SCENARIO ANALYSIS OF CHINESE PASSENGER VEHICLE … · Contemporary Economic Policy (ISSN 1074-3529)...

Contemporary Economic Policy(ISSN 1074-3529)Vol. 21, No. 2, April 2003, 200–217

SCENARIO ANALYSIS OF CHINESE PASSENGERVEHICLE GROWTH

PETER H. KOBOS, JON D. ERICKSON, and THOMAS E. DRENNEN*

This article reports on a simulation and scenario analysis of Chinese passengervehicle growth and resulting energy demand and CO2 emissions. The model includesprovincial level logistic growth functions with saturation levels representative of neigh-boring Asian economies, income growth measured in international dollars, and bothestimated and literature-based income elasticities. Scenarios explore variation in keyparameters, including income and population growth rates, elasticity income ranges,fuel economy, and vehicle saturation. Countrywide base case results estimate growthfrom 4.22 to 54.33 passenger vehicles per thousand people from 1995 to 2025. Result-ing passenger vehicle oil demands and CO2 emissions increase nearly 17-fold. (JELR40, Q40, N70)

I. INTRODUCTION

As personal incomes rise, China is rapidlybecoming more dependent on personal vehi-cle, highway-based, passenger transporta-tion. Among current developing countries,China represents the largest potential modalshift for passenger transport from rail tohighway. Figure 1 highlights an increasein personal transport by highway between1990 and 1997 from 262 to 554 billionpassenger-kilometers, based on data fromthe China Statistical Yearbook (CSY; 1998).Total distance traveled increased 93% (64%of which was by highway) between 1990and 1997. Sinton (1996) reports per capitapassenger-kilometers traveled by highway in1992 were 272 in China, compared with 6985

∗This is a revision of a paper presented at the West-ern Economic Association International 75th AnnualConference, Vancouver, BC, June 29, 2000. This researchwas partially funded by a grant from Sandia NationalLaboratories, contract number BF-7540. The commentsof Kenneth Button, Jack Hou, and an anonymousreviewer are greatly appreciated.Kobos: Staff Economist, Office of the Chief Economist,

Sandia National Laboratories, Albuquerque, NM87185. Phone 1-505-845-7086, Fax 1-505-844-3296,E-mail [email protected]

Erickson: Associate Professor, School of NaturalResources, University of Vermont, BurlingtonVT 05405. Phone 1-802-656-3328, Fax 1-802-656-8683, E-mail [email protected]

Drennen: Assistant Professor, Department of Eco-nomics, Hobart and William Smith Colleges, Geneva,NY 14456; and Senior Economist, Sandia NationalLaboratories, Albuquerque, NM 87185. Phone 1-315-781-3419, Fax 1-315-781-3422, E-mail [email protected]

in Japan, 10,645 in the United States, and2230 in the former Soviet Union. Recentaccession to the World Trade Organization isexpected to magnify this trend. This modalshift of China’s transport sector is similarto that experienced in Taiwan, Republic ofKorea, and Japan, the key distinction beingthe path the latter countries took and thepath China has yet to find. Resulting trafficcongestion, regional air pollution, and depen-dence on foreign oil plague each of China’sAsian neighbors.

Ownership of the vehicle fleet has alsochanged rapidly. Averaging 17.7% growth peryear during the 10-year period from 1984 to1993, much of this new vehicle ownershipis private, compared to past growth primar-ily through state ownership (Sinton, 1996).In many of China’s urban areas, the carhas become a status symbol. Between 1990and 1997, private ownership increased from

ABBREVIATIONS

AAMA: American Automobile ManufacturersAssociation

CSY: China Statistical YearbookGDP: Gross Domestic ProductICP: International Comparison ProjectIPCC: Intergovernmental Panel on Climate

ChangeOECD: Organisation for Economic

Co-operation and DevelopmentPPP: Purchasing Power Parity

200

KOBOS, ERICKSON, & DRENNEN: CHINESE VEHICLE GROWTH 201

FIGURE 1Increasing Passenger Travel by Highway

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

1952 1962 1970 1978 1985 1987 1989 1991 1993 1995 1997

10

0 M

illio

n P

as

se

ng

er

Kilo

me

ters Civil Aviation

Waterways

Highways

Railway

Source: China Statistical Yearbook, 1998.

14.8% to 32.9% of total ownership (CSY,1991, 1998).

As with many Asian countries, more pri-vate vehicles on a capacity-filled road net-work presents a balancing act for spacebetween bicycles, passenger vehicles, andpublic transportation (Greene and Santini,1993). Passenger vehicles, defined here assmall passenger cars and buses, already pro-vide the majority of highway passenger kilo-meters in China compared to motorcyclesand other motor vehicles.1 Although thenumber of Chinese passenger vehicle sales issmall relative to the world automobile mar-ket, the potential exists for rapid expansiondriven by steady per capita income growth.As evidence, several large automobile man-ufacturers have located in China, includingPeugeot, Volkswagen, Chrysler, and Gen-eral Motors. General Motors alone recently

1. Small passenger vehicles have an average of eightseats and are designated cars for this study. The major-ity of passenger vehicles owned by the state or individu-als are minibuses. Passenger cars, as defined in Westerncountries, remain a small percentage of small passengervehicles (see Sinton, 1996).

invested $2 billion to open a factory in China(Fortune, 1999).

Growing passenger vehicle ownershipraises three primary concerns for both Chinaand the international community. First,increased car and bus stocks will exacerbatelocal air pollution problems. Chinese citiesare already among the world’s most pollutedcities. The World Resources Institute (1998)reports that ambient concentrations of sul-fur dioxide exceed the World Health Orga-nization’s recommended guidelines for morethan half of the 88 Chinese cities monitored,and 85 of the cities exceed the guidelines fortotal suspended particulates. Second, as a netimporter of oil since 1993, automobile growthwill continue to increase China’s oil importdemand. A study by Sandia National Labo-ratories (see Conrad et al., 1998; Drennenand Erickson, 1998) forecasts that China’stotal oil import requirements could rivalcurrent U.S. imports, reaching as high as7.1–8.1 million barrels per day (mbbls/day)by 2015. Lacking proven domestic reserves,the China National Petroleum Corporationhas begun to secure oil concessions in Sudan,

202 CONTEMPORARY ECONOMIC POLICY

Venezuela, Iraq, and Kazakhstan and developoil and gas pipelines from Russia and Cen-tral Asia to China (Rashid and Saywell,1998). The potential magnitude of Chineseoil import requirements has serious implica-tions for global geopolitics. Finally, increasesin the vehicle fleet will contribute to bal-looning carbon emissions. The per capitaemissions for China are low relative to devel-oped nations, but China emits about 14%of total world carbon dioxide, ranking Chinaas the world’s second largest emitter afterthe United States (Drennen, 2000). Evenif developed nations in Europe and NorthAmerica agree to greenhouse gas reductionstrategies developed in the Kyoto Protocol,global emissions cannot stabilize or declinewithout the support from developing nations,such as China and India.

