Search Results (14)

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

During the development of the Chengdu–Chongqing Urban Agglomeration (CCUA) in China, PM_2.5 pollution severely threatened public health, presenting a significant environmental challenge. This study employs a novel spatial interpolation method known as High Accuracy Surface Modeling (HASM), along with the geographical detector method, local and regional contributions calculation model, and the Hybrid Single–Particle Lagrangian Integrated Trajectory model to analyze the seasonal spatial distribution of PM_2.5 concentrations and their anthropogenic driving factors from 2014 to 2023. The transport pathway and potential sources of seasonal PM_2.5 concentrations were also examined. The results showed the following: (1) HASM was identified as the most suitable interpolation method for monitoring PM_2.5 concentrations in the CCUA; (2) The PM_2.5 concentrations exhibited a decreasing trend across all seasons, with the highest values in winter and the lowest in summer. Spatially, the concentrations showed a pattern of being higher in the southwest and lower in the southeast; (3) Industrial soot (dust) emissions (ISEs) and industry structure (IS) were the most important anthropogenic driving factors influencing PM_2.5 pollution; (4) The border area between the eastern part of the Tibet Autonomous Region and western Sichuan province in China significantly contribute to PM_2.5 pollution in the CCUA, especially during winter. Full article

China has fully become an aging society, and the scientific response to population aging has become a major task that the country must face in the future. Research on population aging in the Chengdu-Chongqing urban agglomeration (CCUA) can provide a scientific basis for future population management in the CCUA. This paper applies spatial autocorrelation, geodetection, and other methods to analyze the temporal and spatial pattern of population aging and its driving factors in the CCUA from 2000 to 2020, taking districts (counties) as the basic unit and combining them with the spatial structure of the urban agglomeration. The results show that: ① in the time dimension, the population aging in CCUA has gone through the evolution process of “mild–moderate–heavy”; in the spatial dimension, the influence of the urban agglomeration’s development planning axes on the spatial differentiation of the aging population has become more and more prominent. ② The aging level has a strong spatial correlation, and with time, the spatial correlation has changed from weak to strong, and the spatial difference has increased. The dual core city shows a typical spatial pattern of a decreasing aging level in the core area and an increasing aging level in the peripheral area, and the heavily aging area is spreading along the axis. ③ The overall aging speed is high, and the aging speeds of the core cities and node cities are lower than those of other regions. There is a clearer positive correlation between the aging level and the speed of aging, showing the characteristic of “the older the faster”. ④ Endogenous factors such as the aging level and fertility level at the beginning of the period have a significant determining power on the change in the aging level, while exogenous factors such as the in-migration rate and the out-migration rate have a persistent determining power on the urban agglomerations and key areas (core cities, central cities, main axes of development, city belts, and dense urban areas). Full article

Land use change is one of the main factors driving changes in terrestrial carbon storage, which comprises the storage of vegetation carbon and soil carbon. Selecting the Chengdu–Chongqing urban agglomeration (CCUA) as the study area, land use and carbon storage from 2010 to 2030 were analyzed by combining the Future Land Use Simulation (FLUS) model and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model. The main types of land use in CCUA are farmland and forest. The conversion of farmland to built-up land was the most important form of land use transfer between 2010 and 2020. Each type of land use shows the smallest change under the ecological protection scenario, and the degree of the comprehensive land use dynamic is only 0.19%. Under the natural development scenario, the areas of built-up land, wetland, and forest land will increase in 2030. Under the urban development scenario, the built-up land area will increase by 751.24 km², an increase in more than 10.08%, but farmland, forest, and grassland will decrease. The spatial pattern of carbon storage is “high in the east and west, low in the middle”; farmland accounts for the largest proportion of carbon storage at over 60% of the total. Carbon storage decreased by 29.45 × 10⁶ Mg from 2010 to 2020. Grassland showed the most significant decrease in carbon storage, with the proportion decreasing from 7.49% in 2010 to 6.09% in 2020. In 2030, the total carbon storage will reach 1844.68 × 10⁶ Mg under the ecological protection scenario, slightly higher than that in 2020, while it will show a downward trend under the natural development and urban development scenarios. Full article

