R/zchunk_L245.water.demand.municipal.R
In rohmin9122/gcam-korea-release: Build the GCAM-Korea Input Datasets
Defines functions
module_water_L245.water.demand.municipal
Documented in
module_water_L245.water.demand.municipal
#' module_water_L245.water.demand.municipal
#'
#' Expands municipal water information (cost, efficiency, coefficients) across regions and model years
#'
#' @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{L245.Supplysector}, \code{L245.SubsectorLogit}, \code{L245.SubsectorShrwtFllt}, \code{L245.TechShrwt}, \code{L245.TechCoef}, \code{L245.TechCost}, \code{L245.PerCapitaBased}, \code{L245.BaseService}, \code{L245.IncomeElasticity}, \code{L245.PriceElasticity}, \code{L245.aeei}. The corresponding file in the
#' original data system was \code{L245.water.demand.municipal.R} (water level2).
#' @details Describe in detail what this chunk does.
#' @importFrom assertthat assert_that
#' @importFrom dplyr filter mutate select
#' @importFrom tidyr gather spread
#' @author ST August 2017
module_water_L245.water.demand.municipal <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "common/GCAM_region_names",
FILE = "water/A03.sector",
FILE = "water/A45.sector",
FILE = "water/A45.tech_cost",
FILE = "water/A45.demand",
"L145.municipal_water_R_W_Yh_km3",
"L145.municipal_water_cost_R_75USD_m3",
"L145.municipal_water_eff_R_Yh"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L245.Supplysector",
"L245.SubsectorLogit",
"L245.SubsectorShrwtFllt",
"L245.TechShrwt",
"L245.TechCoef",
"L245.TechCost",
"L245.PerCapitaBased",
"L245.BaseService",
"L245.IncomeElasticity",
"L245.PriceElasticity",
"L245.aeei"))
} else if(command == driver.MAKE) {
all_data <- list(...)[[1]]
logit.type <- GCAM_region_ID <- region <- water_type <- coefficient <-
water_sector <- year <- value <- efficiency <- withdrawals <- NULL # silence package check notes
# Load required inputs
GCAM_region_names <- get_data(all_data, "common/GCAM_region_names")
A03.sector <- get_data(all_data, "water/A03.sector")
A45.sector <- get_data(all_data, "water/A45.sector")
A45.tech_cost <- get_data(all_data, "water/A45.tech_cost")
A45.demand <- get_data(all_data, "water/A45.demand")
L145.municipal_water_R_W_Yh_km3 <- get_data(all_data, "L145.municipal_water_R_W_Yh_km3")
L145.municipal_water_cost_R_75USD_m3 <- get_data(all_data, "L145.municipal_water_cost_R_75USD_m3")
L145.municipal_water_eff_R_Yh <- get_data(all_data, "L145.municipal_water_eff_R_Yh")
# ===================================================
# join up all the assumptions into a single tibble and expand for all regions
A45.sector %>%
left_join_error_no_match(A45.tech_cost, by = "supplysector") %>%
bind_cols(A45.demand) %>%
mutate(logit.year.fillout = MODEL_YEARS[1]) %>%
repeat_add_columns(GCAM_region_names) %>%
mutate(logit.type = NA) ->
L245.assumptions_all
L245.assumptions_all %>%
select(LEVEL2_DATA_NAMES$Supplysector, logit.type) ->
L245.Supplysector # Supply sector information
L245.assumptions_all %>%
select(LEVEL2_DATA_NAMES$SubsectorLogit, logit.type) ->
L245.SubsectorLogit # Subsector logit detail
L245.assumptions_all %>%
mutate(year.fillout = MODEL_YEARS[1],
share.weight = 1) %>%
# ^^ share weights are 1 due to no competition
select(LEVEL2_DATA_NAMES$SubsectorShrwtFllt) ->
L245.SubsectorShrwtFllt # Subsector shareweights
L245.assumptions_all %>%
mutate(share.weight = 1) %>%
# ^^ share weights are 1 due to no competition
repeat_add_columns(tibble(year = MODEL_YEARS)) %>%
