IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v322y2025ics0360544225013878.html
   My bibliography  Save this article

Behavioural nudging for greener travel: A discrete choice experiment in the Greater Toronto Area

Author

Listed:
  • Wang, Kaili
  • Li, Melvyn
  • Xu, Junshi
  • Hatzopoulou, Marianne
  • Nurul Habib, Khandker

Abstract

Environmental management through greenhouse gas (GHG) emission mitigation strategies is a collective responsibility shared by governments, communities, and individuals. Vehicular traffic is one of the leading causes of GHG emissions. Therefore, accelerating the shift from private vehicles to greener alternatives is crucial for achieving carbon neutrality. This study conducts controlled stated preference (SP) choice experiments on travellers' mode choices to nudge sustainable travel behaviours. The experiment aims to provide a quantitative understanding of the effectiveness of nudging for greener behaviour through environmental externality information (GHG emissions) and self-interest information (health implications). The SP experiment was conducted in the Greater Toronto Area, Canada, and had 606 valid samples. An error-component mixed logit model is empirically estimated to capture respondents' behavioural changes during various experiment stages. Based on parameter estimation results, the study also reports the Value of Greener (VOGer), defined as the willingness to pay for comparative GHG emission reductions achieved through greener travel modes. The study finds that the design of nudging interventions plays a crucial role in maximizing individuals’ VOGer. Travellers simultaneously aware of environmental and health-related implications are more likely to use greener travel modes (e.g., transit, biking, and walking).

