Articles | Volume 16, issue 2
https://doi.org/10.5194/esd-16-423-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/esd-16-423-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Mar 2025
Research article |
| 13 Mar 2025
| 13 Mar 2025
Exploring the opportunities and challenges of using large language models to represent institutional agency in land system modelling
Yongchao Zeng
CORRESPONDING AUTHOR
Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology, 82467 Garmisch-Partenkirchen, Germany
Calum Brown
Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology, 82467 Garmisch-Partenkirchen, Germany
Highlands Rewilding Limited, The Old School House, Bunloit, Drumnadrochit IV63 6XG, UK
Joanna Raymond
Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology, 82467 Garmisch-Partenkirchen, Germany
Mohamed Byari
Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology, 82467 Garmisch-Partenkirchen, Germany
Ronja Hotz
Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology, 82467 Garmisch-Partenkirchen, Germany
Mark Rounsevell
Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology, 82467 Garmisch-Partenkirchen, Germany
Institute of Geography and Geo-ecology, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
School of Geosciences, University of Edinburgh, Drummond Street, Edinburgh EH8 9XP, UK
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Understanding environmental policy interventions is challenging due to complex institutional actor interactions. Large language models (LLMs) offer new solutions by mimicking the actors. We present InsNet-CRAFTY v1.0, a multi-LLM-agent model coupled with a land system model, simulating competing policy priorities. The model shows how LLM agents can simulate decision-making in institutional networks, highlighting both their potential and limitations in advancing land system modelling.
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Short summary
This study explores using large language models (LLMs) to simulate policy-making in land systems. We integrated LLMs into a land use model and simulated LLM-powered institutional agents steering meat production by taxation. The results show LLMs can generate boundedly rational policy-making behaviours that can hardly be modelled using conventional methods; LLMs can offer the reasoning behind policy actions. We also discussed LLMs’ potential and challenges in large-scale simulations.
This study explores using large language models (LLMs) to simulate policy-making in land...
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