Summary of Interagency Working Comments Received under EO 12866 and EO 13563 review of draft NPRM. Subject to further policy review.



### I **nteragency Working**  **Comments on U.S. Environmental Protection Agency (EPA) and**  **National Highway Traffic Safety Administration (NHTSA) draft proposed rule titled, “Greenhouse Gas Emissions and**  **Fuel Efficiency Standards for Medium**

**- and Heavy** 

**- Duty**  **Engines and Vehicles**

**- Phase 2.” RIN 2060** 

**- AS16; RIN 2127** 

**- AL52** 



### Analysis of the Rebound Effect
The “rebound effect” has been defined a number of ways in the literature, and one common definition states that the rebound effect is the increase in demand for an energy service when the cost of the energy service is reduced due to efficiency improvements. 1

, 2

, 3 In the context of heavy

- duty vehicles (HDVs), this can be interpreted as an increase in HDV fuel consumption resulting from more intensive vehicle use in response to increased vehicle fuel efficiency. 4 Althoug h much of this vehicle use increase is likely to take the form of increases in the number of miles vehicles are driven, it can also take the form of increases in the loaded weight at which vehicles operate or changes in traffic and road conditions vehicles encounter as operators alter their routes and schedules in response to improved fuel efficiency. Because this more intensive use consumes fuel and generates emissions, it reduces the fuel savings and avoided emissions that would otherwise be expected to result from the increases in fuel efficiency this rulemaking proposes.

Unlike the light

- duty vehicle (LDV) rebound effect, the heavy

- duty vehicle (HDV) rebound effect has not been extensively studied. According to a 2010 HDV report published by the Nat ional Research Council of the National Academies (NRC) 5 , it is “not possible to provide a confident measure of the rebound effect,” yet NRC concluded that a HDV rebound effect probably exists and that, “estimates of fuel savings from regulatory standards w ill be somewhat misestimated if the rebound effect is not considered.” Although we believe the HDV rebound effect needs to be studied in more detail, we have nevertheless attempted to capture its potential

1 Winebrake, J.J., Green, E.H., Comer, B., Corbett, J.J., Froman, S., 2012. Estimating the direct rebound effect for

on

- road freight transportation. Energy Policy 48, 252

- 259

. 2 Gree ne, D.L., Kahn, J.R., Gibson, R.C., 1999, “Fuel economy rebound effect for U.S. household veh icles”, The Energy Journal, 20.

3 For a discussion of the wide range of definitions found in the literature, see Appendix D: Discrepancy in Rebound Effect Definiti ons , in EERA (2014), “Research to Inform Analysis of the Heavy

- Duty vehicle Rebound Effect”, Draft Final Report of Phase I under EPA contract EP

- C

- 13

- 025. (Docket ID: EPA

- HQ

- OAR

- 2014

- 0827 ) . See also Greening, L.A., Greene, D.L., Difiglio, C., 2000, “Ener gy efficiency and consumption

— the rebound effect

— a survey”, Energy Policy, 28, 389

- 401

. 4 We discuss other potential rebound effects in section IX.D.3, such as the indirect and economy

- wide rebound effects.

5 Committee to Assess Fuel Economy Technologies for Medium

- and Heavy

- Duty Vehicles; National Research Council; Transportation Research Board (2010). “Technologies and Approaches to Reducing the Fuel Consumption of Medium

- and Heavy

- Duty Vehicles,” . Washington, D.C. The National Academies Press. Available electronically from the National Academies Press Website at http://www.nap.edu/catalog.php?record_id=12845 (last accessed September 10, 2010).Summary of Interagency Working Comments Received under EO 12866 and EO 13563 review of draft NPRM. Subject to further policy review.

effect in our analysis of this proposed rule, rat her than to await further study. We have elected to do so because the magnitude of the rebound effect is an important determinant of the actual fuel savings and emission reductions that are likely to result from adopting stricter fuel efficiency and GHG e missions standards.

In our analysis and discussion below, we focus on one widely

- used metric to estimate the rebound effect associated with all types of more intensive vehicle use, the increase in vehicle miles traveled (VMT) that results from improved fu el efficiency. VMT can often provide a reasonable approximation for all types of more intensive vehicle use. For simplicity, we refer to this as “the VMT rebound effect” or “VMT rebound” throughout this section, although we acknowledge that it is an appr oximation to the rebound effect associated with all types of more intensive vehicle use. The agencies use our VMT rebound estimates to generate VMT inputs that are then entered into the EPA MOVES national emissions inventory model and the Volpe Center’s H D CAFE model. Both of these models use these inputs along with many others to generate projected emissions and fuel consumption changes resulting from each of the regulatory alternatives analyzed.

Using VMT rebound to approximate the fuel consumption impa ct from all types of more intensive vehicle use may not be completely accurate. Many factors other than distance traveled

– for example, a vehicle’s loaded weight

– play a role in determining its fuel consumption, so it is also important to consider how c hanges in these factors are correlated with variation in vehicle miles traveled. Empirical estimates of the effect of weight on HDV fuel consumption vary, but universally show that loaded weight has some effect on fuel consumption that is independent of d istance traveled. Therefore, the product of vehicle payload and miles traveled, which typically is expressed in units of “ton

- miles” or “ton

- kilometers”, has also been considered as a metric to approximate the rebound effect. Because this metric’s value depends on both payload and distance, it is important to note that changes in these two variables can have different impacts on HDV fuel consumption. This is because the fuel consumed by HDV freight transport is determined by several vehicle attributes in cluding engine and accessory efficiencies, aerodynamic characteristics, tire rolling resistance and total vehicle mass

— including payload carried, if any.

Other factors such as vehicle route and traffic patterns can also affect how each of these vehicle att ributes contributes to the overall fuel consumption of a vehicle. While it seems intuitive that if all of these other conditions remain constant, a vehicle driving the same route and distance twice will consume twice as much fuel as driving that same rout e once. However, because of the other vehicle attributes, it is less intuitive how a change in vehicle payload would affect vehicle fuel consumption. We request comment on how the agencies should consider the relationship between changes in vehicle miles traveled, changes in vehicle ton

- miles achieved, and overall fuel consumption when considering how best to measure the rebound effect.

Because the factors influencing HDV VMT rebound are generally different from those affecting LDV VMT rebound, much of the research on the LDV sector is likely to not apply to the HDV sector. For example, the owners and operators of LDVs would likely view may respond to the costs and benefits associated with changes in their personal vehicle’s fuel efficiency very differentl y than a HDV fleet owner or operator would view the costs and benefits (e.g., profits, offering more competitive prices for services) associated with changes in theirSummary of Interagency Working Comments Received under EO 12866 and EO 13563 review of draft NPRM. Subject to further policy review.

