赖苹,曹国华.基于直觉模糊联盟合作博弈的我国主要流域水资源优化配置研究[J].中国环境管理,2024,16(4):71-79,90. LAI Ping,CAO Guohua.Study on the Optimal Allocation of Water Resources in Major Basins of China Based on Intuitionistic Fuzzy Coalition Cooperative Game[J].Chinese Journal of Environmental Management,2024,16(4):71-79,90. |
基于直觉模糊联盟合作博弈的我国主要流域水资源优化配置研究 |
Study on the Optimal Allocation of Water Resources in Major Basins of China Based on Intuitionistic Fuzzy Coalition Cooperative Game |
DOI:10.16868/j.cnki.1674-6252.2024.04.071 |
中文关键词: 直觉模糊联盟 合作博弈 水资源优化配置 |
英文关键词:intuitionistic fuzzy coalition cooperative game optimal allocation of water resources |
基金项目:国家社会科学基金项目“长江上游地区生态资源资本化价值核算与实现路径研究”(22BJY083);重庆市社会科学规划项目“推动建立长江上游地区水生态产品价值核算及价值实现机制研究”(2021NDYB085);高等教育科学研究规划课题“将生态文明价值实现机制融入《环境与自然资源经济学》课程教学的路径研究”(23STWM0408)。 |
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中文摘要: |
我国水资源地区分布不均,呈现南多北少的特点。随着人口增长和社会经济的不断发展,水资源需求量日益增加。为了提高水资源的综合利用效率,在完成水资源初始配置后迫切需要开展水资源跨区调配实现二次优化配置。如何选择水资源调配合作对象,如何兼顾公平与效率,如何共享合作收益,成为水资源再配置需要解决的核心问题。本文提出将直觉模糊联盟合作博弈运用到水资源优化配置问题中,通过构建基于清晰联盟合作博弈和模糊联盟合作博弈的水资源优化配置模型及合作收益函数,运用直觉模糊集、区间Choquet积分、连续有序加权平均算子等方法,推导出基于直觉模糊联盟合作博弈的水资源优化配置模型及合作收益函数,运用直觉模糊Shapley值求解最优解。选取我国南方地区的长江流域和北方地区的黄河、海河和淮河流域等水资源一级区作为研究对象,选择工业用水作为调配用水,将工业用水收益率高低作为水资源配置“优先原则”,比较分析三种不同的模型下我国主要流域水资源调配的合作对象及合作收益分配方式。结果表明:①各地区参与水资源二次配置,都能取得相较于不合作更好的合作收益;②参与直觉模糊合作联盟获得的收益全部都高于参与模糊合作联盟获得的收益;③在直觉模糊合作联盟中,不应盲目追求联盟的规模,应根据地区自身实际情况决定。以上研究结果表明,直觉模糊联盟合作博弈方法为解决跨区域流域水资源配置提供了一种新的解决方案,这将为我国主要流域开展跨区域水资源调配,优化水资源配置提供科学决策依据。 |
英文摘要: |
The distribution of water resources in China is uneven, showing the characteristics of more in the south and less in the north. With the growth of population and the continuous development of social economy, the demand for water resources is increasing. In order to improve the comprehensive utilization efficiency of water resources, it is urgent to carry out cross-regional allocation of water resources to achieve secondary optimal allocation after the completion of the initial allocation. How to select the cooperation objects, how to balance fairness and efficiency, and how to share the cooperation benefits have become the core issues to be solved in water resources reallocation. This paper proposes the application of intuitionistic fuzzy coalition cooperative game to water resources optimization allocation problems. By constructing water resources optimization allocation models and cooperation benefit functions based on clear coalition cooperative game and fuzzy coalition cooperative game, and using methods such as intuitionistic fuzzy set, interval Choquet integral and continuous ordered weighted average operator, water resources optimization allocation model and cooperation benefit function based on intuitionistic fuzzy coalition cooperative game are derived. Optimal solutions are solved using intuitive fuzzy Shapley value. The Yangtze River basin in the southern region and the Yellow River, Hai River, Huai River basins in the northern region are selected as research objects. Industrial water is selected as allocation water, and return rate of industrial water is taken as the“ priority principle” for water resources allocation. Compare and analyze the cooperation objects and benefit distribution of water resources allocation in major river basins under three different models. The results indicate that:①After completing the secondary allocation of water resources in each region, better cooperation benefits have been achieved compared to non-cooperation.②The benefits obtained by participating in intuitionistic fuzzy cooperative coalitions are all higher than those obtained by participating in fuzzy cooperative coalitions.③In intuitionistic fuzzy cooperative coalitions, the size of the coalitions should not be blindly pursued, but should be determined based on the actual situation of the region. The above research results indicate that the intuitionistic fuzzy coalition cooperative game provides a new solution for solving cross regional water resources allocation in river basins, which will provide a scientific decision-making basis for major river basins in China to carry out and optimize cross regional water resources allocation. |
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