Scenario projections of South Asian migration patterns amidst environmental and socioeconomic change
利用机器学习随机森林模型，基于2001-2019年历史数据预测南亚至2050年的净移民场景，发现温度和建成区是主要驱动因素，并指出多模型方法有助于理解未来移民的不确定性。
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.