Battery Electric Vehicle adoption in regions without strong policies

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Working Paper

Battery Electric Vehicle adoption in regions without strong policies

Author(s):

Brückmann, Gracia; Willibald, Fabian; Blanco Gonzalez, Victor Publication Date:

2020-12-09 Permanent Link:

https://doi.org/10.3929/ethz-b-000456016

Originally published in:

OSF Preprints , http://doi.org/10.31219/osf.io/u45dg

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Battery Electric Vehicle adoption in regions without strong policies (Br¨ uckmann, Willibald, Blanco 2020)

Highlights

• Revealed preferences from battery electric vehicle and conventional car holders

• Survey data combined with home location data such as charger density

• Mixed-effects model shows technology affinity is strongest predictor

• Similarly green party preferences and home ownership increase adop- tion probability

• No significant effects from home locations’ population or charging sta- tion density

1

This is a pre-print of an accepted version. Differences from the later published version

are all errors from Gracia Br¨ uckmann, who serves as the corresponding author. Feel free

to contact my via email, if you spot mistakes in this version.

Battery Electric Vehicle adoption in regions without strong policies

Gracia Br¨ uckmann

, Fabian Willibald

, Victor Blanco

a

Institute of Science, Technology and Policy (ISTP), ETH Zurich, Universit¨ atstrasse 41, Zurich 8092, Switzerland, bgracia@ethz.ch

b

Center for Comparative and International Studies (CIS), ETH Zurich, Haldeneggsteig 4, Zurich 8092, Switzerland

c

Planning of Landscape and Urban Systems, Institute for Spatial and Landscape Planning, ETH Zurich, Haldeneggsteig 4, Zurich 8092, Switzerland

Abstract

Individual motorized transport is a major source of emissions and needs to be reduced to meet international agreements. Although alternatives to internal combustion engine vehicles are already on the market without extensive po- litical support, electric vehicle (EV) adoption remains low. Understanding the drivers of adoption of alternative technologies is key to develop effec- tive measures to accelerate their diffusion. This paper presents individual consumer characteristics and home-location based spatial characteristics of current battery electric vehicle (BEV) and internal combustion engine ve- hicle holders, in a region free from strong EV policies. Using a generalized linear mixed-effects logistic model on this revealed preference data, we find that BEV adoption is predicted by technology affinity, high income, green party preferences, and living in one’s own house. Altogether, the study offers

∗

Corresponding author

E-mail address: gracia.brueckmann@istp.ethz.ch

insights on the characteristics of early adopters of BEVs that can be valuable to policymakers, energy grid and charging infrastructure operators, as well as the automotive industry.

Keywords: Plug-in electric vehicle, Revealed preferences, Individual adopters’ characteristics, Consumer demand, Early adopter, Battery electric vehicle market

1. Introduction

Decarbonisation has become one of the major global challenges of our time (Rogelj et al., 2015; Schellnhuber et al., 2016; Rockstr¨ om et al., 2017). As of December 2015, 195 countries who ratified the United Nations Framework Convention on Climate Change have agreed to limit anthropogenic global warming to 1.5-2 ° C (UNFCCC, 2015). Transportation is a leading carbon contributor worldwide (Abergel et al., 2017) that could reduce its emissions through electrification (Pietzcker et al., 2014). As the generation of electric- ity increasingly moves towards renewable energy sources, high adoption rates of battery electric vehicles (BEVs) can be pivotal in reducing these emissions (Ajanovic and Haas, 2016).

Globally, electric vehicle (EV) adoption is currently highly dependent on

strong (demand-side orientated) EV policies (Sierzchula et al., 2014; L´ evay

et al., 2017; M¨ unzel et al., 2019). These strong policies include a wide spec-

trum from road space privileges (bus or HOV lanes), preferential parking and

charging options, free use of ferries for BEV drivers, tax reductions to feebate

systems, financial purchase subsidies and exemptions, and exemptions from license plate lotteries (Wolbertus et al., 2018; Hardman et al., 2018; Hard- man, 2019; Zhuge and Shao, 2019; Br¨ uckmann and Bernauer, 2020). Most previous studies on EV adoption also take place in areas with strong policies encouraging EV adoption in place (e.g. California, Canada, China, Germany, Norway). Some of these areas are also home to EV producing firms (e.g.

California, China, Germany), which is another factor previously identified as increasing EV adoption (Sierzchula et al., 2014). However, in the European Union and Switzerland the target maximum emissions of 95 g CO

/km for newly registered car fleets by 2021, is currently far out of reach. Electrifica- tion of vehicles is especially low where EV policies are lacking. Therefore, this paper uncovers factors affecting BEV uptake in an area without strong EV policies. Previous studies on EV adoption have mostly used stated-preference (SP) surveys to characterize potential EV uptake (e.g. Egbue and Long, 2012;

Carley et al., 2013; Bailey et al., 2015; Noppers et al., 2015; Lane et al., 2018;

Patt et al., 2019; Simsekoglu and Nayum, 2019). Only a few studies have used revealed preference (RP) approaches based on actual car registrations or ownership. While decisions in SP and RP studies are likely to be deter- mined by similar underlying factors, their ability to predict actual adoption is likely to differ substantially (Schuitema et al., 2013), because SP studies

2

A feebate system is a combination of fees and rebates, therefore, in this case, reducing

the costs of EVs and increasing the costs of highly emitting cars.

are usually biased due to gaps between preferences and actual behaviour.

Consequently, RP studies are often favoured by economists and policymak- ers (Carlsson, 2010).

The energy efficiency of battery electric vehicles (BEVs) is higher than that of other EVs (Helmers and Marx, 2012), and their battery capacity is greater, leading to potentially greater implications for electricity grids when recharged at peak hours and peak locations (Brenna et al., 2012; Schey et al., 2012; Jakobsson et al., 2016; Hardman et al., 2018; Moon et al., 2018; Wolber- tus et al., 2018). Furthermore, with rising EV demand, it will be important to understand the spatial patterns of EV adoption to minimize the risks asso- ciated with spatial accumulation of EV drivers and EV-related peak energy demand Nicolson et al. (2017), such as transmission congestion (Hu et al., 2017), and important voltage drops (Hoogsteen et al., 2015).

Therefore, this study looks into the individual and spatial characteris-

tics determining BEV adoption in a setting free from strong EV policies or

manufacturer presence, using unique RP data. B¨ uhler et al. (2014) note

that EV uptake depends on consumer perceptions. Hence, we empirically

analyse private car adoption to examine consumer preferences. Combining

unique survey data with the spatial characteristics of the car holders’ area of

residence in a logit choice model allows us to explore individual and spatial

factors that explain EV adoption behaviour. We refer to actual purchases

as ‘unforced adoption’, as did Rezvani et al. (2015), in contrast to results from ‘forced-choice’ stated-preference methods such as choice experiments, to address the demand for “studies using representative samples and not only focusing on intention to adopt, but actual (‘unforced’) adoption” (Rezvani et al., 2015, p.133) of EVs. Therefore, this paper uses data from a mixed- mode survey of both, holders of officially registered internal combustion en- gine vehicles (ICEV) and BEVs. Furthermore, this study allows to assess whether the lessons learned from adopters of other kinds of EVs can also be transferred to BEV adoption. We study this in Switzerland, a country ab- sent from strong EV policies. While other countries, such as Australia (Webb et al., 2019), also have no strong EV policies, we opted for a country without manufacturer presence to minimise contamination. Lastly, many other areas feature large proportions of home-owners, who are more likely to adopt EVs (Bailey et al., 2015; Axsen et al., 2016; Javid and Nejat, 2017; Nazari et al., 2018), while homeownership is rather uncommon in our chosen area of study.

