A Framework for Assessing the Potential of Artificial Intelligence in the Circular Bioeconomy

Abstract

The circular bioeconomy (CBE) is an evolving paradigm that promotes sustainable economic development. Artificial intelligence (AI) emerges as an important enabler within this paradigm, offering capabilities that could significantly enhance operational efficiencies and innovation. Despite its recognized potential, the full value of Al across the diverse areas of the CBE remains underexplored. This paper introduces a novel framework for assessing and harnessing the role of Al to facilitate a transition towards a CBE. The framework was developed through an interdisciplinary literature review and conceptual modeling. The framework maps ten key CBE domains against eight core AI functions (such as prediction, optimization, and discovery) that can be leveraged to enhance the circularity of bioeconomic processes. A case study on biowaste valorization, employing a framework-guided literature review methodology, demonstrates the framework’s utility in identifying research gaps and opportunities in using AI. The case study reveals a current emphasis on AI for prediction and optimization tasks, while highlighting significant underutilization in discovery and design functions. The framework can help guide researchers, policymakers, and industry stakeholders in identifying and deploying AI-driven solutions that help support a more sustainable bioeconomy.

1. Introduction

As the world faces urgent challenges from resource depletion, climate change, and environmental degradation, there is a critical need to rethink how societies produce, consume, and manage natural resources [1,2,3]. Traditional economic models, built on a linear “take–make–dispose” model, have proven inadequate in addressing these interlinked crises [3]. These models not only accelerate the depletion of finite resources but also generate vast amounts of waste and greenhouse gas emissions, further destabilizing the planet’s ecological systems [1,2,3].

In this context, the concept of a circular bioeconomy (CBE) is gaining attention for its potential to address environmental challenges while fostering sustainable economic growth [4]. It integrates the principles of the bioeconomy, which promotes the use of renewable biological resources, with those of the circular economy, which emphasizes waste elimination, resource efficiency, and regenerative design (see Figure 1). Together, these approaches form a systemic model that seeks to maximize the value derived from biological resources and biomass through cascading use, valorization of waste streams, and the creation of closed-loop resource utilization in resource-efficient value chains [4,5]. The CBE is increasingly seen as a pathway to decarbonize the economy, reduce dependency on fossil inputs, regenerate ecosystems, and stimulate green economic growth, particularly in sectors such as agriculture, forestry, food, and bio-based industries. Its relevance is further amplified by its alignment with global sustainability strategies, including the United Nations Sustainable Development Goals (UN SDGs) [4,5].

Despite the growing momentum behind the CBE, realizing its full potential remains a complex challenge [1,5]. The transition to a CBE involves managing highly interconnected, dynamic, and data-rich systems such as supply chains for biomass, waste valorization processes, and ecosystem service flows. These systems require constant monitoring, real-time optimization, and multi-criteria decision-making across environmental, economic, and social dimensions. Yet, many CBE initiatives currently rely on siloed approaches, limited data and insight integration, and policy tools, which constrain their scalability and effectiveness [6].

Artificial intelligence (AI) is increasingly recognized as a key enabler for overcoming these limitations [1,5,6,7]. With AI’s ability to analyze large, heterogeneous datasets, generate actionable insights, support autonomous systems, and optimize complex processes, AI holds significant potential to unlock new levels of circularity, efficiency, and innovation across bio-based sectors [7,8]. Recent advances in AI, including the democratization of generative AI tools like ChatGPT 4o, have made these technologies more accessible and applicable across domains critical to the CBE, such as agriculture, forestry, energy, and waste management (OpenAI, 2023) [1,9,10].

Despite AI’s recognized potential as an enabling force in transitioning to a CBE, its integrations across the diverse areas of the CBE remain fragmented, poorly understood, and underutilized, an issue which is often overlooked in both research and practice [11,12,13]. There is a lack of structured frameworks to guide how, where, and to what extent AI can be effectively deployed across circular bioeconomic systems. Moreover, the use of AI in a sustainability context should be approached with care. While offering innovative solutions, AI systems may also introduce new socio-ecological risks. These include the environmental footprint of AI infrastructure, data privacy concerns, algorithmic bias, and implications for labor markets [8,14,15,16,17]. Within the context of the CBE, these risks are not yet well understood and mapped. These gaps limit the effective use of AI technologies in tackling the challenges the CBE aims to resolve.

This paper addresses these gaps by proposing a novel conceptual framework to systematically assess the roles of AI in enabling and accelerating the transition towards a CBE. The framework selected ten key CBE domains such as biowaste valorization and eco-design and maps them against eight core AI functions. The framework also considers enabling conditions for AI readiness and provides actionable guidance to navigate the complexities and opportunities at the AI–CBE interface.

Specifically, the paper aims to accomplish the following:

• Develop a framework that aligns AI capabilities with CBE value domains;
• Identify key enabling conditions that influence AI readiness and value creation in circular systems;
• Demonstrate the framework’s practical application using a case study in biowaste valorization;
• Offer strategic insights to guide public- and private-sector decision-making and to highlight areas where investments in AI are needed to support the CBE transition.

The central research question guiding this inquiry is the following: How can AI technologies be leveraged to enhance circularity, efficiency, and innovation within the circular bioeconomy?

To develop this framework, we draw on an interdisciplinary body of literature outlining the challenges and opportunities at the intersection of AI and the CBE, leveraging the insights and methodologies from foundational works [1,7,9,12,18,19]. While some prior studies have examined the role of digital technologies in the circular economy (e.g., [20]) and there are a few studies that explore specific use cases for AI [7,12], approaches that systematically examine the potential for AI-based solutions within the CBE are lacking. The presented framework addresses this dearth by focusing on the intersection of the circular economy, bioeconomy, and artificial intelligence (see Figure 1).

The presented framework is intended to serve as a strategic tool for researchers, policymakers, and industry stakeholders to navigate the complex, data-rich landscape of the CBE and identify high-impact intervention points for AI applications. By capturing the potential of AI to support circularity, regenerative design, and sustainability-oriented innovation, the framework contributes to ongoing global efforts toward sustainable development. By integrating insights from sustainability science, bioeconomy policy, and AI research, this work provides a foundation for future strategy development, investment prioritization, and implementation at the AI–CBE interface.

The remainder of the paper is organized as follows. Section 2 provides a conceptual background of the circular bioeconomy, artificial intelligence, literature review and research gaps. Section 3 introduces the framework and explains how it can be used to assess AI’s potential roles in the CBE, followed by an example application of the framework in Section 4. Section 5 discusses the practical implications of the paper’s findings and gives recommendations for leveraging AI to support sustainable bioeconomic transformations. Future research directions and opportunities for integrating AI into the CBE are also presented. Finally, Section 6 summarizes the paper’s contributions.

2. Background

2.1. Circular Bioeconomy

The CBE refers to a wide range of sectors and systems dependent on biological resources, such as animals, plants, micro-organisms, and biomass derived from organic waste ([19]; see Figure 2). This includes land and marine ecosystems and the services they provide, primary production sectors that use and produce biological resources (e.g., agriculture, forestry, fisheries, and aquaculture), and economic and industrial sectors that employ biological resources and processes to create food, feed, bio-based products, energy, and services [1,2,3,19].

