| Global warming has drawn the extensive attention all over the world and climate change has become one of the great challenges for human survival and development.Mankind has entered electrification age with the production modes and lifestyles undergoing great changes since industrial revolution.Since human development has entered the era of electrification,the production methods and lifestyles have undergone tremendous changes since the Industrial Revolution,and the high-carbon growth model dominated by fossil energy has gradually become unsustainable.As it is emphasized by the Paris Agreement that all the parties should hold the increase in the global average temperature to well below2°C above pre-industrial levels and pursue efforts to limit the temperature increase to 1.5°C above pre-industrial levels;at the same time,it is required all the parties to reach global peaking of greenhouse gas emissions as soon as possible and to assure zero greenhouse gas emissions.Among the greenhouse gases,carbon dioxide is a typical representative,and reducing its emissions has always been the goal of global temperature increase control emphasized by governments in the Paris Agreement.As the largest developing country in the world,China has always attached great importance to the issue of climate change and has played an important role in addressing climate change.In 1992,China became one of the first parties to undersign the United Nations Framework Convention on Climate Change.In 2002,the Chinese government ratified the"Kyoto Protocol".In 2015,General Secretary Xi Jinping emphasized the need to achieve a higher level of global sustainable development in his speech at the opening ceremony of the Paris Conference on Climate Change,and promised that“China’s total carbon emissions will reach a peak around 2030,and strive to achieve it as soon as possible;unit GDP Carbon dioxide emissions（i.e.carbon emission intensity）will be reduced by60%-65%compared to 2005”,which is China’s“dual”goal of reducing carbon emission intensity and peaking the total amount.On September 22,2020,Chinese President Xi Jinping proposed that China aims to peak its CO2 emissions by 2030 and strives to become carbon neutral by 2060.This is of positive significance for the world’s joint efforts to tackle climate change.On March 15,2021,at the ninth meeting of the Central Finance and Economics Committee,carbon peaking and carbon neutrality were included in the overall layout of ecological civilization construction during the"14th Five-Year Plan"period.During the13th Five-Year Plan period（2016-2020）,China’s carbon emissions per unit of GDP decreased by 18%,o the ecological environment was gradually ameliorated.However,to achieve the 1.5°C temperature control target,the pressure is still very high.According to a report by the International Energy Agency（IEA）,due to the outbreak of the COVID-19pandemic,global oil demand has plummeted by 8.6%in 2020,and the corresponding carbon emission has dropped by nearly 1.1 billion tons,which is more than half of the global carbon emission reduction.The International Energy Agency（IEA）,in its March 2021 report"Global Energy Overview:Carbon Dioxide Emissions 2020",pointed out that the transport sector is the"largest"oil consumer,typically accounting for around 60%of oil demand,while transport Emissions have the greatest impact on carbon emissions.As an important carrier of industry and population flow and aggregation,transportation is inseparable from oil consumption.In 2021,China’s GDP exceeded 110 trillion yuan,and its economic growth rate was among the highest in the global economy.The accelerated progress China has made in its overall economic strength is of great significance.The transportation industry has always been a pillar industry of China’s national economy,and it is also a key area of energy conservation and emission reduction.According to existing research,the energy efficiency improvement potential of the transportation industry can reach 50%,while the energy efficiency improvement potential of the industrial sector can only reach about 10%to 20%.Under the premise that China’s economic development and energy consumption have not yet been decoupled,it is of great practical significance to tap the carbon emission reduction potential of the transportation industry and improve the emission reduction space of the transportation industry for China to achieve the overall"dual carbon"goal.By combing the measurement methods,influencing factors and carbon emission reduction paths of the transportation industry,this thesis combined the theory of sustainable development,low-carbon transportation theory and system theory,and comprehensively used social network analysis,spatial econometric analysis,machine learning,and scenario simulation to explore the carbon emission reduction path of the transportation industry.The main research contents are as follows.Firstly,for the measurement model of carbon emissions in the transportation industry,this thesis first uses a"top-down"model to measure China and 30 provinces（excluding Tibet Autonomous Region,Macao Special Administrative Region of China,Taiwan Region of China,and Hong Kong Special Administrative Region of China）carbon emissions from transportation based on energy consumption.Then,the carbon emissions of cargo transportation and passenger transportation of different modes of transportation are measured respectively.On this basis,we quantitatively analyzed the relationship between the carbon emissions and economic growth of the transportation industry in various provinces in China,and further explored the dynamic evolution relationship between the transportation structure and the carbon emissions of the transportation industry.Secondly,taking the spatial characteristics of transportation carbon emissions as the starting point of the thesis,introducing a revised gravity model from a system perspective to build a spatial correlation network of transportation carbon emissions in various provinces;then using social network analysis methods to analyze select feature indicators from three perspectives:overall network,individual network and block model.Then the thesis analyzed the network structure characteristics of carbon emissions in the transportation industry in each province;on this basis,based on the correlation coefficient clustering,the block model analysis of the carbon emission network of