select(LEVEL2_DATA_NAMES$TechShrwt) ->
L245.TechShrwt
# Expand the municipal water efficiencies to all model years
# Keep historical years prior to extrapolating in case there are historical years that aren't model years
L245.municipal_water_eff_R_Y <-
group_by(L145.municipal_water_eff_R_Yh, GCAM_region_ID) %>%
complete(year = sort(unique(c(HISTORICAL_YEARS, MODEL_YEARS)))) %>%
mutate(efficiency = approx_fun(year, efficiency, rule = 2)) %>%
ungroup() %>%
filter(year %in% MODEL_YEARS)
# This is confusing; in GCAM consumptive uses are represented as a fractional input on the withdrawal volumes, so
# the model parameter is an input-output coefficient rather than an efficiency
L245.assumptions_all %>%
select(GCAM_region_ID, one_of(LEVEL2_DATA_NAMES$Tech)) %>%
# ^^ unrestricted left_join allows row expansion for all model years
left_join(L245.municipal_water_eff_R_Y, by = "GCAM_region_ID") %>%
mutate(market.name = region) %>%
# ^^ repeats tibble for consumption and withdrawal coefficients
repeat_add_columns(tibble(water_type = c("water consumption", "water withdrawals"))) %>%
# ^^ withdrawal coefficient is 1; consumption coefficient is fraction of withdrawal
mutate(coefficient = if_else(water_type == "water withdrawals", 1,
round(efficiency, water.DIGITS_MUNI_WATER)),
water_sector = "Municipal",
minicam.energy.input = set_water_input_name(water_sector, water_type, A03.sector)) %>%
select(LEVEL2_DATA_NAMES$TechCoef) ->
L245.TechCoef # municipal water technology withdrawals and consumption efficiencies
L245.assumptions_all %>%
left_join_error_no_match(L145.municipal_water_cost_R_75USD_m3, by = "GCAM_region_ID") %>%
repeat_add_columns(tibble(year = MODEL_YEARS)) %>%
select(LEVEL2_DATA_NAMES$TechCost) ->
L245.TechCost # Municipal water non-energy cost
L245.assumptions_all %>%
select(LEVEL2_DATA_NAMES$PerCapitaBased) ->
L245.PerCapitaBased # used to set final demand as per-capita based
L245.assumptions_all %>%
left_join(L145.municipal_water_R_W_Yh_km3, by = "GCAM_region_ID") %>%
# ^^ non-restrictive join used to allow expansion across multiple years
filter(year %in% MODEL_BASE_YEARS) %>%
rename(base.service = withdrawals) %>%
select(LEVEL2_DATA_NAMES$BaseService) ->
L245.BaseService # municipal water withdrawals for base years
L245.assumptions_all %>%
repeat_add_columns(tibble(year = MODEL_FUTURE_YEARS)) %>%
select(LEVEL2_DATA_NAMES$IncomeElasticity) ->
L245.IncomeElasticity # income elasticity projections
L245.assumptions_all %>%
repeat_add_columns(tibble(year = MODEL_FUTURE_YEARS)) %>%
select(LEVEL2_DATA_NAMES$PriceElasticity) ->
L245.PriceElasticity # price elasticity projections
L245.assumptions_all %>%
repeat_add_columns(tibble(year = MODEL_FUTURE_YEARS)) %>%
select(LEVEL2_DATA_NAMES$aeei) ->
L245.aeei # demand efficiency projections
#==== OUTPUT ===========
L245.Supplysector %>%
add_title("Sector information (municipal water)") %>%
add_units("Unitless") %>%
add_comments("A selection of columns from the input water assumptions") %>%
add_legacy_name("L245.Supplysector") %>%
add_precursors("common/GCAM_region_names",
"water/A45.sector",
"water/A45.tech_cost",
"water/A45.demand") ->
L245.Supplysector
L245.SubsectorLogit %>%
add_title("Subsector logit detail (municipal water)") %>%
add_units("Unitless") %>%
add_comments("A selection of columns from the input tibbles") %>%
add_legacy_name("L245.SubsectorLogit") %>%
add_precursors("common/GCAM_region_names",
"water/A45.sector",
"water/A45.tech_cost") ->
L245.SubsectorLogit
