R/zchunk_L244.building_USA.R
In JGCRI/gcamdata: Build the GCAM Input Datasets
Defines functions
module_gcamusa_L244.building_USA
Documented in
module_gcamusa_L244.building_USA
# Copyright 2019 Battelle Memorial Institute; see the LICENSE file.
#' module_gcamusa_L244.building_USA
#'
#' Creates GCAM-USA building output files for writing to xml.
#'
#' @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.DeleteConsumer_USAbld}, \code{L244.DeleteSupplysector_USAbld}, \code{L244.SubregionalShares_gcamusa},
#' \code{L244.PriceExp_IntGains_gcamusa}, \code{L244.Floorspace_gcamusa}, \code{L244.DemandFunction_serv_gcamusa}, \code{L244.DemandFunction_flsp_gcamusa},
#' \code{L244.Satiation_flsp_gcamusa}, \code{L244.SatiationAdder_gcamusa}, \code{L244.ThermalBaseService_gcamusa}, \code{L244.GenericBaseService_gcamusa},
#' \code{L244.ThermalServiceSatiation_gcamusa}, \code{L244.GenericServiceSatiation_gcamusa}, \code{L244.Intgains_scalar_gcamusa},
#' \code{L244.ShellConductance_bld_gcamusa}, \code{L244.Supplysector_bld_gcamusa}, \code{L244.FinalEnergyKeyword_bld_gcamusa}, \code{L244.SubsectorShrwt_bld_gcamusa},
#' \code{L244.SubsectorShrwtFllt_bld_gcamusa}, \code{L244.SubsectorInterp_bld_gcamusa}, \code{L244.SubsectorInterpTo_bld_gcamusa},
#' \code{L244.SubsectorLogit_bld_gcamusa}, \code{L244.StubTech_bld_gcamusa}, \code{L244.StubTechCalInput_bld_gcamusa}, \code{L244.StubTechMarket_bld},
#' \code{L244.GlobalTechIntGainOutputRatio}, \code{L244.GlobalTechInterpTo_bld}, \code{L244.GlobalTechEff_bld},
#' \code{L244.GlobalTechShrwt_bld_gcamusa}, \code{L244.GlobalTechCost_bld_gcamusa}, \code{L244.GlobalTechSCurve_bld}, \code{L244.HDDCDD_A2_GFDL_USA},
#' \code{L244.HDDCDD_AEO_2015_USA}, \code{L244.HDDCDD_constdds_USA}, \code{L244.GompFnParam_gcamusa}.
#' The corresponding file in the original data system was \code{L244.building_USA.R} (gcam-usa level2).
#' @details Creates GCAM-USA building output files for writing to xml.
#' @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 replace_na
#' @author RLH November 2017
module_gcamusa_L244.building_USA <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "energy/A44.gcam_consumer",
FILE = "energy/A44.sector",
FILE = "gcam-usa/calibrated_techs_bld_usa",
FILE = "gcam-usa/states_subregions",
FILE = "gcam-usa/A44.bld_shell_conductance",
FILE = "gcam-usa/A44.demandFn_flsp",
FILE = "gcam-usa/A44.demandFn_serv",
FILE = "gcam-usa/A44.gcam_consumer",
FILE = "gcam-usa/A44.satiation_flsp",
FILE = "gcam-usa/A44.sector",
FILE = "gcam-usa/A44.subsector_interp",
FILE = "gcam-usa/A44.subsector_logit",
FILE = "gcam-usa/A44.subsector_shrwt",
FILE = "gcam-usa/A44.globaltech_cost",
FILE = "gcam-usa/A44.globaltech_eff",
FILE = "gcam-usa/A44.globaltech_eff_avg",
FILE = "gcam-usa/A44.globaltech_shares",
FILE = "gcam-usa/A44.globaltech_intgains",
FILE = "gcam-usa/A44.globaltech_retirement",
FILE = "gcam-usa/A44.globaltech_shrwt",
FILE = "gcam-usa/A44.globaltech_interp",
FILE = "gcam-usa/A44.demand_satiation_mult",
FILE = "gcam-usa/A44.hab_land_flsp_usa",
"L144.flsp_param",
"L144.flsp_bm2_state_res",
"L144.flsp_bm2_state_comm",
"L144.in_EJ_state_comm_F_U_Y",
"L144.in_EJ_state_res_F_U_Y",
"L143.HDDCDD_scen_state",
"L100.Pop_thous_state",
"L100.pcGDP_thous90usd_state"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L244.DeleteConsumer_USAbld",
"L244.DeleteSupplysector_USAbld",
"L244.SubregionalShares_gcamusa",
"L244.PriceExp_IntGains_gcamusa",
"L244.Floorspace_gcamusa",
"L244.DemandFunction_serv_gcamusa",
"L244.DemandFunction_flsp_gcamusa",
"L244.Satiation_flsp_gcamusa",
"L244.SatiationAdder_gcamusa",
"L244.ThermalBaseService_gcamusa",
"L244.GenericBaseService_gcamusa",
"L244.ThermalServiceSatiation_gcamusa",
"L244.GenericServiceSatiation_gcamusa",
"L244.Intgains_scalar_gcamusa",
"L244.ShellConductance_bld_gcamusa",
"L244.Supplysector_bld_gcamusa",
"L244.FinalEnergyKeyword_bld_gcamusa",
"L244.SubsectorShrwt_bld_gcamusa",
"L244.SubsectorShrwtFllt_bld_gcamusa",
"L244.SubsectorInterp_bld_gcamusa",
"L244.SubsectorInterpTo_bld_gcamusa",
"L244.SubsectorLogit_bld_gcamusa",
"L244.StubTech_bld_gcamusa",
"L244.StubTechCalInput_bld_gcamusa",
"L244.StubTechMarket_bld",
"L244.GlobalTechIntGainOutputRatio",
"L244.GlobalTechInterpTo_bld",
"L244.GlobalTechEff_bld",
"L244.GlobalTechShrwt_bld_gcamusa",
"L244.GlobalTechCost_bld_gcamusa",
"L244.GlobalTechSCurve_bld",
"L244.HDDCDD_A2_GFDL_USA",
"L244.HDDCDD_constdds_USA",
"L244.HDDCDD_AEO_2015_USA",
"L244.GompFnParam_gcamusa"))
} else if(command == driver.MAKE) {
# Silence package checks
GCM <- Scen <- base.building.size <- base.service <- calibrated.value <- comm <-
degree.days <- efficiency <- efficiency_tech1 <- efficiency_tech2 <- fuel <-
gcam.consumer <- grid_region <- half_life_new <- half_life_stock <- input.cost <-
input.ratio <- internal.gains.market.name <- internal.gains.output.ratio <-
internal.gains.scalar <- market.name <- minicam.energy.input <- multiplier <-
object <- pcFlsp_mm2 <- pcGDP <- pcflsp_mm2cap <- pop <- region <- resid <-
satiation.adder <- satiation.level <- sector <- sector.name <- service <- share <-
share.weight <- share_tech1 <- share_tech2 <- share_type <- state <- steepness_new <-
steepness_stock <- stockavg <- subsector <- subsector.name <- supplysector <-
tech_type <- technology <- technology1 <- technology2 <-
thermal.building.service.input <- to.value <- value <- year <- year.fillout <- . <-
pop_year <- Sector <- pop_share <- growth <- flsp_growth <- area_gcam <- misc_land_usda <-
area_thouskm2 <- flsp <- pop_thous <- flsp_pc <- tot.dens <- unadjust.satiation <-
land.density.param <- b.param <- income.param <- gdp_pc <- flsp_est <- base_flsp <-
bias.adjust.param <- state_name <- NULL
all_data <- list(...)[[1]]
# Load required inputs
A44.gcam_consumer_en <- get_data(all_data, "energy/A44.gcam_consumer", strip_attributes = TRUE)
A44.sector_en <- get_data(all_data, "energy/A44.sector", strip_attributes = TRUE)
calibrated_techs_bld_usa <- get_data(all_data, "gcam-usa/calibrated_techs_bld_usa", strip_attributes = TRUE)
states_subregions <- get_data(all_data, "gcam-usa/states_subregions", strip_attributes = TRUE)
A44.bld_shell_conductance <- get_data(all_data, "gcam-usa/A44.bld_shell_conductance", strip_attributes = TRUE)
A44.demandFn_flsp <- get_data(all_data, "gcam-usa/A44.demandFn_flsp", strip_attributes = TRUE)
A44.demandFn_serv <- get_data(all_data, "gcam-usa/A44.demandFn_serv", strip_attributes = TRUE)
A44.gcam_consumer <- get_data(all_data, "gcam-usa/A44.gcam_consumer", strip_attributes = TRUE)
A44.satiation_flsp <- get_data(all_data, "gcam-usa/A44.satiation_flsp", strip_attributes = TRUE)
A44.sector <- get_data(all_data, "gcam-usa/A44.sector", strip_attributes = TRUE)
A44.subsector_interp <- get_data(all_data, "gcam-usa/A44.subsector_interp", strip_attributes = TRUE)
A44.subsector_logit <- get_data(all_data, "gcam-usa/A44.subsector_logit", strip_attributes = TRUE)
A44.subsector_shrwt <- get_data(all_data, "gcam-usa/A44.subsector_shrwt", strip_attributes = TRUE)
A44.globaltech_cost <- get_data(all_data, "gcam-usa/A44.globaltech_cost", strip_attributes = TRUE)
A44.globaltech_eff <- get_data(all_data, "gcam-usa/A44.globaltech_eff", strip_attributes = TRUE) %>%
gather_years()
A44.globaltech_eff_avg <- get_data(all_data, "gcam-usa/A44.globaltech_eff_avg", strip_attributes = TRUE)
A44.globaltech_shares <- get_data(all_data, "gcam-usa/A44.globaltech_shares", strip_attributes = TRUE)
A44.globaltech_intgains <- get_data(all_data, "gcam-usa/A44.globaltech_intgains", strip_attributes = TRUE)
A44.globaltech_retirement <- get_data(all_data, "gcam-usa/A44.globaltech_retirement", strip_attributes = TRUE)
A44.globaltech_shrwt <- get_data(all_data, "gcam-usa/A44.globaltech_shrwt", strip_attributes = TRUE)
A44.globaltech_interp <- get_data(all_data, "gcam-usa/A44.globaltech_interp", strip_attributes = TRUE)
A44.demand_satiation_mult <- get_data(all_data, "gcam-usa/A44.demand_satiation_mult", strip_attributes = TRUE)
L144.flsp_bm2_state_res <- get_data(all_data, "L144.flsp_bm2_state_res", strip_attributes = TRUE)
L144.flsp_bm2_state_comm <- get_data(all_data, "L144.flsp_bm2_state_comm", strip_attributes = TRUE)
L144.in_EJ_state_comm_F_U_Y <- get_data(all_data, "L144.in_EJ_state_comm_F_U_Y", strip_attributes = TRUE)
L144.in_EJ_state_res_F_U_Y <- get_data(all_data, "L144.in_EJ_state_res_F_U_Y", strip_attributes = TRUE)
L143.HDDCDD_scen_state <- get_data(all_data, "L143.HDDCDD_scen_state", strip_attributes = TRUE)
L100.Pop_thous_state <- get_data(all_data, "L100.Pop_thous_state", strip_attributes = TRUE)
L100.pcGDP_thous90usd_state <- get_data(all_data, "L100.pcGDP_thous90usd_state", strip_attributes = TRUE)
L144.hab_land_flsp_usa<- get_data(all_data, "gcam-usa/A44.hab_land_flsp_usa", strip_attributes = TRUE)
L144.flsp_param <- get_data(all_data, "L144.flsp_param", strip_attributes = TRUE)
# ===================================================
# Data Processing
# Note: Building energy demands and floorspace are calculated endogenously - these are undergoing review
# per-capita demand = (satiation.level - satiation.adder) * (1 - exp( -log2 / satiation.impedance * Demand.Driver)) + satiation.adder)
#
# floorspace = (satiation.level - satiation.adder) *
# [1 - exp{(-ln(2) * per-capita-GDP/satiation.impedance) * (energy_cost/base_energy_cost)^price_effect_exponent}] + satiation.adder
#
# satiation.level: maximum per-capita demand that can be achieved
# satiation.adder: value that allow the starting position of any region to be set along the demand function
# satiation.impedance: shape parameter
# Need to delete the buildings sector in the USA region (gcam.consumers and supplysectors)
L244.DeleteConsumer_USAbld <- tibble(region = gcam.USA_REGION, gcam.consumer = A44.gcam_consumer_en$gcam.consumer)
L244.DeleteSupplysector_USAbld <- tibble(region = gcam.USA_REGION, supplysector = A44.sector_en$supplysector)
# L244.SubregionalShares_gcamusa: subregional population and income shares (not currently used)
L244.SubregionalShares_gcamusa <- write_to_all_states(A44.gcam_consumer, c("region", "gcam.consumer")) %>%
mutate(pop.year.fillout = min(MODEL_BASE_YEARS),
inc.year.fillout = min(MODEL_BASE_YEARS),
subregional.population.share = 1,
subregional.income.share = 1)
# L244.PriceExp_IntGains_gcamusa: price exponent on floorspace and naming of internal gains trial markets
L244.PriceExp_IntGains_gcamusa <- write_to_all_states(A44.gcam_consumer, LEVEL2_DATA_NAMES[["PriceExp_IntGains"]])
# L244.Floorspace_gcamusa: base year floorspace
# Keep all historical years for now - these are needed in calculating satiation adders later on
# Residential floorspace
L244.Floorspace_resid <- L144.flsp_bm2_state_res %>%
rename(base.building.size = value,
region = state,
gcam.consumer = sector) %>%
mutate(base.building.size = round(base.building.size, energy.DIGITS_FLOORSPACE)) %>%
left_join_error_no_match(A44.gcam_consumer, by = "gcam.consumer") %>%
select(LEVEL2_DATA_NAMES[["Floorspace"]])
# Commercial floorspace
L244.Floorspace_comm <- L144.flsp_bm2_state_comm %>%
rename(base.building.size = value,
region = state,
gcam.consumer = sector) %>%
mutate(base.building.size = round(base.building.size, energy.DIGITS_FLOORSPACE)) %>%
left_join_error_no_match(A44.gcam_consumer, by = "gcam.consumer") %>%
select(LEVEL2_DATA_NAMES[["Floorspace"]])
L244.Floorspace_full <- bind_rows(L244.Floorspace_resid, L244.Floorspace_comm)
# Final output only has base years
L244.Floorspace_gcamusa <- filter(L244.Floorspace_full, year %in% MODEL_BASE_YEARS)
# L244.DemandFunction_serv_gcamusa and L244.DemandFunction_flsp_gcamusa: demand function types
L244.DemandFunction_serv_gcamusa <- write_to_all_states(A44.demandFn_serv, LEVEL2_DATA_NAMES[["DemandFunction_serv"]])
L244.DemandFunction_flsp_gcamusa <- write_to_all_states(A44.demandFn_flsp, LEVEL2_DATA_NAMES[["DemandFunction_flsp"]])
# L244.Satiation_flsp_gcamusa: Satiation levels assumed for floorspace
L244.Satiation_flsp_gcamusa <- A44.satiation_flsp %>%
gather(gcam.consumer, value, resid, comm) %>%
rename(region = state) %>%
# Need to make sure that the satiation level is greater than the floorspace in the final base year
left_join_error_no_match(L244.Floorspace_gcamusa %>%
filter(year == max(MODEL_BASE_YEARS)), by = c("region", "gcam.consumer")) %>%
left_join_error_no_match(L100.Pop_thous_state %>% rename(pop = value), by = c("region" = "state", "year")) %>%
mutate(year = as.integer(year),
# value.y = population
pcflsp_mm2cap = base.building.size / pop,
# Satiation level = must be greater than the observed value in the final calibration year, so if observed value is
# greater than calculated, multiply observed by 1.001
satiation.level = round(pmax(value * CONV_THOUS_BIL, pcflsp_mm2cap * 1.001), energy.DIGITS_SATIATION_ADDER)) %>%
left_join_error_no_match(A44.gcam_consumer, by = c("gcam.consumer", "nodeInput", "building.node.input")) %>%
