The comprehensive resource and environment carrying capacity (RECC) evaluation is an important method for measuring the rationality of the population, resource, and environment allocation, which is an important scientific guidance for scientific research and the judgment of regional economic and social development potential and the optimization of the national land spatial pattern. This paper constructs a comprehensive evaluation index system of the RECC under the new situation of climate policy and high-quality economic development; it analyzes the factors influencing the RECC, the overall level, the spatial difference, and the carrying status by using the TOPSIS model based on the entropy weight method, and it identifies the shortcomings; then, it analyzes the characteristics of regional dynamic change and sustainable development trend, and finally, it simulates the optimal spatial pattern under the scenario simulated by the FLUS model. The conclusions are as follows: ① the resource factors have the greatest influence on the carrying capacity of the resources and the environment, followed by economic factors. Among them, per capita water resources, forest coverage rate, and health institutions have the highest impact on RECC. ② The overall level of comprehensive RECC from 2015 to 2020 shows an upward trend, and although the positive impact of resource-led provinces on the level of economic development power and RECC is greater than the negative one, the environmental support is the shortcoming of the future development of the regional economy. ③ The overall spatial performance of RECC is characterized as being high in Guanzhong, second in northern Shaanxi, and low in southern Shaanxi. The northern area of Yulin in the Guanzhong Plain City Cluster, which is an important national energy chemical base, is the core of the national-level urbanization development areas, and the northern area of the Guanzhong Plain City Cluster is the key choice of the provincial-level urbanization development areas. The area along the west bank of the Yellow River in the Qinba Mountain area in southern Shaanxi and the Baiyu Mountain area in northern Shaanxi can be positioned as national key ecological function areas.

Traditional villages are the crystallization of the wisdom of human beings when living harmoniously with nature. The spatial texture of villages is complex, vague, and uncertain, making it difficult to describe with traditional Euclidean geometric spatial measurement methods. Based on the fractal theory, this study analyzes the texture of traditional Miao villages in Qiandongnan, Guizhou, China. It constructs an index system that is suitable for the fractal characteristics of traditional Miao villages. This study uses aggregation fractal dimension and capacity fractal dimension to reflect the spatial aggregation and complexity of the traditional villages, and employs the Analytic Hierarchy Process (AHP) to explore the influencing factors of spatial texture, thereby revealing the characteristics of the spatial texture of traditional villages and their relationship with the ecological environment in the process of continuous development. The research results show the following: (1) There is a significant coupling relationship among the capacity fractal dimensions of the research objects in the sampled villages, indicating that the village textures exhibit clear fractal characteristics. The villages, whether in terms of location selection or development degree, show a good fit with the surrounding environment, maintaining a relatively good original state. (2) Human factors have the greatest impact on the texture of the traditional villages, followed by natural and historical factors, economic policies, and social factors. This study provides specific development strategies for traditional Miao villages in Qiandongnan, Guizhou, offering a scientific basis for their sustainable development. It also explores a new approach for the study and conservation of the spatial morphology in traditional villages of ethnic minorities in China.

Amidst the transition from agriculture to tourism, ethnic villages are facing issues of “misalignment” and “disequilibrium” between economic growth and cultural heritage conservation. Previous research has often isolated the effects of cultural heritage conservation or socio-economic development, neglecting their reciprocal influences within the coupling coordination. This study addresses this gap by assessing 43 villages in Leishan County, quantifying the preservation status of the cultural landscape (PSCL) and socio-economic level (SEL) using a comprehensive evaluation model and revealing the coupling coordination relationship between PSCL and SEL in each village with the coupling coordination degree (CCD) model. Spatial autocorrelation and the geographical detector model reveal the spatial agglomeration characteristics and influential factors of the CCD. The results highlight three findings. (1) The majority of villages (93%) are in a moderate imbalance stage or a serious imbalance stage, underscoring an urgent need for targeted policies. (2) Spatial autocorrelation analysis exposes significant clustering, with the northwest corner exhibiting the most pronounced high-value aggregation, which contrasts with the southern region’s low-value clustering. A ‘low–high’ clustering pattern is observed in the northern region, while the southwestern corner presents a ‘high–low’ distribution. (3) Key determinants affecting the CCD include the tourism revenue, tourist volume, resident participation in tourism, village hollowing level, and number of historical buildings. The interactions between driving factors show non-linear and two-factor enhancement effects. This study concludes with policy recommendations advocating for context-specific strategies to enhance the CCD, emphasizing the importance of aligning cultural preservation with economic progress in ethnic villages.

