Behavioral Economics of Disaster Risk Perception: Underestimating Flood Risks During a Non-Landfalling Tropical Cyclone and Acceptance of AI-Based Weather Forecasting in the Philippines’ Typhoon Landfall Region

Communities in typhoon-prone regions may underestimate rainfall-driven flood risks from non-landfalling tropical cyclones because they often rely on traditional storm cues such as landfall location and wind intensity. In the Philippines, particularly in the Bicol Region widely regarded as the country’s typhoon landfall belt, repeated exposure to tropical cyclones has shaped local perceptions and behavioral responses toward storm hazards. This study examines flood risk perception, exposure, preparedness behavior, and trust in disaster risk communication, including artificial intelligence (AI)-based weather forecasting, during Tropical Storm Ada (Nokaen), the first tropical cyclone of the 2026 season and a rare early-season, non-landfalling storm. Using survey data from 640 residents in the Partido District of Camarines Sur, the study employed behavioral econometric analysis to analyze factors influencing disaster-related decision-making. Results showed moderate overall risk perception (WM = 3.71) but high preparedness behavior (WM = 4.20). Respondents strongly relied on traditional storm indicators such as landfall and wind intensity (WM = 3.69), contributing to a perception–reality gap (WM = 3.92). Although most respondents reported no flooding, 28.12% experienced localized flooding caused primarily by prolonged rainfall. Regression results revealed that risk perception (β = 0.7734, p < 0.001) and residence in flood-prone areas (β = 0.8534, p < 0.001) significantly increased preparedness behavior, while prior flood experience negatively influenced preparedness (β=−0.5929, p = 0.009), suggesting normalization bias. The study proposes a Behavioral Flood Risk Communication and AI-Integrated Preparedness Framework emphasizing rainfall-centered, impact-based, and behaviorally informed disaster communication integrated with trusted institutional advisories to strengthen preparedness in flood-prone communities.

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Journal
Economics of Disasters and Climate Change
Published
2026-06-13
DOI
https://doi.org/10.1007/s41885-026-00202-1
Primary Topic
Flood Risk Assessment and Management
Type
article
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article

Behavioral Economics of Disaster Risk Perception: Underestimating Flood Risks During a Non-Landfalling Tropical Cyclone and Acceptance of AI-Based Weather Forecasting in the Philippines’ Typhoon Landfall Region

Emmanuel A. Onsay
Economics of Disasters and Climate Change
Flood Risk Assessment and Management
article

Behavioral Economics of Disaster Risk Perception: Underestimating Flood Risks During a Non-Landfalling Tropical Cyclone and Acceptance of AI-Based Weather Forecasting in the Philippines’ Typhoon Landfall Region

Emmanuel A. Onsay
article en

Abstract

Communities in typhoon-prone regions may underestimate rainfall-driven flood risks from non-landfalling tropical cyclones because they often rely on traditional storm cues such as landfall location and wind intensity. In the Philippines, particularly in the Bicol Region widely regarded as the country’s typhoon landfall belt, repeated exposure to tropical cyclones has shaped local perceptions and behavioral responses toward storm hazards. This study examines flood risk perception, exposure, preparedness behavior, and trust in disaster risk communication, including artificial intelligence (AI)-based weather forecasting, during Tropical Storm Ada (Nokaen), the first tropical cyclone of the 2026 season and a rare early-season, non-landfalling storm. Using survey data from 640 residents in the Partido District of Camarines Sur, the study employed behavioral econometric analysis to analyze factors influencing disaster-related decision-making. Results showed moderate overall risk perception (WM = 3.71) but high preparedness behavior (WM = 4.20). Respondents strongly relied on traditional storm indicators such as landfall and wind intensity (WM = 3.69), contributing to a perception–reality gap (WM = 3.92). Although most respondents reported no flooding, 28.12% experienced localized flooding caused primarily by prolonged rainfall. Regression results revealed that risk perception (β = 0.7734, p < 0.001) and residence in flood-prone areas (β = 0.8534, p < 0.001) significantly increased preparedness behavior, while prior flood experience negatively influenced preparedness (β=−0.5929, p = 0.009), suggesting normalization bias. The study proposes a Behavioral Flood Risk Communication and AI-Integrated Preparedness Framework emphasizing rainfall-centered, impact-based, and behaviorally informed disaster communication integrated with trusted institutional advisories to strengthen preparedness in flood-prone communities.

Economics of Disasters and Climate ChangeVol. 10(2)
Partido State University (PH)
Climate action
Openalex Percentile: Top 6%
Flood Risk Assessment and Management
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Behavioral Economics of Disaster Risk Perception: Underestimating Flood Risks During a Non-Landfalling Tropical Cyclone and Acceptance of AI-Based Weather Forecasting in the Philippines’ Typhoon Landfall Region — Emmanuel A. Onsay · Economics of Disasters and Climate Change (2026) | TGRS Research Map | TGRS