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ISSN 1450-2267 Vol. 56 No 4 July, 2018, pp.364-382 http://www.europeanjournalofsocialsciences.com/

Modelling the Demand for Electric Vehicles

Mohammad M. Hamed

Corresponding Author, Professor of Civil and Transportation Engineering Lecturer at the Civil Engineering Department

Al al-Bayt University, Mafraq, Jordan E-mail: mmshamed@gmail.com

Basel M. Al-Eideh

Associate Professor, Quantitative Methods & Information Systems Department College of Business Administration, Kuwait University, Kuwait

E-mail: basel@cba.edu.kw

Abstract

This paper addresses the demand for electric vehicles (EV). More specifically, the paper develops a number of disaggregate models to estimate the future demand for EV in Jordan.

Two approaches were developed to estimate the future demand. The first relates to Information Indices (II) and the second relates to Poisson regression model. Data from a random sample of 253 respondents were used to estimate the models. Analyses conducted in this paper clearly provide insights into some of the factors that play a role in the demand for EVs in Jordan. Empirical outcomes of this paper confirm earlier findings reported in the literature. Unlike other papers, we estimated Poisson regression and Information indices models and provided forecasts of the demand for EV in the near and medium future (one to four years from now).

Estimation results presented in this paper clearly show that both EV technological limitations (limited driving range), lack of charging stations and un-availability of proper repair facilities play a key role in the demand for EVs in Jordan’s market. More specifically, with the availability of charging stations and proper repair facilities/shops consumers are more likely to choose shorter durations to purchase EV. If these two factors were unavailable, consumers are likely to wait and not purchase now. Other factors that turned out to be significant and affect the demand are: age, gender, total household income level, driver’s occupation and driving experience. It is important to note also that drivers who currently own an EV or a hybrid car are more likely to have shorter durations to buy EV when compared with those without EV or hybrid car. Results from this study indicate that consumers in Jordan are likely to consider EVs as an alternative to conventional or hybrid cars (with internal combustion engine cars) during the purchasing process.

Keywords: Electric vehicles, demand modeling, Information Indices, Regression 1. Introduction

The global energy field is rapidly changing. Instrumental issues inducing this rapid change include:

uncertain energy prices, international pressures to reduce carbon emissions, energy security and economic and security benefits of shifting to clean energy. The global energy sector has changed since the middle of 2014. According to World Energy Councilreport [1],oil price fell by 76% from $115.71 a

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barrel on 19 June of 2014, to $27.10 on 20 January 2016. The ARA coal contract dropped from $84 a ton on 28 April 2014 to $36.30 on 17 February 2016. The US Henry Hub NG price dropped from around $4.50 per MMBtu in June 2014 to $1.91 in February 2016. Between 2014 and 2015, the average electric vehicle (EV) battery cost fell by about 35%, thus setting the stage for the EV to become a mass marketed product. Total EVs sold in 2015 was around 462000 compared with 289000 EVs sold in 2014 (59.86% increase). Most EV sales took place in North America, China, Europe, and Japan. Leading car manufacturers have announced their intention to invest billions of US$ in the production of electric vehicles (EVs). In addition, they are introducing new EV models for 2018. The stage for mass production of EV is underway. This is despite the fact that EVs has encountered difficulties in developing into a mass marketed commodity [2]. EVs in this paper are defined as those vehicles which are powered by electricity and have no internal-combustion engine.

The growth in EV sales is likely to have instrumental impact on global emissions over the coming decades. Some however present the argument regarding the source of electricity used to charge EVs. If such source is coming from coal-fired electricity, then the net effect on emissions of using EVs rather than gasoline cars will be much less than if the EVs are charged with electricity from renewable sources of energy (solar and wind).

A number of countries have issued policies banning and phasing out gasoline and diesel cars starting as early as 2025. Great Britain has announced a ban to sales of new cars that run on diesel and gasoline starting 2040. Furthermore, by the year 2050 all cars in the UK will need to have zero emissions. Other countries who declared ban for such cars include, France, India, Sweden, Denmark, Germany and Norway. More countries are likely to follow. Japan plans to increase the penetration rate of electric vehicles and plug-in hybrid vehicles to 15–20 % of total new car sales by 2020 [3].In terms of policies, some EU countries, US and China offer incentives for plug-in vehicles, such as tax credits and highoccupancy vehicle (HOV) lane exemptions.

The electrification (Green transportation modes) of the transportation sector through the introduction of EVs is a great mitigation measure to reduce air pollution and strengthen energy security [4]. [5] reported that there will be 140 million new energy vehicles in China in 2020, which can save 32.29 million tons oil, which are equivalent to 22.7% reduction in oil consumption for cars. This is a major step forward to protect the environment, reduce climate change and reduce reliance on fossil fuels [6].[7] reported that the annual reduction in CO2 emissions per EV is 1959 kg-CO2. Thus, the reduction rate of CO2per EV is83.5% (1959/2345 kg-CO2).

