Peace Research Institute Oslo, Oslo, Norway.
Department of Sociology and Political Science, Norwegian University of Science and Technology, Trondheim, Norway.
Nat Commun. 2021 Apr 6;12(1):2067. doi: 10.1038/s41467-021-22255-4.
Recent research suggests that climate variability and change significantly affect forced migration, within and across borders. Yet, migration is also informed by a range of non-climatic factors, and current assessments are impeded by a poor understanding of the relative importance of these determinants. Here, we evaluate the eligibility of climatic conditions relative to economic, political, and contextual factors for predicting bilateral asylum migration to the European Union-form of forced migration that has been causally linked to climate variability. Results from a machine-learning prediction framework reveal that drought and temperature anomalies are weak predictors of asylum migration, challenging simplistic notions of climate-driven refugee flows. Instead, core contextual characteristics shape latent migration potential whereas political violence and repression are the most powerful predictors of time-varying migration flows. Future asylum migration flows are likely to respond much more to political changes in vulnerable societies than to climate change.
最近的研究表明,气候变异性和变化显著影响了境内和跨境的被迫迁移。然而,迁移也受到一系列非气候因素的影响,目前的评估受到对这些决定因素相对重要性的理解不足的阻碍。在这里,我们评估了气候条件相对于经济、政治和背景因素在预测向欧盟的双边庇护移民方面的资格——这种形式的被迫迁移已经与气候变异性有因果关系。机器学习预测框架的结果表明,干旱和温度异常是庇护移民的弱预测因素,这挑战了气候驱动难民流动的简单观念。相反,核心背景特征塑造了潜在的迁移潜力,而政治暴力和镇压是时变迁移流的最有力预测因素。未来的庇护移民流动可能更多地受到弱势社会的政治变化的影响,而不是气候变化的影响。