update weighting for the Aggregate score based on WEO2023

data-raw/remaining_carbon_budgets.R

@@ -1,15 +1,50 @@
 # styler: off
-remaining_carbon_budgets <- tibble::tribble(
-  ~scenario_source,  ~ald_sector, ~remaining_carbon_budget,           ~unit, ~weighting_factor,
-        "GECO2021",       "coal",                 84934.79, "Mt CO2 (2019)",              0.25,
-        "GECO2021",        "oil",                 82987.79, "Mt CO2 (2019)",              0.24,
-        "GECO2021",        "gas",                 49094.21, "Mt CO2 (2019)",              0.14,
-        "GECO2021",      "power",                 91121.17, "Mt CO2 (2019)",              0.27,
-        "GECO2021",      "steel",                 10746.03, "Mt CO2 (2019)",              0.03,
-        "GECO2021", "automotive",                  11338.9, "Mt CO2 (2019)",              0.03,
-        "GECO2021",        "hdv",                  7388.01, "Mt CO2 (2019)",              0.02,
-        "GECO2021",   "aviation",                  5963.32, "Mt CO2 (2019)",              0.02
+# The Extended file data for WEO 2023 is stored here:
+# https://portal.azure.com/#view/Microsoft_Azure_FileStorage/FileShareMenuBlade/~/browse/storageAccountId/%2Fsubscriptions%2Ffeef729b-4584-44af-a0f9-4827075512f9%2FresourceGroups%2FRMI-SP-PACTA-PROD%2Fproviders%2FMicrosoft.Storage%2FstorageAccounts%2Fpactarawdata/path/scenario-sources/protocol/SMB
+# The specific file is 'weo_2023-20240222/WEO2023 extended data/WEO2023_Extended_Data.xlsx'
+# Please download the file and replace the dummy path in variable 'file_scenario_emissions_data'
+# to the path on your computer before running the code.
+file_scenario_emissions_data <- "PATH/TO/WEO2023/SCENARIO/EXTENDED/DATA"
+carbon_emissions <- readxl::read_xlsx(file_scenario_emissions_data,
+                              sheet = "World CO2 Emissions",
+                              range = "J7:AP45") %>%
+  filter(!row_number() %in% c(12, 13, 14, 18, 19, 20)) %>%
+  rename(
+    sector = "...1",
+    emissions_2022 = "2022...25",
+    emissions_2030 = "2030...26",
+    emissions_2035 = "2035...27",
+    emissions_2040 = "2040...28",
+    emissions_2045 = "2045...29",
+    emissions_2050 = "2050...30",
+  ) %>%
+  select(c("sector", "2010", "2015", "2021", "emissions_2022", "emissions_2030", "emissions_2035", "emissions_2040", "emissions_2045", "emissions_2050")) %>%
+  filter(sector %in% c("Coal", "Oil", "Natural gas", "Electricity and heat sectors", "Iron and steel**", "Cement**", "Passenger cars", "Aviation")) %>%
+  mutate(
+    scenario_source = "WEO2023",
+    unit = "Mt CO2 (2022)"
   )
+
+remaining_carbon_budgets <- carbon_emissions %>%
+  mutate(
+    # Remaining carbon budget for a sector is the sum of scenario emissions from 2022 until 2030.
+    # We interpolate the emissions linearly between points for which we have scenario values (2022 and 2030).
+    # The sum of 7 linearly interpolated points between x and y (including x and y) is equal to 4x + 5y:
+    # x + 7*x + ((1 + 2 + 3 + 4 + 5 + 6 + 7)/7)*(y-x) + y = 8x + 5y - 4x = 4x + 5y
+    remaining_carbon_budget = emissions_2022 * 4 + emissions_2030 * 5,
+    weighting_factor = remaining_carbon_budget/ sum(remaining_carbon_budget)
+         ) %>%
+  mutate(ald_sector = case_when(
+    sector == "Coal" ~ "coal",
+    sector == "Oil" ~ "oil",
+    sector == "Natural gas" ~ "gas",
+    sector == "Electricity and heat sectors" ~ "power",
+    sector == "Iron and steel**" ~ "steel",
+    sector == "Passenger cars" ~ "automotive",
+    sector == "Aviation" ~ "aviation",
+    sector == "Cement**" ~ "cement"
+  )) %>%
+  select("scenario_source", "unit", "remaining_carbon_budget", "weighting_factor", "ald_sector")
 # styler: on
 
 usethis::use_data(remaining_carbon_budgets, overwrite = TRUE)