REGISTRO DOI: 10.69849/revistaft/ra10202509071652
Eduardo Nunes da Silva
Abstract
The digital circular economy represents the convergence of sustainability principles and advanced technologies to enable transparent, efficient, and resource-optimized production systems. Blockchain, the Internet of Things (IoT), and artificial intelligence (AI) are increasingly recognized as key enablers of this transition. IoT provides real-time data across product lifecycles, enabling monitoring, predictive maintenance, and reverse logistics. Blockchain ensures data integrity, provenance, and verifiable sustainability claims across fragmented supply chains, while AI transforms complex datasets into actionable strategies for eco-design, remanufacturing, and resource optimization. Together, these technologies facilitate the implementation of product-level traceability mechanisms, such as the Digital Product Passport mandated in the European Union, and create integrated infrastructures for sustainable manufacturing. Despite challenges related to interoperability, governance, and organizational adoption, the integration of digital tools is decisive in accelerating the shift from linear to circular economic models. The literature consistently highlights digitalization not only as a facilitator but also as a prerequisite for scaling circular economy practices globally.
Keywords: Circular economy; blockchain; Internet of Things; artificial intelligence; traceability; digital product passport; transparency; sustainable manufacturing; digital twins; resource optimization.
The transition from a linear to a circular economy has become a pressing priority for industries and policymakers, requiring new approaches to resource management, product lifecycle extension, and sustainable manufacturing practices. Central to this transformation is the integration of digital technologies that enable transparency, traceability, and efficiency across supply chains. Recent literature underscores digitalization as the primary driver of circular economy transitions, positioning blockchain, the Internet of Things (IoT), and artificial intelligence (AI) as essential enablers (Bressanelli et al., 2022; Saberi et al., 2019). These technologies collectively create a digital infrastructure capable of addressing key barriers to circularity, including data fragmentation, lack of trust among stakeholders, and inefficiencies in resource utilization.
IoT provides the foundational layer of observability in circular systems. Through sensors, RFID tags, and connected devices, products and materials can be continuously monitored across their lifecycle, capturing data on usage, environmental conditions, and material composition. This high-resolution data enables predictive maintenance, facilitates remanufacturing, and supports reverse logistics, extending product life and improving resource efficiency (Prasad et al., 2025). When integrated into manufacturing systems, IoT creates the conditions for dynamic resource allocation, allowing firms to adapt processes in real time and minimize waste generation. Importantly, IoT-generated data also underpins initiatives such as the European Union’s Digital Product Passport (DPP), designed to make detailed information on product composition, repairability, and recyclability accessible throughout supply chains (Scantrust, 2024).
Blockchain complements IoT by ensuring the integrity, traceability, and trustworthiness of data across organizational boundaries. In circular supply chains, where products and materials may move across multiple actors and jurisdictions, blockchain provides tamper-resistant records of provenance, ownership, and material flows (Saberi et al., 2019). This transparency reduces information asymmetries, supports regulatory compliance, and facilitates consumer trust in sustainability claims such as recycled content or ethical sourcing. Moreover, blockchain enables the creation of tokenized incentives for repair, return, or recycling, thus aligning stakeholder interests with circular economy objectives (Kouhizadeh, Zhu & Sarkis, 2020). Despite challenges related to scalability, energy consumption, and governance, hybrid approaches that combine blockchain with off-chain data storage are emerging as practical solutions for balancing transparency and efficiency (Aung et al., 2023).
AI, meanwhile, transforms the vast datasets generated by IoT and validated by blockchain into actionable insights for optimization and decision-making. Machine learning algorithms can predict product failure, optimize remanufacturing processes, classify secondary materials, and model reverse logistics networks for minimal environmental impact. AI-driven design tools also support eco-design, offering recommendations for disassembly, modularity, and material substitution to enhance product circularity (Charnley et al., 2019). At the system level, AI contributes to carbon-aware decision-making, routing products and materials to the most resource-efficient recovery pathways. When linked with blockchain-based records, AI outputs gain verifiability, increasing their value for compliance and stakeholder trust (Olumide et al., 2025).
The convergence of IoT, blockchain, and AI is particularly powerful when applied within digital twins, virtual representations of products and processes that synchronize real-time operational data with predictive models. Digital twins enable scenario simulations—such as the impact of refurbishment or recycling strategies—before decisions are implemented physically. This capability enhances resource optimization, extends asset life, and accelerates the scaling of circular practices (Pal et al., 2024). Furthermore, as global regulations like the ESPR mandate product-level transparency through tools such as DPPs, companies will increasingly rely on integrated digital infrastructures to remain competitive in sustainable markets (European Commission, 2024).
Nevertheless, significant challenges remain in implementing digital circular economy architectures. Issues of interoperability, data governance, privacy, and organizational readiness hinder adoption. Firms must navigate trade-offs between transparency and confidentiality, while ensuring the reliability of IoT-generated data and managing the energy footprint of blockchain networks. Additionally, cultural and organizational transformations are needed, requiring new skills, roles, and incentive structures to fully realize the benefits of digital circularity (Kouhizadeh et al., 2020). Overcoming these challenges will demand collaborative standard-setting, cross-sectoral innovation, and the development of governance frameworks that balance economic and environmental objectives.
The flowchart illustrates how digital technologies—IoT, blockchain, and AI—collectively enable the transition to a circular economy by addressing key barriers in resource management and supply chains. IoT ensures observability through real-time data collection on product usage and conditions, blockchain guarantees trust and transparency by securing and verifying data across stakeholders, and AI provides optimization by analyzing datasets to enhance decision-making and efficiency. Together, these technologies create a digital infrastructure that supports sustainable practices, facilitates product lifecycle extension, and fosters a resource-efficient circular economy.
Figure 1. Digital Technologies as Enablers of the Circular Economy.
Source: Created by author.
In conclusion, the integration of blockchain, IoT, and AI forms a digital backbone for the circular economy, enabling unprecedented levels of traceability, transparency, and efficiency. IoT captures the real-world state of products and materials, blockchain guarantees data integrity and trust, and AI generates optimization strategies that close material loops. Together, these technologies operationalize the principles of circular manufacturing and create the infrastructure for sustainable, resource-efficient production systems. With regulatory momentum and growing societal demand for transparency, the digitalization of circular economy practices will be decisive in achieving a sustainable industrial future.
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