R/zchunk_L102.GDP.R
In bpbond/gcamdata: Build the GCAM Input Datasets
Defines functions
join.gdp.ts
Documented in
join.gdp.ts
# Copyright 2019 Battelle Memorial Institute; see the LICENSE file.
#' Join past GDP time series to future.
#'
#' When we have to join two GDP time series, we usually find that they don't
#' match up at year of overlap (the "base year"). What we do in these cases is
#' we compute, for the later time series, ratios of GDPs in the future years to
#' those in the base year. We then multiply the future ratios by the past base
#' year value. That future time series can then be grafted onto the past
#' without leaving a seam.
#'
#' In practice, the past is often a single time series, while the future is
#' often a collection of scenarios. Therefore, we assume that the past time
#' series has no scenario column. If the future does not have a scenario
#' column, it is given a dummy one, which is dropped before the new table is
#' returned. Note that we look for lower-case 'scenario' for this.
#'
#' The base year is calculated automatically. It is the maximum of the years
#' that overlap between the two data sets.
#'
#' We also have to know how to group the data for calculating the gdp ratios.
#' Normally this will be either by country ('iso') or by GCAM region
#' ('GCAM_region_ID'). The choice of which is passed in as the 'grouping'
#' argument.
#'
#' Finally, although we have discussed this function in terms of joining two GDP
#' time series, in the future time series we use only the ratios of GDP to base
#' year GDP. Therefore, any time series with the correct ratios will work. For
#' example, if we have a time series of growth rates, we can convert those to
#' ratios using \code{\link[base]{cumprod}} and pass those ratios as the future
#' time series. For similar reasons, even if the two time series have different
#' units (e.g., different dollar-years or PPP vs. MER), they can still be
#' joined. The units of the output time series will be the same as the units of
#' \code{past}.
#'
#' @param past Tibble with the past time series (year, gdp, and grouping).
#' @param future Tibble with the future data (year, gdp, scenario, and
#' grouping).
#' @param grouping Name of the grouping column (generally either 'iso' or
#' 'GCAM_region_ID', but could be anything
#' @return Time series with the past and future joined as described in details.
join.gdp.ts <- function(past, future, grouping) {
year <- gdp <- base.gdp <- gdp.ratio <- . <- scenario <-
NULL # silence notes on package check.
if(! 'scenario' %in% names(future)) {
## This saves us having to make a bunch of exceptions below when we
## include 'scenario' among the columns to join by.
future$scenario <- 'scen'
drop.scenario <- TRUE
}
else {
drop.scenario <- FALSE
}
## Find the base year
base.year <- max(intersect(past$year, future$year))
assert_that(is.finite(base.year))
## Base year gdp from the future dataset
baseyear.future.gdp <- filter(future, year == base.year) %>%
rename(base.gdp = gdp) %>%
select(-year)
gdp.future.ratio <- filter(future, year > base.year) %>%
left_join_error_no_match(baseyear.future.gdp, by = c('scenario', grouping)) %>%
mutate(gdp.ratio = gdp / base.gdp) %>%
select('scenario', grouping, 'year', 'gdp.ratio')
## add the scenario column to the past
gdp.past <- tidyr::crossing(past, scenario = unique(gdp.future.ratio[['scenario']]))
baseyear.past.gdp <- filter(gdp.past, year == base.year) %>%
rename(base.gdp = gdp) %>%
select(-year)
rslt <- left_join(baseyear.past.gdp, gdp.future.ratio,
by = c('scenario', grouping)) %>%
mutate(gdp = base.gdp * gdp.ratio) %>%
select('scenario', grouping, 'year', 'gdp') %>%
bind_rows(gdp.past, .)
if(drop.scenario) {
select(rslt, -scenario)
}
else {
rslt
}
}
#' module_socioeconomics_L102.GDP
#'
#' Prepare historical and future GDP time series. On the historical side, this
#' amounts to aggregating country-level GDP to GCAM regions. On the future side
#' we create a time series for each of a variety of future scenarios. The
#' outputs include GDP, pcGDP, and the PPP-MER conversion factor, all tabulated
#' by GCAM region.
#'
#' The scenarios generated include the SSPs and the gSSPs (SSPs modified by
#' near-term IMF projections). GDP outputs are in millions of 1990 USD, Market
#' Exchange Rate (measured in 2010) is used for foreign currency. Per-capita
#' values are in thousands of 1990 USD.
#'
#' @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{L102.gdp_mil90usd_Scen_R_Y}, \code{L102.pcgdp_thous90USD_Scen_R_Y}, \code{L102.gdp_mil90usd_GCAM3_R_Y}, \code{L102.gdp_mil90usd_GCAM3_ctry_Y}, \code{L102.pcgdp_thous90USD_GCAM3_R_Y}, \code{L102.pcgdp_thous90USD_GCAM3_ctry_Y}, \code{L102.PPP_MER_R}. The corresponding file in the
#' original data system was \code{L102.GDP.R} (socioeconomics level1).
#' @importFrom assertthat assert_that
#' @importFrom dplyr arrange bind_rows distinct filter full_join if_else intersect group_by left_join mutate one_of select summarise transmute
#' @importFrom tidyr complete gather nesting replace_na
#' @author RPL March 2017
module_socioeconomics_L102.GDP <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "common/iso_GCAM_regID",
FILE = "socioeconomics/SSP_database_v9",
FILE = "socioeconomics/IMF_GDP_growth",
FILE = "socioeconomics/GCAM3_GDP",
"L100.gdp_mil90usd_ctry_Yh",
"L101.Pop_thous_GCAM3_R_Y",
"L101.Pop_thous_GCAM3_ctry_Y",
"L101.Pop_thous_R_Yh",
"L101.Pop_thous_Scen_R_Yfut"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L102.gdp_mil90usd_Scen_R_Y",
"L102.pcgdp_thous90USD_Scen_R_Y",
"L102.PPP_MER_R",
"L102.gdp_mil90usd_GCAM3_R_Y",
"L102.gdp_mil90usd_GCAM3_ctry_Y",
"L102.pcgdp_thous90USD_GCAM3_R_Y",
"L102.pcgdp_thous90USD_GCAM3_ctry_Y"))
} else if(command == driver.MAKE) {
iso <- GCAM_region_ID <- value <- year <- gdp <- MODEL <- VARIABLE <-
UNIT <- SCENARIO <- scenario <- gdp.rate <- gdp.ratio <- population <-
pcgdp <- MER <- PPP <- region_GCAM3 <- agg_val <- share <-
GCAM3_value <- base <- ratio <- value.x <- value.y <- NULL # silence package check.
