CHAPTER 11: NATIONAL IMPACT ANALYSIS

TABLE OF CONTENTS

11.1 INTRODUCTION

......................................................................................................... 11-1 11.2 BASE CASE AND STANDARDS CASE FORECASTED EFFICIENCIES

.............. 11-2 11.3 NATIONAL ENERGY SAVINGS

............................................................................... 11-4 11.3.1 NES Definition

.......................................................................................................... ..... 11-4 11.3.2 NES Inputs

.............................................................................................................. ....... 11-5 11.3.2.1 Annual Energy Consumption per Unit

........................................................ 11-5 11.3.2.2 Shipments

..................................................................................................... 11-7 11.3.2.3 Equipment Stock

.......................................................................................... 11-8 11.3.2.4 National Annual Energy Consumption

........................................................ 11-8 11.3.2.5 Energy Site-to-Source Conversion Factors

.................................................. 11-8 11.4 NET PRESENT VALUE

............................................................................................... 11-9 11.4.1 NPV Definition

.......................................................................................................... .... 11-9 11.4.2 NPV Inputs

.............................................................................................................. ..... 11-11 11.4.2.1 Total Installed Cost per Unit

...................................................................... 11-11 11.4.2.2 Annual Operating Cost Savings per Unit

................................................... 11-12 11.4.2.3 Total Annual Installed Cost Increases

....................................................... 11-13 11.4.2.4 Total Annual Operatin g Cost Savings

....................................................... 11-14 11.4.2.5 Discount Factors

........................................................................................ 11-14 11.4.2.6 Present Value of Costs

............................................................................... 11-15 11.4.2.7 Present Value of Savings

........................................................................... 11-15 11.5 NES AND NPV RESULTS

......................................................................................... 11-15 11.5.1 NES and NPV Input Summary

.................................................................................... 11-15 11.5.2 NES Results

............................................................................................................. .... 11-16 11.5.3 Annual Costs and Savings

........................................................................................... 11-17 11.5.4 NPV Results

............................................................................................................. .... 11-18 11.6 IMPACT OF STANDARDS ON NATURAL GAS AND ELECTRICITY PRICES AND ASSOCIATED CONSUMER BENEFITS

.......................................... 11-19 11.6.1 Impact on Natural Gas Prices

...................................................................................... 11-19 11.6.2 Impact on Electricity Prices

......................................................................................... 11- 23

LIST OF TABLES

Table 11.2.1 Top-Loading Commercial Clothes Washers: Base Case and Standards Case Efficiency Distributions in 2013

............................................................... 11-3 Table 11.2.2 Front-Loading Commercial Clothe s Washers: Base Case and Standards Case Efficiency Distributions in 2013

............................................................... 11-4 Table 11.3.1 National Energy Saving Inputs

.......................................................................... 11-5

11-iTable 11.3.2 Top-Loading Commercial Clothes Washers: Base Case and Standards Case Shipment-Weighted Average Per-Unit Annual Energy and Water Consumption in 2013

......................................................................................... 11-6 Table 11.3.3 Front-Loading Commercial Clothe s Washers: Base Case and Standards Case Shipment-Weighted Average Per-Unit Annual Energy and Water Consumption in 2013

......................................................................................... 11-7 Table 11.3.4 Site-to-Source Conve rsion Factors for Electricity and Natural Gas

.................. 11-9 Table 11.4.1 Net Present Value Inputs

................................................................................. 11-11 Table 11.4.2 Top-Loading Commercial Clothes Washers: Base Case and Standards Case Shipment-Weighted Average Per-Un it Total Installed Costs in 2013

.... 11-12 Table 11.4.3 Front-Loading Commercial Clothe s Washers: Base Case and Standards Case Shipment-Weighted Average Per-Un it Total Installed Costs in 2013

.... 11-12 Table 11.4.4 Top-Loading Commercial Clothes Washers: Base Case and Standards Case Shipment-Weighted Average Pe r-Unit Annualized Repair Costs in 2013

.................................................................................................................. 11-13 Table 11.4.5 Front-Loading Commercial Clothe s Washers: Base Case and Standards Case Shipment-Weighted Average Pe r-Unit Annualized Repair Costs in 2013

.................................................................................................................. 11-13 Table 11.5.1 NES and NPV Inputs

....................................................................................... 11-16 Table 11.5.2 Commercial Clothe s Washers: Cumulative NES and NWS Results

