R/metis.assumptions.R

In zarrarkhan/metis: Flexible Scale Multi-Sector Modeling

#' metis.assumptions
#'
#' This function loads holds the different assumptions used throughout the metis package.
#'
#' List of Assumptions
#' \itemize{
#' \item GCAMbaseYear,
#' \item convEJ2MTOE,
#' \item convEJ2TWh,
#' \item convEJ2GW,
#' \item convEJ2GWh,
#' \item convGW_kW,
#' \item conv_C_CO2,
#' \item conv_MT_GT,
#' \item hydro_cap_fact,
#' \item hydro_cost_GW,
#' \item convUSD_1975_2010,
#' \item convUSD_1975_2015,
#' \item conv1975USDperGJ22017USDperMWh,
#' \item conv1975USDperGJ22017USDperMBTU,
#' \item convertGgTgMTC,
#' \item GWP,
#' \item US52=US52}
#' @keywords assumptions
#' @return A list of assumptions
#' @export
#' @examples
#' library(metis)
#' a<-metis.assumptions()
#' a # will give full list of assumptions
#' @importFrom magrittr %>%

metis.assumptions <- function() {

  #------------------
  # Conversions
  #------------------
  GCAMbaseYear <- 2010
  convEJ2MTOE<-23.8845897  #https://www.iea.org/statistics/resources/unitconverter/
  convEJ2TWh<-277.77777777778
  convEJ2GW<-convEJ2TWh*1000/8760
  convEJ2GWh <- 277777.778
  convGW_kW <- 1e6
  conv1975USDperGJ22017USDperMWh<- (103.015/28.485)/0.2777778 # (13.02) Deflators 1975 (28.485) to 2017 (103.015) from World Bank https://data.worldbank.org/indicator/NY.GDP.DEFL.ZS?locations=US&view=chart
  conv1975USDperGJ22017USDperMBTU<- (103.015/28.485)/0.947 # (3.82) Deflators 1975 (28.485) to 2017 (103.015) from World Bank https://data.worldbank.org/indicator/NY.GDP.DEFL.ZS?locations=US&view=chart
  convUSD_1975_2010	<- 91.718/28.485 # (3.22) Deflators 1975 (28.485) to 2010 (91.718) from World Bank https://data.worldbank.org/indicator/NY.GDP.DEFL.ZS?locations=US&view=chart
  convUSD_1975_2015	<- 100/28.485 # (3.51) Deflators 1975 (28.485) to 2015 (100) from World Bank https://data.worldbank.org/indicator/NY.GDP.DEFL.ZS?locations=US&view=chart
  conv_C_CO2 <- 44/12
  conv_MT_GT <- 1e-3

  # Emissions Conversion to CO2eq
  # GWP conversions - uses 100-yr GWPs from IPPC AR4 and AR5
  # https://www.ghgprotocol.org/sites/default/files/ghgp/Global-Warming-Potential-Values%20%28Feb%2016%202016%29_1.pdf
  # Does not include all covnersions. Add them if they are tracked in GCAM
  # Mean HFC numbers GWP %>% dplyr::filter(grepl("HFC",ghg)) %>% summarize_at(vars(names(GWP)[!grepl("ghg",names(GWP))]),funs(mean))
  # GTP AR5 Box 3.2 Table 1 https://www.ipcc.ch/site/assets/uploads/2018/02/SYR_AR5_FINAL_full.pdf

  GWP<- tibble::tribble(
    ~ghg, ~GWPSAR, ~GWPAR4,~GWPAR5,~GTPAR5,
    "CO2",44/12,44/12,44/12,44/12,
    "CH4",21,25,28,4,
    "CH4_AGR", 21,	25, 28, 28,
    "CH4_AWB", 21,	25, 28, 28,
    "N2O",310,298,265,234,
    "N2O_AGR",310,298,265,234,
    "N2O_AWB",310,298,265,234,
    "C2F6",9200,12200,11100,NA,
    "CF4",6500,7390,6630,8040,
    "HFC125",2800,3500,3170,NA,
    "HFC134a",1300,1430,1300,NA,
    "HFC245fa",1030,1030,858,NA,
    "HFC143a",3800,4470,4800,NA,
    "HFC152a",140,124,138,19,
    "HFC227ea",2900,3220,3350,NA,
    "HFC23",11700,14800,12400,NA,
    "HFC236fa",6300,9810,8060,NA,
    "HFC32",650,675,677,NA,
    "HFC365mfc",794,794,804,NA,
    "HFCs", 3141, 3985, 3555,NA,
    "SF6",23900,22800,23500,NA
  )%>%as.data.frame;

