Improving Occupant Wellness in Commercial Office Buildings through Energy Conservation Retrofits
Bibliographic record
Abstract
There is increasing literature demonstrating the link between building indoor environmental quality, and occupant health and productivity, driving the corporate real estate industry to investigate how to integrate wellness features in both new and existing building stock. Meanwhile, new voluntary standards to promote occupant health are becoming adopted alongside sustainability standards. As commercial building owners and tenants seek to improve occupant conditions and incorporate wellness, apparently conflicting priorities must be balanced, particularly improving indoor environmental conditions has the potential to increase energy. This paper presents a framework to consider retrofits holistically and considering the benefit of improved conditions both qualitatively and quantitatively. Where poor conditions exist, published literature demonstrates a lost productivity cost that exceeds typical building energy costs, and this is quantified in the financial analysis presented. Energy retrofits provide a unique opportunity to integrate wellness-enabling features because the energy savings can offset marginal energy or operating cost increases for particular wellness interventions. This paper presents a flexible, customizable framework to develop potential retrofit bundles and evaluate them considering economic, sustainability, wellness, risk and occupant experience factors to identify the optimal zone of retrofit. An illustrative case study using real building data demonstrates how the framework might be applied to a real project and customized to achieve unique stakeholder priorities.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".