select(LEVEL2_DATA_NAMES[["BldNodes"]], "satiation.level")
# L244.SatiationAdder_gcamusa: Satiation adders in floorspace demand function
# Required for shaping the future floorspace growth trajectories in each region
# Match in the per-capita GDP, total floorspace, and population (for calculating per-capita floorspace)
# We will filter GDP to energy.SATIATION_YEAR, but this may be greater than the historical years present
# under timeshift conditions. So we adjust energy.SATIATION_YEAR
energy.SATIATION_YEAR <- min(max(MODEL_BASE_YEARS), energy.SATIATION_YEAR)
L244.SatiationAdder_gcamusa <- L244.Satiation_flsp_gcamusa %>%
# Add per capita GDP
left_join_error_no_match(L100.pcGDP_thous90usd_state %>%
filter(year == energy.SATIATION_YEAR), by = c("region" = "state")) %>%
rename(pcGDP = value) %>%
# Add floorspace
left_join_error_no_match(L244.Floorspace_full, by = c("region", "gcam.consumer", "year", "nodeInput", "building.node.input")) %>%
# Add population
left_join_error_no_match(L100.Pop_thous_state, by = c("region" = "state", "year")) %>%
rename(pop = value) %>%
# Calculate per capita floorspace
mutate(pcFlsp_mm2 = base.building.size / pop,
# Calculate the satiation adders
satiation.adder = round(satiation.level - (
exp(log(2) * pcGDP / energy.GDP_MID_SATIATION) * (satiation.level - pcFlsp_mm2)),
energy.DIGITS_SATIATION_ADDER),
# The satiation adder (million square meters of floorspace per person) needs to be less than the per-capita demand in the final calibration year
satiation.adder = if_else(satiation.adder > pcFlsp_mm2, pcFlsp_mm2 * 0.999, satiation.adder)) %>%
select(LEVEL2_DATA_NAMES[["SatiationAdder"]])
#------------------------------------------------------
# Updated floorspace Gompertz function
# - Calculate the bias correction parameter (k)
# - Write parameters for the updated floorspace function
L144.flsp_param_usa<-L144.flsp_param %>%
filter(region=="USA") %>%
select(-region) %>%
repeat_add_columns(tibble(region=gcamusa.STATES))
# First calculate the habitable land
L144.hab_land_flsp_usa_fin<-L144.hab_land_flsp_usa %>%
rename(state_name=state)%>%
left_join(states_subregions %>% select(state,state_name),by="state_name") %>%
filter(state %in% gcamusa.STATES) %>%
mutate(area_thouskm2=(area_gcam-misc_land_usda)/1E3) %>%
select(region=state,area_thouskm2)
# Write the function parameters
L244.GompFnParam_gcamusa<-L144.flsp_param_usa %>%
left_join_error_no_match(L144.hab_land_flsp_usa_fin,by="region") %>%
mutate(year=MODEL_FINAL_BASE_YEAR) %>%
left_join_error_no_match(L100.pcGDP_thous90usd_state %>% rename(region=state), by=c("region","year")) %>%
rename(gdp_pc=value) %>%
left_join_error_no_match(L100.Pop_thous_state %>% rename(region=state), by=c("region","year")) %>%
rename(pop_thous=value) %>%
left_join_error_no_match(L144.flsp_bm2_state_res %>% rename(region=state), by=c("region","year")) %>%
rename(flsp=value) %>%
mutate(flsp_pc=(flsp*1E9)/(pop_thous*1E3),
base_flsp=flsp_pc,
tot.dens=round(pop_thous/area_thouskm2,0),
#correct 0 population density to avoid NaN
tot.dens=if_else(tot.dens==0,1,tot.dens),
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"]])
#================================================================
# Heating and cooling degree days (thermal services only)
# 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.globaltech_intgains$supplysector)
thermal_services <- dplyr::setdiff(unique(A44.sector$supplysector), generic_services)
# L244.HDDCDD: Heating and cooling degree days by scenario
L244.HDDCDD_scen_state <- L143.HDDCDD_scen_state %>%
rename(region = state,
degree.days = value)
# Let's make a climate normal (historical average) 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_state <- L244.HDDCDD_scen_state %>%
filter(year %in% seq(1981, 2000),
# The AEO_2015 scenario changes this "normal climate" for each region,
# which is not desirable since it does not incldue historical data
# and is not the standard reference assumption. Thus, we remove it
# from this calculation.
Scen != "AEO_2015") %>%
group_by(region, variable) %>%
summarise(degree.days = mean(degree.days)) %>%
ungroup()
# Subset the heating and cooling services, separately
heating_services <- thermal_services[grepl("heating", thermal_services)]
cooling_services <- thermal_services[grepl("cooling", thermal_services)]
L244.HDDCDD_temp <- tidyr::crossing(region = gcamusa.STATES, thermal.building.service.input = thermal_services) %>%
# Add in gcam.consumer
left_join_error_no_match(calibrated_techs_bld_usa %>%
select(service, gcam.consumer = sector) %>%
distinct(), by = c("thermal.building.service.input" = "service")) %>%
# Add in nodeInput and building.node.input
left_join_error_no_match(A44.gcam_consumer, by = "gcam.consumer") %>%
select(LEVEL2_DATA_NAMES[["BldNodes"]], thermal.building.service.input) %>%
# Add in model years
repeat_add_columns(tibble(year = MODEL_YEARS)) %>%
# Add HDD/CDD so that we can join with L244.HDDCDD_scen_state, remove at end
mutate(variable = if_else(thermal.building.service.input %in% heating_services, "HDD", "CDD")) %>%
# Add in degree days
# L244.HDDCDD_scen_state has multiple scenarios, rows in this tbl_df are intended to be duplicated for each scenario
# left_join_error_no_match throws an error when rows are duplicated (as intended), so left_join is used
left_join(L244.HDDCDD_scen_state, by = c("region", "variable", "year")) %>%
mutate(degree.days = round(degree.days, energy.DIGITS_HDDCDD))
L244.HDDCDD_constdds_USA <- L244.HDDCDD_temp %>%
filter(Scen == "constdds") %>%
select(-Scen, -GCM, -variable)
L244.HDDCDD_A2_GFDL_USA <- L244.HDDCDD_temp %>%
filter(Scen == "A2") %>%
select(-Scen, -GCM, -variable)
L244.HDDCDD_AEO_2015_USA <- L244.HDDCDD_temp %>%
filter(Scen == "AEO_2015") %>%
select(-Scen, -GCM, -variable)
# L244.ShellConductance_bld_gcamusa: Shell conductance (inverse of shell efficiency)
L244.ShellConductance_bld_gcamusa <- A44.bld_shell_conductance %>%
# Convert to long form
gather_years() %>%
# Interpolate to model years
complete(gcam.consumer, year = c(year, MODEL_YEARS)) %>%
group_by(gcam.consumer) %>%
mutate(value = round(approx_fun(year, value), energy.DIGITS_EFFICIENCY)) %>%
ungroup() %>%
filter(year %in% MODEL_YEARS) %>%
# Repeat for all states
write_to_all_states(names = c(names(.), "region")) %>%
# Add nodeInput and building.node.input
left_join_error_no_match(A44.gcam_consumer, by = "gcam.consumer") %>%
mutate(floor.to.surface.ratio = energy.FLOOR_TO_SURFACE_RATIO,
shell.year = year) %>%
# Rename columns
rename(shell.conductance = value) %>%
select(LEVEL2_DATA_NAMES[["ShellConductance"]])
# The remainder of the building-level parameters require information about the output of each service, which we do not have yet
# L244.Supplysector_bld: Supplysector info for buildings
L244.Supplysector_bld_gcamusa <- write_to_all_states(A44.sector, c(LEVEL2_DATA_NAMES[["Supplysector"]], LOGIT_TYPE_COLNAME))
# L244.FinalEnergyKeyword_bld: Supply sector keywords for detailed building sector
L244.FinalEnergyKeyword_bld_gcamusa <- write_to_all_states(A44.sector, LEVEL2_DATA_NAMES[["FinalEnergyKeyword"]])
# L244.SubsectorLogit_bld: Subsector logit exponents of building sector
L244.SubsectorLogit_bld_gcamusa <- write_to_all_states(A44.subsector_logit, c(LEVEL2_DATA_NAMES[["SubsectorLogit"]], LOGIT_TYPE_COLNAME))
# L244.SubsectorShrwt_bld and L244.SubsectorShrwtFllt_bld: Subsector shareweights of building sector
if(any(!is.na(A44.subsector_shrwt$year))) {
L244.SubsectorShrwt_bld_gcamusa <- write_to_all_states(A44.subsector_shrwt %>%
filter(!is.na(year)), LEVEL2_DATA_NAMES[["SubsectorShrwt"]])
}
if(any(!is.na(A44.subsector_shrwt$year.fillout))) {
L244.SubsectorShrwtFllt_bld_gcamusa <- write_to_all_states(A44.subsector_shrwt %>%
filter(!is.na(year.fillout)), LEVEL2_DATA_NAMES[["SubsectorShrwtFllt"]])
}
# L244.SubsectorInterp_bld and L244.SubsectorInterpTo_bld: Subsector shareweight interpolation of building sector
if(any(is.na(A44.subsector_interp$to.value))) {
L244.SubsectorInterp_bld_gcamusa <- write_to_all_states(A44.subsector_interp %>%
filter(is.na(to.value)), LEVEL2_DATA_NAMES[["SubsectorInterp"]])
}
if(any(!is.na(A44.subsector_interp$to.value))) {
L244.SubsectorInterpTo_bld_gcamusa <- write_to_all_states(A44.subsector_interp %>%
filter(!is.na(to.value)), LEVEL2_DATA_NAMES[["SubsectorInterpTo"]])
}
# L244.StubTech_bld_gcamusa: Identification of stub technologies for buildings
L244.StubTech_bld_gcamusa <- A44.globaltech_eff %>%
select(supplysector, subsector, technology) %>%
distinct() %>%
write_to_all_states(LEVEL2_DATA_NAMES[["Tech"]]) %>%
rename(stub.technology = technology)
# L244.GlobalTechEff_bld: Assumed efficiencies (all years) of buildings technologies
L244.end_use_eff <- A44.globaltech_eff %>%
complete(nesting(supplysector, subsector, technology, minicam.energy.input), year = c(year, MODEL_YEARS)) %>%
group_by(supplysector, subsector, technology, minicam.energy.input) %>%
mutate(value = approx_fun(year, value)) %>%
ungroup() %>%
filter(year %in% MODEL_YEARS) %>%
mutate(value = round(value, energy.DIGITS_CALOUTPUT)) %>%
rename(efficiency = value)
# Note - this code assumes that base-year efficiences are identical (should fix to copy over to make sure)
L244.GlobalTechEff_bld <- L244.end_use_eff %>%
rename(sector.name = supplysector,
subsector.name = subsector) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechEff"]])
# L244.StubTechMarket_bld: Specify market names for fuel inputs to all technologies in each state
L244.StubTechMarket_bld <- L244.end_use_eff %>%
mutate(market.name = gcam.USA_REGION) %>%
rename(stub.technology = technology) %>%
write_to_all_states(LEVEL2_DATA_NAMES[["StubTechMarket"]]) %>%
# For fuels with state markets, set the market.name to the region
mutate(market.name = if_else(minicam.energy.input %in% gcamusa.STATE_FUEL_MARKETS, region, market.name)) %>%
# replace market name with the grid region name if the minicam.energy.input is
# considered a regional fuel market
left_join_error_no_match(states_subregions, by = c("region" = "state")) %>%
mutate(market.name = if_else(minicam.energy.input %in% gcamusa.REGIONAL_FUEL_MARKETS,
grid_region, market.name)) %>%
select(LEVEL2_DATA_NAMES[["StubTechMarket"]])
# L244.StubTechCalInput_bld: Calibrated energy consumption by buildings technologies
# Combine residential and commercial energy data
L244.in_EJ_R_bld_serv_F_Yh <- bind_rows(L144.in_EJ_state_res_F_U_Y, L144.in_EJ_state_comm_F_U_Y) %>%
filter(year %in% MODEL_YEARS) %>%
mutate(calibrated.value = round(value, energy.DIGITS_CALOUTPUT)) %>%
rename(supplysector = service) %>%
# Add subsector and energy.input
left_join_error_no_match(calibrated_techs_bld_usa %>%
select(sector, supplysector, fuel, subsector, minicam.energy.input) %>%
distinct(), by = c("sector", "supplysector", "fuel")) %>%
select(region = state, supplysector, subsector, minicam.energy.input, year, calibrated.value)
# Shares allocated to partitioned technologies need to be computed first using efficiencies
L244.globaltech_eff_prt <- A44.globaltech_eff %>%
semi_join(A44.globaltech_eff_avg, by = c("supplysector", "subsector")) %>%
filter(year == gcamusa.EFFICIENCY_PARTITION_YEAR) %>%
select(supplysector, subsector, technology, efficiency = value)
# Calculate technology shares using efficiency values
L244.globaltech_shares <- A44.globaltech_eff_avg %>%
# Adding specific technology efficiency to stock average efficiency
left_join_error_no_match(L244.globaltech_eff_prt, by = c("supplysector", "subsector", "technology1" = "technology")) %>%
rename(efficiency_tech1 = efficiency) %>%
left_join_error_no_match(L244.globaltech_eff_prt, by = c("supplysector", "subsector", "technology2" = "technology")) %>%
rename(efficiency_tech2 = efficiency) %>%
# Calculate technology shares using stock average efficiency and individual technology efficiencies
# Equation can be derived by solving following system of equations:
# stockavg = efficiency_tech1 * share_tech1 + efficiency_tech2 * share_tech2
# share_tech1 + share_tech2 = 1
mutate(share_tech1 = (stockavg - efficiency_tech2) / (efficiency_tech1 - efficiency_tech2),
share_tech2 = 1 - share_tech1) %>%
# Keep only same names as A44.globaltech_shares and bind with A44.globaltech_shares
select(names(A44.globaltech_shares)) %>%
bind_rows(A44.globaltech_shares) %>%
# Clunky, but we want only one technology and share value, currently have technology1, technology2, share1, share2
gather(share_type, share, share_tech1, share_tech2)%>%
gather(tech_type, technology, technology1, technology2) %>%
# Filter for same technology and share number, then remove tech_type and share_type columns
filter(substr(tech_type, nchar(tech_type), nchar(tech_type)) == substr(share_type, nchar(share_type), nchar(share_type))) %>%
select(-tech_type, -share_type)
