R/zchunk_L203.demand_input.R
In rohmin9122/gcam-korea-release: Build the GCAM-Korea Input Datasets
Defines functions
module_aglu_L203.demand_input
Documented in
module_aglu_L203.demand_input
#' module_aglu_L203.demand_input
#'
#' Builds agriculture demand inputs for the core and each SSP scenario.
#'
#' @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{L203.SectorLogitTables[[ curr_table ]]$data}, \code{L203.Supplysector_demand}, \code{L203.SubsectorLogitTables[[ curr_table ]]$data}, \code{L203.SubsectorAll_demand}, \code{L203.StubTech_demand}, \code{L203.GlobalTechCoef_demand}, \code{L203.GlobalTechShrwt_demand}, \code{L203.StubTechProd_food_crop}, \code{L203.StubTechProd_food_meat}, \code{L203.StubTechProd_nonfood_crop}, \code{L203.StubTechProd_nonfood_meat}, \code{L203.StubTechProd_For}, \code{L203.StubTechFixOut_exp}, \code{L203.StubCalorieContent_crop}, \code{L203.StubCalorieContent_meat}, \code{L203.PerCapitaBased}, \code{L203.BaseService}, \code{L203.IncomeElasticity}, \code{L203.PriceElasticity}, \code{L203.FuelPrefElast_ssp1}, \code{L203.IncomeElasticity_SSP1}, \code{L203.IncomeElasticity_SSP2}, \code{L203.IncomeElasticity_SSP3}, \code{L203.IncomeElasticity_SSP4}, \code{L203.IncomeElasticity_SSP5}. The corresponding file in the
#' original data system was \code{L203.demand_input.R} (aglu level2).
#' @details This chunk specifies the input tables for agriculture demand: generic information for supply sector, subsector and technology,
#' food and non-food demand in calibration years, forest product demand, net exports and caloric contents in calibration and future years,
#' income elasticities for future years in core and SSP scenarios, as well as price elasticities.
#' @importFrom assertthat assert_that
#' @importFrom dplyr filter mutate select
#' @importFrom tidyr gather spread
#' @author RC July 2017
module_aglu_L203.demand_input <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "common/GCAM_region_names",
FILE = "aglu/A_demand_supplysector",
FILE = "aglu/A_demand_subsector",
FILE = "aglu/A_demand_technology",
FILE = "aglu/A_fuelprefElasticity_ssp1",
"L101.ag_Food_Pcal_R_C_Y",
"L101.ag_kcalg_R_C_Y",
"L105.an_Food_Pcal_R_C_Y",
"L105.an_kcalg_R_C_Y",
"L109.ag_ALL_Mt_R_C_Y",
"L109.an_ALL_Mt_R_C_Y",
"L110.For_ALL_bm3_R_Y",
"L134.pcFood_kcald_R_Dmnd_Y",
"L134.pcFood_kcald_R_Dmnd_Y_ssp1",
"L134.pcFood_kcald_R_Dmnd_Y_ssp2",
"L134.pcFood_kcald_R_Dmnd_Y_ssp3",
"L134.pcFood_kcald_R_Dmnd_Y_ssp4",
"L134.pcFood_kcald_R_Dmnd_Y_ssp5",
"L101.Pop_thous_R_Yh",
"L102.pcgdp_thous90USD_Scen_R_Y"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L203.Supplysector_demand",
"L203.SubsectorAll_demand",
"L203.StubTech_demand",
"L203.GlobalTechCoef_demand",
"L203.GlobalTechShrwt_demand",
"L203.StubTechProd_food_crop",
"L203.StubTechProd_food_meat",
"L203.StubTechProd_nonfood_crop",
"L203.StubTechProd_nonfood_meat",
"L203.StubTechProd_For",
"L203.StubTechFixOut_exp",
"L203.StubCalorieContent_crop",
"L203.StubCalorieContent_meat",
"L203.PerCapitaBased",
"L203.BaseService",
"L203.IncomeElasticity",
"L203.PriceElasticity",
"L203.FuelPrefElast_ssp1",
"L203.IncomeElasticity_SSP1",
"L203.IncomeElasticity_SSP2",
"L203.IncomeElasticity_SSP3",
"L203.IncomeElasticity_SSP4",
"L203.IncomeElasticity_SSP5"))
} else if(command == driver.MAKE) {
all_data <- list(...)[[1]]
GCAM_commodity <- GCAM_region_ID <- element <- item <- value <- year <- aglu_demand_calyears <- aglu_demand_futureyears <-
Prod_colnames <- names_FuelPrefElasticity <- Cons_bm3 <- GCAM_demand <- NetExp_Mt <- OtherUses_Mt <-
core <- SSP2 <- base.service <- calOutputValue <- efficiency <- energy.final.demand <- fixedOutput <-
gdp.ratio <- income.elasticity <- logit.type <- price.elasticity <- ratio <- region <- scenario <-
subsector <- supplysector <- technology <- NULL # silence package check notes
# Load required inputs
GCAM_region_names <- get_data(all_data, "common/GCAM_region_names")
A_demand_supplysector <- get_data(all_data, "aglu/A_demand_supplysector")
A_demand_subsector <- get_data(all_data, "aglu/A_demand_subsector")
A_demand_technology <- get_data(all_data, "aglu/A_demand_technology")
A_fuelprefElasticity_ssp1 <- get_data(all_data, "aglu/A_fuelprefElasticity_ssp1")
L101.ag_Food_Pcal_R_C_Y <- get_data(all_data, "L101.ag_Food_Pcal_R_C_Y")
L101.ag_kcalg_R_C_Y <- get_data(all_data, "L101.ag_kcalg_R_C_Y")
L105.an_Food_Pcal_R_C_Y <- get_data(all_data, "L105.an_Food_Pcal_R_C_Y")
L105.an_kcalg_R_C_Y <- get_data(all_data, "L105.an_kcalg_R_C_Y")
L109.ag_ALL_Mt_R_C_Y <- get_data(all_data, "L109.ag_ALL_Mt_R_C_Y")
L109.an_ALL_Mt_R_C_Y <- get_data(all_data, "L109.an_ALL_Mt_R_C_Y")
L110.For_ALL_bm3_R_Y <- get_data(all_data, "L110.For_ALL_bm3_R_Y")
L134.pcFood_kcald_R_Dmnd_Y <- get_data(all_data, "L134.pcFood_kcald_R_Dmnd_Y")
L134.pcFood_kcald_R_Dmnd_Y_ssp1 <- get_data(all_data, "L134.pcFood_kcald_R_Dmnd_Y_ssp1")
L134.pcFood_kcald_R_Dmnd_Y_ssp2 <- get_data(all_data, "L134.pcFood_kcald_R_Dmnd_Y_ssp2")
L134.pcFood_kcald_R_Dmnd_Y_ssp3 <- get_data(all_data, "L134.pcFood_kcald_R_Dmnd_Y_ssp3")
L134.pcFood_kcald_R_Dmnd_Y_ssp4 <- get_data(all_data, "L134.pcFood_kcald_R_Dmnd_Y_ssp4")
L134.pcFood_kcald_R_Dmnd_Y_ssp5 <- get_data(all_data, "L134.pcFood_kcald_R_Dmnd_Y_ssp5")
L101.Pop_thous_R_Yh <- get_data(all_data, "L101.Pop_thous_R_Yh")
L102.pcgdp_thous90USD_Scen_R_Y <- get_data(all_data, "L102.pcgdp_thous90USD_Scen_R_Y")
# Build tables
# NOTE: This is somewhat complicated. Unlike demands in the energy system, the aglu demands are treated as exogenous
# in all historical periods, regardless of which model base years are used. Processing data for all historical years
# that are also model years (i.e. not just the model base years)
aglu_demand_calyears <- HISTORICAL_YEARS[HISTORICAL_YEARS %in% MODEL_YEARS]
aglu_demand_futureyears <- c(max(aglu_demand_calyears), MODEL_YEARS[!MODEL_YEARS %in% aglu_demand_calyears])
# Build L203.Supplysector_demand: generic info for demand sectors by region
A_demand_supplysector %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["Supplysector"]], LOGIT_TYPE_COLNAME), GCAM_region_names = GCAM_region_names) %>%
filter(!region %in% aglu.NO_AGLU_REGIONS) -> # Remove any regions for which agriculture and land use are not modeled
L203.Supplysector_demand
# Build L203.SubsectorAll_demand: generic info for demand subsectors by region
A_demand_subsector %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["SubsectorAll"]], LOGIT_TYPE_COLNAME), GCAM_region_names = GCAM_region_names) %>%
filter(!region %in% aglu.NO_AGLU_REGIONS) -> # Remove any regions for which agriculture and land use are not modeled
L203.SubsectorAll_demand
# Build L203.StubTech_demand: identification of stub technologies for demands by region
A_demand_technology %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["Tech"]], GCAM_region_names = GCAM_region_names) %>%
rename(stub.technology = technology) %>%
filter(!region %in% aglu.NO_AGLU_REGIONS) -> # Remove any regions for which agriculture and land use are not modeled
L203.StubTech_demand
# Build L203.GlobalTechCoef_demand: input names of demand technologies
A_demand_technology %>%
repeat_add_columns(tibble(year = MODEL_YEARS)) %>%
rename(sector.name = supplysector, subsector.name = subsector) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechCoef"]]) ->
L203.GlobalTechCoef_demand
# Build L203.GlobalTechShrwt_demand: shareweights of demand technologies
L203.GlobalTechCoef_demand %>%
select(LEVEL2_DATA_NAMES[["GlobalTechYr"]]) %>%
mutate(share.weight = 1) ->
L203.GlobalTechShrwt_demand
# Calibrated food and nonfood demands of crops and meat
# Create table of regions, technologies and all base years
# NOTE: Easiest if the model base years are subsetted from a full table as a last step in the construction of each of these tables
A_demand_technology %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["Tech"]], "minicam.energy.input", "market.name"), GCAM_region_names = GCAM_region_names) %>%
mutate(market.name = region, stub.technology = technology) ->
A_demand_technology_R
# Add all base years
A_demand_technology_R %>%
repeat_add_columns(tibble(year = aglu_demand_calyears)) ->
