CHAPTER 17. REGULATORY IMPACT ANALYSIS

TABLE OF CONTENTS

17.1 INTRODUCTION ......................................................................................................... 17-1

17.2 NON

- REGULATORY POLICIES ................................................................................ 17-1

17.2.1 Methodology ...................................................................................................... 17-1

17.2.2 Assumptions Regarding Non- Regulatory Policies ............................................ 17-2

17.2.3 Policy Interactions ............................................................................................. 17-3

17.3 NON

- REGULATORY POLICY AS SUMPTIONS ...................................................... 17-4

17.3.1 No New Regulatory Action ............................................................................... 17-4

17.3.2 Consumer Rebates ............................................................................................. 17-4

17.3.2.1 Methodology ............................................................................................ 17-4

17.3.2.2 Analysis .................................................................................................... 17-5

17.3.3 Consumer Tax Credits ....................................................................................... 17-9

17.3.4 Manufacturer Tax Credits ................................................................................ 17-11

17.3.5 Voluntary Energy Efficiency Targets .............................................................. 17-12

17.3.6 Bulk Government Purchases ............................................................................ 17-14

17.4 IMPACTS OF NON

- REGULATORY ALTERNATIVES ......................................... 17-16

REFERENCES ........................................................................................................................ 17-20

LIST OF TABLES

Table 17.1.1 Non

- Regulatory Alternatives to National Standards ................................... 17-1

Table 17.2.1 Proposed Efficien cy Levels for CUACs and CUHPs .................................. 17-3

Table 17.3.1 Effect of Consumer Rebates on Benefit/Cost Ratios of CUACs ................. 17-6

Table 17.3.2 Effect of Consumer Rebates on Benefit/Cost Ratios of CUHPs ................. 17-6

Table 17.3.3 Market Penetrations in 2019 Attributable to Consumer Rebates for CUACs (TSL 3) ........................................................................................... 17-9

Table 17.3.4 Market Penetrations in 2019 Attributable to Consumer Rebates for CUHPs (TSL 3)

................................ ............................................................ 17-9

Table 17.3.5 Market Penetrations in 2019 Attributable to Consumer Tax Credits for CUACs (TSL 3) ......................................................................................... 17-11

Table 17.3.6 Market Penetrations in 2019 Attributable to Consumer Tax Credits for CUHPs (TSL 3)

................................ .......................................................... 17-11

Table 17.3.7 Market Penetrations in 2019 Attributable to Manufacturer Tax Credits for CUACs (TSL 3) ................................................................................... 17-12

Table 17.3.8 Market Penetrations in 2019 Attributable to Manufacturer Tax Credits for CUHPs (TSL 3) .................................................................................... 17-12

17-iTable 17.3.9 Mar ket Penetrations in 2019 Attributable to Voluntary Energy Efficiency Targets for CUACs (TSL 3) ..................................................... 17-13

Table 17.3.10 Market Penetra tions in 2019 Attributable to Voluntary Energy Efficiency Targets for CUHPs (TSL 3) ..................................................... 17-13

Table 17.3.11 Market Penetrations in 2019 Attributable to Bulk Government Purchases for CUACs (TSL 3) .................................................................. 17-15

Table 17.3.12 Market Penetrations in 2019 Attributable to Bulk Government Purchases for CUHPs (TSL 3) ................................................................... 17-15

Table 17.4.1 Impacts of Non- Regulatory Alternatives for CUACs (TSL 3) .................. 17-19

Table 17.4.2 Impacts of Non- Regulatory Alternatives for CUHPs (TSL 3) .................. 17-19

LIST OF FIGURES

Figure 17.3.1 Market Penetration Curves for CUACs [Top to down: 65,000-135,000 Btu/h; 135,000-240,000 Btu/h; 240,000-760,000 Btu/h] ............................. 17-7 Figure 17.3.2 Market Penetration Curves for CUHPs [Top to down: 65,000-135,000 Btu/h; 135,000-240,000 Btu/h; 240,000-760,000 Btu/h] ............................. 17-8 Figure 17.4.1 Market Penetration of CUACs in Policy Cases [Top to down: 65,000- 135,000 Btu/h; 135,000-240,000 Btu/h; 240,000-760,000 Btu/h] ............. 17-17 Figure 17.4.2 Market Penetration of CUHPs in Policy Cases [Top to down: 65,000- 135,000 Btu/h; 135,000-240,000 Btu/h; 240,000-760,000 Btu/h] ............. 17-18

17- iiCHAPTER 17. REGULATORY IMPACT ANALYSIS

17.1 INTRODUCTION

The U.S. Department of Energy (DOE) has determined that energy conservation

standards for consumers of commercial unitary air conditioners (CUAC) and heat pumps (CUHP) constitute an “economically significant regulatory action” under Executive Order (E.O.) 12866, Regulatory Planning and Review. 58 FR 51735 (October 4, 1993). This RIA, which DOE has prepared pursuant to E.O. 12866, evaluates potential non- regulatory alternatives, co mparing the costs and benefits of each to those of the proposed standard s. 58 FR 51735, 51741.

For this a nalysis , DOE used an integrated National Impact Analysis ( NIA )- RIA model

built on the NIA model discussed in C hapter 10 for its analysis. DOE studied the impacts of the

non- regulatory policies on all CUAC and CUHP equipment classes.

DOE identified five non- regulatory policy alternatives that possibly could provide

incentives for the same energy efficiency levels as the proposed standards for the equipment that are the subject of this rulemaking. The non- regulatory policy alternatives are listed in 0 . DOE

evaluated each alt ernative in terms of its ability to achieve significant e nergy savings at a reasonable cost, and compared the effectiveness of each to the effectiveness of the proposed

standard

.

Table 17.1.1 Non -Regulatory Alternatives to National Standards No New Regulatory Action

Consumer Rebates

Consumer Tax Credits

Manufacturer Tax Credits
Voluntary Energy Efficiency Targets

Bulk Government Purchases

Sections 17.2 and 17.3 discuss the analysis of five selected policies listed above (excluding the alternative of no new regulatory action). Section 17.4 present s the results of the policy alternatives

.

