Exports Widen the Regional Inequality of Health Burdens and Economic Benefits in India
Bibliographic record
Abstract
Both the ever-complex international and subnational supply chains could relocate health burdens and economic benefits across India, leading to the widening of regional inequality. Here, we simultaneously track the unequal distribution of fine particle matter (PM 2.5 ) pollution, health costs, and value-added embodied in inter- and intranational exports for Indian states in 2015 by integrating a nested multiregional input-output (MRIO) table constructed based on EXIOBASE and an Indian regional MRIO table, Emissions Database for Global Atmospheric Research (EDGAR), the Community Multi-Scale Air Quality (CMAQ) model, and a concentration-response function. The results showed that the annual premature deaths associated with PM 2.5 pollution embodied in inter- and intranational exports were 757,356 and 388,003 throughout India, accounting for 39% and 20% of the total premature deaths caused by PM 2.5 pollution, respectively. Richer south and west coastal states received around half of the national Gross Domestic Product (GDP) induced by exports with a quarter of the health burden, while poorer central and east states bear approximately 60% of the health burden with less than a quarter of national GDP. Our findings highlight the role of exports in driving the regional inequality of health burdens and economic benefits. Therefore, tailored strategies (e.g., air pollution compensation, advanced technology transfer, and export structure optimization) could be formulated.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".