Search Results (24)

The integrated development of urban agglomerations strengthens regional connectivity and supports balanced regional development. However, population shrinkage persists in some counties of the Chengdu–Chongqing Economic Circle despite increasingly close regional linkages. From the perspective of regional network embeddedness, this study examines the relationship between different types of regional linkages and the heterogeneous patterns of county-level population shrinkage. Taking 142 counties and districts in the CCEC as the research units, and drawing on multi-source panel data from 2002 to 2022, this study constructs four types of regional linkages: transportation linkages, corporate organizational linkages, executive interlock linkages, and ownership-based corporate investment linkages. Fixed-effects panel models and ordered logistic regression models are then employed to examine the associations of different network linkages with county-level population change and the degree of population shrinkage. By comparing these four network layers within the same county-level framework, the study distinguishes the roles of accessibility, corporate organization, executive relations, and ownership ties. Network evolution was uneven. The dual-core pattern weakened in the transportation and executive interlock networks, while corporate investment became more centralized. These changes describe network topology, whereas population growth remained concentrated in Chengdu, Chongqing, and adjoining districts. Between 2002 and 2022, 89 of the 142 counties in the CCEC experienced population shrinkage, accounting for 62.7% of the sample. These shrinking counties gradually clustered in peripheral areas, including northeastern Sichuan, southern Sichuan, and northeastern Chongqing, resulting in the coexistence of core growth and peripheral shrinkage. Moran’s I increased from 0.0015 in 2002–2007 to 0.1659 in 2017–2022, indicating increasing spatial clustering. The full-sample panel analysis indicates that higher centrality in the transportation, corporate organizational, and corporate investment networks is significantly associated with higher county-level population change rates, indicating either stronger growth or a smaller decline, whereas executive interlock linkages are negatively associated with population change. Further analysis of shrinking counties shows that higher corporate organizational and corporate investment centrality is significantly associated with lower shrinkage severity. By contrast, transportation and executive interlock centrality are not significantly associated with shrinkage severity. Therefore, the positive full-sample association for transportation centrality should not be interpreted as evidence that transportation network expansion generally compensates for metropolitan concentration. These findings suggest that county-level population shrinkage does not simply result from a lack of regional connections, but is closely related to the specific types of networks in which counties are embedded. Corporate organizational and corporate investment networks exhibit more stable statistical associations across both model settings, indicating that increased regional connectivity does not necessarily lead to balanced county-level development. Full article

Taking 563 national key rural tourism villages across 12 provinces, autonomous regions, and municipalities in western China as the research object, this study integrates multi-source data on physical geography, transportation location, socioeconomic conditions, and historical culture based on the ArcGIS platform. It comprehensively applies kernel density analysis, spatial autocorrelation analysis, buffer analysis, Spearman correlation analysis, Geodetector, and the relative enrichment index to examine the spatial distribution characteristics of these villages and their associated spatial factors. The results show that key rural tourism villages in western China exhibit an overall clustered and uneven distribution, forming a spatial pattern characterized by “high concentration in core areas, extension along secondary corridors, and sparse distribution across vast hinterlands.” The core agglomeration areas are mainly located in the Sichuan Basin, the Chongqing metropolitan area, and the Guanzhong Plain. In terms of physical geography, the distribution of key villages shows certain spatial associations with major river basins, low-slope areas, and low-relief terrain. In terms of human factors, population density and road network density are important associated factors, and the combined population–transportation conditions have strong explanatory power for the spatial differentiation of key village density. With regard to historical culture, folk-custom inheritance villages and red-culture heritage villages account for relatively high proportions, while different cultural types show certain regional agglomeration or corridor-like distribution characteristics. The findings can provide references for zoned optimization, transportation connectivity, cultural resource integration, and coordinated regional development of key rural tourism villages in western China. Full article

Health risks associated with groundwater deterioration have become increasingly prominent worldwide. Accurate assessment of human health risks associated with groundwater is a critical component of groundwater development and utilization, particularly in large metropolitan areas with high water resource demands. In our study, 37 groundwater samples were collected from the main urban areas of Chongqing, the largest city in southwest China, to identify the groundwater driving factors and their associated human health risk. The primary hydrochemical facies in the study area is Ca–HCO₃. Groundwater hydrochemistry is primarily controlled by silicate weathering, carbonate (dolomite and calcite) dissolution, and anthropogenic activities such as industrial and agricultural activities. The hazard index (HI) caused by NO₃⁻ and NO₂⁻ was higher than the safety standard and exhibited potentially noncarcinogenic risk for children in the north and the west of the study area. The KDE-based Monte Carlo simulation method showed a high reliability in human health risk assessment, with all mean values of the original dataset falling within their corresponding 95% confidence intervals (CIs) of generated data. The achievement can provide valuable insights for groundwater risk mitigation and resource management in Chongqing’s main urban areas, as well as in other metropolitan regions worldwide. Full article