Our identification of both the individual characteristics and the spatial

characteristics affecting the distribution of BEVs can be of interest to elec-

tricity providers and automotive manufacturers, as well as regional planning

authorities and policymakers. In the following sections, we review the lit-

erature on EV uptake studies to formulate hypotheses and guide variable

selection. We then briefly introduce the methods used and describe the re-

sults obtained before discussing our findings.

2. Literature review and hypotheses

Most studies on EV adoption are based on hypothetical vehicle choices to assess consumers’ attitudes towards buying an EV (e.g. Bailey et al., 2015;

Junquera et al., 2016; Bennett and Vijaygopal, 2018; Priessner et al., 2018).

In contrast, only very few studies (e.g. Axsen et al., 2016; Javid and Nejat, 2017) investigate the characteristics of actual EV holders compared to those of ICEV holders. Some studies analyse characteristics of owners of different kinds of EVs jointly, without differentiating between them (Higgins et al., 2012; Axsen et al., 2015, 2016). A common focus is plug-in electric vehi- cles (PEVs), a category that includes both BEVs and plug-in hybrid electric vehicles. However, it is important to distinguish between different types of electric cars since their technologies differ (Lane et al., 2018; Almeida Neves et al., 2019). For instance, for plug-in hybrids, the range-anxiety associated with BEVs is absent (Lane et al., 2018).

Previous studies suggest that EV adoption can be linked to technologies,

consumer characteristics, and context (Sierzchula et al., 2014). An overview

of previous studies can be found in Liao et al. (2017); Javid and Nejat (2017)

and Li et al. (2017b). Besides consumer characteristics, the context con-

sists of population density, charging infrastructure, policies, energy mix, and

electricity/gas prices (Sierzchula et al., 2014; Axsen et al., 2016; Priessner

et al., 2018; Almeida Neves et al., 2019; Li et al., 2017c). We focus our lit-

erature review on scholarly debates in the areas of consumer characteristics

and contextual criteria that vary within our regional scope (e.g., energy mix or gas price do not vary within a small, single market such as Switzerland).

Besides these factors, we acknowledge the critical link between EV adoption and critical consumption (Yates, 2011), politics of purchasing ( ¨ Ozt¨ urk et al., 2019), ethical consumption, and political consumerism (Micheletti, 2010), which point in the same direction regarding consumer characteristics.

2.1. Consumer characteristics

Studies on EV uptake, as well as on buycotts, a movement of political

consumerism and ethical consumption (Stolle et al., 2005; Jacobsen and Dul-

srud, 2007; Hoffmann et al., 2018), found several socio-demographic factors

to increase uptake probabilities. Many RP studies on PEVs (Axsen and Ku-

rani, 2013; Tal et al., 2014; Axsen et al., 2016; Nazari et al., 2018; Lee et al.,

2019), SP studies on BEVs (Hidrue et al., 2011; Pl¨ otz et al., 2014; Zhuge and

Shao, 2019), as well as most notable RP studies on BEVs (Almeida Neves

et al., 2019), have found higher income and higher education to increase the

likelihood of EV adoption. However, in two RP PEV-studies at the national

level, educational achievements and income were found to be insignificant

(Sierzchula et al., 2014; Li et al., 2017b). Bernards et al. (2018) found a

strong effect of income that levels off as it increases. Moreover, some SP

studies have found income to be insignificant or less important for BEVs

(Hidrue et al., 2011) and PEVs (Carley et al., 2013; Bailey et al., 2015)

, which is understandable, as in SP studies no (expensive) purchases are

made. Altogether, given the higher predictive power of RP studies compared to SP ones, and our focus on BEVs, we formulate the following hypothesis:

H1: Unforced early adoption of BEVs is associated with both higher ed- ucational attainment and income.

Manski and Sherman (1980) claim that household size matters for ve-

hicle selection (through the number of seats and trunk size). This was also

found for PEVs (Javid and Nejat, 2017; Bernards et al., 2018; Priessner et al.,

2018). Since there are still fewer combinations of seat-numbers (Zhang et al.,

2016) and trunk sizes available on the market for BEVs, compared to con-

ventional vehicles, household size may affect the choice for purchasing BEVs

(Pl¨ otz et al., 2014; Almeida Neves et al., 2019; Lee et al., 2019). More cars

in the household make EVs more acceptable (Tamor et al., 2015; Jakobsson

et al., 2016; Karlsson, 2017) as an ICEV can overcome range anxiety and

long recharging times. In RP studies (on PEVs) findings are mixed: while

it matters in Canada (Axsen et al., 2016), this is not the case in the US

(Javid and Nejat, 2017; Nazari et al., 2018). Car sharing usage, especially

the usage of electric car sharing, was found to be positively influencing the

perceived usefulness of EVs (Schl¨ uter and Weyer, 2019),, notably in an RP

study (Javid and Nejat, 2017). Focusing on the studies closest to our setting

(RP on BEV uptake), we formulate the following hypothesis:

H2: BEV adoption is positively associated with both multi-car house- hold as well as usage of car sharing.

Similarly, owning a complete public transport (local trains, trams, ferries and busses as well as long-distance trains) subscription is linked to more open- ness towards new mobility tools (Guidon et al., 2019; Meyer de Freitas et al., 2019) and may play an analogous role in BEV adoption decisions. Lastly, some RP studies (Javid and Nejat, 2017; Nazari et al., 2018; Thøgersen and Ebsen, 2019) show that people in smaller households are more likely to adopt an EV. In SP studies (with some RP cases from European countries) there are result in the same direction (Priessner et al., 2018) as well as the oppo- site direction, that mainly families adopt BEVs (Pl¨ otz et al., 2014). As these factors are not overall clear, we abstain from making hypotheses about them.

Another variable associated with socio-economic status is homeowner-

ship, especially of single-family detached houses. Its effect on EV adoption

is found positive in all studies reviewed (e.g. Axsen et al., 2016; Bailey et al.,

2015; Nazari et al., 2018), except for one recent RP study on PEVs in Cali-

fornia (Javid and Nejat, 2017). In places with a high proportion of tenants,

e.g. Switzerland where 59% of households live in a rented flat/apartment

and only 27.3% live in single-family homes (BFS, 2019), BEV readiness may

also be affected by the increased transaction costs for installing recharging

facilities in rented dwellings.

H3: Owner-occupied houses are the most likely homes of BEV holders.

Most studies have shown environmental attitudes to matter for BEV support in SP studies (?Noppers et al., 2015), as well as for actual PEV adoption (Axsen et al., 2016) and intent (Carley et al., 2013; Schuitema et al., 2013;

Priessner et al., 2018). Still, further research on consumers’ environmental consciousness continues to be encouraged (Almeida Neves et al., 2019). Sim- ilarly, previous studies found the interest in using new technologies to be strongly linked to EV uptake (Egbue and Long, 2012; Long et al., 2019).

