_rate[map$RegionCode[which(map$CountryCode %in% zero_countries)],,phase])
she_rate[zero_countries,,phase] <- as.numeric(reg_she_rate[map$RegionCode[which(map$CountryCode %in% zero_countries)],,phase])
}
#
# #Regional average cancellation and shelving rates, weighted by each country's total projects from 2015-2020
# reg_can_she_rate <- toolAggregate(can_she_rate,rel=map,weight=dimSums(mtran[,,getNames(can_she_rate)],dim=3))
# reg_she_rate <- toolAggregate(she_rate,rel=map,weight=dimSums(mtran[,,getNames(she_rate)],dim=3))
# reg_can_rate <- toolAggregate(can_rate,rel=map,weight=dimSums(mtran[,,getNames(can_rate)],dim=3))
#
#Immaturity index formula to determine nascent coal consuming nations, where >50% of all coal plants are in the current pipeline
immaturity <- (cap_sum[,,"Announced"] + cap_sum[,,"Pre-permit"] + cap_sum[,,"Permitted"] + cap_sum[,,"Construction"] + cap_sum[,,"Shelved"]) /
(dimSums(cap_sum,dim=3))
#Nascent coal consumers AND countries without any projects in the past 6 years are assigned their regional cancellation/shelving rates
can_rate[which(immaturity>0.5)] <- reg_can_rate[map$RegionCode[which(map$CountryCode %in% getItems(dim = 1, x = immaturity)[which(immaturity>0.5)])],,]
can_rate[which(can_rate==0.5)] <- reg_can_rate[map$RegionCode[which(map$CountryCode %in% getItems(dim = 1, x = immaturity)[which(immaturity>0.5)])],,]
comp_rate <- new.magpie(getItems(dim = 1, x = can_she_rate),getYears(mtran),getNames(can_she_rate),fill=NA)
comp_rate_brown <- new.magpie(getItems(dim = 1, x = can_she_rate),getYears(mtran),getNames(can_she_rate),fill=NA)
reg_comp_rate <- new.magpie(getItems(dim = 1, x = reg_can_rate),getYears(mtran),getNames(can_she_rate),fill=NA)
reg_comp_rate_brown <- new.magpie(getItems(dim = 1, x = reg_can_rate),getYears(mtran),getNames(can_she_rate),fill=NA)
glo_comp_rate <- new.magpie("GLO",getYears(mtran),getNames(can_she_rate),fill=NA)
glo_comp_rate_brown <- new.magpie("GLO",getYears(mtran),getNames(can_she_rate),fill=NA)
for (status in getNames(can_she_rate)) {
nonzero_countries <- getItems(dim = 1, x = mtran)[which(mtran[,,status]!=0)]
zero_countries <- getItems(dim = 1, x = mtran)[which(mtran[,,status]==0)]
can_she_rate[,,status][which(immaturity>0.5)] <- reg_can_she_rate[map$RegionCode[which(map$CountryCode %in% getItems(dim = 1, x = immaturity)[which(immaturity>0.5)])],,status]
she_rate[,,status][which(immaturity>0.5)] <- reg_she_rate[map$RegionCode[which(map$CountryCode %in% getItems(dim = 1, x = immaturity)[which(immaturity>0.5)])],,status]
# Derive completion rates to be used in the 2025 capacity estimation
if (status=="shelved") {
comp_rate[,,status] <- 1 - can_rate[,,status]
comp_rate_brown[,,status] <- 1 - 0.5*can_rate[,,status]
}else {
comp_rate[,,status] <- 1 - can_she_rate[,,status]
comp_rate_brown[,,status] <- 1 - 0.5*can_she_rate[,,status]
}
glo_comp_rate[,,status] <- weighted.mean(comp_rate[nonzero_countries,,status],mtran[nonzero_countries,,status])
glo_comp_rate_brown[,,status] <- weighted.mean(comp_rate_brown[nonzero_countries,,status],mtran[nonzero_countries,,status])
if (length(nonzero_countries)) {
#Calculate regional rates
tmp <- toolAggregate(comp_rate[nonzero_countries,,status],filter(map,CountryCode %in% nonzero_countries),mtran[nonzero_countries,,status])
tmp_brown <- toolAggregate(comp_rate_brown[nonzero_countries,,status],filter(map,CountryCode %in% nonzero_countries),mtran[nonzero_countries,,status])
#For regions which had zero projects in a certain status, assign the global weighted mean
reg_comp_rate[,,status] <- suppressWarnings(mbind(tmp,new.magpie(getItems(dim = 1, x = reg_comp_rate)[which(!(getItems(dim = 1, x = reg_comp_rate)) %in% getItems(dim = 1, x = tmp))],getYears(comp_rate),status,glo_comp_rate)))
reg_comp_rate_brown[,,status] <- suppressWarnings(mbind(tmp_brown,new.magpie(getItems(dim = 1, x = reg_comp_rate_brown)[which(!(getItems(dim = 1, x = reg_comp_rate_brown)) %in% getItems(dim = 1, x = tmp_brown))],getYears(comp_rate_brown),status,glo_comp_rate_brown)))
}else {
reg_comp_rate[,,status] <- 0
reg_comp_rate_brown[,,status] <- 0
}
}
# Countries which have indicated an anti-coal recovery plan are assumed to halve their BAU completion rates in the brown and BAU recovery scenarios
# In the green recovery scenario, their completion rates will be 0 (except for under-construction plants which will be built at BAU rates)
# comp_rate_brown[c(nonOECDppca,greenNations),,] <- 0.5 * comp_rate_brown[c(nonOECDppca,greenNations),,]
# comp_rate[c(nonOECDppca,greenNations),,] <- 0.5 * comp_rate[c(nonOECDppca,greenNations),,]
# # Under-construction plants in anti-coal nations will all be completed in both scenarios
# comp_rate[c(nonOECDppca,greenNations),,"newcon"] <- 2 * comp_rate[c(nonOECDppca,greenNations),,"newcon"]
# comp_rate_brown[c(nonOECDppca,greenNations),,"newcon"] <- 2 * comp_rate_brown[c(nonOECDppca,greenNations),,"newcon"]
#
# #OECD PPCA members cannot complete any pipeline projects to meet their 2030 phase-out target
# comp_rate[oecdPPCA,,] <- 0
# comp_rate_brown[oecdPPCA,,] <- 0
#OECD PPCA members and green recovery nations are assumed to cancel all shelved projects and retire mothballed ones.
comp_rate[c(greenNations,oecdPPCA,nonOECDppca),,"shelved"] <- 0
comp_rate_brown[c(greenNations,oecdPPCA,nonOECDppca),,"shelved"] <- 0
cap_sum[c(oecdPPCA,greenNations,nonOECDppca),,"Mothballed"] <- 0
# browser()
# Return completion rate magpies
if (grepl("comp_rates",subtype)) {
getNames(comp_rate) <- c("Shelved","Construction","Announced","Pre-permit","Permitted")
getNames(comp_rate_brown) <- c("Shelved_brown","Construction_brown","Announced_brown","Pre-permit_brown","Permitted_brown")
getNames(glo_comp_rate) <- c("Shelved","Construction","Announced","Pre-permit","Permitted")
getNames(glo_comp_rate_brown) <- c("Shelved_brown","Construction_brown","Announced_brown","Pre-permit_brown","Permitted_brown")
getNames(reg_comp_rate) <- c("Shelved","Construction","Announced","Pre-permit","Permitted")
getNames(reg_comp_rate_brown) <- c("Shelved_brown","Construction_brown","Announced_brown","Pre-permit_brown","Permitted_brown")
getNames(she_rate) <- c("Shelved_shelving","Construction_shelving","Announced_shelving","Pre-permit_shelving","Permitted_shelving")
getNames(reg_she_rate) <- c("Shelved_shelving","Construction_shelving","Announced_shelving","Pre-permit_shelving","Permitted_shelving")
# getNames(glo_she_rate) <- c("Shelved_shelving","Construction_shelving","Announced_shelving","Pre-permit_shelving","Permitted_shelving")
if (grepl("reg",subtype)) {
reg_mtran <- toolAggregate(
setNames(mtran[,,c("shelved","newcon","ann","pre","perm")],
c("Shelved","Construction","Announced","Pre-permit","Permitted")),map,NULL)
reg_all_comp_rate <- toolAggregate(reg_comp_rate,weight=reg_mtran,dim=3,rel=data.frame(from=getNames(reg_mtran),to=rep("all",5)))
out <- suppressWarnings(mbind(reg_comp_rate,reg_all_comp_rate,reg_she_rate))
}else if (grepl("glo",subtype)) {
out <- suppressWarnings(mbind(glo_comp_rate,glo_comp_rate_brown))
# out <- suppressWarnings(mbind(glo_comp_rate,glo_comp_rate_brown,glo_she_rate)
}else out <- suppressWarnings(mbind(comp_rate,comp_rate_brown,she_rate))
return(out)
}
# *********************************Derive Coal capacity in 2025******************************
# browser
cap2025_brown <- cap_sum[,,"Operating"] +
cap_sum[,,"Mothballed"]*0.5 +
cap_sum[,,"Announced"] * comp_rate_brown[,,"ann"] +
cap_sum[,,"Pre-permit"] * comp_rate_brown[,,"pre"] +
cap_sum[,,"Permitted"] * comp_rate_brown[,,"perm"] +
cap_sum[,,"Construction"] * comp_rate_brown[,,"newcon"] +
comp_rate_brown[,,"shelved"] * (cap_sum[,,"Shelved"] +
cap_sum[,,"Announced"] * (she_rate[,,"ann"]) +
cap_sum[,,"Pre-permit"] * (she_rate[,,"pre"]) +
cap_sum[,,"Permitted"] * (she_rate[,,"perm"]) +
cap_sum[,,"Construction"] * (she_rate[,,"newcon"])) -
retireBrown[,,"Retiring_Cap"]
cap2025_BAU <- cap_sum[,,"Operating"] +
cap_sum[,,"Mothballed"]*0.5 +
cap_sum[,,"Announced"] * comp_rate[,,"ann"] +
cap_sum[,,"Pre-permit"] * comp_rate[,,"pre"] +
cap_sum[,,"Permitted"] * comp_rate[,,"perm"] +
cap_sum[,,"Construction"] * comp_rate[,,"newcon"] +
comp_rate[,,"shelved"] * (cap_sum[,,"Shelved"] +
cap_sum[,,"Announced"] * (she_rate[,,"ann"]) +
cap_sum[,,"Pre-permit"] * (she_rate[,,"pre"]) +
cap_sum[,,"Permitted"] * (she_rate[,,"perm"]) +
cap_sum[,,"Construction"] * (she_rate[,,"newcon"])) -
retireBAU[,,"Retiring_Cap"]
cap2025_green <- cap_sum[,,"Operating"] +
cap_sum[,,"Announced"] * comp_rate[,,"ann"]/2 +
cap_sum[,,"Pre-permit"] * comp_rate[,,"pre"]/2 +
cap_sum[,,"Permitted"] * comp_rate[,,"perm"]/2 +
0.5 * comp_rate[,,"newcon"] * cap_sum[,,"Construction"] +
0.5 * comp_rate[,,"shelved"] * cap_sum[,,"Construction"] * (she_rate[,,"newcon"]) -
retireBAU[,,"Retiring_Cap"]
cap2025_norm <- cap_sum[,,"Operating"] +
cap_sum[,,"Mothballed"]*0.5 +
cap_sum[,,"Announced"] +
cap_sum[,,"Pre-permit"] +
cap_sum[,,"Permitted"] +
cap_sum[,,"Construction"] +
cap_sum[,,"Shelved"] -
retireNorm[,,"Retiring_Cap"]
cap2025_brown[which(cap2025_brown<0)] <- 0
cap2025_green[which(cap2025_green<0)] <- 0
cap2025_norm[which(cap2025_norm<0)] <- 0
cap2025_BAU[which(cap2025_BAU<0)] <- 0
getNames(cap2025_brown) <- "Brown"
getNames(cap2025_green) <- "Green"
getNames(cap2025_norm) <- "Norm"
getNames(cap2025_BAU) <- "Neutral"
getYears(cap2025_brown) <- "y2025"
getYears(cap2025_green) <- "y2025"
getYears(cap2025_norm) <- "y2025"
getYears(cap2025_BAU) <- "y2025"
mcap2025 <- suppressWarnings(mbind(cap2025_brown/1000,cap2025_green/1000,cap2025_BAU/1000,cap2025_norm/1000))
# Return Capacity scenarios
if (grepl("future",subtype) | grepl("FinEx",subtype)) {
return(mcap2025)
}
###### PEGASOS Policy Brief Calcs ##########
if (grepl("PEGASOS",subtype)) {
