Modeling Choice Behaviors for Ridesplitting under a Carbon Credit Scheme

Xiaomei Li, Yiwen Zhang, Zijie Yang, Yijun Zhu, Cihang Li and Wenxiang Li ()
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Xiaomei Li: Business School, University of Shanghai for Science and Technology, Shanghai 200093, China
Yiwen Zhang: Business School, University of Shanghai for Science and Technology, Shanghai 200093, China
Zijie Yang: Business School, University of Shanghai for Science and Technology, Shanghai 200093, China
Yijun Zhu: Business School, University of Shanghai for Science and Technology, Shanghai 200093, China
Cihang Li: Business School, University of Shanghai for Science and Technology, Shanghai 200093, China
Wenxiang Li: Business School, University of Shanghai for Science and Technology, Shanghai 200093, China

Sustainability, 2023, vol. 15, issue 16, 1-17

Abstract: Ridesplitting, a form of shared ridesourcing service, has the potential to significantly reduce emissions. However, its current adoption rate among users remains relatively low. Policies such as carbon credit schemes, which offer rewards for emission reduction, hold great promise in promoting ridesplitting. This study aimed to quantitatively analyze the choice behaviors for ridesplitting under a carbon credit scheme. First, both the socio-demographic and psychological factors that may influence the ridesplitting behavioral intention were identified based on the theory of planned behavior, technology acceptance model, and perceived risk theory. Then, a hybrid choice model of ridesplitting was established to model choice behaviors for ridesplitting under a carbon credit scheme by integrating both structural equation modeling and discrete choice modeling. Meanwhile, a stated preference survey was conducted to collect the socio-demographic and psychological information and ridesplitting behavioral intentions of transportation network company (TNC) users in 12 hypothetical scenarios with different travel distances and carbon credit prices. Finally, the model was evaluated based on the survey data. The results show that attitudes, subjective norms, perceived behavioral control, low-carbon values, and carbon credit prices have significant positive effects on the choice behavior for ridesplitting. Specifically, increasing the carbon credit price could raise the probability of travelers choosing ridesplitting. In addition, travelers with higher low-carbon values are usually more willing to choose ridesplitting and are less sensitive to carbon credit prices. The findings of this study indicate that a carbon credit scheme is an effective means to incentivize TNC users to choose ridesplitting.

Keywords: ridesplitting; carbon credit scheme; hybrid choice model; structural equation model; low-carbon value (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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