R/zchunk_L244.building_det.R
In bpbond/gcamdata: Build the GCAM Input Datasets
Defines functions
module_energy_L244.building_det
Documented in
module_energy_L244.building_det
# Copyright 2019 Battelle Memorial Institute; see the LICENSE file.
#' module_energy_L244.building_det
#'
#' Creates level2 data for the building sector.
#'
#' @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{L244.SubregionalShares}, \code{L244.PriceExp_IntGains}, \code{L244.Floorspace}, \code{L244.DemandFunction_serv},
#' \code{L244.DemandFunction_flsp}, \code{L244.Satiation_flsp}, \code{L244.SatiationAdder}, \code{L244.ThermalBaseService}, \code{L244.GenericBaseService},
#'\code{L244.ThermalServiceSatiation}, \code{L244.GenericServiceSatiation}, \code{L244.Intgains_scalar}, \code{L244.ShellConductance_bld},
#' \code{L244.Supplysector_bld}, \code{L244.FinalEnergyKeyword_bld}, \code{L244.SubsectorShrwt_bld}, \code{L244.SubsectorShrwtFllt_bld}, \code{L244.SubsectorInterp_bld},
#' \code{L244.SubsectorInterpTo_bld}, \code{L244.SubsectorLogit_bld}, \code{L244.FuelPrefElast_bld}, \code{L244.StubTech_bld}, \code{L244.StubTechEff_bld},
#' \code{L244.StubTechCalInput_bld}, \code{L244.StubTechIntGainOutputRatio}, \code{L244.GlobalTechShrwt_bld}, \code{L244.GlobalTechCost_bld},
#' \code{L244.DeleteGenericService}, \code{L244.Satiation_flsp_SSP1}, \code{L244.SatiationAdder_SSP1}, \code{L244.GenericServiceSatiation_SSP1},
#' \code{L244.Satiation_flsp_SSP2}, \code{L244.SatiationAdder_SSP2}, \code{L244.GenericServiceSatiation_SSP2}, \code{L244.Satiation_flsp_SSP3},
#' \code{L244.SatiationAdder_SSP3}, \code{L244.GenericServiceSatiation_SSP3}, \code{L244.FuelPrefElast_bld_SSP3}, \code{L244.Satiation_flsp_SSP4},
#' \code{L244.SatiationAdder_SSP4}, \code{L244.GenericServiceSatiation_SSP4}, \code{L244.FuelPrefElast_bld_SSP4}, \code{L244.Satiation_flsp_SSP5},
#' \code{L244.SatiationAdder_SSP5}, \code{L244.GenericServiceSatiation_SSP5}, \code{L244.FuelPrefElast_bld_SSP15}, \code{L244.DeleteThermalService},
#' \code{L244.HDDCDD_A2_CCSM3x}, \code{L244.HDDCDD_A2_HadCM3}, \code{L244.HDDCDD_B1_CCSM3x}, \code{L244.HDDCDD_B1_HadCM3},
#' \code{L244.HDDCDD_constdd_no_GCM} and \code{L244.GompFnParam}.
#' The corresponding file in the original data system was \code{L244.building_det.R} (energy level2).
#' @details Creates level2 data for the building sector.
#' @importFrom assertthat assert_that
#' @importFrom dplyr bind_rows distinct filter if_else group_by left_join mutate select semi_join summarise
#' @importFrom tidyr complete gather nesting unite
#' @author RLH September 2017
module_energy_L244.building_det <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "common/GCAM_region_names",
FILE = "energy/calibrated_techs_bld_det",
FILE = "energy/A_regions",
FILE = "energy/A44.sector",
FILE = "energy/A44.subsector_interp",
FILE = "energy/A44.subsector_logit",
FILE = "energy/A44.subsector_shrwt",
FILE = "energy/A44.fuelprefElasticity",
FILE = "energy/A44.fuelprefElasticity_SSP3",
FILE = "energy/A44.fuelprefElasticity_SSP4",
FILE = "energy/A44.fuelprefElasticity_SSP15",
FILE = "energy/A44.globaltech_shrwt",
FILE = "energy/A44.gcam_consumer",
FILE = "energy/A44.demandFn_serv",
FILE = "energy/A44.demandFn_flsp",
FILE = "energy/A44.internal_gains",
FILE = "energy/A44.satiation_flsp",
FILE = "energy/A44.satiation_flsp_SSPs",
FILE = "energy/A44.demand_satiation_mult",
FILE = "energy/A44.demand_satiation_mult_SSPs",
"L144.flsp_bm2_R_res_Yh",
"L144.flsp_bm2_R_comm_Yh",
"L144.base_service_EJ_serv",
"L144.in_EJ_R_bld_serv_F_Yh",
"L144.end_use_eff",
"L144.shell_eff_R_Y",
"L144.NEcost_75USDGJ",
"L144.internal_gains",
"L143.HDDCDD_scen_R_Y",
"L101.Pop_thous_R_Yh",
"L102.pcgdp_thous90USD_Scen_R_Y",
"L144.flsp_param",
"L144.hab_land_flsp_fin"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L244.SubregionalShares",
"L244.PriceExp_IntGains",
"L244.Floorspace",
"L244.DemandFunction_serv",
"L244.DemandFunction_flsp",
"L244.Satiation_flsp",
"L244.SatiationAdder",
"L244.ThermalBaseService",
"L244.GenericBaseService",
"L244.ThermalServiceSatiation",
"L244.GenericServiceSatiation",
"L244.Intgains_scalar",
"L244.ShellConductance_bld",
"L244.Supplysector_bld",
"L244.FinalEnergyKeyword_bld",
"L244.SubsectorShrwt_bld",
"L244.SubsectorShrwtFllt_bld",
"L244.SubsectorInterp_bld",
"L244.SubsectorInterpTo_bld",
"L244.SubsectorLogit_bld",
"L244.FuelPrefElast_bld",
"L244.StubTech_bld",
"L244.StubTechEff_bld",
"L244.StubTechCalInput_bld",
"L244.StubTechIntGainOutputRatio",
"L244.GlobalTechShrwt_bld",
"L244.GlobalTechCost_bld",
"L244.DeleteGenericService",
"L244.Satiation_flsp_SSP1",
"L244.SatiationAdder_SSP1",
"L244.GenericServiceSatiation_SSP1",
"L244.Satiation_flsp_SSP2",
"L244.SatiationAdder_SSP2",
"L244.GenericServiceSatiation_SSP2",
"L244.Satiation_flsp_SSP3",
"L244.SatiationAdder_SSP3",
"L244.GenericServiceSatiation_SSP3",
"L244.FuelPrefElast_bld_SSP3",
"L244.Satiation_flsp_SSP4",
"L244.SatiationAdder_SSP4",
"L244.GenericServiceSatiation_SSP4",
"L244.FuelPrefElast_bld_SSP4",
"L244.Satiation_flsp_SSP5",
"L244.SatiationAdder_SSP5",
"L244.GenericServiceSatiation_SSP5",
"L244.FuelPrefElast_bld_SSP15",
"L244.DeleteThermalService",
"L244.HDDCDD_A2_CCSM3x",
"L244.HDDCDD_A2_HadCM3",
"L244.HDDCDD_B1_CCSM3x",
"L244.HDDCDD_B1_HadCM3",
"L244.HDDCDD_constdd_no_GCM",
"L244.GompFnParam"))
} else if(command == driver.MAKE) {
# Silence package checks
building.service.input <- calibrated.value <- comm <- degree.days <- floorspace_bm2 <- gcam.consumer <-
internal.gains.market.name <- internal.gains.output.ratio <- multiplier <- nodeInput <- pcFlsp_mm2 <-
pcFlsp_mm2_fby <- pcGDP_thous90USD <- pop_thous <- region <- region.class <- resid <- satiation.adder <-
satiation.level <- scalar_mult <- sector <- service <- service.per.flsp <- share.weight <- shell.conductance <-
subs.share.weight <- subsector <- supplysector <- technology <- thermal.building.service.input <- to.value <-
value <- year <- year.fillout <- GCM <- NEcostPerService <- SRES <- SSP <- TRN_SSP <- base.building.size <-
base.service <- building.node.input <- . <- GCAM_region_ID <- L244.Satiation_flsp_SSP1 <-
L244.SatiationAdder_SSP1 <- L244.GenericServiceSatiation_SSP1 <- L244.Satiation_flsp_SSP2 <-
L244.SatiationAdder_SSP2 <- L244.GenericServiceSatiation_SSP2 <- L244.Satiation_flsp_SSP3 <-
L244.SatiationAdder_SSP3 <- L244.GenericServiceSatiation_SSP3 <-L244.Satiation_flsp_SSP4 <-
L244.SatiationAdder_SSP4 <- L244.GenericServiceSatiation_SSP4 <- L244.Satiation_flsp_SSP5 <-
L244.SatiationAdder_SSP5 <- L244.GenericServiceSatiation_SSP5 <- scenario <- L244.HDDCDD_A2_CCSM3x <-
L244.HDDCDD_A2_HadCM3 <- L244.HDDCDD_B1_CCSM3x <- L244.HDDCDD_B1_HadCM3 <- L244.HDDCDD_constdd_no_GCM <-
area_thouskm2 <- flsp <- flsp_pc <- unadjust.satiation <- land.density.param <- tot.dens <-
b.param <- income.param <- gdp_pc <- flsp_est <- base_flsp <- bias.adjust.param <- NULL
all_data <- list(...)[[1]]
# Load required inputs
GCAM_region_names <- get_data(all_data, "common/GCAM_region_names")
calibrated_techs_bld_det <- get_data(all_data, "energy/calibrated_techs_bld_det")
A_regions <- get_data(all_data, "energy/A_regions")
A44.sector <- get_data(all_data, "energy/A44.sector", strip_attributes = TRUE)
A44.subsector_interp <- get_data(all_data, "energy/A44.subsector_interp", strip_attributes = TRUE)
A44.subsector_logit <- get_data(all_data, "energy/A44.subsector_logit", strip_attributes = TRUE)
A44.subsector_shrwt <- get_data(all_data, "energy/A44.subsector_shrwt", strip_attributes = TRUE)
A44.fuelprefElasticity <- get_data(all_data, "energy/A44.fuelprefElasticity", strip_attributes = TRUE)
A44.fuelprefElasticity_SSP3 <- get_data(all_data, "energy/A44.fuelprefElasticity_SSP3", strip_attributes = TRUE)
A44.fuelprefElasticity_SSP4 <- get_data(all_data, "energy/A44.fuelprefElasticity_SSP4", strip_attributes = TRUE)
A44.fuelprefElasticity_SSP15 <- get_data(all_data, "energy/A44.fuelprefElasticity_SSP15", strip_attributes = TRUE)
A44.globaltech_shrwt <- get_data(all_data, "energy/A44.globaltech_shrwt") %>%
gather_years
A44.gcam_consumer <- get_data(all_data, "energy/A44.gcam_consumer", strip_attributes = TRUE)
A44.demandFn_serv <- get_data(all_data, "energy/A44.demandFn_serv", strip_attributes = TRUE)
A44.demandFn_flsp <- get_data(all_data, "energy/A44.demandFn_flsp", strip_attributes = TRUE)
A44.internal_gains <- get_data(all_data, "energy/A44.internal_gains")
A44.satiation_flsp <- get_data(all_data, "energy/A44.satiation_flsp")
A44.satiation_flsp_SSPs <- get_data(all_data, "energy/A44.satiation_flsp_SSPs")
A44.demand_satiation_mult <- get_data(all_data, "energy/A44.demand_satiation_mult")
A44.demand_satiation_mult_SSPs <- get_data(all_data, "energy/A44.demand_satiation_mult_SSPs")
L144.flsp_bm2_R_res_Yh <- get_data(all_data, "L144.flsp_bm2_R_res_Yh", strip_attributes = TRUE)
L144.flsp_bm2_R_comm_Yh <- get_data(all_data, "L144.flsp_bm2_R_comm_Yh", strip_attributes = TRUE)
L144.base_service_EJ_serv <- get_data(all_data, "L144.base_service_EJ_serv", strip_attributes = TRUE)
L144.in_EJ_R_bld_serv_F_Yh <- get_data(all_data, "L144.in_EJ_R_bld_serv_F_Yh")
L144.end_use_eff <- get_data(all_data, "L144.end_use_eff", strip_attributes = TRUE)
L144.shell_eff_R_Y <- get_data(all_data, "L144.shell_eff_R_Y", strip_attributes = TRUE)
L144.NEcost_75USDGJ <- get_data(all_data, "L144.NEcost_75USDGJ", strip_attributes = TRUE)
L144.internal_gains <- get_data(all_data, "L144.internal_gains", strip_attributes = TRUE)
L143.HDDCDD_scen_R_Y <- get_data(all_data, "L143.HDDCDD_scen_R_Y")
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") # year comes in as double
L144.flsp_param <- get_data(all_data, "L144.flsp_param", strip_attributes = TRUE)
L144.hab_land_flsp_fin<-get_data(all_data, "L144.hab_land_flsp_fin", strip_attributes = TRUE)
# ===================================================
# Subregional population and income shares: need to be read in because these default to 0
# L244.SubregionalShares: subregional population and income shares (not currently used)
L244.SubregionalShares <- write_to_all_regions(A44.gcam_consumer, LEVEL2_DATA_NAMES[["DeleteConsumer"]],
GCAM_region_names = GCAM_region_names) %>%
mutate(pop.year.fillout = min(MODEL_BASE_YEARS),
inc.year.fillout = min(MODEL_BASE_YEARS),
subregional.population.share = 1,
subregional.income.share = 1)
# Internal gains
# L244.PriceExp_IntGains: price exponent on floorspace and naming of internal gains trial markets
L244.PriceExp_IntGains <- write_to_all_regions(A44.gcam_consumer, LEVEL2_DATA_NAMES[["PriceExp_IntGains"]],
GCAM_region_names = GCAM_region_names)
# L244.Floorspace: base year floorspace
# Building residential floorspace in the base years
# Keep all historical years for now - these are needed in calculating satiation adders later on
A44.gcam_consumer_resid <- A44.gcam_consumer %>%
filter(grepl("res", A44.gcam_consumer$gcam.consumer))
L244.Floorspace_resid <- L144.flsp_bm2_R_res_Yh %>%
mutate(base.building.size = round(value, energy.DIGITS_FLOORSPACE)) %>%
select(-value) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
mutate(gcam.consumer = A44.gcam_consumer_resid$gcam.consumer,
nodeInput = A44.gcam_consumer_resid$nodeInput,
building.node.input = A44.gcam_consumer_resid$building.node.input) %>%
select(region, gcam.consumer, nodeInput, building.node.input, year, base.building.size)
# Commercial floorspace
A44.gcam_consumer_comm <- A44.gcam_consumer %>%
filter(grepl("comm", A44.gcam_consumer$gcam.consumer))
L244.Floorspace_comm <- L144.flsp_bm2_R_comm_Yh %>%
mutate(base.building.size = round(value, energy.DIGITS_FLOORSPACE)) %>%
select(-value) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
mutate(gcam.consumer = A44.gcam_consumer_comm$gcam.consumer,
nodeInput = A44.gcam_consumer_comm$nodeInput,
building.node.input = A44.gcam_consumer_comm$building.node.input) %>%
select(region, gcam.consumer, nodeInput, building.node.input, year, base.building.size)
L244.Floorspace <- bind_rows(L244.Floorspace_resid, L244.Floorspace_comm) %>%
filter(year %in% MODEL_BASE_YEARS)
# Demand function
# L244.DemandFunction_serv and L244.DemandFunction_flsp: demand function types
L244.DemandFunction_serv <- write_to_all_regions(A44.demandFn_serv, LEVEL2_DATA_NAMES[["DemandFunction_serv"]],
GCAM_region_names = GCAM_region_names)
L244.DemandFunction_flsp <- write_to_all_regions(A44.demandFn_flsp, LEVEL2_DATA_NAMES[["DemandFunction_flsp"]],
GCAM_region_names = GCAM_region_names)
# Floorspace demand satiation
# L244.Satiation_flsp: Satiation levels assumed for floorspace
L244.Satiation_flsp_class <- A44.satiation_flsp %>%
gather(sector, value, resid, comm) %>%
# Converting from square meters per capita to million square meters per capita
mutate(satiation.level = value * CONV_THOUS_BIL) %>%
select(-value)
L244.Satiation_flsp <- write_to_all_regions(A44.gcam_consumer, c("region", "gcam.consumer", "nodeInput", "building.node.input"), # replace with LEVEL2_DATA_NAMES[["BldNodes]]
GCAM_region_names = GCAM_region_names) %>%
# Match in the region class, and use this to then match in the satiation floorspace
left_join_error_no_match(A_regions %>% select(region, region.class),
by = "region") %>%
# Residential floorspace does not use the satiation demand function, so filter the commercial floorspace
filter(!grepl("resid",gcam.consumer)) %>%
left_join_error_no_match(L244.Satiation_flsp_class, by = c("region.class", "gcam.consumer" = "sector")) %>%
select(LEVEL2_DATA_NAMES[["Satiation_flsp"]])
# Satiation adder - Required for shaping the future floorspace growth trajectories in each region
# The satiation adder allows the starting (final calibration year) position of any region and sector to be set along the satiation demand function
# L244.SatiationAdder: Satiation adders in floorspace demand function
# First, prepare socioeconomics tables by adding region names
L102.pcgdp_thous90USD_Scen_R_Y <- L102.pcgdp_thous90USD_Scen_R_Y %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
rename(pcGDP_thous90USD = value)
L101.Pop_thous_R_Yh <- L101.Pop_thous_R_Yh %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
rename(pop_thous = value)
# In order to pass timeshift, we need floorspace for energy satiation year, which we don't have under timeshift conditions
# So instead, we will take floorspace for maximum year (this ONLY affects the data in the timeshift right now)
Floorspace_timeshift_pass <- L244.Floorspace %>%
filter(year == max(L244.Floorspace$year)) %>%
select(-year)
# Match in the per-capita GDP, total floorspace, and population (for calculating per-capita floorspace)
L244.SatiationAdder <- L244.Satiation_flsp %>%
left_join_keep_first_only(L102.pcgdp_thous90USD_Scen_R_Y %>%
filter(year == energy.SATIATION_YEAR), by = "region") %>%
left_join_error_no_match(Floorspace_timeshift_pass, by = c("region", "gcam.consumer", "nodeInput", "building.node.input")) %>%
left_join_error_no_match(L101.Pop_thous_R_Yh, by = c("region", "year", "GCAM_region_ID")) %>%
mutate(pcFlsp_mm2 = base.building.size / pop_thous,
# We now have all of the data required for calculating the satiation adder in each region
satiation.adder = round(satiation.level - exp(log(2) * pcGDP_thous90USD / energy.GDP_MID_SATIATION) *
(satiation.level - pcFlsp_mm2), energy.DIGITS_SATIATION_ADDER),
pcFlsp_mm2_fby = base.building.size / pop_thous,
# The satiation adder (million square meters of floorspace per person) needs to be less than the per-capita demand in the final calibration year
