Achieving deep reductions in US transport greenhouse gas emissions: Scenario analysis and policy implications

This paper investigates the potential for making deep cuts in US transportation greenhouse gas (GHG) emissions in the long-term (50-80% below 1990 levels by 2050). Scenarios are used to envision how such a significant decarbonization might be achieved through the application of advanced vehicle technologies and fuels, and various options for behavioral change. A Kaya framework that decomposes GHG emissions into the product of four major drivers is used to analyze emissions and mitigation options. In contrast to most previous studies, a relatively simple, easily adaptable modeling methodology is used which can incorporate insights from other modeling studies and organize them in a way that is easy for policymakers to understand. Also, a wider range of transportation subsectors is considered here-light- and heavy-duty vehicles, aviation, rail, marine, agriculture, off-road, and construction. This analysis investigates scenarios with multiple options (increased efficiency, lower-carbon fuels, and travel demand management) across the various subsectors and confirms the notion that there are no silver bullet strategies for making deep cuts in transport GHGs. If substantial emission reductions are to be made, considerable action is needed on all fronts, and no subsectors can be ignored. Light-duty vehicles offer the greatest potential for emission reductions; however, while deep reductions in other subsectors are also possible, there are more limitations in the types of fuels and propulsion systems that can be used. In all cases travel demand management strategies are critical; deep emission cuts will not likely be possible without slowing growth in travel demand across all modes. Even though these scenarios represent only a small subset of the potential futures in which deep reductions might be achieved, they provide a sense of the magnitude of changes required in our transportation system and the need for early and aggressive action if long-term targets are to be met. (C) 2009 Elsevier Ltd. All rights reserved.