Search Results (47)

Natural processes are substantially impacted by human activity, and assessing human activity has significant ramifications for regional ecological conservation. The study developed an extended human footprint (HF) assessment model based on the theory of ecological effects and human pressures to evaluate human disturbances in the urban agglomerations of the Yellow River Basin using data from 2005 to 2020, revealing the spatiotemporal pattern in the region. The conclusions show that the HF value of urban agglomeration in the Yellow River Basin has steadily increased from 2005 to 2020, primarily driven by mining intensity and road construction. High HF areas are primarily concentrated in urban areas in the south-central of the region, with a tendency to spread outward. Medium HF areas are mainly distributed in the eastern part of the study area, and the spatial distribution increases year by year, extending outward from the center area. The moderately low and HF areas are mostly found in the mountainous areas of the northwest. Among the urban agglomerations in the Yellow River Basin, the Central Plains UA and Shandong Peninsula UA are the areas most heavily affected by human disturbance. The conclusions are instructive for the high-quality development of urban agglomerations in the Yellow River Basin. Full article

In this study, the spatial–temporal characteristics of AOD in the Yellow River Basin (YRB) and urban agglomerations within the basin were analyzed at a 1 km scale from 2011 to 2020 based on the MCD19A2 AOD dataset. This study shows the following: (1) From 2011 to 2020, the AOD value of the YRB showed a declining trend, with 96.011% of the zones experiencing a decrease in AOD. The spatial distribution of AOD displayed a pattern of high in the east, low in the west, high in the south, and low in the north. The rate of decline showed a distribution pattern of fast in the southeast and slow in the northwest. (2) The AOD in the YRB showed similar characteristics in different seasons: the south and east were consistently higher than the north and west. The seasonal AOD values in the YRB showed the following pattern: summer > spring > autumn > winter. The AOD values of urban agglomeration were basically larger in spring and summer. (3) The SDE and mean center of the yearly AOD were located in the southeast and Shanxi Province, with the movement from southeast to northwest. It can be divided into three stages based on the movement trajectory: northeast–southwest round-trip movement (2011–2014), one-way movement to the northwest (2014–2018), and southeast–northwest round-trip movement (2018–2020). Full article

This study develops an evaluation system to assess the integration levels of the seven urban agglomerations in the Yellow River Basin. Based on the weighted comprehensive indicator-based evaluation and Dagum’s Gini decompositions, it evaluates the integration of these urban agglomerations as well as their regional disparities from 2010 to 2022. The results show the following: (1) During the study period, the overall integration level of these urban agglomerations exhibited a general upward trend, although significant gaps still exist, with a spatial pattern of “lower reaches > middle reaches > upper reaches”. Moreover, after 2019, the integration accelerated markedly, indicating that the Yellow River Strategy has positively influenced the integration of these urban agglomerations. (2) Significant differences exist between the urban agglomerations in different dimensions of integration, although the gap has shown a fluctuating but narrowing trend. In addition, the degree of integration across different dimensions has been increasing annually for all urban agglomerations, except for the Shandong Peninsula Urban Agglomeration. The focus of integration varies among these urban agglomerations due to their differing stages of development. (3) In terms of regional disparities, the overall Gini coefficient displayed a “reverse U-shaped” decline, suggesting that while the gap in integration between the urban agglomerations has been narrowing over time, imbalances persist. Inter-group differences are the primary source contributing to the overall disparities in the integration levels of the urban agglomerations in the Yellow River Basin. Full article

The high-quality development paradigm, which emphasizes the organic unity of efficiency, equity, and sustainability, has gained increasing global recognition as an extension of the concept of sustainable green development. In this study, we use green development efficiency as a metric of high-quality development and employ a three-stage Stacks-based Measure Model (SBM) in order to assess the true green development efficiency (GDE) levels of urban agglomerations in China’s Yellow River Basin (YRB) from 2011 to 2020. The results indicate that external environmental factors significantly impacted the green development efficiency levels of these urban agglomerations; after removing these factors, their green development efficiency shifted from trendless fluctuations to more consistent upward trends. Additionally, the disparities between different urban agglomerations are the primary sources of overall differences in green development efficiency in the YRB. Influenced by economic development levels and administrative divisions, the degree of internal development imbalance varies among urban agglomerations; however, regional disparities show a decreasing trend, indicating a catch-up effect. Based on these findings, we further propose relevant policy recommendations in this paper. The results of this study help us to understand the current status and trends of high-quality development in the urban agglomerations of the YRB, providing empirical evidence for policy formulation. Full article

