Browsing by Person "Eigenbrod, F."

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    Causal machine learning methods for understanding land use and land cover change
    (2026) Eigenbrod, F.; Alexander, Peter; Apfel, Nicolas; Athanasiadis, Ioannis N.; Berger, Thomas; Bullock, James M.; Duveiller, Gregory; Equihua, Julian; Menezes, Isaura; Moreira, Rodrigo; Paudel, Dilli; Sitokonstantinou, Vasileios; Reichstein, Markus; Willcock, Simon; Woodman, Tamsin; Eigenbrod, F.; School of Geography and Environmental Science, University of Southampton, SSO17 1BJ, Southampton, UK; Alexander, Peter; School of Geosciences, University of Edinburgh, EH9 3JW, Edinburgh, UK; Apfel, Nicolas; Department of Economics, University of Innsbruck, 6020, Innsbruck, Austria; Athanasiadis, Ioannis N.; Artificial Intelligence, Wageningen University and Research, Wageningen, The Netherlands; Berger, Thomas; Department of Land Use Economics, University of Hohenheim, 70599, Stuttgart, Germany; Bullock, James M.; UK Centre for Ecology & Hydrology, OX10 8BB, Wallingford, UK; Duveiller, Gregory; Max Planck Institute for Biogeochemistry, 07745, Jena, Germany; Equihua, Julian; Department of Computational Landscape Ecology, Helmholtz Centre for Environmental Research (UFZ), 04318, Leipzig, Germany; Menezes, Isaura; Artificial Intelligence, Wageningen University and Research, Wageningen, The Netherlands; Moreira, Rodrigo; Environmental Sciences Graduate Program, Federal University of Rondônia, 76900-726, Ji-Paraná, RO, Brazil; Paudel, Dilli; Artificial Intelligence, Wageningen University and Research, Wageningen, The Netherlands; Sitokonstantinou, Vasileios; Image Processing Laboratory, Universitat de València, Paterna, 46980, València, Spain; Reichstein, Markus; Max Planck Institute for Biogeochemistry, 07745, Jena, Germany; Willcock, Simon; School of Environmental and Natural Sciences, Bangor University, Bangor, LL57 2DG, Gwynedd, UK; Woodman, Tamsin; School of Geosciences, University of Edinburgh, EH9 3JW, Edinburgh, UK
    Context: Understanding the roles of different drivers in land use and land cover change (LULCC) is a critical research challenge. However, as LULCC is the result of complex, socio-ecological processes and is highly context dependent, achieving such understanding is difficult. This is particularly true for causal modelling approaches that are critical for effective policy formulation. Causal machine learning (ML) methods could help address this challenge, but are as yet poorly understood or applied by the LULCC community. Objectives: To provide an accessible introduction to the state of the art for causal ML methods, their limitations, and their potential applications understanding LULCC. Methods: We conducted two workshops where we identified the most promising ML methods for increasing understanding of LULCC dynamics. Results: We provide a brief overview of the challenges to causal modelling of LULCC, including a simple example, and the most relevant causal ML approaches for addressing these challenges, as well as their limitations. Conclusions: Causal ML methods hold considerable promise for improving causal modelling of LULCC. However, the complexity of LULCC dynamics mean that such methods must be combined with domain understanding and qualitative insights for effective policy design.