Articles | Volume 16, issue 2
https://doi.org/10.5194/esd-16-423-2025
https://doi.org/10.5194/esd-16-423-2025
Research article
 | 
13 Mar 2025
Research article |  | 13 Mar 2025

Exploring the opportunities and challenges of using large language models to represent institutional agency in land system modelling

Yongchao Zeng, Calum Brown, Joanna Raymond, Mohamed Byari, Ronja Hotz, and Mark Rounsevell

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Latest update: 15 Apr 2025
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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.
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