HDVs’ fuel efficiency. To the extent the response differs, such differe n ces may be smaller for The potential exception is HD pick

- ups and vans, which share some similarities with LDV. As discussed in the 2010 NRC HD report, one difference from the LDV case is that when calculating the change in HDV costs that causes the rebou nd effect, it is more important to consider all components of HDV operating costs. The costs of labor and fuel generally constitute the two largest shares of HDV operating costs, depending on the price of petroleum, distance traveled, type of vehicle, and commodity transported (if any). 6
, 7 Equipment depreciation costs associated with the purchase or lease of an HDV are another significant component of total operating costs. Even when HDV purchases involve upfront, one

- time payments, HDV operators must re cover the depreciation in the value of their vehicles resulting from their use, so this is likely to be considered as an operating cost they will attempt to pass on to final consumers of HDV operator services.

Estimates of the impact of fuel efficiency s tandards on HDV VMT, and hence fuel consumption, should account for changes in all of these components of HDV operating costs. The higher the net savings in total operating costs is, the higher the expected rebound effect would be. Conversely, if higher HDV purchase costs outweigh future cost savings and total operating costs increase, HDV costs could rise, which would likely result in a decrease in HDV VMT. In theory, other cost changes resulting from any requirement to achieve higher fuel efficiency, s uch as changes in maintenance costs or insurance rates, should also be taken into account, although information on these elements of HDV operating costs is extremely limited. In this analysis, the agencies adapt estimates of the VMT rebound effect to proj ect the response of HDV use to the estimated changes in total operating costs that result from the proposed Phase 2 standards. We seek comment and data on how our proposed standards could impact these and other types of HDV operating costs, as well as on our procedure for adapting the VMT rebound effect to estimate the response of HDV use to changes in total operating costs.

Since businesses are profit

- driven, one would expect their decisions to be based on the costs and benefits of different operating de cisions, both in the near

- term and long

- term. Specifically, one would expect commercial HDV operators to take into account changes in overall operating costs per mile when making decisions about HDV use and setting rates they charge for their services. I f demand for those services is sensitive to the rates HDV operators charge, HDV VMT could change in response to the effect of higher fuel efficiency on the rates HDV operators charge. If demand for HDV services is insensitive to price (e.g., due to lack o f good substitutes), however, or if changes in HDV operating costs due to the proposed standards are not passed on to final consumers of HDV operator services, the proposed standards may have a limited impact on HDV VMT.

The following sections describe the factors affecting the magnitude of HDV VMT rebound; review the econometric and other evidence related to HDV VMT rebound; and summarize how we estimated the HDV rebound effect for this proposal.

6 American Transportation Research Institute, An Analysis of the Operational Costs of Trucking

, September 2013 (Docket ID: EPA

- HQ

- OAR

- 2014

- 0827 ).

7 Transport Canada, Operating Cost of Trucks, 2005. See http://www.tc.gc.ca/eng/policy/report

- acg

operatingco st2005

- 2005

- e

- 2

- 1727.htm , accessed on July 16, 2010 (Docket ID: EPA

- HQ

- OAR

- 2010

- 0162 ).Summary of Interagency Working Comments Received under EO 12866 and EO 13563 review of draft NPRM. Subject to further policy review.

Factors Affecting the Magnitude of HDV VMT Rebound

The ma gnitude and timing of HDV VMT rebound result from the interaction of many different factors. 8 Fuel savings resulting from fuel efficiency standards may cause HDV operators and their customers to change their patterns of HDV use and fuel consumption in a

v ariety of ways. For example, HDV operators may pass on the fuel cost savings to their customers by decreasing prices for shipping products or providing services, which in turn could stimulate more demand for those products and services (e.g., increases in freight output), and result in higher VMT. Alternatively, HDV operators could simply use fuel savings to spend more on other costs such as driver labor or vehicle capital (e.g., holding freight output constant), which may not result in higher VMT. As discussed later in this section, HDV VMT rebound estimates determined via other proxy elasticities vary widely, but in no case has there been an estimate that more than offsets the fuel saved due to efficiency improvements (i.e., no rebound effect great er than 100 percent).

If fuel cost savings are passed on to the HDV operators’ customers (e.g., logistics businesses, manufacturers, retailers, municipalities, utilities consumers), those customers might reorganize their logistics and distribution network s over time to take advantage of lower operating costs. For example, customers might demand more ship more frequent ly shipments

, ship products or materials over longer distances, or shift traffic to HDVs from other shipping modes such as rail, barge or ai r. In addition, customers might choose to reduce their number of warehouses, reduce shipment rates or make smaller but more frequent shipments, all of which could lead to an increase in HDV VMT. Ultimately, fuel cost savings could ripple through the enti re economy, thus increasing demand for goods and services shipped by trucks, and therefore increase HDV VMT due to increased gross domestic product (GDP).

Conversely, if fuel efficiency standards lead to net increases in the total costs of HDV operation be cause fuel cost savings do not fully offset the increase in HDV purchase prices and associated depreciation costs, then the price of HDV services could rise. This is likely to spur a decrease in HDV VMT, and perhaps a shift to alternative shipping modes. These effects could also ripple through the economy and affect GDP.

Also, as discussed in Chapter 8.3.3 of the Draft RIA, the magnitude of the rebound effect is likely to be influenced by the extent of any market failures that affect the demand for mor e fuel efficient HDVs, as well as by HDV operators’ responses to their perception of the tradeoff between higher upfront HDV purchase costs versus lower but uncertain future expenditures on fuel.

Econometric and Other Evidence Related to HDV VMT Rebound

As discussed above, HDV VMT rebound is defined as the change in HDV VMT that occurs in response to an increase in HDV fuel efficiency. We are not aware of any studies that

8 These factors are discussed more fully in a report to EPA from Energy and Environmental Research Associates, which illustrates in a series of diagrams the complex sy stem of decisions and decision

- makers that could influence the magnitude and timing of the rebound effect. See Sections 2.2.2, 2.2.3, 2.2.4, and 2.3 in EERA (2014), “Research to Inform Analysis of the Heavy

- Duty Vehicle Rebound Effect”, Draft Final Report of Phase I under EPA contract EP

- C

- 13

- 025. (Docket ID: EPA

- HQ

- OAR

- 2014

- 0827). Commented [A1]: Commenters understand that t his behavior, as described here, would not be consistent with profit maximization in a competitive market. No profit maximizer takes savings from one input and gives it to another input for no apparent reason. Commenter requests that you either p lease either delete or provide a
t estable explanation of how it is in fact consistent with profit maximization.

Commented [A2]: Commenters note that f or this to be the case wouldn’t technology costs have to be greater than after

- tax fuel process? Is that consistent with the analysis supporting the rule?Summary of Interagency Working Comments Received under EO 12866 and EO 13563 review of draft NPRM. Subject to further policy review.

directly estimate this elasticity 9 for the U.S. This section discusses econometri c analyses of other related elasticities that could potentially be used as a proxy for measuring HDV VMT rebound, as well as other analyses that may provide insight into the magnitude of HDV VMT rebound. We seek comment on the applicability of the finding s from these analyses, as well as additional data and research on the topic of HDV VMT rebound.

