A QUANTITATIVE STUDY ON BEV AND PHEV ADOPTION IN THE NETHERLANDS

Brenda Janssen Student number: 358527 MSc Business Information Management 2015/2016 June 16th 2016

Coach: Micha Kahlen, MSc Co-reader: Dr. Ksenia Koroleva Brenda Janssen – June 16th 2016

The author declares that the content presented in this master thesis is original and that no sources other than those mentioned in the text and its references have been used in creating the master thesis. The copyright of this master thesis rests with the author. The author is responsible for its contents. Rotterdam School of Management, Erasmus University, is only responsible for the educational coaching and cannot be held liable for the content.

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Acknowledgements First and foremost, I would like to thank my coach Micha Kahlen for his advice, guidance and specially his quick and honest feedback throughout the thesis process. Secondly, I would like to thank my co-reader Ksenia Koroleva for her time and feedback on, among others, the statistical challenges I had to tackle. Also, I would like to thank my Stedin coaches Baerte de Brey and Henk Fidder for their time, their feedback, for sharing their knowledge and their challenging questions which kept me focused. Furthermore, I would like to thank Harm-Jan Idema for his time, ideas and insights in the ever changing world of electric vehicles.

I am grateful to my parents for their unconditional support during the past years. Finally, special thanks go to Sander who has supported me throughout the thesis trajectory and was always willing to be my sparring partner and motivator.

Brenda Davidse-Janssen June 16th 2016

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Abstract Purpose: Over the past two years the amount of registered plug-in hybrid (PHEV) and battery electric vehicles (BEV) in The Netherlands has more than doubled, inter alia, due to subsidies and fiscal benefits provided by the Dutch government. However, it is still rather uncertain which other factors, apart from financial incentives, drive (PH)EV adoption. Besides, what role does the degree of urbanization play? DSOs need an estimate on how the corresponding demand for electricity will develop, in order to prepare for, for example grid infrastructure. On the other hand, governments need insights in the measures that should be taken in order to reach the aim of an emission free Dutch fleet by 2050. Furthermore, cities that face severe air pollution need insights in how to decrease pollution as effectively as possible. Thus, this study will answer the following research question: What demographical, municipal and context factors influence the adoption of (Plug-in Hybrid) Electric Vehicles for very highly urbanized to non-urban municipalities?

Methods: Adoption likelihood for plug-in hybrid and battery electric vehicles is analyzed based on demographic, municipal, context and urban drivers. The multiple regression model is based on factors such as age, standardized spendable household income, the number of persons per household and the number of cars per household, the ability to request a charging pole, the number of public charging poles and the degree of urbanization. Whereas data on demographic, context and urban drivers has been obtained from the Centraal Bureau voor de Statistiek, the municipal data has been made available through APPM. The purchase likelihood, measured by the number of BEVs and PHEVs out of the total number of passenger cars per municipality, is estimated for the short-term. Finally, this estimation will be applied to the municipality of Rotterdam in order to forecast both BEV and PHEV adoption until 2020.

Findings: For BEV adoption, standardized spendable household income is the most important factor (β=0.310), followed by the relative presence of 40 to 50 year olds (β=0.124). The number of public charging poles is an important influencing factor as well (β=0.339), nevertheless its relation is somewhat ambiguous as it represents an endogeneity problem. Standardized spendable household income is an even more important factor for the purchase likelihood of plug-in hybrid vehicles (β=0.3459), followed by the number of cars per household (β=0.328), income from an own enterprise (β=0.196), and the relative presence of 50 to 60 year olds (β=-0.195). The difference in the importance of household income is likely caused by the relatively higher purchase price of PHEVs. While the degree of urbanization is not found significant for BEV adoption, it appears to have a positive association with PHEV adoption (β=0.275). Furthermore, for highly urban municipalities, the number of cars per household is an important factor for both BEV and PHEV adoption. The number of persons per household is found to be positively related to BEV adoption for moderately and little urban municipalities.

Implications: Local governments can predict based on the above described findings which neighborhoods will most likely see the highest BEV and PHEV adoption rates. This could give insights into the level of air quality or could clarify which inhabitants and neighborhoods might require additional incentives to stimulate (PH)EV purchase likelihood. For DSOs these findings can assist in the prioritization of investments related to infrastructure upgrades: neighborhoods with relatively higher incomes are more likely to adopt a BEV or PHEV and thus might demand a higher number of charging poles. Car manufacturers can use the

3 Brenda Janssen – June 16th 2016 findings of this study to target the right consumer segment and look ahead for other segments that might be interested in cheaper or more advanced versions. Finally, this study gives insights in how (PH)EV adoption will develop, ceteris paribus, and what additional incentives or technological improvements might be needed in order to achieve acceptable adoption rates. In terms of theoretical implications, future research should continue to remain BEV and PHEV adoption separated and continue to take the degree of urbanization into account.

Limitations: The focus of this study is on demographic, municipal, context and urban factors influencing the purchase likelihood of BEVs and PHEVs. Consequently, the scope of this research is limited and cannot aim to cover the full range of predictive factors. Future research could incorporate the effects of the changing fiscal and financial benefits, qualitative factors such as range anxiety or charging behavior and fast charging infrastructure. Finally, the forecasts for the municipality of Rotterdam have been based on assumptions due to the absence of growth figures.

Keywords: Battery Electric Vehicle, Charging Infrastructure, Context Drivers, Degree of Urbanization, Demographic Drivers, Distribution System Operator, Forecast 2020, Non-financial Municipal Incentives, Plug-in Hybrid Vehicle

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Table of content Acknowledgements ...... 2 Abstract ...... 3 Introduction ...... 7 Hypothesis Development ...... 10 2.1 Demographic drivers ...... 10 2.2 Municipal drivers ...... 11 2.3 Context drivers ...... 12 2.4 Degree of urbanization ...... 13 Research Design ...... 15 3.1 Methodology ...... 15 3.2 Sample & data ...... 16 3.2.1 Sample ...... 16 3.2.2 Dependent variable: percentage battery electric and plug-in hybrid vehicles ...... 16 3.2.3 Independent variables: demographic, municipal, context and urban drivers ...... 18 Results ...... 21 4.1 Descriptives ...... 21 4.2 Estimating BEV adoption ...... 22 4.2.1 Estimating demographic drivers on BEV adoption ...... 22 4.2.2 Municipal drivers ...... 24 4.2.3 Context drivers ...... 25 4.2.4 Degree of urbanization ...... 27 4.3 Estimating PHEV adoption ...... 28 4.3.1 Estimating demographic drivers on PHEV adoption ...... 28 4.3.2 Municipal drivers ...... 31 4.3.3 Context drivers ...... 32 4.3.4 Degree of urbanization ...... 33 4.4 Case study: Rotterdam ...... 35 4.4.1 BEV adoption in Rotterdam ...... 35 4.4.2 PHEV adoption in Rotterdam ...... 37 Discussion ...... 39 5.1 Practical implications ...... 39 5.2 Future developments ...... 40

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5.3 Theoretical implications ...... 41 5.4 Limitations and future research ...... 41 Conclusion ...... 43 Bibliography ...... 45 Appendices ...... 48 A. Overview of all municipalities ...... 48 B. Initiatives by local governments ...... 51 C. Estimating (PH)EV adoption ...... 61 C.1 Estimating demographic, municipal and context drivers on BEV adoption ...... 61 C.2 Estimating demographic, municipal and context drivers on PHEV adoption ...... 62 D. Estimating BEV adoption per urban degree ...... 63 D.1 Highly urban municipalities (code 4 & 5) ...... 63 D.2 Moderately urban municipalities (code 3) ...... 64 D.3 Little urban municipalities (code 2) ...... 65 D.4 Non-urban municipalities (code 1) ...... 66 D.5 Moderating effect of urbanization for BEV adoption ...... 67 E. Estimating PHEV adoption per urban degree ...... 68 E.1 Highly urban municipalities (code 4 & 5) ...... 68 E.2 Moderately urban municipalities (code 3) ...... 69 E.3 Little urban municipalities (code 2) ...... 70 E.4 Non-urban municipalities (code 1) ...... 71 E.5 Moderating effect of urbanization for BEV adoption ...... 72 F. Case study: Rotterdam ...... 73 F.1 Predicted BEV adoption per district of Rotterdam ...... 73 F.2 Predicted PHEV adoption per district of Rotterdam ...... 74

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Introduction Starting in the 90’s, politics have become increasingly aware of the changing climate and the severe consequences this can have for the planet. This has led to the promotion of, among others, alternative energy sources and (semi-)electric vehicles. Moreover, the European Union has joined forces and has established targets related to air quality and any subsequent punishments. Interestingly, back in 2012 this specific European target related to air quality has stimulated the municipality of Amsterdam to promote electric transport. Other municipalities such as Groningen who did not have this urgency of improving air quality needed to promote electric driving based on different arguments such as a reduction of the dependency on crude oil (Linnenkamp, 2012).

The Netherlands is one of the frontrunners when it comes to EV uptake and even saw the largest increase of alternative fuel vehicle registrations in the last quarter of 2015. However, the EV uptake in The Netherlands is still limited to urban areas. While cities could simply be the frontrunners when it comes to EV uptake, it could also be the case that different factors influence the (PH)EV adoption of different degrees of urbanization. Additionally, the total number of newly registered electric vehicles represents a fairly small share of the total number of newly registered vehicles (9.7% in 2015) (RVO, 2015). Yet, the amount of registered electric vehicles has more than doubled over the past two years (fully hybrid vehicles excluded). With an increase from nearly 30.000 vehicles by the end of 2013 to slightly over 90.000 vehicles by December 31st 2015, the amount of public and semi-public charging poles has also nearly tripled. The Dutch government envisions an increase in the number of electric vehicles to 200.000 by 2020 and 1 million by 2025 (ACEA, 2016; Agentschap NL, 2011; RVO, 2015).

As can be concluded from figure 1, changing fiscal benefits have led, up until now, to increased BEV and PHEV adoption in the last two months of 2013 and 2015 (Van Mil et al, 2016). However, as stated in the Autobrief II (2015), the Dutch government aims to increase the fiscal surcharge over the coming years for hybrid vehicles. Hybrid vehicles will also be subject to a graduate elimination of the vehicle tax exemption that has been in force in recent years. On the other hand, the Dutch government aims for a Dutch fleet that consists solely of emission free vehicles by 2050. This leads to the question which other factors, besides subsidies and fiscal benefits, drive the adoption of battery electric and plug-in hybrid vehicles. And, which consumers still value a BEV or PHEV over an ICEV when oil prices are relatively low and thus do not differ largely from low energy tariffs?

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Figure 1: Total new registrations of BEVs and PHEVs in The Netherlands (RVO 2016)

For distribution system operators an increase in the number of plug-in hybrid and battery electric vehicles means an increased demand for public charging poles and an increased use of private charging poles, and consequently an increased demand for electricity during peak hours. However, since the electricity grid is limited to a maximum capacity of electricity that it can provide, higher demand raises the necessity for either an investment in capacity increase of the infrastructure, or a more even distribution of demand. To spread these significant investments over a larger period of time it would be helpful to predict which cities, neighborhoods or areas are most likely to see increases in the adoption of (PH)EVs during the coming years (RVO, 2015; Siskens, 2015).

Although governments have subsidized and supported (PH)EV adoption in the past years, this financial support will be limited for PHEVs in the coming years. On the other hand, we see that technology is progressing as for example a company such as Tesla now is planning to offer its newest model – model 3 – for $35,000 (Tesla Motors, 2016). Furthermore, charging infrastructure is developing and companies such as FastNed are also offering fast charging facilities. However, these factors are not the sole drivers of (PH)EV adoption as adoption might also be influenced by specific demographic or context factors. Therefore, the objective of this study is to investigate what other factors, apart from financial benefits, are associated with BEV or PHEV adoption. This leads to the following research question:

What demographical, municipal and context factors influence the adoption of (Plug-in Hybrid) Electric Vehicles for very highly urbanized to non-urban municipalities?

For governments worldwide, and especially the Dutch government and local governments, it is valuable to know which factors are associated with BEV and PHEV adoption and to what extent they can be associated with increases or decreases of (PH)EV adoption. Local governments of urban municipalities that face severe air pollution will be interested in knowing which neighborhoods are likely to see air improvements in the short-term due to low-emission vehicles and which neighborhoods will require other

8 Brenda Janssen – June 16th 2016 measures. Moreover, for distribution system operators it is important to anticipate increases in electricity demand so that they can prepare for the accompanying infrastructure upgrades. Being able to predict when and where these increases will occur can assist in prioritizing budgets and maintenance. Finally, the results of this study might be helpful for electric car manufacturers to decide on which consumer segments can best be targeted.

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Hypothesis Development While the first electric vehicle has been introduced over a hundred years ago, its popularity has risen during the past few decades. The main advantages of (PH)EVs include reduced emissions such as carbon monoxide and efficiency gains due to a more efficient energy use. However, electric vehicles require a larger financial investment compared to ICVs and resale values or costs for replacing the electric batteries are unclear. Furthermore, its limited driving range and the uncertainty regarding the availability of charging stations also represent barriers to adoption (Daziano and Chiew, 2012).

Due to the increased popularity of (PH)EVs that has been partially fueled by governmental incentives such as tax rebates, studies have been conducted by, among others, Gallagher and Muehlegger (2011), Diamond (2009), and Sierzchula (2014) on the drivers and barriers related to (hybrid) electric vehicle adoption, the effectiveness of governmental incentives and other psychological factors. These studies have indicated that driving range, financial incentives, charging time and (PH)EV price are the main stimulators for the adoption of both hybrid and battery electric vehicles.

In general three categories of factors that influence hybrid and battery electric vehicle adoption can be found according to Sierzchula et al. (2014). Firstly, the technology category is related to aspects of the vehicle itself such as battery costs, driving range and charging time. Due to the fact that the technology of the battery is still developing and economies of scale will most likely arise in the coming years due to investments of car manufacturers in electric vehicles, this category will be omitted from the analysis. Secondly, customer characteristics or demographics such as education, income and the degree of environmentalism are also correlated to the likelihood of purchasing an electric vehicle. Finally, context factors influence adoption rates. This category includes factors such as fuel and energy prices, or the availability of charging poles. The availability of non-financial governmental incentives seems to be included in the technological category as these often reduce the total cost of ownership for the vehicle’s owner. However, as these incentives are not related to car-specific aspects such as the range and costs, a separate category for the governmental incentives will be created for the purpose of this study.

2.1 Demographic drivers In various studies the effect of consumer characteristics is not taken into account. Sierzchula et al. (2014), for example, show that socio-demographic variables such as education and income are not necessarily a good predictor of adoption levels when data of a large variety of countries is aggregated. Although these groups of consumers might be more likely to purchase an electric vehicle, they represent a too small portion of the entire population. However, younger or middle aged respondents have an increased EV- orientation as found by Hidrue et al. (2011) and Gallagher and Muehlegger (2011). In contrast, the Dutch report ‘Niet autoloos maar auto later’ (2014) shows that young adults aged between twenty and thirty contribute to a decreasing growth of mobility. A possible reason for this could be the increased popularity of car sharing. This report also states that higher educated consumers are more likely to adopt an electric vehicle, which might be due to a higher standardized income or the availability of green lease cars (KiM, 2014).

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Therefore: H1a: Consumer age is negatively associated with (PH)EV purchase likelihood

Contrary to expectations, both the variables income and multicar household have a negative impact on the adoption of an (PH)EV according to Hidrue et al. (2011), although this does not show any statistical significance. Since one would expect that with increasing income at least the price barrier would be lowered, this effect is rather counterintuitive. In contrast, per-capita income is significantly correlated with the sales of hybrid vehicles as concluded by Gallagher and Muehlegger (2011). When it comes to the number of cars per household, one might expect that a larger number of passenger cars reduces the range barrier. This is due to the fact that the likelihood of being able to cover large distances is enlarged by the availability of multiple cars. This line of thought results in the following two hypotheses:

H1b: Household income is positively associated with (PH)EV purchase likelihood And, H1c: The number of passenger cars in a household is positively associated with (PH)EV purchase likelihood

Larger households seem to have a preference for vans and SUVs over (PH)EVs according to Musti and Kockelman (2011). This is possibly caused by the fact that both vans and SUVs offer larger cargo space and seating capacities compared to for example a hatchback. Furthermore, larger households might be faced with other costs and consequently prefer a passenger car in a lower price range. Thus:

H1d: The number of persons (both adults and non-adults) in a household is negatively associated with (PH)EV purchase likelihood

Building upon the hypothesized positive association of household income with (PH)EV purchase likelihood, it is logical that households with public aid are the least likely to purchase a (PH)EV compared to households with income from other sources.

