The Impact Of China’s Export Trade On Carbon Emission Intensity

In order to clarify the impact of China’s export trade on carbon emission intensity and its spatial effect,this paper used panel data of 30 provinces in China from 2008 to 2017,The main methods used are statistical description method,comparative analysis method,and panel data empirical analysis method.The main models are Spatial Durbin Model,dynamic Spatial Durbin Model and Threshold Model.The main content is to reveal the temporal and spatial characteristics of carbon emission intensity through spatial autocorrelation analysis.The Moran’I index analysis shows that there is a spatial asymmetric pattern of carbon emissions,and the carbon emission intensity shows that there is a spatial correlation.On this basis,the adjacency space weight matrix,geospatial weight matrix,and nested space weight matrix are constructed for the static and dynamic Spatial Durbin Model,This paper comprehensively analyzes the interference of trade dependence,energy structure,commodity structure,industrial structure,technology,government management,then carried out the partial differential effect decomposition and the long-term effect decomposition and the short-term effect decomposition,empirical analysis of the spatial spillover effect of China’s provincial export trade on carbon emission intensity,and then constructs the panel threshold model to further explore the non-linear relationship between export trade and carbon emission intensity.Based on the existing literature,this study has made some contributions.Different from the existing literature using section data and panel data,this paper uses the latest 10-year continuous panel data,which is more comprehensive,updated,persuasive and reliable.The existing research only stops in the analysis of the factors that influence carbon emissions from export trade,and there is no further research on whether the impact will produce spatial spillover.Even though a small number of literatures analyze carbon emission intensity through spatial measurement,they do not analyze its long-term and short-term effects from a dynamic perspective,in addition,this paper discusses the impact of carbon emission intensity in a more comprehensive non-linear way.The results show that:(1)From 2008 to 2017,the total amount of carbon emissions increased year by year,and the intensity of carbon emissions decreased year by year.The intensity of carbon emission shows a significant imbalance between regions,and the overall trend is low in the East and high in the West.(2)There is a significant spatial dependence on the intensity of China’s provincial carbon emissions,and the export trade has a negative spillover effect on the intensity of carbon emissions.(3)Economic level,industrial structure,energy structure,commodity structure and technology are the main factors that affect the carbon emission intensity.Economic level,commodity structure and technology level have significant negative effect on adjacent regions,industrial structure,energy structure and government management have significant positive effect on adjacent regions,the long-term "spillover effect" of technology is not obvious.(4)The carbon emission intensity has both the cumulative effect in time and the radiation driving effect in space.The short-term impact is not significant,but the long-term impact is significant.(5)There is a non-linear relationship between export trade and carbon emission intensity in China,and there are many threshold.At last,this paper puts forward some policy suggestions,such as building the best demonstration area of carbon intensity emission reduction in the eastern region,introducing differentiated carbon emission reduction policies in the central and western regions according to local conditions,optimizing the structure of export commodities,introducing advanced clean technologies,strengthening the government’s encouragement and restriction,building special funds,establishing long-term cooperation mechanism of energy conservation and emission reduction among regions,reducing carbon emission intensity,etc.