R/readRutovitz2015.R

In pik-piam/mrremind: MadRat REMIND Input Data Package

#' Employment factors for various power production technologies from Rutovitz et al. 2015
#' @description Rutovitz, J., Dominish, E., & Downes, J. (2015). Calculating global energy sector jobs—2015 methodology update. Institute for Sustainable Futures, University of Technology, Sydney. https://opus.lib.uts.edu.au/bitstream/10453/43718/1/Rutovitzetal2015Calculatingglobalenergysectorjobsmethodology.pdf
#' @author Aman Malik
#' @importFrom  tidyr pivot_longer
#' @importFrom dplyr rename add_row filter_ mutate_ select_ left_join filter mutate
#' @importFrom readr read_csv
#' @return magpie object of employment factors for different technologies and activities in Jobs/MW (all except fuel_supply) or Jobs/PJ (fuel_supply). Subtype "regional_mult" is a regional multiplier without units.
#' @param subtype Either "oecd_ef","regional_ef","coal_ef","gas_ef", "regional_mult"


readRutovitz2015 <- function(subtype) {
  # note: Employment factors from rutovitz2015 et al. are wt. average values from OECD countries. (Regional) Exceptions are input in subtype "regional_ef", "coal_ef" and "gas_ef"
  CI <- NULL # construction and installation
  Fuel_supply <- NULL # process of extraction of resource
  Manf <- NULL # manufacturing
  OM <- NULL # operation and maintenance
  activity <- NULL # all 4 categories above are "activities"
  region <- NULL
  tech <- NULL # power production technology
  duration <- NULL # average time (years) from start of construction to commission.
  Productivity <- NULL
  Year <- NULL
  value <- NULL
  if (subtype == "oecd_ef") {
    input <- read_csv(file = "oecd_ef.csv", na = "", col_types = "cddddc") %>%
      rename(tech = 1, duration = 2, CI = 3, Manf = 4, OM = 5, Fuel_supply = 6) %>%
      filter(!is.na(tech)) %>%
      add_row(tech = "CoalHP", duration = 5, CI = 11.2, Manf = 5.4, OM = 0.14 * 1.5, Fuel_supply = "Regional") %>%  # for *HP technologies, multiply OM EF by 1.5 as in rutovitz
      add_row(tech = "GasHP", duration = 2, CI = 1.3, Manf = 0.93, OM = 0.14 * 1.5, Fuel_supply = "Regional") %>%
      add_row(tech = "BiomassHP", duration = 2, CI = 14, Manf = 2.9, OM = 1.5 * 1.5, Fuel_supply = "Regional") %>%
      add_row(tech = "Oil", CI = 1.3, Manf = 0.93, OM = 0.14, Fuel_supply = "Regional", duration = 2) %>% # oil EF= Gas EF as in rutovitz
      filter(!tech %in% grep("Ocean|decommissioning|heat|diesel",
                             x = tech, value = TRUE)) %>%  # removing  techs not relevant
      mutate(tech = mgsub::mgsub(tech, c("Solar Photovoltaics", "Solar thermal"),
                             c("Solar|PV", "Solar|CSP"))) %>%  ## renaming techs
      mutate(across(c(CI, Manf, OM, duration), as.numeric)) %>%
      mutate(Fuel_supply = ifelse(Fuel_supply == "0.001 jobs/GWh final demand", 0.001, Fuel_supply))  %>%
      # mutate_(CI=~CI/duration) %>% # dividing employment intensity by construction period
      # mutate_(Manf=~Manf/duration) %>%
      select(-duration)   %>%
      mutate(`Fuel_supply` = ifelse(`Fuel_supply` == "Regional", 0, `Fuel_supply`),
             `Fuel_supply` = as.numeric(`Fuel_supply`)) %>%
      pivot_longer(names_to =  "activity", values_to = "value", c(2:5))   %>%
      # regional values exist for coal and gas and are read later
      mutate(across(c(tech, activity), as.factor)) %>%
      mutate(value = as.numeric(value))

      x <- as.magpie(input, temporal = NULL, spatial = NULL, datacol = 3)
    return(x)
  }


  if (subtype == "regional_ef") {

    input <- read_csv("regional_ef.csv", na = "", col_types = "ccdddd") %>%
      rename(tech = 1, region = 2, CI = 3, Manf = 4, OM = 5, Fuel_supply = 6) %>%
      filter(!is.na(tech)) %>%
   #   mutate(across(c(tech,region),as.character)) %>%
    #  mutate(across(c(CI,Manf,OM,Fuel_supply),as.numeric)) %>%
      mutate(tech = mgsub::mgsub(tech, c("Solar PV", "Solar Thermal power", "Wind-offshore", "Wind-onshore"),
                                     c("Solar|PV", "Solar|CSP", "Wind offshore", "Wind onshore"))) %>%
      pivot_longer(c("CI", "Manf", "OM", "Fuel_supply"), names_to = "activity", values_to = "value") %>%
      filter(!grepl("average", region)) %>%  # removed OECD average values
      na.omit() %>%
      # left_join(const,by="tech") %>%
      # mutate(region=ifelse(region=="OECD North America","OECD Americas",region))
      # mutate(value=ifelse(!activity %in% c("Fuel_supply","OM"),value/duration,value)) %>%
      select(region, tech, activity, value)


    x <- as.magpie(input, spatial = 1, temporal = NULL, datacol = 4)

    return(x)
  }

  if (subtype == "coal_ef") {
   input <- read_csv("coal_ef.csv", col_types = "cddd") %>%
     select(-Year, -Productivity) %>%
     rename(region = 1, value = 2) %>%
     filter(!is.na(region)) %>%
     mutate(value = ifelse(region == "Middle East", 39.7, value)) %>%
     filter(!grepl("World", region)) %>%
     mutate(region = ifelse(region == "Eastern Europe/Eurasia", "Eurasia", region)) %>%
     mutate(tech = "Coal") %>%
     mutate(activity = "Fuel_supply") %>%
     mutate(value = as.numeric(value))
   input <- input[, c("region", "tech", "activity", "value")]

   x <- as.magpie(input, temporal = NULL, datacol = 4, spatial = 1)

   return(x)
 }

  if (subtype == "gas_ef") {
  input <- read_csv("gas_ef.csv", na = "", col_types = "cdcc") %>%
    rename(region = 1, value = 2, notes = 3, Sources = 4) %>%
    select(region, value) %>%
    rbind(data.frame(region = c("India", "Latin America", "Developing Asia", "Middle East"), value = 15.1)) %>% # using world average values from dataset
    filter(!is.na(value)) %>%
    filter(!grepl("World", region))  %>%
    mutate(region = ifelse(region == "Eastern Europe/Eurasia", "Eurasia", region)) %>%
    mutate(tech = "Gas") %>%
    mutate(activity = "Fuel_supply")

  input <- input[, c("region", "tech", "activity", "value")]

  x <- as.magpie(input, temporal = NULL, datacol = 4, spatial = 1)

  return(x)
}
  if (subtype == "regional_mult") {
    input <- read_csv("regional_mult.csv", na = "", col_types = "c") %>%
    pivot_longer(cols = 2:4, names_to = "year") %>%
    mutate(region = ifelse(region == "Eastern Europe/Eurasia", "Eurasia", region))

  x <- as.magpie(input, spatial = 1, temporal = 2)

  return (x)

  }

}