This dataset is curated as a byproduct of the "Material and Vehicle Design for High-Value Recycling of Aluminum and Steel Automotive Sheet" project, funded by the REMADE Institute of the Department of Energy and referred to as the "Clean Sheet Project" in the file "electricity scenarios slides.pptx." The dataset presents projected U.S. electricity emission factors (MJ primary energy or gCO2/kWh electricity delivered) under various scenarios, including different levels of uptake of the U.S. Inflation Reduction Act. The projections are based on estimated trends in the U.S. electricity generation mix, along with the authors' analysis of the energy and emission intensities of relevant power sources. The dataset supports research—particularly life cycle assessment—relying on U.S. regional energy profile and emissions factors.
We apply Bayesian inference to reduce network structure uncertainty on material flow analysis (MFA) and demonstrate the methodology through a case study on U.S. steel flow. In addition, we derive an input/output-based analysis to conduct decision-making based on the uncertainty results from MFA
Liao, Jiankan, Deng, Sidi, Xun Huan, and Daniel Cooper. "Bayesian Model Selection for Network Discrimination and Risk-informed Decision Making in Material Flow Analysis." arXiv preprint arXiv:2501.05556 (2025).
