R/zchunk_L252.MACC.R
In rohmin9122/gcam-korea-release: Build the GCAM-Korea Input Datasets
Defines functions
module_emissions_L252.MACC
Documented in
module_emissions_L252.MACC
#' module_emissions_L252.MACC
#'
#' Creates marginal abatement cost curves "MACC", for fossil resources, agriculture, animals, and processing.
#'
#' @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{L252.ResMAC_fos}, \code{L252.AgMAC}, \code{L252.MAC_an}, \code{L252.MAC_prc},
#' \code{L252.MAC_higwp}, \code{L252.MAC_Ag_TC_SSP1}, \code{L252.MAC_An_TC_SSP1}, \code{L252.MAC_prc_TC_SSP1},
#' \code{L252.MAC_res_TC_SSP1}, \code{L252.MAC_Ag_TC_SSP2}, \code{L252.MAC_An_TC_SSP2}, \code{L252.MAC_prc_TC_SSP2},
#' \code{L252.MAC_res_TC_SSP2}, \code{L252.MAC_Ag_TC_SSP5}, \code{L252.MAC_An_TC_SSP5}, \code{L252.MAC_prc_TC_SSP5},
#' \code{L252.MAC_res_TC_SSP5}. The corresponding file in the
#' original data system was \code{L252.MACC.R} (emissions level2).
#' @details Creates marginal abatement cost curves "MACC", for fossil resources, agriculture, animals, and processing.
#' @importFrom assertthat assert_that
#' @importFrom dplyr filter mutate select
#' @importFrom tidyr gather spread
#' @author RH August 2017
module_emissions_L252.MACC <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "emissions/A_regions",
FILE = "emissions/A_MACC_TechChange",
FILE = "emissions/mappings/GCAM_sector_tech",
FILE = "emissions/HFC_Abate_GV",
FILE = "emissions/GV_mac_reduction",
"L152.MAC_pct_R_S_Proc_EPA",
"L201.ghg_res",
"L211.AGREmissions",
"L211.AnEmissions",
"L211.AGRBio",
"L232.nonco2_prc",
"L241.hfc_all",
"L241.pfc_all"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L252.ResMAC_fos",
"L252.AgMAC",
"L252.MAC_an",
"L252.MAC_prc",
"L252.MAC_higwp",
"L252.MAC_Ag_TC_SSP1",
"L252.MAC_An_TC_SSP1",
"L252.MAC_prc_TC_SSP1",
"L252.MAC_res_TC_SSP1",
"L252.MAC_Ag_TC_SSP2",
"L252.MAC_An_TC_SSP2",
"L252.MAC_prc_TC_SSP2",
"L252.MAC_res_TC_SSP2",
"L252.MAC_Ag_TC_SSP5",
"L252.MAC_An_TC_SSP5",
"L252.MAC_prc_TC_SSP5",
"L252.MAC_res_TC_SSP5"))
} else if(command == driver.MAKE) {
# Silence package checks
. <- AgProductionTechnology <- AgSupplySector <- AgSupplySubsector <- EPA_MACC_Sector <- EPA_region <-
GV_year <- MAC_region <- Non.CO2 <- PCT_ABATE <- Process <- Species <- Year <- bio_N2O_coef <-
depresource <- emiss.coef <- input.emissions <- mac.control <- mac.reduction <- region <-
scenario <- sector <- stub.technology <- subsector <- supplysector <- tax <- tech_change <-
market.name <- year <- NULL
all_data <- list(...)[[1]]
# Load required inputs
A_regions <- get_data(all_data, "emissions/A_regions")
A_MACC_TechChange <- get_data(all_data, "emissions/A_MACC_TechChange")
GCAM_sector_tech <- get_data(all_data, "emissions/mappings/GCAM_sector_tech")
HFC_Abate_GV <- get_data(all_data, "emissions/HFC_Abate_GV")
GV_mac_reduction <- get_data(all_data, "emissions/GV_mac_reduction")
L152.MAC_pct_R_S_Proc_EPA <- get_data(all_data, "L152.MAC_pct_R_S_Proc_EPA")
L201.ghg_res <- get_data(all_data, "L201.ghg_res")
L211.AGREmissions <- get_data(all_data, "L211.AGREmissions")
L211.AnEmissions <- get_data(all_data, "L211.AnEmissions")
L211.AGRBio <- get_data(all_data, "L211.AGRBio")
L232.nonco2_prc <- get_data(all_data, "L232.nonco2_prc")
L241.hfc_all <- get_data(all_data, "L241.hfc_all")
L241.pfc_all <- get_data(all_data, "L241.pfc_all")
# ===================================================
# Prepare the table with all MAC curves for matching
# This contains all tax and mac.reduction values
L252.MAC_pct_R_S_Proc_EPA <- L152.MAC_pct_R_S_Proc_EPA %>%
gather(tax, mac.reduction, matches("^[0-9]+$")) %>%
mutate(tax = as.numeric(tax)) %>%
rename(mac.control = Process)
MAC_taxes <- unique(L252.MAC_pct_R_S_Proc_EPA$tax)
# This is a function to add in the mac.reduction curves to data
# Function needed because these steps are repeated 5 times
mac_reduction_adder <- function(df, order, error_no_match = TRUE) {
df <- df %>%
# Add tax values
repeat_add_columns(tibble(tax = MAC_taxes)) %>%
arrange_("region", order) %>%
# Join in EPA regions
left_join_error_no_match(A_regions %>%
select(region, EPA_region = MAC_region),
by = "region")
# Next, add in mac.reduction values
if(error_no_match) {
# Usually we use left_join_error_no_match
df <- df %>%
left_join_error_no_match(L252.MAC_pct_R_S_Proc_EPA, by = c("EPA_region", "mac.control", "tax")) %>%
mutate(mac.reduction = round(mac.reduction, 3))
} else {
# There are times where the data does not match, so using left_join is necessary
df <- df %>%
