In order to offer policy references for promoting the green total factor productivity (GTFP) in the Yellow River Basin driven by digital finance, this study utilized the panel data of 72 prefecture-level cities in the Yellow River Basin from 2011 to 2022. Utilizing a comprehensive analytical framework that integrates individual and time fixed-effects models, threshold regression models, and spatial econometric models, the research investigates the impact effects of digital finance on GTFP in the Yellow River Basin and elucidates its underlying mechanisms.The results indicate that: a) Digital finance significantly promotes the enhancement of GTFP in the Yellow River Basin, with the green effect from the digitalization level of digital finance being the most substantial. b)Both a capable government and an effective market exhibit dual threshold effects in the process of digital finance influencing green total factor productivity in the Yellow River Basin. Moreover, the positive effect of digital finance on GTFP demonstrates nonlinear incremental enhancement as government governance capacity and marketization levels improve. c) The positive effect of digital finance on GTFP in the Yellow River Basin demonstrates regional heterogeneity and resource dependency heterogeneity, specifically manifested as a greater positive effect in midstream and downstream cities compared to upstream cities, and a larger positive effect in non-resource-based cities than in resource-based cities. d) The development of digital finance not only elevates the GTFP of the local city but also radiates to enhance that of neighboring cities. The study proposes the following policy recommendations: Persistently advance market-oriented reforms in both breadth and depth to elevate marketization levels. Support green development in the Yellow River Basin through improvements in new infrastructure and structural tax and fee reductions. Optimize measures for digital finance development according to local conditions. Strengthen regional cooperation among cities within the basin, leveraging the “Belt and Road” Initiative as a linkage.

The development of new quality productivity is the key to promoting the high-quality development of the Yellow River Basin. In order to explore the improvement path of the new quality productivity of the manufacturing industry in the Yellow River Basin, based on the TOE theoretical framework, this paper takes 359 listed manufacturing enterprises in the Yellow River Basin in 2022 as research samples and constructs the driving model of the new quality productivity from the three levels of technology, organization, and environment. Two methods, NCA and fsQCA, are used to study and analyze the influencing factors and configuration paths of new quality productivity. The results show that, first, the improvement of new quality productivity is affected by the coordination and matching effects of technology, organization, and environment, and no single factor can constitute new quality productivity. Second, there are three configurations for the improvement path of new quality productivity, namely, the technology-driven type composed of technological innovation, digital-intelligence transformation, and government subsidies; the single-factor comprehensive synergy type composed of technological innovation, equity restriction, and government subsidies; and the multi-factor comprehensive synergy type composed of digital-intelligence transformation, equity restriction, supply chain relationships, and government subsidies. Third, technological innovation and government subsidies play a key role in the path of new quality productivity improvement; digital-intelligence transformation and equity restriction have substitution effects, which can improve new quality productivity in the same way.

Accurately assessing the carbon storage of the ecosystem in the Henan section of the Yellow River Basin is of great significance for promoting low-carbon sustainable development in the region and achieving the “dual carbon” goals. Based on carbon density sampling data, a spatial density distribution dataset of carbon density in the Henan section of the Yellow River Basin was constructed. Combined with remote sensing data on land use, a systematic assessment was conducted of the carbon storage patterns and spatio-temporal evolution laws of the ecosystem in the Henan section of the Yellow River Basin for the years 1980, 1990, 2000, 2005, 2010, 2015, and 2020. The Geographic Detector was utilized to explore the influence of natural and socio-economic factors on carbon storage. The results indicate that the average carbon storage in the Henan section of the Yellow River Basin over the past 40 years was 431.16×106 t, with a spatial distribution pattern showing a slight increase from east to west and a decreasing trend from northeast to southwest, with high-value agglomeration areas mainly distributed in the downstream floodplain of the Yellow River. From 1980 to 2000, the ecosystem carbon storage in the Henan section of the Yellow River Basin decreased, followed by an increase, but overall, the carbon storage in the Yellow River Basin from 1980 to 2020 showed a downward trend. Between 1980 and 2020, 82.05% of the region in the Henan section of the Yellow River Basin maintained unchanged carbon storage, while 10.05% experienced a decrease and 7.90% an increase. Among human factors, changes in land use type were the key drivers of dynamic changes in carbon storage in this region, especially the continuous expansion of construction land and encroachment on farmland and grassland, which were the main reasons for the significant decline in carbon storage. Among natural factors, altitude and temperature also had a certain impact on changes in carbon storage.

