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Suggested Citation:"13 Alternative and Complementary Regulatory Approaches." National Academies of Sciences, Engineering, and Medicine. 2019. Reducing Fuel Consumption and Greenhouse Gas Emissions of Medium- and Heavy-Duty Vehicles, Phase Two: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/25542.
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Suggested Citation:"13 Alternative and Complementary Regulatory Approaches." National Academies of Sciences, Engineering, and Medicine. 2019. Reducing Fuel Consumption and Greenhouse Gas Emissions of Medium- and Heavy-Duty Vehicles, Phase Two: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/25542.
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Suggested Citation:"13 Alternative and Complementary Regulatory Approaches." National Academies of Sciences, Engineering, and Medicine. 2019. Reducing Fuel Consumption and Greenhouse Gas Emissions of Medium- and Heavy-Duty Vehicles, Phase Two: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/25542.
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Suggested Citation:"13 Alternative and Complementary Regulatory Approaches." National Academies of Sciences, Engineering, and Medicine. 2019. Reducing Fuel Consumption and Greenhouse Gas Emissions of Medium- and Heavy-Duty Vehicles, Phase Two: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/25542.
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Suggested Citation:"13 Alternative and Complementary Regulatory Approaches." National Academies of Sciences, Engineering, and Medicine. 2019. Reducing Fuel Consumption and Greenhouse Gas Emissions of Medium- and Heavy-Duty Vehicles, Phase Two: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/25542.
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Suggested Citation:"13 Alternative and Complementary Regulatory Approaches." National Academies of Sciences, Engineering, and Medicine. 2019. Reducing Fuel Consumption and Greenhouse Gas Emissions of Medium- and Heavy-Duty Vehicles, Phase Two: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/25542.
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Suggested Citation:"13 Alternative and Complementary Regulatory Approaches." National Academies of Sciences, Engineering, and Medicine. 2019. Reducing Fuel Consumption and Greenhouse Gas Emissions of Medium- and Heavy-Duty Vehicles, Phase Two: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/25542.
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Suggested Citation:"13 Alternative and Complementary Regulatory Approaches." National Academies of Sciences, Engineering, and Medicine. 2019. Reducing Fuel Consumption and Greenhouse Gas Emissions of Medium- and Heavy-Duty Vehicles, Phase Two: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/25542.
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Suggested Citation:"13 Alternative and Complementary Regulatory Approaches." National Academies of Sciences, Engineering, and Medicine. 2019. Reducing Fuel Consumption and Greenhouse Gas Emissions of Medium- and Heavy-Duty Vehicles, Phase Two: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/25542.
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Suggested Citation:"13 Alternative and Complementary Regulatory Approaches." National Academies of Sciences, Engineering, and Medicine. 2019. Reducing Fuel Consumption and Greenhouse Gas Emissions of Medium- and Heavy-Duty Vehicles, Phase Two: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/25542.
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Suggested Citation:"13 Alternative and Complementary Regulatory Approaches." National Academies of Sciences, Engineering, and Medicine. 2019. Reducing Fuel Consumption and Greenhouse Gas Emissions of Medium- and Heavy-Duty Vehicles, Phase Two: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/25542.
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Suggested Citation:"13 Alternative and Complementary Regulatory Approaches." National Academies of Sciences, Engineering, and Medicine. 2019. Reducing Fuel Consumption and Greenhouse Gas Emissions of Medium- and Heavy-Duty Vehicles, Phase Two: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/25542.
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Suggested Citation:"13 Alternative and Complementary Regulatory Approaches." National Academies of Sciences, Engineering, and Medicine. 2019. Reducing Fuel Consumption and Greenhouse Gas Emissions of Medium- and Heavy-Duty Vehicles, Phase Two: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/25542.
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Suggested Citation:"13 Alternative and Complementary Regulatory Approaches." National Academies of Sciences, Engineering, and Medicine. 2019. Reducing Fuel Consumption and Greenhouse Gas Emissions of Medium- and Heavy-Duty Vehicles, Phase Two: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/25542.
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Suggested Citation:"13 Alternative and Complementary Regulatory Approaches." National Academies of Sciences, Engineering, and Medicine. 2019. Reducing Fuel Consumption and Greenhouse Gas Emissions of Medium- and Heavy-Duty Vehicles, Phase Two: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/25542.
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Suggested Citation:"13 Alternative and Complementary Regulatory Approaches." National Academies of Sciences, Engineering, and Medicine. 2019. Reducing Fuel Consumption and Greenhouse Gas Emissions of Medium- and Heavy-Duty Vehicles, Phase Two: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/25542.
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Suggested Citation:"13 Alternative and Complementary Regulatory Approaches." National Academies of Sciences, Engineering, and Medicine. 2019. Reducing Fuel Consumption and Greenhouse Gas Emissions of Medium- and Heavy-Duty Vehicles, Phase Two: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/25542.
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Suggested Citation:"13 Alternative and Complementary Regulatory Approaches." National Academies of Sciences, Engineering, and Medicine. 2019. Reducing Fuel Consumption and Greenhouse Gas Emissions of Medium- and Heavy-Duty Vehicles, Phase Two: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/25542.
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Suggested Citation:"13 Alternative and Complementary Regulatory Approaches." National Academies of Sciences, Engineering, and Medicine. 2019. Reducing Fuel Consumption and Greenhouse Gas Emissions of Medium- and Heavy-Duty Vehicles, Phase Two: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/25542.
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Suggested Citation:"13 Alternative and Complementary Regulatory Approaches." National Academies of Sciences, Engineering, and Medicine. 2019. Reducing Fuel Consumption and Greenhouse Gas Emissions of Medium- and Heavy-Duty Vehicles, Phase Two: Final Report. Washington, DC: The National Academies Press. doi: 10.17226/25542.
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Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

13 Alternative and Complementary Regulatory Approaches 13.1 INTRODUCTION: WHY CONSIDER ALTERNATIVE APPROACHES The approach used for regulating the fuel consumption and greenhouse gas (GHG) emissions of medium- and heavy-duty vehicles (MHDVs) under the Phase I and Phase II rules (EPA and NHTSA, 2011, 2016) employs what economists call a quantity instrument (a standard) rather than a price instrument (an economic incentive). Moreover, compared to the quantity instruments used in other regulatory interventions, including light-duty vehicles, the approach used for MHDVs has some significant limitations. Except for the lightest vehicles in the MHDV spectrum, the regulation is implemented though pre-sale certification of the engine (if made by a company that then sells it to the vehicle assembler) or the vehicle—the latter being based on a computerized simulation of the vehicle’s fuel use and GHG emissions (i.e., the Greenhouse Gas Emissions Model) but with no direct measurement of those variables. This approach was adopted for Phase I because it was readily implementable, would not mandate the purchase of extensive testing equipment, and was not supplanted by a technologically feasible alternative. Changes made for Phase II significantly improve the sophistication of the engine simulation, and extend its use to simulate the fuel economy of some, but not all, trailers when combined with tractors. But, even with the Phase II changes, the regulation remains ex ante, not ex post; and it relies on pre-sale simulation, not vehicle-level testing, of fuel economy and GHG emissions, the items intended to be controlled. There is a crucial assumption that the ex ante simulation provides a reasonably good approximation of what actually occurs in the real world over the vehicle’s lifetime. Without that assumption, there can be no assurance of program effectiveness. With light-duty vehicles, there is pre-sale testing of fuel economy and GHG emissions, and this is complemented by testing of vehicles after sale at the rate of a few thousand vehicles per year to determine if they continue to comply with standards when actually in use. If a test group is determined to be failing, the vehicle model may be recalled provided the problem is deemed repairable. The in-use testing assesses whether light-duty vehicles continue to comply with the laboratory tests that were required pre-sale (DOE and EPA, n.d.). With MHDVs, as noted in Chapter 2, once they are sold, in-use testing is lacking. Even if an on-road test suggested that a particular MHDV exceeded the standards, it is not clear that this could be used as a basis for enforcement since the vehicle was originally certified using a simulation model. The existing regulatory approach, were it to remain a stand-alone approach, would thus have lower efficacy than if it were to be combined with an ex post testing regime. However, as pointed out in Chapter 3, it would not be easy to apply to MHDVs the in-use testing used with light-duty vehicles because of the major differences in how the two types of vehicles are produced and used. The distinctive feature of the MHDV sector is the tremendous heterogeneity of both the manufacturing companies and the vehicles they produce. While there are many different types of light-duty vehicle, there is a far greater diversity of MHDV types, sizes, and duty cycles. Heavy-duty vehicle production is driven by customer specification of the sort which can lead to far greater variety of pairings between major vehicle components. Unlike light-duty vehicles, the production of a MHDV is typically split between different manufacturers, including the producer of the engine, the producer of the chassis, and another manufacturer which purchases the chassis and adds a body and special equipment. With Class 8 vehicles, the tractor and the trailer are never built by the same company, and they are often not owned by the same company when the truck is operated. On the supply side, the responsibility for a MHDV’s fuel consumption is highly fragmented: quite often, no single company may be responsible for the design and manufacture of the complete vehicle. Because of these considerations, Chapter 3 observed that (i) simulation represents a more versatile approach than whole-vehicle testing for MHD vehicles and is a reasonable approach in the short and mid term, but (ii) in time, the growing need to reduce fuel use on all types of operation over a full vehicle life span will require in-use methods of compliance. The purpose of this chapter is to consider Prepublication Copy – Subject to Further Editorial Correction 13-1

