Water scarcity is the core bottleneck restricting socio-economic high-quality development and ecological protection in the Yellow River Basin. To address the water resource challenges in the basin and support decision-making for ecological protection and high-quality development, this study systematically analyzes the multi-dimensional contradictions among water, food, energy, and ecology within the basin, revealing that the current water supply and demand are in a state of “tight balance,” and the gap is expected to continue widening in the future. However, existing measures-such as water conservation, engineered storage and transfer, and inter-basin water diversion-are constrained by the total available water resources and limited coverage, making it difficult to fundamentally alleviate the structural water shortage. Therefore, guided by the water governance principle of “water conservation priority, spatial equilibrium, systematic governance, and dual emphasis on government and market mechanisms,” water safety guarantee a four-pronged integrated approach of “water saving, water diversion, water allocation, and water management” is proposed: 1) Deepening water conservation across all sectors to promote a transformative shift from efficiency enhancement to an efficiency-to-effectiveness revolution. 2) Advancing major strategic projects, particularly accelerating the feasibility assessment and construction of the Western Route of the South-to-North Water Diversion Project. 3) Incorporating water resource carrying capacity as a binding constraint into territorial spatial planning, industrial layout, and urban development frameworks to achieve broader spatial equilibrium. 4) Leveraging the synergistic roles of government and market in water governance to optimize water allocation and ensure sustainable utilization.

Abstract: As global temperatures continue to rise and extreme weather events occur with increasing frequency, climate change has become an urgent global challenge. Against the backdrop of the shift of global climate governance toward an “implementation-oriented” phase, this study explores synergistic mechanisms between energy transition pathways and watershed ecological governance, drawing on thematic presentations from the China Pavilion side event at the 30th Conference of the Parties to the United Nations Framework Convention on Climate Change (COP30) and practical experience in watershed ecological management. The findings indicate that climate change has intensified the dual challenges of global energy security and watershed ecosystem sustainability, making the coordinated advancement of energy transition and ecological governance a core pathway for achieving green and low-carbon development and ensuring ecological security. Reducing energy consumption represents the most economically viable option for energy transition, while multi-energy complementarity provides an innovative direction for watershed-level energy transformation. As a mature renewable energy source, hydropower plays a significant role in peak regulation and in maintaining the stability of power supply. This role is particularly prominent in the Yellow River Basin, characterized by “low water and high sediment,” where existing and planned hydraulic projects exert substantial influence on basin-wide water-sediment regulation. Under current conditions of reduced sediment inflow, hydropower generation, ecological protection, and optimized water resources allocation can deliver even greater benefits. Using turbulence research as an example, a velocity distribution formula derived from the turbulent eddy model demonstrates cross-disciplinary applicability in fields such as hydraulic engineering, aeolian sand control, and energy efficiency optimization in aero-engines, underscoring the critical role of fundamental scientific breakthroughs in supporting applied research and technological implementation. The objectives of energy transition and watershed ecological governance are inherently aligned, and the eco-economy emerging from their integration can leverage capital mechanisms to achieve a win-win outcome between ecological protection and economic development. Finally, the study emphasizes that successful energy transition requires the establishment of a full-chain collaborative system spanning from fundamental research to integrated governance, strengthening innovation at the source, activating energy efficiency markets, and ensuring policy support to drive a broader transformation of the development paradigm.

The long-term changes in sediment concentration in the five rivers of the Poyang Lake Basin significantly affect water quality, the stability of lake ecosystems, and downstream water resource utilization. This study employs a Long Short-Term Memory (LSTM) model to analyze the evolution characteristics and driving mechanisms of sediment concentration based on data from 1966 to 2018. The model demonstrated high fitting accuracy during both the training and validation periods, with Nash-Sutcliffe efficiency (NSE) ranging from 0.89 to 0.94 and 0.82 to 0.93, respectively, and root mean square error (RMSE) values between 0.01 and 0.02 g/L, indicating good robustness and applicability. Scenario analysis revealed a significant decline in sediment concentration in the five rivers after 1990, with human activities having a greater impact than climate change. The reduction in sediment concentration in the Ganjiang and Xinjiang Rivers was primarily driven by human activities, with a contribution rate exceeding 80%. while in the Rao River (represented Changjiang and Le'an Rivers), climate change dominated, with a contribution rate of approximately 40%. Seasonal analysis showed that sediment concentration changes during the flood season were mainly controlled by human activities, while those during the dry season were largely influenced by climate change.

