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Global industrial activities contribute significantly to carbon emissions, impacting climate change and necessitating innovative methods for precise emission monitoring and management at both regional and international levels. Based on nighttime light data, POI data, land use data and energy statistics, this study calculated the carbon emissions of different industrial categories in the Yellow River Basin from 2005 to 2020 and analyzed the temporal and spatial characteristics of their changes to reveal the carbon emission patterns of different industrial categories in the basin. This study analyzes the carbon emissions of various industrial categories from a spatial perspective, addressing the limitations of traditional industrial carbon emission assessments at the spatial scale. The results showed that although the growth rate of industrial carbon emissions in the Yellow River Basin has slowed down significantly, it has not yet reached the peak, with the carbon emissions increasing from 400,0647t in 2005 to 519,216,200t in 2020. The mechanical and electronic manufacturing industry had the largest carbon emissions, which accounting for 37.08% of the total carbon emissions. Medical pharmaceuticals had the fewest, only accounting for 1.16% of the total carbon emissions. The spatial distribution of carbon emissions showed a cluster distribution, and the emissions gradually decrease from the center to the periphery. In addition, the carbon emissions of the construction industry, medical pharmaceutical industry and mechanical and electronic manufacturing industry were concentrated in and around the cites, and were closely related to urban development, infrastructure and technological progress. Furthermore, the study reveals that the relationship between carbon emissions and population structure across different industrial categories is complex. A stable relationship exists between carbon emissions and the population within the mechanical and electronic manufacturing, metallurgy, and chemical industries. However, for the clothing, furniture, and pharmaceutical industries, population is not the sole influencing factor on their carbon emissions. This study provides a new perspective on low-carbon green and sustainable development strategies for industrial carbon emissions in the Yellow River Basin, and emphasizes the importance of constructing detailed, diversified and innovative management strategies in the face of climate change challenges.

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The ecological environment of wetlands in semi-arid regions has deteriorated, and vegetation succession has accelerated due to climate warming-induced aridification and human interference. The nutrient acquisition strategies and biomass allocation patterns reflect plant growth strategies in response to environmental changes. However, the impact of nutrient acquisition strategies on biomass allocation in successional vegetation remains unclear. We investigated 87 plant communities from 13 wetland sites in the semi-arid upper Yellow River basin. These communities were divided into three successional sequences: the herbaceous community (HC), the herbaceous-shrub mixed community (HSC), and the shrub community (SC). The nutrient composition of stems and leaves, as well as the biomass distribution above and belowground, were investigated. Results revealed that aboveground biomass increased with succession while belowground biomass decreased. Specifically, SC exhibited the highest stem biomass of 1,194.53 g m-2, while HC had the highest belowground biomass of 2,054.37 g m-2. Additionally, significant positive correlations were observed between leaf and stem biomasses in both HC and SC. The nitrogen (N) and phosphorus (P) contents within aboveground parts displayed an evident upward trend along the succession sequence. The highest N and P contents were found in SC, followed by HSC, and the lowest in HC. Stem N was negatively correlated with stem, leaf, and belowground biomass but positively correlated with root-shoot ratio. Leaf P displayed positive correlations with aboveground biomass while showing negative correlations with belowground biomass and root-shoot ratio. The ratios of C:N, C:P, and N:P in stem and leaf exhibited positive correlations with belowground biomass. The random forest model further demonstrated that stem N and leaf P exerted significant effects on aboveground biomass, while leaf P, stem N and P, and leaf C:P ratio had significant effects on belowground components. Additionally, the root-shoot ratio was significantly influenced by leaf P, leaf C:P ratio, and stem N, P, and C:P ratio. Therefore, the aboveground and belowground biomasses exhibited asynchronism across successional sequences, while plant nutrient acquisition strategies, involving nutrient levels and stoichiometric ratios, determined the biomass allocation pattern. This study offers valuable insights for assessing vegetation adaptability and formulating restoration plans in the semi-arid upper Yellow River basin.