Technological enthusiasm is correlated with intentions for PEV adoption (Pl¨ otz et al., 2014; Smith et al., 2017) as well as actual BEV adoption (Lane et al., 2018). Apart from these, we also consider party preferences. We go beyond Sintov et al. (2020) who study only potential early adopters and their adoption intention, Kahn (2007), who studied registered Green Party voters and hybrid electric vehicle owners, and Sovacool et al. (2018) who studied Nordic countries and only used party groups (“main categories”) (p. 221).

We expect that representation through Switzerland’s Green (GPS) or Green Liberal (GLP) party is likely to be positively associated with BEV uptake.

H4: BEV uptake probability is positively linked to environmentalism,

technology affinity and green party preferences.

2.2. Spatial characteristics

(Electric) vehicle travel patterns and the link towards the built environ- ment are on the research agenda of many scholars (Higgins et al., 2012; Li et al., 2017b). Although the EC (2014) understands BEVs as a phenomenon of high-density areas, the empirical evidence is mixed. Rather small effects of density were found on PEV adoption in California (Javid and Nejat, 2017) and insignificant effects on intentions for PEV uptake in Austria (Priessner et al., 2018). Also, while Pl¨ otz et al. (2014) showed that BEVs are most likely to be found in smaller settlements, i.e. in rural areas or suburbs, studies on a country level (International Energy Agency, 2017; Li et al., 2017b) found support for the statement of the EC. Therefore, univocal clarification on this topic is imperative (Bernards et al., 2018).

H5: BEV adoption is linked to population density.

Sperling and Kitamura (1986) acknowledged the need for refuelling infras-

tructure for the deployment of new transportation possibilities. This is shown

to be especially important in the purchase decision of vehicles running on al-

ternative fuels (Dagsvik et al., 2002). Nowadays, public (fast) chargers are an

important part in making BEVs more attractive for future users (Neaimeha

et al., 2017). A significant positive correlation between BEV sales data and

public charging infrastructure was found recently (Zhang et al., 2016). In

two revealed preference studies in the US, Li et al. (2017a) find that more

chargers are related to more PEVs, and the effect of charging infrastructure is found to be much higher for BEVs than for PHEVs (Narassimhan and Johnson, 2018). Multiple cross-country RP studies (e.g. Li et al., 2017b), RP studies on PEVs (Javid and Nejat, 2017; Nazari et al., 2018), as well as in SP studies (Achtnicht et al., 2012; Egbue and Long, 2012; Carley et al., 2013; Hackbarth and Madlener, 2013; Bailey et al., 2015) find that public charging stations are the drivers of electric mobility. In contrast, Bailey et al. (2015) conclude that the relationship between the availability of out- of-home charging facilities and the uptake of EVs is not yet fully understood, which, for now, remains true for actual adoption (RP) of BEVs and small- scale spatial resolutions. To close this gap, we investigate the last hypothesis:

H6: Public charging infrastructure availability is positively related to BEV adoption.

3. Data and Methods

This paper uses unique RP data from official sources on the adoption of

BEVs. The sample consists of BEV holders as well as holders of conventional

cars with internal combustion engines. We apply a Generalized Linear Mixed-

Effects Model for regression analysis of BEV adoption. In the next sections,

we describe the case selection, data collection and empirical (mixed-effects

logit model) approach.

3.1. Case Selection

Within the framework of the Paris agreement, Switzerland has set a goal to reduce greenhouse gas emissions by 50% until 2030 and by 75% to 80%

until 2050 (compared to 1990 levels) (Burkhardt, 2016). Transportation ac- counts for 32% of the overall carbon (CO2) emissions in Switzerland (FOEN, 2017). Switzerland has a high potential to reduce transportation emissions through the diffusion of BEVs, as it already has a high share of renewable energies and plans on substituting all non-renewable energy sources by 2050 SFOE (2018).

To attain the emissions targets, the electrification of the passenger trans- portation sector (Pietzcker et al., 2014) has been recognized as a political goal and challenge in the countries affected, such as Switzerland (FEDRO, 2016). The goal of 95 g CO

/km fleet emissions by 2021 seems out of reach and would need widespread EV adoption for its achievement (SFOE, 2017).

Therefore, many countries have applied strong demand-side policies, such

as purchase premia of around 4000 € in Germany and the UK (4000 £ ) or

substantial tax reductions as in Ireland or Norway (Yan, 2018). In Switzer-

land, strong policies that support EV adoption are not yet enacted. The

absence of policy-induced influences and manufacturers make Switzerland a

relevant case study to understand the individual and spatial determinants of

EV adoption without confounding policy effects, and allows implications for

many countries worldwide.

Despite factors that are typically related to high EV uptake, such as a high GDP/capita (IMF, 2018) and an environmentally friendly population (Franzen and Vogl, 2013), EV sales in Switzerland remain low, accounting for only 1.7% of new registrations in 2018 (SFOE, 2019). Therefore, the question as to why a low uptake of EVs persists is puzzling. As Switzerland’s policy- makers and the private sector unite their efforts to promote EVs (DETEC and Communications, 2018), understanding current uptake patterns can assist the design of adequate policies, also to minimize spatial accumulation and associated risks (Nicolson et al., 2017), e.g. transmission congestion (Hu et al., 2017) and voltage drops (Hoogsteen et al., 2015).

3.2. Survey

We use data from the survey of Br¨ uckmann and Bernauer (2020). Their survey population is a random sample of car holders registered in the German- speaking Swiss Cantons Aargau, Schwyz, Zug, and Zurich as well as all (that is to say, a census or a random sample with inclusion probability of p = 1) BEV holders in the same Swiss Cantons. The intentional oversampling of BEV holders is due to their sparse representation in the overall car holding population as of the time of data collection (2018). In Switzerland, each Can- ton has a car registry and car registration with the car registry is mandatory.

In each study canton, 5000 car holders that had no BEV, but an ICEV

, reg-

3

The ICEV holder dataset includes all persons that have a car with internal combustion

engine or possibly (some) electrification, e.g. (plug-in) hybrids, registered – i.e., all car

istered were randomly selected by the car registries and their postal address was delivered to Br¨ uckmann and Bernauer (2020). Additionally, the same address information on all BEV holders at that time was transferred. Both groups of car holders were invited by postal mail to take part in a mixed- mode (online and pen-and-paper) survey. In total, 22’627 survey invitations were sent to the postal addresses provided by official sources (cantonal car registries). These cantons were selected as they include Switzerland’s largest city, the canton with the largest population (Zurich), as well as suburban and rural areas encompassing various topologies, and allowing for a survey in only one language (German). While the cantons do have slightly differ- ent vehicle taxes

(to encourage the use of energy-efficient cars) there were no substantial incentive policies in place in any canton at the time of the study. The mixed-mode survey could be completed online or on print (pen- and-paper). The data-collection started in May 2018 and ended in October 2018. There were 5325 responses obtained, and a minimum response rate (AAPOR, 2016) of 23.5% was reached. For a detailed survey, we refer to Br¨ uckmann and Bernauer (2020), including the questionnaire in their Sup- plementary Information.

holders but those with a battery electric vehicle.

4

In the Supplementary Information SI2 of Br¨ uckmann and Bernauer (2020) this is

outlined in more detail.

