@@ -480,7 +480,9 @@ Our results show that both of these factors play a role \@ref(fig:figure2). Lowe
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).
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.
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 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 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].
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 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. 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].
[*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 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.
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*]
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. 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.
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.
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).
