LowCarbonTrade: Carbon Market Data Analysis

Project Overview

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.

Key Features

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

Technologies Used

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

Key Insights

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

Getting Started

Prerequisites

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

Installation

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

Install dependencies:
```
pip install -r requirements.txt
```
Launch Jupyter Notebook:
```
jupyter notebook data_analysis.ipynb
```
Run all cells in the notebook to reproduce the analysis

Data Source

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.

Project Structure

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

Documentation

📖 Data Dictionary

Complete guide to all columns, indicators, and data quality notes. Essential for understanding the dataset structure and metrics.

📊 Analysis Findings

Comprehensive report of key insights, statistical tests, growth trends, and market analysis from the 30-year study.

Key Findings Summary:

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

Using the Analysis Scripts

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_countries

Run 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

Future Work

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)

Contact

Swanand - GitHub Profile

License

This project is licensed under the MIT License - see the LICENSE file for details.

Swanand33/LowCarbonTrade-EDA