# For calibration table, start with global tech efficiency table, repeat by states, and match in tech shares.
L244.StubTechCalInput_bld_gcamusa <- L244.GlobalTechEff_bld %>%
filter(year %in% MODEL_BASE_YEARS) %>%
write_to_all_states(names = c(names(.), "region")) %>%
rename(supplysector = sector.name, subsector = subsector.name, stub.technology = technology) %>%
# Using left_join because we don't have shares for all technologies, NAs will be set to 1
left_join(L244.globaltech_shares, by = c("supplysector", "subsector", "stub.technology" = "technology")) %>%
replace_na(list(share = 1)) %>%
# Add energy by state/service/fuel
left_join_error_no_match(L244.in_EJ_R_bld_serv_F_Yh, by = c("region", "supplysector", "subsector", "minicam.energy.input", "year")) %>%
# calibrated.value = energy * share
mutate(calibrated.value = round(share * calibrated.value, energy.DIGITS_CALOUTPUT),
share.weight.year = year,
calOutputValue = calibrated.value) %>%
# Set subsector and technology shareweights
set_subsector_shrwt() %>%
mutate(tech.share.weight = if_else(calibrated.value > 0, 1, 0)) %>%
select(LEVEL2_DATA_NAMES[["StubTechCalInput"]])
# L244.GlobalTechShrwt_bld_gcamusa: Default shareweights for global building technologies
L244.GlobalTechShrwt_bld_gcamusa <- A44.globaltech_shrwt %>%
gather_years(value_col = "share.weight") %>%
complete(nesting(supplysector, subsector, technology), year = c(year, MODEL_YEARS)) %>%
group_by(supplysector, subsector, technology) %>%
mutate(share.weight = approx_fun(year, share.weight)) %>%
ungroup() %>%
filter(year %in% MODEL_YEARS) %>%
rename(sector.name = supplysector,
subsector.name = subsector) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechYr"]], share.weight)
# L244.GlobalTechInterpTo_bld: Technology shareweight interpolation (selected techs only)
L244.GlobalTechInterpTo_bld <- A44.globaltech_interp %>%
set_years() %>%
mutate(sector.name = supplysector,
subsector.name = subsector) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechInterpTo"]])
# L244.GlobalTechCost_bld: Non-fuel costs of global building technologies
L244.GlobalTechCost_bld_gcamusa <- A44.globaltech_cost %>%
gather_years(value_col = "input.cost") %>%
complete(nesting(supplysector, subsector, technology), year = c(year, MODEL_YEARS)) %>%
group_by(supplysector, subsector, technology) %>%
mutate(input.cost = approx_fun(year, input.cost)) %>%
ungroup() %>%
filter(year %in% MODEL_YEARS) %>%
rename(sector.name = supplysector, subsector.name = subsector) %>%
mutate(minicam.non.energy.input = "non-energy") %>%
select(LEVEL2_DATA_NAMES[["GlobalTechCost"]])
# L244.GlobalTechSCurve_bld: Retirement rates for building technologies
L244.GlobalTechSCurve_bld <- L244.GlobalTechCost_bld_gcamusa %>%
filter(year %in% c(max(MODEL_BASE_YEARS), MODEL_FUTURE_YEARS),
sector.name %in% A44.globaltech_retirement$supplysector) %>%
# Add lifetimes and steepness
left_join_error_no_match(A44.globaltech_retirement, by = c("sector.name" = "supplysector")) %>%
# Set steepness/halflife values to stock for base years, new for future years
mutate(steepness = if_else(year == max(MODEL_BASE_YEARS), steepness_stock, steepness_new),
half.life = if_else(year == max(MODEL_BASE_YEARS), half_life_stock, half_life_new)) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechSCurve"]])
# L244.GlobalTechIntGainOutputRatio: Output ratios of internal gain energy from non-thermal building services
calibrated_techs_bld_usa_consumer <- calibrated_techs_bld_usa %>%
select(gcam.consumer = sector, supplysector) %>%
distinct()
L244.GlobalTechIntGainOutputRatio <- A44.globaltech_intgains %>%
repeat_add_columns(tibble(year = MODEL_YEARS))%>%
# Add gcam.consumer (sector)
left_join_error_no_match(calibrated_techs_bld_usa_consumer, by = "supplysector") %>%
rename(sector.name = supplysector,
subsector.name = subsector) %>%
# Add internal.gains.market.name
left_join_error_no_match(A44.gcam_consumer, by = "gcam.consumer") %>%
# Add efficiency
left_join_error_no_match(L244.GlobalTechEff_bld,
by = c("sector.name", "subsector.name", "technology", "year")) %>%
mutate(internal.gains.output.ratio = round(input.ratio / efficiency, energy.DIGITS_EFFICIENCY)) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechYr"]], internal.gains.output.ratio, internal.gains.market.name)
# L244.GenericBaseService and L244.ThermalBaseService: Base year output of buildings services (per unit floorspace)
# Base-service: Multiply energy consumption by efficiency for each technology, and aggregate by service
L244.base_service <- L244.StubTechCalInput_bld_gcamusa %>%
# Add in efficiency by technology
left_join_error_no_match(L244.GlobalTechEff_bld,
by = c("supplysector" = "sector.name", "subsector" = "subsector.name",
"stub.technology" = "technology", "year", "minicam.energy.input")) %>%
# Calculate base.service = calibrated.value(energy) * efficiency
mutate(base.service = round(calibrated.value * efficiency, energy.DIGITS_CALOUTPUT)) %>%
# Aggregate base service by service (supplysector)
group_by(region, supplysector, year) %>%
summarise(base.service = sum(base.service)) %>%
ungroup() %>%
# Add gcam.consumer (sector)
left_join_error_no_match(calibrated_techs_bld_usa_consumer, by = "supplysector") %>%
# Add nodeInput and building.node.input
left_join_error_no_match(A44.gcam_consumer, by = "gcam.consumer")
# Separate thermal and generic services into separate tables with different ID strings
L244.GenericBaseService_gcamusa <- L244.base_service %>%
filter(supplysector %in% generic_services) %>%
rename(building.service.input = supplysector) %>%
select(LEVEL2_DATA_NAMES[["GenericBaseService"]])
L244.ThermalBaseService_gcamusa <- L244.base_service %>%
filter(supplysector %in% thermal_services) %>%
rename(thermal.building.service.input = supplysector) %>%
select(LEVEL2_DATA_NAMES[["ThermalBaseService"]])
# L244.GenericServiceSatiation_gcamusa: Satiation levels assumed for non-thermal building services
# Just multiply the base-service by an exogenous multiplier
L244.GenericServiceSatiation_gcamusa <- L244.GenericBaseService_gcamusa %>%
filter(year == max(MODEL_BASE_YEARS)) %>%
# Add floorspace
left_join_error_no_match(L244.Floorspace_gcamusa, by = c(LEVEL2_DATA_NAMES[["BldNodes"]], "year")) %>%
# Add multiplier
left_join_error_no_match(A44.demand_satiation_mult, by = c("building.service.input" = "supplysector")) %>%
# Satiation level = service per floorspace * multiplier
mutate(satiation.level = round(base.service / base.building.size * multiplier, energy.DIGITS_COEFFICIENT)) %>%
select(LEVEL2_DATA_NAMES[["GenericServiceSatiation"]])
# L244.ThermalServiceSatiation: Satiation levels assumed for thermal building services
L244.ThermalServiceSatiation_gcamusa <- L244.ThermalBaseService_gcamusa %>%
filter(year == max(MODEL_BASE_YEARS)) %>%
# Add floorspace
left_join_error_no_match(L244.Floorspace_gcamusa, by = c(LEVEL2_DATA_NAMES[["BldNodes"]], "year")) %>%
# Add multiplier
left_join_error_no_match(A44.demand_satiation_mult, by = c("thermal.building.service.input" = "supplysector")) %>%
# Satiation level = service per floorspace * multiplier
mutate(satiation.level = round(base.service / base.building.size * multiplier, energy.DIGITS_COEFFICIENT)) %>%
select(LEVEL2_DATA_NAMES[["ThermalServiceSatiation"]])
# L244.Intgains_scalar: Scalers relating internal gain energy to increased/reduced cooling/heating demands
variable <- c("HDD", "CDD")
scalar <- c(energy.INTERNAL_GAINS_SCALAR_USA_H, energy.INTERNAL_GAINS_SCALAR_USA_C)
DDnorm <- c(gcamusa.BASE_HDD_USA, gcamusa.BASE_CDD_USA)
US.base.scalar <- tibble(variable, scalar, DDnorm)
threshold_HDD <- 500
L244.Intgains_scalar_gcamusa <- L244.ThermalServiceSatiation_gcamusa %>%
# Assign HDD or CDD
mutate(variable = if_else(thermal.building.service.input %in% heating_services, "HDD", "CDD")) %>%
# Add DDnorm & scalar
left_join_error_no_match(US.base.scalar, by = "variable") %>%
# Add degree days
left_join_error_no_match(L244.HDDCDD_normal_state, by = c("region", "variable")) %>%
mutate(internal.gains.scalar = round(scalar * degree.days / DDnorm, energy.DIGITS_HDDCDD),
# Prevent very warm places from having negative heating demands, using exogenous threshold
internal.gains.scalar = if_else(variable == "HDD" & degree.days < threshold_HDD, 0, internal.gains.scalar)) %>%
select(LEVEL2_DATA_NAMES[["Intgains_scalar"]])
# ===================================================
# Produce outputs
L244.DeleteConsumer_USAbld %>%
add_title("Deletes building sector in USA region to rewrite with GCAM-USA data") %>%
add_units("NA") %>%
add_comments("gcam.consumer column from A44.gcam_consumer") %>%
add_legacy_name("L244.DeleteConsumer_USAbld") %>%
add_precursors("energy/A44.gcam_consumer") ->
L244.DeleteConsumer_USAbld
L244.DeleteSupplysector_USAbld %>%
add_title("Deletes building sector in USA region to rewrite with GCAM-USA data") %>%
add_units("NA") %>%
add_comments("supplysector column from A44.sector") %>%
add_legacy_name("L244.DeleteSupplysector_USAbld") %>%
add_precursors("energy/A44.sector") ->
L244.DeleteSupplysector_USAbld
L244.SubregionalShares_gcamusa %>%
add_title("Subregional population and income shares") %>%
add_units("Unitless") %>%
add_comments("Default values used for years and shares") %>%
add_legacy_name("L244.SubregionalShares") %>%
add_precursors("gcam-usa/A44.gcam_consumer") ->
L244.SubregionalShares_gcamusa
L244.PriceExp_IntGains_gcamusa %>%
add_title("Price exponent on floorspace and naming of internal gains trial markets") %>%
add_units("Unitless") %>%
add_comments("A44.gcam_consumer written to all states") %>%
add_legacy_name("L244.PriceExp_IntGains") %>%
add_precursors("gcam-usa/A44.gcam_consumer") ->
L244.PriceExp_IntGains_gcamusa
L244.Floorspace_gcamusa %>%
add_title("base year floorspace") %>%
add_units("billion m2") %>%
add_comments("Data from L144.flsp_bm2_state_res and L144.flsp_bm2_state_comm") %>%
add_legacy_name("L244.Floorspace") %>%
add_precursors("L144.flsp_bm2_state_res", "L144.flsp_bm2_state_comm", "gcam-usa/A44.gcam_consumer") ->
L244.Floorspace_gcamusa
L244.DemandFunction_serv_gcamusa %>%
add_title("Service demand function types") %>%
add_units("NA") %>%
add_comments("A44.demandFn_serv written to all states") %>%
add_legacy_name("L244.DemandFunction_serv") %>%
add_precursors("gcam-usa/A44.demandFn_serv") ->
L244.DemandFunction_serv_gcamusa
L244.DemandFunction_flsp_gcamusa %>%
add_title("Floorspace demand function types") %>%
add_units("NA") %>%
add_comments("A44.demandFn_flsp written to all states") %>%
add_legacy_name("L244.DemandFunction_flsp") %>%
add_precursors("gcam-usa/A44.demandFn_flsp") ->
L244.DemandFunction_flsp_gcamusa
L244.Satiation_flsp_gcamusa %>%
add_title("Satiation levels assumed for floorspace") %>%
add_units("million m2 / person") %>%
add_comments("Values from A44.satiation_flsp or L244.Floorspace_gcamusa/L100.Pop_thous_state") %>%
add_comments("Whichever is larger") %>%
add_legacy_name("L244.Satiation_flsp") %>%
add_precursors("gcam-usa/A44.satiation_flsp", "gcam-usa/A44.gcam_consumer", "L100.Pop_thous_state",
"L144.flsp_bm2_state_res", "L144.flsp_bm2_state_comm") ->
L244.Satiation_flsp_gcamusa
L244.SatiationAdder_gcamusa %>%
add_title("Satiation adders in floorspace demand function") %>%
add_units("million m2 / person") %>%
add_comments("Calculated with function dependent on satiation level; per capita floorspace; and per capita GDP") %>%
add_legacy_name("L244.SatiationAdder") %>%
add_precursors("gcam-usa/A44.satiation_flsp", "gcam-usa/A44.gcam_consumer", "L100.Pop_thous_state",
"L144.flsp_bm2_state_res", "L144.flsp_bm2_state_comm", "L100.pcGDP_thous90usd_state") ->
L244.SatiationAdder_gcamusa
L244.GompFnParam_gcamusa %>%
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_gcamusa") %>%
add_precursors("L144.flsp_param", "L100.Pop_thous_state","L100.pcGDP_thous90usd_state",
"gcam-usa/A44.hab_land_flsp_usa","L144.flsp_bm2_state_res" ) ->
L244.GompFnParam_gcamusa
L244.HDDCDD_A2_GFDL_USA %>%
add_title("Heating and Cooling Degree Days by State for GFDL A2") %>%
add_units("Fahrenheit Degree Days") %>%
