An AI-driven platform that helps individuals and businesses reduce their environmental footprint by providing personalized, actionable recommendations for sustainable living. It combines data science, behavioral psychology, and environmental science to make sustainability easy and engaging.
What it does:
Users input data (e.g., transportation habits, diet, energy usage).
AI calculates their annual carbon footprint in kg CO₂.
Example:
"You drive 50 km/week → 2.4 tons CO₂/year. Switching to an EV could save 1.5 tons!"
- Personalized Eco-Tips What it does:
AI analyzes user behavior and suggests customized sustainability actions.
Uses NLP (like GPT) to generate relatable tips.
Example:
"Based on your location, try solar panels! You could save $200/year and reduce energy emissions by 30%."
- Progress Tracking & Gamification What it does:
Tracks user progress (e.g., "Recycled 20 times this month").
Awards badges and ranks users on a leaderboard.
Component Technology Used Why We Chose It
Frontend Next.js (React), Tailwind CSS Fast, SEO-friendly, responsive UI Backend Node.js + Express (Python for AI) Scalable, easy Firebase integration Database Firestore (NoSQL) Real-time updates, seamless with Firebase AI/ML Python (scikit-learn, Hugging Face) Pre-trained NLP models for eco-tips Auth Firebase Authentication Secure, free tier, OAuth support Deployment Vercel (Frontend), Railway (Backend) Easy CI/CD, serverless
Built With
- bolt
- bolt.new
- next.js-(react)
- or-apple-ai/ml-hugging-face-transformers
- python-(ai/ml)-scalable-api-routes-and-ai-model-processing-database-firebase-firestore-(nosql)-real-time-user-data-sync-and-storage-authentication-firebase-auth-secure-sign-in/sign-up-with-email
- railway
- responsive-ui-with-server-side-rendering-backend-node.js-+-express-(api)
- scikit-learn-generate-eco-tips-and-predict-carbon-footprints-deployment-vercel-(frontend)
- tailwind-css
- typescript-fast

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