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Building sustainability through a novel exploration of dynamic LCA uncertainty
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Resumo(s)
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.
Descrição
Funding Information: Understanding and managing uncertainty in DLCA is critical for informed decision-making regarding the environmental sustainability of products. Effective uncertainty analysis helps in identifying the key parameters that significantly influence the assessment results, guiding efforts to prioritize data collection and model refinement. Studies like those by Chen et al. (2018) demonstrated the importance of representing and visualizing data uncertainty to support decision-making in LCA. By leveraging an array of publicly available data, they developed methods to quantify and propagate uncertainty, enhancing the reliability of LCA models [13]. Furthermore, a critical perspective by Lo Piano and Benini evaluated the approaches for uncertainty appraisal and sensitivity analysis in LCA, emphasizing the need for comprehensive methods to address both stochastic and epistemic uncertainties to support robust decision-making [14]. Additionally, Herrmann et al. discussed strategies to confront and manage uncertainty in LCA used for decision support, suggesting that incorporating quantitative uncertainty analysis into LCA can significantly enhance its reliability as a decision-support tool [15].Using BIM, LCA practitioners can access a wealth of information about materials, construction processes, and building operations, which allows for more accurate and detailed environmental impact assessments. For example, Jalaei et al. integrated BIM with a decision-making approach to select optimal materials at the conceptual design stage [246]. Other studies have combined BIM with optimization techniques to find the best solutions for building components [247]. Some researchers have used BIM design tools and LCA technology to develop templates for assessing the embodied environmental impacts, such as Lee et al. who created a green template for this purpose [248]. These applications demonstrate that the synergy between BIM and LCA can effectively guide sustainable design decisions early in the project lifecycle. Also, Curry et al. utilized BIM to store cloud-based building data and manage the operational energy of office buildings [249], while Yang and Wang linked BIM to LCA to assess the operational energy consumption of residential buildings [250]. These studies highlight the capability of BIM to support comprehensive operational analysis and enhance energy efficiency throughout the building's lifecycle.Based on the literature, three major conclusions can be drawn. First, BIM simplifies data acquisition for LCA studies and provides effective feedback tools, with numerous applications demonstrating the potential of BIM-LCA integration. Second, the adoption of DLCA is necessary and requires a greater amount of multidisciplinary knowledge compared to traditional LCA. BIM's ability to superimpose multidisciplinary information within a single model enhances the synergy between BIM and DLCA, creating opportunities to incorporate various sustainability measures. Third, while BIM supports LCA studies at different phases of the building lifecycle, most studies have focused on the design and operation phases. There is a scarcity of research on comprehensive, cradle-to-grave BIM-based assessment models that provide quantification and management across the entire building lifecycle. Addressing these gaps will enhance the application of BIM-LCA integration, promoting sustainable building practices from inception to demolition.The authors acknowledge the EEA and Norway grants for the financial support through project LT07-1-EIM-K01-003 (\u201CDevelopment of a less polluting, automated fa\u00E7ade system integrated into building management systems\u201D). The second author is grateful for the Foundation for Science and Technology's support through funding UIDB/04625/2020 from the research unit CERIS (DOI: 10.54499/UIDB/04625/2020). Funding Information: The authors acknowledge the EEA and Norway grants for the financial support through project LT07-1-EIM-K01-003 (\u201CDevelopment of a less polluting, automated fa\u00E7ade system integrated into building management systems\u201D). The second author is grateful for the Foundation for Science and Technology's support through funding UIDB/04625/2020 from the research unit CERIS (DOI: 10.54499/UIDB/04625/2020). Publisher Copyright: © 2024
Palavras-chave
Building information modeling Building sustainability Dynamic life cycle assessment Internet of Things Machine learning Uncertainty management Environmental Engineering Civil and Structural Engineering Geography, Planning and Development Building and Construction SDG 7 - Affordable and Clean Energy SDG 9 - Industry, Innovation, and Infrastructure SDG 12 - Responsible Consumption and Production SDG 13 - Climate Action SDG 17 - Partnerships for the Goals