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The Yellow River Basin has been instrumental in advancing ecological preservation and fostering national high-quality development. However, since the advent of China's reform and opening-up policies, the basin has faced severe environmental pollution issues. This study leverages remote sensing data from 1998 to 2019. As per the "Basin Scope and Its Historical Changes" published by the Yellow River Conservancy Commission of the Ministry of Water Resources, the Yellow River Basin is categorized into upstream, midstream, and downstream regions for analysis of their spatial and temporal distribution traits using spatial autocorrelation methods. Additionally, we employed probes to study the effects of 10 factors, including mean surface temperature and air pressure, on PM2.5. The study findings reveal that (1) the annual average concentration of PM2.5 in the Yellow River Basin exhibited a fluctuating trend from 1998 to 2019, initially increasing, then decreasing, followed by another increase before ultimately declining. (2) The air quality in the Yellow River Basin is relatively poor, making it challenging for large-scale areas with low PM2.5 levels to occur. (3) The PM2.5 concentration in the Yellow River Basin exhibits distinct high and low-value concentration areas indicative of air pollution. Low-value areas are predominantly found in the sparsely populated central and southwestern plateau regions of Inner Mongolia, characterized by a better ecological environment. In contrast, high-value areas are prevalent in the inland areas of Northwest China, with poorer natural conditions, as well as densely populated zones with high energy demand and a relatively developed economy. (4) The overall population density in the Yellow River Basin, as well as in the upstream, midstream, and downstream regions, serves as a primary driving factor. (5) The primary drivers in the middle reaches and the entire Yellow River Basin remain consistent, whereas those in the upper and lower reaches have shifted. In the upstream, air pressure emerges as a primary driver of PM2.5, while in the downstream, NDVI and precipitation become the main influencing factors.

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Agricultural production and sustainable human livelihoods in large river basins are threatened by climate change, human activities, and resource constraints. However, due to the complexity of socio-ecological interactions and agricultural sustainability, current studies are still limited by a priori knowledge and systematic analyses, as well as by the lack of quantification and identification of key factors and valuable pathway structures for agricultural production activities. Here, we combined observation-based causal inference and network analysis to quantify and assess the complex interactions in agricultural production in the Yellow River Basin (YRB) based on data from 12 factors relevant to agriculture over 40 years. We quantitatively assessed the leveraging and hindering roles of the factors in the interacting network system and provided managers with optimization priorities and possible causal pathways to achieve sustainable agriculture in the basin. For example, the fruit yield and income of rural households were identified as leveraging factors that positively affect the agricultural economy. Groundwater was seen as a hindering factor in dampening the negative impacts of the system, highlighting the importance of preventing groundwater depletion. Moreover, the findings suggest that spatially diverse causal interaction structures exist in the YRB and have shaped a variety of distinctive agricultural development modes. Our research ideas and results highlight both systemic considerations and the amplifying or dampening role of factors in interaction pathways, providing valuable quantitative insights into the management and intervention of sustainable agriculture in large river basins. Owing to replaceable and extensible network models, the methodology has the potential to be utilized in a variety of study areas and topics with complex socio-ecological interactions.

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Land use change stands as the primary factor influencing habitat quality (HQ). Clarifying the spatiotemporal change and the obstacle factors of the coupling relationship between HQ and urbanization level (UL) can provide imperative references for achieving sustainability in the Yellow River Basin (YRB). This study is based on the InVEST model, spatial autocorrelation, and obstacle factor analysis to measure the spatiotemporal dynamics and impediments of the coupling relationship between HQ and UL from 2000 to 2020 in the YRB. The findings were as follows: (1) From 2000 to 2020, the HQ showed a tendency of rise first and then fall, with the pattern of "High in the middle and west, low in the east"; (2) from 2000 to 2020, the UL had an upward trend, with the pattern of "Low in the west, high in the middle and east"; (3) the coupling and coordination level of HQ and UL in the YRB changed from extreme incoordination to verge of coordination, and it had a distribution pattern of "High in the east, low in the west", with the high-value area expanding to the east and the low-value area shrinking to the west. (4) Location condition, climate, proportion of construction land, vegetation index, and proportion of non-agricultural employment are the main obstacle factors that determined the coupling and coordination of the HQ and UL.

