The state of California has set aggressive greenhouse gas emissions targets across all sectors of the economy over the next 40 years. The first of these targets occurs in 2020, when California plans to have reduced statewide greenhouse gas emission from their current levels to 1990 levels. As the largest single contributor to emissions, and the sector with the fastest growing emissions, transportation has been targeted for steep reductions. In particular, the state’s policies concentrate on passenger travel, the sector’s largest source of emissions. This dissertation applies robust decision methods to evaluate California’s policies within a framework that considers multiple views of the future, and identifies strategies that consistently reduce emissions at acceptable costs regardless of future conditions. Rather than preferring policies that are “optimal” under a narrow set of assumptions, the methodology identifies strategies which instead perform reasonably well over a wide range of potential future conditions. The study finds that California’s current set of policies is vulnerable to high emissions and cost overruns in a large set of plausible scenarios, and suggests adaptive strategies that can be used to improve policy performance when challenging conditions arise. In particular, efforts to control the growth of vehicle miles traveled are a key component of all adaptive strategies, but have been largely absent from the state’s plan so far.