*3.2. Basic Results*

We conducted a simple pooled regression model of all the countries over the years to describe the effectiveness of different policies and other influential factors. After performing the White test, we verified that the data conform to the OLS homoscedasticity assumption, that is, the variance of the errors in the regression models is constant, which means that the model was well defined. Due to the relatively large number of explanatory variables, in order to assess the possible effects of

multicollinearity, the model was tested for variance inflation factor (VIF) [27]. The results are shown in Table 6. The maximum value of the variance expansion factor is 5.12 and the average value is 2.54, all less than the commonly used threshold of 10. Thus, it can be considered that there is no multicollinearity problem in the pooled model.

**Figure 3.** Market share and numbers of slow and fast chargers per million population in 2018.

**Figure 4.** Market share and numbers of all public chargers per million population in 2018.



Using market share along with sales volume can partly eliminate the interference of external macro factors, such as income growth and economic growth [22]. We still controlled two macro factors in our regression model in order to measure the impact of macro factors. In the regression with EV share as dependent variable (the left column in Table 7), only slow-charger density was positively significant while in the regression of EV sales, mandate, waiver, fast-charger density and purchasing restriction policies showed a significant positive impact. The significant variables vary a lot if changing the dependent variable, but could be explained as follows.

For EV promotion, Europe usually started early in the history for higher citizens' environmental awareness and thus showed a higher EV uptake. In the early days, only slow chargers other than fast chargers were available, so the more public slow chargers, the more it showed that the countries took earlier actions, which is highly related to the high market share nowadays. On the other hand, EV sales are more related to the size of the market, as is shown in Figure 1, meaning that China and the U.S. performed best. Purchasing restriction for traditional cars is the policy used only in China and is proved to be significantly effective [22]. Mandate policies were also used mainly in China and North America, and the large market scale makes the mandate policy remarkable. Waivers on access restrictions are the convenience EVs brought to the driver, especially in densely populated cities. It is good to know that the policy is effective in the history of promoting EV sales volume.

An explanation for the insignificancy of subsidies and other non-financial policies is that most of the countries offered high subsidies and other preferential treatments over the years; thus, those do not make a significant difference now. We will use panel data to analyze the impact thoroughly in the next section.


**Table 7.** Policy impact on total EV market using pooled regression.

\*\* *p* < 0.01, \* *p* < 0.05.

After performing the Hausman test mentioned in Section 2, the result of test showed that the *p*-value is small (less than 0.05) and rejected the null hypothesis, which is that the preferred model is random effects. The result shows significant differences between the coefficients for the fixed effects and random effects model. Therefore, we used the fixed effects model in the study with EV market share and EV sales as dependent variables, respectively.

In Table 8, regression coefficients are tabulated based on four models, in which seven and nine independent variables are utilized, respectively. Compared to pooled regression, the panel model controlled the country fixed effect; thus, we deleted purchasing restriction for highly multicollinearity with the dummy variable China, the only country conducted the restriction policy. Moreover, we will not include target in the regression since the target set does not change over time, meaning that it is not suitable for panel models.


**Table 8.** Policy impact on total EV market.

\*\* *p* < 0.01, \* *p* < 0.05.

By gradually introducing a socioeconomic factor in the later model, the possible impact of the factor can be observed more clearly. Referring to Table 8, the adjusted R square value for each model is above 0.90. That is, the confidence in explaining the cross-country EV share/sales is above 90% for the variables tested.

Most variables are not significant referring to Table 8. Only the density of fast chargers is significantly positive for all models. The parameter means that 1% increase in density of fast chargers can cause a 0.63% increase for EV uptake or a 0.36% increase for EV sales. Our study uses data from 2015–2018, when fast-charging infrastructures rather than slow chargers were constructed rapidly worldwide and contributed considerably to the global EV development. Besides, the result that other policy incentives are not significant may also due to the time period we studied. In the post subsidy era, policies have experienced a declining marginal effect. Taking the waiver on access restrictions as an example, we see from the pooled regression that the policy did show some positive impact on EV sales in the history; however, the effect was declining over the years, because HOV and bus lanes got crowded as the number of electric vehicles increased. In other words, the benefits diminished across time for some non-financial policies. According to the literature, in post subsidy era, it is chargers' density and fuel price that really counts [8]. In this study, we will shed more light on the effect of charging infrastructures.