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As research on the full spectrum of ecosystem service (ES) generation and utilization within coupled human and natural systems (CHANS) has expanded, many studies have shown that the spatiotemporal dynamics of ESs are managed and influenced by human activities. However, there is insufficient research on how ESs are affected by bidirectional coupling between societal and ecological factors during spatial flow, particularly in terms of cross-scale impacts. These bidirectional influences between humans and nature are closely related to the utilization and transfer of ESs and affect the perception of spatiotemporal patterns of ESs and the formulation of management strategies. To fill this research gap, this study focuses on the Yellow River Basin (YRB), using network models to track the spatial dynamics of ES flows (ESFs) and the interactions between ecosystems and socio-economic systems within the basin on an annual scale from 2000 to 2020. The results highlight cross-scale impacts and feedback processes between local subbasins and the larger regional basin: As the supply-demand ratios of freshwater ESs, soil conservation ESs, and food ESs increase within individual subbasins of the YRB, more surplus ESs flow among subbasins. This not only alleviates spatial mismatches in ES supply and demand across the entire basin but also enhances the connectivity of the basin's ESF network. Subsequently, the cascading transfer and accumulation of ESs feedback into local socio-ecological interactions, with both socio-economic factors and the capacity for ES output within subbasins becoming increasingly reliant on external ES inflows. These results underscore the crucial role of ESFs within the CHANS of the YRB and imply the importance of cross-regional cooperation and cross-scale management strategies in optimizing ES supply-demand relationships. Furthermore, this study identifies the potential risks and challenges inherent in highly coupled systems. In conclusion, this work deepens the understanding of the spatial flow characteristics of ESs and their socio-ecological interactions; the analytical methods used in this study can also be applied to research on large river basins like the YRB, and even larger regional ecosystems.

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Maintaining ecosystem health (EH) in watersheds is crucial for building a national pattern of ecological security. However, a comprehensive diagnosis of watershed EH and an exploration of its driving mechanisms are still lacking. This study proposed an EH assessment model from a vitality-organization-resilience-service-environment (VORSE) perspective. Taking the Yellow River Basin of Shaanxi Province (YRBS), China, as a research object, the spatiotemporal evolution trend of EH from 2000 to 2020 was quantified. At the same time, we also quantified the respective contributions of climate change (CC) and human activities (HA) to the EH dynamics based on residual analysis. The results showed that EH in the YRBS increased by 11.80 % from 2000 to 2020, and the spatial distribution of the EH was higher in the southern region than in the northern part. At the pixel scale, areas with improving trends accounted for 90.57 % of the YRBS, while 9.43 % deteriorated, with the improving areas mainly in northern Shaanxi and the deteriorating areas in the Guanzhong region. The correlation between the EH and precipitation was primarily positive, while the correlation between the EH and temperature was mainly negative. The residual analysis showed that the contribution rate of CC to EH changes was 78.54 %, while that of HA was 21.46 %, indicating that CC was the dominant driver of EH changes in the YRBS. Specifically, 82.64 % of the improvement in EH was attributed to CC and 17.36 % to HA. Conversely, 65.30 % of the deterioration in EH was attributed to CC and 34.70 % to HA. Furthermore, CC, HA, and CC&HA dominated EH changes in 26.85 %, 3.77 %, and 69.38 % of the YRBS area, respectively. In addition, the Hurst exponent analysis identified six types of future EH development scenarios, each requiring different restoration strategies. This study provides valuable insights for future EH diagnosis, EH restoration efforts, and the formulation of sustainable development goals in other watersheds.

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Clarifying the spatiotemporal evolution of sustainable water resources utilization (SWU) and its decoupling effects from economic growth (EG) is essential for the effective management of water ecosystems and sustainable development of basins. However, the traditional Ecological Footprint model limits the ability to compare SWU within a basin, and existing studies need to pay more attention to the importance of water renewability in quantifying SWU. Based on the capital flow and capital stock perspectives, this study constructed an evaluation method for SWU and its decoupling effect from EG by combining the three-dimensional Water Ecological Footprint (WEF), sustainable reclassification, and the Tapio model, and explored different types of SWU enhancement strategies. The results indicate that: (1) From 2010 to 2022, the SWU of the Yellow River Basin (YRB) shows a decreasing and then increasing trend and is generally in water ecological deficit, with a lower SWU in the middle and lower reaches. Overall, the per capita WEFsize decreased by 0.73% per year, while the WEFdepth increased by 0.26% per year, the pressure and stress on the SWU of the YRB are still significant. (2) Agricultural freshwater use and domestic greywater discharge dominate the WEF of the basin, and the problem of inversion of the water use structure with the industrial structure is evident. (3) Spatial differentiation within the basin is apparent, and SWU shows a spatial distribution of western strength and eastern weakness, with significant consumption of water capital stock due to insufficient water capital flow as the main reason. (4) Topio decoupling analysis shows that WEF and EG are mainly strongly decoupled, with WEF lagging behind EG; the decoupling relationship between SWU and EG evolves from END-SD-WD, reduces the consumption of water capital stock and increasing water capital flow is a reasonable way to realise its stable strong decoupling. This study is essential for SWU studies of large river basins in arid and semi-arid regions. It provides insights into the sustainable management and rational allocation of water resources in the YRB and other similar basins worldwide.

