Qualitative Evaluation of the Drivers and Barriers of Sustainable Construction Practices in Canada: A Case Study
Bibliographic record
Abstract
Sustainable building adoption is increasingly prioritized to mitigate climate change, reduce energy consumption, and enhance environmental performance.However, decision-making in sustainable design remains complex, influenced by economic, environmental, and social factors.This case study investigates the key drivers and barriers shaping sustainability decisions through stakeholder interviews and qualitative analysis using NVIVO.Focusing on the Engineering Expansion Building at the University of Victoria, the research integrates insights from industry professionals, policymakers, and academics to identify real-world challenges beyond those documented in existing literature.The findings reveal that institutional sustainability targets, regulatory mandates, and financial incentives act as key drivers, motivating stakeholders to pursue carbon reduction, operational efficiency, and high-performance building standards.However, significant barriers persist, including high upfront costs, cost-benefit uncertainties, extended project timelines, and technical knowledge gaps in sustainable materials and lifecycle cost integration.The generated coding hierarchy and clustering maps highlight the interdependencies between financial, regulatory, and technical constraints, with regulatory mandates emerging as both an enabler and a challenge.The study underscores the need for more flexible policy frameworks, enhanced financial mechanisms, and data-driven tools to support sustainability integration.Addressing these barriers through stakeholder collaboration, regulatory refinements, and post-occupancy performance assessments will be crucial in advancing more effective and widespread adoption of sustainable building practices.These findings contribute to refining sustainability decision-making strategies and inform future research on optimizing sustainability performance in the built environment.
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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 | 0.012 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".