Benefits of Decreased Mortality Risk from Reductions in Primary Mobile Source Fine Particulate Matter: A Limited Data Approach for Urban Areas Worldwide
Bibliographic record
Abstract
We developed an approach to estimate the public health benefits resulting from transportation projects or environmental actions that reduce mobile source fine particulate matter (PM2.5 ) in select urban areas worldwide when input data are limited or when a rapid order-of-magnitude assessment is needed. For a given reduction in direct PM2.5 emissions, we can use this approach to quantify (1) the subsequent reduction in ambient primary PM2.5 concentration in the urban area; (2) the public health benefits associated with mortality risk reductions, measured in terms of avoided premature deaths; and (3) the economic value of the reduced mortality risk. To illustrate our approach, we estimated the impact of a 100-metric-ton reduction in primary PM2.5 mobile source emissions in the year 2010 for 42 large, global cities. Our estimates of public health benefits and their economic value varied by city, as did the sensitivity to key assumptions and inputs. The estimated number of premature deaths avoided per 100-metric-ton reduction in PM2.5 emissions ranged from 12 to 202. City-level variability in these estimates was driven by the magnitude of the reduction in ambient PM2.5 concentration, the size of the urban population, and the baseline PM2.5 concentration. The economic value of mortality risk reductions per 100-metric-ton reduction in PM2.5 emissions ranged from $2 million to $328 million in 2010 U.S. dollars. Income per capita was the most important driver of the variability in the economic values across countries.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".