IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i18p7924-d1475617.html
   My bibliography  Save this article

Exploring Psychological Factors Influencing the Adoption of Sustainable Public Transit Considering Preference Heterogeneity

Author

Listed:
  • Gyeongjae Lee

    (Department of Urban Planning, Hongik University, Seoul 04066, Republic of Korea)

  • Sujae Kim

    (Department of Urban Design & Planning, Hongik University, Seoul 04066, Republic of Korea)

  • Jahun Koo

    (Department of Urban Planning, Hongik University, Seoul 04066, Republic of Korea)

  • Sangho Choo

    (Department of Urban Design & Planning, Hongik University, Seoul 04066, Republic of Korea)

Abstract

Carbon emission reduction strategies are being implemented in the transportation sector by encouraging the adoption of eco-friendly vehicles and introducing demand management policies such as Mobility as a Service (MaaS). Nevertheless, the efficacy of MaaS in reducing carbon emissions remains uncertain. This study introduces Sustainable Public Transit (SPT) as a public transit alternative consisting of only green modes to promote sustainability. We explore the preferences of SPT in a commuting context, incorporating individual preference heterogeneity in a discrete choice model. We systematically identify the relationship between choice behaviors and individual heterogeneity in alternative attributes and psychological factors stemming from socio-demographic characteristics. The integrated choice and latent variable (ICLV) model with a mixed logit form is adopted, and the key findings can be summarized as follows: Preference heterogeneity is observed in the travel cost variable, which can be explained by characteristics such as the presence of a preschooler, household size, and income. CO 2 emissions do not have a statistically significant impact on choices. Furthermore, psychological factors are also explained through socio-demographic characteristics, and it is found that low-carbon knowledge positively influences low-carbon habits. Psychological factors significantly affect choices. Respondents who dislike transfers and prioritize punctuality are less likely to choose SPT, while those who have positive low-carbon attitudes are more likely to do so. Finally, scenario analysis is conducted to forecast mode share based on improvements in SPT alternative attributes and variations in attribute levels. Policy implications are then provided to enhance the acceptability of SPT.