A_demand_technology_R_Yh
# Add all model years
A_demand_technology_R %>%
repeat_add_columns(tibble(year = MODEL_YEARS)) ->
A_demand_technology_R_Y
# Build L203.StubTechProd_food_crop and L203.StubTechProd_food_meat: crop and meat food demand by technology and region
L101.ag_Food_Pcal_R_C_Y %>%
# Combine crop and meat food demand in Pcal
bind_rows(L105.an_Food_Pcal_R_C_Y) %>%
filter(year %in% aglu_demand_calyears) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") ->
L203.ag_an_Food_Pcal_R_C_Y
A_demand_technology_R_Yh %>%
# Select food demand
filter(supplysector %in% c("FoodDemand_Crops", "FoodDemand_Meat")) %>%
# Map in food demand by region / commodity / year
left_join_error_no_match(L203.ag_an_Food_Pcal_R_C_Y, by = c("region", "technology" = "GCAM_commodity", "year")) %>%
mutate(calOutputValue = round(value, aglu.DIGITS_CALOUTPUT),
share.weight.year = year,
# Subsector and technology shareweights (subsector requires the year as well)
subs.share.weight = if_else(calOutputValue > 0, 1, 0),
tech.share.weight = if_else(calOutputValue > 0, 1, 0)) %>%
select(LEVEL2_DATA_NAMES[["StubTechProd"]]) %>%
filter(!region %in% aglu.NO_AGLU_REGIONS) %>% # Remove any regions for which agriculture and land use are not modeled
filter(year %in% MODEL_BASE_YEARS) -> # Also subset the calibration tables to only the model base years
L203.StubTechProd_food
# Build L203.StubTechProd_nonfood_crop and L203.StubTechProd_nonfood_meat: crop and meat nonfood demand by technology and region
L109.ag_ALL_Mt_R_C_Y %>%
# Combine the balance tables of crop and meat in Mt
bind_rows(L109.an_ALL_Mt_R_C_Y) %>%
filter(year %in% aglu_demand_calyears) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
select(GCAM_region_ID, region, GCAM_commodity, year, OtherUses_Mt, NetExp_Mt) -> # only nonfood and net exports data will be used
L203.ag_an_ALL_Mt_R_C_Y
A_demand_technology_R_Yh %>%
# Select nonfood demand
filter(supplysector %in% c("NonFoodDemand_Crops", "NonFoodDemand_Meat")) %>%
# Map in nonfood demand by region / commodity / year
left_join_error_no_match(L203.ag_an_ALL_Mt_R_C_Y, by = c("region", "technology" = "GCAM_commodity", "year")) %>%
mutate(calOutputValue = round(OtherUses_Mt, aglu.DIGITS_CALOUTPUT),
share.weight.year = year,
# Subsector and technology shareweights (subsector requires the year as well)
subs.share.weight = if_else(calOutputValue > 0, 1, 0),
tech.share.weight = if_else(calOutputValue > 0, 1, 0)) %>%
select(LEVEL2_DATA_NAMES[["StubTechProd"]]) %>%
filter(!region %in% aglu.NO_AGLU_REGIONS) %>% # Remove any regions for which agriculture and land use are not modeled
filter(year %in% MODEL_BASE_YEARS) -> # Also subset the calibration tables to only the model base years
L203.StubTechProd_nonfood
# Build L203.StubTechFixOut_exp: animal exports for net exporting regions in all periods
A_demand_technology_R_Y %>%
filter(supplysector == "Exports_Meat") %>%
# Create NAs for future years, use left_join instead
left_join(L203.ag_an_ALL_Mt_R_C_Y, by = c("region", "technology" = "GCAM_commodity", "year")) %>%
# Replace negative net exports with zero
mutate(fixedOutput = pmax(0, round(NetExp_Mt, aglu.DIGITS_CALOUTPUT))) %>%
# For each region / commodity
group_by(region, subsector) %>%
# Hold the animal exports constant in the future, so set value for future years at the final base year value
mutate(fixedOutput = replace(fixedOutput, year > max(MODEL_BASE_YEARS), fixedOutput[year == max(MODEL_BASE_YEARS)]),
share.weight.year = year,
# Subsector and technology shareweights (subsector requires the year as well)
subs.share.weight = 0, tech.share.weight = 0) %>%
ungroup() %>%
select(LEVEL2_DATA_NAMES[["StubTechFixOut"]]) %>%
filter(!region %in% aglu.NO_AGLU_REGIONS) -> # Remove any regions for which agriculture and land use are not modeled
L203.StubTechFixOut_exp
# Build L203.StubTechProd_For: Forest product demand by technology and region
L110.For_ALL_bm3_R_Y %>%
unique() %>%
filter(year %in% aglu_demand_calyears) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
ungroup() %>%
select(GCAM_region_ID, region, year, Cons_bm3) -> # Select forest demand
L203.For_ALL_bm3_R_Y
A_demand_technology_R_Yh %>%
filter(supplysector == "NonFoodDemand_Forest") %>%
# Map in forest product demand in bm3
left_join_error_no_match(L203.For_ALL_bm3_R_Y, by = c("region", "year")) %>%
mutate(calOutputValue = round(Cons_bm3, aglu.DIGITS_CALOUTPUT),
share.weight.year = year,
# Subsector and technology shareweights (subsector requires the year as well)
subs.share.weight = if_else(calOutputValue > 0, 1, 0),
tech.share.weight = if_else(calOutputValue > 0, 1, 0)) %>%
select(LEVEL2_DATA_NAMES[["StubTechProd"]]) %>%
filter(!region %in% aglu.NO_AGLU_REGIONS) %>% # Remove any regions for which agriculture and land use are not modeled
filter(year %in% MODEL_BASE_YEARS) -> # Also subset the calibration tables to only the model base years
L203.StubTechProd_For
# Build L203.StubCalorieContent_crop and L203.StubCalorieContent_meat:
# calorie content of food crops (incl secondary products) and meat commodities
L101.ag_kcalg_R_C_Y %>%
# Combine the weigted average caloric content of crop and meat products
bind_rows(L105.an_kcalg_R_C_Y) %>%
filter(year %in% aglu_demand_calyears) %>%
left_join(GCAM_region_names, by = "GCAM_region_ID") ->
L203.ag_an_kcalg_R_C_Y
A_demand_technology_R_Y %>%
filter(supplysector %in% c("FoodDemand_Crops", "FoodDemand_Meat")) %>%
# Create NAs for future years, use left_join instead
left_join(L203.ag_an_kcalg_R_C_Y, by = c("region", "technology" = "GCAM_commodity", "year")) %>%
mutate(efficiency = round(value, aglu.DIGITS_CALOUTPUT)) %>%
# For each region / commodity,
group_by(region, subsector) %>%
# Calorie content are held constant in the future, so set value for future years at the final base year value
mutate(efficiency = replace(efficiency, year > max(MODEL_BASE_YEARS), efficiency[year == max(MODEL_BASE_YEARS)])) %>%
ungroup() %>%
select(LEVEL2_DATA_NAMES[["StubTechCalorieContent"]]) %>%
filter(!region %in% aglu.NO_AGLU_REGIONS) -> # Remove any regions for which agriculture and land use are not modeled
L203.StubCalorieContent
# FINAL DEMANDS
# Build L203.PerCapitaBased: per-capita final demand attributes that do not vary by time period
A_demand_supplysector %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["PerCapitaBased"]], GCAM_region_names = GCAM_region_names) %>%
filter(!region %in% aglu.NO_AGLU_REGIONS) -> # Remove any regions for which agriculture and land use are not modeled
L203.PerCapitaBased
# Build L203.BaseService: base service of final demands
Prod_colnames <- c("region", "supplysector", "year", "calOutputValue")
L203.StubTechFixOut_exp %>%
filter(year %in% MODEL_BASE_YEARS) %>%
rename(calOutputValue = fixedOutput) %>%
select(Prod_colnames) %>%
# Combine all food and nonfood demand
bind_rows(L203.StubTechProd_food[Prod_colnames], L203.StubTechProd_nonfood[Prod_colnames], L203.StubTechProd_For[Prod_colnames]) %>%
group_by(region, supplysector, year) %>%
# Sum the total of all commodities by region and supply sector
summarise(calOutputValue = sum(calOutputValue)) %>%
ungroup() %>%
rename(energy.final.demand = supplysector, base.service = calOutputValue) %>%
filter(!region %in% aglu.NO_AGLU_REGIONS) %>% # Remove any regions for which agriculture and land use are not modeled
filter(year %in% MODEL_BASE_YEARS) -> # also subset the calibration tables to only the model base years
L203.BaseService
# L203.IncomeElasticity: Income elasticities
# NOTE: food demands are taken as given in historical validation runs
# Note: these steps contain processing normally considered level 1, but are done here because unlike other quantities computed in level 1,
# the income elasticities depend on the timestep length chosen, and as such are dependent on the modeltime, which is not a level 1 attribute
# Step 1: Build historical estimates of changes in per-capita food demands by region and demand type
L101.Pop_thous_R_Yh %>%
filter(year %in% aglu_demand_calyears) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") ->
L203.Pop_thous_R_Yh # Population by region in calibration years
L203.BaseService %>%
