CarbonCALC: Real-Time Carbon Footprint Monitoring and Predictive Reporting Cloud Solution

A comprehensive web-based platform for calculating, tracking, and reducing carbon footprints with personalized biosafety and sustainability recommendations.

Features

Core Functionality

• 👥 Multi-User Support: Individual, Institution, and Corporation user types
• 📊 Carbon Footprint Calculator: Comprehensive calculation across energy, transportation, waste, food, water, and corporate operations
• 🌱 Personalized Recommendations: AI-powered biosafety and sustainability feedback tailored to your footprint
• 📈 Interactive Dashboard: Real-time visualization of your carbon footprint trends
• 📋 Historical Tracking: Track your emissions over time and measure improvements

Research-Grade Features

• 🤖 Machine Learning Predictions: Ensemble models (Random Forest + Gradient Boosting) for forecasting future carbon footprints
• 📡 IoT Sensor Integration: Real-time sensor network simulation for continuous emission monitoring
• 📊 Comparative Benchmarking: Statistical comparison against industry standards with percentile analysis
• 🔬 Predictive Analytics: Time-series forecasting with confidence intervals and trend analysis
• 📈 Advanced Analytics: Effect size calculations, performance ratings, and improvement potential quantification
• 📄 Research Reports: Comprehensive reports with statistical analysis and predictive insights

Architecture

┌─────────────┐
│ IoT Sensors │──┐
└─────────────┘  │
                 ├──> [Cloud Backend] ──> [ML Models] ──> [Dashboard]
┌─────────────┐  │
│ Data Sources│──┘
└─────────────┘

Tech Stack

• Backend: FastAPI (Python)
• ML Framework: Scikit-learn (CPU-optimized, no GPU required)
• Database: SQLite (production-ready for PostgreSQL)
• Frontend: HTML/CSS/JavaScript Dashboard
• Cloud: Free deployment ready (Render.com, Railway)
• Compute: CPU-only (works on free cloud hosting)

Installation

1. Clone the repository or navigate to the project directory

2. Install dependencies:

pip install -r requirements.txt

3. Initialize the database:

python init_db.py

4. Start the server:

python main.py

Or use uvicorn directly:

uvicorn main:app --reload --host 0.0.0.0 --port 8000

5. Open the web application:

• Navigate to http://localhost:8000 in your browser
• Register a new account or login
• Start calculating your carbon footprint!

Project Structure

CarbonCalc/
├── main.py                 # FastAPI backend application
├── models/                 # ML models directory
│   ├── carbon_predictor.py
│   └── trained_models/
├── iot/                    # IoT sensor simulation
│   └── sensor_simulator.py
├── database/               # Database models and setup
│   ├── models.py
│   └── database.py
├── api/                    # API routes
│   ├── routes.py
│   └── websocket.py
├── dashboard/              # Frontend dashboard
│   ├── index.html
│   └── static/
├── utils/                  # Utility functions
│   ├── data_processor.py
│   └── analytics.py
└── requirements.txt

API Endpoints

Authentication

• POST /api/auth/register - Register new user
• POST /api/auth/login - Login and get access token
• GET /api/auth/me - Get current user information

Carbon Footprint

• POST /api/calculate - Calculate carbon footprint
• GET /api/entries - Get user's carbon footprint entries
• GET /api/entries/{id} - Get specific entry
• GET /api/recommendations - Get sustainability recommendations

Analytics & Research

• GET /api/analytics/summary - Get analytics summary
• POST /api/predict - Predict future carbon footprint using ML models
• GET /api/benchmark/compare - Compare against industry benchmarks
• GET /api/research/report - Generate comprehensive research report

IoT Integration

• GET /api/iot/sensors - Get IoT sensor network readings
• GET /api/iot/sensors/history - Get historical IoT sensor data

License

MIT License