Changes in climate caused by changes in anthropogenic (i.e. “man-made”) greenhouse gas (GHG) emissions have become a major public policy issue in countries all over the world. With an estimated 28.4% of these emissions attributed to the transportation sector, attention is being focused on strategies aimed at reducing transportation GHG emissions. Quantifying the change in GHG emissions due to such strategies is one of the most challenging aspects of integrating GHG emissions and climate change into transportation planning and policy analysis; the inventory techniques and methods for estimating the impact of different strategies and policies are still relatively unsophisticated.

This research developed a method for estimating intercity passenger transportation energy and carbon footprints and applied this method to three corridors in the U.S.--San Francisco/Los Angeles/San Diego; Seattle/Portland/Eugene, and Philadelphia/Harrisburg/Pittsburg. These corridors are all US DOT-designated high speed rail (HSR) corridors. The methodology consists of estimating the number of trips by mode, estimating the direct CO₂ emissions, and estimating indirect CO₂ emissions.

For each study corridor the impacts of different strategies and policies on carbon dioxide emissions were estimated as an illustration of the policy application of the developed methodology. The largest gain in CO₂ savings can be achieved by strategies aiming at automobile emissions, due to its sizeable share as main mode and access/egress mode to and from airports and bus and train stations: an average fuel economy of 35.5 mpg would result in a 38–42% savings of total CO₂ emissions; replacing 25% of gasoline use with cellulosic ethanol can have a positive impact on CO ₂ emissions of about 13.4–14.5%; and a 10% market share for electric vehicles would result in potential CO₂ savings of 3.4–7.8%. The impact of a 20% or 35% improvement in aircraft efficiency on CO₂ savings is much lower (0.88–3.65%) than the potential impacts of the policies targeting automobile emissions. Three HSR options were analyzed using Volpe's long-distance demand model: HSR125, HSR150, and HSR200. Only the HSR150 and HSR200 would result in CO₂ savings, and then just for two of the three corridors: the Pacific Northwest (1.5%) and California (0.6–0.9%). With increased frequency and load factors, a HSR150 system could result in CO₂ savings of 3.3% and 2.1% for the Pacific Northwest and California, respectively. This would require a mode shift from auto of 5–6%. This shift in auto mode share would mainly be a result of pricing strategies. One such pricing strategy, a carbon tax, could have a positive impact on auto diversion towards HSR. However, even a carbon tax of $400/tC, a multiple of 10 compared to today's tax, would not result in a diversion higher than 0.5%. There are no visible CO₂ savings due to this tax. From these results, HSR may not be such an obvious choice, however, with increased ridership and diversions from other modes, CO₂ savings increase significantly due to the lower emissions per passenger mile for HSR. Higher diversion may occur once a HSR rail system is built, as was seen in several other countries. The framework developed in this study has the ability to determine the GHG emissions for such HSR options and increased diversions.

Recommendations and areas for further research to better understand or estimate the CO₂ emission inventories and potential strategy impacts include: improving long-distance demand modeling and data, energy and emissions data, and life-cycle data; analyzing the cost-effectiveness of policies, future scenarios, pricing strategies to divert auto trips to HSR, network effects, other GHGs, and the impact of aircraft emissions at altitude; and including access and egress emissions.