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.

The Spatial Influence Research of the Energy Efficiency in China — an Empirical Analysis Based on the Spatial Panel Data Model about the Provinces of China

2011 ◽  
Vol 361-363 ◽  
pp. 1071-1079 ◽  
Author(s):  
Wei Wei ◽  
Yi Hong Song ◽  
Zhi Hong Liu
Keyword(s):  
Energy Efficiency ◽  
Panel Data ◽  
Data Model ◽  
Influential Factors ◽  
Panel Data Model ◽  
Energy Price ◽  
Spatial Panel ◽  
Spatial Panel Data Model ◽  
Spatial Panel Data ◽  
The Government

On the basis of the spatial panel data model, this article takes a empirical study on the energy efficiency. The results are as follows. (1) The energy efficiency has the obvious spatial dependence among the provinces of China, and the influential factors of the neighboring provinces will have impacts on the special province. (2) In various factors, the most intense are the energy price and the government intervention, which displays the inverse correlation relations with the energy efficiency. Therefore, more attention must be paid to the cooperation in provinces, the marketability reform of the energy price as well as government's unreasonable intervention in the process of the improvement of the energy efficiency.

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