Articles | Volume 18, issue 15
https://doi.org/10.5194/gmd-18-4983-2025
© 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/gmd-18-4983-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
15 Aug 2025
Model description paper |
| 15 Aug 2025
| 15 Aug 2025
InsNet-CRAFTY v1.0: integrating institutional network dynamics powered by large language models with land use change simulation
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
Mohamed Byari
Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology, 82467 Garmisch-Partenkirchen, Germany
Joanna Raymond
Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU), Karlsruhe Institute of Technology, 82467 Garmisch-Partenkirchen, Germany
Thomas Schmitt
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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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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Grasslands shape the landscape in many parts of the world and serve as the main source of fodder for livestock. There is a lack of comprehensive data on grassland yield, though highly valuable for authorities and research. By applying three approaches to estimate grassland yields, namely a satellite data model, a biogeochemical model and a field measurements approach, we provide annual grassland yield maps for the Ammer region in 2019 and highlight potentials and limitations of the approaches.
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The variety of human and natural processes in the land system can be modelled in many different ways. However, little is known about how and why basic model assumptions affect model results. We compared two models that represent land use in completely distinct ways and found several results that differed greatly. We identify the main assumptions that caused these differences and therefore key issues that need to be addressed for more robust model development.
Sam S. Rabin, Peter Alexander, Roslyn Henry, Peter Anthoni, Thomas A. M. Pugh, Mark Rounsevell, and Almut Arneth
Earth Syst. Dynam., 11, 357–376, https://doi.org/10.5194/esd-11-357-2020, https://doi.org/10.5194/esd-11-357-2020, 2020
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We modeled how agricultural performance and demand will shift as a result of climate change and population growth, and how the resulting adaptations will affect aspects of the Earth system upon which humanity depends. We found that the impacts of land use and management can have stronger impacts than climate change on some such
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Earth Syst. Dynam., 10, 809–845, https://doi.org/10.5194/esd-10-809-2019, https://doi.org/10.5194/esd-10-809-2019, 2019
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Concerns are growing that human activity will lead to social and environmental breakdown, but it is hard to anticipate when and where such breakdowns might occur. We developed a new model of land management decisions in Europe to explore possible future changes and found that decision-making that takes into account social and environmental conditions can produce unexpected outcomes that include societal breakdown in challenging conditions.
Derek T. Robinson, Alan Di Vittorio, Peter Alexander, Almut Arneth, C. Michael Barton, Daniel G. Brown, Albert Kettner, Carsten Lemmen, Brian C. O'Neill, Marco Janssen, Thomas A. M. Pugh, Sam S. Rabin, Mark Rounsevell, James P. Syvitski, Isaac Ullah, and Peter H. Verburg
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Understanding the complexity behind the rapid use of Earth’s resources requires modelling approaches that couple human and natural systems. We propose a framework that comprises the configuration, frequency of interaction, and coordination of communication between models along with eight lessons as guidelines to increase the success of coupled human–natural systems modelling initiatives. We also suggest a way to expedite model coupling and increase the longevity and interoperability of models.
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Carbon Monitor Power-Simulators (CMP-SIM v1.0) across countries: a data-driven approach to simulate daily power generation
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Geosci. Model Dev., 18, 4805–4822, https://doi.org/10.5194/gmd-18-4805-2025, https://doi.org/10.5194/gmd-18-4805-2025, 2025
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Tommi Ekholm, Nadine-Cyra Freistetter, Aapo Rautiainen, and Laura Thölix
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Muhammad Awais, Adriano Vinca, Edward Byers, Stefan Frank, Oliver Fricko, Esther Boere, Peter Burek, Miguel Poblete Cazenave, Paul Natsuo Kishimoto, Alessio Mastrucci, Yusuke Satoh, Amanda Palazzo, Madeleine McPherson, Keywan Riahi, and Volker Krey
Geosci. Model Dev., 17, 2447–2469, https://doi.org/10.5194/gmd-17-2447-2024, https://doi.org/10.5194/gmd-17-2447-2024, 2024
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Utkan M. Durdağ
Geosci. Model Dev., 17, 2187–2196, https://doi.org/10.5194/gmd-17-2187-2024, https://doi.org/10.5194/gmd-17-2187-2024, 2024
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Michaja Pehl, Felix Schreyer, and Gunnar Luderer
Geosci. Model Dev., 17, 2015–2038, https://doi.org/10.5194/gmd-17-2015-2024, https://doi.org/10.5194/gmd-17-2015-2024, 2024
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We extend the REMIND model (used to investigate climate mitigation strategies) by an industry module that represents cement, chemical, steel, and other industries. We also present a method for deriving scenarios of industry subsector activity and energy demand, consistent with established socioeconomic scenarios, allowing us to investigate the different climate change mitigation challenges and strategies in industry subsectors in the context of the entire energy–economy–climate system.
Chen Chris Gong, Falko Ueckerdt, Robert Pietzcker, Adrian Odenweller, Wolf-Peter Schill, Martin Kittel, and Gunnar Luderer
Geosci. Model Dev., 16, 4977–5033, https://doi.org/10.5194/gmd-16-4977-2023, https://doi.org/10.5194/gmd-16-4977-2023, 2023
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To mitigate climate change, the global economy must drastically reduce its greenhouse gas emissions, for which the power sector plays a key role. Until now, long-term models which simulate this transformation cannot always accurately depict the power sector due to a lack of resolution. Our work bridges this gap by linking a long-term model to an hourly model. The result is an almost full harmonization of the models in generating a power sector mix until 2100 with hourly resolution.
