Promoting circular practices in fashion: strategies for sustainable business growth
Bibliographic record
Abstract
Purpose This study aims to systematically investigate the landscape of circular economy (CE) models within the fashion industry, with a focus on evaluating their effectiveness, applicability, and implications for sustainable business practices. The research seeks to identify which CE models are most viable for enhancing environmental performance, profitability, and long-term competitiveness in the fashion sector. Design/methodology/approach A comprehensive and systematic literature review was conducted, drawing on peer-reviewed academic sources and industry reports. The study critically examines a range of CE business models, including rental and subscription services, product customization, recycling, remanufacturing and repair, recommerce or peer-to-peer second-hand services, and transparency-focused models. Comparative analysis was used to assess their practical implementation, scalability, and sustainability outcomes. Findings The findings reveal that all examined models possess substantial potential for driving both environmental and economic value in the fashion industry. Among them, transparency-based business models emerge as particularly effective and feasible, providing multidimensional benefits for both established enterprises and start-ups. Transparency enhances consumer trust, strengthens accountability, and facilitates stakeholder engagement, thereby positioning it as a critical enabler of sustainable transformation within the sector. Originality/value This study provides a nuanced and integrative perspective on circular economy practices in the fashion industry, offering theoretical and practical insights into how businesses can strategically select and combine models to achieve sustainability goals. It contributes to the ongoing discourse by identifying transparency as a central mechanism for operationalizing circularity and fostering consumer-driven sustainability.
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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 | 0.021 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".