Management of construction and demolition waste in the Region of Waterloo
Bibliographic record
Abstract
This paper examines the existing construction waste management program in the Region of Waterloo to identify strategies for increasing waste diversion rates. The construction industry is a major economic driver in the Province of Ontario but it is also one of the largest contributors to landfill usage. The industry generates a large amount of solid waste, of which only a small fraction is recycled. The diversion rate for construction waste is currently at 16%, which is significantly below the 60% target specified in the 2004 Ontario Waste Diversion Goals. In this study, diversion options are identified for six waste streams: wood, concrete, steel, drywall, asphalt, and shingles. An economic evaluation of the cost of recycling these materials was also performed. Research findings indicate that there is poor monitoring of construction waste management in the region. Recycling costs can be minimized by increasing diversion rates for four waste streams (concrete, steel, drywall, and asphalt) rather than by imposing 60% diversion across all streams. However, even with optimized diversion, there is little incentive for the construction industry to recycle - the minimum cost of 60% diversion is 15% more costly than landfilling options. An assessment of the landfill disposal fee structure is recommended to identify strategies for incentivising waste diversion; for example, imposing higher landfill fees may encourage higher waste diversion rates.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".