A comparative approach for the identification and selection of sustainable energy alternatives based on green criteria: Classical and fuzzy WASPAS method

Increasing energy demand and environmental concerns are guiding decision-makers toward sustainable energy systems. In this study, classical and fuzzy WASPAS methods were jointly employed to evaluate sustainable energy sources and determine the most suitable alternative for Kilis, Turkey. The alternatives considered include solar energy (A1), wind energy (A2), biomass energy (A3), hydroelectric energy (A4), and geothermal energy (A5). The criteria selected are carbon emissions (C1), investment cost (C2), energy efficiency (C3), resource continuity (C4), environmental impact (C5), and local acceptance and social impact (C6). For the classical WASPAS method, criterion weights were assigned as C1=0.15, C2=0.15, C3=0.20, C4=0.20, C5=0.15, and C6=0.15, while for fuzzy WASPAS, the criterion weights were defined using triangular fuzzy numbers. According to the classical WASPAS results, the Q_i scores for the alternatives were A1=1.00000, A2=0.85859, A3=0.63089, A4=0.77590, and A5=0.68140, with solar energy ranking first. In the fuzzy WASPAS analysis, the E(Q) scores were A1=2.188, A2=1.542, A3=1.137, A4=1.278, and A5=1.188, confirming the superiority of solar energy when uncertainties are considered. The results obtained from both classical and fuzzy WASPAS methods were compared, and taking into account the geographical, economic, and social conditions of Kilis, solar energy emerged as the most advantageous alternative. The comparative performance analysis indicates that evaluations conducted in a fuzzy environment provide a more realistic and flexible assessment by incorporating uncertainties. This approach offers decision-makers strategic information based on robust foundations for sustainable energy planning. The study demonstrates the effectiveness of the fuzzy WASPAS method in multi-criteria decision-making and contributes to the development of sustainable energy policies at the local level. The results allow decision-makers to balance environmental, economic, and social factors while ensuring practical guidance for regional sustainable energy strategies. Furthermore, the flexibility provided by fuzzy WASPAS in handling uncertainties enables more reliable and robust evaluations in multi-criteria decision-making processes.