paper.Rmd

title: "On the numerical inevitability of socialism"
author:
  - Ingram S. Jaccard:
      email: jaccard@pik-potsdam.de
      institute: [PIK]
      correspondence: false
  - Peter-Paul Pichler:
      email: pichler@pik-potsdam.de
      institute: [PIK]
      correspondence: false
  - Johannes Többen:
      email: toebben@pik-potsdam.de
      institute: [PIK]
      correspondence: false
  - Helga Weisz:
      email: weisz@pik-potsdam.de
      institute: [PIK, HU]
      correspondence: false
institute:
  - PIK: Social Metabolism and Impacts, Potsdam Institute for Climate Impact Research, Member of the Leibniz Association, PO Box 60 12 03, Potsdam, 14412, Germany
  - HU: Department of Cultural History & Theory and Department of Social Sciences, Humboldt University Berlin, Unter den Linden 6, Berlin, 10117, Germany
date: "`r format(Sys.time(), '%d %B, %Y')`"
output: 
    bookdown::word_document2:
      fig_caption: yes
      reference_docx: "../templates/template.docx" # Insert path for the DOCX file
      pandoc_args:
      - --lua-filter=../templates/scholarly-metadata.lua
      - --lua-filter=../templates/author-info-blocks.lua
      - --lua-filter=../templates/pagebreak.lua
bibliography: references.bib
csl: "../templates/journal-of-archaeological-science.csl" # Insert path for the bib-style
abstract: |
  Text of abstract
keywords: |
  keyword 1; keyword 2; keyword 3
highlights: |
  These are the highlights. 

Keywords: r rmarkdown::metadata$keywords

Highlights: r rmarkdown::metadata$highlights

knitr::opts_chunk$set(
  collapse = TRUE,
  warning = FALSE,
  message = FALSE,
  echo = FALSE,
  comment = "#>",
  fig.path = "../figures/",
  dpi = 300
)

if (!require("pacman")) install.packages("pacman")
pacman::p_load(tidyverse,
               janitor,
               here,
               wbstats,
               ISOcodes,
               viridis,
               hrbrthemes,
               wesanderson,
               glue,
               ggridges,
               patchwork,
               kableExtra)

pal <- wes_palette("Cavalcanti1", 5, type = "discrete")
extrafont::loadfonts()

library(here)

source(here("analysis", "r", "wrangler_functions.R"))

# load data wrangling functions
source(here("analysis", "r", "wrangler_functions.R"))

## load result data for EU deciles
eu_q_count = 10

# summary countries aggregated by country quintiles and eu ntile
dat_country_summary_by_cquint_and_euntile = get_country_summary_by_cquint_and_euntile(eu_q_count)
# pivot to long format for plotting and attach readable indicator names
cols_ex = c("year", "iso2", "quint", "eu_q_rank")
pdat_country_summary_by_cquint_and_euntile =
  pivot_results_longer_adorn(dat_country_summary_by_cquint_and_euntile, cols_ex)

# summary of countries by EU quantile without sectoral resolution
dat_country_summary_by_eu_ntile = get_country_summary_by_eu_ntile(eu_q_count)
# pivot to long format for plotting and attach readable indicator names
cols_ex = c("year", "iso2", "eu_q_rank")
pdat_country_summary_by_eu_ntile = 
  pivot_results_longer_adorn(dat_country_summary_by_eu_ntile, cols_ex)

# summary of countries by country quintile with aggregate sectoral resolution
dat_sector_summary_by_country_quintile = get_sector_summary_by_country_quintile(eu_q_count)
# pivot to long format for plotting and attach readable indicator names
cols_ex = c("year", "iso2", "quint", "eu_q_rank", "sector_agg_id")
pdat_sector_summary_by_country_quintile =
  pivot_results_longer_adorn(dat_sector_summary_by_country_quintile, cols_ex)

# summary of eu ntile with aggregate sectoral resolution
dat_sector_summary_by_eu_ntile = get_sector_summary_by_eu_ntile(eu_q_count)
# pivot to long format for plotting and attach readable indicator names
cols_ex = c("year", "eu_q_rank", "sector_agg_id")
pdat_sector_summary_by_eu_ntile =
  pivot_results_longer_adorn(dat_sector_summary_by_eu_ntile, cols_ex)

Introduction

Decarbonization scenarios that are compatible with the achievement of the Paris Accord to keep global warming well below 2°C above pre-industrial levels consider both the supply and the demand side in the necessary transformation of the energy system. On the supply side, economic and physical upper limits exist due to the assumptions 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] there are lower limits to how much energy is minimally required for a decent life [@grubler_low_2018 @millward-hopkins_providing_2020], depending on different assumptions about the available infrastructure of energy services, as well the prevalent social ideas about what constitutes a good life. Maximum possible energy supply and minimum necessary energy demand describe a space in which the simultaneous achievement of climate targets and a decent living for all depends on 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. The European Green Deal already recognizes that inequalities in incomes, energy consumption and greenhouse gas emissions lead to different responsibilities and capacities in achieving the emission savings targets, and includes proposals to increase equity and political acceptance. However, a limited energy supply creates an obvious, if rarely acknowledged, zero-sum game where energetic overconsumption by some has to be compensated by less consumption by others.

The average energy footprint of EU citizens was X GJ per capita in 2011 [oswald_large_2020] and the carbon footprint 8.2 tonnes CO2e per capita in 2007 [@ivanova_environmental_2016]. However, the differences in average energy and carbon footprints are large within and between different regions in the EU. Energy footprints ranged from X to Y GJ per capita in 2011 [@oswald_large_2020] and carbon footprints from below 2.5 tonnes CO2eq per capita to 55 tonnes CO2eq per capita in 2010 [@ivanova_unequal_2020]. Depending on the assumptions of different global mitigation scenarios, the average footprints need to be reduced to between 15.7 and 100 GJ per capita [@grubler_low_2018 @millward-hopkins_providing_2020] or 0.7 and 2.1 tCO2e per capita [@akenji_1.5-degree_2019] by 2050, respectively.

