Bibliometric Analysis of Electric Vehicle Adoption Research: Trends, Implications, and Future Directions
Bibliographic record
Abstract
The study aims to comprehensively understand the research landscape surrounding electric vehicle (EV) adoption.Through bibliometric analysis, the research explores critical questions related to EV adoption trends, leading countries and universities, subject areas of interest, and potential research gaps.The study utilizes data from the Scopus database, covering the period from 2017 to 2023, resulting in 181 publications.The findings reveal a positive trend in EV adoption research, reflecting the growing interest in sustainable transportation solutions.The dominance of the United States and China in EV adoption research highlights their proactive approach towards addressing climate change and advancing EV adoption.Meanwhile, Indonesia's limited representation indicates research gaps within the country's unique context.The research highlights the significance of environmental impact and charging infrastructure in promoting sustainable EV integration.The implications suggest opportunities for further investigation into the environmental benefits of EV adoption and technological advancements.Future research directions include conducting in-depth studies in Indonesia, investigating the economic and financial implications of EV adoption, and assessing the long-term impact of EV adoption on transportation and the environment.Collaborative interdisciplinary research is crucial for developing effective strategies for accelerating EV adoption and achieving sustainable transportation systems globally.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.045 | 0.056 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".