Flood risk is increasing worldwide and there is a growing need to better understand the co-benefits of investments in disaster resilience. Utilizing a multinational community flood resilience dataset, this paper takes a systems approach to understanding community-level flood resilience. Using a cluster analysis and bivariate correlation methods, we develop a typology of community flood resilience capacity based on community characteristics and five capitals (human, financial, natural, physical, and social). Our results reinforce the importance of context-specific policymaking and give recommendations of four distinct clusters to investigate the relationship between flood resilience and prevailing development conditions. We especially find that communities with higher interactions between their capital capacities tend to have higher flood resilience levels. Additionally, there are indications that stronger interactions between community capacities can help to induce multiple co-benefits when investing in disaster resilience. Our results also have important policy implications on the individual community level. For example, based on our results, we suggest that communities with lower flood resilience capacities and interactions can best build resilience on leveraging their relatively higher human capital capacities to strengthen the financial and social capitals. Negative effects might happen for urban communities when co-benefits of natural and physical capital are not fully integrated. The highest flood resilience capacity is found in communities with a well-balanced household income distribution which is likely a contributing factor to the importance of financial capital for this cluster. Our results emphasize the importance of an integrative approach to management when implementing systematic flood disaster resilience metrics and development measures.

Background: Life insurance markets are experiencing unprecedented transformation in the wake of economic disruption, evolving consumer expectations, and behavioral shifts following the COVID-19 pandemic. Traditional economic models often fail to capture the complex interplay of attitudinal, and cognitive factors that now shape insurance demand and premium selection. Methods: This study analyzes nationally representative survey data from over 3600 U.S. adults (2024 NAIC Financial Inclusion Survey). It uses a weighted full maximum likelihood Heckman selection model to identify determinants of life insurance uptake and premiums. The main innovation is modeling psychological price, a composite of perceived affordability, with higher-order polynomials. The design integrates psychometrically validated measures of financial knowledge and risk tolerance. Political ideology, race and ethnicity, and sources of financial advice serve as exclusion restrictions in the selection equation. Results: Psychological price shows an inverse-U relation with term outcomes: uptake rises at low to moderate affordability and declines at high affordability; among purchasers, term premiums rise at low to mid affordability and decline at high levels. For cash value policies, premiums decrease as psychological price increases. Financial knowledge and risk tolerance increase term uptake; financial knowledge reduces cash premiums. Education and income increase term uptake and term premiums. Compared with respondents reporting no ideology, conservative and centrist respondents have lower term uptake and higher cash uptake; using a professional advisor is associated with higher cash uptake. The selection correlation is positive for term ($\rho \approx 0.98$) and negative for cash ($\rho \approx -0.38$), indicating non-random selection in both markets. Implications: In order to reduce disparities, insurers should target the mid-affordability threshold with term offerings, streamline options for high-affordability consumers, offer pricing support and guidance for low-affordability households, increase uptake through advice channels and financial education, and address affordability barriers. Conclusions: Nonlinear affordability effects shape both market entry and pricing choices. Modeling psychological price with higher-order polynomials identifies thresholds and turning points that linear specifications miss. The results support targeted product design and outreach when perceived affordability drives insurance participation and premium choices.