Enhanced decision model for sustainable energy solutions under bipolar hesitant fuzzy soft aggregation information

Energy sustainability is described as an ability to get the energy supplies without diminishing the ability of future generations to provide themselves with any energy. Preceded by the mentioned notion, this paper will be focused on bipolar hesitant fuzzy soft sets (BHFSS) related to the problem of energy sustainability. It actually was a proposed new mathematical method able to conquer ambiguity and uncertainty while determining the different choices in energy-related decisions. In this way, it lead to more informative and better choices to be made, thus leading to the utilization of sustainable energy systems. The paper introduced basic operations and comparison rules for BHFSS. Furthermore, algebraic norms-based aggregation operators were proposed to make the model more robust and flexible so that it was adaptable to a wide range of energy sustainability decisions. Main characteristics of the BHFSS aggregation operators were discussed in detail. Last but not least, this paper also provided a comparison of the BHFSS-based approach with one of the most popular multi-criteria decision-making (MCDM) approaches known as compromise solution (CoCoSo). This comparison confirmed how BHFSS can control for uncertainty and how it can reflect preferences in a mapped way, which afforded it strengths in uses like choosing renewable power and strategy for lowering $C{O}_2$ emissions.