From: "Achanta, Chandana L." <Chandana_L._Achanta@omb.eop.gov>

To: William Charmley/AA/USEPA/US@EPA, "'Neil.Eisner@dot.gov'" <Neil.Eisner@dot.gov>, "'rebecca.yoon@dot.gov'"

<rebecca.yoon@dot.gov>, "'james.tamm@dot.gov'" <james.tamm@dot.gov>, "'Steve.Wood@dot.gov'"

<Steve.Wood@dot.gov>, "'Joanne.Petrie@dot.gov'" <Joanne.Pe trie@dot.gov>, Maureen Delaney/DC/USEPA/US@EPA,

Robin Moran/AA/USEPA/US@EPA

Date: 09/30/2010 11:35 AM

Subject: RE: Summary of interagency working comments received on draft Notice and Technical Assessment under EO 12866 review

Hi All,

Please find attached additional interagency working comments we received under EO 12866 review of

the draft notice and technical assessment.

They are for your consideration and subject to further policy review.Summary of Interagency Working Comments received on draft Notice of Intent under EO 12866 review

Below is a summary of interagency comments received during interagency review of the draft Notice of

Intent and the draft Technical Assessment Report for MY 2017

‐ 2025 under EO 12866. They are for your

consideration and subject to further policy review.

1) Interagency commenter requests change to NOI:

“EPA will consider whether it is appropriate to retain this approach in the MYs 2017

‐

2025 rulemaking

, or to consider other approaches such as those that account for

upstream emissions and carbon sequestration .”

TAR COMMENTS:

2) Interagency commenter requests that the following sentence also include FFV's. When it does,

the sentence will be discussing next

‐ generation and alternative

‐ fuel vehicles. There is nothing in

Section 7 that limits the discussion to next generation technology, indeed the first sentence of

that section says, " In the assessment of potential future ranges of stringency presented in

Chapter 6 of this report, we based our analysis on the tailpipe emissions from all vehicles ...".

Therefore, interagency commenter requests the following edit:

Page 73: Add FFVs

“...EPA will be fully evaluating this issue for the MY 2017

‐ 2025 light

‐ duty vehicle GHG emissions

proposal based on the status of commercialization of EVs, PHEVs, and FCVs, and FFVs the potential

of these technologies to provide long

‐ term GHG emissions savings, the status of and outlook for

upstream GHG control programs, and other relevant factors...”

3) TAR 3.2.7. Costs Effects due to Learning

The agencies have also reconsidered learning effects. For this assessment, we continue to

reflect the phenomenon of volume

‐ based learning curve cost reductions in our modeling using

two algorithms

– “volume

‐ based” for newer technologies and “time

‐ based” for mature

technologies. The observed phenomenon in the economic literature which supports

manufacture learning cost reductions are based on reductions in costs as production volumes

increase, and the economic literature suggests these cost reductions occur indefinitely, though

the absolute magnitude of the cost reductions decrease as production volumes increase (withSummary of Interagency Working Comments received on draft Notice of Intent under EO 12866 review

the highest absolute cost reduction occurring with the first doubling of production). 1 The

agencies use the terminology “volume

‐ based” and “time

‐ based” to distinguish among newer

technologies and more mature technologies, and how we apply learning costs reductions in our

assessment. Our volume

‐ based learning algorithm applies for the early steep portion of the

learning curve and is estimated to result in 20 percent lower costs after two full years of

implementation (i.e., a 2014 MY cost would be 20 percent lower than the 2012 and 2013 model

year costs for a new technology being implemented in 2012). Our time

‐ based learning

algorithm applies for the flatter portion of the learning curve and is estimated to result in 3

percent lower costs in each year following first introduction of a given technology. Once two

volume

‐ based learning steps have occurred (for technologies having volume

‐ based learning

applied), time based learning would begin. For technologies to which time based learning is

applied, learning would begin in year 2 at 3 percent per year for 5 years. Beyond 5 years of

time

‐ based learning at 3 percent per year, 5 years of time

‐ based learning at 2 percent per year,

then 5 at 1 percent per year become effective. Going forward, the agencies intend to

investigate industry learning curves in more detail and whether the use of time

‐ based learning

should be used to reflect “mature” technology given the paucity of support in the literature for

this concept continue to be applied indefinitely, and how long it is appropriate to use the

learning discounts in cost estimation procedure. or whether cost r eductions due to learning

should go to zero after some period of time. In addition, the agencies are exploring to what

extent the learning discount is reflected in the market price for trucks. The learning curve used

in this assessment may be modified for the rule making.

1 See “Learning Curves in Manufacturing”, L. Argote and D. Epple, Science, Volume 247; “Toward Cost Buy down

Via Learning

‐ by

‐ Doing for Environmental Energy Technologies, R. Williams, Princeton University, Workshop on

Learning

‐ by

‐ Doing in Energy Technologies, June 2003; “Industry Learning Environmental and the Heterogeneity of

Firm Performance, N. Balasubramanian and M. Lieberman, UCLA Anderson School of Management, December

2006, Discussion Papers, Center for Economic Studies, Washington DC.