Digital Enablers of the Circular Economy: A Systematic Review of Applications, Barriers, and Future Directions

Abstract

This systematic review examines how digital technologies enable circular economy (CE) transitions across sectors and value chains. Analysing 266 peer-reviewed publications (2016–2025), we develop a comprehensive taxonomy of digital enablers—including IoT, AI, blockchain, cloud computing, additive manufacturing, and digital platforms—and map their applications to circular strategies such as reuse, remanufacturing, and recycling. Our findings reveal that data-driven technologies dominate CE implementation, with 89% of studies involving data collection, storage, analysis, or sharing functions. IoT emerges as the foundational technology for real-time tracking and monitoring, while AI and big data analytics optimise circular processes and predict maintenance needs. Blockchain ensures traceability and trust in circular supply chains, and cloud computing provides scalable infrastructure for collaboration. Manufacturing (41%) and construction (15.5%) are the most studied sectors, with strong European research leadership reflecting policy drivers such as Digital Product Passports. We identify three impact types: enabling (process optimisation), disruptive (business model innovation), and facilitating (ecosystem collaboration). Key barriers include technical complexity, organisational resistance, high implementation costs, and regulatory gaps. The review concludes with recommendations for integrated, multi-stakeholder approaches to realise a digitally enabled circular economy.

1. Introduction

1.1. Background and Urgency of Circular Economy Transition

The world is confronting an unprecedented sustainability crisis. Human activity has already increased global temperatures by roughly 1.1 °C, edging dangerously close to irreversible climate thresholds. This warming is driving more frequent extreme weather events, biodiversity loss, and profound impacts on human well-being, especially among vulnerable communities [1]. At the same time, resource consumption continues to soar alongside population and economic growth. Traditional linear economic models—often summarised as “take, make, dispose”—are straining planetary limits by depleting natural resources and generating unsustainable waste [2]. Recent analyses estimate that the global economy is only ~7.2% circular, meaning over 90% of material throughput is wasted or dispersed, a proportion that has declined from a few years ago [3]. This trend highlights the need for transitioning to more regenerative models that can decouple prosperity from resource depletion.

In this context, the circular economy (CE) has emerged as a promising solutions framework [4]. CE is an economic paradigm aimed at keeping products, components, and materials at their highest utility and value for as long as possible through strategies such as reuse, repair, remanufacturing, and recycling [5]. The evolution of R-hierarchies within the CE reflects the growing need to prioritise high-value resource strategies and minimise environmental impact through structured intervention. The 5R framework, typically comprising Refuse, Reduce, Reuse, Repurpose, and Recycle, offers a basic yet actionable set of strategies for extending product life and reducing waste generation [2]. This model emphasises early-stage preventive actions, particularly in product design and consumption, where Refuse and Reduce aim to eliminate unnecessary resource use. Building on this, the 7R framework integrates additional layers such as Repair and Remanufacture, enabling greater retention of product functionality and encouraging circular loops in both consumer and industrial contexts [6]. The 9R framework, as formalised by Potting et al. (2017) [7], further expands this hierarchy by organising strategies into high (e.g., Refuse, Rethink), medium (e.g., Reuse, Repair, Refurbish), and low value-retention categories (e.g., Recycle, Recover), thereby providing a more granular and policy-relevant tool for assessing circularity potential. These frameworks not only inform eco-design principles but also support policy development and industrial planning by highlighting where interventions can maximise circular outcomes [8]. Their structured progression, from basic waste reduction to systemic resource regeneration, serves as a roadmap for shifting from linear to circular models across sectors.

By maintaining an ongoing flow of materials in closed loops, a CE minimises the need for virgin resource extraction, reduces waste and pollution, and lowers the overall environmental footprint of production and consumption [9]. Notably, the primary objective of a circular economy is to decouple economic growth from the consumption of finite resources, thereby establishing a sustainable basis for development [5]. Figure 1 illustrates these principles through the well-known “butterfly diagram” developed by the Ellen MacArthur Foundation, which distinguishes between technical cycles (for durable materials such as metals and plastics) and biological cycles (for renewable, biodegradable materials) within a circular system. By redesigning products and services from the ground up (e.g., for durability, modularity, and recyclability), companies can “future-proof” their business models against resource scarcity while creating competitive advantage and new value streams.

Over the past decade, the circular economy concept has rapidly gained traction in both policy and industry. Originally popularised by the Ellen MacArthur Foundation in the 2010s, the CE framework has been embraced by the European Union as a pillar of its sustainability agenda and is influencing strategies worldwide [10]. The appeal lies not only in environmental benefits but also in the robust business case for CE: by recirculating products and materials, firms can reduce costs, develop new markets (e.g., secondary materials and product-as-a-service models), and strengthen supply chain resilience [11,12]. Nevertheless, implementing CE at scale remains challenging. Firms face technological and operational barriers in redesigning products and processes, a lack of supportive regulatory frameworks, and often-fragmented supply chains that impede circular flows [13]. Transitioning from linear to circular systems frequently requires significant upfront investments and organisational change, which can be daunting for businesses. These challenges underscore the need for novel approaches to embed circular principles into current economic structures effectively [14]. In particular, leveraging modern technology and innovation is seen as essential to overcoming barriers and accelerating the circular transition. This is where the digital revolution—encapsulated in concepts like Industry 4.0 and the emerging Industry 5.0—becomes critically important.

Figure 1. The circular economy “butterfly diagram” visualises continuous material flows in two cycles: the technical cycle (right side, for non-consumable materials which are recovered, remanufactured, or recycled) and the biological cycle (left side, for biodegradable materials which are cascaded or composted) [15].

Figure 1. The circular economy “butterfly diagram” visualises continuous material flows in two cycles: the technical cycle (right side, for non-consumable materials which are recovered, remanufactured, or recycled) and the biological cycle (left side, for biodegradable materials which are cascaded or composted) [15].

1.2. Role of Digital Technologies in CE and Industry 4.0/5.0 Convergence

The rise of advanced digital technologies in recent years offers powerful tools to enable and scale up circular economy strategies. Technologies associated with Industry 4.0, such as the IoT, big data analytics (BDA), artificial intelligence (AI), blockchain, cloud computing, and 3D printing, are revolutionising the way products are designed, manufactured, used, and recovered [16]. These technologies enable physical objects and systems to be deeply integrated with the digital realm. For example, IoT sensors and connectivity facilitate real-time tracking of products and materials throughout their life cycle, while AI and data analytics optimise processes and predict maintenance needs. Such capabilities are directly applicable to the implementation of the circular economy [16]. For instance, smart manufacturing systems can minimise waste and energy use (supporting circular production), digital platforms can facilitate product sharing or secondary markets (enabling new usage models), and sensor networks can improve waste collection and recycling logistics [17]. In essence, digital innovations provide the data and connectivity needed to manage circular resource loops efficiently and at scale. If managed well, the twin transformations of digitalisation and circularity can be mutually reinforcing: data-driven decision making and connectivity can make circular business models viable where previously they were not, by improving transparency, coordination, and efficiency across value chains [18]. Digital solutions can also enhance information sharing (e.g., digital product passports conveying material data), streamline circular processes (e.g., automated sorting of waste via AI computer vision), and empower consumers with feedback on product sustainability [19]. A conceptual illustration or framework could map these relationships—for example, visualising how core Industry 4.0 technologies feed into various CE loops (sensors enabling asset tracking for reuse/recycling, machine learning optimising product design for longevity, blockchain ensuring transparent circular supply chains, etc.). Such a diagram would highlight that the convergence of digitalisation and circular economy is not coincidental but complementary: digital tools can accelerate and scale up circular strategies, while the pursuit of circularity provides new use cases and demands for digital innovation [16].

Recognising this potential, researchers and practitioners have begun to discuss the integration of Industry 4.0 and CE as a cohesive, transformative agenda. Indeed, the European Commission and thought leaders increasingly emphasise aligning the “digital transition” with the “green transition” to achieve sustainable, competitive economies. However, it is also acknowledged that digitalisation per se will not automatically yield sustainability benefits—deliberate alignment and supportive policy are required [20]. This realisation has given rise to the concept of Industry 5.0, which can be viewed as an evolution that builds on Industry 4.0 technologies while explicitly incorporating human-centric and sustainable objectives. Industry 5.0, a term gaining currency in the mid-2020s, shifts the focus from pure automation and efficiency (the hallmarks of Industry 4.0) toward technology that works in harmony with humans and the environment. In contrast to Industry 4.0′s emphasis on autonomous systems and digitisation, Industry 5.0 envisions collaboration between humans and machines to ensure technological progress is aligned with societal needs and environmental stewardship. This paradigm introduces or highlights concepts like resilience, inclusion, and regeneration as key goals alongside productivity [21]. Crucially, many of the same digital technologies are leveraged—AI, robotics, IoT, digital twins, etc.—but now directed toward outcomes such as resource efficiency, worker well-being, and circular resource flows. Recent studies indicate that Industry 5.0 and circular economy agendas are converging strongly. For example, Rejeb et al. [4] demonstrate that technologies like automation, machine learning and 3D printing, when applied in a human-centric Industry 5.0 context, can optimise resource efficiency and waste reduction in line with CE principles. They highlight how tools such as blockchain and “human-centric AI” are enabling closed-loop systems by ensuring transparency, traceability and stakeholder trust in circular supply chains.

The collaboration between humans and intelligent machines is seen as crucial to balance productivity with environmental responsibility. These insights suggest that the fifth industrial revolution (Industry 5.0) provides a framework for fully harnessing digital innovations to achieve circular and sustainable outcomes. In sum, the rapid advancement of digital technologies offers unprecedented opportunities to support the transition to the circular economy. Scholarly interest in this intersection is growing accordingly. Recent literature explores, for instance, how IoT and data analytics can facilitate circular supply chain management [22], or how additive manufacturing (3D printing) can enable product-life extension and localised recycling [23]. Early review studies have identified numerous synergies and called for deeper exploration of this digital-CE convergence. If one were to depict this conceptually, one might imagine overlapping spheres of “Industry 4.0 + 5.0” and “Circular Economy,” with the overlap representing digital-enabled circular strategies (some authors even refer to this fusion as “Digital Circular Economy” or “CE 4.0/5.0”) [24]. The present work is situated within this emerging discourse, examining how exactly digital technologies are being applied to advance circular economy goals.

1.3. Recent Drivers: Policy, Regulation, and Post-Pandemic Digitalisation

Alongside technological developments, policy and market drivers in the past few years have markedly accelerated interest in digital solutions for the circular economy. Foremost among these are government policies and regulations aimed at sustainability, which increasingly incorporate explicit digital elements [25]. The EU has been a leader in this regard. Its comprehensive Circular Economy Action Plan (adopted in 2020 as part of the European Green Deal) set ambitious targets for waste reduction, resource efficiency, and product longevity, and crucially emphasises digital innovation as a key enabler of these goals [26]. The EU’s policy framework encourages measures like digital product tracking, data sharing across supply chains, and the development of platform-based circular business models. A prime example is the upcoming requirement for Digital Product Passports (DPP) on a wide range of products sold in Europe. In 2024, under the EU’s new Ecodesign for Sustainable Products Regulation, manufacturers were mandated to include a digital passport for products, initially covering batteries, electronics, and other priority sectors, that recorded information about the product’s materials, components, repairability, and end-of-life handling [27]. This policy is designed to enhance transparency and traceability across product value chains, making it easier to recover materials and ensure compliant recycling or reuse. The DPP initiative exemplifies how regulatory drivers are leveraging digital tools (like databases, QR codes, and cloud platforms) to operationalise circular economy principles. Similarly, initiatives around the “Right to Repair” and extended producer responsibility are emerging, which will likely rely on digital information systems to monitor product lifecycles and enforce compliance [28].

Beyond Europe, many other countries are also introducing policies that connect digital innovation with circular outcomes. For instance, China’s national circular economy programs have incorporated industrial internet platforms to track resource flows, and nations like Japan and Canada have launched digital strategies as part of their circular economy roadmaps (though varying in scope) [29]. Internationally, the United Nations Sustainable Development Goals (SDGs), especially SDG 12 (Responsible Consumption and Production), have spurred governments to adopt data-driven approaches to monitor and achieve circularity targets [30]. This policy push—from public procurement rules favouring circular designs to standards for product sustainability data—is creating an environment where companies have both carrots and sticks to adopt digital solutions that support circularity.

Meanwhile, market and societal drivers have also evolved in the wake of the COVID-19 pandemic. The pandemic shock in 2020–2022 exposed vulnerabilities in global supply chains and just-in-time production systems, prompting a renewed focus on resilience and adaptability [31]. One outcome was a dramatic acceleration of digital transformation across industries as companies sought to build more responsive, data-informed operations. In fact, the COVID-19 crisis is widely regarded as a catalyst for digital adoption, forcing businesses to implement remote monitoring, automation, and other Industry 4.0 technologies faster than they otherwise would have. Studies report that during the immediate post-pandemic period, roughly two-thirds of organisations significantly increased their investments in digital tools and infrastructure. Moreover, the strategic goals of digitalisation started to shift: whereas pre-pandemic digitisation was often driven primarily by efficiency and cost reduction, after 2020, the emphasis expanded to include flexibility, agility, and risk management. According to one survey, over 78% of firms indicated that enhancing responsiveness to disruptions became a top driver of their digital initiatives in the post-COVID era. This pivot aligns closely with circular economy thinking [31]. A circular approach inherently boosts resilience—for example, by diversifying supply sources through reuse and recycling, maintaining buffer stocks of remanufactured parts, or localising production via 3D printing. Thus, the post-pandemic digitalisation wave created new opportunities to embed circular practices as part of building robust, tech-enabled supply chains. Companies are increasingly leveraging IoT and data analytics to gain visibility into supply networks and material flows, enabling them to identify circular opportunities (such as recycling scrap or by-products) and respond rapidly to disruptions. Digital and circular strategies together can improve a supply chain’s agility, transparency, and collaboration—qualities that proved vital during the pandemic and remain so in an era of continued volatility [32]. For example, blockchain-based platforms are now used to trace materials (ensuring provenance and compliance with recycling standards), and AI-driven demand forecasting helps optimise inventory with circular options in mind (like redeployment of returned goods). The intersection of digitalisation and circular economy is further reinforced by shifting consumer behaviours (e.g., the rise of e-commerce and sharing platforms during the pandemic) and corporate sustainability commitments, which often rely on digital metrics and reporting.

A confluence of recent drivers is propelling the integration of digital technologies in circular economy initiatives. Regulatory frameworks are mandating greater transparency and circularity (with digital systems as the backbone for compliance and implementation), and the post-pandemic emphasis on resilience is directing corporate digital transformation toward sustainability goals. There is a growing recognition that digitalisation is not only about efficiency but also a key enabler of systemic sustainability and resilience [31]. This creates a timely impetus for our research: understanding how digital tools are being applied to meet circular economy goals, and how these twin transformations—digital and circular—can best complement each other.

1.4. Research Gap and Objectives

Despite the clear potential and growing interest in digital-enabled circular economy solutions, the existing literature has yet to fully chart the landscape of opportunities and challenges at this interface. Many prior studies address the intersection in a piecemeal way. On one hand, there are papers focusing on specific digital technologies within a circular economy context—for example, examining the use of IoT for product tracking in remanufacturing, or blockchain for improving recycling supply chains. On the other hand, some works take a broader view of “digitalisation for sustainability” but remain high-level in their treatment of circular economy outcomes. What is missing is a comprehensive synthesis that maps which digital technologies are used for which circular strategies and their impacts. Recent literature reviews highlight this gap. Neri et al. [33], for instance, note that while digital technologies are widely recognised as a crucial enabler of the circular economy, there is still a lack of clear guidance on how to deploy these tools effectively to support circular implementation in practice. Similarly, frameworks defining the so-called “Smart Circular Economy” or “Digital Circular Economy” paradigms have been proposed, but they often fail to detail the functions of specific technologies or empirically compare their contributions. Bressanelli et al. [34] outline the conceptual foundations of a digital-powered circular economy, and Rejeb et al. [4] explore certain aspects, such as consumer acceptance of digital circular business models. However, these studies tend to adopt either an overly general perspective or focus on narrow aspects, neglecting a technology-by-technology analysis of how each contributes to circularity. In short, the literature lacks an integrative review that bridges the technical and the strategic: we need to catalogue the various digital tools (from sensors to AI to advanced manufacturing) being applied in CE contexts and understand the roles they play and impacts they have on circular transitions. The scholarly gap is also practical: businesses and policymakers could benefit from clearer evidence of which digital interventions yield which circular benefits, and where further opportunities lie.

