Scenario projections of South Asian migration patterns amidst environmental and socioeconomic change

19978 6 107409 2 325405 document 107409 1-s2.0-S0959378024001249-main.pdf application/pdf 6957637497df09aabe70dcdbf7555013 MD5 9209481 2024-09-09 08:06:44 https://pure.iiasa.ac.at/id/eprint/19978/1/1-s2.0-S0959378024001249-main.pdf 19978 1 1 application/pdf text en public cc_by_4

1-s2.0-S0959378024001249-main.pdf

published 107415 1 325416 document 107415 indexcodes.txt text/x-c++ 4038af49393671be2a833d6d3ee354aa MD5 17273 2024-09-09 08:16:25 https://pure.iiasa.ac.at/id/eprint/19978/2/indexcodes.txt 19978 2 2 text/x-c++ other Generate index codes conversion from text to indexcodes en public

indexcodes.txt

lightbox.jpg

preview.jpg

medium.jpg

small.jpg

http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/107409 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/107409 http://eprints.org/relation/issmallThumbnailVersionOf https://pure.iiasa.ac.at/id/document/107409 archive 353 disk0/00/01/99/78 2024-09-09 08:08:01 2024-09-09 08:08:01 2024-09-09 08:08:01 article show de Bruin S. Hoch J. de Bruijn J. 8884 0000-0003-3961-6382 Hermans K. Maharjan A. Kummu M. van Vliet J. Scenario projections of South Asian migration patterns amidst environmental and socioeconomic change pub BNR WAT Climate change; Environmental change; Machine-learning; Migration; Scenario projections; South Asia 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. 2024-09 published Elsevier doi:10.1016/j.gloenvcha.2024.102920 3164 public Global Environmental Change 88 e102920 TRUE 1872-9495 no info:eu-repo/semantics/article