edit ms

analysis/paper/paper.Rmd

@@ -405,7 +405,7 @@ pdat_basket = get_sector_summary_by_eu_ntile_direct(eu_q_count) %>%
          value) %>%
   mutate(indicator = "Expenditure share (%)")
 
-pdat_int_co2 = get_sector_summary_by_eu_ntile_direct(eu_q_count) %>%
+pdat_int_co2eq = get_sector_summary_by_eu_ntile_direct(eu_q_count) %>%
   ungroup() %>%
   filter(year==2015) %>%
   left_join(read_csv(here("analysis/data/derived/sectors_agg_method1_ixi.csv")), by="sector_agg_id") %>% 
@@ -424,7 +424,7 @@ pdat_int_energy = get_sector_summary_by_eu_ntile_direct(eu_q_count) %>%
   mutate(indicator = "Energy intensity (MJ/€)")
 
 library(viridis)
-pdat = pdat_int_co2 %>%
+pdat = pdat_int_co2eq %>%
   bind_rows(pdat_int_energy) %>%
   bind_rows(pdat_basket)
 
@@ -443,7 +443,7 @@ a = ggplot(pdat, aes(x=value, y=five_sectors, color=eu_q_rank)) +
 ggsave(here("analysis", "figures", "figure2-test.pdf"))
 ```
 
-```{r figure2, out.width="100%", fig.cap="Sectoral expenditure shares and carbon intensities of European expenditure deciles. Share of expenditure per final demand sector of total spending per decile in percent (a) and carbon intensity per final demand sector in kgCO2/€."}
+```{r figure2, out.width="100%", fig.cap="Sectoral expenditure shares and carbon intensities of European expenditure deciles. Share of expenditure per final demand sector of total spending per decile in percent (a) and carbon intensity per final demand sector in kgCO2eq/€."}
 knitr::include_graphics(here::here("analysis", "figures", "figure2-test.pdf"))
 ```
 
@@ -453,9 +453,9 @@ exp_share_shelter_bottom_decile = round((pdat_basket %>% filter(eu_q_rank == 1,
 
 exp_share_shelter_top_decile = round((pdat_basket %>% filter(eu_q_rank == 10, five_sectors == "shelter"))$value, digits = 1)
 
-int_co2_shelter_bottom_decile = round((pdat_int_co2 %>% filter(eu_q_rank == 1, five_sectors == "shelter"))$value, digits = 1)
+int_co2eq_shelter_bottom_decile = round((pdat_int_co2eq %>% filter(eu_q_rank == 1, five_sectors == "shelter"))$value, digits = 1)
 
-int_co2_shelter_top_decile = round((pdat_int_co2 %>% filter(eu_q_rank == 10, five_sectors == "shelter"))$value, digits = 1)
+int_co2eq_shelter_top_decile = round((pdat_int_co2eq %>% filter(eu_q_rank == 10, five_sectors == "shelter"))$value, digits = 1)
 
 exp_share_services_bottom_decile = round((pdat_basket %>% filter(eu_q_rank == 1, five_sectors == "services"))$value, digits = 1)
 
@@ -463,7 +463,7 @@ exp_share_services_top_decile = round((pdat_basket %>% filter(eu_q_rank == 10, f
 
 ```
 
-Our data show that both of these factors play a role \@ref(fig:figure2). Poorer households on average, spend larger shares of their expenditure in the shelter sector. The bottom and top deciles spend an average of `r exp_share_shelter_bottom_decile`% and `r exp_share_shelter_top_decile`% of their household expenditures on shelter, respectively. Overall, with increasing expenditure decile, the shares of transport and services expenditures increase and the shares of shelter, food and manufactured goods decrease. At the same time, shelter is by far the most carbon intensive sector with the highest variance between expenditure deciles. In our sample, the intensity of all sectors decreases with expenditure level but the shelter sector stands out with a carbon intensity that is more than 3 times higher in the bottom decile (`r int_co2_shelter_bottom_decile` kgCO2eq/€) than in the top decile (`r int_co2_shelter_top_decile` kgCO2eq/€). Households in the top decile spend about `r exp_share_services_top_decile`% in the service sector that has the lowest carbon intensity, compared to `r exp_share_services_bottom_decile`% in the bottom decile (we have included the EXIOBASE production sector 'real estate services' in our aggregated 'services' sector, not the aggregated 'shelter' sector). Single country studies using MRIO models with national resolution can pick up on differences in consumption baskets but due to the homogeneous technology assumption cannot represent differences in technology between expenditure deciles.
+Our data show that both of these factors play a role \@ref(fig:figure2). Poorer households on average, spend larger shares of their expenditure in the shelter sector. The bottom and top deciles spend an average of `r exp_share_shelter_bottom_decile`% and `r exp_share_shelter_top_decile`% of their household expenditures on shelter, respectively. Overall, with increasing expenditure decile, the shares of transport and services expenditures increase and the shares of shelter, food and manufactured goods decrease. At the same time, shelter is by far the most carbon intensive sector with the highest variance between expenditure deciles. In our sample, the intensity of all sectors decreases with expenditure level but the shelter sector stands out with a carbon intensity that is more than 3 times higher in the bottom decile (`r int_co2eq_shelter_bottom_decile` kgCO2eq/€) than in the top decile (`r int_co2eq_shelter_top_decile` kgCO2eq/€). Households in the top decile spend about `r exp_share_services_top_decile`% in the service sector that has the lowest carbon intensity, compared to `r exp_share_services_bottom_decile`% in the bottom decile (we have included the EXIOBASE production sector 'real estate services' in our aggregated 'services' sector, not the aggregated 'shelter' sector). Single country studies using MRIO models with national resolution can pick up on differences in consumption baskets but due to the homogeneous technology assumption cannot represent differences in technology between expenditure deciles.
 
 The tendency that the emission intensity for direct energy consumption decreases with increasing affluence can be observed at the global level (XXX) between countries and also applies within Europe. In some of the Eastern European countries, between 80% and 100% of the population belong to the four lowest European expenditure deciles. This compares to less than 20% of the population in the richer European countries (Scandinavia, Germany, France, Austria, the Netherlands, Belgium, the UK, and Ireland). Note here that our analysis is based on average expenditure data from five income groups at the national level. This aggregation cuts off the lower and upper ends of the respective national expenditure distributions (Supplementary Note and Map).