Evaluation of renewable energy sources using spherical fuzzy multi-criteria decision-making methods

The increasing global demand for energy, the environmental impacts of fossil fuels, and sustainability goals have placed renewable energy sources at the core of energy policies. The effective management of these resources necessitates multi-criteria decision-making (MCDM) systems that can simultaneously evaluate technical, economic, environmental, and social dimensions. However, information deficiencies in such decision environments increase uncertainty, rendering classical methods inadequate and highlighting the need for fuzzy logic-based approaches. In this context, the Spherical Fuzzy Best-Worst Method (SF-BWM) was developed to model decision-makers' judgments more effectively under uncertainty. For evaluating decision consistency, a Score-Based Consistency Index was proposed. According to the criteria weights obtained through SF-BWM, the most prioritized criterion was efficiency (C2), followed by cost (C1). In the alternative evaluation process, three novel Spherical Fuzzy MCDM methods were developed as original contributions to the literature: Spherical Fuzzy ARAS (SF-ARAS), Spherical Fuzzy CRADIS (SF-CRADIS), and Spherical Fuzzy COBRA (SF-COBRA). These methods were restructured based on spherical fuzzy set theory and are capable of modeling uncertainty in a multidimensional way. The performance of these proposed methods was comparatively assessed alongside widely used methods such as Spherical Fuzzy TOPSIS (SF-TOPSIS) and Spherical Fuzzy WASPAS (SF-WASPAS). Ranking results were tested using Spearman correlation analysis, and the findings revealed a high level of agreement among all methods. Among the alternatives, A1 (Solar Energy) was identified as the top priority. This study thus contributes to the literature by introducing novel methods and offers a practical decision support framework for renewable energy planning under uncertainty.