There is a growing body of literature that addresses EVrate of market penetration and the many different factors affecting the consumer decision making process towards these vehicles. It has been reported in the literature that improvements in EV range, reductions in battery prices, and the availability of tax-based incentives and other incentives, are likely to increase EV rate of penetration to different markets.

A number of studies conducted in different parts of the world havehighlighted the factors influencing the demand for EVs from the demand side. [8] conducted a study in the US and [9]

conducted a study in Malaysia. Factors revealed include social, environmental, financial and the readiness of supporting EV infrastructure. Other studies reported that EV limited driving range, lack of choices in EVs, speed, protectionism, recharging time, and battery problems were the factors discouraging the purchase of full electric vehicles (see [10], [11], [12], [13],[4] and [14]).

[15]suggested that socio-economic and socio-demographic variables are motivational factors for EV use. While [16] reported that drivers put more weights on the environmental benefits of EVs but not the economic and social benefits. [17] on the other hand addressed consumer preference of EV through a stated preference data. Respondents were asked to choose between their preferred gasoline vehicle and two electric versions of that preferred vehicle. The study estimated a latent class random utility model and then estimated the willingness of the consumer to pay for five electric vehicle attributes:

driving range, charging time, fuel cost saving, pollution reduction, and performance.

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Different forecasts sales of EVs have been reported in the literature. [18] study predicted EV sales to be in the range of 1.8 to 7.3 million vehicles by the year 2023. While [19] predicted EV fleet to constitute around 7.6% of the total vehicle fleet by the year 2030.

In his extensive review of past research[6] reported that charging inconvenience, short battery range, cost, and psychological factors are instrumental for the consumer purchasing decision making process. [20] addressed consumer preferences for conventional, hybrid electric, plug-in hybrid electric (PHEV), and battery electric (BEV) vehicle technologies in both the US and China. The study used data from choice-based conjoint surveys fielded in 2012–2013 in both countries.[21] indicated that key barriers for EV rate of penetration were the perceived high purchasing value of EVs and other benefits associated with the use of a conventional vehicle. [8] argued that driving range and chargingduration are barriers to electing to purchase an EV in the US.

[22] addressed the factors influencing the deployment of electric vehicles for the purpose of drawing policy implications for promoting the deployment of EVs. The study considered 24 countries that are promoting EVs. The dataincludedfactors such as policy support and environmental factors. The study adopted fuzzy-set qualitative comparative analysis methodology, to compare the factors affecting the deployment of EVs. The study utilized country-based aggregate data to make inferences. The results show that, among other things, charging infrastructure is sufficient condition to adopt EVs.

A number of reasons have been cited for such market change to EV. For example [23]

concluded that purchase tax relives, low gas emissions, and monetary costs are the main factors that encouraged people to buy more environmental friendly EVs. In general, it has been reported in the literature that a significant number of factors are likely to influence driver’s intention to purchase EV or be as an impediment to the penetration of EVs. Three main categories of variables were identified in the literature. These include: situational, demographic and psychological [6].

Penetration rate of EVs and public acceptance has been different in each country ([22] and [9]).

[9] reported that electric vehicles acceptance in Malaysia can be explained as being significantly related to social influences, performance attributes, financial benefits, environmental concerns, demographics, infrastructure readiness and government interventions. Research on the impediments to EV penetration included the driving range of EV, lack of charging stations, charging time, and lack of specialized repair facilities (see [24]).

A number of incentives have been reported in the literature. These include tax-based policies, lane access that is specially designed for EV, free parking or electricity and better insurance products ([4]).[25] provide a detailed review of the relevant policies for EV development in China. [7]

conducted a conjoint analysis to examine consumers’ stated preferences on the basis of a large-scale survey administered in Japan. The expected market penetration rate for EVs was addressed in the context of incentives policies. The consumer willingness to pay to EVs was estimated. [22] reported that policy support, such as tax benefit and subsidy payment influence the spread of electric vehicles.

Despite all the advantages EV present, the share of EV as a percent of total passenger vehicles still very low. For example, although in Norway the rate of penetration of electric vehicles in Norway is high (28.8 %), EV market share is about 2% of all passenger vehicles [26].

In this paper we develop a statistically sound methodology capable of predicting the demand for EV in Jordan. We also highlight the different factors influencing the demand as well as any impediment to the penetration of EV to the market. Stated preference survey data, intentions of car users to adopt EV, will be used to estimate the developed models. Unlike other studies, who estimate choice or regression models to uncover influencing factors, we estimate models and provide forecast of the demand for EV in the near and medium future (one to four years from now). Jordan market was chosen as a representative of the North Africa and Middle East (MENA) region.

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2. Methodology

Since EV is fairly new and still has not reached the status of being mass marketed product, past research is based on stated preference data to measure consumers’ purchasing propensity. In some countries EVs are rarely available. Still, the literature has shown a somewhat clear picture of the current demand status of EVs and the likely factors that influence this demand. Furthermore, there are established factors that prevented the high penetration of EV to the different markets.

