| At present,China’s tertiary industry is developing rapidly,and logistics has become a pillar industry to promote the national economic development.However,the overall level of development of logistics industry is not high,and the development mode is relatively extensive,which is one of the main industries of energy consumption and carbon dioxide emission in China.Therefore,this thesis takes the green total factor energy efficiency of the logistics industry as a research breakthrough,analyzes the time and space difference of the green total factor energy efficiency of the logistics industry,and predicts the peak of carbon emissions,calculates the regional carbon emission reduction potential index,and coordinates the overall coordinated development of the logistics industry Provide reasonable advice.The main research contents are as follows:(1)Measuring the green total factor energy efficiency of the logistics industry: 30 provinces and cities in China are divided into eight regions.Based on the panel data of the interprovincial logistics industry from 2008 to 2017,a three-stage SBM-DEA model considering nonexpected output is constructed to calculate the green total factor energy efficiency of the logistics industry.The results show that the external environmental factors and random variables have significant influence on different regions to varying degrees,and the influence depends on the specific environment in which the economic zones are distributed,and the external environmental factors and random error will reduce the actual GTFEE value of the logistics industry.Among the eight comprehensive economic zones,the southern coastal economic zone has been kept above the production frontier,and the GTFEE mean value of the national logistics industry has been gradually rising with the passing of time.(2)Exploring the spatial and temporal differences in the green total factor energy efficiency of the logistics industry:Through the Malmquist index model and exploratory spatial data analysis method,constructing the geographic weight matrix of adjacency relations,and using the global Moran’s I index to test the spatial correlation of GTFEE in the logistics industry.The results show that the GTFEE index of the logistics industry fluctuates in the upward and downward state in time series,and the improvement of technological progress will effectively promote the development of the logistics industry.The national logistics industry shows the characteristics of spatial agglomeration.Beijing,Tianjin,Shandong,Fujian and Guangdong are always the core driving areas of GTFEE.The regional efficiency fault is obvious and the polarization is serious.(3)Predicting the carbon emissions of the logistics industry in 2018-2050: STIRPAT model is introduced to set nine scenarios.The prediction results show that the promotion intensity of population and economic level of logistics industry is greater than the inhibition intensity of energy structure and carbon emission intensity.The smaller the promotion intensity of positive factors and the greater the inhibition intensity of negative factors,the easier it is to push the peak time of carbon to be completed in advance,and the lower the carbon peak.The study on the peak controllability and optimal path of carbon emission shows that the positive factors have a more significant promoting effect on carbon emission than the negative factors.This thesis proposes the optimal way to control the total population,improve the quality of economic development,adjust the energy structure and reduce the carbon emission intensity.(4)Calculating the emission reduction potential of the logistics industry in each region: Based on the principle of equity and efficiency of carbon emission reduction,divide each province into four categories.With the help of ACI model,the carbon emission reduction potential index of each region is calculated and three different scenarios are set.The results show that different preference of decision makers will lead to different allocation mechanism of carbon emission reduction in different provinces.The greater the proportion of index weight,the higher the corresponding emission reduction task of each province will be. |
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