some alternatives to the current approach used for MHDVs. By the time of Phase III, the current approach will have been in place for well over a decade, and its limitations may have been seen as problematic. It regulates the manufacturer of the engine and the chassis, not the up-fitter who may subsequently modify the body (e.g., into a motor coach or tow truck) and not the owner/operator of the vehicle. And it applies only to new MHDVs and leaves the fleet of older trucks untouched. The narrowness of the approach may eventually become counterproductive. As described below, the experience of another regulatory program, the Clean Water Act, shows that continued narrow-spectrum regulation can generate diminishing returns with regard to attaining regulatory objectives. As stated in Chapter 2, the problem of reducing MHDV fuel consumption and GHG emissions will ultimately need to be addressed as a societal system optimization and a portfolio of solutions will be needed, including some—perhaps many—for which the National Highway Traffic Safety Administration (NHTSA) and Environmental Protection Agency (EPA) do not have direct authority. The issue will then arise of coordinating the policy instruments used by NHTSA and EPA to promote fuel economy and emissions reduction with the policies and instruments adopted by other actors to achieve those same goals. In that context, a narrow reliance by NHTSA and EPA on pre-sale simulation may become counterproductive. The chapter is organized as follows. Section 13.2 describes some general principles applicable to the design of regulations of the type being considered in this report. Section 13.3 examines the role that price signals can play to incentivize behaviors needed to meet the Phase III goals. Section 13.4 concludes with a discussion of some regulatory efforts that could complement a possible Phase III regulation issued by NHTSA and EPA. 13.2 SOME GENERAL PRINCIPLES FOR REGULATION The question of how to frame regulations that shift people’s behavior in order to achieve objectives set by the regulator is a topic that has been examined in the economics literature and other policy literatures for at least 50 years. Several general principles have emerged from those literatures. Here we review three such general principles: (1) a regulation that takes the form of a performance standard is generally likely to be superior to other approaches; (2) a regulatory approach that targets a narrow subset of the factors that cause the outcome of interest to the regulator is likely to be less effective, and less economically efficient, than one that targets a broader subset; and (3) any regulatory initiative can have some unintended consequences that diminish its effectiveness or its efficiency but this often comes about because a trade-off has to be made in the choice of a regulatory approach. 13.2.1 The Desirability of a Performance Standard If a regulation takes the form of a performance standard, it specifies a target outcome, but not the method by which that outcome should be attained. Examples of performance standards for MHDVs could include an annual level of fuel consumption per ton-mile, or an annual level of emissions per ton-mile (these are performance rate standards), or, more directly, a level of annual fuel consumption or a level of annual emissions (these are performance mass standards). The current approach in Phases I and II sets a rate standard for the engine in pre-sale testing. A performance standard would regulate the actual fuel consumption or actual emissions in operation on the road, which is really what one wants to reduce. With such a performance standard, the regulated entity would be the vehicle owner/operator, whose actions actually determine the fuel consumption and emissions that are to be controlled. The performance standard leaves it up to the owner/operator to decide how to comply with the standard—by changing the age profile of the fleet, making the vehicles more aerodynamic, further reducing the rolling resistance of the tires, increasing the vehicles’ cargo capacities, reducing the energy consumption of accessory equipment, changing the routes traveled, investing more in driver education, making greater use of telematics that improve vehicle operation, and so forth. Rather than singling out a specific pathway, the performance standard leaves the owner/operator free to choose any combination of measures that meets Prepublication Copy – Subject to Further Editorial Correction 13-2

the standard. It is a reasonable presumption that owners/operators will choose the most cost-effective approach for their particular circumstances. An equipment mandate or a technology mandate, by contrast, lacks this flexibility and therefore may turn out to be more costly. Moreover, as the National Research Council (NRC, 2010) report Technologies and Approaches to Reducing the Fuel Consumption of Medium- and Heavy-Duty Vehicles (henceforth the “NRC Phase One Report”) noted, a technology mandate that is not appropriately “tuned” to the operational aspects of a particular class of vehicles may fail to achieve the desired outcome due to incompatibility between the technology and how the vehicle is used. In such cases, especially when characteristics or operations are uncertain, a performance standard offers a surer way of achieving the desired outcome. The drawback of a performance standard is that it has to be possible to measure the performance outcome. This is a challenge with MHDVs. A pre-sale equipment mandate or a technology mandate is much easier to monitor for compliance, which is why that approach was adopted in Phases I and II of the MHDV regulations. But, the failure to measure performance outcomes is a two-edged sword. It precludes the use of a performance standard and it leaves uncertainty as to whether the equipment or technology mandate is actually achieving the desired regulatory goal over the operating life of the regulated vehicles. The longer this uncertainty persists, the stronger the case for introducing some system of outcome measurement. It is that observation which underpins Chapter 3’s conclusion of a need, ultimately, for effective measurement of fuel consumption in use. Below we explore how some form of in-use measurement of fuel consumption could also open the way to a having a performance standard regulation as a complement or substitute for engine or vehicle certification. Finding: If it can be implemented, a performance standard, which specifies a target outcome but leaves the regulated entity free to choose the best way achieve that outcome, is generally the most efficient form of regulation. Finding: The current approach to regulating fuel consumption and GHG emissions of MHDVs under the Phase I and Phase II rules is not a performance standard. The policy objective is fuel consumption and/or emissions in the vehicles’ use phase. At present these are not measured (except for the lightest MHDVs) and they are not regulated. Recommendation 13-1: If and when widespread adoption of in-use measurement of fuel use or emissions comes about, serious consideration should be given for future phases of MHDV rulemaking to the adoption of regulation via a performance standard, such as a cap and trade system or fuel taxes, that directly targets fuel consumption or emissions. Such consideration should evaluate the potential gains from a performance standard as well as the practical difficulties and costs on implementing such a system. 13.2.2 The Clean Water Act: A Case Study of Diminishing Returns When Continuing to Adopt a Narrow Approach The modern law governing surface water pollution in the United States is the Clean Water Act (CWA), enacted in 1972. The 1972 CWA had as its objective “to restore and maintain the chemical, physical and biological integrity of the Nation’s waters.” To this end, it set the goal that the discharge of pollutants into the waters of the United States be eliminated by 1985. It also set an interim goal of achieving water quality sufficient for “the protection and propagation of fish, shellfish, and wildlife, and [that] provides for recreation in and on the water” (“the fishable, swimmable goal”) by July 1983. The Act prohibits the “discharge of any pollutant by any person” into the waters of the United States unless the discharger has obtained a permit and complies with its conditions, including restrictions on which pollutants may be discharged and how much. However, the permit system contains several important exemptions which, in effect, create an opening for the unregulated discharge of water pollutants. The two most important exemptions are that (1) the requirement for a permit is restricted to Prepublication Copy – Subject to Further Editorial Correction 13-3

“point sources” of pollution and not other (“nonpoint”) sources and (2) most agricultural point sources became exempt from the requirement for a permit. Congress made a conscious decision to limit the permit requirement to point sources, defined as a source of water pollution carried by a “discernible, confined, and discrete conveyance.” This definition primarily covers discharges by industrial sources (including manufacturing, mining, and oil and gas extraction facilities), government facilities (including publicly owned wastewater treatment plants and military bases), and some agricultural facilities. The Act itself does not define a nonpoint source, but this is usually considered to be pollution “caused by diffuse sources” and associated with rainfall and runoff, including “agricultural, silvicultural and urban runoff, runoff from construction activities, etc.” 1 The 1972 CWA held back from active federal intervention to control nonpoint sources; section 208 left it to the states to decide whether some action was needed for their nonpoint sources and, if so, what. While some states regulated some forms of nonpoint source pollution, in most cases the 208 effort was left to voluntary action. Little was accomplished, and EPA looked on passively (for example, see Gould, 1990, pp. 463, 473-474, 489-491). Because of the perceived ineffectiveness of section 208, when Congress revised the CWA in 1987, in the Water Quality Act (WQA), it created a new section 319. This still left the responsibility with the states, but it directed them to identify waters impaired by nonpoint source pollution, determine the sources of that impairment, and develop Best Management Plans (BMPs) to address the nonpoint source problem. However, most commentators have concluded that section 319, too, has been ineffective (Andreen and Jones, 2008, pp. 27-28; Gould, 1990, pp. 463, 473-474, 489-491; Ruhl, 2000, pp. 298-299). The original, 1972 CWA defined “pollutant” to include “agricultural waste discharged into water.” It specifically included discharges from a “concentrated animal feeding operation” within the definition of a point source. Presumably irrigation return flows, discharged through pipes, ditches, or channels, would also meet the definition of a point source. However, EPA decided to exempt irrigation return flows from the category of point source pollution. In 1975, a federal court held that EPA exceeded its authority in creating this exemption, and EPA moved to include irrigation return flows in the point source permitting program. In 1977, Congress amended the CWA to undo this. For good measure, it added an amendment specifically prohibiting EPA from requiring a permit for discharges “composed entirely of return flows from irrigated agriculture” and, elsewhere, it described irrigation return flows as “agriculturally … related nonpoint sources of pollution” (Gould, 1990, p. 474; Ruhl, 2000, pp. 293-295). Hence, farms that discharge sediment, animal wastes, fertilizers, or pesticides via their irrigation return flows can do so freely (Ruhl, 2000, p. 295). For the first two decades of the point source permit program, EPA focused on issuing permits to control discharges from industrial sources and sewage treatment plants. The CWA had also provided EPA with the authority, under certain conditions, to require permits for stormwater discharged through point sources, but EPA did not exercise this authority. The 1987 WQA dealt with this and created a framework for the permitting of municipal and industrial stormwater discharges. In doing so, however, it explicitly excluded agricultural stormwater discharges (Ruhl, 2000, pp. 295-296). In summary, the 1972 CWA was the first comprehensive effort by the federal government to control water pollution in the United States. It set the ambitious goal of eliminating, by 1985, the discharge of all water pollutants. But, in implementing that goal, a rather narrow approach was adopted which focused on controlling discharges from primarily nonagricultural point sources. There were some reasons for this as a first approach. Regulating some 60,000 point sources was a much more manageable task than trying to regulate other forms of pollution that were pervasive, diffuse, and highly site specific in nature. The control of nonpoint source pollution was seen as challenging and not susceptible to a 1 Nonpoint pollution also includes sediment from erosion, runoff and siltation from mining activities, saltwater intrusion, and changes in the flow of water caused by the construction of dams and flow diversion facilities (hydromodification). Prepublication Copy – Subject to Further Editorial Correction 13-4

