Energy Policy 50 (2012) 217–227

Contents lists available at SciVerse ScienceDirect

Energy Policy

0301-42

http://d

n Corr

E-m

journal homepage: www.elsevier.com/locate/enpol

Demand for mini cars and large cars; decay effects, and gasolinedemand in Japan

David Bonilla a,n, Klaus E. Schmitz b,c, Atsushi Akisawa d

a Transport Studies Unit, School of Geography and the Environment, and Oxford Martin School Oxford University, Oxford OX1 3QY, UKb Harvard Medical School, Childrens Hospital—Genetics, Harvard University, Boston, MA, USAc Enders 626, 300 Longwood Avenue, Boston, MA 02115, USAd Bio-Applications and Systems Engineering Department, Tokyo University of Agriculture and Technology, 2–24-16 Naka-machi Koganei-shi, Tokyo 184-8588, Japan

a r t i c l e i n f o

Article history:

Received 9 January 2012

Accepted 29 June 2012Available online 25 August 2012

Keywords:

Car sales

Gasoline demand

Large cars

15/$ - see front matter & 2012 Published by

x.doi.org/10.1016/j.enpol.2012.06.061

esponding author. Tel.: þ44 01865 285 545.

ail address: david.bonilla@ouce.ox.ac.uk (D. B

a b s t r a c t

This article explains why: (a) consumers underinvest in new car fuel economy by opting to buy large

vehicles; (b) macro shifts in vehicle classes have occurred in the last decades; and how (c) the effects of

vehicle fuel economy and shifts in vehicle type influence the growth path of gasoline demand, which is

the key to designing effective energy efficiency goals for transport. From 2008, 1.9 EXJ (Exajoules) of

energy were consumed in Japan by private vehicles producing 124 MtCO2 emissions. For the period 1980

to 2008, we estimated: (1) gasoline demand for three vehicle sizes; (2) vehicle sales; (3) new car fuel

economy changes (the ‘real’ technical change); and (4) vehicle stocks. Using a data sample for 1980–2008

we found that: (a) in the short term consumers buy fuel economy, that is sales of mini and small cars

increase, but this is not sustained in the long term: and (b) consumers increasingly traded in their cars for

larger cars. A further finding was that gasoline demand is projected to increase to 2.3 EXJ by 2035, even

with a growing number of mini cars. The policy implication is clear: Japan’s policy to reduce oil

dependency to 80% by 2030 is in peril as long as buyers prefer larger cars and drive ever longer distances.

& 2012 Published by Elsevier Ltd.

1. Introduction

In 2008, 54 million Japanese cars (passenger and light two-wheeled vehicles) were driven approximately 523 billion vehicle-km (VKM) on Japanese roads (Ministry of Land Infrastructure andTransport (MLIT), 2008) (Table 1). To power this volume of privatecars in that year 1.9 EXJ (Exajoules) of motor fuels (gasoline) wereneeded in Japan. A disaggregated model of gasoline demand andcar sales was built using data on vehicle age and class. Theanalysis covers mini, small and standard vehicles that are pri-vately owned in Japan. The model explained the future growthtrajectory of gasoline demand and of new car fleet fuel economy.Fuel economy is defined as the increase in the ratio of VKM-driven per litre of gasoline consumed for new passenger vehicles.Fig. 1 illustrates the rapid growth in gasoline consumption until2002, with consumption falling in 2003–2008.

We have three objectives: the first is to estimate how changesin market shares of efficient vehicles produce a given level of fueleconomy (for all Japanese vehicle). The second is to estimate howfuel economy of new cars will influence gasoline demand fore-casts by 2035; and the third is to estimate vehicle stocks. Vehiclestock models effectively complement gasoline demand models as

Elsevier Ltd.

onilla).

the stock models can explain how old and less efficient cars arereplaced by new ones. During the period 1973–2008 the small carclass lost a considerable amount of the market share as con-sumers increasingly preferred to buy large cars.

There are three key policy drivers that aim to reduce gasolinedemand in Japan. The first is the Kyoto protocol targets, the second isthe National Energy Strategy, and the third is the ‘‘Top RunnerProgramme’’ (IEA, 2008; Energy Conservation Center of Japan(ECCJ), 2005). The most effective plan so far is the latter policy.However, since most of the CO2 emissions are generated by technol-ogy choices that are long lived it is unlikely that Japan will mitigateenough CO2 emissions or meet its long term target 80% for oildependency by 2030 for the transport sector. In addition, Japan is amember of the Kyoto Agreement and so is required to reduceemissions by 6% during 2008–2012, with 1990 targeted as the baseyear. As part of the Kyoto protocol target programme the countryaims to reduce emissions of passenger vehicles by 21 MT–CO2 by2035. The Kyoto commitment refers to six gases, and not only to CO2

emissions, and the commitments end in the 2008–2012 period forthe entire economy. However, transport is the only sector whereemissions have continued to grow for much of the last 30 years.

Strikingly, gasoline demand (exajoules or EXJ); using data ofInstitute of Energy Economics, Japan, 2010, has continued toincrease (Fig. 1), in most years since 1973 even in the 1990s,which was a period of stagnant growth in Japanese householdincomes and of widespread contraction in Japanese economic

Table 1Petroleum product demand (EXJ) in Japan: 1990–2008 (Source: MILT, IEA, 2010, IEEJ (2010)a.

1973 1980 1990 2005 2008

% share of road transport of final energy use (all transport) 35 41 49 54 53

Km/l (all new cars) 11.4 12.8 12.4 14.1 16.1

Vehicle-km-driven (billion) 190 257 366 526 523

On road fuel economy (km/l: all fuels) 10.41 9.57 8.49 7.1 8.57

Final energy consumption passenger cars (gasoline, diesel, LPG, electricity) (EXJ) 0.64 0.94 1.51 2.08 1.9

Energy consumption (all transport) (EXJ) 1.82 2.30 3.11 3.85 3.52

Fuel price (Consumer Price Index) US-cents/l gasoline, 2005¼100) 56.2 105.2 103.1 102.5 116.7

CO2 emissions (in million tonnes) of passenger cars electricity, gasoline diesel and others 42 61 99 136 124

CO2 emissions (in million tonnes) transport sector 119 150 203 252 230

a 4.1868 (�104) megajoules equals one tonne of oil equivalent (TOE); one EXJ equals 1018 J.

0.00

0.50

1.00

1.50

2.00

2.50

1973 1978 1983 1988 1993 1998 2003 2008

Ener

gy c

onsu

mpt

ion

(Exa

joul

es)

Fig. 1. Fuel consumption: passenger cars. Source: Author using data of IEEJ (2010)

and MLIT (2008).

D. Bonilla et al. / Energy Policy 50 (2012) 217–227218

activity. In 2000–2008, gasoline demand was stimulated by:(a) growth in VKM-driven private cars; (b) lower fuel prices(Table 1); (c) the larger stock of large cars: and the relativerecovery of the Japanese economy.

The increase in VKM-driven during the 1973–2008 of passen-ger cars sector has caused the CO2 emissions level to rise in thesame period despite improvements in new car fuel economy inthis period (Table 1). The contribution of the passenger sector tooverall transport CO2 emissions continued to be large in thisperiod and has not declined. Total transport CO2 emissions havestabilised recently but their absolute level remains high. Table 1shows that overall energy use of passenger transport is heavilydominated by fossil fuels.

