Integrating Product Stewardship into the Clothing and Textile Industry: Perspectives of New Zealand Stakeholders
Bibliographic record
Abstract
The clothing and textile industry has become one of the world’s greatest polluters as tremendous volumes of clothing are produced, used, and disposed of at alarming rates. The industry must transition from its linear take-make-waste model towards a circular economy where textile products are kept in circulation and waste is minimized or eliminated. Product stewardship, an environmental management strategy where producers take responsibility for their products through design to the end-of-life stage, is one option to enable the circular economy. The aim of this research was to explore stakeholder drivers, barriers, and strategies for product stewardship participation in New Zealand. Qualitative data gathered through interview and survey methods of 25 stakeholder perspectives including designers, manufacturers, and retailers were analysed for emerging themes. Key findings suggest that product stewardship operating within the current linear system can only go so far; changes must occur at every stage of the value chain with all stakeholders making efforts towards circularity. Given the complexity of the product stewardship system, greater government regulation and incentive policies are likely needed to mainstream product stewardship and increase its material impact. The results of this study highlight the importance of contextual factors and capacities for tailoring regional product stewardship schemes to local needs.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".