add_comments("L143.HDDCDD_scen_state assigned to GCAM subsectors") %>%
add_legacy_name("L244.HDDCDD_A2_GFDL") %>%
add_precursors("L143.HDDCDD_scen_state", "gcam-usa/A44.sector",
"gcam-usa/calibrated_techs_bld_usa", "gcam-usa/A44.gcam_consumer") ->
L244.HDDCDD_A2_GFDL_USA
L244.ThermalBaseService_gcamusa %>%
add_title("Base year output of thermal buildings services") %>%
add_units("EJ per unit floorspace") %>%
add_comments("Multiplied energy consumption by efficiency for each technology, then aggregated by service") %>%
add_legacy_name("L244.ThermalBaseService") %>%
add_precursors("L144.in_EJ_state_res_F_U_Y", "L144.in_EJ_state_comm_F_U_Y", "gcam-usa/calibrated_techs_bld_usa",
"gcam-usa/A44.globaltech_eff", "gcam-usa/A44.globaltech_eff_avg", "gcam-usa/A44.globaltech_shares",
"gcam-usa/A44.gcam_consumer") ->
L244.ThermalBaseService_gcamusa
L244.GenericBaseService_gcamusa %>%
add_title("Base year output of generic buildings services") %>%
add_units("EJ per unit floorspace") %>%
add_comments("Multiplied energy consumption by efficiency for each technology, then aggregated by service") %>%
add_legacy_name("L244.GenericBaseService") %>%
add_precursors("L144.in_EJ_state_res_F_U_Y", "L144.in_EJ_state_comm_F_U_Y", "gcam-usa/calibrated_techs_bld_usa",
"gcam-usa/A44.globaltech_eff", "gcam-usa/A44.globaltech_eff_avg", "gcam-usa/A44.globaltech_shares",
"gcam-usa/A44.gcam_consumer") ->
L244.GenericBaseService_gcamusa
L244.GenericServiceSatiation_gcamusa %>%
add_title("Satiation levels assumed for non-thermal building services") %>%
add_units("EJ/billion m2 floorspace") %>%
add_comments("Satiation level = base service / floorspace * exogenous multiplier") %>%
add_legacy_name("L244.GenericServiceSatiation") %>%
add_precursors("L144.in_EJ_state_res_F_U_Y", "L144.in_EJ_state_comm_F_U_Y", "gcam-usa/calibrated_techs_bld_usa",
"gcam-usa/A44.globaltech_eff", "gcam-usa/A44.globaltech_eff_avg", "gcam-usa/A44.globaltech_shares",
"gcam-usa/A44.gcam_consumer", "L144.flsp_bm2_state_res", "L144.flsp_bm2_state_comm",
"gcam-usa/A44.demand_satiation_mult") ->
L244.GenericServiceSatiation_gcamusa
L244.ThermalServiceSatiation_gcamusa %>%
add_title("Satiation levels assumed for thermal building services") %>%
add_units("EJ/billion m2 floorspace") %>%
add_comments("Satiation level = base service / floorspace * exogenous multiplier") %>%
add_legacy_name("L244.ThermalServiceSatiation") %>%
add_precursors("L144.in_EJ_state_res_F_U_Y", "L144.in_EJ_state_comm_F_U_Y", "gcam-usa/calibrated_techs_bld_usa",
"gcam-usa/A44.globaltech_eff", "gcam-usa/A44.globaltech_eff_avg", "gcam-usa/A44.globaltech_shares",
"gcam-usa/A44.gcam_consumer", "L144.flsp_bm2_state_res", "L144.flsp_bm2_state_comm",
"gcam-usa/A44.demand_satiation_mult") ->
L244.ThermalServiceSatiation_gcamusa
L244.Intgains_scalar_gcamusa %>%
add_title("Scalers relating internal gain energy to increased/reduced cooling/heating demands") %>%
add_units("Unitless") %>%
add_comments("internal.gains.scalar = exogenous scalar * degree.days / exogenous degree day norm") %>%
add_legacy_name("L244.Intgains_scalar") %>%
add_precursors("L144.in_EJ_state_res_F_U_Y", "L144.in_EJ_state_comm_F_U_Y", "gcam-usa/calibrated_techs_bld_usa",
"gcam-usa/A44.globaltech_eff", "gcam-usa/A44.globaltech_eff_avg", "gcam-usa/A44.globaltech_shares",
"gcam-usa/A44.gcam_consumer", "L144.flsp_bm2_state_res", "L144.flsp_bm2_state_comm",
"gcam-usa/A44.demand_satiation_mult", "L143.HDDCDD_scen_state") ->
L244.Intgains_scalar_gcamusa
L244.ShellConductance_bld_gcamusa %>%
add_title("Shell conductance (inverse of shell efficiency) by state") %>%
add_units("Unitless") %>%
add_comments("values from A44.bld_shell_conductance") %>%
add_legacy_name("L244.ShellConductance_bld") %>%
add_precursors("gcam-usa/A44.bld_shell_conductance", "gcam-usa/A44.gcam_consumer") ->
L244.ShellConductance_bld_gcamusa
L244.Supplysector_bld_gcamusa %>%
add_title("Supplysector info for buildings") %>%
add_units("Unitless") %>%
add_comments("A44.sector written to all states") %>%
add_legacy_name("L244.Supplysector_bld") %>%
add_precursors("gcam-usa/A44.sector") ->
L244.Supplysector_bld_gcamusa
L244.FinalEnergyKeyword_bld_gcamusa %>%
add_title("Supply sector keywords for detailed building sector") %>%
add_units("NA") %>%
add_comments("A44.sector written to all states") %>%
add_legacy_name("L244.FinalEnergyKeyword_bld") %>%
add_precursors("gcam-usa/A44.sector") ->
L244.FinalEnergyKeyword_bld_gcamusa
if(exists("L244.SubsectorShrwt_bld")) {
L244.SubsectorShrwt_bld_gcamusa %>%
add_title("Subsector shareweights of building sector") %>%
add_units("Unitless") %>%
add_comments("A44.subsector_shrwt written to all regions") %>%
add_legacy_name("L244.SubsectorShrwt_bld") %>%
add_precursors("gcam-usa/A44.subsector_shrwt") ->
L244.SubsectorShrwt_bld_gcamusa
} else {
missing_data() %>%
add_legacy_name("L244.SubsectorShrwt_bld") ->
L244.SubsectorShrwt_bld_gcamusa
}
if(exists("L244.SubsectorShrwtFllt_bld_gcamusa")) {
L244.SubsectorShrwtFllt_bld_gcamusa %>%
add_title("Subsector shareweights of building sector") %>%
add_units("Unitless") %>%
add_comments("A44.subsector_shrwt written to all regions") %>%
add_legacy_name("L244.SubsectorShrwtFllt_bld") %>%
add_precursors("gcam-usa/A44.subsector_shrwt") ->
L244.SubsectorShrwtFllt_bld_gcamusa
} else {
missing_data() %>%
add_legacy_name("L244.SubsectorShrwtFllt_bld") ->
L244.SubsectorShrwtFllt_bld_gcamusa
}
if(exists("L244.SubsectorInterp_bld_gcamusa")) {
L244.SubsectorInterp_bld_gcamusa %>%
add_title("Subsector shareweight interpolation of building sector") %>%
add_units("NA") %>%
add_comments("A44.subsector_interp written to all regions") %>%
add_legacy_name("L244.SubsectorInterp_bld") %>%
add_precursors("gcam-usa/A44.subsector_interp") ->
L244.SubsectorInterp_bld_gcamusa
} else {
missing_data() %>%
add_legacy_name("L244.SubsectorInterp_bld") ->
L244.SubsectorInterp_bld_gcamusa
}
if(exists("L244.SubsectorInterpTo_bld_gcamusa")) {
L244.SubsectorInterpTo_bld_gcamusa %>%
add_title("Subsector shareweight interpolation of building sector") %>%
add_units("NA") %>%
add_comments("A44.subsector_interp written to all regions") %>%
add_legacy_name("L244.SubsectorInterpTo_bld") %>%
add_precursors("gcam-usa/A44.subsector_interp") ->
L244.SubsectorInterpTo_bld_gcamusa
} else {
missing_data() %>%
add_legacy_name("L244.SubsectorInterpTo_bld") ->
L244.SubsectorInterpTo_bld_gcamusa
}
L244.SubsectorLogit_bld_gcamusa %>%
add_title("Subsector logit exponents of building sector") %>%
add_units("Unitless") %>%
add_comments("A44.subsector_logit written to all states") %>%
add_legacy_name("L244.SubsectorLogit_bld") %>%
add_precursors("gcam-usa/A44.subsector_logit") ->
L244.SubsectorLogit_bld_gcamusa
L244.StubTech_bld_gcamusa %>%
add_title("Identification of stub technologies for buildings") %>%
add_units("NA") %>%
add_comments("A44.globaltech_eff written to all states") %>%
add_legacy_name("L244.StubTech_bld") %>%
add_precursors("gcam-usa/A44.globaltech_eff") ->
L244.StubTech_bld_gcamusa
L244.StubTechCalInput_bld_gcamusa %>%
add_title("Calibrated energy consumption and share weights by buildings technologies") %>%
add_units("calibrated.value: EJ/yr; shareweights: Unitless") %>%
add_comments("Energy consumption multiplied by shares to get calibrated energy") %>%
add_comments("Shares calculated using efficiency averages") %>%
add_legacy_name("L244.StubTechCalInput_bld") %>%
add_precursors("L144.in_EJ_state_res_F_U_Y", "L144.in_EJ_state_comm_F_U_Y", "gcam-usa/calibrated_techs_bld_usa",
"gcam-usa/A44.globaltech_eff", "gcam-usa/A44.globaltech_eff_avg", "gcam-usa/A44.globaltech_shares") ->
L244.StubTechCalInput_bld_gcamusa
L244.StubTechMarket_bld %>%
add_title("market names for fuel inputs to all technologies in each state") %>%
add_units("NA") %>%
add_comments("Categories from A44.globaltech_eff written to all states") %>%
add_comments("Market set to states for electricity") %>%
add_legacy_name("L244.StubTechMarket_bld") %>%
add_precursors("gcam-usa/states_subregions") ->
L244.StubTechMarket_bld
L244.GlobalTechIntGainOutputRatio %>%
add_title("Output ratios of internal gain energy from non-thermal building services") %>%
add_units("Unitless") %>%
add_comments("internal.gains.output.ratio = input.ratio from A44.globaltech_intgains divided by efficiency from L244.GlobalTechEff_bld") %>%
add_legacy_name("L244.GlobalTechIntGainOutputRatio") %>%
add_precursors("gcam-usa/A44.globaltech_intgains", "gcam-usa/calibrated_techs_bld_usa",
"gcam-usa/A44.gcam_consumer", "gcam-usa/A44.globaltech_eff") ->
L244.GlobalTechIntGainOutputRatio
L244.GlobalTechInterpTo_bld %>%
add_title("Technology shareweight interpolation") %>%
add_units("NA") %>%
add_comments("Directly from A44.globaltech_interp") %>%
add_legacy_name("L244.GlobalTechInterpTo_bld") %>%
add_precursors("gcam-usa/A44.globaltech_interp") ->
L244.GlobalTechInterpTo_bld
L244.GlobalTechEff_bld %>%
add_title("Assumed efficiencies (all years) of buildings technologies") %>%
add_units("Unitless") %>%
add_comments("Values from A44.globaltech_eff") %>%
add_legacy_name("L244.GlobalTechEff_bld") %>%
add_precursors("gcam-usa/A44.globaltech_eff") ->
L244.GlobalTechEff_bld
L244.GlobalTechShrwt_bld_gcamusa %>%
add_title("Default shareweights for global building technologies") %>%
add_units("Unitless") %>%
add_comments("Values interpolated to model years from A44.globaltech_shrwt") %>%
add_legacy_name("L244.GlobalTechShrwt_bld") %>%
add_precursors("gcam-usa/A44.globaltech_shrwt") ->
L244.GlobalTechShrwt_bld_gcamusa
L244.GlobalTechCost_bld_gcamusa %>%
add_title("Non-fuel costs of global building technologies") %>%
add_units("1975$/GJ") %>%
add_comments("Values from A44.globaltech_cost") %>%
add_legacy_name("L244.GlobalTechCost_bld") %>%
add_precursors("gcam-usa/A44.globaltech_cost") ->
L244.GlobalTechCost_bld_gcamusa
L244.GlobalTechSCurve_bld %>%
add_title("Retirement rates for building technologies") %>%
add_units("lifetime/half.life = years") %>%
add_comments("Lifetime, steepness, and half.life from A44.globaltech_retirement") %>%
add_legacy_name("L244.GlobalTechSCurve_bld") %>%
add_precursors("gcam-usa/A44.globaltech_cost", "gcam-usa/A44.globaltech_retirement") ->
L244.GlobalTechSCurve_bld
L244.HDDCDD_AEO_2015_USA %>%
add_title("Heating and Cooling Degree Days by State consistent with AEO 2015") %>%
add_units("Fahrenheit Degree Days") %>%
add_comments("L143.HDDCDD_scen_state assigned to GCAM residential / commercial building consumers") %>%
add_legacy_name("L244.HDDCDD_QER_QER") %>%
same_precursors_as("L244.HDDCDD_A2_GFDL_USA") ->
L244.HDDCDD_AEO_2015_USA
L244.HDDCDD_constdds_USA %>%
add_title("Heating and Cooling Degree Days by State - constant at historical levels") %>%
add_units("Fahrenheit Degree Days") %>%
add_comments("L143.HDDCDD_scen_state assigned to GCAM subsectors") %>%
same_precursors_as("L244.HDDCDD_A2_GFDL_USA") ->
L244.HDDCDD_constdds_USA
return_data(L244.DeleteConsumer_USAbld,
L244.DeleteSupplysector_USAbld,
L244.SubregionalShares_gcamusa,
L244.PriceExp_IntGains_gcamusa,
L244.Floorspace_gcamusa,
L244.DemandFunction_serv_gcamusa,
L244.DemandFunction_flsp_gcamusa,
L244.Satiation_flsp_gcamusa,
L244.SatiationAdder_gcamusa,
L244.ThermalBaseService_gcamusa,
L244.GenericBaseService_gcamusa,
L244.ThermalServiceSatiation_gcamusa,
L244.GenericServiceSatiation_gcamusa,
L244.Intgains_scalar_gcamusa,
L244.ShellConductance_bld_gcamusa,
L244.Supplysector_bld_gcamusa,
L244.FinalEnergyKeyword_bld_gcamusa,
L244.SubsectorShrwt_bld_gcamusa,
L244.SubsectorShrwtFllt_bld_gcamusa,
L244.SubsectorInterp_bld_gcamusa,
L244.SubsectorInterpTo_bld_gcamusa,
L244.SubsectorLogit_bld_gcamusa,
L244.StubTech_bld_gcamusa,
L244.StubTechCalInput_bld_gcamusa,
L244.StubTechMarket_bld,
L244.GlobalTechIntGainOutputRatio,
L244.GlobalTechInterpTo_bld,
L244.GlobalTechEff_bld,
L244.GlobalTechShrwt_bld_gcamusa,
L244.GlobalTechCost_bld_gcamusa,
L244.GlobalTechSCurve_bld,
L244.HDDCDD_A2_GFDL_USA,
L244.HDDCDD_AEO_2015_USA,
L244.HDDCDD_constdds_USA,
L244.GompFnParam_gcamusa)
} else {
stop("Unknown command")
}
}
JGCRI/gcamdata documentation built on March 21, 2023, 2:19 a.m.
