Search Results (8)

Land use and cover change (LUCC) profoundly impacts the carbon cycle and carbon storage. Under the goal of “carbon neutrality”, studying the mechanisms linking LUCC with terrestrial ecosystem carbon storage is of significant importance for ecological protection and regional development. Using the central Shanxi urban agglomeration as a case study, this research employs various quantitative models based on land cover data to analyze changes in LUCC and carbon storage from 2000 to 2035. The study scientifically explores the impact of the spatial and temporal distribution characteristics of LUCC on carbon storage. The study indicates the following: (1) Over the past 20 years, the land types in the central Shanxi urban agglomeration are primarily grassland, cropland, and forest land. The two primary land transformations are the conversion of cropland to grassland and the conversion of grassland to cropland and forest land; (2) The carbon storage in the study area has shown a declining trend over the past two decades. Spatially, this decline exhibits a “two mountains and one valley” distribution pattern influenced by land use types. The reduction of grassland and cropland is the primary reason for the decline in carbon storage; (3) By 2035, under three different scenarios, carbon storage is projected to decrease compared to 2020. Among these, the scenario focused on cropland protection (CP) shows the least decline, while the naturally developing scenario (ND) shows the most significant decline. The research demonstrates that under scenarios of cropland protection and ecological conservation, strategies such as environmental restoration, development of unused land, and reclamation of built-up land for greening significantly enhance regional carbon storage and improve carbon sequestration capacity. Full article

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

Increases in land surface temperature (LST) and the urban heat island effect have become major challenges in the process of urban development. However, few studies have examined variations in LST between different urban agglomerations (UAs). Based on MODIS LST data, we quantitatively analyzed the spatial and temporal evolution patterns of LST in three different UAs in China from 2000 to 2020—Beijing–Tianjin–Hebei (BTH) at the national level, the Shandong Peninsula (SP) at the regional level, and Central Shanxi (CS) at the city level—by employing urban agglomeration built-up area intensity (UABI), linear regression analyses, and geodetic detector models. The results showed the following: (1) The spatial and temporal evolution pattern of the LST in BTH was the most regularized; the spatial pattern of the LST in SP gradually evolved from “two points” to “a single branch”; and the LST of CS was easily influenced by the neighboring big cities. (2) The best-fitting coefficients for BTH, SP, and CS were R²_BTH = 0.58, R²_SP = 0.66, and R²_CS = 0.58, respectively; every 10% increase in UABI warmed the LSTs in BTH, SP, and CS by 1.47 °C, 1.27 °C, and 1.83 °C, respectively. (3) The ranking of single-factor influence was DEM (digital elevation model) > UABI > NDVI > T_2m (air temperature at 2 m) > POP (population). The UABI interacting with DEM had the strongest warming effect on LST, with the maximum value q(UABI ∩ DEM) _BTH = 0.951. All factor interactions showed an enhancement of the LST in CS, but factors interacting with POP showed a weaker effect in BTH and SP, for which q(NDVI ∩ POP) _BTH = 0.265 and q(T_2m ∩ POP) _SP = 0.261. As the development of UAs gradually matures, the interaction with POP might have a cooling effect on the environment to a certain degree. Full article

Exploring spatiotemporal evolution features and factors affecting pollution reduction and carbon abatement on the urban agglomeration scale is helpful to better understand the interaction between ecological environment and economic development in urban agglomerations. In this study, we constructed an evaluation index system for collaborative governance of pollution reduction and carbon abatement in urban agglomerations. In addition, we employed the correlation coefficient matrix, the composite system synergy model, the Gini coefficient, and the Theil index to evaluate the level of and regional differences in collaborative governance of pollution reduction and carbon abatement in seven urban agglomerations in the Yellow River Basin from 2006 to 2020. Moreover, we explored the factors affecting collaborative governance of pollution reduction and carbon abatement in urban agglomerations in the basin. The following findings were obtained: (1) the order degree of collaborative governance of pollution reduction and carbon abatement in the seven urban agglomerations exhibited a significant growing trend, representing a spatial evolution feature of “high in the west and low in the east”; (2) the internal differences in collaborative governance synergy of pollution reduction and carbon abatement decreased in Lanzhou–Xining Urban Agglomeration, Hohhot–Baotou–Ordos–Yulin Urban Agglomeration, Central Shanxi Urban Agglomeration, Zhongyuan Urban Agglomeration, and Shandong Peninsula Urban Agglomeration, while the internal differences basically remained stable in Guanzhong Urban Agglomeration and the Urban Agglomeration along the Yellow River in Ningxia; (3) the variances in environmental regulation and industrial structure among urban agglomerations had a significant positive effect on collaborative governance of pollution reduction and carbon abatement in urban agglomerations in the basin, and the variances in economic growth had a significant inhibitory effect. In addition, the variances in energy consumption, greening construction, and opening-up had an inhibitory impact on collaborative governance of pollution reduction, but the impact was not significant. Finally, this study proposes various recommendations to improve collaborative governance for pollution reduction and carbon abatement in urban agglomerations in the basin in terms of promoting industrial structure upgrading, strengthening regional cooperation, and reducing regional differences. This paper represents an empirical reference for formulating differentiated collaborative governance strategies for pollution reduction and carbon abatement, comprehensive green and low-carbon economic and social transformation programs, and high-quality green development paths in urban agglomerations, which is of certain theoretical and practical significance. 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

