🌍 Carbon Emission Prediction & Neutrality Cost Forecasting

🔎 Project Overview

This project focuses on forecasting carbon emissions and estimating the cost of achieving carbon neutrality for top polluting countries. Leveraging a CRISP-DM methodology, the analysis uses time-series modeling to support sustainable policy decisions and emission management strategies.

📊 Datasets Used

• World Data: Contains global carbon emission indicators collected from the World Bank, covering 245 countries and 80+ environmental features.
• USA Data: Includes national-level carbon emission metrics sourced from the U.S. Energy Information Administration (EIA), provided as multiple time-series records.

⚙️ Modeling Approach

We explored and evaluated multiple machine learning models:

• Hybrid CNN-IBFA
• Support Vector Regression (SVR)
• Long Short-Term Memory (LSTM)

Based on model performance, the LSTM model- achieving an MSE of 0.0038 and R² of 0.8892, was selected for final forecasting due to its effectiveness in capturing long-term temporal dependencies in emission trends.

🧪 Evaluation Metrics

Each model was assessed using:

• MSE (Mean Squared Error)
• MAE (Mean Absolute Error)
• R² (R-squared)

The LSTM model achieved the best overall results, making it ideal for predicting both emissions and neutrality cost scenarios.

📈 Key Features

• Time-series forecasting of carbon emissions using LSTM
• Emission trend analysis across key global and national datasets
• Carbon neutrality cost prediction for top polluting countries
• Web-based dashboard to visualize emission forecasts and policy-level cost implications

🧰 Tech Stack

• Python
• LSTM, SVR, Hybrid CNN-IBFA
• NumPy, Pandas, Matplotlib, Seaborn
• Jupyter Notebook

🚀 How to Run

1. Clone the Repository

   git clone https://github.com/saivivek55/Carbon-Emission-Prediction-and-Neutrality-Cost-Forecasting.git
   cd Carbon-Emission-Prediction-and-Neutrality-Cost-Forecasting

2. Install Dependencies
3. Execute the python files

🔍 Key Insights

• Datasets: Global and national datasets revealed region-specific emission behavior trends.
• Robust Forecasting: Achieved low MSE with a high R² value using the LSTM model.
• Actionable Outcomes: Provided precise carbon neutrality cost forecasts, guiding policy formulation and sustainable planning.

📄 License

This project is licensed under the Apache 2.0 License.