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Game modeling and policy research on the system dynamics-based tripartite evolution for government environmental regulation

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Abstract

Government regulation and policy strategies play very important roles in environmental pollution control. In this study on the evolutionary game theories and the relationship between the government, businesses, and the overall interests of society, we build two system dynamics-based tripartite evolutionary game models: a government environmental regulation-static punishment model and a dynamic punishment model. By factoring various policy strategies in the two models, including adjustments to the “Budget of pollution inspection”, “Reward for no pollution discharge”, “Enterprise production gain”, and “Punishment coefficient” and additional combinations of the adjustment schemes; this study observes the changes in the action and the data outputs of the two models. Finally, the operation of the two models under the same policy strategy is compared and analyzed. The results show that loss of integrated social benefit and the type of punishment mechanism will significantly impact the selection of the environmental regulation strategies. However, compared with a single strategy, a combination of policy strategies could work better in promoting the environmental regulatory model to achieve an “ideal state”.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (Project Numbers 71262022 and 71162015) and Inner Mongolia Natural Science Foundation (Project Numbers: 20090401).

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Authors and Affiliations

  1. Chemical Engineering College, Inner Mongolia University of Technology, Hohhot, 010051, China

    Wei Duan & Pei Zhang

  2. Management College, Inner Mongolia University of Technology, Hohhot, 010051, China

    Changqing Li & Qing Chang

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  1. Wei Duan
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Correspondence to Wei Duan.

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Duan, W., Li, C., Zhang, P. et al. Game modeling and policy research on the system dynamics-based tripartite evolution for government environmental regulation. Cluster Comput 19, 2061–2074 (2016). https://doi.org/10.1007/s10586-016-0642-1

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