To assess the green development effect of new quality productivity and explore its path to promoting the high-quality economic development of cities in the Yellow River Basin, based on the panel data of 77 prefecture-level cities in the Yellow River Basin from 2011 to 2021, the entropy Weight-TOPSIS method was used to measure the development level of new quality productivity. A Spatial Durbin Model was constructed to empirically analyze the promoting effect of new quality productivity on the high-quality economic development of cities and its spatial spillover effect. A mechanism test model was constructed to explore the transmission mechanism of new quality productivity promoting the high-quality economic development of cities. The results show that: a) During the research period, the development of new quality productivity in each city in the Yellow River Basin not only promoted the high-quality economic development of the city itself, but also had a significant spatial spillover effect on the high-quality economic development of neighboring cities. b) The development of new quality productivity promotes the high-quality economic development of cities in the Yellow River Basin through intermediary channels such as environmental regulation, energy conservation and the upgrading of industrial structure. c) The direct and indirect effects of new quality productivity promoting the high-quality economic development of cities are the strongest in downstream cities, followed by middle reaches cities, and the weakest in upstream cities. Suggestions: Optimize and improve the top-level design for the development of new quality productivity, give full play to the synergy between new quality productivity and the “dual carbon” goals, strengthen the research and development of core technologies such as 5G networks, the lnternet of things, and artificial intelligence, and enhance the development momentum of new quality productivity.

To broaden the research horizon of how the digital economy empowers low-carbon development and to inform decision-making for ecological protection and high-quality growth in the Yellow River Basin, this study constructs an indicator system and quantifies the digital-economy development level by using panel data of the nine basin provinces from 2012 to 2022. On this basis, we specify a benchmark econometric model in which carbon-lock-in intensity is the explained variable and digital-economy development the core explanatory variable, treat industrial-structure upgrading as the mediating variable and urbanisation as the moderating variable, and further build mediation and moderation-effect models to empirically identify the carbon-unlocking effect of the digital economy and its underlying mechanisms.The findings indicate: a) The development of the digital economy in the Yellow River Basin exhibits a robust carbon-unlocking effect; the effect is markedly stronger in the middle-lower reaches, in areas hosting innovation-oriented industrial clusters, and in regions where resource-intensive industries account for a smaller share of output. b) The digital-economy development delivers its carbon-unlocking impact by propelling industrial-structure upgrading, which significantly and positively mediates the Basin-wide effect. c) during the sample period, urbanization negatively moderates the carbon-unlocking effect of the digital economy in the Yellow River Basin.Policy implications: a) Accelerate digital-economy expansion to reinforce its carbon-unlocking capacity. b) Implement region-specific strategies that coordinate digital-economy growth with green low-carbon industries across the Yellow River Basin. c) Further optimize the industrial structure to promote green low-carbon sectors. d) Pursue green urbanization to create low-carbon living spaces.

The problem of distortion in remote sensing vegetation indices due to terrain effects is more prominent in the hilly and loess plateau areas with intense terrain changes. To obtain the true information about vegetation growth, it is necessary to eliminate the terrain effects in remote sensing images and perform terrain correction. Taking the complete ratio-type, non-complete ratio-type, and non-ratio-type vegetation indices (NDVI, EVI, GVI) in the loess hilly area as the research objects, the SCS+C correction model was used to perform terrain correction for the terrain effects caused by the local solar incidence angle, the spectral variations of the same land type caused by the terrain effects, and the abnormality of vegetation indices caused by the aspect. The improvement effect of terrain correction on the three vegetation indices was investigated, and the correlations between the terrain correctioned vegetation indices and terrain factors (elevation, slope, aspect, etc.) and the measured leaf area index were analyzed. The results show: a) Among the three vegetation indices, GVI is less affected by terrain effects, while EVI and NDVI are relatively more affected by terrain effects. When using EVI and NDVI in related studies, terrain correction must be performed. b) After terrain correction, the regularity of the three vegetation indices changing with terrain factors and the correlation with the measured leaf area index have both been enhanced. c) Using the SCS+C correction model for terrain correction is feasible and effective. The terrain correction effect of the three vegetation indices is as follows: EVI > NDVI > GVI.

