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Commit eefbd8be authored by Ingram Jaccard's avatar Ingram Jaccard
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analysis/preprocessing/full_code.Rmd
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...@@ -7481,6 +7481,8 @@ write_rds(results_formatted_with_direct_FD_fp, paste0(data_dir_income_stratified ...@@ -7481,6 +7481,8 @@ write_rds(results_formatted_with_direct_FD_fp, paste0(data_dir_income_stratified
# European expenditure deciles # European expenditure deciles
- need to download EUROSTAT households and Norway households
```{r european-expenditure-deciles, eval = FALSE} ```{r european-expenditure-deciles, eval = FALSE}
# set target number of quantiles # set target number of quantiles
...@@ -7488,7 +7490,7 @@ target_eu_ntiles = 10 ...@@ -7488,7 +7490,7 @@ target_eu_ntiles = 10
##### main paper results (main paper method, EXIOBASE industry-by-industry version) ##### main paper results (main paper method, EXIOBASE industry-by-industry version)
# 1) load income-stratified-footprints formatted results file # 1) load income-stratified-footprints formatted results file from previous code chunk
dat_results_raw = read_rds(here("analysis", "preprocessing", "income-stratified-footprints", dat_results_raw = read_rds(here("analysis", "preprocessing", "income-stratified-footprints",
"results_formatted_method1_ixi.rds")) %>% "results_formatted_method1_ixi.rds")) %>%
ungroup() %>% ungroup() %>%
...@@ -7498,7 +7500,7 @@ dat_results_raw = read_rds(here("analysis", "preprocessing", "income-stratified- ...@@ -7498,7 +7500,7 @@ dat_results_raw = read_rds(here("analysis", "preprocessing", "income-stratified-
# get iso3 country codes to join with household data # get iso3 country codes to join with household data
country_codes = ISOcodes::ISO_3166_1 %>% country_codes = ISOcodes::ISO_3166_1 %>%
select(iso2 = Alpha_2, iso3 = Alpha_3) %>% select(iso2 = Alpha_2, iso3 = Alpha_3) %>%
# resolve inconsistency between Eurostat and ISO for Greece and UK/Great Britain # resolve inconsistency between EUROSTAT and ISO for Greece and UK/Great Britain
mutate(iso2 = if_else(iso2=="GR", "EL", iso2)) %>% mutate(iso2 = if_else(iso2=="GR", "EL", iso2)) %>%
mutate(iso2 = if_else(iso2=="GB", "UK", iso2)) mutate(iso2 = if_else(iso2=="GB", "UK", iso2))
...@@ -7618,7 +7620,7 @@ df_expenditure_long = read_csv(here("analysis", ...@@ -7618,7 +7620,7 @@ df_expenditure_long = read_csv(here("analysis",
select(-mean_expenditure) %>% select(-mean_expenditure) %>%
ungroup() ungroup()
## Calculate adult equivalents per household ## calculate adult equivalents per household
df_adult_e_p_hh = df_expenditure_long %>% df_adult_e_p_hh = df_expenditure_long %>%
rename(iso2 = geo) %>% rename(iso2 = geo) %>%
pivot_wider(id_cols = c(iso2, year, quintile, imputed), pivot_wider(id_cols = c(iso2, year, quintile, imputed),
...@@ -7630,7 +7632,6 @@ df_adult_e_p_hh = df_expenditure_long %>% ...@@ -7630,7 +7632,6 @@ df_adult_e_p_hh = df_expenditure_long %>%
mutate(iso3 = if_else(iso2 == "XK", "XKX", iso3), mutate(iso3 = if_else(iso2 == "XK", "XKX", iso3),
quint = parse_number(quintile)) quint = parse_number(quintile))
## add quintile population data ## add quintile population data
mrio_results_with_adult_eq_all = dat_results_raw %>% mrio_results_with_adult_eq_all = dat_results_raw %>%
filter(year %in% c(2005, 2010, 2015)) %>% filter(year %in% c(2005, 2010, 2015)) %>%
...@@ -7644,8 +7645,7 @@ mrio_results_with_adult_eq_all = dat_results_raw %>% ...@@ -7644,8 +7645,7 @@ mrio_results_with_adult_eq_all = dat_results_raw %>%
mutate(ae_quintile = hh_quintile * adult_e_p_hh) %>% mutate(ae_quintile = hh_quintile * adult_e_p_hh) %>%
select(-c(hh_quintile, adult_e_p_hh)) select(-c(hh_quintile, adult_e_p_hh))
## for the European expenditure deciles we use only countries with data in 2005, 2010 and 2015. This excludes Luxembourg and Italy.
## for the european expenditure deciles we use only countries with data in 2005, 2010 and 2015. This excludes Luxembourg and Italy.
complete_countries = mrio_results_with_adult_eq_all %>% complete_countries = mrio_results_with_adult_eq_all %>%
group_by(year, iso2) %>% group_by(year, iso2) %>%
summarise(co2_kg = sum(co2_kg)) %>% summarise(co2_kg = sum(co2_kg)) %>%
...@@ -7661,8 +7661,8 @@ df_adult_e_p_hh %>% ...@@ -7661,8 +7661,8 @@ df_adult_e_p_hh %>%
filter(iso2 %in% complete_countries, year<=2015, year>=2005) %>% filter(iso2 %in% complete_countries, year<=2015, year>=2005) %>%
mutate(quint = parse_number(quintile)) mutate(quint = parse_number(quintile))
# calculate EU expenditure tiles based on loaded mrio result file and adult equivalents. # calculate European expenditure deciles based on loaded income-stratified-footprints result file and adult equivalents.
