A comprehensive review of fuzzy logic applications in ESG investment decision-making with a novel hybrid model application

In recent years, there has been a rise in the significance of environmental, social, and governance (ESG) considerations when making investment decisions, suggesting a shift towards more moral and sustainable investing practices. The application of fuzzy logic (FL) in ESG investment decision-making is thoroughly examined in this review, which highlights FL's inherent capability to handle the subjectivity and uncertainty present in ESG data. We examine several FL approaches, such as fuzzy inference systems, fuzzy multi-criteria decision-making (MCDM), and FL-based grading models, and show how they contribute to a more adaptable and sophisticated assessment of corporate sustainability. This work presents a novel Hybrid Fuzzy-Neural Network (Fuzzy-NN) Model for advanced ESG analysis, building on these insights. In contrast to more conventional methods such as ANFIS, our model empirically validates the effectiveness and improved performance of this hybrid method in predicting financial outcomes using real NIFTY 50 time series financial data (2020-2024) with ESG details extrapolated from 2023-2024. The findings demonstrate how it can improve investment decisions by offering both reliable financial projections and easily comprehensible ESG evaluations. Lastly, we address relevant research gaps and suggest potential avenues for future work in the developing field of sustainable finance.