conventional engineering solution. 2 In many cases it could be addressed only through land use controls, traditionally the prerogative of the states. Agriculture, a major source of nonpoint source pollution, was politically powerful (Craig and Roberts, 2015, p. 2; Zaring, 1996, pp. 523-524). As Andreen (2004, p. 562) expressed the problem, “What was the EPA supposed to do, tell farmers how to farm?” With respect to the pollution sources that were targeted, the CWA was quite successful. Two decades later, twice as many people as in 1972 were being served by municipal wastewater treatment plants providing secondary treatment or better. Less than 1 percent of municipal wastewater was being discharged with no treatment (EPA, 1990). This upgrading of sewage treatment plants resulted in a 46 percent reduction in the discharge of oxygen-consuming pollutants. Had the improvements not occurred, those discharges would have increased by 191 percent due to population growth (ASIWPCA, 1985, as cited in Baker, 1992). But, although there was considerable progress, the goals of eliminating the discharge of pollutants or rendering the waters of the United States fishable and swimmable have not been attained. An assessment of streams and small rivers and reported that 67 percent of U.S. stream miles are in poor or fair condition (EPA, 2006). Overall, about 45 percent of river and stream miles assessed by states and 47 percent of assessed lake acres do not meet applicable water quality standards and are impaired for one or more desired uses (EPA, 2007). While the regulation of point sources has not been without flaws, continuing unregulated nonpoint source pollution is the dominant factor in the failure to attain the CWA goals (Andreen and Jones, 2008, pp. 15-17). Nonpoint sources are the primary cause of impairment for three-quarters of all impaired waters. Among nonpoint sources, agriculture is the leading source of impairment for rivers, streams, and lakes nationwide (EPA, 2011). As early as 1977, a U.S. Government Accountability Office (GAO) report had warned that, if left uncontrolled, “nonpoint source pollution will prevent attainment of national water quality goals and will continue to grow in significance as point sources of pollution such as factories and municipal waste treatment plants are brought under control” (GAO, 1977, p. i). One might ask: What should have been done? Should EPA have indeed tried to tell farmers how to farm? Wouldn’t that be like telling MHDV users how to operate their vehicles? In both cases, what needs to be done is to find a way to measure emissions, or to measure behaviors tightly linked to emissions, and then set a performance standard, leaving it up to the regulated parties to determine how best to meet the standard. The lack of measurement is the crucial flaw: what is not measured cannot be regulated. Restricting regulation to what can readily be measured—and failing to improve measurement over time—will distort choices by the regulated parties and lead to unintended, but counterproductive, consequences. Moreover, the longer a source of emissions remains unmeasured and unregulated, the more likely it is that a strong constituency will emerge in support of prolonging the limited regulatory approach. 13.3 UNINTENDED CONSEQUENCES Any action, including any regulatory action, can always have some unintended adverse (or beneficial) consequences. The challenge is to design the action so as to minimize the chance of unintended adverse consequences. There are at least three possible pathways by which unintended adverse consequences could arise from fuel efficiency and emission regulation of MHDVs: narrow (selective) regulation, regulatory transitions, and the rebound effect. 2 A view strongly challenged by Houck (2002, p. 87), who argues that nonpoint source pollution is no more varied, site specific, or technically difficult to control than most point sources. Prepublication Copy – Subject to Further Editorial Correction 13-5

13.3.1 Narrow (Selective) Regulation An important consideration here is the breadth of activities covered by the regulation relative to the full range of activities that could impact attainment of the regulatory objective. Regulating point but not nonpoint source pollution is one example of an unduly narrow approach to regulation. Another is the differentially less stringent regulation of the fuel efficiency of light trucks versus passenger cars by NHTSA under Corporate Average Fuel Economy (CAFE) standards between 1975 and 2005. The narrower the regulatory focus, the greater the chance of some unintended consequence. This can occur because regulated parties deliberately switch away from regulated choices to unregulated (or less regulated) alternatives, a possibility emphasized by the National Research Council (NRC, 2010) in its Phase One Report, Technologies and Approaches to Reducing the Fuel Consumption of Medium- and Heavy-Duty Vehicles, in the context of vehicle class shifting by consumers. The report warned against adopting fuel efficiency regulations that significantly increase the cost, or decrease the performance, for one class of trucks compared to another (NRC, 2010, p. 152) so as not to invite the possibility that differential regulation across vehicle classes would distort consumer choices. A buyer may decide to purchase a different class of vehicle to offset the cost increase or the performance loss with the consequence that a less efficient vehicle is placed into use—exactly the opposite effect of what an efficiency standard aims to achieve. Even if there were no distortion of consumer choice such as induced class shifting, new opportunities or technologies may emerge spontaneously that, with a narrow regulatory approach, have a freer rein in the unregulated sector and become established there. These are not mutually exclusive. Arguably, both factors—regulation-induced shifts in consumer choice and also exogenous innovations which flourished in the less regulated sector—were at play with both nonpoint source pollution and light trucks. 3 At any rate, while regulation-induced class shifting can occur, as yet there appears to be no empirical evidence from which to assess its magnitude. Because of its concern with class shifting, the NRC Phase One Report concluded: “While it may seem expedient to focus initially on those classes of vehicles with the largest fuel consumption (i.e., Class 8, Class 6, and Class 2b, which together account for approximately 90 per cent of fuel consumption of MHDVs), the committee believes that selectively regulating only certain vehicle classes would lead to very serious unintended consequences and would compromise the intent of the regulation” (NRC, 2010, p. 189, Finding 8-1). In truth, however, there may be a trade-off. Some sectors may be more responsive to regulation than others. The potential regulatory burden may be lower for some sectors than others. 4 Or it may be more practical to formulate a performance standard for some sectors than others. Those could be valid reasons for an approach that regulates some sectors differently than others. Otherwise, with the same approach applied across all sectors, the regulation would be determined by the lowest common denominator. There is a trade-off between a partial but more efficacious regulation covering some sectors and the risk of class switching with some unintended consequences. If there were to be differentiated regulation of some MHDV sectors, the important lesson of the CWA is the necessity to press forward with improving the measurement of outcomes in the less regulated and unregulated sectors so that the unintended consequences can be monitored and the regulatory framework extended over time. 3 A key development with the latter was the emergence in the mid-1980s of two new vehicle types, the minivan and the sport-utility vehicle, which were car-like in their performance, albeit regulated as light trucks. Greene (1998, p. 601) notes the uncertainty as to whether the rise of these new vehicle types was due to the stricter fuel economy standards imposed on passenger cars or exogenous social and demographic trends. 4 Thus, with the CAFE standards, the differential treatment of light trucks versus passenger cars was in part intentional, “reflecting the belief that light trucks function more as utility vehicles and face more demanding load- carrying and towing requirements” (NRC, 2002, p. 21). Prepublication Copy – Subject to Further Editorial Correction 13-6

13.3.2 Regulatory Transitions Another form of unintended consequences is associated with what might be called regulatory transitions—when regulations are introduced for the first time or are being modified to a significant degree. If the new regulation, or the modified regulation, significantly raises the cost of a new vehicle, this could have counterproductive consequences in either of two ways. In anticipation of the change, some vehicle owners may decide to accelerate their purchase schedule, obtaining a new vehicle before the new standard goes into effect (“pre-buy”). Or, instead of purchasing a new vehicle, some owners may choose to maintain their existing vehicle and prolong its life (“delayed retirement”). Both behaviors can lead to the phenomenon of a reduction in vehicle purchases immediately following the new standard (“low-buy”). In either case, a less efficient vehicle is substituted for the use of a new, more efficient vehicle and the overall effect of the regulation is dampened, at least in the short run. Delayed retirement following the adoption of new emission standards for CO and NO x was identified as a factor for passenger cars by Gruenspecht (1982) and by Harrison (2008) with respect to NO x and particulate matter (PM) standards for MHDVs in 2007. An econometric analysis by Rittenhouse and Zaragoza Watkins (2017) finds robust evidence of pre-buy and low-buy for trucks following implementation of the 2007 standards, but not following the 1998, 2002, or 2010 pollutant standards, which were less costly to comply with. The 2007 standards caused several thousand more trucks to be sold in each of the months prior to introduction of the standards and approximately the same number of fewer trucks in each of the months after the standards. The pre-buy and low-buy impacts were short-lived and small in volume relative to the estimates of Harrison (2008), and they roughly canceled out, leaving an insignificant net impact on sales (Rittenhouse and Zaragoza-Watkins, 2017, pp. 4, 33). In general, delayed retirement and pre-buy are likely to be relatively short-lived phenomena. They are unavoidable if regulations are to be updated to stay abreast of changing economic and technological circumstances. Even a price signal such as a tax may require adjustment over time, opening it up to the consequences of a regulatory transition. 13.3.3 The Rebound Effect This refers to the phenomenon whereby improved fuel efficiency lowers the cost of operating the vehicle sufficiently that it induces the operator to use the vehicle more (travel more miles), thereby causing an increase in the amount of fuel consumed. 5 The increase in fuel consumption from the extra usage of the vehicle diminishes the saving from its increased fuel efficiency, although it may not eliminate it. This is an inevitable consequence of employing what is an indirect approach to the regulation of fuel use or emissions via the pre-sale regulation of engine fuel efficiency. The direct approach would be to regulate fuel use or emissions in actual use, whether through a price instrument (a gas tax or a price on emissions) or a quantity instrument (e.g., a cap on emissions). An indirect approach may be justified, for example, because it is difficult to measure directly what is intended to be regulated (fuel use or emissions from individual trucks). There is a trade-off: the convenience of indirect regulation versus the opportunity for an unintended consequence through the rebound effect. The size of the rebound effect, and therefore the magnitude of the unintended consequence, is an empirical question. It depends on (i) the extent to which the overall cost of using the vehicle is reduced, given other costs of operation, and (ii) the price elasticity of the vehicle usage decision. 6 By way of 5 In addition to a change in vehicle miles traveled (VMT), the rebound effect could also take the form of an increase in the loaded weight at which vehicles are operated, or a change in road and traffic conditions encountered as routes or schedules are altered in response to increased fuel efficiency. 6 In the case of trucking, the usage decision probably lies not with the truck owner but with the shipper who chooses trucking as the mode of transportation. The impact on VMT, therefore, depends on how much of the cost reduction is passed on to the customer. The 2010 report suggests that, because trucking is a very competitive industry, any savings in fuel costs will likely be passed on to shippers. Prepublication Copy – Subject to Further Editorial Correction 13-7