17.2 NON

- REGULATORY POLICIES

This section describes the method DOE used to analyze the energy savings and cost

effectiveness of the five non- regulatory policy alternatives for CUACs and CUHPs . This section

also describes the assumptions underlying the analysis.

17.2.1 Methodology

DOE used its integrated NIA

- RIA spreadsheet model to calculate the national energy savings (NES) and net present value (NPV) associated with each non-regulatory policy

17-1alternative. Chapter 10 of the technical support document (TSD) describes the NIA spreadsheet model. Appendix 17-A, Section 17-A.3, discusse s the NIA -RIA integrated model approach.

DOE quantified the effect of each alternative on the purchase of equipment that meet the target efficiency level , which is defined as the efficiency level in the proposed standard s. After establishing the quantitative assumptions underlying each alternative, DOE appropriately revised inputs to the NIA- RIA spreadsheet model. The primary model inputs revised were market shares

of equipment meeting the target efficiency level o f each equipment class . The shipments of equipment for any given year reflect a distribution of efficiency levels. DOE assumed that the proposed standards would affect 100 percent of the shipments of equipment that did not meet the target level in the bas e case, a whereas the non

- regulatory policies would affect a smaller percentage of those shipments. DOE made certain assumptions about the percentage of shipments affected by each alternative policy

. DOE used those percentages to calculate the shipment- weig hted average energy consumption and costs of CUACs and CUHPs attributable to each policy alternative.

Increasing the efficiency of a product often increases its average installed cost. However

, operating costs generally decrease because energy consumption declines. DOE therefore

calculated an NPV for each non

- regulatory alternative in the same way it did for the proposed

standard s. In some policy scenarios, increases in total installed cost are mitigated by government

rebates or tax credits. Because DOE assumed that rebates and tax credits would be re- paid in some way (such as additional taxes) DOE did not include the total national amount of rebates or tax credits as a benefit when calculating national NPV. DOE’s analysis also excluded any administrative costs for the non-regulatory policies; including such costs would decrease the NPVs slightly.

The following are key measures for evaluating the impact of each alternative.

• National Energy Savings , given in quadrillion Btus (quads), describes the cumulative national primary energy savings for equipment purchased during the 30- year analysis period starting from the effective date of the poli cy (2019-2048 ).

• Net

- Present Value

, represents the value of net monetary savings in 2013, expressed in 2012$, from equipment purchased during the 30- year analysis period starting from the effective date of the policy ( 2019-2048 ). DOE calculated the NPV as the difference between the present value of installed product cost and operating expenditures in the base case and the present value of those costs in each policy case. DOE calculated operating expenses (including energy costs) f or the life of the product.

17.2.2 Assumptions Regarding Non- Regulatory Policies

The effects of non -regulatory policies are by nature uncertain, because they depend on program implementation

, marketing efforts , and on consumers’ responses to a program. Because the projected effects depend on assumptions regarding the rate of consumer participation, they

a T he base case for the NIA is a market

- weighted average of units at several efficiency levels.

17-2are subject to more uncertainty than are the impacts of mandatory standards, which DOE

assumes will be met with full compliance. To increase the robustness of the analysis, DOE conducted a literature review regarding each non -regulatory policy and consulted with recognized experts to gather information on similar incentive programs that have been implemented in the United States. By studying experiences wi th the various types of programs, DOE sought to make credible assumptions regarding potential market impacts. Section 17.3 presents the sources DOE relied on in developing assumptions about each alternative policy and reports DOE’s conclusions as they affected the assumptions that underlie the modeling of each policy.

Each non-regulatory policy that DOE considered would improve the average efficiency

of CUACs and C UHPs marketed relative to their base case efficiency scenario (which involves no new regulatory action). The analysis considered that each alternative policy would induce consumers to purchase units h aving the same efficiency level as required by the propo sed standards (the target level ). As opposed to the standards case, however, the alternative policy cases may not lead to 100 percent market penetration of units that meet the target level

.

Table 17.2.1 shows the efficiency level stipulated in the proposed T rial S tandard L evel (TSL) for CUACs and CUHPs

.

Table 17.2.1 Proposed Efficiency Levels for CUACs and CUHPs

Equipment Class Efficiency Levels ( I EER )

for Trial Standard Level 3

Commercial Unitary Air Conditioners

CUAC ≥65,000 Btu/h and <135,000 Btu/h 14.8

CUAC ≥135,000 Btu/h and <240,000 Btu/h 14.2

CUAC ≥240,000 Btu/h and <760,000 Btu/h 13.5
Commercial Unitary Heat Pumps

CUHP ≥65,000 Btu/h and <135,000 Btu/h 14.6

CUHP ≥135,000 Btu/h and <240,000 Btu/h 13.7

CUHP ≥240,000 Btu/h and <760,000 Btu/h 13 .0

DOE assumed that the effects of non- regulatory policies would last from the effective date of standards—2019—through the end of the analysis period, which is 2048.

17.2.3 Policy Interactions

DOE calculated the effects of each non -regulatory policy separately from those of the other policies. In practice, some policies are most effective when implemented in combinati on, such as voluntary efficiency targets implemented with consumer rebates or tax credits . However, DOE attempted to make conservative assumptions to avoid double-counting policy impacts. The resulting policy impacts are not additive , and t he combined effect of several or all policies cannot be inferred from summing their results.

17-3Section 17.4 presents graphs that show the market penetration estimated under each nonregulatory policy for each CUAC and CUHP equipment class

.

17.3 NON

- REGULATORY POLICY ASSUMPTIONS

The following subsections describe DOE’s analysis of the impacts of the five nonregulatory policy alternatives to proposed standard s for CUACs and CUHPs

. (Because the alternative of No New Regulatory Action has no energy or economic impacts, essentially representing the NIA base case, DOE did not perform any additional analysis for that alternative.) DOE developed estimate s of the market penetration of high

- efficiency equipment both with and without each of the non-regulatory policy a lternatives

.

