Help or hindrance? The travel, energy and carbon impacts of highly automated vehicles

Wadud, Zia; MacKenzie, Don; Leiby, Paul · 2016 · OpenAlex-citations

DOI: 10.1016/j.tra.2015.12.001

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Summary

This paper investigates the potential impacts of highly automated vehicles on travel demand, energy consumption, and greenhouse gas (GHG) emissions in the United States. Motivated by the rapid advancement of autonomous technology and the lack of systematic quantification of its environmental consequences, the authors aim to identify specific mechanisms through which automation alters transportation systems. The study seeks to determine whether automation will serve as a help or hindrance to emission reduction goals, providing bounds on these effects to inform policy and future research. The authors employ the Activity-Share-Intensity-Fuel (ASIF) framework to decompose emissions into four drivers: activity level, modal share, energy intensity, and fuel carbon content. They review existing literature and conduct engineering and economic analyses to estimate the magnitude of various mechanisms, such as eco-driving, platooning, vehicle right-sizing, and changes in highway speeds. These estimates are expressed as fractional changes in ASIF components for light-duty and heavy-duty vehicles. The study assumes near-complete penetration of automated vehicles by 2050 and combines these multipliers multiplicatively to create illustrative scenarios, acknowledging that the framework captures first-order impacts while excluding complex equilibrium feedbacks and cross-modal shifts. The findings reveal that automation’s net impact is highly uncertain and dependent on which mechanisms dominate. The authors estimate that automation could plausibly reduce road transport GHG emissions and energy use by nearly half or nearly double them. Specific mechanisms include significant energy intensity reductions from platooning (up to 25% for light-duty vehicles) and eco-driving, as well as potential increases from higher highway speeds (7–22% increase). Vehicle right-sizing and reduced safety equipment weight could further lower intensity, while induced demand from reduced travel costs and increased mobility for underserved groups could raise total vehicle kilometers traveled. The analysis suggests that many energy-reduction benefits are achievable through partial automation, whereas the major downside risks, such as substantial induced demand, are more likely associated with full automation. The significance of this work lies in its demonstration that the environmental outcome of automated vehicles is not predetermined but contingent on design choices and policy interventions. The authors conclude that policymakers must actively guide the transition to ensure automation supports emission reduction goals, rather than inadvertently increasing energy demand. The paper highlights the need for further research to better understand secondary interactions and equilibrium effects, emphasizing that without careful management, the widespread adoption of automated vehicles could lead to substantial increases in carbon emissions.

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