Recent advancements in prospective life cycle assessment: Current practices, trends, and implications for future research

Abstract

Prospective Life Cycle Assessment (pLCA) is gaining interest due to its inherent future-oriented feature, which is an essential component of every decision-oriented life cycle assessment. Previous studies have highlighted challenges in conducting pLCA for emerging technologies, categorizing them into issues of comparability, data availability, scaling, and uncertainty and propose general frameworks to address these challenges. Accordingly, the application of pLCA is rapidly growing in recent years, with emerging methods addressing the limitations, and improving pLCA. This review study aims to compile and analyze emerging pLCA methods from scientific literature, identifying best practices and limitations to guide future research. It discusses methodological advancements in pLCA, including prospective life cycle inventory (pLCI) database, foreground modeling, scenario development and prospective life cycle impact assessment, offering insights for practitioners. While changes in background systems are increasingly addressed in pLCA studies, some, particularly earlier or less systematic ones, fall short of fully integrating nuanced future scenarios. The reviewed studies highlight that incorporating future scenarios related to the transformation of energy, material, transport, and industrial systems can significantly influence LCA outcomes, reinforcing the importance of explicitly integrating such scenarios into pLCA to ensure reliable and meaningful results. To ensure robust LCA studies, it is important to consider the use of pLCI databases, accounting for varying technology maturity levels, their improvement and diffusion rate, and incorporating spatial considerations. Yet, integrating pLCI databases with standard LCA tools remains complex, with a lack of practitioner guidance. Moreover, the interlinkage between climate change and various impact categories is a key source of uncertainty in future assessments, highlighting the need to improve both prospective inventory modeling and impact assessment. The findings call for future research to further explore the spatiotemporal effect of climate change on pLCA quantification, developing future-oriented characterization factors, expanding pLCI databases, as well as enhancing the applicability of pLCA studies through the integration of new analytical tools and models.