2.7 Vehicle Emissions

The previous two sections estimated the total VMTs due to commuting for Stockton workers from 2011 to 2017, and non-commuting travel for 2018, which can be used to forecast VMTs through 2040. While VMTs themselves are an important indicator of quality of life and economic vitality, from a GHG perspective we need to convert vehicle miles to emissions.

We followed the standard method for calculating vehicle emissions in California using the California Air Resources Board (CARB) Emission Factors (EMFAC) model. Using the EMFAC2017 model, we downloaded emissions rates data for 2013-2018, 2020, 2025, 2030, and 2040 in the San Joaquin Valley air basin. The data includes CARB’s model estimates for daily vehicles across passenger cars and trucks of different fuel types, along with emissions per mile for CO2 and other pollutants. A summary of the EMFAC data is provided below.

emfac <- read_csv("C:/Users/derek/Google Drive/City Systems/Stockton Green Economy/emfac.csv")

emfac_summary <-
  emfac %>% 
  dplyr::select(
    Year = `Calendar Year`,
    `Vehicle Category`,
    `Fuel Type` = Fuel,
    Population,
    CO2_RUNEX,
    CO2_STREX
  ) %>% 
  group_by(Year) %>% 
  mutate(
    `Percent Vehicles` = round(Population/sum(Population)*100,2) %>% as.numeric(),
    `gCO2 Running Exhaust` = CO2_RUNEX %>% as.integer(),
    `gCO2 Start Exhaust` = CO2_STREX %>% as.integer()
  ) %>% 
  ungroup() %>% 
  dplyr::select(-Population, -CO2_RUNEX, -CO2_STREX) %>% 
  mutate(
    `Fuel Type` = 
      case_when(
        `Fuel Type` == "GAS" ~ "Gasoline",
        `Fuel Type` == "DSL" ~ "Diesel",
        TRUE ~ "Electric"
      ),
    `Vehicle Category` = 
      case_when(
        `Vehicle Category` == "LDA" ~ "Passenger Car",
        TRUE ~ "Light Duty Truck"
      )
  ) %>% 
  group_by(Year, `Vehicle Category`, `Fuel Type`) %>% 
  summarize_all(sum)
  