H1e: Households with income from either an own enterprise or from labor are more likely to adopt a (PH)EV than households with a transfer income

2.2 Municipal drivers In the context of municipal drivers, mainly the effectiveness of financial incentives provided by the government has been widely studied. For example, both the generosity and the type of tax incentive affect (PH)EV adoption according to Gallagher and Muehlegger (2011). This is confirmed by, among others, Sierzchula et al. (2014), Diamond (2009), Bočkarjova et al. (2013), who state that financial incentives are positive and significant in predicting adoption rates for electric vehicles. Due to the fact that a (PH)EV often represents a larger investment than an ICV, subsidies or fiscal benefits lower this investment and thereby also lower the barrier to adoption. However, as the Dutch government is planning to reduce fiscal and

11 Brenda Janssen – June 16th 2016 financial benefits for plug-in hybrid and battery electric vehicles over the coming years, it might be interesting to study which non-financial initiatives by municipalities are associated with (PH)EV adoption.

Currently research on the effectiveness of non-financial incentives is limited and does not necessarily apply to Dutch conditions. For example, the effect of access to HOV lanes in the United States of America has been studied by Diamond (2009) and Gallagher and Muehlegger (2011), who found conflicting results. Nevertheless, Dutch (PH)EV drivers are not granted this access to HOV lanes. In contrast, various Dutch organizations such as ElaadNL have provided public charging poles and other (PH)EV drivers have been able to request a charging pole in the public area at their municipality. This leads to the assumption that a consumer who has the guarantee that a public charging pole will be placed in his direct surroundings, is more likely to adopt a (PH)EV than a consumer in a municipality that does not provide this guarantee. Resulting in the following hypothesis:

H2a: The possibility to request a charging pole in the public area of a municipality is positively associated with (PH)EV purchase likelihood in that same municipality

In the context of sustainability, municipalities might expand their fleet with (semi-)electric vehicles. This could possibly encourage inhabitants of that municipality to also purchase a (PH)EV. This line of thinking assumes that inhabitants perceive their municipal councilors as trendsetters. Therefore, the final hypothesis related to municipal drivers is the following:

H2b: (PH)EV adoption of a municipality is positively associated with (PH)EV purchase likelihood in that same municipality

2.3 Context drivers An increase in the number of charging stations positively and significantly affects EV adoption according to a study by Sierzchula et al. (2014). However, this has been based on a wide variety of countries and does not take into account differences between rural and urban areas. Other studies, such as by Boonen (2013), have concluded that a relation exists between the number of charging poles and the adoption of hybrid and battery electric vehicles. Nonetheless, this might be caused by an endogeneity problem due to the fact that an increase in charging poles could stimulate the purchase of a (PH)EV but an increase in electric vehicles will also increase the number of requests for charging poles. This means that a loop of causality between the dependent variable (PH)EV adoption and the independent variable the number of public charging poles might be present.

H3: The number of public charging poles in a municipality is positively associated with (PH)EV purchase likelihood

Secondly, proximity to highways and train stations is a variable that has not been taken into account when predicting (PH)EV adoption. Assuming that range remains a barrier in the adoption of both plug-in hybrid and battery electric vehicles, as confirmed by e.g. Lieven et al. (2011), Hidrue et al. (2011) and Wilmink

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(2015), this would result in a lesser likelihood of adoption for those that live away from highways. Resulting in the following hypothesis:

H4: The average distance to the access of a highway is negatively associated with (PH)EV purchase likelihood

In contrast, commuters living close to public transport might possibly be more likely to adopt a (PH)EV for short distance trips while they use public transport for larger distances.

H5: The average distance to any train station is positively associated with (PH)EV purchase likelihood

2.4 Degree of urbanization An important focus of this study is to determine whether the degree of urbanization has an effect on (PH)EV purchase likelihood. Urban inhabitants might be more likely to be confronted with milieu zones, as in place in for example the city of Rotterdam, and are thus forced to adopt vehicles with low emission. On the other hand, non-urban inhabitants might have more options to place for example solar panels on their rooftops providing them with relatively cheap electricity. However, as we see that (PH)EV adoption is currently centered around urban areas, we expect the following relation:

H6: The degree of urbanization, with 1 being non-urban and 5 being very highly urbanized, is positively associated with (PH)EV purchase likelihood

Finally, we assume that urban citizens are more dependent on the availability of public charging poles due to the fact that they often have no drive on which they could place their private charging pole. Consequently, the degree of urbanization will have a positive moderating effect on the availability of public charging poles. This leads to the following final hypothesis:

H6a: The degree of urbanization has a positive moderating effect on the positive association of the number of public charging poles with (PH)EV adoption

In conclusion, the above mentioned hypotheses lead to the following conceptual model (figure 2). As can be seen, consumer age, average household size and the average distance to a highway are expected to be negatively associated to (PH)EV adoption. In contrast, household income, number of cars per household, the ability to request a charging pole, municipal (PH)EV adoption, the number of charging poles, the mean distance to a train station and the degree of urbanization are expected to be positively associated with (PH)EV adoption. The variable main source of income is expected to have different effects given different conditions.

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Figure 2: Conceptual model showing hypothesized effects of independent variables on dependent variable.

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Research Design 3.1 Methodology First, the effect of demographic, municipal and context drivers on both BEV and PHEV adoption will be estimated using multiple regression analysss. Municipality is indexed as i, leading to the following regression model:

푃푒푟푐푒푛푡푎푔푒 퐵푎푡푡푒푟푦 퐸푙푒푐푡푟𝑖푐 푉푒ℎ𝑖푐푙푒푠 𝑖푛 푇표푡푎푙 푃푎푠푠푒푛푔푒푟 퐶푎푟푠 = 훼 +

훽퐷퐷퐷푒푚표푔푟푎푝ℎ𝑖푐 퐷푟𝑖푣푒푟푠푖 + 훽푀퐷푀푢푛𝑖푐𝑖푝푎푙 퐷푟𝑖푣푒푟푠푖 + 훽퐶퐷퐶표푛푡푒푥푡 퐷푟𝑖푣푒푟푠푖 + 휀

And, 푃푒푟푐푒푛푡푎푔푒 푃푙푢푔 − 𝑖푛 퐻푦푏푟𝑖푑 푉푒ℎ𝑖푐푙푒푠 𝑖푛 푇표푡푎푙 푃푎푠푠푒푛푔푒푟 퐶푎푟푠 = 훼 +

훽퐷퐷퐷푒푚표푔푟푎푝ℎ𝑖푐 퐷푟𝑖푣푒푟푠푖 + 훽푀퐷푀푢푛𝑖푐𝑖푝푎푙 퐷푟𝑖푣푒푟푠푖 + 훽퐶퐷퐶표푛푡푒푥푡 퐷푟𝑖푣푒푟푠푖 + 휀 where α denotes the constant and ε is the error variable. The 훽′푠 are the coefficients for the different drivers and lead to the estimation of the predictive value of these drivers.

Secondly, this study will research whether degree of urbanization influences the above presented regression models. The following categories will be used to indicate the degree of urbanization: very highly urbanized, highly urban, moderately urban, little urban, and non-urban (CBS, 2016). This leads to the following model for all categories:

푃푒푟푐푒푛푡푎푔푒 퐵푎푡푡푒푟푦 퐸푙푒푐푡푟𝑖푐 푉푒ℎ𝑖푐푙푒푠 𝑖푛 푇표푡푎푙 푃푎푠푠푒푛푔푒푟 퐶푎푟푠 = 훼 +

훽퐷퐷퐷푒푚표푔푟푎푝ℎ𝑖푐 퐷푟𝑖푣푒푟푠푖 + 훽푀퐷푀푢푛𝑖푐𝑖푝푎푙 퐷푟𝑖푣푒푟푠푖 + 훽퐶퐷퐶표푛푡푒푥푡 퐷푟𝑖푣푒푟푠푖 + 훽푈퐷퐷푒푔푟푒푒 표푓 푢푟푏푎푛𝑖푧푎푡𝑖표푛푖 + 휀

And, 푃푒푟푐푒푛푡푎푔푒 푃푙푢푔 − 𝑖푛 퐻푦푏푟𝑖푑 푉푒ℎ𝑖푐푙푒푠 𝑖푛 푇표푡푎푙 푃푎푠푠푒푛푔푒푟 퐶푎푟푠 = 훼 +

훽퐷퐷퐷푒푚표푔푟푎푝ℎ𝑖푐 퐷푟𝑖푣푒푟푠푖 + 훽푀퐷푀푢푛𝑖푐𝑖푝푎푙 퐷푟𝑖푣푒푟푠푖 + 훽퐶퐷퐶표푛푡푒푥푡 퐷푟𝑖푣푒푟푠푖 + 훽푈퐷퐷푒푔푟푒푒 표푓 푢푟푏푎푛𝑖푧푎푡𝑖표푛푖 + 휀

Finally, these models will be applied to the municipality of Rotterdam to indicate neighborhoods and areas that will most likely see an increase in either BEV or PHEV adoption in the short-term.

This study is subject to several sources of potential measurement errors. First, it could be the case that the dataset with the municipal incentives is incomplete due to underreporting by municipalities. If contrary to the data a municipality has or has not provided an incentive, the regression results could be affected. Furthermore, the ability to request a charging pole is endogenous – little urban and non-urban inhabitants have presumably more possibilities to place a private charging pole opposed to highly urban inhabitants who are more dependent on public charging infrastructure. This could possibly lead to highly urban municipalities having a larger incentive to provide this ability to request a charging pole than non-urban municipalities. Finally, endogeneity may arise for the variable number of charging poles since more electric vehicles could lead to more public charging poles.

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3.2 Sample & data 3.2.1 Sample The quantitative analysis conducted in this study is based on the 393 municipalities of The Netherlands on January 1st 2015. These municipalities cover the entire Netherlands and include urban municipalities such as Amsterdam as well as non-urban municipalities such as Baarle-Nassau and the islands. To compare urban to less urban and non-urban adoption rates these municipalities have been divided along the five categories of urbanization that the CBS has established. Table 1 shows an overview of these categories, while a list of all municipalities and their urban degree can be found in Appendix A.

Category Characteristics Mean inhabitants Value Very highly urbanized ≥2500 addresses / km2 217,045.60 17 Highly urban 1500 – 2500 addresses / km2 72,622.38 72 Moderately urban 1000 – 1500 addresses / km2 35,804.34 82 2 Little urban 500 – 1000 addresses / km 26,139.33 136 Non-urban ≤ 500 addresses / km2 17,339.94 86

Table 1: Description of urban degrees

By reason of merges of some municipalities over the past five years, some data of municipalities had to be combined into a new municipality. This has been done by computing the mean of all the municipalities that have merged into a single municipality. An overview of the municipalities that have (e-)merged in the past five years can be found in Appendix A.

3.2.2 Dependent variable: percentage battery electric and plug-in hybrid vehicles The data on the total number of passenger cars per municipality has been obtained via the CBS. The data on the total number of plug-in hybrid and battery electric vehicles is gathered by APPM and also available via the Klimaatmonitor database. The totals of passenger cars, PHEVs and BEVS per municipality on January 1st 2015 have been used to calculate the percentage of both PHEVs and BEVs on the total number of passenger cars. Consequently, three variables are created from this dataset. First, two separate variables for BEVs and PHEVs are created to explore whether the adoption of battery electric vehicles or plug-in hybrid vehicles is determined by different independent variables.

푛푢푚푏푒푟 표푓 퐵퐸푉푠 푝푒푟 푚푢푛𝑖푐𝑖푝푎푙𝑖푡푦 퐵퐸푉 푎푑표푝푡𝑖표푛 푝푒푟 푚푢푛𝑖푐𝑖푝푎푙𝑖푡푦 = ∗ 100% 푛푢푚푏푒푟 표푓 푡표푡푎푙 푝푎푠푠푒푛푔푒푟푐푎푟푠 푝푒푟 푚푢푛𝑖푐𝑖푝푎푙𝑖푡푦

Figure 3 shows the six municipalities with BEV adoption rate of 0.2% or more in 2015.

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Figure 3: BEV adoption for municipalities with adoption rates higher than 0.20% in 2015

And,

푛푢푚푏푒푟 표푓 푃퐻퐸푉푠 푝푒푟 푚푢푛𝑖푐𝑖푝푎푙𝑖푡푦 푃퐻퐸푉 푎푑표푝푡𝑖표푛 푝푒푟 푚푢푛𝑖푐𝑖푝푎푙𝑖푡푦 = ∗ 100% 푛푢푚푏푒푟 표푓 푡표푡푎푙 푝푎푠푠푒푛푔푒푟푐푎푟푠 푝푒푟 푚푢푛𝑖푐𝑖푝푎푙𝑖푡푦

Figure 4 shows the ten municipalities that had a PHEV adoption rate of 1.0% or more in 2015. Compared to the BEV adoption rates the PHEV adoption rates are considerably higher with a maximum of 2.0% PHEVs out of the total number of passenger cars in Eindhoven. For BEVs Amsterdam is the municipality with the largest adoption rate of BEVs, namely 0.6% out of the total number of passenger cars.

Figure 4: PHEV adoption for municipalities with adoption rates higher than 1.0% in 2015

17 Brenda Janssen – June 16th 2016

To make an estimation and prediction of the total demand for charging poles, the BEV and PHEV adoption rates are combined into the following model:

푛푢푚푏푒푟 표푓 (푃퐻)퐸푉푠 푝푒푟 푚푢푛𝑖푐𝑖푝푎푙𝑖푡푦 (푃퐻)퐸푉 푎푑표푝푡𝑖표푛 푝푒푟 푚푢푛𝑖푐𝑖푝푎푙𝑖푡푦 = ∗ 100% 푛푢푚푏푒푟 표푓 푡표푡푎푙 푝푎푠푠푒푛푔푒푟푐푎푟푠 푝푒푟 푚푢푛𝑖푐𝑖푝푎푙𝑖푡푦

The municipalities Almere, Amersfoort, Amsterdam, Breda, Eindhoven, Geldermalsen, Haarlemmermeer, Houten, Oldenzaal, Rozendaal, Smallingerland, Utrecht, Zeewolde, and Zeist have total adoption rates of 1.0% or higher in 2015. This is only roughly 3.5% of all municipalities and they thus represent a very small number within the total sample of 393 municipalities. Interestingly, these municipalities vary in urbanity from very highly urban to little urban. Solely in the category of non-urban municipalities adoption rates of 1.0% or higher are not found.

3.2.3 Independent variables: demographic, municipal, context and urban drivers The independent variables consumer age, household income, number of passenger cars per household, average household size, and main source of income are all collected from the CBS database Statline. For the variable household income, the data on the years 2014 and 2015 has not yet been made available. Thus, this variable has been computed using the average standardized spendable household income over the years 2011 to 2013. The same holds for the variable main source of income: it is also computed using the mean of the years 2011 to 2013. All other variables are measured at the 1st of January 2015.

The municipal variables have been collected by APPM through a survey in the second half of 2014 until the first quarter of 2015. Missing data was either obtained by direct contact with the municipality in question or through consultation of the municipality’s website. The ability to request a charging pole has been recorded as either yes or no. Out of 393 municipalities data is available for 315 municipalities. 119 out of these 315 municipalities offer the ability to request a charging pole. Regarding the (PH)EV adoption of municipalities, data has been collected for 204 municipalities, out of which 93 municipalities have added BEVs or PHEVs to their fleet. Due to incomplete data on the actual number of BEVs, PHEVs and total vehicles in a municipality’s fleet, adoption has been recorded as either yes or no. Table 18 in Appendix B shows an overview on which municipalities have offered the ability to request a charging pole and/or have adopted BEVs or PHEVs in their fleet. Furthermore, this table will show how the data has been collected.

The context variables have been collected by APPM and through Statline. First, the number of public charging pole comes from APPM and has been obtained at the RVO. It includes all public charging poles at January 1st 2015. Secondly, the average distance to the access of a highway or a train station has been gathered from Statline. This ranges from being 0.4 to 34.7 kilometers away from a highway, or being 1 to 51 kilometers away from a train station. Because this data is not yet available for January 2015, the data of January 2014 has been used.

As explained in section 3.1, the degree of urbanization has been collected from the Statline database, and consists of the values one to five, with the first being non-urban and the latter being very highly urban.

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Finally, the number of inhabitants, the average value of immovable properties, population density and the presence of a top ten or green lease company have been included as control variables. While the first three have been gathered through Statline for January 1st 2015, the latter has been collected through VNA. The logarithm of the variable inhabitants has also been included to level out substantial differences.

An overview of all variables, their definition and source can be found in table 2.

