Understanding the structure and behavior of emissions in building systems is the first step toward improving the reliability of the environmental impact assessment of buildings. The shortcomings of current building lifecycle assessment (LCA) research is the lack of understanding of embodied emissions and static analysis. This study presents a methodology for the dynamic LCA of buildings, combined with the system dynamics technique. Dynamic factors related to recurrent embodied emissions are explored through a literature review. Applying the dynamic factors based on the review, a causal map and stock-flow diagram are invented. Collecting the field data and establishing the assumptions based on the literature, a case study is performed for the proposed model. As a result, through dynamic analysis, it was found that recurrent embodied emissions have a considerably different behavior from static ones during their whole life. Additionally, it was found that the environmental impacts changed by more than 10%, according to the variation of the users’ required performance level in sensitivity analysis. This result thoroughly addressed the necessity and appropriateness of dynamic LCA. The dynamic LCA model developed in this study can contribute to the long-term behavioral understanding of the embodied environmental impacts of building LCA.

The current method of estimating CO₂ emissions during the construction phase does not consider the variability that can occur in actual work. Therefore, this study aims at probabilistic CO₂ estimation dealing with the statistical characteristics in activity data of building construction work, focused on concrete pouring work and based on field data. The probabilistically estimated CO₂ emissions have some differences from CO₂ emissions measured by current deterministic methods. The results revealed that the minimum difference was 11.4%, and the maximum difference was 132.7%. This study also used Monte Carlo simulations to derive information on a probability model of CO₂ emissions. Results of the analysis revealed that there is a risk of underestimating emissions because the amount of emissions was estimated at a level that exceeds the 95% confidence interval of the simulation results. In addition, the probability that CO₂ emissions using the measured activities data were less than the estimated CO₂ emissions using the bill of quantity was 73.2% in the probability distribution model.

To maintain railway facilities in an appropriate state, systematic management based on mid- and long-term maintenance plans through future performance prediction must be carried out. To this end, it is necessary to establish and utilize a model that can predict mid- to long-term performance changes of railway facilities by predicting performance changes of individual sub-facilities. However, predicting changes in the performance of all sub-facilities can be difficult as it requires large volumes of data, and railway facilities are a collection of numerous sub-facilities. Therefore, in this study, a framework for a model that can predict mid- to long-term performance changes of railway facilities through analysis of continuously accumulated performance evaluation results is proposed. The model is a system with a series of flows that can classify performance evaluation results by individual sub-facilities, predict performance changes by each sub-facility using statistical methods, and predict mid- to long-term performance changes of the facility. The developed framework was applied to 36,537 sub-facilities comprising 12 lines of two urban railways in South Korea to illustrate the model and verify its applicability and effectiveness. This study contributes in terms of its methodology in establishing a framework for predicting mid- to long-term performance changes, providing the basis for the development of an automated model able to continuously predict performance changes of individual sub-facilities. In practical terms, it is expected that railway facility managers who allow trade-off between reliability and usability can contribute to establishing the mid- to long-term maintenance plans by utilizing the model proposed in this study, instead of subjectively building them.