.............. 11-17 Table 11.5.3 Commercial Clot hes Washers: Cumulativ e NES and NWS Results Discounted at Seven Percen t and Three Percent

.............................................. 11-17 Table 11.5.4 Commercial Clothe s Washers: Cumulative NP V Results based on SevenPercent and Three-Percen t Discount Rates

...................................................... 11-19 Table 11.6.1 Impact of Max Tech Standard Levels on Natural Gas Consumption and Average Wellhead Pr ice in 2030

..................................................................... 11-21 Table 11.6.2 Implicit Natural Gas Inverse Pri ce Elasticity from Seven Energy Models

..... 11-23 Table 11.6.3 Impact of Max Tech Standard Levels on Electricity Consumption and Price in 2030

.................................................................................................... 11-25

LIST OF FIGURES

Figure 11.5.1 Non-Discounted Annual Insta lled Cost Increases and Annual Operating Cost Savings for Top-Loading Clothes Washers, TSL 3

................................. 11-18 Figure 11.6.1 Wellhead Natural Gas Price Change in 2030 Resulting from Reduced Natural Gas Consumption Associated w ith Max Tech Standard Level

.......... 11-21 Figure 11.6.2 Inverse Natural Gas Price Elas ticity from NEMS Analysis for Commercial Clothes Washer Max Tech Standard Level

..................................................... 11-22 Figure 11.6.3 Electricity Price Change in 2030 Resulting from Reduced Electricity Consumption Associated with Max Tech Standard Level

............................... 11-24 Figure 11.6.4 Inverse Electricity Price Elas ticity from NEMS Analysis for Commercial Clothes Washer Max Tech Standard Levels

.................................................... 11-25

11-iiCHAPTER 11. NATIONAL IMPACT ANALYSIS

11.1 INTRODUCTION

This chapter describes the method for estima ting national impacts for commercial clothes washers, i.e., the quantity and net value to consumers of future national energy savings (NES) and water savings from possible tria l standard levels (TSL). Ch apter 9 provides identifies and describes the TSLs for commercial clothes washers. Results described here include: (1) national energy and water consumption and savings, (2) m onetary value of energy savings to the Nation due to TSLs, (3) increased total installed costs to the Nation due to TSLs, and (4) the net present value (NPV) of energy savings (difference betwee n value of energy savings and increased total installed costs). When NES is discussed thr oughout this chapter, it includes national water savings in addition to national energy savings.

DOE determined both the NES and NPV for all of the TSLs considered for commercial clothes washers. It performed all calculations for each of the considered products using a Microsoft Excel spreadsheet model, which is accessible on the Internet ( http://www.eere.energy.gov/buildings/appliance_standards / ). The spreadsheet model, referred to as the National Impact Analysis (NIA) Model, combined the calculations for determining the NES and NPV as well as input from the relevant Sh ipments Model. Details and instructions for using each of the NIA Models are provided in Appendix 11A.

The important facets of national energy c onsumption of include: (1) shipments of the equipment, (2) customer response to changes in total installed cost (i.e., purchase price plus installation costs), operating expense, and household income, and (3) voluntary programs promoting higher energy e fficiency of equipment.

Chapter 10 provides a detailed description of the Shipments Models that DOE used to forecast future purchases of co mmercial clothes washers. Include d in the discussion are detailed descriptions of consumers’ sensitivities to total installed cost, operating expense, and income (otherwise known as elasticities), and how DOE captured them within the model.

Concerning voluntary programs, although they may increase the share of energy efficient equipment prior to the implementation date of any new TSLs, DOE was not able to obtain information that quantified how such programs aff ect equipment efficiencies on a national basis. Consequently, DOE did not explicitly incorporate the impact of market-based initiatives into the shipments forecasts detailed in Chapter 10.

