Methodology for improving the net environmental impacts of new buildings through product recovery management
Bibliographic record
Abstract
Buildings contribute significantly to the global environmental load caused by human activities. There has been a growing interest in improving a building's performance over all of the life-cycle stages (production, construction, operation, and End-of-Life [EoL]). Several studies have recognized the importance of the EoL stage in buildings in terms of sustainability and Circular Economy (CE). A methodology for improving the net environmental impacts of new buildings through Product Recovery Management (PRM) is presented in this thesis. It starts with a CE perspective that emphasizes the importance of adaptive reuse of buildings over new construction. Context is established with a relevant case study in the Waterloo Region. Then, product recovery planning methods that meet environmental life-cycle objectives as well as cost objectives are presented that enhance the attractiveness of adaptive reuse as an alternative. Validation of the proposed methods is achieved through functional demonstration with case studies. Together, these methods form a rational approach to improve the net environmental impact of buildings in our economy. The overall proposed framework in this thesis have demonstrated to be effective to improve sustainability in the construction industry by providing a better understanding of the net environmental impacts and economic potential benefits of buildings' adaptive reuse. Finally, this thesis marks a reference for the development of innovative user-friendly methods and tools for reducing inefficiencies in the process of adaptive reuse through PRM.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".