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Increased CO2 emissions from urban energy consumption pose a significant challenge to regional carbon mitigation policies. In this paper, we integrated two nighttime light (NTL) data: the Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (NPP) composite data to estimate the energy carbon emissions from 2000 to 2019. Then the spatiotemporal dynamics of carbon footprint and deficit in the Yellow River Basin were analyzed at the provincial, municipal, and county scales combined with NPP data. The study shows that (1) the total amount of energy consumption CO2 emissions in the Yellow River Basin had increased from 1332 Mt in 2000 to 6469 Mt in 2019, but the average annual growth rate slowed down after 2010 from 11.5 to 5.61%. (2) From 2000 to 2018, the provinces with the highest carbon footprint and carbon deficit were concentrated in Inner Mongolia and Shanxi. In 2018, Inner Mongolia's carbon footprint was 1366.91 × 104 km2, accounting for 22.8% of the total. Cities with high carbon footprint were mainly economic centers and energy-intensive areas of various provinces. High-carbon deficit counties were mainly distributed in the western region. In 2018, 954 counties exhibited carbon deficits. (3) The carbon footprint in the Yellow River Basin at the municipal and county scales have a significant spatial correlation. The H–H clusters of the carbon footprint on the municipal scale were distributed in the middle reaches of the Yellow River Basin. At the county scale, the L-L clusters were mainly in Sichuan and eastern Henan regions. Through the analysis of the spatial and temporal evolution of carbon footprint and carbon deficit in the Yellow River Basin, it is significant to measure the degree of comprehensive coordination of carbon sources and sinks in the basin, to grasp the differences in the level of regional carbon emissions, and to promote synergistic regional governance, assist in the formulation of more precise carbon emission reduction policies, and to promote green and high-quality development. [ABSTRACT FROM AUTHOR]

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Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Yellow River basin (YRB) plays an important role in China's economic and social growth. Based on different dimensions, we adopted the radial basis function (RBF) neural network model and the obstacle degree model to examine the water resource carrying capacity (WRCC) of the YRB. From 2005 to 2020, the WRCC of the entire YRB, as well as the upstream and midstream regions, improved, but the WRCC of the downstream region remained poor, revealing spatial differences. The overall improvement in the WRCC of the Yellow River's nine provinces is good, but the WRCC of Inner Mongolia and Henan is poor, suggesting regional differences. From the standpoint of obstacle factors, the development and usage rate of surface water resources are the main challenges. In 2020, the obstacle degree of the YRB reached 87.4871%. The irrigated area rate in Gansu was the primary obstacle factor, and the obstacle degree reached 73.0238%. Qinghai's industrial aspects mostly hindered the improvement of its WRCC, with an obstacle degree of 31.36%. The results provide a theoretical reference for the high-quality development of the YRB. [ABSTRACT FROM AUTHOR]

