Operational Performance of Sustainable Measures in Public Buildings
Bibliographic record
Abstract
Buildings in the United States account for 40% of the national annual energy consumption, 39% of carbon footprint, and 13% of water consumption. To minimize these negative environmental impacts and improve life-cycle cost performance, an increasing number of public and private owners are requiring that their existing and new buildings incorporate more sustainable building measures. Despite the recent increase in the use of these sustainable building measures, there is a pressing need to explore their actual operational performance. This paper presents the findings of a recent study that evaluated the performance of sustainable measures in public buildings, such as rest areas, including energy efficient fluorescent and LED lights, solar photovoltaic systems and water heaters, daylight tubes, geothermal heat pumps, wind power technology, motion-activated lighting, double-pane glass, energy-efficient hand dryers, water-conserving fixtures, rain gardens, and graywater systems. The study conducted a comprehensive survey of state departments of transportation (DOTs) to gather and analyze personnel’s experiences in implementing various green measures in their buildings. A total of 30 state DOTs in the United States participated in the survey in addition to one response from the Ontario Ministry of Transportation in Canada. The primary contributions this research makes to the body of knowledge are the new knowledge it provides on the actual operational performance of various green building measures in state DOT buildings including the frequency of their use, user satisfaction, ease of maintenance, encountered problems, repair costs, reductions in electricity/water usage, and payback periods. While there are different ways of evaluating the performance of the green measures, this study concentrates on the feedback and experiences of the personnel who have actually implemented these green measures in their facilities. These important findings based on user experiences on the performance of building measures are useful to both researchers and professionals in the construction industry and will contribute to better selection and use of these measures toward improving building sustainability.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".