Incorporating Greenhouse Gas Emissions into the Collaborative Decision-Making Process (2012)

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89 a believable forecast of impact to one that is considered implau- sible. Insufficient data and inadequate methodologies can limit an understanding of the amount and sources of trans- portation GHG emissions currently emitted, as well as expected emissions in the future if current trends continue and no actions are taken to alter these trends. Key research topics are detailed below. Mismatch between fuel-based and activity-based inventories. Fuel sales data provide the most accurate baseline emissions inventory, but they cannot easily be disaggregated by mode or substate region. They also are unsuitable for forecasting given that future emissions forecasts would have to rely on future fuel sales, something that presents its own forecasting challenge. However, inventories developed using travel activity and emissions factor data typically show discrepancies (usually a shortfall, and often significant) in estimated emissions com- pared with inventories based on fuel sales. These discrepancies illustrate that lab-measured fuel efficiency data and models are far from perfect. Limited knowledge of how travel conditions and characteris- tics (e.g., VMT per capita) will change. Although some factors (such as economic growth or population growth at a regional level) are forecast with potentially high levels of uncertainty, analysts’ ability to understand and model factors such as how changing demographics, communications technology, logis- tics trends, consumption habits, and urban form are likely to affect passenger and freight travel patterns in the future need improvement. Differing assumptions about potential changes in future year motor vehicle fuel economy. This can be a large source of uncertainty in fuel use and GHG forecasts. Analysts often have substantial inconsistencies in how they incorporate the influence of future fuel economy standards. These differences in assumptions can result from not understanding the difference between fuel economy standards and in-use fuel economy; confusing new vehicle fuel economy standards with existing vehicle fuel economy; not understanding the new fuel economy This research lays out a technical framework that can be used to develop a baseline analysis, as well as an assessment of likely GHG emissions given the use of different mitigation strategies. As government agencies, communities, and the public itself become more concerned about the role that GHG emissions play in climate change, the need for credible and transparent analysis tools will become even more apparent. As found in this research, there are still many gaps in analysts’ knowledge that limit their ability to measure and project future GHG emissions and estimate potential reductions from transporta- tion strategies. In a 2010 workshop and conference on environ- mental research needs organized by the Transportation Research Board (Transportation Research Circular 2010), workshop participants ranked climate change and GHG analysis models and methodologies as second in a list of the 10 top-ranked research needs, and a larger set of conference participants ranked this issue as fifth. Many of the key knowledge gaps identified by the research team during this project can be classified in three categories: • Data and methodological limitations for the development of inventories and baseline forecasts; • Limitations on basic knowledge regarding strategy effec- tiveness; and • Limitations in tools and methods for analyzing strategy effectiveness. Each of these areas is discussed in the following sections. Data and Methodological Limitations for Development of Inventories and Baseline Forecasts Data and credible methods are almost always two critical constraints of any problem-solving exercise. In the context of GHG emissions analysis, they can be the difference between C h a p t e r 6 Knowledge Gaps and Research Needs

90 standards from the perspective of how they align with model years; and not knowing what the fuel economy standards will be beyond the adopted time frame. Limited data on travel activity by nonpassenger modes, especially trucks, freight rail, and marine. Freight travel is a significant part of a region’s GHG emissions inventory and could become even more important in the future. Similar to the GHG emissions estimating process for passenger vehicles, emissions estimation for freight modes depends on the level of activity that each freight mode actually incurs. Obtaining this data has traditionally been challenging, although surrogate values such as cargo value can be used to reverse estimate the number of rail cars or fleet trucks actually on the move. Major improvements in estimating freight flow (e.g., the Freight Analysis Framework 2) are underway, and when completed they should improve the reliability of travel activity input into the GHG emissions analysis process. Intercity travel activity. Although the Freight Analysis Framework allows planners to obtain some estimate (albeit at a high level) of intercity freight flows, nothing similar exists for intercity passenger transportation (although FHWA has research underway currently to produce a similar approach). Thus, analysts interested in developing GHG emissions inventories for intercity travel have to rely on airline, rail, and bus schedules for activity data and gross estimates for intercity automobile flows. In addition, estimating passenger origin– destination pairs can be a challenge; Amtrak, for example, knows how many passengers board and