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A STUDY OF GRAPHICAL REPRESENTATIONS OF UNCERTAINTY IN LCA GUIDE

Published online by Cambridge University Press:  27 July 2021

Melissa Tensa*
Affiliation:
School of Mechanical, Industrial and Manufacturing Engineering Oregon State University, Oregon, USA
Jenna Wang
Affiliation:
Stanford Engineering, 475 Via Ortega, Stanford, CA, USA
Roscoe Harris III
Affiliation:
Stanford Engineering, 475 Via Ortega, Stanford, CA, USA
Jeremy Faludi
Affiliation:
Industrial Design Engineering, Landbergstraat 15, 2628 CE Delft, The Netherlands
Bryony DuPont
Affiliation:
School of Mechanical, Industrial and Manufacturing Engineering Oregon State University, Oregon, USA
*
Tensa, Melissa, Oregon State University, MIME, United States of America, [email protected]

Abstract

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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.

Type
Article
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2021. Published by Cambridge University Press

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