The analysis of the spatial distribution and influencing factors of key rural tourism villages in the Yellow River Basin of China is of significant strategic importance for the coordinated implementation of the rural revitalization strategy, the safeguarding of the basin’s ecological barrier function, and the activation of regional cultural heritage in the basin. Using spatial analysis, this study quantitatively analyzed the spatial distribution of 244 national key rural tourism villages across nine provinces/autonomous regions and 65 cities in the Yellow River Basin in 2023. Integrating the random forest regression model and geodetector, this study systematically reveals their spatial distribution and influencing factors. The findings reveal the following. (1) Key rural tourism villages in the Yellow River Basin exhibit significant spatial agglomeration and imbalance, forming a distribution pattern of a multi-core clustering with belt-shaped extension. (2) Random forest regression and geodetector analyses show that the distance from provincial capital cities, amount of intangible cultural heritages, and elevation are key driving factors that affect the spatial differentiation of key rural tourism villages, reflecting the dominant driving mechanism of location-resource-nature synergy. (3) Factor interaction detection shows that the interaction between road network density, urbanization rate, and other factors forms the dominant factor combination that affects the spatial differentiation between key rural tourism villages. The interaction effect between distance from provincial capital cities and annual average precipitation provides the highest spatial explanatory power, revealing the coexistence of basin differentiation and the development contradictions among key villages.

Xinjiang requires timely and accurate monitoring of soil salinization dynamics to support effective management and sustainable land use. This study examines the Weigan-Kuqa River Delta Oasis (Weiku Oasis) in Xinjiang. Based on Sentinel-1 synthetic aperture radar imagery and Sentinel-2 optical imagery from July 2022, characteristic parameters that were significantly correlated with measured soil salinity values were extracted and optimized. Six feature space models were constructed, including three polarization combination models from Sentinel-1 ([V²-H]-[H], [V²-H]-[(V²+H²)/V], and [V²-H]-[V-H]) and three spectral index models from Sentinel-2 (CRSI-COSRI, CRSI-NDWI, and CRSI-GARI). Using the optimal model, soil salinity in the study area was inversely estimated, revealing its spatial distribution patterns and enabling precise monitoring of typical salinized areas within the Weigan-Kuqa River Delta Oasis. The results indicate that (1) The Sentinel-2-based CRSI-COSRI model achieved the best inversion performance, with a correlation coefficient (r) of 0.639 and a coefficient of determination (R²) of 0.670, significantly outperforming all other models. (2) The simulated spatial distribution of soil salinization indicated that the overall degree of soil salinization in the study area is relatively high, with an increasing trend from west to east. This study verifies the effectiveness of feature space models in the remote sensing-based inversion of soil salinization in arid regions, providing reliable data support and methodological references for regional salinized soil monitoring and management.

The synergistic development of urban space and the built environment is essential for achieving sustainable urban development. This study examines Lanzhou, a river valley city, by constructing a community built environment quality evaluation index system using multi-source data to analyze its urban spatial structure and influencing factors. Key findings include: (1) From the perspective of the built environment, Lanzhou’s urban space exhibits an east-west axial, belt-like “core-periphery” structure, characterized by a spatial distribution pattern combining multiple centers with a fan-shaped expansion area dominated by the axial belt. (2) Regression analysis indicates that land price, mean elevation, distance from communities to the district administrative center, and distance to transportation stations significantly influence the urban spatial structure, while distance to subway stations, community road network density, land use type, public space availability, and population size have secondary impacts. (3) The influence of land price and land use type on the urban spatial structure has steadily increased. These findings offer theoretical and practical insights for optimizing urban spatial configurations in river valley cities and promoting high-quality urban development.

Changes to glaciers in the Ulugh Muztagh region significantly impact water resources in southern Xinjiang. This study extracted glacier area and terminus changes in Ulugh Muztagh region glaciers using multi-source remote sensing data from 1960 to 2023 and deep learning methods. Differences and driving factors were analyzed from the perspectives of glacier units and topographic features. The results show that (1) Overall, Ulugh Muztagh glaciers retreated from 1960 to 2023, with an average annual area loss of 0.83 km²·a^-1 and a terminus retreat rate of 2.21 m·a^-1. (2) In terms of intra-glacier regional differences, the retreat rate of glacier area was higher on south- and east-facing slopes and decreased with increasing elevation and slope. The terminus retreat rate was greater on east- and west-facing slopes (4.42 m·a^-1 and 4.01 m·a^-1, respectively) than on south- and north-facing slopes (1.63 m·a^-1 and 1.45 m·a^-1, respectively). (3) The spatial differences in glacier area and terminus changes in the Ulugh Muztagh region are mainly influenced by topography and climate. Sunny slopes receive more solar radiation, accelerating glacier melt, while lower elevations are prone to retreat due to rising temperatures. These findings provide a scientific basis for understanding the mechanisms by which alpine glaciers respond to climate change and offer important references for water resource management and ecological security assessment in southern Xinjiang.

Exploring the impact of territorial spatial ecological protection and restoration on the economic development of China’s poverty-alleviated regions provides valuable insights for promoting sustainable economic development and rural revitalization in these areas during the post-poverty era. Using the double-difference propensity score matching (PSM-DID) model, this study analyzed economic panel data from 58 national-level poverty-alleviated regions involved in the first and second batches of national integrated protection and restoration projects for mountains, rivers, forests, farmlands, lakes, grasslands, and deserts (the “Shanshui Project”) during China’s 13^th and 14^th Five-Year Plans. These were compared with data from 182 non-participating poverty-alleviated regions that had similar natural, social, and economic conditions from 2010 to 2020. This study assesses the impact of ecological protection and restoration on the economic development of these regions. The results indicate the following: (1) Under otherwise unchanged conditions, the economic development rate of poverty-alleviated regions implementing territorial spatial ecological protection and restoration increased significantly by 0.0329 compared to regions that did not implement such measures. This conclusion remains robust after balance tests, parallel trend tests, and placebo tests. (2) In the implementation of spatial ecological protection and restoration, both short-term and long-term economic development effects should be considered. Realizing the value of ecological products is a key driver of sustainable economic development in poverty-alleviated regions. To achieve this, increased policy support is recommended for this region, with a focus on improving systems for realizing the value of ecological products. This approach would enable these areas to successfully transform “green waters and mountains” into “golden mountains and silver mountains”.

This study aims to explore the mechanism of co-evolution between new quality productivity and new urbanization and alleviate the contradictions between them to contribute to achieving Chinese-style modernization. For this purpose, this study considers 31 provinces (including municipalities and autonomous regions) in China as research objects, constructing a theoretical framework for the co-evolution of “new quality productivity-new urbanization”, and uses the CRITIC weight analysis method and the Haken co-evolution model to measure new quality productivity and new urbanization levels and analyze their co-evolution mechanisms. The following results are obtained: (1) During the observation period, new urbanization and new quality productivity levels significantly improved, and the characteristics of spatial differentiation have become more obvious, presenting a regional development trend of “the east is leading, the middle is catching up, and the west is lagging behind”. (2) New quality productivity plays a dominant role in the dual-new complex system and is the core variable driving the system’s orderly evolution. Meanwhile, there are positive feedback mechanisms within and between the systems, and the cumulative and self-enhancing nature of the internal elements are key driving forces for the formation of positive feedback mechanisms. (3) The co-evolution of the dual-new system has undergone two stages: Rapid advancement (2014—2020) and stable operations (2020—2023). Spatially, it continues to show a distribution feature of “strong in the east and weak in the west”. Cultivating new quality productivity and building new urbanization in a way that is tailored to the local conditions, optimizing the co-evolution mechanism of the dual-new system, and continuously injecting new elements into the system are key paths toward achieving upward evolution. The findings provide a reference for achieving coordinated development of new quality productivity and new urbanization, resolving the contradictions between economic efficiency and social equity, and achieving Chinese-style modernization.