Stemming from these three related issues,the purpose of this article is to estimate pas-senger vehicle growth in China, along withcorresponding oil requirements and carbondioxide emissions.

II. CHINESE INCOME GROWTHAND PASSENGER VEHICLE DEMAND

Passenger vehicle stocks vary widelyaround the world and are indicative of severalfactors, including income levels, governmentpolicy toward car ownership, population den-sity, fuel prices, and the availability of substi-tutes, such as bicycles and mass transit. TheAmerican Automobile Manufacturers Asso-ciation (AAMA, 1995) last reported approx-imately 568 passenger vehicles per thousandpeople in the United States in 1993, com-pared to China’s stock of roughly 4 passengervehicles per thousand people in 1997 (a num-ber that varies widely by province). Althoughit is unlikely that China could ever supportthe number of vehicles per capita in currentlydeveloped nations, it is possible that Chinawill follow the path of other Asian countries.The AAMA (1994) reported that Japan, Tai-wan, and the Republic of Korea had 314,114, and 79 cars per thousand people, respec-tively, in 1992.

Each of these Asian countries is at a differ-ent level of development. Figure 2 plots theirpassenger vehicle ownership levels againstaverage income purchasing power (measuredin international dollars) using Organisation

for Economic Co-operation and Develop-ment (OECD) estimates from Maddison(1995, 1998). China is still well below incomelevels required for vehicle ownership lev-els representative of Japan or Taiwan. TheWorld Resources Institute (1998) reportedthe average Chinese per capita income ofUS$572 in 1995. The per capita incomeneeded to purchase even lower-end car mod-els in China has been estimated at US$4000(Asiaweek, 1993). However, due to recentaccession to the World Trade Organiza-tion, China is expected to reduce tariffs onimported autos and allow foreign car compa-nies to offer car loans, making it far easierfor the average Chinese citizen to considercar ownership. Furthermore, the dominantlystate-owned automobile industry will likelyopen to greater foreign ownership (Liu andWoo, 2001), opening the door to productivitygains and price competitive domestic models.

This relationship between income andvehicle ownership has been the most reliablein previous passenger vehicle ownership stud-ies. Past studies differ in how to measureincome, with measures including disposablepersonal income (Dargay and Gately, 1999),household income (Button et al., 1980), andper capita income in constant prices (Tanner,1978). Regardless of the measurement, a con-stant current year is used to account fordomestic inflation, and a common currencyunit (most often the U.S. dollar) is used whenmultiple countries are compared. However,a shortcoming of previous multiple coun-try vehicle ownership models and a pointof difference in the current study has beena neglect of any adjustment for purchasingpower. For instance, often cited internationaltransportation studies by Dargay and Gately(1997, 1999) and Button et al. (1993) adjustto a common currency but do not account forthe purchasing power of that currency in thestudy countries.

This article builds from these past stud-ies, utilizing international dollar units (alsoknown as Geary-Khamis dollars) to calcu-late income elasticities and to capture vehi-cle ownership trends in Japan, Republic ofKorea, Taiwan, and China. Conversion tointernational dollars is based on a multilat-eral comparison to account for both pur-chasing power parity (PPP) of currenciesand international average prices of commodi-ties, as compared to the bilateral approach


FIGURE 2Passenger Vehicle Ownership and Income Growth over Time for China,

Republic of Korea, Taiwan, and Japan

0

50

100

150

200

250

300

350

0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000

Per Capita GDP (1990 International Dollars)

Pas

sen

ger

Veh

icle

s P

er 1

000

Peo

ple

China (1978-1995) South Korea (1964-1992)

Taiwan (1966-1992) Japan (1955-1992)

Source: Maddison, 1995, 1998.

(i.e., benchmarked to one country) of theWorld Bank’s PPP estimates. The more cur-rent international dollar conversions formthe basis of the International ComparisonProject (ICP) of the United Nations, aprocess in which China has not yet fullyparticipated, making conversion from yuanproblematic (Keidel, 2001). The present con-version of Chinese yuan to international dol-lars (described in more detail later) builds onthe ICP framework and from the most recentwork of renowned economic growth historianAngus Maddison (1995, 1998) of the OECD.2

2. Maddison’s (1998) recent effort specific to Chinabuilds on the work of Ren Ruoen, also of the OECD.Ruoen (1997) produced a binary currency conversionto compare Chinese and U.S. gross domestic product.By adapting Ruoen’s binary estimates to a multilateral,ICP-type conversion, Maddison reestimated Chinese percapita GDP, providing an explicit yuan to internationaldollar conversion.

III. MODEL STRUCTURE AND ESTIMATION

Figure 3 outlines the structure of passen-ger vehicle growth simulation for Chineseprovinces from 1995 to 2025.3 Equation num-bers refer to equations (1) through (7)detailed later. The model estimates val-ues outlined in square boxes, includingprovincial passenger vehicles per thousand(v), total passenger vehicle stock (V ), fuelrequirements (F ), and carbon emissions (C).The variables in ovals—including provincialincome (I), income elasticities (n), passen-ger vehicle saturation (S), provincial popu-lation (P ), fuel efficiency (e), and annualtravel (m)—can each be varied in scenarioanalysis. Fleet shares of mini-buses and cars(�) and carbon content (�) are fixed (spec-ified by diamonds) in all model runs. Data

3. Thirty provinces included; all except Chongqingdue to insufficient data.


FIGURE 3Schematic of Provincial Passenger Vehicle Growth Simulation Model

Income Elasticities (Eq. 4)

Income (Eq. 2)

Passenger Vehicle Saturation

Passenger Vehicles

per Thousand

(Eq. 1)

Total Passenger Vehicle Stock

(Eq. 5)

v

Population (Eq. 3)

Fuel Requirements

(Eq. 6) Fuel Efficiency

Annual Travel

V

Carbon Emissions

(Eq. 7)

F

Carbon Content

I

n

S

P

e

m

θC

P

Fleet Shares λ


were compiled at the provincial level primar-ily from CSY (years 1990 through 1998) andthe China Energy Databook (Sinton, 1996).

