Published In

International Journal of Disaster Risk Reduction

Document Type

Article

Publication Date

8-1-2025

Subjects

Flood risk communication, Disaster Risk

Abstract

Despite the crucial role of flood warning systems in mitigating disaster impacts, their economic value—particularly the value of specific attributes—remains poorly understood. This study evaluates public preferences and willingness to pay (WTP) for three flood warning attributes: advance notice, warning reliability, and geographical precision, using a choice experiment conducted in Tokyo's Koto 5 Wards. To address social desirability bias, the study employs both standard and inferred valuation methods, with the latter asking respondents to predict their neighbors' choices. The findings reveal that warning reliability is the most highly valued attribute, with an inferred WTP of ¥2,345 per household annually for improving warning accuracy from 30 % to 80 %. In contrast, significant improvements in geographical precision show a negative inferred WTP, reflecting privacy concerns associated with highly granular, location-specific warnings. Advance notice, while positively valued in the standard valuation, is not statistically significant in the inferred valuation—likely due to concerns about increased false alarms associated with longer lead times. The estimated societal benefit of enhancing warning reliability across the study area amounts to ¥3.07 billion annually. These results suggest that policymakers should prioritize reliability improvements while maintaining the current levels of lead time and spatial granularity. Transparent communication about data usage and privacy is essential to fostering public trust and maximizing the effectiveness of disaster management systems.

Rights

Copyright (c) 2025 The Authors Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License.

DOI

10.1016/j.ijdrr.2025.105571

Persistent Identifier

https://archives.pdx.edu/ds/psu/43817

Publisher

Elsevier BV

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