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-,jaccard,jaccard-Latitude-E6440,08.02.2021 18:22,file:///home/jaccard/.config/libreoffice/4;
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diff --git a/analysis/paper/paper.Rmd b/analysis/paper/paper.Rmd
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@@ -11,7 +11,7 @@ author:
       correspondence: false
   - Johannes Többen:
       email: toebben@pik-potsdam.de
-      institute: [PIK]
+      institute: [PIK, GWS]
       correspondence: false
   - Helga Weisz:
       email: weisz@pik-potsdam.de
@@ -20,6 +20,7 @@ author:
 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
   - HU: Department of Cultural History & Theory and Department of Social Sciences, Humboldt University Berlin, Unter den Linden 6, Berlin, 10117, Germany
+  - GWS: Gesellschaft für Wirtschaftliche Strukturforschung (GWS) mbH, Heinrichstraße 30, 49080 Osnabrück, Germany
 output: 
     bookdown::word_document2:
       fig_caption: yes
@@ -143,7 +144,7 @@ While the European Green Deal already recognizes that inequalities in income, en
 
 ## Income-stratified national household energy and carbon footprints
 
-We used the EE-MRIO model EXIOBASE for 2015 (version3, industry-by-industry) [@stadler_exiobase_2018] and the European household budget survey (HBS) macro-data from EUROSTAT for 2015 [@eurostat_database_nodate] to calculate income-stratified national household energy and cabron footprints (together denoted as environmental footprints in this paper). The EUROSTAT HBS publishes mean household expenditure by income quintile, in purchasing power standard (PPS), by COICOP consumption category, country and year. We choose 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 (see SI - table 5), its detailed satellite extension data, and its year coverage. 
+We used the EE-MRIO model EXIOBASE for 2015 (version3, industry-by-industry) [@stadler_exiobase_2018] and the European household budget survey (HBS) macro-data from EUROSTAT for 2015 [@eurostat_database_nodate] to calculate income-stratified national household energy and carbon footprints (together denoted as environmental footprints in this paper). The EUROSTAT HBS publishes mean household expenditure by income quintile, in purchasing power standard (PPS), by COICOP consumption category, country and year. We choose 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 (see SI - table 5), its detailed satellite extension data, and its year coverage. 
 
 To integrate HBS data into EXIOBASE we created correspondence tables between the EXIOBASE sectors and the matching COICOP consumption categories used in HBS (see SI, p xx for details). We then used the relative shares of the COICOP consumption categories of each income quintile in the HBS to decompose the matching EXIOBASE national household final demand expenditure per sector and per income quintile. Using standard input-output techniques (see SI) we calculated ‘total’ (i.e. indirect supply chain) energy use and carbon intensities per EXIOBASE sector and multiplied them with the income-stratified EXIOBASE national household expenditure, to estimate the supply chain part of national household energy and carbon footprints by national income quintile. 
 
