LCA Design Guide
a project to facilitate green product design
Remote Internship, TU Delft Industrial Design Engineering with Prof. Jeremy Faludi and Prof. Bryony DuPont, 13 week team project
Challenge
Test different data visualizations for environmental impact, then use them to create a website designed to aid industry in creating more environmentally friendly products.
My Role
Designer, user researcher
Skills
User testing
Data and statistical analysis
HTML and CSS
Research paper writing
Teamwork
About Life Cycle Assessment
Life cycle assessment (LCA) is a method to quantify the environmental impact at each stage of a product’s life. It takes into account many different types of impacts, such as transportation, material intake and outtake, land use, and more.
The Problem
LCAs can be an extremely powerful tool for designers to determine the environmental impact of products, but only when performed in the later stages of design, after the design has been solidified and there is little uncertainty. However, designers have much greater potential to reduce environmental impact in the early stages of design where there is more flexibility. In order to bring the quantitative power of LCA’s to early stage designers, we decided to create a design guide where we will have aggregated and averaged data of general product categories and curated suggestions for green design strategies based on each product.
User Research
One of the issues with generalizing product categories is the variation between products, which causes a lot of uncertainty within the data. Rather than ignore this, we want designers to embrace this uncertainty and understand that there are multiple ways to reduce environmental impact. We conducted user research with over 30 participants to determine what style of data visualization best conveys this uncertainty.
User Research Results
After statistical and qualitative analysis, we determined that two graphs, “error bars” (graph 1) and “gradient” (graph 4) were the most effective. While the “error bars” graph was statistically more effective, after looking through qualitative results, the “gradient” graph was most effective in forcing users to acknowledge the uncertainty. We ended up going with the gradient graph on the website, but further testing would be needed to determine which of the two is better. A conference paper going more in depth into the results was written for the 23rd International Conference on Engineering Design (ICED).
The Website
In order to make this data widely available for people, we created a simple website where people can easily find and learn more about it. We were given a barebones prototype, and from there we prototyped a new visual style in Figma, then coded the final website in HTML and CSS. Check out the finished website at ProductDesign.green!
The original prototype we were given was a barebones layout with the information that Prof. Faludi wanted in the final website.
The original website prototype we were given
From there, we prototyped many different visual ideas and layouts. Through our explorations in the design, we discovered that using icons rather than images of the graphs gave a much cleaner look that was also easier to read. Using strategies such as varying word size and style also created a much more readable structure.
First round of prototype iterations
Final tweaks on the chosen prototype
After designing the website in Figma, we moved to HTML and CSS to code the final website and do final tweaks. See the final website here!