Operationalising Digital Circularity: A Critical Systematic Review of Artificial Intelligence Applications to Material and Digital Product Passports

This systematic review maps how Artificial Intelligence (AI) operationalises Material and Digital Product Passports (MP/DPP) for circular construction (January 2020–March 2026). However, most existing AI-to-passport implementations lack standardised reporting metrics, interoperability frameworks, and benchmarks for end-of-life decision support, leaving a critical operational gap that this review systematically addresses. The review follows the PRISMA 2020 guidelines to ensure methodological transparency and reproducibility. Of 2810 records identified across Web of Science, ScienceDirect, and Scopus, 49 peer-reviewed studies met the inclusion criteria for explicitly linking AI to MP/DPP under a circular economy lens. Evidence is synthesised across AI task families (computer vision, NLP including large language models, machine learning, knowledge graphs, digital twin/IoT), lifecycle phases (design, construction, operation, end-of-life), and circularity functions (traceability, lifecycle data enrichment, reuse/recycling readiness, recovery/EoL planning). The literature concentrates on traceability and lifecycle enrichment, while decision support for reuse and end-of-life remains sparse. Methodological weaknesses include narrow field validation, limited reporting of passport-level service metrics, and weak interoperability between AI pipelines and passport schemas. The regulatory landscape has intensified: Regulation (EU) 2024/3110 (Construction Products Regulation) entered into force in January 2025, and Regulation (EU) 2024/1781 (ESPR) launched its first product groups in April 2025, transforming AI-enabled MP/DPP from a prospective research topic into an immediate operational requirement. This review also notes the emergence of Large Language Models and blockchain as pivotal technologies for NLP-based field extraction and governed trust in twin-to-passport pipelines, respectively. Three framework elements are contributed and formalised as the Digital Circularity AI Framework (DCAF): (i) a minimum reporting bundle for AI-to-passport pipelines; (ii) a governance pack for twin-to-passport updates covering provenance, versioning, latency, and blockchain-trust; and (iii) open benchmark definitions for reuse grading, deconstruction sequencing, and residual value estimation. Together, these elements aim to shift MP/DPP from identification-oriented tools toward actionable decision support for circular recovery.