Addressing Grand Challenges in Life Cycle Assessment Through AI
Location: Beaty Museum Allan Yap Theatre (Basement, 2212 Main Mall). Please check in at front desk on main floor before going downstairs.
No food or drinks allowed in the Theatre.
Time: 12:30pm to 1:20pm
Click here to register for Zoom link. Zoom will be terminated if we encounter tech problems 5 to 10 mins into the seminar.
Talk summary:
Life cycle assessment (LCA) is essential for evaluating the environmental performance of technologies and policies, but it faces challenges like missing data and inconsistent data matching. Traditional methods, including process simulations and existing machine learning approaches, have limitations in scalability and generalizability. Large language models (LLMs) offer a solution by leveraging their vast, diverse knowledge base for automating life cycle inventory (LCI) data curation and enabling multi-modal analysis. This presentation outlines how LLMs can address these challenges and discusses future research directions to enhance their use in LCI modeling.
Bio:
Dr. Qingshi Tu is an Assistant Professor of Industrial Ecology at Department of Wood Science at UBC. Dr. Tu has a strong record of life cycle assessment (LCA) and techno-economic analysis (TEA) research on a variety of topics. Dr. Tu’s research focuses on: 1) creating open-source databases and models for evaluating the environmental, economic and social impacts of emerging technologies, 2) transforming knowledge into user-friendly tools and educational materials, 3) engaging different stakeholders to collaborate on sustainable bioeconomy projects.