Suggested Citation

  • Wang, Kaili & Li, Melvyn & Xu, Junshi & Hatzopoulou, Marianne & Nurul Habib, Khandker, 2025. "Behavioural nudging for greener travel: A discrete choice experiment in the Greater Toronto Area," Energy, Elsevier, vol. 322(C).
    • Handle: RePEc:eee:energy:v:322:y:2025:i:c:s0360544225013878
    DOI: 10.1016/j.energy.2025.135745
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225013878
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.135745?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Wang, Luojia & Du, Kerui & Shao, Shuai, 2024. "Transportation infrastructure and carbon emissions: New evidence with spatial spillover and endogeneity," Energy, Elsevier, vol. 297(C).
    2. Gabriel D. Carroll & James J. Choi & David Laibson & Brigitte C. Madrian & Andrew Metrick, 2009. "Optimal Defaults and Active Decisions," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 124(4), pages 1639-1674.
    3. Beck, Matthew J. & Hess, Stephane & Cabral, Manuel Ojeda & Dubernet, Ilka, 2017. "Valuing travel time savings: A case of short-term or long term choices?," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 100(C), pages 133-143.
    4. Rashedi, Zohreh & Mahmoud, Mohamed & Hasnine, Sami & Habib, Khandker Nurul, 2017. "On the factors affecting the choice of regional transit for commuting in Greater Toronto and Hamilton Area: Application of an advanced RP-SP choice model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 105(C), pages 1-13.
    5. Luo, Rachel & Fan, Yichun & Yang, Xin & Zhao, Jinhua & Zheng, Siqi, 2021. "The impact of social externality information on fostering sustainable travel mode choice: A behavioral experiment in Zhengzhou, China," Transportation Research Part A: Policy and Practice, Elsevier, vol. 152(C), pages 127-145.
    6. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, Enero-Abr.
    7. Bhat, Chandra R., 2003. "Simulation estimation of mixed discrete choice models using randomized and scrambled Halton sequences," Transportation Research Part B: Methodological, Elsevier, vol. 37(9), pages 837-855, November.
    8. Wang, Kaili & Gao, Ya & Liu, Yicong & Nurul Habib, Khandker, 2023. "Exploring the choice between in-store versus online grocery shopping through an application of Semi-Compensatory Independent Availability Logit (SCIAL) model with latent variables," Journal of Retailing and Consumer Services, Elsevier, vol. 71(C).
    9. Schmid, Basil & Axhausen, Kay W., 2019. "In-store or online shopping of search and experience goods: A hybrid choice approach," Journal of choice modelling, Elsevier, vol. 31(C), pages 156-180.
    10. Katherine L. Milkman & John Beshears & James J. Choi & David Laibson & Brigitte C. Madrian, 2011. "Using Implementation Intentions Prompts to Enhance Influenza Vaccination Rates," NBER Working Papers 17183, National Bureau of Economic Research, Inc.
    11. Hensher, David A., 2010. "Hypothetical bias, choice experiments and willingness to pay," Transportation Research Part B: Methodological, Elsevier, vol. 44(6), pages 735-752, July.
    12. Guevara, C. Angelo, 2015. "Critical assessment of five methods to correct for endogeneity in discrete-choice models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 82(C), pages 240-254.
    13. Víctor Cantillo & Juan de Dios Ortúzar & Huw C. W. L. Williams, 2007. "Modeling Discrete Choices in the Presence of Inertia and Serial Correlation," Transportation Science, INFORMS, vol. 41(2), pages 195-205, May.
    14. Allcott, Hunt, 2011. "Social norms and energy conservation," Journal of Public Economics, Elsevier, vol. 95(9-10), pages 1082-1095, October.
    15. Balbontin, Camila & Hensher, David A. & Beck, Matthew J., 2022. "Advanced modelling of commuter choice model and work from home during COVID-19 restrictions in Australia," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 162(C).
    16. Zhao, Chuyun & Tang, Jinjun & Gao, Wenyuan & Zeng, Yu & Li, Zhitao, 2024. "Many-objective optimization of multi-mode public transportation under carbon emission reduction," Energy, Elsevier, vol. 286(C).
    17. Hess, Stephane & Train, Kenneth E., 2011. "Recovery of inter- and intra-personal heterogeneity using mixed logit models," Transportation Research Part B: Methodological, Elsevier, vol. 45(7), pages 973-990, August.
    18. Cass Sunstein, 2014. "Nudging: A Very Short Guide," Journal of Consumer Policy, Springer, vol. 37(4), pages 583-588, December.
    19. Wang, Kaili & Salehin, Mohammad Faizus & Nurul Habib, Khandker, 2021. "A discrete choice experiment on consumer’s willingness-to-pay for vehicle automation in the Greater Toronto Area," Transportation Research Part A: Policy and Practice, Elsevier, vol. 149(C), pages 12-30.
    20. Allcott, Hunt, 2011. "Social norms and energy conservation," Journal of Public Economics, Elsevier, vol. 95(9), pages 1082-1095.
    21. Eric P. Bettinger & Bridget Terry Long & Philip Oreopoulos & Lisa Sanbonmatsu, 2012. "The Role of Application Assistance and Information in College Decisions: Results from the H&R Block Fafsa Experiment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 127(3), pages 1205-1242.
    Full references (including those not matched with items on IDEAS)

