Artificial Intelligence and Digital Product Passports: Enabling Circular Business Models for Sustainability
Bravo-Fabian, Marco Antonio
Artificial Intelligence and Digital Product Passports: Enabling Circular Business Models for Sustainability - 2025.
62
This paper explores how the integration of Artificial Intelligence (AI) and the Digital Product Passport (DPP), introduced under the European Union’s Ecodesign for Sustainable Products Regulation (ESPR), can serve as a catalyst for circular business model innovation and sustainable competitiveness. The DPP provides structured, traceable, and interoperable lifecycle data – from material sourcing and manufacturing to use, repair, and end-of-life – that, when combined with AI and machine learning, enables new forms of data-driven decision-making. Anchored in the framework of dynamic capabilities (sensing, seizing, transforming), the analysis demonstrates how AI operationalizes DPP data to identify circular opportunities, design circular business models, and guide organizational transformation toward circularity. Beyond Europe, the paper assesses the opportunities and challenges for small and medium enterprises (SMEs), highlighting gaps in digital infrastructure, data governance, and regulatory readiness, while pointing to the potential of regional dataspaces, SMEs, and sectoral clusters. A roadmap is proposed for SMEs, emphasizing data sovereignty, semantic interoperability, and supportive policy instruments. The paper concludes by framing the DPP not as a compliance tool, but as a strategic data infrastructure capable of enabling sustainable innovation ecosystems across regions and sectors.
Artificial Intelligence and Digital Product Passports: Enabling Circular Business Models for Sustainability - 2025.
62
This paper explores how the integration of Artificial Intelligence (AI) and the Digital Product Passport (DPP), introduced under the European Union’s Ecodesign for Sustainable Products Regulation (ESPR), can serve as a catalyst for circular business model innovation and sustainable competitiveness. The DPP provides structured, traceable, and interoperable lifecycle data – from material sourcing and manufacturing to use, repair, and end-of-life – that, when combined with AI and machine learning, enables new forms of data-driven decision-making. Anchored in the framework of dynamic capabilities (sensing, seizing, transforming), the analysis demonstrates how AI operationalizes DPP data to identify circular opportunities, design circular business models, and guide organizational transformation toward circularity. Beyond Europe, the paper assesses the opportunities and challenges for small and medium enterprises (SMEs), highlighting gaps in digital infrastructure, data governance, and regulatory readiness, while pointing to the potential of regional dataspaces, SMEs, and sectoral clusters. A roadmap is proposed for SMEs, emphasizing data sovereignty, semantic interoperability, and supportive policy instruments. The paper concludes by framing the DPP not as a compliance tool, but as a strategic data infrastructure capable of enabling sustainable innovation ecosystems across regions and sectors.
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