# Need to match in the demand in the final calibration year to check this.
satiation.adder = if_else(satiation.adder > pcFlsp_mm2_fby, pcFlsp_mm2_fby * 0.999, satiation.adder)) %>%
select(LEVEL2_DATA_NAMES[["SatiationAdder"]])
# L244.Satiation_flsp_SSPs: Satiation levels assumed for floorspace in the SSPs
L244.Satiation_flsp_class_SSPs <- A44.satiation_flsp_SSPs %>%
gather(sector, value, resid, comm) %>%
mutate(satiation.level = value * CONV_THOUS_BIL)
L244.Satiation_flsp_SSPs <- write_to_all_regions(A44.gcam_consumer, c("region", "gcam.consumer", "nodeInput", "building.node.input"), # replace with LEVEL2_DATA_NAMES[["BldNodes]]
GCAM_region_names = GCAM_region_names) %>%
repeat_add_columns(tibble(SSP = c("SSP1", "SSP2", "SSP3", "SSP4", "SSP5"))) %>%
# Match in the region class, and use this to then match in the satiation floorspace
left_join_error_no_match(A_regions %>% select(region, region.class), by = "region") %>%
# Residential floorspace does not use the satiation demand function, so filter the commercial floorspace
filter(!grepl("resid",gcam.consumer)) %>%
left_join_error_no_match(L244.Satiation_flsp_class_SSPs, by = c("SSP", "region.class", "gcam.consumer" = "sector")) %>%
# Calculate pcFlsp and make sure it is smaller than the satiation level
left_join_error_no_match(L102.pcgdp_thous90USD_Scen_R_Y %>%
# This used to be filtered to energy.SATIATION_YEAR, changed to pass timeshift test
filter(year == max(MODEL_BASE_YEARS)), by = c("region", "SSP" = "scenario")) %>%
left_join_error_no_match(L244.Floorspace, by = c("region", "gcam.consumer", "year", "nodeInput", "building.node.input")) %>%
left_join_error_no_match(L101.Pop_thous_R_Yh, by = c("GCAM_region_ID", "year", "region")) %>%
mutate(pcFlsp_mm2 = base.building.size / pop_thous,
satiation.level = if_else(pcFlsp_mm2 > satiation.level, 1.001 * pcFlsp_mm2, satiation.level))
L244.Satiation_flsp_SSPs.split <- L244.Satiation_flsp_SSPs%>%
select(region, gcam.consumer, nodeInput, building.node.input, satiation.level, SSP) %>%
# Split by SSP, creating a list with a tibble for each SSP, then add attributes
split(.$SSP) %>%
lapply(function(df) {
select(df, -SSP) %>%
add_units("Million squared meters per capita") %>%
add_comments("Values from A44.satiation_flsp_SSPs added to A44.gcam_consumer written to all regions") %>%
add_comments("Then, per-capita floorspace calculated to ensure satiation levels above per-capita floorspace") %>%
add_precursors("energy/A44.satiation_flsp_SSPs", "energy/A44.gcam_consumer", "common/GCAM_region_names",
"energy/A_regions", "L102.pcgdp_thous90USD_Scen_R_Y", "L101.Pop_thous_R_Yh",
"L144.flsp_bm2_R_res_Yh", "L144.flsp_bm2_R_comm_Yh")
})
# Assign each tibble in list
for(i in names(L244.Satiation_flsp_SSPs.split)) {
assign(paste0("L244.Satiation_flsp_", i), L244.Satiation_flsp_SSPs.split[[i]] %>%
add_title(paste0("Floorspace demand satiation: ", i)) %>%
add_legacy_name(paste0("L244.Satiation_flsp_", i)))
}
# Satiation adder - Required for shaping the future floorspace growth trajectories in each region
# L244.SatiationAdder_SSPs: Satiation adders in floorspace demand function for the SSPs
L244.SatiationAdder_SSPs <- L244.Satiation_flsp_SSPs %>%
# Calculate satiation.adder
mutate(satiation.adder = round(satiation.level - (exp(log(2) * pcGDP_thous90USD / energy.GDP_MID_SATIATION) *
(satiation.level - pcFlsp_mm2)),
energy.DIGITS_SATIATION_ADDER)) %>%
select(LEVEL2_DATA_NAMES[["SatiationAdder"]], SSP) %>%
# Split by SSP, creating a list with a tibble for each SSP, then add attributes
split(.$SSP) %>%
lapply(function(df) {
select(df, -SSP) %>%
add_units("Unitless") %>%
add_comments("Satiation adder compute using satiation level, per-capita GDP and per-capita floorsapce") %>%
add_precursors("energy/A44.satiation_flsp_SSPs", "energy/A44.gcam_consumer", "common/GCAM_region_names",
"energy/A_regions", "L102.pcgdp_thous90USD_Scen_R_Y", "L101.Pop_thous_R_Yh",
"L144.flsp_bm2_R_res_Yh", "L144.flsp_bm2_R_comm_Yh")
})
# Assign each tibble in list
for(i in names(L244.SatiationAdder_SSPs)) {
assign(paste0("L244.SatiationAdder_", i), L244.SatiationAdder_SSPs[[i]] %>%
add_title(paste0("Satiation adders in floorspace demand function: ", i)) %>%
add_legacy_name(paste0("L244.SatiationAdder_", i)))
}
#------------------------------------------------------
# Updated floorspace Gompertz function
# - Calculate the bias correction parameter (k)
# - Write parameters for the updated floorspace function
# Write the function parameters
L244.GompFnParam<-L144.flsp_param %>%
left_join_error_no_match(L144.hab_land_flsp_fin %>% filter(year==MODEL_FINAL_BASE_YEAR),by="region") %>%
rename(area_thouskm2=value) %>%
left_join_error_no_match(GCAM_region_names, by="region") %>%
left_join_error_no_match(L102.pcgdp_thous90USD_Scen_R_Y %>% filter(scenario== socioeconomics.BASE_GDP_SCENARIO), by=c("GCAM_region_ID","year")) %>%
rename(gdp_pc=pcGDP_thous90USD) %>%
left_join_error_no_match(L101.Pop_thous_R_Yh, by=c("GCAM_region_ID","year")) %>%
left_join_error_no_match(L144.flsp_bm2_R_res_Yh,by=c("GCAM_region_ID","year")) %>%
rename(flsp=value) %>%
mutate(tot.dens=pop_thous/area_thouskm2,
flsp_pc=(flsp*1E9)/(pop_thous*1E3),
base_flsp=flsp_pc,
flsp_est=(`unadjust.satiation` +(-`land.density.param`*log(tot.dens)))*exp(-`b.param`
*exp(-`income.param`*log(gdp_pc))),
bias.adjust.param=flsp_pc-flsp_est,
base_flsp=round(base_flsp,energy.DIGITS_FLOORSPACE),
bias.adjust.param=round(bias.adjust.param,energy.DIGITS_FLOORSPACE),
gcam.consumer="resid",
nodeInput="resid",
building.node.input="resid_building") %>%
rename(pop.dens=tot.dens,
habitable.land=area_thouskm2,
base.pcFlsp=base_flsp) %>%
select(LEVEL2_DATA_NAMES[["GompFnParam"]])
#================================================================
# L244.GenericBaseService and L244.ThermalBaseService: Base year output of buildings services (per unit floorspace)
# First, separate the thermal from the generic services. Generic services will be assumed to produce
# internal gain energy, so anything in the internal gains assumptions table will be assumed generic
generic_services <- unique(A44.internal_gains$supplysector)
thermal_services <- dplyr::setdiff(unique(A44.sector$supplysector), generic_services)
# Base-service: filter only the model base years and change names as indicated in calibrated_techs_bld_det
L244.base_service <- L144.base_service_EJ_serv %>%
rename(base.service = value) %>%
mutate(base.service = round(base.service, energy.DIGITS_CALOUTPUT)) %>%
filter(year %in% MODEL_BASE_YEARS) %>%
left_join_keep_first_only(calibrated_techs_bld_det, by = c("sector", "service")) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
select(LEVEL2_DATA_NAMES[["BldNodes"]], building.service.input = supplysector, year, base.service)
# Separate thermal and generic services into separate tibbles
L244.GenericBaseService <- L244.base_service %>%
filter(building.service.input %in% generic_services)
L244.ThermalBaseService <- L244.base_service %>%
filter(building.service.input %in% thermal_services) %>%
rename(thermal.building.service.input = building.service.input)
# L244.HDDCDD: Heating and cooling degree days by scenario
L244.all_sres_gcm <- unique(L143.HDDCDD_scen_R_Y[c("SRES", "GCM")]) # These HDD/CDD scenarios are pretty old, should be updated eventually
L244.HDDCDD_scen_R_Y <- L143.HDDCDD_scen_R_Y %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID")
# Let's make a climate normal (average climate conditions) for each region, using a selected interval of years
# Don't want to just set one year, because we want average values for all regions
L244.HDDCDD_normal_R_Y <- L244.HDDCDD_scen_R_Y %>%
filter(year %in% energy.CLIMATE_NORMAL_YEARS,
SRES == L244.all_sres_gcm$SRES[1],
GCM == L244.all_sres_gcm$GCM[1]) %>%
group_by(region, variable) %>%
summarise(degree.days = mean(value)) %>%
ungroup()
# Subset the heating and cooling services, separately
heating_services <- thermal_services[grepl("heating", thermal_services)]
cooling_services <- thermal_services[grepl("cooling", thermal_services)]
# Add HDDCDD for L244.ThermalBaseService categories and model years
L244.HDDCDD <- L244.ThermalBaseService %>%
select(-year, -base.service) %>%
distinct() %>%
# Add model years
repeat_add_columns(tibble(year = MODEL_YEARS)) %>%
# Add SRES and GCM variables
repeat_add_columns(L244.all_sres_gcm) %>%
mutate(variable = if_else(thermal.building.service.input %in% heating_services, "HDD", "CDD")) %>%
# Add HDD and CDD
left_join_error_no_match(L244.HDDCDD_scen_R_Y, by = c("region", "SRES", "GCM", "variable", "year")) %>%
mutate(degree.days = round(value, energy.DIGITS_HDDCDD)) %>%
select(-variable, -value, -GCAM_region_ID) %>%
# Join SRES and GCM so that we can split by unique ESM scenario
unite(scenario, SRES, GCM) %>%
split(.$scenario) %>%
lapply(function(df) {
select(df, -scenario) %>%
add_units("Fahrenheit Degree Days") %>%
add_comments("Degree days are from L143.HDDCDD_scen_R_Y") %>%
add_precursors("L143.HDDCDD_scen_R_Y", "common/GCAM_region_names",
"energy/A44.internal_gains", "energy/A44.sector", "L144.base_service_EJ_serv",
"energy/calibrated_techs_bld_det")
})
# Assign each tibble in list
for(i in names(L244.HDDCDD)) {
assign(paste0("L244.HDDCDD_", i), L244.HDDCDD[[i]] %>%
add_title(paste0("Heating and cooling degree days: ", i)) %>%
add_legacy_name(paste0("L244.HDDCDD_", i)))
}
# L244.GenericServiceSatiation: Satiation levels assumed for non-thermal building services
# First, calculate the service output per unit floorspace in the USA region
L244.ServiceSatiation_USA <- L144.base_service_EJ_serv %>%
filter(GCAM_region_ID == gcam.USA_CODE) %>%
# Using left_join_keep_first_only b/c there are repeats and we only need to keep generic columns
left_join_keep_first_only(calibrated_techs_bld_det, by = c("sector", "service")) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
select(region, sector, service, gcam.consumer, nodeInput, building.node.input, building.service.input = supplysector, year, value)
# Floorspace should be matched in for a specified year, from the full floorspace table (i.e. not one that is subsetted to model base years)
L144.flsp_bm2_R_res_Yh <- L144.flsp_bm2_R_res_Yh %>%
mutate(gcam.consumer = A44.gcam_consumer$gcam.consumer[grepl("res", A44.gcam_consumer$gcam.consumer)]) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID")
L144.flsp_bm2_R_comm_Yh <- L144.flsp_bm2_R_comm_Yh %>%
mutate(gcam.consumer = A44.gcam_consumer$gcam.consumer[grepl("com", A44.gcam_consumer$gcam.consumer)]) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID")
L244.flsp_bm2_R <- bind_rows(L144.flsp_bm2_R_res_Yh, L144.flsp_bm2_R_comm_Yh) %>%
# Again, used to be energy.SATIATION_YEAR, changed to pass timeshift test
filter(year == max(MODEL_BASE_YEARS)) %>%
select(-year)
L244.ServiceSatiation_USA <- L244.ServiceSatiation_USA %>%
left_join_error_no_match(L244.flsp_bm2_R %>%
rename(floorspace_bm2 = value), by = c("region", "gcam.consumer")) %>%
left_join_error_no_match(A44.demand_satiation_mult, by = c("building.service.input" = "supplysector")) %>%
group_by(region, sector, service) %>%
mutate(satiation.level = round(value[year == max(HISTORICAL_YEARS)] * multiplier / floorspace_bm2, energy.DIGITS_CALOUTPUT)) %>%
ungroup() %>%
select(-multiplier)
# Generic services: read these values to all regions because they're all the same
L244.GenericServiceSatiation <- L244.ServiceSatiation_USA %>%
filter(building.service.input %in% generic_services) %>%
select(-region, -GCAM_region_ID, -year, -value) %>%
distinct() %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["GenericServiceSatiation"]], GCAM_region_names = GCAM_region_names)
# Need to match in the floorspace into the base service table, divide to calculate the service demand
# per unit floorspace in the final calibration year. This (increased slightly) is then the minimum satiation level that needs to be read in.
L244.BS <- L244.GenericBaseService %>%
left_join_error_no_match(L244.Floorspace, by = c(LEVEL2_DATA_NAMES[["BldNodes"]], "year")) %>%
mutate(service.per.flsp = base.service / base.building.size) %>%
filter(year == max(MODEL_BASE_YEARS)) %>%
select(LEVEL2_DATA_NAMES[["BldNodes"]], building.service.input, service.per.flsp)
L244.GenericServiceSatiation <- L244.GenericServiceSatiation %>%
left_join_error_no_match(L244.BS, by = c(LEVEL2_DATA_NAMES[["BldNodes"]], "building.service.input")) %>%
mutate(satiation.level = pmax(satiation.level, service.per.flsp * 1.0001)) %>%
select(-service.per.flsp)
# L244.GenericServiceSatiation_SSPs: Satiation levels assumed for non-thermal building services in the SSPs
# First, calculate the service output per unit floorspace in the USA region
L244.ServiceSatiation_USA_SSPs <- L244.ServiceSatiation_USA %>%
filter(building.service.input %in% generic_services) %>%
repeat_add_columns(tibble(SSP = c("SSP1", "SSP2", "SSP3", "SSP4", "SSP5"))) %>%
left_join_error_no_match(A44.demand_satiation_mult_SSPs, by = c("SSP", "building.service.input" = "supplysector")) %>%
group_by(region, sector, service) %>%
mutate(satiation.level = round(value[year == max(HISTORICAL_YEARS)] * multiplier / floorspace_bm2, energy.DIGITS_CALOUTPUT)) %>%
ungroup()
# Generic services: read these values to all regions because they're all the same
L244.GenericServiceSatiation_SSPs <- L244.ServiceSatiation_USA_SSPs %>%
select(LEVEL2_DATA_NAMES[["GenericServiceSatiation"]], SSP, -region) %>%
distinct() %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["GenericServiceSatiation"]], "SSP"), GCAM_region_names = GCAM_region_names)
# Need to match in the floorspace into the base service table, divide to calculate the service demand
# per unit floorspace in the final calibration year. This (increased slightly) is then the minimum satiation level that needs to be read in.
