Exposing disparities in flood adaptation for equitable future interventions in the USA

As governments race to implement new climate adaptation solutions that prepare for more frequent flooding, they must seek policies that are effective for all communities and uphold climate justice. This requires evaluating policies not only on their overall effectiveness but also on whether they benefit all communities. Using the USA as an example, we illustrate the importance of considering such disparities for flood adaptation through a FEMA dataset of ~ 2.5 million flood insurance claims. We use CAUSAL.FLOW, a causal inference method based on deep generative models, to estimate the treatment effect of flood adaptation interventions based on a community's income, racial demographics, population, flood risk, educational attainment, and precipitation. We find that the program saves communities $5,000–15,000 per household. However, these savings are not evenly spread across communities. For example, for low-income communities savings sharply decline due to risk increases in contrast to their high-income counterparts. Even among low-income communities, savings are >$6,000 per household higher in predominantly white communities. Future flood adaptation efforts should go beyond reducing losses overall and aim to equitably support communities in the race for climate adaptation.