left_join(L252.MAC_pct_R_S_Proc_EPA, by = c("EPA_region", "mac.control", "tax")) %>%
mutate(mac.reduction = round(mac.reduction, 3))
}
return(df)
}
# L252.ResMAC_fos: Fossil resource MAC curves
# NOTE: only applying the fossil resource MAC curves to the CH4 emissions
L252.ResMAC_fos <- L201.ghg_res %>%
select(-emiss.coef) %>%
filter(Non.CO2 == "CH4") %>%
# Add in mac.control
left_join_error_no_match(GCAM_sector_tech %>%
filter(sector == "out_resources") %>%
select(mac.control = EPA_MACC_Sector, subsector),
by = c("depresource" = "subsector")) %>%
mac_reduction_adder(order = "depresource") %>%
# Add column for market variable
mutate(market.name = emissions.MAC_MARKET) %>%
# Remove EPA_Region - useful up to now for diagnostic, but not needed for csv->xml conversion
select(region, depresource, Non.CO2, mac.control, tax, mac.reduction, market.name)
# L252.AgMAC: Agricultural abatement (including bioenergy)
L252.AgMAC <- L211.AGREmissions %>%
select(-input.emissions) %>%
bind_rows(L211.AGRBio %>%
select(-bio_N2O_coef)) %>%
filter(year == min(L211.AGREmissions$year),
Non.CO2 %in% emissions.AG_MACC_GHG_NAMES) %>%
# Add in mac.control
left_join_error_no_match(GCAM_sector_tech %>%
select(mac.control = EPA_MACC_Sector, supplysector) %>%
distinct, # taking distinct values because there were repeats for AEZs
by = c("AgSupplySector" = "supplysector")) %>%
mac_reduction_adder(order = "AgProductionTechnology") %>%
# Add column for market variable
mutate(market.name = emissions.MAC_MARKET) %>%
# Remove EPA_Region - useful up to now for diagnostic, but not needed for csv->xml conversion
select(region, AgSupplySector, AgSupplySubsector, AgProductionTechnology, year, Non.CO2,
mac.control, tax, mac.reduction, market.name)
# L252.MAC_an: Abatement from animal production
L252.MAC_an <- L211.AnEmissions %>%
select(-input.emissions) %>%
filter(year == min(L211.AnEmissions$year),
Non.CO2 %in% emissions.AG_MACC_GHG_NAMES) %>%
# Add in mac.control
left_join_error_no_match(GCAM_sector_tech %>%
select(mac.control = EPA_MACC_Sector, supplysector) %>%
distinct, # taking distinct values because there are repeats for different technologies
by = "supplysector") %>%
mac_reduction_adder(order = c("supplysector", "subsector", "stub.technology", "Non.CO2")) %>%
# Add column for market variable
mutate(market.name = emissions.MAC_MARKET) %>%
# Remove EPA_Region - useful up to now for diagnostic, but not needed for csv->xml conversion
select(region, supplysector, subsector, stub.technology, year, Non.CO2, mac.control,
tax, mac.reduction, market.name)
# L252.MAC_prc: Abatement from industrial and urban processes
L252.MAC_prc <- L232.nonco2_prc %>%
select(-input.emissions) %>%
filter(year == min(L232.nonco2_prc$year),
Non.CO2 %in% emissions.GHG_NAMES) %>%
# Add in mac.control
# Using left_join b/c mac.control for "other industrial processes" is NA
left_join(GCAM_sector_tech %>%
select(mac.control = EPA_MACC_Sector, supplysector, subsector, stub.technology),
by = c("supplysector", "subsector", "stub.technology")) %>%
mac_reduction_adder(order = c("supplysector", "subsector", "stub.technology", "Non.CO2"),
# error_no_match is F, which means we use left_join(L252.MAC_pct_R_S_Proc_EPA)
# because not all mac.controls and regions in L252.MAC_pct_R_S_Proc_EPA
error_no_match = FALSE) %>%
na.omit() %>%
# Add column for market variable
mutate(market.name = emissions.MAC_MARKET) %>%
# Remove EPA_Region - useful up to now for diagnostic, but not needed for csv->xml conversion
select(region, supplysector, subsector, stub.technology, year, Non.CO2, mac.control, tax, mac.reduction, market.name)
# L252.MAC_higwp: Abatement from HFCs, PFCs, and SF6
L252.MAC_higwp <- bind_rows(L241.hfc_all, L241.pfc_all) %>%
select(-input.emissions) %>%
filter(year == min(.$year)) %>%
# Add in mac.control
# Using left_join b/c mac.control for "other industrial processes" is NA
left_join(GCAM_sector_tech %>%
select(mac.control = EPA_MACC_Sector, supplysector, subsector, stub.technology),
by = c("supplysector", "subsector", "stub.technology")) %>%
mac_reduction_adder(order = c("supplysector", "subsector", "stub.technology", "Non.CO2"),
# error_no_match is F, which means we use left_join(L252.MAC_pct_R_S_Proc_EPA)
# because not all mac.controls and regions in L252.MAC_pct_R_S_Proc_EPA
error_no_match = FALSE) %>%
na.omit() %>%
# Add column for market variable
mutate(market.name = emissions.MAC_MARKET) %>%