illustration, suppose that a new standard raises fuel efficiency by 20 percent; this reduces the amount of fuel used per mile traveled (and the fuel component of operating cost per mile) by about 17 percent (= 0.2/1.2). To incorporate the rebound effect, suppose that fuel accounts for about 34 percent of the total operating cost per mile for a truck (ATRI, 2015, Table 10). Then, the total operating cost per mile drops by about 6 percent (= 0.17×0.34). 7 If the price elasticity of miles traveled is 0.5 in absolute value, the reduction in the operating cost per mile triggers a 3 percent increase in miles traveled (= 0.5×0.17×0.34), and the net effect is a reduction in fuel consumption of only 14 percent (= 17 percent – 3 percent). With a smaller absolute value of the price elasticity, the rebound adjustment is smaller and conversely with a larger value of the price elasticity. Compared to the literature for light-duty vehicles (LDVs), the literature on the price elasticity of MHDV activity is very sparse. There are a few studies of the price elasticity of ton-miles shipped but these results vary widely depending on the type of product being shipped, the geography of the shipments, trip length, and other factors. There are two recent sets of studies estimating the price elasticity of truck VMT for the United States. One pair of studies by Winebrake et al. (2015a,b) uses annual data for the period 1970-2012 on aggregate national VMT and fuel consumption by combination trucks (tractor- trailers) and also by vocational (single-unit) truck operations. They find that, for both types of trucks, the price elasticity of VMT is now close to zero, having changed after 1980 when trucking was deregulated. The other study by Leard et al. (2015) uses a large panel of truck-level microdata for the United States from the Vehicle Inventory and Use Survey (VIUS) in 1982, 1987, 1992, 1997, and 2002, and estimates price elasticities of VMT of 0.18 for combination trucks and 0.12 for vocational trucks. In the RIA for the Phase II rule, NHTSA and EPA chose to place more weight on the studies by Winebrake et al.; they assumed a price elasticity of 5 percent for combination trucks, and an elasticity of 15 percent for vocational vehicles. The reasons given were that Leard et al. (2015) was not yet published in a peer- reviewed journal and it uses older data from the VIUS surveys which terminated in 2002. In fact, however, for estimating the price elasticity of VMT, the VIUS truck-level data are likely to be far superior to the coarse national-level aggregate data used by Winebrake et al. The fact that the VIUS effort was discontinued after 2002 is particularly unfortunate, given the need for a regulatory baseline and the importance of tracking the impact of developments in the trucking industry since 2002. In its first report, Reducing the Fuel Consumption and Greenhouse Gas Emissions of Medium- and Heavy-Duty Vehicles, Phase Two: First Report, the committee highlighted the failure to restart VIUS as a significant weakness in the current regulation of truck fuel efficiency and safety (NRC, 2014, p. 11). Finding: A finding of the committee’s 2014 report was that there are no existing data that meet the needs of a baseline for the MHDV fleet. Unless a reliable, repeatable data collection process is established (as recommended in that report), there will be no way to obtain a future measure of whether there is meaningful progress in meeting the regulatory goals of the Phase I and Phase II rules. Recommendation 13-2: NHTSA should establish a reliable, repeatable survey for truck operators. NHTSA and EPA should also reinstitute the VIUS, as the committee recommended in its first report, to provide an accurate baseline for forecasting vehicle number and vehicle technology penetration by class. The major manufacturers should be contacted for accurate numbers today and for their forecasted penetration rates in the next decade. 7 In the rebound analysis performed for the Phase II Regulatory Impact Analysis (RIA), the vehicle purchase cost was allocated on a per-mile basis over the expected lifetime number of vehicle miles and included in the cost per mile, thereby reducing the percentage cost saving when calculating the rebound effect due to improved fuel efficiency (NHTSA and EPA, 2016, p. 8-17). Prepublication Copy – Subject to Further Editorial Correction 13-8

13.3.4 Other Unintended Consequences A commonality linking the rebound effect and the selective regulation discussed above is the mismatch between the formulation of the regulation and the regulatory objective animating it: if the regulation does not target what the regulator aims to control, unintended consequences can arise that impede attainment of the underlying regulatory objective. Another situation in which unintended consequences can occur is if the regulation does not adequately account for the heterogeneity of the behaviors being regulated—in the case of MHDVs, the heterogeneity of the utilization and duty requirements for different trucks. An example was recently noted by Knittel and Ryan (2015) in the case of LDVs. They demonstrate that, if one is using a uniform gas tax as an indirect way to control vehicle emissions, a substantial economic welfare loss can arise when per-gallon emissions differ widely across passenger cars. What is needed to avoid that outcome, they show, is either a direct tax on vehicle emissions or a gas tax differentiated based on vehicle type. The formulation of a regulatory policy often presents a trade-off: there may be physical, economic, or political obstacles which prevent direct regulation of what the regulator aims to control. The result is a regulation that may be only partially effective or excessively costly, including some costs associated with unintended consequences. 13.4 USING A PRICE SIGNAL There are several ways a price signal could be designed so as to incentivize a reduction in MHDV fuel use or emissions. The signal could take the form of an increase in the price of fuel (raising the gas tax) or it could be implemented by setting a price on emissions, directly or implicitly. Because fuel consumptions and emissions are linked, a price on one would have some effect on the other, although not necessarily as strong and, for the reason mentioned above, not necessarily efficient in economic terms. 13.4.1 Raising the Fuel Price Most economists would say that raising the price of fuel, whether through a higher fuel tax or by some other means, is the best way to lower fuel consumption and reduce GHG emissions. Whether this is so depends on the criterion by which the policy intervention is being evaluated. In economics, the criterion commonly applied is that the policy chosen should maximize aggregate national net benefit (i.e., aggregate benefits minus aggregate costs, regardless of to whom they accrue). An alternative, less stringent, criterion would be that aggregate benefits exceed aggregate costs (net benefit being positive, rather than being maximized). But in the real world, governments sometimes apply a different criterion, and policies are sometimes adopted regardless of whether they pass a net benefit test. For example, a government may set a target of reducing emissions by 30 percent by 2030 (as was done by the European Union for CO 2 emissions) regardless of whether this is known to pass a net benefit test. It may be done because the available information is insufficient to determine whether a net benefit test would be passed. It may be done because meeting that target is considered a moral imperative, or is desirable in other way. This may or may not be regrettable, but it happens sometimes. If a MHDV policy is seen as motivated by the objective of meeting a fuel use or emissions reduction target, the question to be addressed is this: If the price of fuel were raised, how much of a reduction in fuel use would that generate (this might be called the efficacy of a price instrument)? 8 If a MHDV policy is seen as requiring a net benefit test, the question to be addressed is this: What is the economic cost and consequent reduction in social welfare when the price of fuel is used to reduce fuel consumption (i.e., the economic efficiency of a price instrument)? Of course, both considerations may underlie the MHDV policy in question. But policy makers should make clear what objectives or 8 As explained below, there may be ways to influence consumption other than by raising the price; for example, it may be possible to alter nonprice attributes that influence consumption decisions. Prepublication Copy – Subject to Further Editorial Correction 13-9

combination of objectives is being pursued, so that the trade-offs between efficiency and efficacy can be evaluated. 13.4.1.1 Impact of Price on Fuel Consumption The aggregate MHDV fuel consumption and emissions depend on decisions made by many actors, no one of whom alone exercises a decisive influence. They include the vehicle manufacturers, equipment manufacturers, vehicle buyers, vehicle owners, vehicle operators, shipping managers, and shipping customers. Reducing fuel consumption and emissions at an aggregate scale is a coordination problem: the multiple actors have to be in sync. For example, it does not help if vehicle owners want a particular type of fuel efficient truck but the truck manufacturers do not produce that type of truck—or, conversely, if the vehicle manufacturers produce a particular type of truck but fleet owners do not buy it. Similarly, if drivers do not operate the vehicle well, the fuel savings designed into it may not materialize. In this context, it is clear that the price of fuel has the potential to influence the broadest possible set of actors. In addition to influencing fuel use, a higher fuel price would tend to lower miles driven, thereby reducing emissions. Compared to a technology mandate, the price of fuel influences not only vehicle technology but also vehicle operation and it affects vehicles already on the road, not just new vehicles. Just how responsive various actors are to a price signal is an empirical question. One would expect this to vary with the actor and, perhaps, with the circumstances. However, compared to light-duty vehicles, one would expect commercial trucking to be more responsive to a change in the price of fuel because the cost of fuel is a large component of total operating expenses and, therefore, more likely to be salient to decision makers in the trucking industry. 9 That said, there are still some factors that may mute the impact of price signals with MHDVs. These factors occur with varying degrees of frequency in any situation where people are making choices, whether they are consumers or firms. Nevertheless, they have received particular attention in the context of energy efficiency investments which people fail to make even though these would appear to save money—a phenomenon referred to as the energy efficiency gap or the energy efficiency paradox. 10 One explanation is that those investments really would not save money; another is that people are being irrational. Both are possible explanations, but they by no means exhaust the possibilities. There are some other explanations that are consistent with richer models of decision making and rationality. In no special order, these include the split incentives, capital constraints, uncertainty, and the use of heuristics and simplified decision procedures. 13.4.1.2 Split Incentives It can happen that there is a mismatch between who pays for an investment and who receives the benefit from it, or who determines the magnitude of the benefit. Such a mismatch lies at the heart of the split-incentives problem (also known as the principal-agent problem). It can cause the investment not to be made even though it is—or it can become—profitable in the aggregate, just not for the actor who would have to pay for it. In the case of trucking, three examples have been identified involving (i) the role of the driver, (ii) leased trailers, and (ii) the resale of tractors after a few years of use by the initial purchaser. Driver behavior, including nonoptimal route choice, idling, and driving techniques such as aggressive acceleration driving and suboptimal vehicle speed or choice of gear shift points, can significantly affect overall fuel consumption. If drivers do not pay their fuel costs, they may lack the incentive to minimize their fuel consumption, thereby creating a principal-agent problem. Vernon and Meier (2012) estimate that 95 percent of all tractors are operated by drivers who do not pay fuel costs; they account for 91 percent of the miles traveled by the trucking industry. One solution is for employers to offer drivers a bonus for reducing their consumption. In 1997, about 23 percent of drivers were offered such bonuses, especially by larger trucking firms. That leaves about 68 percent of the miles traveled by 9 To the extent that fuel surcharges are imposed by trucking companies, this passes the burden of price spikes to shippers and reduces the trucking companies’ sensitivity to fuel prices. 10 For a survey, see Gillingham and Palmer (2014). Prepublication Copy – Subject to Further Editorial Correction 13-10

