The proposed research fills a significant gap in the decision-making technique for evaluating geothermal energy systems in industrial processes by introducing a new approach involving Hesitant Bipolar Fuzzy (HBF) Sets (HBFSs) with Dombi operators. The existing literature has mostly focused on uncertainty only, overlooking the aspect that decisions tend to be imprecise, bipolar, and hesitant in reality. To overcome this gap, we first introduce Dombi operators in the context of HBFSs, thereby improving the parametric flexibility in handling more complex uncertain information. Based on these operators, we establish an HBF Multi-Criteria Decision-Making (MCDM) method for the ranking of geothermal energy systems. The applicability of our proposed methodology for prioritizing different types of geothermal energy systems for industrial applications is illustrated in a detailed case study that supports the theoretical framework. The benefit of the suggested method is also supplemented by the comparison of the proposed method with the previous methods and evidence of the capability to handle uncertainty and make more precise and confident decisions. This study offers an important theoretical as well as practical contribution to decision-making practices and the choice of sustainable energy systems for geothermal energy options under uncertainty, offering decision-makers a robust framework of analysis. Moreover, we have the following key findings or outcomes of proposed research. • Development of HBF Dombi Weighted Averaging (HBFDWA) operators. • Development of HBF Dombi Ordered Weighted Averaging (HBFDOWA) operators. • Development of HBF Dombi Weighted Geometric (HBFDWG) operators. • Development of HBF Dombi Ordered Weighted Geometric (HBFDOWG) operators. • A case study is performed based on the developed operators to rank geothermal energy systems. • A comparative analysis is performed to show the superiority of the proposed approach. • A sensitivity analysis is discussed to show the influences of the parameter.

Energy Storage System (ESS) is a talented solution to overcome the intermittency (that they do not produce energy all the time) and demand-supply misalliance problems in different renewable energy systems. Selecting the most optimal ESS requires the consideration of different conflicting criteria under uncertainty. This study presents a novel Multi-Criteria Decision-Making (MCDM) framework based on Complex Intuitionistic Fuzzy Rough Sets (CIFRSs) and Schweizer-Sklar aggregation operators to facilitate a more comprehensive and flexible ESS selection process. Specifically, we develop new aggregation operators namely, the Complex Intuitionistic Fuzzy Rough (CIFR) Schweizer-Sklar weighted average and the CIFR Schweizer-Sklar weighted geometric operators to model imprecise, vague, and inconsistent information. CIFR-MCDM methodology captures the intuitionistic, roughness and extra related fuzzy information in one structure. A case study is performed to illustrate the applicability of the suggested method in ranking different ESS alternatives. Comparative analysis with existing approaches confirms the robustness and effectiveness of the proposed framework in handling complex decision environments. The results highlight the potential of the CIFR-MCDM methodology to support informed and reliable ESS selection in renewable energy applications.