Modeling ESG-driven industrial value chain dynamics using directed graph neural networks

Zhizhong Tan, Siyang Liu, Qiang Liu, Min Hu, Xiang Zhang, Wenyong Wang and Bin Liu ()
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Zhizhong Tan: Macau University of Science and Technology
Siyang Liu: Southwestern University of Finance and Economics
Qiang Liu: Macau University of Science and Technology
Min Hu: Southwestern University of Finance and Economics
Xiang Zhang: Southwestern University of Finance and Economics
Wenyong Wang: Macau University of Science and Technology
Bin Liu: Southwestern University of Finance and Economics

Financial Innovation, 2025, vol. 11, issue 1, 1-23

Abstract: Abstract This study explores the dynamics of industrial value chains from the perspective of directed graph neural networks (DGNNs). This study focuses on the effects of environmental, social, and governance (ESG) factors on industrial value extension. The industrial value chain, which encompasses a comprehensive network ranging from raw material procurement to final product distribution, is characterized by intricate interconnections between internal and external stakeholders. Traditional quantitative methods, such as input–output (I–O) analysis, often fail to capture the complexity of these relationships. We model the shock propagation across the industrial chain by employing DGNNs, defining two types of hidden representations: demanding (incoming) and supplying (outgoing) embeddings. This dual representation enables a nuanced understanding of how ESG shocks influence the extension of industrial value from downstream and upstream industrial sectors. Our findings highlight potential areas for optimization and value-added opportunities, offering insights that can support more informed decision-making by corporations and policymakers as they navigate the evolving industrial landscape.

Keywords: Industrial chain; ESG; GNNs; Cross-attention (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1186/s40854-025-00783-y

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