Forecasting the Ecology Effects of Electric Cars Deployment in Krasnodar Region: Learning Curves Approach
Abstract
One of the most urgent problems of modern urban agglomerations is the optimization of the structure and technological maintenance of transport systems. As one of the options to solve this problem, the development of electric vehicles (EV) is usually suggested. But the scientific community has still not developed a clear understanding of whether electric vehicles are a better alternative to traditional cars, considering all environmental indicators. The aim of this work is to develop a method of forecasting the environmental effects of diffusion of EV technologies and test it on the example of the Krasnodar region of Russia as a region with the highest motorization ratios in the country, a complicated ecologic situation in large cities, a high population density and a modern structure for energy generation. The technical progress in energy efficiency of each technology is taken into consideration. We use learning theory as a methodological framework, which is common for solution of problems of forecasting technological development. According to the calculations, the total emissions from private motor vehicles, with an increase in energy efficiency of vehicles with internal combustion engine and increase penetration of electric vehicles should decrease in 2025 by 15% comparing business-as-usual scenario, despite a significant increase in the level of motorization (almost 65%). Thus, a wide spread of EV technologies is preferable from an environmental point of view. The proposed approach to predict the environmental effects of diffusion of EV technologies allows us to estimate the reduction in emissions from road transport in any region while maintaining the direction and speed of the following key trends: the growth of energy efficiency and environmental performance of traditional cars with combustion engines, the growth of the level of motorization of the population in Russia, and reduction of EVs costs. Additional effects of stimulating (or de-stimulating) policies are not considered in this model.
References
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[2] Bergman, N., Schwanen, T., Sovacool, B.K. 2017. Imagined people, behaviour and future mobility: Insights from visions of electric vehicles and car clubs in the United Kingdom. Transport Policy 59:165-173. DOI: https://doi.org/10.1016/j.tranpol.2017.07.016
[3] Carlsen, L., Bruggemann, R., Kenessov, B. 2018. Use of partial order in environmental pollution studies demonstrated by urban BTEX air pollution in 20 major cities worldwide. Science of The Total Environment, 610-611: 234-243. DOI: https://doi.org/10.1016/j.scitotenv.2017.08.029
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[5] Davidsson, S., Grandell, L., Wachtmeister, H., Höök, M. 2014. Growth curves and sustained commissioning modelling of renewable energy: Investigating resource constraints for wind energy. Energy Policy, 73: 767-776. DOI: http://dx.doi.org/10.1016/j.enpol.2014.05.003
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[9] Hu, K., Chen, Y. 2016 Technological growth of fuel efficiency in European automobile market 1975–2015. Energy Policy, 98:142-148. DOI: http://dx.doi.org/10.1016/j.enpol.2016.08.024
[10] Jamasb, T., 2007. Technical change theory and learning curves: Patterns of progress in electricity generation technologies. Energy Journal, 28:51–72. DOI: https://doi.org/10.5547/ISSN0195-6574-EJ-Vol28-No3-4
[11] Janic, M. 2017 An assessment of the potential of alternative fuels for “greening” commercial air transportation. Journal of Air Transport Management. DOI: https://doi.org/10.1016/j.jairtraman.2017.09.002
[12] Junginger, M., Faaij, A., Turkenburg, W.C. 2005. Global experience curves for wind farms. Energy Policy 33: 133–150. DOI: https://doi.org/10.1016/S0301-4215(03)00205-2
[13] Kholod, N., Evans, M. 2016 Reducing black carbon emissions from diesel vehicles in Russia: An assessment and policy recommendations. Environmental Science & Policy, 56: 1-8. DOI: https://doi.org/10.1016/j.envsci.2015.10.017