To reveal the relationship between agricultural water resource consumption and grain production in the Yellow River Basin, the irrigation water productivity (WPI), crop water productivity (WPC), total inflow water productivity (WPT), and eleven influencing factors were selected. The spatial and temporal distribution characteristics and driving factors of water productivity of the main crops in the Yellow River Basin were analyzed with the spatial autocorrelation analysis, grey correlation analysis, sensitivity analysis, and relative contribution rate. The results showed that the minimum mean values of WPI, WPC, and WPT were 0.22, 0.35, and 0.18 kg/m3 in Qinghai, respectively, the maximum mean value of WPI was 2.11 kg/m3 in Henan, and the maximum mean values of WPC and WPT were 0.71 and 0.61 kg/m3 in Shandong, respectively. The changing trends in WPI and WPT in Qinghai and in WPC in Shandong were insignificant, whereas the WPI, WPC, and WPT in other provinces showed a significant increasing trend. Water productivity displayed a certain spatial clustering feature in the Yellow River Basin in different years, such as a high-high (H-H) aggregation in Henan in 2005, and an H-H aggregation in Shanxi in 2015 for WPI. The water productivity had a significant positive correlation with the consumption of chemical fertilizer with a 100% effective component (CFCEC), effective irrigated area (EIA), plastic film used for agriculture (PFUA), and total power of agricultural machinery (AMTP), while it had a significant negative correlation with the persons engaged in rural areas (PERA). There was a large grey correlation degree between the water productivity and the average annual precipitation (AAP), CFCEC, PFUA, consumption of chemical pesticides (CFC), and AMTP in the Yellow River Basin, but their sensitivity was relatively small. The main driving factors were EIA (8.98%), agricultural water (AW, 15.55%), AMTP (12.64%), CFCEC (12.06%), and CPC (9.77%) for WPI; AMTP (16.46%), CFCEC (13.25%), average annual evaporation (AAE, 12.94%), EIA (10.49%), and PERA (10.19%) for WPC; and EIA (14.26%), AMTP (13.38%), AAP (12.30%), CFCEC (10.49%), and PFUA (9.69%) for WPT in the Yellow River Basin. The results can provide support for improving the utilization efficiency of agricultural water resources, optimizing the allocation of water resources, and implementing high-quality agricultural developments in the Yellow River Basin. [ABSTRACT FROM AUTHOR]

It is related to the ecological well-being of the people and the high-quality development of the Yellow River Basin to protect the ecological environment of the Yellow River Basin and promote the construction of ecological belts. Henan Province is located in the middle and lower reaches of the Yellow River Basin. This article constructs an index system from five dimensions: residential environment, economic environment, public services, infrastructure and ecological environment. Based on the statistical yearbook of Henan Province in the past five years and the data of China Rural Statistical Yearbook, the entire Henan Province and the main 18 prefecture-level cities were evaluated using the entropy method for rural human settlement environment. On the whole, the rural human settlement environment in Henan Province has been in a slow growth stage in the past five years, and all cities have been in a fluctuating growth stage. The government should adjust measures to local conditions and strengthen ecological environment control on the premise of maintaining sustained and stable economic development. [ABSTRACT FROM AUTHOR]

Establishment of a watershed ecological compensation mechanism between multiple subjects is an effective means to realize the collaborative governance of water pollution and maintain the security of water ecology. This paper breaks through the conventional upstream and downstream perspectives of watershed ecological compensation design research and combines them with uncertainty factors. The watershed ecological compensation mechanism for the mainstream and branches was established based on the evolutionary game and the random process. Then, taking the midstream of the Yellow River as an example, some constraint conditions and influencing factors were explored. Results show that: (1) The branch government (i.e., the Shanxi provincial government) is the key to establishing an ecological compensation mechanism between the river mainstream and branches. (2) The proportion of pollution transferred by other branches, the initial probability and the random factors are the main factors affecting the decision-making of branch governments (Shanxi and Shaanxi provincial governments). (3) The compensation and reward of the mainstream government to the branch government and the compensation of the branch government to the mainstream government are the main factors affecting the decision-making of mainstream and branch governments (Shanxi–Henan provincial governments, Shaanxi–Henan provincial governments). The study may provide scientific guidance for the construction of a watershed ecological compensation mechanism between mainstream and multiple branches. [ABSTRACT FROM AUTHOR]

Rapid urbanization has led to the increasing scarcity of land resources in China. Exploring the spatial-temporal characteristics and influencing factors of urban land use efficiency (LUE) is of great significance for optimizing the allocation efficiency of land resources and promoting regional sustainable development. In this study, the Super-SBM model was used to calculate the urban LUE of the Yellow River Basin from 2009 to 2018. The regional differences and agglomeration characteristics of LUE in the Yellow River Basin were analyzed. Moreover, a panel regression model was used to analyze the influencing factors of LUE. The results showed that the LUE in the Yellow River Basin experienced a process of fluctuation decline during the study period. The regional difference of LUE in the Yellow River Basin was as follows: upper reaches > middle reaches > lower reaches. The hot and cold spots of LUE were relatively stable in spatial distribution during the study period. The hot spots were mainly distributed in Ordos in the upper reaches and Yulin in the middle reaches, while the cold spots were mainly distributed in Henan Province in the lower reaches. Globalization had a positive impact on LUE in the lower reaches. Marketization had a positive impact on LUE in the whole basin and lower reaches, and a negative impact on LUE in the middle reaches. Decentralization had a positive impact on the LUE of the whole basin and the upper reaches, and a negative impact on the LUE of the lower reaches. [ABSTRACT FROM AUTHOR]

