Synergistic evolution of multi-stakeholder strategies in the promotion of green building materials, quadrilateral evolutionary game and system dynamics
Bibliographic record
Abstract
Purpose Sustained urbanization poses challenges to decarbonizing the building sector, which can be addressed through the adoption of green building materials (GBMs). The purpose of this research is to refine regulatory measures to facilitate the promotion of GBMs by multi-stakeholders. Design/methodology/approach A quadrilateral evolutionary game model involving the government, building materials enterprises (BMEs), building developers (BDs) and building consumers (BCs) is developed. The Lyapunov first method is utilized to analyze evolutionarily stable strategies. System dynamics simulations are conducted to assess the impact of game parameters. Findings The findings demonstrate that the evolutionarily stable strategy of the quadrilateral game is that the government chooses strong supervision, BMEs choose green production, BDs choose green development, and BCs choose green consumption. Increasing stakeholders' initial green preferences, adjusting government subsidies and penalties, reducing GBMs’ certification costs, enhancing BCs' perceived benefits of GBMs and carbon inclusion, optimizing stakeholders’ risk avoidance and perceived bias of gains and losses, allocating properly more synergy gains to BMEs and increasing the proportion of non-green stakeholders' loss in non-synergized states promote green synergy evolution. Research limitations/implications The implications suggest increasing initial green preferences for BMEs and BDs based on industry alliances, imposing appropriate incentives and constraints through subsidies and taxes, improving certification mechanisms for GBMs and reducing certification costs, empowering BCs on the demand side to increase the implicit perceived value of GBMs, adjusting stakeholders’ perceived biases of gains and losses through publicity and properly allocating more synergy gains to BMEs and providing support to enterprises that prioritize green. Originality/value This research innovatively constructs a quadrilateral evolutionary game model integrated with system dynamics, considers perceived bias in stakeholders' decision-making using prospect theory, contributes to the field by expanding the application of evolutionary game theory and system dynamics and offers recommendations for the promotion of GBMs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".