R/readIEA_WEO.R
In pik-piam/mrremind: MadRat REMIND Input Data Package
Defines functions
readIEA_WEO
Documented in
readIEA_WEO
#' Read IEA World Energy Outlook data
#'
#' @description Read-in IEA WEO 2016 data for investment costs, O&M costs and
#' Efficiency of different technologies, and WEO 2017 data for historical
#' electricity capacities (GW), generation (TWh) or emissions (Mt CO2).
#' WEO 2019 data for PE and FE (Mtoe).
#' @param subtype data subtype. Either "Capacity", "Generation", "Emissions",
#' "Investment Costs", "O&M Costs" or "Efficiency"
#' @return magpie object of the WEO data on generation (TWh), capacities (GW),
#' emissions (Mt CO2) or disaggregated investment cost as magpie object
#' @author Renato Rodrigues, Aman Malik, and Jerome Hilaire
#' @seealso \code{\link{readSource}}
#' @examples
#' \dontrun{
#' a <- readSource(type = "WEO", subtype = "Capacity")
#' }
#'
#' @importFrom tidyr gather
#' @importFrom readxl read_excel
readIEA_WEO <- function(subtype) {
period <- NULL
value <- NULL
variable <- NULL
model <- NULL
scenario <- NULL
region <- NULL
unit <- NULL
if ((subtype == "Invest_Costs") || (subtype == "O&M_Costs") || (subtype == "Efficiency")) {
# read WEO 2016 input files- values are from the New Policy scenario,
# except for CCS costs which are from 450 scenario.
# Coal
input_coal <- read.csv(file = "WEO_2016-coal.csv", na.strings = "n.a.", stringsAsFactors = FALSE)
input_coal$maintech <- "Coal"
# Gas
input_gas <- read.csv(file = "WEO_2016-gas.csv", na.strings = "n.a.", stringsAsFactors = FALSE)
input_gas$maintech <- "Gas"
# Tech with CCS
input_ccs <- read.csv(file = "WEO_2016-ccs.csv", na.strings = "n.a.", stringsAsFactors = FALSE)
input_ccs$maintech <- "CCS"
# Tech with NUC
input_nuc <- read.csv(file = "WEO_2016-nuclear.csv", na.strings = "n.a.", stringsAsFactors = FALSE)
input_nuc$maintech <- "Nuclear"
# Tech with REN
input_ren <- read.csv(file = "WEO_2016-ren.csv", na.strings = "n.a.", stringsAsFactors = FALSE)
input_ren$maintech <- "Renewables"
# Special case for hydro, values are taken not from the IEA database.
input_hydro <- read.csv(file = "hydro.csv", stringsAsFactors = FALSE)
# removing 2025 values
input_hydro <- input_hydro %>% select(-5)
# 2040 values same as 2030 values
input_hydro$X2040 <- input_hydro$X2030
input_hydro <- cbind(input_hydro, input_ren[1:12, 7:14])
input_hydro[, 7:14] <- 0
input_hydro$maintech <- "Hydro_2"
input_all <- bind_rows(input_coal, input_gas, input_ccs, input_nuc, input_ren, input_hydro)
if (subtype == "Invest_Costs") {
input <- input_all[, c(15, 1:6)]
colnames(input)[4:7] <- c(2015, 2020, 2030, 2040)
input <- input %>%
gather(4:7, key = "Year", value = "costs")
x <- as.magpie(input, spatial = 3, temporal = 4, datacol = 5)
} else if (subtype == "O&M_Costs") {
input <- input_all[, c(15, 1:2, 7:10)]
colnames(input)[4:7] <- c(2015, 2020, 2030, 2040)
input <- input %>%
gather(4:7, key = "Year", value = "costs")
x <- as.magpie(input, spatial = 3, temporal = 4, datacol = 5)
} else if (subtype == "Efficiency") {
input <- input_all[, c(15, 1:2, 11:14)]
colnames(input)[4:7] <- c(2015, 2020, 2030, 2040)
input <- input %>%
gather(4:7, key = "Year", value = "efficiency")
x <- as.magpie(input, spatial = 3, temporal = 4, datacol = 5)
}
} else if ((subtype == "Capacity") || (subtype == "Generation") || (subtype == "Emissions")) {
if (subtype == "Capacity") {
data <- read.csv("WEO_2017/WEO-capacity.csv", sep = ";")[, c(2, 3, 4, 5)]
} else if (subtype == "Generation") {
data <- read.csv("WEO_2017/WEO-generation.csv", sep = ";")[, c(2, 3, 4, 5)]
} else if (subtype == "Emissions") {
data <- read.csv("WEO_2017/WEO-EmiCO2.csv", sep = ";")[, c(2, 3, 4, 5)]
}
# creating capacity, generation or emissions magpie object
x <- as.magpie(data, temporal = 2, spatial = 1, datacol = 4)
} else if ((subtype == "PE") || (subtype == "FE")) {
# read-in sheet names from the excel file
data_weo2019_sheets <- readxl::excel_sheets("WEO2019_AnnexA.xlsx")
sheets_balance <- grep("Balance", data_weo2019_sheets, value = TRUE)
tmp_all <- list()
for (ksheet in sheets_balance) {
kreg <- substr(ksheet, 1, nchar(ksheet) - nchar("_Balance"))
tmp_sps <- readxl::read_excel("WEO2019_AnnexA.xlsx", sheet = ksheet, range = "A5:H56", .name_repair = "unique_quiet")
tmp_cps <- readxl::read_excel("WEO2019_AnnexA.xlsx", sheet = ksheet, range = "M5:Q56", .name_repair = "unique_quiet")
tmp_sds <- readxl::read_excel("WEO2019_AnnexA.xlsx", sheet = ksheet, range = "U5:X56", .name_repair = "unique_quiet")
names(tmp_sps)[1] <- "variable"
names(tmp_cps)[1] <- "variable"
tmp_sds <- cbind(
tmp_cps[, 1],
tmp_sds
)
# Primary energy demand
tmp1 <- tmp_sps[which(tmp_sps[, 1] == "Total primary demand"):(which(tmp_sps[, 1] == "Power sector") - 1), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Stated Policies Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = ifelse(variable == "Natural gas", "Gas", variable)) %>%