A number of approaches have been used in the literature to address the factors influencing the consumers’ decision to purchase EVs. [5] used panel data modeling. [4] applied univariate and multivariate time-series models to forecast future EV sales. Other researchers have examined alternative fuel vehicles by combining stated and revealed preference data using multinomial logit and mixed logit models ([7], [23], [27] [28]).

Other methods have used interactive surveys to investigate consumer awareness of and preferences towards these vehicles. [29] applied multinomial probit models to model consumer choice of potential car purchasing including EV.[20] used both the multinomial logit (MNL) and mixed logit (MXL) models to address the consumer preferences for EVs in both the US and China.[17] estimated a latent class random utility model using choice data from a survey sample. [22] used the fuzzy-set qualitative comparative analysis address factors influencing the deployment of EVs. [9] estimated a multiple regression model to address the factors that affect the usage of EV in Malaysia.

2.1 EV Demand Information Index (EVDII)

Electric Vehicle Demand Information Index (EVDII) is developed to measure the demand for EV in the years ahead (one year from 2017, two years from 2017, three years from 2017 and four years from 2017). Therefore, EVDII is defined as the average of all respondent indices of information (willingness to purchase) regarding the demand for EV such that:

M1-EVDII is the mean information index for those respondents who stated their intention to purchase an EV after one year from 2017.M2-EVDII is the mean information index for those respondents who stated their intention to buy an EV after two years from 2017. M3-EVDII is the mean information index for those respondents who stated their intention to buy an EV after three years from 2017.M4-EVDII is the mean information index for those respondents who stated their intention to buy an electric vehicle after four years from 2017.TOT-EVDII isthe mean information index for all respondents who stated their intention to buy an electric vehicle regardless of the number of years starting from 2017.

The EVDII index measures the average of the personal intention ratesto all items and sub items offered by the Sample Survey. This gives quantitatively the propensity a respondent is willing to purchase an EV (see[30] and [31]).

Let Y be the individual information index for person i i, i=1,2,...,n. Then

=

=

p

j ij

i I

Y p

1

1

, i=1,2,...,n.

(2.1)

Where Iijis defined as the respondent response such that if Iijis 0, it means the respondent has no intention of purchasing an EV within the indicated timeframe (after one year, two years, etc.) with a 100% probability. If the response (Iij) is 1, it means the respondent has an intention of purchasing an EV within the indicated timeframe with a100% probability.

p j

n i

Iij , 1,2,..., ; 1,2,...,

EV an buy to intention an

has

; 1

EV an buy to intention no

;

0 = =



= (2.2)

Wherepis the number of categorical variables (four groups of respondents: the first category represents those who stated their intention to buy an EV after a duration of one year, the second category represents those who stated their intention to buy an EV after a duration of two years from

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now, the third category represents those who stated their intention to buy an EV after a duration of three years from now and the fourth category represents those who stated their intention to buy an EV after a duration of four years from now) related to the model under study.

Now we define, TOT-EVDIIto be the EVDII of the total number of categorical variables related to the total of four models, M1-EVDII to be the EVDII of the number of categorical variables related to model 1, M2-EVDII to be the EVDII of the number of categorical variables related to model 2, M3- EVDII to be the EVDII of the number of categorical variables related to model 3 and M4-EVDII to be the EVDII of the number of categorical variables related to model 4.Then

=

=

=

=

=

=

=

=

=

=

n

i i n

i i n

i i n

i i n

i i

n Y EVDII M

n Y EVDII M

n Y EVDII M

n Y EVDII M

n Y EVDII TOT

1 1 1 1 1

4 1 3 1 2 1 1 1

1

(2.3)

Note that 0≤ EVDII≤1for all the above cases.As such, if M1- EVDII turned out to have a value of 0.4, then it means on average respondents have a 40% propensity to purchase an EV after one year from now. In addition, if TOT-EVDII came out to be 0.3, then it means on average respondents have 30% propensity to purchase an EV, one to four years from now. EVDII value increases as more respondents state their intention of purchasing an EV in the indicated timeframe.

2.2 Poisson Regression Model

Since the response variable is a count variable representing the stated intention of respondents when they are likely to purchase an EV. Data also showed that the mean and variance of the dependent variable is nearly the same (not over dispersed). The Poisson regression model was statistically the most appropriate one. Below is a discussion of the Poisson regression model formulation.

Assume the response variable Ybe the expected number of years to buy an EV (denoted by Period) such that:

=

years After for

years After for

years After for

year After for

Y

4 4

3 3

2 2

1 1

(2.4)

Figure 1.shows the frequency of durations (one year, two years, three years and four years)given by respondents. 5.5% of the sample intend to buy an EV after a duration of one year from 2017, 20.5% of the sample intend to buy an EV after a duration of two years from 2017, 22.4% of the sample intend to buy an EV after a duration of three years from 2017 and 25.2% of the sample intend to buy an EV after a duration of four years from 2017, whereas 26.4% of the sample have no intention of buying an EV.