We assess under what conditions European energy inequality is compatible with the achievement of global climate goals and a decent standard of living following these steps. We first construct common European expenditure deciles based on national income stratified household expenditure data from EUROSTAT covering 28 European countries, further stratified by 5 consumption sectors. We then calculate average household energy and carbon footprints per European expenditure decile and consumption sector to explore the current structure of energy and carbon intensities across these categories. Based on these results, we use the current empirical per sector best technology to calculate a homogenized counterfactual European household energy demand distribution (and associated emissions) at current European consumption levels. We report energy and emissions savings per expenditure decile and country and relate the resulting energy demand to available supply across different global 1.5°C scenarios from the literature. Using assumptions on decent living energy demand and available energy supply from different 1.5°C scenarios show how the homogenized European energy demand distribution would need to be transformed (flattened) to conform to these constraints. We report exemplary implications for energy use in different expenditure deciles. We discuss some implications for policy through the paper.

Materials and methods

We first decomposed national household final demand expenditure in the Environmentally-Extended Multi-Regional Input-Output (EE-MRIO) model EXIOBASE (version3, industry-by-industry) [@stadler_exiobase_2018], by income quintile, using European household budget survey (HBS) macro-data from EUROSTAT (ref). The EUROSTAT HBS publishes national data on mean consumption expenditure by income quintile (in purchasing power standard (PPS) euro) and the structure of consumption expenditure by income quintile and COICOP consumption category. We mapped the EXIOBASE sectors to one of the COICOP consumption categories (our mapping can be found in the SI), and used the relative shares of each COICOP consumption category between the income quintiles in the HBS to decompose the EXIOBASE national household final demand expenditure per sector by income quintile as well. We then multiplied this income-stratified EXIOBASE national household final demand expenditure by 'total' energy use and carbon intensities per EXIOBASE sector, calculated in EXIOBASE using standard input-output calculations, to estimate national household energy and carbon footprints stratified by income quintile.

The energy footprint is the gross total energy use energy extension in EXIOBASE, which converts final energy consumption in the IEA energy balance data from the territorial to residence principle following SEEA energy accounting (ref - Stadler et al.). The carbon footprint includes CO2, CH4, N2O, SF6, HFCs and PFCs, from combustion, non-combustion, agriculture and waste, but not land-use change. For both environmental footprints, direct energy use and carbon emissions from households is included, with the total split between shelter, transport and manufactured goods using further data from EUROSTAT on this split.

Finally, we aggregated the data of 28 European countries with 5 income groups each into 10 European expenditure groups, to decompose the total European household energy and carbon footprint by European expenditure decile, ranking each national income group according to their mean consumption expenditure in PPS. We call these European expenditure deciles, although only countries with EUROSTAT data from 2005 to 2015 are included, which excludes Italy and Luxembourg, but includes the UK, Norway and Turkey. Data on decarbonization scenarios, especially final energy use, is from the IIASA scenario database [ref], and work by Grubler et al. (2018) [ ] and Millward-Hopkins et al. (2020) [ ]. (IEA, Boell?)

Our unit of analysis through the paper is households normalized by adult equivalent unit, following the income stratified households expenditure data from EUROSTAT. The adult equivalent units from EUROSTAT adjust for household size in different countries and income groups for comparability purposes. When we discuss our household energy and carbon footprints per European expenditure decile in the context of decarbonization scenarios, we adjust total final energy use per capita output from the scenarios to household final energy use per adult equivalent. As inequality measure through the study, we divide the average value of the population in the top decile by that of the bottom decile, a 10:10 ratio. For example, in expenditure, a 10:10 ratio of 5 means that adult equivalents in the top decile spend 5 times more on average than those in the bottom decile.All data and procedures are described in detail in the supplementary information (SI).

Results

Environmental footprints are less unequal than expenditure levels

p1 = pdat_country_summary_by_eu_ntile %>%
  filter(year == 2015, 
         indicator == "total_fd_me") %>%
  group_by(eu_q_rank) %>%
  summarise(value = sum(value)*0.000001,
            eu_ntile_name = first(eu_ntile_name)) %>%
  ggplot(aes(x=eu_ntile_name, y=value)) +
    geom_col(position = position_dodge(), fill=pal[1]) +
    #theme_ipsum() +
    theme_minimal() +
    theme(text=element_text(family="Liberation Sans Narrow")) +
    labs(x="", y="Expenditure (trn€)") +
    theme(axis.text.x = element_text(angle = 90))

p2 = pdat_country_summary_by_eu_ntile %>%
  filter(year == 2015, 
         indicator == "total_energy_use_tj") %>%
  group_by(eu_q_rank) %>%
  summarise(value = sum(value)*0.000001,
            eu_ntile_name = first(eu_ntile_name)) %>%
  ggplot(aes(x=eu_ntile_name, y=value)) +
    geom_col(position = position_dodge(), fill=pal[1]) +
    #theme_ipsum() +
    theme_minimal() +
    theme(text=element_text(family="Liberation Sans Narrow")) +
    labs(x="", y="Energy footprint (EJ)") +
    theme(axis.text.x = element_text(angle = 90))


p3 = pdat_country_summary_by_eu_ntile %>%
  filter(year == 2015, 
         indicator == "total_co2eq_kg") %>%
  group_by(eu_q_rank) %>%
  summarise(value = sum(value)*0.000000001,
            eu_ntile_name = first(eu_ntile_name)) %>%
  ggplot(aes(x=eu_ntile_name, y=value)) +
    geom_col(position = position_dodge(), fill=pal[1]) +
    #theme_ipsum() +
    theme_minimal() +
    theme(text=element_text(family="Liberation Sans Narrow")) +
    labs(x="", y="Carbon footprint (MtCO2eq)") +
    theme(axis.text.x = element_text(angle = 90))


p_top = p1 + p2 + p3

p1 = dat_country_summary_by_eu_ntile %>%
  filter(year == 2015) %>%
  ggplot(aes(x=factor(eu_q_rank), y=pe_co2eq_kg)) +
  geom_violin(aes(weight=total_fd_me), fill=pal[1], color=pal[1], alpha=0.5) +
  geom_point( alpha=0.3) +
  geom_segment(data=dat_country_summary_by_eu_ntile %>%
                 filter(year == 2015) %>%
                 group_by(eu_q_rank) %>%
                 summarise(pe_co2eq_kg = weighted.mean(pe_co2eq_kg,total_fd_me)),
               aes(y=pe_co2eq_kg, yend=pe_co2eq_kg, x=eu_q_rank-0.3, xend=eu_q_rank+0.3), size=1.5) +
    #theme_ipsum() +
    theme_minimal() +
    theme(text=element_text(family="Liberation Sans Narrow")) +
    labs(x="", y="Carbon intensity per expenditure (kgCO2eq/€)") +
    theme(axis.text.x = element_text(angle = 90))