17.3.1 No New Regulatory Action

The case in which no new regulatory action is taken with regard to the energy efficiency

of CUACs and CUHPs constitutes the base case, as described in Chapter 10, National Impact Analysis. The base case provides the basis of comparison for all other policies. By definition, no new regulatory action yields zero NES and an NPV of zero dollars.

17.3.2 Consumer Rebates

DOE considered th e scenario in which the Federal government would provide financial

incentives in the form of rebate s to consumers for purchasing energy- efficient appliances

. This policy provides a consumer rebate for purchasing a CUAC or CUHP that operate s at (or above) the same efficiency level as the one stipulated for t he proposed TSL ( target level ).

17.3.2.1 Methodology

To inform its estimate of the market impacts of consumer rebates, DOE performed a thorough nationwide search for existing rebate programs for air -cooled unitary air conditioners and heat pumps with cooling capacity between 65,000 Btu/h and 760,000 Btu/h. I t gathered data on utility and agency rebates throughout the nation.

DOE based its evaluation methodology for consumer rebates on a comprehensive study of California’s potential for achieving energy efficiency . This study, performed by XENERGY,
Inc., b summarize d experiences with various utility rebate programs. 1 XENERGY’s analytical

method utilized graphs, or penetration curves
, that estimate the market penetration of a technology based on its benefit/cost (B/C) ratio. DOE consulted with experts and reviewed other methods of estimating the effect of consumer rebate programs on the market penetration of

efficient technolo gies. The other methods, developed after the referenced XENERGY report was published, 2

, 3

, 4

, 5

, 6

, 7 used different approaches: other economic parameters (e.g., payback period), expert surveys, or model calibration based on specific utility program data rather than

multi

- utility data . Some models in use by energy efficiency program evaluation experts were so

client

- specific that generic relationships between economic parameters and consumer res ponse
could not be established. 5 DOE decided that the most appropriate available method for this RIA

b XENERGY is now owned by KEMA, Inc. ( www.kema.com )

17-4analysis was the XENERGY approach of penetration curves based on B/C ratio, which incorporates lifetime operating cost savings.

XENERGY’s model estimates market impacts induced by financial incentives based on the premise that two types of information diffusion drive the adoption of new technologies.

Internal sources of information encourage consumers to purchase new products prima rily through word-of-mouth from early adopters. External sources affect consumer purchase decisions through marketing efforts and information from outside the consumer group. Appendix 17-A, Section 17-A.4.1, contains additional details on internal and external information

diffusion.

XENERGY’s model equation accounts for the influences of both internal and external sources of information by superimposing the two components. Combining the two mechanisms for information diffusion, XENERGY’s model generates a set of penetration (or implementation)

curves for a policy measure. XENERGY calibrated the curves based on participation data from utility rebate programs. The curves illustrate the increased penetration (i.e., increased market share) of efficient products driven by consumer response to changes in B/C ratio induced by rebate programs. The penetration curves depict various diffusion patterns based on perceived barriers (from “no barriers ” to “ extremely high barriers ”) to consumer purchase of highefficiency products.

DOE adjusted the XENERGY penetration curves based on expert advice founded on more recent utility program experience. 5

, 8 DOE al so used the interpo lation method pr esented in Blum et al ( 2011) 9 to create penetration curves based on relationships between the actual base case market penetrations and actual B/C ratios

.

DOE modeled the effects of a consumer rebate policy for CUACs and CUHPs by determining the increase in market penetration of equipment meeting the target level relative to their market penetration in the base case. It did this using the interpolated penetration curve

crea ted for each CUAC and CUHP based on the methodology presented in Blum et al (2011) 9 to best reflect the market barrier level faced by each equipment class. S ection 17.3.2.2 shows the interpolated curves used in the analysis.

17.3.2.2 Analysis

For each CUAC and CUHP equipment class it analyzed , DOE estimated t he effect of increasing its B/ C ratio via a rebate that would pay part of the increased installed cost of a unit

that met the target efficiency level comp ared to one meeting the baseline efficiency level

. c DOE

based the rebate amount s on a sample of utility and agency rebate programs for air -cooled unitary air conditioners and heat pumps with cooling capacity between 65,000 Btu/h and 760,000

Btu/h . DOE gathered data on 602 rebates initiated by 86 utilities or agencies in various States. (Appendix 17- A, S ection 17- A.5

, identifies the rebate programs .) The rebate programs cover a

c The baseline technology for each product class is defined in the e ngineering analysis, C hapter 5, as the technology that represents the basic characteristics of products in that class . A baseline unit typically is one that just meets current Federal energy conservation standards and provides basic consumer utility

.

17-5wide range of tonnage and efficiency levels. To represent the rebate level fo r each CUAC and

CUHP equipment class , DOE performed statistical analysis on the data it collected . Appendix

17- A, Section 17- A. 5, describes the regression model DOE used and the corresponding results

for each equipment class

. DOE assumed that rebates would remain in effect at the same levels throughout the analysis period (2019–2048 ).

For each equipment class

, DOE first calculated the B/C ratio without a rebate using the

difference in total incremental installed costs (C) and lifetime operating cost savings (B) between

the unit meeting the target level and the baseline unit. It then calculated the B/C ratio given a rebate for the unit meeting the target efficiency level

. Because the rebate reduced the incremental cost, the unit receiving the rebate had a l arger B/C ratio. Table 17.3.1 and Table 17.3.2 show respectively the effect of con sumer rebates on the B/C ratio s for CUACs and CUHPs

. The B/C values for units given rebates re fer to the efficiency level to which the rebate would apply.

