Subsector comparison to MPCA state inventory

.gitignore

@@ -1146,6 +1146,30 @@ _transportation/data-raw/~$_source_method_compare.xlsx
 _transportation/data-raw/epa/SCCformat=CSV&sortFacet=scc%20level%20one&filename=SCCDownload-2024-0812-144242.csv
 
 
+_meta/data-raw/mpca_ghg_inventory.csv
+_meta/data-raw/Minnesota GCAM Modeling Data.xlsx
+
+_industrial/data-raw/emissions_by_unit_and_fuel_type_c_d_aa.xlsb
+_industrial/data-raw/emissions_by_unit_and_fuel_type_c_d_aa.xlsx
+_industrial/data-raw/MPCA_industrial_fuel_throughput.xlsx
+_industrial/data-raw/ghgrp/ghgp_data_2010.xlsx
+_industrial/data-raw/ghgrp/ghgp_data_2011.xlsx
+_industrial/data-raw/ghgrp/ghgp_data_2012.xlsx
+_industrial/data-raw/ghgrp/ghgp_data_2013.xlsx
+_industrial/data-raw/ghgrp/ghgp_data_2014.xlsx
+_industrial/data-raw/ghgrp/ghgp_data_2015.xlsx
+_industrial/data-raw/ghgrp/ghgp_data_2016.xlsx
+_industrial/data-raw/ghgrp/ghgp_data_2017.xlsx
+_industrial/data-raw/ghgrp/ghgp_data_2018.xlsx
+_industrial/data-raw/ghgrp/ghgp_data_2019.xlsx
+_industrial/data-raw/ghgrp/ghgp_data_2020.xlsx
+_industrial/data-raw/ghgrp/ghgp_data_2021.xlsx
+_industrial/data-raw/ghgrp/ghgp_data_2022.xlsx
+_industrial/data-raw/ghgrp/ghgp_data_2023.xlsx
+_meta/data-raw/state-ghg-inventory.csv
+
+
+
 _transportation/data-raw/mndot/city_route_system/
 
 _transportation/data-raw/mndot/updated_VMT_County_City_Route_System/
@@ -1154,3 +1178,28 @@ _transportation/data-raw/mndot/updated_VMT_County_City_Route_System.zip
 
 _agriculture/data-raw/ag-module.xlsx
 
+
+
+
+_energy/data-raw/nrel_slope/MNWI
+
+_energy/data-raw/dataExplorer.twb
+
+*.twbr
+
+_energy/data-raw/modelingDocs
+
+_energy/data-raw/nrel_slope
+
+_energy/data-raw/USDA_ERS_AgriculturalResourceManagementSurvey
+
+_energy/data-raw/XcelCommunityReports
+
+_energy/data-raw/mn_elec_utility_reporting_state
+
+_energy\data-raw\XcelCommunityReports\electricity_all_years_city.csv
+
+_energy/data-raw/GRE_individualReports
+
+_energy/data-raw/xcel_community_reports
+

