Assessing the Environmental Impact: Office Building Reuse as a Sustainable Alternative to Demolition
Bibliographic record
Abstract
Building reuse is key to sustainability, given its potential to conserve resources, reduce waste, and foster sustainable development.However, determining the precise environmental impact of existing buildings through the quantification of embodied carbon in structures remains a challenge.To further our understanding of this impact's magnitude, this research quantifies the embodied carbon in an existing office building by examining three scenarios: "doing nothing," "building reuse," and "new construction."Through a combination of manual and tool-based calculations, the study compares the limitations of methods and the reliability and accuracy of results.Despite the variability of results, the findings still reveal that choosing the path of building reuse over demolition and reconstruction can mitigate the release of significant quantities of carbon into the atmosphere.These findings highlight the environmental implications of building reuse while advocating for standardized methodology, policies, and incentives.I would like to express my deepest appreciation to everyone who played a significant role in assisting me throughout the completion of my thesis.First and foremost, I extend my appreciation to my supervisor, Dr. Mario Santana, as well as Dr. Mariana Esponda and Professor Susan Ross.Their guidance and support were instrumental in helping me discover a research path that I truly enjoyed.Another person I cannot express my appreciation for more is Karim Abuawad the Coordinator of Graduate Professional Development at Carleton.His guidance, encouragement, and constant support allowed me to accomplish my goals.I would also like to extend my thanks to
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".