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ME1 19138030v.1 Questions below are regarding the spreadsheet EERE

- 2014

- BT

- STD

- 0031

- 002 unless otherwise noted.

1) The 2011 LCC spreadsheet predicted lower LCC savings
, especially for replacement s in the south region and for the highest efficiency (98%) condensing furnace cases
, compared to the 2014 LCC spreadsheet even though t he 2011 LCC spreadsheet did not include the potential of fuel switching.

a) What are the major reasons for the significa nt changes in LCC savings?

b) Why do consumer impacts for replacements in the south not sum to 100% in the 2014 LCC spreadsheet ?

c) The 2014 LCC spreadsheet predicts first year operations cost savings averaging between $54 and $88 depending on the mandat ed efficiency level while the fuel switching impact analysis model provided by AGA and dated 7/11/2104 predicts a first year cost increase of $62 even though both consider fuel switching impacts . What are potential reason s for this significant discrepancy in top level results ?10/28/2014 2

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ME1 19138030v.1 2) 2014 LCC analysis Sheet: Statistics and 2011 LCC Sheet: Forecast Cells

a) The annual heating load in the 2014 LCC analysis using the RECS 2009 database is significantly higher , especially in the southern region, than the 2011 LCC analysis ( based on the RECS 2005).
What is the underlying rationale for the significant increase in heating load s , especially in the South region ?

b) The LCC spreadsheet contains over 100 Crystal Ball Monte Carlo simulation

- controlled
variables . It also contains parametric ass umptions that are not evident in the spreadsheet , for example:

The approximate percentage of total heating load in the southern region of the total national heating load for buildings selected for analysis can be estimated by multiplying buildings’ LBNL modified weighting factors by their annual heating NG consumption provided in the RECS 2005 database. In the 2011 LCC spreadsheet that numb ers is 8.6%. It is relatively close to the 9.5% calculated for the same buildings with the RECS 2005 original weighing factors before they were modified by LBNL. This suggests that the ratio of building heating loads in the southern region to
the rest of t he country in the LBNL sample is similar to RECS 2005 weighting factors

.

A similar calculation in the 2014 LCC spreadsheet shows that this number is 17% , or almost double that of the 2011 version. It is also much larger than the 12.2% calculated with the RECS
20 09 original weighing factors demonstrating more aggressive modification of weighting factors
in the southern region by LBNL in favor of increasing these loads. The processed/static weighting factor numbers in the 2014 LCC spreadsheet ( in contrast t o the 2011 version where factors are calculated in the spreadsheet ) makes it impossible to evaluate the methodology used.

What is the methodology that was used to adjust these weighting factors and what is the justification for adjusting them?

3) DOE 2011 Furnace LCC Sheet “ Forecast Cells ” Installed Price (H8 to H12) , 2009 Installed Price DOE 2014 Furnace LCC Sheet “Labels” (M36 to M40) DOE 2014 Furnace LCC Sheet “Forecast Cells” sum of non

- switching retail price (O3123 to O3127) and installation cost (O3163 to O3167)
, 2013 Installed Price;

What is the basis of the large differential increase in the installed cost of a baseline 80% NWGF vs. the installed cost increase of condensing NWGFs in the 2014 LCC when compared with the 2011 LCC (in switching or non

- switching configuration)?10/28/2014 6

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ME1 19138030v.1 4) Sheet: Forecast Cells. Rows 3143

- 3152 and 3183

- 3192

The installation and retail cost of electric water heating equipment predicted by the model is higher than the cost of gas equipment . This is an unexpected result. F or example

, RSMeans
costs for electric water heaters are lower than for comparable gas water heaters, as expected.
What is the rationale for higher electric water heater costs ?

The source of equipment cost data in the 2014 LCC spreadsheet is “CAC and HP

- 2011 Direct Final Rule; EWH and GWH 2010 Heating Products Final Rule” (see “Equip Price” sheet Table located at Y27 and listed below).

Numbers from this table are first multiplied by local Total Distr./Const. Markup and Sales T ax than by Learning Curve Coeff. and by GDP Deflator. (See “Equip Price” sheet AD8 to 12 and listed below)10/28/2014 11

ME1 19138030v.1 Implicit Price Deflators for Gross Domestic Product (Gross private domestic Investment 2013 vs. 2009)
used is 1.04039.

Source: http://www.bea.gov/national/pdf/dpga.pdf

5 ) Sheet: NWGF Switching. D48 and D49

a) What is the rationale for choosing a single payback (3.5 years) as the basis of fuel switching
decisions ?

b) Why is a single time peri od used for all buildings and owners rather than one dependent on discount rates, income, etc.? For example, t here are discount rates in Sheet: Discount Rate.
Rows 26

- 116 that could be used to create a distribution of payback periods for this decision

.