#PEGASOS policy brief scenario: Popular choice in literature has been to use a globally uniform 35-yr lifespan
retirePEGA <- retire %>% select(Country,`Planned Retire`,`Plant Age`,`Capacity (MW)`,Status,Avg_Ret_Age,`Heat rate`,`Emission factor`) %>%
filter(Status %in% c("Operating","operating")) %>%
filter(`Plant Age` >= 25) %>%
filter(!is.na(`Capacity (MW)`)) %>%
group_by(Country) %>% summarise(Retiring_Cap=sum(`Capacity (MW)`))
retirePEGA$Retiring_Cap[which(is.na(retirePEGA$Retiring_Cap))] <- 0
retirePEGA <- as.magpie(retirePEGA,spatial=1)
retirePEGA <- toolCountryFill(retirePEGA,fill=0,no_remove_warning = "KOS",verbosity=2)
retireNorm <- retire %>% select(Country,`Planned Retire`,`Plant Age`,`Capacity (MW)`,Status,Avg_Ret_Age,`Heat rate`,`Emission factor`) %>%
filter(Status %in% c("Operating","operating")) %>%
filter(`Plant Age` >= 30) %>%
filter(!is.na(`Capacity (MW)`)) %>%
group_by(Country) %>% summarise(Retiring_Cap=sum(`Capacity (MW)`))
retireNorm$Retiring_Cap[which(is.na(retireNorm$Retiring_Cap))] <- 0
retireNorm <- as.magpie(retireNorm,spatial=1)
retireNorm <- toolCountryFill(retireNorm,fill=0,no_remove_warning = "KOS",verbosity=2)
retire1p5C <- retire %>% select(Country,`Planned Retire`,`Plant Age`,`Capacity (MW)`,Status,Avg_Ret_Age) %>%
filter(Status %in% c("Operating","operating")) %>%
filter(`Plant Age` >= 10) %>%
filter(!is.na(`Capacity (MW)`)) %>%
group_by(Country) %>% summarise(Retiring_Cap=sum(`Capacity (MW)`))
retire1p5C$Retiring_Cap[which(is.na(retire1p5C$Retiring_Cap))] <- 0
retire1p5C <- as.magpie(retire1p5C,spatial=1)
retire1p5C <- toolCountryFill(retire1p5C,fill=0,no_remove_warning = "KOS",verbosity=2)
getYears(retireBAU) <- "y2025"
getYears(retireGreen) <- "y2025"
getYears(retireBrown) <- "y2025"
getYears(retirePEGA) <- "y2025"
getYears(retire1p5C) <- "y2025"
getYears(retireNorm) <- "y2025"
for (ts in seq(2030,2070,5)) {
#BAU COVID recovery scenario: Countries maintain their historical average lifespans
retireBAUts <- retire %>% select(Country,`Planned Retire`,`Plant Age`,`Capacity (MW)`,Status,Avg_Ret_Age) %>%
filter(Status %in% c("Operating","operating")) %>%
filter(`Planned Retire`<=ts | `Plant Age` >= (Avg_Ret_Age-(ts-2020))) %>%
filter(!is.na(`Capacity (MW)`)) %>%
group_by(Country) %>% summarise(Retiring_Cap=sum(`Capacity (MW)`))
retireBAUts$Retiring_Cap[which(is.na(retireBAUts$Retiring_Cap))] <- 0
retireBAUts <- as.magpie(retireBAUts,spatial=1)
retireBAUts <- toolCountryFill(setYears(retireBAUts,ts),fill=0,no_remove_warning = "KOS",verbosity=2)
#Green COVID recovery scenario: Countries maintain their historical average lifespans
retireGreents <- retire %>% select(Country,`Planned Retire`,`Plant Age`,`Capacity (MW)`,Status,Avg_Ret_Age) %>%
filter(Status %in% c("Operating","operating")) %>%
filter(`Planned Retire`<=ts | `Plant Age` >= (Avg_Ret_Age-(ts-2015))) %>%
filter(!is.na(`Capacity (MW)`)) %>%
group_by(Country) %>% summarise(Retiring_Cap=sum(`Capacity (MW)`))
retireGreents$Retiring_Cap[which(is.na(retireGreents$Retiring_Cap))] <- 0
retireGreents <- as.magpie(retireGreents,spatial=1)
retireGreents <- toolCountryFill(setYears(retireGreents,ts),fill=0,no_remove_warning = "KOS",verbosity=2)
#Brown COVID recovery scenario: Countries maintain their historical average lifespans
retireBrownts <- retire %>% select(Country,`Planned Retire`,`Plant Age`,`Capacity (MW)`,Status,Avg_Ret_Age) %>%
filter(Status %in% c("Operating","operating")) %>%
filter(`Planned Retire`<=ts | `Plant Age` >= (Avg_Ret_Age-(ts-2025))) %>%
filter(!is.na(`Capacity (MW)`)) %>%
group_by(Country) %>% summarise(Retiring_Cap=sum(`Capacity (MW)`))
retireBrownts$Retiring_Cap[which(is.na(retireBrownts$Retiring_Cap))] <- 0
retireBrownts <- as.magpie(retireBrownts,spatial=1)
retireBrownts <- toolCountryFill(setYears(retireBrownts,ts),fill=0,no_remove_warning = "KOS",verbosity=2)
#PEGASOS policy brief scenario: Popular choice in literature has been to use a globally uniform 35-yr lifespan
retirePEGAts <- retire %>% select(Country,`Planned Retire`,`Plant Age`,`Capacity (MW)`,Status,Avg_Ret_Age) %>%
filter(Status %in% c("Operating","operating")) %>%
filter(`Plant Age` + (ts-2020) >= 35) %>%
filter(!is.na(`Capacity (MW)`)) %>%
group_by(Country) %>% summarise(Retiring_Cap=sum(`Capacity (MW)`))
retirePEGAts$Retiring_Cap[which(is.na(retirePEGAts$Retiring_Cap))] <- 0
retirePEGAts <- as.magpie(retirePEGAts,spatial=1)
retirePEGAts <- toolCountryFill(setYears(retirePEGAts,ts),fill=0,no_remove_warning = "KOS",verbosity=2)
#Another common literature figure has been to use a globally uniform 40-yr lifespan
retireNormts <- retire %>% select(Country,`Planned Retire`,`Plant Age`,`Capacity (MW)`,Status,Avg_Ret_Age) %>%
filter(Status %in% c("Operating","operating")) %>%
filter(`Plant Age` + (ts-2020) >= 40) %>%
filter(!is.na(`Capacity (MW)`)) %>%
group_by(Country) %>% summarise(Retiring_Cap=sum(`Capacity (MW)`))
retireNormts$Retiring_Cap[which(is.na(retireNormts$Retiring_Cap))] <- 0
retireNormts <- as.magpie(retireNormts,spatial=1)
retireNormts <- toolCountryFill(setYears(retireNormts,ts),fill=0,no_remove_warning = "KOS",verbosity=2)
#20 year lifespans are roughly compatible with Hi-1p5C scenarios
retire1p5Cts <- retire %>% select(Country,`Planned Retire`,`Plant Age`,`Capacity (MW)`,Status,Avg_Ret_Age) %>%
filter(Status %in% c("Operating","operating")) %>%
filter(`Plant Age` + (ts-2020) >= 20) %>%
filter(!is.na(`Capacity (MW)`)) %>%
group_by(Country) %>% summarise(Retiring_Cap=sum(`Capacity (MW)`))
retire1p5Cts$Retiring_Cap[which(is.na(retire1p5Cts$Retiring_Cap))] <- 0
retire1p5Cts <- as.magpie(retire1p5Cts,spatial=1)
retire1p5Cts <- toolCountryFill(setYears(retire1p5Cts,ts),fill=0,no_remove_warning = "KOS",verbosity=2)
retireBAU <- mbind(retireBAU,retireBAUts)
retireGreen <- mbind(retireGreen,retireGreents)
retireBrown <- mbind(retireBrown,retireBrownts)
retirePEGA <- mbind(retirePEGA,retirePEGAts)
retire1p5C <- mbind(retire1p5C,retire1p5Cts)
retireNorm <- mbind(retireNorm,retireNormts)
}
if (grepl("pubfinex",subtype)) {
pubfinex <- read.csv("C:/Users/stephenb/Downloads/madrat_main/sources/GCPT/Public_Foreign_Finance_Cap_2021.csv",sep = ";")
if (grepl("REsub",subtype)) {
output <- new.magpie(pubfinex$Country,c(2025,2030),NULL,fill = 0)
output[,2025,] <- as.numeric(pubfinex$Foreign) * (1-as.numeric(pubfinex$Ann_share))
output[,2030,] <- as.numeric(pubfinex$Foreign) * as.numeric(pubfinex$Ann_share)
return(toolCountryFill(output,fill = 0, verbosity = 2))
}
for (iii in c("Ann","Pre","Perm")) {
cap_sum[pubfinex$Country,,grepl(iii,getNames(cap_sum),ignore.case = F)] <- pubfinex[,paste0(iii,"_dom")]
}
}
############ CALCULATE FUTURE CAPACITY TRAJECTORIES #################
brown_PEGA <- cap_sum[,,"Operating"] +
cap_sum[,,"Mothballed"]*0.5 +
cap_sum[,,"Announced"] * comp_rate[,,"ann"] +
cap_sum[,,"Pre-permit"] * comp_rate[,,"pre"] +
cap_sum[,,"Permitted"] * comp_rate[,,"perm"] +
cap_sum[,,"Construction"] * comp_rate[,,"newcon"] +
comp_rate[,,"shelved"] * (cap_sum[,,"Shelved"] +
cap_sum[,,"Announced"] * (she_rate[,,"ann"]) +
cap_sum[,,"Pre-permit"] * (she_rate[,,"pre"]) +
cap_sum[,,"Permitted"] * (she_rate[,,"perm"]) +
cap_sum[,,"Construction"] * (she_rate[,,"newcon"])) -
retireBAU[,,"Retiring_Cap"]
cap_green <- cap_sum[,,"Operating"] +
cap_sum[,,"Announced"] * comp_rate[,,"ann"]/2 +
cap_sum[,,"Pre-permit"] * comp_rate[,,"pre"]/2 +
cap_sum[,,"Permitted"] * comp_rate[,,"perm"]/2 +
0.5 * comp_rate[,,"newcon"] * cap_sum[,,"Construction"] +
0.5 * comp_rate[,,"shelved"] * cap_sum[,,"Construction"] * (she_rate[,,"newcon"]) -
retireGreen[,,"Retiring_Cap"]
upper_gray_PEGA <- cap_sum[,,"Operating"] +
cap_sum[,,"Mothballed"]*0.5 +
cap_sum[,,"Announced"] * comp_rate_brown[,,"ann"] +
cap_sum[,,"Pre-permit"] * comp_rate_brown[,,"pre"] +
cap_sum[,,"Permitted"] * comp_rate_brown[,,"perm"] +
cap_sum[,,"Construction"] * comp_rate_brown[,,"newcon"] +
comp_rate_brown[,,"shelved"] * (cap_sum[,,"Shelved"] +
cap_sum[,,"Announced"] * (she_rate[,,"ann"]) +
cap_sum[,,"Pre-permit"] * (she_rate[,,"pre"]) +
cap_sum[,,"Permitted"] * (she_rate[,,"perm"]) +
cap_sum[,,"Construction"] * (she_rate[,,"newcon"])) -
retireBrown[,,"Retiring_Cap"]
if(grepl("PEGASOS",subtype)) {
red_PEGA <- cap_sum[,,"Operating"] +
cap_sum[,,"Mothballed"]*0.5 +
cap_sum[,,"Announced"] +
cap_sum[,,"Pre-permit"] +
cap_sum[,,"Permitted"] +
cap_sum[,,"Construction"] +
cap_sum[,,"Shelved"] -
retirePEGA[,,"Retiring_Cap"]
}else {
red_PEGA <- cap_sum[,,"Operating"] +
cap_sum[,,"Mothballed"]*0.5 +
cap_sum[,,"Announced"] +
cap_sum[,,"Pre-permit"] +
cap_sum[,,"Permitted"] +
cap_sum[,,"Construction"] +
cap_sum[,,"Shelved"] -
retireNorm[,,"Retiring_Cap"]
}
silver_PEGA <- setYears(cap_sum[,,"Operating"],NULL) - retirePEGA
lower_gray_PEGA <- setYears(cap_sum[,,"Operating"],NULL) - retire1p5C
green_PEGA <- setYears(cap_sum[,,"Operating"],NULL) - retireGreen
gold_PEGA <- setYears(cap_sum[,,"Operating"],NULL) - retireGreen
bronze_PEGA <- setYears(cap_sum[,,"Operating"],NULL) - retireBrown
silver_PEGA[which(silver_PEGA<0)] <- 0
cap_green[which(cap_green<0)] <- 0
gold_PEGA[which(gold_PEGA<0)] <- 0
brown_PEGA[which(brown_PEGA<0)] <- 0
getYears(silver_PEGA) <- seq(2025,2070,5)
getYears(cap_green) <- seq(2025,2070,5)
getYears(gold_PEGA) <- seq(2025,2070,5)
getYears(brown_PEGA) <- seq(2025,2070,5)
getYears(bronze_PEGA) <- seq(2025,2070,5)
getYears(upper_gray_PEGA) <- seq(2025,2070,5)
getYears(red_PEGA) <- seq(2025,2070,5)
getYears(green_PEGA) <- seq(2025,2070,5)
getYears(lower_gray_PEGA) <- seq(2025,2070,5)
### Time Series: Announced and pre-permit projects finish in 2030 ##
if (!grepl("2025",subtype)) {
brown_PEGA[,2025,] <- brown_PEGA[,2025,] - (cap_sum[,,"Announced"] * comp_rate[,,"ann"] +
cap_sum[,,"Pre-permit"] * comp_rate[,,"pre"])
cap_green[,2025,] <- cap_green[,2025,] - (cap_sum[,,"Announced"] * comp_rate[,,"ann"] +