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Defines functions module_gcamusa_L244.building_USA
Documented in module_gcamusa_L244.building_USA
# Copyright 2019 Battelle Memorial Institute; see the LICENSE file.
#' module_gcamusa_L244.building_USA
#'
#' Creates GCAM-USA building output files for writing to xml.
#'
#' @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.DeleteConsumer_USAbld}, \code{L244.DeleteSupplysector_USAbld}, \code{L244.SubregionalShares_gcamusa},
#' \code{L244.PriceExp_IntGains_gcamusa}, \code{L244.Floorspace_gcamusa}, \code{L244.DemandFunction_serv_gcamusa}, \code{L244.DemandFunction_flsp_gcamusa},
#' \code{L244.Satiation_flsp_gcamusa}, \code{L244.SatiationAdder_gcamusa}, \code{L244.ThermalBaseService_gcamusa}, \code{L244.GenericBaseService_gcamusa},
#' \code{L244.ThermalServiceSatiation_gcamusa}, \code{L244.GenericServiceSatiation_gcamusa}, \code{L244.Intgains_scalar_gcamusa},
#' \code{L244.ShellConductance_bld_gcamusa}, \code{L244.Supplysector_bld_gcamusa}, \code{L244.FinalEnergyKeyword_bld_gcamusa}, \code{L244.SubsectorShrwt_bld_gcamusa},
#' \code{L244.SubsectorShrwtFllt_bld_gcamusa}, \code{L244.SubsectorInterp_bld_gcamusa}, \code{L244.SubsectorInterpTo_bld_gcamusa},
#' \code{L244.SubsectorLogit_bld_gcamusa}, \code{L244.StubTech_bld_gcamusa}, \code{L244.StubTechCalInput_bld_gcamusa}, \code{L244.StubTechMarket_bld},
#' \code{L244.GlobalTechIntGainOutputRatio}, \code{L244.GlobalTechInterpTo_bld}, \code{L244.GlobalTechEff_bld},
#' \code{L244.GlobalTechShrwt_bld_gcamusa}, \code{L244.GlobalTechCost_bld_gcamusa}, \code{L244.GlobalTechSCurve_bld}, \code{L244.HDDCDD_A2_GFDL_USA},
#' \code{L244.HDDCDD_AEO_2015_USA}, \code{L244.HDDCDD_constdds_USA}, \code{L244.GompFnParam_gcamusa}.
#' The corresponding file in the original data system was \code{L244.building_USA.R} (gcam-usa level2).
#' @details Creates GCAM-USA building output files for writing to xml.
#' @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 replace_na
#' @author RLH November 2017
module_gcamusa_L244.building_USA <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "energy/A44.gcam_consumer",
FILE = "energy/A44.sector",
FILE = "gcam-usa/calibrated_techs_bld_usa",
FILE = "gcam-usa/states_subregions",
FILE = "gcam-usa/A44.bld_shell_conductance",
FILE = "gcam-usa/A44.demandFn_flsp",
FILE = "gcam-usa/A44.demandFn_serv",
FILE = "gcam-usa/A44.gcam_consumer",
FILE = "gcam-usa/A44.satiation_flsp",
FILE = "gcam-usa/A44.sector",
FILE = "gcam-usa/A44.subsector_interp",
FILE = "gcam-usa/A44.subsector_logit",
FILE = "gcam-usa/A44.subsector_shrwt",
FILE = "gcam-usa/A44.globaltech_cost",
FILE = "gcam-usa/A44.globaltech_eff",
FILE = "gcam-usa/A44.globaltech_eff_avg",
FILE = "gcam-usa/A44.globaltech_shares",
FILE = "gcam-usa/A44.globaltech_intgains",
FILE = "gcam-usa/A44.globaltech_retirement",
FILE = "gcam-usa/A44.globaltech_shrwt",
FILE = "gcam-usa/A44.globaltech_interp",
FILE = "gcam-usa/A44.demand_satiation_mult",
FILE = "gcam-usa/A44.hab_land_flsp_usa",
"L144.flsp_param",
"L144.flsp_bm2_state_res",
"L144.flsp_bm2_state_comm",
"L144.in_EJ_state_comm_F_U_Y",
"L144.in_EJ_state_res_F_U_Y",
"L143.HDDCDD_scen_state",
"L100.Pop_thous_state",
"L100.pcGDP_thous90usd_state"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L244.DeleteConsumer_USAbld",
"L244.DeleteSupplysector_USAbld",
"L244.SubregionalShares_gcamusa",
"L244.PriceExp_IntGains_gcamusa",
"L244.Floorspace_gcamusa",
"L244.DemandFunction_serv_gcamusa",
"L244.DemandFunction_flsp_gcamusa",
"L244.Satiation_flsp_gcamusa",
"L244.SatiationAdder_gcamusa",
"L244.ThermalBaseService_gcamusa",
"L244.GenericBaseService_gcamusa",
"L244.ThermalServiceSatiation_gcamusa",
"L244.GenericServiceSatiation_gcamusa",
"L244.Intgains_scalar_gcamusa",
"L244.ShellConductance_bld_gcamusa",
"L244.Supplysector_bld_gcamusa",
"L244.FinalEnergyKeyword_bld_gcamusa",
"L244.SubsectorShrwt_bld_gcamusa",
"L244.SubsectorShrwtFllt_bld_gcamusa",
"L244.SubsectorInterp_bld_gcamusa",
"L244.SubsectorInterpTo_bld_gcamusa",
"L244.SubsectorLogit_bld_gcamusa",
"L244.StubTech_bld_gcamusa",
"L244.StubTechCalInput_bld_gcamusa",
"L244.StubTechMarket_bld",
"L244.GlobalTechIntGainOutputRatio",
"L244.GlobalTechInterpTo_bld",
"L244.GlobalTechEff_bld",
"L244.GlobalTechShrwt_bld_gcamusa",
"L244.GlobalTechCost_bld_gcamusa",
"L244.GlobalTechSCurve_bld",
"L244.HDDCDD_A2_GFDL_USA",
"L244.HDDCDD_constdds_USA",
"L244.HDDCDD_AEO_2015_USA",
"L244.GompFnParam_gcamusa"))
} else if(command == driver.MAKE) {
# Silence package checks
GCM <- Scen <- base.building.size <- base.service <- calibrated.value <- comm <-
degree.days <- efficiency <- efficiency_tech1 <- efficiency_tech2 <- fuel <-
gcam.consumer <- grid_region <- half_life_new <- half_life_stock <- input.cost <-
input.ratio <- internal.gains.market.name <- internal.gains.output.ratio <-
internal.gains.scalar <- market.name <- minicam.energy.input <- multiplier <-
object <- pcFlsp_mm2 <- pcGDP <- pcflsp_mm2cap <- pop <- region <- resid <-
satiation.adder <- satiation.level <- sector <- sector.name <- service <- share <-
share.weight <- share_tech1 <- share_tech2 <- share_type <- state <- steepness_new <-
steepness_stock <- stockavg <- subsector <- subsector.name <- supplysector <-
tech_type <- technology <- technology1 <- technology2 <-
thermal.building.service.input <- to.value <- value <- year <- year.fillout <- . <-
pop_year <- Sector <- pop_share <- growth <- flsp_growth <- area_gcam <- misc_land_usda <-
area_thouskm2 <- flsp <- pop_thous <- flsp_pc <- tot.dens <- unadjust.satiation <-
land.density.param <- b.param <- income.param <- gdp_pc <- flsp_est <- base_flsp <-
bias.adjust.param <- state_name <- NULL
all_data <- list(...)[[1]]
# Load required inputs
A44.gcam_consumer_en <- get_data(all_data, "energy/A44.gcam_consumer", strip_attributes = TRUE)
A44.sector_en <- get_data(all_data, "energy/A44.sector", strip_attributes = TRUE)
calibrated_techs_bld_usa <- get_data(all_data, "gcam-usa/calibrated_techs_bld_usa", strip_attributes = TRUE)
states_subregions <- get_data(all_data, "gcam-usa/states_subregions", strip_attributes = TRUE)
A44.bld_shell_conductance <- get_data(all_data, "gcam-usa/A44.bld_shell_conductance", strip_attributes = TRUE)
A44.demandFn_flsp <- get_data(all_data, "gcam-usa/A44.demandFn_flsp", strip_attributes = TRUE)
A44.demandFn_serv <- get_data(all_data, "gcam-usa/A44.demandFn_serv", strip_attributes = TRUE)
A44.gcam_consumer <- get_data(all_data, "gcam-usa/A44.gcam_consumer", strip_attributes = TRUE)
A44.satiation_flsp <- get_data(all_data, "gcam-usa/A44.satiation_flsp", strip_attributes = TRUE)
A44.sector <- get_data(all_data, "gcam-usa/A44.sector", strip_attributes = TRUE)
A44.subsector_interp <- get_data(all_data, "gcam-usa/A44.subsector_interp", strip_attributes = TRUE)
A44.subsector_logit <- get_data(all_data, "gcam-usa/A44.subsector_logit", strip_attributes = TRUE)
A44.subsector_shrwt <- get_data(all_data, "gcam-usa/A44.subsector_shrwt", strip_attributes = TRUE)
A44.globaltech_cost <- get_data(all_data, "gcam-usa/A44.globaltech_cost", strip_attributes = TRUE)
A44.globaltech_eff <- get_data(all_data, "gcam-usa/A44.globaltech_eff", strip_attributes = TRUE) %>%
gather_years()
A44.globaltech_eff_avg <- get_data(all_data, "gcam-usa/A44.globaltech_eff_avg", strip_attributes = TRUE)
A44.globaltech_shares <- get_data(all_data, "gcam-usa/A44.globaltech_shares", strip_attributes = TRUE)
A44.globaltech_intgains <- get_data(all_data, "gcam-usa/A44.globaltech_intgains", strip_attributes = TRUE)
A44.globaltech_retirement <- get_data(all_data, "gcam-usa/A44.globaltech_retirement", strip_attributes = TRUE)
A44.globaltech_shrwt <- get_data(all_data, "gcam-usa/A44.globaltech_shrwt", strip_attributes = TRUE)
A44.globaltech_interp <- get_data(all_data, "gcam-usa/A44.globaltech_interp", strip_attributes = TRUE)
A44.demand_satiation_mult <- get_data(all_data, "gcam-usa/A44.demand_satiation_mult", strip_attributes = TRUE)
L144.flsp_bm2_state_res <- get_data(all_data, "L144.flsp_bm2_state_res", strip_attributes = TRUE)
L144.flsp_bm2_state_comm <- get_data(all_data, "L144.flsp_bm2_state_comm", strip_attributes = TRUE)
L144.in_EJ_state_comm_F_U_Y <- get_data(all_data, "L144.in_EJ_state_comm_F_U_Y", strip_attributes = TRUE)
L144.in_EJ_state_res_F_U_Y <- get_data(all_data, "L144.in_EJ_state_res_F_U_Y", strip_attributes = TRUE)
L143.HDDCDD_scen_state <- get_data(all_data, "L143.HDDCDD_scen_state", strip_attributes = TRUE)
L100.Pop_thous_state <- get_data(all_data, "L100.Pop_thous_state", strip_attributes = TRUE)
L100.pcGDP_thous90usd_state <- get_data(all_data, "L100.pcGDP_thous90usd_state", strip_attributes = TRUE)
L144.hab_land_flsp_usa<- get_data(all_data, "gcam-usa/A44.hab_land_flsp_usa", strip_attributes = TRUE)
L144.flsp_param <- get_data(all_data, "L144.flsp_param", strip_attributes = TRUE)
# ===================================================
# Data Processing
# Note: Building energy demands and floorspace are calculated endogenously - these are undergoing review
# per-capita demand = (satiation.level - satiation.adder) * (1 - exp( -log2 / satiation.impedance * Demand.Driver)) + satiation.adder)
#
# floorspace = (satiation.level - satiation.adder) *
# [1 - exp{(-ln(2) * per-capita-GDP/satiation.impedance) * (energy_cost/base_energy_cost)^price_effect_exponent}] + satiation.adder
#
# satiation.level: maximum per-capita demand that can be achieved
# satiation.adder: value that allow the starting position of any region to be set along the demand function
# satiation.impedance: shape parameter
# Need to delete the buildings sector in the USA region (gcam.consumers and supplysectors)
L244.DeleteConsumer_USAbld <- tibble(region = gcam.USA_REGION, gcam.consumer = A44.gcam_consumer_en$gcam.consumer)
L244.DeleteSupplysector_USAbld <- tibble(region = gcam.USA_REGION, supplysector = A44.sector_en$supplysector)
# L244.SubregionalShares_gcamusa: subregional population and income shares (not currently used)
L244.SubregionalShares_gcamusa <- write_to_all_states(A44.gcam_consumer, c("region", "gcam.consumer")) %>%
mutate(pop.year.fillout = min(MODEL_BASE_YEARS),
inc.year.fillout = min(MODEL_BASE_YEARS),
subregional.population.share = 1,
subregional.income.share = 1)
# L244.PriceExp_IntGains_gcamusa: price exponent on floorspace and naming of internal gains trial markets
L244.PriceExp_IntGains_gcamusa <- write_to_all_states(A44.gcam_consumer, LEVEL2_DATA_NAMES[["PriceExp_IntGains"]])
# L244.Floorspace_gcamusa: base year floorspace
# Keep all historical years for now - these are needed in calculating satiation adders later on
# Residential floorspace
L244.Floorspace_resid <- L144.flsp_bm2_state_res %>%
rename(base.building.size = value,
region = state,
gcam.consumer = sector) %>%
mutate(base.building.size = round(base.building.size, energy.DIGITS_FLOORSPACE)) %>%
left_join_error_no_match(A44.gcam_consumer, by = "gcam.consumer") %>%
select(LEVEL2_DATA_NAMES[["Floorspace"]])
# Commercial floorspace
L244.Floorspace_comm <- L144.flsp_bm2_state_comm %>%
rename(base.building.size = value,
region = state,
gcam.consumer = sector) %>%
mutate(base.building.size = round(base.building.size, energy.DIGITS_FLOORSPACE)) %>%
left_join_error_no_match(A44.gcam_consumer, by = "gcam.consumer") %>%
select(LEVEL2_DATA_NAMES[["Floorspace"]])
L244.Floorspace_full <- bind_rows(L244.Floorspace_resid, L244.Floorspace_comm)
# Final output only has base years
L244.Floorspace_gcamusa <- filter(L244.Floorspace_full, year %in% MODEL_BASE_YEARS)
# L244.DemandFunction_serv_gcamusa and L244.DemandFunction_flsp_gcamusa: demand function types
L244.DemandFunction_serv_gcamusa <- write_to_all_states(A44.demandFn_serv, LEVEL2_DATA_NAMES[["DemandFunction_serv"]])
L244.DemandFunction_flsp_gcamusa <- write_to_all_states(A44.demandFn_flsp, LEVEL2_DATA_NAMES[["DemandFunction_flsp"]])
# L244.Satiation_flsp_gcamusa: Satiation levels assumed for floorspace
L244.Satiation_flsp_gcamusa <- A44.satiation_flsp %>%
gather(gcam.consumer, value, resid, comm) %>%
rename(region = state) %>%
# Need to make sure that the satiation level is greater than the floorspace in the final base year
left_join_error_no_match(L244.Floorspace_gcamusa %>%
filter(year == max(MODEL_BASE_YEARS)), by = c("region", "gcam.consumer")) %>%
left_join_error_no_match(L100.Pop_thous_state %>% rename(pop = value), by = c("region" = "state", "year")) %>%
mutate(year = as.integer(year),
# value.y = population
pcflsp_mm2cap = base.building.size / pop,
# Satiation level = must be greater than the observed value in the final calibration year, so if observed value is
# greater than calculated, multiply observed by 1.001
satiation.level = round(pmax(value * CONV_THOUS_BIL, pcflsp_mm2cap * 1.001), energy.DIGITS_SATIATION_ADDER)) %>%
left_join_error_no_match(A44.gcam_consumer, by = c("gcam.consumer", "nodeInput", "building.node.input")) %>%
