R/reportEmployment.R
In pik-piam/remind: The REMIND R Package
Defines functions
reportEmployment
Documented in
reportEmployment
#' Computes the employment values (jobs) across different sectors
#' @param gdx A gdx file output from a REMIND run
#' @param type either "created" or "per_year". The former outputs jobs created in that year whereas the latter outputs total jobs in that year
#' @param improvements Either "None", "CEEW", "Dias", "Rutovitz_aus","Solar_found" or "All". Use "All" for all improvements.
#' @param subtype Subtype for how shares of solar rooftop, wind offshore, and small hydro are assumed in the future. Options "current", "irena", and "expert". See calcDspvShare for more information.
#' @param share Either "current" or "local". Current implies current shares of world manufacture remain same until 2050, current means that in 2050 all countries manufacture required components locally.
#' @param multiplier controls how the regional multiplier for non-oecd countries changes with time.
#' @param decline How should the employment factors change over time? "capcosts" means according to capital costs. "static" means it doesn't change
#' @description This function returns a magpie object containing the reporting the jobs for different technologies
#' @return A magpie object
#' @author Aman Malik
#' @examples
#'
#' \dontrun{
#' reportEmployment(gdx,improvements="All",multiplier="own",subtype="expert",share="local",decline="capcosts")
#' }
#' @importFrom madrat calcOutput
#' @importFrom magclass getNames add_columns getItems
#' @export
reportEmployment <- function(gdx,improvements,multiplier,subtype,share,decline,type){
# employment factors
x <- madrat::calcOutput("Employmentfactors",improvements=improvements,multiplier=multiplier)
x <- x[,,"HP",pmatch=T,invert=T] # excluding (at the moment) jobs in combined heat and power plants
## DECLINE FACTORS------------------------
if (decline=="capcosts"){
# capital costs and OM fixed costs evolution over time for different techs, used to calculated the decline factor
report_tech <- reportTechnology(gdx)
report_tech <- collapseNames(report_tech)
var <- c("Tech|Electricity|Coal|PC|w/o CCS|Capital Costs (US$2005/kW)",
"Tech|Electricity|Gas|CC|w/o CCS|Capital Costs (US$2005/kW)",
"Tech|Electricity|Hydro|Capital Costs (US$2005/kW)",
"Tech|Electricity|Nuclear|Capital Costs (US$2005/kW)" ,
"Tech|Electricity|Solar|PV|Capital Costs (US$2005/kW)",
"Tech|Electricity|Solar|CSP|Capital Costs (US$2005/kW)",
"Tech|Electricity|Wind|Capital Costs (US$2005/kW)",
"Tech|Electricity|Biomass|IGCC|w/o CCS|Capital Costs (US$2005/kW)",
"Tech|Electricity|Storage|Battery|For PV|Capital Costs (US$2005/kW)",
"Tech|Electricity|Oil|DOT|Capital Costs (US$2005/kW)",
"Tech|Electricity|Geothermal|Capital Costs (US$2005/kW)",
"Tech|Electricity|Coal|PC|w/o CCS|OM Cost|fixed (US$2005/kW/yr)",
"Tech|Electricity|Gas|CC|w/o CCS|OM Cost|fixed (US$2005/kW/yr)",
"Tech|Electricity|Hydro|OM Cost|fixed (US$2005/kW/yr)",
"Tech|Electricity|Nuclear|OM Cost|fixed (US$2005/kW/yr)" ,
"Tech|Electricity|Solar|PV|OM Cost|fixed (US$2005/kW/yr)",
"Tech|Electricity|Solar|CSP|OM Cost|fixed (US$2005/kW/yr)",
"Tech|Electricity|Wind|OM Cost|fixed (US$2005/kW/yr)",
"Tech|Electricity|Biomass|IGCC|w/o CCS|OM Cost|fixed (US$2005/kW/yr)",
"Tech|Electricity|Oil|DOT|OM Cost|fixed (US$2005/kW/yr)",
"Tech|Electricity|Geothermal|OM Cost|fixed (US$2005/kW/yr)",
"Tech|Electricity|Storage|Battery|For PV|OM Cost|fixed (US$2005/kW/yr)",NULL
)
# only capital costs
cap_costs <- report_tech[,seq(2015,2050,5),grep(x = var,pattern = "Capital",value = T)]
cap_costs <- cap_costs["GLO",,invert=T]# removing GLO
cap_costs <- cap_costs[,,]/setYears(cap_costs[,"y2020",],NULL)# costs relative to 2015
# the job intensity decreases with capital costs for techs, relative to 2015
var_in_x <- getItems(x,dim=3.1) # variables from EF magpie object
var_in_rem <- c("Coal","Gas","Nuclear","Biomass","Hydro","Hydro","Wind","Wind","Solar|PV",
"Geothermal","Solar|CSP","Oil","Solar|PV","Storage") # variables from remind
var_com <- paste(var_in_x,var_in_rem,sep = ".") # concatenating the two for looping to work
for (i in var_com){
x[,,sub("\\..*", "", i)][,,c("CI","Manf")] <- x[,,sub("\\..*", "", i)][,,c("CI","Manf")]*setNames(cap_costs[,getYears(x),sub(".*\\.", "", i),pmatch=T],NULL)
}
# OM fixed costs used to calculate decline factors for O&M employment factors
fixed_costs <- report_tech[,seq(2015,2050,5),grep(x = var,pattern = "fixed",value = T)]
fixed_costs <- fixed_costs["GLO",,invert=T]# removing GLO
fixed_costs <- fixed_costs[,,]/setYears(fixed_costs[,"y2020",],NULL)# costs relative to 2015
for (i in var_com){
x[,,sub("\\..*", "", i)][,,"OM"] <- x[,,sub("\\..*", "", i)][,,"OM"]*setNames(fixed_costs[,getYears(x),sub(".*\\.", "", i),pmatch=T],NULL)