cap_sum[,,"Pre-permit"] * comp_rate[,,"pre"])
upper_gray_PEGA[,2025,] <- upper_gray_PEGA[,2025,] - (cap_sum[,,"Announced"] * comp_rate[,,"ann"] +
cap_sum[,,"Pre-permit"] * comp_rate[,,"pre"])
red_PEGA[,2025,] <- red_PEGA[,2025,] - (cap_sum[,,"Announced"] * comp_rate[,,"ann"] +
cap_sum[,,"Pre-permit"] * comp_rate[,,"pre"])
}
# getNames(silver_PEGA) <- "BAU_can"
getNames(cap_green) <- "Green"
# getNames(gold_PEGA) <- "Green_can"
getNames(brown_PEGA) <- ifelse(grepl("REM",subtype), "Neutral", "Brown")
# getNames(bronze_PEGA) <- "Brown_can"
getNames(upper_gray_PEGA) <- ifelse(grepl("REM",subtype), "Brown", "upGray")
getNames(red_PEGA) <- "Red"
getNames(green_PEGA) <- "Green"
getNames(lower_gray_PEGA) <- "lowGray"
if(grepl("REM",subtype)) {
mcap <- mbind(brown_PEGA,upper_gray_PEGA,cap_green)/1000
mcap <- mcap[,getYears(mcap)<="y2030",]
}else {
mcap <- mbind(brown_PEGA,upper_gray_PEGA,red_PEGA,lower_gray_PEGA,cap_green)/1000
}
# Return Capacity scenarios
return(mcap)
}
# Prepare completion rate data frames
getNames(comp_rate) <- c("Shelved","Construction","Announced","Pre-permit","Permitted")
df_comp <- as.data.frame(comp_rate)
colnames(df_comp)[2] <- "Country"
df_comp <- df_comp[,-3]
df_comp <- df_comp[,-1]
colnames(df_comp)[2] <- "Status"
getNames(comp_rate_brown) <- c("Shelved","Construction","Announced","Pre-permit","Permitted")
df_comp_brown <- as.data.frame(comp_rate_brown)
colnames(df_comp_brown)[2] <- "Country"
df_comp_brown <- df_comp_brown[,-3]
df_comp_brown <- df_comp_brown[,-1]
colnames(df_comp_brown)[2] <- "Status"
#Calculate committed emissions
# Select necessary columns from plant-level database
emi_data <- retire %>% select(Country,`Plant Age`,`Capacity (MW)`,Status,`Heat rate`,`Emission factor`,`Combustion technology`,RETIRED,Year) %>%
filter(!(Status %in% c("Retired","Cancelled","retired","cancelled")) & !is.na(`Capacity (MW)`) & is.na(RETIRED) & !is.na(`Emission factor`)) %>%
left_join(avgRetAge,by="Country") %>% mutate(Brown_Ret_Age=Avg_Ret_Age+5) %>% mutate(Norm_Ret_Age=40)
#Convert Year column to numeric, first handling the special non-numeric cases
emi_data$Year[which(grepl("after",emi_data$Year,ignore.case=TRUE))] <-
as.numeric(gsub("after ","",emi_data$Year[which(grepl("after",emi_data$Year,ignore.case=TRUE))],ignore.case=TRUE))+1
emi_data$Year[which(grepl("/",emi_data$Year,fixed=TRUE) | grepl("-",emi_data$Year,fixed=TRUE))] <-
substr(emi_data$Year[which(grepl("/",emi_data$Year,fixed=TRUE) | grepl("-",emi_data$Year,fixed=TRUE))],1,4)
emi_data$Year <- as.numeric(emi_data$Year)
# Set operating plants with an unknown age to 50% of the national average lifespan
emi_data <- emi_data %>%
mutate(`Plant Age`=
ifelse(is.na(Year) & Status %in% c("Operating","operating"),
0.5 * Avg_Ret_Age,
`Plant Age`))
# emi_data[which(is.na(emi_data$`Plant Age`)),] <- emi_data[which(is.na(emi_data$`Plant Age`)),] %>% mutate(`Plant Age`=Avg_Ret_Age/2)
# Set pipeline plants to appropriate ages
for (Phase in getNames(comp_rate)) {
emi_data <- emi_data %>%
mutate(`Plant Age` =
ifelse(grepl(Phase,Status,ignore.case=F) & is.finite(Year) & Year >=2020,
2020 - Year,
`Plant Age`))
meanAge <- emi_data %>%
filter(grepl(Phase,Status,ignore.case=F) & is.finite(Year) & Year >=2020) %>%
summarise(age=mean(`Plant Age`))
# emi_data$`Plant Age`[which(emi_data$Status==Phase & is.finite(emi_data$Year) & emi_data$Year>=2020)] <-
# 2020 - emi_data$Year[which(emi_data$Status==Phase & is.finite(emi_data$Year) & emi_data$Year>=2020)]
# meanAge <- mean(emi_data$`Plant Age`[which(emi_data$Status==Phase & is.finite(emi_data$Year) & emi_data$Year>=2020)])
emi_data$`Plant Age`[which(emi_data$Status==Phase & is.na(emi_data$Year))] <- meanAge$age
}
# Read in national average capacity factor assumption for each 5-year time-step
capFac <- calcOutput("CapacityFactor",aggregate=F)[,seq(2020,2100,5),"pc"]
capFac <- removeColNa(as.data.frame(capFac))[,-3]
colnames(capFac) <- c("Country","Period","Cap_Factor")
capFac$Period <- as.numeric(as.character(capFac$Period))
# Assign each coal plant its country's average capacity factor per time-step
emi_data <- emi_data %>% left_join(capFac,by="Country") %>% mutate(`Plant Age`=`Plant Age`+(Period-2020))
# Calculate emissions from each plant for each 5-yr timestep in each scenario using GCPT methodology
# 5 * MW * cf * Btu/kWh * kgCO2/TJ * 9.2427e-12
emi_data <- emi_data %>% mutate(green_5yr_emi=ifelse(emi_data$`Plant Age`>=0 & (emi_data$`Plant Age` < emi_data$Avg_Ret_Age),
5 * `Capacity (MW)` * Cap_Factor * `Heat rate` * `Emission factor`*9.2427e-12, 0)) %>%
mutate(bau_5yr_emi=ifelse(emi_data$`Plant Age`>=0 & (emi_data$`Plant Age` < emi_data$Avg_Ret_Age),
5 * `Capacity (MW)` * Cap_Factor * `Heat rate` * `Emission factor`*9.2427e-12, 0)) %>%
mutate(brown_5yr_emi=ifelse(emi_data$`Plant Age`>=0 & (emi_data$`Plant Age` < emi_data$Brown_Ret_Age),
5 * `Capacity (MW)` * Cap_Factor * `Heat rate` * `Emission factor`*9.2427e-12, 0)) %>%
mutate(norm_5yr_emi=ifelse(emi_data$`Plant Age`>=0 & (emi_data$`Plant Age` < emi_data$Norm_Ret_Age),
5 * `Capacity (MW)` * 0.53 * `Heat rate` * `Emission factor`*9.2427e-12, 0))
# Calculate total emissions (MtCO2) over the lifespan of plants in operation and development for each scenario
green_emi <- emi_data %>% group_by(Country,.add=TRUE) %>% group_by(Status,.add=TRUE) %>% summarise(green_emi = sum(green_5yr_emi))
bau_emi <- emi_data %>% group_by(Country,.add=TRUE) %>% group_by(Status,.add=TRUE) %>% summarise(BAU_emi = sum(bau_5yr_emi))
brown_emi <- emi_data %>% group_by(Country,.add=TRUE) %>% group_by(Status,.add=TRUE) %>% summarise(brown_emi = sum(brown_5yr_emi))
norm_emi <- emi_data %>% group_by(Country,.add=TRUE) %>% group_by(Status,.add=TRUE) %>% summarise(norm_emi = sum(norm_5yr_emi))
# Calculate total emissions from all operating & pipeline plants with 40-yr lifespans
norm_emi <- norm_emi %>% group_by(Country) %>% summarise(norm_total_emi=sum(norm_emi)/1000)
glo_norm_emi <- norm_emi %>% summarise(norm_global_emi=sum(norm_total_emi))
# Calculate committed emissions reduced by PPCA members based on norm assumptions
ppca_emi <- emi_data %>%
filter(Period>=2030 & Country %in% ppca) %>%
mutate(norm_5yr_emi=ifelse(Period==2030,norm_5yr_emi/2,norm_5yr_emi)) %>%
summarise(avoided_emissions=sum(norm_5yr_emi)/1000)
# Calculate total emissions from all operating & pipeline plants
all_emi <- bau_emi %>% group_by(Country) %>% summarise(All_total_emi=sum(BAU_emi)/1000)
glo_all_emi <- all_emi %>% summarise(All_global_emi=sum(All_total_emi))
# Calculate total emissions from operating & pipeline plants in each recovery scenario
bau_emi <- left_join(bau_emi,df_comp,by=c("Country","Status"))
bau_emi$Value[which(bau_emi$Status=="Operating")] <- 1
bau_emi$Value[which(bau_emi$Status=="Mothballed")] <- 0.5
bau_emi <- bau_emi %>% group_by(Country) %>% summarize(BAU_total_emi=sum(BAU_emi*Value)/1000)
glo_bau_emi <- bau_emi %>% summarise(Global_BAU_emi=sum(BAU_total_emi))
green_emi <- left_join(green_emi,df_comp,by=c("Country","Status"))
green_emi$Value <- 0.5 * green_emi$Value
green_emi$Value[which(green_emi$Status=="Operating")] <- 1
green_emi$Value[which(green_emi$Status=="Mothballed")] <- 0
green_emi <- green_emi %>% group_by(Country) %>% summarize(green_total_emi=sum(green_emi*Value)/1000)
glo_green_emi <- green_emi %>% summarise(Global_green_emi=sum(green_total_emi))
brown_emi <- left_join(brown_emi,df_comp_brown,by=c("Country","Status"))
brown_emi$Value[which(brown_emi$Status=="Operating")] <- 1
brown_emi$Value[which(brown_emi$Status=="Mothballed")] <- 0.5
brown_emi <- brown_emi %>% group_by(Country) %>% summarize(brown_total_emi=sum(brown_emi*Value)/1000)
glo_brown_emi <- brown_emi %>% summarise(Global_brown_emi=sum(brown_total_emi))
# Calculate capacity-weighted mean emission factor by country and plant status
# emifac <- emi_data %>% group_by(Country,.add=TRUE) %>% group_by(Status,.add=TRUE) %>%
# summarise(EF_w_mean = weighted.mean(`Emission factor`,`Capacity (MW)`))
bau_emi <- toolCountryFill(as.magpie(bau_emi),fill=0,verbosity=2)
green_emi <- toolCountryFill(as.magpie(green_emi),fill=0,verbosity=2)
brown_emi <- toolCountryFill(as.magpie(brown_emi),fill=0,verbosity=2)
norm_emi <- toolCountryFill(as.magpie(norm_emi),fill=0,verbosity=2)
if (grepl("emissions",subtype)) {
return(mbind(bau_emi,green_emi,brown_emi,norm_emi))
}else if (grepl("ppca_emi",subtype)) {
return(as.magpie(ppca_emi))
}
}
pik-piam/mrremind documentation built on March 30, 2024, 3:37 a.m.
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Defines functions readGCPT
Documented in readGCPT
#' Data from the Global Coal Plant Tracker January 2021 release by Global Energy Monitor (formerly EndCoal/CoalSwarm)
#' @description Historical data of operating, under-construction, planned and announced Coal Plants by country (in MW)
#' from the Global Energy Monitor's Global Coal Plant Tracker, and extrapolations for 2025 capacity scenarios
#' @param subtype Options are status, historical, future, lifespans, comp_rates and emissions
#' @author Stephen Bi
#' @importFrom readxl read_excel
#' @importFrom dplyr %>% filter select mutate summarize group_by left_join across everything starts_with
#' @importFrom magclass replace_non_finite
#' @importFrom quitte removeColNa
#' @aliases readEndCoal
readGCPT <- function(subtype) {
map <- toolGetMapping(getConfig("regionmapping"), type="regional", where = "mappingfolder")
year <- NULL
if (!(subtype %in% c("early_retire","historical","status","future","lifespans","emissions","comp_rates","reg_comp_rates","glo_comp_rates","meanAge","ppca_emi","G20_FinEx",
"PEGASOS","PEGASOS_pubfinex","PEGASOS_prefinex","REM_PEGASOS_pubfinex","REM_PEGASOS_pubfinex_REsub"))) {
stop("Invalid subtype!")