all_data <- list(...)[[1]]
# Load required inputs
iso_GCAM_regID <- get_data(all_data, "common/iso_GCAM_regID")
SSP_database_v9 <- get_data(all_data, "socioeconomics/SSP_database_v9")
IMF_GDP_growth <- get_data(all_data, "socioeconomics/IMF_GDP_growth")
GCAM3_GDP <- get_data(all_data, "socioeconomics/GCAM3_GDP") %>%
gather_years %>%
mutate(value = as.numeric(value))
L100.gdp_mil90usd_ctry_Yh <- get_data(all_data, "L100.gdp_mil90usd_ctry_Yh")
L101.Pop_thous_GCAM3_R_Y <- get_data(all_data, "L101.Pop_thous_GCAM3_R_Y")
L101.Pop_thous_GCAM3_ctry_Y <- get_data(all_data, "L101.Pop_thous_GCAM3_ctry_Y")
L101.Pop_thous_R_Yh <- get_data(all_data, "L101.Pop_thous_R_Yh")
L101.Pop_thous_Scen_R_Yfut <- get_data(all_data, "L101.Pop_thous_Scen_R_Yfut")
## iso--region lookup without extraneous data. We'll use this several times.
iso_region32_lookup <- select(iso_GCAM_regID, iso, GCAM_region_ID)
## Add region IDs to historical GDP and aggregate by region. Hang onto the country-level data
## because we will use them again when we make the IMF adjustments
gdp_mil90usd_ctry <-
left_join_error_no_match(L100.gdp_mil90usd_ctry_Yh, iso_region32_lookup, by = 'iso') %>%
rename(gdp = value)
gdp_mil90usd_rgn <- gdp_mil90usd_ctry %>%
filter(year %in% HISTORICAL_YEARS) %>%
group_by(GCAM_region_ID, year) %>%
summarise(gdp = sum(gdp)) %>%
ungroup()
## gdp_mil90usd_ctry: iso, GCAM_region_ID, year, gdp
## gdp_mil90usd_rgn: GCAM_region_ID, year, gdp
## Get the future GDP in the SSP scenarios. These are PPP values in 2005 dollars
gdp_bilusd_rgn_Yfut <-
filter(SSP_database_v9, MODEL == 'OECD Env-Growth' & VARIABLE == 'GDP|PPP') %>%
standardize_iso('REGION') %>%
change_iso_code('rou', 'rom') %>%
left_join_error_no_match(iso_region32_lookup, by = 'iso') %>%
protect_integer_cols %>%
dplyr::select_if(function(x) {!any(is.na(x))}) %>% # apparently the SSP database has some missing in it; filter these out.
unprotect_integer_cols %>%
select(-MODEL, -iso, -VARIABLE, -UNIT) %>%
gather_years(value_col = "gdp") %>%
mutate(gdp = as.numeric(gdp),
scenario = substr(SCENARIO, 1, 4)) %>% # Trim the junk off the end of
# the scenario names, leaving us with
# just SSP1, SSP2, etc.
group_by(scenario, GCAM_region_ID, year) %>%
summarise(gdp = sum(gdp)) %>%
select(scenario, GCAM_region_ID, year, gdp) %>%
ungroup() %>%
# The steps below write out the data to all future years, starting from the final socio historical year
complete(nesting(scenario, GCAM_region_ID), year = c(socioeconomics.FINAL_HIST_YEAR, FUTURE_YEARS)) %>%
group_by(scenario, GCAM_region_ID) %>%
mutate(gdp = approx_fun(year, gdp)) %>%
ungroup()
## Units are billions of 2005$
gdp.mil90usd.SSP.rgn.yr <- join.gdp.ts(gdp_mil90usd_rgn, gdp_bilusd_rgn_Yfut, 'GCAM_region_ID')
## Get the IMF GDP growth rates. Some countries are missing, so we have to
## add them in with an assumed zero growth rate.
imfgdp.growth <-
select(IMF_GDP_growth, one_of(c('ISO', socioeconomics.IMF_GDP_YEARS))) %>%
standardize_iso('ISO') %>%
change_iso_code('rou', 'rom') %>%
gather(year, gdp.rate, -iso) %>%
full_join(gdp_mil90usd_ctry %>% select(iso) %>% unique, by = 'iso') %>%
mutate(gdp.rate = if_else(gdp.rate == 'n/a', '0', gdp.rate), # Treat string 'n/a' as missing.
year = as.integer(year),
gdp.rate = as.numeric(gdp.rate)) %>%
#kbn 2020-03-26 Updated below to the last year in the IMF_GDP_YEARS so that latest GDP growth rates are picked up
replace_na(list(year = max(socioeconomics.IMF_GDP_YEARS))) %>% # have to do this for `complete` to work as expected.
complete(iso, year) %>%
replace_na(list(gdp.rate = 0.0))
imfgdp.ratio <-
imfgdp.growth %>%
mutate(gdp.ratio = 1.0 + gdp.rate / 100.0) %>%
arrange(year) %>% group_by(iso) %>%
mutate(gdp = cumprod(gdp.ratio)) %>% # actually ratio of gdp to base-year
ungroup() %>%
# gdp, but we're calling it "gdp" so
# that join.gdp.ts() can work with it.
select(iso, year, gdp)
gdp.mil90usd.imf.country.yr <-
gdp_mil90usd_ctry %>%
# filter gdp data so that it ends right at the first year of the IMF ratio data
filter(year <= min(imfgdp.ratio$year), year >= min(HISTORICAL_YEARS)) %>%
select(iso, year, gdp) %>%
join.gdp.ts(imfgdp.ratio, 'iso')
## columns: iso, year, gdp
## Aggregate by GCAM region
gdp.mil90usd.imf.rgn.yr <-
left_join_error_no_match(gdp.mil90usd.imf.country.yr, iso_region32_lookup, by = 'iso') %>%
group_by(GCAM_region_ID, year) %>%
summarise(gdp = sum(gdp)) %>%
ungroup() %>%
filter(year %in% c(HISTORICAL_YEARS, FUTURE_YEARS))
## columns: GCAM_region_ID, year, gdp
## join the IMF near future up to the SSP distant future, rename scenarios
## to gSSP*
gdp.mil90usd.gSSP.rgn.yr <-
join.gdp.ts(gdp.mil90usd.imf.rgn.yr, gdp_bilusd_rgn_Yfut, 'GCAM_region_ID') %>%
mutate(scenario = paste0('g', scenario))
## columns: scenario, GCAM_region_ID, year, gdp
## combine SSP and gSSP scenarios into a single table (this will be one of
## our final outputs)
gdp.mil90usd.scen.rgn.yr <-
bind_rows(gdp.mil90usd.SSP.rgn.yr, gdp.mil90usd.gSSP.rgn.yr)
## Construct a table of population by scenario, region, and year. We have a
## table of historical population, and a table of future population by
## scenario, both in wide form. Convert to long form and filter to the years
## we need. Add a scenario column to historical years, and combine the
## whole thing into a single table.
pop.thous.fut <-
rename(L101.Pop_thous_Scen_R_Yfut, population = value) %>%
filter(year %in% FUTURE_YEARS)
pop.thous.hist <-
rename(L101.Pop_thous_R_Yh, population = value) %>%
filter(year %in% HISTORICAL_YEARS) %>%
tidyr::crossing(scenario = unique(pop.thous.fut[['scenario']]))
pop.thous.scen.rgn.yr <-
bind_rows(pop.thous.hist, pop.thous.fut) %>%
mutate(year = as.integer(year),
population = as.numeric(population)) %>%
select(scenario, GCAM_region_ID, year, population)
## calculate per-capita GDP. This is another final output
pcgdp.thous90usd.scen.rgn.yr <-
left_join(gdp.mil90usd.scen.rgn.yr, pop.thous.scen.rgn.yr,
by = c('scenario', 'GCAM_region_ID', 'year')) %>%
mutate(pcgdp = gdp / population) %>%
select(scenario, GCAM_region_ID, year, pcgdp)
