Balancing urbanization with ecological carrying capacity is essential for sustainable urban development. Traditional land use prediction and urban growth boundary (UGB) delineation methods often overlook ecological assessments and fail to address policy conflicts. This study proposes an integrated model combining urban spatial suitability (USS) and ecological carrying capacity (ECC) evaluations with cellular automata (CA) model to improve simulation accuracy and support scenario-based UGB delineation. First, we identify spatial variations in urban development potential under different scenarios by adjusting the weights of USS and ECC. Then, a multi-objective planning model is used to optimize the future land-use structure, maximizing overall benefits. Finally, the development potential and optimized land allocation are incorporated into the CA model to simulate future land use and delineate UGB for each scenario. Results show that integrating USS and ECC evaluations improves simulation accuracy, with the Kappa coefficient increasing from 0.836 (with only USS evaluation) to 0.908 and overall accuracy reaching 94.1%. While the economic development scenario yields the highest economic benefits, a stronger emphasis on ECC produces more compact and spatially organized urban forms, characterized by higher aggregation and lower fragmentation. This framework provides a robust basis for multi-scenario urban simulation and offers valuable guidance for the scientific UGB delineation.

To promote the harmonious coexistence of cities and lakes and to achieve Sustainable Development Goals in the Taihu Lake Basin, we developed an analytical framework for city-lake symbiosis (CLS) on the basis of symbiosis theory. Using the Lotka-Volterra (L-V) model and a coordination degree model, we assessed the state and evolution of the CLS relationship. The findings reveal that urban development levels increased steadily from 27.36 in 1980 to 78.90 in 2020, whereas the ecological conditions of Taihu Lake initially decreased, followed by slow and fluctuating recovery. Overall, cities and Taihu Lake exhibited a “mutualism” relationship, with α and β values of -1.89 and -1.77, respectively, and a general upward trend in the degree of coordination over the study period. However, during the periods 1980-1998 and 2012-2016, the relationship displayed a pattern of “mutual damage”. The adverse effects of urban development accumulated gradually, in contrast to the rapid and abrupt deterioration observed in the lake. Ecological recovery in Taihu Lake progressed slowly and unevenly, stabilizing only after 2016 into a phase of sustained improvement. We recommend enhanced and coordinated efforts in ecological restoration and environmental governance to support this positive trajectory.

Wind and water compound erosion (WWCE) has aggregated the hazards of soil erosion on cropland in the black soil region of northeastern China. The present study employed novel methodology to characterize the spatio-temporal variations in WWCE at the regional scale, using a classification scheme consisting of four levels and three types based on the Revised Universal Soil Loss Equation (RUSLE) and the Revised Wind Erosion Equation (RWEQ). The results showed that between 2001 and 2020, wind-dominated compound erosion (WIDCE) was the dominant type of WWCE, with the relative area decreasing from 73.3% to 55.5%. The significant (p<0.05) driving factors of spatial variation in WWCE included wind speed, precipitation, air temperature, slope gradient, and elevation in 2001, while the anthropogenic factor of land use/land cover was included since 2010. The total area of WWCE and WIDCE decreased initially and then increased from 2001 to 2020, while water-dominated compound erosion (WDCE) and wind-water equivalent compound erosion (WWECE) showed increasing trends during this period. The Moderate and Intensive degree areas of WIDCE, WDCE, and WWECE showed dramatic increases from 2001 to 2020. The implications are discussed, and hotspots are identified for the improvement of future soil and water conservation measures in the black soil region of northeastern China.

Driven by climate change and anthropogenic activities, watershed hydrological processes are undergoing significant nonstationary changes, with increasing variability and more frequent extreme events. These changes have triggered cross-regional hydrological concurrence, which remains understudied compared with single-watershed anomaly analyses. To address this gap, daily runoff data from 27 small watersheds in the Yellow River Water Conservation Area are analysed in this study to identify subseasonal and seasonal anomalies, reveal the spatio-temporal patterns of multitype hydrological concurrence, and quantify key drivers using an XGBoost-SHAP explainable model. The results indicate that subseasonal anomalies occur more frequently and exhibit greater spatial heterogeneity, whereas seasonal anomalies are longer lasting, suggesting the presence of both short-term and cumulative hydrological risks. Hydrological concurrence shows clear seasonal prevalence, with higher probabilities at the seasonal scale. From a temporal perspective, the frequency of concurrence increased, particular for pluvial and drought concurrence at the subseasonal scale. From a spatial perspective, concurrence was more common in the eastern part of the study area, occurring most often in the Weihe South Mountain Region. Driver analysis revealed that temperature dominated subseasonal concurrence, whereas vegetation and hydrothermal conditions played a greater role at the seasonal scale. These findings highlight the complex and scale-dependent nature of hydrological concurrence under a combination of climatic and anthropogenic influences.

