Review of the project management practices from the contractors' perspective to respond to the identified critical challenges in the Canadian green construction industry
Bibliographic record
Abstract
Green construction has been adopted by the Canadian construction industry as a result of the demand for sustainable building techniques. However, green projects must overcome various challenges in order to be successful. \n \nThis study identifies and analyzes the challenges encountered by green building contractors in Canada, as well as the strategies employed to respond to these challenges. \n \nTen contractors were interviewed in addition to a literature review and the Fuzzy analytic hierarchy process (AHP) method. The literature review revealed that there are 34 challenges that fall into six categories. The Fuzzy AHP analysis ranked these categories as follows: market trends, time-related, financial, construction process-related, government-related, and technology challenges. \n \nThe interviews revealed that contractors employ a variety of strategies to overcome such challenges, such as keeping up with industry trends, increasing efficiency, conducting research and development, implementing effective project management, advocating for government incentives, and fostering collaboration among stakeholders. \n \nThese findings highlight the need for continued research and advocacy to promote green building practices and green construction incentives in Canada.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.020 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| 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".