Sustainable procurement in the Canadian construction industry: challenges and benefits
Bibliographic record
Abstract
Disregard of triple bottom line (TBL) of sustainability is one of the major drawbacks of current construction procurement practices. Sustainable procurement is an emergent concept that can improve procurement practices and enhance the sustainability performance of the construction industry as a whole. Presently, sustainable procurement is still not fully utilized in the Canadian construction industry. A comprehensive literature review showed that the construction industry is still not fully aware about the benefits of using sustainable procurement or ways of implementing the same. This study evaluates challenges encountered in implementing sustainable procurement in the Canadian construction industry. In addition, this study investigates perceptions of construction professionals on the benefits of using sustainable procurement for construction projects. A country wide questionnaire survey and semi-structured interviews were conducted to collect required data. A statistical analysis was performed to rank the challenges and benefits of sustainable procurement. Findings from semi-structured interviews were used to validate the results observed in the statistical analysis. This study revealed that lack of funding is the main challenge for implementing sustainable procurement, while reducing harmful emissions and waste generation was identified as the main benefit. It was concluded that the leadership and commitment of the project owners is the key to fully establish sustainable procurement in the Canadian construction industry.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.016 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".