3.3. Spatial Data

The survey data was complemented with data on the spatial character- istics of respondents’ residential zip codes. In contrast to municipality data, zip (postal) code areas are finer-grained in urban areas and coarser in rural areas. Multiple spatial characteristics were initially considered, but only pop- ulation density and availability of charging stations were kept after checking for collinearity. Spatial data was merged with the individual survey data by the respondents’ zip codes. Data on spatial variables, especially popula- tion density, was collected from the cantonal statistical offices, and charging infrastructure data was provided by a private company (lemnet.org).

3.4. Model

In our study, we predict the binary outcome of individuals’ BEV-ownership (denoted with 1) or no BEV-ownership (denoted with 0) from a set of ex- planatory variables. These explanatory variables are data on individuals, nested within their residential ZIP-code areas and cantons. This is referred to as “clustered survey data” (Maimon and Kuhl, 2008), and it is organ- ised in a hierarchical data structure (Cameron and Trivedi, 2005). We have within effects

that occur at level 1, the individual level, and we have be- tween effects or context effects that occur at the level 2 (Howard, 2015) and additionally on level 3 (Rodr´ıguez and Goldman, 2001). Level 2 in our con-

5

Note, we use the term “effect” e.g. in the phrase “within effect”, “between effect”,

“random effect” and “fixed effect” without them implying the existence of causal effects.

text is ZIP code and Level 3 is cantonal. We perform a mixed-effects model with canton fixed effects and ZIP code random effects.

logit(π

) = β

+ β

x

+ β

x

+ u

(1)

Here, x

, x

and x

represent (vectors of) observed characteristics at the individual, ZIP code area and cantonal levels, with corresponding fixed effects β

and β

and β

. For example, there is an individual level invariant level 2 independent variable, such as population density by ZIP code, with its corresponding fixed effects. For the third level, by definition, variables only have “between effects” – level 3 variables (here: canton) cannot have within effects, as there is no variation within higher-level entities (Bell et al., 2019).

U

is an unobserved random effect for ZIP code areas attached to the inter- cept, specifying heterogeneity at level 2 and can be important to avoid biases, particularly in standard errors. We assume them to be normally distributed.

We opted for a mixed-effects model, as fixed effects for level-2 entities would be unreliable with small level-2 entities (Bell et al., 2019). For a comparison with different models, see the Appendix A 1.

All estimation was done in R R Core Team (2018) using the packages

lme4 (Bates et al., 2015), lrtest (Zeileis and Hothorn, 2002) , margins (Leeper

et al., 2018), pROC (Robin et al., 2011), and pscl (Jackman, 2020).

3.5. Variable Selection

For the variable choice, we first used the information from our litera- ture review. We started with a broad set of spatial variables, namely pop- ulation density, the share of built-up area per municipality, the share of single-family houses per municipality, job/population accessibility (based on Hansen (1959)), municipality type (based on FSO (2017)) and availability of charging stations. We then checked for multicollinearity in the data through the application of variance inflation factors (Fox and Monette, 1992). As mul- ticollinearity was found for most of the spatial characteristics, we dropped all variables except for population density and availability of charging stations, which are also most prominent in previous literature. Population density and charging stations are only slightly correlated (correlation coefficient r

= 0.52). To choose which variables to drop we used different sets of vari- ables for our regression and estimated model fit using likelihood ratio tests (Korner-Nievergelt et al., 2015) as well as AIC (Sakamoto et al., 1986). Af- ter checking for multicollinearity, we chose the set of variables for our model that provided the lowest AIC and that allowed straight forward interpreta- tion. We also tried interactions between the variables as well as non-linear terms (squared) but this did not improve the model fit.

To estimate the overall fit of our model we used the ROC curve as a vi-

sual performance indicator. First, we estimated the overall fit of our model

using the Receiver operating characteristic (ROC) curve and area under the

curve (AUC). A ROC curve illustrates the performance of a binary regression with discrete output and shows the specificity (the proportion of correctly classified negative observations) and sensitivity (the proportion of correctly classified positive observations) as the output threshold is moved over the range of all possible values. In simple terms, it plots the “false alarm” rate versus the “hit rate”. ROC curves do not depend on class probabilities, which allows for interpretation and comparison across different data sets. A higher AUC means a better classification (Robin et al., 2011, p.1).

Once multicollinearity was addressed (see above), we selected 17 vari- ables that are expected to influence BEV adoption, which is our dependent variable. The respondents’ education is reflected in ‘Higher Education’, a dummy reflecting attained tertiary education. Mean monthly household in- come is the household’s average gross monthly income (see Table 1 for de- tails). The variable ‘Property ownership’ reflects the three possibilities that the household members own the house they live in, live in an owner-occupied flat, or live in a rented home. ‘Environmental concern’ stems from a five- point Likert-scale, that is factorised using principal component analysis as described in Appendix A2, summarizing the respondent’s environmental con- cern (Diekmann and Meyer, 2009), which is then classified into three groups, based on the alpha-score value tercentiles. Similarly, there is the ‘Tech Scale’

referring to technology interest (Bauer et al., 2005) and also measured on

a five-point Likert-scale. Both attitudinal scales are shown and the factor

analysis is described in the Appendix A2. ‘Party preference’ refers to the (Swiss) political party the respondent best feels represented by. The base category is the Swiss People’s Party

, the party with the largest share in the Swiss parliament, considered as a far-right party. ‘Population density’ refers to the population density in the respondent’s zip code, as indicated by the address we received from the car registries, coded following Eurostat (2019) Degree of Urbanization. ‘Charging availability’ refers to the number of EV recharging facilities per 1000 inhabitants within their zip code area. The respondents’ ‘gender’, as indicated by the survey respondents, was coded as a Boolean (dummy) variable (1 = female, 0 = men and people who opted for a gender other than male or female). ‘Age’ was sorted into three groups and the variable employed reflects whether a respondent is working or unem- ployed. ‘Cars per household’ is the number of vehicles permanently available to the household including cars, minivans, SUVs, and business cars. ‘Per- sons per household’ refers to the sum of all people including the respondent and under-aged persons living in the household. ‘Season ticket ownership’

is a dummy for owning a complete public transport subscription

(1 = yes), similar to ‘Carsharing’ for membership in a carsharing program. Lastly, all observations fall into one of four ‘Cantons’, depending on their residency.

6

We coded rare party mentions (below 50) together with the option “other”. They were Evangelical People’s Party (EVP), Federal Democratic Union (EDU), Party of Labour (PdA). We checked it, and this did not affect substantially any of the results shown here.

7

The Swiss public transport network offers subscriptions for both local public transport

and all national railways and busses. This is commonly referred to as “Generalabonement

(GA)”.

