Can Industrial Collaborative Agglomeration Reduce Haze Pollution? City-Level Empirical Evidence from China

We analyze the mechanism for industrial co-agglomeration in Chinese 283 cities to affect haze pollution from 2003 to 2016 and examine the possible mediating effects of urbanization and energy structure between haze pollution and industrial co-agglomeration, finally obtaining the following results. First, industrial co-agglomeration and haze pollution across China, including central and eastern regions keep a typical inverted U-shaped curve relationship. That is, industrial co-agglomeration first promotes haze pollution and then restrains it. However, the impact of industrial co-agglomeration on haze pollution in western China is still on the left side of the inverted U-shaped curve, reflecting a promotion effect. Second, industrial co-agglomeration has a significant spatial spillover effect on haze pollution. Additionally, industrial co-agglomeration can promote haze pollution in local regions but inhibit it in surrounding regions in both the short and long run. In contrast, when the industrial co-agglomeration index exceeds the inflection point (3.6531), it benefits the reduction of haze pollution in local regions, while not being conducive to it in the neighboring regions. Third, industrial co-agglomeration can affect haze pollution through urbanization and energy structure, that is, urbanization and energy structure play an intermediary role between them.

Download Full-text

Can Green Finance Optimize Energy Structure: A Spatial Econometric Analysis based on China's Traditional and Renewable Energy Consumption

10.21203/rs.3.rs-1087594/v1 ◽

2021 ◽

Author(s):

Hui Wang ◽

Lili Jiang ◽

Hongjun Duan ◽

Yifeng Wang ◽

Yichen Jiang

Keyword(s):

Renewable Energy ◽

Energy Consumption ◽

Spillover Effect ◽

Energy Structure ◽

Renewable Energy Consumption ◽

Spatial Spillover ◽

Consumption Structure ◽

Green Finance ◽

The Impact ◽

Spatial Spillover Effect

Abstract This paper studies the impact of the development of green finance on China’s energy consumption structure. In terms of the construction of the green finance index (GFI), this paper selects 17 basic indexes from the three aspects of economy, finance, and environment, uses the improved entropy weight method to construct the GFI, and studies the spatial spillover effect of the GFI of China's provinces. This paper further studies the impact of green finance on traditional and renewable energy consumption. We first uses panel regression to determine that the development of green finance has a positive effect on the slowdown of traditional energy consumption and acceleration of renewable energy consumption, and then further studies the spatial characteristics of green finance development on energy consumption by using spatial Durbin model. The results show that there is a positive spatial spillover effect in the development of green finance among provinces in China. The development of green finance contributes to the conversion of traditional to renewable energy consumption. The effect of green finance on the transformation of energy consumption structure is mainly reflected in the direct effect. Therefore, the government should support the green finance, reduce traditional energy consumption and increase renewable energy consumption.

Download Full-text

Will Policy Uncertainty Deteriorate Haze Pollution? A Spatial Spillover Perspective

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph181910229 ◽

2021 ◽

Vol 18 (19) ◽

pp. 10229

Author(s):

Xiulin Qi ◽

Xin Wang ◽

Xiao Jin ◽

Zhenyu M. Wang ◽

Beibei Zhang ◽

...

Keyword(s):

Direct Effect ◽

Local Level ◽

Spillover Effect ◽

Sustainable Growth ◽

Policy Uncertainty ◽

Severe Problem ◽

Spatial Spillover ◽

Haze Pollution ◽

The Impact ◽

Spatial Spillover Effect

Haze has been a severe problem in China for some time, jeopardizing air quality, public health and sustainable growth. This paper examines the direct effect and spatial spillover effect of policy uncertainty on haze pollution with a spatial panel model, using prefecture-level data from 2004 to 2016. This study shows that: (1) policy uncertainty has increased the level of local haze pollution and has a significant spatial spillover effect on surrounding areas; (2) although local policy uncertainty has increased the haze pollution in geographically adjacent cities, it only affects the cities within the province with similar economic distances; and (3) the policy at the central level can effectively alleviate the impact of policy uncertainty at the local level on haze pollution, especially in relation to the spatial spillover effect, but still has limitations in eliminating the direct effect, which is due to the ineradicable nature of policy uncertainty.

