Modeling Future Vehicle Sales and Stock in China

Energy Policy 43 (2012) 17–29

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Energy Policy

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Modeling future vehicle sales and stock in China

Hong Huo a,b,n, Michael Wang c a Institute of Energy, Environment and Economy, Tsinghua University, Beijing 100084, China b China Automotive Energy Research Center (CAERC), Tsinghua University, Beijing 100084, China c Center for Transportation Research, Argonne National Laboratory, 9700 South Cass Ave., Argonne, IL 60439, USA article info abstract

Article history: This article presents an updated and upgraded methodology, the Fuel Economy and Environmental Received 10 February 2011 Impacts (FEEI) model (http://www.feeimodel.org/), to project vehicle sales and stock in China on the Accepted 26 September 2011 basis of our previous studies. The methodology presented has the following major improvements: it Available online 24 October 2011 simulates private car ownership on an income-level basis, takes into account car purchase prices, Keywords: separates sales into purchases for ﬂeet growth and for replacements of scrapped vehicles, and examines Vehicle stock various possible vehicle scrappage patterns for China. The results show that the sales of private light- Vehicle projection duty passenger vehicles in China could reach 23–42 million by 2050, with the share of new-growth China purchases representing 16–28%. The total vehicle stock may be 530–623 million by 2050. We compare this study to other publicly available studies in terms of both projection methodology and results. A sensitivity analysis shows that vehicle sales are more affected than levels of vehicle stock by the model parameters, which makes projecting sales more difﬁcult owing to the lack of reliable input data for key model parameters. Because it considers key factors in detail, the sales and stock projection module of the FEEI model offers many advantages over previous models and is capable of simulating various policy scenarios. & 2011 Elsevier Ltd. All rights reserved.

1. Brief background for this series of articles motor-vehicle growth (Wang et al., 2006; Huo et al., 2007). However, as the energy and environmental issues associated with

The increasing energy use and CO2 emissions associated with China’s on-road transport are becoming more challenging, many on-road vehicles have become a major challenge in China, as the policies other than fuel-economy standards need to be included in Chinese motor-vehicle ﬂeet is experiencing tremendous growth. the policy portfolio; therefore, a new, expanded model is needed It is important to understand the energy demand and environ- to meet the new requirements. mental impacts of Chinese on-road vehicles, as well as the effects The FEEI model improves both the methodology and data of potential policies to deal with Chinese transportation energy signiﬁcantly over our previous studies with respect to vehicle sales and environmental challenges. For this purpose, the Fuel Econ- and stock projection, vehicle use, vehicle fuel-consumption rates, omy and Environmental Impacts (FEEI) model has been developed and policy options for evaluation. The FEEI model development and to project future vehicle sales and stock levels, vehicle use, and applications are presented here in detail through four articles on-road energy demand and CO2 emissions in China up to the published in this issue. The present article introduces the metho- year 2050. With these key elements projected, the FEEI model dology of projecting vehicle sales and stock levels; the second enables the evaluation of various policy options for alternative describes the work we have done on determining current vehicle

Chinese vehicle growth and for energy and CO2 effects. The FEEI use and projecting future levels (Huo et al., this issue-a); the third model can be accessed at http://www.feeimodel.org/. explores the real-world fuel-consumption rates of Chinese vehicles Similar work done previously by the same authors has helped (Huo et al., this issue-b); and the fourth estimates the future in the implementation and evaluation of the ﬁrst mandatory energy demand and CO2 emissions of Chinese motor vehicles and national fuel-consumption standards for passenger vehicles in provides a quantitative evaluation of the effects of potential policy China (the Passenger Vehicle Fuel Consumption Limits, GB19578– options in China (Huo et al., this issue-c). 2004; He et al., 2005) and in establishing a trajectory of Chinese

2. Introduction

n Corresponding author at: Institute of Energy, Environment and Economy, Tsinghua University, Beijing 100084, China. Projecting vehicle markets and vehicle stock levels in China E-mail address: [email protected] (H. Huo). is of great interest not only to private industry but also to

0301-4215/$ - see front matter & 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.enpol.2011.09.063 18 H. Huo, M. Wang / Energy Policy 43 (2012) 17–29 policy-makers and researchers who are dealing with the potential growth trend in the developed countries and employed the energy demand and environmental impacts that are resulting Gompertz curve – an S-shaped growth curve that relates per- from the tremendous growth of the number of vehicles in China, capita vehicle ownership to per-capita GDP – to project the where on-road vehicles have become one of the largest oil vehicle stock in China through 2050, and then back-calculated consumers and air-pollution sources. vehicle sales from the projected vehicle stock and survival rates Vehicle stock level is a key factor in determining the total (Wang et al., 2006; Huo et al., 2007). The experiences in a number amount of energy demand and air-pollutant emissions of the of developed countries show that the Gompertz curve is able to transport sector. Also, the dynamics of vehicle-fleet turnover, represent the growth trend of vehicle stock, but back-calculating which describes the number of new vehicles coming into the fleet sales makes the methodology somewhat inaccurate in modeling and the number of vehicles retired from the fleet as well as the the dynamic turnover of the fleet. Some studies started with model-year (MY) distribution of in-fleet vehicles each year, is an projecting sales in China by assuming that the annual growth essential variable for assessing the energy and environmental rates of vehicle sales would gradually decrease until 2050, and effects of vehicle fleets. This is especially true since certain then calculated vehicle stock from the sales and survival rates of policies (e.g., fuel-economy standards, emission-control stan- vehicles (Ou et al., 2010; Yan and Crookes, 2009), but this sales- dards, and economic measures to encourage the market penetra- projection approach was based on little evidence and thus could tion of high-efficiency and low-emission vehicle technologies have caused unpredictable uncertainties. While these recent such as electric-drive technologies) differentially affect vehicles studies have accumulated valuable primary data on vehicle stock of different vintages. Vehicle sales and survival rates (defined as and sales, they left some major issues unaddressed: the proportion of vehicles of a given MY still in operation at a given age) are the key parameters used to simulate the dynamics (1) The dynamic turnover of the fleet was not modeled explicitly; of vehicle-fleet turnover. (2) per-capita GDP used in these projections was the national Long-term vehicle-stock forecasting has a long history in average per-capita GDP, which ignored the potential differ- developed countries, starting from as early as the 1930s. As ences in elasticity at different levels of per-capita GDP across reviewed by De Jong et al. (2004), a wide range of econometric regions in China, and thus may create uncertainties; and and empirical methodologies have been developed on the basis of (3) the influence of vehicle price on vehicle ownership was not an abundance of available time-series statistical data and surveys taken into account quantitatively. in major developed economies, such as the U.S. and European countries. It is commonly acknowledged that the methodology On the basis of our previous work (He et al., 2005; Wang et al.; chosen for vehicle-stock projection depends upon the availability 2006; Huo et al., 2007), together with the new data and evidence of data, so one practical barrier in China to applying some of the that we have collected, we have established an improved meth- mature methodologies is the difficulty of acquiring the necessary odology that attempts to address the issues stated above. The new data. The poor data availability and the short data-accumulation methodology is a key feature of the FEEI model. period in China only allow for the use of simpler methods. Nevertheless, a few studies have attempted to project vehicle- stock levels and sales in China using limited data. In earlier work, 3. Vehicle classification vehicle stock in China was projected using a simple economic– elasticity method on the basis of historical data on per-capita The vehicle classification in China’s statistical databases is not vehicle ownership and per-capita GDP (Wang and He, 2000; He internally consistent over time. Chinese official statistics (e.g., the et al., 2005; Wang et al., 2007). This approach is somewhat Chinese Statistical Yearbooks and local statistics released by acceptable for short-term projections but could be problematic official authorities) classify vehicles into freight vehicles (trucks) if applied for projecting long-term trends, because the history of and passenger vehicles (buses and cars). Starting in 2002, the vehicle-stock growth in China may not be long enough to allow former classification was further categorized into heavy-duty extrapolation. One of our recent studies used the historical trucks, medium-duty trucks, light-duty trucks, and mini trucks;

