Simultaneous estimation of battery electric vehicle adoption with endogenous willingness to pay

Abstract

Abstract To alleviate climate change and health issues caused by the transportation sector, a number of countries and sub-national regions have enacted policies to promote adoption of plug-in vehicles, including policies encouraging the battery electric vehicle (BEV) adoption. Despite the potential benefits of BEVs to both society and consumers, barriers such as high purchase price and limited driving range impede the widespread adoption of BEVs, as evidenced by the small market share of BEVs at present. There is a need to better understand the process in which people make vehicle decisions, and more importantly the value they are willing to pay for purchasing a vehicle. This study employs a bivariate model, wherein a binary probit model of BEV adoption is simultaneously estimated with a log-linear regression of willingness to pay (WTP). The model accounts for the endogeneity of WTP to the equation of BEV adoption and also captures the cross-equation error correlation between the two model components. The estimation results using the 2016 California Vehicle Survey determines the associated influence of factors such as socio-economic characteristics, residential energy sources and prices, and travel behavior, as well as two sets of attitudinal/preferential factors including importance of vehicle attributes and BEV concern factors. The sensitivity analysis of the results reveals that BEV owners are more inclined towards adding another BEV to their households than those with other fuel types and it can be inferred that consumers’ familiarity with the benefits and costs of BEVs may lead to its higher adoption rates.