A study of graphical representations of uncertainty in LCA guide
Melissa Tensa (Oregon State University)
Jenna Wang (Stanford University)
Roscoe Harris (Stanford University)
Jeremy Faludi (TU Delft - Industrial Design Engineering)
Bryony DuPont (Oregon State University)
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Abstract
This study user-tested different data visualizations for highly uncertain life cycle assessments (LCAs) to determine what best supported decision-making. Precise LCAs can only be performed once designs are finalized, due to the information necessary to complete them, but design changes in such late stages are costly. If designers could have environmental impact data earlier in the process, sustainable design choices could instead be built into the initial designs. We compiled LCAs for various product categories, finding the best means of visualizing the data for online and printable dissemination. Because this LCA data varied widely within each product category, it was necessary to display uncertainty and require users to acknowledge the uncertainty. Here, four different data visualizations were tested with engineering, design, and STEM students and professionals; both quantitative and qualitative analysis determined what visualizations were most favored and forced users to consider uncertainty. We hope that this research helps LCA data be more accessible to designers and engineers in the early phases of design, allowing those without the resources or ability to perform LCA to benefit from it and design more sustainably.