Analysis of the Evolutionary Game Between Enterprises and Local Governments under Pollution Control
Bibliographic record
Abstract
The strategic choices made by the government and enterprises, as the main participants in environmental governance, decide the direction of environmental governance. Meanwhile, the strategic choices of enterprises will also change the strategic layout of the government. Therefore, current research mainly focuses on what impact the government has on enterprises when it participates in the game, and how will the interested parties make optimal decisions. In this paper, from the perspective of the evolutionary game theory, the strategic choices of the interaction between pollutant-discharging enterprises and between local governments and pollutant-discharging enterprises are discussed; the model of the evolutionary game between pollutant-discharging enterprises and local governments is also built. According to the replicated dynamic equation, the evolutionarily stable strategy of the participants is obtained. The research results indicate that: the higher the return on pollution control is for an enterprise, the more inclined it will be towards pollution control and environmental protection. Environmental governance should reduce the expected benefits of enterprises without treatment of pollutants and should require local governments to strictly implement environmental protection policies, perform their duty, and identify and punish violators in a timely manner.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".