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
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 |