Carbon Intensity, Traceability, and AI in Energy Supply Chains
Carbon Intensity, Traceability, and AI in Energy Supply Chains
Session ID#: 281238
Session Description:
Global energy supply chains are being reshaped by evolving infrastructure and trade patterns, low-carbon technology transitions, and growing applications of Artificial Intelligence (AI). This session invites contributions that advance measurement-informed analysis of energy systems through integration of monitoring data, engineering-based models, techno-economic analysis (TEA), life-cycle assessment (LCA), and AI-enabled methods. Topics include carbon-intensity benchmarking and differentiation and traceability across oil, gas, LNG, electricity, hydrogen, CCUS, and other emerging energy pathways; methods for uncertainty quantification and attribution; technoeconomic analysis of energy supply under stress; and physics-informed, machine-learning, and optimization approaches for forecasting and system analysis. We welcome studies that improve scientific understanding of supply-chain emissions, resilience, and technology performance across scales, from assets and facilities to regional and global networks.
Co-Sponsor(s):
- A - Atmospheric Sciences
- IN - Informatics
Index Terms:
1610 Atmosphere [GLOBAL CHANGE]
1622 Earth system modeling [GLOBAL CHANGE]
1630 Impacts of global change [GLOBAL CHANGE]
1942 Machine learning [INFORMATICS]
Primary Convener: Wennan Long, University of Pittsburgh, The Chemical and Petroleum Engineering, Pittsburgh, PA, United States
Conveners: Adam R Brandt, Stanford University, Department of Energy Science and Engineering, Stanford, CA, United States, Joule Bergerson, University of Notre Dame, Energy Systems Engineering, Notre Dame, United States and Michael Wang, Argonne National Laboratory, Lemont, United States
See more of: Global Environmental Change