kable(
  emfac_summary,
  booktabs = TRUE,
  caption = 'EMFAC2017 emission rates for vehicles in San Joaquin Valley, 2013 through 2040.'
) %>%
  kable_styling() %>%
  scroll_box(width = "100%", height = "500px")
Table 2.20: EMFAC2017 emission rates for vehicles in San Joaquin Valley, 2013 through 2040.
Year Vehicle Category Fuel Type Percent Vehicles gCO2 Running Exhaust gCO2 Start Exhaust
2013 Light Duty Truck Diesel 0.03 809 0
2013 Light Duty Truck Electric 0.01 0 0
2013 Light Duty Truck Gasoline 31.52 817 177
2013 Passenger Car Diesel 0.33 260 0
2013 Passenger Car Electric 0.07 0 0
2013 Passenger Car Gasoline 68.05 327 70
2014 Light Duty Truck Diesel 0.03 791 0
2014 Light Duty Truck Electric 0.00 0 0
2014 Light Duty Truck Gasoline 31.32 801 173
2014 Passenger Car Diesel 0.34 249 0
2014 Passenger Car Electric 0.08 0 0
2014 Passenger Car Gasoline 68.24 321 68
2015 Light Duty Truck Diesel 0.05 783 0
2015 Light Duty Truck Electric 0.00 0 0
2015 Light Duty Truck Gasoline 31.16 789 170
2015 Passenger Car Diesel 0.38 243 0
2015 Passenger Car Electric 0.14 0 0
2015 Passenger Car Gasoline 68.26 316 67
2016 Light Duty Truck Diesel 0.05 780 0
2016 Light Duty Truck Electric 0.01 0 0
2016 Light Duty Truck Gasoline 31.03 770 165
2016 Passenger Car Diesel 0.35 242 0
2016 Passenger Car Electric 0.21 0 0
2016 Passenger Car Gasoline 68.34 310 65
2017 Light Duty Truck Diesel 0.06 768 0
2017 Light Duty Truck Electric 0.03 0 0
2017 Light Duty Truck Gasoline 30.70 751 161
2017 Passenger Car Diesel 0.38 234 0
2017 Passenger Car Electric 0.33 0 0
2017 Passenger Car Gasoline 68.50 303 63
2018 Light Duty Truck Diesel 0.08 752 0
2018 Light Duty Truck Electric 0.05 0 0
2018 Light Duty Truck Gasoline 30.37 729 156
2018 Passenger Car Diesel 0.42 226 0
2018 Passenger Car Electric 0.45 0 0
2018 Passenger Car Gasoline 68.63 295 62
2020 Light Duty Truck Diesel 0.10 729 0
2020 Light Duty Truck Electric 0.11 0 0
2020 Light Duty Truck Gasoline 29.73 686 147
2020 Passenger Car Diesel 0.50 213 0
2020 Passenger Car Electric 0.73 0 0
2020 Passenger Car Gasoline 68.83 281 58
2025 Light Duty Truck Diesel 0.15 655 0
2025 Light Duty Truck Electric 0.42 0 0
2025 Light Duty Truck Gasoline 28.54 584 125
2025 Passenger Car Diesel 0.65 185 0
2025 Passenger Car Electric 1.79 0 0
2025 Passenger Car Gasoline 68.45 244 51
2030 Light Duty Truck Diesel 0.18 578 0
2030 Light Duty Truck Electric 0.74 0 0
2030 Light Duty Truck Gasoline 27.95 512 109
2030 Passenger Car Diesel 0.73 167 0
2030 Passenger Car Electric 2.88 0 0
2030 Passenger Car Gasoline 67.51 217 45
2035 Light Duty Truck Diesel 0.20 530 0
2035 Light Duty Truck Electric 0.97 0 0
2035 Light Duty Truck Gasoline 27.69 472 99
2035 Passenger Car Diesel 0.78 158 0
2035 Passenger Car Electric 3.63 0 0
2035 Passenger Car Gasoline 66.73 202 41
2040 Light Duty Truck Diesel 0.21 505 0
2040 Light Duty Truck Electric 1.12 0 0
2040 Light Duty Truck Gasoline 27.62 452 93
2040 Passenger Car Diesel 0.80 154 0
2040 Passenger Car Electric 4.07 0 0
2040 Passenger Car Gasoline 66.18 195 39

\(~\)

Emissions from N2O and CH4, along with emissions from other types of vehicles, were left out of this analysis.

We applied the CO2 emission rates above to our 2017 commute VMTs to estimate the total and average GHG emissions for Stockton workers commuting to each of the top 15 counties (filtering out counties further than 3 hours away). We made the following assumptions:

  • Assuming that the distribution vehicle types for Stockton commuters matches the EMFAC breakdown, the average GHG emissions is about 440 g/mile running exhaust, and 93 g/trip start exhaust.
  • For daily VMT, each vehicle was assumed to take 2 one-way trips.
  • To convert daily VMT to annual VMT, the same annualization factor was used as ICLEI: 369.39. The factor appears to account for both expected reductions in VMT because of sick days, as well as expected increases in VMT because of chained trips.

The following table is sorted by total annual GHG from transportation.

load("C:/Users/derek/Google Drive/City Systems/Stockton Green Economy/LODES/stockton_lodes_2017.Rdata")

stockton_lodes_h_counties_ghg <- 
  stockton_lodes_h_mode %>%
  st_set_geometry(NULL) %>% 
  mutate(
    Workplace = ifelse(
      workplace %in% stockton_bgs_full$GEOID,
      "75000",
      substr(workplace,3,5)
    )
  ) %>%
  group_by(Workplace) %>%
  summarise_at(
    vars(jobs,person_miles,person_hours,vehicles,vmt),
    sum, na.rm=T
  ) %>% 
  mutate(
    perc_jobs = jobs/sum(jobs),
    annual_vmt = vmt*2*369.39,
    annual_ghg = annual_vmt*439.462*1.1023e-6 + 2*369.39*vehicles*92.582*1.1023e-6,
    perc_ghg = annual_ghg/sum(annual_ghg),
    avg_ghg = annual_ghg/jobs
  ) %>% 
  left_join(ca_counties_and_stockton %>% 
  dplyr::select(COUNTYFP, NAME), by = c("Workplace" = "COUNTYFP")) %>% 
  dplyr::select(-Workplace) %>%
  dplyr::select(NAME,everything()) %>% 
  st_as_sf() %>% 
  arrange(desc(annual_ghg))
  