The systematic analysis of the factors influencing urban ecological resilience and improvement in its levels are essential for achieving sustainable urban development and fostering “resilient cities”. Based on panel data from 281 prefecture-level cities of China collected between 2005 and 2021, this study constructed evaluation indexes across three dimensions: resistance, adaptability, and resilience. The spatial and temporal evolution of urban ecological resilience were characterized, and spatial and temporal geographically weighted regression models were applied. In addition, a global trend analysis was conducted to explore the spatial and temporal heterogeneity of influencing factors. The results showed the following. (1) From 2005 to 2021, the ecological resilience level of Chinese cities increased from 0.0207 to 0.0248, with an average annual growth rate of 1.140%. Over this period, the center of gravity for ecological resilience shifted along a northeast-southwest direction. The difference in ecological resilience levels initially expanded but then narrowed. (2) Economic development, industrial structure, topographical factors, environmental regulations, and human capital demonstrated positive effects on urban ecological resilience. However, the influence of these factors exhibited obvious spatial and temporal heterogeneity, with their effects varying significantly in terms of magnitude and direction across different regions. (3) The impact of influencing factors on urban ecological resilience exhibited a greater variation along the east-west direction compared to the north-south direction. The ecological resilience was observed to be most sensitive to changes in industrial structure along the east-west direction, while environmental regulation had the greatest sensitivity along the north-south direction.

While the synergistic development of rural public services and rural tourism is critical for advancing rural revitalization, academic research focusing on coupling coordinated development between these two domains remains notably scarce. This study analyzes coupling coordination between rural public services and rural tourism and its impact on rural revitalization, This study uses methods including the coupling coordination degree model, the gravity center and standard deviation ellipse model, and spatial econometric models to quantitatively measure the spatiotemporal characteristics of the coupling coordination degree between the two in western China from 2010 to 2023, as well as their impacts and spatial spillover effects on rural revitalization. The findings reveal that the coupling coordination between rural public services and rural tourism in western China has increased year by year, with the overall level shifting from extreme disorder to intermediate coordination, and presenting a spatial differentiation pattern of high coupling coordination in the south and northwest, and low in the northeast. The gravity center of the coupling coordination degree exhibited a southwestward migration trend, while the standard deviation ellipse consistently maintained a northwest-southeast orientation, demonstrating a trend of first clustering and then dispersing. Furthermore, the degree of coupling coordination has a significant positive impact on rural revitalization and exhibits spatial spillover effects. The research findings can provide a theoretical basis and policy recommendations for the coordinated development of the two aspects and the region’s overall rural revitalization.

It is of great significance to explore the causes of the spatial distribution of tourism and leisure blocks for the healthy development of blocks. Taking 689 tourism and leisure blocks in China in 2024 as the research object, the spatial distribution and causes of tourism and leisure blocks were analysed by kernel density analysis, Dagum Gini coefficient and geodetector. The results show that: (1) According to the functions and characteristics, tourism and leisure blocks can be divided into natural recreation type, cultural leisure type, entertainment and recreation type, and special leisure type, and the quantitative relationship is sorted in descending order: cultural leisure type>entertainment and recreation type>natural recreation type>special leisure type. (2) The overall pattern of tourism and leisure blocks in the country presents a pattern of “bipolar dispersion and multi-core dot piece”. The poles are mainly distributed according to the urban agglomeration, and the Beijing-Tianjin-Hebei urban agglomeration is in the pole position among all types by virtue of its advantages such as complementary functions and rapid circulation of factors. (3) From the perspective of distribution differences, the various differences of tourism and leisure blocks are ranked in order from large to small: inter-provincial, sub-sample cross-overlapping phenomenon and intra-provincial, most of the differences between regions are paired across regions, and the overall performance is obvious east-west difference, and the distribution difference within the region is characterised by “low east-west and high in the middle”. (4) From the perspective of cause analysis, the degree of influence of production-living-ecology spaces on tourism and leisure blocks is as follows: life function>production function>ecological function, and the interaction of various factors significantly improves the interpretation of tourism and leisure blocks, among which life function is the dominant factor, production function is the key factor, and ecological function is the positive catalytic factor.

Using daily rainfall data from 45 national meteorological observation stations in southern Xinjiang of China from 1961 to 2022, we analyzed the spatiotemporal distribution and variability of rainfall, identified extreme rainfall thresholds, and examined extreme rainfall characteristics. The results show that (1) Over the past 62 years, the region with the highest and most rapidly increasing annual average rainfall in southern Xinjiang has been located in the middle mountain belt at an altitude of 1500-2500 m, with a growth rate of 5.4 mm·(10a)^-1. This increase is mainly reflected in the greater number of rainfall days, particularly large-scale rainfall days, while the increase in heavy-rainstorm-level rainfall and above is especially pronounced. (2) Based on a comparative analysis using the percentile method, the 99^th percentile was identified as the threshold for extreme rainfall in southern Xinjiang, corresponding to 14.1-35.4 mm. Thresholds in the western and mountain-adjacent regions are higher than those in the eastern basin-desert areas, and the extreme rainfall threshold has increased at a rate of 0.7 mm·(10a)^-1. Stations with the most rapid increases are primarily located in the mountainous areas of Kashgar, western Hotan, and northern Bayingol Mongolian Autonomous Prefecture. (3) Over the past 62 years, the number of extreme rainfall events in southern Xinjiang has generally ranged from 10-20 times, while in the Tianshan Mountains, events can persist for more than 20 times. Extreme rainfall events have increased in frequency at a rate of 0.9 events per decade.

This study addresses the critical need for precise agricultural population distribution data to inform modern agricultural system development and policy formulation. We integrated country-level demographic data from the Seventh National Population Census with the China Land Use Remote Sensing Monitoring Dataset to develop a novel grid-based spatial distribution model. This methodological approach enabled the first comprehensive visualization of China’s agricultural population density at a 1 km resolution, with validation metrics confirming the high statistical reliability of the results. Our spatial analysis revealed three significant geographic patterns: (1) The Hu Huanyong Line (Hu Line) presents a profound demographic divide, with southeastern regions exhibiting an average agricultural population density (30.57 persons·km^-2) nearly 15.9 times higher than northwestern regions (1.92 persons·km^-2). (2) Agricultural population distribution follows China’s three-tiered topographical gradient, with densities increasing significantly from the western plateau (0.98 persons·km^-2) through the central highlands (11.27 persons·km^-2) to the eastern plains (30.76 persons·km^-2). (3) Environmental determinants strongly influence agricultural settlement patterns, creating a clear dichotomy between densely populated warm, humid, low-altitude agricultural zones, and sparsely distributed cold-arid plateau and hilly agricultural regions. These findings provide an empirical basis for targeted agriculture development strategies: Prioritizing digital agriculture technologies in high-density regions like the Huang-Huai Plain; developing integrated agritourism models in ecologically sensitive areas; and implementing specialized training programs to cultivate a professional agricultural workforce adapted to regional conditions.

