R/zchunk_LA1321.cement.R
In JGCRI/gcamdata: Build the GCAM Input Datasets
Defines functions
module_energy_LA1321.cement
Documented in
module_energy_LA1321.cement
# Copyright 2019 Battelle Memorial Institute; see the LICENSE file.
#' module_energy_LA1321.cement
#'
#' Sets up input, output, and IO coefficients for cement and subtracts input energy from industry energy use
#'
#' @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{L1321.out_Mt_R_cement_Yh}, \code{L1321.IO_GJkg_R_cement_F_Yh}, \code{L1321.in_EJ_R_cement_F_Y}, \code{L1321.in_EJ_R_indenergy_F_Yh}. The corresponding file in the
#' original data system was \code{LA1321.cement.R} (energy level1).
#' @details This chunk generates input, output, and IO coefficients for the cement sector. It begins by downscaling Worrell regional data from 1994
#' to set up process emissions factors that are multiplied by country emissions from CDIAC to determine production. Limestone consumption is calculated from the same downscaling.
#' IEA fuelshares and heat and electricity are used to determine energy use by fuel. Energy inputs are then subtracted from industrial energy use and any resulting negative values
#' are dealt with by moving their accounting to the cement sector.
#' @importFrom assertthat assert_that
#' @importFrom dplyr arrange bind_rows filter group_by left_join mutate select semi_join summarise summarise_all
#' @importFrom tidyr complete gather nesting
#' @author CWR Nov 2017
module_energy_LA1321.cement <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "common/iso_GCAM_regID",
FILE = "emissions/A_PrimaryFuelCCoef",
FILE = "energy/mappings/cement_regions",
FILE = "energy/Worrell_1994_cement",
FILE = "energy/IEA_cement_elec_kwht",
FILE = "energy/IEA_cement_TPE_GJt",
FILE = "energy/IEA_cement_fuelshares",
"L100.CDIAC_CO2_ctry_hist",
"L102.CO2_Mt_R_F_Yh",
"L123.in_EJ_R_elec_F_Yh",
"L123.out_EJ_R_elec_F_Yh",
"L132.in_EJ_R_indenergy_F_Yh",
"L1011.en_bal_EJ_R_Si_Fi_Yh"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L1321.out_Mt_R_cement_Yh",
"L1321.IO_GJkg_R_cement_F_Yh",
"L1321.in_EJ_R_cement_F_Y",
"L1321.in_EJ_R_indenergy_F_Yh"))
} else if(command == driver.MAKE) {
# Silence global variable package check
. <- Biomass <- Biomass_EJ <- Coal <- Coal_EJ <- Country <- GCAM_region_ID <- Gas <- Gas_EJ <-
IEA_fuelshare_region <- IEA_intensity_region <- IOelec <- Oil <- Oil_EJ <- TPE_GJkg <-
Worrell_region <- cement_prod_Mt <- country_name <- elec_EJ <- elec_GJkg <-
emiss_ktC <- fuel <- heat_EJ <- heat_GJkg <- in.value <- ind.value <- iso <-
old.year <- out.value <- process_emissions_MtC <- process_emissions_ktC <-
prod_Mt <- prod_emiss_ratio <- reg_process_emissions <- region_GCAM3 <- sector <-
share <- value <- cement <- year <- value.y <- value.x <- neg <- NULL
all_data <- list(...)[[1]]
# Load required inputs
iso_GCAM_regID <- get_data(all_data, "common/iso_GCAM_regID")
A_PrimaryFuelCCoef <- get_data(all_data, "emissions/A_PrimaryFuelCCoef")
cement_regions <- get_data(all_data, "energy/mappings/cement_regions")
Worrell_1994_cement <- get_data(all_data, "energy/Worrell_1994_cement")
IEA_cement_elec_kwht <- get_data(all_data, "energy/IEA_cement_elec_kwht")
IEA_cement_TPE_GJt <- get_data(all_data, "energy/IEA_cement_TPE_GJt")
IEA_cement_fuelshares <- get_data(all_data, "energy/IEA_cement_fuelshares")
L100.CDIAC_CO2_ctry_hist <- get_data(all_data, "L100.CDIAC_CO2_ctry_hist")
L102.CO2_Mt_R_F_Yh <- get_data(all_data, "L102.CO2_Mt_R_F_Yh")
L123.in_EJ_R_elec_F_Yh <- get_data(all_data, "L123.in_EJ_R_elec_F_Yh")
L123.out_EJ_R_elec_F_Yh <- get_data(all_data, "L123.out_EJ_R_elec_F_Yh")
L132.in_EJ_R_indenergy_F_Yh <- get_data(all_data, "L132.in_EJ_R_indenergy_F_Yh", strip_attributes = TRUE)
L1011.en_bal_EJ_R_Si_Fi_Yh <- get_data(all_data, "L1011.en_bal_EJ_R_Si_Fi_Yh")
# ===================================================
# 2. Perform computations
# Set constants used for this chunk
# ---------------------------------
# Extract carbon coefficient for limestone from assumption file
LIMESTONE_CCOEF <- A_PrimaryFuelCCoef$PrimaryFuelCO2Coef[A_PrimaryFuelCCoef$PrimaryFuelCO2Coef.name == "limestone"]
# Determine historical years not available in data set (additional years) to copy values from final available year (final_CO2_year)
ADDITIONAL_YEARS <- HISTORICAL_YEARS[!HISTORICAL_YEARS %in% energy.CDIAC_CO2_HISTORICAL_YEARS]
FINAL_CO2_YEAR <- dplyr::last(energy.CDIAC_CO2_HISTORICAL_YEARS)
# =======================================================================================
# Derivation of cement production and limestone consumption by region and historical year
# =======================================================================================
# Downscale Worrell's process CO2 emissions and cement production in 1994 to country level using CDIAC emissions inventory
# ------------------------------------------------------------------------------------------------------------------------
# Prepare CDIAC country-level emissions data to match to Worrell regions and year (Worrell data only available for 1994)
cement_regions %>%
left_join(L100.CDIAC_CO2_ctry_hist, by = "iso") %>%
filter(year == 1994) %>%
select(iso, Worrell_region, year, cement) %>%
na.omit() %>%
rename(process_emissions_ktC = cement) ->
L1321.Cement_Worrell_ctry
# Aggregate CDIAC country-level process emissions to regional level
L1321.Cement_Worrell_ctry %>%