stockton_lodes_h_counties_ghg_table <- 
  stockton_lodes_h_counties_ghg[1:15,] %>%
  transmute(
    `Workplace (where Stockton residents work)` = NAME,
    `Jobs (held by Stockton residents)` = prettyNum(round(jobs,-2),big.mark=","),
    `Percent Jobs` = paste0(round(perc_jobs*100),"%"),
    `VMT (millions)` = prettyNum(round(annual_vmt/1000000),big.mark=","),
    `Total Annual GHG (tCO2e)` = prettyNum(round(annual_ghg,-3),big.mark=","),
    `Percent Annual GHG` = paste0(round(perc_ghg*100),"%"),
    `Average Annual GHG/worker (tCO2e)` = round(avg_ghg,1)
  )

kable(
  stockton_lodes_h_counties_ghg_table %>% st_set_geometry(NULL),
  booktabs = TRUE,
  caption = 'Top 15 workplaces where Stockton residents work, GHG emissions from transportation. Includes Stockton as a workplace destination separate from the rest of SJC. All other listed workplaces are counties. Los Angeles, San Bernardino, Orange, and San Diego Counties were removed. Data from LODES, 2017.'
) %>%
  kable_styling() %>%
  scroll_box(width = "100%")
Table 2.21: Top 15 workplaces where Stockton residents work, GHG emissions from transportation. Includes Stockton as a workplace destination separate from the rest of SJC. All other listed workplaces are counties. Los Angeles, San Bernardino, Orange, and San Diego Counties were removed. Data from LODES, 2017.
Workplace (where Stockton residents work) Jobs (held by Stockton residents) Percent Jobs VMT (millions) Total Annual GHG (tCO2e) Percent Annual GHG Average Annual GHG/worker (tCO2e)
Alameda 9,100 8% 304 148,000 15% 16.2
Santa Clara 5,700 5% 244 118,000 12% 20.7
Sacramento 8,900 8% 209 102,000 10% 11.4
Stockton 51,500 44% 191 96,000 10% 1.9
San Joaquin (not including Stockton) 17,700 15% 177 87,000 9% 4.9
San Francisco 3,100 3% 143 69,000 7% 22.5
Contra Costa 4,400 4% 123 60,000 6% 13.7
San Mateo 2,300 2% 105 51,000 5% 22.2
Fresno 1,300 1% 92 44,000 4% 33.7
Stanislaus 4,500 4% 76 37,000 4% 8.2
Solano 1,600 1% 53 26,000 3% 15.6
Placer 1,200 1% 42 21,000 2% 17.7
Sonoma 700 1% 40 20,000 2% 26.9
Monterey 500 0% 36 18,000 2% 35.7
Yolo 900 1% 27 13,000 1% 15.1

\(~\)

According to the table above, 59% percent of Stockton residents work in San Joaquin County and collectively contribute 19% of all commute GHGs. The next 14 counties comprise another 39% of Stockton residents, but they collectively contribute 73% of all commute GHGs. This significant difference comes down to difference in average commute distance to each county, which is directly related to average GHG emissions per worker. In a later section, we will explore opportunities to stimulate local job creation so that some of these long-distance commutes can be converted into local commutes, reducing both the toll on the environment and the tolls on health and well-being for Stockton residents.

# map = mapview(stockton_lodes_h_counties_ghg[1:15,c("annual_ghg")], zcol = "annual_ghg", map.types = c("OpenStreetMap"), legend = TRUE, layer.name = 'Total Annual</br>GHG (tCO2e)')
# 
# mapshot(map,url="map-ghg-map-2.html")

knitr::include_url("https://city.systems/stockton-greeneconomy/map-ghg-map-2.html")

Figure 2.29: Top 15 Counties where Stockton residents work - annual GHG emissions from driving. Data from LODES, 2017.