One of the challenges to developing robust econometric analyses of HDV VMT rebound in the U.S. is data limitations. For example, the main source of time

- serie s HDV fuel efficiency data in the U.S. is derived from aggregate fuel consumption and HDV VMT data. This may introduce interdependence or “simultaneity” between measures of HDV VMT and HDV fuel efficiency, because estimates of HDV fuel efficiency are deriv ed partly from HDV VMT. This mutual interdependence makes it difficult to isolate the causal effect of HDV fuel efficiency on HDV VMT and to measure the response of HDV VMT to changes in HDV fuel efficiency.

Data on other important determinants of HDV V MT, such as freight shipping rates, shipment sizes, HDV payloads, and congestion levels on key HDV routes is also limited, of questionable reliability, or unavailable. Additionally, data on HDVs and their use is usually only available at an aggregate leve l, making it difficult to evaluate potential differences in determinants of VMT for different types of HDV operations (e.g., long

- haul freight vs. regional delivery operations) or vehicle sub

- classes (e.g., utility vehicles vs. school buses).

Another challenge inherent in using econometric techniques to measure the response of HDV VMT to HDV fuel efficiency is developing model specifications that incorporate the mathematical form and range of explanatory variables necessary to produce reliable estimate s of HDV VMT rebound. Many different factors can influence HDV VMT, and the complex relationships among those factors should be considered when measuring the rebound effect. 10

In practice, however, most studies have employed simplified models. Many use p rice variables (e.g., price per gallon of fuel, or fuel cost per mile driven) and some measure of aggregate economic activity, such as GDP. However, some of these studies exclude potentially important variables such as the amount of road capacity (which a ffects travel speeds and may be related to other important characteristics of highway infrastructure), or the price or availability of competing forms of freight transport such as rail or barge (i.e., characteristics of the overall freight transport networ k).

9 E lasticity is the measurement of how responsive an economic variable is to a change in another. For example: price elasticity of demand is a measure used in economics to show the responsiveness, or elasticity, of the quantity demanded of a good or service to a change in its price. More precisely, it gives the percentage change in quantity demanded in response to a one percent change in p rice

. 10 A useful framework for understanding how various responses interact to determine the rebound effect is presented in Section 2 and Appendix B of De Borger, B. and Mulalic, I. (2012), “The determinants of fuel use in the trucking industry

– volume, f leet characteristics and the rebound effect”, Transportation Policy, Volume 24, pp. 284

– 295. See also Section 3.4 of EERA (2014), “Research to Inform Analysis of the Heavy

- Duty vehicle Rebound Effect”, Draft Final Report of Phase I under EPA contract EP

- C

- 13

- 025. (Docket ID: EPA

- HQ

- OAR

- 2014

- 0827 )

.Summary of Interagency Working Comments Received under EO 12866 and EO 13563 review of draft NPRM. Subject to further policy review.

Fuel Price and Fuel Cost Elasticities

This sub

- section reviews econometric analyses of the change in HDV use (measured in VMT, ton

- mile, or fuel consumption) in response to changes in fuel price ($/gallon) or fuel cost ($/mile or $/ton

- mile). The st udies presented below attempt to estimate these elasticities in the HDV sector using varying approaches and data sources.

Gately (1990) employed an econometric analysis of U.S. data for the years 1966

– 1988 to examine the relationship between HDV VMT an d average fuel cost per mile, real Gross National Product (GNP), and variables capturing the effects of fuel shortages in 1974 and 1979. 11 The study found no statistically significant relationship between HDV VMT and fuel cost per mile. Gately’s estimates of the elasticity of HDV VMT with respect to fuel cost per mile

were

- 0.035 with and

- 0.029 without the fuel shortage variables, but both estimates had large standard errors. However, Gately’s study was beset by numerous statistical problems, which raise serious questions about the reliability of its results. 12

More recently, Matos and Silva (2011) analyzed road freight transportation sector data for the years 1987

– 2006 in Portugal to identify the determinants of demand for HDV freight transportation. 13 Using a reduced

- form equation relating HDV use (measured in ton

- km) to economic activity (GDP) and the energy cost of HDV use (measured by in fuel cost per ton

- km carried), these authors estimated the elasticity of HDV ton

- km with respect to energy costs t o be

- 0.241. An important strength of Matos and Silva’s study is that it also estimated this same elasticity using a procedure that accounted for the effect of potential mutual causality between HDV ton

- km and energy costs, and arrived at an identical val ue.
Differences between HDV use and the level of highway service in Portugal and in the U.S. might limit the applicability of Matos and Silva’s result to the U.S. The volume and mix of commodities could differ between the two nations, as could the levels of congestion on their respective highway networks, transport distances, the extent of intermodal competition, and the characteristics of HDVs themselves. HDVs also operate over a more limited highway network in Portugal than in the United States. Unfor tunately, it is difficult to anticipate how these differences might cause Matos and Silva’s elasticity estimates to differ from what we might find in the U.S. Finally, their analysis focused on HDV freight transport and did not consider non

freight uses o f HDVs, which somewhat limits its usefulness in the analysis of this proposed rulemaking.

De Borger and Mulalic (2012) examined the determinants of fuel use in the Denmark HDV freight transport sector for the years 1980

– 2007. The authors developed a system of equations that capture linkages among the demand for HDV freight transport, HDV fleet

11 Gately, D., The U.S. Demand for Highway Travel and Motor Fuels, The Energy Journal, Volume 11, No. 3, July 1990, pp.59

- 73.

12 The most important of these problems

– similar historical time trends in the model’s dependent variable and the measures used to explain its historical variation

– can lead to “spurious regressions,” or the appearance of behavioral relationships that are simply artifacts of the similarity (or correlation) in historical trends among the mo del’s variables.
13 Matos, F. J. F., and Silva, F. J. F., “The Rebound Effect on Road Freight Transport: Empirical Evidence from Portugal,” Energy Policy , 39, 2011, pp. 2833

– 2841. Commented [A3]: Commenters note that a ll of the points made in this paragraph taken together appear to indicate UNCERTAINTY, but not necessarily unequivocal BIAS in a particular direction as it

m ight be applied to the U.S. Commenters note that it is not sufficient to establish a degree of uncertainty with regard to their application to the U.S. the agencies must establish unambiguous and substantial bias in these estimates for purposes of applying to this rule. Commenters do not see sufficient justification for giving it zero weight in the analysis of this rule.Summary of Interagency Working Comments Received under EO 12866 and EO 13563 review of draft NPRM. Subject to further policy review.

characteristics, an d HDV fuel consumption. 14 As De Borger and Mulalic state, “we precisely define and estimate a rebound effect of improvements in fuel efficiency in the trucking industry: behavioural adjustments in the industry imply that an exogenous improvement in fuel eff iciency reduces fuel use less than proportionately. Our best estimate of this effect is approximately 10 percent in the short run and 17 percent in the long run, so that a 1 percent improvement in fuel efficiency reduces fuel use by 0.90 percent (short

- ru n) to 0.83 percent (long

- run).”

