Nothing Special   »   [go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v247y2019icp297-308.html
   My bibliography  Save this article

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

Author

Listed:
  • Taiebat, Morteza
  • Stolper, Samuel
  • Xu, Ming
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

  • Taiebat, Morteza & Stolper, Samuel & Xu, Ming, 2019. "Forecasting the Impact of Connected and Automated Vehicles on Energy Use: A Microeconomic Study of Induced Travel and Energy Rebound," Applied Energy, Elsevier, vol. 247(C), pages 297-308.
    • Handle: RePEc:eee:appene:v:247:y:2019:i:c:p:297-308
    DOI: 10.1016/j.apenergy.2019.03.174
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261919305823
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2019.03.174?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Thomas, Brinda A. & Azevedo, Inês L., 2013. "Estimating direct and indirect rebound effects for U.S. households with input–output analysis Part 1: Theoretical framework," Ecological Economics, Elsevier, vol. 86(C), pages 199-210.
    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. Joshua Linn, 2016. "The Rebound Effect for Passenger Vehicles," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    4. Kenneth A. Small & Kurt Van Dender, 2007. "Fuel Efficiency and Motor Vehicle Travel: The Declining Rebound Effect," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 25-52.
    5. Greene, David L., 2012. "Rebound 2007: Analysis of U.S. light-duty vehicle travel statistics," Energy Policy, Elsevier, vol. 41(C), pages 14-28.
    6. Fagnant, Daniel J. & Kockelman, Kara, 2015. "Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations," Transportation Research Part A: Policy and Practice, Elsevier, vol. 77(C), pages 167-181.
    7. 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.
    8. Jeffery B. Greenblatt & Samveg Saxena, 2015. "Autonomous taxis could greatly reduce greenhouse-gas emissions of US light-duty vehicles," Nature Climate Change, Nature, vol. 5(9), pages 860-863, September.
    9. Gerard de Jong & Hugh Gunn, 2001. "Recent Evidence on Car Cost and Time Elasticities of Travel Demand in Europe," Journal of Transport Economics and Policy, University of Bath, vol. 35(2), pages 137-160, May.
    10. Mustapha Harb & Yu Xiao & Giovanni Circella & Patricia L. Mokhtarian & Joan L. Walker, 2018. "Projecting travelers into a world of self-driving vehicles: estimating travel behavior implications via a naturalistic experiment," Transportation, Springer, vol. 45(6), pages 1671-1685, November.
    11. 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.
    12. West, Sarah E. & Williams, R.C.Roberton III, 2004. "Estimates from a consumer demand system: implications for the incidence of environmental taxes," Journal of Environmental Economics and Management, Elsevier, vol. 47(3), pages 535-558, May.
    13. Correia, Gonçalo Homem de Almeida & Looff, Erwin & van Cranenburgh, Sander & Snelder, Maaike & van Arem, Bart, 2019. "On the impact of vehicle automation on the value of travel time while performing work and leisure activities in a car: Theoretical insights and results from a stated preference survey," Transportation Research Part A: Policy and Practice, Elsevier, vol. 119(C), pages 359-382.
    14. Su, Qing, 2012. "A quantile regression analysis of the rebound effect: Evidence from the 2009 National Household Transportation Survey in the United States," Energy Policy, Elsevier, vol. 45(C), pages 368-377.
    15. 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.
    16. Severin Borenstein, 2014. "A Microeconomic Framework for Evaluating Energy Efficiency Rebound and Some Implications," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    17. Wadud, Zia & MacKenzie, Don & Leiby, Paul, 2016. "Help or hindrance? The travel, energy and carbon impacts of highly automated vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 86(C), pages 1-18.
    18. Kayser, Hilke A., 2000. "Gasoline demand and car choice: estimating gasoline demand using household information," Energy Economics, Elsevier, vol. 22(3), pages 331-348, June.
    19. Alobaidi, Mohammad H. & Chebana, Fateh & Meguid, Mohamed A., 2018. "Robust ensemble learning framework for day-ahead forecasting of household based energy consumption," Applied Energy, Elsevier, vol. 212(C), pages 997-1012.
    20. Bösch, Patrick M. & Becker, Felix & Becker, Henrik & Axhausen, Kay W., 2018. "Cost-based analysis of autonomous mobility services," Transport Policy, Elsevier, vol. 64(C), pages 76-91.
    21. Zhou, Meifang & Liu, Yu & Feng, Shenghao & Liu, Yang & Lu, Yingying, 2018. "Decomposition of rebound effect: An energy-specific, general equilibrium analysis in the context of China," Applied Energy, Elsevier, vol. 221(C), pages 280-298.
    22. Wadud, Zia, 2017. "Fully automated vehicles: A cost of ownership analysis to inform early adoption," Transportation Research Part A: Policy and Practice, Elsevier, vol. 101(C), pages 163-176.