To explore the coupling and coordination status and mutual response relationship between digital economy and green development in the Yellow River Basin, and to provide references for ecological protection and green high-quality development in the basin, based on the analysis of the coupling mechanism between digital economy and green development, this paper uses panel data of 64 prefecture-level cities (prefectures) in the Yellow River Basin from 2013 to 2022. The entropy weight method is adopted to measure the development level of digital economy, the Super-efficiency-SBM model is used to measure the efficiency of green development, and the coupling coordination degree model is employed to measure the coupling and coordination status between digital economy and green development. The PVAR model is used to empirically analyze the mutual response relationship between digital economy and green development. The research shows that: a)The development level of digital economy in the Yellow River Basin has steadily increased, but it is still at a relatively low level at the end of the study period. Spatially, it shows the highest level in the downstream region, followed by the middle reaches, and the lowest in the upper reaches, with a circular structure centered on relatively high provincial capital cities. b)The efficiency of green development in the Yellow River Basin has also steadily increased during the study period, but the imbalance in green development among prefecture-level cities is prominent. c) The coupling and coordination level between digital economy and green development in the Yellow River Basin has been increasing year by year, rising from a barely coordinated level at the beginning of the study period to a primary coordinated level at the end. Spatially, the downstream region has the highest level, followed by the upper reaches. d)Both digital economy and green development have strong self-dependency, and a two-way high-quality interaction relationship has not yet been formed between them. The promoting effect of digital economy on green development is relatively strong, but the promoting effect of green development on digital economy is relatively weak. Suggestions: Accelerate the construction of new digital infrastructure in the Yellow River Basin to further improve the development level of digital economy; formulate plans and strengthen cross-regional cooperation to quickly narrow the regional gap in green development; promote the further improvement of the coupling and coordination level between digital economy and green development through reasonable industrial layout and technological innovation.

The Yellow River Basin is the region in China that suffers from the most severe soil and water loss and has the most fragile ecological environment. The water and soil conservation measures in the Yellow River basin have significant ecological, economic and social effects. To provide theoretical support for effectively enhancing the functional value of soil and water conservation, realizing its exchange value, and amplifying its financial value, and to offer references for accelerating the formation of a pattern for realizing the value of ecological products in soil and water conservation in the Yellow River Basin and promoting the high-quality development of soil and water conservation in the Yellow River Basin, this study, from the perspective of sustainable development, reveals three value forms of soil and water conservation effects, namely: ecological products that embody functional value, ecological commodities that embody exchange value, and ecological financial products that embody financial value. Furthermore, it clarifies the value conversion process of soil and water conservation effects through four stages: valorization, productization, commercialization, and financialization. It also expounds on five mechanisms of value transformation, including clarification of property rights, value accounting, value pricing, market absorption, and financial innovation. Finally, it puts forward countermeasures to improve the value transformation mechanism, such as establishing the common ownership of water resources in the basin, optimizing the value accounting model and methods for soil and water conservation, and implementing a differentiated value pricing mechanism.

The Yellow River (Ningmeng section) continues to face sedimentation and shrinkage, resulting in a “new suspended river”, which threatens the safety of regional ice flood control. By combing the critical situation regarding ice prevention and flood control in the Ningmeng section, this study analyzes the causes of sedimentation and the roles and limitations of various management measures, proposing strategies for sediment reduction and management in the Ningmeng section. The research indicates that implementing measures such as soil erosion control, raising river embankments, and river dredging individually can only tackle sedimentation issues in specific segments and timeframes of the Ningmeng section. It is necessary to consider a variety of measures to cooperate with each other and comprehensive management in order to maintain the appropriate scale of the middle-water channel in the Ningmeng section for a long time and ensure the safety of ice prevention and flood control.

Empowering ecological civilization construction with data elements is the underlying logic for promoting ecological environment governance in the Yellow River Basin. Evaluating the coupling and coordination relationship between data element development and ecological civilization construction is particularly important for achieving high-quality development in the Yellow River Basin. To provide theoretical basis and reference decision-making for the coordinated development of data elements and ecological civilization construction policies in the Yellow River Basin, this study focuses on nine provinces (regions) in the Yellow River Basin from 2013 to 2020. The entropy method, coupling coordination degree model, and kernel density function are used to investigate the coupling coordination level between data element development and ecological civilization construction. The results show that: 1) The development of data elements and the overall construction of ecological civilization are steadily improving, but both have characteristics of uneven and insufficient development. 2) The interactive relationship between the development of data elements and the construction of ecological civilization has gradually strengthened, and the coupling and coordination between the two have gone through a process of mild imbalance to near imbalance. 3) The coupling and coordination between the development of data elements and the construction of ecological civilization exhibit non-equilibrium characteristics, with downstream areas>midstream areas>upstream areas, presenting a U-shaped layout from east to west and an increasing layout from north to south. Based on the empirical results, three types of coupling and coordination zones were divided: high data element development high ecological civilization construction zone, low data element development high ecological civilization construction zone, and low data element development low ecological civilization construction zone, and corresponding improvement paths were proposed.

