Assessing the Renewable Energy Sources for Sustainable Energy Generation Systems: Interval-Valued Q-Rung Orthopair Fuzzy SWARA-TOPSIS

Abstract

Renewable Energy Sources (RESs) help decarbonize power systems, but selecting among them is a challenging decision problem due to multiple, often conflicting, technical, economic, environmental, and health-related criteria. Consequently, numerous studies in the literature have attempted to address this decision-making issue using objective, subjective, and fuzzy decision-making procedures. However, there are still unaddressed research gaps in the literature, particularly regarding the explicit modeling of expert hesitation and ambiguity in real-world RES selection cases. The current study develops a decision-making model based on Step-wise Weight Assessment Ratio Analysis (SWARA) and Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) methods integrated with Interval-Valued q-Rung Orthopair Fuzzy Sets (IV-q-ROFSs) to fill these gaps. Unlike previous studies that have predominantly applied conventional fuzzy MCDM techniques, our model introduces the first integration of IV-q-ROFS into RES selection. This novelty enables a more accurate representation of expert hesitation and uncertainty. The study is applied to a real industrial case in Turkey, where six RES alternatives are evaluated across 43 criteria by five senior experts under the supervision of a three-member professionals’ board. Furthermore, the structured robustness check and systematic literature mapping ensure that the proposed approach is methodologically robust and practically relevant for policymakers and energy planners. The application results of the developed model demonstrate that the estimated energy production potential of the RES and the effects of carcinogens generated from utilizing these energy sources are the critical factors influencing the selection of the most appropriate RESs. Solar energy ranked first among the alternatives. The applicability and validity of the developed model are examined by a comprehensive robustness check consisting of tests of sensitivity, comparison, and resilience to the rank reversal problem. Overall, the study provides (i) a novel methodological framework integrating IV-q-ROFS with SWARA and TOPSIS, (ii) empirical evidence from a comprehensive real-world RES selection case, and (iii) policy-relevant insights into the drivers of renewable energy adoption. © 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.