10.5061/DRYAD.47D7WM3HD
Ha, Si
0000-0002-5572-6568
Kyoto University
Fujimi, Toshio
Kyoto University
Jiang, Xinyu
Wuhan University of Technology
Mori, Nobuhito
Kyoto University
Begum, Rawshan
Macquarie University
Watanabe, Masahide
Ryukoku University
Tatano, Hirokazu
Kyoto University
Nakakita, Eiichi
Kyoto University
Estimating household preferences for coastal flood risk mitigation
policies under ambiguity
Dryad
dataset
2022
FOS: Social and economic geography
Coastal flood
risk mitigation
Residents preferences
Ambiguity premium
Decision model
Ministry of Education, Culture, Sports, Science and Technology
https://ror.org/048rj2z13
JPMXD0717935498
Japan Society for the Promotion of Science
https://ror.org/00hhkn466
JP 17K06603
2022-10-05T00:00:00Z
2022-10-05T00:00:00Z
en
https://github.com/HEMLab/hipims
https://doi.org/10.5281/zenodo.7106917
5912991 bytes
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CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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.
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).
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.