This study conducts a systematic literature review on electric vehicle (EV) adoption, mapping critical themes and presenting an integrated framework to advance understanding of EVs' role in sustainable transportation. Drawing on 917 Scopus-indexed articles and 23 stakeholder interviews, it explores economic, environmental, energy, and social (EEES) dynamics through the Theory-Context-Methodology (TCM) framework and causal loop diagrams. Findings reveal dominant theories, such as the Theory of Planned Behavior and Value-Belief Norm Theory, and underscore the importance of methodological approaches like regression analysis and structural equation modeling. Key research themes include adoption intention, green infrastructure, and sustainable transport policy, all aligning with Sustainable Development Goals (SDGs) 7 (Clean Energy), 11 (Sustainable Cities), and 13 (Climate Action). By constructing a causal loop diagram, this study illustrates complex interrelations among EEES factors, highlighting the reinforcing and balancing influences on EV adoption behavior. The proposed integrated framework provides a roadmap for future research, identifying significant gaps and strategic directions for policymakers, industry stakeholders, and researchers. Practical implications include guidelines to foster EV integration into smart cities, addressing infrastructure and environmental challenges while encouraging policy incentives to enhance public adoption. The study's theoretical implications include the expansion of existing behavioral theories, the development of an integrated theoretical framework that combines TCM and CLDs, the introduction of CLDs as a tool for complex systems analysis, and the identification of thematic clusters within EV adoption research. This review supports informed decision-making for sustainable urban mobility, positioning EVs as transformative in the global shift toward eco-friendly transportation solutions.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.jenvman.2024.123415 | DOI Listing |
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!