Search Results (78)

As the world’s third-longest river supporting 40% of China’s population, the Yangtze River Basin exemplifies the critical challenges of balancing riparian development and ecological resilience for major fluvial systems globally. This study analyzed the spatiotemporal evolution, proximity dynamics to the Yangtze River, and driving mechanisms of the “three types of spaces” (urban, agricultural, and ecological) in 130 counties along the Yangtze River mainstem from 2000 to 2020, utilizing an integrated approach incorporating land use transfer matrices, centroid-based distance metrics and GeoDetector models. Key findings reveal: (1) Urban space exhibited significant irreversible expansion while agricultural space continued to shrink, with ecological space maintaining overall stability but showing high-frequency bidirectional conversion with agricultural areas in localized zones. (2) Spatial proximity analysis demonstrated contrasting patterns—eastern riparian counties showed urban spatial agglomeration towards the river, whereas most mid-western regions experienced urban expansion away from the watercourse, with marked regional disparities in agricultural and ecological spatial changes. (3) Driving mechanism analysis identified topography as the dominant natural factor influencing ecological space evolution, while socioeconomic factors exerted stronger impacts on proximity variations of agricultural and urban spaces, with natural–socioeconomic interactive effects showing the most significant explanatory power. These spatial dynamics reflect universal trade-offs between economic development and ecosystem conservation in large river basins worldwide. We advocate differentiated spatial governance strategies, including rigorous riparian ecological redlines, eco-agricultural models in agricultural retreat zones, and proximity-based real-time monitoring for ecological early warning. The integrated methodology and spatial governance framework offer transferable solutions for sustainable management of major fluvial systems under rapid urbanization pressure. These findings provide scientific evidence and implementable pathways for coordinating socioeconomic development with ecosystem resilience in the Yangtze River Economic Belt. Full article

Achieving the synergistic effect of pollution reduction and carbon mitigation (PRCM) is a core pathway for promoting green and low-carbon transition and realizing the “dual carbon” goals, as well as a crucial mechanism for coordinating ecological environment governance with climate action. Based on panel data from three major urban agglomerations (Beijing–Tianjin–Hebei, Yangtze River Delta, and Pearl River Delta) between 2008 and 2019, this study employs network centrality and structural holes to characterize urban network positions (UNP), and systematically investigates the impact mechanisms and spatial heterogeneity of urban network positions on PRCM synergy using a dual fixed-effects model. The findings reveal that (1) urban network positions exert significant inhibitory effects on the overall synergy of PRCM, meaning higher centrality and structural hole advantages hinder synergistic progress. This conclusion remains valid after robustness checks and endogeneity tests using instrumental variables. (2) Heterogeneity analysis shows the inhibitory effects are particularly pronounced in Type I large cities and southern urban agglomerations, attributable to environmental governance path dependence caused by complex industrial structures in metropolises and compounded pressures from export-oriented economies undertaking industrial transfers in southern regions. Northern cities demonstrate stronger environmental resilience due to first-mover advantages in heavy industry transformation. (3) Mechanism testing reveals that cities occupying advantageous network positions tend to reduce environmental regulation stringency and research and development investment levels. Conversely, cities at the network periphery demonstrate late-mover advantages by embedding environmental regulations and building stable technological cooperation partnerships. This study provides a theoretical foundation for optimizing urban network spatial configurations and implementing differentiated environmental governance policies. It emphasizes the necessity of holistically integrating network effects with ecological effects during new-type urbanization, advocating for the establishment of a multi-scale coordinated environmental governance system. Full article

The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) is one of China’s three major urban agglomerations. Over the past thirty years, the region has undergone intensive economic development and urban expansion, resulting in significant changes in its ecological conditions. Due to the region’s humid and rainy climate, traditional remote sensing ecological indexes (RSEIs) struggle to ensure consistency in long-term ecological quality assessments. To address this, this study developed a unified RSEI (URSEI) model, incorporating optimized data selection, composite index construction, normalization using invariant regions, and multi-temporal principal component analysis. Using Landsat imagery from 1990 to 2020, this study examined the spatiotemporal evolution of ecological quality in the GBA. Building on this, spatial autocorrelation analysis was applied to explore the distribution characteristics of the URSEI, followed by geodetector analysis to investigate its driving factors, including temperature, precipitation, elevation, slope, land use, population density, GDP, and nighttime light. The results indicate that (1) the URSEI effectively mitigates the impact of cloudy and rainy conditions on data consistency, producing seamless ecological quality maps that accurately reflect the region’s ecological evolution; (2) ecological quality showed a “decline-then-improvement” trend during the study period, with the URSEI mean dropping from 0.65 in 1990 to 0.60 in 2000, then rising to 0.63 by 2020. Spatially, ecological quality was higher in the northwest and northeast, and poorer in the central urbanized areas; and (3) in terms of driving mechanisms, nighttime light, GDP, and temperature were the most influential, with the combined effect of “nighttime light + land use” being the primary driver of URSEI spatial heterogeneity. Human-activity-related factors showed the most notable variation in influence over time. Full article

