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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年8月7日

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  • Zhizhong Tan,
  • Siyang Liu,
  • Qiang Liu,
  • Min Hu,
  • Xiang Zhang,
  • Wenyong Wang,
  • Bin Liu

摘要

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.