the transportation industry in the province was carried out.,to clarify the role of each sector in the transportation carbon emission network.An interesting phenomenon was found in this process:starting from 2019,the first sector has changed from the previous two-way spillover role to a net spillover role,and the fourth sector has changed from a net spillover role to a net benefit role.Then,taking the spatial spillover effect as the starting point,using the panel data of 30 provinces in China from 1997 to 2019,based on the spatial adjacency matrix and the economic weight matrix,a spatial Durbin model with stronger explanatory power is constructed to analyze the coupling effect of each driving factor.impact on carbon emissions from the transportation industry.On the one hand,the research conclusion verifies the hypothesis of this thesis that the carbon emissions of the transportation industry has a spatial spillover effect.On the other hand,it is found that the influence coefficient of the level of economic development on the carbon emissions of the transportation industry is significantly positive.It has a kenspeckle inhibitory effect on the carbon emission of the transportation industry,and the effect of public transportation and private transportation on the carbon emission of the transportation industry is just the opposite.Thirdly,according to the causal relationship between the elements of the carbon emission system of the transportation industry,the carbon emission system of the transportation industry was divided into three subsystems of road,railway and waterway,and the internal structure of each subsystem and the relationship between the subsystems are determined.The feedback relationship was then drawn,and the system stock-flow diagram was drawn,and the dynamic model of the carbon emission system in the transportation industry was constructed.Then from the practical application level,the dimensional consistency,stability,sensitivity and validity of the model are tested,so as to ensure the interpretability of the simulation model.Fourthly,based on the expanded STIRPAT model of carbon emissions in the transportation industry,with the factors that significantly affect the carbon emissions of the transportation industry as variables,a non-parametric machine learning prediction model STIRPAT-GWO-LLSVM is constructed.Effective forecasting of carbon emissions levels in the industry.Then,the carbon emission reduction policies of the transportation industry under different scenarios were set from the structural,technical and managerial aspects,and the implementation effects of the carbon emission reduction policies in the transportation industry at the national level and in the fourth sector are simulated respectively.The results show that,from the perspective of scope,the policy effect of carbon emission reduction in the national transportation industry is greater than that of the fourth sector.From the perspective of specific policies and measures,adjusting the transport structure had the best carbon emission reduction effect,while the policy effect of increasing the passenger turnover of public transport was the least obvious.The contributions of this thesis is mainly reflected in the following three aspects.Firstly,in the previous research literature on carbon emissions from the transportation industry,more emphasis was placed on building a single model to obtain the time series characteristics and cross-sectional characteristics of the data,and there was a lack of comparison between regions.In terms of method,based on the expanded theoretical model of carbon emission driving factors,this thesis construct and test the spatial Durbin model of transportation carbon emissions under different weight matrices;then combines the analysis of the social network block model to divide the regional system according to whether it is inside the plate or not.,constructing a two-zone spatial panel measurement model to measure the impact of various elements of carbon emissions in the transportation industry on different sectors.The significance of the effect provides a systematic quantitative method for the research on the driving force of the carbon emission influence of the transportation industry.Secondly,when most scholars conducted simulation research on the carbon emissions of the transportation industry,they mainly analyzed the four subsystems of economy,energy,environment and transportation from the perspective of the whole country or a province（city）.The system dynamics simulation of passenger carbon emissions in each city was carried out.China’s"14th Five-Year"Development Plan for Green Transportation proposes that the transportation industry needs to continuously optimize the transportation structure,and further promote the transportation of bulk goods and medium and long-distance goods"road to rail,road to water".A public transport travel system with public transport as the backbone and conventional public transport as the main body.Therefore,in terms of method,this thesis takes the source of carbon emissions as the main line,dividing the carbon emission system of the transportation industry into three specific modules:road carbon emissions,railway carbon emissions and waterway carbon emissions,and built a system dynamics simulation model of the transportation industry.This thesis compares and analyzes the effects of carbon emission policies in the transportation industry from the national level and the fourth sector,so as to provide targeted suggestions for the government to formulate carbon emission reduction policies for the transportation industry.Thirdly,there are uncertainties and inaccuracies in traditional econometric forecasting.In terms of method,this thesis takes the factors that significantly affect the carbon emissions of the transportation industry as predictors,and constructs a machine learning prediction model STIRPAT-GWO-LSSVM that continuously learns and optimizes historical data.predict.On the basis of the dynamic model of the carbon emission system in the transportation industry,with the goal of the"14th Five-Year Plan"modern comprehensive transportation system development plan as the constraint,the effective path of carbon emission reduction in the transportation industry was identified by constructing different policy scenarios,thereby improving the accuracy and validity of the policy scenario simulation. |