select(LEVEL2_DATA_NAMES[["BldNodes"]], "satiation.level")
# L244.SatiationAdder_gcamusa: Satiation adders in floorspace demand function
# Required for shaping the future floorspace growth trajectories in each region
# Match in the per-capita GDP, total floorspace, and population (for calculating per-capita floorspace)
# We will filter GDP to energy.SATIATION_YEAR, but this may be greater than the historical years present
# under timeshift conditions. So we adjust energy.SATIATION_YEAR
energy.SATIATION_YEAR <- min(max(MODEL_BASE_YEARS), energy.SATIATION_YEAR)
L244.SatiationAdder_gcamusa <- L244.Satiation_flsp_gcamusa %>%
# Add per capita GDP
left_join_error_no_match(L100.pcGDP_thous90usd_state %>%
filter(year == energy.SATIATION_YEAR), by = c("region" = "state")) %>%
rename(pcGDP = value) %>%
# Add floorspace
left_join_error_no_match(L244.Floorspace_full, by = c("region", "gcam.consumer", "year", "nodeInput", "building.node.input")) %>%
# Add population
left_join_error_no_match(L100.Pop_thous_state, by = c("region" = "state", "year")) %>%
rename(pop = value) %>%
# Calculate per capita floorspace
mutate(pcFlsp_mm2 = base.building.size / pop,
# Calculate the satiation adders
satiation.adder = round(satiation.level - (
exp(log(2) * pcGDP / energy.GDP_MID_SATIATION) * (satiation.level - pcFlsp_mm2)),
energy.DIGITS_SATIATION_ADDER),
# The satiation adder (million square meters of floorspace per person) needs to be less than the per-capita demand in the final calibration year
satiation.adder = if_else(satiation.adder > pcFlsp_mm2, pcFlsp_mm2 * 0.999, satiation.adder)) %>%
select(LEVEL2_DATA_NAMES[["SatiationAdder"]])
#------------------------------------------------------
# Updated floorspace Gompertz function
# - Calculate the bias correction parameter (k)
# - Write parameters for the updated floorspace function
L144.flsp_param_usa<-L144.flsp_param %>%
filter(region=="USA") %>%
select(-region) %>%
repeat_add_columns(tibble(region=gcamusa.STATES))
# First calculate the habitable land
L144.hab_land_flsp_usa_fin<-L144.hab_land_flsp_usa %>%
rename(state_name=state)%>%
left_join(states_subregions %>% select(state,state_name),by="state_name") %>%
filter(state %in% gcamusa.STATES) %>%
mutate(area_thouskm2=(area_gcam-misc_land_usda)/1E3) %>%
select(region=state,area_thouskm2)
# Write the function parameters
L244.GompFnParam_gcamusa<-L144.flsp_param_usa %>%
left_join_error_no_match(L144.hab_land_flsp_usa_fin,by="region") %>%
mutate(year=MODEL_FINAL_BASE_YEAR) %>%
left_join_error_no_match(L100.pcGDP_thous90usd_state %>% rename(region=state), by=c("region","year")) %>%
rename(gdp_pc=value) %>%
left_join_error_no_match(L100.Pop_thous_state %>% rename(region=state), by=c("region","year")) %>%
rename(pop_thous=value) %>%
left_join_error_no_match(L144.flsp_bm2_state_res %>% rename(region=state), by=c("region","year")) %>%
rename(flsp=value) %>%
mutate(flsp_pc=(flsp*1E9)/(pop_thous*1E3),
base_flsp=flsp_pc,
tot.dens=round(pop_thous/area_thouskm2,0),
#correct 0 population density to avoid NaN
tot.dens=if_else(tot.dens==0,1,tot.dens),
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"]])
#================================================================
# Heating and cooling degree days (thermal services only)
# 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.globaltech_intgains$supplysector)
thermal_services <- dplyr::setdiff(unique(A44.sector$supplysector), generic_services)
# L244.HDDCDD: Heating and cooling degree days by scenario
L244.HDDCDD_scen_state <- L143.HDDCDD_scen_state %>%
rename(region = state,
degree.days = value)
# Let's make a climate normal (historical average) 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_state <- L244.HDDCDD_scen_state %>%
filter(year %in% seq(1981, 2000),
# The AEO_2015 scenario changes this "normal climate" for each region,
# which is not desirable since it does not incldue historical data
# and is not the standard reference assumption. Thus, we remove it
# from this calculation.
Scen != "AEO_2015") %>%
group_by(region, variable) %>%
summarise(degree.days = mean(degree.days)) %>%
ungroup()
# Subset the heating and cooling services, separately
heating_services <- thermal_services[grepl("heating", thermal_services)]
cooling_services <- thermal_services[grepl("cooling", thermal_services)]
L244.HDDCDD_temp <- tidyr::crossing(region = gcamusa.STATES, thermal.building.service.input = thermal_services) %>%
# Add in gcam.consumer
left_join_error_no_match(calibrated_techs_bld_usa %>%
select(service, gcam.consumer = sector) %>%
distinct(), by = c("thermal.building.service.input" = "service")) %>%
# Add in nodeInput and building.node.input
left_join_error_no_match(A44.gcam_consumer, by = "gcam.consumer") %>%
select(LEVEL2_DATA_NAMES[["BldNodes"]], thermal.building.service.input) %>%
# Add in model years
repeat_add_columns(tibble(year = MODEL_YEARS)) %>%
# Add HDD/CDD so that we can join with L244.HDDCDD_scen_state, remove at end
mutate(variable = if_else(thermal.building.service.input %in% heating_services, "HDD", "CDD")) %>%
# Add in degree days
# L244.HDDCDD_scen_state has multiple scenarios, rows in this tbl_df are intended to be duplicated for each scenario
# left_join_error_no_match throws an error when rows are duplicated (as intended), so left_join is used
left_join(L244.HDDCDD_scen_state, by = c("region", "variable", "year")) %>%
mutate(degree.days = round(degree.days, energy.DIGITS_HDDCDD))
L244.HDDCDD_constdds_USA <- L244.HDDCDD_temp %>%
filter(Scen == "constdds") %>%
select(-Scen, -GCM, -variable)
L244.HDDCDD_A2_GFDL_USA <- L244.HDDCDD_temp %>%
filter(Scen == "A2") %>%
select(-Scen, -GCM, -variable)
L244.HDDCDD_AEO_2015_USA <- L244.HDDCDD_temp %>%
filter(Scen == "AEO_2015") %>%
select(-Scen, -GCM, -variable)
# L244.ShellConductance_bld_gcamusa: Shell conductance (inverse of shell efficiency)
L244.ShellConductance_bld_gcamusa <- A44.bld_shell_conductance %>%
# Convert to long form
gather_years() %>%
# Interpolate to model years
complete(gcam.consumer, year = c(year, MODEL_YEARS)) %>%
group_by(gcam.consumer) %>%
mutate(value = round(approx_fun(year, value), energy.DIGITS_EFFICIENCY)) %>%
ungroup() %>%
filter(year %in% MODEL_YEARS) %>%
# Repeat for all states
write_to_all_states(names = c(names(.), "region")) %>%
# Add nodeInput and building.node.input
left_join_error_no_match(A44.gcam_consumer, by = "gcam.consumer") %>%
mutate(floor.to.surface.ratio = energy.FLOOR_TO_SURFACE_RATIO,
shell.year = year) %>%
# Rename columns
rename(shell.conductance = value) %>%
select(LEVEL2_DATA_NAMES[["ShellConductance"]])
# The remainder of the building-level parameters require information about the output of each service, which we do not have yet
# L244.Supplysector_bld: Supplysector info for buildings
L244.Supplysector_bld_gcamusa <- write_to_all_states(A44.sector, c(LEVEL2_DATA_NAMES[["Supplysector"]], LOGIT_TYPE_COLNAME))
# L244.FinalEnergyKeyword_bld: Supply sector keywords for detailed building sector
L244.FinalEnergyKeyword_bld_gcamusa <- write_to_all_states(A44.sector, LEVEL2_DATA_NAMES[["FinalEnergyKeyword"]])
# L244.SubsectorLogit_bld: Subsector logit exponents of building sector
L244.SubsectorLogit_bld_gcamusa <- write_to_all_states(A44.subsector_logit, c(LEVEL2_DATA_NAMES[["SubsectorLogit"]], LOGIT_TYPE_COLNAME))
# L244.SubsectorShrwt_bld and L244.SubsectorShrwtFllt_bld: Subsector shareweights of building sector
if(any(!is.na(A44.subsector_shrwt$year))) {
L244.SubsectorShrwt_bld_gcamusa <- write_to_all_states(A44.subsector_shrwt %>%
filter(!is.na(year)), LEVEL2_DATA_NAMES[["SubsectorShrwt"]])
}
if(any(!is.na(A44.subsector_shrwt$year.fillout))) {
L244.SubsectorShrwtFllt_bld_gcamusa <- write_to_all_states(A44.subsector_shrwt %>%
filter(!is.na(year.fillout)), LEVEL2_DATA_NAMES[["SubsectorShrwtFllt"]])
}
# L244.SubsectorInterp_bld and L244.SubsectorInterpTo_bld: Subsector shareweight interpolation of building sector
if(any(is.na(A44.subsector_interp$to.value))) {
L244.SubsectorInterp_bld_gcamusa <- write_to_all_states(A44.subsector_interp %>%
filter(is.na(to.value)), LEVEL2_DATA_NAMES[["SubsectorInterp"]])
}
if(any(!is.na(A44.subsector_interp$to.value))) {
L244.SubsectorInterpTo_bld_gcamusa <- write_to_all_states(A44.subsector_interp %>%
filter(!is.na(to.value)), LEVEL2_DATA_NAMES[["SubsectorInterpTo"]])
}
# L244.StubTech_bld_gcamusa: Identification of stub technologies for buildings
L244.StubTech_bld_gcamusa <- A44.globaltech_eff %>%
select(supplysector, subsector, technology) %>%
distinct() %>%
write_to_all_states(LEVEL2_DATA_NAMES[["Tech"]]) %>%
rename(stub.technology = technology)
# L244.GlobalTechEff_bld: Assumed efficiencies (all years) of buildings technologies
L244.end_use_eff <- A44.globaltech_eff %>%
complete(nesting(supplysector, subsector, technology, minicam.energy.input), year = c(year, MODEL_YEARS)) %>%
group_by(supplysector, subsector, technology, minicam.energy.input) %>%
mutate(value = approx_fun(year, value)) %>%
ungroup() %>%
filter(year %in% MODEL_YEARS) %>%
mutate(value = round(value, energy.DIGITS_CALOUTPUT)) %>%
rename(efficiency = value)
# Note - this code assumes that base-year efficiences are identical (should fix to copy over to make sure)
L244.GlobalTechEff_bld <- L244.end_use_eff %>%
rename(sector.name = supplysector,
subsector.name = subsector) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechEff"]])
# L244.StubTechMarket_bld: Specify market names for fuel inputs to all technologies in each state
L244.StubTechMarket_bld <- L244.end_use_eff %>%
mutate(market.name = gcam.USA_REGION) %>%
rename(stub.technology = technology) %>%
write_to_all_states(LEVEL2_DATA_NAMES[["StubTechMarket"]]) %>%
# For fuels with state markets, set the market.name to the region
mutate(market.name = if_else(minicam.energy.input %in% gcamusa.STATE_FUEL_MARKETS, region, market.name)) %>%
# replace market name with the grid region name if the minicam.energy.input is
# considered a regional fuel market
left_join_error_no_match(states_subregions, by = c("region" = "state")) %>%
mutate(market.name = if_else(minicam.energy.input %in% gcamusa.REGIONAL_FUEL_MARKETS,
grid_region, market.name)) %>%
select(LEVEL2_DATA_NAMES[["StubTechMarket"]])
# L244.StubTechCalInput_bld: Calibrated energy consumption by buildings technologies
# Combine residential and commercial energy data
L244.in_EJ_R_bld_serv_F_Yh <- bind_rows(L144.in_EJ_state_res_F_U_Y, L144.in_EJ_state_comm_F_U_Y) %>%
filter(year %in% MODEL_YEARS) %>%
mutate(calibrated.value = round(value, energy.DIGITS_CALOUTPUT)) %>%
rename(supplysector = service) %>%
# Add subsector and energy.input
left_join_error_no_match(calibrated_techs_bld_usa %>%
select(sector, supplysector, fuel, subsector, minicam.energy.input) %>%
distinct(), by = c("sector", "supplysector", "fuel")) %>%
select(region = state, supplysector, subsector, minicam.energy.input, year, calibrated.value)
# Shares allocated to partitioned technologies need to be computed first using efficiencies
L244.globaltech_eff_prt <- A44.globaltech_eff %>%
semi_join(A44.globaltech_eff_avg, by = c("supplysector", "subsector")) %>%
filter(year == gcamusa.EFFICIENCY_PARTITION_YEAR) %>%
select(supplysector, subsector, technology, efficiency = value)
# Calculate technology shares using efficiency values
L244.globaltech_shares <- A44.globaltech_eff_avg %>%
# Adding specific technology efficiency to stock average efficiency
left_join_error_no_match(L244.globaltech_eff_prt, by = c("supplysector", "subsector", "technology1" = "technology")) %>%
rename(efficiency_tech1 = efficiency) %>%
left_join_error_no_match(L244.globaltech_eff_prt, by = c("supplysector", "subsector", "technology2" = "technology")) %>%
rename(efficiency_tech2 = efficiency) %>%
# Calculate technology shares using stock average efficiency and individual technology efficiencies
# Equation can be derived by solving following system of equations:
# stockavg = efficiency_tech1 * share_tech1 + efficiency_tech2 * share_tech2
# share_tech1 + share_tech2 = 1
mutate(share_tech1 = (stockavg - efficiency_tech2) / (efficiency_tech1 - efficiency_tech2),
share_tech2 = 1 - share_tech1) %>%
# Keep only same names as A44.globaltech_shares and bind with A44.globaltech_shares
select(names(A44.globaltech_shares)) %>%
bind_rows(A44.globaltech_shares) %>%
# Clunky, but we want only one technology and share value, currently have technology1, technology2, share1, share2
gather(share_type, share, share_tech1, share_tech2)%>%
gather(tech_type, technology, technology1, technology2) %>%
# Filter for same technology and share number, then remove tech_type and share_type columns
filter(substr(tech_type, nchar(tech_type), nchar(tech_type)) == substr(share_type, nchar(share_type), nchar(share_type))) %>%
select(-tech_type, -share_type)
