Carbon Autopilot

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  The main dashboard where users enter lifestyle data &receive an AI-generated carbon footprint analysis with personalized Gemini AI insights.

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  Visual comparison of carbon emissions beforeafter Autopilot optimization, highlighting the measurable impact of AI-driven emission reduction

Inspiration

Climate change awareness is increasing, but real action remains limited because most sustainability tools require constant manual effort. We were inspired to build a solution that removes this friction by using AI to automate carbon footprint analysis and make sustainable decisions easier for everyday users.

What it does

Carbon Autopilot is a web-based AI-powered application that analyzes basic lifestyle data such as commute distance, electricity usage, and food habits to estimate carbon emissions. It uses Google Gemini AI to generate explainable insights and visually demonstrates how automated optimization can reduce carbon impact.

How we built it

We built Carbon Autopilot using Next.js and TypeScript for the frontend, focusing on a clean and responsive user interface. The core carbon calculation logic processes user inputs, while Google Gemini AI is used to generate intelligent, human-readable insights. The application is deployed on Vercel as a live web-based MVP.

Challenges we ran into

One major challenge was balancing simplicity with meaningful insights, ensuring the system remained easy to use without oversimplifying the problem. Another challenge was presenting AI-driven insights in a way that is understandable and visually clear for non-technical users, especially within hackathon time constraints.

Accomplishments that we're proud of

We are proud of building a fully functional, deployed MVP that integrates AI-driven reasoning with a clear visual impact. Successfully implementing an intuitive UI, dark mode, and a live demo-ready workflow within a limited timeframe was a key achievement for our team.

What we learned

Through this project, we learned how to design user-centric AI systems, structure scalable web applications, and effectively communicate AI insights visually. We also gained hands-on experience in deploying and presenting a complete end-to-end product in a hackathon environment.

What's next for Carbon Autopilot

Future plans include integrating real-time energy and mobility data for higher accuracy, enhancing AI models for predictive carbon forecasting, and expanding the platform to support organizational ESG dashboards and smart-city sustainability initiatives.

Built With

• and
• carbon
• firebase-analytics-for-usage-tracking
• firebase-authentication-for-secure-login
• for
• future
• globally.
• google-cloud-infrastructure-readiness
• google-gemini-ai-for-intelligent-insights
• models.
• prediction
• scalable
• support
• tensorflow
• trusted.