Table 1 Vehicle sales and stock by type in China (millions).

Stock a Salesb

Private LDVs Commercial LDVs Buses Trucks Private LDVsc Commercial LDVs Buses Trucks

1980 0.01 0.05 0.01 0.17 1985 0.06 0.23 0.04 0.34 1990 0.04 0.09 0.05 0.33 1995 0.33 0.43 0.06 0.61 2000 0.77 0.52 0.10 0.77 2002 5.8 4.4 1.8 8.1 1.42 0.67 0.18 1.10 2003 8.0 4.9 1.9 8.5 2.26 0.88 0.16 1.21 2004 10.2 5.2 2.0 8.9 2.65 0.83 0.18 1.12 2005 13.3 5.9 2.1 9.6 3.14 0.75 0.18 1.16 2006 17.6 6.4 2.2 9.9 4.21 1.48 0.20 1.32 2007 22.5 7.1 2.3 10.5 5.49 1.53 0.25 1.52 2008 28.1 7.8 2.4 11.3 6.27 1.14 0.25 1.64 2009 38.1 8.5 2.5 13.7 8.46 2.62 0.27 2.25

a From the State Statistical Bureau of China (2005–2010), which has reported vehicle stock by sub-categories since 2002. b Vehicle sales data for LDVs, buses and trucks between 1992 and 2009 are from the China Automotive Industry Yearbooks (China Automotive Technology and Research Center et al., 1991, 1993–2010) and related automobile-industry statistics. Data for sales before 1992 are not available; sales during this period were estimated on the basis of production and imported and exported volume. See Wang et al. (2006) for details. c Sales of private LDVs are estimated from private-LDV-stock data and survival rates (Wang et al., 2006). H. Huo, M. Wang / Energy Policy 43 (2012) 17–29 19 and the latter into large-size buses, medium-size buses, light- GDP (Dyckman, 1965; Romilly et al., 1998; Koopman, 1995). duty passenger vehicles, and mini passenger vehicles. Cars are Table 2 presents the per-capita GDP and per-capita disposal income categorized as a type of light-duty passenger vehicle (or mini in China from 2000 through 2009. The effect of income on car passenger vehicle). ownership in China can be derived from surveys, conducted by Automotive-industry statistics (e.g., the China Automotive China’s Statistical Bureau, of Chineseurbanfamilieswithdifferent Industry Yearbooks published by the China Automotive Technol- income levels who own cars (State Statistical Bureau of China, 2005– ogy and Research Center and Chinese Automotive Manufacturers 2010), as Fig. 1 shows. Not surprisingly, people with higher income Association) initially used size- and weight-based classifications are more likely to possess cars than those with lower income. As the that were roughly the same as those of the official statistics economy develops, rising income is the initial impetus for the except that they separated cars from light-duty buses and mini increase in car ownership. Another observation from Fig. 1 is that buses when reporting production and sales volume (but not for there is a vertical shift between the curves for adjacent years. As we total stock). In 2005, the automotive-industry statistics started to will show later, this shift is primarily caused by changes in car prices. employ a new use-based classification method, which classifies The price of cars has been decreasing since China entered the vehicles into passenger vehicles (passenger cars, minivans with World Trade Organization, and as a result a significant increase in fewer than nine seats, and sport-utility vehicles, all defined as M1 car sales has occurred concurrently in China. We investigated the vehicles in Chinese regulations, which are similar to the European prices of hundreds of vehicle models during recent years. Accord- classification) and commercial vehicles (passenger vehicles with ing to our investigation, for the same car model with the same nine seats and up and all trucks). But the new classification is configuration, and taking the price inflation for the whole econ- somewhat confusing because, in concept, passenger vehicles and omy into account, 2005 MY cars were 20.4% cheaper than 2004 commercial vehicles are not mutually exclusive categories; for instance, taxis are both passenger vehicles and commercial 160 vehicles. Furthermore, with the new classification, the data in the industry statistics are not easily comparable with those of the 140 official statistics. In this study, we classify vehicles in China into four groups 120 based on ownership (private or fleet-owned) and purpose (passenger or freight), owing to their different growth potentials. 100 These groups are (1) private light-duty passenger vehicles (pri- 80 vate M1 vehicles, called private LDVs hereafter); (2) commercial light-duty passenger vehicles (M1 vehicles, including taxis, that 60 2009 are owned by companies and government bodies); (3) commercial 2008 buses (large- and medium-size buses); and (4) commercial trucks 40 2007 2006 (all trucks used for freight transportation). Note that although a Private LDV onwership/1000 people 20 proportion of trucks and large buses are owned by individuals as 2005 2004 private property in China, most of them are operated for com- 0 mercial activities. Using the available statistics and a few adjust- 0 10000 20000 30000 40000 50000 60000 ments, we derive the vehicle sales and stock levels in China Income per capita (2009 RMB) between 1980 and 2009, as presented in Table 1. Fig. 1. Vehicle ownership per 1000 people versus per-capita disposal income in China: growth trend for 2004–2009. Note: The State Statistical Bureau of China (2005–2010) conducted annual surveys on car ownership by Chinese urban 4. Methodology and data families with different income levels. About 60,000 families throughout the country were surveyed every year. These surveys classify families into eight 4.1. Private LDVs groups according to their household income. The average household disposal income and average car ownership per 100 households for each income group were reported; these data were converted to per-capita disposal income and car It is well recognized that income is a primary driving force for ownership per 1000 people in the present study, as shown here. the growth of private car ownership (Dargay, 2001). In order to interpret the vehicle demand explicitly at a personal (or household) level, earlier studies worldwide used per-capita income (or 160 per-capita disposable income) more intensively than per-capita 140

