Keywords
Abstract
Flood buyout policy requires careful attention to household heterogeneity, local fiscal capacity, housing market constraints, and model-based decision support. Federal-local cost-sharing arrangements shape which communities can participate in relocation programs and how benefits are distributed across homeowners. Pre-disaster relocation modeling provides a behavioral foundation for understanding household decisions under flood risk, while urban analytics research shows that housing prices, built environment characteristics, and polycentric development influence relocation feasibility. The addition of model-confidence research highlights the importance of calibrating AI-based planning tools when they are used to support high-stakes policy decisions. Urban sub-centers, commuting accessibility, and street-network morphology further affect whether relocated households can maintain access to employment, services, and social infrastructure. This literature cluster frames flood buyout equity as a combined problem of disaster governance, metropolitan spatial structure, housing-market variation, and trustworthy computational decision support.
References
Zhou, Y. (2026). Equity Implications of Federal-Local Cost-Sharing in Flood Buyouts: A Game-Theoretic Analysis with Heterogeneous Homeowners (No. 2602.23379).
Zhou, Y. (2022). Pre-disaster relocation and agent-based model for flood disaster. The University of Wisconsin-Madison.
Dai, Y. (2026). Rescaling confidence: What scale design reveals about LLM metacognition. arXiv preprint arXiv:2603.09309.
Yang, T., Jin, Y., Yan, L., & Pei, P. (2019). Aspirations and realities of polycentric development: Insights from multi-source data into the emerging urban form of Shanghai. Environment and planning b: urban analytics and city science, 46(7), 1264-1280.
Yang, T., Pan, H., Hewings, G., & Jin, Y. (2019). Understanding urban sub-centers with heterogeneity in agglomeration economies—Where do emerging commercial establishments locate?. Cities, 86, 25-36.
Liu, X., Qiao, R., Wu, Z., Yang, T., Zhang, X., Zhang, X., & Zhu, Z. (2024). Unveiling the spatially varied nonlinear effects of urban built environment on housing prices using an interpretable ensemble learning model. Applied Geography, 173, 103458.
Yang, T. (2020). Understanding commuting patterns and changes: Counterfactual analysis in a planning support framework. Environment and planning b: urban analytics and city science, 47(8), 1440-1455.
Lim, L., Yang, T., Vialard, A., Feng, C., & Peponis, J. (2015). Urban morphology and syntactic structure: A discussion of the relationship of block size to street integration in some settlements in the Provence. The Journal of Space Syntax, 6(1), 142-169.