The inconsistency caused by different life cycle impact assessment (LCIA) methods is a long-term challenge for the life cycle assessment (LCA) community. It is necessary to systematically analyze the differences caused by LCIA methods and facilitate the fair comparison of LCA results. This study proposes an effective method of conversion factors (CFs) for converting the results of 8 LCIA methods for 14 impact categories and then demonstrates its application in the construction sector. Correlation analyses of the datasets of construction materials are conducted to develop CFs for the impact categories. A set of conversion cards are devised to present the CFs and the associated correlation information for the LCIA methods. It is revealed that the differences between LCIA methods are largely caused by the characterization methods, rather than due to the metrics. A comparison based only on the same metrics but ignoring the underlying LCIA mechanisms is misleading. High correlations are observed for the impact categories of climate change, acidification, eutrophication, and resource depletion. The developed CFs and conversion cards can greatly help LCA practitioners in the fair comparison of LCA results from different LCIA methods. Case studies are conducted, and verify that by applying the CFs the seemingly incomparable results from different LCIA methods become comparable. The CF method addresses the inconsistency problem of LCIA methods in a practical manner and helps improve the comparability and reliability of LCA studies in the construction sector. Suggestions are provided for the further development of LCIA conversion factors.

To reduce harm caused by occupational health risks of construction workers exposed to working environments, especially those for interior decoration, it is crucial for them to actively recognize and prevent these risks. Therefore, how to improve their occupational health risks perception and regulate their coping behaviors should be of great concern. However, most prior studies target construction worker safety, and little research focuses on risk analysis from the psychological level of workers. Hence, construction workers’ occupational health risk perception level and coping behavior level in Nanjing and the influencing factors were analyzed through statistical analysis with 341 valid questionnaires. Bootstrapping was applied to test the mediating effects of risk perception on the proposed factors and coping behaviors. This study revealed that construction workers have a high-level of occupational health risk perception, yet low-level coping behavior. Gender, age, education level, and unit qualification cause differences in individual risk perception level. Personal knowledge and group effects significantly affect the level of risk perception, which subsequently affect coping behavior. Education level, monthly income, and personal knowledge influence the coping behavior through risk perception. Recommendations were put forward for risk perception and coping behavior improvement from the perspectives of construction workers themselves, enterprises, and governments. This study sheds new light for research areas of occupational health and risk management and provides beneficial practice for improving construction workers’ responses to occupational health risks.

Previous literature has recognized that workers’ unsafe behavior is the combined result of both isolated individual cognitive processes and their interaction with others. Based on the consideration of both individual cognitive factors and social organizational factors, this paper aims to develop an Agent-Based Modeling (ABM) approach to explore construction workers’ sociocognitive processes under the interaction with managers, coworkers, and foremen. The developed model is applied to explore the causes of cognitive failure of construction workers and the influence of social groups and social organizational factors on the workers’ unsafe behavior. The results indicate that (1) workers’ unsafe behaviors are gradually reduced with the interaction with managers, foremen, and workers; (2) the foreman is most influential in reducing workers’ unsafe behaviors, and their demonstration role can hardly be ignored; (3) the failure of sociocognitive process of construction workers is affected by many factors, and cognitive process errors could be corrected under social norms; and (4) among various social organizational factors, social identity has the most obvious effect on reducing workers’ unsafe behaviors, and preventive measures are more effective than reactive measures in reducing workers’ unsafe behaviors.

With the continuous progress of urbanization, the discharge of municipal solid waste has profoundly affected human production/living and social ecological health, and waste disposal has become one of the key issues all over the world. In the context of intelligent technology, this paper innovates the way of waste disposal according to the development direction of a smart city and the requirement of sustainable development strategy. An intelligent urban waste removal system is developed using a ROS (Route Operation System) robot operating system and RRT (Rapid Exploration of Random Trees) path planning algorithm. With a background of data management, the entire process of intelligent automatic waste removal is triggered by automatic communication from individual waste bins to a waste collection vehicle (WCV) where the bin needs to be emptied, and the automatic collection and transportation by the WCV in response. In combination with the IoT (Internet of Things), the system provides scientific data support for the intelligent layout of communities and even urban waste bins to greatly enhance the development of intelligence communities and smart cities.

The objective of this study was to establish a satisfaction evaluation model of industrial heritage renewal by evaluating the performance of old industrial buildings after renewal, then judging the adaptability and accuracy of the transformation strategy and putting forward targeted improvement suggestions. The data were collected through a questionnaire survey and analyzed by the statistical package for social science (SPSS) software using a structural equation model (SEM) and importance–performance analysis (IPA). The results show that (1) history, culture, and placeness have the largest average satisfaction and a positive and significant impact on overall satisfaction; (2) the building regeneration effect has a significant impact on overall satisfaction; and (3) social influence, external environmental perception, and economic benefits have no significant effect on overall satisfaction, indicating the need to strengthen public participation for further research into environmental creation, market-based operation, and land conservation. This case study reflects the distinctive features of internationalization and localization, and provides an indication of local practice in China for world urban renewal and architectural heritage theories.