Suggested Citation

  • Gyeongjae Lee & Sujae Kim & Jahun Koo & Sangho Choo, 2024. "Exploring Psychological Factors Influencing the Adoption of Sustainable Public Transit Considering Preference Heterogeneity," Sustainability, MDPI, vol. 16(18), pages 1-23, September.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:18:p:7924-:d:1475617
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/18/7924/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/18/7924/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Stephen Hynes & Nick Hanley & Riccardo Scarpa, 2008. "Effects on Welfare Measures of Alternative Means of Accounting for Preference Heterogeneity in Recreational Demand Models," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 90(4), pages 1011-1027.
    2. Greene, William H. & Hensher, David A., 2007. "Heteroscedastic control for random coefficients and error components in mixed logit," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 43(5), pages 610-623, September.
    3. 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.
    4. Marcel Paulssen & Dirk Temme & Akshay Vij & Joan Walker, 2014. "Values, attitudes and travel behavior: a hierarchical latent variable mixed logit model of travel mode choice," Transportation, Springer, vol. 41(4), pages 873-888, July.
    5. Rafael Maldonado-Hinarejos & Aruna Sivakumar & John Polak, 2014. "Exploring the role of individual attitudes and perceptions in predicting the demand for cycling: a hybrid choice modelling approach," Transportation, Springer, vol. 41(6), pages 1287-1304, November.
    6. Hess, Stephane & Train, Kenneth E. & Polak, John W., 2006. "On the use of a Modified Latin Hypercube Sampling (MLHS) method in the estimation of a Mixed Logit Model for vehicle choice," Transportation Research Part B: Methodological, Elsevier, vol. 40(2), pages 147-163, February.
    7. Mills, Bradford & Schleich, Joachim, 2012. "Residential energy-efficient technology adoption, energy conservation, knowledge, and attitudes: An analysis of European countries," Energy Policy, Elsevier, vol. 49(C), pages 616-628.
    8. Daziano, Ricardo A., 2015. "Inference on mode preferences, vehicle purchases, and the energy paradox using a Bayesian structural choice model," Transportation Research Part B: Methodological, Elsevier, vol. 76(C), pages 1-26.
    9. Angel Bujosa & Antoni Riera & Robert Hicks, 2010. "Combining Discrete and Continuous Representations of Preference Heterogeneity: A Latent Class Approach," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 47(4), pages 477-493, December.
    10. Andrew Daly & Stephane Hess & Bhanu Patruni & Dimitris Potoglou & Charlene Rohr, 2012. "Using ordered attitudinal indicators in a latent variable choice model: a study of the impact of security on rail travel behaviour," Transportation, Springer, vol. 39(2), pages 267-297, March.
    11. Zheng, Zuduo & Washington, Simon & Hyland, Paul & Sloan, Keith & Liu, Yulin, 2016. "Preference heterogeneity in mode choice based on a nationwide survey with a focus on urban rail," Transportation Research Part A: Policy and Practice, Elsevier, vol. 91(C), pages 178-194.
    12. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
    13. David Hensher & William Greene, 2003. "The Mixed Logit model: The state of practice," Transportation, Springer, vol. 30(2), pages 133-176, May.
    14. Henry Kaiser, 1974. "An index of factorial simplicity," Psychometrika, Springer;The Psychometric Society, vol. 39(1), pages 31-36, March.
    15. Hélène Bouscasse & Iragaël Joly & Patrick Bonnel, 2018. "How does environmental concern influence mode choice habits? A mediation analysis," Post-Print hal-01868333, HAL.
    16. Bai, Yin & Liu, Yong, 2013. "An exploration of residents’ low-carbon awareness and behavior in Tianjin, China," Energy Policy, Elsevier, vol. 61(C), pages 1261-1270.
    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. Vij, Akshay & Walker, Joan L., 2016. "How, when and why integrated choice and latent variable models are latently useful," Transportation Research Part B: Methodological, Elsevier, vol. 90(C), pages 192-217.
    2. Cerquera Dussán, Daniel & Ullrich, Hannes, 2010. "Consumer welfare and unobserved heterogeneity in discrete choice models: The value of alpine road tunnels," ZEW Discussion Papers 10-095, ZEW - Leibniz Centre for European Economic Research.
    3. Doherty, Edel & Campbell, Danny & Hynes, Stephen, 2012. "Exploring cost heterogeneity in recreational demand," Working Papers 148832, National University of Ireland, Galway, Socio-Economic Marine Research Unit.
    4. Luis Márquez & Víctor Cantillo & Julián Arellana, 2020. "Assessing the influence of indicators’ complexity on hybrid discrete choice model estimates," Transportation, Springer, vol. 47(1), pages 373-396, February.
    5. Franceschinis, Cristiano & Thiene, Mara & Scarpa, Riccardo & Rose, John & Moretto, Michele & Cavalli, Raffaele, 2017. "Adoption of renewable heating systems: An empirical test of the diffusion of innovation theory," Energy, Elsevier, vol. 125(C), pages 313-326.
    6. Jose J. Soto & Victor Cantillo & Julian Arellana, 2018. "Incentivizing alternative fuel vehicles: the influence of transport policies, attitudes and perceptions," Transportation, Springer, vol. 45(6), pages 1721-1753, November.
    7. Stephane Hess, 2014. "Latent class structures: taste heterogeneity and beyond," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 14, pages 311-330, Edward Elgar Publishing.
    8. Sarrias, Mauricio & Daziano, Ricardo A., 2018. "Individual-specific point and interval conditional estimates of latent class logit parameters," Journal of choice modelling, Elsevier, vol. 27(C), pages 50-61.
    9. Fosgerau, Mogens & Bierlaire, Michel, 2007. "A practical test for the choice of mixing distribution in discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 41(7), pages 784-794, August.
    10. Martínez-Pardo, Ana & Orro, Alfonso & Garcia-Alonso, Lorena, 2020. "Analysis of port choice: A methodological proposal adjusted with public data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 136(C), pages 178-193.
    11. Scheufele, Gabriela & Bennett, Jeffrey W., 2010. "Ordering effects and strategic response in discrete choice experiments," Research Reports 107743, Australian National University, Environmental Economics Research Hub.
    12. Czajkowski, Mikołaj & Bartczak, Anna & Giergiczny, Marek & Navrud, Stale & Żylicz, Tomasz, 2014. "Providing preference-based support for forest ecosystem service management," Forest Policy and Economics, Elsevier, vol. 39(C), pages 1-12.
    13. Habtamu Tilahun Kassahun & Bo Jellesmark Thorsen & Joffre Swait & Jette Bredahl Jacobsen, 2020. "Social Cooperation in the Context of Integrated Private and Common Land Management," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 75(1), pages 105-136, January.
    14. Tagliafierro, C. & Boeri, M. & Longo, A. & Hutchinson, W.G., 2016. "Stated preference methods and landscape ecology indicators: An example of transdisciplinarity in landscape economic valuation," Ecological Economics, Elsevier, vol. 127(C), pages 11-22.
    15. Gamal Eldeeb & Moataz Mohamed, 2020. "Understanding the Transit Market: A Persona-Based Approach for Preferences Quantification," Sustainability, MDPI, vol. 12(9), pages 1-20, May.
    16. Gillespie Rob & Kragt Marit E., 2012. "Accounting for Nonmarket Impacts in a Benefit-Cost Analysis of Underground Coal Mining in New South Wales, Australia," Journal of Benefit-Cost Analysis, De Gruyter, vol. 3(2), pages 1-29, May.
    17. Isler, Cassiano Augusto & Blumenfeld, Marcelo & Caldeira, Gabriel Pereira & Roberts, Clive, 2024. "Long-Distance railway mode choice in Brazil: Evidence from a discrete choice experiment," Research in Transportation Economics, Elsevier, vol. 104(C).
    18. Zhou, Fan & Zheng, Zuduo & Whitehead, Jake & Washington, Simon & Perrons, Robert K. & Page, Lionel, 2020. "Preference heterogeneity in mode choice for car-sharing and shared automated vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 132(C), pages 633-650.
    19. Daina, Nicolò & Sivakumar, Aruna & Polak, John W., 2017. "Modelling electric vehicles use: a survey on the methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 68(P1), pages 447-460.
    20. Bouscasse, H., 2018. "Integrated choice and latent variable models: A literature review on mode choice," Working Papers 2018-07, Grenoble Applied Economics Laboratory (GAEL).

    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:gam:jsusta:v:16:y:2024:i:18:p:7924-:d:1475617. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.