    23. Small, Kenneth A., 2012. "Valuation of travel time," Economics of Transportation, Elsevier, vol. 1(1), pages 2-14.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jan C. T. Bieser & Vlad C. Coroamă, 2021. "Direkte und indirekte Umwelteffekte der Informations- und Kommunikationstechnologie [Direct and indirect environmental effects of information and communication technology]," Sustainability Nexus Forum, Springer, vol. 29(1), pages 1-11, March.
    2. Batarce, Marco & Basso, Franco & Basso, Leonardo J., 2023. "The elasticity of demand on urban highways: The case of Santiago," Transport Policy, Elsevier, vol. 133(C), pages 234-241.
    3. Yuan, Zhen & Xu, Jie & Li, Bing & Yao, Tingting, 2022. "Limits of technological progress in controlling energy consumption: Evidence from the energy rebound effects across China's industrial sector," Energy, Elsevier, vol. 245(C).
    4. Harb, Mustapha PhD & Malik, Jai PhD & Circella, Giovanni PhD & Walker, Joan L. PhD, 2022. "Simulating Life with Personally-Owned Autonomous Vehicles through a Naturalistic Experiment with Personal Drivers," Institute of Transportation Studies, Working Paper Series qt79g921rp, Institute of Transportation Studies, UC Davis.
    5. Rasti-Barzoki, Morteza & Moon, Ilkyeong, 2021. "A game theoretic approach for analyzing electric and gasoline-based vehicles’ competition in a supply chain under government sustainable strategies: A case study of South Korea," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    6. Dowds, Jonathan & Sullivan, James & Rowangould, Gregory & Aultman-Hall, Lisa, 2021. "Consideration of Automated Vehicle Benefits and Research Needs for Rural America," Institute of Transportation Studies, Working Paper Series qt4v25q5n9, Institute of Transportation Studies, UC Davis.
    7. Dong, Haoxuan & Zhuang, Weichao & Chen, Boli & Wang, Yan & Lu, Yanbo & Liu, Ying & Xu, Liwei & Yin, Guodong, 2022. "A comparative study of energy-efficient driving strategy for connected internal combustion engine and electric vehicles at signalized intersections," Applied Energy, Elsevier, vol. 310(C).
    8. Max Luke & Priyanshi Somani & Turner Cotterman & Dhruv Suri & Stephen J. Lee, 2020. "No COVID-19 Climate Silver Lining in the US Power Sector," Papers 2008.06660, arXiv.org, revised May 2021.
    9. Pudāne, Baiba & van Cranenburgh, Sander & Chorus, Caspar G., 2021. "A day in the life with an automated vehicle: Empirical analysis of data from an interactive stated activity-travel survey," Journal of choice modelling, Elsevier, vol. 39(C).
    10. Guzzo, D. & Walrave, B. & Videira, N. & Oliveira, I.C. & Pigosso, D.C.A., 2024. "Towards a systemic view on rebound effects: Modelling the feedback loops of rebound mechanisms," Ecological Economics, Elsevier, vol. 217(C).
    11. Alexander Cremer & Katrin Müller & Matthias Finkbeiner, 2021. "A Systemic View of Future Mobility Scenario Impacts on and Their Implications for City Organizational LCA: The Case of Autonomous Driving in Vienna," Sustainability, MDPI, vol. 14(1), pages 1-19, December.
    12. Pan, Shuai & Fulton, Lewis M. & Roy, Anirban & Jung, Jia & Choi, Yunsoo & Gao, H. Oliver, 2021. "Shared use of electric autonomous vehicles: Air quality and health impacts of future mobility in the United States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    13. Nuri C. Onat & Jafar Mandouri & Murat Kucukvar & Burak Sen & Saddam A. Abbasi & Wael Alhajyaseen & Adeeb A. Kutty & Rateb Jabbar & Marcello Contestabile & Abdel Magid Hamouda, 2023. "Rebound effects undermine carbon footprint reduction potential of autonomous electric vehicles," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    14. 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).
    15. Harb, Mustapha PhD & Malik, Jai PhD & Circella, Giovanni PhD & Walker, Joan L. PhD, 2022. "Simulating Life with Personally-Owned Autonomous Vehicles through a Naturalistic Experiment with Personal Drivers," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt79g921rp, Institute of Transportation Studies, UC Berkeley.
    16. Liao, Zitong & Taiebat, Morteza & Xu, Ming, 2021. "Shared autonomous electric vehicle fleets with vehicle-to-grid capability: Economic viability and environmental co-benefits," Applied Energy, Elsevier, vol. 302(C).
    17. 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.
    18. Taiebat, Morteza & Stolper, Samuel & Xu, Ming, 2022. "Widespread range suitability and cost competitiveness of electric vehicles for ride-hailing drivers," Applied Energy, Elsevier, vol. 319(C).
    19. Peer, Stefanie & Müller, Johannes & Naqvi, Asjad & Straub, Markus, 2024. "Introducing shared, electric, autonomous vehicles (SAEVs) in sub-urban zones: Simulating the case of Vienna," Transport Policy, Elsevier, vol. 147(C), pages 232-243.