• Track interannual variations and trends of SWA and TWS in YRB in 1990–2021. • SWA substantially increased (49.7 km2/yr) while TWS continuously decreased (−6.8 mm/yr). • Increasing surface artificial water bodies contributed ∼69 % to SWA expansion. • Expanding surface water bodies were only an illusion of increased water resources. • SWA expansion exacerbated water crisis by accelerating TWS loss through evaporation. Aggravated water shortage limits high-quality and sustainable development in the Yellow River Basin (YRB), an important strategic region with a dense population in China. Surface water area (SWA) and terrestrial water storage (TWS) are two crucial indicators of the overall status of water resources in the region, so monitoring their dynamics and drivers is extremely important for sustainable water management under a changing climate and anthropogenic disturbances. Here, we examined the interannual variabilities and trends of SWA and TWS in the YRB during 1990–2021 utilizing all Landsat-5/7/8 surface reflectance observations, Gravity Recovery and Climate Experiment mascon data, a robust water mapping algorithm based on spectral indices and thresholds, and the Google Earth Engine (GEE) cloud computing platform. We found that SWA continuously and substantially expanded (49.67 ± 9.73 km2/yr) in the YRB. The most rapid expansion of SWA occurred in Shandong Province (20.24 ± 1.00 km2/yr), next in the provinces of Henan (15.09 ± 1.16 km2/yr), Shanxi (13.21 ± 1.11 km2/yr), and Shaanxi (4.11 ± 1.07 km2/yr). Quantitative attribution analysis showed that anthropogenic factors, including constructing and extending artificial reservoirs/lakes (42.4 %) and aquaculture ponds (26.5 %), were the major contributors to increased SWA. However, TWS had a significant downward trend (-6.82 ± 0.99 mm/yr) across the entire YRB. Using water evaporation data, we found that evaporation consistently increased (0.07 ± 0.01 km3/yr or 0.16 ± 0.02 mm/yr), which was mainly caused by SWA expansion in Shandong, Henan, Shanxi, Shannxi, Inner Mongolia. We also identified the hotspots with the most rapid increases in evaporative loss, which were in the provinces of Shandong (0.026 ± 0.002 km3/yr) and Henan (0.018 ± 0.001 km3/yr). These losses resulted in a decline of TWS by 1.37 ± 0.11 mm/yr and 0.5 ± 0.05 mm/yr, respectively. Our findings warn that the expanding surface water bodies were not only an illusion of increased freshwater resources in the YRB but also exacerbated the water crisis by accelerating TWS loss through evaporation. [ABSTRACT FROM AUTHOR]