While De Borger and Mulalic capture a number of important responses that contribute to the rebound effect, some caution is appropriate when using their results to estimate the VMT rebound effect for this proposal. Like the Matos and Silva study, this study examined HDV activity in another country, Denmark, which has a less

- developed highway system, lower levels of freight railroad service than the U.S., and is also likely to have a different composition of freight shipping activity. Although the effect of some of these differences is unclear, greater competition from rail shipping in the U.S. and the resulting potential for lower trucking costs to divert some rail freight to truck could cause the VMT rebound effect to be la rger in the U.S. than De Borger and Mulalic’s estimate for Denmark.

On the other hand, if freight networks are denser and commodity types are more homogenous in Denmark than the U.S., then shippers may have wider freight trucking options. If this is the case, shippers in Denmark might be more sensitive to changes in freight costs, which could cause the rebound effect in Denmark to be larger than the U.S. Like the Matos and Silva study, this analysis also focuses on freight trucking and does not consider non

- freight HDVs (e.g. vocational vehicles). We have been unable to identify adequate data to employ De Borger and Mulalic’s model for the U.S. (mainly because time

- series data on freight carriage by trucks, driver wages, and vehicle prices in the U.S. a re limited).

The Volpe National Transportation Systems Center previously has developed a series of travel forecasting models for the Federal Highway Administration (FHWA). 15 W ork conducted by the Volpe Center during 2009

- 11 to develop the original versio n of FHWA’s forecasting model was presented in the Regulatory Impact Analysis for the HD GHG Phase I rule (see Table 9

- 2 in that document

, which is reproduced below as Table XX ). 16 In the analysis for the Phase 1 rule, Volpe estimated both state

- level and national aggregate models to forecast HDV single unit and combination truck VMT that included fuel cost per mile as an explanatory variable. This

14 De Borger, B. and Mulalic, I., “The determinates of fuel use in the trucking industry

– volume, fleet characteristics and the rebound effect”, Transportation Policy, Volume 24, November 2012, pp. 284

– 295.

15 FHWA Travel Analysis Framework Development of VMT Forecasting Models for Use by the Federal Highway Administration May 12, 2014 http://www.fhwa.dot.gov/policyinformatio n/tables/vmt/vmt_model_dev.pdf . Volpe’s work was advised by a panel of approximately 20 experts in the measurement, analysis, and forecasting of travel, including academic researchers, transportation consultants, and members of local, state, and federal g overnment transportation agencies. It was also summarized in the paper “Developing a Multi

- Level Vehicle Miles of Travel Forecasting Model,” November, 2011, which was presented to the Transportation Research Board’s 91 st Annual Meeting in January, 2012.
16 EPA/NHTSA, August 2011. Chapter 9.3.3, Final Rulemaking to Establish Greenhouse gas Emissions Standards & Fuel Efficiency Standards for Medium

- and Heavy

- Duty Engines and Vehicles, Regulatory Impact Analysis. EPA

420

- R

- 11

- 901. ( http://www.epa.gov/otaq/climate/documents/420r11901.pdf ) Commented [A4]: Previo us comment applies here as well.Summary of Interagency Working Comments Received under EO 12866 and EO 13563 review of draft NPRM. Subject to further policy review.

analysis used data from 1970

– 20 08 for its national aggregate model, and data for the 50 individual states from 199 4

– 20 08 for its state

- level model. 17

, 18

Volpe analysts tested a large number of different specifications for its national and state level models that incorporated the effects of factors such as aggregate economic activity and its composition, the volume of U.S. exports and imports, and factors affecting the cost of producing trucking services (e.g., driver wage rates, truck purchase prices, and fuel costs), and the extent and capacity of the U.S. and states’ highway networks. Table XX summarizes Vo lpe’s Phase 1 estimates of the elasticity of truck VMT with respect to fuel cost per mile. 19 As it indicates, these estimates vary widely, and the estimates based on state

- level and national data differ substantially.

Table XX. Summary of Volpe Center Estimates of Elasticity of Truck VMT with Respect to Fuel Cost per Mile

Truck Type National Data State Data Short Run Long Run Short Run Long Run Single Unit 13

- 22% 28

- 45% 3

- 8% 12

- 21% Combination N/A 12

- 14% N/A 4

- 5%

Volpe staff conducted additional analysis of the models that yielded the estimates of the elasticity of truck VMT with respect to fuel cost per mile reported in Table XX, using updated information on fuel costs and other variables appearing in these models , together with revised historical data on truck VMT provided by DOT’s Federal Highway Administration. The newly

available data, statistical procedures employed in conducting this additional analysis, and its results are summarized in materials that can be found in the docket for this rulemaking. This

17 Combination trucks are defined as “all [Class 7/8] trucks designed to be used in combination with one or more trailers with a gross vehicle weight rati ng over 26,000 lbs.” (AFDC, 2014; ORNL, 2013c). Single

- unit trucks are defined as “single frame trucks that have 2

– axles and at least 6 tires or a gross vehicle weight rating exceeding 10,000 lbs.” (FHWA, 2013).

18 The national

- level and functional class V MT forecasting models utilize aggregate time

- series data for the nation as a whole, so that only a single measure of each variable is available during each time period (i.e., year). In contrast, the state

- level VMT models have an additional data dimension, since both their dependent variable (VMT) and most explanatory variables have 51 separate observations available for each time period (one for each of the 50 states as well as Washington, DC). In this context, the states represent a “cross

- section,” and a continuous annual sequence of these cross

- sections is available.

19 One drawback of the fuel cost measure employed in Volpe’s models is that it is based on estimates of fuel economy derived from truck VMT and fuel consumption, which introduces the potentia l for mutual causality (or “simultaneity”) between VMT and the fuel cost measure and makes the effect of the latter difficult to isolate. This may cause their estimates of the sensitivity of truck VMT to fuel costs to be inaccurate, although the direction of any resulting bias is difficult to anticipate. Commented [A5]: Commenters request clari ty on relevance of this point. Once again this indicates uncertainty but there is no substantiated claim they are biased in one direction of another.Summary of Interagency Working Comments Received under EO 12866 and EO 13563 review of draft NPRM. Subject to further policy review.

new Volpe analysis was not available at the time the agencies selected the values of the rebound effect for this proposal, but the agencies will consider this work and any other new work that becomes availabl e in the final rule.

Finally, EPA has contracted with Energy and Environmental Research Associates (EERA), LLC to analyze the HDV rebound effect for regulatory assessment purposes. Excerpts of EERA’s initial report to EPA are included in the docket and contain detailed qualitative discussions of the rebound effect as well as data sources that could be used in quantitative analysis

. 20 EERA also conducted follow

- on quantitative analyses focused on estimating the impact of fuel prices on VMT and fuel consumption. We have included a working paper in the docket on this work, and we seek comment on this work. 21 Note that EERA’s working paper was not available at the time the agencies conducted the analysis of the rebound effect for this proposal, but the agencies will consider this work and any other work in the final rule.

