A scoping review on e-cigarette environmental impacts
Bibliographic record
Abstract
INTRODUCTION: The use of e-cigarettes has grown in popularity worldwide. From their manufacturing, use, and disposal, the environmental impacts of e-cigarettes present a novel public health concern that needs to be urgently investigated. However, very limited studies have focused on the subject matter. The present study aims to review available studies to identify the environmental impacts of e-cigarettes. METHODS: In this scoping review, we undertook a search in two databases (PubMed and Web of Science) from inception until 21 March 2023, and a gray literature search in Google Scholar. Reference lists of publications included in the scoping review were screened manually for additional relevant publications. Scientific publications that were in English and focused on the potential impacts of e-cigarettes on the environment were included. RESULTS: A total of 693 publications were identified, of which 33 were subjected to full-text review and 9 publications were finally included in the review. The impacts on air quality, water, land use, and animals, water and energy consumption, with associated environmental impacts, increased pollution and emissions due to greater e-cigarette production, having harmful and toxic components, creating pollution and waste issues, and global environmental impacts due to manufacturing and importing ingredients and components from low- and middle-income countries, were identified as the environmental impacts of e-cigarettes. CONCLUSIONS: Despite the emphasis on the environmental threat of e-cigarettes, there are limited scientific studies on the environmental impacts of the e-cigarette life cycle. Considering the rapid expansion of e-cigarette usage, there is an urgent need for a rigorous assessment of their life-cycle environmental burden of the various potential health, environmental, and other consequences.
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.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".