Download Full-text

Reexamining the Impact of Industrial Structure on Haze Pollution Based on the Yangtze River Delta

Atmosphere ◽

10.3390/atmos12050613 ◽

2021 ◽

Vol 12 (5) ◽

pp. 613

Author(s):

Lu Wang ◽

Shumin Jiang ◽

Hua Xu

Keyword(s):

Yangtze River ◽

Industrial Structure ◽

Spillover Effect ◽

Inhibitory Effect ◽

Yangtze River Delta ◽

River Delta ◽

Spatial Spillover ◽

Haze Pollution ◽

The Impact ◽

Spatial Spillover Effect

In this study, the static and dynamic spatial Durbin model between industrial structure and haze pollution in Yangtze River Delta is constructed. Later, the spatial spillover effect and time lag effect of haze pollution in Yangtze River Delta are analyzed. The impact of rationalization and upgrading of industrial structure on haze pollution and its spatial spillover effect are discussed. The results show that: (i) PM2.5 has a significant positive spatial spillover effect and time lag effect; (ii) in the short run, the rationalization and upgrading of industrial structure has no inhibitory effect on haze pollution, while the rationalization and upgrading of industrial structure of surrounding cities has an inhibitory effect on local haze pollution; (iii) in the long run, the rationalization and upgrading of industrial structure of surrounding cities have an inhibitory effect on local haze pollution; (iv) economic growth, FDI, the number of Industrial Enterprises above Designated Size, and population density also have spatial spillover effects on haze pollution. Therefore, considering the spatial spillover effect of haze pollution from the perspective of urban agglomeration and long-term, strengthening the joint prevention and control and comprehensive treatment among cities, further promoting the rationalization and upgrading of industrial structure is conducive to reducing haze pollution.

Download Full-text

Spatial-Temporal Effects of PM2.5 on Health Burden: Evidence from China

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16234695 ◽

2019 ◽

Vol 16 (23) ◽

pp. 4695 ◽

Cited By ~ 4

Author(s):

Zeng ◽

Du ◽

Zhang

Keyword(s):

Direct Effect ◽

Spatial Clustering ◽

Spillover Effect ◽

Spatial Spillover ◽

Regulatory Policies ◽

Health Burden ◽

Lag Effect ◽

Pm2.5 Exposure ◽

The Impact ◽

Spatial Spillover Effect

By collecting the panel data of 29 regions in China from 2008 to 2017, this study used the spatial Durbin model (SDM) to explore the spatial effect of PM2.5 exposure on the health burden of residents. The most obvious findings to emerge from this study are that: health burden and PM2.5 exposure are not randomly distributed over different regions in China, but have obvious spatial correlation and spatial clustering characteristics. The maximum PM2.5 concentrations have a significant positive effect on outpatient expense and outpatient visits of residents in the current period, and the impact of PM2.5 pollution has a significant temporal lag effect on residents’ health burden. PM2.5 exposure has a spatial spillover effect on the health burden of residents, and the PM2.5 concentrations in the surrounding regions or geographically close regions have a positive influence on the health burden in the particular region. The impact of PM2.5 exposure is divided into the direct effect and the indirect effect (the spatial spillover effect), and the spatial spillover effect is greater than that of the direct effect. Therefore, we conclude that PM2.5 exposure has a spatial spillover effect and temporal lag effect on the health burden of residents, and strict regulatory policies are needed to mitigate the health burden caused by air pollution.

Download Full-text

Does China’s transportation infrastructure have an impact on employment in the service sector?

Kybernetes ◽

10.1108/k-04-2019-0253 ◽

2020 ◽

Vol 49 (11) ◽

pp. 2737-2753

Author(s):

Hui Wang ◽

Meiqing Zhang

Keyword(s):

Large Scale ◽

Service Industry ◽

Spillover Effect ◽

Transportation Infrastructure ◽

Content Type ◽

Inland Waterways ◽

Spatial Spillover ◽

Positive Effects ◽

The Impact ◽

Spatial Spillover Effect

Purpose The large-scale construction of China’s transportation infrastructure has driven the flow of elements between regions, which has provided convenient conditions for the accumulation of advantageous resources. Design/methodology/approach Based on the panel data of 31 provinces in China in the past 2003-2017 years, this paper applies the spatial econometric model and partial differential method and empirically analyzes the spatial spillover effect of transportation infrastructure on employment in the service industry under four spatial weighting matrices. Findings The results show that for every 1 per cent increase in the level of transportation infrastructure, the employment density of the service industry in the region can be increased by 0.1274 per cent. It is worth noting that roads promote the employment of the service industry more than railways and inland waterways. However, inland waterways have not shown positive effects. The results on spatial spillover of transportation infrastructure indicate that railway has obvious promotion effect on the employment level of service industry in the surrounding area, while the highway has hindered the effect. The spatial spillover effect of inland waterway is not obvious. Originality/value The value of this paper is to consider the impact of China’s transportation infrastructure on employment in a particular industry, especially in the service industry. The research will help to provide empirical evidence for policymakers. The government needs to invest and build transportation infrastructure based on the stage and development potential of the employment development of the regional service industry.

