R/calcGHGPrices.R
In pik-piam/mrland: MadRaT land data package
Defines functions
calcGHGPrices
Documented in
calcGHGPrices
#' @title calcGHGPrices
#' @description reads in GHG prices from past runs
#'
#' @param emissions which type of emissions shall be returned. ghg just returns
#' n2o, ch4 and co2, pollutants a longer list including also air pollutants
#' @param datasource REMIND for prices from R2M4 coupled runs, REMMAG for old
#' coupled runs, SSPResults for prices from the SSP scenarios from the IIASA
#' database, SSP_and_REM for a combination of REMIND and SSPResults
#' @param rev data revision the output will be produced for (numeric_version).
#' @return list of magpie object with results on country level, weight on
#' country level, unit and description.
#' @author David Chen, Benjamin Leon Bodirsky, David Klein
#' @seealso
#' \code{\link{readSSPResults}}
#' @examples
#' \dontrun{
#' calcOutput("GHGPrices")
#' }
#' @importFrom magpiesets findset
#' @importFrom magclass complete_magpie
#' @importFrom madrat toolFillYears
calcGHGPrices <- function(emissions = "pollutants", datasource = "REMMAG", rev = numeric_version("0.1")) {
if (datasource == "REMIND") {
x <- readSource("REMIND",
subtype = paste0("intensive_",
rev,
"_",
"Price|Carbon (US$2005/t CO2)"))
outC <- x * 44 / 12 # US$2005/tCO2 -> US$2005/tC
outC <- add_dimension(outC, dim = 3.1, nm = "co2_c")
x <- readSource("REMIND",
subtype = paste0("intensive_",
rev,
"_",
"Price|N2O (US$2005/t N2O)"))
outN2oDirect <- x * 44 / 28 # US$2005/tN2O -> US$2005/tN
outN2oDirect <- add_dimension(outN2oDirect, dim = 3.1, nm = "n2o_n_direct")
x <- readSource("REMIND",
subtype = paste0("intensive_",
rev,
"_",
"Price|N2O (US$2005/t N2O)"))
outN2oIndirect <- x[, , ] * 44 / 28 # US$2005/tN2O -> US$2005/tN
outN2oIndirect <- add_dimension(outN2oIndirect, dim = 3.1, nm = "n2o_n_indirect")
x <- readSource("REMIND",
subtype = paste0("intensive_",
rev,
"_",
"Price|CH4 (US$2005/t CH4)"))
outCh4 <- x[, , ]
outCh4 <- add_dimension(outCh4, dim = 3.1, nm = "ch4")
x <- mbind(outN2oDirect, outN2oIndirect, outCh4, outC)
x <- time_interpolate(x, seq(1995, 2150, 5), extrapolation_type = "constant")
# Set prices to zero before and in 2020
x[, getYears(x) <= "y2020", ] <- 0
# find scenario names that have NPi or NDC in their name and set their GHG
# prices to zero (replicating the coupling script)
setToZero <- getNames(x)[grepl("NPi|NDC", getNames(x))]
x[, , setToZero] <- 0
description <- "GHG certificate prices for different scenarios based on data from REMIND-MAgPIE coupling"
#convert from USD05MER to USD17MER based on USA values for all countries as the CO2 price is global.
x <- x * round(GDPuc::toolConvertSingle(1, "USA", unit_in = "constant 2005 US$MER",
unit_out = "constant 2017 US$MER"), 2)
} else if (datasource == "Strefler2021") {
x <- readSource("Strefler2021", subtype = paste0("intensive_", rev))
outC <- x[, , "Price|Carbon (US$2005/t CO2)"] * 44 / 12 # US$2005/tCO2 -> US$2005/tC
getNames(outC, dim = 2) <- "co2_c"
outN2oDirect <- x[, , "Price|Carbon (US$2005/t CO2)"] * 265 * 44 / 28 # US$2005/tN2O -> US$2005/tN
getNames(outN2oDirect, dim = 2) <- "n2o_n_direct"
outN2oIndirect <- x[, , "Price|Carbon (US$2005/t CO2)"] * 265 * 44 / 28 # US$2005/tN2O -> US$2005/tN
getNames(outN2oIndirect, dim = 2) <- "n2o_n_indirect"
outCh4 <- x[, , "Price|Carbon (US$2005/t CO2)"] * 28
getNames(outCh4, dim = 2) <- "ch4"
x <- mbind(outN2oDirect, outN2oIndirect, outCh4, outC)
x <- time_interpolate(x, seq(1995, 2150, 5), extrapolation_type = "constant")
# Set prices to zero before and in 2020
x[, getYears(x) <= "y2020", ] <- 0
# find scenario names that have NPi or NDC in their name and set their GHG
# prices to zero (replicating the coupling script)
setToZero <- getNames(x)[grepl("NPI|NDC", getNames(x))]
x[, , setToZero] <- 0
# swap dimensions (scenario and gas) such that in the output file gas is in lines and scenarios in columns
getNames(x) <- gsub("^([^\\.]*)\\.(.*$)", "\\2.\\1", getNames(x))
#convert from USD05MER to USD17MER based on USA values for all countries as the CO2 price is global.
