Integration of ESG Ratings and Green Fintech Applications: Dynamic Assessment and Decision Support Based on Gradient Boosting Model Optimization

Research Article
Open access

Integration of ESG Ratings and Green Fintech Applications: Dynamic Assessment and Decision Support Based on Gradient Boosting Model Optimization

Junjie Guan 1*
  • 1 School of Management, Harbin Institute of Technology, Harbin, China    
  • *corresponding author 2024112206@stu.hit.edu.cn
AEMPS Vol.170
ISSN (Print): 2754-1177
ISSN (Online): 2754-1169
ISBN (Print): 978-1-80590-019-1
ISBN (Online): 978-1-80590-020-7

Abstract

This study proposes a dynamic assessment framework based on gradient boosting model optimization, which aims to address the core challenges of inefficient modeling of discrete features, insufficient model dynamics, and algorithmic ethical risks in ESG ratings. The dynamic feature engineering by integrating the embedding layer and adaptive sub-boxing, combined with incremental learning and integration strategies, significantly improves the characterization efficiency of high-dimensional sparse features and the real-time response capability of the model. Blockchain technology is introduced to ensure data credibility, and the model robustness is strengthened by Stacking and SMOTE to provide technical support for risk pricing and compliance decision-making of green financial instruments. The empirical results show that the optimized framework has significant advantages in terms of dynamic adaptability, feature processing efficiency, and ethical constraints, which promote the scale application of ESG assessment technology in complex financial scenarios. Its dynamic data flow integration and Stacking integration strategy significantly improve the accuracy of portfolio risk management and help financial institutions optimize ESG investment strategies.

Keywords:

ESG rating, green fintech, gradient enhancement modeling, dynamic assessment, feature engineering

Guan,J. (2025). Integration of ESG Ratings and Green Fintech Applications: Dynamic Assessment and Decision Support Based on Gradient Boosting Model Optimization. Advances in Economics, Management and Political Sciences,170,67-76.
Export citation

References

[1]. Zhang R.H., Zhou Y.H, (2024) Green Finance, Carbon Emission Intensity, and Corporate ESG Performance: An Empirical Study Based on Microdata of Listed Enterprises. Social Science, 03,126-139.

[2]. Zheng Y.Q., Wang Z.K., (2025) Research on the implementation mechanism of an ESG rating system based on ChatGPT. Friends of Accounting, (01),88-91.

[3]. Peter Schwendner, Jan Alexander Posth, (2024) Trends in AI4ESG: AI for Sustainable Finance and ESG Technology. Editorial, 72,7-9.

[4]. Guo S.J., Yan C.F., (2024) Green finance, digital transformation, and corporate ESG performance. Business Research, 01,92-100.

[5]. Zhang Y.N., Zhuo P.Y., Liu Z.J., Liu W., Song Y., (2024) Credit default prediction model based on Transformer encoder and residual network. Computer Applications, S1,329-334.

[6]. Wihan van der Heever, Ranjan Satapathy, Ji Min Park, Eric Cambria., (2024) Understanding Public Opinion Towards ESG and Green Finance with the Use of Explainable Artificial Intelligence. Mathematics, 12(9), 3119,6-8.

[7]. Zhang Y.P., He J., (2023) Green financial innovation under the ESG concept. China Finance, 11,62-63.

[8]. Dong Z.F., Wu H.C., Zhen J., Li X.L., Pan C.J., (2024) A Study on the Progress of ESG Policy Exploration and Practice in China. China Environmental Management, 16(01),7-15.

[9]. Ren X.S., (2024) Green bond issuance and corporate ESG performance. Contemporary Economic Management, 46(04),71-74.

[10]. Tristan L., (2024) Environmental, social and governance (ESG) and artificial intelligence in finance: state-of-the-art and research takeaways. Artificial Intelligence Review, 2(57), 76,25-37.

Cite this article

Guan,J. (2025). Integration of ESG Ratings and Green Fintech Applications: Dynamic Assessment and Decision Support Based on Gradient Boosting Model Optimization. Advances in Economics, Management and Political Sciences,170,67-76.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Volume title: Proceedings of the 9th International Conference on Economic Management and Green Development

ISBN：978-1-80590-019-1(Print) / 978-1-80590-020-7(Online)
Editor：Florian Marcel Nuţă
Conference website: https://2025.icemgd.org/
Conference date: 26 September 2025
Series: Advances in Economics, Management and Political Sciences
Volume number: Vol.170
ISSN：2754-1169(Print) / 2754-1177(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).

References

[1]. Zhang R.H., Zhou Y.H, (2024) Green Finance, Carbon Emission Intensity, and Corporate ESG Performance: An Empirical Study Based on Microdata of Listed Enterprises. Social Science, 03,126-139.

[2]. Zheng Y.Q., Wang Z.K., (2025) Research on the implementation mechanism of an ESG rating system based on ChatGPT. Friends of Accounting, (01),88-91.

[3]. Peter Schwendner, Jan Alexander Posth, (2024) Trends in AI4ESG: AI for Sustainable Finance and ESG Technology. Editorial, 72,7-9.

[4]. Guo S.J., Yan C.F., (2024) Green finance, digital transformation, and corporate ESG performance. Business Research, 01,92-100.

[5]. Zhang Y.N., Zhuo P.Y., Liu Z.J., Liu W., Song Y., (2024) Credit default prediction model based on Transformer encoder and residual network. Computer Applications, S1,329-334.

[6]. Wihan van der Heever, Ranjan Satapathy, Ji Min Park, Eric Cambria., (2024) Understanding Public Opinion Towards ESG and Green Finance with the Use of Explainable Artificial Intelligence. Mathematics, 12(9), 3119,6-8.

[7]. Zhang Y.P., He J., (2023) Green financial innovation under the ESG concept. China Finance, 11,62-63.

[8]. Dong Z.F., Wu H.C., Zhen J., Li X.L., Pan C.J., (2024) A Study on the Progress of ESG Policy Exploration and Practice in China. China Environmental Management, 16(01),7-15.

[9]. Ren X.S., (2024) Green bond issuance and corporate ESG performance. Contemporary Economic Management, 46(04),71-74.

[10]. Tristan L., (2024) Environmental, social and governance (ESG) and artificial intelligence in finance: state-of-the-art and research takeaways. Artificial Intelligence Review, 2(57), 76,25-37.

For authors
Guide for authors Policies for authors Aims and scope Subscription Open access policy
For editors
Policies for editors Editorial board
For reviewers
Policies for reviewers Privacy policy

Cookies are used by this site.
Copyright © 2024 EWA Publishing, its licensors, and contributors. All rights reserved, including text and data mining, AI training, and similar technologies.