mutate(variable = ifelse(variable == "Bioenergy", "Biomass", variable)) %>%
mutate(variable = paste0("Primary Energy|", variable)) %>%
mutate(variable = ifelse(variable == "Primary Energy|Total primary demand", "Primary Energy", variable)) %>%
mutate(unit = "Mtoe") %>%
select("model", "scenario", "region", "variable", "unit", "period", "value")
tmp1 <- rbind(
tmp1,
rbind(
tmp1 %>%
filter(period < 2025) %>%
mutate(scenario = "Current Policies Scenario"),
tmp_cps[which(tmp_sps[, 1] == "Total primary demand"):(which(tmp_sps[, 1] == "Power sector") - 1), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Current Policies Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = ifelse(variable == "Natural gas", "Gas", variable)) %>%
mutate(variable = ifelse(variable == "Bioenergy", "Biomass", variable)) %>%
mutate(variable = paste0("Primary Energy|", variable)) %>%
mutate(variable = ifelse(variable == "Primary Energy|Total primary demand", "Primary Energy", variable)) %>%
mutate(unit = "Mtoe") %>%
select(model, scenario, region, variable, unit, period, value)),
rbind(
tmp1 %>%
filter(period < 2025) %>%
mutate(scenario = "Sustainable Development Scenario"),
tmp_sds[which(tmp_sps[, 1] == "Total primary demand"):(which(tmp_sps[, 1] == "Power sector") - 1), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Sustainable Development Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = ifelse(variable == "Natural gas", "Gas", variable)) %>%
mutate(variable = ifelse(variable == "Bioenergy", "Biomass", variable)) %>%
mutate(variable = paste0("Primary Energy|", variable)) %>%
mutate(variable = ifelse(variable == "Primary Energy|Total primary demand", "Primary Energy", variable)) %>%
mutate(unit = "Mtoe") %>%
select(model, scenario, region, variable, unit, period, value)))
# Power sector
tmp2 <- tmp_sps[which(tmp_sps[, 1] == "Power sector"):(which(tmp_sps[, 1] == "Other energy sector") - 1), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Stated Policies Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = ifelse(variable == "Natural gas", "Gas", variable)) %>%
# mutate(variable = ifelse(variable == "Bioenergy", "Biomass", variable)) %>%
mutate(variable = paste0("Primary Energy|Electricity|", variable)) %>%
mutate(variable = ifelse(variable == "Primary Energy|Electricity|Power Sector", "Primary Energy|Electricity", variable)) %>%
mutate(unit = "Mtoe") %>%
select(model, scenario, region, variable, unit, period, value)
tmp2 <- rbind(
tmp2,
rbind(
tmp2 %>%
filter(period < 2025) %>%
mutate(scenario = "Current Policies Scenario"),
tmp_cps[which(tmp_sps[, 1] == "Power sector"):(which(tmp_sps[, 1] == "Other energy sector") - 1), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Current Policies Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = ifelse(variable == "Natural gas", "Gas", variable)) %>%
# mutate(variable = ifelse(variable == "Bioenergy", "Biomass", variable)) %>%
mutate(variable = paste0("Primary Energy|Electricity|", variable)) %>%
mutate(variable = ifelse(variable == "Primary Energy|Electricity|Power Sector", "Primary Energy|Electricity", variable)) %>%
mutate(unit = "Mtoe") %>%
select(model, scenario, region, variable, unit, period, value)),
rbind(
tmp2 %>%
filter(period < 2025) %>%
mutate(scenario = "Sustainable Development Scenario"),
tmp_sds[which(tmp_sps[, 1] == "Power sector"):(which(tmp_sps[, 1] == "Other energy sector") - 1), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Sustainable Development Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = ifelse(variable == "Natural gas", "Gas", variable)) %>%
# mutate(variable = ifelse(variable == "Bioenergy", "Biomass", variable)) %>%
mutate(variable = paste0("Primary Energy|Electricity|", variable)) %>%
mutate(variable = ifelse(variable == "Primary Energy|Electricity|Power Sector", "Primary Energy|Electricity", variable)) %>%
mutate(unit = "Mtoe") %>%
select(model, scenario, region, variable, unit, period, value)))
# Final energy consumption
tmp3 <- tmp_sps[which(tmp_sps[, 1] == "Total final consumption"):(which(tmp_sps[, 1] == "Industry") - 1), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Stated Policies Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = ifelse(variable == "Natural gas", "Gas", variable)) %>%
# mutate(variable = ifelse(variable == "Bioenergy", "Biomass", variable)) %>%
mutate(variable = paste0("Final Energy|", variable)) %>%
mutate(variable = ifelse(variable == "Final Energy|Total final consumption", "Final Energy", variable)) %>%
mutate(unit = "Mtoe") %>%
select(model, scenario, region, variable, unit, period, value)
tmp3 <- rbind(
tmp3,
rbind(
tmp3 %>%
filter(period < 2025) %>%
mutate(scenario = "Current Policies Scenario"),
