abstract={Climate change and increasing income inequality have emerged as twin threats to contemporary standards of living, peace and democracy. These two problems are usually tackled separately in the policy agenda. A new breed of radical proposals have been advanced to manage a fair low-carbon transition. In this spirit, we develop a dynamic macrosimulation model to investigate the long-term effects of three scenarios: green growth, policies for social equity, and degrowth. The green growth scenario, based on technological progress and environmental policies, achieves a significant reduction in greenhouse gas emissions at the cost of increasing income inequality and unemployment. The policies for social equity scenario adds direct labour market interventions that result in an environmental performance similar to green growth while improving social conditions at the cost of increasing public deficit. The degrowth scenario further adds a reduction in consumption and exports, and achieves a greater reduction in emissions and inequality with higher public deficit, despite the introduction of a wealth tax. We argue that new radical social policies can combine social prosperity and low-carbon emissions and are economically and politically feasible.},
language={en},
number={4},
urldate={2021-02-17},
journal={Nature Sustainability},
author={D’Alessandro, Simone and Cieplinski, André and Distefano, Tiziano and Dittmer, Kristofer},
month=apr,
year={2020},
note={Number: 4
Publisher: Nature Publishing Group},
pages={329--335},
file={D’Alessandro et al_2020_Feasible alternatives to green growth.pdf:/home/jaccard/.mozilla/firefox/67kb6jd5.default/zotero/storage/UFADH7N2/D’Alessandro et al_2020_Feasible alternatives to green growth.pdf:application/pdf;Snapshot:/home/jaccard/.mozilla/firefox/67kb6jd5.default/zotero/storage/QH7JIA2T/s41893-020-0484-y.html:text/html}
abstract={Ambitious carbon pricing reform is needed to meet climate targets. This Perspective argues that effective revenue recycling schemes should prioritize behavioural considerations that are aimed at achieving greater political acceptance.},
language={en},
number={8},
urldate={2021-02-17},
journal={Nature Climate Change},
author={Klenert, David and Mattauch, Linus and Combet, Emmanuel and Edenhofer, Ottmar and Hepburn, Cameron and Rafaty, Ryan and Stern, Nicholas},
@@ -92,15 +92,15 @@ The final demand expenditure, and thus the national footprint, is typically disa
In order to stratify the EE-MRIO final demand expenditure and footprint by income quantile, a second data input is needed with information on the income/expenditure distribution. This could be a household budget survey (HBS) stratified by income quantile or, at a more aggregate level, the national income distribution. In this paper we use:
2) HBS: European household budget survey from EUROSTAT, macro-data, from : https://ec.europa.eu/eurostat/web/household-budget-surveys/database [accessed on 22.05.2020] [@eurostat_database_nodate]
We discuss each of these in turn, including additional data inputs needed to complement the HBS. We then present our methodology for our results from the main paper and some limitations. The final sub-section in 'supplementary materials and methods' presents an alternative methodology, and the final section of this supplementary information document presents supplementary results from the methodology used in the main paper and the alternative methodology. The whole analysis was performed in RStudio [@r_core_team_r:_2020], using a variety of R packages [@wickham_welcome_2019@firke_janitor:_2021@muller_here:_2020@piburn_wbstats:_2020@buchta_isocodes:_2020@garnier_viridis:_2018@rudis_hrbrthemes:_2020@ram_wesanderson:_2018@hester_glue:_2020@wilke_ggridges:_2021@pedersen_patchwork:_2020@zhu_kableextra:_2020@wickham_readxl:_2019@gohel_flextable:_2021@south_rworldmap:_2016@arnold_ggthemes:_2021].
We discuss each of these in turn, including additional data inputs needed to complement the HBS. We then present our methodology for our results from the main paper and some limitations. The final sub-section in 'supplementary materials and methods' presents an alternative methodology, and the final section of this supplementary information document presents supplementary results from the methodology used in the main paper and the alternative methodology. The whole analysis was performed in RStudio [@r_core_team_r:_2020], using a variety of R packages [@wickham_welcome_2019] [@firke_janitor:_2021] [@muller_here:_2020] [@piburn_wbstats:_2020] [@buchta_isocodes:_2020] [@garnier_viridis:_2018] [@rudis_hrbrthemes:_2020] [@ram_wesanderson:_2018] [@hester_glue:_2020] [@wilke_ggridges:_2021] [@pedersen_patchwork:_2020] [@zhu_kableextra:_2020] [@wickham_readxl:_2019] [@gohel_flextable:_2021] [@south_rworldmap:_2016] [@arnold_ggthemes:_2021].
