scholarly journals The characteristics of spatial expansion and driving forces of land urbanization in counties in central China: A case study of Feixi county in Hefei city

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0252331
Author(s):  
Huafu Jiao ◽  
Xiangxiang Zhang ◽  
Chao Yang ◽  
Xianzhong Cao
Keyword(s):  
Driving Forces ◽  
Central City ◽  
Yangtze River Delta ◽  
Central China ◽  
Western China ◽  
Spatial Process ◽  
Spatial Expansion ◽  
City Region ◽  
Urbanization In China ◽  
Land Urbanization

Urbanization in Central and Western China has attracted increasing attention in the advent of new-type urbanization in China and the age of ‘Global Urbanism’. Although land urbanization is at the epicenter of Chinese urbanization, its process and driving forces in counties beyond the Eastern coastal areas are less known. This paper therefore seeks to investigate the spatial expansion and driving forces of land urbanization in Feixi county, a relatively advanced county neighboring Hefei city proper in Anhui province in Central China. Based on the land-use change survey data, remote sensing interpretation data, and statistical yearbook data of Feixi county from 2002 to 2016, it is revealed that the overall scale of urban land in Feixi county increased remarkably, though with obvious temporal and geographical variants. The year 2009 appears to be the cutting line between quantity-based land urbanization and quality-based land urbanization. Land urbanization rate is sensitive to both geographical distance and traffic accessibility to Hefei city proper. Driving forces of land urbanization in Feixi county are summarized as: (1) central city expansion, (2) government-led industrial park construction, and (3) large industrial projects along traffic corridors. A better understanding of urbanization in Feixi county cannot be fulfilled without taking into account the wider spatial process in Hefei city and the Yangtze River Delta city-region.

scholarly journals Spatial differences and driving forces of land urbanization in China

2015 ◽  
Vol 25 (5) ◽  
pp. 545-558 ◽  
Author(s):  
Xueqin Lin ◽  
Yang Wang ◽  
Shaojian Wang ◽  
Dai Wang
Keyword(s):  
Driving Forces ◽  
Spatial Differences ◽  
Urbanization In China ◽  
Land Urbanization

scholarly journals Mapping urban CO2 emissions using DMSP/OLS ‘city lights’ satellite data in China

2016 ◽  
Vol 49 (2) ◽  
pp. 248-251 ◽  
Author(s):  
Yan Wang ◽  
Guangdong Li
Keyword(s):  
Satellite Data ◽  
Emission Reduction ◽  
Yangtze River Delta ◽  
River Delta ◽  
Central China ◽  
Western China ◽  
Eastern Coast ◽  
Level Energy ◽  
National Bureau ◽  
Administrative Units

China, the world’s top CO2 emitter, is faced with pressure of energy-saving emission reduction. In the 2015 Paris Climate Conference (COP21), China announced its plan, aiming to cut down CO2 emissions by 60%–65% per unit of GDP in comparison to 2005’s level by 2030. To achieve this ambitious goal, reliable national, provincial, and city-level statistics are fundamental for multi-scale mitigation policy-makings as well as for the allocation of responsibilities among different administrative units. However, China implemented a top-down energy statistical system. The National Bureau of Statistics only publishes annually both national and provincial energy statistics. Only part of cities released their statistics, which results in missing data in city-level energy statistics. This also affects data transparency and accuracy of energy and CO2 emission statistics, and as a result, increases difficulty in allocation of CO2 emission reduction responsibilities. In order to fill this lacuna, we employed a standardized remote sensing inversion approach for estimating China’s city-level CO2 emissions from energy consumptions by integrating DMSP/OLS ‘city lights’ satellite data and statistical data. The end product is a map of city-level CO2 emissions in China. The most topping CO2 emitters are located in the major urban agglomerations along the more economically developed eastern coast (e.g. Yangtze River Delta, Beijing–Tianjin–Hebei, Shandong Peninsula, and Pearl River Delta). Other regions with high CO2 emissions are Shanxi and Henan in Central China, as well as the Chengdu–Chongqing and Shaanxi in West China. Regions with low CO2 emissions are western China, and most of Central China and South China.

