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.

Ecological well-being performance and influencing factors in China: from the perspective of income inequality

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lijun Zhou ◽  
Zongqing Zhang
Keyword(s):  
Income Inequality ◽  
Influencing Factors ◽  
Spillover Effect ◽  
Well Being ◽  
Spillover Effects ◽  
Content Type ◽  
Spatial Spillover ◽  
Effective Decision ◽  
The Impact ◽  
Spatial Spillover Effect

PurposeChina's increasing income inequality might cause a series of problems, such as the slowdown of economic growth, social and economic tension, the decline of the ecological environment quality and the threat to citizens' health. Consequently, income inequality will inevitably affect the ecological well-being performance (EWP) level of China's provinces through the above aspects. Analyzing the impact of income inequality on EWP and its spatial spillover effects are conducive to improving the level of EWP in China. Therefore, the research purpose of this paper is to use China's provincial data from 2001 to 2017 to analyze the impact of income inequality on EWP and the spatial spillover effect based on the evaluation of the EWP value of each province.Design/methodology/approachAt first, this study utilizes the super efficiency slacks-based measure model (Super-SBM model) to calculate the EWP values of 30 provinces in China, which can evaluate and rank the effective decision units in the SBM model and make up for the defect that the effective decision units cannot be distinguished. Then this study applies the spatial Durbin model and Tobit regression model (SDM-Tobit model) to explore the impact of income inequality and other influencing factors on EWP and the spatial spillover effects in adjacent areas.FindingsFirstly, the average EWP in China fluctuated slightly and showed a downward trend from 2001 to 2017. In addition, the EWP values of the provinces in the western region are usually weaker than those in the eastern and central regions. Moreover, income inequality is negatively correlated with EWP, and the EWP has a spatial spillover effect, which means the EWP level in a region is affected by EWP values in the adjacent regions. Furthermore, the industrial structure and urbanization level are both negatively related to EWP, while technology level, investment openness, trade openness and education level are positively related to EWP.Originality/valueCompared with the existing research, the possible contribution of this research is that it takes income inequality as one of the important influencing factors of EWP and adopts the SDM-Tobit model to analyze the impact mechanism of income inequality on EWP from the perspective of time and space, providing new ideas for improving the EWP of various provinces in China.

Effects of tourism development on economic growth: An empirical study of China based on both static and dynamic spatial Durbin models

2021 ◽  
pp. 135481662110211
Author(s):  
Honghong Liu ◽  
Ye Xiao ◽  
Bin Wang ◽  
Dianting Wu
Keyword(s):  
Economic Growth ◽  
Spillover Effect ◽  
Tourism Development ◽  
Spillover Effects ◽  
Policy Implications ◽  
Long Term Effects ◽  
Spatial Spillover ◽  
Domestic Tourism ◽  
Inbound Tourism ◽  
Spatial Spillover Effect

This study applies the dynamic spatial Durbin model (SDM) to explore the direct and spillover effects of tourism development on economic growth from the perspective of domestic and inbound tourism. The results are compared with those from the static SDM. The results support the tourism-led-economic-growth hypothesis in China. Specifically, domestic tourism and inbound tourism play a significant role in stimulating local economic growth. However, the spatial spillover effect is limited to domestic tourism, and the spatial spillover effect of inbound tourism is not significant. Furthermore, the long-term effects are much greater than the short-term impact for both domestic and inbound tourism. Plausible explanations of these results are provided and policy implications are drawn.

scholarly journals Effect of Complex Road Networks on Intensive Land Use in China’s Beijing-Tianjin-Hebei Urban Agglomeration

Land ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 532
Author(s):  
Chen Zeng ◽  
Zhe Zhao ◽  
Cheng Wen ◽  
Jing Yang ◽  
Tianyu Lv
Keyword(s):  
Land Use ◽  
Road Network ◽  
Spillover Effect ◽  
Urban Agglomeration ◽  
Land Use Intensity ◽  
Spatial Econometric ◽  
Spatial Spillover ◽  
The Road ◽  
Intensive Land Use ◽  
Spatial Spillover Effect

Coupled with rapid urbanization and urban expansion, the spatial relationship between transportation development and land use has gained growing interest among researchers and policy makers. In this paper, a complex network model and land use intensity assessment were integrated into a spatial econometric model to explore the spatial spillover effect of the road network on intensive land use patterns in China’s Beijing–Tianjin–Hebei (BTH) urban agglomeration. First, population density, point of interest (POI) density, and aggregation index were selected to measure land use intensity from social, physical, and ecological aspects. Then, the indicator of average degree (i.e., connections between counties) was used to measure the characteristics of the road network. Under the hypothesis that the road network functions in shaping land use patterns, a spatial econometric model with the road network embedded spatial weight matrix was established. Our results revealed that, while the land use intensity in the BTH urban agglomeration increased from 2010 to 2015, the road network became increasingly complex with greater spatial heterogeneity. The spatial lag coefficients of land use intensity were positively significant in both years and showed a declining trend. The spatially lagged effects of sector structure, fixed asset investment, and consumption were also significant in most of our spatial econometric models, and their contributions to the total spillover effect increased from 2010 to 2015. This study contributes to the literature by providing an innovative quantitative method to analyze the spatial spillover effect of the road network on intensive land use. We suggest that the spatial spillover effect of the road network could be strengthened in the urban–rural interface areas by improving accessibility and promoting population, resource, and technology flows.

