diff --git a/analysis/preprocessing/full_code.Rmd b/analysis/preprocessing/full_code.Rmd
index cfc2851c0a42af734c69d4811ffce426aef71166..e5e5aee96722f7b5adba8652560401130db45926 100644
--- a/analysis/preprocessing/full_code.Rmd
+++ b/analysis/preprocessing/full_code.Rmd
@@ -1117,6 +1117,7 @@ Would be in an 'income-stratified-footprints' preprocessing folder
 # income-stratified-footprints directory
 #data_dir_income_stratified_footprints = paste("/",file.path("data","metab","income-stratified-footprints", fsep=.Platform$file.sep),sep="")
 data_dir_income_stratified_footprints = here("analysis", "preprocessing", "income-stratified-footprints")
+data_dir_exiobase = here("analysis", "preprocessing", "EXIOBASE")
 
 ################################################### !!!! method 1 - PPS HH - RENT NOT MAPPED TO EXIOBASE  !!!! ###########################################
 ##########################################################################################################################################################
@@ -2562,7 +2563,7 @@ fd_exiobase = disaggregated_final_demand %>%
 
 # direct from FD - to go back to results without direct FD fp, do not run this next chunk and do not bind_rows with 'results'
 
-env_ac_pefasu_no_TR = read_csv(paste0(data_dir_income_stratified_footprints, "/data/env_ac_pefasu_1_Data.csv")) %>%
+env_ac_pefasu_no_TR = read_csv(paste0(data_dir_income_stratified_footprints, "/env_ac_pefasu_1_Data.csv")) %>%
   filter(TIME == 2015) %>%
   mutate(geo = dplyr::recode(GEO,"Austria" = "AT", 
                              "Belgium" = "BE",
@@ -2612,7 +2613,7 @@ env_ac_pefasu_TR = env_ac_pefasu_no_TR %>%
 env_ac_pefasu = rbind(env_ac_pefasu_no_TR,env_ac_pefasu_TR) %>%
   gather(sector,share_of_total_energy,-geo)
 
-env_ac_ainah_r2 = read_csv(paste0(data_dir_income_stratified_footprints, "/data/env_ac_ainah_r2_1_Data.csv")) %>%
+env_ac_ainah_r2 = read_csv(paste0(data_dir_income_stratified_footprints, "/env_ac_ainah_r2_1_Data.csv")) %>%
   filter(TIME == 2015) %>%
   mutate(geo = dplyr::recode(GEO,"Austria" = "AT", 
                              "Belgium" = "BE",
@@ -3013,19 +3014,8 @@ results = fd_exiobase %>%
 
 results_with_direct_FD_fp = bind_rows(results,direct_FD_fp_wide)
 
-#write.csv(results, paste0(data_dir_income_stratified_footprints, "/results_no_rent_ixi.csv"))
-
-
 ### create compressed results_ixi rds file
 
-#if (!require("pacman")) install.packages("pacman")
-#pacman::p_load(tidyverse,
-#               janitor,
-#               here)
-
-#dat_all = read_csv(here("data/results_ixi.csv")) %>%
-#  clean_names()
-
 dat_all = results_with_direct_FD_fp %>%
   clean_names()
 
@@ -3035,7 +3025,7 @@ sectors = dat_all %>%
   mutate(sector_id = row_number())
 
 #write_csv(sectors, here("data/sector_labels.csv"))
-write_csv(sectors, paste0(data_dir_income_stratified_footprints, "/sectors_method1_ixi_pps_hh.csv"))
+write_csv(sectors, paste0(here("/analysis/data/derived/sectors_method1_ixi.csv"))) 
 
 # convert aggregated sector labels to IDs
 sectors_agg = dat_all %>%
@@ -3043,7 +3033,7 @@ sectors_agg = dat_all %>%
   mutate(sector_agg_id = row_number())
 