L244.GenericServiceSatiation_SSPs <- L244.GenericServiceSatiation_SSPs %>%
left_join_error_no_match(L244.BS, by = c(LEVEL2_DATA_NAMES[["BldNodes"]], "building.service.input")) %>%
mutate(satiation.level = pmax(satiation.level, service.per.flsp * 1.0001)) %>%
select(-service.per.flsp) %>%
# Split by SSP, creating a list with a tibble for each SSP, then add attributes
split(.$SSP) %>%
lapply(function(df) {
select(df, -SSP) %>%
add_units("EJ/billion m2 floorspace") %>%
add_comments("For USA, calculate satiation level as base year service / base year floorspace times multiplier") %>%
add_comments("USA values written to all regions, then we make sure that no satiation level is below base year service per floorspace") %>%
add_precursors("L144.base_service_EJ_serv", "energy/calibrated_techs_bld_det", "common/GCAM_region_names",
"L144.flsp_bm2_R_res_Yh", "L144.flsp_bm2_R_comm_Yh", "energy/A44.demand_satiation_mult_SSPs")
})
# Assign each tibble in list
for(i in names(L244.GenericServiceSatiation_SSPs)) {
assign(paste0("L244.GenericServiceSatiation_", i), L244.GenericServiceSatiation_SSPs[[i]] %>%
add_title(paste0("Satiation levels for non-thermal building services: ", i)) %>%
add_legacy_name(paste0("L244.GenericServiceSatiation_", i)))
}
# L244.ThermalServiceSatiation: Satiation levels assumed for thermal building services
# Write USA thermal satiation levels to all regions
L244.ThermalServiceSatiation <- L244.ServiceSatiation_USA %>%
filter(building.service.input %in% thermal_services) %>%
select(-region, -GCAM_region_ID, -value, -year) %>%
distinct() %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["GenericServiceSatiation"]], GCAM_region_names = GCAM_region_names) %>%
rename(thermal.building.service.input = building.service.input) %>%
# Thermal service satiation is modified in each region according to the HDD/CDD ratio to the USA in a given year
mutate(variable = if_else(thermal.building.service.input %in% heating_services, "HDD", "CDD")) %>%
left_join_error_no_match(L244.HDDCDD_normal_R_Y, by = c("region", "variable")) %>%
group_by(gcam.consumer, variable) %>%
mutate(satiation.level = round(satiation.level * degree.days / degree.days[region == gcam.USA_REGION], digits = energy.DIGITS_CALOUTPUT)) %>%
ungroup()
# The service satiation in the final cal year can not be lower than the observed demand, so need to use pmax to set a floor on the quantity
# First need to calculate the maximum quantities of demand over the historical time period, expressed per unit floorspace
L244.tmp <- L244.ThermalBaseService %>%
left_join_error_no_match(L244.Floorspace, by = c(LEVEL2_DATA_NAMES[["BldNodes"]], "year")) %>%
mutate(service.per.flsp = base.service / base.building.size) %>%
filter(year == max(MODEL_BASE_YEARS)) %>%
select(-base.service, - base.building.size, -year)
# Then, match in this quantity into the thermal service satiation and take the max
L244.ThermalServiceSatiation <- L244.ThermalServiceSatiation %>%
left_join_error_no_match(L244.tmp, by = c(LEVEL2_DATA_NAMES[["BldNodes"]], "thermal.building.service.input")) %>%
mutate(satiation.level = round(pmax(satiation.level, service.per.flsp * 1.0001),
digits = energy.DIGITS_CALOUTPUT)) %>%
select(LEVEL2_DATA_NAMES[["ThermalServiceSatiation"]])
# L244.ShellConductance_bld: Shell conductance (inverse of shell efficiency)
L244.ShellConductance_bld <- L144.shell_eff_R_Y %>%
rename(shell.conductance = value) %>%
filter(year %in% MODEL_YEARS) %>%
mutate(shell.conductance = round(shell.conductance, digits = energy.DIGITS_EFFICIENCY)) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_error_no_match(A44.gcam_consumer, by = c("supplysector" = "gcam.consumer")) %>%
mutate(gcam.consumer = supplysector,
shell.year = year,
floor.to.surface.ratio = energy.FLOOR_TO_SURFACE_RATIO) %>%
select(LEVEL2_DATA_NAMES[["ShellConductance"]])
# L244.Supplysector_bld: Supplysector info for buildings
L244.Supplysector_bld <- write_to_all_regions(A44.sector, c(LEVEL2_DATA_NAMES[["Supplysector"]], LOGIT_TYPE_COLNAME),
GCAM_region_names = GCAM_region_names)
# L244.FinalEnergyKeyword_bld: Supply sector keywords for detailed building sector
L244.FinalEnergyKeyword_bld <- write_to_all_regions(A44.sector, c(LEVEL2_DATA_NAMES[["FinalEnergyKeyword"]]),
GCAM_region_names = GCAM_region_names) %>%
na.omit()
# Subsector information
## Not all subsectors exist in all regions; tradbio and heat are only modeled in selected regions
## The level1 end-use tech efficiency file has all of the combinations that exist
L244.Tech_bld <- L144.end_use_eff %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
select(region, supplysector, subsector, technology) %>%
distinct()
# L244.SubsectorLogit_bld: Subsector logit exponents of building sector
L244.SubsectorLogit_bld <- write_to_all_regions(A44.subsector_logit, c(LEVEL2_DATA_NAMES[["SubsectorLogit"]], LOGIT_TYPE_COLNAME),
GCAM_region_names = GCAM_region_names) %>%
semi_join(L244.Tech_bld, by = c("region", "supplysector", "subsector"))
# L244.SubsectorShrwt_bld and L244.SubsectorShrwtFllt_bld: Subsector shareweights of building sector
if(any(!is.na(A44.subsector_shrwt$year))) {
L244.SubsectorShrwt_bld <- A44.subsector_shrwt %>%
filter(!is.na(year)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["SubsectorShrwt"]], GCAM_region_names = GCAM_region_names) %>%
semi_join(L244.Tech_bld, by = c("region", "supplysector", "subsector"))
}
if(any(!is.na(A44.subsector_shrwt$year.fillout))) {
L244.SubsectorShrwtFllt_bld <- A44.subsector_shrwt %>%
filter(!is.na(year.fillout)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["SubsectorShrwtFllt"]], GCAM_region_names = GCAM_region_names) %>%
semi_join(L244.Tech_bld, by = c("region", "supplysector", "subsector"))
}
# L244.SubsectorInterp_bld and L244.SubsectorInterpTo_bld: Subsector shareweight interpolation of building sector
if(any(is.na(A44.subsector_interp$to.value))) {
L244.SubsectorInterp_bld <- A44.subsector_interp %>%
filter(is.na(to.value)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["SubsectorInterp"]], GCAM_region_names = GCAM_region_names) %>%
semi_join(L244.Tech_bld, by = c("region", "supplysector", "subsector"))
}
if(any(!is.na(A44.subsector_interp$to.value))) {
L244.SubsectorInterpTo_bld <- A44.subsector_interp %>%
filter(!is.na(to.value)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["SubsectorInterpTo"]], GCAM_region_names = GCAM_region_names) %>%
semi_join(L244.Tech_bld, by = c("region", "supplysector", "subsector"))
}
# L244.FuelPrefElast_bld: Fuel preference elasticities for buildings
L244.FuelPrefElast_bld <- A44.fuelprefElasticity %>%
mutate(year.fillout = min(MODEL_BASE_YEARS)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["FuelPrefElast"]], GCAM_region_names = GCAM_region_names) %>%
semi_join(L244.Tech_bld, by = c("region", "supplysector", "subsector"))
# L244.FuelPrefElast_bld_SSP3: Fuel preference elasticities for buildings in SSP 3
L244.FuelPrefElast_bld_SSP3 <- A44.fuelprefElasticity_SSP3 %>%
mutate(year.fillout = min(MODEL_BASE_YEARS)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["FuelPrefElast"]], GCAM_region_names = GCAM_region_names) %>%
semi_join(L244.Tech_bld, by = c("region", "supplysector", "subsector"))
# L244.FuelPrefElast_bld_SSP4: Fuel preference elasticities for buildings in SSP 4
L244.FuelPrefElast_bld_SSP4 <- A44.fuelprefElasticity_SSP4 %>%
mutate(year.fillout = min(MODEL_BASE_YEARS)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["FuelPrefElast"]], GCAM_region_names = GCAM_region_names) %>%
semi_join(L244.Tech_bld, by = c("region", "supplysector", "subsector"))
# L244.FuelPrefElast_bld_SSP15: Fuel preference elasticities for buildings in SSP 1 & 5
L244.FuelPrefElast_bld_SSP15 <- A44.fuelprefElasticity_SSP15 %>%
mutate(year.fillout = min(MODEL_BASE_YEARS)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["FuelPrefElast"]], GCAM_region_names = GCAM_region_names) %>%
semi_join(L244.Tech_bld, by = c("region", "supplysector", "subsector"))
# L244.StubTech_bld: Identification of stub technologies for buildings
L244.StubTech_bld <- L244.Tech_bld %>%
rename(stub.technology = technology)
# L244.StubTechCalInput_bld: Calibrated energy consumption by buildings technologies
L244.StubTechCalInput_bld <- L144.in_EJ_R_bld_serv_F_Yh %>%
filter(year %in% MODEL_BASE_YEARS) %>%
rename(calibrated.value = value) %>%
mutate(calibrated.value = round(calibrated.value, energy.DIGITS_CALOUTPUT)) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_error_no_match(calibrated_techs_bld_det, by = c("sector", "service", "fuel")) %>%
mutate(share.weight.year = year,
stub.technology = technology) %>%
group_by(region, supplysector, subsector, year) %>%
mutate(subs.share.weight = sum(calibrated.value)) %>%
ungroup() %>%
# If aggregated calibrated value > 0, set subsector shareweight to 1, else set to 0
mutate(subs.share.weight = if_else(subs.share.weight > 0, 1, 0),
# If calibrated value for specific technology > 0 , set tech shareweight to 1, else set to 0
tech.share.weight = if_else(calibrated.value > 0, 1, 0)) %>%
select(LEVEL2_DATA_NAMES[["StubTechCalInput"]])
# L244.StubTechEff_bld: Assumed efficiencies (all years) of buildings technologies
L244.StubTechEff_bld <- L144.end_use_eff %>%
filter(year %in% MODEL_YEARS) %>%
mutate(value = round(value, energy.DIGITS_CALOUTPUT)) %>%
rename(efficiency = value) %>%
# Add region and input
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_error_no_match(calibrated_techs_bld_det, by = c("supplysector", "subsector", "technology")) %>%
mutate(stub.technology = technology,
market.name = region) %>%
select(LEVEL2_DATA_NAMES[["StubTechEff"]])
# L244.GlobalTechShrwt_bld: Default shareweights for global building technologies
L244.GlobalTechShrwt_bld <- A44.globaltech_shrwt %>%
# Repeat for all model years
complete(nesting(supplysector, subsector, technology), year = c(year, MODEL_YEARS)) %>%
# Interpolate
group_by(supplysector, subsector, technology) %>%
mutate(share.weight = approx_fun(year, value, rule = 2)) %>%
ungroup() %>%
filter(year %in% MODEL_YEARS) %>%
rename(sector.name = supplysector,
subsector.name = subsector) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechYr"]], share.weight)
# L244.GlobalTechCost_bld: Non-fuel costs of global building technologies
L244.GlobalTechCost_bld <- L144.NEcost_75USDGJ %>%
mutate(input.cost = round(NEcostPerService, energy.DIGITS_COST)) %>%
repeat_add_columns(tibble(year = MODEL_YEARS)) %>%
rename(sector.name = supplysector,
subsector.name = subsector) %>%
mutate(minicam.non.energy.input = "non-energy") %>%
select(LEVEL2_DATA_NAMES[["GlobalTechCost"]])
# L244.StubTechIntGainOutputRatio: Output ratios of internal gain energy from non-thermal building services
L244.StubTechIntGainOutputRatio <- L144.internal_gains %>%
filter(year %in% MODEL_YEARS) %>%
# Round and rename value
mutate(value = round(value, energy.DIGITS_EFFICIENCY)) %>%
rename(internal.gains.output.ratio = value) %>%
# Add region name
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
# Add building.node.input
left_join_error_no_match(calibrated_techs_bld_det %>%
select(supplysector, building.node.input) %>%
distinct(), by = "supplysector") %>%
# Add internal.gains.market.name
left_join_error_no_match(A44.gcam_consumer, by = "building.node.input") %>%
select(LEVEL2_DATA_NAMES[["TechYr"]], internal.gains.output.ratio, internal.gains.market.name)
# L244.Intgains_scalar: Scalers relating internal gain energy to increased/reduced cooling/heating demands
variable <- c("HDD", "CDD")
InternalGainsScalar_USA <- c(energy.INTERNAL_GAINS_SCALAR_USA_H, energy.INTERNAL_GAINS_SCALAR_USA_C)
US.base.scalar <- tibble(variable, InternalGainsScalar_USA)
L244.Intgains_scalar <- L244.ThermalServiceSatiation %>%
mutate(variable = if_else(thermal.building.service.input %in% heating_services, "HDD", "CDD")) %>%
left_join_error_no_match(US.base.scalar, by = "variable") %>%
left_join_error_no_match(L244.HDDCDD_normal_R_Y, by = c("region", "variable")) %>%
group_by(thermal.building.service.input) %>%
mutate(scalar_mult = degree.days / degree.days[region == gcam.USA_REGION]) %>%
ungroup() %>%
mutate(internal.gains.scalar = round(InternalGainsScalar_USA * scalar_mult, energy.DIGITS_HDDCDD)) %>%
select(LEVEL2_DATA_NAMES[["Intgains_scalar"]])
# Need to remove any services (supplysectors and building-service-inputs) and intgains trial markets for services that don't exist in any years
# L244.DeleteThermalService and L244.DeleteGenericService: Removing non-existent services, likely related to 0 HDD or CDD
L244.DeleteThermalService <- L244.ThermalBaseService %>%
group_by(region, gcam.consumer, nodeInput, building.node.input, thermal.building.service.input) %>%
summarise(base.service = max(base.service)) %>%
ungroup() %>%
filter(base.service == 0) %>%
select(-base.service) %>%
mutate(supplysector = thermal.building.service.input)
L244.DeleteGenericService <- L244.GenericBaseService %>%
group_by(region, gcam.consumer, nodeInput, building.node.input, building.service.input) %>%
summarise(base.service = max(base.service)) %>%
ungroup()
# This tibble is empty because no base.service = 0, so we will make an if statement to produce correct empty tibble
if(any(L244.DeleteGenericService$base.service == 0)) {
L244.DeleteGenericService <- L244.DeleteGenericService%>%
filter(base.service == 0) %>%
select(-base.service) %>%
mutate(supplysector = building.service.input)
} else {
rm("L244.DeleteGenericService")
}
# ===================================================
# Produce outputs
L244.SubregionalShares %>%
add_title("Subregional population and income shares") %>%
add_units("Unitless") %>%
add_comments("A44.gcam_consumer written to all regions") %>%
add_comments("subregional.population.share and subregional.income.share set to 1") %>%
add_legacy_name("L244.SubregionalShares") %>%
add_precursors("common/GCAM_region_names", "energy/A44.gcam_consumer") ->
L244.SubregionalShares
L244.PriceExp_IntGains %>%
add_title("Price exponent on floorspace and naming of internal gains trial markets") %>%
add_units("Unitless") %>%
add_comments("A44.gcam_consumer written to all regions") %>%
add_legacy_name("L244.PriceExp_IntGains") %>%
add_precursors("common/GCAM_region_names", "energy/A44.gcam_consumer")->
L244.PriceExp_IntGains
L244.Floorspace %>%