A. Passenger Vehicle Growthand Saturation

Several authors have effectively demon-strated the usefulness of the logistic func-tion for simulating passenger vehicle growthand market saturation (e.g., Zhongyuan et al.,2002; Dargay and Gately, 1999; Buttonet al., 1993; Tanner, 1962). Accordingly, carsper thousand (v) at the provincial level(i = 1�2 � � � 30) for yearly time steps (t =0�1�2 � � � 30) are modeled as:

vi� t+1 = vi� t +nk�Ii� t/Pi� t�(1)

× ��Ii� t+1/Pi� t+1�− �Ii� t/Pi� t��/�Ii� t/Pi� t�� vi� t�1− �vi� t/Sa��

The simulation runs from 1995 to 2025,with base year provincial vehicle ownership(vi�0) based on the CSY. The growth func-tion includes income elasticity (nk) as a func-tion of yearly provincial per capita income(Ii� t/Pi� t) in 1990 international dollars, wherek equals the number of income rangesassumed (described later). Because this studyis the first to estimate growth in passen-ger vehicle ownership at the provincial level,many challenges were faced in constructing aprovincial level database of income, popula-tion, and other key variables. Unless other-wise noted, official data from the CSY (bothcountry-wide and provincial yearbooks) wasused as a primary source, often supplementedand cross-checked with data from varioussources, including Maddison (1998).

A passenger vehicle saturation level (Sa)determines the horizontal asymptote of thelogistic functional form, with the index aspecifying which of three estimates areassumed. Button et al. (1993) specificallyexamined low-income countries and found asaturation level for developing Asian coun-tries of 350 cars per thousand people. Dargayand Gately (1999) assume a saturation levelof 850 motor vehicles (including cars, buses,and other motorized vehicles) and 620 carsper thousand people for each of the 26 studycountries, including China.

A common technique to develop a sat-uration level of ownership applies a linear

regression on the percentage change of percapita (or per thousand people) vehicle own-ership and the per capita (or per thousandpeople) vehicle ownership. The saturationlevel is then estimated at the x-axis inter-cept. This assumes that as the number ofvehicles per capita increases toward a satu-ration point, the percentage increase in vehi-cle ownership decreases. This method hasbeen used to model moderately to highlydeveloped countries and their respective lev-els of vehicle ownership. In the context ofdeveloping nations, linear extrapolation maybe misleading for countries on the portionof the S-shaped logistic curve between theorigin and the inflection point. It is moreintuitive to model low-income countries andtheir respective growth patterns on othercountries of similar origins and access toresources. It is unlikely that China, India, orother highly populated countries with low percapita income will have the physical spaceand income for the necessary infrastructureexpansion to support 850 vehicles per thou-sand people during this century.

To estimate the saturation level, this arti-cle fits a logistic growth curve to per capitaincome and vehicle ownership data for fourAsian countries in different developmentstages. Using the Loglet Software from theRockefeller Institute for the Environment(Yung et al., 1998), a saturation level of 292passenger vehicles per thousand people wasestimated with a 90% confidence interval of281, 299�. Figure 4 represents the outputof fitting the logistic growth curve to historicpassenger vehicle data for Japan, Republic ofKorea, Taiwan, and China. In scenario analy-sis, the default setting is the authors’ estimateof 292, however, custom values and those foreither the Button et al. or the Dargay andGately study can be selected.

B. Real Income Growth

There is no shortage of forecasts forcountry-wide Chinese real income growth.The OECD estimates of Maddison (1998)assume China’s gross domestic products(GDP) growth rate to slow to an annual aver-age of 5.5%. Others include a 6.5% estimatefrom Ho et al. (1998), and a 7.5% assumptionof Conrad et al. (1998). These pre-WTO esti-mates may be further magnified by China’s


FIGURE 4Estimated Saturation Level of Passenger Vehicle Ownership

Note: Data ranges are 1955–89 for Japan, 1970–92 for Republic of Korea, 1966–91 for Taiwan, and 1964–92 forChina.

formal accession to the World Trade Organi-zation in November 2001. Oxford EconomicForecasting (1999) predicts a World TradeOrganization premium to GDP growth ratesas high as 1.5–2.5% per year. Due to broadeconomic reform, Chow (2000) estimates theChinese economy will continue to grow at 7%annually over the next two decades.

To capture this range of forecasts, realincome growth (in 1990 international dol-lars) was modeled with four goals in mind.First, the average across all provinces shouldequal a country-wide average within therange of low to high projections of variousinternational agencies. Second, this shouldbe a weighted average based on the cur-rent income share of each province. Third,real income growth rate for each province

should be based on the current distribu-tion of growth, so that the current rank ofprovinces by real income growth is main-tained throughout the simulation. Fourth,yearly growth rates should be distributed overthe simulation period such that the 1995base year has the highest rate, each subse-quent year declines at a constant percent-age, and the annual weighted average acrossall 30 provinces equals the specified country-wide average.

With these goals in mind, provincialincome growth takes the form:

Ii� t+1 = �1+�i� t�Ii� t�(2)

where provincial level annual growth rates(�i� t) were defined such that a country-wide


average annual real income growth rate (Ag)would be maintained for the period 1996 to2025 (t= 1 to 30) under one of three assump-tions (Ag = 55�65, or 7.5%).

To maintain a selected average, base-yearprovincial level real growth rates (�i�0) werecalculated for each province based on 1990to 1995 data. Yearly weights (wt) were thencalculated such that:

Ag =[

30∑i=1

30∑t=1

wt�i�0

]/30(2a)

and

wt = �At−1�1− g��/A0�(2b)

where At is the average annual provincialreal income growth rate (i.e., a country-wide average), A0 is the average of weightedprovincial real income growth rates for 1990–95 (equal to 13.4%), and g is the fixedyearly percentage change in At (such thatAg =

∑30t=1 At/30 for Ag = 55�65, or 7.5%).

To develop the income data used in theseincome growth equations and income elas-ticity estimates (described later), provincial-level income data was converted to 1987yuan using deflators from Maddison (1998).A conversion coefficient of 1.402 developedfor this model by Kobos (2000) was thenapplied to convert 1987 yuan to 1990 interna-tional dollars. Although this conversion doesnot account for regional price disparities, itdoes reveal the general trends of regionalreal income growth and follows methodolo-gies developed by Maddison (1995, 1998) andRuoen (1997).

