In recent years, environmental problems in the construction industry have become increasingly prominent and need to be improved. The production and use of building materials remain one of the main causes of various eco-environmental problems in the construction industry. Compared with traditional building materials, green building materials are environmentally friendly and safer to the human body. Therefore, the promotion of green building materials in the construction industry can handle the eco-environmental problems of the industry to a great extent, and promote the green and low-carbon development of the industry. As the main buyer of building materials products, developers shoulder the responsibility of promoting the development of green building materials. The research on the green purchasing behavior of developers is conductive to winning a good reputation for developers and promoting the sustainable development of the construction industry. However, few scholars have focused on the in-depth research on the impact of developers’ green procurement behavior. Based on the theory of planned behavior, this study analyzes the problem of the developers’ green procurement using the structural equation model in the way of the questionnaire. The results showed that subjective norms and perceived behavior control factors exerted a significant influence on developers’ green procurement (GP) behavior attitude. The subjective norm, perceived behavior control, and other factors indirectly affected developers’ GP behavior through behavior intention. The influence mechanism of perceptual behavior control on developers’ GP behavior is more complicated, and the degree of influence is the greatest among the three kinds of factors. According to the research results, this paper puts forward some suggestions to promote developers to actively implement GP behavior. The results help to promote the GP behavior of developers and promote the planning development of green buildings. In addition, on the basis of a quantitative theoretical model, this paper finds out the key factors that influence the behavior of GP from the point of view of developers, enriches the theory and method of GP research, as well as complements the related literature of GP research. Future research will explore GP behavior from the perspective of other stakeholders and from the developers’ internal level.

Recently, construction safety management (CSM) practices and systems have become important topics for stakeholders to take care of human resources. However, few studies have attempted to map the global research on CSM. A comprehensive bibliometric review was conducted in this study based on multiple methods. In total, 1172 CSM-related papers from the Web of Science Core Collection database were examined. The analyses focused on publication year, country–institute, publication source, author and research topics. The results indicated that the USA, China, Australia and the UK took leading positions in CSM research. Two branches of journals were identified, namely the branch of engineering science and that of safety science and social science. Additionally, seven themes together with 28 specific topics were detected to allow researchers to track the main structure and temporal evolution of CSM research. Finally, the main research trends and potential research directions were discussed to guide the future research.

This research developed and tested a model of the social contagion effect of coworkers’ safety violations on individual workers within construction crews. Both situational and routine safety violations were considered in this model. Empirical data were collected from 345 construction workers in China using a detailed questionnaire. The results showed that both types of safety violations made by coworkers were significantly related to individuals’ perceived social support and production pressure. Individuals’ attitudinal ambivalence toward safety compliance mediated the relationships between perceived social support and production pressure and both types of individuals’ safety violations. However, safety motivation only mediated the effects of perceived social support and production pressure on individuals’ situational safety violations. Further, this research supported the differences between situational and routine safety violations. Specifically, we found that individuals were more likely to imitate coworkers’ routine safety violations than their situational safety violations. Coworkers’ situational safety violations had an indirect effect on individuals’ situational safety violations mainly through perceived social support and safety motivation. By contrast, coworkers’ routine safety violations had an indirect effect on individuals’ routine safety violations mainly through perceived production pressure and attitudinal ambivalence. Finally, the theoretical and practical implications, research limitations, and future directions were discussed.

This study focuses on better development of green buildings. The key to the sustainable development of the construction industry is to popularize and promote the spread of green building technologies (GBTS) in the construction market. This study integrates the technology acceptance model (TAM) and the innovation diffusion theory (IDT) to analyze and construct the theoretical model of developers’ GBTS adoption behavior from three dimensions, including the individual factor, product factor and interface factor. This paper discusses the mechanism of GBTS adoption and diffusion in the construction market. The data are collected by questionnaire, and the structural equation model (SEM) is used for empirical analysis. The results show that the developers’ perceived usefulness (PU) and perceived ease of use (PEOU) of GBTS, developers’ innovativeness and sense of community at the individual level, competitive advantage at the product level, as well as government structural guarantees and relevant stakeholders at the interface level have a significant positive impact on the adoption of GBTS by developers. It is proved that the model can explain the basic path of GBTS adoption by developers, and suggestions to promote the adoption and diffusion of GBTS in China are put forward.

As an important component of urban construction, the community sustainable construction is of great significance to the social development. Despite the importance of the social dimension of community sustainability, it has received relatively little attention when compared to the sustainability of environmental and economic dimensions. Based on the literature review, it is found that the social dimension of the sustainable community, such as residents’ psychological feelings, social capital, has a strong effect on the sustainability of communities. This paper, therefore, conducts an empirical study on the mediating role of emotion experience on the associations between social capital and life satisfaction. Research on the relationship between them can help to generate synergies and strengthen the effect of promoting community sustainable development. A total of 229 complete responses from residents in China is used for data analysis. Results from the multiple linear regression analysis indicate that social capital has the significant positive effect on life satisfaction; the influence of social capital on life satisfaction is partly mediated by positive and negative affect, the social capital improves the life satisfaction through promoting positive affect, while it enhances the life satisfaction through restraining the negative affect. Based on the relationship between them, the relevant recommendations for the measures on the sustainable community can be proposed.

Providing accurate prediction of the severity of traffic collisions is vital to improve the efficiency of emergencies and reduce casualties, accordingly improving traffic safety and reducing traffic congestion. However, the issue of both the predictive accuracy of the model and the interpretability of predicted outcomes has remained a persistent challenge. We propose a Random Forest optimized by a Meta-heuristic algorithm prediction framework that integrates the spatiotemporal characteristics of crashes. Through predictive analysis of motor vehicle traffic crash data on interstate highways within the United States in 2020, we compared the accuracy of various ensemble models and single-classification prediction models. The results show that the Random Forest (RF) model optimized by the Crown Porcupine Optimizer (CPO) has the best prediction results, and the accuracy, recall, f1 score, and precision can reach more than 90 %. We found that factors such as Temperature and Weather are closely related to vehicle traffic crashes. Closely related indicators were analyzed interpretatively using a geographic information system (GIS) based on the characteristic importance ranking of the results. The framework enables more accurate prediction of motor vehicle traffic crashes and discovers the important factors leading to motor vehicle traffic crashes with an explanation. The study proposes that in some areas consideration should be given to adding measures such as nighttime lighting devices and nighttime fatigue driving alert devices to ensure safe driving. It offers references for policymakers to address traffic management and urban development issues.