Spatio-temporal variations in ecological spaces and their ecological carrying status in China’s mega-urban agglomerations

Wang, Shihao; Hua, Lin; Xu, Xiaomei; Li, Jiahui

doi:10.1007/s11442-022-2018-1

Cited by 14 publications

(6 citation statements)

References 48 publications

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“…Additionally, we will use the entropy weighting method and the Analytic Hierarchy Process (AHP) to construct a more precise functional evaluation model, analyzing specific socio-economic development aspects [41]. Learning from international research methods on similar subjects [62,63], we plan to use multiple models to assess these functions in other major cities in China and globally [64]. This approach aims to offer more constructive development recommendations for their future growth.…”

Section: Discussionmentioning

confidence: 99%

Functional Measurements, Pattern Evolution, and Coupling Characteristics of “Production-Living-Ecological Space” in the Yangtze Delta Region

Zhu,

Shang,

Long

et al. 2023

Sustainability

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Based on the “Production-Living-Ecological Space” evaluation system, we hereby form its functional index and take the results of corresponding functional measurement to analyze the spatial pattern, functional evolution, and coupling characteristics of the “Production-Living-Ecological Space” of the Yangtze Delta Region. The results show that: (1) From the spatial pattern, the production space is mainly distributed in the plain areas. The living space is concentrated in the dense urban areas along the Yangtze River, the East Sea, and the East Jiangsu–Gansu Line. The ecological space is concentrated in mountainous hill areas. (2) From the spatial transformation, production space expands slightly, living space continues to expand, and ecological space shrinks significantly. (3) The functionality of “Production-Living-Ecological Space” exhibits a high level in hilly mountainous areas and a low level in plains, with an overall downward trend. Notably, the core cities within the Yangtze Delta Region have experienced the most significant decline in functionality. (4) The natural environment is the decisive factor for the overall pattern of “Production-Living-Ecological Space”, while economic and social development is the core driving force of the evolution of the spatial pattern, and regional integration is the catalyst of the evolution of the spatial pattern. (5) The coupling coordination of “production-ecology” is exceptionally strong, with the living function holding an overwhelmingly dominant position.

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Section: Discussionmentioning

confidence: 99%

Functional Measurements, Pattern Evolution, and Coupling Characteristics of “Production-Living-Ecological Space” in the Yangtze Delta Region

Zhu,

Shang,

Long

et al. 2023

Sustainability

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“…Wang classified the age structure of the population into six types: population ageing rate lower than 4.0% is called the young type, 4.0–5.5% is adult Type I, 5.5–7.0% is adult Type II, 7.0–10.0% is old-age Type I, 10.0–14.0% is old-age Type II, and more than 14.0% is old age Type III. There is no classification for 14.0% and above ( 34 ). In this paper, we combine the above two divisions to classify the population ageing rate into less than 4.0% as young, 4.0–7.0% as adult, 7.0–10.0% as low-old-age Type I, 10.0–14.0% as low-old-age Type II, 14.0–18.0% as medium-old-age Type I, and 18.0–22.0% as medium-old-age Type II and 22.0% and above severe geriatric type.…”

Section: Methodsmentioning

confidence: 99%

Study on the development characteristics and spatial and temporal patterns of population ageing in 31 central cities in China

Zhang,

Ren,

2024

Front. Public Health

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BackgroundPopulation ageing is inseparable from technological innovation, social progress and the development of human civilization, and constitutes a new element in the development of contemporary human history.ObjectiveTo dynamically analyses the developmental, structural and growth characteristics of population ageing in 31 provincial capitals and municipalities in China, using the data of the fifth national census in 2000 and the seventh national census in 2020.MethodsThe development characteristics and spatial and temporal patterns of population aging in the 31 cities were measured using the population aging index growth model, Theil’s index, coefficient of variation, population aging index and other analytical methods.Results(1) From 2000 to 2020, the population aging rate of the 31 central cities generally increased, and the population aging level of the cities showed the characteristics of “East-Central-Northeast-West” to “Northeast-East-Central-West” decreasing. (2) Regional differences in the ratio of old to young are relatively high, while regional differences in the level of population ageing are relatively small. The level of population ageing is classified with the indicators of size structure, family structure and age structure in the first and third quadrants, and with the geographic concentration rate in the second and fourth quadrants. (3) China’s population ageing has a T-shaped spatial distribution characteristic pointing along the coast - along the Yangtze Rivers.ConclusionThe 31 central cities are the center of gravity of China’s economy and have strong economic power in dealing with the challenges of population ageing, but how to make population ageing compatible with the economy and society, and then promote sustainable population development, is a topic that needs further attention in the study.

