Yuegang Song

Henan Normal University

Papers

2

Total Citations

84

H-Index

2

About

Dr. Yuegang Song is a leading scholar at the intersection of artificial intelligence, renewable energy systems, and global value chain resilience. Their pioneering research investigates how emerging technologies—particularly AI and industrial robotics—reshape the vulnerability and robustness of energy supply chains and manufacturing networks. In their highly cited 2024 study, Dr. Song analyzed data from 61 countries to demonstrate that AI adoption can significantly reduce renewable energy supply chain vulnerabilities, offering critical insights for policymakers and industry leaders navigating the green transition. This work, with 81 citations, has become a foundational reference in sustainable operations management. Dr. Song’s forward-looking 2026 research further explores how industrial robot adoption enhances the resilience of manufacturing global value chains, revealing the dual role of automation in both disrupting and stabilizing international production networks. Their contributions bridge theoretical frameworks with empirical evidence, providing actionable strategies for building more adaptive and sustainable economic systems. Dr. Song’s work is essential reading for researchers and practitioners seeking to understand the complex interplay between technological innovation, energy security, and global trade dynamics.

Research Focus

Key Achievements

2
H-Index
2
Papers
84
Total Citations
42
Avg Citations/Paper
🏆 Most Cited Paper
Impact of artificial intelligence on renewable energy supply chain vulnerability: Evidence from 61 countries
81 citations · 2024
📈 Most Prolific Year: 2024 (1 Papers)
🤝 Key Collaborators: 5
🏛 Institutions: Henan Normal University

Top Papers

  1. 1
  2. 2

Key Collaborators

Contact & Links

Available for collaboration
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