Enabling Circularity through Dynamic Material Passports: A Framework Integrating Digital Twin Technologies and Data Governance in the Built Environment
Bibliographic record
Abstract
Achieving circularity and sustainability in the built environment requires advanced mechanisms for tracking and managing material flows across the building lifecycle. Material passports (MPs) have emerged as key digital tools to enable material traceability, reuse, and lifecycle intelligence. However, conventional MPs remain static and limited in supporting dynamic decision-making for sustainable construction practices. This study introduces a novel framework for Dynamic Material Passports (DMPs), developed to enhance material transparency, enable circular resource flows, and support lifecycle-oriented sustainability strategies. The framework leverages Building Information Model (BIM)-based digital twin technologies and internet of things to enable real-time updates and continuous alignment between physical building assets and their digital counterparts. It also incorporates structured data governance mechanisms, drawing from emerging practices in interoperability, blockchain, and stakeholder access control to ensure secure, transparent, and scalable information management. The framework is developed in alignment with Canadian digital construction priorities and standards (e.g., CCMC, NMS) but is designed for global applicability. By embedding digital twin integration and data governance directly into the structure of DMPs, the framework addresses key barriers to circularity, including data fragmentation, limited reuse planning, and lack of lifecycle accountability. The proposed approach contributes to advancing sustainable construction practices and enabling more effective circular economy strategies across the built environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.013 | 0.018 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".