Rural areas face increasing threats from water-related disasters yet often lack the infrastructure and resources available in urban areas for effective disaster response and recovery. Understanding and improving rural resilience—the ability to withstand and recover from disasters—is critical for sustainable rural development, especially under pressures from the climate. This study introduces a practical, indicator-based framework to evaluate rural resilience by analyzing five key aspects: stability, hazards, exposure, vulnerability, and adaptability. Using Zhejiang Province in China as a case study, we examined changes in rural resilience from 2000 to 2020. Our findings reveal that regions with a strong economic foundation, diversified livelihoods, and well-developed healthcare and education infrastructure exhibit higher resilience, while those with high exposure to hazards and economic dependency on agriculture remain vulnerable. The study highlights the crucial role of adaptive capacity in driving long-term resilience, emphasizing targeted investments in rural healthcare, education, infrastructure, and economic diversification. By providing data-driven insights, this research contributes to SDG 11 by offering practical strategies for policymakers and local communities to enhance disaster preparedness and rural sustainability. Moreover, the framework developed here can be adapted and applied to other rural regions facing similar hazards globally, enhancing disaster preparedness and promoting sustainable rural development.

Rural shrinkage is increasingly expressed through changing residential mobility, housing under occupancy, and intermittent dwelling use, rather than a simple linear process of permanent outmigration and abandonment. Yet empirical measurement of occupancy dynamics and the service-mediated mechanisms shaping residence stability remains limited. This study proposes an evidence-driven and explainable assessment framework that links energy-informed occupancy dynamics with settlement building area and mechanism identification, using Fuyuan City as a case study. Daily electricity consumption time series from 2021 to 2024 are used to infer occupancy dynamics and detect behavioral signatures of long term residence, seasonal residence, return visits, and vacancy. Shape-based temporal clustering identifies six occupancy trajectories, revealing pronounced heterogeneity in mobility rhythms within the rural settlement system. Settlement vacancy-related built-environment changes are characterized from 2 m remote sensing imagery, using a trained YOLO-based building detection workflow, producing settlement-level total building area as a physical indicator of the development intensity. Integrating these behavioral measures with multi-source spatial factors, the mechanism model shows that development, governance, and environmental conditions influence residence stability primarily through service provision. Among service domains, education services exhibit the strongest direct association with long-term residence stability, while transport and daily life services show modest positive effects and healthcare presents a smaller positive effect. Development conditions positively promote all service types, whereas governance and environmental context display differentiated and, in some pathways, opposing effects across services. Overall, the framework enables interpretable monitoring of rural shrinkage dynamics by jointly quantifying occupancy trajectories, settlement morphology, and service-mediated pathways shaping residential outcomes.

Compound flooding increasingly threatens developed coastal deltas. High risk pressure does not necessarily produce a resilience deficit where capacity is sufficient, whereas moderate-pressure areas may remain vulnerable when capacity is weak. Recent assessments increasingly integrate hazard, exposure, vulnerability, and adaptive capacity within unified risk frameworks such as the IPCC AR5 risk model. However, by collapsing these dimensions into a single composite risk score, such formulations cannot explicitly diagnose where—and by how much—compound flood risk pressure exceeds intrinsic resilience capacity, which is the information most directly needed for prioritizing resilience investment. This study diagnoses compound flood resilience deficits across Jiangsu Province, China, at county scale from 2000 to 2020. We introduce a diagnostic approach that separates risk pressure from intrinsic resilience capacity and quantifies their spatial mismatch. The risk pressure index is evaluated for consistency with observed disaster-loss indicators—direct economic loss and flood-affected area—over 2010–2020, and spatial statistics, time-series clustering, and explainable machine learning identify deficit patterns, pathways, and associated factors. Both the risk-pressure index and the derived deficit index are positively associated with observed losses, confirming that the framework captures major flood impacts. The resilience deficit index reveals persistent risk–resilience mismatch across Jiangsu. Three pathways emerge: capacity-buffered exposure, inland adaptive adjustment, and coastal resilience-deficit lock-in. Land-system conditions, communication access, transport connectivity, and economic recovery capacity are jointly associated with resilience deficits. The framework offers a transferable approach for prioritizing differentiated flood-risk management in coastal deltas.