The Three Rivers Headwaters Region (TRHR) plays a crucial role in regional economic development planning and ecological conservation. This study utilized Remote Sensing Ecological Index (RSEI) complemented by Theil-Sen slope method and Mann-Kendall test to analyze the evolution trend of ecological environment quality (EEQ) in TRHR from 2000 to 2022. Additionally, it employed multiple linear regression and path analysis to investigate the direct, indirect, and comprehensive impacts of various environmental factors on EEQ changes in TRHR. The results indicate: EEQ in TRHR is generally better in the west than the east, and in the north than the south, predominantly categorized as moderate to poor. EEQ in the Yellow River source area surpasses that of the Yangtze River source area and Lancang River source area. Overall, EEQ in TRHR shows a trend of stability, with the northern part experiencing improvement trends before 2010, shifting to stability trends after 2010. EEQ changes in the northwest are primarily driven by dryness and temperature, while in the east, changes are mainly influenced by vegetation greenness and humidity.

To effectively control soil erosion in the Huangfuchuan, improve the regional ecological environment, and promote the construction of Eco-environment friendly small watersheds , the Inner Mongolia section of the Huangfuchuan was selected as the research area.Based on a comprehensive understanding of the natural geographical conditions, land use status, water source quality, river and gully conditions, human settlements and social economic conditions of the study area, following the principle of “first zoning, then classification”,the spatial analysis software ArcGIS was used to conduct superimposed analysis on four layers of land use status, surface slope, river network and water system, and vegetation coverage with a resolution of 30 m.Firstly, the prevention and protection zone, comprehensive control zone and ecological restoration zone were delineated.Then,the configuration principles of measures for each type of prevention and control zone and the configuration of measures for each sub-region of soil erosion in the comprehensive management zone (Loess hills and gully zone, Arsenic Sandstone hills and gully zone) were clarified.Six types of Eco-environment friendly small watersheds construction suitable for the Huangfuchuan were proposed (water source protection type, ecological tourism type, harmonious living type, leisure and health care type, green industry type, and embankment dam system ecological quality improvement and efficiency type), and the construction and development directions of each type of ecological clean small watershed were pointed out. The construction goals of ecological clean small watersheds were proposed from four aspects: soil erosion control, ecological environment improvement, human settlement improvement and social and economic development.

To provide theoretical and practical references for deepening the construction of the Zhengzhou-Luoyang-Xi’an cooperation belt, promoting coordinated development, and advancing high-quality economic development in the region, this study constructs a synergistic analysis framework encompassing economic development, ecological protection, and social quality of life systems. Based on panel data from 15 prefecture-level cities within the cooperation belt from 2009 to 2022, the entropy weight method is employed to measure the comprehensive development level of the region. Kernel density estimation is used to reveal its dynamic evolution characteristics, while coupling coordination degree and obstacle degree models are applied to analyze inter-system synergy mechanisms and inhibiting factors. The results indicate that: 1) The overall development level of the cooperation belt shows a positive trend, albeit with certain periodic fluctuations. 2) The three systems exhibit coordinated development in coupling coordination. From a temporal perspective, they underwent a transition from “low-level equilibrium to partial imbalance, and finally to synergistic optimization.” Spatially, regional development became more balanced, though developmental disparities among cities persist. 3) In terms of pairwise coupling coordination, all systems demonstrated a stable and improving trend over time, while significant spatial heterogeneity was observed. 4) Obstacle degree analysis reveals the following ranking of systemic barriers: economic development > social quality of life > ecological protection. Key obstructive factors include library collections per 100 people, grain-sown area, total electricity consumption, year-end deposits and loans of financial institutions, and the number of patent applications per 10,000 people.

Promoting the high quality development of rural industries is a key task for Ningxia to achieve Chinese modernization transformation. In order to provide references for Ningxia to promote the high quality development of rural industries, the paper built an evaluation index system based on the new development concept from five dimensions: innovation, coordination, green, openness, and sharing. The AHP-entropy TOPSIS method was used to measure the level of high quality development of Ningxia rural industries from 2013 to 2022, the coupling coordination degree model was used to measure the coupling coordination level of five dimensions of high quality development, and the obstacle degree model was used to diagnose the constraining factors affecting the high quality development of rural industries. The results show that: a) The high quality development level of Ningxia rural industries showed a fluctuating upward trend from 2013 to 2022, but the overall level remained relatively low at the end of the research period. b) The coupling coordination degree of each dimension has steadily improved, rising from near imbalance in 2013 to primary coordination in 2022. c) The main factors hindering the high quality development of Ningxia rural industries are the degree of agricultural mechanization and patent authorization in the dimension of innovative development, as well as the dependence on agricultural product import and export in the dimension of open development. Policy suggestions were proposed, such as optimize the rural industrial structure, strengthen technological innovation, and enhance opening up to the outside world.