Spatio-temporal Patterns and Driving Forces of Urban Expansion in Beijing Central City Since 1932

2010 ◽  
Vol 11 (4) ◽  
pp. 428-435 ◽  
Author(s):  
Wenhui KUANG ◽  
Quanqin SHAO ◽  
Jiyuan LIU ◽  
Chaoyang SUN
Keyword(s):  
Driving Forces ◽  
Central City ◽  
Urban Expansion ◽  
Temporal Patterns ◽  
Spatio Temporal

scholarly journals The Spatio-Temporal Analysis of Urban-Rural Coordinated Development and Its Driving Forces in Yangtze River Delta

Land ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 495
Author(s):  
Daizhong Tang ◽  
Mengyuan Mao ◽  
Jiangang Shi ◽  
Wenwen Hua
Keyword(s):  
Influencing Factors ◽  
Yangtze River ◽  
Driving Forces ◽  
Yangtze River Delta ◽  
River Delta ◽  
Urban Agglomeration ◽  
System Level ◽  
Actual State ◽  
Coordinated Development ◽  
Urban Rural

This paper conducts an analytical study on the urban-rural coordinated development (URCD) in the Yangtze River Delta urban agglomeration (YRDUA), and uses data from 2000–2015 of 27 central cities to study the spatial and temporal evolution patterns of URCD and to discover the influencing factors and driving forces behind it through PCA, ESDA and spatial regression models. It reveals that URCD of the YRDUA shows an obvious club convergence phenomenon during the research duration. The regions with high-level URCD gather mainly in the central part of the urban agglomeration, while the remaining regions mostly have low-level URCD, reflecting the regional aggregation phenomenon of spatial divergence. At the same time, we split URCD into efficiency and equity: urban-rural efficient development (URED) also exhibits similar spatiotemporal evolution patterns, but the patterns of urban-rural balanced development (URBD) show some variability. Finally, by analyzing the driving forces in major years during 2000–2015, it can be concluded that: (i) In recent years, influencing factors such as government financial input and consumption no longer play the main driving role. (ii) Influencing factors such as industrialization degree, fixed asset investment and foreign investment even limit URCD in some years. The above results also show that the government should redesign at the system level to give full play to the contributing factors depending on the actual state of development in different regions and promote the coordinated development of urban and rural areas. The results of this study show that the idea of measuring URCD from two dimensions of efficiency and equity is practical and feasible, and the spatial econometric model can reveal the spatial distribution heterogeneity and time evolution characteristics of regional development, which can provide useful insights for urban-rural integration development of other countries and regions.

scholarly journals Does Mobile Phone Penetration Affect Divorce Rate? Evidence from China

2018 ◽  
Vol 10 (10) ◽  
pp. 3701 ◽  
Author(s):  
Jiaping Zhang ◽  
Mingwang Cheng ◽  
Xinyu Wei ◽  
Xiaomei Gong
Keyword(s):  
Mobile Phone ◽  
Eastern China ◽  
Divorce Rate ◽  
Central China ◽  
Western China ◽  
Explanatory Variables ◽  
Information And Communication ◽  
Lag Times ◽  
One Year ◽  
The Impact

Marital happiness is an important symbol of social harmony and can help promote sustainable economic and social development. In recent years, the rapid rise of the divorce rate in China, a country where the divorce rate had previously been low, has attracted wide attention. However, few articles have focused on the popularization of information and communication technology's impact on China’s rising divorce rate in recent years. As a first attempt, the provincial panel data during the period 2001–2016 is applied to study quantitatively the relationship between mobile phone penetration and the divorce rate. In order to get more reliable estimation results, this paper uses two indicators to measure the divorce rate, and quantile regression is applied for further analysis. Additionally, one-year to five-year lag times of the mobile phone penetration are used as the core explanatory variables in order to analyse the lagging effect of mobile phone penetration on divorce rate. The result shows that the correlation between the mobile phone penetration and the divorce rate was statistically positive significant in China during the period 2001–2016. Furthermore, the paper also finds that mobile phone penetration had the greatest impact on divorce rate in central China, followed by eastern China, but it was not obvious in western China during this period. From a technological perspective, this paper provides some possible explanations for the rising divorce rate in China in recent years, and further enriches the relevant research on the impact of the development of information and communication technology on societal changes.