Understanding the driving forces of land use and land cover change (LUCC) is crucial for revealing the coupled dynamics of human–land systems and supporting optimized spatial planning and resource allocation. To overcome the limitations of conventional Geodetector applications in mountainous regions with complex terrain, this study proposes a terrain–population dual-factor adaptive grid designed for use with the Geodetector model. This adaptive grid refines cells in steep and densely populated areas while merging cells in flatter and sparsely populated regions, thus capturing both natural and socioeconomic heterogeneity. Coupled with the Geodetector model, this framework improves the accuracy and computational efficiency of identifying LUCC drivers. Using the Chongqing Metropolitan Area (CMA) as a case study, LUCC dynamics and their driving mechanisms were systematically examined based on five annual land cover datasets (from 2000 to 2020 at five-year intervals.). The results show the following: (1) From 2000 to 2020, cropland, forest land, and built-up land were the dominant land use types. During this period, cropland and forest land declined, whereas built-up land expanded continuously, with the most pronounced changes occurring between 2000 and 2010. (2) The dominant drivers of LUCC shifted over time: socioeconomic factors such as population density and GDP were primary drivers from 2000 to 2010, while both natural and socioeconomic factors exerted strong influence between 2010 and 2020. (3) The proposed terrain–population dual-factor irregular grid performed better than traditional regular grids in detecting socioeconomic drivers while retaining comparable explanatory power for natural factors. Compared with traditional regular grids, with an average q-value improvement of 18.7% and a 55.52% reduction in sampling points, resulting in substantially improved computational efficiency. Overall, the proposed method enhances the applicability of Geodetector in complex mountainous cities and provides practical implications for urban land use regulation and refined spatial management. Full article

This study investigated the spatiotemporal dynamics and driving mechanisms of urban expansion in the Chongqing Metropolitan Area by integrating multi-source big data and employing a suite of quantitative analytical methods. Drawing upon high-resolution remote sensing imagery, land-use datasets, socioeconomic statistics, and transportation network data spanning 2019 to 2023, the research revealed pronounced spatial and temporal heterogeneity in urban growth. Specifically, expansion manifested through a core-periphery spatial structure and temporal imbalances. The findings underscore a growing economic interconnectedness between core urban districts and peripheral cities such as Guang’an and Luzhou, giving rise to a multilayered and increasingly networked spatial-economic system. Moreover, urban expansion is shown to be tightly coupled with industrial distribution, transportation optimization, and regional integration strategies. In particular, the implementation of the Chengdu-Chongqing Twin-City Economic Circle has significantly facilitated cross-regional factor mobility and spatial restructuring, thereby accelerating coordinated development across the metropolitan area. Looking forward, urban expansion in the Chongqing Metropolitan Region is expected to continue leveraging transportation infrastructure and strategic industrial placement to advance regional economic integration. 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

Rural ecosystem health (REH) serves as an effective metric for assessing the damage degree and stability state within rural systems and their components. It reflects the interaction and the balance among rural subsystems, emphasizing the harmonious development of resources, agriculture, environment, economy, and society that are fundamental to sustainable rural development. Most regional-scale ecosystem health assessments primarily focus on either the natural state of the ecosystem or external disturbances affecting it, often neglecting human ecological systems characterized by economic and social dimensions. Taking Chongqing as an example, we established an improved REH assessment framework by integrating ecological integrity from the perspective of a social-economy-natural compound ecosystem. Furthermore, we innovatively incorporated the Sustainable Development Goals (SDGs) into the formulation of the REH indicator system to quantitatively elucidate the spatiotemporal characteristics. The results indicated that: (1) The REH in Chongqing exhibited an evolutionary pattern characterized by a subsequent rise, maintaining values between 0.363–0.872 from 2000 to 2018. This trend reflected a distinct two-stage development characteristic, with the rural socio-economic subsystem contributing the most at 33.36%, followed closely by the rural environmental subsystem at 27.84%; (2) In 2018, the REH across the 36 districts and counties in Chongqing displayed spatial differentiation patterns described as “collapse in the west, high levels in the northeast, and localized surges”. The areas ranked from smallest to largest REH were metropolitan, western, southeastern, and northeastern areas; (3) Four levels (e.g., disease, single health, compound health, and comprehensive health) and twelve sub-levels of REH were defined using a dominant factors method. Finally, we analyzed the driving factors from four aspects of urbanization development: policy regulation, urban-rural factors flow, and regional differences. We also proposed differentiated planning and policies for sustainable rural development in Chongqing. Full article

