A review of fast fashion and environmental research gaps in the top garment-producing countries: a case study of China, Bangladesh, Vietnam, India, Turkey, and Indonesia
Bibliographic record
Abstract
The growth of the fast fashion industry has accelerated over the last 5 decades, leading to serious environmental repercussions, including immense production of wastewater and greenhouse gases, in addition to landfill contributions. The fashion industry has been credited with generating 20% of global wastewater and 8% of global greenhouse gas emissions. In this systematic review, we explore the research gaps in the top garment-producing countries, specifically China, Bangladesh, Vietnam, Turkey, India, and Indonesia. We aim to answer the following research questions: (1) What are the predominant environmental impacts and research areas in each of the top garment-producing countries as a result of the fast fashion industry?; (2) What are the knowledge gaps hindering improved environmental practices?; and (3) What are the primary barriers to implementing sustainable garment production in the top garment-producing countries? First, we identified 2318 studies related to the environment and fast fashion in the top six garment-producing countries published between March 2008 and October 2023 using Web of Science and Scopus. Next, the titles and abstracts were screened using the inclusion criteria, from which articles received a second round of full-text screening. Subsequently, full-text analysis was conducted on the resulting 68 articles. We identified policy, sustainability, and wastewater as key research areas across all the top garment-producing countries. A lack of empirical research methods and consumer knowledge on the environmental impacts of fast fashion are key knowledge gaps hindering improved environmental practices. Investments in education and infrastructure will aid in the progression towards a more sustainable garment-producing industry.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.021 | 0.035 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".