tmp_cps[which(tmp_sps[, 1] == "Total final consumption"):(which(tmp_sps[, 1] == "Industry") - 1), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Current Policies Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = ifelse(variable == "Natural gas", "Gas", variable)) %>%
# mutate(variable = ifelse(variable == "Bioenergy", "Biomass", variable)) %>%
mutate(variable = paste0("Final Energy|", variable)) %>%
mutate(variable = ifelse(variable == "Final Energy|Total final consumption", "Final Energy", variable)) %>%
mutate(unit = "Mtoe") %>%
select(model, scenario, region, variable, unit, period, value)),
rbind(
tmp3 %>%
filter(period < 2025) %>%
mutate(scenario = "Sustainable Development Scenario"),
tmp_sds[which(tmp_sps[, 1] == "Total final consumption"):(which(tmp_sps[, 1] == "Industry") - 1), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Sustainable Development Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = ifelse(variable == "Natural gas", "Gas", variable)) %>%
# mutate(variable = ifelse(variable == "Bioenergy", "Biomass", variable)) %>%
mutate(variable = paste0("Final Energy|", variable)) %>%
mutate(variable = ifelse(variable == "Final Energy|Total final consumption", "Final Energy", variable)) %>%
mutate(unit = "Mtoe") %>%
select(model, scenario, region, variable, unit, period, value)))
# Final energy consumption (Industry)
tmp4 <- tmp_sps[which(tmp_sps[, 1] == "Industry"):(which(tmp_sps[, 1] == "Transport") - 1), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Stated Policies Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = ifelse(variable == "Natural gas", "Gas", variable)) %>%
mutate(variable = paste0("Final Energy|Industry|", variable)) %>%
mutate(variable = ifelse(variable == "Final Energy|Industry|Industry", "Final Energy|Industry", variable)) %>%
mutate(unit = "Mtoe") %>%
select(model, scenario, region, variable, unit, period, value)
tmp4 <- rbind(
tmp4,
rbind(
tmp4 %>%
filter(period < 2025) %>%
mutate(scenario = "Current Policies Scenario"),
tmp_cps[which(tmp_sps[, 1] == "Industry"):(which(tmp_sps[, 1] == "Transport") - 1), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Current Policies Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = ifelse(variable == "Natural gas", "Gas", variable)) %>%
# mutate(variable = ifelse(variable == "Bioenergy", "Biomass", variable)) %>%
mutate(variable = paste0("Final Energy|Industry|", variable)) %>%
mutate(variable = ifelse(variable == "Final Energy|Industry|Industry", "Final Energy|Industry", variable)) %>%
mutate(unit = "Mtoe") %>%
select(model, scenario, region, variable, unit, period, value)),
rbind(
tmp4 %>%
filter(period < 2025) %>%
mutate(scenario = "Sustainable Development Scenario"),
tmp_sds[which(tmp_sps[, 1] == "Industry"):(which(tmp_sps[, 1] == "Transport") - 1), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Sustainable Development Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = ifelse(variable == "Natural gas", "Gas", variable)) %>%
# mutate(variable = ifelse(variable == "Bioenergy", "Biomass", variable)) %>%
mutate(variable = paste0("Final Energy|Industry|", variable)) %>%
mutate(variable = ifelse(variable == "Final Energy|Industry|Industry", "Final Energy|Industry", variable)) %>%
mutate(unit = "Mtoe") %>%
select(model, scenario, region, variable, unit, period, value)))
# Final energy consumption (Transport)
tmp5 <- tmp_sps[which(tmp_sps[, 1] == "Transport"):(which(tmp_sps[, 1] == "Buildings") - 1), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Stated Policies Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = ifelse(variable == "International bunkers", "Oil|International bunkers", variable)) %>%
mutate(variable = paste0("Final Energy|Transportation|", variable)) %>%
mutate(variable = ifelse(variable == "Final Energy|Transportation|Transport", "Final Energy|Transportation", variable)) %>%
mutate(unit = "Mtoe") %>%
select(model, scenario, region, variable, unit, period, value)
tmp5 <- rbind(
tmp5,
rbind(
tmp5 %>%
filter(period < 2025) %>%
mutate(scenario = "Current Policies Scenario"),
tmp_cps[which(tmp_sps[, 1] == "Transport"):(which(tmp_sps[, 1] == "Buildings") - 1), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Current Policies Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = ifelse(variable == "International bunkers", "Oil|International bunkers", variable)) %>%
mutate(variable = paste0("Final Energy|Transportation|", variable)) %>%
mutate(variable = ifelse(variable == "Final Energy|Transportation|Transport", "Final Energy|Transportation", variable)) %>%
mutate(unit = "Mtoe") %>%
select(model, scenario, region, variable, unit, period, value)),
rbind(
tmp5 %>%
filter(period < 2025) %>%
mutate(scenario = "Sustainable Development Scenario"),
