Evolution and Influencing Factors of Social-Ecological System Vulnerability in the Wuling Mountains Area

Abstract

With the wide spread of the concept of sustainable tourism in various countries and regions, the research on tourism poverty alleviation is paying increasingly closer attention to the sustainability of the poverty reduction effect of tourism, and the social-ecosystem theory of tourist destinations has been widely applied in the sustainable development of tourism in backward mountainous areas. However, existing studies lack the dynamic evaluation of social-ecosystem vulnerability in places of tourism, and are devoid of large sample data. This paper aims to analyze the law of spatial and temporal evolution of the social-ecosystem vulnerability in China’s Wuling Mountains area, and to help solve the obstacles and difficulties of realizing the effective connection between poverty alleviation and rural revitalization. The set analysis method (SPA) was used to evaluate the vulnerability and the obstacle degree model was utilized to identify the vulnerability barrier factors. Forty-two national key poverty-alleviation counties in the Wuling Mountains area were selected as the research objects to analyze the spatial and temporal evolution characteristics of social-ecosystem vulnerability based on the valuation model of “Social-Economic-Ecological (S-E-E) model” and the “Vulnerability-Scoping-Diagram (V-S-D) model”. In this paper, we clarified the two types of changes in social-ecosystem vulnerability in the Wuling Mountains area, and analyzed the spatial differences of vulnerability from the perspective of subsystems and counties. In terms of the results of this study, from 2010 to 2019, the overall vulnerability of social ecosystems showed a trend of “slow-rise and steady-decline”, with the vulnerability index peaking in 2014 and declining year by year thereafter. Spatially, the overall vulnerability is smaller in the north than in the south; and social-ecosystem vulnerability is the result of the interaction between system exposure-sensitivity and system adaptive capacity. Based on the change in vulnerability of different subsystems and different counties, and the barrier factors it faced, we make targeted suggestions to help the region to reduce its social-ecosystem vulnerability.