David R. Morrow, Raphael Apeaning, and Garrett Guard
Geosci. Model Dev., 16, 1105–1118, https://doi.org/10.5194/gmd-16-1105-2023, https://doi.org/10.5194/gmd-16-1105-2023, 2023
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GCAM-CDR is a variant of the Global Change Analysis Model that makes it easier to study the roles that carbon dioxide removal (CDR) might play in climate policy. Building on GCAM 5.4, GCAM-CDR adds several extra technologies to permanently remove carbon dioxide from the air and enables users to simulate a wider range of CDR-related policies and controls.
Jarmo S. Kikstra, Zebedee R. J. Nicholls, Christopher J. Smith, Jared Lewis, Robin D. Lamboll, Edward Byers, Marit Sandstad, Malte Meinshausen, Matthew J. Gidden, Joeri Rogelj, Elmar Kriegler, Glen P. Peters, Jan S. Fuglestvedt, Ragnhild B. Skeie, Bjørn H. Samset, Laura Wienpahl, Detlef P. van Vuuren, Kaj-Ivar van der Wijst, Alaa Al Khourdajie, Piers M. Forster, Andy Reisinger, Roberto Schaeffer, and Keywan Riahi
Geosci. Model Dev., 15, 9075–9109, https://doi.org/10.5194/gmd-15-9075-2022, https://doi.org/10.5194/gmd-15-9075-2022, 2022
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Assessing hundreds or thousands of emission scenarios in terms of their global mean temperature implications requires standardised procedures of infilling, harmonisation, and probabilistic temperature assessments. We here present the open-source
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Théo Le Guenedal, Philippe Drobinski, and Peter Tankov
Geosci. Model Dev., 15, 8001–8039, https://doi.org/10.5194/gmd-15-8001-2022, https://doi.org/10.5194/gmd-15-8001-2022, 2022
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The CATHERINA model produces simulations of cyclone-related annualized damage costs at a country level from climate data and open-source socioeconomic indicators. The framework couples statistical and physical modeling of tropical cyclones to bridge the gap between general circulation and integrated assessment models providing a precise description of tropical-cyclone-related damages.
William Atkinson, Sebastian D. Eastham, Y.-H. Henry Chen, Jennifer Morris, Sergey Paltsev, C. Adam Schlosser, and Noelle E. Selin
Geosci. Model Dev., 15, 7767–7789, https://doi.org/10.5194/gmd-15-7767-2022, https://doi.org/10.5194/gmd-15-7767-2022, 2022
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Understanding policy effects on human-caused air pollutant emissions is key for assessing related health impacts. We develop a flexible scenario tool that combines updated emissions data sets, long-term economic modeling, and comprehensive technology pathways to clarify the impacts of climate and air quality policies. Results show the importance of both policy levers in the future to prevent long-term emission increases from offsetting near-term air quality improvements from existing policies.
Chengyong Wu, Kelong Chen, Chongyi E, Xiaoni You, Dongcai He, Liangbai Hu, Baokang Liu, Runke Wang, Yaya Shi, Chengxiu Li, and Fumei Liu
Geosci. Model Dev., 15, 6919–6933, https://doi.org/10.5194/gmd-15-6919-2022, https://doi.org/10.5194/gmd-15-6919-2022, 2022
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The traditional Carnegie–Ames–Stanford Approach (CASA) model driven by multisource data such as meteorology, soil, and remote sensing (RS) has notable disadvantages. We drove the CASA using RS data and conducted a case study of the Qinghai Lake basin alpine grassland. The simulated result is similar to published and measured net primary productivity (NPP). It may provide a reference for simulating vegetation NPP to satisfy the requirements of accounting carbon stocks and other applications.
Rui Ma, Jingfeng Xiao, Shunlin Liang, Han Ma, Tao He, Da Guo, Xiaobang Liu, and Haibo Lu
Geosci. Model Dev., 15, 6637–6657, https://doi.org/10.5194/gmd-15-6637-2022, https://doi.org/10.5194/gmd-15-6637-2022, 2022
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Parameter optimization can improve the accuracy of modeled carbon fluxes. Few studies conducted pixel-level parameterization because it requires a high computational cost. Our paper used high-quality spatial products to optimize parameters at the pixel level, and also used the machine learning method to improve the speed of optimization. The results showed that there was significant spatial variability of parameters and we also improved the spatial pattern of carbon fluxes.
Núria Pérez-Zanón, Louis-Philippe Caron, Silvia Terzago, Bert Van Schaeybroeck, Llorenç Lledó, Nicolau Manubens, Emmanuel Roulin, M. Carmen Alvarez-Castro, Lauriane Batté, Pierre-Antoine Bretonnière, Susana Corti, Carlos Delgado-Torres, Marta Domínguez, Federico Fabiano, Ignazio Giuntoli, Jost von Hardenberg, Eroteida Sánchez-García, Verónica Torralba, and Deborah Verfaillie
Geosci. Model Dev., 15, 6115–6142, https://doi.org/10.5194/gmd-15-6115-2022, https://doi.org/10.5194/gmd-15-6115-2022, 2022
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CSTools (short for Climate Service Tools) is an R package that contains process-based methods for climate forecast calibration, bias correction, statistical and stochastic downscaling, optimal forecast combination, and multivariate verification, as well as basic and advanced tools to obtain tailored products. In addition to describing the structure and methods in the package, we also present three use cases to illustrate the seasonal climate forecast post-processing for specific purposes.
