Search Results (288)

Sustainable reservoir-system management requires coordinated operation to balance economic benefits, sediment regulation, and ecological requirements while maintaining equitable and durable cooperation among participating reservoirs. Focusing on the Wanjiazhai, Sanmenxia, and Xiaolangdi reservoirs in the middle and lower Yellow River Basin, this study develops an integrated framework that links multi-objective reservoir operation, coordination-oriented scheme selection, and compensation allocation under representative dry, normal, and wet hydrological conditions. NSGA-II is used to identify Pareto trade-offs among sediment transport, electricity production, and ecological water-deficit control, while the coupling-coordination degree model is applied to select schemes with balanced overall performance. CRITIC-TOPSIS is then used to allocate compensation by integrating static engineering attributes with operation-induced dynamic responses. The results show that the maximum coupling-coordination degrees reach 0.81, 0.88, and 0.90 under dry, normal, and wet conditions, respectively. Compared with actual operation, the recommended schemes increase total electricity production while reflecting hydrologically dependent trade-offs in sediment transport and ecological water-deficit control. Xiaolangdi Reservoir consistently receives the largest compensation share, followed by Wanjiazhai Reservoir and Sanmenxia Reservoir, and this ranking remains consistent across alternative allocation methods. By linking operational trade-offs with reservoir-specific contributions and compensation priorities, the framework supports more adaptive and equitable joint operation and provides a quantitative decision-support approach for improving the long-term environmental, economic, and institutional sustainability of reservoir-system management in the Yellow River Basin. Full article

The water–energy–food nexus (WEF nexus) is central to sustainable development in arid regions. However, research on nexus evolution based on water consumed during food and energy production remains limited. In this study, the evolutionary patterns and influencing mechanisms of the WEF nexus in an arid region are investigated from a water footprint perspective, taking the middle reaches of the Yellow River Basin in China as a case study. The results show that from 2010 to 2021, the crop water footprint in the study area exhibited distinct varietal differences, with the highest values for legumes and the lowest for tubers, and a spatial pattern characterized by higher values in the north and lower values in the south. The energy–water footprint underwent a structural shift from grey water dominance to blue water dominance in 2015. The coupling coordination degree of the WEF nexus gradually improved from moderate imbalance to mild imbalance. The core obstacle in the water subsystem shifted from water quantity shortage to an imbalance in water use structure, while that in the energy subsystem moved from supply-side constraints to consumption-side constraints. The food subsystem was constrained by rigid factors related to arable land and ecological conditions. Consequently, the core constraint of the WEF nexus has transitioned from independent bottlenecks within individual subsystems to cross-system coupling constraints. Regulating the water footprint on both the production and consumption sides is conducive to the sustainability of the WEF nexus. The findings of this study enrich the regional empirical evidence of the nexus theory and provide a reference for research and practice concerning the optimal allocation of regional resources. Full article

Driven by the strategies of high-quality development and manufacturing transformation and upgrading, China’s economy is gradually shifting to an innovation-driven growth model. Empowering firms to lift innovation quality through new-generation artificial intelligence has become a core priority for industrial development and policy support. This paper adopts unbalanced panel data of A-share listed manufacturing firms from 2012 to 2023 and employs the Double Machine Learning model to systematically investigate the impact and transmission mechanisms of new-generation artificial intelligence on manufacturing firms’ innovation quality. The results reveal that new-generation artificial intelligence can significantly boost manufacturing firms’ innovation quality, and this core conclusion remains valid after a series of robustness tests. Mechanism verification confirms that new-generation artificial intelligence promotes the upgrading of firms’ human capital structure, deepens firms’ digital transformation, and raises firms’ R&D investment intensity. These three channels work synergistically to indirectly empower the upgrading of firms’ innovation quality. Heterogeneity analysis verifies that the innovation quality improvement effect of new-generation artificial intelligence exhibits prominent stratified differences across regions, industrial sectors, and environmental regulatory scenarios. Specifically, the empowerment effect of new-generation artificial intelligence (GAI) is significantly stronger for samples from environmental pilot cities, Southern Coastal, Eastern Coastal, and Northern Coastal regions, as well as high-tech manufacturing firms. In contrast, its positive driving effect is weak or insignificant for samples in the middle reaches of the Yellow River and Northwest China, along with non-high-tech industries. This paper improves the theoretical analytical framework for artificial intelligence empowering micro-firm innovation and enriches relevant research on digital technologies and corporate innovation. It provides a theoretical basis and practical implications for China’s manufacturing sector to break through innovation bottlenecks via intelligent technologies and achieve comprehensive high-quality innovative development across the manufacturing industry. Full article