Table 17.3.1 Effect of Consumer Rebates on Benefit/Cost Ratios of CUACs
≥65,000 Btu/h and

<135,000 Btu/h ≥135,000 Btu/h and <240,000

Btu/h ≥240,000 Btu/h and <760,000

Btu/h

B/C Ratio Without Rebate 84.4 45.8 116.9

Rebate Amount (2012$) 657.92 1245.84 2328.33

B/C Ratio With Rebate 121.3 66.6 217.2

Calculated Market Barrier Curve xHigh xHigh xHigh

Table 17.3.2 Effect of Consumer Rebates on Benefit/Cost Ratios of CUHPs
≥65,000 Btu/h and

<135,000 Btu/h ≥135,000 Btu/h and <240,000

Btu/h ≥240,000 Btu/h and <760,000

Btu/h

B/C Ratio Without Rebate 4.0 1.9 5.9

Rebate Amount (2012$) 628.92 1152.83 2142.30

B/C Ratio With Rebate 5.7 2.6 10.3

Calculated Market Barrier Curve xHigh xHigh xHigh

DOE used the se B/C ratios along with the penetration curves shown in and to estimate the percentage of consumers who would purchase CUACs and CUHPs that meet the target level

both with and without a rebate incentive. The penetration curves calculated by DOE to represent

the market behavior for CUACs and CUHPs are indicated in Figure 17.3.1 and Figure 17.3.2 respectively

.

17-6Figure 17.3.1 Market Penetration Curve s for CUACs [Top to down: 65,000-135,000 Btu/h; 135,000-240,000 Btu/h; 240,000-760,000 Btu/h]

17-7Figure 17.3.2 Market Penetration Curve s for CUHPs [Top to down: 65,000-135,000 Btu/h; 135,000-240,000 Btu/h; 240,000-760,000 Btu/h]

For each equipment class, DOE next estimated the percent increase s represented by the change in penetration rate shown on the corresponding penetration curve. It then added this

17-8percent increase to t he market share of units that meet the target level in the base case to obtain

the mar ket share of units that meet the target level in the rebate policy case. Table 17.3.3 and

Table 17.3.4 summarize the market shares in 2019 for CUACs and CUHPs respectively . DOE

used the resulting annual increase s in market shares as inputs t o represent the rebate policy case

s cenario in its NIA

- RIA model. Appendix 17-A, Section 17-A.2, shows the annual market share increases due to this policy. Section 17.4 presents the resulting market penetration trends for the policy case of consumer rebates for CUACs and CUHPs

.

Table 17.3.3 Market Penetrations in 2019 Attributable to Consumer Rebates for CUACs (TSL 3 )
≥65,000 Btu/h and <135,000 Btu/h ≥135,000 Btu/h and <240,000 Btu/h ≥240,000 Btu/h and <760,000 Btu/h

Base

- Case Market Share 0.3% 0.2% 1.9% Policy Case Market Share 3.2% 5.9% 4.7%

Increased Market Share 2.9% 5.8% 2.8%

Table 17.3.4 Market Penetrations in 2019 Attributable to Consumer R ebates for CU HP s (TSL 3 )
≥65,000 Btu/h and <135,000 Btu/h ≥135,000 Btu/h and <240,000 Btu/h ≥240,000 Btu/h and <760,000 Btu/h

Base

- Case Market Share 0.3% 0.2% 1.9% Policy Case Market Share 1.4% 0.4% 6.0%

Increased Market Share 1.1% 0.2% 4.2%

17.3.3 Consumer Tax Credits

DOE estimated the effects of tax credits on consumer purchases based on its previous analysis of consumer participation in tax credits. DOE supported its approach using data from Oregon State’s tax credit program for energy

- efficient appliances. DOE also incorporated previous research that disaggregated the effe ct of rebates and tax credits into a direct price effect

, which deriv es from the savings in purchase price

, and an announcement effect , which is independent of the amount of the incentive. 10

, 11 The announcement effect derives from the credibility that a technology receives from being included in an incentive program, as well as changes in product marketing and modifications in markup and pricing. DOE assumed that the rebate and consumer tax credit policies would encompass both dire ct price effect s and

announcement effect s, and that half the increase in market penetration associated with either policy would be due to the direct price effe ct and ha lf to the announcement effect.

In estimat ing the effects of a tax credit on purchases of equipment that meet new efficiency standards, DOE assumed the amount of the tax credit would be the same as the corresponding rebate amount discussed above.

DOE estimated that fewer consumers would participate in a tax credit program than would take advantage of a rebate. Research has shown that the delay required for a consumer to receive a tax credit, plus the added time and cost in preparing the tax return, make a tax credit

17-9incentive less effective than a rebate received at the time of purchase. Based on previous analyses, DOE assumed that only 60 percent of the consumers who would take advantage of a rebate would take advantage of a tax credit. 12

In preparing its assumptions, DOE also reviewed other tax credit programs that have been offered at both the Federal and State levels for energy

- efficient appliances.

The Energy Policy Act of 2005 (EPACT 2005) included Federal tax credits for

consumers who purchase energy

- efficient products, including air-conditioners. 13 Those tax

credits were in effect in 2006 and 2007, expired in 2008, were reinstated for 2009–2010 by the

Ameri can Recovery and Reinvestment Act of 2009 ( ARRA ), extended by Congress for 2011

with some modifications , and expired at the end of 2011. 14

, 15 The American Taxpayer Relief Act of 2012 extended, with some modifications, residential tax credits for air conditioners, heat pumps, furnaces, and water heaters placed in service between January 1, 2012 and December 31,

2013. 16 DOE r eviewed Internal Revenue Service data on the numbers of taxpayers who claimed the tax credits during tax years 2006 and 2007. DOE also reviewed data from an earlier Federal energy conservation tax credit program in place in the 1980s. However, DOE did not find data specific enough to commercial air -conditioners to warrant adjusting its analysis method for the Consumer Tax Credits policy case. Appendix 17- A, Section 17- A.6 .1, contains more information

on Federal consumer tax credits.

DOE also reviewed its previous analysis of Oregon’s tax credits for clothes washers to provide support for its assumptions. 17 In that previous analysis, DOE compared the market

shares of ultra -high efficiency (UHE) residential clothes washers in Oregon, which offered both

State tax credits and utilit y rebates, with those in Washington State, which offered only utility rebates during the same period. Based on this analysis, DOE estimated that in Oregon the impact of tax credits was 62 percent of the impact of rebates for UHE clothes washers having equi valent efficiency. This finding supports its original assumption that participation in a tax credit program would be about 60 percent of participation in a rebate program. Additional discussion of State

tax credits for Oregon and other states is in Appendix 17- A, Section 17- A.6 .3.

