Revealing the Hidden Health Costs Embodied in Chinese Exports
Bibliographic record
Abstract
China emits a considerable amount of air pollutants when producing goods for export. Previous efforts have emphasized the magnitude of export-related emissions; however, their health consequences on the Chinese population have not been quantified. Here, we present an interdisciplinary study to estimate the health impact of export-related air pollution. The results show that export-related emissions elevated the annual mean population weighted PM2.5 by 8.3 μg/m(3) (15% of the total) in 2007, causing 157,000 deaths and accounting for 12% of the total mortality attributable to PM2.5-related air pollution. Compared to the eastern coastal provinces, the inner regions experience much larger export-related health losses relative to their economic production gains, owing to huge inter-regional disparities in export structures and technology levels. A shift away from emission-intensive production structure and export patterns, especially in inner regions, could significantly help improve national exports while alleviating the inter-regional cost-benefit inequality. Our results provide the first quantification of health consequences from air pollution related to Chinese exports. The proposed policy recommendations, based on health burden, economic production gains, and emission analysis, would be helpful to develop more sustainable and effective national and regional export strategies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".