6 ) Sheet: NWGF Switching. Column AG

a) What is the decision making criteria for choosing which non

- NWGF option is selected when
fuel switching occurs?

b) What happens when negative values occur (meaning the first year operations cost of the switching op tion is lower than the first year operations cost of the high efficiency NWGF)?

c ) Why does fuel switching remain high even at extreme payback times whether negative values
are excluded or not?

d) Pushing the “payback period” up (changing D48&49 to high va lues) does not eliminate fuel switching. Even at 15 years it remains around 10 %. In part this is because options that have a negative “payback period” will always allow switching and also because some fuel switching options make economic sense even when considering a long time horizon. This switching behavior is entirely rational. A significant fraction of these switching events would be expected to occur in the absence of a DOE rule. Are these rational fuel switching cases included in an
estimate of LCC savings due to a DOE rule change?

e) The switching “payback period” is essentially the ratio of the first cost advantage of a switching option to the annual operation cost disadvantage of same option. Because this analysis is a Monte Carlo analysis t his will tend to choose situations which are low operations cost disadvantage and/or high first cost advantage. This is likely to underestimate the true cost of fuel switching. How is this inherent aspect of the Monte Carlo methodology addressed equitabl y given the “irrational” consumer behavior predicted based on behavioral economics theory?10/28/2014 12

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ME1 19138030v.1 7 ) Sheet: Overall Spreadsheet

How are the negatively and positively impacted homes segmented? This includes north/south, and new construction/replacement segments. Included in the segmentation would be replacement costs of different options in different home locations, sizes, and configurations, as well as other factors that would impact consumer classes differently. Averages do not show the marginal aff ected consumers.

8 ) Sheet: Summary Switching. Columns H and I

How were the GTI survey numbers processed?10/28/2014 14

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ME1 19138030v.1 9 ) Sheet: Bldg Sample and general methodology

The buildings sampled are only those that use natural gas or LPG as a heat source and it is used as a primary or secondary source of heat. Are the re an y other criteria for selecting buildings to specifically select for buildings where an 80% NWGF would have been installed in the absence of a DOE rule mandating higher efficiencies?

If there is not doe s this seem reasonable? For example, is the probability of choosing a high efficiency furnace the same in the case of a high income owner with a large home and the case
of a low income owner of a small home?

10 ) Sheet: Summary Switching

Switching statis tics should be different for different efficiency levels; however

, the spreadsheet shows only one set. I s it a composite, how it is calculated

, and how do the statistics look in each group? It appears that this is supposed to be the content of M35

– R58 on the same sheet but
these all contain #REF! rather than data

.

1 1 ) Sheet : Summary Switching
, National Summary tables Replacement AC18 and New Construction
AC32

Why does the summary of national switching and no switching cases add to 11,129 cases when only 10,000 cases were analyzed?

1 2 ) Using t he 2014 LCC spreadsheet and allowing fuel switching improves LCC savings compared to disallowing fuel switching if the ‘ payback ’ for switching decisions is long (15 years for sheet NWGF Switching cells D48 and D 49 is shown below ) . T he effect is even larger in the south

. D oes this mean that fuel switching should be expected to reduce costs to consumers (e.g., in the South ) as a result of the new minimum efficiency level ?10/28/2014 16

ME1 19138030v.1 1 3 ) Sheet: Statistics and Forecast Cells O3043 to O3052

Why is the national annual fuel usage per efficiency group in the ‘Statistics’ sheet different than
the one listed in the ‘Forecast Cells’ sheet?

1 4 ) Sheet: Energy Price, Table Marginal Energy Prices

What is the source of the margina l electric and gas prices? Did the analysis incorporate any of the marginal gas price information provided by AGA based on its member survey? If so, how did it incorporate the AGA member survey results? If not, why not?

1 5 ) Sheet: Equip Price. AC50

Wh at is the source of the factor of 1/3 tha t is multiplied by the cost differential (EF vs. NWGF)?

1 6 ) Sheet: Base Case AFUE

What is the source of the distributions of furnace efficiencies (new and replacement)?

1 7 ) Sheet: Bldg Sample, E67, E61

Why was a uniform distribution chosen for remaining lifetimes for cooling and water heating equipment?

1 8 ) Sheet: Installation Cost, Columns E and F

The source for the assumptions regarding venting options and conditioned vs. unconditioned space is given as “Consultant Report.” Is this report available?10/28/2014 17

ME1 19138030v.1 19) She et NWGF Switching and LCC&PB Cal cs

It appears that in NWGF Switching column AA, equipment age related discounting incorporates a present worth factor from columns AJ and AK. But the source of the cos t numbers is generated in the LCC&PB Calcs sheet, where the same present worth factor is used to discount costs in cells D44 and D46. Is this double counting the present worth factor?

20 ) Summary Sheet

a) Why are the National, North and South Region Installed Prices in “Summary” sheet (row K) different (smaller) than the calculated weighted average of Replacement and New values (rows AA and AP)?

b) 2014 LCC CB 10,000 cases default simulation run includes 9,717 residential and 283 commercial buildings. Are the commercial buildings results included in the Simulation Results
NATIONAL

- 10000 samples tables in the “Summary” sheet? If so, what is the approach to dealing with that unique market segment?