Search Results (2,898)

Accurate precipitation estimation is essential for hydrological modeling and water resource management in arid regions; however, complex terrain and sparse meteorological station networks introduce substantial uncertainties into gridded precipitation datasets. This study evaluates the performance of nine widely used precipitation products in the arid region of Northwest China (ARNC) at both the meteorological station scale and the sub-basin scale, and applies the Bayesian Model Averaging (BMA) approach to merge multi-source precipitation estimates. The results reveal pronounced spatial heterogeneity and significant differences in performance among datasets, with the Integrated Multi-Satellite Retrievals for the Global Precipitation Measurement mission performing best at the station scale and the Famine Early Warning Systems Network Land Data Assimilation System performing best at the sub-basin scale. Compared with individual products, the BMA-merged precipitation demonstrates substantial improvements at both scales, providing higher coefficients of determination and agreement indices, and lower relative mean absolute error and relative root mean square error, indicating enhanced accuracy and robustness. The BMA-merged precipitation product generally exhibits superior and more spatially consistent performance than the individual datasets across the ARNC, thereby providing a more reliable basis for regional hydrological and climate-related applications. The merged dataset shows that the mean annual precipitation in the ARNC during 2000–2024 is approximately 230.4 mm, exhibiting a statistically significant increasing trend of 1.4 mm per year, with the strongest increases occurring in the Tianshan and Qilian Mountains. This study provides a reliable foundation for hydrological modeling and climate-change assessments in data-limited arid environments. Full article

Green finance is increasingly recognized as an important instrument for improving sustainable development. Existing research has focused on green finance’s impact on corporate environmental performance, failing to account for the complex regional mechanisms that shape its contribution to systemic sustainability. This study fills the gaps by examining the mechanism and spatial heterogeneity of green finance’s influences on regional sustainability measured by environmental performance. Using panel data from 30 Chinese provinces during 2010–2022, it shows that green finance increased from 0.318 to 0.539, while environmental performance improved from 0.441 to 0.656. The empirical evidence demonstrates that green finance has a robust positive effect on environmental performance, acting as an effective tool for environmental governance. This impact is primarily channeled through technological innovation and green consumption, with environmental regulation providing a synergistic moderating role. Furthermore, significant regional heterogeneity in sustainability outcomes is observed, while the effect is strongest in eastern China, unstable or negligible in old industrial bases, and unexpectedly negative in ecologically fragile Northwest China. The disparities are attributed to variations in local economic structure, institutional capacity, and development stage. Corresponding policy recommendations include improving the institutional framework, channeling financial resources to green technology R&D and sustainable consumption incentives, integrating green finance with environmental policies, and implementing region-specific strategies. This study offers practical benchmarks for China and other developing economies to leverage green finance as a driver of sustainable development. Full article

The imbalance between water supply and demand in the arid and semi-arid regions of northwest China has become increasingly severe, highlighting the urgent need to develop and utilize unconventional water resources. Return flow, originating from canal leakage and field drainage, is widely distributed in these regions. However, as it contains a certain amount of salts, long-term use of return flow can lead to soil salinization and degradation of soil structure. Therefore, the scientific utilization of return flow has become a key issue for achieving sustainable agricultural development and efficient water use in arid areas. This study was conducted in the Yinbei Irrigation District, Ningxia, northwest China. Water samples were collected from the main and branch drainage ditches and analyzed to evaluate the feasibility of using return flow irrigation in the area. In addition, based on two years of continuous field monitoring and HYDRUS model simulations, the long-term dynamics of soil salinity under moderate return flow irrigation over the next 20 years were predicted. The results show that the total salinity of the main return ditches consistently remained below the agricultural irrigation water quality standard of 2000 mg/L, with Na⁺ and SO₄²⁻ as the predominant ions. Seasonal variations in return flow salinity were notable, with higher levels observed in spring compared to summer. Simulation results based on field trial data indicated that soil salinity displayed regular seasonal fluctuations. During the rice-growing season, strong leaching kept the salinity in the plough layer (0–40 cm) low. However, after irrigation ceased, evaporation in autumn and winter led to an increase in surface soil salinity, creating annual peaks. Long-term simulations showed that soil salinity throughout the entire profile (0–100 cm) followed a pattern of “slight increase—gradual decrease—dynamic stability.” Specifically, winter salinity peaks slightly increased during the first two years but then gradually declined, stabilizing after approximately 15 years. This indicates that long-term return-flow irrigation does not result in the accumulation of soil salinity in the plough layer. Full article

