Evaluating sustainable and green building designs using human factor approaches
Bibliographic record
Abstract
In response to government requirements for zero carbon emissions for existing and new buildings, a number of organizations committed to explore the most efficient ways to build new buildings or renovate their aging infrastructure, and to implement the necessary measures and technologies supporting net zero standards and sustainable building designs. In many cases, this means deep energy retrofits within buildings, including upgrades to the exterior and the interior building design features. By using modelling techniques and following standard specifications, a building’s performance can be optimized through a number of energy efficient measures and implementation of sustainable, net zero technologies. However, research has shown that in many cases the modelled performance is not often easily achievable in real life settings. This can be specifically relevant to cases where the comfort requirements are surpassed by an increased focus on energy efficiency measures. Methodology: This paper outlines a case study where the National Research Council Canada (NRC) has committed to complete a pre- and post-renovation evaluation of the Ontario Association of Architects (OAA) headquarter building, which was retrofitted to achieve net zero emissions. The main methodologies used during the data collection included occupant surveys, physical environment measurements and energy monitoring across the various stages of the project. Findings: This paper outlines the methodology used during the pre- and post-renovation data collection. The post-renovation data collection is currently in progress, therefore, only data from the pre-renovation phase is currently discussed. The results identified many opportunities for improvement through renovation, including a variety of occupant satisfaction and comfort dimensions related to the physical indoor environmental conditions.Conclusion: By using human factor methodologies and user-centric approaches, we can improve our understanding of the human factor impacts caused by sustainable and green building design practices. Successfully completed projects present great examples of how buildings, old or new, could meet modern-day needs, such as net zero standards and carbon neutrality, whilst at the same time providing efficient workplaces that support occupant wellbeing and productivity.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".