IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i20p14809-d1258522.html
   My bibliography  Save this article

Daily Travel Mode Choice Considering Carbon Credit Incentive (CCI)—An Application of the Integrated Choice and Latent Variable (ICLV) Model

Author

Listed:
  • Lei Gong

    (College of Urban Transportation and Logistics, Shenzhen Technology University, Shenzhen 518118, China)

  • Tianxu Wang

    (College of Urban Transportation and Logistics, Shenzhen Technology University, Shenzhen 518118, China)

  • Tian Lei

    (College of Urban Transportation and Logistics, Shenzhen Technology University, Shenzhen 518118, China)

  • Qin Luo

    (College of Urban Transportation and Logistics, Shenzhen Technology University, Shenzhen 518118, China)

  • Zhu Han

    (College of Urban Transportation and Logistics, Shenzhen Technology University, Shenzhen 518118, China)

  • Yihong Mo

    (College of Urban Transportation and Logistics, Shenzhen Technology University, Shenzhen 518118, China)

Abstract

There have been many implementations of carbon credit incentives (CCIs) for promoting green travel, but research on quantifying the effectiveness remains limited. To fill this gap, this study focuses on residents’ daily transportation mode choices under the incentive of carbon credits by employing an integrated choice and latent variable (ICLV) model to adequately address the role of attitudinal variables related to carbon credits, such as perceived usefulness, perceived ease of use, and behavioral intentions. Data from a questionnaire survey show that the ICLV model provides a richer and more nuanced understanding of the green mode choice than a traditional multinomial logit (MNL) model, where the AIC value of the ICLV model (3901.17) is smaller than that of the MNL model (3910.09). Carbon credits exhibit diverse impacts across various modes of eco-friendly transportation and specific demographic groups. Commuting trips reveal noteworthy positive associations between carbon credits and the use of bicycles as well as metro/bus services. Moreover, carbon credits exert a more pronounced influence on individuals with higher education levels, older age groups, and owners of new energy vehicles, whereas their impact on high-income individuals is relatively constrained. Furthermore, perceptions of carbon credits are pivotal, with perceived utility emerging as the most influential factor. The results provide a scientific basis for formulating more effective policies regarding carbon credit and incentive measures in the future.

Suggested Citation

  • Lei Gong & Tianxu Wang & Tian Lei & Qin Luo & Zhu Han & Yihong Mo, 2023. "Daily Travel Mode Choice Considering Carbon Credit Incentive (CCI)—An Application of the Integrated Choice and Latent Variable (ICLV) Model," Sustainability, MDPI, vol. 15(20), pages 1-16, October.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:20:p:14809-:d:1258522
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/20/14809/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/20/14809/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Niemeier, D. & Gould, Gregory & Karner, Alex & Hixson, Mark & Bachmann, Brooke & Okma, Carrie & Lang, Ziv & Heres Del Valle, David, 2008. "Rethinking downstream regulation: California's opportunity to engage households in reducing greenhouse gases," Energy Policy, Elsevier, vol. 36(9), pages 3436-3447, September.
    2. Mao, Zidan & Ettema, Dick & Dijst, Martin, 2016. "Commuting trip satisfaction in Beijing: Exploring the influence of multimodal behavior and modal flexibility," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 592-603.
    3. Jennifer Dill & Cynthia Mohr & Liang Ma, 2014. "How Can Psychological Theory Help Cities Increase Walking and Bicycling?," Journal of the American Planning Association, Taylor & Francis Journals, vol. 80(1), pages 36-51, January.
    4. Wadud, Zia, 2011. "Personal tradable carbon permits for road transport: Why, why not and who wins?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(10), pages 1052-1065.
    5. Hess, Stephane & Spitz, Greg & Bradley, Mark & Coogan, Matt, 2018. "Analysis of mode choice for intercity travel: Application of a hybrid choice model to two distinct US corridors," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 547-567.
    6. Schwanen, Tim & Mokhtarian, Patricia L., 2005. "What Affects Commute Mode Choice: Neighborhood Physical Structure or Preferences Toward Neighborhoods?," University of California Transportation Center, Working Papers qt4nq9r1c9, University of California Transportation Center.
    7. 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.
    8. Jiangping Zhou & Yin Wang & Jiangyue Wu, 2018. "Mode Choice of Commuter Students in a College Town: An Exploratory Study from the United States," Sustainability, MDPI, vol. 10(9), pages 1-18, September.
    9. Hossain Mohiuddin & Md Musfiqur Rahman Bhuiya & Shaila Jamal & Zhi Chen, 2022. "Exploring the Choice of Bicycling and Walking in Rajshahi, Bangladesh: An Application of Integrated Choice and Latent Variable (ICLV) Models," Sustainability, MDPI, vol. 14(22), pages 1-20, November.
    10. Potoglou, Dimitris & Palacios, Juan-Francisco & Feijóo, Claudio, 2015. "An integrated latent variable and choice model to explore the role of privacy concern on stated behavioural intentions in e-commerce," Journal of choice modelling, Elsevier, vol. 17(C), pages 10-27.
    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. Marek Drliciak & Michal Cingel & Jan Celko & Zuzana Panikova, 2024. "Research on Vehicle Congestion Group Identification for Evaluation of Traffic Flow Parameters," Sustainability, MDPI, vol. 16(5), pages 1-16, February.

