Optimization Of China’s Transportation Structure Based On Carbon Emission Reduction

Since 2014,the national economy has entered a new normal,and China has gradually shifted from the pursuit of high-speed development to the pursuit of high-quality development,while a series of problems brought by the previous development are gradually exposed.The previous social development held the idea of "pollution first,treatment later",but now entering the stage of high-quality development,the country has put environmental governance on the agenda.As the pillar of social and economic development,the environmental pollution of transportation industry can not be ignored.In order to meet the double goals of increasing transportation demand and low carbon emission reduction,it is necessary to start from the transportation internal,and explore the transportation system composition which is more conducive to the green transformation of transportation industry.Due to the relatively easy adjustment of the transportation structure,the transportation structure has become an important point of low carbon emission reduction in the transportation industry in a short time.Firstly,this thesis quantifies the impact of transportation structure to the transport carbon emissions by means of the extended STIRPAT model regression using data between 2000 and2019.Secondly,on the premise that the four modes of transportation can be substituted with each other,a dual-objective programming model of minimizing transportation carbon emissions and maximizing total transportation turnover is constructed from the perspectives of total transport turnover volume and component,energy consumption,environmental pollution degree,policy inclination,reasonable line utilization rate,etc.NSGA2 intelligent optimization algorithm was used to solve the problem,entropy weight-topsis method was used to make objective decision and sensitivity analysis of the model was carried out.Finally,the empirical study on the existing transportation structure in 2019 and model transformation are used to try to explore the optimal transportation structure under the constraint of carbon peak in 2030.The results show that:(1)The change of transportation structure does have an impact on transportation carbon emissions.The increase of the proportion of road transport and air transport will promote the generation of transportation carbon emissions,while the increase of the proportion of railway transport will inhibit transportation carbon emissions.(2)The empirical findings of the existing transportation structure show that the ratio of road,railway,water and air transport to the original transportation structure decreased by 4.51%,increased by 0.81%,increased by 3.76% and decreased by 0.05%,respectively.The optimized transportation structure is obviously better than the before one,and the optimization of transportation structure has greater carbon emission reduction potential.(3)The empirical study of transport structure under the constraint of carbon peak in 2030 shows that the optimized transportation structure compared with the original transportation structure can reduce the emission of 106 million tons of carbon dioxide and consumption of 75.36 million tons of standard coal when the transportation turnover is the same.The transportation structure has changed from the original pattern of "water + road" to that of "water + rail".If priority is given to transportation turnover targets,priority should be given to rail transport;If energy consumption targets are given priority,emphasis should be placed on increasing the share of water transport.(4)The sensitivity analysis of uncertain parameters in the model shows that the existing transportation structure optimization model is not sensitive to parameter changes,and the carbon emission reduction effect obtained by transportation structure adjustment is relatively stable.The energy saving effect of future transportation structure optimization model largely depends on parameter changes,so it is necessary to carry out sensitivity analysis of uncertain parameters in the model in the follow-up research and need to strengthen the accuracy of the prediction of uncertain parameters.