Urbanization has been accompanied by rapid growth in population, energy consumption, and carbon emissions. To forecast the peak of energy-related carbon emissions in the Yellow River Basin under urbanization and provide a reference for advancing ecological conservation and high-quality development in the basin, this study calculates energy-related carbon emissions based on energy consumption data of eight major energy types across provinces (regions) in the Yellow River Basin from 2000 to 2021. The LMDI model is employed to decompose the factors influencing energy-related carbon emissions in the basin, while an extended STIRPAT model is used to forecast peak energy-related carbon emissions under multiple scenarios (business-as-usual, energy-saving, low-carbon, and high-energy-consumption). The results indicate that: 1) From 2000 to 2021, total energy carbon emissions in the Yellow River Basin continued to grow, but at a declining rate. Economic development, urbanization, and population growth promoted energy carbon emissions, while optimization of energy structure, energy intensity, and industrial structure suppressed them. Economic development was the primary driver of emission growth, whereas energy intensity was the main factor suppressing emissions. 2) Under the current, energy-saving, and low-carbon development models, the peak range for energy-related carbon emissions in the Yellow River Basin is estimated to be between 1.80 and 1.87 billion tons, with the peak occurring between 2030 and 2035. In contrast, under a high-energy-consumption development model, the peak is unlikely to occur before 2040. 3) Regulating carbon emission intensity helps reduce peak energy carbon emissions in the Yellow River Basin, while optimizing the energy structure and improving energy efficiency can facilitate an earlier peak. Recommendations include optimizing urban planning, promoting industrial structure upgrades, strengthening public education on low-carbon awareness, advocating low-carbon lifestyles, expanding renewable energy adoption, and optimizing the energy consumption structure.

Promoting the green transformation of resource-based cities is crucial for achieving high-quality regional development. To provide a theoretical reference for the green transformation of resource-based cities in the Shanxi-Shaanxi-Inner Mongolia region and the formulation of relevant national policies, this study measures the green transformation performance based on panel data from 21 resource-based cities in the Shanxi-Shaanxi-Inner Mongolia region from 2012 to 2022. The Entropy Weight-TOPSIS method is applied to measure green transformation performance from four dimensions: economic development, social welfare, environmental greenness, and green technology innovation. Furthermore, a modified gravity model and Social Network Analysis are used to analyze the structure of the green transformation performance spatial correlation network. The results indicate that: a) The overall green transformation performance of resource-based cities in the Shanxi-Shaanxi-Inner Mongolia region shows a slow, fluctuating upward trend. By the end of the study period, the overall level remains low, leaving significant room for improvement. From the perspective of individual cities, the distribution is “olive-shaped”, with few cities exhibiting high or low performance and many cities showing medium-low and medium-high performance. A significant “core-periphery”spatial distribution is evident, with Baotou and Ordos forming a“dual core”. The performance gap between core and peripheral areas is substantial. b) Regarding the characteristics of the green transformation performance correlation network, Jinzhong City holds a central position, playing a vital bridging role and acting as a “central actor”. In contrast, peripheral cities such as Tongchuan, Wuhai, Jincheng, and Baoji show weak correlations, and their green transformation relies mainly on factor inflows, exhibiting a“Matthew effect”where the strong grow stronger. Recommendations are as follows: a) Formulate targeted support policies and financial incentives to encourage green technology innovation. Provide special funds to support green industries and the construction of environmental protection facilities, thereby promoting the rapid enhancement of green transformation performance in Shanxi-Shaanxi-Inner Mongolia resource-based cities. b) Adhering to a holistic “one chessboard” mindset, establish linkage and cooperation mechanisms for green transformation between “green pole”cities and“green basin” cities. Through technical exchange, joint innovation, and the driving effect of core cities, the green transformation performance gap among resource-based cities should be narrowed to foster coordinated and balanced green transformation development across the Shanxi-Shaanxi-Inner Mongolia region.

This study prepared glass-ceramics using Yellow River sediment as the primary raw material. Combined with performance testing methods such as X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), fourier-transform infrared spectroscopy (FTIR), and hardness testing. The results demonstrate that: The main crystalline phase precipitated in the glass-ceramics made from Yellow River sediment is ferrite silicate. Crystallization begins inside the particles and gradually extends to the exterior. The primary influencing factors for the flexural strength, vickers hardness, bulk density, acid resistance, alkali resistance, and thermal properties of the glass-ceramics are crystallization time, nucleation time, crystallization time, nucleation temperature, crystallization temperature, and nucleation time, respectively. The optimal nucleation and crystallization regime is nucleation temperature 750 ℃ (60 min) and crystallization temperature 910 ℃ (180 min). The glass-ceramics made from Yellow River sediment has a flexural strength of 79.07 MPa, young's modulus of 51.59 GPa, vickers hardness of 6.37 GPa, and fracture toughness of 1.39 MPa·m^1/2. These properties comply with the “Glass-ceramics for Architectural Decoration” standard. Under nucleation temperature 720 ℃ (60 min) and crystallization temperature 880 ℃ (180 min), the dielectric constant and dielectric loss of the microcrystalline glass made from Yellow River sediment are 7.43 and 0.88, respectively. These values meet the performance requirements of the “Structural Ceramic Materials for Electronic Components” standard.

