Carbon Sense

Inspiration

What it does# CarbonSenseAI — Project Story

About the Project

CarbonSenseAI was inspired by a simple but important problem: many people want to live more sustainably, but they do not know how their everyday choices translate into carbon impact. Climate information is often too technical, too broad, or too hard to act on. We wanted to build something that turns climate awareness into clear, personalized action.

Our project helps users estimate their annual carbon footprint based on habits in transportation, food, home energy, and shopping. Instead of stopping at a number, CarbonSenseAI uses AI to recommend realistic steps users can take to reduce their emissions. The goal was to make sustainability feel practical, understandable, and achievable.

What Inspired Us

We were inspired by the gap between caring about climate change and knowing what to do about it. A lot of people hear general advice like “drive less” or “eat greener,” but that advice is not always specific enough to fit real life. We wanted to create a tool that meets people where they are and gives them guidance based on their own lifestyle.

We also liked the idea of building something that combines technology with social impact. CarbonSenseAI is not just about calculations — it is about helping users make informed choices in a way that feels personal and motivating.

How We Built It

We built CarbonSenseAI as a mobile app with a backend carbon analysis engine.

On the frontend, users complete a short lifestyle quiz with around 10 questions. These questions cover major sources of personal emissions, including how they travel, what they eat, how they use energy at home, and how often they shop. We designed the interface to be simple and fast, with a progress bar and step-based flow so it feels easy to complete.

On the backend, we created a footprint calculation system that estimates a user’s annual emissions in tons of carbon dioxide equivalent ((tCO_2e)). The total footprint is based on contributions from each category:

[ \text{Total Footprint} = E_{\text{transport}} + E_{\text{food}} + E_{\text{home}} + E_{\text{shopping}} ]

We used emissions factors from trusted public climate sources and added fallback logic so the app could still return a useful estimate even when detailed inputs were unavailable. After the calculation, the app shows both the total footprint and a category breakdown, helping users understand where their emissions come from.

We then added an AI-powered recommendation layer that takes the user’s results and generates personalized suggestions. For example, if transportation is their biggest source of emissions, the app can recommend actions related to commuting, driving habits, or travel choices. This made the project feel more like a climate coach than just a calculator.

Challenges We Faced

One of the biggest challenges was balancing accuracy with simplicity. Carbon footprint analysis can become very detailed, but we only had a short amount of time to build an MVP that users could understand quickly. We had to decide which questions mattered most and how to keep the experience informative without making it overwhelming.

Another challenge was working with emissions data. Different sources use different assumptions, categories, and units, so we had to make sure our calculations stayed consistent. It was also difficult to design recommendations that felt specific and helpful instead of generic.

We also faced the usual hackathon challenge of building fast while still keeping the project polished. That meant making tradeoffs, prioritizing the most important features, and staying focused on the core user experience.

What We Learned

We learned that building a useful product is not just about making the technology work — it is also about making the results meaningful to the user. A carbon number alone is not enough. Users need context, comparison, and realistic next steps.

We also learned a lot about teamwork and rapid development. Our team had to divide responsibilities, connect the frontend and backend, and make decisions quickly under time pressure. Through that process, we gained experience in product design, technical integration, and building for real-world impact.

Most importantly, we learned that technology can make complex issues like climate change feel more understandable and actionable when it is designed with the user in mind.

Final Reflection

CarbonSenseAI showed us how software can be used to support positive behavior change. We are proud that our project takes a complex global issue and turns it into something personal, practical, and easy to understand. More than just a footprint calculator, it is a tool that helps users see where they are and where they can improve.