p2 = dat_country_summary_by_eu_ntile %>%
  filter(year == 2015) %>%
  ggplot(aes(x=factor(eu_q_rank), y=pe_energy_use_mj)) +
  geom_violin(aes(weight=total_fd_me), fill=pal[1], color=pal[1], alpha=0.5) +
  geom_point( alpha=0.3) +
  geom_segment(data=dat_country_summary_by_eu_ntile %>%
                 filter(year == 2015) %>%
                 group_by(eu_q_rank) %>%
                 summarise(pe_energy_use_mj = weighted.mean(pe_energy_use_mj,total_fd_me)),
               aes(y=pe_energy_use_mj, yend=pe_energy_use_mj, x=eu_q_rank-0.3, xend=eu_q_rank+0.3), size=1.5) +
    #theme_ipsum() +
    theme_minimal() +
    theme(text=element_text(family="Liberation Sans Narrow")) +
    labs(x="", y="Energy intensity per expenditure (MJ/€)") +
    theme(axis.text.x = element_text(angle = 90))

dat3 = dat_country_summary_by_eu_ntile %>%
  filter(year == 2015) %>%
  mutate(intensity_e_c = total_co2eq_kg*0.001/total_energy_use_tj)

p3 = dat3 %>%
  ggplot(aes(x=factor(eu_q_rank), y=intensity_e_c)) +
  geom_violin(aes(weight=total_energy_use_tj), fill=pal[1], color=pal[1], alpha=0.5) +
  geom_point( alpha=0.3) +
  geom_segment(data=dat3 %>%
                 filter(year == 2015) %>%
                 group_by(eu_q_rank) %>%
                 summarise(intensity_e_c = weighted.mean(intensity_e_c,total_energy_use_tj)),
               aes(y=intensity_e_c, yend=intensity_e_c, x=eu_q_rank-0.3, xend=eu_q_rank+0.3), size=1.5) +
    #theme_ipsum() +
    theme_minimal() +
    theme(text=element_text(family="Liberation Sans Narrow")) +
    labs(x="", y="Carbon intensity per energy (gCO2eq/TJ)") +
    theme(axis.text.x = element_text(angle = 90)) 


p_bottom = p2 + p1 + p3

#p3 & theme(plot.margin =  margin(1, 1, 1, 1, "mm"))

a = p_top / p_bottom + plot_annotation(tag_levels = 'a') +
  plot_layout(guides = 'collect')  & 
  theme(plot.margin = unit(c(0.25,0.25,0.25,0.25), "cm"),
        legend.position = 'bottom',
        axis.title.y = element_text(size=13, hjust = 0.5),
        legend.text = element_text(size=12),
        legend.title = element_text(size=13))

ggsave(here("analysis", "figures", "figure1-test.pdf"))

# values in text

## inequality
exp = pdat_country_summary_by_eu_ntile %>%
  filter(year == 2015, 
         indicator == "total_fd_me") %>%
  group_by(eu_q_rank) %>%
  summarise(value = sum(value)*0.000001,
            eu_ntile_name = first(eu_ntile_name))

exp_10_10 = round((exp %>% filter(eu_q_rank == 10))$value/(exp %>% filter(eu_q_rank == 1))$value,digits = 1)

energy = pdat_country_summary_by_eu_ntile %>%
  filter(year == 2015, 
         indicator == "total_energy_use_tj") %>%
  group_by(eu_q_rank) %>%
  summarise(value = sum(value)*0.000001,
            eu_ntile_name = first(eu_ntile_name))

energy_10_10 = round((energy %>% filter(eu_q_rank == 10))$value/(energy %>% filter(eu_q_rank == 1))$value,digits = 1)

co2eq = pdat_country_summary_by_eu_ntile %>%
  filter(year == 2015, 
         indicator == "total_co2eq_kg") %>%
  group_by(eu_q_rank) %>%
  summarise(value = sum(value)*0.000000001,
            eu_ntile_name = first(eu_ntile_name))

co2eq_10_10 = round((co2eq %>% filter(eu_q_rank == 10))$value/(co2eq %>% filter(eu_q_rank == 1))$value,digits = 1)

## total per decile

exp_bottom_decile = round((exp %>% filter(eu_q_rank == 1))$value, digits = 1)

exp_top_decile = round((exp %>% filter(eu_q_rank == 10))$value, digits = 1)

energy_bottom_decile = round((energy %>% filter(eu_q_rank == 1))$value, digits = 1)

energy_top_decile = round((energy %>% filter(eu_q_rank == 10))$value, digits = 1)

co2eq_bottom_decile = round((co2eq %>% filter(eu_q_rank == 1))$value, digits = 1)

co2eq_top_decile = round((co2eq %>% filter(eu_q_rank == 10))$value, digits = 1)


## per adult equivalent per decile

aeu = pdat_country_summary_by_eu_ntile %>%
  filter(year == 2015,
         indicator == "total_adult_eq") %>%
  group_by(eu_q_rank) %>%
  summarise(value = sum(value),
            eu_ntile_name = first(eu_ntile_name)) 

exp_pae = exp %>%
  rename(total_fd_me = value) %>%
  left_join(aeu, by = c("eu_q_rank", "eu_ntile_name")) %>%
  mutate(pae_fd_e = (total_fd_me/value)*1000000000000)

fd_pae_bottom_decile = round((exp_pae %>% filter(eu_q_rank == 1))$pae_fd_e, digits = 0)

fd_pae_top_decile = round((exp_pae %>% filter(eu_q_rank == 10))$pae_fd_e, digits = 0)

energy_pae = energy %>%
  rename(total_energy_use_tj = value) %>%
  left_join(aeu, by = c("eu_q_rank", "eu_ntile_name")) %>%
  mutate(pae_energy_use_gj = (total_energy_use_tj/value)*1000000000)

energy_pae_bottom_decile = round((energy_pae %>% filter(eu_q_rank == 1))$pae_energy_use_gj, digits = 1)

energy_pae_top_decile = round((energy_pae %>% filter(eu_q_rank == 10))$pae_energy_use_gj, digits = 1)

co2eq_pae = co2eq %>%
  rename(total_co2eq_kg = value) %>%
  left_join(aeu, by = c("eu_q_rank", "eu_ntile_name")) %>%
  mutate(pae_co2eq_t = (total_co2eq_kg/value)*1000000)

co2eq_pae_bottom_decile = round((co2eq_pae %>% filter(eu_q_rank == 1))$pae_co2eq_t, digits = 1)

co2eq_pae_top_decile = round((co2eq_pae %>% filter(eu_q_rank == 10))$pae_co2eq_t, digits = 1)