@@ -407,13 +408,15 @@ mean_co2eq_of_energy_intens_top_decile = round((mean_co2eq_of_energy_intens %>%
 
 ```
 
-Consumption-based indicators such as the energy and carbon footprint of households are largely determined by their spending levels. An inequality of household expenditures in a population therefore implies an inequality of their environmental footprints. Figures 1a-c show European households by decile of expenditure and their associated footprints for energy and carbon in 2015. The figures show that increasing expenditure generally translated into larger footprints, but that the inequality decreased from expenditure to energy to carbon, with 10:10 ratios (the top decile divided by the bottom decile) of `r exp_10_10`, `r energy_10_10` and `r co2eq_10_10`, respectively. Total expenditure ranged from `r exp_bottom_decile` trn€ to `r exp_top_decile` trn€ (or `r fd_pae_bottom_decile`€ to `r fd_pae_top_decile`€ per adult equivalent) across bottom and top decile, the energy footprint from `r energy_bottom_decile` EJ to `r energy_top_decile` EJ (or `r energy_pae_bottom_decile` GJ/ae to `r energy_pae_top_decile` GJ/ae), and the carbon footprint from `r co2eq_bottom_decile` MtCO2eq to `r co2eq_top_decile` MtCO2eq (or `r co2eq_pae_bottom_decile` tCO2eq/ae to `r co2eq_pae_top_decile` tCO2eq/ae). The reason for this is evident from figures 1d-f. Both the energy intensity of consumption, measured as energy use per € expenditure (d), and the carbon intensity of energy, measured as carbon per unit of energy use (f), gradually decrease from bottom to top expenditure decile. The weighted average energy intensity of consumption decreased from `r mean_energy_intens_bottom_decile` MJ/€ in the bottom decile to less than half (`r mean_energy_intens_top_decile` MJ/€) in the top decile. Additionally, the carbon intensity of energy was also higher in the bottom decile (`r mean_co2eq_of_energy_intens_bottom_decile` gCO2eq/TJ) compared to the top decile (`r mean_co2eq_of_energy_intens_top_decile` gCO2eq/TJ). There is a clear trend of decreasing intensities across expenditure deciles even though the variance in the lower deciles is much higher. The carbon intensity of consumption (figure 1e) combines the effects of the intensities of 1d and 1f. The higher carbon intensity of energy is likely due to a larger share of emission intensive energy carriers in the energy system. The decreasing energy intensity of consumption is due to either inefficient energy technologies or energy subsidies in lower-income areas in Europe.
+Increasing expenditure generally translated into larger environmental footprints across European expenditure deciles (Figure 1a). However, the energy and carbon inequality was much lower than the expenditure inequality (Figure 1b and 1c). The top decile divided by the bottom decile (the 10:10 ratio) was `r exp_10_10` for expenditure, `r energy_10_10` for energy and `r co2eq_10_10` for carbon (Figure 1a-c). Total expenditure ranged from `r exp_bottom_decile` trn€ to `r exp_top_decile` trn€ between bottom and top decile, or `r fd_pae_bottom_decile`€ to `r fd_pae_top_decile`€ per adult equivalent (ae), the energy footprint from `r energy_bottom_decile` EJ to `r energy_top_decile` EJ (or `r energy_pae_bottom_decile` GJ/ae to `r energy_pae_top_decile` GJ/ae), and the carbon footprint from `r co2eq_bottom_decile` MtCO2eq to `r co2eq_top_decile` MtCO2eq (or `r co2eq_pae_bottom_decile` tCO2eq/ae to `r co2eq_pae_top_decile` tCO2eq/ae).
+
+The reason for this is evident from Figures 1d-f. Both the energy intensity of consumption, measured as energy use per € expenditure (d), and the carbon intensity of energy, measured as carbon footprint per energy footprint (f), decreased from bottom to top expenditure decile. The population weighted average energy intensity of consumption decreased from `r mean_energy_intens_bottom_decile` MJ/€ in the bottom decile to less than half (`r mean_energy_intens_top_decile` MJ/€) in the top decile. Likewise, the carbon intensity of energy was higher in the bottom decile (`r mean_co2eq_of_energy_intens_bottom_decile` gCO2eq/TJ) compared to the top decile (`r mean_co2eq_of_energy_intens_top_decile` gCO2eq/TJ). The carbon intensity of consumption in Figure 1f combines the effects of the intensities displayed in Figure 1d and 1e. Across all population weighted intensities per deciles, the variance in the lower four deciles is much higher (Figure 1d-f). 
 
 ```{r figure1, out.width="98%", fig.cap="Household expenditure and environmental footprints and intensities across European expenditure deciles. Total expenditures (a), energy footprint (b), and carbon footprint (c) per decile. Energy intensity of consumption as energy footprint per expenditure (d), carbon intensity of consumption as carbon footprint per expenditure (e), and carbon intensity of energy as carbon footprint per energy footprint (f)."}
 knitr::include_graphics(here::here("analysis", "figures", "figure1.pdf"))
 ```
 
-Figures 1d-e show that energy and carbon intensities of consumption are particularly high in the lower four deciles, while the higher deciles do not show large differences in weighted average energy and carbon intensity. The different intensities of household consumption across European expenditure deciles can be attributed to a combination of two plausible causes: first, if the composition of consumption baskets systematically differs according to the level of household expenditure. Second, if energy and carbon intensity within individual consumption sectors systematically differs according to the level of household expenditure.
+The different intensities of household consumption across European expenditure deciles can be attributed to a combination of two plausible causes: first, the composition of consumption baskets could systematically differ according to the level of household expenditure. Second, the energy and carbon intensity within individual consumption sectors could systematically differ according to the level of household expenditure.
 
