Merge pull request #93 from Metropolitan-Council/gwp-ar6

Update GWP values to AR6, add county population data back to 2005 (v1.1.1)

.gitignore

@@ -7,6 +7,8 @@
 *_cache
 .DS_Store
 
+**/desktop.ini
+
 _transportation/report_transportation_files/figure-html/
 _waste/data-raw/score_summary.csv
 _waste/data-raw/ghg-emission-factors-hub-2021.xlsx
@@ -327,6 +329,18 @@ _nature/data-raw/land_cover_stock.csv
 site_libs/
 _nature/_natural_systems_files/*
 
+_energy/data-raw/nhgis_blk2000_blk2010_MN/
+
+_energy/data-raw/nhgis_blk2000_blk2010_WI/
+
+_energy/data-raw/EIA861
+
+_energy/data-raw/libraryDocs_2005
+
+_meta/data-raw/nrel_slope/scenario_planner/
+
+_meta/data-raw/nrel_slope/scenario_planner.zip
+
 _meta/data-raw/nrel_slope_emissions/scenario_planner/
 
 _meta/data-raw/nrel_slope_emissions/
@@ -336,3 +350,7 @@ _meta/data-raw/nrel_slope/scenario_planner/
 _meta/data-raw/nrel_slope/emissions/
 
 _meta/data-raw/nrel_slope/scenario_planner.zip
+
+_transportation/data-raw/ghg_protocol/Emission_Factors_for_Cross_Sector_Tools_V2.0_0.xlsx
+
+_transportation/data-raw/ghg_protocol/ghg-uncertainty.xlsx

R/global_warming_potential.R

@@ -1,13 +1,28 @@
 # global warming potential
-# 100-year, accurate to AR5
-# * Following  revised reporting requirements under the UNFCCC, this tool presents CO2 equivalent values based on the IPCC Fifth Assessment Report (AR5) GWP values.
-# see Box 3.2, Table 1
-# Zotero: ipccClimateChange2014
+# 100-year, accurate to AR6
+# * Following  revised reporting requirements under the UNFCCC, this tool presents CO2 equivalent values based on the IPCC Sixth Assessment Report (AR6) GWP values.
+# see Table 7.SM.7 in the Supplementary Materials for Chp.7 of the Climate Change 2021: The Physical Science Basis report prepared by Working Group I for the AR6 -- https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_Chapter07_SM.pdf -- for full data table of GWPs
+# Zotero: ipccAR62021
 gwp <-
   list(
     "co2" = 1,
-    "ch4" = 28,
-    "n2o" = 265,
-    "cf4" = 6630,
-    "HFC-152a" = 138
+    "ch4" = 27.9,
+    "n2o" = 273,
+    "cf4" = 7380,
+    "HFC-152a" = 164
   )
+
+
+# OLD VALUES do not use ------
+# 100-year, accurate to AR5
+# * Following  revised reporting requirements under the UNFCCC, this tool presents CO2 equivalent values based on the IPCC Fifth Assessment Report (AR5) GWP values.
+# see Box 3.2, Table 1
+# Zotero: ipccClimateChange2014
+# gwp <-
+#   list(
+#     "co2" = 1,
+#     "ch4" = 28,
+#     "n2o" = 265,
+#     "cf4" = 6630,
+#     "HFC-152a" = 138
+#   )

