Application of Adjoint Sensitivity Analysis for Performance Enhancement of Power Plants’ Nitrogen Oxides Control Policies
Bibliographic record
Abstract
Significant investments in nitrogen oxide (NO x ) emission controls in the U.S.have led to a substantial reduction in emissions.However, it is unclear whether these programs have optimally reduced ozone concentrations and their corresponding health damages.Current cap-and-trade program allocates emission quotas to participants and allows the trade of quotas on a one-to-one basis.However, it does not account for spatial and temporal differences in health damage of NO x emissions.This shortcoming in the current U.S. NO x control policy is explored in this research.Spatial and temporal differences in NO x emissions can be included in policy design if emission quotas are valued differently (exchange rate policy) or if polluters pay time-or location-specific emission fees (taxation policy).The main objective of this work is to develop a decision support system model for evaluating different policies.The proposed model includes an optimization platform to predict the polluters' behavior, and an air quality model and its adjoint (or backward) sensitivity model to calculate the derivatives of the environmental or health damage function with respect to NO x emissions used for emission differentiations.The results from a case study of U.S. power plants show that exchange rate trading outperforms current indiscriminate trading policies.These findings imply that by implementing exchange rate trading or taxation policies, current improvements in air quality could have been achieved at lower costs, or alternatively, more substantial improvements could have been reached at little to no additional costs.Furthermore, the results indicate that setting the emission fees on an hourly basis leads to a outcome ii comparable to setting fees based on location.Moreover, the per ton health benefit of NO x emission reductions is found to increase as emissions are reduced.This finding is particularly important from an environmental policy perspective as it impacts the optimal NO x emission reduction target.Our results also indicate that power plants in the restructured electricity market are willing to pay more for emission quotas.Uncertainties involved in the proposed model, challenges for implementation of the proposed policies, and inclusion of health impacts caused by exposure to particulate matter are main directions for future research.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.002 |
| 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".