Impact of Progress of AI on Circular Bioeconomy: Applications, Challenges, and Future Directions

Charles Cao (), Jie Zhuang (), Jie Jayne Wu () and Wenjun Zhou ()
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Charles Cao: The University of Tennessee, Department of Electrical Engineering and Computer Science
Jie Zhuang: The University of Tennessee, Department of Biosystems Engineering and Soil Science
Jie Jayne Wu: The University of Tennessee, Department of Electrical Engineering and Computer Science
Wenjun Zhou: University of Tennessee, Haslam College of Business

Chapter Chapter 18 in Handbook of Circular Bioeconomy, 2026, pp 397-440 from Springer

Abstract: Abstract The global economy faces unprecedented pressure from climate change, resource depletion, and environmental degradation, demanding a shift toward sustainable models. Circular Bioeconomy (CBE) has emerged as a transformative strategy, integrating principles of the bioeconomy (sustainable use of renewable biological resources) with the circular economy (designing out waste, keeping materials in use, regenerating natural systems) to address these multifaceted challenges. Simultaneously, Artificial Intelligence (AI) is rapidly evolving, with recent advancements in Generative AI (GenAI) for novel design, Large Language Models (LLMs) for knowledge synthesis and communication, and AI Agents for autonomous decision-making and system control, offering powerful new capabilities. This chapter explores the critical intersection of these two dynamic domains. It examines how the advanced AI, including these specific technologies, can significantly accelerate CBE transition by enabling more sophisticated approaches to the design of bio-based products and processes, optimization of complex value chains, predictive modeling of environmental and economic impacts, intelligent automation of bioprocessing and waste valorization, enhanced monitoring of resources, and fostering improved collaboration among diverse stakeholders. Furthermore, the chapter investigates key applications such as AI-driven material discovery, supply chain resilience, precision agriculture, and decision support systems, while also addressing the inherent challenges, including data scarcity, model reliability, and implementation hurdles, that must be navigated to fully harness AI’s potential for a sustainable and circular future.

Keywords: Circular Bioeconomy (CBE); Artificial Intelligence (AI); Generative AI; Large Language Models (LLMs); AI Agents; Sustainable development; Resource optimization; Knowledge synthesis (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-032-07112-5_18

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