Tourism urbanization has become an important pathway for promoting regional economic growth, optimizing urban spatial structures and enhancing residents’ quality of life, especially in the context of sustainable development. Balancing the relationship between tourism urbanization and residents’ well-being in China’s Central Plains Urban Agglomeration is a key objective for the promotion of sustainable regional development in the context of rapid tourism development. However, few studies have quantitatively explored the spatiotemporal coupling dynamics between tourism urbanization and residents’ well-being at the urban agglomeration scale, leaving a significant gap in understanding their integrated evolution. Therefore, in this study, we constructed an evaluation index system for tourism urbanization and residents’ well-being. Next, we explored the coupling relationship between tourism urbanization and residents’ well-being and its influencing factors in the Central Plains Urban Agglomeration from 2005 to 2022 via the coupling coordination degree and random forest approaches. The study’s three major findings are as follows: (1) First, in terms of development level, the tourism urbanization of the Central Plains Urban Agglomeration from 2005 to 2019 generally showed a steady upwards trend, and the well-being of residents as a whole showed a steady development trend; however, there were significant regional differences in the level of development. The spatial differentiation between tourism urbanization and residents’ well-being was characterized by “high in the west and low in the east” and “high in the middle and low in the surroundings”, and the degree of spatial differentiation tended to gradually narrow over time. (2) In terms of the level of coupling coordination, the overall coordination between tourism urbanization and residents’ well-being in the Central Plains Urban Agglomeration increased annually and reached the stages of running-in and high coordination. (3) The key factors affecting the coupled coordination of tourism urbanization and residents’ well-being in the Central Plains Urban Agglomeration differed significantly over time. The importance of the number of tourists, policy support, and fiscal balance ratio increased significantly over time, whereas the importance of the vegetation index and the distance to the nearest provincial capital city decreased. These findings have valuable implications for urban planning, governance optimization, and the formulation of sustainable development strategies, highlighting the need to strengthen resilience and promote synergistic growth between tourism development and residents’ well-being. Full article

Urban activities are a significant source of global carbon emissions. Balancing economic development and environmental protection during urban construction and development has become a common concern worldwide. The urban form structure, as the spatial foundation of urban activities, is critical for both current and future urban development. In this study, an urban economic activities perspective is taken to examine the relationship between the urban form structure and carbon emission efficiency in 63 prefecture-level cities within China’s three major urban agglomerations from 2013 to 2022. Two dimensions are considered: land resource development and the urban spatial layout. The research findings indicate that (1) the built-up areas of the three major urban agglomerations in China generally exhibit a pattern of “core cities expanding outwards and peripheral cities emerging sporadically”. (2) Various urban form structure indicators have different effects on carbon emission efficiency, with interaction detection via geographic detectors showing a dual-factor enhancement effect. (3) Urban form structure influences carbon emission efficiency through the mediating pathway of economic agglomeration. This study enriches empirical research on the impact of the urban form structure on carbon emission efficiency from an economic activities perspective and provides empirical evidence for urban spatial planning and achieving sustainable development. Full article

Spanning China’s eastern, central, and western regions, the Yangtze River Economic Belt (YREB) is a pivotal area for economic growth and carbon emissions, with its three major urban agglomerations serving as key hubs along the upper, middle, and lower reaches of the Yangtze River. Understanding the driving factors of carbon emissions and simulating carbon peak scenarios in these regions are critical for informing low-carbon development strategies across China’s diverse geographical zones. This study employs Grey Relational Analysis to identify key drivers and applies the Logarithmic Mean Divisia Index (LMDI) decomposition method to quantify the contributions of various factors to carbon emissions from 2005 to 2021. Furthermore, the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model is utilized to project future emission trends under multiple scenarios. The results indicate that (1) the growth rate of carbon emissions in the three urban agglomerations has generally decelerated during the study period; (2) the influence of driving factors varies significantly across regions, with economic development, urbanization, and population size positively correlating with carbon emissions, while energy structure and energy intensity exhibit mitigating effects; and (3) tailored emission reduction strategies for each urban agglomeration—namely, the Yangtze River Delta Urban Agglomeration (YRD), the Middle Reaches of the Yangtze River Urban Agglomeration (TCC), and the Chengdu-Chongqing Urban Agglomeration (CCA)—can enable all three to achieve carbon peaking by 2030. These findings provide a robust foundation for region-specific policy-making to support China’s carbon neutrality goals. Full article