This study aims to fill that research gap by providing a systematic review of the applications and impacts of digital technologies in the circular economy. In doing so, we extend previous reviews by covering a broad range of digital technologies and linking them to specific circular economy strategies across multiple sectors, drawing on the latest research (including studies from 2023–2025 which have not been covered in earlier reviews). The objective is to develop a comprehensive taxonomy of digital technology uses in CE and to evaluate how these technologies influence or enable the transition toward circular business models and practices. Specifically, our review addresses the following overarching research question:

RQ: What functions do different digital technologies serve, and how do they impact the transition towards a circular economy?

To answer this question, we systematically analysed over a decade of academic publications at the intersection of digital tech and CE (yielding a dataset of more than 266 relevant studies). We identify the key categories of digital technologies being employed (e.g., data-related technologies like IoT and AI, versus other enabling technologies like additive manufacturing or digital platforms) and map them to the circular economy activities or strategies they support (such as circular design, sustainable sourcing, product-life extension, waste recovery, etc.). We then assess the impact patterns reported—for example, whether a given technology primarily improves operational efficiency (an incremental impact), enables new circular business models (a more transformative impact), or facilitates system-level coordination (an infrastructural impact). Through this analysis, we develop an integrative framework illustrating how digital technologies collectively contribute to circular economy goals. The intended contributions of our study are twofold. First, we offer scholars a consolidated reference on the state of the art at the CE–digital interface, highlighting what has been accomplished so far and where knowledge gaps remain (e.g., understudied technology applications or insufficient evidence of impact). Second, we provide practitioners and decision-makers with insights into choosing and implementing digital solutions for circular initiatives: which tools are most useful for which purposes, what benefits can be expected, and what limitations or challenges might be encountered. By shedding light on the functions and impacts of different digital technologies in advancing a circular economy, this review seeks to support more informed and effective integration of digital innovation into sustainability strategies.

1.5. Structure of the Paper

The paper systematically reviews how digital technologies enable and shape circular economy (CE) practices. It begins by detailing the review methodology, search strategy, and analytical framework (Section 2), followed by an overview of publication trends, research domains, and sectoral coverage (Section 3). It then develops a taxonomy of digital technologies—such as IoT, AI, blockchain, and additive manufacturing—highlighting their specific applications across circular strategies (Section 4). The analysis extends to the impacts of these technologies, distinguishing between incremental, transformative, and ecosystem-level effects (Section 5). Recent trends (Section 6) reveal emerging technology integrations, new sectoral applications, links to ESG and policy frameworks, and a shift toward Industry 5.0 principles. Key challenges (Section 7) span technical, organisational, financial, and regulatory barriers, alongside gaps for future research. The paper concludes (Section 8) with theoretical, practical, and policy implications, underscoring the importance of integrated, multi-stakeholder approaches to fully realise a digitally enabled circular economy.

2. Methodology

Figure 2, the PRISMA 2020 flow diagram, shows the overall methodology of the systematic review. The PRISMA checklist is included in the Supplementary Materials.

2.1. Literature Search

We conducted a comprehensive systematic literature review following established guidelines [18]. In line with the PRISMA methodology for transparent reporting, we defined our review scope and search strategy to capture studies that link digital technologies to the circular economy. We performed a keyword-based search across major scholarly databases (primarily Scopus and Web of Science) to ensure broad coverage of relevant literature. The search query combined terms related to circular economy (e.g., “circular economy”, “circularity”) with terms related to digitalisation and Industry 4.0 technologies (e.g., “digital”*, “Industry 4.0”, “Internet of Things”, “IoT”, “Artificial Intelligence”, “AI”) [33]. We applied this query to publication titles, abstracts, and keywords, restricting results to English-language, peer-reviewed publications. No strict start date was enforced (effectively covering literature from the mid-2010s onward, as early work in this domain began appearing then), and the search was updated through mid-2025 to include the most recent studies. For example, an initial search (through 2023) was later extended using the same keywords to capture publications from 2024 and 2025. This approach ensured that our review included both foundational studies and the latest research contributions in this rapidly evolving area.

2.2. Screening and Selection

All retrieved records from the database searches were exported and merged, and duplicate entries were removed. The remaining unique references (numbering in the high hundreds) underwent a two-stage screening process in accordance with PRISMA’s recommended phases (screening, eligibility, and inclusion). In the first stage (title/abstract screening), we examined each reference to determine whether it explicitly addressed both digital technologies and circular economy topics. We excluded any works that were clearly out of scope. For instance, studies focusing solely on circular economy (with no mention of digital tools) or solely on digital technologies (with no linkage to circularity) were filtered out at this stage [18]. In practice, many initial hits were removed because they did not address the intersection of the two domains (e.g., purely technical IoT studies with no sustainability context, or circular economy papers with no digital aspect). The second stage (full-text eligibility) involved retrieving and reading the full texts of the remaining candidate papers. Using predefined inclusion criteria, we verified that each study indeed discussed how one or more digital technologies enable or impact circular economy practices. To avoid sample mixing and ensure conceptual consistency, we operationalised the “enabling role” of digital technologies using a functional inclusion criterion. Studies were included only if digital technologies were explicitly analysed as mechanisms that support, enhance, optimise, or transform circular economy strategies (e.g., enabling traceability, reverse logistics optimisation, predictive maintenance for life extension, or digital platforms for material exchange). Papers that merely referenced digitalisation or circular economy as contextual background, without demonstrating a functional or causal linkage between the two, were excluded during the full-text eligibility stage. This ensured that the final dataset reflects substantive digital–circular integration rather than thematic co-occurrence. We further confirmed that only original research articles and relevant review or conceptual papers were included (non-research items such as editorials or unrelated case reports were excluded). This two-stage filtering was performed by multiple reviewers working independently to minimise bias [35]. Any disagreements or borderline cases were resolved through discussion, ensuring a consensus on the final set of included studies. We also employed snowballing techniques—checking the reference lists and citations of included papers—to identify any additional relevant studies that the database search might have missed (yielding a few extra inclusions that met our criteria). After the entire selection process, 266 publications met all inclusion criteria and were included in the review for analysis. These spanned 2016 through 2025, reflecting growing research interest in digital technologies for the circular economy.

As this review aims to provide a comprehensive mapping and thematic synthesis of the field rather than a statistical meta-analysis, we did not apply citation-based weighting or exclude studies based on citation counts. All included publications were peer-reviewed journal articles or rigorously reviewed academic contributions, ensuring a baseline level of scholarly quality. Each study was treated equally during coding and thematic analysis, with patterns and themes emerging through frequency and conceptual convergence rather than citation volume.

2.3. Data Extraction and Analysis

For each of the 266 included studies, we carried out a systematic data extraction and qualitative synthesis. We catalogued important details of each paper, including:

• Bibliographic information—author(s), year, publication outlet, and document type (to characterise the source of the research).
• Digital technologies addressed—we noted which digital or Industry 4.0 technologies were involved (for example, Internet of Things (IoT) for data collection, AI or BDA for data analysis, Cloud computing or blockchain for data storage/sharing, and other relevant technologies like robotics, automation, digital platforms, digital twins, etc.). To handle the diversity of technologies, we grouped them into broad functional categories (such as data collection, data storage, data analysis, data sharing, and advanced manufacturing technologies) based on each technology’s primary role [18]. This functional classification helped compare studies focusing on similar types of digital enablers.
• Circular economy focus—we recorded which aspect of the circular economy each study targeted, such as which stage of the product lifecycle or supply chain (design, sourcing/procurement, production, consumption or waste management) or which circular strategy/principle (e.g., recycle, reuse, remanufacture, product-service systems, etc.) was being enabled or examined. This allowed us to identify where digital interventions were applied within the circular economy framework.
• Research context and method—we captured the study’s context (the industry sector or application domain, geographic focus, and scale of application, such as whether it involved small firms or large enterprises) [35]. We also noted the research approach (e.g., empirical case study, survey, experiment, interview-based qualitative study, theoretical framework, or literature review) to understand the nature of the available evidence.
• Key findings and themes—we extracted qualitative insights from each paper regarding how digital technologies enable circular economy outcomes. In particular, we summarised the reported benefits/impacts of adopting digital solutions for circularity (e.g., improvements in resource efficiency, waste reduction, new circular business models), noted any identified challenges or barriers (e.g., implementation difficulties, skills or infrastructure gaps, economic or regulatory hurdles), and recorded emerging trends or patterns the authors highlighted (such as increasing integration of certain technologies or cross-industry collaborations). We also captured any proposed future research directions or open questions, as these indicate knowledge gaps. These summaries were used to synthesise overarching themes across studies.

We conducted a descriptive and thematic analysis to map and interpret the reviewed literature. First, we examined publication trends, key journals, and citation patterns to characterise the field’s growth and influence. Next, we performed a thematic synthesis to integrate findings across studies, grouping evidence on similar applications—such as IoT-based waste tracking or AI-driven predictive maintenance—to identify how digital technologies advance circular outcomes. This iterative process ensured that the emerging themes were grounded in the literature. The resulting review offers both quantitative insights into the research landscape and a qualitative understanding of how digital technologies enable, challenge, and shape circular economy practices.

2.4. Analysis Framework

In order to systematically examine the selected literature, we established an analysis framework consisting of two complementary classification schemes: (1) a taxonomy of digital technology (DT) application functions in CE, and (2) a typology of their impact on circular economy outcomes. Each paper was coded according to this framework, which allowed us to map specific technologies to their primary roles and effects in facilitating CE transitions. Below, we outline the taxonomy of DT applications we employed, followed by the impact typology used to categorise how these technologies influence circular practices. Coding allowed multiple technologies per study (non-exclusive coding). Quantification, therefore, reports frequency of occurrence across studies and across lifecycle stages/impact categories rather than allocating a single study to a single technology.

2.4.1. Taxonomy of Digital Technology Applications in CE

Building on patterns observed in prior studies, we grouped the diverse digital technologies into categories based on their primary functions in a circular economy context. A consistent finding in the literature is that most DTs serve data-driven functions—capturing, processing, or sharing information—which are crucial for optimising resource loops [36]. At the same time, we account for other enabling technologies that contribute to circularity through physical or user-oriented innovations. The taxonomy is defined as depicted in Figure 3 (with examples of technologies in each category).

Notably, our taxonomy highlights the centrality of data in digital CE applications—nearly all identified DTs involve capturing, analysing, or sharing information to enable better circular decisions. This is consistent with recent frameworks that emphasise data as the “fuel” of circular economy initiatives, where “waste + information = resource”. At the same time, we recognise that certain physical-digital technologies (such as 3D printing and AR) play a pivotal enabling role beyond data handling, directly reshaping production-consumption systems in line with CE principles. The above taxonomy provides a structured lens to categorise each technology’s function in our review, laying the groundwork for analysing its impacts.

2.4.2. Impact Typology of DTs on Circular Economy

In parallel, we developed a typology to classify the nature of the impact that each digital technology application has on circular economy implementation. This typology is inspired by the extent to which a technology-driven initiative alters existing business models or value networks, as highlighted in both the innovation literature and CE case studies. We identified three overarching impact types, aligning with the categories outlined in our research design (enabling, disruptive, and facilitating impacts). These are defined as:

• Enabling (Process Optimisation): Digital technologies improve efficiency and resource use without changing the core business model. Examples include IoT and AI for predictive maintenance, waste reduction, and real-time monitoring, helping firms extend product lifetimes and cut costs while maintaining existing operations.
• Disruptive (Business Model Innovation): Technologies drive new circular value streams and shift from product sales to service-based or platform models (e.g., product-as-a-service, sharing systems). Blockchain, digital twins, and AR/VR enable new inter-firm collaborations and the delivery of non-material value, transforming traditional linear business structures.
• Facilitating (Ecosystem Collaboration): Technologies create shared platforms and networks for cross-stakeholder cooperation, transparency, and data exchange. Examples include digital product passports, industrial symbiosis platforms, and data hubs connecting manufacturers, recyclers, and consumers. These tools form the infrastructure for scaling circular ecosystems.

These three impact types often overlap; a single technology may enhance efficiency while also enabling new models or collaborations. For clarity, we classified the primary impact highlighted in each study. The typology spans a spectrum from incremental improvements (enabling) to transformative change (disruptive) and system-wide collaboration (facilitating), illustrating how digital technologies advance circular economy transitions at different scales.

Figure 4 below provides a conceptual mapping of the DT application category taxonomy against the impact typology, illustrating how the two dimensions of our framework interrelate.

In this schematic matrix, the rows represent the major technology application categories (as defined in the taxonomy), and the columns correspond to the three impact types defined above. A check mark indicates that technologies in that category are predominantly associated with that impact in the literature. For instance, as shown, data-focused functions (top rows) most commonly act as enablers that optimise processes (under Enabling for Data Collection/Storage/Analysis)—reflecting that much of IoT, cloud, and AI usage in CE is initially aimed at improving efficiency and monitoring within existing operations. In contrast, the Data Sharing category stands out as primarily facilitating multi-actor collaboration (under Facilitating), as sharing platforms and interoperability tools are central to connecting stakeholders in a circular value chain. Meanwhile, technologies oriented towards user experience (e.g., AR/VR under Experience/Engagement) are often linked to disruptive new models (under Disruptive), such as virtual services or enhanced product-service systems. This visual framework underscores how different groups of technologies map to varying types of impact—a data-centric tool might mainly improve internal efficiency. In contrast, a platform technology might enable an ecosystem solution, although overlaps exist, and some tools can play multiple roles.

3. Descriptive Analysis

3.1. Distribution by Year, Journal, and Citations

The reviewed literature spans 2000–2025, with an apparent acceleration in publication volume over the last decade. Early years saw only a handful of relevant papers, but output surged notably after 2015, reflecting growing scholarly and practical interest in digital technology applications for CE. A timeline plot of publications per year (Figure 5) would show a sharp rise from 2016 onward, peaking in the early 2020s. This trend corresponds with the broader emergence of CE in policy and research agendas during that period, as well as rapid advancements in Industry 4.0 technologies. By 2023, annual publication counts are an order of magnitude higher than in the mid-2010s, indicating that this topic has gained significant momentum.

In terms of publication outlets, the 266 papers are distributed across a wide range of journals, though a few sustainability-focused journals account for a large share. Figure 6 illustrates the top contributing journals. Notably, sustainability has the highest count with 35 papers, reflecting its openness to emerging CE topics.

This is followed by outlets such as Business Strategy and the Environment (19 papers) and the Journal of Cleaner Production (15 papers), both of which are well-established in sustainable innovation research. Other influential journals include the Journal of Environmental Management (9 papers), Resources, Conservation and Recycling (8 papers), and Sustainable Production and Consumption (7 papers). These figures show that most studies appear in environmental sustainability and operations management journals, reflecting the field’s interdisciplinary scope. Highly cited papers in the Journal of Cleaner Production mark key foundational work, while the recent surge in Sustainability and Resources, Conservation and Recycling indicates growing scholarly interest.

3.2. Sectoral Distribution and Regional Focus

The application domains of digital technologies in CE are diverse, covering many industry sectors. Figure 7 illustrates the sectoral distribution of the reviewed papers.

Manufacturing is by far the most studied sector, accounting for about 41% of the publications—a testament to the emphasis on circular production systems and Industry 4.0 practices in manufacturing contexts. The construction and built environment sector is the second most represented (around 15.5%), reflecting interest in smart construction, BIM, and material reuse in that industry. An “ICT/Digital Technology” category itself constitutes roughly 12% of the papers, often focusing on the development of digital platforms or tools that are cross-cutting in nature. Other notable sectors include waste management and general circular economy initiatives (approximately 9% of papers), as well as cross-sectoral or multi-sector studies (~9%) that address broad CE strategies not limited to a single industry. Agriculture and food systems (6% of papers) form another important application domain, for instance, in precision agriculture for waste reduction and farm-to-fork traceability. Energy and utilities (4%) and services/retail (2%) are less common focal areas, and only a handful of papers (<1% each) specifically examine transport & logistics or public sector (governance and policy) applications. This spread indicates that while manufacturing and construction lead the conversation, digital CE principles are being explored in virtually every economic sector. The accompanying sector distribution chart visually reinforces the dominance of manufacturing and highlights the breadth of sectors involved.