# Filter total final food demand in Pcal
filter(energy.final.demand %in% c("FoodDemand_Crops", "FoodDemand_Meat")) %>%
# Map in total population in thousands
left_join_error_no_match(L203.Pop_thous_R_Yh, by = c("region", "year")) %>%
# Calculate daily per-capita food demand (Mcal / thous = kcal)
mutate(value = base.service * (1 / CONV_MCAL_PCAL) * CONV_DAYS_YEAR / value) %>%
select(-base.service) %>%
group_by(region, GCAM_region_ID, energy.final.demand) %>%
# Calculate per capita food demand changes (ratios)
mutate(ratio = value / lag(value)) %>%
replace_na(list(ratio = 1)) ->
L203.pcFoodRatio_R_Yh
# Step 2: Calculate future changes (ratios) in caloric demands by region and demand type
L134.pcFood_kcald_R_Dmnd_Y_ssp1$scenario <- "SSP1"
L134.pcFood_kcald_R_Dmnd_Y_ssp2$scenario <- "SSP2"
L134.pcFood_kcald_R_Dmnd_Y_ssp3$scenario <- "SSP3"
L134.pcFood_kcald_R_Dmnd_Y_ssp4$scenario <- "SSP4"
L134.pcFood_kcald_R_Dmnd_Y_ssp5$scenario <- "SSP5"
L134.pcFood_kcald_R_Dmnd_Y %>%
# Combine per capita food caloric demand of core and all SSP scenarios
mutate(scenario = "core") %>%
bind_rows(L134.pcFood_kcald_R_Dmnd_Y_ssp1, L134.pcFood_kcald_R_Dmnd_Y_ssp2,
L134.pcFood_kcald_R_Dmnd_Y_ssp3, L134.pcFood_kcald_R_Dmnd_Y_ssp4,
L134.pcFood_kcald_R_Dmnd_Y_ssp5) %>%
filter(year %in% aglu_demand_futureyears) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
# Create category crop vs. meat
mutate(energy.final.demand = "FoodDemand_Crops",
energy.final.demand = replace(energy.final.demand, GCAM_demand == "meat", "FoodDemand_Meat")) %>%
# For each region / category / scenario,
group_by(GCAM_region_ID, region, energy.final.demand, scenario) %>%
# Calculate per capita food demand changes (ratios)
mutate(ratio = value / lag(value)) %>%
replace_na(list(ratio = 1)) %>%
ungroup() %>%
select(-value) ->
L203.pcFoodRatio_R_Dmnd_Yfut
# Step 4: Calculate the per-capita GDP trajectories over the same time period
# NOTE: only computing elasticities based on the specified GDP scenario
L102.pcgdp_thous90USD_Scen_R_Y %>%
filter(year %in% MODEL_YEARS) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
# For each region / scenario,
group_by(GCAM_region_ID, region, scenario) %>%
# Calculate per capita GDP changes (ratios)
mutate(gdp.ratio = value / lag(value)) %>%
replace_na(list(gdp.ratio = 1)) %>%
select(-value) %>%
spread(scenario, gdp.ratio) %>%
# Set the core scenario's per capita GDP changes the same as the SSP2
mutate(core = SSP2) %>%
gather(scenario, gdp.ratio, -region, -GCAM_region_ID, -year) ->
L203.pcgdpRatio_R_Y
# Step 5: Solve for the income elasticities in each time period
L203.pcFoodRatio_R_Dmnd_Yfut %>%
# Taiwan's pcap food demand are missing there are NAs, use left_join instead
left_join(L203.pcgdpRatio_R_Y, by = c("GCAM_region_ID", "region", "year", "scenario")) %>%
mutate(income.elasticity = round(log(ratio) / log(gdp.ratio), aglu.DIGITS_INCELAS)) %>%
# NAs for Taiwan, replace with zero, but Taiwan is dropped later
replace_na(list(income.elasticity = 0)) %>%
# Step 6: Convert to appropriate formats
filter(year %in% MODEL_FUTURE_YEARS) %>%
select(scenario, region, energy.final.demand, year, income.elasticity) %>%
# Adjust income elasticity values between [0,1] for all the SSP scenarios
mutate(income.elasticity = replace(income.elasticity, scenario != "core" & income.elasticity > 1, 1),
income.elasticity = replace(income.elasticity, scenario != "core" & income.elasticity < 0, 0)) %>%
filter(!region %in% aglu.NO_AGLU_REGIONS) -> # Remove any regions for which agriculture and land use are not modeled
L203.IncomeElasticity_allScen
# Build L203.PriceElasticity: Price elasticities
L203.PriceElasticity <- write_to_all_regions(A_demand_supplysector, c(LEVEL2_DATA_NAMES[["EnergyFinalDemand"]], "price.elasticity"),
GCAM_region_names = GCAM_region_names) %>%
repeat_add_columns(tibble(year = MODEL_FUTURE_YEARS)) %>% # Price elasticities are only read for future periods
# Set the USA meat food price elasticity to a region-specific value
mutate(price.elasticity = replace(price.elasticity, region == gcam.USA_REGION & energy.final.demand == "FoodDemand_Meat", aglu.FOOD_MEAT_P_ELAS_USA)) %>%
select(LEVEL2_DATA_NAMES[["PriceElasticity"]]) %>%
filter(!region %in% aglu.NO_AGLU_REGIONS) -> # Remove any regions for which agriculture and land use are not modeled
L203.PriceElasticity
# Fuel preference elasticity
# Build L203.FuelPrefElast_ssp1: Fuel preference elasticities for meat in SSP1
names_FuelPrefElasticity <- c("region", "supplysector", "subsector", "year.fillout", "fuelprefElasticity")
A_fuelprefElasticity_ssp1 %>%
mutate(year.fillout = min(MODEL_BASE_YEARS)) %>%
write_to_all_regions(names_FuelPrefElasticity, GCAM_region_names = GCAM_region_names) %>%
filter(!region %in% aglu.NO_AGLU_REGIONS) -> # Remove any regions for which agriculture and land use are not modeled
L203.FuelPrefElast_ssp1
L203.Supplysector_demand %>%
add_title("Generic information for agriculture demand sectors") %>%
add_units("Unitless") %>%
add_comments("Specify generic info for demand sectors") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.Supplysector_demand") %>%
add_precursors("common/GCAM_region_names",
"aglu/A_demand_supplysector") ->
L203.Supplysector_demand
L203.SubsectorAll_demand %>%
add_title("Generic information for agriculture demand subsectors") %>%
add_units("Unitless") %>%
add_comments("Specify generic info for demand subsectors") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.SubsectorAll_demand") %>%
add_precursors("common/GCAM_region_names",
"aglu/A_demand_subsector") ->
L203.SubsectorAll_demand
L203.StubTech_demand %>%
add_title("Identification for stub technologies for agriculture demands") %>%
add_units("Unitless") %>%
add_comments("Specify identification of stub technologies for demands") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.StubTech_demand") %>%
add_precursors("common/GCAM_region_names",
"aglu/A_demand_technology") ->
L203.StubTech_demand
L203.GlobalTechCoef_demand %>%
add_title("Input names of agriculture demand technologies") %>%
add_units("Unitless") %>%
add_comments("Specify input names of demand technologies") %>%
add_legacy_name("L203.GlobalTechCoef_demand") %>%
add_precursors("aglu/A_demand_technology") ->
L203.GlobalTechCoef_demand
L203.GlobalTechShrwt_demand %>%
add_title("Shareweights of agriculture demand technologies") %>%
add_units("Unitless") %>%
add_comments("Specify shareweights of agriculture demand technologies") %>%
add_legacy_name("L203.GlobalTechShrwt_demand") %>%
same_precursors_as(L203.GlobalTechCoef_demand) ->
L203.GlobalTechShrwt_demand
L203.StubTechProd_food %>%
filter(supplysector == "FoodDemand_Crops") %>%
add_title("Crop food demand by technology and region") %>%
add_units("Pcal") %>%
add_comments("Map in crop food demand in calibration years by region / commodity") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.StubTechProd_food_crop") %>%
add_precursors("common/GCAM_region_names",
"aglu/A_demand_technology",
"L101.ag_Food_Pcal_R_C_Y") ->
L203.StubTechProd_food_crop
L203.StubTechProd_food %>%
filter(supplysector == "FoodDemand_Meat") %>%
add_title("Meat food demand by technology and region") %>%
add_units("Pcal") %>%
add_comments("Map in meat food demand in calibration years by region / commodity") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.StubTechProd_food_meat") %>%
add_precursors("common/GCAM_region_names",
"aglu/A_demand_technology",
"L105.an_Food_Pcal_R_C_Y") ->
L203.StubTechProd_food_meat
L203.StubTechProd_nonfood %>%
filter(supplysector == "NonFoodDemand_Crops") %>%
add_title("Crop non-food demand by technology and region") %>%
add_units("Mt") %>%
add_comments("Map in crop non-food demand in calibration years by region / commodity") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.StubTechProd_nonfood_crop") %>%
add_precursors("common/GCAM_region_names",
"aglu/A_demand_technology",
"L109.ag_ALL_Mt_R_C_Y") ->
L203.StubTechProd_nonfood_crop
L203.StubTechProd_nonfood %>%
filter(supplysector == "NonFoodDemand_Meat") %>%
add_title("Meat non-food demand by technology and region") %>%
add_units("Mt") %>%