x <- x * round(GDPuc::toolConvertSingle(1, "USA", unit_in = "constant 2005 US$MER",
unit_out = "constant 2017 US$MER"), 2)
description <- paste("GHG certificate prices for different scenarios taken",
"from Strefler et al 2021 (DOI 10.1038/s41467-021-22211-2)")
} else if (datasource == "REMMAG") {
x <- readSource("REMMAG", "ghgprices")
if (emissions == "pollutants") {
pollutants <- findset("pollutants")
y <- add_columns(x, dim = 3.1, addnm = setdiff(pollutants, getNames(x, dim = 1)))
y[, , ] <- 0
y[, , "ch4"] <- x[, , "ch4"]
y[, , "co2_c"] <- x[, , "co2_c"]
y[, , c("n2o_n_direct", "n2o_n_indirect")] <- collapseNames(x[, , "n2o_n"])
x <- y[, , pollutants]
} else if (emissions != "ghg") {
stop("unknown emission type")
}
#convert from USD05MER to USD17MER based on USA values for all countries as the CO2 price is global.
x <- x * round(GDPuc::toolConvertSingle(1, "USA", unit_in = "constant 2005 US$MER",
unit_out = "constant 2017 US$MER"), 2)
description <- "ghg certificate prices for different scenarios based on data from REMIND-MAgPIE-coupling"
} else if (datasource == "SSPResults") {
x <- readSource("SSPResults")
x <- collapseNames(x[, , "Price|Carbon (US$2017/t CO2)"])
x <- x * 44 / 12
pollutants <- findset("pollutants")
y <- add_dimension(x, dim = 3.1, nm = "co2_c")
y <- add_columns(y, dim = 3.1, addnm = setdiff(pollutants, getNames(y, dim = 1)))
y[, , ] <- 0
y[, , "co2_c"] <- x
y[, , "ch4"] <- (x * 28 * 12 / 44)
y[, , c("n2o_n_direct", "n2o_n_indirect")] <- collapseNames(x) * 265 * 44 / 28 * 12 / 44
x <- y[, , c(pollutants)]
if (emissions == "ghg") {
x <- x[, , c("co2_c", "n2o_n_direct", "ch4")]
getNames(x, dim = 3) <- c("co2_c", "n2o_n", "ch4")
} else if (emissions != "pollutants") {
stop("emissions has to be set to ghg or pollutants")
}
x[, c(2005, 2010), ] <- 0
x <- time_interpolate(dataset = x[, 2010 + (0:9) * 10, ],
interpolated_year = c(1965 + (0:8) * 5, 2015 + (0:8) * 10),
integrate_interpolated_years = TRUE, extrapolation_type = "constant")
x <- toolHoldConstantBeyondEnd(x)
description <- paste("ghg certificate prices for different scenarios based",
"on the multimodel SSP results from the IIASA DB")
} else if (datasource == "S4N_project") {
# Carbon price (in USD2005 per t CO2) at country level
x <- readSource("S4Nproject_input", subtype = "co2prices", convert = "onlycorrect")
# Extend years and set to 0 before 2030
x <- toolFillYears(x, paste0("y", seq(1995, 2100, by = 5)))
x[, paste0("y", seq(1995, 2025, by = 5)), ] <- 0
# Convert in USD2005 per tons of carbon
x <- x * 44 / 12
# Add pollutants dimension to magpie object
pollutants <- findset("pollutants")
y <- add_dimension(x, dim = 3.1, nm = "co2_c")
y <- add_columns(y, dim = 3.1, addnm = setdiff(pollutants, getNames(y, dim = 1)))
# Fill object with pollutant prices (including transformation to CO2-equivalents)
y[, , ] <- 0
# CO2 in USD2005 per tons of C
y[, , "co2_c"] <- x
# CH4 in USD2005 per tons of CO2-equivalent
y[, , "ch4"] <- (x * 28 * 12 / 44)
# N2O in USD2005 per tons of N
y[, , c("n2o_n_direct", "n2o_n_indirect")] <- collapseNames(x) * 265 * 44 / 28 * 12 / 44
x <- y[, , c(pollutants)]
# pollutant prices to be returned
if (emissions == "ghg") {
x <- x[, , c("co2_c", "n2o_n_direct", "ch4")]
getNames(x, dim = 1) <- c("co2_c", "n2o_n", "ch4")
} else if (emissions != "pollutants") {
stop("emissions has to be set to ghg or pollutants")
}
# co2 pricing starts in 2015
x[, c(2005, 2010), ] <- 0
# fill missing years in the future
x <- time_interpolate(x, seq(1995, 2150, 5), extrapolation_type = "constant")
#convert from USD05MER to USD17MER based on USA values for all countries as the CO2 price is global.