Table 2 120 Historical data on per-capita GDP and per-capita disposal income in China (based on the 2009 value of the Chinese RMB). 100 Source: State Statistical Bureau of China (2005–2010). 80 Per-capita Per-capita GDP 2009 disposal income 60 2008 2007 2000 7866 11,044 40 2001 8076 11,873 2006 Private LDV onwership/1000 people 2002 8168 12,865 20 2005 2003 8411 14,068 2004 2004 8858 15,395 0 2005 9205 17,035 0 20000 40000 60000 80000 100000 120000 2006 9472 19,089 Income per capita (2009 RMB)/vehicle price index 2007 10,041 21,677 2008 10,717 23,644 Fig. 2. Vehicle ownership per 1000 people versus per-capita disposal income/car 2009 10,754 25,575 price index in China: growth trend for 2004–2009. 20 H. Huo, M. Wang / Energy Policy 43 (2012) 17–29

MY cars, 2006 cars were 15.3% cheaper than 2005 cars, and 2007 (Jørgensen and Wentzel-Larsen, 1990), especially in emerging cars were 14.7% cheaper than 2006 cars. In this study, we economics such as China (Souma, 2001). Fig. 3 presents a log introduce the term ‘‘vehicle price index’’ (VPI), which is the price normal distribution function f(x), where the x-axis represents the ratio of cars in a given year to those of the same models in 2004 level of income per capita and the y-axis represents the percen- (VPI of 2004 MY cars¼1). We use the quotient of income and VPI tage of the population. The function can be described as to represent the vehicle purchasing power for a given income; 1 2 2 plotting the ownership data of Fig. 1 against this quotient yields f ðxÞ¼ pffiffiffiffiffiffi eðln ðx=x0Þ=2s Þ ð4Þ Fig. 2. A clear and consistent relationship is observed between car 2psx ownership and income-specific vehicle purchasing power. where x is income per capita; x0 is a function parameter that is Obviously, income coupled with car price correlates to car own- related to the mean value of the function; and s is another ership much better than per-capita income alone. Use of vehicle function parameter that determines the shape of the curve. purchasing power instead of income is especially important in The Lorenz curve g(x)(asshowninFig. 4) is like an accumulative economies where vehicle prices, incomes, and inflation (or defla- function of the income distribution function f(x). The Lorenz curve can tion) have great disparities, which is the case in China and other generate the Gini index, which is commonly used to evaluate the emerging economies. income inequality of regions. In this study, we use the Gini values to Therefore, the total car stock can be calculated using Eq. (1): determine the s value in f(x), as expressed by Equation Set (5). Each Z 1 Gini value will generate one and only one corresponding s. Car_Stocki ¼ TPi ½f ðxÞisðx,yiÞdx ð1Þ 8 R x ¼ 0 > xi > yi ¼ TPi x ¼ 0 f ðxÞdx <> R where Car_Stocki represents the total car stock in year i; x gðy Þ¼TP xi ½f ðxÞxdx i i x ¼R0 ð5Þ represents per-capita income; f(x)i is the income distribution > TP > i gðyÞdy > ¼ function for year i; yi represents the value of VPI in year i; s(x, : Gini ¼ 12 y 0 TIiTPi yi) is the function of vehicle ownership per 1000 people vs. per- capita disposal income (‘‘per-capita income’’ for short hereafter) where Gini represents the Gini value; xi represents a random point in and VPI; and TPi is the total population in year i. People purchase new cars primarily for two reasons: to own one or more cars as their income rises (new-growth purchase) or to replace old cars they already own (replacement purchase; De Wolff, 1938). The number of new-car purchases motivated by the N first reason in year i (Car_Salesi ) is the accumulation of differ- ential new purchases as the mean income increases from mi1 (mean per-capita income of year i1) to mi (mean per-capita Total Income Comparison curve income of year i), as expressed in Eq. (2), and those motivated by R the second reason (Car_Salesi ) can be calculated on the basis of Lorenz curve the car sales of the years prior to year i (Car_Sales) and the A survival function r(x), expressed as Eq. (3): Z Z B mi 1 ( ) i N 0 g ni Car_Salesi ¼ TPi ½f ðxÞisðx,yiÞdxdm ð2Þ mi1 x ¼ 0 ni X30 Total Population R Car_Salesi ¼ fCar_Salesij½rðj1ÞrðjÞg ð3Þ j ¼ 1 Fig. 4. An illustration of the Lorenz curve. With Eqs. (1)–(3), private car stock levels and vehicle sales can be calculated. In the rest of this section, we present methods for Table 3 obtaining the key functions and parameters in Eqs. (1)–(3). Projection of population, per-capita income and the Gini index in China.