}
# Data files to be read in
status_changes_encoding <- 'UTF-8'
if (is.null(year)) {
summary_data <- "GCPT_data_Jan2023.xlsx"
status_changes <-
"Jan 2023 GCPT Status Changes - 2014 - 2022 (final).csv"
status_changes_encoding <- 'ANSI_X3.4-1986'
plant_data <- "Global-Coal-Plant-Tracker-January-2023.xlsx"
lastCol <- "X"
sep = ";"
latest_col <- 3
} else if (grepl('2021', year)) {
summary_data <- "GCPT_data_Jan2021.xlsx"
status_changes <- "GCPT Status Changes_1Feb2021.csv"
plant_data <- "January 2021 Global Coal Plant Tracker.xlsx"
lastCol <- "V"
sep = ","
latest_col <- 3
} else if (grepl('2022', year)) {
summary_data <- "GCPT_data_Jan2022.xlsx"
status_changes <-
"Jan 2022 GCPT Status Changes - 2014 - 2021 (b).csv"
status_changes_encoding <- 'ANSI_X3.4-1986'
plant_data <- "Global-Coal-Plant-Tracker-Jan-2022.xlsx"
lastCol <- "W"
sep = ";"
latest_col <- 3
} else if (grepl('2020', year)) {
summary_data <- "GCPT_data_Jul2020.xlsx"
status_changes <- "GCPT Status Changes H1 2015 to H1 2020.xlsx"
plant_data <- "July 2020 Global Coal Plant Tracker.xlsx"
lastCol <- "V"
sep = ","
plant_status <-
read_excel("GCPT Status Changes H1 2015 to H1 2020.xlsx", sheet = 2)
plant_status <-
plant_status %>% select(Country, MW, Jul20, Jan20, Jan19, Jan18, Jan17, Jan16, Jan15)
colnames(plant_status)[3:ncol(plant_status)] <- c(2020:2014)
latest_col <- 3
}
plant_status <- read.csv(status_changes, sep = sep,
stringsAsFactors = FALSE)
plant_status <- plant_status %>%
select(Country, MW, starts_with("H2")) %>%
# mutate(across(where(is.character), ~iconv(., from = status_changes_encoding, to = "UTF-8"))) %>%
mutate(Country = ifelse(grepl("rkiye", Country), "Turkey", Country),
Country = ifelse(grepl("Ivoire", Country), "Cote d'Ivoire", Country))
colnames(plant_status)[3:ncol(plant_status)] <- as.numeric(gsub("H2.","",colnames(plant_status)[3:ncol(plant_status)]))
present_year <- as.numeric(substr(summary_data,nchar(summary_data)-8,nchar(summary_data)-5))
`Announced + Pre-permit + Permitted` <- NULL
. <- NULL
H2.2014 <- NULL
H2.2015 <- NULL
H2.2016 <- NULL
H2.2017 <- NULL
H2.2018 <- NULL
H2.2019 <- NULL
H2.2020 <- NULL
H2.2021 <- NULL
Jan15 <- NULL
Jan16 <- NULL
Jan17 <- NULL
Jan18 <- NULL
Jan19 <- NULL
Jan20 <- NULL
Jul20 <- NULL
Country <- NULL
CountryCode <- NULL
MW <- NULL
`2015` <- NULL
`2016` <- NULL
`2017` <- NULL
`2018` <- NULL
`2019` <- NULL
`2020` <- NULL
`Plant Age` <- NULL
`Capacity (MW)` <- NULL
Status <- NULL
RETIRED <- NULL
`Planned Retire` <- NULL
Avg_Ret_Age <- NULL
`Heat rate` <- NULL
`Emission factor` <- NULL
`Combustion technology` <- NULL
Year <- NULL
Period <- NULL
Cap_Factor <- NULL
green_5yr_emi <- NULL
bau_5yr_emi <- NULL
brown_5yr_emi <- NULL
norm_5yr_emi <- NULL
BAU_emi <- NULL
All_total_emi <- NULL
Value <- NULL
BAU_total_emi <- NULL
green_total_emi <- NULL
brown_total_emi <- NULL
norm_total_emi <- NULL
brown_emi <- NULL
green_emi <- NULL
norm_emi <- NULL
# Capacity by country as of January 2022, with select manual updates according to the Global Coal Finance Tracker and news media
cap_sum <- read_excel(summary_data,sheet="Summary",range="A4:K112") %>%
filter(!grepl("Total",Country) & !grepl("World",Country))
# cap_sum <- ifelse(grepl("2020",subtype) | grepl("2021",subtype),
# read_excel(summary_data,sheet="Summary",range="A4:K112"),
# read_excel(summary_data,sheet="Summary"))
cap_sum <- cap_sum %>% select(-`Announced + Pre-permit + Permitted`)
cap_sum <- as.magpie(cap_sum,spatial=1)
cap_sum <- toolCountryFill(setItems(dim = 1, x = cap_sum,
value = toolCountry2isocode(mapping = c("DR Congo" = "COD"),
country = getItems(dim = 1, x = cap_sum))),
fill=0,
no_remove_warning = "KOS",
verbosity=2)
cap_sum_oper <- cap_sum[,,"Operating"]
###################################################
### Historical capacity additions & retirements ###
###################################################
additions <- read_excel(summary_data,sheet="Additions",range = c(paste0("A4:",lastCol,"112"))) %>%
filter(!grepl("Total",Country) & !grepl("World",Country))
# colnames(additions)[length(colnames(additions))]="2020"
retirements <- read_excel(summary_data,sheet="Retirements",range = c(paste0("A4:",lastCol,"112"))) %>%
filter(!grepl("Total",Country) & !grepl("World",Country))
add_startyr <- as.numeric(colnames(additions)[2])
add_endyr <- as.numeric(colnames(additions)[length(additions)])
status_startyr <- as.numeric(colnames(plant_status)[ncol(plant_status)])
status_endyr <- as.numeric(colnames(plant_status)[3])
# Filter for all plants that were mothballed during this period
mothballed <- plant_status %>%
filter(rowSums(across(everything(),~grepl("Moth",.)))>0) %>%
filter(rowSums(across(everything(),~grepl("Oper",.)))>0)
# Calculate capacity that was mothballed or restarted each year since 2014 (to be fed into back-calculation of annual capacity below)
cap_moth <- new.magpie(getItems(dim = 1, x = cap_sum),years=as.numeric(colnames(additions)[2:length(additions)]),names=c("Operating","pre_status"),fill=0)
for (j in 4:ncol(mothballed)) {
# Mothballed plants in 2020 which were operating earlier
oper_moth <- mothballed %>%
filter(mothballed[,j]=="Operating" & mothballed[,3]=="Mothballed") %>%
group_by(Country) %>%
summarize(sum=sum(MW))
oper_moth$Country <- toolCountry2isocode(mapping = c("DR Congo" = "COD"), country = oper_moth$Country)
oper_moth <- suppressWarnings(
toolCountryFill(as.magpie(oper_moth,spatial=1,temporal=as.numeric(colnames(mothballed)[j]),datacol=2),verbosity=2,fill=0,no_remove_warning = "KOS")
)
# Retired plants in 2020 which were mothballed earlier
moth_ret <- mothballed %>%
filter(mothballed[,j]=="Mothballed" & mothballed[,3]=="Retired") %>%
group_by(Country) %>%
summarize(sum=sum(MW))
moth_ret$Country <- toolCountry2isocode(mapping = c("DR Congo" = "COD"), country = moth_ret$Country)
moth_ret <- suppressWarnings(
toolCountryFill(as.magpie(moth_ret,spatial=1,temporal=as.numeric(colnames(mothballed)[j]),datacol=2),verbosity=2,fill=0,no_remove_warning = "KOS")
)
# Operating plants in 2020 which were mothballed earlier
moth_oper <- mothballed %>%
filter(mothballed[,j]=="Mothballed" & mothballed[,3]=="Operating") %>%
group_by(Country) %>%
summarize(sum=sum(MW))
moth_oper$Country <- toolCountry2isocode(mapping = c("DR Congo" = "COD"), country = moth_oper$Country)
moth_oper <- suppressWarnings(
toolCountryFill(as.magpie(moth_oper,spatial=1,temporal=as.numeric(colnames(mothballed)[j]),datacol=2),verbosity=2,fill=0,no_remove_warning = "KOS")
)
constr_moth <- mothballed %>%
filter(mothballed[,3]=="Mothballed" & mothballed[,j]!="Mothballed" & mothballed[,j]!="Operating") %>%
group_by(Country) %>%
summarize(sum=sum(MW))
constr_moth$Country <- toolCountry2isocode(mapping = c("DR Congo" = "COD"), country = constr_moth$Country)
constr_moth <- suppressWarnings(
toolCountryFill(as.magpie(constr_moth,spatial=1,temporal=as.numeric(colnames(mothballed)[j]),datacol=2),verbosity=2,fill=0,no_remove_warning = "KOS")
)
# Annual flux of mothballing/restarting
cap_moth[,as.numeric(colnames(mothballed)[j]),"Operating"] <- oper_moth + constr_moth - moth_oper - moth_ret
}
# Capacity with mothballed status in the first and last year shall be added as operating capacity in years prior to status tracking
moth_moth <- mothballed %>%
filter(mothballed[,3]=="Mothballed" & mothballed[,j]=="Mothballed") %>%
group_by(Country) %>%
summarize(sum=sum(MW))
moth_moth$Country <- toolCountry2isocode(mapping = c("DR Congo" = "COD"), country = moth_moth$Country)
moth_moth <- suppressWarnings(
toolCountryFill(as.magpie(moth_moth,spatial=1,temporal=as.numeric(colnames(mothballed)[j]),datacol=2),verbosity=2,fill=0,no_remove_warning = "KOS")
)
cap_moth[,add_startyr:(as.numeric(colnames(mothballed)[j])-1),"pre_status"] <- oper_moth + constr_moth + moth_moth
# Back-calculate annual capacity by adding retired and mothballed plants and subtracting added plants each year
cap <- new.magpie(getItems(dim = 1, x = cap_sum),years=as.numeric(colnames(additions)[2:length(additions)]),fill = 0)
# j <- ncol(mothballed)
for (i in add_startyr:(add_endyr-1)) {
cap_add <- additions %>% select(Country,as.character(i+1):as.character(add_endyr)) %>%
mutate(Operating=rowSums(.[-1])) %>%
mutate(Country=toolCountry2isocode(mapping = c("DR Congo" = "COD"), country = Country))
cap_add <- as.magpie(cap_add[,c("Country","Operating")])
cap_add <- toolCountryFill(cap_add,verbosity=2,fill=0,no_remove_warning = "KOS")
getItems(cap_add,dim=2) <- i
cap_ret <- retirements %>% select(Country,as.character(i+1):as.character(add_endyr)) %>%
mutate(Operating=rowSums(.[-1])) %>%
mutate(Country=toolCountry2isocode(mapping = c("DR Congo" = "COD"), country = Country))
cap_ret <- as.magpie(cap_ret[,c("Country","Operating")])
cap_ret <- toolCountryFill(cap_ret,verbosity=2,fill=0,no_remove_warning = "KOS")
getItems(cap_ret,dim=2) <- i
cap[,i,] <- cap_sum[,,"Operating"] - cap_add[,,"Operating"] + cap_ret[,,"Operating"] + cap_moth[,i,"Operating"] + cap_moth[,i,"pre_status"]
}
cap[,add_endyr,] <- cap_sum_oper
if (grepl("historical",subtype)) {
return(cap/1000)
}else if (grepl("status",subtype)) {
return(cap_sum/1000)
}
#Plant-level data detailing capacities, commissioning and retirement dates, location and more.
retire <- suppressWarnings(read_excel(plant_data,sheet=2))
retire$`Capacity (MW)` <- as.numeric(retire$`Capacity (MW)`)
retire$`Planned Retire` <- suppressWarnings(as.numeric(retire$`Planned Retire`))
retire$Country <- toolCountry2isocode(mapping = c("DR Congo" = "COD"), country = retire$Country)
retire <- retire[-which(retire$Country=="KOS"),]
if (present_year!=2020) {
retire <- retire %>% mutate(`Plant Age` =
ifelse(is.na(Year),
NA,
ifelse(grepl("retire",Status,ignore.case=T),
ifelse(is.na(RETIRED),
NA,
RETIRED - Year),
present_year - Year)))
}
retAge <- retire %>% select(Country,`Plant Age`,`Capacity (MW)`,Status) %>%
filter(Status %in% c("Retired","retired")) %>% filter(!is.na(`Plant Age`)) %>% filter(`Plant Age` > 0) %>% filter(!is.na(`Capacity (MW)`))
avgRetAge <- retAge %>% group_by(Country) %>% summarise(Avg_Ret_Age=weighted.mean(`Plant Age`,`Capacity (MW)`))
retCap <- retAge %>% group_by(Country) %>% summarise(Retired_Cap=sum(`Capacity (MW)`))
avgRetAge <- avgRetAge[do.call(order,avgRetAge),]
retCap <- retCap[do.call(order,retCap),]
mavgRetAge <- as.magpie(avgRetAge,spatial=1)
mavgRetAge <- toolCountryFill(mavgRetAge,fill=0,no_remove_warning = "KOS",verbosity=2)
retCap <- as.magpie(retCap,spatial=1)
retCap <- toolCountryFill(retCap,fill=0,no_remove_warning = "KOS",verbosity=2)
regAvgRetAge <- toolAggregate(mavgRetAge,map,weight=retCap)
regAvgRetAge[which(regAvgRetAge==0)] <- mean(regAvgRetAge)
mavgRetAge[getItems(dim = 1, x = mavgRetAge)[mavgRetAge == 0],,] <- as.vector(regAvgRetAge[map$RegionCode[which(map$CountryCode %in% getItems(dim = 1, x = mavgRetAge)[mavgRetAge == 0])],,])
if (grepl("lifespans",subtype)) {
return(mavgRetAge)
}else if (grepl("meanAge",subtype)) {
meanAge <- retire %>% select(Country,`Plant Age`,`Capacity (MW)`,Status) %>%
filter(Status %in% c("Operating","operating") & !is.na(`Capacity (MW)`) & !is.na(`Plant Age`))
