The effectiveness of Metro Vancouver’s green bin program
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.010 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".