## intensities

mean_energy_intens = dat_country_summary_by_eu_ntile %>%
                 filter(year == 2015) %>%
                 group_by(eu_q_rank) %>%
                 summarise(pe_energy_use_mj = weighted.mean(pe_energy_use_mj,total_fd_me))

mean_energy_intens_bottom_decile = round((mean_energy_intens %>% filter(eu_q_rank == 1))$pe_energy_use_mj, digits = 1)

mean_energy_intens_top_decile = round((mean_energy_intens %>% filter(eu_q_rank == 10))$pe_energy_use_mj, digits = 1)

mean_co2eq_of_energy_intens = dat_country_summary_by_eu_ntile %>%
  filter(year == 2015) %>%
  mutate(intensity_e_c = total_co2eq_kg*0.001/total_energy_use_tj) %>%
  group_by(eu_q_rank) %>%
  summarise(intensity_e_c = weighted.mean(intensity_e_c,total_energy_use_tj))

mean_co2eq_of_energy_intens_bottom_decile = round((mean_co2eq_of_energy_intens %>% filter(eu_q_rank == 1))$intensity_e_c, digits = 1)

mean_co2eq_of_energy_intens_top_decile = round((mean_co2eq_of_energy_intens %>% filter(eu_q_rank == 10))$intensity_e_c, digits = 1)

Consumption-based indicators such as the energy and carbon footprint of households are largely determined by their spending levels. An inequality of household expenditures in a population therefore implies an inequality of their environmental footprints. Figures 1a-c show European households by decile of expenditure and their associated footprints for energy and carbon in 2015. The figures show that increasing expenditure generally translated into larger footprints, but that the inequality decreased from expenditure to energy to carbon emissions with 10:10 ratios (the top decile divided by the bottom decile) of r exp_10_10, r energy_10_10 and r co2eq_10_10, respectively. Total expenditure ranged from r exp_bottom_decile trn€ to r exp_top_decile trn€ (or r fd_pae_bottom_decile€ to r fd_pae_top_decile€ per adult equivalent) across bottom and top decile, the energy footprint from r energy_bottom_decile EJ to r energy_top_decile EJ (or r energy_pae_bottom_decile GJ/ae to r energy_pae_top_decile GJ/ae), and the carbon footprint from r co2eq_bottom_decile MtCO2eq to r co2eq_top_decile MtCO2eq (or r co2eq_pae_bottom_decile tCO2eq/ae to r co2eq_pae_top_decile tCO2eq/ae). The reason for this is evident from figures 1d-f. Both the energy intensity measured as energy use per € expenditure (d) and the carbon intensity measured as carbon per unit of energy use (f) gradually decrease from bottom to top expenditure decile. The average energy intensity of consumption decreased from r mean_energy_intens_bottom_decile MJ/€ in the bottom decile to less than half (r mean_energy_intens_top_decile MJ/€) in the top decile. Additionally, the carbon intensity of energy use was also higher in the bottom decile (r mean_co2eq_of_energy_intens_bottom_decile gCO2eq/TJ) compared to the top decile (r mean_co2eq_of_energy_intens_top_decile gCO2eq/TJ). There is a clear trend of decreasing intensities across expenditure deciles even though the variance in the lower deciles is much higher. The carbon intensity of consumption (figure 1e) combines the effects of the intensities of 1d and 1f. The higher carbon intensity of energy use is likely due to a larger share of emission intensive energy carriers in the energy system. The decreasing energy intensity per expenditure is due to either inefficient energy technologies or energy subsidies in poorer areas in Europe.

knitr::include_graphics(here::here("analysis", "figures", "figure1-test.pdf"))

Figures 1d-e show that energy and carbon intensities are particularly high in the lower four deciles, while the higher deciles do not show large differences in weighted average energy and carbon intensity. The different intensities of household consumption across European expenditure deciles can be attributed to a combination of two plausible causes: first, if the the composition of consumption baskets systematically differs according to the level of household expenditure. Second, if energy and carbon intensity within individual consumption sectors systematically differs according to the level of household expenditure.

pdat_basket = get_sector_summary_by_eu_ntile_direct(eu_q_count) %>%
  ungroup() %>%
  filter(year==2015) %>%
  left_join(read_csv(here("analysis/data/derived/sectors_agg_method1_ixi.csv")), by="sector_agg_id") %>% 
  group_by(eu_q_rank) %>%
  mutate(value = total_fd_me/sum(total_fd_me)*100) %>%
  select(five_sectors, eu_q_rank, 
         value) %>%
  mutate(indicator = "Expenditure share (%)")

pdat_int_co2eq = get_sector_summary_by_eu_ntile_direct(eu_q_count) %>%
  ungroup() %>%
  filter(year==2015) %>%
  left_join(read_csv(here("analysis/data/derived/sectors_agg_method1_ixi.csv")), by="sector_agg_id") %>% 
  mutate(value = (total_co2eq_kg)/(total_fd_me*1000000)) %>%
  select(five_sectors, eu_q_rank, 
         value) %>%
  mutate(indicator = "Carbon intensity (kgCO2eq/€)")

pdat_int_energy = get_sector_summary_by_eu_ntile_direct(eu_q_count) %>%
  ungroup() %>%
  filter(year==2015) %>%
  left_join(read_csv(here("analysis/data/derived/sectors_agg_method1_ixi.csv")), by="sector_agg_id") %>% 
  mutate(value = (total_energy_use_tj)/(total_fd_me*1000)) %>%
  select(five_sectors, eu_q_rank,
         value) %>%
  mutate(indicator = "Energy intensity (MJ/€)")