Ensuring the harmonization between urbanization and water environment systems is imperative for fostering sustainable regional development in the future. With urban agglomerations and metropolitan areas increasingly dominating urbanization trends in China, it is crucial to explore the interdependent relationship between urbanization and the water environment. Such exploration holds significant implications for water resource management and the formulation of urbanization policies. This study utilizes a comprehensive index system encompassing urbanization and the water environment. It examines the coupled and coordinated spatial and temporal dynamics of these systems within the Chengdu-Chongqing Urban Agglomeration from 2011 to 2019. This analysis employs the Coupled Coordination Degree model alongside the spatial autocorrelation model. The results show that there is still much room for improving the urbanization development level and the water environment quality. During the study period, a nonlinear and nearly U-shaped evolutionary trajectory was observed between the two systems. The results suggest that there is a progression from basic to more advanced coordination between urbanization and water environment at the city cluster scale. Urbanization appears to generally lag behind the water environment in terms of coordination. At the municipal scale, there is a gradient in which some cities show better coordination compared to others. Spatially, the coupling and coordination of this region exhibited dual-core development characteristics centered around Chengdu and Chongqing. The region is in the transition stage towards a core-type networked and decentralized development mode, which has not yet formed an integrated pattern. This offers a theoretical and technical framework for harmonizing water environments and urbanization in similar regions globally. Full article

China’s urban–rural dichotomy has resulted in a widening gap between urban and rural areas, posing significant challenges to rural development. This study aims to investigate the spatio-temporal differentiation and driving mechanisms of rural industry integration within the Chengdu–Chongqing Economic Circle in China. Using panel data from 2011 to 2020, we employed the entropy weight TOPSIS method to construct a comprehensive index that charts the evolution of rural industry integration across various districts and counties. Additionally, we utilized fixed-effect and spatio-temporally weighted regression models to analyze the underlying driving forces behind this integration. Our findings reveal a dynamic and varied landscape of rural industry integration, with different levels of depth and breadth across various subsystems. Spatially, we observed a transition from a dispersed to a more concentrated agglomeration pattern within the Chengdu–Chongqing Economic Circle. This shift suggests a diffusion effect emanating from core metropolitan areas, as well as an attracting force exerted by adjacent metropolitan circles. In terms of drivers, market demand, openness level, financial development, policy support, and agricultural insurance breadth significantly contribute to rural industry integration. However, technological progress and rural human capital exhibit a weaker correlation. Notably, our models identified pronounced spatial–temporal heterogeneity among these influencing factors, highlighting a nuanced and dynamic relationship between them. Overall, our study emphasizes the crucial role of rural industry integration in bridging the urban–rural divide and fostering sustainable agricultural development and rural revitalization. The insights gained from this research provide valuable guidance for policymakers and stakeholders seeking to optimize rural development strategies and unlock the potential of integrated rural industries. Full article

Land use/cover change (LUCC) refers to the phenomenon of changes in the Earth’s surface over time. Accurate prediction of LUCC is crucial for guiding policy formulation and resource management, contributing to the sustainable use of land, and maintaining the health of the Earth’s ecosystems. LUCC is a dynamic geographical process involving complex spatiotemporal dependencies. Existing LUCC simulation models suffer from insufficient spatiotemporal feature learning, and traditional cellular automaton (CA) models exhibit limitations in neighborhood effects. This study proposes a cellular automaton model based on spatiotemporal feature learning and hotspot area pre-allocation (VST-PCA). The model utilizes the video swin transformer to acquire transformation rules, enabling a more accurate capture of the spatiotemporal dependencies inherent in LUCC. Simultaneously, a pre-allocation strategy is introduced in the CA simulation to address the local constraints of neighborhood effects, thereby enhancing the simulation accuracy. Using the Chongqing metropolitan area as the study area, two traditional CA models and two deep learning-based CA models were constructed to validate the performance of the VST-PCA model. Results indicated that the proposed VST-PCA model achieved Kappa and FOM values of 0.8654 and 0.4534, respectively. Compared to other models, Kappa increased by 0.0322–0.1036, and FOM increased by 0.0513–0.1649. This study provides an accurate and effective method for LUCC simulation, offering valuable insights for future research and land management planning. Full article

