Pollution Accounting for Corporate Actions: Quantifying the Air Emissions and Impacts of Transportation System Choices Case Study: Food Freight and the Grocery Industry in Los Angeles
Abstract
:1. Executive Summary
2. Background
3. Purpose: Accounting for Air Pollution by Sector and Company
4. Top-Down Approach
4.1. Purpose and Intent
4.2. Method for Quantifying Total Emissions
- 1.
- Data Sources
- 2.
- Geographic Boundary Setting
- 3.
- Commodity Freight Flow Data
- 4.
- Distance Estimation
- 5.
- Calculating Emissions
- 6.
- Translating to monetary and health impacts
4.3. Method for Apportioning Emissions to Companies
- 1.
- Grocery industry and store share of food consumption
- 2.
- Corporate attribution
4.4. Analytical Demonstration of Top-Down Method
4.5. Results of Top-Down Approach Application
- 1.
- Food freight tonnage and ton-miles
- 2.
- Emissions impact
- 618,000 tons of CO2,
- 1549 tons of NOx and
- 24.6 tons of PM2.5.
- 3.
- Apportionment to individual companies
5. Bottom-Up Approach
5.1. Purpose and Intent
5.2. Method for Quantifying Corporate-Level Emissions
- 1.
- Distribution segment analytical framework
- Transport from a good’s first point of entry to first destination in region of interest
- Pickup and transport from manufacturing and processing centers
- Pickup and transport from distribution centers
- Drop off at retail/wholesale stores
- Final drop off at customer establishment after pick-up at retail/wholesale store
- 2.
- Scaling to single-truck-level emissions:
- 3.
- Scaling to corporation-level emissions:
- 4.
- Translating to monetary and health impacts
5.3. Areas of Uncertainty and Data Gaps
- 1.
- Distance travelled
- 2.
- Idling time
- 3.
- Emissions factors
- 4.
- Scaling
6. Results
6.1. Summary of Analytical Findings
- 1.
- Top-down method
- 2.
- Bottom-up method
6.2. Discussion: Further Areas of Analysis for Accuracy and Scaling
- First, a comprehensive analysis of goods movement emissions in the Los Angeles area that expands the top-down approach beyond the food sector and then compares observations with the findings of this paper.
- Second, a truck-level analysis that quantifies the air quality benefits from a single zero-emissions truck and the damages of a single diesel truck, depending on VMT and truck type.
- Third, community-level proximity mapping that highlights the disproportionate risk from goods movement emissions on communities of color and provides a detailed spatial assessment of where individual corporations can prioritize investments in local communities.
- Fourth, emissions analysis that evaluates the appropriateness of current emissions factors used for vehicles and ensures those factors accurately represent in-use truck emissions
- 1.
- Comprehensive Goods Movement Emissions Analysis
- 2.
- Truck-Level Analysis
- 3.
- Proximity Mapping
- 4.
- Emissions Factor Analysis
6.3. Relevance to Corporate Actors and Investors
6.4. Relevance to Academic Institutions and Researchers
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. Source Data Summary
Appendix A.1. SCAQMD Air Emissions Inventories
Appendix A.2. Multiple Air Toxics Exposure Study
Appendix A.3. AB617 Community Air Initiatives
Appendix A.4. Freight Analysis Framework
Appendix A.5. Corporate Sustainability Reporting
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Source Category | Population | VMT (Miles/Day) | VOC (Tons/Day) | VOC (% of Total) | NOx (Tons/Day) | NOx (% of Total) |
---|---|---|---|---|---|---|
Light HD Gas Trucks-1 (8501–10,000 lb.) | 274,553 | 11,988,596 | 6.58 | 48% | 15.01 | 11% |
Light HD Gas Trucks-2 (10,001–14,000 lb.) | 29,078 | 1,261,404 | 0.66 | 5% | 1.49 | 1% |
