R/calcGAINS.R
In pik-piam/moinput: Model Operations Input Data Library
Defines functions
calcGAINS
#' calcGAINS
#'
#' Calculates air pollutant emissions and emission factors (user can choose)
#' based on GAINS emissions and activity data. Result is given on GAINS sector level.
#' User can choose between aggregated and extended sectoral resolution. Results are
#' given for multiple scenarios. Scenario design is partly taken from the GAINS data
#' and partly created in this function (particularly the SSPs).
#'
#' @param subtype decides whether emissions or emission factors are returned
#' @param sectoral_resolution aggreaged or extenden (uses different GAINS input data)
#' @importFrom dplyr group_by_ summarise_ ungroup mutate_ rename_ filter_ select_
#' @importFrom magclass as.magpie getCells getSets<- getNames<- getSets getRegions<- mselect<- setNames write.magpie
#' @importFrom tidyr gather_
#' @importFrom utils read.csv read.csv2
#' @importFrom quitte as.quitte
calcGAINS <- function(subtype="emission_factors", sectoral_resolution="extended") {
if (!(subtype %in% c("emission_factors", "emissions"))) stop('subtype must be in c("emission_factors", "emissions")')
# local functions
# country to region
allocate_c2r_ef <- function(id_ef, ip_region, ip_country, ip_year, ip_scenario) {
dummy <- id_ef[ip_region, ip_year, ip_scenario]
dummy[,,] <- setCells(id_ef[ip_country, ip_year, ip_scenario], "GLO")
#names(dimnames(dummy)) <- c("region", "years", "data1.data2.species.scenario")
return(dummy)
}
#
allocate_min2r_ef <- function(id_ef, ip_region, ip_countryGroup, ip_year, ip_scenario) {
dummy <- id_ef[ip_region, ip_year, ip_scenario]
# Get minimum values across country group
tmp <- as.quitte(id_ef[ip_countryGroup,ip_year,ip_scenario]) %>%
group_by_(~data1,~data2) %>%
summarise_(value=~ifelse(all(value == 0) , 0, min(value[value >0 ],na.rm= TRUE))) %>% # a value 0 is often a sign for a NA that has been replaced with 0 for small countries
ungroup() %>%
as.data.frame() %>%
as.quitte() %>%
as.magpie()
# Allocate minimum values to region
dummy[ip_region, ip_year, ip_scenario] <- setYears(tmp)
return(dummy)
}
# DK: deleted unused function
# conversion factors
conv_ktSO2_to_ktS <- 1/2 # 32/(32+2*16)
conv_kt_per_PJ_to_Tg_per_TWa <- 1e-3 / (1e15/(365*24*60*60)*1e-12)
conv_PJ_to_Twa <- (1e15/(365*24*60*60)*1e-12)
# user-defined parameters
time <- c(seq(2005,2055,5), seq(2060,2110,10), 2130, 2150)
scenario <- c("SSP1","SSP2","SSP5","FLE", "MFR", "CLE") #,"SSP3","SSP4", "MFR_Transports", "GlobalEURO6", "FLE_building_transport", "SLCF_building_transport")
p_dagg_year <- 2005
p_dagg_pop <- "pop_SSP2"
p_dagg_gdp <- "gdp_SSP2"
p_dagg_map <- "regionmappingGAINS.csv"
p_countryCategories <- "useGAINSregions" # "perCountry"
# list of OECD countries
#TODO: may want to place this in a mapping file or in a R library
r_oecd <- c("AUS", "AUT", "BEL", "CAN", "CHL", "CZE", "DNK", "EST", "FIN", "FRA", "DEU", "GRC", "HUN", "ISL", "IRL", "ISR", "ITA",
"JPN", "KOR", "LUX", "MEX", "NLD", "NZL", "NOR", "POL", "PRT", "SVK", "SVN", "ESP", "SWE", "CHE", "TUR", "GBR", "USA")
# set of sectors for which no emission factor will be computed (because there is no activity reported, or not in terms of energy)
dimSector_skipEF = c("AACID", "CEMENT", "CHEM", "CHEMBULK", "CUSM", "NACID", "PAPER", "STEEL",
"Losses_Coal", "Losses_Distribution_Use",
"Transformations_Coal", "Transformations_HLF", "Transformations_HLF_Refinery", "Transformations_NatGas")
dimSector_skipEF_edge = c("End_Use_Industry_Bio_Trad", "End_Use_Industry_Coal", "End_Use_Industry_HLF", "End_Use_Industry_LLF",
"End_Use_Industry_NatGas", "End_Use_Residential_Bio_Mod", "End_Use_Residential_Bio_Trad", "End_Use_Residential_Coal",
"End_Use_Residential_HLF", "End_Use_Residential_LLF", "End_Use_Residential_NatGas", "End_Use_Services_Bio_Trad",
"End_Use_Services_Coal")
dimSector_skipEF_edge = c("")
dimSector_skipEF = c("")
#-- READ IN ECLIPSE (GAINS) DATA ------------------
activities <- readSource("ECLIPSE", subtype=paste0("activities.", sectoral_resolution))
activities <- activities[,c(2005,2010,2020,2030,2050),]
emissions <- readSource("ECLIPSE", subtype=paste0("emissions.", sectoral_resolution))
emissions <- emissions[,c(2005,2010,2020,2030,2050),]
# read in sectoral mapping (ECLIPSE (IMAGE) <> REMIND)
# DK map_sectors_ECLIPSE2Agg <- read.csv(toolMappingFile("sectoral", "mappingECLIPSEtoAggREMINDsectors.csv"), stringsAsFactors=TRUE)
# DK map_sectors_Agg2REMIND <- read.csv(toolMappingFile("sectoral", "mappingAggREMINDtoREMINDsectors.csv"), stringsAsFactors=TRUE)
#map_sectors <- map_sectors[which(!is.na(map_sectors$EDGE)),] # Remove transport sector (which is not represented in EDGE)
# read in regional map (select ISO and GAINS codes only). This is required for the construction of the SSPs
map_regions <- read.csv2(toolMappingFile("regional", p_dagg_map), stringsAsFactors=TRUE)[,c(2,3)]
map_regions <- map_regions %>%
filter_(~CountryCode != "ANT") %>% # Remove Netherland Antilles (not in REMIND regional mapping)
filter_(~RegionCode != "") %>%
mutate_(RegionCode = ~gsub("\\ \\+", "\\+", gsub("^\\s+|\\s+$", "", gsub("[0-9]", "", RegionCode)))) %>%
mutate_(CountryCode = ~factor(CountryCode))
# read in population and GDP data. required to compute gdp per cap
pop <- calcOutput("Population",aggregate=FALSE)[,p_dagg_year,p_dagg_pop]