add_comments("Map in meat non-food demand in calibration years by region / commodity") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.StubTechProd_nonfood_meat") %>%
add_precursors("common/GCAM_region_names",
"aglu/A_demand_technology",
"L109.an_ALL_Mt_R_C_Y") ->
L203.StubTechProd_nonfood_meat
L203.StubTechProd_For %>%
add_title("Forest product demand by technology and region") %>%
add_units("bm3") %>%
add_comments("Map in forest demand in calibration years by region / commodity") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.StubTechProd_For") %>%
add_precursors("common/GCAM_region_names",
"aglu/A_demand_technology",
"L110.For_ALL_bm3_R_Y") ->
L203.StubTechProd_For
L203.StubTechFixOut_exp %>%
add_title("Animal exports for net exporting regions in all periods") %>%
add_units("Mt") %>%
add_comments("Map in animal products exports in calibartion years by region / commodity") %>%
add_comments("Net importing countries are set to zero") %>%
add_comments("Future years are set to the final base year value") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.StubTechFixOut_exp") %>%
add_precursors("common/GCAM_region_names",
"aglu/A_demand_technology",
"L109.an_ALL_Mt_R_C_Y") ->
L203.StubTechFixOut_exp
L203.StubCalorieContent %>%
filter(supplysector == "FoodDemand_Crops") %>%
add_title("Caloric content of food crops") %>%
add_units("kcal/g") %>%
add_comments("Map in weighted average of caloric content in calibration years by region / commodity") %>%
add_comments("Future years are set to the final base year value") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.StubCalorieContent_crop") %>%
add_precursors("common/GCAM_region_names",
"aglu/A_demand_technology",
"L101.ag_kcalg_R_C_Y") ->
L203.StubCalorieContent_crop
L203.StubCalorieContent %>%
filter(supplysector == "FoodDemand_Meat") %>%
add_title("Caloric content of meat commodities") %>%
add_units("kcal/g") %>%
add_comments("Map in weighted average of caloric content in calibration years by region / commodity") %>%
add_comments("Future years are set to the final base year value") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.StubCalorieContent_meat") %>%
add_precursors("common/GCAM_region_names",
"aglu/A_demand_technology",
"L105.an_kcalg_R_C_Y") ->
L203.StubCalorieContent_meat
L203.PerCapitaBased %>%
add_title("Per-capita final agriculture demand attributes") %>%
add_units("Unitless") %>%
add_comments("Attributes do not vary by time period") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.PerCapitaBased") %>%
add_precursors("common/GCAM_region_names",
"aglu/A_demand_supplysector") ->
L203.PerCapitaBased
L203.BaseService %>%
add_title("Base service of final demands") %>%
add_units("Pcal/Mt/bm3") %>%
add_comments("Calculate the total final demands by supply sector in each region") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.BaseService") %>%
add_precursors("common/GCAM_region_names",
"aglu/A_demand_supplysector",
"aglu/A_demand_technology",
"L101.ag_Food_Pcal_R_C_Y",
"L105.an_Food_Pcal_R_C_Y",
"L109.ag_ALL_Mt_R_C_Y",
"L109.an_ALL_Mt_R_C_Y",
"L110.For_ALL_bm3_R_Y") ->
L203.BaseService
L203.IncomeElasticity_allScen %>%
filter(scenario == "core") %>%
select(-scenario) %>%
add_title("Core scenario future income elasticity of crop and meat food demand by region") %>%
add_units("Unitless") %>%
add_comments("Calculate income elasticity based on core scenario food demand and SSP2 GDP growth trajectory") %>%
add_comments("Values are between zero and 1") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.IncomeElasticity") %>%
add_precursors("common/GCAM_region_names",
"L134.pcFood_kcald_R_Dmnd_Y",
"L101.Pop_thous_R_Yh",
"L102.pcgdp_thous90USD_Scen_R_Y") ->
L203.IncomeElasticity
L203.PriceElasticity %>%
add_title("Price elasticities of crop and meat demand by supply sector") %>%
add_units("Unitless") %>%
add_comments("Set the USA meat food price elasticity to a region-specific value") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.PriceElasticity") %>%
add_precursors("aglu/A_demand_supplysector") ->
L203.PriceElasticity
L203.FuelPrefElast_ssp1 %>%
add_title("Fuel preference elasticities for meat in SSP1") %>%
add_units("Unitless") %>%
add_comments("Specify the minimum base year value") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.FuelPrefElast_ssp1") %>%
add_precursors("aglu/A_fuelprefElasticity_ssp1") ->
L203.FuelPrefElast_ssp1
L203.IncomeElasticity_allScen %>%
filter(scenario == "SSP1") %>%
select(-scenario) %>%
add_title("SSP1 future income elasticity of crop and meat food demand by region") %>%
add_units("Unitless") %>%
add_comments("Calculate income elasticity based on SSP1 food demand and GDP growth trajectory") %>%
add_comments("Values are between zero and 1") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.IncomeElasticity_SSP1") %>%
add_precursors("common/GCAM_region_names",
"L134.pcFood_kcald_R_Dmnd_Y_ssp1",
"L101.Pop_thous_R_Yh",
"L102.pcgdp_thous90USD_Scen_R_Y") ->
L203.IncomeElasticity_SSP1
L203.IncomeElasticity_allScen %>%
filter(scenario == "SSP2") %>%
select(-scenario) %>%
add_title("SSP2 future income elasticity of crop and meat food demand by region") %>%
add_units("Unitless") %>%
add_comments("Calculate income elasticity based on SSP2 food demand and GDP growth trajectory") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.IncomeElasticity_SSP2") %>%
add_precursors("common/GCAM_region_names",
"L134.pcFood_kcald_R_Dmnd_Y_ssp2",
"L101.Pop_thous_R_Yh",
"L102.pcgdp_thous90USD_Scen_R_Y") ->
L203.IncomeElasticity_SSP2
L203.IncomeElasticity_allScen %>%
filter(scenario == "SSP3") %>%
select(-scenario) %>%
add_title("SSP3 future income elasticity of crop and meat food demand by region") %>%
add_units("Unitless") %>%
add_comments("Calculate income elasticity based on SSP3 food demand and GDP growth trajectory") %>%
add_comments("Values are between zero and 1") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.IncomeElasticity_SSP3") %>%
add_precursors("common/GCAM_region_names",
"L134.pcFood_kcald_R_Dmnd_Y_ssp3",
"L101.Pop_thous_R_Yh",
"L102.pcgdp_thous90USD_Scen_R_Y") ->
L203.IncomeElasticity_SSP3
L203.IncomeElasticity_allScen %>%
filter(scenario == "SSP4") %>%
select(-scenario) %>%
add_title("SSP4 future income elasticity of crop and meat food demand by region") %>%
add_units("Unitless") %>%
add_comments("Calculate income elasticity based on SSP4 food demand and GDP growth trajectory") %>%
add_comments("Values are between zero and 1") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.IncomeElasticity_SSP4") %>%
add_precursors("common/GCAM_region_names",
"L134.pcFood_kcald_R_Dmnd_Y_ssp4",
"L101.Pop_thous_R_Yh",
"L102.pcgdp_thous90USD_Scen_R_Y") ->
L203.IncomeElasticity_SSP4
L203.IncomeElasticity_allScen %>%
filter(scenario == "SSP5") %>%
select(-scenario) %>%
add_title("SSP5 future income elasticity of crop and meat food demand by region") %>%
add_units("Unitless") %>%
add_comments("Calculate income elasticity based on SSP5 food demand and GDP growth trajectory") %>%
add_comments("Values are between zero and 1") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.IncomeElasticity_SSP5") %>%
add_precursors("common/GCAM_region_names",
"L134.pcFood_kcald_R_Dmnd_Y_ssp5",
"L101.Pop_thous_R_Yh",
"L102.pcgdp_thous90USD_Scen_R_Y") ->
L203.IncomeElasticity_SSP5
return_data(L203.Supplysector_demand, L203.SubsectorAll_demand, L203.StubTech_demand, L203.GlobalTechCoef_demand, L203.GlobalTechShrwt_demand, L203.StubTechProd_food_crop, L203.StubTechProd_food_meat, L203.StubTechProd_nonfood_crop, L203.StubTechProd_nonfood_meat, L203.StubTechProd_For, L203.StubTechFixOut_exp, L203.StubCalorieContent_crop, L203.StubCalorieContent_meat, L203.PerCapitaBased, L203.BaseService, L203.IncomeElasticity, L203.PriceElasticity, L203.FuelPrefElast_ssp1, L203.IncomeElasticity_SSP1, L203.IncomeElasticity_SSP2, L203.IncomeElasticity_SSP3, L203.IncomeElasticity_SSP4, L203.IncomeElasticity_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_aglu_L203.demand_input
Documented in module_aglu_L203.demand_input
#' module_aglu_L203.demand_input
#'
#' Builds agriculture demand inputs for the core and each SSP scenario.
