Forecasting the Ecology Effects of Electric Cars Deployment in Krasnodar Region Learning Curves Approach

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