In the overall national security concept in the new era, historical blocks are facing dual challenges of disturbances and resilience building. Taking the historical block of Kashi Old City as the research object, this studybuilds a pressure-state-response model comprising six elements: Extreme disaster pressure, climate environment pressure, alley composition capacity, alley component density, infrastructure response, and public service response. It selects 44 key factors closely related to resilience to natural disturbances and uses the entropy weight method and the analytic hierarchy process to establish a multidimensional resilience evaluation system for historical blocks. The evaluation results show that (1) The comprehensive resilience index of the historical block of Kashi Old City is 1.84 (Level II), and the overall coping ability is weak. The resilience of the state layer (38.83%) is significantly higher than that of the response layer (27.45%), indicating its strong self-regulating ability but weak postdisaster management. (2) The key restrictive factors are the flood inundation radius, historical street and alley greening rate, and average summer solar radiation intensity, highlighting the synergistic risks between high-density built environments and climate sensitivity. (3) The wisdom of traditional construction is not linked to modern disaster prevention needs, leading to a contradiction between protection and safety. The results reveal a quantitative framework for integrated multi-hazard defense and living conservation of historic districts in arid zones, thereby promoting the synergistic development of cultural heritage conservation and ecological security.

To investigate the impact of new quality productive forces on the high-quality development of tourism, clarify their mutual influence, and improve the high-quality development of tourism. This study used nine provinces and autonomous regions in the Yellow River Basin as the research subject to establish a measurement index system for new quality productive forces and the high-quality development of tourism. Entropy values, fixed-effects models, and mediation effect models were used to measure new quality productive forces and the high-quality development of tourism in the nine provinces and autonomous regions from 2013 to 2022, analyzing their mutual influence. The results reveal that (1) New quality productive forces significantly and positively affect the high-quality development of tourism. (2) Cultural-tourism integration and technological progress mediate the relationship between new quality productive forces and the high-quality development of tourism. (3) New quality productive forces exert a more pronounced promotional effect on the high-quality development of tourism in regions with lower economic development levels. (4) Each component of new-type productive forces (new technologies, laborers, objects of labor, and means of labor) promotes the high-quality development of tourism, with laborers and means of labor playing more prominent roles at present. Therefore, localities should completely recognize the guiding values of new quality productive forces, formulate differentiated developmental strategies based on regional realities, and advance the high-quality development of tourism to help achieve the goals of Chinese modernization.

Grassland vegetation biomass is a key indicator of grassland ecosystem productivity and carbon storage. Its spatial distribution pattern and driving mechanisms are of great scientific significance for understanding the maintenance of regional grassland ecosystem structure and function and their responses to climate change. Taking the Qinghai Lake Basin as the study area, this research integrates field sampling data (collected in July-August 2023) and remote sensing data to analyze the spatial distribution characteristics of grassland vegetation biomass (including aboveground and belowground components) and to explore its driving pathways using statistical analysis, correlation analysis, and structural equation modeling. The results reveal (1) Significant differences in biomass among different vegetation types, with meadow types demonstrating higher values than steppe types. Aboveground biomass is highest in mountain shrub meadows (311.54 g·m^-2) and lowest in mountain solifluction meadows (64.67 g·m^-2), whereas belowground biomass is highest in dwarf kobresia meadows (3534.05 g·m^-2) and lowest in rhodiola desert (339.12 g·m^-2). (2) High aboveground biomass values are primarily concentrated in the middle and lower reaches of the Shaliu River Basin and the southern region surrounding the lake, whereas the high-value areas for belowground and total biomass are primarily located in the middle reaches of the Buha River, the Quanji River, the Qiadangqu River Basin and middle reaches of the Shaliu River. Lower altitude areas provide more suitable temperatures and fertile soil, thereby promoting the growth of aboveground parts. Conversely, due to colder conditions and poorer soils, higher altitude regions drive plants to enhance root system development and improve resource acquisition capacity. (3) Structural equation modeling revealed that ecosystem carbon use efficiency (total effect: -0.44) and soil bulk density (total effect: -0.59) were direct factors affecting both aboveground and belowground biomass of grassland vegetation. In conclusion, vegetation type, and regional environment collectively affected grassland vegetation in the Qinghai Lake Basin, identifying ecosystem carbon use efficiency and soil bulk density as the primary determinants. This study provides critical data and scientific support for understanding vegetation biomass spatial patterns and for guiding grassland conservation and restoration in the Qinghai Lake Basin.

Under the influence of global warming, the disaster risks associated with extreme precipitation events have intensified. These risks pose serious threats to regional socioeconomic development and the safety of life and property. Considering historical daily meteorological data from 1981 to 2024, this study examines the spatiotemporal distribution patterns of extreme precipitation indices in the Ili River Basin, Xinjiang, China. Furthermore, using data from multiple models under the Coupled Model Intercomparison Project Phase 6 (CMIP6) and applying the multimodel ensemble mean and Sen’s slope estimation method, this study estimates the changes in these indices under different scenarios from 2025 to 2050. The results indicate that: (1) From 1981 to 2024, most extreme precipitation indices in the Ili River Basin exhibited an increasing trend, especially in the eastern and southwestern mountainous regions. (2) From 2025 to 2050, under the SSP245 and SSP585 scenarios, extreme precipitation is estimated to result in large fluctuations but in an upward trend. In particularly, under the SSP585 scenario, precipitation events are expected to become more frequent and intense, with remarkable increases in annual precipitation and extreme precipitation events in the eastern and southern mountainous regions of the Ili River Basin, indicating a stronger upward trend and increased risk. By contrast, extreme precipitation events are projected to be relatively fewer in the northern and central plains. This spatial heterogeneity may have differentiated impacts on the frequency of natural disasters and agricultural and pastoral production in the region. The findings provide a scientific basis for preventing and mitigating extreme precipitation hazards and support high-quality regional development in the Ili River Basin.

The assessment of inclusive green development efficiency (IGDE) using a water cycle perspective is pivotal for the promotion of ecological sustainability and high-quality growth in the Yellow River Basin. This study addresses regional water scarcity challenges, incorporating indicators of the water cycle into an IGDE evaluation framework, relying on a super-efficiency slacks-based measure model to analyze IGDE's spatiotemporal evolution across 77 cities from 2013 to 2022. Methods of spatial autocorrelation and geographically temporally weighted regression were used to identify regional disparities and dynamic mechanisms of influence. Key findings include the following: (1) IGDE demonstrated a persistent upward trajectory, including accelerated growth, following the implementation of a national strategy for basin development, highlighting the role that policy interventions play. (2) Significant spatial heterogeneity emerged, revealing efficiency gaps between upper-basin and middle-lower basin regions. (3) High-efficiency clusters centered on economically advanced cities exhibited weakening spatial dependency over time. (4) Urbanization, natural resource endowments, institutional governance, and social innovation were identified as core drivers, and their impacts displayed distinct spatiotemporal variabilities. Theoretically, the research in this study advances sustainability science by integrating water cycle dynamics into green development metrics in a way that addresses limitations in conventional efficiency assessments. Methodologically, it establishes a novel spatiotemporal analytical framework that combines the measurement of efficiency, spatial econometrics, and regression modeling to decipher complex regional interactions. In a practical perspective, the findings provide actionable insight to optimize resource allocation, enhancing cross-regional governance and formulating adaptive policies tailored to basin-specific conditions. Bridging ecological objectives and socioeconomic inclusivity, this study offers empirical evidence that supports the coordinated management of water resources and green development in the Yellow River Basin.