Variable Description (all per municipality) Source BEV adoption Percentage of battery electric vehicles in total passenger cars, on APPM & January 1st 2015 CBS PHEV adoption Percentage of plug-in hybrid vehicles in total passenger cars, on APPM & January 1st 2015 CBS Total adoption Percentage of battery electric and plug-in hybrid vehicles in total APPM & passenger cars, on January 1st 2015 CBS Control variables Inhabitants Number of inhabitants, on January 1st 2015 CBS Logarithm of The base 10 logarithm of the number of inhabitants, on January 1st / inhabitants 2015 Mean WOZ Average value of immovable properties, on January 1st 2015 CBS Lease dummy 1 if a top-10 lease company or a green lease company is located in VNA the municipality, or else 0 lease Population density Number of inhabitants per km2, on January 1st 2015 CBS Demographic variables Consumer age Relative number of inhabitants per 10yrs age category starting from CBS 20 up until 80, on January 1st 2015 Household income Average standardized income per household (excl. students) CBS (x€1000) on January 1st 2011 to January 1st 2013 No. of cars per Average number of passenger cars per household, on January 1st CBS household 2015 Average household Average number of persons in private households, on January 1st CBS size 2015 Main source of income Average percentage of households (excl. students) that receive CBS income either from labor, an own enterprise or from public aid, on January 1st 2011 to January 1st 2013 Municipal variables Charging pole request 1 if an inhabitant can request a public charging pole; or else 0 APPM Municipal (PH)EV 1 if the municipality has added (PH)EVs to its fleet; or else 0 APPM adoption Context variables Public charging poles The number of public charging poles, on January 1st 2015 APPM

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Average distance to Average distance in kilometers of all inhabitants to the access of a CBS highway turnpike road, on January 1st 2014 Average distance to Average distance in kilometers of all inhabitants to a train station, CBS train on January 1st 2014 Urban variables Urban code 5 if a municipality is very highly urban, 4 if the municipality is highly CBS urban, 3 if the municipality is moderately urban, 2 if little urban and 1 for non-urban (reversed for interpretation purposes) Table 2: Description of all variables

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Results Analysis of the data was done separately for the adoption of battery electric vehicles and the adoption of plug-in hybrid vehicles in order to determine their distinct drivers. First, a descriptive analysis of all variables included in this study has been performed. Next, the multiple regression models for the estimation of BEV and PHEV adoption and their findings are presented. Finally, the BEV and PHEV models will be applied to the municipality of Rotterdam in order to predict BEV and PHEV adoption rates.

4.1 Descriptives In the present study quantitative data is collected from the CBS database Statline, the Klimaatmonitor database and APPM. Table 3 presents the summary statistics for the demographic, municipal and context drivers. For the municipal variables, municipal adoption and charge pole request, there are missing cases as can be seen from the table. This is because some data has not been provided by the municipalities.

Obs Mean Std. Dev. Min Max BEV_adoption 393 0.058 0.053 0 0.56 PHEV_adoption 393 0.358 0.228 0 2.030 Control variables log_inhabitants 393 4.453 0.363 2.967 5.915 mean_woz 391 221.476 55.127 120.0 538.0 lease 393 0.040 0.198 0 1 population_density 393 818.323 996.076 25.0 6,289.0 Demographic variables relative_age_20_30 393 0.1056 0.0254 0.0345 0.2845 relative_age_30_40 393 0.1050 0.0157 0.0488 0.1749 relative_age_40_50 393 0.1472 0.0119 0.0775 0.2785 relative_age_50_60 393 0.1513 0.0161 0.0862 0.3083 relative_age_20_65 393 0.5759 0.0382 0.3028 1.0707 relative_age_65 393 0.1959 0.0333 0.0869 0.4242 mean_householdincome 393 24.72 2.42 19.93 38.03 mean_cars_perhousehold 392 1.16 0.18 0.50 2.40 mean_householdsize 393 2.30 0.18 1.60 3.30 mean_%_income_labour 393 0.51 0.04 0.38 0.62 mean_%_income_enterprise 393 0.14 0.03 0.07 0.23 mean_%_income_transfer 393 0.36 0.04 0.21 0.47 Municipal variables municipal_adoption 204 0.46 0.50 0 1 chargepole_request 315 0.38 0.49 0 1 Context variables no._of_chargingpoles 393 17.10 65.86 0 1099.00 distance_highway 386 1.75 2.63 0.40 34.70 distance_trainstation 393 6.97 6.79 1.00 51.00 Urban variables urban_code_reverse 393 2.49 1.15 1 5 Table 3: Summary statistics

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4.2 Estimating BEV adoption First, the model estimating the effect of demographic, municipal, context and urbanization drivers on the adoption of battery electric variables has been carried out as described in section 3.1. Although the Durbin- Watson of 1.715 remains within the upper and lower limits, the inclusion and exclusion of different variables leads to different variables either being significant or not. All 23 predictors are a little correlated and testing all of them on a sample size of 180 leads to a model that is too crowded and complex. Thus, the demographic, municipal and context drivers have been included in separate regression models. The total models including the 23 predictors can be found in Appendix C.1.

4.2.1 Estimating demographic drivers on BEV adoption Due to signs of multi-collinearity (VIF of the variables related to age all above 10.0 and tolerance close to 0.0) changes have been made. Instead of the number of inhabitants within a certain age-group, the relative presence of a specific age-group within the total number of inhabitants has been used as a variable.

For the likelihood of purchasing a BEV, this results in a dataset that is free from error terms (Durbin Watson = 1.795 with n=390 and k=14) and free from multi-collinearity (VIF all below 6.861 and tolerance all over 0.146). As table 4 displays, the independent variables of the final BEV model – model 4 – explain 31.3% of the variance in performance, indicating a moderate fit of the model. Adding the R2 change of model 2 and 3 indicates that the predictive variables age, household size, cars per household, household income, and main source of income account for 4.1% of the variance in performance, whereas 27.1% is explained by the control variables. Lastly, the degree of urbanization does not result in any changes.

Model R R2 Adjusted Std. R2 R2 Error change 1 Control variables only 0.521 0.272 0.264 0.045 0.272 2 Control & age, household size, cars per 0.550 0.303 0.282 0.045 0.031 household, household income 3 Control & all demographic variables 0.560 0.313 0.289 0.044 0.010 4 Control, demographic variables & urban degree 0.560 0.313 0.287 0.045 0.000 Table 4: Demographic BEV regression model summary. Statistically significant at α=0.01

Table 5 shows the regression coefficients for the independent variables. Concerning the control variables, the logarithm of the number of inhabitants and the lease dummy appear to have a significant effect (α=0.01) on BEV adoption. The positive association of the presence of a lease company with the purchase likelihood of a BEV is expected as almost 90% of (PH)EV purchases is driven by business demand (ING Economisch Bureau, 2016). The logarithm of the number of inhabitants is also positively associated with BEV adoption, which can be explained by the fact that in urban areas BEVs might be more popular as city citizens are less restricted by the range limit. On the other hand, one would expect that the degree of urbanization then also shows a positive association towards BEV adoption. However, this is not the case and neither is a moderating effect found.

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Regarding the predictive variables, the average household income and inhabitants aged 40 to 50 show to have a significant effect (respectively for α=0.01 and α=0.05) on the purchase likelihood of a BEV. The specific results per hypothesis are discussed below.

Model B Standardized T Sig. Beta Coefficient 4 (Constant) -0.168 -1.522 0.129 log_inhabitants 0.026 0.180 2.628 0.009** mean_woz 0.000 0.084 0.785 0.433 lease 0.062 0.227 4.419 0.000** population_density 0.000 0.012 0.165 0.869 mean_householdincome 0.007 0.310 2.829 0.005** mean_householdsize -0.011 -0.036 -0.803 0.422 mean_cars_perhousehold 0.008 0.026 0.402 0.688 relative_age_30_40 0.362 0.107 1.159 0.247 relative_age_40_50 0.549 0.124 2.066 0.039* relative_age_50_60 -0.373 -0.114 -1.620 0.106 relative_age_65 -0.184 -0.115 -1.036 0.301 mean_relative_income_labour -0.202 -0.151 -1.350 0.178 mean_relative_income_enterprise 0.085 0.053 0.566 0.572 urban_code_reverse 0.000 0.009 0.096 0.924 Table 5: Demographic regression coefficients for BEV purchase likelihood * Statistically significant at α=0.05, ** Statistically significant at α=0.01

Consumer age (H1a: Consumer age is negatively associated with (PH)EV purchase likelihood) The age categories 30 to 40 and 40 to 50 show a positive effect, while the age categories above 50 show a negative effect. However, only the variable relative age 40 to 50 is found to be significant. The question arises at what age the distinction between young and old can be made. People until the age of 50 are often still quite actively involved in society, might fulfill management positions and can thus be regarded relatively young. Thus, although the effect is little, it confirms hypothesis H1a partly. Due to the fact that the negative effects for ages above 50 are not significant, we cannot fully confirm the hypothesis.

Household income (H1b: Household income is positively associated with (PH)EV purchase likelihood) Household income has a moderate positive influence on the purchase likelihood of a BEV, confirming hypothesis H1b. As expected, this can be due to the fact that these households do have the budget available for the relatively higher investment of a BEV compared to an ICE. While the latter often also has plenty of second hand options, the supply of second hand BEVs might be significantly lower. Furthermore, when looking at the top six BEV models that have been registered at the end of 2014, we can conclude that the Tesla Model S has been registered most often. As this relates to an even higher investment compared to for example the Nissan leaf, it is even more intuitive that households with higher income are more likely to purchase a BEV (RVO, 2015).

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Number of passenger cars (H1c: The number of passenger cars in a household is positively associated with (PH)EV purchase likelihood) For BEV purchase likelihood, hypothesis H1c is rejected due to the absence of any significant effect. This can be explained by the fact that a BEV represents a significant investment and might not always be available as second hand option. Thus, households might be more likely to add a cheaper option to their fleet instead of a BEV.

Household size (H1d: The number of persons (both adults and non-adults) in a household is negatively associated with BEV purchase likelihood) Although the number of persons in a household does indeed show to have a negative effect on BEV purchase likelihood, it is not a significant effect. Thus, based on the results of the regression analysis, hypothesis H1d is rejected. Possibly, other predictors such as income are more important in predicting BEV adoption and hold regardless of household size or other household characteristics.

Main source of income (H1e: Households with income from either an own enterprise or from labor are more likely to adopt a (PH)EV than households with a transfer income) Based on the results presented in table 5, hypothesis H1e is rejected. The variables income from labor and income from enterprise do not show a significant effect, while the variable transfer income is excluded from the analysis due to signs of correlation. A possible explanation for the missing significant effect is that the income level is more important than the source of income. Households with income from labor or an own enterprise can potentially reach the same levels and thus do not have any influence on BEV adoption.

4.2.2 Municipal drivers With a sample size of 184 and 7 predictors, the Durbin Watson score of 1.783 is slightly (2.12%) over the upper limit of 1.746. This should be taken into account when analyzing the results. The dataset is however free from multi-collinearity (VIF all below 3.234 and tolerance all over 0.309).

Model R R2 Adjusted Std. R2 R2 Error change 1 Control variables only 0.593 0.352 0.337 0.044 0.352 2 Control & municipal variables 0.598 0.358 0.336 0.044 0.006 3 Control, municipal variables & urban 0.599 0.359 0.334 0.044 0.002 degree Table 6: Municipal BEV regression model summary. Statistically significant at α=0.01

Table 6 displays the final municipal BEV model – model 3 -, which is significant at α= 0.01. The independent variables explain 36.0% of the variance in performance, indicating a moderately good fit of the model. The municipal variables and the urban degree account for only 0.8% of the variance in performance, while the control variables explain the remaining variance in performance (35.2%). Looking at table 7, this can be explained by the fact that only the control variables, except population density, are significant. The predictive variables municipal adoption and charge pole effect show a minimal positive effect that is insignificant. Possible explanations for this are discussed below.

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Charging pole request (H2a: The possibility to request a charging pole in the public area of a municipality is positively associated with (PH)EV purchase likelihood in that same municipality) Although a positive effect is found, Hypothesis H2a cannot be confirmed based on the above presented results because the effect is not significant. This is interesting, since the municipalities that offer the ability to request a public charging pole have an average adoption rate that is 50.0% higher than the adoption rate of municipalities without the option to request a public charging pole. A possible explanation could be that there is a delay between the request and the actual placement of the charging pole. Many municipalities have outsourced the exploitation of charging poles to commercial partners, meaning that some time will pass by before a request is forwarded to the respective partner.

Municipal (PH)EV adoption (H2b: (PH)EV adoption of a municipality is positively associated with (PH)EV purchase likelihood in that same municipality) The expected positive association of municipal (PH)EV adoption with (PH)EV purchase likelihood as stated in hypothesis H2b cannot be confirmed based on the regression results. This could be due to the fact that inhabitants do not see their local municipalities as trendsetters or influencers. Furthermore, the electric vehicles in a municipalities’ fleet are not necessarily passenger cars but can also be for example garbage trucks, scooters or tricycles. Thus, inhabitants might possibly not directly relate this to battery or plug-in hybrid vehicles.

Model B Standardized Beta T Sig. Coefficient 3 (Constant) -0.195 -3.486 0.001** log_inhabitants 0.038 0.287 2.987 0.003** mean_woz 0.000 0.349 5.234 0.000** lease 0.067 0.295 4.261 0.000** population_density 0.000 0.098 1.080 0.282 municipal_adoption 0.006 0.057 0.892 0.374 chargepole_request 0.008 0.070 1.044 0.298 urban_code_reverse -0.003 -0.072 -0.666 0.506 Table 7: Municipal regression coefficients for BEV purchase likelihood * Statistically significant at α=0.05, ** Statistically significant at α=0.01

4.2.3 Context drivers The Durbin Watson for the context dataset equals 1.781 which means that it falls within the lower and upper limits for a sample size of 384 and 8 predictors. The VIF is for all variables below 3.359 and tolerance all above 0.298 which indicates that the context dataset also does not show signs of multi-collinearity. The final context BEV model – model 3 –, is significant at α= 0.01 and can be found in table 8. The model fits moderately good as 35.3% of the variance in performance is explained by the independent variables. Out of this 35.3%, the control variables account for 27.6% while the context variables explain 7.4% of the variance in performance. The degree of urbanization adds very little to this (0.4%).

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Model R R2 Adjusted Std. R2 R2 Error change 1 Control variables only 0.525 0.276 0.268 0.046 0.276 2 Control & context variables 0.591 0.350 0.338 0.043 0.074 3 Control and context variables & urban 0.594 0.353 0.339 0.043 0.004 degree Table 8: Context BEV regression model summary. Statistically significant at α=0.01

As table 9 displays, the control variables mean WOZ and lease are significant. While the effect of the average value of immovable properties equals zero, the presence of a lease company has a small positive association with BEV adoption. Further results and their corresponding hypothesis are discussed next.

Model B Standardized Beta T Sig. Coefficients 3 (Constant) -0.113 -2.633 0.009** log_inhabitants 0.018 0.126 1.924 0.055 mean_woz 0.000 0.350 7.995 0.000** lease 0.040 0.151 3.028 0.003** population_density 0.000 -0.055 -0.827 0.409 no._of_chargingpoles 0.000 0.339 6.670 0.000** distance_highway 0.000 0.003 0.061 0.951 distance_trainstation 0.000 0.009 0.191 0.848 urban_code_reverse 0.005 0.110 1.442 0.150 Table 9: Context regression coefficients for BEV purchase likelihood * Statistically significant at α=0.05, ** Statistically significant at α=0.01

Public charging poles (H3: The number of public charging poles in a municipality is positively associated with (PH)EV purchase likelihood) The number of public charging poles shows a medium positive association with the purchase likelihood of BEVs. This could indicate that the number of public charging poles triggers the adoption of BEVs, but could also indicate a reverse association, meaning that an increase in the adoption of BEVs results in an increased number of public charging poles. By the beginning of 2014, the number of BEVs (6,825) exceeded the number of public charging points (5,421) which leads to the assumption that more BEVs rather trigger the placement of public charging poles than vice versa (RVO, 2015).

Proximity to transport facilities (H4: The average distance to the access of a highway is negatively associated with (PH)EV purchase likelihood; and H5: The average distance to any train station is positively associated with (PH)EV purchase likelihood) Although the average distance to a highway shows a negative effect and the average distance to a train station a positive effect, both the hypotheses H4 and H5 are rejected based on insignificant results. This could possibly be explained by the fact that proximity to transport facilities is not necessarily taken into account when deciding on the purchase of a battery electric vehicle. Furthermore, the range of these vehicles has increased and is still increasing and fast charging facilities have expanded along highways. This

26 Brenda Janssen – June 16th 2016 possibly diminishes the range barrier and thus decreases the importance of the proximity to transport facilities.

4.2.4 Degree of urbanization All the above presented multiple regression analyses have included the degree of urbanization in their last model. This section will discuss the effect of the degree of urbanization and its probable moderating effect on the number of public charging poles.

Degree of urbanization (H6: The degree of urbanization, with 1 being non-urban and 5 being very highly urbanized, is positively associated with (PH)EV purchase likelihood) Looking at the regression analysis results that are presented in tables 5, 7, and 9, in neither of the analyses a significant effect of the degree of urbanization on BEV purchase likelihood is found. This can be explained by the fact that apart from the highest degree of urbanization, the remaining degrees of urbanization show relatively similar BEV adoption rates (0.065 to 0.043).