 ```{r , fig.width=8, fig.height=2.5}
 
@@ -504,13 +507,11 @@ 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 \@ref(fig:figure2). Lower-income 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 expenditure 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 of consumption 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, which has the lowest carbon intensity, compared to `r exp_share_services_bottom_decile`% in the bottom decile. Single country studies using EE-MRIO models with national resolution can pick up on differences in consumption baskets, but due to the homogeneous technology assumption in EE-MRIOs, cannot represent differences in technology between expenditure deciles.
-
-The tendency for energy and carbon intensity to decrease with increasing affluence can be observed at the global level (ref) between countries and also applies within Europe [@sommer_carbon_2017]. 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 higher-income 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 higher ends of the respective national expenditure distributions (see SI - Supplementary Note and Map).
+Our results show that both of these factors play a role (Figure 2). Lower-income households, on average, spend larger shares of their expenditure in the shelter sector. The bottom decile spent an average of `r exp_share_shelter_bottom_decile`% of their household expenditure on shelter, while the top decile spent `r exp_share_shelter_top_decile`%. The shelter sector stands out with a carbon intensity of consumption 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/€). At the same time, shelter is by far the most carbon intensive sector and has the highest variance in carbon intensity among expenditure deciles. Overall, with increasing decile, the shares of transport and services expenditures increase and the shares of shelter, food and manufactured goods decrease. Households in the top decile spend about `r exp_share_services_top_decile`% in the service sector, which has the lowest carbon intensity of all consumption sectors, compared to `r exp_share_services_bottom_decile`% in the bottom decile. 
 
-The high intensities in the bottom four European expenditure deciles can be attributed in large part to more inefficient and dirtier domestic energy supplies 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]. These differences in energy and carbon intensities in basic needs sectors (especially shelter) account for the smaller inequality between expenditure deciles, in terms of environmental footprints compared to raw expenditures. [*do we need to mention subsidies also?*]
+The tendency for energy and carbon intensity to decrease with increasing affluence has been reported for the global level (ref - Hubacek?) between countries and also within Europe [@sommer_carbon_2017]. 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 expenditure data from national income quintiles. This aggregation cuts off the lower and higher tails of the respective national expenditure distributions (see SI - Supplementary Note and Map). 
 
-[*The consumption basket aspect has been extensively studied and mostly found to be intuitively true. This is a line of inquiry we do not currently pursue but I just remembered the analysis we did on this which is actually quite interesting: This common sense knowledge could be challenged because it is true mostly in western countries with high demand for heating and cooling and mobility both mostly fossil based and subsidized. In this case, necessities especially shelter (maybe and car based mobility (accessible to most)) have a higher intensity compared to "luxury spending" ie the average intensity of the international supply chain for manufactured goods etc.. It is not true in rich countries with high renewable energy shares (e.g. Norway) where the domestic energy system is more resource efficient than the international supply chain. It is possibly also not true in countries with low heating/cooling demand. We may want to check if that flips after applying the best technology transformation.*]
+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]. We did not account here for subsidies which could also have attributed to high energy and carbon intensities (see SI limitations, pp xx). 
 
 ## Inequality across final consumption sectors
 
@@ -617,7 +618,7 @@ food_energy_10_10 = round((energy_per_sector %>% filter(eu_q_rank == 10, five_se
 
 ```
 
-In absolute terms, the final consumption sectors contribute very differently to the total environmental footprint of households (Figure 3). On average, shelter and transport are the two largest sectors, accounting for nearly two thirds of both footprints. However, there are big differences between the sectors when looking at the respective contributions of each expenditure decile. For shelter there is very little difference, in both the energy and carbon footprint, between deciles. The lowest four deciles even have higher carbon footprints from shelter than most higher deciles, which can be explained by the extreme differences in intensity shown in Figure 2. Transport was the most unequal sector, with footprints in the top decile `r transport_energy_10_10` times higher than the bottom decile (corroborating findings in [@ivanova_quantifying_2020] and [@oswald_large_2020]). Manufactured goods was the second most unequal final consumption sector (10:10 ratios around `r man_goods_energy_10_10` for both footprints), followed by services (10:10 ratios of `r services_energy_10_10` for energy and `r services_co2eq_10_10` for carbon) and then food (10:10 ratios of `r food_energy_10_10` for both footprints).
+The final consumption sectors (transport, food, housing, manufactured goods and services) contributed very differently to the total environmental footprint of European households in 2015 (Figure 3). On average, shelter and transport are the two largest sectors, accounting for nearly two thirds of both the energy and carbon footprints. However, there are big differences between the sectors when looking at the respective contributions of each expenditure decile. For shelter there is very little difference between deciles in both the energy and the carbon footprint. The bottom four deciles even have higher carbon footprints from shelter than most top deciles, which can be explained by the extreme differences in intensity shown in Figure 2. Transport was the most unequal sector, with footprints in the top decile 10 times higher than the bottom decile, corroborating findings in [@ivanova_quantifying_2020] and [@oswald_large_2020].  Manufactured goods was the second most unequal final consumption sector (10:10 ratios around `r man_goods_energy_10_10` for both footprints), followed by services (10:10 ratios of `r services_energy_10_10` for energy and `r services_co2eq_10_10` for carbon) and then food (10:10 ratios of `r food_energy_10_10` for both footprints).
 
