R/zchunk_L272.nonghg_elc_USA.R
In JGCRI/gcamdata: Build the GCAM Input Datasets
Defines functions
module_gcamusa_L272.elc_nonghg_USA
Documented in
module_gcamusa_L272.elc_nonghg_USA
#' module_gcamusa_L272.elc_nonghg_USA
#'
#' Calculate Non-GHG emissions parameters for electricity technologies in the USA
#'
#' @param command API command to execute
#' @param ... other optional parameters, depending on command
#' @return Depends on \code{command}: either a vector of required inputs,
#' a vector of output names, or (if \code{command} is "MAKE") all
#' the generated outputs: \code{L2721.nonghg_elec_tech_coeff_USA},\code{L2721.elec_MAC_CSAPR}. The corresponding file in the
#' original data system was \code{L272.elc_nonghg_USA.R} (gcam-usa level2).
#' @details This chunk calculates Non-GHG emissions parameters for electricity technologies in the USA and the electricity MAC curve
#' @importFrom assertthat assert_that
#' @importFrom dplyr filter mutate select
#' @importFrom tidyr gather spread
#' @author AJS and BY August 2019, MAW March 2022
module_gcamusa_L272.elc_nonghg_USA <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "gcam-usa/emissions/BC_OC_assumptions",
FILE = "gcam-usa/emissions/BCOC_PM25_ratios",
FILE = "gcam-usa/emissions/EPA_state_egu_emission_factors_ktPJ_post2015",
FILE = "gcam-usa/emissions/EPA_state_egu_emission_factors_ktPJ",
FILE = "gcam-usa/states_subregions",
"L123.in_EJ_state_elec_F",
"L270.nonghg_tg_state_elec_F_Yb",
FILE = "gcam-usa/A23.elecS_tech_mapping_cool",
FILE = "gcam-usa/A23.elecS_tech_availability",
"L2234.StubTechMarket_elecS_USA",
"L2233.StubTechProd_elecS_cool_USA"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L272.nonghg_elec_tech_coeff_USA"))
} else if(command == driver.MAKE) {
# silence package check
supplysector <- subsector <- technology <- elec_supplysector <- elec_subsector <-
elec_technology <- BC_fraction <- OC_fraction <- Non.CO2 <- emiss.coef <-
State <- ElectricityFuel <- Reduction <- Cost <- state <- state_name <-
grid_region <- CSAPR <- sector <- fuel <- year <- value <- output <- input <-
stub.technology <- region <- input.emissions <- fuel_input <- emiss.coef.x <-
emiss.coef.y <- supplysector.y <- subsector.y <- emiss.coef <- NULL
all_data <- list(...)[[1]]
# Load required inputs
BC_OC_assumptions <- get_data(all_data, "gcam-usa/emissions/BC_OC_assumptions", strip_attributes = TRUE)
BCOC_PM25_ratios <- get_data(all_data, "gcam-usa/emissions/BCOC_PM25_ratios") %>%
# removing columns we do not use, and sectors that aren't mapped to GCAM sectors
select( -c( "Region", "Gains_Sector" ) ) %>%
filter( !is.na( sector ) )
EPA_state_egu_emission_factors_ktPJ_post2015 <- get_data(all_data, "gcam-usa/emissions/EPA_state_egu_emission_factors_ktPJ_post2015", strip_attributes = TRUE)
EPA_state_egu_emission_factors_ktPJ <- get_data(all_data, "gcam-usa/emissions/EPA_state_egu_emission_factors_ktPJ", strip_attributes = TRUE)
states_subregions <- get_data(all_data, "gcam-usa/states_subregions", strip_attributes = TRUE)
L123.in_EJ_state_elec_F <- get_data(all_data, "L123.in_EJ_state_elec_F", strip_attributes = TRUE)
L270.nonghg_tg_state_elec_F_Yb <- get_data(all_data, "L270.nonghg_tg_state_elec_F_Yb", strip_attributes = TRUE)
L2234.StubTechMarket_elecS_USA <- get_data(all_data, 'L2234.StubTechMarket_elecS_USA', strip_attributes = TRUE)
L2233.StubTechProd_elecS_cool_USA <- get_data(all_data, 'L2233.StubTechProd_elecS_cool_USA', strip_attributes = TRUE)
A23.elecS_tech_mapping_cool <- get_data(all_data, 'gcam-usa/A23.elecS_tech_mapping_cool', strip_attributes = TRUE)
A23.elecS_tech_availability <- get_data(all_data, 'gcam-usa/A23.elecS_tech_availability', strip_attributes = TRUE)
load_segments <- unique(A23.elecS_tech_mapping_cool$Electric.sector)
#Electric s/s/t mapping file
A23.elecS_tech_mapping_cool %>%
anti_join(A23.elecS_tech_availability,
by = c("Electric.sector.technology" = "stub.technology")) %>%
mutate(Electric.sector = factor(Electric.sector, levels = load_segments),
