si.Rmd

            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 == 2005) %>%
                 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 == 2005) %>%
  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)

```

In 2005, total household final demand was `r exp_total` trn€, the energy footprint `r energy_total` EJ, and the carbon footprint `r co2eq_total` MtCO2eq. We estimated the 10:10 ratios at `r exp_10_10` for expenditure, `r energy_10_10` for the energy footprint, and `r co2eq_10_10` for the carbon footprint. 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 GHG 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 average energy intensity of consumption was `r mean_energy_intens_bottom_decile` MJ/€ in the bottom decile and `r mean_energy_intens_top_decile` MJ/€ in the top decile. The GHG intensity of energy use was `r mean_co2eq_of_energy_intens_bottom_decile` gCO2eq/TJ in the bottom decile, and `r mean_co2eq_of_energy_intens_top_decile` gCO2eq/TJ in the top decile.

```{r figureS1-test, fig.width=12, fig.height=8, out.width="98%", fig.cap="Expenditure and resource footprints and intensities across European expenditure deciles in 2005. Total expenditures (a), energy footprint (b), and GHG footprint (c) per decile. Energy intensity as energy footprint per expenditure (d), GHG intensity as GHG footprint per expenditure (e), and GHG intensity as GHG footprint per energy footprint (f)."}

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),
        axis.text.x = element_text(size = 12),
        axis.text.y = element_text(size = 12),
        legend.text = element_text(size=12),
        legend.title = element_text(size=13))
a

ggsave(here("analysis", "figures", "figureS1.pdf"), device=cairo_pdf)

```

## 2010 using main method, EXIOBASE industry-by-industry

```{r ntiles-total-2010-ixi}

p1 = pdat_country_summary_by_eu_ntile %>%
  filter(year == 2010, 
         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_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 == 2010, 
         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_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 == 2010, 
         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_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

```

```{r ntiles-intensity-violin-2010-ixi}

p1 = dat_country_summary_by_eu_ntile %>%
  filter(year == 2010) %>%
  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 == 2010) %>%
                 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_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 == 2010) %>%
  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 == 2010) %>%
                 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_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 == 2010) %>%
  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 == 2010) %>%
                 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_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

```

```{r values-in-text-2010-ixi}

# values in text

## inequality
exp = pdat_country_summary_by_eu_ntile %>%
  filter(year == 2010, 
         indicator == "total_fd_me") %>%
  group_by(eu_q_rank) %>%
  summarise(value = sum(value)*0.000001,
            eu_ntile_name = first(eu_ntile_name))

exp_total = (exp %>% summarise(value = sum(value)))$value

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 == 2010, 
         indicator == "total_energy_use_tj") %>%
  group_by(eu_q_rank) %>%
  summarise(value = sum(value)*0.000001,
            eu_ntile_name = first(eu_ntile_name))

energy_total = (energy %>% summarise(value = sum(value)))$value

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 == 2010, 
         indicator == "total_co2eq_kg") %>%
  group_by(eu_q_rank) %>%
  summarise(value = sum(value)*0.000000001,
            eu_ntile_name = first(eu_ntile_name))

co2eq_total = (co2eq %>% summarise(value = sum(value)))$value

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 == 2010,
         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 == 2010) %>%
                 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 == 2010) %>%
  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)

```

In 2010, total household final demand was `r exp_total` trn€, the energy footprint `r energy_total` EJ, and the carbon footprint `r co2eq_total` MtCO2eq. We estimated the 10:10 ratios at `r exp_10_10` for expenditure, `r energy_10_10` for the energy footprint, and `r co2eq_10_10` for the carbon footprint. 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 GHG 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 average energy intensity of consumption was `r mean_energy_intens_bottom_decile` MJ/€ in the bottom decile and `r mean_energy_intens_top_decile` MJ/€ in the top decile. The GHG intensity of energy use was `r mean_co2eq_of_energy_intens_bottom_decile` gCO2eq/TJ in the bottom decile, and `r mean_co2eq_of_energy_intens_top_decile` gCO2eq/TJ in the top decile.

```{r figureS2-test, fig.width=12, fig.height=8, out.width="98%", fig.cap="Expenditure and resource footprints and intensities across European expenditure deciles in 2010. Total expenditures (a), energy footprint (b), and GHG footprint (c) per decile. Energy intensity as energy footprint per expenditure (d), GHG intensity as GHG footprint per expenditure (e), and GHG intensity as GHG footprint per energy footprint (f)."}

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),
        axis.text.x = element_text(size = 12),
        axis.text.y = element_text(size = 12),
        legend.text = element_text(size=12),
        legend.title = element_text(size=13))

a

ggsave(here("analysis", "figures", "figureS2.pdf"), device=cairo_pdf)

```

## 2010 using main method, EXIOBASE product-by-product

```{r load-data2, include=FALSE}
# load data wrangling functions
source(here("analysis", "R", "si", "wrangler_functions_pxp.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)

```