## Calculate the PPP-MER conversion factor in base year for each region.
## Our PPP values are in billions of 2005$, so we make that conversion
## here too.
#kbn 2020-03-26 Using model final base year here below
PPP.MER.baseyr <- MODEL_FINAL_BASE_YEAR
mer.rgn <- gdp_mil90usd_ctry %>%
filter(year == PPP.MER.baseyr) %>%
group_by(GCAM_region_ID) %>%
mutate(MER = gdp * gdp_deflator(2005, 1990) * CONV_MIL_BIL) %>%
summarise(MER = sum(MER))
## columns: GCAM_region_ID, MER
## The future data is given by SSP scenario, but the final table is scenario
## independent, as it should be, since this base year is meant to be a
## historical year. Likewise, the GDP in the PPP/MER base year should also
## be scenario-independent, and mostly it is, except for the region containing
## the Palestinian Territories, which has four slightly different values across
## the 5 SSPs. (It's SSP 2 and 4 that are the same). The value
## actually used in the old data system is the one for SSP1, so that's the
## one we'll use here. Arguably we should average the values over the 5
## scenarios, but the differences are only 1 part in 10^4, so we can just
## let it slide.
ppp.rgn <- gdp_bilusd_rgn_Yfut %>%
ungroup %>%
filter(year == PPP.MER.baseyr, scenario == 'SSP1') %>%
rename(PPP = gdp) %>%
select(GCAM_region_ID, PPP)
ppp.mer.rgn <-
left_join_error_no_match(mer.rgn, ppp.rgn, by = 'GCAM_region_ID') %>%
mutate(PPP_MER = PPP / MER)
# GDP by GCAM region from GCAM 3.0 GDPs.
# Downscaling GCAM 3.0 GDP by GCAM 3.0 region to countries, using SSP2 GDP scenario
# GDP by GCAM 3.0 region - downscale to country according to actual shares in the historical periods, and SSPbase in the future periods
# Future GDP
gdp_bilusd_ctry_Yfut <- SSP_database_v9 %>%
filter(MODEL == 'OECD Env-Growth' & VARIABLE == 'GDP|PPP') %>%
standardize_iso('REGION') %>%
change_iso_code('rou', 'rom') %>%
mutate(scenario = substr(SCENARIO, 1, 4)) %>%
# Only Base SSP
filter(scenario == socioeconomics.BASE_POP_SCEN) %>%
gather_years %>%
mutate(value = as.numeric(value)) %>%
select(iso, year, value) %>%
complete(nesting(iso), year = c(socioeconomics.FINAL_HIST_YEAR, FUTURE_YEARS)) %>%
group_by(iso) %>%
mutate(value = approx_fun(year, value)) %>%
ungroup() %>%
filter(year %in% FUTURE_YEARS)
# Historical GDP
gdp_mil90usd_ctry_Yh <- L100.gdp_mil90usd_ctry_Yh %>%
filter(year %in% HISTORICAL_YEARS,
iso %in% gdp_bilusd_ctry_Yfut$iso)
gdp_mil90usd_ctry_Yh <- gdp_mil90usd_ctry_Yh %>%
# Only take future GDP if iso has historical GDP
bind_rows(gdp_bilusd_ctry_Yfut %>%
filter(iso %in% gdp_mil90usd_ctry_Yh$iso)) %>%
left_join_error_no_match(iso_GCAM_regID %>%
select(iso, region_GCAM3), by = "iso")
# Aggregating GDP by GCAM3 region
gdp_mil90usd_SSPbase_RG3_Y <- gdp_mil90usd_ctry_Yh %>%
group_by(year, region_GCAM3) %>%
summarise(agg_val = sum(value))
# Calculate shares of each country within its region
gdpshares_ctryRG3_Y <- gdp_mil90usd_ctry_Yh %>%
group_by(year, region_GCAM3) %>%
mutate(share = value / sum(value)) %>%
ungroup()
# Interpolate GCAM3 GDP data to all historical and future years
gdp_mil90usd_GCAM3_RG3_Y <- iso_GCAM_regID %>%
select(region_GCAM3) %>%
distinct() %>%
repeat_add_columns(tibble(year = as.integer(c(HISTORICAL_YEARS, FUTURE_YEARS)))) %>%
left_join(GCAM3_GDP, by = c("region_GCAM3", "year")) %>%
# Add in historical values to match in for NA values
left_join_error_no_match(gdp_mil90usd_SSPbase_RG3_Y, by = c("region_GCAM3", "year")) %>%
group_by(region_GCAM3) %>%
# Extending GCAM 3.0 scenario to first historical year using historical GDP ratios by GCAM 3.0 region
mutate(value = replace(value, year == min(year),
value[year == min(GCAM3_GDP$year)] * agg_val[year == min(year)] / agg_val[year == min(GCAM3_GDP$year)]),
value = approx_fun(year, value, rule = 1)) %>%
ungroup() %>%
select(region_GCAM3, year, GCAM3_value = value)
# Multiply these GDP numbers by the shares of each country within GCAM region
gdp_mil90usd_GCAM3_ctry_Y <- gdpshares_ctryRG3_Y %>%
left_join_error_no_match(gdp_mil90usd_GCAM3_RG3_Y, by = c("year", "region_GCAM3")) %>%
transmute(iso, year, value = share * GCAM3_value)
# rebasing GDP to 2010 USD at country level
# Calculate the GDP ratios from the first year in the projections. Use this ratio to project GDP from historical dataset in final historical period
gdpRatio_GCAM3_ctry_Yfut <- gdp_mil90usd_GCAM3_ctry_Y %>%
group_by(iso) %>%
mutate(base = value[year == socioeconomics.FINAL_HIST_YEAR]) %>%
ungroup() %>%
filter(year %in% FUTURE_YEARS) %>%
transmute(iso, year, ratio = value / base)
# Use these ratios to build the GDP trajectories by old GCAM3
gdp_mil90usd_GCAM3_ctry_Y <- gdp_mil90usd_ctry_Yh %>%
left_join(gdpRatio_GCAM3_ctry_Yfut, by = c("iso", "year")) %>%
group_by(iso) %>%
mutate(value = if_else(year %in% FUTURE_YEARS, value[year == socioeconomics.FINAL_HIST_YEAR] * ratio, value)) %>%
ungroup() %>%
select(iso, year, value)
# Aggregating by GCAM4 region
gdp_mil90usd_GCAM3_R_Y <- gdp_mil90usd_GCAM3_ctry_Y %>%
left_join_error_no_match(iso_region32_lookup, by = "iso") %>%