Electric.sector = as.character(Electric.sector)) %>%
filter( Electric.sector != "elect_td_bld" ) ->
A23.elecS_tech_mapping
### Electricity technology emission coefficients in base years
# First clean up the fuel inputs
agg_fuel_input_elec_Yb <- L123.in_EJ_state_elec_F %>%
# filter for the base years, which NEI input emissions are being applied for
filter(year %in% MODEL_BASE_YEARS) %>%
rename(fuel_input = value)
# Take the table containing the emissions, match on the fuel inputs, and compute the emission coefficients
agg_nonghg_elec_emiss_coeffs_F_Yb <- L270.nonghg_tg_state_elec_F_Yb %>%
# filter for the base years
filter(year %in% MODEL_BASE_YEARS) %>%
rename(input.emissions = value) %>%
left_join_error_no_match(agg_fuel_input_elec_Yb, by = c("state", "fuel", "year")) %>%
mutate(emiss.coef = input.emissions / fuel_input) %>%
select(state, fuel, Non.CO2, year, emiss.coef, fuel_input)
###MISSING VALUES: NAs and Infs generated. Keep these for now
# Assign emission coefficients to the electricity technologies in the base years
L272.nonghg_elec_tech_coeff_Yb_USA.NAs <- L2233.StubTechProd_elecS_cool_USA %>%
select(-c(calOutputValue, share.weight.year, subs.share.weight, tech.share.weight)) %>%
filter(year %in% MODEL_BASE_YEARS,
# only retain entries for subsectors that we have fuel consumption for
subsector0 %in% agg_fuel_input_elec_Yb$fuel) %>%
# Add pollutant column
repeat_add_columns(tibble::tibble(Non.CO2=unique(L270.nonghg_tg_state_elec_F_Yb$Non.CO2))) %>%
# Match on emission factors, must use left_join as there are several NAs - technologies that do not have emissions
left_join(agg_nonghg_elec_emiss_coeffs_F_Yb, by = c("region" = "state", "subsector0" = "fuel", "Non.CO2", "year" ) ) %>%
rename( stub.technology = technology ) %>%
select(region, supplysector, subsector0, subsector, stub.technology, year, Non.CO2, emiss.coef)
# Generate national median emissions factors for base years
# Remove NAs so as to not skew the median
L272.nonghg_elec_tech_coeff_Yb_USA.median.true <- L272.nonghg_elec_tech_coeff_Yb_USA.NAs %>%
filter(!is.na(emiss.coef)) %>%
group_by(year, Non.CO2, supplysector, subsector0, subsector, stub.technology) %>%
summarise(emiss.coef = median(emiss.coef)) %>%
ungroup() %>%
rename(nationalEF = emiss.coef)
# Some year / pollutant / sector / subsector / tech are NA for all entries, and should be set to 0
L272.nonghg_elec_tech_coeff_Yb_USA.median.skewed <- L272.nonghg_elec_tech_coeff_Yb_USA.NAs %>%
replace_na(list(emiss.coef = 0)) %>%
group_by(year, Non.CO2, supplysector, subsector0, subsector, stub.technology) %>%
summarise(emiss.coef = median(emiss.coef)) %>%
ungroup() %>%
rename(nationalEF = emiss.coef)
# We want to join these tables so that only the entries not in median.true are retained from median.skewed
# These all have EFs of 0
L272.nonghg_elec_tech_coeff_Yb_USA.median <- L272.nonghg_elec_tech_coeff_Yb_USA.median.skewed %>%
anti_join( L272.nonghg_elec_tech_coeff_Yb_USA.median.true, by=c("year", "Non.CO2", "supplysector", "subsector0", "subsector", "stub.technology") ) %>%
# rebind to median.true
bind_rows(L272.nonghg_elec_tech_coeff_Yb_USA.median.true)
# Replace all emissions factors above a given value (20 * median) or that are NAs with the national median emissions factor for that year, non.CO2, and technology
L272.nonghg_elec_tech_coeff_Yb_USA.noBCOC <- L272.nonghg_elec_tech_coeff_Yb_USA.NAs %>%
left_join_error_no_match(L272.nonghg_elec_tech_coeff_Yb_USA.median, by = c("year", "Non.CO2", "supplysector", "subsector0", "subsector", "stub.technology")) %>%
# create a new column that has the threshold value
mutate( threshold = nationalEF * 20,
emiss.coef = if_else(emiss.coef > threshold | is.na(emiss.coef), nationalEF, emiss.coef)) %>%
select(region, Non.CO2, year, supplysector, subsector0, subsector, stub.technology, emiss.coef)
# Use fractions of PM2.5 to calculate BC/OC emissions.
# We need to modify the BC_OC_assumptions table, as the BCOC_PM25_ratios table has updated values that are time dependent
