Assessment of renewable energy alternatives for sustainable resource policies with knowledge-based expert prioritized quantum picture fuzzy rough modelling

Abstract

Renewable energy investments aim to provide the highest efficiency with limited budgets and resources. This makes it critical to make the most appropriate choice among alternatives. Hence, a priority analysis should be carried out to satisfy this missing gap in the literature. This study aims to identify the most suitable renewable energy alternative for sustainable resource policies by addressing the challenge of uncertainty in decision-making. A novel approach integrating quantum picture fuzzy rough sets, k-means clustering, and multi stepwise weight assessment ratio analysis (M−SWARA) and multi-objective optimization on the basis of ratio analysis (MOORA) methodologies is proposed. Experts are prioritized using K-means clustering based on demographic factors such as education and experience, ensuring realistic weight assignments. Quantum theory is employed to handle uncertainty and minimize information loss, while the M−SWARA method accounts for cause-and-effect relationships in weighting criteria. The findings highlight the critical importance of aligning expert prioritization and advanced fuzzy modeling for robust and adaptive decision-making in renewable energy investments. This study contributes to sustainable resource management by providing a more accurate and adaptable framework for decision-making under uncertain conditions. The integration of quantum theory and fuzzy systems minimizes uncertainty and information loss, which in turn enables more realistic results to be obtained in decision-making processes.