group_by(Worrell_region) %>%
summarise(reg_process_emissions = sum(process_emissions_ktC)) %>%
ungroup() ->
L1321.Cement_Worrell_reg
# Read in country and regional emissions, select Worrell's cement production and process emissions to multiply by country level shares
# Compute country-level shares of regional emissions and multiply the region-level process emissions
# and cement production by these shares to get the country-level estimates
# (1994 cement data source: Table 4 in Worrell et al. 2001 Ann Rev Energ Env 26: 303-329)
L1321.Cement_Worrell_ctry %>%
left_join_error_no_match(L1321.Cement_Worrell_reg, by = "Worrell_region") %>%
mutate(share = process_emissions_ktC / reg_process_emissions) %>%
left_join_error_no_match(select(Worrell_1994_cement, Country, cement_prod_Mt, process_emissions_MtC), by = c("Worrell_region" = "Country")) %>%
mutate(cement_prod_Mt = cement_prod_Mt * share, process_emissions_MtC = process_emissions_MtC * share) %>%
# Now match and aggregate Worrell's process CO2 emissions and cement production in 1994 by GCAM region and compute the emissions ratio
# which is assumed to be constant for all other years
left_join_error_no_match(iso_GCAM_regID, by = "iso") %>%
select(-country_name, -region_GCAM3) %>%
group_by(GCAM_region_ID) %>%
summarise(cement_prod_Mt = sum(cement_prod_Mt), process_emissions_MtC = sum(process_emissions_MtC)) %>%
mutate(prod_emiss_ratio = cement_prod_Mt / process_emissions_MtC) %>%
ungroup() ->
L1321.Cement_Worrell_R
# Calculate cement production over time using ratio of production to emissions for L1321.out_Mt_R_cement_Yh
# assuming that this ratio is constant over time for each region
# ---------------------------------------------------------------------------------------------------------
# If the CO2 emissions inventories do not go to the latest historical time period, copy the last available year
L102.CO2_Mt_R_F_Yh %>%
filter(fuel == "limestone") ->
L1321.CO2_Mt_R_F_Yh_base
L1321.CO2_Mt_R_F_Yh_base %>%
filter(year == FINAL_CO2_YEAR) %>%
rename(old.year = year) %>%
repeat_add_columns(tibble(year = ADDITIONAL_YEARS)) %>%
select(-old.year) %>%
bind_rows(L1321.CO2_Mt_R_F_Yh_base) ->
L1321.CO2_Mt_R_F_Yh
# Calculate cement production over time by multiplying production emissions ratio by emissions
L1321.Cement_Worrell_R %>%
mutate(sector = "cement") %>%
left_join(L1321.CO2_Mt_R_F_Yh, by = "GCAM_region_ID") %>%
mutate(value = prod_emiss_ratio * value) %>%
select(GCAM_region_ID, sector, year, value) ->
L1321.out_Mt_R_cement_Yh
# Calculate limestone consumption by region and fuel
# --------------------------------------------------
# Use the assumed limestone fuel carbon content (same in all regions) to calculate the limestone consumption
# and limestone to cement IO coefficients in each region
L1321.CO2_Mt_R_F_Yh %>%
mutate(sector = "cement", in.value = value / LIMESTONE_CCOEF) %>%
select(-value) ->
L1321.in_Cement_Mt_R_limestone_Yh
# Calculate input-output coefficients
L1321.in_Cement_Mt_R_limestone_Yh %>%
left_join_error_no_match(L1321.out_Mt_R_cement_Yh, by = c("GCAM_region_ID", "sector", "year")) %>%
mutate(value = in.value / value) %>%
select(-in.value) ->
L1321.IO_Cement_R_limestone_Yh
# Derive energy inputs to cement production by region and historical year -
# Calculate average electric and TPE intensity for each GCAM region (use process emissions as a weighting factor)
# ---------------------------------------------------------------------------------------------------------------
# Interpolate available data on electricity intensity to all historical years
IEA_cement_elec_kwht %>%
gather_years() %>%
complete(nesting(Country), year = c(year, HISTORICAL_YEARS)) %>%
arrange(Country, year) %>%
group_by(Country) %>%
mutate(value = approx_fun(year, value, rule = 1) * CONV_KWH_GJ / CONV_T_KG) %>%
ungroup() ->
L1321.IEA_cement_elec_intensity
# Interpolate available data on total primary energy intensity to all historical years by region
IEA_cement_TPE_GJt %>%
gather_years() %>%
complete(nesting(Country), year = c(year, HISTORICAL_YEARS)) %>%
arrange(Country, year) %>%
group_by(Country) %>%
mutate(value = approx_fun(year, value, rule = 1) / CONV_T_KG) %>%
ungroup() ->
L1321.IEA_cement_TPE_intensity
# Calculate the average electricity generation efficiencies by region to be added to L1321.Cement_ALL_ctry_Yh
# -----------------------------------------------------------------------------------------------------------
# Calculate average regional input energy for electricity across all fuels
L123.in_EJ_R_elec_F_Yh %>%
group_by(GCAM_region_ID, year) %>%
summarise(in.value = sum(value)) %>%
ungroup() ->
L1321.in_EJ_R_elec_Yh
# Calculate average regional output energy for electricity across all fuels matching input, join to input energy, and calculate the IO coefficient
L123.out_EJ_R_elec_F_Yh %>%
# Filter out electricity fuel outputs with no matching energy inputs - by default removes non-fossil, non-bio energy
semi_join(L123.in_EJ_R_elec_F_Yh, by = "fuel") %>%
group_by(GCAM_region_ID, year) %>%
summarise(out.value = sum(value)) %>%
ungroup() %>%
left_join_error_no_match(L1321.in_EJ_R_elec_Yh, by = c("GCAM_region_ID", "year")) %>%
mutate(value = in.value / out.value) %>%
# NOTE: below replicates the old data system by interpolating IO value to all historical years, but this step can be skipped with current inputs (no new values generated)
