Transition to a Circular Economy in Off-Site Construction Supply Chain: Perspectives on Economic Viability
Bibliographic record
Abstract
The construction industry is among the most resource-intensive sectors, generating the highest levels of waste and emissions.Implementing Circular Economy (CE) principles offers a promising pathway toward a more sustainable built environment.However, a successful transition requires a robust assessment framework to support the development and adoption of CE strategies.Addressing this need, the present study evaluates the financial feasibility of integrating CE principles into the Modular and Offsite Construction (MOC) supply chain.Through system dynamics modeling, it examines material flows from end-of-life (EoL) buildings to MOC factories, identifying the key conditions necessary to maximize economic benefits.The research highlights the crucial roles of demolition contractors, recycling facilities, and secondary material markets in enhancing material recovery efficiency.A case study on a wood-frame panelized wall factory illustrates the model's utility, while the framework also demonstrating its potential applicability to other building types.Findings show that circular strategies can enhance profitability while reducing material waste and carbon emissions, even in the absence of government intervention.By detailing both profitable and non-profitable scenarios, this study provides valuable insights into various EoL scenarios and collaborative strategies among MOC supply chain stakeholders, including new players like deconstruction contractors.The proposed framework aims to guide decision-makers in simulating costbenefit analyses of collaborative, CE-compliant scenarios, thereby facilitating the construction sector's transition to a CE.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".