考虑城市与群体异质的新能源车激励策略有效性研究

doi:10.16097/j.cnki.1009-6744.2025.01.001

摘要/Abstract

摘要： 制定针对城市低碳发展阶段和居民特征的差异化政策，是优化激励策略和促进居民转向绿色出行的重要途径。本文综合考虑空气质量、新能源汽车渗透率和充电设施成熟度等因素，选取4种类别典型城市的异质性居民人群，量化评估新能源车激励策略的有效性；利用隐含狄利克雷分布(LDA)模型分析社交媒体热点数据，设计用户调查问卷；构建潜在类别有序Logit模型(LCOL)定量分析不同城市类别下潜在类别人群对车辆电动化激励策略的偏好程度，辨识不同策略的核心作用群体。结果表明，即时效应激励，例如，限行豁免和大额财政补贴，更能有效提升新能源车接受度较低居民的购车意愿，接受度较高的居民对常态化低额补贴更为敏感。在城市类别维度上，相较新能源车渗透率高的大城市(60%)，渗透率较低的中小城市居民在政策激励下，购买新能源车概率为65%，更具提升潜力；充电设施欠缺的城市，优化充电设施可显著提升居民购车意愿，减少1min寻电时间，概率提高1%，但在充电桩覆盖率高的城市，效果有限；机动车限号的大城市，实施新能源车限行豁免政策时，居民购车概率提高3.5%。定量化的研究结论可为不同城市新能源车推广策略的制定提供决策依据和科学度量参考。

关键词: 城市交通, 低碳激励政策, 潜在类别有序Logit模型, 隐含狄利克雷分布模型, 群体异质性, 城市类别

Abstract: Formulating policies tailored to urban low-carbon development phases and resident characteristics is essential for optimizing incentive structures and promoting green mobility. This study evaluates new energy vehicle (NEV) incentive strategies across four city categories, considering factors such as air quality, NEV penetration, and charging infrastructure maturity. It analyzes social media data using the Latent Dirichlet Allocation (LDA) model and designs user surveys. A Latent Class Ordered Logit Model (LCOL) is employed to assess different urban populations' preferences for vehicle electrification incentives, identifying key impacted groups. The results indicate that immediate incentives, such as driving ban exemptions and significant fiscal subsidies, effectively enhance the purchasing intent of NEVs among less receptive residents. Conversely, more receptive residents respond better to regular, smaller subsidies. Cities with low NEV penetration exhibit a higher probability of purchasing under incentives, highlighting greater potential for improvement. Enhancing charging infrastructure significantly boosts purchasing intentions in infrastructure-deficient cities, with a 1% increase in likelihood for every minute reduction in charging time. However, this effect diminishes in cities with extensive charging networks. In metropolises with vehicle access restrictions, exempting NEVs from these increases purchasing probabilities by 3.5%. These insights guide NEV promotional strategy development in diverse urban settings.

Key words: urban traffic, low-carbon incentives, latent class ordered Logit model, LDA model, population heterogeneity, urban category

中图分类号:

U121

翁剑成, 周慧缘, 张梦媛, 于江波. 考虑城市与群体异质的新能源车激励策略有效性研究[J]. 交通运输系统工程与信息, 2025, 25(1): 2-14.

WENG Jiancheng, ZHOU Huiyuan, ZHANG Mengyuan, YU Jiangbo. Effectiveness of New Energy Vehicle Incentive Strategies Considering Urban and Population Heterogeneity[J]. Journal of Transportation Systems Engineering and Information Technology, 2025, 25(1): 2-14.

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http://www.tseit.org.cn/CN/Y2025/V25/I1/2

参考文献

[1]交通运输部科学研究院.典型城市绿色出行发展研究报告[R]. 北京: 交通运输部科学研究院, 2022.[Academy of Transport, Ministry of Transport. Research report on the development of green travel in typical cities[R]. Beijing: Academy of Transport, 2022.]

