With the United Nations climate conference in Glasgow, the world is focused on the consequences of a climate crisis and how we can still change course.
With the United Nations climate conference in Glasgow, the world is focused on the consequences of a climate crisis and how we can still change course. Yet while climate-driven migration has been deemed a major threat in public discourse and academic research, comprehensive studies that take into account both environmental and social factors globally have been scarce. Now, with the help of machine learning, a research team led by Aalto University has drawn a clearer picture of the factors involved in migration for 178 countries.
Traditionally, research on climate change-related migration has taken a linear approach, concentrating on whether or not environmental stress is directly related to migration—typically for one country or set of countries at a time. Researchers have known that social factors must also play a role, but studying both at a global scale, with all necessary information for all countries and sub-regions, has been a major challenge.
‘Perhaps the most surprising finding from our study is that, when we look at the overall picture, social factors are more important than environmental factors in explaining migration. And regardless of the level of income involved, gross national income was the key factor in explaining net-migration in half of countries,’ says Venla Niva, a doctoral student at Aalto University and lead author of the study published in Environmental Research Letters.
Read more at Aalto University
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