meanAge_c <- meanAge %>% group_by(Country) %>% summarise(meanAge=weighted.mean(`Plant Age`,`Capacity (MW)`))
meanAge_c <- toolCountryFill(as.magpie(meanAge_c,spatial=1),fill=0, verbosity = 2)
capWeight <- meanAge %>% group_by(Country) %>% summarise(Capacity=sum(`Capacity (MW)`))
capWeight <- toolCountryFill(as.magpie(capWeight,spatial=1),fill=0, verbosity = 2)
return(toolAggregate(meanAge_c,map,capWeight))
}
# Return national average lifespans to calculate early retirement adjustment factors
if (grepl("early_retire",subtype)) {
retired <- retire %>%
select(Country,`Plant Age`,`Capacity (MW)`,RETIRED) %>%
left_join(avgRetAge,by="Country")
#Sum up capacity retired in each country in each year 2000-2020
early_ret <- retired %>%
filter(!is.na(RETIRED) & !is.na(`Plant Age`)) %>%
filter(`Plant Age` < Avg_Ret_Age) %>%
group_by(Country,.add=TRUE) %>%
group_by(RETIRED,.add=TRUE) %>%
summarise(Retired_cap=sum(`Capacity (MW)`))
early_ret <- as.magpie(early_ret,spatial=1,temporal=2)
early_ret <- toolCountryFill(early_ret,fill=0,no_remove_warning = "KOS",verbosity = 0)
ret_rate <- new.magpie(getItems(dim = 1, x = mavgRetAge),seq(2000,2015,5),names="Early_Retirement",fill=0)
for (t in seq(2000,2015,5)) {
ret_rate[,t,] <- dimSums(early_ret[,t:(t+5),],dim=2)/cap[,t,]
}
ret_rate <- replace_non_finite(ret_rate)
max_ret <- new.magpie(getItems(dim = 1, x = ret_rate),NULL,names="Max_Early_Retirement",fill=0)
for (reg in getItems(dim = 1, x = ret_rate)) {
max_ret[reg,,] <- max(ret_rate[reg,,])
}
return(max_ret)
}
#Use the national lifespans to derive scenarios of 2025 coal capacity
avgRetAge <- data.frame(Country=getItems(dim = 1, x = mavgRetAge),Avg_Ret_Age=array(mavgRetAge))
retire <- left_join(retire,avgRetAge,by="Country")
#Neutral COVID recovery scenario: Countries maintain their historical average lifespans
retireBAU <- retire %>% select(Country,`Planned Retire`,`Plant Age`,`Capacity (MW)`,Status,Avg_Ret_Age) %>%
filter(Status %in% c("Operating","operating")) %>%
filter(`Planned Retire`<=2028 | `Plant Age` >= (Avg_Ret_Age-5)) %>%
filter(!is.na(`Capacity (MW)`)) %>%
group_by(Country) %>% summarise(Retiring_Cap=sum(`Capacity (MW)`))
retireBAU$Retiring_Cap[which(is.na(retireBAU$Retiring_Cap))] <- 0
retireBAU <- as.magpie(retireBAU,spatial=1)
retireBAU <- toolCountryFill(retireBAU,fill=0,no_remove_warning = "KOS",verbosity=2)
#Brown COVID recovery scenario: Countries extend their average lifespans by 5 years
retireBrown <- retire %>% select(Country,`Planned Retire`,`Plant Age`,`Capacity (MW)`,Status,Avg_Ret_Age) %>%
filter(Status %in% c("Operating","operating")) %>%
filter(`Planned Retire`<=2028 | `Plant Age` >= (Avg_Ret_Age)) %>%
filter(!is.na(`Capacity (MW)`)) %>%
group_by(Country) %>% summarise(Retiring_Cap=sum(`Capacity (MW)`))
retireBrown$Retiring_Cap[which(is.na(retireBrown$Retiring_Cap))] <- 0
retireBrown <- as.magpie(retireBrown,spatial=1)
retireBrown <- toolCountryFill(retireBrown,fill=0,no_remove_warning = "KOS",verbosity=2)
#Green COVID recovery scenario: Countries reduce historical average lifespans by 5 years
retireGreen <- retire %>% select(Country,`Planned Retire`,`Plant Age`,`Capacity (MW)`,Status,Avg_Ret_Age) %>%
filter(Status %in% c("Operating","operating")) %>%
filter(`Planned Retire`<=2028 | `Plant Age` >= (Avg_Ret_Age-10)) %>%
filter(!is.na(`Capacity (MW)`)) %>%
group_by(Country) %>% summarise(Retiring_Cap=sum(`Capacity (MW)`))
retireGreen$Retiring_Cap[which(is.na(retireGreen$Retiring_Cap))] <- 0
retireGreen <- as.magpie(retireGreen,spatial=1)
retireGreen <- toolCountryFill(retireGreen,fill=0,no_remove_warning = "KOS",verbosity=2)
#Literature norm scenario: Popular choice in literature has been to use a globally uniform 40-yr lifespan
retireNorm <- retire %>% select(Country,`Planned Retire`,`Plant Age`,`Capacity (MW)`,Status,Avg_Ret_Age) %>%
filter(Status %in% c("Operating","operating")) %>%
filter(`Plant Age` >= 35) %>%
filter(!is.na(`Capacity (MW)`)) %>%
group_by(Country) %>% summarise(Retiring_Cap=sum(`Capacity (MW)`))
retireNorm$Retiring_Cap[which(is.na(retireNorm$Retiring_Cap))] <- 0
retireNorm <- as.magpie(retireNorm,spatial=1)
retireNorm <- toolCountryFill(retireNorm,fill=0,no_remove_warning = "KOS",verbosity=2)
#Relevant regional mappings and country classifications
#Current members of PPCA
PPCAmap <- toolGetMapping("regionmappingPPCA.csv",type = "regional")
#Current OECD members
OECDmap <- toolGetMapping("regionmappingOECD.csv",type = "regional")
ppca <- PPCAmap$CountryCode[which(PPCAmap$RegionCode=="PPCA")]
oecd <- OECDmap$CountryCode[which(OECDmap$RegionCode=="OECD")]
EU27 <- map$CountryCode[which(map$RegionCode=="EUR")]
nonoecd <- OECDmap$CountryCode[which(OECDmap$RegionCode=="NON")]
#The PPCA stipulates that OECD and EU members must phase out coal power by 2030
oecdPPCA <- ppca[which(ppca %in% oecd | ppca %in% EU27)]
#While non-OECD members must phase out coal power by 2050
nonOECDppca <- ppca[which(!(ppca %in% oecdPPCA))]
#There are several countries which have explicit plans to abandon coal power in their COVID recovery packages
greenNations <- c(EU27[-which(EU27=="POL")][-which(EU27 %in% ppca)],"EGY","AUS","KOR","CHL")
if (subtype!="PEGASOS") greenNations <- c(greenNations,"BGD","PAK","PHL","VNM")
###########################
### ANNUAL PLANT STATUS ###
###########################
# Remove any plants with unknown (NA) capacity from plant status data frame
plant_status <- plant_status %>% filter(!is.na(MW))
tran <- data.frame(Country=unique(plant_status$Country),shelved=0,she2she=0,she2can=0,she2oper=0,she2ann=0,she2pre=0,pre2she=0,pre2con=0,
she2perm=0,she2con=0,newcon=0,con2she=0,con2she2oper=0,con2she2con=0,con2she2can=0,con2can=0,perm2she=0,perm2con=0,
con2oper=0,con2she2she=0,plan2she=0,ann=0,ann2oper=0,ann2can=0,ann2she=0,ann2con=0,pre=0,pre2oper=0,pre2can=0,perm=0,
perm2oper=0,perm2can=0,moth=0,moth2ret=0,moth2oper=0)
for (i in 1:nrow(plant_status)) {
j <- ncol(plant_status)
if (plant_status[i,j]=="XXX") {
plant_status[i,which(plant_status[i,]=="XXX")] <- "Announced"
}
while (j > latest_col) {
if (grepl("She",plant_status[i,j])) {
if (j==ncol(plant_status) || !any(grepl("She",plant_status[i,j:ncol(plant_status)]))) {
tran$shelved[which(tran$Country==plant_status$Country[i])] <- tran$shelved[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
if (grepl("Can",plant_status[i,latest_col])) {
tran$she2can[which(tran$Country==plant_status$Country[i])]<- tran$she2can[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (grepl("She",plant_status[i,latest_col])) {
tran$she2she[which(tran$Country==plant_status$Country[i])]<- tran$she2she[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (grepl("Oper",plant_status[i,latest_col])) {
tran$she2oper[which(tran$Country==plant_status$Country[i])]<- tran$she2oper[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (grepl("Ann",plant_status[i,latest_col])) {
tran$she2ann[which(tran$Country==plant_status$Country[i])]<- tran$she2ann[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (grepl("Pre",plant_status[i,latest_col])) {
tran$she2pre[which(tran$Country==plant_status$Country[i])]<- tran$she2pre[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (grepl("Perm",plant_status[i,latest_col])) {
tran$she2perm[which(tran$Country==plant_status$Country[i])]<- tran$she2perm[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (grepl("Con",plant_status[i,latest_col])) {
tran$she2con[which(tran$Country==plant_status$Country[i])]<- tran$she2con[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}
}
}else if (grepl("Con",plant_status[i,j])) {
if (j==ncol(plant_status) || !any(grepl("Con",plant_status[i,(j+1):ncol(plant_status)]))) {
tran$newcon[which(tran$Country==plant_status$Country[i])] <- tran$newcon[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
if (grepl("Can",plant_status[i,latest_col])) {
tran$con2can[which(tran$Country==plant_status$Country[i])] <- tran$con2can[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (grepl("She",plant_status[i,latest_col])) {
tran$con2she2she[which(tran$Country==plant_status$Country[i])]<- tran$con2she2she[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (grepl("Oper",plant_status[i,latest_col])) {
tran$con2oper[which(tran$Country==plant_status$Country[i])]<- tran$con2oper[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (grepl("Con",plant_status[i,latest_col])) {
tran$con2con[which(tran$Country==plant_status$Country[i])]<- tran$con2con[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}
}
}else if (grepl("Ann",plant_status[i,j])) {
if (j==ncol(plant_status) || !any(grepl("Ann",plant_status[i,(j+1):ncol(plant_status)]))) {
tran$ann[which(tran$Country==plant_status$Country[i])] <- tran$ann[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
if (grepl("Can",plant_status[i,latest_col])) {
tran$ann2can[which(tran$Country==plant_status$Country[i])] <- tran$ann2can[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (grepl("She",plant_status[i,latest_col])) {
tran$ann2she[which(tran$Country==plant_status$Country[i])]<- tran$ann2she[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (grepl("Oper",plant_status[i,latest_col])) {
tran$ann2oper[which(tran$Country==plant_status$Country[i])]<- tran$ann2oper[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (grepl("Con",plant_status[i,latest_col])) {
tran$ann2con[which(tran$Country==plant_status$Country[i])]<- tran$ann2con[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}
}
}else if (grepl("Pre",plant_status[i,j])) {
if (j==ncol(plant_status) || !any(grepl("Pre",plant_status[i,(j+1):ncol(plant_status)]))) {
tran$pre[which(tran$Country==plant_status$Country[i])] <- tran$pre[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
if (grepl("Can",plant_status[i,latest_col])) {
tran$pre2can[which(tran$Country==plant_status$Country[i])] <- tran$pre2can[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (grepl("She",plant_status[i,latest_col])) {
tran$pre2she[which(tran$Country==plant_status$Country[i])]<- tran$pre2she[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (grepl("Oper",plant_status[i,latest_col])) {
tran$pre2oper[which(tran$Country==plant_status$Country[i])]<- tran$pre2oper[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (grepl("Con",plant_status[i,latest_col])) {
tran$pre2con[which(tran$Country==plant_status$Country[i])]<- tran$pre2con[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}
}
}else if (grepl("Perm",plant_status[i,j])) {
if (j==ncol(plant_status) || !any(grepl("Perm",plant_status[i,(j+1):ncol(plant_status)]))) {
tran$perm[which(tran$Country==plant_status$Country[i])] <- tran$perm[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
if (grepl("Can",plant_status[i,latest_col])) {
tran$perm2can[which(tran$Country==plant_status$Country[i])] <- tran$perm2can[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (grepl("She",plant_status[i,latest_col])) {
tran$perm2she[which(tran$Country==plant_status$Country[i])]<- tran$perm2she[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (grepl("Oper",plant_status[i,latest_col])) {
tran$perm2oper[which(tran$Country==plant_status$Country[i])]<- tran$perm2oper[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (grepl("Con",plant_status[i,latest_col])) {
