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Record W2598088721 · doi:10.47339/ephj.2014.144

The effectiveness of Metro Vancouver’s green bin program

2014· article· en· W2598088721 on OpenAlexfundvenueaboutno aff
Alex Lui, Environmental Health BCIT School of Health Sciences, Helen Heacock

Bibliographic record

VenueBCIT Environmental Public Health Journal · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsnot available
FundersBritish Columbia Institute of Technology
KeywordsResidenceGarbageBinApartmentDemographicsBusinessOperations managementTransport engineeringEngineeringMarketingSocioeconomicsWaste managementDemographySociologyCivil engineering

Abstract

fetched live from OpenAlex


 Background and Aims Metro Vancouver is implementing a disposal ban on all food scraps from entering the landfills and incinerators by the year 2015. In order to prepare the city’s residents, a food scraps recycling program, known as the Green Bin Program, was initiated in 2013 for all single family households. The aim of this research project was to measure public knowledge and awareness of the program across various demographics and collect data on the general opinion of it. Methods An online survey was created using SurveyMonkey, a survey generating website, and distributed online via Facebook and e-mail. The results from these surveys were analyzed using NCSS software to determine statistical significance via a chi-squared analysis with alpha (a) = 0.05. Results There were a total of 70 respondents. Of these, 68% of the respondents indicated that the Green Bin Program should stay the way it currently is without any further changes. 8% of the respondents were in favour of stopping the program and the remaining 24% indicated that the program needed some modifications such as more education/promotional material, implementing the program into apartment complexes and more garbage pickup days to prevent pest and odor problems. Age category, location of residence, and educational background were analyzed against other variables in the survey that tested the knowledge and usefulness of the Green Bin Program. Looking at these 3 variables in relation to knowledge: there was no association between location of residence, age, and educational background, with knowledge of what could go into the green bin (p= 0.76, p= 0.53, p= 0.33, respectively). These same 3 demographic variables were also analyzed against frequency of food scraps recycling and there was a positive association between age and frequency (p= 0.037), indicating that respondents aged 19-29 were recycled food scraps more than respondents over the age of 29. However, there was no association between location/education and frequency (p= 0.32 and p= 0.10, respectively). Non demographic variables were also analyzed, such as determining if household size and garbage bin size had an effect on frequency of food scraps recycling: household size did not have a significant association (p=0.70) while garbage bin size did have a positive association (p= 0.025), showing that residences with smaller garbage bins were more likely to recycle their food scraps. Conclusion These results indicated limited knowledge of the Green Bin program and pinpointed deterrents (mostly pests and odors) from participating in it. Environmental Health Officers’ involvement would be important as educators to emphasize that certain organic wastes (like pet fecal matter) should not go into the green bin as they create health hazards. EHOs can also collaborate with the municipality to promote the program. Several participants reported recycling their food scraps; as a result, the Metro Vancouver Green Bin Program has achieved some of its aims in creating a greener and more sustainable city.

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 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)0.008
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.871
Threshold uncertainty score0.853

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.000
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 science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.264
Teacher spread0.247 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes3
Has abstractyes

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