AI-Powered Life Cycle Assessment Course

The AI-Powered Life Cycle Assessment course bridges environmental science, industrial engineering, and AI-driven analytics. LCA traditionally quantifies environmental impacts from raw material extraction to end-of-life disposal. Integrating AI enables practitioners to manage large, heterogeneous datasets, identify patterns, predict impacts under varying scenarios, and optimize for sustainability.

More accurately, this course addresses the gap between conventional LCA workflows—which can be labor-intensive and static—and the potential of predictive, computationally enhanced assessments that inform both policy and industrial design. Participants gain both theoretical grounding in LCA principles and practical experience implementing AI models to evaluate environmental performance.

Environmental pressures, regulatory requirements, and corporate sustainability mandates have elevated LCA from an academic tool to a critical industrial practice. Challenges include:

• Increasing complexity of global supply chains
• High-volume environmental datasets requiring computational efficiency
• Need for scenario-based predictions for product redesign and circular economy strategies
• Cross-disciplinary relevance: combining data science, environmental engineering, and industrial management

AI integration enables actionable insights in LCA, from predicting carbon footprints to identifying optimization opportunities in production or logistics. That distinction matters: LCA is only as useful as the data and interpretation supporting decision-making.

Python libraries: pandas, scikit-learn, TensorFlow/Keras R packages for LCA and environmental modeling LCA databases: ecoinvent, OpenLCA AI techniques: regression, classification, clustering, neural networks Data visualization and impact reporting tools

• Predicting carbon and water footprints of industrial processes
• Sustainability assessment in product design and supply chains
• Scenario modeling for circular economy strategies
• Policy-oriented environmental impact reports
• Integration of AI-LCA workflows in R&D or consultancy projects

• Environmental engineers and sustainability analysts
• Researchers and postgraduate students in environmental science or industrial engineering
• Data scientists working on sustainability applications
• Policy analysts or corporate sustainability teams interested in data-driven LCA

Prerequisites or Recommended Background: Familiarity with environmental science or LCA concepts. Basic programming knowledge in Python or R recommended. Understanding of datasets and statistical analysis preferred.