[2]KONG N, HARDMAN S. Electric vehicle incentives in 13 leading electric vehicle markets[R]. California:University of California, 2019.

[3]FENG J. Development and impacts of the incentive policies for electric vehicles in China[M]// Singapore:Springer Nature Singapore, 2023.

[4]QIU Y Q, ZHOU P, SUN H C. Assessing the effectiveness of city-level electric vehicle policies in China[J]. Energy Policy, 2019, 130: 22-31.

[5]IVANOVA G, MOREIRA A C. Antecedents of electric vehicle purchase intention from the consumer's perspective:A systematic literature review[J].Sustainability, 2023, 15: 2878.

[6]YANG S, CHENG P, LI J, et al. Which group should policies target? Effects of incentive policies and product cognitions for electric vehicle adoption among Chinese consumers[J]. Energy Policy, 2019, 135(12): 111009.1111009.9.

[7]WANG S, JING W, LI J, et al. Policy implications for promoting the adoption of electric vehicles: Do consumer's knowledge, perceived risk and financial incentive policy matter?[J]. Transportation Research Part A: Policy & Practice, 2018, 117: 58-69.

[8]GENG J, LONG R, CHEN H, et al. Exploring the motivation-behavior gap in urban residents' green travel behavior: A theoretical and empirical study[J]. Resources Conservation & Recycling, 2017, 125: 282-292.

[9]MA S C, XU J H, FAN Y. Willingness to pay and preferences for alternative incentives to EV purchase subsidies: An empirical study in China[J]. Energy Economics, 2019, 81: 197-215.

[10]刘建荣, 郝小妮.考虑出行者异质性的绿色出行行为研究[J]. 华南理工大学学报(自然科学版),2019,47(7):99-104, 120. [LIU J R, HAO X N. Research on green travel behavior considering traveler heterogeneity[J]. Journal of South China University of Technology (Natural Science Edition), 2019, 47(7): 99-104, 120. ]

[11]周雨阳,王沛钰,陈艳艳.考虑自我呈现与环保意识的出行方式选择行为异质性分析[J].交通运输系统工程与信息,2023, 23(3): 30-38. [ZHOU Y Y, WANG P Y, CHEN Y Y. Heterogeneity analysis of travel mode choice behavior considering self-presentation and environmental awareness[J]. Journal of Transportation Systems Engineering and Information Technology, 2023, 23(3):30-38.]

[12] LI Q Q, ZHANG Q, LI Y, et al. The image of China's car hailing policy in the eyes of the public: A social media analytics perspective[J]. Case Studies on Transport Policy, 2022, 10(3): 1651-1660.

[13] DING Y, KOROLOV R, WALLACE W. How are sentiments on autonomous vehicles influenced? An analysis using Twitter feeds[J]. Transportation Research Part C: Emerging Technologies, 2021, 131: 103356.

[14] TRAIN K E. EM algorithms for nonparametric estimation of mixing distributions[J]. Journal of Choice Modelling, 2008, 1(1): 40-69.

[15]乔珂, 赵鹏,文佳星.基于潜在类别模型的高铁旅客市场细分[J]. 交通运输系统工程与信息,2017,17(2):28-34. [QIAO K, ZHAO P, WEN J X. High-speed rail passenger market segmentation based on latent category model[J]. Journal of Transportation Systems Engineering and Information Technology, 2017, 17(2): 28-34.]

[16]陈川. 碳中和研究阶段性成果:乘用车电动化路线图2035研究[R]. 天津: 中汽数据有限公司,2021. [CHEN C. Phased results of carbon neutrality research: 2035 research on the electrification roadmap of passenger vehicles[R]. Tianjin: China Automotive Data Co Ltd, 2021.]

[17] LI W, LONG R, CHEN H, et al. Would personal carbon trading enhance individual adopting intention of battery electric vehicles more effectively than a carbon tax?[J]. Resources, Conservation and Recycling, 2019, 149: 638645.