tran$perm2con[which(tran$Country==plant_status$Country[i])]<- tran$perm2con[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}
}
}else if (grepl("Moth",plant_status[i,j])) {
if (j==ncol(plant_status) || !any(grepl("Moth",plant_status[i,(j+1):ncol(plant_status)]))) {
tran$moth[which(tran$Country==plant_status$Country[i])] <- tran$moth[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
if (grepl("Ret",plant_status[i,latest_col])) {
tran$moth2ret[which(tran$Country==plant_status$Country[i])] <- tran$moth2ret[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (any(grepl("Oper",plant_status[i,latest_col:(j-1)]))) {
tran$moth2oper[which(tran$Country==plant_status$Country[i])] <- tran$moth2oper[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}
}
}
j <- j-1
if (grepl("She",plant_status[i,j])) {
if (plant_status[i,j+1]=="Con") {
tran$con2she[which(tran$Country==plant_status$Country[i])]<- tran$con2she[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
if (grepl("Oper",plant_status[i,latest_col])) {
tran$con2she2oper[which(tran$Country==plant_status$Country[i])] <- tran$con2she2oper[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (grepl("Con",plant_status[i,latest_col])) {
tran$con2she2con[which(tran$Country==plant_status$Country[i])] <- tran$con2she2con[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}else if (grepl("Can",plant_status[i,latest_col])) {
tran$con2she2can[which(tran$Country==plant_status$Country[i])] <- tran$con2she2can[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}
}else if (grepl("Ann",plant_status[i,j+1]) | grepl("Pre",plant_status[i,j+1]) | grepl("Perm",plant_status[i,j+1])) {
tran$plan2she[which(tran$Country==plant_status$Country[i])] <- tran$plan2she[which(tran$Country==plant_status$Country[i])] + plant_status$MW[i]
}
}
}
}
tran <- tran[-which(tran$Country=="Kosovo"),]
mtran <- as.magpie(tran,spatial=1)
getYears(mtran) <- "y2020"
mtran <- setItems(dim = 1, x = mtran,
value = toolCountry2isocode(mapping = c("DR Congo" = "COD"), country = getItems(dim = 1, x = mtran)))
mtran <- toolCountryFill(mtran,fill=0,verbosity=2)
# Combined cancellation + shelving rates of each project phase, to be used for completion rate calculation
can_she_rate <- new.magpie(getItems(dim = 1, x = mtran),getYears(mtran),getNames(mtran)[which(!grepl("2",getNames(mtran)) & getNames(mtran)!="moth")],fill=NA)
# Shelving rate of projects in each phase
she_rate <- new.magpie(getItems(dim = 1, x = mtran),getYears(mtran),getNames(can_she_rate),fill=NA)
# Cancellation rate of projects in the shelving phase
can_rate <- new.magpie(getItems(dim = 1, x = mtran),getYears(mtran),"shelved",fill=NA)
reg_can_rate <- new.magpie(getItems(dim = 1, x = regAvgRetAge),getYears(mtran),getNames(can_rate))
reg_can_she_rate <- new.magpie(getItems(dim = 1, x = regAvgRetAge),getYears(mtran),getNames(can_she_rate))
reg_she_rate <- new.magpie(getItems(dim = 1, x = regAvgRetAge),getYears(mtran),getNames(she_rate))
for (phase in getNames(can_she_rate)) {
nonzero_countries <- getItems(dim = 1, x = mtran)[which(mtran[,,phase]!=0)]
zero_countries <- getItems(dim = 1, x = mtran)[which(mtran[,,phase]==0)]
if (phase=="newcon") {
can_she_rate[,,phase] <- (mtran[,,"con2can"] + mtran[,,"con2she2she"])/mtran[,,"newcon"]
she_rate[,,phase] <- mtran[,,"con2she2she"]/mtran[,,"newcon"]
}else if (phase=="shelved") {
can_she_rate[,,phase] <- (mtran[,,"she2can"] + mtran[,,"she2she"])/mtran[,,phase]
she_rate[,,phase] <- mtran[,,"she2she"]/mtran[,,"shelved"]
can_rate[nonzero_countries,,phase] <- mtran[nonzero_countries,,"she2can"]/mtran[nonzero_countries,,"shelved"]
#Calculate global mean rates
glo_can_rate <- weighted.mean(can_rate[nonzero_countries,,phase],mtran[nonzero_countries,,phase])
#Calculate regional rates
tmp <- toolAggregate(can_rate[nonzero_countries,,phase],rel=map[which(map$CountryCode %in% nonzero_countries),],mtran[nonzero_countries,,phase])
#For regions which had zero projects in a certain phase, assign the global weighted mean
reg_can_rate <- suppressWarnings(mbind(tmp,new.magpie(getItems(dim = 1, x = reg_can_rate)[which(!(getItems(dim = 1, x = reg_can_rate)) %in% getItems(dim = 1, x = tmp))],getYears(can_rate),phase,glo_can_rate)))
#Assign regional rates to countries without projects in the present phase
can_rate[zero_countries,,phase] <- as.numeric(reg_can_rate[map$RegionCode[which(map$CountryCode %in% zero_countries)],,phase])
}else {
can_she_rate[,,phase] <- (mtran[,,paste0(phase,"2can")] + mtran[,,paste0(phase,"2she")])/mtran[,,phase]
she_rate[,,phase] <- mtran[,,paste0(phase,"2she")]/mtran[,,phase]
}
#Calculate global mean rates
glo_can_she_rate <- weighted.mean(can_she_rate[nonzero_countries,,phase],mtran[nonzero_countries,,phase])
glo_she_rate <- weighted.mean(she_rate[nonzero_countries,,phase],mtran[nonzero_countries,,phase])
#Calculate regional rates
tmp_can_she <- toolAggregate(can_she_rate[nonzero_countries,,phase],rel=map[which(map$CountryCode %in% nonzero_countries),],mtran[nonzero_countries,,phase])
tmp_she <- toolAggregate(she_rate[nonzero_countries,,phase],rel=map[which(map$CountryCode %in% nonzero_countries),],mtran[nonzero_countries,,phase])
#For regions which had zero projects in a certain phase, assign the global weighted mean
reg_can_she_rate[,,phase] <- suppressWarnings(mbind(tmp_can_she,
new.magpie(getItems(dim = 1, x = reg_can_she_rate)[which(!(getItems(dim = 1, x = reg_can_she_rate)) %in% getItems(dim = 1, x = tmp_can_she))],getYears(mtran),phase,glo_can_she_rate)))
reg_she_rate[,,phase] <- suppressWarnings(mbind(tmp_she,
new.magpie(getItems(dim = 1, x = reg_she_rate)[which(!(getItems(dim = 1, x = reg_she_rate)) %in% getItems(dim = 1, x = tmp_she))],getYears(mtran),phase,glo_she_rate)))
#Assign regional rates to countries without projects in the present phase
can_she_rate[zero_countries,,phase] <- as.numeric(reg_can_she_rate[map$RegionCode[which(map$CountryCode %in% zero_countries)],,phase])
she_rate[zero_countries,,phase] <- as.numeric(reg_she_rate[map$RegionCode[which(map$CountryCode %in% zero_countries)],,phase])
}
#
# #Regional average cancellation and shelving rates, weighted by each country's total projects from 2015-2020
# reg_can_she_rate <- toolAggregate(can_she_rate,rel=map,weight=dimSums(mtran[,,getNames(can_she_rate)],dim=3))
# reg_she_rate <- toolAggregate(she_rate,rel=map,weight=dimSums(mtran[,,getNames(she_rate)],dim=3))
# reg_can_rate <- toolAggregate(can_rate,rel=map,weight=dimSums(mtran[,,getNames(can_rate)],dim=3))
#
#Immaturity index formula to determine nascent coal consuming nations, where >50% of all coal plants are in the current pipeline
immaturity <- (cap_sum[,,"Announced"] + cap_sum[,,"Pre-permit"] + cap_sum[,,"Permitted"] + cap_sum[,,"Construction"] + cap_sum[,,"Shelved"]) /
(dimSums(cap_sum,dim=3))
#Nascent coal consumers AND countries without any projects in the past 6 years are assigned their regional cancellation/shelving rates
can_rate[which(immaturity>0.5)] <- reg_can_rate[map$RegionCode[which(map$CountryCode %in% getItems(dim = 1, x = immaturity)[which(immaturity>0.5)])],,]
can_rate[which(can_rate==0.5)] <- reg_can_rate[map$RegionCode[which(map$CountryCode %in% getItems(dim = 1, x = immaturity)[which(immaturity>0.5)])],,]
comp_rate <- new.magpie(getItems(dim = 1, x = can_she_rate),getYears(mtran),getNames(can_she_rate),fill=NA)
comp_rate_brown <- new.magpie(getItems(dim = 1, x = can_she_rate),getYears(mtran),getNames(can_she_rate),fill=NA)
reg_comp_rate <- new.magpie(getItems(dim = 1, x = reg_can_rate),getYears(mtran),getNames(can_she_rate),fill=NA)
reg_comp_rate_brown <- new.magpie(getItems(dim = 1, x = reg_can_rate),getYears(mtran),getNames(can_she_rate),fill=NA)
glo_comp_rate <- new.magpie("GLO",getYears(mtran),getNames(can_she_rate),fill=NA)
glo_comp_rate_brown <- new.magpie("GLO",getYears(mtran),getNames(can_she_rate),fill=NA)
for (status in getNames(can_she_rate)) {
nonzero_countries <- getItems(dim = 1, x = mtran)[which(mtran[,,status]!=0)]
zero_countries <- getItems(dim = 1, x = mtran)[which(mtran[,,status]==0)]
can_she_rate[,,status][which(immaturity>0.5)] <- reg_can_she_rate[map$RegionCode[which(map$CountryCode %in% getItems(dim = 1, x = immaturity)[which(immaturity>0.5)])],,status]
she_rate[,,status][which(immaturity>0.5)] <- reg_she_rate[map$RegionCode[which(map$CountryCode %in% getItems(dim = 1, x = immaturity)[which(immaturity>0.5)])],,status]
# Derive completion rates to be used in the 2025 capacity estimation
if (status=="shelved") {
comp_rate[,,status] <- 1 - can_rate[,,status]
comp_rate_brown[,,status] <- 1 - 0.5*can_rate[,,status]
}else {
comp_rate[,,status] <- 1 - can_she_rate[,,status]
comp_rate_brown[,,status] <- 1 - 0.5*can_she_rate[,,status]
}
glo_comp_rate[,,status] <- weighted.mean(comp_rate[nonzero_countries,,status],mtran[nonzero_countries,,status])
glo_comp_rate_brown[,,status] <- weighted.mean(comp_rate_brown[nonzero_countries,,status],mtran[nonzero_countries,,status])
if (length(nonzero_countries)) {
#Calculate regional rates
tmp <- toolAggregate(comp_rate[nonzero_countries,,status],filter(map,CountryCode %in% nonzero_countries),mtran[nonzero_countries,,status])
tmp_brown <- toolAggregate(comp_rate_brown[nonzero_countries,,status],filter(map,CountryCode %in% nonzero_countries),mtran[nonzero_countries,,status])
#For regions which had zero projects in a certain status, assign the global weighted mean
reg_comp_rate[,,status] <- suppressWarnings(mbind(tmp,new.magpie(getItems(dim = 1, x = reg_comp_rate)[which(!(getItems(dim = 1, x = reg_comp_rate)) %in% getItems(dim = 1, x = tmp))],getYears(comp_rate),status,glo_comp_rate)))
reg_comp_rate_brown[,,status] <- suppressWarnings(mbind(tmp_brown,new.magpie(getItems(dim = 1, x = reg_comp_rate_brown)[which(!(getItems(dim = 1, x = reg_comp_rate_brown)) %in% getItems(dim = 1, x = tmp_brown))],getYears(comp_rate_brown),status,glo_comp_rate_brown)))
}else {
reg_comp_rate[,,status] <- 0
reg_comp_rate_brown[,,status] <- 0
}
}
# Countries which have indicated an anti-coal recovery plan are assumed to halve their BAU completion rates in the brown and BAU recovery scenarios
# In the green recovery scenario, their completion rates will be 0 (except for under-construction plants which will be built at BAU rates)
# comp_rate_brown[c(nonOECDppca,greenNations),,] <- 0.5 * comp_rate_brown[c(nonOECDppca,greenNations),,]
# comp_rate[c(nonOECDppca,greenNations),,] <- 0.5 * comp_rate[c(nonOECDppca,greenNations),,]
# # Under-construction plants in anti-coal nations will all be completed in both scenarios
# comp_rate[c(nonOECDppca,greenNations),,"newcon"] <- 2 * comp_rate[c(nonOECDppca,greenNations),,"newcon"]
# comp_rate_brown[c(nonOECDppca,greenNations),,"newcon"] <- 2 * comp_rate_brown[c(nonOECDppca,greenNations),,"newcon"]
#
# #OECD PPCA members cannot complete any pipeline projects to meet their 2030 phase-out target
# comp_rate[oecdPPCA,,] <- 0
# comp_rate_brown[oecdPPCA,,] <- 0
#OECD PPCA members and green recovery nations are assumed to cancel all shelved projects and retire mothballed ones.