Ecological well-being performance (EWP) is a key indicator of sustainable development and has garnered significant research attention. This study measures the overall and stage-by-stage efficiency of the urban agglomerations in the Yellow River Basin based on the ends–means framework of steady-state economics. This study then delves into the spatiotemporal transfer characteristics of EWP through Moran’s I, and spatial Markov chains. Additionally, this research investigates the factors influencing EWP using a random forest model. The findings indicate a notable enhancement in EWP in the urban agglomerations in the YRB from 2006 to 2021, showing clear spatial agglomeration patterns. The shift in EWP types displays a “path dependence” effect, with distinct evolutionary paths influenced by spatial lag effects. Ecological input emerges as a key internal driver of EWP, while urbanization and technological advancements are highlighted as significant external factors. Industrial agglomeration and industrial structure also contribute to improving EWP. The findings of this study help to clarify the spatial and temporal characteristics of ecological welfare performance and its driving mechanisms in the urban agglomerations of the Yellow River Basin. This is conducive to the achievement of high-quality urban transformation and regional green development, and it provides a reference for the construction of an ecological civilization. Full article

Land use change has a significant impact on the sustainability of ecosystems, and ecological security patterns (ESPs) can improve environmental quality through spatial planning. This study explored a multi-scenario ESP framework by integrating future land use simulation (FLUS) and minimum cumulative resistance (MCR) for urban agglomeration along the Yellow River Basin (YRB) in Ningxia. The research involved simulating land use change in 2035 under four development scenarios, identifying ecological security networks, and evaluating network stability for each scenario. The study revealed that the ecological sources under different development scenarios, including a natural development scenario (NDS), an economic development scenario (EDS), a food security scenario (FSS), and an ecological protection scenario (EPS), were 834.82 km², 715.46 km², 785.56 km², and 1091.43 km², respectively. The overall connectivity values (O_G) for these scenarios were 0.351, 0.466, 0.334, and 0.520, respectively. It was found that under an EPS, the ESPs had the largest area of ecological sources and the most stable ecological network structure, which can effectively protect natural habitats. This study provides a valuable method for identifying ESPs that can respond to diversity and the uncertainty of future development. It can assist decision-makers in enhancing the ecological quality of the study area while considering various development scenarios. Full article

Continued urban expansion (UE) has long been regarded as a huge challenge for climate change mitigation. However, much less is known about how UE affects carbon emissions (CEs), especially in the urban agglomerations of the Yellow River Basin (UAYRB), China. In this regard, this study introduced kernel density analysis, the Gini coefficient, and Markov chains to reveal the UE patterns and carbon emissions intensity (CEI) in the UAYRB at the county level, and explored the spatial heterogeneity of the impact of UE on CEI with the geographically and temporally weighted regression model. The results show that both CEI and UE in the UAYRB showed a steady growing trend during the study period. The kernel density of CEI and UE revealed that CEI in the UAYRB was weakening, while the UE rate continuously slowed down. The Gini coefficients of both CEI and UE in the UAYRB region were at high levels, indicating obvious spatial imbalance. The Markov transfer probability matrix for CEI with a time span of five years showed that CEI growth will still occur over the next five years, while that of UE was more obvious. Meanwhile, counties with a regression coefficient of UE on CEI higher than 0 covered the majority, and the distribution pattern remained quite stable. The regression coefficients of different urban landscape metrics on CEI in the UAYRB varied greatly; except for the landscape shape index, the regression coefficients of the aggregation index, interspersion and juxtaposition index, and patch density overall remained positive. These findings can advance the policy enlightenment of the high-quality development of the Yellow River Basin. Full article

