Research Pinpoints “Hot Spots” of Vulnerability for Children Around the World

With climate change expected to increasingly have an impact on child nutrition, new research led by University of Maryland geographers pinpoints areas of the globe where children could face the biggest risk from events like severe drought or periods of extreme rainfall.

In a study published in the Proceedings of the National Academies of Sciences (PNAS), researchers combined geolocated nutrition data from demographic and health surveys on half a million children across 53 countries with data on rainfall anomalies to map “hot spots” of climate vulnerability around the globe. The information could help policymakers prepare to support the at-risk locations in poor countries.

“Our study provides further evidence that climate change will impact already vulnerable populations, particularly children, in poor parts of the world that are contributing the least to greenhouse gas emissions,” said Matthew Cooper, a geographical sciences doctoral student and lead author on the PNAS paper.

They discovered that droughts in particular were detrimental to child nutrition and that the most vulnerable children live in areas of Africa, central Asia and the Middle East in countries that are poor, arid and politically unstable with little trade and weak government support. Children were deemed by researchers to be the most at risk in countries including Chad, Sudan, South Sudan, Eritrea, Somalia and Yemen.

In order to identify these global “hot spots,” researchers considered the factors that are known to increase both vulnerability and resilience to drought and excessive rainfall, such as diverse agricultural systems, staple crop production, international trade and effective governance.

While similar studies have been conducted at national scales, Cooper and colleagues from the International Institute for Applied Systems Analysis in Austria are the first to draw on a dataset of this magnitude—560,000 children—to examine climate-nutrition linkages. The data was processed and analyzed using the Microsoft Azure cloud computing system and supported by resources provided through a Microsoft AI for Earth grant.