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.

The present study utilized a random parameter logit (RPL) model to explore the nonlinear relationship between explanatory variables and the likelihood of expressway crash severity. The potential unobserved heterogeneity of data brought by China’s road traffic characteristics was fully considered. A total of 1154 crashes happened on Hang-Jin-Qu Expressway from 2013 to 2018 were analyzed. In addition to the conventional impact factors considered in the past, variables related to road geometry were also introduced, which contributed to expressway accidents significantly. The overall stability of the model estimation was examined by likelihood ratio test. Then, the average elastic coefficient of the significant factors at each severity level was also calculated. Several factors that significantly increase the fatal crash probability were highlighted: rainy/snowy/cloudy weather condition, low visibility (100– m), night without light, wet-skid road surface, being female, aged 41+ years, collision with a rigid barrier and some other obstacles, radius and length of horizontal curve, and longitudinal gradient. The parameters of four factors were random and obeyed normal distribution: night without light, being female, driving experience with 10 + years and with large vehicle responsible. These findings provide insights for better understanding of expressway crash severity. Some countermeasures were proposed about driver education, traffic law enforcement, vehicle and road design, environmental improvement, and so on.

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.