Environmental impact assessment for renewable energy investments through integrated reinforcement learning and molecular fuzzy-based decision-making algorithm

Environmental impact assessment is a significant component of renewable energy project planning. However, the identification of key environmental performance indicators remains underexplored. In the literature, most existing studies do not adequately prioritize these environmental factors. This situation creates a significant research gap in the renewable energy literature. This study addresses this gap by proposing a novel hybrid decision-making model to identify the most effective investment strategies for improving the environmental performance of renewable energy projects. First, the balanced expert dataset has been constructed by Q-learning algorithm. The second stage is related to the evaluation of the criteria with molecular fuzzy (MF) Bayesian networks (BANEW)-based weighting. Finally, alternatives are ranked by MF multi-objective particle swarm optimization (MOPSO). This study fills an important gap in the literature on increasing the environmental sustainability of renewable energy investments by integrating molecular geometry-based fuzzy decision-making techniques and Q-learning supported expert weighting method in prioritizing environmental impacts. The use of molecular geometry and fuzzy multi-criteria decision-making analysis together reduces the uncertainty in the solution process of complex problems more effectively. The use of the Q-learning algorithm in the model reduces subjectivity in the decision-making process by providing a dynamic structure based on learning in the weighting of expert opinions. The findings show that biodiversity is the most effective environmental impact of renewable energy investments is mostly on biodiversity. On the other side, it is also identified that the most optimal option for assessing the environmental impact of renewable energy investments is life cycle assessment.