An MCDM-based Optimization Approach for Identifying Efficient Renewable Energy Sources under Fermatean Fuzzy Environment

In recent decades, the growing global demand for energy and the depletion of conventional fossil fuel resources have intensified the need for sustainable and renewable energy alternatives. Consequently, identifying efficient renewable energy sources has become a critical challenge for policymakers and energy planners worldwide. This study evaluates major renewable energy sources, including solar, wind, tidal, hydropower, geothermal, and biomass energy, with the aim of ranking them according to their technical efficiency. To achieve this objective, the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is applied within a Fermatean fuzzy framework to address uncertainty and ambiguity in expert judgments. Furthermore, a comparative analysis under different uncertainty scenarios is conducted to validate the robustness and reliability of the obtained results. The proposed approach provides a systematic decision-support framework for researchers, energy-sector authorities, and policymakers to facilitate sustainable and informed energy planning