Environmental resource management

Electrical engineering

Business

Energy security

Government (linguistics)

Sustainable development

Biology

Risk analysis (engineering)

Engineering

Sustainable transport

Environmental economics

Philosophy

Economics

Renewable energy

Ecology

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Sustainability

Decision Analytics Journal

The rapid growth of the global economy and advanced technology has modernized our life, but it has taken its toll on ecology and environmental sustainability. Over the last few decades, the world has experienced increased emissions of toxic gases, severe global warming, and ambient air pollution. Mass dependency on internal combustion engine (ICE) vehicles is rightfully criticized for increasing air pollution, jeopardizing societal health outcomes, and perpetuating the use of fossil fuels-all of which threaten sustainable development. Amid this situation, to ensure economic and social growth as well as help to achieve sustainable development goals (SDGs) by improving energy security, electric vehicles (EVs) are the green alternatives to conventional high-emission vehicles. To successfully promote EV adoption and diffusion in emerging economies, the key drivers that may foster the adoption process must be identified and evaluated. However, there exists a conspicuous literature gap in this domain. This study presents an intelligent multi-criteria decision-making (MCDM) approach, integrating Principal Component Analysis (PCA), Interpretive Structural Modeling (ISM), and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) to identify, examine, and classify the drivers of successful adoption of EVs in the emerging economies. Based on the literature review and expert feedback, a total of seventeen drivers were identified and later prioritized by using PCA. The top twelve drivers have been further analyzed using ISM-MICMAC to examine their interrelationships. The findings reveal that vehicle performance and reliability, adequate power and charging infrastructure, and government policies are the most influential factors for EV adoption. Finally, this study offers several managerial implications and prospects, which may aid governments and the automotive sectors in taking strategic measures to capture this booming market and pursue more EV customers for a sustainable future.

An integrated Principal Component Analysis and Interpretive Structural Modeling approach for electric vehicle adoption decisions in sustainable transportation systems