the trucking industry, which are driven by drivers with little or no motivation to reduce their fuel consumption. Whether this is still an important problem in the trucking industry today is less clear. The survey of stakeholders by Roeth et al. found conflicting perspectives, with some stakeholders seeing split incentives as still being a problem and others believing that it is no longer a problem (2013, p. 51).Industry participants interviewed by Klemick et al. (2015, p. 161) were aware of the issue and many had attempted to address it by offering drivers rewards for better fuel efficiency, or through driver training, or by adopting technologies such as cruise control and speed limiters that control vehicle performance and narrow the variation in fuel economy across drivers. Another example is the use of leased or rented trailers. Vernon and Meier (2012, p. 272) estimate that about 23 percent of trailers fall into this category. The firm leasing the trailer chooses the type of trailer to buy but does not itself incur the fuel charges associated with use of the trailer, thereby giving it an incentive to ignore trailer characteristics that reduce fuel consumption. 11 By way of a third example, Klemick et al. (2015, p. 162) suggest that split incentives may be present if the resale market does not value fuel economy features on vehicles, thereby truncating the return on investment for the first owner. 12 13.4.1.3 Up-Front Capital Cost Split incentives provide one explanation why what appears to be a profitable investment in fuel efficiency is not undertaken. Another possibility is that, even though the party that pays for the investment would receive a benefit greater than its cost, the item is expensive and requires an up-front investment which is too large to be readily financed. For example, a new long-haul tractor with a sleeper cab costs about $125,000 to $145,000; driver features added to the truck may add another $10,000 (Roeth et al., 2013, p. 29). Financing this large a cost may be a challenge for a small trucking company with limited access to credit. Also, companies often self-ration their access to credit by imposing an internal requirement that an investment satisfy a payback period test (i.e., the value of the fuel savings from the investment must make up for the additional capital cost and other costs of that technology within a set period of time). This is used in many industries as a crude but simple criterion for selecting capital projects and rationing limited capital. In trucking, according to Roeth et al. (2013, pp. 24-26), large and medium for-hire carriers and lease/rental fleets typically require a payback period of between 12 and 24 months; private fleets require a payback period of 24 to 36 months. This heuristic may be a way to deal with uncertainty regarding the effectiveness of technologies in actual use as well as uncertainty about future fuel prices. It may also reflect the fact, noted above, that fuel saving is often less valued in secondary markets, a state of affairs which makes it important that the investment pays off during the ownership of the first buyer. In trucking, there appears to be a norm to make the payback period roughly one-half of the vehicle ownership cycle. Thus, large for-hire carriers tend to keep their trucks for 4 to 6 years. 13 The trucks average about 120,000 miles per year, and the companies tend to replace them when the maintenance costs reach an unacceptable level at about 500,000 to 700,000 miles. 14 The relatively short payback period is a formidable obstacle to investment in improved fuel efficiency. 11 This could change if carriers priced differentially according to the fuel efficiency of the leased trailer, but that does not appear to happen. 12 Klemick et al. note that this could arise because secondary owners often put tractors to different uses than those for which they were originally purchased. Whether or not the resale pricing is inefficient depends on whether or not there is continuing social value to the fuel economy of the used vehicle. 13 According to Roeth et al. (2013, p. 24), the ownership cycle used to be about 4 years, was extended during the Great Recession to 5, 6, and 7 years, and is now being lowered back to about 4 years. 14 The old truck is then sold into the used truck market, where it is purchased by smaller fleets, owner/operators, or agricultural operators, and put to lower-mileage uses. Prepublication Copy – Subject to Further Editorial Correction 13-11

13.4.1.4 Consideration of Other Attributes So far we have focused on the financial cost and benefit of an investment in fuel efficiency as though that were the only consideration in a firm’s decision making. But firms are often concerned with other, nonmonetary aspects of an investment decision as well. In the case of trucking, such considerations may include safety, reliability, ease and convenience of maintenance, driver comfort and acceptance, the increased weight of the vehicle,15 supplier relationship (a preference to purchase from a familiar supplier or manufacturer), and, in the case of alternative fuels, the length of refueling time or the availability of a fueling infrastructure. 16 The unfamiliarity of a new technology may itself be seen as a negative attribute. Whatever the nonmonetary attributes may be, they are weighed alongside the financial net benefit of a technology. The weight they receive is a matter of the decision maker’s interests and preferences, not a matter of rationality. In any event, they can trigger a decision against adopting a fuel technology that reduces costs. 13.4.1.5 Uncertainty or Lack of Credible Information Roeth et al. (2013) identify uncertainty and unfamiliarity as major factors reducing the willingness to invest in new technologies in the trucking industry. There are a variety of duty cycles being performed under very different operating conditions in different parts of the country, and reliability is a crucial consideration for the industry. Past experience is the surest way to know that a given piece of equipment will function reliably and provide the required service. Any new equipment or technology is necessarily suspect. 17 Fleet managers value their own testing experience or that of other firms above that of information provided by manufacturers. 18 Smaller fleets usually do not have the resources to investigate and test new technologies themselves. And, while not all innovators are large fleets, most fleets of all types choose to be followers rather than early adopters of new technologies. Roeth et al. (2013, p. 38) summarize the obstacles to the deployment of new technology caused by the users’ lack of familiarity and uncertainty about its performance: “[V]erifying performance in the early stages of technology deployment is challenging, duplicated testing efforts by multiple fleets and manufacturers [are] commonplace, and data is not often shared [with] or trusted by outside parties.” The uncertainty may impede adoption through two separate economic mechanisms: risk aversion (i.e., unwillingness to take certain gambles that nevertheless have a positive expected net payoff) and loss aversion (i.e., the potential monetary loss if the investment does not work out is given more weight than the potential upside gain if it does, even if those outcomes are seen as equally likely). 13.4.1.6 Simplified Choice Procedures When economists think of rational decision making, they tend to think in terms of a decision maker performing a global optimization: the decision maker systematically considers all alternatives, and all the possible attributes associated with each alternative. In order to give due consideration to his budget constraint, he considers every other possible use of the money that would have to be spent on the item in question. He makes a thorough assessment of these things, and chooses the best course of action. 15 Klemick et al. (2015, p. 164) note that, given federal restrictions on the gross maximum weight of heavy-duty trucks, fuel technologies that add weight to the truck (e.g., automatic transmission, compressed natural gas, auxiliary power units, and some fairings) have to pass a higher bar for adoption than those that lighten the truck. 16 To be sure, these various attributes can have a monetary dimension. But the firm may place a weight on them that goes beyond their monetary cost; for nonmonetary reasons, it may have a willingness to pay for attributes that exceeds their out-of-pocket cost. 17 “Another factor contributing to the payback gap is uncertainty how technologies will perform in practice. Participants expressed concerns that ‘a lot of things can go wrong’”(Klemick et al., 2015, p. 159). 18 Roeth et al. (2013, p. 33) quote one interviewee as stating: “Fleets have a general mistrust of standardized drive cycles and the associated test results, as they will not reflect the [actual] performance of the technologies in their operations.” Another respondent, a vice president in a large fleet corporation, said, “We only buy what we test— [we] only trust ourselves.” The cost of performing in-house testing is an additional financial cost associated with the adoption of a new technology. Prepublication Copy – Subject to Further Editorial Correction 13-12

