Can rental platforms contribute to more sustainable fashion consumption? Evidence from a mixed-method study
Bibliographic record
Abstract
This study presents a case study of fashion rental platforms in Canada, drawing upon two unique, yet complementary, datasets: a qualitative analysis based upon semi-structured interviews with the rental platform entrepreneurs and a life cycle assessment (LCA) of 11 garment designs simulating garments offered by the platforms. Fast fashion has not only made garments more accessible to all parts of society, but also made them more disposable. To counteract the sustainability issue of fashion, rental platforms are emerging as a potential solution. While fashion rental platforms are often described as being “sustainable alternatives”, their business practices and the quantitative impact remains largely untested. This study posed four research questions to address this gap: 1) How do fashion rental platform entrepreneurs see their contribution to enhance sustainability with their provided service?,2) What are the item purchase criteria of rental platforms and their relation to environmental sustainability of fashion consumption?, 3) How do factors such as garment type, season, fabric composition and style influence the greenhouse gas (GHG) emissions of a garment when owned versus rented?, 4) What are the research gaps between business practices and evidence of environmental impact? To answer these questions, we combined semi-structured interviews with rental entrepreneurs and an LCA. The interviews provided basic understanding in fashion rental operations and their reasons, which assisted in modeling the environmental impact of rented garments using LCA. As a result, qualitative findings indicate that rental entrepreneurs recognize provision of rental service itself contributes to sustainable fashion. From the LCA, the embodied GHG of garments varied significantly depending on the design and fiber content. When owning and renting were compared, rented garments had a greater life cycle GHG per piece when the garment is dry-cleaned. Also, the GHG emission per wear is tremendously reduced for garments that increase lifetime wear through renting such as dresses. Our mixed-method study suggests the need to further analyze the role of the garment category to consumer behavior, rebound effects, and garment design for rental platforms.
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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 | 0.045 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".