Health damages from air pollution in China
Bibliographic record
Abstract
This study evaluates air pollution-related health impacts on the Chinese economy by using an expanded version of the Emissions Prediction and Policy Analysis model. We estimated that marginal welfare impact to the Chinese economy of ozone and particulate-matter concentrations above background levels increased from 1997 US$22 billion in 1975 to 1997 US$112 billion in 2005, despite improvements in overall air quality. This increase is a result of the growing urban population and rising wages that thus increased the value of lost labor and leisure. In relative terms, however, welfare losses from air pollution decreased from 14% of the historical welfare level to 5% during the same period because the total size of the economy grew much faster than the absolute air pollution damages. In addition, we estimated that particulate-matter pollution alone led to a gross domestic product loss of 1997 US$64 billion in 1995. Given that the World Bank's comparable estimate drawn from a static approach was only 1997 US$34 billion, this result suggests that conventional static methods neglecting the cumulative impact of pollution-caused welfare damage are likely to underestimate pollution-health costs substantially. However, our analysis of uncertainty involved in exposure-response functions suggests that our central estimates are susceptible to significantly large error bars of around ±80%. © 2011 Elsevier Ltd.
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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 | 0.001 |
| 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) | 0.001 | 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".