Olexandr Balyk, James Glynn, Vahid Aryanpur, Ankita Gaur, Jason McGuire, Andrew Smith, Xiufeng Yue, and Hannah Daly
Geosci. Model Dev., 15, 4991–5019, https://doi.org/10.5194/gmd-15-4991-2022, https://doi.org/10.5194/gmd-15-4991-2022, 2022
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Ireland has significantly increased its climate mitigation ambition, with a recent commitment to reduce greenhouse gases by an average of 7 % yr-1 in the period to 2030 and a net-zero target for 2050. This article describes the TIMES-Ireland model (TIM) developed to inform Ireland's energy system decarbonisation challenge. The paper also outlines a priority list of future model developments to better meet the challenge, taking into account equity, cost-effectiveness, and technical feasibility.
Haiyan Jiang, Slobodan P. Simonovic, and Zhongbo Yu
Geosci. Model Dev., 15, 4503–4528, https://doi.org/10.5194/gmd-15-4503-2022, https://doi.org/10.5194/gmd-15-4503-2022, 2022
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The Yangtze Economic Belt is one of the most dynamic regions of China. The fast urbanization and strong economic growth in the region pose severe challenges for its sustainable development. To improve our understanding of the interactions among coupled human–natural systems in the Belt and to provide the foundation for science-based policy-making for the sustainable development of the Belt, we developed an integrated system-dynamics-based simulation model (ANEMI_Yangtze) for the Belt.
Thi Lan Anh Dinh and Filipe Aires
Geosci. Model Dev., 15, 3519–3535, https://doi.org/10.5194/gmd-15-3519-2022, https://doi.org/10.5194/gmd-15-3519-2022, 2022
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We proposed the leave-two-out method (i.e. one particular implementation of the nested cross-validation) to determine the optimal statistical crop model (using the validation dataset) and estimate its true generalization ability (using the testing dataset). This approach is applied to two examples (robusta coffee in Cu M'gar and grain maize in France). The results suggested that the simple models are more suitable in crop modelling where a limited number of samples is available.
Matthew Binsted, Gokul Iyer, Pralit Patel, Neal T. Graham, Yang Ou, Zarrar Khan, Nazar Kholod, Kanishka Narayan, Mohamad Hejazi, Son Kim, Katherine Calvin, and Marshall Wise
Geosci. Model Dev., 15, 2533–2559, https://doi.org/10.5194/gmd-15-2533-2022, https://doi.org/10.5194/gmd-15-2533-2022, 2022
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GCAM-USA v5.3_water_dispatch is an open-source model that represents key interactions across economic, energy, water, and land systems in a global framework, with subnational detail in the United States. GCAM-USA can be used to explore future changes in demand for (and production of) energy, water, and crops at the state and regional level in the US. This paper describes GCAM-USA and provides four illustrative scenarios to demonstrate the model's capabilities and potential applications.
Colm Duffy, Remi Prudhomme, Brian Duffy, James Gibbons, Cathal O'Donoghue, Mary Ryan, and David Styles
Geosci. Model Dev., 15, 2239–2264, https://doi.org/10.5194/gmd-15-2239-2022, https://doi.org/10.5194/gmd-15-2239-2022, 2022
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The GOBLIN (General Overview for a Backcasting approach of Livestock INtensification) model is a new high-resolution integrated
bottom-upbiophysical land use model capable of identifying broad pathways towards climate neutrality in the agriculture, forestry, and other land use (AFOLU) sector. The model is intended to bridge the gap between hindsight representations of national emissions and much larger globally integrated assessment models.
Samuel Lüthi, Gabriela Aznar-Siguan, Christopher Fairless, and David N. Bresch
Geosci. Model Dev., 14, 7175–7187, https://doi.org/10.5194/gmd-14-7175-2021, https://doi.org/10.5194/gmd-14-7175-2021, 2021
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In light of the dramatic increase in economic impacts due to wildfires, the need for modelling impacts of wildfire damage is ever increasing. Insurance companies, households, humanitarian organisations and governmental authorities are worried by climate risks. In this study we present an approach to modelling wildfire impacts using the open-source modelling platform CLIMADA. All input data are free, public and globally available, ensuring applicability in data-scarce regions of the Global South.
Lavinia Baumstark, Nico Bauer, Falk Benke, Christoph Bertram, Stephen Bi, Chen Chris Gong, Jan Philipp Dietrich, Alois Dirnaichner, Anastasis Giannousakis, Jérôme Hilaire, David Klein, Johannes Koch, Marian Leimbach, Antoine Levesque, Silvia Madeddu, Aman Malik, Anne Merfort, Leon Merfort, Adrian Odenweller, Michaja Pehl, Robert C. Pietzcker, Franziska Piontek, Sebastian Rauner, Renato Rodrigues, Marianna Rottoli, Felix Schreyer, Anselm Schultes, Bjoern Soergel, Dominika Soergel, Jessica Strefler, Falko Ueckerdt, Elmar Kriegler, and Gunnar Luderer
Geosci. Model Dev., 14, 6571–6603, https://doi.org/10.5194/gmd-14-6571-2021, https://doi.org/10.5194/gmd-14-6571-2021, 2021
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This paper presents the new and open-source version 2.1 of the REgional Model of INvestments and Development (REMIND) with the aim of improving code documentation and transparency. REMIND is an integrated assessment model (IAM) of the energy-economic system. By answering questions like
Can the world keep global warming below 2 °C?and, if so,
Under what socio-economic conditions and applying what technological options?, it is the goal of REMIND to explore consistent transformation pathways.
