Investigating the factors influencing the uptake of electric vehicles in Beijing, China: Statistical and spatial perspectives

被引:66
|
作者
Zhuge, Chengxiang [1 ,2 ]
Shao, Chunfu [3 ,4 ]
机构
[1] Univ Cambridge, Dept Geog, Downing Pl, Cambridge CB2 3EN, England
[2] Univ East Anglia, Tyndall Ctr Climate Change Res, Norwich NR1 7TJ, Norfolk, England
[3] Beijing Jiaotong Univ, Key Lab Transport Ind Big Data Applicat Technol C, 3 Shangyuancun, Beijing 100044, Peoples R China
[4] Beijing Jiaotong Univ, MOE Key Lab Urban Transportat Complex Syst Theory, 3 Shangyuancun, Beijing 100044, Peoples R China
基金
欧洲研究理事会; 中国国家自然科学基金;
关键词
Electric vehicle (EV); Purchase behaviour; Influential factors; Multinomial logit (MNL) model; Spatial analysis; ALTERNATIVE-FUEL VEHICLES; CONSUMER PURCHASE INTENTIONS; WILLINGNESS-TO-PAY; SOCIAL-INFLUENCE; MARKET PENETRATION; EARLY ADOPTERS; MORANS-I; CHOICE; AGENT; ADOPTION;
D O I
10.1016/j.jclepro.2018.12.099
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Electrifying urban transportation through the adoption of Electric Vehicles (EVs) has great potential to mitigate two global challenges, namely climate change and energy scarcity, and also to improve local air quality and further benefit human health. This paper was focused on the six typical factors potentially influencing the purchase behaviour of EVs in Beijing, China, namely vehicle price, vehicle usage, social influence, environmental awareness, purchase-related policies and usage-related policies. Specifically, this study used the data collected in a paper-based questionnaire survey in Beijing from September 2015 to March 2016, covering all of the 16 administrative regions, and tried to quantify the relative importance of the six factors, based on their weights (scores) given by participants. Furthermore, Multinomial Logit (MNL) models and Moran's l (a measure of global spatial autocorrelation) were used to analyse the weights of each factor from statistical and spatial perspectives, respectively. The results suggest that 1) vehicle price and usage tend to be more influential among the six factors, accounting for 32.3% and 28.1% of the importance; 2) Apart from the weight of social influence, the weights of the other five factors are closely associated with socio-demographic characteristics, such as individual income and the level of education; 3) people having similar attitudes towards vehicle usage (Moran's l = 0.10) and purchase restriction (Moran's l = 0.14) tend to live close to each other. This paper concludes with a discussion on applying the empirical findings in policy making and modelling of EV purchase behaviour. (C) 2018 Published by Elsevier Ltd.
引用
收藏
页码:199 / 216
页数:18
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