IDEAS home Printed from https://ideas.repec.org/a/aiy/jnjaer/v22y2023i3p497-521.html
   My bibliography  Save this article

Predictive Assessment of the Potential Electric Vehicle Market and the Effects of Reducing Greenhouse Gas Emissions in Russia

Author

Listed:
  • Nelly S. Kolyan
  • Alexander E. Plesovskikh
  • Roman V. Gordeev

Abstract

In recent decades, the global adoption of alternative fuel vehicles, which may contribute to carbon emission reduction due to the use of alternative energy sources has stirred particular interest. Despite a significant body of scientific literature in Russia about electric vehicle adoption, the approaches used in papers lack quantitative estimates of the Russian market's potential. This paper aims to fill this gap as it provides a long-term electric vehicle market forecast in Russia as well as assesses the environmental effects. The following hypotheses are tested: (1) the Bass model is applicable to predict the long-term electric vehicle diffusion process in Russia; (2) the transition to electric cars will have a significant impact on greenhouse gas emission reduction. The Bass model, a widely used tool for predicting the innovation diffusion process, serves as a methodological base for the research. The long-term forecast of the Russian electric car fleet includes several scenarios. The most realistic scenario suggests that the Russian electric vehicle market is estimated to grow, reaching 5.62 million units by 2060. Furthermore, the environmental effects associated with electric vehicle adoption were identified. Two scenarios for changes in the energy generation structure were taken into consideration. The expected carbon emission reduction is estimated to reach 14.08 million tons in CO2-eq. if an accelerated transition to low-carbon energy sources is implemented, the baseline scenario suggests 12.86 million tons in CO2-eq. carbon emission reduction. The estimates of the transport diffusion in Russia as well as of environmental effects associated with this process form the theoretical value of the study. The practical significance of the study suggests developing electric vehicle demand forecasts that might be utilized while implementing measures to achieve goals stated in the Strategy of Social and Economic Development with a Low Level of Greenhouse Gas Emissions until 2050 in the Russian Federation.

Suggested Citation

  • Nelly S. Kolyan & Alexander E. Plesovskikh & Roman V. Gordeev, 2023. "Predictive Assessment of the Potential Electric Vehicle Market and the Effects of Reducing Greenhouse Gas Emissions in Russia," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, vol. 22(3), pages 497-521.
    • Handle: RePEc:aiy:jnjaer:v:22:y:2023:i:3:p:497-521
    DOI: https://doi.org/10.15826/vestnik.2023.22.3.021
    as

    Download full text from publisher

    File URL: https://journalaer.ru//fileadmin/user_upload/site_15934/2023/02_Koljan_Plesovskikh_Gordeev.pdf
    Download Restriction: no
    File URL: https://libkey.io/https://doi.org/10.15826/vestnik.2023.22.3.021?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
    ---><---