For example, in the 1980s gasoline prices remained stable inreal terms (after a 49% increase in the previous decade), whereasin the 1990s a further 20% decline was recorded (Consumer PriceIndex in Japanese Yen terms). During 2000–2008 a price recoveryof 30% occured but gasoline demand was largely insensitive to thisas were vehicle sales. In 2008 1.9 EXJ were consumed by privatecars compared to nearly 1.12 EXJ in 1985 and 0.64 EXJ in 1973.

In 2008, on-road vehicle fuel economy of Japanese privatevehicles stood at 8.57 (km/l or 11.01 l/100 km; MLIT, 2008) (usingdata on gasoline, diesel and LPG, electricity).This is only slightlybetter than the 1990 level (8.49 km/l) (Table 1). The on road fueleconomy was higher, at 10.41 (km/l) in 1973 than in 2008,despite improved fuel economy standards for new vehicles anda highly taxed vehicle ownership system. 1

1 This implies that on-road fuel economy is 41.4% below new car fuel

economy. Sano (2008) reports a fuel economy gap of 30% for 2005, implying that

on-road fuel economy is equal to 70% of new car fuel economy. Schipper (2007)

reports a gap of 38.7% using a figure of 15.34 km/l, of new cars, and 9.43 km/l for

on-road cars, for 2005 and of 28% for 1995. Recent figures show a slight

improvement in on-road fuel economy perhaps owing to the rising market share

of mini cars.

The lack of accelerated improvement in on-road fuel economy hasled to a rapid growth in gasoline consumption so increasing Japan’soil dependency and its vulnerability to oil price variations. Table 1shows that the transport sector (all modes) consumed 54% of totalpetroleum products in 2008; this figure was only 35% in 1973 and41% in 1980 (Institute of Energy Economics, Japan, 2010). 2 Motor fueluse (mainly gasoline and LPG) accounted for half of total energy usein transport in 2008. Japan’s motor fleet continues to grow inimportance in terms of its overall energy use, its emissions ofgreenhouse gases, local air emissions and its weight in the nationaleconomy.3

Standard top-down models (numerical or econometric) ofgasoline demand, and vehicle energy efficiency, do not linkchanges in physical depreciation (decay rates) of fuel economy(of new cars) to changes in vehicle stock and vehicle sales; acommon practice is to simulate changes over time in gasolinedemand without feedback effects linked to vehicle sales, or tovehicle size and vehicle stocks. This paper uses disaggregate dataon vehicle sales to rectify these deficiencies that are found in theliterature of gasoline demand models.

2. Research framework and data

2.1. Gasoline demand model: A review of studies

Our paper provides several advances beyond the existingliterature on gasoline demand models. First, our data set includesnational sales of inefficient and efficient cars for the period 1980–2008, the price of these vehicles, household income, gasolineprices and vehicle use by vehicle class. The data allows us to studywhy consumers choose to buy a mini or a larger car and then toproject gasoline demand. Second we propose a model of gasolinedemand that interacts with vehicle sales changes. To our knowl-edge this is the only study that examines disaggregate fueleconomy effects on gasoline demand using this data set.

Studies of gasoline demand have been numerous and the fieldhas been heavily researched. Espey (1998) reviews the field as farback as 1929. However, many of the key findings on thebehaviour of gasoline demand are based on studies with a topdown approach covering the 1980s, and earlier, as shown inHughes et al. (2009). Structural changes in the vehicle market

2 In Japan motor fuels consumption accounts for the largest proportion of

energy consumption of the transport sector. LPG and diesel car fleets have

increased but their market share remains low. For example the share of LPG fell

from 6% of total fuel consumption to 4%, in 2005, and diesel from 12% to 6%. Diesel

cars (more fuel efficient) are less popular in Japan than in Europe.3 In 2003, the entire transport sector (private vehicles, freight vehicles, shipping

and aircraft) was responsible for almost one fourth (23%) of Japan’s CO2 emissions

with private vehicles accounting for 14% of total CO2 Japanese emissions IEEJ (2010;

pp. 44). In 2010, the share of transport is 38% (IEEJ 2010; pp. 44).

D. Bonilla et al. / Energy Policy 50 (2012) 217–227 219

have caused changes in gasoline demand and so to accuratelypredict gasoline demand it is best to apply a disaggregate model,with a bottom up perspective.

Unlike our model (Section 3 onwards), top down models ofgasoline demand do not incorporate vehicle characteristics, orvehicle market dynamics and vehicle stock changes across timeand space. Top-down models of gasoline demand are described inDahl (1995) and Graham and Glaister (2002).

Many studies do not take into account the heterogeneity of thevehicle market (Gately, 1990; Johansson and Schipper, 1997 andSmall and Van Dender, 2006). Johansson and Schipper (1997) is theonly study to compare the effect of taxes on gasoline demand usinga sample of the Organisation for Economic Co-operation & Devel-opment region (OECD), however, the study does not incorporate thedisaggregation used in our study. The Small and Van Dender studyintroduced a gasoline demand model using vehicle stock effects viaa simultaneous model with km-driven, vehicle stock and aggregatefuel economy for 1966–2001. These studies are not, however,sufficiently disaggregated and so it is difficult to provide policyadvice using models from the top-down perspective.

Blomqvist and Haessel (1978) estimate how much gasolinedemand can be mitigated by stimulating sales of fuel efficientcars. Bonilla and Foxon (2009) estimates whether fuel demandincreased after fuel economy and income gains (by fuel type forcar stocks) for the British experience. In an exceptionally largestudy, Busse et al. (2009) examined the new and used car marketto estimate the market shares and to predict fuel economy bymodel type for the US case. These studies have the drawback ofignoring dynamic vehicle price effects on the demand for smalland mini cars, nor do they use data samples over several decadesto represent income effects on the demand for efficient cars.

A model similar to ours is the study by Sakaguchi (2000)although our study differs because we incorporate time seriesdata in our analysis, and represent the ‘real’ technical progressusing data on the historical change of new car fuel economywithin 1980–2008 by class of vehicle.4

Gasoline demand has been estimated using numerous econo-metric techniques (co integration, dynamic panel models, quali-tative choice, cross sectional, error correction) and with single orsimultaneous equations. Recent studies have relied on ever largerdata sets and more sophisticated econometric models but theapproach continues to be top-down.

The following studies do not examine changes, at a decadelevel, of vehicle characteristics (Schmalensee and Stoker, 1999;Kazimi, 1997; Oladosu, 2003; Archibald and Guillingham, 1980;Kayser, 2000; Greening et al., 2000; Nicol, 2003). An exception isthe work by Barla et al. (2009) whose estimates are based onrecent historical data of fuel efficiency, vehicle mileage and therebound effect. Historical changes (e.g., changes beyond the spanof a decade) in car markets are closely connected to today’s levelof new car fuel economy and of gasoline demand but this iscommonly ignored in much of the literature. Innovative work(Greene, 2010) uses the reference dependent choice paradigm toexplain why consumers underinvest in fuel economy i.e., lossaversion or reference dependent choices, uncertainty in fuelprices and in vehicle use. This paradigm complements the moretraditional insights, that correlate vehicle sales to householdbudget, fuel expenditures, mileage, and fuel prices. Our paperquantifies the investment decision for fuel economy.

