Changqing Song

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About

Changqing Song is a leading researcher at the intersection of artificial intelligence, energy economics, and sustainable supply chain management. Their seminal work, "Impact of artificial intelligence on renewable energy supply chain vulnerability: Evidence from 61 countries" (2024, 81 citations), provides groundbreaking empirical evidence on how AI technologies can both mitigate and exacerbate vulnerabilities in global renewable energy networks. This study, which analyzes cross-national data, has become a cornerstone for policymakers and scholars seeking to balance technological innovation with supply chain resilience. Song’s research uniquely bridges the gap between machine learning applications and energy security, offering actionable frameworks for reducing systemic risks in clean energy transitions. Their work is widely cited for its rigorous methodology and timely relevance, particularly as nations accelerate decarbonization efforts. By quantifying AI’s dual role—as a tool for optimization and a potential source of new fragility—Song has shaped critical debates in energy policy and operations management. This contribution positions them as a vital voice in understanding how emerging technologies can be harnessed to build more robust, sustainable energy systems for the future.