# For calibration table, start with global tech efficiency table, repeat by states, and match in tech shares.
L244.StubTechCalInput_bld_gcamusa <- L244.GlobalTechEff_bld %>%
filter(year %in% MODEL_BASE_YEARS) %>%
write_to_all_states(names = c(names(.), "region")) %>%
rename(supplysector = sector.name, subsector = subsector.name, stub.technology = technology) %>%
# Using left_join because we don't have shares for all technologies, NAs will be set to 1
left_join(L244.globaltech_shares, by = c("supplysector", "subsector", "stub.technology" = "technology")) %>%
replace_na(list(share = 1)) %>%
# Add energy by state/service/fuel
left_join_error_no_match(L244.in_EJ_R_bld_serv_F_Yh, by = c("region", "supplysector", "subsector", "minicam.energy.input", "year")) %>%
# calibrated.value = energy * share
mutate(calibrated.value = round(share * calibrated.value, energy.DIGITS_CALOUTPUT),
share.weight.year = year,
calOutputValue = calibrated.value) %>%
# Set subsector and technology shareweights
set_subsector_shrwt() %>%
mutate(tech.share.weight = if_else(calibrated.value > 0, 1, 0)) %>%
select(LEVEL2_DATA_NAMES[["StubTechCalInput"]])
# L244.GlobalTechShrwt_bld_gcamusa: Default shareweights for global building technologies
L244.GlobalTechShrwt_bld_gcamusa <- A44.globaltech_shrwt %>%
gather_years(value_col = "share.weight") %>%
complete(nesting(supplysector, subsector, technology), year = c(year, MODEL_YEARS)) %>%
group_by(supplysector, subsector, technology) %>%
mutate(share.weight = approx_fun(year, share.weight)) %>%
ungroup() %>%
filter(year %in% MODEL_YEARS) %>%
rename(sector.name = supplysector,
subsector.name = subsector) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechYr"]], share.weight)
# L244.GlobalTechInterpTo_bld: Technology shareweight interpolation (selected techs only)
L244.GlobalTechInterpTo_bld <- A44.globaltech_interp %>%
set_years() %>%
mutate(sector.name = supplysector,
subsector.name = subsector) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechInterpTo"]])
# L244.GlobalTechCost_bld: Non-fuel costs of global building technologies
L244.GlobalTechCost_bld_gcamusa <- A44.globaltech_cost %>%
gather_years(value_col = "input.cost") %>%
complete(nesting(supplysector, subsector, technology), year = c(year, MODEL_YEARS)) %>%
group_by(supplysector, subsector, technology) %>%
mutate(input.cost = approx_fun(year, input.cost)) %>%
ungroup() %>%
filter(year %in% MODEL_YEARS) %>%
rename(sector.name = supplysector, subsector.name = subsector) %>%
mutate(minicam.non.energy.input = "non-energy") %>%
select(LEVEL2_DATA_NAMES[["GlobalTechCost"]])
# L244.GlobalTechSCurve_bld: Retirement rates for building technologies
L244.GlobalTechSCurve_bld <- L244.GlobalTechCost_bld_gcamusa %>%
filter(year %in% c(max(MODEL_BASE_YEARS), MODEL_FUTURE_YEARS),
sector.name %in% A44.globaltech_retirement$supplysector) %>%
# Add lifetimes and steepness
left_join_error_no_match(A44.globaltech_retirement, by = c("sector.name" = "supplysector")) %>%
# Set steepness/halflife values to stock for base years, new for future years
mutate(steepness = if_else(year == max(MODEL_BASE_YEARS), steepness_stock, steepness_new),
half.life = if_else(year == max(MODEL_BASE_YEARS), half_life_stock, half_life_new)) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechSCurve"]])
# L244.GlobalTechIntGainOutputRatio: Output ratios of internal gain energy from non-thermal building services
calibrated_techs_bld_usa_consumer <- calibrated_techs_bld_usa %>%
select(gcam.consumer = sector, supplysector) %>%
distinct()
L244.GlobalTechIntGainOutputRatio <- A44.globaltech_intgains %>%
repeat_add_columns(tibble(year = MODEL_YEARS))%>%
# Add gcam.consumer (sector)
left_join_error_no_match(calibrated_techs_bld_usa_consumer, by = "supplysector") %>%
rename(sector.name = supplysector,
subsector.name = subsector) %>%
# Add internal.gains.market.name
left_join_error_no_match(A44.gcam_consumer, by = "gcam.consumer") %>%
# Add efficiency
left_join_error_no_match(L244.GlobalTechEff_bld,
by = c("sector.name", "subsector.name", "technology", "year")) %>%
mutate(internal.gains.output.ratio = round(input.ratio / efficiency, energy.DIGITS_EFFICIENCY)) %>%
select(LEVEL2_DATA_NAMES[["GlobalTechYr"]], internal.gains.output.ratio, internal.gains.market.name)
# L244.GenericBaseService and L244.ThermalBaseService: Base year output of buildings services (per unit floorspace)
# Base-service: Multiply energy consumption by efficiency for each technology, and aggregate by service
L244.base_service <- L244.StubTechCalInput_bld_gcamusa %>%
# Add in efficiency by technology
left_join_error_no_match(L244.GlobalTechEff_bld,
by = c("supplysector" = "sector.name", "subsector" = "subsector.name",
"stub.technology" = "technology", "year", "minicam.energy.input")) %>%
# Calculate base.service = calibrated.value(energy) * efficiency
mutate(base.service = round(calibrated.value * efficiency, energy.DIGITS_CALOUTPUT)) %>%
# Aggregate base service by service (supplysector)
group_by(region, supplysector, year) %>%
summarise(base.service = sum(base.service)) %>%
ungroup() %>%
# Add gcam.consumer (sector)
left_join_error_no_match(calibrated_techs_bld_usa_consumer, by = "supplysector") %>%
# Add nodeInput and building.node.input
left_join_error_no_match(A44.gcam_consumer, by = "gcam.consumer")
# Separate thermal and generic services into separate tables with different ID strings
L244.GenericBaseService_gcamusa <- L244.base_service %>%
filter(supplysector %in% generic_services) %>%
rename(building.service.input = supplysector) %>%
select(LEVEL2_DATA_NAMES[["GenericBaseService"]])
L244.ThermalBaseService_gcamusa <- L244.base_service %>%
filter(supplysector %in% thermal_services) %>%
rename(thermal.building.service.input = supplysector) %>%
select(LEVEL2_DATA_NAMES[["ThermalBaseService"]])
# L244.GenericServiceSatiation_gcamusa: Satiation levels assumed for non-thermal building services
# Just multiply the base-service by an exogenous multiplier
L244.GenericServiceSatiation_gcamusa <- L244.GenericBaseService_gcamusa %>%
filter(year == max(MODEL_BASE_YEARS)) %>%
# Add floorspace
left_join_error_no_match(L244.Floorspace_gcamusa, by = c(LEVEL2_DATA_NAMES[["BldNodes"]], "year")) %>%
# Add multiplier
left_join_error_no_match(A44.demand_satiation_mult, by = c("building.service.input" = "supplysector")) %>%
# Satiation level = service per floorspace * multiplier
mutate(satiation.level = round(base.service / base.building.size * multiplier, energy.DIGITS_COEFFICIENT)) %>%
select(LEVEL2_DATA_NAMES[["GenericServiceSatiation"]])
# L244.ThermalServiceSatiation: Satiation levels assumed for thermal building services
L244.ThermalServiceSatiation_gcamusa <- L244.ThermalBaseService_gcamusa %>%
filter(year == max(MODEL_BASE_YEARS)) %>%
# Add floorspace
left_join_error_no_match(L244.Floorspace_gcamusa, by = c(LEVEL2_DATA_NAMES[["BldNodes"]], "year")) %>%
# Add multiplier
left_join_error_no_match(A44.demand_satiation_mult, by = c("thermal.building.service.input" = "supplysector")) %>%
# Satiation level = service per floorspace * multiplier
mutate(satiation.level = round(base.service / base.building.size * multiplier, energy.DIGITS_COEFFICIENT)) %>%
select(LEVEL2_DATA_NAMES[["ThermalServiceSatiation"]])
# L244.Intgains_scalar: Scalers relating internal gain energy to increased/reduced cooling/heating demands
variable <- c("HDD", "CDD")
scalar <- c(energy.INTERNAL_GAINS_SCALAR_USA_H, energy.INTERNAL_GAINS_SCALAR_USA_C)
DDnorm <- c(gcamusa.BASE_HDD_USA, gcamusa.BASE_CDD_USA)
US.base.scalar <- tibble(variable, scalar, DDnorm)
threshold_HDD <- 500
L244.Intgains_scalar_gcamusa <- L244.ThermalServiceSatiation_gcamusa %>%
# Assign HDD or CDD
mutate(variable = if_else(thermal.building.service.input %in% heating_services, "HDD", "CDD")) %>%
# Add DDnorm & scalar
left_join_error_no_match(US.base.scalar, by = "variable") %>%
# Add degree days
left_join_error_no_match(L244.HDDCDD_normal_state, by = c("region", "variable")) %>%
mutate(internal.gains.scalar = round(scalar * degree.days / DDnorm, energy.DIGITS_HDDCDD),
# Prevent very warm places from having negative heating demands, using exogenous threshold
internal.gains.scalar = if_else(variable == "HDD" & degree.days < threshold_HDD, 0, internal.gains.scalar)) %>%
select(LEVEL2_DATA_NAMES[["Intgains_scalar"]])
# ===================================================
# Produce outputs
L244.DeleteConsumer_USAbld %>%
add_title("Deletes building sector in USA region to rewrite with GCAM-USA data") %>%
add_units("NA") %>%
add_comments("gcam.consumer column from A44.gcam_consumer") %>%
add_legacy_name("L244.DeleteConsumer_USAbld") %>%
add_precursors("energy/A44.gcam_consumer") ->
L244.DeleteConsumer_USAbld
L244.DeleteSupplysector_USAbld %>%
add_title("Deletes building sector in USA region to rewrite with GCAM-USA data") %>%
add_units("NA") %>%
add_comments("supplysector column from A44.sector") %>%
add_legacy_name("L244.DeleteSupplysector_USAbld") %>%
add_precursors("energy/A44.sector") ->
L244.DeleteSupplysector_USAbld
L244.SubregionalShares_gcamusa %>%
add_title("Subregional population and income shares") %>%
add_units("Unitless") %>%
add_comments("Default values used for years and shares") %>%
add_legacy_name("L244.SubregionalShares") %>%
add_precursors("gcam-usa/A44.gcam_consumer") ->
L244.SubregionalShares_gcamusa
L244.PriceExp_IntGains_gcamusa %>%
add_title("Price exponent on floorspace and naming of internal gains trial markets") %>%
add_units("Unitless") %>%
add_comments("A44.gcam_consumer written to all states") %>%
add_legacy_name("L244.PriceExp_IntGains") %>%
add_precursors("gcam-usa/A44.gcam_consumer") ->
L244.PriceExp_IntGains_gcamusa
L244.Floorspace_gcamusa %>%
add_title("base year floorspace") %>%
add_units("billion m2") %>%
add_comments("Data from L144.flsp_bm2_state_res and L144.flsp_bm2_state_comm") %>%
add_legacy_name("L244.Floorspace") %>%
add_precursors("L144.flsp_bm2_state_res", "L144.flsp_bm2_state_comm", "gcam-usa/A44.gcam_consumer") ->
L244.Floorspace_gcamusa
L244.DemandFunction_serv_gcamusa %>%
add_title("Service demand function types") %>%
add_units("NA") %>%
add_comments("A44.demandFn_serv written to all states") %>%
add_legacy_name("L244.DemandFunction_serv") %>%
add_precursors("gcam-usa/A44.demandFn_serv") ->
L244.DemandFunction_serv_gcamusa
L244.DemandFunction_flsp_gcamusa %>%
add_title("Floorspace demand function types") %>%
add_units("NA") %>%
add_comments("A44.demandFn_flsp written to all states") %>%
add_legacy_name("L244.DemandFunction_flsp") %>%
add_precursors("gcam-usa/A44.demandFn_flsp") ->
L244.DemandFunction_flsp_gcamusa
L244.Satiation_flsp_gcamusa %>%
add_title("Satiation levels assumed for floorspace") %>%
add_units("million m2 / person") %>%
add_comments("Values from A44.satiation_flsp or L244.Floorspace_gcamusa/L100.Pop_thous_state") %>%
add_comments("Whichever is larger") %>%
add_legacy_name("L244.Satiation_flsp") %>%
add_precursors("gcam-usa/A44.satiation_flsp", "gcam-usa/A44.gcam_consumer", "L100.Pop_thous_state",
"L144.flsp_bm2_state_res", "L144.flsp_bm2_state_comm") ->
L244.Satiation_flsp_gcamusa
L244.SatiationAdder_gcamusa %>%
add_title("Satiation adders in floorspace demand function") %>%
add_units("million m2 / person") %>%
add_comments("Calculated with function dependent on satiation level; per capita floorspace; and per capita GDP") %>%
add_legacy_name("L244.SatiationAdder") %>%
add_precursors("gcam-usa/A44.satiation_flsp", "gcam-usa/A44.gcam_consumer", "L100.Pop_thous_state",
"L144.flsp_bm2_state_res", "L144.flsp_bm2_state_comm", "L100.pcGDP_thous90usd_state") ->
L244.SatiationAdder_gcamusa
L244.GompFnParam_gcamusa %>%
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_gcamusa") %>%
add_precursors("L144.flsp_param", "L100.Pop_thous_state","L100.pcGDP_thous90usd_state",
"gcam-usa/A44.hab_land_flsp_usa","L144.flsp_bm2_state_res" ) ->
L244.GompFnParam_gcamusa
L244.HDDCDD_A2_GFDL_USA %>%
add_title("Heating and Cooling Degree Days by State for GFDL A2") %>%
add_units("Fahrenheit Degree Days") %>%