C. Population Growth

Population by province by year growsaccording to

Pi� t = �1+�z�i�Pi� t�(3)

where initial provincial population levels(Pi�0) are based on official statistics in theCSY (1990 through 1998). To capture dif-ferences at the provincial level, 30 separateannual population growth rates (�i) are set totheir respective average of provincial annualgrowth rates over the base period 1990–97.However, these fixed averages do not accountfor preliminary evidence of a decreasing rate

of population growth country-wide and insome provinces. The country-wide trend ismost reflected in the 2000 CSY estimate ofa 0.043 average percentage point decline peryear in annual population growth rates duringthe base period.4 However, according to CSYstatistics, a country-wide rate of decrease wasinsignificant and provincial growth rates werein decline in only 13 of the 30 provinces whileremaining statistically flat for the remain-ing 17. The provinces with declining ratesof growth are predominately near Beijing,perhaps reflecting the influence of rural tourban migration. Beijing itself has the highestannual population growth rate over the baseperiod at nearly 2%.

To investigate hypotheses of both anurban migration and a country-wide slow-ing of population growth, the i by t matrix�z is used in scenario analysis. The base-case (z= 1) assumes population growth ratesremain fixed at �i. The urban migration case(z= 2) assumes the 13 provinces with signif-icant downward trends in growth rates con-tinue this linear trend through the simulationperiod, while the remaining 17 provincialgrowth rates remain fixed at base-case values.Finally, the scenario of country-wide slowing(z = 3) assumes all provincial growth rateslinearly decline by 0.043 percentage pointsper year. This scenario eventually drives17 provinces to negative annual populationgrowth by 2020 and 20 provinces by 2025.

D. Income Elasticity

Various studies have estimated incomeelasticities associated with transportation,although not many focus specifically on carownership. In a comparison of over 50 studiesof income elasticities of various energy typesin the developing world, Dahl (1994) notesthat energy for transport is more incomeelastic than total energy demand, with along-run average of 1.64. For auto registra-tions, the estimates of Dunkerly and Hoch(1987) range from 1.3 for industrialized coun-tries and 1.5 for middle-income developingcountries to 1.7 for low-income developingcountries. Walls (1997) estimates a long runincome elasticity for vehicle stock in Hong

4. This rate of decline is based on a reviewer’s com-ments to an early draft, however, is much less and sta-tistically insignificant in the nation-wide growth ratesreported in the CSY.


Kong of 1.7. Stares and Zhi (1995) summa-rize income elasticities reported from vari-ous sources ranging from 1.09 to 1.95 forvehicle ownership in free-market and OECDcountries. Specifically, they report incomeelasticities for cars or passenger cars rang-ing from 1.02 to 1.58 and lower elastici-ties for commercial and total motor vehicles.Dargay and Gately (1999) estimate short-run income elasticities for cars at 1.2 anda long-run income elasticity in China for2015 of 2.34, numbers that seem to agreemore with estimates for industrialized coun-tries. For example, Johannson and Schipper(1997) estimate short-run income elasticitiesfor vehicle stocks at 1.23 and long-run elastic-ities of 1.0 in OECD countries. Last, Buttonet al. (1993) estimate income elasticities forlow-income countries in five categories rang-ing from 0.57 to 1.60.

The shortcoming of using a single valueis that income elasticities are only likely tohold within a certain income range and areunlikely to be constant across all levels ofincome. For this article elasticity measureswere estimated using point elasticities fol-lowed by an arc elasticity estimate to accountfor the endpoints of the income ranges. Forthe point elasticity estimates, data on pas-senger vehicle ownership in Japan, Republicof Korea, Taiwan, and China were regressed

TABLE 1Regression Results for Point

Elasticity Estimates

Per CapitaIncome Range(1990 International Income AdjustedDollars) Constant Coefficient R2

Two-income rangemodel −1825 246 096I < 10�000 �−4868� �5396�I ≥ 10�000 −1011 161 083

�−725� �1099�

Four-income rangemodel −1594 215 096I < 5�000 �−3093� �3277�5�000 ≤ I −1888 253 086

< 10�000 �−1053� �1258�10�000 ≤ I −1744 240 078

< 15�000 �−582� �751�15�000 ≤ I −584 117 095

< 20�000 �−623� �1220�

Note: t-statistics shown in parentheses.

against international dollars per capita GDPlevels. Table 1 reports regression results forboth a two-income and four-income rangemodel, with the natural log of passengervehicles per 1000 people as the dependentvariable and the natural log of per capitaincome in 1990 international dollars as theindependent variable.

An arc elasticity method was then usedto account for the endpoints of each incomerange. This method is based on the averageincome and vehicle ownership levels withinthe specified range.5 The general intuitionbehind relying on arc elasticity estimates isthe extreme sensitivity of point estimates overthe critical low-income range of 1000 to 5000international dollars per capita. At the begin-ning of the model’s time frame in 1990, 29of the 30 provinces in China lie within thisrange. By 2005 under the low growth assump-tion, 11 of the 30 provinces still remain withinthis range of per capita income. Income elas-ticity estimates will greatly affect the resultsof the model over this dominant range. Thusthe more conservative approach is to use thearc elasticity method of calculation.

In scenario analysis, the model can useelasticity estimates for one income rangebased on Dargay and Gately (1999), or two orfour income ranges based on the arc elastic-ity results. The one income range assumption(k= 1) assumes a gradual increase in elastic-ity from the base year value of 1.21 to a fixedlevel of 2.34 in 2015.

n1�Ii� t/Pi� t�= 121 for t = 0�(4a)

= nt�Ii� t/Pi� t�

+ �n20�Ii�20/Pi�20�

−n0�Ii�0/Pi�0��/20

for 0 < t < 20�

= 234 for t ≥ 20

where provincial income (Ii� t) and population(Pi� t) grow according to equations (2) and(3), with trends specified in scenario analysis.

5. Using the arc elasticity method, the incomeelasticity (n) between any two income (I) and quan-tity (V ) points is given by: n = � V /�05�V1 + V2��/� I/�05�I1 + I2��. For example, over the 1000–5000international dollars per capita range: n = 141 =�106025/�05�109448 + 03423��/�4000/�05�5000 +1000��. The quantities of passenger vehicles perthousand people were estimated using the regressionequations.