To explore the influence of variable speed adjustment and valve regulation on the energy consumption of pump stations under different water demand flows, this paper took the first-level pump station in Shitie Irrigation District, Yuci District, Jinzhong City, as the basis, introduced the variable speed ratio and pipeline head loss coefficient, and constructed a two-stage optimization mathematical model for the energy-saving operation of the pump station with the goal of minimum annual waste water and minimum power. The hydraulic parameters, such as annual waste water, pump station power, pump head, and pump station efficiency, were calculated under different operation modes. The results show that under different water demand flow rates, the optimized operation mode of the pump station reduce the average annual waste water volume by 4 267 872 m3 and the pump station average power by 24.1%. In the optimal operating mode, the efficiency of the pump station shows an overall trend of first increasing, then decreasing, and then increasing again with the increase of water demand flow rate, and the pump head increases with the increase of water demand flow rate. The use of the same gear ratio for variable speed regulation in parallel operation of multiple water pumps is not optimal, and it is necessary to determine the gear ratios of different water pumps to avoid causing an increase in pump station power.

This study investigates the factors influencing new quality productivity in the Yellow River Basin to support high-quality regional development. An evaluation system for new quality productivity was established, and data from 56 prefecture-level cities across seven urban clusters in the basin (2010-2022) were analyzed. The entropy method measured new quality productivity levels, social network analysis identified spatial characteristics, and the spatial Durbin model explored influencing factors. Results show: a)New quality productivity in the basin increased overall during the study period, but regional disparities persisted, with downstream areas > midstream areas > upstream areas, challenging coordinated development; b)New quality productivity exhibits spatial spillover effects and networked patterns. Regional hubs like Zhengzhou, Qingdao, and Xi'an act as key nodes, generating both radiation (positive spillovers) and siphon (resource concentration) effects; c)Key drivers include economic development and industrial upgrading, which directly boost local new quality productivity. Urbanization and government intervention enhance local productivity and benefit neighboring regions via spillovers, while market openness slightly hinders new quality productivity growth in both local and adjacent areas. Recommendations: Optimize spatial network structures to enhance regional coordination; implement targeted policies for balanced new quality productivity development; refine institutional frameworks to safeguard growth; leverage digital tools to accelerate green transitions.

Microbially induced calcium carbonate precipitation (MICP) technology has attracted wide attention as an economical, environmentally friendly, and durable method for wind prevention and sand control. To provide new technical support for applying MICP in desertification control, two curing methods (PVC board and baffle) were designed, and urease-producing bacteria together with cementation solutions at two concentrations (1.5 mol/L and 2.0 mol/L) were introduced to prepare microbially consolidated sand barriers. The optimal curing method was explored by comparing the dry density and calcium carbonate content of the barriers under different curing conditions. The results showed that: a) MICP consolidation generated a large amount of calcium carbonate crystals between sand particles, cementing them together and forming microbially consolidated sand barriers; b) the baffle curing method achieved good consolidation in the shallow layer (0-10 cm) but was less effective and uniform in deeper layers (>10 cm), whereas the PVC board curing method maintained effective and uniform consolidation across different depths; c) increasing the cementation solution concentration enhanced both dry density and calcium carbonate content, but no significant difference was observed between 1.5 mol/L and 2.0 mol/L, indicating that solution concentration had no notable effect on the consolidation performance.

The “Ji Bend” of the Yellow River region is renowned as an important “energy granary” in northern China. In the context of the “dual carbon” goals, it faces the challenge of reducing emissions from traditional high-energy-consuming industries and transitioning its resource-based economy. This necessitates the promotion of green and low-carbon development through technological innovation. To provide insights for ecological protection and high-quality development in this region and the entire Yellow River basin, this study focuses on 15 prefecture-level cities in the “Ji Bend” of the Yellow River, with a research period from 2012 to 2022. Using an optimized four-stage SBM-DEA model, the low-carbon innovation efficiency of each city is calculated. The study also explores the spatio-temporal differences in low-carbon innovation efficiency among these cities using methods such as the Dagum Gini coefficient, standard deviation ellipse, and gravity model, along with σ-convergence and β-convergence tests. The results show that: a) From 2012 to 2022, the overall low-carbon innovation efficiency of the cities in the “Ji Bend” of the Yellow River remained low, with N-shaped fluctuations. Only Zhongwei and Taiyuan achieved DEA efficiency, while other cities did not; b) There is limited collaborative cooperation in low-carbon innovation among the cities, and the level of regional integration is low, leading to a phenomenon of “individual efforts” in low-carbon innovation; c) The spatial distribution of low-carbon innovation efficiency in the “Ji Bend” cities roughly follows a “higher in the south, lower in the north” pattern, with polarization between the cities. A “catch-up” effect exists, and by the end of the research period, the number of high-efficiency cities increased, though significant differences still remain between the cities; d) Improvements in education levels have a significant positive impact on the low-carbon innovation efficiency of the cities, whereas higher economic development levels and urbanization rates have a suppressive effect on this efficiency. Recommendations are made to address the issues in low-carbon innovation in the cities of the “Ji Bend”.