complete(nesting(GCAM_region_ID), year = c(year, HISTORICAL_YEARS)) %>%
arrange(GCAM_region_ID, year) %>%
group_by(GCAM_region_ID) %>%
mutate(value = approx_fun(year, value)) %>%
filter(year %in% HISTORICAL_YEARS) %>%
select(GCAM_region_ID, year, value) %>%
ungroup() ->
L1321.IO_R_elec_Yh
# Set cap on IO coefficients for regions and years exceeding maximum value
# Generation technologies with extremely low efficiency indicate that a significant portion of the power sector
# in the given region/year is combined heat and power systems.
# Since we don't account for that here, we use this constant to set a reasonable limit on how much of the
# total primary energy in IEA's estimate is electricity-related. (see issue #833)
L1321.IO_R_elec_Yh$value[L1321.IO_R_elec_Yh$value > energy.MAX_IOELEC] <- energy.MAX_IOELEC
# Build data frame including all above calculated values for cement production - intensity, fuel shares, energy for heat and electricity
# --------------------------------------------------------------------------------------------------------------------------------------
# Start with CO2 emissions from cement
L100.CDIAC_CO2_ctry_hist %>%
filter(year %in% HISTORICAL_YEARS) %>%
select(iso, year, cement) %>%
rename(emiss_ktC = cement) %>%
# Match in region IDs by iso code
left_join_error_no_match(iso_GCAM_regID, by = "iso") %>%
# Replace process emissions with actual cement production
left_join_error_no_match(L1321.Cement_Worrell_R, by = "GCAM_region_ID") %>%
mutate(prod_Mt = emiss_ktC * prod_emiss_ratio * CONV_KT_MT) %>%
# add region names for intensity and fuelshare
left_join_error_no_match(cement_regions, by = "iso") %>%
# add above calculated energy intensities
left_join_error_no_match(L1321.IEA_cement_elec_intensity, by = c("IEA_intensity_region" = "Country", "year")) %>%
left_join_error_no_match(L1321.IEA_cement_TPE_intensity, by = c("IEA_intensity_region" = "Country", "year")) %>%
rename(elec_GJkg = value.x, TPE_GJkg = value.y) %>%
# remove unneeded columns from various left_joins
select(iso, year, emiss_ktC, GCAM_region_ID, prod_Mt, IEA_intensity_region, elec_GJkg, TPE_GJkg, IEA_fuelshare_region) %>%
# Match in IO coefficients by region and year
left_join_error_no_match(L1321.IO_R_elec_Yh, by = c("GCAM_region_ID", "year")) %>%
rename(IOelec = value) %>%
# Match in fuelshares of (by default) coal, gas, oil, and biomass
left_join_error_no_match(IEA_cement_fuelshares, by = c("IEA_fuelshare_region" = "Country")) %>%
# Calculate heat intensity of energy and electricity, as well as total heat for each
mutate(heat_GJkg = TPE_GJkg - elec_GJkg * IOelec, heat_EJ = heat_GJkg * prod_Mt, elec_EJ = elec_GJkg * prod_Mt) %>%
# Calculate total heat by fuel using fuelshares
mutate(Coal_EJ = Coal * heat_EJ, Oil_EJ = Oil * heat_EJ, Gas_EJ = Gas * heat_EJ, Biomass_EJ = Biomass * heat_EJ) ->
L1321.Cement_ALL_ctry_Yh
# ===============================================================================================================================================
# Now that country level data has been built and downscaled into L1321.Cement_ALL_ctry_Yh, calculate needed GCAM input energy and IO coefficients
# ===============================================================================================================================================
# Calculate aggregated regional data on IO coefficients for cement production by fuel for heat and electricity
# ------------------------------------------------------------------------------------------------------------
# Aggregate country data to the regional level
L1321.Cement_ALL_ctry_Yh %>%
select(GCAM_region_ID, year, prod_Mt, heat_EJ, elec_EJ, Coal_EJ, Oil_EJ, Gas_EJ, Biomass_EJ) %>%
group_by(GCAM_region_ID, year) %>%
summarise_all(sum) %>%
ungroup() %>%
mutate(heat_GJkg = heat_EJ / prod_Mt, elec_GJkg = elec_EJ / prod_Mt) ->
L1321.Cement_ALL_R_Yh
# Separate regional electricity and heat coefficients, first removing all unneeded columns
L1321.Cement_ALL_R_Yh %>%
select(GCAM_region_ID, year, elec_GJkg, heat_GJkg) ->
L1321.Cement_ALL_R_Yh_base
# Copy final year value to any historical period values not contained in the data set
L1321.Cement_ALL_R_Yh_base %>%
filter(year == FINAL_CO2_YEAR) %>%
rename(old.year = year) %>%
repeat_add_columns(tibble(year = ADDITIONAL_YEARS)) %>%
select(-old.year) %>%
bind_rows(L1321.Cement_ALL_R_Yh_base) ->
L1321.IO_Cement_GJkg_R_ALL_Yh
# Assign sector and fuel names for heat and electricity data
L1321.IO_Cement_GJkg_R_ALL_Yh %>%
select(GCAM_region_ID, year, elec_GJkg) %>%
mutate(sector = "cement", fuel = "electricity") %>%
rename(value = elec_GJkg) ->
L1321.IO_Cement_GJkg_R_elec_Yh
L1321.IO_Cement_GJkg_R_ALL_Yh %>%
select(GCAM_region_ID, year, heat_GJkg) %>%
mutate(sector = "cement", fuel = "heat") %>%
rename(value = heat_GJkg) ->
L1321.IO_Cement_GJkg_R_heat_Yh
# Compile electricity, heat, and limestone IO coefficients in L1321.IO_GJkg_R_cement_F_Yh
L1321.IO_Cement_GJkg_R_elec_Yh %>%
bind_rows(L1321.IO_Cement_GJkg_R_heat_Yh) %>%
bind_rows(L1321.IO_Cement_R_limestone_Yh) ->
L1321.IO_GJkg_R_cement_F_Yh
# Calculate input energy for cement production by region, fuel, and year for L1321.in_EJ_R_cement_F_Y
# ---------------------------------------------------------------------------------------------------
# Compile regional historical data on energy use for cement by fuel
L1321.Cement_ALL_R_Yh %>%
select(GCAM_region_ID, year, elec_EJ, Coal_EJ, Oil_EJ, Gas_EJ, Biomass_EJ) %>%
gather(fuel, value, elec_EJ, Coal_EJ, Oil_EJ, Gas_EJ, Biomass_EJ) %>%
# Match fuel names to GCAM, removing _EJ and replacing oil and elec
mutate(sector = "cement", fuel = tolower(gsub("_EJ", "", fuel)), fuel = gsub("elec", "electricity", fuel),
fuel = gsub("oil", "refined liquids", fuel)) ->
L1321.in_EJ_R_cement_F_Y_base
# Copy final year value to any historical period values not contained in the data set
L1321.in_EJ_R_cement_F_Y_base %>%
filter(year == FINAL_CO2_YEAR) %>%
rename(old.year = year) %>%
repeat_add_columns(tibble(year = ADDITIONAL_YEARS)) %>%
select(-old.year) %>%
bind_rows(L1321.in_EJ_R_cement_F_Y_base) ->
L1321.in_EJ_R_cement_F_Y
# ---------------------------------------------------------------------------------------------------------------------
## 7/30/21: Modification for detailed industry
## Manually adjust coal use in South Korea, so there is enough left for iron and steel sector
## Upper bound is IEA coal consumption in non-metallic minerals
L1321.in_EJ_R_cement_F_Y %>%
left_join(L1011.en_bal_EJ_R_Si_Fi_Yh %>% filter(sector == "cement") %>% select(-sector),
by = c("GCAM_region_ID", "year", "fuel")) %>%
mutate(value = if_else(fuel == "coal" & GCAM_region_ID == 28 & value.x > value.y, value.y, value.x),
