edit refs

analysis/paper/paper.Rmd

@@ -132,7 +132,7 @@ pdat_sector_summary_by_eu_ntile =
 
 Decarbonising the energy system in accordance with the Paris agreement requires a deep transformation of both the supply and the demand side [@riahi_shared_2017] [@grubler_low_2018]. On both sides, however, necessary transformation is restricted by different factors. On the supply side, there exist economic and physical upper limits of how much energy can be provided from renewable sources on the one hand, and how much CO2 removal infrastructure is used to compensate for remaining emissions from fossil fuels on the other. On the demand side [@creutzig_towards_2018], by contrast, there are lower limits to how much energy is minimally required for a decent standard of living [@grubler_low_2018] [@millward-hopkins_providing_2020], depending on different assumptions about infrastructures and service provision [@creutzig_towards_2018], as well as the prevalent social ideas about what constitutes decent living [@rao_energy_2019] [@millward-hopkins_providing_2020]. Maximum possible energy supply and minimum required energy demand describe the corridor in which the simultaneous achievement of climate targets and a decent standard of living for all is possible and, at the same time, restricts the distribution of available energy services among the population. If this dual objective is taken seriously in European climate policy, then there are practical limits to how unequal the society of the future can be, which go beyond the purely political [@leach_equity_2018]. In fact, a limited energy supply creates an obvious, if rarely acknowledged, zero-sum game where energetic over-consumption by some must be compensated with less consumption by others. 
 
-The average household energy footprint of European citizens was around 170 gigajoules (GJ) per capita in 2015 [@stadler_exiobase_2018] [@eurostat_eurostat_nodate-3], and the household carbon footprint around 7 tonnes CO2-equivalence (tCO2eq) per capita [@eurostat_eurostat_nodate-4]. However, the differences in household energy and carbon footprints are large within and between different regions in Europe [@ivanova_mapping_2017] [@gore_t._confronting_2020] [@oswald_large_2020]. Energy footprints ranged from less than 100 GJ per capita to over 300 GJ per capita [@oswald_large_2020], and carbon footprints from below 2.5 tCO2eq per capita to 55 tCO2eq per capita [@ivanova_unequal_2020]. Depending on the assumptions of different global decarbonisation scenarios, the average footprints likely need to be reduced to somewhere below 100 GJ per capita [@riahi_shared_2017] [@grubler_low_2018] [@millward-hopkins_providing_2020], and below 2.1 tCO2eq per capita [@akenji_1.5-degree_2019] by 2050, respectively.
+The average household energy footprint of European citizens was around 170 gigajoules (GJ) per capita in 2015 [@stadler_exiobase_2018] [@eurostat_eurostat_2021-1], and the household carbon footprint around 7 tonnes CO2-equivalence (tCO2eq) per capita [@eurostat_eurostat_2021-2]. However, the differences in household energy and carbon footprints are large within and between different regions in Europe [@ivanova_mapping_2017] [@gore_t._confronting_2020] [@oswald_large_2020]. Energy footprints ranged from less than 100 GJ per capita to over 300 GJ per capita [@oswald_large_2020], and carbon footprints from below 2.5 tCO2eq per capita to 55 tCO2eq per capita [@ivanova_unequal_2020]. Depending on the assumptions of different global decarbonisation scenarios, the average footprints likely need to be reduced to somewhere below 100 GJ per capita [@riahi_shared_2017] [@grubler_low_2018] [@millward-hopkins_providing_2020], and below 2.1 tCO2eq per capita [@akenji_1.5-degree_2019] by 2050, respectively.
 
 In this paper, we assess under what conditions European energy inequality is compatible with the achievement of global climate goals and a decent standard of living, taking both inequality within and between European countries into account. To this end, we first construct household energy and carbon footprints for harmonized European expenditure deciles in 2015, combining data from EUROSTAT's Household Budget Survey (HBS) with the Environmentally-Extended Multi-Regional Input-Output (EE-MRIO) model EXIOBASE. We analyze the distribution of energy and carbon intensities across European expenditure deciles and final consumption categories, and compare this current structure to a counterfactual situation where all European expenditure deciles use the best technology available in Europe. Finally, we examine how the energy inequality across European expenditure deciles would need to change in order to achieve the dual goal of climate protection and a decent standard of living for all. 
 