# If there are sector/subsector/tech combos that are in BCOC_PM25_ratios, we want to replace those entries in
# the BC_OC_assumptions table. We also need to extend the data.
# Extrapolate the data to future model years, and format the table
BCOC_PM25_ratios_ext <- BCOC_PM25_ratios %>%
gather_years() %>%
complete(nesting(Parameter,sector,subsector,technology), year = MODEL_YEARS) %>%
# extrapolate missing years
group_by(Parameter,sector,subsector,technology) %>%
mutate(value = approx_fun(year, value, rule = 2)) %>%
ungroup() %>%
spread( Parameter, value ) %>%
rename( "BC_fraction" = `BC Fraction`,
"OC_fraction" = `OC Fraction`)
BC_OC_assumptions_ext <- BC_OC_assumptions %>%
mutate( year = min( MODEL_BASE_YEARS ) ) %>%
complete(nesting(sector,subsector,technology, BC_fraction, OC_fraction), year = MODEL_YEARS)
# Join the tables, keeping values from BC_OC_assumptions_ext that do not appear in BCOC_PM25_ratios
BC_OC_assumptions_years <- BC_OC_assumptions_ext %>%
anti_join( BCOC_PM25_ratios_ext, by = c("sector", "subsector", "technology", "year") ) %>%
# bind to BCOC_PM25_assumptions
bind_rows( BCOC_PM25_ratios_ext )
# Compute BC and OC EFs based off of PM2.5
L272.nonghg_elec_tech_coeff_Yb_USA_all_techs <- compute_BC_OC_elc(L272.nonghg_elec_tech_coeff_Yb_USA.noBCOC, BC_OC_assumptions_years) %>%
# at this point we have some NA EFs due to renewable technologies not having BC/OC emissions
# we can omit these
na.omit()
# Remove IGCC and CCS technologies in the base years and filter out 1975
L272.nonghg_elec_tech_coeff_Yb_USA <- L272.nonghg_elec_tech_coeff_Yb_USA_all_techs %>%
filter( !grepl( 'IGCC|CCS', subsector ),
year > min( MODEL_BASE_YEARS ) )
### Electricity technology emission coefficients in future vintages
# Create a table containing future emission factors, starting with exhaustive list of all technologies
L272.nonghg_elec_tech_coeff_Yf_USA.No.NH3 <- L2233.StubTechProd_elecS_cool_USA %>%
select(-c(calOutputValue, share.weight.year, subs.share.weight, tech.share.weight, year)) %>%
# only retain entries for subsectors that we have fuel consumption for
filter(subsector0 %in% agg_fuel_input_elec_Yb$fuel) %>%
# add future years, we only need the minimum future year, as the EF will pass forward in the model
repeat_add_columns(tibble::tibble(year = unique(min(MODEL_FUTURE_YEARS)))) %>%
# add pollutants
repeat_add_columns(tibble::tibble(Non.CO2 = unique(L270.nonghg_tg_state_elec_F_Yb$Non.CO2))) %>%
# must use left_join as there are several NAs - technologies that do not have emissions
left_join(A23.elecS_tech_mapping, by = c("supplysector" = "Electric.sector",
"subsector0" = "subsector",
"subsector" = "Electric.sector.technology",
"technology" = "to.technology")) %>%
# emission factors from EPA for electric power plants built post-2015
left_join(EPA_state_egu_emission_factors_ktPJ_post2015, by=c("subsector0" = "fuel", "technology.y" = "technology", "Non.CO2")) %>%
rename(emiss.coef = emiss.coeff,
stub.technology = technology) %>%
select( c( region, supplysector, subsector0, subsector, stub.technology, year, Non.CO2, emiss.coef ) )
###Filter out all NH3 NA emission factors which will replaced by EPA-ORD data, and are not included in the post2015 table
L272.nonghg_elec_tech_coeff_Yf_USA.NA.NH3 <- L272.nonghg_elec_tech_coeff_Yf_USA.No.NH3 %>%
filter(is.na(emiss.coef) & Non.CO2=="NH3")
#Get NH3 emission factors from EPA-ORD
L272.nonghg_elec_tech_coeff_Yf_USA_NH3_EPA <- EPA_state_egu_emission_factors_ktPJ %>%
# Convert to long format
tidyr::gather(variable, value, -state_name, -fuel) %>%
separate(variable, into = c("year", "Non.CO2"), sep = "_") %>%
mutate(year = as.numeric(year),
# NOTE: for now change oil to refined liquids
fuel = gsub("oil", "refined liquids", fuel)) %>%
# state code & select relevant columns
left_join_error_no_match(states_subregions %>% select(state, state_name),
by = c("state_name")) %>%
mutate(sector = "elec_heat") %>%
filter(year %in% MODEL_FUTURE_YEARS,
Non.CO2 == "NH3") %>%
select(state, sector, fuel, Non.CO2, year, value)
L272.nonghg_elec_tech_coeff_Yf_USA.NH3 <- L272.nonghg_elec_tech_coeff_Yf_USA.NA.NH3 %>%
# must use left_join as there are several NAs - technologies that do not have emissions
left_join(L272.nonghg_elec_tech_coeff_Yf_USA_NH3_EPA, by=c("region" = "state", "subsector0" = "fuel" ,"Non.CO2", "year")) %>%
select(-emiss.coef, -sector) %>%
rename(emiss.coef = value)
###Add these back to the L272.nonghg_elec_tech_coeff_Yf_USA.No.NH3 table
L272.nonghg_elec_tech_coeff_Yf_USA.NAs <- L272.nonghg_elec_tech_coeff_Yf_USA.No.NH3 %>%
#filter out NH3 because this dataframe has EFs as NA for all NH3 entries
filter(Non.CO2 != "NH3") %>%
#bind to table that has EFs for NH3
bind_rows(L272.nonghg_elec_tech_coeff_Yf_USA.NH3)
# Generate national median emissions factors for future years
# Remove NAs so as to not skew the median
L272.nonghg_elec_tech_coeff_Yf_USA.median.true <- L272.nonghg_elec_tech_coeff_Yf_USA.NAs %>%
filter(!is.na(emiss.coef)) %>%
group_by(year, Non.CO2, supplysector, subsector0, subsector, stub.technology) %>%
summarise(emiss.coef = median(emiss.coef)) %>%
ungroup() %>%
rename(nationalEF = emiss.coef)
# Some year / pollutant / sector / subsector / tech are NA for all entries, and should be set to 0
L272.nonghg_elec_tech_coeff_Yf_USA.median.skewed <- L272.nonghg_elec_tech_coeff_Yf_USA.NAs %>%
replace_na(list(emiss.coef = 0)) %>%
group_by(year, Non.CO2, supplysector, subsector0, subsector, stub.technology) %>%
summarise(emiss.coef = median(emiss.coef)) %>%
ungroup() %>%
rename(nationalEF = emiss.coef)
# We want to join these tables so that only the entries not in median.true are retained from median.skewed
# These all have EFs of 0
L272.nonghg_elec_tech_coeff_Yf_USA.median <- L272.nonghg_elec_tech_coeff_Yf_USA.median.skewed %>%
anti_join( L272.nonghg_elec_tech_coeff_Yf_USA.median.true, by=c("year", "Non.CO2", "supplysector", "subsector0", "subsector", "stub.technology") ) %>%
# rebind to median.true
bind_rows( L272.nonghg_elec_tech_coeff_Yf_USA.median.true)
# Replace all emissions factors above a given value (20 * median) or that are NAs with the national median emissions factor for that year, non.CO2, and technology
L272.nonghg_elec_tech_coeff_Yf_USA.noBCOC <- L272.nonghg_elec_tech_coeff_Yf_USA.NAs %>%
left_join_error_no_match(L272.nonghg_elec_tech_coeff_Yf_USA.median, by = c("year", "Non.CO2", "supplysector", "subsector0", "subsector", "stub.technology")) %>%
# create a new column that has the threshold value
mutate( threshold = nationalEF * 20,
emiss.coef = if_else(emiss.coef > threshold | is.na(emiss.coef), nationalEF, emiss.coef)) %>%
select(region, Non.CO2, year, supplysector, subsector0, subsector, stub.technology, emiss.coef) %>%
mutate(emiss.coef = if_else(is.infinite(emiss.coef), 1, emiss.coef))
