Examining the spatial mode in the early market for electric vehicles adoption: evidence from 41 cities in China

ABSTRACT

This study explores the spatial mode of electric vehicle (EV) adoption in the early market of 41 key cities in China among which there is a high spatial heterogeneity . A spatial econometric model is established to illustrate the relative importance of regional variables on EV adoption. Most independent variables have significant direct positive effects on EV adoption. Specifically, the number of charging piles and per capita income had the greatest effects, followed by population density and university degree. These results indicate that areas with dense populations, higher education levels, higher income, and a complete charging infrastructure tend to dominate the early market for EVs. In comparison, the indirect effects of most independent variables are not significant, except for the population density and number of charging piles. Finally, this study concludes with the application of empirical results to policymaking.

KEYWORDS:

• Spatial mode
• early market
• electric vehicle adoption
• china
• transformation of traffic sustainability

Acknowledgments

The authors gratefully acknowledges the financial support of National Natural Science Foundation of China, Grant/Award Numbers: 71974083, 71673238, 71904067; Ministry of Education Humanities and Social Sciences Planning Fund, Grant/Award Number: 19YJA790024; Jiangsu Social Science Fund, Grant/Award Number: 18EYB014; Jiangsu Education Department, Grant/Award Number: 2018SJZDI089, Research Support Project for Doctoral Degree Teachers of Jiangsu Normal University of China (Grant 18XWRS017).

Data Availability Statement

The data that support the findings of this study are openly available in figshare at http://doi.org/10.6084/m9.figshare/14453526.v1.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. In November 2013, a list of the first batch of cities or regions for the promotion of EVs, including 28 cities or regions such as Beijing, was published by the Ministry of Finance, the Ministry of Science and Technology, the Ministry of Industry and Information Technology and the National Development and Reform Commission. In February 2014, the four Ministries and Commissions approved the second batch of lists of promoting cities, which included 12 cities or regions.

2. According to the Notice on Financial Support Policy for the Promotion and Application of New Energy Vehicles in 2016–2020 (hereinafter referred to as the Notice of the Four Ministries and Committees) published by the Ministry of Finance, the Ministry of Science and Technology, the Ministry of Industry and Information Technology and the National Development and Reform Commission on April 22nd, 2015, the direct financial subsidy standard for EVs will gradually be phased out based on the effect of energy saving and emission reduction and considering the factors of production cost, scale effect, and technological progress among others. Specifically, the subsidy standard for EVs in 2017–2018 dropped by 20% since 2016; the subsidy standard in 2019–2020 dropped by 40% on the basis of 2016; the direct financial subsidy was completely abolished by 2021.

3. The reason for the variable is that researchers believe that if the length of the common boundary of the spatial units is different, the intensity of its spatial effect may be different, so it is necessary to incorporate the length of the common boundary into the calculation process of the spatial weights to make them more accurate, and of course, it is common to set the boundary sharing effect coefficient b to zero(Wang 2013).

4. For this analysis, weak correlations are considered as those which are between 0.2 and 0.4; moderate correlations are those which are between0.4 and 0.6; and strong correlations are those which are greater than 0.6.

5. Freund et al. (2006) showed that if $0<VIF<10$, multicollinearity is acceptable; $10\le VIF<100$, implies multicollinearity; and$VIF\ge 100$, implies strong multicollinearity.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [71974083]; National Natural Science Foundation of China [71673238]; National Natural Science Foundation of China [71904067]; Ministry of Education Humanities and Social Sciences Planning Fund [19YJA790024]; Jiangsu Social Science Fund [18EYB014]; Jiangsu Education Department [2018SJZDI089].