00_compile_industrial_emissions.R

@@ -0,0 +1,185 @@
+##### run industrial scripts; compile county and CTU industrial emissions
+
+source("./R/global_warming_potential.R")
+ind_fuel_combustion <- readRDS("./_industrial/data/fuel_combustion_emissions_by_gas.rds")
+ind_flight_emissions <- readRDS("./_industrial/data/flight_industrial_point_sources_ctu.rds")
+ind_nei_emissions <- readRDS("./_industrial/data/nei_county_industrial_emissions.rds")
+
+### first start with county level emission estimates
+### The workflow is split out industrial fuel combustion into gas, oil, coal
+### subtract away fuel emissions from FLIGHT and call it process
+### subtract away flight from NEI and call it 'small industrial'
+### If flight > NEI, call 'small industrial' 0
+
+### need to do more research into what 'fuel gas' is
+ind_fuel_combustion <- ind_fuel_combustion %>% 
+  mutate(fuel_type = case_when(
+    specific_fuel_type == "Fuel Gas" ~ "Fuel Gas",
+    TRUE ~ general_fuel_type
+  ))
+
+county_fuel_combustion <- ind_fuel_combustion %>% 
+  filter(units_emissions != "avg_activity") %>% 
+  mutate(mt_co2e = case_when(
+    units_emissions == "mt_ch4" ~ values_emissions * gwp$ch4,
+    units_emissions == "mt_n2o" ~ values_emissions * gwp$n2o,
+    TRUE ~ values_emissions
+  )) %>% 
+  group_by(county_name, reporting_year, fuel_type) %>% 
+  summarize(mt_co2e = sum(mt_co2e)) %>% 
+  mutate(data_source = "EPA FLIGHT Subpart C Analysis",
+         factor_source = "EPA Emission Factor Hub",
+         sector = "Industrial",
+         category = "Fuel combustion") %>% 
+  select(county_name,
+         inventory_year = reporting_year,
+         sector,
+         category,
+         source = fuel_type,
+         data_source,
+         factor_source,
+         mt_co2e)
+
+county_process_emissions <- ind_flight_emissions %>% 
+  filter(doublecount == "No") %>% 
+  group_by(county_name, inventory_year) %>% 
+  summarize(mt_co2e = sum(value_emissions)) %>% 
+  left_join(county_fuel_combustion %>% 
+              group_by(county_name, inventory_year) %>% 
+              summarize(mt_co2e = sum(mt_co2e)),
+            by = c("county_name",
+                  "inventory_year"),
+            suffix = c("_total","_fuel_combustion")) %>% 
+  #2010 is absent for fuel combustion
+  filter(inventory_year != 2010) %>% 
+  mutate(mt_co2e_fuel_combustion = coalesce(mt_co2e_fuel_combustion, 0),
+         mt_co2e = mt_co2e_total - mt_co2e_fuel_combustion,
+         sector = "Industrial",
+         category = "Process",
+         source = "Process", #This could be filled in from subparts with some effort
+         data_source = "EPA FLIGHT",
+         factor_source = "EPA Emission Factor Hub") %>% 
+  select(inventory_year, county_name, data_source,
+         factor_source, mt_co2e, sector,
+         category,
+         source)
+
+# to estimate smaller industrial emissions, we'll subtract all FLIGHT
+# emissions away from NEI estimates. Negative numbers will be corrected
+# to zero until a better data source is identified
+county_small <- ind_nei_emissions %>% 
+  group_by(inventory_year, county_name) %>% 
+  summarize(mt_co2e_all = as.numeric(sum(values_emissions))) %>% 
+  left_join(ind_flight_emissions %>% 
+              group_by(county_name, inventory_year) %>% 
+              summarize(mt_co2e_big = sum(value_emissions))) %>% 
+  mutate(mt_co2e_big =coalesce(mt_co2e_big, 0),
+         mt_co2e = mt_co2e_all - mt_co2e_big,
+         mt_co2e = if_else(mt_co2e < 0, 0, mt_co2e),
+         sector = "Industrial",
+         category = "Other",
+         source = "Small point source",
+         data_source = "EPA NEI",
+         factor_source = "EPA NEI") %>% 
+  select(inventory_year, county_name, data_source,
+         factor_source,mt_co2e, sector,
+         category,
+         source)
+
+county_industrial_emission = bind_rows(
+  county_fuel_combustion,
+  county_process_emissions,
+  county_small) %>% 
+  mutate(units_emissions = "Metric tons CO2e") %>% 
+  rename(values_emissions = mt_co2e)
+
+county_industrial_emission_meta <-
+  tibble::tribble(
+    ~"Column", ~"Class", ~"Description",
+    "county_name", class(county_industrial_emission$county_name), "County name",
+    "inventory_year", class(county_industrial_emission$inventory_year), "Year of activity",
+    "sector", class(county_industrial_emission$sector), "Sector of emissions",
+    "category", class(county_industrial_emission$category), "Category of emissions",
+    "source", class(county_industrial_emission$source), "Specific source of emissions",
+    "data_source", class(county_industrial_emission$data_source), "Activity data source",
+    "factor_source", class(county_industrial_emission$factor_source), "Emissions factor data source",
+    "values_emissions", class(county_industrial_emission$values_emissions), "Numerical value of emissions data",
+    "units_emissions", class(county_industrial_emission$units_emissions), "Units of emissions data"
+  )
+
+saveRDS(county_industrial_emission, "./_industrial/data/county_industrial_emissions.rds")
+saveRDS(county_industrial_emission_meta, "./_industrial/data/county_industrial_emissions_meta.rds")
+
+
+## do the same for cities, excepting the nei source
+
+
+city_fuel_combustion <- ind_fuel_combustion %>% 
+  filter(units_emissions != "avg_activity") %>% 
+  mutate(mt_co2e = case_when(
+    units_emissions == "mt_ch4" ~ values_emissions * gwp$ch4,
+    units_emissions == "mt_n2o" ~ values_emissions * gwp$n2o,
+    TRUE ~ values_emissions
+  )) %>% 
+  group_by(city_name, reporting_year, fuel_type) %>% 
+  summarize(mt_co2e = sum(mt_co2e)) %>% 
+  mutate(data_source = "EPA FLIGHT Subpart C Analysis",
+         factor_source = "EPA Emission Factor Hub",
+         sector = "Industrial",
+         category = "Fuel combustion") %>% 
+  select(city_name,
+         inventory_year = reporting_year,
+         sector,
+         category,
+         source = fuel_type,
+         data_source,
+         factor_source,
+         mt_co2e)
+
+city_process_emissions <- ind_flight_emissions %>% 
+  filter(doublecount == "No") %>% 
+  group_by(city_name, inventory_year) %>% 
+  summarize(mt_co2e = sum(value_emissions)) %>% 
+  left_join(city_fuel_combustion %>% 
+              group_by(city_name, inventory_year) %>% 
+              summarize(mt_co2e = sum(mt_co2e)),
+            by = c("city_name",
+                   "inventory_year"),
+            suffix = c("_total","_fuel_combustion")) %>% 
+  #2010 is absent for fuel combustion
+  filter(inventory_year != 2010) %>% 
+  mutate(mt_co2e_fuel_combustion = coalesce(mt_co2e_fuel_combustion, 0),
+         mt_co2e = mt_co2e_total - mt_co2e_fuel_combustion,
+         mt_co2e = if_else(mt_co2e < 0, 0, mt_co2e),
+         sector = "Industrial",
+         category = "Process",
+         source = "Process", #This could be filled in from subparts with some effort
+         data_source = "EPA FLIGHT",
+         factor_source = "EPA Emission Factor Hub") %>% 
+  select(inventory_year, city_name, data_source,
+         factor_source, mt_co2e, sector,
+         category,
+         source)
+
+city_industrial_emission = bind_rows(
+  city_fuel_combustion,
+  city_process_emissions) %>% 
+  mutate(units_emissions = "Metric tons CO2e") %>% 
+  rename(values_emissions = mt_co2e)
+
+city_industrial_emission_meta <-
+  tibble::tribble(
+    ~"Column", ~"Class", ~"Description",
+    "city_name", class(city_industrial_emission$city_name), "County name",
+    "inventory_year", class(city_industrial_emission$inventory_year), "Year of activity",
+    "sector", class(city_industrial_emission$sector), "Sector of emissions",
+    "category", class(city_industrial_emission$category), "Category of emissions",
+    "source", class(city_industrial_emission$source), "Specific source of emissions",
+    "data_source", class(city_industrial_emission$data_source), "Activity data source",
+    "factor_source", class(city_industrial_emission$factor_source), "Emissions factor data source",
+    "values_emissions", class(city_industrial_emission$values_emissions), "Numerical value of emissions data",
+    "units_emissions", class(city_industrial_emission$units_emissions), "Units of emissions data"
+  )
+
+saveRDS(city_industrial_emission, "./_industrial/data/city_industrial_emissions.rds")
+saveRDS(city_industrial_emission_meta, "./_industrial/data/city_industrial_emissions_meta.rds")