gdp <- calcOutput("GDPppp", aggregate=FALSE)[,p_dagg_year,p_dagg_gdp]
#co <- map_regions$CountryCode[map_regions$RegionCode %in% c("Northern Africa","Middle East","Asia-Stan","Russia+")]
#e <- dimSums(emissions["IRN",,"End_Use_Industry_Coal.VOC"],dim=1)
#a <- dimSums(activities["IRN",,"End_Use_Industry_Coal"],dim=1)
#-- PROCESS DATA ------------------
# set of sectors for which emission factors are computed
dimSector_EF <- getNames(activities)[!getNames(activities) %in% c(dimSector_skipEF, dimSector_skipEF_edge)]
# calculate gdp per capita
gdp_cap <- gdp/pop
gdp_cap[is.na(gdp_cap)] <- 0 # set NA to 0
# Regional selections
# select one country pertaining to WEU (all WEU countries should have the same EF). Used for SSP scenario rules
select_weu <- paste(map_regions[which(map_regions$RegionCode == "Western Europe")[1],1])
# Retrieve Transport names
#transportNames <- getNames(activities)[grepl("End_Use_Transport", getNames(activities))]
#buildingNames <- getNames(activities)[grepl("End_Use_Industry|End_Use_Residential|End_Use_Services", getNames(activities))]
# convert SO2 emission from TgSO2 to TgS
#emissions[,,"SO2"] <- emissions[,,"SO2"]*conv_ktSO2_to_ktS
# define missing SLE scenario (assumed to be 3/4 of the distance between CLE and MFR, according to discussion with Zig Klimont on 18th Feb 2016)
cle = emissions[,,"CLE"]
getNames(cle) = gsub("CLE", "MFR", getNames(cle))
sle = cle - (cle - emissions[,,"MFR"])*0.75
getNames(sle) = gsub("MFR", "SLE", getNames(sle))
emissions=mbind(emissions, sle)
rm(cle,sle)
# calculate emission factors (only for power and end-use sectors, and not empty activities) and convert from kt/PJ to Tg/Twa
ef_eclipse <- emissions[,,dimSector_EF] / activities[,,dimSector_EF] * conv_kt_per_PJ_to_Tg_per_TWa
getSets(ef_eclipse) <- c("region", "year", "data1", "data2", "data3")
# DK: NAs in ef_eclipse: There are two potential reasons for NAs in ef_eclipse:
# 1) ef = emi / activitiy => if the activity is zero the ef gets NA. The activity is disaggregated from 24 GAINS regions to the 249 ISO
# countries using population as weight. If there is no population data for a country the activity (and also the emissions) of this country
# are zero. In this case ef for this country is NA for ALL sectors and species => Jerome's command below that collects regions in NAregions
# which have only NAs works! The NAs are replaced with ef from countries that belong to the same GAINS region and thus have identical ef.
# After replacing these kind of NAs there may remain NAs for another reason:
# 2) There is no activity data for a particular sector and GAINS region in the source data => activity will be zero for this sector and all
# ISO countries belonging to this GAINS region => ef will be NA for those sectors and countries. Set those NAs to zero. When ef is reaggregated
# to REMIND regions this is done using the activites as weight. And the activity is zero for the countries and sectors that have zeros due to zero
# activity and thus have no effect on the result.
#
# some regions/countries have NA values everywhere (pop data is zero). Allocate EF of another country that belongs to the same GAINS region (except for Antartica)
# Find countries that have NA for all sectors and species (then probably due to missing population data, see above)
ef_eclipse["ATA",,] = 0 # Antartica -> 0
NAregions <- c("AIA","ALA","ATF","BES","BLM","BVT","CCK","COK","CXR","ESH","FLK","GGY","GIB","GLP","GUF","HMD","IOT","JEY","MSR","MTQ","MYT","NFK",
"NIU","NRU","PCN","REU","SGS","SHN","SJM","SPM","TKL","TWN","UMI","VAT","VGB","WLF")
# NAregions <- names(which(
# sapply(
# getRegions( which(is.na(ef_eclipse), arr.ind = TRUE)), function(k) all(is.na(as.numeric(ef_eclipse[k,,])))
# )
# )
# )
#NAregions <- c("AIA", "ATF", "BVT", "CCK", "COK", "CXR", "ESH", "FLK", "GIB", "GLP", "GUF",
# "HMD", "IOT", "MSR", "MTQ", "MYT", "NFK", "NIU", "NRU", "PCN",
# "REU", "SGS", "SHN", "SJM", "SPM", "TKL", "TWN", "UMI", "VAT", "VGB", "WLF")
MissingRegions <- c("ALA", "BES", "BLM", "CUW", "GGY", "IMN", "JEY", "MAF", "PSE", "SSD", "SXM")
#AssociatedGAINSregions <- c("Western Europe", "Rest Central America", "Rest Central America", "Rest Central America", "Western Europe", "Western Europe", "Western Europe",
# "Rest Central America", "Middle East", "Northern Africa", "Rest Central America")
# for countries that have NAs everywhere (missing population, see reason 1 above) replace NA with ef of country that belongs to same GAINS region
cat("ef_eclipse: NA values in first country replaced with ef of second country:\n")
for (kregi in NAregions) {
# suche die countrycodes aller countries, die zu der region gehören, zu der auch kregi gehört, lasse die Regionen aus NAregions und missingRegions weg
# take the value of the first country since all countries that belong to the same GAINS region have the same ef
subsitute_region = map_regions$CountryCode[map_regions$RegionCode == map_regions$RegionCode[map_regions$CountryCode == kregi] &
!map_regions$CountryCode %in% c(NAregions, MissingRegions)][1]
cat(kregi,as.character(subsitute_region),"\n")
#subsitute_region<-subsitute_region[-which(as.character(subsitute_region)=="ANT")] # remove ANT
# if (all(is.na(ef_eclipse[subsitute_region,,]))) {
# # NAs in all countries
# } elese { }
tmp <- ef_eclipse[subsitute_region,,]