_energy/data-raw/MNWI_2005_CensusCrosswalk_UtilityAllocation.R

@@ -0,0 +1,110 @@
+source("R/_load_pkgs.R")
+source("_meta/data-raw/cprg_geography.R")
+library(tidycensus)
+options(tidycensus.cache = TRUE)
+
+
+# Fetch data for the 2000 decennial census for Wisconsin and Minnesota
+population_data_2000 <- get_decennial(
+  geography = "block",
+  variables = "P001001", # Total population
+  state = c("WI", "MN"),
+  year = 2000,
+  geometry = TRUE, # Include geometry for spatial operations
+  output = "wide" # Outputs data in a 'wide' format, each variable as a separate column
+)
+
+# Fetch data for the 2010 decennial census for Wisconsin and Minnesota
+population_data_2010 <- get_decennial(
+  geography = "block",
+  variables = "P001001", # Total population
+  state = c("WI", "MN"),
+  year = 2010,
+  geometry = TRUE, # Include geometry for spatial operations
+  output = "wide" # Outputs data in a 'wide' format, each variable as a separate column
+)
+
+
+# Calculating geographic crosswalks from 2000 blocks to 2010 blocks
+# 1) Obtain data of interest for 2000 blocks (see population_data_2000 above) and download crosswalk
+crosswalkMN <- read_csv(here("_energy", "data-raw", "nhgis_blk2000_blk2010_MN", "nhgis_blk2000_blk2010_ge_27.csv"))
+crosswalkWI <- read_csv(here("_energy", "data-raw", "nhgis_blk2000_blk2010_WI", "nhgis_blk2000_blk2010_ge_55.csv"))
+CombinedCrosswalk <- rbind(crosswalkMN, crosswalkWI) %>%
+  mutate(
+    GEOID00 = as.character(GEOID00),
+    GEOID10 = as.character(GEOID10)
+  )
+
+# 2) Join the 2000-block-to-2010-block crosswalk to the 2000 block data of interest
+crosswalkPop_2000_to_2010 <- population_data_2000 %>%
+  left_join(CombinedCrosswalk,
+    by = join_by(GEOID == GEOID00)
+  ) %>%
+  # 3) Multiply the 2000 block counts by the crosswalk's interpolation weights, producing estimated counts for all 2000-2010 block intersections, or "atoms"
+  mutate(
+    pop2000_inAtom = P001001 * WEIGHT
+  ) %>%
+  # 4) Sum these atom counts for each 2010 block, join to 2010 data of interest (population_data_2010)
+  group_by(GEOID10) %>%
+  summarise(
+    popIn2000_on2010blocks = sum(pop2000_inAtom)
+  )
+
+crosswalkPop_2000_to_2010_centroids <- st_centroid(crosswalkPop_2000_to_2010) %>%
+  mutate(
+    GEOID10 = as.character(GEOID10)
+  ) %>%
+  mutate(
+    state = case_when(
+      substr(as.character(GEOID10), 1, 2) == "27" ~ "MN",
+      substr(as.character(GEOID10), 1, 2) == "55" ~ "WI",
+      TRUE ~ NA_character_
+    )
+  )
+
+
+GEOID10_2000_2005_2010_population_MNWI <- crosswalkPop_2000_to_2010_centroids %>%
+  left_join(st_drop_geometry(population_data_2010),
+    by = join_by(GEOID10 == GEOID)
+  ) %>%
+  rename(
+    totalPop2010 = P001001,
+    totalPop2000 = popIn2000_on2010blocks
+  ) %>%
+  mutate(
+    totalPop2005_interpolated = ((totalPop2000 + totalPop2010) / 2)
+  )
+
+GEOID10_2005_population_MNWI <- GEOID10_2000_2005_2010_population_MNWI %>%
+  select(-totalPop2000, -NAME, -totalPop2010)
+
+est_state_pop_2005 <- st_drop_geometry(GEOID10_2005_population_MNWI) %>%
+  group_by(state) %>%
+  summarize(
+    state_population = sum(totalPop2005_interpolated)
+  )
+
+
+# rejoin back to cprg_county to hold onto necessary reference data
+cprg_county_population2005_export <- cprg_county %>%
+  left_join((st_drop_geometry(intercensal_pop_2005_MNWI)),
+    by = join_by(NAMELSAD == county_name)
+  ) %>%
+  mutate(
+    year = 2005,
+    county_population = round(population_2005_censusInterp)
+  ) %>%
+  group_by(STATE) %>%
+  mutate(
+    state_population = sum(county_population)
+  ) %>%
+  ungroup() %>%
+  mutate(
+    county_proportion_of_state_pop = county_population / state_population,
+    population_data_source = "Interpolation of data from Summmary File 1, 2000/2010 Decennial Census based on 2000-2010 Geographic Crosswalk from IPUMS NHGIS, University of Minnesota"
+  ) %>%
+  select(-population_2005_censusInterp)
+
+
+# write_sf(crosswalkPop_2000_to_2010,here("_energy", "data-raw", "geoCrosswalk","crosswalkPop_2000_to_2010_blocks_MNWI.shp"))
+# write_sf(GEOID10_2000_2005_2010_population_MNWI,here("_energy", "data-raw", "geoCrosswalk","GEOID10_2000_2005_2010_population_MNWI.shp"))