a 4.1.1. Income distribution function f(x) Population Per-capita Gini (billions) income Indexc Many studies have shown that the relationship of population (2009 RMB)b distribution (in percentage) to income per capita can be described by a log normal distribution function in various economies 2000 1.27 7866 0.46 2005 1.31 9205 0.47 2009 1.33 10,754 0.48 2010 1.34 12,258 0.49 2020 1.44 27,347 0.45 f(m ) 2030 1.47 46,670 0.40 i Log Normal Distribution 2040 1.47 65,052 0.38 i 2050 1.46 81,931 0.35

a Future population is projected by the Energy Research Institute of the National Development and Reform Commission of China (2009). b Per-capita income is projected on the basis of the current Chinese government’s plan of reaching the per-capita income level of ‘‘medium-developed’’ countries by 2050 (10,000–15,000 U.S. dollars per capita) and the projections by

Population distribution (%) the Development Research Center of the State Council in China (Li et al., 2010). c The Gini Index in China was reported to be 0.456 in 1999 (Wang, 2004), mi 0.469 in 2007 (United Nations Development Programme, 2008), and 0.48 in 2008 Income per capita (Development Research Center of the State Council, 2010). Future Gini Index values are projected according to studies by the Institute of Sociology, Chinese Fig. 3. An illustration of the income distribution function. Academy of Social Sciences (2003) and Lu (2007). H. Huo, M. Wang / Energy Policy 43 (2012) 17–29 21

0.35% 250 Historical data 0.30% 2010 Gompertz 2020 0.25% Logistic 2030 Richards, n=0.1 0.20% 2040 200 2050 0.15%

0.10% Share of population 150 0.05%

0.00% 0 50000 100000 150000 200000 250000 300000 350000 Income level (2008 RMB) 100

Fig. 5. Income distributions in China from 2010 to 2050. Vehicle/1000 people

f(x); TIi represents total income in year i; and other variables are as 50 defined above. With total population, total income, and the Gini index, Equation Set (5) can be solved analytically by a computer program. Necessary parameters, including population, per-capita 0 income, and the Gini index in China through 2050, are obtained 0 20000 40000 60000 80000 100000 120000 140000 on the basis of previous socio-economic studies accomplished by Income per household (2009 RMB)/price index key organizations in China, as presented in Table 3. Fig. 5 depicts Fig. 6. Multiple functions fit to car-ownership data. the variation of the income distribution in China from 2000 to 2050. 1 0.9 4.1.2. Function of car ownership s(x,y) Projection for China, 2004 price=1 0.8 The new finding from Fig. 2 is used to establish the car Historical data of the U.S. 1906 price=1 0.7 ownership model, with car ownership as a function of per-capita income and car price. Many other factors could also influence car 0.6 ownership. Such factors include fuel prices, urban structure, and 0.5 public-transportation availability, but empirical data for these 0.4

