edit si

analysis/data/raw/.~lock.scenarios.xlsx#

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analysis/paper/paper.Rmd

@@ -514,7 +514,7 @@ exp_share_services_top_decile = round((pdat_basket %>% filter(eu_q_rank == 10, f
 
 Our results show that both of these factors play a role (Figure 2). The housing sector stands out with a carbon intensity of consumption more than 6 times higher in the bottom decile (`r int_co2eq_housing_bottom_decile` kgCO2eq/€) than in the top decile (`r int_co2eq_housing_top_decile` kgCO2eq/€). Housing has the highest variance in energy and carbon intensity among expenditure deciles, and for the bottom deciles, it is the most energy and carbon intensive category. Overall, with increasing expenditure decile, the shares of mobility, services and housing expenditures increase and the shares of food and goods decrease. The bottom decile spent an average of `r exp_share_housing_bottom_decile`% of their household expenditure on housing, while the top decile spent `r exp_share_housing_top_decile`%. Households in the top decile spent about `r exp_share_services_top_decile`% on services, which has the lowest energy and carbon intensities of all final consumption categories, compared to `r exp_share_services_bottom_decile`% in the bottom decile. 
 
-The tendency for energy and carbon intensity to decrease with increasing affluence has been reported for the global level between countries [@hubacek_global_2017 @berthe_mechanisms_2015 @scruggs_political_1998] and also within Europe [@sommer_carbon_2017 @bianco_understanding_2019]. Our results show that the four lowest European expenditure deciles make up 80% to 100% of the population in Poland, Romania, Bulgaria and the Czech Republic, while less than 20% of the population in the higher-income European countries (Scandinavia, Germany, France, Austria, the Netherlands, Belgium, the UK, and Ireland) are in the lowest European expenditure deciles. Note here that this does not imply that there are no high-income households in Eastern Europe. Our analysis is based on average household expenditure data from national income quintiles. This aggregation thus cuts off the lower and higher tails of the respective national expenditure distributions (see SI). 
+The tendency for energy and carbon intensity to decrease with increasing affluence has been reported for the global level between countries [@hubacek_global_2017 @berthe_mechanisms_2015 @scruggs_political_1998] and also within Europe [@sommer_carbon_2017 @bianco_understanding_2019]. Our results show that the four lowest European expenditure deciles make up over 80% of the population in Eastern European countries, while less than 20% of the population in the higher-income European countries (Scandinavia, Germany, France, Austria, the Netherlands, Belgium, the UK, and Ireland) are in the lowest European expenditure deciles (see SI, Figure S1). 
 
 The high intensities in the bottom four European expenditure deciles can be attributed in large part to more inefficient and dirtier domestic energy supply and demand technologies for heating and electricity generation in Poland, Bulgaria, the Czech Republic, and Romania. Poland alone was responsible for about 40% of total coal combustion for heat production in Europe in 2015 [@eurostat_eurostat_nodate-2], and had a higher average intensity of carbon per MJ of heat delivered than both Europe and the world [@werner_international_2017]. 
 
@@ -987,13 +987,13 @@ ggsave(here("analysis", "figures", "figure5.pdf"))
 
 Based on this counterfactual distribution of the energy footprint using homogeneous supply technologies, we can now scale down the energy footprint across European expenditure deciles to meet supply constraints and, where necessary, "squeeze" the distribution to not undershoot minimum energy requirements in any decile (Figure 5). 
 
-Both the DLE and LED scenarios satisfy final energy demand for a decent standard of living and are compatible with the 1.5°C target without resorting to CCS technologies [@millward-hopkins_providing_2020 @grubler_low_2018]. The DLE scenario explicitly envisions absolute global equality (a 10:10 ratio of 1) in energy consumption, except for small differences in required energy consumption based on climatic and demographic factors, as well as differences in population density [@millward-hopkins_providing_2020]. The LED scenario does not explicitly discuss distributional aspects beyond giving different final energy values for the Global North (around 53 household GJ/ae) and the Global South (around 27 household GJ/ae) [@grubler_low_2018]. However, due to the bottom-up construction of this demand scenario, these values can be interpreted as estimates for minimum required final energy. The energy supply scenarios do not include specific details about how the energy footprints are distributed within countries [@riahi_shared_2017]. They achieve energy savings through the replacement of carbon-intensive fossil fuels by cleaner alternatives, efficiency improvements including the electrification of final energy, and some measures towards energy conservation [@riahi_shared_2017]. 
+Both the DLE and LED scenarios satisfy final energy demand for a decent standard of living and are compatible with the 1.5°C target without resorting to CCS technologies [@millward-hopkins_providing_2020 @grubler_low_2018]. The DLE scenario explicitly envisions absolute global equality (a 10:10 ratio of 1) in energy consumption, except for small differences in required energy consumption based on climatic and demographic factors, as well as differences in population density [@millward-hopkins_providing_2020]. The LED scenario does not explicitly discuss distributional aspects beyond giving different final energy values for the Global North (around 53 household GJ/ae) and the Global South (around 20 household GJ/ae) [@grubler_low_2018]. However, due to the bottom-up construction of this demand scenario, these values can be interpreted as estimates for minimum required final energy. The energy supply scenarios do not include specific details about how the energy footprints are distributed within countries [@riahi_shared_2017]. They achieve energy savings through the replacement of carbon-intensive fossil fuels by cleaner alternatives, efficiency improvements including the electrification of final energy, and some measures towards energy conservation [@riahi_shared_2017]. 
 
 ```{r figure5, out.width="70%", fig.align="center", fig.cap="Mean energy available for Europe in decarbonisation scenarios, positioned in option space between a range of minimum energy requirements and range of associated maximum inequality. All expenditure deciles have 'best technology' already."}
 knitr::include_graphics(here::here("analysis", "figures", "figure5.pdf"))
 ```
 