# Use fractions of PM2.5 to calculate BC/OC emissions.
L272.nonghg_elec_tech_coeff_Yf_USA <- compute_BC_OC_elc(L272.nonghg_elec_tech_coeff_Yf_USA.noBCOC, BC_OC_assumptions_years) %>%
# at this point we have some NA EFs due to renewable technologies not having BC/OC emissions
# we can omit these
na.omit()
### Electricity emission factors in all years, for all technologies
L272.nonghg_elec_tech_coeff_USA_all_techs <- bind_rows(L272.nonghg_elec_tech_coeff_Yb_USA, L272.nonghg_elec_tech_coeff_Yf_USA) %>%
distinct(region, supplysector, subsector0, subsector, stub.technology, year, Non.CO2, emiss.coef) %>%
select(region, supplysector, subsector0, subsector, stub.technology, year, Non.CO2, emiss.coef)
# Create a table that has region/supplysector/subsector0/subsector/technology
# that should be kept for vintage and retire subsectors
# Remove technologies from both base and future years
# that have a calOutput = 0 in ALL base years, for vintage and retire subsectors
nonzero_cal_output <- L2233.StubTechProd_elecS_cool_USA %>%
# filtering for the retire and vintage subsectors (these contain 19XX or 20XX)
filter( grepl( '19|20', subsector )) %>%
# making a sum column, if sum = 0, that region/supplysector/subsector0/subsector/technology should be removed
group_by( region, supplysector, subsector0, subsector, technology ) %>%
mutate( sum = sum( calOutputValue ) ) %>%
ungroup() %>%
# do not keep the entries that have sum = 0
filter( sum > 0 ) %>%
select( -c( sum, year, share.weight.year, subs.share.weight, tech.share.weight, calOutputValue ) ) %>%
distinct( region, supplysector, subsector0, subsector, technology )
# Subset the vintage and retire subsectors
L272.vintage_retire_techs <- L272.nonghg_elec_tech_coeff_USA_all_techs %>%
# filtering for the retire and vintage subsectors (these contain 19XX or 20XX)
filter( grepl( '19|20', subsector ) ) %>%
rename( technology = stub.technology )
# Subset the non vintage and retire subsectors
L272.regular_techs <- L272.nonghg_elec_tech_coeff_USA_all_techs %>%
# filtering out the retire and vintage subsectors (these contain 19XX or 20XX)
filter( !grepl( '19|20', subsector ) ) %>%
rename( technology = stub.technology )
# Keep only the region/supplysector/subsector0/subsector/technology with nonzero calOutput
L272.vintage_retire_techs_nonzero <- nonzero_cal_output %>%
left_join( L272.vintage_retire_techs, by = c("supplysector", "subsector0", "subsector", "region", "technology"))
# Bind the two technologies together back together
L272.nonghg_elec_tech_coeff_USA_no_driver <- bind_rows( L272.regular_techs, L272.vintage_retire_techs_nonzero )
# Add an input name column to drive emissions
L272.nonghg_elec_tech_coeff_USA <- L272.nonghg_elec_tech_coeff_USA_no_driver %>%
# L2234.StubTechMarket_elecS has the fuel inputs that should be used to drive emissions
left_join_error_no_match( L2234.StubTechMarket_elecS_USA %>% select( c( "supplysector", "subsector", "stub.technology",
"minicam.energy.input" ) ) %>%
distinct( supplysector, subsector, minicam.energy.input ),
by = c("supplysector", "subsector0" = "subsector") ) %>%
#rename to input.name to match header
rename( input.name = minicam.energy.input) %>%
#change SO2 to SO2_1
mutate( Non.CO2 = gsub( "SO2", "SO2_1", Non.CO2 ) ) %>%
# remove 1975
filter( year > min(MODEL_BASE_YEARS) ) %>%
# distinct to remove any potential duplicate rows
distinct()
# Produce outputs
L272.nonghg_elec_tech_coeff_USA %>%
add_title("Non-GHG emissions parameters for electricity technologies in the USA") %>%
add_units("Tg/EJ") %>%
add_comments("Non-GHG emissions parameters for electricity technologies in the USA") %>%
add_legacy_name("L272.nonghg_elec_tech_coeff_USA") %>%
add_precursors("gcam-usa/emissions/BC_OC_assumptions",
"gcam-usa/emissions/BCOC_PM25_ratios",
"gcam-usa/emissions/EPA_state_egu_emission_factors_ktPJ_post2015",
"gcam-usa/emissions/EPA_state_egu_emission_factors_ktPJ",
"gcam-usa/states_subregions",
"L123.in_EJ_state_elec_F",
"L270.nonghg_tg_state_elec_F_Yb",
"gcam-usa/A23.elecS_tech_mapping_cool",
"gcam-usa/A23.elecS_tech_availability",
"L2234.StubTechMarket_elecS_USA",
"L2233.StubTechProd_elecS_cool_USA") ->
L272.nonghg_elec_tech_coeff_USA
return_data(L272.nonghg_elec_tech_coeff_USA)
} else {
stop("Unknown command")
}
}
JGCRI/gcamdata documentation built on March 21, 2023, 2:19 a.m.
