Conference Agenda

This paper examines how the issuance and escalation of flood-related municipal evacuation orders affect residents' evacuation behavior. We exploit a 2022 flood in Hamamatsu, Japan, where a high-urgency alert (Level 5) was issued for the first time in a major metropolitan area under the revised Basic Act on Disaster Management. Using mobile phone population counts and vehicle traffic volumes, we find that a first evacuation order (Level 4) leaves traffic unchanged but increases the number of people staying. This is consistent with residents remaining in place rather than departing. Following the Level 5 alert, by contrast, the staying population falls within the first hour while traffic rises only with a delay. We read this pattern cautiously, but it points to an active behavioral response to the more urgent alert. Heterogeneity analysis reveals suggestive differences by hazard-map designation. These findings suggest that escalating alert levels trigger distinct behavioral responses, with implications for emergency communication.

Large-scale infrastructure, such as flood-control dams, is a central strategy for climate adaptation. However, while these projects are engineered to protect downstream assets, the distributional consequences of their adaptation costs remain poorly understood. Using a spatial regression discontinuity design (RDD) on geocoded manufacturing firms across China, I document a stark asymmetry in adaptation outcomes. First, I confirm the physical efficacy of this infrastructure: dams successfully decouple downstream production from upstream rainfall shocks, effectively mitigating the hydrological propagation of extreme weather. However, this protection generates unequal economic returns. Firms immediately upstream exhibit 27–36% lower total factor productivity (TFP) growth compared to their downstream counterparts. I show that this gap is driven by the unequal burden of defensive adaptation. Lacking physical protection, upstream firms are incentivized to divert resources into non-productive buffers—specifically, excessive inventory and flood insurance—which decouple input intensification from output growth. Furthermore, these efficiency losses are amplified by cross-sectoral spillovers from agriculture-dependent supply chains. These findings highlight the hidden costs of adaptation borne by unprotected regions, suggesting that infrastructure-based solutions, while physically effective, can exacerbate spatial inequality by distorting firm-level resource allocation.

Using a discrete choice experiment, this study examines household preferences for staying versus relocating under future coastal flood risk in metropolitan France. We estimate mixed logit models to analyse how experimentally varied environmental and economic attributes, together with selected self-reported behavioural variables, shape residential relocation preferences. Flood frequency, beach amenities, insurance premiums, local economic conditions, perceived ability to relocate, homeownership, and prior network exposure significantly influence relocation choices. Counterfactual simulations show that insurance premiums provide a strong price signal: changes in insurance premiums generate substantial predicted changes in staying probabilities, even relative to large increases in flood probability. At the same time, high-income households exhibit slightly higher average staying probabilities across all policy scenarios. These findings suggest that the uniform pricing structure of the French CatNat system may weaken location-specific financial incentives and contribute to socioeconomic differences in coastal exposure while spreading risk-related costs more broadly. As a possible reform direction, we propose finer-scale risk-based pricing complemented by means-tested vouchers to preserve affordability.

Flooding is a persistent threat, especially to communities along the coast of the United States. Due to rising sea levels and intensifying storms, these communities face elevated risks of property damage and economic instability. The impact of flood risks on price capitalization in the real estate industry has been a focus of climate risk research studies. However, such studies often overlook the impact on housing market liquidity. This study

investigates the relationship between coastal flooding and days-on-market (DOM), a key measure of housing market liquidity. We use National Oceanic and Atmospheric Administration (NOAA) tidal records with real estate market data for East U.S. coastal zip codes from July 2017 to July 2024. DOM and tidal flooding can be simultaneously affected by factors such as precipitation and flood mitigation policies. To estimate causal effects, we employ a two-stage least squares (2SLS) approach, using onshore wind speed as an instrument. Findings show that 1% increase in flood exposure extends the DOM by 1.78 days; a 2.26% increase relative to the average. The effect is more than three times larger in areas with lower Special Flood Hazard Area (SFHA) coverage, where flood insurance and disclosure requirements are less uniformly applied. This pattern indicates that tidal flooding generates meaningful housing market liquidity frictions, particularly in moderate-risk coastal markets. Scaled across all active listings, these

delays lock up an estimated $1.05–$2.11 billion in housing capital annually, slowing market turnover and affecting local tax bases. Results underscore the growing economic footprint of tidal flooding and highlight the need for policy tools that address liquidity risks both inside and beyond officially mapped flood zones.