Bayesian Analysis of Air Emission Violations from Waste Incineration and Coincineration Plants
Bibliographic record
Abstract
Waste incineration and coincineration plants in most European countries have frequently updated their flue gas cleaning systems, surpassing in most cases E.U. air emission standards. At the same time, in most developing countries, cement and other coincineration facilities follow less stringent emission regulations and have a mixed record of protecting air quality. The European Union, the United States, and Canada have established penalties for air emission violations that account for the harm done to the environment and to human health and aiming to remove the economic benefit reaped as a result of noncompliance. Despite their legal completeness, these regulations do not adequately address the probabilistic nature of air pollution. This article recasts the issue of air pollution penalties in a Bayesian decision-making framework with the aspiration that the assessment of penalties on a rigorous mathematical framework can assist in alleviating the mistrust by sections of the public on the effectiveness of air pollution regulations. Integration of economic analyses into risk assessments of emission violations can help clarify to policymakers the effect of environmental policies. Our analysis indicates that the penalty structure of the United States appears to favor the update of emission systems more often than the corresponding European Commission's penalties. Our study advances the use of the loss function as a risk analysis tool that can be used as a public policy instrument to promote environmentally friendlier air emission choices. A parabolic, compared to a linear, loss function was seen to justify higher expenses in gas cleaning systems.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".