In reality, choices—by firms as well as by households—often fall short of this idealized conceptualization. This was first pointed out in economics by Herbert Simon (1955), who argued that the conventional characterization of decision making is unrealistic and lacks psychological veracity. To perform the implied analysis would be computationally demanding, if not intractable; would require information that might not always be available; and would demand an implausibly large amount of time and attention for processing information and calculating the optimum. Instead, Simon proposed that people display “bounded rationality” and, when making decisions, they “satisfice.” 19 These terms embody two notions. First, people often find ways to simplify their decision making. They may limit the set of options considered; they reduce the attributes to a subset that is salient and easy to grasp; and they employ heuristics or rules of thumb to evaluate the options and their attributes. Second, instead of seeking the optimal choice, they look for a choice that is good enough. Commenting on decision making in business organizations, Simon wrote that decision makers can satisfice “either by finding the optimal solutions for a simplified world, or by finding satisfactory solutions for a more realistic world. Neither approach, in general, dominates the other, and both have continued to co-exist in the world of management science” (Simon, 1979, p. 498). In the untidy process of decision making under bounded rationality, the financially optimal choice of fuel technology may not get chosen; in fact, it may not even be considered.20 13.4.1.7 The Economic Efficiency of a Fuel Price Signal The factors listed above provide reasons why decision makers, including firms, may make choices regarding technology adoption that appear to work against their own financial interest. Put simply, their own financial self-interest is in fact more complicated than a simple analysis suggests (e.g., split incentives, up-front capital cost); they are motivated by other factors in addition to financial considerations; they face uncertainty or lack information; or they find ways of simplifying their decision process. These phenomena are likely to play out differently with different decision makers. But, to the extent that they occur, they shroud the visibility of price as a signal for guiding behavior. The notion that, in making a choice, people are inattentive to some types of incentives has received significant attention in economics and has been demonstrated empirically in many contexts. Most of the empirical literature has focused on decision making by consumers, but there are also applications to decision making in organizations. 21 Firms have more resources available for processing information than individual consumers and are less likely to be inattentive to business-relevant considerations, but they are not necessarily immune to these phenomena. Nonprice regulation provides a way of circumventing some of those phenomena. It shortcuts the decision process by increasing the saliency of certain attributes, removing certain items from the set of alternatives considered by the decision maker, forcing other items into that consideration set, modifying the default outcome if no alternative is chosen explicitly, or otherwise changing what is referred to as the architecture of choice, a term popularized by Thaler and Sunstein (2009) in their book Nudge. To be sure, there is a wide variety of potential regulatory interventions that differ in the extent to which they override individual freedom of choice. 22 All are potentially subject, in principle, to criticism on ethical and economic grounds. The ethical objection is along the lines that a regulatory intervention leads actors to make a choice they otherwise would not have made, thereby reducing their well-being. 23 We focus here on the economic objection. 19 Satisfice was coined as a mixture of satisfy and suffice. 20 Marketers use the term “consideration set” to refer to the (typically small) subset of alternatives actually considered by a consumer when making a purchase decision. 21 See, for example, DellaVigna (2009) and Jones (2003). 22 Thaler and Sunstein argue that their policy recommendations meet a criterion of libertarian paternalism, seeking “to expand or maintain freedom of choice as far as possible.” 23 For example, see White (2013). It could be argued that whether or not the intervention reduces well-being depends on the particular factor causing the behavior that is being corrected. If it is due to ignorance or inertia, for example, overriding an actor’s free choice might not actually reduce his well-being (Bovens, 2013). There is also the possible question of preference change. After the behavior change induced by the intervention, the actor comes to Prepublication Copy – Subject to Further Editorial Correction 13-13

The economic argument for using a price signal to change behavior rather than any other form of intervention is that, other things being equal, it ensures economic efficiency and minimizes the aggregate cost of whatever reduction in fuel consumption or emissions comes about. Even if another type of intervention could achieve the same overall reduction in fuel consumption or emissions, it would be liable to do so at a higher cost than with a price signal. This holds true if every actor is actively pursuing the financial objective of maximizing profit or minimizing cost. That objective should lead each actor to choose a level of fuel consumption such that the marginal profit/marginal cost from the next gallon of fuel used just equals the price of fuel. If all actors face the same price signal, the marginal profit/marginal cost from the next gallon of fuel used will be equalized across all fuel consumers. Technically, that is the condition to maximize economic efficiency. This is the basis for the argument that the use of a price signal brings about economic efficiency. Another form of regulatory intervention would not necessarily ensure equalization of the marginal profit/marginal cost from the next gallon of fuel used across all fuel consumers, and therefore fails the test of economic efficiency. The argument rests on the assumptions that all actors are global optimizers, in the manner discussed (and questioned) above, and that they are all motivated solely by consideration of financial costs. If some actors care about nonfinancial attributes—for example, driver comfort and acceptance or relationship with a preferred supplier—the economically optimal public policy does not necessarily require conformity with the principle of equalizing the marginal profit/marginal cost from the next gallon of fuel across all fuel consumers. It is certainly true that some nonprice interventions can be economically wasteful and far less efficient that using a price signal, but the simple presumption that a price signal is always superior fails. The advantages and disadvantages of alternative regulatory approaches depend on the specific contexts in which they operate. 13.5 CAP AND TRADE Some of the desirable features of a price signal can also be captured through what is known as a cap and trade system. This combines features of regulation via a quantity instrument with those of regulation via a price instrument. Around the time of the 2010 report, legislation employing this type of approach to regulate CO 2 emissions nationally was being discussed in the U.S. Senate, having been passed by the House of Representatives in 2009. The legislation was patterned after the system introduced to control SO 2 emissions in the 1990 Amendments to the Clean Air Act and subsequently adapted by the EU to regulate CO 2 emissions in 2005. H.R. 2454 set limits on CO 2 emissions that would have covered about 85 percent of total U.S. emissions, including emissions from domestic oil refining. Under the proposed legislation, the government would have set a limit (a cap) on the amount of CO 2 that could be emitted annually by each regulated firm, in a manner similar to the limits on emissions of criteria air pollutants by power plants under the Clean Air Act or the limits on the discharge of water pollutants by point sources under the Clean Water Act. Unlike those limits (except for SO 2 ), however, the limits in a cap and trade system would not necessarily be binding: if a firm could obtain unused emission permits from another regulated firm, it could exceed its own cap up to the amount of the number of permits thus obtained. This does three things. It places a binding limit on the total quantity of emissions across all regulated firms (like a quantity instrument). However, it offers flexibility to an individual regulated firm, permitting it to exceed its limit if it obtains (purchases) unused permits from other firms. And, it creates an economic incentive (like a price instrument) for each regulated firm: the firm can make money by reducing emissions and selling unused permits; it thus has a financial incentive to reduce its emissions. 24 place a high value on an attribute that previously he valued little. Hence, while his well-being was reduced ex ante, it is increased ex post. 24 The revenue from selling permits goes to the firm, not to the government as under a tax on emissions. Under H.R. 2454, 85 percent of the emissions allowances would have been allotted by the government for free, but the remainder would have been auctioned. Prepublication Copy – Subject to Further Editorial Correction 13-14

In the end, the U.S. Senate did not enact its version of H.R. 2454 and the idea of using a cap and trade system to regulate CO 2 emissions has languished in the United States. 25 However, the cap and trade system has some merit and it is something that conceivably could be employed in the future to regulate fuel consumption or CO 2 emissions in the heavy-duty trucking sector. This would become possible only with in-use measurement of fuel use in that sector via some sort of telematics system. Telematic systems provide real-time monitoring of the location, movements, engine performance, and behavior of a vehicle or fleet of vehicles. This is achieved through a network of electronic monitoring devices and cameras on the vehicle combined with a GPS receiver and an electronic Global System for Mobile communication device. These systems are obtainable in new trucks and also can readily be retrofitted for existing trucks. They are being adopted not only because they monitor and save fuel use but also because they can reduce accidents, improve driver performance, streamline maintenance, and provide better operational efficiency and improved customer service. It has been estimated that 12.6 percent of all commercial vehicles in the United States now have a telematics unit on board. 26 Adoption of these systems is growing rapidly. 27 Adoption will be further stimulated when federal regulations for the use of electronic logging devices to track compliance with hours-of-service regulations in the commercial truck and bus industries take effect in 2019. Consequently, telematics might well be in common use in several MHDV sectors, especially Class 8 vehicles, by the time of the Phase III regulations. Where, and when, this type of in-use monitoring is adopted, it might then be possible to develop some form of a performance standard for reduced fuel consumption and CO 2 emissions. 28 A cap and trade program would be one form of performance standard. It could be implemented in various ways. The cap could apply to fuel use, to vehicle emissions, or to both. The cap could be differentiated by type of vehicle and duty cycle. The cap could be set annually or for some other length of time. Small fleets could be exempted. Unused permits could be allowed to be carried over for use in a future year. The permits could be allotted for free or some portion could be auctioned (this could be set to change over time). To control price fluctuations in the market for permits there could be a price ceiling and/or a price floor. In short, there would be considerable flexibility in determining the details of a cap and trade system for trucking. 25 There are exceptions at the state level. In 2005, Connecticut, Delaware, Maine, New Hampshire, New Jersey, New York, and Vermont agreed to establish the Regional Greenhouse Gas Initiative (RGGI), a cap and trade program covering CO2 emissions from electric power generation. Subsequently, New Jersey left RGGI, while Maryland, Massachusetts, and Rhode Island joined it. The cap and trade program went into operation in January 2009 and continues to the present. In 2006, California enacted legislation which requires greenhouse gas emissions from all sources in California to be reduced to the 1990 level by 2020. This was to be accomplished through a cap and trade program together with a variety of regulatory restrictions. The cap and trade program in California holds refiners responsible for the GHG emissions from the transportation fuels they sell. The cap and trade program started in January 2013 and continues to the present. 26 Fleetmatics. 2014. “Fleetbeat Report: Economic Impact of Telematics Adoption by Commercial Fleets,” Waltham, MA: Fleetmatics. April. 27 For example, Frost & Sullivan projects that 35 million light-, medium-, and heavy-duty trucks globally will have some sort of telematics connectivity by 2020 (Lau, 2015). “Although telematics technologies and services were first adopted by the long-haul trucking industry in the United States more than 20 years ago, adoption in the non-trucking sector is expected to equal the trucking industry in terms of numbers of connected vehicles by the end of 2014 and will dominate the commercial fleet telematics industry by the end of 2019, accounting for almost 50 million vehicles globally” (ABI, 2014a). “The number of subscribers to OnBoard Diagnostics aftermarket telematics solutions is expected to increase from 9.5 million in 2014 to 117.8 million in 2019” (ABI, 2014b). “Telematics will be an indispensable element of 2020’s truck, impacting everything about the vehicle and its usage” (Rishi et al., 2009, p. 9). 28 A telematics system is not necessarily a panacea since there may be issues with the precision of measurement and also with which variable is being measured. If the goal is to regulate fuel use per ton-mile, for example, it is necessary to measure simultaneously fuel use, vehicle weight, and distance traveled. Prepublication Copy – Subject to Further Editorial Correction 13-15

