Prompt-Driven ChatGPT Carbon Calculator for Dental Practices: Estimation and Tailored Improvement Strategies

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Demonstrates application of LLMs for carbon footprint estimation in a niche sector, prompting further research on prompt optimization and accuracy validation.

🏢実務担当者:Provides a practical method for dental clinics to estimate emissions without specialized expertise, facilitating sustainability integration into daily operations.

🏛政策担当者:Illustrates potential for AI-assisted tools to support SME decarbonization, suggesting policy support for open emission factor databases and AI literacy.

Introduction and aims This study investigates the feasibility of applying ChatGPT, a generative artificial intelligence (AI) language model, to develop a user-friendly carbon footprint calculator tailored for dental practices. Building on a previously developed Excel-based tool, the research aimed to evaluate ChatGPT’s capacity to generate accurate emissions estimates and sustainability recommendations using different prompting strategies. Methods Three prompting variants were tested. Variant 1 employed an unstructured request to assess general responses. Variant 2 used structured data entry with predefined emission factors. Variant 3 combined structured input with instructions to rely exclusively on outputs from a previously validated sustainability tool. ChatGPT-generated results were compared with the Excel benchmark, focusing on accuracy, contextual relevance and alignment with peer-reviewed guidance. Results Unstructured prompts (Variant 1) produced general recommendations of limited contextual relevance. Structured prompts improved both accuracy and specificity. Variant 2 generated tailored outputs using emission factors, while Variant 3 provided detailed, evidence-based recommendations consistent with established literature. Across variants, ChatGPT’s carbon footprint estimates were largely comparable to the Excel benchmark, with only minor discrepancies in waste-related emissions. Conclusion Structured prompting significantly enhances ChatGPT’s performance in generating reliable carbon footprint data and recommendations for dental practices. When supported by transparent emission factors and credible literature, generative AI tools can increase access to environmental data, support sustainability decision-making and facilitate climate action in clinical contexts. However, limitations remain, including risks of inaccurate outputs (‘hallucinations’) and regional generalisations. Effective use requires prompt literacy and open access to validated emission factor databases to maximise impact and reliability. Clinical relevance AI-driven calculators such as ChatGPT can help dental teams without carbon accounting expertise to understand and reduce their environmental impacts, supporting the integration of sustainability into routine clinical practice.

• semanticscholar https://doi.org/10.1016/j.identj.2025.103979first seen 2026-06-29 08:54:45