In order to conclude on the importance of the degree of urbanization, regression analyses have been conducted per degree of urbanization. The two highest degrees of urbanization have been combined to ensure that the samples are of comparable size. All results can be found in Appendix D.

For the highly urban municipalities, the number of charging pole is strongly and positively associated with BEV adoption (β=0.939 at α=0.01). This strong association holds both ways, meaning that an increased BEV adoption could also lead to an increased number of public charging poles which is a more intuitive line of argumentation. Furthermore, the number of cars per household is moderately positive associated with BEV adoption in highly urban municipalities. In contrast, for the moderately urban municipalities, only the demographic variable average household size is positively associated with BEV adoption. This is inconsistent with the expectations, but with an average household size of 2.32 persons for the moderately urban municipalities it is not an impossible outcome. Interestingly, this positive association of household size with BEV adoption is also found for the little urban municipalities. Furthermore, household income and the number of public charging poles are also found to have a positive association with BEV purchase likelihood. Finally, for the non-urban municipalities, a negative association with inhabitants aged twenty to sixty-five is found. Due to the fact that this variable contains both young, middle-aged and senior inhabitants nothing can be concluded on the effect of age on BEV adoption for non-urban municipalities.

Compared to the demographic, municipal and context results that are discussed above, it is interesting to see that the average number of cars is found to be positively associated with BEV adoption but only for the highly urban municipalities. Due to the fact that these households live in highly urban municipalities, they might be more aware of air pollution and emissions and thus more likely to purchase a battery electric vehicle. On the other hand, the control variable lease is insignificant in all urban regression analyses, which is mainly caused by the fact that for the lower degrees of urbanization there simply is no lease company. Finally, in contrast to the expectations and theory, household size appears to be positively associated with BEV adoption for moderately and little urban municipalities. With average household sizes of 2.32 persons

27 Brenda Janssen – June 16th 2016 this can be easily explained by the fact that these households do not require large vans and would purchase a regular passenger car.

Moderating effect of urbanization (H6a: The degree of urbanization has a positive moderating effect on the positive association of the number of public charging poles with (PH)EV adoption) As displayed in table 9 in section 4.2.3, the number of public charging poles shows to have a significant effect on BEV adoption although the actual B is equal to zero. Furthermore, the degree of urbanization does not appear to be significant for the demographic, municipal and context model.

The model fit with the interaction effect included is slightly lower than the initial context model (0.348 < 0.353) but fits moderately well. Table 31 in Appendix D.5 shows that the interaction of the variables number of public charging poles and urban degree account for 7.2% of the variance in performance, compared to the control variables that explain 27.6% of this variance in performance. All regression coefficients can be found in table 32 in Appendix D.5. As expected, the interaction of the variables degree of urbanization and the number of public charging poles is found to be significant and positively associated with BEV adoption. However, this positive effect is minimal (B=0.00004). Furthermore, compared to the regression model presented in section 4.2.3, the control variable log inhabitants is significant while all other associations remain the same. To conclude, based on the results we can accept hypothesis H6a.

4.3 Estimating PHEV adoption For the regression model that estimates PHEV adoption the effects of the demographic, municipal, context and urbanization drivers have been measured in separate models. The total model, including all 23 predictors, can be found in Appendix C.2.

4.3.1 Estimating demographic drivers on PHEV adoption Similar to the changes made in the model for BEV adoption, the age categories have been replaced with relative age categories for the estimation of PHEV adoption. This resulted, for the likelihood of purchasing a PHEV, in a dataset that is free from error terms (Durbin Watson = 1.923 with n=390 and k=14) and free from multi-collinearity (VIF all below 6.861 and tolerance all over 1.46).

As can be seen from table 10, the independent variables in the final PHEV model for the demographic drivers – model 4 – indicate a medium fit as they explain 49.3% of the variance in performance. The control variables account for 32.0% of this variance in performance, while all demographic variables account for 16.0% of the variance in performance. Similar to the BEV model, the degree of urbanization accounts for only a very small part of this variance in performance (0.2%).

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Model R R2 Adjusted Std. R2 R2 Error change 1 Control variables only 0.565 0.320 0.313 0.186 0.320 2 Control & age, household size, cars per 0.691 0.477 0.462 0.166 0.158 household, household income 3 Control & all demographic variables 0.692 0.479 0.461 0.165 0.002 4 Control, demographic variables & urban 0.702 0.493 0.475 0.163 0.014 degree Table 10: Demographic PHEV regression model summary. Statistically significant at α=0.01

The regression coefficients for the independent variables can be found in table 11. Regarding the control variables, the average value of immovable property and the lease dummy appear to have a significant effect (at α=0.05 and α=0.01 respectively). For the positive association of the lease dummy with PHEV adoption the same line of argumentation can be followed as for its positive association with BEV adoption. Due to the fact that still a large share of (PH)EV purchases is driven by business demand, it is logical that the presence of a lease company has a moderate positive effect on PHEV adoption. Although the negative association of the average value of immovable property with PHEV adoption might be counterintuitive at first, it could possibly be explained by the fact that the areas with lower adoption rates turn out to be less urban municipalities. These municipalities might have a relatively larger share of detached houses and farms for example, leading to a higher average value of immovable property.

Concerning the predictive variables, the average household income, the average number of cars per household, inhabitants aged 50 to 60, households with income from an own enterprise and the degree of urbanization are found to have a significant effect (at α=0.05) on PHEV adoption. The specific results per hypothesis are discussed below.

Consumer age (H1a: Consumer age is negatively associated with (PH)EV purchase likelihood) Out of the four age categories, only the ages 50 to 60 have a significant effect on PHEV adoption as table 11 displays. This effect is negative, which confirms the expectation on which hypothesis H1a is based. When looking at the other age categories, although their effects are insignificant, they show a similar pattern. While the age categories until 50 show a positive effect, the ages over 50 show a negative effect. Based on these results we accept hypothesis H1a with the footnote that the positive relation for the lower ages is not confirmed.

Household income (H1b: Household income is positively associated with (PH)EV purchase likelihood) Based on the results in table 11, hypothesis H1b is accepted. Household income shows to be medium positively associated with the purchase likelihood of a PHEV. This can easily be explained by the fact that plug-in hybrid vehicles require a higher investment over battery electric vehicles (ANWB, 2016). Thus, PHEVs represent a larger share of a household’s spendable income. Furthermore, as a larger share of PHEV adoption is driven by business demand, we can also conclude that those that drive a PHEV often also have a higher position in business.

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Model B Standardized T Sig. Beta Coefficient 4 (Constant) -1.498 -3.706 0.000** log_inhabitants 0.041 0.066 1.114 0.266 mean_woz -0.001 -0.186 -2.017 0.044* lease 0.312 0.268 6.082 0.000** population_density 0.000 -0.037 -0.611 0.541 mean_householdincome 0.042 0.459 4.873 0.000** mean_householdsize -0.071 -0.057 -1.483 0.139 mean_cars_perhousehold 0.410 0.328 5.986 0.000** relative_age_30_40 1.436 0.100 1.260 0.208 relative_age_40_50 1.522 0.081 1.569 0.118 relative_age_50_60 -2.719 -0.195 -3.230 0.001** relative_age_65 -0.006 -0.001 -0.010 0.992 mean_relative_income_labour 0.381 0.067 0.699 0.485 mean_relative_income_enterprise 1.351 0.196 2.460 0.014* urban_code_reverse 0.054 0.275 3.265 0.001** Table 11: Demographic regression coefficients for PHEV purchase likelihood * Statistically significant at α=0.05, ** Statistically significant at α=0.01

Number of passenger cars (H1c: The number of passenger cars in a household is positively associated with (PH)EV purchase likelihood) As the average number of cars per household is found to be significant, hypothesis H1c can be accepted. The results show a moderate positive association with PHEV adoption, which aligns with the argumentation on which this hypothesis is based. Households that already own an ICEV are less restricted by the range barrier and could therefore be more likely to purchase a (PH)EV. However, one would expect that this effect is more present for BEV adoption but no significant effect is found for BEV adoption. This could indicate that although households are less restricted by range limitations, it still plays an important role and possibly leads to a preference for PHEVs over BEVs.

Household size (H1d: The number of persons (both adults and non-adults) in a household is negatively associated with BEV purchase likelihood) Although the number of persons in a household does indeed show to have a negative effect on PHEV purchase likelihood, it is not a significant effect. Thus, based on the results of the regression analysis, hypothesis H1d is rejected. A possible explanation could be that the number of large households (>5 persons) is a too small share of the total population and therefore does not have a significant effect on PHEV adoption.

Main source of income (H1e: Households with income from either an own enterprise or from labor are more likely to adopt a (PH)EV than households with a transfer income) Similar to the BEV demographic multiple regression analysis, the variable transfer income has been excluded from the analysis. Income from labor does not show any significant effect, while income from an own enterprise appears to have a positive significant effect on PHEV adoption. This could be due to the

30 Brenda Janssen – June 16th 2016 fiscal benefits (that will be phased out over the coming years) on PHEVs for entrepreneurs. Combined with a relative higher price of PHEVs, this little positive influence can be explained. Because no effect is found for income from labor or transfer income, hypothesis H1e can only be partly confirmed.

4.3.2 Municipal drivers The Durbin Watson equaling 2.166 exceeds the upper limit for a sample size of 184 and 7 predictors by around 24.0%. This should be taken into account when analyzing the results. However, all VIFs are below 3.234 and tolerance all over 0.309 indicating that the dataset is free from multi-collinearity.

The final municipal PHEV model – model 3 – can be found in table 12 and is significant at α= 0.01. The model fit is moderately good because the independent variables explain 39.8% of the variance in performance. The control variables account for a large part to this (38.6%), whereas the municipal variables and the urban degree both account for only 0.6% of this variance in performance. This can be explained by the fact that only three out of four control variables are found to be significant compared to no significant effects for the predictive variables. The control variables log inhabitants, mean WOZ and lease are found to be significant, which is a similar to the results for the municipal BEV model. All municipal regression results are discussed below.

Model R R2 Adjusted Std. R2 R2 Error change 1 Control variables only 0.621 0.386 0.372 0.170 0.386 2 Control & municipal variables 0.626 0.392 0.371 0.170 0.006 3 Control, municipal variables & urban 0.631 0.398 0.374 0.170 0.006 degree Table 12: Municipal PHEV regression model summary. Statistically significant at α=0.01

Charging pole request (H2a: The possibility to request a charging pole in the public area of a municipality is positively associated with (PH)EV purchase likelihood in that same municipality) Although a minimal positive effect is found, Hypothesis H2a cannot be confirmed based on the above presented results as this effect is not significant. Similar to the missing effect on BEV adoption, this is interesting since the municipalities that offer the ability to request a public charging pole have an average adoption rate that is 32.9% higher than those municipalities that do not offer such option. Similarly, this could possibly be explained due to a delay between the request and the actual placement of the charging pole. However, a possible explanation could also be that PHEVs are less dependent on the availability of charging poles and are thus less affected by the presence or absence of such an initiative.

Municipal (PH)EV adoption (H2b: (PH)EV adoption of a municipality is positively associated with (PH)EV purchase likelihood in that same municipality) Contrary to the expected positive association of municipal (PH)EV adoption with (PH)EV purchase, the results show a minimal negative effect, although it is not significant. Based on these results, hypothesis H2b is rejected. Comparable to the absence of any significant effect for the likelihood of purchasing an BEV, this could be due to the fact that apparently municipalities are not considered to be trendsetters or

31 Brenda Janssen – June 16th 2016 influencers. Furthermore, as explained in section 4.2.2, not all vehicles in a municipality’s fleet are passenger cars and thus might not lead to an increased presence and visibility of PHEVs.

Model B Standardized Beta T Sig. Coefficient 3 (Constant) -0.577 -2.678 0.008** log_inhabitants 0.118 0.224 2.409 0.017* mean_woz 0.001 0.369 5.703 0.000** lease 0.312 0.346 5.157 0.000** population_density 0.000 -0.069 -0.783 0.435 municipal_adoption -0.001 -0.003 -0.047 0.962 chargepole_request 0.033 0.077 1.182 0.239 urban_code_reverse 0.024 0.139 1.326 0.187 Table 13: Municipal regression coefficients for PHEV purchase likelihood * Statistically significant at α=0.05, ** Statistically significant at α=0.01

4.3.3 Context drivers With n=384 and k = 8, the Durbin Watson of 1.885 can be accepted. Besides being free from errors, the dataset is also free from multi-collinearity (VIF all below 3.359 and tolerance all above 0.298). Table 14 displays the final context PHEV model – model 3 – which fits moderately good (R2 = 0.386) and is significant at α= 0.01. The control variables explain 33.5% of the variance in performance, while the context variables account for 4.8% of the variance in performance. All results are discussed below.

Model R R2 Adjusted Std. R2 R2 Error change 1 Control variables only 0.579 0.335 0.328 0.188 0.335 2 Control & context variables 0.619 0.384 0.372 0.182 0.048 3 Control, context variables & urban degree 0.622 0.386 0.373 0.182 0.003 Table 14: Context PHEV regression model summary. Statistically significant at α=0.01

Public charging poles (H3: The number of public charging poles in a municipality is positively associated with (PH)EV purchase likelihood) Surprisingly, the multiple regression analysis results in table 15 show that the number of public charging poles is negatively associated with the purchase likelihood of plug-in hybrid vehicles. Although the effect is relatively small, it is counterintuitive that an increase in public charging poles will slightly reduce the number of PHEVs in a municipality. This result will be further explored in the multiple regression analyses per degree of urbanization to find if it holds for all degrees of urbanization, for one urban degree or for none of the urban degrees.

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Proximity to transport facilities (H4: The average distance to the access of a highway is negatively associated with (PH)EV purchase likelihood; and H5: The average distance to any train station is positively associated with (PH)EV purchase likelihood) Based on the regression analysis results, both the hypotheses H4 and H5 are rejected as no significant results are found. The absence of a significant effect could be due to the fact that these variables simply are not important in predicting PHEV adoption. Possibly, households are more interested in the actual range of the vehicle than the range they will have to cover.

Model B Standardized Beta T Sig. Coefficients 2 (Constant) -0.453 -2.501 0.013* log_inhabitants 0.096 0.153 2.397 0.017* mean_woz 0.001 0.354 8.307 0.000** lease 0.555 0.483 9.921 0.000** population_density 0.000 0.037 0.579 0.563 no._of_chargingpoles -0.001 -0.237 -4.782 0.000** distance_highway -0.003 -0.035 -0.794 0.428 distance_trainstation -0.001 -0.025 -0.518 0.605 urban_code_reverse 0.019 0.095 1.281 0.201 Table 15: Context regression coefficients for PHEV purchase likelihood * Statistically significant at α=0.05, ** Statistically significant at α=0.01

4.3.4 Degree of urbanization The degree of urbanization has been included in the above presented multiple regression analyses. This section will focus on the effect of the urban degree on PHEV adoption and discuss whether it has a moderating effect on the number of public charging poles. Furthermore, as the number of charging poles showed a surprising negative association with PHEV purchase likelihood, this section will also focus on explaining this.

Degree of urbanization (H6: The degree of urbanization, with 1 being non-urban and 5 being very highly urbanized, is positively associated with (PH)EV purchase likelihood) Tables 11, 13, and 15 present the regression analysis results for the effect of demographic, municipal and context drivers on the purchase likelihood of a PHEV. As can be seen in table 11, the degree of urbanization is only found to be moderately significant for the demographic model. When looking at the raw data, the PHEV adoption rates are all around 4.5% for the three highest urban degrees and 3.2% and 2.6% respectively for the little and non-urban municipalities. These relatively similar adoption rates might explain the little effect of the degree of urbanization on PHEV adoption. Furthermore, the significance of the degree of urbanization could rather also be caused by interaction with another variable in this model.

To study whether some predictive variables are more or less associated with PHEV adoption for varying degrees of urbanization, regression analyses for each of the degrees of urbanization have been carried

33 Brenda Janssen – June 16th 2016 out. To ensure similar sample sizes, the two highest degrees of urbanization have been combined. All analyses and results can be found in Appendix E.

In contrast to the overall demographic regression analysis results, the number of cars per household are highly and positively associated with PHEV adoption for the highly urban municipalities (β=0.914 at α=0.01). Similar to the argumentation for BEV adoption, we could assume that urban citizens are more conscious of air quality and thus might be more likely to purchase a zero emission vehicle next to their ICEV. Furthermore, the average distance to a highway is found to be negatively associated with PHEV purchase likelihood. A longer drive to a highway and thus to your destination makes a driver more dependent on the internal combustion engine and thus reduces the use of electricity. This reduces the advantage of a PHEV over an ICEV and could possibly explain this negative effect. The moderately urban model does not show any results beside a medium positive association of the average value of immovable property with PHEV purchase likelihood. For the municipalities with the lowest degrees of urbanization, household income shows to have a strong positive effect on PHEV adoption. This could be due to the fact that PHEV drivers are often faced with an even larger investment due to the fact that they have to provide their own charging facilities compared to public charging facilities that often can be found in cities. Finally, ages above 65 are found to be negatively related to PHEV adoption as expected.