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Defines functions module_gcamusa_L272.elc_nonghg_USA
Documented in module_gcamusa_L272.elc_nonghg_USA
#' module_gcamusa_L272.elc_nonghg_USA
#'
#' Calculate Non-GHG emissions parameters for electricity technologies in the USA
#'
#' @param command API command to execute
#' @param ... other optional parameters, depending on command
#' @return Depends on \code{command}: either a vector of required inputs,
#' a vector of output names, or (if \code{command} is "MAKE") all
#' the generated outputs: \code{L2721.nonghg_elec_tech_coeff_USA},\code{L2721.elec_MAC_CSAPR}. The corresponding file in the
#' original data system was \code{L272.elc_nonghg_USA.R} (gcam-usa level2).
#' @details This chunk calculates Non-GHG emissions parameters for electricity technologies in the USA and the electricity MAC curve
#' @importFrom assertthat assert_that
#' @importFrom dplyr filter mutate select
#' @importFrom tidyr gather spread
#' @author AJS and BY August 2019, MAW March 2022
module_gcamusa_L272.elc_nonghg_USA <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "gcam-usa/emissions/BC_OC_assumptions",
FILE = "gcam-usa/emissions/BCOC_PM25_ratios",
FILE = "gcam-usa/emissions/EPA_state_egu_emission_factors_ktPJ_post2015",
FILE = "gcam-usa/emissions/EPA_state_egu_emission_factors_ktPJ",
FILE = "gcam-usa/states_subregions",
"L123.in_EJ_state_elec_F",
"L270.nonghg_tg_state_elec_F_Yb",
FILE = "gcam-usa/A23.elecS_tech_mapping_cool",
FILE = "gcam-usa/A23.elecS_tech_availability",
"L2234.StubTechMarket_elecS_USA",
"L2233.StubTechProd_elecS_cool_USA"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L272.nonghg_elec_tech_coeff_USA"))
} else if(command == driver.MAKE) {
# silence package check
supplysector <- subsector <- technology <- elec_supplysector <- elec_subsector <-
elec_technology <- BC_fraction <- OC_fraction <- Non.CO2 <- emiss.coef <-
State <- ElectricityFuel <- Reduction <- Cost <- state <- state_name <-
grid_region <- CSAPR <- sector <- fuel <- year <- value <- output <- input <-
stub.technology <- region <- input.emissions <- fuel_input <- emiss.coef.x <-
emiss.coef.y <- supplysector.y <- subsector.y <- emiss.coef <- NULL
all_data <- list(...)[[1]]
# Load required inputs
BC_OC_assumptions <- get_data(all_data, "gcam-usa/emissions/BC_OC_assumptions", strip_attributes = TRUE)
BCOC_PM25_ratios <- get_data(all_data, "gcam-usa/emissions/BCOC_PM25_ratios") %>%
# removing columns we do not use, and sectors that aren't mapped to GCAM sectors
select( -c( "Region", "Gains_Sector" ) ) %>%
filter( !is.na( sector ) )
EPA_state_egu_emission_factors_ktPJ_post2015 <- get_data(all_data, "gcam-usa/emissions/EPA_state_egu_emission_factors_ktPJ_post2015", strip_attributes = TRUE)
EPA_state_egu_emission_factors_ktPJ <- get_data(all_data, "gcam-usa/emissions/EPA_state_egu_emission_factors_ktPJ", strip_attributes = TRUE)
states_subregions <- get_data(all_data, "gcam-usa/states_subregions", strip_attributes = TRUE)
L123.in_EJ_state_elec_F <- get_data(all_data, "L123.in_EJ_state_elec_F", strip_attributes = TRUE)
L270.nonghg_tg_state_elec_F_Yb <- get_data(all_data, "L270.nonghg_tg_state_elec_F_Yb", strip_attributes = TRUE)
L2234.StubTechMarket_elecS_USA <- get_data(all_data, 'L2234.StubTechMarket_elecS_USA', strip_attributes = TRUE)
L2233.StubTechProd_elecS_cool_USA <- get_data(all_data, 'L2233.StubTechProd_elecS_cool_USA', strip_attributes = TRUE)
A23.elecS_tech_mapping_cool <- get_data(all_data, 'gcam-usa/A23.elecS_tech_mapping_cool', strip_attributes = TRUE)
A23.elecS_tech_availability <- get_data(all_data, 'gcam-usa/A23.elecS_tech_availability', strip_attributes = TRUE)
load_segments <- unique(A23.elecS_tech_mapping_cool$Electric.sector)
#Electric s/s/t mapping file
A23.elecS_tech_mapping_cool %>%
anti_join(A23.elecS_tech_availability,
by = c("Electric.sector.technology" = "stub.technology")) %>%
mutate(Electric.sector = factor(Electric.sector, levels = load_segments),