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Agricultural drought (AD) is the main environmental factor affecting vegetation productivity (VP) in the Yellow River Basin (YRB). In recent years, the nonlinear effects of AD on VP in the YRB have attracted much attention. However, it is still unclear whether fluctuating AD will have complex nonlinear effects on VP in the YRB, and there are scant previous studies at large scale on whether there is a threshold for nonlinear effects of AD on VP in the YRB. Therefore, this study used a newly developed agricultural drought index to explore nonlinear effects on VP revealing the nonlinear effects of AD on VP in the YRB. First, we developed a kernel temperature vegetation drought index (kTVDI) based on kernel normalized difference vegetation index (kNDVI) and land surface temperature data to study the spatiotemporal variation of AD in the YRB. Second, we used GPP data from solar-induced chlorophyll fluorescence inversion as an indicator to explore the spatiotemporal variation of VP in the YRB. Finally, we used several statistical indicators and a distributed lag nonlinear model (DLNM) to analyze the nonlinear effect of AD on VP in the YRB. The results showed that AD decreased significantly during 2000-2020, mainly in the southeast of the Loess Plateau, while GPP increased significantly in 80.93 % of the YRB. Meanwhile, moderate and severe AD stress limited VP growth, with the negative effects gradually decreasing, while mild AD had an increasingly positive promoting effect on VP. AD stress resulted in a VP decrease of 69.78 %, and severe AD stress resulted in a VP decrease of 65.52 %, mainly distributed in the northern Loess and Ordos Plateau. AD had significant nonlinear effects on VP. The effects of moderate and severe AD on the sustained nonlinear lag of vegetation were more obvious, and those of moderate and severe AD on the nonlinear lag of VP were the largest when the lag was approximately 1 month and 7 months. The effect of AD on the nonlinear hysteresis of VP in YRB was significantly different under different vegetation types, and forests were more able to withstand longer and more severe droughts than grasslands and croplands. The results of the study provide a theoretical basis for evaluating AD and analyzing the nonlinear impact of AD on VP. This will provide scientific basis for studying the mechanism of drought effect on vegetation in other regions.

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Assessing and monitoring the green total factor energy efficiency (GTFEE) of cities while considering technology heterogeneity is crucial for the development of energy-conservation and emission-reduction policies. Considering that the heterogeneity of production technologies encompasses several dimensions, this paper proposes a 3E3S (Economy-Environment-Energy-Society-Science-Space) heterogeneity framework and integrates it with the improved meta-frontier global SBM-undesirable to analyze GTFEE and its decomposition. Empirical analysis of cities in the Yellow River Basin of China (YRBC) highlights the following: (1) The 3E3S heterogeneity framework facilitates the classification of all cities into three distinct groups, a finding that contrasts significantly with previous outcomes documented in the literature that relied solely on criteria such as geographic location. (2) The three groups identified under the meta-frontier exhibit substantial energy-saving potentials of 24.49%, 35.17%, and 52.46%, respectively. Additionally, there are spatiotemporal variations in GTFEE, with cities located in the central part of YRBC, particularly those in Shanxi province, demonstrating poor GTFEE performance. (3) The decomposition analysis of GTFEE indicates that technological progress plays a pivotal role in enhancing GTFEE on the whole, albeit with varying approaches for improving GTFEE depending on individual cities.