factors in China are too scarce to be assessed, so they are not Vehicl price index 0.3 included in the FEEI model. 0.2 The growth of car ownership in response to economic factors 0.1 (e.g. per-capita GDP or per-capita income) is believed to follow an 0 S-shaped curve, with three phases: a slow-growth period in the China: 2004 2014 2024 2034 2044 2054 beginning (when income levels are low), a boom period, and a US: 1906 1916 1926 1936 1946 1956 saturated period (when car growth approaches a saturation level). Previous studies have employed many sigmoid functions to Fig. 7. Projected vehicle price indexes for China versus historical data of the U.S. simulate the growth of car ownership, such as the modified logistic function (Button et al., 1993; Ingram and Liu, 1997) and the Gompertz function (Dargay and Gately, 1999; Zachariadis Determining the ultimate saturation level of car ownership a is et al., 1995). One of our previous studies also used the Gompertz a crucial component in projecting vehicle stock. Many developed function (Wang et al., 2006). We here examine three growth countries have reached the saturation point of car ownership; for functions, i.e., the Gompertz function, the logistic function, and instance, it is 800 per 1000 people (including cars and light-duty the Richards function, shown in Eqs. (6)–(8). trucks) in the U.S., 450–600 in European countries, and 440 in cx Gompertz Function : y ¼ aeðbe Þ ð6Þ Japan (Davis, et al., 2009; European Commission, 2003–2009; Statistics Bureau of Japan, 2007). According to the population a Logistic Function : y ¼ ð7Þ density and potential future urban development patterns in 1þeðbcxÞ China, we assume two scenarios for the growth of private car a ownership in China, a low-growth scenario and a high-growth Richards Function : y ¼ ð8Þ scenario, in which the saturation level of private car ownership ½1þeðbcxÞ1=N per 1000 people is 400 and 500, respectively. where y represents car ownership; x represents an economic Car price is difficult to predict, since it is subject to many indicator, here per-capita income over VPI; a represents the factors, such as the prices of materials used to make cars (steel, ultimate saturation level of vehicle ownership; and b and c are rubber, etc.) and technology changes. In China, cars are more two parameters that determine the shape of the S-curve. N is a expensive than those in developed countries with the same parameter of the Richards Function; if N¼1, the Richards function performance and quality. However, the prices of cars in China will become the logistic function, and if N tends to zero, the are decreasing rapidly and they are expected to continue to fall in Richards function will approximate the Gompertz function. the foreseeable future, which is the trend observed in the early Fig. 6 plots the original Chinese data and fitted values for the motorization stage of developed countries (Davis et al., 2009). We three functions. The Gompertz function fits the original data assumed that car prices in China would drop gradually to a level better than the other two. comparable to that of developed countries during 2015–2020 and 22 H. Huo, M. Wang / Energy Policy 43 (2012) 17–29 then follow the same car-price variation trend as in the developed scrapped (see Fig. 8). In recent years, as more cars have been countries, as illustrated in Fig. 7. owned by private citizens, China has reconsidered the scrappage requirement for private LDVs to emphasize safety and performance rather than service period. As requested by the 4.1.3. Survival function r(x) Ministry of Communications of China, private LDVs would not The determination of vehicle survival rates normally requires be subjected to a mandatory scrappage period, but cars of age 15 substantive historical information about vehicle fleets. Because and older would undergo two inspections per year. Table 4 they have had a long period to accumulate registration informa- summarizes the draft mandatory standards for vehicle scrappage tion and survey data, developed countries have been able to (Ministry of Commerce of China, 2006). generate survival rates for their vehicle fleets. Yang et al. (2003) Recently, in order to remove inefficient, high-emissions vehi- generated the survival rates for LDVs in Beijing by using the total cles from the road and stimulate the automobile industry, many number of registered vehicles, newly registered vehicles, and countries worldwide have introduced government-subsidized scrapped vehicles. However, in most Chinese regions, information scrappage programs to accelerate the replacement of old cars such as the number of scrapped vehicles by vehicle type in each with modern ones (Wikipedia, 2010). These scrappage programs, year is not easy to obtain. though with different names, typically operate by offering a Fig. 8 presents the vehicle survival rates in Beijing, Japan, scrappage premium or tax rebate to car owners who scrap their Germany and the United States. According to the statistics in the old cars earlier than required. The U.S. and many European United States (Davis et al., 2009) and Japan (Takita, 2001), cars countries had large-scale scrappage programs as an economic from later MYs survive longer because of the improved technol- stimulus to increase market demand for cars during the global ogies and performance of newer car models. The variation in recession that began in 2008. Taking Germany as an example, survival patterns of cars across countries can be attributed mainly every owner of a car older than 9 years was entitled to a to the differences in the vehicle management and scrappage scrappage premium of 3320 USD when buying a new car. This requirements in different countries. For example, the life span program has successfully raised car sales by 40% in Germany of cars in Japan is shorter because Japan has very strict inspection (Wikipedia, 2010). The United States Congress devised a scrap- requirements for cars, which make it more cost-effective to page program, referred to as ‘‘cash for clunkers,’’ under which purchase a new car than to keep an old one. China has mandatory consumers could trade in their old, ‘‘gas-guzzling’’ vehicles and scrappage standards for all vehicle types under which vehicles are receive vouchers worth up to $4500 to help pay for new, more required to be scrapped when they reach a given age or mileage fuel-efficient cars and light-duty trucks (U.S. House of (e.g., 15 years for cars), and this factor causes a sudden decline in Representatives, 2009). Japan introduced a program in 2009 the survival-rate curve around the age when vehicles must be offering up to 2500 USD for car owners to trade in vehicles 13 years old or older for new, environmentally friendly, fuel-efficient 100 Japan-General Cars, 2004 cars (Wikipedia, 2010). China is also making efforts to speed up 90 Japan-Small Cars, 2004 retirement of old cars. In 2009, a nationwide scrappage program US - Average was implemented, offering rebates of 450–900 USD for trading in 80 US - 1970 MY cars old, heavily polluting cars and light-duty trucks (known as US - 1980s MY cars 70 yellow-labeled vehicles) that could not meet the ‘‘Euro I’’ emis- US - 1990s MY cars sion standards for new ones, and the rebates were doubled 60 Germany, 2005 (tripled for some vehicle types) in 2010 (Ministry of Commerce China, 2003 of China et al., 2009, 2010). While these incentive programs affect 50 car scrappage in the near term, they may not affect vehicle 40 scrappage, and thus vehicle survival rates, in the long run. In the future, cars will last longer in China as technologies 30 improve, but also will be scrapped sooner if the government takes 20 strong measures to accelerate car retirement to promote high- efficiency cars and stimulate the automobile industry. In the FEEI 10 model, we considered four possible survival patterns in China for impacts on car sales, fleet technology distribution, and the overall 0 0 5 10 15 20 25 30 35 energy demand and emissions of cars: (1) the China pattern: the survival rates will be unchanged until 2050; (2) the Japan pattern: Fig. 8. Comparison of vehicle survival rates in different countries. China will take strong actions, including both strict inspection Sources: Data for Japan are from the Japanese Automobile Inspection and requirements and scrappage incentives, such that survival rates of Registration Association (2006), for the U.S. from Davis et al. (2009), for Germany from the TREMOD model developed by the Institute for Energy and Environmental Chinese cars in 2050 will be the same as the current level in Research Heidelberg (2010), and for China from Yang et al. (2003). Japan; (3) the Europe pattern: China will introduce moderate

Table 4 Mandatory standards for motor vehicle scrappage proposed by the Ministry of Commerce of China (2006) (Partial).

Category Vehicle type Scrappage mileage (km) Scrappage period (yr)

Not-for-revenue cars 600,000 Not requested For-revenue cars Taxis 600,000 8 Rental cars 500,000 10 Driving-school training cars 500,000 10 Other purposes 600,000 8 Urban public buses 400,000 13 Long-distance buses 600,000 15 Not-for-revenue buses 500,000 20 H. Huo, M. Wang / Energy Policy 43 (2012) 17–29 23

100% 1980-2000 1980-2050 2000-2020 80% 2020-2050 Japan 60%

40%

20%

0% 100% 1980-2000 1980-2000 2000-2020 80% 2000-2020 2020-2050 2020-2050 Germany US-1990 MY cars 60%

40%

20%

0% 0 5 10 15 20 25 30 0 5 10 15 20 25 30

Fig. 9. Scenarios for survival patterns of cars in China. (a) China Pattern, (b) Japan Pattern, (c) Europe Pattern and (d) US Pattern.

Table 5 of commercial cars out of the total car ﬂeet is less than 1% in Japan Values of T and b for four car scrappage patterns. (Statistics Bureau of Japan, 2007), 3.5% in Korea (Korea National Statistical Ofﬁce, 2005), and 4.5% in the U.S. (U.S. Energy 1980–2000 2000–2020 2020–2050 Information Administration, 2010). The Energy Research TbT bTb Institute (ERI) of the National Development and Reform Comm- ission of China (2009) estimated that the share would be 6% in China pattern 26 11 26 11 26 11 China in 2050. We assume that the share of commercial cars in Japan pattern 26 11 20 5 18 4 China will drop to 5% by 2050 for the low-growth scenario and to Europe pattern 26 11 23.5 8 21 5.5 U.S. pattern 26 11 24.5 6 23 3 4% for the high-growth scenarios.