Geographically, the research has a strong regional bias toward Europe and other select economies, as seen in Figure 8. Research on digital technologies for the circular economy is heavily concentrated in Europe, led by Italy, Germany, and the UK—reflecting the EU’s strong policy support and funding for CE initiatives. Emerging economies such as China, India, and Brazil follow, while North America shows limited engagement, with few studies from the US and Canada. Africa and Oceania are also underrepresented. Overall, the regional distribution mirrors where CE and digital innovation are most advanced, highlighting research gaps in developing regions and North America.

The reviewed literature shows a clear regional concentration in Europe, which likely reflects the stronger and earlier regulatory push for circularity and digital traceability (e.g., DPP-related initiatives and harmonisation efforts). This policy environment has influenced the research emphasis on interoperability, lifecycle data sharing, and cross-actor transparency. In contrast, studies from other regions more frequently frame digital CE adoption through industrial competitiveness, supply-chain resilience, or resource-security lenses, with fewer references to product-passport-style mandates. This suggests that regional policy mixes shape not only the pace of adoption but also the dominant application patterns (e.g., compliance-driven traceability in Europe versus efficiency- and productivity-driven deployments elsewhere). Future work would benefit from systematic cross-region comparative studies to validate these patterns.

3.3. Research Designs and Methods Used

The literature employs diverse research methods, with a clear dominance of qualitative case-based studies (≈56%), reflecting the exploratory and context-specific nature of digital CE research. Surveys and interviews appear in ≈27% of studies, while ≈29% use analytical or simulation models to assess circular scenarios, signalling growing quantitative engagement. About 41% explicitly focus on theory or framework development, aiming to generalise insights and advance conceptual understanding. Overall, mixed-method approaches are common, but the field remains primarily qualitative and exploratory.

3.4. Real-Life vs. Potential Applications

The reviewed literature employs a diverse range of research methods, with qualitative case-based studies being the most prominent. Over half of the papers (≈56%) use case studies as their primary design, often analysing one or more organisations to understand digital CE implementation. This prevalence highlights the exploratory and context-specific nature of research in this emerging field. Survey and interview-based studies are less common (≈27%), suggesting that while some collect quantitative or stakeholder data at scale, most rely on detailed qualitative insights from particular projects or firms. About 29% of studies employ analytical or simulation models—such as optimisation frameworks, digital prototypes, or system simulations—indicating a growing but still secondary quantitative strand. Notably, ≈41% of the papers contribute to theory building or framework development, aiming to generalise findings and strengthen conceptual underpinnings (e.g., taxonomies of digital CE tools or technology-enabled business models). Overall, the methods are diverse, with frequent combinations of qualitative and quantitative approaches, but the field continues to lean toward exploratory, case-oriented designs.

3.5. Technology–Sector Network Mapping

To better understand how specific digital technologies intersect with different sectors of the circular economy, we mapped the occurrence of technologies against the industries in which they are applied. Figure 9 presents a network diagram of these technology–sector interactions, where each node represents either a technology or a sector, and links indicate that a given technology is applied in a given sector (based on our review coding). Technologies are depicted in one colour (e.g., green nodes), and sectors in another (e.g., blue nodes) for clarity. This network visualisation offers a holistic view of the landscape, revealing clusters and key connection points: for instance, certain digital tools serve as enablers across multiple sectors, while some sectors leverage a wide array of different technologies. The density of connections in the diagram highlights the multidisciplinary nature of digital CE solutions—many technologies are not confined to a single industry domain, and similarly, many sectors employ a suite of digital innovations rather than just one. By examining the structure of this network, readers can quickly grasp which technologies are most pervasive and which sectoral applications are drawing the most technological support.

Some prominent technology–sector linkages are evident from the network, including:

• Internet of Things (IoT): IoT appears as a central hub technology, with strong links to multiple sectors such as manufacturing, waste management, and agriculture. For example, IoT-based sensor networks are widely used in manufacturing processes for real-time monitoring and in smart waste collection systems to optimise recycling and disposal routes. This prevalence (also reflected by IoT being identified in a large number of studies) underscores IoT’s role in data collection and tracking across the CE spectrum.
• Mobile applications: Mobile apps show prominent connections with consumer-facing sectors, including retail and mobility (transportation), as well as urban development (smart city initiatives). This indicates their versatile application in engaging end-users for circular practices—for instance, apps for product sharing or return schemes in retail, ride-sharing and mobility services, and platforms that involve citizens in urban recycling programs.
• Advanced manufacturing and 3D printing: These production technologies are closely associated with the manufacturing sector, but the network also shows links to specific industries such as textiles and chemicals. This reflects how additive manufacturing (3D printing) and other advanced production techniques are being used to enable on-demand production, customisation, and materials efficiency in textiles and chemical manufacturing (e.g., producing components with less waste or using recycled feedstock). Such technologies thus drive circular outcomes by optimising production and enabling remanufacturing of parts across these sectors.
• Artificial intelligence and robotics: AI, often alongside technologies like machine learning and computer vision, as well as robotics, are strongly connected to construction and general manufacturing in the network. These technologies facilitate automation, precision, and intelligent decision-making—for example, AI-driven algorithms and robotic systems are used in construction for efficient building demolition and material sorting, and in factories for predictive maintenance and automated disassembly of products. Their central position in the network diagram highlights their importance in improving process efficiency and reducing waste in core industrial sectors. Additionally, technologies such as AR and VR cluster around design-intensive and training applications in construction and manufacturing, indicating use in prototyping, worker training, and maintenance visualisation to support circular practices.
• Digital platforms and blockchain: Digital platform technologies (including online marketplaces, IoT platforms, and other collaboration portals) form a nexus connecting multiple sectors (often represented in the network as links to a “cross-sectoral” node). These platforms enable circular business models, such as product-service systems and sharing economies, by linking producers, consumers, and recyclers. For instance, several studies highlight platforms for industrial symbiosis or material exchange that span across industries. Likewise, blockchain technology connects to supply chain-intensive sectors (e.g., food and agriculture, electronics) through its role in improving traceability and trust in circular supply chains (e.g., tracking recycled material content or product histories).
• Waste management and resource recovery: In the network, the waste management sector is connected with numerous digital technologies, including IoT, automation systems, drones, and AI-based analytics. This reflects that managing waste and recycling is a key area where multiple digital solutions converge—sensors and IoT enable smart bins and real-time waste tracking, drones can monitor landfill or hazardous waste sites, and AI can improve sorting of recyclables. The strong interconnectedness around waste management emphasises its critical role in the CE and how technology is enhancing efficiency at this end-of-life stage across various material streams. Notably, remanufacturing activities (closely related to waste management in extending product lifecycles) are linked in the diagram to robotics and service-oriented technologies. This highlights, for example, the use of robotic automation in disassembly for remanufacturing, and digital service platforms that facilitate maintenance or take-back programs—underscoring the synergy between technology and circular strategies aimed at keeping products in use longer.

The technology sector network (Figure 9) visually illustrates the complex interplay between digital solutions and circular economy applications. It shows that core technologies like IoT and data analytics serve as broad enablers across sectors, while others, such as AR/VR in design or blockchain in supply chain transparency, play more specialised roles. Mapping these linkages helps identify pivotal technologies and potential cross-sector opportunities (e.g., applying AI from manufacturing to agriculture). Overall, the network provides a high-level view of digital CE integration, highlighting both established and underexplored connections, supported by accompanying figures for clarity and context.

4. Digital Technologies as Enablers in the Circular Economy

A clear insight from the reviewed literature is that data lies at the heart of digitalisation for the CE. Nearly all studies emphasise some form of data handling. In fact, about 236 of the 266 papers (~89%) involve technologies for data collection, storage, analysis, or sharing, underscoring that effective data management is the linchpin of most digital CE initiatives. By contrast, around 105 papers (~40%) highlight other non-data-centric digital solutions (e.g., robotics, 3D printing, drones), often in combination with data-focused tools. This suggests that while specialised technologies are essential, the primary role of digital tools in CE is to generate and leverage data for better decision-making, resource optimisation, and collaboration. In line with this finding, we structure our discussion into two parts: data-driven digital technologies and other innovative digital technologies. This structuring is an analytical device for presentation rather than mutually exclusive classes. Many technologies (e.g., drones, robotics, digital twins) can generate data while also directly enabling circular operations; therefore, studies may be coded under multiple technologies and multiple functional roles. To maintain consistency, each technology is classified by its primary function in the CE context reported by the study, while co-occurring technologies are recorded as secondary enablers. Accordingly, frequency counts represent occurrences across the dataset and can sum to more than 266. The former subsection details how digital technologies enable the collection, storage, analysis, and sharing of data to support circular strategies. The latter then covers additional technologies (e.g., automation, additive manufacturing, robotics) that directly facilitate circular practices. This structure allows us to highlight high-level patterns and trends in the literature while also giving concrete examples of how specific technologies are applied in case studies. Overall, the literature reveals that digital tools—especially those managing data flows—are catalysing circular business model innovation and operational improvements, from tracking materials in supply chains to creating new platforms for collaborative waste reduction. By synthesising both frequently cited works and the most recent contributions (2024–2025), the following sections illustrate these patterns in detail, balancing broad trends with illustrative examples from practice.

4.1. Data-Driven Digital Technologies in CE

Digital technologies that collect, store, analyse, and share data are widely recognised as critical enablers for circular economy implementation. These data-centric tools allow companies to monitor products and resources in real time, extract insights to improve circular processes, and coordinate actions across value networks. In our review, an overwhelming majority of papers highlight one or more of these data-related functions. We discuss each in turn, noting key trends and case examples for how they support circular strategies.

4.1.1. Data Collection

Data collection is the foundation for all data-driven initiatives in the CE. Modern enterprises rely on timely, granular data on products, materials, and processes to enable circular strategies such as reuse, remanufacturing, and recycling. The IoT—networks of sensors, smart devices, and connectivity—emerges as a pivotal technology in this context. Over half of the reviewed papers (roughly 152 out of 266) explicitly highlight IoT as a means of gathering essential data for circular operations. IoT devices are deployed throughout production lines, supply chains, products, and even waste bins to continuously capture information such as location, usage conditions, performance, and environmental parameters. This real-time data collection capability is indispensable for monitoring resource flows and product lifecycles in a circular economy (e.g., tracking a component’s status to decide when it should be repaired or recycled).

A typical IoT application in CE is asset tracking and condition monitoring. Sensors and RFID tags attached to products or containers feed data to centralised systems, enabling companies to know where an item is and what state it is in. For instance, enterprises can monitor product usage and health to optimise maintenance or trigger take-back for refurbishment [37,38]. IoT-generated data on wear, temperature, or performance can signal when a product is nearing end-of-life or is eligible for upgrade, thereby facilitating product life extension and timely recycling. Studies also show that IoT-based tracking is crucial for reverse logistics, ensuring that materials return to the production cycle rather than become waste [39]. Additionally, IoT supports environmental monitoring by measuring factors such as energy consumption, emissions, and waste levels in real time [40]. This helps organisations identify inefficiencies and intervene quickly to reduce environmental impact. IoT systems have been used in smart manufacturing and even in smart city initiatives (e.g., sensor-equipped waste bins and water systems) to provide data that optimise resource use and improve sustainability. By connecting devices and infrastructure, IoT also enhances coordination across the value chain—for example, sharing supply/demand data with suppliers or recyclers to better align production with circular supply flows [41]. In short, IoT provides the data streams that enable many circular practices (such as predictive maintenance, inventory sharing, and efficient collection of end-of-life products). Other data-collection approaches noted in the literature include mobile applications that crowdsource user data (e.g., apps where consumers report product conditions or return items) and smart sensors/drones for environmental data, though these are mentioned less frequently than IoT sensor networks (mobile apps appear in ~18 papers as a data source, often for consumer engagement). Overall, establishing robust data collection via IoT is seen as a first step toward any data-driven circular initiative, as it bridges the physical world with digital systems and provides the raw information needed to fuel analysis and decision-making [42].

4.1.2. Data Storage

Once data is collected, it must be stored securely and accessible to be useful for circular economy applications. Our review shows that cloud computing and blockchain technologies dominate discussions of data storage in CE contexts. Around 60 of the 266 papers (~23%) discuss the use of cloud-based systems for managing circular data, and about 52 (~20%) highlight blockchain or distributed ledgers for storing and safeguarding information. These storage technologies ensure that the massive volume of data generated (often in real time) can be retained, organised, and retrieved by relevant stakeholders throughout a product’s lifecycle.

Cloud computing is frequently characterised as the “backbone” of data storage for circular systems. By leveraging scalable cloud infrastructures, organisations can aggregate large, diverse data streams—from IoT sensors on factory equipment to customer return logistics data—into a central repository [43]. Cloud platforms provide virtually unlimited storage capacity and on-demand computing power, which is crucial for circular initiatives that often involve large datasets (e.g., tracking thousands of products or materials over many years). Moreover, cloud services enhance data security and accessibility: companies can implement backups, encryption, and user access controls, while still enabling authorised actors (within the firm or across the supply chain) to access the information they need anytime, anywhere. For example, a manufacturer and its recycling partners might share a cloud-based database of components’ material composition and usage history to coordinate recycling efforts. The literature notes that cloud storage improves transparency and auditing in CE practices—data such as origin of materials, recycling rates, or carbon footprint can be logged and later reviewed to ensure compliance with sustainability goals [43]. Overall, cloud solutions are seen as flexible, cost-effective data hubs that support real-time analytics and inter-firm collaboration in a circular economy [44,45].

Blockchain technology, on the other hand, is valued for ensuring data integrity, traceability, and trust in circular ecosystems. A blockchain is a decentralised ledger where transactions or data entries are cryptographically linked and immutable. In about 20% of the papers, blockchain is proposed as a solution to securely store critical information about products and materials—for instance, recording each time a product changes hands, is serviced, or is recycled [46]. This tamper-proof record is extremely useful for preventing fraud (e.g., false claims of recycled content) and for verifying the history of a circular product. Traceability is a key requirement for circular supply chains (e.g., to ensure that recycled materials meet quality standards or to validate a product’s origin for reuse), and blockchain offers a reliable way to achieve it by design [47,48]. Several case studies highlight blockchain-based platforms where stakeholders (suppliers, manufacturers, consumers, recyclers) write and read transactions about material flows, thereby creating a shared, trustworthy source of truth for the circular lifecycle [49,50]. For example, a recycling plant can confidently identify a part and its material composition by using a blockchain record from the manufacturer, thereby improving sorting and recycling efficiency. Additionally, smart contracts (self-executing code on blockchains) are noted as tools to automate circular transactions—such as automatically releasing a deposit refund when a customer returns a product for recycling—without requiring intermediaries [51]. While blockchain does not replace relational databases or cloud storage for large-scale data, it complements them by providing data assurance for critical information (such as certifications, ownership, or compliance documents) in a multi-party, circular network. Together, cloud and blockchain technologies therefore address the dual need in CE for vast data handling and secure, transparent record-keeping. By using these tools, companies can maintain the integrity of their circular data and ensure that all partners have trustworthy access to the information necessary for collaboration. For instance, cloud databases might hold detailed sensor datasets and analytics dashboards, while a blockchain ledger anchors the veracity of key lifecycle events (production, repairs, recycling) for each product [40,52]. Both types of storage solutions are pivotal for scaling up circular economy practices, as they address the distributed, data-intensive nature of these practices across product life cycles and value chains.