scholarly journals Regional Green Development and Spatial Evolution in China: Abiding by Theory of Dissipative Structure

2018 ◽  
Vol 53 ◽  
pp. 04008
Author(s):  
Fumin Deng ◽  
Hui Zhu ◽  
Xuedong Liang
Keyword(s):  
Economic Development ◽  
Dissipative Structure ◽  
Eastern China ◽  
Indicator System ◽  
Entropy Method ◽  
Central China ◽  
Western China ◽  
Brusselator Model ◽  
Energy Factors ◽  
Green Development

Regional green development can commendably abide by the theory of dissipative structure. The relative dissipative characteristics taken on by regional development are analyzed, in which the energy factors and resources factors are incorporated into the green economic development and green environment support subsystems (2GE system) in line with the definite input characteristics. A more representative indicator system is established, with positive and negative entropy indexes involved. As Brusselator model and information entropy method are employed to calculate the data of 30 China’s provinces from 2008 to 2015, the findings bespeak that green development in China lays particular stress on green economic development assuming higher environment pressure and cost. The development among various regions is getting progressively and evidently different, which is manifested as potent economic base and abundant natural resources in the Eastern China; the backward green economic development and the progress of green environment in Central China; the pursuit of green economic development at the expense of the green environment in Western China and Northeastern China.

scholarly journals Research on Population-Land-Industry Relationship Pattern in Underdeveloped Regions: Gansu Province of Western China as an Example

2019 ◽  
Vol 11 (8) ◽  
pp. 2434 ◽  
Author(s):  
Libang Ma ◽  
Meimei Chen ◽  
Xinglong Che ◽  
Fang Fang
Keyword(s):  
Three Dimensional ◽  
Gansu Province ◽  
Three Dimensions ◽  
Western China ◽  
Sustainable Urban Development ◽  
Economic Changes ◽  
Industry Relationship ◽  
Population Urbanization ◽  
Urbanization Pattern ◽  
Land Urbanization

Urbanization is a three-dimensional process including population, spatial, and economic changes. The coordination among the three dimensions is the key to sustainable urban development. Here, a population-land-industry index system of urbanization is constructed, and the degree of coupling and mutual feedback among population urbanization, land urbanization, and industrial urbanization are analyzed. The urbanization patterns and their spatiotemporal variation are identified. The results show that: (1) Population and land urbanization proceeded slowly in Gansu Province and their trends were similar, whereas industry urbanization proceeded faster than the two. From a spatial perspective, population, land, and industrial urbanization levels (PUi, LUi, and IUi) decreased from southwest to northeast. The coupling degree of population, land, and industrial urbanization increased from 1998 to 2016 and showed significant spatial variation, decreasing from northwest to southeast. (2) Population, land, and industry all play a role in urbanization. PUi was significantly and positively correlated with LUi. However, there was no significant correlation between IUi and PUi and between IUi and LUi. The improvement of PUi, LUi, and IUi effectively promoted the coupling degree of population, land, and industrial urbanization. (3) Seven urbanization patterns were identified in Gansu Province and evaluation units with the same urbanization pattern tended to be spatially close to each other. IUi > PUi > LUi (IX), IUi > LUi > PUi (X) and IUi > PUi = LUi (XI) were the dominant urbanization patterns. There was crisscross distribution of various urbanization patterns and, thus, it was not easy to observe the agglomeration center of certain urbanization pattern. (4) The urbanization pattern of the same evaluation unit changed with time. This change was mainly reflected in the change of relationship between population and land urbanization. Urbanization pattern changed more significantly in 2008–2016 than in 1998–2008. The changes were dominant by IX→XI, X→XI, XI→IX, and XI→X.

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