add_title("Base year floorspace") %>%
add_units("billion m2") %>%
add_comments("Values taken from L144.flsp_bm2_R_res_Yh and L144.flsp_bm2_R_comm_Yh") %>%
add_legacy_name("L244.Floorspace") %>%
add_precursors("common/GCAM_region_names", "energy/A44.gcam_consumer",
"L144.flsp_bm2_R_res_Yh", "L144.flsp_bm2_R_comm_Yh") ->
L244.Floorspace
L244.DemandFunction_serv %>%
add_title("Demand function for building service") %>%
add_units("NA") %>%
add_comments("A44.demandFn_serv written to all regions") %>%
add_comments("can be multiple lines") %>%
add_legacy_name("L244.DemandFunction_serv") %>%
add_precursors("common/GCAM_region_names", "energy/A44.demandFn_serv")->
L244.DemandFunction_serv
L244.DemandFunction_flsp %>%
add_title("Demand function for building floorspace") %>%
add_units("NA") %>%
add_comments("A44.demandFn_flsp written to all regions") %>%
add_comments("can be multiple lines") %>%
add_legacy_name("L244.DemandFunction_flsp") %>%
add_precursors("common/GCAM_region_names", "energy/A44.demandFn_flsp") ->
L244.DemandFunction_flsp
L244.Satiation_flsp %>%
add_title("Floorspace demand satiation") %>%
add_units("Million squared meters per capita") %>%
add_comments("Values from A44.satiation_flsp added to A44.gcam_consumer written to all regions") %>%
add_legacy_name("L244.Satiation_flsp") %>%
add_precursors("energy/A44.satiation_flsp", "energy/A44.gcam_consumer", "common/GCAM_region_names", "energy/A_regions") ->
L244.Satiation_flsp
L244.SatiationAdder %>%
add_title("Satiation adders in floorspace demand function") %>%
add_units("Unitless") %>%
add_comments("Satiation adder compute using satiation level, per-capita GDP and per-capita floorsapce") %>%
add_legacy_name("L244.SatiationAdder") %>%
add_precursors("energy/A44.satiation_flsp", "energy/A44.gcam_consumer", "common/GCAM_region_names", "energy/A_regions",
"L102.pcgdp_thous90USD_Scen_R_Y", "L101.Pop_thous_R_Yh",
"L144.flsp_bm2_R_res_Yh", "L144.flsp_bm2_R_comm_Yh") ->
L244.SatiationAdder
L244.GompFnParam %>%
add_title("Parameters for the floorspace Gompertz function") %>%
add_units("Unitless") %>%
add_comments("Computed offline based on data from RECS and IEA") %>%
add_legacy_name("L244.GompFnParam") %>%
add_precursors("common/GCAM_region_names",
"L144.flsp_param",
"L102.pcgdp_thous90USD_Scen_R_Y", "L101.Pop_thous_R_Yh",
"L144.flsp_bm2_R_res_Yh","L144.hab_land_flsp_fin") ->
L244.GompFnParam
L244.ThermalBaseService %>%
add_title("Historical building heating and cooling energy output") %>%
add_units("EJ/yr") %>%
add_comments("L144.base_service_EJ_serv rounded and renamed") %>%
add_legacy_name("L244.ThermalBaseService") %>%
add_precursors("energy/A44.internal_gains", "energy/A44.sector", "L144.base_service_EJ_serv",
"energy/calibrated_techs_bld_det", "common/GCAM_region_names") ->
L244.ThermalBaseService
L244.GenericBaseService %>%
add_title("Historical building `other` energy output") %>%
add_units("EJ/yr") %>%
add_comments("L144.base_service_EJ_serv rounded and renamed") %>%
add_legacy_name("L244.GenericBaseService") %>%
same_precursors_as(L244.ThermalBaseService) ->
L244.GenericBaseService
L244.ThermalServiceSatiation %>%
add_title("Satiation levels for thermal building services") %>%
add_units("EJ/billion m2 floorspace") %>%
add_comments("For USA, calculate satiation level as base year service / base year floorspace times multiplier") %>%
add_comments("USA values written to all regions, which are multiplied by ratio of degree days in each region to degree days in USA") %>%
add_comments("then we make sure that no satiation level is below base year service per floorspace") %>%
add_legacy_name("L244.ThermalServiceSatiation") %>%
add_precursors("L144.base_service_EJ_serv", "energy/calibrated_techs_bld_det", "common/GCAM_region_names",
"L144.flsp_bm2_R_res_Yh", "L144.flsp_bm2_R_comm_Yh", "energy/A44.demand_satiation_mult",
"L143.HDDCDD_scen_R_Y", "energy/A44.internal_gains", "energy/A44.sector", "energy/A44.gcam_consumer") ->
L244.ThermalServiceSatiation
L244.GenericServiceSatiation %>%
add_title("Satiation levels for non-thermal building services") %>%
add_units("EJ/billion m2 floorspace") %>%
add_comments("For USA, calculate satiation level as base year service / base year floorspace times multiplier") %>%
add_comments("USA values written to all regions, then we make sure that no satiation level is below base year service per floorspace") %>%
add_legacy_name("L244.GenericServiceSatiation") %>%
add_precursors("L144.base_service_EJ_serv", "energy/calibrated_techs_bld_det", "common/GCAM_region_names",
"L144.flsp_bm2_R_res_Yh", "L144.flsp_bm2_R_comm_Yh", "energy/A44.demand_satiation_mult") ->
L244.GenericServiceSatiation
L244.Intgains_scalar %>%
add_title("Scalers relating internal gain energy to increased/reduced cooling/heating demands") %>%
add_units("Unitless") %>%
add_comments("USA base scalar assumption multiplied by ratio of degree days to USA degree days") %>%
add_legacy_name("L244.Intgains_scalar") %>%
same_precursors_as(L244.ThermalServiceSatiation) ->
L244.Intgains_scalar
L244.ShellConductance_bld %>%
add_title("Shell conductance (inverse of shell efficiency)") %>%
add_units("Unitless") %>%
add_comments("Shell conductance from L144.shell_eff_R_Y") %>%
add_legacy_name("L244.ShellConductance_bld") %>%
add_precursors("L144.shell_eff_R_Y", "common/GCAM_region_names", "energy/A44.gcam_consumer") ->
L244.ShellConductance_bld
L244.Supplysector_bld %>%
add_title("Supplysector info for buildings") %>%
add_units("NA") %>%
add_comments("A44.sector written to all regions") %>%
add_legacy_name("L244.Supplysector_bld") %>%
add_precursors("energy/A44.sector", "common/GCAM_region_names") ->
L244.Supplysector_bld
L244.FinalEnergyKeyword_bld %>%
add_title("Supply sector keywords for detailed building sector") %>%
add_units("NA") %>%
add_comments("A44.sector written to all regions") %>%
add_legacy_name("L244.FinalEnergyKeyword_bld") %>%
same_precursors_as(L244.Supplysector_bld) ->
L244.FinalEnergyKeyword_bld
if(exists("L244.SubsectorShrwt_bld")) {
L244.SubsectorShrwt_bld %>%
add_title("Subsector shareweights for building sector") %>%
add_units("Unitless") %>%
add_comments("A44.subsector_shrwt written to all regions") %>%
add_legacy_name("L244.SubsectorShrwt_bld") %>%
add_precursors("energy/A44.subsector_shrwt", "common/GCAM_region_names", "L144.end_use_eff") ->
L244.SubsectorShrwt_bld
} else {
missing_data() %>%
add_legacy_name("L244.SubsectorShrwt_bld") ->
L244.SubsectorShrwt_bld
}
if(exists("L244.SubsectorShrwtFllt_bld")) {
L244.SubsectorShrwtFllt_bld %>%
add_title("Subsector shareweights for building sector") %>%
add_units("Unitless") %>%
add_comments("A44.subsector_shrwt written to all regions") %>%
add_legacy_name("L244.SubsectorShrwtFllt_bld") %>%
add_precursors("energy/A44.subsector_shrwt", "common/GCAM_region_names", "L144.end_use_eff") ->
L244.SubsectorShrwtFllt_bld
} else {
missing_data() %>%
add_legacy_name("L244.SubsectorShrwtFllt_bld") ->
L244.SubsectorShrwtFllt_bld
}
if(exists("L244.SubsectorInterp_bld")) {
L244.SubsectorInterp_bld %>%
add_title("Subsector shareweight interpolation for building sector") %>%
add_units("NA") %>%
add_comments("A44.subsector_interp written to all regions") %>%
add_legacy_name("L244.SubsectorInterp_bld") %>%
add_precursors("energy/A44.subsector_interp", "common/GCAM_region_names", "L144.end_use_eff") ->
L244.SubsectorInterp_bld
} else {
missing_data() %>%
add_legacy_name("L244.SubsectorInterp_bld") ->
L244.SubsectorInterp_bld
}
if(exists("L244.SubsectorInterpTo_bld")) {
L244.SubsectorInterpTo_bld %>%
add_title("Subsector shareweight interpolation for building sector") %>%
add_units("NA") %>%
add_comments("A44.subsector_interp written to all regions") %>%
add_legacy_name("L244.SubsectorInterpTo_bld") %>%
add_precursors("energy/A44.subsector_interp", "common/GCAM_region_names", "L144.end_use_eff") ->
L244.SubsectorInterpTo_bld
} else {
missing_data() %>%
add_legacy_name("L244.SubsectorInterpTo_bld") ->
L244.SubsectorInterpTo_bld
}
L244.SubsectorLogit_bld %>%
add_title("Subsector logit exponents of building sector") %>%
add_units("Unitless") %>%
add_comments("A44.subsector_logit written to all regions") %>%
add_legacy_name("L244.SubsectorLogit_bld") %>%
add_precursors("energy/A44.subsector_logit", "common/GCAM_region_names", "L144.end_use_eff") ->
L244.SubsectorLogit_bld
L244.FuelPrefElast_bld %>%
add_title("Fuel preference elasticities for buildings") %>%
add_units("Unitless") %>%
add_comments("A44.fuelprefElasticity written to all regions") %>%
add_legacy_name("L244.FuelPrefElast_bld") %>%
add_precursors("energy/A44.fuelprefElasticity", "common/GCAM_region_names", "L144.end_use_eff") ->
L244.FuelPrefElast_bld
L244.StubTech_bld %>%
add_title("Identification of stub technologies for buildings") %>%
add_units("NA") %>%
add_comments("Technologies from L144.end_use_eff") %>%
add_legacy_name("L244.StubTech_bld") %>%
add_precursors("L144.end_use_eff") ->
L244.StubTech_bld
L244.StubTechEff_bld %>%
add_title("Assumed efficiencies of buildings technologies") %>%
add_units("Unitless efficiency") %>%
add_comments("Efficiencies taken from L144.end_use_eff") %>%
add_legacy_name("L244.StubTechEff_bld") %>%
add_precursors("L144.end_use_eff", "common/GCAM_region_names", "energy/calibrated_techs_bld_det") ->
L244.StubTechEff_bld
L244.StubTechCalInput_bld %>%
add_title("Calibrated energy consumption by buildings technologies") %>%
add_units("calibrated.value: EJ; shareweights: Unitless") %>%
add_comments("Calibrated values directly from L144.in_EJ_R_bld_serv_F_Yh") %>%
add_comments("Shareweights are 1 if subsector/technology total is non-zero, 0 otherwise") %>%
add_legacy_name("L244.StubTechCalInput_bld") %>%
add_precursors("L144.in_EJ_R_bld_serv_F_Yh", "common/GCAM_region_names", "energy/calibrated_techs_bld_det") ->
L244.StubTechCalInput_bld
L244.StubTechIntGainOutputRatio %>%
add_title("Output ratios of internal gain energy from non-thermal building services") %>%
add_units("Unitless output ratio") %>%
add_comments("Values from L144.internal_gains") %>%
add_legacy_name("L244.StubTechIntGainOutputRatio") %>%
add_precursors("L144.internal_gains", "common/GCAM_region_names",
"energy/calibrated_techs_bld_det", "energy/A44.gcam_consumer") ->
L244.StubTechIntGainOutputRatio
L244.GlobalTechShrwt_bld %>%
add_title("Default shareweights for global building technologies") %>%
add_units("Unitless") %>%
add_comments("Values interpolated from A44.globaltech_shrwt") %>%
add_legacy_name("L244.GlobalTechShrwt_bld") %>%
add_precursors("energy/A44.globaltech_shrwt") ->
L244.GlobalTechShrwt_bld
L244.GlobalTechCost_bld %>%
add_title("Non-fuel costs of global building technologies") %>%
add_units("1975$/GJ-service") %>%
add_comments("Costs from L144.NEcost_75USDGJ expanded to model years") %>%
add_legacy_name("L244.GlobalTechCost_bld") %>%
add_precursors("L144.NEcost_75USDGJ") ->
L244.GlobalTechCost_bld
if(exists("L244.DeleteGenericService")) {
L244.DeleteGenericService %>%
add_title("Removing non-existent services") %>%
add_units("NA") %>%
add_comments("Categories from L244.GenericBaseService with no base.service") %>%
add_legacy_name("L244.DeleteGenericService") %>%
same_precursors_as(L244.GenericBaseService) ->
L244.DeleteGenericService
} else {
missing_data() %>%
add_legacy_name("L244.DeleteGenericService") ->
L244.DeleteGenericService
}
L244.FuelPrefElast_bld_SSP3 %>%
add_title("Fuel preference elasticities for buildings: SSP3") %>%
add_units("Unitless") %>%
add_comments("A44.fuelprefElasticity_SSP3 written to all regions") %>%
add_legacy_name("L244.FuelPrefElast_bld_SSP3") %>%
add_precursors("energy/A44.fuelprefElasticity_SSP3", "common/GCAM_region_names", "L144.end_use_eff") ->
L244.FuelPrefElast_bld_SSP3
L244.FuelPrefElast_bld_SSP4 %>%
add_title("Fuel preference elasticities for buildings: SSP4") %>%
add_units("Unitless") %>%
add_comments("A44.fuelprefElasticity_SSP4 written to all regions") %>%
add_legacy_name("L244.FuelPrefElast_bld_SSP4") %>%
add_precursors("energy/A44.fuelprefElasticity_SSP4", "common/GCAM_region_names", "L144.end_use_eff") ->
L244.FuelPrefElast_bld_SSP4
L244.FuelPrefElast_bld_SSP15 %>%
add_title("Fuel preference elasticities for buildings: SSP 1 & 5") %>%
add_units("Unitless") %>%
add_comments("A44.fuelprefElasticity_SSP15 written to all regions") %>%
add_legacy_name("L244.FuelPrefElast_bld_SSP15") %>%
add_precursors("energy/A44.fuelprefElasticity_SSP15", "common/GCAM_region_names", "L144.end_use_eff") ->
L244.FuelPrefElast_bld_SSP15
L244.DeleteThermalService %>%
add_title("Removing non-existent thermal services") %>%
add_units("NA") %>%
add_comments("Categories from L244.ThermalBaseService with no base.service") %>%
add_legacy_name("L244.DeleteThermalService") %>%
same_precursors_as(L244.ThermalBaseService) ->
L244.DeleteThermalService
return_data(L244.SubregionalShares, L244.PriceExp_IntGains, L244.Floorspace, L244.DemandFunction_serv, L244.DemandFunction_flsp,
L244.Satiation_flsp, L244.SatiationAdder, L244.ThermalBaseService, L244.GenericBaseService, L244.ThermalServiceSatiation,
L244.GenericServiceSatiation, L244.Intgains_scalar, L244.ShellConductance_bld,
L244.Supplysector_bld, L244.FinalEnergyKeyword_bld, L244.SubsectorShrwt_bld, L244.SubsectorShrwtFllt_bld, L244.SubsectorInterp_bld,
L244.SubsectorInterpTo_bld, L244.FuelPrefElast_bld,
L244.StubTech_bld, L244.StubTechEff_bld, L244.StubTechCalInput_bld, L244.GlobalTechShrwt_bld,
L244.GlobalTechCost_bld, L244.DeleteGenericService, L244.Satiation_flsp_SSP1, L244.SatiationAdder_SSP1,
L244.GenericServiceSatiation_SSP1, L244.Satiation_flsp_SSP2,
L244.SatiationAdder_SSP2, L244.GenericServiceSatiation_SSP2, L244.Satiation_flsp_SSP3, L244.SatiationAdder_SSP3,
L244.GenericServiceSatiation_SSP3, L244.FuelPrefElast_bld_SSP3, L244.Satiation_flsp_SSP4,
L244.SatiationAdder_SSP4, L244.GenericServiceSatiation_SSP4, L244.FuelPrefElast_bld_SSP4,
L244.Satiation_flsp_SSP5, L244.SatiationAdder_SSP5, L244.GenericServiceSatiation_SSP5, L244.FuelPrefElast_bld_SSP15,
L244.DeleteThermalService, L244.SubsectorLogit_bld, L244.StubTechIntGainOutputRatio,
L244.HDDCDD_A2_CCSM3x, L244.HDDCDD_A2_HadCM3, L244.HDDCDD_B1_CCSM3x, L244.HDDCDD_B1_HadCM3, L244.HDDCDD_constdd_no_GCM,
L244.GompFnParam)
} else {
stop("Unknown command")
}
}
bpbond/gcamdata documentation built on March 22, 2023, 4:52 a.m.