[18] QIAN L, GRISOLÍA J M, SOOPRAMANIEN D. The impact of service and government-policy attributes on consumer preferences for electric vehicles in China[J]. Transportation Research Part A: Policy and Practice,2019, 122: 70-84.

[19]李文博.电动汽车激励政策的消费者响应及其体系优化研究[D]. 徐州: 中国矿业大学, 2018. [LI W B. Research on consumer response and system optimization of electric vehicle incentive policy[D]. Xuzhou: China University of Mining and Technology, 2018.]

[20] YE F, KANG W, LI L, et al. Why do consumers choose to buy electric vehicles? A paired data analysis of purchase intention configurations[J]. Transportation Research Part A: Policy and Practice, 2021, 147: 14-27.

[21] ROTARIS L, GIANSOLDATI M, SCORRANO M. The slow uptake of electric cars in Italy and Slovenia: Evidence from a stated-preference survey and the role of knowledge and environmental awareness[J]. Transportation Research Part A: Policy and Practice, 2021, 144: 1-18.

[22] QIU Y Q, NG A T S, ZHOU P. Optimizing urban electric vehicle incentive policy mixes in China: Perspective of residential preference heterogeneity[J]. Applied Energy, 2022, 313: 118794.

[23]李礼旭.中国城市居民电动汽车采纳行为的影响因素研究[D]. 广州: 华南理工大学,2021. [LI L X. Research on the influencing factors of electric vehicle adoption behavior of urban residents in China[D]. Guangzhou: South China University of Technology, 2021.]

[24] LI J, JIAO J, TANG Y. An evolutionary analysis on the effect of government policies on electric vehicle diffusion in complex network[J]. Energy Policy, 2019, 129: 1-12.

[25] KONG D, XIA Q, XUE Y, et al. Effects of multi policies on electric vehicle diffusion under subsidy policy abolishment in China: A multi-actor perspective[J]. Applied Energy, 2020, 266: 114887.

[26]周耀东,李宛华,孙昭昱.“尾号限行”策略对居民机动车出行选择的影响研究[J].北京交通大学学报(社会科学版), 2022, 21(2): 100-112. [ZHOU Y D, LI W H, SUN Z Y. Research on the influence of "Tail Number Restriction" strategy on residents' motor vehicle travel choice[J]. Journal of Beijing Jiaotong University (Social Sciences Editing), 2022, 21(2): 100-112

[27] WANG Y, YAO E, PAN L. Electric vehicle drivers' charging behavior analysis considering heterogeneity and satisfaction[J]. Journal of Cleaner Production, 2021, 286: 124982.

[28] ZHANG Y, LIU X, WEI W, et al. Mobile charging: A novel charging system for electric vehicles in urban areas [J]. Applied Energy, 2020, 278: 115648.

[29] LI L, WANG Z, XIE X. From government to market? A discrete choice analysis of policy instruments for electric vehicle adoption[J]. Transportation Research Part A: Policy and Practice, 2022, 160: 143-159.

[30] NYLUND GIBSON K, CHOI A Y. Ten frequently asked questions about latent class analysis[J]. Translational Issues in Psychological Science, 2018, 4(4): 440.

[31] SUN M, SUN X, SHAN D. Pedestrian crash analysis with latent class clustering method[J]. Accident Analysis & Prevention, 2019, 124: 50-57.

[32] MARCUCCI E, GATTA V, SCACCIA L. Urban freight, parking and pricing policies: An evaluation from a transport providers' perspective[J]. Transportation Research Part A: Policy and Practice, 2015, 74: 239249.

[33] POTOGLOU D, WHITTLE C, TSOUROS I, et al. Consumer intentions for alternative fuelled and autonomous vehicles: A segmentation analysis across six countries[J]. Transportation Research Part D: Transport and Environment, 2020, 79: 102243.