4.1.3. Data Analysis (Including Digital Twins)

Raw data alone has little value without analysis to derive insights and drive decisions. Accordingly, technologies for data processing, analytics, and modelling are extensively discussed in the CE literature. Out of the 266 works, we found around 169 (~64%) that involve some form of data analysis tool—primarily AI/Machine Learning, BDA, and simulation-based approaches (including digital twins). These technologies enable organisations to make sense of complex datasets and inform actions that enhance circularity, such as optimising processes, predicting maintenance needs, and redesigning products for longevity. Notably, about 101 papers (38%) highlight uses of AI and machine learning, 115 (~43%) highlight big data techniques, and ~30 (11%) discuss simulation or digital twin applications. Often, multiple techniques are used together (for example, big-data platforms feeding AI algorithms, or AI models embedded in a digital twin), so these categories do overlap. Below, we synthesise how these analytical tools contribute to CE, along with examples:

• AI-driven optimisation of processes: A growing number of case studies demonstrate that AI can analyse large, complex datasets from production and product use to identify inefficiencies and recommend improvements. For instance, machine learning models can tune manufacturing parameters to minimise waste and energy consumption while maintaining product quality [53,54]. In one example, AI algorithms applied in a factory setting helped reduce scrap rates and cut energy usage, directly improving circular outcomes by conserving materials [55]. AI is also used to optimise supply chain logistics—e.g., routing vehicles to reduce transport emissions or suggesting the best times to remanufacture vs. recycle an item—thereby increasing overall resource efficiency.
• Predictive maintenance and product life extension: AI (particularly predictive analytics) is often cited as a tool for extending the useful life of products and assets. By analysing sensor data on equipment or products in use, AI models can predict failures or maintenance needs before they occur [56,57]. This capability allows companies to service products proactively, preventing costly breakdowns and keeping items in circulation longer. For example, an AI-based predictive maintenance system in a manufacturing plant might forecast when a machine part will wear out and schedule its refurbishment, avoiding an unplanned outage and prolonging the machine’s life [58]. Similarly, for consumer products such as appliances or vehicles, connected data analysed by AI can alert users or service centres to perform repairs, thereby avoiding premature disposal. Such AI-driven maintenance not only reduces waste but also saves costs, creating a win–win incentive for circular business models.
• Material flow and recycling optimisation: Data analytics and AI also assist in managing material streams in a circular economy. Several studies describe using AI to sort waste, identify recyclable materials, or even dynamically set up closed-loop supply chains by matching waste outputs from one process as inputs to another [59,60]. For instance, computer vision (an AI technique) can automate the identification and separation of different types of recyclable plastics on a conveyor belt, improving recycling purity and efficiency. In broader supply chain planning, AI and big data tools help companies forecast the availability of secondary materials, optimise the inventory of refurbished parts, or simulate the impact of different circular strategies on resource flows [61]. These insights help decision-makers choose circular interventions that maximise reuse and minimise waste across the entire network.

Recent literature further extends digital twin applications by integrating immersive virtual environments and metaverse-based interfaces to simulate and optimise circular operations across complex industrial systems, strengthening collaborative and real-time circular decision-making [62].

In addition to AI and machine learning, BDA is often cited as the engine powering these insights. BDA platforms enable organisations to process and query large datasets generated by IoT devices, customer interactions, and operational systems [6]. By leveraging big data techniques, companies can uncover patterns—for example, correlations between usage behaviour and product failure, or hotspots of material waste in a production line—that would be impossible to see otherwise. Big data tools also facilitate data-driven decision-making aligned with CE principles, such as identifying opportunities for product-as-a-service models or optimising recycling logistics based on real-time data [63,64]. Furthermore, big data supports collaborative planning: by pooling data across partners, stakeholders can jointly model scenarios like urban resource management or industry-wide material loops [65]. Some papers in 2024–2025 emphasise the role of data analytics in the twin transition (digital + green), arguing that mastering big data is key to simultaneously achieving digital transformation and sustainability goals [66,67].

Another critical set of analysis tools in the CE context are simulation technologies and digital twins. Simulation has long been used to model complex systems, and in circular economy research, it is used in applications such as virtual product design, process simulation, and scenario analysis. For example, simulations can model a manufacturing process under different circular strategies (e.g., recycled vs. virgin materials or varying product modularity) to predict outcomes such as waste generated or costs incurred [68,69]. Urban planners have used simulation to visualise city-scale resource flows, helping to plan infrastructure for waste management and recycling [70]. Building on traditional simulation, digital twins have gained attention as a cutting-edge approach: a digital twin is a live, digital replica of a physical product, process, or system, continuously updated with real-world data. Several of the recent papers, such as [71,72,73], explore how digital twins can support circular objectives. By linking sensors on physical assets to a virtual model, digital twins enable real-time monitoring, predictive analytics, and testing of “what-if” scenarios without disrupting the actual system. In a CE setting, a digital twin of a factory or product can be used to test improvements virtually—for instance, engineers can simulate a product’s performance if a component is replaced with a recycled material, or how a machine would run with a different maintenance schedule, before implementing it in reality. This helps in optimising design for circularity and maintenance cycles in a risk-free manner [74]. Digital twins also facilitate better information sharing (as discussed next) because the live model can be accessed by multiple stakeholders (e.g., a manufacturer and a recycler both examining the twin to decide on the best end-of-life option). In summary, data analysis capabilities—from AI-driven analytics to digital twin simulations—are the “brain” of digital circular economy solutions. They convert the raw data (collected and stored in the previous steps) into actionable intelligence, enabling businesses to innovate their models (such as shifting to predictive services or closed-loop supply chains) and continuously improve circular performance with data-driven evidence. By blending advanced algorithms with domain knowledge, these tools help navigate the complexity of circular systems and maximise value from resources at every lifecycle stage.

4.1.4. Data Sharing (and Digital Product Passports)

Effective circular ecosystems often involve multiple independent actors—suppliers, manufacturers, distributors, consumers, recyclers, and even regulatory authorities. Thus, securely and efficiently sharing data among stakeholders is crucial for coordination and transparency in the circular economy. This need is reflected in the literature, with approximately 70 of the reviewed papers (≈26%) explicitly discussing technologies for data sharing and interoperability, ranging from blockchain-based networks to collaborative platforms and standards, such as DPPs. The overarching goal is to ensure that the right information (e.g., a part’s material composition or real-time inventory levels of a reusable component) is available to the right parties at the right time to facilitate circular decisions. Several digital solutions are highlighted in this context, each addressing different facets of the data-sharing challenge in CE:

• Blockchain networks for trust: As mentioned earlier, blockchain not only stores data but also enables peer-to-peer sharing of verified information in a network. By using blockchain, organisations can exchange data (such as certificates of recycling or origin of materials) with built-in trust and security, since each update is validated and immutable. This is particularly valuable when many partners are involved, and no single entity governs the data. For example, a blockchain-based material registry can allow brands, consumers, and recyclers to access a product’s history (origin of raw materials, repair records, etc.) with confidence in its accuracy [40]. Blockchain thus lays a foundation of trust among stakeholders, reducing hesitancy in sharing data that might otherwise be considered sensitive [75]. It also supports smart contracts to automate collaborative processes—e.g., automatically transferring ownership or issuing recycling incentives when conditions are met—streamlining multi-party workflows in the circular value chain.
• Cloud-based platforms for real-time access: Cloud services are not just for storage; they also act as shared platforms where stakeholders can retrieve or input data concurrently. Many papers mention cloud-based dashboards or IoT platforms that provide real-time data access across geographies [76,77]. For instance, a global firm with multiple production sites can use a cloud platform to share live data on resource consumption and waste generation with headquarters or supply chain partners. This ensures that everyone sees a “single source of truth” and can respond quickly to changes (like a sudden shortage of a recycled material). Real-time sharing through cloud APIs is essential for dynamic circular activities—imagine a network of factories adjusting their schedules based on live feeds of available recyclable feedstock. In such scenarios, cloud infrastructure enables the speed and scalability required for data-intensive coordination.
• Digital platforms and APIs: Beyond generic cloud storage, digital platforms tailored for circular ecosystems are highlighted as key enablers. These platforms often include APIs that allow different software systems to communicate and exchange data automatically [78]. By standardising data exchange (using APIs or common data formats), they break down silos between organisations’ IT systems. For example, a circular marketplace platform might connect a manufacturer’s inventory database with a recycler’s demand system via APIs, so that excess materials are instantly listed for recycling companies to procure. Such interoperability is crucial for efficient resource loops. Researchers note that digital platforms can host marketplaces for secondary materials, facilitate industrial symbiosis by matching waste-to-input uses, and support joint tracking of products through take-back programs [79,80]. In essence, these platforms act as hubs for collaboration, where multiple stakeholders converge to share data (and often transact) in support of circular outcomes.
• Building Information Modelling (BIM): In the construction sector—a sector strongly represented in CE research—BIM is frequently cited as a tool for sharing rich data among stakeholders over a building’s lifecycle. BIM software creates a digital model of a building that includes detailed information on every component (materials, dimensions, suppliers, etc.). This digital model can be continuously updated and accessed by architects, engineers, contractors, and facilities managers. In a circular economy context, BIM enables stakeholders to plan for material reuse or recycling from the design phase and to track those materials during construction, use, and demolition [81]. For instance, if a building is deconstructed, the BIM data can inform what materials (steel beams, fixtures, etc.) are available for reuse and their exact specifications. Studies show that using BIM greatly improves cross-disciplinary collaboration and transparency in projects, ensuring that sustainability and circularity goals (like reusing 30% of materials from an old building in a new one) are achieved through shared information [82]. Thus, BIM exemplifies how structured data sharing within an ecosystem (here, the construction project ecosystem) can unlock circular opportunities.
• Digital Twins: While we discussed digital twins as an analysis tool, they also play a role in data sharing. A digital twin, by its nature, serves as a shared, real-time data representation of a physical asset or system. Different stakeholders can subscribe to or query the twin to get up-to-the-minute information and insights. For example, consider a digital twin of a wind turbine: the manufacturer, operator, and maintenance service provider could all access the twin to monitor performance and schedule repairs or upgrades that extend the turbine’s life. In a CE setting, such an arrangement means that knowledge about the asset’s condition and remaining life is not siloed—it is collaboratively used to maximise the asset’s productive lifespan [71]. In our literature sample, digital twins are often discussed in industrial symbiosis or smart city contexts, where a digital twin of a system (e.g., a factory, city district) enables multiple parties to coordinate in managing resources [73]. By sharing the twins’ data, participants can align their decisions (for example, adjusting production schedules based on energy availability or waste heat utilisation, as shown by a city’s energy twin). In summary, digital twins enhance data sharing by creating a common lens through which all stakeholders can observe and influence a circular system simultaneously.
• Cyber-Physical Systems (CPS): CPS refer to integrations of physical processes with computational control and networking. In practice, a CPS might be a smart factory where sensors, machines, and software are tightly integrated to autonomously adjust operations. CPS contribute to data sharing by enabling machine-to-machine communication and decentralised decision-making. For instance, in a CPS recycling facility, sorting machines equipped with sensors could automatically redirect materials and inform upstream systems of the quality of recyclables, without human intervention [83]. Such systems generate and exchange data on the fly, allowing different components of a circular process to self-optimise. The literature indicates that CPS can make circular operations more agile and responsive, as data is not only collected but also immediately acted upon by interconnected devices [84]. Essentially, CPS blur the line between data sharing and action—the data is shared across the cyber-physical network to trigger real-time adjustments that enhance efficiency and reduce waste.
• Digital Product Passports (DPPs): A notable emerging concept in recent papers (especially in 2024–2025 studies) is the DPP, which is a standardised digital record for individual products. A DPP contains all relevant information about a product’s components, materials, repair history, and end-of-life instructions. The idea is that whenever the product changes hands or reaches a new stage (sale, repair, disposal), its passport is updated and travels with it [85]. This ensures that each stakeholder—whether a reseller, a recycler, or even a consumer—can easily access the data needed to make sustainable decisions. For example, a DPP for an electronic device would tell a recycler what valuable materials it contains and how to extract them, improving recycling outcomes safely. The European Union is actively pushing for DPPs for batteries and electronics, seeing it as key to enabling circular flows of products through transparency. In our literature set, DPPs are highlighted as a way to operationalise data sharing at the product level: instead of broad databases, the information travels with the product in a secure digital format [19]. It’s effectively a shared digital identity for products that links all lifecycle stages. Several papers suggest implementing DPPs using technologies like blockchain (for security) or QR codes/IoT tags that link to cloud records. As DPPs become more common, they could greatly reduce the information barriers that currently hinder circular business models—every product will come with the data needed for repairing, reusing, or recycling it, accessible to all authorised parties. This dedicated section on data sharing underscores a trend toward product-specific data transparency as a driver of circularity.

Data sharing technologies form the connective tissue of circular ecosystems. They ensure that the insights and information gained (about products, materials, processes) do not remain isolated within one organisation but are disseminated to all stakeholders who can benefit from them. Whether through distributed ledgers that provide trust, collaborative platforms that enable interoperability, or emerging standards like product passports that ensure transparency, the literature consistently points to the need for robust data-sharing mechanisms. By deploying these digital solutions, circular economy participants can synchronise their efforts—aligning supply with demand for secondary materials, verifying compliance with circular standards, and co-managing assets and infrastructure—thereby achieving the level of coordination necessary for circular models to succeed at scale. Each technology (blockchain, cloud platforms, APIs, BIM, digital twins, CPS, DPPs) addresses different challenges in data sharing, but together they help break down information silos and foster an ecosystem-wide approach to sustainability.

4.2. Other Enabling Technologies

Beyond the core digital pillars (e.g., IoT, analytics, blockchain) discussed earlier, a range of other technologies is powering circular-economy applications. These include advancements in production (like robotics and additive manufacturing), immersive tools for engagement (AR/VR and gamified apps), and innovations in logistics (drones, smart containers, etc.). This section reviews how such technologies—often in combination with digital intelligence—enable circular practices, with recent examples illustrating their impact.

4.2.1. Production & Manufacturing

In manufacturing, robotics, automation and advanced production technologies are being leveraged to increase material efficiency and facilitate closed-loop processes. Industrial robots can execute tasks with high precision and consistency, reducing production scrap and enabling difficult circular operations like product disassembly and remanufacturing. For instance, case studies in heavy industries found that integrating robotic disassembly into end-of-life processes can significantly improve the recovery of components and materials for reuse [86]. Robots enable partial or full automation of disassembly, inspection, and reassembly tasks, thereby enhancing the productivity, precision, and quality of these circular operations [87]. A recent multi-case study across construction and manufacturing demonstrated that combining robotics with digital intelligence (sensors and models) yields substantial benefits for component reuse—from optimised dismantling processes to better quality control—ultimately advancing circular value chains. Thakuri et al. [86] identify that robots, when used alongside IoT and digital twin data, contribute to improved operational efficiency (“improved physical processes”) and lower costs in component refurbishing and remanufacturing, which helps make circular strategies more economically viable. In short, automation technologies are maturing to handle not only mass production but also reverse flows (disassembly, sorting, remanufacturing), thus supporting closed-loop manufacturing at scale.

Another key enabler in production is AM, including 3D printing (and emerging 4D printing). AM enables the production of highly complex or personalised products on demand with minimal waste, aligning production with circular economy principles [23]. By building products layer by layer, additive methods can use only the material needed and even incorporate recycled feedstock, reducing waste. The study notes that 3D/4D printing opens new opportunities for “circular economy–compliant production” of personalised goods. In their framework, manufacturers co-design products with consumers for a “market of one,” then use AM to produce these tailored items efficiently, potentially eliminating overproduction and inventory waste. Such capability also supports product life extension, since spare parts can be printed on demand for repairs or older models. Recent literature highlights that the Industry 4.0 to 5.0 evolution (toward human-centric, sustainable manufacturing) positions AM and robotics together as tools to achieve net-zero waste goals in production [88]. For example, smart 3D printing systems can be integrated with robotics to enable automated “print-remanufacture” cells, where worn components are scanned and repaired or rebuilt additively. Early implementations in the aerospace and automotive domains (e.g., printing obsolete spare parts for vehicles) show promising resource savings and reduced raw-material requirements. Overall, advanced manufacturing technologies—from robotised assembly lines to AM-based production of parts—are increasingly tailored to support circular strategies like remanufacturing, component reuse, and mass customisation for longevity.

4.2.2. Experience & Engagement

Digital technologies are not only transforming processes, but also people’s experience and engagement with circular economy practices. In particular, tools like AR, VR, and gamified mobile applications are being used to educate and motivate both workers and consumers toward more circular behaviours. These technologies provide immersive or interactive experiences that can make abstract sustainability concepts tangible and personally relevant. For example, AR has been deployed as a citizen engagement tool to improve public awareness of recycling and reuse. In one recent study, a mobile AR application was introduced to residents of a municipality to visualise waste sorting and upcycling processes in their community [89]. The results were compelling—the AR tool “successfully brought CE principles and benefits to the public’s attention” and increased participants’ interest and confidence in circular practices. Notably, engagement gains were highest among people who had little prior exposure to the circular economy, suggesting that AR can lower the entry barrier and create an engaging introduction to sustainability. Similarly, researchers have integrated VR with digital twin models to simulate circular processes, such as virtual disassembly of products or interactive visualisations of material flows [52]. Rocca et al. [90] demonstrated a laboratory case where a VR environment linked to a digital twin allowed users to experiment with circular scenarios (e.g., virtually “recycling” a component) to understand outcomes before implementing them in reality. Such immersive training scenarios are increasingly used in industry: for instance, an EU project “Allview” developed an XR (extended reality) training lab for the woodworking sector to promote circular skills like furniture refurbishing and material efficiency [91]. By using VR headsets and AR overlays in vocational training, workers could practice repair or remanufacturing tasks in a risk-free, game-like setting, which improved motivation and knowledge retention [52]. These examples illustrate how XR technologies make circular economy education more effective—whether by engaging students in sustainable design, or helping technicians learn new circular operations with fewer real-world resources.