DOE applied the assumed 60 percent participation described above to the penetration rates estimated for the rebate policy to estimate penetration rates attributable to consumer tax credits. In doing so, DOE incorporated the assumptions for consumer response to financial

incent ives from the penetration curves calculated for CUACs and CUHPs

.

Table 17.3.5 and Table 17.3.6 summarize DOE’s assumptions for CUACs and CUHPs

r egarding the market penetration of units in 2019 tha t meet the target efficiency level given a

consumer tax credit.

17-10Table 17.3.5 Market Penetrations in 2019 Attributable to Consumer Tax Credits for CUACs (TSL 3 )
≥65,000 Btu/h and <135,000 Btu/h ≥135,000 Btu/h and <240,000 Btu/h ≥240,000 Btu/h and <760,000 Btu/h

Base

- Case Market Share 0.3% 0.2% 1.9% Policy Case Market Share 2.0% 3.6% 3.6%

Increased Market Share 1.8% 3.5% 1.7%

Table 17.3.6 Market Penetrations in 2019 Attributable to Consumer Tax Credits for CUHPs (TSL 3)
≥65,000 Btu/h and <135,000 Btu/h ≥135,000 Btu/h and <240,000 Btu/h ≥240,000 Btu/h and <760,000 Btu/h

Base

- Case Market Share 0.3% 0.2% 1.9% Policy Case Market Share 0.9% 0.3% 4.4%

Increased Market Share 0.7% 0.1% 2.5%

DOE assumed that this policy would transform the market permanently, so that the

increase in market share seen in the first year of the program would be maintained throughout the forecast period. The increased market shares attributable to consumer tax cre dits shown in Table

17.3.5 and Table 17.3.6 were used as inputs in the NIA

- RIA model. Appendix 17-A, Section 17- A.2, shows the annual market share increases due to this policy. Section 17.4 presents the resulting market penetration tre nds for the policy case of consumer tax credits for CUACs and

CUHPs that meet the target efficiency level

.

17.3.4 Manufacturer Tax Credits

To analyze the potential effects of a policy that offers tax credits to manufacturers that produce CUACs or CUHPs that meet the target efficiency level, DOE assumed that a manufacturer tax credit would lower the consumer’s purchase cost by an amount equivalent to that provided by the consumer rebates or tax credits described above. DOE further assumed that manufacturers would pass on some of their reduced costs to consumers, causing a direct price effect. DOE assumed that no announcement effect would occur, because the program would not

be visible to consumers. d Because the direct price effect is approximately equiva lent to the

announcement effect

, 10 DO E estimated that a manufacturer tax credit would induce half the number of consumers assumed to take advantage of a consumer tax credit to purcha se more efficient equipment. Thus the assumed participation rate is equal to 30 percent of the number of consumers who would participate in a rebate program.

DOE attempted to in vestigate manufacturer response to the Energy Efficient Appliance Credits for manufacturers mandated by EPACT 2005. 18 Those manufacturer tax credits have

d Note that this is a conservative assumpt ion, since it is possible that manufacturers or utility/agency efficiency programs might promote the models for which manufacturers increase production due to the tax credits, which in turn might induce some announcement effect. However, DOE found no data on such programs on which to base an estimate of the magnitude of this possible announcement effect on consumer behavior.

17-11been in effect for dishwashers, clothes washers and refrigerators produced beginning in 2009.

DOE was unable to locate data from the Internal Revenue Service or other sources on manufacturer response to the Federal credits. Appendix 17-A, Section 17- A.6 .2, presents deta ils

on Federal manufacturer tax credits.

DOE applied the assumption of 30 percent participation to the penetration rates predicted for the rebate policy to estimate the effects of a manufacturer tax credit policy. In doing so, the Department incorporated the assumptions for consumer response to financial incentives from the penetration curves calculated for CUACs and CUHPs

.

Table 17.3.7 and Table 17.3.8 summarize DOE’s assumptions for CUACs and CUHPs respectively regarding the market pen etration of units in 2019 meeting the target efficiency level given a manufacturer tax credit.

Table 17.3.7 Market Penetrations in 2019 Attributable to Manufacturer Tax Credits for CUACs (TSL 3)
≥65,000 Btu/h and <135,000 Btu/h ≥135,000 Btu/h and <240,000 Btu/h ≥240,000 Btu/h and <760,000 Btu/h

Base

- Case Market Share 0.3% 0.2% 1.9% Policy Case Market Share 1.2% 1.9% 2.7%

Increased Market Share 0.9% 1.7% 0.8%

Table 17.3.8 Market Penetrations in 2019 Attributable to Manufacturer Tax Credits for CUHPs (TSL 3 )
≥65,000 Btu/h and <135,000 Btu/h ≥135,000 Btu/h and <240,000 Btu/h ≥240,000 Btu/h and <760,000 Btu/h

Base

- Case Market Share 0.3% 0.2% 1.9% Policy Case Market Share 0.6% 0.2% 3.1%

Increased Market Share 0.3% 0.1% 1.3%

DOE assumed that this policy would transform the market permanently, so that the

increase s in market share seen in the first year of the program would be maintained throughout the forecast period. The increased market shares attributable to a manufacturer tax credit shown

in Table 17.3.7 and Table 17.3.8 were used as inputs in the NIA

- RIA model. Appendix 17-A,

Section 17-A.2, shows the an nual market share increases due to this policy

. Section 17.4 presents the resulting market penetration trends for the policy case of manufacturer tax credits for CUACs

and CUHPs

.

