As a national strategic development area, the Yellow River Basin (YRB) has seen progress in research on the synergy efficiency of pollution reduction and carbon reduction (SEPCR). However, there are still notable gaps. The theoretical framework for this area is lacking, leading to diverse and inconsistent conclusions. Additionally, difficulties in data collection and processing, along with incomplete and inconsistent data, negatively affect the accuracy of research findings. Current studies tend to focus on single aspects and lack a comprehensive and systematic analysis of the SEPCR across the entire basin. There is insufficient understanding of key network nodes, connections, and overall structural characteristics. A scientific assessment of its spatial correlation structure has far-reaching implications for the national battle against pollution and the realization of “dual carbon” goals. This study is based on panel data from 75 cities in the YRB from 2006 to 2022. It employs an ultra-efficiency SBM model to measure the SEPCR. Additionally, it utilizes a modified gravity model and social network analysis to explore the spatial network correlation structure in depth. Furthermore, the QAP model is used to clarify the mechanisms of various influencing factors. The research findings indicate that there is an imbalance in the spatial and temporal distribution of the SEPCR in the YRB. Although there is a fluctuating upward trend over time, significant internal spatial disparities exist. While the gaps between regions are gradually narrowing, there are still evident research disparities. Moreover, the spatial connectivity of the SEPCR in the YRB is gradually strengthening, with overall network connectivity also improving, yet there remains a considerable distance from an ideal state. The network density shows a decreasing trend from the downstream to the midstream and then to the upstream regions, with significant differences in spatial network centrality among these areas, particularly pronounced between the midstream and upstream regions. Differences in economic development levels, technological development levels, and industrial structure development levels promote the formation of spatial correlations in SEPCR, while disparities in energy utilization have a suppressive effect.

Assessing the suitability of human settlements is of great significance for promoting pastoral development, improving herders’ livelihoods, and advancing the construction of beautiful villages in agro-pastoral regions. Focusing on ten pastoral banners within Xilingol League, a representative pastoral region in northern China, this study employed the Google Earth Engine (GEE) platform combined with statistical datasets to evaluate settlement suitability using an optimized MaxEnt model. Fourteen key influencing factors were identified, and the spatiotemporal dynamics of settlement suitability in 2017 and 2024 were analyzed, together with predictions of suitable area distribution. The results showed that the model achieved the highest accuracy when using a linear combination of linear, quadratic, hinge, product, and threshold features with a regularization multiplier of 5.0. Suitable areas were mainly located in the southern part of the League, characterized by higher elevation, moderate temperatures, sufficient water resources, and relatively developed economies, while unsuitable areas were concentrated along the northwestern and northeastern borders with Mongolia. Spatially, settlement suitability exhibited a decreasing gradient from the southwest to the northeast. Furthermore, the dominant driving factors have gradually shifted from ecological conditions to socio-economic conditions. Overall, the suitability of pastoral human settlements in Xilingol League has continued to improve, providing new insights for suitability evaluation and spatial restructuring in pastoral regions.

Population migration and agricultural expansion have profoundly reshaped the rural characteristics of Xilingol League, a typical pastoral region in northern China. From the perspective of historical geography, this study investigates the settlement evolution in Xilingol League during 1915–1980. Drawing on declassified statistical data and historical documents, and employing kernel density estimation, hotspot analysis, and the geographical detector, we reconstruct and interpret the settlement patterns during the period without remote sensing imagery. The results reveal that: (1) Between 1915 and 1980, settlement expansion in Xilingol League exhibited a fan-shaped trajectory centered on Taibus Banner and Duolun County, with clustering intensity diminishing with increasing distance. (2) The 42°N parallel demarcates pastoral areas from agro-pastoral transitional zones, with settlements concentrated mainly in the transitional zone and showing a fragmented northeastward expansion into pastoral areas over time. (3) Socio-economic conditions were the dominant drivers of settlement evolution during this period, with cultivated land area ( q = 0.6266) and livestock numbers ( q = 0.6215) exerting the strongest explanatory power. This study provides new insights into the processes and driving mechanisms of modern settlement evolution in Xilingol League.