Multi-criteria assessment of climate change due to green house effect based on Sugeno Weber model under spherical fuzzy Z-numbers

Abstract

Using the multi-criteria decision-making (MCDM) approach, this research piece addresses the urgent problems of environmental degradation and climate change. The method provides a structured way to examine and compare different criteria and options, which improves the precision of decision-making. In order to make this method even better, we combine Zadeh's Z´-numbers with limitations and reliability factors in an effort to fill the current knowledge vacuum about their maximum potential. We investigate Z´-numbers, which are unexpected and fuzzy, in an effort to use them to our advantage by combining them with spherical fuzzy sets (SFSs). We present the idea of spherical fuzzy Z´-numbers (SFZ´Ns) that allow for flexible and adaptive handling of uncertain data by facilitating fast pairwise comparisons of decision-making options. We develop a full set of operational rules and create aggregation operators (AOs) based on the Sugeno Weber Γ¯-norm and Γ¯-conorm to formalize this new method. We use the average and geometric AOs, two of these operators, to show how our suggested technique works and how practical it is. In decision-making situations including climate change assessment, our comparison study highlights the importance of the suggested operators and approaches, especially in relation to the greenhouse effect. Our study helps us understand Z´-numbers better and how they might be used to handle uncertain data by tackling the interaction between fuzziness and randomness. By emphasizing clarity and specificity, our research provides a solid basis for better decision-making and handling of ambiguity in several fields.