A novel pythagorean fuzzy distance measure for multicriteria decision making in renewable energy resource selection

Selecting the most suitable renewable energy resource is vital for ensuring long-term sustainability, reducing environmental impact, and decreasing dependence on conventional fossil fuels. However, this selection process involves managing complex, uncertain, and imprecise information arising from diverse evaluation criteria. Pythagorean fuzzy sets (PFSs), combined with distance measures, offer a more expressive and flexible framework than traditional fuzzy or intuitionistic fuzzy sets for modeling such uncertainty, particularly in multicriteria decision-making (MCDM) problems. However, existing distance measures for PFSs often suffer from limitations such as zero-divisor problems, counterintuitive outcomes, and violations of axiomatic conditions. To address these issues, this study introduces a novel distance measure for PFSs, whose mathematical properties are thoroughly analyzed to ensure consistency, boundedness, and interpretability. A hybrid Pythagorean fuzzy-stepwise weight assessment ratio analysis?technique for order preference by similarity to ideal solution (PF-SWARA?TOPSIS) framework is proposed, where the SWARA method determines the importance weights of the criteria, and the TOPSIS method ranks the alternatives using the proposed distance measure. The approach is applied to evaluate five renewable energy resources based on multiple criteria. The results indicate that solar energy (alternative D1) is the most preferred option, followed by the ranking order: D1 > D3 > D4 > D2 > D5. Comparative analysis with existing methods validates the robustness and effectiveness of the proposed model, offering a reliable tool for sustainable energy planning under uncertainty. Finally, the study highlights the future potential of the proposed distance measure and methodology, providing insights for further advancements in renewable energy resource selection.