Based on optical remote sensing, two challenges should be highlighted in research on reservoir water resources in mountainous areas: (1) the isolation of climatic influences from anthropogenic impacts during impoundment periods, and (2) the discernment of circulation mechanisms to improve early-warning-system capabilities against extreme climate events. To address these challenges, we developed an enhanced automated water-detection framework using Landsat TOA data (1986-2023) that allowed us to analyse the spatiotemporal variations and driving factors of the water surface area of the Danjiangkou (DJK) Reservoir—China’s key source of the South to North Water Diversion. The results showed that anthropogenically induced changes in water surface area were evident through infrastructure developments, notably the increase in the height of the DJK dam (contributing 34.0% of the variance) and the construction of the Wangfuzhou Hydrojunction (contributing 51.9% of the variance). Following ensemble empirical mode decomposition and first-order difference detrending to reduce the impacts from human activity, a significantly enhanced positive (negative) correlation between autumn precipitation (potential evaporation) and water surface area was revealed; this demonstrated the climate-driven controls on reservoir dynamics at the interannual and decadal scales. Importantly, atmospheric pressure anomalies over the Tibetan Plateau and the area of the Asian polar vortex are two effective indicators of autumn precipitation anomalies for the DJK Reservoir. Our research framework has the potential to support the development of early warning systems, which have direct applications to the DJK Reservoir and, more broadly, to reservoir systems across Eurasia.

Coordinating socioeconomic development and environmental conservation is essential for ensuring sustainable development. Previous studies have primarily focused on evaluating key dimensions of coordination, i.e., performance alignment and progress synchronization, in isolation, thus limiting a comprehensive understanding. Assessment methods also face limitations because of the lack of reliable and comparable performance evaluation criteria, insufficient differentiation in coordination measurement, and symmetry issues that conceal distinct coordination patterns. To address these gaps, a dual-dimensional coordination analytical framework is developed in which Sustainable Development Goals (SDG)-based criteria and refined coordination measurement methods are incorporated. Application of this framework to the Qinghai‒Tibet Plateau reveals that environmental and socioeconomic goal achievement increased by 15% from 2000 to 2020, reaching 59% and 41%, respectively. The degree of performance alignment remained moderate, and progress synchronization was generally high, with both improving over time despite persistent regional disparities. Under the assumption of SDG achievement by 2030, projections indicate that only counties on the eastern plateau and in parts of Lhasa are likely to sustain coordinated progress, while 51% of counties face dual-dimensional coordination challenges, which poses barriers to SDG attainment. These findings highlight the need for phased and region-specific policy strategies. This framework provides a systematic approach for analysing coordination challenges and guiding policy formulation, with broader applicability across regions.

The Himalayas, as a global biodiversity hotspot, are crucial for the sustainable development of the Qinghai-Tibet Plateau and South Asia. In this study, the effects of human activity expansion on habitat fragmentation were investigated. Although the encroachment on ecological space in the Himalayas is mainly due to the expansion of cultivated land, in India and Pakistan, human activities affect mainly woodlands, whereas in Nepal and Bhutan, they affect mainly grasslands. Moreover, anthropogenic activities have a more significant impact on agriculture-driven regions such as Nepal, Bhutan, South Xizang, and Sikkim. Finally, the expansion of anthropogenic activities in areas unsuitable for urban and rural development may lead to more severe ecological consequences. Therefore, the expansion of anthropogenic activities in rural areas of the Himalayas warrants careful attention, especially in regions that are not suitable for such development.