neg = if_else(fuel == "coal" & GCAM_region_ID == 28 & value.x > value.y, value.y - value.x, 0)) ->
L1321.in_EJ_R_cement_F_Y_adj
# extra coal use will be moved to biomass
L1321.in_EJ_R_cement_F_Y_adj %>%
filter(neg < 0) %>%
select(GCAM_region_ID, sector, fuel, year, value=neg) ->
korea_coal_neg_en
# Replace cement energy data frame with adjusted version
L1321.in_EJ_R_cement_F_Y_adj %>%
select(-value.x, -value.y, -neg) ->
L1321.in_EJ_R_cement_F_Y
# ---------------------------------------------------------------------------------------------------------------------
# Calculate remaining industrial energy use (input), subtracting cement production energy from energy balances
# ------------------------------------------------------------------------------------------------------------
# Subtract input energy to cement sector from industrial energy
L132.in_EJ_R_indenergy_F_Yh %>%
rename(ind.value = value) %>%
left_join(select(L1321.in_EJ_R_cement_F_Y, -sector), by = c("GCAM_region_ID", "fuel", "year")) %>%
mutate(value = ind.value - value) %>%
select(-ind.value) ->
L1321.in_EJ_R_indenergy_F_Yh_NAs
# Replace NA values in sectors with no match in cement with original values from L132.in_EJ_R_indenergy_F_Yh
L1321.in_EJ_R_indenergy_F_Yh_NAs %>%
filter(is.na(value)) %>%
select(-value) %>%
left_join(select(L132.in_EJ_R_indenergy_F_Yh, -sector), by = c("GCAM_region_ID", "fuel", "year")) %>%
bind_rows(filter(L1321.in_EJ_R_indenergy_F_Yh_NAs, !is.na(value))) ->
L1321.in_EJ_R_indenergy_F_Yh_negbio
# This dataset may now have negative values. If it's biomass, these can be changed without breaking energy balances, so we set the rest-of-industry to an exogenous minimum value
L1321.in_EJ_R_indenergy_F_Yh_negbio %>%
filter(value < 0, fuel == "biomass") %>%
mutate(value = energy.MIN_IN_EJ_IND) %>%
bind_rows(filter(L1321.in_EJ_R_indenergy_F_Yh_negbio, value >= 0 | fuel != "biomass")) ->
L1321.in_EJ_R_indenergy_F_Yh
# Negative values in non-bio industrial energy use are problematic and have to be zeroed out
# In the below method, these negative values are zeroed in industrial energy and subtracted from positive values in fossil fuel use for cement
# and then offset with a positive adjustment to cement biomass fuel use. This preserves the total energy balances while removing negative values
# ----------------------------------------------------------------------------------------------------------------------------------------------
# Currently offending negative values are mostly developing regions with (probably incorrectly) low bio shares
# Check if any of these values exist and then conditionally perform the adjustments
# Subset regions and years where any fuels are negative
# and aggregate those negative values (in case one region/year has multiple fuels) as a positive adjustment to biomass
L1321.in_EJ_R_indenergy_F_Yh %>%
filter(value < -0) %>%
mutate(sector = "cement") ->
L1321.cement_adj_neg
L1321.cement_adj_neg %>%
# STEEL DECARONBONIZATION MODIFICATION: add korea excess coal to biomass
bind_rows(korea_coal_neg_en) %>%
mutate(fuel = "biomass") %>%
group_by(GCAM_region_ID, sector, fuel, year) %>%
summarise(value = sum(value) * -1) %>%
ungroup() ->
L1321.cement_adj_pos
# Reset the negative values to 0 in the industrial energy table
L1321.in_EJ_R_indenergy_F_Yh[, "value"][L1321.in_EJ_R_indenergy_F_Yh[, "value"] < 0] <- 0
# Add in the adjustments to the table of cement energy consumption.
L1321.in_EJ_R_cement_F_Y %>%
bind_rows(L1321.cement_adj_neg) %>%
bind_rows(L1321.cement_adj_pos) %>%
group_by(GCAM_region_ID, sector, fuel, year) %>%
summarise(value = sum(value)) %>%
ungroup() ->
L1321.in_EJ_R_cement_F_Y
# ===================================================
# Produce outputs
L1321.out_Mt_R_cement_Yh %>%
add_title("Historical cement outputs by region, fuel, and year") %>%
add_units("Mt cement") %>%
add_comments("Outputs are calculated by by downscaling Worrell regions using CDIAC country emissions and then aggregating to GCAM regions") %>%
add_comments("Final outputs are a product of regional emissions times the production emissions ratio") %>%
add_legacy_name("L1321.out_Mt_R_cement_Yh") %>%
add_precursors("emissions/A_PrimaryFuelCCoef", "energy/Worrell_1994_cement", "energy/mappings/cement_regions", "L100.CDIAC_CO2_ctry_hist", "L102.CO2_Mt_R_F_Yh") ->
L1321.out_Mt_R_cement_Yh
L1321.IO_GJkg_R_cement_F_Yh %>%
add_title("Input-output coefficients for cement production") %>%
add_units("GJ/kg cement") %>%
add_comments("IO coefficients for heat energy, electricity, and limestone consumption are calculated from weighted IEA fuel shares, CDIAC emissions data, and Worrell cement production") %>%
add_legacy_name("L1321.IO_GJkg_R_cement_F_Yh") %>%
add_precursors("emissions/A_PrimaryFuelCCoef", "energy/Worrell_1994_cement", "energy/mappings/cement_regions", "L100.CDIAC_CO2_ctry_hist", "L102.CO2_Mt_R_F_Yh", "L123.in_EJ_R_elec_F_Yh", "L123.out_EJ_R_elec_F_Yh", "energy/IEA_cement_elec_kwht",
"energy/IEA_cement_TPE_GJt", "energy/IEA_cement_fuelshares", "common/iso_GCAM_regID") ->
L1321.IO_GJkg_R_cement_F_Yh
L1321.in_EJ_R_cement_F_Y %>%
add_title("Historical input energy use for the cement sector") %>%
add_units("Exajoules") %>%
add_comments("Input energy by fuel calculated from weighted fuel shares using energy intensity values for heat and electricity") %>%
add_comments("Multiplied by raw fuel shares, all from IEA") %>%
add_legacy_name("L1321.in_EJ_R_cement_F_Y") %>%
add_precursors("L100.CDIAC_CO2_ctry_hist", "L102.CO2_Mt_R_F_Yh", "L123.in_EJ_R_elec_F_Yh", "L123.out_EJ_R_elec_F_Yh", "energy/IEA_cement_elec_kwht",
"energy/IEA_cement_TPE_GJt", "energy/IEA_cement_fuelshares", "common/iso_GCAM_regID", "L1011.en_bal_EJ_R_Si_Fi_Yh") ->
L1321.in_EJ_R_cement_F_Y
L1321.in_EJ_R_indenergy_F_Yh %>%
add_title("Adjusted historical input energy balances for industrial energy use") %>%
add_units("Exajoules") %>%
add_comments("Subtracted cement energy use from industrial energy use values in L132.in_EJ_R_indenergy_F_Yh") %>%
add_comments("To determine adjusted input energy for industrial energy use") %>%
add_legacy_name("L1321.in_EJ_R_indenergy_F_Yh") %>%
add_precursors("L100.CDIAC_CO2_ctry_hist", "L102.CO2_Mt_R_F_Yh", "L123.in_EJ_R_elec_F_Yh", "L123.out_EJ_R_elec_F_Yh", "energy/IEA_cement_elec_kwht",
"energy/IEA_cement_TPE_GJt", "energy/IEA_cement_fuelshares", "L132.in_EJ_R_indenergy_F_Yh", "common/iso_GCAM_regID") ->
L1321.in_EJ_R_indenergy_F_Yh
return_data(L1321.out_Mt_R_cement_Yh, L1321.IO_GJkg_R_cement_F_Yh, L1321.in_EJ_R_cement_F_Y, L1321.in_EJ_R_indenergy_F_Yh)
} else {
stop("Unknown command")
}
}
JGCRI/gcamdata documentation built on March 21, 2023, 2:19 a.m.