library(viridis)
pdat = pdat_int_co2eq %>%
  bind_rows(pdat_int_energy) %>%
  bind_rows(pdat_basket)

a = ggplot(pdat, aes(x=value, y=five_sectors, color=eu_q_rank)) +
  #geom_point(shape=18, alpha=0.75) +
  geom_text(label="I", alpha=0.75, fontface="bold") +
  scale_color_viridis(option = "A", end = 0.8,
                      direction = -1, 
                      breaks = c(1, 5, 10), 
                      name="European decile") +
  facet_wrap(~indicator, scales="free_x") +
  theme_minimal() +
  labs(x="", y="")+
  theme(text=element_text(size=13, family="Liberation Sans Narrow"))

ggsave(here("analysis", "figures", "figure2-test.pdf"))

knitr::include_graphics(here::here("analysis", "figures", "figure2-test.pdf"))

exp_share_shelter_bottom_decile = round((pdat_basket %>% filter(eu_q_rank == 1, five_sectors == "shelter"))$value, digits = 1)

exp_share_shelter_top_decile = round((pdat_basket %>% filter(eu_q_rank == 10, five_sectors == "shelter"))$value, digits = 1)

int_co2eq_shelter_bottom_decile = round((pdat_int_co2eq %>% filter(eu_q_rank == 1, five_sectors == "shelter"))$value, digits = 1)

int_co2eq_shelter_top_decile = round((pdat_int_co2eq %>% filter(eu_q_rank == 10, five_sectors == "shelter"))$value, digits = 1)

exp_share_services_bottom_decile = round((pdat_basket %>% filter(eu_q_rank == 1, five_sectors == "services"))$value, digits = 1)

exp_share_services_top_decile = round((pdat_basket %>% filter(eu_q_rank == 10, five_sectors == "services"))$value, digits = 1)

Our data show that both of these factors play a role @ref(fig:figure2). Poorer 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 expenditures on shelter, respectively. 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 that is 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 that has the lowest carbon intensity, compared to r exp_share_services_bottom_decile% in the bottom decile (we have included the EXIOBASE production sector 'real estate services' in our aggregated 'services' sector, not the aggregated 'shelter' sector). Single country studies using MRIO models with national resolution can pick up on differences in consumption baskets but due to the homogeneous technology assumption cannot represent differences in technology between expenditure deciles.

The tendency that the emission intensity for direct energy consumption decreases 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 richer 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 upper ends of the respective national expenditure distributions (Supplementary Note and Map).

The high intensities in the bottom four European expenditure deciles can be attributed in large part to inefficient 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 and had a higher average carbon intensity per MJ of heat delivered than both Europe and the world (XXX). These differences in specific energy and carbon intensities in basic services sectors (especially shelter) account for the smaller inequality between expenditure deciles in terms of environmental footprints compared to raw expenditures.

Inequality across final consumption sectors

pdat = get_sector_summary_by_eu_ntile_direct(eu_q_count) %>%
  ungroup() %>%
  filter(year==2015) %>%
  left_join(read_csv(here("analysis/data/derived/sectors_agg_method1_ixi.csv")), by="sector_agg_id") %>% 
  select(five_sectors, eu_q_rank, contains("pae_co2eq"), contains("pae_energy")) %>%
  pivot_longer(cols=-c(five_sectors, eu_q_rank), names_to="indicator", values_to="value") %>%
  mutate(indicator_type = if_else(str_detect(indicator, "co2eq"), 
                                  "tCO2eq per adult eq", 
                                  "Energy GJ per adult eq")) %>%
  filter(indicator != "pae_co2eq_t", 
         indicator != "pae_energy_use_gj")
  
  
pal <- wes_palette("Zissou1", 4, type = "discrete")
pal = pal[c(2,1,3,4)]
  
p1 = ggplot(pdat %>% filter(indicator_type == "tCO2eq per adult eq"), 
       aes(x=(eu_q_rank), y=value, fill=indicator)) +
  geom_col(position = position_stack()) +
  facet_wrap(~five_sectors, ncol = 5) +
  scale_fill_manual(values = pal, name="Location", labels=c("Direct", "Domestic", "Europe", "non-Europe")) +
  scale_x_continuous(breaks = c(1,2,3,4,5,6,7,8,9,10), 
                     labels = c("Q1","Q2","Q3","Q4","Q5","Q6","Q7","Q8","Q9","Q10")) +
  #theme_ipsum() +
  theme_minimal() +
  theme(text=element_text(family="Liberation Sans Narrow")) +
  labs(x="", y="Carbon footprint tCO2eq per adult eq") +
  theme(legend.position = "bottom", 
          axis.text.x = element_text(angle = 90))


p2 = ggplot(pdat %>% filter(indicator_type == "Energy GJ per adult eq"), 
       aes(x=(eu_q_rank), y=value, fill=indicator)) +
  geom_col(position = position_stack()) +
  facet_wrap(~five_sectors, ncol = 5) +
  scale_fill_manual(values = pal, name="Location", labels=c("Direct", "Domestic", "Europe", "non-Europe")) +
  scale_x_continuous(breaks = c(1,2,3,4,5,6,7,8,9,10), 
                     labels = c("Q1","Q2","Q3","Q4","Q5","Q6","Q7","Q8","Q9","Q10")) +
  #theme_ipsum() +
  theme_minimal() +
  theme(text=element_text(family="Liberation Sans Narrow")) +
  labs(x="", y="Energy footprint GJ per adult eq") +
  theme(legend.position = "bottom", 
          axis.text.x = element_text(angle = 90))

a = p1 / p2 + plot_layout(guides = "collect") & theme(legend.position = 'bottom', 
                                                  plot.margin =  margin(1, 1, 1, 1, "mm"),
                                                  panel.spacing = unit(c(1, 1, 1, 1), "mm"))


hm = pdat %>% filter(indicator_type == "Energy GJ per adult eq") %>%
  group_by(eu_q_rank) %>%
  summarise(value = sum(value))

ggsave(here("analysis", "figures", "figure3-test.pdf"))

