Waste Management Practices in the Textile Industry: A Review
Bibliographic record
Abstract
Several studies have been carried out to highlight the challenges the improper disposal of textile waste poses to the environment and human life. Other studies were done on the effective waste management strategies (WMS) to adopt for sustainable waste management. However, there remains a paucity of knowledge on the eco-friendly and sustainable WMS to adopt for managing waste in the textile industry. This review was therefore carried out to investigate the appropriate WMS to apply in handling textile waste so as to promote the circular economy (CE), since the focus in the world is now shifting from the linear economy to CE. In order to accomplish this, the systematic literature review (SLR) method was used, in which the VOSviewer software was used to assess the co-occurrence of keywords in 35 publications that were retrieved from the Scopus online database. The documents used for the study span a period of 15 years, from 2009 to 2024. The findings reveal that the 3R principle of waste management, smart waste management (SWM), and waste valorization are the best WMS to adopt in a CE. The study recommends that stakeholders in the textile value chain as well as governments all over the world should put practical measures in place (i.e., the drafting of a waste management policy and the provision of financial support to the textile industry) to ensure the full adoption of the preferred WMS by the textile industry. In the long run, this will lessen the negative consequences that textile waste has on both the environment and people.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".