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Defines functions module_energy_LA1321.cement
Documented in module_energy_LA1321.cement
# Copyright 2019 Battelle Memorial Institute; see the LICENSE file.
#' module_energy_LA1321.cement
#'
#' Sets up input, output, and IO coefficients for cement and subtracts input energy from industry energy use
#'
#' @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{L1321.out_Mt_R_cement_Yh}, \code{L1321.IO_GJkg_R_cement_F_Yh}, \code{L1321.in_EJ_R_cement_F_Y}, \code{L1321.in_EJ_R_indenergy_F_Yh}. The corresponding file in the
#' original data system was \code{LA1321.cement.R} (energy level1).
#' @details This chunk generates input, output, and IO coefficients for the cement sector. It begins by downscaling Worrell regional data from 1994
#' to set up process emissions factors that are multiplied by country emissions from CDIAC to determine production. Limestone consumption is calculated from the same downscaling.
#' IEA fuelshares and heat and electricity are used to determine energy use by fuel. Energy inputs are then subtracted from industrial energy use and any resulting negative values
#' are dealt with by moving their accounting to the cement sector.
#' @importFrom assertthat assert_that
#' @importFrom dplyr arrange bind_rows filter group_by left_join mutate select semi_join summarise summarise_all
#' @importFrom tidyr complete gather nesting
#' @author CWR Nov 2017
module_energy_LA1321.cement <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "common/iso_GCAM_regID",
FILE = "emissions/A_PrimaryFuelCCoef",
FILE = "energy/mappings/cement_regions",
FILE = "energy/Worrell_1994_cement",
FILE = "energy/IEA_cement_elec_kwht",
FILE = "energy/IEA_cement_TPE_GJt",
FILE = "energy/IEA_cement_fuelshares",
"L100.CDIAC_CO2_ctry_hist",
"L102.CO2_Mt_R_F_Yh",
"L123.in_EJ_R_elec_F_Yh",
"L123.out_EJ_R_elec_F_Yh",
"L132.in_EJ_R_indenergy_F_Yh",
"L1011.en_bal_EJ_R_Si_Fi_Yh"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L1321.out_Mt_R_cement_Yh",
"L1321.IO_GJkg_R_cement_F_Yh",
"L1321.in_EJ_R_cement_F_Y",
"L1321.in_EJ_R_indenergy_F_Yh"))
} else if(command == driver.MAKE) {
# Silence global variable package check
. <- Biomass <- Biomass_EJ <- Coal <- Coal_EJ <- Country <- GCAM_region_ID <- Gas <- Gas_EJ <-
IEA_fuelshare_region <- IEA_intensity_region <- IOelec <- Oil <- Oil_EJ <- TPE_GJkg <-
Worrell_region <- cement_prod_Mt <- country_name <- elec_EJ <- elec_GJkg <-
emiss_ktC <- fuel <- heat_EJ <- heat_GJkg <- in.value <- ind.value <- iso <-
old.year <- out.value <- process_emissions_MtC <- process_emissions_ktC <-
prod_Mt <- prod_emiss_ratio <- reg_process_emissions <- region_GCAM3 <- sector <-
share <- value <- cement <- year <- value.y <- value.x <- neg <- NULL
all_data <- list(...)[[1]]
# Load required inputs
iso_GCAM_regID <- get_data(all_data, "common/iso_GCAM_regID")
A_PrimaryFuelCCoef <- get_data(all_data, "emissions/A_PrimaryFuelCCoef")
cement_regions <- get_data(all_data, "energy/mappings/cement_regions")
Worrell_1994_cement <- get_data(all_data, "energy/Worrell_1994_cement")
IEA_cement_elec_kwht <- get_data(all_data, "energy/IEA_cement_elec_kwht")
IEA_cement_TPE_GJt <- get_data(all_data, "energy/IEA_cement_TPE_GJt")
IEA_cement_fuelshares <- get_data(all_data, "energy/IEA_cement_fuelshares")
L100.CDIAC_CO2_ctry_hist <- get_data(all_data, "L100.CDIAC_CO2_ctry_hist")
L102.CO2_Mt_R_F_Yh <- get_data(all_data, "L102.CO2_Mt_R_F_Yh")
L123.in_EJ_R_elec_F_Yh <- get_data(all_data, "L123.in_EJ_R_elec_F_Yh")
L123.out_EJ_R_elec_F_Yh <- get_data(all_data, "L123.out_EJ_R_elec_F_Yh")
L132.in_EJ_R_indenergy_F_Yh <- get_data(all_data, "L132.in_EJ_R_indenergy_F_Yh", strip_attributes = TRUE)
L1011.en_bal_EJ_R_Si_Fi_Yh <- get_data(all_data, "L1011.en_bal_EJ_R_Si_Fi_Yh")
# ===================================================
# 2. Perform computations
# Set constants used for this chunk
# ---------------------------------
# Extract carbon coefficient for limestone from assumption file
LIMESTONE_CCOEF <- A_PrimaryFuelCCoef$PrimaryFuelCO2Coef[A_PrimaryFuelCCoef$PrimaryFuelCO2Coef.name == "limestone"]
# Determine historical years not available in data set (additional years) to copy values from final available year (final_CO2_year)
ADDITIONAL_YEARS <- HISTORICAL_YEARS[!HISTORICAL_YEARS %in% energy.CDIAC_CO2_HISTORICAL_YEARS]
FINAL_CO2_YEAR <- dplyr::last(energy.CDIAC_CO2_HISTORICAL_YEARS)
# =======================================================================================
# Derivation of cement production and limestone consumption by region and historical year
# =======================================================================================
# Downscale Worrell's process CO2 emissions and cement production in 1994 to country level using CDIAC emissions inventory
# ------------------------------------------------------------------------------------------------------------------------
# Prepare CDIAC country-level emissions data to match to Worrell regions and year (Worrell data only available for 1994)
cement_regions %>%
left_join(L100.CDIAC_CO2_ctry_hist, by = "iso") %>%
filter(year == 1994) %>%
select(iso, Worrell_region, year, cement) %>%
na.omit() %>%
rename(process_emissions_ktC = cement) ->
L1321.Cement_Worrell_ctry
# Aggregate CDIAC country-level process emissions to regional level
L1321.Cement_Worrell_ctry %>%
group_by(Worrell_region) %>%