Optimizing land use structure in urban agglomerations is essential to mitigating climate change and achieving carbon neutrality. However, the studies on low-carbon (LC) land use in the urban agglomeration based on carbon neutrality are still limited and lack the consideration of the optimized land ecological benefits. To reduce land use carbon emissions (LUCEs) and improve the ecological benefits of urban agglomerations, we constructed the framework of land use structure optimization (LUSO) under carbon neutrality. Then, in view of land use quantity structure and spatial distribution, we compared the results of LUCEs and the ecological benefits of the Chengdu–Chongqing urban agglomeration (the CCUA) in 2030 under different scenarios. The results showed that in 2030, the LUCEs of the CCUA is 3481.6632 × 10⁴ t under the carbon neutral scenario (CN_Scenario), which is significantly lower than the baseline scenario (BL_Scenario) and 2020. In the CN_Scenario, the land use/cover change (LUCC) in the CCUA is more moderate, the aggregation degree of the forestland (FL), grassland (GL), wetland (WL), and water (WTR) patch area deepens, and the overall landscape spreading degree is increased, which is more conducive to play the ecological benefit of carbon sink land. The results can provide a reference for the more efficient use of land resource areas and the formulation of land use and spatial planning. Full article

China is attaching increasing importance to the creation of regional integration, high-quality economic development and ecological civilization. An accurate grasp of the traction effect of the increased level of integration of urban agglomerations on the eco-efficiency (EE) of cities in the region will help to promote the steady improvement of urban economic development and the ecological environment. This paper constructs an index system to measure the level of integration of the Chengdu–Chongqing urban agglomeration (CCUA) and the EE of each city within it from 2011 to 2020 and explores the impact of regional integration on urban EE and its mechanism of action. The study presents the follow findings: (1) The level of integration of the CCUA increased nearly 10 times from 2011 to 2020, with the government playing a significant leading role. (2) The positive and negative effects of the level of integration of the CCUA on urban EE depend on factors such as the level of economic development, the stage of development and the location. There are several relationships between the level of intra-regional integration and urban EE: first, a nearly linear increase, as in Chongqing and Chengdu; second, an increase in fluctuation, as in Dazhou, Guang’an and Leshan; and third, a fluctuation, decrease, flat or even no real increase, as in Luzhou, Ya’an and Zigong. (3) Based on this, this paper considers the mechanism of the level of integration within the region on urban EE in terms of both economic and eco-environmental effects, with a view to exploring the future green development path of the CCUA. Full article

The rapid economic development (ED) of the Yangtze River Economic Belt (YREB) has had a significant negative impact on regional ecosystem services (ES). Accurately understanding and properly handling the relationship between ES and ED is critical to achieving coordinated regional development of the YREB. Restricted by a minimal number of research units, traditional studies have not fully considered the spatial heterogeneity of the influencing factors, leading to results with poor accuracy and applicability. To address these problems, this paper introduces a spatial econometric model to explore the impact of influencing factors on the level of coordinated development in the YREB. For the 1013 counties in the YREB, we used the value equivalent method, the entropy weight method, and the coupling coordination model to quantify the coupling coordination relationship between the ecosystem services value (ESV) and ED from 2010 to 2020. The multi-scale geographically weighted regression model (MGWR) was adopted to analyze the role of influencing factors. The results showed the following: (1) The coupling coordination degree (CCD) of ESV and ED along the YREB demonstrated significant spatial heterogeneity, with Sichuan and Anhui provinces forming a low-value lag. The average CCD from high to low were found in the Triangle of Central China (TOCC), the Yangtze River Delta urban agglomeration (YRDUA), and the Chengdu–Chongqing urban agglomeration (CCUA). (2) There was spatial autocorrelation in the distribution of CCD, with high–high clustering mainly distributed in Hunan, Jiangxi, and Zhejiang provinces. The counties with high–high clustering were expanding, mainly centering on Kunming City in Yunnan Province and expanding outward. (3) There was significant spatial heterogeneity in the impact of each influencing factor on CCD. Per capita fiscal expenditure was sensitive to low–low clustering areas of CCD; per capita, food production was a negative influence, and the rate of urbanization transitioned from negative to positive values from west to east. Full article

Air pollution endangers human health and sustainable socio-economic development, especially in urban agglomeration (UA). The Chinese government has implemented a series of policies and standards to improve air quality. However, few studies have compared variations in PM_2.5 concentrations across multiple UAs, and current research often lacks analysis relative to the clean air policies implemented by the government. In this study, we used econometric and geostatistical methods to assess the distribution and spatial evolution of PM_2.5 concentrations in five UAs (the Beijing–Tianjin–Hebei UA (BTHUA), middle reaches of the Yangtze River UA (MYRUA), Chengdu–Chongqing UA (CCUA), Harbin Changchun UA (HCUA), and Beibu Gulf UA (BGUA)) in China from 2000 to 2021 to explore the effectiveness of the clean air policies implemented by the government on air pollution control, to analyze the ambient air quality of UAs, and to make recommendations for public outdoor activities. The results indicated that the clean air policy implemented by the Chinese government in 2013 achieved significant treatment results. PM_2.5 concentrations were plotted as an inverted U-shaped curve based on time, which showed an upward trend before 2013 and a downward trend after 2013. PM_2.5 concentrations showed a similar seasonal pattern, with a single-valley “V” shape. PM_2.5 concentration was the highest in winter and the lowest in summer. The PM_2.5 concentration of HCUA and BGUA was lower than that of CCUA, MYRUA, and BTHUA. The increase in PM_2.5 concentration mainly occurred in autumn and winter, while the decrease mainly occurred in spring. In 2021, the PM_2.5 air quality compliance rates (<35 µg/m³) in BTHUA, MYRUA, CCUA, HCUA, and BGUA were 44.57%, 80.00%, 82.04%, 99.74%, and 100%, respectively. However, in 2021, 19.19% of the five UAs still had an ambient air quality of Grade II (i.e., 50 < AQI_PM2.5 < 100). People with abnormally sensitive breathing in these areas should reduce their outdoor activities. These results contribute to epidemiological studies on human health and disease prevention and suggest reasonable pathways by which governments can improve air quality through sustainable urban planning. Full article