R/cprg_colors.R

@@ -10,34 +10,52 @@ cprg_colors <- list(
 )
 
 # sector colors
-sector_colors <- list(
-  "Transportation" = "#8e440b",
-  "Energy" = "#163c6a",
-  "Waste" = "#8d0c3b"
-)
+sector_colors <- list("Electricity" = "#1f77b4", 
+                       "Transportation" = "#191970", 
+                       "Residential" = "#9467bd", 
+                       "Commercial" = "#d62728", 
+                       "Industrial" = "slategray",
+                       "Waste" = "#8c564b",
+                       "Agriculture" = "#8fb910", 
+                       "Natural Systems" = "#006f3c")
+
 
 # category
 category_colors <-
   list(
-    # GnBu
-    "Residential energy" = "#225ea8",
-    "Commercial energy" = "#41b6c4",
-    "Industrial energy" = "#7fcdbb",
-    "Liquid stationary fuels" = "#a8ddb5",
-
-
+
+    #Pu
+    "Residential natural gas" = "#483248",
+    "Residential electricity" = "#CF9FFF",
+
+    "Commercial natural gas" = "#800000",
+    "Commercial fuel combustion" = "#DE3163",
+    "Commercial electricity" = "#FAA0A0",
+
+    #Gray
+    "Industrial electricity" = "#E5E4E2",
+    "Industrial natural gas" = "#36454F",
+    "Industrial processes" = "#B2BEB5",
+    "Industrial fuel combustion" = "#818589",
+    "Refinery processes" = "#708090",
+
     # YlOrBr
-    "Passenger vehicles" = "#993404",
-    "Buses" = "#E9967A",
-    "Trucks" = "#fe9929",
-
-    # PuRd
-    "Solid waste" = "#ce1256",
-    "Wastewater" = "#c994c7",
-
+    "Passenger vehicles" = "#0047AB",
+    "Buses" = "#6F8FAF",
+    "Trucks" = "#6495ED",
+    "Aviation" = "#191970",
+
+    # Rd
+    "Solid waste" = "#8B4513",
+    "Wastewater" = "#CD853F",
+
+    # Green
+    "Cropland" = "#c7e960",
+    "Livestock" = "#8fb910",
+
     # Gn
-    "Sequestration" = "#006f3c",
-    "Stock" = "#27b376"
+    "Urban greenery" = "#006f3c",
+    "Natural systems" = "#27b376"
   )