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@@ -141,11 +141,11 @@ We assess under what conditions European energy inequality is compatible with th
 
 # Materials and methods
 
-We first decomposed national household final demand expenditure in the Environmentally-Extended Multi-Regional Input-Output (EE-MRIO) model EXIOBASE (version3, industry-by-industry) (ref), by income quintile, using European household budget survey (HBS) macro-data from EUROSTAT (ref). The EUROSTAT HBS publishes national data on mean consumption expenditure by income quintile (in purchasing power standard (PPS) euro) and the structure of consumption expenditure by income quintile and COICOP consumption category. We mapped the EXIOBASE sectors to one of the COICOP consumption categories (our mapping can be found in the SI), and used the relative shares of each COICOP consumption category between the income quintiles in the HBS to decompose the EXIOBASE national household final demand expenditure per sector by income quintile as well. We then multiplied this income-stratified EXIOBASE national household final demand expenditure by 'total' energy use and carbon intensities per EXIOBASE sector, calculated in EXIOBASE using standard input-output calculations, to estimate national household energy and carbon footprints stratified by income quintile. 
+We first decomposed national household final demand expenditure in the Environmentally-Extended Multi-Regional Input-Output (EE-MRIO) model EXIOBASE (version3, industry-by-industry) (ref), by income quintile, using European household budget survey (HBS) macro-data from EUROSTAT (ref). The EUROSTAT HBS publishes national data on mean consumption expenditure by income quintile (in purchasing power standard (PPS) euro) and the structure of consumption expenditure by income quintile and COICOP consumption category. We mapped the EXIOBASE sectors to one of the COICOP consumption categories (our mapping can be found in the SI), and used the relative shares of each COICOP consumption category between the income quintiles in the HBS to decompose the EXIOBASE national household final demand expenditure per sector by income quintile as well. We then multiplied this income-stratified EXIOBASE national household final demand expenditure by 'total' energy use and carbon intensities per EXIOBASE sector, calculated in EXIOBASE using standard input-output calculations, to estimate national household energy and carbon footprints stratified by income quintile.
 
 The energy footprint is the gross total energy use energy extension in EXIOBASE, which converts final energy consumption in the IEA energy balance data from the territorial to residence principle following SEEA energy accounting (ref - Stadler et al.). The carbon footprint includes CO2, CH4, N2O, SF6, HFCs and PFCs, from combustion, non-combustion, agriculture and waste, but not land-use change. For both footprints, direct energy use and carbon emissions from households is included, with the total split between shelter, transport and manufactured goods using further data from EUROSTAT on this split.
 
-Finally, we aggregated the data of 30 European countries with 5 income groups each into 10 European expenditure groups, to decompose the total European household energy and carbon footprint by European expenditure decile, ranking each national income group according to their mean consumption expenditure in PPS. We call these European expenditure deciles, although only countries with EUROSTAT data from 2005 to 2015 are included, which excludes Italy and Luxembourg, but includes the UK, Norway and Turkey. 
+Finally, we aggregated the data of 30 European countries with 5 income groups each into 10 European expenditure groups, to decompose the total European household energy and carbon footprint by European expenditure decile, ranking each national income group according to their mean consumption expenditure in PPS. We call these European expenditure deciles, although only countries with EUROSTAT data from 2005 to 2015 are included, which excludes Italy and Luxembourg, but includes the UK, Norway and Turkey.
 
 We use households normalized by adult equivalent unit as the unit of analysis, following the EUROSTAT HBS. The adult equivalent units from EUROSTAT adjust for household size in different countries and income groups. As inequality measure through the study, we divide the average value of the population in the top decile by that of the bottom decile, a 10:10 ratio. For example, in expenditure, a 10:10 ratio of 5 means that adult equivalent units in the top decile spend 5 times more on average than those in the bottom decile. The data and procedures are described in detail in the supplementary information (SI).
 
@@ -393,7 +393,7 @@ Consumption-based indicators such as the energy and greenhouse gas footprint of
 knitr::include_graphics(here::here("analysis", "figures", "figure1.pdf"))
 ```
 
-Figures 1d-e show that energy and GHG intensities are particularly high in the lower four deciles, while the higher deciles do not show large differences in weighted average resource intensity. The background to this is the unequal distribution of income in Europe, which, especially in the East, is much lower than in Central and Northern Europe. In most of Eastern European countries, between 80-100% of the population falls within the bottom four European deciles. In Scandinavia, Germany, France, Austria, the Netherlands, Belgium, the UK, and Ireland fewer than 20% of the population belong to the bottom four European deciles (Supplementary figure map). [*Here a sentence quoting literature or EXIOBASE to show that a number of Eastern European countries have much higher intensities due to, e.g. coal use.*] Note 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.
+Figures 1d-e show that energy and GHG intensities are particularly high in the lower four deciles, while the higher deciles do not show large differences in weighted average resource intensity. The different intensities of household consumption across European expenditure deciles can be attributed to a combination of two plausible causes: first, if the the composition of consumption baskets systematically differs according to the level of household expenditure. Second, if resource intensity within individual consumption sectors systematically differs according to the level of household expenditure.
 
