Urbanization inequality: evidence from vehicle ownership in Chinese cities

Urbanization, while crucial for sustainable development, presents the challenge of inequality in its outcomes. This study focuses on vehicle ownership as a key indicator of urbanization, given its role in mobility and access to services. China’s rapid vehicle ownership growth since 2000, despite still being lower per capita than in developed economies, raises the question of future peak levels. Existing city-level studies often concentrate on correlations between socioeconomic factors and vehicle ownership, neglecting the temporal evolution and spatial disparities. This study aims to fill this gap by analyzing long-term (2001–2018) vehicle ownership data for 283 Chinese prefecture-level cities, providing a robust empirical foundation for more accurate vehicle demand forecasting, environmental impact assessment, and sustainable urbanization strategies. The concept of "in-use stocks," central to socioeconomic metabolism, frames the analysis, emphasizing the active role of vehicles in driving material cycles and shaping societal patterns. The research addresses the limitations of existing studies which primarily focus on statistical relationships between various factors and vehicle ownership without considering temporal growth patterns and spatial disparities.

Prior research on urban inequality has largely focused on economic disparities, neglecting other indicators. Studies on vehicle ownership in China have examined the relationship between vehicle ownership and various socioeconomic determinants like per capita GDP, built-up area, population density, and road network density. These studies, while informative, have often lacked a longitudinal perspective and detailed spatial analysis. This research builds upon existing work by providing a long-term, city-level analysis, accounting for temporal changes and geographical variations in vehicle ownership.

Data on vehicle ownership, population, GDP, and urbanization rate for 283 prefecture-level cities in China (covering 93% of the national population, 96% of national vehicle ownership, and 97% of national GDP in 2018) were collected from national, provincial, and city-level statistical yearbooks. Vehicle ownership includes passenger vehicles, trucks, and low-speed vehicles. The Theil index and its decomposition into within-region and between-region components were used to measure inequality in vehicle ownership rates. The ARIMA method was employed to identify four distinct growth patterns of vehicle ownership rates (Type A-D, representing initial, take-off, accelerating, and slowdown stages). Pearson correlation analysis assessed the relationships between vehicle ownership rate, GDP per capita, and urbanization rate. China was divided into eight regions for regional analysis, and four major urban agglomerations were also examined separately.

The study found a rapid increase in China's vehicle stocks since 2000, exceeding the United States in 2020 but with significantly lower per capita ownership than developed countries. Vehicle stocks are unevenly distributed, concentrated in northern and eastern coastal cities and provincial capitals. The Theil index showed a declining trend in inequality in vehicle ownership rates both nationally and regionally from 2001-2018. The growth of vehicle ownership rates followed an S-shaped curve, with most cities in the early stages of motorization. Four distinct stages of motorization (Type A-D) were identified. China is likely to have a lower saturation level of vehicle ownership than developed countries. A strong positive correlation (0.80) was found between vehicle ownership rate and GDP per capita, and a positive correlation (0.63) was found between vehicle ownership rate and urbanization rate. However, even with similar GDP per capita and urbanization rates, substantial disparities in vehicle ownership remained, influenced by factors beyond economic output. Megacities with vehicle purchase restrictions showed lower ownership rates despite high GDP and urbanization. The decrease in inequality indexes reflects the positive effects of balanced regional development strategies. Spatial patterns show high ownership rates in coastal cities and provincial capitals, reflecting migration and economic development. The S-shaped growth pattern suggests a lower saturation point for vehicle ownership rates in China compared to developed countries.

The findings highlight the complex interplay between urbanization, economic development, and vehicle ownership in China. The declining inequality in vehicle ownership rates reflects the success of balanced regional development strategies, but significant disparities remain. The S-shaped growth pattern and lower projected saturation level suggest that China has the opportunity to adopt more sustainable transportation strategies, including increased investment in public transport and strategies for compact cities, to mitigate environmental and social issues associated with high levels of vehicle ownership. The findings are relevant for policy-makers seeking to balance economic development with environmental sustainability and social equity.

This study provides a comprehensive analysis of vehicle ownership patterns across Chinese cities, revealing spatial inequalities and a projected lower saturation level than in developed countries. The S-shaped growth pattern offers valuable insights for forecasting future vehicle demand and assessing environmental impacts. Policy implications include promoting public transport and considering the influence of urban planning on vehicle ownership to achieve sustainable transportation and balanced regional development. Future research could delve deeper into the role of electric vehicles and other specific policies.

While the study covers a large number of Chinese cities, some relatively underdeveloped cities were excluded, potentially underestimating vehicle stocks in certain regions. Data uncertainties related to data collection and reporting methods may have affected the results. Differences between registered vehicles and actual vehicle stocks also exist. More current, comprehensive data are needed to enhance the study's accuracy and generalizability.