    20. Moneim Massar & Imran Reza & Syed Masiur Rahman & Sheikh Muhammad Habib Abdullah & Arshad Jamal & Fahad Saleh Al-Ismail, 2021. "Impacts of Autonomous Vehicles on Greenhouse Gas Emissions—Positive or Negative?," IJERPH, MDPI, vol. 18(11), pages 1-23, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tilov, Ivan & Weber, Sylvain, 2023. "Heterogeneity in price elasticity of vehicle kilometers traveled: Evidence from micro-level panel data," Energy Economics, Elsevier, vol. 127(PA).
    2. Silvia Tiezzi & Stefano F. Verde, 2017. "The signaling effect of gasoline taxes and its distributional implications," RSCAS Working Papers 2017/06, European University Institute.
    3. Gillingham, Kenneth & Munk-Nielsen, Anders, 2019. "A tale of two tails: Commuting and the fuel price response in driving," Journal of Urban Economics, Elsevier, vol. 109(C), pages 27-40.
    4. Wadud, Zia & Mattioli, Giulio, 2021. "Fully automated vehicles: A cost-based analysis of the share of ownership and mobility services, and its socio-economic determinants," Transportation Research Part A: Policy and Practice, Elsevier, vol. 151(C), pages 228-244.
    5. Kassens-Noor, Eva & Dake, Dana & Decaminada, Travis & Kotval-K, Zeenat & Qu, Teresa & Wilson, Mark & Pentland, Brian, 2020. "Sociomobility of the 21st century: Autonomous vehicles, planning, and the future city," Transport Policy, Elsevier, vol. 99(C), pages 329-335.
    6. Hymel, Kent M. & Small, Kenneth A., 2015. "The rebound effect for automobile travel: Asymmetric response to price changes and novel features of the 2000s," Energy Economics, Elsevier, vol. 49(C), pages 93-103.
    7. Kent M. Hymel & Kenneth Small, 2014. "The Rebound Effect for Automobile Travel:Asymmetric Response to Price Changes and Novel Features of the 2000s," Working Papers 141503, University of California-Irvine, Department of Economics.
    8. Silvia Tiezzi & Stefano F. Verde, 2019. "The signaling effect of gasoline taxes and its distributional implications," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 17(2), pages 145-169, June.
    9. Abe, Ryosuke, 2019. "Introducing autonomous buses and taxis: Quantifying the potential benefits in Japanese transportation systems," Transportation Research Part A: Policy and Practice, Elsevier, vol. 126(C), pages 94-113.
    10. Liu, Weiwei, 2015. "Gasoline taxes or efficiency standards? A heterogeneous household demand analysis," Energy Policy, Elsevier, vol. 80(C), pages 54-64.
    11. De Borger, Bruno & Mulalic, Ismir & Rouwendal, Jan, 2016. "Measuring the rebound effect with micro data: A first difference approach," Journal of Environmental Economics and Management, Elsevier, vol. 79(C), pages 1-17.
    12. Hirte, Georg & Laes, Renée & Gerike, Regine, 2023. "Working from self-driving cars," Transportation Research Part A: Policy and Practice, Elsevier, vol. 176(C).
    13. Shaw, Charles, 2020. "Econometric Analysis of Demand for Petrol in India, 1966-2019," MPRA Paper 104797, University Library of Munich, Germany.
    14. Dimitropoulos, Alexandros & Oueslati, Walid & Sintek, Christina, 2018. "The rebound effect in road transport: A meta-analysis of empirical studies," Energy Economics, Elsevier, vol. 75(C), pages 163-179.
    15. Matsushima, Hiroshi & Khanna, Madhu, 2022. "Estimating Medium-run Direct Rebound Effects of the Footprint-based CAFE Standard," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322420, Agricultural and Applied Economics Association.
    16. Stapleton, Lee & Sorrell, Steve & Schwanen, Tim, 2016. "Estimating direct rebound effects for personal automotive travel in Great Britain," Energy Economics, Elsevier, vol. 54(C), pages 313-325.
    17. Mattioli, Giulio & Wadud, Zia & Lucas, Karen, 2018. "Vulnerability to fuel price increases in the UK: A household level analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 113(C), pages 227-242.
    18. Hössinger, Reinhard & Link, Christoph & Sonntag, Axel & Stark, Juliane, 2017. "Estimating the price elasticity of fuel demand with stated preferences derived from a situational approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 103(C), pages 154-171.
    19. Barla, Philippe & Herrmann, Markus & Ordas-Criado, Carlos & Miranda-Moreno, Luis F., 2015. "Are Gasoline Demand Elasticities Different across Cities?," Working Papers 208360, University of Laval, Center for Research on the Economics of the Environment, Agri-food, Transports and Energy (CREATE).
    20. Silvia Tiezzi & Stefano F. Verde, 2019. "The signaling effect of gasoline taxes and its distributional implications," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 17(2), pages 145-169, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:247:y:2019:i:c:p:297-308. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.