Role and limitations of the in-store waste collection system at supermarkets
Bibliographic record
Abstract
In-store collection is defined as the activity of installing collection boxes at retail stores, such as supermarkets, for the collection of recyclables. The use of in-store collection reduces the burden of garbage collection in municipalities, which may reduce administrative and environmental burdens and costs. Previous discussions on in-store collection have ignored environmental impacts and the costs to consumers and that supermarkets should become players in the collection of recyclables. Therefore, it is necessary to clarify whether the use of in-store collection effectively contributes to a reduction in environmental burdens and costs for society. This study aimed to analyze the environmental burden and costs associated with integrating in-store collection into municipal solid waste (MSW) management systems. A total of 1734 municipalities in Japan were classified into six clusters using cluster analysis to analyze the characteristics of municipal and in-store collection by municipality. Model cities representing each cluster were created, and three scenarios were established to analyze the CO 2 emissions and costs associated with municipal and in-store collection. The scenarios were cases where recyclables were collected through in-store collection (Scenario 1), recyclables were collected through municipal collection (Scenario 2), and both in-store collection and municipal collection were combined, similar to the current system (Scenario 3). The reduction in CO 2 emissions in each model city in Scenario 1 was −37.0 to 53.5% compared to that in Scenario 3. There was a 0.90–1.96-fold increase in cost in Scenario 1 relative to Scenario 3. Suggestions for the appropriate implementation of in-store collection are proposed based on these results. For example, an increase in in-store collection reduces CO 2 emissions but leads to an increase in costs. When integrating in-store collection into an MSW management system, reviewing the municipal collection system is necessary.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".