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

Ao, Yang, Chen, Wang, 2019, in Journal of Transport Geography

doi:10.1016/j.jtrangeo.2018.11.002
Location Sichuan Province, China
Population General
Sample size 374
Factor analysis type principal components, varimax rotation
Stepwise regression no
Removal of insignificant variables no
Reviewed by LCM

Abstract

With the rapid infrastructure development and economic growth in China, household car ownership in the country's rural areas has changed dramatically in the past 16 years. The total number of cars owned by households in rural areas is currently 12 times higher than it was 16 years ago. The exploration of the effects of the built environment on household car ownership in China's rural areas is worthwhile. However, few studies have investigated this topic. To fill in the research gap, this work collected 374 household data from rural areas in China to examine the effects of the built environment in Sichuan's rural areas on the number of cars in a household. It considered family structure, socioeconomic characteristics, and individual's perceptions of the built environment, preferences towards the built environment and attitudes towards car ownership (shortened to perceptions, preferences and attitudes from now on). Geographic information system (GIS) technology, combined with on-site measurement, was used for data collection. The multinomial logit model was applied for estimation. Household structure and the built environment (including the perceived built environment and the objective built environment) significantly influence the number of cars in a household. By contrast, preference and attitude attributes have less influence on car ownership. Most of the findings are in line with the literature in the context of Chinese cities. Nevertheless, new results are also found. For example, rural hukou, and building density have significant positive impacts on household car ownership in China's rural areas, which is in contrast with their effects on cities. As the first study on rural areas in China, this research provides some insights for rural planners and policymakers to understand better the relationship between built environment and household car ownership. © 2018 Elsevier Ltd

Factors

Variable Pattern loading
No traffic incident (Safety) 0.812
No criminal incident (Safety) 0.752

