Localized or Regional? Urban Housing Policy Spillover in China’s Urban Agglomerations 2010–2018

2020 ◽  
Vol 8 (4) ◽  
pp. 325-345
Author(s):  
Xiangfei Li ◽  
Hongli Han ◽  
Minghan Sun
Keyword(s):  
Spillover Effects ◽  
Urban Housing ◽  
Event Analysis ◽  
Central Cities ◽  
Real Estate Markets ◽  
Urban Agglomerations ◽  
Housing Regulation ◽  
Spatio Temporal ◽  
Regional Housing Market ◽  
Residential Market

AbstractSpatio-temporal model and event analysis were integrated in this paper, with 156 prefecture level cities’ housing transaction data and 167 items policies proposed by 10 central cities between January, 2010 and December, 2018 as samples. This paper studied the regional and cross-regional spillover effects of central cities’ urban housing regulation policies to the peripheral cities in the scope of urban agglomerations, as well as the policy-driven interactions of different regional real estate markets. The results indicated that: China’s regional housing market has obvious characteristics of policy orientation, of which the regulation measures on some central cities can affect the residential market and produce certain spillover interference on the market fluctuations of peripheral cities in time and space dimension. When geographical factor was considered, the 10 central cities had different degree of policy spillover effects caused by distinct policy types in their respective urban agglomerations. When ignoring spatial factors, restrictive policies in Beijing, Shanghai, Zhengzhou, Xi’an, Wuhan and Shenzhen had significant cross-regional spillover effects and drove the surrounding housing markets to have geared interactions, which to a certain extent revealed the flowing way of population and wealth in China’s regional economy during the past dozen years.

scholarly journals Spatio-Temporal Coupling Characteristics and the Driving Mechanism of Population-Land-Industry Urbanization in the Yangtze River Economic Belt

Land ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 400
Author(s):  
Liejia Huang ◽  
Peng Yang ◽  
Boqing Zhang ◽  
Weiyan Hu
Keyword(s):  
Economic Development ◽  
Spatial Correlation ◽  
Yangtze River ◽  
Three Dimensional ◽  
Driving Mechanism ◽  
The Yangtze River ◽  
Central Cities ◽  
Urban Agglomerations ◽  
Development Level ◽  
Coordination Degree

The purpose of this paper is to probe into the coupled coordination of urbanization in population, land, and industry to improve urbanization quality. A coupled coordination degree model, spatial analysis method and spatial metering model are employed. The study area is 110 prefecture-level cities in the Yangtze River Economic Belt. The study shows that: (1) the coupling degree of the population-land-industry urbanization grew very slowly between 2006 and 2016. On the whole, the three-dimensional urbanization is in a running-in period, and land-based urbanization dominates, while population-based urbanization and industry-based urbanization are relatively lagging behind. (2) The three major urban agglomerations, the Chengdu-Chongqing, the middle reaches of the Yangtze River and the Yangtze River Delta, are parallel to the whole area in terms of the coupling degree of the three dimensional urbanization with a well-ordered structure, especially in the central cities of the three major urban agglomerations. (3) There is significant spatial correlation in the coupling degree and coordination degree of the three-dimensional urbanization. The high value of coupling degree and coordination degree are clustered continuously in developed cities, provincial capitals, and central cities of the downstream reaches of the Yangtze River. (4) The coordinated degree has significant positive spatial autocorrelation, showing obvious spatial agglomeration characteristics: H-H agglomeration areas are concentrated in the downstream developed areas such as Jiangsu, Zhejiang, and Shanghai. L-L agglomeration areas are mainly concentrated in upstream undeveloped areas, but the number of their cities shows a decreasing trend. (5) The coordination degree of the three-dimensional urbanization is the result of the comprehensive effect of economic development level, the government’s decision-making behavior, and urban location. Among them, the economic development level, urbanization investment, traffic condition, and urban geographical location play a decisive role. This paper contributes to the existing literatures by exploring urbanization quality, spatial correlation and influencing factors from the perspectives of the three-dimensional urbanization in the Yangtze River Economic Belt. The conclusion might be helpful to promote the coupling and coordinated development of urbanization in population-land-industry, and ultimately to improve urbanization quality in the Yangtze River Economic Belt.

