The impact of credit guarantee on enterprises' willingness to undertake environmental responsibilities: Evidence from a dynamic evolutionary game model
Bibliographic record
Abstract
Evolutionary game is a powerful tool for exploring the interactive strategies of enterprises, governments, and third-party platform institutions that provide credit guarantees. In the context of big data and the Internet of Things, as more and more enterprises participate in the collective process of credit shaping, this series of platform-based credit ratings have a profound impact on the environmental willingness of enterprises. This article constructs an evolutionary game model of enterprise government with the introduction of credit guarantee mechanism, explores the evolution of participant behavior and their Evolutionary Stable Strategies (ESS), and uses MATLAB tools for evolutionary simulation to explore the impact of relevant parameters on the system evolution results. The results have shown that the selection of environmental protection strategies by enterprises is a dynamic process of continuous adjustment and optimization; The government can help the evolutionary game converge to an ideal state by providing reward subsidies and strengthening punishment measures; The intervention of credit guarantees has influenced the strategic choices of both parties, promoting the willingness of enterprises to actively fulfill their environmental responsibilities.
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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 | 0.014 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".