In the summer of 2023, the Hexi Corridor, northwest China experienced its most intense regional high-temperature drought in nearly 60 years, accompanied by frequent wind and dust events. Notably, on September 6—7, Minqin County recorded its most severe sandstorm in almost 40 years. This study investigates two significant sandstorm events (on August 16, on September 6—7) in the Hexi Corridor during the summer and autumn of 2023. The evolution of meteorological elements, synoptic conditions, and boundary layer characteristics preceding the events is analyzed. The sources and transport pathways of dust are determined using the HYSPLIT model’s backward trajectory analysis and Sunflower 8 dust monitoring. The findings are as follows: (1) The regional high temperatures and drought provided ample material conditions for sandstorm development. (2) Synoptic analysis reveals that low-pressure troughs between 65°-85°E and 50°-58°N, combined with a stepped trough pattern from eastern Xinjiang to the Hexi Corridor and mesoscale shear, contributed to the events. At 500 hPa, the cold center intensity was ≤−20 ℃; at 700 hPa, it was ≤0 ℃. The mid-level jet exceeded 20 m·s⁻¹, the low-level jet exceeded 14 m·s⁻¹, and the K index was ≥21 ℃. Convergence occurred below 700 hPa (divergence ≤-0.75×10⁻⁵ s⁻¹), and divergence occurred between 700-650 hPa (divergence ≥0.75×10⁻⁵ s⁻¹). Ascending motion was observed between 800-600 hPa with intensity ≤−0.6 hPa·s⁻¹. Below 700 hPa was a region of positive vorticity with intensity ≥0.75×10⁻⁵ s⁻¹. (3) When the dust source is primarily local, near-surface humidity is the key factor influencing sandstorm intensity. (4) Compared to spring events, sandstorms in summer and autumn require stronger vertical motion, higher wind speeds, and larger 3 h pressure differentials for formation.

Using the CN05.1 precipitation dataset and the NCEP/NCAR reanalysis data, this study examines the spatiotemporal characteristics of summer precipitation on the Tibetan Plateau from 1981 to 2022 and explores the influence of westerly-monsoon synergy on this precipitation. The results show that: (1) Average summer precipitation over the past four decades is 216.6 mm, with a spatial pattern that decreases from the southeast to the northwest and an increasing trend of 4.85 mm per decade. The first empirical orthogonal function (EOF) mode indicates a spatially coherent change across the plateau, with a notable increase in wetness observed in the late 2010s, while the second EOF mode reveals pronounced interdecadal variability. (2) The westerly-monsoon synergy index provides a more robust indicator of summer precipitation over the plateau than individual westerly and monsoon indices, with a correlation coefficient of 0.5, effectively capturing interannual variability. (3) The westerly-monsoon synergy exerts a significant influence on summer precipitation over the Tibetan Plateau. When the synergistic effect strengthens, the upper-level divergent westerly circulation interacts with low-level warm and moist air transported by the westerlies, enhancing vertical ascent and promoting precipitation. Conversely, when the synergy is weak, anomalous easterly circulation dominates, accompanied by low-level dry and cold northerly flows, under which subsidence and moisture divergence suppress precipitation formation.

This study constructs a comprehensive evaluation index system for the new quality productive factors of cultural tourism in Xinjiang of China from three dimensions of laborers, labor materials, and labor objects and uses the entropy method, spatial autocorrelation, geographic detector, and other research methods to explore the spatiotemporal evolution characteristics and driving factors of the development level of the new quality productive factors of cultural tourism in 14 prefectures and regions of Xinjiang from 2013 to 2023. The results indicate that (1) From 2013 to 2023, the overall level of the new quality productivity in Xinjiang’s cultural tourism industry experienced a continuous growth trend, and the development levels of all dimensions have improved. The dimension of labor materials in cultural tourism witnessed the highest increase. (2) The development level of the new quality productivity in Xinjiang’s cultural tourism industry presents a spatial development pattern of “northern Xinjiang>southern Xinjiang>eastern Xinjiang”. The degree of spatial agglomeration has gradually weakened, but its overall characteristics are still evident. (3) Economic development level, industrial development level, market demand, scientific and technological investment intensity, education level, and transportation conditions are important driving factors for the development of new quality productivity in Xinjiang’s cultural tourism industry, among which transportation conditions and market demand have the most significant driving effects. The results provide data support for cultivating new quality productivity in Xinjiang’s cultural and tourism industry and help realize its high-quality development.

Ice and snow tourism has emerged as a crucial driving force for regional economic and cultural revitalization. This study employs accessibility measurement, hotspot analysis, and geographic detectors to evaluate the accessibility of ice and snow tourism destinations in the Yellow River Basin. We also examine their spatial distribution characteristics and influencing factors. The main findings show that: (1) Ice and snow tourism destinations in the Yellow River Basin exhibit an uneven spatial distribution characterized by a “dense east and sparse west” pattern. Moreover, clustering tendencies are apparent, wherein the downstream region contains the highest number of destinations, whereas the upstream region has the fewest. At the provincial scale, Shandong, Shanxi, and Henan have relatively more destinations, and at the municipal scale, Jinan, Zhengzhou, and Qingdao lead in the number of destinations. (2) Accessibility to ice and snow tourism destinations in the Yellow River Basin reveals a spatial gradient, described as “convenient in the downstream, moderate in the midstream, and restricted in the upstream areas”. The experiential tourism destinations dominate numerically; however, their overall accessibility remains relatively weak. (3) Hotspots and subhotspots of accessibility to ice and snow tourism destinations in the Yellow River Basin are concentrated in cities within Shandong and northern Henan. In contrast, cold spots and subcold spots mainly occur in cities located within the upstream and midstream regions. (4) A combination of social, economic, and ecological factors jointly influences accessibility to ice and snow tourism destinations in the Yellow River Basin. Specifically, road mileage, urbanization rate, and annual average snowfall comprise the primary determinants. These findings provide a scientific reference for optimizing resource allocation and promoting coordinated regional development in the Yellow River Basin’s ice and snow tourism sector.

Evaluating rural development from a resilience perspective is significant for breaking the urban-rural dual structure and promoting comprehensive rural revitalization. This article takes 86 counties in Gansu Province as the research object and constructs a rural resilience evaluation index system based on three dimensions: “resilience, adaptability, and reconstruction capability”. Using spatial autocorrelation analysis and geographic detector models, it reveals the spatiotemporal evolution of rural resilience and its driving mechanisms in counties of Gansu Province from 2010 to 2022. The results indicated that (1) The resilience level of rural areas in Gansu Province has significantly improved, with obvious spatial differentiation characteristics. The resilience of rural areas in central and eastern Gansu is relatively high, while some counties in Hexi, Gannan, and Linxia have lower resilience. The number of high-resilience counties continues to increase, while the number of low-resilience counties continues to decrease. (2) There is a spatial positive correlation in rural resilience, and the correlation is gradually weakening. The degree of spatial agglomeration is declining, with high-agglomeration areas shrinking toward the central Gansu region and low-agglomeration areas concentrated in counties such as Gannan and Linxia. (3) The level of rural social services, economic development, and agricultural production has a significant impact on rural resilience. The explanatory power of rural industrial structure for rural resilience is steadily increasing, and rural resilience is driven by economic development, resource optimization, industrial transformation, and service guarantee mechanisms. This study’s results provide scientific reference for the implementation of rural resilience development in Gansu Province.