In the context of promoting high-quality development of mountainous urban areas, it is of great significance to explore the evolutionary trajectory of habitat quality in the future based on policy-driven backgrounds, particularly for the protection of the Western mountainous ecosystem. This study takes the Chongqing metropolitan area, a typical southwestern mountainous city, as the study area. Based on land use data from 1990 to 2020, the study combines the InVEST and PLUS models, considering the constraints imposed by urban construction planning and ecological control policies, to investigate the spatiotemporal variations of habitat quality from 1990 to 2030. The findings are as follows: (1) From 1990 to 2020, there was a significant decrease in cultivated land area in the study area, while forestland and unused land showed a declining trend. Conversely, built-up land, grassland, and water bodies exhibited an increasing trend. In the land use simulation for 2030, under the scenarios of natural growth and ecological protection, the cultivated land area further decreased, while forestland and grassland received a certain degree of protection. In the scenario of development, a large amount of cultivated land was converted into built-up land. (2) From 1990 to 2030, significant overall habitat quality changes were observed among different regions within the study area. Except for Nanchuan District and Qijiang District, other administrative regions experienced a certain degree of decline in habitat quality. The distribution of habitat quality exhibited significant spatial heterogeneity. The low-value habitat areas were centered in the middle of the metropolitan area and gradually expanded outward. The high-value habitat areas were concentrated in the study area, including the Huaying Mountain range and other mountainous ecological corridor regions. (3) Habitat quality in the study area showed a decreasing trend with an increasing slope gradient. With the development of urbanization, habitat quality degradation gradually spread to high-altitude and steep-slope areas. (4) The expansion of built-up land is the main cause of habitat degradation in the study area. From 1990 to 2030, against the background of development strategies in the study area, the expansion of built-up land encroached upon cultivated land and forestland. In the habitat quality prediction for 2030, habitat degradation in the region will continue to intensify. This study provides scientific references and the basis for promoting regional sustainable land use and ecological conservation. Full article

Rapid urbanization in China has led to an exponential increase in the stocks of metals used in cities. Exploring their amount and growth patterns is an important way to forecast future metal demand and identify the potential for urban mining. Here, we use a combination of bottom-up and GIS tools to estimate the amount of in-use stocks and scrap metal of steel, copper, and aluminum in 366 regions of mainland China from 2010 to 2020. We then downscaled the 2020 metal scrap volume based on a multi-source dataset of socioeconomic factors. Finally, the accessibility of the urban mining pilot base (UMPB) was calculated using the two-step floating catchment area method (2SFCA), and the spatial layout assessment analysis of the UMPB was conducted under the supply–demand balance perspective. The results showed that the total in-use stocks of steel, copper, and aluminum increased from an initial 3186 million tons to 5216 million tons, with a corresponding trend of continued growth in the amount of metal scrap. The high value of scrap metal in 2020 is concentrated in the Beijing–Tianjin–Hebei urban agglomeration, the Yangtze River Delta region, and the Chengdu–Chongqing metropolitan area. The accessibility results show that the road network distance-based accessibility covered a smaller area than the Euclidean distance-based accessibility, but when the UMPB service radius was set to 300 km, the road network distance-based accessibility could also cover most of the eastern part of China. The spatial evaluation results of UMPB show that for service radii of 200 km and 300 km, low-supply and high-demand areas account for 6.32 percent and 5.89 percent, respectively. Full article