Urban coordinated development is an important aspect of regional development. The high-quality development of the Yellow River Basin cannot be separated from the coordinated and sustainable development of its inner cities. However, the network connection and spatial structure of cities in the Yellow River Basin have not received sufficient attention. Therefore, this study considered 11 prefecture-level cities in Shanxi Province, an underdeveloped region in the Yellow River Basin, as case areas and selected data on traffic, migration, and information flow that can better represent the urban spatial network structure and depict the spatial connection between cities. Based on the flow intensity calculation, flow direction judgment, spatial structure index, and social network analysis, the spatial structural characteristics of Shanxi Province were comprehensively analyzed from the perspective of flow space. The results showed the following: (1) Cities in Shanxi Province present a development trend of “one core and multiple centers.” The strong connection concerns mostly Taiyuan and radiates outward and presents a Chinese character “大”—shaped spatial connection pattern. (2) Taiyuan is the first connecting city of most cities in Shanxi Province, and the element flows particularly towards the central city and geographical proximity. (3) The urban spatial pattern of Shanxi Province presents an obvious unipolar development trend, where the network structure is an “absence-type pyramid.” The imbalance of the urban network connection strength is prominent in Shanxi Province, which is strong and numerous in the south but opposite in the north. (4) The overall network element flow density is low, the network connection is weak, Taiyuan agglomeration and radiation are the strongest, and Changzhi centrality ranks second, but the gap between Changzhi and Taiyuan is wide, and the polarization phenomenon is serious. Future research should focus on the rapidly developing provincial capital city of Taiyuan, coordinating the steady development of the central Shanxi city cluster, and driving the common development of neighboring cities. Full article

Central Shanxi is one of the nine urban agglomerations proposed in China’s latest national planning, which has great development potential and represents a major opportunity for Shanxi Province to rise in central China. How to determine the existing problems and promote better-coordinated development is the goal of this article. Therefore, an improved gravity model, industrial structure similarity coefficient and population–economic growth elasticity method were used to analyze and study the coordinated development of urban agglomerations in central Shanxi from the perspectives of economy, industry and population–economy. The research conclusion is that there are three problems: a low level of coordinated economic development, strong dependence on coal resources, and uncoordinated population development and economic growth. Therefore, this paper discusses and puts forward the main strategies for the government to strengthen economic planning, improve the level of economic development, optimize and upgrade the industrial structure, end dependence on coal resources and strengthen regional ties, and improve the level of population and economy coordination so that the urban agglomeration in central Shanxi becomes the growth pole and important support point of regional economic and social development. Full article

We measured the health resource agglomeration capacities of 31 Chinese provinces (or municipalities) in 2004–2018 based on the entropy weight method. Using a modified spatial gravity model, we constructed and analyzed the spatial correlation network of these health resource agglomeration capacities and their influencing factors through social network analysis. We found that: (i) China’s health resource agglomeration capacity had a gradual strengthening trend, with capacity weakening from east to west (strongest in the eastern region, second strongest in the central region, and weakest in the western region). (ii) The spatial network of such capacities became more densely connected, and the network density and level (efficiency) showed an upward (downward) trend. (iii) In terms of centrality, the high-ranking provinces (or municipalities) were Beijing, Shanghai, Jiangsu, Zhejiang, Guangdong, Shandong, Hunan, Hubei, Fujian, Anhui, Jiangxi, and Tianjin, while the low-ranking were Tibet, Qinghai, Gansu, Ningxia, Inner Mongolia, Heilongjiang, Yunnan, Guizhou, Xinjiang, Hainan, Shaanxi, and Shanxi. (iv) Block 1 (eight provinces or municipalities), including Beijing, Tianjin, and Hebei, had a “net spillover” effect in the spatial network of health resource agglomeration capacities; Block 2, (seven provinces or municipalities), including Shanghai, Jiangsu, and Zhejiang, had a “bidirectional spillover” effect in the spatial network; Block 3 (seven provinces or municipalities), including Anhui, Hubei, and Hunan, had a “mediator” effect in the network; and Block 4, (nine provinces or municipalities), including Sichuan, Guizhou, and Tibet, had a “net beneficial” effect in the network. (v) The economic development, urbanization wage, and financial health expenditure levels, and population size had significant positive correlations with the spatial network of health resource agglomeration capacities. Policy recommendations to enhance the radiating role of health resources in core provinces (or municipalities), rationally allocate health resources, and transform ideas to support public health resource services were provided. Full article