Demand for environmentally friendly vehicles: A review and new evidence

Abstract

Although the need for more environmentally friendly vehicles was recognized some decades ago, this new market has not yet established itself. Consumer behavior needs to be studied to ascertain when people will decide to purchase hybrid or electric vehicles rather than conventional ones. An in-depth review of the state-of-the-art has identified existing deficiencies and these are addressed in this paper, proposing a new approach that is applied to the case of Santander in Spain. Emphasis is placed on the role of citizens in researching the local market and their requirements with respect to such vehicles; our model assumes variability in user preferences, an utmost requirement as concluded from the literature review. Results suggest that the highest demand for cleaner vehicles would be achieved in two ways: firstly, by penalizing conventional vehicles in terms of costs/km; secondly, by providing incentives directed at lowering the purchasing price of hybrid and electric vehicles. Finally, as demand becomes more elastic, the preferred strategy should initially focus on hybrid vehicles.

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International Journal of Sustainable Transportation
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Demand for environmentally friendly vehicles: A
review and new evidence
Ruben Cordera, Luigi dell'Olio, Angel Ibeas & Juan de Dios Ortúzar
To cite this article: Ruben Cordera, Luigi dell'Olio, Angel Ibeas & Juan de Dios Ortúzar (2018):
Demand for environmentally friendly vehicles: A review and new evidence, International Journal of
Sustainable Transportation, DOI: 10.1080/15568318.2018.1459969
To link to this article: https://doi.org/10.1080/15568318.2018.1459969
Published online: 09 May 2018.
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Demand for environmentally friendly vehicles: A review and new evidence
Ruben Cordera
a
, Luigi dell’Olio
b
, Angel Ibeas
c
, and Juan de Dios Ort
uzar
d
a
University of Cantabria, Department of Transport and Projects Technology, Av. De Los Castros s/n, 39005, Santander, Spain;
b
University of Cantabria,
Department of Transport and Projects Technology, Av. De Los Castros s/n, 39005, Santander, Spain;
c
University of Cantabria, Department of Transport
and Projects Technology, Av. De Los Castros s/n, 39005, Santander, Spain;
d
Pontificia Universidad Cat
olica de Chile, Department of Transport
Engineering and Logistics, Centre for Urban Sustainable Development (CEDEUS), Vicu~
na Mackenna 4860, Mac
ul, Santiago, 7820436, Chile
ARTICLE HISTORY
Received 9 April 2017
Revised 28 March 2018
Accepted 28 March 2018
ABSTRACT
Although the need for more environmentally friendly vehicles was recognized some decades ago, this new
market has not yet established itself. Consumer behavior needs to be studied to ascertain when people
will decide to purchase hybrid or electric vehicles rather than conventional ones. An in-depth review of
the state-of-the-art has identified existing deficiencies and these are addressed in this paper, proposing a
new approach that is applied to the case of Santander in Spain. Emphasis is placed on the role of citizens
in researching the local market and their requirements with respect to such vehicles; our model assumes
variability in user preferences, an utmost requirement as concluded from the literature review. Results
suggest that the highest demand for cleaner vehicles would be achieved in two ways: firstly, by penalizing
conventional vehicles in terms of costs/km; secondly, by providing incentives directed at lowering the
purchasing price of hybrid and electric vehicles. Finally, as demand becomes more elastic, the preferred
strategy should initially focus on hybrid vehicles.
KEYWORDS
Electric vehicles;
heterogeneity; mixed logit;
stated preference
In spite of the higher environmental sustainability offered by
electric motors when they are powered by low polluting elec-
tricity (Tessum, Hill, & Marshall, 2014), sales of alternative
vehicles are still scarce. The first studies on the purchase of
hybrid (HVs) and electric vehicles (EVs) predicted the future
availability of revealed preference (RP) data that would allow
insights based on stated preference (SP) data to be compared.
However, such a situation is still for the future more than the
present. In the case of Spain, the greater proportion of new
vehicle sales is currently taken up by hybrids, with a very weak
market for electric cars (i.e. about 1.5% of all cars are currently
hybrid and less than 0.1% of all car sales are electric vehicles).
The automobile industry continues striving to improve their
efficiency so that cleaner vehicles become more attractive
(Nunes & Bennett, 2010; Zapata & Nieuwenhuis 2010). This
coincides with the goals of governments and local administra-
tions interested in designing a strategy to fulfil long-term emis-
sions targets. But the people have the last word as each citizen
makes frequent transport and mobility related choices. Pur-
chasing a vehicle is one of these choices and has a direct effect
on the above common goal of industry and government, mak-
ing the role of the consumer a key element in the process of
change. For this reason, we are interested in examining the
behavior of consumers when faced with purchasing a new vehi-
cle for their personal use.
In recent years the literature has contributed much to the
knowledge about the factors affecting car purchase choices
and a general conclusion can be drawn: results are very
diverse, sometimes even opposed, but at the same time the
methodological contributions are wide. The complexity of the
issue is such that restrictive approaches will fail to consider
the taste heterogeneity that is a key characteristic of the popu-
lation being studied.
On the other hand, the diverse conclusions that many
research studies have reached might also be a consequence of
different social and economic contexts experienced by the pop-
ulations studied in each case. Unfortunately, not much research
has considered the citizens’household, cultural and purchasing
backgrounds, which should be incorporated from the very start
in any demand study. This is particularly important in the case
of new products, since there is high uncertainty about their
effects on consumers’perception, preferences and demand.
This research aims to overcome previous deficiencies
detected in the literature and to provide a new perspective that,
from the start, considers the behavior and opinions of citizens
(in this case from Santander, Spain). In fact, the first step in
our methodology was designed to fill a gap identified by
Rezvani, Jansson, and Bodin (2015): the need to understand
consumer preferences through group discussions. Actually, one
of the contributions of our approach is the citizen involvement
in focus group (FG) sessions that helped to uncover many
aspects considered by potential buyers when faced with the
hypothetical situation of choosing from a conventional, hybrid
or fully electric vehicle. A second methodological contribution
was in the modelling, using stated choice (SC) data, where we
removed some constraints assumed by most previous research
(e.g. van Rijnsoever et al. (2013)), allowing for different causes
of heterogeneity in the sample.
CONTACT Ruben Cordera corderar@unican.es University of Cantabria, Department of Transport and Projects Technology, Av. De Los Castros s/n, 39005,
Santander, Spain.
© 2018 Taylor & Francis Group, LLC
INTERNATIONAL JOURNAL OF SUSTAINABLE TRANSPORTATION
2018, VOL. 0, NO. 0, 1–14
https://doi.org/10.1080/15568318.2018.1459969