L245.SubsectorShrwtFllt %>%
add_title("Subsector shareweights (municipal water)") %>%
add_units("Unitless") %>%
add_comments("Shareweights of 1 for all regions due to no competition)") %>%
add_legacy_name("L245.SubsectorShrwtFllt") %>%
add_precursors("common/GCAM_region_names",
"water/A45.sector",
"water/A45.tech_cost") ->
L245.SubsectorShrwtFllt
L245.TechShrwt %>%
add_title("Technology share weights (municipal water)") %>%
add_units("Unitless") %>%
add_comments("Shareweights of 1 for all regions/years due to no competition") %>%
add_legacy_name("L245.TechShrwt") %>%
add_precursors("common/GCAM_region_names",
"water/A45.sector",
"water/A45.tech_cost") ->
L245.TechShrwt
L245.TechCoef %>%
add_title("Municipal water technology consumption efficiencies") %>%
add_units("Unitless") %>%
add_comments("Withdrawal efficiencies bound to consumption and appropriate minicam.energy.input appended") %>%
add_legacy_name("L245.TechCoef") %>%
add_precursors("common/GCAM_region_names",
"water/A03.sector",
"water/A45.sector",
"water/A45.tech_cost",
"L145.municipal_water_eff_R_Yh") ->
L245.TechCoef
L245.TechCost %>%
add_title("Municipal water non-energy costs") %>%
add_units("1975USD/m3") %>%
add_comments("Costs joined to sector infromation and expanded for all model years") %>%
add_legacy_name("L245.TechCost") %>%
add_precursors("common/GCAM_region_names",
"water/A45.sector",
"water/A45.tech_cost",
"water/A45.demand",
"L145.municipal_water_cost_R_75USD_m3") ->
L245.TechCost
L245.PerCapitaBased %>%
add_title("Per-capital based final energy demand switch (municipal water)") %>%
add_units("NA") %>%
add_comments("final energy demand category repeated for all regions") %>%
add_comments("can be multiple lines") %>%
add_legacy_name("L245.PerCapitaBased") %>%
add_precursors("common/GCAM_region_names",
"water/A45.demand") ->
L245.PerCapitaBased
L245.BaseService %>%
add_title("Municipal water withdrawals for base years") %>%
add_units("km^3") %>%
add_comments("Withdrawals filtered for base years and combined with demand category") %>%
add_legacy_name("L245.BaseService") %>%
add_precursors("common/GCAM_region_names",
"water/A45.demand",
"L145.municipal_water_R_W_Yh_km3") ->
L245.BaseService
L245.IncomeElasticity %>%
add_title("Income elasticity projections (municipal water)") %>%
add_units("Unitless") %>%
add_comments("Regional income elasticity repeated out for future years") %>%
add_legacy_name("L245.IncomeElasticity") %>%
add_precursors("common/GCAM_region_names",
"water/A45.demand") ->
L245.IncomeElasticity
L245.PriceElasticity %>%
add_title("Price elasticity projections (municipal water)") %>%
add_units("Unitless") %>%
add_comments("Regional price elasticity repeated out for all years") %>%
add_legacy_name("L245.PriceElasticity") %>%
add_precursors("common/GCAM_region_names",
"water/A45.demand") ->
L245.PriceElasticity
L245.aeei %>%
add_title("Demand efficiency projections (municipal water)") %>%
add_units("Unitless") %>%
add_comments("Regional efficiency repeated out for all years") %>%
add_legacy_name("L245.aeei") %>%
add_precursors("common/GCAM_region_names",
"water/A45.demand") ->
L245.aeei
return_data(L245.Supplysector, L245.SubsectorLogit, L245.SubsectorShrwtFllt, L245.TechShrwt, L245.TechCoef, L245.TechCost, L245.PerCapitaBased, L245.BaseService, L245.IncomeElasticity, L245.PriceElasticity, L245.aeei)
} else {
stop("Unknown command")
}
}
rohmin9122/gcam-korea-release documentation built on Nov. 26, 2020, 8:11 a.m.