scholarly journals Information Spillover Effects of Real Estate Markets: Evidence from Ten Metropolitan Cities in China

2021 ◽  
Vol 14 (6) ◽  
pp. 244
Author(s):  
Junjie Li ◽  
Li Zheng ◽  
Chunlu Liu ◽  
Zhifeng Shen
Keyword(s):  
Real Estate ◽  
Rapid Development ◽  
House Price ◽  
Principal Component ◽  
Spillover Effects ◽  
Real Estate Markets ◽  
The Real ◽  
The Real Estate ◽  
Information Interaction ◽  
Information Spillover

With the rapid development of information communication technology and the Internet, information spillover between cities in real estate markets is becoming more frequent. The influence of information spillover in real estate markets is becoming more and more prominent. However, the current research of information spillover between cities is still relatively insufficient. In view of this research gap, this paper builds a research framework on the information conduction effect in the real estate markets of 10 Chinese cities by using Baidu search data, text mining and principal component analysis and analyzes the information interaction and dynamic influence of the real estate markets in each city by using the vector autoregressive model empirically. The results show that the information interaction among the real estate markets in each city has a network pattern and there is a significant two-way information spillover effect in most cities. When the “information distance” becomes closer, the information interaction between the markets of the cities becomes closer and it is easier for cities to influence each other. The results help to explain the information spillover mechanism behind the house price spillover and to improve the ability to predict and analyze the information spillover process in real estate markets.

scholarly journals Does Entrepreneurship Contribute to Innovation Performance When Considering Spatial Spillover Effects? Evidence From the Automobile Industrial Cluster in China

SAGE Open ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 215824402098299
Author(s):  
Haishi Li ◽  
Xiangyi Xu ◽  
Shuaishuai Li
Keyword(s):  
Automobile Industry ◽  
Hot Spots ◽  
Hot Spot ◽  
Empirical Support ◽  
Temporal Patterns ◽  
Spillover Effects ◽  
Industrial Clusters ◽  
Innovation Performance ◽  
Spatial Spillover ◽  
Spatio Temporal

Entrepreneurship, as one of the important factors to promote industrial innovation, is closely related to the development of the regional economy. Based on the methods of Kernel density and standard deviation ellipse, this article presents the spatio-temporal patterns of entrepreneurship and innovation performance. The article also examines the spatial spillover mechanism of entrepreneurship on innovation performance by establishing spatial Durbin models. The heterogeneous results of the spatial regression models in six clusters are also discussed. The final results show that the spatio-temporal patterns of entrepreneurship are gradually presenting three major hot spots and two secondary hot spots while the spatio-temporal patterns of innovation performance are presenting four major hot spots and a secondary hot spot; the spatial distribution of both entrepreneurship and innovation performance are changing regularly; the spillover effects of entrepreneurship and innovation performance are both significant; the spatial spillover mechanisms in six automobile industrial clusters are different. The results can provide empirical support for decision-making in the automobile industry in China in the future.

scholarly journals Research on the Spatio-Temporal Dynamic Evolution Characteristics and Influencing Factors of Electrical Power Consumption in Three Urban Agglomerations of Yangtze River Economic Belt, China Based on DMSP/OLS Night Light Data

2021 ◽  
Vol 13 (6) ◽  
pp. 1150
Author(s):  
Yang Zhong ◽  
Aiwen Lin ◽  
Chiwei Xiao ◽  
Zhigao Zhou
Keyword(s):  
Influencing Factors ◽  
Yangtze River ◽  
Electrical Power ◽  
Random Forest Algorithm ◽  
Temporal Dynamic ◽  
Urban Agglomerations ◽  
Evolution Characteristics ◽  
Spatio Temporal ◽  
Electrical Power Consumption ◽  
Gwr Model

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.

scholarly journals Spatial Spillover Effect and Influencing Factors of Information Flow in Urban Agglomerations—Case Study of China Based on Baidu Search Index

2021 ◽  
Vol 13 (14) ◽  
pp. 8032
Author(s):  
Chengzhuo Wu ◽  
Li Zhuo ◽  
Zhuo Chen ◽  
Haiyan Tao
Keyword(s):  
Influencing Factors ◽  
Information Flow ◽  
Spillover Effect ◽  
Spillover Effects ◽  
Urban Agglomeration ◽  
Coordinated Development ◽  
Spatial Spillover ◽  
Urban Agglomerations ◽  
Search Index ◽  
Spatial Spillover Effect

