A nationwide study of factors associated with household car ownership in China

This paper presents a study of spatio-temporal patterns and city-level determinants of private car ownership in urban China. Car ownership data from 1990 to 2009 were collected from 235 cities of the prefecture-level and above. Panel data models are developed to examine the effects of city's socioeconomic, spatial and transport variables on private car ownership development. We find that car ownership development in Chinese cities has experienced stages of steady and rapid growths in the years before and after 2005; cities of larger sizes as well as in the coastal region have a higher level of car ownership and have experienced much faster growth in the entire period; and the most important determinants of car ownership are economic development related, including gross domestic product per capita and disposable income per capita, which both have positive and significant effects on car ownership in different periods and for different sizes of cities. This study adds to the literature some empirical evidence on the spatio-temporal patterns of private car ownership in urban China and the importance of socioeconomic, spatial and transport-related factors in determining the level of car ownership of cities.

Exploring the effects of the rural built environment on household car ownership after controlling for preference and attitude: Evidence from Sichuan, China

With the economic growth and the improvement of living standards, car ownership in China is increasing rapidly. It exerts enormous pressure on transportation services. It is necessary to forecast China car ownership for urban transport planning, transport infrastructure improvement and traffic management in terms of economic level, urban configuration, traffic situation, and car-concerned policies. In this paper, the main factors affecting car ownership are analyzed and a model to estimate car ownership in China city with the BP neural network technology is developed. The model can take the sudden effect of some external factors such as political and economic factors on car ownership into account.

A diamond effect exists when an individual's utility depends on the exchange value in addition to the intrinsic consumption effects of the good. This concept was first studied by Ng (1987). The existence of the diamond effect justifies a higher tax than a good with no diamond effect, and the stronger the effect is, the higher the tax should be. This paper attempts to test the diamond effect of private car ownership in China. While taxi services are very convenient in most of the large and mediumsize Chinese cities, and the cost of owning a private car is much higher than using a taxi for all travel purposes, many people still choose to buy a private car. This suggests the existence of diamond effects. A survey of 118 private car owners in three Chinese cities was conducted to test this conjecture. The survey results show that the relative income and the purchase decisions of colleagues, friends and relatives may have a strong impact on the purchase decisions of potential car owners, and that the closer the relationship, the stronger the impact. It also suggests that private car ownership may act as a sign of social status in China. In conclusion, the survey is supportive of the existence of a diamond effect in private car ownership in China.

At the beginning of the catching-up phenomenon of personal motorization in China, two very different car ownership policies were implemented in megacities: Beijing adopted a no intervention policy and Shanghai adopted a license quota policy. This difference provides an opportunity to examine policy impacts on car ownership and usage. The effects of the two policies on car ownership levels and delays in the process of personal motorization were examined. The growth pattern of car ownership without intervention was estimated on the basis of city-level aggregate data and a principal component analysis. The results indicate that an increase in income is the dominant factor driving the growth of car ownership in China. The forecast result in Shanghai indicates that the limitation policy has suppressed car ownership by at least twice as many individuals as the number of individuals who actually own cars. The limited car ownership policy also delays the process of personal motorization; this delay provides authorities with more time and chances to improve alternative transport services to fill the demand for motorized travel. The practical experience in Shanghai and public attitude responses in Beijing emphasize the importance of supporting measures to complement the limited car ownership policy and the need to offer equitable car ownership opportunities for all groups.

Numerous functions, especially the Gompertz function, have been predetermined to analyze the growth in vehicle ownership. This study utilizes the data-driven symbolic regression to automatically find a generalized function, named as new equation by symbolic regression (NE-SR), for passenger car ownership in six representative countries including Japan, England, USA, Finland, Poland and Australia. Then the new proposed function is applied for forecasting the passenger car ownership in China up to the year 2060. The experimental results indicate that the NE-SR, as an extension of the Gompertz function, fits better than the classical Gompertz function for car ownership growth. In NE-SR function, three scenarios can be realized by the variation of parameter signs, which are represented by the patterns of Japan, USA and Australia, respectively. The predicted results based on the NE-SR also show that the Chinese car ownership still has a potential to increase after 2060 in the pattern of Japan and Australia, but grows until around 2057 in the pattern of USA. The results can be used to further predict the energy demand and carbon emissions of passenger cars, which can provide a basis for the policymaker to propose transportation and environmental strategies.

Car and vehicle ownership levels in developing countries are gradually increasing, leading to traffic congestion, energy consumption, and air pollution. As a developing country, Turkey encounters many of the issues caused by this rapid increase in car ownership. This paper aims to examine potential determinants of household car and vehicle ownership behavior in urban areas of Turkey by concentrating on three cities (Ankara, Erzurum, and Bayburt) that are distinct in population, geographical region, and public transit services. For this purpose, a written survey was administered to 1389 households living in these cities. The data is analyzed using alternative ordered response models—the generalized ordered logit, partial proportional odds (PPO), and heteroskedastic ordered logit models. To the best of our knowledge, this study is the first attempt to compare alternative ordered response models to determine car and vehicle ownership behavior in Turkey. Consequently, the PPO model was found to be the most parsimonious model among the others. The results demonstrated that household car and vehicle ownership behavior in Turkey differs significantly with urban area size. The estimation results also revealed that several key drivers, including type of dwelling, parking availability, household monthly income, and public transit use, significantly influence Turkish households’ car and vehicle ownership levels. This study’s empirical findings may provide insight to policymakers and stakeholders for future transportation strategies and policies.