[14] Kouvaritakis, N., Soria, A., Isoard, S., 2000. Modeling energy technology dynamics: methodology for adaptive expectations models with learning by doing and learning by searching. International Journal of Global Energy Issues 14: 104–115. DOI: https://doi.org/10.1504/IJGEI.2000.004384
[15] Li, S., Zhang, X., Gao, L., Jin, H., 2012. Learning rates and future cost curves for fossil fuel energy systems with CO2 capture: methodology and case studies. Applied Energy 93: 348–356. DOI: https://doi.org/10.1016/j.apenergy.2011.12.046
[16] Lindman, Å., Söderholm, P., 2012. Wind power learning rates: a conceptual review and meta-analysis. Energy Economics 34: 754–761. DOI: https://doi.org/10.1016/j.eneco.2011.05.007
[17] Lozhkina, O., Lozhkin, V. 2015. Estimation of road transport related air pollution in Saint Petersburg using European and Russian calculation models. Transportation Research Part D: Transport and Environment 36: 178-189. DOI: https://doi.org/10.1016/j.trd.2015.02.013
[18] MacKenzie, D., Heywood, J. 2015. Quantifying efficiency technology improvements in U.S. cars from 1975–2009. Applied Energy, 157: 918-928. DOI: https://doi.org/10.1016/j.apenergy.2014.12.083
[19] Manne, A., Richels, R., 2004. The impact of learning-by-doing on the timing and costs of CO2 abatement. Energy Economics, 26: 603–619. DOI: https://doi.org/10.1016/j.eneco.2004.04.033
[20] McDonald, A., Schrattenholzer, L. 2001. Learning rates for energy technologies. Energy Policy, 29: 255-261. DOI: https://doi.org/10.1016/S0301-4215(00)00122-1
[21] Nizhegorodtsev, R.M., Ratner, S.V. 2016. Trends in the development of industrially assimilated renewable energy: the problem of resource restrictions. Thermal Engineering 63(3): 197-207. DOI: https://doi.org/10.1134/S0040601516030083
[22] Notter, D., Gauch, M., Widmer, R., Wäger, P., Stamp, A., Zah, R., Althaus, H.J. 2010. Contribution of Li Ion Batteries to the Environmental Impact of Electric Vehicles. Environmental Science & Technology, 44 (17): 6550-6556. DOI: https://doi.org/10.1021/es903729a
[23] Offer, G.J., Howey, D., Contestabile, M., Clague, R., Brandon, N.P. 2010 Comparative analysis of battery electric, hydrogen fuel cell and hybrid vehicles in a future sustainable road transport system. Energy Policy, 38: 24-29. DOI: https://doi.org/10.1016/j.enpol.2009.08.040
[24] Ratner, S., Iosifov, V. 2017. Eco-management and eco-standardization in Russia: The perspectives and barriers for development. Journal of Environmental Management and Tourism, Volume VIII, Spring, 1(17): 247-258. DOI: https://doi.org/10.14505/jemt.v8.1(17).23
[25] Ratner, S.V., Dicolov, E.А. 2016. Models of city environment quality. Proc. Int. Sci. Conf. Control for Innovation-2016 Moscow, IPU RAN. 120-130 pp. (in Russ)
[26] Ratner, S.V., Klochkov, V.V. 2017. Scenario Forecast for Wind Turbine Manufacturing in Russia. International Journal of Energy Economics and Policy, 7(2): 144-151
[27] Rubin, E.S., Azevedo, I.M.L., Jaramillo, P., Yeh, S. 2015. A review of learning rates for electricity supply technologies. Energy Policy, 86: 198-218. DOI: https://doi.org/10.1016/j.enpol.2015.06.011
[28] Saisirirat, P., Chollacoop, N. 2017. A Scenario Analysis of Road Transport Sector: The Impact of Recent Energy Efficicency Policies. Energy Procedia 138: 1004-1010. DOI: https://doi.org/10.1016/j.egypro.2017.10.115
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[30] Siskos, P., Capros, P., De Vita, A. 2015. CO2 and energy efficiency car standards in the EU in the context of decarbonization strategy: A model-based policy assesment. Energy Policy 84: 22-34. DOI: https://doi.org/10.1016/j.enpol.2015.04.024
[31] Sun, C., Yuan, L., Jianglong, L. 2018. Urban traffic infrastructure investment and air pollution: Evidence from the 83 cities in China. Journal of Cleaner Production 172: 488-496. DOI: https://doi.org/10.1016/j.jclepro.2017.10.194
