Shaping India’s EV future: a policy framework inspired by global best practices
Bibliographic record
Abstract
This study examines global electric vehicle (EV) policy approaches—driven by cost reduction, emission control, and energy security—to derive actionable insights for India’s EV transition. Through a comparative analysis of EV strategies in countries like China, Germany, the United States, Norway, Japan, Canada, and the Netherlands, this paper highlights how national priorities shape policy effectiveness. While India shows EV market potential with a projected 49% CAGR by 2030, it faces significant challenges, including policy gaps, limited charging infrastructure, and weak incentives. Drawing lessons from global leaders—such as Japan’s infrastructure-first approach and the Netherlands’ awareness campaigns—this study proposes a tailored roadmap for India. Recommendations include refining subsidies, enabling regulatory reforms, and expanding charging networks. The findings support India’s goal of 30% EV penetration by 2030, aligning with its broader climate commitments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".