To gain a deeper understanding of the lower-atmospheric dynamic characteristics in the transition zone on the northeastern margin of the Tibetan Plateau, high-resolution wind profile data collected by a Doppler wind lidar (DWL) at Yinchuan Hedong International Airport from 2021 to 2023 were used to analyze the vertical structure, seasonal variations, and diurnal characteristics of the low-height wind field and wind shear in this region. The results indicate that (1) the data acquisition rate (DAR) below 1.5 km is generally high, exceeding 90% during most periods, and decreases monotonically with height; the 90% DAR contour height exhibits clear seasonal and diurnal variations, with the largest diurnal amplitude in summer and the smallest in winter. (2) The middle- and low-height wind fields are jointly modulated by topographic forcing and local circulations. Below 0.4–0.7 km, north–northeast and south–southwest winds prevail across all seasons, which is consistent with the blocking and splitting effects of the Helan Mountains. At 42 m, the wind direction shows a marked diurnal transition that may reflect the combined influence of the Helan Mountains’ bypass flow, mountain–plain circulation, and thermal contrasts between the Yellow River and surrounding desert/plain surfaces. (3) Horizontal wind speeds are predominantly concentrated below 6 m s⁻¹, and the development height of this low-wind-speed zone varies seasonally. The vertical velocity statistics show weak positive values in parts of the observed layer, but these signals are interpreted cautiously because vertical-velocity retrieval is subject to additional uncertainty. (4) The low-level wind shear intensity reaches its peak below 100 m and generally exhibits a U-shaped vertical distribution; severe wind shear below 100 m occurs most frequently from nighttime to early morning during May–October, whereas its occurrence frequency is lowest in winter. These findings provide observational evidence for aviation meteorological support and boundary-layer studies in semi-arid regions of Northwest China. Full article

Urban low-carbon transition is increasingly shaped by the interaction between digital infrastructure, intelligent applications, land-space allocation, and regional governance. Existing studies have mainly examined whether the digital economy or smart-city development can reduce emissions, but less attention has been paid to the coordination between digitalization and intelligentization, the carbon cost of digital infrastructure, and the spatial consequences of local gains. This research defines digital–intelligent integration as the coupling coordination between digitalization and intelligentization subsystems. Using panel data for 39 prefecture-level cities in the Middle Reaches of the Yellow River Urban Agglomeration from 2013 to 2022, it applies Global Moran’s I, a spatial Durbin model, partial-derivative effect decomposition, alternative spatial weight matrices, alternative dependent variable tests, and multidimensional heterogeneity analysis. The own-city coefficient of digital–intelligent integration in the carbon-efficiency model is positive (0.0282, p < 0.05), whereas the spatial-equilibrium direct effect is statistically insignificant. These quantities are not short- and long-run estimates: the former is a conditional model coefficient, while the latter incorporates spatial feedback. The indirect effect on neighboring carbon efficiency is negative and remains negative under contiguity, economic-distance, and geo-economic nested matrices. Under an otherwise identical fixed-effects specification, digital–intelligent integration lowers local construction-land carbon intensity but raises neighboring intensity. The structural estimates further show that local conversion is weaker in industrially and energy-intensive cities. Digital–intelligent integration should therefore be interpreted as a governance capacity rather than a net-carbon technology; its regional effect depends on industrial lock-in, infrastructure-energy demand, and cross-city responsibility sharing. Full article

Yellow River irrigation districts must maintain food production under variable inflows, rigid diversion quotas, sedimentation, groundwater depletion, and soil salinization. This systematic review synthesized 79 journal articles from Web of Science and CNKI to clarify how resilience can be assessed and managed at the irrigation-district scale. The evidence indicates that resilience is a time-dependent combination of resistance, recovery, adaptability, and transformability within a coupled water source–canal–field–drainage–ecology–institution system. Although composite indices and hydrological–crop models have advanced, three gaps remain: operational thresholds rarely connect indicators to failure and recovery; farmer and institutional feedbacks are weakly represented; and assessments seldom translate into executable schedules. We, therefore, propose an irrigation-district-specific framework that couples water, sediment, salt, crops, ecology, and governance across basin–district–field scales without transferring risk between scales. Management priorities differ spatially: upstream districts require coordinated water–salt control; middle-reach well–canal systems require surface-water substitution and groundwater recovery; and downstream diversion districts require multi-source allocation and adaptive intake. A digital twin-based closed loop—continuous monitoring, forecasting, optimization, operational commands, and feedback correction—can translate diagnosis into canal rotation, recharge, drainage, and emergency actions. This review provides operational indicators and a decision-oriented research agenda for resilient irrigation modernization. Full article