This chapter presents information and results pertaining solely to CCWs. As described in section 1.3 of chapter 1 of this technical s upport document (TSD), DOE is continuing the rulemaking for energy conservation standards fo r microwave oven standby power, and analyses related to these products will be published in a separate TSD. In addition, DOE issued a final rule 1 adopting energy conservation standard s for conventional cooking products ( i.e. , cooktops

11-1and ovens) and microwave oven energy factor (EF), and details of the analyses for these products are contained in the corresponding final rule TSD. 2

11.2 BASE CASE AND STANDARDS CASE FORECASTED EFFICIENCIES

A key component of DOE’s estimates of NE S and NPV is the en ergy efficiencies forecasted over time for the base case (without ne w standards) and each of the standards cases (with new standards). The forecasted efficien cies represent the annual shipment-weighted energy efficiency of the products under consid eration over the forecast period (i.e., from the assumed effective date of a new standard to 30 years after the standard becomes effective). Because key inputs to the calculation of the NES and NPV are dependent on the estimated efficiencies, these efficiencies are of great importance to the analysis. In the case of the NES, the per-unit annual energy and water c onsumption is a direct function of product efficiency. Chapter 6, Energy and Water Use Dete rmination, describes how the per-unit energy and water consumption vary as a function of equipment effi ciency. With regard to the NPV, two inputs, the per-unit total installed cost and the pe r-unit annual operating cost, are dependent on efficiency. The per-unit total installed cost is a direct function of efficiency while the per-unit annual operating cost, because it is a function of the per-unit annu al and water consumption, is indirectly dependent on product efficiency. Se ction 8.2.1 of Chapter 8, Life-Cycle Cost (LCC) and Payback Period (PBP) Analysis, describes ho w per-unit total installed costs vary as a function of efficiency for each of the consider ed products. The above NES and NPV inputs, as well as all other inputs to the calculation of th e NES and NPV, are also discussed below in sections 11.3.1 and 11.4.1.

DOE based the development of the product efficiencies in the base case on the assignment of equipment efficien cies in the base case for the year 2009. In other words, DOE determined the distribution of product efficienci es currently in the marketplace to develop a shipment-weighted efficiency for the year 2009. Fo r a complete discussion of the assignment of efficiencies in the base case for each of the c onsidered products, refer to section 8.2.6 of Chapter 8, LCC and PBP Analysis.

Using the shipment-weight efficiency for the year 2009 as a starting point, DOE developed base case forecasted e fficiencies based on assumptions regarding future efficiency growth. For the period spanning 2009–2013 (2013 be ing the assumed effective date of a new standard), DOE assumed that there would be no growth in the shipment-weighted efficiency (i.e., no change in the distribution of product efficiencies). Because there are no historical data to indicate how product efficiencies have changed over time, DOE estimated that forecasted efficiencies remained frozen at the 2013 efficiency level until th e end of the forecast period (30 years after the assumed effective date—the year 2043). Although DOE recognizes the possibility that product efficiencies may change over time, DOE does not want to speculate as to how these product efficiencies may change without historical information.

11-2For its determination of sta ndards-case forecasted efficiencies, DOE assumed a “roll-up” scenario to establish the shipment-weighted efficiency for the year that standards are assumed to become effective (i.e., 2013). DOE assumed that product efficiencies in the base case that did not meet the standard under consid eration would “roll up” to meet the new standard level. Also, DOE assumed that all product efficiencies in the base case that were above the standard under consideration would not be affected. Using the shipment-weighted efficiency in the year 2013 as a starting point, DOE developed standards-case forecasted efficiencies based on assumptions regarding future efficiency growth. DOE made the same assumptions regarding standards-case efficiency growth rates as for the base case— namely, that forecasted efficiencies remain frozen at the 2013 efficiency level until the end of the fo recast period. By maintaining the same growth rate for forecasted efficiencies in the standards cas e as in the base case (i.e., zero growth), DOE retained a constant efficiency difference or ga p between the two cases over the length of the forecast period. Although frozen trends may not re flect what happens to ba se case and standards case product efficiencies in the future, DOE believes that maintaining a frozen efficiency difference between the base case and standards case provides a reasonable estimate of the impact that standards have on product efficiency. The NIA spreadsheet models for each of the considered products also incorporated user options of one percent and two percent for the growth rates of the base-case and standards-case forecasted efficiencies.

Tables 11.2.1 and 11.2.2 show the ba se case and TSL product efficiency distributions in the year 2013, corresponding to the modified energy factor (MEF) and water factor (WF) of toploading and front-loading clothes washing equipmen t, respectively, that DOE used in its national impact analysis for commercial clothes washers. The TSLs are comprised of the candidate standard levels analyzed in the LCC and PBP An alysis. The TSLs are also shown in Tables 11.2.1 and 11.2.2. Note that DOE considered all the ca ndidate standard levels in the TSLs that it developed for commercial clothes washers. Also included in the table below is the SWEF associated with the base case and each TSL.

