An in-depth exploratory data analysis of carbon trading markets, focusing on trends, patterns, and opportunities in low-carbon investments. This project analyzes historical carbon credit pricing, trading volumes, and market dynamics to provide actionable insights for sustainable investment strategies.
- Comprehensive EDA of carbon market data from multiple exchanges
- Advanced visualizations highlighting market trends and seasonal patterns
- Statistical analysis of price volatility and market correlations
- Sector performance comparison across different industries
- Regional market analysis showing geographic distribution of carbon trading
- Python: Primary programming language
- Pandas & NumPy: Data manipulation and numerical analysis
- Matplotlib & Seaborn: Data visualization
- Jupyter Notebooks: Interactive analysis environment
- Scikit-learn: Statistical modeling and analysis
- Carbon credit prices show a steady upward trend over the analyzed period with significant volatility
- European markets demonstrate the highest trading volumes and price stability
- Energy and transportation sectors represent the largest share of carbon offset projects
- Seasonal patterns indicate higher trading activity in Q4 of each year
- Strong correlation observed between carbon prices and renewable energy stock performance
python >= 3.8
pandas >= 1.3.0
numpy >= 1.20.0
matplotlib >= 3.4.0
seaborn >= 0.11.0
jupyter >= 1.0.0
scikit-learn >= 1.0.0
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Clone the repository:
git clone https://github.com/Swanand33/LowCarbonTrade-EDA.git cd LowCarbonTrade-EDA -
Set up a virtual environment (optional but recommended):
python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate
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Install dependencies:
pip install -r requirements.txt
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Launch Jupyter Notebook:
jupyter notebook data_analysis.ipynb
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Run all cells in the notebook to reproduce the analysis
The cleaned dataset (data/cleaned_data.csv) is included in this repository. The original dataset (Trade_in_Low_Carbon_Technology_Products.csv) is from the UN Department of Economic and Social Affairs and contains trade data for low-carbon technology products from 1994-2023 across 100+ countries.
LowCarbonTrade-EDA/
├── data/ # Raw and processed datasets
│ ├── Trade_in_Low_Carbon_Technology_Products.csv # Original data (2,143 records)
│ └── cleaned_data.csv # Cleaned dataset (443 records)
├── Notebook/ # Utility scripts (reusable functions)
│ ├── data_cleaning.py # Data loading, cleaning, transformation
│ ├── statistical_analysis.py # Statistical tests, growth calculations
│ └── visualization.py # Plotting functions (trends, distributions)
├── data_analysis.ipynb # Main analysis notebook (EDA)
├── DATA_DICTIONARY.md # Column descriptions and data guide
├── FINDINGS.md # Analysis results and insights
├── requirements.txt # Project dependencies
├── .gitignore # Git ignore rules
├── LICENSE # MIT License
└── README.md # Project documentation
Complete guide to all columns, indicators, and data quality notes. Essential for understanding the dataset structure and metrics.
Comprehensive report of key insights, statistical tests, growth trends, and market analysis from the 30-year study.
- 866% growth in low-carbon tech trade (1994-2023)
- Statistically significant increase post-2010 (p < 0.001)
- 26% surge in 2021 (largest single-year growth)
- Average annual growth rate: 7.6% over 30 years
- Market shows resilience despite volatility from financial crises
The Notebook/ folder contains reusable Python modules:
# Data cleaning
from Notebook.data_cleaning import load_raw_data, clean_data, melt_year_columns
# Statistical analysis
from Notebook.statistical_analysis import calculate_growth_rate, perform_ttest
# Visualizations
from Notebook.visualization import plot_yearly_trends, plot_top_countriesRun individual scripts:
python Notebook/data_cleaning.py # Clean and process data
python Notebook/statistical_analysis.py # Run statistical tests
python Notebook/visualization.py # Generate visualizations- Implement predictive modeling for carbon price forecasting
- Develop a machine learning model to identify undervalued carbon credits
- Integrate alternative data sources (news sentiment, policy changes)
- Create an interactive dashboard for real-time market monitoring
- Disaggregate analysis by specific technology types (solar, wind, EV)
Swanand - GitHub Profile
This project is licensed under the MIT License - see the LICENSE file for details.