edit si

analysis/paper/si.Rmd

@@ -3,24 +3,20 @@ title: "The energy and carbon inequality corridor for a 1.5 degree compatible an
 author:
 - Ingram S. Jaccard:
     email: jaccard@pik-potsdam.de
-    institute: PIK
-    correspondence: no
+    institute: [PIK]
+    correspondence: false
 - Peter-Paul Pichler:
     email: pichler@pik-potsdam.de
-    institute: PIK
-    correspondence: no
+    institute: [PIK]
+    correspondence: false
 - Johannes Többen:
     email: toebben@pik-potsdam.de
-    institute:
-    - PIK
-    - GWS
-    correspondence: no
+    institute: [PIK, GWS]
+    correspondence: false
 - Helga Weisz:
     email: weisz@pik-potsdam.de
-    institute:
-    - PIK
-    - HU
-    correspondence: no
+    institute: [PIK, HU]
+    correspondence: false
 institute:
 - PIK: Social Metabolism and Impacts, Potsdam Institute for Climate Impact Research,
     Member of the Leibniz Association, PO Box 60 12 03, Potsdam, 14412, Germany
@@ -616,6 +612,8 @@ To calculate European household expenditure deciles we first ranked the national
 
 This method uses average expenditure data from the national income quintiles, and so one limitation is that this aggregation cuts off the lower and higher tails of the respective national expenditure distributions. 
 
+Different levels of subsidy in different countries could alter pure comparisons between intensities of consumption between countries, although presumably less within countries. 
+
 ## Alternative method
 
 Our methodology used for the main paper, and explained in the sections above, keeps the production sector shares of EE-MRIO household final demand expenditure (and subsequently the footprint) the same as they are found in the original EE-MRIO household final demand expenditure when not decomposed by income quantile. The alternative method is to keep the consumption category shares of total HBS expenditure the same as they are found in the HBS. This means taking the total sum of household final demand expenditure from the EE-MRIO and decomposing it first based on the share of each income quantile's consumption expenditure of the total consumption expenditure as found in the HBS, before decomposing into sectors as well using the HBS 'parts per mille' per sector. This leads to a different total footprint than the original EE-MRIO footprint (when not decomposed by income quantile), because a different amount of final demand expenditure in each sector is now multiplied by the same 'direct and indirect supply chain' intensities per sector that are originally calculated in the EE-MRIO.  
@@ -816,7 +814,9 @@ flextable(footprint_alt_method) %>%
 
 # Supplementary Results
 
-- brief description of each graphic. Can then say something tentative about whether or not inequality increased or decreased, the footprints increased or decreased, differences between methods and versions, and then call for more investigation of effects over time. 
+We ran the same analysis for the years 2015, 2010 and 2005. We presented only 2015 in the main paper as we focused more on the distributional aspect of the environmental footprints and their relationship to decarbonisation scenarios and minimum energy, rather than the time component. As mentioned in the 'limitations' section above, caution is needed when comparing the EUROSTAT HBS data over time, as well as the version of EXIOBASE we used (version3, industry-by-industry). We also estimated the environmental footprints per European expenditure decile using the product-by-product version of EXIOBASE for the year 2010, and then the year 2015 again using the alternative methodology that was explained in the supplementary methods sections in this document above. 
+
+We show each of these in turn in the format of Figure 1 from the main paper: 1) the year 2005 using the main paper method and EXIOBASE industry-by-industry version, then 2) the year 2010 using the main paper method and EXIOBASE industry-by-industry version, 3) the year 2010 using the main paper method but EXIOBASE product-by-product version, and finally 4) the year 2015 using an alternative methodology and EXIOBASE industry-by-industry version. 
 
 ## 2005 using main method, EXIOBASE industry-by-industry
 
@@ -1776,7 +1776,7 @@ ggsave(here("analysis", "figures", "figureS4.pdf"), device=cairo_pdf)
 
 ```
 
-# Unit of analysis
+# Units of analysis
 
 ```{r load-data4, include=FALSE}
 # load data wrangling functions
@@ -1815,7 +1815,7 @@ pdat_sector_summary_by_eu_ntile =
 
 ```
 
-```{r figureSx, fig.cap="**Figure S5:**"}
+```{r }
 
 # ae vs. per capita
 
@@ -1911,9 +1911,13 @@ ae_share_of_pop = ae_vs_pop$total_adult_eq/ae_vs_pop$total_population
 
 ```
 
-to add?: 'For example, we adjust a total final energy use of 53 GJ per capita from the LED scenario (Grubler et al. (2018) [@grubler_low_2018]), first by the household share of the total European energy footprint in 2015 (around 0.62, calculated in EXIOBASE), and then the share of total adult equivalents in the total European population in 2015 (also around 0.62, calculated using the EUROSTAT HBS, number of households per country, and population data per country): a total final energy use of 53 GJ/capita is therefore adjusted to a household final energy use of 53 GJ/adult equivalent in Europe ((53 total GJ/capita * 0.62 household share of total footprint)/0.62 adult equivalent share of population = 53 household GJ/adult equivalent).'
+Through the main paper we use household per adult equivalent as our unit of analysis, following the EUROSTAT HBS. This meant that we adjusted decarbonisation scenario final energy numbers from total per capita to household per adult equivalent to better compare them with our environmental footprint estimates. We adjusted them for 1) the household share of the total footprint, and 2) the adult equivalent share of the total population.
+
+As a numerical example, we adjust a total final energy of 53 GJ per capita from the LED scenario (Grubler et al. (2018) [@grubler_low_2018]), first by the household share of the total European energy footprint in 2015 (around 0.62, calculated in EXIOBASE), and then the share of total adult equivalents in the total European population in 2015 (also around `r ae_share_of_pop`, calculated using the EUROSTAT HBS, number of households per country, and population data per country). A total final energy of 53 GJ/capita is therefore adjusted to a household final energy of 53 GJ/adult equivalent in Europe ((53 total GJ/capita * 0.62 household share of total footprint)/0.62 adult equivalent share of total population = 53 household GJ/adult equivalent).
+
+Our European expenditure deciles were constructed having the exact same number of adult equivalents per decile. When comparing with external per capita numbers, however, there are not the same number of population per decile because of differences in non-adult-equivalent-normalized people per household between income quintiles per country, and between countries. In Figure Sx we show an estimate of population per European expenditure decile. We use this to re-estimate our energy footprint per European expenditure decile in per capita terms, and then re-create Figure 5 from the main paper (Figure Sxx below) in per capita terms. 
 
-`r ae_share_of_pop`
+Because we have the number of adult equivalents per country, and the total population per country, we could use both to calculate the total population per adult equivalent ratio for each country. We applied these ratio to the adult equivalents from different countries making up each European expenditure decile, as so we could estimate total population per European expenditure decile taking into account differences in household per capita between countries, but not between income quintiles within each country. Figure Sx shows these population estimates per European expenditure decile. 
 
 ```{r figureSx, fig.cap="**Figure S5:**"}
 
@@ -1929,8 +1933,9 @@ ggsave(here("analysis", "figures", "figureSx.pdf"), device=cairo_pdf)
 
 ```
 
+## Figure 5 from manuscript in household final energy per capita
 
-## Figure 5 from manuscript (population)
+Here we re-create Figure 5 from the main paper after estimating our energy footprint per European expenditure decile in per capita terms (using the population per decile estimates from above), instead of per adult equivalent terms. Now we only adjust the decarbonisation scenario final energy numbers from total GJ per capita to household GJ per capita, so 53 total GJ/capita becomes: 53 * 0.62 = 33 household GJ/capita. 
 
 ```{r }