A two-income range (k = 2) assumption,using arc elasticity estimates, assumes

n2�Ii� t/Pi� t�(4b)

= 121 for Ii� t/Pi� t < 10�000�

= 152 for Ii� t/Pi� t ≥ 10�000

A four-income range (k = 4) assumption,again using arc elasticity estimates, assumes

n4�Ii� t/Pi� t�(4c)

= 141 for Ii� t/Pi� t < 5�000�

= 212 for 5�000 ≤ Ii� t/Pi� t

< 10�000�

= 225 for 10�000 ≤ Ii� t/Pi� t

< 15�000�

= 117 for Ii� t/Pi� t ≥ 15�000

E. Total Passenger Vehicle Stockand Fuel Requirements

Total annual passenger vehicle stock (Vt)is calculated as

Vt =30∑i=1

�vi� tPi� t/1000�(5)

Total annual fuel requirements (Ft) inliters then follows as

Ft = ��Vtmc�1−�b� t��/ec� t�(6)

+ �Vtmb�b� t/eb�

where �b� t and �1 − �b� t� are annual per-centages of mini-buses and cars, respectively,from the total passenger vehicle stock. Mini-buses comprised 20.5%, 18.5%, and 16.8%of all Chinese passenger vehicles in 1990,1991, and 1992, respectively (Sinton, 1996).To reflect a near-term trend of a decliningbus fleet share, the base case assumes �b� t

to fall one percentage point every two yearsfrom 16% in 1993–94 to 10% in 2005, andremains at 10% for the remainder of the sim-ulation. Separating the stock allows for dif-ferent fuel efficiency and average kilometerstraveled assumptions. In the base case sim-ulation, fuel efficiency for cars (ec� t) growsfrom 8 km/L in 1995, based on a 1990 IPCCestimate (Michaelis et al., 1995), to 10.6 km/L

by 2025, based on a constant increase of0.9 km/L every 10 years beginning in 1995.Bus fuel efficiency (eb) remains constant at3.63 km/L, based on Sinton (1996). Annualvehicle kilometers driven by cars (mc) areset at 15,000 km per year for cars accord-ing to Dargay and Gately (1999). Annualvehicle kilometers driven by buses (mb) arebased on an average between Chongqingand Hangzhou cities of 57,689 km per yearaccording to Bushell (1994). Both efficiencyand kilometers driven can be changed in sce-nario analysis.

F. Carbon Emissions

Carbon emissions (C) in million metrictons are calculated as

C = �Ft ∗��/�100∗106��(7)

where � is a carbon factor of 0.647292 kg ofcarbon per liter of fuel based on a commonIPCC assumption (Michaelis et al., 1995).

IV. MODEL RESULTS AND SCENARIO ANALYSIS

The dynamic simulation model allows pol-icy analysts to explore a wide range of pos-sible scenarios for Chinese passenger vehi-cle growth. Examples include variations inprovincial income and population growthrates, income elasticities, vehicle saturationlevel, fuel efficiency of cars, and averageannual distances driven per car or bus. Themodel was programmed with Powersim Con-structor (see www.powersim.com), a dynamicsimulation programming package. The fol-lowing scenario analysis can help illuminatewhere policy may play a role in balanc-ing vehicle ownership with local and inter-national environmental and energy policygoals. Unless otherwise noted, model vari-ables are set to their base-case levels outlinedin Table 2.

A. Scenario Analysis: Income Growth

Table 3 summarizes the results for pas-senger vehicles per thousand people, oilrequirements, and carbon emissions for threeincome scenarios. The scenarios are derivedby changing the income growth assump-tions, where low-, medium-, and high-incomegrowth correspond to an average annual


TABLE 2Base Case Variable Set

Variable Notation Base Case Assumption

Passenger vehicle saturation level Sa 292 passenger vehicles (a= 1)Country-wide income growth rate Ag 6.5% (g = 2)Income elasticity nk Four-income range estimate (k = 4)Scaling matrix for population �z Provincial level population growth rates fixed at

growth rates 1990–1997 average annual growth rates (z= 1)Car fuel efficiency ec� t Improvement of 0.9 km/L per decadeBus fuel efficiency eb 3.63 km/LAverage yearly car travel mc 15,000 kmAverage yearly bus travel mb 57,689 km

provincial-weighted income growth rate ofeither 5.5%, 6.5%, or 7.5% for the simulationperiod 1996 through 2025. Historical growthrates are used from 1990 to 1995.

For the selected cases, the estimated num-ber of passenger vehicles grows from 1.6million in 1990 to between 64 and 125 millionin 2025. Given provincial-level populationgrowth trends, the nationwide average vehi-cle ownership expands to between 39 and73 passenger vehicles per thousand. Accom-panying oil requirements and carbon emis-sions amount to just over 3 mbbls per dayand nearly 119 million metric tons of car-bon (MtC) for the low-growth case, and justover 6 mbbls/day and nearly 232 MtC for thehigh-growth case. When considering only carsfor 2025 (i.e., factoring out buses), the modelestimates range from about 58 to 112 millioncars, requiring 1.4 to 2.7 mbbls of oil per dayand emitting 53–103 MtC per year.

Figure 5 illustrates passenger vehicles perthousand people at the provincial level forthe base case scenario. This scenario esti-mates an average of about 41 passenger vehi-cles per thousand people in 2015, increasingto 54 by 2025. Estimates range from just over3 passenger vehicles per thousand people in

TABLE 3Income Growth Scenarios

Passenger Passenger Oil perVehicles Vehicles per Day Carbon

Scenario Year (Millions) Thousand (mbbls) (MtC)

Low 2015 4609 3195 236 8879growth 2025 6391 3872 316 11855

Medium 2015 5963 4063 306 11486growth 2025 9155 5433 452 1698

High 2015 7406 4990 380 14266growth 2025 12484 7275 617 23156

Guizhou province to nearly 119 passengervehicles per thousand people in Fujian. By2025, the lowest and highest estimates are 3.4and 159.9 passenger vehicles per thousand.The provinces with the highest numbers ofvehicles are mostly located along the coastin the more industrialized, rapidly urbanizingareas of China.

B. Scenario Analysis: Income Elasticity

The three types of income elasticitiesincorporated in the model include the esti-mated four- and two-income range elasticitiesand an extrapolation of Dargay and Gately(1999). Figure 6 illustrates the results for thethree options while other variables are heldat base case levels.

The importance of the income relation-ship with new vehicle ownership is reflectedin the magnitude of difference resulting fromvarying the elasticity assumption. The four-income range scenario results in a 2025 esti-mate that is nearly twice the magnitude ofthe two-range option. By incorporating dis-tinct income stages of vehicle demand, modelresults may more accurately reflect stages ofgrowth in China’s vehicle market.

C. Scenario Analysis: VehicleSaturation Level

The default saturation level used for Chinais the authors’ estimate of 292 passenger vehi-cles per thousand people. To illustrate thesensitivity of the model to other saturationlevels, an estimate of 350 by Button et al.(1993) and 620 by Dargay and Gately (1999)are included in Figure 7. With other vari-ables held at base case values, projections to


FIGURE 5Passenger Vehicles per Thousand People by Chinese Province, 2015

Note: Base case scenario. Chongqing not included in model due to limited data; represented 1.4% of nationwide1997 passenger vehicle stock (CSY, 1998).

