The usual suspects of circularity: Unmasking green and circular innovations in the tyre industry using NLP

Traditional patent classification systems use broad labels that are often too imprecise or ill-adapted to distinguish green from circular innovations, particularly in material-intensive sectors with long-standing waste management issues such as tyres. Existing taxonomies frequently conflate resource loop closure (circularity) with broader environmental mitigation (green), resulting in conceptual and practical ambiguity. To address these limitations, we propose a novel method that integrates a transformer-based NLP model (DistilBERT) within an operational framework based on the 3R strategies — reuse, recycling, and recovery. This approach systematically identifies circular patents, revealing patterns and overlaps beyond traditional classifications. Applying our method to 59,298 tyre-sector patent applications filed at the EPO, USPTO, and WIPO, we find that many circular innovations are not captured by previous classifications. Our results demonstrate that transformer-based NLP can provide a scalable and empirically validated approach to delineate the landscape of circular innovation, clarify the boundary between green and circular patents, provide a detailed breakdown of circular patents by 3R-strategies, and derive implications for firms, policymakers, and researchers seeking to monitor the transition to circularity. • DistilBERT maps circular tyre patents using reuse, recycling, and recovery. • Most green tyre patents are circular, but most circular patents are not green. • Recovery-oriented innovation dominates green and Y02W-classified tyre patents. • Advanced NLP enables circular patent taxonomies and supports better policy design.