When comparing these results to the results for BEV adoption, it is interesting to note that the number of charging poles is not significant for any of the degrees of urbanization. This can be explained by the fact that PHEVs are less dependent on the availability of charging poles compared to BEVs. Surprisingly, the effect appears to be negative for all urban degrees except the moderately urban degree. Although it is insignificant, it is interesting to see that apparently the availability of public charging poles is less important for PHEV adoption and even negatively associated with it.

Moderating effect of urbanization (H6a: The degree of urbanization has a positive moderating effect on the positive association of the number of public charging poles with (PH)EV adoption) Interestingly, for PHEV adoption, the number of public charging poles shows a minimal negative effect. The degree of urbanization is found to be significant in the demographic model. Will the interaction of these variables lead to a positive or negative effect?

Both the model summary and the regression coefficients can be found in Appendix E.5. The model fits moderately well (R2 equals 37.4%) and is free from errors and signs of multi-collinearity (VIF all above 1.096 and tolerance all below 0.913. The interaction of the urban degree and the number of public charging poles explains 4.5% of the variance in performance, while the control variables account for the remaining 33.5%. The interaction of the number of public charging poles and the degree of urbanization appears to have a significant effect (at α=0.01) although this effect is close to zero. Consistent with the findings for PHEV adoption, the effect is negative. This indicates that for all degrees of urbanization, the number of public charging poles has a negative influence on the number of charging poles. This is inconsistent with the hypothesis and will need further research. Hypothesis H6a is rejected based on these results.

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4.4 Case study: Rotterdam Although the multiple regression analysis results are interesting and provide an overview of demographic and context factors that either positively or negatively influence BEV and PHEV adoption, it is even of more value to apply this very practically to the municipality of Rotterdam. Based on the results presented in sections 4.2 and 4.3, this section will highlight the neighborhoods and districts that will most likely see an increased BEV and PHEV adoption during the coming years.

This prediction is based on the following assumptions: 1) The number of inhabitants in every neighborhood of Rotterdam has grown along the same rate as the total number of inhabitants of the municipality of Rotterdam. As can be concluded from the growth rates in 2015 this is not the case in reality. However, due to the fact that there are no predictions of the development of the number of inhabitants per neighborhood, the aggregated growth rate will be used (Gemeente Rotterdam, 2016). 2) Similar to assumption one, the relative presence of the age categories has grown along with the growth rate of the municipality. 3) Due to the fact that the most recent trends of household income have not been published, the growth of standardized spendable household income for 2013 to 2014 has been used to predict household income for 2015 to 2020. Because these were post-crisis years we can assume that they are representative for current economic growth. 4) The average value of immovable property will remain at its 2014 value. From 2013 to 2014 the WOZ has slightly in- or decreased, but with renovation and construction projects going on in some neighborhoods these growth rates are expected to change. Thus the 2014 values will provide the best estimate. 5) Due to the fact that data on the relative number of households that earns income from an own enterprise is only available as an average for Rotterdam, this is assumed to be the value for each neighborhood in 2015 and 2020.

4.4.1 BEV adoption in Rotterdam As can be found in section 4.2, the purchase likelihood of battery electric vehicles is positively influenced by the average household income, the relative number of inhabitants aged 40 to 50 and the number of charging poles. While the effect of the latter is minimal (B=0.0002) and could also be due to endogeneity, it is not taken into account when predicting BEV adoption in Rotterdam. As figure 8 in Appendix F.1 shows, charging poles can mainly be found above the river Maas in the north of the city Rotterdam. Moreover, the average value of immovable property is significant but shows an effect equal to zero and therefore is also excluded from the prediction. Looking at the results of the multiple regression analysis for the highly urban municipalities, only the number of cars per household is found to be positively association with BEV adoption. Using this model produces significantly lower adoption rates that are even below adoption rates 0f 2015. Thus can be concluded that the basic model produces a better outcome. The data on the districts and neighborhoods of Rotterdam has been collected via the Rotterdam Buurtmonitor database (2016).

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Inserting these values in the regression model leads to a prediction as displayed in figure 5. A table with the actual adoption rates can be found in Appendix F.1. The average adoption rate of the districts (0.187%), based on the most recent data, is slightly higher than the actual adoption rate for the municipality of Rotterdam at January 2015 (0.136%) and even higher than the adoption rate of April 2016 (0.154%). Thus, the predicted adoption rates are slightly more optimistic than the actual rates. Using the forecast on 2020 generates adoption rates as depicted in figure 6. Adoption rates in 2020 are expected to be highest in the neigborhoods Witte Dorp, Kralingen Oost, Molenlaankwartier, Nieuwe Werk and Terbregge. The neighborhoods which will have the lowest BEV adoption rates can mainly be found in the south of the city Rotterdam; for example, Zuiderpark, Afrikaanderwijk and Hillesluis. The total adoption for the municipality of Rotterdam is estimated at 0.240%, which is fairly low when looking at the aim to achieve an emission- free Dutch fleet in the 30 years after 2020. However, as Rotterdam has put a milieu zone in place, since the beginning of 2016, these adoption rates might be positively affected over time.

Figure 5: Predicted BEV adoption in the municipality of Rotterdam (2015)

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Figure 6: Predicted BEV adoption in the municipality of Rotterdam for 2020

4.4.2 PHEV adoption in Rotterdam Household income and income from an own enterprise are found to be positively associated with PHEV adoption, while inhabitants aged 50 to 60 show a negative association. The number of charging poles shows a minimal negative association with PHEV adoption. Similar to the model for BEV adoption it is not taken into account for predicting PHEV adoption in Rotterdam as the association could also be due to an endogeneity problem. The model for the highly urban municipalities that has been analyzed in section 4.3.4 returns negative adoption rates for all neighborhoods of Rotterdam and is therefore not taken into account when predicting PHEV adoption rates in Rotterdam. Figure 9 in Appendix F.2 shows the PHEV adoption rates based on the most recent data mapped, ranging from 0.061% PHEVs out of total passenger cars in the Wielewaal, to an adoption rate of 1.749% out of the total number of passenger cars in the neighborhood Nieuwe Werk. Similar to the predicted adoption rates of BEVs, the predicted adoption rate of PHEVs based on the most recent data is higher than the actual PHEV adoption rate of January 2015 (0.338% < 0.602%). However, it is below the PHEV adoption rate of April 2016 (0.710%).

The forecast for 2020 is depicted in figure 7. Neigborhoods such as Kralingen Oost, the Nieuwe Werk, the Witte Dorp, Molenlaankwartier and Strand en Duin in Hoek van Holland are expected to see the highest adoption rates (2.027% to 1.851%). Mainly neighborhoods in the south and south-west of the city Rotterdam, such as Wielewaal, Tussendijken, Afrikaanderwijk and Heijplaat will see the lowest adoption rates (around 0.5%).

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Figure 7: Predicted PHEV adoption in the municipality of Rotterdam for 2020

In conclusion, the average BEV adoption rate of the municipality of Rotterdam will be around 0.240% and the PHEV adoption rate around 0.710% in 2020. On the total number of passenger cars in 2015 (214,363 vehicles) this means that, in the municipality of Rotterdam, there will be approximately 514 battery electric vehicles and 1522 plug-in hybrid vehicles. This means that in the years towards 2050 several changes such as lower purchase prices or financial incentives might be needed in order to achieve an emission free fleet.

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Discussion Reflecting on our research question and its main findings, we argue that for BEV adoption standardized spendable household income is the main predictor, followed by age, the number of cars per household and the number of charging poles in a municipality. PHEV adoption is mainly driven by spendable household income and the number of cars per households, while the degree of urbanization, inhabitants aged 50 to 60 and income from an own enterprise are also found to be of importance.

5.1 Practical implications The relative presence of inhabitants aged 40 to 50 and 50 to 60 are found to be influencing factors for BEV and PHEV adoption. While the first is positively associated with BEV adoption, the latter shows a negative association with PHEV adoption. Furthermore, standardized spendable household income proves to be an important factor for both BEV and PHEV adoption, consistent with the findings of e.g. Gallagher and Muehlegger (2011) and confirming hypothesis H1b. Income appears to be more important for PHEV purchase likelihood, which could be explained by the relatively higher purchase price of PHEVs. The number of cars per household is the second most important factor in the purchase decision of PHEVs and shows to have a positive effect on both BEV and PHEV adoption for highly urban municipalities. Moreover, households with income from an own enterprise show a slight positive association with PHEV adoption. For national and local governments, this means that currently BEV and PHEV adoption can be mainly found in municipalities and neighborhoods with a relatively higher income, inhabitants aged below 50 and households with a relatively higher number of cars. Consequently, neighborhoods and municipalities with low incomes and thus a low number of cars per household and a large presence of inhabitants older than 50 might need additional incentives. Furthermore, cities like for example Rotterdam, that aim to improve the air quality of their city center through a milieu zone, might need to take into account the neighborhoods close to the city center but with a relatively large number of households with low income. Car manufacturers should currently target consumers with a relatively higher household income, aged 40 to 50 and with more than 1 car. When more affordable models are introduced, consumers with an average income could be targeted as well. Finally, distribution system operators could prioritize infrastructure investments and upgrades in areas with higher incomes, a higher number of cars and relatively young inhabitants.

Contrary to the expectations, the ability to request a charging pole and the adoption of BEVs and PHEVs in a municipality’s fleet do not confirm our hypotheses. This confirms the ambiguous effect of non-financial incentives as found by Diamond (2009) and Gallagher and Muehlegger (2011). The first could possibly be caused by a delayed effect of the charging pole request. Inhabitants might not directly be aware of this option due to for example limited communication. Furthermore, there could be a delay between the request and the actual placement of the charging pole due to different parties being involved in the process. The latter can be explained by the absence of a trendsetter role of local municipalities. Furthermore, inhabitants might be more driven by direct financial benefits opposed to non-financial benefits. Thus, in order to stimulate BEV and PHEV adoption, municipalities could turn to for example subsidies, as this is proved to be effective according to for example Bočkarjova et al. (2013), Diamond

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(2009), and Sierzchula et al. (2014). Whether the ability to request a public charging pole drives adoption requires additional research in order to capture a possible delayed effect.

The number of public charging poles in a municipality is found to be the most important factor in BEV adoption. However, due to an endogeneity problem the reverse could also hold. Due to the fact that it is more likely that an increased number of BEVs triggers more charging poles than vice versa, this factor is not taken into account when predicting BEV adoption. Remarkably, the purchase likelihood of PHEVs is negatively influenced by the number of charging poles. This could indicate that PHEVs are less dependent on charging poles, but further research on the relation between the number of charging poles and (PH)EV adoption is required. Once again, this study however confirms that an increased BEV adoption rate triggers public charging poles. For both BEV and PHEV adoption an interaction is found between the degree of urbanization and the number of public charging poles. While the first shows a minimal positive association, the latter is found to have a minimal negative association. Due to the fact that these effects are close to zero, no solid conclusions other than the fact that there exists some sort of relation can be made.

The degree of urbanization does not prove to be significant for BEV adoption, due to the fact that all degrees of urbanization show similar adoption rates. In contrast, it is found to be significant in the demographic model for PHEV adoption. The number of cars per household is a major influencing factor for both BEV and PHEV adoption in highly urban municipalities. This is an interesting finding, since this would mean that inhabitants with more than one passenger car are more likely to purchase an additional vehicle that is low in emissions. This will likely have effects on the air quality of these highly urban municipalities. To stimulate this effect, municipalities could opt for a reduction of for example the parking permit of the second car that is low in emissions. Regarding moderately and little urban municipalities, household size is positively associated to BEV adoption, while for PHEV adoption no results are found. Finally, consistent with the demographic models, standardized spendable income is an important factor for BEV adoption in the little urban municipalities and for PHEV adoption in the little and non-urban municipalities. Besides from the results for all degrees of urbanization, both municipalities and distribution system operators should take these associations into account. However, as has been revealed by the case study for the municipality of Rotterdam, the results obtained through the demographic, municipal and context models are more accurate predictors of (PH)EV adoption.

Lastly, the proximity to transport facilities appears to be of little to non-importance for the purchase likelihood of BEVs and PHEVs. For PHEV adoption in highly urban municipalities the average distance to a highway shows a negative effect. While this cannot be related to range anxiety, it could be explained by the fact that a larger distance to the highway increases the dependence on fuel and reduces the effectiveness of the electric power. This should be taken into account by local governments of highly urban municipalities, but it is not an outcome of major importance.

5.2 Future developments As mentioned in the introductory chapter, the Dutch electric vehicle environment will see several changes over the coming years. Among others, fiscal benefits will be reduced for plug-in hybrid vehicles, most likely

40 Brenda Janssen – June 16th 2016 more affordable BEV and PHEV models will be introduced such as the Tesla model 3, technology will most likely improve and milieu zones might come into force in some highly urban municipalities. Most importantly, the Dutch government has set the ambition to exclusively sell emission free cars by 2035 in order to ensure that the Dutch fleet only consists of emission free cars by 2050. These future developments will have their effect on the outcomes of this study. While we can assume that more affordable BEVs and PHEVs will enable more consumers to adopt such electric vehicles, we cannot predict how BEV and PHEV adoption will be affected by other developments.

5.3 Theoretical implications This study is among the first to combine different categories of influencing factors in a quantitative analysis. In comparison to qualitative studies, there are no distinct differences in the factors influencing the purchase likelihood of (PH)EVs. The results show that BEV and PHEV adoption are driven by different factors and to different extents and therefore should continue to be separated in future studies. Additionally, the degree of urbanization has proven to be an important factor for PHEV adoption on a Dutch level and appears to lead to different predictive variables for varying degrees of urbanization. Therefore, the degree of urbanization should remain to be taken into account in future studies.

Finally, the no. of charging poles is an ambiguous factor in this analysis. Its significant effect on BEV and PHEV adoption could be the result of an endogeneity problem. It should remain to be included in the analysis of BEV and PHEV purchase likelihood, but rather in a different form or measurement to ensure that the perceived readiness of the charging infrastructure by a possible BEV or PHEV driver is measured.

5.4 Limitations and future research This study is subject to a few limitations. First, the data on municipal incentives is either missing or incomplete due to missing information from the municipality itself. This could influence the results of this study in case a municipality has been recorded as providing incentives while it has not or vice versa.

The scope of this research is limited to demographic factors, non-financial municipal incentives, context factors and the degree of urbanization, and hence does not cover the full range of predictive factors for BEV and PHEV adoption. Future studies could include for example the effect of the evolving fiscal climate, technological progress, political preferences of a municipality’s inhabitants as well as the important variables of this study. Combining both quantitative insights on demographics and context and qualitative insights on for example range anxiety, charging behavior and the perceived evolution of technology might yield even more precisely the predictive factors of (PH)EV adoption. Moreover, including the parking pressure of a given municipality might provide insights in the field of charging behavior and possible motivating factors to adopt a (PH)EV.

Concerning the case study on the municipality of Rotterdam, the results and forecasts were limited to the data and growth figures available. This resulted in several assumptions regarding the development of for example the number of inhabitants of Rotterdam and their income. Furthermore, the forecasts could not take possible technological innovations, changes in the fiscal environment, subsidies or other evolving factors into account. Future research may wish to administer an analysis on the pattern that BEV adoption

41 Brenda Janssen – June 16th 2016 will need to follow to reach an emission free Dutch fleet by 2050. Finally, the forecasts could not take the effects of the milieu zone that has been put in place since the beginning of 2016 into account. This might possibly function as an additional motivating factor for BEV and PHEV adoption.

Finally, future research might wish to include a factor that captures the readiness of public (fast)charging infrastructure instead of the number of public charging poles. Including factors such as the occupancy rate of public charging poles, international charging infrastructure and charging behavior into one factor could give a better judgement of the effect on BEV and PHEV adoption.

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Conclusion This study presents the results of a quantitative analysis based on the 393 municipalities of the Netherlands and aims to elicit the most important drivers of BEV and PHEV adoption. This has been explicated by means of a multiple regression analysis allowing to identify demographic, municipal, context and urban drivers in (PH)EV adoption.

Data has been gathered for the 393 municipalities on their demographic characteristics, non-financial incentives by the local government, context factors and their degree of urbanization. The findings show that for BEV adoption the standardized spendable income and the relative presence of inhabitants aged 40 to 50 years old are the most influential factors. The variables standardized spendable income, the number of cars per household, the relative presence of inhabitants aged 50 to 60 years old and income earned from an own enterprise are the most influential factors in the purchase consideration of a PHEV. Income proves to be more important for PHEV adoption than for BEV adoption, which can be explained by the fact that PHEVs require a relatively larger investment compared to BEVs.

