edit si - Fig.S7 caption

analysis/paper/si.Rmd

@@ -2187,7 +2187,7 @@ df_all = readRDS(here("analysis/data/derived/si/scenarios_extrafine.rds")) %>%
 
 ```
 
-```{r, fig.width=8, fig.height=4, fig.cap="***Figure S7: The available energy supply (colored scenario lines and dashed elevation lines, in household GJ/capita) in the 1.5°C compatible scenarios together with the assumed minimum energy use (household GJ/capita) for a decent life, determine the maximum level of energy inequality (expressed as 90:10 top-to-bottom decile ratio) while achieving both goals. Energy inequality was calculated for harmonized best technology per final consumption category. All values are in household GJ/capita, meaning the original total GJ/capita values from each scenario were multiplied by 0.65. For further comparison, we added two more SSP2 scenarios to the figure which don't achieve the 1.5°C target (SSP2-3.4, SSP2-4.5) [@riahi_shared_2017]. The original total GJ/capita values for these two scenarios were: 131 GJ/capita (SSP2-3.4) and 140 GJ/capita (SSP2-4.5) [@riahi_shared_2017].***"}
+```{r, fig.width=8, fig.height=4, fig.cap="***Figure S7: The maximum available average energy supply (colored scenario lines and dashed elevation lines, in household GJ/capita) in the 1.5°C compatible scenarios together with the assumed minimum energy use (household GJ/capita) for a decent life, determine the maximum level of energy inequality (expressed as 90:10 top-to-bottom decile ratio) while achieving both goals. Energy inequality was calculated for harmonized best technology per final consumption category. All values refer to final energy. All values are in household GJ/capita, meaning the original total GJ/capita values from each scenario were multiplied by 0.65. For further comparison, we added two more SSP2 scenarios to the figure which don't achieve the 1.5°C target (SSP2-3.4, SSP2-4.5) [@riahi_shared_2017]. The original total GJ/capita values for these two scenarios were: 131 GJ/capita (SSP2-3.4) and 140 GJ/capita (SSP2-4.5) [@riahi_shared_2017].***"}
 
 df_grid = df_all %>%
   filter(!(v_mean %in% df_scenario_info$fe_gj_pc))
@@ -2225,7 +2225,7 @@ library(ggsci)
 
 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) +
+  geom_smooth(aes(linetype="Maximum energy\nsupply (GJ/cap)"), se=FALSE, color="grey", size=0.5) +
   scale_linetype_manual(name="", values = c(2)) +
   scale_size_manual(values = c("yes"=1, "no"=-1))+
   geom_point(data=df_hm, aes(color=labl, size=dle)) +
@@ -2240,7 +2240,7 @@ a = df_grid %>%
   guides(size=FALSE) +
   scale_color_npg(name = "Scenario") +
   lims(x=c(9.5,60), y=c(1,13)) +
-  labs(x= "Minimum energy use (GJ/ae)", y="Energy use inequality (90:10 ratio)") +
+  labs(x= "Minimum energy use (GJ/cap)", y="Final energy use inequality (90:10 ratio)") +
   theme_minimal() +
   theme(text=element_text(family="Liberation Sans Narrow"),
       axis.title.y = element_text(size=13, hjust = 0.5),