Another powerful approach is gamification, which turns circular actions into rewarding games or challenges. Recent literature shows a surge of interest in gamified apps that encourage recycling, responsible consumption, and participation in circular programs. A clear example is the “smart recycling station” pilot in Sweden, which combined a physical household waste sorter with a smartphone app featuring missions, quizzes, and achievement badges [92]. Over a 6-month trial with multiple families, the gamified system succeeded in making recycling a more engaging, even fun, experience. Users reported that the combination of a visual progress dashboard and rewards kept them more involved in proper sorting, and knowledge about recycling improved (especially for previously unengaged participants). The study also identified segments of users (highly engaged vs. not engaged) and noted that game mechanics need to be tailored—for instance, competitive elements might motivate some, while others respond to collaborative or educational aspects. Nevertheless, the overall finding was that gamification can boost recycling participation and learning, supporting circular outcomes in communities. Beyond waste, gamified approaches are being applied to areas such as sustainable fashion (e.g., apps that reward consumers for returning clothes or choosing eco-friendly options) and product life extension (e.g., “repair challenge” leaderboards). Even simple mobile apps and social platforms play a role: for example, online second-hand marketplaces and sharing platforms use rating systems and social rewards to normalise reuse behaviour. A 2025 study by Talukder et al. [93] emphasises that online information sources and social media can significantly shape circular consumption patterns by spreading awareness and influencing norms (e.g., “upcycling challenges” that go viral). In summary, digital engagement technologies—whether immersive AR/VR or interactive apps—act as catalysts for behaviour change. They bridge the gap between knowledge and action by making circular practices more accessible, fun, and rewarding. Practitioners are increasingly harnessing these tools for employee training (e.g., VR for safe recycling line training) and customer engagement (e.g., AR tutorials for product repair, gamified loyalty points for returns).

4.2.3. Logistics & Supply Chain

The logistics and supply chain domain is another frontier where enabling technologies are driving better resource recovery, traceability, and efficiency in circular ecosystems. A primary goal in circular supply chains is to seamlessly handle reverse logistics—the flow of used products or materials back from consumers to producers or recyclers. Digital integration through CPS is helping achieve this. For example, sensors and connectivity (IoT) now allow real-time tracking of products, parts, and waste containers throughout their journey. This traceability is crucial to reducing uncertainties in the timing and quality of returns. As research has shown, IoT-enabled tracking can inform companies exactly when and where a product is ready for take-back, improving collection efficiency and reducing lost materials [22]. Moreover, shared data platforms (sometimes via blockchain or cloud systems) create a transparent record of a component’s life cycle. Thakuri et al. [84] found that deploying a “digital infrastructure” encompassing digital twin models and IoT across multiple firms gave all partners better visibility into material flows and conditions, which, in turn, enabled them to coordinate on reuse and recycling decisions. In their cases, construction components were tagged and monitored during use, so that at end-of-life stakeholders could quickly identify recoverable items and plan disassembly, transport, and reassembly operations in a synchronised manner. This kind of end-to-end information sharing—essentially a CPS network spanning the supply chain—enhances collaboration among suppliers, manufacturers, recyclers, and logistics providers [84]. The outcome is a more “closed-loop” supply chain where materials are traced and managed from production to return, enabling higher reuse rates and lower waste leakage. Indeed, researchers note that when actors have integrated real-time data on inventory and returns, they can make adaptive decisions (e.g., schedule a pickup when a part’s health digital twin indicates end-of-use) and avoid unnecessary new production [86]. Such data-driven coordination is transforming reverse logistics from a traditionally ad-hoc process into a strategic, optimised operation.

Alongside digital connectivity, specialised hardware is improving circular logistics. Smart containers and bins equipped with sensors are one example—these can monitor fill levels, temperatures, or contamination in recyclable waste bins and signal for collection only when needed, thus optimising routes and preserving material quality. Cities and companies adopting smart waste bins report more efficient collection schedules and less overflow or spoilage of recyclables (reducing the amount that ends up landfilled due to delay). On a larger scale, GPS-tracked smart shipping containers help ensure that reusable transit packaging (pallets, totes, etc.) is returned and not lost, supporting reuse loops in logistics. Another exciting frontier is the use of autonomous vehicles and drones in circular supply chains [94]. Drones can be used to inspect and even retrieve materials from locations that are hard to reach by conventional means. For instance, environmental agencies have begun using aerial drones to detect illegal dumping sites and identify materials for cleanup, a task that previously might go unnoticed until severe pollution occurs. Moreover, autonomous waste collection drones are being piloted [95]. A 2025 initiative in Spain tested an aquatic drone in a marina to collect floating plastic debris; the drone, guided by AI and IoT sensors, gathered up to 500 kg of waste per day and relayed data in real-time for sorting and recycling onshore [96]. This pilot not only cleaned the harbour with a lower carbon footprint than manned boats, but also demonstrated how robotic systems can directly reclaim resources from the environment for circular processing. On land, similar concepts include drone networks for e-waste pickup in dense urban areas or the use of autonomous guided vehicles in warehouses to consolidate products for refurbishment.

Perhaps the most dramatic improvements are happening at recycling facilities through AI and automation. Advanced optical sorting machines (powered by machine vision) can now distinguish different plastics or textiles on a conveyor at lightning speed, vastly improving the purity of recycled outputs. Even more cutting-edge are robotic sorting systems that combine AI, cameras, and robotic arms to pick valuable materials from mixed waste streams. For example, Fraunhofer IVV’s recent “iDEAR” project developed an intelligent robotic station for e-waste disassembly that combines knowledge management, computer vision, and robotics to automatically disassemble discarded electronics [97]. The system creates a digital disassembly twin of each device, scanning it with 3D sensors to identify components and optimal disassembly sequences. It then directs a robot to unscrew, detach, and sort parts, such as circuit boards, drives, and batteries, for reuse or recycling.

Such automation not only recovers more high-value materials (such as precious metals on boards) but does so non-destructively, enabling components to be remanufactured or refurbished rather than shredded. Industry observers note that AI-powered robots are approaching the point where they can economically disassemble a variety of products—from smartphones to appliances—which could revolutionise recycling economics in favour of circular reuse [98]. These innovations in sorting and processing are critical, as they address the “last mile” of the circular loop: ensuring that, once a product is discarded, its materials efficiently return to the production cycle.

In summary, the logistics and supply chain arena is witnessing a wave of technological interventions all aimed at tightening the circular loop. Connected systems (IoT/CPS) are delivering transparency and coordination for reverse logistics and product life-cycle management [86]. Physical automation (robots, drones) is boosting the capacity to retrieve and process used goods (from collection to sophisticated sorting) with high efficiency and safety [97]. And intelligent analytics (AI optimisation) are streamlining everything from the routing of collection trucks to inventory management for remanufacturing. These developments point toward a future where materials flow through a digitally orchestrated circular network rather than a linear chain. Figure 10 summarises how various logistics technologies (AI, IoT, drones, etc.) map to improvements in traceability, resource recovery rate, and cost efficiency across different stages of the circular supply chain. Together, these enabling technologies make the practical implementation of circular economy models more feasible by reducing friction and uncertainty in returning materials to productive use. The continued integration of such tools will be vital for scaling up circular economy initiatives from isolated projects to standard business practice across industries.

5. Impact of Digital Technologies on CE Transition

5.1. Enabling Technologies

Digital technologies often serve as enablers that optimise existing processes and designs to embed circular principles without fundamentally altering a firm’s business model. These tools enhance efficiency and data-driven decision-making at various stages of the product lifecycle, thereby facilitating circular strategies within traditional operations. Enabling technologies support circular product design, responsible sourcing of materials, resource-efficient production, and improved consumption and waste management practices. Recent studies consistently show that such technologies primarily help collect, analyse, and share information, leading to smarter resource use and incremental improvements in circularity [59,99]. The net effect is a reduction in waste and better utilisation of assets within the established value chain, laying the groundwork for deeper CE interventions.

5.1.1. Circular Design

At the design stage, digital tools allow companies to incorporate circularity from the outset. Eco-design software and simulation tools [55,100] enable designers to model the environmental impacts of product choices and iterate toward more sustainable options. For example, BIM and digital twin technology have been used to create detailed “digital material passports” for buildings, mapping all components in a structure to facilitate future disassembly and reuse [101]. In one case, a BIM-integrated digital twin of a housing project provided a structured inventory of materials, improving deconstruction planning and maximising component reuse in line with decarbonization goals. Similarly, AI-driven design assistants can analyse material data and suggest product modifications that enhance durability or recyclability [45]. These technologies collectively help optimise products for longer lifespans, modularity, and ease of repair or recycling. By simulating real-world usage and end-of-life scenarios, designers can ensure new products are durable, upgradeable, and easy to disassemble, thus embedding circular economy principles into the blueprint of products and infrastructure [55,102]. The evidence from recent literature underscores that early-stage digital interventions—ranging from AI-based material selection to virtual prototyping—are crucial for “designing out” waste and pollution [74]. This aligns product development with circular outcomes before physical production even begins, setting a strong foundation for the later stages of the lifecycle.

5.1.2. Circular Sourcing

In the sourcing phase, digital technologies enhance transparency and sustainability in procurement. Within a single firm’s supply chain, these tools optimise how materials are selected and acquired, often by ensuring they meet circular criteria (e.g., recycled or ethically sourced content) and by minimising waste in the purchasing process. AI helps analyse supplier data and material options, providing insights into the availability of recycled materials or the environmental performance of suppliers [40,79]. Firms can utilise AI-based decision support to select inputs that have lower footprints or are easier to reclaim later, thereby aligning sourcing with circular economy goals. Blockchain technology, meanwhile, is increasingly being employed to ensure the traceability of materials from origin to the factory gate. By recording each transaction on an immutable ledger, blockchain can certify the recycled content or ethical provenance of inputs, building trust in secondary material markets [54]. For instance, an expert assessment of blockchain in CE found that, under the right governance (industry consortia with shared standards), distributed ledgers can enable end-to-end transparency and even create incentive systems (such as token rewards) for returning products or materials. Additionally, IoT sensors and cloud platforms allow real-time tracking of material shipments and inventory levels [65,103]. This connectivity helps companies tightly monitor their incoming materials, reducing over-ordering and allowing just-in-time use of resources. One study highlights how IoT-based tracking of recyclable inputs (such as plastics or fibres) enabled a manufacturer to seamlessly incorporate post-consumer waste into production, dynamically adjusting orders based on availability [104]. Furthermore, BDA can process procurement and usage data to identify patterns—for example, flagging when certain virgin materials could be substituted with recycled alternatives without compromising quality [46,105]. Even advanced tools like geographic information systems (GIS) and AR have niche applications in sourcing—for instance, mapping regional waste resource hotspots or visualising supply networks—to guide companies toward local, circular material streams. In sum, digital enablers in sourcing create a more transparent and optimised procurement process, ensuring that materials entering production are not only sustainably obtained but also primed for circular use and reuse down the line.

5.1.3. Circular Production

Digital technologies play a pivotal role in transforming production processes to minimise waste and keep resources in use. Within factories and production lines, tools such as IoT, AI, and advanced automation continuously monitor and optimise operations for efficiency and circularity. For example, IoT sensors on industrial equipment provide real-time data on energy and material usage, allowing AI-driven control systems to adjust processes and reduce scrap generation or energy waste [59]. Closed-loop process control becomes feasible when machinery, products, and even by-products are instrumented with sensors. In practice, this means waste outputs from one step can be detected and immediately rerouted as inputs for another step, creating an internal recycling loop. A recent case in the textile industry illustrates this well: a smart production system used IoT-enabled monitoring and digital dashboards to incorporate recycled PET plastics and spent coffee grounds as raw materials for fabrics, dynamically tweaking processing parameters to accommodate these secondary inputs. The result was a measurable reduction in material waste, energy, and water use during manufacturing, while maintaining product quality—an outcome attributed to continuous sensor data and feedback controls [104]. Similarly, additive manufacturing (AM), including 3D printing, is an enabling technology for circular production. AM enables on-demand fabrication of parts with minimal surplus material, supporting strategies such as spare parts printing and product customisation for extended use [23]. By shifting to produce-to-order with digital designs, companies can reduce overproduction and maintain “virtual inventories” of parts that are printed only when needed, thus cutting down warehousing and material obsolescence. Robotics and automation also contribute by efficiently handling disassembly and remanufacturing tasks—robots can be programmed to disassemble used products or sort materials with high precision, thereby improving the yield of usable components and materials. Moreover, digital simulation tools (akin to digital twins of production lines) help plan how to reconfigure processes to use recycled inputs or optimise resource flows within a factory. Multiple studies emphasise that high levels of automation and real-time data in production directly translate to improved circular performance indicators, such as lower waste-to-product ratios and higher rates of internal material reuse [102,106,107]. In summary, the infusion of digital intelligence into production systems enables a shift from linear “take-make-dispose” methods toward closed-loop manufacturing, where resources circulate within or across facilities with minimal losses.

5.1.4. Circular Consumption and Waste Management

Even after a product leaves the factory, digital technologies continue to enable circular outcomes during the use and end-of-life phases. Smart products embedded with IoT sensors can actively influence consumption patterns by providing feedback and data. For instance, smart appliances can optimise their energy and water use, and even prompt users when maintenance or part replacements are needed, thereby extending product longevity and preventing premature disposal [90]. In a circular consumption model, companies use digital tools to stay connected to products in the field—a strategy that enables maintenance, refurbishment, or take-back programs. For example, some electronics manufacturers use remote monitoring (via IoT) to track product performance and trigger preventive maintenance or software updates to keep devices running more efficiently [108]. This not only enhances the customer experience but also delays products entering the waste stream. When it comes to reuse and sharing, digital platforms are key enablers: online marketplaces and rental apps connect owners of underutilised goods with users, increasing the utilisation of products such as tools, vehicles, and electronics [36,109]. These platforms rely on data analytics (for matching and pricing) and smartphone technology (for user access and tracking), illustrating how digital infrastructure supports collaborative consumption models that are inherently more circular than one-off ownership.

On the waste management end, digital innovations improve collection, sorting, and recycling processes, ensuring that materials loop back into production. Municipalities and firms deploy IoT-based solutions, such as smart bins that signal when they are full, enabling optimised collection routes that save fuel and ensure recyclable materials are collected promptly [110]. In recycling facilities, artificial intelligence vision systems and robots increasingly perform automated waste sorting—identifying materials on conveyor belts and separating them for proper recycling streams with accuracy unattainable by humans [111]. Such AI-driven sorting significantly increases the purity of recovered materials, making it easier to reintroduce them into manufacturing. Blockchain is also being piloted to track waste flows, providing an auditable chain of custody from consumers disposing of products to recyclers processing the material [112]. This kind of transparent tracking can help implement extended producer responsibility by verifying that products collected at end-of-life do get recycled or treated as claimed. Another emerging tool is the DPP, a concept gaining traction in the EU: essentially, a digital record accompanying a product that contains information on its composition, repair instructions, and recycling guidelines [19]. These passports, accessible via QR codes or cloud databases, guide both consumers and waste managers on how to handle products at the end of life, ensuring more components are recovered. For instance, a DPP for a piece of electronics might inform a recycler about the precious metals it contains and how to extract them safely, thereby bridging information gaps that often lead to recyclable materials being discarded [113]. Finally, drones and other remote sensing technologies have found a niche in waste management logistics—companies use drones to surveil large stockpiles of waste or recyclables, monitor volumes, and spot issues such as contamination or fires in real time [103]. By integrating these digital solutions, organisations create a connected, data-rich waste management system that not only handles existing waste more effectively but also provides insights to designers and policymakers on how to prevent waste in the first place. In essence, from smart use to smart disposal, enabling technologies ensure that products remain in use longer and that when they are eventually discarded, their materials are efficiently looped back into the economy rather than lost.

