Environmental Assessment of Emerging Technologies: Recommendations for Prospective LCA

TLDR

Assessing emerging technologies with LCA is increasingly challenging and has been widely discussed in the field. The article defines prospective LCA and aims to provide recommendations for conducting such assessments. The authors adapt prospective LCA methods by reviewing case studies and employing predictive scenarios, scenario ranges, and diverse data sources to model foreground and background systems separately. Including future‑relevant technology alternatives is crucial, and separating foreground and background impacts avoids temporal mismatches, enhancing the usefulness of prospective LCA results.

Abstract

Summary The challenge of assessing emerging technologies with life cycle assessment (LCA) has been increasingly discussed in the LCA field. In this article, we propose a definition of prospective LCA: An LCA is prospective when the (emerging) technology studied is in an early phase of development (e.g., small‐scale production), but the technology is modeled at a future, more‐developed phase (e.g., large‐scale production). Methodological choices in prospective LCA must be adapted to reflect this goal of assessing environmental impacts of emerging technologies, which deviates from the typical goals of conventional LCA studies. The aim of the article is to provide a number of recommendations for how to conduct such prospective assessments in a relevant manner. The recommendations are based on a detailed review of selected prospective LCA case studies, mainly from the areas of nanomaterials, biomaterials, and energy technologies. We find that it is important to include technology alternatives that are relevant for the future in prospective LCA studies. Predictive scenarios and scenario ranges are two general approaches to prospective inventory modeling of both foreground and background systems. Many different data sources are available for prospective modeling of the foreground system: scientific articles; patents; expert interviews; unpublished experimental data; and process modeling. However, we caution against temporal mismatches between foreground and background systems, and recommend that foreground and background system impacts be reported separately in order to increase the usefulness of the results in other prospective studies.