Cities in an urban agglomeration closely interact with each other through various flows. Information flow, as one of the important forms of urban interactions, is now increasingly indispensable with the fast development of informatics technology. Thanks to its timely, convenient, and spatially unconstrained transmission ability, information flow has obvious spillover effects, which may strengthen urban interaction and further promote urban coordinated development. Therefore, it is crucial to quantify the spatial spillover effect and influencing factors of information flows, especially at the urban agglomeration scale. However, the academic research on this topic is insufficient. We, therefore, developed a spatial interaction model of information flow (SIM-IF) based on the Baidu Search Index and used it to analyze the spillover effects and influencing factors of information flow in the three major urban agglomerations in China, namely Beijing–Tianjin–Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) in the period of 2014–2019. The results showed that the SIM-IF performed well in all three agglomerations. Quantitative analysis indicated that the BTH had the strongest spillover effect of information flow, followed by the YRD and the PRD. It was also found that the hierarchy of cities had the greatest impact on the spillover effects of information flow. This study may provide scientific basis for the information flow construction in urban agglomerations and benefit the coordinated development of cities.

scholarly journals Spatial income inequality in Toronto: a longitudinal study

2021 ◽  
Author(s):  
Candace Safonovs
Keyword(s):  
Income Inequality ◽  
Fixed Effects ◽  
Spillover Effects ◽  
Spatial Inequality ◽  
Income Segregation ◽  
Inequality Measures ◽  
Housing Characteristics ◽  
Fixed Effects Panel Regression ◽  
Macro Scale ◽  
Housing Regulation

This paper examines the trends and changes in both spatial and non-spatial income inequality in the Toronto CMA between 1985 and 2015 at various geographic scales, including both within and between neighbourhoods. Fixed effects panel regression models are used to uncover which local demographic and housing characteristics are most significant in explaining changes in inequality within neighbourhoods over time. Findings indicate that macro-scale income segregation among neighbourhoods has declined, while micro-scale intra-neighbourhood income segregation has increased since 1985. Further, compared to overall changes in income inequality in the region, neighbourhoods have become more homogenous in terms of their household income distribution. Thus, neighbourhood sorting by households based on income has increased since 1985. Consistent with extant literature, local housing characteristics have spillover effects on income segregation. Specifically, variables associated with greater housing affluence are negatively correlated with intra-neighbourhood inequality measures, and thus positively correlated with income homogenization. This confirms and adds to the literature that local land use regulations impact income spatial inequality. KEYWORDS Spatial Income Inequality; Segregation; Neighbourhoods; Toronto CMA; Fixed Effects Models; Quantitative Analysis; GIS; Housing Regulation

scholarly journals MONITORING CHANGES OF ECOSYSTEM SERVICES SUPPLY AND DEMAND PATTERN IN CENTRAL AND SOUTHERN LIAONING URBAN AGGLOMERATIONS, CHINA USING LANDSAT IMAGES

2018 ◽  
Vol XLII-3 ◽  
pp. 765-770
Author(s):  
B. Li ◽  
F. Huang ◽  
S. Chang ◽  
H. Qi ◽  
H. Zhai
Keyword(s):  
Ecosystem Services ◽  
Population Density ◽  
Foreign Investment ◽  
Driving Forces ◽  
Supply And Demand ◽  
Reference Value ◽  
Landsat Images ◽  
Urban Agglomerations ◽  
Spatio Temporal