#'
#' @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{L203.SectorLogitTables[[ curr_table ]]$data}, \code{L203.Supplysector_demand}, \code{L203.SubsectorLogitTables[[ curr_table ]]$data}, \code{L203.SubsectorAll_demand}, \code{L203.StubTech_demand}, \code{L203.GlobalTechCoef_demand}, \code{L203.GlobalTechShrwt_demand}, \code{L203.StubTechProd_food_crop}, \code{L203.StubTechProd_food_meat}, \code{L203.StubTechProd_nonfood_crop}, \code{L203.StubTechProd_nonfood_meat}, \code{L203.StubTechProd_For}, \code{L203.StubTechFixOut_exp}, \code{L203.StubCalorieContent_crop}, \code{L203.StubCalorieContent_meat}, \code{L203.PerCapitaBased}, \code{L203.BaseService}, \code{L203.IncomeElasticity}, \code{L203.PriceElasticity}, \code{L203.FuelPrefElast_ssp1}, \code{L203.IncomeElasticity_SSP1}, \code{L203.IncomeElasticity_SSP2}, \code{L203.IncomeElasticity_SSP3}, \code{L203.IncomeElasticity_SSP4}, \code{L203.IncomeElasticity_SSP5}. The corresponding file in the
#' original data system was \code{L203.demand_input.R} (aglu level2).
#' @details This chunk specifies the input tables for agriculture demand: generic information for supply sector, subsector and technology,
#' food and non-food demand in calibration years, forest product demand, net exports and caloric contents in calibration and future years,
#' income elasticities for future years in core and SSP scenarios, as well as price elasticities.
#' @importFrom assertthat assert_that
#' @importFrom dplyr filter mutate select
#' @importFrom tidyr gather spread
#' @author RC July 2017
module_aglu_L203.demand_input <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "common/GCAM_region_names",
FILE = "aglu/A_demand_supplysector",
FILE = "aglu/A_demand_subsector",
FILE = "aglu/A_demand_technology",
FILE = "aglu/A_fuelprefElasticity_ssp1",
"L101.ag_Food_Pcal_R_C_Y",
"L101.ag_kcalg_R_C_Y",
"L105.an_Food_Pcal_R_C_Y",
"L105.an_kcalg_R_C_Y",
"L109.ag_ALL_Mt_R_C_Y",
"L109.an_ALL_Mt_R_C_Y",
"L110.For_ALL_bm3_R_Y",
"L134.pcFood_kcald_R_Dmnd_Y",
"L134.pcFood_kcald_R_Dmnd_Y_ssp1",
"L134.pcFood_kcald_R_Dmnd_Y_ssp2",
"L134.pcFood_kcald_R_Dmnd_Y_ssp3",
"L134.pcFood_kcald_R_Dmnd_Y_ssp4",
"L134.pcFood_kcald_R_Dmnd_Y_ssp5",
"L101.Pop_thous_R_Yh",
"L102.pcgdp_thous90USD_Scen_R_Y"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L203.Supplysector_demand",
"L203.SubsectorAll_demand",
"L203.StubTech_demand",
"L203.GlobalTechCoef_demand",
"L203.GlobalTechShrwt_demand",
"L203.StubTechProd_food_crop",
"L203.StubTechProd_food_meat",
"L203.StubTechProd_nonfood_crop",
"L203.StubTechProd_nonfood_meat",
"L203.StubTechProd_For",
"L203.StubTechFixOut_exp",
"L203.StubCalorieContent_crop",
"L203.StubCalorieContent_meat",
"L203.PerCapitaBased",
"L203.BaseService",
"L203.IncomeElasticity",
"L203.PriceElasticity",
"L203.FuelPrefElast_ssp1",
"L203.IncomeElasticity_SSP1",
"L203.IncomeElasticity_SSP2",
"L203.IncomeElasticity_SSP3",
"L203.IncomeElasticity_SSP4",
"L203.IncomeElasticity_SSP5"))
} else if(command == driver.MAKE) {
all_data <- list(...)[[1]]
GCAM_commodity <- GCAM_region_ID <- element <- item <- value <- year <- aglu_demand_calyears <- aglu_demand_futureyears <-
Prod_colnames <- names_FuelPrefElasticity <- Cons_bm3 <- GCAM_demand <- NetExp_Mt <- OtherUses_Mt <-
core <- SSP2 <- base.service <- calOutputValue <- efficiency <- energy.final.demand <- fixedOutput <-
gdp.ratio <- income.elasticity <- logit.type <- price.elasticity <- ratio <- region <- scenario <-
subsector <- supplysector <- technology <- NULL # silence package check notes
# Load required inputs
GCAM_region_names <- get_data(all_data, "common/GCAM_region_names")
A_demand_supplysector <- get_data(all_data, "aglu/A_demand_supplysector")
A_demand_subsector <- get_data(all_data, "aglu/A_demand_subsector")
A_demand_technology <- get_data(all_data, "aglu/A_demand_technology")
A_fuelprefElasticity_ssp1 <- get_data(all_data, "aglu/A_fuelprefElasticity_ssp1")
L101.ag_Food_Pcal_R_C_Y <- get_data(all_data, "L101.ag_Food_Pcal_R_C_Y")
L101.ag_kcalg_R_C_Y <- get_data(all_data, "L101.ag_kcalg_R_C_Y")
L105.an_Food_Pcal_R_C_Y <- get_data(all_data, "L105.an_Food_Pcal_R_C_Y")
L105.an_kcalg_R_C_Y <- get_data(all_data, "L105.an_kcalg_R_C_Y")
L109.ag_ALL_Mt_R_C_Y <- get_data(all_data, "L109.ag_ALL_Mt_R_C_Y")
L109.an_ALL_Mt_R_C_Y <- get_data(all_data, "L109.an_ALL_Mt_R_C_Y")
L110.For_ALL_bm3_R_Y <- get_data(all_data, "L110.For_ALL_bm3_R_Y")
L134.pcFood_kcald_R_Dmnd_Y <- get_data(all_data, "L134.pcFood_kcald_R_Dmnd_Y")
L134.pcFood_kcald_R_Dmnd_Y_ssp1 <- get_data(all_data, "L134.pcFood_kcald_R_Dmnd_Y_ssp1")
L134.pcFood_kcald_R_Dmnd_Y_ssp2 <- get_data(all_data, "L134.pcFood_kcald_R_Dmnd_Y_ssp2")
L134.pcFood_kcald_R_Dmnd_Y_ssp3 <- get_data(all_data, "L134.pcFood_kcald_R_Dmnd_Y_ssp3")
L134.pcFood_kcald_R_Dmnd_Y_ssp4 <- get_data(all_data, "L134.pcFood_kcald_R_Dmnd_Y_ssp4")
L134.pcFood_kcald_R_Dmnd_Y_ssp5 <- get_data(all_data, "L134.pcFood_kcald_R_Dmnd_Y_ssp5")
L101.Pop_thous_R_Yh <- get_data(all_data, "L101.Pop_thous_R_Yh")
L102.pcgdp_thous90USD_Scen_R_Y <- get_data(all_data, "L102.pcgdp_thous90USD_Scen_R_Y")
# Build tables
# NOTE: This is somewhat complicated. Unlike demands in the energy system, the aglu demands are treated as exogenous
# in all historical periods, regardless of which model base years are used. Processing data for all historical years
# that are also model years (i.e. not just the model base years)
aglu_demand_calyears <- HISTORICAL_YEARS[HISTORICAL_YEARS %in% MODEL_YEARS]
aglu_demand_futureyears <- c(max(aglu_demand_calyears), MODEL_YEARS[!MODEL_YEARS %in% aglu_demand_calyears])
# Build L203.Supplysector_demand: generic info for demand sectors by region
A_demand_supplysector %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["Supplysector"]], LOGIT_TYPE_COLNAME), GCAM_region_names = GCAM_region_names) %>%
filter(!region %in% aglu.NO_AGLU_REGIONS) -> # Remove any regions for which agriculture and land use are not modeled
L203.Supplysector_demand
# Build L203.SubsectorAll_demand: generic info for demand subsectors by region
A_demand_subsector %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["SubsectorAll"]], LOGIT_TYPE_COLNAME), GCAM_region_names = GCAM_region_names) %>%
filter(!region %in% aglu.NO_AGLU_REGIONS) -> # Remove any regions for which agriculture and land use are not modeled
L203.SubsectorAll_demand
# Build L203.StubTech_demand: identification of stub technologies for demands by region
A_demand_technology %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["Tech"]], GCAM_region_names = GCAM_region_names) %>%
rename(stub.technology = technology) %>%
filter(!region %in% aglu.NO_AGLU_REGIONS) -> # Remove any regions for which agriculture and land use are not modeled
L203.StubTech_demand
# Build L203.GlobalTechCoef_demand: input names of demand technologies
A_demand_technology %>%
repeat_add_columns(tibble(year = MODEL_YEARS)) %>%
rename(sector.name = supplysector, subsector.name = subsector) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechCoef"]]) ->
L203.GlobalTechCoef_demand
# Build L203.GlobalTechShrwt_demand: shareweights of demand technologies
L203.GlobalTechCoef_demand %>%
select(LEVEL2_DATA_NAMES[["GlobalTechYr"]]) %>%
mutate(share.weight = 1) ->
L203.GlobalTechShrwt_demand
# Calibrated food and nonfood demands of crops and meat
# Create table of regions, technologies and all base years
# NOTE: Easiest if the model base years are subsetted from a full table as a last step in the construction of each of these tables
A_demand_technology %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["Tech"]], "minicam.energy.input", "market.name"), GCAM_region_names = GCAM_region_names) %>%
mutate(market.name = region, stub.technology = technology) ->
A_demand_technology_R
# Add all base years
A_demand_technology_R %>%
repeat_add_columns(tibble(year = aglu_demand_calyears)) ->
A_demand_technology_R_Yh
# Add all model years
A_demand_technology_R %>%
repeat_add_columns(tibble(year = MODEL_YEARS)) ->
A_demand_technology_R_Y
