An agent‐based model for evaluating reforms of the National Flood Insurance Program: A benchmarked model applied to Jamaica Bay, NYC. Issue 2 (18th April 2022)

• Record Type: Journal Article
• Authors: de Ruig, Lars Tjitze; Haer, Toon; de Moel, Hans; Orton, Philip; Botzen, W. J. Wouter; Aerts, Jeroen C. J. H.
• Is Part Of: Risk analysis. Volume 43:Issue 2(2023)
• Page Start: 405
• Page End: 422
• Publication Date: 2022-04-18
• Subjects: affordability -- agent‐based model -- disaster risk reduction -- dynamic adaptive behavior -- flood insurance; Technology -- Risk assessment -- Periodicals; 658.403
• DOI: 10.1111/risa.13905
• Languages: English
• ISSNs: 0272-4332

Abstract

Abstract: Coastal flood risk is expected to increase as a result of climate change effects, such as sea level rise, and socioeconomic growth. To support policymakers in making adaptation decisions, accurate flood risk assessments that account for the influence of complex adaptation processes on the developments of risks are essential. In this study, we integrate the dynamic adaptive behavior of homeowners within a flood risk modeling framework. Focusing on building‐level adaptation and flood insurance, the agent‐based model (DYNAMO) is benchmarked with empirical data for New York City, USA. The model simulates the National Flood Insurance Program (NFIP) and frequently proposed reforms to evaluate their effectiveness. The model is applied to a case study of Jamaica Bay, NY. Our results indicate that risk‐based premiums can improve insurance penetration rates and the affordability of insurance compared to the baseline NFIP market structure. While a premium discount for disaster risk reduction incentivizes more homeowners to invest in dry‐floodproofing measures, it does not significantly improve affordability. A low interest rate loan for financing risk‐mitigation investments improves the uptake and affordability of dry‐floodproofing measures. The benchmark and sensitivity analyses demonstrate how the behavioral component of our model matches empirical data and provides insights into the underlying theories and choices that autonomous agents make.