new figure 5

analysis/paper/figures.Rmd

@@ -570,14 +570,14 @@ knitr::include_graphics(here::here("analysis", "figures", "figure4.pdf"))
 
 ```{r }
 
-pdat_int_energy = get_sector_summary_by_eu_ntile_direct(eu_q_count) %>%
+pdat_intensity = 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_energy_use_losses_tj)/(total_fd_me)) %>%  ## changed to subtract losses to get 'final' energy
+  mutate(final_energy_intensity = (total_energy_use_tj-total_energy_use_losses_tj)/(total_fd_me)) %>%  ## changed to subtract losses to get 'final' energy
   select(five_sectors, eu_q_rank, 
-         intensity_energy) %>%
+         final_energy_intensity) %>%
   filter(eu_q_rank == 10) %>%
   select(-eu_q_rank)
 
@@ -586,19 +586,19 @@ pdat_final_demand = get_sector_summary_by_eu_ntile_direct(eu_q_count) %>%
   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_losses_tj)-total_energy_use_tj_new) %>%  ## changed here too
-  select(eu_q_rank,total_energy_use_tj_new) %>%
+  left_join(pdat_intensity, by="five_sectors") %>%
+  mutate(total_final_energy_use_tj_new = (total_fd_me)*final_energy_intensity) %>%
+  #mutate(total_energy_use_tj_diff = (total_energy_use_tj-total_energy_use_losses_tj)-total_energy_use_tj_new) %>%  ## changed here too
+  select(eu_q_rank,total_final_energy_use_tj_new) %>%
   group_by(eu_q_rank) %>%
-  summarise(total_energy_use_tj_new = sum(total_energy_use_tj_new)) %>%
-  mutate(pae_energy_use_tj = total_energy_use_tj_new/33417583,
-         pae_energy_use_gj = pae_energy_use_tj*1000)
+  summarise(total_final_energy_use_tj_new = sum(total_final_energy_use_tj_new)) %>%
+  mutate(pae_final_energy_use_tj = total_final_energy_use_tj_new/33417583,
+         pae_final_energy_use_gj = pae_final_energy_use_tj*1000)
 
 df_energy_deciles = pdat_final_demand %>%
-  select(eu_q_rank, pae_energy_use_gj)
+  select(eu_q_rank, pae_final_energy_use_gj)
 
-ineq_curr = df_energy_deciles$pae_energy_use_gj[10]/df_energy_deciles$pae_energy_use_gj[1]
+ineq_curr = df_energy_deciles$pae_final_energy_use_gj[10]/df_energy_deciles$pae_final_energy_use_gj[1]
 
 df_scenario_info = read_excel(here("analysis/data/raw/scenarios.xlsx"), sheet="overview") %>%
   select(scenario, fe_gj_aeu = final_energy_gj_per_aeu_2050,
@@ -662,7 +662,7 @@ for (min_energy in seq(from=mer[1], to=mer[2], by=0.1)) {
     if (min_energy <= mean_energy) {
       df_all = df_all %>%
       bind_rows(df_energy_deciles %>%
-                  scaled_quantiles(eu_q_rank, pae_energy_use_gj, min_energy, mean_energy))
+                  scaled_quantiles(eu_q_rank, pae_final_energy_use_gj, min_energy, mean_energy))
     }
   }
 }
@@ -695,14 +695,27 @@ df_all = readRDS(here("analysis/data/derived/scenarios_extrafine.rds")) %>%
 
 df_grid = df_all %>%
   filter(!(v_mean %in% df_scenario_info$fe_gj_aeu))
-df_scenario = df_all %>%
-  filter((v_mean %in% df_scenario_info$fe_gj_aeu))
 
-df_dle = tibble(bin_ratio = 1, v_first = 16, v_mean = 16)
+df_scenario_info = df_scenario_info %>% 
+  mutate(labl = paste0(scenario, " (", fe_gj_aeu, ")"))
 
-scenario_label = df_scenario_info %>%
-  mutate(labl = paste0(scenario, " (", fe_gj_aeu, ")")) %>%
-  pull(labl)
+df_scenario = df_all %>%
+  filter((v_mean %in% df_scenario_info$fe_gj_aeu)) %>% 
+  left_join(df_scenario_info %>% select(labl, v_mean = fe_gj_aeu))
+
+df_scenario$labl = factor(df_scenario$labl, levels = c("DLE (16)",
+                                                       "LED global (28)",
+                                                       "LED North (55)",
+                                                       "GEA-efficiency (66)",
+                                                       "IEA ETP B2DS (87)",
+                                                       "SSP1-1.9 (90)",
+                                                       "SSP2-1.9 (98)",
+                                                       "SSP2-2.6 (119)"))
+
+df_hm = df_scenario %>% 
+  group_by(v_mean) %>% 
+  slice_min(v_first) %>% 
+  mutate(dle = if_else(v_mean == 16, "yes", "no"))
 
 library(ggsci)
 
@@ -710,17 +723,18 @@ a = df_grid %>%
   ggplot(aes(x=v_first, y=bin_ratio, group=v_mean)) +
   geom_smooth(aes(linetype="Maximum energy\nsupply (GJ/ae)"), se=FALSE, color="grey", size=0.5) +
   scale_linetype_manual(name="", values = c(2)) +
-  geom_smooth(data=df_scenario, aes(color=factor(v_mean)), se=FALSE) +
-  geom_point(data=df_dle, aes(color=factor(v_mean))) +
+  scale_size_manual(values = c("yes"=1, "no"=-1))+
+  geom_point(data=df_hm, aes(color=labl, size=dle)) +
+  geom_smooth(data=df_scenario, aes(color=labl), se=FALSE) +
+  #geom_point(data=df_scenario %>% filter(v_mean==16), aes(color=labl)) +
   geom_hline(yintercept = ineq_curr, color="grey") +
   annotate(geom="text",x=56,y=12,label="300", angle=-35, size=3, color="grey32") +
   annotate(geom="text",x=46,y=9.5,label="200", angle=-35, size=3, color="grey32") +
   annotate(geom="text",x=33.5,y=6,label="100", angle=-35, size=3, color="grey32") +
   annotate(geom="text",x=60,y=ineq_curr-0.28,
            label="energy use inequality 2015", size=3.75, hjust=1, color="grey40") +
-  scale_color_npg(
-                     name = "Scenario", 
-                     labels = scenario_label) +
+  guides(size=FALSE) +
+  scale_color_npg(name = "Scenario") +
   lims(x=c(15.5,60), y=c(1,13)) +
   labs(x= "Minimum energy use (GJ/ae)", y="Energy use inequality (90:10 ratio)") +
   theme_minimal() +