# returns country quintiles mapped to EU ntile rank and EU ntile boundaries # returns country quintiles mapped to European ntile rank and European ntile boundaries
# helper function called by function below # helper function called by function below
calculate_eu_ntiles <- function(pyear, pquantile_count=10) { calculate_eu_ntiles <- function(pyear, pquantile_count=10) {
...@@ -7673,14 +7673,14 @@ calculate_eu_ntiles <- function(pyear, pquantile_count=10) { ...@@ -7673,14 +7673,14 @@ calculate_eu_ntiles <- function(pyear, pquantile_count=10) {
mutate(idx = 1:n(), mutate(idx = 1:n(),
eu_q_rank = 0) # later to be filled with euro quintile rank eu_q_rank = 0) # later to be filled with euro quintile rank
# total EU adult equivalents (of included countries) in year # total European adult equivalents (of included countries) in year
total_ae_in_year = sum(country_data_annual_sorted$ae_quintile) total_ae_in_year = sum(country_data_annual_sorted$ae_quintile)
# quantile target ae population # quantile target ae population
eu_decile_adult_eq = total_ae_in_year/pquantile_count eu_decile_adult_eq = total_ae_in_year/pquantile_count
# country quinitles must be split to allocate ae population accorting to eu quantile target ae population # country quintles must be split to allocate ae population according to eu quantile target ae population
# filtering by condition that cant be fulfilled is a lazy way to create an empty dataframe # filtering by condition that can't be fulfilled is a lazy way to create an empty dataframe
# of the same structure as country_data_annual_sorted # of the same structure as country_data_annual_sorted
additional_rows = country_data_annual_sorted %>% additional_rows = country_data_annual_sorted %>%
filter(year==1) filter(year==1)
...@@ -7688,9 +7688,8 @@ calculate_eu_ntiles <- function(pyear, pquantile_count=10) { ...@@ -7688,9 +7688,8 @@ calculate_eu_ntiles <- function(pyear, pquantile_count=10) {
# store quantile split values # store quantile split values
eu_quantile_boundaries = data.frame(euro_q_rank = 1:pquantile_count, p = 0) eu_quantile_boundaries = data.frame(euro_q_rank = 1:pquantile_count, p = 0)
## can't think of a non-loop way to do this, sorry # loops through the ordered dataset, assigns European quantile rank
## loops through the ordered dataset, assignes euro quantile rank # and splits quintiles where necessary
## and splits quintiles where necessary
eu_ae_current = 0 eu_ae_current = 0
euro_q_rank_current = 1 euro_q_rank_current = 1
for (row_idx in 1:nrow(country_data_annual_sorted)) { for (row_idx in 1:nrow(country_data_annual_sorted)) {
...@@ -7700,17 +7699,17 @@ calculate_eu_ntiles <- function(pyear, pquantile_count=10) { ...@@ -7700,17 +7699,17 @@ calculate_eu_ntiles <- function(pyear, pquantile_count=10) {
country_data_annual_sorted[row_idx, "eu_q_rank"] = euro_q_rank_current country_data_annual_sorted[row_idx, "eu_q_rank"] = euro_q_rank_current
} else { } else {
ae_diff = eu_decile_adult_eq - eu_ae_current ae_diff = eu_decile_adult_eq - eu_ae_current
## write rest of this eu decile (split country quintile) ## write rest of this European decile (split country quintile)
new_row = country_data_annual_sorted[row_idx, ] new_row = country_data_annual_sorted[row_idx, ]
new_row[1, "eu_q_rank"] = euro_q_rank_current new_row[1, "eu_q_rank"] = euro_q_rank_current
new_row[1, "ae_quintile"] = ae_diff new_row[1, "ae_quintile"] = ae_diff
## record eu quantile boundary ## record European quantile boundary
eu_quantile_boundaries[eu_quantile_boundaries$euro_q_rank==euro_q_rank_current, "p"] = eu_quantile_boundaries[eu_quantile_boundaries$euro_q_rank==euro_q_rank_current, "p"] =
country_data_annual_sorted[row_idx, "fd_pae_e"] country_data_annual_sorted[row_idx, "fd_pae_e"]
## put first part of population in overflow dataframe ## put first part of population in overflow dataframe
additional_rows = additional_rows %>% additional_rows = additional_rows %>%
bind_rows(new_row) bind_rows(new_row)
## classify rest of country quinitle population to next euro quantile ## classify rest of country quintile population to next European quantile
country_data_annual_sorted[row_idx, "ae_quintile"] = country_data_annual_sorted[row_idx, "ae_quintile"] =
country_data_annual_sorted[row_idx, "ae_quintile"] - (ae_diff+0.0001) country_data_annual_sorted[row_idx, "ae_quintile"] - (ae_diff+0.0001)
euro_q_rank_current = euro_q_rank_current + 1 euro_q_rank_current = euro_q_rank_current + 1
...@@ -7725,21 +7724,17 @@ calculate_eu_ntiles <- function(pyear, pquantile_count=10) { ...@@ -7725,21 +7724,17 @@ calculate_eu_ntiles <- function(pyear, pquantile_count=10) {
arrange(fd_pae_e, eu_q_rank) %>% arrange(fd_pae_e, eu_q_rank) %>%
mutate(idx = 1:n()) mutate(idx = 1:n())
#ad zeroth and nth quantile (min and max) # add zeroth and nth quantile (min and max)
eu_quantile_boundaries[pquantile_count, "p"] = max(country_data_eu_quantiles$fd_pae_e) eu_quantile_boundaries[pquantile_count, "p"] = max(country_data_eu_quantiles$fd_pae_e)
#tmp = data.frame(euro_q_rank = 0, p = min(country_data_eu_quantiles$fd_pae_e)) %>%
# bind_rows(eu_quantile_boundaries)
list("df_q_data" = country_data_eu_quantiles, "df_q_boundaries" = eu_quantile_boundaries) list("df_q_data" = country_data_eu_quantiles, "df_q_boundaries" = eu_quantile_boundaries)
} }
# maps MRIO results to EU ntile ranks, returns mapping and ntile EU boundaries # maps income-stratified-footprint results to European ntile ranks, returns mapping and ntile European boundaries
map_mrio_results_to_eu_ntiles <- function(pyear, ptarget_ntiles) { map_mrio_results_to_eu_ntiles <- function(pyear, ptarget_ntiles) {
df_eu_ntiles = calculate_eu_ntiles(pyear, pquantile_count = ptarget_ntiles) df_eu_ntiles = calculate_eu_ntiles(pyear, pquantile_count = ptarget_ntiles)
df_eu_ntiles_data = df_eu_ntiles$df_q_data df_eu_ntiles_data = df_eu_ntiles$df_q_data
#df_eu_ntiles_p = df_eu_ntiles$df_q_boundaries
sector_mapping = mrio_results_with_adult_eq %>% sector_mapping = mrio_results_with_adult_eq %>%
