Causal machine learning methods for understanding land use and land cover change
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.
Authors
- Rodrigo Martins Moreira (ORCID: https://orcid.org/0000-0001-6794-6026)
- James M. Bullock (ORCID: https://orcid.org/0000-0003-0529-4020)
- Tamsin L. Woodman (ORCID: https://orcid.org/0000-0003-3992-457X)
- M. Reichstein
- Isaura Menezes
- Nicolas Apfel (ORCID: https://orcid.org/0000-0001-5771-6745)
- Ioannis N. Athanasiadis (ORCID: https://orcid.org/0000-0003-2764-0078)
- Vasileios Sitokonstantinou
- Peter Alexander
- F. Eigenbrod
- Dilli Paudel
- Gregory Duveiller
- Julian Equihua
- Simon Willcock
- Thomas Berger
Institutions
- University of Hohenheim (DE)
- Universidade Federal de Rondônia (BR)
- Helmholtz Centre for Environmental Research (DE)
- Bangor University (GB)
- Universität Innsbruck (AT)
- University of Aberdeen (GB)
- Parc Científic de la Universitat de València (ES)
- UK Centre for Ecology & Hydrology (GB)
- Max Planck Institute for Biogeochemistry (DE)
- University of Southampton (GB)
- Wageningen University & Research (NL)
- University of Edinburgh (GB)
Publication Details
- Journal
- Landscape Ecology
- Published
- 2025-12-28
- DOI
- https://doi.org/10.1007/s10980-025-02279-7
- Primary Topic
- Land Use and Ecosystem Services
- Type
- article
- Field-Weighted Citation Impact
- 0.00