Deep Decarbonization of U.S. Passenger Vehicle Transportation: Robust Decisions for Sectoral Transition and Climate Policy

This thesis provides an assessment of the requirements, obstacles, and pathways to achieving deep decarbonization of the United States (U.S.) passenger vehicle sector. First, in a set of two studies, I investigate the requirements for meeting specific climate targets in the U.S. Light Duty Vehicle (LDV) sector. In the first study, I analyze an 80% carbon emissions reduction target by 2050 relative to 2005, a common midcentury decarbonization benchmark target. I find that decarbonizing U.S. passenger vehicle transport to achieve this target under electrification and automation uncertainty has a travel budget. A travel budget limits and reduces total vehicle travel to improve the likelihood of meeting the midcentury climate target. In the second study, I use the global remaining carbon budget (RCB) for different levels of warming use a contraction and convergence (C&C) framework to allocate them to the U.S., and the assign a portion of that national budget to LDVs. Using these RCB constraints by 2050 as a performance metric, I perform a robust decision making (RDM) analysis on the U.S. LDV fleet by generating thousands of future pathways under different technology and policy scenarios. Clustering analysis of those cases revealed that for a robust LDV decarbonization pathway, immediate and synergistic action for low-carbon electricity and a highly electrified fleet was imperative. Increasing the likelihood of staying below the more stringent 1.5 °C budget would require additional efforts to reduce the vehicle travel demand via decreasing vehicle miles traveled (VMT) and vehicle sales from now to 2050. Where the first two studies look at the LDV fleet emissions and the enablers for its decarbonization, my third study considers the implications on the U.S. petroleum refining sector given the current reliance on petroleum-based fuels for transport and primarily gasoline for LDVs. I select eight existing individual U.S. refineries and use Linear Programming (LP) optimization models calibrated to approximate actual operation to test the impact of gasoline price reduction on refinery capacity utilization and carbon intensity (CI). I find that refineries with low complexity and/or no access to low-cost heavy crude oils would be the most negatively affected as they will likely shut down earlier. This heterogeneity in refinery response was also illustrated in how their CIs change. The complex refineries operating with low gasoline prices might considerably increase their CI as they process heavier sour crude to minimize gasoline output, however the total absolute emissions will likely decrease due to lower volumes processed. Taken together, these three studies highlight this century’s massive infrastructure challenge pertaining to mitigating climate change. The robust decisions and insights identified in this thesis help inform future policies aiming towards an efficient and equitable transition to a deeply decarbonized passenger mobility future.