How Can Large Language Models Drive Environmental Sustainability? A Systematic Scoping Review

Currently, Large Language Models (LLMs), exemplified by ChatGPT, are accelerating technological development across various domains, including the environmental domain, owing to their powerful text-generation and information-processing capabilities. With changes in global climate and environmental conditions, environmental sustainability has emerged as a major global challenge. Leveraging LLMs to advance environmental sustainability and mitigate current environmental problems is considered a valuable and effective approach. This study aims to systematically synthesize research progress and core challenges in current LLMs for promoting sustainability-related fields, and to comprehensively analyze the application contexts, impacts, and development potential of various LLMs within the environmental sector. Following the PRISMA-ScR guidelines, a comprehensive search was conducted across six databases: Web of Science (WOS), Scopus, ACM Digital Library, IEEE Xplore, ScienceDirect, and Google Scholar. A total of 20 articles were ultimately included for analysis. The findings indicate that LLMs play a positive role in maintaining environmental sustainability and promoting the low-carbon energy transition. The applications of LLMs span six core domains: the green transition, carbon emission management, air quality assessment, smart city operations, map analysis, and human cognition and behavioral observation. However, the training and operation of current LLMs consume considerable resources, which creates an inherent conflict with the goals of sustainable development. Future efforts must focus on developing a secure, equitable, and scalable LLM support system to advance environmental sustainability. This requires optimizing model energy efficiency and ensuring a balance between performance, reliability, and environmental impact. These endeavors are crucial for addressing environmental problems and guaranteeing the sustainable progression of LLMs across diverse environmental contexts.