The CBE represents a shift away from the linear “take–make–dispose” model towards closed-loop resource utilization [21]. It centers on harnessing renewable biological resources—feedstocks like plants, algae, and microbes—to produce food, materials, and energy while minimizing waste through enhanced recycling, repurposing, and reuse of material and by-products, both pre-consumption (in production processes and the supply chain) and post-consumption [22]. This mimics natural ecosystems, where waste from one organism becomes the sustenance for another, fostering a system of cascading resource utilization and efficient nutrient cycling [23]. Key principles underpinning the CBE thus include maximizing the value extracted from biomass through cascading product cycles [24], extending product life spans through reuse and repurposing [25], and employing innovative biological processes to enhance resource recovery and recycling [26]. In seeking to implement these principles within economic processes, CBE approaches aim to contribute to mitigating climate change, reducing environmental waste and pollution, enhancing resource security, and supporting sustainable economic development through green innovation and job creation in “green” sectors, such as recycling industries, renewable energy, etc. [27] (see Figure 2).

2.2. Artificial Intelligence

At its core, artificial intelligence (AI), sometimes called machine intelligence, is an interdisciplinary field that blends computer science, cognitive science, and advanced mathematics to create systems capable of performing tasks traditionally requiring human intelligence [10]. These tasks range from learning, problem-solving, perception, recognizing patterns, autonomy, and understanding natural languages to making decisions and solving complex problems.

The rise of AI is marked by significant milestones from its conceptual beginnings in the mid-20th century to the current era, where its influence spans across various sectors, including the CBE. This expansion has been fueled by breakthroughs in computational power and big data availability, alongside advancements in deep learning and neural networks, making AI a cornerstone of modern technological innovation [28,29].

AI encompasses several subfields, including machine learning (ML), natural language processing (NLP), robotics, and computer vision, each contributing uniquely to its adaptability and functionality. From enhancing predictive modeling and decision-making in ML [10,30] to facilitating complex interactions between humans and machines in NLP, and from executing physical tasks in robotics to interpreting visual data in computer vision, AI’s versatility is evident [31]. The diversity of AI learning methodologies—supervised, unsupervised, and reinforcement learning—underpins its ability to learn from data, uncover hidden patterns, and make informed decisions with minimal human oversight [32,33]. These methodologies have propelled AI from theoretical exploration to practical utility, heralding a new era of autonomous systems and innovation in technology.

AI systems now have analytical capabilities that surpass human capabilities in some areas, which enable the generation of insights and efficiencies that were previously unattainable [31]. This enhances the ability to analyze, monitor, optimize, predict, classify, and automate complex processes. In doing so, AI is an important tool across diverse industries by enabling novel solutions where technology and human ingenuity converge to address complex challenges and unlock new possibilities [34,35]. Specifically, AI approaches offer the following key advantages:

• Handling large and diverse datasets: AI can integrate vast amounts of data from diverse sources to support complex decision-making processes. This capability is, in many respects, superior to traditional methods that may not effectively handle multi-dimensional data or require simplifications that can omit critical nuances. AI applications in biowaste valorization, for example, analyze data from various sources such as biowaste composition, microbial activity rates, and energy yield outputs from different conversion processes to optimize the conversion of biowaste into bioenergy [36,37]. This enables the maximization of energy output while minimizing residuals that require disposal, thus enhancing sustainability within the CBE.
• Efficiency, speed, automation, and scalability: AI can perform tasks such as data analysis and pattern recognition much faster than human analysts and often more accurately than traditional statistical methods. This efficiency is highly valuable in the CBE, especially in smart biorefineries. For example, AI can optimize operational parameters in real time for the conversion of biomass into biofuels, chemicals, and other valuable products [38]. By continuously analyzing data from sensors monitoring temperature, pressure, chemical concentrations, and machinery performance, AI systems can adjust processes nearly instantaneously to maximize output and energy efficiency [38]. This capability significantly enhances the scalability and economic viability of biorefineries, promoting more sustainable industrial practices.
• Adapting to changing conditions: Unlike traditional methods that require explicit reprogramming to adapt to new data, AI systems, especially those using machine learning, continuously evolve as they process more data [39]. This feature allows AI to improve its predictive accuracy over time without human intervention, offering a dynamic edge in rapidly changing scenarios such as the management of changing agricultural resources in a CBE. For instance, AI can optimize water usage and crop rotation by continuously analyzing data from soil sensors, weather forecasts, and crop yield rates [40,41]. This ongoing learning process can enable more efficient resource use and enhanced crop productivity, which is crucial for sustainable agriculture practices.
• Identifying patterns and relationships: AI models, particularly those leveraging deep learning architectures, are adept at processing and analyzing vast and heterogeneous datasets to extract meaningful patterns and insights that often elude traditional approaches. This capacity to discern subtle correlations within complex data landscapes positions AI as a powerful tool for the CBE. For example, AI can analyze vast datasets from genomic sequencing and metabolic pathways to identify potential enzymes in biowaste that can break down plastics or other persistent pollutants [42,43]. This approach speeds up the discovery process and enhances the development of novel bioproducts by pinpointing enzyme candidates that could be missed using conventional biochemical methods, thereby improving waste management and recycling processes.

While AI offers tremendous potential in the CBE, it is crucial to recognize its limitations and acknowledge that traditional approaches may be a better choice in certain situations. AI models typically excel at solving well-defined problems with clear objectives and quantifiable metrics. When the problem is ill defined, lacking clear goals or measurable outcomes, simpler heuristics that rely on domain knowledge and experience rather than complex data patterns can incorporate qualitative judgment and domain expertise may be more effective [39,44]. Additionally, AI models thrive on large and diverse datasets. When data are scarce, their performance can suffer due to overfitting or an inability to generalize. In such cases, traditional techniques may offer comparable or even superior performance. Finally, in situations where the underlying processes are rapidly changing and unpredictable (e.g., predicting rare events), AI models may struggle. The assumptions made during training might no longer hold, leading to inaccurate predictions. Simpler heuristics can be more robust and adaptable to such volatility. A balanced approach that combines AI with simpler algorithms and domain expertise is often the most effective strategy for tackling complex challenges, such as those associated with CBE transitions.

2.3. Literature Review: Research Gaps and Contributions

The convergence of AI, the circular economy (CE), and the bioeconomy has generated growing scholarly interest as a pathway to achieving sustainability goals. Conceptual frameworks that integrate AI into circular systems, primarily in industrial and manufacturing contexts, have been proposed in the recent scholarly literature. For instance, Madanaguli et al. (2024) synthesized research to propose a framework linking specific AI capabilities such as integrated intelligence, process automation, platform infrastructure, and ecosystem orchestration as crucial drivers for enabling different types of circular business models (CBMs) [45]. Other recent work, based on industrial cases, conceptualizes how AI’s perceptive, predictive, and prescriptive capacities enable specific augmentation or automation CBMs and details the dynamic capabilities needed by firms to commercialize these innovations [46]. These studies underscore AI’s potential for enhancing circularity but sometimes tend to prioritize process-level efficiency over system-wide transformation.