# Build L203.StubTechProd_food_crop and L203.StubTechProd_food_meat: crop and meat food demand by technology and region
L101.ag_Food_Pcal_R_C_Y %>%
# Combine crop and meat food demand in Pcal
bind_rows(L105.an_Food_Pcal_R_C_Y) %>%
filter(year %in% aglu_demand_calyears) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") ->
L203.ag_an_Food_Pcal_R_C_Y
A_demand_technology_R_Yh %>%
# Select food demand
filter(supplysector %in% c("FoodDemand_Crops", "FoodDemand_Meat")) %>%
# Map in food demand by region / commodity / year
left_join_error_no_match(L203.ag_an_Food_Pcal_R_C_Y, by = c("region", "technology" = "GCAM_commodity", "year")) %>%
mutate(calOutputValue = round(value, aglu.DIGITS_CALOUTPUT),
share.weight.year = year,
# Subsector and technology shareweights (subsector requires the year as well)
subs.share.weight = if_else(calOutputValue > 0, 1, 0),
tech.share.weight = if_else(calOutputValue > 0, 1, 0)) %>%
select(LEVEL2_DATA_NAMES[["StubTechProd"]]) %>%
filter(!region %in% aglu.NO_AGLU_REGIONS) %>% # Remove any regions for which agriculture and land use are not modeled
filter(year %in% MODEL_BASE_YEARS) -> # Also subset the calibration tables to only the model base years
L203.StubTechProd_food
# Build L203.StubTechProd_nonfood_crop and L203.StubTechProd_nonfood_meat: crop and meat nonfood demand by technology and region
L109.ag_ALL_Mt_R_C_Y %>%
# Combine the balance tables of crop and meat in Mt
bind_rows(L109.an_ALL_Mt_R_C_Y) %>%
filter(year %in% aglu_demand_calyears) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
select(GCAM_region_ID, region, GCAM_commodity, year, OtherUses_Mt, NetExp_Mt) -> # only nonfood and net exports data will be used
L203.ag_an_ALL_Mt_R_C_Y
A_demand_technology_R_Yh %>%
# Select nonfood demand
filter(supplysector %in% c("NonFoodDemand_Crops", "NonFoodDemand_Meat")) %>%
# Map in nonfood demand by region / commodity / year
left_join_error_no_match(L203.ag_an_ALL_Mt_R_C_Y, by = c("region", "technology" = "GCAM_commodity", "year")) %>%
mutate(calOutputValue = round(OtherUses_Mt, aglu.DIGITS_CALOUTPUT),
share.weight.year = year,
# Subsector and technology shareweights (subsector requires the year as well)
subs.share.weight = if_else(calOutputValue > 0, 1, 0),
tech.share.weight = if_else(calOutputValue > 0, 1, 0)) %>%
select(LEVEL2_DATA_NAMES[["StubTechProd"]]) %>%
filter(!region %in% aglu.NO_AGLU_REGIONS) %>% # Remove any regions for which agriculture and land use are not modeled
filter(year %in% MODEL_BASE_YEARS) -> # Also subset the calibration tables to only the model base years
L203.StubTechProd_nonfood
# Build L203.StubTechFixOut_exp: animal exports for net exporting regions in all periods
A_demand_technology_R_Y %>%
filter(supplysector == "Exports_Meat") %>%
# Create NAs for future years, use left_join instead
left_join(L203.ag_an_ALL_Mt_R_C_Y, by = c("region", "technology" = "GCAM_commodity", "year")) %>%
# Replace negative net exports with zero
mutate(fixedOutput = pmax(0, round(NetExp_Mt, aglu.DIGITS_CALOUTPUT))) %>%
# For each region / commodity
group_by(region, subsector) %>%
# Hold the animal exports constant in the future, so set value for future years at the final base year value
mutate(fixedOutput = replace(fixedOutput, year > max(MODEL_BASE_YEARS), fixedOutput[year == max(MODEL_BASE_YEARS)]),
share.weight.year = year,
# Subsector and technology shareweights (subsector requires the year as well)
subs.share.weight = 0, tech.share.weight = 0) %>%
ungroup() %>%
select(LEVEL2_DATA_NAMES[["StubTechFixOut"]]) %>%
filter(!region %in% aglu.NO_AGLU_REGIONS) -> # Remove any regions for which agriculture and land use are not modeled
L203.StubTechFixOut_exp
# Build L203.StubTechProd_For: Forest product demand by technology and region
L110.For_ALL_bm3_R_Y %>%
unique() %>%
filter(year %in% aglu_demand_calyears) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
ungroup() %>%
select(GCAM_region_ID, region, year, Cons_bm3) -> # Select forest demand
L203.For_ALL_bm3_R_Y
A_demand_technology_R_Yh %>%
filter(supplysector == "NonFoodDemand_Forest") %>%
# Map in forest product demand in bm3
left_join_error_no_match(L203.For_ALL_bm3_R_Y, by = c("region", "year")) %>%
mutate(calOutputValue = round(Cons_bm3, aglu.DIGITS_CALOUTPUT),
share.weight.year = year,
# Subsector and technology shareweights (subsector requires the year as well)
subs.share.weight = if_else(calOutputValue > 0, 1, 0),
tech.share.weight = if_else(calOutputValue > 0, 1, 0)) %>%
select(LEVEL2_DATA_NAMES[["StubTechProd"]]) %>%
filter(!region %in% aglu.NO_AGLU_REGIONS) %>% # Remove any regions for which agriculture and land use are not modeled
filter(year %in% MODEL_BASE_YEARS) -> # Also subset the calibration tables to only the model base years
L203.StubTechProd_For
# Build L203.StubCalorieContent_crop and L203.StubCalorieContent_meat:
# calorie content of food crops (incl secondary products) and meat commodities
L101.ag_kcalg_R_C_Y %>%
# Combine the weigted average caloric content of crop and meat products
bind_rows(L105.an_kcalg_R_C_Y) %>%
filter(year %in% aglu_demand_calyears) %>%
left_join(GCAM_region_names, by = "GCAM_region_ID") ->
L203.ag_an_kcalg_R_C_Y
A_demand_technology_R_Y %>%
filter(supplysector %in% c("FoodDemand_Crops", "FoodDemand_Meat")) %>%
# Create NAs for future years, use left_join instead
left_join(L203.ag_an_kcalg_R_C_Y, by = c("region", "technology" = "GCAM_commodity", "year")) %>%
mutate(efficiency = round(value, aglu.DIGITS_CALOUTPUT)) %>%
# For each region / commodity,
group_by(region, subsector) %>%
# Calorie content are held constant in the future, so set value for future years at the final base year value
mutate(efficiency = replace(efficiency, year > max(MODEL_BASE_YEARS), efficiency[year == max(MODEL_BASE_YEARS)])) %>%
ungroup() %>%
select(LEVEL2_DATA_NAMES[["StubTechCalorieContent"]]) %>%
filter(!region %in% aglu.NO_AGLU_REGIONS) -> # Remove any regions for which agriculture and land use are not modeled
L203.StubCalorieContent
# FINAL DEMANDS
# Build L203.PerCapitaBased: per-capita final demand attributes that do not vary by time period
A_demand_supplysector %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["PerCapitaBased"]], GCAM_region_names = GCAM_region_names) %>%
filter(!region %in% aglu.NO_AGLU_REGIONS) -> # Remove any regions for which agriculture and land use are not modeled
L203.PerCapitaBased
# Build L203.BaseService: base service of final demands
Prod_colnames <- c("region", "supplysector", "year", "calOutputValue")
L203.StubTechFixOut_exp %>%
filter(year %in% MODEL_BASE_YEARS) %>%
rename(calOutputValue = fixedOutput) %>%
select(Prod_colnames) %>%
# Combine all food and nonfood demand
bind_rows(L203.StubTechProd_food[Prod_colnames], L203.StubTechProd_nonfood[Prod_colnames], L203.StubTechProd_For[Prod_colnames]) %>%
group_by(region, supplysector, year) %>%
# Sum the total of all commodities by region and supply sector
summarise(calOutputValue = sum(calOutputValue)) %>%
ungroup() %>%
rename(energy.final.demand = supplysector, base.service = calOutputValue) %>%
filter(!region %in% aglu.NO_AGLU_REGIONS) %>% # Remove any regions for which agriculture and land use are not modeled
filter(year %in% MODEL_BASE_YEARS) -> # also subset the calibration tables to only the model base years
L203.BaseService
# L203.IncomeElasticity: Income elasticities
# NOTE: food demands are taken as given in historical validation runs
# Note: these steps contain processing normally considered level 1, but are done here because unlike other quantities computed in level 1,
# the income elasticities depend on the timestep length chosen, and as such are dependent on the modeltime, which is not a level 1 attribute
# Step 1: Build historical estimates of changes in per-capita food demands by region and demand type
L101.Pop_thous_R_Yh %>%
filter(year %in% aglu_demand_calyears) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") ->
L203.Pop_thous_R_Yh # Population by region in calibration years
L203.BaseService %>%
# Filter total final food demand in Pcal
filter(energy.final.demand %in% c("FoodDemand_Crops", "FoodDemand_Meat")) %>%
