Publication Date:
2023-11-16
Description:
〈title xmlns:mml="http://www.w3.org/1998/Math/MathML"〉Abstract〈/title〉〈p xmlns:mml="http://www.w3.org/1998/Math/MathML" xml:lang="en"〉Floods cause average annual losses of more than US$30 billion in the US and are estimated to significantly increase due to global change. Flood resilience, which currently differs strongly between socio‐economic groups, needs to be substantially improved by proactive adaptive measures, such as timely purchase of flood insurance. Yet, knowledge about the state and uptake of private adaptation and its drivers is so far scarce and fragmented. Based on interpretable machine learning and large insurance and socio‐economic open data sets covering the whole continental US we reveal that flood insurance purchase is characterized by reactive behavior after severe flood events. However, we observe that the Community Rating System helps overcome this behavior by effectively fostering proactive insurance purchase, irrespective of socio‐economic backgrounds in the communities. Thus, we recommend developing additional targeted measures to help overcome existing inequalities, for example, by providing special incentives to the most vulnerable and exposed communities.〈/p〉
Description:
Plain Language Summary: Flood resilience of individuals and communities can be improved by bottom‐up strategies, such as insurance purchase, or top‐down measures like the US National Flood Insurance Program's Community Rating System (CRS). Our interpretable machine learning approach shows that flood insurances are mostly purchased reactively, after the occurrence of a flood event. Yet, reactive behaviors are ill‐suited as more extreme events are expected under future climate, also in areas that were not previously flooded. The CRS counteracts this behavior by fostering proactive adaptation across a widespread range of socio‐economic backgrounds. Future risk management including the CRS should support and motivate individuals' proactive adaptation with a particular focus on highly vulnerable social groups to overcome existing inequalities in flood risk.〈/p〉
Description:
Key Points:
〈list list-type="bullet"〉
〈list-item〉
〈p xml:lang="en"〉Flood insurance purchase in the US is dominated by reactive behavior after severe floods〈/p〉〈/list-item〉
〈list-item〉
〈p xml:lang="en"〉The Community Rating System (CRS) fosters proactive insurance adoption irrespective of socio‐economic background〈/p〉〈/list-item〉
〈list-item〉
〈p xml:lang="en"〉The CRS should further balance existing inequalities by targeting specific population segments〈/p〉〈/list-item〉
〈/list〉
〈/p〉
Description:
https://api.census.gov/data/2018/acs/
Description:
https://www.fema.gov/about/openfema/data-sets#nfip
Description:
https://www.fema.gov/fact-sheet/community-rating-system-overview-and-participation
Description:
https://msc.fema.gov/portal/home
Description:
https://www.fema.gov/case-study/information-about-community-rating-system
Description:
https://doi.org/10.5281/zenodo.8067448
Keywords:
ddc:363.34
;
FEMA
;
machine learning
;
flood insurance
;
human behavior
;
flood resilience
Language:
English
Type:
doc-type:article
