Abstract
Water scarcity is an important issue in many countries, and it is therefore necessary to improve the efficiency and equality of water resource allocation for decision makers. Based on game theory (GT), a bi-level optimization model is developed from the perspective of a leader-follower relationship among agents (stakeholders) of a river basin in this study, which consists of a single-agent GT-based optimization model of common interest and a multi-agent cooperative GT-based model. The Hanjiang River Basin is chosen as a case study, where there are conflicts among different interest agents in this basin. The results show that the proposed bi-level model could attain the same improvement of common interest by 8%, with the conventional optimal model. However, different from the conventional optimal model, since the individual interests have been considered in the bi-level optimization model, the willingness of cooperation of individuals has risen from 20% to 80%. With a slight decrease by 3% of only one agent, the increases of interest of other agents are 14%, 18%, 7%, and 14%, respectively, when using the bi-level optimization model. The conclusion could be drawn that the proposed model is superior to the conventional optimal model. Moreover, this study provides scientific support for the large spatial scale water resource allocation model.
Highlights
Water is essential for human well-being and all activities [1]
Since the imbalance between the supply and demand of water resources is getting more and more prominent, it is urgent for decision makers to solve the conflicts arisen from ineffective and unfair water resource allocation [5]
To enhance the effectiveness and benefits of water resource allocation schemes, a large group of scholars have suggested the use of optimization models
Summary
Water is essential for human well-being and all activities [1]. Owing to the impact of climate change and human activities, water scarcity has become a common problem in many countries, especially in developing countries [2,3,4]. To enhance the effectiveness and benefits of water resource allocation schemes, a large group of scholars have suggested the use of optimization models Optimization techniques, such as linear programming, mixed-integer linear programing, dynamic programming, evolutionary computation, artificial neural networks, and so on [5,6,7,8], have been trying to find the optimal schemes of water resource allocation. These conventional optimization methods usually convert the multi-decision-maker problems of the whole system into a single-decision-maker problem, with a single composite objective [5]. The ideal optimal scheme can’t be realized without the willingness to cooperation of individuals [9,10,11]
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