Application of life-cycle approaches for the evaluation of high performance buildings
Bibliographic record
Abstract
The market shift towards high performance buildings is posing a major challenge to decision- makers, designers and developers. They need to know what constitutes high performance design and practice, what the environmental consequences of decisions are, and how buildings are performing relative to anchored benchmarks. This doctoral dissertation provides building designers and operators methods on how to use life-cycle approaches to inform design and track performance. The research focuses on a case-study of the lifecycle impacts of advanced buildings at UBC, built to various standards of performance including the current best-practices (LEED standards) and the currently emerging ‘regenerative’ standard. Life-cycle approaches are used to explore simulated impact over time in terms of quantified financial and environmental metrics. The research novelty is in the integration of life-cycle models; the aggregation of compatible separate studies to provide a larger overview of building performance. Additionally, the analysis leverages the benchmarking capabilities of the UBC Life-cycle Analysis database - a high-resolution survey of 30 UBC buildings – to show that the contribution of rapid churn building products, such as information technology, contributes a disproportionally high amount to embodied impacts. The study also analyses operational impacts based on utility consumption data for 70 conventional buildings versus 10 best practices (LEED Gold) buildings at UBC with respect to building age and building type. The results show that, in contrast to previous studies, older buildings often outperform new buildings. The dissertation concludes that benchmarking and multi-stakeholder modeling life-cycle approaches are critical for informing expert opinion during decision-making. Attention to modeling construction, and ensuring broad participation is key to ‘useful’ modeling. The process of creating a life-cycle model is often more informative than modeled final results; collective understanding and communication – the basis of good decision-making – improve through participation and stakeholder interaction.
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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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".