 #write_csv(sectors_agg, here("data/sector_agg_labels.csv"))
-write_csv(sectors_agg, paste0(data_dir_income_stratified_footprints, "/sectors_agg_method1_ixi_pps_hh.csv"))
+write_csv(sectors_agg, paste0(here("/analysis/data/derived/sectors_agg_method1_ixi.csv"))) 
 
 # convert COICOP labels to IDs
 coicop = dat_all %>%
@@ -3051,7 +3041,7 @@ coicop = dat_all %>%
   mutate(coicop_id = row_number()) 
 
 #write_csv(sectors_agg, here("data/sector_agg_labels.csv"))
-write_csv(coicop, paste0(data_dir_income_stratified_footprints, "/coicop_method1_ixi_pps_hh.csv"))
+write_csv(coicop, paste0(here("/analysis/data/derived/coicop_method1_ixi.csv"))) 
 
 # replace sector text labels with numerical IDs (save space)
 dat_compressed = dat_all %>%
@@ -3186,25 +3176,15 @@ results_recombined = tmp_fd %>%
   left_join(tmp_energy_domestic, by=c("year", "geo", "sector_id", "quint")) %>%
   left_join(tmp_energy_europe, by = c("year", "geo", "sector_id", "quint"))
 
-
-
 # finally re-join aggregated sector IDs
 results_formatted = results_recombined %>% 
   left_join(sector_mapping, by="sector_id") %>%
   ungroup() %>%
   select(-coicop_id)
 
-#write_rds(results_formated, here("/results_formated.rds"))
-
 write.csv(results_formatted, paste0(data_dir_income_stratified_footprints, "/results_formatted_method1_ixi.csv"))
-
 write_rds(results_formatted, paste0(data_dir_income_stratified_footprints, "/results_formatted_method1_ixi.rds"))
 
-
-#write.csv(results_formatted, paste0(data_dir_income_stratified_footprints, "/results_formatted_method1_ixi_pps_ae.csv"))
-#write_rds(results_formatted, paste0(data_dir_income_stratified_footprints, "/results_formatted_method1_ixi_pps_ae.rds"))
-
-
 ################################################### !!!! method 1 - PXP version - PPS HH NO RENT !!!! ####################################################
 ##########################################################################################################################################################
 ##########################################################################################################################################################
@@ -4544,7 +4524,7 @@ fd_exiobase = disaggregated_final_demand %>%
 
 # direct from FD - to go back to results without direct FD fp, do not run this next chunk and do not bind_rows with 'results'
 
-env_ac_pefasu_no_TR = read_csv(paste0(data_dir_income_stratified_footprints, "/data/env_ac_pefasu_1_Data.csv")) %>%
+env_ac_pefasu_no_TR = read_csv(paste0(data_dir_income_stratified_footprints, "/env_ac_pefasu_1_Data.csv")) %>%
   filter(TIME == 2015) %>%
   mutate(geo = dplyr::recode(GEO,"Austria" = "AT", 
                              "Belgium" = "BE",
@@ -4594,7 +4574,7 @@ env_ac_pefasu_TR = env_ac_pefasu_no_TR %>%
 env_ac_pefasu = rbind(env_ac_pefasu_no_TR,env_ac_pefasu_TR) %>%
   gather(sector,share_of_total_energy,-geo)
 
-env_ac_ainah_r2 = read_csv(paste0(data_dir_income_stratified_footprints, "/data/env_ac_ainah_r2_1_Data.csv")) %>%
+env_ac_ainah_r2 = read_csv(paste0(data_dir_income_stratified_footprints, "/env_ac_ainah_r2_1_Data.csv")) %>%
   filter(TIME == 2015) %>%
   mutate(geo = dplyr::recode(GEO,"Austria" = "AT", 
                              "Belgium" = "BE",
@@ -4994,18 +4974,8 @@ results = fd_exiobase %>%
             energy_total_europe = q1_energy_europe+q2_energy_europe+q3_energy_europe+q4_energy_europe+q5_energy_europe)
 
 results_with_direct_FD_fp = bind_rows(results,direct_FD_fp_wide)
-#write.csv(results, paste0(data_dir_income_stratified_footprints, "/results_no_rent_ixi.csv"))
-
-
-### create compressed results_ixi rds file
-
-#if (!require("pacman")) install.packages("pacman")
-#pacman::p_load(tidyverse,
-#               janitor,
-#               here)
 
