Investment decision-making is often characterized by uncertainty and the subjective weighting of criteria. This study aims to develop a more robust decision support framework by integrating the Generalized Interval-Valued Hesitant Intuitionistic Fuzzy Soft Set (GIVHIFSS) with the Analytic Hierarchy Process (AHP) to objectively weight criteria and handle multi-evaluator hesitancy. In the proposed GIVHIFSS-AHP model, the AHP is employed to derive mathematically consistent criterion weights, which are subsequently embedded into the GIVHIFSS structure to accommodate interval-valued and hesitant evaluations from multiple decision-makers. The model is applied to a numerical case study evaluating five investment alternatives. Its performance is assessed through a comparative analysis with standard GIVHIFSS and GIFSS models, as well as a sensitivity analysis. The results indicate that the model produces financially rational rankings, identifying blue-chip technology stocks as the optimal choice (score: +2.4). The comparative analysis confirms its superiority over existing models, which yielded less-stable rankings. Moreover, the sensitivity analysis demonstrates the robustness of the results against minor perturbations in criterion weights. This research introduces a novel and synergistic integration of the AHP and GIVHIFSS. The key advantage of this approach lies in its ability to address the long-standing issue of arbitrary criterion weighting in Fuzzy Soft Set models by embedding the AHP as a foundational mechanism for ensuring validation and objectivity. This integration results in mathematically derived, consistent weights, thereby yielding empirically validated, more reliable, and defensible decision outcomes compared with existing models.

The Citarum River Basin (DAS Citarum) in Indonesia faces significant challenges in waste management, necessitating a circular economy-based approach to reduce land-based pollution, which is critical for achieving the sustainability goals of the blue economy in the basin. This study addresses the complexity and inherent uncertainty in decision-making processes related to this challenge by developing a novel hybrid model, namely the Weighted Fuzzy N-Soft Set combined with the COmbinative Distance-based Assessment (CODAS) method. The model synergistically integrates the weighted 10R strategies in the circular economy, obtained via the Analytical Hierarchy Process (AHP), the capability of Fuzzy N-Soft Sets to represent uncertainty granularly, and the robust ranking mechanism of CODAS. Applied to a case study covering 16 types of waste in the Citarum River Basin, the model effectively processes expert assessments that are ambiguous regarding the 10R criteria. The results indicate that single-use plastics, particularly plastic bags (HDPE), styrofoam, transparent plastic sheets (PP), and plastic cups (PP), are the top priorities for intervention, in line with the high AHP weights for upstream strategies such as Refuse (0.2664) and Rethink (0.2361). Comparative analysis with alternative models, namely Fuzzy N-Soft Set-CODAS, Weighted Fuzzy N-Soft Set with row-column sum ranking, and Weighted Fuzzy N-Soft Set-TOPSIS, confirms the superiority of the proposed hybrid model in producing ecologically rational priorities, free from purely economic value biases. Further sensitivity analysis shows that the model remains highly robust across various weighting scenarios. This study concludes that the WFN-SS-CODAS framework provides a rigorous, data-driven, and reliable decision support tool for translating circular economy principles into actionable waste management priorities, directly supporting the restoration and sustainability goals of the blue economy in river basins. The findings suggest that targeting the high-priority waste types identified by the model addresses the dominant fraction of riverine pollution, indicating the potential for significant waste volume reduction. This research was conducted to directly contribute to achieving multiple targets under SDG 6 (Clean Water and Sanitation), SDG 12 (Responsible Consumption and Production), and SDG 14 (Life Below Water).

Environmentally friendly (green) stock investment has evolved into a global trend over the past few decades, including in the Indonesian capital market. However, the process of selecting sustainability-oriented stocks involves various complex criteria that are often qualitative, subjective, and uncertain. Therefore, an analytical tool is needed to support the decision-making process more adaptively and objectively. This study proposes the Criteria Importance Through Inter-criteria Correlation–Weighted Fuzzy Soft Set (CRITIC-WFSS) integration model, a decision-making method that combines WFSS with the objective, data-driven weighting mechanism of the CRITIC method. In the proposed model, parameter weights are determined by considering data variation (standard deviation) and inter-criteria correlation, ensuring that more discriminative and informative parameters receive higher weights. The model was applied to data on environmentally friendly stocks in the SRI-KEHATI Index, obtained from the Indonesia Stock Exchange (IDX) official website, to evaluate and identify stocks with optimal performance. The model’s performance is evaluated through a comparative study with the AHP-WFSS and Entropy–WFSS methods, complemented by a sensitivity analysis. The results show that UNVR ranked highest with a perfect score of 1, indicating an optimal balance between financial performance and sustainability. Furthermore, a comparative study demonstrated that CRITIC-WFSS can generate rankings that are more reliable, appropriate, and logical than those generated by two comparison methods. Meanwhile, the results of the sensitivity analysis indicate that the CRITIC-WFSS model demonstrates strong robustness to variations in input parameters, ensuring stable rankings. The model shows significant potential to support more accurate and transparent investment decision-making by generating consistent stock rankings based on a balanced integration of financial, and sustainability (environmental, social, and governance (ESG)) aspects. This research was conducted in order to support the achievement of various goals through SDG 8 (Decent Work and Economic Growth).

