Impacts of the built environment features and comprehensive experience on adaptive behavior in the context of climate change: A partial least squares structural equation modeling approach

Under the combined pressures of global climate change and high-density urbanization, public open spaces (POSs) are critical infrastructure for enhancing urban resilience. However, current "one-size-fits-all" design paradigms often fail to meet users' adaptive needs. To bridge this gap, this study proposes a replicable theoretical and analytical framework, which integrates spatial type, built environment, individual background, comprehensive experience, and adaptive behavior to quantify how environmental features drive differentiated adaptive behaviors. Validating this framework using Lhasa, China, as a test-bed site, the study identifies spatial types through multi-source data fusion and cluster analysis. Furthermore, it systematically verifies hypothesized pathways using Partial Least Squares Structural Equation Modeling (PLS-SEM) and Multi-Group Analysis (PLS-MGA). Empirical results confirm the robustness and interpretability of the framework. While the core psychological cognitive process: "attitude to perception to willingness to adaptive behavior of spatial" remains universal, the framework sensitively captures the "context dependency" of the drivers behind adaptive behavior. Additionally, the study clarifies that the built environment exerts an indirect regulatory effect through psychological mediators, rather than acting as a direct physical determinant. Consequently, this study advocates a paradigm shift from "standardized supply" to "precision support." By providing a scalable research framework, this study empowers policymakers and urban planners to formulate context-aware and resilient spatial governance strategies. These strategies can adapt to diverse geographical and cultural contexts.