Rapid urbanization and the attendant spatial expansion of urban regions can alter the structure of ecosystems, degrade habitat quality, and thereby impact sustainable development. Investigating the effects of urban expansion on habitat quality is crucial for the rational allocation of land use and supporting the restoration and reconstruction of ecosystems. This study focuses on the northern Tianshan Mountain economic zone (NTMEZ) in Xinjiang, China, where future urban expansion scenarios from 2024 to 2050 under different socioeconomic pathways are predicted using the Land Use Scenario Dynamic-Urban (LUSD-urban) model. The spatiotemporal changes in habitat quality from 2000 to 2050 in the region are also assessed. The results show that: (1) Between 2000 and 2023, urban land in the NTMEZ rapidly expanded from 551.72 km² to 1756.11 km², with an annual expansion rate of 54.70 km². From 2023 to 2050, under the fossil fuel pathway, urban land expansion is expected to be most significant, whereas under the regional competition pathway, expansion is expected to be least pronounced. The area of urban land is expected to further expand to between 3176.27 km² and 3859.20 km², with an annual expansion rate ranging from 244.03 km² to 369.38 km². (2) From 2000 to 2023, habitat quality in the NTMEZ declined from 0.551 to 0.520, representing a loss rate of 5.574%. From 2023 to 2050, habitat quality is expected to continue to decrease, with an anticipated loss of 0.57×10^-2‒0.82×10^-2, corresponding to a loss rate of 1.10%~1.58%. (3) The main driver of habitat quality loss is the encroachment of urban land on grasslands. In future urban planning and management, greater emphasis should be placed on the restoration of ecological land in the NTMEZ to ensure the region’s sustainable development.

The water resources of rivers and lakes are essential for human survival and development, and their protection and sustainable use have become a global priority. In this study, rivers and lakes in western Mongolia were investigated through field surveys conducted in the summer of 2023 to evaluate water quality contamination. By measuring chemical parameters, heavy metals, and comprehensive indicators, the spatiotemporal variation in water quality was analyzed using the single-factor evaluation method, the comprehensive pollution index method, and the comprehensive trophic status index. The aim was to provide a scientific basis for ecological protection and the region's sustainable socio-economic development. The results showed that: (1) Surface waters in western Mongolia were alkaline, with spatial variations in different indicators. Except for Uvs Lake, Kyrgyz Lake, and the lower reaches of the Khovd River, the remaining areas complied with relevant water quality standards of the world, Mongolia, and China. (2) The main pollution indicators were the permanganate index and ammonia nitrogen, with excessive organic matter primarily linked to grazing activities and domestic sewage discharge. (3) Significant spatial differences in lake trophic status were observed, with some lakes exhibiting severe eutrophication. Although areas with low nutrient concentrations presented a relatively small risk of eutrophication, continuous monitoring of nitrogen-phosphorus ratios remains necessary.

This research integrates the Flus-Markov model and the equivalent factor method to analyze dynamic changes in the land use patterns in the Tarim River Basin from 2012 to 2032, evaluate the spatiotemporal characteristics of ecosystem service value (ESV), and predict its future trends, based on Landsat remote sensing images and socioeconomic data. The results show that: (1) From 2012 to 2032, the area of unused land and water first decreased and then increased, while the cultivated land area expanded and grassland degraded significantly continuously, resulting in a decrease of 2093.72×10⁸ yuan in the total ESV. The contributions of grassland and water decreased by 2984.17×10⁸ yuan and 38.44×10⁸ yuan, respectively. (2) The spatial distribution of ESV shows a significant gradient, decreasing from high values in the northwest to low values in the southeast. Hydrological regulation, climate regulation, and gas regulation are key service functions. (3) Sensitivity analysis shows that changes in water bodies and grasslands have the greatest impact on ESV. A low value of the elasticity of coefficient correction indicates that the estimation results are robust. Model validation reveals a Kappa coefficient of 0.95, indicating reliable prediction accuracy. (4) Land use and normalized difference vegetation index are the main factors influencing the ESV of the Tarim River Basin. This study reveals the correlation between the degradation of ecological service functions and increased land use in the Tarim River Basin. ESV could be improved by optimizing land use structure, strengthening grassland and water conservation, and providing a scientific basis for ecological security and sustainable development in arid areas.

To promote urban-rural integration in China in the new era, it is necessary to explore the coordinated development of new urbanization in a harmonious villages. Using panel data of 30 provinces in China from 2011 to 2022, we construct a comprehensive index for the levels of new urbanization and harmonious villages and analyze their level of coordinated development, regional differences, and dynamic evolution by employing the coupled coordination degree model, Dagum’s Gini coefficient, Kernel density estimation, and Markov chains. The results show that: (1) The level of new urbanization and harmonious villages shows a growing trend. (2) The coupling between new urbanization and harmonious villages is low and mostly in the stage of near-disorder, and the level of coordination generally decreases from east to west. (3) The differences within the eastern region are the largest, and the inter-regional differences are the main source of the gaps in the levels of coupling of new urbanization and harmonious villages. (4) The absolute differences between regions are decreasing, with a certain gradient effect. (5) There is a tendency for the coupling of new urbanization and harmonious villages to increase, with spatial spillover effects. In the future, we should give full play to the dual promotion of new urbanization and harmonious villages, stimulate local internal energy in accordance with conditions, build a regional learning and exchange platform, and enhance the coordinated development of new urbanization and harmonious villages.

High-intensity coal mining accounts for exacerbated water-resource depletion and ecological vulnerability in arid and semi-arid regions. Therefore, investigating coal-mining-induced surface water and groundwater depletions is pivotal to sustainable water-resource utilization. To address this, static, dynamic, and depletion methods are employed to quantitatively identify water resources depleted by coal mining in 2022 in mining sites within the Jurassic, Carboniferous-Permian, and Triassic coalfields in northern Shaanxi, China. The results reveal the following: (1) Different coalfields exhibit significantly varying hydrogeological characteristics, with the aquifer thickness and coalfield type influencing the variations in the goaf area, water supply degree, and groundwater-damage modulus. (2) Coal mining already accounts for the depletion of 5782.761×10⁵ m³ of static groundwater reserves, specifically facilitating the depletion of 2171.952×10⁵ m³ dynamic reserves in 2022 (mainly in the Salawusu formation aquifer and burned-rock area of the Jurassic coalfield). (3) The total water influx of the mine was 2507.800×10⁵ m³ in 2022, and coal mining triggered the depletions of 486.889×10⁵ m³ and 335.848×10⁵ m³ surface water and groundwater, respectively, with coal mining exerting regional-type impacts on water resources. Overall, these results reveal the quantitative impact of coal mining on water resources, offering a scientific basis for the synergistic management of water-resource protection and development in arid areas.