# Remove EPA_Region - useful up to now for diagnostic, but not needed for csv->xml conversion
select(region, supplysector, subsector, stub.technology, year, Non.CO2, mac.control, tax, mac.reduction, market.name)
# These steps will be completed if we choose to replace our HiGWP data with data from Guus Velders
if(emissions.USE_GV_MAC) {
# L252.MAC_higwp_GV: Abatement from HFCs, PFCs, and SF6 using Guus Velders data for HFCs
# Filter our PFCs
L252.MAC_pfc <- L252.MAC_higwp %>%
filter(Non.CO2 %in% c("C2F6", "CF4", "SF6"))
# Table of abatement potentials
L252.HFC_Abate_GV <- HFC_Abate_GV %>%
filter(Species == "Total_HFCs",
Year %in% unique(GV_mac_reduction$GV_year)) %>%
select(Year, mac.reduction = PCT_ABATE)
L252.MAC_hfc <- L252.MAC_higwp %>%
filter(!(Non.CO2 %in% c("C2F6", "CF4", "SF6")),
tax == 0) %>%
# Remove our tax and mac.reduction
select(-tax, -mac.reduction) %>%
# Add in GV tax and mac.reduction
repeat_add_columns(tibble(tax = GV_mac_reduction$tax)) %>%
left_join_error_no_match(GV_mac_reduction, by = "tax") %>%
# left_join because some GV_years, but not L252.HFC_Abate_GV Years, are 0, indicating that mac.reduction should also be 0
left_join(L252.HFC_Abate_GV, by = c("GV_year" = "Year")) %>%
# Replace mac.reduction for tax 0 with 0
mutate(mac.reduction = replace(mac.reduction, tax == 0, 0)) %>%
select(-GV_year)
L252.MAC_higwp <- bind_rows( L252.MAC_pfc,L252.MAC_hfc)
}
# L252.MAC_TC: Tech Change on MACCs
# For all tibbles, add in scenarios and tech change, then split by scenario and add in documentation
L252.MAC_Ag_TC <- L252.AgMAC %>%
repeat_add_columns(tibble(scenario = unique(A_MACC_TechChange$scenario))) %>%
left_join_error_no_match(A_MACC_TechChange %>% rename(tech.change = tech_change),
by = c("scenario", "mac.control" = "MAC")) %>%
split(.$scenario) %>%
lapply(function(df) {
df %>%
add_title("Marginal Abatement Cost Curves with Technology Changes for Agriculture") %>%
add_units("tax: 1990 USD; mac.reduction: % reduction; tech_change: Unitless") %>%
add_comments("Category data from L211.AGREmissions and L211.AGRBio given tax and mac.reduction data from L152.MAC_pct_R_S_Proc_EPA") %>%
add_comments("Technology change data added in from A_MACC_TechChange") %>%
add_precursors("emissions/A_regions", "emissions/mappings/GCAM_sector_tech",
"L152.MAC_pct_R_S_Proc_EPA", "L211.AGREmissions", "L211.AGRBio", "emissions/A_MACC_TechChange") %>%
select(-scenario)
})
L252.MAC_An_TC <- L252.MAC_an %>%
repeat_add_columns(tibble(scenario = unique(A_MACC_TechChange$scenario))) %>%
left_join_error_no_match(A_MACC_TechChange %>% rename(tech.change = tech_change),
by = c("scenario", "mac.control" = "MAC")) %>%
split(.$scenario) %>%
lapply(function(df) {
df %>%
add_title("Marginal Abatement Cost Curves with Technology Changes for Animals") %>%
add_units("tax: 1990 USD; mac.reduction: % reduction; tech_change: Unitless") %>%
add_comments("Category data from L211.AnEmissions given tax and mac.reduction data from L152.MAC_pct_R_S_Proc_EPA") %>%
add_comments("Technology change data added in from A_MACC_TechChange") %>%
add_precursors("emissions/A_regions", "emissions/mappings/GCAM_sector_tech",
"L152.MAC_pct_R_S_Proc_EPA", "L211.AnEmissions", "emissions/A_MACC_TechChange") %>%
select(-scenario)
})
L252.MAC_prc_TC <- L252.MAC_prc %>%
repeat_add_columns(tibble(scenario = unique(A_MACC_TechChange$scenario))) %>%
left_join(A_MACC_TechChange %>% rename(tech.change = tech_change),
by = c("scenario", "mac.control" = "MAC")) %>%
na.omit %>%
split(.$scenario) %>%
lapply(function(df) {
df %>%
add_title("Marginal Abatement Cost Curves with Technology Changes for Industrial and Urban Processing Greenhouse Gases") %>%
add_units("tax: 1990 USD; mac.reduction: % reduction; tech_change: Unitless") %>%
add_comments("Category data from L232.nonco2_prc given tax and mac.reduction data from L152.MAC_pct_R_S_Proc_EPA") %>%
add_comments("Technology change data added in from A_MACC_TechChange") %>%
add_precursors("emissions/A_regions", "emissions/mappings/GCAM_sector_tech",
"L152.MAC_pct_R_S_Proc_EPA", "L232.nonco2_prc", "emissions/A_MACC_TechChange") %>%
select(-scenario)
})
L252.MAC_res_TC <- L252.ResMAC_fos %>%
repeat_add_columns(tibble(scenario = unique(A_MACC_TechChange$scenario))) %>%
left_join_error_no_match(A_MACC_TechChange %>% rename(tech.change = tech_change),
by = c("scenario", "mac.control" = "MAC")) %>%
split(.$scenario) %>%
lapply(function(df) {
df %>%
add_title("Marginal Abatement Cost Curves with Technology Changes for Fossil Resources") %>%
add_units("tax: 1990 USD; mac.reduction: % reduction; tech_change: Unitless") %>%
