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 quitte removeColNa
#' @aliases readEndCoal
readGCPT <- function(subtype) {
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!")
}
`Announced + Pre-permit + Permitted` <- NULL
. <- NULL
Country <- NULL
CountryCode <- NULL
MW <- 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
Value <- NULL
brown_emi <- NULL
green_emi <- NULL
norm_emi <- NULL
map <- toolGetMapping(getConfig("regionmapping"), type = "regional", where = "mappingfolder")
# Data files to be read in
summary_data <- "GCPT_data_Jan2023.xlsx"
status_changes <- "Jan 2023 GCPT Status Changes - 2014 - 2022 (final).csv"
plant_data <- "Global-Coal-Plant-Tracker-January-2023.xlsx"
lastCol <- "X"
sep <- ";"
latest_col <- 3
plant_status <- read.csv2(status_changes, sep = sep, stringsAsFactors = FALSE, fileEncoding = "ISO-8859-13")
plant_status <- plant_status %>%
select(Country, MW, starts_with("H2")) %>%
mutate("Country" = ifelse(grepl("rkiye", Country), "Turkey", Country),
"Country" = ifelse(grepl("Ivoire", Country), "Cote d'Ivoire", Country),
"MW" = as.numeric(MW))
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))
# 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 <- 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))
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)])
# 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)
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$Country == "KOS"), ]
if (present_year != 2020) {
retire <- retire %>%
mutate(
`Plant Age` =
ifelse(is.na(Year),
NA,
ifelse(grepl("retire", Status, ignore.case = TRUE),
ifelse(is.na(RETIRED),
NA,
RETIRED - Year
),
present_year - Year
)
)
)
}
retAge <- retire %>%
select(Country, `Plant Age`, `Capacity (MW)`, Status) %>%
filter(Status %in% c("Retired", "retired")) %>%
filter(!is.na(`Plant Age`)) %>%
filter(`Plant Age` > 0) %>%
filter(!is.na(`Capacity (MW)`))
avgRetAge <- retAge %>%
group_by(Country) %>%
summarise(Avg_Ret_Age = weighted.mean(`Plant Age`, `Capacity (MW)`))
retCap <- retAge %>%
group_by(Country) %>%
summarise(Retired_Cap = sum(`Capacity (MW)`))
avgRetAge <- avgRetAge[do.call(order, avgRetAge), ]
retCap <- retCap[do.call(order, retCap), ]
mavgRetAge <- as.magpie(avgRetAge, spatial = 1)
mavgRetAge <- toolCountryFill(mavgRetAge, fill = 0, no_remove_warning = "KOS", verbosity = 2)
retCap <- as.magpie(retCap, spatial = 1)
retCap <- toolCountryFill(retCap, fill = 0, no_remove_warning = "KOS", verbosity = 2)
regAvgRetAge <- toolAggregate(mavgRetAge, map, weight = retCap)
regAvgRetAge[which(regAvgRetAge == 0)] <- mean(regAvgRetAge)
mavgRetAge[getItems(dim = 1, x = mavgRetAge)[mavgRetAge == 0], , ] <- as.vector(regAvgRetAge[map$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$Retiring_Cap))] <- 0
retireNorm <- as.magpie(retireNorm, spatial = 1)
retireNorm <- toolCountryFill(retireNorm, fill = 0, no_remove_warning = "KOS", verbosity = 2)
# Relevant regional mappings and country classifications
# Current members of PPCA
PPCAmap <- toolGetMapping("regionmappingPPCA.csv", type = "regional", where = "mappingfolder")
# Current OECD members
OECDmap <- toolGetMapping("regionmappingOECD.csv", type = "regional", where = "mappingfolder")
ppca <- PPCAmap$CountryCode[which(PPCAmap$RegionCode == "PPCA")]
oecd <- OECDmap$CountryCode[which(OECDmap$RegionCode == "OECD")]
EU27 <- map$CountryCode[which(map$RegionCode == "EUR")]
# The PPCA stipulates that OECD and EU members must phase out coal power by 2030
oecdPPCA <- ppca[which(ppca %in% oecd | ppca %in% EU27)]
# While non-OECD members must phase out coal power by 2050
nonOECDppca <- ppca[which(!(ppca %in% oecdPPCA))]
# There are several countries which have explicit plans to abandon coal power in their COVID recovery packages
greenNations <- c(EU27[-which(EU27 == "POL")][-which(EU27 %in% ppca)], "EGY", "AUS", "KOR", "CHL")
if (subtype != "PEGASOS") greenNations <- c(greenNations, "BGD", "PAK", "PHL", "VNM")
###########################
### ANNUAL PLANT STATUS ###
###########################
# Remove any plants with unknown (NA) capacity from plant status data frame
plant_status <- plant_status %>%
filter(!is.na(MW))
tran <- data.frame(Country = unique(plant_status$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])
}
# 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
}
}
# 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
# 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")
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))
} 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(file.path("Coal_Finance_Exit", "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 = FALSE)] <- 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(cap_green) <- "Green"
getNames(brown_PEGA) <- ifelse(grepl("REM", subtype), "Neutral", "Brown")
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`))
# 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 = FALSE) & is.finite(Year) & Year >= 2020,
2020 - Year,
`Plant Age`))
meanAge <- emi_data %>%
filter(grepl(Phase, Status, ignore.case = FALSE) & is.finite(Year) & Year >= 2020) %>%
summarise(age = mean(`Plant Age`))
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 = FALSE)[, 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 #nolint
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)
# 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 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)
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)
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)
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 Sept. 26, 2024, 1:15 p.m.