getRegions(tmp) <- kregi
ef_eclipse[kregi,,] <- tmp
}
# some regions have no population data when disaggregating.
# DK: I think this case was already addressed above (country has NAs everywhere)
# for (kregi in MissingRegions) {
# # warum hier so kompliziert mit AssociatedGAINSregions und nicht automatisch wie oben?
# # es gibt einen Unterschied: oben würde SSD Western Africa zugeordnet, hier wird es Northern Africa zugeordnet
# substitute_region = map_regions$CountryCode[map_regions$RegionCode == AssociatedGAINSregions[which(MissingRegions == kregi)] &
# !map_regions$CountryCode %in% MissingRegions][1]
# tmp <- ef_eclipse[substitute_region,,]
# getRegions(tmp) <- kregi
# ef_eclipse[kregi,,] <- tmp
# }
# for the remaining NAs (0/0 = NaN) and Infs (1/0 = Inf ) just set EF to 0 (activity levels are 0, although in some cases emissions exist)
ef_eclipse[is.na(ef_eclipse)] <- 0
ef_eclipse[is.infinite(ef_eclipse)] <- 0
rm(NAregions, MissingRegions)#, AssociatedGAINSregions)
# DK: deleted outcommented code
# Check ef corresponds to initial data
# define exogenous emission data
emissions_exogenous <- emissions#[,,dimSector_skipEF]
# make output dummy "ef" and "emi" which then has to be filled by the data
ef <- do.call('mbind',
lapply(scenario,
function(s) {new.magpie(getRegions(ef_eclipse),
c(2005,2010,2030,2050,2100),
gsub("CLE", s, getNames(ef_eclipse[,,"CLE"])))
}))
emi <- do.call('mbind',
lapply(scenario,
function(s) {new.magpie(getRegions(emissions_exogenous),
c(2005,2010,2030,2050,2100),
gsub("CLE", s, getNames(emissions_exogenous[,,"CLE"])))
}))
# define country categories
if (p_countryCategories == "perCountry") {
# low income countries (using World Bank definition < 2750 US$(2010)/Cap)
r_L <- dimnames(gdp_cap[getRegions(ef),,])$ISO3[which(gdp_cap[getRegions(ef),,] <= 2750)]
# high and medium income countries
r_HM <- setdiff(getRegions(ef), r_L)
# High-Medium income countries with strong pollution policies in place
r_HMStrong <- c("AUS", "CAN", "USA","JPN") # FIXME which definition???
# High-Medium income countries with lower emissions goals
r_HMRest <- setdiff(r_HM,r_HMStrong)
} else if (p_countryCategories == "useGAINSregions") {
# Compute mean GDP/Cap per GAINS region
regionMean_gdppcap <- sapply(unique(map_regions$RegionCode), function(x) {mean(gdp_cap[map_regions$CountryCode[map_regions$RegionCode == x],,])})
# low income countries (using World Bank definition < 2750 US$(2010)/Cap)
r_L <- map_regions$CountryCode[map_regions$RegionCode %in% names(regionMean_gdppcap[regionMean_gdppcap <= 2750])]
# high and medium income countries
r_HM <- setdiff(getRegions(ef), r_L)
# High-Medium income countries with strong pollution policies in place
r_HMStrong <- map_regions$CountryCode[map_regions$RegionCode %in% c("Western Europe", "Japan")] # FIXME definition taken from JeS matlab script
# High-Medium income countries with lower emissions goals
r_HMRest <- setdiff(r_HM,r_HMStrong)
} else {
stop("Unknown value of p_countryCategories")
}
# generate FLE and SSP scenarios
# -------- Fix all scenarios to CLE in 2005 and 2010 ----------
ef[,c(2005,2010),] <- ef_eclipse[,c(2005,2010),"CLE"]
emi[,c(2005,2010),] <- emissions_exogenous[,c(2005,2010),"CLE"]
# ---------------- FLE ----------------------------------------
# FLE: CLE 2010 emission factors and emissions are held constant
ef[,,"FLE"] <- setYears(ef[,2010,"FLE"], NULL) # NULL is actually the default value, skipping afterwards
emi[,,"FLE"] <- setYears(emi[,2010,"FLE"], NULL)
# ---------------- SSP1 ---------------------------------------
# Emission factors
# low income countries
ef[r_L,2030,"SSP1"] <- ef_eclipse[r_L, 2030, "CLE"] # 2030: CLE30
ef[r_L,2050,"SSP1"] <- pmin(setYears(ef[r_L, 2030, "SSP1"]),
setYears(allocate_c2r_ef(ef_eclipse, r_L, select_weu, 2030, "CLE"))) # 2050: CLE30 WEU, if not higher than 2030 value
ef[r_L,2100,"SSP1"] <- pmin(setYears(ef[r_L, 2050, "SSP1"]), setYears(ef_eclipse[r_L, 2030, "MFR"])) # 2100: SLE30, if not higher than 2050 value
# high income countries
ef[r_HM,2030,"SSP1"] <- 0.75 * ef_eclipse[r_HM, 2030, "CLE"] # 2030: 75% of CLE30
ef[r_HM,2050,"SSP1"] <- pmin(setYears(ef[r_HM, 2030, "SSP1"]), setYears(ef_eclipse[r_HM, 2030, "SLE"])) # 2050: SLE30, if not higher than 2030 value
ef[r_HM,2100,"SSP1"] <- pmin(setYears(ef[r_HM, 2050, "SSP1"]), setYears(ef_eclipse[r_HM, 2030, "MFR"])) # 2100: MFR, if not higher than 2050 value
# Emissions
# low income countries
emi[r_L,2030,"SSP1"] <- emissions_exogenous[r_L, 2030, "CLE"] # 2030: CLE30
emi[r_L,2050,"SSP1"] <- pmin(setYears(emi[r_L, 2030, "SSP1"]), setYears(0.5*emissions_exogenous[r_L, 2030, "CLE"]
+ 0.5*emissions_exogenous[r_L, 2030, "SLE"])) # 2050: CLE30 WEU, if not higher than 2030 value
emi[r_L,2100,"SSP1"] <- pmin(setYears(emi[r_L, 2050, "SSP1"]), setYears(emissions_exogenous[r_L, 2030, "MFR"])) # 2100: SLE30, if not higher than 2050 value
# high income countries
emi[r_HM,2030,"SSP1"] <- 0.75 * emissions_exogenous[r_HM, 2030, "CLE"] # 2030: 75% of CLE30