R Package Documentation
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Defines functions module_water_L245.water.demand.municipal
Documented in module_water_L245.water.demand.municipal
#' module_water_L245.water.demand.municipal
#'
#' Expands municipal water information (cost, efficiency, coefficients) across regions and model years
#'
#' @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{L245.Supplysector}, \code{L245.SubsectorLogit}, \code{L245.SubsectorShrwtFllt}, \code{L245.TechShrwt}, \code{L245.TechCoef}, \code{L245.TechCost}, \code{L245.PerCapitaBased}, \code{L245.BaseService}, \code{L245.IncomeElasticity}, \code{L245.PriceElasticity}, \code{L245.aeei}. The corresponding file in the
#' original data system was \code{L245.water.demand.municipal.R} (water level2).
#' @details Describe in detail what this chunk does.
#' @importFrom assertthat assert_that
#' @importFrom dplyr filter mutate select
#' @importFrom tidyr gather spread
#' @author ST August 2017
module_water_L245.water.demand.municipal <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "common/GCAM_region_names",
FILE = "water/A03.sector",
FILE = "water/A45.sector",
FILE = "water/A45.tech_cost",
FILE = "water/A45.demand",
"L145.municipal_water_R_W_Yh_km3",
"L145.municipal_water_cost_R_75USD_m3",
"L145.municipal_water_eff_R_Yh"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L245.Supplysector",
"L245.SubsectorLogit",
"L245.SubsectorShrwtFllt",
"L245.TechShrwt",
"L245.TechCoef",
"L245.TechCost",
"L245.PerCapitaBased",
"L245.BaseService",
"L245.IncomeElasticity",
"L245.PriceElasticity",
"L245.aeei"))
} else if(command == driver.MAKE) {
all_data <- list(...)[[1]]
logit.type <- GCAM_region_ID <- region <- water_type <- coefficient <-
water_sector <- year <- value <- efficiency <- withdrawals <- NULL # silence package check notes
# Load required inputs
GCAM_region_names <- get_data(all_data, "common/GCAM_region_names")
A03.sector <- get_data(all_data, "water/A03.sector")
A45.sector <- get_data(all_data, "water/A45.sector")
A45.tech_cost <- get_data(all_data, "water/A45.tech_cost")
A45.demand <- get_data(all_data, "water/A45.demand")
L145.municipal_water_R_W_Yh_km3 <- get_data(all_data, "L145.municipal_water_R_W_Yh_km3")
L145.municipal_water_cost_R_75USD_m3 <- get_data(all_data, "L145.municipal_water_cost_R_75USD_m3")
L145.municipal_water_eff_R_Yh <- get_data(all_data, "L145.municipal_water_eff_R_Yh")
# ===================================================
# join up all the assumptions into a single tibble and expand for all regions
A45.sector %>%
left_join_error_no_match(A45.tech_cost, by = "supplysector") %>%
bind_cols(A45.demand) %>%
mutate(logit.year.fillout = MODEL_YEARS[1]) %>%
repeat_add_columns(GCAM_region_names) %>%
mutate(logit.type = NA) ->
L245.assumptions_all
L245.assumptions_all %>%
select(LEVEL2_DATA_NAMES$Supplysector, logit.type) ->
L245.Supplysector # Supply sector information
L245.assumptions_all %>%
select(LEVEL2_DATA_NAMES$SubsectorLogit, logit.type) ->
L245.SubsectorLogit # Subsector logit detail
L245.assumptions_all %>%
mutate(year.fillout = MODEL_YEARS[1],
share.weight = 1) %>%
# ^^ share weights are 1 due to no competition
select(LEVEL2_DATA_NAMES$SubsectorShrwtFllt) ->
L245.SubsectorShrwtFllt # Subsector shareweights
L245.assumptions_all %>%
mutate(share.weight = 1) %>%
# ^^ share weights are 1 due to no competition
repeat_add_columns(tibble(year = MODEL_YEARS)) %>%