Phillip D. Alderman
Geosci. Model Dev., 14, 6541–6569, https://doi.org/10.5194/gmd-14-6541-2021, https://doi.org/10.5194/gmd-14-6541-2021, 2021
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This paper documents a framework for accessing crop model input data directly from spatially referenced file formats and running simulations in parallel across a geographic region using the Decision Support System for Agrotechnology Transfer Cropping Systems Model (a widely used crop model system). The framework greatly reduced the execution time when compared to running the standard version of the model.
Abhijeet Mishra, Florian Humpenöder, Jan Philipp Dietrich, Benjamin Leon Bodirsky, Brent Sohngen, Christopher P. O. Reyer, Hermann Lotze-Campen, and Alexander Popp
Geosci. Model Dev., 14, 6467–6494, https://doi.org/10.5194/gmd-14-6467-2021, https://doi.org/10.5194/gmd-14-6467-2021, 2021
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Timber plantations are an increasingly important source of roundwood production, next to harvest from natural forests. However, timber plantations are currently underrepresented in global land-use models. Here, we include timber production and plantations in the MAgPIE modeling framework. This allows one to capture the competition for land between agriculture and forestry. We show that increasing timber plantations in the coming decades partly compete with cropland for limited land resources.
Cited articles
Acs, S., Hanley, N., Dallimer, M., Gaston, K. J., Robertson, P., Wilson, P., and Armsworth, P. R.: The effect of decoupling on marginal agricultural systems: implications for farm incomes, land use and upland ecology, Land Use Policy, 27, 550–563, https://doi.org/10.1016/j.landusepol.2009.07.009, 2010.
Ariti, A. T., van Vliet, J., and Verburg, P. H.: The role of institutional actors and their interactions in the land use policy making process in Ethiopia, J. Environ. Manage., 237, 235–246, https://doi.org/10.1016/j.jenvman.2019.02.059, 2019.
Arnott, R., De Palma, A., and Lindsey, R.: Information and time-of-usage decisions in the bottleneck model with stochastic capacity and demand, Eur. Econ. Rev., 43, 525–548, https://doi.org/10.1016/S0014-2921(98)00013-0, 1999.
Bainbridge, A., Troppe, T., and Bartley, J.: Responding to research evidence in Parliament: A case study on selective education policy, Rev. Educ., 10, e3335, https://doi.org/10.1002/rev3.3335, 2022.
Banerjee, S., Agarwal, A., and Singla, S.: Llms will always hallucinate, and we need to live with this, arXiv [preprint], https://doi.org/arXiv:2409.05746, 2024.
Barnett, M. N. and Finnemore, M.: The politics, power, and pathologies of international organizations, Int. Organ., 53, 699–732, https://doi.org/10.1162/002081899551048, 1999.
Broussard, A., Dahdouh-Guebas, F., and Hugé, J.: Diversity of perspectives in biodiversity conservation: A case study of port land use in Antwerp and Rotterdam, J. Environ. Manage., 341, 117937, https://doi.org/10.1016/j.jenvman.2023.117937, 2023.
Brown, C., Seo, B., and Rounsevell, M.: Societal breakdown as an emergent property of large-scale behavioural models of land use change, Earth Syst. Dynam., 10, 809–845, https://doi.org/10.5194/esd-10-809-2019, 2019.
Cairney, P., Heikkila, T., and Wood, M.: Making policy in a complex world, Cambridge University Press, https://doi.org/10.1017/9781108679053, 2019.
Callander, S.: Searching for good policies, Am. Polit. Sci. Rev., 105, 643–662, https://www.jstor.org/stable/23275345, 2011.
Calvin, K. V., Snyder, A., Zhao, X., and Wise, M.: Modeling land use and land cover change: using a hindcast to estimate economic parameters in gcamland v2.0, Geosci. Model Dev., 15, 429–447, https://doi.org/10.5194/gmd-15-429-2022, 2022.
Chen, M., Vernon, C. R., Huang, M., Calvin, K. V., and Kraucunas, I. P.: Calibration and analysis of the uncertainty in downscaling global land use and land cover projections from GCAM using Demeter (v1.0.0), Geosci. Model Dev., 12, 1753–1764, https://doi.org/10.5194/gmd-12-1753-2019, 2019.
Cosma, S., Rimo, G., and Cosma, S.: Conservation finance: What are we not doing? A review and research agenda, J. Environ. Manage., J. Environ. Manage., 336, 1–14, https://doi.org/10.1016/j.jenvman.2023.117649, 2023.
Dakin, R. and Ryder, T. B.: Reciprocity and behavioral heterogeneity govern the stability of social networks, P. Natl. Acad. Sci., 117, 2993–2999, https://doi.org/10.1073/pnas.1913284117, 2020.
