A game theoretical model for the stimulation of public cooperation in environmental collaborative governance.

Yinhai Fang, Matjaž Perc, Hui Zhang
Author Information
  1. Yinhai Fang: College of Economics and Management, Nanjing Forestry University, Nanjing 210037, People's Republic of China. ORCID
  2. Matjaž Perc: Faculty of Natural Sciences and Mathematics, University of Maribor, Koroška cesta 160, 2000 Maribor, Slovenia. ORCID
  3. Hui Zhang: College of Economics and Management, Nanjing Forestry University, Nanjing 210037, People's Republic of China.

Abstract

Digital technologies provide a convenient way for the public to participate in environmental governance. Therefore, by means of a two-stage evolutionary model, a new mechanism for promoting public cooperation is proposed to accomplish environmental collaborative governance. Interactive effects of government-enterprise environmental governance are firstly explored, which is the external atmosphere for public behaviour. Second, the evolutionary dynamics of public behaviour is analysed to reveal the internal mechanism of the emergence of public cooperation in environmental collaborative governance projects. Simulations reveal that the interaction of resource elements between government and enterprise is an important basis for environmental governance performance, and that governments can improve this as well as public cooperation by increasing the marginal governance propensity. Similarly, an increase in the government's fixed expenditure item of environmental governance can also significantly improve government-enterprise performance and public cooperation. And finally, the effect of government's marginal incentive propensity on public environmental governance is moderated by enterprises' marginal environmental governance propensity, so that simply increasing the government's marginal incentive propensity cannot improve the evolutionary stable state of public behaviour under the scenario where enterprises' marginal environmental governance propensity is low.

Keywords

Associated Data

figshare | 10.6084/m9.figshare.c.6280860

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