Scenario-based dynamic evaluation of lifecycle environmental efficiency in renewable energy infrastructure using a novel hybrid multi-facet fuzzy decision-making approach

The efficiency of life cycle environmental impacts of renewable energy infrastructures is extremely important for ensuring sustainability and combating climate change effectively. Although prior studies have addressed various aspects of lifecycle environmental impacts in renewable energy systems, a limited number of studies have systematically identified and prioritized the most critical factors influencing lifecycle environmental efficiency. This limitation may lead to suboptimal allocation of improvement efforts and reduce the effectiveness of sustainability-oriented strategies. The main objective of this study is to determine the most effective strategies to increase environmental efficiency of economic life cycle of renewable energy infrastructures and to identify the most critical environmental impact factors in this process. For this purpose, an original decision-making model integrating dynamic multi-facet fuzzy sets, Bayesian networks and multi-objective optimization on the basis of ratio analysis (MOORA) techniques is developed. This study makes a significant contribution to the literature by presenting an innovative decision support model that systematically identifies the most critical factors for increasing life cycle environmental efficiency in renewable energy infrastructures. Considering multi-facet fuzzy sets allows dynamic redefinition of membership degrees for different scenarios (negative, positive, unstable, natural). This provides better modelling of uncertainty and variability of the decision environment.