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Help or hindrance? The travel, energy and carbon impacts of highly automated vehicles
Open accessAbstract
Experts predict that new automobiles will be capable of driving themselves under limited conditions within 5–10 years, and under most conditions within 10–20 years. Automation may affect road vehicle energy consumption and greenhouse gas (GHG) emissions in a host of ways, positive and negative, by causing changes in travel demand, vehicle design, vehicle operating profiles, and choices of fuels. In this paper, we identify specific mechanisms through which automation may affect travel and energy demand and resulting GHG emissions and bring them together using a coherent energy decomposition framework. We review the literature for estimates of the energy impacts of each mechanism and, where the literature is lacking, develop our own estimates using engineering and economic analysis. We consider how widely applicable each mechanism is, and quantify the potential impact of each mechanism on a common basis: the percentage change it is expected to cause in total GHG emissions from light-duty or heavy-duty vehicles in the U.S. Our primary focus is travel related energy consumption and emissions, since potential lifecycle impacts are generally smaller in magnitude. We explore the net effects of automation on emissions through several illustrative scenarios, finding that automation might plausibly reduce road transport GHG emissions and energy use by nearly half – or nearly double them – depending on which effects come to dominate. We also find that many potential energy-reduction benefits may be realized through partial automation, while the major energy/emission downside risks appear more likely at full automation. We close by presenting some implications for policymakers and identifying priority areas for further research.
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“While AVs could reduce energy consumption-for example, by reducing traffic congestion and inducing efficiency through eco-driving-it is also possible that AVs will lead to an increase in overall road traffic that nullifies efficiency gains.”
From Tackling climate change with machine learning (2023) · cites this paper as [820]
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Self-citation
Metadata
- Authors
- Wadud, Z., MacKenzie, D., & Leiby, P.
- Affiliations
- University of Leeds (GB)
- University of Washington (US)
- Oak Ridge National Laboratory (US)
- Corresponding author
- Wadud, Zia
- Venue
- Transportation Research Part A: Policy and Practice
- Publisher
- Elsevier BV
- Published
- 2016-04
- Volume
- 86
- Pages
- 1-18
- ISSN
- 0965-8564 (ISSN-L)
- OpenAlex
- W2259684855
- Cited by
- 859
- References
- 52
- Citations by year
- 2026: 34 · 2025: 60 · 2024: 83 · 2023: 81 · 2022: 132 · 2021: 118 · 2020: 133 · 2019: 103 · 2018: 76 · 2017: 29 · 2016: 10
- FWCI
- 63.33
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Transportation Research Part A Policy and Practice (publishedVersion, cc-by-nc-nd)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- U.S. Department of Transportation
- Imperial College London
- Federal Highway Administration
- Topics
- Physical Sciences › Engineering › Automotive Engineering › Vehicle emissions and performance
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Energy, Environment, and Transportation Policies
- Social Sciences › Transportation › Transportation and Mobility Innovations
- Keywords
- highly automated vehicles, autonomous vehicles, travel demand, vehicle energy consumption, greenhouse gas emissions, energy decomposition analysis, partial automation, full automation, vehicle design, fuel selection, heavy vehicles, light-duty vehicles, road transport emissions, energy policy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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