There are reasons to be cautious about interpreting the elasticities from the studies reviewed in this section as a measure of VMT rebound resulting from our pro posed standards. For example, vehicle capacity and loaded weight can vary dynamically in the HDV sector

– possibly in response to changes in fuel price and fuel efficiency

– and data on these measures are limited. This makes it difficult to confidently infer a direct relationship between trucking output (e.g., ton

- miles carried) and VMT assuming a constant average payload.

In addition, fuel cost per mile

– calculated by multiplying fuel price per gallon by fuel efficiency in gallons per mile

– and fuel price may be imprecise proxies for an improvement in fuel efficiency, because the response of VMT to these variables may dif fer. For example, if truck operators are more attentive to variation in fuel prices than to changes in fuel efficiency, then fuel price or fuel cost elasticities may overstate the true magnitude of the rebound effect.

Similarly, there is some evidence i n the literature that demand for fuel is more responsive to fuel price increases than to decreases. 22 Since improved fuel efficiency typically causes fuel costs for HDVs to fall (and assuming fuel costs are not fully offset by increases in vehicle purch ase prices), fuel price or cost elasticities derived from historical periods when fuel prices were increasing or fuel efficiency was declining may also overstate the magnitude of the rebound effect. An additional unknown is that HDV operators may factor f uel prices and fuel costs into

20 EERA (2014), “Research to Inform Analysis of the Heavy

- Duty vehicle Rebound Effect”, Excerpts of Draft Final Report of Phase I under EPA contract EP

- C

- 13

- 025. (Docket ID: EPA

- HQ

- OAR

- [insert]).

21 EERA (2015), “Working Paper on Fuel Price Elasticities for Heavy Duty Vehicles”, Draft Final Report of Phase II under EPA contract EP

- C

- 11

- 046. (Docket ID: EPA

- HQ

- OAR

- 2014

- 0827)
22 Gately, D. 1993. The Imperfect Price

- Reversibility of World Oil Demand. Th e Energy Journal, International Association for Energy Economics, vol. 14 (4), pp. 163

- 182; Dargay, J.M., Gately, D. 1997. The demand for transportation fuels: imperfect price

- reversibility? Transportation Research Part B 31(1); and Sentenac

- Chemin, E., 20 12. Is the price effect on fuel consumption symmetric? Some evidence from an empirical study. Energy Policy, vol. 41, pp. 59

- 65. Commented [A6]: Presumably it will be available for the final rule, no? Commenters request clarity on Why not lay out here and seek com ment?

Commented [A7]: Is this evidence on the responsiveness of truck use, vehicle use, or aggregate energy use? Commenters suggest clarifying whether this literature is directly applicable to heavy trucks.Summary of Interagency Working Comments Received under EO 12866 and EO 13563 review of draft NPRM. Subject to further policy review.

their decision

- making about rates to charge for their service differently from the way they incorporate initial vehicle purchase costs.

Despite these limitations, elasticities with respect to fuel price and fuel cost can pr ovide some insight into the magnitude of the HDV VMT rebound effect. The agencies request comment on all of the studies presented in this section.

Freight Price Elasticities

Freight price elasticities measure the percent change in demand for freight in r esponse to a percent change in freight prices, controlling for other variables that may influence freight demand such as GDP, the extent that goods are traded internationally, and road supply and capacity. This type of elasticity is only applicable to the HDV subcategory of freight trucks (i.e., combination tractors and vocational vehicles that transport freight). One desirable attribute of such measures for purposes of this analysis is that they show the response of freight trucking activity to changes t o trucking rates, including changes that result from fuel cost savings as well as increases in HDV technology costs. 23

Freight price elasticities, however, are imperfect proxies for the rebound effect in freight trucks for a number of reasons. 24 For example, in order to apply these elasticities we must assume that our proposed rule’s impact on fuel and vehicle costs is fully reflected in freight rates. This may not be the case if truck operators adjust their profit margins or other operational
p ractices (e.g., loading practices, truck driver’s wages) instead of freight rates. It is not well understood how trucking firms respond to different types of cost changes (e.g., changes to fuel costs versus labor costs).

Freight price elasticity estimates in the literature typically measure freight activity in tons
or ton

- miles, rather than VMT. As discussed in the previous section, average truck capacity and payload in the HDV sector varies dynamically

– possibly in response to changes in fuel price and fuel efficiency

– and data on these measures are limited. This makes it difficult to confidently infer a direct relationship between ton

- miles and VMT by assuming a constant average payload. Inferring a direct relationship between tons and VMT is even les s straightforward. Additionally, there are significant limitations on national freight rate and freight truck ton

- mile data in the U.S., making it difficult to confidently measure the impact of a change in freight rates on ton

miles. 25

Finally, freight p rice elasticity estimates in the literature vary significantly based on commodity type, length of haul, region, availability of alternative modes (discussed further in

23 Note however that a percent change in freight activity in response to a percent change in freight rates should theoretically be larger than a percent change in freight activity in response to a percent change in fuel efficiency because fuel efficiency only impacts a portion of freight operating costs (e.g., fuel and vehicle costs, but not likely driver wages or highway tolls).

24 Winebrake, J.J., Green, E.H., Comer, B., Corbett, J.J., Froman, S., 2012. Estimating the direct rebound effect for

on

- road freight transportation. Energy Policy 48, 252

- 259.

25 See, for example, Appendix E in EERA (2014), “Research to Inform Analysis of t he Heavy

- Duty Vehicle Rebound Effect”, Draft Final Report of Phase I under EPA contract EP

- C

- 13

- 025. (Docket ID: EPA

- HQ

- OAR

2014

- 0827).Summary of Interagency Working Comments Received under EO 12866 and EO 13563 review of draft NPRM. Subject to further policy review.

Section IX.E.b.iii below), and functional form of the model (i.e., log

- linear, linear, t ranslog) making it difficult to confidently apply any single estimate reported in the literature to nationwide freight activity. For example, elasticity estimates for longer trips tend to be larger in magnitude than those for shorter trips, while demand t o ship bulk commodities tends to be less elastic than for non

- bulk commodities.

Although these factors explain some of the differences among reported estimates, much of the observed variation cannot be explained quantitatively. For example, one study th at controlled for mode, commodity class, demand elasticity measure (i.e., tons or ton

- miles), model estimation form, country, and temporal nature of data only accounted for about half of the observed variation. 26

Mode Shift Case Study

Although the total d emand for freight transport is generally determined by economic activity, there is often the choice of shipping freight on modes other than HDVs. This is because the United States has extensive rail, waterway , pipeline, and air transport networks in addit ion to an extensive highway network; these networks often closely parallel each other and are often viable choices for freight transport for many long

- distance shipping routes within the continental U.S. If rates for one mode decline, demand for that mode is likely to increase, and some of this new demand could represent shifts from other modes. 27 The “cross

- price elasticity of demand,” which measures the percentage change in demand for shipping by another mode ( e.g. , rail) given a percentage change in the price of HDV freight transport services, provides a measure of the importance of such mode shifting. Aggregate estimates of cross

- price elasticities vary widely 28

, and there is no general consensus on the most appropriate value to use for analytical purpo ses.
When considering intermodal shift, one of the most relevant kinds of shipments are those that are competitive between rail and HDV modes. These trips generally include long

- haul shipments greater than 500 miles, which weigh between 50,000 and 80,00 0 pounds (the legal road limit in many states). Special kinds of cargo like coal and short

- haul deliveries are of less interest because they are generally not economically transferable between HDV and rail modes, so they would not be expected to shift mod es except under an extreme price change. However, to the best of our knowledge, the total amount of freight that could potentially be subject to mode shifting has not been studied extensively.

