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Do environmental concerns affect commuting choices?: hybrid choice modelling with household survey data

Author

Listed:
  • Jennifer Roberts
  • Gurleen Popli
  • Rosemary J. Harris
Abstract
To meet ambitious climate change goals governments must encourage behavioural change alongside technological progress. Designing effective policy requires a thorough understanding of the factors that drive behaviours. In an effort to understand the role of environmental attitudes better we estimate a hybrid choice model (HCM) for commuting mode choice by using a large household survey data set. HCMs combine traditional discrete choice models with a structural equation model to integrate latent variables, such as attitudes, into the choice process. To date HCMs have utilized small bespoke data sets, beset with problems of selection and limited generalizability. To overcome these problems we demonstrate the feasibility of using this valuable modelling approach with nationally representative data. Our results suggest that environmental attitudes have an important influence on commute mode choice, and this can be exploited by governments looking to add to their climate change policy toolbox in an effort to change travel behaviours.

Suggested Citation

  • Jennifer Roberts & Gurleen Popli & Rosemary J. Harris, 2018. "Do environmental concerns affect commuting choices?: hybrid choice modelling with household survey data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(1), pages 299-320, January.
    • Handle: RePEc:bla:jorssa:v:181:y:2018:i:1:p:299-320
    DOI: 10.1111/rssa.12274
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    References listed on IDEAS

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    Cited by:

    1. Sascha von Behren & Lisa Bönisch & Ulrich Niklas & Bastian Chlond, 2020. "Revealing Motives for Car Use in Modern Cities—A Case Study from Berlin and San Francisco," Sustainability, MDPI, vol. 12(13), pages 1-18, June.
    2. Jindo Jeong & Jiwon Lee & Tae‐Hyoung Tommy Gim, 2022. "Travel mode choice as a representation of travel utility: A multilevel approach reflecting the hierarchical structure of trip, individual, and neighborhood characteristics," Papers in Regional Science, Wiley Blackwell, vol. 101(3), pages 745-765, June.
    3. Yixi Xue & Xiaoyan Zhang & Yi Zhang & Exiang Luo, 2024. "Understanding the Barriers to Consumer Purchasing of Electric Vehicles: The Innovation Resistance Theory," Sustainability, MDPI, vol. 16(6), pages 1-19, March.
    4. Matthew Wigginton Bhagat-Conway & Laura Mirtich & Deborah Salon & Nathan Harness & Alexis Consalvo & Shuyao Hong, 2024. "Subjective variables in travel behavior models: a critical review and Standardized Transport Attitude Measurement Protocol (STAMP)," Transportation, Springer, vol. 51(1), pages 155-191, February.
    5. Tran, Yen & Yamamoto, Toshiyuki & Sato, Hitomi, 2020. "The influences of environmentalism and attitude towards physical activity on mode choice: The new evidences," Transportation Research Part A: Policy and Practice, Elsevier, vol. 134(C), pages 211-226.
    6. Bouscasse, H., 2018. "Integrated choice and latent variable models: A literature review on mode choice," Working Papers 2018-07, Grenoble Applied Economics Laboratory (GAEL).
    7. Salak, B. & Lindberg, K. & Kienast, F. & Hunziker, M., 2021. "How landscape-technology fit affects public evaluations of renewable energy infrastructure scenarios. A hybrid choice model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    8. Yueqi Mao & Qiang Mei & Peng Jing & Ye Zha & Ying Xue & Jiahui Huang & Danning Shao & Pan Luo, 2022. "Factors Affecting the Parental Intention of Using AVs to Escort Children: An Integrated SEM–Hybrid Choice Model Approach," Sustainability, MDPI, vol. 14(18), pages 1-21, September.
    9. Hélène Bouscasse, 2018. "Integrated choice and latent variable models: A literature review on mode choice," Working Papers hal-01795630, HAL.
    10. Henri Kuokkanen & William Sun, 2020. "Companies, Meet Ethical Consumers: Strategic CSR Management to Impact Consumer Choice," Journal of Business Ethics, Springer, vol. 166(2), pages 403-423, October.

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    More about this item

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • Q50 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - General
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise

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