Analysis Of Spatial Spillover Effects And Influencing Factors Of China's Provincial Carbon Emissions

Energy is an important part of the human society for survival and development,and it is also one of the most basic elements to promote economic growth.China is now in a period of rapid development,and this is closely related to energy consumption.However,a huge number of the consumption of carbon containing energy will directly lead to global warming and the deterioration of the ecological environment.This will undoubtedly bring serious challenges to human survival and economic development.How to solve the problem of global warming has become a concerted effort of all the governments.Therefore,this paper focuses on the research of carbon emissions in China,and it has important theoretical and practical significance to deal with global climate change.In this paper,the empirical research part will use provinces as region units,province energy consumption data will be collected from 2005 to 2014 to calculate carbon dioxide emissions,and also collect other variables.In view of this,what this paper will do is shown as follow:First of all,this paper will use the carbon emissions coefficient and also calculation method of carbon emissions of IPCC to get the total carbon emissions of thirty provinces(except Macao,Hongkong,Tibet and Taiwan)from 2005 to 2014.We find that in the past ten years,China's total carbon emissions are in a generally increasing trend,of which 2005-2010 growth is relatively steep,while 2010-2014 growth is slow.In 2013,compared with the carbon emissions in 2012 is less,which may be China's energy-saving emission reduction policies have achieved initial success.As can be seen from the data visualization,there are differences between the north and south of China's carbon emissions and also between east and west.That is,the eastern carbon emissions higher than the west,the north carbon emissions higher than the south,and the difference between east and west is greater than the difference between north and south.Then,two spatial weight matrices are constructed: spatial contiguity weight(rook contiguity)and spatial distance weight.Theses two spatial weights are used tocalculate the global spatial correlation of global carbon emissions.To compare the results obtained,we find that the contiguity weight is better,so the contiguity weight is used as the spatial weight matrix.According to the results obtained from the global auto-correlation,it is concluded that China's provincial carbon emissions have spatial auto-correlation.In the local spatial correlation test,Moran's I scatter plots divide the 30 provinces into four parts: high-high aggregation,low-low aggregation,high-low aggregation and low-high aggregation,then validate the conclusion by using LISA clustering and LISA significance map.It is obvious that China's carbon emissions do exist high-high aggregation and low-low aggregation,and the high-high aggregation areas are mainly in the eastern Bohai region.This paper will use STIRPAT model to find the Factors affecting carbon emissions in the province.At last,the three variables are: provincial per capital GDP,the total population of the province and the intensity of energy consumption in the province.Finally,before using the spatial econometric model,the ordinary OLS estimation method is used to estimate the coefficients of the panel model,and we get the preliminary conclusion that there is a positive correlation between the three selected variables and carbon emissions.Because of the fact that there is a spatial auto-correlation in China's provincial carbon emissions,the two spatial econometric models are used to model: spatial lagged model and spatial error model.At the same time,the correlation between sections makes the ordinary OLS estimation is no longer trial,so the maximum likelihood estimation method is used to estimate the spatial econometric model.The results show that the spatial and period fixed spatial error model(SEM)is the best,so all the conclusions of this paper are based on this model.At the same time,it is proved that the four factors that affect the carbon emissions are significant,so the population,per capital GDP and energy consumption intensity have a positive impact on carbon emissions.