emi[r_HM,2050,"SSP1"] <- pmin(setYears(emi[r_HM, 2030, "SSP1"]), setYears(emissions_exogenous[r_HM, 2030, "SLE"])) # 2050: SLE30, if not higher than 2030 value
emi[r_HM,2100,"SSP1"] <- pmin(setYears(emi[r_HM, 2050, "SSP1"]), setYears(emissions_exogenous[r_HM, 2030, "MFR"])) # 2100: MFR, if not higher than 2050 value
# ----------------- SSP2 --------------------------------------
# Emission factors
# High-Medium income countries with strong pollution policies in place
ef[r_HMStrong,2030,"SSP2"] <- ef_eclipse[r_HMStrong,2030,"CLE"] # 2030: CLE30
ef[r_HMStrong,2050,"SSP2"] <- pmin(setYears(ef[r_HMStrong, 2030,"SSP2"]),
setYears(ef_eclipse[r_HMStrong,2030,"SLE"])) # 2050: SLE30
ef[r_HMStrong,2100,"SSP2"] <- pmin(setYears(ef[r_HMStrong, 2050,"SSP2"]),
setYears(allocate_min2r_ef(ef_eclipse, r_HMStrong, r_oecd, 2030, "SLE"))) # 2100: Lowest SLE30 or lower
# High-Medium income countries with lower emissions goals
ef[r_HMRest,2030,"SSP2"] <- ef_eclipse[r_HMRest,2030,"CLE"] # 2030: CLE30
ef[r_HMRest,2050,"SSP2"] <- pmin(setYears(ef[r_HMRest, 2030,"SSP2"]),
setYears(allocate_min2r_ef(ef_eclipse, r_HMRest, r_HMRest, 2030, "CLE"))) # 2050: Min CLE30
ef[r_HMRest,2100,"SSP2"] <- pmin(setYears(ef[r_HMRest,2050,"SSP2"]),
setYears(allocate_c2r_ef(ef_eclipse, r_HMRest, select_weu, 2030, "SLE"))) # 2100: SLE30 WEU
# low income countries
ef[r_L,2030,"SSP2"] <- setYears(ef_eclipse[r_L, 2020, "CLE"]) # 2030: CLE20
ef[r_L,2050,"SSP2"] <- pmin(setYears(ef[r_L, 2030,"SSP2"]),
setYears(allocate_min2r_ef(ef_eclipse, r_L, r_L, 2030, "CLE"))) # 2050: Min CLE30
ef[r_L,2100,"SSP2"] <- pmin(setYears(ef[r_L,2050,"SSP2"]),
setYears(allocate_c2r_ef(ef_eclipse, r_L, select_weu, 2030, "CLE"))) # 2100: CLE30 WEU
# Emissions
# High-Medium income countries with strong pollution policies in place
emi[r_HMStrong,2030,"SSP2"] <- emissions_exogenous[r_HMStrong,2030,"CLE"] # 2030: CLE30
emi[r_HMStrong,2050,"SSP2"] <- pmin(setYears(emi[r_HMStrong, 2030,"SSP2"]),
setYears(emissions_exogenous[r_HMStrong,2030,"SLE"])) # 2050: SLE30
emi[r_HMStrong,2100,"SSP2"] <- pmin(setYears(emi[r_HMStrong, 2050,"SSP2"]),
setYears(emissions_exogenous[r_HMStrong,2030,"SLE"]*0.8)) # 2100: Lowest SLE30 or lower -> 0.8*SLE30
# High-Medium income countries with lower emissions goals
emi[r_HMRest,2030,"SSP2"] <- emissions_exogenous[r_HMRest,2030,"CLE"] # 2030: CLE30
emi[r_HMRest,2050,"SSP2"] <- pmin(setYears(emi[r_HMRest, 2030,"SSP2"]),
setYears(emissions_exogenous[r_HMRest,2030,"SLE"])) # 2050: Min CLE30 -> SLE30
emi[r_HMRest,2100,"SSP2"] <- pmin(setYears(emi[r_HMRest,2050,"SSP2"]),
setYears(emissions_exogenous[r_HMRest,2030,"SLE"]*0.8)) # 2100: SLE30 WEU -> 0.8*SLE30
# low income countries
emi[r_L,2030,"SSP2"] <- setYears(emissions_exogenous[r_L, 2020, "CLE"]) # 2030: CLE20
emi[r_L,2050,"SSP2"] <- pmin(setYears(emi[r_L, 2030,"SSP2"]),
setYears(emissions_exogenous[r_L, 2030, "CLE"])) # 2050: Min CLE30 -> CLE30
emi[r_L,2100,"SSP2"] <- pmin(setYears(emi[r_L,2050,"SSP2"]),
setYears(emissions_exogenous[r_L, 2030, "SLE"]*0.95)) # 2100: CLE30 WEU -> 0.95*SLE30
# DK: deleted outcommented code
# -----------------SSP1<SSP2-----------------------------------
ef[,2030,"SSP1"] <- pmin(setYears(ef[,2030,"SSP2"]),setYears(ef[,2030,"SSP1"]))
ef[,2050,"SSP1"] <- pmin(setYears(ef[,2050,"SSP2"]),setYears(ef[,2050,"SSP1"])) # make sure SSP1 is not higher than SSP2
# ----------------- SSP5 --------------------------------------
# set SSP5 to the values of SSP1
ef[,,"SSP5"] <- ef[,,"SSP1"]
emi[,,"SSP5"] <- emi[,,"SSP1"] # does not really make sense...
# DK: deleted outcommented code:
# ----------------- CLE and MFR -------------------------------
ef[,c(2005,2010,2030,2050),c("CLE","MFR")] <- ef_eclipse[,c(2005,2010,2030,2050),c("CLE","MFR")]
ef[,2100,c("CLE","MFR")] <- setYears(ef_eclipse[,2050,c("CLE","MFR")]) # for 2100, take the same values as in 2050
emi[,c(2005,2010,2030,2050),c("CLE","MFR")] <- emissions_exogenous[,c(2005,2010,2030,2050),c("CLE","MFR")]
emi[,2100,c("CLE","MFR")] <- setYears(emissions_exogenous[,2050,c("CLE","MFR")]) # for 2100, take the same values as in 2050
# DK: deleted outcommented code:
# DK select the scenario before returning
if (subtype == "emissions") {
result <- time_interpolate(emi, interpolated_year=time, integrate_interpolated_years=TRUE, extrapolation_type="constant")
result <- result / 1000 # kt -> Mt
getSets(result) <- c("region","year","sector","emi","scenario")
w <- NULL
} else if (subtype == "emission_factors") {
common_y <- intersect(getYears(ef),getYears(activities))
result <- time_interpolate(ef[,common_y,], interpolated_year=time, integrate_interpolated_years=TRUE, extrapolation_type="constant")
getSets(result) <- c("region","year","sector","emi","scenario")
#
w <- time_interpolate(activities[,common_y,getNames(result,dim=1)], interpolated_year=time, integrate_interpolated_years=TRUE, extrapolation_type="constant")
getSets(w) <- c("region","year","sector")
} else { stop("Unknown subtype ",subtype,"!")}
return(list(x = result,
weight = w,
unit = "Mt",
description = "Scenario for emissions or emission factors calculated based on ECLIPSE (GAINS) data."))
