Key Drivers and Barriers to Circular Economy Practices in the Global Textile and Fashion Industries: Sustainable Strategies for the Indonesian Batik Industry
Bibliographic record
Abstract
The global textile and fashion industries, including Indonesia's batik sector, significantly contribute to environmental pollution and resource depletion, particularly water waste.This makes adopting circular economy and sustainability practices in the batik industry crucial.The study identifies key drivers, barriers, and strategies for sustainability in the global textile, fashion, and batik industries to recommend strategies for Indonesia's batik industry.Using a combination of SLR with PRISMA, Pareto analysis, and SWOT analysis, four strategic models were developed and validated through in-depth interviews with relevant stakeholders.The strongest, S-O strategy (based on internal and external drivers), emphasizes leveraging knowledge of circular economy principles and utilizing government policies and subsidies for technological investments and improving production efficiency.The weakest, W-T strategy (based on internal and external barriers), calls for internal training and collaboration with the government to enhance skills and secure financial access, aiding the transition to more sustainable practices.Compared to the S-O and W-T strategies, the S-T and W-O strategies are at an intermediate level in terms of strengths and weaknesses.Implementing these strategies will help the batik industry improve sustainability, align with global trends, and overcome existing challenges.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| 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".