tmp_sds[which(tmp_sps[, 1] == "Transport"):(which(tmp_sps[, 1] == "Buildings") - 1), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Sustainable Development Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = ifelse(variable == "International bunkers", "Oil|International bunkers", variable)) %>%
mutate(variable = paste0("Final Energy|Transportation|", variable)) %>%
mutate(variable = ifelse(variable == "Final Energy|Transportation|Transport", "Final Energy|Transportation", variable)) %>%
mutate(unit = "Mtoe") %>%
select(model, scenario, region, variable, unit, period, value)))
# Final energy consumption (Buildings)
tmp6 <- tmp_sps[which(tmp_sps[, 1] == "Buildings"):(which(tmp_sps[, 1] == "Other") - 1), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Stated Policies Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = ifelse(variable == "Traditional biomass", "Bioenergy|Traditional biomass", variable)) %>%
mutate(variable = paste0("Final Energy|Buildings|", variable)) %>%
mutate(variable = ifelse(variable == "Final Energy|Buildings|Buildings", "Final Energy|Buildings", variable)) %>%
mutate(unit = "Mtoe") %>%
select(model, scenario, region, variable, unit, period, value)
tmp6 <- rbind(
tmp6,
rbind(
tmp6 %>%
filter(period < 2025) %>%
mutate(scenario = "Current Policies Scenario"),
tmp_cps[which(tmp_sps[, 1] == "Buildings"):(which(tmp_sps[, 1] == "Other") - 1), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Current Policies Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = ifelse(variable == "Traditional biomass", "Bioenergy|Traditional biomass", variable)) %>%
mutate(variable = paste0("Final Energy|Buildings|", variable)) %>%
mutate(variable = ifelse(variable == "Final Energy|Buildings|Buildings", "Final Energy|Buildings", variable)) %>%
mutate(unit = "Mtoe") %>%
select(model, scenario, region, variable, unit, period, value)),
rbind(
tmp6 %>%
filter(period < 2025) %>%
mutate(scenario = "Sustainable Development Scenario"),
tmp_sds[which(tmp_sps[, 1] == "Buildings"):(which(tmp_sps[, 1] == "Other") - 1), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Sustainable Development Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = ifelse(variable == "Traditional biomass", "Bioenergy|Traditional biomass", variable)) %>%
mutate(variable = paste0("Final Energy|Buildings|", variable)) %>%
mutate(variable = ifelse(variable == "Final Energy|Buildings|Buildings", "Final Energy|Buildings", variable)) %>%
mutate(unit = "Mtoe") %>%
select(model, scenario, region, variable, unit, period, value)))
# Final energy consumption (Other)
tmp7 <- tmp_sps[which(tmp_sps[, 1] == "Other"):which(tmp_sps[, 1] == "Petrochem. feedstock"), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Stated Policies Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = paste0("Final Energy|Other|", variable)) %>%
mutate(variable = ifelse(variable == "Final Energy|Other|Other", "Final Energy|Other", variable)) %>%
mutate(unit = "Mtoe") %>%
select(model, scenario, region, variable, unit, period, value)
tmp7 <- rbind(
tmp7,
rbind(
tmp7 %>%
filter(period < 2025) %>%
mutate(scenario = "Current Policies Scenario"),
tmp_cps[which(tmp_sps[, 1] == "Other"):which(tmp_sps[, 1] == "Petrochem. feedstock"), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Current Policies Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = paste0("Final Energy|Other|", variable)) %>%
mutate(variable = ifelse(variable == "Final Energy|Other|Other", "Final Energy|Other", variable)) %>%
mutate(unit = "Mtoe") %>%
select(model, scenario, region, variable, unit, period, value)),
rbind(
tmp7 %>%
filter(period < 2025) %>%
mutate(scenario = "Sustainable Development Scenario"),
tmp_sds[which(tmp_sps[, 1] == "Other"):which(tmp_sps[, 1] == "Petrochem. feedstock"), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Sustainable Development Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = paste0("Final Energy|Other|", variable)) %>%
mutate(variable = ifelse(variable == "Final Energy|Other|Other", "Final Energy|Other", variable)) %>%
mutate(unit = "Mtoe") %>%
select(model, scenario, region, variable, unit, period, value))
)
tmp_all[[kreg]] <- rbind(tmp1, tmp2, tmp3, tmp4, tmp5, tmp6, tmp7)
}
data_weo2019 <- do.call("rbind", tmp_all)
data_weo2019 <- data_weo2019[, c(3, 6, 1, 2, 5, 4, 7)]
data_weo2019$period <- as.numeric(data_weo2019$period)
data_weo2019$variable <- gsub(pattern = "\\.", replacement = "_", x = data_weo2019$variable)
if (subtype == "PE") {
data_weo2019 <- data_weo2019 %>% filter(variable %in%
grep("Primary Energy", unique(data_weo2019$variable), value = TRUE))
} else if (subtype == "FE") {
data_weo2019 <- data_weo2019 %>% filter(variable %in%
grep("Final Energy", unique(data_weo2019$variable), value = TRUE))
}
x <- as.magpie(data_weo2019, spatial = 1, temporal = 2, datacol = 7)
} else {
stop("Not a valid subtype!")
}
return(x)
}
pik-piam/mrremind documentation built on March 30, 2024, 3:37 a.m.
