IDEAS home Printed from https://ideas.repec.org/a/eee/trapol/v134y2023icp128-138.html
   My bibliography  Save this article

External benefits of a road transportation system with vehicle-to-everything communications

Author

Listed:
  • Lee, Hye-Jeong
  • Yoo, Seung-Hoon
  • Lim, Sesil
  • Huh, Sung-Yoon

Abstract

Greenhouse gas (GHG) emissions on roads accounts for about three-quarters of the total emissions of the transport sector, and therefore it is vital to reduce them. South Korea plans to expand 30% of all roads to the cooperative intelligent transport system (C-ITS). This transport system uses vehicle-to-everything communication technologies to reduce GHG emissions by 2030. This study analysed the external benefits of expanding C-ITS using the preference data from a choice experiment. A mixed logit model is used to analyse the heterogeneity of the respondents’ preferences. We find that the marginal willingness to pay for reducing traffic congestion, reducing traffic accidents, improving fuel economy, reducing particulate matter emissions and reducing GHG emissions are KRW 137.63/%, KRW 189.92/%, KRW 82.71/%, KRW 447.47/%, and KRW 81.03/%, respectively. It is expected that this result will provide basic data and implications for related transportation policies.

Suggested Citation

  • Lee, Hye-Jeong & Yoo, Seung-Hoon & Lim, Sesil & Huh, Sung-Yoon, 2023. "External benefits of a road transportation system with vehicle-to-everything communications," Transport Policy, Elsevier, vol. 134(C), pages 128-138.
    • Handle: RePEc:eee:trapol:v:134:y:2023:i:c:p:128-138
    DOI: 10.1016/j.tranpol.2023.02.015
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0967070X23000409
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tranpol.2023.02.015?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 search for a different version of it.