The rest of the paper is organized as follows. In section 2,we
discuss the state of knowledge on car purchase choices and the
factors that have been shown to influence preferences for
cleaner alternatives. At the end of this section we enumerate
some contributions towards solving many of the literature’s
current deficiencies. Section 3presents the proposed methodol-
ogy for modelling vehicle-purchasing choice along with an
analysis of the explanatory variables within the choice process
as applied to the city of Santander. Section 4computes the
demand elasticity of each type of vehicle with respect to the
influential variables considered in the choice process. Section 5
describes different demand scenarios obtained from the esti-
mated model and section 6summarizes our main conclusions.
2. Review of literature
Initial research on alternative fuel vehicles was conducted by
Brownstone, Bunch, Golob, and Ren (1996), Gould and Golob
(1998) and Kurani, Turrentine, and Sperling (1996), and took
place after the oil crisis that unfolded in the 1970s. The
increased awareness about the scarcity and price of fossil fuels
provoked industry in North America to work on the develop-
ment of more efficient engines and motors. Cleaner vehicles
based on renewable energy were proposed and consumer
response to this new market scenario became a focus of
research (Beggs, Cardell, & Hausman, 1981; Calfee, 1985).
2.1. Technical issues
Extensive literature is available about motor technology and
mechanics, and the potential contribution that these could
make to reduce emissions (Ahn, Jeong, & Kim, 2008; Karplus,
Paltsev, & Reilly, 2010; Musti & Kockelman, 2011; Shin, Hong,
Jeong, & Lee, 2012). There is no doubt that the technical speci-
fications of alternative fuel vehicles have an effect on consumer
choice. The set of attributes and alternatives considered by
research studies concerned with understanding the demand for
alternatively powered vehicles is large and diverse. In the case
of engines/motors, apart from conventional petrol, completely
electric and hybrid vehicles, different fuels have also been con-
sidered as alternatives, for example, liquid propane gas
(Dagsvik, Wennemo, Wetterwald, & Aaberge, 2002) and
hydrogen fuel cell vehicles (Horne, Jaccard, & Tiedemann,
2005; Mau, Eyzaguirre, Jaccard, Collins-Dodd, & Tiedemann,
2008; Struben & Sterman, 2008). Brownstone et al. (1996) actu-
ally considered four types of fuels: petrol, compressed natural
gas, methanol and electric.
In relation to vehicle size and type, Choo and Mokhtarian
(2004) considered luxury cars, sports vehicles and minivans,
while Adler, Wargelin, Kostyniuk, Kavalec, and Occhiuzzo
(2003); Axsen, Mountain, and Jaccard (2009) and Paul,
Kockelman, and Musti (2011) established an ample range of
alternative sizes and typologies. McCarthy and Tay (1998) also
considered the brand, length of the vehicle and presence of air-
bags as further explanatory variables. Other attributes included
in the demand for alternative fuel vehicles have been mainte-
nance costs (Ahn et al., 2008), vehicle warranty (Mau et al.,
2008) and luggage space (Brownstone et al., 1996).
Other types of technical specifications, such as acceleration
or speed, are less commonly found in the literature. Speed was
considered by Brownstone, Bunch, and Train (2000) and
Dagsvik et al. (2002); and acceleration by Brownstone et al.
(2000) and Ewing and Sarig€
oll€
u(1998). Other authors, such as
Axsen et al. (2009); Bolduc, Boucher, and Alvarez-Daziano
(2008) and Horne et al. (2005) included horsepower among the
group of attributes to explain vehicle choice.
Consumption (of liquid fuel and electricity) is another attri-
bute considered by the majority of case studies. Indeed, the cost
of fuel has also shown to be important in several studies (Musti
& Kockelman, 2011; Turrentine & Kurani, 2007). Ewing and
Sarig€
oll€
u(1998) stand out in that to simplify the choice process
they considered fuel consumption together with parking. On
another hand, together with consumption, manufacturers tend
to inform on the type and amount of gas emissions. This aspect
has been considered by, for instance, Hidrue, Parsons, Kemp-
ton, and Gardner (2011) and Taylor, Daziano, and Bolduc
(2013).
However, in terms of technical features, the limited range of
electric vehicles is clearly one of the major barriers for consum-
ers according to our literature review (Stark, Link, Simic, &
B€
auml, 2015). In fact, Daziano and Chiew (2012;2013) called
“driving range anxiety”the preoccupation experienced by driv-
ers due to the limitations of this EV attribute and estimated
willingness-to-pay (WTP) for improved batteries with longer
ranges. Nevertheless, this limitation has been found less influ-
ential depending on the number of vehicles available, as several
authors have found that the range is not such an issue for
households who already own a petrol vehicle with greater
autonomy and obtained higher willingness to buy an EV
(B€
uhler, Cocron, Neumann, Franke, & Krems, 2014; Kurani
et al., 1996).
Various actions proposed in the literature should be consid-
ered to face the concern about the limited range of EV, making
cleaner alternatives to the petrol car more appealing. For exam-
ple, many authors have suggested the development of recharg-
ing infrastructure; the explanatory value of fuel and recharging
station availability has been measured by Bolduc et al. (2008);
Horne et al. (2005) and Mau et al. (2008). Some studies have
concluded that the possibility of having household or work-
place recharging points would guarantee growth in the EV
market since the range anxiety would lessen (B€
uhler et al.,
2014; Lin & Greene, 2010). The location and availability of
recharging points has also been addressed by Jensen, Cherchi,
Ort
uzar, and de (2014) in a novel SP experiment incorporating
actual use of EV by respondents in Denmark, and by Axsen
and Kurani (2012), who analysed access to home recharging
facilities in San Diego.
The effect of recharging times has also been estimated in the
literature (Ewing & Sarig€
oll€
u, 1998; Hidrue et al., 2011); how-
ever, Greaves, Backman, and Ellison (2014) found that such an
aspect does not affect the feasibility of EV for general travel pat-
terns where the vehicle is parked most of the time. Ito, Takeu-
chi, and Managi (2013) found that an efficient scenario would
be to have battery-exchange stations when electric sales were
over a certain threshold (5.6%) of the total market.
Now, although the above features are important from the
perspective of vehicle usage, the purchase decision may be
2 R. CORDERA ET AL.

determined by the consumer’s travel patterns. Indeed, some
authors have explicitly focused on how the travel needs and
vehicle usage determine consumer behavior. For example,
Greaves et al. (2014) and K€
olbl, Bauer, and Rudloff (2013) con-
cluded that EV and specifically their limited ranges, are actually
feasible for day-to-day driving given actual travel patterns. It
should be mentioned that, in any case, the majority of studies
concerned with factors affecting purchase choice considered
variables that directly or indirectly informed on vehicle usage.
Apart from the specific characteristics of the product the
high price of these new vehicles is considered to be one of the
main causes of their low demand (Lebeau, Van Mierlo, Lebeau,
Mairesse, & Macharis, 2012). The research led by Dagsvik et al.
(2002) highlighted the relevance of the initial purchase price
and the range. Similarly, Hidrue et al. (2011) found that poten-
tial buyers opted for cleaner alternatives only if this meant a
cost saving over a conventional vehicle. They also found that,
apart from the range anxiety and long charging time, the most
important variable for drivers was the price. In fact, Karplus
et al. (2010) defend the idea that, together with battery costs,
vehicle costs should drop to guarantee market penetration.
They state that a 15% higher price for an EV over an internal
combustion vehicle is reasonable, but an 80% higher cost is
not, unless there are really strong policies aimed to deter use of
fossil fuels.
2.2. Understanding the potential of political strategies:
Incentives
All things considered, many studies have measured the effect of
applying purchase incentives for EV sales (Adler et al., 2003;
Horne et al., 2005; Potoglou & Kanaroglou, 2007; Tanaka, Ida,
Murakami, & Friedman, 2014); these are generally regarded as
the most efficient type of incentive to stimulate demand for
cleaner vehicles (Ewing & Sarig€
oll€
u, 1998; Jones, Cherry, Vu, &
Nguyen, 2013; Shin et al., 2012). In fact, even private discount
rates of around 20–25%, have been discussed (Mau et al., 2008).
Other kinds of incentives have also been evaluated. The
most common ones are access to fast lanes or high occupancy
vehicle (HOV) lanes (Diamond, 2009; Horne et al., 2005; Tay-
lor et al., 2013) and parking discounts (Adler et al., 2003; Poto-
glou & Kanaroglou, 2007). For example, Adler et al. (2003)
found that tax-reductions and parking incentives appeared to
be the most efficient drivers for encouraging EV market pene-
tration. Diamond (2009) analysed the stimulant effect of access
to HOV lanes, showing that this policy may not have an impact
on the market if travel times are not significantly improved too.
Bahamonde-Birke and Hanappi (2016) tested policy incentives
such as a Park and Ride subscription and a one-year –ticket for
public transportation in a study case in Austria. The authors
found that these incentives had no significant impact on the
adoption of EV. Gallagher and Muehlegger (2011) considered
the impact of fuel prices and tax incentives on the adoption of
hybrid vehicles; they concluded that both the type and generos-
ity of the incentives influenced purchase choice. Notwithstand-
ing, Potoglou and Kanaroglou (2007) found that although
purchase incentives had a significant positive impact, those
applied to parking or access to fast lanes did not. This conclu-
sion agrees with Diamond (2009), who notes that those
incentives with highest impact are immediate ones, that is,
those applied to the purchase price rather than those that are
perceived over the long term. This result is supported by the
research of Shin et al. (2012), who concluded that tax incentives
were less powerful than price subsidies in the promotion of
cleaner vehicles. Furthermore, both Shepherd, Bonsall, and
Harrison (2012) and Struben and Sterman (2008) studied the
promotion power of incentives in depth, highlighting the need
to evaluate the continuous application of subsidies over a long
enough time period to establish and stabilize the market.
Finally in terms of policy, a yet unanswered question is: are
positive incentives more effective than negative incentives?
That is, is it better to focus on positive incentives such as tax
reductions for EVs and HVs, or on negative incentives such as
tax increases for conventional vehicles? This paper also
attempts to respond to this important question.
2.3. Intangible factors associated with the willingness to
purchase alternative-fuel vehicles
Some authors have identified other causes that explain the lack
of success of alternative fuel vehicles. Jaffe and Stavins (1994)
identified three factors in the field of energy –efficient invest-
ments: information, principal-agent problem and unobserved
costs. The lack of information about new alternatives was
regarded as an important barrier because it is usually costly for
consumers to learn about new technologies and innovations.
The principal –agent problem can arise when the efficient
energy decisions are made by different agents than those who
pay the cost (e.g. landlords and renters), whereas the unob-
served costs may appear in situations in which artificially low
energy prices lead to a disinterest in more efficient solutions.
Struben and Sterman (2008) developed a behavioral model to
understand the spread of these new technologies; they support
the idea that providing information is one way to increase
uptake of alternative fuel vehicles, and also indicated that word
of mouth and social exposure encouraged the purchase of new
products.
As the lack of adequate information increases the aversion to
purchase new alternatives, initiatives have been proposed
worldwide to provide access to hybrid and electric vehicles dur-
ing a period. The idea is to measure the impact that actual use
has on the willingness to buy new alternatives. Gould and
Golob (1998) found that people who had been users of an EV
for two weeks became much more positive about their environ-
mental effects. This result led them to suggest the promotion of
public access to demonstrations and tests of new technology
vehicles, partly to also face the great suspicion found about the
long-term costs of fuel or batteries for each type of vehicle. Tur-
rentine and Kurani (2007) and Kurani et al. (1996) emphasized
the importance of providing adequate information when
launching a new technology product, so that potential buyers
could form a realistic opinion about its attributes and the bene-
fits it might bring. In their research, a video demonstrating
how to use and recharge an EV accompanied a household sur-
vey; they found significant WTP for increased autonomy under
certain activity patterns and uses of the private car, and cau-
tioned that the market for EV could shrink if there was
INTERNATIONAL JOURNAL OF SUSTAINABLE TRANSPORTATION 3