In Poisson regression the response/outcome variable Y is a count with explanatory variables, X

= (X1, X2, … , Xk), can be continuous or a combination of continuous and categorical variables.

Convention is to call such a model “Poisson Regression”. For simplicity, with a single explanatory variable, we write: log(μ)=α+β x where α and β is the intercept and the slope of log(μ) on x. This is equivalent to:

μ=exp(α+β x )=exp(α)exp(β x )

The parameter estimates can be interpreted as:

exp(α) = effect on the mean of Y, that is μ, when X = 0

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exp(β) = with every unit increase in X, the predictor variable has multiplicative effect of exp(β) on the mean of Y, that is μ

If β = 0, then exp(β) = 1, and the expected count, μ = E(y) = exp(α), and Y and X are not related.

If β > 0, then exp(β) > 1, and the expected count μ = E(y) is exp(β) times larger than when X = 0

If β < 0, then exp(β) < 1, and the expected count μ = E(y) is exp(β) times smaller than when X = 0

In this case, note that Ydepends only on the predictors (independent variables):

29 28 27 15 11 10 8 7 4 3

1,X ,X ,X ,X ,X ,X ,X ,X ,X ,X

X . Assumeµ =E

( )

Y , then the Poisson regression

model will be:

( )





+ +

+ +

+

+ +

+ +

= +

29 11 28 10 27 9 15 8 11 7

10 6 8 5 7 4 4 3 3 2 1

exp 1

exp X X X X X

X X

X X

X X

β β

β β

β

β β

β β

β α β

µ (2.5)

3. Empirical Setting

3.1 Statistical Population and Study Sample

Around1200 questionnaires were distributed in the summer of 2017, 253 completed questionnaires.

The response rate was 21.2%. The survey took place in Amman, Jordan’s capital. The statistical population consists of respondents of ages greater than or equal to eighteen years old. The study sample is selected using simple random sampling of size 253 representing persons of ages greater than or equal to eighteen years old. The questionnaire listed 15 items with 32 sub items. The respondent would only check the listed items with the sub items that he/she related to him/her.

Jordan is a key country in the MENA region. It continues to play a pivotal role in the Middle East for two main reasons: its key geographical location and its political stability that make the country one of the safest in the whole region. Currently Jordan’s population is around 9.5 million people, nearly 4.2 million of which live in Amman. The population of Jordan is young. More than 70% of the population is under 34 years of age, while those between the age of 15 and 24 comprise around 20% of the population.

Jordan is open economy country, with key natural resources base like oil shale, phosphate, potash, and uranium.TheJordanian economy is mainly driven by the services sector, many of which are tourism-related (trade, hotels and restaurants, transport and communications). The annual growth rate for the time period 2013 to 2016was between 2.4 and 3.0 %, while the forecasted growth for 2017 is around 2.6%.Poverty and unemployment are among the key challenges facing Jordan. Unemployment has grown from 12.5 % in 2012 to around 16% in 2017. Moreover, the conflict in Syria has resulted in a large influx of refugees (around 1.6 million refugees) which aggravated the unemployment in the country.This paper takes Jordan as a case study representing the MENA region to investigate the factors affecting the demand for EVs.

3.2 Energy Sector

Jordan currently imports around 92% of its energy needs. In 2016, total cost of primary energy relative to Jordan’s GDP was around 7% compared with 21.1% in 2012. The huge drop is mainly due to natural gas being used to generate electricity. The natural gas is coming from the newly built (mid 2015) LNG terminal located in Aqaba.

Until mid-2014, most of electricity generated is coming from non-renewable sources of energy.

The annual growth in electricity consumption is considered above the international average of 5.3%. In 2016, total available capacity was 4419MW and the peak load was 3250MW [32]. The average (KWh) consumed per capita was 1719.

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Jordan now is pursuing an ambitious renewable energy program to increase its energy independence, reduce costs and reduce carbon emissions. The Ministry of Energy and Mineral Resources has signed a number of renewable energy projects (solar and wind) with the above stated aims. Currently, the largest wind project in the Middle East of 117 MW is in operation and is located in the southern part of Jordan. This is in addition to a significant number of solar projects now in operation. Total installed capacity from renewable energy (wind and solar) until 2017 is around 500MW. By the end of 2018 total capacity of renewable energy projects will be around 1132MW representing around 20% of installed capacity and 9% of total electricity generated. Other local sources of electricity will be coming, in few years’ time, from oil shale (470MW). According to Jordan’s national energy strategy (2015-2025) 40% of total energy will be produced locally compared with 8% in 2017.

The electricity sector in Jordan is operating under the single buyer model and three subsectors, generation, transmission and distribution. The transmission is the responsibility of the National Electric Power Company (NEPCO). The residential sector consumes around 36% of total electric power consumption. This is followed by the industrial sector, the commercial sector, and the water pumping sector.

3.3 Transportation Sector

Transportation contributes 38% of Jordan’s fuel bill, which is mainly imported. This percentage is expected to rise if car ownership levels continue to increase at current rates (8.5%). Currently, there are around 1.5 million private cars and public transportation mode share is low (about 14%).

