Search Results (87)

Under the dual pressures of global climate warming and rapid urbanization, the Yangtze River Basin, as the world’s largest urban agglomeration, is facing intensifying thermal environmental stress. Although river ecosystems demonstrate significant thermal regulation functions, their spatial thresholds of cooling effects and multiscale driving mechanisms have remained to be systematically elucidated. This study retrieved land surface temperature (LST) using the split window algorithm and quantitatively analyzed the changes in the river cold island effect and its driving mechanisms in the Yangtze River Basin by combining multi-ring buffer analysis and the optimal parameter-based geographical detector model. The results showed that (1) forest land is the main land use type in the Yangtze River Basin, with built-up land having the largest area increase. Affected by natural, socioeconomic, and meteorological factors, the summer temperatures displayed a spatial pattern of “higher in the east than the west, warmer in the south than the north”. (2) There are significant differences in the cooling magnitude among different land types. Forest land has the maximum daytime cooling distance (589 m), while construction land has the strongest cooling magnitude (1.72 °C). The cooling effect magnitude is most pronounced in upstream areas of the basin, reaching 0.96 °C. At the urban agglomeration scale, the Chengdu–Chongqing urban agglomeration shows the greatest temperature reduction of 0.90 °C. (3) Elevation consistently demonstrates the highest explanatory power for LST spatial variability. Interaction analysis shows that the interaction between socioeconomic factors and elevation is generally the strongest. This study provides important spatial decision support for formulating basin-scale ecological thermal regulation strategies based on refined spatial layout optimization, hierarchical management and control, and a “natural–societal” dual-dimensional synergistic regulation system. Full article

With the development of urban scale and economic growth, the challenges posed by limited resources and insufficient environmental carrying capacity become increasingly severe, making the sustainable improvement of production efficiency an urgent requirement. Based on panel data for cities in the Dianzhong Urban Agglomeration and the Chengdu–Chongqing Economic Circle in Southwest China (2012–2021), this study elucidates the positive effect of urban compactness on green total factor productivity (GTFP). By constructing a composite index to measure urban compactness and employing an SBM model to quantify GTFP, we find that a 1% increase in urban compactness leads to a 0.65% increase in GTFP. A mediating-effect analysis reveals that green technological innovation serves as a significant mediator, with a mediating effect value of 0.363. Heterogeneity analysis uncovers differing mechanisms of influence: urban compactness exerts a positive effect in regions with higher levels of economic development, while its impact is not significant in regions with lower economic development, indicating that the effect of compactness varies with economic context; the impact of urban compactness on GTFP is statistically insignificant in regions with higher tertiary sector shares (p > 0.1), whereas it exhibits a highly significant positive effect in regions with lower tertiary sector presence (β = 1.49, p < 0.01). These results collectively demonstrate that the influence of urban compactness on GTFP varies significantly with industrial structure composition. Threshold-effect analysis further shows that there is a threshold in the proportion of industrial output value, beyond which the influence of compactness on GTFP becomes even stronger. Our research quantitatively explores both linear and nonlinear relationships between urban compactness and GTFP, clarifying the linkage between urban spatial dynamics and green production efficiency, and provides empirical evidence and scholarly support for urban planning and economic development. Full article

As a pivotal node in urban spatial restructuring, the evolution of three-dimensional (3D) compactness in high-speed rail station areas is crucial for sustainable development. However, the existing research predominantly focuses on two-dimensional forms and lacks dynamic analysis and models that are adaptable to complex terrains. This study develops an enhanced 3D gravitational model that integrates satellite imagery and Gaode building data to quantify the spatiotemporal heterogeneity and carry out multidimensional classification of the compactness across 16 stations in the Chengdu-Chongqing urban agglomeration (2015–2025), with driving factors being identified through correlation and regression analyses. The key findings reveal the following: (1) The mean compactness increased by 22.41%, exhibiting nonlinear heterogeneity characterized by high initial values with low growth rates versus low initial values with high growth rates. Spatially, the southern line evolved from a dual-core pattern at the terminals to multigradient development, while the northern line maintained stable growth despite gradient discontinuities. These spatial differentiations resulted from synergistic effects of urban sizes (station hierarchy), terrain features, administrative divisions, and the line affiliation. (2) The built-up land area (under equal study conditions) and vertical development emerged as key drivers, with the building height diversity demonstrating dual spatial effects (enhancing both compactness and aesthetic richness). Complex terrain characteristics were found to promote clustered urban land use and compact efficiency during initial development phases. This study proposes a planning framework that integrates morphology-adaptive zoning control, ecology-responsive compactness principles, and urban–rural integrated settlement patterns, providing quantitative tools for mountainous station development. These findings offer theoretical and practical support for achieving urban sustainability goals and meeting the 3D compactness and transit-oriented development requirements in territorial spatial planning. Full article