A further innovation is that we assume that fuel economydegrades overtime as in Fischer et al. (2007) and we adopt the

4 Baltagi and Griffin (1983) estimated the effect of car fuel economy on

gasoline demand for OECD economies but this is also an aggregate fuel economy

study.

same approach to explain how vehicle age lowers engine perfor-mance. We improve upon previous models of gasoline demand,by breaking up the components of demand with observed vehicletypes, household income, car usage, vehicle size and vehicle age.For example, sales for new vehicles with lower fuel economy(fewer km per litre of fuel) have increased their market share inrecent years. To our knowledge this is the first study thatdisaggregates gasoline demand and that links such demand tochanges in vehicle sales using time series data for the small, miniand standard car classes.

2.2. Trends in vehicle sales: Japan’s passenger cars

Fig. 2 depicts the time trend of annual sales (in million vehicleregistrations) of mini, small and medium-large (standard) cars;total sales (all vehicles) peak in 1990. Fig. 2 highlights the upwardtrend in mini-car sales (with fuel economy of 17 km/l), thedecreasing trend in sales of small cars (with fuel economy of15 km/l in 2001) and the increasing trend in sales of standard cars(with fuel economy of 9.85 km/l). Larger cars are heavier, havemore accessories, hold more on board electronics; all of whichdecreases their fuel economy. Large (standard) cars are those witha 2000 cm3 (cubic centimetres) engine; small ones with4660 cm3 engine but less than 2000 cm3; and mini cars withless than 660 cm3.

Fig. 2 also shows that new car sales (all vehicles) increasedstrongly in the 1980s and later stabilised at approximately3.9 million registrations per year in 2008. It is clear from Fig. 2that sales of the energy intensive cars are higher in 2008 than inthe 1980s. Mini vehicles have been defined by engine size butthat size has altered since 1989 allowing relatively larger vehiclesto qualify as ‘‘mini’’.5 For example, the share of (new) smallvehicles, in the entire Japanese vehicle fleet, fell from 87% in 1990to 45% in 2003 as consumer preferences shifted from small tolarger vehicles (Japan Automobile Manufacturers Association(JAMA), 2008). These changes in the market shares—have pre-vented the reduction in gasoline demand over time.

The increase in the sales of mini cars is real and not only aresult of the changed definition. The increase in total salesoccurred during the 1980s whereas sales stabilized in the1990s. The volume of sales is large by international standards.

The data on fuel economy of new cars (mini, small andstandard) are plotted in Fig. 3 in time series form: the data areused in our model of gasoline demand (Fig. 1). The mini car offersthe best fuel economy of the three car types, with a fuel economyperformance of more than 20 km/l (gasoline), a substantialadvantage over the fuel economy of a standard car. Fuel economyis assumed to increase by 1% per year in the projection period,following the historical average. In Section 3 we explain howthese projections are produced (Figs. 2 and 3).

In the following sections we introduce a gasoline model thatsimulates how difficult it can be to reduce gasoline demand if thesales of more efficient cars decline while those of standard ones,and less fuel efficient, increase. In our model, sales of small carsfall, this in turn, leads to a decline in their vehicle stock translat-ing into less fuel saved. Although the effect of vehicle stock ofsmall cars outweighs that of the mini car, the model predicts adecline in aggregate gasoline demand to 2021 followed by asubsequent increase by the year 2035.

The gasoline demand model, applied to data on three vehicletypes and the results presented in this paper, enables a detailed

5 Santini (1994; pp. 23) notes that from 1976 to 1989 mini-sized cars were

defined as those with 550 cm3 engines; after 1989 mini cars with engines of size

660 cm3 were still classified as mini-sized cars.

Fig. 2. Vehicle sales in Japan (by class). Source: IEEJ (2010).

Fig. 3. New car fuel economy (by class). Source: JAMA, (Guidebook, various years) (10.15 drive cycle test and IEEJ (2010). Source: This author for years 2002–2035.

D. Bonilla et al. / Energy Policy 50 (2012) 217–227220

examination of car market trends and policies that affect gasolinedemand, on-road vehicles and key technological characteristicsthat determine on-road fuel economy.6 The model is tailored tocapture predicted changes in vehicle sales, by size class, andvehicle prices as well as by household income growth, all ofwhich impact on gasoline demand. The complete model is madeup of numerical equations (inputs) producing vehicle stock andeconometric equations using time series data. The first stepinvolves developing model inputs (for vehicle stock, and newcar fuel economy) and estimations (by Ordinary least squares) ofvehicle sales. Estimations for vehicles sales feed the vehicle stockprojections. Both projections of vehicle stock and of new car fueleconomy feed the gasoline demand models. Projected car salesfeed the vehicle stock numerical solution to 2035.

In addition, unlike the cited literature, our model of gasolinedemand is a multi-equation system, that considers, explicitly,historical change in new car fuel economy levels by vehicle class,and distinguishes between large and small vehicles stock. Ourmodel draws on data from sixteen data items (time series)spanning two decades.7 To our knowledge few studies have

6 The literature has consistently modeled fuel economy change by means of a

time trend (Bonilla 2006).7 Studies also fail to incorporate the effect of fuel economy of the car stock on

such demand. In contrast to those studies the effects of the vehicle stock and

vehicle fuel economy in this work are modelled to project gasoline demand.

simultaneously simulated such effects on gasoline demandin Japan.

3. Overview of methods and model structure

The model is split into a set of numerical equations andeconometric relationships. Numerical equations provide the fore-casts using econometric results. We first estimate vehicle stockand the surviving vehicle stock, using sales data by vehicle classsize and scrappage. Second, we estimate market shares by classsize of more or less efficient cars. We, then, simulate gasolinedemand after accounting for the effects of VKM-driven, survivingcar stock and the weighted mean of new car fuel economy. Salesand car stock are projected to the year 2035 by using thecalculated coefficients from the time series equations and byassuming future trends in fuel economy improvements in thenew vehicles.

3.1. Estimating the stock of Japanese vehicles

Gasoline demand is a derived demand as it depends on thestock of gasoline fuelled vehicles. In this section, we explain howvehicle sales and vehicle stock are simulated. Below, we estimatea demand function of vehicle sales by econometric equations.

D. Bonilla et al. / Energy Policy 50 (2012) 217–227 221

Economic theory suggests that vehicle stock is a function ofpast investment (in vehicles), the utilisation rate of older vehicles,investment (sales) in new vehicles and their current use (Berndtand Botero, 1983; pp. 5). For this reason total investment (carstock) needs to be estimated. In this formulation, vehicle sales actas inputs to estimate Japan’s vehicle stock to 2035. We estimatethe surviving vehicle stock assuming such stock is a function ofvehicle sales using survival rates of vehicles that vary with the ageof the vehicle. Vehicle age is used because it determines:(a) physical wear and tear; and (b) the quantity of vehicles thatare scrapped, as described in Greenspan and Cohen (1996). Stockmodels (Eq. (1)) allow us to estimate how quickly old vehicles(less fuel efficient) are replaced by new vehicles (more fuelefficient).

3.2. Surviving vehicle stock

The model starts at the level of vehicle sales (observed andactual) for year ‘‘i’’ for three different vehicles:

Vsalesik

where i is the year of observation (1978o io2035) and k¼1,2,3is the vehicle type, standard, small or mini, respectively.

