Influence Factors And Guidance Strategy Of Low Carbon Travel For Urban Resident In China

With the rapid development of urbanization and the expansion of city, traditionalcity traffic systems in China become more and more unsuitable for the developmentof cities and the different demand level of residents. The contradiction among urbantraffic, energy and environment seriously restricts the normal operation of residents’life as well as the sustainable development of cities. As the main implementor oftraffic activities, the urban resident plays an important role in the construction anddevelopment of low carbon traffic system. Therefore, it is necessary to study theinfluence of residents’ travel behavior on carbon emissions from urban transport, anddiscuss how to guide the residents to choose the low-carbon transportation mode.Based on the related research at home and abroad, this paper discusses theimpact factor and mechanism of carbon emission from urban transport on theperspective of residents’ low carbon travel behavior. We further investigate thefeasible policies that encourage urban residents to select the low carbon transportationmode, as well as make them to transfer from traditional oil-powered automobiles toelectric vehicles. Both quantitative and qualitative approaches are used in this paperwith the consideration of the theoretical and empirical research. The main contentsand contributions are displayed as follows:Firstly, based on the summarization of disaggregate theory and utility theory, thispaper constructs a multi-nomial logit model of travel choice behavior to study theurban resident travel behavior characteristics and decision-making mechanisms.Using the survey data of Beijing residents’ travel preferences, this paper empiricallystudies the influence factors of resident travel choice behavior in Beijing. It providesthe supporting data for understanding the characteristics of urban residents travelbehavior and a scientific explanation for the travel mode choice behavior, which has acertain reference value for establishing traffic demand management measures.Secondly, in order to calculate the carbon emissions from household daily travel,this paper develops a modified transportation carbon emission accounting methodbased on the changing emission coefficient and travel distance year by year ofdifferent vehicles. We further construct LMDI decomposition model to analyze the effects of population, per capita economic activity, transportation intensity,transportation mode share, vehicle-use intensity and emission coefficient oncarbonemissions of urban passenger transportation in Beijing and Shanghai. The resultsshow that per capita disposable income effect is the leading factor contributing to theincrease in household daily travel carbon emission. Both vehicle-use intensity andpopulation effect are other main drivers that increase household daily travel carbonemissions, while transportation intensity and transportation mode share havesignificant effects on the reduction of carbon emissions. Emission coefficient playsopposite effect on the carbon emissions in Beijing and Shanghai.Thirdly, on the theoretical background of the concept of sustainable consumptionand the theory of planned behavior, this paper uses the structural equation model toinvestigate the determinants on personal purchase intention of electric vehicles inurban China, which exploits the internal causes of the poor sales status of electricvehicles. It is found out that the personal purchase intention of electric vehicles inChina is significantly influenced by attitude toward behavior, perceived behavioralcontrol, relevant policy and the conception of green consumption. In particular, theinfluence of relevant policy on personal purchase intention is insignificant, whichimplies that the existing policy hasn’t gotten its expected effect.Fourthly, this paper takes the passenger transport in China as a case, anddevelops a system dynamics model for policy assessment and transportation carbonemission mitigation potential analysis. It provides the evolutionary path for thechanges of resident’s transportation carbon emission under different scenarios ofpolicy profile, such as the traffic demand management policy, public transit prioritypolicy, road supply capacity expansion policy and so on. Results show that thetransportation carbon emission mitigation will be best achieved by slowing down thegrowth rate of private car ownership, together with accelerating the development ofpublic transportation network and increasing the price of fuel oil.