Models

Dependent variable Number of cars owned
Model type MNL
Sample size 374.0
R2 0.503
Adjusted R2
Pseudo R2 (nan) nan
AIC nan
BIC nan
Log-likelihood at zero nan
Log-likelihood at constants -330.754
Log-likelihood at convergence -164.493
1 car
Variable Coefficient p-value
Constant -7.825 0
Household size -0.085 0.655
Resident population -0.071 0.616
Population under 18 0.338 0.211
Household highest education -0.267 0.084
Household number of workers 0.118 0.468
Number of driver license holders 1.173 0
Household income 0.359 0
Number of housing units -0.017 0.955
Household parking space 1.109 0.002
Rural hukou 0.496 0.435
Motorcycle ownership -0.573 0.071
Ebike ownership 0.122 0.69
Bicycle ownership -0.047 0.851
Holding a driver's license 0.937 0.037
Can ride motorcycle -0.229 0.474
Can ride bicycle 0.875 0.093
Can ride ebike -0.525 0.281
Independent on car -0.203 0.274
Economy, status symbol 0.011 0.953
Cost -0.241 0.162
Fuel efficiency and road 0.083 0.638
Safety 0.139 0.434
Accessibility 0.071 0.679
Public space 0.031 0.849
Good neighborhood and service -0.275 0.1
Accessibility 0.053 0.788
Public space and services -0.088 0.669
Good neighborhood environment -0.08 0.648
Physical activity options -0.011 0.949
Few bad accidents 0.472 0.019
Building density 0.117 0.097
Road density 0.926 0.013
Distance to transit mix index 0.149 0.646
Destination accessibility 0.137 0.849
Living style 0.228 0.671
2+ cars
Variable Coefficient p-value
Constant -27.08 0
Household size 0.862 0.064
Resident population -0.573 0.151
Population under 18 1.777 0.024
Household highest education -0.67 0.129
Household number of workers -0.034 0.95
Number of driver license holders 2.744 0
Household income 0.715 0
Number of housing units 0.045 0.947
Household parking space 3.119 0.013
Rural hukou 6.761 0.037
Motorcycle ownership 0.351 0.675
Ebike ownership -0.384 0.712
Bicycle ownership -1.227 0.161
Holding a driver's license 1.085 0.352
Can ride motorcycle -3.133 0.02
Can ride bicycle 4.852 0.018
Can ride ebike -0.166 0.915
Independent on car -0.948 0.062
Economy, status symbol -0.328 0.592
Cost -0.673 0.255
Fuel efficiency and road 0.137 0.782
Safety -0.413 0.492
Accessibility 0.813 0.236
Public space -0.271 0.575
Good neighborhood and service -0.881 0.091
Accessibility 1.889 0.005
Public space and services -0.185 0.775
Good neighborhood environment 0.732 0.22
Physical activity options 0.229 0.672
Few bad accidents 1.231 0.042
Building density 0.579 0.023
Road density 1.339 0.191
Distance to transit mix index -2.766 0.068
Destination accessibility -1.239 0.571
Living style -1.984 0.285
Dependent variable Number of cars owned
Model type Ordered logit model
Sample size 374.0
R2 0.433
Adjusted R2
Pseudo R2 (nan) nan
AIC nan
BIC nan
Log-likelihood at zero nan
Log-likelihood at constants -330.754
Log-likelihood at convergence -187.461
Variable Coefficient p-value
Constant -4.17 0
Household size -0.069 0.444
Resident population -0.038 0.571
Population under 18 0.415 0.002
Household highest education -0.087 0.246
Household number of workers 0.054 0.482
Number of driver license holders 0.65 0
Household income 0.17 0
Number of housing units 0.086 0.488
Household parking space 0.607 0
Rural hukou 0.622 0.04
Motorcycle ownership -0.001 0.996
Ebike ownership 0.139 0.324
Bicycle ownership -0.129 0.283
Holding a driver's license 0.438 0.025
Can ride motorcycle -0.381 0.036
Can ride bicycle 0.77 0.001
Can ride ebike -0.39 0.1
Independent on car -0.174 0.047
Economy, status symbol -0.064 0.462
Cost -0.154 0.074
Fuel efficiency and road 0.055 0.511
Safety 0.014 0.881
Accessibility 0.094 0.279
Public space 0.014 0.866
Good neighborhood and service -0.13 0.112
Accessibility 0.217 0.019
Public space and services -0.003 0.98
Good neighborhood environment 0.004 0.963
Physical activity options -0.06 0.472
Few bad accidents 0.209 0.01
Building density 0.081 0.018
Road density 0.355 0.038
Distance to transit mix index -0.139 0.375
Destination accessibility -0.088 0.792
Living style -0.05 0.85
Dependent variable Number of cars owned
Model type Ordered probit model
Sample size 374.0
R2 0.433
Adjusted R2
Pseudo R2 (nan) nan
AIC nan
BIC nan
Log-likelihood at zero nan
Log-likelihood at constants -330.754
Log-likelihood at convergence -187.637
Variable Coefficient p-value
Constant -7.002 0
Household size -0.124 0.448
Resident population -0.047 0.694
Population under 18 0.705 0.003
Household highest education -0.171 0.199
Household number of workers 0.112 0.419
Number of driver license holders 1.145 0
Household income 0.316 0
Number of housing units 0.162 0.445
Household parking space 1.018 0.001
Rural hukou 1.076 0.043
Motorcycle ownership 0.01 0.966
Ebike ownership 0.23 0.373
Bicycle ownership -0.227 0.285
Holding a driver's license 0.69 0.05
Can ride motorcycle -0.628 0.06
Can ride bicycle 1.326 0.002
Can ride ebike -0.699 0.1
Independent on car -0.342 0.031
Economy, status symbol -0.108 0.473
Cost -0.249 0.104
Fuel efficiency and road 0.143 0.34
Safety 0.01 0.952
Accessibility 0.145 0.348
Public space 0.039 0.788
Good neighborhood and service -0.242 0.095
Accessibility 0.381 0.02
Public space and services 0.035 0.845
Good neighborhood environment -0.001 0.995
Physical activity options -0.1 0.499
Few bad accidents 0.376 0.008
Building density 0.152 2.47
Road density 0.56 0.071
Distance to transit mix index -0.291 0.301
Destination accessibility -0.262 0.663
Living style -0.102 0.828

The Attitudes and Travel Database is produced with support from the Center for Teaching Old Models New Tricks at Arizona State University, a University Transportation Center sponsored by the US Department of Transportation through Grant No. 69A3551747116.

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