}
# decline factors for coal fuel supply until 2050 are projected depending on
# historical patterns and convergence between some countries.
# See calcLabourProductivity for more information.
coal_ef <- calcOutput("CoalLabourProductivity",subtype = "Employment_factor")
coal_ef <- coal_ef/setYears(coal_ef[,2020,],NULL)
coal_ef[which(coal_ef=="NaN")] <- 0
x[,getYears(x),"Coal.Fuel_supply"] <- x[,getYears(x),"Coal.Fuel_supply"]*setNames(coal_ef[,getYears(x),],NULL)
}
# if (decline=="capcosts_50"){
# report_tech <- reportTechnology(gdx)
# report_tech <- collapseNames(report_tech)
#
# var <- c("Tech|Electricity|Coal|PC|w/o CCS|Capital Costs (US$2005/kW)",
# "Tech|Electricity|Gas|CC|w/o CCS|Capital Costs (US$2005/kW)",
# "Tech|Electricity|Hydro|Capital Costs (US$2005/kW)",
# "Tech|Electricity|Nuclear|Capital Costs (US$2005/kW)" ,
# "Tech|Electricity|Solar|PV|Capital Costs (US$2005/kW)",
# "Tech|Electricity|Solar|CSP|Capital Costs (US$2005/kW)",
# "Tech|Electricity|Wind|Capital Costs (US$2005/kW)",
# "Tech|Electricity|Biomass|IGCC|w/o CCS|Capital Costs (US$2005/kW)",
# "Tech|Electricity|Storage|Battery|For PV|Capital Costs (US$2005/kW)",
# "Tech|Electricity|Oil|DOT|Capital Costs (US$2005/kW)",
# "Tech|Electricity|Geothermal|Capital Costs (US$2005/kW)",
#
# "Tech|Electricity|Coal|PC|w/o CCS|OM Cost|fixed (US$2005/kW/yr)",
# "Tech|Electricity|Gas|CC|w/o CCS|OM Cost|fixed (US$2005/kW/yr)",
# "Tech|Electricity|Hydro|OM Cost|fixed (US$2005/kW/yr)",
# "Tech|Electricity|Nuclear|OM Cost|fixed (US$2005/kW/yr)" ,
# "Tech|Electricity|Solar|PV|OM Cost|fixed (US$2005/kW/yr)",
# "Tech|Electricity|Solar|CSP|OM Cost|fixed (US$2005/kW/yr)",
# "Tech|Electricity|Wind|OM Cost|fixed (US$2005/kW/yr)",
# "Tech|Electricity|Biomass|IGCC|w/o CCS|OM Cost|fixed (US$2005/kW/yr)",
# "Tech|Electricity|Oil|DOT|OM Cost|fixed (US$2005/kW/yr)",
# "Tech|Electricity|Geothermal|OM Cost|fixed (US$2005/kW/yr)",
# "Tech|Electricity|Storage|Battery|For PV|OM Cost|fixed (US$2005/kW/yr)",NULL
# )
# # only capital costs
# cap_costs <- report_tech[,seq(2015,2050,5),grep(x = var,pattern = "Capital",value = T)]
# cap_costs <- cap_costs["GLO",,invert=T]# removing GLO
#
# cap_costs <- cap_costs[,,]/setYears(cap_costs[,"y2020",],NULL)# costs relative to 2020
# cap_costs[,seq(2025,2050,5),] <- cap_costs[,seq(2025,2050,5),]*1.50
#
# # the job intensity decreases with capital costs for techs, relative to 2020
# var_in_x <- getItems(x,dim=3.1) # variables from EF magpie object
# var_in_rem <- c("Coal","Gas","Nuclear","Biomass","Hydro","Hydro","Wind","Wind","Solar|PV",
# "Geothermal","Solar|CSP","Oil","Solar|PV","Storage") # variables from remind
# var_com <- paste(var_in_x,var_in_rem,sep = ".") # concatenating the two for looping to work
#
# for (i in var_com){
# x[,,sub("\\..*", "", i)][,,c("CI","Manf")] <- x[,,sub("\\..*", "", i)][,,c("CI","Manf")]*setNames(cap_costs[,getYears(x),sub(".*\\.", "", i),pmatch=T],NULL)
# }
# # OM fixed costs used to calculate decline factors for O&M employment factors
# fixed_costs <- report_tech[,seq(2015,2050,5),grep(x = var,pattern = "fixed",value = T)]
# fixed_costs <- fixed_costs["GLO",,invert=T]# removing GLO
#
# fixed_costs <- fixed_costs[,,]/setYears(fixed_costs[,"y2020",],NULL)# costs relative to 2015
# for (i in var_com){
# x[,,sub("\\..*", "", i)][,,"OM"] <- x[,,sub("\\..*", "", i)][,,"OM"]*setNames(fixed_costs[,getYears(x),sub(".*\\.", "", i),pmatch=T],NULL)
# }
#
#
#
# # decline factors for coal fuel supply
# coal_ef <- calcOutput("CoalLabourProductivity",subtype = "Employment_factor")
# coal_ef <- coal_ef/setYears(coal_ef[,2020,],NULL)
# coal_ef[which(coal_ef=="NaN")] <- 0
# x[,getYears(coal_ef),"Coal.Fuel_supply"] <- x[,getYears(coal_ef),"Coal.Fuel_supply"]*setNames(coal_ef[,,],NULL)
#
#
# }
# }
if (decline=="static"){
# do nothing, as employment factors have already been read
}
# the above loops are based on the expanded code below (2 shown for example)-
# x[,,"Coal"][,,c("CI","Manf")] <- x[,,"Coal"][,,c("CI","Manf")]*setNames(cap_costs[,getYears(x),"Coal",pmatch=T],NULL)
# x[,,"Gas"][,,c("CI","Manf")] <- x[,,"Gas"][,,c("CI","Manf")]*setNames(cap_costs[,getYears(x),"Gas",pmatch=T],NULL)
##--------------------------------------------------------
## Other external parameters--------------------------
# share of exports to world installed capacity
prod_share <- calcOutput("ProdShares")
prod_share <- prod_share[,"y2019",] # use 2019 as 2018 values have NAs for important countries
cols_to_add <- c("Solar|PV-utility","Solar|PV-rooftop","Wind offshore","Wind onshore")
prod_share <- add_columns(prod_share,addnm = cols_to_add,dim=3.1)
prod_share[,,c("Solar|PV-utility","Solar|PV-rooftop")] <- prod_share[,,"spv"]
prod_share[,,c("Wind offshore","Wind onshore")] <- prod_share[,,"wind"]
# share of rooftop in total spv, wind offshore in total wind, and hydro small in total hydro
share <- calcOutput("DspvShare")
