Search Results (8)

Vegetation coverage is a critical indicator for assessing urban ecosystems and is essential for sustainable development. However, the evolution patterns and driving mechanisms of vegetation change at the urban agglomeration scale remain underexplored. This study used the Google Earth Engine (GEE) to compute the kernel Normalized Difference Vegetation Index (kNDVI) for the Beibu Gulf Urban Agglomeration (BGUA), an important emerging coastal urban cluster in southern China, from 2000 to 2022. Trend analysis was employed to examine spatiotemporal changes in kNDVI, and an interpretable machine learning framework was applied to quantify the nonlinear, spatially heterogeneous effects of environmental and anthropogenic drivers. The results show that (1) kNDVI showed a general increasing trend, with medium-to-high kNDVI predominating. Approximately 91.91% of the region maintained an improving trend, whereas vegetation degradation concentrated in the core urban areas. (2) The Categorical Boosting model demonstrated superior performance in predicting kNDVI compared to other machine learning models. (3) The SHAP analysis identified land cover, elevation, and nighttime lights as the primary determinants of kNDVI change. These factors exhibited significant spatial heterogeneity in their nonlinear effects. These findings provide theoretical insights and practical guidance for ecological planning and environmental management in urban agglomerations. Full article

The ecological environment is crucial for human survival and development. As ecological issues become more pressing, studying the spatiotemporal evolution of ecological quality (EQ) and its driving mechanisms is vital for sustainable development. This study, based on MODIS data from 2000 to 2022 and the Google Earth Engine platform, constructs a remote sensing ecological index for the Beibu Gulf Urban Agglomeration and analyzes its spatiotemporal evolution using Theil–Sen trend analysis, Hurst index (HI), and geographic detector. The results show the following: (1) From 2000 to 2010, EQ improved, particularly from 2005 to 2010, with a significant increase in areas of excellent and good quality due to national policies and climate improvements. From 2010 to 2015, EQ degraded, with a sharp reduction in areas of excellent quality, likely due to urban expansion and industrial pressures. After 2015, EQ rebounded with successful governance measures. (2) The HI analysis indicates that future changes will continue the past trend, especially in areas like southeastern Chongzuo and northwestern Fangchenggang, where governance efforts were effective. (3) EQ shows a positive spatial correlation, with high-quality areas in central Nanning and Fangchenggang, and low-quality areas in Nanning and Beihai. After 2015, both high–high and low–low clusters showed changes, likely due to ecological governance measures. (4) NDBSI (dryness) is the main driver of EQ changes (q = 0.806), with significant impacts from NDVI (vegetation coverage), LST (heat), and WET (humidity). Urban expansion’s increase in impervious surfaces (NDBSI rise) and vegetation loss (NDVI decline) have a synergistic effect (q = 0.856), significantly affecting EQ. Based on these findings, it is recommended to control construction land expansion, optimize land use structure, protect ecologically sensitive areas, and enhance climate adaptation strategies to ensure continuous improvement in EQ. Full article

Cultivated land intensive utilization (CLIU) exhibits spatial heterogeneity that is influenced by both natural and anthropogenic factors, with land dissected into different scale systems; however, CLIU has not yet been systematically explored. This study takes the Beibu Gulf urban agglomeration, a national-level model area for integrated land and sea development in China, as an example to investigate the spatial heterogeneity of CLIU and explore its driving factors through multiple econometrical and geographical methods, including identifying its underlying mechanisms. The results indicate that (1) the CLIU index is 0.334, its Gini coefficient is 0.183, and its comprehensive level has a low intensity and obvious spatial nonequilibrium characteristics. Hypervariable density (50.33%) and the intraprovincial gap (45.6%) are the main sources. (2) Among the independent effects of single factors, the multiple cropping index (0.57), labor force index (0.489), and intensification of construction land (0.375) exert the most influence on CLIU spatial variation. The interaction effects of two factors primarily manifested as nonlinear enhancements, with the interaction between the labor force index and multiple cropping index being particularly noteworthy (0.859). (3) The geographically weighted regression coefficients reveal that temperature (0.332), multiple cropping index (0.211), and labor force index (0.209) have relatively large and positive impacts on CLIU, while slope (−0.1), precipitation (−0.087), and population urbanization (−0.039) have relatively small and negative impacts; all factors exhibit spatial nonstationarity. The spatial heterogeneity of CLIU in the Beibu Gulf urban agglomeration is characterized by patterns’ nonequilibrium and factors’ nonstationarity. The driving mode of multiple factors on CLIU is manifested as follows: natural factors of cropland utilization provide basic guarantees, internal factors of CLIU provide positive enhancement, and external factors of land intensive utilization provide auxiliary promotion. Full article