2025 differ by over 15 million passenger vehi-cles. Even during the next 30 years, Chinaremains far below any of the saturation esti-mates. In fact, by 2025 some 27 of the 30provinces remain below 146 passenger vehi-cles per thousand people, the midpoint of thelogistic curve for a 292 saturation level.

D. Scenario Analysis: Population Growth

Growth in provincial populations indi-rectly affects passenger vehicle ownershipby jointly determining provincial per capitaincome with provincial income growth. Thebase case (z= 1) assumes population growthrates fixed at their respective provincial1990–97 average. Two scenarios investigate

the impact of a declining rate of popula-tion growth, the first (z = 2) reflecting evi-dence of urban migration, and the second(z = 3) reflecting preliminary evidence ofa nationwide slowing of population growthrates. Figure 8 plots total population (in mil-lions) over the simulation period for eachcase.

The impact of lowering population growthrates is seen mostly through higher estimatesof passenger vehicles per thousand. Com-pared to base case nationwide averages of41 and 54 passenger vehicles per thousandin 2015 and 2025, the urban migration caseresults in 45 and 67, and the nationwide slow-ing case results in 44 and 68. At the provinciallevel, the differences are more revealing. By


FIGURE 6Income Elasticity Scenarios

Year

Mill

ion

Pas

seng

er V

ehic

les

1990 1995 2000 2005 2010 2015 2020 2025

10

20

30

40

50

60

70

80

90

100

110

120

130

123 123123

1

23

1

2

3

1

2

3

1

2

3

1

2

3

1 Four income ranges 2 Two income ranges 3 Dargay and Gately (1999)

2025, the base case results in five coastalprovinces (Jiangsu, Zhejiang, Fujian, Guang-dong, Hainan) reaching over 100 passen-ger vehicles per thousand. In contrast, themigration case adds the coastal provinces ofTianjin and Guangxi to the over-100 club,with Tianjin (near Beijing) joining Fujian(north of Guangdong) as the two provinceswith over 150 passenger vehicles per thou-sand. The nationwide slowing case results ineight provinces with over 100 passenger vehi-cles per thousand, replacing Tianjin with Bei-jing and adding the coastal province of Shan-dong. Under this case, Beijing and Fujianreach a dubious level of 179 and 189 passen-ger vehicles per thousand by 2025.

Despite clear implications at the provin-cial level, the overall impact on the total pas-senger vehicle stock (Vt) is negligible. This isdue to the nearly canceling effects of the mul-tiplication of higher passenger vehicles perthousand (vi� t) and lower population (Pi� t) inequation (5).

E. Scenario Analysis: Fuel Efficiencyand Annual Travel

Table 4 illustrates several fuel efficiencyand annual distance driven scenarios. Resultsreflect the medium income growth scenario(6.5%), with 2025 values of 92 million pas-senger vehicles and 56 passenger vehicles perthousand. Efficiency and annual travel sce-narios only apply to the car portion of thepassenger vehicle fleet. Small buses makeup the remainder of the fleet, and theirshare (10% for 2005–2025), annual travel(57,689 km), and fuel efficiency (3.63 km/L)remain constant throughout this simulation.

A policy aimed at a 33% improvement incar fuel efficiency by 2025 reduces oil useand carbon emissions by between 10% and14%. Under a constant fuel efficiency sce-nario, oil use and carbon emissions drop bynearly 30% between the high and low annualtravel scenarios. When increasing fuel effi-ciency is assumed, the percentage decreaseis about 25%. The decrease in oil use andcarbon emissions from the high travel/low


FIGURE 7Saturation Level Scenarios

Year

Mill

ion

Pas

seng

er V

ehic

les

1990 1995 2000 2005 2010 2015 2020 20250

10

20

30

40

50

60

70

80

90

100

110

120

130

123 123123

123

123

123

12

31

3

1 Kobos (2000) 2 Button (1993) 3 Dargay and Gately (1999)

efficiency scenario (worst case) to the lowtravel/high efficiency scenario (best case) isroughly 36%.

V. DISCUSSION

Growth in passenger vehicle ownershiphas been modeled predominately on income,particularly in developing countries. Thischoice has both practical and theoretical sig-nificance. In theory, income is a major factorin the purchase of durable goods that occupya large portion of consumer budgets. In prac-tice, data on nonincome factors of vehicledemand are scarce in developing nations,particularly for China. However, factors suchas traffic congestion, road infrastructure, andaccess to public transportation may not haveas much of an influence on early stagesof Chinese passenger vehicle demand. Forinstance, higher levels of vehicle ownershipexist in China’s city centers, contrary todeveloped country vehicle ownership trends.In 1993, China’s national average for all typesof motor vehicle ownership per thousand

people was 7.4, whereas in Beijing, Shang-hai, and Guangzhou it was 47.8, 24.3, and 41motor vehicles per thousand people, respec-tively (Stares and Zhi, 1995). Demand forvehicles tends to mirror the high concentra-tion of urban wealth in China.

However, other limiting factors couldeventually overwhelm income effects, evenin these early stages of market development.For example, roadway infrastructure develop-ment in China is one of the most restrictiveparameters of the transportation network.Virtually every city in China has insuffi-cient roadway networks to support Western-style car ownership levels. Bus, bicycle, andpedestrian lanes all support the day-to-daycommuting. Road networks are growing, butdisproportionately in urban over rural China,building on an urban income effect. The totallength of urban roads increased from 29,485km in 1980 to 104,897 km in 1993, repre-senting 10.2% annual growth (Ganshi, 1995).In most provinces, rural roads are virtuallynonexistent or are of very low quality. Provin-cial level simulation of passenger vehicle


FIGURE 8Population Scenarios

Year

Mill

ions

of P

eopl

e

1990 1995 2000 2005 2010 2015 2020 2025

1,150

1,200

1,250

1,300

1,350

1,400

1,450

1,500

1,550

1,600

1,650

1,700

123

123

123

123

1

23

1

23

1

23

1

2

1 Base Case 2 Urban Migration 3 Declining National Avg.

ownership (highlighted in Figure 5) reflectsthese trends.