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Defines functions module_energy_L244.building_det
Documented in module_energy_L244.building_det
# Copyright 2019 Battelle Memorial Institute; see the LICENSE file.
#' module_energy_L244.building_det
#'
#' Creates level2 data for the building sector.
#'
#' @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{L244.SubregionalShares}, \code{L244.PriceExp_IntGains}, \code{L244.Floorspace}, \code{L244.DemandFunction_serv},
#' \code{L244.DemandFunction_flsp}, \code{L244.Satiation_flsp}, \code{L244.SatiationAdder}, \code{L244.ThermalBaseService}, \code{L244.GenericBaseService},
#'\code{L244.ThermalServiceSatiation}, \code{L244.GenericServiceSatiation}, \code{L244.Intgains_scalar}, \code{L244.ShellConductance_bld},
#' \code{L244.Supplysector_bld}, \code{L244.FinalEnergyKeyword_bld}, \code{L244.SubsectorShrwt_bld}, \code{L244.SubsectorShrwtFllt_bld}, \code{L244.SubsectorInterp_bld},
#' \code{L244.SubsectorInterpTo_bld}, \code{L244.SubsectorLogit_bld}, \code{L244.FuelPrefElast_bld}, \code{L244.StubTech_bld}, \code{L244.StubTechEff_bld},
#' \code{L244.StubTechCalInput_bld}, \code{L244.StubTechIntGainOutputRatio}, \code{L244.GlobalTechShrwt_bld}, \code{L244.GlobalTechCost_bld},
#' \code{L244.DeleteGenericService}, \code{L244.Satiation_flsp_SSP1}, \code{L244.SatiationAdder_SSP1}, \code{L244.GenericServiceSatiation_SSP1},
#' \code{L244.Satiation_flsp_SSP2}, \code{L244.SatiationAdder_SSP2}, \code{L244.GenericServiceSatiation_SSP2}, \code{L244.Satiation_flsp_SSP3},
#' \code{L244.SatiationAdder_SSP3}, \code{L244.GenericServiceSatiation_SSP3}, \code{L244.FuelPrefElast_bld_SSP3}, \code{L244.Satiation_flsp_SSP4},
#' \code{L244.SatiationAdder_SSP4}, \code{L244.GenericServiceSatiation_SSP4}, \code{L244.FuelPrefElast_bld_SSP4}, \code{L244.Satiation_flsp_SSP5},
#' \code{L244.SatiationAdder_SSP5}, \code{L244.GenericServiceSatiation_SSP5}, \code{L244.FuelPrefElast_bld_SSP15}, \code{L244.DeleteThermalService},
#' \code{L244.HDDCDD_A2_CCSM3x}, \code{L244.HDDCDD_A2_HadCM3}, \code{L244.HDDCDD_B1_CCSM3x}, \code{L244.HDDCDD_B1_HadCM3},
#' \code{L244.HDDCDD_constdd_no_GCM} and \code{L244.GompFnParam}.
#' The corresponding file in the original data system was \code{L244.building_det.R} (energy level2).
#' @details Creates level2 data for the building sector.
#' @importFrom assertthat assert_that
#' @importFrom dplyr bind_rows distinct filter if_else group_by left_join mutate select semi_join summarise
#' @importFrom tidyr complete gather nesting unite
#' @author RLH September 2017
module_energy_L244.building_det <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "common/GCAM_region_names",
FILE = "energy/calibrated_techs_bld_det",
FILE = "energy/A_regions",
FILE = "energy/A44.sector",
FILE = "energy/A44.subsector_interp",
FILE = "energy/A44.subsector_logit",
FILE = "energy/A44.subsector_shrwt",
FILE = "energy/A44.fuelprefElasticity",
FILE = "energy/A44.fuelprefElasticity_SSP3",
FILE = "energy/A44.fuelprefElasticity_SSP4",
FILE = "energy/A44.fuelprefElasticity_SSP15",
FILE = "energy/A44.globaltech_shrwt",
FILE = "energy/A44.gcam_consumer",
FILE = "energy/A44.demandFn_serv",
FILE = "energy/A44.demandFn_flsp",
FILE = "energy/A44.internal_gains",
FILE = "energy/A44.satiation_flsp",
FILE = "energy/A44.satiation_flsp_SSPs",
FILE = "energy/A44.demand_satiation_mult",
FILE = "energy/A44.demand_satiation_mult_SSPs",
"L144.flsp_bm2_R_res_Yh",
"L144.flsp_bm2_R_comm_Yh",
"L144.base_service_EJ_serv",
"L144.in_EJ_R_bld_serv_F_Yh",
"L144.end_use_eff",
"L144.shell_eff_R_Y",
"L144.NEcost_75USDGJ",
"L144.internal_gains",
"L143.HDDCDD_scen_R_Y",
"L101.Pop_thous_R_Yh",
"L102.pcgdp_thous90USD_Scen_R_Y",
"L144.flsp_param",
"L144.hab_land_flsp_fin"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L244.SubregionalShares",
"L244.PriceExp_IntGains",
"L244.Floorspace",
"L244.DemandFunction_serv",
"L244.DemandFunction_flsp",
"L244.Satiation_flsp",
"L244.SatiationAdder",
"L244.ThermalBaseService",
"L244.GenericBaseService",
"L244.ThermalServiceSatiation",
"L244.GenericServiceSatiation",
"L244.Intgains_scalar",
"L244.ShellConductance_bld",
"L244.Supplysector_bld",
"L244.FinalEnergyKeyword_bld",
"L244.SubsectorShrwt_bld",
"L244.SubsectorShrwtFllt_bld",
"L244.SubsectorInterp_bld",
"L244.SubsectorInterpTo_bld",
"L244.SubsectorLogit_bld",
"L244.FuelPrefElast_bld",
"L244.StubTech_bld",
"L244.StubTechEff_bld",
"L244.StubTechCalInput_bld",
"L244.StubTechIntGainOutputRatio",
"L244.GlobalTechShrwt_bld",
"L244.GlobalTechCost_bld",
"L244.DeleteGenericService",
"L244.Satiation_flsp_SSP1",
"L244.SatiationAdder_SSP1",
"L244.GenericServiceSatiation_SSP1",
"L244.Satiation_flsp_SSP2",
"L244.SatiationAdder_SSP2",
"L244.GenericServiceSatiation_SSP2",
"L244.Satiation_flsp_SSP3",
"L244.SatiationAdder_SSP3",
"L244.GenericServiceSatiation_SSP3",
"L244.FuelPrefElast_bld_SSP3",
"L244.Satiation_flsp_SSP4",
"L244.SatiationAdder_SSP4",
"L244.GenericServiceSatiation_SSP4",
"L244.FuelPrefElast_bld_SSP4",
"L244.Satiation_flsp_SSP5",
"L244.SatiationAdder_SSP5",
"L244.GenericServiceSatiation_SSP5",
"L244.FuelPrefElast_bld_SSP15",
"L244.DeleteThermalService",
"L244.HDDCDD_A2_CCSM3x",
"L244.HDDCDD_A2_HadCM3",
"L244.HDDCDD_B1_CCSM3x",
"L244.HDDCDD_B1_HadCM3",
"L244.HDDCDD_constdd_no_GCM",
"L244.GompFnParam"))
} else if(command == driver.MAKE) {
# Silence package checks
building.service.input <- calibrated.value <- comm <- degree.days <- floorspace_bm2 <- gcam.consumer <-
internal.gains.market.name <- internal.gains.output.ratio <- multiplier <- nodeInput <- pcFlsp_mm2 <-
pcFlsp_mm2_fby <- pcGDP_thous90USD <- pop_thous <- region <- region.class <- resid <- satiation.adder <-
satiation.level <- scalar_mult <- sector <- service <- service.per.flsp <- share.weight <- shell.conductance <-
subs.share.weight <- subsector <- supplysector <- technology <- thermal.building.service.input <- to.value <-
value <- year <- year.fillout <- GCM <- NEcostPerService <- SRES <- SSP <- TRN_SSP <- base.building.size <-
base.service <- building.node.input <- . <- GCAM_region_ID <- L244.Satiation_flsp_SSP1 <-
L244.SatiationAdder_SSP1 <- L244.GenericServiceSatiation_SSP1 <- L244.Satiation_flsp_SSP2 <-
L244.SatiationAdder_SSP2 <- L244.GenericServiceSatiation_SSP2 <- L244.Satiation_flsp_SSP3 <-
L244.SatiationAdder_SSP3 <- L244.GenericServiceSatiation_SSP3 <-L244.Satiation_flsp_SSP4 <-
L244.SatiationAdder_SSP4 <- L244.GenericServiceSatiation_SSP4 <- L244.Satiation_flsp_SSP5 <-
L244.SatiationAdder_SSP5 <- L244.GenericServiceSatiation_SSP5 <- scenario <- L244.HDDCDD_A2_CCSM3x <-
L244.HDDCDD_A2_HadCM3 <- L244.HDDCDD_B1_CCSM3x <- L244.HDDCDD_B1_HadCM3 <- L244.HDDCDD_constdd_no_GCM <-
area_thouskm2 <- flsp <- flsp_pc <- unadjust.satiation <- land.density.param <- tot.dens <-
b.param <- income.param <- gdp_pc <- flsp_est <- base_flsp <- bias.adjust.param <- NULL
all_data <- list(...)[[1]]
# Load required inputs
GCAM_region_names <- get_data(all_data, "common/GCAM_region_names")
calibrated_techs_bld_det <- get_data(all_data, "energy/calibrated_techs_bld_det")
A_regions <- get_data(all_data, "energy/A_regions")
A44.sector <- get_data(all_data, "energy/A44.sector", strip_attributes = TRUE)
A44.subsector_interp <- get_data(all_data, "energy/A44.subsector_interp", strip_attributes = TRUE)
A44.subsector_logit <- get_data(all_data, "energy/A44.subsector_logit", strip_attributes = TRUE)
A44.subsector_shrwt <- get_data(all_data, "energy/A44.subsector_shrwt", strip_attributes = TRUE)
A44.fuelprefElasticity <- get_data(all_data, "energy/A44.fuelprefElasticity", strip_attributes = TRUE)
A44.fuelprefElasticity_SSP3 <- get_data(all_data, "energy/A44.fuelprefElasticity_SSP3", strip_attributes = TRUE)
A44.fuelprefElasticity_SSP4 <- get_data(all_data, "energy/A44.fuelprefElasticity_SSP4", strip_attributes = TRUE)
A44.fuelprefElasticity_SSP15 <- get_data(all_data, "energy/A44.fuelprefElasticity_SSP15", strip_attributes = TRUE)
A44.globaltech_shrwt <- get_data(all_data, "energy/A44.globaltech_shrwt") %>%
gather_years
A44.gcam_consumer <- get_data(all_data, "energy/A44.gcam_consumer", strip_attributes = TRUE)
A44.demandFn_serv <- get_data(all_data, "energy/A44.demandFn_serv", strip_attributes = TRUE)
A44.demandFn_flsp <- get_data(all_data, "energy/A44.demandFn_flsp", strip_attributes = TRUE)
A44.internal_gains <- get_data(all_data, "energy/A44.internal_gains")
A44.satiation_flsp <- get_data(all_data, "energy/A44.satiation_flsp")
A44.satiation_flsp_SSPs <- get_data(all_data, "energy/A44.satiation_flsp_SSPs")
A44.demand_satiation_mult <- get_data(all_data, "energy/A44.demand_satiation_mult")
A44.demand_satiation_mult_SSPs <- get_data(all_data, "energy/A44.demand_satiation_mult_SSPs")
L144.flsp_bm2_R_res_Yh <- get_data(all_data, "L144.flsp_bm2_R_res_Yh", strip_attributes = TRUE)
L144.flsp_bm2_R_comm_Yh <- get_data(all_data, "L144.flsp_bm2_R_comm_Yh", strip_attributes = TRUE)
L144.base_service_EJ_serv <- get_data(all_data, "L144.base_service_EJ_serv", strip_attributes = TRUE)
L144.in_EJ_R_bld_serv_F_Yh <- get_data(all_data, "L144.in_EJ_R_bld_serv_F_Yh")
L144.end_use_eff <- get_data(all_data, "L144.end_use_eff", strip_attributes = TRUE)
L144.shell_eff_R_Y <- get_data(all_data, "L144.shell_eff_R_Y", strip_attributes = TRUE)
L144.NEcost_75USDGJ <- get_data(all_data, "L144.NEcost_75USDGJ", strip_attributes = TRUE)
L144.internal_gains <- get_data(all_data, "L144.internal_gains", strip_attributes = TRUE)
L143.HDDCDD_scen_R_Y <- get_data(all_data, "L143.HDDCDD_scen_R_Y")
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") # year comes in as double
L144.flsp_param <- get_data(all_data, "L144.flsp_param", strip_attributes = TRUE)
L144.hab_land_flsp_fin<-get_data(all_data, "L144.hab_land_flsp_fin", strip_attributes = TRUE)
# ===================================================
# Subregional population and income shares: need to be read in because these default to 0
# L244.SubregionalShares: subregional population and income shares (not currently used)
L244.SubregionalShares <- write_to_all_regions(A44.gcam_consumer, LEVEL2_DATA_NAMES[["DeleteConsumer"]],
GCAM_region_names = GCAM_region_names) %>%
mutate(pop.year.fillout = min(MODEL_BASE_YEARS),
inc.year.fillout = min(MODEL_BASE_YEARS),
subregional.population.share = 1,
subregional.income.share = 1)
# Internal gains
# L244.PriceExp_IntGains: price exponent on floorspace and naming of internal gains trial markets
L244.PriceExp_IntGains <- write_to_all_regions(A44.gcam_consumer, LEVEL2_DATA_NAMES[["PriceExp_IntGains"]],
GCAM_region_names = GCAM_region_names)
# L244.Floorspace: base year floorspace
# Building residential floorspace in the base years
# Keep all historical years for now - these are needed in calculating satiation adders later on
A44.gcam_consumer_resid <- A44.gcam_consumer %>%
filter(grepl("res", A44.gcam_consumer$gcam.consumer))
L244.Floorspace_resid <- L144.flsp_bm2_R_res_Yh %>%
mutate(base.building.size = round(value, energy.DIGITS_FLOORSPACE)) %>%
select(-value) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
mutate(gcam.consumer = A44.gcam_consumer_resid$gcam.consumer,
nodeInput = A44.gcam_consumer_resid$nodeInput,
building.node.input = A44.gcam_consumer_resid$building.node.input) %>%
select(region, gcam.consumer, nodeInput, building.node.input, year, base.building.size)
# Commercial floorspace
A44.gcam_consumer_comm <- A44.gcam_consumer %>%
filter(grepl("comm", A44.gcam_consumer$gcam.consumer))
L244.Floorspace_comm <- L144.flsp_bm2_R_comm_Yh %>%
mutate(base.building.size = round(value, energy.DIGITS_FLOORSPACE)) %>%
select(-value) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
mutate(gcam.consumer = A44.gcam_consumer_comm$gcam.consumer,
nodeInput = A44.gcam_consumer_comm$nodeInput,
building.node.input = A44.gcam_consumer_comm$building.node.input) %>%
select(region, gcam.consumer, nodeInput, building.node.input, year, base.building.size)
L244.Floorspace <- bind_rows(L244.Floorspace_resid, L244.Floorspace_comm) %>%
filter(year %in% MODEL_BASE_YEARS)
# Demand function
# L244.DemandFunction_serv and L244.DemandFunction_flsp: demand function types
L244.DemandFunction_serv <- write_to_all_regions(A44.demandFn_serv, LEVEL2_DATA_NAMES[["DemandFunction_serv"]],
GCAM_region_names = GCAM_region_names)
L244.DemandFunction_flsp <- write_to_all_regions(A44.demandFn_flsp, LEVEL2_DATA_NAMES[["DemandFunction_flsp"]],
GCAM_region_names = GCAM_region_names)
# Floorspace demand satiation
# L244.Satiation_flsp: Satiation levels assumed for floorspace
L244.Satiation_flsp_class <- A44.satiation_flsp %>%
gather(sector, value, resid, comm) %>%
# Converting from square meters per capita to million square meters per capita
mutate(satiation.level = value * CONV_THOUS_BIL) %>%
select(-value)
L244.Satiation_flsp <- write_to_all_regions(A44.gcam_consumer, c("region", "gcam.consumer", "nodeInput", "building.node.input"), # replace with LEVEL2_DATA_NAMES[["BldNodes]]
GCAM_region_names = GCAM_region_names) %>%
# Match in the region class, and use this to then match in the satiation floorspace
left_join_error_no_match(A_regions %>% select(region, region.class),
by = "region") %>%
# Residential floorspace does not use the satiation demand function, so filter the commercial floorspace
filter(!grepl("resid",gcam.consumer)) %>%
left_join_error_no_match(L244.Satiation_flsp_class, by = c("region.class", "gcam.consumer" = "sector")) %>%
select(LEVEL2_DATA_NAMES[["Satiation_flsp"]])
# Satiation adder - Required for shaping the future floorspace growth trajectories in each region
# The satiation adder allows the starting (final calibration year) position of any region and sector to be set along the satiation demand function
# L244.SatiationAdder: Satiation adders in floorspace demand function
# First, prepare socioeconomics tables by adding region names
L102.pcgdp_thous90USD_Scen_R_Y <- L102.pcgdp_thous90USD_Scen_R_Y %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
rename(pcGDP_thous90USD = value)
L101.Pop_thous_R_Yh <- L101.Pop_thous_R_Yh %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
rename(pop_thous = value)
# In order to pass timeshift, we need floorspace for energy satiation year, which we don't have under timeshift conditions
# So instead, we will take floorspace for maximum year (this ONLY affects the data in the timeshift right now)
Floorspace_timeshift_pass <- L244.Floorspace %>%
filter(year == max(L244.Floorspace$year)) %>%
select(-year)
# Match in the per-capita GDP, total floorspace, and population (for calculating per-capita floorspace)
L244.SatiationAdder <- L244.Satiation_flsp %>%
left_join_keep_first_only(L102.pcgdp_thous90USD_Scen_R_Y %>%
filter(year == energy.SATIATION_YEAR), by = "region") %>%
left_join_error_no_match(Floorspace_timeshift_pass, by = c("region", "gcam.consumer", "nodeInput", "building.node.input")) %>%
left_join_error_no_match(L101.Pop_thous_R_Yh, by = c("region", "year", "GCAM_region_ID")) %>%
mutate(pcFlsp_mm2 = base.building.size / pop_thous,
# We now have all of the data required for calculating the satiation adder in each region
satiation.adder = round(satiation.level - exp(log(2) * pcGDP_thous90USD / energy.GDP_MID_SATIATION) *
(satiation.level - pcFlsp_mm2), energy.DIGITS_SATIATION_ADDER),
pcFlsp_mm2_fby = base.building.size / pop_thous,
# The satiation adder (million square meters of floorspace per person) needs to be less than the per-capita demand in the final calibration year