group_by(sector_id) %>%# group_by(sector_id) %>%#
...@@ -7763,7 +7758,7 @@ map_mrio_results_to_eu_ntiles <- function(pyear, ptarget_ntiles) { ...@@ -7763,7 +7758,7 @@ map_mrio_results_to_eu_ntiles <- function(pyear, ptarget_ntiles) {
energy_use_europe_TJ, energy_use_europe_TJ,
ae_quintile) %>% ae_quintile) %>%
filter(year==pyear) %>% filter(year==pyear) %>%
# calc per adult aequivalent values in quintiles # calculate per adult equivalent values in quintiles
mutate(fd_pae_e = fd_me*1000000/ae_quintile, mutate(fd_pae_e = fd_me*1000000/ae_quintile,
co2_pae_kg = co2_kg/ae_quintile, co2_pae_kg = co2_kg/ae_quintile,
co2_pae_dom_kg = co2_domestic_kg/ae_quintile, co2_pae_dom_kg = co2_domestic_kg/ae_quintile,
...@@ -7789,7 +7784,7 @@ map_mrio_results_to_eu_ntiles <- function(pyear, ptarget_ntiles) { ...@@ -7789,7 +7784,7 @@ map_mrio_results_to_eu_ntiles <- function(pyear, ptarget_ntiles) {
full_join(df_eu_ntiles_data %>% full_join(df_eu_ntiles_data %>%
rename(fd_pae_e_quint_tmp = fd_pae_e), by=c("iso2", "quint")) %>% rename(fd_pae_e_quint_tmp = fd_pae_e), by=c("iso2", "quint")) %>%
rename(adult_eq = ae_quintile) %>% # country quintile and their split fraction population rename(adult_eq = ae_quintile) %>% # country quintile and their split fraction population
# recalc totals # recalculate totals
mutate(fd_me = fd_pae_e*adult_eq/1000000, mutate(fd_me = fd_pae_e*adult_eq/1000000,
co2_kg = co2_pae_kg*adult_eq, co2_kg = co2_pae_kg*adult_eq,
co2_dom_kg = co2_pae_dom_kg*adult_eq, co2_dom_kg = co2_pae_dom_kg*adult_eq,
...@@ -7807,24 +7802,22 @@ map_mrio_results_to_eu_ntiles <- function(pyear, ptarget_ntiles) { ...@@ -7807,24 +7802,22 @@ map_mrio_results_to_eu_ntiles <- function(pyear, ptarget_ntiles) {
} }
# filter only countries with complete info for years 2005, 2010, 2015
### Filter only countries with complete info for years 2005, 2010, 2015
mrio_results_with_adult_eq = mrio_results_with_adult_eq_all %>% mrio_results_with_adult_eq = mrio_results_with_adult_eq_all %>%
filter(iso2 %in% complete_countries) filter(iso2 %in% complete_countries)
## summarize final demand per adult equvalent per quintile across all sectors as basis for eurodeciles for complete countries # summarize final demand per adult equivalent per quintile across all sectors as basis for European deciles for complete countries
summary_country_fd = mrio_results_with_adult_eq %>% summary_country_fd = mrio_results_with_adult_eq %>%
group_by(iso2, year, quint) %>% group_by(iso2, year, quint) %>%
summarise(ae_quintile = first(ae_quintile), summarise(ae_quintile = first(ae_quintile),
fd_pae_e = sum(fd_me*1000000)/(ae_quintile)) fd_pae_e = sum(fd_me*1000000)/(ae_quintile))
## summarize final demand per adult equvalent per quintile across all sectors as basis for eurodeciles for all countries # summarize final demand per adult equivalent per quintile across all sectors as basis for European deciles for all countries
summary_country_fd_all = mrio_results_with_adult_eq_all %>% summary_country_fd_all = mrio_results_with_adult_eq_all %>%
group_by(iso2, year, quint) %>% group_by(iso2, year, quint) %>%
summarise(ae_quintile = first(ae_quintile), summarise(ae_quintile = first(ae_quintile),
fd_pae_e = sum(fd_me*1000000)/(ae_quintile)) fd_pae_e = sum(fd_me*1000000)/(ae_quintile))
df_mapped_result_2005 = map_mrio_results_to_eu_ntiles(2005, target_eu_ntiles) df_mapped_result_2005 = map_mrio_results_to_eu_ntiles(2005, target_eu_ntiles)
df_mapped_result_2005_data = df_mapped_result_2005$df_mapped_data df_mapped_result_2005_data = df_mapped_result_2005$df_mapped_data
df_mapped_result_2005_ntiles = df_mapped_result_2005$df_ntile_boundaries df_mapped_result_2005_ntiles = df_mapped_result_2005$df_ntile_boundaries
...@@ -7844,19 +7837,20 @@ df_mapped_result_data = df_mapped_result_2005_data %>% ...@@ -7844,19 +7837,20 @@ df_mapped_result_data = df_mapped_result_2005_data %>%
write_csv(df_mapped_result_data, write_csv(df_mapped_result_data,
here(paste0("analysis/data/derived/mrio_results_eu_ntile_mapped_n_", target_eu_ntiles, ".csv"))) here(paste0("analysis/data/derived/mrio_results_eu_ntile_mapped_n_", target_eu_ntiles, ".csv")))
###### using EXIOBASE product-by-product version
# 1) load MRIO result file ###### SI results, main paper method, EXIOBASE product-by-product version
# 1) load income-stratified-footprints formatted results file
dat_results_raw = read_rds(here("analysis", "preprocessing", "income-stratified-footprints", dat_results_raw = read_rds(here("analysis", "preprocessing", "income-stratified-footprints",
"results_formatted_method1_pxp.rds")) %>% "results_formatted_method1_pxp.rds")) %>%
ungroup() %>% ungroup() %>%
mutate(year= strtoi(year)) %>% mutate(year= strtoi(year)) %>%
rename(iso2 = geo) rename(iso2 = geo)
# get iso3 country codes to join with hh data # get iso3 country codes to join with household data
country_codes = ISOcodes::ISO_3166_1 %>% country_codes = ISOcodes::ISO_3166_1 %>%
select(iso2 = Alpha_2, iso3 = Alpha_3) %>% select(iso2 = Alpha_2, iso3 = Alpha_3) %>%
# resolve inconsistency between Eurostat and ISO for Greece and UK/Great Britain # resolve inconsistency between EUROSTAT and ISO for Greece and UK/Great Britain
mutate(iso2 = if_else(iso2=="GR", "EL", iso2)) %>% mutate(iso2 = if_else(iso2=="GR", "EL", iso2)) %>%
mutate(iso2 = if_else(iso2=="GB", "UK", iso2)) mutate(iso2 = if_else(iso2=="GB", "UK", iso2))
...@@ -7949,7 +7943,7 @@ write_csv(total_private_households, here("/analysis/preprocessing/income-stratif ...@@ -7949,7 +7943,7 @@ write_csv(total_private_households, here("/analysis/preprocessing/income-stratif
## return to 2 digits per value ## return to 2 digits per value
options(digits=2) options(digits=2)
# 2) load Eurostat household data # 2) load merged total private households data
hh_data = read_csv(here("analysis", "preprocessing", "income-stratified-footprints", hh_data = read_csv(here("analysis", "preprocessing", "income-stratified-footprints",
"total_private_households.csv")) %>% "total_private_households.csv")) %>%
