Influence of land use and street characteristics on car ownership and use: Evidence from Jinan, China

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Highlights

  • Land use and street characteristics influence car ownership and use.

  • Parking supply, neighborhood permeability and street design affect car ownership.

  • Most land use and street variables influence car use.

Abstract

This study examines the effects of land use and street characteristics on household car ownership and use, based on a travel survey of 2540 households in 104 neighborhoods of Jinan, China. We performed two-step instrumental variable models, composed of a multinomial logistic model predicting the car ownership in the first step and a double-hurdle model predicting the car traveling distances in the second step with the use of car ownership as a mediating variable. It is found that parking supply, neighborhood permeability and street design influence car ownership. Most land use and street variables along dimensions of density, diversity and design, as well as the proximity to regional transport infrastructures, influence either the participation probability or the distance travelled by car. Overall, our study points to the importance of strategic urban planning favoring bus rapid transit development, mixed land uses, human friendly streets and restrictive parking to reduce car dependency in rapidly motorizing Chinese cities.

Introduction

In many cities of the developing world, car ownership levels are increasing rapidly thanks to urban sprawl, economic growth, rising incomes and strong car purchase intentions among residents (Pucher et al., 2007, Belgiawan et al., 2014). Meanwhile, an explosive growth of car use is taking place in these cities, evident from the travel mode shifting to cars from other modes (i.e., transit, walk and cycle) as well as longer car travel distances in families (Darido et al., 2013, Pourbaix, 2011, Cervero, 2013, Guerra, 2014). The combination of these trends has triggered a series of negative consequences at the local level, such as traffic congestion, road accidents, air pollution and heavy financial burdens for expanding and maintaining large-scale transportation infrastructures. At the global level, current motorization paths make developing countries the main contributor to the growth of transport-related energy consumption and greenhouse gas emissions (GHG), imposing great pressures on the fight against world-wide energy crisis and climate change (Sperling and Clausen, 2002, Figueroa et al., 2013, Lyu et al., 2015).

To address the aforementioned challenges, promoting urban development patterns that can reduce car dependency have gained wide interests among policy makers and scholars (Cervero, 2013). From a theoretical perspective, people choose their vehicle ownership and travel patterns to maximize the derived net utility, and the built environment can influence both travel costs (disutility) and potential activity realization benefit at destinations (positive utility) (Crane, 1996, Maat et al., 2005). While cities in US and Europe are largely built up with structures and mobility cultures established already, cities in the developing world like China are still experiencing rapid urbanization and shaping their spatial forms, a trend likely to continue for decades (UN DESA, 2010, Wang and He, 2015). If empirical evidences confirm that the influence of the built environment on travel behavior does exist in the developing cities, there would be a much larger scale-up potential for them than developed countries to intervene in the form of urban development in order to purposefully influence travel behaviors and outcomes.

Unfortunately, literature on the relationship between built environment and travel behavior has mainly focused on cities in the developed countries. Since urban development strategies such as new urbanism, smart growth, compact cities and transit oriented development (TOD) are mostly originated from US and Europe, advocating and implementing those strategies in the context of developing-world cities is subject to transferability concerns. For example, empirical precedents in Santiago de Chile suggest that residential density can assert little influence on car use (Zegras, 2010). For China, the very few research precedents were limited by crude measurement on built environment and/or relatively small sample size of neighborhoods due to data availability, making policy implications inconclusive and difficult to be generalized. Furthermore, previous research in the developing world focused mostly on the land use of neighborhoods but rarely quantified the physical form of streets. Thus they failed to separate the potential effect of street characteristics on household car ownership and use.

This paper aims at filling the gap described above using a travel survey of 2540 households from 104 neighborhoods in Jinan, China. We endeavor to shed light on answers to following questions. What neighborhood characteristics are associated with car ownership and use after controlling for socio demographics and residential self selection in the rapidly urbanizing context of Jinan? How big are the impacts of built environment on car ownership and use respectively? Do street characteristics impose an independent impact on car ownership and use? This research, to our knowledge, presents one of the first efforts of developing integrated household car ownership and use models for a Chinese city and at the same time quantifying the effect of built environment at a decent level. The remainder of this paper is organized as follows. The next section reviews previous studies on the relationships between the built environment and car ownership and use. Section 3 describes data and variables used in this study. Section 4 presents the modeling approach. Section 5 discusses results. Section 6 concludes with the key findings and policy implications.

Section snippets

Literature review

In the past decades, numerous studies have been conducted in the developed countries using empirical data and a wide variety of methodological approaches to explore the relationship between built environment and travel (including car ownership and use). Earlier studies were featured of comparative analysis as they directly compared aggregate data of travel patterns across different neighborhood settings (Cervero and Gorham, 1995, Dagang and Loudon, 1995, Friedman et al., 1994). Yet findings

Data and variables

In this study, we focused on the city of Jinan, China. Jinan is the capital of Shandong Province and a city with history dating back to the earliest beginning of civilization, now positioned itself as the center city of Yellow River downstream. The city represents the typical image of a Chinese city which is still under fast urbanization. Jinan accommodates 3.5 million urban residents in 2010 and expects to reach an urban population of 4.3 million by 2020 (Jinan Urban Planning Bureau, 2014).

Two-step modeling

For a more rigorous causality study on the effect of built environment on travel behavior, we identified potential self-selection associated with two related forms of bias in our study: simultaneity bias and omitted variable bias. The simultaneity bias results from the endogeneity between residential location choice and travel choice (both car purchase and car travel distance), since households might make those two choices simultaneously with attitudinal factors playing an important role. We

Household car ownership model

Table 6 presents results of the model. The coefficients of most socio-demographic variables showed expected signs and statistical significance. For example, increase of income and household size will lead to higher probability of owning cars. Households having children or workers tend to own cars. Interestingly, households with rural Hukou (i.e., household registration) were associated with lower probability for owning a car. This might be explained by that rural migrant households in Chinese

Conclusion

This research disentangles the influences of neighborhood-level land use and street characteristics on household car ownership and use under a rapidly urbanizing and motorizing context using the Jinan, China for a case study. Factor analyses were conducted to combine collinear variables of built environment along different dimensions as well as household living and traveling preferences. Two-step instrumental variable models, composed of a multinomial logistic (MNL) model predicting the car

Acknowledgement

This work is supported by the National Natural Science Foundation of China (No. 51378278) and the Energy Foundation. We acknowledge our research partners at Shandong University for the administration of household travel surveys and street image visual surveys. We also thank the Jinan urban planning bureau for providing local land use information for our analysis. Finally, we thank the International Association for China Planning (IACP) for organizing this special issue and a coaching symposium

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