A comparison of indoor air quality and employee absenteeism in ‘local’ and ‘imported’ green building standards
Bibliographic record
Abstract
Abstract Buildings are responsible for a quarter of global carbon emissions. In the developing world, the desire to reduce energy consumption initially resulted in the adoption of ‘imported’ standards such as LEED and BREEAM and, over time, the development of several ‘localised’ standards that either supplant or compete with the imported standards. However, such standards have often been implicated in the unintended consequence of reduced indoor air quality resulting from lowered ventilation rates, in turn affecting employee productivity and absenteeism. Here, we systematically review and compare the performance of office buildings built to the localised Jordanian Green Building Guide (JGBG) and the well-known international LEED standard. We measure building performance in terms of the indoor air quality (via CO2 concentration) and occupant absenteeism during winter 2019. Results show that the JGBG building had a significantly lower mean indoor CO2 concentration than the LEED building during working hours (p < 0.00). In addition, the occupants in the JGBG building reported 20% more working hours (p < 0.03) and approximately 9 hours less of absolute absenteeism. These initial results suggest that further development of localised codes is likely to bring greater benefit to the performance of building and occupants compared to imported standards.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".