Organiser:
Biography: Young-Choon Kim is a Professor at the Graduate School of Technological Innovation Management, Ulsan National Institute of Science and Technology (UNIST), Republic of Korea. His research examines how organizations innovate and adapt in response to technological and institutional change, integrating perspectives from technology and innovation management. His recent work focuses on artificial intelligence (AI) and climate technologies with particular interest in how AI can accelerate clean innovation. His research has been published in global journals, including Organization Science, Management Science, and Research Policy.
Biography: Ji-Bum Chung is a Professor at the Department of Civil, Urban, Earth, and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Republic of Korea. His research areas include risk communication, disaster management, climate change, and AI applications. He is a member of several government advisory committees. His current and future research interests include domain specific benchmark for safe AI and Longitudinal Public Opinion Research and Tracking of Reactions, Attitudes, and Insights Toward AI (PORTRAIT-AI Project).
Introduction:
Artificial intelligence (AI) and the
energy transition are among the most
transformative forces shaping contemporary
societies. While AI is accelerating
innovation across industries, it is also
driving unprecedented demand for
electricity, computing infrastructure, and
critical resources. Conversely, the
transition toward low-carbon energy systems
is increasingly influencing the development,
deployment, and governance of AI
technologies. Rather than evolving
independently, AI and energy systems are
co-evolving through complex interactions
among technological innovation,
organizations, markets, institutions, and
public policy.
This special session provides an
interdisciplinary forum for advancing an
evidence-based understanding of the
governance of AI-energy co-evolution. We
invite research that examines how
organizations, governments, industries, and
society respond to the opportunities and
challenges arising from these intertwined
transformations. The session particularly
welcomes empirical studies that leverage
large-scale datasets, patent and publication
databases, firm-level information, network
analysis, longitudinal data, simulation, and
other quantitative or mixed-method
approaches to generate rigorous evidence on
AI-energy interactions.
By integrating perspectives from
sustainability transitions, innovation
studies, organizational research, public
policy, finance, and operations, the session
seeks to develop a coherent understanding of
how AI and energy systems evolve together
and how effective governance can foster
resilient, low-carbon, and sustainable
societies.
Topics:
Related topics for this special
session (but not limited to):
AI-Energy Co-evolution and Socio-technical
Systems
• Co-evolution of AI and energy systems
• AI-enabled sustainability transitions
• Socio-technical system transformation
• Technological innovation and energy
transition
• Innovation ecosystems for sustainable
development
Governance, Organizations, and Markets
• Governance of AI-energy systems
• Organizational adaptation to AI and energy
transition
• Public policy and technology governance
• Sustainable finance and investment for AI
and energy transition
• Industrial transformation and business
model innovation
• Supply chains and operations for
sustainable AI ecosystems
• Responsible AI and environmental
governance