Nighttime lights remote sensing has a significant advantage in exploring the economic development of cities. Based on nighttime lighting data, this study employed spatial direction analysis, exploratory spatial data analysis, and social network analysis to explore the spatial characteristics of economic development and analyzed the economic connection network structures within urban agglomerations in the New Western Land-sea Corridor (NWLSC) in western China. The results show that the spatial pattern of the Tianshan North slope urban agglomeration, Guanzhong Plain urban agglomeration, and Lanzhou–Xining urban agglomeration shrank, while other urban agglomerations expanded. The city economy of the Chengdu–Chongqing urban agglomeration (CCUA) and the Beibu Gulf urban agglomeration varied dramatically according to a LISA space-time transition analysis, which indicates a strong spatial dependence between cities in the local space. Within urban agglomerations, the economic connection between cities increased significantly, and central cities were at the core of the network and significantly influenced other cities. Among the urban agglomerations, economic connections among neighboring urban agglomerations in geographic space increased during the study period. The CCUA gradually developed into the center of the economic network in the NWLSC. Network density positively influenced economic connections. The degree centrality, closeness centrality, and betweenness centrality significantly enhanced the economic connections between city agglomerations. The study’s conclusions and methods can serve as the policy support for the cooperative development of urban agglomerations in NWLSC serve as a guideline for the development of other economically underdeveloped regions in the world. Full article

The ecological carrying capacity (ECC) is a prerequisite for China’s regional and green developments. Since the Chengdu–Chongqing urban agglomeration (CCUA) is an important economic area, it is important to study the development of its ECC in order to establish its green development and to promote its regionally coordinated development in China. This paper first establishes the ECC evaluation index system based on the Pressure–State–Response (PSR) model and AHP-TOPSIS. Secondly, it estimates the ECC of the CCUA between 2000 and 2018. Thirdly, it constructs a system dynamics model of the ECC and, finally, it simulates and predicts the ECC from 2021 to 2050 based on shared socioeconomic pathways. The results show that the ECC indices of 16 cities in the CCUA have increased significantly in 18 years and the annual ECC indices from 2021 to 2050 all show significant growth trends. This paper will show that the CCUA should select the most suitable development mode to be adopted in the different periods. The development should follow SSP2 from 2021 to 2025, SSP1 from 2026 to 2035, and the development characteristics of SSP5 should be referred to at levels between 2036 and 2050, based on the CCUA’s overall development in accordance with SSP1. Full article

Recent years have seen the global proliferation and integration of transportation systems in urban agglomeration (UA), suggesting that transportation networks have become more prominent in the sustainable development of UA. Core cities play a radiating and driving role in affecting their adjacent cities to formulate transportation networks. Such a phenomenon is called the radiation effect of transportation networks and can be imaged using a field strength model as proposed in the study. The field strength model was verified using the Chengdu–Chongqing urban agglomeration (CCUA) as a case. Case data concerning transportation routes and traffic volume were collected for the past 20 years. The data analyses results indicate a relatively stable pattern of transportation networks in the UA. UA cities’ radiation effects follow the same compactness trend. The core cities’ radiation spheres go beyond their territories, and they can envelop the surrounding cities, highlighting the core cities’ dominance in the entire transportation network. Moreover, two development stages of UA transportation—focus and spillover—are also identified. This study contributes to the literature by providing an innovative quantitative method to detect the interaction between a city’s transportation system and peripheral cities or regions. The radiation effect of cities’ transportation systems should be considered in the UA transportation development plan, so as to meet the needs of spatial structure planning and coordinated development of the UA. Full article