The integration of Industry 4.0 technologies with CE principles, sometimes termed the smart circular economy (SCE), is recognized as crucial for accelerating the transition to CE practices [47,48]. Research in this area explores not only integration frameworks but also the key enablers needed for successful SCE adoption by firms [48]. In this context, Digital technologies including Internet of Things (IoT), blockchain, and AI have also been framed as key enablers of the CE [49]. Liu et al. (2022) provide a framework identifying key digital functions (e.g., monitoring, optimizing) enabled by technologies like AI, IoT, and big data, mapping these functions onto specific CE strategies based on the 9R framework [20]. However, while these frameworks provide valuable guidance, they often overlook the unique properties and regenerative cycles central to the CBE. Furthermore, reviews indicate that while the CBE holds potential, its practical adoption faces significant hurdles, and its impact on holistic sustainable performance requires further analysis, often being hindered by factors like technological gaps and supply chain immaturity [49].

Studies focused explicitly on the CBE, such as Stegmann et al. (2020) [50] and Carus and Dammer (2021) [51], articulate foundational elements of the bioeconomy, including renewable biomass use, cascading systems, and alignment with sustainability initiatives like the European Green Deal. Yet, AI or digital tools are rarely integrated into their strategic models. The intersection between AI and CBE thus remains under theorized, with limited domain-specific frameworks addressing how AI can support nutrient recycling, biowaste valorization, and regenerative agriculture.

Several key research gaps emerged from the literature review. First, AI is often applied tactically rather than strategically; its use as a tool for reconfiguring circular bio-based value chains is still in its infancy [20,52]. Second, while ethical challenges including algorithmic bias, data privacy, and energy consumption are acknowledged in broader sustainability discussions [14,15,16,17,53,54,55], they are not structurally embedded in most CE or CBE frameworks. Third, current models rarely provide implementation methodologies or readiness assessment tools for deploying AI in real-world bioeconomic contexts. Finally, existing frameworks are seldom multi-scalar, overlooking the need to assess AI impacts from micro-level operations to system-wide ecological outcomes. This paper addresses these gaps by proposing a novel framework tailored to the CBE. By extending existing digital circular frameworks (e.g., [19,20]) into bio-based systems, and emphasizing ethical deployment and system resilience, this study provides a holistic and actionable model to guide AI adoption across the CBE landscape.

3. Framework to Assess the Potential of Artificial Intelligence in the Circular Bioeconomy

In the quest for sustainable development, integrating AI into the CBE presents a fertile ground for innovation. Yet, as discussed in Section 2.3, the path to harnessing AI’s full potential within this domain is filled with complexities, and existing frameworks often lack specificity for the unique aspects of the bioeconomy. To navigate these intricacies, our proposed framework offers a structured approach to assessing and leveraging AI’s capabilities to facilitate the transition towards a CBE. It achieves this by systematically integrating two primary components: ten key CBE areas where interventions are needed (detailed in Section 3.1) and eight core AI functions offering potential solutions (detailed in Section 3.2). Unlike existing methodologies that overlook the nuanced interplay between AI capabilities and the challenges the CBE is meant to address [5,41,56,57], our framework helps to identify and match specific AI technologies to specific bioeconomy challenges, guides the assessment of the technologies’ potential and applicability, and supports their implementation in a manner that is both sustainable and effective.

These components were identified through a comprehensive review [18,19,20,56,57,58] to cover the broad spectrum of opportunities and challenges at the intersection of AI and CBE. See

Table 1. A framework for assessing the potential of AI in the circular bioeconomy with example use cases.

Artificial Intelligence (AI) Functions
Circular Bioeconomy Areas		Analysis	Prediction	Monitoring	Optimization	Automation	Classification	Interaction	Discovery and Design
	Sustainable Food System	Insights for precision agriculture operation	Predict crop yield, weather, diseases	Hyperspectral imaging for crop health monitoring	Optimize irrigation and fertilization	Robots and AI for precision agriculture	Crop quality grading	Personalized suggestions for interventions	Discover novel plant varieties, etc.
	Biowaste Valorization	Understand valorization processes	Predict biogas yield	Bioprocess monitoring	Optimize valorization bioprocesses	Smart biorefineries	Classify biowaste streams	Waste disposal guidance chatbots	New enzymes for bioconversion
	Biobased Products	Market trends for bioproducts	Predict quality	Bioprocess monitoring for quality control	Optimal biomaterial combinations	Automated adjustment of bioprocess parameters	Sort recyclable biomaterials	Sustainable product choices	Discover new designs for bio-based products
	Eco-Design	Product lifecycle analysis	Predict product performance	Track product performance	Optimize products for end-of-life management	Smart prototyping	Classify product designs on sustainability metrics	-	Design for longevity of circular lifecycles
	Renewable and Efficient Energy Use	Analyze energy usage	Energy production forecasting	Monitor energy production infrastructure	Optimize energy distribution	Smart grids for energy distribution	Classify renewable energy sources	Interface for energy sharing	Renewable energy mix design
	Ecosystem Protection	Remote sense data analysis	Early warning of environmental threats	Monitor habitats & wildlife	Optimize ecosystem restoration	Drones for conservation monitoring	Classify biodiversity species	-	Discover critical habitats
	Circular Business Models	Data for circularity	Predict resource availability	Monitor supply chains	Optimize supply chains	Smart contracts	Classify material on circularity potential	Stakeholder engagementtools	Discover circular business models
	Job Creation	Job market and skills analysis	-	Employment and skill trend and KPIs	-	-	-	Education and training chatbots	-
	Consumer Behavior	Consumer behavior trend analysis	Predict consumer preferences	-	-	-	Segmenting consumers on sustainability attitude	Personalized promotion of sustainable consumption	-
	Policy and Regulatory Framework	Analysis of previous policy documents	Policy impact prediction	Monitor KPIs	-	-	Classify policy documents on scope	Public participation in policy dev	Discover policy synergies & regulatory optimization

and Figure 2 for a visual overview of the framework structure, its components, and example applications across the CBE areas. The framework’s core mechanism involves the systematic mapping outlined in Table 1. This table aligns the capabilities of specific AI functions (detailed in Section 3.2) with the unique challenges and opportunities presented by each CBE area (detailed in Section 3.1), thereby illuminating targeted strategies for AI deployment. By outlining how AI can be used to develop solutions that advance the goals of a sustainable, efficient, and resilient CBE, the framework provides researchers, policymakers, and industry stakeholders with a comprehensive tool to navigate the complexities and opportunities at the AI–CBE interface. The key components are described as follows.

3.1. Circular Bioeconomy Areas

This section introduces the key areas within the CBE, identifying sectors where AI’s capabilities can be most effectively deployed to foster sustainability. These areas are categorized based on the work of Palahí et al. (2020) to present ten critical areas where AI’s integration holds the promise of catalyzing significant advancements [19].