 ```{r , fig.width=8, fig.height=2}
 
@@ -435,8 +435,6 @@ a = ggplot(pdat, aes(x=value, y=five_sectors, color=eu_q_rank)) +
 #ggsave(here("analysis", "figures", "figure2-test.pdf"))
 ```
 
-The different intensities of household consumption across European expenditure deciles can be attributed to a combination of two plausible causes: first, if the the composition of consumption baskets systematically differs according to the level of household expenditure. Second, if resource intensity within individual consumption sectors systematically differs according to the level of household expenditure.
-
 ```{r figure2, out.width="100%", fig.cap="Sectoral expenditure shares and GHG intensities of European expenditure deciles. Share of expenditure per final demand sector of total spending per decile in percent (a) and GHG intensity per final demand sector in kgCO2/€."}
 knitr::include_graphics(here::here("analysis", "figures", "figure2.pdf"))
 ```
@@ -457,7 +455,11 @@ exp_share_services_top_decile = round((pdat_basket %>% filter(eu_q_rank == 10, f
 
 ```
 
-Figure \@ref(fig:figure2) shows that both of these factors play a role. 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 GHG 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 GHG 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 lowset GHG intensity, compared to `r exp_share_services_bottom_decile`% in the bottom decile [*wow that is high. correct?*]. 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 quantiles.
+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 GHG 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 GHG 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 GHG intensity, compared to `r exp_share_services_bottom_decile`% in the bottom decile [*wow that is high. correct?*]. 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).
+
+The high resource intensities in the bottom four European expenditure deciles can be attributed in large part to inefficient 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 and had a higher average GHG intensity per MJ of heat delivered than both Europe and the world (XXX). These differences in specific energy and emissions intensities in basic services sectors (especially shelter) account for the smaller inequality between expenditure deciles in terms of resource footprints compared to raw expenditures [*do we need to mention subsidies also?*].
 
