Shunfeng Song

Papers

Total Citations

232

H-Index

About

Shunfeng Song is a leading scholar at the intersection of artificial intelligence, environmental economics, and sustainable development. His most-cited work, "The effect of artificial intelligence on carbon intensity: Evidence from China's industrial sector" (2021), has garnered over 230 citations, establishing him as a pivotal voice in understanding how AI technologies can drive decarbonization. Song’s research rigorously examines the dual role of AI in industrial systems—both as a tool for optimizing energy efficiency and as a potential driver of increased consumption. By leveraging empirical evidence from China, he demonstrates that AI adoption can significantly reduce carbon intensity, offering critical insights for policymakers and industry leaders navigating the green transition. Beyond this landmark study, Song’s broader portfolio explores urban economics, regional development, and the socioeconomic impacts of technological change. His work is widely cited for its methodological rigor and policy relevance, making him an essential reference for researchers studying the nexus of innovation and environmental sustainability. Song continues to shape debates on how emerging technologies can be harnessed to achieve climate goals without compromising economic growth.