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Based on the panel data of 75 cities in the Yellow River Basin from 2000 to 2020, this manuscript measures the agricultural low-carbon production efficiency scientifically through the Super-SBM model. In addition, the deviation degree of agricultural industry is used as the index of structural adjustment. Finally, the spatial Durbin model is used to analyze the effect direction and degree of structural adjustment, financial support, and their synergistic effect on agricultural low-carbon production efficiency. The results show that ① the agricultural low-carbon production efficiency in the Yellow River Basin shows a trend of fluctuating downward and a spatial distribution pattern of "high in the east and low in the west". ② Structural adjustment in local region and adjacent areas has a significantly negative impact on agricultural low-carbon production, and the inhibitory effect in adjacent areas is more obvious, and the negative spatial spillover effect is strong. Financial support has a significantly positive impact on agricultural low-carbon production, but the spatial spillover effect of adjacent areas is not obvious. ③ By region, structural adjustment has a significantly negative impact on low-carbon agricultural production in the midstream and downstream regions, while financial support has a significantly positive impact on low-carbon agricultural production in the upstream region. The impact of control variables on agricultural low-carbon production varies from region to region. ④ The synergistic effect of structural adjustment and financial support in the whole and midstream region shows a significantly positive impact on agricultural low-carbon production, indicating that financial support has a certain correction effect on structural adjustment. The coefficient between the upstream and downstream regions is positive but not significant. The conclusions have important reference significance for promoting the ecological protection and high-quality development and agricultural low-carbon development in the Yellow River Basin.

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Priority ecological reserves (PER) aim to protect areas with significant ecological value and crucial ecological functions, optimizing resource allocation to maximize the benefits of ecological conservation. However, most previous studies have considered only ecosystem services (ESs) in delineating PER, neglecting eco-environmental quality (EEQ). This study used the Remote Sensing-based Ecological Index (RSEI) to represent EEQ and combined it with ESs to delineate PER at the county scale in the Yellow River Basin (YRB). Additionally, it employed Multiscale Geographically Weighted Regression to identify the driving factors influencing the ESs and EEQ of PER. The results showed that: (1) From 2000 to 2020, both RSEI and the Comprehensive ESs (CES) in the YRB exhibited a fluctuating upward trend; (2) Three types of PER were extracted, with ESs reserve mainly distributed in the upstream region, EEQ reserve primarily in the middle and lower reaches, and integrated ecological reserve mainly in the midstream region, all dominated by vegetation land-use types; (3) Within the extracted PER, RSEI was mainly influenced by soil, aspect, population (pop), PM2.5, temperature (tmp), and potential evapotranspiration (pet), while CES was affected by soil, pop, PM2.5, slope, tmp, precipitation, and pet. To enhance the EEQ and ESs of the YRB, it was recommended to incorporate at least 105,379 km2 into the existing protected areas in the YRB. These areas should be subdivided based on their ecological status, with specific management measures for different types of PER. This study provides recommendations for environmental protection and land planning in the YRB, actively responding to current policies on high-quality development and ecological environmental protection in the YRB.

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The aim of this study was to comprehensively understand the water environment quality status and its change trend in the Inner Mongolia section of the Yellow River Basin. To analyze the water quality in recent years,the water quality data in the Yellow River basin from 2003 to 2020 were firstly collected from five typical monitoring stations．Various data analysis methods, including principal component analysis, cluster analysis, and a long short-term memory model, were used along with an improved comprehensive water quality identification index to explore the spatiotemporal characteristics of water quality in the Yellow River Basin. The results showed that the overall water quality in the basin has improved and stabilized over time. In terms of temporal variation, there was a distinction between the wet season and dry season, with a better status observed during the wet season due to increased agricultural irrigation and higher water volume. Spatially, the five monitoring sections could be divided into three categories based on strong natural factors that maintained their temporal characteristics during the wet season； however, significant differences were observed during the dry season due to urban water usage patterns. Analysis using LSTM models revealed that ammonia nitrogen will continue to decline and have a decreasing impact on the comprehensive water quality. These findings provide valuable insights for the comprehensive management of water quality in Inner Mongolia's Yellow River Basin.

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This study investigates the Dynamic Coupling between Land Use Economic Efficiency (LUEE) and Urban Ecological Resilience (UER) in the Yellow River Basin (YRB). This exploration is pivotal for elucidating the interaction mechanisms between economic growth and ecological governance. Furthermore, understanding this relationship is essential for fostering high-quality, sustainable urban development in the YRB. Utilizing panel data from 56 cities spanning 2003 to 2020, this study employed the coupling coordination degree (CCD) model, spatial correlation analysis, Kernel density estimation, convergence model, and Geodetector to systematically analyze the spatio-temporal distribution, dynamic trend, and determinants of the CCD between LUEE and UER in the YRB. The findings indicate that: (1) A general upward trend in both LUEE and UER, accompanied by a steady improvement in their CCD. (2) Significant spatial disparities in their CCD, with higher levels in the lower reaches. (3) Marked positive spatial autocorrelation, predominantly characterized by clusters where high (low) values are surrounded by high (low) values. (4) Regarding the impact of individual factors, government fiscal budget expenditure demonstrates the most robust explanatory power for the CCD within the YRB. Concerning the effects of two-factor interactions, the interplay between industrial structure upgrading and government fiscal budget expenditure emerges as the most significant determinant in influencing the CCD between LUEE and UER. This study enhances our comprehensive understanding of the interplay between economic and ecological systems. It offers scientific insights and strategic direction for harmonizing ecological governance with urban economic growth at both the regional and global scales.