The Yellow River Basin is an important ecological security barrier and agricultural production area in China. Using panel data for nine sprovincial-level regions from 2010 to 2022, this study constructs a multidimensional development index of rural industrial integration and examines its association with regional environmental pollution. Regional pollution pressure is measured from total wastewater discharge, sulfur dioxide emissions, and general industrial solid-waste generation; the measure therefore captures broad regional pollution linked to agricultural and related industrial chains rather than agricultural non-point-source pollution alone. Two-way fixed-effects estimates show that higher integration scores are significantly associated with lower pollution levels. This association is statistically evident in the upper reaches, whereas the middle- and lower-reach estimates are not statistically significant and are interpreted as exploratory because each subsample contains only two provinces. Exploratory channel regressions suggest that agricultural technological progress, rural labor mobility, and agricultural industrial scale may help explain the observed association, but the regressions do not establish causal mediation. The findings indicate potential synergies between rural industrial integration and environmental governance, while also requiring caution regarding causal interpretation, composite-index boundaries, and small-sample regional comparisons. Full article

Vegetation carbon sinks are an important component of the terrestrial carbon cycle, and net ecosystem productivity (NEP) is widely used to indicate ecosystem carbon-sink strength. However, the long-term dynamics and environmental associations of vegetation carbon sinks in the middle and lower Yellow River Basin remain insufficiently understood. Based on remote-sensing net primary productivity (NPP) and an empirical heterotrophic-respiration model, annual NEP was estimated for 2001–2024. Theil–Sen trend analysis, the Mann–Kendall test, coefficient of variation, optimal-parameter geographical detector (OPGD), and regression residual analysis were applied. Basin-mean annual NEP increased from approximately 214 to 425 g C m⁻² yr⁻¹, with significantly increasing areas accounting for 89.93% of the study area. Areas with NEP above 300 g C m⁻² yr⁻¹ expanded from 18.51% to 78.86%. Precipitation and solar radiation showed the highest explanatory power for NEP spatial differentiation, with mean q values of 0.538 and 0.490, and their interaction reached 0.722. Comparison with a published NEP product, parameter-sensitivity analysis, and an NPP-based robustness test supported the main temporal patterns and factor rankings. The residual-derived non-climatic component exceeded 40% of the combined component-trend magnitude across approximately 94% of the study area, but should not be interpreted as a direct measure of human activities. These findings support regional carbon-sink monitoring, water-constrained ecological restoration, and land-use management. Full article

The water–energy–carbon (WEC) nexus is central to sustainability, yet the role of water quality as a potential moderator within this nexus remains empirically underexplored. Using a fixed-effects panel regression model applied to provincial-level data from the Yellow River Basin over the period of 2007–2022, this study provides systematic evidence that water quality significantly moderates the WEC nexus. The empirical findings indicate that (1) water quality negatively affects the WEC nexus significantly; (2) the moderating effect presents spatial heterogeneity, showing greater carbon reduction potential in middle and lower reaches; and (3) the marginal impact of water cycle energy consumption on carbon emissions declines monotonically with improvements in water quality, with a sample-specific estimated critical point identified at a compliance rate of 88.159%—below which the marginal effect remains positive and above which it turns negative. (4) The moderating effect exhibits pronounced spatial heterogeneity, being significantly positive in the middle and lower reaches but insignificant in the upper reaches, and is significantly strengthened after the implementation of China’s strictest water resource management policy in 2013. These results suggest that water quality serves as a strategic lever for synergizing WEC governance. The findings offer theoretical and practical insights for adjusting the tightly coupled WEC nexus in the Yellow River Basin, with implications for region-specific and threshold-oriented policy design. Full article