-#dat_all = read_csv(here("data/results_ixi.csv")) %>%
-#  clean_names()
+### create compressed results_pxp rds file
 
 dat_all = results_with_direct_FD_fp %>%
   clean_names()
@@ -5015,24 +4985,22 @@ sectors = dat_all %>%
   distinct(sector) %>%
   mutate(sector_id = row_number())
 
-#write_csv(sectors, here("data/sector_labels.csv"))
-write_csv(sectors, paste0(data_dir_income_stratified_footprints, "/sectors_method1_pxp_pps_hh.csv"))
+# if interested in looking at a sectoral breakdown of the product-by-product version results, un-comment line below
+#write_csv(sectors, paste0(here("/analysis/data/derived/si/sectors_method1_pxp.csv")))
 
 # convert aggregated sector labels to IDs
 sectors_agg = dat_all %>%
   distinct(five_sectors) %>%
   mutate(sector_agg_id = row_number())
 
-#write_csv(sectors_agg, here("data/sector_agg_labels.csv"))
-write_csv(sectors_agg, paste0(data_dir_income_stratified_footprints, "/sectors_agg_method1_pxp_pps_hh.csv"))
+#write_csv(sectors_agg, paste0(here("analysis/data/derived/si/sectors_agg_method1_pxp.csv")))
 
 # convert COICOP labels to IDs
 coicop = dat_all %>%
   distinct(coicop) %>%
   mutate(coicop_id = row_number()) 
 
-#write_csv(sectors_agg, here("data/sector_agg_labels.csv"))
-write_csv(coicop, paste0(data_dir_income_stratified_footprints, "/coicop_method1_pxp_pps_hh.csv"))
+#write_csv(coicop, paste0(here("analysis/data/derived/si/coicop_method1_pxp.csv")))
 
 # replace sector text labels with numerical IDs (save space)
 dat_compressed = dat_all %>%
@@ -5175,23 +5143,11 @@ results_formatted = results_recombined %>%
   ungroup() %>%
   select(-coicop_id)
 
-#write_rds(results_formated, here("/results_formated.rds"))
-
-write.csv(results_formatted, paste0(data_dir_income_stratified_footprints, "/results_formatted_method1_pxp_pps_hh_no_rent.csv"))
-
-#write_rds(results_formatted, paste0(data_dir_income_stratified_footprints, "/results_formatted_method1_pxp_pps_hh_no_rent.rds"))
+write.csv(results_formatted, paste0(data_dir_income_stratified_footprints, "/results_formatted_method1_pxp.csv"))
+write_rds(results_formatted, paste0(data_dir_income_stratified_footprints, "/results_formatted_method1_pxp.rds"))
 
 
-#write.csv(results_formatted, paste0(data_dir_income_stratified_footprints, "/results_formatted_method1_pxp_pps_ae.csv"))
-#write_rds(results_formatted, paste0(data_dir_income_stratified_footprints, "/results_formatted_method1_pxp_pps_ae.rds"))
-
-
-
-
-
-
-
-################################################### !!!! method 2 !!!! - IXI version - PPS HH - RENT NOT MAPPED TO EXIOBASE !!!!! #############################
+################################################### !!!! method 2 !!!! - IXI version #############################
 ###############################################################################################################################################################
 ###############################################################################################################################################################
 
