Driven by economic growth, globalization and e-commerce, freight per capita in the United States has been consistently increasing in recent decades. Projecting to 2050, we explore the emissions, and health and climate impacts of US freight truck and rail transport under various policy scenarios. We predict that, overall, air pollutant emissions and health impacts from the freight-truck-rail system will be greatly reduced from 2010 to 2030, while long-term climate forcing will continue to increase if petroleum is the fuel source. A carbon tax could shift freight shipments from trucking to energy-efficient rail, providing the greatest reduction in long-term forcing among all policies (24%), whereas a policy enforcing truck fleet maintenance would cause the largest reduction in air pollutant emissions, offering the largest reduction in mortalities (36%). Increasing urban compactness could reduce freight activity but increase population exposure per unit emission, offering slight health benefits over the current urban sprawl trend (13%).
Subscribe to Journal
Get full journal access for 1 year
only $8.67 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Bureau of Transportation Statistics. National Transportation Statistics http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/national_transportation_statistics/index.html (2014).
Laden, F., Neas, L. M., Dockery, D. W. & Schwartz, J. Association of fine particulate matter from different sources with daily mortality in six US cities. Environ. Health Perspect. 108, 941–947 (2000).
Pope, C. A. III et al. Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. JAMA 287, 1132–1141 (2002).
Unger, N. et al. Attribution of climate forcing to economic sectors. Proc. Natl Acad. Sci. USA 107, 3382–3387 (2010).
Bickford, E. et al. Emissions and air quality impacts of truck-to-rail freight modal shifts in the Midwestern United States. Environ. Sci. Technol. 48, 446–454 (2014).
Hankey, S. & Marshall, J. D. Impacts of urban form on future US passenger-vehicle greenhouse gas emissions. Energy Policy 38, 4880–4887 (2010).
Frank, L. D., Stone, B. Jr & Bachman, W. Linking land use with household vehicle emissions in the central Puget Sound: methodological framework and findings. Transp. Res. D Transp. Environ. 5, 173–196 (2000).
Stone, B., Mednick, A. C., Holloway, T. & Spak, S. N. Is compact growth good for air quality? J. Am. Plann. Assoc. 73, 404–418 (2007).
Marshall, J. D. Energy-efficient urban form. Environ. Sci. Technol. 42, 3133–3137 (2008).
van der Waals, J. The compact city and the environment: a review. Tijdschr. Econ. Soc. Geogr. 91, 111–121 (2000).
Marshall, J. D., McKone, T. E., Deakin, E. & Nazaroff, W. W. Inhalation of motor vehicle emissions: effects of urban population and land area. Atmos. Environ. 39, 283–295 (2005).
Federal Highway Administration. Freight Analysis Framework 3 https://ops.fhwa.dot.gov/freight/freight_analysis/faf/faf3/userguide/ (2011).
Muratori, M. et al. Role of the freight sector in future climate change mitigation scenarios. Environ. Sci. Technol. 51, 3526–3533 (2017).
You, S. I. et al. Air Pollution Impacts of Shifting San Pedro Bay Ports Freight from Truck to Rail in Southern California (University of California Transportation Center, UC Berkeley, 2010).
Park, M., Regan, A. & Yang C.-H. Emissions impacts of a modal shift: a case study of the Southern California ports region. J. Int. Logist. Trade (Online) 5, 67–81 (2007).
Ewing, R., Pendall, R. & Chen, D. Measuring sprawl and its transportation impacts. Transp. Res. Rec. 1831, 175–183 (2003).
Stone, B. Jr Urban sprawl and air quality in large US cities. J. Environ. Manage. 86, 688–698 (2008).
Fisher-Vanden, K., Schu, K., Sue Wing, I. & Calvin, K. Decomposing the impact of alternative technology sets on future carbon emissions growth. Energy Econ. 34, S359–S365 (2012).
Hewings, G. J. D. On the accuracy of alternative models for stepping-down multi-county employment projections to counties. Econ. Geogr. 52, 206–217 (1976).
Cascetta, E. Transportation Systems Analysis Models and Applications 2nd edn (Springer, New York, 2009).
Cohen, H, Horowitz, A. & Pendyala, R. M. Forecasting Statewide Freight Toolkit. (Transportation Research Board, Washington DC, 2008).
Liu, L. et al. Emission projections for long-haul freight trucks and rail in the United States through 2050. Environ. Sci. Technol. 49, 11569–11576 (2015).
Elhorst, J. P. Dynamic models in space and time. Geogr. Anal. 33, 119–140 (2001).
