Assessing the health benefits of urban air pollution reductions associated with climate change mitigation (2000-2020): Santiago, São Paulo, México City, and New York City.
Bibliographic record
Abstract
To investigate the potential local health benefits of adopting greenhouse gas (GHG) mitigation policies, we develop scenarios of GHG mitigation for México City, México; Santiago, Chile; São Paulo, Brazil; and New York, New York, USA using air pollution health impact factors appropriate to each city. We estimate that the adoption of readily available technologies to lessen fossil fuel emissions over the next two decades in these four cities alone will reduce particulate matter and ozone and avoid approximately 64,000 (95% confidence interval [CI] 18,000-116,000) premature deaths (including infant deaths), 65,000 (95% CI 22,000-108,000) chronic bronchitis cases, and 46 million (95% CI 35-58 million) person-days of work loss or other restricted activity. These findings illustrate that GHG mitigation can provide considerable local air pollution-related public health benefits to countries that choose to abate GHG emissions by reducing fossil fuel combustion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".