select(LEVEL2_DATA_NAMES$TechShrwt) ->
L245.TechShrwt
# Expand the municipal water efficiencies to all model years
# Keep historical years prior to extrapolating in case there are historical years that aren't model years
L245.municipal_water_eff_R_Y <-
group_by(L145.municipal_water_eff_R_Yh, GCAM_region_ID) %>%
complete(year = sort(unique(c(HISTORICAL_YEARS, MODEL_YEARS)))) %>%
mutate(efficiency = approx_fun(year, efficiency, rule = 2)) %>%
ungroup() %>%
filter(year %in% MODEL_YEARS)
# This is confusing; in GCAM consumptive uses are represented as a fractional input on the withdrawal volumes, so
# the model parameter is an input-output coefficient rather than an efficiency
L245.assumptions_all %>%
select(GCAM_region_ID, one_of(LEVEL2_DATA_NAMES$Tech)) %>%
# ^^ unrestricted left_join allows row expansion for all model years
left_join(L245.municipal_water_eff_R_Y, by = "GCAM_region_ID") %>%
mutate(market.name = region) %>%
# ^^ repeats tibble for consumption and withdrawal coefficients
repeat_add_columns(tibble(water_type = c("water consumption", "water withdrawals"))) %>%
# ^^ withdrawal coefficient is 1; consumption coefficient is fraction of withdrawal
mutate(coefficient = if_else(water_type == "water withdrawals", 1,
round(efficiency, water.DIGITS_MUNI_WATER)),
water_sector = "Municipal",
minicam.energy.input = set_water_input_name(water_sector, water_type, A03.sector)) %>%
select(LEVEL2_DATA_NAMES$TechCoef) ->
L245.TechCoef # municipal water technology withdrawals and consumption efficiencies
L245.assumptions_all %>%
left_join_error_no_match(L145.municipal_water_cost_R_75USD_m3, by = "GCAM_region_ID") %>%
repeat_add_columns(tibble(year = MODEL_YEARS)) %>%
select(LEVEL2_DATA_NAMES$TechCost) ->
L245.TechCost # Municipal water non-energy cost
L245.assumptions_all %>%
select(LEVEL2_DATA_NAMES$PerCapitaBased) ->
L245.PerCapitaBased # used to set final demand as per-capita based
L245.assumptions_all %>%
left_join(L145.municipal_water_R_W_Yh_km3, by = "GCAM_region_ID") %>%
# ^^ non-restrictive join used to allow expansion across multiple years
filter(year %in% MODEL_BASE_YEARS) %>%
rename(base.service = withdrawals) %>%
select(LEVEL2_DATA_NAMES$BaseService) ->
L245.BaseService # municipal water withdrawals for base years
L245.assumptions_all %>%
repeat_add_columns(tibble(year = MODEL_FUTURE_YEARS)) %>%
select(LEVEL2_DATA_NAMES$IncomeElasticity) ->
L245.IncomeElasticity # income elasticity projections
L245.assumptions_all %>%
repeat_add_columns(tibble(year = MODEL_FUTURE_YEARS)) %>%
select(LEVEL2_DATA_NAMES$PriceElasticity) ->
L245.PriceElasticity # price elasticity projections
L245.assumptions_all %>%
repeat_add_columns(tibble(year = MODEL_FUTURE_YEARS)) %>%
select(LEVEL2_DATA_NAMES$aeei) ->
L245.aeei # demand efficiency projections
#==== OUTPUT ===========
L245.Supplysector %>%
add_title("Sector information (municipal water)") %>%
add_units("Unitless") %>%
add_comments("A selection of columns from the input water assumptions") %>%
add_legacy_name("L245.Supplysector") %>%
add_precursors("common/GCAM_region_names",
"water/A45.sector",
"water/A45.tech_cost",
"water/A45.demand") ->
L245.Supplysector
L245.SubsectorLogit %>%
add_title("Subsector logit detail (municipal water)") %>%
add_units("Unitless") %>%
add_comments("A selection of columns from the input tibbles") %>%
add_legacy_name("L245.SubsectorLogit") %>%
add_precursors("common/GCAM_region_names",
"water/A45.sector",
"water/A45.tech_cost") ->