Electric.sector = as.character(Electric.sector)) %>%
filter( Electric.sector != "elect_td_bld" ) ->
A23.elecS_tech_mapping
### Electricity technology emission coefficients in base years
# First clean up the fuel inputs
agg_fuel_input_elec_Yb <- L123.in_EJ_state_elec_F %>%
# filter for the base years, which NEI input emissions are being applied for
filter(year %in% MODEL_BASE_YEARS) %>%
rename(fuel_input = value)
# Take the table containing the emissions, match on the fuel inputs, and compute the emission coefficients
agg_nonghg_elec_emiss_coeffs_F_Yb <- L270.nonghg_tg_state_elec_F_Yb %>%
# filter for the base years
filter(year %in% MODEL_BASE_YEARS) %>%
rename(input.emissions = value) %>%
left_join_error_no_match(agg_fuel_input_elec_Yb, by = c("state", "fuel", "year")) %>%
mutate(emiss.coef = input.emissions / fuel_input) %>%
select(state, fuel, Non.CO2, year, emiss.coef, fuel_input)
###MISSING VALUES: NAs and Infs generated. Keep these for now
# Assign emission coefficients to the electricity technologies in the base years
L272.nonghg_elec_tech_coeff_Yb_USA.NAs <- L2233.StubTechProd_elecS_cool_USA %>%
select(-c(calOutputValue, share.weight.year, subs.share.weight, tech.share.weight)) %>%
filter(year %in% MODEL_BASE_YEARS,
# only retain entries for subsectors that we have fuel consumption for
subsector0 %in% agg_fuel_input_elec_Yb$fuel) %>%
# Add pollutant column
repeat_add_columns(tibble::tibble(Non.CO2=unique(L270.nonghg_tg_state_elec_F_Yb$Non.CO2))) %>%
# Match on emission factors, must use left_join as there are several NAs - technologies that do not have emissions
left_join(agg_nonghg_elec_emiss_coeffs_F_Yb, by = c("region" = "state", "subsector0" = "fuel", "Non.CO2", "year" ) ) %>%
rename( stub.technology = technology ) %>%
select(region, supplysector, subsector0, subsector, stub.technology, year, Non.CO2, emiss.coef)
# Generate national median emissions factors for base years
# Remove NAs so as to not skew the median
L272.nonghg_elec_tech_coeff_Yb_USA.median.true <- L272.nonghg_elec_tech_coeff_Yb_USA.NAs %>%
filter(!is.na(emiss.coef)) %>%
group_by(year, Non.CO2, supplysector, subsector0, subsector, stub.technology) %>%
summarise(emiss.coef = median(emiss.coef)) %>%
ungroup() %>%
rename(nationalEF = emiss.coef)
# Some year / pollutant / sector / subsector / tech are NA for all entries, and should be set to 0
L272.nonghg_elec_tech_coeff_Yb_USA.median.skewed <- L272.nonghg_elec_tech_coeff_Yb_USA.NAs %>%
replace_na(list(emiss.coef = 0)) %>%
group_by(year, Non.CO2, supplysector, subsector0, subsector, stub.technology) %>%
summarise(emiss.coef = median(emiss.coef)) %>%
ungroup() %>%
rename(nationalEF = emiss.coef)
# We want to join these tables so that only the entries not in median.true are retained from median.skewed
# These all have EFs of 0
L272.nonghg_elec_tech_coeff_Yb_USA.median <- L272.nonghg_elec_tech_coeff_Yb_USA.median.skewed %>%
anti_join( L272.nonghg_elec_tech_coeff_Yb_USA.median.true, by=c("year", "Non.CO2", "supplysector", "subsector0", "subsector", "stub.technology") ) %>%
# rebind to median.true
bind_rows(L272.nonghg_elec_tech_coeff_Yb_USA.median.true)
# Replace all emissions factors above a given value (20 * median) or that are NAs with the national median emissions factor for that year, non.CO2, and technology
L272.nonghg_elec_tech_coeff_Yb_USA.noBCOC <- L272.nonghg_elec_tech_coeff_Yb_USA.NAs %>%
left_join_error_no_match(L272.nonghg_elec_tech_coeff_Yb_USA.median, by = c("year", "Non.CO2", "supplysector", "subsector0", "subsector", "stub.technology")) %>%
# create a new column that has the threshold value
mutate( threshold = nationalEF * 20,
emiss.coef = if_else(emiss.coef > threshold | is.na(emiss.coef), nationalEF, emiss.coef)) %>%
select(region, Non.CO2, year, supplysector, subsector0, subsector, stub.technology, emiss.coef)
# Use fractions of PM2.5 to calculate BC/OC emissions.
# We need to modify the BC_OC_assumptions table, as the BCOC_PM25_ratios table has updated values that are time dependent