x <- x * round(GDPuc::toolConvertSingle(1, "USA", unit_in = "constant 2005 US$MER",
unit_out = "constant 2017 US$MER"), 2)
description <- "ghg certificate prices for different scenarios based on CO2 prices provided by IMAGE"
} else if (datasource == "SSP_and_REM") {
ssp <- calcOutput("GHGPrices", datasource = "SSPResults", aggregate = FALSE, rev = rev)
rem <- calcOutput("GHGPrices", datasource = "REMIND", aggregate = FALSE, rev = rev)
if (rev > numeric_version("4.58")) {
strefler <- calcOutput("GHGPrices", datasource = "Strefler2021", aggregate = FALSE, rev = rev)
} else {
strefler <- NULL
}
x <- mbind(ssp[, getYears(rem), ], rem, strefler)
x <- complete_magpie(x, fill = 0)
# sort scenarios alphabetically
x <- x[, , sort(getNames(x, dim = 2))]
description <- paste("ghg certificate prices for different scenarios based",
"on data from REMIND-MAgPIE-coupling and the multimodel SSP results from the IIASA DB")
}
pop <- calcOutput("Population", aggregate = FALSE)
return(list(x = x, weight = pop[, 2010, 1],
unit = "US$ 2017 per t N2O-N CH4 and CO2-C",
description = description,
note = "As weight for aggregation currently population data from 2010 is used.",
min = 0,
max = 10^7))
}
pik-piam/mrland documentation built on Nov. 23, 2024, 11:37 a.m.
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Defines functions calcGHGPrices
Documented in calcGHGPrices
#' @title calcGHGPrices
#' @description reads in GHG prices from past runs
#'
#' @param emissions which type of emissions shall be returned. ghg just returns
#' n2o, ch4 and co2, pollutants a longer list including also air pollutants
#' @param datasource REMIND for prices from R2M4 coupled runs, REMMAG for old
#' coupled runs, SSPResults for prices from the SSP scenarios from the IIASA
#' database, SSP_and_REM for a combination of REMIND and SSPResults
#' @param rev data revision the output will be produced for (numeric_version).
#' @return list of magpie object with results on country level, weight on
#' country level, unit and description.
#' @author David Chen, Benjamin Leon Bodirsky, David Klein
#' @seealso
#' \code{\link{readSSPResults}}
#' @examples
#' \dontrun{
#' calcOutput("GHGPrices")
#' }
#' @importFrom magpiesets findset
#' @importFrom magclass complete_magpie
#' @importFrom madrat toolFillYears
calcGHGPrices <- function(emissions = "pollutants", datasource = "REMMAG", rev = numeric_version("0.1")) {
if (datasource == "REMIND") {
x <- readSource("REMIND",
subtype = paste0("intensive_",
rev,
"_",
"Price|Carbon (US$2005/t CO2)"))
outC <- x * 44 / 12 # US$2005/tCO2 -> US$2005/tC
outC <- add_dimension(outC, dim = 3.1, nm = "co2_c")
x <- readSource("REMIND",
subtype = paste0("intensive_",
rev,
"_",
"Price|N2O (US$2005/t N2O)"))
outN2oDirect <- x * 44 / 28 # US$2005/tN2O -> US$2005/tN
outN2oDirect <- add_dimension(outN2oDirect, dim = 3.1, nm = "n2o_n_direct")
x <- readSource("REMIND",
subtype = paste0("intensive_",
rev,
"_",
"Price|N2O (US$2005/t N2O)"))
outN2oIndirect <- x[, , ] * 44 / 28 # US$2005/tN2O -> US$2005/tN
outN2oIndirect <- add_dimension(outN2oIndirect, dim = 3.1, nm = "n2o_n_indirect")
x <- readSource("REMIND",
subtype = paste0("intensive_",
rev,
"_",
"Price|CH4 (US$2005/t CH4)"))
outCh4 <- x[, , ]
outCh4 <- add_dimension(outCh4, dim = 3.1, nm = "ch4")
x <- mbind(outN2oDirect, outN2oIndirect, outCh4, outC)
x <- time_interpolate(x, seq(1995, 2150, 5), extrapolation_type = "constant")
# Set prices to zero before and in 2020
x[, getYears(x) <= "y2020", ] <- 0
# find scenario names that have NPi or NDC in their name and set their GHG
# prices to zero (replicating the coupling script)
setToZero <- getNames(x)[grepl("NPi|NDC", getNames(x))]
x[, , setToZero] <- 0
description <- "GHG certificate prices for different scenarios based on data from REMIND-MAgPIE coupling"
#convert from USD05MER to USD17MER based on USA values for all countries as the CO2 price is global.
