An empirical study on spatial spillover of carbon emissions and financial development based on provinces data and spatial panel econometrics model

Author(s):  
Tong Xin ◽  
Wu Yuming ◽  
Li Xuesen ◽  
Tong Lin
Author(s):  
Jin Guo ◽  
Yingzhi Xu ◽  
Zhengning Pu

Urbanization is considered as a main indicator of regional economic development due to its positive effect on promoting industrial development; however, many regions, especially developing countries, are troubled by its negative effect — the aggravating environmental pollution. Many researchers have indicated that rapid urbanization stimulated the expansion of industrial production scale and increased industrial pollutant emissions. However, this judgement contains a grave deficiency in that urbanization not only expands industrial production scales but can also increase industrial labour productivity and change the industrial structure. To modify this deficiency, we first decompose the influence which urbanization impacts on industrial pollutant emissions into the scale effect, the intensive effect and the structure effect by using the Kaya Identity and the LMDI Method; second, we perform an empirical study of the three effects’ impacts by applying the spatial panel model with data from 282 Chinese cities between 2003 and 2013. Our results indicate that (1) there are significant reverse U-shapes between Chinese urbanization rate and its industrial pollutant emissions; (2) the scale effect and the structure effect have aggravated Chinese industrial waste water discharge, sulphur dioxide emissions and soot (dust) emissions, while the intensive effect has generated a decreasing and ameliorative impact on that aggravated trend. The definite relationship between urbanization and industrial pollutant emissions depends on the combined influence of the scale effect, the intensive effect and the structure effect; (3) there are significant spatial autocorrelations of industrial pollutant emissions between Chinese cities, but the spatial spillover effect from other cities does not aggravate local urban industrial pollutant emissions, we offer an explanation to this contradiction that the vast rural areas surrounding Chinese cities have served as sponge belts and have absorbed the spatial spillover of cities’ industrial pollutant emissions. According to the results, we argue that this type of decomposition of the influence into three effects is necessary and meaningful, it establishes a solid foundation for understanding the relationship between urbanization and industrial pollutant emissions, and effectively helps to meet relative policy making.


2018 ◽  
Vol 63 (02) ◽  
pp. 447-464 ◽  
Author(s):  
LING XIONG ◽  
SHAOZHOU QI

Using the panel data of 30 provinces in China between 1997 and 2011, we employed the extended STIRPAT model and spatial panel econometrics methods to investigate the relationship between financial development and carbon emissions and test the influence of financial development as well as other factors on provincial carbon emissions per capita among Chinese provinces. The estimation results show that: (i) spatial spillover effects play a role in provincial carbon emissions in China; and (ii) the sum of technical effect and structure effect of financial development surpass its’ sum of direct effect and wealth effect in China, which suggests that financial development reduces carbon emissions per capita. China should pay more attention to the integration of green finance policy and environmental regulation, and establish appropriate mechanisms to strengthen inter-provincial interaction and coordinated development.


2018 ◽  
Vol 10 (12) ◽  
pp. 4739 ◽  
Author(s):  
Xin Tong ◽  
Xuesen Li ◽  
Lin Tong ◽  
Xuan Jiang

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.


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