    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. Samira Ramezani & Tiina Laatikainen & Kamyar Hasanzadeh & Marketta Kyttä, 2021. "Shopping trip mode choice of older adults: an application of activity space and hybrid choice models in understanding the effects of built environment and personal goals," Transportation, Springer, vol. 48(2), pages 505-536, April.
    2. Jie Gao & Dick Ettema & Marco Helbich & Carlijn B. M. Kamphuis, 2019. "Travel mode attitudes, urban context, and demographics: do they interact differently for bicycle commuting and cycling for other purposes?," Transportation, Springer, vol. 46(6), pages 2441-2463, December.
    3. Dogterom, Nico & Ettema, Dick & Dijst, Martin, 2018. "Behavioural effects of a tradable driving credit scheme: Results of an online stated adaptation experiment in the Netherlands," Transportation Research Part A: Policy and Practice, Elsevier, vol. 107(C), pages 52-64.
    4. Kim, Seheon & Rasouli, Soora, 2022. "The influence of latent lifestyle on acceptance of Mobility-as-a-Service (MaaS): A hierarchical latent variable and latent class approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 159(C), pages 304-319.
    5. Kumagai, Junya & Managi, Shunsuke, 2019. "Environmental behaviour and choice of sustainable travel mode in urban areas: comparative evidence from commuters in Asian cities," MPRA Paper 97493, University Library of Munich, Germany.
    6. Fangqing Song & Stephane Hess & Thijs Dekker, 2024. "Uncovering the link between intra-individual heterogeneity and variety seeking: the case of new shared mobility," Transportation, Springer, vol. 51(2), pages 371-406, April.
    7. Starkey, Richard, 2012. "Personal carbon trading: A critical survey," Ecological Economics, Elsevier, vol. 73(C), pages 7-18.
    8. De Vos, Jonas, 2018. "Do people travel with their preferred travel mode? Analysing the extent of travel mode dissonance and its effect on travel satisfaction," Transportation Research Part A: Policy and Practice, Elsevier, vol. 117(C), pages 261-274.
    9. Masiero, Lorenzo & Hrankai, Richard, 2022. "Modeling tourist accessibility to peripheral attractions," Annals of Tourism Research, Elsevier, vol. 92(C).
    10. De Vos, Jonas & Witlox, Frank, 2017. "Travel satisfaction revisited. On the pivotal role of travel satisfaction in conceptualising a travel behaviour process," Transportation Research Part A: Policy and Practice, Elsevier, vol. 106(C), pages 364-373.
    11. An, Kangxin & Zhang, Shihui & Huang, Hai & Liu, Yuan & Cai, Wenjia & Wang, Can, 2021. "Socioeconomic impacts of household participation in emission trading scheme: A Computable General Equilibrium-based case study," Applied Energy, Elsevier, vol. 288(C).
    12. Anna-Katharina Kothe & Alexander Kuptel & Roman Seidl, 2021. "Simulating Personal Carbon Trading (PCT) with an Agent-Based Model (ABM): Investigating Adaptive Reduction Rates and Path Dependence," Energies, MDPI, vol. 14(22), pages 1-15, November.
    13. John Buckell & David A Hensher & Stephane Hess, 2021. "Kicking the habit is hard: A hybrid choice model investigation into the role of addiction in smoking behavior," Health Economics, John Wiley & Sons, Ltd., vol. 30(1), pages 3-19, January.
    14. Schwanen, Tim & Banister, David & Anable, Jillian, 2011. "Scientific research about climate change mitigation in transport: A critical review," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(10), pages 993-1006.
    15. Edwin Woerdman & Jan Willem Bolderdijk, 2017. "Emissions trading for households? A behavioral law and economics perspective," European Journal of Law and Economics, Springer, vol. 44(3), pages 553-578, December.
    16. H. M. Abdul Aziz & Satish V. Ukkusuri & Xianyuan Zhan, 2017. "Determining the Impact of Personal Mobility Carbon Allowance Schemes in Transportation Networks," Networks and Spatial Economics, Springer, vol. 17(2), pages 505-545, June.
    17. Jeremy van Dijk & Mehdi Farsi & Sylvain Weber, 2020. "Travel mode choices in a greening market: the impact of electric vehicles and prior investments," IRENE Working Papers 20-04, IRENE Institute of Economic Research.
    18. Figliozzi, Miguel & Unnikrishnan, Avinash, 2021. "Exploring the impact of socio-demographic characteristics, health concerns, and product type on home delivery rates and expenditures during a strict COVID-19 lockdown period: A case study from Portlan," Transportation Research Part A: Policy and Practice, Elsevier, vol. 153(C), pages 1-19.
    19. Namgung, Hyewon & Chikaraishi, Makoto & Fujiwara, Akimasa, 2023. "Influence of real and video-based experiences on stated acceptance of connected public transportation and autonomous vehicles in a transit mall: A hybrid choice modeling approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 173(C).
    20. Uusitalo, V. & Huttunen, A. & Kareinen, E. & von Wright, T. & Valjakka, M. & Pitkänen, A. & Levänen, J., 2022. "Using personal carbon trading to reduce mobility emissions: A pilot in the Finnish city of Lahti," Transport Policy, Elsevier, vol. 126(C), pages 177-187.

    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:15:y:2023:i:20:p:14809-:d:1258522. 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.