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“…The YRD, with 2.2% of China's land area and 11.0% of its population, generates 18.5% of China's GDP. Finally, the GBA, with only 0.6% of China's land area and 5.1% of its population, contributes 12.5% to China's GDP [44]. Due to rapid urbanization, various issues, such as low resource utilization rate, ecosystem degradation, and frequent natural disasters, have emerged in many regions.…”

Section: Study Areamentioning

confidence: 99%

Identification of Urban Clusters Based on Multisource Data—An Example of Three Major Urban Agglomerations in China

Wang

et al. 2023

Land

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Accurately identifying the boundary of urban clusters is a crucial aspect of studying the development of urban agglomerations. This process is essential for comprehending and optimizing smart and compact urban development. Existing studies often rely on a single category of data, which can result in coarse identification boundaries, insufficient detail accuracy, and slight discrepancies between the coverage and the actual conditions. To accurately identify the extent of urban clusters, this study proposes and compares the results of three methods for identifying dense urban areas of three major agglomerations in China: Beijing–Tianjin–Hebei, the Yangtze River Delta, and the Guangdong–Hong Kong–Macao Greater Bay Area. The study then integrates the results of these methods to obtain a more effective identification approach. The social economic method involved extracting a density threshold based on the fused nuclear density of socio-economic vitality data, including population, GDP, and POI, while the remote sensing method evaluated feature indices based on remote sensing images, including the density index, continuity index, gradient index, and development index. The traffic network method utilizes land transportation networks and travelling speeds to identify the minimum cost path and delineate the boundary by 20–30 min isochronous circles. The results obtained from the three methods were combined, and hotspots were identified using GIS overlay analysis and spatial autocorrelation analysis. This method integrates the multi-layered information from the previous three methods, which more comprehensively reflects the characteristics and morphology of urban clusters. Finally, the accuracy of each identification result is verified and compared. The results reveal that the average overall accuracy (OA) of the three areas delineated by the first three methods are 57.49%, 30.88%, and 33.74%, respectively. Furthermore, the average Kappa coefficients of these areas are 0.4795, 0.2609, and 0.2770, respectively. After performing data fusion, the resulting average overall accuracy (OA) was 85.34%, and the average Kappa coefficient was 0.7394. These findings suggest that the data fusion method can effectively delineate dense urban areas with greater accuracy than the previous three methods. Additionally, this method can accurately reflect the scope of urban clusters by depicting their overall boundary contour and the distribution of internal details in a more scientific manner. The study proposes a feasible method and path for the identification of urban clusters. It can serve as a starting point for formulating spatial planning policies for urban agglomerations, aiding in precise and scientific control of boundary growth. This can promote the rational allocation of resources and optimization of spatial structure by providing a reliable reference for the optimization of urban agglomeration space and the development of regional spatial policies.

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Spatio-temporal variations in ecological spaces and their ecological carrying status in China’s mega-urban agglomerations

Cited by 14 publications

References 48 publications

Functional Measurements, Pattern Evolution, and Coupling Characteristics of “Production-Living-Ecological Space” in the Yangtze Delta Region

Functional Measurements, Pattern Evolution, and Coupling Characteristics of “Production-Living-Ecological Space” in the Yangtze Delta Region

Study on the development characteristics and spatial and temporal patterns of population ageing in 31 central cities in China

Identification of Urban Clusters Based on Multisource Data—An Example of Three Major Urban Agglomerations in China

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