Given this backdrop, this article outlined avehicle transportation model for China at theprovincial level. Modifications over past stud-ies included new estimates of saturation level,income elasticity, and purchasing power. Inaddition, according to the knowledge of theauthors, this model was the first to evaluatevehicle ownership at the provincial level. The

TABLE 4Car Fuel Efficiency and Annual Travel Scenarios, 2025

Fuel Efficiency Scenario

Constant Increasing

Average Annual Carbon CarbonCar Travel Oil per Day Emissions Oil per Day Emissions(km/year) (mmbls) (MtC) (mmbls) (MtC)

10,000 429 16119 385 1447215,000 518 19452 452 1698020,000 607 22784 519 19489

Note: Income growth, population growth, small bus fleet share, annual travel, and fuelefficiency held to base case levels.

results are consistent with other country-wideestimates. Dargay and Gately (1999) esti-mated that by 2015 China will have 40 carsper thousand people, similar to the medium-growth scenario of Table 3. A report in theBeijing Review (1994) estimated that Chinacould reach 40 cars per thousand peopleby 2010, faster growth than estimated bythis study’s high growth scenario of 33 carsper thousand people by 2010. A tangential


analysis to our own by Zhongyuan et al.(2002) estimated that by 2020 China willhave between 71.6 and 99.46 million cars(measured as “automobiles”). Conrad et al.(1998) assume that as a percentage of GDP,by 2025 the transport sector will increaseonly slightly, from 5.6% to 6%. However,based on assumptions about declining energyintensities and continued economic growth,their model estimates oil consumption forthe transportation sector to increase from0.53 mbbls/day in 1995 to 2.8 mbbls/day by2015 and 4.8 mbbls/day by 2025. The presentresults suggest that economy-wide models(such as Conrad et al.’s) may actually under-estimate future oil import requirements.

This study also picked up where otherson Chinese vehicle ownership have left off.Rapid growth in passenger vehicles in Chinahas implications for domestic air quality,oil import requirements, and internationalefforts to limit greenhouse gas emissions. Theresults of this model suggest a substantialincrease in the number of passenger vehiclesas Chinese income increases. However, vehi-cle and car ownership levels do not approachthe levels experienced in other countries.The argument that China has the “right” todevelop as others have is most often used indiscussions over international environmentalagreements. Table 5 summarizes 1995 oilconsumption for China, Republic of Korea,Japan, Taiwan, and the United States forthe transport sector. The Chinese consumedapproximately 0.68 mbbls/day for transport(all types), compared with 7.7 mbbls/day inthe United States. The average Japanese

TABLE 5Select Asian Nations versus United States

Oil Consumption in the TransportSector, 1995

Per Capita OilOil Consumption Consumption(1000 bbls/day) (bbls/year)

China 680 02Republic 163 13

of KoreaJapan 765 22Taiwan 137 23United States 7,789 108

Source: Drennen and Kobos, 1998.

citizen consumed 11 times what the aver-age Chinese citizen did in 1995; the averageU.S. citizen consumed 54 times as much. Thehigh-income growth scenario suggests thatChinese oil demand from increased vehicleownership could reach 2.7 mbbls/day for carsand 6.2 mbbls/day for total passenger vehi-cles by 2025. Though it is a large number, itis still well below 1995 U.S. consumption fortransportation.

However, these dramatic differencesbetween China and developed nations, suchas the United States, do not negate the factthat small changes in per capita consumptionin China can bring about large aggregateincreases in terms of energy use and car-bon emissions. Drennen (2000) estimatestotal carbon emissions in China as high as2,677 MtC by 2020. The total carbon emis-sions from passenger vehicles under the high-growth scenario in this article reach nearly189 MtC by 2020, or about 7% of total emis-sions. At the sectoral level, transportationin China for 1991 emitted only 24.5 MtC,or 3.7% of the national total carbon emis-sions from energy use (Lin and Polenske,1998). For perspective, the emissions fromthe transportation sector in the United Statesfor 1997 were 473 MtC, accounting for 32%of carbon emissions from all energy use(Energy Information Administration, 1999).

Clearly, China’s growing contribution toglobal greenhouse gas emissions is not largelydue to its transportation sector. In 1990, forexample, due to the coal-dominant structureof energy consumption in China, 83.4% oftotal carbon emitted was from coal. Totaloil use made up only 15% of 1990 carbonemissions (Zhang, 1998). However, alterna-tive transportation development paths canhave significant domestic benefits to a grow-ing Chinese economy. Increases in passen-ger vehicle use in China will place seriousstrain on land use, urban air pollution (in par-ticular ground-level ozone), and regional oilrequirements. Urban air pollution, the mostserious short-term problem, may reach mon-umental proportions with an expanding carfleet in China. In Guangzhou, the capitalof the Guangdong province, ground-level airpollution from coal combustion has been sur-passed by motor vehicle emissions. Alreadyit is estimated that the “emissions fromChinese-made motor vehicles are at least ten


times above the Japanese and U.S. standardsfor all pollutants” (Lin and Polenske, 1998).

Modest improvements in fuel efficiency(perhaps through technology transfer) canreduce oil requirements and carbon emis-sions, however, only by 12.7% over ano-improvement scenario. Annual travelreductions (perhaps through public transportexpansion) have a larger and more imme-diate effect but would require a reversal intrends that higher personal incomes tend tostrengthen. As is the case in neighboringAsian economies, the income effect on vehi-cle demand and use is perhaps the singlemost overwhelming variable. Lower incomegrowth rates and weaker income elasticitieshave the most substantial effect on amelio-rating growing personal vehicle ownership.However, to the contrary, with China’s recentaccession to the World Trade Organizationwill likely follow greater opportunities forpersonal income growth, more mass mediapressure on consumer preferences for per-sonal car ownership, reduced prices on for-eign auto imports, and broader access to for-eign capital for personal auto loans.

In conclusion, China’s strong macroeco-nomic performance, increasing currency pur-chasing power, and rapidly evolving con-sumer tastes for automobiles all point towarda clear need for an integrated transportationpolicy with broad implications for domes-tic air quality, oil import requirements, andinternational efforts to limit greenhouse gasemissions. This article presents a modeldeveloped with the policy maker in mind. Byallowing the user to change key assumptionsabout the Chinese market at the provinciallevel, it offers a tool for helping policy makersunderstand both the implications of increasedcar use and the opportunities for limiting theenvironmental and energy security problemsassociated with the personal transport sector.

REFERENCES

American Automobile Manufacturers Association. WorldMotor Vehicle Data. Detroit, MI: AAMA, 1994.

. World Motor Vehicle Data. Detroit, MI: AAMA,1995.

Asiaweek. “Start Your Engines.” Asiaweek, February 24,1993, 53–65.

Beijing Review. “China’s Strategy for Development ofPrivate Cars.” Beijing Review, December 1994,11–16.

Bushell, C., ed. Jane’s Urban Transport Systems, 13th ed..Surrey, UK: Coulsdon, 1994.