### EXIOBASE
We use standard input-output calculations to calculate 'direct and indirect supply chain' intensity vectors in EXIOBASE [@miller_input-output_1985]. EXIOBASE publishes the A matrix, the final demand matrix, the satellite extensions matrix, and satellite extensions direct from final demand matrix. We use the industry-by-industry (ixi) EXIOBASE data tables from EXIOBASE version3. This means 163 industry production sectors and 6 final demand categories for 49 regions worldwide (44 countries and 5 rest-of-world regions), from 1995 - 2016. All monetary units are in million current Euros. Stadler et al. (2018) @stadler_exiobase_2018 describe the EXIOBASE version3 compilation procedure in detail, including nine supporting information documents with further detailed information on the compilation of the monetary tables (S1), energy (S2), emissions (S3), and others.
We use standard input-output calculations to calculate 'direct and indirect supply chain' intensity vectors in EXIOBASE [@miller_input-output_1985]. EXIOBASE publishes the A matrix, the final demand matrix, the satellite extensions matrix, and satellite extensions direct from final demand matrix. We use the industry-by-industry (ixi) EXIOBASE data tables from EXIOBASE version3. This means 163 industry production sectors and 6 final demand categories for 49 regions worldwide (44 countries and 5 rest-of-world regions), from 1995 - 2016. All monetary units are in million current Euros. Stadler et al. (2018) [@stadler_exiobase_2018] describe the EXIOBASE version3 compilation procedure in detail, including nine supporting information documents with further detailed information on the compilation of the monetary tables (S1), energy (S2), emissions (S3), and others.
For each year, we first load the A matrix and calculate the Leontief inverse (the inverse of the A matrix). We load the final demand matrix and calculate total output ($x$) by pre-multiplying the Leontief inverse ($L$) by the row sums of the final demand matrix ($Y$):
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@@ -138,13 +138,13 @@ $$
The environmental extensions we use are emissions of CO2-equivalence (in kilograms) and 'gross total energy use' (in terajoules). We create the CO2-equivalence extension by summing together the greenhouse gases CO2, CH4, N2O, SF6, HFCs, and PFCs, from combustion, noncombustion, agriculture and waste. We use Global Warming Potential (GWP) values for a 100-year time horizon taken from the IPCC Fifth Assessment Report [@myhre_g._anthropogenic_2013 (p.73-79)]: 28 for CH4, 265 for N2O and 23500 for SF6 (HFCs and PFCs are in CO2-equivalence already in the EXIOBASE environmental extensions).
The 'gross total energy use' extension in EXIOBASE converts final energy consumption in the International Energy Agency (IEA) energy balance data from the territorial to residence principle following System of Environmental Economic Accounting (SEEA) energy accounting principles. In their Supporting Information 2, Stadler et al. (2018) @stadler_exiobase_2018 describe the compilation of the energy extensions in EXIOBASE version3. Energy supply and use tables from the IEA are converted from the territory to the residence principle before being allocated to the EXIOBASE industries and final demand categories.
The 'gross total energy use' extension in EXIOBASE converts final energy consumption in the International Energy Agency (IEA) energy balance data from the territorial to residence principle following System of Environmental Economic Accounting (SEEA) energy accounting principles. In their Supporting Information 2, Stadler et al. (2018) [@stadler_exiobase_2018] describe the compilation of the energy extensions in EXIOBASE version3. Energy supply and use tables from the IEA are converted from the territory to the residence principle before being allocated to the EXIOBASE industries and final demand categories.
The conversion to the residence principle means that the EXIOBASE energy extensions refer to the functional border of a country's economy. In this case, the system border is defined by the 'residence' of the agent. This means that energy supply and use from international transport by ships, airplanes, fishing vessels, cars and trucks is allocated to the resident units of a country, independent from where these activities take place. In EXIOBASE version3, because emissions from these transport activities are estimated from the energy extensions via emission factors, the emissions extensions follow the residence principle as well.
