Probabilistic life cycle assessment in buildings: A systematic literature review

Uncertainties can lead to significant discrepancies between building life cycle assessment (LCA) results and actual environmental impacts, potentially misleading design decisions. While probabilistic methods can offer comprehensive insights into uncertainties and promote informed decision-making, they are not yet widely applied in building LCA due to practical challenges. This study presents a systematic review of the application of probabilistic LCA in buildings, answers questions on how to implement and utilize probabilistic LCA to support design decisions, and proposes strategies for its widespread adoption. First, this study examines the current application status of probabilistic LCA in buildings, and demonstrates its effectiveness in simultaneously handling multiple uncertain parameters. Subsequently, systematic guidance on applying probabilistic LCA in buildings is provided. For implementation guidelines, uncertainty characterization and propagation methods are evaluated, and probabilistic LCA tools adopted across various application contexts are compared. For decision-making approaches, basic statistical and specialized design comparison indicators are synthesized, and frameworks for both manual and automatic optimization are formulated. Notably, automatic optimization methods based on probabilistic LCA show promising potential in identifying robust design solutions and balancing environmental performance with design flexibility. Finally, three main challenges that hinder the widespread adoption of probabilistic LCA in buildings are identified—computational intensity, operational difficulty, and decision-making complexity—and six corresponding strategies are proposed to address them. This study contributes to facilitating the application of probabilistic LCA in buildings, thus advancing decarbonization efforts in the building sector.