comp_rate[c(greenNations,oecdPPCA,nonOECDppca),,"shelved"] <- 0
comp_rate_brown[c(greenNations,oecdPPCA,nonOECDppca),,"shelved"] <- 0
cap_sum[c(oecdPPCA,greenNations,nonOECDppca),,"Mothballed"] <- 0
# browser()
# Return completion rate magpies
if (grepl("comp_rates",subtype)) {
getNames(comp_rate) <- c("Shelved","Construction","Announced","Pre-permit","Permitted")
getNames(comp_rate_brown) <- c("Shelved_brown","Construction_brown","Announced_brown","Pre-permit_brown","Permitted_brown")
getNames(glo_comp_rate) <- c("Shelved","Construction","Announced","Pre-permit","Permitted")
getNames(glo_comp_rate_brown) <- c("Shelved_brown","Construction_brown","Announced_brown","Pre-permit_brown","Permitted_brown")
getNames(reg_comp_rate) <- c("Shelved","Construction","Announced","Pre-permit","Permitted")
getNames(reg_comp_rate_brown) <- c("Shelved_brown","Construction_brown","Announced_brown","Pre-permit_brown","Permitted_brown")
getNames(she_rate) <- c("Shelved_shelving","Construction_shelving","Announced_shelving","Pre-permit_shelving","Permitted_shelving")
getNames(reg_she_rate) <- c("Shelved_shelving","Construction_shelving","Announced_shelving","Pre-permit_shelving","Permitted_shelving")
# getNames(glo_she_rate) <- c("Shelved_shelving","Construction_shelving","Announced_shelving","Pre-permit_shelving","Permitted_shelving")
if (grepl("reg",subtype)) {
reg_mtran <- toolAggregate(
setNames(mtran[,,c("shelved","newcon","ann","pre","perm")],
c("Shelved","Construction","Announced","Pre-permit","Permitted")),map,NULL)
reg_all_comp_rate <- toolAggregate(reg_comp_rate,weight=reg_mtran,dim=3,rel=data.frame(from=getNames(reg_mtran),to=rep("all",5)))
out <- suppressWarnings(mbind(reg_comp_rate,reg_all_comp_rate,reg_she_rate))
}else if (grepl("glo",subtype)) {
out <- suppressWarnings(mbind(glo_comp_rate,glo_comp_rate_brown))
# out <- suppressWarnings(mbind(glo_comp_rate,glo_comp_rate_brown,glo_she_rate)
}else out <- suppressWarnings(mbind(comp_rate,comp_rate_brown,she_rate))
return(out)
}
# *********************************Derive Coal capacity in 2025******************************
# browser
cap2025_brown <- cap_sum[,,"Operating"] +
cap_sum[,,"Mothballed"]*0.5 +
cap_sum[,,"Announced"] * comp_rate_brown[,,"ann"] +
cap_sum[,,"Pre-permit"] * comp_rate_brown[,,"pre"] +
cap_sum[,,"Permitted"] * comp_rate_brown[,,"perm"] +
cap_sum[,,"Construction"] * comp_rate_brown[,,"newcon"] +
comp_rate_brown[,,"shelved"] * (cap_sum[,,"Shelved"] +
cap_sum[,,"Announced"] * (she_rate[,,"ann"]) +
cap_sum[,,"Pre-permit"] * (she_rate[,,"pre"]) +
cap_sum[,,"Permitted"] * (she_rate[,,"perm"]) +
cap_sum[,,"Construction"] * (she_rate[,,"newcon"])) -
retireBrown[,,"Retiring_Cap"]
cap2025_BAU <- cap_sum[,,"Operating"] +
cap_sum[,,"Mothballed"]*0.5 +
cap_sum[,,"Announced"] * comp_rate[,,"ann"] +
cap_sum[,,"Pre-permit"] * comp_rate[,,"pre"] +
cap_sum[,,"Permitted"] * comp_rate[,,"perm"] +
cap_sum[,,"Construction"] * comp_rate[,,"newcon"] +
comp_rate[,,"shelved"] * (cap_sum[,,"Shelved"] +
cap_sum[,,"Announced"] * (she_rate[,,"ann"]) +
cap_sum[,,"Pre-permit"] * (she_rate[,,"pre"]) +
cap_sum[,,"Permitted"] * (she_rate[,,"perm"]) +
cap_sum[,,"Construction"] * (she_rate[,,"newcon"])) -
retireBAU[,,"Retiring_Cap"]
cap2025_green <- cap_sum[,,"Operating"] +
cap_sum[,,"Announced"] * comp_rate[,,"ann"]/2 +
cap_sum[,,"Pre-permit"] * comp_rate[,,"pre"]/2 +
cap_sum[,,"Permitted"] * comp_rate[,,"perm"]/2 +
0.5 * comp_rate[,,"newcon"] * cap_sum[,,"Construction"] +
0.5 * comp_rate[,,"shelved"] * cap_sum[,,"Construction"] * (she_rate[,,"newcon"]) -
retireBAU[,,"Retiring_Cap"]
cap2025_norm <- cap_sum[,,"Operating"] +
cap_sum[,,"Mothballed"]*0.5 +
cap_sum[,,"Announced"] +
cap_sum[,,"Pre-permit"] +
cap_sum[,,"Permitted"] +
cap_sum[,,"Construction"] +
cap_sum[,,"Shelved"] -
retireNorm[,,"Retiring_Cap"]
cap2025_brown[which(cap2025_brown<0)] <- 0
cap2025_green[which(cap2025_green<0)] <- 0
cap2025_norm[which(cap2025_norm<0)] <- 0
cap2025_BAU[which(cap2025_BAU<0)] <- 0
getNames(cap2025_brown) <- "Brown"
getNames(cap2025_green) <- "Green"
getNames(cap2025_norm) <- "Norm"
getNames(cap2025_BAU) <- "Neutral"
getYears(cap2025_brown) <- "y2025"
getYears(cap2025_green) <- "y2025"
getYears(cap2025_norm) <- "y2025"
getYears(cap2025_BAU) <- "y2025"
mcap2025 <- suppressWarnings(mbind(cap2025_brown/1000,cap2025_green/1000,cap2025_BAU/1000,cap2025_norm/1000))
# Return Capacity scenarios
if (grepl("future",subtype) | grepl("FinEx",subtype)) {
return(mcap2025)
}
###### PEGASOS Policy Brief Calcs ##########
if (grepl("PEGASOS",subtype)) {
#PEGASOS policy brief scenario: Popular choice in literature has been to use a globally uniform 35-yr lifespan
retirePEGA <- retire %>% select(Country,`Planned Retire`,`Plant Age`,`Capacity (MW)`,Status,Avg_Ret_Age,`Heat rate`,`Emission factor`) %>%
filter(Status %in% c("Operating","operating")) %>%
filter(`Plant Age` >= 25) %>%
filter(!is.na(`Capacity (MW)`)) %>%
group_by(Country) %>% summarise(Retiring_Cap=sum(`Capacity (MW)`))
retirePEGA$Retiring_Cap[which(is.na(retirePEGA$Retiring_Cap))] <- 0
retirePEGA <- as.magpie(retirePEGA,spatial=1)
retirePEGA <- toolCountryFill(retirePEGA,fill=0,no_remove_warning = "KOS",verbosity=2)
retireNorm <- retire %>% select(Country,`Planned Retire`,`Plant Age`,`Capacity (MW)`,Status,Avg_Ret_Age,`Heat rate`,`Emission factor`) %>%
filter(Status %in% c("Operating","operating")) %>%
filter(`Plant Age` >= 30) %>%
filter(!is.na(`Capacity (MW)`)) %>%
group_by(Country) %>% summarise(Retiring_Cap=sum(`Capacity (MW)`))
retireNorm$Retiring_Cap[which(is.na(retireNorm$Retiring_Cap))] <- 0
retireNorm <- as.magpie(retireNorm,spatial=1)
retireNorm <- toolCountryFill(retireNorm,fill=0,no_remove_warning = "KOS",verbosity=2)
retire1p5C <- retire %>% select(Country,`Planned Retire`,`Plant Age`,`Capacity (MW)`,Status,Avg_Ret_Age) %>%
filter(Status %in% c("Operating","operating")) %>%
filter(`Plant Age` >= 10) %>%
filter(!is.na(`Capacity (MW)`)) %>%
group_by(Country) %>% summarise(Retiring_Cap=sum(`Capacity (MW)`))
retire1p5C$Retiring_Cap[which(is.na(retire1p5C$Retiring_Cap))] <- 0
retire1p5C <- as.magpie(retire1p5C,spatial=1)
retire1p5C <- toolCountryFill(retire1p5C,fill=0,no_remove_warning = "KOS",verbosity=2)
getYears(retireBAU) <- "y2025"
getYears(retireGreen) <- "y2025"
getYears(retireBrown) <- "y2025"
getYears(retirePEGA) <- "y2025"
getYears(retire1p5C) <- "y2025"
getYears(retireNorm) <- "y2025"
for (ts in seq(2030,2070,5)) {
#BAU COVID recovery scenario: Countries maintain their historical average lifespans
retireBAUts <- retire %>% select(Country,`Planned Retire`,`Plant Age`,`Capacity (MW)`,Status,Avg_Ret_Age) %>%
filter(Status %in% c("Operating","operating")) %>%
filter(`Planned Retire`<=ts | `Plant Age` >= (Avg_Ret_Age-(ts-2020))) %>%
filter(!is.na(`Capacity (MW)`)) %>%
group_by(Country) %>% summarise(Retiring_Cap=sum(`Capacity (MW)`))
retireBAUts$Retiring_Cap[which(is.na(retireBAUts$Retiring_Cap))] <- 0
retireBAUts <- as.magpie(retireBAUts,spatial=1)
retireBAUts <- toolCountryFill(setYears(retireBAUts,ts),fill=0,no_remove_warning = "KOS",verbosity=2)
#Green COVID recovery scenario: Countries maintain their historical average lifespans
retireGreents <- retire %>% select(Country,`Planned Retire`,`Plant Age`,`Capacity (MW)`,Status,Avg_Ret_Age) %>%
filter(Status %in% c("Operating","operating")) %>%
filter(`Planned Retire`<=ts | `Plant Age` >= (Avg_Ret_Age-(ts-2015))) %>%
filter(!is.na(`Capacity (MW)`)) %>%
group_by(Country) %>% summarise(Retiring_Cap=sum(`Capacity (MW)`))
retireGreents$Retiring_Cap[which(is.na(retireGreents$Retiring_Cap))] <- 0
retireGreents <- as.magpie(retireGreents,spatial=1)
retireGreents <- toolCountryFill(setYears(retireGreents,ts),fill=0,no_remove_warning = "KOS",verbosity=2)
#Brown COVID recovery scenario: Countries maintain their historical average lifespans
retireBrownts <- retire %>% select(Country,`Planned Retire`,`Plant Age`,`Capacity (MW)`,Status,Avg_Ret_Age) %>%
filter(Status %in% c("Operating","operating")) %>%
filter(`Planned Retire`<=ts | `Plant Age` >= (Avg_Ret_Age-(ts-2025))) %>%
filter(!is.na(`Capacity (MW)`)) %>%
group_by(Country) %>% summarise(Retiring_Cap=sum(`Capacity (MW)`))
retireBrownts$Retiring_Cap[which(is.na(retireBrownts$Retiring_Cap))] <- 0
retireBrownts <- as.magpie(retireBrownts,spatial=1)
retireBrownts <- toolCountryFill(setYears(retireBrownts,ts),fill=0,no_remove_warning = "KOS",verbosity=2)
#PEGASOS policy brief scenario: Popular choice in literature has been to use a globally uniform 35-yr lifespan
retirePEGAts <- retire %>% select(Country,`Planned Retire`,`Plant Age`,`Capacity (MW)`,Status,Avg_Ret_Age) %>%
filter(Status %in% c("Operating","operating")) %>%
filter(`Plant Age` + (ts-2020) >= 35) %>%
filter(!is.na(`Capacity (MW)`)) %>%
group_by(Country) %>% summarise(Retiring_Cap=sum(`Capacity (MW)`))
retirePEGAts$Retiring_Cap[which(is.na(retirePEGAts$Retiring_Cap))] <- 0
retirePEGAts <- as.magpie(retirePEGAts,spatial=1)
retirePEGAts <- toolCountryFill(setYears(retirePEGAts,ts),fill=0,no_remove_warning = "KOS",verbosity=2)
#Another common literature figure has been to use a globally uniform 40-yr lifespan
retireNormts <- retire %>% select(Country,`Planned Retire`,`Plant Age`,`Capacity (MW)`,Status,Avg_Ret_Age) %>%
filter(Status %in% c("Operating","operating")) %>%
filter(`Plant Age` + (ts-2020) >= 40) %>%
filter(!is.na(`Capacity (MW)`)) %>%
group_by(Country) %>% summarise(Retiring_Cap=sum(`Capacity (MW)`))
retireNormts$Retiring_Cap[which(is.na(retireNormts$Retiring_Cap))] <- 0
retireNormts <- as.magpie(retireNormts,spatial=1)
retireNormts <- toolCountryFill(setYears(retireNormts,ts),fill=0,no_remove_warning = "KOS",verbosity=2)
#20 year lifespans are roughly compatible with Hi-1p5C scenarios
retire1p5Cts <- retire %>% select(Country,`Planned Retire`,`Plant Age`,`Capacity (MW)`,Status,Avg_Ret_Age) %>%
filter(Status %in% c("Operating","operating")) %>%
filter(`Plant Age` + (ts-2020) >= 20) %>%
filter(!is.na(`Capacity (MW)`)) %>%
group_by(Country) %>% summarise(Retiring_Cap=sum(`Capacity (MW)`))
retire1p5Cts$Retiring_Cap[which(is.na(retire1p5Cts$Retiring_Cap))] <- 0
retire1p5Cts <- as.magpie(retire1p5Cts,spatial=1)
retire1p5Cts <- toolCountryFill(setYears(retire1p5Cts,ts),fill=0,no_remove_warning = "KOS",verbosity=2)
retireBAU <- mbind(retireBAU,retireBAUts)
retireGreen <- mbind(retireGreen,retireGreents)
retireBrown <- mbind(retireBrown,retireBrownts)
retirePEGA <- mbind(retirePEGA,retirePEGAts)
retire1p5C <- mbind(retire1p5C,retire1p5Cts)
retireNorm <- mbind(retireNorm,retireNormts)
}
if (grepl("pubfinex",subtype)) {
pubfinex <- read.csv("C:/Users/stephenb/Downloads/madrat_main/sources/GCPT/Public_Foreign_Finance_Cap_2021.csv",sep = ";")
if (grepl("REsub",subtype)) {
output <- new.magpie(pubfinex$Country,c(2025,2030),NULL,fill = 0)
output[,2025,] <- as.numeric(pubfinex$Foreign) * (1-as.numeric(pubfinex$Ann_share))
output[,2030,] <- as.numeric(pubfinex$Foreign) * as.numeric(pubfinex$Ann_share)
return(toolCountryFill(output,fill = 0, verbosity = 2))
}
for (iii in c("Ann","Pre","Perm")) {
cap_sum[pubfinex$Country,,grepl(iii,getNames(cap_sum),ignore.case = F)] <- pubfinex[,paste0(iii,"_dom")]
}
}
############ CALCULATE FUTURE CAPACITY TRAJECTORIES #################
brown_PEGA <- cap_sum[,,"Operating"] +
cap_sum[,,"Mothballed"]*0.5 +
cap_sum[,,"Announced"] * comp_rate[,,"ann"] +
cap_sum[,,"Pre-permit"] * comp_rate[,,"pre"] +
