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- Title
Green Development of Traditional Villages: Stakeholder Game Perspectives Under Reward and Punishment Policies.
- Authors
Wang, Song; Zhu, Dongliang; Li, Yaru; Liu, Chang
- Abstract
Government, companies, and residents are the most important stakeholders in the sustainable development of traditional villages. Balancing the participants' interests is a challenge for the green development of traditional villages. By constructing a tripartite evolutionary game model and introducing reward and punishment policies under the stakeholder theory, this study examined changes in traditional village conservation strategies regarding the timing of the regulation in the development process. Based on the reform data of Wan Liushu Village and government support policies, the proposed theoretical model was employed using numerical simulations, and the influence of critical parameters on stakeholders' decisions was discussed. The results show that (1) four stable points exist in the theoretical model, but only the (0,1,0) strategy point is consistent with traditional village conservation and development interests. At this point, the government adopts weak regulation, the company adopts green development, and the residents choose not to participate in the strategy. (2) Only when the government incentives exceed the companies' illegal benefits. The companies will choose the conservation and development strategy for stability. (3) The government has to ensure that penalties are more substantial and significant than the cost of regulation to avoid the (0,0,0) strategy point. At the (0,0,0) point, the government adopts weak regulation, the company adopts a no green development strategy, and the residents adopt a non-participation strategy. (4) Under the government's limited regulatory strategy, residents can only be motivated if their incentives exceed participation costs. This study can provide a reference for the green development of traditional villages.
- Subjects
SUSTAINABLE development; VILLAGES; STAKEHOLDER theory; GOVERNMENT policy; EVOLUTIONARY models; PUNISHMENT
- Publication
Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ), 2024, Vol 49, Issue 5, p7395
- ISSN
2193-567X
- Publication type
Article
- DOI
10.1007/s13369-023-08229-2