Daugbjerg, C. and Feindt, P. H.: Post-exceptionalism in public policy: transforming food and agricultural policy, J. Eur. Public Pol., 24, 1565–1584, https://doi.org/10.1080/13501763.2017.1334081, 2017.
Davidson, M. R., Filatova, T., Peng, W., Verbeek, L., and Kucuksayacigil, F.: Simulating institutional heterogeneity in sustainability science, P. Natl. Acad. Sci., 121, e2215674121, https://doi.org/10.1073/pnas.2215674121, 2024.
Díez-Echavarría, L., Villegas-Palacio, C., Arango-Aramburo, S., and Ezzine-de-Blas, D.: Decoupling in governance: the land governance network in a region of the Colombian Andes, Land Use Policy, 133, 106880, https://doi.org/10.1016/j.landusepol.2023.106880, 2023.
Djekic, I.: Environmental Impact of Meat Industry – Current Status and Future Perspectives, Procedia Food Sci., 5, 61–64, https://doi.org/10.1016/j.profoo.2015.09.025, 2015.
Duffy, C., Prudhomme, R., Duffy, B., Gibbons, J., O'Donoghue, C., Ryan, M., and Styles, D.: GOBLIN version 1.0: a land balance model to identify national agriculture and land use pathways to climate neutrality via backcasting, Geosci. Model Dev., 15, 2239–2264, https://doi.org/10.5194/gmd-15-2239-2022, 2022.
Ekholm, T., Freistetter, N.-C., Rautiainen, A., and Thölix, L.: CLASH – Climate-responsive Land Allocation model with carbon Storage and Harvests, Geosci. Model Dev., 17, 3041–3062, https://doi.org/10.5194/gmd-17-3041-2024, 2024.
Fan, W., Ding, Y., Ning, L., Wang, S., Li, H., Yin, D., Chua, T.-S., and Li, Q.: A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models, arXiv [preprint], https://doi.org/10.48550/arXiv.2405.06211, 2024.
Feindt, P. H.: Policy-Learning and Environmental Policy Integration in the Common Agricultural Policy, 1973–2003, Public Admin., 88, 296–314, https://doi.org/10.1111/j.1467-9299.2010.01833.x, 2010.
Fischer, M., Ingold, K., Sciarini, P., and Varone, F.: Dealing with bad guys: Actor- and process-level determinants of the “devil shift” in policy making, J. Public Policy, 36, 309–334, https://doi.org/10.1017/S0143814X15000021, 2016.
Fischer, P., Schulz-Hardt, S., and Frey, D.: Selective exposure and information quantity: how different information quantities moderate decision makers' preference for consistent and inconsistent information, J. Pers. Soc. Psychol., 94, 231, https://doi.org/10.1037/0022-3514.94.2.94.2.231, 2008.
Gallegos, I. O., Rossi, R. A., Barrow, J., Tanjim, M. M., Kim, S., Dernoncourt, F., Yu, T., Zhang, R., and Ahmed, N. K.: Bias and fairness in large language models: A survey, Comput. Linguist., 50, 1097–1179, 2024.
Gigerenzer, G. and Goldstein, D. G.: Reasoning the fast and frugal way: models of bounded rationality, Psychol. Rev., 103, 650–669, https://doi.org/10.1037/0033-295X.103.4.650, 1996.
González, V. B.: Modelling adaptation strategies for Swedish forestry under climate and global change, University of Edinburgh, http://hdl.handle.net/1842/25380 (last access: 7 June 2024), 2016.
Groq: Groq – AI Acceleration Platform, https://groq.com/, last access: 22 August 2024.
Guo, J., Li, F. Y., Tuvshintogtokh, I., Niu, J., Li, H., Shen, B., and Wang, Y.: Past dynamics and future prediction of the impacts of land use cover change and climate change on landscape ecological risk across the Mongolian plateau, J. Environ. Manage., 355, 120365, https://doi.org/10.1016/j.jenvman.2024.120365, 2024.
Hallerberg, M. and Von Hagen, J.: Electoral institutions, cabinet negotiations, and budget deficits in the European Union, National Bureau of Economic Research, Cambridge, Mass., USA, https://doi.org/10.3386/w6341, 1997.
He, J., Rungta, M., Koleczek, D., Sekhon, A., Wang, F. X., and Hasan, S.: Does Prompt Formatting Have Any Impact on LLM Performance?, arXiv [preprint], https://doi.org/10.48550/arXiv.2411.10541, 2024.
Henle, K., Alard, D., Clitherow, J.,Cobb, P., Firbank,L., Kull, T., McCracken, D., Moritz, R., Niemelä, J., Rebane, M., Wascher, D., Watt, A., and Young, J.: Identifying and managing the conflicts between agriculture and biodiversity conservation in Europe – A review, Agr. Ecosyst. Environ., 124, 60–71, https://doi.org/10.1016/j.agee.2007.09.005, 2008.
Holzhauer, S., Brown, C., and Rounsevell, M.: Modelling dynamic effects of multi-scale institutions on land use change, Reg. Environ. Change, 19, 733–746, https://doi.org/10.1007/s10113-018-1424-5, 2019.