With the rapid development of China’s economy, the process of industrialization and urbanization is accelerating, and environmental pollution is becoming more and more serious. The urban agglomerations (UAs) are the fastest growing economy and are also areas with serious air pollution. Based on the monthly mean PM_2.5 concentration data of 20 UAs in China from 2015 to 2019, the spatiotemporal distribution characteristics of PM_2.5 were analyzed in UAs. The effects of natural and social factors on PM_2.5 concentrations in 20 UAs were quantified using the geographic detector. The results showed that (1) most UAs in China showed the most severe pollution in winter and the least in summer. Seasonal differences were most significant in the Central Henan and Central Shanxi UAs. However, the PM_2.5 was highest in March in the central Yunnan UA, and the Harbin-Changchun and mid-southern Liaoning UAs had the highest PM_2.5 in October. (2) The highest PM_2.5 concentrations were located in northern China, with an overall decreasing trend of pollution. Among them, the Beijing-Tianjin-Hebei, central Shanxi, central Henan, and Shandong Peninsula UAs had the highest concentrations of PM_2.5. Although most of the UAs had severe pollution in winter, the central Yunnan, Beibu Gulf, and the West Coast of the Strait UAs had lower PM_2.5 concentrations in winter. These areas are mountainous, have high temperatures, and are subject to land and sea breezes, which makes the pollutants more conducive to diffusion. (3) In most UAs, socioeconomic factors such as social electricity consumption, car ownership, and the use of foreign investment are the main factors affecting PM_2.5 concentration. However, PM_2.5 in Beijing-Tianjin-Hebei and the middle and lower reaches of the Yangtze River are chiefly influenced by natural factors such as temperature and precipitation. Full article

To understand the allocation efficiency and fairness of natural capital in the Guangxi Beibu Gulf urban agglomeration, its ecological footprint from 2007 to 2020 was calculated based on the emergy ecological footprint (EEF) model, and the 10,000 Yuan GDP and Gini coefficient were introduced. The results show that (1) in the past 14 years, the per capita ecological footprint of the urban agglomeration slowly increased, the ecological pressure index rapidly increased with an average annual growth rate of 6.55%, and the regional ecological safety showed an unsafe trend. (2) The regional ecological footprint was mainly based on cultivated land, construction land and fossil energy land, of which the latter two significantly increased. For construction land, the average annual per capita growth rate in the central city of Nanning and the coastal cities (Fangchenggang, Beihai and Qinzhou) exceeded 10%, ranging from 11.39%–25.70%. For fossil energy land, the annual average per capita growth rate in Fangchenggang and Chongzuo exceeded 10%, at 19.64% and 11.40%, respectively. During urbanization, increasing population density leads to increased regional consumption of electricity and energy, thus affecting the regional ecological security. (3) The resource utilization efficiency improved annually, and the resource allocation was generally fair. Nanning and Beihai had high economic contributions and low ecological carrying capacities, Qinzhou and Chongzuo had low economic contributions and high ecological carrying capacities, and Yulin and Fangchenggang had low economic contributions and low ecological carrying capacities. These results clarify the differences among cities in the development of the Guangxi Beibu Gulf urban agglomeration, improve the efficiency of natural resource allocation, and provide a reference for the achievement of regional sustainable development. 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

Sustainable Development Goals (SDGs) target 11.a is a good vision for the coordinated development of the economy, society and environment in urban agglomerations. However, there was an extreme lack of indicators, data or case studies for SDG target 11.a, since it is a vague “process target”, which is not conducive to the implementation of SDG target 11.a. It is important to propose a quantitative, convenient, and local policies relevant method to promote the realization or to test the implementation effects of SDG target 11.a. Combined with socio-economic data and land use data, this study uses the methods of comprehensive evaluation model, coupling and coordination degree, and comparative advantage degree methods to study the pattern evolution, coordination characteristics and advantageous areas of production–living–ecological (PLE) functions in the Guangxi Beibu Gulf Urban Agglomeration (GBG_UA) from 1995 to 2019. The results showed that, (1) considering the spatiotemporal distribution of PLE functions, the study area has a relatively stable ecological function as well as fluctuating production and living functions. Considering the coordination characteristics of PLE functions, high–high and low–low clustering effects were observed, and primary coordination maintained the highest proportion, accounting from 55.26% in 1995 to 71.05% in 2019, indicating the SDG target 11.a level in the GBG_UA was poor. Considering the advantageous areas for PLE functions, the region mostly comprises single-function advantageous areas and a few multifunction advantageous areas, including 20 single-function advantage counties (accounting for 52%), 15 dual-function advantage counties (accounting for 39%), and three multi-function advantage counties (accounting for 7.8%), which indicates the lack of diversified land use structures in this region. (2) Optimization suggestions for the coordinated development and realization of SDG target 11.a for the GBG_UA were provided. Suggestions were made based on the radiation and driving role of Nanning city to guide the coordinated development of surrounding counties (districts). Suggestions were also made to improve the design of the integrated transportation network as well as to optimize allocation according to the resource endowment of land and to realize an upgraded ecology as well as agricultural products and services. (3) The evaluation of PLE functions is a quantitative and convenient method that can optimize national and regional development planning and test the implementation effects of SDG target 11.a. This study offers foundational knowledge for the realization of SDG target 11.a in the GBG_UA and provides a reference for the research and implementation of SDG target 11.a in other regions around the world. Full article