Driving factors on corporate green investments behaviors: from the strategic intersection of governments regulation and public participation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it