This study aims to investigate the temporal and spatial distribution of the agricultural nonpoint source of pollutant emission in the Yellow River Basin of China. The supplementary calculation was used to analyze the census data of the first and second pollution sources. Influencing factors were explored, including fertilizer usages, quantity of straw resources, pollutant loads from livestock and poultry breeding, and efficiency of pollutant treatment. The targeted prevention and control measures were proposed on the agricultural nonpoint source pollution in the Yellow River Basin. The results showed that: 1) The total amount of pollutant emission in the Yellow River Basin decreased obviously in 2017, compared with 2006. The estimated total amount of chemical oxygen demand, ammonia nitrogen, total nitrogen, and total phosphorus pollutant were 1 705, 53, 185, and 17 thousand tons, respectively, while the amount produced by agricultural production was 962, 12, 79, and 11 thousand tons. There was a significant increase in the proportion of agricultural nonpoint source pollution in the total amount. Inner Mongolia contributed the largest amount of agricultural nonpoint source pollution, but Henan and Shandong provinces presented the largest pollution intensity. Livestock and poultry breeding contributed the largest amount of chemical oxygen demand in 2017, whereas fertilizer was the main pollution source for the ammonia nitrogen, total nitrogen, and phosphorus pollutant. 2) There was a significant linear relationship between different pollutant indices and control factors. The total amount of fertilizer usage increased 83% in Inner Mongolia in 2017, compared with 2016. The reason was that the fertilizer usage increased 41% per unit of the cultivated land, as the cultivated land increased 30%. Additionally, there were too many higher fertilizer usages per unit of the cultivated land in Henan, Shaanxi, and Shandong provinces, because of the large-scale planting of vegetables and fruits with substantial production, especially in Henan Province. 3) Compared with 2006, the straw resources in the Yellow River in 2017 increased 46% with an amount of 0.113 billion tons, where most straw was produced in Inner Mongolia, due mainly to the largest cultivated land in the Basin and the large planting area of sugar and oil crops. In the number of straw resources per unit of the cultivated land, the top three were Henan, Shandong, and Ningxia Hui Autonomous Region. Specifically, the straw resources were attributed the most to the corn and wheat in Henan and Shandong provinces, while the corn was in Ningxia Hui Autonomous Region. 4) The emission amounts from the livestock and poultry breeding decreased by 27% in 2017 than that in 2006, mainly because of the national policy on animal husbandry, such as no breeding areas. 5) More efforts can be made to improve the efficiency of pollutant treatment for all pollution sources in Henan Province, and the fertilizer utilization rate in Shanxi Province. The overall control policy and the targeted strategies can be combined with the measures of agricultural nonpoint source pollution in the Yellow River Basin. The most effective measures can be adopted to control the fertilizer usages of vegetables and fruits in Henan, Shaanxi, and Shandong provinces, together with the comprehensive utilization of straw resources, such as corns in all provinces, and oil crops in Qinghai and Henan provinces and Inner Mongolia Autonomous Region. The treatment can be used to improve the utilization of livestock and poultry breeding, especially for cows and sheep in western provinces and pigs in the middle and lower reaches of the Yellow River. A better ecological cycle can be strengthened to combine planting with livestock and poultry breeding. [ABSTRACT FROM AUTHOR]

Understanding the historical change trajectory of Land Use/Land Cover Change (LUCC) is helpful to analyze the land use trends under the interference of human activities and changes in the natural environment, thereby helping decision makers to eliminate the negative impact of unreasonable land use patterns to the greatest extent. Yellow River Basin is the fifth longest river in the world, its ecological environment continues to deteriorate due to the rapid population growth and urban expansion, which has become one of the regions with the most serious soil erosion in China. Aiming to promote the ecological quality and high-quality development of social economy under the human interference and natural environment change, we analyzed the spatiotemporal dynamic of the LUCC in the Yellow River Basin (Henan section) from the watershed scale and proposed policy recommendations. We introduced the chord diagram visualization model to intuitively show the flow, direction and diversity of land cover changes, which would enrich the visual research method system of the land cover quantity transfer trajectory. The land use change index, chord diagram model and gravity center transfer model were used in this paper to analyze the spatial-temporal evolution trajectory and policy implications of the land use pattern in the Yellow River Basin (Henan section) in the past 30 years based on the land use remote sensing data in 1990, 2000, 2010 and 2018. The results showed that: 1) Ecological conservation land (35%) was mainly concentrated in the mountainous areas with higher elevation in the middle reaches; food security land (55%) and production and living land (10%) were mainly distributed in the central and eastern plains. 2) The outflows and inflows of dry land from 1990 to 2018 showed significant dominance; paddy field, water and grassland were dominated by outflow; urban land, rural settlements and other construction land were dominated by inflow. 3) The land use change in central cities had significant regional driving effects throughout the period. 4) The gravity center of food security land and ecological conservation land moved to the West and the area showed a decreasing trend, which proved that the high-quality cultivated land in the plain area in the lower reaches decreased and the ecosystem service function gradually weakened; The shift of gravity center of urban land to the East indicated that the social and economic development in the lower reaches were gradually active. The shift of gravity center of rural residential and other construction land to the West indicated that the population scale in the middle reaches was gradually increasing. [ABSTRACT FROM AUTHOR]