Domestic vehicle sales (actually observed sales) in Japan(Fig. 1), start from 1978o io2007. Vehicle sales are forecasted,in the next section, for 2008o io2035 using a set of estimatedeconometric equations. Domestic vehicle sales are plotted inFig. 2.

We define the vehicle stock in the year i as follows.

Si,j,k

where j is the years of use of the vehicle since it was acquired. Weassume that cars are scrapped when they reach 18 years old,hence

0o jo18

Si,j,k ¼ 0 for j418

Integrating the vehicle stock and vehicle sales, we have

Si,0,k ¼ Vsalesi,k

and using survival rates of vehicles (based on US data in Davis andDiegel (2007), we have:

Siþ1,jþ1,k ¼ ðSi,j,kÞgj,k ð1Þ

We use subscripts in order to represent the stock of vehicles ofJapan. For example, to work out the total vehicle stock (allvehicles)s in the year i, we have

Si ¼X18

j ¼ 0

X3

k ¼ 1

Si,j,k ð1:1Þ

Function 1 is applied to data for years 1978 to 2035 and thisfunction assumes the vehicle stock begins in 1978; in other wordsvehicle stock is equal to vehicle sales in 1978. For example, forvehicles surviving into 1996 car sales data for 1978 until 1996 aremultiplied by a survival rate (as in Eq. (1)) yielding the survivingvehicle stock in years 1996. Eq. (1) is plugged into Eq. (1.1) into adynamic equation of vehicle stock where vehicle stock, of age j, isa subset of surviving vehicle stock. The total surviving vehiclestock, for example,1996, is the sum (Eq. (1.1)) of vehicle stocks ofages 0 to 18 of k vehicle. For example, survived vehicle stock ofage 1 is computed by multiplying the previous year’s car sales(year 1995) times its corresponding survival rate. The vehiclestock and gasoline demand models are solved in a spread-sheetformat.

Surviving vehicle stock (Eq. (1.1)) yields the vehicle stock.Total stock for any year would be the sum of the three stocks (ofthree vehicle types). To project the growth of vehicle stocks bysize to the year 2035 as shown in Eq. (1) vehicle sales are firstdetermined using time series equations (Table 4). Relevant data isavailable in Japan Automobile Manufacturers Association (JAMA)(2003a) as well as in Institute of Energy Economics, Japan (2010).The time-series data is derived on the basis of actual data for only3 vehicle types for the period 1980–2007. Fitted data for vehiclesales are obtained through estimated equations and it is thesefigures which are then added to surviving vehicle stock. Projec-tions of vehicle stock (by type) are subsequently fed into gasolinedemand equations described below.

3.3. Decay in fuel economy of new cars and gasoline demand

Data of new car fuel economy (Fig. 3) is taken from carmanufacturer sources (Japan Automobile ManufacturersAssociation (JAMA) (2003b); Institute of Energy Economics,Japan, 2010). Using the data obtained from Japan and assumingthat the fuel economy of new vehicles in future years improves by1% per year during the period 2008–2035, we obtain Fig. 3.

An extra layer of uncertainty is introduced (Eqs. (2) and (3)) tocapture a the decaying effect of new car fuel economy on totalgasoline demands. Because the fuel economy of an older car, forexample, a 9 year old car is significantly below that of a new one,an extra variable is introduced for physical depreciation:

Ei,j,k ¼ Ei,k � yj ð2Þ

where E i, j, k is new car fuel economy of k vehicle, of j years, of i

year of observation; y j is a depreciation (given in percent) of theengine’s car, for j years of age, and represents the decay in fueleconomy over time. Using the following expression we get:

yj þ1 ¼ 1 � fð Þ yj for 0ryjr1 ð3Þ

where f is the depreciation rate (given in percent) of theefficiency of the engine’s car. For example, if f takes a value of1% (per year), by the time a car is 18 years old, the decay in its fueleconomy (efficiency) will be 83% of the fuel economy of a new car.(This is a conservative assumption as engine efficiency candecline more markedly with time. Hence, it is assumed that fueleconomy declines by 1% for every extra year of vehicle age). The1% assumption is based on historical average improvements innew car fuel economy.

The calibration of gasoline demand by vehicle class is deter-mined, on the basis of co-dependent economic and technologicalrelationships, as follows. In Eq. (4) gasoline demand is obtainedby an equation that includes vehicle stock times average km-driven per vehicle, and fuel economy of the new car stock for thekth vehicle.

Gasoline demand forecasts are obtained by applying Eq. (4)and assuming fuel economy by class of vehicle, once projectionshave been performed on vehicle stock (Eq. (1)) and usage:

Aggregate gasoline demand, G, can be calculated as:

Gi,j,k ¼Si,j,k � bi,k

Ei,j,k

� �ð4Þ

where bi, j is the average kilometers driven per vehicle at year i forevery type of vehicle k. Eq.(5) completes the picture. Again usingthe definition in our model, we can compute the total gasolineconsumption of the Japanese vehicle stock by:

Gi, k ¼X18

j ¼ 0Gi,j,k ð5Þ

As we show in the next sections fuel economy of new cars(denoted by E, in Eq. (4)) is forecastable to 2035. This fueleconomy variable is used subsequently to project gasoline

Table 2Descriptive statistics of vehicle sales equations.

Mean Standard deviation

Sales 000’s standard) 394.0 155.09

Sales 000’s (small) 2873 594.75

Sales 000’s (mini) 473 417

Capital cost (index) standard 119.0 20.79

Capital cost (index) small 113.72 21.13

Capital cost (index) mini 113.83 20.14

Household income 10647.03 1021.59

Km/car 10,038 177.58

Gasoline price (yen/l) 129.52 32.54

Table 3Time series model of vehicle sales: 1980–2007. Dependent variable: vehicle sales.

Estimated by SPSS software. Long run model for vehicle sales.

Standard

vehicle (a)

Small

vehicles (b)

Mini

vehicles (c)

Intercept 118.02 131.65 196.3605

log of CAPCO–HI �5.34n�1.23n

�4.75n

t-value (�7.44) (�3.35) (6.74)

Log of GP 0.50 0.64n 1.20n

t-value (0.71) (1.83) (1.49)

D. Bonilla et al. / Energy Policy 50 (2012) 217–227222

demand, by vehicle size, to 2035. Eq. (5) is applied to stock datafor every year from 1978 to 2035 for all vehicles.

Total gasoline demand, involving vehicles k of ages j, issimulated by applying Eq. (5). We calibrate to existing totals forspecific years.

Eq. (4) includes the vehicle stock and household effects andbehaviour of drivers. (VKM-driven per car and per year). Each ofthe variables of Eq. (4) are forecastable to the year 2035. Asshown in Baltagi and Griffin (1983, pp. 119) Eq. (4) has theadvantage of incorporating both short (mileage) and long termeffects (fuel economy). In particular, car fuel economy effectsneed a long period to have an impact on vehicle stock, whilechanges of usage do not necessarily impact on car stock. It shouldbe noted that Eq. (4) uses the past fuel economy of new vehiclesto calculate gasoline demand. This equation computes gasolinedemand for vehicle stock k that survives at every vehicle age j.Each model year is linked to past fuel economy. In other wordssimulated gasoline demand accounts for the profile of both pastand current fuel economy levels. The estimation of gasolinedemand for small and mini cars is conducted in a similar way.

