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I can’t believe your attitude: a joint estimation of best worst attitudes and electric vehicle choice

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  • Matthew J. Beck

    (University of Sydney)

  • John M. Rose

    (University of South Australia)

  • Stephen P. Greaves

    (University of Sydney)

Abstract

The number of conventionally fuelled motor vehicles in use is increasing worldwide despite warnings about finite fossil fuel and the detrimental impacts of burning such fuels. While electric vehicles, the subject of much research, generate far less emissions and offer the potential for power from renewable sources, they are yet to significantly penetrate the market. Tangible barriers such as price and vehicle range still exist, but consumer attitudes also drive behaviour. This paper examines attributes in a framework relatively new to transportation and energy policy; best–worst scaling. This method is widely considered an improvement over traditional methods of eliciting attitudes and beliefs, where respondents select attitudes they find best or worst from a set of attitudinal statements. To avoid potential endogeneity bias, we jointly model attitudes and choice for the first time with best–worst data. It is found that energy crisis, air quality and climate change concerns influence behaviour with respect to vehicle range and that travel behaviour change and forms of government incentives are needed influences on behaviour with respect to vehicle emissions. It is argued that correctly modelling attitudes reduces the error term of the vehicle choice model and provides policy makers with an improved lens for assessing behaviour. Additionally, the methods described within can easily be adapted to other policy scenarios.

Suggested Citation

  • Matthew J. Beck & John M. Rose & Stephen P. Greaves, 2017. "I can’t believe your attitude: a joint estimation of best worst attitudes and electric vehicle choice," Transportation, Springer, vol. 44(4), pages 753-772, July.
  • Handle: RePEc:kap:transp:v:44:y:2017:i:4:d:10.1007_s11116-016-9675-9
    DOI: 10.1007/s11116-016-9675-9
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    Cited by:

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    6. MacDonald, Darla Hatton & Rose, John M. & Johnston, Robert J. & Bark, Rosalind H. & Pritchard, Jodie, 2019. "Managing groundwater in a mining region: an opportunity to compare best-worst and referendum data," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 63(4), October.
    7. Patyal, Vishal Singh & Kumar, Ravi & Kushwah, Shiksha, 2021. "Modeling barriers to the adoption of electric vehicles: An Indian perspective," Energy, Elsevier, vol. 237(C).
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    9. Moon-Koo Kim & Jong-Hyun Park & Kyungsoo Kim & Byoungkyu Park, 2020. "Identifying factors influencing the slow market diffusion of electric vehicles in Korea," Transportation, Springer, vol. 47(2), pages 663-688, April.
    10. Zhu, Xianwen & Xia, Mingchao & Chiang, Hsiao-Dong, 2018. "Coordinated sectional droop charging control for EV aggregator enhancing frequency stability of microgrid with high penetration of renewable energy sources," Applied Energy, Elsevier, vol. 210(C), pages 936-943.
    11. Danielis, Romeo & Scorrano, Mariangela & Giansoldati, Marco & Rotaris, Lucia, 2019. "A meta-analysis of the importance of the driving range in consumers’ preference studies for battery electric vehicles," Working Papers 19_2, SIET Società Italiana di Economia dei Trasporti e della Logistica.
    12. Wei Wei & Ming Cao & Qianling Jiang & Sheng-Jung Ou & Hong Zou, 2020. "What Influences Chinese Consumers’ Adoption of Battery Electric Vehicles? A Preliminary Study Based on Factor Analysis," Energies, MDPI, vol. 13(5), pages 1-15, February.
    13. Rahman, Moshiur & Yasmin, Shamsunnahar & Eluru, Naveen, 2019. "Controlling for endogeneity between bus headway and bus ridership: A case study of the Orlando region," Transport Policy, Elsevier, vol. 81(C), pages 208-219.
    14. Anna Fernández-Antolín & Matthieu Lapparent & Michel Bierlaire, 2018. "Modeling purchases of new cars: an analysis of the 2014 French market," Theory and Decision, Springer, vol. 84(2), pages 277-303, March.
    15. Paolo Delle Site & Karim Kilani & Valerio Gatta & Edoardo Marcucci & André de Palma, 2018. "Estimation of Logit and Probit models using best, worst and best-worst choices," Working Papers hal-01953581, HAL.
    16. Higueras-Castillo, Elena & Kalinic, Zoran & Marinkovic, Veljko & Liébana-Cabanillas, Francisco J., 2020. "A mixed analysis of perceptions of electric and hybrid vehicles," Energy Policy, Elsevier, vol. 136(C).
    17. Gaofeng Gu & Xiaofeng Pan, 2023. "A Study on the Interdependence in Sustainable Mobility Tools and Home Energy Equipment Choices," Energies, MDPI, vol. 16(3), pages 1-16, January.
    18. Gabriela D. Oliveira & Luis C. Dias, 2019. "Influence of Demographics on Consumer Preferences for Alternative Fuel Vehicles: A Review of Choice Modelling Studies and a Study in Portugal," Energies, MDPI, vol. 12(2), pages 1-33, January.
    19. Weibo Li & Maria Kamargianni, 2020. "An Integrated Choice and Latent Variable Model to Explore the Influence of Attitudinal and Perceptual Factors on Shared Mobility Choices and Their Value of Time Estimation," Transportation Science, INFORMS, vol. 54(1), pages 62-83, January.
    20. Echaniz, Eneko & Ho, Chinh Q. & Rodriguez, Andres & dell'Olio, Luigi, 2019. "Comparing best-worst and ordered logit approaches for user satisfaction in transit services," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 752-769.
    21. Cristina Sousa & Evaldo Costa, 2022. "Types of Policies for the Joint Diffusion of Electric Vehicles with Renewable Energies and Their Use Worldwide," Energies, MDPI, vol. 15(20), pages 1-19, October.

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