Amid escalating urbanization, devising rational commercial space layouts is a critical challenge. By leveraging machine learning, this study used a backpropagation (BP) neural network to optimize commercial spaces in Weinan City’s central urban area. The results indicate an increased number of commercial facilities with a trend of multi-centered agglomeration and outward expansion. Based on these findings, we propose a strategic framework for rational commercial space development that emphasizes aggregation centers, development axes, and spatial guidelines. This strategy provides valuable insights for urban planners in small- and medium-sized cities in the Yellow River Basin and metropolitan areas, ultimately showcasing the power of machine learning in enhancing urban planning. Full article

With the growing influences of anthropogenic activities and climatic change, the problem concerning the vulnerability of the Water-Energy-Food-Carbon (WEFC) system in the Yellow River Basin is prominent. Using the VSD framework, the WEFC system vulnerability evaluation index system was constructed with 60 cities in the Yellow River Basin as the samples, and the WEFC system vulnerability of each city was measured from 2010 to 2019. Kernel density estimation, Theil index, and spatial correlation analysis were employed to investigate spatio-temporal evolution characteristics. Geodetector was utilized to analyze the driving factors of WEFC system vulnerability. The results demonstrate that: (1) The vulnerability of the WEFC system in the Yellow River Basin tends to decrease, with a spatial pattern of “low in the middle and high on both sides”; the vulnerability is largest in the upper and lower reaches, while smallest in the middle reaches. (2) The spatial difference in vulnerability narrows in the middle and lower reaches, while expanding in the upper reaches. The differences among the three major regions mainly originate from within the region, with the upper reaches having the largest difference and contribution; the vulnerability is featured with a significant spatial correlation, with the upper and lower reaches cities mostly displaying a “high-high” agglomeration and the middle reaches mainly showing a “low-low” one. (3) Factors, including the carbon and ecological carrying capacity coefficient, water resource development and utilization rate, and urbanization rate, mainly influence the WEFC system vulnerability; the spatial heterogeneity of core drivers at the regional scale is obvious, with the upper reaches regions being more strongly influenced by factors of the water resources system, while the middle and lower reaches regions are more sensitive to factors concerning industrial pollution of the energy subsystem. The explanatory power of carbon ecological carrying capacity reaches its peak in the middle reaches. The interaction of factors increases the strength of the impact on vulnerability. This study provides decision support and policy suggestions for achieving a balanced and coordinated development of water resource utilization, energy development, food production, and carbon cycle system in the Yellow River Basin. Investigating WEFC system vulnerability to support SDG 11 provided valuable insights and policy strategies for building cities that are inclusive, secure, resource-efficient, and resilient in the face of climate change and disaster risks. Full article

The United Nations Sustainable Development Goals (SDGs) and the rise of global sustainability science have led to the increasing recognition of basins as the key natural geographical units for human–land system coupling and spatial coordinated development. The effective measurement of spatiotemporal patterns of urban connectivity within a basin has become a key issue in achieving basin-related SDGs. Meanwhile, China has been actively working toward co-ordinated regional development through in-depth implementation of the Yellow River Basin’s ecological protection and high-quality development. Urban connectivity has been trending in urban planning, and significant progress has been made on different scales according to the flow space theory. Nevertheless, few studies have been conducted on the multiscale spatiotemporal patterns of urban agglomeration connectivity. In this study, the urban network in the Yellow River Basin was constructed using Tencent population migration data from 2015 and 2019. It was then divided into seven distinct communities to enable analysis at both the basin and community scales. Centrality, symmetry, and polycentricity indices were employed, and the multiscale spatiotemporal patterns of urban agglomerations in the Yellow River Basin were identified using community detection, complex networks, and the migration kaleidoscope method. Community connectivity was notably concentrated at the basin scale with a centripetal pattern and spatial heterogeneity. Additionally, there was a symmetrical and co-ordinated relationship in population migration between the eastern and western regions of the basin, as well as between the internal and external parts of the basin. At the community scale, there was significant variation in the extent of central agglomeration among different communities, with few instances of similar-level, long-distance, and interregional bilateral links. The utilization of multiscale spatiotemporal patterns has the potential to enhance the comprehension of economic cooperation between various cities and urban agglomerations. This understanding can aid decision-makers in formulating sustainable development policies that foster the spatial integration of the basin. Full article