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Defines functions readIEA_WEO
Documented in readIEA_WEO
#' Read IEA World Energy Outlook data
#'
#' @description Read-in IEA WEO 2016 data for investment costs, O&M costs and
#' Efficiency of different technologies, and WEO 2017 data for historical
#' electricity capacities (GW), generation (TWh) or emissions (Mt CO2).
#' WEO 2019 data for PE and FE (Mtoe).
#' @param subtype data subtype. Either "Capacity", "Generation", "Emissions",
#' "Investment Costs", "O&M Costs" or "Efficiency"
#' @return magpie object of the WEO data on generation (TWh), capacities (GW),
#' emissions (Mt CO2) or disaggregated investment cost as magpie object
#' @author Renato Rodrigues, Aman Malik, and Jerome Hilaire
#' @seealso \code{\link{readSource}}
#' @examples
#' \dontrun{
#' a <- readSource(type = "WEO", subtype = "Capacity")
#' }
#'
#' @importFrom tidyr gather
#' @importFrom readxl read_excel
readIEA_WEO <- function(subtype) {
period <- NULL
value <- NULL
variable <- NULL
model <- NULL
scenario <- NULL
region <- NULL
unit <- NULL
if ((subtype == "Invest_Costs") || (subtype == "O&M_Costs") || (subtype == "Efficiency")) {
# read WEO 2016 input files- values are from the New Policy scenario,
# except for CCS costs which are from 450 scenario.
# Coal
input_coal <- read.csv(file = "WEO_2016-coal.csv", na.strings = "n.a.", stringsAsFactors = FALSE)
input_coal$maintech <- "Coal"
# Gas
input_gas <- read.csv(file = "WEO_2016-gas.csv", na.strings = "n.a.", stringsAsFactors = FALSE)
input_gas$maintech <- "Gas"
# Tech with CCS
input_ccs <- read.csv(file = "WEO_2016-ccs.csv", na.strings = "n.a.", stringsAsFactors = FALSE)
input_ccs$maintech <- "CCS"
# Tech with NUC
input_nuc <- read.csv(file = "WEO_2016-nuclear.csv", na.strings = "n.a.", stringsAsFactors = FALSE)
input_nuc$maintech <- "Nuclear"
# Tech with REN
input_ren <- read.csv(file = "WEO_2016-ren.csv", na.strings = "n.a.", stringsAsFactors = FALSE)
input_ren$maintech <- "Renewables"
# Special case for hydro, values are taken not from the IEA database.
input_hydro <- read.csv(file = "hydro.csv", stringsAsFactors = FALSE)
# removing 2025 values
input_hydro <- input_hydro %>% select(-5)
# 2040 values same as 2030 values
input_hydro$X2040 <- input_hydro$X2030
input_hydro <- cbind(input_hydro, input_ren[1:12, 7:14])
input_hydro[, 7:14] <- 0
input_hydro$maintech <- "Hydro_2"
input_all <- bind_rows(input_coal, input_gas, input_ccs, input_nuc, input_ren, input_hydro)
if (subtype == "Invest_Costs") {
input <- input_all[, c(15, 1:6)]
colnames(input)[4:7] <- c(2015, 2020, 2030, 2040)
input <- input %>%
gather(4:7, key = "Year", value = "costs")
x <- as.magpie(input, spatial = 3, temporal = 4, datacol = 5)
} else if (subtype == "O&M_Costs") {
input <- input_all[, c(15, 1:2, 7:10)]
colnames(input)[4:7] <- c(2015, 2020, 2030, 2040)
input <- input %>%
gather(4:7, key = "Year", value = "costs")
x <- as.magpie(input, spatial = 3, temporal = 4, datacol = 5)
} else if (subtype == "Efficiency") {
input <- input_all[, c(15, 1:2, 11:14)]
colnames(input)[4:7] <- c(2015, 2020, 2030, 2040)
input <- input %>%
gather(4:7, key = "Year", value = "efficiency")
x <- as.magpie(input, spatial = 3, temporal = 4, datacol = 5)
}
} else if ((subtype == "Capacity") || (subtype == "Generation") || (subtype == "Emissions")) {
if (subtype == "Capacity") {
data <- read.csv("WEO_2017/WEO-capacity.csv", sep = ";")[, c(2, 3, 4, 5)]
} else if (subtype == "Generation") {
data <- read.csv("WEO_2017/WEO-generation.csv", sep = ";")[, c(2, 3, 4, 5)]
} else if (subtype == "Emissions") {
data <- read.csv("WEO_2017/WEO-EmiCO2.csv", sep = ";")[, c(2, 3, 4, 5)]
}
# creating capacity, generation or emissions magpie object
x <- as.magpie(data, temporal = 2, spatial = 1, datacol = 4)
} else if ((subtype == "PE") || (subtype == "FE")) {
# read-in sheet names from the excel file
data_weo2019_sheets <- readxl::excel_sheets("WEO2019_AnnexA.xlsx")
sheets_balance <- grep("Balance", data_weo2019_sheets, value = TRUE)
tmp_all <- list()
for (ksheet in sheets_balance) {
kreg <- substr(ksheet, 1, nchar(ksheet) - nchar("_Balance"))
tmp_sps <- readxl::read_excel("WEO2019_AnnexA.xlsx", sheet = ksheet, range = "A5:H56", .name_repair = "unique_quiet")
tmp_cps <- readxl::read_excel("WEO2019_AnnexA.xlsx", sheet = ksheet, range = "M5:Q56", .name_repair = "unique_quiet")
tmp_sds <- readxl::read_excel("WEO2019_AnnexA.xlsx", sheet = ksheet, range = "U5:X56", .name_repair = "unique_quiet")
names(tmp_sps)[1] <- "variable"
names(tmp_cps)[1] <- "variable"
tmp_sds <- cbind(
tmp_cps[, 1],
tmp_sds
)
# Primary energy demand
tmp1 <- tmp_sps[which(tmp_sps[, 1] == "Total primary demand"):(which(tmp_sps[, 1] == "Power sector") - 1), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Stated Policies Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = ifelse(variable == "Natural gas", "Gas", variable)) %>%
