Air Pollution and Mortality Impacts

Zhe Michelle Dong, Han Lin Shang and Aaron Bruhn
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Zhe Michelle Dong: Research School of Finance, Actuarial Studies and Statistics, Australian National University, Canberra, ACT 2601, Australia
Aaron Bruhn: Research School of Finance, Actuarial Studies and Statistics, Australian National University, Canberra, ACT 2601, Australia

Risks, 2022, vol. 10, issue 6, 1-21

Abstract: This study quantifies the air quality impact on population mortality from an actuarial perspective, considering implications to the industry through the application of findings. The study focuses on the increase in mortality from air quality changes due to extreme weather impacts. We conduct an empirical study using monthly Californian climate and mortality data from 1999 to 2019 to determine whether adding PM2.5 as a factor improves forecast excess mortality. Expected mortality is defined using the rolling five-year average of observed mortality for each county. We compared three statistical models, namely a Generalised Linear Model (GLM), a Generalised Additive Model (GAM), and an Extreme Gradient Boosting (XGB) regression model. We find including PM2.5 improves the performance of all three models and that the GAM performs the best in terms of predictive accuracy. Change points are also considered to determine whether significant events trigger changes in mortality over extended periods. Based on several identified change points, some wildfires trigger heightened excess mortality.

Keywords: climate risk; wildfires; change-point detection; Multivariate Time Series; machine learning (search for similar items in EconPapers)
JEL-codes: C G0 G1 G2 G3 K2 M2 M4 (search for similar items in EconPapers)
Date: 2022
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