This study aimed to examine the interventional effects of positive psychology expressive writing (PPEW) on adolescents’ time attitudes and mental health. A total of 285 adolescents from Northwest China (M = 14.13, SD = 1.075; 53.3% female) were randomly assigned to either a PPEW group (n = 148) or a control group (n = 137). The PPEW group completed a six-week positive psychology expressive writing intervention, while the control group engaged in neutral writing tasks. All participants were assessed on time attitudes, positive affect, and depressive symptoms before and after the intervention. The results showed that, compared to the control group, the PPEW group scored significantly higher on Past Positive, Present Positive, and Future Positive, and significantly lower on Present Negative at post-test; however, a significant improvement in Past Negative was observed only within the PPEW group itself. Regarding mental health, depressive symptoms were significantly reduced in the PPEW group relative to the control group at post-test, but no significant change was observed in positive affect. In conclusion, positive psychology expressive writing can effectively foster the positive development of time attitudes in adolescents and may serve as a feasible approach to alleviating depressive symptoms. Full article

Photovoltaic (PV) power generation is essential for achieving carbon neutrality and advancing renewable energy development. In Northwest China, the rapid expansion of PV installations requires accurate and timely spatial data to support effective monitoring and planning. Addressing the limitations of existing datasets in spatiotemporal resolution and driver analysis, this study develops a scalable solar facility inventory framework on the Google Earth Engine (GEE) platform. The framework integrates Sentinel-1 SAR, Sentinel-2 multispectral imagery, and interpretable machine learning. Feature redundancy is first assessed using correlation-based metrics, after which a Random Forest classifier is applied to generate a 10 m resolution distribution map of utility-scale photovoltaic power plants as of December 2023. To elucidate model behavior, SHAP (SHapley Additive exPlanations) is used to identify key predictors, and MaxEnt is incorporated to provide a preliminary quantitative assessment of spatial drivers of PV deployment. The RFECV-optimized model, retaining 44 key features, achieves an overall accuracy of 98.4% and a Kappa coefficient of 0.96. The study region contains approximately 2560 km² of PV installations, with pronounced clusters in northern Ningxia, central Shaanxi, and parts of Xinjiang and Gansu. SHAP analysis highlights the Enhanced Photovoltaic Index (EPVI), the Normalized Difference Built-up Index (NDBI), Sentinel-2 Band 8A, and related texture metrics as primary contributors to model predictions. High EPVI, NDBI, and Sentinel-2 Band 8A values contribute positively to PV classification, whereas vegetation-related indices (e.g., NDVI) exhibit predominantly negative contributions; these results indicate that PV mapping relies on the integrated discrimination of multiple spectral and texture features rather than on a single dominant variable. MaxEnt results indicate that grid accessibility and land-use constraints (e.g., nighttime light intensity reflecting human activity) are dominant drivers of PV clustering, often exerting more influence than solar irradiance alone. This framework provides robust technical support for PV monitoring and offers high-resolution spatial distribution data and driver insights to inform sustainable energy management and regional renewable-energy planning. Full article