scholarly journals The Closer the Better? Modeling Spatial Spillover Effects of Ecological Externalities in Coastal Mangroves

2021 ◽  
Vol 8 ◽  
Author(s):  
Han Wang ◽  
Xiaoyu Yang ◽  
Shuang Li ◽  
Qiwen Zheng ◽  
Xin Nie
Keyword(s):  
Environmental Economics ◽  
Spillover Effect ◽  
Spillover Effects ◽  
Spatial Distance ◽  
Spatial Spillover ◽  
Ecological Protection ◽  
Environmental Attributes ◽  
High Degree ◽  
Geographical Accessibility ◽  
Spatial Spillover Effect

As an important part of ecological externalities, the spatial spillover effect has attracted the attention of researchers in the field of environmental economics. However, the traditional view that the spillover mechanism of ecological externalities generally decreases in line with increases in distance remains to be thoroughly proven. Effective ecological management requires an understanding of the relationship between the natural environment and human communities. In this study, the concept of geographical accessibility and a two-step mobile search model are introduced in order to connect ecosystems and humans by a spatial distance. This model can fully demonstrate the external spatial spillover effect of ecology. Based on research from the Beihai Wetland Reserve, Guangxi, China, this study found that the change in the ecological externality spillover mechanism is not only affected by spatial distance but is also affected by the pro-environmental attributes of individual residents around the region. Under the same conditions, residents with a high degree of interaction with ecological protection zones can display a stronger spatial spillover effect. The conclusion of this study provides a more accurate understanding of the changes in the spillover effect of ecological externalities, which in turn can help managers to formulate more adequate ecological protection policies that are based on the specific conditions of different residents. This is crucial for the successful management of protected ecological areas that are highly linked to human communities.

scholarly journals Direct Rebound Effect for Electricity Consumption of Urban Residents in China Based on the Spatial Spillover Effect

Energies ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 2069 ◽  
Author(s):  
Ying Han ◽  
Jianhua Shi ◽  
Yuanfan Yang ◽  
Yaxin Wang
Keyword(s):  
Energy Efficiency ◽  
Rebound Effect ◽  
Spillover Effect ◽  
Electricity Consumption ◽  
Spillover Effects ◽  
Local Region ◽  
Urban Residents ◽  
Spatial Spillover ◽  
Direct Rebound Effect ◽  
Spatial Spillover Effect

Based on methods of price decomposition and spatial econometrics, this paper improves the model for calculating the direct energy rebound effect employing the panel data of China’s urban residents’ electricity consumption for an empirical analysis. Results show that the global spatial correlation of urban residents’ electricity consumption has a significant positive value. The direct rebound effect and its spillover effects are 37% and 13%, respectively. Due to the spatial spillover effects, the realization of energy-saving targets in the local region depends on the implementation effect of energy efficiency policies in the surrounding areas. However, the spatial spillover effect is low, and the direct rebound effect induced by the local region is still the dominant factor affecting the implementation of energy efficiency. The direct rebound effect for urban residents’ electricity consumption eliminating the spatial spillover effect does not show a significant downward trend. The main reason is that the rapid urbanization process at the current stage has caused a rigid residents’ electricity demand and large-scale marginal consumer groups, which offsets the inhibition effect of income growth on the direct rebound effect.

scholarly journals Spatial Spillover Effects of Air Pollution on the Health Expenditure of Rural Residents: Based on Spatial Durbin Model

2021 ◽  
Vol 18 (13) ◽  
pp. 7058
Author(s):  
Bo Sun ◽  
Bo Wang
Keyword(s):  
Air Pollution ◽  
Direct Effect ◽  
Health Expenditure ◽  
Spillover Effect ◽  
Spillover Effects ◽  
Spatial Durbin Model ◽  
Rural Residents ◽  
Spatial Spillover ◽  
The Impact ◽  
Spatial Spillover Effect

Background: Air pollution is one source of harm to the health of residents, and the impact of air pollution on health expenditure has become a hot topic worldwide. However, few studies aim at the spatial spillover effects of air pollution on the health expenditure of rural residents (HE-RR), including the impact on the health expenditure in neighboring areas. Objective: Based on the existing research, this paper further introduces the spatial dimension and uses the Spatial Durbin model to discuss the impact of environmental pollution on the health expenditure of rural residents (HE-RR). Methods: Based on provincial panel data during 2002–2015 in China, the Spatial Durbin model was used to investigate the spatial spillover effect of the average annual concentration of PM2.5 (AAC-PM2.5) on the health expenditure of rural residents (HE-RR). Results: There was a significant positive correlation between AAC-PM2.5 and health expenditure of rural residents (HE-RR) in neighboring areas at a significant level of 5% (COEF: 2.546, Z:2.340), that is, AAC-PM2.5 has a spatial spillover effect on PC-HE-RR in neighboring areas, and the spatial spillover effect is greater than the direct effect. The migration and diffusion of PM2.5 pollution will affect the air quality of neighboring areas, leading to the health risk not only from the local PM2.5 pollution but also the nearby PM2.5 pollution. Conclusion: The results show a significant positive relationship between air pollution and HE-RR in neighboring areas, and the spatial spillover effect is greater than the direct effect.

