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

Author

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. Stoneman, P, 1981. "Intra-Firm Diffusion, Bayesian Learning and Profitability," Economic Journal, Royal Economic Society, vol. 91(362), pages 375-388, June.
    3. Dan Horsky & Leonard S. Simon, 1983. "Advertising and the Diffusion of New Products," Marketing Science, INFORMS, vol. 2(1), pages 1-17.
    4. Shlomo Kalish, 1985. "A New Product Adoption Model with Price, Advertising, and Uncertainty," Management Science, INFORMS, vol. 31(12), pages 1569-1585, December.
    5. Bruce Robinson & Chet Lakhani, 1975. "Dynamic Price Models for New-Product Planning," Management Science, INFORMS, vol. 21(10), pages 1113-1122, June.
    6. 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.
    7. 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.
    8. 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.
    9. 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).
    10. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    11. 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.
    12. 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.
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