mutate(variable = ifelse(variable == "Bioenergy", "Biomass", variable)) %>%
mutate(variable = paste0("Primary Energy|", variable)) %>%
mutate(variable = ifelse(variable == "Primary Energy|Total primary demand", "Primary Energy", variable)) %>%
mutate(unit = "Mtoe") %>%
select("model", "scenario", "region", "variable", "unit", "period", "value")
tmp1 <- rbind(
tmp1,
rbind(
tmp1 %>%
filter(period < 2025) %>%
mutate(scenario = "Current Policies Scenario"),
tmp_cps[which(tmp_sps[, 1] == "Total primary demand"):(which(tmp_sps[, 1] == "Power sector") - 1), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Current Policies Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = ifelse(variable == "Natural gas", "Gas", variable)) %>%
mutate(variable = ifelse(variable == "Bioenergy", "Biomass", variable)) %>%
mutate(variable = paste0("Primary Energy|", variable)) %>%
mutate(variable = ifelse(variable == "Primary Energy|Total primary demand", "Primary Energy", variable)) %>%
mutate(unit = "Mtoe") %>%
select(model, scenario, region, variable, unit, period, value)),
rbind(
tmp1 %>%
filter(period < 2025) %>%
mutate(scenario = "Sustainable Development Scenario"),
tmp_sds[which(tmp_sps[, 1] == "Total primary demand"):(which(tmp_sps[, 1] == "Power sector") - 1), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Sustainable Development Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = ifelse(variable == "Natural gas", "Gas", variable)) %>%
mutate(variable = ifelse(variable == "Bioenergy", "Biomass", variable)) %>%
mutate(variable = paste0("Primary Energy|", variable)) %>%
mutate(variable = ifelse(variable == "Primary Energy|Total primary demand", "Primary Energy", variable)) %>%
mutate(unit = "Mtoe") %>%
select(model, scenario, region, variable, unit, period, value)))
# Power sector
tmp2 <- tmp_sps[which(tmp_sps[, 1] == "Power sector"):(which(tmp_sps[, 1] == "Other energy sector") - 1), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Stated Policies Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = ifelse(variable == "Natural gas", "Gas", variable)) %>%
# mutate(variable = ifelse(variable == "Bioenergy", "Biomass", variable)) %>%
mutate(variable = paste0("Primary Energy|Electricity|", variable)) %>%
mutate(variable = ifelse(variable == "Primary Energy|Electricity|Power Sector", "Primary Energy|Electricity", variable)) %>%
mutate(unit = "Mtoe") %>%
select(model, scenario, region, variable, unit, period, value)
tmp2 <- rbind(
tmp2,
rbind(
tmp2 %>%
filter(period < 2025) %>%
mutate(scenario = "Current Policies Scenario"),
tmp_cps[which(tmp_sps[, 1] == "Power sector"):(which(tmp_sps[, 1] == "Other energy sector") - 1), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Current Policies Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = ifelse(variable == "Natural gas", "Gas", variable)) %>%
# mutate(variable = ifelse(variable == "Bioenergy", "Biomass", variable)) %>%
mutate(variable = paste0("Primary Energy|Electricity|", variable)) %>%
mutate(variable = ifelse(variable == "Primary Energy|Electricity|Power Sector", "Primary Energy|Electricity", variable)) %>%
mutate(unit = "Mtoe") %>%
select(model, scenario, region, variable, unit, period, value)),
rbind(
tmp2 %>%
filter(period < 2025) %>%
mutate(scenario = "Sustainable Development Scenario"),
tmp_sds[which(tmp_sps[, 1] == "Power sector"):(which(tmp_sps[, 1] == "Other energy sector") - 1), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Sustainable Development Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = ifelse(variable == "Natural gas", "Gas", variable)) %>%
# mutate(variable = ifelse(variable == "Bioenergy", "Biomass", variable)) %>%
mutate(variable = paste0("Primary Energy|Electricity|", variable)) %>%
mutate(variable = ifelse(variable == "Primary Energy|Electricity|Power Sector", "Primary Energy|Electricity", variable)) %>%
mutate(unit = "Mtoe") %>%
select(model, scenario, region, variable, unit, period, value)))
# Final energy consumption
tmp3 <- tmp_sps[which(tmp_sps[, 1] == "Total final consumption"):(which(tmp_sps[, 1] == "Industry") - 1), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Stated Policies Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = ifelse(variable == "Natural gas", "Gas", variable)) %>%
# mutate(variable = ifelse(variable == "Bioenergy", "Biomass", variable)) %>%
mutate(variable = paste0("Final Energy|", variable)) %>%
mutate(variable = ifelse(variable == "Final Energy|Total final consumption", "Final Energy", variable)) %>%
mutate(unit = "Mtoe") %>%
select(model, scenario, region, variable, unit, period, value)
tmp3 <- rbind(
tmp3,
rbind(
tmp3 %>%
filter(period < 2025) %>%
mutate(scenario = "Current Policies Scenario"),