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Improving the efficiency of agricultural technology innovation is an inevitable requirement for ecological protection and sustainable agricultural development in the Yellow River Basin. Under the background of prominent water resources constraints in the Yellow River Basin, the actual level and regional differences of agricultural technology innovation efficiency in the basin are analyzed, which will provide theoretical basis and data reference for the development plan of agricultural modernization in the Yellow River Basin. The construction of evaluation index system and the choice of method are the main factors that affect the measurement results. Therefore, this paper first selects input indicators such as water-saving technology level and output indicators such as total agricultural output value to construct an innovative evaluation index system of agricultural technology innovation efficiency in the Yellow River Basin under the consideration of water resources constraints. Secondly, the three-stage DEA model was used to eliminate the interference of external environmental variables, and the overall and regional agricultural technology innovation efficiency of the Yellow River Basin during 2011-2020 was measured with the data of prefecture-level cities under the constraint of water resources. Finally, from the perspective of the whole and the region, the spatio-temporal dynamic evolution law is analyzed and evaluated. The results show that: on the whole, the agricultural technology innovation efficiency in the Yellow River Basin is good and rising in a wave trend, the adjusted pure technology efficiency increases, while the scale efficiency decreases. From the regional perspective, although there is regional heterogeneity, the overall development trend is good, and the lower Yellow River has the highest efficiency, followed by the upper reaches and the middle reaches. From the perspective of the change law, the spatio-temporal evolution distribution shows a multi-polarization trend, and the different regions in the upper, middle and lower reaches show great differences. Therefore, promoting the innovation of water-saving technology and the integration of agricultural science and technology, promoting the integration of production, study and research, and strengthening the regional linkage mechanism are helpful to improve the competitiveness of agricultural science and technology and achieve sustainable agricultural development in the Yellow River Basin.

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Human activities are a triggering factor for landslides in the Yellow River Basin (YRB, China). However, the extent to which the spatial distribution of landslides is affected by human activities is unclear. We constructed a human activity intensity index (HAII) based on nighttime light data and land cover data. Regression and dominance analyses were used to compare the effects of the HAII, precipitation, distance to river, distance to fault, topographic relief and slope on the landslides spatial density (LSD). The results showed that in the YRB, the HAII, as a dominance influencing factor, had a significant positive influence on the LSD. Moreover, regional differences in the human disturbance of nature intensify the spatial variation of LSD. To quantify the intensity of human disturbance to nature, a human-nature conflict index (HNCI) is constructed by quantifying the difference between the slope distributions of artificial and natural landscapes. The results show that in the middle section of the YRB, humans are developing more steep mountainous areas, leading to more dense landslides. This study provides a reference for landslide risk management and land use planning in the YRB.

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Improving the green electricity efficiency (GEE), is an important issue for China's high-quality economic development. This study presents a spatial correlation network of urban GEE in the Yellow River Basin from 2012 to 2021, constructed using an improved gravity model. Social network analysis and the quadratic assignment procedure method are employed to analyze the spatial correlation characteristics and influencing factors. The findings indicate that urban GEE in the Yellow River Basin exhibits complex and stable network characteristics. The spatial network analysis reveals that Jiayuguan City, Dongying City, Dingxi City, Zibo City, and Shizuishan City occupy central positions within the network. The results indicate that spatial adjacency, GDP per capita, industrial structure, and the level of science and technology expenditure are positively related to urban GEE, while environmental regulation and average temperature are negatively related. The findings of the study have led to policy recommendations aimed at enhancing urban GEE in the Yellow River Basin.