As a crucial engineering measure safeguarding the lives and property of hundreds of millions of people, the modernized operation and management of the lower Yellow River levees are pivotal for ensuring the safety and stability of the Yellow River and supporting the high-quality development of the river basin. Confronted with challenges such as intensifying extreme weather events, the prominent perched river characteristics, and the efficacy bottlenecks of traditional management models, there is an urgent need to transform the management paradigm towards intelligence, resilience, and collaboration. This paper systematically analyzes the bottleneck issues in the current management of Yellow River levees, including information sensing, risk prediction, emergency coordination, engineering resilience, and institutional mechanisms. It innovatively proposes a modern management framework centered on comprehensive intelligent perception-digital twin empowerment-resilient engineering foundations-intelligent collaborative governance. Key measures are elaborated, including constructing an integrated “space-air-ground-river-engineering” monitoring network, building a high-fidelity digital twin levee decision-making hub, integrating ecological and engineering resilience enhancement technologies, and establishing a flattened inter-departmental emergency command system. A phased implementation path is also presented. The research aims to provide a systemic solution for building a safer, smarter, and more resilient “digital twin levee”, contributing to the long-term safety and stability of the Yellow River.

Research on the spatiotemporal changes in carbon storage is an important prerequisite for achieving regional low-carbon, high-quality development based on the “dual carbon” goals. This study employs a method combining supervised classification with field surveys to extract land use information of Bayannur City using high-resolution satellite imagery data. The study quantifies the spatiotemporal changes in carbon storage in Bayannur City from 2015 to 2021 based on the InVEST model. The results show that: a) Carbon storage in Bayannur City increased by a total of 97.55 million tons from 2015 to 2021, with high-value areas concentrated in Dengkou District, Hangjin District, Linhe District, and Wuyuan District, and low-value areas concentrated in Urad Front Banner District and Urad Middle Banner District. b) Changes in land use are consistent with changes in carbon storage. Compared to 2015, cultivated land and grassland areas in Bayannur City increased significantly by 1 740.00 km² and 11 099.09 km², respectively, while unused land decreased by 13 143.96 km². The area of water bodies showed an initial increase followed by a decrease over the six years. Significant changes were observed in the conversion of unused land to grassland in Urad Rear Banner and Urad Middle Banner, resulting in carbon storage increases of 36.72 million tons and 58.70 million tons, respectively.

Carbon compensation serves as an effective mechanism for sustaining ecological stability, fostering equitable and coordinated regional development, and facilitating the achievement of carbon emission reduction targets. Focusing on the nine provinces and autonomous regions within the Yellow River Basin as the research area, this study integrates the carbon quota allocation under emission reduction targets with interprovincial carbon compensation, identifies the carbon compensation subject and object based on the carbon quota amount, carbon absorption amount and actual carbon emissions, and the carbon compensation value of the Yellow River Basin from 2021 to 2030 is estimated. The results show that: the satisfaction rate of the inter-provincial carbon quota allocation scheme in the Yellow River Basin exceeding 94%, which is easy for each region to accept. The carbon compensation heterogeneity among the regions is large, and the overall spatial pattern is“compensation from the middle and lower reaches to the upper reaches”. From 2021 to 2030, the overall carbon offset value of the Yellow River Basin will decrease year by year, and the funds requiring subsidies from the central government will show a downward trend. Based on this, the study suggests that enhancing the supporting infrastructure for inter-provincial carbon compensation, instituting a comprehensive ‘vertical and horizontal’ coordination mechanism, and exploring the differentiated low-carbon development models for the regions.