With the global increase in disaster risks, enhancing urban resilience has become a critical strategy for risk mitigation and sustainable development. This study develops a two-dimensional indicator framework based on urban systems and resilience capacity from the perspective of the disaster management cycle and applies an improved CRITIC-TOPSIS method to evaluate the resilience levels of the Chengdu–Chongqing urban agglomeration, China. The spatiotemporal evolution of urban resilience from 2010 to 2022 is systematically examined. Furthermore, the dynamics of urban resilience transitions are investigated using a spatial Markov chain model, and the driving factors behind the spatial distribution of resilience are explored through the Geo-detector method. The results indicate the following: (1) Comprehensive resilience demonstrated a steady upward trend during the study period, with Chengdu and Chongqing, as core cities, driving regional resilience improvement and reducing disparities within the urban agglomeration. (2) Significant spatial heterogeneity was observed in the distribution of the comprehensive resilience index and the indices of individual resilience dimensions. (3) The Markov chain analysis revealed a distinct “club convergence” pattern in the dynamic transitions of resilience levels, with development trends closely tied to spatial factors. (4) The Geo-detector model analysis highlighted that infrastructure development and technological innovation exert long-term and substantial impacts on resilience improvement. These findings provide valuable insights for enhancing resilience and promoting sustainable development in the Chengdu–Chongqing region and other similar urban systems. 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

New quality productive forces serve as a catalyst for high-quality development and act as a critical driver of Chinese-style modernization. This study evaluated the degree of new quality productive force in China’s five major urban agglomerations between 2013 and 2022 using the entropy approach. Additionally, it utilized kernel density estimation, the Dagum Gini coefficient, and Markov chain analysis to explore the spatial and temporal dynamics of these forces and their evolutionary trends. The findings revealed the following: (1) Overall, the new quality productive forces in China’s five major urban agglomerations have exhibited a steady upward trend, although the overall level remains relatively low. Among these regions, the Pearl River Delta ranks the highest, followed by the Yangtze River Delta, Beijing–Tianjin–Hebei, Chengdu–Chongqing, and the Urban Cluster in the Middle Reaches of the Yangtze River. Nevertheless, significant potential for improvement persists. (2) The traditional Markov probability transfer matrix suggests that the new quality productive forces in these urban agglomerations are relatively stable, with evidence of “club convergence”. Meanwhile, the spatial Markov transfer probability matrix indicates that transfer probabilities are influenced by neighborhood contexts. (3) Over time, the new quality productive forces in Chinese urban agglomerations show a tendency to concentrate at higher levels, reflecting gradual improvement. The developmental state and evolutionary patterns of new quality productive forces in Chinese urban agglomerations are thoroughly evaluated in this paper, along with advice for accelerating their growth to promote Chinese-style modernization. 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

As an important region in western China, the Chengdu–Chongqing region has rich sports tourism resources and huge development potential. Based on relevant prior studies and the principle of data collectability, a sports tourism evaluation index system was constructed from four aspects: overall scale, market entity, development foundation, and government support. Using the coupled coordination model, the trend of the coordination and evolution of sports tourism in the Chengdu–Chongqing region from 2015 to 2020 was analyzed, and the primary obstacles affecting the coordinated development of sports tourism were identified through the obstacle degree model. The results show that sports tourism in Chengdu and Chongqing achieved great success based on the geographical environment, sports resources, and policy support. However, problems such as imperfect sports tourism infrastructure, unbalanced regional development, insufficient industrial integration, and shortage of professional talents have restricted the further development of the industry. This study holds that the Chengdu–Chongqing region can achieve high-quality and coordinated development of sports tourism by strengthening urban integration, expanding open cooperation, enhancing further industrial agglomeration, boosting policy support, and improving transportation networks. The findings offer insights for policymakers and stakeholders aiming to leverage sports tourism for economic and social benefits in similar regions. Full article

Understanding the long-term land use changes in the Chengdu–Chongqing urban agglomeration, a vital element of the Yangtze River Economic Belt and a key economic growth center in western China, can offer scientific basis and policy direction for improving regional ecosystem service values (ESVs), environmental conservation, and sustainable development. This study investigated the features connected to land use change, changes in landscape patterns, and variations in ESV across this region by means of time series of remote sensing and socio-economic data from 1990 to 2020. Additionally, the correlations between landscape pattern indices and ESV were examined. The findings indicated that: (1) cultivated land and grassland in the Chengdu–Chongqing urban agglomeration exhibited a declining trend, while forest, water, and construction land demonstrated increasing trends; (2) human activities significantly affected the Chengdu–Chongqing urban agglomeration, resulting in severe landscape fragmentation, heterogeneity, complexity of types, and patch separation, although the predominant patch types maintained strong connectivity; (3) the ESV of the Chengdu–Chongqing urban agglomeration initially decreased, reaching a minimum of CNY 5.017 billion in 2000, and subsequently rose again. Forest land accounted for approximately 69% of the overall ESV in the region over the preceding decades. The policy of reverting farmland to forest, the establishment of wetland parks, and the increased awareness of environmental protection have led to notable alterations in land use types and landscape patterns within the Chengdu–Chongqing urban agglomeration, which can significantly improve the regional ecosystem service value; however, the extent of influence and trends among various landscape indices are disparate. Full article

