The vast majority of accidents in construction are generated by unsafe behaviors. Some researches also find that the behaviors could be influenced by the awareness and safety climate. The safety behavior and awareness belong to individual levels, while the safety climate belongs to the organization level. Previous studies mainly focus on the relationships between safety climate, safety awareness, and safety behavior without considering their different respective levels and the interaction between levels. This study establishes a hierarchical linear model (HLM) of safety climate, individual safety awareness, and safety behavior to examine the multilevel relationships between them. Data were collected using questionnaire from workers in different teams on the construction site in China. The results indicate that organizational safety climates affect individual safety behavior and safety awareness. In addition, there is a positive correlation between individual safety awareness and safety behavior, and the safety climates have a positive moderating effect on the relationship between them. The final conclusion offers a path for the current practice of safety management in the construction industry.

Purpose: This study aims to evaluate the effectiveness of different prefabricated construction (PC) policies using a case study in Wuhan, considering the local context.Design/methodology/approach: The effectiveness of PC policies is falling behind expectations. The main reason lies in an insufficient understanding of the policy impacts. An agent-based model was built by choosing the residential sector in a typical large city of Wuhan, China, as the study case. Different cost reduction scenarios were introduced for investigating the PC policy effectiveness. The proposed model and simulation approach can be used for other cities and generalized to the whole Chinese PC industry with the potential to include more local policies and corresponding data.Findings: Simulation results show that carbon emission reduction will be between 60,000 and 80,000 tons with policy incentives, nearly double that of the no policy intervention scenario. The target of 30% PC in all new buildings by 2026 in China is achievable with the subsidy policies of linear cost reduction, or cost reduction conforms to the learning curve.Practical implications: Simulation results of three kinds of policy show that subsidy policy optimization is necessary regarding reducing the level of subsidy needed. The carbon credit policy is not essential since it has little influence on PC development. Implementing the project procurement restriction policy is not recommended if the scale of development of PC is more important than achieving the development target.Originality/value: This study can help the government and developers make better policy and strategic decisions on PC development and boost the sustainability transition of the construction industry.

Data privacy in smart homes is receiving increasing attention due to the growing adoption of smart appliances. Adoption of smart appliances can bring benefits, including energy consumption reduction. This study investigates how people made the trade-offs between sharing privacy-sensitive data and the potential environmental and economic benefits of smart home energy appliances using discrete choice modeling. The findings reveal that the trade-off is mainly affected by four product attributes: the type of data that is processed, the reason why this data is processed, the data sharing frequency, and the financial benefit gained from the smart home appliances. Specifically, individuals tend to share less data daily for their daily routine convenience and demand a (theoretical) financial compensation for the data sharing. The results also show that privacy attitudes are not related to data sharing preferences, while socio-demographics, including gender, age, and income, are. The results emphasize the gap between people's attitudes and behaviors regarding data privacy. This research serves as a foundation for further investigations and can be used by smart appliance retailers, manufacturers, and governments for designing research and development focus and energy reduction incentives, respectively.

Purpose: Housing energy consumption is a significant contributor to climate change. Encouraging the adoption of energy-efficient products can be an effective way to reduce energy consumption. The impacts of social influences, such as peer effects and social norms, on energy efficiency adoptions were identified; however, these social influences are not quantified and compared with each other or with other influences. This study aims to investigate the choice of energy-efficient product adoption with different costs and how different social influences affect the choice through different processes and paths. Design/methodology/approach: Two stated choice experiments were employed in Wuhan, China, to examine the impact of social influences on energy-efficient product adoption in low-cost and high-cost scenarios. Appliance packages (including fridges and washing machines) and heating and cooling systems were used for the two cost scenarios, respectively. The social influences are evaluated in three aspects: positive versus negative information, physical versus online social networks and peer effects versus social norms. Findings: The study revealed how various factors, including social influences, impact energy-efficient product choices. Research results show that: (1) social influences have greater and wider impacts in the low-cost scenario than in the high-cost scenario; (2) negative information decreases the adoption of low-cost energy-efficient products, while positive information boosts high-cost energy-efficient product adoption and (3) people value the information provided by those they know personally and are more influenced by physical social networks. Originality/value: This study contributes to a better understanding of social influence in energy-efficient product adoption with different costs. This study provides a comprehensive framework to investigate social influences comparing the impact of different processes, paths and types of information. The findings can also provide practical implications for policymakers to accelerate the energy transition in the built environment.