Life Cycle Assessment (LCA) is a powerful tool for the quantification of environmental and social impacts of products, allowing comparison, contribution analysis, and integration with other tools. Over the past few decades, decarbonization and climate change mitigation have become central to the sustainability and environmental conversation. The urgency of climate change has spurred new products, production pathways, and concepts—many of which are early-stage technologies with limited or no production data available to use for environmental assessments. This leads to a challenge in conducting LCA, where practice assumes detailed, high-quality data from existing production to complete a thorough product assessment.

At early Technology Readiness Level (TRL), the guidelines for performing LCA or related methods are not as clear as for established technologies. Researchers have devised ex -ante, anticipatory and other types of LCA methods to assess early-stage technologies; this array of exploratory approaches has been called ‘alphabet soup.’ The lack of consensus on appropriate methods (e.g., upscaling and uncertainty assessment) and analysis scope (e.g., how to compare to incumbent technologies) creates significant challenges: with no clear guidelines, practitioners may feel uncomfortable performing LCA, and the findings can be difficult to interpret. In the worst case, poorly executed or inconsistent LCA can be manipulated and used selectively to greenwash or justify preferred investments, highlighting the critical need for standardized guidelines.

In many related areas, tiered systems have been developed to allow practitioners and clients to communicate the strengths and weaknesses of their analysis. For example, the Cost Estimate Classification System is relied upon for Techno-Economic Assessment (TEA), front-end loading for program management; similarly, the Intergovernmental Panel on Climate Change (IPCC) offers tiers to help guide analysis and bound expectations. Various data pedigree matrices have also emerged to indicate the robustness of support and conclusions, but rarely do they explicitly address the additional data quality considerations for use with low-TRL technologies.

In this special session, we will introduce a preliminary diagnostic matrix for characteristics of LCA of emerging technologies, which is under development by the ACLCA LCA for Emerging Technologies Working Group. This matrix attempts to define the aspects of LCA for which implementation to early-stage technologies varies the most, and how this affects interpretation. Participants will engage in a series of interactive discussions and activities using this matrix, breaking into groups to outline case studies and offer critical review. The goal of this session is to catalyze a conversation on f