tmp_cps[which(tmp_sps[, 1] == "Total final consumption"):(which(tmp_sps[, 1] == "Industry") - 1), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Current Policies Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = ifelse(variable == "Natural gas", "Gas", variable)) %>%
# mutate(variable = ifelse(variable == "Bioenergy", "Biomass", variable)) %>%
mutate(variable = paste0("Final Energy|", variable)) %>%
mutate(variable = ifelse(variable == "Final Energy|Total final consumption", "Final Energy", variable)) %>%
mutate(unit = "Mtoe") %>%
select(model, scenario, region, variable, unit, period, value)),
rbind(
tmp3 %>%
filter(period < 2025) %>%
mutate(scenario = "Sustainable Development Scenario"),
tmp_sds[which(tmp_sps[, 1] == "Total final consumption"):(which(tmp_sps[, 1] == "Industry") - 1), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Sustainable Development Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = ifelse(variable == "Natural gas", "Gas", variable)) %>%
# mutate(variable = ifelse(variable == "Bioenergy", "Biomass", variable)) %>%
mutate(variable = paste0("Final Energy|", variable)) %>%
mutate(variable = ifelse(variable == "Final Energy|Total final consumption", "Final Energy", variable)) %>%
mutate(unit = "Mtoe") %>%
select(model, scenario, region, variable, unit, period, value)))
# Final energy consumption (Industry)
tmp4 <- tmp_sps[which(tmp_sps[, 1] == "Industry"):(which(tmp_sps[, 1] == "Transport") - 1), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Stated Policies Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = ifelse(variable == "Natural gas", "Gas", variable)) %>%
mutate(variable = paste0("Final Energy|Industry|", variable)) %>%
mutate(variable = ifelse(variable == "Final Energy|Industry|Industry", "Final Energy|Industry", variable)) %>%
mutate(unit = "Mtoe") %>%
select(model, scenario, region, variable, unit, period, value)
tmp4 <- rbind(
tmp4,
rbind(
tmp4 %>%
filter(period < 2025) %>%
mutate(scenario = "Current Policies Scenario"),
tmp_cps[which(tmp_sps[, 1] == "Industry"):(which(tmp_sps[, 1] == "Transport") - 1), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Current Policies Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = ifelse(variable == "Natural gas", "Gas", variable)) %>%
# mutate(variable = ifelse(variable == "Bioenergy", "Biomass", variable)) %>%
mutate(variable = paste0("Final Energy|Industry|", variable)) %>%
mutate(variable = ifelse(variable == "Final Energy|Industry|Industry", "Final Energy|Industry", variable)) %>%
mutate(unit = "Mtoe") %>%
select(model, scenario, region, variable, unit, period, value)),
rbind(
tmp4 %>%
filter(period < 2025) %>%
mutate(scenario = "Sustainable Development Scenario"),
tmp_sds[which(tmp_sps[, 1] == "Industry"):(which(tmp_sps[, 1] == "Transport") - 1), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Sustainable Development Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = ifelse(variable == "Natural gas", "Gas", variable)) %>%
# mutate(variable = ifelse(variable == "Bioenergy", "Biomass", variable)) %>%
mutate(variable = paste0("Final Energy|Industry|", variable)) %>%
mutate(variable = ifelse(variable == "Final Energy|Industry|Industry", "Final Energy|Industry", variable)) %>%
mutate(unit = "Mtoe") %>%
select(model, scenario, region, variable, unit, period, value)))
# Final energy consumption (Transport)
tmp5 <- tmp_sps[which(tmp_sps[, 1] == "Transport"):(which(tmp_sps[, 1] == "Buildings") - 1), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Stated Policies Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = ifelse(variable == "International bunkers", "Oil|International bunkers", variable)) %>%
mutate(variable = paste0("Final Energy|Transportation|", variable)) %>%
mutate(variable = ifelse(variable == "Final Energy|Transportation|Transport", "Final Energy|Transportation", variable)) %>%
mutate(unit = "Mtoe") %>%
select(model, scenario, region, variable, unit, period, value)
tmp5 <- rbind(
tmp5,
rbind(
tmp5 %>%
filter(period < 2025) %>%
mutate(scenario = "Current Policies Scenario"),
tmp_cps[which(tmp_sps[, 1] == "Transport"):(which(tmp_sps[, 1] == "Buildings") - 1), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Current Policies Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = ifelse(variable == "International bunkers", "Oil|International bunkers", variable)) %>%
mutate(variable = paste0("Final Energy|Transportation|", variable)) %>%
mutate(variable = ifelse(variable == "Final Energy|Transportation|Transport", "Final Energy|Transportation", variable)) %>%
mutate(unit = "Mtoe") %>%
select(model, scenario, region, variable, unit, period, value)),
rbind(
tmp5 %>%
filter(period < 2025) %>%
mutate(scenario = "Sustainable Development Scenario"),
tmp_sds[which(tmp_sps[, 1] == "Transport"):(which(tmp_sps[, 1] == "Buildings") - 1), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Sustainable Development Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = ifelse(variable == "International bunkers", "Oil|International bunkers", variable)) %>%