Land use/cover change (LUCC) models are essential for studying the profound impact of land use/cover dynamics on various aspects of the natural and social environment. Cellular Automata (CA) is widely used in the dynamic modeling of complex LUCC systems. In the traditional machine learning CA model, when using statistical methods to obtain neighborhood features, there is usually the problem that the spatio-temporal feature learning of neighborhood factors is insufficient. At the same time, the CA dynamic iteration module using the random seed selection mechanism often has the problem that the seed selection efficiency is very low. In this paper, taking the Chongqing Metropolitan Area as an example, convolutional neural networks (CNN)-Long Short-Term Memory Network (LSTM) is introduced to improve the learning effect of the traditional random forest (RF)-CA model in the spatial and temporal characteristics of neighborhood factors. CNN is used to extract the spatial dimension features of LUCC in the neighborhood, and the LSTM model is used to extract the time dimension features and long-term dependencies. At the same time, a high-quality seed selection iterative algorithm (HQSSIA) is used to improve the accuracy of the multi-land-use dynamic change model and the efficiency of the iterative algorithm. The results show that, the proposed model performs better than other models in simulating the LUCC from 2015 to 2020 (Kappa = 0.9684, FOM = 0.1744, Accuracy = 0.9829, F1 = 0.9641, Hamming = 0.0171) and from 2010 to 2020 (Kappa = 0.9599, FOM = 0.4662, Accuracy = 0.9785, F1 = 0.8113, Hamming = 0.0214). After introducing the CNN-LSTM model, the Figure of Merit (FOM) increased by 1.56% and 18.88% for 2015–2020 and 2010–2020. Compared with the CA model-based random seed selection algorithm, the FOM of the model using HQSSIA in the dynamic iteration module are improved by 11.60% and 24.79% for 2015–2020 and 2010–2020, and the operation efficiency of the dynamic iteration module is improved by about 19 times. Compared with the current mainstream LUCC models PLUS and FLUS, the proposed model has improved 14.38%, 37.55%, and 14.93%, 37.74% in FOM, respectively, for 2015–2020 and 2010–2020. The research shows that: (1) RF-CNN-LSTM-CA model not only retains the interpretability advantage of the traditional RF-CA model, but also improves the accuracy of the whole model by improving the spatio-temporal characteristics of neighborhood factors through in-depth learning; (2) the HQSSIA can quickly and accurately search for cells to be converted with higher conversion probability in the observed data, which can not only significantly reduce the time complexity of the model, but also improve the accuracy of LUCC simulation. Full article

Rapid urban expansion and economic development lead to the deterioration of ecosystems, which not only aggravates regional ecological risks but also leads to the degradation of ecosystem functions. It is of great significance to rationally divide regions and provide targeted management strategies for realizing the sustainability of regional economic development and ecological maintenance. Taking southwest China (Sichuan, Yunnan, Guizhou and Chongqing) as an example, land use data from 2000, 2010 and 2020 were used to evaluate the value of landscape ecological risk (LER) and ecosystem services, and comprehensive zoning was divided according to their spatial correlation. The socio-economic development characteristics of each zone were analyzed, and differentiated and targeted sustainable development paths were proposed. The results showed that the overall LER level of southwest China increased, and the gap of internal LER narrowed gradually. The ecosystem service value (ESV) per unit area showed an increasing trend, but the core metropolitan areas and northwest Sichuan had little change. According to the differences in population, industrial structure and land use, the low-ESV zone was densely populated, while the high-ESV zone was sparsely populated, and the population from the high-LER zone gradually migrated to the low-LER zone. The economic development of the low-ESV zone was better than that of the high-ESV zone, and secondary industry was an important driving force of regional economic development. Large-scale forestland can alleviate the LER, but the increase in cultivated land and grassland further aggravated the LER. According to the social and economic characteristics of each zone, this study put forward a differentiated development strategy for southwest China and also provided reference for the coordinated development of ecological protection and social economy in other key ecological regions. Full article

Revealing the spatial dynamics of vegetation change in Chongqing and their driving mechanisms is of major value to regional ecological management and conservation. Using several data sets, including the SPOT Normalized Difference Vegetation Index (NDVI), meteorological, soil, digital elevation model (DEM), human population density and others, combined with trend analysis, stability analysis, and geographic detectors, we studied the pattern of temporal and spatial variation in the NDVI and its stability across Chongqing from 2000 to 2019, and quantitatively analyzed the relative contribution of 18 drivers (natural or human variables) that could influence vegetation dynamics. Over the 20-year period, we found that Chongqing region’s NDVI had an annual average value of 0.78, and is greater than 0.7 for 93.52% of its total area. Overall, the NDVI increased at a rate of 0.05/10 year, with 81.67% of the areas undergoing significant expansion, primarily in the metropolitan areas of Chongqing’s Three Gorges Reservoir Area (TGR) and Wuling Mountain Area (WMA). The main factors influencing vegetation change were human activities, climate, and topography, for which the most influential variables respectively were night light brightness (NLB, 51.9%), annual average air temperature (TEM, 47%), and elevation (ELE, 44.4%). Furthermore, we found that interactions between differing types of factors were stronger than those arising between similar ones; of all pairwise interaction types tested, 92.9% of them were characterized by two-factor enhancement. The three most powerful interactions detected were those for NLB ∩ TEM (62.7%), NLB ∩ annual average atmospheric pressure (PRS, 62.7%), and NLB ∩ ELE (61.9%). Further, we identified the most appropriate kind or range of key elements shaping vegetation development and dynamics. Altogether, our findings can serve as a timely scientific foundation for developing a vegetative resource management strategy for the Yangtze River basin that duly takes into account local climate, terrain, and human activity. Full article