This study adopted an empirical analysis to explore social resilience to major natural disasters along the Tea-Horse Road (THR) in southwest China and to understand why and how the THR and its connected communities maintained and developed over a long period. A set of archive data, literature re-analysis, statistical data, monitoring data, and surveyed materials were collected and qualitatively and quantitatively analysed to support a holistic investigation of disaster impacts and social resilience. The results indicate that (a) natural disasters occurred frequently but were distributed over place and time and had various impacts, which left possibilities for maintaining social development with diverse and specific coping strategies; (b) strong central and local governance continually improved infrastructure and engineering technologies, and collaboration in social networks with local experience and disaster cultures were the major contributing factors that enhanced social resilience at various levels; (c) the THR area demonstrated various features of social resilience to natural disasters in terms of spatial-temporal scales, where the combination of multiple resilience measures enabled the resilience of the entire social system at various places over long time periods. Generally, larger social systems with diverse response capabilities were more resilient than small and individual entities over a long time scale. The study highlights that the THR region withstood frequent natural disasters but maintained a general development of social economy, transportation, and advanced technologies, and performed a positive transformation to a more resilient status. Overall, this paper describes the scale effects of multiple resilience measures along the THR and calls for specific studies on social resilience and transformation of diverse social entities over multiple spatial-temporal scales.

Understanding the driving factors of land-use spatio-temporal change is important for the guidance of rational land-use management. Based on land-use data, household surveys and social economic data in 2000, 2005, 2010, and 2015, this study adopted the Binary Logistic Regression Model (BLRM) to analyze the driving factors of land-use spatio-temporal change in a large artificial forest area in the Ximeng County, Yunnan province, in Southwest China. Seventeen factors were used to reflect the socio-economic and natural environment conditions in the study area. The results show a land use pattern composed of forestland, dry cropland, and rubber plantation in Ximeng County. Over the past fifteen years, the area of artificial forests increased rapidly due to the “Grain for Green” policy, which has led to increases in rubber plantations, tea gardens, eucalyptus forests, etc. In contrast, the area of natural forest and dry cropland decreased due to reclamations for farming and constructions. The BLRM approach helped to identify the main driving factors of land-use spatio-temporal change, which includes land-use policies (protection of basic farmlands and natural reserves), topography (elevation and slope), accessibility (distance to the human settlements), and potential productivity (fertility and irrigation). The study revealed the relationship between land-use spatio-temporal change and its driving factors in mountainous Southwest China, providing a decision-making basis for rational land-use management and optimal allocation of land resources.