-The colored curves in Figure 5 represent constant average household energy footprints according to the different scenarios. The slopes of the curves connect different assumptions about minimum required energy for a decent standard of living (on the x-axis), to the corresponding energy inequality that is consistent with the average energy availability. It is clear from Figure 5 that at current inequality levels, only the two scenarios with heavier CCS deployment (SSP2-1.9, SSP1-1.9) and GEA efficiency are possible, and only if we assume in addition an extremely low minimum energy requirement below 27 GJ/ae, which is roughly the value the LED scenario gives for the Global South in 2050. If we use the value given for the Global North at around 53 GJ/ae as minimum energy requirement, which still requires strong demand-side measures, then inequality would need to be zero in the LED scenario and more than halved in all other scenarios. 
+The colored curves in Figure 5 represent constant average household energy footprints according to the different scenarios. The slopes of the curves connect different assumptions about minimum required energy for a decent standard of living (on the x-axis), to the corresponding energy inequality that is consistent with the average energy availability. It is clear from Figure 5 that at current inequality levels, only the two scenarios with heavier CCS deployment (SSP2-1.9, SSP1-1.9) and GEA efficiency are possible, and only if we assume in addition an extremely low minimum energy requirement below 27 GJ/ae, which is roughly the value the LED scenario gives for the world in 2050. If we use the value given for the Global North at around 53 GJ/ae as minimum energy requirement, which still requires strong demand-side measures, then inequality would need to be zero in the LED scenario and more than halved in all other scenarios. 
 
 # Conclusions
 

analysis/paper/si.Rmd

@@ -603,8 +603,7 @@ flextable(eemrio_bp) %>%
 
 To calculate European household expenditure deciles we first ranked the national income quintiles (140 in total: 28 European countries x 5 national income quintiles each) according to their mean household expenditure in PPS and then aggregated the result to 10 European expenditure groups. This distribution allowed us to analyze the total European household energy and carbon footprints per these European expenditure deciles. We included only those countries with EUROSTAT HBS and EXIOBASE data in 2015, 2010, and 2005, which excludes Italy (no 2010 or 2015 necessary EUROSTAT HBS data, i.e. no data per income quintile) and Luxembourg (no 2010 EUROSTAT HBS data), but includes the UK, Norway and Turkey. 
 
-Because this method uses average expenditure data from the national income quintiles, the aggregation cuts off the lower and higher tails of the respective national expenditure distributions. 
-
+Each national income quintile is thus allocated to one of the 10 European expenditure deciles (some national income quintiles at the boundaries between deciles are split between two deciles). Figure Sx shows the population share of each country in our bottom 4 European expenditure deciles. A 100% share thus means that all 5 national income quintiles of that country fall within the bottom 4 European expenditure deciles. This does not imply that there are no high-income households in those countries, but because this method uses average expenditure data from the national income quintiles, the aggregation cuts off the lower and higher tails of the respective national expenditure distributions. There are likely some higher-income households, in those countries 100% within the bottom 4 European expenditure deciles, who are somewhere higher than the bottom 4 deciles which we do not capture with this method. 
 
 ```{r load-data-0, include=FALSE}
 # load data wrangling functions
@@ -684,7 +683,7 @@ map1 = ggplot() +
   
 ```
 
-```{r , fig.cap="Figure : Percentage of population in bottom 4 European deciles.", out.width="99%", cache=F, fig.width=8, fig.height = 4}
+```{r , fig.cap="**Figure S1: Percentage of population in bottom 4 European deciles.**", out.width="99%", cache=F, fig.width=8, fig.height = 4}
 
 map1
 
@@ -693,13 +692,13 @@ ggsave(here("analysis", "figures", "figureSxxx.pdf"), device=cairo_pdf)
 