add_comments("Category data from L201.ghg_res given tax and mac.reduction data from L152.MAC_pct_R_S_Proc_EPA") %>%
add_comments("Technology change data added in from A_MACC_TechChange") %>%
add_precursors("emissions/A_regions", "emissions/mappings/GCAM_sector_tech",
"L152.MAC_pct_R_S_Proc_EPA", "L201.ghg_res", "emissions/A_MACC_TechChange") %>%
select(-scenario)
})
# ===================================================
# Produce outputs
L252.ResMAC_fos %>%
add_title("Marginal Abatement Cost Curves for Fossil Resources") %>%
add_units("tax: 1990 USD; mac.reduction: % reduction") %>%
add_comments("Category data from L201.ghg_res given tax and mac.reduction data from L152.MAC_pct_R_S_Proc_EPA") %>%
add_legacy_name("L252.ResMAC_fos") %>%
add_precursors("emissions/A_regions", "emissions/mappings/GCAM_sector_tech",
"L152.MAC_pct_R_S_Proc_EPA", "L201.ghg_res") ->
L252.ResMAC_fos
L252.AgMAC %>%
add_title("Marginal Abatement Cost Curves for Agriculture") %>%
add_units("tax: 1990 USD; mac.reduction: % reduction") %>%
add_comments("Category data from L211.AGREmissions and L211.AGRBio given tax and mac.reduction data from L152.MAC_pct_R_S_Proc_EPA") %>%
add_legacy_name("L252.AgMAC") %>%
add_precursors("emissions/A_regions", "emissions/mappings/GCAM_sector_tech",
"L152.MAC_pct_R_S_Proc_EPA", "L211.AGREmissions", "L211.AGRBio") ->
L252.AgMAC
L252.MAC_an %>%
add_title("Marginal Abatement Cost Curves for Animals") %>%
add_units("tax: 1990 USD; mac.reduction: % reduction") %>%
add_comments("Category data from L211.AnEmissions given tax and mac.reduction data from L152.MAC_pct_R_S_Proc_EPA") %>%
add_legacy_name("L252.MAC_an") %>%
add_precursors("emissions/A_regions", "emissions/mappings/GCAM_sector_tech",
"L152.MAC_pct_R_S_Proc_EPA", "L211.AnEmissions") ->
L252.MAC_an
L252.MAC_prc %>%
add_title("Marginal Abatement Cost Curves for Industrial and Urban Processing Greenhouse Gases") %>%
add_units("tax: 1990 USD; mac.reduction: % reduction") %>%
add_comments("Category data from L232.nonco2_prc given tax and mac.reduction data from L152.MAC_pct_R_S_Proc_EPA") %>%
add_legacy_name("L252.MAC_prc") %>%
add_precursors("emissions/A_regions", "emissions/mappings/GCAM_sector_tech",
"L152.MAC_pct_R_S_Proc_EPA", "L232.nonco2_prc") ->
L252.MAC_prc
L252.MAC_higwp %>%
add_title("Marginal Abatement Cost Curves for High GWP Gases") %>%
add_units("tax: 1990 USD; mac.reduction: % reduction") %>%
add_comments("Category data from L241.hfc_all and L241.pfc_all given tax and mac.reduction data from L152.MAC_pct_R_S_Proc_EPA") %>%
add_comments("If using Guus Velders data, tax and mac.reduction values taken from HFC_Abate_GV and GV_mac_reduction") %>%
add_legacy_name("L252.MAC_higwp") %>%
add_precursors("emissions/A_regions", "emissions/mappings/GCAM_sector_tech",
"L152.MAC_pct_R_S_Proc_EPA", "L241.hfc_all", "L241.pfc_all",
"emissions/HFC_Abate_GV", "emissions/GV_mac_reduction") ->
L252.MAC_higwp
L252.MAC_Ag_TC[["SSP1"]] %>%
add_legacy_name("L252.MAC_Ag_TC_SSP1") ->
L252.MAC_Ag_TC_SSP1
L252.MAC_An_TC[["SSP1"]] %>%
add_legacy_name("L252.MAC_An_TC_SSP1") ->
L252.MAC_An_TC_SSP1
L252.MAC_prc_TC[["SSP1"]] %>%
add_legacy_name("L252.MAC_prc_TC_SSP1") ->
L252.MAC_prc_TC_SSP1
L252.MAC_res_TC[["SSP1"]] %>%
add_legacy_name("L252.MAC_res_TC_SSP1") ->
L252.MAC_res_TC_SSP1
L252.MAC_Ag_TC[["SSP2"]] %>%
add_legacy_name("L252.MAC_Ag_TC_SSP2") ->
L252.MAC_Ag_TC_SSP2
L252.MAC_An_TC[["SSP2"]] %>%
add_legacy_name("L252.MAC_An_TC_SSP2") ->
L252.MAC_An_TC_SSP2
L252.MAC_prc_TC[["SSP2"]] %>%
add_legacy_name("L252.MAC_prc_TC_SSP2") ->
L252.MAC_prc_TC_SSP2
L252.MAC_res_TC[["SSP2"]] %>%
add_legacy_name("L252.MAC_res_TC_SSP2") ->
L252.MAC_res_TC_SSP2
L252.MAC_Ag_TC[["SSP5"]] %>%
add_legacy_name("L252.MAC_Ag_TC_SSP5") ->
L252.MAC_Ag_TC_SSP5
L252.MAC_An_TC[["SSP5"]] %>%
add_legacy_name("L252.MAC_An_TC_SSP5") ->
L252.MAC_An_TC_SSP5
L252.MAC_prc_TC[["SSP5"]] %>%
add_legacy_name("L252.MAC_prc_TC_SSP5") ->
L252.MAC_prc_TC_SSP5
L252.MAC_res_TC[["SSP5"]] %>%
add_legacy_name("L252.MAC_res_TC_SSP5") ->
L252.MAC_res_TC_SSP5
return_data(L252.ResMAC_fos, L252.AgMAC, L252.MAC_an, L252.MAC_prc, L252.MAC_higwp, L252.MAC_Ag_TC_SSP1,
L252.MAC_An_TC_SSP1, L252.MAC_prc_TC_SSP1, L252.MAC_res_TC_SSP1, L252.MAC_Ag_TC_SSP2,
L252.MAC_An_TC_SSP2, L252.MAC_prc_TC_SSP2, L252.MAC_res_TC_SSP2, L252.MAC_Ag_TC_SSP5,
L252.MAC_An_TC_SSP5, L252.MAC_prc_TC_SSP5, L252.MAC_res_TC_SSP5)
} else {
stop("Unknown command")
}
}
rohmin9122/gcam-korea-release documentation built on Nov. 26, 2020, 8:11 a.m.