```{r ntiles-total-2010-pxp}

p1 = pdat_country_summary_by_eu_ntile %>%
  filter(year == 2010, 
         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_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 == 2010, 
         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_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 == 2010, 
         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_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

```

```{r ntiles-intensity-violin-2010-pxp}

p1 = dat_country_summary_by_eu_ntile %>%
  filter(year == 2010) %>%
  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 == 2010) %>%
                 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_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 == 2010) %>%
  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 == 2010) %>%
                 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_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 == 2010) %>%
  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 == 2010) %>%
                 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_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

```

```{r values-in-text-2010-pxp}

# values in text

## inequality
exp = pdat_country_summary_by_eu_ntile %>%
  filter(year == 2010, 
         indicator == "total_fd_me") %>%
  group_by(eu_q_rank) %>%
  summarise(value = sum(value)*0.000001,
            eu_ntile_name = first(eu_ntile_name))

exp_total = (exp %>% summarise(value = sum(value)))$value

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 == 2010, 
         indicator == "total_energy_use_tj") %>%
  group_by(eu_q_rank) %>%
  summarise(value = sum(value)*0.000001,
            eu_ntile_name = first(eu_ntile_name))

energy_total = (energy %>% summarise(value = sum(value)))$value

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 == 2010, 
         indicator == "total_co2eq_kg") %>%
  group_by(eu_q_rank) %>%
  summarise(value = sum(value)*0.000000001,
            eu_ntile_name = first(eu_ntile_name))

co2eq_total = (co2eq %>% summarise(value = sum(value)))$value

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 == 2010,
         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 == 2010) %>%
                 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 == 2010) %>%
  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)

```

In 2010 but using the product-by-product version of EXIOBASE, total household final demand was `r exp_total` trn€, the energy footprint `r energy_total` EJ, and the GHG footprint `r co2eq_total` MtCO2eq. We estimated the 10:10 ratios at `r exp_10_10` for expenditure, `r energy_10_10` for the energy footprint, and `r co2eq_10_10` for the carbon footprint. 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 GHG 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 average energy intensity of consumption was `r mean_energy_intens_bottom_decile` MJ/€ in the bottom decile and `r mean_energy_intens_top_decile` MJ/€ in the top decile. The GHG intensity of energy use was `r mean_co2eq_of_energy_intens_bottom_decile` gCO2eq/TJ in the bottom decile, and `r mean_co2eq_of_energy_intens_top_decile` gCO2eq/TJ in the top decile.

```{r figureS3-test, fig.width=12, fig.height=8, out.width="98%", fig.cap="Expenditure and resource footprints and intensities across European expenditure deciles in 2010 (product-by-product EXIOBASE version). Total expenditures (a), energy footprint (b), and GHG footprint (c) per decile. Energy intensity as energy footprint per expenditure (d), GHG intensity as GHG footprint per expenditure (e), and GHG intensity as GHG footprint per energy footprint (f)."}

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),
        axis.text.x = element_text(size = 12),
        axis.text.y = element_text(size = 12),
        legend.text = element_text(size=12),
        legend.title = element_text(size=13))

a

ggsave(here("analysis", "figures", "figureS3.pdf"), device=cairo_pdf)

```

## 2015 using alternative method, EXIOBASE industry-by-industry

There is relatively good agreement between the alternative method footprints and the EXIOBASE footprints, except in Bulgaria, where the alternative method footprint was around 3 times larger. Including Bulgaria, the alternative method footprints were on average 20% larger than the EXIOBASE footprints. With Bulgaria removed they were 10% larger on average. Eastern European countries especially had larger alternative method footprints due to high intensities in electricity production and hot water supply especially, and then more expenditure in CP045 multiplied by these intensities than the expenditure in EXIOBASE. This is why the figure shows such high footprints in the bottom European deciles compared to the method keeping EXIOBASE footprints the same (ms results).

```{r load-data3, include=FALSE}
# load data wrangling functions
source(here("analysis", "R", "si", "wrangler_functions_method2_ixi.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", "coicop")
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", "coicop")
pdat_sector_summary_by_eu_ntile =
  pivot_results_longer_adorn(dat_sector_summary_by_eu_ntile, cols_ex)

```

```{r ntiles-total-2015-method2}

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_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_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_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

```

```{r ntiles-intensity-violin-2015-method2}

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_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_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_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

```

```{r figureS4-test, fig.width=12, fig.height=8, out.width="98%", fig.cap="Expenditure and resource footprints and intensities across European expenditure deciles in 2015 (EXIOBASE industry-by-industry version, but using alternative method). Total expenditures (a), energy footprint (b), and GHG footprint (c) per decile. Energy intensity as energy footprint per expenditure (d), GHG intensity as GHG footprint per expenditure (e), and GHG intensity as GHG footprint per energy footprint (f)."}

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),
        axis.text.x = element_text(size = 12),
        axis.text.y = element_text(size = 12),
        legend.text = element_text(size=12),
        legend.title = element_text(size=13))

a

ggsave(here("analysis", "figures", "figureS4.pdf"), device=cairo_pdf)

```

\newpage

# References 

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<div id="refs"></div>