Transportation challenges in Amman include: population growth, increase in intensity of land use, rapid expansion of metropolitan area, overloaded road system, poorly public transport system, majority of public transportation trips are made by car-based services, either shared taxi or regular taxi. Current public transportation system can be characterized as being: fragmented and not well-planned, not responsive to mobility needs, lacks characteristics of modern systems. Jordan hopes to increase public transport mode share from14% to 40% in 2025. Furthermore, reduce dependence on private cars, and reduce negative environmental impacts of transportation.

The Government of Jordan (GoJ) initiated tax-based policies as incentives to give hybrid (powered from battery and some from gasoline) cars higher rate of penetration. It is been reported that 60 to 70% of low to medium income groups are using these hybrid cars. With car manufacturers’

production of EVs, the GoJ is trying to pass on all kinds of tax-based incentives to EVs purchases. This move by the GoJ is amid at creating high penetration rate for EVs. In addition, the Government granted licenses to investors to open charging facilities (stations) and have set tariff for electricity bought at charging stations.

All kinds of tax-based incentives given to hybrid cars will cease by the end of January 2018.

Currently, full EVs represent around 0.35% of the total private vehicle fleet. Private car ownership is high in Jordan. The growth in private car ownership is about 8.5% annually. This high growth is a direct result of many factors: poor public transportation system (including intercity transportation), private sector car sales marketing strategies, bank strategies aimed at facilitating the issuance of personnel loans, and the public sector policies which allow the importation of used cars to the market.

3.4 Respondents Characteristics

Table 1shows demographic attributes of respondents who took part in the survey.Respondents who completed the questionnaires included: consultants, teachers working at public schools, pharmacists, public sector employees and university faculty members. The majority (88%) of respondent were educated (with a university degree). Respondents were asked to state their preference regarding the purchase of an EV from a set of choices: one year fromnow (2017), two years from now, three years from now and four years from now. This constituted our demand variable.

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Table 1shows that 74% of respondents were male with females making up to26%. Most of the respondents were within the age group of 26-45 (51%); this was followed by the age group 46-65 (45%). The table also shows about 82% of the total respondents were married. About 52% of respondents have total household monthly income level between 1800-2500 US$. The majority of respondents (55%) are public sector employees followed by 26% who works as teachers in the public sector. Table 1 also shows that only 2.77% of respondents own and drive full EV. Furthermore, 88%

have a positive perception of EVs and about 86% have the intention of replacing their current car with an EV. In addition, about 88% of respondents wish to see more penetration of EVs to Jordan market.

This is a major outcome from the survey as EV market is greatly influenced by customer perception and awareness.

Respondents (69%) indicated that the availability of charging facilities is a major factor to the penetration of EVs to Jordan’s market. Furthermore, 22% of respondents believe improving the driving range of the EV is likely to improve EVs presence in the market and capture more market share.

Figure 1 shows that the majority of respondents (25.30%) intend to purchase an EV after four years from now, while 20.55% and 22.53% of respondents intend to purchase an EV after two and three years from now, respectively. Only 5.53% of respondents have the intention of buying an EV after one year from 2017.

Table 1: Descriptive statistics of sample

Respondents profile Frequency/(average) Percentage (%)

Gender Male

Female

188 65

74.31 25.69 Age groups

Age group 1: 18-25 Age group 2: 26-45 Age group 3: 46-65 Age group 4: Above 65

10 129 114 0

3.95 50.99 45.06 0.00 Total household monthly

income (US$) groups

Income level 1: 700-1100 Income level 2: 1100-1800 Income level 3: 1800-2500 Income level 4: 2500-3200 Income level 5: Over 3200

12 52 131

44 14

4.74 20.55 51.78 17.39 5.54 Number of children in

household 1.10

Number of cars in

household 1.88

Driving license

100% of

respondents had a driving license Access to a private vehicle

99% of respondents had access to a private vehicle

Occupation

Occupation group 1: Consultant Occupation group 2: Secretary Occupation group 3: Public sector employee

Occupation group 4: Pharmacist Occupation group 5: University Faculty Member

Occupation group 6: Engineering Office Occupation group 7: Teacher

4 4 140

8 13 17 67

1.58 1.58 55.34

3.16 5.14 6.72 26.48

Driving experience (years) 26.21

Owning full EV 7

Social status Married Single

207 46

81.82 18.18

Perception about EV Positive 224 88.54

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Respondents profile Frequency/(average) Percentage (%)

Negative 29 11.46

Intend to replace your current car with EV

Yes No

218 35

86.17 13.83 Wish to see more

penetration of EV to the market

Yes No

Do not know

222 5 26

87.75 1.98 10.27

Impediment elements

Charging facilities (stations) No spare parts

No repair shops Limited driving range Size of EV

175 9 8 56

5

69.17 3.56 3.16 22.13

1.98 Figure 1: Respondents intention to purchase EV after one, two, three or four years

4. Estimation Results and Analysis 4.1 Information Indices

Table 2 shows mean information indices outcomes for the four categories of respondents. The table shows that the mean information index for the first group (5.5% of respondents who intend to buy an EV after duration of one year from 2018) is 0.4286. This means that this group of respondents has a willing propensity of 0.4286 to buy an EV after one year from 2017. While, respondents (25.30%) who intend to buy an EV after duration four years have a willing propensity of 0.3733. For the whole sample and for all respondents who indicated their intention to buy an EV, their propensity to buy an EV sometime in the future (between one and four years) is 0.3156.