  # https://nepis.epa.gov/Exe/ZyNET.exe/P1001YTS.txt?ZyActionD=ZyDocument&Client=EPA&Index=2000%20Thru%202005&Docs=&Query=&Time=&EndTime=&SearchMethod=1&TocRestrict=n&Toc=&TocEntry=&QField=&QFieldYear=&QFieldMonth=&QFieldDay=&UseQField=&IntQFieldOp=0&ExtQFieldOp=0&XmlQuery=&File=D%3A%5CZYFILES%5CINDEX%20DATA%5C00THRU05%5CTXT%5C00000017%5CP1001YTS.txt&User=ANONYMOUS&Password=anonymous&SortMethod=h%7C-&MaximumDocuments=1&FuzzyDegree=0&ImageQuality=r75g8/r75g8/x150y150g16/i425&Display=hpfr&DefSeekPage=x&SearchBack=ZyActionL&Back=ZyActionS&BackDesc=Results%20page&MaximumPages=1&ZyEntry=3
  # https://www.firescience.gov/projects/09-2-01-9/supdocs/09-2-01-9_Appendix_C_-_Unit_Conversion_and_Other_Tables.pdf
  # MTC is megatonnes (10^6 tonnes) of Carbon.
  # Tg one terragram = 1 Megatonne = 10^6 tonnes
  # Convert to Mega tonnes of CO2 eq.

  convertGgTgMTC<- tibble::tribble(
    ~Units,~Convert,
    "Gg",0.001*1,
    "Tg",1,
    "MTC",1,
    "MtC/yr",1
  )%>%as.data.frame;


  # Data source for capacity factor:
  # https://hub.globalccsinstitute.com/publications/renewable-power-generation-costs-2012-overview/52-capacity-factors-hydropower
  # Data source for hydropower capital cost
  # https://hub.globalccsinstitute.com/publications/renewable-power-generation-costs-2012-overview/51-hydropower-capital-costs
  hydro_cap_fact <- 0.38
  hydro_cost_GW <- 1.5


  # GCAM USA
  US52 <- c("AK","AL","AR","AZ","CA","CO","CT","DC","DE","FL","GA",
            "HI","IA","ID","IL","IN","KS","KY","LA","MA","MD","ME",
            "MI","MN","MO","MS","MT","NC","ND","NE","NH","NJ","NM",
            "NV","NY","OH","OK","OR","PA","PR","RI","SC","SD","TN",
            "TX","UT","VA","VT","WA","WI","WV","WY")


  return(list(
         GCAMbaseYear=GCAMbaseYear,
         convEJ2MTOE=convEJ2MTOE,
         convEJ2TWh=convEJ2TWh,
         convEJ2GW=convEJ2GW,
         convEJ2GWh=convEJ2GWh,
         convGW_kW=convGW_kW,
         conv_C_CO2=conv_C_CO2,
         conv_MT_GT=conv_MT_GT,
         hydro_cap_fact=hydro_cap_fact,
         hydro_cost_GW=hydro_cost_GW,
         convUSD_1975_2010=convUSD_1975_2010,
         convUSD_1975_2015=convUSD_1975_2015,
         conv1975USDperGJ22017USDperMWh=conv1975USDperGJ22017USDperMWh,
         conv1975USDperGJ22017USDperMBTU=conv1975USDperGJ22017USDperMBTU,
         convertGgTgMTC=convertGgTgMTC,
         GWP=GWP,
         US52=US52))
}