_energy/data-raw/_run_energy.R

@@ -56,4 +56,5 @@ electric_natgas_nrel_proportioned <- electric_raw %>%
   ) %>%
   mutate(category = str_to_sentence(category))
 
+waldo::compare(electric_natgas_nrel_proportioned, readRDS("_energy/data/electric_natgas_nrel_proportioned.RDS"))
 saveRDS(electric_natgas_nrel_proportioned, "_energy/data/electric_natgas_nrel_proportioned.RDS")

_energy/data-raw/kerosene-estimate-2021.R

@@ -57,7 +57,7 @@ wi_kero_use <- as.numeric(eia2020[6, 12])
 #   print(n = 10000)
 
 # get number of households in each county using propane
-mn_kero_hh <- get_acs(
+mn_kero_hh <- tidycensus::get_acs(
   geography = "county",
   variables = "B25040_005",
   state = "MN",
@@ -73,7 +73,7 @@ mn_kero_hh <- get_acs(
   )
 
 # repeat for WI
-wi_kero_hh <- get_acs(
+wi_kero_hh <- tidycensus::get_acs(
   geography = "county",
   variables = "B25040_005",
   state = "WI",
@@ -94,4 +94,5 @@ kero_county
 total_regional_kerosene_emissions <- sum(kero_county$CO2e) # total regional emissions of the 11 county area
 total_regional_kerosene_emissions
 
+waldo::compare(kero_county, readRDS("_energy/data-raw/kerosene_use_county.RDS"))
 saveRDS(kero_county, "_energy/data-raw/kerosene_use_county.RDS")

_energy/data-raw/propane-estimate-2021.R

@@ -53,7 +53,7 @@ wi_prop_use <- eia2020[64, 15] # 66.5
 #   print(n = 10000)
 
 # get number of households in each county using propane
-mn_prop_hh <- get_acs(
+mn_prop_hh <- tidycensus::get_acs(
   geography = "county",
   variables = "B25040_003",
   state = "MN",
@@ -69,7 +69,7 @@ mn_prop_hh <- get_acs(
   )
 
 # repeat for WI
-wi_prop_hh <- get_acs(
+wi_prop_hh <- tidycensus::get_acs(
   geography = "county",
   variables = "B25040_003",
   state = "WI",
@@ -93,4 +93,5 @@ total_regional_propane_emissions <- sum(prop_county$CO2e) # total regional emiss
 
 
 total_regional_propane_emissions
+waldo::compare(prop_county, readRDS("_energy/data-raw/propane_use_county.RDS"))
 saveRDS(prop_county, "_energy/data-raw/propane_use_county.RDS")

_meta/data-raw/cprg_county_proportions.R

@@ -93,16 +93,69 @@ decennial_proportions <- tidycensus::get_decennial("county",
     population_data_source
   )
 