    References listed on IDEAS

    as
    1. Joel Huber and Kenneth Train., 2000. "On the Similarity of Classical and Bayesian Estimates of Individual Mean Partworths," Economics Working Papers E00-289, University of California at Berkeley.
    2. Clara Benevolo & Renata Paola Dameri & Beatrice D’Auria, 2016. "Smart Mobility in Smart City," Lecture Notes in Information Systems and Organization, in: Teresina Torre & Alessio Maria Braccini & Riccardo Spinelli (ed.), Empowering Organizations, edition 1, pages 13-28, Springer.
    3. Suhaiza Zailani & Mohammad Iranmanesh & Sunghyup Sean Hyun & Mohd Helmi Ali, 2019. "Applying the Theory of Consumption Values to Explain Drivers’ Willingness to Pay for Biofuels," Sustainability, MDPI, vol. 11(3), pages 1-13, January.
    4. Ho, Chinh Q. & Hensher, David A. & Mulley, Corinne & Wong, Yale Z., 2018. "Potential uptake and willingness-to-pay for Mobility as a Service (MaaS): A stated choice study," Transportation Research Part A: Policy and Practice, Elsevier, vol. 117(C), pages 302-318.
    5. Lee, Jinwoong & Yoon, Yoojung, 2021. "Indicators development to support intelligent road infrastructure in urban cities," Transport Policy, Elsevier, vol. 114(C), pages 252-265.
    6. Mohammed Elhenawy & Sébastien Glaser & Andy Bond & Andry Rakotonirainy & Sébastien Demmel & Mahmoud Masoud, 2020. "A framework for testing independence between lane change and cooperative intelligent transportation system," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-18, February.
    7. Malte Welling & Ewa Zawojska & Julian Sagebiel, 2022. "Information, Consequentiality and Credibility in Stated Preference Surveys: A Choice Experiment on Climate Adaptation," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 82(1), pages 257-283, May.
    8. David Hensher & William Greene, 2003. "The Mixed Logit model: The state of practice," Transportation, Springer, vol. 30(2), pages 133-176, May.
    9. Zhao, Congyu & Wang, Kun & Dong, Xiucheng & Dong, Kangyin, 2022. "Is smart transportation associated with reduced carbon emissions? The case of China," Energy Economics, Elsevier, vol. 105(C).
    10. Gupta, Monika, 2016. "Willingness to pay for carbon tax: A study of Indian road passenger transport," Transport Policy, Elsevier, vol. 45(C), pages 46-54.
    11. Huh, Sung-Yoon & Jo, Manseok & Shin, Jungwoo & Yoo, Seung-Hoon, 2019. "Impact of rebate program for energy-efficient household appliances on consumer purchasing decisions: The case of electric rice cookers in South Korea," Energy Policy, Elsevier, vol. 129(C), pages 1394-1403.
    12. Cui, Nan & Chen, Bokui & Zhang, Kai & Zhang, Yi & Liu, Xiaotong & Zhou, Jun, 2019. "Effects of route guidance strategies on traffic emissions in intelligent transportation systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 513(C), pages 32-44.
    13. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, January.
    14. Balcombe, Kelvin & Chalak, Ali & Fraser, Iain, 2009. "Model selection for the mixed logit with Bayesian estimation," Journal of Environmental Economics and Management, Elsevier, vol. 57(2), pages 226-237, March.
    15. Ju-Hee Kim & Hyo-Jin Kim & Seung-Hoon Yoo, 2020. "External benefits of increasing bioethanol consumption: a choice experiment study," Applied Economics Letters, Taylor & Francis Journals, vol. 27(6), pages 447-450, March.
    16. Jobert, Arthur & Laborgne, Pia & Mimler, Solveig, 2007. "Local acceptance of wind energy: Factors of success identified in French and German case studies," Energy Policy, Elsevier, vol. 35(5), pages 2751-2760, May.
    17. Woo, JongRoul & Shin, Jungwoo & Kim, Hongbum & Moon, HyungBin, 2022. "Which consumers are willing to pay for smart car healthcare services? A discrete choice experiment approach," Journal of Retailing and Consumer Services, Elsevier, vol. 69(C).
    18. Zawieska, Jakub & Pieriegud, Jana, 2018. "Smart city as a tool for sustainable mobility and transport decarbonisation," Transport Policy, Elsevier, vol. 63(C), pages 39-50.
    19. Wustenhagen, Rolf & Wolsink, Maarten & Burer, Mary Jean, 2007. "Social acceptance of renewable energy innovation: An introduction to the concept," Energy Policy, Elsevier, vol. 35(5), pages 2683-2691, May.
    20. Yoo, Sunbin & Koh, Kyung Woong & Yoshida, Yoshikuni, 2020. "Are consumers abandoning diesel automobiles because of contrasting diesel policies? Evidence from the Korean automobile market," Energy Economics, Elsevier, vol. 92(C).
    21. Train, Kenneth, 2016. "Mixed logit with a flexible mixing distribution," Journal of choice modelling, Elsevier, vol. 19(C), pages 40-53.
    22. Liu, Wenling & Wang, Can & Mol, Arthur P.J., 2013. "Rural public acceptance of renewable energy deployment: The case of Shandong in China," Applied Energy, Elsevier, vol. 102(C), pages 1187-1196.
    23. Kim, Ju-Hee & Kim, Hyo-Jin & Yoo, Seung-Hoon, 2019. "Willingness to pay for fuel-cell electric vehicles in South Korea," Energy, Elsevier, vol. 174(C), pages 497-502.
    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. Tomislav Letnik & Katja Hanžič & Matej Mencinger & Drago Sever, 2023. "Impact of Transport Trends on Sustainability in the Western Balkans: A Future-Oriented Business Sector Perspective," Sustainability, MDPI, vol. 16(1), pages 1-19, December.

