Abstract
Electric vehicles (EVs) offer major sustainability benefits through reductions in greenhouse gas emissions and noise pollution. This study presents a bibliometric analysis of the literature on EV adoption and environmental sustainability from 2014 to 2024. The aim is to uncover publication trends, influential countries, research themes, gaps, and future directions. A systematic search of Scopus yielded 121 documents for analysis using VOSviewer. Results reveal an upward trajectory in publications, signaling intensifying research attention, especially post-2019. The US and China dominate research outputs. However, developing countries are underrepresented, indicating a contextual knowledge gap. Analysis of keywords and citations shows emphasis on environmental impacts, consumer attitudes, charging infrastructure, incentives, costs, and policy. Key themes represent research priorities for scholars and stakeholders. Multi-country collaborations and studies grounded in developing world contexts can enrich the discourse. Interdisciplinary perspectives embracing technical, social, economic and policy dimensions are valuable. The study provides a foundation to advance research and evidence-based policies for accelerated EV adoption globally through targeted strategies addressing region-specific barriers.
| Original language | English |
|---|---|
| Title of host publication | Studies in Computational Intelligence |
| Publisher | Springer Science and Business Media Deutschland GmbH |
| Pages | 43-58 |
| Number of pages | 16 |
| DOIs | |
| State | Published - 2024 |
| Externally published | Yes |
Publication series
| Name | Studies in Computational Intelligence |
|---|---|
| Volume | 1161 |
| ISSN (Print) | 1860-949X |
| ISSN (Electronic) | 1860-9503 |
Bibliographical note
Publisher Copyright:© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
Keywords
- Adoption
- Bibliometric analysis
- Developing countries
- Electric vehicle
- Environmental sustainability
- Trends
ASJC Scopus subject areas
- Artificial Intelligence