mutate(imputed = if_else(is.na(total_private_households), TRUE, FALSE)) %>% mutate(imputed = if_else(is.na(total_private_households), TRUE, FALSE)) %>%
...@@ -7960,7 +7954,7 @@ hh_data = read_csv(here("analysis", "preprocessing", "income-stratified-footprin ...@@ -7960,7 +7954,7 @@ hh_data = read_csv(here("analysis", "preprocessing", "income-stratified-footprin
left_join(country_codes, by="iso2") %>% left_join(country_codes, by="iso2") %>%
select(-total_private_households) select(-total_private_households)
#3) Eurostat mean expenditures per household income quintile per household and per adult equivalent #3) load EUROSTAT mean expenditures per household income quintile per household and per adult equivalent (written and saved in the previous, income-stratified-footprints code chunk)
df_expenditure_long = read_csv(here("analysis", "preprocessing", "income-stratified-footprints", df_expenditure_long = read_csv(here("analysis", "preprocessing", "income-stratified-footprints",
"mean_expenditure_by_quintile_long.csv"), "mean_expenditure_by_quintile_long.csv"),
na = ":") %>% na = ":") %>%
...@@ -7974,7 +7968,7 @@ df_expenditure_long = read_csv(here("analysis", "preprocessing", "income-stratif ...@@ -7974,7 +7968,7 @@ df_expenditure_long = read_csv(here("analysis", "preprocessing", "income-stratif
select(-mean_expenditure) %>% select(-mean_expenditure) %>%
ungroup() ungroup()
## Calculate adult equivalents per household ## calculate adult equivalents per household
df_adult_e_p_hh = df_expenditure_long %>% df_adult_e_p_hh = df_expenditure_long %>%
rename(iso2 = geo) %>% rename(iso2 = geo) %>%
pivot_wider(id_cols = c(iso2, year, quintile, imputed), pivot_wider(id_cols = c(iso2, year, quintile, imputed),
...@@ -7986,7 +7980,7 @@ df_adult_e_p_hh = df_expenditure_long %>% ...@@ -7986,7 +7980,7 @@ df_adult_e_p_hh = df_expenditure_long %>%
mutate(iso3 = if_else(iso2 == "XK", "XKX", iso3), mutate(iso3 = if_else(iso2 == "XK", "XKX", iso3),
quint = parse_number(quintile)) quint = parse_number(quintile))
# add quintile population data ## add quintile population data
mrio_results_with_adult_eq_all = dat_results_raw %>% mrio_results_with_adult_eq_all = dat_results_raw %>%
filter(year %in% c(2005, 2010, 2015)) %>% filter(year %in% c(2005, 2010, 2015)) %>%
left_join(hh_data, by=c("iso2", "year")) %>% left_join(hh_data, by=c("iso2", "year")) %>%
...@@ -7999,8 +7993,7 @@ mrio_results_with_adult_eq_all = dat_results_raw %>% ...@@ -7999,8 +7993,7 @@ mrio_results_with_adult_eq_all = dat_results_raw %>%
mutate(ae_quintile = hh_quintile * adult_e_p_hh) %>% mutate(ae_quintile = hh_quintile * adult_e_p_hh) %>%
select(-c(hh_quintile, adult_e_p_hh)) select(-c(hh_quintile, adult_e_p_hh))
## for the European expenditure deciles we use only countries with data in 2005, 2010 and 2015. This excludes Luxembourg and Italy.
#### ONLY COUNTRIES THAT HAVE DATA FOR 2005, 2010, and 2015
complete_countries = mrio_results_with_adult_eq_all %>% complete_countries = mrio_results_with_adult_eq_all %>%
group_by(year, iso2) %>% group_by(year, iso2) %>%
summarise(co2_kg = sum(co2_kg)) %>% summarise(co2_kg = sum(co2_kg)) %>%
...@@ -8016,8 +8009,8 @@ df_adult_e_p_hh %>% ...@@ -8016,8 +8009,8 @@ df_adult_e_p_hh %>%
filter(iso2 %in% complete_countries, year<=2015, year>=2005) %>% filter(iso2 %in% complete_countries, year<=2015, year>=2005) %>%
mutate(quint = parse_number(quintile)) mutate(quint = parse_number(quintile))
# calculate EU expenditure tiles based on loaded mrio result file and adult equivalents. # calculate European expenditure deciles based on loaded income-stratified-footprints result file and adult equivalents.
# returns country quintiles mapped to EU ntile rank and EU ntile boundaries # returns country quintiles mapped to European ntile rank and European ntile boundaries
# helper function called by function below # helper function called by function below
calculate_eu_ntiles <- function(pyear, pquantile_count=10) { calculate_eu_ntiles <- function(pyear, pquantile_count=10) {
...@@ -8028,13 +8021,13 @@ calculate_eu_ntiles <- function(pyear, pquantile_count=10) { ...@@ -8028,13 +8021,13 @@ calculate_eu_ntiles <- function(pyear, pquantile_count=10) {
mutate(idx = 1:n(), mutate(idx = 1:n(),
eu_q_rank = 0) # later to be filled with euro quintile rank eu_q_rank = 0) # later to be filled with euro quintile rank
# total EU adult equivalents (of included countries) in year # total European adult equivalents (of included countries) in year
total_ae_in_year = sum(country_data_annual_sorted$ae_quintile) total_ae_in_year = sum(country_data_annual_sorted$ae_quintile)
# quantile target ae population # quantile target ae population
eu_decile_adult_eq = total_ae_in_year/pquantile_count eu_decile_adult_eq = total_ae_in_year/pquantile_count
# country quinitles must be split to allocate ae population accorting to eu quantile target ae population # country quintiles must be split to allocate ae population according to European quantile target ae population
# filtering by condition that cant be fulfilled is a lazy way to create an empty dataframe # filtering by condition that cant be fulfilled is a lazy way to create an empty dataframe
# of the same structure as country_data_annual_sorted # of the same structure as country_data_annual_sorted
additional_rows = country_data_annual_sorted %>% additional_rows = country_data_annual_sorted %>%
...@@ -8043,9 +8036,8 @@ calculate_eu_ntiles <- function(pyear, pquantile_count=10) { ...@@ -8043,9 +8036,8 @@ calculate_eu_ntiles <- function(pyear, pquantile_count=10) {
# store quantile split values # store quantile split values
eu_quantile_boundaries = data.frame(euro_q_rank = 1:pquantile_count, p = 0) eu_quantile_boundaries = data.frame(euro_q_rank = 1:pquantile_count, p = 0)
## can't think of a non-loop way to do this, sorry # loops through the ordered dataset, assignes euro quantile rank
## loops through the ordered dataset, assignes euro quantile rank # and splits quintiles where necessary
## and splits quintiles where necessary
eu_ae_current = 0 eu_ae_current = 0