}
pik-piam/moinput documentation built on June 9, 2020, 12:23 p.m.
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Defines functions calcGAINS
#' calcGAINS
#'
#' Calculates air pollutant emissions and emission factors (user can choose)
#' based on GAINS emissions and activity data. Result is given on GAINS sector level.
#' User can choose between aggregated and extended sectoral resolution. Results are
#' given for multiple scenarios. Scenario design is partly taken from the GAINS data
#' and partly created in this function (particularly the SSPs).
#'
#' @param subtype decides whether emissions or emission factors are returned
#' @param sectoral_resolution aggreaged or extenden (uses different GAINS input data)
#' @importFrom dplyr group_by_ summarise_ ungroup mutate_ rename_ filter_ select_
#' @importFrom magclass as.magpie getCells getSets<- getNames<- getSets getRegions<- mselect<- setNames write.magpie
#' @importFrom tidyr gather_
#' @importFrom utils read.csv read.csv2
#' @importFrom quitte as.quitte
calcGAINS <- function(subtype="emission_factors", sectoral_resolution="extended") {
if (!(subtype %in% c("emission_factors", "emissions"))) stop('subtype must be in c("emission_factors", "emissions")')
# local functions
# country to region
allocate_c2r_ef <- function(id_ef, ip_region, ip_country, ip_year, ip_scenario) {
dummy <- id_ef[ip_region, ip_year, ip_scenario]
dummy[,,] <- setCells(id_ef[ip_country, ip_year, ip_scenario], "GLO")
#names(dimnames(dummy)) <- c("region", "years", "data1.data2.species.scenario")
return(dummy)
}
#
allocate_min2r_ef <- function(id_ef, ip_region, ip_countryGroup, ip_year, ip_scenario) {
dummy <- id_ef[ip_region, ip_year, ip_scenario]
# Get minimum values across country group
tmp <- as.quitte(id_ef[ip_countryGroup,ip_year,ip_scenario]) %>%
group_by_(~data1,~data2) %>%
summarise_(value=~ifelse(all(value == 0) , 0, min(value[value >0 ],na.rm= TRUE))) %>% # a value 0 is often a sign for a NA that has been replaced with 0 for small countries
ungroup() %>%
as.data.frame() %>%
as.quitte() %>%
as.magpie()
# Allocate minimum values to region
dummy[ip_region, ip_year, ip_scenario] <- setYears(tmp)
return(dummy)
}
# DK: deleted unused function
# conversion factors
conv_ktSO2_to_ktS <- 1/2 # 32/(32+2*16)
conv_kt_per_PJ_to_Tg_per_TWa <- 1e-3 / (1e15/(365*24*60*60)*1e-12)
conv_PJ_to_Twa <- (1e15/(365*24*60*60)*1e-12)
# user-defined parameters
time <- c(seq(2005,2055,5), seq(2060,2110,10), 2130, 2150)
scenario <- c("SSP1","SSP2","SSP5","FLE", "MFR", "CLE") #,"SSP3","SSP4", "MFR_Transports", "GlobalEURO6", "FLE_building_transport", "SLCF_building_transport")
p_dagg_year <- 2005
p_dagg_pop <- "pop_SSP2"
p_dagg_gdp <- "gdp_SSP2"
p_dagg_map <- "regionmappingGAINS.csv"
p_countryCategories <- "useGAINSregions" # "perCountry"
# list of OECD countries
#TODO: may want to place this in a mapping file or in a R library
r_oecd <- c("AUS", "AUT", "BEL", "CAN", "CHL", "CZE", "DNK", "EST", "FIN", "FRA", "DEU", "GRC", "HUN", "ISL", "IRL", "ISR", "ITA",
"JPN", "KOR", "LUX", "MEX", "NLD", "NZL", "NOR", "POL", "PRT", "SVK", "SVN", "ESP", "SWE", "CHE", "TUR", "GBR", "USA")
# set of sectors for which no emission factor will be computed (because there is no activity reported, or not in terms of energy)
dimSector_skipEF = c("AACID", "CEMENT", "CHEM", "CHEMBULK", "CUSM", "NACID", "PAPER", "STEEL",
"Losses_Coal", "Losses_Distribution_Use",
"Transformations_Coal", "Transformations_HLF", "Transformations_HLF_Refinery", "Transformations_NatGas")
dimSector_skipEF_edge = c("End_Use_Industry_Bio_Trad", "End_Use_Industry_Coal", "End_Use_Industry_HLF", "End_Use_Industry_LLF",
"End_Use_Industry_NatGas", "End_Use_Residential_Bio_Mod", "End_Use_Residential_Bio_Trad", "End_Use_Residential_Coal",
"End_Use_Residential_HLF", "End_Use_Residential_LLF", "End_Use_Residential_NatGas", "End_Use_Services_Bio_Trad",
"End_Use_Services_Coal")
dimSector_skipEF_edge = c("")
dimSector_skipEF = c("")
#-- READ IN ECLIPSE (GAINS) DATA ------------------
activities <- readSource("ECLIPSE", subtype=paste0("activities.", sectoral_resolution))
activities <- activities[,c(2005,2010,2020,2030,2050),]
emissions <- readSource("ECLIPSE", subtype=paste0("emissions.", sectoral_resolution))
emissions <- emissions[,c(2005,2010,2020,2030,2050),]
# read in sectoral mapping (ECLIPSE (IMAGE) <> REMIND)
# DK map_sectors_ECLIPSE2Agg <- read.csv(toolMappingFile("sectoral", "mappingECLIPSEtoAggREMINDsectors.csv"), stringsAsFactors=TRUE)
# DK map_sectors_Agg2REMIND <- read.csv(toolMappingFile("sectoral", "mappingAggREMINDtoREMINDsectors.csv"), stringsAsFactors=TRUE)
#map_sectors <- map_sectors[which(!is.na(map_sectors$EDGE)),] # Remove transport sector (which is not represented in EDGE)
# read in regional map (select ISO and GAINS codes only). This is required for the construction of the SSPs
map_regions <- read.csv2(toolMappingFile("regional", p_dagg_map), stringsAsFactors=TRUE)[,c(2,3)]
map_regions <- map_regions %>%
filter_(~CountryCode != "ANT") %>% # Remove Netherland Antilles (not in REMIND regional mapping)
filter_(~RegionCode != "") %>%
mutate_(RegionCode = ~gsub("\\ \\+", "\\+", gsub("^\\s+|\\s+$", "", gsub("[0-9]", "", RegionCode)))) %>%
mutate_(CountryCode = ~factor(CountryCode))
# read in population and GDP data. required to compute gdp per cap
pop <- calcOutput("Population",aggregate=FALSE)[,p_dagg_year,p_dagg_pop]