@@ -142,7 +142,7 @@ While the European Green Deal already recognizes that inequalities in income, en
 
 ## Income-stratified national household energy and carbon footprints
 
-We first used the EE-MRIO model EXIOBASE for 2015 (version3.7, industry-by-industry) [@stadler_exiobase_2018] and the European 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 standards (PPS), by COICOP consumption category, country and year. We chose EXIOBASE as the EE-MRIO for this study because of its European focus, with nearly all countries in the EUROSTAT HBS also found as stand-alone countries in EXIOBASE, its detailed environmental extension data, and its year coverage. 
+We first used the EE-MRIO model EXIOBASE for 2015 (version3.7, industry-by-industry) [@stadler_exiobase_2018] and the European HBS macro-data from EUROSTAT for 2015 [@eurostat_database_2021] 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 standards (PPS), by COICOP consumption category, country and year. We chose EXIOBASE as the EE-MRIO for this study because of its European focus, with nearly all countries in the EUROSTAT HBS also found as stand-alone countries in EXIOBASE, its detailed environmental 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 the HBS. We then used the relative expenditure shares of each income quintile on the COICOP consumption categories in the HBS, to stratify the matching EXIOBASE national household final demand expenditure per sector by income quintile, according to those relative HBS expenditure shares. Using standard input-output techniques we calculated ‘total’ (i.e. direct and indirect supply chain) energy and carbon intensities per EXIOBASE sector, and multiplied them with the now income-stratified EXIOBASE national household final demand expenditure, to estimate the supply chain part of national household energy and carbon footprints by national income quintile. 
 
@@ -156,13 +156,13 @@ To calculate European household expenditure deciles, we first ranked the nationa
 
 The unit of analysis for our energy and carbon footprint calculations is the household. We normalized our results to average adult equivalent per household and per national income quintile, as this is how the EUROSTAT HBS publishes its data. The first adult in the household is given a weight of 1.0, each adult thereafter 0.5, and each child 0.3 [@eurostat_description_2016]. 
 
-For our calculations of attainable corridors for achieving the dual goal of climate protection and a decent standard of living for all, we adjusted the total per capita results from published 1.5°C decarbonisation scenarios to household adult equivalents in order to better compare them with our environmental footprint estimates. Estimates of minimum final energy required for a decent standard of living are from Grubler et al. (2018) [@grubler_low_2018] and Millward-Hopkins et al. (2020) [@millward-hopkins_providing_2020], while maximum final energy compatible with the 1.5°C target is from the decarbonisation scenarios in the International Institute for Applied Systems Analysis (IIASA) scenario database [@riahi_shared_2017] [@gea_gea_nodate].
+For our calculations of attainable corridors for achieving the dual goal of climate protection and a decent standard of living for all, we adjusted the total per capita results from published 1.5°C decarbonisation scenarios to household adult equivalents in order to better compare them with our environmental footprint estimates. Estimates of minimum final energy required for a decent standard of living are from Grubler et al. (2018) [@grubler_low_2018] and Millward-Hopkins et al. (2020) [@millward-hopkins_providing_2020], while maximum final energy compatible with the 1.5°C target is from the decarbonisation scenarios in the International Institute for Applied Systems Analysis (IIASA) scenario database [@riahi_shared_2017] [@iiasa_gea_2012].
 
 As inequality measure we use the 10:10 ratio, i.e. the expenditure or the environmental footprint of the top European expenditure decile divided by that of the bottom European expenditure decile. Thus, an expenditure 10:10 ratio of 5 means that one adult equivalent in the top decile spent 5 times more on average than one adult equivalent in the bottom decile. 
 
 ## Computing maximum permissible inequality
 
-Based on a counterfactual best available technology distribution across European expenditure deciles, for each value combination of maximum energy supply from four scenarios [@riahi_shared_2017] [@gea_gea_nodate] and minimum energy requirements from refs. [@grubler_low_2018] and [@millward-hopkins_providing_2020], the maximum permissible inequality was calculated as a 10:10 ratio using the formula [insert formula]. All data and procedures are described in more detail in the supplementary information (SI).
+Based on a counterfactual best available technology distribution across European expenditure deciles, for each value combination of maximum energy supply from four scenarios [@riahi_shared_2017] [@iiasa_gea_2012] and minimum energy requirements from refs. [@grubler_low_2018] and [@millward-hopkins_providing_2020], the maximum permissible inequality was calculated as a 10:10 ratio using the formula [insert formula]. All data and procedures are described in more detail in the supplementary information (SI).
 
 # Results and discussion
 
@@ -546,7 +546,7 @@ Our results show that both of these factors play a role (Figure 2). The housing
 
 The tendency for energy and carbon intensity to decrease with increasing affluence has been reported for the global level between countries [@hubacek_global_2017] [@berthe_mechanisms_2015] [@scruggs_political_1998] [@weber_quantifying_2008] and also within Europe [@sommer_carbon_2017] [@bianco_understanding_2019] [@kerkhof_determinants_2009]. Our results show that the four lowest European expenditure deciles make up over 80% of the population in Eastern European countries, while less than 20% of the population in the higher-income European countries (Scandinavia, Germany, France, Austria, the Netherlands, Belgium, the UK, and Ireland) are in the lowest European expenditure deciles (see SI, Figure S1). 
 
