AI for Circular Economy: Intelligent Algorithms for Sustainable Resource Recovery

The accelerating growth of electronic waste (e-waste), industrial byproducts, and natural resources depletion highlights the urgent need for intelligent and scalable solutions in waste management. In parallel, artificial intelligence (AI) has emerged as a transformative force capable of optimizing decision-making, automation, and predictive modeling across the recycling value chain. This Research Topic invites contributions at the intersection of AI algorithms and sustainable innovation, with the goal of advancing circular economy practices and addressing the challenges of waste electrical and electronic equipment (WEEE), battery recycling, and critical raw material (CRM) recovery.

We welcome submissions that develop and apply state-of-the-art AI methods including machine learning, computer vision, deep learning, optimization, and edge computing for real-world applications in sustainable disassembly, resource recovery, and waste treatment. This topic serves as a platform for interdisciplinary research bridging algorithmic development and impactful applications aligned with the European Green Deal and the United Nations Sustainable Development Goals (SDGs), particularly in areas such as responsible consumption (SDG 12), industry innovation (SDG 9), and climate action (SDG 13).

Contributions may cover a wide range of topics, including, but not limited to, the following:
• AI-driven sorting and disassembly systems for electronic, municipal, and industrial waste
• Real-time AI inference and edge computing for automation in recycling and waste processing
• Machine learning and optimization algorithms for predictive recyclability assessment
• Deep learning techniques for hyperspectral and multisensor-based material classification
• Robotic integration and smart automation for circular economy applications (e.g., WPCBs, batteries, plastics, textiles)
• Generative AI for sustainable product design and reverse manufacturing workflows
• Blockchain, digital twins, and intelligent supply chain algorithms for circular resource tracking
• Case studies and lifecycle assessments evaluating algorithmic impact on sustainability metrics
• Algorithmic frameworks for reducing environmental impact and improving process efficiency
• Data-driven decision-making for policy and logistics in sustainable waste management

This Article Collection will provide a platform for researchers, academics, and industry professionals to share their latest advancements in AI and computational methods as applied to sustainable resource management and circular economy systems. We welcome both theoretical developments and practical applications that demonstrate how AI-driven approaches such as machine learning, computer vision, robotics, and optimization can address complex challenges in electronic waste recycling, CRMs recovery, and environmentally conscious engineering. Contributions highlighting real-world case studies, innovative disassembly and sorting techniques, and interdisciplinary frameworks that support the transition to circular, low-carbon systems are particularly encouraged.

A note on article types: this Research Topic particularly encourages submissions as Original Research, Brief Research Report, Methods, Review, Systematic Review, and Technology and Code articles, reflecting the applied and technical nature of the topic. Submissions as Conceptual Analysis, Data Report, Editorial, FAIR² Data, FAIR² Data Direct Submission, General Commentary, Mini Review, or Hypothesis and Theory are not encouraged for this Research Topic.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Review
• Systematic Review
• Technology and Code

Keywords: Artificial Intelligence, Machine Learning, Deep Learning, Computer Vision, Smart Recycling, E-Waste Management, Circular Economy, Resource Recovery, Critical Raw Materials

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.