The wind–water erosion crisscross region in the middle reaches of the Yellow River is a typical ecologically fragile area in China, which seriously restricts the sustainable development of the regional ecological environment. For the soil wind erosion issue in typical wind–water compound erosion zones, this study selected three representative watersheds from different sub-regions, namely Liudaogou, Zhifanggou, and Hailesitaigou, as the research objects. Indoor wind tunnel experiments were conducted to investigate the driving mechanisms of wind speed, soil moisture content, and vegetation coverage on wind erosion processes under different underlying surface conditions, and to compare near-surface soil wind erosion responses among three typical watersheds with different soil backgrounds. The results indicated that when soil moisture content increased from 0.01 to 0.05 g/g, the threshold wind velocities for sand entrainment in Liudaogou, Zhifanggou and Hailesitaigou increased by 5%, 10%, and 5%, respectively. Regression analysis was adopted to establish the empirical formulas of critical sand-blowing wind speed for each watershed, and soil moisture content exerted the most significant influence on the critical wind speed in the Zhifanggou watershed. At a constant wind speed, the increases in soil moisture content and vegetation coverage could markedly reduce wind erosion yield. Under the wind speed of 12–18 m/s, wind erosion yield was strongly dominated by wind speed and weakly affected by soil moisture content. The improvement of vegetation coverage could substantially mitigate wind erosion intensity. Additionally, soil moisture content and vegetation coverage presented more prominent inhibitory effects on near-surface sediment transport. The three watersheds exhibited different wind erosion response patterns and sensitivity characteristics, indicating that soil type and underlying surface conditions should be fully considered when conducting wind erosion risk assessment and vegetation restoration planning in the wind–water erosion crisscross region. Full article

The digital economy is profoundly reshaping the global economic landscape; however, its impact on the innovation capacity of cultural and tourism integration—particularly within developing regions—remains insufficiently empirically tested. Utilizing panel data from 43 cities in the middle and lower reaches of the Yellow River (2009–2023), this study constructs indices for the digital economy and the innovation capacity of cultural and tourism integration using the entropy weight method. Employing a two-way fixed effects model, instrumental variable approach (using lagged variables and historical interaction terms), and mediation effect models, we empirically examine the impact, transmission mechanisms, and heterogeneity across city scales. The findings reveal that: (1) The digital economy significantly enhances the innovation capacity of cultural and tourism integration, with a benchmark regression coefficient of 0.683 (significant at the 1% level). (2) Mediation analysis indicates two transmission pathways—enhancing development potential of culture and tourism and promoting digitalization of cultural and tourism enterprises—with mediating effects accounting for 15.95% and 2.62%, respectively. (3) Heterogeneity analysis shows a significant negative effect in megacities (coefficient = −1.3726, significant at the 10% level), contrasting with positive effects observed in super-large and large cities. This study elucidates the differentiated pathways through which the digital economy empowers cultural–tourism integration innovation, providing empirical evidence for tailored digital–cultural–tourism policies across cities of varying sizes, with implications for other developing regions exhibiting similar urban hierarchies. Full article

Against the backdrop of China’s advancing strategy for ecological protection and high-quality development in the Yellow River Basin, improving urban land green-use efficiency is critical to reconciling ecological conservation, resource efficiency, and coordinated regional development. Using data for 64 cities across seven urban agglomerations, this study applies a Super-SBM model to estimate urban land green-use efficiency from 2012 to 2024 and combines Dagum Gini decomposition, kernel density estimation, and conventional and spatial Markov chains to examine its spatial disparities and dynamic evolution. Four principal findings emerge. First, efficiency increased overall but exhibited a clear spatial gradient, with higher levels in the upper and middle reaches and lower levels downstream. Second, overall disparities initially widened, subsequently narrowed, and rebounded slightly toward the end of the study period. Between-agglomeration disparities were the largest component on average and during most of the study period; however, their contribution declined, and transvariation density became the largest component in 2023–2024, indicating greater overlap among the efficiency distributions of urban agglomerations. Third, both within- and between-agglomeration disparities exhibited marked heterogeneity, with distinct trajectories across and within urban agglomerations. Fourth, the conventional Markov-chain analysis revealed strong state persistence, a pronounced tendency for high-efficiency states to persist, and transitions occurring predominantly between adjacent classes. The spatial Markov results further showed that local transition probabilities varied across neighborhood efficiency conditions. Overall, efficiency disparities did not exhibit sustained unidirectional convergence; instead, they were characterized by phased adjustment, increasing cross-agglomeration overlap, and neighborhood-conditioned state transitions. By establishing a sequential framework of “efficiency measurement–disparity decomposition–distributional evolution–state transition,” this study provides empirical evidence for understanding the spatial disparities and dynamic evolution of urban land green-use efficiency in the Yellow River Basin. Full article