5.2. Disruptive Technologies

While enabling technologies optimise the status quo, disruptive digital technologies catalyse new business models and value propositions that fundamentally depart from the traditional linear model. The two prominent modes of digital disruption in the circular economy are servitisation and virtualisation. By focusing on service-oriented models and the virtualisation of products and processes, companies can shift from selling physical goods to delivering outcomes and experiences, decoupling value creation from material consumption. These models promote product longevity (through maintenance and reuse incentives), reduce waste (by avoiding unnecessary production), and often enhance customer relationships through continuous services and engagement [107]. In other words, digital disruption enables companies to reimagine their roles—from manufacturers and retailers to service providers and platform orchestrators—thereby driving substantial business transformation aligned with circular economy principles. Recent studies demonstrate that when firms embrace servitisation or virtualise their offerings, they tend to redesign products for durability, establish mechanisms for take-back and refurbishment, and leverage data to continuously improve performance [114]. As a result, digital disruption not only changes how revenue is generated but also aligns profit motives with keeping products and materials circulating for longer. Below, we detail how servitisation and virtualisation models—empowered by digital tools—are pioneering such novel circular business models.

Disruptive digital technologies profoundly alter value generation logics in circular economy systems, in addition to their operational uses. For example, digitally enabled servitisation changes revenue models from owning a product to providing value based on performance. This changes the incentives for everyone in the supply chain and could even move power from manufacturers to service providers who own data. However, old business models, capital lock-in, organisational opposition, and opaque revenue-sharing procedures still make it hard for servitisation to really shake things up. Digital platforms that make it easier to trade goods and handle returns can also affect the way competition works by lowering transaction costs and making the market more open. But they might also give more authority to the biggest platform operators, which would change the balance of power between big manufacturers, small and medium-sized businesses (SMEs), and recyclers. Disruption in circular systems is not only technological but also institutional and structural, influenced by governance models, data ownership frameworks, and ecosystem-level coordination.

5.2.1. Servitisation Models (Product-as-a-Service, Leasing, Predictive Maintenance)

Servitisation refers to the shift from selling products to providing them as services, and digital technology is a key enabler of this shift. In a circular economy context, Product-as-a-Service (PaaS) and leasing models incentivise providers to maintain products for longevity and to reclaim them for reuse or recycling, rather than simply pushing new sales. Digital tools provide the infrastructure needed to make these models viable at scale. For instance, IoT sensors embedded in leased equipment or appliances allow the provider to monitor usage, performance, and wear in real time. This data feeds into predictive maintenance algorithms (often powered by AI and BDA) that can foresee when a machine is likely to fail or decline in efficiency [106]. By performing timely maintenance or part replacements, the service provider can prevent breakdowns, thereby keeping the product in service longer and satisfying customers with high uptime. Several studies have highlighted predictive maintenance as a linchpin of successful servitisation: it not only reduces downtime but also ensures that components are used to their full lifespan, aligning operational savings with waste reduction. For example, in one automotive fleet leasing case, IoT telematics tracked vehicle health indicators and identified optimal servicing windows, extending vehicle lifespans by years and significantly reducing material consumption and costs [114]. Here, the digital capability (telematics + analytics) directly enabled the business model (leasing with uptime guarantees) and delivered circular outcomes (reducing the need for new vehicles over time).

Beyond maintenance, digital platforms facilitate the overall management of PaaS offerings. Companies deploy service management platforms that handle customer subscriptions, usage tracking, consumption-based billing, and reverse logistics for returned products. These platforms often integrate multiple stakeholders—the customer, maintenance crews, logistics providers, etc.—coordinating them through a single digital interface. For instance, a heating-as-a-service provider might use a platform to remotely control home heating systems, charge users per unit of heat delivered, and dispatch technicians when IoT sensors indicate a drop in performance. Such arrangements flip the traditional incentive: the provider profits by keeping the product (e.g., a heater) running efficiently for as long as possible, not by selling a new heater every few years. One empirical study in 2025 found that networked digital tools strongly support platform-based servitisation and that even emerging technologies such as the metaverse are being considered to enhance these service ecosystems [107]. In that study, institutional factors like “metaverse readiness” and strong digital collaboration networks significantly drove the adoption of platform-centric servitisation in Chinese service firms. The idea is that immersive digital environments could further streamline interactions between providers and customers (for example, virtual showrooms or remote assistance via AR), lowering the transaction costs of service delivery and improving user engagement without physical resource use. More immediately, however, we see simpler forms of digital support in servitisation: for example, manufacturers of industrial equipment are using blockchain-based platforms to log usage and maintenance history for leased machines, ensuring transparency and trust when machines change hands or when performance-based contracts are settled [115]. Smart contracts on such blockchain systems can automatically enforce service agreements—e.g., triggering a payment or dispatching maintenance when certain conditions are met—thus reducing administrative overhead and fostering trust in multi-party service models [51]. Overall, servitisation models enabled by digital tech represent a disruptive change: value is delivered through continuous service and outcomes rather than product sales. This change is profoundly circular because it aligns the provider’s incentives with extending product life and reclaiming materials. However, it also requires a robust digital backbone (IoT, analytics, platforms, and, sometimes, distributed ledgers) to manage the complexity of service relationships and capture the data that drives circular value creation.

5.2.2. Virtualisation Models (Digital Twins, AR/VR, Virtual Inventory)

Virtualisation in the context of the circular economy refers to the use of digital technology to replace or augment physical products and processes, thereby reducing the material intensity of delivering value. A classic example is the dematerialisation of media (books, CDs) into digital formats, but in industry, virtualisation takes more advanced forms. Digital twins exemplify this concept: a digital twin is a virtual replica of a physical asset (product, machine, or even an entire facility) that mirrors its state in real time. By deploying digital twins, companies can conduct testing, monitoring, and optimisation in the virtual realm, which reduces the need for physical prototypes, trial-and-error experimentation on real machines, or conservative overdesign. For instance, a factory might maintain a digital twin of a production line to simulate changes in process settings or to predict equipment fatigue under different scenarios, all without physically interrupting production or risking wear and tear on actual machinery [55]. In the construction sector, a recent study found that integrating BIM with digital twin technology enabled stakeholders to monitor building performance and predict maintenance needs virtually, leading to fewer site inspections and more targeted interventions [116]. That digital twin not only enabled operational efficiencies but also identified opportunities to retrofit and repurpose building components, thereby supporting the circular use of construction materials. The virtual inventory concept extends this idea to spare parts and products: instead of stocking every part in physical warehouses (which can lead to excess and waste), companies keep digital models of parts and rely on additive manufacturing to produce them [23]. This virtualisation of inventory, supported by CAD files, cloud databases, and 3D printers, dramatically cuts down the resources tied up in unused stock and ensures that when a part is needed (for repair, upgrade, etc.), it can be made available without mass production. It represents a shift from physical stockpiling to digital stockpiling of design information—a less resource-intensive strategy aligned with circular principles.

AR and VR technologies contribute to circularity by virtualising experiences that would otherwise require physical materials or travel. AR can provide digital overlays for maintenance technicians or consumers, guiding them through repair procedures step-by-step without the need for printed manuals or repeated expert visits. This not only empowers users to maintain products (extending their life) but also reduces the carbon footprint associated with sending technicians on-site for minor issues. VR and mixed reality, on the other hand, enable virtual training and collaboration. A company can train its workforce on a complex circular process (such as e-waste disassembly or remanufacturing) in a VR environment, thereby avoiding the costs and waste of using real hardware for training sessions. Likewise, product developers from different sectors can convene in a virtual environment to co-design a product for circularity, rather than flying in prototypes or travelling for meetings. While these may seem like marginal gains, at scale they contribute to significant material and energy savings. The notion of a “metaverse” for industry—an immersive, persistent virtual space where business interactions occur—is emerging as a potential accelerator for such practices. If designers, engineers, and suppliers can meet in a virtual space to collaboratively test and validate a product design, they may iterate faster and more sustainably. Early evidence suggests that companies experimenting with metaverse-like platforms report improved cross-functional collaboration and fewer physical prototypes, pointing to both efficiency and circular benefits [107]. Moreover, virtualisation supports new business opportunities such as virtual product services. For example, consider a scenario in which a customer purchases a digital NFT (non-fungible token) of an art piece—the value is delivered without physical production (though the sustainability of NFTs is another matter). In a more practical vein, high-end manufacturers are exploring virtual showrooms and digital product trials: customers can “try out” a product in VR or via digital simulation before committing, which can reduce the production of display models and the likelihood of returns. All these instances embody the same principle: decoupling the value proposition from physical resource use by leveraging rich digital representations and experiences. Virtualisation thus complements servitisation as a disruptive pathway—where servitisation changes what is sold (a service instead of a product), virtualisation changes how it is sold or operated (digitally instead of physically). Both lead to potentially large reductions in material throughput and waste generation, as evidenced by the growing literature on digital transformation for sustainability in the past two years. However, it is worth noting that virtualisation relies on underlying digital infrastructure and energy, which must be managed responsibly to ensure the net environmental gains remain positive.

5.3. Facilitating Technologies

The third category of impact is where digital technologies act as facilitators, extending beyond individual firms to connect multiple stakeholders and enable system-level circular-economy initiatives. These technologies don’t just optimise or transform a single business model; instead, they provide platforms, data architectures, and standards that allow different organisations—often across sectors—to collaborate towards circular goals. In a circular economy, the success of one player (e.g., a recycler) often depends on the actions of others (manufacturers, consumers, regulators), making ecosystem-wide coordination vital. Facilitating technologies enhance this coordination by improving information flow, trust, and alignment among diverse actors in the value chain. As recent studies emphasise, digital platforms, data-sharing tools, and blockchain-based systems can break down traditional silos and foster new networks of exchange [79,117]. The result is a more integrated circular ecosystem in which materials, knowledge, and revenue flow across company and industry borders more freely. Importantly, these facilitating tools also help align circular practices with policy and compliance requirements, ensuring that collaborations not only make business sense but also meet regulatory standards ([19]). In this section, we discuss three key facets: (1) multi-sided platforms and marketplaces that connect stakeholders, (2) regulatory and compliance technologies that build trust and accountability, and (3) cross-sector collaboration enablers that support broader ecosystem integration. Together, they illustrate how digital innovation is underpinning the collaborative infrastructure of the circular economy.

Facilitating impacts, while less radical than disruptive ones, play a crucial systemic role in enabling coordination and reducing friction across circular ecosystems. Digital traceability systems, shared platforms, and interoperability standards enhance information symmetry and trust among actors who traditionally operate in fragmented value chains. However, their effectiveness depends heavily on data governance frameworks, standardisation efforts, and stakeholder alignment. Without clear incentives and equitable access to digital infrastructure, facilitating technologies risk reinforcing existing asymmetries rather than enabling inclusive circular transitions. Therefore, facilitation should be understood not only as technological enablement but as socio-technical mediation within evolving circular business ecosystems.

5.3.1. Ecosystem Platforms and Marketplaces

Digital ecosystem platforms are virtual hubs where multiple stakeholders can interact, exchange resources, and co-create value. In the circular economy, such platforms often take the form of marketplaces for secondary materials or asset-sharing networks. By centralising information about available resources (like recyclable materials, surplus products, or idle equipment) and connecting those who have them with those who need them, digital platforms dramatically reduce the transaction costs and friction that typically hinder circular exchanges. A clear example is the emergence of online marketplaces for industrial waste: a manufacturer’s waste by-product can be listed on a platform where another company, possibly from a different sector, can purchase it as a raw material. One study documents a digital marketplace that enabled manufacturers and recyclers to trade waste plastics and metals, resulting in higher uptake of recycled feedstock and reducing virgin material purchase [117]. The platform provided transparency on material quality and availability, and facilitated logistics by coordinating pickups and deliveries—services that smaller firms would struggle to arrange bilaterally. Another example is in the construction industry, where platforms now connect demolition contractors, material stockists, and builders to circulate reclaimed construction components. Through a shared database (often BIM-based) listing available beams, windows, or fittings from demolished buildings, interested parties can source these for new projects, effectively creating a reuse marketplace for construction materials. Lavagna et al. [79] describe how such a platform in Europe allowed multiple construction firms to achieve cost savings and waste reduction by substituting new materials with reclaimed ones, coordinated via the digital system.

Beyond material exchanges, ecosystem platforms also support service-based circular models that span organisations. For instance, product-sharing platforms (such as tool-sharing apps or B2B equipment pools) bring together users and owners in a multi-sided network, maximising the utilisation of each item. These platforms rely on features like user ratings, secure payments, and identity verification—all digital functionalities that build trust so that strangers can safely and conveniently share products. Importantly, they also collect data (e.g., how often an item is used, failure rates, user preferences) that can be fed back to manufacturers to improve product design for durability. In effect, the platform becomes a repository of knowledge about product life in the wild, accessible to multiple stakeholders. Moreover, the data-rich nature of platforms helps track environmental impact: some circular platforms provide dashboards showing how much waste or emissions have been avoided through exchanges facilitated, which can further incentivise participation by quantifying the benefits [118]. A compelling case of ecosystem orchestration is reported in a 2025 study of a coffee waste-to-textile supply chain: a digital platform was used to link coffee shops (providing spent coffee grounds), a recycler (processing those grounds into oils), and a textile manufacturer (using those oils to produce synthetic leather) in a seamless loop [104]. The platform tracked real-time inputs from each actor, aligned them with shared sustainability KPIs, and ensured that information (such as the volume of waste collected, the quality of processed material, and inventory at the manufacturer) was visible to all participants. This level of coordination, only feasible with a robust digital backbone, was crucial to balancing supply and demand across a novel cross-industry value chain and was cited as a key to the project’s success.

In summary, ecosystem platforms and marketplaces act as collaborative infrastructure for the circular economy. They aggregate supply and demand for secondary resources, enable industrial symbiosis (where the waste of one becomes input for another), and support new forms of collaborative consumption. By doing so, they unlock circular opportunities that no single firm could easily pursue on its own. Research from 2024–2025 strongly indicates that such platforms are shifting from experimental to mainstream, with more industries launching shared digital hubs to collectively tackle waste and improve resource efficiency [106]. The challenge ahead lies in scaling these platforms and ensuring interoperability—something that often requires alignment on data standards and trust, which brings us to the next set of facilitating technologies.

5.3.2. Regulatory and Compliance Tools (Digital Passports, Blockchain)

As companies and ecosystems adopt circular practices, digital technologies that ensure trust, transparency, and compliance have become indispensable. Two technologies stand out in this regard: blockchain (distributed ledger technology) and DPPs. Both serve to provide verifiable information about products and processes, thereby aligning business actions with regulatory requirements and stakeholder expectations.

Blockchain provides a secure and tamper-proof method for recording transactions and tracking assets throughout supply chains. In a circular economy context, blockchain can be used to certify the provenance of materials (e.g., confirming that a batch of recycled plastic actually came from a verified recycler) and to maintain an unbroken chain of custody as materials flow through reuse or recycling loops. This capability is especially valuable for meeting regulatory standards and consumer assurances—for example, the EU’s recycling targets or laws against illegal waste export require solid proof of compliance. A blockchain-based system can automatically log each handoff of a material, including timestamps and participant signatures, creating an audit trail readily available to regulators or auditors [40]. Santolin et al. [54] provide a nuanced view of how blockchain can enable circular transparency: their work filters through the hype to identify realistic scenarios where blockchain adds value, such as in multi-stakeholder electronics recycling programs where trust is low. In those cases, a consortium blockchain (governed by an industry coalition) underpinned the sharing of sensitive data (product compositions, recycling rates) in a way that no single party could falsify, thereby aligning all participants with a common set of verified facts. Blockchain’s role in incentivising circular actions is also notable—with smart contracts, one can automate reward mechanisms, for instance, depositing a reward or recycling fee reimbursement to a consumer’s account when they return a product for recycling [51]. Such smart contracts enforce agreements without intermediaries, ensuring, for example, that a deposit-refund system for beverage containers operates transparently and efficiently. Another area is carbon and sustainability reporting: as companies strive to substantiate their circular claims (such as recycled content percentages or product carbon footprints blockchain can serve as the backbone for immutable record-keeping, reducing the risk of greenwashing by anchoring claims in traceable data [119].

