What influences residents’ intention to participate in the electric vehicle battery recycling? Evidence from China

• Published in: Energy (Oxford) 2023-08, Vol.276, p.127563, Article 127563
• Main Authors: Tang, Yanyan, Zhang, Qi, Liu, Boyu, Li, Yan, Ni, Ruiyan, Wang, Yi
• Format: Article
• Language: English
• Subjects: Agent-based model; Electric vehicle battery; Recycling intention; Structural equation model
• ISSN: 0360-5442
• DOI: 10.1016/j.energy.2023.127563

A large number of Electric Vehicle (EV) batteries are intensively reaching their end-of-life, implying a significant risk of environmental pollution and sustainable development. Guiding the residents to select the formal recycling channels is the key to realizing the closed-loop supply chain. Therefore, a solid questionnaire is designed to further investigate their recycling intention. In the context of a China case study, a total of 1083 valid samples are obtained. By establishing the structural equation model and agent-based model, the impacts of critical rational and bounded rational determinants are identified and analyzed. The results show that: (i) perceived behavior control exerts the highest impacts on recycling intention, followed by economic incentive, subjective norm and self-identity; (ii) residents who lack the recycling cognition hold vague judgment on the difficulty of transfer behavior, and require more auxiliary information; (iii) every unit increase in the recycling price, the number of service outlets and the ratio of residents with positive attitude and high cognition will lead to an improvement of 6.93%, 1.89% and 2.69% in the recycling rate, respectively. Eventually, practical policy implications are proposed for the government to strengthen public participation. •Rational and bounded rational determinants for EV battery recycling are explored.•A SEM and agent-based analytical framework are formulated.•Heterogeneous motivation of recycling attitude and cognition are considered.•Residents with positive attitude and high cognition rely less on incentives.