Sulfur dioxide (SO2) emission reduction and its spatial spillover effect in high-tech industries: based on panel data from 30 provinces in China

With ever-increasing economic globalization and rapid advancement of science and technology, developing high-tech industries have become an important way for many countries to achieve sustainable and environmentally friendly economic development. In this article, we aim to empirically test the critical factors, which can influence the spatial spillover of a country’s high-tech industries. Using data from the high-tech industries in China during the years of 2007–2016, we establish a space lag model and a space error model to examine the space fixed effect, the time fixed effect, and the space-time double mixed effect in spatial spillover in high-tech industries. We compare the results of these two spatial panel models with those from a general panel model and find that the spatial spillover effect within high-tech industries is rather significant. Moreover, we find that the spatial-time double mixed of the spatial lag model is the best fitting effect. Our empirical results also show that the research and development (R&D) investment and international trade can positively promote spatial spillover of high-tech industries among different regions. In terms of policy insights, our results imply that the government can establish a technology transfer platform to promote the spillover in high-tech industries. This can help achieve a sustainable and balanced development of high-tech industries.

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Environmental Regulation, Resource Misallocation and Industrial Total Factor Productivity: A Spatial Empirical Study Based on China’s Provincial Panel Data

Sustainability ◽

10.3390/su13042390 ◽

2021 ◽

Vol 13 (4) ◽

pp. 2390

Author(s):

Xu Dong ◽

Yali Yang ◽

Xiaomeng Zhao ◽

Yingjie Feng ◽

Chenguang Liu

Keyword(s):

Panel Data ◽

Environmental Regulation ◽

Total Factor Productivity ◽

Spillover Effect ◽

Factor Productivity ◽

Resource Reallocation ◽

Spatial Spillover ◽

Resource Misallocation ◽

Capital Misallocation ◽

Spatial Spillover Effect

A vast theoretical and empirical literature has been devoted to exploring the relationship between environmental regulation and total factor productivity (TFP), but no consensus has been reached and the reason may be attributed to the fact that the resource reallocation effect of environmental regulation is ignored. In this paper, we introduce resource misallocation in the process of discussing the impact of environmental regulation on TFP, taking China’s provincial industrial panel data from 1997 to 2017 as a sample, and the spatial econometric method is employed to investigate whether environmental regulation has a resource reallocation effect and affects TFP. The results indicate that there is a U-shaped relationship between environmental regulation and industrial TFP and a negative spatial spillover effect of environmental regulation on industrial TFP at the provincial level in China. Both capital misallocation and labor misallocation will lead to the loss of industrial TFP. Capital misallocation has a negative spatial spillover effect on industrial TFP, while labor misallocation is just the opposite. Environmental regulation can produce a positive resource reallocation effect, which in turn promotes the industrial TFP in the range of 28% to 33%, while capital misallocation and labor misallocation are only partial mediator.

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Carbon Emission Trading Scheme, Carbon Emissions Reduction and Spatial Spillover Effects: Quasi-Experimental Evidence From China

Frontiers in Environmental Science ◽

10.3389/fenvs.2021.824298 ◽

2022 ◽

Vol 9 ◽

Author(s):

Zhaofu Yang ◽

Yongna Yuan ◽

Qingzhi Zhang

Keyword(s):

Emission Reduction ◽

Carbon Emission ◽

Spillover Effect ◽

Emission Trading ◽

Coal Consumption ◽

Difference In Differences ◽

Spatial Spillover ◽

Trading Scheme ◽

Carbon Emission Trading ◽

Spatial Spillover Effect

The carbon emission trading scheme (ETS) is an essential policy tool for accomplishing Chinese carbon targets. Based on the Chinese provincial panel data from 2003 to 2019, an empirical study is conducted to measure the effects of carbon emission reduction and spatial spillover effect by adopting the difference-in-differences (DID) model and spatial difference-in-differences (SDID) model. The research findings show that: 1) The ETS effectively reduced the total carbon emissions as well as emissions from coal consumption; 2) such effects come mainly from the reduction of coal consumption and the optimization of energy structure, rather than from technological innovation and optimization of industrial structure in the pilot regions; and 3) the ETS pilot regions have a positive spatial spillover effect on non-pilot regions, indicating the acceleration effect for carbon emission reduction. Geographic proximity makes the spillover effect decrease due to carbon leakage.

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Spatial spillover effect of green innovation on economic development quality in China: Evidence from a panel data of 270 prefecture-level and above cities

Sustainable Cities and Society ◽

10.1016/j.scs.2021.102863 ◽

2021 ◽

pp. 102863

Author(s):

Wenbin Peng ◽

Yong Yin ◽

Change Kuang ◽

Zezhou Wen ◽

Jinsong Kuang

Keyword(s):

Economic Development ◽

Panel Data ◽

Spillover Effect ◽

Green Innovation ◽

Spatial Spillover ◽

Spatial Spillover Effect

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

Tourism Economics ◽

10.1177/13548166211021175 ◽

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.

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Research on spatial spillover effect and regional economic growth of high-speed railway network —— Take the cities along the Beijing-Shanghai high-speed railway as an example

2020 2nd International Conference on Economic Management and Model Engineering (ICEMME) ◽

10.1109/icemme51517.2020.00172 ◽

2020 ◽

Author(s):

Shuo Lei

Keyword(s):

Economic Growth ◽

High Speed ◽

Spillover Effect ◽

Regional Economic Growth ◽

Railway Network ◽

High Speed Railway ◽

Regional Economic ◽

Spatial Spillover ◽

Spatial Spillover Effect

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

Sustainability ◽

10.3390/su13148032 ◽

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.

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