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Forecasting the Impact of Connected and Automated Vehicles on Energy Use A Microeconomic Study of Induced Travel and Energy Rebound

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  • Morteza Taiebat
  • Samuel Stolper
  • Ming Xu

Abstract

Connected and automated vehicles (CAVs) are expected to yield significant improvements in safety, energy efficiency, and time utilization. However, their net effect on energy and environmental outcomes is unclear. Higher fuel economy reduces the energy required per mile of travel, but it also reduces the fuel cost of travel, incentivizing more travel and causing an energy "rebound effect." Moreover, CAVs are predicted to vastly reduce the time cost of travel, inducing further increases in travel and energy use. In this paper, we forecast the induced travel and rebound from CAVs using data on existing travel behavior. We develop a microeconomic model of vehicle miles traveled (VMT) choice under income and time constraints; then we use it to estimate elasticities of VMT demand with respect to fuel and time costs, with fuel cost data from the 2017 United States National Household Travel Survey (NHTS) and wage-derived predictions of travel time cost. Our central estimate of the combined price elasticity of VMT demand is -0.4, which differs substantially from previous estimates. We also find evidence that wealthier households have more elastic demand, and that households at all income levels are more sensitive to time costs than to fuel costs. We use our estimated elasticities to simulate VMT and energy use impacts of full, private CAV adoption under a range of possible changes to the fuel and time costs of travel. We forecast a 2-47% increase in travel demand for an average household. Our results indicate that backfire - i.e., a net rise in energy use - is a possibility, especially in higher income groups. This presents a stiff challenge to policy goals for reductions in not only energy use but also traffic congestion and local and global air pollution, as CAV use increases.

Suggested Citation

  • Morteza Taiebat & Samuel Stolper & Ming Xu, 2019. "Forecasting the Impact of Connected and Automated Vehicles on Energy Use A Microeconomic Study of Induced Travel and Energy Rebound," Papers 1902.00382, arXiv.org, revised May 2019.
  • Handle: RePEc:arx:papers:1902.00382
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    References listed on IDEAS

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    1. Morteza Taiebat & Austin L. Brown & Hannah R. Safford & Shen Qu & Ming Xu, 2019. "A Review on Energy, Environmental, and Sustainability Implications of Connected and Automated Vehicles," Papers 1901.10581, arXiv.org, revised Feb 2019.
    2. Christopher R. Knittel & Ryan Sandler, 2018. "The Welfare Impact of Second-Best Uniform-Pigouvian Taxation: Evidence from Transportation," American Economic Journal: Economic Policy, American Economic Association, vol. 10(4), pages 211-242, November.
    3. Wadud, Zia & Graham, Daniel J. & Noland, Robert B., 2009. "Modelling fuel demand for different socio-economic groups," Applied Energy, Elsevier, vol. 86(12), pages 2740-2749, December.
    4. Zia Wadud & Daniel J. Graham & Robert B. Noland, 2010. "Gasoline Demand with Heterogeneity in Household Responses," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 47-74.
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    Cited by:

    1. Rasti-Barzoki, Morteza & Moon, Ilkyeong, 2020. "A game theoretic approach for car pricing and its energy efficiency level versus governmental sustainability goals by considering rebound effect: A case study of South Korea," Applied Energy, Elsevier, vol. 271(C).
    2. Möller, Jasmin & Daschkovska, Kateryna & Bogaschewsky, Ronald, 2019. "Sustainable city logistics: rebound effects from self-driving vehicles," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Jahn, Carlos & Kersten, Wolfgang & Ringle, Christian M. (ed.),Digital Transformation in Maritime and City Logistics: Smart Solutions for Logistics. Proceedings of the Hamburg International Conference of Logistics, volume 28, pages 299-337, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    3. Max Luke & Priyanshi Somani & Turner Cotterman & Dhruv Suri & Stephen J. Lee, 2020. "No COVID-19 Climate "Silver Lining" in the U.S. Power Sector: CO$_2$ Emissions Reductions Not Statistically Significant, Additional Risk to Coal Generators is Minimal," Papers 2008.06660, arXiv.org.
    4. Jan C. T. Bieser & Vlad C. Coroamă, 0. "Direkte und indirekte Umwelteffekte der Informations- und Kommunikationstechnologie
      [Direct and indirect environmental effects of information and communication technology]
      ," NachhaltigkeitsManagementForum | Sustainability Management Forum, Springer, vol. 0, pages 1-11.

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