euro_q_rank_current = 1 euro_q_rank_current = 1
for (row_idx in 1:nrow(country_data_annual_sorted)) { for (row_idx in 1:nrow(country_data_annual_sorted)) {
...@@ -8055,17 +8047,17 @@ calculate_eu_ntiles <- function(pyear, pquantile_count=10) { ...@@ -8055,17 +8047,17 @@ calculate_eu_ntiles <- function(pyear, pquantile_count=10) {
country_data_annual_sorted[row_idx, "eu_q_rank"] = euro_q_rank_current country_data_annual_sorted[row_idx, "eu_q_rank"] = euro_q_rank_current
} else { } else {
ae_diff = eu_decile_adult_eq - eu_ae_current ae_diff = eu_decile_adult_eq - eu_ae_current
## write rest of this eu decile (split country quintile) ## write rest of this European decile (split country quintile)
new_row = country_data_annual_sorted[row_idx, ] new_row = country_data_annual_sorted[row_idx, ]
new_row[1, "eu_q_rank"] = euro_q_rank_current new_row[1, "eu_q_rank"] = euro_q_rank_current
new_row[1, "ae_quintile"] = ae_diff new_row[1, "ae_quintile"] = ae_diff
## record eu quantile boundary ## record European quantile boundary
eu_quantile_boundaries[eu_quantile_boundaries$euro_q_rank==euro_q_rank_current, "p"] = eu_quantile_boundaries[eu_quantile_boundaries$euro_q_rank==euro_q_rank_current, "p"] =
country_data_annual_sorted[row_idx, "fd_pae_e"] country_data_annual_sorted[row_idx, "fd_pae_e"]
## put first part of population in overflow dataframe ## put first part of population in overflow dataframe
additional_rows = additional_rows %>% additional_rows = additional_rows %>%
bind_rows(new_row) bind_rows(new_row)
## classify rest of country quinitle population to next euro quantile ## classify rest of country quintile population to next European quantile
country_data_annual_sorted[row_idx, "ae_quintile"] = country_data_annual_sorted[row_idx, "ae_quintile"] =
country_data_annual_sorted[row_idx, "ae_quintile"] - (ae_diff+0.0001) country_data_annual_sorted[row_idx, "ae_quintile"] - (ae_diff+0.0001)
euro_q_rank_current = euro_q_rank_current + 1 euro_q_rank_current = euro_q_rank_current + 1
...@@ -8080,21 +8072,17 @@ calculate_eu_ntiles <- function(pyear, pquantile_count=10) { ...@@ -8080,21 +8072,17 @@ calculate_eu_ntiles <- function(pyear, pquantile_count=10) {
arrange(fd_pae_e, eu_q_rank) %>% arrange(fd_pae_e, eu_q_rank) %>%
mutate(idx = 1:n()) mutate(idx = 1:n())
#ad zeroth and nth quantile (min and max) # add zeroth and nth quantile (min and max)
eu_quantile_boundaries[pquantile_count, "p"] = max(country_data_eu_quantiles$fd_pae_e) eu_quantile_boundaries[pquantile_count, "p"] = max(country_data_eu_quantiles$fd_pae_e)
#tmp = data.frame(euro_q_rank = 0, p = min(country_data_eu_quantiles$fd_pae_e)) %>%
# bind_rows(eu_quantile_boundaries)
list("df_q_data" = country_data_eu_quantiles, "df_q_boundaries" = eu_quantile_boundaries) list("df_q_data" = country_data_eu_quantiles, "df_q_boundaries" = eu_quantile_boundaries)
} }
# maps MRIO results to EU ntile ranks, returns mapping and ntile EU boundaries # maps income-stratified-footprint results to European ntile ranks, returns mapping and ntile European boundaries
map_mrio_results_to_eu_ntiles <- function(pyear, ptarget_ntiles) { map_mrio_results_to_eu_ntiles <- function(pyear, ptarget_ntiles) {
df_eu_ntiles = calculate_eu_ntiles(pyear, pquantile_count = ptarget_ntiles) df_eu_ntiles = calculate_eu_ntiles(pyear, pquantile_count = ptarget_ntiles)
df_eu_ntiles_data = df_eu_ntiles$df_q_data df_eu_ntiles_data = df_eu_ntiles$df_q_data
#df_eu_ntiles_p = df_eu_ntiles$df_q_boundaries
sector_mapping = mrio_results_with_adult_eq %>% sector_mapping = mrio_results_with_adult_eq %>%
group_by(sector_id) %>%# group_by(sector_id) %>%#
...@@ -8118,7 +8106,7 @@ map_mrio_results_to_eu_ntiles <- function(pyear, ptarget_ntiles) { ...@@ -8118,7 +8106,7 @@ map_mrio_results_to_eu_ntiles <- function(pyear, ptarget_ntiles) {
energy_use_europe_TJ, energy_use_europe_TJ,
ae_quintile) %>% ae_quintile) %>%
filter(year==pyear) %>% filter(year==pyear) %>%
# calc per adult aequivalent values in quintiles # calculate per adult equivalent values in quintiles
mutate(fd_pae_e = fd_me*1000000/ae_quintile, mutate(fd_pae_e = fd_me*1000000/ae_quintile,
co2_pae_kg = co2_kg/ae_quintile, co2_pae_kg = co2_kg/ae_quintile,
co2_pae_dom_kg = co2_domestic_kg/ae_quintile, co2_pae_dom_kg = co2_domestic_kg/ae_quintile,
...@@ -8144,7 +8132,7 @@ map_mrio_results_to_eu_ntiles <- function(pyear, ptarget_ntiles) { ...@@ -8144,7 +8132,7 @@ map_mrio_results_to_eu_ntiles <- function(pyear, ptarget_ntiles) {
full_join(df_eu_ntiles_data %>% full_join(df_eu_ntiles_data %>%
rename(fd_pae_e_quint_tmp = fd_pae_e), by=c("iso2", "quint")) %>% rename(fd_pae_e_quint_tmp = fd_pae_e), by=c("iso2", "quint")) %>%
rename(adult_eq = ae_quintile) %>% # country quintile and their split fraction population rename(adult_eq = ae_quintile) %>% # country quintile and their split fraction population
# recalc totals # recalculate totals
mutate(fd_me = fd_pae_e*adult_eq/1000000, mutate(fd_me = fd_pae_e*adult_eq/1000000,
co2_kg = co2_pae_kg*adult_eq, co2_kg = co2_pae_kg*adult_eq,
co2_dom_kg = co2_pae_dom_kg*adult_eq, co2_dom_kg = co2_pae_dom_kg*adult_eq,
...@@ -8162,17 +8150,17 @@ map_mrio_results_to_eu_ntiles <- function(pyear, ptarget_ntiles) { ...@@ -8162,17 +8150,17 @@ map_mrio_results_to_eu_ntiles <- function(pyear, ptarget_ntiles) {
} }
### Filter only countries with complete info for years 2005, 2010, 2015 # filter only countries with complete info for years 2005, 2010, 2015
mrio_results_with_adult_eq = mrio_results_with_adult_eq_all %>% mrio_results_with_adult_eq = mrio_results_with_adult_eq_all %>%
filter(iso2 %in% complete_countries) filter(iso2 %in% complete_countries)
## summarize final demand per adult equvalent per quintile across all sectors as basis for eurodeciles for complete countries # summarize final demand per adult equivalent per quintile across all sectors as basis for European deciles for complete countries
summary_country_fd = mrio_results_with_adult_eq %>% summary_country_fd = mrio_results_with_adult_eq %>%