persistent publicity about their low range and longer recharging
times in comparison with petrol engines.
On another vein, Jensen, Cherchi, and Mabit (2013) and
Jensen, Cherchi, and Ort
uzar (2014) performed an interesting
experiment. They allowed people to use an electric vehicle for
three months with the aim of discovering if there was any
change in attitudes or preferences before and after the real
experience. The authors found that the individual preferences
changed significantly after using an EV and that the driving
range was a critical factor. As the years have passed, research
has been conducted on the impact of public and private initia-
tives giving access to hybrid and electric cars during a period of
several weeks or months. Regan, Labeye, Brusque, Hugot, and
Adrian (2013) informed that drivers adapted their needs to the
possibilities of the car (i.e. finding an increasing number of
short trips and charging batteries overnight at home), and con-
firmed the importance of promoting household recharging
facilities. The range preference was relaxed after a 3-months
trial in the research conducted by Franke and Krems (2013),
who found that range needs and preferences were more accu-
rately related after the real experience. Furthermore, as occurs
with any new product, the greater the market penetration, the
higher the value placed on it. Mau et al. (2008) and Axsen et al.
(2009) studied this phenomenon in the case of cleaner vehicles.
The authors defined the greater value and probability of buying
an EV as “neighbor effect”when these vehicles are familiar to
potential buyers due to the increasing presence of alternative
cars.
Some of the factors already mentioned can indirectly be con-
nected with attitudes. The inclusion of cognitive psychology in
econometric modelling, through attitudes, has been found to
shed some light on the behavior towards purchasing cleaner
vehicles and has introduced new sources of heterogeneity. This
perspective requires the specification of latent factors to
describe the subjective nature of attitudes. These are measured
through observable indicators that, in the literature, have been
associated with issues such as: (i) the symbolism involved in
the purchase of a recently launched vehicle; (ii) saving money;
(iii) the opposition towards fossil fuel producers; (iv) the pos-
session of the latest technology (Heffner, Kurani, & Turrentine,
2007); (v) safety perceptions and status seeking (Jensen et al.,
2013). Notwithstanding, the major focus has been environmen-
tal awareness and the preoccupation about emissions. In partic-
ular, Daziano and Bolduc (2013) found that women were more
worried about the environment, as were elderly people, more
highly educated people and users of public transport, and that
such preoccupation had a positive impact on the probability of
buying cleaner vehicles. On the other hand, several studies
argue that environmental awareness is not as important as
range anxiety or high purchase prices (Bolduc et al., 2008;
Gould & Golob, 1998; Kurani et al., 1996).
In the case of organizations/companies, the uptake of EV
has been found to be related with non-tangible factors such as
their public image, but may be also affected by other traditional
aspects, such as government incentives and pollution reduction
(Sierzchula, 2014).
The transitions theory and the niche management approach
are explored by Steinhilber, Wells, and Thankappan (2013).
These frameworks try to integrate the results achieved in policy
practice explaining why different technologies have been suc-
cessful or not. The data used by the authors was obtained from
interviews with important agents in the automotive and public
sectors. The results showed that the penetration of EV is hin-
dered by several barriers including the limitations of the cur-
rent technology, the lack of a good infrastructure and the
inadequacy of the regulation. The authors recommended
changing the regulation in order to make the innovations more
attractive to both, producers and consumers.
Finally, it should also be mentioned that even if new vehicles
are not the most attractive option to buyers, penalizing the use
of fossil-fuel vehicles could stimulate demand towards more
sustainable alternatives. This is considered by many authors as
the factor that could have the greatest impact on purchase
choice. Ewing and Sarig€
oll€
u(1998) mention taxes on polluting
vehicles, while Daziano and Bolduc (2013) highlight the role of
fuel taxes and road tolls as ways to encourage purchasing an
EV.
2.4. Approaches to study the demand for cleaner vehicles
A wide variety of methods has been proposed to predict the
demand for alternative fuel vehicles. For instance, binomial
and multinomial logit (MNL) models were the choice of
Ewing and Sarig€
oll€
u(1998, 2000); Brownstone et al. (2000),
Struben and Sterman (2008) and Musti and Kockelman (2011).
On the other hand, Adler et al. (2003), McCarthy and Tay
(1998), Potoglou and Kanaraglou (2007), and Lin and Greene
(2010) have used nested logit (NL) models, allowing for corre-
lation among certain alternatives. Dagsvik et al. (2002) esti-
mated a ranked ordered logit model and Daziano and Chiew
(2013) proposed a more flexible probit model with a Bayesian
estimator. The presence of heterogeneity in preferences has
been considered by Brownstone and Train (1999) and Brown-
stone et al. (2000) through the use of mixed logit (ML) models,
and by Ziegler (2012) with a multinomial probit model. Finally,
Bolduc et al. (2008), and Daziano and Bolduc (2013) proposed
hybrid choice models (HCM), including latent variables, for
the purchase choice. Latent variables, together with stated pref-
erences were also originally gathered by Jensen et al. (2014)ina
panel survey to test the change in preferences after a real expe-
rience with the new battery powered electric vehicles.
Other approaches proposed in the literature for modelling
the choice process associated with buying a new car have been
ordinary least squares (OLS) and weighted least squares (Dia-
mond, 2009; Dimitropoulos, Rietveld, & van Ommeren, 2013),
as well as energy-economy models allowing the costs and
effects of a given policy to be simulated (Axsen et al., 2009;
Horne et al., 2005; Mau et al., 2008). Choo and Mokhtarian
(2004) applied ANOVA analysis and x
2
tests to determine the
differences in choice according to different attitudes and social
demographic factors.
Considering the demand target, the literature has examined
a rich range of potential buyers. Most research has centered on
the household decision to buy a new vehicle (Brownstone et al.,
1996; Lin & Greene, 2010; Turrentine & Kurani, 2007; Daziano
and Bolduc, 2013; Musti & Kockelman, 2011); Potoglou and
Kanaroglou (2007) introduced household characteristics such
as income, dwelling size, education standard, gender and age,
4 R. CORDERA ET AL.