4.3. Commercial buses and trucks economic incentives and the survival pattern by 2050 will be the same as that of Germany; and (4) the U.S. pattern: China will not Fig. 10 presents the ownership of commercial buses and trucks interfere in car retirement, and cars will last longer, gradually per 1000 people in China and other countries from 1970 to 2007. reaching the survival level of U.S. 1990s-MY cars by 2050. Fig. 9 The variations in ownership of buses and trucks in different illustrates the four survival scenarios. countries can probably be attributed to differences in patterns In the study by Zachariadis et al. (1995), survival rates were of commercial transportation. For instance, buses are more simulated using a Weibull distribution, which is deﬁned as popular than trains in Norway because the terrain in Norway follows: favors the construction of highways rather than railways. In "# contrast, people in Germany rely on trains more than on buses xþb b rðxÞ¼exp ð9Þ for travel, which is why Germany has much lower bus ownership T than Norway (1 bus per 1000 people in Germany versus 5 buses per 1000 people in Norway in 2007; European Commission, where T is a parameter associated with vehicle service life and b is 2003–2009). a parameter that affects the shape of the curve. Table 5 provides Obviously, the competition between highways and railways to the values of T and b for the four above-mentioned vehicle some extent inﬂuences the ownership of commercial vehicles. survival patterns. According to the ‘‘Mid- and Long-Term Development Plan of Railway Nets of China’’ developed by the Ministry of Railways of 4.2. Commercial LDVs China (2008) and the ‘‘Plan of National Highway Nets of China’’ developed by the Ministry of Communications of China (2004), Besides private LDVs, the present study considers the category both railways and highways will be major modes of transporting of commercial LDVs, i.e., government- and company-owned cars passengers and goods in China in the future. The growth of and taxis. The share of commercial cars in China has dramatically commercial buses and trucks in China will depend on the overall decreased in recent years as the number of private LDVs has level and pace of development of railway transportation versus increased. At present, commercial cars account for about 19% of highway transportation. In order to relate the railway capacity to total car stock in China (see Table 1). On the other hand, the share the ownership of commercial vehicles, Fig. 11 compares the ratios 24 H. Huo, M. Wang / Energy Policy 43 (2012) 17–29

9 China 3.0 8 Japan China Germany Denmark Germany Italy France UK 7 2.5 Norway Japan Australia UK Korea 6 France Italy 2.0 5 Australia 4 Norway 1.5 Denmark 3 1.0

2 of roads to railways

1 Ratio of passenger-kilometers 0.5 Bus ownership per 1000 people 0 1970 1975 1980 1985 1990 1995 2000 2005 0.0 19701980 1990 2000 2010

180 China 20 160 China Germany Korea 18 Denmark Italy 140 Japan 16 France UK UK Norway US France 14 Japan Australia 120 Korea Italy 12 100 Australia US 10 80 Germany 8 Norway 60 roads to railways 6

40 Ratio of ton-kilometers 4 20 2 Truck ownership per 1000 people 0 0 1980 1985 1990 1995 2000 2005 2010 1970 1975 1980 1985 1990 1995 2000 2005 Fig. 11. Ratios of transport volume of roads to railways in selected countries. Fig. 10. Bus and truck ownership per 1000 people in selected countries. (a) passenger–kilometers and (b) tonne–kilometers. Sources: State Statistical Bureau of China, 2005–2010; Korea National Statistical Office, Sources: State Statistical Bureau of China, 2005–2010; Korea National Statistical Office, 2005; Statistics Bureau of Japan, 2010; U.S. Federal Highway Administration, 1993– 2005; Statistics Bureau of Japan, 2010; U.S. Federal Highway Administration, 1993– 2009; European Commission, 2003–2009; Bureau of Infrastructure, Transport and 2009; European Commission, 2003–2009; Bureau of Infrastructure, Transport and Regional Economics of Australia, 2009. Regional Economics of Australia, 2009. of transport volume by roads to that by railways in some selected the U.S. Energy Information Administration (EIA) (2010), China’s countries. Apparently, countries with higher commercial-vehicle LDV sales will exceed those of the U.S. before 2016 under the low- ownership tend to have larger ratios. Besides, several developed growth scenario (or 2013 under the high-growth scenario). countries have observed a steady decline in the ratio of passen- As mentioned above, vehicle sales are divided into two groups, ger-kilometers of roads to railways. For China, the passenger- new-growth purchases and replacement purchases, the split kilometer ratio increased significantly from 1970 to 1995, then between which is a criterion for determining whether an auto became stable after 1995, and now is at roughly the same level as market is mature. The larger the share of replacement purchases, in Australia, Denmark, and the U.K. We assume that the passen- the more mature the auto market. For instance, as a mature auto ger-kilometer ratio will follow the same variation trend of these market, the U.S. has a share of replacement purchases of more developed countries and bus ownership in China (Fig. 10) will than 99% (derived from the VISION model; Singh et al., 2003). increase gradually to 3 per 1000 people (the average 2007 level of Because China is an emerging auto market, new-growth pur- Australia, Denmark, and the UK) by 2050. Similarly, from an chases will account for the dominant proportion in the near examination of the tonne-kilometer ratios in China and other future. As shown in Fig. 12, new-growth purchases are predicted countries, we assume that truck ownership in China will increase to peak around 2020, and then decline slowly. Replacement to 25 per 1000 people (the average 2007 level of the U.S., purchases will gradually capture the majority of the market Australia, and Germany) by 2050. beginning in 2030, indicating that by then the Chinese auto market will start to become mature. However, there is still new-growth purchase potential in China in 2050, representing 5. Results and analysis 16–28% of total sales, which implies that the Chinese auto market will not reach a complete maturity level even by 2050. 5.1. Sales of private LDVs Replacement purchases are subjected to scrappage schemes. Under the Japan scrappage pattern, cars are retired more quickly, Sales of Chinese passenger cars are expected to dramatically so more replacement sales occur than under other scrappage increase in the future, as shown in Fig. 12. Our study shows that patterns. Cars are retired slowly under the U.S. scrappage pattern, sales of private LDVs in China will reach 23–34 million and 29–42 which could make car sales in China saturated after 2035 (see million by 2050 under the low- and high-growth scenarios, Fig. 12). The difference in LDV sales between the Japan and U.S. respectively. According to our projection and the projection by patterns is 11.4–13.8 million a year in 2050. Note that the total H. Huo, M. Wang / Energy Policy 43 (2012) 17–29 25

