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Life Cycle Assessment is necessary for evaluating the environmental impacts of buildings throughout their life cycle, considering factors such as energy consumption, emissions, and resource utilization. However, Dynamic Life Cycle Assessment introduces a temporal dimension, acknowledging that a building's environmental performance evolves due to technological advancements, occupancy behavior, and changing environmental conditions. This paper reviews DLCA, focusing on uncertainties arising from parameter, scenario, and model variability, and emphasizes the integration of technologies like Building Information Modeling, the Internet of Things, and machine learning to enhance real-time data collection and predictive analytics. An extensive review of 430 papers, refined to 180, reveals that 55 % of publications are in environmental sciences, with significant contributions from the United Kingdom (27.8 %), France (24.1 %), and China (18.1 %). Key findings include significant variations in embodied greenhouse gas emissions for materials like aluminum and the dynamic aspects of transportation impacts, which extend beyond traditional metrics to include operational efficiency over time. Uncertainties in all LCA stages (A1 to D) are addressed, focusing on service life, operational energy and water use, and transportation needs. Advanced methodologies, including a proposed framework for a hybrid LCA approach that integrates process-based and input-output methods, are suggested to enhance the comprehensiveness of assessments. The integration of real-time monitoring and predictive analytics further improves the adaptability and precision of LCA models, emphasizing the necessity of continuous updates and scenario analyses to capture future conditions accurately. This study paves the way for future research aimed at mitigating major sources of uncertainty, promoting more sustainable building practices, and advancing the field of dynamic LCA.
Hosamo et al. (Thu,) studied this question.