mutate(variable = paste0("Final Energy|Transportation|", variable)) %>%
mutate(variable = ifelse(variable == "Final Energy|Transportation|Transport", "Final Energy|Transportation", variable)) %>%
mutate(unit = "Mtoe") %>%
select(model, scenario, region, variable, unit, period, value)))
# Final energy consumption (Buildings)
tmp6 <- tmp_sps[which(tmp_sps[, 1] == "Buildings"):(which(tmp_sps[, 1] == "Other") - 1), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Stated Policies Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = ifelse(variable == "Traditional biomass", "Bioenergy|Traditional biomass", variable)) %>%
mutate(variable = paste0("Final Energy|Buildings|", variable)) %>%
mutate(variable = ifelse(variable == "Final Energy|Buildings|Buildings", "Final Energy|Buildings", variable)) %>%
mutate(unit = "Mtoe") %>%
select(model, scenario, region, variable, unit, period, value)
tmp6 <- rbind(
tmp6,
rbind(
tmp6 %>%
filter(period < 2025) %>%
mutate(scenario = "Current Policies Scenario"),
tmp_cps[which(tmp_sps[, 1] == "Buildings"):(which(tmp_sps[, 1] == "Other") - 1), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Current Policies Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = ifelse(variable == "Traditional biomass", "Bioenergy|Traditional biomass", variable)) %>%
mutate(variable = paste0("Final Energy|Buildings|", variable)) %>%
mutate(variable = ifelse(variable == "Final Energy|Buildings|Buildings", "Final Energy|Buildings", variable)) %>%
mutate(unit = "Mtoe") %>%
select(model, scenario, region, variable, unit, period, value)),
rbind(
tmp6 %>%
filter(period < 2025) %>%
mutate(scenario = "Sustainable Development Scenario"),
tmp_sds[which(tmp_sps[, 1] == "Buildings"):(which(tmp_sps[, 1] == "Other") - 1), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Sustainable Development Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = ifelse(variable == "Traditional biomass", "Bioenergy|Traditional biomass", variable)) %>%
mutate(variable = paste0("Final Energy|Buildings|", variable)) %>%
mutate(variable = ifelse(variable == "Final Energy|Buildings|Buildings", "Final Energy|Buildings", variable)) %>%
mutate(unit = "Mtoe") %>%
select(model, scenario, region, variable, unit, period, value)))
# Final energy consumption (Other)
tmp7 <- tmp_sps[which(tmp_sps[, 1] == "Other"):which(tmp_sps[, 1] == "Petrochem. feedstock"), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Stated Policies Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = paste0("Final Energy|Other|", variable)) %>%
mutate(variable = ifelse(variable == "Final Energy|Other|Other", "Final Energy|Other", variable)) %>%
mutate(unit = "Mtoe") %>%
select(model, scenario, region, variable, unit, period, value)
tmp7 <- rbind(
tmp7,
rbind(
tmp7 %>%
filter(period < 2025) %>%
mutate(scenario = "Current Policies Scenario"),
tmp_cps[which(tmp_sps[, 1] == "Other"):which(tmp_sps[, 1] == "Petrochem. feedstock"), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Current Policies Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = paste0("Final Energy|Other|", variable)) %>%
mutate(variable = ifelse(variable == "Final Energy|Other|Other", "Final Energy|Other", variable)) %>%
mutate(unit = "Mtoe") %>%
select(model, scenario, region, variable, unit, period, value)),
rbind(
tmp7 %>%
filter(period < 2025) %>%
mutate(scenario = "Sustainable Development Scenario"),
tmp_sds[which(tmp_sps[, 1] == "Other"):which(tmp_sps[, 1] == "Petrochem. feedstock"), ] %>%
gather(period, value, -variable) %>%
mutate(period = as.numeric(period)) %>%
mutate(model = "IEA-WEM2019") %>%
mutate(scenario = "Sustainable Development Scenario") %>%
mutate(region = kreg) %>%
mutate(variable = paste0("Final Energy|Other|", variable)) %>%
mutate(variable = ifelse(variable == "Final Energy|Other|Other", "Final Energy|Other", variable)) %>%
mutate(unit = "Mtoe") %>%
select(model, scenario, region, variable, unit, period, value))
)
tmp_all[[kreg]] <- rbind(tmp1, tmp2, tmp3, tmp4, tmp5, tmp6, tmp7)
}
data_weo2019 <- do.call("rbind", tmp_all)
data_weo2019 <- data_weo2019[, c(3, 6, 1, 2, 5, 4, 7)]
data_weo2019$period <- as.numeric(data_weo2019$period)
data_weo2019$variable <- gsub(pattern = "\\.", replacement = "_", x = data_weo2019$variable)
if (subtype == "PE") {
data_weo2019 <- data_weo2019 %>% filter(variable %in%
grep("Primary Energy", unique(data_weo2019$variable), value = TRUE))
} else if (subtype == "FE") {
data_weo2019 <- data_weo2019 %>% filter(variable %in%
grep("Final Energy", unique(data_weo2019$variable), value = TRUE))
}
x <- as.magpie(data_weo2019, spatial = 1, temporal = 2, datacol = 7)
} else {
stop("Not a valid subtype!")
}
return(x)
}
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