A comparison of municipal waste collection policies to optimize recycling rates: Evidence from England and Wales
Bibliographic record
Abstract
• Municipalities with less frequent garbage collection had higher recycling rates. • Weekly food waste collection significantly increased recycling rates. • Free yard waste services improved recycling rates more than paid yard waste services. • Sorting and collection frequency for recyclables did not affect recycling rates. • Recycling success correlated with demographic factors, such as education levels. This study investigated the effectiveness of municipal waste collection policies within England and Wales by examining how variations in local waste management strategies correlate with recycling rates. Using data from 297 council districts, we analysed the impact of different policy variables (frequency of residual waste and recycling collection, sorting requirements for recyclables, and the availability of food and yard waste collections) on recycling rates. We applied a logistic transformation to the dependent variable and fitted a linear regression model using the gathered predictors to evaluate policy effectiveness, while controlling for demographic factors. We validated the model with a series of beta regression models. The findings indicate that less frequent residual waste collection, the availability of weekly organic food waste and free organic yard waste significantly enhance recycling outcomes. Moreover, the research highlights the influence of socio-demographic factors. The results provide actionable insights for policymakers to optimise waste management practices and recycling rates within the framework of existing policies.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".