##----------------------------------------------
## New and Absolute capacity from REMIND---------------------------------
# filtering required capacity variables i.e., only new and existing coal capacities
remind_output <- reportCapacity(gdx)
rem_filter <- remind_output[,,c("Cumulative","Idle","Total Cap","CC","Hydrogen","Liquids",
"Estimated","Non-Biomass","For Wind","For PV","Onshore","CT"),pmatch=T,invert=T]# removing cumulative and idle variables in capacity
rem_filter <- rem_filter[,,c("Cap|Electricity (GW)",# total sum of all techs and not needed
"New Cap|Electricity (GW)",
"Cap|Electricity|Solar (GW)",# is sum of pv and csp and not needed
"New Cap|Electricity|Solar (GW)"),invert=T]
# selecting variables only related to power or electricity
rem_filter <- rem_filter[,,][,,"Electricity",pmatch=T]
# filtering required regions
regions <- setdiff(getRegions(remind_output),"GLO")# all regions except world
rem_filter <- rem_filter[regions,seq(2015,2050,5),]
## disaggregating certain variables/adding new variables
cols_to_add <- c("Solar|PV-utility","Solar|PV-rooftop","Hydro-large","Hydro-small",
"Wind offshore","Wind onshore")
for (j in cols_to_add){
rem_filter <- add_columns(rem_filter,addnm = paste0("New Cap|Electricity|",j," (GW)"),dim = 3.1)
rem_filter <- add_columns(rem_filter,addnm = paste0("Cap|Electricity|",j," (GW)"),dim = 3.1)
}
# adding values to the new variables
for (p in c("New Cap|Electricity|","Cap|Electricity|")){
rem_filter[,,paste0(p,"Solar|PV-rooftop"," (GW)")] <- rem_filter[,,paste0(p,"Solar|PV"," (GW)")]*share[,,"spv"]
rem_filter[,,paste0(p,"Solar|PV-utility"," (GW)")] <- rem_filter[,,paste0(p,"Solar|PV"," (GW)")]*(1-share[,,"spv"])
rem_filter[,,paste0(p,"Hydro-small"," (GW)")] <- rem_filter[,,paste0(p,"Hydro"," (GW)")]*share[,,"hydro"]
rem_filter[,,paste0(p,"Hydro-large"," (GW)")] <- rem_filter[,,paste0(p,"Hydro"," (GW)")]*(1-share[,,"hydro"])
rem_filter[,,paste0(p,"Wind offshore"," (GW)")] <- rem_filter[,,paste0(p,"Wind"," (GW)")]*share[,,"wind"]
rem_filter[,,paste0(p,"Wind onshore"," (GW)")] <- rem_filter[,,paste0(p,"Wind"," (GW)")]*(1-share[,,"wind"])
}
# added/installed capacity from REMIND, for years > 2010
added_cap <- rem_filter[regions,getYears(rem_filter)>2010,"New Cap",pmatch=T]# only new capacities
added_cap_sum <- dimSums(added_cap,dim = 1) # world added capacity for each tech
# existing/operating capacity from REMIND
cap_tot <- rem_filter[,,"New Cap",invert=T,pmatch=T]# only existing capacities
##-----------------------------------------------------
# share of world capacity addition by region
shr_GLO_add <- added_cap[,,cols_to_add,pmatch=T]/added_cap_sum[,,cols_to_add,pmatch=T]
prod_share_tmp <- new.magpie(regions,c(2015,2020,2050),names = c("Solar|PV-utility","Solar|PV-rooftop","Wind offshore","Wind onshore"))
if (share=="local"||share=="current"){
## prod shares complete local manufacture of wind and solar components in 2050. Change is linear.
# assigning 2050 regional share values as prod shares
prod_share_tmp[,"y2050",] <- as.numeric(shr_GLO_add[,"y2050",getNames(prod_share_tmp),pmatch=T])
# 2015 and 2020 values same as 2019 values
prod_share_tmp[,c(2015,2020),] <- prod_share[,,getNames(prod_share_tmp)]
# from 2020 to 2050, linearly interpolate
prod_share <- time_interpolate(prod_share_tmp,seq(2025,2045,5),integrate_interpolated_years = T)
if (share=="current"){
prod_share[,,] <- prod_share[,2020,] # shares remain same throughout = 2020
}
}
# Obtaining fuel supply variables
remind_prod <- reportExtraction(gdx)["GLO",,,invert=T] # remove GLO
vars <- c("PE|Production|Biomass (EJ/yr)",
"PE|Production|Gross|Coal (EJ/yr)" ,
"PE|Production|Gross|Oil (EJ/yr)" ,
"PE|Production|Gross|Gas (EJ/yr)" ,
"PE|Production|Gross|Uranium [Energy] (EJ/yr)"
)
remind_prod <- remind_prod[regions,getYears(x),vars]# period >2010 and only select variables
pe <- reportPE(gdx)[regions,getYears(x),"PE|Biomass|Traditional (EJ/yr)"]
# removing the traditional biomass component, where biomass isn't sold, thus no employment
# in conventional sense
remind_prod[,,"PE|Production|Biomass (EJ/yr)"] <- remind_prod[,,"PE|Production|Biomass (EJ/yr)"] - setNames(pe[,,],NULL)
#getNames(remind_prod) <- gsub(pattern = "\\[Energy\\]",replacement = "",x = getNames(remind_prod))
getNames(remind_prod) <- gsub(pattern = " \\(EJ\\/yr\\)",replacement = " \\(EJ\\/yr\\)",x = getNames(remind_prod))# removing space
getNames(remind_prod) <- gsub(pattern = "Uranium",replacement = "Nuclear",x = getNames(remind_prod))# removing space
## Calculating jobs--------------------------------
techs <- c("Solar|PV-utility","Solar|PV-rooftop","Hydro-large","Hydro-small",
"Wind offshore","Wind onshore","Coal","Biomass","Gas","Storage","Oil","Nuclear","Geothermal","Solar|CSP")
techs_exp <- c("Solar|PV-utility","Solar|PV-rooftop","Wind offshore","Wind onshore") # technologies with export component