Table 1: Summary of explanatory variables

Variable Description Levels All ICEV BEV

Higher Education Degree in higher education [yes/no] no 1351 1120 231

yes 982 656 326

Mean monthly household income Mean monthly gross income [CHF]

<4000 75 72 3

4001-8000 654 574 80

8001-12000 714 549 165

12001-16000 453 315 138

>16000 437 266 171

Property ownership House owner or rental of house or apartment

rented house/flat 806 695 111

own house 1043 707 336

owner-occupied flat 484 374 110

Environmental concern Scale

low 867 723 144

medium 746 583 163

high 720 470 250

Tech scale Scale

low 932 894 38

medium 749 611 138

high 652 271 381

Party preference Political party that best represents one’s opinion

Swiss People’s Party (SVP) 298 248 50

Green Liberal Party (GLP) 313 177 136

Green Party (GPS) 95 47 48

Conservative Democratic Party (BDP) 71 61 10 Christian Democratic People’s Party (CVP) 196 172 24

The Liberals (FDP) 506 399 107

Social Democratic Party (SP) 257 204 53

other 79 52 27

none 518 416 102

Population density Population density of residents’ ZIP-code area [inhabitants/kmˆ2]

rural 333 251 82

agglo 1574 1198 376

urban 426 327 99

Charging availability Number of charging stations of residents’ ZIP-code area [count/kmˆ2] continuous: Mean 0.23 0.22 0.25

standard deviation 0.36 0.35 0.38

Gender Female or male (or other) male (or other) 1658 1188 470

female 675 588 87

Age [years]

<40 370 298 72

40-65 1315 936 379

>65 648 542 106

Employed State of Employment [yes/no] no 717 591 126

yes 1616 1185 431

Cars per household Number of cars available per household [count]

1 1066 890 176

2 1045 729 316

>2 222 157 65

Persons per household Number of persons permanently living in household[count]

1 387 316 71

2 1090 850 240

>2 856 610 246

Season ticket ownership Owner of an annual season ticket (unlimited travel on Swiss trains) [yes/no] no 2089 1602 487

yes 244 174 70

Carsharing Active carsharing membership [yes/no] no 2194 1713 481

yes 139 63 76

Canton Canton of residence

Zurich 773 456 317

Aargau 551 391 160

Schwyz 510 464 46

Zug 499 465 34

4. Results

4.1. Regression results

The regression results are summarized in Table 2. For the regression, only complete cases were retained, which reduces the dataset to 2333 observations which fall into 424 ZIP codes. There is a total of 557 BEV holders and 1776 ICEV holders.

In Fig. 1, we present the predicted probabilities for our different groups of variables, using average marginal effects. A baseline category is always omitted for discrete variables.

4.1.1. Consumer characteristics

There is a positive effect of higher education on BEV adoption but the effect is not statistically significant. Therefore, we cannot reject the first part of the null for H1 (i.e. that unforced early adoption of BEVs is not associated with higher educational attainment). The average marginal effect of higher education on BEV adoption is around 1.7%. The mixed-effects logit model shows significant positive effects for the higher income groups. The average marginal effects plot for household income show that a higher income group is always associated with higher probabilities of BEV adoption. Overall, we acknowledge evidence in favour of our first hypothesis:

H1: Unforced early adoption of BEVs is associated with both higher ed-

ucational attainment and income.

Table 2: Summary of Generalized linear mixed model results

Groups Coefficients Estimate Std.Error z value Pr(>|z|)

Higher education yes 0.170 0.151 1.127 0.260

Mean monthly household income

4001-8000 0.990 0.694 1.426 0.154

8001-12000 1.388 0.695 1.996 0.046 *

12001-16000 1.637 0.705 2.322 0.020 *

>16000 1.896 0.709 2.674 0.008 **

Property ownership owner-occupied flat 1.025 0.218 4.696 0.000 ***

own house 1.204 0.202 5.965 0.000 ***

Tech scale medium 1.658 0.211 7.872 0.000 ***

high 3.280 0.212 15.475 0.000 ***

Environmental concern medium 0.163 0.174 0.939 0.348

high 0.566 0.178 3.182 0.001 **

Party preference

Green Liberal Party (GLP) 0.930 0.273 3.406 0.001 ***

Green Party (GPS) 1.458 0.381 3.827 0.000 ***

Conservative Democratic Party (BDP) -0.493 0.498 -0.991 0.322 Christian Democratic People’s Party (CVP) 0.110 0.344 0.319 0.750

The Liberals (FDP) -0.001 0.259 -0.004 0.997

Social Democratic Party (SP) 0.132 0.299 0.442 0.659

other 1.251 0.400 3.127 0.002 **

none 0.407 0.258 1.577 0.115

Population Density agglo 0.052 0.204 0.255 0.798

urban -0.134 0.280 -0.479 0.632

Charging availability charging 0.028 0.202 0.137 0.891

Gender female -0.521 0.176 -2.970 0.03 **

Age 40-65 0.084 0.215 0.392 0.695

>65 -0.842 0.346 -2.431 0.015 *

Employed yes -0.375 0.265 -1.415 0.157

Cars per Household 2 0.877 0.168 5.213 0.000 ***

>2 0.773 0.254 3.047 0.002 **

Persons per household 2 -0.600 0.221 -2.715 0.007 **

>2 -1.135 0.245 -4.627 0.000 ***

Carsharing yes 1.129 0.257 4.400 0.000 ***

Annual season ticket holder yes -0.285 0.224 -1.273 0.203

Canton Aargau -0.427 0.175 -2.436 0.015 *

Schwyz -1.932 0.216 -8.943 0.000 ***

Zug -2.191 0.231 -9.485 0.000 ***

(Intercept) -4.587 0.814 -5.635 0.000 ***

Log Likelihood -7211.1 Akaike Inf. Crit. 1516.3 Bayesian Inf. Crit. 1729.2

Significance codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1

Fig. 1: Average Marginal Effects (AME) plot on the probability of BEV adop-

tion. Point-estimates (points) with 95%-confidence intervals (whiskers) and a dashed

(red) line at 0, n = 2333.

Multi-car households appear to be significantly more likely to have a BEV, both having two or more than two cars. Similarly, Carsharing is significantly positively associated with BEV uptake. Therefore, we find support for the second hypothesis:

H2: BEV adoption is positively associated with both multi-car house- hold as well as usage of car sharing.

Living in an owner-occupied house, in comparison to a rented house or flat, or an owner-occupied flat shows a positive relationship to BEV adoption (see Fig. 1). These effects are highly significant, see Table 2. Moving from a rented house/flat to an owner-occupied flat already increases BEV adoption probability by more than 10% and to an owned house by around 12%. We, therefore, find support for our third hypothesis:

H3: Owner-occupied houses are the most likely homes of BEV holders.

Compared to the base category (Swiss People’s Party), the green par- ties, most notably the Green Party of Switzerland and the Green Liberal Party, are associated with a significantly higher likelihood of BEV uptake.

Moreover, preferences for Conservative Democratic Party and the Liberals

go in the opposite direction, compared to the baseline category. Green party

preference is one of the single strongest predictors for BEV adoption in our sample, as it yields average marginal effects about 15%. Given that envi- ronmental concern is also clearly and significantly positively linked to BEV adoption, this is in line with our third hypothesis. The strongest, single pre- dictor, is a strong affinity for new technologies. Tech enthusiasts are nearly 40% more likely to adopt a BEV, on average, compared to those with lowest technology interest. Therefore, we can reject the corresponding null hypoth- esis in favour of:

H4: BEV uptake probability is positively linked to environmentalism, technology affinity and green party preferences.