 ```{r figure3, out.width="100%", fig.cap="Energy and carbon footprints by final consumption sector and European expenditure decile in 2015, further broken down by emission source location."}
 knitr::include_graphics(here::here("analysis", "figures", "figure3.pdf"))
@@ -645,7 +646,7 @@ shelter_energy_direct = round(((energy_per_source %>% filter(five_sectors == "sh
 
 ```
 
-Figure 3 also shows the inequality in geographical source of the household energy and carbon footprints across final consumption sector. The shelter footprint was almost entirely domestic, with `r shelter_co2eq_direct`/`r shelter_energy_direct`% coming from direct household energy use/emissions from heating and cooling, and the rest embedded primarily along the domestic supply chain. The transport footprint, on the other hand, was around 1/4 non-European. The majority of the transport footprint, above 60%, came from vehicle fuel, either directly, or indirectly embedded along its supply chain. The manufactured goods footprint was mostly non-European, while services and food were both around 1/3 non-European. These results suggest that proposed future carbon border-adjustment mechanisms will especially impact the manufactured goods and transport footprints of the higher deciles, and to a lesser extent the food and services footprints, depending on mechanism design [@european_commission_communication_2019]. 
+The geographical source of the household energy and carbon footprints also varies with consumption categories (Figure 3). The shelter footprint was almost entirely domestic, with `r shelter_co2eq_direct`% and `r shelter_energy_direct`% respectively coming from direct household energy use and carbon emissions from heating and cooling, and the rest embedded primarily along the domestic supply chain. The transport footprint, on the other hand, was around one fourth non-European. The majority of the transport footprint, above 60%, came from vehicle fuel, either directly from household, or indirectly, i.e. embedded along household's supply chains. The manufactured goods footprint was mostly non-European, while services and food were both around one third non-European. These results suggest that proposed future carbon border-adjustment mechanisms [@european_commission_communication_2019] will especially impact the manufactured goods and transport footprints of the higher deciles, and to a lesser extent the food and services footprints.
 
 # Counterfactual: a 1.5°C compatible Europe
 
@@ -670,13 +671,11 @@ flextable(df_scenario_info) %>%
   width(width = 3) 
 ```
 
-The various global supply side scenarios (SSP1-1.9, SSP2-1.9, GEA efficiency, IEA ETP B2DS)[@riahi_shared_2017 @gea_gea_nodate @grubler_low_2018] envisage household European energy use falling from the 2015 level of 92 EJ to around 21-31 EJ by 2050, equivalent to a per household reduction from a current average of 250 GJ to 64-94 GJ per adult equivalent. The differences in energy use in 2050 in the scenarios reflect different model assumptions about the rate of expansion of renewable energy and CCS capacity. These scenarios rely on substantial amounts of CCS starting in 2020, which is still a fairly speculative technology, and we therefore interpret them as ranges for the upper limits of 1.5°C-compatible energy supply [@riahi_shared_2017 @gea_gea_nodate].
+The various global supply side scenarios (SSP1-1.9, SSP2-1.9, GEA efficiency, IEA ETP B2DS)[@riahi_shared_2017 @gea_gea_nodate @grubler_low_2018] envisage household European energy use falling from the 2015 level of 92 EJ to around 21-31 EJ by 2050, equivalent to a per household reduction from a current average of 250 GJ to 64-94 GJ per adult equivalent. The differences in energy use in 2050 in the scenarios reflect different model assumptions about the rate of expansion of renewable energy and CCS capacity. These scenarios rely on substantial amounts of CCS, which is still a fairly speculative technology, and we therefore interpret them as ranges for the upper limits of 1.5°C-compatible energy supply [@riahi_shared_2017 @gea_gea_nodate].
 