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Quantifying the source contributions of sediments in large fluvial systems with active wind erosion problems has crucial implications for understanding morphological evolution and ecological progression in the Earth system. Much effort have been focused on characterizing sediments of the Yellow River, but quantitation of the sediment source proportions at the basin-wide scale is lacking. To this end, the research aims to quantitatively elucidate the potential source contributions of sediments in the Yellow River based on geochemical characteristics and sediment fingerprinting technique, in order to identify sedimentary mixing effect and propose sustainable development strategies. In total, samples of four source groups (n = 107) and target floodplain sediments (n = 61) were collected and tested for elemental composition, grain size, magnetic susceptibility, and quartz grain microtextures. The results indicated that the optimal tracer combination was determined as P, Zn, and Ca. The average contributions of the "Tibetan Plateau", "Sandy deserts-Loess Plateau", "Loess Plateau", and "Loess Plateau-Qinling Mountains" source groups to the target sediments were 23.0 %, 21.5 %, 31.6 %, and 23.9 %, respectively. The accuracy of source apportionments was supported by the goodness of fit (GOF) and virtual mixtures tests. Meanwhile, large amounts of debris from surrounding mountains was transported to the Loess Plateau through fluvial processes and ultimately mixed with aeolian deposits, leading to sedimentary mixing effect. To maintain water balance and minimize erosion risk, the drought-resistant perennial planting and moderate grazing were recommended. The findings are instrumental in promoting soil and water conservation and disclosing fluvial and aeolian interaction on a global scale.

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The complex global context, including globalization, rapid urbanization, and climate change, poses significant challenges to urban stability and development. Balancing urban land use efficiency and resilience is crucial for sustainable progress. Focusing on the vulnerable Yellow River Basin (YRB), this study examines the interplay between urban resilience and land use efficiency. Panel data from 2013 to 2020 for 54 cities in the YRB were used, it quantifies the Coupling Coordination Degree of Urban Resilience and Urban Land Use Efficiency (CCDUU), explores its spatiotemporal evolution and influencing factors. Key findings include: The CCDUU exhibits a sustained and discernible growth trend. Notably, CCDUU is higher in downstream areas in comparison to the middle reaches, reaching its lowest point in the upstream areas; however, the increase in CCDUU in the upstream areas surpasses that observed in other regions. Concurrently, regional disparities in CCDUU are diminishing. Despite the presence of a notable positive spatial correlation in CCDUU within the YRB, the strength of this spatial association is not sufficiently robust. Of paramount importance factor is the role of regional innovation, which significantly influences the enhancement of CCDUU. Following closely is the degree of openness, whereas the positive effects of government support and population density are concentrated predominantly in the upper YRB region. In contrast, urban-rural disparity exerts an adverse impact on CCDUU in most regions. Policy recommendations for enhancing CCDUU in YRB cities include strengthening government support and planning control, particularly in upstream regions, to achieve efficient resource utilization and environmental protection. Implementing population density management policies, encouraging rational movement, and promoting population migration to upstream areas can alleviate pressure in downstream cities. Enhancing openness, attracting foreign investment, and promoting innovation and industrial upgrading will drive economic structural upgrades and improve CCDUU.

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Studying the carbon effect of land use in watersheds is important for mitigating global warming, promoting coordinated emission reduction in different regions within the watersheds, and realizing high-quality development of the watersheds. Although a number of scholars have carried out relevant studies in the past, they mainly focused on carbon emissions, rarely involved the carbon balance formed by carbon sources and sinks, and lacked relevant studies on the development of low-carbon economy sub-region. Based on this, this study takes the Yellow River Basin as an example, explores the spatial and temporal evolution of carbon emissions from land use in counties in the Yellow River Basin from 1980 to 2020, and predicts the spatial pattern of carbon income and expenditure from land use under natural conditions in 2030 and 2060 using the PLUS model; and then superimposes on the main functional area planning, divides 735 counties in the Yellow River Basin into six low-carbon economic development subregions, and analyzes their economic development The model of their economic development is analyzed. The results show that: (1) the spatial and temporal differentiation of land use carbon balance in the Yellow River Basin has changed greatly over the past 40 years, (2) the spatial distribution pattern of land use carbon balance in the natural context in 2030 and 2060 is more similar to that in 1990, (3) the carbon emission reduction potentials and pattern optimization of the different low-carbon economic development subregions differ greatly, and they have different low-carbon economic development patterns. The results of this study provide a theoretical basis for scientifically and rationally formulating economic policies for low-carbon development in the counties of the Yellow River Basin, and also provide an important reference for related studies in other similar basins or regions in the world.