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Defines functions module_emissions_L252.MACC
Documented in module_emissions_L252.MACC
#' module_emissions_L252.MACC
#'
#' Creates marginal abatement cost curves "MACC", for fossil resources, agriculture, animals, and processing.
#'
#' @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{L252.ResMAC_fos}, \code{L252.AgMAC}, \code{L252.MAC_an}, \code{L252.MAC_prc},
#' \code{L252.MAC_higwp}, \code{L252.MAC_Ag_TC_SSP1}, \code{L252.MAC_An_TC_SSP1}, \code{L252.MAC_prc_TC_SSP1},
#' \code{L252.MAC_res_TC_SSP1}, \code{L252.MAC_Ag_TC_SSP2}, \code{L252.MAC_An_TC_SSP2}, \code{L252.MAC_prc_TC_SSP2},
#' \code{L252.MAC_res_TC_SSP2}, \code{L252.MAC_Ag_TC_SSP5}, \code{L252.MAC_An_TC_SSP5}, \code{L252.MAC_prc_TC_SSP5},
#' \code{L252.MAC_res_TC_SSP5}. The corresponding file in the
#' original data system was \code{L252.MACC.R} (emissions level2).
#' @details Creates marginal abatement cost curves "MACC", for fossil resources, agriculture, animals, and processing.
#' @importFrom assertthat assert_that
#' @importFrom dplyr filter mutate select
#' @importFrom tidyr gather spread
#' @author RH August 2017
module_emissions_L252.MACC <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "emissions/A_regions",
FILE = "emissions/A_MACC_TechChange",
FILE = "emissions/mappings/GCAM_sector_tech",
FILE = "emissions/HFC_Abate_GV",
FILE = "emissions/GV_mac_reduction",
"L152.MAC_pct_R_S_Proc_EPA",
"L201.ghg_res",
"L211.AGREmissions",
"L211.AnEmissions",
"L211.AGRBio",
"L232.nonco2_prc",
"L241.hfc_all",
"L241.pfc_all"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L252.ResMAC_fos",
"L252.AgMAC",
"L252.MAC_an",
"L252.MAC_prc",
"L252.MAC_higwp",
"L252.MAC_Ag_TC_SSP1",
"L252.MAC_An_TC_SSP1",
"L252.MAC_prc_TC_SSP1",
"L252.MAC_res_TC_SSP1",
"L252.MAC_Ag_TC_SSP2",
"L252.MAC_An_TC_SSP2",
"L252.MAC_prc_TC_SSP2",
"L252.MAC_res_TC_SSP2",
"L252.MAC_Ag_TC_SSP5",
"L252.MAC_An_TC_SSP5",
"L252.MAC_prc_TC_SSP5",
"L252.MAC_res_TC_SSP5"))
} else if(command == driver.MAKE) {
# Silence package checks
. <- AgProductionTechnology <- AgSupplySector <- AgSupplySubsector <- EPA_MACC_Sector <- EPA_region <-
GV_year <- MAC_region <- Non.CO2 <- PCT_ABATE <- Process <- Species <- Year <- bio_N2O_coef <-
depresource <- emiss.coef <- input.emissions <- mac.control <- mac.reduction <- region <-
scenario <- sector <- stub.technology <- subsector <- supplysector <- tax <- tech_change <-
market.name <- year <- NULL
all_data <- list(...)[[1]]
# Load required inputs
A_regions <- get_data(all_data, "emissions/A_regions")
A_MACC_TechChange <- get_data(all_data, "emissions/A_MACC_TechChange")
GCAM_sector_tech <- get_data(all_data, "emissions/mappings/GCAM_sector_tech")
HFC_Abate_GV <- get_data(all_data, "emissions/HFC_Abate_GV")
GV_mac_reduction <- get_data(all_data, "emissions/GV_mac_reduction")
L152.MAC_pct_R_S_Proc_EPA <- get_data(all_data, "L152.MAC_pct_R_S_Proc_EPA")
L201.ghg_res <- get_data(all_data, "L201.ghg_res")
L211.AGREmissions <- get_data(all_data, "L211.AGREmissions")
L211.AnEmissions <- get_data(all_data, "L211.AnEmissions")
L211.AGRBio <- get_data(all_data, "L211.AGRBio")
L232.nonco2_prc <- get_data(all_data, "L232.nonco2_prc")
L241.hfc_all <- get_data(all_data, "L241.hfc_all")
L241.pfc_all <- get_data(all_data, "L241.pfc_all")
# ===================================================
# Prepare the table with all MAC curves for matching
# This contains all tax and mac.reduction values
L252.MAC_pct_R_S_Proc_EPA <- L152.MAC_pct_R_S_Proc_EPA %>%
gather(tax, mac.reduction, matches("^[0-9]+$")) %>%
mutate(tax = as.numeric(tax)) %>%
rename(mac.control = Process)
MAC_taxes <- unique(L252.MAC_pct_R_S_Proc_EPA$tax)
# This is a function to add in the mac.reduction curves to data
# Function needed because these steps are repeated 5 times
mac_reduction_adder <- function(df, order, error_no_match = TRUE) {
df <- df %>%
# Add tax values
repeat_add_columns(tibble(tax = MAC_taxes)) %>%
arrange_("region", order) %>%
# Join in EPA regions
left_join_error_no_match(A_regions %>%
select(region, EPA_region = MAC_region),
by = "region")
# Next, add in mac.reduction values
if(error_no_match) {
# Usually we use left_join_error_no_match
df <- df %>%
left_join_error_no_match(L252.MAC_pct_R_S_Proc_EPA, by = c("EPA_region", "mac.control", "tax")) %>%
mutate(mac.reduction = round(mac.reduction, 3))
} else {
# There are times where the data does not match, so using left_join is necessary
df <- df %>%