and Gao and Kitirattragarn (2008) estimated the preferences of
taxi drivers for purchasing hybrid-electric vehicles. On the
other hand, organizations and companies were the target group
of Sierzchula (2014), whilst Jones et al. (2013) focused on the
potential market for electric motorcycles in Hanoi, Vietnam.
Some authors have obtained consumer profiles, that is, the
characteristics of the people more likely to purchase hybrid or
electric cars. The literature shows contradicting results in this
sense, probably caused by cultural, social and economic differ-
ences describing the diverse societies analysed. For example,
age appears to be a discordant aspect (Ziegler, 2012); there is
evidence that age has a negative effect on the willingness to pur-
chase cleaner vehicles (Ewing & Sarig€
oll€
u1998; Potoglou &
Kanaroglou 2007), but also that the influence is positive (Musti
& Kockelman, 2011). A similar thing happens with gender as
some authors found that females show a higher probability of
buying cleaner vehicles (Dagsvik et al., 2002; McCarthy & Tay,
1998), whilst Ziegler (2012) found the opposite. McCarthy and
Tay (1998) also identified lower income households, non-white
buyers, and drivers living in more densely populated areas as
more prone to buying an EV. On the contrary, several authors
found evidence that higher educated people are more likely to
choose cleaner alternatives (He, Chen, & Conzelmann, 2012;
Hidrue et al., 2011; Potoglou & Kanaroglou, 2007).
Pl€
otz, Schneider, Globisch, and D€
utschke (2014) recently
characterized early adopters as having both electric and fuel
cars available and being middle-aged male workers with a tech-
nical education, living in rural areas or in the outskirts of cities,
who travel long distances to commute, thereby being willing to
avoid the kilometer penalties applied to conventional vehicles.
In contrast, (Tamor, Moraal, Reprogle, & Mila
ci
c, 2015) Hoen
and Koetse (2014) and Li, Clark, Jensen, Yen, and English
(2013) found that preferences for EV decreased as the annual
distance driven increased.
Various authors have reviewed the literature on the demand
for alternatives to conventional vehicles. Daziano and Chiew
(2012), Hidrue et al. (2011) and Daziano and Bolduc (2013)
discuss the attributes considered in different case studies to
model purchase choice. Also, an interesting meta-analysis has
been done by Dimitropoulos et al. (2013), focusing on the
approaches proposed to measure the WTP for driving range.
Finally, for a detailed description of the selection of attributes,
methods and perspectives in the specific research of EV adop-
tion, the review written by Rezvani et al. (2015) is very exten-
sive, particularly in the narrative about emotional and
attitudinal factors.
2.5. Literature summary and research proposal
The above review of the international literature provides several
interesting conclusions. The attributes that are most frequently
considered in EV choice are motor and vehicle type, consump-
tion, range, speed, acceleration and emissions. Range, accessi-
bility to fuel, recharging conditions and price have all turned
out to be significant in various studies. Nevertheless, the pur-
chase decision is not a function of technical variables only.
Choice variability is ever present in the literature and a wide
range of causes of heterogeneity have been identified and mea-
sured through different methods. Overall, differences in
preferences have been shown to depend on many aspects: loca-
tion, available and/or provided information, the experience of
respondents, the group of attributes considered, the question-
naire design and the model specification chosen.
Methods proposed to treat heterogeneity are diverse. One of
the most widely applied is the introduction of factors that
describe the choice context and may have an influence on the
final decision (Brownstone et al., 1996; McCarthy & Tay, 1998;
Musti & Kockelman, 2011; Paul et al.,2011). These can be
socioeconomic variables, personality and lifestyle characteris-
tics or mobility aspects (Choo & Mokhtarian, 2004), travel pat-
terns (Ewing & Sarig€
oll€
u, 1998) and economic and cultural
circumstances, as highlighted by Tanaka et al. (2014) in the
comparison of several US states with Japan. Consequently,
there is a need for more flexible modelling in order to allow for
a correct treatment of the intrinsic variability in tastes and pref-
erences that characterize this type of choice (Ziegler, 2012).
In an attempt to better adjust models to the real world situa-
tion, we propose a methodology to determine the purchase
decision as a function of a set of variables uncovered in citizen
participation sessions, together with the consideration of vari-
ous potential sources of heterogeneity. The approach places rel-
evance on the local context (i.e. economic situation and
cultural lifestyle), considers citizen participation (through a
direct exploration of their preoccupations and influential fac-
tors), and proposes an advanced modelling approach flexible
enough to consider several sources of variability.
Our research work was applied to the city of Santander
(Spain). The implementation of the proposed approach led to
insights on the policies that would cause the highest positive
impact of a shift to cleaner vehicles. Our work also intended to
shed light on the dilemma raised by Rezvani et al. (2015),
whether policies should focus on promoting new alternatives
or in preventing the purchase of conventional ones.
3. Proposed methodology
This section is concerned with explaining the process of model-
ling vehicle purchase choice from three types of engines/motors
currently available on the market: internal combustion, hybrid
(pluginable and non –pluginable) and fully electric.
3.1. Focus groups
Each individual region may be affected by intrinsic condition-
ing factors and may be subject to different levels of information
about new products; this may also affect the way in which cer-
tain aspects are perceived. It might also cause differences with
respect to other regions, urban centers or countries, as con-
cluded by Dimitropoulos et al. (2013) after comparing results
from nine different countries. Therefore, the first step in this
research was to hold focus group (FG) sessions to uncover the
a priori qualitative perception of the people of Santander about
purchasing hybrid or electric vehicles (EV).
Rea and Parker (2014) provided a methodology for the cor-
rect design and development of focus groups: identify the goal
of the FG, identify the participants, to establish the required
number of FGs, choice of location and choice of day and time
for holding the session.
INTERNATIONAL JOURNAL OF SUSTAINABLE TRANSPORTATION 5

The main aim of these sessions was to discover which varia-
bles were consciously considered by citizens in relation to pur-
chasing an EV. The process allowed existing worries and
barriers to be uncovered, as well as some advantages felt by
potential buyers (Ibeas, Dell’Olio, & Montequ
ın, 2011). Two
focus groups were organized from among the citizens of
Santander. Both groups had 10 members recruited from the
neighborhood associations in the city. We decided to hold two
focus groups in order to represent all the associations. The
members of both groups were given information about electric
vehicles and they were asked about their main advantages and
disadvantages as well as about their willingness to change and
about what measures they would take to encourage greater use
of electric vehicles. The FGs were led by an expert in citizen
participation and sustainable mobility at the University of Can-
tabria. Both FGs were held on consecutive working days at
7 PM to guarantee that people who were working would be
able to attend.
The sessions involved discussions about the pros and
cons of electric/hybrid vehicles. Advantages were medium/
long-term economic and environmental savings; although a
lack of detailed information was evident among people.
Apart from the initial costs, the disadvantages included the
preoccupation with the limited range provided by EV and
its resulting uncertainty, together with –as far as they were
aware –the lack of a recharging network. Access to
recharging stations was not only worrisome for urban jour-
neys, but even more so for long distance trips where people
feared having little or no knowledge about available infra-
structure. FG members coincided on the need for more
information about EV and their features, but assumed that
EV were not currently competitive with petrol or diesel-
powered vehicles. They also expressed the view that any
changeover to cleaner vehicles would be a gradual process,
starting with an increasing demand for hybrids to be later
followedupbyfullEV(andthesewouldbeusedinitially
mainly in urban areas). FG members highlighted the need
for information campaigns and indicated that public institu-
tions should provide an initial example by adopting these
kinds of vehicles. Finally, they suggested that providing
incentives to reduce their weaknesses should be the way to
promote this new market.
Few studies reported in the literature have used FG (Dagsvik
et al., 2002; Ewing & Sarig€
oll€
u, 1998; Hidrue et al., 2011). Not-
withstanding, in spite of the results coming from these sessions
in the majority of cases the final survey forms were designed on
the basis of pre-defined variables supported by the literature,
placing less importance on the results obtained from the FGs.
In our study, on the other hand, the variables used in the
subsequent survey were defined during the discussions held
at the FG sessions. We assumed that the attributes and con-
siderations that verbally arose during the FG were those
being consciously considered by individuals when compar-
ing alternatives. The FG meetings also allowed us to under-
stand that individuals were not able to evaluate a large
number of variables simultaneously, unless the discussion
was specifically geared to stimulate the consideration of
new factors. By incorporating this knowledge, we expect
ourmodelstoreflect local individual perceptions better and
eventually allow us to design efficient strategies for answer-
ing the needs of our specific setting.
3.2. Data collection
Ewing and Sarig€
oll€
u(1998) predicted the future availability of
revealed preference (RP) data to validate the results of predic-
tions made by models based on stated preference (SP) experi-
ments. However, this prediction has not yet come true, at least
in Spain. As Daziano and Chiew (2013) indicate, access to RP
data is still limited due to the low sales of alternative vehicles.
Therefore, our research involved the design of an SP survey to
recreate different hypothetical choice frameworks based on the
current supply of EV. The SP surveys are particularly useful in
choice situations in which the respondents have little experi-
ence with EV.
After a pilot test, the final survey was applied to 181 ran-
domly chosen households. The results of the preliminary model
estimated using pilot survey data showed that all the parame-
ters could be estimated at a 95% confidence level obtaining at
least 100 completed questionnaires. We specified that a house-
hold member with the decision power to buy a vehicle should
answer the survey. Two different survey forms were designed:
one contained specifications about medium sized vehicles and
the other about large vehicles, so that data was more precisely
customized to the needs of each respondent and that choice
scenarios resembled the preoccupations of potential buyers
more realistically. Respondents had to answer one or the other
questionnaire depending on the type of vehicle they were think-
ing of acquiring in the future.
The survey followed an efficient design based on the RSC
(Relabeling, Swapping, Cycling) algorithm and presented
eight choice situations (scenarios) to each respondent (Rose
& Bliemer, 2009). RSC is an iterative search algorithm for
experimental designs based on columns. The columns of
the experimental design are created at each iteration from
three criteria used for processing the attribute levels: Relab-
eling, Swapping and Cycling (Hensher, Rose, & Greene,
2015, Ch. 6). The D-Error indicator is evaluated for each
design obtained and the design with the minimum D-Error
is chosen. Therefore, we choose the design which allows us
to estimate the parameters of the discrete choice model
with the least possible standard error. This type of design
has advantages over orthogonal design as it allows us to
estimate the models with fewer questionnaires and at the
same time provides parameters with higher significance
levels.
In each scenario, respondents were asked to choose the
alternative they would buy, among: (i) a car with an internal
combustion (C) engine, (ii) a petrol-battery hybrid (H), or (iii)
a completely electric (E) vehicle. The hybrid vehicle alternative
considered both, the pluginable and non –pluginable types
currently available on the market. Table 1 shows the variables
considered in the experiment for each vehicle type as a result of
the FG work. Table 2 presents the levels used in the case of
medium sized vehicles.
In the survey form, the price was presented as the amount of
money that finally had to be paid (i.e. after applying the dis-
count as a purchase incentive) for each type of vehicle. The
6 R. CORDERA ET AL.

