Scholarlink

<p>Predicting rare yet high-impact risks in low-carbon supply chains (LCSCs) is critical for sustainable operations but challenged by data imbalance and complex interdependencies. This study proposes a Meta-Learning-based Graph Convolutional Network on Prototype Space (ML-GCNPS) to address these issues. Using a real-world, multi-modal dataset of firm-level carbon emissions and supply-chain relationships, we model the LCSC as a graph. The model first extracts node features and embeds them into a prototype space for enhanced class discrimination. An adaptive Vertex-to-Edge (V2E) network then constructs the graph topology, enabling risk propagation via GCN. Extensive experiments demonstrate that ML-GCNPS significantly outperforms state-of-the-art baselines, achieving an AUPRC of 0.850 and reducing the False Negative Rate (FNR) to 0.080. These results, coupled with a low Weighted Average Cost (WAC) of 45, confirm its practical value for accurate and cost-effective risk early-warning.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Low-carbon supply chain logistics risk prediction using meta-learning-based graph convolutional network on prototype space

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月24日

查看全文
  • Yueqi Wang,
  • Yonghe Sun

摘要

Predicting rare yet high-impact risks in low-carbon supply chains (LCSCs) is critical for sustainable operations but challenged by data imbalance and complex interdependencies. This study proposes a Meta-Learning-based Graph Convolutional Network on Prototype Space (ML-GCNPS) to address these issues. Using a real-world, multi-modal dataset of firm-level carbon emissions and supply-chain relationships, we model the LCSC as a graph. The model first extracts node features and embeds them into a prototype space for enhanced class discrimination. An adaptive Vertex-to-Edge (V2E) network then constructs the graph topology, enabling risk propagation via GCN. Extensive experiments demonstrate that ML-GCNPS significantly outperforms state-of-the-art baselines, achieving an AUPRC of 0.850 and reducing the False Negative Rate (FNR) to 0.080. These results, coupled with a low Weighted Average Cost (WAC) of 45, confirm its practical value for accurate and cost-effective risk early-warning.