Log of VKM-driven–vehicle stock

(vehicle class) �1.24 0.09 0.81n

t-value (0.07) (0.06) (3.30)time trend �0.04n

�0.06n -0.096n

t-value (�1.71) (�3.83) (1.52)Adj. R2 0.97 0.50 0.96

DW 1.00 0.54 0.70

Period (all equations) 1980–07

Values in() are¼t statistics. Notes: Data: annual time series. HI: household income;

CAPCO: capital cost by vehicles class. GP: real gasoline price; total VKM-driven:

km per car per year. The model for mini cars includes (column c) VKM-driven only,

unlike models ‘‘a’’ and ‘‘b’’.The F-test accepts the first equation since the F-ratio is

larger than the tabled F value.Number of sample observations: 28.n Denotes a statistical significant coefficient at 5% significance level.

Table 4Coefficients fit of the model, diagnostic and forecasts tests for the model estimated

over the 1980–2008 sample. Short run elasticity values are produced by the first

differences model for sales. T values in brackets. The table reports standardized

coefficients in standard deviation units. Estimated by SPSS software. T-values in

brackets.

Large Small Mini

DCapco–HI 0.028 �0.538 0.685

(0.111) (2.639) (3.037)

DGP 0.007 �0.059 �0.01

(0.034) (0.336) (0.06)

Dkm/stock 0.111 0.101 0.083

(0.449) (0.597) (-0.391)

Trend �0.007 �0.031 0.340

(�0.025) (0.153) (1.484)

R2 (adjusted) �0.16 0.280 0.206

Durbin Watson 1.83 1.17 1.39

Observations 28 28 28

3.4. Vehicle sales regressions

To identify vehicle stocks as explained in Section 3.2, vehiclesales need to be forecast to 2035. Regressions are estimated toexplain Japan’s demand for cars (by class size) using the followingexplanatory variables in logarithms: real car price index dividedby (real) household income, (real) gasoline price, km-driven(kilometres per vehicle ‘‘in use’’), and a time trend. Data iscollected for household income based on GDP data fromInstitute of Energy Economics, Japan (2010) and number ofhouseholds using data from National Institute of Population andSocial Security Research (2010). Vehicles sales data is sourcedfrom JAMA (2008, 2003a,b) Handbook. km-driven from Instituteof Energy Economics, Japan (2010), gasoline price from Instituteof Energy Economics, Japan (2010) and the capital cost of vehiclesfrom Japan Statistical Bureau (2010). Table 2 depicts the summaryof statistics of data for the econometric analysis. Appendix Ashows the data used for this analysis.

Eq. (6) estimated a joint hypothesis for each of the three cases(by class of vehicle). The long run regression for car sales for theunrestricted model is:

lnðsaleskÞ ¼ aþb1 lnðcostk=HincÞ

þb2lnðGaspÞþb3lnðKmkÞþb4 trendþet ð6Þ

where sales is vehicle sales; cost is cost of the vehicles; Hinc isreal household income; Gasp is gas price and Km VKM driven bycar for k (mini, small and standard) vehicles; and an error termrepresented by epsilon. Table 3 shows the results of Eq. (6). lnstands for natural logarithms and the coefficients of Eq. (6) can beseen as long run elasticities of car sales. The elasticities that

Eq. (6) produces can show whether consumers underinvested infuel economy during 1980–2007.

Table 3 shows the econometric results of logged car sales usingtime series data for 1980–2007.

Eq. (6) is used to represent vehicle sales responses. Resultsshow that most fitted equations explain more than 90% of thevariation of vehicle sales, with high R2 statistics, except for thesmall car equation. The t-tests show that the fraction of capitalcost to household income is statistically significant from zero(Table 3). The estimated equations (Eq. (6)) also show that mostkey variables are statistically significant. The estimated coeffi-cients (capital cost), returned by the estimated equations, show anegative sign for all vehicle sizes (Table 3). These results (Table 3)can, however, be spurious or non authentic and so other modelsare used to remove the non stationarity characteristics of the dataseries. Non stationarity occurs if one of the explanatory variablestrends upwards or downwards (Thomas, 1997, p. 373) and whereits mean, variance and covariance are not constant over time. Thefirst difference models are shown in Table 4.

The values in Table 3 indicate strong autocorrelation for allthree models, so these are not used to project vehicles sales to theyear 2035. An examination of the data series used in the

D. Bonilla et al. / Energy Policy 50 (2012) 217–227 223

estimation of Eq. (6) indicates non stationarity or the presence ofa unit root. The non stationarity feature in the data series isrevealed by Augmented Dickey and Fuller tests; the tests valueranges from �2.2. to �2.56 which is not negative enough toreject non stationarity in the series. Table 2 tabulates the dataseries that are used in this analysis.

Since the equations shown in Table 3 are found to have nonstationarity properties, that is the data series do not return totheir long run mean, the estimation strategy now relies on modelsfor the proportionate change in sales of vehicles k (Table 4).

Table 4 shows the short run estimates which are computed byusing a model of first differences to explain vehicles sales. Theelasticities are statistically significant (t value 41.6) but only fora few variables. The first difference model, to represent theproportionate changes in sales of k cars, is explained in theequation below.

where,

D ðsalestÞ ¼ ð salest�salest�1Þ

D ðcaptÞ ¼ ð cos t=Hinct�cos t=Hinct�1Þ

DGaspt ¼ Gaspt�Gaspt�1

DKmt ¼ KmtþKmt�1

k¼ vehicletype ðmin i,small,stan dardÞ

D¼ f irstdif f erence

t¼ year; t�1¼ previousyear

and coef f icients

bk ¼ regressioncoef f icients

nt ¼ et�et�1

D ðsalesk tÞ ¼ aþb1 ðDCapk tÞ

þb2 ðDGasptÞþb3 ðDKmk tÞþb4 trendþnt ð7Þ

The decision rule is that if buyers of mini, or small cars, reactnegatively to increases in capital cost or to gasoline price theyunderinvest in fuel economy and gasoline demand will be higherin the long term. On the other hand, if those buyers do not reactnegatively to price changes they invest in fuel economy in thelong term.

Fitted equations (Tables 3 and 4) give the consumer response ofsales with respect to capital cost as a proportion of household income.A ratio is used to capture the capital cost effect on the purchasingdecision (vehicle sales variable) since the variables on the right handside are highly collinear (Eqs. (6) and (7)). Inclusion of capital costs insales equations follow the approaches of Lave and Train (1979),Mannering and Mahmassani (1985) and McCarthy (1996).

The econometric evidence suggests that car buyers will under-invest in fuel economy in the case of the mini car class (Table 3)since capital cost is associated with a decrease in sales of thisspecific car class. In addition there is evidence that consumerswill underinvest in fuel economy in the long term as sales of smallcars are negatively associated with increases in capital costs inthe 1980–2007 period (Table 3). Sales of large cars, however, arenot negatively affected by increases in capital costs in the shortterm (Table 4).