By the time of the Phase III regulations, it is unlikely that this type of system could be applied to all MHDV classes, from Class 2b through Class 8. It would have to be limited to the subset of MHD vehicles with a high penetration of telematics systems. This would most likely be Class 8b vehicles, which account for about two-thirds of fuel consumption by MHDVs. Finding: In dealing with MHDV fuel consumption and emissions, the question arises of whether to adopt the same regulatory strategy for all classes of MHDV vehicles or different strategies for different classes. If one adopts different strategies, there is a risk of artificially distorting owner choices. Because of differential regulatory impacts on vehicle cost or performance, a buyer may shift to a less regulated class, causing a less efficient vehicle to end up in use, thereby frustrating the purpose of the regulation. On the other hand, if there is significant heterogeneity across classes, applying the same regulatory strategy to all classes may lead to an outcome determined by the lowest common denominator. In particular, there is reason to anticipate that telematics systems which permit in-use measurement of vehicle fuel use will be widely adopted in Class 8b vehicles well ahead other classes, making it more practical to formulate a performance standard for those sectors. Recommendation 13-3: The 2010 report concluded that selectively regulating only certain vehicle classes should be avoided because of the risk of class shifting. This is a trade-off that needs to be revisited when the Phase III rules are formulated. In particular, consideration should be given to implementing a performance standard first in Class 8 over-the-road vehicles, for which the telematics capability exists, before it is widely adopted in other vehicle classes. Finding: A cap and trade system is a performance standard that combines some of the desirable features of a price signal (e.g., fuel tax) with those of a quantity instrument directly regulating fuel consumption and/or emissions. This could become possible only if fuel consumption or emissions were measurable in use (e.g., via some form of telematics). Cap and trade involves greater complexity than, say, a gas tax but it could possibly be more efficacious than a gas tax. It also raises design issues that would have to be resolved. 13.6 COMPLEMENTARY REGULATIONS One could conceive of existing complementary regulations that, like the pre-sale certification currently in effect for MHDVs, would have the objective of reducing fuel consumption and emissions related to fuels produced and used in vehicles. Some such regulations lie potentially within the authority of federal agencies; others lie elsewhere. Already in the United States, there is a renewable fuel standard, and California and Oregon have a low-carbon fuel standard, which has also been considered, but not adopted, by the U.S. Congress. 13.6.1 Renewable Fuel Standard/Low‐Carbon Fuel Standard The Renewable Fuel Standard (RFS) program was first authorized by the Energy Policy Act of 2005 and subsequently amended and expanded by the Energy Independence and Security Act of 2007 (the expanded program is referred to as RFS2). These programs require that a certain volume of renewable fuels be used annually and blended into the fuels being used by vehicles in the United States. The specific amount required to be used each year is determined by EPA. The initial RFS mandated that a minimum of 4 billion gallons be used in 2006, with this minimum usage to rise to 7.5 billion gallons by 2012. Under RFS2, the total amount mandated to be used in 2008 was raised to 9 billion gallons, and this will rise to 36 billion gallons in 2022. The renewable fuel amount proposed by EPA for 2015 was 16.3 billion gallons. The RFS2 requirement is broken down into individual mandates for four separate categories: total renewable fuels, advanced biofuels, biomass-based diesel, and cellulosic biofuel. Biofuels qualifying under each category must achieve certain minimum thresholds of life-cycle greenhouse gas emissions Prepublication Copy – Subject to Further Editorial Correction 13-16

reduction (i.e., reduction in the emissions associated with the production, transportation, and use of that fuel): 20 percent reduction for conventional biofuels (primarily ethanol from corn starch), 50 percent reduction for advanced biofuels, and 60 percent for cellulosic biofuel. Under RFS2, all renewable fuel must be made from feedstocks that meet a new definition of renewable biomass, including certain land use restrictions. Issues subsequently raised regarding the operation of the RFS/REFS2 programs include whether the rapid expansion of U.S. corn ethanol production had an undesirable effect on food prices in the United States and elsewhere, whether there were environmentally harmful impacts in the United States or elsewhere due to the conversion of grassland and other noncropland for growing corn (indirect land use impacts), whether there will be sufficient distribution infrastructure to deliver the expanding biofuels mandate to users in the United States, whether the goals set for the supply of noncorn biofuels are feasible, and whether the assumed reductions in life-cycle GHG emissions are correct and, therefore, just how much emissions reduction will actually be accomplished. 29 To implement the system, fuel refiners and importers are the parties regulated; they are required to turn in (retire) Renewable Identification Numbers (RINs) when they sell gasoline or diesel into the domestic surface transportation market. RINs are generated upon production or impost of a qualifying renewable fuel and are typically detached from that fuel when it is blended or sold into the fuel supply system. Each of the four categories of renewable fuels has its own RINs. RINs are tradable and, subject to certain restrictions, durable. The trading of RINs introduces an economic component into what is a quantity mandate for renewable fuels through the potential influence of RIN prices on the market prices of gasoline or diesel. A Low-Carbon Fuel Standard (LCFS) is a regulation to reduce the carbon intensity of transportation fuels. The regulation of fuel chemistry is not unprecedented. 30 Versions of a LCFS exist in the UK (adopted in 2008), in the EU (adopted in 2009), in British Columbia (adopted in 2010), in Oregon (adopted in 2012), and, since 2007, in California (effective in January 2011). In California, the LCFS regulates transport fuel providers, including refiners, blenders, producers, and importers. It requires them incrementally to reduce the carbon intensity of the fuel mix used in California, starting with a 0.25 percent reduction in 2011 and rising to a 10 percent reduction by 2020. There is an emission factor for each type of fuel, whether a type of fossil fuel or a renewable fuel, accounting for its life-cycle greenhouse gas emissions and including an allowance for the indirect effects on greenhouse gas emissions from changes in land use for some biofuels. Each fuel provider receives a certain number of credits per gallon of fuel, reflecting the carbon reduction target; at the end of the year, the producer must either meet the reduction target or make up the shortfall by obtaining credits from other fuel producers. As with RINs, there is an active market for these credits, which thus has the potential to generate a differential in the market prices of gasoline or diesel fuels based on their carbon intensity. Operationally, a difference between the RFS2 and the LCFS is that the former is closer to a technology standard while the latter is a performance standard. The California LCFS discriminates between fossil fuels of differing carbon intensity (e.g., oil from Canadian tar sands versus oil from Saudi Arabian oil fields) 31: the incentive to reduce emissions applies not only between renewable fuels and fossil fuels but also within the broad category of fossil fuels. 13.6.2 Other Complementary Regulations Besides the Phase II standards, administered by NHTSA and EPA, there are regulatory authorities at the federal, state, and local levels that affect how, when, and where MHDVs are used, thereby 29 See NRC (2011) for a discussion of some of these issues. Also see, for example, Stock (2015) and U.S. Congressional Research Service (2010). 30 For example, the 2010 report noted that EPA currently regulates the sulfur content of on-road diesel fuel. EPA also required the use of certain oxygenates—one of which, methyl tertiary-butyl ether, was later banned—pursuant to the 1977 Clean Air Act. 31 The carbon intensity of each fuel is calculated using a modification of the GREET (Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation) model developed at the Argonne National Laboratory. Prepublication Copy – Subject to Further Editorial Correction 13-17

influencing their fuel consumption and their emissions. There is a relationship between these other regulations and the regulation of MHDV fuel consumption and GHG emissions by NHTSA and EPA; in the case of regulation of tailpipe emissions, the relationship is particularly strong and can make compliance with the Phase II standard more difficult. Other regulations may focus not on vehicle technologies but rather on vehicle operation and driver behavior: The innovative technologies for intelligent transportation systems discussed in Chapter 9 might require regulatory action by agencies other than NHTSA and EPA in order to promote their adoption. To take another example, recent experiences in several countries have demonstrated that congestion pricing could significantly improve traffic flow in cities and thereby reduce fuel consumption and emissions by vehicles in Classes 2b through 8a. 32 Authorizing congestion pricing within cities would fall squarely within the jurisdiction of local and state government. 33 The various regulatory agencies—federal, state, and local—co-determine the actual fuel consumption and emissions of MHDVs in use. In this context, in-use measurement of fuel consumption post-sale becomes of great importance. If such measurement is in place and is being used in connection with the Phase III regulations, it would also provide a tool for assessing the effectiveness of regulations imposed by other agencies, whether federal, state, or local. And without such measurement, it will be difficult or impossible to assess whether the Phase III regulations, or regulations by other agencies, are having a meaningful impact on fuel consumption and emissions by MHDVs. If such measurement happens to come into being, it would benefit all efforts to improve fuel efficiency. 13.7 COSTS OF DELAY The fuel consumption and GHG emissions in MHDVs are presently controlled with an incremental strategy organized around successive phases of regulation. Thus, measures not adopted in one phase can be reconsidered for adoption in a later phase. Nevertheless, several features of these vehicles and their GHG emissions militate against being too cautious or myopic at each phase. First, the current regulatory approach impacts only new vehicles from a given model year onward; the existing stock of vehicles on the road is unaffected. Since MHDVs typically have working lives of at least 10 years, and in some cases much longer, the overall impact of a new phase of regulations on aggregate fleet emissions and fuel consumption is highly circumscribed. Second, unlike criteria pollutants which are typically flow pollutants (i.e., the harm is associated mainly with the instantaneous flow of emissions), GHG emissions are stock pollutants: they accumulate and remain in the atmosphere (or the ocean) and cause harm for long periods of time, often hundreds of years. Third, as described in Section 12.4.4, the cumulative experience in producing a technology tends to generate improvements and cost reductions over time. The learning process is set back when the introduction of the technology is postponed. In short, delaying the adoption of a more forceful regulation can have some long-lasting consequences. 13.8 REFERENCES Allied Business Intelligence (ABI). 2014a. Almost 50 Million Non-trucking Commercial Fleet Vehicles Equipped with Telematics by the End of 2019. March 24. Oyster Bay. https://www.abiresearch.com/press/almost-50-million-non-trucking-commercial-fleet-ve/. Allied Business Intelligence (ABI). 2014b. OBD Aftermarket Telematics Subscribers to Exceed 117 Million Globally by 2019. March 24. Oyster Bay. https://www.abiresearch.com/press/obd- aftermarket-telematics-subscribers-to-exceed-1/. 32 See, for example, Small and Verhoef (2007, Chap. 4). 33 Federal agencies could take steps to promote the use of congestion pricing on Interstate and other highways that receive federal funds, for example, by modifying conditions attached to federal highway funding programs (Congressional Budget Office, 2009; GAO, 2012). Prepublication Copy – Subject to Further Editorial Correction 13-18

