Optimal Ozone Control with Inclusion of Spatiotemporal Marginal Damages and Electricity Demand
Bibliographic record
Abstract
Marginal damage (MD), or damage per ton of emission, is a policy metric used for effective pollution control and reducing the corresponding adverse health impacts. However, for a pollutant such as NOx, the MD varies by the time and location of the emissions, a complication that is not adequately accounted for in the currently implemented economic instruments. Policies accounting for MD information would aim to encourage emitters with large MDs to reduce their emissions. An optimization framework is implemented to account for NOx spatiotemporal MDs calculated through adjoint sensitivity analysis and to simulate power plants' behavior under emission and simplified electricity constraints. The results from a case study of U.S. power plants indicate that time-specific MDs are high around noon and low in the evening. Furthermore, an emissions reduction of about 40% and a net benefit of about $1200 million can be gained for this subset of power plants if a larger fraction of the electricity demand is supplied by power plants at low-damage times and in low-damage locations. The results also indicate that the consideration of temporal effects in NOx control policies results in a comparable net benefit to the consideration of spatial or spatiotemporal effects, thus providing a promising option for policy development.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".