A Pollution Prevention Pathway Evaluation Methodology Based on Systematic Collaborative Control
Bibliographic record
Abstract
To improve the efficiency of air pollution control, in this research, a systematic air pollution collaborative governance pathway system was developed from a systemic perspective. The sequencing of air pollution control pathways in the system can significantly affect its efficiency, so the order of the sequence was optimized. To develop the system, first, two case studies on coordinated air pollution control in the U.S. and China were conducted to demonstrate the importance of systematic collaborative governance. Next, based on the analysis of these two cases and a review of the related literature, a systematic coordinated air pollution control mechanism was proposed. The priorities of collaborative governance pathways were evaluated using the Analytic Hierarchy Process (AHP) methodology. The input to the AHP was data from in-depth interviews with established scholars and practitioners in air pollution prevention and control. Several policy suggestions are put forward based on the expert ranking of the results of the priorities of the collaborative governance pathways. These policy suggestions include identifying the most critical pathways in the cooperative control of air pollution and their order of implementation as well as measures that can effectively reduce pollution. The theoretical contributions of this research include the establishment of a cooperative governance mechanism and the analysis of governance pathways to help develop an efficient air pollution pathway system. The practical contributions of this research include policy suggestions to improve the efficiency of collaborative air pollution treatment and lower its costs.
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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 | 0.036 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.016 | 0.010 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".