• Sustainable food systems: Focus on optimizing food supply chains—from production to consumption—to reduce waste, ensure food security, and promote sustainable agricultural practices [40,59,60]. AI can enable precision agriculture techniques and smart supply chain management to enhance yield, minimize losses, and support sustainable practices [61].
• Biowaste valorization: Focus on transforming organic waste into valuable resources. This sector challenges traditional waste management paradigms by leveraging biowaste for the production of bioenergy, biofuels, and bioplastics, thus contributing to a sustainable economic model while mitigating environmental impacts [22,62]. AI technologies play a critical role by enhancing the efficiency of biowaste processing through intelligent sorting, predictive analytics for biogas production optimization, and the identification of novel valorization pathways [63,64]. By streamlining the conversion of biowaste into usable forms of energy and materials, AI can foster environmental sustainability and opens new avenues for economic growth within the bioeconomy.
• Bio-based products: Offer potential sustainable alternatives to fossil fuel-derived materials, driven by environmental concerns and sustainability goals [65,66]. This area focuses on leveraging renewable biological resources such as plants, algae, and microbes to manufacture a diverse range of products, from biochemicals and bioplastics to biofuels and biopharmaceuticals [22,62,67]. Key examples include platform chemicals like microbially produced succinic acid [68], functional polymers like bio-based thermosetting resins and epoxies designed for specific properties such as flame retardancy or recyclability [66,69], bioplastics used in durable consumer goods [65], and bio-based building materials derived from wood, hemp, or straw [70]. The integration of AI can assist in the identification of novel biomaterials and the optimization of their production processes. Specifically, AI and machine learning hold potential to predictively design materials with desired functionalities based on the structural diversity of bio-based feedstocks [66]. AI can facilitate the exploration of untapped biological resources and the enhancement of biomanufacturing efficiencies, thereby driving the innovation of new bio-based products with reduced environmental footprints [63,64].
• Eco-design: A proactive approach that integrates environmental considerations into product lifecycles, from conception to end-of-life management. It emphasizes the creation of products that are not only efficient and sustainable but also designed with their eventual reuse, recycling, or biodegradation in mind [71,72]. AI can help to optimize product design for minimal environmental impact. This involves developing computational models for the intelligent selection of materials, enhancement of product longevity, and facilitation of recycling or biodegradability. AI-enabled tools and models can simulate product lifecycles, assess environmental footprints, and generate insights for sustainable design practices, thus fostering the development of products that align with the principles of a circular and sustainable economy [71].
• Renewable and efficient energy use: Focus on harnessing renewable energy, such as bioenergy, solar, and wind power, and improving energy efficiency across all bioeconomic sectors [73]. The integration of AI can assist in making energy management and utilization processes efficient, from predicting energy demand and optimizing renewable energy production to enhancing energy distribution networks. By leveraging AI to optimize renewable energy use and improve energy efficiency, the CBE can make significant strides towards sustainability, reducing dependency on fossil fuels and mitigating climate change impacts [73].
• Ecosystem protection: Prioritizes actions and technologies that mitigate environmental impacts, preserve biodiversity, and enhance the resilience of ecosystems [74,75,76,77,78]. AI offers advanced computer vison tools for monitoring biodiversity, assessing ecosystem health, and predicting environmental changes. Through satellite imagery analysis and environmental sensor data, AI algorithms can automate the detection of changes in land use, monitor wildlife populations, and track the health of forests and oceans with high precision and scale [76,77]. This can ensure that economic activities harmonize with the planet’s ecological balance, promoting a sustainable future where both human and natural systems thrive.
• Circular business models: Focus on restructuring traditional linear business and value chain models into circular systems that enhance the lifecycle value of products and materials [79,80]. AI with technologies like IoT and blockchain can help provide the analytical tools and insights necessary to facilitate the development of new business models that are transparent, efficient, resilient, economically viable, and environmentally sustainable [49]. For instance, natural language processing algorithms, video and image processing algorithms, and speech-to-text algorithms could aid in matching suppliers and buyers of rare by-products across a wide range of media and platforms [76,81]. Automated data collection and curation techniques could empower analysts to efficiently analyze vast and diverse datasets, thereby enabling more robust forecasts of the supply and demand for recycled materials [76,81]. Machine learning models can optimize logistics for resource recovery, such as by quickly identifying the most fuel-efficient route amid changing traffic conditions.
• Job creation: Transitioning towards a CBE may also offer economic opportunities for new industries and sectors to emerge, possibly leading to job creation and economic diversification alongside achieving improved environmental outcomes in areas such as food waste recovery [82,83,84]. While it remains unclear how AI technologies will affect specific segments of the labor market [85], employing AI strategically is instrumental in the CBE by optimizing processes, enabling new business models, and creating high-value jobs in green tech and sustainability sectors. For instance, AI-driven analytics can help identify growth sectors, forecast employment trends, and assist in developing skill-based training programs tailored to the needs of an evolving workforce.
• Consumer behavior: Engaging consumers in sustainable practices is key to driving the demand for sustainable products and encourages responsible consumption behaviors [86,87,88]. To realize CBE targets, dominant consumption patterns in many populations around the world will require significant adjustments, such as different modes of diet or mobility preferences [89]. Many existing economic models are inadequate to predict the demands and impacts of fundamental changes in consumption patterns. AI can significantly contribute to improving the recognition and forecasting of changing consumer demands, enhancing consumer awareness and fostering sustainable behaviors through personalized recommendations, targeted marketing campaigns, and interactive educational platforms. For instance, AI can support initiatives that encourage consumer participation in recycling programs or community-based sustainability projects through customized information, further embedding CBE principles into daily life [86,87,88].
• Policy and regulatory framework: It is crucial to ensure that CBE-specific innovation is supported, sustainability goals are met, and bioeconomic activities are regulated in a manner that protects the environment and promotes social welfare [90,91,92]. While policies, sector strategies, and legislative changes can drive CBE targets [3], the more profound transformation towards a wider CBE is not a simple replacement of existing production and consumption patterns but a complex adaptation process across existing and new value chains [89]. AI can provide policymakers with advanced tools for data analysis and simulation to model these complex interactions, enabling more informed decision-making [89,93]. For example, AI models can analyze environmental, economic, and social data to predict the outcomes of policy changes, help identify the most effective regulatory approaches, and simulate the impact of new laws on CBE activities. Furthermore, AI can assist in monitoring compliance with regulations, streamlining regulatory processes through automation.

Identifying these areas is instrumental in setting the groundwork for exploring AI’s potential contributions to the CBE. Each area presents unique challenges and opportunities for AI to make meaningful impacts. The subsequent sections describe AI functions and their applications within these identified areas, demonstrating the framework’s utility in harnessing AI for a sustainable CBE.

3.2. Key Functions of AI

This section presents the key capabilities of AI technologies that can be harnessed to address the unique challenges of the CBE [20,94,95]. By mapping these AI functions to the specific needs and opportunities within the CBE, we offer a comprehensive framework for leveraging AI to foster sustainability, efficiency, and innovation across the bio-based sectors [57]. Each function of AI is not just a standalone capability but a building block towards a more intelligent, adaptable, and sustainable bioeconomy.

Before outlining these AI functions, it is crucial to recognize their underlying interdependencies and the collective potential they offer when aligned with CBE objectives. Through a detailed exploration of each function, this section aims to articulate a clear and actionable insight into how AI can be effectively applied in the key CBE areas identified above. These insights can guide more targeted research, policy-making, and industrial innovation towards the realization of an integrated and thriving CBE [90,92].