pilot survey was used to check that this method allowed the
interviewees to better understand the survey and were able to
more easily compare the alternatives.
As well as choosing an alternative in each scenario, individu-
als were also asked to answer a series of questions about their
household and the characteristics and use made of the cars
available to its members. Table 3 summarizes the answers.
Most respondents were men (64%) and their age distri-
bution showed that more than 70% were between 25 and
64 years old. Also, more than 70% of households were
made up of one or two members and the average monthly
household income was less than €2,500. On the other
hand, 80% of the households interviewed owned only one
car and 12% had two (a little over 3% of the households
had three or more cars). In order to guarantee the represen-
tativeness of the sample a Pearson’s chi squared test was
performed on the variables: number of household members
andnumberofvehiclesatthehousehold.Inbothcaseswe
could not reject the null hypothesis of no difference
between the distributions of the population and the sample,
thereby showing evidence that the sample was representa-
tive of the population. The response rate for the survey was
0.8. Where a response was not received from a household,
efforts were made to get a response from the nearest possi-
ble household.
While quite a considerable 22% did not know if there were
any recharging points in the area around their home, around
70% confirmed that there were none. When asked about their
next purchase of a new vehicle only 10% said they would do it
within the next three years and 36% did not know. When asked
about the size of car they would buy, 71% declared it would be
a medium-urban sized car.
A whole section of the survey was related to the use and
conditions of the first of the household’s current vehicles
that would be replaced. Around 39% replied that it had a
garage located at home, followed by 35% replying that it
used unreserved street parking. The fact that 70% declared
that this vehicle was required for mostly urban or mixed
urban-interurban usage is striking, along with the report
that over 60% of cases reported a daily frequency of use.
This is interesting additional information because it
addresses the current situation, providing RP data that can
help determining the reasons behind systematic variations
in the perception of some attributes considered in the SP
experiment. Therefore, as in the case of Brownstone et al.
(1996), we do not model SP and RP data together, but
rather use a SP choice experiment supported by RP data
about the use and ownership of the households’current
vehicles.
Table 1. Attributes describing the alternatives in the SP choice experiment.
Variable and description Measure Alternatives
Price: amount to pay after the discount
(incentive) is applied
Euros (€)C,H,E
Incentive: price discount Euros (€)H,E
Consumption: fuel consumption costs Euros/km (€/km) C, H, E
Range: distance the vehicle can travel
without needing to recharge
Kilometers (km) C, H, E
Street parking discount Percentage over the
fee (%)
E
Availability of recharging points in the area
around the house
Available (1) or not
(0)
E
Table 2. Attribute levels in the SP choice experiment for medium sized vehicles.
S Alternative
Price
(€)
Consumption
(€/100 km)
Street
Parking
Discount (%)
Range
(km)
Availability
recharging points
1 Combustion 12500 5.7 0 1700 0
Hybrid 20250 4.9 0 1500 0
Electric 18700 1.5 100 600 There are enough
2 Combustion 15500 4.2 0 1400 0
Hybrid 18250 5.4 0 1800 0
Electric 21250 1.8 20 200 There are enough
3 Combustion 17000 4.2 0 1700 0
Hybrid 24700 5.4 0 1400 0
Electric 23750 1.2 50 200 There are not
enough
4 Combustion 14000 6.2 0 1700 0
Hybrid 21350 4.9 0 1500 0
Electric 21250 1.2 20 800 There are enough
5 Combustion 15500 5.7 0 1400 0
Hybrid 17150 3.9 0 1700 0
Electric 25000 1.5 100 400 There are enough
6 Combustion 14000 5.2 0 1400 0
Hybrid 25800 3.9 0 1800 0
Electric 20000 1.8 30 400 There are not
enough
7 Combustion 17000 6.2 0 1400 0
Hybrid 20400 5.9 0 1400 0
Electric 14950 1.7 50 600 There are not
enough
8 Combustion 12500 5.2 0 1700 0
Hybrid 19300 5.9 0 1700 0
Electric 27500 1.7 30 800 There are not
enough
INTERNATIONAL JOURNAL OF SUSTAINABLE TRANSPORTATION 7

