Sustainable Plastic Waste Collection and Distribution Strategies in Operations Management
Bibliographic record
Abstract
The global plastic waste crisis necessitates effective collection and distribution strategies aligned with principles of Operations Management and Sustainability. This study investigates efficient methods for managing plastic waste, focusing on mechanisms such as buy-back facilities, door-to-door collection systems, and reverse vending machines (RVMs). Among the various types of plastics, only PET, HDPE, PP, and LDPE are widely recyclable, with PET being the most preferred due to its high recyclability and potential for reuse in manufacturing. A mixed-methods approach was adopted, combining both qualitative and quantitative data collection techniques. The study involved a comparative analysis of different plastic collection strategies across various countries and regions, including deployments of RVMs in the UK, Sweden, Australia, Canada, the USA, and selected Indian cities such as Mumbai, Delhi, and Chennai. Findings reveal that RVMs offer a superior method for plastic collection due to their integrated sorting capabilities and user-friendly design. The global proliferation of over 100,000 RVM units illustrates their scalability and acceptance. Furthermore, the study highlights the environmental and economic benefits of optimized plastic waste collection, including natural resource conservation, energy savings, job creation, and reduced ecological impact. The integration of sustainable collection strategies, particularly through the deployment of RVMs, holds significant promise for enhancing waste management systems. The study emphasizes the importance of selecting appropriate technologies and infrastructures to support a circular economy. These insights contribute to operational improvements in waste logistics and support long-term sustainability goals.
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 | 0.000 | 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 | 0.001 |
| 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".