The sustainable management of land resources and the formulation of land policies are closely linked to the stability and health of terrestrial ecological systems, which in turn underpin sustainable regional economic, social, and environmental development. However, land use change has a time effect on the environment and requires long-term observation to discover its impact on landscape patterns. The Yellow River Basin functions as a critical ecological barrier in northern China, where land use changes are particularly intense in the transitional zone between its middle and lower reaches. Using Landsat imagery as the data source, this study adopts the Random Forest (RF) algorithm to classify eight sets of sequential data covering a 35-year period from 1990 to 2025 in the study area. Landscape pattern metrics and transfer matrices are employed to conduct qualitative and quantitative analyses of the spatiotemporal dynamics of land use changes. Additionally, land expansion analysis strategies and the RF algorithm are applied to identify the relative importance of different driving factors. The results show that: (1) The classification accuracy based on the Google Earth Engine (GEE) cloud platform remains consistently high, exceeding 90% across all phases. (2) Patch density decreases significantly, while the largest patch index continues to decline; the Shannon diversity index shows a fluctuating upward trend, and the aggregation index exhibits a slight increase. (3) Mutual conversions among farmland, forest, and grassland are the dominant processes driving land use changes in the region. (4) The Digital Elevation Model (DEM), construction land area distribution, and distance to primary roads are the key factors influencing land use patterns, with human activities acting as the primary driver of land use type transformations in the area. Full article

Accurate prediction of reservoir discharge and sediment load is essential for optimizing reservoir operations and mitigating downstream flood risks. In the middle reaches of the Yellow River, water–sediment evolution exhibits significant nonlinearity and temporal dependency. However, existing models often neglect the dynamic coupling between water–sediment evolution and storage–discharge states and lack hydrological prior constraints. To improve performance under complex conditions, this study developed models for the Sanmenxia and Xiaolangdi reservoirs based on historical hydrological data from 2002 to 2022, with features selected via Pearson correlation. By incorporating variables such as reservoir capacity, we evaluated four deep learning architectures: CNN, LSTM, CNN-LSTM, and TCN. Furthermore, a Genetic Algorithm (GA) was employed to optimize the hyperparameters, utilizing a custom fitness function that integrates Nash–Sutcliffe Efficiency (NSE) with non-negative boundary constraints to ensure the simultaneous optimization of predictive accuracy and hydrological consistency. Experimental results demonstrate that the CNN-LSTM model achieved superior performance. Specifically, the test set NSE values for discharge and sediment reached 0.927 and 0.821 for Sanmenxia, and 0.949 and 0.843 for Xiaolangdi, respectively. In outflow prediction, the feature weights of inflow and reservoir capacity for Sanmenxia exceed 45% and 35%, while for Xiaolangdi, those of upstream and local inflow exceed 40% and 55%, respectively. In sediment prediction, almost all feature weights exceed 15%. This research provides a valuable reference for intelligent reservoir management in the Yellow River, advancing deep learning applications in complex hydrological systems. Full article

The middle reaches of the Yellow River Basin are ecologically fragile and face increasing tension between ecological restoration and socioeconomic development. This study developed an integrated risk-service framework to assess spatiotemporal ecological security, identify dominant drivers, and support zoning-based management. A Comprehensive Ecological Security Index combining landscape ecological risk with four InVEST-derived ecosystem services showed a steady improvement from 2000 to 2025, especially after 2010. Higher ecological security was mainly concentrated in the southern and southeastern mountainous areas, whereas lower values persisted in the central plains and northern semi-arid areas. The index showed significant positive spatial autocorrelation (Moran’s I = 0.413–0.474, p < 0.001). Forest coverage was the strongest driver (q = 0.504), with a threshold effect at 40–50% coverage. ${\mathrm{P}\mathrm{M}}_{2.5}$ concentration and population density had negative effects, and most driver interactions showed nonlinear enhancement. Five ecological security zones were identified for differentiated management. The proposed framework links ecological risk, ecosystem service capacity, interpretable machine learning, and zoning-based governance. It provides a transferable approach for ecological security assessment and spatial management in the Middle Yellow River Basin and comparable dryland watersheds. Full article