The Yellow River Basin is an important energy base and economic belt in China, but its water resources are scarce, its ecology is fragile, and the task of achieving the goal of carbon peak and carbon neutrality is arduous. Carbon compensation potential can also be used to study the path to achieving carbon neutrality, which can clarify the potential of one region’s carbon sink surplus to be compensated to the other areas. Still, there needs to be more research on the carbon compensation potential of the Yellow River Basin. Therefore, this study calculated the carbon compensation potential using the β convergence test and parameter comparison method. With the help of spatial measurement tools such as GIS, GeoDa, Stata, and social network analysis methods, the spatiotemporal pattern and network structure of the carbon compensation potential in the Yellow River Basin were studied from the perspective of urban agglomeration. The results demonstrate the following: (1) The overall carbon compensation rate of the YRB showed a downward trend from 2005 to 2019, falling by 0.94, and the specific pattern was “high in the northwest and low in the southeast”. The spatial distribution is roughly spread along the east–west axis, and the distribution axis and the center of gravity keep shifting to the northwest. It also showed a weak divergence and a bifurcation trend. (2) The carbon compensation rate in the YRB passed the spatial correlation and β convergence tests, demonstrating the existence of spatial correlation and a “catch-up effect” among cities. (3) The overall distribution pattern of the carbon compensation potential in the YRB is a “low in the west and high in the east” pattern, and its value increased by 8.86% during the sampled period. (4) The network correlation of carbon compensation potential in the YRB has been significantly enhanced, with the downstream region being more connected than the upstream region. (5) The Shandong Peninsula Urban Agglomeration has the largest network center, followed by the Central Plains Urban Agglomeration, and the Ningxia along the Yellow River Urban Agglomeration has the fewest linked conduction paths. According to the research results, accurate and efficient planning and development suggestions are proposed for urban agglomeration in the Yellow River Basin. Full article

The Yellow River Basin is an essential ecological barrier in China, but it is relatively underdeveloped. The human–land relationship needs to be coordinated, and the ecological environment is fragile, which seriously restricts the sustainable development of the urban agglomeration in the Yellow River Basin. In this study, a “five-dimensional integrated” comprehensive carrying capacity evaluation model is constructed using the five dimensions of water, land, ecology, monitoring, and early warning to evaluate its resource carrying capacity quantitatively. It constructs an early warning system of the resource carrying capacity based on the quantitative evaluation results and monitors the state of the resource carrying capacity. The results show that (1) seven major urban agglomerations’ populations, grain productions, and land are surplus, and 50.85% of prefecture-level cities have food surpluses regarding human–food relationships. (2) There are shortages in the urban agglomeration’s water resources and a deficit in the water resource carrying capacity. (3) The average ecological carrying capacity index is 0.519, indicating a state of ecological affluence. (4) The comprehensive resource carrying capacity is defined as level-three heavy-load conditions, while 67%, 22%, and 14% of cities have level-one, -two, and -three heavy-load conditions, respectively. This study can aid in the monitoring of the resource carrying status of the Yellow River Basin. These results provide a scientific basis for effectively restraining the utilization and development of natural resources in the Yellow River Basin. It can also provide a research paradigm for the world’s river basins, as well as the sustainable development of man and nature in the world. Full article

Since the ecological protection and high-quality development of the Yellow River basin in China have become a major national strategy, reducing carbon emissions has become pivotal. Therefore, based on the relevant data of 53 cities from 2008 to 2021 in seven urban agglomerations in the Yellow River basin, this paper explores the overall situation and spatio-temporal evolution of environmental governance and carbon emissions in the urban agglomerations in the Yellow River basin using the entropy method, ArcGIS, slacks-based measurement models (SBM models), etc. Additionally, this paper quantitatively analyzes the pathways by which environmental governance affects carbon emissions in the urban agglomerations in the Yellow River basin. The results show that carbon emissions increased year on year from 2008 to 2021, the growth rate slowed down gradually and exhibited a downward trend, and the largest amount of carbon was emitted in 2019, at 3495 million tons. Before 2017, the growth rate of carbon emissions showed a trend of increasing year by year, with the largest increase rate being 11.17% in 2010. After that, the growth rate of carbon emissions continued to decrease and entered a stage of fluctuation. The growth rate of carbon emissions in 2020 was the lowest, reaching −5.66%. The environmental governance effect of urban agglomerations in the Yellow River basin exhibits a large gap; the regional difference is obvious, and the overall trend is rising. Environmental governance has a significant negative effect on carbon emissions in urban agglomerations in the Yellow River basin. The cross-terms of environmental governance, the energy consumption structure, industrial structure upgrading, green technological innovation, and foreign direct investment (FDI) have significant negative impacts on carbon emissions, while the indirect impacts on urban agglomerations have shown regional heterogeneity. The goal of reducing carbon emissions in urban agglomerations in the Yellow River basin is being realized gradually. Based on research conclusions, policy suggestions are put forward, hoping to provide ideas for environmental protection and high-quality development of urban agglomerations in the Yellow River basin. Full article