# If there are sector/subsector/tech combos that are in BCOC_PM25_ratios, we want to replace those entries in
# the BC_OC_assumptions table. We also need to extend the data.
# Extrapolate the data to future model years, and format the table
BCOC_PM25_ratios_ext <- BCOC_PM25_ratios %>%
gather_years() %>%
complete(nesting(Parameter,sector,subsector,technology), year = MODEL_YEARS) %>%
# extrapolate missing years
group_by(Parameter,sector,subsector,technology) %>%
mutate(value = approx_fun(year, value, rule = 2)) %>%
ungroup() %>%
spread( Parameter, value ) %>%
rename( "BC_fraction" = `BC Fraction`,
"OC_fraction" = `OC Fraction`)
BC_OC_assumptions_ext <- BC_OC_assumptions %>%
mutate( year = min( MODEL_BASE_YEARS ) ) %>%
complete(nesting(sector,subsector,technology, BC_fraction, OC_fraction), year = MODEL_YEARS)
# Join the tables, keeping values from BC_OC_assumptions_ext that do not appear in BCOC_PM25_ratios
BC_OC_assumptions_years <- BC_OC_assumptions_ext %>%
anti_join( BCOC_PM25_ratios_ext, by = c("sector", "subsector", "technology", "year") ) %>%
# bind to BCOC_PM25_assumptions
bind_rows( BCOC_PM25_ratios_ext )
# Compute BC and OC EFs based off of PM2.5
L272.nonghg_elec_tech_coeff_Yb_USA_all_techs <- compute_BC_OC_elc(L272.nonghg_elec_tech_coeff_Yb_USA.noBCOC, BC_OC_assumptions_years) %>%
# at this point we have some NA EFs due to renewable technologies not having BC/OC emissions
# we can omit these
na.omit()
# Remove IGCC and CCS technologies in the base years and filter out 1975
L272.nonghg_elec_tech_coeff_Yb_USA <- L272.nonghg_elec_tech_coeff_Yb_USA_all_techs %>%
filter( !grepl( 'IGCC|CCS', subsector ),
year > min( MODEL_BASE_YEARS ) )
### Electricity technology emission coefficients in future vintages
# Create a table containing future emission factors, starting with exhaustive list of all technologies
L272.nonghg_elec_tech_coeff_Yf_USA.No.NH3 <- L2233.StubTechProd_elecS_cool_USA %>%
select(-c(calOutputValue, share.weight.year, subs.share.weight, tech.share.weight, year)) %>%
# only retain entries for subsectors that we have fuel consumption for
filter(subsector0 %in% agg_fuel_input_elec_Yb$fuel) %>%
# add future years, we only need the minimum future year, as the EF will pass forward in the model
repeat_add_columns(tibble::tibble(year = unique(min(MODEL_FUTURE_YEARS)))) %>%
# add pollutants
repeat_add_columns(tibble::tibble(Non.CO2 = unique(L270.nonghg_tg_state_elec_F_Yb$Non.CO2))) %>%
# must use left_join as there are several NAs - technologies that do not have emissions
left_join(A23.elecS_tech_mapping, by = c("supplysector" = "Electric.sector",
"subsector0" = "subsector",
"subsector" = "Electric.sector.technology",
"technology" = "to.technology")) %>%
# emission factors from EPA for electric power plants built post-2015
left_join(EPA_state_egu_emission_factors_ktPJ_post2015, by=c("subsector0" = "fuel", "technology.y" = "technology", "Non.CO2")) %>%
rename(emiss.coef = emiss.coeff,
stub.technology = technology) %>%
select( c( region, supplysector, subsector0, subsector, stub.technology, year, Non.CO2, emiss.coef ) )
###Filter out all NH3 NA emission factors which will replaced by EPA-ORD data, and are not included in the post2015 table
L272.nonghg_elec_tech_coeff_Yf_USA.NA.NH3 <- L272.nonghg_elec_tech_coeff_Yf_USA.No.NH3 %>%
filter(is.na(emiss.coef) & Non.CO2=="NH3")
#Get NH3 emission factors from EPA-ORD
L272.nonghg_elec_tech_coeff_Yf_USA_NH3_EPA <- EPA_state_egu_emission_factors_ktPJ %>%
# Convert to long format
tidyr::gather(variable, value, -state_name, -fuel) %>%
separate(variable, into = c("year", "Non.CO2"), sep = "_") %>%
mutate(year = as.numeric(year),
# NOTE: for now change oil to refined liquids
fuel = gsub("oil", "refined liquids", fuel)) %>%
# state code & select relevant columns
left_join_error_no_match(states_subregions %>% select(state, state_name),
by = c("state_name")) %>%
mutate(sector = "elec_heat") %>%
filter(year %in% MODEL_FUTURE_YEARS,
Non.CO2 == "NH3") %>%
select(state, sector, fuel, Non.CO2, year, value)
L272.nonghg_elec_tech_coeff_Yf_USA.NH3 <- L272.nonghg_elec_tech_coeff_Yf_USA.NA.NH3 %>%
# must use left_join as there are several NAs - technologies that do not have emissions
left_join(L272.nonghg_elec_tech_coeff_Yf_USA_NH3_EPA, by=c("region" = "state", "subsector0" = "fuel" ,"Non.CO2", "year")) %>%
select(-emiss.coef, -sector) %>%
rename(emiss.coef = value)
###Add these back to the L272.nonghg_elec_tech_coeff_Yf_USA.No.NH3 table
L272.nonghg_elec_tech_coeff_Yf_USA.NAs <- L272.nonghg_elec_tech_coeff_Yf_USA.No.NH3 %>%
#filter out NH3 because this dataframe has EFs as NA for all NH3 entries
filter(Non.CO2 != "NH3") %>%
#bind to table that has EFs for NH3
bind_rows(L272.nonghg_elec_tech_coeff_Yf_USA.NH3)
# Generate national median emissions factors for future years
# Remove NAs so as to not skew the median
L272.nonghg_elec_tech_coeff_Yf_USA.median.true <- L272.nonghg_elec_tech_coeff_Yf_USA.NAs %>%
filter(!is.na(emiss.coef)) %>%
group_by(year, Non.CO2, supplysector, subsector0, subsector, stub.technology) %>%
summarise(emiss.coef = median(emiss.coef)) %>%
ungroup() %>%
rename(nationalEF = emiss.coef)
# Some year / pollutant / sector / subsector / tech are NA for all entries, and should be set to 0
L272.nonghg_elec_tech_coeff_Yf_USA.median.skewed <- L272.nonghg_elec_tech_coeff_Yf_USA.NAs %>%
replace_na(list(emiss.coef = 0)) %>%
group_by(year, Non.CO2, supplysector, subsector0, subsector, stub.technology) %>%
summarise(emiss.coef = median(emiss.coef)) %>%
ungroup() %>%
rename(nationalEF = emiss.coef)
# We want to join these tables so that only the entries not in median.true are retained from median.skewed
# These all have EFs of 0
L272.nonghg_elec_tech_coeff_Yf_USA.median <- L272.nonghg_elec_tech_coeff_Yf_USA.median.skewed %>%
anti_join( L272.nonghg_elec_tech_coeff_Yf_USA.median.true, by=c("year", "Non.CO2", "supplysector", "subsector0", "subsector", "stub.technology") ) %>%
# rebind to median.true
bind_rows( L272.nonghg_elec_tech_coeff_Yf_USA.median.true)
# Replace all emissions factors above a given value (20 * median) or that are NAs with the national median emissions factor for that year, non.CO2, and technology
L272.nonghg_elec_tech_coeff_Yf_USA.noBCOC <- L272.nonghg_elec_tech_coeff_Yf_USA.NAs %>%
left_join_error_no_match(L272.nonghg_elec_tech_coeff_Yf_USA.median, by = c("year", "Non.CO2", "supplysector", "subsector0", "subsector", "stub.technology")) %>%
# create a new column that has the threshold value
mutate( threshold = nationalEF * 20,
emiss.coef = if_else(emiss.coef > threshold | is.na(emiss.coef), nationalEF, emiss.coef)) %>%
select(region, Non.CO2, year, supplysector, subsector0, subsector, stub.technology, emiss.coef) %>%
mutate(emiss.coef = if_else(is.infinite(emiss.coef), 1, emiss.coef))