# Map in total population in thousands
left_join_error_no_match(L203.Pop_thous_R_Yh, by = c("region", "year")) %>%
# Calculate daily per-capita food demand (Mcal / thous = kcal)
mutate(value = base.service * (1 / CONV_MCAL_PCAL) * CONV_DAYS_YEAR / value) %>%
select(-base.service) %>%
group_by(region, GCAM_region_ID, energy.final.demand) %>%
# Calculate per capita food demand changes (ratios)
mutate(ratio = value / lag(value)) %>%
replace_na(list(ratio = 1)) ->
L203.pcFoodRatio_R_Yh
# Step 2: Calculate future changes (ratios) in caloric demands by region and demand type
L134.pcFood_kcald_R_Dmnd_Y_ssp1$scenario <- "SSP1"
L134.pcFood_kcald_R_Dmnd_Y_ssp2$scenario <- "SSP2"
L134.pcFood_kcald_R_Dmnd_Y_ssp3$scenario <- "SSP3"
L134.pcFood_kcald_R_Dmnd_Y_ssp4$scenario <- "SSP4"
L134.pcFood_kcald_R_Dmnd_Y_ssp5$scenario <- "SSP5"
L134.pcFood_kcald_R_Dmnd_Y %>%
# Combine per capita food caloric demand of core and all SSP scenarios
mutate(scenario = "core") %>%
bind_rows(L134.pcFood_kcald_R_Dmnd_Y_ssp1, L134.pcFood_kcald_R_Dmnd_Y_ssp2,
L134.pcFood_kcald_R_Dmnd_Y_ssp3, L134.pcFood_kcald_R_Dmnd_Y_ssp4,
L134.pcFood_kcald_R_Dmnd_Y_ssp5) %>%
filter(year %in% aglu_demand_futureyears) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
# Create category crop vs. meat
mutate(energy.final.demand = "FoodDemand_Crops",
energy.final.demand = replace(energy.final.demand, GCAM_demand == "meat", "FoodDemand_Meat")) %>%
# For each region / category / scenario,
group_by(GCAM_region_ID, region, energy.final.demand, scenario) %>%
# Calculate per capita food demand changes (ratios)
mutate(ratio = value / lag(value)) %>%
replace_na(list(ratio = 1)) %>%
ungroup() %>%
select(-value) ->
L203.pcFoodRatio_R_Dmnd_Yfut
# Step 4: Calculate the per-capita GDP trajectories over the same time period
# NOTE: only computing elasticities based on the specified GDP scenario
L102.pcgdp_thous90USD_Scen_R_Y %>%
filter(year %in% MODEL_YEARS) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
# For each region / scenario,
group_by(GCAM_region_ID, region, scenario) %>%
# Calculate per capita GDP changes (ratios)
mutate(gdp.ratio = value / lag(value)) %>%
replace_na(list(gdp.ratio = 1)) %>%
select(-value) %>%
spread(scenario, gdp.ratio) %>%
# Set the core scenario's per capita GDP changes the same as the SSP2
mutate(core = SSP2) %>%
gather(scenario, gdp.ratio, -region, -GCAM_region_ID, -year) ->
L203.pcgdpRatio_R_Y
# Step 5: Solve for the income elasticities in each time period
L203.pcFoodRatio_R_Dmnd_Yfut %>%
# Taiwan's pcap food demand are missing there are NAs, use left_join instead
left_join(L203.pcgdpRatio_R_Y, by = c("GCAM_region_ID", "region", "year", "scenario")) %>%
mutate(income.elasticity = round(log(ratio) / log(gdp.ratio), aglu.DIGITS_INCELAS)) %>%
# NAs for Taiwan, replace with zero, but Taiwan is dropped later
replace_na(list(income.elasticity = 0)) %>%
# Step 6: Convert to appropriate formats
filter(year %in% MODEL_FUTURE_YEARS) %>%
select(scenario, region, energy.final.demand, year, income.elasticity) %>%
# Adjust income elasticity values between [0,1] for all the SSP scenarios
mutate(income.elasticity = replace(income.elasticity, scenario != "core" & income.elasticity > 1, 1),
income.elasticity = replace(income.elasticity, scenario != "core" & income.elasticity < 0, 0)) %>%
filter(!region %in% aglu.NO_AGLU_REGIONS) -> # Remove any regions for which agriculture and land use are not modeled
L203.IncomeElasticity_allScen
# Build L203.PriceElasticity: Price elasticities
L203.PriceElasticity <- write_to_all_regions(A_demand_supplysector, c(LEVEL2_DATA_NAMES[["EnergyFinalDemand"]], "price.elasticity"),
GCAM_region_names = GCAM_region_names) %>%
repeat_add_columns(tibble(year = MODEL_FUTURE_YEARS)) %>% # Price elasticities are only read for future periods
# Set the USA meat food price elasticity to a region-specific value
mutate(price.elasticity = replace(price.elasticity, region == gcam.USA_REGION & energy.final.demand == "FoodDemand_Meat", aglu.FOOD_MEAT_P_ELAS_USA)) %>%
select(LEVEL2_DATA_NAMES[["PriceElasticity"]]) %>%
filter(!region %in% aglu.NO_AGLU_REGIONS) -> # Remove any regions for which agriculture and land use are not modeled
L203.PriceElasticity
# Fuel preference elasticity
# Build L203.FuelPrefElast_ssp1: Fuel preference elasticities for meat in SSP1
names_FuelPrefElasticity <- c("region", "supplysector", "subsector", "year.fillout", "fuelprefElasticity")
A_fuelprefElasticity_ssp1 %>%
mutate(year.fillout = min(MODEL_BASE_YEARS)) %>%
write_to_all_regions(names_FuelPrefElasticity, GCAM_region_names = GCAM_region_names) %>%
filter(!region %in% aglu.NO_AGLU_REGIONS) -> # Remove any regions for which agriculture and land use are not modeled
L203.FuelPrefElast_ssp1
L203.Supplysector_demand %>%
add_title("Generic information for agriculture demand sectors") %>%
add_units("Unitless") %>%
add_comments("Specify generic info for demand sectors") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.Supplysector_demand") %>%
add_precursors("common/GCAM_region_names",
"aglu/A_demand_supplysector") ->
L203.Supplysector_demand
L203.SubsectorAll_demand %>%
add_title("Generic information for agriculture demand subsectors") %>%
add_units("Unitless") %>%
add_comments("Specify generic info for demand subsectors") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.SubsectorAll_demand") %>%
add_precursors("common/GCAM_region_names",
"aglu/A_demand_subsector") ->
L203.SubsectorAll_demand
L203.StubTech_demand %>%
add_title("Identification for stub technologies for agriculture demands") %>%
add_units("Unitless") %>%
add_comments("Specify identification of stub technologies for demands") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.StubTech_demand") %>%
add_precursors("common/GCAM_region_names",
"aglu/A_demand_technology") ->
L203.StubTech_demand
L203.GlobalTechCoef_demand %>%
add_title("Input names of agriculture demand technologies") %>%
add_units("Unitless") %>%
add_comments("Specify input names of demand technologies") %>%
add_legacy_name("L203.GlobalTechCoef_demand") %>%
add_precursors("aglu/A_demand_technology") ->
L203.GlobalTechCoef_demand
L203.GlobalTechShrwt_demand %>%
add_title("Shareweights of agriculture demand technologies") %>%
add_units("Unitless") %>%
add_comments("Specify shareweights of agriculture demand technologies") %>%
add_legacy_name("L203.GlobalTechShrwt_demand") %>%
same_precursors_as(L203.GlobalTechCoef_demand) ->
L203.GlobalTechShrwt_demand
L203.StubTechProd_food %>%
filter(supplysector == "FoodDemand_Crops") %>%
add_title("Crop food demand by technology and region") %>%
add_units("Pcal") %>%
add_comments("Map in crop food demand in calibration years by region / commodity") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.StubTechProd_food_crop") %>%
add_precursors("common/GCAM_region_names",
"aglu/A_demand_technology",
"L101.ag_Food_Pcal_R_C_Y") ->
L203.StubTechProd_food_crop
L203.StubTechProd_food %>%
filter(supplysector == "FoodDemand_Meat") %>%
add_title("Meat food demand by technology and region") %>%
add_units("Pcal") %>%
add_comments("Map in meat food demand in calibration years by region / commodity") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.StubTechProd_food_meat") %>%
add_precursors("common/GCAM_region_names",
"aglu/A_demand_technology",
"L105.an_Food_Pcal_R_C_Y") ->
L203.StubTechProd_food_meat
L203.StubTechProd_nonfood %>%
filter(supplysector == "NonFoodDemand_Crops") %>%
add_title("Crop non-food demand by technology and region") %>%
add_units("Mt") %>%
add_comments("Map in crop non-food demand in calibration years by region / commodity") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.StubTechProd_nonfood_crop") %>%
add_precursors("common/GCAM_region_names",
"aglu/A_demand_technology",
"L109.ag_ALL_Mt_R_C_Y") ->
L203.StubTechProd_nonfood_crop
L203.StubTechProd_nonfood %>%
filter(supplysector == "NonFoodDemand_Meat") %>%
add_title("Meat non-food demand by technology and region") %>%
add_units("Mt") %>%