group_by(GCAM_region_ID, year) %>%
summarise(value = sum(value)) %>%
ungroup()
# Calculate per-capita GDP
pcgdp_thous90USD_GCAM3_R_Y <- gdp_mil90usd_GCAM3_R_Y %>%
left_join_error_no_match(L101.Pop_thous_GCAM3_R_Y, by = c("year", "GCAM_region_ID")) %>%
transmute(GCAM_region_ID, year, value = value.x / value.y)
pcgdp_thous90USD_GCAM3_ctry_Y <- gdp_mil90usd_GCAM3_ctry_Y %>%
left_join_error_no_match(L101.Pop_thous_GCAM3_ctry_Y, by = c("year", "iso")) %>%
transmute(iso, year, value = value.x / value.y)
## Produce outputs
gdp.mil90usd.scen.rgn.yr %>%
ungroup %>%
filter(year %in% c(HISTORICAL_YEARS, FUTURE_YEARS)) %>%
rename(value = gdp) %>%
mutate(year = as.integer(year)) %>%
add_title("Gross Domestic Product (GDP) by scenario, region, and year.") %>%
add_units("Millions of 1990 USD (MER)") %>%
add_comments("For the SSP scenarios, SSP GDP projections are scaled to match ") %>%
add_comments("historical values in the base year (2010). For the gSSP scenarios ") %>%
add_comments("IMF growth projections are applied from 2010-2020, and the SSP projections ") %>%
add_comments("are scaled to match the 2020 values resulting from this process.") %>%
add_legacy_name("L102.gdp_mil90usd_Scen_R_Y") %>%
add_precursors("common/iso_GCAM_regID",
"socioeconomics/SSP_database_v9",
"socioeconomics/IMF_GDP_growth",
"L100.gdp_mil90usd_ctry_Yh") ->
L102.gdp_mil90usd_Scen_R_Y
pcgdp.thous90usd.scen.rgn.yr %>%
ungroup %>%
filter(year %in% c(HISTORICAL_YEARS, FUTURE_YEARS)) %>%
rename(value = pcgdp) %>%
add_title("Gross Domestic Product (GDP) per capita, by scenario, region, and year.") %>%
add_units("Thousands of 1990 USD (MER)") %>%
add_comments("Computed as GDP/population. Values prior to the base year (2010) are ") %>%
add_comments("historical; values subsequent are from SSP projections.") %>%
add_legacy_name("L102.pcgdp_thous90USD_Scen_R_Y") %>%
add_precursors("common/iso_GCAM_regID",
"socioeconomics/SSP_database_v9",
"socioeconomics/IMF_GDP_growth",
"L100.gdp_mil90usd_ctry_Yh",
"L101.Pop_thous_R_Yh",
"L101.Pop_thous_Scen_R_Yfut") ->
L102.pcgdp_thous90USD_Scen_R_Y
ppp.mer.rgn %>%
add_title("Purchasing Power Parity (PPP) to Market Exchange Rate (MER) GDP conversions, by region") %>%
add_units("unitless") %>%
add_comments("Calculated as GDP(PPP) / GDP(MER) in the base year (2010) for each region. The") %>%
add_comments("table also contains PPP-GDP and MER-GDP in billions of 2005 USD for the base year, ") %>%
add_comments("because they were included in the original table, but I'm not sure they are used for, ") %>%
add_comments("or useful for, anything besides calculating the ratio.") %>%
add_legacy_name("L102.PPP_MER_R") %>%
add_precursors("common/iso_GCAM_regID",
"socioeconomics/SSP_database_v9",
"socioeconomics/IMF_GDP_growth",
"L100.gdp_mil90usd_ctry_Yh") ->
L102.PPP_MER_R
gdp_mil90usd_GCAM3_R_Y %>%
add_title("GDP by GCAM3 Region") %>%
add_units("Million 1990 USD") %>%
add_comments("Scales historical SSP GDP data to GCAM3 GDP data.") %>%
add_comments("Calculates future GDP based on ratio of GCAM3 future to 2010 value.") %>%
add_legacy_name("L102.gdp_mil90usd_GCAM3_R_Y") %>%
add_precursors("common/iso_GCAM_regID",
"socioeconomics/SSP_database_v9",
"L100.gdp_mil90usd_ctry_Yh",
"socioeconomics/GCAM3_GDP") ->
L102.gdp_mil90usd_GCAM3_R_Y
gdp_mil90usd_GCAM3_ctry_Y %>%
add_title("GCAM3 GDP by Country") %>%
add_units("Million 1990 USD") %>%
add_comments("Scales historical SSP GDP data to GCAM3 GDP data.") %>%
add_comments("Calculates future GDP based on ratio of GCAM3 future to 2010 value.") %>%
add_legacy_name("L102.gdp_mil90usd_GCAM3_ctry_Y") %>%
add_precursors("common/iso_GCAM_regID",
"socioeconomics/SSP_database_v9",
"L100.gdp_mil90usd_ctry_Yh",
"socioeconomics/GCAM3_GDP") ->
L102.gdp_mil90usd_GCAM3_ctry_Y
pcgdp_thous90USD_GCAM3_R_Y %>%
add_title("Per-Capita GDP by GCAM3 Region") %>%
add_units("Thousand 1990 USD") %>%
add_comments("L102.gdp_mil90usd_GCAM3_R_Y divided by population from L101.Pop_thous_GCAM3_R_Y") %>%
add_legacy_name("L102.pcgdp_thous90USD_GCAM3_R_Y") %>%
add_precursors("common/iso_GCAM_regID",
"socioeconomics/SSP_database_v9",
"L100.gdp_mil90usd_ctry_Yh",
"socioeconomics/GCAM3_GDP",
"L101.Pop_thous_GCAM3_R_Y") ->
L102.pcgdp_thous90USD_GCAM3_R_Y
pcgdp_thous90USD_GCAM3_ctry_Y %>%
add_title("GCAM3 Per-Capita GDP by Country") %>%
add_units("Thousand 1990 USD") %>%
add_comments("L102.gdp_mil90usd_GCAM3_ctry_Y divided by population from L101.Pop_thous_GCAM3_ctry_Y") %>%
add_legacy_name("L102.pcgdp_thous90USD_GCAM3_ctry_Y") %>%
add_precursors("common/iso_GCAM_regID",
"socioeconomics/SSP_database_v9",
"L100.gdp_mil90usd_ctry_Yh",
"socioeconomics/GCAM3_GDP",
"L101.Pop_thous_GCAM3_ctry_Y") ->
L102.pcgdp_thous90USD_GCAM3_ctry_Y
return_data(L102.gdp_mil90usd_Scen_R_Y, L102.pcgdp_thous90USD_Scen_R_Y, L102.PPP_MER_R, L102.gdp_mil90usd_GCAM3_R_Y, L102.gdp_mil90usd_GCAM3_ctry_Y, L102.pcgdp_thous90USD_GCAM3_R_Y, L102.pcgdp_thous90USD_GCAM3_ctry_Y)
} else {
stop("Unknown command")
}
}
bpbond/gcamdata documentation built on March 22, 2023, 4:52 a.m.
