Beyond green chemistry: a comprehensive review of how sustainability has been integrated into cosmetic research
Bibliographic record
Abstract
Abstract Non-technical summary Cosmetics, including makeup, perfumes, and facial care products, have a significant impact on the environment and society, particularly as they are used by many consumers daily. The industry's continued growth further contributes to this impact. This paper reviews 365 articles on existing research on sustainable cosmetics. Findings of this review showed that Italy, Brazil, and Spain are the countries with the highest number of research articles. It was also noted that many studies were from chemical and pharmaceutical disciplines, whereas there is minimal research through a social science lens. These insights provide avenues for future sustainability research in the cosmetics industry. Technical summary Cosmetics have become an essential part of daily life, but their impact on the environment and society cannot be ignored. With the cosmetics industry experiencing almost continuous growth, it is imperative to ensure its sustainability. While several studies have examined various aspects of cosmetics and sustainability, there is no comprehensive overview of the literature in this field. To address this gap, this review aims to categorize the extant literature thematically and identify areas that require further research. A systematic review of 365 selected journal articles published from 1992 to 2022 revealed several insights. Firstly, the number of publications in this area has increased significantly over the years. Secondly, Italy has the highest number of publications, and Sustainability is the most popular publication outlet. Thirdly, research output from chemistry, chemical engineering, and pharmacy disciplines is abundant, while social science disciplines have comparatively few studies. Fourthly, experimental procedures are the most commonly used research methods. Finally, ‘process and technology’ is the most studied area, while ‘stakeholder behavior’ is the least studied area. These findings highlight research gaps and suggest future research directions to promote sustainability in the cosmetics industry. Social media summary This review looks at 30 years of research on sustainable cosmetics and identifies areas that need to be explored.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".