# Use fractions of PM2.5 to calculate BC/OC emissions.
L272.nonghg_elec_tech_coeff_Yf_USA <- compute_BC_OC_elc(L272.nonghg_elec_tech_coeff_Yf_USA.noBCOC, BC_OC_assumptions_years) %>%
# at this point we have some NA EFs due to renewable technologies not having BC/OC emissions
# we can omit these
na.omit()
### Electricity emission factors in all years, for all technologies
L272.nonghg_elec_tech_coeff_USA_all_techs <- bind_rows(L272.nonghg_elec_tech_coeff_Yb_USA, L272.nonghg_elec_tech_coeff_Yf_USA) %>%
distinct(region, supplysector, subsector0, subsector, stub.technology, year, Non.CO2, emiss.coef) %>%
select(region, supplysector, subsector0, subsector, stub.technology, year, Non.CO2, emiss.coef)
# Create a table that has region/supplysector/subsector0/subsector/technology
# that should be kept for vintage and retire subsectors
# Remove technologies from both base and future years
# that have a calOutput = 0 in ALL base years, for vintage and retire subsectors
nonzero_cal_output <- L2233.StubTechProd_elecS_cool_USA %>%
# filtering for the retire and vintage subsectors (these contain 19XX or 20XX)
filter( grepl( '19|20', subsector )) %>%
# making a sum column, if sum = 0, that region/supplysector/subsector0/subsector/technology should be removed
group_by( region, supplysector, subsector0, subsector, technology ) %>%
mutate( sum = sum( calOutputValue ) ) %>%
ungroup() %>%
# do not keep the entries that have sum = 0
filter( sum > 0 ) %>%
select( -c( sum, year, share.weight.year, subs.share.weight, tech.share.weight, calOutputValue ) ) %>%
distinct( region, supplysector, subsector0, subsector, technology )
# Subset the vintage and retire subsectors
L272.vintage_retire_techs <- L272.nonghg_elec_tech_coeff_USA_all_techs %>%
# filtering for the retire and vintage subsectors (these contain 19XX or 20XX)
filter( grepl( '19|20', subsector ) ) %>%
rename( technology = stub.technology )
# Subset the non vintage and retire subsectors
L272.regular_techs <- L272.nonghg_elec_tech_coeff_USA_all_techs %>%
# filtering out the retire and vintage subsectors (these contain 19XX or 20XX)
filter( !grepl( '19|20', subsector ) ) %>%
rename( technology = stub.technology )
# Keep only the region/supplysector/subsector0/subsector/technology with nonzero calOutput
L272.vintage_retire_techs_nonzero <- nonzero_cal_output %>%
left_join( L272.vintage_retire_techs, by = c("supplysector", "subsector0", "subsector", "region", "technology"))
# Bind the two technologies together back together
L272.nonghg_elec_tech_coeff_USA_no_driver <- bind_rows( L272.regular_techs, L272.vintage_retire_techs_nonzero )
# Add an input name column to drive emissions
L272.nonghg_elec_tech_coeff_USA <- L272.nonghg_elec_tech_coeff_USA_no_driver %>%
# L2234.StubTechMarket_elecS has the fuel inputs that should be used to drive emissions
left_join_error_no_match( L2234.StubTechMarket_elecS_USA %>% select( c( "supplysector", "subsector", "stub.technology",
"minicam.energy.input" ) ) %>%
distinct( supplysector, subsector, minicam.energy.input ),
by = c("supplysector", "subsector0" = "subsector") ) %>%
#rename to input.name to match header
rename( input.name = minicam.energy.input) %>%
#change SO2 to SO2_1
mutate( Non.CO2 = gsub( "SO2", "SO2_1", Non.CO2 ) ) %>%
# remove 1975
filter( year > min(MODEL_BASE_YEARS) ) %>%
# distinct to remove any potential duplicate rows
distinct()
# Produce outputs
L272.nonghg_elec_tech_coeff_USA %>%
add_title("Non-GHG emissions parameters for electricity technologies in the USA") %>%
add_units("Tg/EJ") %>%
add_comments("Non-GHG emissions parameters for electricity technologies in the USA") %>%
add_legacy_name("L272.nonghg_elec_tech_coeff_USA") %>%
add_precursors("gcam-usa/emissions/BC_OC_assumptions",
"gcam-usa/emissions/BCOC_PM25_ratios",
"gcam-usa/emissions/EPA_state_egu_emission_factors_ktPJ_post2015",
"gcam-usa/emissions/EPA_state_egu_emission_factors_ktPJ",
"gcam-usa/states_subregions",
"L123.in_EJ_state_elec_F",
"L270.nonghg_tg_state_elec_F_Yb",
"gcam-usa/A23.elecS_tech_mapping_cool",
"gcam-usa/A23.elecS_tech_availability",
"L2234.StubTechMarket_elecS_USA",
"L2233.StubTechProd_elecS_cool_USA") ->
L272.nonghg_elec_tech_coeff_USA
return_data(L272.nonghg_elec_tech_coeff_USA)
} else {
stop("Unknown command")
}
}
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