#' metis.assumptions
#'
#' This function loads holds the different assumptions used throughout the metis package.
#'
#'@param name Default=NULL. Name of assumption object.
#' 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,
#' \item US49,
#' \item countryToGCAMReg32}
#' @keywords assumptions
#' @return A list of assumptions
#' @export
#' @examples
#' library(metis)
#' a<-metis.assumptions("US49")
#' @importFrom magrittr %>%
metis.assumptions <- function(name=NULL) {
#------------------
# 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;
#--------------------------------------------------------------------------------------------------
# GCAM USA Regions
#--------------------------------------------------------------------------------------------------
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")
# GCAM USA 49. Excludes Alaska, Hawaii and Puerto Rico. Includes DC.
US49 <- c("AL","AR","AZ","CA","CO","CT","DC","DE","FL","GA",
"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","RI","SC","SD","TN",
"TX","UT","VA","VT","WA","WI","WV","WY")
#--------------------------------------------------------------------------------------------------
# Return Data
#--------------------------------------------------------------------------------------------------
listx <- 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,
convUSD_1975_2010=convUSD_1975_2010,
convUSD_1975_2015=convUSD_1975_2015,
conv1975USDperGJ22017USDperMWh=conv1975USDperGJ22017USDperMWh,
conv1975USDperGJ22017USDperMBTU=conv1975USDperGJ22017USDperMBTU,
convertGgTgMTC=convertGgTgMTC,
GWP=GWP,
US52=US52,
US49=US49)
if(!is.null(name)){returnx <- listx[[name]]} else {returnx <- listx }
return(returnx)
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.