Affected by multiple factors, differentiated capital investment and power games have led to the uneven regional development of rural areas at cultural heritage sites. Therefore, it is urgent to reflect on the spatial equity and justice of heritage sites from the perspective of social space. This study took the territorial function of rural areas (TFRA) as its research objective, utilizing the UNESCO World Cultural Heritage Site—the Qinshihuang Mausoleum—and the Qianling Mausoleum and the Qiaoling Mausoleum in Shaanxi Province as cases. Based on the theory of spatial production, an index system of TFRA was constructed using social and spatial dimensions. Factor analysis and social–spatial differentiation indices were employed to identify the spatial structure and differentiation of TFRA. The results show the following: (1) the case areas reflect eight principal components, and these components have obvious spatial differentiation characteristics; (2) the principal components related to heritage display and utilization have had a sustained impact over the past decade, which have driven the development of related industries, but the scores of principal components related to the restrictions of heritage protection have been decreased; (3) six functional type zones are designated, and the spatial pattern of TFRA presented a concentric circle with a core-periphery structure, which is influenced by heritage protection zoning; (4) high-value-added functional spaces form and expand along transportation lines; (5) the inherent demand for capital proliferation is the fundamental driving force for the differentiation and evolution of TFRA at heritage sites. Based on the results of the above analysis, strategies of coordinated development between society and space are proposed to address the issue of uneven regional development at heritage sites.

The ethnic villages in karst regions, where the ecosystem and social systems are intricately linked, face the dual challenges of poverty and ecological sustainability. Tourism, as an emerging strategy adopted for poverty alleviation, has inevitably posed complex impacts on social–ecological systems (SES). However, due to the particularity of the SES in this region, the mechanisms through which tourism influences social–ecological systems remain unclear, hindering the achievement of eco-friendly economic growth. In this study, we first applied the vulnerability spectrum diagram (VSD) model assessment framework to various remotely sensed and socially sensed data to evaluate ecological and social vulnerability, taking Leishan County, a typical karst region in Guizhou, southwest China as a case study. Then, advanced geospatial analysis methods were adopted to investigate the spatial characteristics of the vulnerability index. Finally, we utilized the geographical detector to identify influencing factors and investigated their synergistic effects. Our results reveal that, within the studied area, social vulnerability is generally lower in the north than the south, while ecological vulnerability shows the other way around. Social vulnerability is significantly influenced by several tourism-related factors, such as transportation convenience and the preservation of traditional dwellings. These factors collectively exert a pronounced effect on social vulnerability mitigation. Moreover, ecological vulnerability, with the exception of rocky desertification, shows significant co-directional changes with social vulnerability, reflecting the fact that tourism factors indirectly shape the ecosystem. The development of ethnic village tourism in villages with better socio-economic conditions tends to effectively improve the quality of the ecological environment, whereas those with poorer conditions tend to exacerbate ecological damage. The findings drawn from this study convey important practical implications that assist in identifying key vulnerable areas in karst ethnic villages and support their sustainable development goals.

The ecological efficiency of industrial linkage considering unexpected output is an important indicator to measure the coordinated development of industrial linkage, resources, and the environment. It is an important basis for realizing the sustainable development of industry linkage. Taking the composite index of carbon emissions of the logistics industry and pollution emissions of the manufacturing industry as the unexpected output, we used the unexpected SBM model to evaluate the ecological efficiency of industrial linkage between the logistics industry and the manufacturing industry in Northeast China from 2011 to 2019, and used the spatial autocorrelation analysis method to analyze the spatial differences in industrial linkage efficiency. The results show that (1) considering the unexpected output, in Northeast China, the ecological efficiency cannot reach a high level of linkage development stage. (2) The results of the spatial correlation show that there are spatial differences between H-H agglomeration and L-L agglomeration in the linkage ecological efficiency of the two industries, and the spatial agglomeration attribute is relatively stable. (3) The analysis results of spatial agglomeration characteristics show that the spatial agglomeration of the two industries has a spatial evolution process from the southern coastal area to the central region. (4) Spatial trend analysis shows that in Northeast China, the western region is slightly higher than the eastern region, while the southern region is higher than the northern region. (5) From the development trend of linkage ecological efficiency, the linkage ecological efficiency of the study area will be improved in the future, but in the short term, the linkage ecological development level is not high and may still be at the primary linkage level.