\(~\)

Similar to Table 2.14 and Table 2.15, the following table disaggregates annual GHG/commuter by NAICS industry and wage tier.

stockton_naics_ghg <-
  stockton_lodes_h_join_wac_normalize %>% 
  left_join(stockton_lodes_h_mode %>%
  st_set_geometry(NULL), by = c("h_bg" = "residence", "w_bg" = "workplace")) %>% 
  mutate(
    annual_vmt = vmt*2*369.39,
    annual_ghg = annual_vmt*439.462*1.1023e-6 + 2*369.39*vehicles*92.582*1.1023e-6
  ) %>% 
  group_by(w_bg) %>% 
  summarize_at(
    vars(S000,SE01,SE02,SE03,high_CNS01:mid_CNS20,annual_ghg),
    sum, 
    na.rm=T
  ) %>% 
  mutate(jobs = S000) %>% 
  mutate_at(
    .vars = vars(S000,SE01,SE02,SE03,high_CNS01:mid_CNS20),
    .funs = list(~ .*annual_ghg/jobs)
  )

stockton_naics_ghg_summary <-
  rbind(
    stockton_naics_jobs[,-1] %>% colSums(),
    stockton_naics_ghg[,-1] %>% dplyr::select(-annual_ghg,-jobs) %>% colSums(na.rm=T)
  ) %>% 
  t() %>%
  as.data.frame() %>% 
  rownames_to_column() %>% 
  mutate(
    ghg_per_worker = round(V2/V1,1),
    rowname = 
      case_when(
        rowname == "SE01" ~ "low_Total",
        rowname == "SE02" ~ "mid_Total",
        rowname == "SE03" ~ "high_Total",
        TRUE ~ rowname
      )
  ) %>% 
  dplyr::select(-V1,-V2) %>% 
  filter(!rowname %in% c("S000")) %>% 
  separate(
    rowname,
    into = c("wage","industry"),
    sep = "_"
  ) %>% 
  mutate(
    industry =
      case_when(
        industry == "CNS01" ~ "Agriculture, Forestry, Fishing and Hunting",
        industry == "CNS02" ~ "Mining, Quarrying, and Oil and Gas Extraction",
        industry == "CNS03" ~ "Utilities",
        industry == "CNS04" ~ "Construction",
        industry == "CNS05" ~ "Manufacturing",
        industry == "CNS06" ~ "Wholesale Trade",
        industry == "CNS07" ~ "Retail Trade",
        industry == "CNS08" ~ "Transportation and Warehousing",
        industry == "CNS09" ~ "Information",
        industry == "CNS10" ~ "Finance and Insurance",
        industry == "CNS11" ~ "Real Estate and Rental and Leasing",
        industry == "CNS12" ~ "Professional, Scientific, and Technical Services",
        industry == "CNS13" ~ "Management of Companies and Enterprises",
        industry == "CNS14" ~ "Administrative and Support and Waste Management and Remediation Services",
        industry == "CNS15" ~ "Educational Services",
        industry == "CNS16" ~ "Health Care and Social Assistance",
        industry == "CNS17" ~ "Arts, Entertainment, and Recreation",
        industry == "CNS18" ~ "Accommodation and Food Services",
        industry == "CNS20" ~ "Public Administration",
        industry == "CNS19" ~ "Other Services",
        TRUE ~ industry
      )
  ) %>%  
  spread(wage,ghg_per_worker) %>% 
  dplyr::select(
    `NAICS Industry` = industry,
    `Annual GHG per Stockton worker with earnings $1250/month or less` = low,
    `Annual GHG per Stockton worker with earnings $1251/month to $3333/month` = mid,
    `Annual GHG per Stockton worker with earnings greater than $3333/month` = high
  )

stockton_naics_ghg_summary_table <-
  rbind(
    stockton_naics_ghg_summary[which(stockton_naics_ghg_summary$`NAICS Industry`=="Total"),],
    stockton_naics_ghg_summary[which(stockton_naics_ghg_summary$`NAICS Industry`!="Total"),]
  )