# cons <- new.magpie(getRegions(x),names = techs)
# average construction time for different technologies
# cons[] <- rep(c(1,1,4,3,4,2,5,2,2,1,2,10,2,2),each=12)
# 1. Manufacturing jobs for techs with export component.
# 1a) Total manf jobs (per region) for these techs are equal to total world addition X share of
# world exports (for that region)
if (type=="created"){
jobs_manf <- new.magpie(regions,getYears(x),techs,fill=0)
for (i in techs_exp){
#manf <- paste0(i,".","Manf")
jobs_manf[,,i] <- added_cap_sum[,getYears(x),i,pmatch=T]*1000
jobs_manf[,getYears(x),i] <- jobs_manf[,getYears(x),i] * setNames(x[,,i][,,"Manf"],NULL) * prod_share[,,i]
}
for (i in setdiff(techs,techs_exp)){
# manf <- paste0(i,".","Manf")
jobs_manf[,,i] <- added_cap[,getYears(x),i,pmatch=T]*1000
jobs_manf[,getYears(x),i] <- jobs_manf[,getYears(x),i] * setNames(x[,,i,pmatch=T][,,"Manf"],NULL)
}
jobs_manf <- add_dimension(jobs_manf,dim=3.2,add="type",nm = "Manf")
getSets(jobs_manf) <- c("region","year","variable","type")
# 2. Construction and Installation jobs
jobs_ci <- new.magpie(regions,getYears(x),techs)
for (i in techs){
#inst <- paste0(i,".","CI")
jobs_ci[,,i] <- added_cap[,getYears(x),i,pmatch=T]*1000
jobs_ci[,,i] <- jobs_ci[,getYears(x),i] * setNames(x[,,i,pmatch=T][,,"CI"],NULL)
}
jobs_ci <- add_dimension(jobs_ci,dim=3.2,add="type",nm = "CI")
getSets(jobs_ci) <- c("region","year","variable","type")
}
if (type=="per_year"){
jobs_manf <- new.magpie(regions,getYears(x),techs,fill=0)
for (i in techs_exp){
#manf <- paste0(i,".","Manf")
jobs_manf[,,i] <- added_cap_sum[,getYears(x),i,pmatch=T]*1000
jobs_manf[,getYears(x),i] <- (jobs_manf[,getYears(x),i] * setNames(x[,,i][,,"Manf"],NULL) * prod_share[,,i])
}
for (i in setdiff(techs,techs_exp)){
# manf <- paste0(i,".","Manf")
jobs_manf[,,i] <- added_cap[,getYears(x),i,pmatch=T]*1000
jobs_manf[,getYears(x),i] <- (jobs_manf[,getYears(x),i] * setNames(x[,getYears(x),i,pmatch=T][,,"Manf"],NULL))
}
jobs_manf <- add_dimension(jobs_manf,dim=3.2,add="type",nm = "Manf")
getSets(jobs_manf) <- c("region","year","variable","type")
# 2. Construction and Installation jobs
jobs_ci <- new.magpie(regions,getYears(x),techs,fill = 0)
for (i in techs){
#inst <- paste0(i,".","CI")
jobs_ci[,,i] <- added_cap[,getYears(x),i,pmatch=T]*1000
#jobs_ci[,,i] <- (jobs_ci[,getYears(x),i] * setNames(x[,,i,pmatch=T][,,"CI"],NULL))
jobs_ci[,,i] <- jobs_ci[,getYears(x),i] * setNames(x[,,i,pmatch=T][,,"CI"],nm = NULL)
}
jobs_ci <- add_dimension(jobs_ci,dim=3.2,add="type",nm = "CI")
getSets(jobs_ci) <- c("region","year","variable","type")
}
## 3. Jobs in O & M
# jobs = existing capacity per tech * emp factor per tech
jobs_om <- new.magpie(regions,getYears(x),techs)
for (i in techs){
# inst <- paste0(i,".","OM")
jobs_om[,,i] <- cap_tot[,getYears(x),i,pmatch=T]*1000
jobs_om[,,i] <- jobs_om[,,i] * setNames(x[,,i,pmatch=T][,,"OM"],NULL)
}
jobs_om <- add_dimension(jobs_om,dim=3.2,add="type",nm = "OM")
getSets(jobs_om) <- c("region","year","variable","type")
## 4. Jobs in Fuel supply
#jobs = total production (in EJ/yr) per fuel * employment factor per fuel (Jobs/PJ)
fuels <- c("Coal","Gas","Biomass","Oil")
jobs_prod <- new.magpie(regions,getYears(x),techs,fill=0)
for (i in fuels){
#fs <- paste0(i,".","Fuel_supply")
jobs_prod[,,i] <- remind_prod[,getYears(x),i,pmatch=T] * 1000
jobs_prod[,,i] <- jobs_prod[,,i] * setNames(x[,,i][,,"Fuel_supply"],NULL)
}
jobs_prod <- add_dimension(jobs_prod,dim=3.2,add="type",nm = "Fuel_supply")
getSets(jobs_prod) <- c("region","year","variable","type")
## JObs in Fuel supply, nuclear which are given in terms of SE
rem_se_nuc <- reportSE(gdx)[regions,getYears(x),"Nuclear",pmatch=T]*277777.778 # EJ to GWh
jobs_prod[,,"Nuclear.Fuel_supply"] <- setNames(rem_se_nuc[,,],NULL)*setNames(x[,,"Nuclear.Fuel_supply"],NULL)
####### put all together #############
jobs <- mbind(jobs_ci,jobs_om,jobs_prod,jobs_manf)
return (jobs)
}
pik-piam/remind documentation built on Sept. 9, 2021, 1:09 p.m.