# Need to match in the demand in the final calibration year to check this.
satiation.adder = if_else(satiation.adder > pcFlsp_mm2_fby, pcFlsp_mm2_fby * 0.999, satiation.adder)) %>%
select(LEVEL2_DATA_NAMES[["SatiationAdder"]])
# L244.Satiation_flsp_SSPs: Satiation levels assumed for floorspace in the SSPs
L244.Satiation_flsp_class_SSPs <- A44.satiation_flsp_SSPs %>%
gather(sector, value, resid, comm) %>%
mutate(satiation.level = value * CONV_THOUS_BIL)
L244.Satiation_flsp_SSPs <- write_to_all_regions(A44.gcam_consumer, c("region", "gcam.consumer", "nodeInput", "building.node.input"), # replace with LEVEL2_DATA_NAMES[["BldNodes]]
GCAM_region_names = GCAM_region_names) %>%
repeat_add_columns(tibble(SSP = c("SSP1", "SSP2", "SSP3", "SSP4", "SSP5"))) %>%
# Match in the region class, and use this to then match in the satiation floorspace
left_join_error_no_match(A_regions %>% select(region, region.class), by = "region") %>%
# Residential floorspace does not use the satiation demand function, so filter the commercial floorspace
filter(!grepl("resid",gcam.consumer)) %>%
left_join_error_no_match(L244.Satiation_flsp_class_SSPs, by = c("SSP", "region.class", "gcam.consumer" = "sector")) %>%
# Calculate pcFlsp and make sure it is smaller than the satiation level
left_join_error_no_match(L102.pcgdp_thous90USD_Scen_R_Y %>%
# This used to be filtered to energy.SATIATION_YEAR, changed to pass timeshift test
filter(year == max(MODEL_BASE_YEARS)), by = c("region", "SSP" = "scenario")) %>%
left_join_error_no_match(L244.Floorspace, by = c("region", "gcam.consumer", "year", "nodeInput", "building.node.input")) %>%
left_join_error_no_match(L101.Pop_thous_R_Yh, by = c("GCAM_region_ID", "year", "region")) %>%
mutate(pcFlsp_mm2 = base.building.size / pop_thous,
satiation.level = if_else(pcFlsp_mm2 > satiation.level, 1.001 * pcFlsp_mm2, satiation.level))
L244.Satiation_flsp_SSPs.split <- L244.Satiation_flsp_SSPs%>%
select(region, gcam.consumer, nodeInput, building.node.input, satiation.level, SSP) %>%
# Split by SSP, creating a list with a tibble for each SSP, then add attributes
split(.$SSP) %>%
lapply(function(df) {
select(df, -SSP) %>%
add_units("Million squared meters per capita") %>%
add_comments("Values from A44.satiation_flsp_SSPs added to A44.gcam_consumer written to all regions") %>%
add_comments("Then, per-capita floorspace calculated to ensure satiation levels above per-capita floorspace") %>%
add_precursors("energy/A44.satiation_flsp_SSPs", "energy/A44.gcam_consumer", "common/GCAM_region_names",
"energy/A_regions", "L102.pcgdp_thous90USD_Scen_R_Y", "L101.Pop_thous_R_Yh",
"L144.flsp_bm2_R_res_Yh", "L144.flsp_bm2_R_comm_Yh")
})
# Assign each tibble in list
for(i in names(L244.Satiation_flsp_SSPs.split)) {
assign(paste0("L244.Satiation_flsp_", i), L244.Satiation_flsp_SSPs.split[[i]] %>%
add_title(paste0("Floorspace demand satiation: ", i)) %>%
add_legacy_name(paste0("L244.Satiation_flsp_", i)))
}
# Satiation adder - Required for shaping the future floorspace growth trajectories in each region
# L244.SatiationAdder_SSPs: Satiation adders in floorspace demand function for the SSPs
L244.SatiationAdder_SSPs <- L244.Satiation_flsp_SSPs %>%
# Calculate satiation.adder
mutate(satiation.adder = round(satiation.level - (exp(log(2) * pcGDP_thous90USD / energy.GDP_MID_SATIATION) *
(satiation.level - pcFlsp_mm2)),
energy.DIGITS_SATIATION_ADDER)) %>%
select(LEVEL2_DATA_NAMES[["SatiationAdder"]], SSP) %>%
# Split by SSP, creating a list with a tibble for each SSP, then add attributes
split(.$SSP) %>%
lapply(function(df) {
select(df, -SSP) %>%
add_units("Unitless") %>%
add_comments("Satiation adder compute using satiation level, per-capita GDP and per-capita floorsapce") %>%
add_precursors("energy/A44.satiation_flsp_SSPs", "energy/A44.gcam_consumer", "common/GCAM_region_names",
"energy/A_regions", "L102.pcgdp_thous90USD_Scen_R_Y", "L101.Pop_thous_R_Yh",
"L144.flsp_bm2_R_res_Yh", "L144.flsp_bm2_R_comm_Yh")
})
# Assign each tibble in list
for(i in names(L244.SatiationAdder_SSPs)) {
assign(paste0("L244.SatiationAdder_", i), L244.SatiationAdder_SSPs[[i]] %>%
add_title(paste0("Satiation adders in floorspace demand function: ", i)) %>%
add_legacy_name(paste0("L244.SatiationAdder_", i)))
}
#------------------------------------------------------
# Updated floorspace Gompertz function
# - Calculate the bias correction parameter (k)
# - Write parameters for the updated floorspace function
# Write the function parameters
L244.GompFnParam<-L144.flsp_param %>%
left_join_error_no_match(L144.hab_land_flsp_fin %>% filter(year==MODEL_FINAL_BASE_YEAR),by="region") %>%
rename(area_thouskm2=value) %>%
left_join_error_no_match(GCAM_region_names, by="region") %>%
left_join_error_no_match(L102.pcgdp_thous90USD_Scen_R_Y %>% filter(scenario== socioeconomics.BASE_GDP_SCENARIO), by=c("GCAM_region_ID","year")) %>%
rename(gdp_pc=pcGDP_thous90USD) %>%
left_join_error_no_match(L101.Pop_thous_R_Yh, by=c("GCAM_region_ID","year")) %>%
left_join_error_no_match(L144.flsp_bm2_R_res_Yh,by=c("GCAM_region_ID","year")) %>%
rename(flsp=value) %>%
mutate(tot.dens=pop_thous/area_thouskm2,
flsp_pc=(flsp*1E9)/(pop_thous*1E3),
base_flsp=flsp_pc,
flsp_est=(`unadjust.satiation` +(-`land.density.param`*log(tot.dens)))*exp(-`b.param`
*exp(-`income.param`*log(gdp_pc))),
bias.adjust.param=flsp_pc-flsp_est,
base_flsp=round(base_flsp,energy.DIGITS_FLOORSPACE),
bias.adjust.param=round(bias.adjust.param,energy.DIGITS_FLOORSPACE),
gcam.consumer="resid",
nodeInput="resid",
building.node.input="resid_building") %>%
rename(pop.dens=tot.dens,
habitable.land=area_thouskm2,
base.pcFlsp=base_flsp) %>%
select(LEVEL2_DATA_NAMES[["GompFnParam"]])
#================================================================
# L244.GenericBaseService and L244.ThermalBaseService: Base year output of buildings services (per unit floorspace)
# First, separate the thermal from the generic services. Generic services will be assumed to produce
# internal gain energy, so anything in the internal gains assumptions table will be assumed generic
generic_services <- unique(A44.internal_gains$supplysector)
thermal_services <- dplyr::setdiff(unique(A44.sector$supplysector), generic_services)
# Base-service: filter only the model base years and change names as indicated in calibrated_techs_bld_det
L244.base_service <- L144.base_service_EJ_serv %>%
rename(base.service = value) %>%
mutate(base.service = round(base.service, energy.DIGITS_CALOUTPUT)) %>%
filter(year %in% MODEL_BASE_YEARS) %>%
left_join_keep_first_only(calibrated_techs_bld_det, by = c("sector", "service")) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
select(LEVEL2_DATA_NAMES[["BldNodes"]], building.service.input = supplysector, year, base.service)
# Separate thermal and generic services into separate tibbles
L244.GenericBaseService <- L244.base_service %>%
filter(building.service.input %in% generic_services)
L244.ThermalBaseService <- L244.base_service %>%
filter(building.service.input %in% thermal_services) %>%
rename(thermal.building.service.input = building.service.input)
# L244.HDDCDD: Heating and cooling degree days by scenario
L244.all_sres_gcm <- unique(L143.HDDCDD_scen_R_Y[c("SRES", "GCM")]) # These HDD/CDD scenarios are pretty old, should be updated eventually
L244.HDDCDD_scen_R_Y <- L143.HDDCDD_scen_R_Y %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID")
# Let's make a climate normal (average climate conditions) for each region, using a selected interval of years
# Don't want to just set one year, because we want average values for all regions
L244.HDDCDD_normal_R_Y <- L244.HDDCDD_scen_R_Y %>%
filter(year %in% energy.CLIMATE_NORMAL_YEARS,
SRES == L244.all_sres_gcm$SRES[1],
GCM == L244.all_sres_gcm$GCM[1]) %>%
group_by(region, variable) %>%
summarise(degree.days = mean(value)) %>%
ungroup()
# Subset the heating and cooling services, separately
heating_services <- thermal_services[grepl("heating", thermal_services)]
cooling_services <- thermal_services[grepl("cooling", thermal_services)]
# Add HDDCDD for L244.ThermalBaseService categories and model years
L244.HDDCDD <- L244.ThermalBaseService %>%
select(-year, -base.service) %>%
distinct() %>%
# Add model years
repeat_add_columns(tibble(year = MODEL_YEARS)) %>%
# Add SRES and GCM variables
repeat_add_columns(L244.all_sres_gcm) %>%
mutate(variable = if_else(thermal.building.service.input %in% heating_services, "HDD", "CDD")) %>%
# Add HDD and CDD
left_join_error_no_match(L244.HDDCDD_scen_R_Y, by = c("region", "SRES", "GCM", "variable", "year")) %>%
mutate(degree.days = round(value, energy.DIGITS_HDDCDD)) %>%
select(-variable, -value, -GCAM_region_ID) %>%
# Join SRES and GCM so that we can split by unique ESM scenario
unite(scenario, SRES, GCM) %>%
split(.$scenario) %>%
lapply(function(df) {
select(df, -scenario) %>%
add_units("Fahrenheit Degree Days") %>%
add_comments("Degree days are from L143.HDDCDD_scen_R_Y") %>%
add_precursors("L143.HDDCDD_scen_R_Y", "common/GCAM_region_names",
"energy/A44.internal_gains", "energy/A44.sector", "L144.base_service_EJ_serv",
"energy/calibrated_techs_bld_det")
})
# Assign each tibble in list
for(i in names(L244.HDDCDD)) {
assign(paste0("L244.HDDCDD_", i), L244.HDDCDD[[i]] %>%
add_title(paste0("Heating and cooling degree days: ", i)) %>%
add_legacy_name(paste0("L244.HDDCDD_", i)))
}
# L244.GenericServiceSatiation: Satiation levels assumed for non-thermal building services
# First, calculate the service output per unit floorspace in the USA region
L244.ServiceSatiation_USA <- L144.base_service_EJ_serv %>%
filter(GCAM_region_ID == gcam.USA_CODE) %>%
# Using left_join_keep_first_only b/c there are repeats and we only need to keep generic columns
left_join_keep_first_only(calibrated_techs_bld_det, by = c("sector", "service")) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
select(region, sector, service, gcam.consumer, nodeInput, building.node.input, building.service.input = supplysector, year, value)
# Floorspace should be matched in for a specified year, from the full floorspace table (i.e. not one that is subsetted to model base years)
L144.flsp_bm2_R_res_Yh <- L144.flsp_bm2_R_res_Yh %>%
mutate(gcam.consumer = A44.gcam_consumer$gcam.consumer[grepl("res", A44.gcam_consumer$gcam.consumer)]) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID")
L144.flsp_bm2_R_comm_Yh <- L144.flsp_bm2_R_comm_Yh %>%
mutate(gcam.consumer = A44.gcam_consumer$gcam.consumer[grepl("com", A44.gcam_consumer$gcam.consumer)]) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID")
L244.flsp_bm2_R <- bind_rows(L144.flsp_bm2_R_res_Yh, L144.flsp_bm2_R_comm_Yh) %>%
# Again, used to be energy.SATIATION_YEAR, changed to pass timeshift test
filter(year == max(MODEL_BASE_YEARS)) %>%
select(-year)
L244.ServiceSatiation_USA <- L244.ServiceSatiation_USA %>%
left_join_error_no_match(L244.flsp_bm2_R %>%
rename(floorspace_bm2 = value), by = c("region", "gcam.consumer")) %>%
left_join_error_no_match(A44.demand_satiation_mult, by = c("building.service.input" = "supplysector")) %>%
group_by(region, sector, service) %>%
mutate(satiation.level = round(value[year == max(HISTORICAL_YEARS)] * multiplier / floorspace_bm2, energy.DIGITS_CALOUTPUT)) %>%
ungroup() %>%
select(-multiplier)
# Generic services: read these values to all regions because they're all the same
L244.GenericServiceSatiation <- L244.ServiceSatiation_USA %>%
filter(building.service.input %in% generic_services) %>%
select(-region, -GCAM_region_ID, -year, -value) %>%
distinct() %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["GenericServiceSatiation"]], GCAM_region_names = GCAM_region_names)
# Need to match in the floorspace into the base service table, divide to calculate the service demand
# per unit floorspace in the final calibration year. This (increased slightly) is then the minimum satiation level that needs to be read in.
L244.BS <- L244.GenericBaseService %>%
left_join_error_no_match(L244.Floorspace, by = c(LEVEL2_DATA_NAMES[["BldNodes"]], "year")) %>%
mutate(service.per.flsp = base.service / base.building.size) %>%
filter(year == max(MODEL_BASE_YEARS)) %>%
select(LEVEL2_DATA_NAMES[["BldNodes"]], building.service.input, service.per.flsp)
L244.GenericServiceSatiation <- L244.GenericServiceSatiation %>%
left_join_error_no_match(L244.BS, by = c(LEVEL2_DATA_NAMES[["BldNodes"]], "building.service.input")) %>%
mutate(satiation.level = pmax(satiation.level, service.per.flsp * 1.0001)) %>%
select(-service.per.flsp)
# L244.GenericServiceSatiation_SSPs: Satiation levels assumed for non-thermal building services in the SSPs
# First, calculate the service output per unit floorspace in the USA region
L244.ServiceSatiation_USA_SSPs <- L244.ServiceSatiation_USA %>%
filter(building.service.input %in% generic_services) %>%
repeat_add_columns(tibble(SSP = c("SSP1", "SSP2", "SSP3", "SSP4", "SSP5"))) %>%
left_join_error_no_match(A44.demand_satiation_mult_SSPs, by = c("SSP", "building.service.input" = "supplysector")) %>%
group_by(region, sector, service) %>%
mutate(satiation.level = round(value[year == max(HISTORICAL_YEARS)] * multiplier / floorspace_bm2, energy.DIGITS_CALOUTPUT)) %>%
ungroup()
# Generic services: read these values to all regions because they're all the same
L244.GenericServiceSatiation_SSPs <- L244.ServiceSatiation_USA_SSPs %>%
select(LEVEL2_DATA_NAMES[["GenericServiceSatiation"]], SSP, -region) %>%
distinct() %>%
write_to_all_regions(c(LEVEL2_DATA_NAMES[["GenericServiceSatiation"]], "SSP"), GCAM_region_names = GCAM_region_names)
# Need to match in the floorspace into the base service table, divide to calculate the service demand
# per unit floorspace in the final calibration year. This (increased slightly) is then the minimum satiation level that needs to be read in.
