Navigating Green Ship Recycling: A Systematic Review and Implications for Circularity and Sustainable Development
Bibliographic record
Abstract
The shipping industry is the cornerstone that facilitates the movement of approximately 90% of international commercial goods. However, environmental challenges, particularly in the ship recycling (SR) industry, have become increasingly evident. Via closed-loop production patterns within an economic system, a circular economy aims to improve resource-use efficiency by focusing on urban and industrial waste to achieve better balance and harmony between the economy, environment, and society. A key element in this process is a well-executed disassembly that enables reuse, remanufacturing, high-value recycling, and implementing other circular strategies. Based on a systematic literature review, this paper delineates the SR process, identifies influential scholarly works on recycling end-of-life ships, discusses factors affecting shipowners’ decision to recycle, and opportunities for sustainability and circularity in SR processes. The results confirm the increasing need for green SR to reduce shipbreaking waste. Also discussed is how greening SR could be integrated into sustainable development goals under proper environmental and safety regulations and an aligned cultural mindset for stakeholders.
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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".