x <- x * round(GDPuc::toolConvertSingle(1, "USA", unit_in = "constant 2005 US$MER",
unit_out = "constant 2017 US$MER"), 2)
} else if (datasource == "Strefler2021") {
x <- readSource("Strefler2021", subtype = paste0("intensive_", rev))
outC <- x[, , "Price|Carbon (US$2005/t CO2)"] * 44 / 12 # US$2005/tCO2 -> US$2005/tC
getNames(outC, dim = 2) <- "co2_c"
outN2oDirect <- x[, , "Price|Carbon (US$2005/t CO2)"] * 265 * 44 / 28 # US$2005/tN2O -> US$2005/tN
getNames(outN2oDirect, dim = 2) <- "n2o_n_direct"
outN2oIndirect <- x[, , "Price|Carbon (US$2005/t CO2)"] * 265 * 44 / 28 # US$2005/tN2O -> US$2005/tN
getNames(outN2oIndirect, dim = 2) <- "n2o_n_indirect"
outCh4 <- x[, , "Price|Carbon (US$2005/t CO2)"] * 28
getNames(outCh4, dim = 2) <- "ch4"
x <- mbind(outN2oDirect, outN2oIndirect, outCh4, outC)
x <- time_interpolate(x, seq(1995, 2150, 5), extrapolation_type = "constant")
# Set prices to zero before and in 2020
x[, getYears(x) <= "y2020", ] <- 0
# find scenario names that have NPi or NDC in their name and set their GHG
# prices to zero (replicating the coupling script)
setToZero <- getNames(x)[grepl("NPI|NDC", getNames(x))]
x[, , setToZero] <- 0
# swap dimensions (scenario and gas) such that in the output file gas is in lines and scenarios in columns
getNames(x) <- gsub("^([^\\.]*)\\.(.*$)", "\\2.\\1", getNames(x))
#convert from USD05MER to USD17MER based on USA values for all countries as the CO2 price is global.
x <- x * round(GDPuc::toolConvertSingle(1, "USA", unit_in = "constant 2005 US$MER",
unit_out = "constant 2017 US$MER"), 2)
description <- paste("GHG certificate prices for different scenarios taken",
"from Strefler et al 2021 (DOI 10.1038/s41467-021-22211-2)")
} else if (datasource == "REMMAG") {
x <- readSource("REMMAG", "ghgprices")
if (emissions == "pollutants") {
pollutants <- findset("pollutants")
y <- add_columns(x, dim = 3.1, addnm = setdiff(pollutants, getNames(x, dim = 1)))
y[, , ] <- 0
y[, , "ch4"] <- x[, , "ch4"]
y[, , "co2_c"] <- x[, , "co2_c"]
y[, , c("n2o_n_direct", "n2o_n_indirect")] <- collapseNames(x[, , "n2o_n"])
x <- y[, , pollutants]
} else if (emissions != "ghg") {
stop("unknown emission type")
}
#convert from USD05MER to USD17MER based on USA values for all countries as the CO2 price is global.
