The economic and environmental consequences of the electric vehicle transition in India
Bibliographic record
Abstract
India is pursuing a 'twin-transition trajectory' with the expansion of RE sources in the power sector and the expansion of Electric Vehicles (EVs) in the transport sector. This strategy is in line with SDG 2030 goals 7, 11 and 13. This study intends to analyse the macroeconomic impact of EV battery manufacturing and the energy and environmental impact of EV battery charging with coal and solar power for the years 2022 and 2030. A net-positive economy-wide impact across Output, GDP and Employment of 0.5 %, 0.08 % and 0.34 %, respectively is observed with minerals such as copper, manganese and bauxite benefitting the most. Through the deployment of 16 million EVs, health cost savings from reduced on-road emissions is expected to be $2.1 million per vehicle substituted with a cumulative reduction in imports of approximately 30 million barrels of crude oil. Solar-powered EV battery charging vehicle lead to 12 % to 25 % reduction in emissions in different scenarios. It is pivotal to ensure the synergy between power and transport sectors for constructing a decarbonization roadmap with the long-run target of achieving net-zero emissions by 2070.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".