L244.GenericServiceSatiation_SSPs <- L244.GenericServiceSatiation_SSPs %>%
left_join_error_no_match(L244.BS, by = c(LEVEL2_DATA_NAMES[["BldNodes"]], "building.service.input")) %>%
mutate(satiation.level = pmax(satiation.level, service.per.flsp * 1.0001)) %>%
select(-service.per.flsp) %>%
# Split by SSP, creating a list with a tibble for each SSP, then add attributes
split(.$SSP) %>%
lapply(function(df) {
select(df, -SSP) %>%
add_units("EJ/billion m2 floorspace") %>%
add_comments("For USA, calculate satiation level as base year service / base year floorspace times multiplier") %>%
add_comments("USA values written to all regions, then we make sure that no satiation level is below base year service per floorspace") %>%
add_precursors("L144.base_service_EJ_serv", "energy/calibrated_techs_bld_det", "common/GCAM_region_names",
"L144.flsp_bm2_R_res_Yh", "L144.flsp_bm2_R_comm_Yh", "energy/A44.demand_satiation_mult_SSPs")
})
# Assign each tibble in list
for(i in names(L244.GenericServiceSatiation_SSPs)) {
assign(paste0("L244.GenericServiceSatiation_", i), L244.GenericServiceSatiation_SSPs[[i]] %>%
add_title(paste0("Satiation levels for non-thermal building services: ", i)) %>%
add_legacy_name(paste0("L244.GenericServiceSatiation_", i)))
}
# L244.ThermalServiceSatiation: Satiation levels assumed for thermal building services
# Write USA thermal satiation levels to all regions
L244.ThermalServiceSatiation <- L244.ServiceSatiation_USA %>%
filter(building.service.input %in% thermal_services) %>%
select(-region, -GCAM_region_ID, -value, -year) %>%
distinct() %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["GenericServiceSatiation"]], GCAM_region_names = GCAM_region_names) %>%
rename(thermal.building.service.input = building.service.input) %>%
# Thermal service satiation is modified in each region according to the HDD/CDD ratio to the USA in a given year
mutate(variable = if_else(thermal.building.service.input %in% heating_services, "HDD", "CDD")) %>%
left_join_error_no_match(L244.HDDCDD_normal_R_Y, by = c("region", "variable")) %>%
group_by(gcam.consumer, variable) %>%
mutate(satiation.level = round(satiation.level * degree.days / degree.days[region == gcam.USA_REGION], digits = energy.DIGITS_CALOUTPUT)) %>%
ungroup()
# The service satiation in the final cal year can not be lower than the observed demand, so need to use pmax to set a floor on the quantity
# First need to calculate the maximum quantities of demand over the historical time period, expressed per unit floorspace
L244.tmp <- L244.ThermalBaseService %>%
left_join_error_no_match(L244.Floorspace, by = c(LEVEL2_DATA_NAMES[["BldNodes"]], "year")) %>%
mutate(service.per.flsp = base.service / base.building.size) %>%
filter(year == max(MODEL_BASE_YEARS)) %>%
select(-base.service, - base.building.size, -year)
# Then, match in this quantity into the thermal service satiation and take the max
L244.ThermalServiceSatiation <- L244.ThermalServiceSatiation %>%
left_join_error_no_match(L244.tmp, by = c(LEVEL2_DATA_NAMES[["BldNodes"]], "thermal.building.service.input")) %>%
mutate(satiation.level = round(pmax(satiation.level, service.per.flsp * 1.0001),
digits = energy.DIGITS_CALOUTPUT)) %>%
select(LEVEL2_DATA_NAMES[["ThermalServiceSatiation"]])
# L244.ShellConductance_bld: Shell conductance (inverse of shell efficiency)
L244.ShellConductance_bld <- L144.shell_eff_R_Y %>%
rename(shell.conductance = value) %>%
filter(year %in% MODEL_YEARS) %>%
mutate(shell.conductance = round(shell.conductance, digits = energy.DIGITS_EFFICIENCY)) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_error_no_match(A44.gcam_consumer, by = c("supplysector" = "gcam.consumer")) %>%
mutate(gcam.consumer = supplysector,
shell.year = year,
floor.to.surface.ratio = energy.FLOOR_TO_SURFACE_RATIO) %>%
select(LEVEL2_DATA_NAMES[["ShellConductance"]])
# L244.Supplysector_bld: Supplysector info for buildings
L244.Supplysector_bld <- write_to_all_regions(A44.sector, c(LEVEL2_DATA_NAMES[["Supplysector"]], LOGIT_TYPE_COLNAME),
GCAM_region_names = GCAM_region_names)
# L244.FinalEnergyKeyword_bld: Supply sector keywords for detailed building sector
L244.FinalEnergyKeyword_bld <- write_to_all_regions(A44.sector, c(LEVEL2_DATA_NAMES[["FinalEnergyKeyword"]]),
GCAM_region_names = GCAM_region_names) %>%
na.omit()
# Subsector information
## Not all subsectors exist in all regions; tradbio and heat are only modeled in selected regions
## The level1 end-use tech efficiency file has all of the combinations that exist
L244.Tech_bld <- L144.end_use_eff %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
select(region, supplysector, subsector, technology) %>%
distinct()
# L244.SubsectorLogit_bld: Subsector logit exponents of building sector
L244.SubsectorLogit_bld <- write_to_all_regions(A44.subsector_logit, c(LEVEL2_DATA_NAMES[["SubsectorLogit"]], LOGIT_TYPE_COLNAME),
GCAM_region_names = GCAM_region_names) %>%
semi_join(L244.Tech_bld, by = c("region", "supplysector", "subsector"))
# L244.SubsectorShrwt_bld and L244.SubsectorShrwtFllt_bld: Subsector shareweights of building sector
if(any(!is.na(A44.subsector_shrwt$year))) {
L244.SubsectorShrwt_bld <- A44.subsector_shrwt %>%
filter(!is.na(year)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["SubsectorShrwt"]], GCAM_region_names = GCAM_region_names) %>%
semi_join(L244.Tech_bld, by = c("region", "supplysector", "subsector"))
}
if(any(!is.na(A44.subsector_shrwt$year.fillout))) {
L244.SubsectorShrwtFllt_bld <- A44.subsector_shrwt %>%
filter(!is.na(year.fillout)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["SubsectorShrwtFllt"]], GCAM_region_names = GCAM_region_names) %>%
semi_join(L244.Tech_bld, by = c("region", "supplysector", "subsector"))
}
# L244.SubsectorInterp_bld and L244.SubsectorInterpTo_bld: Subsector shareweight interpolation of building sector
if(any(is.na(A44.subsector_interp$to.value))) {
L244.SubsectorInterp_bld <- A44.subsector_interp %>%
filter(is.na(to.value)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["SubsectorInterp"]], GCAM_region_names = GCAM_region_names) %>%
semi_join(L244.Tech_bld, by = c("region", "supplysector", "subsector"))
}
if(any(!is.na(A44.subsector_interp$to.value))) {
L244.SubsectorInterpTo_bld <- A44.subsector_interp %>%
filter(!is.na(to.value)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["SubsectorInterpTo"]], GCAM_region_names = GCAM_region_names) %>%
semi_join(L244.Tech_bld, by = c("region", "supplysector", "subsector"))
}
# L244.FuelPrefElast_bld: Fuel preference elasticities for buildings
L244.FuelPrefElast_bld <- A44.fuelprefElasticity %>%
mutate(year.fillout = min(MODEL_BASE_YEARS)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["FuelPrefElast"]], GCAM_region_names = GCAM_region_names) %>%
semi_join(L244.Tech_bld, by = c("region", "supplysector", "subsector"))
# L244.FuelPrefElast_bld_SSP3: Fuel preference elasticities for buildings in SSP 3
L244.FuelPrefElast_bld_SSP3 <- A44.fuelprefElasticity_SSP3 %>%
mutate(year.fillout = min(MODEL_BASE_YEARS)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["FuelPrefElast"]], GCAM_region_names = GCAM_region_names) %>%
semi_join(L244.Tech_bld, by = c("region", "supplysector", "subsector"))
# L244.FuelPrefElast_bld_SSP4: Fuel preference elasticities for buildings in SSP 4
L244.FuelPrefElast_bld_SSP4 <- A44.fuelprefElasticity_SSP4 %>%
mutate(year.fillout = min(MODEL_BASE_YEARS)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["FuelPrefElast"]], GCAM_region_names = GCAM_region_names) %>%
semi_join(L244.Tech_bld, by = c("region", "supplysector", "subsector"))
# L244.FuelPrefElast_bld_SSP15: Fuel preference elasticities for buildings in SSP 1 & 5
L244.FuelPrefElast_bld_SSP15 <- A44.fuelprefElasticity_SSP15 %>%
mutate(year.fillout = min(MODEL_BASE_YEARS)) %>%
write_to_all_regions(LEVEL2_DATA_NAMES[["FuelPrefElast"]], GCAM_region_names = GCAM_region_names) %>%
semi_join(L244.Tech_bld, by = c("region", "supplysector", "subsector"))
# L244.StubTech_bld: Identification of stub technologies for buildings
L244.StubTech_bld <- L244.Tech_bld %>%
rename(stub.technology = technology)
# L244.StubTechCalInput_bld: Calibrated energy consumption by buildings technologies
L244.StubTechCalInput_bld <- L144.in_EJ_R_bld_serv_F_Yh %>%
filter(year %in% MODEL_BASE_YEARS) %>%
rename(calibrated.value = value) %>%
mutate(calibrated.value = round(calibrated.value, energy.DIGITS_CALOUTPUT)) %>%
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_error_no_match(calibrated_techs_bld_det, by = c("sector", "service", "fuel")) %>%
mutate(share.weight.year = year,
stub.technology = technology) %>%
group_by(region, supplysector, subsector, year) %>%
mutate(subs.share.weight = sum(calibrated.value)) %>%
ungroup() %>%
# If aggregated calibrated value > 0, set subsector shareweight to 1, else set to 0
mutate(subs.share.weight = if_else(subs.share.weight > 0, 1, 0),
# If calibrated value for specific technology > 0 , set tech shareweight to 1, else set to 0
tech.share.weight = if_else(calibrated.value > 0, 1, 0)) %>%
select(LEVEL2_DATA_NAMES[["StubTechCalInput"]])
# L244.StubTechEff_bld: Assumed efficiencies (all years) of buildings technologies
L244.StubTechEff_bld <- L144.end_use_eff %>%
filter(year %in% MODEL_YEARS) %>%
mutate(value = round(value, energy.DIGITS_CALOUTPUT)) %>%
rename(efficiency = value) %>%
# Add region and input
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
left_join_error_no_match(calibrated_techs_bld_det, by = c("supplysector", "subsector", "technology")) %>%
mutate(stub.technology = technology,
market.name = region) %>%
select(LEVEL2_DATA_NAMES[["StubTechEff"]])
# L244.GlobalTechShrwt_bld: Default shareweights for global building technologies
L244.GlobalTechShrwt_bld <- A44.globaltech_shrwt %>%
# Repeat for all model years
complete(nesting(supplysector, subsector, technology), year = c(year, MODEL_YEARS)) %>%
# Interpolate
group_by(supplysector, subsector, technology) %>%
mutate(share.weight = approx_fun(year, value, rule = 2)) %>%
ungroup() %>%
filter(year %in% MODEL_YEARS) %>%
rename(sector.name = supplysector,
subsector.name = subsector) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechYr"]], share.weight)
# L244.GlobalTechCost_bld: Non-fuel costs of global building technologies
L244.GlobalTechCost_bld <- L144.NEcost_75USDGJ %>%
mutate(input.cost = round(NEcostPerService, energy.DIGITS_COST)) %>%
repeat_add_columns(tibble(year = MODEL_YEARS)) %>%
rename(sector.name = supplysector,
subsector.name = subsector) %>%
mutate(minicam.non.energy.input = "non-energy") %>%
select(LEVEL2_DATA_NAMES[["GlobalTechCost"]])
# L244.StubTechIntGainOutputRatio: Output ratios of internal gain energy from non-thermal building services
L244.StubTechIntGainOutputRatio <- L144.internal_gains %>%
filter(year %in% MODEL_YEARS) %>%
# Round and rename value
mutate(value = round(value, energy.DIGITS_EFFICIENCY)) %>%
rename(internal.gains.output.ratio = value) %>%
# Add region name
left_join_error_no_match(GCAM_region_names, by = "GCAM_region_ID") %>%
# Add building.node.input
left_join_error_no_match(calibrated_techs_bld_det %>%
select(supplysector, building.node.input) %>%
distinct(), by = "supplysector") %>%
# Add internal.gains.market.name
left_join_error_no_match(A44.gcam_consumer, by = "building.node.input") %>%
select(LEVEL2_DATA_NAMES[["TechYr"]], internal.gains.output.ratio, internal.gains.market.name)
# L244.Intgains_scalar: Scalers relating internal gain energy to increased/reduced cooling/heating demands
variable <- c("HDD", "CDD")
InternalGainsScalar_USA <- c(energy.INTERNAL_GAINS_SCALAR_USA_H, energy.INTERNAL_GAINS_SCALAR_USA_C)
US.base.scalar <- tibble(variable, InternalGainsScalar_USA)
L244.Intgains_scalar <- L244.ThermalServiceSatiation %>%
mutate(variable = if_else(thermal.building.service.input %in% heating_services, "HDD", "CDD")) %>%
left_join_error_no_match(US.base.scalar, by = "variable") %>%
left_join_error_no_match(L244.HDDCDD_normal_R_Y, by = c("region", "variable")) %>%
group_by(thermal.building.service.input) %>%
mutate(scalar_mult = degree.days / degree.days[region == gcam.USA_REGION]) %>%
ungroup() %>%
mutate(internal.gains.scalar = round(InternalGainsScalar_USA * scalar_mult, energy.DIGITS_HDDCDD)) %>%
select(LEVEL2_DATA_NAMES[["Intgains_scalar"]])
# Need to remove any services (supplysectors and building-service-inputs) and intgains trial markets for services that don't exist in any years
# L244.DeleteThermalService and L244.DeleteGenericService: Removing non-existent services, likely related to 0 HDD or CDD
L244.DeleteThermalService <- L244.ThermalBaseService %>%
group_by(region, gcam.consumer, nodeInput, building.node.input, thermal.building.service.input) %>%
summarise(base.service = max(base.service)) %>%
ungroup() %>%
filter(base.service == 0) %>%
select(-base.service) %>%
mutate(supplysector = thermal.building.service.input)
L244.DeleteGenericService <- L244.GenericBaseService %>%
group_by(region, gcam.consumer, nodeInput, building.node.input, building.service.input) %>%
summarise(base.service = max(base.service)) %>%
ungroup()
# This tibble is empty because no base.service = 0, so we will make an if statement to produce correct empty tibble
if(any(L244.DeleteGenericService$base.service == 0)) {
L244.DeleteGenericService <- L244.DeleteGenericService%>%
filter(base.service == 0) %>%
select(-base.service) %>%
mutate(supplysector = building.service.input)
} else {
rm("L244.DeleteGenericService")
}
# ===================================================
# Produce outputs
L244.SubregionalShares %>%
add_title("Subregional population and income shares") %>%
add_units("Unitless") %>%
add_comments("A44.gcam_consumer written to all regions") %>%
add_comments("subregional.population.share and subregional.income.share set to 1") %>%
add_legacy_name("L244.SubregionalShares") %>%
add_precursors("common/GCAM_region_names", "energy/A44.gcam_consumer") ->
L244.SubregionalShares
L244.PriceExp_IntGains %>%
add_title("Price exponent on floorspace and naming of internal gains trial markets") %>%
add_units("Unitless") %>%
add_comments("A44.gcam_consumer written to all regions") %>%
add_legacy_name("L244.PriceExp_IntGains") %>%
add_precursors("common/GCAM_region_names", "energy/A44.gcam_consumer")->
L244.PriceExp_IntGains
L244.Floorspace %>%