L245.SubsectorLogit
L245.SubsectorShrwtFllt %>%
add_title("Subsector shareweights (municipal water)") %>%
add_units("Unitless") %>%
add_comments("Shareweights of 1 for all regions due to no competition)") %>%
add_legacy_name("L245.SubsectorShrwtFllt") %>%
add_precursors("common/GCAM_region_names",
"water/A45.sector",
"water/A45.tech_cost") ->
L245.SubsectorShrwtFllt
L245.TechShrwt %>%
add_title("Technology share weights (municipal water)") %>%
add_units("Unitless") %>%
add_comments("Shareweights of 1 for all regions/years due to no competition") %>%
add_legacy_name("L245.TechShrwt") %>%
add_precursors("common/GCAM_region_names",
"water/A45.sector",
"water/A45.tech_cost") ->
L245.TechShrwt
L245.TechCoef %>%
add_title("Municipal water technology consumption efficiencies") %>%
add_units("Unitless") %>%
add_comments("Withdrawal efficiencies bound to consumption and appropriate minicam.energy.input appended") %>%
add_legacy_name("L245.TechCoef") %>%
add_precursors("common/GCAM_region_names",
"water/A03.sector",
"water/A45.sector",
"water/A45.tech_cost",
"L145.municipal_water_eff_R_Yh") ->
L245.TechCoef
L245.TechCost %>%
add_title("Municipal water non-energy costs") %>%
add_units("1975USD/m3") %>%
add_comments("Costs joined to sector infromation and expanded for all model years") %>%
add_legacy_name("L245.TechCost") %>%
add_precursors("common/GCAM_region_names",
"water/A45.sector",
"water/A45.tech_cost",
"water/A45.demand",
"L145.municipal_water_cost_R_75USD_m3") ->
L245.TechCost
L245.PerCapitaBased %>%
add_title("Per-capital based final energy demand switch (municipal water)") %>%
add_units("NA") %>%
add_comments("final energy demand category repeated for all regions") %>%
add_comments("can be multiple lines") %>%
add_legacy_name("L245.PerCapitaBased") %>%
add_precursors("common/GCAM_region_names",
"water/A45.demand") ->
L245.PerCapitaBased
L245.BaseService %>%
add_title("Municipal water withdrawals for base years") %>%
add_units("km^3") %>%
add_comments("Withdrawals filtered for base years and combined with demand category") %>%
add_legacy_name("L245.BaseService") %>%
add_precursors("common/GCAM_region_names",
"water/A45.demand",
"L145.municipal_water_R_W_Yh_km3") ->
L245.BaseService
L245.IncomeElasticity %>%
add_title("Income elasticity projections (municipal water)") %>%
add_units("Unitless") %>%
add_comments("Regional income elasticity repeated out for future years") %>%
add_legacy_name("L245.IncomeElasticity") %>%
add_precursors("common/GCAM_region_names",
"water/A45.demand") ->
L245.IncomeElasticity
L245.PriceElasticity %>%
add_title("Price elasticity projections (municipal water)") %>%
add_units("Unitless") %>%
add_comments("Regional price elasticity repeated out for all years") %>%
add_legacy_name("L245.PriceElasticity") %>%
add_precursors("common/GCAM_region_names",
"water/A45.demand") ->
L245.PriceElasticity
L245.aeei %>%
add_title("Demand efficiency projections (municipal water)") %>%
add_units("Unitless") %>%
add_comments("Regional efficiency repeated out for all years") %>%
add_legacy_name("L245.aeei") %>%
add_precursors("common/GCAM_region_names",
"water/A45.demand") ->
L245.aeei
return_data(L245.Supplysector, L245.SubsectorLogit, L245.SubsectorShrwtFllt, L245.TechShrwt, L245.TechCoef, L245.TechCost, L245.PerCapitaBased, L245.BaseService, L245.IncomeElasticity, L245.PriceElasticity, L245.aeei)
} else {
stop("Unknown command")
}
}
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