DPPs mandated by forthcoming EU legislation for certain products are essentially structured digital records that accompany a product throughout its life cycle. A DPP might include information such as the product’s bill of materials, chemical composition, repair manuals, warranty status, and recycling instructions. The passport concept directly addresses a major barrier in the circular economy: information asymmetry. Often, those at later stages (second-hand markets, recyclers, waste managers) lack crucial knowledge about how a product was made and how it can be safely handled or disassembled. By making this information accessible (via a QR code or an online database keyed to a product ID), DPPs help overcome that gap. Jensen et al. [19] highlight that implementing DPPs requires harmonised data standards and industry cooperation, but once in place, the passports enable a host of circular activities—from consumers easily finding repair services (since the DPP tells which spare part is needed) to recyclers identifying valuable components. A practical illustration comes from the electronics sector: a digital passport for a smartphone could log each repair and refurbishment it undergoes, along with the original specifications for its components. When the phone finally reaches a recycler, the DPP can inform the recycler exactly which parts can be harvested (e.g., a high-capacity battery that was replaced a year ago) and how to safely extract them. This significantly improves recycling efficiency and safety, as confirmed by pilot projects in Europe [113]. Additionally, regulatory tools like DPPs and blockchain often work best in tandem. For instance, the authenticity and updates of a digital passport could be secured using blockchain, preventing tampering with its data history.

Finally, we must note the role of data standards and interoperability as facilitating mechanisms in the regulatory domain. The value of blockchain or DPPs is maximised only when many actors agree on common protocols [54]. Initiatives like the EU’s Circular Economy Action Plan are spurring the development of standardised taxonomies for materials and APIs for data exchange, essentially creating a digital language that companies and regulators can all speak. Such standards, though not “technologies” in the hardware sense, are enabled by and embedded in digital systems—and they greatly enhance cross-company collaboration on compliance. In conclusion, regulatory and compliance-oriented digital tools build the trust infrastructure of the circular economy. They reassure each participant (and regulators and customers) that claims of circularity are backed by solid data, and they reduce companies’ risk of engaging in innovative circular partnerships by providing clear rules and automated enforcement. The past two years have seen a notable increase in research and pilot implementations in this space, indicating a maturation of the idea that “transparent data = accountability” for circular ecosystems [19].

5.3.3. Cross-Sectoral Collaboration Enablers

Cross-sector collaboration is often heralded as the next frontier for the circular economy because many circular solutions (such as industrial symbiosis, circular supply chains, or city-level resource loops) require participants from different industries, government bodies, and communities to work together. Digital enablers are making such broad collaboration more feasible by bridging communication gaps and aligning efforts across traditional boundaries. One fundamental enabler is the rise of data-sharing platforms and consortium databases. Whereas Section 5.3.1 discussed market-style platforms for transactions, here we speak of platforms for information exchange and joint planning. For example, in regional industrial symbiosis programs, firms and municipalities may contribute data on their waste outputs and resource needs to a common platform, which then uses algorithms to identify potential matches. Ali et al. [106] describe how, in BRICS economies, an “Industrial Symbiosis Information System” facilitated cross-industry exchanges by analysing data from various sectors and suggesting where one firm’s waste could substitute for another firm’s raw material. Such a system needed the involvement of public agencies (for policy support), multiple industries (for data input), and logistics providers—a true cross-sector endeavour underpinned by a digital tool that acted as a neutral broker. The study found significantly improved rates of material exchange once the digital system was in place, compared to earlier manual coordination attempts.

Another set of enablers falls under communication and co-creation tools. Consider the challenge of designing a product for circularity that involves suppliers from different sectors (materials, manufacturing, recycling). Digital collaboration platforms—from simple shared workspaces to sophisticated co-design software- allow these stakeholders to interact in real time, share specifications, and collectively make decisions. In practice, companies are using cloud-based project management and version control systems (such as shared CAD environments or even blockchain for IP protection) to jointly develop circular products. This could be as straightforward as a fashion brand working with a textile recycler on a new garment line: using an online platform, they can iterate on fabric choices that balance style and recyclability, with the recycler providing input on end-of-life processing for each option. Such cross-sector dialogues used to be rare and cumbersome, but digital connectivity makes them routine and documented. Some researchers note that the open innovation paradigm in sustainability is growing, with firms openly soliciting ideas or partners for circular initiatives via digital networks. For instance, a consumer goods company might host an online challenge to redesign packaging that invites material scientists, waste management experts, and entrepreneurs to submit solutions. The submissions and ensuing collaboration are managed digitally, effectively crowdsourcing innovation across sectoral boundaries. Studies have found that companies with strong “digital openness” and engagement in these networks tend to achieve more radical improvements in their circular performance, likely because they can tap into diverse expertise and implement ideas more quickly [120].

Lastly, we consider the role of government and policy interfaces as collaboration enablers. Governments are launching digital portals and databases to connect with businesses on circular economy programs—for example, national electronic waste registries or circular procurement portals where businesses can find information on tenders that require circular criteria. These public-sector digital tools encourage private companies to align with policy goals by making it easier to understand and participate in relevant initiatives. Additionally, public data (such as satellite land-use data or materials flow statistics) are increasingly made open and accessible through APIs, enabling cross-sector analysis and innovation. An illustration of this is the use of satellite imagery and IoT data by both environmental agencies and mining companies to identify sites for urban mining (recovering materials from landfills)—a collaboration that only materialised once the data were shared on a common analytical platform [121].

In essence, cross-sectoral collaboration enablers are the digital “glue” that binds various pieces of the circular economy puzzle. They ensure that an insight or opportunity in one domain (e.g., agricultural waste) can be applied in another (e.g., bio-based materials for packaging) by connecting the right stakeholders with the right information. The ecosystem note from many 2024–2025 papers is clear: those efforts which succeeded in scaling up circular solutions often did so by leveraging digital systems to orchestrate complex networks of players [54,79,106]. Conversely, where such digital support was absent, collaborations remained ad hoc and struggled to sustain momentum. Therefore, investing in shared digital infrastructure, common data standards, and inclusive platforms is increasingly seen as crucial to facilitating the cross-sector partnerships required by a circular economy. The convergence of Industry 4.0 and circular economy, as this review has shown, is not just about smart factories or products—it is equally about smart networks and communities coalescing around circular goals, empowered by information technologies.

6. Emerging Trends and Future Directions in Digital Technologies for CE

6.1. Convergence of Multiple Digital Technologies

A prominent trend is the integration of various Industry 4.0 technologies (IoT, AI/ML, BDA, blockchain, etc.) into unified solutions for circular economy challenges [122]. Rather than deploying single-purpose tools in isolation, recent studies demonstrate that combining data-driven IoT sensing with AI-driven analytics and blockchain traceability can amplify circular outcomes. For example, one study (Ada2023 [ID]) highlights how IoT networks feeding big data platforms enable predictive maintenance and resource optimisation, while blockchain secures the shared data for multi-party reuse decisions. Similarly, Adisorn et al. [113] report an emerging practice of using digital twins synchronised with real-time IoT data to model product lifecycles, thereby improving efficiency in recycling and remanufacturing processes. By converging such technologies, organisations build more robust digital capabilities that span the value chain, moving beyond siloed applications toward systemic digital solutions [108]. This integrated approach is expected to continue growing, as it provides the technological foundation for advanced circular strategies [122]. Future research is exploring even tighter integration—for instance, linking additive manufacturing (3D printing) with AI-optimised material designs and cloud-based sharing of fabrication data—pointing to increasingly synergistic digital ecosystems that can rapidly adapt to changing resource flows.

6.2. Data-Driven and AI-Powered Circular Strategies

Data analytics and artificial intelligence are becoming central to circular economy initiatives, marking a shift toward data-driven decision-making. A number of papers show that firms are leveraging predictive analytics, machine learning, and big data to optimise circular processes. For instance, Choudhuri et al. [61] document how big data systems analyse usage and return patterns to inform inventory pooling and product life extension strategies. AI techniques (e.g., machine learning models) are being used to forecast material demand and component failures, enabling preemptive recycling or reuse actions [85]. The literature emphasises that real-time data from IoT sensors, combined with AI, can significantly improve efficiency. One case describes an AI tool that dynamically adjusts a reverse logistics network based on waste collection data, reducing both costs and emissions. Moving forward, this trend is expected to accelerate: enterprises plan to harness advanced AI not only for operational optimisation but also for designing circular products (through generative design) and predicting consumer behaviour to enhance circular offerings [123]. Such data-driven approaches can also personalise services (e.g., maintenance scheduling or upgrade suggestions), thus increasing user engagement in circular programs. Overall, the growing analytics capabilities in organisations are positioning data as a key asset for the success of the circular economy, enabling evidence-based strategies and continuous learning loops.

6.3. Collaborative Platforms and Ecosystem Integration

Another clear trend is the rise of digital platforms and inter-organisational information systems that connect stakeholders across the value chain. Rather than each company acting in isolation, firms are adopting blockchain-based ledgers, online marketplaces, and data-sharing platforms to foster transparency and collaboration in circular ecosystems [123]. For example, blockchain applications [49,124] are used to create tamper-proof records of product materials and ownership transfers, thereby building trust among suppliers, recyclers, and consumers. Similarly, digital platforms [79] are emerging as secondary material marketplaces where companies can exchange scrap, spare parts, or recycled feedstock, thereby closing resource loops. One study [125] highlights a platform enabling a network of manufacturers and recyclers to share real-time inventory and demand data, aligning their processes for waste valorisation. The literature indicates that by using such collaborative tools, linear supply-chain relationships are evolving into dynamic networks, with vertical and horizontal integration supporting circular flows [123]. This trend is poised to grow, especially as standard data schemas (e.g., through product passports) make information exchange easier. In the future, we can expect more ecosystem-level digital solutions—for instance, industry consortia using common data spaces or IoT-enabled sharing systems—that coordinate circular activities across multiple partners, cities, or regions for greater scale and impact.

6.4. Digital-Enabled Servitisation and New Circular Business Models

Many enterprises are leveraging digital tech to transform their business models from selling products to delivering product-service systems and circular value propositions. This servitisation trend is evident in cases where IoT and analytics enable companies to remain connected to products throughout their use phase, facilitating models such as leasing, sharing, or pay-per-use. For example, Akarsu [117] describes a company that implemented smart sensors in its equipment, enabling predictive maintenance and a performance-based rental model that keeps the equipment in use longer rather than prematurely discarding it. Digital tools also support reverse logistics and take-back schemes, making it viable for firms to offer trade-in, refurbishment, or remanufacturing services [126]. Several papers [6,127] note that digital connectivity with customers (via apps and platforms) helps companies monitor product health and incentivise returns, thereby closing the loop. Crucially, digitalisation provides the data transparency and automation needed for these circular business models to be profitable and scalable [122]. It not only optimises operations but also enhances the customer experience—for instance, firms can use usage data to offer personalised upgrades or resale options, aligning with consumer preferences [123]. Going forward, we see a blended approach in which technologies such as blockchain tokens and smart contracts might underpin circular incentive schemes (e.g., token rewards for recycling), while AI-driven insights help firms continuously refine their service-oriented strategies. The mix of digital and circular innovation is unlocking new revenue streams while extending product life, signalling a robust shift toward service- and outcome-based business models in the circular economy.

6.5. Human-Centric Design and the Rise of Industry 5.0

Beyond the current Industry 4.0 paradigm, researchers and practitioners are looking toward Industry 5.0 principles that emphasise human-centric, sustainable design alongside advanced automation. This emerging trend recognises that achieving circularity is not only a technical challenge but also a human and social one. Industry 5.0 envisions greater collaboration between humans and machines (e.g., cobots in recycling facilities or AI decision support for designers), aiming to marry efficiency with worker well-being and creativity. Kazancoglu et al. [83] discuss how a human-centric approach can improve the adoption of circular practices, for example, by designing intuitive digital tools for employees on the factory floor to identify waste or by incorporating customer co-creation in product design via VR. The “digital twin” of Industry 4.0 is evolving into a “digital teammate” in Industry 5.0, meaning technology will assist rather than replace human decision-makers in circular initiatives. This is evident in pilot projects where AR interfaces guide workers in disassembly and sorting processes, improving recovery rates while reducing training time. The literature also notes a push for skill development and organisational culture that values sustainability—digital transformation efforts are increasingly aligning with corporate social responsibility and employee engagement programs. In sum, the Industry 5.0 trend adds a forward-looking, human-centred layer to digital CE efforts [122]. It suggests that future digital systems will be designed with user experience, workforce empowerment, and societal impacts in mind, ultimately creating solutions that are not just smart and circular but also broadly inclusive and resilient.

6.6. Policy Drivers and the Advent of Digital Product Passports

Finally, a significant trend shaping the future is the influence of policy and regulatory frameworks that mandate or incentivise the use of digital tools for circular economy transparency. Notably, the European Union’s Circular Economy Action Plan and proposed Ecodesign for Sustainable Products Regulation (ESPR) are accelerating the development of DPPs as a standard for data sharing across product lifecycles [128]. A DPP is essentially a detailed electronic record that travels with a product, capturing information on its materials, components, repair history, and end-of-life handling. Jensen et al. [19] argue that DPP systems will become a crucial infrastructure for circular supply chains, as they enable seamless communication among manufacturers, retailers, recyclers, and even consumers. Early implementations are underway in sectors such as electronics and batteries, often spurred by regulatory requirements for documenting recycled content or disposal. Policy is also driving the adoption of standards for data formats and interoperability, ensuring that different stakeholders’ IT systems (e.g., waste management databases and factory ERP systems) can exchange circularity data. This top-down push is evident beyond Europe as well—for example, China’s emerging guidelines on “Digital CE” encourage unified platforms for tracking resource usage. The convergence of policy and technology is thus a key future direction: governments are recognising that without digital traceability and metrics, circular economy goals are hard to achieve at scale [129]. In response, companies are investing in compliance-oriented tech like material tracking software and blockchain-based certification of recycled content. Over the next few years, we can expect DPPs and similar regulatory tech tools to become widespread, fundamentally embedding circular criteria into product information systems and making transparency a default feature of all products in the market [122]. This will not only help enforce circular economy policies but also unlock new opportunities for innovation in eco-design, recycling technologies, and consumer engagement, enabled by greater data visibility.

7. Challenges, Barriers, and Gaps

7.1. Technical Challenges: Interoperability, Scalability, and Data Quality

One of the most persistent challenges identified in the literature concerns the technical limitations of current digital technologies when applied to circular economy systems. A recurring theme is the lack of interoperability across platforms and data standards, which hinders the seamless integration of digital tools throughout supply chains. Studies indicate that while IoT devices, digital twins, and blockchain platforms have shown promise, they often operate in silos with incompatible architectures, resulting in fragmented digital ecosystems that limit data sharing and circular value creation [19,85]. Interoperability gaps are especially pronounced in cross-sectoral collaborations, where companies use heterogeneous systems that fail to communicate efficiently, resulting in high transaction costs for data harmonisation.

Another critical issue relates to scalability. Many pilot projects demonstrate the potential of digital twins or blockchain-based product passports, but struggle when scaled up to larger systems or across industries. Case studies reveal that solutions proven in niche contexts—such as construction or fashion—often face bottlenecks when scaled due to infrastructure demands, computational complexity, or limitations in handling large volumes of data [73,130]. This limits the ability of digital technologies to achieve systemic impact, keeping many CE applications at experimental or small-scale stages.

Closely tied to interoperability and scalability is the issue of data quality and reliability. Several papers highlight that real-time data streams from IoT sensors or user-facing apps are prone to inaccuracies, incomplete records, or biased reporting, which compromise the validity of AI-driven decisions in circular systems [39,61]. Low-quality data not only undermines optimisation but also erodes trust among stakeholders who must rely on these datasets for critical decisions, such as sourcing secondary materials or obtaining recycling certification.

Looking ahead, researchers propose several pathways to overcome these technical barriers. Calls are growing for internationally aligned data standards and ontologies to enable interoperability across CE platforms and to support the adoption of DPPs [19]. There is also interest in developing scalable architectures leveraging cloud–edge hybrid models, which would allow the computational load to be distributed more efficiently and ensure responsiveness at scale [42]. Regarding data quality, scholars recommend using AI-driven anomaly detection and blockchain-based validation to secure, clean, and authenticate datasets, thereby increasing trust in CE-relevant data flows [46]. In summary, while interoperability, scalability, and data quality remain significant hurdles, the literature suggests that coordinated progress on standards, infrastructure design, and data governance could substantially mitigate these challenges and enable digital technologies to deliver on their potential to drive the circular economy.

Digital technology can help with circular methods, but the literature also talks about possible negative effects that need to be taken into account to avoid shifting the load. First, some digital solutions, like blockchain-based traceability and data-heavy AI, can use more energy and cause more emissions if they are not built on energy-efficient designs or powered by low-carbon electricity. Second, if IoT sensors and connected devices are used on a wide scale, they could lead to more electronic waste and material criticality, which could make it harder to reach circular goals unless the devices are built to last, be modular, be easy to repair, and be responsibly disposed of at the end of their lives [131]. Third, digitalisation could make the “digital divide” worse [132]. This is when smaller businesses, informal actors, or less important supply-chain members don’t have the technology, expertise, or money to join data ecosystems like DPPs and platforms. This could lead to exclusion or new dependency. These trade-offs show why we need life-cycle evaluation, “green AI” methods, and inclusive governance frameworks to make sure that digital CE projects have a net positive effect on the environment and society.

