A probabilistic approach for estimating environmental impacts over the life cycle of buildings
Bibliographic record
Abstract
There is increased awareness and concern regarding human activities with high environmental impacts caused by the construction, operation, maintenance and decommissioning of the built environment. The work presented in this thesis helps predict holistically the environmental impact indicators of different building design options. A probabilistic framework, applicable to multiple building function types, is proposed to estimate the environmental metrics of energy, water and global warming potential. The environmental impact indicators are studied at varying resolutions of data quality. The proposed framework differs from alternate tools by explicitly accounting for uncertainty through the use of random variables in its models. The modeling approach emphasizes greater transparency of the environmental impact intensity values that relate known information about the building, such as material quantities, with respective environmental impacts. Explicit environmental impact models are presented for each of the building’s life cycle phases, including extraction, manufacture, on-site construction, operation, maintenance, and end of life. The methodology is then demonstrated by analyzing a sample residence in Ontario. The environmental impacts associated with the entire life cycle of the building are reported and possible improvements to the methodology are identified. The ability to analyze the probability of exceeding an environmental impact threshold is a feature of this work that is useful in the refinement of environmental performance rating systems. The general lack of public information about the environmental impact of the manufacturing of building components in North America, as well as uncertainty about component replacement frequency and the building service life continue to pose a challenge for environmental impact analysis. However, this thesis presents a new probabilistic framework in which this uncertainty is explicitly identified and addressed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".