Heatwaves pose increasing risks to human health and socio-economic systems, yet their spatiotemporal organization and underlying synergistic mechanisms remain insufficiently understood, particularly with respect to daytime and nighttime processes. Using a dual identification framework combining absolute and relative temperature thresholds, this study systematically investigates the spatiotemporal evolution of daytime and nighttime heatwaves across China during 1961–2022. A complex network approach is further introduced to characterize the interannual co-variability and interdecadal structural evolution of heatwave activity from a system-level perspective. Results reveal a pronounced interdecadal transition in the early 1990s, accompanied by a fundamental reorganization of heatwave co-occurrence networks. Heatwave frequency exhibits a clear post-transition desynchronization, characterized by a sharp decline in network connectivity and fragmented local clustering, indicating a shift from large-scale, circulation-dominated coherence toward increasingly localized and heterogeneous heatwave occurrences. In contrast, heatwave duration shows an opposite evolution, with significantly enhanced spatial synchronization after the transition. Degree centrality and clustering coefficients increase markedly, and high-connectivity cores expand from coastal regions into inland areas, including North, Central, and Northwest China. This coexistence of desynchronized heatwave occurrence and strongly synchronized persistence suggests an emerging high-risk regime in which heatwaves occur more randomly but, once initiated, tend to persist coherently across large regions. Furthermore, a dual-layer network analysis reveals previously undocumented cross-temporal coupling between daytime and nighttime heatwaves, with pronounced regional differences. The middle and lower reaches of the Yangtze River are more strongly influenced by local processes, whereas northern China is increasingly governed by large-scale circulation control and enhanced regional clustering after the transition. These findings demonstrate that complex network analysis provides a powerful framework for uncovering hidden structural changes in extreme heat events and offer new insights into the evolving risks of compound and persistent heatwaves under climate change. Full article

River and lake ice are sensitive indicators of climate change and important components of hydrological and ecological systems in cold regions. In this study, we develop a simple and transferable “surface water + land surface temperature (LST)” framework on Google Earth Engine to map potential winter ice area across China from 1990 to 2020. The framework enables consistent, large-scale, long-term monitoring without relying on complex remote sensing models or region-specific thresholds. Our results show that, despite a pronounced northwestward shift in the freezing-zone boundary, more than 400 km in the Northeast Plain and about 13 km per year along the eastern coast, the total ice-covered area increased by approximately 1.1% per year. At the same time, the average ice season became slightly shorter. This indicates asynchronous spatial and temporal responses of potential winter ice to warming. We identify a persistent “Northwest–Northeast dual-core” spatial pattern with strong positive spatial autocorrelation, characterized by increasing ice cover in Tibet, Qinghai, Xinjiang, Inner Mongolia, and Northeast China, and decreasing ice cover mainly in Beijing and Yunnan, where intense urbanization and low-latitude warming dominate. Random Forest modeling further shows that water area fraction, nighttime lights, built-up area, altitude, and water–heat indices are the main controls on potential winter ice. These findings highlight the combined influence of hydrological and thermal conditions and urbanization in reshaping potential winter ice patterns under climate change. Full article

This study investigates the characteristics and causes of a low-level wind shear (LLWS) event induced by downward momentum transport at Xining Airport, China on 5 April 2023. By utilizing Doppler Wind Lidar (DWL), Automated Weather Observing System (AWOS), and ERA5 reanalysis data, the detailed structure and synoptic-scale mechanisms of the event were analyzed. The LLWS manifested as a non-convective, meso-γ scale (2–20 km) directional wind shear, characterized by horizontal variations in wind direction. The system moved from northwest to southeast and persisted for approximately three hours. The shear zone was characterized by westerly flow to the west and easterly flow to the east, with their convergence triggering upward motion. The Range Height Indicator (RHI) and Doppler Beam Swinging (DBS) modes of the DWL clearly revealed the features of westerly downward momentum transport. Diagnostic analysis of the synoptic-scale environment reveals that a developing 300-hPa trough steered the merging of the subtropical and polar front jets. This interaction provided a robust source of momentum. The secondary circulation excited in the jet entrance region promoted active vertical motion, facilitating the exchange of momentum and energy between levels. Simultaneously, the development of the upper-level trough led to the intrusion of high potential vorticity (PV) air from the upper levels (100–300 hPa) into the middle troposphere (approximately 500 hPa), which effectively transported high-momentum air downward and dynamically induced convergence in the low-level wind field. Furthermore, the establishment of a deep dry-adiabatic mixed layer in the afternoon provided a favorable thermodynamic environment for momentum transport. These factors collectively led to the occurrence of the LLWS. This study will further deepen the understanding of the formation mechanism of momentum-driven LLWS at plateau airports, and provide a scientific basis for improving the forecasting and warning of such hazardous aviation weather events. Full article