• Analysis and Insights: Through advanced machine learning, deep learning, and natural language processing algorithms, AI can transform complex and often disparate datasets typical of bioeconomic systems into actionable knowledge, offering high speed and accuracy compared to the traditional methods [57,96,97,98]. This function is crucial for enhancing resource use and sustainability, enabling stakeholders to make informed decisions that significantly improve bioeconomic efficiency [98,99]. For instance, AI’s precise analysis of agricultural data informs targeted interventions to increase crop yields and minimize waste, directly contributing to sustainable food systems [95]. Similarly, in waste management, AI’s insights into material flow data facilitate the identification of optimal recycling and upcycling pathways, underscoring its potential to support circular value chains and business models [94,100,101].
• Prediction: AI’s predictive capabilities can leverage historical data and machine learning models to forecast future trends and events with accuracy in certain contexts [12,102,103,104], which is particularly valuable in the CBE for navigating inherent biological variability and environmental dependencies. This function is essential for anticipatory decision-making and proactive planning within the CBE, enabling stakeholders to proactively address challenges and seize opportunities [103,104]. For instance, AI can model and predict the best conditions for fermentation processes that produce biogas, ensuring that the microbes are efficient in converting organic materials into energy [100,101,103]. Similarly, predictive analytics are crucial in waste management for forecasting waste generation patterns. This enables more efficient waste collection and recycling processes, supporting the development of circular value chains by ensuring that resources are reused and recycled at optimal levels [100,101].
• Monitoring: AI can perform real-time monitoring, tracking, and tracing, thus playing a crucial role across the CBE, enabling continuous oversight of complex, often opaque biological processes [58,105,106]. By leveraging machine learning, deep learning, and data from Internet of Things sensors, AI can process and analyze vast amounts of indirect or proxy data (e.g., temperature, pH, spectral signatures) to infer the state of difficult-to-measure biological parameters, such as microbial community health in a bioreactor or crop water stress across a field, in real time [58,105,106]. This offers immense potential for monitoring bioprocesses, biological materials, and waste products through supply chains. For example, AI-enabled monitoring in biorefineries ensures that production processes operate at their optimum, guaranteeing product quality and enhancing the sustainability and economic viability of biofuel production [38,107].
• Optimization: Sophisticated AI algorithms can be employed to enhance the efficiency and effectiveness of bioeconomic processes and systems particularly those characterized by non-linear responses and multi-objective goals. By analyzing vast datasets and predicting outcomes, AI algorithms can empower stakeholders to utilize resources, time, and effort with efficiency [94,95]. AI-driven optimization focuses on maximizing resource efficiency and minimizing waste across the value chain. From streamlining production processes in biorefineries by dynamically adjusting parameters to account for the non-linear kinetics of bioconversion based on real-time data [94,95], to optimizing complex supply chain logistics for seasonally variable biomass feedstocks, AI can ensure the sustainable and efficient utilization of biological resources. In biorefinery operations, for example, AI’s capability to predict optimal bioproduction conditions translates into more effective conversion of biological waste into valuable resources [94,95]. This optimization extends beyond production, affecting every facet of the CBE, from reducing inputs in agricultural settings to improving energy use in industrial processes.
• Automation: This involves the creation of intelligent systems to perform tasks without human intervention [108,109,110]. This function leverages AI algorithms to analyze data, make decisions, and carry out actions based on learned patterns and predictive analytics, which is particularly useful in complex decision-making scenarios [111]. This includes the automation of routine tasks, process optimization, and control of complex systems [109]. In the CBE, AI-driven automation can boost recycling, waste management, and production efficiency, streamlining bioprocessing, waste sorting, and renewable energy optimization [108,112]. For instance, in the cultivation and processing of microalgae, AI-driven automation can streamline operations and enhance biofuel production efficiency and sustainability by enabling real-time adjustments to cultivation conditions [109]. This showcases AI’s role in automating complex bioeconomic processes, reducing the need for human intervention, and ensuring optimal resource use and minimal environmental impact.
• Classification: This involves categorizing bioproducts or biowaste material into predefined groups or classes based on certain characteristics or patterns [113,114]. This enables systems to make sense of complex datasets and make decisions or predictions accordingly. In the CBE, AI systems can delve into vast datasets, distinguishing and classifying biological materials and waste into predefined categories based on specific characteristics [114,115,116]. This function is critical for effective recycling and waste management, where AI can classify materials for appropriate processing and reuse, aiding in the efficient recycling and repurposing of resources. For example, AI-driven sorting technologies based on computer vision enhance the accuracy of waste segregation, significantly boosting recycling rates and minimizing contamination, which are crucial goals of the CBE [101].
• Interaction: This represents a shift in how technology interfaces with human experiences, enabling more nuanced understanding and response to human sentiments, personalizing experiences, and accurately predicting behaviors [117,118,119,120]. This is particularly evident in AI-driven chatbots, recommendation systems, and sentiment analysis tools, which collectively enhance user engagement by tailoring interactions based on individual preferences and feedback [121,122,123]. In the context of the CBE, AI can gauge consumer attitudes towards bio-based products, analyze market trends, and personalize consumer experiences, thereby helping to encourage sustainable consumption patterns and greater acceptance of circular economy principles. For example, natural language processing (NLP) chatbots can enable better interaction between different stages of the supply chain by predicting and communicating demand fluctuations to optimize production and distribution [124,125]. This predictive communication can help minimize overproduction and underutilization of food products [124]. Moreover, AI-tools such as ChatGPT 4o can be used for personalized education and engagement with stakeholders about the benefits of the CBE, thereby shaping a more sustainable and inclusive economic model. By improving these interactions, AI contributes to a more responsive and efficient CBE and fosters a culture of sustainability and responsible consumption [101].
• Discovery and Design: This involves a focus on human–machine collaboration in the discovery and design process, creating new knowledge and innovative solutions using generative AI models [126,127,128,129], which can accelerate the exploration of vast biological diversity for new CBE solutions. AI can identify novel patterns or compounds in research and development, leading to new scientific discoveries or product designs. In creative thinking, AI can assist in the design process, from generating ideas to prototyping, offering novel approaches that augment human creativity [130,131,132]. In, C.B.E.; AI can assist in designing new bio-based materials, optimizing product designs for longevity and recyclability, and discovering novel approaches to waste valorization, driving innovation and sustainability [133,134,135]. For instance, AI can assist in discovering new methods for recycling materials or repurposing food waste into valuable products [101,136]. Furthermore, AI can help in designing new forms of biodegradable packaging to replace plastics, which is a significant step towards a more sustainable food system [57].

3.3. Concerns and Challenges

While offering innovative solutions for sustainability and environmental challenges, AI’s integration into the CBE is not without obstacles. AI holds promise in mitigating climate change, enhancing resource management, and promoting sustainable practices, but its large-scale implementation also raises environmental concerns due to the high computational resource demands and associated carbon and water footprints [16,17,54,55]. In a CBE, AI’s potential to optimize bioprocesses and enable circularity must be balanced against the ethical considerations of increased carbon emissions, data privacy issues, algorithmic bias, potential job displacement, and associated social disruptions [14,15].