Newell, G. F. & Daganzo, C. F. Design of multiple-vehicle delivery tours—I a ring-radial network. Transp. Res. Part B Method. 20, 345–363 (1986).
Lee, S. & Hwang, T. Estimating emissions from regional freight delivery under different urban development scenarios. Sustainability 10, 1188 (2018).
Yan, F., Winijkul, E., Jung, S., Bond, T. C. & Streets, D. G. Global emission projections of particulate matter (PM): I. Exhaust emissions from on-road vehicles. Atmos. Environ. 45, 4830–4844 (2011).
Tessum, C. W., Hill, J. D. & Marshall, J. D. InMAP: a model for air pollution interventions. PLoS ONE 12, e0176131 (2017).
Ewing, R., Bartholomew, K., Winkelman, S., Walters, J. & Chen, D. Growing cooler: the evidence on urban development and climate change. RRJ 25, 6–13 (2009).
Yan, F., Winijkul, E., Bond, T. C. & Streets, D. G. Global emission projections of particulate matter (PM): II. Uncertainty analyses of on-road vehicle exhaust emissions. Atmos. Environ. 87, 189–199 (2014).
Stone, B. Jr., Mednick, A. C., Holloway, T. & Spak, S. N. Mobile source CO2 mitigation through smart growth development and vehicle fleet hybridization. Environ. Sci. Technol. 43, 1704–1710 (2009).
Lee, S. & Lee, B. The influence of urban form on GHG emissions in the US household sector. Energy Policy 68, 534–549 (2014).
Ansari, A. S. & Pandis, S. N. Response of inorganic PM to precursor concentrations. Environ. Sci. Technol. 32, 2706–2714 (1998).
United States Environmental Protection Agency. User’s Manual for the Co-Benefits Risk Assessment (COBRA) Screening Model https://www.epa.gov/statelocalenergy/users-manual-co-benefits-risk-assessment-cobra-screening-model (2015).
The Benefits and Costs of the Clean Air Act from 1990 to 2020 (United States Environmental Protection Agency, 2011); https://www.epa.gov/sites/production/files/2015-07/documents/summaryreport.pdf
Fann, N., Fulcher, C. M. & Baker, K. The recent and future health burden of air pollution apportioned across US sectors. Environ. Sci. Technol. 47, 3580–3589 (2013).
EMFAC2014 Web Database v1.0.7 (California Air Resources Board, 2015); https://www.arb.ca.gov/emfac/2014/
National Research Council. Technologies and Approaches to Reducing the Fuel Consumption of Medium- and Heavy-Duty Vehicles (National Academies Press, Washington DC, 2010).
Ubanwa, B., Burnette, A., Kishan, S. & Fritz, S. G. Exhaust particulate matter emission factors and deterioration rate for in-use motor vehicles. J. Eng. Gas Turbine Power 125, 513–523 (2003).
Zachariadis, T., Ntziachristos, L. & Samaras, Z. The effect of age and technological change on motor vehicle emissions. Transp. Res. D Transp. Environ. 6, 221–227 (2001).
Ban-Weiss, G. A., Lunden, M. M., Kirchstetter, T. W. & Harley, R. A. Measurement of black carbon and particle number emission factors from individual heavy-duty trucks. Environ. Sci. Technol. 43, 1419–1424 (2009).
Zhang, Y., Stedman, D. H., Bishop, G. A., Guenther, P. L. & Beaton, S. P. Worldwide on-road vehicle exhaust emissions study by remote-sensing. Environ. Sci. Technol. 29, 2286–2294 (1995).
Yan, F. et al. Global emission projections for the transportation sector using dynamic technology modeling. Atmos. Chem. Phys. 14, 5709–5733 (2014).
United States Environmental Protection Agency. National Emissions Inventory (NEI) Air Pollutant Emissions Trends Data https://www.epa.gov/air-emissions-inventories/national-emissions-inventory-nei (2010).
Burgard, D. A., Bishop, G. A., Stedman, D. H., Gessner, V. H. & Daeschlein, C. Remote sensing of in-use heavy-duty diesel trucks. Environ. Sci. Technol. 40, 6938–6942 (2006).
Harvey, C. A. et al. A Study of the Potential Impact of Some Unregulated Motor Vehicle Emissions. SAE Technical Paper 830987 (SAE International, 1983).
Pierson, W. R. & Brachaczek, W. W. Emissions of ammonia and amines from vehicles on the road. Environ. Sci. Technol. 17, 757–760 (1983).
United States Environmental Protection Agency. Diesel Fuel Standards & Rulemakings https://www.epa.gov/diesel-fuel-standards/diesel-fuel-standards-rulemakings (2016).