[Display omitted] • The conceptual framework of the interaction between TD and ESV was constructed. • The coupling coordination degree between TD and ESV in the YRB was evaluated. • The spatiotemporal evolution of coupling coordination between TD and ESV was analyzed. • Obstacle factors of CCD between TD and ESV were identified at the basin and city level. The rapid development of tourism has caused a series of impacts on the ecosystem. Although studies on the relationship between tourism development (TD) and the ecological environment are rich, few scholars have focused on the coupling coordination between them from the perspective of ecosystem services value (ESV). Given this research gap, we evaluated the levels of TD and ESV in 78 cities of the Yellow River Basin (YRB), China based on statistical and land-use data. Then, the coupling coordination degree (CCD) model, exploratory spatial data analysis (ESDA), the spatial variogram model, and the obstacle degree model were used to analyze the spatiotemporal evolution of coupling coordination between TD and ESV and its obstacle factors. The results show the following: (1) The comprehensive TD level shows an upward trend and presents significant spatial differentiation characteristics. The total ESV of the YRB shows a fluctuating upward trend, and the average ESV presents the spatial pattern of uplift in the middle reaches. (2) The comprehensive CCD of the YRB has gradually improved, but the gap between cities has widened. The global spatial autocorrelation of CCD is significantly positive, and the spatial variability has gradually increased. An "uplift area" in southern Shaanxi always exists and has gradually expanded to Shanxi and western Henan, while eastern Henan and western Shandong become a "subsidence area". The coupling coordination level has changed from uncoordinated to coordinated in most counties, but the problem of TD and ESV imbalance generally exists. (3) For the whole basin, TD is always the main obstacle factor at the system layer, and tourism scale and tourism economic benefit are the most significant obstacles at the category layer. At the city level, the proportion of cities where TD is the main obstacle factor exceeds 80% by 2019. Specifically, insufficient tourism scale and low tourism economic efficiency restrict the improvement of CCD in most cities. This research provides valuable references for studying the coupling coordination relationship between TU and ESV in big river basins worldwide. [ABSTRACT FROM AUTHOR]

• A theoretical framework for the relationship among resources, economy, and ecology is built. • The coupling coordination level exhibits geographical heterogeneity, with superior performance in upstream provinces. • Spatial variation and key issues are discussed in detail with geographical detector model. • Factors affecting coupling coordination are becoming balanced and diversified. • Three development suggestions are proposed to promote coupling coordination level. The development of the Yellow River Basin (YRB) has been constrained by resource utilization, ecological conservation, and economic growth. The aim of this study was to establish a theoretical framework for characterizing the relationships among resources, economy, and ecology in the YRB using panel data and remote sensing data from nine provinces in the basin from 2002 to 2022. Furthermore, a coupling coordination degree model and geographical detector model were used to evaluate the spatiotemporal coupling and relevant factors. Our findings were manifold. (1) The coupling coordination degree in the YRB and its provinces has improved to varying degrees, with upstream provinces showing stronger coordination than midstream and downstream provinces. (2) The most significant growth rates were observed during 2002–2006 and 2014–2018. However, a noticeable decline in the coupling coordination degree occurred during 2006–2010, indicating that ecological constraints and negative effects were pronounced when resource constraints were not apparent. (3) The average coupling coordination degree of resources, economy, and ecology in the YRB in 2022 was 0.684. Excellent coordination was observed in Shandong Province and Henan Province, and good coordination was observed in Shaanxi Province and Shanxi Province. (4) In the initial part of the study period, the water resource supply, demand, and allocation had strong effects on the coordination degree. Over time, the focus shifted from a single system to multiple systems, which increased the similarity in the explanatory power of different factors. Additionally, an in-depth analysis using the coupling coordination degree and relevant factors was conducted to characterize the policy orientations of different periods in the YRB. Policy recommendations were provided on the basis of the natural conditions and socio-economic conditions of the YRB. The findings of this study have implications for coupling coordination relationships and high-quality development in the YRB. Generally, our results provide key insights that will aid coordinated and sustainable regional development. [ABSTRACT FROM AUTHOR]