+# intercensal population estimates provided by US Census for 2005 at county level
+# downloaded from https://www.census.gov/data/tables/time-series/demo/popest/intercensal-2000-2010-counties.html and cleaned to read/save as CSV
+
+intercensal_pop_2005_MN <- read_csv(here("_energy", "data-raw", "nhgis_blk2000_blk2010_MN", "co-est00int-01-27.csv")) %>%
+  mutate(
+    county_name = sub("^\\.", "", county_name),
+    state = "MN"
+  )
+intercensal_pop_2005_WI <- read_csv(here("_energy", "data-raw", "nhgis_blk2000_blk2010_WI", "co-est00int-01-55.csv")) %>%
+  mutate(
+    county_name = sub("^\\.", "", county_name),
+    state = "WI"
+  )
+
+# calculate total state populations in 2005 and add back as constant column
+# MN
+total_pop_MN <- intercensal_pop_2005_MN %>%
+  summarise(state_population = sum(`2005`, na.rm = TRUE))
+
+intercensal_pop_2005_MN <- intercensal_pop_2005_MN %>%
+  mutate(
+    year = 2005,
+    state_population = total_pop_MN$state_population
+  ) %>%
+  rename(
+    county_population = `2005`
+  )
+
+# WI
+total_pop_WI <- intercensal_pop_2005_WI %>%
+  summarise(state_population = sum(`2005`, na.rm = TRUE))
+
+intercensal_pop_2005_WI <- intercensal_pop_2005_WI %>%
+  mutate(
+    year = 2005,
+    state_population = total_pop_WI$state_population
+  ) %>%
+  rename(
+    county_population = `2005`
+  )
+
+intercensal_pop_2005_MNWI <- rbind(intercensal_pop_2005_MN, intercensal_pop_2005_WI) %>%
+  mutate(
+    year = as.character(year),
+    county_proportion_of_state_pop = county_population / state_population,
+    population_data_source = "US Census County Intercensal Tables: 2000-2010 (2005)"
+  )
+
+cprg_county_population2005 <- cprg_county %>%
+  left_join((intercensal_pop_2005_MNWI),
+    by = join_by(NAMELSAD == county_name, STATE_ABB == state)
+  ) %>%
+  st_drop_geometry() %>%
+  select(-NAMELSAD)
 
 cprg_county_proportions <-
   bind_rows(
     acs_proportions,
-    decennial_proportions
+    decennial_proportions,
+    cprg_county_population2005
   )
 
 
-
-
 cprg_county_proportions_meta <- bind_rows(
   cprg_county_meta,
   tribble(

_meta/data-raw/epa_nei.R

@@ -1,7 +1,6 @@
 # get data from the 2020 national emissions inventory
 # graphic model here: https://www.epa.gov/enviro/nei-model
 source("R/_load_pkgs.R")
-source("R/global_warming_potential.R")
 library(httr2)
 cprg_county <- readRDS("_meta/data/cprg_county.RDS")
 

_meta/data-raw/nrel_slope_emissions.R

@@ -39,8 +39,10 @@ city_emissions <- raw_city_emissions %>%
       stringr::str_remove(" village, WI")
   ) %>%
   # filter to our states, our cities
-  filter(state_name %in% c("MN", "WI"),
-         city %in% cprg_city$CTU_NAME)
+  filter(
+    state_name %in% c("MN", "WI"),
+    city %in% cprg_city$CTU_NAME
+  )
 
 
 city_emissions

_meta/data-raw/nrel_slope_scenario.R

@@ -18,7 +18,7 @@
 # The scenario planning tool is interactive, so we can't download directly
 # from a single link
 # I downloaded each state and county manually and added each to our MS Team site
-# 
+#
 source("R/_load_pkgs.R")
 if (!file.exists("_meta/data-raw/nrel_slope/scenario_planner/anoka_reference/c03e2f7f40cf72b29fdb11fe595cc6851bf4ed23c592b02d0014c6db-CO2-emissions-county.csv")) {
   cli::cli_abort("You need to download the raw data from MS Teams. Contact a project lead if you need help")
@@ -66,12 +66,12 @@ nrel_reference_county <- bind_rows(
     county = "Dakota"
   ),
   read_nrel_scenario("_meta/data-raw/nrel_slope/scenario_planner/hennepin_reference/d037c812b9dd6dc0d9b42c6bbb97213172391853a9ac4e83ec5c3628_county.csv",
-                     state = "MN",
-                     county = "Hennepin"
+    state = "MN",
+    county = "Hennepin"
   ),
   read_nrel_scenario("_meta/data-raw/nrel_slope/scenario_planner/ramsey_reference/4e0b8987e8d3b66893a4e348f049eed5af27c7d000b4e7c1f28cb2eb-CO2-emissions-county.csv",
-                     state = "MN",
-                     county = "Ramsey"
+    state = "MN",
+    county = "Ramsey"
   ),
   read_nrel_scenario("_meta/data-raw/nrel_slope/scenario_planner/pierce_reference/a4794ae3e72bcab0cd614f877d587092b9f5944e137d9574704875b4-CO2-emissions-county.csv",
     state = "WI",