The Yellow River has long been a capricious and troublesome river. Coordinating the human-water relationship in the Yellow River Basin and achieving harmonious coexistence between human and water are urgent needs for the protection and governance of the Yellow River in the new era. In order to coordinate the human-water relationship in the Yellow River Basin in a sound manner, this paper summarized the evolution process of the human-water relationship, elaborated on the research background and significance, and proposed an overall research idea. The key issues from four aspects (i.e., cognition, theory, method, and practice) were identified, including insufficient understanding of action mechanisms and evolution processes, inadequate research on theoretical foundations and the theory of harmonious coexistence, the need to deepen research on monitoring-assessment-simulation-regulation, and the need to expand multi-level paths toward harmonious coexistence. The key research areas were clarified, including river ethics, intelligent perception equipment, water balance, improvement paths, threshold adjustment, “defining city, land, population, and industry based on water”, simulators, water network construction, engineering layout, and high-quality development. Combining the key issues and research areas, strategies for coordinating the human-water relationship in the Yellow River Basin were provided and prospects were presented. The results can promote the understanding and recognition of the human-water relationship in the Yellow River Basin, and provide scientific support for handling the relationship between human and water and implementing the major national strategy of the Yellow River.

The upper reaches of the Yellow River (UPYR) serve as the primary source area for the basin’s runoff. It is essential to quantify the impacts of climate change and anthropogenic activities on the variation patterns of runoff in this region to enhance effective water resource management and support informed decision-making within the Yellow River Basin. In this study, we developed the SWAT hydrological model for the upper reaches of the Yellow River, calibrating and validating it from the base period of 1964 to 1980. We systematically evaluated the effects of climate change and human activities—including water usage, reservoir regulation, and land use—on runoff changes from 1981 to 2020. The findings indicate that a) The basin is currently undergoing a significant increase in both precipitation and temperature, with precipitation levels rising at a rate of 8.11 mm per decade and a corresponding warming rate of 0.35 ℃ per decade. It is important to note that there is spatial heterogeneity in the intensity of the impacts of climate change. In the source area, the contribution rate of the Jimai climate factor is 94%. In contrast, in the headway region, the contribution rate decreases to 21%. b) The influence of human activities on runoff attenuation exhibits spatial gradient characteristics, with variations ranging from 60% to 80% between Lanzhou and Toudaoguai. Human water consumption is identified as the principal contributing factor, accounting for approximately 40% to 45% of this phenomenon. The establishment and operation of the reservoir have resulted in a temporal redistribution of runoff, leading to a decrease of 18.11% ± 6.27% during the flood season and an increase of 12.33% ± 4.2% during the non-flood season. c) Between 1964 and 2020, the annual runoff in the upstream region of the Yellow River experienced a decline of 148 million cubic meters per decade. An analysis of the factors contributing to runoff reveals that precipitation recharge is the primary determinant, accounting for approximately 80% ± 11.33%. This is followed by contributions from snow and ice melt, thawing of frozen soil, and groundwater recharge. The findings of this study elucidate the nonlinear superposition effects of climate change and anthropogenic activities in the upper reaches of the Yellow River, thereby providing theoretical support for understanding the variations in upstream runoff in the context of climate change.

In response to the complex characteristics of rivers with abundant sediment in the north, such as the Yellow River, traditional flow measurement technologies face challenges such as inaccuracy due to complex morphologies and susceptibility of measurement capabilities to environmental factors. To address these challenges, this paper proposes a novel intelligent and precise measurement method for open channel flow based on the concept of digital twin and the Bayesian hierarchical model. This method integrates time series prediction and intelligent management technology. Field tests conducted in the "Yellow River Water Diversion to Hebei Province for Replenishing Baiyangdian Lake" project in the Yellow River basin have demonstrated significant advantages of this method. Compared with existing technologies, this method not only solves the problem of inaccurate flow measurement under conditions of unstable flow velocity and scouring and deposition changes but also achieves precise prediction of flow data over small time scales in the future.

