Driving uneven development: The emerging geography of India's electric vehicle transition
Bibliographic record
Abstract
One of India's most important decarbonization strategies involves transitioning its automobile industry from combustion engine to electric vehicles. In this paper, we examine the emerging geography of India's nascent EV sector toward understanding how policy and technological changes surrounding the energy transition are intersecting with regional development pathways, and with what implications for uneven development. We utilize sectoral and workforce data on firms, employment and skills; qualitative interviews with industry experts; and a policy analysis of state-level industrial strategies to attract and grow the EV sector. Our findings indicate that India's ICE-to-EV transition has the potential to amplify regional disparities in India's economic development patterns. The mechanism underlying this effect is the skill-biased technological change inherent in the EV transition, which benefits regions such as southern India, where high-skilled workers and information technology firms are clustered. If India's EV industry continues to concentrate in its most prosperous and innovative regions, this may accelerate advancements in low-carbon technologies, but it will sharpen the country's patterns of uneven development. The paper calls for discourses on the “spatially just” transition to look beyond the energy and resources sector itself, examining the wide spatial-economic reverberations of decarbonization and consequences for spatial inequality.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, 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".