revision 0.1

analysis/paper/figures.Rmd

@@ -314,7 +314,8 @@ pdat = get_sector_summary_by_eu_ntile_direct(eu_q_count) %>%
                                   "tCO2eq per adult eq", 
                                   "Energy GJ per adult eq")) %>%
   filter(indicator != "pae_co2eq_t", 
-         indicator != "pae_energy_use_gj")
+         indicator != "pae_energy_use_gj") %>% 
+  filter(!str_detect(indicator, "losses"))
   
   
 pal <- wes_palette("Zissou1", 4, type = "discrete")
@@ -382,7 +383,8 @@ knitr::include_graphics(here::here("analysis", "figures", "figure3.pdf"))
 
 # Fig.4
 
-```{r}
+```{r, eval=FALSE}
+# old figure 4a, delete
 pal <- wes_palette("Zissou1", 4, type = "discrete")
 pal = pal[c(2,1,3,4)]
 
@@ -427,6 +429,66 @@ p1 = ggplot(pdat_final_demand, aes(x=eu_q_rank, y=value, fill=indicator, alpha=i
 
 ```
 
+
+
+```{r fig4a-alt}
+pal <- wes_palette("Zissou1", 4, type = "discrete")
+pal = pal[c(2,1,3,4)]
+
+pdat_int_energy_best = 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(int_energy_final = (total_energy_use_tj-total_energy_use_losses_tj)/(total_fd_me),
+         int_energy_losses = (total_energy_use_losses_tj)/(total_fd_me),
+         int_energy_net = (total_energy_use_tj)/(total_fd_me)) %>%
+  select(five_sectors, eu_q_rank, 
+         int_energy_final, int_energy_losses, int_energy_net) %>%
+  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_best, by="five_sectors") %>%
+  mutate(total_energy_use_new_final_tj = (total_fd_me)*int_energy_final,
+         total_energy_use_new_losses_tj = (total_fd_me)*int_energy_losses) %>%
+  mutate(total_energy_use_final_tj = total_energy_use_tj-total_energy_use_losses_tj,
+         total_energy_use_final_tj_diff = total_energy_use_final_tj-total_energy_use_new_final_tj,
+         total_energy_use_losses_tj_diff = total_energy_use_losses_tj-total_energy_use_new_losses_tj) %>%
+  select(five_sectors, eu_q_rank, 
+         total_energy_use_new_final_tj, total_energy_use_final_tj_diff,
+         total_energy_use_new_losses_tj, total_energy_use_losses_tj_diff) %>%
+  group_by(eu_q_rank) %>%
+  summarise(total_energy_use_new_final_tj = sum(total_energy_use_new_final_tj)*0.000001,
+            total_energy_use_final_tj_diff = sum(total_energy_use_final_tj_diff)*0.000001,
+            total_energy_use_new_losses_tj = sum(total_energy_use_new_losses_tj)*0.000001,
+            total_energy_use_losses_tj_diff = sum(total_energy_use_losses_tj_diff)*0.000001) %>%
+  pivot_longer(-c(eu_q_rank), names_to = "indicator", values_to = "value") %>% 
+  mutate(technology = if_else(str_detect(indicator, "new"), "new", "diff"),
+         energy = if_else(str_detect(indicator, "final"), "Final energy", "Losses"))
+
+
+p1 = ggplot(pdat_final_demand, aes(x=eu_q_rank, y=value, fill=technology, alpha=technology)) +
+  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("D01","D02","D03","D04","D05","D06","D07","D08","D09","D10")) +
+  labs(y="Energy footprint (EJ)", x="") +
+  theme_minimal() +
+  coord_flip() +
+  theme(legend.position="bottom") +
+  facet_wrap(~energy, ncol = 2)
+
+```
+
+
+
 ```{r}
 
 pdat_energy_country = get_sector_summary_by_country_quintile_direct() %>%
@@ -434,8 +496,8 @@ pdat_energy_country = get_sector_summary_by_country_quintile_direct() %>%
   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) %>%
+  left_join(pdat_int_energy_best, by="five_sectors") %>%
+  mutate(total_energy_use_tj_new = (total_fd_me)*int_energy_net) %>%
   #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) %>%