Table 2: Mean information indices

N Minimum Maximum Mean Std. Deviation

M1-EVII 14 .17 .50 .4286 .10770

M2-EVII 52 .13 .63 .5110 .12563

M3-EVII 57 .11 .56 .3352 .09484

M4-EVII 64 .00 .50 .3733 .11611

TOT-EVII 253 .15 .38 .3156 .04805

Results in Table 3 estimated using SPSS Software (see [33] and [34])

Figures 2 and 3 show the autocorrelation and partial autocorrelation functions of the observedEVDII obtained from the survey for respondents.

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Figure 2: Autocorrelation function (ACF)for observed EVDII.

Figure 3: Partial autocorrelation function (PACF) for observed EVDII

The ACF and PACF shown in Figures 2 and 3 respectively suggest differencing of the data first. An ARIMA (0, 1, 1) model was suggested to fit the EVDII data.

The ARIMA (0, 1, 1) model is given by

t t t

t EVDII

EVDII0 + 1−θ1ε 1−ε . (4.1) Using the SPSS Software, the model parameter estimates were as follows: ˆ 0.001

0 =

φ and

703 . ˆ 0

1 =

θ with p-value 0.000, which is significant at 0.05 level of significant. The fitted model is then given by

t t t

t EVII

EVDII =0.001+ 1−0.703ε 1−ε (4.2) Figures 4 and 5 show the autocorrelation and partial autocorrelation functions of the error term of the fitted model.

The ACF and PACF plots (Figures 4 and 5) below clearly support the fact that the fitted ARIMA (0, 1, 1) model is statistically adequate representation of the EVDII data.

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Figure 4: The ACF of the residual of the EVDII model

Figure 5: The PACF of the residual of the EVDII model

The original (stated) and the forecasted models are shown in the Figure 6.

Figure 6: Fitted respondents intentions of purchasing an EV using Information Indices approach(EVDII)

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The next task would be to predict for each respondent the expected number of years, from 2017, to purchase an EV using the estimated EVDII values. If we define the dependent variable Y as the expected number of years to buy an EV (denoted by Period) such that:





+

=

years for

years After

for

years After

for

year After for

Y

3 4

3 3

2 2

1 1

Using the model selection approach in time series modeler in the SPSS, we find that the best fitted model will by the autoregressive model of order 1 shown below:

t t

t EVII

Y01 +ε (4.3)

From the result of the SPSS analysis, we get the estimated parameters as ˆ 2.925

0 =

φ and

456 . ˆ 0

1 =

φ where both are significant with probability values 0.000 compared to significant level 0.05.

Thus the predicted model will be as follows:

t

t EVII

Yˆ =2.925+0.456 (4.4)

For the compatibility of the fitted model with original observations, we use the autocorrelation function ACF and the partial autocorrelation function PACF for the residuals (Figures 7 and 8) of the fitted model above.

The ACF and PACF plots of the residuals shown in Figures 7 and 8 respectively clearly indicate that the fitted autoregressive model of order 1 (AR(1)) is statistically appropriate and is capable of predicting the respondent’s expected number of years needed to buy EV.

Figure 7: The ACF of the residual of the AR(1) model

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Figure 8: The PACF of the residual of the AR(1) model

Figure 9 shows the original and the forecasted expected number of years for respondents.

Figure 9: Fitted the expected number of years to purchase an EV using Information Indices approach (EVDII)

4.2 Poisson Regression Model

The regression coefficients are estimated using the method of maximum likelihood. Suppose

(

X1,X3,X4,X7,X8,X10,X11,X15,X27,X28,X29

)

X′=

be the random vector of the independent variables and β′=

(

α,β12,11

)

be the parameter vector. Using this notation, the fundamental Poisson regression model for an observation I is written as

( ) ( )

| ! Pr

i y i i

i

i y

y e Y

i µ i

µ

µ

=

= (4.5)

where

(

β

)

µ µi = X ′i 

The regression coefficients are estimated using the method of maximum likelihood. The logarithm of the likelihood function is

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( )

=

 ( (

) )

 (

)

( )

i i i

i i

i

i X X y

y y

L , ln ln !

ln  β µ  β µ  β (4.6)

In this case we will use the SPSS software to compute the estimated coefficients as shown in table 4 below.

Table 3 shows the Poisson regression model estimation results. All of the variables included in Table 2 arewithinlow to moderate correlation.Therewas no multicollinearity problem to report. Most of parameters estimates are significant at the 0.05 or 0.1 level.

