Benefit Sharing in Hydropower Development: A Model Using Game Theory and Cost–Benefit Analysis

Abstract

Globally, hydropower is one of the most important energy sources, but its development often entails population displacement. Traditional economics cannot fundamentally resolve disputes over relocation compensation. In the present study, we use the game theory to model the relationship between stakeholders. The main issue is the distribution of benefits between hydropower developers and the affected population. To distribute benefits more fairly and rationally, we model the benefits and costs for the developers and the affected people over the full life cycle of the project, consider the affected people as project investors, adjust benefit distribution based on the internal rate of return, and assess the rationality of the resulting plan. Under this benefit-sharing model, hydropower developers and affected people can share benefits and risks, effectively eliminate conflicts, and ensure project success. The model provides guidance for the formulation of compensation for affected people, and a new paradigm for the study of benefit-sharing mechanisms for hydropower development.

Model and Simulation of Benefit Distribution of Collaborative Cooperation in the Supply Chain of General Contracting Projects

“…A large number of cooperative game studies can be further divided into two typesex-ante allocation and ex-post allocation-in which the specific forms of ex-ante allocation generally include revenue sharing contracts [25][26][27][28][29][30], quantity flexibility contracts [31], repurchase contracts [32][33][34], etc. ; export profit allocation is carried out using the Shapley value method [10,11,16,[35][36][37][38][39][40][41], the minimum core method [35], the bargaining model [42], the Nash bargaining model [43], and the maximum entropy method [17] among other models.…”

“…; export profit allocation is carried out using the Shapley value method [10,11,16,[35][36][37][38][39][40][41], the minimum core method [35], the bargaining model [42], the Nash bargaining model [43], and the maximum entropy method [17] among other models. Raza, S.A. [25] proposed solutions for control decisions by analyzing the demand profiles of a quantitative model of joint pricing, inventory (order quantity), and investment used for supply chain social responsibility decisions under three games of decentralized, centralized, and revenue-sharing contracts; Heydari, J. et al [26] applied quantity elasticity contracts to study the stochastic demand situation of a two-level supply chain consisting of one product and two members (manufacturer and retailer); Luo, C.L. [31] analyzed that when a supply chain can be coordinated through a buy-back contract, the coordination benefits of the supply chain can be arbitrarily distributed between suppliers and retailers; Xiao, Q. and Ma, S.H.…”

Optimal Scheduling of Cascade Reservoirs Based on an Integrated Multistrategy Particle Swarm Algorithm

“…and N max i,t denote the upper and lower limits of the output of the ith hydropower reservoir in the tth stage, respectively. (9) Initial and final water level control constraints…”

“…Therefore, to facilitate the efficient allocation of resources, rational utilization and the development of hydropower energy are crucial. In this context, the optimal operation of cascade reservoirs has emerged as a major research area in recent years [3], with research efforts mainly focused on deriving reservoir scheduling regulations [4][5][6], identifying targets for reservoir scheduling models [7][8][9][10], and revising and utilizing optimization techniques for reservoir scheduling [11][12][13].…”