The Taihang Mountains serve as a critical geographical barrier in northern China, delineating two major 2.5-micrometer particulate matter (PM_2.5) pollution hotspots in the Beijing–Tianjin–Hebei region and the Fenwei Plain. This study examines the underlying mechanisms and interregional dynamic transport pathways of a severe PM_2.5 pollution event that occurred in the urban agglomerations of the Central Taihang Mountains (CTHM) from 8–13 December 2021. The WRF-HYSPLIT simulation was employed to analyze a broader range of potential pollution sources and transport pathways. Additionally, a new river network analysis module was developed and integrated with the Atmospheric Pollutant Transport Quantification Model (APTQM). This module is capable of identifying localized, small-scale (interplot) pollution transport processes, thereby enabling more accurate identification of potential source areas and transport routes. The findings indicate that the persistence of low temperatures, high humidity, and stagnant atmospheric conditions facilitated both the local accumulation and cross-regional transport of PM_2.5. The eastern urban agglomerations, such as Shijiazhuang and Xingtai, were predominantly influenced by northwesterly air masses originating from Inner Mongolia and Shanxi, with pollution levels intensified due to topographic blocking and subsidence effects east of the Taihang Mountains. In contrast, western urban centers, including Taiyuan and Yangquan, experienced pollution primarily from short-range transport within the Fen River Basin, central Inner Mongolia, and Shaanxi, compounded by basin-induced stagnation. Three principal transport pathways were identified: (1) a northwestern pathway from Inner Mongolia to Hebei, (2) a southwestern pathway following the Fen River Basin, and (3) a southward inflow from Henan. The trajectory analysis revealed that approximately 68% of PM_2.5 in eastern receptor cities was transported through topographic channels within the Taihang Transverse Valleys, whereas 43% of pollution in the western regions originated from intra-basin emissions and basin-capture circulation. Furthermore, APTQM-PM_2.5 identified major pollution source regions, including Ordos and Chifeng in Inner Mongolia, as well as Taiyuan and the Fen River Basin. This study underscores the synergistic effects of basin topography, regional circulation, and anthropogenic emissions in shaping pollution distribution patterns. The findings provide a scientific basis for formulating targeted, regionally coordinated air pollution mitigation strategies in complex terrain areas. Full article

China’s rapid urbanization has spurred economic growth and posed environmental challenges. We investigate the relationship between multidimensional urbanization and environmental pollution by a fixed effect model based on the panel data of 70 cities in three major urban agglomerations in the Yangtze River Economic Belt during 2005–2020. Overall, environmental pollution aggravates and then decreases in three major urban agglomerations, which is closely related to China’s environmental pollution control policies. Environmental pollution shows obvious spatial heterogeneities by five levels in three major urban agglomerations, which have been gradually changed from high-value levels to low-value levels. In the Yangtze River Economic Belt, environmental pollution is dominated by “High–High” and “Low–Low” clusters. Among them, “High–High” clusters move eastwards, while “Low–Low” clusters gradually concentrate southeastwards. Multidimensional urbanization helps to alleviate regional environmental pollution. Economic, social, and land urbanization reduces environmental pollution in three urban agglomerations to a certain extent. Population urbanization has a non-significant effect on environmental pollution. Social urbanization has improved environmental pollution in the Chengdu–Chongqing urban agglomeration (CC) and the Yangtze River Delta (YRD), which is increased by population urbanization in the middle reaches of the Yangtze River (MYR) and is improved by comprehensive urbanization in CC. Full article