The effect of capital cost has the greatest effect on car sales inthe short (Eq. (7)) and long term models (See Tables 3 and 4). Thecoefficient on capital cost is statistically significant from zero andit is the largest coefficient in all equations; the calculated t valueis greater than 1.6 at a 5% significance level. This result allows toreject the null hypothesis of no effect of this variable on vehiclesales. The coefficient on capital cost ranges from a unitary priceelasticity (�1.23) to a highly elastic response (�5.34) using a log-linear equation without first differences. The vehicle cost elasti-cities of Table 3 are inside the range of studies for the U.S. A. carmarket; the latter elasticities lie between �0.51 and �6.13. (Laveand Train, 1979; Mannering and Mahmassani, 1985; McCarthy,1996)). In short the econometric evidence shows that vehicle

capital cost has been the key consideration, in the last threedecades, for car choice among Japanese buyers.

Table 3 shows that an increase in sales of larger cars occurs ifpeople drive longer distances (km-driven), or when they use theircars more frequently. Vehicle size was sensitive to gasoline pricechanges in the 1980–2007 period: higher gasoline prices encour-aged mini car sales because such cars were fuel efficient and soreduced running costs. Therefore, the higher the fuel prices arethe higher the sales of fuel efficient cars will be. This shows theimportance of raising fuel prices so that buyers favour fuelefficient cars (the mini or the small car).

Table 3 shows that energy rebound effects appear in the smalland in the mini car equations, which means that a recovery indemand for gasoline is likely for personal transport in Japan.Substitution effects show that if consumers buy standard carsthey will also buy mini cars (cross elasticity of �5.42) but ifconsumers buy mini cars they will move away from small cars.Substitution estimates also show that consumers switch from thesmall to the large car (cross price elasticity of 0.42). Thesesubstitution possibilities will change if Japan’s population con-tinues to age and if the composition of the vehicle stock changesin next decades. The models presented in Table 3 show thatsubstitution possibilities among buyers of small, mini and stan-dard cars are a key parameter in explaining the future level of fueleconomy of new car fleets and of future gasoline demand. Thegrowth in the stock of large cars also increases the demand forgasoline more than in the previous decades when the small cardominated the Japanese car market.

4. Results: Projections for vehicle sales by vehicles class

4.1. Vehicle sales and gasoline demand 2035

Our projections on vehicle sales (Fig. 4) shows that the smallcar class will continue to lose market share whilst the other twovehicle types will gain market share; the overall demand, how-ever, for motor fuels will not decline by 2035 (Figs. 5 and 6explain this point further).

Our projection methodology, for vehicle sales, is explained inSection 3.

4.2. Forecasting vehicle stocks for Japanese cars

Following the projection of vehicle sales to 2035 (based on theestimates reported in Table 4) and the application of Eq. (5),projections can be made for vehicle stock to 2035 (by applyingthe vehicle stock model (Eqs. (1) and (1.1)). Projections forgasoline demands (Eqs. (4) and (5)) can also be produced basedon Eqs. (1) to (5) for each vehicle type. Two projections areproposed. The first of these shows the level of gasoline demandwhilst fuel economy decays over time (labelled DFE); the secondprojection relies on the assumption that vehicle fuel economy willnot decay in the 2008–2035 period (labelled FEI). All projectionstake into account the low rate of growth in new households asJapan’s population continues to age.

4.2.1. Assumptions of gasoline demand

The projections require a number of assumptions on:

1.

Household income (using published forecasts on real GDP byIEEJ (2010);

2.

Vehicle sales and vehicle stock and vehicle use (using Eqs.(1)–(1.1); (7));

3.

Vehicle fuel economy by vehicle class (Eqs. (2)–(4)); 4. Car price by vehicle class;

Fig. 5. Gasoline demand under decaying fuel economy (DFE).(By vehicle size assuming decay effects for fuel economy of new cars) (see Eq. (3)). Data is corrected to remove

the gap between on road and test fuel economy (new cars).

Fig. 6. Projections for gasoline demand Data is corrected to remove the gap

between on road and test fuel economy (new cars).

Table 5Assumptions for gasoline demand 2008–2035

(Institute of Energy Economics. Japan, 2006).

Source: Authors based on data of IEEJ, 2006

High growth

(%/year)

GDP growth (%) 1.1

Households (number) 0.2

Capital cost (vehicle class) Constant

Vehicle-km per car (use) Constant

Fig. 4. Projections for vehicle sales new cars: small car loses market share.

D. Bonilla et al. / Energy Policy 50 (2012) 217–227224

Figs. 4 and 5 show the assumptions and outcomes. Table 5tabulates the assumptions that are used to build the projections.

4.3. Projection for gasoline demand by vehicle class

Fig. 5 describes the projection for the entire gasoline demandof Japan by vehicle size for 2008–2035 and it assumes that thefuel economy of new cars decays over time. Eqs. (1)–(5) and theeconometric results (Table 4) produce the projection of gasolineuse (Fig. 5). The dominance of gasoline use in the standard car

D. Bonilla et al. / Energy Policy 50 (2012) 217–227 225

segment is due to its higher mileage (þ30%) compared to themini car and the small car. In other words, despite the increase invehicles sales of mini cars the standard car class becomes the topgasoline consumer in the projection plotted in Fig. 5.

Since the projection exercise includes changes in new car fueleconomy (by vehicle class), two cases are generated in theprojection: a fuel economy improvement (FEI), without decayingfuel economy of cars and a decaying fuel economy (DFE) (Fig. 6).The FEI case applies where historical trends in fuel economywould continue into the future.

Under the DFE (decay effect for fuel economy of new cars) weproduce several cases for each car class. In the DFE case, themodel shows that the effect of including decay effects for fueleconomy shifts the demand for gasoline downwards and thenupwards by 2035 (Fig. 6).

In the DFE projection, we assume policy and market failure, oran assumption that new car fuel economy, km per litre, (subjectto car manufacturer behaviour) will not improve in the projectionperiod as much as expected: Under DFE, the assumption is thatvehicle technology degrades overtime.

Fig. 5 is a disaggregated picture of Fig. 6 which shows asimulation of gasoline demand using Eqs. (1)–(5) and the carsales models (Eq. (7)). For example, following an improvement infuel economy of 1% per year for each vehicle class, the exerciseconcludes that gasoline demand approaches 1.86 EXJ by 2020 and2.3 EXJ by 2035 (Figs. 5 and 6). (Vehicle use is held constantthroughout the projection). Fig. 5 shows that the growth ingasoline demand is a result of the mini and standard carsreplacing the growth from the small car class by 2035.

Furthermore, the model can explain the effects after improvingthe average fuel economy by class of vehicle (in the FEI case) in0.17 km/l increments. Fuel economy reaches up to 16.7 km/l(weighted average of 3 vehicles) by 2020 and by 2035. In allprojections no rebound effect is assumed (this is defined as theincrease in VKM-driven given a decrease in the cost per kilometre,in turn, led by declines in gasoline prices (Greening 2000,pp. 394). Vehicle usage is assumed to remain flat and drivers dovalue (rank) fuel economy highly in their driving decisions.

The projections (2008–2035) for gasoline consumption, by vehicleclass, are generated using the stylised facts (Eqs. (5) and (7)) as wenext show. The projections raise two issues. First, vehicle prices havea strong effect on the volume of vehicle stock (via sales of these).Second, the projections (Figs. 5 and 6) show that it is not feasible toachieve reductions in gasoline consumption providing consumerschoose, or buy, more energy efficient vehicles, even assuming modestimprovements in fuel economy of sold cars (Figs. 5 and 6).

