Dynamic Optimal Control of Transboundary Pollution Abatement under Learning-by-Doing Depreciation
This paper analyzes a dynamic Stackelberg differential game model of watershed transboundary water pollution abatement and discusses the optimal decision-making problem under non-cooperative and cooperative differential game, in which the accumulation effect and depreciation effect of learning-by-do...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/3763684 |
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| _version_ | 1849398711012032512 |
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| author | Zhigang Chen Rongwei Xu Yongxi Yi |
| author_facet | Zhigang Chen Rongwei Xu Yongxi Yi |
| author_sort | Zhigang Chen |
| collection | DOAJ |
| description | This paper analyzes a dynamic Stackelberg differential game model of watershed transboundary water pollution abatement and discusses the optimal decision-making problem under non-cooperative and cooperative differential game, in which the accumulation effect and depreciation effect of learning-by-doing pollution abatement investment are taken into account. We use dynamic optimization theory to solve the equilibrium solution of models. Through numerical simulation analysis, the path simulation and analysis of the optimal trajectory curves of each variable under finite-planning horizon and long-term steady state were carried out. Under the finite-planning horizon, the longer the planning period is, the lower the optimal emission rate is in equilibrium. The long-term steady-state game under cooperative decision can effectively reduce the amount of pollution emission. The investment intensity of pollution abatement in the implementation of non-cooperative game is higher than that of cooperative game. Under the long-term steady state, the pollution abatement investment trajectory of the cooperative game is relatively stable and there is no obvious crowding out effect. Investment continues to rise, and the optimal equilibrium level at steady state is higher than that under non-cooperative decision making. The level of decline in pollution stock under finite-planning horizon is not significant. Under the condition of long-term steady state, the trajectories of upstream and downstream pollution in the non-cooperative model and cooperative model are similar, but cooperative decision-making model is superior to the non-cooperative model in terms of the period of stabilization and steady state. |
| format | Article |
| id | doaj-art-a55572e65e944443b1905bbdc8e800a2 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-a55572e65e944443b1905bbdc8e800a22025-08-20T03:38:31ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/37636843763684Dynamic Optimal Control of Transboundary Pollution Abatement under Learning-by-Doing DepreciationZhigang Chen0Rongwei Xu1Yongxi Yi2Institute of Regional and Urban-Rural Development, Wuhan University, Wuhan, ChinaInstitute of Regional and Urban-Rural Development, Wuhan University, Wuhan, ChinaSchool of Economics, Management & Law, University of South China, Hengyang, ChinaThis paper analyzes a dynamic Stackelberg differential game model of watershed transboundary water pollution abatement and discusses the optimal decision-making problem under non-cooperative and cooperative differential game, in which the accumulation effect and depreciation effect of learning-by-doing pollution abatement investment are taken into account. We use dynamic optimization theory to solve the equilibrium solution of models. Through numerical simulation analysis, the path simulation and analysis of the optimal trajectory curves of each variable under finite-planning horizon and long-term steady state were carried out. Under the finite-planning horizon, the longer the planning period is, the lower the optimal emission rate is in equilibrium. The long-term steady-state game under cooperative decision can effectively reduce the amount of pollution emission. The investment intensity of pollution abatement in the implementation of non-cooperative game is higher than that of cooperative game. Under the long-term steady state, the pollution abatement investment trajectory of the cooperative game is relatively stable and there is no obvious crowding out effect. Investment continues to rise, and the optimal equilibrium level at steady state is higher than that under non-cooperative decision making. The level of decline in pollution stock under finite-planning horizon is not significant. Under the condition of long-term steady state, the trajectories of upstream and downstream pollution in the non-cooperative model and cooperative model are similar, but cooperative decision-making model is superior to the non-cooperative model in terms of the period of stabilization and steady state.http://dx.doi.org/10.1155/2020/3763684 |
| spellingShingle | Zhigang Chen Rongwei Xu Yongxi Yi Dynamic Optimal Control of Transboundary Pollution Abatement under Learning-by-Doing Depreciation Complexity |
| title | Dynamic Optimal Control of Transboundary Pollution Abatement under Learning-by-Doing Depreciation |
| title_full | Dynamic Optimal Control of Transboundary Pollution Abatement under Learning-by-Doing Depreciation |
| title_fullStr | Dynamic Optimal Control of Transboundary Pollution Abatement under Learning-by-Doing Depreciation |
| title_full_unstemmed | Dynamic Optimal Control of Transboundary Pollution Abatement under Learning-by-Doing Depreciation |
| title_short | Dynamic Optimal Control of Transboundary Pollution Abatement under Learning-by-Doing Depreciation |
| title_sort | dynamic optimal control of transboundary pollution abatement under learning by doing depreciation |
| url | http://dx.doi.org/10.1155/2020/3763684 |
| work_keys_str_mv | AT zhigangchen dynamicoptimalcontroloftransboundarypollutionabatementunderlearningbydoingdepreciation AT rongweixu dynamicoptimalcontroloftransboundarypollutionabatementunderlearningbydoingdepreciation AT yongxiyi dynamicoptimalcontroloftransboundarypollutionabatementunderlearningbydoingdepreciation |