7.2. Organisational Barriers: Skills Gaps and Resistance to Change

While digital technologies hold promise for enabling circular economy transitions, many studies emphasise that organisational factors often slow or prevent adoption. A recurring challenge is the skills gap. Firms, particularly in manufacturing and construction, often lack personnel with the expertise to deploy, manage, and interpret advanced tools such as AI-driven analytics, digital twins, or blockchain systems [133]. Even when technical staff are available, organisations frequently struggle to integrate circularity knowledge with digital expertise, leading to partial or ineffective implementation. Several papers note that this dual competency—understanding both sustainability and digital systems—is still rare, creating a bottleneck in scaling circular strategies through technology [134].

Beyond technical competencies, cultural and organisational resistance emerges as a major barrier. Many companies remain entrenched in linear business models, with performance metrics tied to sales volume rather than resource efficiency or product longevity. Studies highlight cases in which managers perceived digital circular initiatives as risky or peripheral to the core business, leading to a reluctance to invest or a tendency to run pilots without committing to systemic change [85,124]. Resistance also stems from fears of disruption: servitisation models, for instance, require significant shifts in organisational routines, incentive structures, and customer relationships, which employees and managers may view as threatening [114].

Researchers propose several strategies to address these barriers. First, capacity-building programs are widely recommended, targeting not only technical staff but also managers and decision-makers to align digital innovation with sustainability objectives [106]. Upskilling initiatives are suggested to focus on cross-disciplinary training that bridges CE principles with digital skillsets, creating a workforce able to design, operate, and evaluate digital circular solutions. Second, scholars stress the importance of organisational change management and leadership commitment. Embedding CE objectives into corporate strategy, aligning key performance indicators (KPIs) with circular outcomes, and establishing cross-functional teams are seen as critical enablers for overcoming resistance [135]. Finally, future research is encouraged to explore organisational learning pathways, how firms can transition iteratively, and how they can learn from small-scale digital CE projects and progressively integrate lessons into broader strategies [136].

7.3. Financial and Resource Constraints (Especially SMEs)

A frequently reported barrier to the adoption of digital technologies to support circular economy initiatives is their financial and resource-intensive nature. Many of the technologies reviewed—such as digital twins, blockchain infrastructures, and advanced robotics—require substantial upfront investment in hardware, software, and integration capacity. For SMEs in particular, these costs present a prohibitive challenge. Case evidence shows that SMEs often lack not only the capital to purchase and maintain advanced systems but also the internal IT resources to configure them effectively [134]. Even where pilot projects are subsidised, firms may hesitate to commit to full-scale rollouts without clear evidence of a return on investment, especially in competitive markets with thin margins [85].

Beyond capital investment, ongoing costs for digital infrastructure also emerge as barriers. Maintaining cloud platforms, ensuring cybersecurity, and regularly upgrading AI or IoT systems all require recurring expenses that SMEs struggle to sustain. Several papers highlight cases in which promising pilots stalled after initial funding ended due to a lack of a viable financial model for long-term operations [133]. Moreover, uncertainty about economic benefits—for example, the financial returns from improved traceability or recycling efficiency—compounds reluctance. Without robust business cases, many managers perceive circular digitalisation as an added cost rather than a strategic investment, reinforcing short-term decision-making patterns. Recent contributions emphasise the importance of structured multicriteria risk assessment models to support sustainability-oriented investment decisions in industrial contexts, particularly in sectors such as agri-food where digital and circular transitions involve significant uncertainty [137].

The literature also highlights inequalities in access to financial resources and support mechanisms. Larger corporations often benefit from government subsidies, innovation partnerships, or internal R&D budgets, while SMEs are left underfunded or reliant on short-lived grants. This creates a gap between technological leaders and laggards, undermining the systemic impact of digital CE initiatives. Future research suggests that financial models tailored to SMEs are urgently needed, such as shared digital infrastructures (e.g., regional cloud-based CE platforms), pay-per-use models for digital tools, or public-private co-investments in sector-wide digital ecosystems [138]. Additionally, scholars call for more comprehensive cost–benefit analyses that quantify not only economic but also environmental and social returns from digital circular projects, providing stronger evidence for investment decisions [19,106].

From a practical point of view, SMEs might be better off using phased and low-cost digital entry strategies instead of trying to make big changes to their digital systems all at once. Entry-level solutions like cloud-based inventory management systems, shared digital traceability platforms, or joining data cooperatives managed by the industry can lower the initial cost and allow for incremental competence building. Pay-per-use SaaS models, modular IoT installations, and collaboration within sectoral digital ecosystems may enable SMEs to access advanced functions without incurring the complete infrastructure cost. These incremental strategies are in line with the resource limitations discussed in the literature [133], allowing SMEs to test, learn, and grow digital circular initiatives gradually while reducing their financial risk.

Regarding scale implementation, the evidence indicates that SME adoption is a critical bottleneck because many circular digital solutions require interoperable data infrastructure, skills, and sustained financing. Scaling beyond pilots, therefore, depends on mechanisms that reduce fixed costs for SMEs, such as shared regional data platforms, interoperable open standards, sectoral data cooperatives, and public–private capacity-building programs. These approaches can help shift digital CE from isolated demonstrations to repeatable, ecosystem-level deployment by enabling smaller actors (e.g., repairers, recyclers, and material brokers) to participate in trusted data exchanges at manageable cost.

7.4. Regulatory and Legal Issues (Data Ownership, IP Rights)

Alongside technical and financial hurdles, numerous studies highlight the regulatory and legal complexities of implementing digital technologies in the circular economy. Chief among these are unresolved questions around data ownership, access rights, and IP. Circular strategies depend on seamless data sharing across firms and sectors, yet companies are often reluctant to disclose sensitive product, material, or operational data without clear safeguards. For example, studies note that manufacturers fear that opening product composition data—critical for recyclers and remanufacturers—could expose proprietary designs or weaken competitive advantages [19]. Similarly, supply-chain actors may hesitate to contribute data to blockchain or DPPs if the legal framework for ownership and usage rights is ambiguous.

Another recurring concern relates to liability and compliance. When data-driven systems underpin circular practices—for instance, when a blockchain record certifies recycled content or when IoT sensors track waste flows—the legal accountability for errors or fraud is often unclear. Papers highlight that regulators and companies alike lack tested frameworks for assigning responsibility in multi-stakeholder digital environments [73]. This creates uncertainty that discourages wider adoption. In addition, international differences in regulatory approaches—for example, between the European Union’s forthcoming DPP mandate and less prescriptive frameworks in other regions—further complicate cross-border collaborations and the deployment of technology.

To address these issues, researchers call for harmonised regulatory frameworks and clearer governance mechanisms. Future research is encouraged to explore models of data trusts and cooperative governance structures that balance confidentiality with transparency [133]. Several scholars also argue that progressive disclosure models—where firms reveal only the data necessary for a specific circular function, while safeguarding sensitive information—could reconcile CE goals with IP protection [46]. Blockchain and smart contracts are seen as promising technical aids here, providing traceability and auditability while enabling fine-grained access controls. However, the literature emphasises that technical fixes must be embedded within robust legal and institutional frameworks to gain industry trust and regulatory approval.

7.5. Interdependencies Among Barriers

Importantly, these barriers should not be viewed as independent constraints but as mutually reinforcing systemic challenges. Technical limitations such as poor data quality and lack of interoperability (Section 7.1) often increase operational complexity and the cost of data cleaning, integration, and validation, disproportionately affecting SMEs with limited financial and human resources (Section 7.3). Similarly, skills gaps and organisational resistance (Section 7.2) can exacerbate technical shortcomings by limiting the effective implementation of digital infrastructures. Regulatory uncertainty regarding data ownership and intellectual property (Section 7.4) further amplifies investment risks, discouraging firms from committing capital to circular digital transformation. These interdependencies suggest that the digital circular transition is constrained by a network of interacting barriers rather than isolated obstacles, reinforcing the need for integrated policy, governance, and capacity-building approaches.

A cross-case reading of the literature illustrates how these barriers materialise in practice. For example, in construction-sector BIM implementations, interoperability gaps between contractors’ software systems increased data harmonisation costs, disproportionately burdening smaller subcontractors and delaying ecosystem-wide adoption. In manufacturing-focused IoT and predictive maintenance projects, firms reported that insufficient in-house data science skills led to underutilisation of collected data, weakening the business case and reinforcing financial hesitancy for further investment. Similarly, blockchain-based traceability pilots often stalled not due to technical infeasibility but because of unresolved data ownership and liability concerns among supply-chain actors, which amplified organisational resistance and slowed scaling efforts. These examples suggest that digital circular initiatives frequently fail or remain at pilot scale not because of a single barrier, but because technical, financial, organisational, and regulatory obstacles interact in reinforcing feedback loops.

7.6. Under-Researched Areas and Methodological Gaps

Despite the growing literature on digital technologies in the circular economy, several areas remain underexplored or methodologically fragmented. A recurrent observation is that most existing studies rely on conceptual frameworks, pilot projects, or single case studies, with limited large-scale or longitudinal evidence. For example, many blockchain and DPP applications are discussed in terms of theoretical potential, but very few studies systematically assess their performance across industries or over extended periods [73]. Similarly, simulation and digital twin research often remains confined to laboratory contexts, lacking validation in real production or waste-management environments [74]. This creates a methodological gap between visionary claims and demonstrated outcomes.

Another under-researched area concerns the social and behavioural dimensions of digital circular transitions. While technical feasibility is frequently addressed, fewer papers examine how consumers, employees, or communities actually engage with digital CE tools. For instance, gamified recycling apps or AR-based repair guidance systems show promise, but evidence on long-term behavioural impacts, inclusivity, or unintended consequences remains scarce. This gap is particularly notable given the increasing emphasis on Industry 5.0 and human-centric design. Without a stronger empirical base, it is difficult to assess whether these digital tools truly shift practices in durable and equitable ways.

Finally, there is a lack of comparative and cross-sectoral studies. Most research is sector-specific—focusing on construction, manufacturing, or textiles—but rarely draws connections across industries that share similar challenges, such as material traceability or product life extension. Scholars call for more interdisciplinary approaches that integrate engineering, business, and the social sciences, and for greater consistency in the use of robust methodologies such as mixed-methods research, longitudinal studies, and cross-country comparisons [134]. Addressing these gaps would not only improve academic rigour but also provide decision-makers with stronger evidence for scaling digital solutions across diverse contexts.

7.7. Future Research Directions

Building on the challenges identified above, the literature highlights several future research priorities to advance the role of digital technologies in circular economy transitions. First, researchers consistently call for work on interoperability and standardisation. Studies emphasise the need for harmonised data ontologies, protocols for DPPs, and cross-platform integration strategies that can be tested and validated across industries [19]. Progress in this area is critical to moving beyond fragmented pilot projects and enabling system-wide scalability.

Second, there is a strong demand for new business and governance models that clarify data ownership, IP rights, and liability in multi-stakeholder digital ecosystems. Several papers recommend experimenting with data trusts, cooperative governance mechanisms, and progressive disclosure frameworks to balance transparency with competitive concerns [139]. Research could also examine the regulatory implications of these models across different legal jurisdictions, particularly as policy drivers such as the EU’s DPP gain momentum.

Third, future studies should address the organisational and social dimensions of digital CE adoption. Scholars note the need for longitudinal research into how firms build digital and sustainability skills, how employees and consumers engage with digital CE tools over time, and what cultural or behavioural factors accelerate or impede uptake [140]. These perspectives would complement the current technical focus and ensure that human factors are adequately considered in circular transitions.

Finally, the literature calls for more rigorous and comparative methodologies. Large-scale empirical studies, cross-sectoral comparisons, and mixed-methods research designs are considered essential for substantiating the performance, scalability, and societal impacts of digital CE solutions [74]. Such methodological innovation would provide the evidence base needed for both policymakers and practitioners to invest confidently in digital circular strategies.

The literature suggests that future research should move beyond isolated case studies and conceptual models toward systemic, empirical, and human-centred investigations that address interoperability, governance, organisational change, and methodological rigour. Only by bridging these gaps can digital technologies fully realise their transformative potential for a circular economy.

Furthermore, Large Language Models (LLMs) and agentic AI (autonomous AI agents) hold significant promise for advancing circular economy initiatives. These technologies can rapidly analyse and synthesise complex information, offering expert-level insights and even autonomous decision-making to support circular strategies. For instance, one recent study used a GPT-4-based LLM to classify and analyse over 20,000 social media posts about public-sector CE initiatives, revealing patterns in citizen engagement [141]. In the future, LLM-powered autonomous agents could proactively coordinate circular processes—an emerging paradigm of “agentic AI,” where AI systems perceive, reason, and act towards goals with minimal human intervention. Such agents might optimise supply chains in real time, suggest eco-design improvements, or dynamically manage resource recovery loops. However, these powerful tools also come with new challenges and risks. LLMs may hallucinate information (e.g., misidentifying material codes or environmental data), and fully automated AI decisions can be opaque. Ensuring transparency and accountability will be crucial—for example, by developing explainable AI techniques that enable agents to provide human-understandable justifications for their actions (through natural-language rationales, audit trails, etc.). Likewise, it is essential that autonomous AI aligns with human values and sustainability principles, lest it produce biased or unintended outcomes [142]. Balancing these opportunities and risks will be a key focus of future research. With robust safeguards for transparency, ethics, and human oversight, LLM-driven agentic AI could ultimately unlock innovative solutions and significant efficiency gains for the circular economy’s next generation of challenges.

7.8. Policy Implications

The findings of this review suggest that policymakers play a central role in enabling digital technologies to effectively support circular economy transitions. First, governments can facilitate interoperability (Section 7.1) by investing in the development of open-source data standards, shared ontologies, and publicly accessible digital infrastructure, reducing fragmentation and lowering entry barriers for SMEs. Supporting neutral data spaces and common digital platforms can prevent market dominance by proprietary systems and promote inclusive participation.

Second, public policy can address organisational barriers (Section 7.2) by embedding digital and circular economy competencies into vocational training, higher education curricula, and continuous professional development programs. Incentivising cross-disciplinary training and providing subsidies for SME upskilling initiatives can help bridge the digital–sustainability skills gap identified in the literature.

Third, financial instruments such as targeted grants, tax incentives, and public–private co-investment schemes can mitigate the high upfront costs of digital circular technologies (Section 7.3), particularly for SMEs. Policymakers may also encourage experimentation through regulatory sandboxes that allow firms to pilot blockchain-based traceability or AI-driven CE models under supervised conditions.

Finally, regulatory clarity around data ownership, liability, and IP (Section 7.4) is essential to build trust in digital ecosystems. Progressive disclosure frameworks, harmonised standards for Digital Product Passports, and support for cooperative governance models such as data trusts can align transparency objectives with competitive safeguards. Taken together, these policy actions can accelerate systemic digital–circular transformation while ensuring inclusivity and long-term sustainability.

8. Summary

This systematic review analyses how digital technologies enable circular economy (CE) transitions by examining 266 peer-reviewed publications from 2016 to 2025. The study develops a comprehensive taxonomy of digital enablers and maps their applications across sectors and circular strategies.

• Data-driven dominance: 89% of studies involve data collection, storage, analysis, or sharing functions
• Technology roles: IoT provides foundational real-time tracking and monitoring; AI and big data analytics optimise circular processes and enable predictive maintenance; blockchain ensures traceability and supply chain trust; cloud computing offers scalable collaborative infrastructure
• Sectoral focus: Manufacturing (41%) and construction (15.5%) are the most studied, with strong European research leadership driven by policies like Digital Product Passports
• Three impact types identified: Enabling (process optimisation), disruptive (business model innovation), and facilitating (ecosystem collaboration)
• Major barriers: Technical complexity, organisational resistance, high implementation costs, and regulatory gaps

Digital technologies are critical catalysts for achieving circular economy goals at scale. The review demonstrates that successful CE implementation requires integrated, multi-stakeholder approaches combining technological innovation with supportive policy frameworks and organisational transformation. Future progress depends on addressing identified barriers through collaborative efforts among industry, policymakers, and researchers to realise a truly digitally enabled circular economy.