50 New purchase High ownership scenario 45 China scrappage pattern a = 500 cars/1000 people Japan scrappage pattern Europe scrappage pattern 40 US scrappage pattern Private LDV sales in the U.S.(EIA,2010) 35 Projection of LDV sales for China by IEA (2009) Low ownership 30 scenario a = 400 / 1000 people 25 20 15

Private LDV sales (million) 10 5 0 2010 2020 2030 2040 2050 2010 2020 2030 2040 2050

Fig. 12. Annual sales of private LDVs in China under the two growth scenarios and the four scrappage patterns. Note: the IEA’s sale results include both private and commercial LDVs. As the number of commercial LDVs is small, the differences that might be caused can be neglected in the comparison. stock under these scrappage patterns is the same. Therefore, more 700 rapid retirement of cars can keep the auto industry growing This study, base year=2009 600 without increasing the burden on roads. High, a=500 Fig. 12 also provides the projection results from the Interna- Low, a=400 500 tional Energy Agency’s (IEA) Sustainable Mobility Project (SMP) Approach in Wang et al (2006) with the model (IEA, 2009) for comparison purposes. Although the FEEI base year updated from 2004 to 2009 High, Europe pattern model and the SMP model could result in equivalent sales levels 400 Medium, Japan pattern by 2050, their growth trajectories are different. Our study shows a Low, low growth rapid decrease in the annual growth rate of car sales from 15% to 300 0.1–0.8% (depending on the scrappage patterns) between 2010 LDVs stock (million) 200 and 2050, while it decreases from 6.8% to 4.4% during the same period in IEA’s SMP model. The principal reason for this observa- ERI, 2009 100 tion is that the IEA’s method simply assumes a moderate growth Ng and Schipper, 2006 rate for sales without further exploring the driving forces that Projection of US LDV stock (EIA, 2010) 0 influence the sales. The FEEI model starts by analyzing the 2000 2010 2020 2030 2040 2050 intrinsic factors that cause the growth of car sales (e.g., shift in income distribution, changes in car price, and scrappage Fig. 13. Stock of Private LDVs in China. schemes). Although the results of the IEA’s projection may be possible for China under certain assumptions, such as slower earlier. The projection by ERI (2009) did not account for the car income growth and unchanged car prices, the methodology itself price, so it shows a growth curve that is consistent with the results of is crude and may not be capable of examining the effects of the Wang et al. (2006) method for the medium-growth case. Ng and certain policies. Schipper (2006) used purchasing power parity (PPP) as the economic surrogate, which requires a reliable PPP estimation. It is difficult to 5.2. Stock of LDVs conduct a PPP projection for China owing to the many uncertainties. To date, PPP projections by Chinese academic societies or official Fig. 13 presents the stock of LDVs in China projected by the research institutes have rarely been made public. FEEI model and compares the results from several other studies. According to our projection, the stock of LDVs in China will 5.3. Stock of all vehicles in the fleet gradually increase to 490–580 million by 2050, and will exceed the U.S. level (EIA, 2010) by 2022–2024. Fig. 14 illustrates the projection of total stock and its break- The method in one of our previous studies (Wang et al., 2006) down in China. The total vehicle stock in China will be 530–623 also used the Gompertz function to simulate vehicle-ownership million by 2050, exceeding the U.S. vehicle stock by 2022–2024. growth, but it used the average per-capita GDP as the economic The percentage of LDVs will gradually increase from less than 80% surrogate, whereas the FEEI model uses both income and car price in 2010 to 93–94% in 2030, which is the current LDV percentage as the economic surrogate. The two methods show a fair agree- in developed countries. ment in the car-stock projection, but the major difference is that Fig. 15 compares the vehicle-stock projections made in various vehicle ownership grows faster during the first half-period with studies. As was the case for the car-stock projection (see Fig. 13), the FEEI model (the results for years 2010–2030 generated from the ERI’s projection of the total vehicle stock lies between the FEEI are higher than those from the method of Wang et al., 2006), results of the two scenarios addressed in the present study. The and the reverse is true during the second half-period (the method method applied in our previous study (Wang et al., 2006) generated of Wang et al., 2006 generates faster growth of car stock for 2030– higher vehicle-stock levels because it simulated the ownership of 2050). The reason is that taking the decline in car prices into both private LDVs and commercial vehicles using the Gompertz consideration makes the rapid growth of car ownership come function. However, unlike private LDVs, the ownership of commercial 26 H. Huo, M. Wang / Energy Policy 43 (2012) 17–29

700 with IEA’s study that uses 2000 as base year, and this is the major reason that IEA’s projection is lower than other studies. Wang et al. 600 (2011) generated higher vehicle stock result than this study, because 2010 2020 2030 2050 of the higher GDP growth assumed. 500 Table 6 provides vehicle sales and stock levels projected by the FEEI model. 400

300 Additional private LDVs if a=500 6. Sensitivity analysis Private LDVs if a=400 200 Commercial LDVs Trucks For a vehicle growth-projection model such as FEEI, it is