Average In-Use Emissions from Heavy-Duty Trucks (EPA420-F-08-027) (United States Environmental Protection Agency, 2008).
Land-Use Scenarios: National-Scale Housing-Density Scenarios Consistent with Climate Change Storylines (Final Report) (EPA/600/R-08/076F) (United States Environmental Protection Agency, 2009).
Collins, W. D. et al. The formulation and atmospheric simulation of the Community Atmosphere Model version 3 (CAM3). J. Clim. 19, 2144–2161 (2006).
Byun, D. W. & Ching, J. K. S. Science Algorithms of the EPA Models-3 Community Multiscale Air Quality (CMAQ) Modeling System (US Environmental Protection Agency, Office of Research and Development, Washington DC, 1999).
Grell, G. A. et al. Fully coupled “online” chemistry within the WRF model. Atmos. Environ. 39, 6957–6975 (2005).
Tessum, C. W., Hill, J. D. & Marshall, J. D. Twelve-month, 12 km resolution North American WRF-Chemv3.4 air quality simulation: performance evaluation. Geosci. Model Dev. 8, 957–973 (2015).
Krewski, D. et al. Extended Follow-up and Spatial Analysis of the American Cancer Society Study Linking Particulate Air Pollution and Mortality (Health Effects Institute, 2009).
Muller, N. Z. & Mendelsohn, R. Efficient pollution regulation: getting the prices right. Am. Econ. Rev. 99, 1714–1739 (2009).
Heo, J., Adams, P. J. & Gao, H. O. Reduced-form modeling of public health impacts of inorganic PM2.5 and precursor emissions. Atmos. Environ. 137, 80–89 (2016).
United States Census Bureau. County Population Totals and Components of Change 2010–2017 https://www.census.gov/data/tables/2017/demo/popest/counties-total.html (2018).
Centers for Disease Control and Prevention. Compressed Mortality File https://www.cdc.gov/nchs/data_access/cmf.htm (2018).
Quantitative Health Risk Assessment for Particulate Matter (EPA-452/R-10-005) (United States Environmental Protection Agency, 2010).
Kelly, F. J. & Fussell, J. C. Size, source and chemical composition as determinants of toxicity attributable to ambient particulate matter. Atmos. Environ. 60, 504–526 (2012).
Muller, N. Z. Linking policy to statistical uncertainty in air pollution damages. B E J. Econom. Anal. Policy 11, 1–29 (2011).
Myhre, G. et al. in Climate Change 2013: The Physical Science Basis Ch. 2 (eds Stocker, T. F. et al.) (IPCC, Cambridge Univ. Press, 2013).
Bond, T. C. et al. Bounding the role of black carbon in the climate system: a scientific assessment. J. Geophys. Res. Atmos. 118, 5380–5552 (2013).
Kvalevåg, M. M. & Myhre, G. Human impact on direct and diffuse solar radiation during the industrial era. J. Clim. 20, 4874–4883 (2007).
Shindell, D. T. et al. Climate forcing and air quality change due to regional emissions reductions by economic sector. Atmos. Chem. Phys. 8, 7101–7113 (2008).
USA State Boundaries (Esri.com, accessed 15 January 2018); https://www.arcgis.com/home/item.html?id=540003aa59b047d7a1f465f7b1df1950
This publication was supported by assistance agreement nos. EPA RD-83428001 and R835873 (Center for Clean Air Climate Solutions) awarded by the EPA. It has not been formally reviewed by the EPA. The views expressed in this document are solely those of the authors and do not necessarily reflect those of the Agency. The EPA does not endorse any products or commercial services mentioned in this publication. C. Barkan shared the observation about mode-shifting in response to fuel price increase that inspired the long-haul freight modelling. Additional support was provided by the PNNL Global Technology Strategy Program for S.J.S. We thank R. Minjares of the International Council for Clean Transportation for critical feedback on the work, and Y. Cui and C. Roney for their helpful comments.
The authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Liu, L., Hwang, T., Lee, S. et al. Health and climate impacts of future United States land freight modelled with global-to-urban models. Nat Sustain 2, 105–112 (2019). https://doi.org/10.1038/s41893-019-0224-3
Energy Policy (2020)
Estimating transboundary economic damages from climate change and air pollution for subnational incentives for green on-road freight
Transportation Research Part D: Transport and Environment (2020)
Nature Sustainability (2019)
Journal of the American Planning Association (2019)
The air quality and health impacts of projected long-haul truck and rail freight transportation in the United States in 2050
Environment International (2019)