High-altitude mountainous counties face compound geological hazards and chronic socioeconomic and ecological pressures, yet existing resilience assessments rarely integrate external disturbance, internal adaptive capacity, and subsystem coordination within a dynamic county-level framework. Taking 73 medium- and high-altitude counties in Sichuan Province, China, as the study area, this study develops an EDE–TA–CCD framework that combines Environmental Disturbance Exposure, Total Adaptive Capacity, and Coupling Coordination Degree. Using panel data from 2007 to 2022, an improved coupling coordination model, spatial autocorrelation analysis, and K-means clustering were applied to examine the spatiotemporal evolution, spatial dependence, and typological differentiation of geological disaster resilience. The results show that regional resilience improved overall during the study period, mainly driven by the continuous enhancement of TA and CCD, while EDE remained relatively stable. Spatially, resilience displayed significant clustering and path dependence, with higher resilience concentrated in counties with stronger development foundations and reconstruction support, and lower resilience persisting in remote mountainous areas. Static classification and dynamic trajectory analysis further identified three resilience patterns and five evolutionary pathways, revealing differentiated processes such as low-level lock-in, stable saturation, post-disaster remodeling, rapid catch-up, and high-pressure stress climbing. The proposed framework provides a practical tool for identifying resilience disparities and supporting differentiated disaster risk governance in high-altitude mountainous regions.

High-altitude mountainous counties face compound geological hazards and chronic socioeconomic and ecological pressures, yet existing resilience assessments rarely integrate external disturbance, internal adaptive capacity, and subsystem coordination within a dynamic county-level framework. Taking 73 medium- and high-altitude counties in Sichuan Province, China, as the study area, this study develops an EDE–TA–CCD framework that combines Environmental Disturbance Exposure, Total Adaptive Capacity, and Coupling Coordination Degree. Using panel data from 2007 to 2022, an improved coupling coordination model, spatial autocorrelation analysis, and K-means clustering were applied to examine the spatiotemporal evolution, spatial dependence, and typological differentiation of geological disaster resilience. The results show that regional resilience improved overall during the study period, mainly driven by the continuous enhancement of TA and CCD, while EDE remained relatively stable. Spatially, resilience displayed significant clustering and path dependence, with higher resilience concentrated in counties with stronger development foundations and reconstruction support, and lower resilience persisting in remote mountainous areas. Static classification and dynamic trajectory analysis further identified three resilience patterns and five evolutionary pathways, revealing differentiated processes such as low-level lock-in, stable saturation, post-disaster remodeling, rapid catch-up, and high-pressure stress climbing. The proposed framework provides a practical tool for identifying resilience disparities and supporting differentiated disaster risk governance in high-altitude mountainous regions.

The population dynamics of high-altitude mountainous areas are shaped by a complex interplay of socioeconomic and environmental drivers. Despite their significance, such regions have received limited scholarly attention. This research identifies and examines the principal determinants of population changes in the high-altitude mountainous zones of Sichuan Province, China. Utilizing a robust quantitative framework, we introduce the Sustainable Population Migration Index (SPMI) to systematically analyze the migration potential over two decades. The findings indicate healthcare accessibility as the most significant determinant influencing resident and rural population changes, while economic factors notably impact urban populations. The SPMI reveals a pronounced deterioration in migration attractiveness, decreasing by 0.27 units on average from 2010 to 2020. Furthermore, a fixed-effects panel regression confirmed the predictive capability of SPMI regarding population trends, emphasizing its value for demographic forecasting. We also develop a Digital Twin-based Simulation and Decision-support Platform (DTSDP) to visualize policy impacts effectively. Scenario simulations suggest that targeted enhancements in healthcare and infrastructure could significantly alleviate demographic pressures. This research contributes critical insights for sustainable regional development strategies and provides an effective tool for informed policymaking.

Population change in high-altitude mountainous areas is influenced by various socio-economic and environmental factors, yet relevant research remains limited. This study examines the key driving forces of population change in high-altitude mountainous areas, using Sichuan Province, China, as a case study, to provide insights for regional planning. By employing Geodetector and spatial autocorrelation analysis, this study evaluates the impact of economic, infrastructure, and healthcare factors on population change based on 14 indicators from the Sichuan Statistical Yearbook. The results indicate that healthcare resources and economic factors are the primary drivers of population change in high-altitude areas. Resident and rural population are most influenced by healthcare resources, while urban population change is driven by both economic and healthcare factors. In recent years, the dominant driving force of population growth has shifted from economic factors to healthcare resources, with high-altitude areas experiencing a higher population growth rate than low-altitude areas. The findings highlight the critical role of healthcare accessibility and economic development in shaping population dynamics in high-altitude areas, providing empirical support for regional development policies.