Table 3: Poisson regression model parameter estimates

Parameter B Std.

Error

95% Wald

Confidence Interval Hypothesis Test

Exp (B)

95% Wald Confidence Interval for Exp (B) Lower Upper Wald Chi-

Square Df Sig. Lower Upper

Intercept 1.698 .4844 .748 2.647 12.287 1 .000*** 5.462 2.114 14.115 Introducing more

charging stations -.239 .0953 -.426 -.052 6.298 1 .012* .787

.653 .949

Presence of repair

facilities -.447 .1555 -.752 -.143 8.279 1 .004** .639

.471 .867

Improving EV

driving range -.294 .0995 -.489 -.099 8.719 1 .003** .745

.613 .906

Income level 3 .225 .0650 .097 .352 11.932 1 .001* 1.252 1.102 1.422 Income level 4 .596 .1000 .400 .792 35.513 1 .000* 1.815 1.492 2.208

Age group 2 -.182 .0962 -.371 .006 3.590 1 .058+ .833 .690 1.006

Age group 4 -.317 .1338 -.580 -.055 5.627 1 .018* .728 .560 .946

Occupation group 1 .658 .2084 .250 1.067 9.974 1 .002** 1.931 1.284 2.906 Occupation group 3 .230 .0827 .068 .392 7.725 1 .005** 1.259 1.070 1.480 Occupation group 7 -.154 .0939 -.338 .030 2.687 1 .101+ .857 .713 1.031 Owner of EV -.572 .1949 -.954 -.190 8.614 1 .003** .564 .385 .827 Gender (1 male) -.142 .0796 -.298 .014 3.169 1 .075+ .868 .743 1.014 Number of children

in household -.055 .0265 -.107 -.003 4.253 1 .039* .947

.899 .997

Years of driving

experience -.009 .0040 -.017 -.002 5.464 1 .019* .991

.983 .998

Dependent Variable: Period

a. Computed based on the Pearson chi-square.

*** p< 0.001,** p < 0.01, * p < 0.05, +almost significant at p< 0.1.

Table 4 below shows residual analysis Table 4: One-Sample Kolmogorov-Smirnov Test

Raw Residual

N 187

Normal Parametersa,,b Mean .00000

Std. Deviation .746353

Most Extreme Differences

Absolute .055

Positive .055

Negative -.052

Kolmogorov-Smirnov Z .752

Asymp. Sig. (2-tailed) .624

a. Test distribution is Normal.

b. Calculated from data.

Table 4 shows that the distribution of residuals is Normal according to Kolmogorov-Smirnov Test since the p-value is 0.624 at significance level of 0.05. Figure10 shows the observed and the predicted values of the number of years to purchase an EV using the Poisson regression model.

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Figure 10: Stated and predicted number of years to purchase an EV

Estimation results in Table 3clearly show that several demographic variables are strong predictors of EV demand or the intention to purchase EV few years from now. Respondent who’s age above 45 are more likely to have shorter durations to purchase EV when compared with young respondents. Males are more likely to have shorter time period to purchase EV when compared with their female counterpart while holding all other variables constant. [8]reported that men are 11.5%

more interested than women in purchasing EV. Public employee are more likely to have higher (about 1.3 times) time period to purchase EV when compared with other occupations. Households with more children are likely to have lower period (about 0.947 year) to purchase EV when compared with households with lower number of children while holding all other variables constant. Drivers with higher driving experience are more likely to have lower period (about 0.991 year)to purchase EV when compared with those with lesser driving experience.Similar findings were reported by [29] who reported that younger car buyers have a higher stated preference for hydrogen and EVs, males have a higher stated choice of hydrogen vehicles, and environmentally aware potential car buyers have a higher stated preference for hydrogen and EVs.

Results in Table 3 also indicate that respondent with high monthly income are more likely to have higher durations (about 1.3 to 1.8 times)to purchase EV compared with those of lower level of income. Other studies found out that income is not significantly related to intend to purchase EV (see [8]). Environmentally conscious respondents and who currently own EV are more likely to have less time period to purchase EV when compared with those do not own EV. Similar results were revealed by [8] who indicated that respondents who express interest in purchasing EVs are typically highly educated, previous owners of conventional hybrids, environmentally sensitive, and concerned about dependence on foreign oil

Our results clearly show strong respondents concerns about limited driving range, lack of charging facilities and absence of proper repair shops. Increasing charging facilities/stations (enhancing EV infrastructure) is likely to shorten the duration for respondents to purchase EV. In fact, 69% of respondents indicated that the lack of charging facilities is a major impediment to the purchasing process of EVs in Jordan. This outcome is in line with reported outcomes in the literature which indicate the lack of charging facilities is likely to deter people from purchasing EV. [29]

indicated that the low stated preference for electric, hydrogen, and hybrid vehicles is mainly caused by lacking battery charging stations, high battery costs, short driving range, and short battery service life.