The Zhundong region in Xinjiang, a typical arid and semi-arid area, is a vital national energy base with abundant coal resources. However, intensive coal mining and related industrial activities have caused severe ecological issues, including land desertification, salinization, and PM_2.5 pollution, intensifying the conflict between resource exploitation and ecological preservation. To accurately evaluate the ecological environment quality (EEQ) of this region, an arid modified remote sensing ecological index (ARSEI) is proposed herein. The ARSEI improves upon the traditional remote sensing ecological index by incorporating indicators for salinity, desertification, and air pollution, besides greenness, wetness, and heat. Using the Google Earth Engine platform, Landsat 5/8 images from 2000 to 2023 were processed to generate the ARSEI. The first principal component derived via principal component analysis was used to construct the ARSEI, with the direction of the wetness component applied to resolve the eigenvector ambiguity. Temporal trends were analyzed using Sen’s slope estimator and Mann-Kendall trend test, and ecological vulnerability was assessed based on the sensitivity and adaptability of the ARSEI. The optimal parameter-based geographical detector was employed to identify key driving factors and their interactions. Results indicated that the ARSEI of the Zhundong region showed a trend of initial decline followed by a partial recovery from 2000 to 2023, with its mean value decreasing from 0.368 in 2000 to 0.225 in 2018, before increasing to 0.289 in 2023. Spatially, the EEQ was generally poor, with over 60% of the area classified as “poor” or “relatively poor”, exhibiting a clear north-south gradient where the southern part exhibited better ecological conditions. Trend analysis revealed that 83.5% of the region experienced significant ecological degradation, whereas only 4.6% showed improvement, mainly in the southern agricultural and urban areas with low ecological vulnerability. Single-factor detection identified human activity intensity (q>0.6) and evapotranspiration (q>0.35) as the primary drivers. The interaction between temperature and human activity intensity had the strongest explanatory power (q>0.7), indicating nonlinear enhancement effects. In summary, large-scale resource development has exerted considerable pressure on the already fragile ecosystem. Although ecological restoration measures since 2019 have led to a partial recovery, the northern mining areas remain highly vulnerable due to water scarcity and soil degradation. The ARSEI model demonstrates enhanced applicability in arid resource-based regions and offers a scientific basis for ecological protection and sustainable development strategies.

Scientifically measuring economic resilience in the Yellow River region is essential for enhancing regional development quality and sustainability. This study constructed a comprehensive indicator system to evaluate economic resilience across the Yellow River region from 2003 to 2022, examining three critical dimensions: Resistance, resilience, and transformation. We employed the Theil index to quantify regional disparities and the Moran’s index to analyze spatial correlation patterns. Our analysis revealed four key findings: (1) Regional performance: The Yellow River region demonstrated stable economic operation with strong overall resilience during the study period, though a significant gap persists compared to national economic resilience levels. Among the dimensional subsystems, resilience scored highest while transformation scored lowest. (2) Spatial distribution: Economic resilience exhibits a clear center-periphery structure at the prefecture level. Higher resilience areas are concentrated in the Shandong Peninsula, provincial capitals in central and western China, and mineral-rich cities. Lower resilience characterizes Qinghai, Ningxia, most of Gansu, Shaanxi, Henan, western Inner Mongolia, and western Shandong. (3) Temporal trends: The overall difference in economic resilience across the Yellow River region showed a decreasing trend over most of the study year. Regional differences contributed more than 75% to overall inequality, highlighting the need for coordinated intra-regional strategies. (4) Spatial correlation: Positive Moran’s index values throughout the study period indicate significant autocorrelation in economic resilience. Local spatial clustering revealed predominant “low-low” agglomeration (concentrated in Qinghai, Gansu, and Ningxia) and “high-high” agglomeration (primarily in Shandong).

Railways are vital external transportation infrastructures in modern cities and play an important role in urban development. However, they also cause certain negative impacts, such as the fragmentation of urban patterns and disruption to residents’ lives. The study focused on Lanzhou City, Gansu, China, a linear city, and investigated the not-in-my-backyard (NIMBY) impacts of railway facilities employing a price characteristic model and a geographically weighted regression model. The study concluded the following. (1) Railway facilities significantly reduce housing prices. With every 1% reduction in distance between railway and railway stations, housing prices fall by 0.175% and 0.017%, respectively. (2) The NIMBY influence ranges approximately 1.2 km from the railway and approximately 0.6 km from the railway station. Owing to Lanzhou’s linear urban layout (a belt along the same direction), which runs parallel to the railway, several residential blocks (55.24% of total housing in the city), are within the NIMBY-affected area. (3) The spatial effects of the railway and railway stations vary significantly. Houses closer to the city center, particularly those related along the inner side of the railway, experience greater negative impacts. Furthermore, development areas are more impacted by the railway, and properties in high-price areas are more strongly affected compared to those located in the suburbs. (4) The “dock area” surrounding railroad stations has a maximum influence of 1.6 km, while the “municipal support area” affects up to 0.8 km. This implies that the positive effect of the station has a significant offsetting effect on the NIMBY effect.

As China accelerates the construction of new development models and advances high-quality development, human capital has become a key driver of intercity competitiveness. In recent years, the Lanzhou-Xining urban agglomeration has experienced significant population decline and insufficient human capital competitiveness, which have directly weakened its urban economic resilience. This study examines six representative cities within the Lanzhou-Xining (Lanxi) urban agglomeration, employing a linear mixed-effects model to analyze the effects and mechanisms of differentiated population growth and decline on urban economic resilience in 2010, 2015, and 2020. The findings reveal the following characteristics of urban economic resilience in the Lanzhou-Xining urban agglomeration. (1) Between 2010 and 2020, urban economic resilience generally increased across the Lanxi region, forming a “core-edge” structure, with Lanzhou City and Xining City at the core and other cities at the periphery. (2) Variations in population growth significantly affected urban economic resilience, with population inflows exerting positive externalities on regional resilience. (3) The rationalization of the industrial structure did not moderate the impact of population change on urban economic resilience. In cities experiencing population loss, initial declines facilitated some degree of industrial restructuring; however, this mode of adjustment was not sustainable over time. (4) Population change also influenced the level of local informatization, which in turn affected urban economic resilience. Population growth supported the development of urban informatization, thereby strengthening urban economic resilience. Based on these findings, it is recommended to promote coordinated regional development, enhance human capital accumulation, accelerate industrial transformation and upgrading, and advance population policy in tandem with informatization initiatives to improve the economic resilience of major cities in the Lanzhou-Xining urban agglomeration.

CO₂ emission reduction is a globally shared concern today, and the voluntary carbon offset (or sequestration increase) market for natural ecosystems, along with its operational models, holds immense development potential. As one of the key natural carbon sinks, grasslands have received widespread attention. This study focuses on the Xilin Gol League in Inner Mongolia of China as the research area, utilizing the Google Earth Engine platform to obtain MODIS data, meteorological reanalysis data, and vegetation coverage data. Based on the Carnegie-Ames-Stanford approach (CASA) model, the spatiotemporal dynamics of grassland net primary productivity (NPP) from 2001 to 2024 in Xilin Gol League were analyzed. The study investigates the impact of land cover change and climate change on grassland NPP through land use dynamics, partial correlation, and contribution index methods, providing a reference for achieving carbon peak and carbon neutrality goals in the region. The results indicate that: (1) From 2001 to 2024, grassland NPP in Xilin Gol League showed a fluctuating upward trend, with a multi-year average of 302.86 g C∙m^-2∙a^-1 The annual NPP distribution displayed a trend of gradual decrease from the northeast to the southwest. (2) The grassland NPP in Xilin Gol League was mainly driven by changes in land use and climate, with a minor influence from geographic factors, with precipitation having the greatest contribution, reaching 0.84. (3) Over the years, the impact of climate change on grassland NPP gradually outweighed that of land use change, especially in meadow steppe, typical steppe, and desert steppe, where contribution values reached 96.01%, 63.97%, and 93.05%, respectively.