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Defines functions reportEmployment
Documented in reportEmployment
#' Computes the employment values (jobs) across different sectors
#' @param gdx A gdx file output from a REMIND run
#' @param type either "created" or "per_year". The former outputs jobs created in that year whereas the latter outputs total jobs in that year
#' @param improvements Either "None", "CEEW", "Dias", "Rutovitz_aus","Solar_found" or "All". Use "All" for all improvements.
#' @param subtype Subtype for how shares of solar rooftop, wind offshore, and small hydro are assumed in the future. Options "current", "irena", and "expert". See calcDspvShare for more information.
#' @param share Either "current" or "local". Current implies current shares of world manufacture remain same until 2050, current means that in 2050 all countries manufacture required components locally.
#' @param multiplier controls how the regional multiplier for non-oecd countries changes with time.
#' @param decline How should the employment factors change over time? "capcosts" means according to capital costs. "static" means it doesn't change
#' @description This function returns a magpie object containing the reporting the jobs for different technologies
#' @return A magpie object
#' @author Aman Malik
#' @examples
#'
#' \dontrun{
#' reportEmployment(gdx,improvements="All",multiplier="own",subtype="expert",share="local",decline="capcosts")
#' }
#' @importFrom madrat calcOutput
#' @importFrom magclass getNames add_columns getItems
#' @export
reportEmployment <- function(gdx,improvements,multiplier,subtype,share,decline,type){
# employment factors
x <- madrat::calcOutput("Employmentfactors",improvements=improvements,multiplier=multiplier)
x <- x[,,"HP",pmatch=T,invert=T] # excluding (at the moment) jobs in combined heat and power plants
## DECLINE FACTORS------------------------
if (decline=="capcosts"){
# capital costs and OM fixed costs evolution over time for different techs, used to calculated the decline factor
report_tech <- reportTechnology(gdx)
report_tech <- collapseNames(report_tech)
var <- c("Tech|Electricity|Coal|PC|w/o CCS|Capital Costs (US$2005/kW)",
"Tech|Electricity|Gas|CC|w/o CCS|Capital Costs (US$2005/kW)",
"Tech|Electricity|Hydro|Capital Costs (US$2005/kW)",
"Tech|Electricity|Nuclear|Capital Costs (US$2005/kW)" ,
"Tech|Electricity|Solar|PV|Capital Costs (US$2005/kW)",
"Tech|Electricity|Solar|CSP|Capital Costs (US$2005/kW)",
"Tech|Electricity|Wind|Capital Costs (US$2005/kW)",
"Tech|Electricity|Biomass|IGCC|w/o CCS|Capital Costs (US$2005/kW)",
"Tech|Electricity|Storage|Battery|For PV|Capital Costs (US$2005/kW)",
"Tech|Electricity|Oil|DOT|Capital Costs (US$2005/kW)",
"Tech|Electricity|Geothermal|Capital Costs (US$2005/kW)",
"Tech|Electricity|Coal|PC|w/o CCS|OM Cost|fixed (US$2005/kW/yr)",
"Tech|Electricity|Gas|CC|w/o CCS|OM Cost|fixed (US$2005/kW/yr)",
"Tech|Electricity|Hydro|OM Cost|fixed (US$2005/kW/yr)",
"Tech|Electricity|Nuclear|OM Cost|fixed (US$2005/kW/yr)" ,
"Tech|Electricity|Solar|PV|OM Cost|fixed (US$2005/kW/yr)",
"Tech|Electricity|Solar|CSP|OM Cost|fixed (US$2005/kW/yr)",
"Tech|Electricity|Wind|OM Cost|fixed (US$2005/kW/yr)",
"Tech|Electricity|Biomass|IGCC|w/o CCS|OM Cost|fixed (US$2005/kW/yr)",
"Tech|Electricity|Oil|DOT|OM Cost|fixed (US$2005/kW/yr)",
"Tech|Electricity|Geothermal|OM Cost|fixed (US$2005/kW/yr)",
"Tech|Electricity|Storage|Battery|For PV|OM Cost|fixed (US$2005/kW/yr)",NULL
)
# only capital costs
cap_costs <- report_tech[,seq(2015,2050,5),grep(x = var,pattern = "Capital",value = T)]
cap_costs <- cap_costs["GLO",,invert=T]# removing GLO
cap_costs <- cap_costs[,,]/setYears(cap_costs[,"y2020",],NULL)# costs relative to 2015
# the job intensity decreases with capital costs for techs, relative to 2015
var_in_x <- getItems(x,dim=3.1) # variables from EF magpie object
var_in_rem <- c("Coal","Gas","Nuclear","Biomass","Hydro","Hydro","Wind","Wind","Solar|PV",
"Geothermal","Solar|CSP","Oil","Solar|PV","Storage") # variables from remind
var_com <- paste(var_in_x,var_in_rem,sep = ".") # concatenating the two for looping to work
for (i in var_com){
x[,,sub("\\..*", "", i)][,,c("CI","Manf")] <- x[,,sub("\\..*", "", i)][,,c("CI","Manf")]*setNames(cap_costs[,getYears(x),sub(".*\\.", "", i),pmatch=T],NULL)
}
# OM fixed costs used to calculate decline factors for O&M employment factors
fixed_costs <- report_tech[,seq(2015,2050,5),grep(x = var,pattern = "fixed",value = T)]
fixed_costs <- fixed_costs["GLO",,invert=T]# removing GLO
fixed_costs <- fixed_costs[,,]/setYears(fixed_costs[,"y2020",],NULL)# costs relative to 2015
for (i in var_com){
x[,,sub("\\..*", "", i)][,,"OM"] <- x[,,sub("\\..*", "", i)][,,"OM"]*setNames(fixed_costs[,getYears(x),sub(".*\\.", "", i),pmatch=T],NULL)