Hong, S., Zhuge, M., Chen, J., Zheng, X., Cheng, Y., Zhang, C., Wang, J., Wang, Z., Yau, S. K. S., Lin, Z., Zhou, L., Ran, C., Xiao, L., Wu, C., and Schmidhuber, J.: MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework, arXiv [preprint], https://doi.org/10.48550/arXiv.2308.00352,2023.
IEC 61131-7: IEC 61131-7, https://plcopen.org/iec-61131-7, last access: 22 August 2024.
Ji, Z., Yu, T., Xu, Y., Lee, N., Ishii, E., and Fung, P.: Towards mitigating LLM hallucination via self reflection, in: Findings of the Association for Computational Linguistics: EMNLP 2023, https://doi.org/10.18653/v1/2023.findings-emnlp.123, 1827–1843, 2023.
Jones, B. D.: Bounded Rationality and Political Science: Lessons from Public Administration and Public Policy, J. Publ. Adm. Res. Theory, 13, 395–412, https://doi.org/10.1093/jpart/mug028, 2003.
Kimmich, C., Ehlers, M.-H., Kellner, E., Oberlack, C., Thiel, A., and Villamayor-Tomas, S.: Networks of action situations in social–ecological systems: current approaches and potential futures, Sustain. Sci., 18, 1–10, https://doi.org/10.1007/s11625-022-01278-w, 2023.
LangChain: LangChain, https://python.langchain.com/v0.1/docs/get_started/introduction/, last access: 9 May 2024.
LangGraph: LangGraph Introduction, https://www.langchain.com/langgraph, last access: 8 August 2025.
Lawrence, A., Houghton, J., Thomas, J., and Weldon, P.: Where Is the Evidence? Realising the Value of Grey Literature for Public Policy and Practice, Discuss. Pap., University of Nebraska-Lincoln, https://digitalcommons.unl.edu/scholcom (last access: 6 May 2024), 2014.
Lee, J., Kim, S., and Lee, J.: Public vs. Public: Balancing the Competing Public Values of Participatory Budgeting, Public Adm. Q., 46, 39–66, https://doi.org/10.37808/paq.46.1.3, 2022.
Levy, M., Alon, J., and Yoav, G.: Same task, more tokens: the impact of input length on the reasoning performance of large language models, arXiv [preprint], https://doi.org/10.48550/arXiv.2402.14848, 2024.
Llama: Meta-Llama-3-70B, https://www.llama.com/, last access: 22 August 2024.
Li, X., Wang, S., Zeng, S., Wu, Y., and Yang, Y.: A survey on LLM-based multi-agent systems: workflow, infrastructure, and challenges, Vicinagearth, 1, 9, https://doi.org/10.1007/s44336-024-00009-2, 2024.
Li, Y., Krishnamurthy, R., Raghavan, S., Vaithyanathan, S., and Jagadish, H. V.: Regular expression learning for information extraction. In Proceedings of the 2008 conference on empirical methods in natural language processing, 21–30, https://doi.org/10.5555/1613715.1613719, 2008.
Lindblom, C. E.: The science of muddling through, Read. Manag. Psychol., 4, 117–131, https://doi.org/10.2307/973677, 1989.
Liu, Z.: Cultural Bias in Large Language Models: A Comprehensive Analysis and Mitigation Strategies, J. Transcult. Commun., 3, 224–244, https://doi.org/10.1515/jtc-2023-0019, 2024.
Liu, Z., Yao, W., Zhang, J., Yang, L., Liu, Z., Tan, J., Choubey, P. K., Lan, T., Wu, J., Wang, H., Heinecke, S., Xiong, C., and Savarese, S.: AgentLite: A Lightweight Library for Building and Advancing Task-Oriented LLM Agent System, arXiv [preprint], https://doi.org/10.48550/arXiv.2402.15538, 2024.
Machovina, B., Feeley, K. J., and Ripple, W. J.: Biodiversity conservation: The key is reducing meat consumption, Sci. Total Environ., 536, 419–431, https://doi.org/10.1016/j.scitotenv.2015.07.022, 2015.
Maes, J., Paracchini, M., Zulian, G., Dunbar, M., and Alkemade, R.: Synergies and trade-offs between ecosystem service supply, biodiversity, and habitat conservation status in Europe, Biol. Conserv., 155, 1–12, https://doi.org/10.1016/j.biocon.2012.06.016, 2012.
Mattison, E. H. and Norris, K.: Bridging the gaps between agricultural policy, land-use and biodiversity, Trends Ecol. Evol., 20, 610–616, https://doi.org/10.1016/j.tree.2005.08.011, 2005.
McGinnis, M. D.: Networks of Adjacent Action Situations in Polycentric Governance, Policy Stud. J., 39, 51–78, https://doi.org/10.1111/j.1541-0072.2010.00396.x, 2011.
Minaee, S., Mikolov, T., Nikzad, N., Chenaghlu, M., Socher, R., Amatriain, X., and Gao, J.: Large Language Models: A Survey, arXiv [preprint], https://doi.org/10.48550/arXiv.2402.06196, 2024.
Mizrahi, M., Kaplan, G., Malkin, D., Dror, R., Shahaf, D., and Stanovsky, G.: State of what art? A call for multi-prompt LLM evaluation, Trans. Assoc. Comput. Linguist., 12, 933–949, 2024.
