edit paper

analysis/data/raw/.~lock.scenarios.xlsx#

+,jaccard,jaccard-Latitude-E6440,12.01.2021 12:05,file:///home/jaccard/.config/libreoffice/4;
\ No newline at end of file

analysis/paper/paper.Rmd

@@ -737,7 +737,7 @@ ineq_curr = df_energy_deciles$pae_energy_use_gj[10]/df_energy_deciles$pae_energy
 
 ```
 
-```{r}
+```{r old, eval = FALSE}
 
 library(readxl)
 
@@ -757,7 +757,7 @@ c_mean_mer = round((mer[1]+mer[2])/2)
 
 ```
 
-```{r, fig.width=7, fig.height=5.5}
+```{r old2, fig.width=7, fig.height=5.5, eval = FALSE}
 
 round_by = 10
 
@@ -791,11 +791,69 @@ a = df_all %>%
   scale_color_manual(values=wes_palette("Darjeeling1")) +
   labs(x="Minimum energy requirement (GJ/cap)", y="max inequality (10:10 ratio)")+
   theme_ipsum()
-a
+
 #ggsave(here("figures", "figure5.pdf"))
 
 ```
 
+```{r}
+
+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$fe_gj_aeu),53)
+
+c_mean_mea = round((27+mea[2])/2)
+c_mean_mer = round((mer[1]+mer[2])/2)
+
+```
+
+```{r}
+
+round_by = 10
+
+df_all = readRDS(here("analysis/data/raw/scenarios_fine.rds")) %>%
+  filter(eu_q_rank %in% c(1,10)) %>%
+  group_by(v_mean, v_first) %>%
+  summarise(ratio = last(scaled)/first(scaled)) %>%
+  mutate(bin_ratio = if_else((ratio*100)%%round_by > round_by*0.5, 
+                             ratio*100+(round_by-(ratio*100)%%round_by),
+                             ratio*100-(ratio*100)%%round_by)) %>%
+  group_by(bin_ratio, v_first) %>%
+  summarise(v_mean = mean(v_mean)) %>%
+  mutate(bin_ratio = bin_ratio*0.01)
+
+df_scenario = df_all %>%
+  filter(v_mean %in% df_scenario_info$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,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")) +
+  labs(x="Minimum energy requirement (GJ/aeu)", y="max inequality (10:10 ratio)")+
+  theme_ipsum()
+
+#ggsave(here("figures", "figure5.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. [*space permitting, give examples of the rather extreme nature of demand interventions here or in in scenario description/table above*]