Accurate prediction of future car ownership is of great importance for road traffic planning, automobile industry development planning and the formulation of related policies. Therefore, this paper constructs a machine learning-based analysis and prediction model of the factors influencing private car ownership. First, the XGBoost method is used to identify the factors affecting private car ownership based on the data published by the National Bureau of Statistics. Then, comparing the prediction effects of three methods, XGBoost, random forest and neutral network, we found that neural network has better prediction accuracy in the private car ownership prediction model. Finally, based on the neural network method, the future private car ownership in China is predicted. The results of the study showed that GDP per capita and urbanization rate are the two most important factors affecting private car ownership; by 2030, China's private car ownership is expected to reach 438.3 million, 452.56 million and 469.42 million under the low, medium and high development scenarios, respectively.

In order to study the effect of average car price on city-level private car ownership in China, a panel data of 204 target cities in China for the period of 2006 to 2015 is collected and investigated. Nine variables including average car price to average income ratio (ACP/AI), economic characteristics, urban characteristics, and transportation characteristics of cities are selected as potential explanatory variables. Pooled regression model, fixed effect model and random effect model are adopted and compared by fitting the panel data. The results of the Hausman test indicate that the fixed effect model fits the data better. Gross domestic product per capita, population density, highway density, per capita area of urban road, number of taxis per 10,000 population and number of buses per 10,000 population have significantly positive effects on private car ownership while ACP and has significantly negative effects on private car ownership. Among them, ACP/AI which reflect the proportion of car prices in people's purchasing power has the highest coefficient. When ACP/AI decreases by 1%, the city-level private car ownership increases by 0.590%, while other variables are controlled. This finding could provide reference for policy makers to make a better balance between controlling for the car ownership in a city by adjusting the car price and ensuring GDP and tax collection from vehicle industry as a certain level.

The impact of fixed and variable costs on household car ownership

With the improvement of living standards, the demand for residents’ travel has grown rapidly. At present, China has surpassed the U.S. to become the world’s largest vehicle sales country. By the end of 2018, there had been over 200 million private passenger cars in China. Meanwhile, the increase in the number of cars has also brought a series of other problems: energy consumption, air pollution, traffic congestion, etc. Therefore, some first-tier cities have successively introduced motor vehicle purchase restriction policies to constrain the surge of local private cars. However, existing researches have overemphasized the factors that promote the development of China’s motor vehicle market and ignored the importance of the purchase restriction policies. In this study, policies in Beijing, Tianjin, Shanghai, and Guangzhou are introduced, and their impacts on local private passenger car stock are analyzed. The results indicate that purchase restriction policies kept the car ownership per thousand people in these cities in a relatively stable level with growing economy. Therefore, as the number of cities with restriction policies increases, it is necessary to take those policies into consideration in the forecast of possession. Meanwhile, the local governments should still think over policy contents from more aspects, like number of issued plates every year, special measures for new energy vehicles, and travel demand of residents.

The rising prevalence of private cars in the developing world is causing serious congestion and pollution. In China, private cars started to emerge as an important travel mode in the past decade. Prospective research on the relationship between urban form and car ownership is relatively uncommon in the developing world, and China offers a unique study opportunity, given the tremendous increases in private cars and fast-paced urbanization over the past decade. This study investigates the influence of urban form on car ownership as well as the impact of other socioeconomic and demographic factors on private car ownership across megacities in China. Analysis was conducted through the use of data from 36 megacities and two household survey data sets collected in Beijing and the city of Chengdu, China. Ordinary least squares regression and discrete choice models were employed to execute the aggregate and disaggregate analysis of the urban form impact on private car ownership across cities. The statistical model results demonstrate that urban affluence, urban scale, and road infrastructure supply factors have significant positive effects on the city level of private car ownership across cities. Population density calculated at the subdistrict level, however, had a significant negative effect on private car ownership across cities. Households with private cars were found to prefer to live close to urban centers where amenities were readily available. The results provide evidence for urban planners and policy makers.

Assessing one-way carsharing’s impacts on vehicle ownership: Evidence from Shanghai with an international comparison

Carsharing is a service in which members of an organization have access to vehicles for predetermined periods of time (usually with short duration). One of the main impacts of this service in a city is said to be the reduction of car ownership. However, most studies used surveys of carsharing members to evaluate this effect, and these surveys may contain a bias because of the members’ interpretation of reality. This study proposes a first assessment of the reduction of car ownership in an area served by station-based carsharing service; the study used historical empirical data describing the population (Canadian census), typical travel behaviors, and car ownership (origin–destination surveys). Multiple regression models are used to study the relation between household and individual car ownership and exposure to carsharing, while controlling for other variables known to also influence ownership. Although more complex model formulations need to be tested to enhance the analysis, the results obtained in this analysis using linear regression models indicate that the number of shared vehicles in a 500-m radius is negatively correlated with car ownership.

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