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Record W4220962167 · doi:10.3390/w14071073

The Environmental Impacts of Fast Fashion on Water Quality: A Systematic Review

2022· review· en· W4220962167 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueWater · 2022
Typereview
Languageen
FieldBusiness, Management and Accounting
TopicSustainable Supply Chain Management
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaYork University
KeywordsSustainabilityGreenhouse gasChinaBusinessNatural resource economicsCorporate governanceEnvironmental impact assessmentCarbon footprintWater qualityEnvironmental planningEnvironmental scienceEnvironmental resource managementEnvironmental economicsPolitical scienceEconomicsEcology

Abstract

fetched live from OpenAlex

The fashion industry is the second most polluting industry, contributing 8% of all carbon emissions and 20% of all global wastewater, with an anticipated 50% increase in greenhouse gas emissions by 2030. To gain a better understanding of the state of the academic literature on the environmental impacts of the fast fashion industry, we systematically identified 65 publications from 1996 to November 2021 that were subjected to (i) bibliometric, (ii) text, and (iii) content analysis. We found that there is a growing research interest surrounding fast fashion and water quality, with 74% of the articles published in the last 5 years, and the majority of publications and citations are from China and European countries. We summarise the evaluation of production processes, such as carbon and water footprints, along with recycling practices aimed to increase the sustainability of the fashion industry. Circular economy, social environmental responsibility, and sustainability governance are key areas for future research in this growing field.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.906
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.030
GPT teacher head0.272
Teacher spread0.242 · 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