summarise(reg_process_emissions = sum(process_emissions_ktC)) %>%
ungroup() ->
L1321.Cement_Worrell_reg
# Read in country and regional emissions, select Worrell's cement production and process emissions to multiply by country level shares
# Compute country-level shares of regional emissions and multiply the region-level process emissions
# and cement production by these shares to get the country-level estimates
# (1994 cement data source: Table 4 in Worrell et al. 2001 Ann Rev Energ Env 26: 303-329)
L1321.Cement_Worrell_ctry %>%
left_join_error_no_match(L1321.Cement_Worrell_reg, by = "Worrell_region") %>%
mutate(share = process_emissions_ktC / reg_process_emissions) %>%
left_join_error_no_match(select(Worrell_1994_cement, Country, cement_prod_Mt, process_emissions_MtC), by = c("Worrell_region" = "Country")) %>%
mutate(cement_prod_Mt = cement_prod_Mt * share, process_emissions_MtC = process_emissions_MtC * share) %>%
# Now match and aggregate Worrell's process CO2 emissions and cement production in 1994 by GCAM region and compute the emissions ratio
# which is assumed to be constant for all other years
left_join_error_no_match(iso_GCAM_regID, by = "iso") %>%
select(-country_name, -region_GCAM3) %>%
group_by(GCAM_region_ID) %>%
summarise(cement_prod_Mt = sum(cement_prod_Mt), process_emissions_MtC = sum(process_emissions_MtC)) %>%
mutate(prod_emiss_ratio = cement_prod_Mt / process_emissions_MtC) %>%
ungroup() ->
L1321.Cement_Worrell_R
# Calculate cement production over time using ratio of production to emissions for L1321.out_Mt_R_cement_Yh
# assuming that this ratio is constant over time for each region
# ---------------------------------------------------------------------------------------------------------
# If the CO2 emissions inventories do not go to the latest historical time period, copy the last available year
L102.CO2_Mt_R_F_Yh %>%
filter(fuel == "limestone") ->
L1321.CO2_Mt_R_F_Yh_base
L1321.CO2_Mt_R_F_Yh_base %>%
filter(year == FINAL_CO2_YEAR) %>%
rename(old.year = year) %>%
repeat_add_columns(tibble(year = ADDITIONAL_YEARS)) %>%
select(-old.year) %>%
bind_rows(L1321.CO2_Mt_R_F_Yh_base) ->
L1321.CO2_Mt_R_F_Yh
# Calculate cement production over time by multiplying production emissions ratio by emissions
L1321.Cement_Worrell_R %>%
mutate(sector = "cement") %>%
left_join(L1321.CO2_Mt_R_F_Yh, by = "GCAM_region_ID") %>%
mutate(value = prod_emiss_ratio * value) %>%
select(GCAM_region_ID, sector, year, value) ->
L1321.out_Mt_R_cement_Yh
# Calculate limestone consumption by region and fuel
# --------------------------------------------------
# Use the assumed limestone fuel carbon content (same in all regions) to calculate the limestone consumption
# and limestone to cement IO coefficients in each region
L1321.CO2_Mt_R_F_Yh %>%
mutate(sector = "cement", in.value = value / LIMESTONE_CCOEF) %>%
select(-value) ->
L1321.in_Cement_Mt_R_limestone_Yh
# Calculate input-output coefficients
L1321.in_Cement_Mt_R_limestone_Yh %>%
left_join_error_no_match(L1321.out_Mt_R_cement_Yh, by = c("GCAM_region_ID", "sector", "year")) %>%
mutate(value = in.value / value) %>%
select(-in.value) ->
L1321.IO_Cement_R_limestone_Yh
# Derive energy inputs to cement production by region and historical year -
# Calculate average electric and TPE intensity for each GCAM region (use process emissions as a weighting factor)
# ---------------------------------------------------------------------------------------------------------------
# Interpolate available data on electricity intensity to all historical years
IEA_cement_elec_kwht %>%
gather_years() %>%
complete(nesting(Country), year = c(year, HISTORICAL_YEARS)) %>%
arrange(Country, year) %>%
group_by(Country) %>%
mutate(value = approx_fun(year, value, rule = 1) * CONV_KWH_GJ / CONV_T_KG) %>%
ungroup() ->
L1321.IEA_cement_elec_intensity
# Interpolate available data on total primary energy intensity to all historical years by region
IEA_cement_TPE_GJt %>%
gather_years() %>%
complete(nesting(Country), year = c(year, HISTORICAL_YEARS)) %>%
arrange(Country, year) %>%
group_by(Country) %>%
mutate(value = approx_fun(year, value, rule = 1) / CONV_T_KG) %>%
ungroup() ->
L1321.IEA_cement_TPE_intensity
# Calculate the average electricity generation efficiencies by region to be added to L1321.Cement_ALL_ctry_Yh
# -----------------------------------------------------------------------------------------------------------
# Calculate average regional input energy for electricity across all fuels
L123.in_EJ_R_elec_F_Yh %>%
group_by(GCAM_region_ID, year) %>%
summarise(in.value = sum(value)) %>%
ungroup() ->
L1321.in_EJ_R_elec_Yh
# Calculate average regional output energy for electricity across all fuels matching input, join to input energy, and calculate the IO coefficient
L123.out_EJ_R_elec_F_Yh %>%
# Filter out electricity fuel outputs with no matching energy inputs - by default removes non-fossil, non-bio energy
semi_join(L123.in_EJ_R_elec_F_Yh, by = "fuel") %>%
group_by(GCAM_region_ID, year) %>%
summarise(out.value = sum(value)) %>%
ungroup() %>%
left_join_error_no_match(L1321.in_EJ_R_elec_Yh, by = c("GCAM_region_ID", "year")) %>%
mutate(value = in.value / out.value) %>%
# NOTE: below replicates the old data system by interpolating IO value to all historical years, but this step can be skipped with current inputs (no new values generated)
