Green Building Construction Practices: Review of Environmental Management from the Contractor Perspective in the Canadian Industry
Bibliographic record
Abstract
Green building is a practice applied to the whole life cycle of a building, which means its environmental performance is not only defined by design, but also by the management processes in the construction, operation, and maintenance of a building. Over the past 10 years, research in green buildings has been increasing, mostly focusing on the design and energy efficiency. Contractors now represent a critical role in sustainability, but they have received less support and attention than the design community. This paper focuses on three main areas: practices on site, project management, and organizational support. While contractors in Canada are focusing on the implementation of green practices mainly established by LEED; a main problem is that the concept of "green" remains unclear and ambiguous in on-site practices. This research started with the identification of green building construction practices available to contractors by reviewing and comparing green building standards and literature review. Next, this research considered the management perspective by conducting semi-structured interviews in four construction organizations in order to document how LEED is integrated into project management and what issues companies are facing in the implementation of green construction practices. In addition, this research identified best practices in how these companies are supporting the reduction of environmental impacts during the construction phase. Overall, this paper highlights the different green construction practices that Canadian contractors can apply to advance their environmental sustainability, along with four case studies to document LEED construction management and an overview of the organizational tools used by companies for their environmental management.
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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.038 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".