co2eq_per_sector = pdat %>%
  filter(indicator_type == "tCO2eq per adult eq") %>%
  group_by(five_sectors, eu_q_rank) %>%
  summarise(value = sum(value))

transport_co2eq_10_10 = round((co2eq_per_sector %>% filter(eu_q_rank == 10, five_sectors == "transport"))$value/
                                (co2eq_per_sector %>% filter(eu_q_rank == 1, five_sectors == "transport"))$value,digits = 0)

man_goods_co2eq_10_10 = round((co2eq_per_sector %>% filter(eu_q_rank == 10, five_sectors == "manufactured goods"))$value/
                                (co2eq_per_sector %>% filter(eu_q_rank == 1, five_sectors == "manufactured goods"))$value,digits = 0)

services_co2eq_10_10 = round((co2eq_per_sector %>% filter(eu_q_rank == 10, five_sectors == "services"))$value/
                                (co2eq_per_sector %>% filter(eu_q_rank == 1, five_sectors == "services"))$value,digits = 1)

food_co2eq_10_10 = round((co2eq_per_sector %>% filter(eu_q_rank == 10, five_sectors == "food"))$value/
                                (co2eq_per_sector %>% filter(eu_q_rank == 1, five_sectors == "food"))$value,digits = 1)

energy_per_sector = pdat %>%
  filter(indicator_type == "Energy GJ per adult eq") %>%
  group_by(five_sectors, eu_q_rank) %>%
  summarise(value = sum(value))

transport_energy_10_10 = round((energy_per_sector %>% filter(eu_q_rank == 10, five_sectors == "transport"))$value/
                                (energy_per_sector %>% filter(eu_q_rank == 1, five_sectors == "transport"))$value,digits = 0)

man_goods_energy_10_10 = round((energy_per_sector %>% filter(eu_q_rank == 10, five_sectors == "manufactured goods"))$value/
                                (energy_per_sector %>% filter(eu_q_rank == 1, five_sectors == "manufactured goods"))$value,digits = 0)

services_energy_10_10 = round((energy_per_sector %>% filter(eu_q_rank == 10, five_sectors == "services"))$value/
                                (energy_per_sector %>% filter(eu_q_rank == 1, five_sectors == "services"))$value,digits = 1)

food_energy_10_10 = round((energy_per_sector %>% filter(eu_q_rank == 10, five_sectors == "food"))$value/
                                (energy_per_sector %>% filter(eu_q_rank == 1, five_sectors == "food"))$value,digits = 1)

In absolute terms, the various final consumption sectors contribute very differently to the total environmental footprint of households (Figure 3). On average, shelter and transport are the two largest sectors, accounting for nearly two thirds of both footprints. However, there are big differences between the sectors when looking at the respective contributions in the expenditure quantiles. For shelter there is almost no difference (neither in the carbon nor in the energy footprint). Especially the lower four expenditure deciles have high carbon emissions, which can be explained by the extreme differences in intensity shown in Figure 2. Transport was the most unequal sector, with footprints r transport_energy_10_10 times higher in the top decile compared to the bottom deciles (corroborating findings in [@ivanova_quantifying_2020] and [@oswald_large_2020]). Manufactured goods was the second most unequal consumption category (10:10 ratios around r man_goods_energy_10_10 for both footprints), followed by services (10:10 ratios of r services_co2eq_10_10 for carbon and r services_energy_10_10 for energy) and then food (10:10 ratios of r food_energy_10_10 for both footprints).

knitr::include_graphics(here::here("analysis", "figures", "figure3-test.pdf"))

co2eq_per_source = pdat %>%
  filter(indicator_type == "tCO2eq per adult eq") %>%
  group_by(five_sectors, indicator) %>%
  summarise(value = sum(value)) %>%
  mutate(share = value/sum(value))

shelter_co2eq_direct = round(((co2eq_per_source %>% filter(five_sectors == "shelter", 
                                                          indicator == "pae_co2eq_direct_t"))$share)*100, digits = 0)

energy_per_source = pdat %>%
  filter(indicator_type == "Energy GJ per adult eq") %>%
  group_by(five_sectors, indicator) %>%
  summarise(value = sum(value)) %>%
  mutate(share = value/sum(value))

shelter_energy_direct = round(((energy_per_source %>% filter(five_sectors == "shelter", 
                                                          indicator == "pae_energy_use_direct_gj"))$share)*100, digits = 0)

Figure 3 also shows the inequality in geographical source of the household energy and carbon footprints across final consumption sector. The shelter footprint was almost entirely domestic, with r shelter_co2eq_direct/r shelter_energy_direct% coming from direct household emissions/energy use for heating and cooling, and the rest embedded primarily along the domestic supply chain. The transport footprint was just under 2/3rds domestic. The majority of the transport footprint, above 60%, came from vehicle fuel, either burned directly or indirectly embedded along its supply chain. More than half of the transport footprint's foreign 1/3rd came from outside Europe. The manufactured goods footprint was mostly non-European, while services and food were both around half domestic. These results suggest that proposed future carbon border-adjustment mechanisms will especially impact the manufactured goods and transport footprints of the higher deciles, and to a lesser extent the food and services footprints, depending on mechanism design.

Counterfactual: a 1.5°C compatible Europe

Global 1.5°C compatible decarbonisation scenarios achieve a similar climate outcome with different assumptions about the transformation of energy supply and demand, from renewable capacity, deployment of carbon-capture-and-storage (CCS), and socio-technological demand transformation.

library(readxl)

df_scenario_info = read_excel(here("analysis/data/raw/scenarios.xlsx"), sheet="overview") %>%
  select(scenario, fe_gj_aeu = final_energy_gj_per_aeu_2050,
         ccs_required = primary_energy_fossil_w_ccs2050_ej,
         description) %>%
  arrange(fe_gj_aeu) %>%
  mutate(fe_gj_aeu = round(fe_gj_aeu),
         ccs_required = round(ccs_required)) 

library(flextable) 

regulartable(df_scenario_info) %>%
  autofit()

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.

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.

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.