The rapid growth of Digital Financial Services (DFSs), including what is occurring in Indonesia, necessitates evaluation methods that are capable of objectively and systematically handling multiple assessment criteria. Therefore, this study aimed to propose a hybrid FSS–CRITIC–TOPSIS framework for selecting optimal DFSs. Fuzzy soft sets (FSSs) were used to model uncertainty and subjectivity in criterion assessments. The Criteria Importance Through Inter-criteria Correlation (CRITIC) method determined the weights objectively based on the degree of contrast and inter-criteria correlation. Subsequently, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was used to rank the alternatives based on the closeness to the ideal solution. The incorporation led to a formally defined decision operator, $\mathcal{T}$, which mapped FSS to complete preference orderings while ensuring provable stability and strong discriminative properties. The framework was applied to five major Indonesian digital wallets, namely ShopeePay, GoPay, OVO, LinkAja, and DANA, as well as being evaluated across five criteria. This framework identified DANA as the optimal alternative, with a score of 0.9282, followed by ShopeePay (0.8354) and GoPay (0.6958). Comparative analysis with other methods showed a near-perfect ranking correlation ($\rho =0.9–1$), with a more proportional score distribution and ranking results that reflected actual conditions. Sensitivity analysis also confirmed robustness, with ranking changes remaining logically consistent underweight variations. In conclusion, the FSS-CRITIC-TOPSIS framework provided an effective, mathematically rigorous method for multi-criteria decision-making (MCDM) under uncertainty, which applied to digital wallet selection as well as potential extension to broader evaluation contexts supporting SDGs 8, 9, and 10.

Effective environmental sustainability decision-making requires the consideration of both positive and negative information, as well as the integration of weighting methods, to ensure decisions are accurate and representative of real-world conditions. This study presents a Systematic Literature Review (SLR) on the development of the Weighted Bipolar Fuzzy Soft Set (WBFSS) framework for environmental sustainability decision-making. Articles were retrieved from three databases: Scopus, ScienceDirect, and Dimensions. The screening process adhered to PRISMA 2020 guidelines and identified 27 relevant articles. VOSviewer was subsequently used to conduct a bibliometric analysis, mapping keyword co-occurrences and the structural landscape of research topics. The analysis examined the evolution of Bipolar Fuzzy Soft Set (BFSS) frameworks, their application domains, and the integration of weighting methods within BFSS and related Fuzzy Soft Set (FSS) frameworks. The review found that, although 11 studies addressed environmental sustainability applications, only three explicitly employed BFSS-based frameworks, indicating that the application of BFSS in this domain remains limited. Furthermore, the incorporation of explicit weighting techniques within BFSS remains scarce, particularly for objective, data-driven weighting approaches. These findings provide a comprehensive overview of current research trends, identify important methodological gaps, and support the conceptual development of the WBFSS framework as a direction for future research rather than an established decision-making framework. This study highlights opportunities to advance decision-support methods for environmental sustainability, which may support future climate-related and sustainability-oriented decision-making in the context of Sustainable Development Goal 13 (Climate Action).

The evaluation of corporate bond yields is a multi-criteria decision-making problem involving parameter-based uncertainty and heterogeneity in expert judgments. Approaches, including N-soft sets, hesitant N-soft sets, and hesitant fuzzy N-soft sets, remain limited in their ability to simultaneously represent uncertainty at the level of discrete grade assignment and fuzzy membership degrees. To address these limitations, we proposed a hybrid hesitant fuzzy N-soft set (HHFNSS) framework integrated with a soft yield structure. The proposed model represented each object-parameter pair as a set of possible grade combinations along with corresponding sets of membership values, thereby accommodating two-layer hesitancy within a unified algebraic structure. A formal reduction mechanism was developed to transform the set-based representation into a reduced fuzzy N-soft set, which was subsequently processed using a soft yield-generating function to produce a scalar evaluative index. Based on this framework, a structured decision-making algorithm was constructed to rank alternatives. An application to corporate bond evaluation demonstrated that the proposed model yields stable and discriminative rankings that are consistent with the fundamental financial characteristics of the evaluated firms. Comparative analysis indicated that the framework provides a more comprehensive representation of uncertainty than existing models. In contrast, sensitivity analysis confirmed the robustness of the ranking results against variations in input parameters. Overall, the proposed HHFNSS-based soft yield framework offers a flexible and effective approach for multicriteria evaluation under complex uncertainty conditions. This research aligns with Decent Work and Economic Growth (SDG) 8, specifically Target 8.10, by contributing to the strengthening of domestic financial institutions' capacity for transparent and replicable corporate bond evaluation. The proposed framework supports more efficient capital allocation, which is foundational to sustained economic growth and the creation of decent work opportunities.