Projections of gasoline demand, based on new car fuel econ-omy to 2035, are further adjusted according to Eqs. (2) and (3).This can be seen in Fig. 6. If the model includes the decay of fueleconomy (Eq. (2)) two effects emerge: (1) it offsets the fuel savingeffect, of new car fuel economy in the projection period: and (2) itproduces lower gasoline savings for all three vehicles. Decayeffects of fuel economy reflect real world conditions of efficiencyimprovements of new cars.

Figs. 5 and 6 give levels for gasoline demand to 2035; thefactor of 0.7 is used to correct the gap between test fuel economy(new cars fuel economy) and on road fuel economy. Projectionsfor national gasoline use to 2035 show gasoline savings of 10%compared to 2008 in the 2008–2020 period or an increase of11.5% by 2035 (Fig. 6).

We have introduced a gasoline model that simulates gasolinedemand to 2035. The models confirm that savings of gasolinedemand will be challenging, if the sales of more efficient carsdecline while those of standard ones, and less fuel efficient,increase. In our model, sales of small cars fall which, in turn leadsto a decline in their vehicle stock translating into less fuel saved.

Although the effect of the stock of small cars outweighs that ofthe mini car, the model predicts a decline in aggregate gasolinedemand to 2035.

5. Discussion

In 2008, vehicle use continues to clog Japanese roads, increas-ing gasoline demand, leading to high oil dependency and to everhigher CO2 emissions even though there have been strongimprovements in new car fuel economy of Japanese car fleetsand tax discounts are available for buyers of small and mini cars.The policy implications are related to: (a) the models conclusions;and (b) to the analysis that goes beyond the data on car sales andgasoline demand trends.

Understanding how consumers choose fuel efficient carsfollowing cost changes, gasoline price effects and householdbudget levels, during 1980–2008, is the key for building moreaccurate gasoline demand projections. The econometric evidencefor underinvestment in fuel economy is mixed. On the one handconsumers underinvest in fuel economy (small car class) as salesare negatively associated with increases in capital costs in theshort term. On the other, consumers do buy, and prefer, fueleconomy (of mini cars) despite the increase in capital costs in theshort term. In the short term, buyers of large cars do not preferfuel economy since they are not sensitive to capital costsincreases. In contrast to the short term, in the long termconsumers do not invest in fuel economy as they are moresensitive to capital cost increases, this is specially so for the smalland mini car classes. Large vehicle buyers however, also revealsensitivity to capital costs in the long term. In summary, con-sumers do not always underinvest in fuel economy.

In the long term consumers prefer fuel economy following anincrease in fuel prices during 1980–2008. Consumers substitutedsmall for large vehicles and small ones for mini vehicles but thisneeds further exploration in future work. The model shows thatthe vehicle mix, on the road and at the point of sales, is asimportant as the level of fuel economy and the level of vehicletraffic. The projections produced show that the vehicle mix is thekey in any gasoline reduction strategy.

The projection to 2035, for on the road car fleets shows that thelarge and mini cars segments will dominate the car market. Theprojections confirm that large gasoline savings will be achieved bythe year 2020 but these savings will be reversed by 2035. In theprojection to the year 2035, consumers do invest in fuel economyand this is reflected in the increase in sales of mini cars.

The disaggregated model of gasoline demand and car salesleads to five policy implications. First, Japan’s goal of decreasingthe oil dependency of the transport sector is in peril so long asbuyers of large cars are unresponsive to gasoline price and tocapital cost levels in the short term. Second, policy makers shouldset targets not only for fuel economy levels of new cars but targetthe market share of more energy efficient cars such as the miniand the small cars that we have modelled in the paper. The toprunner programme, currently used, targets the new car marketbut not the on-road car fleet. A national congestion chargescheme is also an effective tool to reduce the traffic generatedby the large car class.

Third, policy makers should set behavioural campaigns, alongwith the financial education of car buyers, to incentivise furthersales of energy efficient vehicles; fourth, policy makers shouldconsider possible decay effects on fuel economy performancewhen building projections of gasoline demands. Fifth, to furtherreduce the high capital cost burden, consumers could be givenlower taxes on car ownership especially for buyers of small ormini cars.

Table 6List of variables for models, parameters and coefficients employed in the gasoline models.

Abbreviation Variable name Units

HI Real household income (GDP/household) Proportion

H Number of households Millions

GP Gasoline price index Index: 2000¼100

CPI Consumer price index Index: 2000¼100

ANR Sales standard vehicle 1000 car

ANS Sales small vehicle 1000 car

ANM Sales mini car vehicles 1000 car

CAPCO Capital cost by vehicle class Index: 1985¼100

MSRActual Mileage of both standard and small vehicles; (also miles

per vehicle) and average over both classes000 km

MM Actual Mileage of mini vehicles km

RE Fuel economy new standard vehicle km/l

RS Fuel economy new small vehicle km/l

RM Fuel economy new mini vehicle km/l

G Estimated gasoline demand of kth vehicle Exajoules

S Surviving vehicle stock 1000s vehicle

g Survival rate of vehicles (by age of vehicle) Rate

E New vehicle fuel economy (vehicle class) km/l

TAEFF New vehicle fuel economy (all vehicles, average) km/l

B Kilometers per vehicle 1000 km/car

j Index for vehicle age 1–18 years

k Index of vehicle types: standard, small and mini 1–3

i–1 Previous year

D. Bonilla et al. / Energy Policy 50 (2012) 217–227226

We can draw the following conclusions beyond the model andthe data. New perspectives that help to explain why consumersunderinvest in fuel economy have emerged (Greene, 2010) butthese have not been fully incorporated in projections of gasolinedemand. The evidence on underinvestment in fuel economy,gathered in this paper, is mixed in the case of Japan.

The current model could be extended by incorporating a largerpool of vehicle types, a disaggregation of household incomes, andof vehicle fuel economy levels. Future models should includevariables for vehicle choice (by car class) as this affects drivingdistance and fuel use.

Acknowledgements

This work is funded by Oxford Martin School, (The Institute ofCarbon and Energy Reduction in Transport), University of Oxford).The funding source has had no involvement in the study design,the collection, analysis and interpretation of data; in the writingof the report; and in the decision to submit the paper forpublication. This draft benefits from comments that were kindlyoffered in 2010 by Dr. L. Schipper (Precourt Energy efficiencyCenter-Stanford University) on an earlier draft. Akira Yanagisawathe (International Energy Agency/Institute of Energy Economics,Japan) provided the recent Handbook of Energy & EconomicsStatistics in Japan.

Appendix A

See Table 6

References

Archibald, R, Guillingham, R., 1980. Analysis of the short run consumer demand forgasoline using household survey data. Review of Economics and Statistics 62(4), 622–628.

Baltagi, B, Griffin, J., 1983. Gasoline demand in the OECD: an application of poolingand testing procedures. European Economic Review 22 117-37. (1983).

Barla, P., Lamande, B., Miranda Moreno, L.B., Boucher, N., 2009. Travelled distance,stock and fuel efficiency private vehicles in Canada: price elasticities and therebound effect. Transportation 36, 386–402.

Berndt, E, Botero, G., 1983. Energy demand in transportation sector of Mexico.Journal of Development Economics 17, 219–238.