alight at each station, but does not know the actual origin–destination pair for each traveler. This becomes an important constraint when under- taking policy studies that examine the feasibility of alternative, GHG-reducing modes (such as high-speed rail). Improved data collection and models for intercity travel are an important research need. Uncertainty over life-cycle emissions. This is a particular problem for biofuels, for which land use and other indirect impacts are highly uncertain. However, it is also true for other fuel types, especially since life-cycle emissions can vary by the specific production process, which is not typically accounted for in today’s inventories. Lack of knowledge regarding noninventoried GHGs. A num- ber of other gases are known to have climate change effects, either through their direct warming potential or because they influence the formation or destruction of other GHGs. These gases include ozone, nonmethane volatile organic compounds, particulate matter, and aerosols. The contri- bution of the transportation sector to some of these gases is significant. However, there is no agreed-on method to estimate the global warming potential of gases that are short-lived, spatially variable, or have only indirect effects on radiative forcing. Limitations on Basic Knowledge regarding Strategy effectiveness One of the most important products of GHG analyses is producing a set of strategies that will reduce GHG emissions in a cost-effective way. Inherent to developing such methods or analysis tools is an understanding of the underlying relation- ships between human and firm behavior and the incentives or disincentives used to influence travel-related behavior. Transportation researchers have been studying these relation- ships for decades and have developed a strong foundation of understanding of what influences household and individual travel decisions. However, strategies for reducing GHG emissions could include fundamentally different responses to strategies than what has been seen before (e.g., a heavy reliance on pricing strategies might result in significantly different responses than simply focusing on a single facility toll). The major gaps in fundamental understanding are listed below according to whether they relate to travel activity strategies, system efficiency improvements, vehicle and fuel technologies, or cross-cutting issues. The reader will recognize many of these gaps from other research in the transportation field. They are listed here as gaps and needed research simply because of the unique circumstances surrounding the imple- mentation of GHG emission strategies. Clearly, many areas among travel behavior research and GHG emissions research will overlap. Travel Activity Strategies Synergies among pricing, land use, transit, transportation demand management (TDM), and nonmotorized improvements. These strategies are believed to be more effective when applied in combination, but little quantitative evidence exists to support this belief or to show how synergies vary for different combi- nations of these strategies in different contexts. This gap in knowledge is particularly important in that a state or regional GHG emissions reduction strategy will likely have to use multiple initiatives and actions to produce any noticeable impact on the level of GHG emissions. Most of the research and studies to date on combined strategies to reduce GHG emissions have made assumptions of how individual strategies might affect the GHG emissions reduction potential of other strategies. Research on this topic is an important first step in identifying feasible approaches to GHG emissions reduction programs. Potential for vehicle travel reduction in smaller or low-density regions with limited congestion, limited transit, and/or slow growth. By far, and not surprisingly, most of the research on GHG emissions reduction strategies has focused on metro- politan areas or at the national and state levels. Even here,

91 much of this research has relied on assumptions relating to likely effectiveness of different strategies. Very little attention has been given to nonurban areas. Aside from pricing, do any strategies to reduce travel activity (such as transit, TDM, nonmotorized travel, and land use change) have significant potential in such regions? Will this change in the future if energy prices increase substantially? If there is to be a national transportation-related strategy for reducing GHG emissions, and if some portion of this strategy focuses on reducing VMT, what role will medium- to small-sized MPOs have in plan- ning and assessing the effectiveness of metropolitan GHG programs? Effects of land use patterns and smart growth on freight and commercial vehicle travel, particularly urban and local freight distribution. The freight industry responds to changing market pressures and government requirements concerning environ- mental issues. And given that freight-related GHG emissions will continue to be an important part of a GHG emissions inventory, how will the dynamic nature of the freight industry affect the effectiveness of GHG emissions reduction strategies? Can reductions in local goods movement and commercial vehicle movement be obtained proportionate to similar reduc- tions in passenger travel? To what extent will freight villages tend to reduce GHG emissions from freight flows? Potential of new information systems and communications technologies (telematics) to change travel habits. One of the most important characteristics of future transportation systems will be the application of network and vehicle technologies to make