Life Cycle Assessment (LCA) results are typically presented using default visualization and communication approaches without acknowledging: the goals of the end-user, the end-user’s level of knowledge in LCA, the qualitative explanation supporting the visual, and the uncertainty in the process. The motivating hypothesis of this research is that the way practitioners communicate and visualize LCA results poses a risk to the interpretations of the end-users, especially when the goal of the study is not of focus when designing the visuals. Different LCA goals, whether it is for comparisons, hotspot identifications, or environmental declarations, require different visualization designs. To test this, studies were conducted with a variety of participants by giving them several visual representations of LCA results and asking them to share their interpretations of them. The participants’ interpretations of each visual were compared to the opinions of a panel of LCA experts and to the author’s intended use of it. This research gives insight on where misalignments or enhancements in the interpretation of results can occur based on the visual representations used in a certain goal category and the other factors previously mentioned. The results also provided three more key findings: 1) The majority of visuals that accurately presented and communicated the results were in the same goal category that the authors intended the visuals to be used for, suggesting that visuals are more effective when designed with the goal of the study in mind. 2) Several visuals suggested misconceptions in the presentation of results which included a misconception of the participants, a misconception of the authors, or a misconception between all groups. 3) None of the visuals in the environmental declarations category received a consensus from the panel of experts that they were well-suited for that purpose which suggests a significant research gap in accurately visualizing results for these purposes. These results aided the development of guidance documents to suggest both what to consider and what to avoid based on the goal of the study. The findings from this study can assist in bridging the gap in communication between the practitioner and the end-user.

Comparative life cycle assessment (LCA) evaluates the relative performance of multiple products, services, or technologies with the purpose of selecting the least impactful alternative. Nevertheless, characterized results are seldom conclusive. When one alternative performs best in some aspects, it may also performs worse in others. These tradeoffs among different impact categories make it difficult to identify environmentally preferable alternatives. To help reconcile this dilemma, LCA analysts have the option to apply normalization and weighting to generate comparisons based upon a single score. However, these approaches can be misleading because they suffer from problems of reference dataset incompletion, linear and fully compensatory aggregation, masking of salient tradeoffs, weight insensitivity and difficulties incorporating uncertainty in performance assessment and weights. Consequently, most LCA studies truncate impacts assessment at characterization, which leaves decision-makers to confront highly uncertain multi-criteria problems without the aid of analytic guideposts. This study introduces Stochastic Multi attribute Analysis (SMAA), a novel approach to normalization and weighting of characterized life-cycle inventory data for use in comparative Life Cycle Assessment (LCA). The proposed method avoids the bias introduced by external normalization references, and is capable of exploring high uncertainty in both the input parameters and weights.

Life Cycle Assessment (LCA) quantifies environmental impacts of products in raw material extraction, processing, manufacturing, distribution, use and final disposal. The findings of an LCA can be used to improve industry practices, to aid in product development, and guide public policy. Unfortunately, existing approaches to LCA are unreliable in the cases of emerging technologies, where data is unavailable and rapid technological advances outstrip environmental knowledge. Previous studies have demonstrated several shortcomings to existing practices, including the masking of environmental impacts, the difficulty of selecting appropriate weight sets for multi-stakeholder problems, and difficulties in exploration of variability and uncertainty. In particular, there is an acute need for decision-driven interpretation methods that can guide decision makers towards making balanced, environmentally sound decisions in instances of high uncertainty. We propose the first major methodological innovation in LCA since early establishment of LCA as the analytical perspective of choice in problems of environmental management. We propose to couple stochastic multi-criteria decision analytic tools with existing approaches to inventory building and characterization to create a robust approach to comparative technology assessment in the context of high uncertainty, rapid technological change, and evolving stakeholder values. Namely, this study introduces a novel method known as Stochastic Multi-attribute Analysis for Life Cycle Impact Assessment (SMAA-LCIA) that uses internal normalization by means of outranking and exploration of feasible weight spaces.