The calculation of ecological compensation (EC) constitutes the core element of the EC mechanism, which is related to the practicability of the mechanism. Based on the comprehensive index of water quality and quantity, this study establishes a measurement method for EC across administrative divisions. First, the improved Nemerow pollution index (NPI) is adopted to assess the water quality of specific sections, while the water rights and GDP indicators are employed to characterize the water quantity of each section. Secondly, given the spatial relationship of each region within the basin, two compensation allocation schemes are established referring to the water intake and grey water footprint indicators. Finally, the EC amounts of each region in the Yellow River Basin from 2016 to 2021 are calculated using the proposed method. The results show that: (1) the improved NPI is significantly higher in the upstream than in the downstream, implying better water quality in the upstream region; (2) From 2016 to 2021, both Gansu and Ningxia exhibit negative EC coefficients, suggesting superior water quality in the upper section of these regions, while Henan Province demonstrates the opposite trend; (3) During the same period, Qinghai, Gansu, Shanxi, Shaanxi, and Shandong experience an EC deficit, while Ningxia, Inner Mongolia, Henan, and Yellow River Conservancy Commission exhibit an EC surplus. Using water quality and quantity indicators, this study includes the implementation of basin unification and regional environmental comprehensive management into the scope of responsibility of the basin government, which can effectively solve the drawbacks of unclear responsibilities of stakeholders and ineffective implementation of compensation in the basin EC. [Display omitted] • The Nemerow pollution index is improved to evaluate water quality. • An EC measurement model coupled with water quality and quantity is established. • The water quality of the Fenglingdu section needs to be monitored with attention. • Ningxia, Inner Mongolia, and Henan are in EC surplus, while others are in reverse. [ABSTRACT FROM AUTHOR]

Economic density refers to the degree of labor, population, and tangible capital that concentrate in a physical space. It represents the efficiency of economic activities and intensity of land use in an area. This article presents an in-depth analysis spatial disparity of economic densities in Yellow River Basin in China by using 2000 and 2014 country level data. A composite index of economic density that integrates a suite of population, land, and production indicators through a grey-relation entropy model was applied for the spatial analysis in economic densities. The overall economic density levels in Henan Province and Shandong Province were higher than those of other provinces. Other major spatial agglomerations of counties with high economic density were in central parts of Inner Mongolia and Henan Province, the south of Shanxi Province, and the middle regions of Shaanxi Province. Finally, the indicators that might have influenced the level of economic density and the formation spatial economic disparity within Yellow River Basin were identified as the basic environmental conditions, transportation infrastructure, social and economic infrastructure, level and structure of industrialization level, and strategic policies for regional economic development. The results show that there is indeed spatial economic disparity in the studied region and that it may have been mainly affected by the geographically uneven distribution of per capita income and per capita land use intensity. [ABSTRACT FROM AUTHOR]

