Evolutionary game analysis of environmental pollution control under the government regulation.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
10 01 2022
10 01 2022
Historique:
received:
13
08
2021
accepted:
20
12
2021
entrez:
11
1
2022
pubmed:
12
1
2022
medline:
12
1
2022
Statut:
epublish
Résumé
This paper studied a tripartite evolutionary game of stakeholders in environmental pollution control. Most previous studies on this issue are limited to a focus on system dynamics with two-party game problems and lack a spatial analysis of strategy evolution. The parameters adopted are too few, and the influencing factors considered are too simple. The purpose of the paper is to introduce more parameters to study, which will have an important impact on the strategy choices of participants and the evolution path of the strategy over time. We construct a tripartite evolutionary game model of sewage enterprises, governments and the public. We establish a payment matrix and replicator equations as our method, and we also implement parameter simulations in MATLAB. In summary, we found that the reward and punishment mechanism plays an important role in environmental pollution control. Specifically: intensifying rewards and penalties will help encourage sewage enterprises to meet the discharge standard and the public to participate in pollution control action. However, increased rewards will reduce government's willingness to adopt incentive strategies; Government's reward for public's participation in the action must be greater than the increased cost of participation; Reducing the cost of sewage enterprise can also encourage them to implement standard emissions. The research presented in this paper further improves standard emissions and designs reasonable reward and punishment mechanism.
Identifiants
pubmed: 35013497
doi: 10.1038/s41598-021-04458-3
pii: 10.1038/s41598-021-04458-3
pmc: PMC8748631
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
474Subventions
Organisme : National Natural Science Foundation of China
ID : 72061014
Informations de copyright
© 2022. The Author(s).
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