Meeting urban GHG reduction goals with waste diversion: multi-residential buildings
Bibliographic record
Abstract
Waste diversion targets are a common characteristic of municipal climate change mitigation plans because about two-thirds of residential waste sent to landfills is degradable and thus contributes to greenhouse gas (GHG) emissions. This paper focuses on the challenge of achieving waste diversion targets in multi-residential buildings because their diversion rates are much lower than those for single-family homes. A case study of 15 high-rise condominium and cooperative housing buildings compares modes of governance by the City of Toronto and by multi-residential buildings to address waste diversion challenges. City responses to the challenges included mandatory building standards making waste diversion as convenient as garbage disposal, voluntary standards for in-suite storage of recyclables and organics, phase-in of organics collection and pay-as-you-throw collection fees, and delivery of promotion and education programs. For buildings, the responses were fines for poor-quality sorting, conversion of the garbage chute to an organics chute, the delivery of education material to residents, and monitoring bin capacity. Despite these initiatives, Toronto is very unlikely to meet its target of diverting 70% of residential waste away from disposal in landfill by 2030. Seven actions are recommended to increase the rate of diversion. Policy relevance Recommended actions for Toronto and other municipalities facing similar waste diversion deficits in the multi-residential sector include: studying the potential for converting garbage chutes to organic chutes, assessing the effectiveness of different chute systems, modifying waste collection service agreements or city bylaws to incorporate obligations for promotion and education around waste diversion, revising building standards to require more space for diversion facilities inside buildings, adopting voluntary building standards for building operations, and advocating with higher levels of government to regulate packaging complexity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".