add_title("Base year floorspace") %>%
add_units("billion m2") %>%
add_comments("Values taken from L144.flsp_bm2_R_res_Yh and L144.flsp_bm2_R_comm_Yh") %>%
add_legacy_name("L244.Floorspace") %>%
add_precursors("common/GCAM_region_names", "energy/A44.gcam_consumer",
"L144.flsp_bm2_R_res_Yh", "L144.flsp_bm2_R_comm_Yh") ->
L244.Floorspace
L244.DemandFunction_serv %>%
add_title("Demand function for building service") %>%
add_units("NA") %>%
add_comments("A44.demandFn_serv written to all regions") %>%
add_comments("can be multiple lines") %>%
add_legacy_name("L244.DemandFunction_serv") %>%
add_precursors("common/GCAM_region_names", "energy/A44.demandFn_serv")->
L244.DemandFunction_serv
L244.DemandFunction_flsp %>%
add_title("Demand function for building floorspace") %>%
add_units("NA") %>%
add_comments("A44.demandFn_flsp written to all regions") %>%
add_comments("can be multiple lines") %>%
add_legacy_name("L244.DemandFunction_flsp") %>%
add_precursors("common/GCAM_region_names", "energy/A44.demandFn_flsp") ->
L244.DemandFunction_flsp
L244.Satiation_flsp %>%
add_title("Floorspace demand satiation") %>%
add_units("Million squared meters per capita") %>%
add_comments("Values from A44.satiation_flsp added to A44.gcam_consumer written to all regions") %>%
add_legacy_name("L244.Satiation_flsp") %>%
add_precursors("energy/A44.satiation_flsp", "energy/A44.gcam_consumer", "common/GCAM_region_names", "energy/A_regions") ->
L244.Satiation_flsp
L244.SatiationAdder %>%
add_title("Satiation adders in floorspace demand function") %>%
add_units("Unitless") %>%
add_comments("Satiation adder compute using satiation level, per-capita GDP and per-capita floorsapce") %>%
add_legacy_name("L244.SatiationAdder") %>%
add_precursors("energy/A44.satiation_flsp", "energy/A44.gcam_consumer", "common/GCAM_region_names", "energy/A_regions",
"L102.pcgdp_thous90USD_Scen_R_Y", "L101.Pop_thous_R_Yh",
"L144.flsp_bm2_R_res_Yh", "L144.flsp_bm2_R_comm_Yh") ->
L244.SatiationAdder
L244.GompFnParam %>%
add_title("Parameters for the floorspace Gompertz function") %>%
add_units("Unitless") %>%
add_comments("Computed offline based on data from RECS and IEA") %>%
add_legacy_name("L244.GompFnParam") %>%
add_precursors("common/GCAM_region_names",
"L144.flsp_param",
"L102.pcgdp_thous90USD_Scen_R_Y", "L101.Pop_thous_R_Yh",
"L144.flsp_bm2_R_res_Yh","L144.hab_land_flsp_fin") ->
L244.GompFnParam
L244.ThermalBaseService %>%
add_title("Historical building heating and cooling energy output") %>%
add_units("EJ/yr") %>%
add_comments("L144.base_service_EJ_serv rounded and renamed") %>%
add_legacy_name("L244.ThermalBaseService") %>%
add_precursors("energy/A44.internal_gains", "energy/A44.sector", "L144.base_service_EJ_serv",
"energy/calibrated_techs_bld_det", "common/GCAM_region_names") ->
L244.ThermalBaseService
L244.GenericBaseService %>%
add_title("Historical building `other` energy output") %>%
add_units("EJ/yr") %>%
add_comments("L144.base_service_EJ_serv rounded and renamed") %>%
add_legacy_name("L244.GenericBaseService") %>%
same_precursors_as(L244.ThermalBaseService) ->
L244.GenericBaseService
L244.ThermalServiceSatiation %>%
add_title("Satiation levels for thermal building services") %>%
add_units("EJ/billion m2 floorspace") %>%
add_comments("For USA, calculate satiation level as base year service / base year floorspace times multiplier") %>%
add_comments("USA values written to all regions, which are multiplied by ratio of degree days in each region to degree days in USA") %>%
add_comments("then we make sure that no satiation level is below base year service per floorspace") %>%
add_legacy_name("L244.ThermalServiceSatiation") %>%
add_precursors("L144.base_service_EJ_serv", "energy/calibrated_techs_bld_det", "common/GCAM_region_names",
"L144.flsp_bm2_R_res_Yh", "L144.flsp_bm2_R_comm_Yh", "energy/A44.demand_satiation_mult",
"L143.HDDCDD_scen_R_Y", "energy/A44.internal_gains", "energy/A44.sector", "energy/A44.gcam_consumer") ->
L244.ThermalServiceSatiation
L244.GenericServiceSatiation %>%
add_title("Satiation levels for non-thermal building services") %>%
add_units("EJ/billion m2 floorspace") %>%
add_comments("For USA, calculate satiation level as base year service / base year floorspace times multiplier") %>%
add_comments("USA values written to all regions, then we make sure that no satiation level is below base year service per floorspace") %>%
add_legacy_name("L244.GenericServiceSatiation") %>%
add_precursors("L144.base_service_EJ_serv", "energy/calibrated_techs_bld_det", "common/GCAM_region_names",
"L144.flsp_bm2_R_res_Yh", "L144.flsp_bm2_R_comm_Yh", "energy/A44.demand_satiation_mult") ->
L244.GenericServiceSatiation
L244.Intgains_scalar %>%
add_title("Scalers relating internal gain energy to increased/reduced cooling/heating demands") %>%
add_units("Unitless") %>%
add_comments("USA base scalar assumption multiplied by ratio of degree days to USA degree days") %>%
add_legacy_name("L244.Intgains_scalar") %>%
same_precursors_as(L244.ThermalServiceSatiation) ->
L244.Intgains_scalar
L244.ShellConductance_bld %>%
add_title("Shell conductance (inverse of shell efficiency)") %>%
add_units("Unitless") %>%
add_comments("Shell conductance from L144.shell_eff_R_Y") %>%
add_legacy_name("L244.ShellConductance_bld") %>%
add_precursors("L144.shell_eff_R_Y", "common/GCAM_region_names", "energy/A44.gcam_consumer") ->
L244.ShellConductance_bld
L244.Supplysector_bld %>%
add_title("Supplysector info for buildings") %>%
add_units("NA") %>%
add_comments("A44.sector written to all regions") %>%
add_legacy_name("L244.Supplysector_bld") %>%
add_precursors("energy/A44.sector", "common/GCAM_region_names") ->
L244.Supplysector_bld
L244.FinalEnergyKeyword_bld %>%
add_title("Supply sector keywords for detailed building sector") %>%
add_units("NA") %>%
add_comments("A44.sector written to all regions") %>%
add_legacy_name("L244.FinalEnergyKeyword_bld") %>%
same_precursors_as(L244.Supplysector_bld) ->
L244.FinalEnergyKeyword_bld
if(exists("L244.SubsectorShrwt_bld")) {
L244.SubsectorShrwt_bld %>%
add_title("Subsector shareweights for building sector") %>%
add_units("Unitless") %>%
add_comments("A44.subsector_shrwt written to all regions") %>%
add_legacy_name("L244.SubsectorShrwt_bld") %>%
add_precursors("energy/A44.subsector_shrwt", "common/GCAM_region_names", "L144.end_use_eff") ->
L244.SubsectorShrwt_bld
} else {
missing_data() %>%
add_legacy_name("L244.SubsectorShrwt_bld") ->
L244.SubsectorShrwt_bld
}
if(exists("L244.SubsectorShrwtFllt_bld")) {
L244.SubsectorShrwtFllt_bld %>%
add_title("Subsector shareweights for building sector") %>%
add_units("Unitless") %>%
add_comments("A44.subsector_shrwt written to all regions") %>%
add_legacy_name("L244.SubsectorShrwtFllt_bld") %>%
add_precursors("energy/A44.subsector_shrwt", "common/GCAM_region_names", "L144.end_use_eff") ->
L244.SubsectorShrwtFllt_bld
} else {
missing_data() %>%
add_legacy_name("L244.SubsectorShrwtFllt_bld") ->
L244.SubsectorShrwtFllt_bld
}
if(exists("L244.SubsectorInterp_bld")) {
L244.SubsectorInterp_bld %>%
add_title("Subsector shareweight interpolation for building sector") %>%
add_units("NA") %>%
add_comments("A44.subsector_interp written to all regions") %>%
add_legacy_name("L244.SubsectorInterp_bld") %>%
add_precursors("energy/A44.subsector_interp", "common/GCAM_region_names", "L144.end_use_eff") ->
L244.SubsectorInterp_bld
} else {
missing_data() %>%
add_legacy_name("L244.SubsectorInterp_bld") ->
L244.SubsectorInterp_bld
}
if(exists("L244.SubsectorInterpTo_bld")) {
L244.SubsectorInterpTo_bld %>%
add_title("Subsector shareweight interpolation for building sector") %>%
add_units("NA") %>%
add_comments("A44.subsector_interp written to all regions") %>%
add_legacy_name("L244.SubsectorInterpTo_bld") %>%
add_precursors("energy/A44.subsector_interp", "common/GCAM_region_names", "L144.end_use_eff") ->
L244.SubsectorInterpTo_bld
} else {
missing_data() %>%
add_legacy_name("L244.SubsectorInterpTo_bld") ->
L244.SubsectorInterpTo_bld
}
L244.SubsectorLogit_bld %>%
add_title("Subsector logit exponents of building sector") %>%
add_units("Unitless") %>%
add_comments("A44.subsector_logit written to all regions") %>%
add_legacy_name("L244.SubsectorLogit_bld") %>%
add_precursors("energy/A44.subsector_logit", "common/GCAM_region_names", "L144.end_use_eff") ->
L244.SubsectorLogit_bld
L244.FuelPrefElast_bld %>%
add_title("Fuel preference elasticities for buildings") %>%
add_units("Unitless") %>%
add_comments("A44.fuelprefElasticity written to all regions") %>%
add_legacy_name("L244.FuelPrefElast_bld") %>%
add_precursors("energy/A44.fuelprefElasticity", "common/GCAM_region_names", "L144.end_use_eff") ->
L244.FuelPrefElast_bld
L244.StubTech_bld %>%
add_title("Identification of stub technologies for buildings") %>%
add_units("NA") %>%
add_comments("Technologies from L144.end_use_eff") %>%
add_legacy_name("L244.StubTech_bld") %>%
add_precursors("L144.end_use_eff") ->
L244.StubTech_bld
L244.StubTechEff_bld %>%
add_title("Assumed efficiencies of buildings technologies") %>%
add_units("Unitless efficiency") %>%
add_comments("Efficiencies taken from L144.end_use_eff") %>%
add_legacy_name("L244.StubTechEff_bld") %>%
add_precursors("L144.end_use_eff", "common/GCAM_region_names", "energy/calibrated_techs_bld_det") ->
L244.StubTechEff_bld
L244.StubTechCalInput_bld %>%
add_title("Calibrated energy consumption by buildings technologies") %>%
add_units("calibrated.value: EJ; shareweights: Unitless") %>%
add_comments("Calibrated values directly from L144.in_EJ_R_bld_serv_F_Yh") %>%
add_comments("Shareweights are 1 if subsector/technology total is non-zero, 0 otherwise") %>%
add_legacy_name("L244.StubTechCalInput_bld") %>%
add_precursors("L144.in_EJ_R_bld_serv_F_Yh", "common/GCAM_region_names", "energy/calibrated_techs_bld_det") ->
L244.StubTechCalInput_bld
L244.StubTechIntGainOutputRatio %>%
add_title("Output ratios of internal gain energy from non-thermal building services") %>%
add_units("Unitless output ratio") %>%
add_comments("Values from L144.internal_gains") %>%
add_legacy_name("L244.StubTechIntGainOutputRatio") %>%
add_precursors("L144.internal_gains", "common/GCAM_region_names",
"energy/calibrated_techs_bld_det", "energy/A44.gcam_consumer") ->
L244.StubTechIntGainOutputRatio
L244.GlobalTechShrwt_bld %>%
add_title("Default shareweights for global building technologies") %>%
add_units("Unitless") %>%
add_comments("Values interpolated from A44.globaltech_shrwt") %>%
add_legacy_name("L244.GlobalTechShrwt_bld") %>%
add_precursors("energy/A44.globaltech_shrwt") ->
L244.GlobalTechShrwt_bld
L244.GlobalTechCost_bld %>%
add_title("Non-fuel costs of global building technologies") %>%
add_units("1975$/GJ-service") %>%
add_comments("Costs from L144.NEcost_75USDGJ expanded to model years") %>%
add_legacy_name("L244.GlobalTechCost_bld") %>%
add_precursors("L144.NEcost_75USDGJ") ->
L244.GlobalTechCost_bld
if(exists("L244.DeleteGenericService")) {
L244.DeleteGenericService %>%
add_title("Removing non-existent services") %>%
add_units("NA") %>%
add_comments("Categories from L244.GenericBaseService with no base.service") %>%
add_legacy_name("L244.DeleteGenericService") %>%
same_precursors_as(L244.GenericBaseService) ->
L244.DeleteGenericService
} else {
missing_data() %>%
add_legacy_name("L244.DeleteGenericService") ->
L244.DeleteGenericService
}
L244.FuelPrefElast_bld_SSP3 %>%
add_title("Fuel preference elasticities for buildings: SSP3") %>%
add_units("Unitless") %>%
add_comments("A44.fuelprefElasticity_SSP3 written to all regions") %>%
add_legacy_name("L244.FuelPrefElast_bld_SSP3") %>%
add_precursors("energy/A44.fuelprefElasticity_SSP3", "common/GCAM_region_names", "L144.end_use_eff") ->
L244.FuelPrefElast_bld_SSP3
L244.FuelPrefElast_bld_SSP4 %>%
add_title("Fuel preference elasticities for buildings: SSP4") %>%
add_units("Unitless") %>%
add_comments("A44.fuelprefElasticity_SSP4 written to all regions") %>%
add_legacy_name("L244.FuelPrefElast_bld_SSP4") %>%
add_precursors("energy/A44.fuelprefElasticity_SSP4", "common/GCAM_region_names", "L144.end_use_eff") ->
L244.FuelPrefElast_bld_SSP4
L244.FuelPrefElast_bld_SSP15 %>%
add_title("Fuel preference elasticities for buildings: SSP 1 & 5") %>%
add_units("Unitless") %>%
add_comments("A44.fuelprefElasticity_SSP15 written to all regions") %>%
add_legacy_name("L244.FuelPrefElast_bld_SSP15") %>%
add_precursors("energy/A44.fuelprefElasticity_SSP15", "common/GCAM_region_names", "L144.end_use_eff") ->
L244.FuelPrefElast_bld_SSP15
L244.DeleteThermalService %>%
add_title("Removing non-existent thermal services") %>%
add_units("NA") %>%
add_comments("Categories from L244.ThermalBaseService with no base.service") %>%
add_legacy_name("L244.DeleteThermalService") %>%
same_precursors_as(L244.ThermalBaseService) ->
L244.DeleteThermalService
return_data(L244.SubregionalShares, L244.PriceExp_IntGains, L244.Floorspace, L244.DemandFunction_serv, L244.DemandFunction_flsp,
L244.Satiation_flsp, L244.SatiationAdder, L244.ThermalBaseService, L244.GenericBaseService, L244.ThermalServiceSatiation,
L244.GenericServiceSatiation, L244.Intgains_scalar, L244.ShellConductance_bld,
L244.Supplysector_bld, L244.FinalEnergyKeyword_bld, L244.SubsectorShrwt_bld, L244.SubsectorShrwtFllt_bld, L244.SubsectorInterp_bld,
L244.SubsectorInterpTo_bld, L244.FuelPrefElast_bld,
L244.StubTech_bld, L244.StubTechEff_bld, L244.StubTechCalInput_bld, L244.GlobalTechShrwt_bld,
L244.GlobalTechCost_bld, L244.DeleteGenericService, L244.Satiation_flsp_SSP1, L244.SatiationAdder_SSP1,
L244.GenericServiceSatiation_SSP1, L244.Satiation_flsp_SSP2,
L244.SatiationAdder_SSP2, L244.GenericServiceSatiation_SSP2, L244.Satiation_flsp_SSP3, L244.SatiationAdder_SSP3,
L244.GenericServiceSatiation_SSP3, L244.FuelPrefElast_bld_SSP3, L244.Satiation_flsp_SSP4,
L244.SatiationAdder_SSP4, L244.GenericServiceSatiation_SSP4, L244.FuelPrefElast_bld_SSP4,
L244.Satiation_flsp_SSP5, L244.SatiationAdder_SSP5, L244.GenericServiceSatiation_SSP5, L244.FuelPrefElast_bld_SSP15,
L244.DeleteThermalService, L244.SubsectorLogit_bld, L244.StubTechIntGainOutputRatio,
L244.HDDCDD_A2_CCSM3x, L244.HDDCDD_A2_HadCM3, L244.HDDCDD_B1_CCSM3x, L244.HDDCDD_B1_HadCM3, L244.HDDCDD_constdd_no_GCM,
L244.GompFnParam)
} else {
stop("Unknown command")
}
}
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