Moreover, female respondents are significantly less likely to adopt a BEV

in our study, while previous findings are ambiguous (see e.g. Habich-Sobiegalla

et al., 2018; Sovacool et al., 2018, for an overview). Owners of complete pub-

lic transport subscriptions are (not statistically significant) less likely to have

a BEV. Our model results also suggest that the higher the number of people

living in a household, the (significantly) less likely BEV adoption is. Aside

from that, employment status is not significantly altering BEV uptake. Age

above 65 years is significantly negatively influencing BEV uptake, while mid-

dle age (40-65) is slightly, but insignificantly, increasing the chance to adopt

a BEV.

4.1.2. Spatial characteristics

EV adoption does not seem to be driven by the density of residential areas. There is no significant effect of population density on BEV uptake.

While not significant, urban areas tend to be less likely homes for BEV own- ers and agglomerations are more likely, compared to rural areas. Therefore, we find no support for our fourth hypothesis.

H5: BEV adoption is linked to population density.

Areas with higher availability of charging infrastructure show a positive link to BEV adoption. However, this effect is small and not significant.

Hence, we find no support for our last hypothesis.

H6: Public charging infrastructure availability is positively related to BEV adoption.

4.1.3. Goodness of fit

The ROC curve for our model illustrates its’ specificity and sensitivity

(see Fig. 2). For our model, the AUC is 0.914, which means that there is a

91% chance that the model will be able to distinguish between BEV and no

BEV holders. These results indicate a reasonably good model fit.

Fig. 2: ROC curve of the fitted mixed-effects logit model , n = 2333.

5. Discussion and conclusion

In this paper, we analyse the effect of individual and spatial characteristics on BEV adoption in four Swiss cantons. The analysis is based on a revealed preference survey with actual BEV and ICEV holders, in an area not affected by strong EV policies. We apply a mixed-effects logistic maximum-likelihood model of BEV adoption based on variables present in the literature on EV adoption and find clear support for most of our hypotheses. Our assessment of transferability of knowledge generated from other EVs to BEVs reveals that different types of EVs should be investigated separately, in line with suggestions from other studies (Almeida Neves et al., 2019; Lane et al., 2018).

We note, however, some differences with the previous literature, mostly at- tributed to the uniqueness of our dataset (i.e. random samples from official sources, revealed preferences, BEVs only, a combination of survey data and spatial data) and propose ideas for further research.

As our findings show, actual BEV adoption strongly (and positively) de-

pends on personal characteristics, such as income, as well as having multiple

cars in the household, and usage of carsharing are positively related to BEV

adoption. The importance of income, especially in Switzerland, where gov-

ernmental support for EV adoption is low, can be explained by higher pur-

chase prices of new BEVs and less available second-hand cars (L´ evay et al.,

2017; Berkeley et al., 2018) compared to ICEVs. BEVs seem to be only ac-

cessible to the rich (Sovacool et al., 2019). This finding differs from those

in previous literature using SP studies, both for BEVs (Hidrue et al., 2011) and PEVs (Carley et al., 2013; Bailey et al., 2015), which are the combi- nation of BEVs and PHEVs. This could be a result of hypothetical bias in SP studies (Schuitema et al., 2013). Cross-country studies on PEVs have not found income to be significant (Sierzchula et al., 2014; Li et al., 2017c), potentially due to coarser data and/or insufficiently capturing the effects of governmental market interventions.

In contrast to owning a flat, we find that home-ownership of detached dwellings is positively linked to BEV adoption. Given that home-ownership is rather uncommon in Switzerland, as most households (59%) live in rented flats (BFS, 2019), this finding may serve as a possible explanation for slow overall uptake of BEVs. This is especially prominent, as home-ownership is strongly associated with possibilities for parking and recharging of electric cars. As the readiness for EV adoption is strongly interlinked with the op- portunity for private home charging: “for those who utilize public parking spaces, [. . . ] problematic access to overnight charging at or near their home could make owning an EV substantially more challenging” (Patt et al., 2019, p.2). Accordingly, most previous studies show that EV users prefer to charge at home overnight (see Hardman et al., 2018, for a review). Nevertheless, even though we controlled for homeownership, we did not control for own- ership of garages with recharging infrastructure (Franke and Krems, 2013;

Bailey et al., 2015; Labeye et al., 2016; Funke et al., 2019). Future studies

should control for the access to recharging infrastructure at overnight park- ing near respondents’ home.

We find several other particularities where BEV uptake and PEV uptake could differ, as mentioned above for household income. Most notably, as in Lane et al. (2018), we find that affinity for new technologies as well as envi- ronmental awareness are strong predictors for BEV uptake. Note that, it is disadvantageous to use self-reported scales as they serve only as a proxy for personal attitudes, which however are latent variables and are not directly represented by the scale. We also note, that using a general environmental concern scale, is unlikely to explain all effects related to pro-environmental travel behaviour, as e.g. Hunecke et al. (2014)). This leaves room for further research, even though this study proxies pro-environmental travel behaviour with car sharing membership, season ticket ownership and green political party preferences. Moreover, we find that the number of cars per household, as well as the usage of carsharing, are significantly positively linked to BEV uptake. As pointed out earlier (Lane et al., 2018), this effect is stronger for BEVs than PEVs.

Our results on the effects of (green) political party preferences also con-

firm the corresponding hypothesis and much of previous research (Axsen

et al., 2016; Carley et al., 2013; Kahn, 2007; Noppers et al., 2015; Pl¨ otz

et al., 2014; Priessner et al., 2018). We contribute to the literature by taking

a broader view on policy preferences, compared to e.g. Kahn (2007) focus- ing on registered Green Party voters in California, by analysing respondents’

party preferences. Apart from the green parties’ positive effect on BEV adop-

tion (compared to the right-wing Swiss People’s Party, reference category),

more central parties like the Liberals and the Conservative Democratic Party

are likely to be reluctant to BEV adoption. This could be due to lower cli-

mate change awareness in these parties. More surprisingly, results for the

Social Democratic Party (that is usually also in favour of ‘green’ policies)

also do not have a significant positive effect compared to the baseline. These

findings might be explained by the survey’s sampling strategy, which only

includes car holders, i.e., it does not include Social Democratic Party voters

who do not have a car, generally for environmental reasons or to signal their

car abstention to their peers. Therefore, respondents leaning towards the

Social Democratic Party might rather represent the traditional workers and

union wing who are predominantly voting for this party due to social rather

than green policy issues. The baseline category (Swiss People’s Party) was

chosen as the largest party in parliament. However, it is also a right-wing

party appealing to both low and (very) high-income individuals, that tend

to have a low climate change awareness. The Swiss People’s Party is also the

descendant of the former Motorists’ or Automobile Party Stadelmann-Steffen

(2011), further hinting towards anti-environmentalist positions. Altogether,

it appears that green party sympathizers can act upon their political beliefs

through costly consumption decisions and by buying electric cars. This adds

interesting insights into the scientific literature on politicized consumption.

One of our hypotheses is not supported by the results, as the likelihood of BEV adoption in middle to high-density areas does not significantly differ from that in rural areas. Altogether, our findings do not corroborate the inconclusive findings in the literature on the effects of population density.

Only one study that focused solely on BEVs (?) finds more BEVs in rural areas. We argue that these differences stem from the fact that our study mainly covers densely populated areas and rural areas are underrepresented, which entails that differences in population densities are simply too small to have a significant impact in our model. Moreover, due to the microscale view on the individual car holders in our study, plenty of the effect of the variance of density is absorbed in our model by individual-level data (such as socio- demographic profiles, mobility-tool ownership and political preferences).