Download Full-text

The Impact of Multi-Dimensional Urbanization on China’s Carbon Emissions Based on the Spatial Spillover Effect

Polish Journal of Environmental Studies ◽

10.15244/pjoes/114508 ◽

2020 ◽

Vol 29 (5) ◽

pp. 3317-3327

Author(s):

Zhi-Qiang Sun ◽

Tao Sun

Keyword(s):

Carbon Emissions ◽

Spillover Effect ◽

Spatial Spillover ◽

The Impact ◽

Spatial Spillover Effect

Download Full-text

The Impact of Government Competition on Regional R&D Efficiency: Does Legal Environment Matter in China’s Innovation System?

Sustainability ◽

10.3390/su10124401 ◽

2018 ◽

Vol 10 (12) ◽

pp. 4401 ◽

Cited By ~ 4

Author(s):

Baocheng He ◽

Jiawei Wang ◽

Jiaoyang Wang ◽

Kun Wang

Keyword(s):

Local Governments ◽

High Efficiency ◽

Eastern China ◽

Innovation System ◽

Spillover Effect ◽

Western China ◽

Legal Environment ◽

Promoting Effect ◽

Spatial Panel Data ◽

The Impact

Local governments are encouraged to compete in R&D investments and activities in China’s innovation system. We aim to understand the influence of government competition on regional R&D efficiency. We are also interested in examining how the attributes of legal environment act as a moderating variable for the relationship between government competition and R&D efficiency. We developed Tobit spatial models with spatial panel data of 30 provinces of China in 2008–2016. The results show that: (1) There exists spatial dependence of R&D efficiency, and the regions with high efficiency have “spillover effect” on the surrounding areas. (2) Government competition has a significant promoting effect on R&D efficiency and/or R&D efficiency spillover. Specifically, government competition has both R&D efficiency promotion and R&D efficiency “spillover” promotion in Eastern China, only R&D efficiency positive spillover promotion in Middle-area and R&D efficiency promotion but negative spillover in Western China. (3) The impact of government competition on efficiency is affected by the legal environment, and the promotion effect of government competition only exists in good legal environment. The results of this study reveal an important way to improve R&D efficiency by establishing a new R&D competition mechanism for local government which is oriented by efficiency and ruled by the legal environment.

Download Full-text

Analysis and Evaluation of the Regional Characteristics of Carbon Emission Efficiency for China

Sustainability ◽

10.3390/su12083138 ◽

2020 ◽

Vol 12 (8) ◽

pp. 3138 ◽

Cited By ~ 3

Author(s):

Jinkai Li ◽

Jingjing Ma ◽

Wei Wei

Keyword(s):

Carbon Dioxide Emissions ◽

Carbon Emission ◽

Government Regulation ◽

Industrial Structure ◽

Western China ◽

Energy Structure ◽

Analysis And Evaluation ◽

Emission Efficiency ◽

Technological Efficiency ◽

The Impact

To promote economic and social development with reduced carbon dioxide emissions, the key lies in determining how to improve carbon emission efficiency (CEE). We first measured the CEE of each province by using the input-oriented three-stage Data Envelopment Analysis (DEA) and DEA-Malmquist model for the panel data of 30 provinces in China during 2000–2017. Then we explored the CEE differences and characteristics of different regions obtained by using hierarchical clustering of each province’s CEE. Finally, based on the regression model, we conducted an empirical analysis of the impact of each factor of total factor productivity (TFP) on CEE. The main findings of this research are as follows: (1) The industrial structure, energy structure, government regulation, technological innovation, and openness had a significant impact on CEE; (2) The variation trends of CEE and TFP in the eight regions we studied were convergent, while the variations of CEE among regions were diverse and all distributed stably in different ranges; (3) The eight regions’ efficiency basically showed a downward trend of eastern, central and western China; (4) Technological regression was the main reason for the decline in TFP. Technological progress and technological efficiency can contribute to an improvement in CEE. Based on the findings above, we provide decision-making references for comprehensively improving the efficiency of various regions and accelerating China’s energy conservation, emissions reduction, and coordinated development.

Download Full-text

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

Kybernetes ◽

10.1108/k-06-2021-0452 ◽

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.

Download Full-text