Examining Shanghai consumer preferences for electric vehicles and their attributes

Nie, Wang, Guo, Shen, 2018, in Sustainability (Switzerland)

doi:10.3390/su10062036
Location Shanghai, China
Population Other (specify)
Sample size 760
Factor analysis type exploratory factor analysis, unknown rotation
Stepwise regression no
Removal of insignificant variables no
Reviewed by LCM

Abstract

In this study, we conducted a stated choice survey in Shanghai in order to examine the attitudes of Shanghai residents towards electric vehicles and their attributes. Multinomial Logit and Random Parameter Logit models were used to analyze the response data for three samples-the full sample, a subsample of potential electric vehicle purchasers, and a subsample of unlikely electric vehicle purchasers. We found that the respondents in each of the three groups preferred electric vehicles with a longer driving range, a shorter charging time, a faster maximum speed, lower pollution emissions, lower fuel cost, and a lower price. However, an overlong driving range seems not to be a must for potential electric vehicles (EV) purchasers. In addition, a comparison of the two subsamples showed that potential electric vehicle purchasers were willing to pay more than their counterparts for enhancing vehicle attributes. We also investigated the determinants of likely electric vehicle purchase and found a number of demographic characteristics that were statistically significant. © 2018 by the authors.

Factors

Variable Pattern loading
I am willing to pay more to buy environmentally friendly products (Environmentalism) 0.6085
I am willing to pay more to buy products with new technology (Lifestyle) 0.6111
Variable Pattern loading
Environmental problems never bother me because I think environmental pollution problems are overestimated (Environmentalism) 0.6147
I have little or no fear that environmental problems will have an impact on myself and my family's health (Environmentalism) 0.6062
I can accept some of the developing countries like China have several pollution problems (Environmentalism) 0.3505
Variable Pattern loading
I think that our consumption should be responsible for the environment (Environmentalism) 0.4377
Driving new energy vehicles can reduce the current environmental pollution (Environmentalism) 0.5891
I think that decreasing pollutant emissions is important for me to choose a new energy vehicle (Environmentalism) 0.6024