In order to explore the potential for HDV fuel efficiency st andards to produce economic conditions that favor a mode shift from rail to HDVs, EPA commissioned GIFT Solutions, LLC to perform case studies on the HD GHG Phase 1 rule using a number of data sources, including the Commodity Flow Survey, interviews with t rucking firms, and the Geospatial Intermodal

26 Li, Z., D.A. Hensher, and J.M. Rose, Identifying sources of systematic variation in direct price elasticities from revealed preference studies of inter

- city freight demand. Transport Policy, 2011

. 27 Rail lines in parts of the U.S. are thought to be currently oversubscribed. If that is the ca se, and new freight demand is already being satisfied by trucks, then this would limit the potential for intermodal freight shifts between trucks and rail as the result of this proposed rule.

28 Winebrake, J.J., Green, E.H., Comer, B., Corbett, J.J., Froma n, S., 2012. Estimating the direct rebound effect for

on

- road freight transportation. Energy Policy 48, 252

- 259.Summary of Interagency Working Comments Received under EO 12866 and EO 13563 review of draft NPRM. Subject to further policy review.

Freight Transportation (GIFT) model developed by Winebrake and Corbett, which includes information on infrastructure and other route characteristics in the U.S. 29

, 30

A central assumption in the case studies was that economic conditions would favor a shift from rail to HDVs if either the price per ton

- mile to ship a commodity by HDV, or the price to ship a given quantity of a commodity by HDV, became lower relative to rail transport options post

- regulation. The r esults of the case studies indicate that the HD Phase I rule would not seem to create obvious economic conditions that lead to a mode shift from rail to truck, but there are a number of limitations and caveats to this analysis, which are discussed in the f inal report to EPA by GIFT. 31

, 32 For example, even if trucking did not become less expensive than rail post

regulation, a relative decrease in the truck versus rail rates might be enough to produce a shift, given that other factors could influence shippers’ decisions on modal choice. The study did not, however, consider these other factors such as time

- of

- delivery and modal capacity. As another example, the analysis assumes all fuel cost savings and incremental vehicle costs from the HD Phase 1 rule would b e passed on to shippers via changes in freight rates, even though the analysis found some evidence that this might not occur (in two cases, the charges for shipping a truckload over a given route and distance were the same despite differences in payloads t hat should have been reflected in their fuel costs). Given these limitations, more work is needed in this area to explore the potential for mode shift in response to HD fuel efficiency standards.

Case Study Using Freight Price Elasticities

Cambridge System atics, Inc. (CSI) employed a case study approach using freight price elasticity estimates in the literature to show several examples of the magnitude of the HDV rebound effect. 33 In their unpublished paper commissioned by the National Research Council of the National Academies in support of its 2010 HDV report, CSI estimated the effect on HDV VMT from a net decrease in operating costs associated with fuel efficiency improvements, using two different technology cost and fuel savings scenarios for Class 8 co mbination tractors. Scenario 1 increased average fuel efficiency of the tractor from 5.59 miles per gallon to 6.8 miles per gallon, with an additional cost of $22,930 for purchasing the improved tractor. Scenario 2 increased the average fuel efficiency t o 9.1 miles per gallon, at an incremental cost of $71,630 per tractor. Both of these scenarios were based on the technologies and targets from a report

29 Winebrake, James and James J. Corbett (2010). “Improving the Energy Efficiency and Environmental Performance of Goods Movement,” in Sperling , Daniel and James S. Cannon (2010) Climate and Transportation Solutions: Findings from the 2009 Asilomar Conference on Transportation and Energy Policy. See http://www.its.ucdavis.edu/events/2009book/Chapter13.pdf (Docket ID: EPA

- HQ

- 2014

- 0827 )

30 Winebrake, J. J.; Corbett, J. J.; Falzarano, A.; Hawker, J. S.; Korfmacher, K.; Ketha, S.; Zilora, S., Assessing Energy, Enviro nmental, and Economic Tradeoffs in Intermodal Freight Transportation, Journal of the Air & Waste Management Association , 58(8), 2008 (Docket ID: EPA

- HQ

- OAR

- 2010

- 0162

- 0008).

31 See GIFT Solutions, LLC, “Potential for Mode Shift due to Heavy Duty Vehicle Fuel Efficiency Improvements”. February, 2012.

32 Winebrake, James, J. Corbett, J. Silberman, E. Erin, & B. Comer, 2012. Potential for Mode Shift due to Heavy Duty Vehicle Fuel E fficiency Improvements: A Case Study Approach. GIFT Solutions, LLC. (Docket ID: EPA

HQ

- OAR

- 2014

- 0827)
33 Cambridge Systematics, Inc., Assessment of Fuel Economy Technologies for Medium and Heavy Duty Vehicles: Commissioned Paper on Indirect Costs and Al ternative Approaches, 2009.Summary of Interagency Working Comments Received under EO 12866 and EO 13563 review of draft NPRM. Subject to further policy review.

authored by the Northeast States Center for a Clean Air Future (NESCCAF) and International Council on C lean Transportation (ICCT). 34

The CSI estimates were based on a range of direct (or “own

- price”) freight elasticities (

- 0.5 to

- 1.5) 35 and cross

- price freight elasticities (0.35 to 0.59) 36 obtained from the literature. 37 In their calculations, CSI assumed 1 42,706 million miles of tractor VMT and 1,852 billion ton

miles were affected. The tractor VMT was based on the Bureau of Transportation Statistics’ (BTS) estimate of highway miles for combination tractors in 2006, and the rail ton

- miles were based on the BTS estimate of total railroad miles during 2006. This assumption is likely to overstate the rebound effect, since not all freight shipments occur on routes where tractors and rail service shipments compete directly. Nevertheless, this assumption appea rs to be reasonable in the absence of more detailed information on the percentage of total miles and ton

- miles that are subject to potential mode shifting.

For CSI’s calculations, all costs except fuel costs and vehicle costs were taken from a 2008 ATRI study. 38 It is not clear from the report how the new vehicle costs were incorporated into CSI’s calculations of per

- mile tractor operating costs. For example, neither the ATRI report nor the CSI report discusses assumptions about depreciation, useful life times of tractors, and the opportunity cost of capital.

