Nie Qiyang, Liu Tong
Graduate School of Environmental Science, Hokkaido University, Sapporo 060-0810, Japan.
Graduate School of Environmental Science, Hokkaido University, Sapporo 060-0810, Japan; Faculty of Environmental Earth Science, Hokkaido University, Sapporo 060-0810, Japan.
J Environ Manage. 2025 Feb;375:124373. doi: 10.1016/j.jenvman.2025.124373. Epub 2025 Feb 1.
This study represents the first exploration of Large Language Models (LLMs) in environmental decision-making, examining their potential benefits and limitations. To address environmental issues, we propose and compare two generalizable frameworks: an LLMs-assisted framework that leverages LLMs to augment human expertise and coding in traditional decision workflows, and an LLMs-driven framework that aims to automate optimization. Through a water engineering case study focusing on PFAS control, where Environmental Fluid Dynamics Code (EFDC) was used for water environment simulation, we illustrate how to instantiate these frameworks and assess their performance. The case study reveals generalizable insights about these frameworks. Results indicate that both frameworks can contribute to environmental decision-making optimization to varying degrees, though their applicability differs significantly when facing complex decision scenarios. The LLMs-assisted framework, which effectively regulates flow rates and achieves higher PFAS interception, demonstrates how AI can enhance human decision-making while preserving the essential role of domain expertise and professional judgment. In contrast, the LLMs-driven framework faces challenges in handling complex parameter optimization tasks due to constraints such as context window and maximum output length. The findings emphasize the advantages of integrating Artificial Intelligence (AI) with conventional environmental modeling and management practices. This work confirms a crucial principle: LLMs should enhance rather than replace human expertise, with the ultimate responsibility for environmental decisions remaining with humans. The originality of this research lies in its innovative methodological approach, which leverages process design and prompt engineering to integrate cutting-edge AI with conventional environmental models, establishing a foundation for responsible human-AI collaboration in environmental decision-making. While examining current strengths and limitations, this framework robustly generates optimized environmental decision strategies, marking a new exploration in the field.
本研究首次探索了大语言模型（LLMs）在环境决策中的应用，考察了其潜在的优势和局限性。为解决环境问题，我们提出并比较了两个可推广的框架：一个是大语言模型辅助框架，该框架利用大语言模型增强人类专业知识并应用于传统决策工作流程中的编码；另一个是大语言模型驱动框架，旨在实现优化过程的自动化。通过一个以全氟辛烷磺酸（PFAS）控制为重点的水利工程案例研究，我们展示了如何实例化这些框架并评估其性能。该案例研究揭示了关于这些框架的可推广见解。结果表明，尽管在面对复杂决策场景时它们的适用性有显著差异，但两个框架都能在不同程度上有助于环境决策优化。大语言模型辅助框架有效地调节了流速并实现了更高的PFAS截留率，展示了人工智能如何在保留领域专业知识和专业判断的关键作用的同时增强人类决策。相比之下，由于上下文窗口和最大输出长度等限制，大语言模型驱动框架在处理复杂参数优化任务时面临挑战。这些发现强调了将人工智能（AI）与传统环境建模和管理实践相结合的优势。这项工作证实了一个关键原则：大语言模型应增强而非取代人类专业知识，环境决策的最终责任仍由人类承担。本研究的创新性在于其创新的方法论方法，该方法利用流程设计和提示工程将前沿人工智能与传统环境模型相结合，为环境决策中负责任的人机协作奠定了基础。在考察当前的优势和局限性的同时，该框架有力地生成了优化的环境决策策略，标志着该领域的新探索。
