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An Evolutionary Game Model for Industrial Pollution Management under Two Punishment Mechanisms
In recent years, with the rapid development of the economy, industrial pollution problems have become more and more serious. This paper constructs an evolutionary game model for industrial pollution between the local governments and enterprises to study the dynamic evolution path of a game system an...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6696192/ https://www.ncbi.nlm.nih.gov/pubmed/31382549 http://dx.doi.org/10.3390/ijerph16152775 |
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author | Wang, Chuansheng Shi, Fulei |
author_facet | Wang, Chuansheng Shi, Fulei |
author_sort | Wang, Chuansheng |
collection | PubMed |
description | In recent years, with the rapid development of the economy, industrial pollution problems have become more and more serious. This paper constructs an evolutionary game model for industrial pollution between the local governments and enterprises to study the dynamic evolution path of a game system and the evolutionary stable strategy under two punishment mechanisms. The results show that, in a static punishment mechanism (SPM), the strategy between governments and enterprises is uncertain. Moreover, the evolutionary trajectory between governments and enterprises is uncertain. However, under the dynamic punishment mechanism (DPM), the evolution path between governments and enterprises tends to converge to a stable value. Thus, the DPM is more conducive than the SPM for industrial pollution control. |
format | Online Article Text |
id | pubmed-6696192 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-66961922019-09-05 An Evolutionary Game Model for Industrial Pollution Management under Two Punishment Mechanisms Wang, Chuansheng Shi, Fulei Int J Environ Res Public Health Article In recent years, with the rapid development of the economy, industrial pollution problems have become more and more serious. This paper constructs an evolutionary game model for industrial pollution between the local governments and enterprises to study the dynamic evolution path of a game system and the evolutionary stable strategy under two punishment mechanisms. The results show that, in a static punishment mechanism (SPM), the strategy between governments and enterprises is uncertain. Moreover, the evolutionary trajectory between governments and enterprises is uncertain. However, under the dynamic punishment mechanism (DPM), the evolution path between governments and enterprises tends to converge to a stable value. Thus, the DPM is more conducive than the SPM for industrial pollution control. MDPI 2019-08-03 2019-08 /pmc/articles/PMC6696192/ /pubmed/31382549 http://dx.doi.org/10.3390/ijerph16152775 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Wang, Chuansheng Shi, Fulei An Evolutionary Game Model for Industrial Pollution Management under Two Punishment Mechanisms |
title | An Evolutionary Game Model for Industrial Pollution Management under Two Punishment Mechanisms |
title_full | An Evolutionary Game Model for Industrial Pollution Management under Two Punishment Mechanisms |
title_fullStr | An Evolutionary Game Model for Industrial Pollution Management under Two Punishment Mechanisms |
title_full_unstemmed | An Evolutionary Game Model for Industrial Pollution Management under Two Punishment Mechanisms |
title_short | An Evolutionary Game Model for Industrial Pollution Management under Two Punishment Mechanisms |
title_sort | evolutionary game model for industrial pollution management under two punishment mechanisms |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6696192/ https://www.ncbi.nlm.nih.gov/pubmed/31382549 http://dx.doi.org/10.3390/ijerph16152775 |
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