In order to understand the water resource vulnerability status of Shaanxi Province, an evaluation index system was constructed based on the Pressure-State-Response (PSR) model. Utilizing statistical data from Shaanxi Province from 2013 to 2022, the fuzzy set pair analysis method was applied to assess the vulnerability grade of its water resources. By analyzing the relationships between individual indicators and the vulnerability grade of the criterion layers, as well as the trends in criterion layer vulnerability and overall system vulnerability, the underlying causes of changes in water resource vulnerability were thoroughly investigated. The results demonstrate that Shaanxi Province's water resources transitioned from an extremely vulnerable state in 2013 to a mildly vulnerable state by 2022, indicating continuous improvement in water resource conditions. The vulnerability grades of the criterion layer indicators and the criterion layers themselves largely aligned with the trend in the comprehensive vulnerability grade. Notably, the vulnerability grade of the Response criterion layer was closest to the province's overall comprehensive vulnerability grade. Based on the identified water resource vulnerability status and its causative factors, recommendations were proposed, including: establishing a trinity management system integrating pressure identification, state early warning, and resilience response; and innovating a multi-stakeholder governance model involving government, market, and society for addressing water resource vulnerability.

This study aimed to explore the influence of landscape patterns on soil erosion in the Yihe River Basin and provide references for formulating new-era soil and water conservation plans. Using data from 2018 to 2022 including DEM, precipitation, soil erodibility, NDVI, and land use, we calculated multiple landscape indices at a 10 m×10 m grid scale. The soil erosion modulus was determined through the CSLE. The proportion of soil erosion area in each sub-basin was set as the dependent variable, with landscape indices and socio-economic factors as independent variables. The Optimal Parameter-based Geographical Detector was employed to quantify the explanatory power of individual variables and their interactions. Finally, we analyzed how landscape patterns influence soil erosion dynamics. The results indicated that: a) Although both the area and intensity of soil erosion in the Yihe River Basin exhibited a decreasing trend, there remained a risk of slight and mild erosion escalating to higher severity levels. b) The fragmentation of landscape patches in the Yihe River Basin has intensified, evidenced by increased patch numbers and density across cultivated land, forest land, grassland, construction land, and water bodies. This fragmentation process has been accompanied by reduced connectivity among these land use types. Notably, while landscape patches in cultivated land, grassland, and construction land exhibited orderly and regular configurations, those in forest land and water bodies displayed more irregular and complex morphologies. c) andscape fragmentation significantly influences the spatio-temporal heterogeneity of soil erosion in the Yi River Basin. This relationship is evidenced by the strong explanatory capacity of key landscape metrics-including patch edge density, dispersion index, and perimeter-area fractal dimension-in interpreting the observed spatio-temporal variations in soil erosion patterns. d) Socio-economic factors significantly contribute to the spatio-temporal heterogeneity of soil erosion. Anthropogenic modifications through surface disturbances alter landscape configurations, consequently driving spatio-temporal erosion dynamics. This mechanism is substantiated by the enhanced explanatory power generated through interactions between socio-economic drivers and landscape pattern metrics.

Based on the development of method, theory, and the legislation in the process of Yellow River management, this paper proposes the philosophical concept of “method Theory legislation Daoism” in Yellow River management, analyzes the basic principles and rules of dialectical materialism reflected in it, and demonstrates its philosophical scientificity, rationality, and the dialectical relationship between them. Through reviewing the ancient river management ideas, analyzing the Yellow river management strategies of the people at different stages, and comprehending the profound essence of the new era’s water management ideas in Yellow River management, we aim to explore the concepts of “method Theory legislation Daoism” in it, Exploring its dialectical development in the practice of Yellow River control in various historical periods. Analyzing that the Yellow River control is a comprehensive process of the method progress, the ideological development, and the legislation improvement, which is a historical practice of coordinated development of “method Theory legislation Daoism”. The protection and management of the Yellow River should innovate the method of river management, study the theory of water management, clarify the legislation of valley harnessing, and follow the Daoism of harmonious coexistence between humans and water.

