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

Chuke Chen¹, Nan Li^1,2, Jianchuan Qi¹

¹School of Environment, Tsinghua University, Beijing 100084, P. R. China.

Environmental Science & Technology

|January 1, 2026

Summary

This summary is machine-generated.

Generative artificial intelligence (AI) shows promise for environmental analysis. A layered AI deployment strategy, combining fine-tuned models for specific tasks and generalist models for complex decisions, offers a scalable solution.

Keywords:

agentic workflows environmental complexity fine-tuning generative artificial intelligence large language models

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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.