Furthermore, the number of public charging poles in a municipality shows to be positively related to BEV adoption, while it shows a negative association with PHEV adoption. As this effect represents an endogeneity problem, its effect on BEV and PHEV adoption is not taken into account. Future research may wish to incorporate a factor that is rather related to the perceived readiness of charging infrastructure by a (PH)EV driver than the absolute number of public charging poles.

The degree of urbanization appears to have no influence on BEV purchase likelihood. However, for the varying degrees of urbanization, different influencing factors were found. For highly urban municipalities, BEV adoption is mainly predicted by the number of cars per household. This could be due to the fact that urban citizens are more aware of air pollution and thus more likely to balance the emission of their fleet. Interestingly and contrary to expectations, household size appeared to be the main determinant for moderately and little urban municipalities. With average household sizes of 2.32 persons this is approved as a valid outcome due to the fact that these households do not face any limitations for vehicle size. For the little and non-urban municipalities, results were quite consistent with the results of the main model. A positive moderating effect of the degree of urbanization on the number of public charging poles was found, although the actual influence is minimal (B=0.00004). The larger dependence of highly urban municipalities can be explained by the limited availability of private charging options compared to little and non-urban municipalities. In contrast to the results for BEV adoption, the degree of urbanization is found to be positively associated with PHEV adoption. PHEV adoption in highly urban municipalities is mainly influenced by the number of cars per household and the average distance to a highway. The latter shows a negative association, which will need to be further explored in future analyses. For the little urban and non-urban municipalities, standardized spendable household income is the main influential factor in the purchase consideration of an PHEV. This could be due to the fact that on top of the significant investment in the vehicle itself, inhabitants of these municipalities often have to arrange their own private charging infrastructure.

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The case study on the municipality of Rotterdam showed that in 2020 the average BEV adoption rate will, ceteris paribus, be 0.240% and the average PHEV adoption rate 1.045%. The neighborhoods that will see the highest adoption rates are, among others, Kralingen Oost, Molenlaankwartier, Terbregge and Nieuwe Werk. Municipalities and distribution system operators can anticipate on increasing (PH)EV adoption rates in areas with higher incomes.

44 Brenda Janssen – June 16th 2016

Bibliography ACEA 2016, Alternative Fuel Vehicle registrations: +20.0% in 2015; +21.1% in Q4, Available at: [Accessed 10 June 2016]

Agentschap NL 2011, Elektrisch rijden in de versnelling, plan van aanpak 2011-2015, Agentschap NL, Available at: [Accessed 3 December 2015]

ANWB 2016, Welke elektrische auto’s zijn er op de markt?, Available at: [Accessed 10 June 2016]

Bočkarjova, M, Rietveld, P & Knockaert, J 2013, Adoption of Electric Vehicle in the Netherlands – A Stated Choice Experiment. PhD. Tinbergen Institute.

Boonen, A 2013, The (Electric) Power of Municipalities.

Bryman, A 2011, Business research methods, Oxford University Press, 2011

CBS.nl 2016, Begrippen, Available at: [Accessed 29 April 2016]

CBS Statline 2016, Database, Available at: [Accessed 14 June 2016]

Citethisforme.com, (n.d.) Introduction to Harvard Referencing. [online] Available at: https://www.citethisforme.com/harvard-referencing [Accessed 28 January 2016]

Daziano, RA & Chiew, E 2012, Electric vehicles rising from the dead: Data needs for forecasting consumer response toward sustainable energy sources in personal transportation, Energy Policy, Volume 51(12), p. 876–894. Available at: [Accessed 22 December 2015]

Diamond, D 2009, The impact of government incentives for hybrid-electric vehicles, Energy Policy, Volume 37(3), p. 972-983, Available at: [Accessed 22 December 2015]

45 Brenda Janssen – June 16th 2016

Gallagher, KS & Muehlegger, E 2011, Giving green to get green? Incentives and consumer adoption of hybrid vehicle technology, Journal of Environmental Economics and Management, Volume 61(1), p. 1-15, Available at: [Accessed 28 Dec. 2015]

Gemeente Rotterdam 2016, Feitenkaart, Available at: [Accessed 13 June 2016]

Gemeente Rotterdam 2016, Feitenkaart: bevolkingsmonitor januari 2016, Availabe at: [Accessed 13 June 2016]

Hidrue, MK, Parsons, GR, Kempton, W & Gardner, MP 2011, Willingness to pay for electric vehicles and their attributes, Resource and Energy Economics, Volume 33(3), p. 686-705, Available at: [Accessed 29 December 2015]

ING Economisch Bureau 2016, Vooruitzicht Automotive, ING, Available at:

Keller, G 2012, Managerial statistics, 9th edition, South-Western

Kennisinstituut voor Mobilitieitsbeleid 2014, Niet autoloos, maar auto later. Den Haag: Ministerie van Infrastructuur en Milieu, Available at: [Accessed 27 January 2016]

Klimaatmonitor 2016, Database, Rijkswaterstaat: Ministerie van Infrastrctuur en Milieu, Available at: [Accessed 13 June 2016]

Lieven, T, Mühlmeier, S, Henkel, S & Waller, JF 2011, Who will buy electric cars? An empirical study in Germany, Transportation Research Part D: Transport and Environment, Volume 16(3), p. 236-243. Available at: [Accessed 22 Dec. 2015]

Linnenkamp, M. 2012, Elektrische auto moet binnenstedelijke luchtkwaliteit redden, Verkeerskunde. Available at: [Accessed 27 January 2016]

Mil, B van, Schelven, R van, & Kuiperi, F 2016, Terugblik en vooruitblik op het beleid voor elektrisch vervoer, Kwink Group, Available at: [Accessed 9 June 2016]

46 Brenda Janssen – June 16th 2016

Ministerie van Algemene Zaken 2015, Autobrief II: Eenvoudiger, stabieler en meer milieuwinst, Den Haag: Rijksoverheid, Available at: [Accessed 15 April 2016]

Musti, S & Kockelman, KM 2011, Evolution of the household vehicle fleet, Transportation Research Part A, Volume 45, p. 707-720, Available at: [Accessed 14 May 2015]

Oplaadpalen.nl 2016, Available at: http://www.oplaadpalen.nl/link/Wb77f1fffff000000fff/51.9351/4.4814/12 [Accessed 10 June 2016]

Rijksdienst voor Ondernemend Nederland 2015, Cijfers Elektrisch Vervoer, Available at: [Accessed 4 December 2015]

Rotterdam Buurtmonitor 2016, Rotterdam-Rijnmond in Cijfers, Available at: [Accessed 13 June 2016]

Sekaran, U & Bougie, R 2009, Research Methods for Business, 5th edition, John Wiley & Sons Ltd, The United Kingdom

Sierzchula, W, Bakker, S, Maat, K & Wee, B van 2014, The influence of financial incentives and other socio-economic factors on electric vehicle adoption, Energy Policy, Volume 68(5), p. 183-194, Available at: [Accessed 22 December 2015]

Siskens, R 2015, Incentives for off-peak charging of electric vehicles.

Tesla Motors 2016, Model 3, Available at: [Accessed 10 June 2016]

Unibo.it 2016, Durbin-Watson Significance tables, Available at: [Accessed 15 June 2016]

Wilmink, K 2015, A study on the factors influencing the adoption of Hybrid and Electric Vehicles in The Netherlands.

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Appendices A. Overview of all municipalities Very highly urban Amstelveen Groningen Rijswijk (ZH.) Utrecht Amsterdam Haarlem Rotterdam Vlaardingen Beverwijk Hilversum Schiedam Delft Leiden ’s-Gravenhage Dordrecht Leidschendam- Tilburg Voorburg Highly urban Alkmaar Eindhoven Katwijk 's-Hertogenbosch Almelo Enschede Kerkrade Sliedrecht Almere Etten-Leur Krimpen aan den IJssel Veenendaal Alphen aan de Rijn Gorinchem Leeuwarden Veldhoven Amersfoort Gouda Leiderdorp Velsen Apeldoorn Haarlemmermeer Lisse Venlo Arnhem Heemskerk Maassluis Vlissingen Assen Heemstede Maastricht Voorschoten Baarn Heerhugowaard Middelburg (Z.) Waddinxveen Barendrecht Heerlen Nieuwegein Wageningen Bergen op Zoom Hellevoetsluis Nijmegen Weesp Breda Helmond Nissewaard Zaanstad Brunssum Hendrik-Ido-Ambacht Oegstgeest Zandvoort Bussum Hengelo (O.) Oosterhout Zeist Capelle aan den Ijssel Hillegom Papendrecht Zoetermeer Den Helder Hoorn Purmerend Zutphen Deventer Huizen Ridderkerk Zwijndrecht Diemen IJsselstein Roosendaal Zwolle Moderately urban Alblasserdam Goes Nijkerk Uden Albrandswaard Goirle Noordwijk Uitgeest Appingedam Harderwijk Noordwijkerhout Uithoorn Best Harlingen Nuenen, Gerwen en Urk Nederwetten Bloemendaal Heerenveen Oldenzaal Valkenswaard Bodegraven-Reeuwijk Heiloo Oostzaan Veghel Borne Heusden Oss Vught Boxtel Hoogeveen Oud-Beijerland Waalwijk Bunschoten Hoogezand-Sappemeer Ouder-Amstel Wassenaar Castricum Houten Pijnacker-Nootdorp Weert Culemborg Kampen Rheden Westervoort De Bilt Landgraaf Rijssen-Holten Westland Doetinchem Lansingerland Roermond Wijchen Dongen Laren (NH.) Schijndel Wijk bij Duurstede Duiven Leerdam Sittard-Geleen Winterswijk Edam-Volendam Lelystad Smallingerland Woerden

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Ede Leusden Soest Wormerland Enkhuizen Loon op Zand Stede Broec Zevenaar Geertruidenberg Meppel Stichtse Vecht Zuidplas Geldrop-Mierlo Midden-Delfland Teylingen Gilze en Rijen Naarden Tiel Little urban Aalsmeer Eersel Lochem Sint-Michielsgestel Aalten Elburg Losser Sint-Oedenrode Asten Emmen Medemblik Someren Barneveld Epe Meerssen Son en Breugel Bedum Ermelo Moerdijk Stadskanaal Beek (L.) Franekeradeel Montferland Steenbergen Beemster Geldermalsen Montfoort Steenwijkerland Beesel Gemert-Bakel Nederweert Stein (L.) Bergeijk Gennep Noordenveld Strijen Bergen (NH.) Goeree-Overflakkee Noordoostpolder Súdwest-Fryslân Berkelland Grave Nunspeet Terneuzen Bernheze Haaksbergen Nuth Twenterand Beuningen Haarlemmerliede en Oirschot Utrechtse Heuvelrug Spaarnwoude Binnenmaas Halderberge Oisterwijk Vaals Bladel Hardenberg Oldambt Valkenburg aan de Geul Blaricum Hardinxveld- Oldebroek Veendam Giessendam Boxmeer Haren Oost Gelre Venray Brielle Hattem Oude IJsselstreek Vianen Brummen Heerde Oudewater Voerendaal Bunnik Heeze-Leende Overbetuwe Voorst Cranendonck Hellendoorn Peel en Maas Waalre Cromstrijen Heumen Pekela Waterland Cuijk Hilvarenbeek Putten Werkendam De Friese Meren Hof van Twente Raalte Weststellingwerf De Ronde Venen Horst aan de Maas Reimerswaal Westvoorne Delfzijl Kaag en Braassem Renkum Wierden Deurne Kapelle Reusel-De Mierden Wijdemeren Doesburg Krimpenerwaard Rhenen Woensdrecht Dongeradeel Laarbeek Rozendaal Woudenberg Drimmelen Landsmeer Rucphen Zaltbommel Dronten Langedijk Schagen Zeewolde Druten Leek Scherpenzeel Zoeterwoude Echt-Susteren Leeuwarderadeel Schouwen-Duiveland Zundert Eemnes Lingewaard Simpelveld Zwartewaterland Non-urban Aa en Hunze Eijsden-Margraten Menterwolde Slochteren Aalburg Ferwerderadiel Midden-Drenthe Sluis Achtkarspelen Giessenlanden Mill en Sint Hubert Staphorst

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Alphen-Chaam Grootegast Molenwaard Ten Boer Ameland Gulpen-Wittem Mook en Middelaar Terschelling Baarle-Nassau Haaren Muiden Texel Bellingwedde het Bildt Neder-Betuwe Tholen Berg en Dal Hollands Kroon Neerijnen Tubbergen Bergen (L.) Hulst Nieuwkoop Tynaarlo Boekel Koggenland Noord-Beveland Tytsjerksteradiel Borger-Odoorn Kollumerland en Olst-Wijhe Veere Nieuwkruisland Borsele Korendijk Ommen Vlagtwedde Bronckhorst Landerd Onderbanken Vlieland Buren Leudal Ooststellingwerf West Maas en Waal Coevorden Lingewaal Opmeer Westerveld Dalfsen Littenseradiel Opsterland Winsum Dantumadiel Lopik Renswoude Woudrichem De Marne Loppersum Rijnwaarden Zederik De Wolden Maasdriel Roerdalen Zeevang Dinkelland Maasgouw Schiermonnikoog Zuidhorn Drechterland Marum Schinnen Eemsmond Menameradiel Sint Anthonis Table 16: All municipalities of 2015 by their degree of urbanization (CBS, 2016)

Municipality at January 1st Consists of Since 2015 Alkmaar Graft-De Rijp and Schermer 2014 Berg en Dal Groesbeek, Millingen aan de Rijn, and Ubbergen Krimpenerwaard Bergambacht, Nederlek, Ouderker, Schoonhoven, and Vlist Nissewaard Bernisse and Spijkenisse ’s-Hertogenbosch Maasdonk Alphen aan den Rijn Boskoop en Rijnwoude 2013 De Friese Meren Boornsterhem, Gaasterlan Sleat, Lemsterland, and Skarsterlan Goeree-Overflakkee Dirksland, Goedereede, Middelharnis, and Oostflakkee 2012 Molenwaard Graafstroom, Liesveld, and Nieuw-Lekkerland Schagen Harenkarspel, Schagen, and Zijpen Hollands Kroon Anna Paulowna, Niedorp, Wieringen, and Wieringermeer 2011 Table 17: Merges of municipalities as of 2011 (CBS, 2016)

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B. Initiatives by local governments (PH)EV in Charging pole Source fleet? request? Aa en Hunze Website municipality Aalburg X X Enquete q1-2015 Aalsmeer X Website municipality Aalten Direct contact with municipality April ‘15 Achtkarspelen X Enquete q1-2015 Alblasserdam Website municipality Albrandswaard X Website municipality Alkmaar X Enquete q3 2014 Almelo Enquete q1-2015 Almere X X Enquete q1-2015 Alphen aan de Rijn X Website municipality Alphen-Chaam X Enquete q1-2015 Ameland X Enquete q1-2015 Amersfoort X Enquete q1-2015 Amstelveen X Enquete q3 2014 Amsterdam X X Enquete q1-2015 Apeldoorn X X Enquete q1-2015 Appingedam X Enquete q1-2015 Arnhem X Website municipality Assen Asten Enquete q1-2015 Baarle-Nassau Enquete q1-2015 Baarn Enquete q1-2015 Barendrecht X X Enquete q1-2015 Barneveld X Website municipality Bedum Enquete q1-2015 Beek (L.) Beemster Website municipality Beesel Bellingwedde Enquete q1-2015 Berg en Dal Enquete q1-2015 Bergeijk X Website municipality Bergen (L.) Bergen (NH.) Bergen op Zoom X Website municipality Berkelland Bernheze X X Enquete q1-2015 Best X Enquete q1-2015 Beuningen X Enquete q1-2015

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Beverwijk X Enquete q3 2014 Binnenmaas Website municipality Bladel X Enquete q1-2015 Blaricum X Enquete q3 2014 Bloemendaal X X Enquete q1-2015 Bodegraven-Reeuwijk Enquete q1-2015 Boekel Website municipality Borger-Odoorn X Enquete q1-2015 Borne Borsele Boxmeer Website municipality Boxtel X Enquete q1-2015 Breda X X Enquete q1-2015 Brielle Website municipality Bronckhorst X Enquete q1-2015 Brummen Enquete q1-2015 Brunssum Enquete q1-2015 Bunnik Website municipality Bunschoten Direct contact with municipality April ‘15 Buren X Enquete q1-2015 Bussum X Enquete q1-2015 Capelle aan den IJssel X X Enquete q1-2015 Castricum X X Enquete q1-2015 Coevorden Website municipality Cranendonck Website municipality Cromstrijen Cuijk Enquete q1-2015 Culemborg X Direct contact with municipality April ‘15 Dalfsen Direct contact with municipality April ‘15 Dantumadiel De Bilt X Enquete q1-2015 De Friese Meren De Marne X Enquete q1-2015 De Ronde Venen X X Enquete q1-2015 De Wolden Delft X X Enquete q1-2015 Delfzijl X Enquete q1-2015 Den Helder X Enquete q1-2015 Deurne X Website municipality Deventer Enquete q1-2015 Diemen X Enquete q1-2015