Based on the “six elements” theory of tourism, this study explores the spatial distribution characteristics and influencing factors of tourism elements in the counties of Inner Mongolia, China, in 2023. Specifically, it utilizes tourism point of interest data at the county level and employs methods such as the nearest neighbor index, kernel density estimation, entropy method, comprehensive index, spatial autocorrelation, and geographical detector. The findings reveal the following. (1) The tourism elements in counties of Inner Mongolia exhibit significant clustering characteristics, with “food”, “accommodation”, and “shopping” being the most clustered elements, followed by “entertainment” and “transportation”, whereas the “tourism” element is relatively dispersed. (2) An imbalance in the density distribution of tourism elements is observed, where areas south of the fitting line (Alagxa Left Banner to Jalaid Banner) generally have higher densities than those in the northern regions. Notably, cities such as Hohhot-Baotou-Erdos, Chifeng, and Tongliao, have dense clusters of tourism elements. (3) Tourism elements demonstrate significant positive global autocorrelation, which includes four local autocorrelation patterns: low-low, low-high, high-high, and high-low. (4) The factors influencing the spatial distribution characteristics of tourism elements in the counties of Inner Mongolia span multiple dimensions, including economic, social, cultural, and natural environments, with economic and cultural factors identified as the core influences.

The complex relationship between dust storms and meteorological factors is the primary driver of particulate matter formation and distribution. These dust storms also significantly affect transportation, agriculture and animal husbandry, and population health. Using the HYSPLIT, wavelet coherence, and random forest models, the transport paths of two strong dust storms in northern China, one from March 10 to 25, 2021 (SD1) and the other from March 16 to 28, 2023 (SD2), were investigated. In addition, the effects of meteorological factors on urban PM₁₀ were investigated. The findings revealed the following. (1) A high aerosol optical thickness (AOD) belt stretching across Xinjiang, Gansu, Shaanxi, and Shandong formed in northern China during these two dust storm events. This phenomenon was primarily attributed to the southward movement of the Mongolian cyclone and the strong westerly wind circulation in Xinjiang. (2) The overall airflow transmission moved from northwest to northeast. Lanzhou was found to be susceptible to Xinjiang cyclones, while Beijing was notably affected by Mongolian cyclones. During SD1, airflow in Lanzhou primarily originated from Qinghai and Xinjiang, with 53.64% of Beijing’s air mass being transmitted to northeast regions of China and extending into northeastern Russia. During SD2, 51.16% of Lanzhou’s airflow stemmed from Inner Mongolia, while 49.41% of Beijing’s airflow moved toward areas such as Shandong and Jiangsu. (3) PM₁₀ variations in SD1 exhibited greater sensitivity to meteorological factors over longer time scales. In contrast, SD2 displayed diverse responses to meteorological factors over shorter time scales. (4) Lanzhou served as the collision zone for the two cyclones, showing unstable pressure and temperature changes. Conversely, Beijing acted as the endpoint for dust storm input, primarily influenced by temperature. These results facilitate a better understanding of dust storm formation mechanisms in northern China and of the interactions between meteorological factors and particulate matter.

Exploring the mechanism through which the digital economy influences the urban-rural income gap holds significant importance for promoting integrated urban-rural development and achieving common prosperity. Based on relevant data from 14 cities (prefectures) in Gansu Province of China from 2012 to 2022, this study employs a fixed effects model and a spatial econometric model to empirically analyze the impact mechanism and spatial effects of the digital economy on urban-rural income gap. The findings are as follows: (1) The impact of the digital economy on the urban-rural income gap in Gansu Province exhibits a nonlinear “narrowing-widening-narrowing” pattern and has entered a critical phase since 2022, with the digital economy widening the urban-rural income gap. (2) Regional heterogeneity analysis reveals that the influence of the digital economy on the urban-rural income gap varies across different regions of Gansu Province. Specifically, a pronounced “inverted N-shaped” relationship is observed in the southeastern region of Gansu, while the impact is relatively weak in the central region and statistically insignificant in the Hexi Corridor region. (3) Mechanism analysis indicates that industrial structure upgrading and agricultural socialized services are crucial channels through which the digital economy affects the urban-rural income gap in Gansu Province. (4) There is a notable spatial clustering phenomenon in the urban-rural income gap across Gansu Province. The direct, indirect, and total effects of the digital economy on the urban-rural income gap all demonstrate an “inverted N-shaped” relationship, implying the presence of spatial spillover effects in the digital economy’s influence on the urban-rural income gap.

As the core spatial units of oasis systems—hosting concentrated population functions, economic activities, and public services—the expansion of oasis cities is constrained by ecological patterns while simultaneously driving oasis evolution. Systematically identifying the spatial-temporal evolution of oasis and oasis cities is fundamental for coordinating ecological protection and spatial development in arid zones. This study delineates the spatial distribution of oasis in northwest China from 2000 to 2023 using evapotranspiration and the normalized difference vegetation index, while urban expansion data are extracted from the annual China land cover dataset. A synergy assessment is conducted by constructing an oasis and city synergy index (OCSI) to quantify the coordination between oasis and urban expansion across 31 typical oasis cities. Further, the impacts of various urban expansion patterns on oasis landscape structures are classified. The findings reveal that: (1) From 2000 to 2023, oasis areas increased by 0.90×10⁵ km², representing a 36.89% expansion. Concurrently, the number of oasis patches declined from 135 to 101, indicating a trend toward larger and more consolidated oasis, mainly through edge expansion and infilling. Urban areas expanded by 4.34×10³ km²—a 361.60% increase—outpacing oasis growth. Urban spatial expansion was categorized into four patterns: Infilling, edge expansion, leapfrogging, and river-dependent expansion. (2) OCSI analysis indicates that the urban and oasis expansion rates were similar in 8 cities, while critical disparities existed in the remaining 23. Terrain complexity and resource limitations contributed to mismatches, whereas economically developed or well-managed cities exhibited higher synergy levels. (3) Cities dominated by edge or infilling expansion modes displayed higher OCSI values, reflecting stronger spatial synergy with oasis expansion. By contrast, cities characterized by leapfrogging or river-dependent expansion showed low and fluctuating synergy owing to their dispersed spatial patterns or linear development along water bodies. This study elucidates the complex spatial interaction between urban development and oasis evolution in the northwest arid zone. Although these processes are dynamically coupled, coordinated development does not spontaneously occur. Achieving sustainable long-term balance necessitates region-specific strategies to optimize urban and oasis spatial configurations, thereby fostering synergistic evolution and ensuring ecological security and sustainable development in arid regions.

