Our data 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 (our aggregated 'shelter' sector does not include rental payments. We have included the EXIOBASE production sector 'real estate services', which includes rental payments, in our aggregated 'services' sector, not the aggregated 'shelter' sector - see SI Table 4). 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.
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 (our aggregated 'shelter' sector does not include rental payments. We have included the EXIOBASE production sector 'real estate services', which includes rental payments, in our aggregated 'services' sector, not the aggregated 'shelter' sector - see SI Table 4). 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 (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 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 tendency for energy and carbon intensity to decrease 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 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 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.
The various global supply side scenarios (SSP1-1.9, SSP2-1.9, GEA efficiency) envisage total EU (*or our sample*) energy use 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 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.
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) 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. 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 in 2015 even though they fell almost entirely within the Eurostat definition of severe material deprivation.
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) 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. 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 in 2015 even though they fell almost entirely within the Eurostat definition of severe material deprivation.
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 2). 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.
Our results show that in 2015, higher-income people in higher-income countries had access to the most energy-efficient energy services across all final consumption sectors (Figure 2). 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 consumption sector as the top decile. Around 17 EJ would have been saved in total, and the energy footprint of the bottom 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.
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.
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@@ -822,7 +822,7 @@ Based on this counterfactual distribution of the energy footprint using homogene
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.
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.
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 quantiles 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. 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 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).
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).