In this paper, based on electrical power consumption (EPC) data extracted from DMSP/OLS night light data, we select three national-level urban agglomerations in China’s Yangtze River Economic Belt(YREB), includes Yangtze River Delta urban agglomerations(YRDUA), urban agglomeration in the middle reaches of the Yangtze River(UAMRYR), and Chengdu-Chongqing urban agglomeration(CCUA) as the research objects. In addition, the coefficient of variation (CV), kernel density analysis, cold hot spot analysis, trend analysis, standard deviation ellipse and Moran’s I Index were used to analyze the Spatio-temporal Dynamic Evolution Characteristics of EPC in the three urban agglomerations of the YREB. In addition, we also use geographically weighted regression (GWR) model and random forest algorithm to analyze the influencing factors of EPC in the three major urban agglomerations in YREB. The results of this study show that from 1992 to 2013, the CV of the EPC in the three urban agglomerations of YREB has been declining at the overall level. At the same time, the highest EPC value is in YRDUA, followed by UAMRYR and CCUA. In addition, with the increase of time, the high-value areas of EPC hot spots are basically distributed in YRDUA. The standard deviation ellipses of the EPC of the three urban agglomerations of YREB clearly show the characteristics of “east-west” spatial distribution. With the increase of time, the correlations and the agglomeration of the EPC in the three urban agglomerations of the YREB were both become more and more obvious. In terms of influencing factor analysis, by using GWR model, we found that the five influencing factors we selected basically have a positive impact on the EPC of the YREB. By using the Random forest algorithm, we found that the three main influencing factors of EPC in the three major urban agglomerations in the YREB are the proportion of secondary industry in GDP, Per capita disposable income of urban residents, and Urbanization rate. Full article

The ecological environment of urban water resources in the Yangtze River Economic Belt (YREB) is in a huge challenge; yet, while myriad studies have investigated the carrying capacity or sustainable utilization of Chinese water resources, few to none have looked at the inclusive sustainable development of water resources. In this article, a new concept of urban water inclusive sustainability (UWIS) is firstly proposed, and the conceptual framework of ASFII and a five-dimensional indicator system are developed, integrating availability, sustainability, friendliness, inclusiveness and innovation. The panel data of 38 cities in the YREB are adopted from 2008 to 2018 to measure UWIS and five development indexes by the TOPSIS (technique for order preference by similarity to ideal solution) and entropy method. Moreover, the characteristics of the temporal–spatial evolution of the overall system and subsystems coupling coordination are described using the coupling coordination degree model. Key factors that hinder UWIS are identified through the obstacle degree model. The results indicate that the overall UWIS showed a gradual upward trend at a lower to medium level from 2008 to 2018 and a friendliness > sustainability > inclusiveness > innovation > availability index. The UWIS from high to low is YRDUA (Yangtze River Delta urban agglomeration), MRYRUA (middle reaches of the Yangtze River urban agglomeration) and CCUA (Chengdu–Chongqing urban agglomeration). The 38 cities are in low-level coordination, and their temporal characteristics show a trend of economy > science and technology > water resources > environment > societal system, YRDUA > MRYRUA > CCUA. The spatial differentiation is manifested as high in the east and low in the west. The main obstacles come from 12 factors, such as the water resources utilization rate, etc. The findings of our study will be a scientific reference for the Chinese government to track UWIS and ensure urban water resources security in the YREB. Full article

Vehicular emissions have become one of the important sources of air pollution, and their effective control is essential to protect the environment. The Chengdu-Chongqing Urban Agglomeration (CCUA), a less developed area located in the southwest of China with higher vehicle population and special topographic features, was selected as the research area. The aims of this study were to establish multi-year vehicular emission inventories for ten important air pollutants in this area and to analyze emission control policy scenarios based on the inventories. The results showed that the ten vehicular pollutant emissions had differences during the past decade, and CO₂ and NH₃ increased markedly between 1999 and 2015. Chengdu and Chongqing were the dominant contributors of vehicular emissions in the CCUA. Eight scenarios based on these inventories were designed and the alternative energy replacement scenario was studied from the life-cycle perspective. Compared with the business as usual scenario, elimination of substandard vehicles scenario is the most effective policy to control NO_x, PM_2.5, PM₁₀, and CH₄ emissions; the radical alternative energy replacement scenario could decrease the vehicular NMVOC, CO₂, N₂O, and NH₃ emissions; the elimination of motorcycles scenario could decrease the vehicular CO emissions; and the raising fuel standards scenario could reduce vehicular SO₂ emissions significantly (by 94.81%). The radical integrated scenario (combining all of the reduction control measures mentioned above) would achieve the maximum emission reduction of vehicular pollutants CO, NMVOC, NO_x, PM_2.5, PM₁₀, CO₂, N₂O, and NH₃ compared with any scenario alone. Full article