Studying the spatial distribution of land use/land cover (LULC) and habitat quality (HQ), influenced by both climate change and socio-economic factors, holds immense importance for fostering ecological sustainability. The previous scale setting was based on changes in granularity and division of spatial ranges, without considering the differences in land quantity structure and spatial expansion under different spatial ranges. Therefore, this study is based on climate and economic data at different spatial scales to determine the various land demands of provinces (YRB-P) and integration of provinces (YRB-I) in the Yellow River Basin, and to limit the expansion of LULC in corresponding regions. At the same time, we have also established three future scenarios representing different development speeds based on the latest path of shared socio-economic development in CMIP6. We found exhibit significant characteristics in ecological responses under combinations of different scales and scenarios. Shandong and Henan Provinces are the main gathering (38.7–41.7%, 24.1–26.5%) and expansion (68.54–85.99 × 102km2, 18.89–34.12 × 102km2) provinces of built-up land under the YRB-P scale, and their HQ (0.260–0.397) are significantly lower than the average HQ (0.619–0.654). Forest land, grassland, and high value regions of HQ show "45°" distribution at two scales, with high and low values clearly clustered (Moran's I is 0.5440–0.580). The HQ evolution region is larger and more dispersed at the YRB-P scale, but accumulates in local areas at the YRB-I scale. In addition, the highest and lowest HQ mean values appear under the low speed development scenario at the YRB-P scale (0.721) and the rapid development scenario at the YRB-I scale (0.689), respectively. This study helps decision-makers control different scales and development scenarios to improve the ecological level of the study area. [Display omitted] • Couple the SD-PLUS-InVEST model to simulate land use/land cover and habitat quality. • Determine the scale from the perspective of regional individualized and integrated development. • Different development scenarios were set based on the latest path of CMIP6 and related policies. • Identification of ecological change characteristics and sensitive regions under two development models. [ABSTRACT FROM AUTHOR]

• Water quality induced water scarcity is incorporated. • Quantify local and virtual water scarcity risks for YRB through MRIO. • Pollution reduces the resilience of supply chains in the YRB to water scarcity. Water scarcity poses economic risks and affects the quality of national and regional development. The risk of local water scarcity can be conveyed to downstream economies through interregional trade for potential economic losses. However, most previous water scarcity studies focused on the availability of adequate freshwater supplies, ignoring the economic losses associated with inadequate water quality. With the aggravation of water pollution, water scarcity caused by inadequate water quality is exacerbated. This study incorporates water quality and quantity into local water scarcity risk assessments, revealing the virtual water scarcity risks and transmission pathways of the cities and sectors in the Yellow River Basin (YRB) in China. The results show that the number of cities with severe water stress increases from 13 to 53 when water quality is considered. Virtual water scarcity risks transfer from upstream cities to downstream cities in Henan and Shandong Provinces, and high-risk cities in Henan can be identified by considering water quality. Key transmission pathways for intersectoral transfer are identified. This study validates the significance of considering water quality factors in water scarcity risk assessments and provides a perspective for policy decisions to mitigate water scarcity and thus ensure water security in the YRB. [ABSTRACT FROM AUTHOR]

[Display omitted] • Cultivated land use efficiency (CLUE) was assessed based on carbon emissions. • The overall CLUE values in the YRB increased and subsequently decreased with time. • Per capita grain possession was main driver of CLUE in most areas. • Road network density was the key driver of CLUE in the QTP. • Results will assist China for food security and cultivated land use carbon emission reduction. China is accelerating green and low-carbon transformations of cultivated land to achieve a carbon peak by 2030 and to actively respond to global climate change. Evaluating the cultivated land use efficiency (CLUE) and determining its driving factors help reduce cultivated land-use carbon emissions and promote green and low-carbon transformations of cultivated land. However, previous carbon emission-based studies of CLUE and its driving factors have been insufficient. Therefore, in this study, we explored the spatiotemporal characteristics and driving factors of CLUE in the Yellow River Basin (YRB), China, based on carbon emissions. A slacks-based model with undesirable (bad) outputs, spatial autocorrelation models, and geographic detector models was used for the analysis, with statistical data from 2005 to 2017 as inputs. The overall CLUE values in the YRB increased and subsequently decreased with time. The Huang-Huai-Hai Plain (HHHP) consistently had the highest values, followed by the northern arid and semi-arid regions (NASR), the Loess Plateau (LP), and the Qinghai-Tibet Plateau (QTP). According to the global spatial autocorrelation, the CLUE had a positive association with themselves. The major cluster types moved from high–high and low–low to high–high cluster types in eastern Henan and southern Shanxi provinces, according to local spatial autocorrelation. Per capita grain possession was the main driver of CLUE in the YRB, HHHP, LP, and NASR, and road network density was the key driver of CLUE in the QTP, according to Geodetector analysis. Furthermore, non-linear enhancement dominates the driving factors of CLUE in the YRB, with non-linear enhancement dominating the HHHP, LP, and NASR, and bi-factor enhancement dominating the QTP. The findings of this study can be used to assist policymakers in formulating feasible, green, and efficient cultivated land-use policies. [ABSTRACT FROM AUTHOR]

