Material passports promise to unlock AI-driven circular markets in construction, but adoption is stalling: fragmented definitions, scarce interoperable data and unclear governance are the main bottlenecks, and while digital tools can bridge many gaps, widespread deployment will need unified standards, stakeholder coordination and regulatory support.

Main Finding

Material Passports (MPs) are a critical data infrastructure for enabling circular economy (CE) outcomes in the Architecture, Engineering, and Construction (AEC) sector, but adoption is being slowed by data scarcity, fragmented definitions and scopes, weak links to existing construction datasets, unclear responsibilities among stakeholders, and absence of unified standards and regulatory support. Digital tools can mitigate many barriers by enabling dynamic, interoperable, and cohesive MP implementations, yet large-scale deployment will require coordinated strategies, stakeholder collaboration, and policy action.

Key Points

• Study type: Systematic Literature Review (PRISMA) of peer-reviewed literature from Scopus (screened 54 items; 46 included; analysis dated April 2025).
• Focus of included studies: 65% primarily on Material Passports; 35% on MPs within broader Circular Economy contexts.
• Research landscape: recent, rapidly growing, and heterogeneous; definitions and scopes of MPs vary across authors.
• Core functions of MPs: record material-level data, enable traceability, support reuse and recovery decisions, and act as a bridge between physical assets and digital lifecycle records.
• Major barriers to adoption:
  • Data scarcity and limited integration with existing construction datasets (particularly acute for existing buildings).
  • Lack of industry-wide consensus on MP definitions, formats, and scope.
  • Fragmented stakeholder responsibilities and unclear governance models.
  • Insufficient regulatory frameworks and incentives for mass uptake.
• Opportunities via digital tools:
  • Improve cohesion across stakeholders and phases (design, construction, use, end-of-life).
  • Enable dynamic updates (changes over time, maintenance, repairs).
  • Enhance interoperability across systems (MPs, Digital Product Passports, Digital Building Logbooks).
• Theoretical contribution: clarifies relationships and gaps among MPs, Digital Product Passports (DPPs), and Digital Building Logbooks (DBLs), highlighting overlap and points of divergence.
• Practical recommendations from literature: develop unified standards, coordinate stakeholder collaboration, assign clear responsibilities, and pursue regulatory support and incentives for scale-up.

Data & Methods

• Methodology: Systematic Literature Review following PRISMA guidelines.
• Source and scope: Scopus database search; 54 peer-reviewed articles and book chapters screened; 46 included for in-depth analysis (April 2025).
• Classification: Papers categorized by primary focus (MP-centered vs. MP-in-CE) and analyzed for definitions, implementation challenges, links to digital tools, interoperability concerns, and policy/organizational recommendations.
• Synthesis: Qualitative thematic analysis highlighting heterogeneity, implementation barriers (especially for existing stock), and potential digital solutions.

Implications for AI Economics

• Data as foundational infrastructure and economic asset
  • MPs convert dispersed, tacit construction-material information into structured, reusable datasets. For AI economics, this creates a new class of productive data that enables ML-driven optimization across lifecycle and markets.
  • Lack of standardized, high-quality MPs increases transaction costs and raises entry barriers for AI firms that need labeled, interoperable inputs.
• Market structure and platform economics
  • Standardized MPs can create network effects: platforms that aggregate passports and provide matching/marketplace services (for reclaimed materials, components, or services) may exhibit winner-take-most dynamics.
  • Policy and standards will shape market concentration risks and competition dynamics (who controls the passport layer — private platforms, consortia, or public infrastructure).
• Investment and business-model implications
  • There is economic value in investing in data curation, integration tools, and automated generation of MPs (computer vision for on-site material identification, NLP for extracting material specs from documents, IoT for dynamic updates).
  • Business models can include subscription data services, certification fees, transaction commissions in reuse marketplaces, and AI analytics for circularity scoring.
• AI product and service opportunities
  • Automated MP generation (vision/NLP), predictive maintenance and lifespan estimation, material reuse matching, lifecycle carbon and cost optimization, and digital-twin-based simulations for reuse scenarios.
  • Interoperability enables composable AI services (e.g., a reuse marketplace calling an AI model that scores material condition based on MP sensor data).
• Research and measurement priorities
  • Quantify economic benefits of MPs (reduced material procurement costs, avoided waste, labor savings, carbon reductions) and model how MPs affect adoption incentives.
  • Study adoption dynamics (thresholds, complementarities, tipping points) and design incentive-compatible policies (subsidies, mandates, data trusts).
  • Assess data governance: privacy, proprietary data, compensation for data producers (manufacturers, owners), and implications for competition and innovation.
• Policy levers to unlock AI-driven value
  • Promote open standards and APIs to reduce integration costs for AI developers and lower lock-in risk.
  • Fund seed datasets and public MP infrastructure to address initial data-scarcity problems and to catalyze private markets.
  • Require minimal MP standards in regulations (e.g., for deconstruction permits or public procurement) to create demand-side pull.
  • Support certification and auditability frameworks to ensure AI models using MPs are reliable and contestable.
• Risks and considerations for AI economists
  • Data heterogeneity and low coverage (especially for existing buildings) limit training data quality and generalizability of ML models; selection bias may favor new builds.
  • Proprietary MPs and platform control could concentrate data rents; antitrust and data portability policies matter.
  • Algorithmic opacity and measurement error could mis-price material value or circularity, producing perverse incentives.
  • Need to evaluate distributional impacts (which firms/regions benefit) and occupational effects in AEC.

Suggested next research questions for AI economics - What is the estimated economic value (cost savings, carbon abatement, market size) unlocked by full MP adoption in an urban building stock? - How do different governance models for MP data (open public infrastructure vs. platform-owned) affect competition, innovation, and welfare? - Which incentive designs (mandates, subsidies, procurement rules) most cost-effectively accelerate MP coverage of existing buildings? - How robust are AI models trained on MPs to dataset heterogeneity and missingness — and what are best practices for bias mitigation and validation?

If you want, I can draft a short note outlining a research agenda or an economic model (stylized) to quantify MP-driven impacts on material markets and AI service markets.