Blomqvist, A.G., Haessel, W., 1978. Small cars, large cars, and the price of gasoline.Canadian Journal of Economics 11, 470–489, august.

Bonilla, D. 2006. The demand for gasoline of passenger vehicles in Japan to 2010: avehicle stock and vehicle efficiency approach. In: The International Associationof Energy Economics (IAEE), 29th International Conference, Securing Energy inInsecure Times, 7–10 June, 2006. Potsdam, Germany.

Bonilla, D., Foxon, T., 2009. Demand for new car fuel economy in the UK, 1970–2005. Journal of Transport Economics and Policy 43 (1), 55–83.

Busse M. R., Knittel C. R., Zettelmeyer, F. 2009. Pain at the pump: on new and usedautomobile markets. Working Paper, 15590. National Bureau of EconomicResearch, Cambridge, MA. NBER Working Paper Series. Available at: /http://www.nber.org/papers/w15990S.

Dahl, C, 1995. Demand for transportation fuels: a survey of demand elasticitiesand their components. The Journal Energy Literature 2 (I), 3–27.

Davis, S., Diegel, S. 2007. Transportation Energy Data Book: Edition 26. Oak RidgeNational Laboratory, Center for Transportation Analysis. Oakridge TN.

Energy Conservation Center of Japan (ECCJ) 2005. What is the Top RunnerProgramme?. Agency of Natural Resources and Energy, Japan, 2005. See also:/www.enecho.meti.go.jpS.

Espey, M., 1998. Gasoline demand revisited: an international meta-analysis ofelasticities. Energy Economics 20, 273–295.

Fischer, C., Harrington, W., Parry, I.W.H., 2007. Should automobile fuel economystandards be tightened? The Energy Journal 28 (4), 1–29 2007.

Gately, D., 1990. The U.S. demand for highway travel and motor fuel. The EnergyJournal 3 (11), 59–72.

Graham, DJ., Glaister, S., 2002. The demand for automobile fuel. Journal ofTransport Economics and Policy 36 (Part 1), 1–26.

Greene, D. (2010) Why the New Market for New Passenger Cars GenerallyUndervalues Fuel Economy. JTRC Discussion Paper 2010–7.

Greening, LD, Greene, D, Difiglio, C., 2000. Energy Efficiency and Consumption –the rebound effect – a survey. Energy Policy 28, 389–401.

Greenspan, A., Cohen, D. 1996. Motor Vehicle Stocks, Scrappage, and Sales. 1–33,Federal Reserve. See also /www.federalreserve.gov/pubs/feds/1996/199640pap.pdfS.

Hughes, J.E., Knittel, C.R., Sperling, 2009. Evidence of a shift in the short run priceelasticity of gasoline demand. The Energy Journal 29 (1), 93–114.

IEA (2010) Energy Balances of OECD Countries. Paris.IEA (2008) Japan 2008 Review, Energy Policies of IEA Countries. Paris, France, 1–220.Institute of Energy Economics, Japan, 2010. Handbook of Energy Economics and

Statistics, EDMC. The Energy Conservation Center, Tokyo.Institute of Energy Economics. Japan. 2006. Japan Long Term Energy Outlook: A

Projection up to 2030 under Environmental Constraints and Changing EnergyMarkets. EDMC, 2006 IEEJ, K. Ito., Y., Morita, Yanagisawa, A., Suehiro, S. June. In394th Forum on Research Works on April 26. See also: /http://eneken.ieej.or.jp/en/whatsnew/394.htmS.

Japan Automobile Manufacturers Association (JAMA) 2008. ‘‘2008 the MotorIndustry of Japan’’ See also: /www.JAMA-english.jp/index.htmlS.

Japan Automobile Manufacturers Association (JAMA) 2003a. Jidousha NennkannHandbook. 2002–2003 Edition, various years), Nikkan Jidousha Shinbun-sha,Nippon Jidousha Kaigisho, ISBN:4901830082.

D. Bonilla et al. / Energy Policy 50 (2012) 217–227 227

Japan Automobile Manufacturers Association (JAMA). 2003b. Automobile (Jidousha)Guidebok. 2003.

Japan Statistical Bureau 2010. Bukka Shisuu Nennppou (Consumer Price Index(CPI)). Available at: /http://www.stat.go.jp/data/cpi/S.

Johansson, O., Schipper, L., 1997. Separate estimations of vehicle stock mean fuelintensity and mean annual driving distance. Journal of Transport Economicsand Policy 31 (3), 277–292.

Kayser, A., 2000. Gasoline demand and car choice: estimating gasoline demandusing household information. Energy Economics 22 (3), 331–348.

Kazimi, C., 1997. Evaluating the environmental impact of alternative fuel vehicles.Journal of Environmental Economics Management 33, 163–185.

Lave, C.A, Train, K.A., 1979. A disaggregate model of auto type choice. Transporta-tion Research 13A, 1–9.

Mannering, F, Mahmassani, H., 1985. Consumer valuation of foreign and domesticvehicle attributes: econometric analysis and implications for auto demand.Transportation Research A 19A, 243–251.

McCarthy, P.S., 1996. Market price and income elasticities of new vehicledemands. The Review of Economics Statistics 78 (3), 543–547.

Ministry of Land Infrastructure and Transport (MLIT). 2008. Survey on Transport ofEnergy. Information and Research Department, Policy Bureau. See also:/http://www.mlit.go.jp/english/S.

National Institute of Population and Social Security Research. 2010. PopulationProjections. See also: /www.ipss.go.jp/index-e.htmlS.

Nicol, C.J., 2003. Elasticities of demand in Canada and the United States. EnergyEconomics 25 (2), 201–214.

Oladosu, G., 2003. An almost ideal demand system of household vehicle fuelexpenditure allocation in the United States. The Energy Journal 24 (1), 1–21.

Sakaguchi, T. 2000. Influence for Fuel Efficient Motor Vehicles on Gasoline Demandfor Individual User Owned Passenger Cars. Institute of Energy Economics,Japan. See also: /http://eneken.ieej.or.jp/en/S.

Sano, M. 2008. Fuel Efficiency of Passenger Cars in Japan. Paper Presented at theNew Energy Indicator for Transport: The Way Forward. 28–29th, IEA, Paris.

Santini, D. 1994.Transportation technology transitions and macroeconomicgrowth-contemporary evidence. Center for Transportation Research, ArgonneNational Laboratory, U.S. ANL/ES/CP-84/328. CONF-9411164-1. In: Interna-tional Workshop on the Motor Vehicle and the Global Environment Problem,Tokyo 1994. p. 1–31.

Schipper, L. 2007. Automobile Fuel Economy and CO2 Emissions in IndustrialisedCountries: Troubling Trends Through 2005–2006. Embarq, The WorldResources Institute Center for Sustainable Transport. Washington DC, U.S.

Schmalensee, R., Stoker, T.M., 1999. Household gasoline demand in the UnitedStates. Econometrica 67 (3), 645–662.

Small, K. A., Van Dender, K. 2006. Fuel Efficiency and Motor Vehicle Travel: TheDeclining Rebound Effect. UCI Department of Economics. Working Paper 05 06 03.

Thomas, R.L., 1997. Modern Econometrics: An Introduction. Prentice Hall, London,pp 1-531.