rownames(stockton_naics_ghg_summary_table) <- NULL

kable(
  stockton_naics_ghg_summary_table, 
  booktabs = TRUE, 
  caption = 'Annual commute tCO2e per Stockton commuter, by NAICS industry and wage tier. Data from LODES, 2017.'
  ) %>% 
  kable_styling() %>%
  scroll_box(width = "100%")
Table 2.22: Annual commute tCO2e per Stockton commuter, by NAICS industry and wage tier. Data from LODES, 2017.
NAICS Industry Annual GHG per Stockton worker with earnings $1250/month or less Annual GHG per Stockton worker with earnings $1251/month to $3333/month Annual GHG per Stockton worker with earnings greater than $3333/month
Total 5.9 6.1 6.9
Accommodation and Food Services 7.1 7.7 9.1
Administrative and Support and Waste Management and Remediation Services 7.6 7.6 9.1
Agriculture, Forestry, Fishing and Hunting 6.4 6.4 7.1
Arts, Entertainment, and Recreation 8.9 8.6 10.2
Construction 7.2 7.5 10.1
Educational Services 5.3 5.3 5.8
Finance and Insurance 9.5 9.3 11.2
Health Care and Social Assistance 6.0 6.2 8.2
Information 12.4 11.8 13.5
Management of Companies and Enterprises 7.9 8.5 10.0
Manufacturing 6.7 6.6 9.4
Mining, Quarrying, and Oil and Gas Extraction 8.9 10.1 10.3
Other Services 8.9 8.3 9.9
Professional, Scientific, and Technical Services 11.8 12.0 14.1
Public Administration 6.6 5.4 5.9
Real Estate and Rental and Leasing 10.3 9.8 11.6
Retail Trade 9.8 9.4 10.5
Transportation and Warehousing 7.9 6.6 7.7
Utilities 10.2 7.1 7.6
Wholesale Trade 9.1 7.6 8.1

\(~\)

To conclude this section, we plotted changes in emissions per mile (just running exhaust) and the percentage of electric vehicles in San Joaquin County from 2013-2040. These projections will contribute to modeling business-as-usual GHG emissions.

vehicle_emissions_trend <-
  emfac_summary %>% 
    mutate(
      product = `Percent Vehicles`/100*`gCO2 Running Exhaust`,
      product2 = `Percent Vehicles`/100*`gCO2 Start Exhaust`
    ) %>% 
    group_by(Year) %>% 
    summarize(
      `gCO2/mile` = sum(product),
      `gCO2/trip` = sum(product2)
    )

vehicle_emissions_trend %>% 
  ggplot(
    aes(
      x = Year,
      y = `gCO2/mile`
    )
  )+
  geom_line(size = 2, colour = "orange") + 
  labs(title = "San Joaquin Valley Air Basin", y = "gCO2/mile running exhaust")
Average GHG emissions per mile for vehicles in San Joaquin Valley Air Basin, 2013-2040. Source: EMFAC2017.

Figure 2.30: Average GHG emissions per mile for vehicles in San Joaquin Valley Air Basin, 2013-2040. Source: EMFAC2017.

\(~\)

vehicle_electric_trend <-
  emfac_summary %>% 
  mutate(
    `Fuel Type` = ifelse(`Fuel Type` == "Electric", "Electric", "Other")
  ) %>% 
  group_by(Year, `Fuel Type`) %>% 
  summarize(count = sum(`Percent Vehicles`)) %>% 
  filter(`Fuel Type` == "Electric")

vehicle_electric_trend %>% 
  ggplot(
    aes(
      x = Year,
      y = `count`
    )
  )+
  geom_line(size = 2, colour = "forest green") + 
  labs(title = "San Joaquin Valley Air Basin", y = "Percentage electric vehicles on road")
Percentage of electric vehicles in San Joaquin Valley Air Basin, 2013-2040. Source: EMFAC2017.

Figure 2.31: Percentage of electric vehicles in San Joaquin Valley Air Basin, 2013-2040. Source: EMFAC2017.

\(~\)

The projected busines-as-usual increase in electric vehicles on the road is a driver of the reduction in emissions/mile (along with fuel efficiency improvements and other factors). Later, one of our strategies will model the GHG reductions associated with even more progressive EV adoption beyond 5% by 2040.