Sustainability rating systems for large infrastructure projects : good management practices for the inclusion of Envision in the Metro Vancouver Regional District
Bibliographic record
Abstract
Municipalities in the Metro Vancouver Regional District are beginning to adopt sustainable rating systems for large infrastructure projects. These systems act as process tools to assess sustainable practices throughout the planning and design of a project. The dominant rating system being applied for Metro Vancouver projects is Envision. Envision is a holistic framework of 5 categories and 60 sustainability credits that is based on the sustainable triple-bottom-line principle of economy, environment and society. Despite its growing recognition as a viable assessment tool for managing sustainability in Metro Vancouver, wide spread adoption of the system is slow because there is no guide on how to include rating systems in current management systems. The objective of this thesis is to conduct a comparative analysis of the lessons learned during the planning and design of two projects – one transportation project and one wastewater facility project – to highlight good management practices for the inclusion of Envision. The methods used to conduct the research study include a literature review of sustainability management in Canada and of Envision lesson-learned case studies and a series of interviews with various members of each project team. The results are presented in tables based on the Project Management Body of Knowledge and organized by the knowledge management areas to highlight the challenges, successes, impacts and good management practices for future inclusion of Envision for other projects. The results indicate the criticality of obtaining senior leadership approval; the importance of conducting a comparative decision analysis; the significance of having experienced Envision Certified Professionals; the need for determining appropriate benchmarking systems; and the need to account for potential schedule delays in project planning using Envision. The concluded results will provide good management practices for the inclusion of Envision to plan and design sustainable, large-scale infrastructure projects.
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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 | 0.027 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".