 ## Limitations
 
-While the EUROSTAT HBS is compiled for cross-country comparison purposes and aims for harmonization, there remains imperfect harmonization in the frequency of surveys, timing, content and structure between countries and years @eurostat_eu_2020. Some types of households may also be excluded from the samples, including super-rich households, for example Germany, which excludes households with over €18,000 monthly net income @eurostat_eu_2020. Sensitive goods and services, such as alcohol, may be under-reported in household budget surveys, while expenditure on infrequent purchases such as a vehicle may create artificially large expenditure differences between households depending on the timing of the survey @eurostat_eu_2020. The EUROSTAT HBS macro-data also does not report direct foreign purchases, and we assumed that the expenditure shares between income quintiles of direct final demand for foreign goods and services was the same as direct final demand for domestic goods and services.
+While the EUROSTAT HBS is compiled for cross-country comparison purposes and aims for harmonization, there remains imperfect harmonization in the frequency of surveys, timing, content and structure between countries and years [@eurostat_eu_2020]. Some types of households may also be excluded from the samples, including super-rich households, for example Germany, which excludes households with over €18,000 monthly net income [@eurostat_eu_2020]. Sensitive goods and services, such as alcohol, may be under-reported in household budget surveys, while expenditure on infrequent purchases such as a vehicle may create artificially large expenditure differences between households depending on the timing of the survey [@eurostat_eu_2020]. The EUROSTAT HBS macro-data also does not report direct foreign purchases, and we assumed that the expenditure shares between income quintiles of direct final demand for foreign goods and services was the same as direct final demand for domestic goods and services.
 
 We used EXIOBASE as the EE-MRIO for this study because of its European focus, with nearly all countries in the EUROSTAT HBS also found as stand-alone countries in EXIOBASE, its detailed satellite extension data, and its year coverage (specifically version3, industry-by-industry), but there are well known limitations when using and selecting an EE-MRIO [@moran_convergence_2014]. The production sectors in EXIOBASE are harmonized across countries and years, but needing to map the EUROSTAT HBS to EXIOBASE meant that the most recent year of 2015 could only use the industry-by-industry version of EXIOBASE version3. This version assumes fixed product sales. Furthermore, because EXIOBASE version3 is extrapolated beyond 2011, caution should be used when comparing results over time. This, and the fact that harmonization guidelines in the EUROSTAT HBS have changed over time, were the justification for presenting only the 2015 results in the main paper, and presenting 2005 and 2010 results only here in the SI. We also show 2010 results using the product-by-product version of EXIOBASE version3 in the final section of this SI document. 
 
-Mapping the EUROSTAT HBS to EXIOBASE means mapping the COICOP consumption categories in the HBS to industry production sectors in EXIOBASE, which is not one-to-one. Both the EUROSTAT HBS and EXIOBASE are limited in their consumption category/production sector level of detail. The share of each consumption category/production sector in the total amount of expenditure is also not identical between the HBS and EXIOBASE. As discussed in the 'methods for main paper results' section of this SI document, there are alternative methods for decomposing the EXIOBASE household final demand expenditure: one that keeps the EXIOBASE production sector shares of total expenditure intact, and one that keeps the HBS consumption category shares of total expenditure intact. Our results in the main paper use the first method, keeping the EXIOBASE sectoral shares of total expenditure intact, which means that the total footprint is identical to when it is calculated in EXIOBASE without any decomposition by income quantile. The alternative method, on the other hand, results in a different total footprint because a different amount of final demand expenditure in each sector is multiplied by the same original 'direct and indirect supply chain' intensities, but stays faithful to the original HBS consumption category shares of total HBS expenditure. We show the alternative method below, and some results in the last sections of this SI document.
+Mapping the EUROSTAT HBS to EXIOBASE means mapping the COICOP consumption categories in the HBS to industry production sectors in EXIOBASE, which is not one-to-one. Both the EUROSTAT HBS and EXIOBASE are limited in their consumption category/production sector level of detail. The share of each consumption category/production sector in the total amount of expenditure is also not identical between the HBS and EXIOBASE. As discussed in the 'methods for main paper results' section of this SI document, there are alternative methods for decomposing the EXIOBASE household final demand expenditure: one that keeps the EXIOBASE production sector shares of total expenditure intact, and one that keeps the HBS consumption category shares of total expenditure intact. Our results in the main paper use the first method, keeping the EXIOBASE sectoral shares of total expenditure intact, which means that the total footprint is identical to when it is calculated in EXIOBASE without any decomposition by income quantile. The alternative method, on the other hand, results in a different total footprint because a different amount of final demand expenditure in each sector is multiplied by the same original 'direct and indirect supply chain' intensities, but stays faithful to the original HBS consumption category shares of total HBS expenditure. We show the alternative method below, and some results in the last sections of this SI document. We also stay at a relatively aggregated level for our energy and carbon footprints, as our primary goal was to connect aspects of the aggregate European household environmental footprint distribution with the decarbonisation and minimum energy requirement scenarios. Work specifically investigating distributional aspects of the European carbon footprint, sometimes at a finer sectoral level than we do here, can be found in the refs. [@ivanova_unequal_2020], [@gore_t._confronting_2020], [@sommer_carbon_2017] and [@bianco_understanding_2019]. 
 
