A Strategic Methodological Roadmap for Designing Circular Economy Data Systems: From Integrated Architecture to Indicator Prioritization

Despite growing global interest in Circular Economy (CE) strategies, developing reliable and scalable CE data systems remains challenging, due to methodological gaps. These include the absence of stepwise planning frameworks, lack of integrated cross-sectoral data architectures, and inadequate mechanisms for indicator prioritization. This theoretical and conceptual study introduces a comprehensive three-layered methodological framework, TRIADS ( Three-layer Integrated Architecture for Decision-Support in Circular Systems ), to support the strategic design, operational structure, and adaptive evaluation of CE data systems. Layer 1 defines a Strategic Roadmap involving planning, stakeholder engagement, and iterative system development. Layer 2 establishes an Integrated Data Architecture that enables data acquisition, storage, interoperability, and delivery in compliance with privacy regulations. Layer 3 focuses on CE Indicator Development using structured literature mining, and Multi-Criteria Decision Analysis (MCDA), along with AI-assisted ranking techniques. The proposed roadmap, TRIADS, was systematically compared against 13 existing CE frameworks using 20 evaluation criteria derived through literature review and text mining analysis. TRIADS appears to address most evaluation criteria compared to existing frameworks. Key findings indicate that current CE frameworks suffer gaps in advanced implementation capabilities, which TRIADS successfully addresses. TRIADS provides practitioners with standardized protocols for CE system design, reducing implementation time and enabling cross-sector benchmarking through unified metrics. By embedding stakeholder feedback and contextual adaptation, this unified framework enables evidence-based CE strategy implementation within diverse operational environments. While this theoretical study focuses on framework development rather than empirical cases, inclusive evaluation suggests practical implementation potential, pending empirical validation.