DOI: 10.5061/dryad.47d7wm3hd
title: Estimating household preferences for coastal flood risk mitigation policies under ambiguity

description: Risk mitigation policies (like dike rising) are essential to address
 increasing coastal flood risks due to global warming. Furthermore, the
 optimal level of risk mitigation policy should be determined by public
 preferences for risk reduction. However, it is difficult to reveal public
 preferences for coastal flood risk reduction because projections of
 coastal flood risks inevitably involve uncertainty. This study aims to
 estimate household preference for coastal flood reduction under ambiguity
 and multiple projections of coastal flood risks. By coupling storm surge
 inundation simulations and stated preference experiments with decision
 models, we estimate the expected loss reduction, risk premium, and
 ambiguity premium for coastal flood risk mitigation policies. Results of
 the study show that ignoring the ambiguity premium causes significant
 undervaluation of coastal flood risk mitigation, and the ambiguity premium
 stems from households' over-concern about the worst projection, which
 may lead to an over-allocation of resources to prevent inundation damage
 caused from the worst-case flood before a disaster. The study concludes
 that a risk mitigation policy combining public insurance for the worst
 projection and pre-disaster prevention measures can be effective and
 efficient.
description: To assess inundation risks and ambiguity
 in coastal areas, we proposed a framework: (1) conduct a simulation of
 typhoon generations for 200 years using a global stochastic tropical
 cyclone model (GSTCM); (2) the total number of typhoons in this study
 generated over half a million in the Western Pacific Ocean, to understand
 the uncertainty of typhoon storm surge inundation, also to avoid
 unnecessary inundation simulations, the significant four typhoon ensembles
 (each ensemble including 25 typhoon cases) are selected (after fulfilling
 the conditions), and the storm surges of Osaka Bay are simulated by a
 full-coupled surge-wave-tide coupled model (SuWAT); (3) predict the
 inundation depth due to storm surges using the inundation simulation
 model; (4) repeat step (1) to (3), get 25 projections of the inundation
 risk for each dike level: current level and rising by 0.5m, 1.0m, 1.5m,
 and 2.0m (25×5); (5) the average and worst projections of the inundation
 risks are specified by each zip-code in the web-based survey; (6) estimate
 households' preferences by asking them to choose whether to buy
 hypothetical insurance to cover all losses from coastal flooding by
 presenting the average and worst scenarios of the inundation risks to
 their houses; (7) by using the choice experiment data, a decision model is
 applied for estimating risk premiums and ambiguity premiums; (8) analyze
 the geographical distribution of risk premiums and ambiguity premiums by
 geographic information system (GIS).
description: Typhoon data sets for this research are available through Mori et
 al. (2019). Inundation simulation model codes are
 available at: https://github.com/HEMLab/hipims. Please refer to Liang
 (2010) for further information on this model.