Meeting the Challenge: A Stochastic Assessment of the U.S. Light-Duty Vehicle Fuel Economy Standards Available to Purchase

The U.S. Department of Transport and EPA have recently proposed further regulation of the light-duty vehicle corporate average fuel economy and GHG emissions for model years 2017 to 2025. Policy makers are setting more stringent targets out to 2025 in a context of significant uncertainty. These uncertainties need to be quantified and taken into account systematically when evaluating policies. In this paper, a stochastic technology and market vehicle fleet analysis is carried out, using the STEP (Stochastic Transport Emissions Policy model), to assess the probability of meeting the proposed CAFE targets in 2016 and 2025, and identify factors that play key roles in the near and midterm. Our results indicate that meeting the proposed targets requires (a) aggressive technological progress rate and deployment, (b)aggressive market penetration of advanced engines and powertrains, (c) aggressive vehicle downsizing and weight reduction, and (d) a high emphasis on reducing fuel consumption. Three scenarios are examined to assess the likelihood of meeting the proposed targets. The targets examined here, 32.5 and 34.1 mpg in 2016 and 44 and 54.5 mpg in 2025, are reduced from the nominal CAFE values after allowing for the various credits in the proposed rulemaking. The results show that there is about a 42.5% likelihood of the passenger cars average fuel economy falling below 32.5 mpg and a 5.3% likelihood of it exceeding 34.1 mpg in 2016, and about a 4% chance of it exceeding 44 mpg in 2025, under the plausible-ambitious scenario. Under the EPA/DOT preferred alternative scenario, the likelihood of passenger cars average fuel economy meeting or exceeding 34.1 mpg in 2016 and 44 mpg in 2025 increases to about 74% and 34.5% respectively. The probability of meeting these combined CAFE targets drops to less than 1% in both near and mid terms, once light trucks are included in the mix. This analysis quantifies the probability of meeting the targets therefore to enable risk-based contingency planning, and identifies key drivers of uncertainty where further strategic research is needed.