With the global issues of extreme climate and urbanization, the ecological security patterns (ESPs) in the Qinling Mountains are facing prominent challenges. As a crucial ecological barrier in China, understanding the characteristics of ESPs in the Qinling Mountains is vital for achieving sustainable development. This study focuses on Yangxian and employs methods such as machine learning (ML), remote sensing (RS), geographic information systems (GISs), analytic hierarchy process and principal component analysis (AHP–PCA), and the minimum cumulative resistance (MCR) model to construct an ecological security network based on multi-factor ecological sensitivity (ES) and conduct quantitative spatial analysis. The results demonstrate that the AHP–PCA method based on ML overcomes the limitations of the single-weighting method. The ESPs of Yangxian were established, consisting of 21 main and secondary ecological sources with an area of 592.81 km2 (18.55%), 41 main and secondary ecological corridors with a length of 738.85 km, and 33 ecological nodes. A coupling relationship among three dimensions was observed: comprehensive ecological sensitivity, ESPs, and administrative districts (ADs). Huangjinxia Town (1.43 in C5) and Huayang Town (7.28 in C4) likely have significant areas of ecological vulnerability, while Machang Town and Maoping Town are important in the ESPs. ADs focus on protection and management. The second corridor indicated high-quality construction, necessitating the implementation of strict protection policies in the study area. The innovation lies in the utilization of quantitative analysis methods, such as ML and RS technologies, to construct an ecological spatial pattern planning model and propose a new perspective for the quantitative analysis of ecological space. This study provides a quantitative foundation for urban and rural ecological spatial planning in Yangxian and will help facilitate the sustainable development of ecological planning in the Qinling region.
The coronavirus disease (COVID-19) pandemic has led to a surge in rural tourism, catering to consumers during the pandemic. However, rural tourism faces severe issues of homogeneity and environmental degradation owing to excessive development. Sustainable development of rural tourism is an urgent problem. This study, based on the average variable cost (AVC) theory, aims to explore the sustainable development of rural tourism landscapes with a focus on the Shijing area. A landscape evaluation system was established through factor analysis and weight calculations, with ten principal components contributing to a cumulative contribution rate of 77.196%. The weighted values for attractiveness, vitality, and resilience were 0.539, 0.297, and 0.164, respectively. The findings indicate that Caijiapo Village had the highest comprehensive score of 88.79 (good level of performance), whereas Laoyukou Village had the lowest comprehensive score of 80.25 (average level of performance). Caijiapo and Liyukou exhibited the strongest overall strength, whereas Liyuanpo and Xiazhuang had moderate overall strength, and Laoyukou had the weakest overall strength. The results reveal that all five villages possess rich natural landscapes and favorable geographical conditions, demonstrating the potential and attractiveness of rural tourism development. However, the overall carrying capacity was moderate and vitality was relatively weak. This supports the AVC theory application in rural tourism research and emphasizes the importance of rural landscape quality and economic vitality. The main contributions of this study are as follows: (1) the establishment of a rural tourism landscape evaluation system based on the AVC theory, providing a scientific assessment method for sustainable development; (2) the case evaluation in the Shiying area provides decision-makers with reference for development strategies; (3) emphasis on the importance of ecological conservation in rural tourism and providing recommendations to address issues of homogenization and environmental degradation.
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