The Qinling-Daba Mountains are located at the convergence of the climatic transition zone between northern and southern China and the ecotone between warm-temperate and subtropical regions. As a climate-sensitive area, investigating the coupling relationships between vegetation and climate in this area is critical for understanding the evolutionary mechanisms of ecosystems under climate change. This study employs a multiple linear regression model to predict kernel normalized difference vegetation index (kNDVI) values under three Shared Socioeconomic Pathways scenarios from 2024 to 2100, based on MODIS data and climatic factor datasets from 2001 to 2023. The Theil Sen Median estimator and Mann-Kendall test were used to analyze spatiotemporal trends of vegetation changes, and path analysis was applied to dissect the driving mechanisms of key climatic factors. The results reveal that (1) Temperature is the dominant factor driving vegetation changes, spatially covering 67.27% of the study area, with its positive effects concentrated in the Qinling-Daba Mountain region, whereas the impacts of evapotranspiration and precipitation exhibit significant spatial heterogeneity. (2) The vegetation kNDVI increased by 0.1 from 2001 to 2023, demonstrating a “rapid initial growth followed by a gradual slowdown” trend, with degradation areas concentrated in low-altitude urbanized zones and high-altitude regions constrained by water-heat limitations. (3) Future scenario simulations reveal that vegetation dynamics stabilize under SSP119, whereas SSP585 demonstrates divergent trends, with the direct inhibitory effects of evapotranspiration coexisting with indirect facilitative effects driven by increased temperatures. (4) The replenishment efficiency of precipitation for vegetation diminishes with increasing climate extremes, whereas the direct climatic forcing of temperature significantly intensifies under elevated emission scenarios. (5) Regional vegetation responses indicate significant spatial heterogeneity, requiring differentiated ecological restoration strategies. These strategies should prioritize high-altitude vulnerable zones, low-altitude areas disturbed by human activities, and evapotranspiration-sensitive regions in the central-eastern sectors. This study reveals the nonlinear response of vegetation to climate change in the Qinling-Daba Mountains, thereby confirming the ecological stability advantages of the low-carbon pathway (SSP119) and providing spatially optimized strategies for vegetation conservation and carbon sequestration enhancement under regional carbon neutrality goals.

This study employs the theory of the “six elements” of tourism and utilizes spatial analysis methods, including nearest neighbor index, kernel density analysis, bivariate spatial autocorrelation, and Ripley’s K-function, to examine the spatial distribution and correlation characteristics of point of interest data related to tourism elements in the urban agglomeration on the northern slope of the Tianshan Mountains in Xinjiang of China based on data collected in April 2024. In addition, we explore the influencing factors using a geographical detector. The results show the following. (1) The spatial distribution characteristics of each tourism element exhibit significant concentration, with the degree of spatial agglomeration ranking from high to low as follows: “food”>“shopping”>“accommodation”>“transportation”>“entertainment”>“tourism”. (2) Each tourism element demonstrates weak spatial continuity, resulting in a distribution pattern characterized by “one core, one axis, and multiple centers”. At the county level, the spatial correlation among tourism elements is generally weak; however, a strong correlation exists between the “transportation” element and other elements, whereas the “tourism” element exhibits weak correlations, indicating a need for optimization in the spatial distribution of tourism elements. (3) The characteristic value of the overall spatial agglomeration scale of the “six elements” of tourism is 33.83 km. Among the different elements, the “tourism” factor shows the largest spatial agglomeration scale eigenvalue (42.95 km), whereas the “accommodation” factor has the smallest (18.48 km). (4) The influence of the interaction between each factor on the spatial pattern of tourism elements is significantly greater than that of any single factor. This research highlights the effects of multi-dimensional factors, including economic development level, infrastructure, and population on the spatial pattern of tourism elements, with GDP, night light index, number of A-level scenic spots, population density, and the proportion of the tertiary industry having the most significant effects.

An ecological vulnerability assessment provides the foundation for sustainable regional development and restoration. This study utilized remote sensing data and socioeconomic statistics to construct an ecological vulnerability evaluation index system for the Tarim Populus euphratica Forest Park (Xinjiang, China). Based on the sensitivity-resilience-pressure model, the system incorporated 18 indicators, including ecological function, and was analyzed using a combined hierarchical analysis-entropy weighting method. This approach revealed the spatiotemporal characteristics and driving factors of the park’s ecological vulnerability from 2000 to 2020. The results are as follows: (1) The park’s average ecological vulnerability index was 0.30 in 2020, indicating a mild vulnerability level, with a spatial distribution of higher vulnerability in the eastern, western, and central areas and lower vulnerability in the southeast. (2) The park’s ecological vulnerability showed a fluctuating downward trend, moving from an index of 0.33 in 2000 to 0.47 in 2010, before improving to 0.29 in 2015 and then slightly degrading to 0.30 in 2020. Over this period, 44.52% of the area saw a decrease in vulnerability, while 28.60% experienced an increase. (3) A significant positive spatial correlation was consistently observed, with a Global Moran’s I greater than 0 (Z>2.58, P<0.01), indicating prominent low-low and high-high clustering. (4) Key single-factor drivers of vulnerability included the degree of water change, desertification, water conservation, and the vegetation index. The interactions among the factors demonstrated a non-linear and two-factor enhancement relationship. Remarkably, the synergistic effect between the degree of water change and water source conservation was the most significant driver of the park’s ecological vulnerability. These results offer a scientific reference for the ecological restoration, environmental protection, and sustainable management of the Tarim P. euphratica Forest Park.

Promoting high-quality urban development in the Jiziwan metropolitan area of the Yellow River is crucial for establishing a significant growth pole within China’s Yellow River Basin. This study focuses on the 15 prefecture-level cities in the Jiziwan metropolitan area, developing a comprehensive evaluation index system for high-quality urbanization based on four dimensions: population, economy, society, and ecology. To analyze the spatial and temporal divergence characteristics and driving forces of high-quality urbanization from 2012 to 2021, the study employs the entropy method, Theil index, standard deviation ellipse, and geographic detector research method. The findings reveal the following. (1) The overall level of high-quality urbanization in the Jiziwan metropolitan area of the Yellow River exhibits a fluctuating upward trend from 2012 to 2021. (2) High-quality urbanization levels in this region shows significant spatial disparities, following a “core-edge” distribution pattern. Overall differences in development levels initially rise before falling, with intra-regional disparities being the primary contributor to overall variances in high-quality urbanization. The standard deviation ellipse indicates a spatial distribution directed from “northeast to southwest”, with its center of gravity located in Ordos City, highlighting notable spatial clustering. (3) The economic base serves as the dominant factor influencing high-quality urbanization, whereas the market environment, government regulatory capacity, and scientific innovation also play critical supporting roles. This study offers theoretical insights and serves as a reference for promoting high-quality urbanization and coordinated regional development in the Jiziwan metropolitan area of the Yellow River.

Desertification is among the most pressing environmental challenges affecting human society. Understanding the temporal and spatial changes in desertification sensitivity and revealing its driving factors are crucial for implementation of effective control measures. This study utilized the MEDALUS model, geographic information system (GIS) spatial analysis, geographic detectors, and other methods to develop a desertification sensitivity assessment model. By integrating various natural and human activity factors, this study aimed to identify desertification-sensitive areas and reveal the spatiotemporal evolution patterns and driving mechanisms of desertification sensitivity in the Hulun Buir grasslands, located in Hulun Buir City, Inner Mongolia Autonomous Region, China. The results showed the following. (1) From 2001 to 2020, the desertification sensitivity in the Hulun Buir grassland showed the trend of declining, rising and declining. From 2001 to 2010, the sensitivity exhibited a declining pattern, and then followed by an rising trend from 2010 to 2015, and finally, another phase of decline from 2015 to 2020. (2) Spatial analysis revealed that the desertification sensitivity decreased from west to east across the Hulun Buir grasslands. The sensitivity levels were classified as extreme, high, moderate, mild, and insensitive. (3) Climate and vegetation and soil factors were found to exert the greatest influence on desertification sensitivity. Human interference factors played a secondary role, while topographic factors had the least influence. Furthermore, the interaction between any two factors demonstrated a greater effect than individual factors alone. The results of this study can offer valuable insights for desertification control and strategy formulation in the Hulun Buir grasslands.