[32] Tarasenko, A.B., Popel’, O.S. 2015. Manufacturing technologies for photovoltaics and possible means of their development in Russia (Review). Part 1: General approach to the development of photoelectric converters and basic silicon technologies. Thermal Engineering, 62(11): 825-832. DOI: https://doi.org/10.1134/S0040601515110087
[33] Thomas, C.E. 2009. Fuel cell and battery electric vehicles compared. International Journal of hydrogen energy, 34: 6005 – 6020. DOI: https://doi.org/10.1016/j.ijhydene.2009.06.003
[34] Triantafyllopoulos, G., Kontses, A., Tsokolis, D., Ntziachristos, L., Samaras, Z. 2017. Potential of energy efficiency technologies in reducing vehicle consumption under type approval and real world conditions. Energy 140: 365-373. DOI: https://doi.org/10.1016/j.energy.2017.09.023
[35] Turconi, R., Boldrin, A., Astrup, T. 2013. Life cycle assessment (LCA) of electricity generation technologies: Overview, comparability and limitations. Renewable and Sustainable Energy Reviews, 28: 555–565. DOI: https://doi.org/10.1016/j.rser.2013.08.013
[36] Williams, E., Hittinger, E., Carvalho, R., Williams, R. 2017. Wind power costs expected to decrease due to technological progress. Energy Policy, 106: 427-435. DOI: https://doi.org/10.1016/j.enpol.2017.03.032
[37] Winyuchakrit, P., Yod Sukamongkol, Y., Limmeechokchai, B. 2017. Do Electric Vehicles Really Reduce GHG Emissions in Thailand? Energy Procedia 138: 348-353. DOI: https://doi.org/10.1016/j.egypro.2017.10.137
[38] Wright, T.P. 1936. Factors affecting the cost of airplanes. Journal of the Aeronautical Sciences, 3 (4): 122–128, Available at: http://www.uvm.edu/pdodds/research/papers/others/1936/wright1936a.pdf
[39] Yeh, S., Rubin, E. 2012. A review of uncertainties in technology experience curves. Energy Economics, 34: 762–771. DOI: https://doi.org/10.1016/j.eneco.2011.11.006
[40] Zhuk, A.Z., Buzoverov, Е.А., Sheydlin, А.Е. 2015. Distributed Energy Storage Systems on the Basis of ElectricVehicle Fleets. Thermal Engineering, 62(1): 1-6. DOI: https://doi.org/10.1134/S0040601515010127
***EPA, 2018. Light-Duty Automotive Technology, Carbon Dioxide Emissions, and Fuel Economy Trends: 1975 Through 2017. Available at: https://www.epa.gov/sites/production/files/2018-01/documents/420r18001.pdf
***IEA. 2016. Energy Technology Perspectives. International Energy Agency, Paris, France.
***IEA. 2017. Global EV Outlook 2017. Two million and counting. International Energy Agency, Paris, France. 2017. Available at: https://www.iea.org/publications/freepublications/publication/GlobalEVOutlook2017.pdf
***IEA. 2000. Experience curves for energy technology policy. International Energy Agency, Paris, France.
***METI. 2016. Compilation of the road map for EVs and PHVs toward the dissemination of electric vehicles and plug-in hybrid vehicles. Ministry of Economy, Trade and Industry, Japan.
***Ministry of Natural Recourses of Krasnodar Region. 2016. Annual Report on the state of natural resources and environmental protection of the Krasnodar Territory in 2016. Available at: http://mprkk.ru/media/main/attachment/attach/6__doklad_ob_oos_kk_v_2016.pdf
***OECD. 2015. Domestic incentive measures for environmental goods with possible trade implications: Electric vehicles and batteries. Organisation for Economic Co-operation and Development.
***OGK-2. 2016. OGK-2 Annual Report. 2016. Available at http://www.ogk2.ru/upload/iblock/051/051da51bd1c9587af3ce06d1fab07ca7.pdf (in Russ) (accessed 15.12.2017)
***UNFCCC. 2015. Paris Declaration on Electro-Mobility and Climate Change and Call to Action. UNFCCC, Paris, France.
Published
2018-06-22
How to Cite
RATNER, Svetlana; ZARETSKAYA, Marina.
Forecasting the Ecology Effects of Electric Cars Deployment in Krasnodar Region: Learning Curves Approach.
Journal of Environmental Management and Tourism, [S.l.], v. 9, n. 1, p. 82-94, june 2018.
ISSN 2068-7729.
Available at: <https://journals.aserspublishing.eu/jemt/article/view/2067>. Date accessed: 20 apr. 2024.
doi: https://doi.org/10.14505//jemt.v9.1(25).11.
Section
Article
Copyright© 2023 The Author(s). Published by ASERS Publishing 2023. This is an open access article distributed under the terms of CC-BY 4.0 license.