The global value chain (GVC) is a central driver of the international division of labor and a critical lens for understanding global economic dynamics. This study constructs a long-term GVC network dataset using multi-regional input-output (MRIO) tables and input-output analysis techniques. By integrating advanced network analysis algorithms, this study characterizes the macrostructure and spatiotemporal evolution of the GVC network, examines its topological features, and evaluates the evolution of its structural resilience. The main findings are as follows: (1) Value flows among major economies form a hierarchical structure, and value distribution is highly uneven across space. The GVC network has expanded and densified over time, with rising value flows in East Asia, evolving from a dual-center structure centered on Europe and North America to a tri-polar structure comprising Europe, North America, and East Asia. (2) The core-periphery structure of the GVC network is pronounced, and a node’s position is determined by both the scale of its participation and the diversification of its value linkages. (3) The backbone structure of the GVC network has shifted from a single core centered on the United States to a dual-core system led by China and the United States, while Europe and Japan provide stabilizing support. Over time, the backbone structure has evolved with a trend of eastward shift and increasing globalization characteristics. (4) Overall resilience of the GVC network is limited and follows a four-stage evolutionary trajectory, with major international shocks serving as turning points. These findings elucidate the node and structural characteristics of the GVC network, contributing to a deeper understanding of global economic dynamics and the process of economic globalization.

Addressing poverty among the elderly is essential for achieving sustainable development objectives. Despite considerable research progress on elderly poverty, studies that explore this topic from the perspective of spatial changes in urban-rural differences are scarce. This study aims to fill this gap by utilising data from the Chinese Longitudinal Healthy Longevity Survey covering the years 2011, 2014 and 2018. This research applies the Alkire- Foster and Geodetector methods and investigates the spatial variations and determinants of urban-rural differences in elderly poverty (URDEP). Results reveal that rural elderly individuals experience higher poverty levels than their urban counterparts. In addition, URDEP is lower in developed areas and more pronounced in less-developed regions. This study identifies several factors that influence spatial changes in URDEP, including urban-rural income gap, economic level and quality of community elderly care services. These findings offer valuable insights into reducing elderly poverty and enriching the understanding of mechanisms driving spatial changes in URDEP from a geographical perspective.

The spatial mismatch between tourism supply and demand poses a significant challenge to sustainable rural development. China’s rapid yet uneven rural tourism development presents a representative case for examining such spatial imbalances. Using a national dataset of characteristic villages alongside multi-source geospatial data, this study employs a coupling coordination model and geographical detector analysis to assess the supply-demand relationship in China’s characteristic villages. The findings indicate a pronounced eastward clustering of villages and a diamond-shaped spatial pattern of tourism facilities linking four major cities (Beijing, Chengdu, Guangzhou, and Shanghai). Crucially, we identify a widespread undersupply of facilities around high-demand characteristic villages in the eastern region. The overall coupling coordination between tourism supply and demand was low and exhibited significant spatial heterogeneity in characteristic villages. High levels of coordination were found in the Beijing-Tianjin-Hebei, Chengdu-Chongqing, Yangtze River Midstream, Yangtze River Delta, and Guangdong-Hong Kong-Macao Greater Bay Area urban agglomerations. The density of tourism facilities and GDP emerged as critical variables affecting the spatial differentiation of supply-demand coordination degree. The study offers a scalable method for diagnosing supply-demand imbalances and guiding targeted planning interventions in rural tourism.

Understanding the complex interactions among armed conflict events is important for the simulation and prediction of conflict risks. Many studies have suggested that conflict events affect neighbouring regions. Here we find that conflicts exhibit not only localized effects but also significant teleconnections. By applying complex network analysis, the spatiotemporal scale of conflict teleconnections is quantified, revealing a characteristic spatial distance of approximately 1500 km and a rapid propagation delay of about 10 days. Moreover, distinct teleconnection patterns are identified through network coefficients. Eight major hubs, located in South Asia, the Middle East, and Sub-Saharan Africa, emerge and exhibit pronounced spatial heterogeneity in their interactions. Furthermore, analysis of the underlying driving mechanisms indicates that differences in teleconnection patterns are likely associated with the flows of energy, materials, and information, with information playing the predominant role in shaping these interactions. These findings provide insights into long-distance interactions in armed conflict, offering a better understanding of conflict risk.