Evaluating the effectiveness, costs, and challenges of deposit return systems for beverage containers: A meta-analysis
Bibliographic record
Abstract
This study conducts a comprehensive meta-analysis to evaluate the effectiveness, economic costs, and long-term sustainability of deposit return systems (DRS) for beverage containers across various countries. DRS are recognized as a critical strategy to enhance recycling rates, reduce environmental waste, and support the transition toward a circular economy. While empirical evidence from countries like Germany, Norway, and Lithuania indicates that DRS can achieve recycling rates exceeding 90%, challenges such as high setup costs, stakeholder resistance, policy inconsistency, and adaptability to market changes complicate their implementation and sustainability. The analysis synthesizes data from diverse geographic contexts, highlighting the factors that contribute to the success or failure of DRS, including public engagement, policy stability, technological adaptation, and effective stakeholder collaboration. The findings suggest that while DRS can provide substantial environmental and economic benefits, their long-term success is contingent upon sustained public participation, consistent policies, adaptability to market shifts, and robust stakeholder engagement. This study offers critical insights for policymakers, environmental advocates, and industry stakeholders seeking to optimize DRS as a tool for sustainable waste management.
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 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 | 0.047 | 0.090 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.058 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".