Models

Dependent variable Preference for EV characteristics
Model type MNL
Sample size 3040.0
R2 nan
Adjusted R2
Pseudo R2 (nan) nan
AIC nan
BIC nan
Log-likelihood at zero nan
Log-likelihood at constants nan
Log-likelihood at convergence -2955.81
Variable Coefficient p-value
EV1 Constant 0.316 0.024
EV2 Constant 0.141 0.001
Driving range: 200 km 0.37 0.0
Driving range: 300 km 0.633 0.0
Driving range: 400 km 0.848 0.0
Charging time: 3 h 0.197 0.81
Charging time: 1 h 0.283 0.0
Charging time: 10 min 0.547 0.0
Pollution degree 0.747 0.0
Maximum speed 2.611 0.0
Fuel costs -0.982 0.001
Relative price -0.126 0.0
Dependent variable Preference for EV characteristics
Model type MNL
Sample size 1656.0
R2 nan
Adjusted R2
Pseudo R2 (nan) nan
AIC nan
BIC nan
Log-likelihood at zero nan
Log-likelihood at constants nan
Log-likelihood at convergence -1499.5
Variable Coefficient p-value
EV1 Constant -0.318 0.478
EV2 Constant 0.209 0.0
Driving range: 200 km 0.485 0.0
Driving range: 300 km 0.606 0.0
Driving range: 400 km 0.822 0.0
Charging time: 3 h 0.472 0.826
Charging time: 1 h 0.319 0.003
Charging time: 10 min 0.595 0.0
Pollution degree 0.745 0.0
Maximum speed 3.138 0.0
Fuel costs -0.7 0.085
Relative price -0.1 0.0
Dependent variable Preference for EV characteristics
Model type MNL
Sample size 1296.0
R2 nan
Adjusted R2
Pseudo R2 (nan) nan
AIC nan
BIC nan
Log-likelihood at zero nan
Log-likelihood at constants nan
Log-likelihood at convergence -1282.76
Variable Coefficient p-value
EV1 Constant 0.832 0.0
EV2 Constant 0.503 0.459
Driving range: 200 km 0.263 0.044
Driving range: 300 km 0.605 0.0
Driving range: 400 km 0.865 0.0
Charging time: 3 h 0.066 0.617
Charging time: 1 h 0.319 0.016
Charging time: 10 min 0.56 0.0
Pollution degree 0.899 0.0
Maximum speed 1.421 0.012
Fuel costs -1.45 0.003
Relative price -0.173 0.0
Dependent variable Preference for EV characteristics
Model type MNL
Sample size 3040.0
R2 nan
Adjusted R2
Pseudo R2 (nan) nan
AIC nan
BIC nan
Log-likelihood at zero nan
Log-likelihood at constants nan
Log-likelihood at convergence -2945.83
Variable Coefficient p-value
EV1 Constant 0.172 0.441
EV2 Constant 0.174 0.004
Driving range: 200 km 0.505 0.0
Driving range: 300 km 0.864 0.0
Driving range: 400 km 1.123 0.0
Charging time: 3 h 0.073 0.509
Charging time: 1 h 0.361 0.001
Charging time: 10 min 0.697 0.0
Pollution degree 0.93 0.0
Maximum speed 3.241 0.0
Fuel costs -1.23 0.006
Relative price -0.157 0.0
Dependent variable Preference for EV characteristics
Model type MNL
Sample size 1656.0
R2 nan
Adjusted R2
Pseudo R2 (nan) nan
AIC nan
BIC nan
Log-likelihood at zero nan
Log-likelihood at constants nan
Log-likelihood at convergence -1483.76
Variable Coefficient p-value
EV1 Constant -0.904 0.153
EV2 Constant 0.329 0.009
Driving range: 200 km 0.863 0.001
Driving range: 300 km 1.161 0.0
Driving range: 400 km 1.405 0.001
Charging time: 3 h 0.186 0.327
Charging time: 1 h 0.584 0.01
Charging time: 10 min 1.224 0.001
Pollution degree 1.316 0.001
Maximum speed 5.604 0.001
Fuel costs -1.615 0.08
Relative price -0.714 0.0
Dependent variable Preference for EV characteristics
Model type MNL
Sample size 1296.0
R2 nan
Adjusted R2
Pseudo R2 (nan) nan
AIC nan
BIC nan
Log-likelihood at zero nan
Log-likelihood at constants nan
Log-likelihood at convergence -1279.15
Variable Coefficient p-value
EV1 Constant 0.807 0.002
EV2 Constant 0.051 0.535
Driving range: 200 km 0.304 0.049
Driving range: 300 km 0.69 0.0
Driving range: 400 km 0.992 0.0
Charging time: 3 h 0.023 0.897
Charging time: 1 h 0.336 0.026
Charging time: 10 min 0.592 0.0
Pollution degree 0.928 0.0
Maximum speed 1.517 0.02
Fuel costs -1.73 0.004
Relative price -0.193 0.0
Dependent variable Determinants of being a potential EV purchaser
Model type Logit regression
Sample size 680.0
R2 nan
Adjusted R2
Pseudo R2 (nan) nan
AIC nan
BIC nan
Log-likelihood at zero nan
Log-likelihood at constants nan
Log-likelihood at convergence -5072.3
Variable Coefficient p-value
Constant term -0.046 nan
Male 0.202 <0.01
Age -0.004 nan
Masters degree or above 0.479 <0.01
Individual annual income 0.092 <0.01
Mid-level or manager -0.351 <0.01
Salariat 0.126 <0.1
Entrepreneur -0.05 nan
Civil servant -0.079 nan
Professionals (teacher, doctor, lawyer, etc.) -0.056 nan
Family with cars -0.149 <0.01
Pay attention to policies related to NEVs 0.712 <0.01
Green consumption consciousness 0.258 <0.01
Acceptance of new product and new technology 0.261 <0.01
Environmental protection awareness -0.102 <0.01

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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