Urban agglomerations, as hubs of population, economic activity, and energy consumption, significantly contribute to greenhouse gas emissions. The interconnected infrastructure, energy networks, and shared economic systems of these regions create complex emission dynamics that cannot be effectively managed through isolated city-level strategies. However, these regions also present unique opportunities for innovation, policy implementation, and resource optimization, making them crucial focal points in efforts to reduce carbon emissions. This study examines China’s three major urban agglomerations: the Yangtze River Delta, the Pearl River Delta, and the Beijing–Tianjin–Hebei region. Utilizing data from 2005 to 2020 and a comprehensive evaluation model (BCPCAM), the research offers more profound insights into the socio-economic factors and collaborative mechanisms influencing emission trends, facilitating the development of targeted strategies for sustainable development and carbon neutrality. The findings indicate that (1) economic development and carbon control can progress synergistically to some extent, as economically advanced cities like Beijing and Shanghai have achieved their carbon peaks earlier; (2) natural resource endowment significantly affects urban carbon emissions, with resource-rich cities such as Tangshan and Handan, where fossil fuels dominate the energy mix, facing considerable challenges in reducing emissions; and (3) notable differences exist in the growth patterns of carbon emissions between urban and rural buildings, underscoring the need for tailored carbon reduction policies. Full article

Eliminating poverty, reducing inequality, and achieving balanced development are one of the United Nations Sustainable Development Goals. Objectively and accurately measuring regional economic vitality and development equilibrium is a pressing scientific issue that needs to be addressed in order to achieve common prosperity. Nighttime light (NTL) remote sensing data have been proven to be a good proxy variable for socio-economic development, and are widely used due to their advantages of convenient access and wide spatial coverage. Based on multi-source data, this study constructs an Economic Development Index (EDI) that comprehensively reflects regional economic vitality from two aspects, economic quality and development potential, combines the Nighttime Light Development Index (NLDI) as the evaluation indicators to measure the economic vitality and development equilibrium, analyzes the economic vitality and development equilibrium of 300 district and county units in China’s three major urban agglomerations from 2000 to 2020 and their temporal and spatial variation characteristics, and discusses the connotation of EDI and its availability. The results show the following: (1) From 2000 to 2020, the average growth rate of EDI in China’s three major urban agglomerations reached 36.32%, while the average decrease rate of NLDI reached 38.75%; both economic vitality and the development equilibrium have been continuously enhanced. Among them, the Yangtze River Delta (YRD) urban agglomeration experienced the fastest economic growth, while the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) exhibited the strongest economic strength. (2) Both economic vitality and the development equilibrium in these three urban agglomerations exhibited distinct spatial agglomeration characteristics, namely center-surrounding distribution, coastal–inland distribution, and radial belt–pole distribution, respectively. (3) Over the past two decades, the economic development of these three urban agglomerations has progressed towards the pattern of regional coordinated development, pole-driven development and urban–rural integrated development. The research results can provide new research perspectives and scientific support for promoting regional balanced development, achieving sustainable development goals, and reducing inequality. Full article

The Yangtze River Economic Belt (YREB), spanning 11 provinces and municipalities across China, is of paramount importance due to its high economic development and strategic role in national distribution. However, the YREB, which has experienced rapid economic growth, faces challenges resulting from its previously expansive development model, including regional resource and environmental issues. Consequently, a systematic analysis encompassing socio-economic, ecological, and resource-environmental aspects is vital for a comprehensive and quantitative understanding of the YREB’s overall condition. This study explores resource and environmental carrying capacity (RECC) by constructing an integrated framework that includes remote sensing data, geographic information data and social statistical data, which allows for a precise analysis of RECC dynamics from 2010 to 2020. The findings demonstrate an upward trend in the overall quality of RECC from 2010 to 2020, achieving higher grades over time. However, there is significant spatial heterogeneity, with a notable decrease in RECC levels moving from the eastern to the western regions within the YREB. Moreover, low-level RECC areas situated in the northwest of the YREB, show a trend of moving toward regions of higher altitude from 2010 to 2020 based on analysis using the standard deviation ellipse (SDE) method. When considering to the three major urban agglomerations within the YREB, overall RECC in middle and lower agglomerations is generally stable and on an upward trend while cities in upper reaches exhibit significant variation and fluctuations, highlighting them as areas requiring future focus. Therefore, specific indicators were applied to monitor RECC risk for each of these three agglomerations, respectively, after which optimized strategies could be proposed based on different early warning levels. Ultimately this study allows local authorities to implement timely and effective interventions to mitigate risks and promote sustainable development. Full article

Breakthrough innovation and incremental innovation have different impacts on economic development. For regional development, it is important to find a balance in dual innovation, which entails effective coordination of allocating innovation resources and managing risks. However, little attention has been given to the spatial relationship and differentiation mechanisms between breakthrough innovation and incremental innovation. Therefore, our research takes China’s three major urban agglomerations as examples, aiming to explore the spatial–temporal pattern evolution, influencing factors, spatial relationship, and spatial organizational patterns of breakthrough innovation and incremental innovation from 2000 to 2021. The research found that the spatial distribution of urban dual innovation is affected by the law of distance decay, and the spatial distribution of incremental innovation is more polycentric than that of breakthrough innovation. In terms of the differentiation mechanism, breakthrough innovation is more affected by the innovation atmosphere, while incremental innovation is more likely to be affected by the economic foundation and built environment. Our research effectively supplements the shortcomings in the spatial relationship research of breakthrough innovation and incremental innovation and provides references for formulating innovation policies. Full article

