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Record W4317581955 · doi:10.1016/j.clrc.2023.100103

Can rental platforms contribute to more sustainable fashion consumption? Evidence from a mixed-method study

2023· article· en· W4317581955 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCleaner and Responsible Consumption · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSharing Economy and Platforms
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRentingFast fashionClothingSustainabilityBusinessSharing economyConsumption (sociology)MarketingService (business)EngineeringComputer scienceSociologyCivil engineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.043
GPT teacher head0.304
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it