R Package Documentation
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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 quitte removeColNa
#' @aliases readEndCoal
readGCPT <- function(subtype) {
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!")
}
`Announced + Pre-permit + Permitted` <- NULL
. <- NULL
Country <- NULL
CountryCode <- NULL
MW <- 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
Value <- NULL
brown_emi <- NULL
green_emi <- NULL
norm_emi <- NULL
map <- toolGetMapping(getConfig("regionmapping"), type = "regional", where = "mappingfolder")
# Data files to be read in
summary_data <- "GCPT_data_Jan2023.xlsx"
status_changes <- "Jan 2023 GCPT Status Changes - 2014 - 2022 (final).csv"
plant_data <- "Global-Coal-Plant-Tracker-January-2023.xlsx"
lastCol <- "X"
sep <- ";"
latest_col <- 3
plant_status <- read.csv2(status_changes, sep = sep, stringsAsFactors = FALSE, fileEncoding = "ISO-8859-13")
plant_status <- plant_status %>%
select(Country, MW, starts_with("H2")) %>%
mutate("Country" = ifelse(grepl("rkiye", Country), "Turkey", Country),
"Country" = ifelse(grepl("Ivoire", Country), "Cote d'Ivoire", Country),
"MW" = as.numeric(MW))
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))
# 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 <- 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))
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)])
# 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)
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$Country == "KOS"), ]
if (present_year != 2020) {
retire <- retire %>%
mutate(
`Plant Age` =
ifelse(is.na(Year),
NA,
ifelse(grepl("retire", Status, ignore.case = TRUE),
ifelse(is.na(RETIRED),
NA,
RETIRED - Year
),
present_year - Year
)
)
)
}
retAge <- retire %>%
select(Country, `Plant Age`, `Capacity (MW)`, Status) %>%
filter(Status %in% c("Retired", "retired")) %>%
filter(!is.na(`Plant Age`)) %>%
filter(`Plant Age` > 0) %>%
filter(!is.na(`Capacity (MW)`))
avgRetAge <- retAge %>%
group_by(Country) %>%
summarise(Avg_Ret_Age = weighted.mean(`Plant Age`, `Capacity (MW)`))
retCap <- retAge %>%
group_by(Country) %>%
summarise(Retired_Cap = sum(`Capacity (MW)`))
avgRetAge <- avgRetAge[do.call(order, avgRetAge), ]
retCap <- retCap[do.call(order, retCap), ]
mavgRetAge <- as.magpie(avgRetAge, spatial = 1)
mavgRetAge <- toolCountryFill(mavgRetAge, fill = 0, no_remove_warning = "KOS", verbosity = 2)
retCap <- as.magpie(retCap, spatial = 1)
retCap <- toolCountryFill(retCap, fill = 0, no_remove_warning = "KOS", verbosity = 2)
regAvgRetAge <- toolAggregate(mavgRetAge, map, weight = retCap)
regAvgRetAge[which(regAvgRetAge == 0)] <- mean(regAvgRetAge)
mavgRetAge[getItems(dim = 1, x = mavgRetAge)[mavgRetAge == 0], , ] <- as.vector(regAvgRetAge[map$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$Retiring_Cap))] <- 0
retireNorm <- as.magpie(retireNorm, spatial = 1)
retireNorm <- toolCountryFill(retireNorm, fill = 0, no_remove_warning = "KOS", verbosity = 2)
# Relevant regional mappings and country classifications
# Current members of PPCA
PPCAmap <- toolGetMapping("regionmappingPPCA.csv", type = "regional", where = "mappingfolder")
# Current OECD members
OECDmap <- toolGetMapping("regionmappingOECD.csv", type = "regional", where = "mappingfolder")
ppca <- PPCAmap$CountryCode[which(PPCAmap$RegionCode == "PPCA")]
oecd <- OECDmap$CountryCode[which(OECDmap$RegionCode == "OECD")]
EU27 <- map$CountryCode[which(map$RegionCode == "EUR")]
# The PPCA stipulates that OECD and EU members must phase out coal power by 2030
oecdPPCA <- ppca[which(ppca %in% oecd | ppca %in% EU27)]
# While non-OECD members must phase out coal power by 2050
nonOECDppca <- ppca[which(!(ppca %in% oecdPPCA))]
# There are several countries which have explicit plans to abandon coal power in their COVID recovery packages
greenNations <- c(EU27[-which(EU27 == "POL")][-which(EU27 %in% ppca)], "EGY", "AUS", "KOR", "CHL")
if (subtype != "PEGASOS") greenNations <- c(greenNations, "BGD", "PAK", "PHL", "VNM")
###########################
### ANNUAL PLANT STATUS ###
###########################
# Remove any plants with unknown (NA) capacity from plant status data frame
plant_status <- plant_status %>%
filter(!is.na(MW))
tran <- data.frame(Country = unique(plant_status$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])
}
# 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
}
}
# 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
# 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")
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))
} 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(file.path("Coal_Finance_Exit", "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 = FALSE)] <- 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(cap_green) <- "Green"
getNames(brown_PEGA) <- ifelse(grepl("REM", subtype), "Neutral", "Brown")
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`))
# 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 = FALSE) & is.finite(Year) & Year >= 2020,
2020 - Year,
`Plant Age`))
meanAge <- emi_data %>%
filter(grepl(Phase, Status, ignore.case = FALSE) & is.finite(Year) & Year >= 2020) %>%
summarise(age = mean(`Plant Age`))
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 = FALSE)[, 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 #nolint
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)
# 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 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)
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)
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)
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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