17.3.5 Voluntary Energy Efficiency Targets

For each equipment class , DOE assumed that voluntary energy efficiency targets would be achieved as manufacturers gradually stop producing units that operate below the target efficiency level . DOE assumed that the impetus for phasing out production of low- efficiency units would be a program similar to the ENERGY STAR labeling program, conducted by the Environmental Protection Agency (EPA) and DOE, and the Better Building Alliance program, conducted by DOE in conjunction with industry partners . The ENERGY STAR program specifie s the minimum energy efficienc ies that various products must have to receive the

17-12ENERGY STAR label . ENERGY STAR encourages consumers to purchase efficient products via marketing that promotes consumer label recognition, various incentive programs that adopt

the ENERGY STAR specifications, and manufacturers’ promotion of their qualifying appliances. The Better Building Alliance introduced in 2011 the High Performance Rooftop Unit Challe nge

. The initiative is based on a design specification for 10- ton capacity commercial air conditioning units “expected to reduce energy use by as much as 50 percent compared to the current ASHRAE 90.1 standard.” This voluntary energy efficiency target prog ram, similar to the ENERGY STAR labeling program, is designed to encourage manufacturers to develop higher efficiency commercial air conditioners as a result of commercial buyer’s support for high

performance equipment manufactured in compliance with the a forementioned specification. 19

DOE believes that informational incentive programs

– like ENERGY STAR , the High Performance Rooftop Unit Challenge, or any other labeling program sponsored by industry or other organizations – are likely to reduce the market barriers to more efficient equipment over

time . During the rebate analysis, when assessing the B/C ratio and market penetration in the base

case for each equipment class, DOE observed that the market barriers for the six equipment

classes are extremel y hig h. DOE estimates that voluntary energy efficiency targets could reduce these barriers to a moderate level over 10 years , and followed the methodology presented by Blum et al ( 2011) 9 to evaluate the effects that such a reduction in market barriers have on the market penetration of each equipment class of CUAC and CUHP . The methodology relies on interpolated market penetration curves to calculate

– given a B/C ratio

– how the market penetration of more efficient u nits increases as the market barrier level to those units decreases.

Table 17.3.9 and Table 17.3.10 summarize DOE’s assumptions for CUACs and CUHPs respectively regarding the market penetration of units in 2019 meeting the target efficiency level

under the influence of a voluntary energy efficiency targets program

.

Table 17.3.9 Market Penetrations in 2019 Attributable to Voluntary Energy Efficiency Targets for CUACs (TSL 3 )
≥65,000 Btu/h and

<135,000 Btu/h ≥135,000 Btu/h and <240,000 Btu/h ≥240,000 Btu/h and <760,000 Btu/h

Base

- Case Market Share 0.3% 0.2% 1.9% Policy Case Market Share 46.7% 40.0% 48.7%

Increased Market Share 46.4% 39.8% 46.8%

Table 17.3.10 Market Penetrations in 2019 Attributable to Voluntary Energy Efficiency Targets for CUHPs (TSL 3 )
≥65,000 Btu/h and

<135,000 Btu/h ≥135,000 Btu/h and <240,000 Btu/h ≥240,000 Btu/h and <760,000 Btu/h

Base

- Case Market Share 0.3% 0.2% 1.9% Policy Case Market Share 1.9% 0.3% 4.1%

Increased Market Share 1.6% 0.2% 2.2%

DOE estimated that the program developed in support of the voluntary energy efficiency

targets policy would increase market shares of ef ficient units . Appendix 17-A, Section 17-A.2, shows the annual market share increases due to this policy used as inputs to the NIA-RIA model.

17-13Section 17.4 presents the resulting market penetration trends for the policy case of voluntary energy efficiency targets for CUACs and CUHPs.

17.3.6 Bulk Government Purchases

Bulk government purchases can lead to Federal, State, and local governments purchasing large quantities of equipment that meet the target efficiency level . Combining the market

demands of mu ltiple public sectors also can provide a market signal to manufacturers and

vendors that some of the ir largest customers seek equipment that meet an efficiency target at favorable prices. Such a program also can induce “market pull,” whereby manufacturers and vendors would achieve economies of scale for high efficiency equipment.

Most of the previous bulk government purchase (procurement) initiatives at the Federal, State, and municipal levels have not tracked data on numbers of purchases or degree of compliance with procurement specifications. In many cases, procurement programs are decentralized, be ing part of larger State or regional initiatives. DOE based its assumptions regarding the effects of this policy on studies the Federal Energy Management Program (FEMP) performed regarding the savings potential of its procurement specifications for applian ces and other equipment. FEMP, however, does not track purchasing data, because of the complex range of purchasing systems, large number of vendors, and so on. States, counties, and municipalities have demonstrated increasing interest and activity in “green purchasing." Although many of the programs target office equipment, the growing infrastructure for developing and applying efficient purchasing specifications indicates that bulk government purchase programs are

feasible

. 20

, 21

DOE assumed that government agencies would administer bulk purchasing programs for

CUACs and CUHPs. The policy also would be incorporated into the FEMP program. FEMP currently has procurement specifications in place for CUACs and CUHPs based on ENERGY STAR requirements. These specifications however do not cover all covered equipment classes. 22

D OE assumed t he program would be reviewed to meet the efficien cy levels corresponding to

TSL 3 for all covered equipment classes

. DOE reviewed its own previous research on the potential for market transformation through bulk government purchases. Its major study analyzed several scenarios based on the assumption that 20 percent of Federal equipment purchas es in 2000 already incorporated energy efficiency requirements based on FEMP guidelines. One scenario in the DOE report showed energy efficient purchasing ram ping up during 10 years from 20 percent to 80 percent of all Federal purchases. 23 Based on this study , DOE estimated that a bulk government purchase program instituted within a 10-year period would result in at least 80 percent of governmentpurchased CUACs and CUHPs meeting target efficiency levels.

DOE assumed that bulk government purchases would affect the subset of governmentowned commercial buildings cooled by packaged air -conditioners. This subset consist s primarily

of government-owned buildings used for offices, public assembly, education, food s ervice and

other services. DOE defined this subset based on the primary building activities used for the energy use analysis (see Chapter 7 ). DOE used t he 2007 Commercial Buildings Energy Consumption Survey ( CBECS 2007 ) to calculate the percentage of the total commercial floor space cooled by packaged air -conditioners that corresponds to government -owned buildings. The

17-14calculat ion resulted in 22 percent . DOE assumed that percentage as the share of the market to which this policy would apply.