    References listed on IDEAS

    as
    1. Dan Horsky & Leonard S. Simon, 1983. "Advertising and the Diffusion of New Products," Marketing Science, INFORMS, vol. 2(1), pages 1-17.
    2. Frank M. Bass & Trichy V. Krishnan & Dipak C. Jain, 1994. "Why the Bass Model Fits without Decision Variables," Marketing Science, INFORMS, vol. 13(3), pages 203-223.
    3. Park, Sang Yong & Kim, Jong Wook & Lee, Duk Hee, 2011. "Development of a market penetration forecasting model for Hydrogen Fuel Cell Vehicles considering infrastructure and cost reduction effects," Energy Policy, Elsevier, vol. 39(6), pages 3307-3315, June.
    4. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    5. Stephen P. Holland & Erin T. Mansur & Nicholas Z. Muller & Andrew J. Yates, 2016. "Are There Environmental Benefits from Driving Electric Vehicles? The Importance of Local Factors," American Economic Review, American Economic Association, vol. 106(12), pages 3700-3729, December.
    6. Secinaro, Silvana & Calandra, Davide & Lanzalonga, Federico & Ferraris, Alberto, 2022. "Electric vehicles’ consumer behaviours: Mapping the field and providing a research agenda," Journal of Business Research, Elsevier, vol. 150(C), pages 399-416.
    7. Stoneman, P, 1981. "Intra-Firm Diffusion, Bayesian Learning and Profitability," Economic Journal, Royal Economic Society, vol. 91(362), pages 375-388, June.
    8. Shlomo Kalish, 1985. "A New Product Adoption Model with Price, Advertising, and Uncertainty," Management Science, INFORMS, vol. 31(12), pages 1569-1585, December.
    9. Bruce Robinson & Chet Lakhani, 1975. "Dynamic Price Models for New-Product Planning," Management Science, INFORMS, vol. 21(10), pages 1113-1122, June.
    10. Massiani, Jérôme & Gohs, Andreas, 2015. "The choice of Bass model coefficients to forecast diffusion for innovative products: An empirical investigation for new automotive technologies," Research in Transportation Economics, Elsevier, vol. 50(C), pages 17-28.
    11. Shafiei, Ehsan & Davidsdottir, Brynhildur & Stefansson, Hlynur & Asgeirsson, Eyjolfur Ingi & Fazeli, Reza & Gestsson, Marías Halldór & Leaver, Jonathan, 2019. "Simulation-based appraisal of tax-induced electro-mobility promotion in Iceland and prospects for energy-economic development," Energy Policy, Elsevier, vol. 133(C).
    12. Fabio Carlucci & Andrea Cirà & Giuseppe Lanza, 2018. "Hybrid Electric Vehicles: Some Theoretical Considerations on Consumption Behaviour," Sustainability, MDPI, vol. 10(4), pages 1-11, April.
    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. Ferreira, Kevin D. & Lee, Chi-Guhn, 2014. "An integrated two-stage diffusion of innovation model with market segmented learning," Technological Forecasting and Social Change, Elsevier, vol. 88(C), pages 189-201.
    2. Barnes, Belinda & Southwell, Darren & Bruce, Sarah & Woodhams, Felicity, 2014. "Additionality, common practice and incentive schemes for the uptake of innovations," Technological Forecasting and Social Change, Elsevier, vol. 89(C), pages 43-61.
    3. Avagyan, Vardan & Esteban-Bravo, Mercedes & Vidal-Sanz, Jose M., 2014. "Licensing radical product innovations to speed up the diffusion," European Journal of Operational Research, Elsevier, vol. 239(2), pages 542-555.
    4. Shun-Chen Niu, 2006. "A Piecewise-Diffusion Model of New-Product Demands," Operations Research, INFORMS, vol. 54(4), pages 678-695, August.
    5. Chang, Byeong-Yun & Li, Xu & Kim, Yun Bae, 2014. "Performance comparison of two diffusion models in a saturated mobile phone market," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 41-48.
    6. Saurabh Panwar & P. K. Kapur & Ompal Singh, 2020. "Modeling technology diffusion: a study based on market coverage and advertising efforts," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(2), pages 154-162, July.
    7. Ruiz-Conde, Enar & Wieringa, Jaap E. & Leeflang, Peter S.H., 2014. "Competitive diffusion of new prescription drugs: The role of pharmaceutical marketing investment," Technological Forecasting and Social Change, Elsevier, vol. 88(C), pages 49-63.
    8. Dong, Changgui & Sigrin, Benjamin & Brinkman, Gregory, 2017. "Forecasting residential solar photovoltaic deployment in California," Technological Forecasting and Social Change, Elsevier, vol. 117(C), pages 251-265.
    9. Teng, Jinn-Tsair & Thompson, Gerald L., 1996. "Optimal strategies for general price-quality decision models of new products with learning production costs," European Journal of Operational Research, Elsevier, vol. 93(3), pages 476-489, September.
    10. Orbach Yair & Fruchter Gila E., 2010. "A Utility-Based Diffusion Model Applied to the Digital Camera Case," Review of Marketing Science, De Gruyter, vol. 8(1), pages 1-28, June.
    11. Meade, Nigel & Islam, Towhidul, 2006. "Modelling and forecasting the diffusion of innovation - A 25-year review," International Journal of Forecasting, Elsevier, vol. 22(3), pages 519-545.
    12. Dragan Lazarević & Libor Švadlenka & Valentina Radojičić & Momčilo Dobrodolac, 2020. "New Express Delivery Service and Its Impact on CO 2 Emissions," Sustainability, MDPI, vol. 12(2), pages 1-29, January.
    13. Xu, Mei & Xie, Pu & Xie, Bai-Chen, 2020. "Study of China's optimal solar photovoltaic power development path to 2050," Resources Policy, Elsevier, vol. 65(C).
    14. Martin Hewing, 2012. "A Theoretical and Empirical Comparison of Innovation Diffusion Models Applying Data from the Software Industry," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 3(2), pages 125-141, June.
    15. Jinah Yang & Daiki Min & Jeenyoung Kim, 2020. "The Use of Big Data and Its Effects in a Diffusion Forecasting Model for Korean Reverse Mortgage Subscribers," Sustainability, MDPI, vol. 12(3), pages 1-17, January.
    16. Swami, Sanjeev & Dutta, Arindam, 2010. "Advertising strategies for new product diffusion in emerging markets: Propositions and analysis," European Journal of Operational Research, Elsevier, vol. 204(3), pages 648-661, August.
    17. E. J. Dockner & G. E. Fruchter, 2004. "Dynamic Strategic Pricing and Speed of Diffusion," Journal of Optimization Theory and Applications, Springer, vol. 123(2), pages 331-348, November.
    18. Samuel Sale, R. & Mesak, Hani I. & Inman, R. Anthony, 2017. "A dynamic marketing-operations interface model of new product updates," European Journal of Operational Research, Elsevier, vol. 257(1), pages 233-242.
    19. Y. Li & C.J.M. Kool & P.J. Engelen, 2016. "Hydrogen-Fuel Infrastructure Investment with Endogenous Demand: A Real Options Approach," Working Papers 16-12, Utrecht School of Economics.
    20. Olivier Toubia & Jacob Goldenberg & Rosanna Garcia, 2014. "Improving Penetration Forecasts Using Social Interactions Data," Management Science, INFORMS, vol. 60(12), pages 3049-3066, December.

    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:aiy:jnjaer:v:22:y:2023:i:3:p:497-521. 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: Natalia Starodubets (email available below). General contact details of provider: https://edirc.repec.org/data/seurfru.html .

    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.