Medium HD Gas Trucks (14,001–33,000 lb.) | 23,181 | 960,000 | 1.18 | 9% | 2.43 | 2% |
Heavy HD Gas Trucks (>33,000 lb.) | 1585 | 186,000 | 0.19 | 1% | 1.02 | <1% |
Light HD Diesel Trucks-1 (8501–10,000 lb.) | 86,598 | 3,679,455 | 0.56 | 4% | 17.48 | 12% |
Light HD Diesel Trucks-2 (10,001–14,000 lb.) | 29,299 | 1,231,545 | 0.19 | 1% | 5.69 | 4% |
Medium HD Diesel Trucks (14,001–33,000 lb.) | 80,061 | 4,101,000 | 0.94 | 7% | 23.30 | 16% |
Heavy HD Diesel Trucks (>33,001 lb.) | 72,411 | 8,216,000 | 3.29 | 24% | 76.43 | 54% |
Total | 596,766 | 31,624,000 | 13.59 | 142.85 |
Retailer | Market Share (2017) |
---|---|
Company A | 21% |
Company B | 19% |
Company C | 13% |
Company D | 11% |
Company E | 9% |
Company F | 6% |
Company G | 6% |
Company H | 3% |
Variable | Definition | Value | Source |
---|---|---|---|
di | Distance from shipment i’s entry point to central LA | 65, 75, or 225 miles | Google Maps |
mi | 1 for internal LA shipment, 0 otherwise | FAF | |
fi | 1 for mixed freight, 0 otherwise | FAF | |
S | Food share of mixed freight | 35.6% | CFS |
Ti | Ton miles for shipment i | FAF | |
Ki | Kilotons in shipment i | FAF | |
G | Industry share of food shipments | Grocery: 67% Food Service: 33% | Saksena et al., 2018 |
A | Company’s grocery market share | Table 2 grocery market share (%) | LA Times, 2017 |
O | Emissions: g/ton-mile CO2 | 87.75 | EPA SmartWay |
N | Emissions: g/ton-mile NOx | 0.22 | EPA SmartWay |
P | Emissions: g/ton-mile PM2.5 | 0.0035125 | EPA SmartWay |
Retailer | CO2 Emissions (Tons Per Year) | NOx Emissions (Tons Per Year) | PM2.5 Emissions (Tons Per Year) | Local Air Pollution Cost (USD Millions Per Year) | Total Social Cost (USD Millions Per Year) |
---|---|---|---|---|---|
Company A | 84,769 | 212 | 3.4 | 7.5 | 11.3 |
Company B | 76,951 | 193 | 3.1 | 6.8 | 10.2 |
Company C | 54,730 | 137 | 2.2 | 4.8 | 7.3 |
Company D | 46,500 | 117 | 1.9 | 4.1 | 6.2 |
Other Grocery | 148,552 | 372 | 5.9 | 13.1 | 19.8 |
Grocery Total | 411,501 | 1031 | 16.4 | 36.2 | 54.7 |
Company I | 102,875 | 258 | 4.1 | 9.1 | 13.7 |
Company J | 51,438 | 129 | 2.1 | 4.5 | 6.8 |
Other Food Services | 51,438 | 129 | 2.1 | 4.5 | 6.8 |
Food Services Total | 205,750 | 516 | 8.2 | 18.1 | 27.4 |
Grand Total | 617,251 | 1547 | 24.6 | 54.3 | 82.1 |
Variable | Definition |
---|---|
dx | Truck ton-miles associated with segment “x” |
tx | Idling time associated with segment “x” |
O | Driving emissions: g/mile CO2 |
N | Driving emissions: g/mile NOx |
P | Driving emissions: g/mile PM |
Oh | Idling emissions: g/h CO2 |
Nh | Idling emissions: g/h NOx |
Ph | Idling emissions: g/h PM |
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Nowlan, A.; Fine, J.; O’Connor, T.; Burget, S. Pollution Accounting for Corporate Actions: Quantifying the Air Emissions and Impacts of Transportation System Choices Case Study: Food Freight and the Grocery Industry in Los Angeles. Sustainability 2021, 13, 10194. https://doi.org/10.3390/su131810194
Nowlan A, Fine J, O’Connor T, Burget S. Pollution Accounting for Corporate Actions: Quantifying the Air Emissions and Impacts of Transportation System Choices Case Study: Food Freight and the Grocery Industry in Los Angeles. Sustainability. 2021; 13(18):10194. https://doi.org/10.3390/su131810194
Chicago/Turabian StyleNowlan, Aileen, James Fine, Timothy O’Connor, and Spencer Burget. 2021. "Pollution Accounting for Corporate Actions: Quantifying the Air Emissions and Impacts of Transportation System Choices Case Study: Food Freight and the Grocery Industry in Los Angeles" Sustainability 13, no. 18: 10194. https://doi.org/10.3390/su131810194