Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/302109 
Year of Publication: 
2024
Citation: 
[Journal:] Global Environmental Change [ISSN:] 1872-9495 [Volume:] 88 [Article No.:] 102920 [Publisher:] Elsevier [Place:] Amsterdam [Year:] 2024 [Pages:] 1-12
Publisher: 
Elsevier, Amsterdam
Abstract: 
Projecting migration is challenging, due to the context-specific and discontinuous relations between migration and the socioeconomic and environmental conditions that drive this process. Here, we investigate the usefulness of Machine Learning (ML) Random Forest (RF) models to develop three net migration scenarios in South Asia by 2050 based on historical patterns (2001–2019). The model for the direction of net migration reaches an accuracy of 75%, while the model for the magnitude of migration in percentage reaches an R² value of 0.44. The variable importance is similar for both models: temperature and built-up land are of primary importance for explaining net migration, aligning with previous research. In all scenarios we find hotspots of in-migration North-western India and hotspots of out-migration in eastern and northern India, parts of Nepal and Sri Lanka, but with disparities across scenarios in other areas. These disparities underscore the challenge of obtaining consistent results from different approaches, which complicates drawing firm conclusions about future migration trajectories. We argue that the application of multi-model approaches is a useful avenue to project future migration dynamics, and to gain insights into the uncertainty and range of plausible outcomes of these processes.
Subjects: 
environmental change
climate change
migration
South Asia
machine-learning
scenario projections
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article
Document Version: 
Published Version

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