Evaluating renewable energy options under spherical fuzzy environment: A novel integrated DEMATEL, SWARA, and PROMETHEE approach

Abstract The selection of renewable energy sources (RES) constitutes a strategic decision with significant implications for national sustainability objectives and energy security. This process is inherently complex due to the need to consider multiple interdependent criteria and the subjective, often uncertain, evaluations of decision-makers (DMs). To address these complexities, recent research emphasizes the development of advanced analytical frameworks capable of capturing uncertainty and elucidating causal relationships among evaluation criteria. This study employs Spherical Fuzzy Sets (SFS) to more effectively represent ambiguity in expert opinions. Given that subtraction is not defined within SFS, a distance-based mechanism for set comparison is utilized. This approach facilitates the development of two enhanced techniques: Improved Spherical Fuzzy Step-Wise Weight Assessment Ratio Analysis (I-SF-SWARA) and Improved Spherical Fuzzy Preference Ranking Organisation Method for Enrichment Evaluations (I-SF-PROMETHEE). The integrated framework initially examines causal interrelationships among criteria using the SF-DEMATEL (Spherical Fuzzy Decision-Making Trial and Evaluation Laboratory) method, determines the relative importance of influencing criteria via I-SF-SWARA, and subsequently ranks renewable energy alternatives using I-SF-PROMETHEE. Empirical results from the Istanbul case study indicate that wind energy is the most suitable alternative, achieving the highest net outranking flow value (1.5978), closely followed by solar energy (1.5717), while other alternatives demonstrate comparatively lower performance. Furthermore, the weighting analysis identifies technological complexity (0.1723), technological maturity (0.1605), and reliability (0.1379) as the most influential criteria in the decision-making process. The applicability of the proposed model is demonstrated through an empirical case study for Istanbul, complemented by a sensitivity assessment to evaluate the robustness of the results.