-The high intensities in the bottom four European expenditure deciles can be attributed in large part to more inefficient and dirtier domestic energy supply and demand technologies for heating and electricity generation in Poland, Bulgaria, the Czech Republic, and Romania. Poland alone was responsible for about 40% of total coal combustion for heat production in Europe in 2015 [@eurostat_eurostat_nodate-2], and had a higher average intensity of carbon per MJ of heat delivered than both Europe and the world [@werner_international_2017]. We did not account for energy subsidies here, but different subsidy levels in different countries could also contribute to higher energy and carbon intensities [@sovacool_reviewing_2017].
+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_2021], 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 for energy subsidies here, but different subsidy levels in different countries could also contribute to higher energy and carbon intensities [@sovacool_reviewing_2017].
 
 ## Inequality across final consumption categories
 
@@ -709,9 +709,9 @@ flextable(df_scenario_info) %>%
   width(width = 2.1) 
 ```
 
-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] would see the European household energy footprint falling from the 2015 level of `r energy_total_hh` EJ to around 21-31 EJ by 2050, equivalent to a per adult equivalent reduction from a 2015 average of `r energy_pae_mean` GJ to around 64-94 GJ. The differences in final energy in 2050 in the scenarios reflect different model assumptions about the rate of expansion of renewable energy, efficiency improvements and conservation, and CCS capacity. These scenarios rely on 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].
+The various global supply-side scenarios (SSP1-1.9, SSP2-1.9, GEA-efficiency, IEA ETP B2DS) [@riahi_shared_2017] [@iiasa_gea_2012] [@grubler_low_2018] would see the European household energy footprint falling from the 2015 level of `r energy_total_hh` EJ to around 21-31 EJ by 2050, equivalent to a per adult equivalent reduction from a 2015 average of `r energy_pae_mean` GJ to around 64-94 GJ. The differences in final energy in 2050 in the scenarios reflect different model assumptions about the rate of expansion of renewable energy, efficiency improvements and conservation, and CCS capacity. These scenarios rely on 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] [@iiasa_gea_2012].
 
-It is more difficult to determine a lower limit for the minimum amount of energy required for a decent standard of living. Such a lower limit depends strongly on the prevalent socio-cultural idea of what constitutes a decent standard of living, and, perhaps even more strongly, on the physical infrastructure available to deliver this. The two global demand-side scenarios, Low Energy Demand (LED) [@grubler_low_2018] and Decent Living Energy (DLE) [@millward-hopkins_providing_2020], that attempt to define such a limit conclude that, in principle, a very low energy footprint, between around 15-53 household GJ per 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 numbers in perspective, the average household energy footprint in our sample was `r energy_pae_mean` GJ per adult equivalent in 2015, about a factor 5 above the high estimate. Households in the bottom European expenditure decile, many falling within the EUROSTAT definition of severe material deprivation [@eurostat_living_nodate], still had an energy footprint of `r energy_pae_bottom_decile` GJ per adult equivalent in 2015 (roughly 80 GJ/capita), a factor of 2.5 above the high estimate. 
+It is more difficult to determine a lower limit for the minimum amount of energy required for a decent standard of living. Such a lower limit depends strongly on the prevalent socio-cultural idea of what constitutes a decent standard of living, and, perhaps even more strongly, on the physical infrastructure available to deliver this. The two global demand-side scenarios, Low Energy Demand (LED) [@grubler_low_2018] and Decent Living Energy (DLE) [@millward-hopkins_providing_2020], that attempt to define such a limit conclude that, in principle, a very low energy footprint, between around 15-53 household GJ per 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 numbers in perspective, the average household energy footprint in our sample was `r energy_pae_mean` GJ per adult equivalent in 2015, about a factor 5 above the high estimate. Households in the bottom European expenditure decile, many falling within the EUROSTAT definition of severe material deprivation [@eurostat_living_2020], still had an energy footprint of `r energy_pae_bottom_decile` GJ per adult equivalent in 2015 (roughly 80 GJ/capita), a factor of 2.5 above the high estimate. 
 
 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 and a decent standard of living 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. 
 

analysis/paper/references.bib

@@ -680,11 +680,12 @@ Publisher: Nature Publishing Group},
 	year = {2013}
 }
 
-@misc{gea_gea_nodate,
+@misc{iiasa_gea_2012,
 	title = {{GEA} {Scenario} database (public)},
-	url = {https://www.iiasa.ac.at/web-apps/ene/geadb/dsd?Action=htmlpage&page=about},
+	url = {https://tntcat.iiasa.ac.at/geadb/dsd?Action=htmlpage&page=about},
 	urldate = {2021-01-20},
-	author = {{GEA}},
+	author = {{IIASA}},
+	year = {2012},
 	file = {GEA Scenario database (public):/home/jaccard/.mozilla/firefox/67kb6jd5.default/zotero/storage/VYWCLUIW/dsd.html:text/html}
 }