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Record W4312471508 · doi:10.5334/bc.277

Meeting urban GHG reduction goals with waste diversion: multi-residential buildings

2022· article· en· W4312471508 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueBuildings and Cities · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsUniversity of GuelphUniversity of Toronto
Fundersnot available
KeywordsGarbageMunicipal solid wastePromotion (chess)BusinessWaste collectionGreenhouse gasEnvironmental planningWaste managementEngineeringEnvironmental science

Abstract

fetched live from OpenAlex

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. <em><strong>Policy relevance</strong></em> 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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.486
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.010
GPT teacher head0.203
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it