#### Environmental extensions direct from households
For CO2-equivalence emissions and energy use direct from households, the EXIOBASE extensions provide one number of physical emissions and energy use direct from households per country. This number is made up of some mix between, primarily, direct household vehicle fuel use and direct household fuel use for housing heating and cooling, along with some other, smaller miscellaneous uses. To estimate the split between these three activities, we use two further EUROSTAT emissions and energy tables to split 'Total activities by households' between heating/cooling (HH_HEAT), transport activities (HH_TRA), and other (HH_OTH). Full definitions of what is included in these can be found in EUROSTAT's 'manual for air emissions accounts' (2015, p.66) @eurostat_manual_2015. While this disaggregation exists for nearly all EUROSTAT HBS countries, 2015 is the earliest year in our sample with complete coverage (except for Turkey in energy, which we impute using the Bulgarian splits). Therefore, we also use the 2015 splits between these 3 categories for our 2005 and 2010 estimates (the 2005 and 2010 results are shown only in this SI document). The two data tables are:
For CO2-equivalence emissions and energy use direct from households, the EXIOBASE extensions provide one number of physical emissions and energy use direct from households per country. This number is made up of some mix between, primarily, direct household vehicle fuel use and direct household fuel use for housing heating and cooling, along with some other, smaller miscellaneous uses. To estimate the split between these three activities, we use two further EUROSTAT emissions and energy tables to split 'Total activities by households' between heating/cooling (HH_HEAT), transport activities (HH_TRA), and other (HH_OTH). Full definitions of what is included in these can be found in EUROSTAT's 'manual for air emissions accounts' (2015, p.66) [@eurostat_manual_2015]. While this disaggregation exists for nearly all EUROSTAT HBS countries, 2015 is the earliest year in our sample with complete coverage (except for Turkey in energy, which we impute using the Bulgarian splits). Therefore, we also use the 2015 splits between these 3 categories for our 2005 and 2010 estimates (the 2005 and 2010 results are shown only in this SI document). The two data tables are:
1) For energy, the EUROSTAT data table 'Energy supply and use by NACE Rev. 2 activity' [env_ac_pefasu] at: http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=env_ac_pefasu [accessed on 03.06.2020]. [@eurostat_eurostat_2020]
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@@ -158,7 +158,7 @@ The EUROSTAT HBS compiles national household budget surveys from European countr
2) 'Structure of consumption expenditure by income quintile and COICOP consumption purpose (hbs_str_t223)'
'Mean consumption expenditure by income quintile' is expressed in purchasing power standards (PPS) per year, country and income quintile, in two possible units; per household and per adult equivalent. Purchasing power standard (PPS) is a common currency measure that eliminates differences in national price-levels. The income quintiles are determined by household, ie. all households in the sample are ranked by income and evenly distributed into five quintiles, thus there are the same number of households in each income quintile. For per adult equivalent, the mean consumption expenditure by income quintile is adjusted for household size between countries, years, and income quintiles, using the modified OECD scale: 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.
'Mean consumption expenditure by income quintile' is expressed in purchasing power standards (PPS) per year, country and income quintile, in two possible units; per household and per adult equivalent. Purchasing power standard (PPS) is a common currency measure that eliminates differences in national price-levels. The income quintiles are determined by household, ie. all households in the sample are ranked by income and evenly distributed into five quintiles, thus there are the same number of households in each income quintile. For per adult equivalent, the mean consumption expenditure by income quintile is adjusted for household size between countries, years, and income quintiles, using the modified OECD scale: 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].
The 'Structure of consumption expenditure by income quintile and COICOP consumption purpose' table gives the distribution of consumption expenditure across COICOP consumption categories in each income quintile, expressed in 'parts per mille (pm)'. There are three 'levels' of COICOP breakdown. All countries have level 1 (12 consumption categories) and 2, but there are only a few with level 3. For the current analysis, we select our own mix of COICOP level 1 and 2. We use COICOP level 1, except for the categories of food (CP01), housing (CP04) and mobility (CP07), where we use level 2.