the system more efficient and safer. Large-scale implementation of intelligent transportation systems (ITS) technologies has occurred in most major U.S. metropolitan areas. Research on such technologies has shown that they can reduce levels of congestion, make pathfinding much more efficient, and promote multimodal coordination. To what extent might technology-based strategies such as neighborhood vehicles, dynamic ridesharing, real-time multimodal wireless infor- mation, and demand-responsive (intelligent) transit assist in mode shifting or other carbon-intensive travel reduction in different contexts? How could such technologies be used to promote a GHG emissions reduction strategy for a metro- politan area? Potential for eco-driving in the United States. European GHG-reduction strategies have included a strong emphasis on eco-driving. Although the evidence suggests that continual reinforcement to individual drivers of the eco-driving ethic could result in notable reductions in GHG vehicle emissions, it is not clear how such a strategy would work in the United States. Some research on eco-driving is taking place through the U.S. DOT ITS Joint Program Office’s Applications for the Environment: Real-Time Information Synthesis program. This 5-year program, initiated in 2010, is examining how applications of vehicle–infrastructure integration can benefit the environment (ITS Joint Program Office 2012). This research will provide important insights on what will work in the U.S. context. Important questions include, what fraction of the population can realistically be reached with comprehensive eco-driving training? How might this fraction vary based on the cost of fuel, vehicle technology, and other factors? System Efficiency Strategies Short- and long-term induced demand effects of strategies that improve traffic flow. This issue is not limited to GHG emissions reduction efforts; it has been studied for a variety of reasons over the past several decades. However, in the context of GHG emissions strategies, additional questions need to be considered as part of such research. To what extent are the fuel efficiency benefits of congestion-relief improvements such as bottle- neck removal, signal coordination, and incident management offset by long-term increases in travel in response to improved travel conditions? How does this trade-off vary for capacity versus operational strategies, for strategies affecting recurring versus nonrecurring congestion, and for passenger versus freight travel? Effect of new and/or emerging vehicle technologies on the effectiveness of system efficiency improvements. The introduction of new technologies into the vehicle fleet will have a potentially significant impact on both travel behavior and vehicle emis- sions. In addition, as noted above, ITS technologies have been employed for many years as a strategy for improving system efficiency. One of the key issues with respect to vehicle tech- nology relates to the ability of electric-drive vehicles to reduce the fuel efficiency losses of low-speed travel. To what extent will full or partial electric-drive vehicles (including hybrid electric, plug-in hybrid, battery-powered electric, and fuel cell) reduce or even eliminate the fuel efficiency and GHG benefits of congestion relief? Potential for freight mode shifting to rail. Rail is approxi- mately three times more energy efficient than truck transport on an energy per ton-mile basis. Yet much of these efficiencies are lost for short trips (which still require truck transport at either end), and it is widely assumed that movement by rail is most cost-effective only for longer-distance and/or lower- value goods movement. Yet, given the large contribution of the nation’s truck fleet to GHG emissions, it would be worthwhile to examine different strategies for reducing GHG emissions from this component of the vehicle fleet. This raises several issues: how much cargo could be shifted to rail as a result of widespread investment in freight rail and intermodal facilities? What realistic reduction in GHG emissions would result? How does this potential depend on the price of fuel, roadway congestion, and other influencing factors? Benefits of emerging ITS strategies, advanced traffic manage- ment, integrated corridor management, and vehicle–infrastructure

92 integration. Concepts such as eco-adaptive cruise control, eco-routing, and green platooning, as well as system operations, may potentially transform travel. As these types of concepts take hold in the U.S. environment, transportation professionals need to have a better understanding of their effects on system management, and thus cumulatively what their effect is with respect to GHG emissions reduction. Vehicle and Fuel Technology Strategies Research on vehicle and fuel technologies occurs in a variety of settings, with orders of magnitude levels of investment over what is invested by transportation agencies in GHG reduction strategies. It is clear from numerous studies that changes in vehicle and fuel technologies will have by far the most signifi- cant impact on reducing GHG emissions from the transporta- tion sector. However, from the perspective of GHG emissions analysis, one important issue stands out: the effect of govern- ment interventions (e.g., pricing, infrastructure deployment) on technology advancement. In addition to federal investments in research and deployment, many local and state governments are interested in how they can provide incentives to help accelerate the adoption of new vehicle technologies. What are the most important interventions that local governments can