Based on these two scenarios, CSI estimated the change in tractor VMT in response to a net decrease in operating costs (i.e., accounting for fuel cost and changes in tractor purchase costs) associat ed with fuel efficiency improvement of 11

- 31 percent for Scenario 1 and 5

- 16 percent for Scenario 2, without accounting for any fuel savings from reduced rail service. When the fuel savings from reduced rail usage were included in the calculations, they e stimated the change in tractor VMT in response to a net decrease in operating costs associated with fuel efficiency improvement would be 9

- 30 percent for Scenario 1, and 3

- 15 percent for Scenario 2.

Note that these estimates reflect changes to tractor VM T with respect to total operating costs, so they should theoretically be larger than a percent change in tractor VMT with respect to a percent change in fuel efficiency because fuel efficiency only impacts a portion of truck operating costs (e.g., fuel and vehicle costs, but not likely driver wages or highway tolls).

CSI included caveats associated with these calculations. For example, their report states that freight price elasticity estimates derived from the literature are “heavily reliant on factors

34 Northeast States Center for a Clean Air Future, Southeast Research Institute, TIAX, LLC., and International Council on Clean Transportation, Reducing Heavy

- Duty Long Haul Truck Fuel Consumption and CO2 Emissions, September 20 09. See http://www.nescaum.org/documents/heavy

- duty

- truck

- ghg_report_final

- 200910.pdf

35 Graham and Glaister, “Road Traffic Demand Elasticity Estimates: A Review, ” Transport Reviews Volume 24, 3, pp. 261

- 274, 2004. 36 Based upon a study for the National Cooperative Highway Research Program by Cambridge Systematics, Inc., Characteristics and Changes in Freight Transportation Demand: A Guidebook for Planners and Policy Analysts Phase II Report, National Cooperative Highway Research Program Project 8

- 30, June 1995.

37 The own (i.e., self) price elasticity provides a measure f or describing how the volume of truck shipping (demand) changes with its price while the cross

- price elasticity provides a measure for describing how the volume of rail shipping changes with truck price. In general, an elasticity describes the percent chan ge in one variable (e.g. demand for trucking) in response to a percent

- change in another (e.g. price of truck operations).

38 American Transportation Research Institute , “ An Analysis of the Operational Costs of Trucking ” , October 2008.Summary of Interagency Working Comments Received under EO 12866 and EO 13563 review of draft NPRM. Subject to further policy review.

in cluding the type of demand measures analyzed (vehicle

- miles of travel, ton

- miles, or tons), geography, trip lengths, markets served, and commodities transported.” These factors can increase variability in the results. Also, estimates in CSI’s study have t he limitation of using freight price elasticities to estimate the HDV rebound effect discussed previously in Section IV.D.2.b.

Simulation Model Study Using Freight Price Elasticities

Guerrero (2014) constructs a freight simulation model of the California trucking sector to measure the impact of fuel saving investments and fleet management on GHG emissions. 39 Rather than estimating these impacts using econometric analysis of raw data, the study uses values from the existing literature. Guerrero determines that “...improving the performance of trucking also increases the number of trips demanded because the market price also decreases. This ‘rebound’ effect offsets around 40

- 50 percent of these vehicle efficiency emission reductions, with 9

- 14 percent of the effect coming from increased pavement deterioration and 31

- 36 percent coming from increased fuel combustion.” Note that to the extent that trip lengths also vary in response to improvements in HDV fuel efficiency, changes in the number of HDV trips may not exactly reflect changes in the total number of miles the vehicles are operated.

However, these findings are based on freight price elasticities, which

– as we discuss in Section IV.D.2.b and in the context of the CSI study above

– have significant limita tions. The study also simulates only one state’s freight network (California), which may not be a good representation of national activity.

How the Agencies Estimated the HDV Rebound Effect for this Proposal

(1) Values Used in the Phase 1 Analysis At the time the agencies conducted their analysis of the Phase 1 fuel efficiency and GHG emissions standards, the only evidence on the HD V rebound effect were the previously described studies by CSI and the Volpe Center. 40 On the basis of this evidence, th e agencies chose rebound effects of 15 percent for vocational vehicles and 5 percent for combination tr actors , both of which were toward the lower end of the range of values f ro m these studies. The agencies found no evidence on the rebound effect for HD pickup trucks and vans, but concluded it would be inappropriate to use the values selected for vocational vehicles or combination tr actors for those vehicles. Because the usage patterns of HD pickup trucks and vans can more closely resemble those of larg e light

- duty vehicles, the agencies used the 10% rebound effect we had employed in our most recent light

- duty rulemaking to analyze the Phase 1 standards for 2b/3 vehicles.

39 Guerrero, Sebastian. Modeling fuel saving investments and fleet management in the trucking industry: The impact of shipment performance on GHG emissions. Transportation Research Part E, May 2014.

40 The Gately study was also available, however, the agencie s were not aware of the work at the time.Summary of Interagency Working Comments Received under EO 12866 and EO 13563 review of draft NPRM. Subject to further policy review.

(2) How the Agencies Analyze d VMT Rebound in this Proposal

After considering the new evidence that has become available since the HD Phase 1 final rule

, the agencies elected to continue using the rebound effect estimates we used previously in the HD Phase 1 rule in our analysis of Phase 2 proposed standards. In arriving at this decision, the agencies considered the shortcomings and limitations of the newl y

- available studies described previously, particularly the limited applicability of the two published studies using data from European nations to the U.S. context. After weighing these attributes of the more recent studies

, the agencies concluded that we had insufficient evidence to justify revising the rebound effect values that were used in the Phase 1 analysis.

In our assessment , we do not discern between short

- run and long

- run rebound effects, although these effects may differ. The vocational and c ombination truck estimates are based on the Volpe Center analysis presented in the HD Phase 1 rule and the case study from CSI. As with the HD Phase 1 rule, we did not find any literature specifically examining the HD pickup and truck sector. Since these vehicles are used for very different purposes than combination tractors and vocational vehicles, and they are more similar in use to large light

- duty vehicles, we have chosen the light

- duty rebound effect of 10 percent used in the final rule establishing fuel economy and GHG standards for MYs 2017

- 2025 light

- duty vehicles in our analysis of HD pickup trucks and vans.

While for this proposal, the agencies have selected to use these rebound effect values, we acknowledge the literature shows a wide range of rebound effect estimates. Therefore, we will review and consider revising these estimates in the final rule, taking into consideration all available data and analysis, including submissions from public commenters and new research on the rebound effect.

I t should be noted that the rebound estimates we have selected for our analysis represent the VMT impact from our proposed standards with respect to changes in the fuel cost per mile driven. As described previously, the HDV rebound effect should ideally be a measure of the change in fuel consumed with respect to the change in overall operating costs due to a change in HDV fuel efficiency. Such a measure would incorporate all impacts from our proposal, including those from incremental increases in vehicle p rices that reflect costs for improving their fuel efficiency. Therefore, VMT rebound estimates with respect to fuel costs per mile must be “scaled” to apply to total operating costs, by dividing them by the fraction of total operating costs accounted for by fuel.