gdp <- calcOutput("GDPppp", aggregate=FALSE)[,p_dagg_year,p_dagg_gdp]
#co <- map_regions$CountryCode[map_regions$RegionCode %in% c("Northern Africa","Middle East","Asia-Stan","Russia+")]
#e <- dimSums(emissions["IRN",,"End_Use_Industry_Coal.VOC"],dim=1)
#a <- dimSums(activities["IRN",,"End_Use_Industry_Coal"],dim=1)
#-- PROCESS DATA ------------------
# set of sectors for which emission factors are computed
dimSector_EF <- getNames(activities)[!getNames(activities) %in% c(dimSector_skipEF, dimSector_skipEF_edge)]
# calculate gdp per capita
gdp_cap <- gdp/pop
gdp_cap[is.na(gdp_cap)] <- 0 # set NA to 0
# Regional selections
# select one country pertaining to WEU (all WEU countries should have the same EF). Used for SSP scenario rules
select_weu <- paste(map_regions[which(map_regions$RegionCode == "Western Europe")[1],1])
# Retrieve Transport names
#transportNames <- getNames(activities)[grepl("End_Use_Transport", getNames(activities))]
#buildingNames <- getNames(activities)[grepl("End_Use_Industry|End_Use_Residential|End_Use_Services", getNames(activities))]
# convert SO2 emission from TgSO2 to TgS
#emissions[,,"SO2"] <- emissions[,,"SO2"]*conv_ktSO2_to_ktS
# define missing SLE scenario (assumed to be 3/4 of the distance between CLE and MFR, according to discussion with Zig Klimont on 18th Feb 2016)
cle = emissions[,,"CLE"]
getNames(cle) = gsub("CLE", "MFR", getNames(cle))
sle = cle - (cle - emissions[,,"MFR"])*0.75
getNames(sle) = gsub("MFR", "SLE", getNames(sle))
emissions=mbind(emissions, sle)
rm(cle,sle)
# calculate emission factors (only for power and end-use sectors, and not empty activities) and convert from kt/PJ to Tg/Twa
ef_eclipse <- emissions[,,dimSector_EF] / activities[,,dimSector_EF] * conv_kt_per_PJ_to_Tg_per_TWa
getSets(ef_eclipse) <- c("region", "year", "data1", "data2", "data3")
# DK: NAs in ef_eclipse: There are two potential reasons for NAs in ef_eclipse:
# 1) ef = emi / activitiy => if the activity is zero the ef gets NA. The activity is disaggregated from 24 GAINS regions to the 249 ISO
# countries using population as weight. If there is no population data for a country the activity (and also the emissions) of this country
# are zero. In this case ef for this country is NA for ALL sectors and species => Jerome's command below that collects regions in NAregions
# which have only NAs works! The NAs are replaced with ef from countries that belong to the same GAINS region and thus have identical ef.
# After replacing these kind of NAs there may remain NAs for another reason:
# 2) There is no activity data for a particular sector and GAINS region in the source data => activity will be zero for this sector and all
# ISO countries belonging to this GAINS region => ef will be NA for those sectors and countries. Set those NAs to zero. When ef is reaggregated
# to REMIND regions this is done using the activites as weight. And the activity is zero for the countries and sectors that have zeros due to zero
# activity and thus have no effect on the result.
#
# some regions/countries have NA values everywhere (pop data is zero). Allocate EF of another country that belongs to the same GAINS region (except for Antartica)
# Find countries that have NA for all sectors and species (then probably due to missing population data, see above)
ef_eclipse["ATA",,] = 0 # Antartica -> 0
NAregions <- c("AIA","ALA","ATF","BES","BLM","BVT","CCK","COK","CXR","ESH","FLK","GGY","GIB","GLP","GUF","HMD","IOT","JEY","MSR","MTQ","MYT","NFK",
"NIU","NRU","PCN","REU","SGS","SHN","SJM","SPM","TKL","TWN","UMI","VAT","VGB","WLF")
# NAregions <- names(which(
# sapply(
# getRegions( which(is.na(ef_eclipse), arr.ind = TRUE)), function(k) all(is.na(as.numeric(ef_eclipse[k,,])))
# )
# )
# )
#NAregions <- c("AIA", "ATF", "BVT", "CCK", "COK", "CXR", "ESH", "FLK", "GIB", "GLP", "GUF",
# "HMD", "IOT", "MSR", "MTQ", "MYT", "NFK", "NIU", "NRU", "PCN",
# "REU", "SGS", "SHN", "SJM", "SPM", "TKL", "TWN", "UMI", "VAT", "VGB", "WLF")
MissingRegions <- c("ALA", "BES", "BLM", "CUW", "GGY", "IMN", "JEY", "MAF", "PSE", "SSD", "SXM")
#AssociatedGAINSregions <- c("Western Europe", "Rest Central America", "Rest Central America", "Rest Central America", "Western Europe", "Western Europe", "Western Europe",
# "Rest Central America", "Middle East", "Northern Africa", "Rest Central America")
# for countries that have NAs everywhere (missing population, see reason 1 above) replace NA with ef of country that belongs to same GAINS region
cat("ef_eclipse: NA values in first country replaced with ef of second country:\n")
for (kregi in NAregions) {
# suche die countrycodes aller countries, die zu der region gehören, zu der auch kregi gehört, lasse die Regionen aus NAregions und missingRegions weg
# take the value of the first country since all countries that belong to the same GAINS region have the same ef
subsitute_region = map_regions$CountryCode[map_regions$RegionCode == map_regions$RegionCode[map_regions$CountryCode == kregi] &
!map_regions$CountryCode %in% c(NAregions, MissingRegions)][1]
cat(kregi,as.character(subsitute_region),"\n")
#subsitute_region<-subsitute_region[-which(as.character(subsitute_region)=="ANT")] # remove ANT
# if (all(is.na(ef_eclipse[subsitute_region,,]))) {
# # NAs in all countries
# } elese { }
tmp <- ef_eclipse[subsitute_region,,]
