AI holds untapped power in building a circular, sustainable bioeconomy

The study identifies ten strategic domains within the circular bioeconomy, ranging from sustainable food systems and biowaste valorization to ecosystem protection and policy development, where AI could offer transformative benefits. These sectors are all deeply interdependent, often involving complex, data-rich operations such as biomass supply chains, renewable energy systems, or bio-based manufacturing processes.

As the world scrambles to align economic growth with climate action, the circular bioeconomy -  a model which merges biological resource use with circular economy principles - has emerged as a beacon for sustainability. It promises to reduce environmental footprints while regenerating ecosystems, yet its implementation remains fragmented, and the digital tools required to optimize it, especially artificial intelligence, are often underutilized or misaligned with real-world challenges.

A new study published in Sustainability titled "A Framework for Assessing the Potential of Artificial Intelligence in the Circular Bioeconomy" addresses this critical gap by introducing a comprehensive framework that systematically maps AI capabilities across ten core bioeconomy domains. The research, led by Munir Shah, Mark Wever, and Martin Espig, positions artificial intelligence not just as a support tool but as a strategic lever to restructure bio-based value chains, optimize resource flows, and elevate system-wide sustainability outcomes.

Where can AI have the greatest impact in the circular bioeconomy?

The study identifies ten strategic domains within the circular bioeconomy, ranging from sustainable food systems and biowaste valorization to ecosystem protection and policy development, where AI could offer transformative benefits. These sectors are all deeply interdependent, often involving complex, data-rich operations such as biomass supply chains, renewable energy systems, or bio-based manufacturing processes.

For example, in biowaste valorization, AI enables the real-time classification of waste types, optimization of anaerobic digestion processes, and discovery of new biocatalysts for waste-to-energy conversion. In food systems, AI models are used for predicting crop yields, monitoring soil health through hyperspectral imaging, and enhancing irrigation strategies, all of which contribute to reducing agricultural water use and food loss.

Meanwhile, AI’s analytical and predictive capabilities are being applied to design and refine bio-based products, from biodegradable packaging to green building materials. These functions are particularly critical in eco-design, where life cycle assessments and product performance simulations are required to close material loops and extend product lifespans.

In total, the framework proposes eight key AI functions, analysis, prediction, monitoring, optimization, automation, classification, interaction, and discovery/design, each mapped to the bioeconomy domains where they can generate the most impact. This structured approach allows stakeholders to identify precisely where to deploy AI tools, and for what purpose, in ways that are actionable and scalable.

How mature are these AI applications and what are the gaps?

Despite significant promise, the study reveals that AI adoption in the circular bioeconomy is still in its early stages. Most of the AI models in use are prototype-level applications confined to controlled settings. The authors note a particularly strong reliance on prediction and optimization tasks, with less attention given to AI’s potential in discovery and design functions such as generating novel materials or system architectures.

This underutilization is most apparent in a framework-led case study on biowaste valorization. An analysis of 50 recent publications showed AI is predominantly used to forecast energy outputs, optimize feedstock use, and classify compost maturity. However, critical applications such as stakeholder engagement, AI-driven system design, or the discovery of new enzymes and biological pathways remain rare.

Moreover, the digital infrastructure necessary to support AI - high-resolution sensors, real-time data feeds, and interoperable databases - is often lacking, especially in rural and developing regions. Many AI models also demand large, high-quality datasets that are difficult to obtain due to the biological variability of feedstocks or the complexity of natural ecosystems.

There are also technological and economic barriers. High-performance AI systems can be costly to deploy, requiring advanced hardware and significant computational power. In many cases, these costs can outweigh immediate benefits, especially for smallholder farmers or underfunded municipalities. As such, the authors advocate for more low-complexity, scalable AI solutions that are tailored for diverse operational contexts across the globe.

What are the strategic priorities for responsible AI in the bioeconomy?

Recognizing the risks of unchecked AI integration, the study emphasizes the importance of ethical and responsible deployment. While AI can be used to monitor biodiversity, optimize material flows, and personalize sustainable consumption recommendations, it also introduces new environmental and social concerns. These include the carbon footprint of AI infrastructures, data privacy vulnerabilities, algorithmic biases, and potential labor displacement.

The authors propose a set of guiding principles to ensure AI in the bioeconomy remains aligned with sustainability goals. These include fostering interdisciplinary collaboration between data scientists, environmental engineers, and policy makers; developing open-access datasets for training robust AI models; and implementing ethical design standards that promote transparency and inclusiveness.

In the case of biowaste, for example, future research should prioritize not only energy optimization but also the design of AI systems that support novel discovery pathways, such as enzyme engineering or AI-assisted biochar development. The study also highlights the potential of combining science-based heuristics with machine learning to improve model interpretability, essential for building trust and validating predictions in dynamic, real-world settings.