The agencies made simplifying assumptions in the VMT rebound analysis for this proposal, similar to the approach taken during the development of the HD GHG Phase 1 final rule. However, for the HD Phase 2 final rulemaking, we plan to use a more comprehensive approach. Due to timing constraints during the development of this proposal, the agencies did not have the technology package costs for each of the alternatives prior to the need to conduct the inventory analysis, except for the pickup truck and van category in analysis Method A. Therefore, the same "overall" VMT rebound values were used for Alternatives 2 through 5 (as discussed in Chapter 8. 3 .3 of the Draft RIA and analyzed in Chapter 6 of the Draft RIA), despite the fact that each alternative results in a different change in incremental technology and fuel Commented [A8]: Commenters want to know if you mean “differentiate” here ?Summary of Interagency Working Comments Received under EO 12866 and EO 13563 review of draft NPRM. Subject to further policy review.

costs . For the final rulemaking, we plan to determine VMT rebound separately for each HDV category and for each alternative. Tables 64 through 66 in Chapter 7 of the Draft RIA p resent VMT rebound for each HDV sector that we estimated for the preferred alternative. These VMT impacts are reflected in the estimates of total fuel savings and reductions in emissions of GHG and other air pollutants presented in Section VI and VII of th is preamble for all categories.

Section 9.3.3 in the draft RIA provides more details on our assessment of HDV VMT rebound. We invite comment on our approach, the rebound estimates, and the related assumptions we made. In particular, we invite comment on the most appropriate methodology for factoring new vehicle purchase or leasing costs into the per

- mile operating costs. Also, for the purposes of this proposal, we have not taken into account any potential fuel savings or GHG emission reductions from the rail sector due to mode shift because estimates of this effect seem too speculative at this time. We invite comment on this assumption, as well as suggestions on alternative modeling frameworks that could be used to assess mode shifting implications of th ese proposed regulations.

Note that while we focus on the VMT rebound effect in our analysis of this proposed rule, there are at least two other types of rebound effects discussed in the economics literature. 41 In addition to VMT rebound effects, there are “indirect” rebound effects, which refers to the purchase of other goods or services (that consume energy) with the costs savings from energy efficiency improvements; and “economy

- wide” rebound effects, which refers to the increased demand for energy throughout the economy in response to the reduced market price of energy that happens as a result of energy efficiency improvements.

Research on indirect and economy

- wide rebound effects is nascent, and we have no t identified any that attempts to quantify indirect or economy

- wide rebound effects for HDVs. In particular, the agencies are not aware of any data to indicate that the magnitude of indirect or
economy

- wide rebound effects, if any, would be significant fo r this proposed rule. 42 Therefore, we rely the same analysis of vehicle miles traveled to estimate the rebound effect in this proposal that we did for the HD Phase 1 rule, where we attempted to quantify only rebound effects from our rule that impact HDV VM T. We welcome comments and any new work in this area that helps to assess and quantify different rebound effects that could result from improvements in HDV efficiency, including different types of more intensive truck usage that affect fuel consumption bu t not VMT such as loaded weight, truck routing, and scheduling.

In order to test the effect of alternative assumptions about the rebound effect, NHTSA examined the sensitivity of its estimates of benefits and costs of the Phase 2 Preferred

41 Note that there is more than one way to measure HDV energy services. The agencies’ analyses use VMT as a measure of HDV energy services; other potential measures include ton

- miles, cube

- miles, and fuel consumpti on.
42 One entity sought reconsideration of the Phase 1 rule on the grounds that indirect rebound effects had not been considered by the agencies and could negate all of the benefits of the standards. This assertion rested on an unsupported affidavit lack ing any peer review or other indicia of objectivity. This affidavit cited only one published study. The study cited did not deal with vehicle efficiency, has methodological limitations (many of them acknowledged), and otherwise was not pertinent. EPA and NHTSA thus declined to reconsider the Phase 1 rule based on these speculative assertions. See generally 77 FR 51703

- 04 (Aug. 27, 2012) and 77 FR 51502

- 03 (Aug. 24, 2012).Summary of Interagency Working Comments Received under EO 12866 and EO 13563 review of draft NPRM. Subject to further policy review.

Alternative for HD pickups and vans to alternative assumptions about the rebound effect. While the main analysis for pickups and vans assumes a 10 percent rebound effect, the sensitivity analysis estimates the benefits and costs of the proposed standards under the assumption s of 5, 15, and 20 percent rebound effect s

. Alternative values of the rebound effect change the estimates of benefits and costs from the proposed standards in three ways. First, h igher values of the rebound effect increase the amount of additio nal VMT that results from improved fuel efficiency; this increases costs associated with additional congestion, accidents, and noise, thus increasing total costs associated with the proposed standards. Conversely, smaller values of the rebound effect redu ce costs from additional congestion, accidents, and noise, so they reduce total costs of the proposed standards.
L arger increase s in VMT associated with higher values of the rebound effect reduce the value of fuel savings and related benefits ( such as red uctions in GHG emissions ) by progressively larger amounts, while smaller values of the rebound effect cause smaller reductions in these benefits. At the same time, however, a higher rebound effect generates larger benefits from increased vehicle use , whil e a smaller rebound effect reduces these benefits compared to the base case. Thus the impact of alternative values of the rebound effect on total benefits from the proposed standards depends on the exact magnitudes of these latter two effects. On balance, these three effects can cause net benefits to increase or decrease for alternative values of the rebound effect.

Table Error! No text of specified style in document.

- 1 Sensitivity of Preferred Alternative Impacts Under Different Assumptions About Rebound Effect for Pickups and Vans, using 3% Discount Rate

HD Pickups and Vans Rebound Effect

Main Analysis Sensitivity Cases Using Alternative Rebound Assumptions
10 % 5 % 15 % 20 % Fuel Reductions (Billion Gallons) 7.8 8.2 7.5 7.1 GHG Reductions (MMT CO 2 eq) 94.1 95.7 87.2 83.0 Total Costs ($ billion) 5.5 5.0 6.5 7.2 Total Benefits ($ billion) 23.5 23.0 22.9 22.8 Net Benefits ($ billion) 18.0 18.0 16.4 15.5

Table XX summarizes the impact of these alternative assumptions on fuel and GHG emissions savings, total costs, total benefits, and net benefits. As it indicates, a 5 percent value for the rebound effect reduces benefits and costs of the proposed standards by identical amounts, leaving net benefits unaffected. Higher values of the rebound effect increase costs and reduce benefits from their values in the main analysis, thus reducing net benefits of the pr oposedSummary of Interagency Working Comments Received under EO 12866 and EO 13563 review of draft NPRM. Subject to further policy review.

standards. Nevertheless, the preferred alternative has significant net benefits under each alternative assumption about the magnitude of the rebound effect for HD pickups and vans

. Thus, these alternative value s of the rebound effect would not have affect ed the agencies’ selection of the preferred alternative, as th at selection is based on NHTSA’s assessment of the maximum feasible fuel efficiency standards and EPA’s selection of appropriate GHG standards to address energy security and the environm ent

.