DOE estimated that, starting in 2019, each year of a bulk government purchase policy would result in an increasing percent of shipments of government- purchased units beyond the base case would meet target efficiency levels. DOE estimated that within 10 years ( by the end of 2028), bulk government purchasing programs would result in 80 percent of the CUAC and

CUHP market for government-owned commercial buildings meeting the target level . DOE modeled the bulk government purchase program assuming that the market share for each product achieved in 2028 would be maintained throughout the rest of the analysis period. Appendix 17- A, Section 17-A.2, shows the annual market share increases due to this policy used as inputs to

the NIA -RIA model. Section 17.4 below presents the resulting mark et penetration trends for the policy case of bulk government purchase of CUACs and CUHPs

.

Table 17.3.11 and Table 17.3.12 summarize DOE’s assumptions for CUACs and CUHPs respectively regarding the market p enetration of units in 2019 meeting the target efficiency level under the influence of a bulk government purchasing program

Table 17.3.11 Market Penetrations in 2019 Attributable to Bulk Government Purchases for CUACs (TSL 3 )
≥65,000 Btu/h and <135,000 Btu/h ≥135,000 Btu/h and <240,000 Btu/h ≥240,000 Btu/h and <760,000 Btu/h

Base

- Case Market Share 0.3% 0.2% 1.9% Policy Case Market Share 2.0% 1.9% 3.6%

Increased Market Share 1.8% 1.8% 1.7%

Table 17.3.12 Market Penetrations in 2019 Attributable to Bulk Government Purchases for CUHPs (TSL 3 )
≥65,000 Btu/h and <135,000 Btu/h ≥135,000 Btu/h and <240,000 Btu/h ≥240,000 Btu/h and <760,000 Btu/h

Base

- Case Market Share 0.3% 0.2% 1.9% Policy Case Market Share 2.0% 1.9% 3.6%

Increased Market Share 1.8% 1.8% 1.7%

DOE estimated that the program developed in support of the bulk government purchases policy would increase market shares of ef ficient units . Appendix 17- A, Section 17-A.2, shows the annual market share increases due to this policy used as inputs to the NIA-RIA model.

Section 17.4 presents the re sulting market penetration trends for the policy case of bulk government purchases for CUACs and CUHPs.

17-1517.4 IMPACTS OF NON

- REGULATORY ALTERNATIVES

Figure 17.4.1 and Figure 17.4.2 show the effects of each non -regulatory policy on market penetration of CUACs and C UHPs respectively. R elative to the base case, the policy cas es increase the market sha re of units that meet the target level. Recall that the proposed standards (not shown in the figures) would result in a 100- percent market penetration of units that meet the target efficiency level.

17-16Figure 17.4.1 Market Penetration of CUACs in Policy Cases [Top to down: 65,000-135,000 Btu/h; 135,000-240,000 Btu/h; 240,000-760,000 Btu/h] 0% 10% 20% 30% 40% 50% 60% 70%201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045204620472048 Consumer Rebate

Consumer Tax Credit

Manufacturer Tax Credit

Voluntary Efficiency Targets

Govt Bulk Purchasing

Base Case

0% 10% 20% 30% 40% 50% 60% 70%201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045204620472048 Consumer Rebate

Consumer Tax Credit

Manufacturer Tax Credit

Voluntary Efficiency Targets

Govt Bulk Purchasing

Base Case

0% 10% 20% 30% 40% 50% 60% 70%201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045204620472048 Consumer Rebate

Consumer Tax Credit

Manufacturer Tax Credit

Voluntary Efficiency Targets

Govt Bulk Purchasing

Base Case

17-17Figure 17.4.2 Market Penetration of CUHPs in Policy Cases [Top to down: 65,000-135,000 Btu/h; 135,000-240,000 Btu/h; 240,000-760,000 Btu/h] 0% 10% 20% 30% 40% 50% 60% 70%201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045204620472048 Consumer Rebate

Consumer Tax Credit

Manufacturer Tax Credit

Voluntary Efficiency Targets

Govt Bulk Purchasing

Base Case

0% 10% 20% 30% 40% 50% 60% 70%201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045204620472048 Consumer Rebate

Consumer Tax Credit

Manufacturer Tax Credit

Voluntary Efficiency Targets

Govt Bulk Purchasing

Base Case

0% 10% 20% 30% 40% 50% 60% 70%201920202021202220232024202520262027202820292030203120322033203420352036203720382039204020412042204320442045204620472048 Consumer Rebate

Consumer Tax Credit

Manufacturer Tax Credit

Voluntary Efficiency Targets

Govt Bulk Purchasing

Base Case

17-18Table 17.4.1 and Table 17.4.2 show NES and NPV for five non-regulatory polic ies analyzed in detail for CUACs and CU HPs . T he target level for each policy equal s the efficiency

level proposed in TSL 3 for each equipment class

.

The case in which no regulatory action is taken with regard to CUACs and CUHPs

constitute s the base case (or "No New Regulatory Action" scenario ), in which energy and

economic savings are zero by definition. For comparison, the tables include the impacts of the proposed standards. Energy savings are given in quadrillion British thermal units (quads). The

NPV s shown in Table 17.4.1 and Table 17.4.2 are based on two discount rates, 7 percent and 3

percent

.

The poli cies with the highest projected cumulative energy and economic savings are voluntary energy efficiency targets and bulk government purchases . Market barriers to more efficient CUACs and CUHPs have been estimated to be extremely high , and this would prevent rebates and tax credits to lead to greater market penetration. Voluntary energy efficiency targets were assumed to reduce these barriers and, therefore, to lead to a more significant market penetration of energy efficient units. Government bulk purchases are not subject to market

barriers and would affect 22 percent of the market lead ing to market penetration s that are greater than the ones resulting from rebates and tax credits.