add_comments("Map in meat non-food demand in calibration years by region / commodity") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.StubTechProd_nonfood_meat") %>%
add_precursors("common/GCAM_region_names",
"aglu/A_demand_technology",
"L109.an_ALL_Mt_R_C_Y") ->
L203.StubTechProd_nonfood_meat
L203.StubTechProd_For %>%
add_title("Forest product demand by technology and region") %>%
add_units("bm3") %>%
add_comments("Map in forest demand in calibration years by region / commodity") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.StubTechProd_For") %>%
add_precursors("common/GCAM_region_names",
"aglu/A_demand_technology",
"L110.For_ALL_bm3_R_Y") ->
L203.StubTechProd_For
L203.StubTechFixOut_exp %>%
add_title("Animal exports for net exporting regions in all periods") %>%
add_units("Mt") %>%
add_comments("Map in animal products exports in calibartion years by region / commodity") %>%
add_comments("Net importing countries are set to zero") %>%
add_comments("Future years are set to the final base year value") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.StubTechFixOut_exp") %>%
add_precursors("common/GCAM_region_names",
"aglu/A_demand_technology",
"L109.an_ALL_Mt_R_C_Y") ->
L203.StubTechFixOut_exp
L203.StubCalorieContent %>%
filter(supplysector == "FoodDemand_Crops") %>%
add_title("Caloric content of food crops") %>%
add_units("kcal/g") %>%
add_comments("Map in weighted average of caloric content in calibration years by region / commodity") %>%
add_comments("Future years are set to the final base year value") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.StubCalorieContent_crop") %>%
add_precursors("common/GCAM_region_names",
"aglu/A_demand_technology",
"L101.ag_kcalg_R_C_Y") ->
L203.StubCalorieContent_crop
L203.StubCalorieContent %>%
filter(supplysector == "FoodDemand_Meat") %>%
add_title("Caloric content of meat commodities") %>%
add_units("kcal/g") %>%
add_comments("Map in weighted average of caloric content in calibration years by region / commodity") %>%
add_comments("Future years are set to the final base year value") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.StubCalorieContent_meat") %>%
add_precursors("common/GCAM_region_names",
"aglu/A_demand_technology",
"L105.an_kcalg_R_C_Y") ->
L203.StubCalorieContent_meat
L203.PerCapitaBased %>%
add_title("Per-capita final agriculture demand attributes") %>%
add_units("Unitless") %>%
add_comments("Attributes do not vary by time period") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.PerCapitaBased") %>%
add_precursors("common/GCAM_region_names",
"aglu/A_demand_supplysector") ->
L203.PerCapitaBased
L203.BaseService %>%
add_title("Base service of final demands") %>%
add_units("Pcal/Mt/bm3") %>%
add_comments("Calculate the total final demands by supply sector in each region") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.BaseService") %>%
add_precursors("common/GCAM_region_names",
"aglu/A_demand_supplysector",
"aglu/A_demand_technology",
"L101.ag_Food_Pcal_R_C_Y",
"L105.an_Food_Pcal_R_C_Y",
"L109.ag_ALL_Mt_R_C_Y",
"L109.an_ALL_Mt_R_C_Y",
"L110.For_ALL_bm3_R_Y") ->
L203.BaseService
L203.IncomeElasticity_allScen %>%
filter(scenario == "core") %>%
select(-scenario) %>%
add_title("Core scenario future income elasticity of crop and meat food demand by region") %>%
add_units("Unitless") %>%
add_comments("Calculate income elasticity based on core scenario food demand and SSP2 GDP growth trajectory") %>%
add_comments("Values are between zero and 1") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.IncomeElasticity") %>%
add_precursors("common/GCAM_region_names",
"L134.pcFood_kcald_R_Dmnd_Y",
"L101.Pop_thous_R_Yh",
"L102.pcgdp_thous90USD_Scen_R_Y") ->
L203.IncomeElasticity
L203.PriceElasticity %>%
add_title("Price elasticities of crop and meat demand by supply sector") %>%
add_units("Unitless") %>%
add_comments("Set the USA meat food price elasticity to a region-specific value") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.PriceElasticity") %>%
add_precursors("aglu/A_demand_supplysector") ->
L203.PriceElasticity
L203.FuelPrefElast_ssp1 %>%
add_title("Fuel preference elasticities for meat in SSP1") %>%
add_units("Unitless") %>%
add_comments("Specify the minimum base year value") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.FuelPrefElast_ssp1") %>%
add_precursors("aglu/A_fuelprefElasticity_ssp1") ->
L203.FuelPrefElast_ssp1
L203.IncomeElasticity_allScen %>%
filter(scenario == "SSP1") %>%
select(-scenario) %>%
add_title("SSP1 future income elasticity of crop and meat food demand by region") %>%
add_units("Unitless") %>%
add_comments("Calculate income elasticity based on SSP1 food demand and GDP growth trajectory") %>%
add_comments("Values are between zero and 1") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.IncomeElasticity_SSP1") %>%
add_precursors("common/GCAM_region_names",
"L134.pcFood_kcald_R_Dmnd_Y_ssp1",
"L101.Pop_thous_R_Yh",
"L102.pcgdp_thous90USD_Scen_R_Y") ->
L203.IncomeElasticity_SSP1
L203.IncomeElasticity_allScen %>%
filter(scenario == "SSP2") %>%
select(-scenario) %>%
add_title("SSP2 future income elasticity of crop and meat food demand by region") %>%
add_units("Unitless") %>%
add_comments("Calculate income elasticity based on SSP2 food demand and GDP growth trajectory") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.IncomeElasticity_SSP2") %>%
add_precursors("common/GCAM_region_names",
"L134.pcFood_kcald_R_Dmnd_Y_ssp2",
"L101.Pop_thous_R_Yh",
"L102.pcgdp_thous90USD_Scen_R_Y") ->
L203.IncomeElasticity_SSP2
L203.IncomeElasticity_allScen %>%
filter(scenario == "SSP3") %>%
select(-scenario) %>%
add_title("SSP3 future income elasticity of crop and meat food demand by region") %>%
add_units("Unitless") %>%
add_comments("Calculate income elasticity based on SSP3 food demand and GDP growth trajectory") %>%
add_comments("Values are between zero and 1") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.IncomeElasticity_SSP3") %>%
add_precursors("common/GCAM_region_names",
"L134.pcFood_kcald_R_Dmnd_Y_ssp3",
"L101.Pop_thous_R_Yh",
"L102.pcgdp_thous90USD_Scen_R_Y") ->
L203.IncomeElasticity_SSP3
L203.IncomeElasticity_allScen %>%
filter(scenario == "SSP4") %>%
select(-scenario) %>%
add_title("SSP4 future income elasticity of crop and meat food demand by region") %>%
add_units("Unitless") %>%
add_comments("Calculate income elasticity based on SSP4 food demand and GDP growth trajectory") %>%
add_comments("Values are between zero and 1") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.IncomeElasticity_SSP4") %>%
add_precursors("common/GCAM_region_names",
"L134.pcFood_kcald_R_Dmnd_Y_ssp4",
"L101.Pop_thous_R_Yh",
"L102.pcgdp_thous90USD_Scen_R_Y") ->
L203.IncomeElasticity_SSP4
L203.IncomeElasticity_allScen %>%
filter(scenario == "SSP5") %>%
select(-scenario) %>%
add_title("SSP5 future income elasticity of crop and meat food demand by region") %>%
add_units("Unitless") %>%
add_comments("Calculate income elasticity based on SSP5 food demand and GDP growth trajectory") %>%
add_comments("Values are between zero and 1") %>%
add_comments("Remove any regions for which agriculture and land use are not modeled") %>%
add_legacy_name("L203.IncomeElasticity_SSP5") %>%
add_precursors("common/GCAM_region_names",
"L134.pcFood_kcald_R_Dmnd_Y_ssp5",
"L101.Pop_thous_R_Yh",
"L102.pcgdp_thous90USD_Scen_R_Y") ->
L203.IncomeElasticity_SSP5
return_data(L203.Supplysector_demand, L203.SubsectorAll_demand, L203.StubTech_demand, L203.GlobalTechCoef_demand, L203.GlobalTechShrwt_demand, L203.StubTechProd_food_crop, L203.StubTechProd_food_meat, L203.StubTechProd_nonfood_crop, L203.StubTechProd_nonfood_meat, L203.StubTechProd_For, L203.StubTechFixOut_exp, L203.StubCalorieContent_crop, L203.StubCalorieContent_meat, L203.PerCapitaBased, L203.BaseService, L203.IncomeElasticity, L203.PriceElasticity, L203.FuelPrefElast_ssp1, L203.IncomeElasticity_SSP1, L203.IncomeElasticity_SSP2, L203.IncomeElasticity_SSP3, L203.IncomeElasticity_SSP4, L203.IncomeElasticity_SSP5)
} else {
stop("Unknown command")
}
}
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