Track your carbon footprint with AI-powered insights, 3D visualizations, and actionable sustainability goals.

🚀 Live Demo

EcoTrack is an intelligent carbon footprint tracking dashboard that helps you understand, monitor, and reduce your environmental impact. Built with pure HTML, CSS, and JavaScript — no frameworks, no dependencies, no API keys.

The average person emits 13.1 kg CO₂ per day, but most people have no idea where their emissions come from. Without data, there's no way to make informed decisions about reducing your carbon footprint.

EcoTrack gives you a real-time, personalized dashboard that tracks your daily activities across 4 categories (Transport, Energy, Food, Shopping), analyzes your patterns with a local AI engine, and gives you actionable, data-driven recommendations to reduce emissions.

• HTML5 — Semantic markup, accessibility-first
• CSS3 — Custom properties, 3D transforms, glassmorphism, animations
• Vanilla JavaScript — Modular architecture, zero dependencies
• Chart.js — Data visualization (CDN)
• GSAP — Landing page animations (CDN)
• Lenis — Smooth scrolling (CDN)
• Lucide Icons — Icon system (CDN)

EcoTrack's AI advisor runs entirely in the browser — no API calls, no external services, no internet required.

• analyzeTopCategory() — Identifies your highest-impact emission category
• generateTip() — Creates personalized recommendations using your actual numbers
• generateWeeklySummary() — Compares this week vs last week with specific insights
• chatRespond() — Keyword-matched responses referencing your real data

All responses generated in < 5ms with zero network latency.

ecotrack/
├── index.html        # Animated landing page (GSAP + Lenis)
├── app.html          # Main dashboard application
├── css/
│   └── style.css     # Complete design system
└── js/
    ├── utils.js      # Helper functions
    ├── data.js       # Emission factors + LocalStorage
    ├── ai.js         # Local AI intelligence engine
    ├── charts.js     # Chart.js configuration
    └── app.js        # Main app controller

1. Clone the repository:

   git clone https://github.com/guptaaashrestha-jpg/ecotrack.git

2. Open index.html in your browser
3. That's it — no build step, no server, no installation

All emission factors are sourced from peer-reviewed data:

ML Empowerment Build Challenge 2026 — A global initiative helping students learn AI and apply it to real-world projects.

MIT License — free to use, modify, and distribute.