3.3. Modelling purchase choice
Probably due to the novelty of the product and because the
information provided about its characteristics is generally het-
erogeneous or limited, a great variability in consumer preferen-
ces exists as was concluded from the literature review. For these
reasons, a mixed logit (ML) discrete choice model (Train,
2009) was proposed to explain the purchase choice among the
three alternatives: gasoline-powered, hybrid and electric
vehicles.
ML models are fairly complex and may contain different
forms of randomness (Greene, Hensher, & Rose, 2006) to relax
the less realistic hypotheses assumed in simpler choice models
such as the MNL or NL, in relation to caveats such as indepen-
dence among alternatives, heteroscedasticity and taste varia-
tions (Ort
uzar, de, & Willumsen, 2011, Ch. 7 and 8).
Correlation between alternatives and heterogeneity in per-
ceptions need to be considered to adapt future marketing cam-
paigns to the diverse preferences found among the population.
The basic utility specification of our ML model was as follows:
Uiqt DX
k
.bkq ¢xkiqt/Cui¢Eiq Ceiqt (1)
where U
iqt
is the utility of alternative ifor individual qin choice
situation t;xkiqt is the value of attribute kof alternative ifor
individual qin choice situation t; bkq is the value of its parame-
ter for individual qand eiqt is a random error term, which is
assumed to be independent and identically distributed (IID)
extreme value type I. Eiq are alternative specific random indi-
vidual effects, that is, the variability induced by the alternatives
themselves that is not considered by the attributes in the model
(Greene, 2007). For the model to explicitly explain this varia-
tion, the effect is represented by ui, which is the standard devia-
tion estimated by the model, and made explicit for
convenience.
The parameters bqrepresent the importance that individual
qplaces on the attribute to which the parameter is associated
with and –in our specification –are made up of various ele-
ments:
bqDbCbF¢FqCG¢vqDbCbF¢FqChq(2)
where bis the mean (population) parameter; Fqare factors
behind the systematic taste variation; bFare parameters to be
estimated that weigh the effect of the Fqfactors on the mean
parameter b(Ort
uzar et al., 2011, page 279); hqdistributes
among individuals according to a random variable vq(generally
assumed to distribute normal, lognormal, uniform or triangu-
lar) and G, represents the elements of the Cholesky matrix,
which allow for correlation between random parameters
(Train, 2009).
Equation (2) contains the diverse forms of randomness that
can exist in the subject population and which may be consid-
ered in the specification of the ML model (Walker, Ben-Akiva,
& Bolduc, 2007). On the one hand, the random distribution of
the parameter allows each individual to value the importance
of each attribute differently. If the stated importance does not
show any randomness among the population, then the parame-
ter is defined by its population average b. On the other hand,
the most general ML model also allows for correlation
between those parameters showing significant randomness,
given that this is another factor of variability in the perception
of variables. This effect is controlled by the Gelements in the
Cholesky matrix, which is a triangular matrix where the main
diagonal elements represent the existing randomness in the
perception of the associated variables, and those beneath the
main diagonal report on the randomness due to correlation
between parameters; that is, if a below-diagonal element is sig-
nificant, it implies that there is correlation between the impor-
Table 3. Distribution of observations in the sample.
Percentage
Gender Female 36.0
Age 24 or younger 1.7
25 to 34 years old 13.3
35 to 44 years old 18.8
45 to 54 years old 28.2
55 to 64 years old 18.2
65 or older 19.9
Household members 1 30.9
2 40.9
3 20.5
4 7.7
Monthly household income 1000 €24.1
1000 –2500 €54.3
2500 –5000 €17.3
>5000 €4.3
Number of cars None, but willing to
purchase
3.3
1 80.7
2 12.7
3 2.2
4 1.1
Are there any recharging points in the area
around the household?
Yes 6.7
No 71.3
Do not know 22.1
When are you going to buy a new car? Within the next three
years
10
Within three and five
years
9.4
In more than five years 44.5
Do not know 36.1
Vehicle size in the next purchase Medium –urban 71.3
Large 28.7
Parking situation of current vehicle that will
be the next to replace
Garage at home 38.9
Garage near home 17.2
Free street parking 35.6
Reserved parking 8.3
Use made of current vehicle that will be the
next to replace
Exclusively urban use 8.3
Mostly urban use 31.1
Mixed urban-
interurban use
42.2
Mostly interurban use 15
Exclusively interurban
use
3.3
Frequency of use of current
vehicle that will be the next
to replace
Daily use (all day travel
to work)
24.4
Daily use 41.1
Weekly use 30
Monthly use 4.4
8 R. CORDERA ET AL.

tance placed on the two variables associated with that element of
the Cholesky matrix. Finally, the Fqelements represent the group
of factors that may have a systematic observable effect on the
average importance of the attributes. These factors normally rep-
resent socio-economic or other conditioning characteristics pres-
ent in the choice framework. They were introduced as
interactions of the demographic variables with the attributes of
the SP experimental design in the proposed model.
The unobserved components in equations (1) and (2) allow
for correlation between the model parameters and relax the
independence from irrelevant alternatives (IIA) constraint of
the MNL model. Finally, given that eight choice scenarios were
presented to each individual, the data was specified as a pseudo
panel to consider the interdependence in the responses made
by the same individual (Ort
uzar et al., 2011, section 8.6.5).
The ML model can be estimated by simulated maximum
likelihood. In this case, we used the NLOGIT software package
and specified for 400 Halton points; the estimation results are
presented in Table 4. A total of 1,448 observations were
counted as each household replied to 8 scenarios.
3.4. Estimation results
The results show that, as other studies have concluded, the pur-
chase price significantly affects the choice among the three
alternatives. The incentive itself did not yield significant param-
eters although its amount was clearly indicated. So, it would
appear that, independently of the incentive, buyers only consid-
ered the final price they have to pay. The importance of the
price coincides with previous research and allows us to confirm
that one of the main barriers to purchasing alternative fuel
vehicles seems to be the price. Nevertheless, the importance of
this attribute has a heterogeneous Normal distribution
across the population. The reasons for this dispersion are
revealed by the elements in the Cholesky matrix (Table 4); these
suggest the existence of correlation between the importance of
price and the range in the case of combustion-powered and
electric vehicles.
Another technical characteristic included in the SP survey
was the cost associated with the fuel or battery consumption.
Although this does not have any bearing on the final choice of
an EV, it does in the case of the other two options, where its
perception is homogenous among the population. In addition
to this, an interaction effect that proved significant was the con-
sumption of the conventional car, which has an additional
weight in the case of people who indicated that they planned to
buy a new car within the next three years. This interesting
result implies that the perception of the traditional vehicle’s
consumption is more negative for those households planning
to renew their cars in the immediate future, reducing their will-
ingness to purchase the currently most preferred option.
Interestingly, when the car is planned to be renewed in the
long term (i.e. in more than five years), the utility of the hybrid
vehicle increases (estimated parameter: 0.853). This result may
be due to lack of confidence concerning the actual features of
the new alternatives which, according to Turrentine and Kurani
(2007), reside in the uncertainty about the long term costs of
fuel and batteries. Given this result, it would appear that there
could be a much higher future demand for hybrid vehicles and,
as a consequence, marketing strategies might be better directed
to the hybrid alternative first.
Regarding charging infrastructure, the model confirms that
the availability of battery recharging points has a positive effect
on the utility of EV. The discount in the cost of on-street park-
ing is also an influential factor in favor of EV but not as much
as the latter (estimated parameter: 1.437D-02 versus 1.681 for
recharging points). This result also supports previous research
findings concluding that the immediacy of incentives has a
higher impact on the sales of new cars than other measures,
such as discounts in on-street parking, the benefits of which are
perceived only in small quantities as time goes by (Potoglou &
Kanaroglou, 2007; Shin et al., 2012).
The term representing ignorance about the existence of bat-
tery recharging points in the surroundings of households
clearly reduces the attractiveness of the electric alternative. This
sign of the parameter is also consistent with previous findings
about the negative effect, even barrier, which a lack of informa-
tion has on newer alternatives. In this case, it is the ignorance
about the existence of a service network, which represents an
uncertainty associated with the range anxiety (Daziano &
Chiew, 2012;2013) and which decreases the utility of the EV.
Afinal analysis of the results in Table 4 relates to the error
components, EC, specified to account for unobservable variabil-
ity in utility. Three nests were introduced in the model, one for
each pair of alternatives, so that the presence of correlation
between alternatives may be detected through the common var-
iability in the error of the utilities. As shown in Table 4, the
terms EC (Combustion –Hybrid) and EC (Hybrid –Electric)
were significant. This suggests a common heterogeneity in the
Table 4. Estimated parameters describing purchase choice.
Variable (alternative where it applies) Coefficient t-test
Non-random parameters
Constant (Combustion) 7.121 5.374
Consumption (Combustion, Hybrid)¡0.762 ¡8.176
Proposed change of car in less than
3 years

