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Managing the Traffic-Related Air Pollution (TRAP) Effects of Urban Warehousing Near Historically Marginalized Communities: A Scenario Analysis of Technology and Land Use Based Strategies

Ecommerce’s far-reaching impacts have prompted cities and companies to introduce strategies that advance urban freight transport’s environmental accountability. Many of these strategies have implications for equity. Warehousing and distribution centers (W&Ds) have concentrated in socially marginalized communities, in part, due to historical, racialized urban development practices. W&Ds generate high volumes of freight trips that are a prominent emitter of health-adverse, criteria air pollutants that burden nearby communities and workers. With the rapid proliferation of these facilities due to ecommerce-related demands, there is a need to evaluate and manage the traffic-related air pollution (TRAP) effect of these strategies on local communities. Most urban freight management strategies center on technological approaches (e.g., electrification), with limited implications for land use based strategies (e.g., zoning) that influence the spatial organization of W&Ds. Therefore, the proposed project endeavors to evaluate the distributional impacts of possible local policy interventions within ecommerce-related transport and land use systems with a focus on populations identified by federal Justice40 guidelines and steering committee input.

The methodology employs a novel, model-based approach to estimate the distribution of ecommerce’s TRAP-related health effects across population subgroups. Methodological procedures include household-level demand modeling using publicly available household travel surveys and population synthesis, traffic simulation (TransModeler), EPA MOVES4, and InMAP modeling with assumptions and parameters informed by interviews with experts from delivery companies, city planning agencies, and W&D operators. The model projects scenarios of future adoption of low- and zero-emission commercial vehicles and alternative W&D locations/characteristics, using sensitivity analyses to capture the effects of uncertainty in model parameters. The project identifies Seattle and New York City as case studies, due the states’ recent adoption of California’s Advanced Clean Truck Program, New York’s proposed Indirect Source Rule that targets W&D-derived pollution in historically marginalized communities, and both cities’ innovative efforts to analyze and mitigate the impacts of ecommerce. The findings and employed methods have long-term applicability for local and regional policymakers’ strategic equity goals concerning comprehensive urban mobility and land use planning.

The project’s objectives seek to capture the breadth of disparate impacts resulting from decisions made by consumers, delivery companies, state and local policymakers.

The UFL spearheads the project, with strategic leadership by Dr. Anne Goodchild (PI) and statistical leadership by Dr. Giacomo Dalla Chiara. Dr. Julian Marshall (co-PI) advises pollutant exposure and health effect estimation methods and interpretations. Travis Fried, Ph.D. student and RA, directs the methodological workflow and written production of results. Dr. Lianne Sheppard (UW Department of Environmental and Occupational Health Sciences and Biostatistics) serves as scientific advisor on the steering committee, providing additional support for interpretation of results and review of written materials.

Presentation

Using a GIS-based Emissions Minimization Vehicle Routing Problem with Time Windows (EVRPTW) Model to Evaluate CO2 Emissions and Costs: Two Case Studies Comparing Changes Within and Between Fleets

Publication: Transportation Research Board 90th Annual Meeting
Publication Date: 2010
Summary:

Growing pressure to limit greenhouse gas emissions is changing the way businesses operate. A model was developed in ArcGIS to evaluate the trade-offs between cost, service quality (represented by time window guarantees), and emissions of urban pickup and delivery systems under these changing pressures.

A specific case study involving a real fleet with specific operational characteristics is modeled as an emissions minimization vehicle routing problem with time windows (EVRPTW). Analyses of different external policies and internal operational changes provide insight into the impact of these changes on cost, service quality, and emissions. Specific considerations of the influence of time windows, customer density, and vehicle choice are included.

The results show a stable relationship between monetary cost and kilograms of CO2, with each kilogram of CO2 associated with a $3.50 increase in cost, illustrating the influence of fuel use on both cost and emissions. In addition, customer density and time window length are strongly correlated with monetary cost and kilograms of CO2 per order. The addition of 80 customers or extending the time window 100 minutes would save approximately $3.50 and 1 kilogram of CO2 per order. Lastly, the evaluation of four different fleets illustrates significant environmental and monetary gains can be achieved through the use of hybrid vehicles.

Authors: Erica Wygonik
Recommended Citation:
Wygonik, Erica and Anne V. Goodchild. “Using a GIS-based emissions minimization vehicle routing problem with time windows (EVRPTW) model to evaluate emissions and cost trade-offs in a case study of an urban delivery system.” Proc., 90th Annual Meeting of the Transportation Research Board, Transportation Research Board, Washington, DC.
Chapter

Comparison of Vehicle Miles Traveled and Pollution from Three Goods Movement Strategies

Publication: Sustainable Logistics: Transport and Sustainability (Emerald Group Publishing Limited)
Volume: Volume 6
Pages: 63-82
Publication Date: 2014
Summary:

This chapter provides additional insight into the role of warehouse location in achieving sustainability targets and provides a novel comparison between delivery and personal travel for criteria pollutants.

Purpose: To provide insight into the role and design of delivery services to address CO2, NO x , and PM10 emissions from passenger travel.Methodology/approach: A simulated North American data sample is served with three transportation structures: last-mile personal vehicles, local-depot-based truck delivery, and regional warehouse-based truck delivery. CO2, NO x , and PM10 emissions are modeled using values from the US EPA’s MOVES model and are added to an ArcGIS optimization scheme.Findings: Local-depot-based truck delivery requires the lowest amount of vehicle miles traveled (VMT), and last-mile passenger travel generates the lowest levels of CO2, NO x , and PM10. While last-mile passenger travel requires the highest amount of VMT, the efficiency gains of the delivery services are not large enough to offset the higher pollution rate of the delivery vehicle as compared to personal vehicles.

Practical implications: This research illustrates the clear role delivery structure and logistics have in impacting the CO2, NO x , and PM10 emissions of goods transportation in North America.

Social implications: This research illustrates the tension between goals to reduce congestion (via VMT reduction) and CO2, NO x , and PM10 emissions.

Originality/value: This chapter provides additional insight into the role of warehouse location in achieving sustainability targets and provides a novel comparison between delivery and personal travel for criteria pollutants.

Authors: Dr. Anne Goodchild, Erica Wygonik
Recommended Citation:
Wygonik, Erica, and Anne Goodchild. "Comparison of vehicle miles traveled and pollution from three goods movement strategies." Sustainable Logistics, pp. 63-82. Emerald Group Publishing Limited, 2014.