getRegions(tmp) <- kregi
ef_eclipse[kregi,,] <- tmp
}
# some regions have no population data when disaggregating.
# DK: I think this case was already addressed above (country has NAs everywhere)
# for (kregi in MissingRegions) {
# # warum hier so kompliziert mit AssociatedGAINSregions und nicht automatisch wie oben?
# # es gibt einen Unterschied: oben würde SSD Western Africa zugeordnet, hier wird es Northern Africa zugeordnet
# substitute_region = map_regions$CountryCode[map_regions$RegionCode == AssociatedGAINSregions[which(MissingRegions == kregi)] &
# !map_regions$CountryCode %in% MissingRegions][1]
# tmp <- ef_eclipse[substitute_region,,]
# getRegions(tmp) <- kregi
# ef_eclipse[kregi,,] <- tmp
# }
# for the remaining NAs (0/0 = NaN) and Infs (1/0 = Inf ) just set EF to 0 (activity levels are 0, although in some cases emissions exist)
ef_eclipse[is.na(ef_eclipse)] <- 0
ef_eclipse[is.infinite(ef_eclipse)] <- 0
rm(NAregions, MissingRegions)#, AssociatedGAINSregions)
# DK: deleted outcommented code
# Check ef corresponds to initial data
# define exogenous emission data
emissions_exogenous <- emissions#[,,dimSector_skipEF]
# make output dummy "ef" and "emi" which then has to be filled by the data
ef <- do.call('mbind',
lapply(scenario,
function(s) {new.magpie(getRegions(ef_eclipse),
c(2005,2010,2030,2050,2100),
gsub("CLE", s, getNames(ef_eclipse[,,"CLE"])))
}))
emi <- do.call('mbind',
lapply(scenario,
function(s) {new.magpie(getRegions(emissions_exogenous),
c(2005,2010,2030,2050,2100),
gsub("CLE", s, getNames(emissions_exogenous[,,"CLE"])))
}))
# define country categories
if (p_countryCategories == "perCountry") {
# low income countries (using World Bank definition < 2750 US$(2010)/Cap)
r_L <- dimnames(gdp_cap[getRegions(ef),,])$ISO3[which(gdp_cap[getRegions(ef),,] <= 2750)]
# high and medium income countries
r_HM <- setdiff(getRegions(ef), r_L)
# High-Medium income countries with strong pollution policies in place
r_HMStrong <- c("AUS", "CAN", "USA","JPN") # FIXME which definition???
# High-Medium income countries with lower emissions goals
r_HMRest <- setdiff(r_HM,r_HMStrong)
} else if (p_countryCategories == "useGAINSregions") {
# Compute mean GDP/Cap per GAINS region
regionMean_gdppcap <- sapply(unique(map_regions$RegionCode), function(x) {mean(gdp_cap[map_regions$CountryCode[map_regions$RegionCode == x],,])})
# low income countries (using World Bank definition < 2750 US$(2010)/Cap)
r_L <- map_regions$CountryCode[map_regions$RegionCode %in% names(regionMean_gdppcap[regionMean_gdppcap <= 2750])]
# high and medium income countries
r_HM <- setdiff(getRegions(ef), r_L)
# High-Medium income countries with strong pollution policies in place
r_HMStrong <- map_regions$CountryCode[map_regions$RegionCode %in% c("Western Europe", "Japan")] # FIXME definition taken from JeS matlab script
# High-Medium income countries with lower emissions goals
r_HMRest <- setdiff(r_HM,r_HMStrong)
} else {
stop("Unknown value of p_countryCategories")
}
# generate FLE and SSP scenarios
# -------- Fix all scenarios to CLE in 2005 and 2010 ----------
ef[,c(2005,2010),] <- ef_eclipse[,c(2005,2010),"CLE"]
emi[,c(2005,2010),] <- emissions_exogenous[,c(2005,2010),"CLE"]
# ---------------- FLE ----------------------------------------
# FLE: CLE 2010 emission factors and emissions are held constant
ef[,,"FLE"] <- setYears(ef[,2010,"FLE"], NULL) # NULL is actually the default value, skipping afterwards
emi[,,"FLE"] <- setYears(emi[,2010,"FLE"], NULL)
# ---------------- SSP1 ---------------------------------------
# Emission factors
# low income countries
ef[r_L,2030,"SSP1"] <- ef_eclipse[r_L, 2030, "CLE"] # 2030: CLE30
ef[r_L,2050,"SSP1"] <- pmin(setYears(ef[r_L, 2030, "SSP1"]),
setYears(allocate_c2r_ef(ef_eclipse, r_L, select_weu, 2030, "CLE"))) # 2050: CLE30 WEU, if not higher than 2030 value
ef[r_L,2100,"SSP1"] <- pmin(setYears(ef[r_L, 2050, "SSP1"]), setYears(ef_eclipse[r_L, 2030, "MFR"])) # 2100: SLE30, if not higher than 2050 value
# high income countries
ef[r_HM,2030,"SSP1"] <- 0.75 * ef_eclipse[r_HM, 2030, "CLE"] # 2030: 75% of CLE30
ef[r_HM,2050,"SSP1"] <- pmin(setYears(ef[r_HM, 2030, "SSP1"]), setYears(ef_eclipse[r_HM, 2030, "SLE"])) # 2050: SLE30, if not higher than 2030 value
ef[r_HM,2100,"SSP1"] <- pmin(setYears(ef[r_HM, 2050, "SSP1"]), setYears(ef_eclipse[r_HM, 2030, "MFR"])) # 2100: MFR, if not higher than 2050 value
# Emissions
# low income countries
emi[r_L,2030,"SSP1"] <- emissions_exogenous[r_L, 2030, "CLE"] # 2030: CLE30
emi[r_L,2050,"SSP1"] <- pmin(setYears(emi[r_L, 2030, "SSP1"]), setYears(0.5*emissions_exogenous[r_L, 2030, "CLE"]
+ 0.5*emissions_exogenous[r_L, 2030, "SLE"])) # 2050: CLE30 WEU, if not higher than 2030 value
emi[r_L,2100,"SSP1"] <- pmin(setYears(emi[r_L, 2050, "SSP1"]), setYears(emissions_exogenous[r_L, 2030, "MFR"])) # 2100: SLE30, if not higher than 2050 value
# high income countries
emi[r_HM,2030,"SSP1"] <- 0.75 * emissions_exogenous[r_HM, 2030, "CLE"] # 2030: 75% of CLE30
emi[r_HM,2050,"SSP1"] <- pmin(setYears(emi[r_HM, 2030, "SSP1"]), setYears(emissions_exogenous[r_HM, 2030, "SLE"])) # 2050: SLE30, if not higher than 2030 value
