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

Author

Listed:
  • Meili Tang

    (Jiangxi Normal University)

  • Xiaoyuan Li

    (Jiangxi Normal University)

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.

Suggested Citation

  • Meili Tang & Xiaoyuan Li, 2025. "Environmental Risk Identification and Green Finance Development Based on Multi-scale Fusion Recognition Network," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(1), pages 1291-1306, March.
    • Handle: RePEc:spr:jknowl:v:16:y:2025:i:1:d:10.1007_s13132-024-01996-9
    DOI: 10.1007/s13132-024-01996-9
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