scholarly journals The Moderating Effect of Innovation on the Relationship between Urbanization and CO2 Emissions: Evidence from Three Major Urban Agglomerations in China

2019 ◽  
Vol 11 (6) ◽  
pp. 1633 ◽  
Author(s):  
Yanwen Sheng ◽  
Yi Miao ◽  
Jinping Song ◽  
Hongyan Shen
Keyword(s):  
Co2 Emissions ◽  
Spillover Effect ◽  
Yangtze River Delta ◽  
River Delta ◽  
Policy Implications ◽  
Spatial Spillover ◽  
Urban Agglomerations ◽  
The Yrd ◽  
The Relationship ◽  
Spatial Spillover Effect

This study investigates the relationship between urbanization, innovation, and CO2 emissions, with particular attention paid to the issue of how innovation influences the effect of urbanization on CO2 emissions in urban agglomerations, considering the spatial spillover effect between cities. Therefore, based on panel data on 48 cities in the three major urban agglomerations in China from 2001–2015, a spatial econometric model is used to estimate the effect of urbanization and innovation on CO2 emissions. The empirical results indicate that the relationship between urbanization and CO2 emissions follows a U-shaped curve in the Beijing-Tianjin-Hebei (BTH), an N-shaped curve in the Yangtze River Delta (YRD) and an inverted N-shaped pattern in the Pearl River Delta (PRD). Additionally, innovation shows a significantly positive effect on reducing CO2 emissions in the YRD, but does not exert a significantly direct effect on CO2 emissions in the BTH and the PRD. More importantly, innovation played an important moderating role between urbanization and CO2 emissions in the YRD and PRD, suggesting that reducing the positive impacts of urbanization on CO2 emissions depends on innovative development. In addition, urban CO2 emissions presented a clearly negative spatial spillover effect among the cities in the three urban agglomerations. These findings and the following policy implications will contribute to reducing CO2 emissions.

scholarly journals Spatial Spillover and the Influencing Factors Relating to Provincial Carbon Emissions in China Based on the Spatial Panel Data Model

2018 ◽  
Vol 10 (12) ◽  
pp. 4739 ◽  
Author(s):  
Xin Tong ◽  
Xuesen Li ◽  
Lin Tong ◽  
Xuan Jiang
Keyword(s):  
Influencing Factors ◽  
Carbon Emissions ◽  
Data Model ◽  
Spillover Effect ◽  
Panel Data Model ◽  
Spatial Spillover ◽  
Spatial Panel ◽  
Spatial Panel Data Model ◽  
Spatial Panel Data ◽  
Spatial Spillover Effect

From the perspective of spatial geography, this paper verifies the spatial dependence of China’s provincial carbon emissions. The contribution of impact factors with different fields of view to carbon emissions’ growth is estimated based on the spatial panel data model, t. The study found that during 2000–2015, China’s energy-related carbon emissions in the provinces were dependent on the spatial, and the spatial spillover effect of carbon emissions and its influencing factors in the neighboring provinces are obvious. It was also found that economic growth, industrial structure, financial development, and urbanization rates are positive, and the effect of the population and technological progress on reducing carbon emissions is significant. The effect of source price, export dependence, and fiscal decentralization on carbon emissions’ growth did not pass a significance test. In the formulation of carbon emission-related policies and development plans, the government must consider the effect of the influencing factors affecting the carbon emissions in the adjacent area and combine the carbon emissions and spatial spillover effect of the related factors in order to reduce carbon emissions in the time dimension and the spatial dimension of China as a whole.

scholarly journals Measuring the Spatial Spillover Effects of Multimodel Transit System in Beijing: A Structural Spatial Vector Autoregressive Approach

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Honghai Li ◽  
Xiaolei Ma ◽  
Xian Zhang ◽  
Xin Li ◽  
Weihan Xu
Keyword(s):  
Spillover Effect ◽  
Spillover Effects ◽  
Transport Systems ◽  
Traffic Patterns ◽  
Transit System ◽  
Vector Autoregressive ◽  
Spatial Spillover ◽  
Passenger Flow ◽  
Study Results ◽  
Spatial Spillover Effect

Changes in local transit passenger flow may cause a spatial spillover effect across the involved regions and affect traffic patterns in other regions. To identify the affected areas and the traffic patterns, this study develops an enhanced spatial vector autoregressive (SpVAR) model to investigate relations in public transport systems in the case of sudden large passenger flow impact. The proposed model captures the interacted correlation within different transit models in separated regions. Three representative commuting regions in Beijing, namely, Zhongguancun, Guomao, and Huilongguan, are employed for empirical study. Results confirm the existence of spatial spillover effect in the commuter regions and reveal heterogeneous effects of multimodal transit system on regions with different distances.

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