Current empirical best technology per sector

pal <- wes_palette("Zissou1", 4, type = "discrete")
pal = pal[c(2,1,3,4)]

pdat_int_energy = get_sector_summary_by_eu_ntile_direct(eu_q_count) %>%
  ungroup() %>%
  filter(year==2015) %>%
  left_join(read_csv(here("analysis/data/derived/sectors_agg_method1_ixi.csv")), 
            by="sector_agg_id") %>% 
  mutate(intensity_energy = (total_energy_use_tj)/(total_fd_me)) %>%
  select(five_sectors, eu_q_rank, 
         intensity_energy) %>%
  filter(eu_q_rank == 10) %>%
  select(-eu_q_rank)

pdat_final_demand = get_sector_summary_by_eu_ntile_direct(eu_q_count) %>%
  ungroup() %>%
  filter(year==2015) %>%
  left_join(read_csv(here("analysis/data/derived/sectors_agg_method1_ixi.csv")), 
            by="sector_agg_id") %>%
  left_join(pdat_int_energy, by="five_sectors") %>%
  mutate(total_energy_use_tj_new = (total_fd_me)*intensity_energy) %>%
  mutate(total_energy_use_tj_diff = total_energy_use_tj-total_energy_use_tj_new) %>%
  select(five_sectors, eu_q_rank, 
         total_energy_use_tj_diff, total_energy_use_tj_new) %>%
  group_by(eu_q_rank) %>%
  summarise(total_energy_use_tj_new = sum(total_energy_use_tj_new)*0.000001,
            total_energy_use_tj_diff = sum(total_energy_use_tj_diff)*0.000001) %>%
  pivot_longer(-c(eu_q_rank), names_to = "indicator", values_to = "value")


p1 = ggplot(pdat_final_demand, aes(x=eu_q_rank, y=value, fill=indicator, alpha=indicator)) +
  geom_col(position = position_stack()) +
  scale_fill_manual(values=c(pal[1], pal[2]), 
                    labels=c("2015", "Best technology"), name="Energy\nfootprint") +
  scale_alpha_manual(values=c(0.3,1),labels=c("2015", "Best technology"), name="Energy\nfootprint")+
  scale_x_continuous(breaks = c(1,2,3,4,5,6,7,8,9,10), 
                     labels = c("Q1","Q2","Q3","Q4","Q5","Q6","Q7","Q8","Q9","Q10")) +
  labs(y="Energy footprint (EJ)", x="") +
  theme_minimal() +
  coord_flip() +
  theme(legend.position="bottom")

pdat_energy_country = get_sector_summary_by_country_quintile_direct() %>%
  ungroup() %>%
  filter(year==2015) %>%
  left_join(read_csv(here("analysis/data/derived/sectors_agg_method1_ixi.csv")), 
            by="sector_agg_id") %>%
  left_join(pdat_int_energy, by="five_sectors") %>%
  mutate(total_energy_use_tj_new = (total_fd_me)*intensity_energy) %>%
  #mutate(total_energy_use_tj_save = total_energy_use_tj_new/total_energy_use_tj*100) %>%
  select(iso2, 
         total_energy_use_tj_new, total_energy_use_tj) %>%
  group_by(iso2) %>%
  summarise(total_energy_use_tj_save = 100 - sum(total_energy_use_tj_new)/sum(total_energy_use_tj)*100)

library(rworldmap)
library(ggthemes)



df_country_mean = pdat_energy_country %>%
  mutate(iso2 = if_else(iso2 == "EL", "GR", iso2)) %>%
  mutate(iso2 = if_else(iso2 == "UK", "GB", iso2)) %>%
  left_join(ISO_3166_1 %>% select(iso2 = Alpha_2, iso3 = Alpha_3), by="iso2")


quantile_rank_map = joinCountryData2Map(df_country_mean, 
                                        joinCode = "ISO_A3", 
                                        nameJoinColumn = "iso3")
quantile_rank_map_poly = fortify(quantile_rank_map) #extract polygons 

quantile_rank_map_poly = merge(quantile_rank_map_poly, quantile_rank_map@data, by.x="id", by.y="ADMIN", all.x=T)
quantile_rank_map_poly = quantile_rank_map_poly %>% arrange(id, order)

map = ggplot() + 
  geom_polygon(data = quantile_rank_map_poly, aes(long, lat, group = group), color="black") +
  geom_polygon(data = quantile_rank_map_poly, aes(long, lat, group = group, 
                                        fill=total_energy_use_tj_save)) + 
  #scale_fill_gradient(name="Mean decile \nrank", low="skyblue", high = "skyblue4", na.value = "#EEEEEE") +
  scale_fill_viridis(direction = -1, discrete = F, na.value = "white", name="Energy\nsavings (%)") +
  theme_map() +
  labs(x="",y="")+
  coord_map("bonne", lat0 = 50,xlim = c(-9, 40), ylim = c(38, 68), clip="on") +
  theme(legend.position = c(0.85, 0.6))
  

a = p1 + map + plot_annotation(tag_levels = 'a') +
  theme(plot.margin = unit(c(0.25,0.25,0.25,0.25), "cm"),
        legend.position = 'bottom',
        axis.title.y = element_text(size=13, hjust = 0.5),
        legend.text = element_text(size=12),
        legend.title = element_text(size=13))

ggsave(here("analysis", "figures", "figure4-test.pdf"))

knitr::include_graphics(here::here("analysis", "figures", "figure4-test.pdf"))

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.

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.