However, these concerns are not insurmountable. Ongoing research is exploring energy-efficient AI algorithms and hardware, as well as strategies for optimizing model training and deployment to minimize environmental impact [15]. Additionally, initiatives are underway to develop ethical and regulatory guidelines for the responsible development and implementation of AI, covering issues like bias, transparency, and data privacy [17]. By addressing these concerns through more efficient algorithms, renewable energy sources, and green AI practices, we can ensure that the benefits of AI are realized in a manner that is economically viable, socially acceptable, and environmentally responsible [15,54].

4. Application of the Proposed Framework

This section outlines a practical example application to demonstrate the framework’s utility from a researcher’s perspective, providing a structured methodology for conducting a literature review. Such reviews can guide focused investigations into the current state of AI within CBE domains—here, biowaste valorization—to identify research gaps and emerging opportunities for AI to address complex challenges. This case study highlights the complex interplay between AI and the CBE. Through this detailed exploration, we offer a pathway for researchers to leverage the framework in uncovering innovative solutions and strategies for biowaste valorization, contributing to the broader objectives of the CBE.

4.1. Biowaste Valorization: A Framework-Led Literature Review

Biowaste represents both a significant challenge and an opportunity for the CBE, encompassing a wide range of organic materials discarded from various sources. The transformation of biowaste into valuable resources, such as bioenergy, biofuels, and bioproducts, through reduction and valorization processes, mitigates environmental impacts and contributes to economic growth and resource sustainability. Despite its potential, the complexity of biowaste streams and the inefficiencies in current management practices underscore the need for innovative solutions. Figure 3 presents an overview of a waste management system and the context of biowaste valorization, which is the present focus.

4.1.1. Application of the Framework

Adopting the proposed framework, this literature review involves querying databases for peer-reviewed articles and reports that explicitly mention AI applications in biowaste processes. We searched peer-reviewed research articles at the intersection of biowaste valorization and AI in two prominent scientific databases, Google Scholar and Science Direct. Keywords and phrases included “biowaste valorization and AI”, “machine learning in biowaste treatment”, and “AI-driven biowaste solutions”. We also created eight search queries by combining the word “biowaste” with each of the identified AI functions in this paper. This search was augmented by a snowballing technique of pertinent references in key articles.

Inclusion criteria encompassed peer-reviewed articles published between 2019 and 2023, ensuring the review’s relevance. We removed studies that did not directly address AI’s role in biowaste valorization or that lacked empirical evidence of AI applications. We found 127 research publications initially. After reviewing abstracts, keywords, and introductions, we discarded publications where biowaste and AI were not a central topic. This left 50 publications for in-depth analysis, ranging from predictive analytics for biowaste conversion to bioenergy process optimization to AI-enhanced bioprocessing techniques.

Each sampled study was analyzed through the framework’s structured lens. For the “circular bioeconomy areas” component, studies were categorized based on the specific area of biowaste valorization challenge they addressed. Simultaneously, the “AI functions’‘ component facilitated a deeper examination of how various AI methodologies were employed to tackle challenges within these areas. This dual-faceted analysis enabled a nuanced synthesis of how AI contributes to advancing biowaste valorization practices, highlighting innovative applications and pinpointing areas ripe for further exploration. The following discussion encapsulates a detailed examination of the contributions of AI functions to biowaste reduction and valorization, underscoring their collective impact on advancing the goals of the CBE.

4.1.2. Key Findings from the Framework-Led Literature Review

The literature review, guided by the framework, identifies areas where further exploration and application of AI could drive substantial impact within biowaste valorization.

Table 2. Key findings from the literature review.

Artificial Intelligence Functions
Biowaste valorization sub-fields		Analysis	Prediction	Monitoring	Optimization	Automation	Classification	Interaction	Discovery and Design
	Planning & Pretreatment	AI-driven analysis of experimental data. e.g., analyze how temperature, pH, and substrate concentration changes impact biogas production rates. Feedstock analysis.	Predict the higher heating value of biomass. Predicted the amount of municipal solid waste generated. Predict syngas yield from wet waste.	-	-	Automated segregation of biowaste.	Biomass compositions Characterizing biomass feedstock selection.	-	Enhanced catalyst design through AI-driven methods.
	Bioprocesses	Interpret the complex dataset from hydrothermal liquefaction of various food wastes using ML.	Predict the performance of the anaerobic reactor.	AI & IoTs for real time monitoring conversion processes.	Optimal operating conditions for maximizing efficiency.	Automated process parameter control in biorefineries.	Classify the maturity of compost using CNNs.	-	-
	Post-processing	Analyzes biochar properties. Performance analysis of bio-hydrogen.	Predicts output (yield, nitrogen content) predict nutrient recovery in vermicompost.	-	-	Automated control of biorefineries.	Characterize properties of products like biochar and hydrochar.	-	-
	Research	Identifies and analyzes emerging trends in biowaste treatment using NLP.	Predicting kinetics of hydrothermal carbonization of selective biomass.	-	-	-	-	-	AI-enabled analysis to identify key factors impacting bioproduct production.
	System design	Assess environmental impacts rapid evaluation of many different combinations of metals in catalysts.	-	Monitor the quality of bioproducts.	-	-	-	-	New system design for biowaste valorization.

presents key themes and details of how different AI functions are leveraged to address specific challenges in biowaste management.

Prediction: A significant focus lies in leveraging machine learning and deep learning algorithms to predict biowaste conversion outcomes and properties including yield, properties of products derived from biowaste, and the efficiency of energy production processes [137,138]. Models have been developed to forecast biogas yield [103,139,140,141,142], biochar properties [138,143], bio-oil characteristics [144,145], and syngas generation [146]. Additionally, researchers predict the higher heating value of biomass [147] and nutrient recovery from vermicompost [148] and kinetics of hydrothermal carbonization of biomass [149]. These predictive capabilities enable informed decision-making about feedstock selection, process design, and the tailoring of bioproducts for specific applications.

Optimization: From the literature, it is evident that the AI plays an important role in optimizing biowaste conversion process conditions and parameters across technologies like pyrolysis, gasification, hydrothermal processes, and anaerobic digestion [150]. Researchers employ various ML techniques to fine-tune process parameters [151,152]. This optimization targets maximizing bioenergy yields [153,154,155], improving product quality [156,157], and enhancing overall process efficiency [100,158,159].

Classification: This topic received considerable attention but is not as extensively explored as prediction and optimization. For instance, the integration of computer vision techniques like convolutional neural networks (CNNs) is used to facilitate the advanced characterization of biowastes, such as classifying compost maturity [160].

Analysis and insights: AI-driven analysis using techniques like principal component analysis and feature importance is mainly used for the analyzing large and complex experimental datasets, which aids in understanding complex biowaste conversion processes, such as hydrothermal liquefaction [161]. These insights advance feedstock evaluation and process design. The studies by Anisa et al. (2023) and Yatim et al. (2022) underscore a growing trend in leveraging AI for analysis and insights, highlighting the shift towards data-driven decision-making in biowaste valorization processes [100,147]. However, the potential for AI to analyze complex datasets to uncover deeper interactions within bioprocesses or between bioprocesses and external environmental factors remains largely untapped.