add_comments("L143.HDDCDD_scen_state assigned to GCAM subsectors") %>%
add_legacy_name("L244.HDDCDD_A2_GFDL") %>%
add_precursors("L143.HDDCDD_scen_state", "gcam-usa/A44.sector",
"gcam-usa/calibrated_techs_bld_usa", "gcam-usa/A44.gcam_consumer") ->
L244.HDDCDD_A2_GFDL_USA
L244.ThermalBaseService_gcamusa %>%
add_title("Base year output of thermal buildings services") %>%
add_units("EJ per unit floorspace") %>%
add_comments("Multiplied energy consumption by efficiency for each technology, then aggregated by service") %>%
add_legacy_name("L244.ThermalBaseService") %>%
add_precursors("L144.in_EJ_state_res_F_U_Y", "L144.in_EJ_state_comm_F_U_Y", "gcam-usa/calibrated_techs_bld_usa",
"gcam-usa/A44.globaltech_eff", "gcam-usa/A44.globaltech_eff_avg", "gcam-usa/A44.globaltech_shares",
"gcam-usa/A44.gcam_consumer") ->
L244.ThermalBaseService_gcamusa
L244.GenericBaseService_gcamusa %>%
add_title("Base year output of generic buildings services") %>%
add_units("EJ per unit floorspace") %>%
add_comments("Multiplied energy consumption by efficiency for each technology, then aggregated by service") %>%
add_legacy_name("L244.GenericBaseService") %>%
add_precursors("L144.in_EJ_state_res_F_U_Y", "L144.in_EJ_state_comm_F_U_Y", "gcam-usa/calibrated_techs_bld_usa",
"gcam-usa/A44.globaltech_eff", "gcam-usa/A44.globaltech_eff_avg", "gcam-usa/A44.globaltech_shares",
"gcam-usa/A44.gcam_consumer") ->
L244.GenericBaseService_gcamusa
L244.GenericServiceSatiation_gcamusa %>%
add_title("Satiation levels assumed for non-thermal building services") %>%
add_units("EJ/billion m2 floorspace") %>%
add_comments("Satiation level = base service / floorspace * exogenous multiplier") %>%
add_legacy_name("L244.GenericServiceSatiation") %>%
add_precursors("L144.in_EJ_state_res_F_U_Y", "L144.in_EJ_state_comm_F_U_Y", "gcam-usa/calibrated_techs_bld_usa",
"gcam-usa/A44.globaltech_eff", "gcam-usa/A44.globaltech_eff_avg", "gcam-usa/A44.globaltech_shares",
"gcam-usa/A44.gcam_consumer", "L144.flsp_bm2_state_res", "L144.flsp_bm2_state_comm",
"gcam-usa/A44.demand_satiation_mult") ->
L244.GenericServiceSatiation_gcamusa
L244.ThermalServiceSatiation_gcamusa %>%
add_title("Satiation levels assumed for thermal building services") %>%
add_units("EJ/billion m2 floorspace") %>%
add_comments("Satiation level = base service / floorspace * exogenous multiplier") %>%
add_legacy_name("L244.ThermalServiceSatiation") %>%
add_precursors("L144.in_EJ_state_res_F_U_Y", "L144.in_EJ_state_comm_F_U_Y", "gcam-usa/calibrated_techs_bld_usa",
"gcam-usa/A44.globaltech_eff", "gcam-usa/A44.globaltech_eff_avg", "gcam-usa/A44.globaltech_shares",
"gcam-usa/A44.gcam_consumer", "L144.flsp_bm2_state_res", "L144.flsp_bm2_state_comm",
"gcam-usa/A44.demand_satiation_mult") ->
L244.ThermalServiceSatiation_gcamusa
L244.Intgains_scalar_gcamusa %>%
add_title("Scalers relating internal gain energy to increased/reduced cooling/heating demands") %>%
add_units("Unitless") %>%
add_comments("internal.gains.scalar = exogenous scalar * degree.days / exogenous degree day norm") %>%
add_legacy_name("L244.Intgains_scalar") %>%
add_precursors("L144.in_EJ_state_res_F_U_Y", "L144.in_EJ_state_comm_F_U_Y", "gcam-usa/calibrated_techs_bld_usa",
"gcam-usa/A44.globaltech_eff", "gcam-usa/A44.globaltech_eff_avg", "gcam-usa/A44.globaltech_shares",
"gcam-usa/A44.gcam_consumer", "L144.flsp_bm2_state_res", "L144.flsp_bm2_state_comm",
"gcam-usa/A44.demand_satiation_mult", "L143.HDDCDD_scen_state") ->
L244.Intgains_scalar_gcamusa
L244.ShellConductance_bld_gcamusa %>%
add_title("Shell conductance (inverse of shell efficiency) by state") %>%
add_units("Unitless") %>%
add_comments("values from A44.bld_shell_conductance") %>%
add_legacy_name("L244.ShellConductance_bld") %>%
add_precursors("gcam-usa/A44.bld_shell_conductance", "gcam-usa/A44.gcam_consumer") ->
L244.ShellConductance_bld_gcamusa
L244.Supplysector_bld_gcamusa %>%
add_title("Supplysector info for buildings") %>%
add_units("Unitless") %>%
add_comments("A44.sector written to all states") %>%
add_legacy_name("L244.Supplysector_bld") %>%
add_precursors("gcam-usa/A44.sector") ->
L244.Supplysector_bld_gcamusa
L244.FinalEnergyKeyword_bld_gcamusa %>%
add_title("Supply sector keywords for detailed building sector") %>%
add_units("NA") %>%
add_comments("A44.sector written to all states") %>%
add_legacy_name("L244.FinalEnergyKeyword_bld") %>%
add_precursors("gcam-usa/A44.sector") ->
L244.FinalEnergyKeyword_bld_gcamusa
if(exists("L244.SubsectorShrwt_bld")) {
L244.SubsectorShrwt_bld_gcamusa %>%
add_title("Subsector shareweights of building sector") %>%
add_units("Unitless") %>%
add_comments("A44.subsector_shrwt written to all regions") %>%
add_legacy_name("L244.SubsectorShrwt_bld") %>%
add_precursors("gcam-usa/A44.subsector_shrwt") ->
L244.SubsectorShrwt_bld_gcamusa
} else {
missing_data() %>%
add_legacy_name("L244.SubsectorShrwt_bld") ->
L244.SubsectorShrwt_bld_gcamusa
}
if(exists("L244.SubsectorShrwtFllt_bld_gcamusa")) {
L244.SubsectorShrwtFllt_bld_gcamusa %>%
add_title("Subsector shareweights of building sector") %>%
add_units("Unitless") %>%
add_comments("A44.subsector_shrwt written to all regions") %>%
add_legacy_name("L244.SubsectorShrwtFllt_bld") %>%
add_precursors("gcam-usa/A44.subsector_shrwt") ->
L244.SubsectorShrwtFllt_bld_gcamusa
} else {
missing_data() %>%
add_legacy_name("L244.SubsectorShrwtFllt_bld") ->
L244.SubsectorShrwtFllt_bld_gcamusa
}
if(exists("L244.SubsectorInterp_bld_gcamusa")) {
L244.SubsectorInterp_bld_gcamusa %>%
add_title("Subsector shareweight interpolation of building sector") %>%
add_units("NA") %>%
add_comments("A44.subsector_interp written to all regions") %>%
add_legacy_name("L244.SubsectorInterp_bld") %>%
add_precursors("gcam-usa/A44.subsector_interp") ->
L244.SubsectorInterp_bld_gcamusa
} else {
missing_data() %>%
add_legacy_name("L244.SubsectorInterp_bld") ->
L244.SubsectorInterp_bld_gcamusa
}
if(exists("L244.SubsectorInterpTo_bld_gcamusa")) {
L244.SubsectorInterpTo_bld_gcamusa %>%
add_title("Subsector shareweight interpolation of building sector") %>%
add_units("NA") %>%
add_comments("A44.subsector_interp written to all regions") %>%
add_legacy_name("L244.SubsectorInterpTo_bld") %>%
add_precursors("gcam-usa/A44.subsector_interp") ->
L244.SubsectorInterpTo_bld_gcamusa
} else {
missing_data() %>%
add_legacy_name("L244.SubsectorInterpTo_bld") ->
L244.SubsectorInterpTo_bld_gcamusa
}
L244.SubsectorLogit_bld_gcamusa %>%
add_title("Subsector logit exponents of building sector") %>%
add_units("Unitless") %>%
add_comments("A44.subsector_logit written to all states") %>%
add_legacy_name("L244.SubsectorLogit_bld") %>%
add_precursors("gcam-usa/A44.subsector_logit") ->
L244.SubsectorLogit_bld_gcamusa
L244.StubTech_bld_gcamusa %>%
add_title("Identification of stub technologies for buildings") %>%
add_units("NA") %>%
add_comments("A44.globaltech_eff written to all states") %>%
add_legacy_name("L244.StubTech_bld") %>%
add_precursors("gcam-usa/A44.globaltech_eff") ->
L244.StubTech_bld_gcamusa
L244.StubTechCalInput_bld_gcamusa %>%
add_title("Calibrated energy consumption and share weights by buildings technologies") %>%
add_units("calibrated.value: EJ/yr; shareweights: Unitless") %>%
add_comments("Energy consumption multiplied by shares to get calibrated energy") %>%
add_comments("Shares calculated using efficiency averages") %>%
add_legacy_name("L244.StubTechCalInput_bld") %>%
add_precursors("L144.in_EJ_state_res_F_U_Y", "L144.in_EJ_state_comm_F_U_Y", "gcam-usa/calibrated_techs_bld_usa",
"gcam-usa/A44.globaltech_eff", "gcam-usa/A44.globaltech_eff_avg", "gcam-usa/A44.globaltech_shares") ->
L244.StubTechCalInput_bld_gcamusa
L244.StubTechMarket_bld %>%
add_title("market names for fuel inputs to all technologies in each state") %>%
add_units("NA") %>%
add_comments("Categories from A44.globaltech_eff written to all states") %>%
add_comments("Market set to states for electricity") %>%
add_legacy_name("L244.StubTechMarket_bld") %>%
add_precursors("gcam-usa/states_subregions") ->
L244.StubTechMarket_bld
L244.GlobalTechIntGainOutputRatio %>%
add_title("Output ratios of internal gain energy from non-thermal building services") %>%
add_units("Unitless") %>%
add_comments("internal.gains.output.ratio = input.ratio from A44.globaltech_intgains divided by efficiency from L244.GlobalTechEff_bld") %>%
add_legacy_name("L244.GlobalTechIntGainOutputRatio") %>%
add_precursors("gcam-usa/A44.globaltech_intgains", "gcam-usa/calibrated_techs_bld_usa",
"gcam-usa/A44.gcam_consumer", "gcam-usa/A44.globaltech_eff") ->
L244.GlobalTechIntGainOutputRatio
L244.GlobalTechInterpTo_bld %>%
add_title("Technology shareweight interpolation") %>%
add_units("NA") %>%
add_comments("Directly from A44.globaltech_interp") %>%
add_legacy_name("L244.GlobalTechInterpTo_bld") %>%
add_precursors("gcam-usa/A44.globaltech_interp") ->
L244.GlobalTechInterpTo_bld
L244.GlobalTechEff_bld %>%
add_title("Assumed efficiencies (all years) of buildings technologies") %>%
add_units("Unitless") %>%
add_comments("Values from A44.globaltech_eff") %>%
add_legacy_name("L244.GlobalTechEff_bld") %>%
add_precursors("gcam-usa/A44.globaltech_eff") ->
L244.GlobalTechEff_bld
L244.GlobalTechShrwt_bld_gcamusa %>%
add_title("Default shareweights for global building technologies") %>%
add_units("Unitless") %>%
add_comments("Values interpolated to model years from A44.globaltech_shrwt") %>%
add_legacy_name("L244.GlobalTechShrwt_bld") %>%
add_precursors("gcam-usa/A44.globaltech_shrwt") ->
L244.GlobalTechShrwt_bld_gcamusa
L244.GlobalTechCost_bld_gcamusa %>%
add_title("Non-fuel costs of global building technologies") %>%
add_units("1975$/GJ") %>%
add_comments("Values from A44.globaltech_cost") %>%
add_legacy_name("L244.GlobalTechCost_bld") %>%
add_precursors("gcam-usa/A44.globaltech_cost") ->
L244.GlobalTechCost_bld_gcamusa
L244.GlobalTechSCurve_bld %>%
add_title("Retirement rates for building technologies") %>%
add_units("lifetime/half.life = years") %>%
add_comments("Lifetime, steepness, and half.life from A44.globaltech_retirement") %>%
add_legacy_name("L244.GlobalTechSCurve_bld") %>%
add_precursors("gcam-usa/A44.globaltech_cost", "gcam-usa/A44.globaltech_retirement") ->
L244.GlobalTechSCurve_bld
L244.HDDCDD_AEO_2015_USA %>%
add_title("Heating and Cooling Degree Days by State consistent with AEO 2015") %>%
add_units("Fahrenheit Degree Days") %>%
add_comments("L143.HDDCDD_scen_state assigned to GCAM residential / commercial building consumers") %>%
add_legacy_name("L244.HDDCDD_QER_QER") %>%
same_precursors_as("L244.HDDCDD_A2_GFDL_USA") ->
L244.HDDCDD_AEO_2015_USA
L244.HDDCDD_constdds_USA %>%
add_title("Heating and Cooling Degree Days by State - constant at historical levels") %>%
add_units("Fahrenheit Degree Days") %>%
add_comments("L143.HDDCDD_scen_state assigned to GCAM subsectors") %>%
same_precursors_as("L244.HDDCDD_A2_GFDL_USA") ->
L244.HDDCDD_constdds_USA
return_data(L244.DeleteConsumer_USAbld,
L244.DeleteSupplysector_USAbld,
L244.SubregionalShares_gcamusa,
L244.PriceExp_IntGains_gcamusa,
L244.Floorspace_gcamusa,
L244.DemandFunction_serv_gcamusa,
L244.DemandFunction_flsp_gcamusa,
L244.Satiation_flsp_gcamusa,
L244.SatiationAdder_gcamusa,
L244.ThermalBaseService_gcamusa,
L244.GenericBaseService_gcamusa,
L244.ThermalServiceSatiation_gcamusa,
L244.GenericServiceSatiation_gcamusa,
L244.Intgains_scalar_gcamusa,
L244.ShellConductance_bld_gcamusa,
L244.Supplysector_bld_gcamusa,
L244.FinalEnergyKeyword_bld_gcamusa,
L244.SubsectorShrwt_bld_gcamusa,
L244.SubsectorShrwtFllt_bld_gcamusa,
L244.SubsectorInterp_bld_gcamusa,
L244.SubsectorInterpTo_bld_gcamusa,
L244.SubsectorLogit_bld_gcamusa,
L244.StubTech_bld_gcamusa,
L244.StubTechCalInput_bld_gcamusa,
L244.StubTechMarket_bld,
L244.GlobalTechIntGainOutputRatio,
L244.GlobalTechInterpTo_bld,
L244.GlobalTechEff_bld,
L244.GlobalTechShrwt_bld_gcamusa,
L244.GlobalTechCost_bld_gcamusa,
L244.GlobalTechSCurve_bld,
L244.HDDCDD_A2_GFDL_USA,
L244.HDDCDD_AEO_2015_USA,
L244.HDDCDD_constdds_USA,
L244.GompFnParam_gcamusa)
} else {
stop("Unknown command")
}
}
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