Environmental scientists face a growing problem: there is more research available than ever before, but much of the useful information remains scattered across papers, tables, figures, and inconsistent reporting formats. A new review suggests that large language models, or LLMs, could help researchers organize this fragmented evidence more efficiently, provided that their outputs remain traceable, validated, and subject to expert review.
Published in Artificial Intelligence & Environment, the review examines how LLMs can support environmental research through three connected tasks: systematic literature screening, relational knowledge mining, and quantitative data extraction. Together, these approaches could help transform unstructured scientific literature into structured information that can be reused in databases, models, risk assessments, and environmental decision-making.
"Large language models have the potential to reduce the enormous amount of manual work required to organize environmental evidence, but their greatest value lies in assisting experts rather than replacing them," said corresponding author Jing Guo of Nanjing University. "Reliable applications need clear task definitions, structured constraints, traceable evidence, and human verification."
One promising application is literature screening. Environmental reviews may involve thousands of papers spanning pollutants, ecosystems, exposure pathways, toxicological effects, and treatment technologies. LLMs can interpret context, recognize synonyms and implicit expressions, and apply multiple inclusion criteria at once. In one study highlighted by the review, GPT-4 achieved 100% recall when screening nearly 12,000 records and reduced manual screening time by about 50% at the corresponding threshold. However, the authors emphasize that final decisions about whether studies should be included should remain with human reviewers.
LLMs may also help researchers uncover relationships buried across scientific texts. These include connections between pollution sources and exposure, links between chemicals and toxicological outcomes, and relationships among treatment conditions and pollutant removal performance. Instead of simply identifying individual terms, models can help organize these connections into relationship networks, knowledge graphs, and other structured resources.
A third opportunity is quantitative data extraction. Environmental papers contain enormous numbers of concentrations, toxicity endpoints, degradation rates, removal efficiencies, and experimental conditions. LLMs can help reconstruct these scattered values into complete records that preserve the links among chemicals, conditions, measurements, units, and evidence sources. Multimodal models may even recover information from figures, although the review cautions that figure interpretation remains less reliable and still requires careful validation.
The authors stress that full automation is not the goal. LLM outputs can contain incorrect numbers, mismatched units, unsupported relationships, or missing context. A more practical workflow combines model-based extraction with rule-based validation and expert review.
Looking ahead, the review calls for task-specific benchmarks, stronger integration with environmental databases and ontologies, improved source traceability, and standardized evaluation methods. The authors conclude that LLMs are most useful as collaborative tools that help scientists navigate rapidly expanding literature while preserving scientific quality and accountability.
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Journal reference: Li Y; Guo J; Shi W. Large language models for environmental research: systematic literature screening, relational knowledge mining, and quantitative data extraction. AI Environ. 2026, 1(3): xx-xx. DOI: 10.66178/aie-0026-0019
https://www.the-newpress.com/aie/article/doi/10.66178/aie-0026-0019
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About the Journal:
Artificial Intelligence & Environment is an international multidisciplinary platform for communicating advances in fundamental and applied research on the intersection of environmental science and artificial intelligence (AI). It is dedicated to serving as an innovative, efficient and professional platform for researchers in the cross-discipline fields of earth and environmental sciences, big data science and AI around the world to deliver findings from this rapidly expanding field of science. It is a peer-reviewed, open-access journal that publishes critical review, original research, rapid communication, view-point, commentary and perspective papers.
