A scoping review exploring carbon emissions in dentistry—a step towards sustainability
Bibliographic record
Abstract
Abstract Background In the context of advancing sustainability in healthcare, in alignment with Sustainable Development Goal 13 on Climate Action, there is a necessity to enhance our understanding of the carbon footprint associated with dental services to establish an environmentally conscious system. This insight could help devise strategies for policymakers, oral health practitioners, and researchers to establish more sustainable dental practices. Therefore, the present scoping review was conducted to analyse the current body of literature exploring carbon emissions generated from dental health care services with the aim of promoting sustainable dental practices. Methods This scoping review was conducted in line with Arksey and O’Malley’s Framework and was reported using the PRISMA-ScR guidelines. A systematic database search was conducted across four databases: PubMed, Scopus, Web of Science, and EMBASE, to collect original research published in English up to June 2024, focusing on carbon emissions in dentistry. Review articles, letters to editors, and editorials were excluded. Selected articles underwent critical appraisal using Crowe’s Critical Appraisal Tool, followed by data charting and thematic analysis using Atlas.ti for Mac version 24.1.0. Results A total of 15 articles were included in this review based on the eligibility criteria. The qualitative thematic analysis of these articles revealed four main themes: sources of carbon emissions, strategies to promote sustainable dental practices, challenges for sustainability, and future research areas. Conclusions The review provides a comprehensive understanding of the various sources of carbon emissions associated with dental practices along with the challenges faced towards sustainable clinical practices. The key themes were explored across four hierarchical levels: structural, dental practice, dental practitioner, and method or product. The findings further guided us toward potential strategies for integrating sustainability into dental care, including stakeholder collaboration, policy reform, management practices, infrastructural improvements, and the adoption of environmentally-friendly materials. Additionally, the review highlights the need for future research to explore sustainable alternatives, conduct life cycle impact assessments, and undertake more qualitative studies to inform best practices.
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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 | 0.023 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.023 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".