The state of the art and future of climate risk insurance modeling.

This study provides a comprehensive review of the literature on climate risk insurance modeling to identify lessons learned and knowledge gaps to be addressed by future research. These models are increasingly relevant due to the rising losses attributable to climate change. Insurance models estimate risk for different perils and simulate risk-related parameters for insurance schemes, such as premiums and deductibles.

A global dataset of 7 billion individuals with socio-economic characteristics.

In global impact modeling, there is a need to address the heterogeneous characteristics of households and individuals that drive different behavioral responses to, for example, environmental risk, socio-economic policy changes and spread of diseases. In this research, we present GLOPOP-S, the first global synthetic population dataset with 1,999,227,130 households and 7,335,881,094 individuals for the year 2015, consistent with population statistics at an administrative unit 1 level. GLOPOS-S contains the following attributes: age, education, gender, income/wealth, settlement type (urban/rural), household size, household type, and for selected countries in the Global South, ownership of agricultural land and dwelling characteristics.