Button, K., A. S. Fowkes, and A. D. Pearman. “Disag-gregate and Aggregate Car Ownership Forecastingin Great Britain.” Transportation Research Part A,14A, 1980, 263–73.

Button, K., N. Ngoe, and J. Hine. “Modeling VehicleOwnership and Use in Low Income Countries.”Journal of Transport Economics and Policy, 14A,1993, 51–67.

China Statistical Yearbook. State Statistical Bureau of thePeople’s Republic of China, 1990 through 2000 edi-tions.

Chow, G. C. “China’s Economic Reform and Policies atthe Beginning of the Twenty-First Century.” ChinaEconomic Review, 11, 2000, 427–31.

Conrad, S. H., T. E. Drennen, D. Engi, D. L. Harris,D. M. Jeppeson, and R. P. Thomas. Measuring theInfrastructure Dynamics of China—Water, Agricul-ture, Energy, and Greenhouse Gases. Albuquerque,NM: Sandia National Laboratories, 1998.

Dahl, C. “A Survey of Energy Demand Elasticities forthe Developing World.” Journal of Energy andDevelopment, 25, 1994, 1121–27.

Dargay, J., and D. Gately. “Vehicle Ownership to2015: Implications for Energy Use and Emissions.”Energy Policy, 25(14-15), 1997, 1121–27.

. “Income’s Effect on Car and Vehicle Own-ership, Worldwide: 1960–2015.” TransportationResearch Part A, 33, 1999, 101–38.

Drennen, T. E. China Energy and Greenhouse GasModel—Version 2.0. Albuquerque, NM: SandiaNational Laboratories, 2000.

Drennen, T. E., and J. D. Erickson. “Who Will FuelChina?” Science, 270(5), 1998, 1483.

Drennen, T., and P. Kobos. “Fueling China’s Cars:Growth in Passenger Vehicle Ownership, OilDemand and Carbon Emission,” in Proceedingsof the 19th Annual North American Conference ofthe International Association for Energy Economics.Albuquerque, NM, October 18–21, 1998.

Dunkerly, J., and I. Hoch. “Energy for Transport inDeveloping Countries.” Energy Journal, 8(3), 1987,57–72.

Energy Information Administration. Emissions of Green-house Gases in the United States in 1998.Washington, DC: Energy Information Administra-tion, 1999 (available online at www.eia.doe.gov).

Fortune Magazine. “China’s Car Guy.” Fortune,October 11, 1999, 238–46.

Ganshi, Z. “Urban Transport Problems in ChineseCities: Causes, Trends and Options.” World BankDiscussion Paper 352, 1995, 35–42.

Greene, D. L., and D. J. Santini. Transportationand Global Climate Change. Washington, DC:American Council for an Energy-Efficient Econ-omy, 1993.

Ho, M. S., D. W. Jorgenson, and D. H. Perkins. “EnergyUse and Air-Pollution Impacts of China’s Trans-portation Growth,” in Energizing China, edited byM. McElroy, C. Nielson, and P. Lydon. Newton,MA: Harvard University Committee on Environ-ment, 1998, 301–41.

Johannson, O., and L. Shipper. “Measuring the LongRun Fuel Demand of Cars: Separate Estimationsof Vehicle Stock, Mean Fuel Intensity, and MeanAnnual Driving Distances.” Journal of TransportEconomics and Policy, September 1997, 277–92.


Keidel, A. “China’s GDP Expenditure Accounts.” ChinaEconomic Review, 12, 2001, 355–67.

Kobos, P. H. Growth in Passenger Vehicle Ownership inChina: Oil Demand, Carbon Emissions and Pol-icy Implications. Thesis, Department of Economics,Rensselaer Polytechnic Institute, Troy, NY, 2000.

Lin, X. J., and K. R. Polenske. “Energy Use andAir-Pollution Impacts of China’s Transporta-tion Growth,” in Energizing China, edited byM. McElroy, C. Nielson, and P. Lydon. Newton,MA: Harvard University Committee on Environ-ment, 1998, 201–38.

Liu, G. S., and W. T. Woo. “How Will Ownership inChina’s Industrial Sector Evolve with WTO Acces-sion?” China Economic Review, 12, 2001, 137–61.

Maddison, A. Monitoring the World Economy 1820–1992.Paris: OECD, 1995.

. Chinese Economic Performance in the Long Run.Paris: OECD, 1998.

Michaelis, L., D. Bleviss, J.-P. Orfeuil, R. Pischinger,J. Crayston, O. Davidson, T. Kram, N. Nakicenovic,and L. Schipper. “Mitigation Options in the Trans-portation Sector,” in Second Assessment Report—Working Group II. Intergovernmental Panel on Cli-mate Change, 1995, chap. 21.

Oxford Economic Forecasting. “China: Implications ofWTO Entry.” Oxford Economic Review, August1999.

Rashid, A., and T. Saywell. “Beijing Gusher.” Far EasternEconomic Review, February 26, 1998.

Ruoen, R. China’s Economic Performance in an Interna-tional Perspective. Paris: OECD, 1997.

Sinton, J. E., ed. China Energy Databook. LBL-32822Rev. 4, Lawrence Berkeley National Laboratory,1996.

Stares, S., and L. Zhi. “Motorization in Chinese Cities:China’s Urban Transport Development Strategy.”World Bank Discussion Paper 352, 1995, 43–104.

Tanner, J. C. “Forecasts of Future Numbers of Vehiclesin Great Britain.” Roads and Road Construction, 40,1962, 263–74.

. “Long-Term Forecasting of Vehicle Ownershipand Road Traffic.” Journal of the Royal StatisticalSociety, 141(1), 1978, 14–63.

Walls, W. D. “Vehicle Controls and Petrol Demand inHong Kong.” Journal of Energy and Development,21(2), 1997, 217–28.

World Resources Institute. World Resources 1998–1999. Washington, DC: World Resources Institute,1998.

Yung, J., P. S. Meyer, and J. Ausubel. Loglet Lab—Version 1.1.4. New York, NY: Rockefeller Univer-sity, Program for the Human Environment, 1998.

Zhang, Z. The Economics of Energy Policy in China:Implications for Global Climate Change. Chel-tenham, UK: Edward Elgar, 1998.

Zhongyuan, S., K. Ito, and J. Zhidong. “Outlook forChina’s Motorization and Energy Consumption.”Research Paper, Institute of Energy Economics,Japan, 2002.

SCENARIO ANALYSIS OF CHINESE PASSENGER VEHICLE … · Contemporary Economic Policy (ISSN 1074-3529)...

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