@@ -146,7 +146,7 @@ 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 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.
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 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 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.
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@@ -166,7 +166,7 @@ As inequality measure we use the 10:10 ratio, i.e. the expenditure or the enviro
## Computing maximum permissible inequality
Based on an hypothetical current best technology distribution across European household expenditure deciles, for each value combination of maximum energy supply from [xx] scenarios [REF] and minimum energy use requirements from [REF], the maximum permissible inequality was calculated as a 10:10 ratio using the formula [insert formula]. The remaining global emissions budget to achieve the 1.5 degree target from the scenarios was allocated in proportion to population (equal per capita allocation). All data and procedures are described in detail in the supplementary information (SI).
Based on an hypothetical current best technology distribution across European household expenditure deciles, for each value combination of maximum energy supply from [xx] scenarios [@riahi_shared_2017 @gea_gea_nodate] and minimum energy use requirements from [@grubler_low_2018 @millward-hopkins_providing_2020], the maximum permissible inequality was calculated as a 10:10 ratio using the formula [insert formula]. The remaining global emissions budget to achieve the 1.5 degree target from the scenarios was allocated in proportion to population (equal per capita allocation). All data and procedures are described in detail in the supplementary information (SI).
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 housing sector. The bottom decile spent an average of `r exp_share_housing_bottom_decile`% of their household expenditure on housing, while the top decile spent `r exp_share_housing_top_decile`%. The housing sector stands out with a carbon intensity of consumption more than 3 times higher in the bottom decile (`r int_co2eq_housing_bottom_decile` kgCO2eq/€) than in the top decile (`r int_co2eq_housing_top_decile` kgCO2eq/€). At the same time, housing 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 mobility and services expenditures increase and the shares of housing, food and 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 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 tendency for energy and carbon intensity to decrease with increasing affluence has been reported for the global level [@hubacek_global_2017] 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 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).
