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.

Strategic Planning of Regional Energy System Based on Life Cycle Assessment Methodology

“…If the goal of environmental policy is to reduce greenhouse gas emissions (for example, in the framework of fulfilling obligations under international climate agreements), then it makes sense to equip wind parks with energy storage systems, of which ZEBRA batteries and lithium-ion batteries are the best alternatives for most other impact environmental categories rather than nickel metal hydride batteries. If the goal of regional environmental policy is to reduce eco-toxicity (which is consistent with the real environmental situation in the Krasnodar Region as can be seen from the publications of Russian scientists (Ratner and Zaretskaya, 2018), then the refusal to equip wind farms with energy storage systems is a highly reasonable option for organizing a regional energy system.…”

Barriers of Consumer Behavior for the Development of the Circular Economy: Empirical Evidence from Russia

“…Since some age categories of respondents were less represented in the sample, we used nonparametric statistical methods for processing research results that allow working with small samples [47,48]. Calculation of nonparametric correlation coefficients Spearman (R) and tau-Kendall (K) revealed the presence of some statistically significant relationships between the age of the respondent and his attitude to various practices of proecological behavior (Table 3).…”

The Linear Programming Problem of Regional Energy System Optimization

“…This region was chosen for testing for several reasons: firstly, the region has significant potential for the development of various types of renewable energy sources, including solar and wind energy, hydro-and geothermal energy, rich biological resources, as well as a large number of biowaste that can be considered as resources for bioenergy . Secondly, the Krasnodar Territory is currently one of the most densely populated and dynamically developing regions of Russia, experiencing a serious energy shortage and problems with air quality in large cities (Ratner and Zaretskaya, 2018). Therefore, solving the problem of optimizing the energy balance of a given region is an urgent practical task.…”