Determinants of Heat Risk in an Aging Population: A Machine Learning Approach

Hannah Klauber () and Nicolas Koch ()
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Hannah Klauber: Mercator Research Institute on Global Commons and Climate Change (MCC)
Nicolas Koch: Mercator Research Institute on Global Commons and Climate Change (MCC)

No 15996, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: This paper identifies individual and regional risk factors for hospitalizations caused by heat within the German population over 65 years of age. Using administrative insurance claims data and a machine-learning-based regression model, we causally estimate heterogeneous heat effects and explore the geographic, morbidity, and socioeconomic correlates of heat vulnerability. Our results indicate that health effects distribute highly unevenly across the population. The most vulnerable are more likely to suffer from chronic diseases such as dementia and Alzheimer's disease and live in rural areas with more old-age poverty and less nursing care. We project that unabated climate change might bring heat to areas with particularly vulnerable populations, which could lead to a five-fold increase in heat-related hospitalization by 2100.

Keywords: heat; climate change; hospitalization; risk factors; adaptation; machine learning (search for similar items in EconPapers)
JEL-codes: I14 I18 Q51 Q54 Q58 (search for similar items in EconPapers)
Pages: 48 pages
Date: 2023-03
New Economics Papers: this item is included in nep-age, nep-big, nep-cmp, nep-env and nep-hea
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