The mobility and openness of smart cities characterize them as particularly complex networks, necessitating the resilience enhancement of smart city regions from a network structure perspective. Taking the Chengdu–Chongqing urban agglomeration as a case study, this research constructs economic, information, population, and technological intercity networks based on the complex network theory and gravity model to evaluate their spatial structure and resilience over five years. The main conclusions are as follows: (1) subnetworks exhibit a ‘core/periphery’ structure with a significant evolution trend, particularly the metropolitan area integration degree of capital cities has significantly improved; (2) the technology network is the most resilient but was the most affected by COVID-19, while the population and information networks are the least resilient, resulting from poor hierarchy, disassortativity, and agglomeration; (3) network resilience can be improved through system optimization and node enhancement. System optimization should focus more on improving the coordinated development of population, information, and technology networks due to their low synergistic level of resilience, while node optimization should adjust strategies according to the dominance, redundancy, and network role of nodes. This study provides a reference framework to assess the resilience of smart cities, and the assessment results and enhancement strategies can provide valuable regional planning information for resilience building in smart city regions. Full article

Urban Agglomerations (UAs), as the primary form of China’s new urbanization and an essential spatial unit for promoting coordinated regional development, play a crucial role in measuring the sustainable and healthy development of urban clusters through the assessment of spatial network connections among cities within the UAs. Taking the 16 prefecture-level cities of the Chengdu-Chongqing Urban Agglomeration (CCUA) as the research subject, this study constructs six types of element flow networks, including population flow, logistics, and information flow. Employing network visualization analysis, the Self-Organizing Maps (SOM) neural network machine learning models, and Quadratic Assignment Procedure (QAP) relational regression models, the research analyzes the spatial network characteristics of the CCUA from the perspective of multi-dimensional element flows and explores the influencing factors of the UA’s connectivity pattern. The results indicate that: The various element flows within the CCUA exhibit a bipolar spatial network characteristic with Chengdu and Chongqing as the poles. In the element network grouping features, a multi-centered group differentiation structure is presented, and the intensity of internal element flow varies. Based on the results of the SOM neural network machine learning model, the connectivity capabilities of cities within the CCUA are divided into five levels. Among them, Chengdu and Chongqing have the strongest comprehensive connectivity capabilities, showing a significant difference compared to other cities, and there is an imbalance in the connectivity capabilities between cities. In terms of the influencing factors of the urban connectivity pattern within the CCUA, the differences in permanent population size and urbanization rates have a significant negative impact on the information flow network, technology flow network, and capital flow network. The differences in the secondary industrial structure and public budget expenditures have a significant positive impact on the intensity of inter-city element flows, and the differences in per capita consumption expenditures have a significant negative impact, collectively influencing the formation of the spatial connectivity pattern of the CCUA. The findings of this study can provide a scientific basis for the construction and optimization of the spatial connectivity pattern of the CCUA. Full article

Air pollution is influenced by a combination of pollutant emissions and meteorological conditions. Anthropogenic emissions and meteorological conditions are the two main causes of atmospheric pollution, and the contribution of meteorology and emissions to the reduction of PM_2.5 concentrations across the country has not yet been comprehensively examined. This study used the Kolmogorov–Zurbenko (KZ) filter and random forest (RF) model to decompose and reconstruct PM_2.5 time series in five major urban agglomerations in China, analyzing the impact of meteorological factors on PM_2.5 concentrations. From 2015 to 2021, PM_2.5 concentrations significantly decreased in all urban agglomerations, with annual averages dropping by approximately 50% in Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD), Pearl River Delta (PRD), Central Plain (CP), and Chengdu–Chongqing (CC). This reduction was due to both favorable meteorological conditions and emission reductions. The KZ filter effectively separated the PM_2.5 time series, and the RF model achieved high squared correlation coefficient (R²) values between predicted and observed values, ranging from 0.94 to 0.98. Initially, meteorological factors had a positive contribution to PM_2.5 reduction, indicating unfavorable conditions, but this gradually turned negative, indicating favorable conditions. By 2021, the rates of meteorological contribution to PM_2.5 reduction in BTH, YRD, PRD, CP, and CC changed from 14.3%, 16.9%, 7.2%, 12.2%, and 11.5% to −36.5%, −31.5%, −26.9%, −30.3%, and −23.5%, respectively. Temperature and atmospheric pressure had the most significant effects on PM_2.5 concentrations. The significant decline in PM_2.5 concentrations in BTH and CP after 2017 indicated that emission control measures were gradually taking effect. This study confirms that effective pollution control measures combined with favorable meteorological conditions jointly contributed to the improvement in air quality. Full article

