Environmental Risk Identification and Green Finance Development Based on Multi-scale Fusion Recognition Network

Meili Tang and Xiaoyuan Li ()
Additional contact information
Meili Tang: Jiangxi Normal University
Xiaoyuan Li: Jiangxi Normal University

Journal of the Knowledge Economy, 2025, vol. 16, issue 1, No 46, 1306 pages

Abstract: Abstract This paper aims to enhance the resilience of financial enterprises against environmental risks by leveraging financial data analysis tools. The approach involves designing environmental risk assessment indicators and rating criteria. The study utilizes a convolutional neural network model extended by a multi-scale feature fusion module to analyze environmental risk information in the industry. The proposed model achieves impressive results with accuracy (Acc), precision (P), recall (R), and F1 scores reaching 99.09, 96.31, 95.32, and 95.64, respectively. These metrics outperform those of comparison models. The success of this model is anticipated to pave the way for the transformation of green finance through automated industry-level environmental risk assessment. Furthermore, the method’s adaptability extends beyond environmental risks, offering a scalable solution for identifying and assessing environmental risks in various contexts.

Keywords: Environmental risks; Convolutional neural network; Green investment; Feature fusion (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13132-024-01996-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jknowl:v:16:y:2025:i:1:d:10.1007_s13132-024-01996-9

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/13132

DOI: 10.1007/s13132-024-01996-9

Access Statistics for this article

Journal of the Knowledge Economy is currently edited by Elias G. Carayannis

More articles in Journal of the Knowledge Economy from Springer, Portland International Center for Management of Engineering and Technology (PICMET)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().