As China’s urbanization rate continues to rise, the scale of high-rise residences also grows, emerging as one of the main sources of building energy consumption and carbon emissions. It is therefore crucial to conduct energy-efficient design tailored to local climate and resource endowments during the schematic design phase. At the same time, consideration should also be given to its impact on economic efficiency and environmental comfort, so as to achieve synergistic optimization of energy, carbon emissions, and economic and environmental performance. This paper focuses on typical high-rise residences in three cities across China’s northwestern region, each with distinct climatic conditions and solar energy resources. The optimization objectives include building energy consumption intensity (BEI), useful daylight illuminance (UDI), life cycle carbon emissions (LCCO2), and life cycle cost (LCC). The optimization variables include 13 design parameters: building orientation, window–wall ratio, horizontal overhang sun visor length, bedroom width and depth, insulation layer thickness of the non-transparent building envelope, and window type. First, a parametric model of a high-rise residence was created on the Rhino–Grasshopper platform. Through LHS sample extraction, performance simulation, and calculation, a sample dataset was generated that included objective values and design parameter values. Secondly, an SVM prediction model was constructed based on the sample data, which was used as the fitness function of MOPSO to construct a multi-objective optimization model for high-rise residences in different cities. Through iterative operations, the Pareto optimal solution set was obtained, followed by an analysis of the optimization potential of objective performances and the sensitivity of design parameters across different cities. Furthermore, the TOPSIS multi-attribute decision-making method was adopted to screen optimal design patterns for high-rise residences that meet different requirements. After verifying the objective balance of the comprehensive optimal design patterns, the influence of climate differences on objective values and design parameter values was explored, and parametric models of the final design schemes were generated. The results indicate that differences in climatic conditions and solar energy resources can affect the optimal objective values and design variable settings for typical high-rise residences. This paper proposes a building optimization design framework that integrates parametric design, machine learning, and multi-objective optimization, and that explores the impact of climate differences on optimization results, providing a reference for determining design parameters for climate-adaptive high-rise residences. Full article

Traditional villages are vital carriers of cultural heritage and key foundations for rural revitalization and sustainable development, yet rapid urbanization increasingly threatens their survival, making it necessary to clarify their spatial distribution and driving mechanisms to support effective conservation and rational utilization. Yunnan Province, home to 777 nationally recognized traditional villages and the highest number in China, offers a representative context for such analysis. Methodologically, this study uses a 12 km × 12 km geographic grid (3005 cells) rather than administrative units. The count of catalogued traditional villages in each cell is taken as the dependent variable, and nine indicators selected from five dimensions (traffic accessibility, natural topography, climatic conditions, socioeconomic factors, and historical and cultural factors) serve as explanatory variables. Assuming that relationships between villages and their environment are spatially nonstationary and operate at multiple spatial scales, we combine spatial autocorrelation analysis with a multiscale geographically weighted regression (MGWR) model to detect clustering patterns and estimate location-specific coefficients and bandwidths. The results indicate that: (1) traditional villages in Yunnan exhibit significant clustering, with over 60% concentrated in Dali, Baoshan, Honghe, and Lijiang; (2) the spatial pattern follows a “more in the northwest, fewer in the southeast, dense in mountainous areas” distribution, shaped by both natural and socioeconomic factors; (3) natural geographic factors show the strongest associations, with sunshine duration and water availability strongly promoting village presence, while slope exhibits regionally differentiated effects; (4) socioeconomic development and transportation accessibility are generally negatively associated with village distribution, but in tourism-driven areas such as Dali and Lijiang, road improvements have facilitated protection and revitalization; and (5) historical and cultural factors, particularly proximity to nationally protected cultural heritage sites, contribute to spatial clustering and long-term preservation. The MGWR model achieves strong explanatory power (R² = 0.555, adjusted R² = 0.495) and outperforms OLS and standard GWR, confirming its suitability for analyzing the spatial mechanisms of traditional villages. Finally, the study offers targeted recommendations for the conservation and sustainable development of traditional villages in Yunnan. Full article

