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

Ruchika Lochab; Luckshay Batra

doi:10.3934/jimo.2025134

Article Contents

2025, Volume 21, Issue 11: 6348-6383. Doi: 10.3934/jimo.2025134

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

Ruchika Lochab^1, and
Luckshay Batra^2, ,

1.
IMI Delhi, Qutab Institutional Area, New Delhi-110016, India
2.
School of Engineering and Technology, BML Munjal University, Haryana-122413, India

^*Corresponding author: Luckshay Batra
^*Corresponding author: Luckshay Batra

Received: November 2024

Revised: July 2025

Early access: September 2025

Published online: September 2025

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Abstract

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.

Keywords:

Mathematics Subject Classification: Primary: 03E72; Secondary: 90C70, 90B50, 68T07, 91G50, 91G60.

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Figure 1. Membership functions for input variables and output ESG score

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Figure 2. Numerical results of ANFIS, and the Hybrid Fuzzy-NN Model based on NIFTY 50 data

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Figure 3. Fuzzy ESG score distribution on the basis of various sectors

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Table 1. Fuzzy scale for likelihood and impact

Linguistic Term	Fuzzy Number
Very Low	(0, 0, 0.2)
Low	(0, 0.2, 0.4)
Medium	(0.3, 0.5, 0.7)
High	(0.6, 0.8, 1)
Very High	(0.8, 1, 1)

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Table 2. Fuzzy ESG Score Statistics by Sector

Sector	Median FIS ESG Score	Interquartile Range (IQR)	Number of Companies
Energy	68.50	10.20	15
Industrials	62.10	15.50	12
Healthcare	75.30	8.00	10
Basic Materials	55.80	7.50	8
Financial Services	70.90	12.30	20
Consumer Cyclical	61.20	11.80	15
Communication Services	58.70	9.10	7
Consumer Defensive	65.40	6.20	9
Technology	78.10	5.00	18
Utilities	52.60	4.50	14

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