Monitoring and Automation: These functions are less frequently the focus but are recognized for their potential in streamlining bioprocess [162]. For instance, Agrawal et al. (2023) illustrate the application of various machine learning models for real-time monitoring of anaerobic digestion [158]. Real-time tracking and process optimization stand out as critical AI contributions to enhancing operational efficiencies within waste management and bioprocessing systems. Works by Said et al. (2023) and Tsui et al. (2023) illustrate AI’s role in dynamically adjusting processes in response to changing conditions, thereby optimizing resource use and process outcomes [12,142]. Automation is touched upon in studies that integrate AI to control bioprocessing operations dynamically, though detailed applications are scarce [163].

Interaction and discovery and design: These areas represent significant gaps where AI could bring substantial benefits but is currently underutilized. The role of AI in designing novel bioprocess systems or discovering new bioproducts from underexploited types of biowaste offers a fertile ground for future research. For instance, [164] suggested enhanced catalyst design through AI-driven methods. AI applications offer promising avenues for improving user engagement and fostering innovative strategies in waste management. Mahmoud et al. (2022) note the potential of these AI capabilities in developing new approaches to waste management that can engage stakeholders more effectively and lead to novel valorization pathways [165].

Additional applications of AI in biowaste valorization: Some studies demonstrated an application of AI in the broader context of biowaste management. These include NLP models for patent analysis to track technological trends [166], food waste quantification and prediction [142], evaluation of biogas energy potential [167], and environmental impact assessment of biowaste management strategies [168].

AI techniques: A breadth of machine learning techniques is applied across the reviewed studies, reflecting their versatility for complex bioprocess challenges. Techniques range from artificial neural networks and ensemble methods to random forest, support vector machines, genetic algorithms, and fuzzy logic Systems [151,166,169]. Other approaches like convolutional neural networks and natural language processing are beginning to gain traction (e.g., for compost maturity detection [160] or patent analysis [166]). Notably, newer generative AI models, such as large language models, were not found to be employed in the reviewed biowaste valorization literature, highlighting a potential gap and opportunity.

4.2. Case Study Insights and Reflections

Applying the proposed framework to the biowaste valorization literature yielded clear insights into the current state of AI application in this critical CBE domain. The systematic analysis revealed a strong emphasis on employing AI primarily for prediction and optimization within biowaste conversion processes. Emerging applications were noted in monitoring and classification, but these appear to be less frequently studied.

Crucially, the framework also illuminated significant gaps. AI applications related to comprehensive analysis (integrative lifecycle or system-level insights), interaction (stakeholder engagement, supply chain coordination), and particularly discovery and design (novel processes, enzymes, or bioproducts) were markedly underrepresented in the reviewed literature. Addressing these gaps can propel the field toward more sustainable, efficient, and novel solutions in biowaste management, highlighting the need for a broader application of AI capabilities in future studies. The challenges, specific gaps, and future opportunities identified through this framework-guided literature review are presented in detail in Section 5.

This case study demonstrates the practical utility of the proposed framework. The structural approach facilitated by the framework, i.e., mapping AI functions onto the specific CBE area of biowaste valorization, enabled the identification of dominant research themes, pinpointed specific areas lacking investigation (corresponding to underutilized AI functions within this domain), and highlighted emerging opportunities for innovation. The framework thus served as an effective analytical tool for synthesizing the state of the art in this area and identifying research priorities.

However, the application also suggests potential areas for refining the framework. While Section 3.3 acknowledges general AI concerns, the framework could be enhanced to more explicitly capture and guide the assessment of the potential negative environmental implications of AI integration (e.g., energy consumption of models) within specific CBE contexts and to structure the ethical considerations (e.g., data bias in waste characterization) concerning AI technologies. Future iterations could benefit from incorporating metrics or criteria related to these dimensions, ensuring that AI’s role in advancing the CBE is evaluated holistically against all intended sustainability goals. Researchers are encouraged to utilize the proposed framework as a blueprint for conducting targeted literature reviews or empirical assessments in other CBE domains. Focusing on identified gaps, such as the need for AI applications in discovery and design or the environmental impact assessment of biowaste valorization itself, can propel the field forward.

In summary, this case study not only illustrates Al’s current and potential role in biowaste management but also validates the proposed framework as a valuable tool for structuring research and analysis in the complex intersection of AI and the CBE. The insights gained from this case study can guide future research directions, encouraging a deeper and more systematic exploration of AI’s role in realizing a sustainable and efficient CBE.

5. Discussions, Challenges, and Future Work

5.1. Discussion

This paper advances earlier work on AI and the circular economy by providing a comprehensive, bioeconomy-specific framework that maps AI functions to CBE domains. Prior frameworks such as the Smart CE model [47,49] and the digital function–mechanism matrix proposed by Liu et al. (2022) [20] have laid important groundwork for understanding the enabling role of AI and digital tools in resource optimization and circular manufacturing. However, these studies focus predominantly on industrial systems and lack customization for biological systems and regenerative cycles inherent in the circular bioeconomy. Similarly, Refs. [45,46] introduced a roadmap for AI maturity in sustainable business models but did not address bio-based sectors or ethical governance dimensions.

In contrast, our framework incorporates multi-level integration from process-level AI applications to ecosystem-scale coordination tailored to domains such as biowaste valorization, regenerative agriculture, and bio-based manufacturing. It also introduces a readiness assessment and policy-aligned roadmap for implementation, thereby addressing a critical gap in operationalizing AI across CBE transitions. Moreover, the added focus given in the framework to responsible AI deployment (e.g., by covering issues like energy demand, data governance, and transparency) helps to move beyond the techno-centric orientation of earlier models [20,47] and to identify a more holistic and resilient path forward for sustainable AI adoption in bioeconomic systems.

Therefore, this framework represents a significant conceptual and practical contribution. It moves beyond existing paradigms by providing the first dedicated, structured, and actionable assessment tool specifically designed for navigating the complexities and harnessing the potential of AI within the unique, biologically grounded context of the CBE. Moreover, by providing a structured way to assess AI’s role, our framework addresses the need for more systematic approaches to overcome CBE implementation challenges related to complexity and optimization, which often restrict industries from adopting CBE practices effectively [170].

5.2. Challenges and Future Work

The field of biowaste conversion stands at a critical juncture where the incorporation of AI and machine learning is poised to bring about significant advancements. However, challenges persist that must be addressed to harness the full potential of these technologies.

Practical implementation: While the reviewed studies provide compelling evidence of AI’s potential, a recurrent theme is the challenge of scaling these solutions. Bridging the gap between pilot projects and large-scale applications remains a critical hurdle for realizing the full benefits of AI in biowaste management. This is compounded by broader CBE adoption challenges, including limited technological infrastructure and financial capabilities, particularly noted in developing countries [170,171]. The seamless incorporation of AI into current systems poses both technical and logistical challenges, necessitating innovative approaches and collaboration between technology developers, industry stakeholders, and policymakers. The literature review also highlights the need for greater attention to the ethical implications of AI deployment in biowaste management, including concerns related to data privacy, algorithmic bias, and the environmental impact of AI operations themselves.

Based on these findings, we propose several recommendations: (1) Encourage collaborations across computer science, environmental engineering, and waste management disciplines to develop AI solutions that are not only technologically advanced but also practically applicable and environmentally sustainable. (2) Prioritize the development and testing of AI technologies designed for scalability from the outset, ensuring they can be effectively integrated into diverse waste management contexts. (3) Develop and adhere to guidelines for ethical AI use in environmental applications, focusing on transparency, fairness, and the minimization of ecological footprints.

