Climate sensitivity analysis -- A case study from forty years of US compositional cause of death data

2026-07-05

AI Summary

This study quantifies the impact of extreme climatic factors on cause-specific mortality across diverse U.S. populations to address uncertainties in physical climate risk. Innovatively integrating compositional data analysis (CODA) into climate–mortality research, the authors employ principal component analysis (PCA) to reduce dimensionality of climate variables and construct generalized additive models (GAMs) to capture nonlinear relationships between factors such as temperature and sea-level rise and the proportional distribution of causes of death. The findings reveal that elevated temperatures and rising sea levels significantly increase the proportion of deaths attributable to hypertensive heart disease, with individuals aged 55–95 exhibiting heightened sensitivity. Moreover, the study identifies climate-driven natural hedging effects among different causes of death, offering novel insights for climate risk modeling and the design of insurance products.

Abstract

Emerging climate risks pose pressing challenges for insurers, governments, and businesses worldwide, as they face growing uncertainty in quantifying the impacts of climate change from physical risks. This paper aims to understand the impact of specific climate factors on mortality by cause for subgroups of the United States (US) population, through sensitivity and scenario analysis based on an increase in temperature and sea level extremes. We apply compositional data analysis (CODA) techniques to examine cause-specific deaths, treating the density of deaths as a set of dependent, non-negative values that sum to one. We couple CODA with principal component analysis on the climate factors as a means of dimension reduction, and fit generalised additive models to better reflect the non-linear relationships between the dimension-reduced principal scores and mortality by cause. The results of our analysis indicate climate-related factors have varying impacts by cause and ages within each cause, with more pronounced increases in the proportions of deaths from hypertensive heart disease as temperature and sea level extremes increase. Scenario analysis also indicates that an increase in temperature high extremes, sea level, and rainfall, in conjunction with a decrease in low temperature extremes, lead to offsetting impacts on the proportion of deaths between climate-related causes, but less offsetting by age within causes. Furthermore, the impacts on the proportions of death are more pronounced for ages between 55 and 95, reinforcing the observation that climate-related risks have a greater impact on older (and potentially more vulnerable) subgroups of the population. For life insurers specifically, these results are consistent with the natural hedge that arises between annuity and protection products, in light of increasing climate risk.

Problem

• climate sensitivity
• mortality by cause
• extreme temperature
• sea level rise
• compositional data

Innovation

• Compositional Data Analysis
• Principal Component Analysis
• Generalized Additive Models
• Climate Sensitivity
• Cause-specific Mortality