*2.1. Data Description*

EV market share and sales data were collected from International Energy Agency [2], containing 13 countries over 4 years (2015–2018). These countries are Canada, China, France, Germany, India, Japan, Korea, the Netherlands, Norway, Portugal, Sweden, the United Kingdom and the United States. Electric vehicles in the 13 countries accounted for over 90% worldwide in recent years. The macroeconomic data were collected from the website of World Bank. Details of variables and data sources are provided in Appendix A.

Policies we selected in the paper are considered as important ones to EV adoption or have conflicting results in related literatures. All these countries mentioned above are adopting targeted zero emission vehicle (ZEV) regulations and incentives to accelerate the rate of deployment. Mandate and purchasing restriction are dummy variables, indicating whether it is implemented. The other non-financial policies are categorical variables evaluated by policy intensity. The wider the policy is applied in the country, the higher the scores it gets. Details about how we quantify the effect of non-financial policies by assigning different values are provided in Appendix B.

Table 3 provides the financial and non-financial policies we have considered in our model. Fuel standard is the level of fuel standards/regulations drivers need to meet and has been raised over the years to meet air quality standards and greenhouse gas emission reduction goals. ZEV (Zero emission vehicle) mandates on manufacturers can also result in increased model availability in the market. Insufficient model options can deter consumers from purchasing EVs even after adequate emphasis on consumer incentives and charging infrastructures.

Among the non-financial policy incentives, the variable target evaluates the governments' ambition to promote EVs. Different countries released a different target year to ban internal combustion engine (ICE) sales and achieve 100% ZEV sales. For example, Norway took the lead and promised to replace all fuel cars by 2025 [18], followed by some other European countries, such as Denmark and Iceland, announcing 100% ZEV by 2030. The Netherlands and the UK promised to replace all traditional vehicles later than 2040. The earlier the 100% ZEV target year is, the higher scores the variable target will get. We expected a positive relationship between the goal and EV uptake. [19] has highlighted the importance of policy goals in the UK and Germany to decrease GHG emissions; however, [20] found that the EV climate mitigation strategy was not effective in the United Kingdom. Despite the conflicting conclusions, the policy goal will be considered and tested in our model.


**Table 3.** Financial and non-financial variables.

For better accuracy, after using dummy variables to describe the policies, we collected numerical dollar values to describe the purchasing incentives (usually are tax credits or purchasing subsidies). The numerical incentives are used in the extended analysis part where BEVs and PHEVs are discussed, respectively. BEVs, which totally use electricity were able to receive higher subsidies because the level of subsidies was usually decided by battery capacity across the countries. In this study, we assigned the highest level of money (tax credits or subsidies) a passenger vehicle can receive from the central government to the variable subsidy. Though we did not use median or average values because of data

unavailability, the maximum value can still reflect the intensity of the subsidy, since both manufacturers and consumers will strive for the maximum subsidy. It is also a reason that a government giving a higher upper limit for subsidy standard, tends to provide a higher medium level of subsidies, that is to say, the maximum value of standard should be highly related to the average subsidy consumers get. Furthermore, we used linear regression for analysis; thus, we care much more about the relative relationship between countries and the difference between years, than the absolute value of subsidies. Thus, using the highest level of subsidy standard is reasonable. Besides, the subsidy itself is based on many parameters, and each country or even each province has different standards. For a global scope research involving 13 countries and 4 years, it is too complicated to refine and calculate subsidies and we only divided it into two categories: BEV and PHEV subsidies, which are considered as two variables and will be applied to BEV and PHEV sales, respectively.

For BEVs, Korea and China offered the highest subsidy in Asia. The subsidy in Korea was up to 10,900 dollars and was the highest of all countries over the years, although the standard of getting total subsidy was not easy to meet. China offered a subsidy up to 8900 dollars before 2018, slightly higher than the federal tax credits (USD 7500) in the United States. Norway was the most generous country in Europe to subsidize the promotion of electric vehicles with the highest level of 10,300 dollars over the 4 years. Except for France and Sweden, the other European countries, such as Germany and Portugal, gave lower than average subsidies (USD 6000) to EV consumers. Among all the countries listed in our study, India gave the least subsidies of up to 2000 dollars for BEVs, lower than Portugal (USD 2400), which ranked the second last.

For PHEVs, subsidies were much lower. The UK with USD 7100 subsidies ranked first and Norway with USD 7000 ranked second. Though Korea offered the highest subsidy for BEVs, PHEVs could only get the maximum of USD 860 dollars. When BEVs and PHEVs are considered as a whole, we used the weighted average of subsidies by the sales volume. Appendix B shows the value of some non-financial policy and numerical variables, such as subsidies and charger density, in 2018.

Table 4 below is the summary statistics, which contains the characteristics of all variables. The variables fast/slow chargers per million population means the number of chargers per million people in the country, which can also be presented by chargers' density. EV share is the proportion of electric vehicle sales in total vehicle sales. We implemented a logit transformation to normalize the distribution of EV share because the data are skewed to the right [21]. After the transformation, the data showed a normal distribution and validated the ordinary least squares (OLS) regression we plan to use. This is one of the necessary conditions for a good estimation result, not a sufficient condition. OLS regression also needs to satisfy the assumptions of homoscedasticity of the error term, no multicollinearity, etc. We have tested them below in the study and all results are satisfactory. To solve traffic congestion and air pollution problem, China adopts administrative orders to control the supply of vehicle licenses, while the restrictions is not applicable for EVs [22]. To identify this effect, the variable purchasing restriction is considered in the pooled regression in the basic results part.



In Table 5, correlation coefficients are provided. Between a pair of independent variables, the correlation between slow chargers and fast chargers shows the largest cross-correlation coefficient of 0.804. Thus, there should be no severe linear correlations during regressions.


**Table 5.** Correlation coefficients of variables.

\* *p* < 0.05, \*\* *p* < 0.01, \*\*\* *p* < 0.001.

For an overview of the electric vehicle markets, we used cross-sectional data in 2018 to draw a worldwide map of the sales and market share of EVs as shown in Figure 1. Though both crediting to the governments' effort in promotion, market shares and sales give different information on the countries' EV market. EV sales is more related to the market volume given that China and United States made remarkable achievements, while market shares showed the overall recognition of the citizens for EVs; thus, European countries showed higher EV share, especially Norway, the highest of all. Later in the basic result part, we will interpret the difference with statistical models.

**Figure 1.** EV sales and market share worldwide overview. (**a**) EV sales in 2018; (**b**) market share of EVs in 2018.