emi[r_HM,2100,"SSP1"] <- pmin(setYears(emi[r_HM, 2050, "SSP1"]), setYears(emissions_exogenous[r_HM, 2030, "MFR"])) # 2100: MFR, if not higher than 2050 value
# ----------------- SSP2 --------------------------------------
# Emission factors
# High-Medium income countries with strong pollution policies in place
ef[r_HMStrong,2030,"SSP2"] <- ef_eclipse[r_HMStrong,2030,"CLE"] # 2030: CLE30
ef[r_HMStrong,2050,"SSP2"] <- pmin(setYears(ef[r_HMStrong, 2030,"SSP2"]),
setYears(ef_eclipse[r_HMStrong,2030,"SLE"])) # 2050: SLE30
ef[r_HMStrong,2100,"SSP2"] <- pmin(setYears(ef[r_HMStrong, 2050,"SSP2"]),
setYears(allocate_min2r_ef(ef_eclipse, r_HMStrong, r_oecd, 2030, "SLE"))) # 2100: Lowest SLE30 or lower
# High-Medium income countries with lower emissions goals
ef[r_HMRest,2030,"SSP2"] <- ef_eclipse[r_HMRest,2030,"CLE"] # 2030: CLE30
ef[r_HMRest,2050,"SSP2"] <- pmin(setYears(ef[r_HMRest, 2030,"SSP2"]),
setYears(allocate_min2r_ef(ef_eclipse, r_HMRest, r_HMRest, 2030, "CLE"))) # 2050: Min CLE30
ef[r_HMRest,2100,"SSP2"] <- pmin(setYears(ef[r_HMRest,2050,"SSP2"]),
setYears(allocate_c2r_ef(ef_eclipse, r_HMRest, select_weu, 2030, "SLE"))) # 2100: SLE30 WEU
# low income countries
ef[r_L,2030,"SSP2"] <- setYears(ef_eclipse[r_L, 2020, "CLE"]) # 2030: CLE20
ef[r_L,2050,"SSP2"] <- pmin(setYears(ef[r_L, 2030,"SSP2"]),
setYears(allocate_min2r_ef(ef_eclipse, r_L, r_L, 2030, "CLE"))) # 2050: Min CLE30
ef[r_L,2100,"SSP2"] <- pmin(setYears(ef[r_L,2050,"SSP2"]),
setYears(allocate_c2r_ef(ef_eclipse, r_L, select_weu, 2030, "CLE"))) # 2100: CLE30 WEU
# Emissions
# High-Medium income countries with strong pollution policies in place
emi[r_HMStrong,2030,"SSP2"] <- emissions_exogenous[r_HMStrong,2030,"CLE"] # 2030: CLE30
emi[r_HMStrong,2050,"SSP2"] <- pmin(setYears(emi[r_HMStrong, 2030,"SSP2"]),
setYears(emissions_exogenous[r_HMStrong,2030,"SLE"])) # 2050: SLE30
emi[r_HMStrong,2100,"SSP2"] <- pmin(setYears(emi[r_HMStrong, 2050,"SSP2"]),
setYears(emissions_exogenous[r_HMStrong,2030,"SLE"]*0.8)) # 2100: Lowest SLE30 or lower -> 0.8*SLE30
# High-Medium income countries with lower emissions goals
emi[r_HMRest,2030,"SSP2"] <- emissions_exogenous[r_HMRest,2030,"CLE"] # 2030: CLE30
emi[r_HMRest,2050,"SSP2"] <- pmin(setYears(emi[r_HMRest, 2030,"SSP2"]),
setYears(emissions_exogenous[r_HMRest,2030,"SLE"])) # 2050: Min CLE30 -> SLE30
emi[r_HMRest,2100,"SSP2"] <- pmin(setYears(emi[r_HMRest,2050,"SSP2"]),
setYears(emissions_exogenous[r_HMRest,2030,"SLE"]*0.8)) # 2100: SLE30 WEU -> 0.8*SLE30
# low income countries
emi[r_L,2030,"SSP2"] <- setYears(emissions_exogenous[r_L, 2020, "CLE"]) # 2030: CLE20
emi[r_L,2050,"SSP2"] <- pmin(setYears(emi[r_L, 2030,"SSP2"]),
setYears(emissions_exogenous[r_L, 2030, "CLE"])) # 2050: Min CLE30 -> CLE30
emi[r_L,2100,"SSP2"] <- pmin(setYears(emi[r_L,2050,"SSP2"]),
setYears(emissions_exogenous[r_L, 2030, "SLE"]*0.95)) # 2100: CLE30 WEU -> 0.95*SLE30
# DK: deleted outcommented code
# -----------------SSP1<SSP2-----------------------------------
ef[,2030,"SSP1"] <- pmin(setYears(ef[,2030,"SSP2"]),setYears(ef[,2030,"SSP1"]))
ef[,2050,"SSP1"] <- pmin(setYears(ef[,2050,"SSP2"]),setYears(ef[,2050,"SSP1"])) # make sure SSP1 is not higher than SSP2
# ----------------- SSP5 --------------------------------------
# set SSP5 to the values of SSP1
ef[,,"SSP5"] <- ef[,,"SSP1"]
emi[,,"SSP5"] <- emi[,,"SSP1"] # does not really make sense...
# DK: deleted outcommented code:
# ----------------- CLE and MFR -------------------------------
ef[,c(2005,2010,2030,2050),c("CLE","MFR")] <- ef_eclipse[,c(2005,2010,2030,2050),c("CLE","MFR")]
ef[,2100,c("CLE","MFR")] <- setYears(ef_eclipse[,2050,c("CLE","MFR")]) # for 2100, take the same values as in 2050
emi[,c(2005,2010,2030,2050),c("CLE","MFR")] <- emissions_exogenous[,c(2005,2010,2030,2050),c("CLE","MFR")]
emi[,2100,c("CLE","MFR")] <- setYears(emissions_exogenous[,2050,c("CLE","MFR")]) # for 2100, take the same values as in 2050
# DK: deleted outcommented code:
# DK select the scenario before returning
if (subtype == "emissions") {
result <- time_interpolate(emi, interpolated_year=time, integrate_interpolated_years=TRUE, extrapolation_type="constant")
result <- result / 1000 # kt -> Mt
getSets(result) <- c("region","year","sector","emi","scenario")
w <- NULL
} else if (subtype == "emission_factors") {
common_y <- intersect(getYears(ef),getYears(activities))
result <- time_interpolate(ef[,common_y,], interpolated_year=time, integrate_interpolated_years=TRUE, extrapolation_type="constant")
getSets(result) <- c("region","year","sector","emi","scenario")
#
w <- time_interpolate(activities[,common_y,getNames(result,dim=1)], interpolated_year=time, integrate_interpolated_years=TRUE, extrapolation_type="constant")
getSets(w) <- c("region","year","sector")
} else { stop("Unknown subtype ",subtype,"!")}
return(list(x = result,
weight = w,
unit = "Mt",
description = "Scenario for emissions or emission factors calculated based on ECLIPSE (GAINS) data."))
}
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