Following our last hypothesis, increased out-of-home EV charger avail- ability is also related to higher BEV adoption probabilities. However, this effect is small and not significant. This might be largely driven by the fact that currently, 50-80% (Hardman et al., 2018) of charging takes place at home. Public slow chargers are the closest surrogates for home charging.

They are cheaper to install than fast chargers. Hence, public slow chargers

could be installed in residential areas to facilitate overnight charging and,

therefore, support greater EV adoption. However, our study is unable to

differentiate between slow and fast chargers. Moreover, the relationship we

establish could be co-determined by other factors such as densely-populated

high-income locations (likely spots for public charging infrastructure) or that public charger placement responds to actual demand. Schroeder and Traber (2012) point out that low profitability for public fast-chargers at low rates of EV adoption is an obstacle for the deployment of public charging infras- tructure. (Patt et al., 2019, p.2) point out this “chicken-and-egg” problem, where low numbers of EV prohibit high numbers of chargers, which in turn induce low EV uptake. A better understanding of causal relations between EV adoption and charging station availability is necessary to inform policies and potential plans to incentivize EV adoption. Therefore, future research should explore PEV registration and charging infrastructure expansion in space and, importantly, over time, as well as study the difference between fast and slow chargers.

Understanding the individual and spatial factors that lead to BEV adop-

tion can assist in the design of electric mobility policies. Therefore, our

findings have substantial implications for policymaking. A focus should be

placed on recharging infrastructure, at home and out of home. Information

on the spatial and household characteristics for which adoption is more likely

can be particularly valuable to electricity grid operators. For them, it is im-

portant to know the types of settlement where a greater BEV uptake can be

expected, so that they can assess whether existing infrastructure can handle

the increasing loads during peak EV charging times. Our findings could help

them identify neighbourhoods with certain shared household characteristics

(e.g. high income, green party preferences, technology affinity) that make them more prone to experience growing electricity demands from EVs. This can be of particular importance if other decentralized energy-consuming (e.g.

other types of EVs, heat pumps) and energy-generating technologies (e.g. so- lar PV) follow similar spatial adoption patterns (Bernards et al., 2018).

Besides individual and contextual characteristics, social relations can also

affect adoption. “Neighbourhood effects” on adoption (Kahn, 2007; Mau

et al., 2008; Axsen et al., 2009; Pettifor et al., 2017), as well as herd be-

haviour (Nyborg et al., 2006), could lead to growing spatial clusters of EVs,

resulting in a spatial concentration of electricity demand. Furthermore, the

visibility of EVs is of utmost importance to their adoption. Consequently,

the more their adoption grows, the more attractive EVs will become for

car buyers (Mau et al., 2008; Axsen et al., 2016). As EVs are still mostly

adopted by wealthy customers and home-owners, charging stations should

be deployed in ways that spread EV adoption more evenly in space. Addi-

tionally, in areas of high spatial concentration of electricity demand, grids

may need to be strengthened and dynamic pricing policies (i.e. charging

rates to incentivize EV charging at times of lower electricity demand and/or

higher supply) (Bakker et al., 2014) will likely be necessary. Additionally,

others have suggested policy options such as local information events and

test-driving opportunities to promote BEVs in “lagging” regions (Skippon

and Garwood, 2011; Jensen et al., 2013; B¨ uhler et al., 2014; Schmalfuß et al.,

2017).

For research to assist in accelerating EV adoption, future studies should aim at better-understanding people who currently do not own a BEV but state interest in it as their next car. Axsen et al. (2016) call them “potential early mainstream” in contrast to those who do not wish to buy a BEV next, whom they call “potential late mainstream”. For them, technology enthu- siasm stops being a decisive factor, as the technology is no longer new by the time they decide to buy a BEV. We can, therefore, expect their profiles to differ from those of early adopters, studied here and in every other BEV adoption empirical study to date. Currently, segmenting consumers is hardly done in EV research, with some notable exceptions (Hardman et al., 2016;

Morton et al., 2016; Axsen et al., 2018; Lee et al., 2019). Using consumer segmentation can show how potential new consumer groups might form deci- sions about acquiring EVs. For example, people who buy an EV to couple it with their solar PV (Stokes and Breetz, 2018) might differ from those, who acquire it, due to their travelling profile (Lane et al., 2018). Understanding these patterns allows targeting potential new consumer groups based on their specific needs and demands.

This analysis was purposefully carried out in regions without strong EV

policies, as policies were previously found to have large and significant ef-

fects on EV adoption behaviour, as well as in a country understudied in

cross-country studies (Sierzchula et al., 2014; Li et al., 2017b; Almeida Neves et al., 2019). The question of whether adoption patterns differ between coun- tries with and without (strong) EV policies cannot be directly tackled by this research design. However, comparing this study with studies in countries such as Denmark or Germany, which previously used to have moderate EV poli- cies, show very similar patterns at the time they had moderate EV policies.

A Danish study (Thøgersen and Ebsen, 2019) similar to ours found that men around their fifties who live in houses, not apartments, are likely BEV owners, if they have high incomes. The Netherlands or Norway have rather strong EV policies, with associated high policy incentives (see e.g. Bjerkan et al.

(2016) Table 2; Hardman et al. (2017) Table 1). In the Netherlands, high environmentalism was found as a significant driver of EV adoption Noppers et al. (2015). In Norway, comparisons between EV users and conventional car holders also show similarities in socio-demographics, namely that men, between 36 and 55 years of age, with high education and income are more inclined towards BEVs. In the same study, Bjerkan et al. (2016) also find positive effects for living in the Norwegian capital area, which is far more dense than the study area in this paper. Generally, effects tend to go in the same directions in areas with and without strong policy interventions.

Only the magnitude of the effects might differ, leading to more EVs in in-

centivized areas. However, a full comparison between any two studies, that

have neither the same sampling strategy nor the same questionnaire or anal-

ysis method should always be taken with a grain of salt. Further research

into the (possibly) different adoption patterns that might arise from different policy incentive schemes, therefore, could be done by replicating this study in multiple other countries.

Overall, our findings on BEV adoption by high-income individuals, with technology enthusiasm and environmental concern, living in their own houses fit well with previous scholarly work. However, these findings represent fun- damental patterns of individuals in the adoption of new mobility and energy technologies, especially when far-reaching policies are lacking. The study at hand could be further enhanced by examining neighbourhood effects and purchasing behaviour over time, as well as by looking into the next customer groups for EVs. Our research allows us to derive general recommendations for policy-making (electricity pricing and promotion policies) that can facilitate the transition to electric mobility.

Acknowledgement

We would like to thank participants at the presentation of an earlier ver-

sion of this paper at the 41

IAEE conference in Montreal, Canada. We

are grateful to Kay W. Axhausen, Liam Beiser-McGrath, Thomas Bernauer,

Adrienne Grˆ et-Regamey, Sergio Guidon, Robert A. Huber, Dennis Kolcava,

Lukas Rudolph, Ang´ elica Serrano Galvis, and Michael Wicki for helpful com-

ments in the development of this manuscript and their support. We would

also like to thank Joshua Good, Alen Salihovic, and Ursin Gst¨ ohl for their re-

search assistance while carrying out the survey and the car registries Aargau, Schwyz, Zug and Zurich for the cooperation throughout the data collection.

