A Strategic Decision-Making Model for Hydrogen Energy Technology Selection Under Sustainability Criteria

Abstract

In recent years, the transition toward alternative energy sources has accelerated due to global warming, carbon emissions and dependence on fossil fuels. In this context, hydrogen energy emerges as one of the most prominent energy sources, with features such as near-zero carbon emissions, energy diversity, security of supply, storability, portability, producibility from renewable sources, and high energy density. Hydrogen energy can be derived from different sources using different methods. Because each method has its own merits and drawbacks, selecting the best hydrogen energy production method is a significant challenge for decision-makers. The primary aim of this study is to develop a novel, robust, and integrated Fermatean fuzzy Multi-Criteria Decision-Making (MCDM) framework to identify the most suitable hydrogen production technology under economic, environmental, and socio-political sustainability criteria. While the FF-Step-wise Weight Assessment Ratio Analysis (SWARA) is utilized to determine the weights of the main and sub-criteria in an environment of uncertainty, the FF-Additive Ratio Assessment System (ARTASI) is deployed to evaluate hydrogen production technologies. To analyze the robustness and validation of the suggested framework, sensitivity analyses were performed with respect to the criteria weights and the w and beta parameters in the utility function, which can affect results, especially in the ARTASI method. According to the analysis results, Steam Methane Reforming was identified as the most suitable technology for hydrogen production in T & uuml;rkiye due to its low cost, technological maturity, and compatibility with existing infrastructure. Coal gasification ranked second, while electrolysis and biomass gasification ranked third and fourth, respectively. These findings provide policymakers in T & uuml;rkiye with valuable insights into its sustainable hydrogen strategy and demonstrate the practical applicability of Fermatean fuzzy MCDM methods in complex energy decision-making processes.