Carbonomics-AI is an AI-powered decision support system designed to help institutions measure, analyze, predict, and optimize carbon emissions using machine learning and sustainability analytics.
Unlike conventional carbon management systems that primarily focus on reporting, Carbonomics-AI combines carbon accounting, predictive analytics, scenario simulation, and optimization to support data-driven sustainability decisions.
The objective of Carbonomics-AI is to develop an intelligent platform that enables organizations to:
- Measure carbon emissions accurately
- Predict future emission trends
- Identify major emission drivers
- Evaluate sustainability strategies through simulation
- Optimize emission reduction decisions
- Support long-term environmental planning
Traditional carbon management systems mainly emphasize emission reporting and regulatory compliance. While these systems provide valuable insights into current emissions, they often lack predictive capabilities and decision-support mechanisms.
Carbonomics-AI addresses this limitation by integrating carbon accounting, machine learning, simulation, and optimization into a unified framework that enables organizations to move from analysis to action.
- Carbon Emission Calculation
- GHG-Based Carbon Accounting
- Machine Learning Prediction
- Feature Importance Analysis
- Scenario-Based Simulation
- Optimization Engine
- Decision Support System
- Interactive Dashboard
- Sustainability Reports
- Data Visualization
Raw Data
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Data Processing
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Carbon Accounting
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Machine Learning Prediction
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Feature Analysis
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Scenario Simulation
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Optimization
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Decision Support
- Python
- Pandas
- NumPy
- Scikit-learn
- XGBoost
- Plotly
- Matplotlib
- Streamlit
- PostgreSQL
- SQLite
- Git
- GitHub
- Literature Survey
- Problem Identification
- Research Gap Analysis
- System Architecture
- Research Methodology
- Repository Setup
- Carbon Emission Calculator
- GHG Emission Factors
- Dataset Integration
- Carbon Footprint Report
- Data Preprocessing
- Random Forest Model
- XGBoost Model
- Model Evaluation
- Emission Forecasting
- Feature Importance Analysis
- Interactive Dashboard
- Trend Analysis
- KPI Monitoring
- What-if Analysis
- Renewable Energy Simulation
- Electric Vehicle Adoption
- Energy Efficiency Simulation
- Strategy Comparison
- Constraint-Based Optimization
- Cost-Benefit Analysis
- Recommendation Engine
- Streamlit Dashboard
- CSV Upload
- User Interaction
- Report Generation
- Testing
- Documentation
- Performance Optimization
- Cloud Deployment
Status: Under Active Development
The project is being developed incrementally. Each module will be implemented, tested, documented, and released through regular commits.
Carbonomics-AI is inspired by recent research in:
- Carbon Accounting
- Machine Learning
- Explainable Artificial Intelligence
- Sustainability Analytics
- Optimization Techniques
- Decision Support Systems
Carbonomics-AI/
│── data/
│── notebooks/
│── models/
│── app/
│── docs/
│── assets/
│── README.md
│── requirements.txt
Future versions of Carbonomics-AI will include:
- Real-time IoT data integration
- Carbon credit estimation
- Net-Zero planning
- Renewable energy optimization
- Multi-institution benchmarking
- AI-powered sustainability assistant
This project is licensed under the MIT License.
© 2026 Sanket Chaudhari
This project is developed as an academic and portfolio project. Unauthorized plagiarism or direct submission as one's own academic work is prohibited.
B.Tech Artificial Intelligence & Data Science
GitHub: https://github.com/sanket1035