left_join(L252.MAC_pct_R_S_Proc_EPA, by = c("EPA_region", "mac.control", "tax")) %>%
mutate(mac.reduction = round(mac.reduction, 3))
}
return(df)
}
# L252.ResMAC_fos: Fossil resource MAC curves
# NOTE: only applying the fossil resource MAC curves to the CH4 emissions
L252.ResMAC_fos <- L201.ghg_res %>%
select(-emiss.coef) %>%
filter(Non.CO2 == "CH4") %>%
# Add in mac.control
left_join_error_no_match(GCAM_sector_tech %>%
filter(sector == "out_resources") %>%
select(mac.control = EPA_MACC_Sector, subsector),
by = c("depresource" = "subsector")) %>%
mac_reduction_adder(order = "depresource") %>%
# Add column for market variable
mutate(market.name = emissions.MAC_MARKET) %>%
# Remove EPA_Region - useful up to now for diagnostic, but not needed for csv->xml conversion
select(region, depresource, Non.CO2, mac.control, tax, mac.reduction, market.name)
# L252.AgMAC: Agricultural abatement (including bioenergy)
L252.AgMAC <- L211.AGREmissions %>%
select(-input.emissions) %>%
bind_rows(L211.AGRBio %>%
select(-bio_N2O_coef)) %>%
filter(year == min(L211.AGREmissions$year),
Non.CO2 %in% emissions.AG_MACC_GHG_NAMES) %>%
# Add in mac.control
left_join_error_no_match(GCAM_sector_tech %>%
select(mac.control = EPA_MACC_Sector, supplysector) %>%
distinct, # taking distinct values because there were repeats for AEZs
by = c("AgSupplySector" = "supplysector")) %>%
mac_reduction_adder(order = "AgProductionTechnology") %>%
# Add column for market variable
mutate(market.name = emissions.MAC_MARKET) %>%
# Remove EPA_Region - useful up to now for diagnostic, but not needed for csv->xml conversion
select(region, AgSupplySector, AgSupplySubsector, AgProductionTechnology, year, Non.CO2,
mac.control, tax, mac.reduction, market.name)
# L252.MAC_an: Abatement from animal production
L252.MAC_an <- L211.AnEmissions %>%
select(-input.emissions) %>%
filter(year == min(L211.AnEmissions$year),
Non.CO2 %in% emissions.AG_MACC_GHG_NAMES) %>%
# Add in mac.control
left_join_error_no_match(GCAM_sector_tech %>%
select(mac.control = EPA_MACC_Sector, supplysector) %>%
distinct, # taking distinct values because there are repeats for different technologies
by = "supplysector") %>%
mac_reduction_adder(order = c("supplysector", "subsector", "stub.technology", "Non.CO2")) %>%
# Add column for market variable
mutate(market.name = emissions.MAC_MARKET) %>%
# Remove EPA_Region - useful up to now for diagnostic, but not needed for csv->xml conversion
select(region, supplysector, subsector, stub.technology, year, Non.CO2, mac.control,
tax, mac.reduction, market.name)
# L252.MAC_prc: Abatement from industrial and urban processes
L252.MAC_prc <- L232.nonco2_prc %>%
select(-input.emissions) %>%
filter(year == min(L232.nonco2_prc$year),
Non.CO2 %in% emissions.GHG_NAMES) %>%
# Add in mac.control
# Using left_join b/c mac.control for "other industrial processes" is NA
left_join(GCAM_sector_tech %>%
select(mac.control = EPA_MACC_Sector, supplysector, subsector, stub.technology),
by = c("supplysector", "subsector", "stub.technology")) %>%
mac_reduction_adder(order = c("supplysector", "subsector", "stub.technology", "Non.CO2"),
# error_no_match is F, which means we use left_join(L252.MAC_pct_R_S_Proc_EPA)
# because not all mac.controls and regions in L252.MAC_pct_R_S_Proc_EPA
error_no_match = FALSE) %>%
na.omit() %>%
# Add column for market variable
mutate(market.name = emissions.MAC_MARKET) %>%
# Remove EPA_Region - useful up to now for diagnostic, but not needed for csv->xml conversion
select(region, supplysector, subsector, stub.technology, year, Non.CO2, mac.control, tax, mac.reduction, market.name)
# L252.MAC_higwp: Abatement from HFCs, PFCs, and SF6
L252.MAC_higwp <- bind_rows(L241.hfc_all, L241.pfc_all) %>%
select(-input.emissions) %>%
filter(year == min(.$year)) %>%
# Add in mac.control
# Using left_join b/c mac.control for "other industrial processes" is NA
left_join(GCAM_sector_tech %>%
select(mac.control = EPA_MACC_Sector, supplysector, subsector, stub.technology),
by = c("supplysector", "subsector", "stub.technology")) %>%
mac_reduction_adder(order = c("supplysector", "subsector", "stub.technology", "Non.CO2"),
# error_no_match is F, which means we use left_join(L252.MAC_pct_R_S_Proc_EPA)
# because not all mac.controls and regions in L252.MAC_pct_R_S_Proc_EPA
error_no_match = FALSE) %>%
na.omit() %>%
# Add column for market variable
mutate(market.name = emissions.MAC_MARKET) %>%
