The prioritization of solutions for reducing the influence of climate change on the environment by using the conception of bipolar complex fuzzy power Dombi aggregation operators

This article aims to examine the causes and consequences of climate change on the environment and then prioritize the solution for reducing its influence on the environment under the setting of the bipolar complex fuzzy set (BCFS). Climate change is illustrated by the transformation of wind, temperature, and precipitation; regardless of its natural causes, it is generally connected with human movement and ozone-depleting substances. It is an important task to determine the reasons, effects, and, especially, solutions for reducing the effects of climate change. The prioritization of the solution for reducing the influence of climate change is a multi-attribute decision-making (MADM) dilemma, and for solving such a dilemma, we need a proper MADM technique. Thus, in this study, we first interpreted various aggregation operators (AOs) such as bipolar complex fuzzy (BCF) power Dombi averaging (BCFPDA); BCF power Dombi weighted averaging (BCFPDWA); BCF power Dombi ordered weighted averaging (BCFPDOWA); BCF power Dombi geometric (BCFPDG); BCF power Dombi weighted geometric (BCFPDWG); and BCF power Dombi ordered weighted geometric (BCFPDOWG) and then interpreted an MADM approach based on the invented operators. Furthermore, we studied a numerical example regarding the prioritization of solutions for the reduction of the influence of climate change on the environment and achieved the best solution, i.e., DBCFS−4= ocean and sea protection. Finally, the advantages of this approach are compared with those of other approaches.