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Defines functions join.gdp.ts
Documented in join.gdp.ts
# Copyright 2019 Battelle Memorial Institute; see the LICENSE file.
#' Join past GDP time series to future.
#'
#' When we have to join two GDP time series, we usually find that they don't
#' match up at year of overlap (the "base year"). What we do in these cases is
#' we compute, for the later time series, ratios of GDPs in the future years to
#' those in the base year. We then multiply the future ratios by the past base
#' year value. That future time series can then be grafted onto the past
#' without leaving a seam.
#'
#' In practice, the past is often a single time series, while the future is
#' often a collection of scenarios. Therefore, we assume that the past time
#' series has no scenario column. If the future does not have a scenario
#' column, it is given a dummy one, which is dropped before the new table is
#' returned. Note that we look for lower-case 'scenario' for this.
#'
#' The base year is calculated automatically. It is the maximum of the years
#' that overlap between the two data sets.
#'
#' We also have to know how to group the data for calculating the gdp ratios.
#' Normally this will be either by country ('iso') or by GCAM region
#' ('GCAM_region_ID'). The choice of which is passed in as the 'grouping'
#' argument.
#'
#' Finally, although we have discussed this function in terms of joining two GDP
#' time series, in the future time series we use only the ratios of GDP to base
#' year GDP. Therefore, any time series with the correct ratios will work. For
#' example, if we have a time series of growth rates, we can convert those to
#' ratios using \code{\link[base]{cumprod}} and pass those ratios as the future
#' time series. For similar reasons, even if the two time series have different
#' units (e.g., different dollar-years or PPP vs. MER), they can still be
#' joined. The units of the output time series will be the same as the units of
#' \code{past}.
#'
#' @param past Tibble with the past time series (year, gdp, and grouping).
#' @param future Tibble with the future data (year, gdp, scenario, and
#' grouping).
#' @param grouping Name of the grouping column (generally either 'iso' or
#' 'GCAM_region_ID', but could be anything
#' @return Time series with the past and future joined as described in details.
join.gdp.ts <- function(past, future, grouping) {
year <- gdp <- base.gdp <- gdp.ratio <- . <- scenario <-
NULL # silence notes on package check.
if(! 'scenario' %in% names(future)) {
## This saves us having to make a bunch of exceptions below when we
## include 'scenario' among the columns to join by.
future$scenario <- 'scen'
drop.scenario <- TRUE
}
else {
drop.scenario <- FALSE
}
## Find the base year
base.year <- max(intersect(past$year, future$year))
assert_that(is.finite(base.year))
## Base year gdp from the future dataset
baseyear.future.gdp <- filter(future, year == base.year) %>%
rename(base.gdp = gdp) %>%
select(-year)
gdp.future.ratio <- filter(future, year > base.year) %>%
left_join_error_no_match(baseyear.future.gdp, by = c('scenario', grouping)) %>%
mutate(gdp.ratio = gdp / base.gdp) %>%
select('scenario', grouping, 'year', 'gdp.ratio')
## add the scenario column to the past
gdp.past <- tidyr::crossing(past, scenario = unique(gdp.future.ratio[['scenario']]))
baseyear.past.gdp <- filter(gdp.past, year == base.year) %>%
rename(base.gdp = gdp) %>%
select(-year)
rslt <- left_join(baseyear.past.gdp, gdp.future.ratio,
by = c('scenario', grouping)) %>%
mutate(gdp = base.gdp * gdp.ratio) %>%
select('scenario', grouping, 'year', 'gdp') %>%
bind_rows(gdp.past, .)
if(drop.scenario) {
select(rslt, -scenario)
}
else {
rslt
}
}
#' module_socioeconomics_L102.GDP
#'
#' Prepare historical and future GDP time series. On the historical side, this
#' amounts to aggregating country-level GDP to GCAM regions. On the future side
#' we create a time series for each of a variety of future scenarios. The
#' outputs include GDP, pcGDP, and the PPP-MER conversion factor, all tabulated
#' by GCAM region.
#'
#' The scenarios generated include the SSPs and the gSSPs (SSPs modified by
#' near-term IMF projections). GDP outputs are in millions of 1990 USD, Market
#' Exchange Rate (measured in 2010) is used for foreign currency. Per-capita
#' values are in thousands of 1990 USD.
#'
#' @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{L102.gdp_mil90usd_Scen_R_Y}, \code{L102.pcgdp_thous90USD_Scen_R_Y}, \code{L102.gdp_mil90usd_GCAM3_R_Y}, \code{L102.gdp_mil90usd_GCAM3_ctry_Y}, \code{L102.pcgdp_thous90USD_GCAM3_R_Y}, \code{L102.pcgdp_thous90USD_GCAM3_ctry_Y}, \code{L102.PPP_MER_R}. The corresponding file in the
#' original data system was \code{L102.GDP.R} (socioeconomics level1).
#' @importFrom assertthat assert_that
#' @importFrom dplyr arrange bind_rows distinct filter full_join if_else intersect group_by left_join mutate one_of select summarise transmute
#' @importFrom tidyr complete gather nesting replace_na
#' @author RPL March 2017
module_socioeconomics_L102.GDP <- function(command, ...) {
if(command == driver.DECLARE_INPUTS) {
return(c(FILE = "common/iso_GCAM_regID",
FILE = "socioeconomics/SSP_database_v9",
FILE = "socioeconomics/IMF_GDP_growth",
FILE = "socioeconomics/GCAM3_GDP",
"L100.gdp_mil90usd_ctry_Yh",
"L101.Pop_thous_GCAM3_R_Y",
"L101.Pop_thous_GCAM3_ctry_Y",
"L101.Pop_thous_R_Yh",
"L101.Pop_thous_Scen_R_Yfut"))
} else if(command == driver.DECLARE_OUTPUTS) {
return(c("L102.gdp_mil90usd_Scen_R_Y",
"L102.pcgdp_thous90USD_Scen_R_Y",
"L102.PPP_MER_R",
"L102.gdp_mil90usd_GCAM3_R_Y",
"L102.gdp_mil90usd_GCAM3_ctry_Y",
"L102.pcgdp_thous90USD_GCAM3_R_Y",
"L102.pcgdp_thous90USD_GCAM3_ctry_Y"))
} else if(command == driver.MAKE) {
iso <- GCAM_region_ID <- value <- year <- gdp <- MODEL <- VARIABLE <-
UNIT <- SCENARIO <- scenario <- gdp.rate <- gdp.ratio <- population <-
pcgdp <- MER <- PPP <- region_GCAM3 <- agg_val <- share <-
GCAM3_value <- base <- ratio <- value.x <- value.y <- NULL # silence package check.