Datasets: A persistent challenge is the need for extensive, high-quality datasets that are essential for the effective training and validation of machine learning models [109,172]. Data collection is often constrained by the complexity of biological systems and the variance in feedstock, particularly for lignocellulosic biomass conversion processes [173]. Moreover, the limited availability of comprehensive datasets hinders the development of models capable of capturing the full complexity of bioenergy systems. This is exacerbated by the existing data silos and the lack of a collaborative framework for sharing data and insights across disciplines [172]. Addressing these issues requires collaborative effort to establish extensive, publicly accessible biowaste databases, standardize data collection, and explore data augmentation techniques [109,172,173].

Science-guided machine learning (ML) models and interpretability: The interpretability of ML models also poses a significant challenge, as there is a pressing need to connect AI-derived findings to underlying physical and biological mechanisms for a deeper understanding and trust in the technology [173]. To improve the interpretability of ML models, one area of potential future research is the augmentation of ML models with science-driven models [109,174]. This methodology, blending first-principle scientific models with data-driven ML models, facilitates more accurate, reliable modeling and optimization. The approach is particularly effective in complex system modeling, offering the potential for novel biomaterials and process efficiency improvements. Such a hybrid approach leverages the predictive power of ML while grounding the findings in established scientific principles, enhancing the understanding and efficiency of bioenergy systems [109,172].

Multi-view hybrid models: Further research into the integration of multi-view learning and hybrid modeling approaches are suggested to enhance the performance and reliability of bioenergy systems [162]. There is a call for employing multi-view learning to integrate diverse datasets, which could offer a more holistic understanding of the bioenergy conversion process and uncover hidden correlations that single-view model might overlook [172].

New models: The variability of biological processes, such as microbial community dynamics, poses distinct challenges that require ML models to adapt to fluctuating conditions, potentially affecting performance even under consistent operation [172]. The exploitation of advanced deep learning methods and metaheuristic algorithms could be developed to deal with the intricacies of bioenergy conversion and microalgal reactor optimization. Such approaches promise to uncover new insights, expedite the optimization process, and contribute to the design of cutting-edge bioenergy solutions [109,172]. Although ML has demonstrated its utility in simulating complex bioenergy systems like wastewater treatment, there is an opportunity to extend these applications further [172]. The field would benefit from exploiting alternative ML methodologies for feature extraction, such as bioimage informatics, which could introduce new methodological avenues beyond conventional strategies [172].

Cost of AI models: The development and ongoing maintenance of advanced AI systems, including large language models and computer vision models, encapsulate a significant financial commitment, with costs that often surmounting millions of dollars [175]. For instance, OpenAI’s GPT-3 models cost approximately USD 4.6 million for a single training run [176]. Generally, these models require several experimental runs.

The integration of AI with Internet of Things technologies is another frontier where real-time monitoring and predictive analytics could improve the efficiency and sustainability of bioenergy conversion, as exemplified by the work of [106,157]. However, there is an imperative need for research into AI/ML applications in dynamic system control, predictive maintenance, and expanding the models to include micronutrient effects in microalgal models, where limited studies point to an opportunity for more comprehensive analyses [109].

AI in areas of bioconversion: Despite the advancements, certain areas remain less explored and ripe for future research. For instance, the variability in biological processes, such as those observed in microbial community dynamics during anaerobic digestion, presents a challenge for ML models, suggesting a need for models that can adapt to such changes [172]. Similarly, the current focus on macronutrients in algal cultivation models overlooks the role of micronutrients, representing an opportunity to refine these models for enhanced accuracy [109].

Generative models, discovery and design: The employment of AI in the discovery and design phase of new bioenergy technologies is also a frontier yet to be fully realized. There is immense potential for AI to identify novel pathways and contribute to the innovation of bioenergy solutions, particularly in the optimization of microalgal reactors using advanced techniques like metaheuristic methods [109]. Recent breakthroughs in generative AI techniques such as ChatGPT have the potential to accelerate discovery processes in biology and life science research [127,177]. For instance, by simulating the properties of biological materials, these AI models can expedite the discovery and development of novel bio-based products, offering cost-effective alternatives to conventional materials and supporting a shift towards a CBE. However, it is essential to use these models to complement human judgment rather than replace it and to continuously improve AI models for responsible and ethical use [178].

Interdisciplinary effort is required: A collaborative effort is thus encouraged to overcome these challenges, emphasizing the value of interdisciplinary partnerships that can enhance model interpretability and foster the development of advanced ML applications in bioenergy [172]. It calls for concerted efforts among biological science, chemical science, data science, and computer science researchers to advance ML applications by sharing data, methodologies, and insights [172].

Responsible innovation and implementation: Given AI’s potential to significantly disrupt existing business models, value chains and labor markets, its introduction presents not only economic opportunities but also important societal challenges that need to be adequately understood and proactively addressed in a responsible manner [179]. Developing AI and associated digital technologies through socially inclusive approaches that go beyond exclusively techno-centric framings will be critical to mitigate negative ramifications and build user trust. This is crucial to enable the effective integration of AI technologies across CBE value chains, from agri-food producers to processors and consumers [180,181,182,183].

In summary, future work in this domain should concentrate on overcoming data-related challenges, enhancing model complexity to accommodate variability in biological processes, and leveraging AI for the full lifecycle of bioenergy production from discovery to design and optimization. Developing sustainable, low-energy AI algorithms and ensuring transparent, ethical AI applications will be paramount. As we embark on this journey, fostering interdisciplinary collaboration and engaging with ethical considerations will be key to unlocking AI’s potential in advancing a sustainable and innovative CBE.

6. Conclusions

The integration of AI into the CBE presents a promising frontier for enhancing sustainability and economic efficiency across various bio-based sectors. This paper addressed the need for systematic approaches by presenting a novel framework designed for assessing and maximizing Al’s potential contributions to the CBE. The framework’s primary contribution is to offer a structured approach to map key AI capabilities onto specific CBE domains as a practical tool for researchers, industry stakeholders, and policymakers.

By delving into the multifaceted interactions between AI capabilities and bioeconomic areas, the framework provides a structured approach to identify and leverage AI capabilities. The illustrative case study on biowaste valorization demonstrated the framework’s utility, revealing not only the current dominance of AI applications for prediction and optimization tasks but also critical gaps, particularly the underutilization of AI for discovery and design functions and the need for more system-level analyses. This research offers actionable insights for policymakers and industry leaders aiming to strategically deploy AI in pursuit of a sustainable CBE.

Looking ahead, the research agenda is vast and varied. Realizing AI’s full potential in the CBE requires concerted efforts to address persistent challenges, including enhancing data infrastructure and accessibility, improving model interpretability and transparency, and ensuring the development and deployment of sustainable and ethical AI algorithms. Fostering interdisciplinary collaboration between environmental science, biotechnology, data science, and social sciences will be essential to unlock AI’s full potential in advancing the CBE. By prioritizing responsible innovation towards sustainable AI practices that enable feasible CBE transition pathways, we can create a future where technological innovation and environmental stewardship go hand in hand, paving the way for a more resilient and prosperous bio-based economy.