Table 17.4.1 Impacts of Non- Regulatory Alternatives for CUACs (TSL 3 )

Policy Alternative Primary Energy Savings

quads Net Present Value*

billion 2012 $

7% Disc

. Rate 3% Disc

. Rate

Consumer Rebates 0.032 0.032 0.128

Consumer Tax Credits 0.019 0.019 0.077

Manufacturer Tax Credits 0.010 0.010 0.038
Voluntary Energy Efficiency Targets 0.666 0.651 2.635

Bulk Government Purchases 0.136 0.123 0.527
Proposed Standards 10.713 15.095 46.712

Table 17.4.2 Impacts of Non- Regulatory Alternatives for CUHPs (TSL 3 )

Policy Alternative Primary Energy Savings

quads Net Present Value*
billion 2012$

7% Disc

. Rate 3% Disc

. Rate

Consumer Rebates 0.001 0.002 0.006

Consumer Tax Credits 0.001 0.001 0.004

Manufacturer Tax Credits 0.000 0.000 0.002
Voluntary Energy Efficiency Targets 0.032 0.033 0.126

Bulk Government Purchases 0.007 0.007 0.028
Proposed Standards 0.951 1.446 4.126

17-19REFERENCES

1. Rufo, M. and F. Coito, California's Secret Energy Surplus: The Potential for Energy Efficiency, 2002. XENERGY Inc. (Last accessed March, 2013.)

< http://www.ef.org/documents/Secret_Surplus.pdf >

2. ICF International

, Arizona Public Service Energy Efficient Market Potential Study , 2007. Fairfax, VA.

3. Mosenthal, P., Telephone Conversation with Barbara Atkinson of LBNL, personal

communication. Optimal Energy

. January, 2008.

4. Lee, A., Telephone Conversation with Barbara Atkinson of LBNL, personal

communication. Quantec, LLC

. January, 2008.

5. Rufo, M., Telephone Conversation with Barbara Atkinson of LBNL, personal

communication. Itron, Inc

. January 2008 and March 2009.

6. I tron Inc. and KEMA Inc., California Energy Efficiency Potential Study, 2008. (Last accessed March, 2013.) < http://calmac.org/publications/PGE0264_Final_Report.pdf >

7. Global Energy Partners LLC, AmerenUE Demand Side Management (DSM) Market Potential Study, 2010. (Last accessed March, 2013.)

< http://www.ameren.com/sites/aue/Environment/Renewables/Documents/AmerenUEVol ume2MarketResearchReport2.pdf >

8. Coito, F., E-mail to Barbara Atkinson of LBNL, personal communication. KEMA, Inc

. June, 2010.

9. Blum, H., Atkinson B., and Lekov, A., A Framework for Comparative Assessments of Energy Efficiency Policy Measures, May, 2011.

10. Lawrence Berkeley National Laboratory, Energy End -Use Forecasting: Analysis of Tax Credits for Efficient Equipment, 1998. LBNL Technology Evaluation Modeling and Assessment Group (Last acc essed March, 2013.)

< http://enduse.lbl.gov/Projects/TaxCredits.html >

11. Train, K., Customer Decision Study: Analysis of Residential Customer Equipment

Purchase Decisions , 1994. Prepared for Southern California Edison by Cambridge Systematics, Pacific Consulting Services, The Technology Applications Group, and California Survey Research Services.

12. U.S. Department of Energy: Office of Codes and Standards, Technical Support Document: Energy Efficiency Standards for Consumer Products: Refrigerators, Refrigerator-Freezers, and Freezers, Including Environmental Assessment and

17-20Regulatory Impact Analysis, November, 1995. Washington, DC. Report No. DOE/EE0064.

13. Energy Policy Act of 2005 , in 119 STAT. 594 Public Law 109-58. Section 1333, 26 USC
25C note, . 2005. http://www.gpo.gov/fdsys/pkg/BILLS-109hr6pcs/pdf/BILLS109hr6pcs.pdf

14. American Recovery and Rein vestment Act of 2009, 2009. Government Printing Office. (Last accessed March, 2013.) < http://frwebgate.access.gpo.gov/cgi

bin/getdoc.cgi?dbname=111_cong_bills&docid=f:h1enr.pdf >

15. Tax Incentives Assistance Project, Consumer Tax Incentives: Home Heating & Cooling Equipment., 2011. (Last accessed March, 2013.)

< www.ene rgytaxincentives.org/consumers/heating-cooling.php >

16. Tax Incentives Assistance Project, The Tax Incentives Assistance Project (TIAP), 2013. (Last accessed March, 2013.) < http://energytaxincentives.org/ >

17. U.S. Department of Energy - Energy Efficiency & Renewable Energy, Technical Support Document: Energy Efficiency Program for Consumer Products and Commercial and Industrial Equipment, Residential Dishwashers, Dehumidifiers, and Cooking Products,

and Commercial Clothes Washers , 2008. Washington, DC.

18. Tax Incentives Assistance Project, Manufacturers Incentives, (Last accessed March, 2013.) < http://energytaxincentives.org/builders/appliances.php >

19. Better Buildings Alliance, 2013. United States Department of Energy. (Last accessed September 11, 2013.) < http://www4.eere.energy.gov/alliance/activities/technology

solutions- teams/space

- conditioning/rtu >

20. Smith, N., Telephone conversation with Barbara Atkinson of LBNL, personal

communication. National Association of State Procurement Officials

. April, 2008.

21. Responsible Purchasing Network, (Last accessed March, 2013.)

< www.responsiblepurchasing.org/ >

22. Light Commercial Heating & Cooling Key Product Criteria, 2010. Energy Star. (Last accessed September 12, 2013.)

< http://www.energystar.gov/index.cfm?c=lchvac.pr_crit_lchvac >

23. Harris, J. and F. Johnson, Potential Energy, Cost, and CO2 Savings from EnergyEfficient Government Purchase

. ACEEE: Summer Study on Energy Efficiency in Buildings , 2000: pp. 147-166

17-21