 *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.*
 
@@ -587,19 +589,23 @@ shelter_energy_direct = round(((energy_per_source %>% filter(five_sectors == "sh
 
 ```
 
-[*This needs an intro and polish, not clear at the moment why we say this. Maybe only relevant if we pick up reduction options of non-EU emissions in dscussion.*] The shelter footprint was almost entirely domestic, with `r shelter_co2eq_direct`/`r shelter_energy_direct`% coming from direct household emissions/energy use for heating and cooling, and the rest embedded primarily along the domestic supply chain. The transport footprint was just under 2/3rds domestic. The majority of the transport footprint, above 60%, came from vehicle fuel, either burned directly or indirectly embedded along its supply chain. More than half of the transport footprint's foreign 1/3rd came from outside Europe. The manufactured goods footprint was mostly non-European, while services and food were both around half domestic.
+[*The following paragraph needs an intro and polish, not clear at the moment why we say this. Maybe only relevant if we pick up reduction options of non-EU emissions in dscussion.*] The shelter footprint was almost entirely domestic, with `r shelter_co2eq_direct`/`r shelter_energy_direct`% coming from direct household emissions/energy use for heating and cooling, and the rest embedded primarily along the domestic supply chain. The transport footprint was just under 2/3rds domestic. The majority of the transport footprint, above 60%, came from vehicle fuel, either burned directly or indirectly embedded along its supply chain. More than half of the transport footprint's foreign 1/3rd came from outside Europe. The manufactured goods footprint was mostly non-European, while services and food were both around half domestic.
 
 # Counterfactual: a 1.5°C compatible Europe
 
-Global 1.5°C compatible decarbonisation scenarios achieve a similar climate outcome with different assumptions about the transformation of energy supply and demand, from renewable capacity, deployment of carbon-capture-and-storage (CCS), and socio-technological transformation. 
+Global 1.5°C compatible decarbonisation scenarios achieve a similar climate outcome with different assumptions about the transformation of energy supply and demand, from renewable capacity, deployment of carbon-capture-and-storage (CCS), and socio-technological demand transformation.
 
-The various global supply side scenarios (name them) envisage total EU (*or our sample*) energy consumption falling from the current X EJ to X-Y EJ by 2030 (or 2050), equivalent to a per capita reduction from a current average of 250 GJ to X-Y GJ. The differences in energy consumption in 2050 in the scenarios reflect different model assumptions about the rate of expansion of renewable energy and CCS capacity. Most/all of these scenarios rely on substantial amounts of CCS (*starting from when?*) 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. 
+[*Alternative to converting in the following text between per capita and per adult, we could convert scenarios to rough per adult equvialents and discuss differences across deciles in supplement*]
 
-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 satisfy human needs [*dont want a 'needs' debate with you Helga, just didnt know how to say this differently.*]. The two (*or three with Boell*) global demand side scenarios (LED, DLE) that attempt to define such a limit conclude that, in principle, very low energy consumption (between 16-27 GJ per capita) could be sufficient. However, these scenarios rely on socio-technological transformations on a scale that far exceeds the current political discourse on the subject. All two/three scenarios are 1.5°C compatible without resorting to any CCS but they all implictily (LED) or explicitly (DLE) assume near complete equality of consumption across the population. To put these low energy demand numbers in perspective, households in the first European expenditure quantile had an energy consumption of 130 GJ per adult equivalent (*convert/handle adulteq/capita consistently in mansucript*) in 2015 and fell almost entirely within the Eurostat definition of severe material deprivation.
+[*maybe table with scenario properties*]
+
+The various global supply side scenarios (SSP1-1.9, SSP2-1.9, GEA efficiency) envisage total EU (*or our sample*) energy consumption falling from the current X EJ to X-Y EJ by 2030 (or 2050), equivalent to a per household reduction from a current average of 250 GJ to X-Y GJ per adult equivalent. The differences in energy consumption in 2050 in the scenarios reflect different model assumptions about the rate of expansion of renewable energy and CCS capacity. Most/all of these scenarios rely on substantial amounts of CCS (*starting from when?*) 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.
+
+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 satisfy human needs/to deliver on this life [*dont want a 'needs' debate with you Helga, just didnt know how to say this differently.*]. The two (*or three with Boell*) global demand side scenarios (LED, DLE) that attempt to define such a limit conclude that, in principle, a very low energy footprint (between 16-53 GJ per capita, or X-Y, translated to household adult equivalents) 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. All two/three scenarios are 1.5°C compatible without resorting to any CCS but they all implicitly (LED) or explicitly (DLE) 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 about a factor 5 above the high estimate (250 MJ/aeq). Households in the first European expenditure decile had an energy footprint of 130 GJ per adult equivalent (*give rough per capita*) in 2015 even though they fell almost entirely within the Eurostat definition of severe material deprivation.
 
 [*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 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.
 
 ## Current empirical best technology per sector
 
@@ -718,13 +724,9 @@ a = p1 + map + plot_annotation(tag_levels = 'a') +
 knitr::include_graphics(here::here("analysis", "figures", "figure4.pdf"))
 ```
 
-Our results show that in 2015, rich people in rich countries had access to the most energy-efficient energy services across all final demand sectors (Figure 2b [*todo currently figure shows only GHG*]). Since we are interested in the numerically possible inequality in the distribution of actual consumption of goods and services in the next section, these efficiency differences must first be adjusted. In practice, this corresponds, for example, to the need for large-scale investments in the technical efficiency of heat, electricity and hot water, especially in Eastern Europe. Figure 4 shows the energy footprint savings per decile (Fig. 4a) that would have occurred in 2015 if all deciles had the same efficiency per final demand sector as the 10th decile. Around 17 EJ would have been saved in total, and the energy footprint of the first decile would have been nearly half its 2015 value. Fig. 4b shows saved energy per country, with Eastern European countries especially saving large proportions of their 2015 footprint, over 60% for Bulgaria and Estonia for example.
-
-
-Points to hit:
--   Improving energy efficiency is the most politically uncontroversial step towards mitigation targets. The EU has a bunch of policies for that, old and new. The GD has a transition fund to pay for this for poorer countries, sort of.
-
+Our results show that in 2015, rich people in rich countries had access to the most energy-efficient energy services across all final demand sectors (Figure 2b [*todo currently figure shows only GHG*]). Since we are interested in the numerically possible inequality in the distribution of actual consumption of goods and services in the next section, these efficiency differences must first be adjusted. In practice, this corresponds, for example, to the need for large-scale investments in the technical efficiency of heat, electricity and hot water supply, especially in Eastern Europe. Figure 4 shows the energy footprint savings per decile (Fig. 4a) that would have occurred in 2015 if all deciles had the same efficiency per final demand sector as the 10th decile. Around 17 EJ would have been saved in total, and the energy footprint of the first decile would have been nearly half its 2015 value. Fig. 4b shows saved energy per country, with Eastern European countries especially saving large proportions of their 2015 footprint, over 60% for Bulgaria and Estonia for example.
 
