The emergence of the digital economy has accelerated digital transformation, and digitalization has shown new potential and solutions to increasingly severe environmental challenges. Based on the resource-based view, dynamic capability view, synergy effect and upper echelons theory, the connotation and measurement dimensions of digital capability and green supply chain collaboration are defined and improved. Then, a theoretical model of "digital capability-green supply chain collaboration-green innovation performance" is constructed. The influence mechanism and transmission process of digital capability on green innovation performance from the perspective of green supply chain collaboration is discussed. Meanwhile, the boundary condition of the influence of digital capability on green innovation performance in the view of top management's environmental awareness is explored. Finally, an empirical test is conducted based on the Chinese manufacturing corporates. The results indicate that green innovation performance is significantly and favorably impacted by digital capability, green supply chain collaboration plays a partial mediating role between digital capability and green innovation performance, and top management's environmental awareness can positively moderate the effect of digital capability on green innovation performance. This study offers valuable theoretical and practical enlightenments for manufacturing companies to foster the growth of green innovation through digital capability more effectively.

Electric bicyclists are vulnerable road users and play an important role in traffic safety. The focus of this research is on analyzing cyclists’ injury severity in vehicle-electric bicycle collisions. It is an exploratory analysis that was conducted based on samples obtained from video data provided by the police of Xi’an China. Three types of severity include fatal, injury, and property-damage-only (PDO). A random parameter logit (RPL) model was specified to gain more insights into factors related to the injury severity level, including human behaviors, vehicle characteristics, roadway attributes, and environmental conditions. Some factors not included in previous research were introduced into this study, especially precrash behaviors of drivers and cyclists. The direct pseudo-elasticity effects of variables were compared to investigate the stability of individual parameter estimates on the severity categories. The results indicated that variables that significantly increment the probability of fatal accidents were as follows: driver violation behaviors (speeding, red-light violation, driving in the opposite direction), cyclist violation behaviors (speeding, red-light violation), day of time (nighttime), visibility restrictions (fixed obstacles), and vehicle type (larger bus, small truck, and larger truck). Based on these findings, we suggested measures such as strengthening law enforcement by installing cameras, implementing zero tolerance for cyclist violations, promoting education by completing training courses for cyclists, and enhancing traffic safety awareness through educational activities. The research results can provide a theoretical basis for formulating strategies to improve cyclist safety.

The green supply chain (GSC) has become a major trend that advocates for the sustainability of supply chains. To seek optimal strategies for the GSC, the competition between green and nongreen supply chains (NGSCs), along with the impacts of government subsidies and tax policies and the green preferences of consumers, are discussed in this study. A pricing-strategy model of an NGSC and a GSC was conducted by considering the cross-price effects. The equilibrium strategies (the optimal green-technology level, the pricing, and the profits) were achieved and were comparatively analyzed with backward induction. For more in-depth research, a robust sensitivity analysis was conducted, and the Taguchi method was used to identify the main factors that affect the revenues of NGSCs and GSCs. The results show that the vertical collaboration between manufacturers and retailers can help to improve the greenness of products and bring more benefits to consumers. The government interventions have an effect, and when the government sets a premeditated threshold for subsidies and taxation, GSC members can earn more, and the green products obtain more competitiveness. The green preferences of consumers are the primarily conducive factor to the sustainability and profit increases of GSCs. However, the green-technology-investment cost will not have a significant influence on the equilibrium strategies.

In light of increasingly prominent environmental issues, inspiring green supply chain (GSC) members to engage in collaborative innovation is crucial to improve environmental performance. In this paper, in relation to a two-level GSC consisting of manufacturers and suppliers, differential equations involving the greenness of intermediate and final products as state variables are constructed considering the effect of digital capability on green innovation. Subsequently, designs for three incentive mechanisms—the greenness reward, the R&D effort reward, and the digital construction reward—are presented, and their long-term dynamic effects on the economic, environmental, and social benefits are compared and analyzed. Finally, the impacts of consumer green preference and the contribution of digital capability to the advancement of green innovation are explored. The findings show that all these incentives can boost economic, environmental, and social benefits while motivating the supplier. To achieve the best incentive effect, the reward coefficient should fall within a specific range. The digital construction reward mechanism is the most favourable in the initial stage, while the R&D effort reward mechanism is the most appropriate in the long term. The promotion effects of digital capability on green innovation and consumer green preference have the potential to enhance economic, environmental, and social performance.

The freeway’s operation safety has attracted wide attention. In order to mitigate the losses brought on by traffic accidents on freeways, discrete choice models were constructed based on the statistical analysis method to quantitatively analyze the significance and magnitude of the impact of multiple dimensional factors on crash severity. Based on 1154 accidents that occurred on Zhejiang Province’s Hang-Jin-Qu Fressway from 2013 to 2018, the distribution characteristics of crash severity were analyzed. The dependent variable was the crash injury severity, which was categorized into property damage only (PDO), injury, and fatal. As independent variables, 15 candidate variables representing four aspects, including driver, vehicle, road, and environmental conditions, were chosen. Considering the ordered characteristics of the variables, the models developed included the ordered logit, the generalized ordered logit, and the partial proportional odds models. The Brant test found that the previous two models had difficulty dealing with the problem of partial variables that did not fit the parallel-lines assumption, and the conclusions were finally discussed through the partial proportional odds model results. The findings indicate that 11 factors have significant consequences. Five variables, namely “mountainous”, “female”, “driving experience 2- years”, “large vehicle responsible”, and “vehicle not going straight”, violated the parallel-lines assumption. Female drivers and drivers aged 55+ years were more likely to suffer injuries and fatalities in collisions with guardrails and other objects. Large vehicles being involved and vehicles not going straight enhanced the likelihood of injury and fatal outcomes when drivers had 2- years of experience. Wet-skid road conditions enhanced the likelihood of injury accidents, and driving at nighttime without lighting increased the likelihood of fatal accidents. Departments responsible for traffic management can take full account of these variations and develop focused proposals for improvement.

The closed-loop supply chain (CLSC) focusing on product recycling is getting more and more attention. The question of how to motivate CLSC participants to implement product recycling effectively has come into focus. We study the optimal strategy of a two-echelon CLSC under different incentive mechanisms, characterize the return rate in the form of a state equation for its dynamic behavior and construct a closed-loop supply chain dynamic model. Subsequently, we discuss two incentives—the cooperative promotion incentive and wholesale price discount incentive—based on the no-incentive game and investigate the optimal strategy, steady-state return rate, and revenue of each CLSC player in the three scenarios. Then, the choice of incentives by manufacturers and retailers under different scenarios is determined by comparative analysis. The results indicate that retailers always favor the cooperative promotion incentive. For manufacturers, the wholesale price discount incentive is more advantageous only when the recycling efforts outperform retailers’ promotion efforts in unit cost. In most other cases, the cooperative promotional incentive is more popular since it will generate more revenue for each participant and the entire supply chain. Moreover, there is a Profit-Pareto-improvement region when the CLSC implements the cooperative promotion incentive.