-Finally, the main limitation of the European expenditure deciles is the fact that they use average expenditure data from the national income quintiles. This aggregation cuts off the lower and higher tails of the respective national expenditure distributions. Our constructed European expenditure deciles thus likely *under*estimate European expenditure inequality. Also, when calculating energy and carbon intensities per harmonized European expenditure decile, one aspect not harmonized is the possible effect of different national fossil fuel subsidy levels. Higher subsidies in the lower European expenditure deciles could play a role in the higher energy and carbon intensities observed there. 
+Finally, the main limitation of the European expenditure deciles is the fact that they use average expenditure data from the national income quintiles. This aggregation cuts off the lower and higher tails of the respective national expenditure distributions. Our constructed European expenditure deciles thus likely *under*estimate European expenditure inequality. Also, when calculating energy and carbon intensities per harmonized European expenditure decile, and comparing them across deciles, one aspect not harmonized is the possible effect of different national fossil fuel subsidy levels. Higher subsidies in the lower European expenditure deciles could play a role in the higher energy and carbon intensities observed there. 
 
 ### Alternative method for income-stratified national household energy and carbon footprints
 
@@ -2002,6 +2001,8 @@ Through the main paper we use household per adult equivalent as our unit of anal
 
 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).
 
+The decarbonisation scenario final energy numbers in 2050, presented in Table 1 of the main paper, were originally in total GJ per capita: 94 GJ/capita (SSP2-1.9), 87 GJ/capita (SSP1-1.9), 84 GJ/capita (IEA ETP B2DS), 64 GJ/capita (GEA-efficiency), 53 GJ/capita (LED), and 15.3 GJ/capita (DLE). Because of the similar relative shares of the household part of the total European energy footprint (~0.62), and the adult equivalent share of the total population in our sample (also around 0.62), these final energy numbers end up close to the same when adjusted to household per adult equivalent. The original numbers for the SSP and GEA-efficiency scenarios are from the International Institute for Applied Systems Analysis (IIASA) scenario database [@riahi_shared_2017 @gea_gea_nodate]. The SSP total GJ/capita numbers are for the OECD region, while the GEA-efficiency total GJ/capita number is for their 'West EU' region. The LED total GJ/capita number is from Grubler et al. (2018) [@grubler_low_2018], and the IEA ETP B2DS total GJ/capita number is from the Supplementary Table 27 in the supplementary information document of Grubler et al. (2018) [@grubler_low_2018]. The LED and IEA ETP B2DS total GJ/capita numbers are both for the Global North region. We also refer in the main paper to the LED numbers for the Global South (20 total GJ/capita) and the world (27 total GJ/capita). Finally, the DLE number is one number for the world, and while they give a range of 13-18.4 total GJ/capita, we take their average of 15.3 total GJ/capita [@millward-hopkins_providing_2020].
+
 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. 
 
 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. 
@@ -2022,7 +2023,7 @@ ggsave(here("analysis", "figures", "figureSx.pdf"), device=cairo_pdf)
 
 ## Figure 5 from manuscript in household final energy per capita
 
-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. 
+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, for example, 53 total GJ/capita becomes: 53 * 0.62 = 33 household GJ/capita. 
 
 ```{r }
 

analysis/paper/to-do.Rmd

@@ -17,11 +17,11 @@ output:
 
 - 'weighted' problem - need to explain that it is 'population weighted', explain once in the methods section? (DONE)
 
-- better explanation of deciles
+- better explanation of deciles (DONE - in SI)
 
 - finalize aggregate sector names (DONE)
 
-- line 222 - note on limits of deciles. Discussion on limitations of the deciles...short one in methods section then natural language in its place in the ms? Definitely need to fully discuss in SI limitations section
+- line 222 - note on limits of deciles. Discussion on limitations of the deciles...short one in methods section then natural language in its place in the ms? Definitely need to fully discuss in SI limitations section (DONE - in SI)
 
 - subsidies: discuss in limitations? can we find some external stuff on this?
 
@@ -31,7 +31,7 @@ output:
 
 - new IEA scenario (net-zero emissions 2050 - 1.5°C) - if it exists, include it (can find the explanation of the scenario but having trouble accessing precise final energy in 2050 numbers)
 
-- better discussion on scenarios: where to pick up CCS?
+- pick up CCS in conclusions section somewhere?
 
 - severe material deprivation: Helga reworks sentence (DONE)