*3.4. Extended Analysis: Vector Autoregression*

Infrastructures for facilitating EV sales could be enhanced over years, and factors such as subsidies may influence upon others like chain reactions. Financial aid influences upon the BEV market; meanwhile, the reverse impact could occur. If the feedback relationships exist, vector autoregression should be used to reflect the dynamic relationships [30,33,34]. In this part, we are going to explore if there's reverse causality in EV markets worldwide.

The authors of [8] develop a system dynamics model of China's EV adoption to analyze the effectiveness of EV policies. In the dynamic model, the relationship of government incentives, customers' behavior and infrastructure providers showed complex causality, inspiring us to predict the values of a time series using prior values of another time series. We run a vector autoregression (VAR) model using the three variables mentioned. For the data only contain four periods, we used the first lag of each variable.

We showed the results of the Granger causality test below in Table 10. The results show that all the lagged items do not show significance, even on the 10% level, which means that all lagged variables have no explanatory power for the other variables. The reversed causal relationship is not significant referring to EV adoption; thus, the pooled and panel data regressions we used above are sufficient to explain the causal effect.


**Table 10.** Results of the Granger causality test.

### **4. Conclusions and Policy Implications**

### *4.1. Conclusions*

The purpose of this research is to explore the relationship between government incentives and other related factors to electric vehicle adoption across the main countries with EVs. Using panel data from 2015 to 2018, this paper studies the EV uptake among 13 countries. An econometric model for the uptake is established with eight independent variables and two macro control variables. Five of them showed significantly positive effects on 1% level in different regression models: fast/slow charger density, mandate, purchasing restriction and waiver. Subsidies showed significance only on the 5% level for BEVs. The zero–emission vehicle (ZEV) target set did not have apparent impacts. Descriptive analysis drew the same conclusion that charger infrastructure density predicts best for electric vehicle uptake on the national level.

Fast–charge infrastructures were positively related to EV uptake all the time. In the panel data regression, a 1% increase in the density of fast chargers can cause a 0.63% increase for EV uptake or a 0.36% increase for EV sales. It is reasonable that increasing the number of charging stations contributes to EV adoption, while from another perspective, fast–charger density is a sign that the country invested in public infrastructures in recent years. Slow charge infrastructures were positively significant when using pooled regression, that is to take all years into comparation. 1% increase in the density of slow chargers can cause 0.7% increase for EV uptake. Technology always evolves gradually [35] and even though fast chargers have been common in recent years, slow chargers occupied the market in the primary stage. Those countries with high slow charger density, such as Norway and the Netherlands [18], usually have developed electric vehicle markets for years. Thus, citizens have a higher acceptance for the new mobility tools, which was shown in the national EV uptake.

On the other hand, waiver, mandate and purchasing restriction (for fuel cars) were significantly effective to sales volume though they seemed to have little impact on EV market share, in pooled regression. They all showed significance in the 1% level, and purchasing restriction has the best effect for EV sales, that is a country with the fuel car restriction would have 1.47% more sales than others, with other factors controlled. We believe the three policies were directly intended for the sales volume and because of the mandatory, it should be effective in the short run, thus not enough to effect EV share. Market share is a better indicator for the overall acceptance of EV, requiring the governments' persistent work for years, while EV sales are more sensitive to some powerful mandatory policy in the short term.