all_data <- list(...)[[1]]
# Load required inputs
iso_GCAM_regID <- get_data(all_data, "common/iso_GCAM_regID")
SSP_database_v9 <- get_data(all_data, "socioeconomics/SSP_database_v9")
IMF_GDP_growth <- get_data(all_data, "socioeconomics/IMF_GDP_growth")
GCAM3_GDP <- get_data(all_data, "socioeconomics/GCAM3_GDP") %>%
gather_years %>%
mutate(value = as.numeric(value))
L100.gdp_mil90usd_ctry_Yh <- get_data(all_data, "L100.gdp_mil90usd_ctry_Yh")
L101.Pop_thous_GCAM3_R_Y <- get_data(all_data, "L101.Pop_thous_GCAM3_R_Y")
L101.Pop_thous_GCAM3_ctry_Y <- get_data(all_data, "L101.Pop_thous_GCAM3_ctry_Y")
L101.Pop_thous_R_Yh <- get_data(all_data, "L101.Pop_thous_R_Yh")
L101.Pop_thous_Scen_R_Yfut <- get_data(all_data, "L101.Pop_thous_Scen_R_Yfut")
## iso--region lookup without extraneous data. We'll use this several times.
iso_region32_lookup <- select(iso_GCAM_regID, iso, GCAM_region_ID)
## Add region IDs to historical GDP and aggregate by region. Hang onto the country-level data
## because we will use them again when we make the IMF adjustments
gdp_mil90usd_ctry <-
left_join_error_no_match(L100.gdp_mil90usd_ctry_Yh, iso_region32_lookup, by = 'iso') %>%
rename(gdp = value)
gdp_mil90usd_rgn <- gdp_mil90usd_ctry %>%
filter(year %in% HISTORICAL_YEARS) %>%
group_by(GCAM_region_ID, year) %>%
summarise(gdp = sum(gdp)) %>%
ungroup()
## gdp_mil90usd_ctry: iso, GCAM_region_ID, year, gdp
## gdp_mil90usd_rgn: GCAM_region_ID, year, gdp
## Get the future GDP in the SSP scenarios. These are PPP values in 2005 dollars
gdp_bilusd_rgn_Yfut <-
filter(SSP_database_v9, MODEL == 'OECD Env-Growth' & VARIABLE == 'GDP|PPP') %>%
standardize_iso('REGION') %>%
change_iso_code('rou', 'rom') %>%
left_join_error_no_match(iso_region32_lookup, by = 'iso') %>%
protect_integer_cols %>%
dplyr::select_if(function(x) {!any(is.na(x))}) %>% # apparently the SSP database has some missing in it; filter these out.
unprotect_integer_cols %>%
select(-MODEL, -iso, -VARIABLE, -UNIT) %>%
gather_years(value_col = "gdp") %>%
mutate(gdp = as.numeric(gdp),
scenario = substr(SCENARIO, 1, 4)) %>% # Trim the junk off the end of
# the scenario names, leaving us with
# just SSP1, SSP2, etc.
group_by(scenario, GCAM_region_ID, year) %>%
summarise(gdp = sum(gdp)) %>%
select(scenario, GCAM_region_ID, year, gdp) %>%
ungroup() %>%
# The steps below write out the data to all future years, starting from the final socio historical year
complete(nesting(scenario, GCAM_region_ID), year = c(socioeconomics.FINAL_HIST_YEAR, FUTURE_YEARS)) %>%
group_by(scenario, GCAM_region_ID) %>%
mutate(gdp = approx_fun(year, gdp)) %>%
ungroup()
## Units are billions of 2005$
gdp.mil90usd.SSP.rgn.yr <- join.gdp.ts(gdp_mil90usd_rgn, gdp_bilusd_rgn_Yfut, 'GCAM_region_ID')
## Get the IMF GDP growth rates. Some countries are missing, so we have to
## add them in with an assumed zero growth rate.
imfgdp.growth <-
select(IMF_GDP_growth, one_of(c('ISO', socioeconomics.IMF_GDP_YEARS))) %>%
standardize_iso('ISO') %>%
change_iso_code('rou', 'rom') %>%
gather(year, gdp.rate, -iso) %>%
full_join(gdp_mil90usd_ctry %>% select(iso) %>% unique, by = 'iso') %>%
mutate(gdp.rate = if_else(gdp.rate == 'n/a', '0', gdp.rate), # Treat string 'n/a' as missing.
year = as.integer(year),
gdp.rate = as.numeric(gdp.rate)) %>%
#kbn 2020-03-26 Updated below to the last year in the IMF_GDP_YEARS so that latest GDP growth rates are picked up
replace_na(list(year = max(socioeconomics.IMF_GDP_YEARS))) %>% # have to do this for `complete` to work as expected.
complete(iso, year) %>%
replace_na(list(gdp.rate = 0.0))
imfgdp.ratio <-
imfgdp.growth %>%
mutate(gdp.ratio = 1.0 + gdp.rate / 100.0) %>%
arrange(year) %>% group_by(iso) %>%
mutate(gdp = cumprod(gdp.ratio)) %>% # actually ratio of gdp to base-year
ungroup() %>%
# gdp, but we're calling it "gdp" so
# that join.gdp.ts() can work with it.
select(iso, year, gdp)
gdp.mil90usd.imf.country.yr <-
gdp_mil90usd_ctry %>%
# filter gdp data so that it ends right at the first year of the IMF ratio data
filter(year <= min(imfgdp.ratio$year), year >= min(HISTORICAL_YEARS)) %>%
select(iso, year, gdp) %>%
join.gdp.ts(imfgdp.ratio, 'iso')
## columns: iso, year, gdp
## Aggregate by GCAM region
gdp.mil90usd.imf.rgn.yr <-
left_join_error_no_match(gdp.mil90usd.imf.country.yr, iso_region32_lookup, by = 'iso') %>%
group_by(GCAM_region_ID, year) %>%
summarise(gdp = sum(gdp)) %>%
ungroup() %>%
filter(year %in% c(HISTORICAL_YEARS, FUTURE_YEARS))
## columns: GCAM_region_ID, year, gdp
## join the IMF near future up to the SSP distant future, rename scenarios
## to gSSP*
gdp.mil90usd.gSSP.rgn.yr <-
join.gdp.ts(gdp.mil90usd.imf.rgn.yr, gdp_bilusd_rgn_Yfut, 'GCAM_region_ID') %>%
mutate(scenario = paste0('g', scenario))
## columns: scenario, GCAM_region_ID, year, gdp
## combine SSP and gSSP scenarios into a single table (this will be one of
## our final outputs)
gdp.mil90usd.scen.rgn.yr <-
bind_rows(gdp.mil90usd.SSP.rgn.yr, gdp.mil90usd.gSSP.rgn.yr)
## Construct a table of population by scenario, region, and year. We have a
## table of historical population, and a table of future population by
## scenario, both in wide form. Convert to long form and filter to the years
## we need. Add a scenario column to historical years, and combine the
## whole thing into a single table.
pop.thous.fut <-
rename(L101.Pop_thous_Scen_R_Yfut, population = value) %>%
filter(year %in% FUTURE_YEARS)
pop.thous.hist <-
rename(L101.Pop_thous_R_Yh, population = value) %>%
filter(year %in% HISTORICAL_YEARS) %>%
tidyr::crossing(scenario = unique(pop.thous.fut[['scenario']]))
pop.thous.scen.rgn.yr <-
bind_rows(pop.thous.hist, pop.thous.fut) %>%
mutate(year = as.integer(year),
population = as.numeric(population)) %>%
select(scenario, GCAM_region_ID, year, population)
## calculate per-capita GDP. This is another final output
pcgdp.thous90usd.scen.rgn.yr <-
left_join(gdp.mil90usd.scen.rgn.yr, pop.thous.scen.rgn.yr,
by = c('scenario', 'GCAM_region_ID', 'year')) %>%
mutate(pcgdp = gdp / population) %>%
select(scenario, GCAM_region_ID, year, pcgdp)