}
# decline factors for coal fuel supply until 2050 are projected depending on
# historical patterns and convergence between some countries.
# See calcLabourProductivity for more information.
coal_ef <- calcOutput("CoalLabourProductivity",subtype = "Employment_factor")
coal_ef <- coal_ef/setYears(coal_ef[,2020,],NULL)
coal_ef[which(coal_ef=="NaN")] <- 0
x[,getYears(x),"Coal.Fuel_supply"] <- x[,getYears(x),"Coal.Fuel_supply"]*setNames(coal_ef[,getYears(x),],NULL)
}
# if (decline=="capcosts_50"){
# report_tech <- reportTechnology(gdx)
# report_tech <- collapseNames(report_tech)
#
# var <- c("Tech|Electricity|Coal|PC|w/o CCS|Capital Costs (US$2005/kW)",
# "Tech|Electricity|Gas|CC|w/o CCS|Capital Costs (US$2005/kW)",
# "Tech|Electricity|Hydro|Capital Costs (US$2005/kW)",
# "Tech|Electricity|Nuclear|Capital Costs (US$2005/kW)" ,
# "Tech|Electricity|Solar|PV|Capital Costs (US$2005/kW)",
# "Tech|Electricity|Solar|CSP|Capital Costs (US$2005/kW)",
# "Tech|Electricity|Wind|Capital Costs (US$2005/kW)",
# "Tech|Electricity|Biomass|IGCC|w/o CCS|Capital Costs (US$2005/kW)",
# "Tech|Electricity|Storage|Battery|For PV|Capital Costs (US$2005/kW)",
# "Tech|Electricity|Oil|DOT|Capital Costs (US$2005/kW)",
# "Tech|Electricity|Geothermal|Capital Costs (US$2005/kW)",
#
# "Tech|Electricity|Coal|PC|w/o CCS|OM Cost|fixed (US$2005/kW/yr)",
# "Tech|Electricity|Gas|CC|w/o CCS|OM Cost|fixed (US$2005/kW/yr)",
# "Tech|Electricity|Hydro|OM Cost|fixed (US$2005/kW/yr)",
# "Tech|Electricity|Nuclear|OM Cost|fixed (US$2005/kW/yr)" ,
# "Tech|Electricity|Solar|PV|OM Cost|fixed (US$2005/kW/yr)",
# "Tech|Electricity|Solar|CSP|OM Cost|fixed (US$2005/kW/yr)",
# "Tech|Electricity|Wind|OM Cost|fixed (US$2005/kW/yr)",
# "Tech|Electricity|Biomass|IGCC|w/o CCS|OM Cost|fixed (US$2005/kW/yr)",
# "Tech|Electricity|Oil|DOT|OM Cost|fixed (US$2005/kW/yr)",
# "Tech|Electricity|Geothermal|OM Cost|fixed (US$2005/kW/yr)",
# "Tech|Electricity|Storage|Battery|For PV|OM Cost|fixed (US$2005/kW/yr)",NULL
# )
# # only capital costs
# cap_costs <- report_tech[,seq(2015,2050,5),grep(x = var,pattern = "Capital",value = T)]
# cap_costs <- cap_costs["GLO",,invert=T]# removing GLO
#
# cap_costs <- cap_costs[,,]/setYears(cap_costs[,"y2020",],NULL)# costs relative to 2020
# cap_costs[,seq(2025,2050,5),] <- cap_costs[,seq(2025,2050,5),]*1.50
#
# # the job intensity decreases with capital costs for techs, relative to 2020
# var_in_x <- getItems(x,dim=3.1) # variables from EF magpie object
# var_in_rem <- c("Coal","Gas","Nuclear","Biomass","Hydro","Hydro","Wind","Wind","Solar|PV",
# "Geothermal","Solar|CSP","Oil","Solar|PV","Storage") # variables from remind
# var_com <- paste(var_in_x,var_in_rem,sep = ".") # concatenating the two for looping to work
#
# for (i in var_com){
# x[,,sub("\\..*", "", i)][,,c("CI","Manf")] <- x[,,sub("\\..*", "", i)][,,c("CI","Manf")]*setNames(cap_costs[,getYears(x),sub(".*\\.", "", i),pmatch=T],NULL)
# }
# # OM fixed costs used to calculate decline factors for O&M employment factors
# fixed_costs <- report_tech[,seq(2015,2050,5),grep(x = var,pattern = "fixed",value = T)]
# fixed_costs <- fixed_costs["GLO",,invert=T]# removing GLO
#
# fixed_costs <- fixed_costs[,,]/setYears(fixed_costs[,"y2020",],NULL)# costs relative to 2015
# for (i in var_com){
# x[,,sub("\\..*", "", i)][,,"OM"] <- x[,,sub("\\..*", "", i)][,,"OM"]*setNames(fixed_costs[,getYears(x),sub(".*\\.", "", i),pmatch=T],NULL)
# }
#
#
#
# # decline factors for coal fuel supply
# coal_ef <- calcOutput("CoalLabourProductivity",subtype = "Employment_factor")
# coal_ef <- coal_ef/setYears(coal_ef[,2020,],NULL)
# coal_ef[which(coal_ef=="NaN")] <- 0
# x[,getYears(coal_ef),"Coal.Fuel_supply"] <- x[,getYears(coal_ef),"Coal.Fuel_supply"]*setNames(coal_ef[,,],NULL)
#
#
# }
# }
if (decline=="static"){
# do nothing, as employment factors have already been read
}
# the above loops are based on the expanded code below (2 shown for example)-
# x[,,"Coal"][,,c("CI","Manf")] <- x[,,"Coal"][,,c("CI","Manf")]*setNames(cap_costs[,getYears(x),"Coal",pmatch=T],NULL)
# x[,,"Gas"][,,c("CI","Manf")] <- x[,,"Gas"][,,c("CI","Manf")]*setNames(cap_costs[,getYears(x),"Gas",pmatch=T],NULL)
##--------------------------------------------------------
## Other external parameters--------------------------
# share of exports to world installed capacity
prod_share <- calcOutput("ProdShares")
prod_share <- prod_share[,"y2019",] # use 2019 as 2018 values have NAs for important countries
cols_to_add <- c("Solar|PV-utility","Solar|PV-rooftop","Wind offshore","Wind onshore")
prod_share <- add_columns(prod_share,addnm = cols_to_add,dim=3.1)
prod_share[,,c("Solar|PV-utility","Solar|PV-rooftop")] <- prod_share[,,"spv"]
prod_share[,,c("Wind offshore","Wind onshore")] <- prod_share[,,"wind"]
# share of rooftop in total spv, wind offshore in total wind, and hydro small in total hydro
share <- calcOutput("DspvShare")
