A Robust Decision-Making Approach to Passenger Transport Decarbonization Under Uncertainty

Abstract:

Deeply decarbonizing the transport sector is an essential element in any climate stabilization scenario, but requires a major transition in energy use, vehicle technology, and enabling infrastructure. In a new era of advanced mobility represented by electrification, ride sharing, and automation, passenger transportation is expected to change. This uncertainty presents a challenge for transportation decarbonization decision-making, and identifying the robust pathways to achieving climate policy objectives is needed. This research examines the pathways for meeting transport climate mitigation targets in the U.S. under deep uncertainty by assessing the implications of transitions in the passenger transportation sector. We use a robust decision-making (RDM) method to simulate the U.S. Light Duty Vehicle (LDV) future fleet stock, vehicle miles traveled (VMT), and penetration of new technology, and estimate the implications for liquid fuel consumption, electricity consumption, and CO₂ emissions. We examined two strategies on when to introduce fuel economy improvements, electricity decarbonization, and EV adoption. The Immediate Action strategy represents introducing these improvements immediately, while the Delayed Action follows base case projections, then starts the interventions in year 2024. Further, we considered three distinct scenarios for effects of COVID19 on LDV sales and VMT by assuming varying rates of recovery. We find that even with Immediate Action, only 23% of the 1000 simulated LDV future cases achieve a midcentury climate target of at least 80% CO₂ reduction from 2005 levels. Out of these cases, the requirements for maximum allowable electricity carbon intensity and minimum travel electrification were 220 g CO2/kWh and 69% miles electrification, respectively. Delayed Action increased cumulative CO₂ emissions in more than 98% of the simulated futures and it caused the 2030 emissions reduction goal to be infeasible in all cases. The few cases for which the Delayed Action resulted in emission reductions were ones with delayed interventions being less stringent than the base case projection. By modeling the evolution of the U.S. LDV sector under deep uncertainty, we can identify the most robust pathways and decisions required to achieve energy savings and emissions reduction targets.