Indentifying the spatio-temporal patterns of ecosystem services supply and demand and the driving forces is of great significance to the regional ecological security and sustainable socio-economic development. Due to long term and high-intensity development, the ecological environment in central and southern Liaoning urban agglomerations has been greatly destroyed thereafter has restricted sustainable development in this region. Based on Landsat ETM and OLI images, land use of this urban agglomeration in 2005, 2010 and 2015 was extracted. The integrative index of multiple-ecosystem services (IMES) was used to quantify the supply (IMESs), demand (IMESd) and balance (IMESb) of multiple-ecosystem services, The spatial patterns of ecosystem services and its dynamics for the period of 2005–2015 were revealed. The multiple regression and stepwise regression analysis were used to explore relationships between ecosystem services and socioeconomic factors. The results showed that the IMESs of the region increased by 2.93 %, whereas IMESd dropped 38 %. The undersupplied area was reduced to 2. The IMESs and IMESb were mainly negatively correlated with gross domestic product (GDP), population density, foreign investment and industrial output, while GDP per capita and the number of teachers had significant positive impacts on ecosystem services supply. The positive correlation between IMESd and GDP, population density and foreign investment were found. The ecosystem services models were established. Supply and balance of multiple-ecosystem services were positively correlated with population density, but the demand was the opposite. The results can provide some reference value for the coordinately economic and ecological development in the study area.

Are Industrial Structure Adjustment and Technical Progress Conducive to Environmental Improvement?

2022 ◽  
Vol 30 (6) ◽  
pp. 0-0
Keyword(s):  
Environmental Pollution ◽  
Technical Progress ◽  
Industrial Structure ◽  
Spillover Effects ◽  
Pollutant Emissions ◽  
Temporal Dependence ◽  
Environmental Improvement ◽  
Structure Adjustment ◽  
Industrial Structure Adjustment ◽  
Spatio Temporal

China actively broadens its channels for environmental protection and limits pollutant emissions through industrial structure adjustment and technical progress. Based on panel data of 30 provinces in China from 2003 to 2017, this study investigated the effects of industrial structure adjustment and technical progress on environmental pollution using spatial Dubin models. The findings show the following. (1) As the economy develops, the situation of environmental pollution in various regions deteriorates; moreover, spatio-temporal dependence is an aspect of environmental pollution. (2) Industrial structure adjustment and technical progress are beneficial to environmental improvement. Furthermore, there are spillover effects in factors such as industrial structure and technical progress to varying degrees. Thus, this study suggests that the path of coupling between industrial structure and technical progress should be explored to establish a pollution filtering mechanism, thereby improving environmental quality.

scholarly journals Spatio-Temporal Distribution, Spillover Effects and Influences of China’s Two Levels of Public Healthcare Resources

2019 ◽  
Vol 16 (4) ◽  
pp. 582 ◽  
Author(s):  
Xueqian Song ◽  
Yongping Wei ◽  
Wei Deng ◽  
Shaoyao Zhang ◽  
Peng Zhou ◽  
...  
Keyword(s):  
Industrial Structure ◽  
Healthcare Expenditure ◽  
Government Expenditure ◽  
Spillover Effects ◽  
Public Healthcare ◽  
Long Term Effects ◽  
Healthcare Resources ◽  
Spatio Temporal ◽  
Main Determinants

In China, upper-level healthcare (ULHC) and lower-level healthcare (LLHC) provide different public medical and health services. Only when these two levels of healthcare resources are distributed equally and synergistically can the public’s demands for healthcare be met fairly. Despite a number of previous studies having analysed the spatial distribution of healthcare and its determinants, few have evaluated the differences in spatial equity between ULHC and LLHC and investigated their institutional, geographical and socioeconomic influences and spillover effects. This study aims to bridge this gap by analysing panel data on the two levels of healthcare resources in 31 Chinese provinces covering the period 2003–2015 using Moran’s I models and dynamic spatial Durbin panel models (DSDMs). The results indicate that, over the study period, although both levels of healthcare resources improved considerably in all regions, spatial disparities were large. The spatio-temporal characteristics of ULHC and LLHC differed, although both levels were relatively low to the north-west of the Hu Huanyong Line. DSDM analysis revealed direct and indirect effects at both short-and long-term scales for both levels of healthcare resources. Meanwhile, the influencing factors had different impacts on the different levels of healthcare resources. In general, long-term effects were greater for ULHC and short-term effects were greater for LLHC. The spillover effects of ULHC were more significant than those of LLHC. More specifically, industrial structure, traffic accessibility, government expenditure and family healthcare expenditure were the main determinants of ULHC, while industrial structure, urbanisation, topography, traffic accessibility, government expenditure and family healthcare expenditure were the main determinants of LLHC. These findings have important implications for policymakers seeking to optimize the availability of the two levels of healthcare resources.

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