Multi-objective particle swarm optimization for environmental risk/benefit analysis with pre-assignment strategy

Hydropower is a fundamental renewable energy source, and the Amazon basin represents one of its largest untapped frontiers. However, its expansion in this ecologically sensitive region raises significant environmental challenges, especially concerning greenhouse gas emissions. In this paper, we develop a multi-objective optimization framework that employs a variant of the Multi-Objective Particle Swarm Optimizer to balance the competing objectives represented by the total electricity generation and the reduction of carbon emissions. We analyse a dataset of 509 dams, categorized by geographical and technical features, to assess the impact of site selection and taking into account the pre-assignment of dams already installed. We further inspect the key features of dams that compose the best configurations to maximize energy output while minimizing emissions. In such configurations, the dams are located in highland areas, offering flexible trade-offs and allowing planners to balance sustainability with energy demands. Decision-makers could take advantage of this work by adopting a strategic approach to hydropower expansion that prioritizes energy efficiency and environmental responsibility, showcasing the effectiveness of computational optimization in sustainable energy planning.