Carbon Footprint Modeling: LCA Automation with Python

Aim

Workshop Structure

🐍 Day 1 — Python Basics and LCA Data Preprocessing

• Focus: Setting up Python environment and preprocessing LCA data
• Key Topics:
  • Installing Anaconda, Jupyter Notebooks, and necessary libraries (Pandas, NumPy, Matplotlib)
  • Importing, cleaning, and preprocessing LCA data (CSV, Excel, JSON)
  • Data visualization: creating bar charts, line graphs, pie charts, and scatter plots
• Hands-on (Google Colab):
  • Task: Import, clean, and visualize real-world LCA data (carbon emissions, energy usage)

🔧 Day 2 — Automating LCA Calculations and Data Aggregation

• Focus: Automating LCA calculations and aggregating data
• Key Topics:
  • Writing Python functions for carbon footprint, energy, and water consumption calculations
  • Data aggregation: group, aggregate, and summarize LCA data by product and lifecycle stage
  • Script optimization: improving performance and troubleshooting common errors in large datasets
• Hands-on (Google Colab):
  • Task: Automate LCA impact assessments and aggregate data for multiple life cycle stages

🌍 Day 3 — Advanced Visualization, Reporting, and Real-World Applications

• Focus: Visualizing LCA results, generating reports, and applying LCA automation to real-world cases
• Key Topics:
  • Advanced visualization techniques: creating heatmaps, stacked bar charts, and multi-line graphs
  • Automating LCA reports (PDF/Excel) and creating interactive dashboards with Python
  • Practical applications: LCA automation in real-world industries (manufacturing, energy, product design)
• Hands-on (Google Colab):
  • Task: Build a complete LCA automation workflow (data import, impact calculation, visualization, reporting)

Who Should Enrol?

Important Dates

Workshop Outcomes