A Reproducible Multi-Criteria Decision-Support Framework for Climate-Resilient Urban Refuge Green-Space Planning: An Illustrative Nantong Case

Rapid coastal urbanization increases exposure to multiple hazards while intensifying competition for land essential for safe emergency refuge. This paper proposes a reproducible decision-support framework for planning Disaster Prevention and Relief Accessible Green Spaces (DPRAGS). The framework integrates four components that are usually treated separately: GIS-based multi-hazard risk assessment, tiered green-space suitability screening, phase-specific refuge supply and demand, and accessibility- and capacity-aware location assessment using a Gaussian two-step floating catchment area (Ga2SFCA) model and a capacity-constrained maximal-coverage formulation. The contribution lies in this integration rather than in any individual method. The framework specifies the data architecture, a restrictive multi-hazard eligibility rule, the accessibility and allocation equations, a single tier-specific demand-coverage metric applied to four nested models (M1–M4), and the expert and local inputs that an application must document. Its application is illustrated conceptually using Nantong, China; no empirical hazard, accessibility, allocation, or coverage results for Nantong are reported. Generating and validating these outputs with the identified public datasets and locally elicited expert inputs is the next applied step. What transfers to other cities is the decision architecture and analytical sequence; parameters, thresholds, and demand estimates must be specified locally.