Annual anthropogenic greenhouse gas emissions must be cut by 40-70% by 2050 to limit global warming this century to 2o C above the pre-industrial temperature and avoid the worst consequences of climate change. This cut in global emissions is likely infeasible without U.S. decarbonization efforts equaling the global target. The transport and industry sectors account for 57% of U.S. GHG emissions. These two sectors must decarbonize and match the target if the U.S. is to achieve the necessary cut in emissions.
Emissions from U.S. transport and industry are coupled with advanced transport technologies (e.g., battery electric vehicles (BEVs) with Li-ion batteries) typically requiring emissions-intensive manufacturing. Previous studies have largely ignored the transport-industry emissions nexus. Instead, this thesis presents a parametric fleet-scale production-use-disposal model that combines life cycle assessment with macro-level consumption parameters to calculate consumption based cumulative emissions and global temperature changes attributable to U.S. light duty vehicles (LDVs). Future pathways account for emerging powertrain technologies, electricity decarbonization, transport demand, recycling rates, and vehicle lifespans. Only 3% of the 1,512 modeled pathways meet the emissions target. Without aggressive actions, U.S. LDVs will likely exceed the cumulative emissions budget by 2039. Cumulative emissions are most sensitive to transport demand and the speed of fleet electrification and electricity decarbonization. Increasing production of BEVs to 100% of sales by 2040 (at the latest) is necessary, and early retirement of internal combustion engine vehicles is beneficial. Rapid electricity decarbonization minimizes emissions from BEV use and increasingly energy-intensive vehicle production. Deploying high fuel economy vehicles can increase emissions from the production of BEV batteries and lightweight materials. Increased recycling has only a small effect on these emissions because over the time period there are few batteries and lightweight materials available for recycling.
A quarter of U.S. industry emissions are from the steel and aluminum sectors. Previous studies have shown that there are limited opportunities for further energy efficiency improvements in these upstream industries; however, increased material efficiency might prove fruitful, where services are delivered using less emissions-intensive materials produced from natural resources. Detailed material flow analyses (MFAs) are needed to identify the opportunities for material efficiency and to model the supply chain emissions. MFA construction is time consuming and fraught with missing and contradictory data. This thesis presents an easily updatable nonlinear least squares data reconciliation framework for MFA that is then applied to the annual U.S. steel flow. The MFA reveals key opportunities for U.S. steel material efficiency: increased manufacturing process yields and domestic recycling of landfilled and exported scrap.
To understand the barriers to increased recycling, an optimal reverse supply chain model is derived using linear programming (LP). It shows that U.S. domestic steel and aluminum recycling is already constrained by compositional mismatches between the scrap streams and industry demand. The LP model is coupled with a dynamic material flow analysis to show that the increasing volumes of high-quality wrought aluminum being used in U.S. vehicles are likely to be downcycled or landfilled at vehicle end-of-life. The LP model is revised to show the potential for using emerging scrap separation and refining technologies to increase closed-loop recycling rates towards 90%.
The technical assessments presented here highlight the scope for change. In future work, socioeconomic analyses could be coupled with these models to further assess the viability of the material efficiency strategies highlighted throughout.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/171347/1/yxzhu_1.pd
