CAPSTONE PROJECT
Compass: Equipping climate experts with a tool to guide decisions that reduce carbon emissions
Skills
UX Design, Strategy, Research, Product Thinking, Workshop Facilitation, Testing
Timeline
May 2024 - April 2025
Team
1 Designer, 2 Product Managers,
2 Developers
Context
As part of our engineering capstone project at the University of Waterloo, our team partnered with our client, Abbcari, to address one of Canada’s greatest challenges: achieving net-zero carbon emissions.
Our work focused on the Levelized Cost of Carbon Abatement (LCCA) model, a framework that evaluates how cost-effective different technologies are at reducing greenhouse gas emissions.
🎯 Goal: Transform a highly technical Excel-based model into an intuitive web tool that helps climate experts
make informed, sustainable, and cost-effective decisions.
Tl;dr
Where did I make an impact?
As the team's UX designer, I helped guide aspects of our development process from problem discovery, user research, ideation, designing, and testing.
1.
Problem Discovery
Stakeholder meetings
Interviews
Competitive analysis
2.
Define
User needs mapping
Developing requirements
3.
Ideation
Flow chart
Design jams
4.
Design
Hi-fi designs & prototype
Heuristic evaluation
Focus groups & testing
Current State
A powerful tool trapped in an unusable Excel file
The original LCCA model lived in a dense Excel file with complex formulas, hundreds of parameters, and nested sheets. It required a lot of domain expertise and only the creator of the tool was able to use it effectively. Our client wanted to turn a specialized model into something that any sustainability professional could confidently use.
The output of the LCCA model is highlighted in yellow. Without adequate visualization, its significance gets lost.
Multiple sheets containing vague parameters, including some that users do not need to adjust. This can lead to cognitive overload.
Solution
🚀 Launched and ready to use!
You can explore the live version of Compass, a web application that enables users to evaluate the cost-effectiveness of green technologies through interactive visualization and simplified input parameters.
Discovery
Talking to the experts to guide our approach
Ideating in a new problem space felt daunting, so we prioritized hearing directly from users. Our stakeholders had identified three primary user groups for this product: researchers, policymakers, and academia.
We conducted 12 interviews across these groups, gaining valuable insights into their typical workflows, key considerations when adopting a new tool, and their initial impressions of the LCCA framework in Excel.
“The main problem is it takes [users] a lot of time to understand the interaction between the different units... if you limit your input parameters, it will make life easier for everyone”
“If you're talking about net present value, it still goes right over their head. If you say it's going to cost $1 million today, they'll be like, ‘whoa, okay, I understand what that means’.... at the end of the day, almost all decisions are based on available capital at the present day”
🧑🏽🦱
👩🦰
Looking at what's currently on the market
Since LCCA is a novel parameter, there were no tools using this metric on the market. However, we explored other decision making or data visualization tools that are commonly used by our target groups to help us get a feel of the type of interfaces and processes they are familiar with.
Ideation
✏️ Design jams: sketching our way to big ideas
I facilitated design sessions with my team to collaboratively explore what the interface and flow of the product should look like. In each session, I focused on a specific part of the user flow, presenting relevant findings from our competitive analysis and user research to guide ideation.
Our goal was to visualize how information could be organized across screens, while building on and challenging each other’s ideas. From this, I extracted the strongest concepts and turned them into high-fidelity designs.
Design Jam #1: Performing a new calculation and viewing previous ones
Team member’s sketch:
Section of user flow:
Define
Let's define our problem space
How might we make the model more accessible to improve research and policy decision making to ultimately reduce global greenhouse gas emissions?
Translating research insights into design direction
By combining insights from user interviews, stakeholder discussions, and competitive analysis, we identified the distinct needs of our three user groups. These core insights became the foundation of our design strategy:
🏛️ Policymakers need summarized, interpretable insights
📈 Researchers need transparency in data and calculations
📖 Academics need flexibility to update and test assumptions
Developing requirements
Once we understood our problem space and established clear focus areas, we broke down the feature requirements. Using the MoSCoW method, we then prioritized these features and organized them into sprints.
Awards
🥇 Konrad Capstone Design Award
“Their process closely mirrored the Konrad "way of working," from User Research and Defining Scope to User Flow Design
Diagrams, Prototypes, and High-Fidelity Designs – all within an agile SDLC. Seeing this structured, thoughtful approach in action
really stood out to us!”
🥇 Sustainable Development Capstone Design Award
Recognized for advancing the UN Sustainable Development Goals through innovation in sustainable design and usability.
Learnings
🌱 Getting to know our users changed everything
We began in a technically complex space far from our comfort zone. By choosing to focus on understanding our users first, everything shifted. This project became more manageable and we were able to transform all of our uncertainty into direction. We prioritized what mattered most, simplified the experience, and knew how to pivot when dealing with unexpected constraints.
Outcome
Seeing how our solution holds up
We tested our prototype in moderated user testing sessions with the climate experts we initially spoke to and received valuable feedback that helped us refine our product experience. Our client was thrilled that we were able to create a web application grounded in the insights and validation of the target users.
“The team developed an application that transforms the complex LCCA framework into an intuitive, user-friendly tool—a significant milestone. By lowering the barrier to considering and adopting green technologies, this tool has the potential to serve as a guiding example for sustainable solutions.”
👩
Designs
🙌🏽 Bringing our vision to life through high-fidelity design
From creating a design system to sketching low-fi wireframes, a lot went into this process. Here are some key aspects of our final design that I wanted to highlight:
Focused on introducing the model and demonstrating its value to users through feature highlights, an overview of LCCA, and a clear statement of our mission
Introducing a homepage
LCCA Analysis
Start new analysis
Saved analyses
How it works
FAQ
Help
“P2A vs. HB Analysis” Project
General inputs
LCCA analysis
Start new
P2A Analysis
1
General
2
Electrified Process
3
Natural Gas Process
4
Review
Discount Rate
Value
%
Start year
2024
Plant operating hours
8000
/year
Target year
2034
Province used in analysis
Value
Next
Back
Electrical ammonia demand in target year
Value
pJ
Breaking down the analysis workflow into manageable steps with an interactive wizard
Segmenting the analysis
Use bottom-up cost estimation
Direct costs
Name
$
+ Add a direct cost
Indirect costs
Name
$
+ Add a direct cost
Working capital cost
$
LCCA Analysis
Start new analysis
Saved analyses
How it works
FAQ
Help
Inputs for the Haber-Bosch process
Water requirements
Value
Onsite emissions
Value
Upstream emissions
Value
Subprocesses
CAPEX
+ Add a subtechnology
Baseline cost: 1000
Learning rate: 4
Scaling factor: 2
Installation factor: 2
Energy requirement: 100
Efficiency: 0.7
General
2
Electrified Process
3
Natural Gas Process
4
Review
LCCA analysis
Start new
P2A Analysis
“P2A vs. HB Analysis” Project
PEM
Next
Back
Edit
Baseline cost: 900
Learning rate: 3
Scaling factor: 2
Installation factor: 2
Energy requirement: 100
Efficiency: 0.5
XYZ
Edit
Help
Showing users the modifiable parameters only and prefilling input boxes with recommended defaults
Limiting parameters
Provides explanations for each parameter, showing their role in the calculation, and links to a user manual for more guidance
Using tooltips
Provides users with warnings for potential input errors to prevent incorrect analyses
Error prevention
Highlighting important metrics upfront, providing interactive graphs, and allowing parameter adjustments for sensitivity analysis
Visualization of results