Consumption (Combustion)
¡0.330 ¡2.559
Constant (Hybrid) 7.110 10.366
Proposed change of car over 5 years (Hybrid) 0.853 2.087
Street parking discount (Electric) 1.437 D-02 2.819
Availability of recharging points (Electric) 1.681 6.209
Not knowing if there are recharging points in area
around home address (Electric)
¡1.490 ¡2.120
Random parameters
Range (Combustion, Electric) 2.011 D-03 2.578
Price (Combustion, Hybrid, Electric)¡2.928 D-04 ¡9.307
Diagonal values in Cholesky matrix
Range (Combustion, Electric) 6.223 D-04 2.032
Price (Combustion, Hybrid, Electric) 9.065 D-05 0.068
Below diagonal values in Cholesky matrix
Price (Combustion, Hybrid, Electric)–Range
(Combustion, Electric)
1.483 D-04 3.551
Standard deviations of latent random effects
EC (Combustion –Hybrid) 2.153 5.821
EC (Hybrid –Electric) 2.297 6.460
EC (Combustion –Electric) 0.339 0.314
Standard deviations of parameter distributions
Range (Combustion, Electric) 6.223 D-04 2.032
Price (Combustion, Hybrid, Electric) 1.486 D-04 4.004
Log likelihood value ¡704.292
Restricted log likelihood ¡1532.564
McFadden’s Pseudo R
2
(No coefficients) 0.54
McFadden’s Pseudo R
2
(Constants only) 0.33
N 1448
INTERNATIONAL JOURNAL OF SUSTAINABLE TRANSPORTATION 9

demand for the petrol and hybrid options, as well as between
the alternatives hybrid and electric vehicle. The modelling of
this correlation is relevant for increasing the realism of the sim-
ulated substitution rates between alternatives.
The goodness of fit of the model was good and clearly supe-
rior to the log –likelihood of the model without coefficients
(Pseudo R
2
D0.54) and with constants only (Pseudo R2 D
0.33).
4. Demand elasticity for each type of vehicle
The model parameters take on a practical sense in the analysis
of the demand elasticity for the three alternatives considered:
conventional, hybrid and electric vehicles. The direct demand
elasticity quantifies the variation in the demand of an alterna-
tive as a function of any attribute considered in its utility func-
tion. It is given by the following expression:
Eii D@Piq
@xikq
¢xikq
Piq
(3)
where Eii is the direct elasticity of the demand of alternative i
with respect to attribute kfor user q;P
iq
is the probability that
user qwill choose alternative i; and x
ikq
is the value of attribute
kof alternative ifor user q.
The cross elasticity of demand quantifies the variation in the
demand for an alternative due to the variation of a variable in
the utility of a competing alternative:
Eij D@Piq
@xjkq
¢xjkq
Piq
(4)
where Eij is the cross elasticity of demand for alternative iwith
respect to attribute kof alternative jfor user q.
The average demand elasticity values (calculated using sam-
ple enumeration, see Ort
uzar et al., 2011, Ch. 9) from our
model and sample of observations are shown in Table 5.Itis
important to consider that a 1% increase in some variables for
the different alternatives shows a magnitude difference in abso-
lute terms (e.g. and increase of 1% in range represents more
kilometers for a combustion vehicle than for an electric vehicle
in absolute terms).
These results may be informative for the design of marketing
strategies for alternative-fuel vehicles. Firstly, the direct elastic-
ity of demand for the combustion vehicle with respect to all
attributes that significantly influence its choice is smaller than
one in absolute value; that is, a percentage point in the variation
of each variable implies less than a percentage point in the vari-
ation of the demand for the non-sustainable alternative. This
implies that the demand for the conventional vehicle is fairly
inelastic. This result is consistent with the preference of
respondents to purchase an internal combustion car and
describes the present scenario.
Regarding direct price elasticities, Dagsvik et al. (2002)
obtained similar values; however, in our case study the hybrid
and electric vehicles obtained a purchase elasticity four times
higher than the combustion-powered vehicle elasticity. Obvi-
ously, the time that has passed and the local conditions and
market framework are possibly important causes for such
divergence. In any case, it is important to remark that the
demand of hybrid and electric technology to price is highly
elastic (¡2.230, ¡2.404) whereas the elasticity of the demand
for conventional vehicles is limited with regard to price varia-
tions (the direct elasticity is only ¡0.603).
The elasticity for the EV is also above one for the fuel con-
sumption and the price of the conventional alternative, e.g. an
increase of one percentage point in the cost of fuel would cause
an average increase of 1.418% in the demand for EV. Further-
more, this same variable also has the largest effect after the
price on the demand for the conventional vehicle, but in this
case with a negative sign (¡0.572), correctly meaning that less
efficient engines or higher gasoline or diesel costs would imply
a reduction in the demand for combustion vehicles. In addition,
fuel consumption also presents a high elasticity in the choice
for hybrid vehicles (1.748). The conclusion from these results is
that both, gasoline and diesel costs, are key variables in the
demand for the three alternatives. This is consistent with evi-
dence provided by other authors; for example, Graham-Rowe
et al. (2012) concluded that an increase in liquid fuel prices
would effectively contribute to a changeover, and K€
olbl et al.
(2013) identified energy cost gains together with purchase price
as the variables setting the threshold in favor (or not) of cleaner
alternatives.
In the case of the hybrid alternative, its price causes the
highest elasticity of demand (¡2.230). Another two variables
with elastic effects on the demand for the hybrid vehicle are its
consumption (¡2.206) and the range of the combustion vehicle
(¡1.094). The result for consumption is in line with the operat-
ing cost elasticities found for medium fuel efficiencies in the
work of McCarthy and Tay (1998), whereas the value for the
conventional (low fuel efficient) vehicle are four times lower in
Santander; this suggests that the demand for the traditional
alternative is much more stable there.
In the case of electric cars the greatest effects on their
demand are due to the purchase price (¡2.404) and, after the
fuel consumption, by the price of the conventional vehicles
(1.313), as shown in Table 5.
Finally, it should be remarked that the indirect incentive (in
the form of a street parking discount), did not stimulate a high
Table 5. Elasticities of demand for the three vehicle alternatives.
Variable
(Direct demand elasticity) Combustion Hybrid Electric
Range (Combustion) 0.354 ¡1.094 ¡0.929
Range (Electric)¡0.051 ¡0.100 0.430
Price (Combustion)¡0.603 1.537 1.313
Price (Hybrid) 0.390 ¡2.230 0.622
Price (Electric) 0.219 0.395 ¡2.404
Consumption (Combustion)¡0.572 1.748 1.418
Consumption (Hybrid) 0.326 ¡2.206 0.804
Street parking discount (Electric)¡0.034 ¡0.087 0.386
Availability of recharging points (Electric)¡0.038 ¡0.104 0.445
Proposed change of car in less than 3 years

Consumption (Combustion)
¡0.036 0.058 0.045
Proposed change of car in more
than 5 years (Hybrid)
¡0.037 0.192 ¡0.091
Not knowing if there is any recharging
point in the surroundings of the
home address (Electric)
0.008 0.023 ¡0.213
10 R. CORDERA ET AL.