group_by(iso2, year, quint) %>% group_by(iso2, year, quint) %>%
summarise(ae_quintile = first(ae_quintile), summarise(ae_quintile = first(ae_quintile),
fd_pae_e = sum(fd_me*1000000)/(ae_quintile)) fd_pae_e = sum(fd_me*1000000)/(ae_quintile))
## summarize final demand per adult equvalent per quintile across all sectors as basis for eurodeciles for all countries # summarize final demand per adult equivalent per quintile across all sectors as basis for European deciles for all countries
summary_country_fd_all = mrio_results_with_adult_eq_all %>% summary_country_fd_all = mrio_results_with_adult_eq_all %>%
group_by(iso2, year, quint) %>% group_by(iso2, year, quint) %>%
summarise(ae_quintile = first(ae_quintile), summarise(ae_quintile = first(ae_quintile),
...@@ -8192,9 +8180,10 @@ df_mapped_result_data = df_mapped_result_2005_data %>% ...@@ -8192,9 +8180,10 @@ df_mapped_result_data = df_mapped_result_2005_data %>%
write_csv(df_mapped_result_data, write_csv(df_mapped_result_data,
here(paste0("analysis/data/derived/si/mrio_results_eu_ntile_mapped_n_", target_eu_ntiles, "_pxp.csv"))) here(paste0("analysis/data/derived/si/mrio_results_eu_ntile_mapped_n_", target_eu_ntiles, "_pxp.csv")))
###### alternative method, EXOIBASE industry-by-industry version
# 1) load MRIO result file ##### SI results, alternative method, EXOIBASE industry-by-industry version
# 1) load income-stratified-footprints formatted results file
dat_results_raw = read_rds(here("analysis", "preprocessing", "income-stratified-footprints", dat_results_raw = read_rds(here("analysis", "preprocessing", "income-stratified-footprints",
"results_formatted_method2_ixi.rds")) %>% "results_formatted_method2_ixi.rds")) %>%
ungroup() %>% ungroup() %>%
...@@ -8204,7 +8193,7 @@ dat_results_raw = read_rds(here("analysis", "preprocessing", "income-stratified- ...@@ -8204,7 +8193,7 @@ dat_results_raw = read_rds(here("analysis", "preprocessing", "income-stratified-
# get iso3 country codes to join with hh data # get iso3 country codes to join with hh data
country_codes = ISOcodes::ISO_3166_1 %>% country_codes = ISOcodes::ISO_3166_1 %>%
select(iso2 = Alpha_2, iso3 = Alpha_3) %>% select(iso2 = Alpha_2, iso3 = Alpha_3) %>%
# resolve inconsistency between Eurostat and ISO for Greece and UK/Great Britain # resolve inconsistency between EUROSTAT and ISO for Greece and UK/Great Britain
mutate(iso2 = if_else(iso2=="GR", "EL", iso2)) %>% mutate(iso2 = if_else(iso2=="GR", "EL", iso2)) %>%
mutate(iso2 = if_else(iso2=="GB", "UK", iso2)) mutate(iso2 = if_else(iso2=="GB", "UK", iso2))
...@@ -8297,7 +8286,7 @@ write_csv(total_private_households, here("/analysis/preprocessing/income-stratif ...@@ -8297,7 +8286,7 @@ write_csv(total_private_households, here("/analysis/preprocessing/income-stratif
## return to 2 digits per value ## return to 2 digits per value
options(digits=2) options(digits=2)
# 2) load Eurostat household data # 2) load EUROSTAT household data
hh_data = read_csv(here("analysis", "preprocessing", "income-stratified-footprints", hh_data = read_csv(here("analysis", "preprocessing", "income-stratified-footprints",
"total_private_households.csv")) %>% "total_private_households.csv")) %>%
mutate(imputed = if_else(is.na(total_private_households), TRUE, FALSE)) %>% mutate(imputed = if_else(is.na(total_private_households), TRUE, FALSE)) %>%
...@@ -8308,7 +8297,7 @@ hh_data = read_csv(here("analysis", "preprocessing", "income-stratified-footprin ...@@ -8308,7 +8297,7 @@ hh_data = read_csv(here("analysis", "preprocessing", "income-stratified-footprin
left_join(country_codes, by="iso2") %>% left_join(country_codes, by="iso2") %>%
select(-total_private_households) select(-total_private_households)
#3) Eurostat mean expenditures per household income quintile per household and per adult equivalent #3) EUROSTAT mean expenditures per household income quintile per household and per adult equivalent
df_expenditure_long = read_csv(here("analysis", "preprocessing", "income-stratified-footprints", df_expenditure_long = read_csv(here("analysis", "preprocessing", "income-stratified-footprints",
"mean_expenditure_by_quintile_long.csv"), "mean_expenditure_by_quintile_long.csv"),
na = ":") %>% na = ":") %>%
...@@ -8322,7 +8311,7 @@ df_expenditure_long = read_csv(here("analysis", "preprocessing", "income-stratif ...@@ -8322,7 +8311,7 @@ df_expenditure_long = read_csv(here("analysis", "preprocessing", "income-stratif
select(-mean_expenditure) %>% select(-mean_expenditure) %>%
ungroup() ungroup()
## Calculate adult equivalents per household ## calculate adult equivalents per household
df_adult_e_p_hh = df_expenditure_long %>% df_adult_e_p_hh = df_expenditure_long %>%
rename(iso2 = geo) %>% rename(iso2 = geo) %>%
pivot_wider(id_cols = c(iso2, year, quintile, imputed), pivot_wider(id_cols = c(iso2, year, quintile, imputed),
...@@ -8334,7 +8323,7 @@ df_adult_e_p_hh = df_expenditure_long %>% ...@@ -8334,7 +8323,7 @@ df_adult_e_p_hh = df_expenditure_long %>%
mutate(iso3 = if_else(iso2 == "XK", "XKX", iso3), mutate(iso3 = if_else(iso2 == "XK", "XKX", iso3),
quint = parse_number(quintile)) quint = parse_number(quintile))
# add quintile population data ## add quintile population data
mrio_results_with_adult_eq_all = dat_results_raw %>% mrio_results_with_adult_eq_all = dat_results_raw %>%
filter(year %in% c(2005, 2010, 2015)) %>% filter(year %in% c(2005, 2010, 2015)) %>%
left_join(hh_data, by=c("iso2", "year")) %>% left_join(hh_data, by=c("iso2", "year")) %>%
...@@ -8347,7 +8336,7 @@ mrio_results_with_adult_eq_all = dat_results_raw %>% ...@@ -8347,7 +8336,7 @@ mrio_results_with_adult_eq_all = dat_results_raw %>%
mutate(ae_quintile = hh_quintile * adult_e_p_hh) %>% mutate(ae_quintile = hh_quintile * adult_e_p_hh) %>%
select(-c(hh_quintile, adult_e_p_hh)) select(-c(hh_quintile, adult_e_p_hh))
#### ONLY COUNTRIES THAT HAVE DATA FOR 2005, 2010, and 2015 ## for the European expenditure deciles we use only countries with data in 2005, 2010 and 2015. This excludes Luxembourg and Italy.
