Driving factors on corporate green investments behaviors: from the strategic intersection of governments regulation and public participation
Bibliographic record
Abstract
As the global community confronts the challenges of climate change, businesses face increasing pressure to adopt sustainable practices. This study develops a tripartite game model to investigate the impact of green investments on corporate performance, considering the dynamic interplay between governments regulations and public participation in shaping strategic initiatives. First, the evolutionary stability strategy (ESS) is identified by solving replicator dynamic equations and performing stability analysis of equilibrium points. Next, the practicability and rationality of the evolutionary game model are assessed by analyzing ESSs under various corporate green investment scenarios. Finally, a case-based example is provided to validate the theoretical findings and support the following arguments: there are eight equilibrium points and four potential ESSs in the game model; the selection of each ESS is primarily determined by the trade-off between costs and revenues for each stakeholder; increased governmental regulatory costs prompt a strategic shift, incentivizing corporations to enhance green investments; while rising penalties drive a preference for green options; and corporations recognizing compensatory responsibilities are steered towards sustainable pathways.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".