[Display omitted] • The equivalent coefficient is revised according to ability and willingness to pay. • The Best-worst method is used to determine the weight of the allocated indicators. • A 10 km buffer calculation strip is established in the compensated payment area. • The overflow of hydrological adjusting is greater than that of erosion prevention. • Ecological compensation in 2010–2020 is higher than that in 2000–2010. Coordinated governance and horizontal ecological compensation are important ways to realize ecological protection and high-quality development in the Yellow River basin. Taking the middle and lower reaches of the Yellow River as the research object, this paper analyses the compensation relationship and amount of cities along the line in Shaanxi, Shanxi, Henan and Shandong provinces. Firstly, the spillover value of regulation of water flows and erosion prevention of 32 counties along the Yellow River in Shaanxi and Shanxi provinces were calculated during 2000–2010 and 2010–2020 from the perspective of land use/cover change (LUCC). Secondly, the compensation accounting index system was established based on the classification of ecological services given the beneficiaries' individual preferences, and each indicator's weights were determined based on the Best-worst method. Finally, setting the water ecological service value of the 10 km buffer zone on both sides of the Yellow River as the accounting scope, this paper calculated the amount of ecological compensation paid by 16 cities in Shandong and Henan provinces. The results show that: (1) The spillover value of Shaanxi and Shanxi provinces in 2010–2020 is greater than that in 2000–2010, and the values of erosion prevention in both stages are lower than that of regulation of water flows; (2) From the perspective of providers, the highest is 3.68e + 08 yuan in Lvliang from 2000 to 2010, while the highest is 3.60e + 09 yuan in Yulin from 2010 to 2020; (3) From the perspective of beneficiaries, Zhengzhou has the highest amount of compensation in Henan province, which is 2.28e + 08 yuan in 2000–2010 and 9.41e + 08 yuan in 2010–2020 respectively. In Shandong province, Heze pays the highest value of 1.27e + 08 yuan from 2000 to 2010, and Jinan pays the highest value of 9.56e + 08 yuan from 2010 to 2020. The ecological compensation scheme obtained in this paper is more sustainable than the vertical compensation and expands the scope of the existing ecological compensation standards, which can effectively stimulate the enthusiasm for ecological protection, and reduce the financial pressure on the central government. [ABSTRACT FROM AUTHOR]

A thorough understanding of groundwater recharge source, particularly its rate, is usually a prerequisite for effective water resources management. In this paper, we report the impact of Yellow River water seepage from the North Henan Plain, using both hydrogeochemical and stable isotopic analysis data. Seven Yellow River water samples, 10 groundwater samples from a river-parallel transect, and 36 groundwater samples from four different perpendicular transects to the Yellow River in the western, middle, and eastern plain were collected and analyzed. It inferred that cation exchange of Ca and/or Mg for Na occurred in groundwaters because of the dissolution of carbonate rocks. The hydrogeochemical results indicate that western piedmont lateral groundwater and the Yellow River are both important sources of groundwater recharge for the western transect of the North Henan Plain, while the former is a greater recharge source for the middle transect, and the latter is a greater recharge source for the eastern transect. Stable isotope data support Yellow River water incursion into the groundwater. The approximate distance (based on chloride concentration) from the Yellow River to border of the impact zone is17.43-23.40 km in the western plain, 52.46 km in the middle plain, and 49.82 km in the eastern plain. [ABSTRACT FROM AUTHOR]