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Dinkelland Enquete q1-2015 Doesburg Doetinchem Website municipality Dongen X Website municipality Dongeradeel Dordrecht X Enquete q1-2015 Drechterland Enquete q3 2014 Drimmelen Enquete q3 2014 Dronten X Enquete q1-2015 Druten Duiven Echt-Susteren Direct contact with municipality April ‘15 Edam-Volendam X Enquete q1-2015 Ede X X Enquete q1-2015 Eemnes Enquete q1-2015 Eemsmond Enquete q1-2015 Eersel X X Enquete q1-2015 Eijsden-Margraten Website municipality Eindhoven X Website municipality Elburg X Direct contact with municipality April ‘15 Emmen Enkhuizen Enquete q1-2015 Enschede X Enquete q1-2015 Epe Ermelo Enquete q1-2015 Etten-Leur X Enquete q1-2015 Ferwerderadiel X Enquete q1-2015 Franekeradeel Geertruidenberg X Website municipality Geldermalsen Enquete q1-2015 Geldrop-Mierlo X Enquete q1-2015 Gemert-Bakel Website municipality Gennep Giessenlanden Enquete q1-2015 Gilze en Rijen X Enquete q1-2015 Goeree-Overflakkee X Enquete q1-2015 Goes X Enquete q3 2014 Goirle X Enquete q1-2015 Gorinchem X X Enquete q1-2015 Gouda Enquete q1-2015 Grave Website municipality

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Groningen (gemeente) X Enquete q1-2015 Grootegast Direct contact with municipality April ‘15 Gulpen-Wittem X Direct contact with municipality April ‘15 Haaksbergen Direct contact with municipality April ‘15 Haaren X Enquete q3 2014 Haarlem X X Enquete q1-2015 Haarlemmerliede en Spaarnwoude X Enquete q1-2015 Haarlemmermeer X X Enquete q1-2015 Halderberge Direct contact with municipality April ‘15 Hardenberg X Enquete q1-2015 Harderwijk X Enquete q1-2015 Hardinxveld- X X Enquete q1-2015 Giessendam Haren X Enquete q1-2015 Harlingen Direct contact with municipality April ‘15 Hattem Enquete q1-2015 Heemskerk X Enquete q1-2015 Heemstede X Enquete q1-2015 Heerde Enquete q1-2015 Heerenveen Heerhugowaard X X Enquete q1-2015 Heerlen X Enquete q1-2015 Heeze-Leende X Enquete q1-2015 Heiloo Hellendoorn Direct contact with municipality April ‘15 Hellevoetsluis X Enquete q1-2015 Helmond X X Enquete q1-2015 Hendrik-Ido-Ambacht X Enquete q1-2015 Hengelo (O.) X Enquete q1-2015 het Bildt X Enquete q1-2015 Heumen Heusden Enquete q1-2015 Hillegom Enquete q1-2015 Hilvarenbeek X Enquete q1-2015 Hilversum X X Enquete q3 2014 Hof van Twente Enquete q1-2015 Hollands Kroon Enquete q3 2014 Hoogeveen Hoogezand-Sappemeer Hoorn X Enquete q1-2015 Horst aan de Maas Direct contact with municipality April ‘15 Houten Enquete q3 2014

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Huizen Hulst Enquete q1-2015 IJsselstein Kaag en Braassem Enquete q1-2015 Kampen X Enquete q1-2015 Kapelle Katwijk X Website municipality Kerkrade Direct contact with municipality April ‘15 Koggenland X Enquete q1-2015 Kollumerland en Nieuwkruisland Enquete q1-2015 Korendijk Krimpen aan den IJssel X Enquete q1-2015 Krimpenerwaard Laarbeek Enquete q1-2015 Landerd Website municipality Landgraaf Enquete q1-2015 Landsmeer Enquete q3 2014 Langedijk Enquete q1-2015 Lansingerland Laren (NH.) Enquete q3 2014 Leek X X Enquete q1-2015 Leerdam X X Enquete q1-2015 Leeuwarden X Enquete q1-2015 Leeuwarderadeel Leiden Website municipality Leiderdorp X Enquete q1-2015 Leidschendam-Voorburg Enquete q1-2015 Lelystad Leudal Leusden X Enquete q1-2015 Lingewaal Enquete q1-2015 Lingewaard Lisse Enquete q1-2015 Littenseradiel Direct contact with municipality April ‘15 Lochem X X Enquete q1-2015 Loon op Zand X Enquete q1-2015 Lopik Enquete q1-2015 Loppersum Losser Enquete q1-2015 Maasdriel Enquete q1-2015 Maasgouw Direct contact with municipality April ‘15

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Maassluis X X Enquete q1-2015 Maastricht X Enquete q1-2015 Marum Medemblik X Enquete q3 2014 Meerssen X Enquete q1-2015 Menameradiel Enquete q1-2015 Menterwolde Meppel X Enquete q1-2015 Middelburg (Z.) X X Enquete q1-2015 Midden-Delfland Midden-Drenthe Mill en Sint Hubert Website municipality Moerdijk X Enquete q1-2015 Molenwaard X Enquete q1-2015 Montferland Enquete q1-2015 Montfoort Enquete q3 2014 Mook en Middelaar Muiden X Enquete q1-2015 Naarden Neder-Betuwe X Enquete q1-2015 Nederweert Direct contact with municipality April ‘15 Neerrijnen Direct contact with municipality April ‘15 Nieuwegein X Enquete q3 2014 Nieuwkoop Website municipality Nijkerk Direct contact with municipality April ‘15 Nijmegen Enquete q1-2015 Nissewaard X Noord-Beveland Direct contact with municipality April ‘15 Noordenveld X Enquete q1-2015 Noordoostpolder Noordwijk Website municipality Noordwijkerhout Website municipality Nuenen, Gerwen en Nederwetten X Website municipality Nunspeet X Enquete q1-2015 Nuth Oegstgeest X Website municipality Oirschot Enquete q1-2015 Oisterwijk X Website municipality Oldambt Oldebroek Oldenzaal

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Olst-Wijhe Ommen X Enquete q1-2015 Onderbanken Oost Gelre Oosterhout X X Enquete q3 2014 Ooststellingwerf Oostzaan X X Enquete q1-2015 Opmeer X Enquete q1-2015 Opsterland X Enquete q1-2015 Oss Website municipality Oud-Beijerland X Direct contact with municipality April ‘15 Oude IJsselstreek Ouder-Amstel X X Enquete q1-2015 Oudewater X Enquete q3 2014 Overbetuwe X Direct contact with municipality April ‘15 Papendrecht X Website municipality Peel en Maas X Enquete q1-2015 Pekela Enquete q1-2015 Pijnacker-Nootdorp Website municipality Purmerend X Enquete q1-2015 Putten X Enquete q1-2015 Raalte X Reimerswaal Direct contact with municipality April ‘15 Renkum X Renswoude Enquete q3 2014 Reusel-De Mierden Rheden X Rhenen Ridderkerk Rijnwaarden X Enquete q1-2015 Rijssen-Holten Direct contact with municipality April ‘15 Rijswijk (ZH.) Enquete q1-2015 Roerdalen Roermond Roosendaal X Website municipality Rotterdam X Website municipality Rozendaal Rucphen Website municipality Schagen Enquete q3 2014 Scherpenzeel Direct contact with municipality April ‘15 Schiedam X Website municipality

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Schiermonnikoog Enquete q1-2015 Schijndel Enquete q1-2015 Schinnen Schouwen-Duiveland X Enquete q1-2015 's-Gravenhage X X Enquete q1-2015 's-Hertogenbosch X X Enquete q3 2014 Simpelveld Enquete q1-2015 Sint Anthonis Website municipality Sint-Michielsgestel X Enquete q1-2015 Sint-Oedenrode X Enquete q3 2014 Sittard-Geleen Sliedrecht Website municipality Slochteren Enquete q1-2015 Sluis X Enquete q1-2015 Smallingerland Soest Someren X Enquete q1-2015 Son en Breugel X Enquete q1-2015 Stadskanaal Staphorst Direct contact with municipality April ‘15 Stede Broec Enquete q3 2014 Steenbergen X Enquete q1-2015 Steenwijkerland X Direct contact with municipality April ‘15 Stein (L.) Stichtse Vecht Strijen X Direct contact with municipality April ‘15 Súdwest-Fryslân Enquete q1-2015 Ten Boer Terneuzen Enquete q1-2015 Terschelling Texel Teylingen Direct contact with municipality April ‘15 Tholen X Enquete q1-2015 Tiel X Enquete q1-2015 Tilburg X X Enquete q1-2015 Tubbergen Enquete q1-2015 Twenterand X Enquete q1-2015 Tynaarlo Tytsjerksteradiel Direct contact with municipality April ‘15 Uden X X Enquete q1-2015 Uitgeest

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Uithoorn X Enquete q1-2015 Urk Utrecht X Website municipality Utrechtse Heuvelrug Vaals Valkenburg aan de Geul Direct contact with municipality April ‘15 Valkenswaard Website municipality Veendam Enquete q1-2015 Veenendaal X Veere X Enquete q1-2015 Veghel X Enquete q1-2015 Veldhoven X X Enquete q1-2015 Velsen X Enquete q3 2014 Venlo Enquete q1-2015 Venray Vianen Vlaardingen Website municipality Vlagtwedde Direct contact with municipality April ‘15 Vlieland Enquete q1-2015 Vlissingen Enquete q1-2015 Voerendaal Voorschoten Website municipality Voorst Vught X Website municipality Waalre X Enquete q1-2015 Waalwijk X X Enquete q1-2015 Waddinxveen Website municipality Wageningen X Enquete q1-2015 Wassenaar Waterland Enquete q1-2015 Weert X Enquete q1-2015 Weesp Werkendam X Website municipality West Maas en Waal Direct contact with municipality April ‘15 Westerveld Westervoort X Enquete q1-2015 Westland X Enquete q1-2015 Weststellingwerf Westvoorne Wierden Enquete q1-2015 Wijchen Enquete q1-2015

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Wijdemeren X Enquete q3 2014 Wijk bij Duurstede Enquete q3 2014 Winsum Winterswijk Enquete q1-2015 Woensdrecht Enquete q3 2014 Woerden X Enquete q3 2014 Wormerland Direct contact with municipality April ‘15 Woudenberg Direct contact with municipality April ‘15 Woudrichem X Enquete q1-2015 Zaanstad X Zaltbommel X Zandvoort Direct contact with municipality April ‘15 Zederik Direct contact with municipality April ‘15 Zeevang Zeewolde X Direct contact with municipality April ‘15 Zeist X Enquete q3 2014 Zevenaar X Zoetermeer X X Website municipality Zoeterwoude X Enquete q1-2015 Zuidhorn Zuidplas Zundert Zutphen Zwartewaterland Zwijndrecht X Website municipality Zwolle X X Enquete q1-2015 TOTAL 93 119 Table 18: Initiatives by local governments in 2014 and 2015 (APPM, 2015)

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C. Estimating (PH)EV adoption C.1 Estimating demographic, municipal and context drivers on BEV adoption With n equaling 180 and 19 predictors, the Durbin Watson of 1.699 can be accepted.

Model R R2 Adjusted Std. R2 R2 Error Change 1 Control variables only 0.586 0.343 0.328 0.044 0.343 2 Control & income, household size, 0.605 0.366 0.325 0.045 0.023 number of cars and age 3 Control & all demographic variables 0.634 0.401 0.355 0.044 0.035 4 Control, demographic & municipal 0.637 0.405 0.351 0.044 0.004 variables 5 Control, demographic, municipal & 0.777 0.604 0.560 0.036 0.199 context variables 6 Control, demographic, municipal and 0.788 0.621 0.576 0.035 0.016 context variables & urban degree Table 19: Complete BEV regression model summary. Statistically significant at α=0.01

Model B Standardized Beta t Sig. Coefficients 6 (Constant) -0.127 -0.672 0.502 log_inhabitants 0.007 0.054 0.575 0.566 mean_woz 0.000 -0.300 -2.105 0.037* lease 0.006 0.024 0.363 0.717 population_density 0.000 -0.246 -2.772 0.006** mean_householdincome 0.012 0.546 3.503 0.001** mean_householdsize 0.015 0.051 0.951 0.343 mean_cars_perhousehold 0.066 0.239 2.842 0.005** relative_age_30_40 -0.330 -0.093 -0.693 0.489 relative_age_40_50 -0.190 -0.033 -0.540 0.590 relative_age_50_60 -0.420 -0.098 -1.259 0.210 relative_age_65 -0.267 -0.138 -0.930 0.354 mean_relative_income_labour -0.165 -0.112 -0.830 0.408 mean_relative_income_enterprise 0.125 0.077 0.642 0.522 municipal_adoption 0.005 0.050 0.958 0.340 chargepole_request 0.002 0.016 0.282 0.778 no._of_chargingpoles 0.000 0.700 9.469 0.000** distance_highway 0.002 0.123 1.931 0.055 distance_trainstation 0.000 -0.004 -0.062 0.951 urban_code_reverse 0.016 0.363 2.635 0.009** Table 20: Demographic, municipal and context regression coefficients for BEV purchase likelihood * Statistically significant at α=0.05, ** Statistically significant at α=0.01

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C.2 Estimating demographic, municipal and context drivers on PHEV adoption For a sample size of 180 and 18 predictors, the Durbin Watson exceeds the upper limit by 6.0% (1.983>1.871). The variable household income is removed due to a high VIF score.

Model R R2 Adjusted Std. R2 R2 Error Change 1 Control variables only 0.602 0.362 0.348 0.168 0.362 2 Control & household size, number of 0.700 0.490 0.460 0.153 0.128 cars and age 3 Control & all demographic variables 0.708 0.501 0.465 0.152 0.010 4 Control, demographic & municipal 0.710 0.504 0.462 0.152 0.003 variables 5 Control, demographic, municipal & 0.734 0.539 0.491 0.148 0.035 context variables 6 Control, demographic, municipal and 0.759 0.576 0.528 0.143 0.036 context variables & urban degree Table 21: Complete PHEV regression model summary. Statistically significant at α=0.01

Model B Standardized Beta t Sig. Coefficients 6 (Constant) 0.150 0.197 0.844 log_inhabitants 0.055 0.108 1.102 0.272 mean_woz 0.000 0.087 0.940 0.349 lease 0.255 0.282 4.136 0.000** population_density 0.000 0.102 1.158 0.248 mean_householdsize -0.097 -0.089 -1.586 0.115 mean_cars_perhousehold 0.398 0.377 4.318 0.000** relative_age_30_40 -4.532 -0.336 -2.448 0.015* relative_age_40_50 0.691 0.032 0.503 0.616 relative_age_50_60 -5.115 -0.313 -3.803 0.000** relative_age_65 -0.779 -0.105 -0.720 0.473 mean_relative_income_labour 0.737 0.131 0.992 0.323 mean_relative_income_enterprise 2.408 0.390 3.240 0.001** municipal_adoption -0.016 -0.039 -0.705 0.482 chargepole_request 0.011 0.025 0.430 0.668 no._of_chargingpoles 0.000 -0.128 -1.655 0.100 distance_highway -0.001 -0.019 -0.305 0.761 distance_trainstation -0.002 -0.057 -0.818 0.414 urban_code_reverse 0.089 0.530 3.721 0.000** Table 22: Demographic, municipal and context regression coefficients for PHEV purchase likelihood * Statistically significant at α=0.05, ** Statistically significant at α=0.01

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D. Estimating BEV adoption per urban degree D.1 Highly urban municipalities (code 4 & 5) With n=56 and k=14, the Durbin Watson equaling 1.984 can be accepted.

Model R R2 Adjusted Std. R2 R2 Error Change 1 Control variables only 0.608 0.370 0.320 0.066 0.370 2 Control & demographic variables 0.647 0.419 0.306 0.067 0.049 3 Control, demographic & municipal 0.651 0.424 0.280 0.068 0.005 variables 4 Control, demographic, municipal & 0.858 0.736 0.646 0.048 0.312 context variables Table 23: BEV regression model summary for highly urban municipalities. Statistically significant at α=0.01

Model B Standardized Beta t Sig. Coefficients 4 (Constant) -0.856 -1.813 0.077 log_inhabitants 0.015 0.061 0.416 0.679 mean_woz 0.000 -0.118 -0.529 0.599 lease -0.019 -0.091 -0.748 0.459 population_density 0.000 -0.215 -1.654 0.106 mean_householdincome 0.015 0.391 1.513 0.138 mean_householdsize 0.027 0.066 0.636 0.529 mean_cars_perhousehold 0.129 0.371 2.703 0.010** relative_age_20_65 0.700 0.277 1.350 0.184 relative_age_65 0.064 0.023 0.156 0.876 municipal_adoption 0.013 0.079 0.827 0.413 chargepole_request -0.003 -0.021 -0.227 0.822 no._of_chargingpoles 0.000 0.939 6.737 0.000** distance_highway -0.030 -0.173 -1.759 0.086 distance_trainstation -0.004 -0.133 -1.420 0.163 Table 24: Regression coefficients for BEV purchase likelihood of highly urban municipalities * Statistically significant at α=0.05, ** Statistically significant at α=0.01

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D.2 Moderately urban municipalities (code 3) With n=34 and k=14, the Durbin Watson equaling 1.982 can be accepted.