Modarressi, A., Imani, A., Fayyaz, M., and Schütze, H.: Ret-llm: Towards a general read-write memory for large language models, arXiv [preprint], https://doi.org/10.48550/arXiv.2305.14322, 2023.
Murray-Rust, D., Brown, C., van Vliet, J., Alam, S. J., Robinson, D. T., Verburg, P. H., and Rounsevell, M.: Combining agent functional types, capitals and services to model land use dynamics, Environ. Modell. Softw., 59, 187–201, https://doi.org/10.1016/j.envsoft.2014.05.019, 2014.
Nair, S. and Howlett, M.: Policy myopia as a source of policy failure: adaptation and policy learning under deep uncertainty, Policy Polit., 45, 103–118, https://doi.org/10.1332/030557316X14788776017743, 2017.
Neri, S. and Ropele, T.: Imperfect information, real-time data and monetary policy in the euro area, Econ. J., 122, 651–674, https://doi.org/10.1111/j.1468-0297.2011.02488.x, 2012.
OpenAI: GPT-4o Documentation, https://platform.openai.com/docs/models, last access: 22 August 2024.
Park, J. S., O'Brien, J. C., Cai, C. J., Morris, M. R., Liang, P., and Bernstein, M. S.: Generative Agents: Interactive Simulacra of Human Behavior, arXiv [preprint], https://doi.org/10.48550/arXiv.2304.03442, 2023.
Paz, D. B., Henderson, K., and Loreau, M.: Agricultural land use and the sustainability of social-ecological systems, Ecol. Model., 437, 109312, https://doi.org/10.1016/j.ecolmodel.2020.109312, 2020.
Perkins, O., Kasoar, M., Voulgarakis, A., Smith, C., Mistry, J., and Millington, J. D. A.: A global behavioural model of human fire use and management: WHAM! v1.0, Geosci. Model Dev., 17, 3993–4016, https://doi.org/10.5194/gmd-17-3993-2024, 2024.
Perković, G., Drobnjak, A., and Botički, I.: Hallucinations in LLMs: Understanding and Addressing Challenges, in: 2024 47th MIPRO ICT and Electronics Convention (MIPRO), 20–24 May 2024, Opatija, Croatia, https://doi.org/10.1109/MIPRO60963.2024.10569238, 2024.
Petrovic, Z., Djordjevic, V., Milicevic, D., Nastasijevic, I., and Parunovic, N.: Meat Production and Consumption: Environmental Consequences, Procedia Food Sci., 5, 235–238, https://doi.org/10.1016/j.profoo.2015.09.041, 2015.
Piantadosi, S. T.: Zipf's word frequency law in natural language: A critical review and future directions, Psychon. B. Rev., 21, 1112–1130, https://doi.org/10.3758/s13423-014-0585-6, 2014.
Qi, X., Wang, R. Y., Li, J., Zhang, T., Liu, L., and He, Y.: Ensuring food security with lower environmental costs under intensive agricultural land use patterns: A case study from China, J. Environ. Manage., 213, 329–340, https://doi.org/10.1016/j.jenvman.2018.02.048, 2018.
Qian, C., Liu, W., Liu, H., Chen, N., Dang, Y., Li, J., Yang, C., Chen, W., Su, Y., Cong, X., Xu, J., Li, D., Liu, Z., and Sun, M.: ChatDev: Communicative Agents for Software Development, arXiv preprint, https://doi.org/10.48550/arXiv.2307.07924, 2024.
Radford, A., Narasimhan, K., Salimans, T., and Sutskever, I.: Improving language understanding by generative pre-training, Open AI, https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf (last access: 4 July 2024), 2018.
Rich, R. F.: Selective utilization of social science related information by federal policy-makers, Inquiry, 12, 239–245, 1975.
Simon, H. A.: Theories of bounded rationality, Decis. Organ., 1, 161–176, 1972.
Simon, H. A.: Administrative behavior, Simon and Schuster, ISBN 9780684835822, 2013.
Sinden, A.: In Defense of Absolutes: Combating the Politics of Power in Enviornmental Law, Iowa Law Rev., 90, 1405, https://ssrn.com/abstract=691344, 2004.
Sumers, T. R., Yao, S., Narasimhan, K., and Griffiths, T. L.: Cognitive Architectures for Language Agents, arXiv preprint, https://doi.org/10.48550/arXiv.2309.02427, 2024.
Tan, R., Xiong, C., and Kimmich, C.: An agent-situation-based model for networked action situations: Cap-and-trade land policies in China, Land Use Policy, 131, 106743, https://doi.org/10.1016/j.landusepol.2023.106743, 2023.
Tao, Y., Viberg, O., Baker, R. S., and Kizilcec, R. F.: Cultural bias and cultural alignment of large language models, PNAS Nexus, 3, 346, https://doi.org/10.1093/pnasnexus/pgae346, 2024.
Taubenfeld, A., Dover, Y., Reichart, R., and Goldstein, A.: Systematic biases in LLM simulations of debates, arXiv [preprint], https://doi.org/10.48550/arXiv.2402.04049, 2024.
Tesfaye, M., Kimengsi, J. N., and Giessen, L.: A policy mix for achieving ambitious goals on forest landscape restoration: Analyzing coherence and consistency in Ethiopia forest-related policy, Land Use Policy, 144, 107214, https://doi.org/10.1016/j.landusepol.2024.107214, 2024.
