A Rapid Literature Review of Environmental Performance of Offsite Building Construction Industry
Bibliographic record
Abstract
The construction industry consumes about 36% of the total energy and releases up to 39% of global CO2. If no appropriate measures are taken, the figure may double in the next two decades. Therefore, the construction sector is considering several measures to mitigate carbon emissions. In this regard, modular and panelized construction, which is collectively called offsite construction (OSC), is steadily gaining momentum. Several studies demonstrate that life cycle assessment (LCA) is a practical tool for evaluating the performance of OSC in greenhouse gas (GHG) emission reduction. Although a reasonable number of previous studies on OSC exist in the context of Canada, only a few apply LCA based on actual case studies. Realizing this fact, a collaborative research team from Concordia University, the University of Alberta, and NRC is undertaking an ongoing research project with the goal of decarbonization of the construction industry through OSC. As a foundation of this large ongoing research project, this study aims to review LCA related studies focusing on actual case studies in the context of OSC.
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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.024 | 0.030 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".