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[4] reported that potential EV buyer will not purchase an EV unless charging facilities (stations) are always available. Having proper maintenance facilities for new EVs and improving the range of EV are likely to shorten the duration for respondents to purchase EV. Our finding support similar outcomes by [9], [35], [24], [20], [17], and [36]. [37] reported that low driving range, lack of charging stations and no repair centers are considered key barriers to EV market penetration. The study showed, through stated preference survey, that 68%, 48% and 29% of respondent indicated that low driving range, lack of charging stations and no repair centers are key impediments to EV market penetration respectively.

5. Concluding Remarks and Recommendations

Analyses conducted in this paper provide some insights into a group of factors that play a role in the demand for EVs. Empirical outcomes discussed in this paper confirm earlier findings reported in the literature.In this paper, we estimated Poisson regression and Information indices models and provided forecasts of the demand for EV in the near and medium future (one to four years from now).

As discussed earlier, EVs rate of penetration to worldwide markets is on the rise. Countries are making policies to ban cars with internal combustion engines starting from as early as 2025.

Furthermore, countries are introducing wide ranging policies aimed at providing incentives for consumers to purchase EVs. In addition, reliable, convenient and constantly available charging infrastructure (stations) turned out to be crucial factors for the consumers’ decision making process.

Both private and public sectors are working to remove impediments that prevent EVs to have higher share of the vehicle transportation fleet.

EVs have not yet entered the mass marketed commodity. As such manufacturers can benefit from the wide ranging research by addressing the expectations and concerns of consumers. A significant number of studies including this effort concluded that both EV limited driving range and the lack of proper repair facilities are major impediment to the rate of EV penetration to markets.

This paper presented two approaches to model the demand for EVs in Jordan’s market. The first approach relates to Information Indices and the second utilizes Poisson regression model. The Information Indices approach is fundamentally based on the stated intentions of respondents and does not account for explanatory variables in predicting the willingness probabilities of respondents and the time to purchase an EV. The Poisson regression model on the over hand predicts the durations to purchase an EV by respondents considering the interaction of other independent variables. As such, we have estimates of the duration to purchase EV from two distinct approaches. Both approaches turn out to fit the stated preference data very nicely.

Estimation results in this paper clearly show that both EV technological limitations (limited driving range), lack of charging stations and un-availability of proper repair facilities play a key role in the demand for EVs in Jordan’s market. More specifically, with the availability of charging stations and proper repair facilities/shops consumers are more likely to choose shorter durations to purchase EV. If these two factors were unavailable, consumers are likely to wait and not purchase now.

Other factors that turned out to be significant and affect the demand are: age, gender, total household income level, driver’s occupation and driving experience. It is important to note also that drivers who currently own an EV or a hybrid car are more likely to have shorter durations to buy EV when compared with those without EV or hybrid car. Clearly these consumers have seen the benefits of owning an EV and how much it can save money. Survey and estimation results indicate that consumers’ stated intention towards EV is affected by environmental concern, the positive perception of EVs, and the anticipated of economic benefit.

Survey outcome shows that 88% of respondent have a positive perception of EVs, 87% of respondents wish to see EVs having more penetration to the market, and 86% of them indicated that they have the intention to replace current car with an EV. Furthermore, 5.5% of respondents intend to buy an EV one year from now, 20.5% of respondents intend to buy after two years, 22.4% after three years and 25.2% after four years from now. While 26.4% of respondents indicated they have no

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intention of buying an EV. In addition, willingness probabilities for the groups of respondents who indicated they intend to buy an EV turned out to be low ranging from 0.3352 to 0.5110. These results clearly indicate that consumers in Jordan are considering EVs as an alternative to conventional or hybrid cars (with internal combustion engine cars) during the purchasing process. These outcomes can also be explained in the context of respondents response to the availability and of wide spread of charging stations and the availability of proper repair facilities.

This is a clear message for authorities to develop a well-structured strategy that is aimed at proving incentives to consumers to increase their welling probabilities to buy EVs. At the outset, authorities should contemplatea ban on vehicles running on gasoline or diesel say after 2035 or 2040.

This is crucial step in order to set the stage for cleaner more efficient EVs and to give amble time for the country to develop its national grid and plan for the future demand for electricity. Clearly, smart grids are needed to deal with the new paradigm [7]. The strategy should aim at removing impediments such as better and wide spread charging infrastructure (in urban and rural areas), encouraging the private sector to establish specialized repair facilities, fair electricity prices at charging stations and a wide ranging tax-based policies. The private sector role in this strategy would be to make the needed investments in developing charging stations and repair shops. In addition, the private sector should develop marketing strategies targeting all groups of ages and all income level groups.

To create more penetration for EVs and increase the market share of EVs in the market, the government should pay more attention to the cost of electricity sold at charging stations. Special rates could be part of tax-based policies. A number of studies have identified the cost of electricity as impediment to EVs penetration to the market.

In terms of the limitations of this effort, estimated models do not account for the unobserved variables, such as policy and regulation. Furthermore, results from this paper are totally based on stated preference data collected through a survey. Validation of empirical outcomes would be through a data set from revealed preference.

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References

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