We greatly acknowledge funding through ETH Zurich ISTP Research Incu- bator Grant and the Swiss Federal Office of Energy.

Funding information: The research for this article was funded by the Swiss Federal Office of Energy, and supported by an ETH Zurich ISTP Re- search Incubator Grant.

Declaration of interest: none.

References

AAPOR, T.A.A.f.P.O.R., 2016. Standard Definitions Final Dispositions of Case Codes and Outcome Rates for Surveys 9th Edition.

Abergel, T., Brown, A., Cazzola, P., Dockweiler, S., Dulac, J., Pales, A.F., Gorner, M., Malischek, R., Masanet, E.R., McCulloch, S., Munera, L., Remme, U., Schuitmaker, R., Stanley, T., Teter, J., West, K., 2017. En- ergy Technology Perspectives 2017. volume 303 of Energy Technology Per- spectives. OECD. doi:10.1787/energy{\_}tech-2017-en.

Achtnicht, M., B¨ uhler, G., Hermeling, C., 2012. The impact of fuel availabil- ity on demand for alternative-fuel vehicles. Transportation Research Part D: Transport and Environment doi:10.1016/j.trd.2011.12.005.

Ajanovic, A., Haas, R., 2016. Dissemination of electric vehicles in urban

areas: Major factors for success. Energy 115, 1451–1458. doi:10.1016/j.

energy.2016.05.040.

Almeida Neves, S., Cardoso Marques, A., Alberto Fuinhas, J., 2019. Techno- logical progress and other factors behind the adoption of electric vehicles:

Empirical evidence for EU countries. Research in Transportation Eco- nomics 74, 28–39. doi:10.1016/j.retrec.2018.12.001.

Axsen, J., Bailey, J., Castro, M.A., 2015. Preference and lifestyle het- erogeneity among potential plug-in electric vehicle buyers. Energy Eco- nomics 50, 190–201. URL: https://www.sciencedirect.com/science/

article/pii/S0140988315001553?via%3Dihub, doi:10.1016/j.eneco.

2015.05.003.

Axsen, J., Cairns, J., Dusyk, N., Goldberg, S., 2018. What drives the Pio- neers? Applying lifestyle theory to early electric vehicle buyers in Canada.

Energy Research and Social Science 44, 17–30.

Axsen, J., Goldberg, S., Bailey, J., 2016. How might potential future plug- in electric vehicle buyers differ from current ”Pioneer” owners? Trans- portation Research Part D: Transport and Environment 47, 357–370.

doi:10.1016/j.trd.2016.05.015.

Axsen, J., Kurani, K.S., 2013. Connecting plug-in vehicles with green elec- tricity through consumer demand. Environmental Research Letters 8, 1–

11. doi:10.1088/1748-9326/8/1/014045.

Axsen, J., Mountain, D.C., Jaccard, M., 2009. Combining stated and re- vealed choice research to simulate the neighbor effect: The case of hybrid- electric vehicles. Resource and Energy Economics 31, 221–238.

Bailey, J., Miele, A., Axsen, J., 2015. Is awareness of public charging asso- ciated with consumer interest in plug-in electric vehicles? Transportation Research Part D 36, 1–9. doi:http://dx.doi.org/10.1016/j.trd.2015.

02.001.

Bakker, S., Maat, K., van Wee, B., 2014. Stakeholders interests, expectations, and strategies regarding the development and implementation of electric vehicles: The case of the Netherlands. Transportation Research Part A:

Policy and Practice 66, 52–64. URL: http://dx.doi.org/10.1016/j.

tra.2014.04.018, doi:10.1016/j.tra.2014.04.018.

Bates, D., M¨ achler, M., Bolker, B., Walker, S., 2015. Fitting Linear Mixed- Effects Models Using lme4. Journal of Statistical Software 67. doi:10.

18637/jss.v067.i01.

Bauer, H.H., Reichardt, T., Barnes, S.J., Neumann, M.M., 2005. Driving consumer acceptance of mobile marketing: A theoretical framework and empirical study. Journal of Electronic Commerce Research 6, 188–192.

doi:10.1145/1964921.1964970.

Bell, A., Fairbrother, M., Jones, K., 2019. Fixed and random effects

models: making an informed choice. Quality and Quantity 53, 1051–

1074. URL: https://doi.org/10.1007/s11135-018-0802-x, doi:10.

1007/s11135-018-0802-x.

Bennett, R., Vijaygopal, R., 2018. An assessment of UK drivers’ attitudes regarding the forthcoming ban on the sale of petrol and diesel vehicles.

Transportation Research Part D: Transport and Environment 62, 330–344.

doi:10.1016/J.TRD.2018.03.017.

Berkeley, N., Jarvis, D., Jones, A., 2018. Analysing the take up of battery electric vehicles: An investigation of barriers amongst drivers in the UK.

Transportation Research Part D: Transport and Environment 63, 466–481.

doi:10.1016/j.trd.2018.06.016.

Bernards, R., Morren, J., Slootweg, H., 2018. Development and Implemen- tation of Statistical Models for Estimating Diversified Adoption of En- ergy Transition Technologies. IEEE Transactions on Sustainable Energy 9, 1540–1554. doi:10.1109/TSTE.2018.2794579.

BFS, B.f.S., 2019. Bau- und Wohnungswesen 2017. Technical Re- port. Bundesamt f¨ ur Statistik (BFS). Neuchˆ atel. URL: https:

//www.bfs.admin.ch/bfs/de/home/statistiken/bau-wohnungswesen/

wohnungen/wohnverhaeltnisse/mieter-eigentuemer.assetdetail.

7966565.html.

Bjerkan, K.Y., Nørbech, T.E., Nordtømme, M.E., 2016. Incentives for

promoting Battery Electric Vehicle (BEV) adoption in Norway. Trans-

portation Research Part D: Transport and Environment 43, 169–180.

doi:10.1016/j.trd.2015.12.002.

Brenna, M., Foiadelli, F., Roscia, M., Zaninelli, D., 2012. Synergy between renewable sources and electric vehicles for energy integration in distribu- tion systems, in: 2012 IEEE 15th International Conference on Harmonics and Quality of Power, IEEE. pp. 865–869.

Br¨ uckmann, G., Bernauer, T., 2020. What drives public support for policies to enhance electric vehicle adoption? Environmental Research Letters 15, 094002. doi:10.1088/1748-9326/ab90a5.

B¨ uhler, F., Cocron, P., Neumann, I., Franke, T., Krems, J.F., 2014. Is EV experience related to EV acceptance? Results from a German field study.

Transportation Research Part F: Psychology and Behaviour 25, 34–49.

doi:10.1016/j.trf.2014.05.002.

Burkhardt, A., 2016. Schweizer Klimapolitik, in: Akademien der Wis- senschaften Schweiz (Ed.), Brennpunkt Klima Schweiz. Grundlagen, Fol- gen und Perspektiven. Bern, pp. 194–197.

Cameron, A.C., Trivedi, P.K., 2005. Microeconometrics Methods and Applications. Cambridge University Press, Cambridge. URL:

https://www.cambridge.org/de/academic/subjects/economics/

econometrics-statistics-and-mathematical-economics/