complete(nesting(GCAM_region_ID), year = c(year, HISTORICAL_YEARS)) %>%
arrange(GCAM_region_ID, year) %>%
group_by(GCAM_region_ID) %>%
mutate(value = approx_fun(year, value)) %>%
filter(year %in% HISTORICAL_YEARS) %>%
select(GCAM_region_ID, year, value) %>%
ungroup() ->
L1321.IO_R_elec_Yh
# Set cap on IO coefficients for regions and years exceeding maximum value
# Generation technologies with extremely low efficiency indicate that a significant portion of the power sector
# in the given region/year is combined heat and power systems.
# Since we don't account for that here, we use this constant to set a reasonable limit on how much of the
# total primary energy in IEA's estimate is electricity-related. (see issue #833)
L1321.IO_R_elec_Yh$value[L1321.IO_R_elec_Yh$value > energy.MAX_IOELEC] <- energy.MAX_IOELEC
# Build data frame including all above calculated values for cement production - intensity, fuel shares, energy for heat and electricity
# --------------------------------------------------------------------------------------------------------------------------------------
# Start with CO2 emissions from cement
L100.CDIAC_CO2_ctry_hist %>%
filter(year %in% HISTORICAL_YEARS) %>%
select(iso, year, cement) %>%
rename(emiss_ktC = cement) %>%
# Match in region IDs by iso code
left_join_error_no_match(iso_GCAM_regID, by = "iso") %>%
# Replace process emissions with actual cement production
left_join_error_no_match(L1321.Cement_Worrell_R, by = "GCAM_region_ID") %>%
mutate(prod_Mt = emiss_ktC * prod_emiss_ratio * CONV_KT_MT) %>%
# add region names for intensity and fuelshare
left_join_error_no_match(cement_regions, by = "iso") %>%
# add above calculated energy intensities
left_join_error_no_match(L1321.IEA_cement_elec_intensity, by = c("IEA_intensity_region" = "Country", "year")) %>%
left_join_error_no_match(L1321.IEA_cement_TPE_intensity, by = c("IEA_intensity_region" = "Country", "year")) %>%
rename(elec_GJkg = value.x, TPE_GJkg = value.y) %>%
# remove unneeded columns from various left_joins
select(iso, year, emiss_ktC, GCAM_region_ID, prod_Mt, IEA_intensity_region, elec_GJkg, TPE_GJkg, IEA_fuelshare_region) %>%
# Match in IO coefficients by region and year
left_join_error_no_match(L1321.IO_R_elec_Yh, by = c("GCAM_region_ID", "year")) %>%
rename(IOelec = value) %>%
# Match in fuelshares of (by default) coal, gas, oil, and biomass
left_join_error_no_match(IEA_cement_fuelshares, by = c("IEA_fuelshare_region" = "Country")) %>%
# Calculate heat intensity of energy and electricity, as well as total heat for each
mutate(heat_GJkg = TPE_GJkg - elec_GJkg * IOelec, heat_EJ = heat_GJkg * prod_Mt, elec_EJ = elec_GJkg * prod_Mt) %>%
# Calculate total heat by fuel using fuelshares
mutate(Coal_EJ = Coal * heat_EJ, Oil_EJ = Oil * heat_EJ, Gas_EJ = Gas * heat_EJ, Biomass_EJ = Biomass * heat_EJ) ->
L1321.Cement_ALL_ctry_Yh
# ===============================================================================================================================================
# Now that country level data has been built and downscaled into L1321.Cement_ALL_ctry_Yh, calculate needed GCAM input energy and IO coefficients
# ===============================================================================================================================================
# Calculate aggregated regional data on IO coefficients for cement production by fuel for heat and electricity
# ------------------------------------------------------------------------------------------------------------
# Aggregate country data to the regional level
L1321.Cement_ALL_ctry_Yh %>%
select(GCAM_region_ID, year, prod_Mt, heat_EJ, elec_EJ, Coal_EJ, Oil_EJ, Gas_EJ, Biomass_EJ) %>%
group_by(GCAM_region_ID, year) %>%
summarise_all(sum) %>%
ungroup() %>%
mutate(heat_GJkg = heat_EJ / prod_Mt, elec_GJkg = elec_EJ / prod_Mt) ->
L1321.Cement_ALL_R_Yh
# Separate regional electricity and heat coefficients, first removing all unneeded columns
L1321.Cement_ALL_R_Yh %>%
select(GCAM_region_ID, year, elec_GJkg, heat_GJkg) ->
L1321.Cement_ALL_R_Yh_base
# Copy final year value to any historical period values not contained in the data set
L1321.Cement_ALL_R_Yh_base %>%
filter(year == FINAL_CO2_YEAR) %>%
rename(old.year = year) %>%
repeat_add_columns(tibble(year = ADDITIONAL_YEARS)) %>%
select(-old.year) %>%
bind_rows(L1321.Cement_ALL_R_Yh_base) ->
L1321.IO_Cement_GJkg_R_ALL_Yh
# Assign sector and fuel names for heat and electricity data
L1321.IO_Cement_GJkg_R_ALL_Yh %>%
select(GCAM_region_ID, year, elec_GJkg) %>%
mutate(sector = "cement", fuel = "electricity") %>%
rename(value = elec_GJkg) ->
L1321.IO_Cement_GJkg_R_elec_Yh
L1321.IO_Cement_GJkg_R_ALL_Yh %>%
select(GCAM_region_ID, year, heat_GJkg) %>%
mutate(sector = "cement", fuel = "heat") %>%
rename(value = heat_GJkg) ->
L1321.IO_Cement_GJkg_R_heat_Yh
# Compile electricity, heat, and limestone IO coefficients in L1321.IO_GJkg_R_cement_F_Yh
L1321.IO_Cement_GJkg_R_elec_Yh %>%
bind_rows(L1321.IO_Cement_GJkg_R_heat_Yh) %>%
bind_rows(L1321.IO_Cement_R_limestone_Yh) ->
L1321.IO_GJkg_R_cement_F_Yh
# Calculate input energy for cement production by region, fuel, and year for L1321.in_EJ_R_cement_F_Y
# ---------------------------------------------------------------------------------------------------
# Compile regional historical data on energy use for cement by fuel
L1321.Cement_ALL_R_Yh %>%
select(GCAM_region_ID, year, elec_EJ, Coal_EJ, Oil_EJ, Gas_EJ, Biomass_EJ) %>%
gather(fuel, value, elec_EJ, Coal_EJ, Oil_EJ, Gas_EJ, Biomass_EJ) %>%
# Match fuel names to GCAM, removing _EJ and replacing oil and elec
mutate(sector = "cement", fuel = tolower(gsub("_EJ", "", fuel)), fuel = gsub("elec", "electricity", fuel),
fuel = gsub("oil", "refined liquids", fuel)) ->
L1321.in_EJ_R_cement_F_Y_base
# Copy final year value to any historical period values not contained in the data set
L1321.in_EJ_R_cement_F_Y_base %>%
filter(year == FINAL_CO2_YEAR) %>%
rename(old.year = year) %>%
repeat_add_columns(tibble(year = ADDITIONAL_YEARS)) %>%
select(-old.year) %>%
bind_rows(L1321.in_EJ_R_cement_F_Y_base) ->
L1321.in_EJ_R_cement_F_Y
# ---------------------------------------------------------------------------------------------------------------------
## 7/30/21: Modification for detailed industry
## Manually adjust coal use in South Korea, so there is enough left for iron and steel sector
## Upper bound is IEA coal consumption in non-metallic minerals
L1321.in_EJ_R_cement_F_Y %>%
left_join(L1011.en_bal_EJ_R_Si_Fi_Yh %>% filter(sector == "cement") %>% select(-sector),
by = c("GCAM_region_ID", "year", "fuel")) %>%
mutate(value = if_else(fuel == "coal" & GCAM_region_ID == 28 & value.x > value.y, value.y, value.x),