-It is even more difficult to determine a lower limit for the minimum amount of energy needed for a decent life. This depends strongly on the one hand on the prevalent socio-cultural idea of what constitutes a decent life, and on the other hand, perhaps even more strongly, on the physical infrastructure available to deliver this life. The two global demand side scenarios (LED, DLE)[@grubler_low_2018 @millward-hopkins_providing_2020] that attempt to define such a limit conclude that, in principle, a very low energy footprint (between 16-53 GJ per household adult equivalent) could be sufficient. However, these scenarios rely on socio-technological transformations on a scale that, especially at the lower end, far exceeds the current political discourse on the subject. These scenarios are 1.5°C compatible without resorting to any CCS but they all implicitly (LED)[@grubler_low_2018] or explicitly (DLE)[@millward-hopkins_providing_2020] assume near complete equality of consumption across the population. To put these low energy demand numbers in perspective, the average energy footprint in our sample (250 GJ/ae) is about a factor 5 above the high estimate. Households in the first European expenditure decile had an energy footprint of 130 GJ per adult equivalent in 2015 (roughly 80 GJ/capita) even though they fell almost entirely within the Eurostat definition of severe material deprivation [@eurostat_living_nodate].
+It is even more difficult to determine a lower limit for the minimum amount of energy needed for a decent life. Such a lower limit depends strongly on the prevalent socio-cultural idea of what constitutes a decent life, and, perhaps even more strongly, on the physical infrastructure available to deliver this life. The two global demand side scenarios LED [@grubler_low_2018] and DLE [@millward-hopkins_providing_2020] that attempt to define such a limit conclude that, in principle, a very low energy footprint, between 16-53 GJ per household adult equivalent, could be sufficient. However, these scenarios rely on socio-technological transformations on a scale that, especially at the lower end, far exceed the current political discourse on the subject. These scenarios are 1.5°C compatible without resorting to any CCS but they all implicitly (LED) [@grubler_low_2018] or explicitly (DLE) [@millward-hopkins_providing_2020] assume near complete equality of consumption across the population. To put these low energy demand numbers in perspective, the average energy footprint in our sample is 250 per adult equivalent in 2015, about a factor 5 above the high estimate. Households in the bottom European expenditure decile, which almost entirely fell within the Eurostat definition of severe material deprivation [@eurostat_living_nodate], still had an energy footprint of 130 GJ per adult equivalent in 2015 (roughly 80 GJ/capita), factor 2.5 above the high estimate. 
 
-[*I struggle to separate between energy efficiency in purely technological terms, and energy efficiency of the energy service. This is relevant for the transformation we apply. Do we assume the efficiency differences are only due to inefficient energy carriers and transformation losses, or do we assume this is also due to differences in the demand/provision of energy services, e.g. more rural and car dependent. It would be easier if we could argue the former, which I will do for now.*]
-
-Based on these two constraints, the upper limit on the supply side and the lower limit on the demand side, it is possible to make a generalized estimate of how much inequality in the distribution of energy consumption is numerically possible, if at the same time global warming is to be kept below 1.5°C above pre-industrial levels and a good life for all is to be made possible. Before we can make this evaluation, we must take into account the existing large differences in the technological efficiency of energy provision (Figure 2). Since the European expenditure deciles discussed here include large population groups (\~X persons/households) with different demand structures for energy services (urban/rural, demographic, climatic), we assume that the variation in energy intensity across deciles is largely due to technological efficiency. These differences will be adjusted in the next step.
+Based on these two constraints, the upper limit on the supply side and the lower limit on the demand side, it is possible to make a generalized estimate of how much inequality in the distribution of energy consumption is numerically possible, if at the same time global warming is to be kept below 1.5°C above pre-industrial levels and a decent life for all is to be made possible. Before we can make this evaluation, we must take into account the existing large differences in the technological efficiency of energy provision (Figure 2). These differences will be adjusted in the next step. 
 
