Closing the Gap: A Comprehensive Review of the Literature on Closed-Loop Supply Chains
Bibliographic record
Abstract
Background: Sustainable closed-loop supply chains have emerged as viable answers to supply chain problems. They can handle environmental damages (e.g., waste) and related social impacts. Closed-loop supply chains (CLSCs) are forward and reverse supply chain networks that have gained popularity in recent years. Recovery options such as reusing, remanufacturing and recycling can be considered in CLSCs. Methods: This paper provides a comprehensive evaluation of CLSC journal papers published between 2020 and the present. This study examines and synthesizes 54 papers from major publications in this area, covering a wide range of themes and approaches. This paper aims to respond to the following key questions: (i) What are the current trends and challenges in CLSC research, and how have they evolved since previous literature review papers? (ii) What key variables and objectives have been studied in recent CLSC research, and how have they been operationalized? (iii) What are the gaps and limitations in current CLSC research? To our knowledge, other literature review papers in this field have covered older papers, and recent papers have been ignored in them. Another research contribution of this paper is the taxonomy of it. Results: This review article highlights some developing themes and research gaps in the CLSC literature and makes recommendations for further study. Conclusions: This paper provides a comprehensive review of papers on closed-loop supply chain networks.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".