Purpose This study aims to develop and evaluate a blockchain-enabled traceability framework capable of improving circular steel recovery and reducing embodied carbon within Tata Steel’s supply chain. Study Blockchain technology is increasingly being explored as a digital enabler for circular economy practices and low-carbon industrial supply chains. In the steel sector, fragmented documentation systems, limited material traceability and inefficient scrap recovery mechanisms continue to constrain circularity and embodied carbon reduction, particularly within the Indian construction industry. Despite growing interest in blockchain-enabled traceability, limited research has examined its application within steel supply chains using a case-grounded and quantitatively modelled approach linking traceability improvements with circular steel recovery and embodied carbon reduction. Design/Methodology/Approach A single-case explanatory research design combined with secondary-data-based scenario modelling was adopted using publicly available Tata Steel sustainability disclosures, industry benchmarks and validated emission factors. The proposed framework integrates QR/RFID-enabled batch identities, Internet of Things (IoT)-assisted verification systems, smart contracts and artificial intelligence (AI)-assisted dashboard visualisations to model improvements in traceability and closed-loop recycling performance. Findings Scenario modelling indicates that traceability coverage could improve from approximately 42% to 98%, while verified scrap reuse could increase from 60% to 88%. Using an emission avoidance factor of 1.35 tCO2 per tonne of recycled steel and a conservative annual scrap throughput assumption of 8.0 Mt, the framework estimates a feasibility-oriented embodied carbon reduction of approximately 3.0 MtCO2/year. Originality/Value The study proposes a Tata Steel-specific blockchain circularity framework demonstrating how digital provenance systems can strengthen transparent material governance, enhance circular steel recovery and support feasibility-oriented embodied carbon reduction pathways within industrial steel supply chains. Research Limitations/Implications The findings are based on secondary-data-driven scenario modelling and should therefore be interpreted as feasibility-oriented estimates rather than empirically validated operational outcomes. The framework provides a basis for future empirical assessment and sensitivity analysis under alternative industrial and policy conditions. Practical Implications The framework provides a conceptual decision-support approach for improving batch-level traceability, scrap verification, closed-loop recovery, environmental reporting and supply-chain accountability through blockchain-enabled systems. Social Implications Improved material provenance and verification can strengthen transparency and accountability among supply-chain actors and support more trustworthy circular material governance.
The integration of artificial intelligence (AI), the Internet of Things (IoT), and blockchain may provide a viable approach to tackle persistent operational and informational challenges in cleaner production. This conceptual review synthesizes existing literature and presents an integrated AI-IoT-blockchain framework mapped across the four sequential stages of cleaner production: source reduction, process control, end-of-pipe treatment and recycling, and full-chain traceability. The literature indicates that IoT enables real-time sensing, AI drives predictive and prescriptive analytics, and blockchain ensures tamper-proof record-keeping and stakeholder trust. Together, these technologies may help address long-standing barriers including fragmented data, delayed responses, and a lack of verifiability. Despite challenges such as high costs, technical fragmentation, and organizational resistance, several emerging strategies have been proposed in the literature to address these challenges. These include modular deployment, federated learning, permissioned blockchains, and regulatory sandboxes. The framework’s underlying architecture appears transferable across sectors, subject to industry-specific adaptation, supporting sustainable manufacturing, the circular economy, and low-carbon development.
Katarina Dimić‐Mišić, Shailesh Singh Chouhan, Michael Gasik, Milica P. Marčeta Kaninski · 7 authors
Electronic waste (e-waste) is among the fastest-growing global waste streams, with 62 million tonnes generated in 2022 and only 22.3% formally recycled, while generation is projected to reach 82 million tonnes by 2030. Fragmented record-keeping, weak chain-of-custody controls, and limited transparency enable illegal dumping, fraudulent recycling claims, and loss of recoverable materials. This paper presents a narrative literature review of blockchain and distributed ledger technologies applied to electronics and e-waste management. Following a structured search and relevance screening of major scientific databases, publisher platforms and grey literature (last search was run on 15 July 2026), 110 sources published between 2017 and 2026 were thematically synthesized across four domains: technological developments, sustainability and circular-economy implications, traceability and compliance mechanisms, and e-waste management applications. Blockchain delivers value primarily through an auditable chain-of-custody built on three pillars: standardized item and batch identities, standardized lifecycle event schemas, and licensed-actor attestations supported by off-chain evidence anchored on-chain. Recurring design patterns include permissioned consortium networks, hybrid storage, and role-based access control. Persistent barriers include data-input integrity, scalability, governance, and cost. Blockchain immutability protects stored records but cannot correct falsified inputs, making trusted data capture and governance the decisive factors for reliability. Adoption should be evaluated against measurable indicators, such as traceability completeness, audit-time reduction, fraud rates, and verified material recovery, rather than assumed benefits.