Green Supply Chains: Feature Analysis and Key Node Identification in Multi-Layer Supply Chain Networks Based on Fused Multi-Scale Metric

Nowadays, the stability, efficiency, and costs of supply chains and their adaptability to market fluctuations have become challenging for global enterprises. Still, supply chain experts have paid little attention to how inter-layer information, intrinsic features of real-world networks, and ESG performance affect the significance of node identification. The paper responds to this above gap by proposing [and testing] a novel modified fused multi-scale metric (FMSM) with environmental, social, and governance performance (ESG) indicators to identify important nodes within multi-layer supply chain networks (MLSCNs). Using real-time data (2012-2022) of 4,562 observations (including 2,276 suppliers and 2,109 customers) from Chines A-shared list firms, the model accurately identified 35 enterprises in the middle layer as important nodes, further validated by a cascading failure test. A comparative analysis demonstrated superior network, nodes, and predicting efficiency of the FMSM against other centrality and structural hole metrics, however it is constrained by its dependency on both data quality and quantifiable metrics. The model efficiently highlighted the unexplored complexities of multi-layer supply chain networks characterized by sparsity, hierarchy, and community structures. Additionally, the model indicated that identified important nodes exhibited strong and stable ESG performance, particularly in governance and social responsibility, concentrated in manufacturing, technology, energy, chemicals, and pharmaceutical sectors.