x <- x * round(GDPuc::toolConvertSingle(1, "USA", unit_in = "constant 2005 US$MER",
unit_out = "constant 2017 US$MER"), 2)
description <- "ghg certificate prices for different scenarios based on data from REMIND-MAgPIE-coupling"
} else if (datasource == "SSPResults") {
x <- readSource("SSPResults")
x <- collapseNames(x[, , "Price|Carbon (US$2017/t CO2)"])
x <- x * 44 / 12
pollutants <- findset("pollutants")
y <- add_dimension(x, dim = 3.1, nm = "co2_c")
y <- add_columns(y, dim = 3.1, addnm = setdiff(pollutants, getNames(y, dim = 1)))
y[, , ] <- 0
y[, , "co2_c"] <- x
y[, , "ch4"] <- (x * 28 * 12 / 44)
y[, , c("n2o_n_direct", "n2o_n_indirect")] <- collapseNames(x) * 265 * 44 / 28 * 12 / 44
x <- y[, , c(pollutants)]
if (emissions == "ghg") {
x <- x[, , c("co2_c", "n2o_n_direct", "ch4")]
getNames(x, dim = 3) <- c("co2_c", "n2o_n", "ch4")
} else if (emissions != "pollutants") {
stop("emissions has to be set to ghg or pollutants")
}
x[, c(2005, 2010), ] <- 0
x <- time_interpolate(dataset = x[, 2010 + (0:9) * 10, ],
interpolated_year = c(1965 + (0:8) * 5, 2015 + (0:8) * 10),
integrate_interpolated_years = TRUE, extrapolation_type = "constant")
x <- toolHoldConstantBeyondEnd(x)
description <- paste("ghg certificate prices for different scenarios based",
"on the multimodel SSP results from the IIASA DB")
} else if (datasource == "S4N_project") {
# Carbon price (in USD2005 per t CO2) at country level
x <- readSource("S4Nproject_input", subtype = "co2prices", convert = "onlycorrect")
# Extend years and set to 0 before 2030
x <- toolFillYears(x, paste0("y", seq(1995, 2100, by = 5)))
x[, paste0("y", seq(1995, 2025, by = 5)), ] <- 0
# Convert in USD2005 per tons of carbon
x <- x * 44 / 12
# Add pollutants dimension to magpie object
pollutants <- findset("pollutants")
y <- add_dimension(x, dim = 3.1, nm = "co2_c")
y <- add_columns(y, dim = 3.1, addnm = setdiff(pollutants, getNames(y, dim = 1)))
# Fill object with pollutant prices (including transformation to CO2-equivalents)
y[, , ] <- 0
# CO2 in USD2005 per tons of C
y[, , "co2_c"] <- x
# CH4 in USD2005 per tons of CO2-equivalent
y[, , "ch4"] <- (x * 28 * 12 / 44)
# N2O in USD2005 per tons of N
y[, , c("n2o_n_direct", "n2o_n_indirect")] <- collapseNames(x) * 265 * 44 / 28 * 12 / 44
x <- y[, , c(pollutants)]
# pollutant prices to be returned
if (emissions == "ghg") {
x <- x[, , c("co2_c", "n2o_n_direct", "ch4")]
getNames(x, dim = 1) <- c("co2_c", "n2o_n", "ch4")
} else if (emissions != "pollutants") {
stop("emissions has to be set to ghg or pollutants")
}
# co2 pricing starts in 2015
x[, c(2005, 2010), ] <- 0
# fill missing years in the future
x <- time_interpolate(x, seq(1995, 2150, 5), extrapolation_type = "constant")
#convert from USD05MER to USD17MER based on USA values for all countries as the CO2 price is global.
x <- x * round(GDPuc::toolConvertSingle(1, "USA", unit_in = "constant 2005 US$MER",
unit_out = "constant 2017 US$MER"), 2)
description <- "ghg certificate prices for different scenarios based on CO2 prices provided by IMAGE"
} else if (datasource == "SSP_and_REM") {
ssp <- calcOutput("GHGPrices", datasource = "SSPResults", aggregate = FALSE, rev = rev)
rem <- calcOutput("GHGPrices", datasource = "REMIND", aggregate = FALSE, rev = rev)
if (rev > numeric_version("4.58")) {
strefler <- calcOutput("GHGPrices", datasource = "Strefler2021", aggregate = FALSE, rev = rev)
} else {
strefler <- NULL
}
x <- mbind(ssp[, getYears(rem), ], rem, strefler)
x <- complete_magpie(x, fill = 0)
# sort scenarios alphabetically
x <- x[, , sort(getNames(x, dim = 2))]
description <- paste("ghg certificate prices for different scenarios based",
"on data from REMIND-MAgPIE-coupling and the multimodel SSP results from the IIASA DB")
}
pop <- calcOutput("Population", aggregate = FALSE)
return(list(x = x, weight = pop[, 2010, 1],
unit = "US$ 2017 per t N2O-N CH4 and CO2-C",
description = description,
note = "As weight for aggregation currently population data from 2010 is used.",
min = 0,
max = 10^7))
}
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