# Remove EPA_Region - useful up to now for diagnostic, but not needed for csv->xml conversion
select(region, supplysector, subsector, stub.technology, year, Non.CO2, mac.control, tax, mac.reduction, market.name)
# These steps will be completed if we choose to replace our HiGWP data with data from Guus Velders
if(emissions.USE_GV_MAC) {
# L252.MAC_higwp_GV: Abatement from HFCs, PFCs, and SF6 using Guus Velders data for HFCs
# Filter our PFCs
L252.MAC_pfc <- L252.MAC_higwp %>%
filter(Non.CO2 %in% c("C2F6", "CF4", "SF6"))
# Table of abatement potentials
L252.HFC_Abate_GV <- HFC_Abate_GV %>%
filter(Species == "Total_HFCs",
Year %in% unique(GV_mac_reduction$GV_year)) %>%
select(Year, mac.reduction = PCT_ABATE)
L252.MAC_hfc <- L252.MAC_higwp %>%
filter(!(Non.CO2 %in% c("C2F6", "CF4", "SF6")),
tax == 0) %>%
# Remove our tax and mac.reduction
select(-tax, -mac.reduction) %>%
# Add in GV tax and mac.reduction
repeat_add_columns(tibble(tax = GV_mac_reduction$tax)) %>%
left_join_error_no_match(GV_mac_reduction, by = "tax") %>%
# left_join because some GV_years, but not L252.HFC_Abate_GV Years, are 0, indicating that mac.reduction should also be 0
left_join(L252.HFC_Abate_GV, by = c("GV_year" = "Year")) %>%
# Replace mac.reduction for tax 0 with 0
mutate(mac.reduction = replace(mac.reduction, tax == 0, 0)) %>%
select(-GV_year)
L252.MAC_higwp <- bind_rows( L252.MAC_pfc,L252.MAC_hfc)
}
# L252.MAC_TC: Tech Change on MACCs
# For all tibbles, add in scenarios and tech change, then split by scenario and add in documentation
L252.MAC_Ag_TC <- L252.AgMAC %>%
repeat_add_columns(tibble(scenario = unique(A_MACC_TechChange$scenario))) %>%
left_join_error_no_match(A_MACC_TechChange %>% rename(tech.change = tech_change),
by = c("scenario", "mac.control" = "MAC")) %>%
split(.$scenario) %>%
lapply(function(df) {
df %>%
add_title("Marginal Abatement Cost Curves with Technology Changes for Agriculture") %>%
add_units("tax: 1990 USD; mac.reduction: % reduction; tech_change: Unitless") %>%
add_comments("Category data from L211.AGREmissions and L211.AGRBio given tax and mac.reduction data from L152.MAC_pct_R_S_Proc_EPA") %>%
add_comments("Technology change data added in from A_MACC_TechChange") %>%
add_precursors("emissions/A_regions", "emissions/mappings/GCAM_sector_tech",
"L152.MAC_pct_R_S_Proc_EPA", "L211.AGREmissions", "L211.AGRBio", "emissions/A_MACC_TechChange") %>%
select(-scenario)
})
L252.MAC_An_TC <- L252.MAC_an %>%
repeat_add_columns(tibble(scenario = unique(A_MACC_TechChange$scenario))) %>%
left_join_error_no_match(A_MACC_TechChange %>% rename(tech.change = tech_change),
by = c("scenario", "mac.control" = "MAC")) %>%
split(.$scenario) %>%
lapply(function(df) {
df %>%
add_title("Marginal Abatement Cost Curves with Technology Changes for Animals") %>%
add_units("tax: 1990 USD; mac.reduction: % reduction; tech_change: Unitless") %>%
add_comments("Category data from L211.AnEmissions given tax and mac.reduction data from L152.MAC_pct_R_S_Proc_EPA") %>%
add_comments("Technology change data added in from A_MACC_TechChange") %>%
add_precursors("emissions/A_regions", "emissions/mappings/GCAM_sector_tech",
"L152.MAC_pct_R_S_Proc_EPA", "L211.AnEmissions", "emissions/A_MACC_TechChange") %>%
select(-scenario)
})
L252.MAC_prc_TC <- L252.MAC_prc %>%
repeat_add_columns(tibble(scenario = unique(A_MACC_TechChange$scenario))) %>%
left_join(A_MACC_TechChange %>% rename(tech.change = tech_change),
by = c("scenario", "mac.control" = "MAC")) %>%
na.omit %>%
split(.$scenario) %>%
lapply(function(df) {
df %>%
add_title("Marginal Abatement Cost Curves with Technology Changes for Industrial and Urban Processing Greenhouse Gases") %>%
add_units("tax: 1990 USD; mac.reduction: % reduction; tech_change: Unitless") %>%
add_comments("Category data from L232.nonco2_prc given tax and mac.reduction data from L152.MAC_pct_R_S_Proc_EPA") %>%
add_comments("Technology change data added in from A_MACC_TechChange") %>%
add_precursors("emissions/A_regions", "emissions/mappings/GCAM_sector_tech",
"L152.MAC_pct_R_S_Proc_EPA", "L232.nonco2_prc", "emissions/A_MACC_TechChange") %>%
select(-scenario)
})
L252.MAC_res_TC <- L252.ResMAC_fos %>%
repeat_add_columns(tibble(scenario = unique(A_MACC_TechChange$scenario))) %>%
left_join_error_no_match(A_MACC_TechChange %>% rename(tech.change = tech_change),
by = c("scenario", "mac.control" = "MAC")) %>%
split(.$scenario) %>%
lapply(function(df) {
df %>%
add_title("Marginal Abatement Cost Curves with Technology Changes for Fossil Resources") %>%
add_units("tax: 1990 USD; mac.reduction: % reduction; tech_change: Unitless") %>%
