The impact of FDI on environmental pollution in China: evidence from spatial panel data

2021 ◽  
Author(s):  
Tongxin An ◽  
Chengyun Xu ◽  
Xianchun Liao
Keyword(s):  
Panel Data ◽  
Environmental Pollution ◽  
Spatial Panel ◽  
Spatial Panel Data ◽  
The Impact

scholarly journals Does Environmental Innovation Improve Environmental Productivity?—An Empirical Study Based on the Spatial Panel Data Model of Chinese Urban Agglomerations

2020 ◽  
Vol 17 (17) ◽  
pp. 6022
Author(s):  
Junwei Ma ◽  
Jianhua Wang ◽  
Philip Szmedra
Keyword(s):  
Panel Data ◽  
Data Model ◽  
Spillover Effect ◽  
Value Added ◽  
Environmental Innovation ◽  
Spatial Panel ◽  
Different Types ◽  
Spatial Panel Data ◽  
Pollution Emissions ◽  
The Impact

Environmental productivity comprehensively measures economic growth and environmental quality. Environmental innovation is considered to be the key to solving economic and environmental problems. Therefore, discussing the impact of environmental innovation on environmental productivity will reveal its economic and environmental effects. This paper measures environmental productivity by value added per unit of pollution emissions (four types of emissions are used) using panel data of 10 Chinese urban agglomerations from 2003 to 2016 to analyze the spatial correlation of environmental productivity, and constructs a spatial panel data model to empirically test the impact of environmental innovation on environmental productivity. It was found that environmental productivity measured by value added per unit of carbon dioxide emissions (gross domestic product (GDP)/CO2) had a significant positive spatial spillover effect, and measured by value added per unit of sulfur dioxide emissions (GDP/SO2), smoke (dust) emissions (GDP/SDE), and industrial sewage emissions (GDP/IS) had a significant negative spatial spillover effect. Environmental innovation has a significant negative inhibitory effect on environmental productivity measured by GDP/SDE and GDP/IS, but no obvious effect measured by GDP/CO2 and GDP/SO2. Control variables such as economic development level, industrial agglomeration, foreign direct investment, and endowment structure factor also show significant differences in environmental productivity measured by value added per unit of pollution emissions. In addition, there are significant differences in direct effects of explanatory variables on environmental productivity of local regions and indirect effects on neighboring regions. These differences are also related to the types of pollution emissions. Therefore, policymakers should set different policies for different types of pollution and encourage different types of environmental innovation, so as to achieve reduced pollution emissions and improved environmental productivity.

Modeling the Impact of Technological Innovation on Environmental Efficiency: A Spatial Panel Data Approach

2020 ◽  
Vol 52 (2) ◽  
pp. 231-253 ◽  
Author(s):  
Roberto Benedetti ◽  
Daniela Palma ◽  
Paolo Postiglione
Keyword(s):  
Panel Data ◽  
Technological Innovation ◽  
Environmental Efficiency ◽  
Spatial Panel ◽  
Spatial Panel Data ◽  
The Impact

scholarly journals A Framework for Spatiotemporal Analysis of Regional Economic Agglomeration Patterns

2018 ◽  
Vol 10 (8) ◽  
pp. 2800 ◽  
Author(s):  
Rui Jin ◽  
Jianya Gong ◽  
Min Deng ◽  
Yiliang Wan ◽  
Xuexi Yang
Keyword(s):  
Data Analysis ◽  
Panel Data ◽  
Panel Data Analysis ◽  
Interaction Model ◽  
Spillover Effects ◽  
Spatiotemporal Pattern ◽  
Regional Economic ◽  
Spatial Panel ◽  
Economic Agglomeration ◽  
Spatial Panel Data

Understanding regional economic agglomeration patterns is critical for sustainable economic development, urban planning and proper utilization of regional resources. Taking Guangdong Province of China as the study area, this paper introduces a comprehensive research framework for analyzing regional economic agglomeration patterns and understanding their spatiotemporal characteristics. First, convergence and autocorrelation methods are applied to understand the economic spatial patterns. Then, the intercity spatial interaction model (ISIM) is proposed to measure the strength of interplay among cities, and social network analysis (SNA) based on the ISIM is utilized, which is designed to reveal the network characteristics of economic agglomerations. Finally, we perform a spatial panel data analysis to comprehensively interpret the influences of regional economic agglomerations. The results indicate that from 2001 to 2016, the economy in Guangdong showed a double-core/peripheral pattern of convergence, with strengthened intercity interactions. The strength and external spillover effects of Guangzhou and Shenzhen enhanced, while Foshan and Dongguan had relatively strong absorptive abilities. Moreover, expanding regional communication and cooperation is key to enhancing vigorous economic agglomerations and regional network ties in Guangdong by spatial panel data analysis. Our results show that this is a suitable method of reflecting regional economic agglomeration process and its spatiotemporal pattern.

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