The adaptive reuse of industrial heritage is increasingly recognized as an effective low-carbon strategy that reduces resource consumption, lowers embodied carbon emissions, and supports sustainable urban transitions. Developing appropriate reuse strategies, however, requires a robust understanding of heritage value. As material evidence of China’s modern industrialization, railway-associated industrial heritage possesses the characteristics of linear cultural heritage. Yet systematic and multi-scalar value assessments from a linear heritage perspective remain limited. Focusing on the Guanzhong Section of the Longhai Railway—one of the most representative industrial development axes in Northwest China—this study establishes a two-level value assessment framework and conducts a comprehensive evaluation of fourteen textile industrial heritage units. At the individual level, five dimensions—historical significance, architectural features, structural integrity, authenticity, and rarity—were assessed through field investigation, and type-specific weights were introduced to correct structural imbalances between quantity and value across building categories. At the unit level, the Analytic Hierarchy Process (AHP) was employed to determine the weights of spatial–functional integrity, process completeness, railway connectivity, industrial landscape characteristics, and the integrated individual-level value. The results show that factory workshops and warehouses consistently exhibit the highest value, whereas structures and residential buildings, despite their numerical dominance, contribute relatively little. Spatially, a clear west–east gradient emerges: high-value units cluster in Baoji and Xi’an, medium-value units in Xianyang, and low-value units mainly in Weinan and surrounding counties. The findings indicate that textile industrial heritage along the Guanzhong Section forms a railway-linked linear cultural heritage system rather than isolated sites. The proposed evaluation framework not only supports heritage identification and conservation planning but also provides a theoretical basis for promoting low-carbon adaptive reuse of existing industrial buildings. Full article

Considering the escalating international geopolitical tensions and the ensuing great power maneuvers, China’s oil supply faced unprecedented threats. To safeguard against these risks and harness domestic resources more effectively, addressing the stability of refined oil supply had become an urgent imperative. The complex network theory is integrated into oil product delivery logistics, accounting for transportation volumes, distances, and node importance. Through simulation, we evaluated each scheme’s efficacy using a case study from a province in northwest China. The results demonstrate notable improvements in network robustness across all four strategies. The key node focuses on protection measures emerged as the most effective, followed by the oil depot resource optimization strategy and the network topology optimization strategy, in descending order. By mitigating the risks stemming from international uncertainties, our strategies ensured the timely supply of refined oil products, thereby upholding the stable functioning of the national economy. Full article

The combined effects of climate change and socio-economic development have intensified the risk of water supply–demand imbalance in China. To project future trends, this study develops a multi-scenario coupled prediction framework integrating climate, socio-economic, and human activity drivers, combining data-driven and physically based modeling approaches to assess terrestrial water storage imbalance in nine major river basins under six representative SSP–RCP scenarios through the end of the 21st century. Using ISIMIP multi-model runoff outputs along with GDP and population projections, agricultural, industrial, and domestic water demands were estimated. A Water Conflict Index was proposed by integrating the Water Supply–Demand Stress Index and the Standardized Hydrological Runoff Index to identify high-risk basins. Results show that under high-emission scenarios, the WCI in the Yellow River, Hai River, and Northwest Rivers remains high, peaking during 2040–2069, while low-emission scenarios significantly alleviate stress in most basins. Water allocation inequity is mainly driven by insufficient supply in arid northern regions and limited redistribution capacity in resource-rich southern basins. Targeted strategies are recommended for different risk types, including inter-basin water transfer, optimization of water use structure and pricing policies, and the development of resilient management systems, providing scenario-based quantitative support for future water security and policy-making in China. Full article