@@ -5201,14 +5157,14 @@ write.csv(results_formatted, paste0(data_dir_income_stratified_footprints, "/res
 # aggregate - playing around trying to go the other way
 
 # load 'mean expenditure by quintile' data
-hbs_exp_t133 = read_csv(paste0(data_dir_income_stratified_footprints, "/data/hbs_exp_t133.csv"))
+hbs_exp_t133 = read_csv(paste0(data_dir_income_stratified_footprints, "/hbs_exp_t133.csv"))
 # rename and arrange by country
 mean_expenditure_by_quintile = hbs_exp_t133 %>% 
   rename(geo = 3, quintile = "quantile") %>%
   arrange(geo)
 
 # load 'mean expenditure by quintile and coicop' data
-hbs_str_t223 = read_csv(paste0(data_dir_income_stratified_footprints, "/data/hbs_str_t223.csv"))
+hbs_str_t223 = read_csv(paste0(data_dir_income_stratified_footprints, "/hbs_str_t223.csv"))
 # rename and arrange by country
 mean_expenditure_by_coicop_sector = hbs_str_t223 %>% 
   rename(geo = 4, quintile = "quantile") %>%
@@ -5273,7 +5229,7 @@ join_expenditures = mean_expenditure_by_coicop_sector_long %>%
 
 # load margin tables
 
-trade_and_transport = read.csv(paste0(data_dir_income_stratified_footprints, "/data/SNA_TABLE45_20042020103737298.csv")) %>%
+trade_and_transport = read.csv(paste0(data_dir_income_stratified_footprints, "/SNA_TABLE45_20042020103737298.csv")) %>%
   select(LOCATION, PRODUCT, Product, Year, Value) %>% 
   mutate(geo = dplyr::recode(LOCATION,"AUT" = "AT", 
                              "BEL" = "BE",
@@ -5317,7 +5273,7 @@ trade_and_transport = read.csv(paste0(data_dir_income_stratified_footprints, "/d
 
 
 
-taxes_less_subsidies = read.csv(paste0(data_dir_income_stratified_footprints, "/data/SNA_TABLE45_20042020104120395.csv")) %>%
+taxes_less_subsidies = read.csv(paste0(data_dir_income_stratified_footprints, "/SNA_TABLE45_20042020104120395.csv")) %>%
   select(LOCATION, PRODUCT, Product, Year, Value) %>%
   mutate(geo = dplyr::recode(LOCATION,"AUT" = "AT", 
                              "BEL" = "BE",
@@ -5515,10 +5471,6 @@ shares = join_expenditures %>%
 ##########################################################################################################################################################
 ##########################################################################################################################################################
 
-# pre-processing
-
-data_dir_exiobase = paste("/",file.path("data","metab","Exiobase", fsep=.Platform$file.sep),sep="")
-
 # Exiobase - ixi version
 
 years_exb_ixi = c(2005,2010,2015)
@@ -5601,7 +5553,7 @@ for (i in years_exb_ixi){
   
   # labels
   
-  Exiobase_T_labels = read.csv(paste0(data_dir_income_stratified_footprints, "/data/Exiobase_T_labels_ixi_w_coicop_mapping_no_rent.csv")) %>%
+  Exiobase_T_labels = read.csv(paste0(data_dir_income_stratified_footprints, "/Exiobase_T_labels_ixi_w_coicop_mapping.csv")) %>%
     mutate(V1 = dplyr::recode(V1,"GR" = "EL","GB" = "UK")) 
   
   # TIVs
@@ -6721,22 +6673,6 @@ join_ala = mean_expenditure_by_coicop_sector_long_bp %>%
          pm_bp = as.numeric(pm_bp),
          fd_me = pm_bp*((eurostat_countries_colsums*mean_exp_shares)/1000)) 
 
