为加快电动汽车的推广使用，基于混合Logit模型，应用在北京市收取的问卷数据开展模型实证研究以分析电动汽车购买意愿。首先，从个人属性、车辆属性、充电基础设施、购车与用车激励政策4方面考虑国内消费者电动汽车购买偏好影响因素，并通过网络问卷调查和陈述性偏好实验（Sated Preference, SP）获取相关数据。然后，分别使用混合Logit模型和多项Logit模型（Multinominal Logit Model, MNL）定量分析各类因素对消费者购置意愿的影响，并对比两种方法的拟合优度，结果显示混合Logit模型有更好的拟合优度，可以避免样本间异质性导致的误差。最后，通过对各类因素的参数估计结果、显著性水平及成分效用值的分析可知：除购买价格外，充电服务设施以及车辆牌照制度对电动汽车购买意愿有较为明显的影响；针对电动汽车的购车财政补贴对电动汽车的购买意愿无显著影响，因此补贴政策效果有限，有待进一步调整；电动汽车牌照的排队等候时长对电动汽车的购买意愿有显著影响，未来可以进一步考虑将传统燃油车的部分牌照配额转移至电动汽车。
To accelerate the promotion and use of electric vehicles, based on the Mixed Logit model, an empirical analysis using data collected from Beijing was conducted to analyze the purchase intention of electric vehicles. Firstly, factors which impact domestic consumers' electric vehicle purchase preference were considered from four aspects: personal attributes, vehicle attributes, charging infrastructure and incentive policies. The relevant data was obtained through a network questionnaire survey and a Sated Preference (SP) experiment. Then, the Mixed Logit model and Multinomial Logit model (MNL) were used to quantitatively analyze the influence of various factors on consumers' purchase intention respectively, and the goodness of fit of the two methods was compared. The results showed that the Mixed Logit model had better goodness of fit and could avoid the error caused by the heterogeneity between samples. Finally, the parameter estimation results, significance levels and component utility value of various factors show that besides the purchase price, charging service facilities and vehicle license rules have a significant impact on the purchase intention; the financial subsidies for electric vehicles have no significant impact on the purchase intention of electric vehicles, so the subsidy policy needs to be further adjusted; the waiting time of the license plate of electric vehicles has a significant impact on the purchase intention of electric vehicles, so part of the license plate quota of traditional fuel vehicles could be considered being transferred to electric vehicles in the future.
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