CarbonWise

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Inspiration

Climate change is one of the biggest challenges of our time. While we often hear about "carbon footprint," most people don’t know how their daily choices add up—from electricity bills to fuel usage, food consumption, or shopping habits.

This inspired us to build a simple, AI-assisted tool that could make carbon accounting accessible to everyone—whether it’s a student, a family, or even a company.

What it does

How to convert real-world activities into CO₂ equivalents using emission factors.

The importance of contextual questioning (e.g., asking about fuel use in liters instead of distance traveled makes it easier for users).

How AI can simplify complex data models by turning them into interactive, user-friendly experiences.

That sustainability tools must balance accuracy and simplicity to stay practical.

How we built it

Research Phase: We collected emission factors from environmental studies (e.g., electricity ≈ 0.82 kg CO₂/kWh in India, petrol ≈ 2.31 kg CO₂/liter).

Designing the Flow: The chatbot asks whether the user is an individual or a company, then tailors questions accordingly.

Individuals: electricity, LPG, food habits, clothing/shopping, and fuel consumption.

Companies: energy bills, employee commuting, refrigerants, and business travel.

Estimation Engine: Each response is multiplied with emission factors and annualized (e.g., liters per month × emission factor × 12).

AI Integration: The chatbot interprets user answers, performs calculations, and provides personalized feedback on footprint + suggestions to reduce it.

Visualization: Results are displayed in charts/pie diagrams to help users see which activities contribute most to their emissions.

Challenges we ran into

Data Accuracy: Different sources give slightly different emission factors. We had to balance between global standards (IPCC) and local context (India-specific values).

Simplifying Complex Data: People don’t want to fill long surveys. Designing short but meaningful questions was tough.

Company Footprints: Individual footprints are easier to calculate; company-wide emissions are complex (scope 1, 2, 3 emissions). We simplified them for hackathon scope.

Time Constraint: Building an end-to-end tool (AI chatbot + calculator + visualization) within limited hackathon time was challenging but rewarding.

Accomplishments that we're proud of

Built a working AI-powered carbon footprint calculator within the limited hackathon timeframe.

Simplified a complex sustainability problem into a user-friendly chatbot experience.

Integrated scientific emission factors into real-world calculations.

Designed a tool that can be used by both individuals and organizations.

Created a strong foundation that can grow into a scalable green-tech solution.

What we learned

Translating technical environmental data into something the public can easily understand.

How AI can bridge the gap between raw data and meaningful insights.

The difference between scope 1, 2, and 3 emissions for companies.

The importance of balancing accuracy, simplicity, and user engagement.

How teamwork, rapid prototyping, and clear vision can bring impactful solutions to life.

What's next for CarbonWise

Add real-time regional data (e.g., electricity grid CO₂ intensity by state/country).

Introduce personalized recommendations (e.g., switching to solar, reducing meat intake, public transport suggestions).

Build a mobile app version with gamification (badges, challenges, CO₂ savings leaderboard).

Expand to corporate ESG reporting tools for organizations.

Collaborate with NGOs and schools to raise awareness on sustainable lifestyles.

Built With

• react
• shadcn-ui
• tailwind
• typescript
• vite