make? What critical mass of action across areas is needed to make local government incentives meaningful? There is no certain answer to these questions without understanding fun- damentally what technologies will progress fastest and at what rate. However, through techniques such as system dynamics modeling, different scenarios can be examined to illustrate which interventions might be most likely to be effective, and what levels of subsidy (and for how long) might be needed under different energy price assumptions. Cross-Cutting Issues Ability to adopt new vehicle technologies in different land use and transit contexts. For example, densely populated urban environments may lend themselves more to electric vehicle and neighborhood vehicle technology, because trips are shorter and street space is constrained. However, the barriers to deploy- ment of recharging infrastructure may be greater in environ- ments where opportunities for in-home charging are limited. Are there significant variations in the most effective vehicle technology across urban contexts? Mechanisms through which pricing leads to GHG reductions in different contexts. There is a substantial body of literature on the effects of changes in the cost of fuel on fuel use, as well as the cost of travel on the amount of travel. However, few studies decompose these effects into their components. For example, to what extent would a 5-cents per mile VMT fee lead to greater ridesharing versus transit use versus nonmotorized travel versus trip reduction? How would these effects vary in different geographic contexts? These questions have important implications both for understanding the GHG benefits of pricing strategies and for understanding what types of alterna- tive transportation services might be most needed in different contexts. Welfare and economic benefits and impacts of different strategies, especially related to mobility and accessibility. Many assessments of GHG reduction potential report vehicle oper- ating cost savings as a benefit, but these represent only one component of consumer welfare and business economic impacts. Travel time savings can be measured with the right tools, but a comprehensive assessment of welfare changes must account for effects on mobility reflected in trips taken or not taken, as well as time and cost changes for existing travelers. Equity—the distribution of benefits and impacts across different population groups—is also important but poorly understood. Finally, as with GHG emissions benefits, better understanding is needed of how welfare and economic impacts vary across different combinations of strategies and in different contexts. Limitations in tools and Methods for analyzing Strategy effectiveness These limitations have less to do with basic knowledge of strat- egy impacts than with the ability to incorporate this knowledge into tools that can be easily applied by practitioners, who often operate under severe time and resource constraints. Enhancements to current travel demand models. Current travel demand modeling practice is well-suited for examining major highway and sometimes transit investments, broad land use and spatial patterns, and their interactions; it is not well-suited for small-scale interventions such as traffic flow improve- ments, TMD, or microscale land use and design. Some sketch techniques and models have been developed for strategies such as TDM, transit, and nonmotorized travel. However, there is wide variation in these strategies’ effectiveness, depending on how and in what context they are deployed, which cannot easily be captured by sketch modeling. State-of-practice models in many areas need enhancements for transit, land use, and nonmotorized travel. Enhancements (such as time-of-day models) are also needed to fully examine pricing strategies, including congestion pricing and managed lanes. In addition, few areas have models capable of capturing feedback between transportation and land use. Induced demand may only be partially accounted for even in regional models, and is not accounted for at all in most project- and corridor-level models. Most cost-effective use of simulation models. Traffic simulation models are good tools for capturing the effects of capacity and

93 operations improvements on traffic flow and emissions, espe- cially if used in conjunction with modal emissions models such as MOVES. However, such applications are labor-intensive to develop and therefore feasible only for large projects. Further- more, travel demand is an external input to these models, and therefore taken as a given. Modeling of freight movements. Regional and statewide transportation models are limited in their ability to predict future freight flows and the impacts of freight emissions reduction strategies. Few areas have well-developed freight models at the level of detail that allows a credible analysis of GHG emissions reduction strategies. There is a need to provide analysts with better models, analysis tools, and methods that accurately assesses the contribution of different freight strate- gies to reduced GHG emissions. Lack of intercity passenger models. Similar to the concept discussed above with respect to data, few intercity passenger models are available for use in GHG emissions analysis. When the intercity passenger equivalent of the Freight Analysis Framework is available, this observation may no longer be true, but as it stands today, intercity alternatives (e.g., high-speed rail) represent a choice that does not currently exist for many people, and therefore the possibility of their use as a future option is poorly understood.