Inequality in a 1.5°C compatible Europe

df_energy_deciles = read_csv(here("analysis/data/raw/energy_figure_data.csv")) 

ineq_curr = df_energy_deciles$pae_energy_use_gj[10]/df_energy_deciles$pae_energy_use_gj[1]

library(readxl)

df_scenario_info = read_excel(here("analysis/data/raw/scenarios.xlsx"), sheet="overview") %>%
  select(scenario, fe_gj_aeu = final_energy_gj_per_aeu_2050,
         ccs_required = primary_energy_fossil_w_ccs2050_ej,
         description) %>%
  arrange(fe_gj_aeu) %>%
  mutate(fe_gj_aeu = round(fe_gj_aeu),
         ccs_required = round(ccs_required)) 

mea = c(min(df_scenario_info$fe_gj_aeu),max(df_scenario_info$fe_gj_aeu))
mer = c(min(df_scenario_info$fe_gj_aeu),53)

c_mean_mea = round((27+mea[2])/2)
c_mean_mer = round((mer[1]+mer[2])/2)

round_by = 10

df_all = readRDS(here("analysis/data/raw/scenarios_fine.rds")) %>%
  filter(eu_q_rank %in% c(1,10)) %>%
  group_by(v_mean, v_first) %>%
  summarise(ratio = last(scaled)/first(scaled)) %>%
  mutate(bin_ratio = if_else((ratio*100)%%round_by > round_by*0.5, 
                             ratio*100+(round_by-(ratio*100)%%round_by),
                             ratio*100-(ratio*100)%%round_by)) %>%
  group_by(bin_ratio, v_first) %>%
  summarise(v_mean = mean(v_mean)) %>%
  mutate(bin_ratio = bin_ratio*0.01)

df_scenario = df_all %>%
  filter(v_mean %in% df_scenario_info$fe_gj_aeu) %>%
  left_join(df_scenario_info, by=c("v_mean"="fe_gj_aeu"))

library(wesanderson)

a = df_all %>%
  ggplot(aes(x=v_first, y=bin_ratio, fill=v_mean)) +
  geom_tile() +
  geom_hline(yintercept = ineq_curr, alpha=0.8, color="grey", linetype=2) +
  geom_line(data=df_scenario, aes(color=scenario, group=scenario)) +
  annotate(geom="text", x=max(df_all$v_first)-5.7,y=ineq_curr+0.6,label = "current 10:10 ratio") +
  #scale_fill_viridis("Mean energy\navailable") +
  scale_fill_gradient("Mean energy\navailable",
                      low=wes_palette("Chevalier1")[3], 
                      high = wes_palette("Rushmore1")[4]) +
  scale_color_manual(values=wes_palette("Darjeeling1")) +
  theme_minimal() +
  labs(x="Minimum energy requirement (GJ/aeu)", y="max inequality (10:10 ratio)")+
  theme(text=element_text(family="Liberation Sans Narrow"))# +
  #theme_ipsum()

ggsave(here("analysis", "figures", "figure5-test.pdf"))

Based on this counterfactual distribution of the energy footprint using homogeneous supply technologies, we can now scale down energy consumption across European expenditure deciles to meet supply constraints and, where necessary, "squeeze" the distribution to not undershoot minimum demand constraints in any decile. This means that, based on the current empirical distribution, for each value combination of supply and minimum necessary demand, 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 (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.

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).

knitr::include_graphics(here::here("analysis", "figures", "figure5-test.pdf"))

Conclusions

Estimates of energy and carbon footprint inequality are increasingly being used to assign responsibility for climate change. At a global, regional, and within-country level, energy use and carbon emissions are often highly unequal [@piketty_carbon_2015 @kartha_carbon_2020 @gore_extreme_2015 @hubacek_global_2017-1 @ivanova_unequal_2020 @wiedenhofer_unequal_2017 @golley_income_2012 @steenolsen_carbon_2016 @weber_quantifying_2008 @hardadi_implications_2020 @oswald_large_2020]. The proposed solution is often a call to reduce the carbon or energy inequality by reducing over-consumption, especially by the richest at the top of the economic distribution, which would then also reduce the energy and carbon footprints, everything else held equal. Complicating this picture, however, is the fact that energy and carbon intensities of consumption usually differ between economic groups. This is due to different consumption baskets and different access to technology. That lower-income groups tend to have higher energy and carbon intensities is an important finding from the environmental Kuznet's curve literature [@berthe_mechanisms_2015 @scruggs_political_1998]. This finding has not yet been well integrated with the current carbon and energy footprint inequality literature, that focuses more on assigning responsibility based on aggregate energy and carbon footprint inequality.

In this study, we have found that, for Europe as a whole, lower-economic groups have higher energy and carbon intensities of consumption (although this is not necessarily true within each European country) [@sommer_carbon_2017 @kerkhof_determinants_2009]. These higher intensities come almost entirely from domestic electricity production and heating/cooling for shelter, in a handful of Central and Eastern European countries. This is already an important focus of European climate policy, but reducing these intensities should be a major priority for investment fund allocation going forward, especially within a framework such as the EU's European Green Deal [@bianco_understanding_2019]. Efforts to break consumer lock-in to these high intensities must be occurring alongside policies that seek to reduce aggregate consumption and intensities higher up in the economic distribution. Bringing intensities of consumption for all economic groups in line with those of higher-economic groups in Europe with access to the cleanest and most efficient available technologies, would substantially reduce the European household energy and carbon footprints, everything else held equal. The unequal intensity structure hinders clear conclusions on footprint inequality. We have shown that in an important sector such as shelter, lower-economic groups have almost the same level of footprint as higher-economic groups despite a fraction of the expenditure, because of their higher intensities. This can then be misleading in terms of assigning responsibility for climate change. Bringing energy and carbon intensities of all economic groups in line with the top group, and thus removing the inequality in intensity structure, would reduce the footprint, all else held equal, but increase energy and carbon inequality. The reduction of energy and carbon inequality is not a meaningful goal by itself.

Current consumption inequality, however, is a barrier to achieving both scenario targets and providing minimum energy use (and minimum carbon in the short-term) for decent living to every European. At a global level, there is concern that achieving extreme poverty eradication may put global climate targets at risk [@hubacek_poverty_2017 @woodward_incrementum_2015]. In the European context, although less unequal than the globe as a whole, if/as lower-consumption groups increase their income and consumption, energy use and carbon emissions will increase if more efficient and cleaner technology is not adopted at a fast enough rate. Achieving an average per capita/adult equivalent energy and carbon footprint in Europe, in scenarios that reach the Paris agreement goals, means either doing so at current consumption inequality levels and keeping lower-economic groups near or below minimum energy use levels for decent living, or reducing consumption inequality.

refs to add

demand side solution:

Royston, S., Selby, J. & Shove, E. Invisible energy policies: A new agenda for energy demand reduction. Energy Policy 123, 127--135 (2018).

reduction in demand

Alfredsson, E. et al. Why achieving the Paris Agreement requires reduced overall consumption and production. Sustainability: Science, Practice and Policy 14, 1--5 (2018).

Acknowledgements

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References

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Colophon

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