-
-###################################################
-#%>%
-#  rename(coicop_level1 = coicop)
-
-# TIV only taking the mean
-
-# mean_TIV_with_labels = TIV_with_labels %>% group_by(geo,year,coicop,coicop_level1) %>%
-#  summarise(TIV_CO2 = mean(TIV_CO2)) 
-
-#ok = join_ala %>% left_join(mean_TIV_with_labels, by = c("geo","year","coicop")) %>%
-#  mutate(CO2_normal = exp_normal*TIV_CO2,
-#         CO2_pe = exp_pe*TIV_CO2,
-#         CO2_pi = exp_pi*TIV_CO2)
-##################################################
-
 Eurostat_countries_hh_fd_mean_TIV = as.data.frame(Eurostat_countries_hh_fd) %>% select(-year)
 
 weighted_mean_TIV_with_labels = cbind(TIVs,Eurostat_countries_hh_fd_mean_TIV) %>%
@@ -7098,7 +7034,7 @@ ok = join_ala %>%
 
 # direct from FD - to go back to results without direct FD fp, do not run this next chunk and do not bind_rows with 'results'
 
-env_ac_pefasu_no_TR = read_csv(paste0(data_dir_income_stratified_footprints, "/data/env_ac_pefasu_1_Data.csv")) %>%
+env_ac_pefasu_no_TR = read_csv(paste0(data_dir_income_stratified_footprints, "/env_ac_pefasu_1_Data.csv")) %>%
   filter(TIME == 2015) %>%
   mutate(geo = dplyr::recode(GEO,"Austria" = "AT", 
                              "Belgium" = "BE",
@@ -7148,7 +7084,7 @@ env_ac_pefasu_TR = env_ac_pefasu_no_TR %>%
 env_ac_pefasu = rbind(env_ac_pefasu_no_TR,env_ac_pefasu_TR) %>%
   gather(sector,share_of_total_energy,-geo)
 
-env_ac_ainah_r2 = read_csv(paste0(data_dir_income_stratified_footprints, "/data/env_ac_ainah_r2_1_Data.csv")) %>%
+env_ac_ainah_r2 = read_csv(paste0(data_dir_income_stratified_footprints, "/env_ac_ainah_r2_1_Data.csv")) %>%
   filter(TIME == 2015) %>%
   mutate(geo = dplyr::recode(GEO,"Austria" = "AT", 
                              "Belgium" = "BE",
@@ -7491,15 +7427,6 @@ direct_FD_fp_wide_recombined = tmp_co2 %>%
   clean_names() %>%
   mutate(year = as.numeric(year))
 
-
-
-# something is wrong with 'bah' (don't think so anymore) - the expenditures match the german and now the 
-#shares match the german, but some countries are clearly wrong - with almost 100% shares in CP04, whereas some look 
-#relatively normal - have to figure this out - huge TIVS in the CP045 sector for those weird countries. likely some weird 
-#sector that has a huge TIV but not much expenditure to it so need to do a weighted average - was the case, now have done 
-#with weighted TIV. some eastern european countries like Bulgaria still have huge CP04 emission shares - might be correct 
-#if their electricity is extremely dirty - need to look at the intensities of each country individually
-
 results = ok %>% 
   filter(!(geo %in% c("EA","EA12","EA13","EA17",
                       "EA18","EA19","EEA28","EEA30_2007",
@@ -7550,7 +7477,8 @@ results_formatted = results %>%
 results_formatted_with_direct_FD_fp = bind_rows(results_formatted,direct_FD_fp_wide_recombined)
 
 
-write.csv(results_formatted_with_direct_FD_fp, paste0(data_dir_income_stratified_footprints, "/results_formatted_method2_ixi_pps_hh_no_rent.csv"))
+write.csv(results_formatted_with_direct_FD_fp, paste0(data_dir_income_stratified_footprints, "/results_formatted_method2_ixi.csv"))
+write_rds(results_formatted_with_direct_FD_fp, paste0(data_dir_income_stratified_footprints, "/results_formatted_method2_ixi.rds"))
 
 ```