Low-carbon cities aim to minimize greenhouse gas emissions in the context of climate change in the process of urbanization. Maintaining these cities at an appropriate physical scale has been proven to contribute to carbon reduction. Therefore, this study extended the definition of the city scale to an integrated framework with three dimensions: the construction land area, population, and economy. The urban construction land of 258 cities in China during 2012 to 2019 was divided into commercial, industrial, residential, and traffic sectors, and carbon emissions were calculated for each. The regression relationship between carbon emissions and the urban scale revealed by panel data analysis showed the following conclusions: (1) carbon emissions were concentrated in north China, provincial capital cities, and municipalities directly under the central government during the research period, and the industrial sector was the main emission resource, accounting for more than 85% of the total emissions. (2) Carbon emissions per unit of land decreased with the increasing land scale, regardless of the land-use type. The growth rate of carbon emissions was slower than that of the population, and cities also became more efficient as their economic scale expanded. (3) Compared with small cities, the large ones benefited more from increasing commercial and traffic land areas, whereas industrial emissions for production needs exhibited significant agglomeration characteristics. Overall, low-carbon planning should focus on the driving role of provincial capital cities as large cities tend to be more efficient, and develop the emission reduction potential of major industrial cities as well. Full article

Water resource carrying capacity is indispensable for sustainable development, acting as a crucial determinant for harmonizing ecological preservation with socio-economic development. This study centers on Hubei Province, which is an important water conservation area in the Yangtze River Basin and is one of the core water source areas for the South-to-North Water Diversion Project, and evaluates the water resource carrying capacity of the three major urban agglomerations in Hubei Province from 2005 to 2020 based on the four dimensions of water resources, economics, society, and ecology, using the entropy weighting method and the TOPSIS model to construct an evaluation index system. We then employ the kernel density estimation method, ArcGIS visualization, and the Dagum Gini coefficient method to perform a comprehensive analysis of spatial and temporal differences, dynamic evolution, and contribution sources. The results show that (1) the water resource carrying capacity of Hubei Province as a whole increased from a severe overload to overload level during the study period. The water resource carrying capacity of the three major urban agglomerations shows a regional distribution pattern where the Yi-Jing-Jing-En agglomeration’s capacity surpasses that of the Wuhan urban agglomeration, which is bigger than Xiang-Shi-Sui-Shen urban agglomeration. A lower ecological water use rate primarily constrains the enhancement of the carrying capacity of water resources in Hubei Province. (2) The kernel density estimation reveals an increase in the overall water resource carrying capacity across Hubei Province’s three major urban agglomerations during the study period, alongside a pronounced trend towards polarization. (3) While the overall Gini coefficient, indicating an imbalance in water resource carrying capacity in Hubei Province, remains high, it demonstrates a declining trend, suggesting a growing disparity in water resource carrying capacity across the province’s three major urban agglomerations. Hubei Province’s water resource carrying capacity faces challenges of an overall imbalance and localized vulnerability. Strategies should aim to enhance synergy, address these deficiencies directly, and devise targeted measures tailored to the distinct features of various urban clusters. Full article

With environmental degradation and energy shortages, green and low-carbon development has become an industry trend, especially in regards to cold chain logistics (CCL), where energy consumption and emissions are substantial. In this context, determining how to scientifically evaluate the cold chain logistics efficiency (CCLE) under carbon emission constraints is of great significance for achieving sustainable development. This study uses the three-stage data envelopment analysis (DEA) and the Malmquist index model to analyze the overall level and regional differences regarding CCLE in China’s four major urban agglomerations, under carbon constraints, from 2010 to 2020. Then, the influencing factors of CCLE are identified through Tobit regression. The results reveal that: (1) the CCLE in the four urban agglomerations is overestimated when carbon constraints are not considered; (2) the CCLE in the four urban agglomerations shows an upward trend from 2010 to 2020, with an average annual growth rate of 1.25% in regards to total factor productivity. However, there are significant spatial and temporal variations, with low-scale efficiency being the primary constraint. (3) Different influencing factors have different directions and exert different effects on CCLE in different urban agglomerations, and the improvement of economic development levels positively affects all regions. Full article