📅 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
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Hands-on Lab: Build an automated Python pipeline to track live compute emissions and run Scope 3 supply-chain LCA models using Google Colab.
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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
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Hands-on Lab: Map a circular supply chain using NetworkX to optimize waste recovery and calculate circularity scores using Google Colab.
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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
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Hands-on Lab: Implement a Pyomo optimization model to determine the lowest-cost decarbonization strategy for an industrial system using Google Colab.
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Tools Covered: Pyomo, SciPy Optimize, Pandas, Google Colab
