The Missing Link in the Circular Economy
Why Material Traceability Still Fails (and How AI Can Help)
Circular economy strategies aim to design out waste and keep materials in circulation, but implementation remains uneven. While nearly every major manufacturer now references “circularity” in its sustainability plans, (Kirchherr et al. 2017) found 114 different definitions of the term) each is inconsistent with the others. This lack of alignment makes it difficult for organizations to measure or compare any real progress.
In my past experiences in ESG, I encountered similar challenges … tracing relationships between suppliers, emissions, and disclosures across disconnected systems. Even with advanced analytics, an incredibly AI system and massive transparency, visibility often stopped at the second tier. That experience shaped how I now view material traceability: the failure isn’t technological, it’s structural.
This article is the second in my three-part series exploring how AI can make sustainability measurable. The first focused on transforming EPR compliance into a strategic asset. This one examines how the same principles: semantic alignment, automation, continuous verification, can help make circularity credible and data-driven.
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Why Circular Traceability Falls Short
Circularity often breaks down way before recycling begins. The systems that manage production, sourcing, and sustainability data are not designed to speak the same language. Procurement systems focus on suppliers, sustainability platforms focus on emissions, and manufacturing tools track cost, performance and yield. None share a unified material taxonomy.
This disconnect is why many “recycled content” claims rely on assumptions rather than verifiable data. As is conventional wisdom, Cullen (2017) observed that a lot of materials degrade with each use, meaning perfect circularity is physically impossible. Plastics can typically only be recycled a few times before losing structural integrity. Without a connected data foundation and a clear understanding of material limits, circular economy initiatives struggle to move from aspiration to evidence.
Lessons from Past ESG Work: Connecting the Disconnected
In previous ESG work, I helped design frameworks that combined fragmented data on environment, labor, and governance into unified risk indicators. Each dataset came from different sources, using inconsistent language and metrics. Solving that problem required semantic mapping, data confidence scoring, and ongoing recalibration as new inputs emerged.
The same logic applies to material traceability. Instead of scoring company performance, AI could map material lineages, tracking where materials originate, how they are transformed, and where they end up. Zink and Geyer (2017) warned that even if efficiency improves, consumption can increase, a rebound effect that offsets progress. Building traceability systems that continuously verify material flows is one way to prevent this cycle. A lot of supply chain risk management companies support a similar mechanism, akin to blockchain in fintech.
How AI Can Build Circular Proof
Graph AI for Material Lineage: Maps relationships among suppliers, recyclers, and converters to reveal how materials flow across value chains.
NLP for Disclosure Translation: Extracts recycled-content claims from certificates, invoices, and reports, harmonizing language across regions and standards.
Predictive Circularity Scoring: Estimates recovery rates and future material reuse potential using trade, production, and waste data.
These capabilities point toward a Digital Material Passport, a living system of record that updates dynamically instead of relying on annual audits. Verified data would replace assumptions as the foundation of circular economy reporting.
From Sustainability to Resilience
When circularity is built on data, it becomes more than an environmental dream, it becomes a resilience strategy. Companies that can trace materials through their entire lifecycle gain agility when disruptions occur. They can validate supplier claims, adapt sourcing strategies, and respond confidently to regulatory change.
Circular transparency also helps close the credibility gap. Saidani et al. (2019) found that the absence of standardized circularity metrics makes it “nearly impossible to distinguish between superficial and substantive initiatives”. AI-based validation can bring comparability and accountability back into circular reporting.
Practical Next Steps for Sustainability Leaders
Select a pilot product line and map its full material journey from source to recovery.
Integrate cross-functional data partners to combine supplier, recycler, and certification data.
Apply AI-based entity resolution to unify disparate company and material identifiers.
Establish a continuous feedback loop that updates circular performance as new data is collected.
TLDR
Circular economy commitments cannot succeed on ambition alone. They require interoperability, evidence, and recognition of physical and behavioral limits.
My past ESG experience showed that even fragmented sustainability data can be connected through AI to build measurable, trusted insights. The same principles can be applied to the movement of materials themselves.
AI will not make circularity effortless, but it can make it verifiable and resilient. The organizations that invest now in connected, transparent data systems will lead the next era of circular innovation—one grounded in proof, not promises.
Attribution and Inspiration
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Broken Circles: 10 Critical Failures of the Circular Economy, by Calvin Lakhan, Ph.D, Director, Circular Innovation Hub, Faculty of Environment and Urban Change, March 12, 2025
Stop Asking ‘Is Paper Better Than Plastic?’ Start Asking ‘What Does This Packaging Need to DO?’, Calvin Lakhan, Ph.D, Director, Circular Innovation Hub, Faculty of Environment and Urban Change, July 7, 2025
The Circular Economy, By Don Fullerton, National Bureau Of Economic Research, May 2024