ESG Digital Evaluation System for Small and Medium-sized Enterprises

A Dynamic Rating Method Based on Deep Learning and Game Theory Combined Weighting

DOI

10.2991/978-94-6239-719-4_31How to use a DOI?

Keywords

ESG evaluation; small and medium-sized enterprises; deep learning; game theory; combined weighting; dynamic adaptation; green finance

Abstract

In response to the technical pain points of unstructured ESG information, fragmented data, uneven disclosure quality, insufficient robustness of traditional rating methods, and difficulty in adapting to industry differences, this paper constructs an end-to-end ESG digital evaluation and green finance decision support system. The system takes multi-source heterogeneous data as input, uses a fine-tuned RoBERTa-WWM model to automate the extraction and standardization of unstructured ESG information, forming a computable feature matrix; it concurrently generates subjective and objective weights through the Analytic Hierarchy Process (AHP) and an improved entropy weighting method; introduces game theory mechanisms to solve for Nash equilibrium with the goal of minimizing the sum of squared deviations, obtaining optimal combined weights; based on this, it designs industry dynamic adjustment factors to achieve adaptive calibration of ESG evaluation across industries; ultimately outputting a standardized ESG comprehensive score in the range of [0,1] and mapping it to financial risk control levels. Experimental results show that the proposed system outperforms single weighting methods in weight consistency, score stability, and anti-interference, reducing ESG governance costs for small and medium-sized enterprises, improving evaluation accuracy, and minimizing human interference, providing a lightweight, scalable, and traceable technical path for the micro-implementation of green finance.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Siyuan Shen
AU  - Xinying Pan
AU  - Bei Tang
PY  - 2026
DA  - 2026/07/09
TI  - ESG Digital Evaluation System for Small and Medium-sized Enterprises
BT  - Proceedings of the 2026 6th International Conference on Enterprise Management and Economic Development (ICEMED 2026)
PB  - Atlantis Press
SP  - 268
EP  - 278
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-719-4_31
DO  - 10.2991/978-94-6239-719-4_31
ID  - Shen2026
ER  -