Carbon Storage Projections: Jupyter Notebooks for CO2 Shipping

A collection of Jupyter notebooks analyzing the economic feasibility and cost projections of shipping carbon dioxide from Europe to US ports. This project uses Monte Carlo simulations to model the annual cost and logistics of carbon shipping, providing insights into potential terminal locations and shipping infrastructure.

Features

• Monte Carlo simulation modeling dynamic shipping variables such as distance, capacity, and trip duration
• Analysis of shipping costs for CO2 transport across the Atlantic
• Feasibility studies on carbon shipping infrastructure
• Visualizations and detailed markdown reports accompanying notebooks

Tech Stack

• Jupyter Notebook (Python)
• Markdown for documentation
• Python libraries (assumed: numpy, pandas, matplotlib, scipy for simulations and analysis)

Getting Started

Prerequisites

• Python 3.7 or higher
• Jupyter Notebook

Installation

1. Clone the repository:

git clone https://github.com/justin-napolitano/carbon-storage-projections.git
cd carbon-storage-projections

2. (Optional) Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install required Python packages (assumed requirements):

pip install numpy pandas matplotlib scipy

Running the Notebooks

Launch Jupyter Notebook:

jupyter notebook

Open any of the .ipynb files to explore the analyses:

• shipping_projections.ipynb — Monte Carlo projection of shipping costs
• shipping_carbon_feasibility.ipynb — Feasibility study of shipping carbon
• carbon-storage-projects.ipynb — Additional carbon storage related projects
• europe_ports.ipynb — Data and analysis on European ports

Project Structure

carbon-storage-projections/
├── carbon-storage-projects.ipynb
├── europe_ports.ipynb
├── histogram.png
├── shipping_carbon_feasibility.ipynb
├── shipping_carbon_feasibility.md
├── shipping_carbon_feasibility_files/
├── shipping_projections.ipynb
├── shipping_projections.md
├── shipping_projections_files/
├── README.md

Future Work / Roadmap

• Refine cost of transport modeling by incorporating more accurate and dynamic data sources
• Expand simulation parameters to include additional variables affecting shipping feasibility
• Develop automated testing and validation for simulation outputs
• Integrate visualization dashboards for interactive exploration of results
• Extend analysis to other geographic routes and carbon storage scenarios