##----------------------------------------------
## New and Absolute capacity from REMIND---------------------------------
# filtering required capacity variables i.e., only new and existing coal capacities
remind_output <- reportCapacity(gdx)
rem_filter <- remind_output[,,c("Cumulative","Idle","Total Cap","CC","Hydrogen","Liquids",
"Estimated","Non-Biomass","For Wind","For PV","Onshore","CT"),pmatch=T,invert=T]# removing cumulative and idle variables in capacity
rem_filter <- rem_filter[,,c("Cap|Electricity (GW)",# total sum of all techs and not needed
"New Cap|Electricity (GW)",
"Cap|Electricity|Solar (GW)",# is sum of pv and csp and not needed
"New Cap|Electricity|Solar (GW)"),invert=T]
# selecting variables only related to power or electricity
rem_filter <- rem_filter[,,][,,"Electricity",pmatch=T]
# filtering required regions
regions <- setdiff(getRegions(remind_output),"GLO")# all regions except world
rem_filter <- rem_filter[regions,seq(2015,2050,5),]
## disaggregating certain variables/adding new variables
cols_to_add <- c("Solar|PV-utility","Solar|PV-rooftop","Hydro-large","Hydro-small",
"Wind offshore","Wind onshore")
for (j in cols_to_add){
rem_filter <- add_columns(rem_filter,addnm = paste0("New Cap|Electricity|",j," (GW)"),dim = 3.1)
rem_filter <- add_columns(rem_filter,addnm = paste0("Cap|Electricity|",j," (GW)"),dim = 3.1)
}
# adding values to the new variables
for (p in c("New Cap|Electricity|","Cap|Electricity|")){
rem_filter[,,paste0(p,"Solar|PV-rooftop"," (GW)")] <- rem_filter[,,paste0(p,"Solar|PV"," (GW)")]*share[,,"spv"]
rem_filter[,,paste0(p,"Solar|PV-utility"," (GW)")] <- rem_filter[,,paste0(p,"Solar|PV"," (GW)")]*(1-share[,,"spv"])
rem_filter[,,paste0(p,"Hydro-small"," (GW)")] <- rem_filter[,,paste0(p,"Hydro"," (GW)")]*share[,,"hydro"]
rem_filter[,,paste0(p,"Hydro-large"," (GW)")] <- rem_filter[,,paste0(p,"Hydro"," (GW)")]*(1-share[,,"hydro"])
rem_filter[,,paste0(p,"Wind offshore"," (GW)")] <- rem_filter[,,paste0(p,"Wind"," (GW)")]*share[,,"wind"]
rem_filter[,,paste0(p,"Wind onshore"," (GW)")] <- rem_filter[,,paste0(p,"Wind"," (GW)")]*(1-share[,,"wind"])
}
# added/installed capacity from REMIND, for years > 2010
added_cap <- rem_filter[regions,getYears(rem_filter)>2010,"New Cap",pmatch=T]# only new capacities
added_cap_sum <- dimSums(added_cap,dim = 1) # world added capacity for each tech
# existing/operating capacity from REMIND
cap_tot <- rem_filter[,,"New Cap",invert=T,pmatch=T]# only existing capacities
##-----------------------------------------------------
# share of world capacity addition by region
shr_GLO_add <- added_cap[,,cols_to_add,pmatch=T]/added_cap_sum[,,cols_to_add,pmatch=T]
prod_share_tmp <- new.magpie(regions,c(2015,2020,2050),names = c("Solar|PV-utility","Solar|PV-rooftop","Wind offshore","Wind onshore"))
if (share=="local"||share=="current"){
## prod shares complete local manufacture of wind and solar components in 2050. Change is linear.
# assigning 2050 regional share values as prod shares
prod_share_tmp[,"y2050",] <- as.numeric(shr_GLO_add[,"y2050",getNames(prod_share_tmp),pmatch=T])
# 2015 and 2020 values same as 2019 values
prod_share_tmp[,c(2015,2020),] <- prod_share[,,getNames(prod_share_tmp)]
# from 2020 to 2050, linearly interpolate
prod_share <- time_interpolate(prod_share_tmp,seq(2025,2045,5),integrate_interpolated_years = T)
if (share=="current"){
prod_share[,,] <- prod_share[,2020,] # shares remain same throughout = 2020
}
}
# Obtaining fuel supply variables
remind_prod <- reportExtraction(gdx)["GLO",,,invert=T] # remove GLO
vars <- c("PE|Production|Biomass (EJ/yr)",
"PE|Production|Gross|Coal (EJ/yr)" ,
"PE|Production|Gross|Oil (EJ/yr)" ,
"PE|Production|Gross|Gas (EJ/yr)" ,
"PE|Production|Gross|Uranium [Energy] (EJ/yr)"
)
remind_prod <- remind_prod[regions,getYears(x),vars]# period >2010 and only select variables
pe <- reportPE(gdx)[regions,getYears(x),"PE|Biomass|Traditional (EJ/yr)"]
# removing the traditional biomass component, where biomass isn't sold, thus no employment
# in conventional sense
remind_prod[,,"PE|Production|Biomass (EJ/yr)"] <- remind_prod[,,"PE|Production|Biomass (EJ/yr)"] - setNames(pe[,,],NULL)
#getNames(remind_prod) <- gsub(pattern = "\\[Energy\\]",replacement = "",x = getNames(remind_prod))
getNames(remind_prod) <- gsub(pattern = " \\(EJ\\/yr\\)",replacement = " \\(EJ\\/yr\\)",x = getNames(remind_prod))# removing space
getNames(remind_prod) <- gsub(pattern = "Uranium",replacement = "Nuclear",x = getNames(remind_prod))# removing space
## Calculating jobs--------------------------------
techs <- c("Solar|PV-utility","Solar|PV-rooftop","Hydro-large","Hydro-small",
"Wind offshore","Wind onshore","Coal","Biomass","Gas","Storage","Oil","Nuclear","Geothermal","Solar|CSP")
techs_exp <- c("Solar|PV-utility","Solar|PV-rooftop","Wind offshore","Wind onshore") # technologies with export component