The Yellow River Basin (YRB) stands as one of China’s most significant river basins, and the maintenance of its ecological functionality is of paramount importance for national well-being. The Guanzhong Plain Urban Agglomeration (GPUA), situated in the middle reaches of the YRB, represents the central hub of human activities. The rapid expansion of cities within this region poses formidable challenges to the ecological security framework of the highly sensitive middle reaches of the YRB. In this study, the dynamic equivalent coefficient method was employed to evaluate the changes in Ecological Service Values (ESVs) within the GPUA from 1990 to 2020, as well as the ecological costs incurred due to urban expansion. The results indicate the following: (1) Over the past three decades, the land-use pattern within the GPUA has undergone significant transformations. The area designated for urban development has expanded by a factor of 1.16 compared to its original extent, while the areas encompassing forests, shrubs, agricultural land, grassland, wetland, and bare land have experienced continuous reductions. (2) The ESV of the study area displays a declining trend initially, followed by a subsequent increase over the 30-year period. Forests play a predominant role in contributing to the ESV of the GPUA, with regulating services and supporting services standing out as the primary ecosystem functions. (3) The expansion of the GPUA between 1990 and 2020 has resulted in a net loss of 3772.10 km² of ecological land. The ecological costs associated with urban expansion soar to an astonishing CNY 2.54 billion, with the highest costs attributed to the loss of hydrological regulation and soil conservation services; this issue demands significant attention. The outcomes of this research contribute to a better comprehension of the ecological costs and benefits that accompany the development of urban agglomerations in the middle reaches of the YRB. Furthermore, they provide invaluable insights for decision makers seeking to implement more effective strategies for sustainable land-use management. Full article

Achieving synergistic development of efficient urban land use and the natural environment is crucial in promoting green urbanization. The assessment of land use eco-efficiency (LUEE) and its temporal–spatial changes provides an effective means of quantifying the relationship between the urban ecological environment and land use. Targeting 55 selected cities in the Yellow River Basin (YRB), in this study, we utilize the Super-EBM method to gauge the LUEE. We explore the temporal patterns and the spatial convergence of LUEE utilizing kernel density estimation and spatial econometric methods. Considering the resource and environmental costs of land use, we assumed the industrial pollutant emissions generated during urban land use as the undesired outputs and designed a framework for measuring the level of LUEE under double constraints, which theoretically revealed the formation process and spatial convergence mechanism of LUEE. The results show the following: (1) Throughout the sample period, the LUEE of the YRB urban agglomeration decreased from 0.158 in 2009 to 0.094 in 2020, indicating a decreasing spatial disparity in LUEE over time. Notably, the Lanxi urban cluster exhibited the largest gap in LUEE, whereas the Guanzhong Plain urban agglomeration displayed the smallest gap. The hyper-variable density exceeded the inter-group gap as the main factor leading to the difference in LUEE. (2) Although the LUEE of urban agglomerations has increased, there still exists a noticeable polarization phenomenon. (3) The LUEE of YRB demonstrates a pattern of conditional convergence and exerts a significant spatial spillover effect. Over time, the LUEE of YRB will tend towards an individual steady state. The findings have implications for strengthening linkage and synergy among cities in YRB, promoting factor integration across administrative regions, and formulating heterogeneous policies. Full article