cap_sum[,,"Permitted"] * comp_rate[,,"perm"] +
cap_sum[,,"Construction"] * comp_rate[,,"newcon"] +
comp_rate[,,"shelved"] * (cap_sum[,,"Shelved"] +
cap_sum[,,"Announced"] * (she_rate[,,"ann"]) +
cap_sum[,,"Pre-permit"] * (she_rate[,,"pre"]) +
cap_sum[,,"Permitted"] * (she_rate[,,"perm"]) +
cap_sum[,,"Construction"] * (she_rate[,,"newcon"])) -
retireBAU[,,"Retiring_Cap"]
cap_green <- cap_sum[,,"Operating"] +
cap_sum[,,"Announced"] * comp_rate[,,"ann"]/2 +
cap_sum[,,"Pre-permit"] * comp_rate[,,"pre"]/2 +
cap_sum[,,"Permitted"] * comp_rate[,,"perm"]/2 +
0.5 * comp_rate[,,"newcon"] * cap_sum[,,"Construction"] +
0.5 * comp_rate[,,"shelved"] * cap_sum[,,"Construction"] * (she_rate[,,"newcon"]) -
retireGreen[,,"Retiring_Cap"]
upper_gray_PEGA <- cap_sum[,,"Operating"] +
cap_sum[,,"Mothballed"]*0.5 +
cap_sum[,,"Announced"] * comp_rate_brown[,,"ann"] +
cap_sum[,,"Pre-permit"] * comp_rate_brown[,,"pre"] +
cap_sum[,,"Permitted"] * comp_rate_brown[,,"perm"] +
cap_sum[,,"Construction"] * comp_rate_brown[,,"newcon"] +
comp_rate_brown[,,"shelved"] * (cap_sum[,,"Shelved"] +
cap_sum[,,"Announced"] * (she_rate[,,"ann"]) +
cap_sum[,,"Pre-permit"] * (she_rate[,,"pre"]) +
cap_sum[,,"Permitted"] * (she_rate[,,"perm"]) +
cap_sum[,,"Construction"] * (she_rate[,,"newcon"])) -
retireBrown[,,"Retiring_Cap"]
if(grepl("PEGASOS",subtype)) {
red_PEGA <- cap_sum[,,"Operating"] +
cap_sum[,,"Mothballed"]*0.5 +
cap_sum[,,"Announced"] +
cap_sum[,,"Pre-permit"] +
cap_sum[,,"Permitted"] +
cap_sum[,,"Construction"] +
cap_sum[,,"Shelved"] -
retirePEGA[,,"Retiring_Cap"]
}else {
red_PEGA <- cap_sum[,,"Operating"] +
cap_sum[,,"Mothballed"]*0.5 +
cap_sum[,,"Announced"] +
cap_sum[,,"Pre-permit"] +
cap_sum[,,"Permitted"] +
cap_sum[,,"Construction"] +
cap_sum[,,"Shelved"] -
retireNorm[,,"Retiring_Cap"]
}
silver_PEGA <- setYears(cap_sum[,,"Operating"],NULL) - retirePEGA
lower_gray_PEGA <- setYears(cap_sum[,,"Operating"],NULL) - retire1p5C
green_PEGA <- setYears(cap_sum[,,"Operating"],NULL) - retireGreen
gold_PEGA <- setYears(cap_sum[,,"Operating"],NULL) - retireGreen
bronze_PEGA <- setYears(cap_sum[,,"Operating"],NULL) - retireBrown
silver_PEGA[which(silver_PEGA<0)] <- 0
cap_green[which(cap_green<0)] <- 0
gold_PEGA[which(gold_PEGA<0)] <- 0
brown_PEGA[which(brown_PEGA<0)] <- 0
getYears(silver_PEGA) <- seq(2025,2070,5)
getYears(cap_green) <- seq(2025,2070,5)
getYears(gold_PEGA) <- seq(2025,2070,5)
getYears(brown_PEGA) <- seq(2025,2070,5)
getYears(bronze_PEGA) <- seq(2025,2070,5)
getYears(upper_gray_PEGA) <- seq(2025,2070,5)
getYears(red_PEGA) <- seq(2025,2070,5)
getYears(green_PEGA) <- seq(2025,2070,5)
getYears(lower_gray_PEGA) <- seq(2025,2070,5)
### Time Series: Announced and pre-permit projects finish in 2030 ##
if (!grepl("2025",subtype)) {
brown_PEGA[,2025,] <- brown_PEGA[,2025,] - (cap_sum[,,"Announced"] * comp_rate[,,"ann"] +
cap_sum[,,"Pre-permit"] * comp_rate[,,"pre"])
cap_green[,2025,] <- cap_green[,2025,] - (cap_sum[,,"Announced"] * comp_rate[,,"ann"] +
cap_sum[,,"Pre-permit"] * comp_rate[,,"pre"])
upper_gray_PEGA[,2025,] <- upper_gray_PEGA[,2025,] - (cap_sum[,,"Announced"] * comp_rate[,,"ann"] +
cap_sum[,,"Pre-permit"] * comp_rate[,,"pre"])
red_PEGA[,2025,] <- red_PEGA[,2025,] - (cap_sum[,,"Announced"] * comp_rate[,,"ann"] +
cap_sum[,,"Pre-permit"] * comp_rate[,,"pre"])
}
# getNames(silver_PEGA) <- "BAU_can"
getNames(cap_green) <- "Green"
# getNames(gold_PEGA) <- "Green_can"
getNames(brown_PEGA) <- ifelse(grepl("REM",subtype), "Neutral", "Brown")
# getNames(bronze_PEGA) <- "Brown_can"
getNames(upper_gray_PEGA) <- ifelse(grepl("REM",subtype), "Brown", "upGray")
getNames(red_PEGA) <- "Red"
getNames(green_PEGA) <- "Green"
getNames(lower_gray_PEGA) <- "lowGray"
if(grepl("REM",subtype)) {
mcap <- mbind(brown_PEGA,upper_gray_PEGA,cap_green)/1000
mcap <- mcap[,getYears(mcap)<="y2030",]
}else {
mcap <- mbind(brown_PEGA,upper_gray_PEGA,red_PEGA,lower_gray_PEGA,cap_green)/1000
}
# Return Capacity scenarios
return(mcap)
}
# Prepare completion rate data frames
getNames(comp_rate) <- c("Shelved","Construction","Announced","Pre-permit","Permitted")
df_comp <- as.data.frame(comp_rate)
colnames(df_comp)[2] <- "Country"
df_comp <- df_comp[,-3]
df_comp <- df_comp[,-1]
colnames(df_comp)[2] <- "Status"
getNames(comp_rate_brown) <- c("Shelved","Construction","Announced","Pre-permit","Permitted")
df_comp_brown <- as.data.frame(comp_rate_brown)
colnames(df_comp_brown)[2] <- "Country"
df_comp_brown <- df_comp_brown[,-3]
df_comp_brown <- df_comp_brown[,-1]
colnames(df_comp_brown)[2] <- "Status"
#Calculate committed emissions
# Select necessary columns from plant-level database
emi_data <- retire %>% select(Country,`Plant Age`,`Capacity (MW)`,Status,`Heat rate`,`Emission factor`,`Combustion technology`,RETIRED,Year) %>%
filter(!(Status %in% c("Retired","Cancelled","retired","cancelled")) & !is.na(`Capacity (MW)`) & is.na(RETIRED) & !is.na(`Emission factor`)) %>%
left_join(avgRetAge,by="Country") %>% mutate(Brown_Ret_Age=Avg_Ret_Age+5) %>% mutate(Norm_Ret_Age=40)
#Convert Year column to numeric, first handling the special non-numeric cases
emi_data$Year[which(grepl("after",emi_data$Year,ignore.case=TRUE))] <-
as.numeric(gsub("after ","",emi_data$Year[which(grepl("after",emi_data$Year,ignore.case=TRUE))],ignore.case=TRUE))+1
emi_data$Year[which(grepl("/",emi_data$Year,fixed=TRUE) | grepl("-",emi_data$Year,fixed=TRUE))] <-
substr(emi_data$Year[which(grepl("/",emi_data$Year,fixed=TRUE) | grepl("-",emi_data$Year,fixed=TRUE))],1,4)
emi_data$Year <- as.numeric(emi_data$Year)
# Set operating plants with an unknown age to 50% of the national average lifespan
emi_data <- emi_data %>%
mutate(`Plant Age`=
ifelse(is.na(Year) & Status %in% c("Operating","operating"),
0.5 * Avg_Ret_Age,
`Plant Age`))
# emi_data[which(is.na(emi_data$`Plant Age`)),] <- emi_data[which(is.na(emi_data$`Plant Age`)),] %>% mutate(`Plant Age`=Avg_Ret_Age/2)
# Set pipeline plants to appropriate ages
for (Phase in getNames(comp_rate)) {
emi_data <- emi_data %>%
mutate(`Plant Age` =
ifelse(grepl(Phase,Status,ignore.case=F) & is.finite(Year) & Year >=2020,
2020 - Year,
`Plant Age`))
meanAge <- emi_data %>%
filter(grepl(Phase,Status,ignore.case=F) & is.finite(Year) & Year >=2020) %>%
summarise(age=mean(`Plant Age`))
# emi_data$`Plant Age`[which(emi_data$Status==Phase & is.finite(emi_data$Year) & emi_data$Year>=2020)] <-
# 2020 - emi_data$Year[which(emi_data$Status==Phase & is.finite(emi_data$Year) & emi_data$Year>=2020)]
# meanAge <- mean(emi_data$`Plant Age`[which(emi_data$Status==Phase & is.finite(emi_data$Year) & emi_data$Year>=2020)])
emi_data$`Plant Age`[which(emi_data$Status==Phase & is.na(emi_data$Year))] <- meanAge$age
}
# Read in national average capacity factor assumption for each 5-year time-step
capFac <- calcOutput("CapacityFactor",aggregate=F)[,seq(2020,2100,5),"pc"]
capFac <- removeColNa(as.data.frame(capFac))[,-3]
colnames(capFac) <- c("Country","Period","Cap_Factor")
capFac$Period <- as.numeric(as.character(capFac$Period))
# Assign each coal plant its country's average capacity factor per time-step
emi_data <- emi_data %>% left_join(capFac,by="Country") %>% mutate(`Plant Age`=`Plant Age`+(Period-2020))
# Calculate emissions from each plant for each 5-yr timestep in each scenario using GCPT methodology
# 5 * MW * cf * Btu/kWh * kgCO2/TJ * 9.2427e-12
emi_data <- emi_data %>% mutate(green_5yr_emi=ifelse(emi_data$`Plant Age`>=0 & (emi_data$`Plant Age` < emi_data$Avg_Ret_Age),
5 * `Capacity (MW)` * Cap_Factor * `Heat rate` * `Emission factor`*9.2427e-12, 0)) %>%
mutate(bau_5yr_emi=ifelse(emi_data$`Plant Age`>=0 & (emi_data$`Plant Age` < emi_data$Avg_Ret_Age),
5 * `Capacity (MW)` * Cap_Factor * `Heat rate` * `Emission factor`*9.2427e-12, 0)) %>%
mutate(brown_5yr_emi=ifelse(emi_data$`Plant Age`>=0 & (emi_data$`Plant Age` < emi_data$Brown_Ret_Age),
5 * `Capacity (MW)` * Cap_Factor * `Heat rate` * `Emission factor`*9.2427e-12, 0)) %>%
mutate(norm_5yr_emi=ifelse(emi_data$`Plant Age`>=0 & (emi_data$`Plant Age` < emi_data$Norm_Ret_Age),
5 * `Capacity (MW)` * 0.53 * `Heat rate` * `Emission factor`*9.2427e-12, 0))
# Calculate total emissions (MtCO2) over the lifespan of plants in operation and development for each scenario
green_emi <- emi_data %>% group_by(Country,.add=TRUE) %>% group_by(Status,.add=TRUE) %>% summarise(green_emi = sum(green_5yr_emi))
bau_emi <- emi_data %>% group_by(Country,.add=TRUE) %>% group_by(Status,.add=TRUE) %>% summarise(BAU_emi = sum(bau_5yr_emi))
brown_emi <- emi_data %>% group_by(Country,.add=TRUE) %>% group_by(Status,.add=TRUE) %>% summarise(brown_emi = sum(brown_5yr_emi))
norm_emi <- emi_data %>% group_by(Country,.add=TRUE) %>% group_by(Status,.add=TRUE) %>% summarise(norm_emi = sum(norm_5yr_emi))
# Calculate total emissions from all operating & pipeline plants with 40-yr lifespans
norm_emi <- norm_emi %>% group_by(Country) %>% summarise(norm_total_emi=sum(norm_emi)/1000)
glo_norm_emi <- norm_emi %>% summarise(norm_global_emi=sum(norm_total_emi))
# Calculate committed emissions reduced by PPCA members based on norm assumptions
ppca_emi <- emi_data %>%
filter(Period>=2030 & Country %in% ppca) %>%
mutate(norm_5yr_emi=ifelse(Period==2030,norm_5yr_emi/2,norm_5yr_emi)) %>%
summarise(avoided_emissions=sum(norm_5yr_emi)/1000)
# Calculate total emissions from all operating & pipeline plants
all_emi <- bau_emi %>% group_by(Country) %>% summarise(All_total_emi=sum(BAU_emi)/1000)
glo_all_emi <- all_emi %>% summarise(All_global_emi=sum(All_total_emi))
# Calculate total emissions from operating & pipeline plants in each recovery scenario
bau_emi <- left_join(bau_emi,df_comp,by=c("Country","Status"))
bau_emi$Value[which(bau_emi$Status=="Operating")] <- 1
bau_emi$Value[which(bau_emi$Status=="Mothballed")] <- 0.5
bau_emi <- bau_emi %>% group_by(Country) %>% summarize(BAU_total_emi=sum(BAU_emi*Value)/1000)
glo_bau_emi <- bau_emi %>% summarise(Global_BAU_emi=sum(BAU_total_emi))
green_emi <- left_join(green_emi,df_comp,by=c("Country","Status"))
green_emi$Value <- 0.5 * green_emi$Value
green_emi$Value[which(green_emi$Status=="Operating")] <- 1
green_emi$Value[which(green_emi$Status=="Mothballed")] <- 0
green_emi <- green_emi %>% group_by(Country) %>% summarize(green_total_emi=sum(green_emi*Value)/1000)
glo_green_emi <- green_emi %>% summarise(Global_green_emi=sum(green_total_emi))
brown_emi <- left_join(brown_emi,df_comp_brown,by=c("Country","Status"))
brown_emi$Value[which(brown_emi$Status=="Operating")] <- 1
brown_emi$Value[which(brown_emi$Status=="Mothballed")] <- 0.5
brown_emi <- brown_emi %>% group_by(Country) %>% summarize(brown_total_emi=sum(brown_emi*Value)/1000)
glo_brown_emi <- brown_emi %>% summarise(Global_brown_emi=sum(brown_total_emi))
# Calculate capacity-weighted mean emission factor by country and plant status
# emifac <- emi_data %>% group_by(Country,.add=TRUE) %>% group_by(Status,.add=TRUE) %>%
# summarise(EF_w_mean = weighted.mean(`Emission factor`,`Capacity (MW)`))
bau_emi <- toolCountryFill(as.magpie(bau_emi),fill=0,verbosity=2)
green_emi <- toolCountryFill(as.magpie(green_emi),fill=0,verbosity=2)
brown_emi <- toolCountryFill(as.magpie(brown_emi),fill=0,verbosity=2)
norm_emi <- toolCountryFill(as.magpie(norm_emi),fill=0,verbosity=2)
if (grepl("emissions",subtype)) {
return(mbind(bau_emi,green_emi,brown_emi,norm_emi))
}else if (grepl("ppca_emi",subtype)) {
return(as.magpie(ppca_emi))
}
}
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