    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. Hye-Jeong Lee & Beom Jin Chung & Sung-Yoon Huh, 2023. "Consumer Preferences for Smart Energy Services Based on AMI Data in the Power Sector," Energies, MDPI, vol. 16(9), pages 1-20, May.
    2. Linnerud, K. & Dugstad, A. & Rygg, B.J., 2022. "Do people prefer offshore to onshore wind energy? The role of ownership and intended use," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    3. Ju-Hee Kim & Younggew Kim & Seung-Hoon Yoo, 2021. "Using a choice experiment to explore the public willingness to pay for the impacts of improving energy efficiency of an apartment," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(5), pages 1775-1793, October.
    4. Chen, Tiantian & Fu, Xiaowen & Hensher, David A. & Li, Zhi-Chun & Sze, N.N., 2022. "Air travel choice, online meeting and passenger heterogeneity – An international study on travellers’ preference during a pandemic," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 439-453.
    5. Landmann, D. & Feil, J.-H. & Lagerkvist, C.J. & Otter, V., 2018. "Designing capacity development activities of small-scale farmers in developing countries based on discrete choice experiments," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277738, International Association of Agricultural Economists.
    6. I. G. Ukpong & K. G. Balcombe & I. M. Fraser & F. J. Areal, 2019. "Preferences for Mitigation of the Negative Impacts of the Oil and Gas Industry in the Niger Delta Region of Nigeria," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 74(2), pages 811-843, October.
    7. Mørkbak, Morten Raun & Olsen, Søren Bøye & Campbell, Danny, 2014. "Behavioral implications of providing real incentives in stated choice experiments," Journal of Economic Psychology, Elsevier, vol. 45(C), pages 102-116.
    8. Sung-Yoon Huh & JongRoul Woo & Chul-Yong Lee, 2019. "What Do Potential Residents Really Want When Hosting a Nuclear Power Plant? An Empirical Study of Economic Incentives in South Korea," Energies, MDPI, vol. 12(7), pages 1-17, March.
    9. Ugo Colombino & Marilena Locatelli, 2008. "Parameters Heterogeneity in a Model of Labour Supply: Exploring the Performance of Mixed Logit," CHILD Working Papers wp21_08, CHILD - Centre for Household, Income, Labour and Demographic economics - ITALY.
    10. Akinc, Deniz & Vandebroek, Martina, 2018. "Bayesian estimation of mixed logit models: Selecting an appropriate prior for the covariance matrix," Journal of choice modelling, Elsevier, vol. 29(C), pages 133-151.
    11. 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.
    12. Regier, Dean A. & Ryan, Mandy & Phimister, Euan & Marra, Carlo A., 2009. "Bayesian and classical estimation of mixed logit: An application to genetic testing," Journal of Health Economics, Elsevier, vol. 28(3), pages 598-610, May.
    13. Paz, Alexander & Arteaga, Cristian & Cobos, Carlos, 2019. "Specification of mixed logit models assisted by an optimization framework," Journal of choice modelling, Elsevier, vol. 30(C), pages 50-60.
    14. John V. Colias & Stella Park & Elizabeth Horn, 2021. "Optimizing B2B product offers with machine learning, mixed logit, and nonlinear programming," Journal of Marketing Analytics, Palgrave Macmillan, vol. 9(3), pages 157-172, September.
    15. Kim, Sung Hoo & Mokhtarian, Patricia L., 2023. "Finite mixture (or latent class) modeling in transportation: Trends, usage, potential, and future directions," Transportation Research Part B: Methodological, Elsevier, vol. 172(C), pages 134-173.
    16. Muhammad Aslam Mohd Safari & Nurulkamal Masseran & Alias Jedi & Sohif Mat & Kamaruzzaman Sopian & Azman Bin Abdul Rahim & Azami Zaharim, 2020. "Rural Public Acceptance of Wind and Solar Energy: A Case Study from Mersing, Malaysia," Energies, MDPI, vol. 13(15), pages 1-24, July.
    17. Kim, Doha & Song, Yeosol & Kim, Songyie & Lee, Sewang & Wu, Yanqin & Shin, Jungwoo & Lee, Daeho, 2023. "How should the results of artificial intelligence be explained to users? - Research on consumer preferences in user-centered explainable artificial intelligence," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    18. Ana María González & Harrison Sandoval & Pilar Acosta & Felipe Henao, 2016. "On the Acceptance and Sustainability of Renewable Energy Projects—A Systems Thinking Perspective," Sustainability, MDPI, vol. 8(11), pages 1-21, November.
    19. Carol Mansfield & Jessie Sutphin & Marco Boeri, 2020. "Assessing the impact of excluded attributes on choice in a discrete choice experiment using a follow‐up question," Health Economics, John Wiley & Sons, Ltd., vol. 29(10), pages 1307-1315, October.
    20. Riccardo Scarpa & Cristiano Franceschinis & Mara Thiene, 2017. "A Monte Carlo Evaluation of the Logit-Mixed Logit under Asymmetry and Multimodality," Working Papers in Economics 17/23, University of Waikato.

    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:trapol:v:134:y:2023:i:c:p:128-138. 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.elsevier.com/wps/find/journaldescription.cws_home/30473/description#description .

    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.