Scientific and technological collaboration provides foundational support for high-quality economic development. By continuously introducing new technologies and methods, it enhances production efficiency, reduces costs, promotes industrial upgrading, and injects new vitality into high-quality economic growth. In the Yellow River Basin, the industrial technology level has been steadily rising, the scale of scientific and technological expenditure continues to expand, the number of patent applications and grants is increasing steadily, and the mechanisms for scientific collaboration are improving. However, challenges remain, such as an overly heavy industrial structure, insufficient innovation capacity, uneven economic development among provinces (regions), and a weak foundation for industrial division and collaboration. To address these issues, this paper proposes pathways for scientific and technological collaboration to drive high-quality economic development in the Yellow River Basin: strengthening institutional safeguards for collaboration, elevating the industrial capacity of collaboration, enhancing the effectiveness of collaborative platforms, and fully leveraging the role of talent in scientific collaboration.

To provide a scientific theoretical foundation for promoting the coordinated development of new quality productivity between the Yellow River Basin and Yangtze River Basin, and to offer decision-making references for formulating more rational regional development policies, this study established an evaluation index system for regional new quality productivity development. Employing methods such as entropy method, Theil index, spatial correlation analysis, and QAP analysis, a comparative empirical investigation was conducted on the development levels and influencing factors of new quality productivity in both basins from 2013 to 2022. The results showed that: 1) The Yangtze River Basin exhibited overall higher new quality productivity development indices than the Yellow River Basin, with both basins demonstrating distinct "block segmentation" spatial distribution patterns characterized by incremental improvements from upstream to downstream regions. Notably, the Yellow River Basin exhibited significantly higher polycentric than the Yangtze River Basin. 2) Intra-regional and inter-regional differences in the Yangtze River Basin decreased significantly, with a turning point occurring in 2019 when inter-regional differences fell below intra-regional differences for the first time, signaling a shift toward intra-regional variance dominance. Conversely, differences between eastern and western regions within the Yellow River Basin, along with intra-regional differences in western areas, displayed an expanding trend. 3) Global spatial autocorrelation of new quality productivity development in both basins showed continuous growth during 2013-2022, reflecting intensified spatial agglomeration. Provincial-level spatial correlation intensity in the Yellow River Basin remained weaker than that in the Yangtze River Basin. 4) Regional differences in future-oriented industries and green development emerged as the strongest drivers of new quality productivity differences in the Yangtze River Basin, while innovation-driven industrial development levels and production materials differences constituted the predominant influencing factors in the Yellow River Basin. Based on these findings, the study proposed recommendations including optimizing resource allocation to enhance regional equilibrium, implementing innovation-driven strategies with differentiated approaches for cultivating new quality productivity, refining targeted policy frameworks, and strengthening inter-regional collaborative development mechanisms.

The river channel erosion and undercutting appeared downstream the reservoir, since the operation of reservoir impounding and sediment retention, and even the river channel planform changed, affected the safety of water-related projects. In order to learn the rules of river sediment erosion downstream the reservoir, in this paper, accumulate erosion, average erosion depth, the maximum water depth, cross-section and longitudinal section morphology were analyzed. The erosion efficiency and relationship with effects of water-sediment and channel boundary conditions were investigated. Results of study show that the accumulative erosion of 2.276 billion m³ until October 2022 with the characterization of “more upward and less downward erosion，uneven distribution along the river”. The 70% of the erosion occurred above Gaocun and 30% below Gaocun. Channel longitudinal increased in the upper and lower sections ,while decreased in the middle section, and the whole longitudinal profile became more concave. River bed coarsening occurred, with median grain size increased ranging from 6% to 79%，and the channel erosion efficiency reduced obviously. The channel erosion efficiency below the reservoir is closely related to the average flood flow and the cumulative erosion volume in the preceding period. When the accumulative erosion volume above Huayuankou reach reaches 0.6 billion t and the discharge is 2 000 m³/s, the future erosion efficiency will be reduced to -2.9 kg/m³.