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@@ -2009,7 +2009,7 @@ Through the main paper we use household per adult equivalent as our unit of anal
As a numerical example, we adjust a total final energy of 53 GJ per capita from the Low Energy Demand (LED) scenario (Grubler et al. (2018) [@grubler_low_2018]), first by the household share of the total European energy footprint in 2015 (around 0.63, calculated in EXIOBASE), and then the share of total adult equivalents in the total European population in 2015 (also around `r ae_share_of_pop`, calculated using the EUROSTAT HBS, number of households per country, and population data per country). A total final energy of 53 GJ/capita is therefore adjusted to a household final energy of 53 GJ/adult equivalent in Europe ((53 total GJ/capita * 0.63 household share of total footprint)/0.63 adult equivalent share of total population = 53 household GJ/adult equivalent).
The decarbonisation scenario final energy numbers in 2050, presented in Table 1 of the main paper, were originally in total GJ per capita: 94 GJ/capita (SSP2-1.9), 87 GJ/capita (SSP1-1.9), 84 GJ/capita (IEA ETP B2DS), 64 GJ/capita (GEA-efficiency), 53 GJ/capita (LED), and 15.3 GJ/capita (DLE). Because of the similar relative shares of the household part of the total European energy footprint (around 0.63), and the adult equivalent share of the total population in our sample (also around 0.63), these final energy numbers end up close to the same when adjusted to household per adult equivalent. The original numbers for the SSP and GEA-efficiency scenarios are from the International Institute for Applied Systems Analysis (IIASA) scenario database [@riahi_shared_2017@gea_gea_nodate]. The SSP total GJ/capita numbers are for the OECD region, while the GEA-efficiency total GJ/capita number is for their 'West EU' region. The SSP numbers are from the MESSAGE-GLOBIOM model, and the GEA-efficiency number is from the MESSAGE model. The LED total GJ/capita number is from Grubler et al. (2018) [@grubler_low_2018], and the IEA ETP B2DS total GJ/capita number is from the Supplementary Table 27 in the supplementary information document of Grubler et al. (2018) [@grubler_low_2018]. The LED and IEA ETP B2DS total GJ/capita numbers are both for the Global North region. We also refer in the main paper to the LED numbers for the Global South (20 total GJ/capita) and the world (27 total GJ/capita). Finally, the DLE number is one number for the world, and while they give a range of 13-18.4 total GJ/capita, we take their average of 15.3 total GJ/capita [@millward-hopkins_providing_2020].
The decarbonisation scenario final energy numbers in 2050, presented in Table 1 of the main paper, were originally in total GJ per capita: 94 GJ/capita (SSP2-1.9), 87 GJ/capita (SSP1-1.9), 84 GJ/capita (IEA ETP B2DS), 64 GJ/capita (GEA-efficiency), 53 GJ/capita (LED), and 15.3 GJ/capita (DLE). Because of the similar relative shares of the household part of the total European energy footprint (around 0.63), and the adult equivalent share of the total population in our sample (also around 0.63), these final energy numbers end up close to the same when adjusted to household per adult equivalent. The original numbers for the SSP and GEA-efficiency scenarios are from the International Institute for Applied Systems Analysis (IIASA) scenario database [@riahi_shared_2017] [@gea_gea_nodate]. The SSP total GJ/capita numbers are for the OECD region, while the GEA-efficiency total GJ/capita number is for their 'West EU' region. The SSP numbers are from the MESSAGE-GLOBIOM model, and the GEA-efficiency number is from the MESSAGE model. The LED total GJ/capita number is from Grubler et al. (2018) [@grubler_low_2018], and the IEA ETP B2DS total GJ/capita number is from the Supplementary Table 27 in the supplementary information document of Grubler et al. (2018) [@grubler_low_2018]. The LED and IEA ETP B2DS total GJ/capita numbers are both for the Global North region. We also refer in the main paper to the LED numbers for the Global South (20 total GJ/capita) and the world (27 total GJ/capita). Finally, the DLE number is one number for the world, and while they give a range of 13-18.4 total GJ/capita, we take their average of 15.3 total GJ/capita [@millward-hopkins_providing_2020].
Our European expenditure deciles were constructed having the exact same number of adult equivalents per decile. When comparing with external per capita numbers, however, there is not the same number of population per decile because of differences in non-adult-equivalent-normalized people per household between income quintiles per country, and between countries.