Tonmoy, S. M. T. I., Zaman, S. M. M., Jain, V., Rani, A., Rawte, V., Chadha, A., and Das, A.: A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models, arXiv [preprint], https://doi.org/10.48550/arXiv.2401.01313, 2024.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I.: Attention is all you need, Adv. Neural Inf. Process. Syst., 30, https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf (last access: 12 August 2024), 2017.
Waldron, A., Mooers, A. O., Miller, D. C., Nibbelink, N., Redding, D., Kuhn, T. S., Roberts, J. T., and Gittleman, J. L.: Targeting global conservation funding to limit immediate biodiversity declines, P. Natl. Acad. Sci., 110, 12144–12148, https://doi.org/10.1073/pnas.1221370110, 2013.
Wang, J., Lin, Y., Glendinning, A., and Xu, Y.: Land-use changes and land policies evolution in China's urbanization processes, Land Use Policy, 75, 375–387, https://doi.org/10.1016/j.landusepol.2018.04.011, 2018.
Wang, L., Ma, C., Feng, X., Zhang, Z., Yang, H., Zhang, J., Chen, Z., Tang, J., Chen, X., Lin, Y., Zhao, W. X., Wei, Z., and Wen, J.: A survey on large language model based autonomous agents, Front. Comput. Sci., 18, 186345, https://doi.org/10.1007/s11704-024-40231-1, 2024a.
Wang, Z. Z., Mao, J., Fried, D., and Neubig, G.: Agent workflow memory, arXiv [preprint], https://doi.org/10.48550/arXiv.2409.07429, 2024b.
Wu, Q., Bansal, G., Zhang, J., Wu, Y., Li, B., Zhu, E., Jiang, L., Zhang, X., Zhang, S., Liu, J., Awadallah, A. H., White, R. W., Burger, D., and Wang, C.: AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation, arXiv [preprint], https://doi.org/10.48550/arXiv.2308.08155, 2023.
Yami, M., van Asten, P., Hauser, M., Schut, M., and Pali, P.: Participation without negotiating: Influence of stakeholder power imbalances and engagement models on agricultural policy development in Uganda, Rural Sociol., 84, 390–415, https://doi.org/10.1111/ruso.12229, 2019.
Yang, H., Yue, S., and He, Y.: Auto-GPT for Online Decision Making: Benchmarks and Additional Opinions, arXiv [preprint], https://doi.org/10.48550/arXiv.2306.02224, 2023.
Yao, J.-Y., Ning, K.-P., Liu, Z.-H., Ning, M.-N., and Yuan, L.: LLM Lies: Hallucinations are not Bugs, but Features as Adversarial Examples, arXiv [preprint], https://doi.org/10.48550/arXiv.2310.01469, 2023.
Zeng, Y.: Data for running InsNet-CRAFTY v1.0 (1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.13944650, 2024a.
Zeng, Y.: Code for running InsNet-CRAFTY v1.0 (1.0), Zenodo [code], https://doi.org/10.5281/zenodo.13356487, 2024b.
Zeng, Y. and Byari, M.: Interactive visualization of InsNet-CRAFTY v1.0 agent textual output, https://crafty-abm.github.io/Interactive-visualization-of-InsNet-CRAFTY-v1.0-agent-textual-output/, last access: 10 March 2025.
Zeng, Y., Raymond, J., Brown, C., and Rounsevell, M.: Exploring outcome-driven policymaking on protected areas with an endogenous institutional model, J. Clean. Prod., 501, 145262, https://doi.org/10.1016/j.jclepro.2025.145262, 2025a.
Zeng, Y., Raymond, J., Brown, C., Byari, M., and Rounsevell, M.: Simulating endogenous institutional behaviour and policy implementation pathways within the land system, Ecol. Model., 501, 111032, https://doi.org/10.1016/j.ecolmodel.2025.111032, 2025b.
Zeng, Y., Brown, C., Raymond, J., Byari, M., Hotz, R., and Rounsevell, M.: Exploring the opportunities and challenges of using large language models to represent institutional agency in land system modelling, Earth Syst. Dynam., 16, 423–449, https://doi.org/10.5194/esd-16-423-2025, 2025c.
Zhang, Z., Bo, X., Ma, C., Li, R., Chen, X., Dai, Q., Zhu, J., Dong, Z., and Wen, J.-R.: A survey on the memory mechanism of large language model based agents, arXiv [preprint], https://doi.org/10.48550/arXiv.2404.13501, 2024.
Zhong, W., Guo, L., Gao, Q., Ye, H., and Wang, Y.: Memorybank: Enhancing large language models with long-term memory, in: Proceedings of the AAAI Conference on Artificial Intelligence, 38, 19724–19731, https://doi.org/10.1609/aaai.v38i17.29946, 2024.
Zhou, H., Feng, Z., Zhu, Z., Qian, J., and Mao, K.: UniBias: Unveiling and Mitigating LLM Bias through Internal Attention and FFN Manipulation, arXiv [preprint], https://doi.org/10.48550/arXiv.2405.20612, 2024.
Zhou, X., Li, G., and Liu, Z.: LLM as DBA, arXiv [preprint], https://doi.org/10.48550/arXiv.2308.05481, 2023.
Short summary
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.
Understanding environmental policy interventions is challenging due to complex institutional...
