No abstract

The issue of environmental protection and sustainable development is a key research focus across multiple fields. Employee green behavior is considered to be an important micro-activity to address this. Researchers in the field of organizational behavior and sustainable development have been focusing on the influencing factors of employee green behavior. However, few have explored the beneficial effects of employee green behavior on behavioral implementers. The objective of this study is to investigate the relationships among employee green behavior, self-esteem, perceived organizational support for employee environmental efforts, and employee well-being, and to explore a new dimension of employee green behavior. We empirically examined the underlying framework by conducting two surveys to collect data from 900 employees working in manufacturing, construction, and the service industry in China. We performed multilevel path analysis using SPSS and AMOS software, and confirmed that employee green behavior includes four dimensions: green learning, individual practice, influencing others, and organizational voices. Further, employee green behavior has a significant positive impact on self-esteem, which in turn is converted into employee well-being. Finally, perceived organizational support for employee environmental efforts not only positively moderated the relationship between employee green behavior and self-esteem, but was also confirmed as a moderated mediation model. This study enriches the current literature on the measurement framework and variables of employee green behavior.

The promotion of electric vehicles and their charging facilities to achieve carbon emission reduction is a research hotspot in the field of transportation. Aiming at the comprehensive decision of electric vehicle charging station (EVCS) location, this paper constructs an EVCS location evaluation index system that includes five indexes of grid load, traffic facilities, user preference, construction cost, and service radius. Firstly, we convert the exact number into interval judgment matrix, introduce Shapley fuzzy measure to calculate the weight of factors, and use the two-stage optimization model to further optimize the weight. Then, we combine the multiple criteria decision-making (MCDM) method in the Pythagorean fuzzy environment with partitioned normalized weighted Bonferroni mean (PFPNWBM) operator, and calculate the optimal ranking of alternatives according to the performance function and the accuracy function. Finally, a numerical example is used to analyze the difference between first-order linear optimization and two-stage optimization in alternative scheme evaluation, and the practical value of using model to evaluate EVCS location is verified.