Multi-agent evolutionary game analysis on the implementation of municipal solid waste classification policy
With the rapid development of economy and the improvement of people's living standard, the domestic waste is greatly increasing. Based on the multi-agent evolutionary game theory, an evolutionary game model among the recycling industry, urban management officer and government is established to...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-02-01
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| Series: | Frontiers in Sustainable Cities |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/frsc.2023.954323/full |
| _version_ | 1797901804926664704 |
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| author | Nan Yao Xiwen Tan Yuefan Zhang Yunhan Qu Xue Han Zhi Li |
| author_facet | Nan Yao Xiwen Tan Yuefan Zhang Yunhan Qu Xue Han Zhi Li |
| author_sort | Nan Yao |
| collection | DOAJ |
| description | With the rapid development of economy and the improvement of people's living standard, the domestic waste is greatly increasing. Based on the multi-agent evolutionary game theory, an evolutionary game model among the recycling industry, urban management officer and government is established to address the environmental issues of rising municipal solid waste production and low recycling utilization rates in China. Through numerical example analysis, the relationship between relevant factors on the evolutionary stable state of the system is studied. The findings demonstrate that when the likelihood of severe oversight by the government and of significant enforcement by urban management officers grows, the recycling industry becomes more ready to select waste separation strategy. Additionally, the government is far more affected by the likelihood of significant enforcement by urban management officers than the recycling industries are. In addition, effective acceleration of waste sorting may be achieved by giving the recycling industries the proper government subsidies and increasing the recycling industries' profits from garbage sorting. Moreover, the government's increased fines for the urban management officers can significantly lower the incidence of bribery between the recycling industries and the urban management officers. |
| first_indexed | 2024-04-10T09:07:44Z |
| format | Article |
| id | doaj.art-7365fcdee7904e97bd6ea5c8e89f0b64 |
| institution | Directory Open Access Journal |
| issn | 2624-9634 |
| language | English |
| last_indexed | 2024-04-10T09:07:44Z |
| publishDate | 2023-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Sustainable Cities |
| spelling | doaj.art-7365fcdee7904e97bd6ea5c8e89f0b642023-02-21T06:03:02ZengFrontiers Media S.A.Frontiers in Sustainable Cities2624-96342023-02-01510.3389/frsc.2023.954323954323Multi-agent evolutionary game analysis on the implementation of municipal solid waste classification policyNan Yao0Xiwen Tan1Yuefan Zhang2Yunhan Qu3Xue Han4Zhi Li5School of Economics and Management, Tiangong University, Tianjin, ChinaSchool of Economics and Management, Tiangong University, Tianjin, ChinaSchool of Economics and Management, Tiangong University, Tianjin, ChinaSchool of Economics and Management, Tiangong University, Tianjin, ChinaSchool of Humanities, Tiangong University, Tianjin, ChinaSchool of Economics and Management, Tiangong University, Tianjin, ChinaWith the rapid development of economy and the improvement of people's living standard, the domestic waste is greatly increasing. Based on the multi-agent evolutionary game theory, an evolutionary game model among the recycling industry, urban management officer and government is established to address the environmental issues of rising municipal solid waste production and low recycling utilization rates in China. Through numerical example analysis, the relationship between relevant factors on the evolutionary stable state of the system is studied. The findings demonstrate that when the likelihood of severe oversight by the government and of significant enforcement by urban management officers grows, the recycling industry becomes more ready to select waste separation strategy. Additionally, the government is far more affected by the likelihood of significant enforcement by urban management officers than the recycling industries are. In addition, effective acceleration of waste sorting may be achieved by giving the recycling industries the proper government subsidies and increasing the recycling industries' profits from garbage sorting. Moreover, the government's increased fines for the urban management officers can significantly lower the incidence of bribery between the recycling industries and the urban management officers.https://www.frontiersin.org/articles/10.3389/frsc.2023.954323/fullmunicipal solid wasteclassified recyclingmulti-agentevolutionary gamesimulated analysis |
| spellingShingle | Nan Yao Xiwen Tan Yuefan Zhang Yunhan Qu Xue Han Zhi Li Multi-agent evolutionary game analysis on the implementation of municipal solid waste classification policy Frontiers in Sustainable Cities municipal solid waste classified recycling multi-agent evolutionary game simulated analysis |
| title | Multi-agent evolutionary game analysis on the implementation of municipal solid waste classification policy |
| title_full | Multi-agent evolutionary game analysis on the implementation of municipal solid waste classification policy |
| title_fullStr | Multi-agent evolutionary game analysis on the implementation of municipal solid waste classification policy |
| title_full_unstemmed | Multi-agent evolutionary game analysis on the implementation of municipal solid waste classification policy |
| title_short | Multi-agent evolutionary game analysis on the implementation of municipal solid waste classification policy |
| title_sort | multi agent evolutionary game analysis on the implementation of municipal solid waste classification policy |
| topic | municipal solid waste classified recycling multi-agent evolutionary game simulated analysis |
| url | https://www.frontiersin.org/articles/10.3389/frsc.2023.954323/full |
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