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Evaluating the Factors Affecting Electric Vehicles Adoption Considering the Sustainable Development Level

doi: 10.3390/wevj14050120
Electric vehicles are an important part of governments’ environmental policies, and therefore understanding the factors affecting their market share is very important. So, this research is designed to investigate the factors affecting electric vehicle adoption, considering the effects of the COVID-19 pandemic and sustainable development level. Effective factors have been investigated in three categories. One is the characteristics of electric vehicles; the other is the impact of the COVID-19 pandemic on demand for these vehicles; and finally, the impact of the level of sustainable development of countries on adopting electric vehicles. Our analysis method is based on grey econometric and grey regression methods. The results show that vehicle dimensions, battery warranty conditions, battery life, and charging facilities are effective factors in the field of vehicle characteristics that can increase the adoption of electric vehicles. Also, the analysis shows that the COVID-19 pandemic has reduced the adoption of electric vehicles. Finally, we have shown that the market share of electric vehicles is higher in countries with a higher sustainable development level because of better economic, social, and cultural infrastructures.
- Nanjing University of Aeronautics and Astronautics China (People's Republic of)
- Nanjing University of Aeronautics and Astronautics China (People's Republic of)
TA1001-1280, COVID-19, SDG, sustainable development goals, grey econometrics, TK1-9971, Transportation engineering, electric vehicles; sustainable development goals; grey econometrics; regression; SDG; COVID-19, regression, Electrical engineering. Electronics. Nuclear engineering, electric vehicles
TA1001-1280, COVID-19, SDG, sustainable development goals, grey econometrics, TK1-9971, Transportation engineering, electric vehicles; sustainable development goals; grey econometrics; regression; SDG; COVID-19, regression, Electrical engineering. Electronics. Nuclear engineering, electric vehicles
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