Against the backdrop of intensifying global climate change and expanding human encroachment into mountainous regions, landslides have increased markedly in both frequency and destructiveness, emerging as a key risk to socio-ecological security and development in mountain areas. Rigorous assessment and forward-looking prediction of landslide disaster vulnerability (LDV) are essential for targeted disaster risk reduction and regional sustainability. However, existing studies largely center on landslide susceptibility or risk, often overlooking the dynamic evolution of adaptive capacity within affected systems and its nonlinear responses across temporal and spatial scales, thereby obscuring the complex mechanisms underpinning LDV. To address this gap, we examine Yunnan Province, a landslide-prone region of China where intensified extreme rainfall and the expansion of human activities in recent years have exacerbated landslide risk. Drawing on the vulnerability scoping diagram (VSD), we construct an exposure–sensitivity–adaptive capacity assessment framework to characterize the spatiotemporal distribution of LDV during 2000–2020. We further develop a multi-model, multi-scale integrated prediction framework, benchmarking the predictive performance of four machine learning algorithms—backpropagation neural network (BPNN), support vector machine (SVM), random forest (RF), and XGBoost—across sample sizes ranging from 2500 to 360,000 to identify the optimal model–scale combination. From 2000 to 2020, LDV in Yunnan declined overall, exhibiting a spatial pattern of “higher in the northwest and lower in the southeast.” High-LDV areas decreased markedly, and sustained enhancement of adaptive capacity was the primary driver of the decline. At approximately the 90,000-cell grid scale, XGBoost performed best, robustly reproducing the observed spatiotemporal evolution and projecting continued declines in LDV during 2030–2050, albeit with decelerating improvement; low-LDV zones show phased fluctuations of “expansion followed by contraction”, whereas high-LDV zones continue to contract northwestward. The proposed multi-model, multi-scale fusion framework enhances the accuracy and robustness of LDV prediction, provides a scientific basis for precise disaster risk reduction strategies and resource optimization in Yunnan, and offers a quantitative reference for resilience building and policy design in analogous regions worldwide. Full article

Understanding the formation logic and spatial organization of vernacular settlements requires analytical approaches that capture both morphological structures and the cognitive rules underlying residents’ interactions with space. However, existing research on rural spatial patterns has paid limited attention to the perceptual and cognitive mechanisms through which spatial genes are recognized, maintained, and reproduced. This gap limits the development of generalizable and bottom-up methods for interpreting and transmitting rural spatial characteristics. To address this gap, this study proposes a cognition-driven analytical framework supported by spatial analysis for rural space gene extraction and transmission. The framework consists of five interrelated components: environmental cognition, spatial element identification, system coupling, space gene extraction, and transmission mechanisms. The Guanzhong Region in Northwest China is selected as a representative case to examine the multi-scale spatial structure of vernacular settlements. The results reveal three major findings. (1) The proposed framework effectively links physical spatial features with local perceptual structures, enabling the identification of key elements constituting rural space gene. (2) Three categories of representative space gene and seven core morphological and functional factors are extracted through the coupled analysis of nature–settlement systems. (3) Three adaptive transmission mechanisms—element replication and reinforcement, recombination of disrupted elements, and controlled adjustment of characteristic elements—are identified to support spatial renewal while maintaining local distinctiveness. This research contributes a structured, scalable, and replicable workflow for rural space gene analysis and enhances the application of cognitive principles in geospatial modeling. The findings provide methodological and practical support for rural revitalization, cultural landscape conservation, and vernacular settlement planning in inland agrarian regions undergoing rapid transformation. Full article