## Calculate the PPP-MER conversion factor in base year for each region.
## Our PPP values are in billions of 2005$, so we make that conversion
## here too.
#kbn 2020-03-26 Using model final base year here below
PPP.MER.baseyr <- MODEL_FINAL_BASE_YEAR
mer.rgn <- gdp_mil90usd_ctry %>%
filter(year == PPP.MER.baseyr) %>%
group_by(GCAM_region_ID) %>%
mutate(MER = gdp * gdp_deflator(2005, 1990) * CONV_MIL_BIL) %>%
summarise(MER = sum(MER))
## columns: GCAM_region_ID, MER
## The future data is given by SSP scenario, but the final table is scenario
## independent, as it should be, since this base year is meant to be a
## historical year. Likewise, the GDP in the PPP/MER base year should also
## be scenario-independent, and mostly it is, except for the region containing
## the Palestinian Territories, which has four slightly different values across
## the 5 SSPs. (It's SSP 2 and 4 that are the same). The value
## actually used in the old data system is the one for SSP1, so that's the
## one we'll use here. Arguably we should average the values over the 5
## scenarios, but the differences are only 1 part in 10^4, so we can just
## let it slide.
ppp.rgn <- gdp_bilusd_rgn_Yfut %>%
ungroup %>%
filter(year == PPP.MER.baseyr, scenario == 'SSP1') %>%
rename(PPP = gdp) %>%
select(GCAM_region_ID, PPP)
ppp.mer.rgn <-
left_join_error_no_match(mer.rgn, ppp.rgn, by = 'GCAM_region_ID') %>%
mutate(PPP_MER = PPP / MER)
# GDP by GCAM region from GCAM 3.0 GDPs.
# Downscaling GCAM 3.0 GDP by GCAM 3.0 region to countries, using SSP2 GDP scenario
# GDP by GCAM 3.0 region - downscale to country according to actual shares in the historical periods, and SSPbase in the future periods
# Future GDP
gdp_bilusd_ctry_Yfut <- SSP_database_v9 %>%
filter(MODEL == 'OECD Env-Growth' & VARIABLE == 'GDP|PPP') %>%
standardize_iso('REGION') %>%
change_iso_code('rou', 'rom') %>%
mutate(scenario = substr(SCENARIO, 1, 4)) %>%
# Only Base SSP
filter(scenario == socioeconomics.BASE_POP_SCEN) %>%
gather_years %>%
mutate(value = as.numeric(value)) %>%
select(iso, year, value) %>%
complete(nesting(iso), year = c(socioeconomics.FINAL_HIST_YEAR, FUTURE_YEARS)) %>%
group_by(iso) %>%
mutate(value = approx_fun(year, value)) %>%
ungroup() %>%
filter(year %in% FUTURE_YEARS)
# Historical GDP
gdp_mil90usd_ctry_Yh <- L100.gdp_mil90usd_ctry_Yh %>%
filter(year %in% HISTORICAL_YEARS,
iso %in% gdp_bilusd_ctry_Yfut$iso)
gdp_mil90usd_ctry_Yh <- gdp_mil90usd_ctry_Yh %>%
# Only take future GDP if iso has historical GDP
bind_rows(gdp_bilusd_ctry_Yfut %>%
filter(iso %in% gdp_mil90usd_ctry_Yh$iso)) %>%
left_join_error_no_match(iso_GCAM_regID %>%
select(iso, region_GCAM3), by = "iso")
# Aggregating GDP by GCAM3 region
gdp_mil90usd_SSPbase_RG3_Y <- gdp_mil90usd_ctry_Yh %>%
group_by(year, region_GCAM3) %>%
summarise(agg_val = sum(value))
# Calculate shares of each country within its region
gdpshares_ctryRG3_Y <- gdp_mil90usd_ctry_Yh %>%
group_by(year, region_GCAM3) %>%
mutate(share = value / sum(value)) %>%
ungroup()
# Interpolate GCAM3 GDP data to all historical and future years
gdp_mil90usd_GCAM3_RG3_Y <- iso_GCAM_regID %>%
select(region_GCAM3) %>%
distinct() %>%
repeat_add_columns(tibble(year = as.integer(c(HISTORICAL_YEARS, FUTURE_YEARS)))) %>%
left_join(GCAM3_GDP, by = c("region_GCAM3", "year")) %>%
# Add in historical values to match in for NA values
left_join_error_no_match(gdp_mil90usd_SSPbase_RG3_Y, by = c("region_GCAM3", "year")) %>%
group_by(region_GCAM3) %>%
# Extending GCAM 3.0 scenario to first historical year using historical GDP ratios by GCAM 3.0 region
mutate(value = replace(value, year == min(year),
value[year == min(GCAM3_GDP$year)] * agg_val[year == min(year)] / agg_val[year == min(GCAM3_GDP$year)]),
value = approx_fun(year, value, rule = 1)) %>%
ungroup() %>%
select(region_GCAM3, year, GCAM3_value = value)
# Multiply these GDP numbers by the shares of each country within GCAM region
gdp_mil90usd_GCAM3_ctry_Y <- gdpshares_ctryRG3_Y %>%
left_join_error_no_match(gdp_mil90usd_GCAM3_RG3_Y, by = c("year", "region_GCAM3")) %>%
transmute(iso, year, value = share * GCAM3_value)
# rebasing GDP to 2010 USD at country level
# Calculate the GDP ratios from the first year in the projections. Use this ratio to project GDP from historical dataset in final historical period
gdpRatio_GCAM3_ctry_Yfut <- gdp_mil90usd_GCAM3_ctry_Y %>%
group_by(iso) %>%
mutate(base = value[year == socioeconomics.FINAL_HIST_YEAR]) %>%
ungroup() %>%
filter(year %in% FUTURE_YEARS) %>%
transmute(iso, year, ratio = value / base)
# Use these ratios to build the GDP trajectories by old GCAM3
gdp_mil90usd_GCAM3_ctry_Y <- gdp_mil90usd_ctry_Yh %>%
left_join(gdpRatio_GCAM3_ctry_Yfut, by = c("iso", "year")) %>%
group_by(iso) %>%
mutate(value = if_else(year %in% FUTURE_YEARS, value[year == socioeconomics.FINAL_HIST_YEAR] * ratio, value)) %>%
ungroup() %>%
select(iso, year, value)
# Aggregating by GCAM4 region
gdp_mil90usd_GCAM3_R_Y <- gdp_mil90usd_GCAM3_ctry_Y %>%
left_join_error_no_match(iso_region32_lookup, by = "iso") %>%