    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. Chen, Liyuan & Wang, Kaili & Nurul Habib, Khandker, 2024. "A household-based online cooked meal delivery demand generation model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 190(C).
    2. Schmid, Basil & Becker, Felix & Axhausen, Kay W. & Widmer, Paul & Stein, Petra, 2023. "A simultaneous model of residential location, mobility tool ownership and mode choice using latent variables," Transportation Research Part A: Policy and Practice, Elsevier, vol. 178(C).
    3. Raj Chetty, 2015. "Behavioral Economics and Public Policy: A Pragmatic Perspective," American Economic Review, American Economic Association, vol. 105(5), pages 1-33, May.
    4. Kast, Felipe & Meier, Stephan & Pomeranz, Dina, 2012. "Under-Savers Anonymous: Evidence on Self-Help Groups and Peer Pressure as a Savings Commitment Device," IZA Discussion Papers 6311, Institute of Labor Economics (IZA).
    5. Wang, Kaili & Salehin, Mohammad Faizus & Nurul Habib, Khandker, 2021. "A discrete choice experiment on consumer’s willingness-to-pay for vehicle automation in the Greater Toronto Area," Transportation Research Part A: Policy and Practice, Elsevier, vol. 149(C), pages 12-30.
    6. Robert French & Philip Oreopoulos, 2017. "Applying behavioural economics to public policy in Canada," Canadian Journal of Economics, Canadian Economics Association, vol. 50(3), pages 599-635, August.
    7. Luca Congiu & Ivan Moscati, 2022. "A review of nudges: Definitions, justifications, effectiveness," Journal of Economic Surveys, Wiley Blackwell, vol. 36(1), pages 188-213, February.
    8. Damgaard, Mette Trier & Nielsen, Helena Skyt, 2018. "Nudging in education," Economics of Education Review, Elsevier, vol. 64(C), pages 313-342.
    9. Fanghella, Valeria & Faure, Corinne & Guetlein, Marie-Charlotte & Schleich, Joachim, 2022. "Discriminatory subsidies for energy-efficient technologies and the role of envy," Resource and Energy Economics, Elsevier, vol. 68(C).
    10. Kast, Felipe & Meier, Stephan & Pomeranz, Dina, 2018. "Saving more in groups: Field experimental evidence from Chile," Journal of Development Economics, Elsevier, vol. 133(C), pages 275-294.
    11. W. Kip Viscusi, 2022. "Efficiency criteria for nudges and norms," Public Choice, Springer, vol. 191(3), pages 465-482, June.
    12. Benjamin L. Castleman & Francis X. Murphy & Richard W. Patterson & William L. Skimmyhorn, 2021. "Nudges Don't Work When the Benefits Are Ambiguous: Evidence from a High‐Stakes Education Program," Journal of Policy Analysis and Management, John Wiley & Sons, Ltd., vol. 40(4), pages 1230-1248, September.
    13. Beshears, John & Kosowsky, Harry, 2020. "Nudging: Progress to date and future directions," Organizational Behavior and Human Decision Processes, Elsevier, vol. 161(S), pages 3-19.
    14. Beatty, Timothy K.M. & Katare, Bhagyashree, 2018. "Low-cost approaches to increasing gym attendance," Journal of Health Economics, Elsevier, vol. 61(C), pages 63-76.
    15. Bernadeta Gołębiowska & Anna Bartczak & Mikołaj Czajkowski, 2020. "Energy Demand Management and Social Norms," Energies, MDPI, vol. 13(15), pages 1-20, July.
    16. Caroline Dieckhoener & Harald Hecking, 2012. "Greenhouse Gas Abatement Cost Curves of the Residential Heating Market – a Microeconomic Approach," EWI Working Papers 2012-16, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
    17. Löfgren, Åsa & Nordblom, Katarina, 2020. "A theoretical framework of decision making explaining the mechanisms of nudging," Journal of Economic Behavior & Organization, Elsevier, vol. 174(C), pages 1-12.
    18. Ghesla, Claus & Grieder, Manuel & Schubert, Renate, 2020. "Nudging the poor and the rich – A field study on the distributional effects of green electricity defaults," Energy Economics, Elsevier, vol. 86(C).
    19. Lin, Wen & Nayga, Rodolfo M., 2022. "Green identity labeling, environmental information, and pro-environmental food choices," Food Policy, Elsevier, vol. 106(C).
    20. S. Ceolotto & E. Denny, 2024. "Putting a New ‘Spin’ on Energy Information: Measuring the Impact of Reframing Energy Efficiency Information on Tumble Dryer Choices in a Multi-country Experiment," Journal of Consumer Policy, Springer, vol. 47(1), pages 51-108, March.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:energy:v:322:y:2025:i:c:s0360544225013878. 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.journals.elsevier.com/energy .

    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.