AI-Powered Carbon Footprint & Circular Economy Modeling

To build practical skills in using machine learning and optimization algorithms to measure, model, and reduce carbon footprints across supply chains and industrial systems.

• Automate Scope 1, 2, and 3 GHG emissions tracking with ML
• Predict missing emission factors and forecast dynamic carbon intensity
• Model circular supply chains using graph-based Material Flow Analysis
• Apply ML for waste sorting, decomposition, and end-of-life routing
• Quantify Material Circularity Indicators and embodied carbon savings
• Build multi-objective optimization models for net-zero roadmaps
• Apply reinforcement learning to industrial energy efficiency

📅 Day 1: AI-Driven Carbon Accounting & Life Cycle Assessment (LCA)

Core Objective: Automate Scope 1-3 emissions tracking and Life Cycle Assessment using machine learning.

• Machine learning for automated Scope 1, 2, and 3 GHG emissions mapping
• Predicting missing supply chain emission factors with regression models
• Dynamic carbon intensity forecasting based on real-time grid data

🛠️ Hands-on Lab: Build an automated Python pipeline to track live compute emissions and run Scope 3 supply-chain LCA models using Google Colab.

🧰 Tools Covered: Brightway2, CodeCarbon, EcoLogits, Google Colab

📅 Day 2: Predictive Material Flow Analysis & Circular Economy Design

Core Objective: Model closed-loop recycling networks and track resource flows using graph algorithms.

• Graph-based Material Flow Analysis (MFA) to track raw and waste material lifecycles
• Machine learning for waste sorting, material decomposition, and end-of-life routing
• Quantifying Material Circularity Indicators (MCI) and embodied carbon savings

🛠️ Hands-on Lab: Map a circular supply chain using NetworkX to optimize waste recovery and calculate circularity scores using Google Colab.

🧰 Tools Covered: NetworkX, Scikit-learn, OpenLCA Python API, Google Colab

📅 Day 3: Generative System Optimization & Decarbonization Roadmaps

Core Objective: Generate optimal net-zero strategy pathways using multi-objective optimization algorithms.

• Multi-objective optimization balancing operational cost, resource depletion, and carbon ROI
• Reinforcement learning for industrial energy efficiency and byproduct exchange
• Automated scenario analysis for climate risk reporting and ESG compliance

🛠️ Hands-on Lab: Implement a Pyomo optimization model to determine the lowest-cost decarbonization strategy for an industrial system using Google Colab.

🧰 Tools Covered: Pyomo, SciPy Optimize, Pandas, Google Colab

• Sustainability/ESG analysts and consultants
• Supply chain and operations professionals
• Data scientists/ML engineers entering climate tech
• Environmental engineers and LCA practitioners
• Corporate sustainability and net-zero strategy teams

Participants will be able to:

• Build a Python pipeline to track live compute emissions and run Scope 3 LCA models
• Map circular supply chains and calculate circularity scores using NetworkX
• Build a Pyomo optimization model for lowest-cost decarbonization strategy
• Generate automated scenario analyses for ESG/climate risk reporting
• Walk away with reusable Colab notebooks for carbon and circularity modeling