neg = if_else(fuel == "coal" & GCAM_region_ID == 28 & value.x > value.y, value.y - value.x, 0)) ->
L1321.in_EJ_R_cement_F_Y_adj
# extra coal use will be moved to biomass
L1321.in_EJ_R_cement_F_Y_adj %>%
filter(neg < 0) %>%
select(GCAM_region_ID, sector, fuel, year, value=neg) ->
korea_coal_neg_en
# Replace cement energy data frame with adjusted version
L1321.in_EJ_R_cement_F_Y_adj %>%
select(-value.x, -value.y, -neg) ->
L1321.in_EJ_R_cement_F_Y
# ---------------------------------------------------------------------------------------------------------------------
# Calculate remaining industrial energy use (input), subtracting cement production energy from energy balances
# ------------------------------------------------------------------------------------------------------------
# Subtract input energy to cement sector from industrial energy
L132.in_EJ_R_indenergy_F_Yh %>%
rename(ind.value = value) %>%
left_join(select(L1321.in_EJ_R_cement_F_Y, -sector), by = c("GCAM_region_ID", "fuel", "year")) %>%
mutate(value = ind.value - value) %>%
select(-ind.value) ->
L1321.in_EJ_R_indenergy_F_Yh_NAs
# Replace NA values in sectors with no match in cement with original values from L132.in_EJ_R_indenergy_F_Yh
L1321.in_EJ_R_indenergy_F_Yh_NAs %>%
filter(is.na(value)) %>%
select(-value) %>%
left_join(select(L132.in_EJ_R_indenergy_F_Yh, -sector), by = c("GCAM_region_ID", "fuel", "year")) %>%
bind_rows(filter(L1321.in_EJ_R_indenergy_F_Yh_NAs, !is.na(value))) ->
L1321.in_EJ_R_indenergy_F_Yh_negbio
# This dataset may now have negative values. If it's biomass, these can be changed without breaking energy balances, so we set the rest-of-industry to an exogenous minimum value
L1321.in_EJ_R_indenergy_F_Yh_negbio %>%
filter(value < 0, fuel == "biomass") %>%
mutate(value = energy.MIN_IN_EJ_IND) %>%
bind_rows(filter(L1321.in_EJ_R_indenergy_F_Yh_negbio, value >= 0 | fuel != "biomass")) ->
L1321.in_EJ_R_indenergy_F_Yh
# Negative values in non-bio industrial energy use are problematic and have to be zeroed out
# In the below method, these negative values are zeroed in industrial energy and subtracted from positive values in fossil fuel use for cement
# and then offset with a positive adjustment to cement biomass fuel use. This preserves the total energy balances while removing negative values
# ----------------------------------------------------------------------------------------------------------------------------------------------
# Currently offending negative values are mostly developing regions with (probably incorrectly) low bio shares
# Check if any of these values exist and then conditionally perform the adjustments
# Subset regions and years where any fuels are negative
# and aggregate those negative values (in case one region/year has multiple fuels) as a positive adjustment to biomass
L1321.in_EJ_R_indenergy_F_Yh %>%
filter(value < -0) %>%
mutate(sector = "cement") ->
L1321.cement_adj_neg
L1321.cement_adj_neg %>%
# STEEL DECARONBONIZATION MODIFICATION: add korea excess coal to biomass
bind_rows(korea_coal_neg_en) %>%
mutate(fuel = "biomass") %>%
group_by(GCAM_region_ID, sector, fuel, year) %>%
summarise(value = sum(value) * -1) %>%
ungroup() ->
L1321.cement_adj_pos
# Reset the negative values to 0 in the industrial energy table
L1321.in_EJ_R_indenergy_F_Yh[, "value"][L1321.in_EJ_R_indenergy_F_Yh[, "value"] < 0] <- 0
# Add in the adjustments to the table of cement energy consumption.
L1321.in_EJ_R_cement_F_Y %>%
bind_rows(L1321.cement_adj_neg) %>%
bind_rows(L1321.cement_adj_pos) %>%
group_by(GCAM_region_ID, sector, fuel, year) %>%
summarise(value = sum(value)) %>%
ungroup() ->
L1321.in_EJ_R_cement_F_Y
# ===================================================
# Produce outputs
L1321.out_Mt_R_cement_Yh %>%
add_title("Historical cement outputs by region, fuel, and year") %>%
add_units("Mt cement") %>%
add_comments("Outputs are calculated by by downscaling Worrell regions using CDIAC country emissions and then aggregating to GCAM regions") %>%
add_comments("Final outputs are a product of regional emissions times the production emissions ratio") %>%
add_legacy_name("L1321.out_Mt_R_cement_Yh") %>%
add_precursors("emissions/A_PrimaryFuelCCoef", "energy/Worrell_1994_cement", "energy/mappings/cement_regions", "L100.CDIAC_CO2_ctry_hist", "L102.CO2_Mt_R_F_Yh") ->
L1321.out_Mt_R_cement_Yh
L1321.IO_GJkg_R_cement_F_Yh %>%
add_title("Input-output coefficients for cement production") %>%
add_units("GJ/kg cement") %>%
add_comments("IO coefficients for heat energy, electricity, and limestone consumption are calculated from weighted IEA fuel shares, CDIAC emissions data, and Worrell cement production") %>%
add_legacy_name("L1321.IO_GJkg_R_cement_F_Yh") %>%
add_precursors("emissions/A_PrimaryFuelCCoef", "energy/Worrell_1994_cement", "energy/mappings/cement_regions", "L100.CDIAC_CO2_ctry_hist", "L102.CO2_Mt_R_F_Yh", "L123.in_EJ_R_elec_F_Yh", "L123.out_EJ_R_elec_F_Yh", "energy/IEA_cement_elec_kwht",
"energy/IEA_cement_TPE_GJt", "energy/IEA_cement_fuelshares", "common/iso_GCAM_regID") ->
L1321.IO_GJkg_R_cement_F_Yh
L1321.in_EJ_R_cement_F_Y %>%
add_title("Historical input energy use for the cement sector") %>%
add_units("Exajoules") %>%
add_comments("Input energy by fuel calculated from weighted fuel shares using energy intensity values for heat and electricity") %>%
add_comments("Multiplied by raw fuel shares, all from IEA") %>%
add_legacy_name("L1321.in_EJ_R_cement_F_Y") %>%
add_precursors("L100.CDIAC_CO2_ctry_hist", "L102.CO2_Mt_R_F_Yh", "L123.in_EJ_R_elec_F_Yh", "L123.out_EJ_R_elec_F_Yh", "energy/IEA_cement_elec_kwht",
"energy/IEA_cement_TPE_GJt", "energy/IEA_cement_fuelshares", "common/iso_GCAM_regID", "L1011.en_bal_EJ_R_Si_Fi_Yh") ->
L1321.in_EJ_R_cement_F_Y
L1321.in_EJ_R_indenergy_F_Yh %>%
add_title("Adjusted historical input energy balances for industrial energy use") %>%
add_units("Exajoules") %>%
add_comments("Subtracted cement energy use from industrial energy use values in L132.in_EJ_R_indenergy_F_Yh") %>%
add_comments("To determine adjusted input energy for industrial energy use") %>%
add_legacy_name("L1321.in_EJ_R_indenergy_F_Yh") %>%
add_precursors("L100.CDIAC_CO2_ctry_hist", "L102.CO2_Mt_R_F_Yh", "L123.in_EJ_R_elec_F_Yh", "L123.out_EJ_R_elec_F_Yh", "energy/IEA_cement_elec_kwht",
"energy/IEA_cement_TPE_GJt", "energy/IEA_cement_fuelshares", "L132.in_EJ_R_indenergy_F_Yh", "common/iso_GCAM_regID") ->
L1321.in_EJ_R_indenergy_F_Yh
return_data(L1321.out_Mt_R_cement_Yh, L1321.IO_GJkg_R_cement_F_Yh, L1321.in_EJ_R_cement_F_Y, L1321.in_EJ_R_indenergy_F_Yh)
} else {
stop("Unknown command")
}
}
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