Diminishing Returns or Compounding Benefits of Air Pollution Control? The Case of NO<sub><i>x</i></sub> and Ozone
Bibliographic record
Abstract
UNLABELLED: A common measure used in air quality benefit-cost assessment is marginal benefit (MB), or the monetized societal benefit of reducing 1 ton of emissions. Traditional depictions of MB for criteria air pollutants are such that each additional ton of emission reduction incurs less benefit than the previous ton. Using adjoint sensitivity analysis in a state-of-the-art air quality model, we estimate MBs for NOx emitted from mobile and point sources, characterized based on the estimated ozone-related premature mortality in the U.S. POPULATION: Our findings indicate that nation-wide emission reductions in the U.S. significantly increase NOx MBs for all sources, without exception. We estimate that MBs for NOx emitted from mobile sources increase by 1.5 and 2.5 times, on average, for 40% and 80% reductions in anthropogenic emissions across the U.S. Our results indicate a strictly concave damage function and compounding benefits of progressively lower levels of NOx emissions, providing economic incentive for higher levels of abatement than were previously advisible. These findings suggest that the traditional perception of a convex damage function and decreasing MB with abatement may not hold true for secondary pollutants such as O3.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".