+Points to hit: - Improving energy efficiency is the most politically uncontroversial step towards mitigation targets. The EU has a bunch of policies for that, old and new. The GD has a transition fund to pay for this for poorer countries, sort of.
 
 ## Inequality in a 1.5°C compatible Europe
 
@@ -795,18 +797,17 @@ a = df_all %>%
 
 ```
 
-Points to hit:
-
--   We introduce the main global 1.5°C compatible scenarios with their energy supply and their assumptions for minimal energy demand for a decent life (maybe table).
+Based on this counterfactual distribution of the energy footprint using homogeneous supply technologies, we can now scale down energy consumption across European expenditure deciles to meet supply constraints and, where necessary, "squeeze" the distribution to not undershoot minimum demand constraints in any decile. This means that, based on the current empirical distribution, for each value combination of supply and minimum necessary demand, the maximum permissible inequality can be calculated as a 10:10 ratio (Figure \@ref(fig:figure5). [*Ref to formula*]
 
--   We explain that they all give average values but say little or nothing about distribution. We then explain for one example that if we simply scale the current distribution to the mean value of a medium supply scenario, we run into problems for the lower deciles to achieve decent living energy.
+Starting at the low end of energy supply, both (or all three with Boell) the DLE and LED scenarios satisfy energy demand without resorting to CCS technologies. 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. The LED scenario does not explicitly discuss distributional aspects beyond giving different final energy consumption values for the Global North (53GJ/cap) and the Global South (27GJ/cap). However, due to the bottom-up construction of this demand scenario, these values can be interpreted as estimates for the minimum required energy demand. [*space permitting, give examples of the rather extreme nature of demand interventions here or in in scenario description/table above*]
 
--   We then explain how to do this more generically pointing to figure 5 that shows the scenario space.
+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 quantiles is required.
 
-Text:
+At current inequality levels, only the two scenarios with heavy CCS deployment and GEA efficiency are possible if we assume extremely low minimum energy requirements (below 27 GJ/cap). This 27 GJ/capita is 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/cap (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).
 
+[*Figure supplement: show country map with energy reductions resulting from SSPx supply and LED demand*]
 
-Fig. 5 shows this option space between achieving mean energy in five decarbonisation scenarios, and the trade-off between achieving minimum energy requirements as well (x-axis), and the level of inequality required to achieve both (y-axis). In Figure 5, all deciles have the same technology as the tenth decile, as shown in Figure 4. For example, to achieve mean energy of 87 GJ/cap (as in the SSP1-1.9 scenario) and minimum energy of 27 GJ/cap for all, inequality would need to decrease from the current 10:10 ratio around 7 to just over 6. At current inequality levels, only those scenarios with heavy CCS deployment and GEA efficiency are possible if we assume likely overly optimistic minimum energy requirements (below 27 GJ/cap). This 27 GJ/capita is the value the low-energy demand (LED) scenario (with strong demand-side effort) gives for the global South in 2050, with the global North at 53 GJ/cap. If we assumed minimum energy requirements to be 53 GJ/cap, then inequality would need to be drastically reduced, the 10:10 ratio more than halved, in all scenarios (including those with CCS deployment).
+Mini-conclusion: It is not enough to maximise supply and minimise demand. To hit targets, inequality needs to dramatically reduce under all realistic scenarios.
 
 ```{r figure5, out.width="70%", fig.align="center", fig.cap="Dings. in Figure 5, all deciles have 'best technology' already"}
 knitr::include_graphics(here::here("analysis", "figures", "figure5.pdf"))
@@ -814,13 +815,11 @@ knitr::include_graphics(here::here("analysis", "figures", "figure5.pdf"))
 
 # Discussion and conclusions
 
+[*conclusions - Ingram*]
 
+-   more nuance on carbon-energy inequality - responsibility is important, but cannot say *reduction of inequality is an important goal* without more detail and we try to provide this in the context of Europe
 
-[*conclusions - Ingram*] 
-
-- more nuance on carbon-energy inequality - responsibility is important, but cannot say *reduction of inequality is an important goal* without more detail and we try to provide this in the context of Europe
-
-- investigating the context is especially important concerning two important, potentially conflicting, goals: energy-carbon reduction and energy-carbon minimum use for decent living. Achieving both in Europe likely requires inequality reduction. 
+-   investigating the context is especially important concerning two important, potentially conflicting, goals: energy-carbon reduction and energy-carbon minimum use for decent living. Achieving both in Europe likely requires inequality reduction.
 
 # Acknowledgements
 
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