 ## Current empirical best technology per sector
 
@@ -951,18 +950,18 @@ a = df_all %>%
 ggsave(here("analysis", "figures", "figure5.pdf"))
 ```
 
-Based on this counterfactual distribution of the energy footprint using homogeneous supply technologies, we can now scale down energy use across European expenditure deciles to meet supply constraints and, where necessary, "squeeze" the distribution to not undershoot minimum energy use requirements in any decile. This means that, based on the current empirical distribution, for each value combination of energy supply and minimum energy use requirement, the maximum permissible inequality can be calculated as a 10:10 ratio (Figure \@ref(fig:figure5). [*Ref to formula*]
-
-Starting at the low end of energy supply, both the DLE and LED scenarios satisfy energy demand without resorting to CCS technologies[@millward-hopkins_providing_2020 @grubler_low_2018]. The DLE scenario explicitly envisions absolute global equality (10:10 ratio of 1) in 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 use values for the Global North (53GJ/aeu) and the Global South (27GJ/aeu) [@grubler_low_2018]. However, due to the bottom-up construction of this demand scenario, these values can be interpreted as estimates for the minimum required energy use. [*space permitting, give examples of the rather extreme nature of demand interventions here or in in scenario description/table above*]
+Based on this counterfactual distribution of the energy footprint using homogeneous supply technologies, we can now scale down energy use across European expenditure deciles to meet supply constraints and, where necessary, "squeeze" the distribution to not undershoot minimum energy use requirements in any decile (Figure 5). 
 
-The descriptions of the energy supply scenarios do not include specific details about how the energy footprints are distributed within the population. The energy savings here are achieved primarily through efficiency improvements, and perhaps also generally assumed demand reductions [*I actually know nothing about these scenarios, how do they achieve the reduction, and is energy demand actually resolved by country maybe?*]. However, Figure \@ref(fig:figure5) makes it clear that even with ambitious demand reductions, as in the LED scenario, a large reduction in inequality between the European expenditure deciles is required.
+Both the DLE and LED scenarios satisfy energy demand for decent living and are compatible with the 1.5 degree target without resorting to CCS technologies [@millward-hopkins_providing_2020 @grubler_low_2018]. The DLE scenario explicitly envisions absolute global inequality (10:10 ratio of 1) in 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 (53GJ/ae) and the Global South (27GJ/ae) [@grubler_low_2018]. However, due to the bottom-up construction of this demand scenario, these values can be interpreted as estimates for the minimum required energy use. 
 
-At current inequality levels, only the two scenarios with heavy CCS deployment and GEA efficiency are possible if we assume extremely low minimum energy use requirements (below 27 GJ/aeu). This 27 GJ/aeu is roughly the value the low-energy demand (LED) scenario gives for the Global South in 2050. If we use the value given for the Global North at 53 GJ/aeu (with strong demand side measures) then inequality would need to be drastically reduced, the 10:10 ratio more than halved, in all scenarios (including those with CCS deployment). 
+The descriptions of the energy supply scenarios do not include specific details about how the energy footprints are distributed within the population. The energy savings here are achieved primarily through efficiency improvements, and perhaps also generally assumed demand reductions. 
 
 ```{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 minimal energy for a decent 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 heavy CCS deployment [add scenario acronym] and GEA efficiency are possible and only if we assume in addition extremely low minimum energy use requirements (below 27 GJ/ae). This 27 GJ/ae is roughly the value the low-energy demand (LED) scenario gives for the Global South in 2050. If we use the value given for the Global North at 53 GJ/ae as minimum energy requirements, which still requires strong demand side measures, then inequality would need to be zero in the LED scenario and cut down by factors xx to yyy in all other scenarios. 
+
 # Conclusions
 
 Estimates of energy and carbon footprint inequality are increasingly being used to assign responsibility for climate change. At a global, regional, and within-country level, energy use and carbon emissions are often highly unequal [@piketty_carbon_2015 @kartha_carbon_2020 @gore_extreme_2015 @hubacek_global_2017 @ivanova_unequal_2020 @gore_t._confronting_2020 @wiedenhofer_unequal_2017 @golley_income_2012 @steenolsen_carbon_2016 @weber_quantifying_2008 @hardadi_implications_2020 @oswald_large_2020]. The proposed solution is often a call to reduce the carbon or energy inequality by reducing over-consumption, especially by the richest at the top of the economic distribution, which would then also reduce the energy and carbon footprints, everything else held equal. Complicating this picture, however, is the fact that energy and carbon intensities of consumption usually differ between economic groups. This is due to different consumption baskets and different access to technology. That lower-income groups tend to have higher energy and carbon intensities is an important finding from the environmental Kuznet's curve literature [@berthe_mechanisms_2015 @scruggs_political_1998]. This finding has not yet been well integrated with the current carbon and energy footprint inequality literature, that focuses more on assigning responsibility based on aggregate energy and carbon footprint inequality. 
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