group_by(GCAM_region_ID, year) %>%
summarise(value = sum(value)) %>%
ungroup()
# Calculate per-capita GDP
pcgdp_thous90USD_GCAM3_R_Y <- gdp_mil90usd_GCAM3_R_Y %>%
left_join_error_no_match(L101.Pop_thous_GCAM3_R_Y, by = c("year", "GCAM_region_ID")) %>%
transmute(GCAM_region_ID, year, value = value.x / value.y)
pcgdp_thous90USD_GCAM3_ctry_Y <- gdp_mil90usd_GCAM3_ctry_Y %>%
left_join_error_no_match(L101.Pop_thous_GCAM3_ctry_Y, by = c("year", "iso")) %>%
transmute(iso, year, value = value.x / value.y)
## Produce outputs
gdp.mil90usd.scen.rgn.yr %>%
ungroup %>%
filter(year %in% c(HISTORICAL_YEARS, FUTURE_YEARS)) %>%
rename(value = gdp) %>%
mutate(year = as.integer(year)) %>%
add_title("Gross Domestic Product (GDP) by scenario, region, and year.") %>%
add_units("Millions of 1990 USD (MER)") %>%
add_comments("For the SSP scenarios, SSP GDP projections are scaled to match ") %>%
add_comments("historical values in the base year (2010). For the gSSP scenarios ") %>%
add_comments("IMF growth projections are applied from 2010-2020, and the SSP projections ") %>%
add_comments("are scaled to match the 2020 values resulting from this process.") %>%
add_legacy_name("L102.gdp_mil90usd_Scen_R_Y") %>%
add_precursors("common/iso_GCAM_regID",
"socioeconomics/SSP_database_v9",
"socioeconomics/IMF_GDP_growth",
"L100.gdp_mil90usd_ctry_Yh") ->
L102.gdp_mil90usd_Scen_R_Y
pcgdp.thous90usd.scen.rgn.yr %>%
ungroup %>%
filter(year %in% c(HISTORICAL_YEARS, FUTURE_YEARS)) %>%
rename(value = pcgdp) %>%
add_title("Gross Domestic Product (GDP) per capita, by scenario, region, and year.") %>%
add_units("Thousands of 1990 USD (MER)") %>%
add_comments("Computed as GDP/population. Values prior to the base year (2010) are ") %>%
add_comments("historical; values subsequent are from SSP projections.") %>%
add_legacy_name("L102.pcgdp_thous90USD_Scen_R_Y") %>%
add_precursors("common/iso_GCAM_regID",
"socioeconomics/SSP_database_v9",
"socioeconomics/IMF_GDP_growth",
"L100.gdp_mil90usd_ctry_Yh",
"L101.Pop_thous_R_Yh",
"L101.Pop_thous_Scen_R_Yfut") ->
L102.pcgdp_thous90USD_Scen_R_Y
ppp.mer.rgn %>%
add_title("Purchasing Power Parity (PPP) to Market Exchange Rate (MER) GDP conversions, by region") %>%
add_units("unitless") %>%
add_comments("Calculated as GDP(PPP) / GDP(MER) in the base year (2010) for each region. The") %>%
add_comments("table also contains PPP-GDP and MER-GDP in billions of 2005 USD for the base year, ") %>%
add_comments("because they were included in the original table, but I'm not sure they are used for, ") %>%
add_comments("or useful for, anything besides calculating the ratio.") %>%
add_legacy_name("L102.PPP_MER_R") %>%
add_precursors("common/iso_GCAM_regID",
"socioeconomics/SSP_database_v9",
"socioeconomics/IMF_GDP_growth",
"L100.gdp_mil90usd_ctry_Yh") ->
L102.PPP_MER_R
gdp_mil90usd_GCAM3_R_Y %>%
add_title("GDP by GCAM3 Region") %>%
add_units("Million 1990 USD") %>%
add_comments("Scales historical SSP GDP data to GCAM3 GDP data.") %>%
add_comments("Calculates future GDP based on ratio of GCAM3 future to 2010 value.") %>%
add_legacy_name("L102.gdp_mil90usd_GCAM3_R_Y") %>%
add_precursors("common/iso_GCAM_regID",
"socioeconomics/SSP_database_v9",
"L100.gdp_mil90usd_ctry_Yh",
"socioeconomics/GCAM3_GDP") ->
L102.gdp_mil90usd_GCAM3_R_Y
gdp_mil90usd_GCAM3_ctry_Y %>%
add_title("GCAM3 GDP by Country") %>%
add_units("Million 1990 USD") %>%
add_comments("Scales historical SSP GDP data to GCAM3 GDP data.") %>%
add_comments("Calculates future GDP based on ratio of GCAM3 future to 2010 value.") %>%
add_legacy_name("L102.gdp_mil90usd_GCAM3_ctry_Y") %>%
add_precursors("common/iso_GCAM_regID",
"socioeconomics/SSP_database_v9",
"L100.gdp_mil90usd_ctry_Yh",
"socioeconomics/GCAM3_GDP") ->
L102.gdp_mil90usd_GCAM3_ctry_Y
pcgdp_thous90USD_GCAM3_R_Y %>%
add_title("Per-Capita GDP by GCAM3 Region") %>%
add_units("Thousand 1990 USD") %>%
add_comments("L102.gdp_mil90usd_GCAM3_R_Y divided by population from L101.Pop_thous_GCAM3_R_Y") %>%
add_legacy_name("L102.pcgdp_thous90USD_GCAM3_R_Y") %>%
add_precursors("common/iso_GCAM_regID",
"socioeconomics/SSP_database_v9",
"L100.gdp_mil90usd_ctry_Yh",
"socioeconomics/GCAM3_GDP",
"L101.Pop_thous_GCAM3_R_Y") ->
L102.pcgdp_thous90USD_GCAM3_R_Y
pcgdp_thous90USD_GCAM3_ctry_Y %>%
add_title("GCAM3 Per-Capita GDP by Country") %>%
add_units("Thousand 1990 USD") %>%
add_comments("L102.gdp_mil90usd_GCAM3_ctry_Y divided by population from L101.Pop_thous_GCAM3_ctry_Y") %>%
add_legacy_name("L102.pcgdp_thous90USD_GCAM3_ctry_Y") %>%
add_precursors("common/iso_GCAM_regID",
"socioeconomics/SSP_database_v9",
"L100.gdp_mil90usd_ctry_Yh",
"socioeconomics/GCAM3_GDP",
"L101.Pop_thous_GCAM3_ctry_Y") ->
L102.pcgdp_thous90USD_GCAM3_ctry_Y
return_data(L102.gdp_mil90usd_Scen_R_Y, L102.pcgdp_thous90USD_Scen_R_Y, L102.PPP_MER_R, L102.gdp_mil90usd_GCAM3_R_Y, L102.gdp_mil90usd_GCAM3_ctry_Y, L102.pcgdp_thous90USD_GCAM3_R_Y, L102.pcgdp_thous90USD_GCAM3_ctry_Y)
} else {
stop("Unknown command")
}
}
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