Fermatean fuzzy type entropy-based new integrated decision making method with analysis of energy poverty in T\"urkiye application

Abstract

Income-based poverty indicators are insufficient to fully explain the multifaceted socio-economic issue of energy poverty. The ability of households to obtain and use energy services sustainably is significantly influenced by structural factors like building efficiency, energy prices, climate, and regional disparities. Moreover, the assessment of energy poverty inherently involves uncertainty, vagueness, and subjective judgments, which limit the applicability of classical deterministic evaluation approaches. This paper proposes a novel, integrated MCDM framework based on FFSs to address these issues. To objectively assess the uncertainty present in FF-information, a novel FF-entropy metric is first presented. The proposed entropy employs a nonlinear structure, enabling a more flexible and sensitive representation of fuzziness in complex decision environments. The subjective weights obtained from the FF-PIPRECIA approach are then blended with the entropy-based objective weights. Lastly, options are ranked according to their relative usefulness in relation to ideal and anti-ideal solutions using the FF-MARCOS approach. The proposed framework is applied to the regional evaluation of energy poverty in Turkiye, considering seven geographical regions and six key criteria: income level, energy prices, energy efficiency, building efficiency, climate, and urbanization. Sensitivity analysis, weight perturbation analysis, and entropy-criterion dominance analysis validate the robustness and stability of the suggested model across various parameter settings. For policymakers and practitioners seeking to develop focused, effective, and socially just energy policies amid uncertainty, the proposed method provides a robust, adaptable decision-support tool.