Nox Control for Reducing Ozone Health Effects in a Changing World
Bibliographic record
Abstract
Benefit-cost assessments are key in reaching economically efficient emission reduction targets for reducing health and environmental effects associated with ozone exposure. Current benefit-cost analyses of NOx emission reductions are conducted for a static point in time with limited consideration for the dynamic context within which emission reductions take place. We use reverse emission influence modeling and explore the forces of technological change, changes in emission patterns, and a warming climate on the long-term efficacy of NOx control. In the face of continued advancement of emission control technologies, the cost-per-ton of NOx emission reduction becomes less over time. Second, in the traditional environmental economics literature, the monetary benefit associated with 1 ton of emission reduction of criteria pollutants often diminishes as emission controls become more progressive, and thus high levels of emission reduction are not economically justifiable. Our work suggests the contrary for NOx emission reductions: ozone-based benefits-per-ton increase invariably at progressively lower emissions (e.g., from - $13,000/ton to $11,500/ton after eliminating mobile emissions in Los Angeles for the year 2007). Finally, past work has demonstrated that population exposures to urban ozone are particularly sensitive to a warming climate. We show, using reverse chemical transport modeling, that a uniform 1 degree rise in temperature across the U.S. and Canada results in approximately 370 premature ozone-related mortalities over a summer season; an impact comparable to a 5% change in anthropogenic NOx emissions. We show that urban NOx control may be regarded as a climate change adaptation strategy by reducing the vulnerability of ozone-related mortality to a warming climate. Our discussion draws upon recent studies of ours and others to suggest that the socioeconomic benefits of emission reductions may extend beyond what is currently envisioned.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".