complete_countries = mrio_results_with_adult_eq_all %>% complete_countries = mrio_results_with_adult_eq_all %>%
group_by(year, iso2) %>% group_by(year, iso2) %>%
summarise(co2_kg = sum(co2_kg)) %>% summarise(co2_kg = sum(co2_kg)) %>%
...@@ -8363,8 +8352,8 @@ df_adult_e_p_hh %>% ...@@ -8363,8 +8352,8 @@ df_adult_e_p_hh %>%
filter(iso2 %in% complete_countries, year<=2015, year>=2005) %>% filter(iso2 %in% complete_countries, year<=2015, year>=2005) %>%
mutate(quint = parse_number(quintile)) mutate(quint = parse_number(quintile))
# calculate EU expenditure tiles based on loaded mrio result file and adult equivalents. # calculate European expenditure deciles based on loaded income-stratified-footprints result file and adult equivalents.
# returns country quintiles mapped to EU ntile rank and EU ntile boundaries # returns country quintiles mapped to European ntile rank and European ntile boundaries
# helper function called by function below # helper function called by function below
calculate_eu_ntiles <- function(pyear, pquantile_count=10) { calculate_eu_ntiles <- function(pyear, pquantile_count=10) {
...@@ -8375,23 +8364,22 @@ calculate_eu_ntiles <- function(pyear, pquantile_count=10) { ...@@ -8375,23 +8364,22 @@ calculate_eu_ntiles <- function(pyear, pquantile_count=10) {
mutate(idx = 1:n(), mutate(idx = 1:n(),
eu_q_rank = 0) # later to be filled with euro quintile rank eu_q_rank = 0) # later to be filled with euro quintile rank
# total EU adult equivalents (of included countries) in year # total European adult equivalents (of included countries) in year
total_ae_in_year = sum(country_data_annual_sorted$ae_quintile) total_ae_in_year = sum(country_data_annual_sorted$ae_quintile)
# quantile target ae population # quantile target ae population
eu_decile_adult_eq = total_ae_in_year/pquantile_count eu_decile_adult_eq = total_ae_in_year/pquantile_count
# country quinitles must be split to allocate ae population accorting to eu quantile target ae population # country quintiles must be split to allocate ae population according to European quantile target ae population
# filtering by condition that cant be fulfilled is a lazy way to create an empty dataframe # filtering by condition that can't be fulfilled is a lazy way to create an empty dataframe
# of the same structure as country_data_annual_sorted # of the same structure as country_data_annual_sorted
additional_rows = country_data_annual_sorted %>% additional_rows = country_data_annual_sorted %>%
filter(year==1) filter(year==1)
# store quantile split values # store quantile split values
eu_quantile_boundaries = data.frame(euro_q_rank = 1:pquantile_count, p = 0) eu_quantile_boundaries = data.frame(euro_q_rank = 1:pquantile_count, p = 0)
## can't think of a non-loop way to do this, sorry ## loops through the ordered dataset, assigns European quantile rank
## loops through the ordered dataset, assignes euro quantile rank
## and splits quintiles where necessary ## and splits quintiles where necessary
eu_ae_current = 0 eu_ae_current = 0
euro_q_rank_current = 1 euro_q_rank_current = 1
...@@ -8402,17 +8390,17 @@ calculate_eu_ntiles <- function(pyear, pquantile_count=10) { ...@@ -8402,17 +8390,17 @@ calculate_eu_ntiles <- function(pyear, pquantile_count=10) {
country_data_annual_sorted[row_idx, "eu_q_rank"] = euro_q_rank_current country_data_annual_sorted[row_idx, "eu_q_rank"] = euro_q_rank_current
} else { } else {
ae_diff = eu_decile_adult_eq - eu_ae_current ae_diff = eu_decile_adult_eq - eu_ae_current
## write rest of this eu decile (split country quintile) ## write rest of this European decile (split country quintile)
new_row = country_data_annual_sorted[row_idx, ] new_row = country_data_annual_sorted[row_idx, ]
new_row[1, "eu_q_rank"] = euro_q_rank_current new_row[1, "eu_q_rank"] = euro_q_rank_current
new_row[1, "ae_quintile"] = ae_diff new_row[1, "ae_quintile"] = ae_diff
## record eu quantile boundary ## record European quantile boundary
eu_quantile_boundaries[eu_quantile_boundaries$euro_q_rank==euro_q_rank_current, "p"] = eu_quantile_boundaries[eu_quantile_boundaries$euro_q_rank==euro_q_rank_current, "p"] =
country_data_annual_sorted[row_idx, "fd_pae_e"] country_data_annual_sorted[row_idx, "fd_pae_e"]
## put first part of population in overflow dataframe ## put first part of population in overflow dataframe
additional_rows = additional_rows %>% additional_rows = additional_rows %>%
bind_rows(new_row) bind_rows(new_row)
## classify rest of country quinitle population to next euro quantile ## classify rest of country quintile population to next European quantile
country_data_annual_sorted[row_idx, "ae_quintile"] = country_data_annual_sorted[row_idx, "ae_quintile"] =
country_data_annual_sorted[row_idx, "ae_quintile"] - (ae_diff+0.0001) country_data_annual_sorted[row_idx, "ae_quintile"] - (ae_diff+0.0001)
euro_q_rank_current = euro_q_rank_current + 1 euro_q_rank_current = euro_q_rank_current + 1
...@@ -8427,21 +8415,17 @@ calculate_eu_ntiles <- function(pyear, pquantile_count=10) { ...@@ -8427,21 +8415,17 @@ calculate_eu_ntiles <- function(pyear, pquantile_count=10) {
arrange(fd_pae_e, eu_q_rank) %>% arrange(fd_pae_e, eu_q_rank) %>%