Total Vehicle Stock (million) Buses 100 US vehicle stock(EIA, 2010) important to examine the influence of the variation in major parameters on the modeling results so that the key areas that 0 require improvements can be identified, in particular, when some 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050 parameters (such as the Gini index) are very difficult to project Fig. 14. Projected vehicle stock and breakdown of vehicle types in China. and have large uncertainties. A sensitivity analysis is conducted here for this purpose. To do the analysis, we vary the vehicle price index, mean income and Gini index in Eqs. (1) and (2) separately 900 between 50% and 150% of their initial values in 2050. This study, High, a=500 Fig. 16 illustrates the magnitude of the change in private-car 800 This study, Low, a=400 stock and new-growth purchases per unit change in the three 700 Wang et al. (2006) Medium, base year=2004 parameters. Car stock levels increase as mean income increases, Wang et al. (2006) Medium, base year=2009 but decrease as car price or the Gini value increases. The 600 ERI (2009) magnitude of these changes will be smaller as time passes, which 500 Wang et al.(2011) Ou et al.(2010) indicates that the model will generate more accurate results for 400 IEA (2009) long-term projections. Also, the three parameters have equivalent influences on the stock projection (every 10% change in car price, 300 mean income, and Gini value will result in a change of 4.4%, 4%, 200 and 3.9% in the stock level, respectively). Therefore, all three Total Vehicle Stock (million) Vehicle Total parameters are equally important in projecting car stock levels. In 100 particular, the sensitivity of car stock level to car price shown in 0 Fig. 16(a) is consistent with the earlier finding with respect to the 2000 2010 2020 2030 2040 2050 effect of taking car price into consideration (FEEI method vs. Wang et al. (2006) method; see Fig. 13). Fig. 15. Comparison of vehicle-stock projections made in various studies. Unlike car stock levels, the sensitivity of new-growth purchases to the parameters depicted in Fig. 16 is not monotonic (decreasing or increasing). This result is due to the different Table 6 Projected vehicle sales and stock levels (millions). growth mechanisms for sales vs. stock levels; for example, income growth always means increasing vehicle ownership but 2010 2020 2030 2040 2050 does not necessarily mean increasing potential for new-growth purchases (see Fig. 12; as income grows, new-growth purchases Stocks in China are projected to peak during 2020, and then decline Private LDVs Low 45.1 168.2 335.2 419.9 464.9 High 49.6 188.3 390.3 498.6 557.7 slowly). As Fig. 16 shows, new-growth purchases are more Commercial LDVs 10.3 19.2 22.9 24.3 24.5 sensitive to the parameters than is vehicle stock, which means Trucks 14.6 19.0 25.2 31.0 36.5 that new-growth purchases are more easily affected by external Buses 2.6 3.1 3.6 4.0 4.4 variables; therefore, projecting new-growth purchases is more Total 73–77 209–230 387–442 479–558 530–623 difficult than projecting stock levels because more accurate input Sales data will be needed to obtain reliable projections of new-growth Private LDVs Low – 17–19 22–27 22–30 23–34 purchases. Of the three parameters considered, the mean income High – 19–22 26–32 27–37 29–42 has the strongest influence on new-growth purchases.

7. Discussion vehicles is not well correlated with income, as discussed above (see Fig. 10). Therefore, the use of income to estimate commercial-vehicle The FEEI-model work is an updating and upgrading of our ownership may cause overestimation. This observation also explains previous studies (He et al., 2005; Wang et al., 2006; Huo et al., whythemethodsusedinthepresentstudy(FEEImodel)andin 2007) with more factors accounted for. The new methodology has Wang et al. (2006) can generate similar car-stock levels by 2050, but achieved major improvements over previous methods by simu- fail to generate the same total-vehicle stock levels. Another point lating private-car ownership on an income-level basis, taking into demonstrated by Fig. 15 is the importance of data updating. Using the account car prices, separating sales into new-growth purchases same method with 2004 vs. 2009 as the base year could result in and replacements, examining four possible scrappage patterns for signiﬁcant differences in vehicle-stock projections. China is in a period China, and predicting the growth of commercial vehicles in a of rapid vehicle growth. In particular, since 2004, vehicle sales in more reasonable way. China have increased dramatically. Updating data in a timely manner As was concluded in previous studies, the price and income is an important way to improve the projection results for Chinese elasticity is not constant over time, but instead is dependent on the vehicle growth now and in the near future. Similar issue is observed level of car ownership; therefore, the overall trend of elasticity should H. Huo, M. Wang / Energy Policy 43 (2012) 17–29 27

50% 50%

40% 2050 40% 2050 2030 2030 30% 2020 30% 2020 20%

20% Changes in new purchases Changes in stock 10% 10% Changes in car price Changes in car price 0% 0% -50% -30% -10% 10% 30% 50% -50% -30% -10% 10% 30% 50% -10% -10%

-20% -20%

-30% -30%

-40% -40%

-50% -50% 50% 50% 2050 40% 40% 2050 2030 2030 30% 2020 30% 2020

20% 20% Changes in stock 10% 10% Changes in new purchase 0% 0% -50% -30% -10% 10% 30% 50% -50% -30% -10% 10% 30% 50% -10% -10% Changes in per-capita income Changes in per-capita income -20% -20%

-30% -30%

-40% -40%

-50% -50% 50% 50% 2050 40% 2050 40% 2030 2030 30% 30% 2020 2020 20% 20% Changes in new purchase Changes in stock 10% 10% Changes in Gini index Changes in Gini index 0% 0% -50% -30% -10% 10% 30% 50% -50% -30% -10% 10% 30% 50% -10% -10%

-20% -20%

-30% -30%

-40% -40%

-50% -50%

Fig. 16. Sensitivity of new-growth purchases and stock of private LDVs to three key parameters. (a) car price to stock; (b) car price to new purchases; (c) mean income to stock; (d) mean income to new purchases; (e) gini index to stock and (f) gini index to new purchases.

be characterized for a longer period. We collected time-series data for (see Fig. 2), which was built into the FEEI model to strengthen its six years, 2004–2009, which is not sufﬁcient to extrapolate from the reliability for predicting vehicle growth. Also, the car ownership present into the long-term future. However, since China is in its early function is updatable with new data as they become available. stage of motorization, the fact that the history of data accumulation The methodology in this work separates sales into new- for car growth is so short in China is a real limitation for any Chinese growth purchases and replacement purchases; one important car-ownership model. The important point is that despite the data advantage of the separation is that it can help make policy limitation, the currently available data have shown a clear and evaluation more accurate. New-growth purchases and replace- consistent relationship between car ownership and economic factors ment purchases are generated for different reasons, which could 28 H. Huo, M. Wang / Energy Policy 43 (2012) 17–29

be influenced by different policies. New-growth purchases are the fourteenth ‘‘2050 China Energy and CO2 Emission Report’’ by the Develop- usually stimulated by a rise in personal income or a decline in car ment Research Center of the State Council of China, Energy Research Institute at the National Development and Reform Commission of China, and Institute price or both. Replacement purchases are influenced by vehicle of Nuclear and New Energy Technology at Tsinghua University. Science Press, retirement requirements or people’s desire to upgrade their cars. Beijing. With this methodology, the model can simulate the effects of European Commission, Directorate-General for Energy and Transport, 2003–2009. policies that impact new-growth purchases, such as purchase-tax EU energy and transport in figures (various issues). He, K., Huo, H., Zhang, Q., He, D., An, F., Wang, M., Walsh, M., 2005. 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