elasticity in the demand for EV. Thus, our results support pre-
vious research that concluded that purchase incentives have a
greater impact than indirect ones (Diamond, 2009; Potoglou &
Kanaroglou, 2007; Shin et al., 2012; Struben & Sterman, 2008).
However, this fact could be different in study contexts with
more powerful indirect incentives. For example, in some major
cities of China there are vehicle-licensing regulations in order
to limit the number of new cars (Chen & Zhao, 2013). In these
cases free licensing policy for environmentally friendly vehicles
could have a stronger effect on demand than purchase incen-
tives (Hao, Ou, Du, Wang, & Ouyang, 2014).
5. Simulation of scenarios
In order to increase the evidence derived from the calculated
parameters and elasticities, this section will describe the simula-
tion of changes in market share resulting from the application
of various scenarios. The following variables were modified in
different proportions: vehicle price, consumption, range, street
parking discount and availability of charging points. The pro-
posed scenarios and the resulting market shares are described
in Table 6.
It can be seen that the scenario of increasing the consump-
tion costs (€/km) in the case of the conventional vehicle caused
a significant drop in its market share (¡9.5%) and a corre-
sponding increase in demand for both the electric and, above
this, the hybrid vehicle.
A 25% reduction in purchase price for the Electric and
Hybrid vehicles is the factor that results in the greatest increase
in their market share, although the increase in the Hybrid
choice was clearly superior to the Electric alternative. These
scenarios show that the penalization of the conventional vehicle
and reducing the purchase prices of the more environmentally
friendly vehicles are the two most effective policies to encour-
age greater use of the latter. Over the short-medium term these
changes could be proposed for, above all, the hybrid vehicle
rather than the electric alternative. In terms of cost-benefit, the
most effective measure would surely be to increase the costs
associated with running a conventional vehicle as it would lead
to penalizing the negative environmental externalities gener-
ated by this kind of vehicle and, at the same time, increase pub-
lic income which could be invested in encouraging the use of
more sustainable modes of transport. However, in practice this
could be a difficulty policy to introduce due to its potential
unpopularity. Other measures like free street parking for elec-
tric vehicles or wider availability of recharging points would
only have, according to our estimations, moderate effects on
electric vehicle uptake.
6. Conclusions
Not only the automobile industry, the providers of energy dif-
ferent from oil and the local authorities, but also society in gen-
eral, can benefit from an increased demand for alternative fuel
vehicles. Moving to such engines would imply a cleaner envi-
ronment, a more sustainable way of life and more economic
independence in countries highly dependent on oil imports.
Nevertheless, in spite of over two decades in the market place,
these new alternatives still do not enjoy any real success. Given
that each country and region may show different perceptions
about alternative-fuel vehicles, policies established to promote
their markets should be studied in detail keeping a local focus
to adopt sustainable practices as efficiently as possible.
The goal of our research was bringing to light some questions
still existent in the literature and address aspects that had not
been considered in previous research. The diversity of results in
the literature is probably due to several reasons, such as context,
available information, attributes considered, data collection
methods, and the specification chosen for the demand models.
For this reason, this research gave an important role to citizen
involvement from the initial stages of the methodology. A debate
among consumers is a useful technique to understand which fac-
tors influence their purchasing behavior, thereby, allowing the
design of a SP questionnaire tailor made to the requirements
and preoccupations of potential buyers in the city.
Our model identified some policy priorities for stimulating
demand for hybrid and electric vehicles in the city of Santander
(Spain), both products being practically non-existent in Spain
today. However, as indicated by Bunch et al. (1993) it is impor-
tant to be careful when interpreting predictions under SP
choice scenarios because these introduce hypothetical rather
than existing situations, where the choices stated by individuals
may not correspond to what would be their decisions in real
life. Therefore, the applied methodology only serves to examine
behavior under a hypothetical future market.
With respect to modelling, this research emphasized the
need to consider variability in consumer tastes in order to guar-
antee the success of potential promotional strategies for new
alternative vehicles in Santander. As Jaffe and Stavins (1994)
pointed out, one of the weak points in the market for alterna-
tives to combustion vehicles resides in being able to correctly
consider heterogeneity among the population.
Our results show two specific priorities for the promotion of
cleaner alternative vehicles, especially hybrid cars, in the case of
Santander. Firstly, an important predictable impact on the
demand for alternative fuel vehicles would be obtained through
penalizing traditional petrol-powered engines. This effect would
be even stronger on those individuals who planned to buy a new
vehicle within the next three years. This conclusion partly
answers the question regarding which strategy administrations
should follow; it appears that for our sample, the penalization of
the conventional vehicle should yield a significant impact. While
the price of fuel depends partly on diplomacy and international
relationships, direct taxes based on annual kilometers or fuel
purchase can be expected to stimulate the uptake of cleaner
options. Thus, a complementary policy should incentivize the
development of significantly more efficient hybrid engines than
their purely combustion counterparts.
Table 6. Simulated scenarios of demand for the three vehicle alternatives.
Scenario Combustion Hybrid Electric
Increased Consumption (C25%) (Combustion)¡9.5% C6.0% C3.5%
Reduction of price (¡25%) (Hybrid) ¡10.0% C12.3% ¡2.3%
Reduction of Consumption (¡25%) (Hybrid)¡6.0% C7.4% ¡1.4%
Reduction of price (¡25%) (Electric) ¡5.2% ¡2.4% C7.6%
Increased Autonomy (C50%) (Electric)¡2.0% ¡0.7% C2.7%
Street parking discount (100%) (Electric)¡2.4% ¡0.8% C3.2%
Enough availability of recharging points (Electric)¡2.9% ¡0.8% C3.7%
INTERNATIONAL JOURNAL OF SUSTAINABLE TRANSPORTATION 11

The second priority for increasing the demand for cleaner
vehicles in Santander should aim at enhancing the competitive-
ness of their prices. Therefore, as previously shown by Dia-
mond (2009) and Gallagher and Muehlegger (2011) among
others, any incentives should give priority to the reduction of
purchase price or of the taxes directly associated with the pur-
chase, or with the circulation of cleaner vehicles.
A clear inelasticity appears to exist in the demand for tradi-
tional combustion vehicles. The reasons argued by Ahn et al.
(2008) may explain this, that is, the dominant market position
of the petrol car is due to the stability of its supply, existing
infrastructure and available maintenance network.
On the other hand, the hybrid alternative receives significant
elasticity both direct and crossed, due to changes in its attrib-
utes and of those characterizing the conventional car (e.g. price,
consumption). The model also shows that the hybrid alterna-
tive is an attractive possibility for individuals who stated they
would contemplate buying a new car in the longer term. The
estimated parameters and the analysis of demand elasticities
suggest that hybrid vehicles would be perceived as an attractive
option in Santander, with a consequent increase in market
share.
Furthermore, households planning to buy a car within the
next three years perceive petrol costs more negatively than the
rest, increasing their likelihood to opt for the cleanest alterna-
tives, especially for hybrids. Also, those that would need to buy
a vehicle in the longer term (more than five years) find a higher
utility in the hybrid alternative than the rest of the consumers.
Consequently, the prediction of future demand in Santander is
clearly in favor of the hybrid option; the electric alternative
would only appear a step further on. However, this contrasts
with the conclusions drawn by Shin et al. (2012), who identified
the potential for EV to be greater than that of hybrids and pet-
rol engines in South Korea. In any case, the initial penetration
of hybrid engines could actually stimulate an increase in the
utility of the fully electric option, since the gradual presence of
greener engines will reduce the existent uncertainty regarding
the performance and potential of new vehicles, as claimed in
the literature (Jaffe & Stavins, 1994; Jensen et al., 2013; Kurani
et al., 1996; Struben & Sterman, 2008).
An important point that has been revealed in our work is
that range, in itself, does not have such a strong effect as some
of the aspects mentioned above, and turned out to be a factor
perceived in a highly heterogeneous way. Thus, in line with sev-
eral authors (Greaves et al., 2014;K
€
olbl et al., 2013; Kurani
et al., 1996) we conclude that range is not always decisive due
to usage patterns and the fact that vehicles tend to be parked
most of the time. Notwithstanding, our research identified two
factors that indirectly give importance to the range. Firstly, we
found significant correlation between the perception of the pri-
ces of electric motors and fuel engines and their ranges. Sec-
ondly, evidence was also found that the preference for EV is
smaller for people that do not know whether there are battery
recharging points nearby their homes, evoking the so-called
range anxiety.
Afinal conclusion can be drawn from this research: work
needs to be done on eliminating uncertainty by providing
information about the characteristics of hybrid and electric
vehicles, and on creating an adequate recharging network
infrastructure. An initial strategy could be to push forward the
use of hybrid and electric vehicles through the renewal of the
vehicle fleets providing public services (such as public transport
and taxis), waste collection vehicles and cars used exclusively
by public service employees, with hybrid or electric alternatives.
This would indirectly improve the administrations’public
image (Gao & Kitirattragarn, 2008) and encourage the adop-
tion of cleaner vehicles by the general public.
Finally, as evidenced in previous studies, drivers adjust their
perceptions about new products once they have experienced
them (B€
uhler et al., 2014; Jensen et al.,2014; Mau et al., 2008);
so, the market share for cleaner vehicles should be expected to
gradually increase, making it possible to compare revealed pref-
erence studies with predictions based on the currently more
common stated preference data.
7. Contributions/highlights
Focus on the local framework through a debate among
consumers in focus group sessions.
Using a mixed logit model, heterogeneity is addressed in
various forms.
Insights are provided into the dilemma of ‘negative versus
positive incentives’.
Clear policy implications are drawn.
Acknowledgments
The authors would like to thank the Institute in Complex Engineering Sys-
tems (ICM: P-05-004-F; CONICYT: FBO16), the Centre for Sustainable
Urban Development, CEDEUS (Conicyt/Fondap/15110020) and the Bus
Rapid Transit Centre of Excellence funded by VREF (www.brt.cl), for their
support.
Funding
Centre for Sustainable Urban Development, CEDEUS ID: Conicyt/Fondap/
15110020 Institute in Complex Engineering Systems ID: ICM: P-05-004-F;
CONICYT: FBO16.
ORCID
Ruben Cordera http://orcid.org/0000-0001-8272-6662
Luigi dell’Olio http://orcid.org/0000-0003-0919-7578
Angel Ibeas http://orcid.org/0000-0001-6551-2013
Juan de Dios Ort
uzar http://orcid.org/0000-0003-3452-3574
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