Hybrid LCA for sustainable transitions: principles, applications, and prospects

Hybrid life cycle assessment (LCA) offers an integrated approach, combining process-based and input-output data. As such, it strives for a more comprehensive and precise evaluation of alternative technologies and products, supporting sustainability transitions. A review of 114 studies from 2016 to 2022 reveals that hybrid LCA applications are most commonly focused on energy systems (24 %), with a strong emphasis on assessing climate change impacts. Hybrid LCA is crucial for addressing the data gaps prevalent in environmental evaluations of renewable energy technologies. Methodological innovations, such as combining hybrid LCA with multi-objective optimization, show promise in pinpointing ideal locations and designs for energy plants, such as biorefineries. However, a concerning observation is that many studies employed oversimplified or inadequately documented hybridization procedures, raising questions about their robustness. The review also identifies several remaining challenges in hybrid LCA, including the linearity assumptions, omission of capital goods, price data uncertainties, and inconsistencies in environmental flow data. A significant advancement lies in creating standardized datasets, especially for construction materials. Such datasets could enable large-scale evaluations of embodied impacts across projects, facilitating the selection of construction materials based on comprehensive material trade-offs and supporting net-zero carbon construction. Furthermore, to enhance hybrid LCA in future research, this study presents a streamlined classification of the various methods, clarifying their intended purpose and computational structure. • HLCA integrates micro and macro data for enhanced sustainability assessment. • HLCA addresses critical data gaps in renewable energy assessment. • HLCA combined with optimization reveals the ideal energy plant location and design. • Mathematical methods and terminology in HLCA are streamlined for simplicity. • Future research is guided by rising policy needs and key data challenges.