The mismatch between urban livability and population distribution can result in overcrowding and excessive pressure on ecosystem services if population growth surpasses urban capacity. Conversely, if urban expansion outpaces population needs, it can lead to underutilized infrastructure and inefficient land use. This study aims to assess the coordination between urban livability and population distribution in five major urban agglomerations in China: Beijing–Tianjin–Hebei (BTH), Yangtze River Delta (YRD), Pearl River Delta (PRD), Mid-Yangtze River (MYR), and Chengdu–Chongqing (CC). A comprehensive index for urban livability is established, from the aspects of social–economic development and ecosystem service. Additionally, a Coordination Distance Index (CDI) is developed to measure the relationship between urban livability and population distribution. Data from 2010, 2015, and 2020 are analyzed to evaluate the coordination levels and trends across the five urban agglomerations. The results show that from 2010 to 2020, most cities within these urban agglomerations experience improvements in their coordination levels, with the most notable advancements in the PRD and YRD regions. By 2020, the PRD and YRD were classified as having “high coordination”, while BTH, MYR, and CC were categorized as having “moderate coordination”. However, certain cities, such as Chengde in BTH, Shanghai in YRD, Ya’an in CC, and Zhuhai in PRD, still exhibited “low coordination”, highlighting areas requiring spatial planning adjustments. This study introduces a method for quantitatively assessing the coordination between urban livability and population distribution, providing essential insights for policymakers and urban planners to refine urbanization development strategies and population regulation policies in China’s major urban agglomerations. Full article

A deep understanding of the spatiotemporal changes in ecosystem services (ESs) under the influence of urbanisation, as well as clarifying the trade-offs and synergies between different services and their driving factors, is crucial for sustainable regional development and the formulation of rational urban expansion policies. Dramatic changes in landscape patterns, driven by the interplay of human activities and natural processes, can have profound effects on regional ESs. Existing research primarily discusses the synergistic trade-offs between ESs, with less focus on the interactions among different landscape patterns and the synergies among ESs. In the present study, we established a multi-model fusion method for ES analysis (PLUS-InVEST-Trade-offs/Synergies-Geographical Detectors (GDs)) to explore the synergistic trade-offs of ESs and their driving factors in the Chengdu-Chongqing Economic Circle from an urban agglomeration perspective. Our findings indicated the following. (1) The distribution of synergistic/trade-offs relationships among ESs exhibited spatial variability. The varying responses of different urban clusters to these policies, along with their unique topography and landforms, are the reasons behind the differences in the synergistic/trade-offs relationships of ESs among these urban clusters. (2) In the Chengdu-Chongqing Economic Circle, the driving factors of the synergistic/trade-offs effects among ESs displayed spatial differentiation. In a certain range, the degree of landscape agglomeration interacts with the degree of landscape fragmentation to promote synergistic/trade-offs effects of ESs. Our findings will provide a new analytical perspective for policymakers in the region and serve as a valuable reference for formulating targeted policies in different sub-regions. Full article

The impact of industrial co-agglomeration (ICA) on green total factor productivity (GTFP) has garnered considerable academic attention. However, there remains a gap in research systematically investigating how ICA affects China’s GTFP within the framework of green development, specifically by analyzing transmission mechanisms, regulatory mechanisms, and spatial spillover effects. To address this gap, this study utilizes panel data from 283 Chinese cities, spanning the years 2006 to 2020, and conducts both theoretical and empirical analyses to examine ICA’s influence on GTFP through these three mechanisms. Our findings indicate that ICA significantly enhances GTFP by alleviating the mismatch of capital and energy factors but does not improve GTFP by addressing labor mismatches. Furthermore, when the intensity of local government competition exceeds a threshold of 14.3825, the positive impact of ICA diminishes, whereas an environmental regulation intensity above 0.4381 strengthens ICA’s positive effect on GTFP. ICA was found to substantially increase local GTFP and generate positive spatial spillover effects on surrounding cities within a 100 km radius. Co-agglomeration of both high-end and low-end producer services with manufacturing boosts local GTFP, while co-agglomeration of low-end producer services with manufacturing also enhances GTFP in adjacent cities. In megacities, ICA positively influences both local and nearby GTFP, whereas in large cities, ICA tends to suppress GTFP in neighboring areas. Additionally, with the exception of the Middle Yangtze River and Pearl River Delta city clusters, ICA in urban clusters enhances local GTFP; ICA in the Middle Yangtze River cluster promotes GTFP in neighboring areas, whereas ICA in the Chengdu–Chongqing cluster inhibits neighboring GTFP. Full article