Digital intelligence plays a crucial role in promoting high-quality development in the Yellow River Basin. This study measured the digital intelligence development levels across nine provinces and regions in the Yellow River Basin from 2016 to 2023 based on text analysis. This research investigated the spatiotemporal evolution characteristics of digital intelligence development by employing methodologies such as time series analysis, Dagum Gini coefficient, local Moran's index, spatiotemporal transition analysis, and natural breaks classification. The findings indicate a steady increase in digital intelligence levels within the Yellow River Basin. However, areas with high growth rates are few, and there is significant spatial heterogeneity influenced by resource endowments, leading to notable regional development disparities that tend toward polarization. The downstream eastern regions exhibit higher development levels compared to the central and western basin areas. Consequently, this paper proposes enhancing the construction of basic communication infrastructure, improving supporting policies, strengthening regional cooperation, and establishing data and technology sharing platforms to leverage policy “multiplier effects”, coordinate regional development, and accelerate digital intelligence construction.

The shortage of water resources has become a global challenge, which seriously restricts the economic development and social stability of water-deficient areas. The evaporation loss of water bodies is a major cause of this phenomenon. In order to solve the problem of evaporation loss, domestic and foreign scholars have been constantly exploring effective measures to suppress water surface evaporation to improve the utilization rate of water resources. At present, the most commonly used technologies are chemical reagent method, biological measure method, physical material covering method, floating photovoltaic technology covering method and so on. In this paper, the published research results are reviewed, and the characteristics, main advantages and disadvantages of common technologies are summarized. From the current research results and comprehensive benefits, the physical material covering method is the most suitable anti-evaporation technology for large-scale application, and its inhibition rate of water evaporation is up to 90 % or more. If it is combined with photovoltaic technology to achieve the dual benefits of power generation and reduction of water evaporation, it should be the focus of future research by scholars. In addition, in order to realize the sustainable development of water resources, we not only need to pay attention to economic benefits, but also need to fully consider ecological benefits.

Zhengzhou, as a national hub for road, rail, and waterway transportation, is crucial for the high-quality development of the Zhengzhou Metropolitan Area with the construction of water function networks under the context of developing hub economy. Based on national strategy and regional planning, this study integrates the urban flood control plans, urban modern water network construction plans, irrigation plans, “layout planning of inland waterways and ports in Henan Province (2022-2035)”, and “water resources guarantee and water conservancy facilities construction planning in Zhengzhou metropolitan area”. The main objective is to ensure water security and water environmental safety, and by improving the efficient use of water resources and the reliability of waterway transportation, to create high-standard waterways that connect Zhengzhou to major rivers and seas. This will provide robust support for the development of a hub-based economy, shaping a new development pattern for Henan.

In order to improve the prediction accuracy of seepage water level of hydropower station dam foundation, a BP neural network model based on random forest (RF-BP model) was proposed. Taking Baihetan Hydropower Station as an example, the data of 18 seepage measurement points at the dam foundation from August 1, 2021 to February 23, 2023 were analyzed. The GA (Genetic Algorithm)-BP, PSO (Particle Swarm Optimization)-BP, RF, LSTM (Long Short Term Memory Network)-BP models were selected to compare the prediction accuracy with the RF-BP model. Considering that there was a certain correlation between the seepage water level and the reservoir water level, the Pearson correlation coefficient of the two was calculated. The results show that the RF-BP model has the smallest MAE, RMSE and MAPE and the highest prediction accuracy at the typical measurement points of OH-WML1-1, OH-WML1-2 and OH-WML5-3, which highlights the significant effect of random forest algorithm in optimizing selection factors. The stronger the correlation between the seepage water level and the reservoir water level at the measurement point, the higher the prediction accuracy of the RF-BP model, indicating that the correlation between the seepage water level and the reservoir water level has an important impact on the prediction accuracy.