add_comments("Category data from L201.ghg_res given tax and mac.reduction data from L152.MAC_pct_R_S_Proc_EPA") %>%
add_comments("Technology change data added in from A_MACC_TechChange") %>%
add_precursors("emissions/A_regions", "emissions/mappings/GCAM_sector_tech",
"L152.MAC_pct_R_S_Proc_EPA", "L201.ghg_res", "emissions/A_MACC_TechChange") %>%
select(-scenario)
})
# ===================================================
# Produce outputs
L252.ResMAC_fos %>%
add_title("Marginal Abatement Cost Curves for Fossil Resources") %>%
add_units("tax: 1990 USD; mac.reduction: % reduction") %>%
add_comments("Category data from L201.ghg_res given tax and mac.reduction data from L152.MAC_pct_R_S_Proc_EPA") %>%
add_legacy_name("L252.ResMAC_fos") %>%
add_precursors("emissions/A_regions", "emissions/mappings/GCAM_sector_tech",
"L152.MAC_pct_R_S_Proc_EPA", "L201.ghg_res") ->
L252.ResMAC_fos
L252.AgMAC %>%
add_title("Marginal Abatement Cost Curves for Agriculture") %>%
add_units("tax: 1990 USD; mac.reduction: % reduction") %>%
add_comments("Category data from L211.AGREmissions and L211.AGRBio given tax and mac.reduction data from L152.MAC_pct_R_S_Proc_EPA") %>%
add_legacy_name("L252.AgMAC") %>%
add_precursors("emissions/A_regions", "emissions/mappings/GCAM_sector_tech",
"L152.MAC_pct_R_S_Proc_EPA", "L211.AGREmissions", "L211.AGRBio") ->
L252.AgMAC
L252.MAC_an %>%
add_title("Marginal Abatement Cost Curves for Animals") %>%
add_units("tax: 1990 USD; mac.reduction: % reduction") %>%
add_comments("Category data from L211.AnEmissions given tax and mac.reduction data from L152.MAC_pct_R_S_Proc_EPA") %>%
add_legacy_name("L252.MAC_an") %>%
add_precursors("emissions/A_regions", "emissions/mappings/GCAM_sector_tech",
"L152.MAC_pct_R_S_Proc_EPA", "L211.AnEmissions") ->
L252.MAC_an
L252.MAC_prc %>%
add_title("Marginal Abatement Cost Curves for Industrial and Urban Processing Greenhouse Gases") %>%
add_units("tax: 1990 USD; mac.reduction: % reduction") %>%
add_comments("Category data from L232.nonco2_prc given tax and mac.reduction data from L152.MAC_pct_R_S_Proc_EPA") %>%
add_legacy_name("L252.MAC_prc") %>%
add_precursors("emissions/A_regions", "emissions/mappings/GCAM_sector_tech",
"L152.MAC_pct_R_S_Proc_EPA", "L232.nonco2_prc") ->
L252.MAC_prc
L252.MAC_higwp %>%
add_title("Marginal Abatement Cost Curves for High GWP Gases") %>%
add_units("tax: 1990 USD; mac.reduction: % reduction") %>%
add_comments("Category data from L241.hfc_all and L241.pfc_all given tax and mac.reduction data from L152.MAC_pct_R_S_Proc_EPA") %>%
add_comments("If using Guus Velders data, tax and mac.reduction values taken from HFC_Abate_GV and GV_mac_reduction") %>%
add_legacy_name("L252.MAC_higwp") %>%
add_precursors("emissions/A_regions", "emissions/mappings/GCAM_sector_tech",
"L152.MAC_pct_R_S_Proc_EPA", "L241.hfc_all", "L241.pfc_all",
"emissions/HFC_Abate_GV", "emissions/GV_mac_reduction") ->
L252.MAC_higwp
L252.MAC_Ag_TC[["SSP1"]] %>%
add_legacy_name("L252.MAC_Ag_TC_SSP1") ->
L252.MAC_Ag_TC_SSP1
L252.MAC_An_TC[["SSP1"]] %>%
add_legacy_name("L252.MAC_An_TC_SSP1") ->
L252.MAC_An_TC_SSP1
L252.MAC_prc_TC[["SSP1"]] %>%
add_legacy_name("L252.MAC_prc_TC_SSP1") ->
L252.MAC_prc_TC_SSP1
L252.MAC_res_TC[["SSP1"]] %>%
add_legacy_name("L252.MAC_res_TC_SSP1") ->
L252.MAC_res_TC_SSP1
L252.MAC_Ag_TC[["SSP2"]] %>%
add_legacy_name("L252.MAC_Ag_TC_SSP2") ->
L252.MAC_Ag_TC_SSP2
L252.MAC_An_TC[["SSP2"]] %>%
add_legacy_name("L252.MAC_An_TC_SSP2") ->
L252.MAC_An_TC_SSP2
L252.MAC_prc_TC[["SSP2"]] %>%
add_legacy_name("L252.MAC_prc_TC_SSP2") ->
L252.MAC_prc_TC_SSP2
L252.MAC_res_TC[["SSP2"]] %>%
add_legacy_name("L252.MAC_res_TC_SSP2") ->
L252.MAC_res_TC_SSP2
L252.MAC_Ag_TC[["SSP5"]] %>%
add_legacy_name("L252.MAC_Ag_TC_SSP5") ->
L252.MAC_Ag_TC_SSP5
L252.MAC_An_TC[["SSP5"]] %>%
add_legacy_name("L252.MAC_An_TC_SSP5") ->
L252.MAC_An_TC_SSP5
L252.MAC_prc_TC[["SSP5"]] %>%
add_legacy_name("L252.MAC_prc_TC_SSP5") ->
L252.MAC_prc_TC_SSP5
L252.MAC_res_TC[["SSP5"]] %>%
add_legacy_name("L252.MAC_res_TC_SSP5") ->
L252.MAC_res_TC_SSP5
return_data(L252.ResMAC_fos, L252.AgMAC, L252.MAC_an, L252.MAC_prc, L252.MAC_higwp, L252.MAC_Ag_TC_SSP1,
L252.MAC_An_TC_SSP1, L252.MAC_prc_TC_SSP1, L252.MAC_res_TC_SSP1, L252.MAC_Ag_TC_SSP2,
L252.MAC_An_TC_SSP2, L252.MAC_prc_TC_SSP2, L252.MAC_res_TC_SSP2, L252.MAC_Ag_TC_SSP5,
L252.MAC_An_TC_SSP5, L252.MAC_prc_TC_SSP5, L252.MAC_res_TC_SSP5)
} else {
stop("Unknown command")
}
}
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