Model R R2 Adjusted Std. R2 R2 Error Change 1 Control variables only 0.704 0.495 0.445 0.026 0.495 2 Control & demographic variables 0.803 0.644 0.530 0.024 0.149 3 Control, demographic & municipal 0.812 0.659 0.511 0.024 0.015 variables 4 Control, demographic, municipal & 0.845 0.714 0.529 0.024 0.055 context variables Table 25: BEV regression model summary for moderately urban municipalities. Statistically significant at α=0.01

Model B Standardized Beta t Sig. Coefficients 4 (Constant) -1.191 -2.035 0.055 log_inhabitants 0.004 0.026 0.147 0.885 mean_woz 0.000 0.353 0.714 0.483 population_density 0.000 -0.363 -1.895 0.073 mean_householdincome 0.009 0.753 1.339 0.196 mean_householdsize 0.069 0.416 2.433 0.024* mean_cars_perhousehold -0.042 -0.127 -0.601 0.555 relative_age_20_65 1.365 0.822 1.838 0.081 relative_age_65 0.533 0.349 1.444 0.164 municipal_adoption 0.008 0.115 0.927 0.365 chargepole_request 0.001 0.009 0.048 0.962 no._of_chargingpoles 0.001 0.158 0.816 0.424 distance_highway -0.023 -0.256 -1.527 0.142 distance_trainstation -0.001 -0.116 -0.715 0.483 Table 26: Regression coefficients for BEV purchase likelihood of moderately urban municipalities * Statistically significant at α=0.05, ** Statistically significant at α=0.01

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D.3 Little urban municipalities (code 2) With n=51 and k=14, the Durbin Watson equaling 1.722 can be accepted.

Model R R2 Adjusted Std. R2 R2 Error Change 1 Control variables only 0.548 0.300 0.255 0.030 0.300 2 Control & demographic variables 0.762 0.580 0.500 0.025 0.280 3 Control, demographic & municipal 0.764 0.583 0.479 0.025 0.003 variables 4 Control, demographic, municipal & 0.823 0.678 0.565 0.023 0.095 context variables Table 27: BEV regression model summary for little urban municipalities. Statistically significant at α=0.01

Model B Standardized Beta t Sig. Coefficients 4 (Constant) -0.206 -0.728 0.471 log_inhabitants -0.018 -0.119 -0.853 0.399 mean_woz 0.000 -0.511 -2.070 0.046* population_density 0.000 -0.189 -1.656 0.106 mean_householdincome 0.017 1.149 4.555 0.000** mean_householdsize 0.064 0.297 2.764 0.009** mean_cars_perhousehold 0.062 0.183 1.635 0.111 relative_age_20_65 -0.316 -0.159 -0.924 0.362 relative_age_65 -0.269 -0.186 -1.086 0.284 municipal_adoption -0.012 -0.155 -1.470 0.150 chargepole_request 0.000 -0.004 -0.034 0.973 no._of_chargingpoles 0.006 0.449 3.286 0.002** distance_highway -0.003 -0.051 -0.511 0.613 distance_trainstation 0.000 -0.003 -0.022 0.983 Table 28: Regression coefficients for BEV purchase likelihood of little urban municipalities * Statistically significant at α=0.05, ** Statistically significant at α=0.01

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D.4 Non-urban municipalities (code 1) With n=39 and k=13, the Durbin Watson equaling 2.029 can be accepted.

Model R R2 Adjusted Std. R2 R2 Error Change 1 Control variables only 0.479 0.229 0.163 0.031 0.229 2 Control & demographic variables 0.634 0.402 0.242 0.029 0.172 3 Control, demographic & municipal 0.692 0.479 0.293 0.028 0.077 variables 4 Control, demographic, municipal & 0.780 0.608 0.405 0.026 0.129 context variables Table 29: BEV regression model summary for non-urban municipalities. Statistically significant at α=0.05

Model B Standardized Beta t Sig. Coefficients 4 (Constant) 0.825 2.902 0.008** log_inhabitants 0.025 0.257 1.248 0.223 mean_woz 0.000 0.376 1.143 0.264 population_density 0.000 0.466 2.317 0.029* mean_householdincome -0.007 -0.393 -1.167 0.254 mean_householdsize -0.027 -0.162 -1.091 0.286 mean_cars_perhousehold 0.012 0.061 0.268 0.791 relative_age_20_65 -1.322 -0.626 -3.408 0.002** relative_age_65 -0.101 -0.090 -0.439 0.664 municipal_adoption 0.006 0.092 0.575 0.570 chargepole_request 0.024 0.300 1.864 0.074 no._of_chargingpoles -0.001 -0.130 -0.891 0.381 distance_highway 0.003 0.554 2.447 0.220 distance_trainstation 0.000 0.073 0.430 0.671 Table 30: Regression coefficients for BEV purchase likelihood of non-urban municipalities * Statistically significant at α=0.05, ** Statistically significant at α=0.01

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D.5 Moderating effect of urbanization for BEV adoption With n=384 and k=7, the Durbin Watson equaling 1.797 can be accepted.

Model R R2 Adjusted Std. R2 R2 Error change 1 Control variables only 0.525 0.276 0.268 0.046 0.276 2 Control & context variables 0.526 0.277 0.265 0.046 0.001 3 Control and context variables & urban 0.590 0.348 0.336 0.043 0.071 moderator Table 31: Context BEV with urban moderator regression model summary. Statistically significant at α=0.01

Model B Standardized Beta T Sig. Coefficients 3 (Constant) -0.136 -3.335 0.001** log_inhabitants 0.026 0.176 3.047 0.002** mean_woz 0.000 0.358 8.200 0.000** lease 0.042 0.157 3.135 0.002** population_density 0.000 0.003 0.060 0.952 distance_highway 0.000 0.010 0.222 0.824 distance_trainstation 0.000 -0.004 -0.077 0.939 chargingpoles_X_urban_degree 0.000 0.318 6.402 0.000** Table 32: Regression coefficients for BEV purchase likelihood with interaction of the number of public charging poles and degree of urbanization * Statistically significant at α=0.05, ** Statistically significant at α=0.01

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E. Estimating PHEV adoption per urban degree E.1 Highly urban municipalities (code 4 & 5) With n=56 and k=13, the Durbin Watson equaling 1.870 can be accepted.

Model R R2 Adjusted Std. R2 R2 Error Change 1 Control variables only 0.587 0.345 0.293 0.232 0.345 2 Control & demographic variables 0.830 0.689 0.636 0.167 0.344 3 Control, demographic & municipal 0.836 0.698 0.631 0.168 0.009 variables 4 Control, demographic, municipal & 0.884 0.782 0.715 0.148 0.084 context variables Table 33: PHEV regression model summary for highly urban municipalities. Statistically significant at α=0.01

Model B Standardized Beta t Sig. Coefficients 4 (Constant) -3.886 -3.159 0.003** log_inhabitants 0.268 0.319 2.421 0.020* mean_woz 0.002 0.371 4.082 0.000** lease 0.062 0.087 0.806 0.425 population_density 0.000 0.283 2.792 0.008** mean_householdsize -0.213 -0.151 -1.630 0.111 mean_cars_perhousehold 1.095 0.914 7.442 0.000** relative_age_20_65 3.887 0.444 2.531 0.015* relative_age_65 -1.173 -0.121 -0.921 0.362 municipal_adoption -0.045 -0.081 -0.943 0.351 chargepole_request 0.005 0.008 0.102 0.919 no._of_chargingpoles 0.000 -0.225 -1.888 0.066 distance_highway -0.155 -0.261 -3.019 0.004** distance_trainstation -0.009 -0.083 -1.054 0.298 Table 34: Regression coefficients for PHEV purchase likelihood of highly urban municipalities * Statistically significant at α=0.05, ** Statistically significant at α=0.01

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E.2 Moderately urban municipalities (code 3) With n=34 and k=11, the Durbin Watson equaling 1.851 can be accepted.

Model R R2 Adjusted Std. R2 R2 Error Change 1 Control variables only 0.551 0.303 0.234 0.123 0.303 2 Control & demographic variables 0.621 0.385 0.248 0.122 0.082 3 Control, demographic & municipal 0.679 0.462 0.289 0.118 0.077 variables 4 Control, demographic, municipal & 0.767 0.589 0.383 0.110 0.127 context variables Table 35: PHEV regression model summary for moderately urban municipalities. Statistically significant at α=0.05

Model B Standardized Beta t Sig. Coefficients 4 (Constant) -0.053 -0.089 0.930 log_inhabitants 0.168 0.272 1.485 0.152 mean_woz 0.001 0.506 2.783 0.011* population_density 0.000 0.207 1.123 0.273 mean_householdsize -0.032 -0.049 -0.278 0.784 mean_cars_perhousehold -0.338 -0.254 -1.189 0.247 relative_age_65 -0.731 -0.119 -0.647 0.524 municipal_adoption 0.010 0.035 0.246 0.808 chargepole_request 0.087 0.316 1.631 0.117 no._of_chargingpoles 0.002 0.067 0.337 0.740 distance_highway -0.096 -0.266 -1.580 0.128 distance_trainstation 0.008 0.259 1.452 0.161 Table 36: Regression coefficients for PHEV purchase likelihood of moderately urban municipalities * Statistically significant at α=0.05, ** Statistically significant at α=0.01

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E.3 Little urban municipalities (code 2) With n=51 and k=13, the Durbin Watson equaling 1.995 can be accepted.

Model R R2 Adjusted Std. R2 R2 Error Change 1 Control variables only 0.516 0.267 0.220 0.138 0.267 2 Control & demographic variables 0.651 0.424 0.314 0.129 0.157 3 Control, demographic & municipal 0.678 0.459 0.324 0.128 0.036 variables 4 Control, demographic, municipal & 0.685 0.470 0.283 0.132 0.010 context variables Table 37: PHEV regression model summary for little urban municipalities. Statistically significant at α=0.05

Model B Standardized Beta t Sig. Coefficients 4 (Constant) 0.889 0.545 0.589 log_inhabitants 0.103 0.150 0.839 0.407 mean_woz 0.000 0.038 0.121 0.904 population_density 0.000 -0.067 -0.456 0.651 mean_householdincome 0.048 0.727 2.247 0.031* mean_householdsize 0.023 0.024 0.172 0.864 mean_cars_perhousehold -0.091 -0.060 -0.419 0.678 relative_age_20_65 -2.456 -0.276 -1.245 0.221 relative_age_65 -3.495 -0.538 -2.449 0.019* municipal_adoption 0.000 -0.001 -0.010 0.992 chargepole_request -0.065 -0.202 -1.463 0.152 no._of_chargingpoles -0.008 -0.135 -0.768 0.448 distance_highway 0.005 0.019 0.148 0.883 distance_trainstation -0.001 -0.044 -0.287 0.775 Table 38: Regression coefficients for PHEV purchase likelihood of little urban municipalities * Statistically significant at α=0.05, ** Statistically significant at α=0.01

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E.4 Non-urban municipalities (code 1) With n=39 and k=13, the Durbin Watson equaling 1.304 can be accepted.

Model R R2 Adjusted Std. R2 R2 Error Change 1 Control variables only 0.735 0.540 0.501 0.105 0.540 2 Control & demographic variables 0.876 0.768 0.706 0.081 0.228 3 Control, demographic & municipal 0.883 0.780 0.701 0.082 0.011 variables 4 Control, demographic, municipal & 0.890 0.791 0.683 0.084 0.012 context variables Table 39: PHEV regression model summary for non-urban municipalities. Statistically significant at α=0.01

Model B Standardized Beta t Sig. Coefficients 4 (Constant) 2.358 2.549 0.017* log_inhabitants 0.006 0.015 0.098 0.923 mean_woz 0.000 -0.109 -0.455 0.653 population_density 0.000 0.132 0.898 0.378 mean_householdincome 0.051 0.654 2.662 0.013* mean_householdsize -0.088 -0.117 -1.078 0.291 mean_cars_perhousehold 0.008 0.009 0.052 0.959 relative_age_20_65 -4.488 -0.476 -3.557 0.002** relative_age_65 -2.741 -0.543 -3.649 0.001** municipal_adoption -0.030 -0.099 -0.846 0.406 chargepole_request -0.040 -0.114 -0.967 0.343 no._of_chargingpoles -0.001 -0.013 -0.120 0.905 distance_highway 0.005 0.182 1.102 0.281 distance_trainstation -0.001 -0.076 -0.613 0.546 Table 40: Regression coefficients for PHEV purchase likelihood of non-urban municipalities * Statistically significant at α=0.05, ** Statistically significant at α=0.01

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E.5 Moderating effect of urbanization for BEV adoption With n=384 and k=7, the Durbin Watson equaling 1.886 can be accepted.

Model R R2 Adjusted Std. R2 R2 Error change 1 Control variables only 0.579 0.335 0.328 0.188 0.335 2 Control & context variables 0.583 0.340 0.330 0.188 0.005 3 Control and context variables & urban 0.621 0.385 0.374 0.182 0.045 moderator Table 41: Context PHEV with urban moderator regression model summary. Statistically significant at α=0.01

Model B Standardized Beta T Sig. Coefficients 3 (Constant) -0.523 -3.053 0.002** log_inhabitants 0.120 0.190 3.391 0.001** mean_woz 0.001 0.359 8.485 0.000** lease 0.556 0.485 9.982 0.000** population_density 0.000 0.091 1.787 0.075 distance_highway -0.003 -0.029 -0.669 0.504 distance_trainstation -0.001 -0.037 -0.801 0.423 chargingpoles_X_urban_degree 0.000 -0.253 -5.245 0.000** Table 42: Regression coefficients for PHEV purchase likelihood with interaction of the number of public charging poles and degree of urbanization * Statistically significant at α=0.05, ** Statistically significant at α=0.01

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F. Case study: Rotterdam F.1 Predicted BEV adoption per district of Rotterdam

Disctrict Predicted BEV Predicted BEV Predicted BEV adoption rate adoption rate based adoption rate 2020 2015 on highly urban model 2015 Hillegersberg-Schiebroek 0.241% 0.116% 0.276% Prins Alexander 0.214% 0.103% 0.244% Rotterdam Centrum 0.202% 0.090% 0.235% Kralingen-Crooswijk 0.186% 0.065% 0.216% Hoek van Holland 0.186% 0.129% 0.216% Rozenburg 0.185% 0.129% 0.214% Overschie 0.184% 0.116% 0.213% Hoogvliet 0.183% 0.116% 0.210% Noord 0.181% 0.065% 0.208% IJsselmonde 0.178% 0.090% 0.204% Feijenoord 0.175% 0.077% 0.200% Delfshaven 0.168% 0.065% 0.192% Pernis 0.168% 0.129% 0.196% Charlois 0.164% 0.077% 0.188% Rotterdam (municipality) 0.154%* 0.090% 0.240% Table 43: Predicted BEV adoption rates for the districts of the municipality of Rotterdam *Adoption rate in April 2016

Figure 8: charging infrastructure of the municipality of Rotterdam (Oplaadpalen.nl, 2016)

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F.2 Predicted PHEV adoption per district of Rotterdam

Figure 9: Predicted PHEV adoption in the municipality of Rotterdam (2015)

Disctrict Predicted PHEV Predicted PHEV Predicted PHEV adoption rate adoption rate based adoption rate 2020 2015 on highly urban model 2015 Rotterdam Centrum 0.977% 0.114% 1.134% Hillegersberg-Schiebroek 0.863% 0.087% 1.313% Overschie 0.711% 0.232% 0.844% Delfshaven 0.689% -0.163% 0.808% Kralingen-Crooswijk 0.644% -0.244% 1.058% Hoek van Holland 0.630% 0.017% 1.057% Prins Alexander 0.615% 0.087% 1.035% Noord 0.571% 0.149% 0.979% Rozenburg 0.561% 0.134% 0.985% Hoogvliet 0.515% 0.164% 0.927% Pernis 0.476% 0.008% 0.895% IJsselmonde 0.412% -0.026% 0.807% Feijenoord 0.410% -0.257% 0.800% Charlois 0.353% -0.227% 0.736% Rotterdam (municipality) 0.710%* 0.576% 1.045% Table 44: Predicted PHEV adoption rates for the districts of the municipality of Rotterdam *Adoption rate in April 2016

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