Leveraging LLMs for Environmental Complexity: Structured Fine-Tuning Data Sets and Deployment Strategies.

Area of Science:

• Environmental Science
• Artificial Intelligence
• Data Science

Background:

• Generative artificial intelligence (AI), particularly large language models (LLMs), has potential to advance environmental analysis.
• Deployment challenges include limited structured domain knowledge and unclear decision-making strategies.
• Existing AI models lack adaptability in complex environmental decision contexts.

Purpose of the Study:

• To develop a China-centered environmental knowledge dataset for LLM fine-tuning and benchmarking.
• To evaluate the performance trade-offs between fine-tuned and generalist LLMs in environmental decision-making.
• To propose a layered deployment strategy for LLMs in environmental intelligence.

Main Methods:

• Construction of a textbook-based, hierarchically organized environmental knowledge dataset.
• Fine-tuning LLMs on the specialized dataset for standardized environmental tasks.
• Benchmarking fine-tuned models against state-of-the-art generalist models in agentic workflows and decision tasks.
• Evaluation of model performance in terms of precision, response efficiency, adaptability, and system-level sustainability.

Main Results:

• Fine-tuned models showed modest improvements in precision (+1%) and efficiency (+52%) on standardized tasks but limited adaptability (-3%) in agentic workflows.
• Generalist models outperformed in system-level sustainability and interdisciplinary tasks (+10%), demonstrating superior cross-domain reasoning and tool integration.
• A trade-off exists between specialized fine-tuning and broad adaptability for LLMs in environmental applications.

Conclusions:

• A layered LLM deployment strategy is recommended for environmental intelligence.
• Selective fine-tuning is suitable for stable, regulatory, and verification tasks.
• Agentic workflows with generalist models are effective for dynamic, data-intensive, and interdisciplinary decision-making.
• The study provides a reusable dataset and a framework for deploying LLMs as environmental decision-support tools.