mutate(idx = 1:n()) mutate(idx = 1:n())
#ad zeroth and nth quantile (min and max) # add zeroth and nth quantile (min and max)
eu_quantile_boundaries[pquantile_count, "p"] = max(country_data_eu_quantiles$fd_pae_e) eu_quantile_boundaries[pquantile_count, "p"] = max(country_data_eu_quantiles$fd_pae_e)
#tmp = data.frame(euro_q_rank = 0, p = min(country_data_eu_quantiles$fd_pae_e)) %>%
# bind_rows(eu_quantile_boundaries)
list("df_q_data" = country_data_eu_quantiles, "df_q_boundaries" = eu_quantile_boundaries) list("df_q_data" = country_data_eu_quantiles, "df_q_boundaries" = eu_quantile_boundaries)
} }
# maps MRIO results to EU ntile ranks, returns mapping and ntile EU boundaries # maps income-stratified-footprint results to European ntile ranks, returns mapping and ntile European boundaries
map_mrio_results_to_eu_ntiles <- function(pyear, ptarget_ntiles) { map_mrio_results_to_eu_ntiles <- function(pyear, ptarget_ntiles) {
df_eu_ntiles = calculate_eu_ntiles(pyear, pquantile_count = ptarget_ntiles) df_eu_ntiles = calculate_eu_ntiles(pyear, pquantile_count = ptarget_ntiles)
df_eu_ntiles_data = df_eu_ntiles$df_q_data df_eu_ntiles_data = df_eu_ntiles$df_q_data
#df_eu_ntiles_p = df_eu_ntiles$df_q_boundaries
sector_mapping = mrio_results_with_adult_eq %>% sector_mapping = mrio_results_with_adult_eq %>%
group_by(coicop) %>%# group_by(coicop) %>%#
...@@ -8465,7 +8449,7 @@ map_mrio_results_to_eu_ntiles <- function(pyear, ptarget_ntiles) { ...@@ -8465,7 +8449,7 @@ map_mrio_results_to_eu_ntiles <- function(pyear, ptarget_ntiles) {
energy_use_europe_tj, energy_use_europe_tj,
ae_quintile) %>% ae_quintile) %>%
filter(year==pyear) %>% filter(year==pyear) %>%
# calc per adult aequivalent values in quintiles # calculate per adult equivalent values in quintiles
mutate(fd_pae_e = fd_me*1000000/ae_quintile, mutate(fd_pae_e = fd_me*1000000/ae_quintile,
co2_pae_kg = co2_kg/ae_quintile, co2_pae_kg = co2_kg/ae_quintile,
co2_pae_dom_kg = co2_domestic_kg/ae_quintile, co2_pae_dom_kg = co2_domestic_kg/ae_quintile,
...@@ -8491,7 +8475,7 @@ map_mrio_results_to_eu_ntiles <- function(pyear, ptarget_ntiles) { ...@@ -8491,7 +8475,7 @@ map_mrio_results_to_eu_ntiles <- function(pyear, ptarget_ntiles) {
full_join(df_eu_ntiles_data %>% full_join(df_eu_ntiles_data %>%
rename(fd_pae_e_quint_tmp = fd_pae_e), by=c("iso2", "quint")) %>% rename(fd_pae_e_quint_tmp = fd_pae_e), by=c("iso2", "quint")) %>%
rename(adult_eq = ae_quintile) %>% # country quintile and their split fraction population rename(adult_eq = ae_quintile) %>% # country quintile and their split fraction population
# recalc totals # recalculate totals
mutate(fd_me = fd_pae_e*adult_eq/1000000, mutate(fd_me = fd_pae_e*adult_eq/1000000,
co2_kg = co2_pae_kg*adult_eq, co2_kg = co2_pae_kg*adult_eq,
co2_dom_kg = co2_pae_dom_kg*adult_eq, co2_dom_kg = co2_pae_dom_kg*adult_eq,
...@@ -8503,23 +8487,23 @@ map_mrio_results_to_eu_ntiles <- function(pyear, ptarget_ntiles) { ...@@ -8503,23 +8487,23 @@ map_mrio_results_to_eu_ntiles <- function(pyear, ptarget_ntiles) {
energy_use_dom_tj = energy_use_dom_pae_tj*adult_eq, energy_use_dom_tj = energy_use_dom_pae_tj*adult_eq,
energy_use_eu_tj = energy_use_eu_pae_tj*adult_eq energy_use_eu_tj = energy_use_eu_pae_tj*adult_eq
) #%>% ) #%>%
#left_join(sector_mapping, by="sector_id") #left_join(sector_mapping, by="sector_id") # comment out joining sectors (only working with coicop categories in method2)
list("df_mapped_data" = df_mapped_data, "df_ntile_boundaries" = df_eu_ntiles$df_q_boundaries) list("df_mapped_data" = df_mapped_data, "df_ntile_boundaries" = df_eu_ntiles$df_q_boundaries)
} }
### Filter only countries with complete info for years 2005, 2010, 2015 # filter only countries with complete info for years 2005, 2010, 2015
mrio_results_with_adult_eq = mrio_results_with_adult_eq_all %>% mrio_results_with_adult_eq = mrio_results_with_adult_eq_all %>%
filter(iso2 %in% complete_countries) filter(iso2 %in% complete_countries)
## summarize final demand per adult equvalent per quintile across all sectors as basis for eurodeciles for complete countries # summarize final demand per adult equivalent per quintile across all sectors as basis for European deciles for complete countries
summary_country_fd = mrio_results_with_adult_eq %>% summary_country_fd = mrio_results_with_adult_eq %>%
group_by(iso2, year, quint) %>% group_by(iso2, year, quint) %>%
summarise(ae_quintile = first(ae_quintile), summarise(ae_quintile = first(ae_quintile),
fd_pae_e = sum(fd_me*1000000, na.rm = T)/(ae_quintile)) fd_pae_e = sum(fd_me*1000000, na.rm = T)/(ae_quintile))
## summarize final demand per adult equvalent per quintile across all sectors as basis for eurodeciles for all countries # summarize final demand per adult equivalent per quintile across all sectors as basis for European deciles for all countries
summary_country_fd_all = mrio_results_with_adult_eq_all %>% summary_country_fd_all = mrio_results_with_adult_eq_all %>%
group_by(iso2, year, quint) %>% group_by(iso2, year, quint) %>%
summarise(ae_quintile = first(ae_quintile), summarise(ae_quintile = first(ae_quintile),
......
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