CO₂ Emissions Explorer

A Streamlit-based web app for exploring and forecasting CO₂ emissions per capita.

Project Overview

• Case Studies: Historical trends for Wyoming (WY), North Dakota (ND), and Alaska (AK).
• Prediction: Interactive forecasting based on coal & natural gas production, per capita income, urbanization, and renewable energy usage.
• HASS Reflection: Social and ethical analysis on environmental justice and responsibility.

File Structure

LAST_DTP/
├── app.py
├── case_service.py         # Load & filter merged data for case studies
├── data_service.py         # Fit & apply state-specific regression models
├── assets/
│   ├── All main data (1998 to 2023).xlsx   # Input data for charts & models
├── pages/                    # Streamlit multipage directory
│   ├── 02_case_studies.py    # Case studies page
│   ├── 03_prediction.py      # Prediction interface
│   └── 04_hass_reflection.py # Qualitative reflection page
├── tests/
│   ├── test_data_service.py
│   └── test_case_service.py
├── requirements.txt
├── README.md
└── .gitignore

Quick Start

1. Clone the repository

git clone https://github.com/Kopi-O-Kosong-Beng/DTP-MU-Project/tree/main
cd LAST_DTP

2. Create and activate a virtual environment

# Install virtualenv (to your user site)
pip3 install --user virtualenv

# Create the env
~/.local/bin/virtualenv .venv

# Activate it
source .venv/bin/activate

3. Install dependencies

pip install --upgrade pip
pip install -r requirements.txt

4. Run the Streamlit app

streamlit run app.py

Open your browser at the URL printed (usually http://localhost:8501).

Testing

Run unit tests with pytest:

pytest

Usage

• Home: Intro & navigation.
• Case Studies: View historical CO₂ trends for WY, ND, AK.
• Prediction: Input your parameters and click Run Prediction.
• HASS Reflection: Explore environmental justice themes.

Deployment

• Suitable for platforms supporting Python & Streamlit (Heroku, Streamlit Cloud, Azure).
• Ensure assets/ and .env (if any secrets) are included in deployment.

Contribution

Contributions and improvements are welcome:

1. Fork the repo
2. Create a feature branch (git checkout -b feature-name)
3. Commit your changes (git commit -m "Add new feature")
4. Push to branch (git push origin feature-name)
5. Open a Pull Request