Andreen, W.L. 2004. Water quality today – has the Clean Water Act been a success? Alabama Law Review 55:537-593. Andreen, W.L., and S.C. Jones. 2008. The Clean Water Act: Blueprint for Reform. Center for Progressive Reform White Paper 802. July. ASIWPCA (Association of State and Interstate Water Pollution Control Administrators). 1985. America's Clean Water: The States' Evaluation of Progress, 1972-1982. ASIWPCA, Washington, D.C. ATRI (American Transportation Research Institute). 2015. An Analysis of the Operational Costs of Trucking: 2015 Update. http://atri-online.org/wp-content/uploads/2015/09/ATRI-Operational- Costs-of-Trucking-2015-FINAL-09-2015.pdf. Baker, L.A. 1992. Introduction to nonpoint source pollution in the United States and prospects for wetland use. Ecological Engineering 1(2):1-26. Bovens, L. 2013. Why couldn't I be nudged to dislike a Big Mac? Journal of Medical Ethics 39(8):495- 496. ISSN 0306-6800. Congressional Budget Office (CBO). 2009. Using Pricing to Reduce Traffic Congestion. Publication Number 3133. Craig, R.K., and A.M. Roberts. 2015. When will governments regulate nonpoint source pollution? A comparative perspective. Boston College Environmental Affairs Law Review 42(1):1-64. DOE (Department of Energy) and EPA (Environmental Protection Agency). No date. How Vehicles are Tested. Available at https://www.fueleconomy.gov/feg/how_tested.shtml. Accessed August 9, 2016. EPA (Environmental Protection Agency). 1990. National Water Quality Inventory: 1988 Report to Congress. EPA/440/4-90/003. EPA, Washington, D.C. EPA. 2006. Wadeable Streams Assessment: A Collaborative Survey of the Nation’s Streams. EPA 841- B-05-002. Washington, D.C. EPA. 2007. The National Water Quality Inventory: Report to Congress for the 2002 Reporting Cycle—A Profile. EPA 841-F-07-003. Washington, D.C. EPA. 2011. A National Evaluation of the Clean Water Act Section 319 Program. https://www.epa.gov/sites/production/files/2015-09/documents/319evaluation.pdf. EPA and NHTSA (National Highway Traffic Safety Administration). 2011. Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Medium- and Heavy-Duty Engines and Vehicles; Final Rule. Federal Register 76:57106-57513. September 15. EPA and NHTSA. 2016. Greenhouse Gas Emissions and Fuel Efficiency Standards for Medium- and Heavy-Duty Engines and Vehicles—Phase 2. Federal Register 81:73478-74274. October 25. GAO (Government Accountability Office). 1997. National Water Quality Goals Cannot be Attained Without More Attention to Pollution from Diffused or “Nonpoint” Sources. CED-78-6. GAO. 2012. TRAFFIC CONGESTION Road Pricing Can Help Reduce Congestion, but Equity Concerns May Grow. Report to the Subcommittee on Transportation, Housing, and Urban Development and Related Agencies, Committee on Appropriations, House of Representatives. GAO-12-119. Gillingham, K., and K. Palmer. 2014. Bridging the energy efficiency gap: Policy insights from economic theory and empirical evidence. Review of Environmental Economics and Policy 8(1):18-38. doi: 10.1093/reep/ret021. Gould, G.A. 1990. Agriculture, nonpoint source pollution, and federal law. University of California, Davis Law Review 23(3):461-498. Greene, D.L. 1998. Why CAFE worked. Energy Policy 26(8):595-613. Gruenspecht, H.K. 1982. Differentiated regulation: The case of auto emissions standards. American Economic Review, Papers and Proceedings of the Ninety-Fourth Annual Meeting of the American Economic Association 72(2):328-331. Houck, O. 2002. The Clean Water Act TMDL Program: Law, Policy, and Implementation. Washington, D.C.: Environmental Law Institute. Prepublication Copy – Subject to Further Editorial Correction 13-19

Klemick, H., E. Kopits, A. Wolverton, and K. Sargent. 2015. Heavy-duty trucking and the energy efficiency paradox: Evidence from focus groups and interviews. Transportation Research Part A: Policy and Practice 77:154-166. Lau, W. 2015. “Strategic Outlook of Global Commercial Vehicle Industry: Trends and Impact on ATA Members,” presentation at American Trucking Association Management Conference and Exhibitions. Leard, B., J. Linn, V. McConnell, and W. Raich. 2015. Fuel Costs, Economic Activity, and the Rebound Effect for Heavy-Duty Trucks. Resources for the Future. RFF DP 15-43. NRC. 2002. Effectiveness and Impact of Corporate Average Fuel Economy (CAFE) Standards. Washington, DC: The National Academies Press. https://doi.org/10.17226/10172. NRC. 2010. Technologies and Approaches to Reducing the Fuel Consumption of Medium- and Heavy- Duty Vehicles. Washington, D.C.: The National Academies Press. NRC. 2011. Renewable Fuel Standard: Potential Economic and Environmental Effects of U.S. Biofuel Policy. Washington D.C.: The National Academies Press. NRC. 2014. Reducing the Fuel Consumption and Greenhouse Gas Emissions of Medium- and Heavy- Duty Vehicles, Phase Two: First Report. Washington, D.C.: The National Academies Press. Rishi, S., K. Gyimesi, C. Burek and M. Monday. Truck 2020 Transcending Turbulence. IBM Global Business Services Executive Report. Somers, NY: IBM Institute for Business Value. Rittenhouse, K., and M. Zaragoza-Watkins. 2017. Anticipation and Environmental Regulation. MIT CEEPR Working Paper 2017-004. Roeth, M., D. Kircher, J. Smith, and R. Swim. 2013. Barriers to the Increased Adoption of Fuel Efficiency Technologies in the North American On-Road Freight Sector. Report for the International Council for Clean Transportation. http://www.theicct.org/sites/default/files/publications/ICCT-NACFE- CSS_Barriers_Report_Final_20130722.pdf. Ruhl, J.B. 2000. Farms, their environmental harms, and environmental law. Ecology Law Quarterly 27:263-349. Simon, H. 1955. A behavioral model of rational choice. The Quarterly Journal of Economics 69(1):99- 118. Simon, H. 1979. Rational decision making in business organizations. American Economic Review 69(4):493-513. Small, K., and E. Verhoef. 2007. The Economics of Urban Transportation. Oxfordshire: Routledge. Thaler, R.H., and C.R. Sunstein. 2009. Nudge: Improving Decisions about Health, Wealth, and Happiness. New York: Penguin Books. U.S. Congressional Research Service. Renewable Fuel Standard (RFS): Overview and Issues (R40155, July 14, 2010), by Randy Schnepf and Brent Yacobucci. Text from: Congressional Research Digital Collection. Vernon, D., and A. Meier. 2012. Identification and quantification of principal–agent problems affecting energy efficiency investments and use decisions in the trucking industry. Energy Policy 49:266- 273. Winebrake, J.J., E.H. Green, B. Comer, C. Li, S. Froman, and M. Shelby. 2015a. Fuel price elasticities for single-unit truck operations in the United States. Transportation Research Part D: Transport and Environment 38:166-177. doi: 10.1016/j.trd.2015.04.006. Winebrake, J.J., E.H. Green, B. Comer, C. Li, S. Froman, and M. Shelby. 2015b. Fuel price elasticities for single-unit truck operations in the United States. Transportation Research Part D: Transport and Environment 38:178-187. doi: 10.1016/j.trd.2015.05.003. Zaring, D. 1996. Agriculture, nonpoint source pollution, and regulatory control: The Clean Water Act’s bleak present and future. Harvard Environmental Law Review 20:515-545. Prepublication Copy – Subject to Further Editorial Correction 13-20

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Medium- and heavy-duty trucks, motor coaches, and transit buses - collectively, "medium- and heavy-duty vehicles", or MHDVs - are used in every sector of the economy. The fuel consumption and greenhouse gas emissions of MHDVs have become a focus of legislative and regulatory action in the past few years. This study is a follow-on to the National Research Council's 2010 report, Technologies and Approaches to Reducing the Fuel Consumption of Medium-and Heavy-Duty Vehicles. That report provided a series of findings and recommendations on the development of regulations for reducing fuel consumption of MHDVs.

On September 15, 2011, NHTSA and EPA finalized joint Phase I rules to establish a comprehensive Heavy-Duty National Program to reduce greenhouse gas emissions and fuel consumption for on-road medium- and heavy-duty vehicles. As NHTSA and EPA began working on a second round of standards, the National Academies issued another report, Reducing the Fuel Consumption and Greenhouse Gas Emissions of Medium- and Heavy-Duty Vehicles, Phase Two: First Report, providing recommendations for the Phase II standards. This third and final report focuses on a possible third phase of regulations to be promulgated by these agencies in the next decade.

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