New quality productive forces are the core driving force for the construction of a modern industrial system. To provide references for the deep transformation and upgrading of industries in the Yellow River Basin and the construction of a modern industrial system, this paper analyzes how new quality productive forces enable the deep transformation and upgrading of traditional industries in the Yellow River Basin towards high-end, intelligent, and green development. It also examines the internal logic of how disruptive and frontier technological innovations empower emerging and future industries. Based on the current development status of the Yellow River Basin, this paper identifies issues in the empowerment process of new quality productive forces, such as weak technological innovation foundations, insufficient supply of high-quality labor, high pressure for green transformation, insufficient vitality of data elements, and imperfect new forms of production relationships. Therefore, this paper proposes practical pathways to strengthen the empowerment of new quality productive forces in the Yellow River Basin’s modern industrial system, including strengthening the cultivation of high-quality labor, reinforcing technological support, facilitating the flow of data elements, accelerating green and low-carbon transformation, and deepening institutional and mechanism reforms.

Due to changes in climate and human factors, surface runoff will also change. In this study, in order to measure the contribution of climate and human factors to the change of runoff in the middle and upper reaches of the Huai River from 1982 to 2019, this study first used Mann Kendall trend analysis to conduct a sudden change analysis of the flow data in the basin, and divided the study time period into a baseline period and a mutation period. The contribution of climate and human factors to the runoff changes in the middle and upper reaches of the Huai River was quantitatively measured based on the extended Budyko model at three-time scales: month, season, and year. The results are as follows: a) On the month scale, the months in which climatic factors led to an increase in runoff were January, February, April, May, June, and December, and the months in which they led to a decrease in runoff were March, July, August, September, October, and November; anthropogenic factors increased the runoff depth of the middle and upper Huaihe River in January, October, November, and December, and decreased it in the rest of the months. b) On a seasonal scale, human factors are the dominant factor in reducing runoff in spring, summer, and autumn. Climate factors increase runoff in all four seasons, with the increase being smaller than the decrease caused by human factors. In winter, climate and human factors have a small-scale increasing effect on runoff changes. c) On an annual scale, climatic factors led to an increase in runoff depth of 4.71 mm, and human factors led to a decrease in runoff depth of 89.75 mm, and human factors had a greater effect on runoff change than climatic factors.

To provide insights for accelerating the development of new productive forces in the Yellow River Basin, based on an analysis of the internal mechanisms driving the development of new productive forces, an evaluation index system for new productive forces is constructed from three dimensions: laborers, labor objects, and means of labor. Using panel data from nine provinces in the basin, the vertical and horizontal range method is employed to measure the development level of new productive forces from 2012 to 2022, analyzes regional imbalance with Kernel density estimation and Theil index, and examines spatial patterns and correlation via Moran’s Index and ArcGIS. The result show that: a) The overall level is low but exhibits a good upward trend, with growth across all nine regions. b) Significant disparities exist: Shandong ranks highest, followed by Sichuan and Shaanxi, while Qinghai and Gansu are relatively low; downstream regions outperform the middle reaches, which in turn surpass the upstream. c) Spatial differences are mainly reflected in the gap between upstream and mid-downstream areas, and within the upstream, Sichuan differs greatly from others, with obvious polarization remaining by the end of the study period. d) There is significant positive spatial correlation, with a pattern of “higher in the east and lower in the west” and “higher in the south and lower in the north”. Shandong, Henan and Shaanxi are high-high clusters; Shanxi and Inner Mongolia are low-high clusters; Gansu, Ningxia and Qinghai are low-low clusters; Sichuan is a high-low cluster. The following recommendations are proposed: Adapting to local conditions to leverage strength and address weakness; Promoting regional linkage and coordinated development; Achievement transformation; And advancing industrial upgrading to create more opportunities for new productive forces.

To accelerate the cultivation of new productive forces and provide reference for implementing the major national strategies of ecological protection and high-quality development in the Yellow River Basin, based on the connotation of new productive forces, this paper constructs an “technology-factor-industry” analytical framework to explore the inherent logic of how new productive forces empower ecological protection and high-quality development in the Yellow River Basin. Specifically, revolutionary technological breakthroughs provide new driving forces, innovative allocation of production factors strengthens data empowerment, and in-depth transformation and upgrading of industries provide carrier support. It points out that empowering ecological protection and high-quality development in the Yellow River Basin with new productive forces still faces challenges such as insufficient innovation drive, the need to improve the level of factor integration and allocation, and lagging industrial transformation and upgrading. Given the reality of the Yellow River Basin, this paper proposes an enhancement path for empowering ecological protection and high-quality development in the Yellow River Basin with new productive forces: improving the science and technology innovation system, achieving high-level self-reliance and self-strengthening in science and technology, deepening factor market allocation, promoting the flow of data factors, accelerating the transformation and upgrading of industrial structure, and enhancing the competitive advantage of the modern industrial system.

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 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 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.