# cons <- new.magpie(getRegions(x),names = techs)
# average construction time for different technologies
# cons[] <- rep(c(1,1,4,3,4,2,5,2,2,1,2,10,2,2),each=12)
# 1. Manufacturing jobs for techs with export component.
# 1a) Total manf jobs (per region) for these techs are equal to total world addition X share of
# world exports (for that region)
if (type=="created"){
jobs_manf <- new.magpie(regions,getYears(x),techs,fill=0)
for (i in techs_exp){
#manf <- paste0(i,".","Manf")
jobs_manf[,,i] <- added_cap_sum[,getYears(x),i,pmatch=T]*1000
jobs_manf[,getYears(x),i] <- jobs_manf[,getYears(x),i] * setNames(x[,,i][,,"Manf"],NULL) * prod_share[,,i]
}
for (i in setdiff(techs,techs_exp)){
# manf <- paste0(i,".","Manf")
jobs_manf[,,i] <- added_cap[,getYears(x),i,pmatch=T]*1000
jobs_manf[,getYears(x),i] <- jobs_manf[,getYears(x),i] * setNames(x[,,i,pmatch=T][,,"Manf"],NULL)
}
jobs_manf <- add_dimension(jobs_manf,dim=3.2,add="type",nm = "Manf")
getSets(jobs_manf) <- c("region","year","variable","type")
# 2. Construction and Installation jobs
jobs_ci <- new.magpie(regions,getYears(x),techs)
for (i in techs){
#inst <- paste0(i,".","CI")
jobs_ci[,,i] <- added_cap[,getYears(x),i,pmatch=T]*1000
jobs_ci[,,i] <- jobs_ci[,getYears(x),i] * setNames(x[,,i,pmatch=T][,,"CI"],NULL)
}
jobs_ci <- add_dimension(jobs_ci,dim=3.2,add="type",nm = "CI")
getSets(jobs_ci) <- c("region","year","variable","type")
}
if (type=="per_year"){
jobs_manf <- new.magpie(regions,getYears(x),techs,fill=0)
for (i in techs_exp){
#manf <- paste0(i,".","Manf")
jobs_manf[,,i] <- added_cap_sum[,getYears(x),i,pmatch=T]*1000
jobs_manf[,getYears(x),i] <- (jobs_manf[,getYears(x),i] * setNames(x[,,i][,,"Manf"],NULL) * prod_share[,,i])
}
for (i in setdiff(techs,techs_exp)){
# manf <- paste0(i,".","Manf")
jobs_manf[,,i] <- added_cap[,getYears(x),i,pmatch=T]*1000
jobs_manf[,getYears(x),i] <- (jobs_manf[,getYears(x),i] * setNames(x[,getYears(x),i,pmatch=T][,,"Manf"],NULL))
}
jobs_manf <- add_dimension(jobs_manf,dim=3.2,add="type",nm = "Manf")
getSets(jobs_manf) <- c("region","year","variable","type")
# 2. Construction and Installation jobs
jobs_ci <- new.magpie(regions,getYears(x),techs,fill = 0)
for (i in techs){
#inst <- paste0(i,".","CI")
jobs_ci[,,i] <- added_cap[,getYears(x),i,pmatch=T]*1000
#jobs_ci[,,i] <- (jobs_ci[,getYears(x),i] * setNames(x[,,i,pmatch=T][,,"CI"],NULL))
jobs_ci[,,i] <- jobs_ci[,getYears(x),i] * setNames(x[,,i,pmatch=T][,,"CI"],nm = NULL)
}
jobs_ci <- add_dimension(jobs_ci,dim=3.2,add="type",nm = "CI")
getSets(jobs_ci) <- c("region","year","variable","type")
}
## 3. Jobs in O & M
# jobs = existing capacity per tech * emp factor per tech
jobs_om <- new.magpie(regions,getYears(x),techs)
for (i in techs){
# inst <- paste0(i,".","OM")
jobs_om[,,i] <- cap_tot[,getYears(x),i,pmatch=T]*1000
jobs_om[,,i] <- jobs_om[,,i] * setNames(x[,,i,pmatch=T][,,"OM"],NULL)
}
jobs_om <- add_dimension(jobs_om,dim=3.2,add="type",nm = "OM")
getSets(jobs_om) <- c("region","year","variable","type")
## 4. Jobs in Fuel supply
#jobs = total production (in EJ/yr) per fuel * employment factor per fuel (Jobs/PJ)
fuels <- c("Coal","Gas","Biomass","Oil")
jobs_prod <- new.magpie(regions,getYears(x),techs,fill=0)
for (i in fuels){
#fs <- paste0(i,".","Fuel_supply")
jobs_prod[,,i] <- remind_prod[,getYears(x),i,pmatch=T] * 1000
jobs_prod[,,i] <- jobs_prod[,,i] * setNames(x[,,i][,,"Fuel_supply"],NULL)
}
jobs_prod <- add_dimension(jobs_prod,dim=3.2,add="type",nm = "Fuel_supply")
getSets(jobs_prod) <- c("region","year","variable","type")
## JObs in Fuel supply, nuclear which are given in terms of SE
rem_se_nuc <- reportSE(gdx)[regions,getYears(x),"Nuclear",pmatch=T]*277777.778 # EJ to GWh
jobs_prod[,,"Nuclear.Fuel_supply"] <- setNames(rem_se_nuc[,,],NULL)*setNames(x[,,"Nuclear.Fuel_supply"],NULL)
####### put all together #############
jobs <- mbind(jobs_ci,jobs_om,jobs_prod,jobs_manf)
return (jobs)
}
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