Table 11.2.1 Top-Loading Commercial Clothes Washers : Base Case and Standards Case Efficiency Distributions in 2013

CSL TSL Efficiencies Market Shares

Base Case Trial Standard Level
MEF WF 1 2 3 4 5
Baseline ­ 1.26 9.50 64.8%

- - - - - 1 1, 2 1.42 9.50 33.8% 98.6% 98.6%

- - - 2 3, 4, 5 1.60 8.50 1.4% 1.4% 1.4% 100% 100% 100% SWEF (MEF) 1.32 1.42 1.42 1.60 1.60 1.60

SWEF (WF) 9.49 9.49 9.49 8.50 8.50 8.50

11-3Table 11.2.2 Front-Loading Commercial Clothes Washers: Base Case and Standards Case Efficiency Di stributions in 2013

CSL TSL Efficiencies Market Shares

Base Case Trial Standard Level
MEF WF 1 2 3 4 5
Baseline

- 1.72 8.00 3.5%

- ­ ­ - - 1 1 1.80 7.50 0.0% 3.5%

- - - - 2 2, 3 2.00 5.50 73.7% 73.7% 77.2% 77.2%

- - 3 4 2.20 5.10 22.8% 22.8% 22.8% 22.8% 100%

- 4 5 2.35 4.40 0.0% 0.0% 0.0% 0.0% 0.0% 100% SWEF (MEF) 2.04 2.04 2.05 2.05 2.20 2.35

SWEF (WF) 5.50 5.48 5.41 5.41 5.10 4.40

Based on the assumption of a zero-percent growth rate in the forecasted efficiencies for the base case and all standard s cases, DOE maintained the SWEFs shown above in Tables 11.2.1 and 11.2.2 throughout the entire forecast period, i.e., from 2013 to 2043.

11.3 NATIONAL ENERGY SAVINGS

11.3.1 NES Definition

As shown in the following equation, DOE ca lculates annual national energy savings as the difference between two projections: a base case (without new standards) and a standards case (with new standards). Positive values of NES correspond to energy savings (i.e., national energy consumption with standards is less than national energy consumption in the base case).

_ NES y = AEC BASE AEC STD

Cumulative energy savings are the sum over the forecast period, extending from the assumed effective date, i.e., 2013, to 30 years after the effective date, i.e., 2043, of the annual national energy savings. This calculation is represented by the following equation:

NES = 

## ∑ NES
cum y

DOE calculated the national annual energy consumption by multiplying the number or stock of the given product (by vintage) by its unit energy consumption (also by vintage). The calculation of the national annual energy consump tion is represented by the following equation:

AEC = 

## ∑ STOCK V × UEC V

For the above expressions, DOE defi ned the various quantities as follows:

11-4AEC = Annual national energy consum ption each year in quadrillion British thermal units (Btus) – or quads, summe d over vintages of the product stock, STOCK V , NES = Annual national energy savings (quads), STOCK V

= Stock of product (millions of units) of vintage V surviving in the year for which DOE calculated annual energy c onsumption (vintages range from five to approximately 15.5 years, depending on the retirement function of the washer based on its pr oduct application), UEC V = Annual energy consumption per pr oduct in either kilowatt-hours (kWh) or million Btus (MMBtu) (elect ricity and gas consump tion are converted from site energy to source energy (quads) by applying a time-dependent conversion factor), V

= Year in which the product was purchased as a new unit, and

y = Year in the forecast (i.e., 2013 to 2043).

The stock of equipment is dependent on annual shipments and the lifetime of the equipment. As described in Chapter 10, DOE conducted shipments projections under the base case and standards cases. DOE determined that the shipments projections under the standards cases were slightly lower than those in the base case projection, due to the higher purchase prices of the more-efficient equipment. In other words, DOE believes that the higher purchase prices would cause some consumers to forego new equipment purchases.

DOE assumed that any drop in shipments caused by standards would result in the purchase of used machines. Therefore, DOE explicitly accounted for the energy and water consumption of the used washers in the standards cases.

11.3.2 NES Inputs

Table 11.3.1 lists the inputs for the determination of NES.

Table 11.3.1 National Energy Saving Inputs Annual Energy Consumption per Unit ( UEC ) Shipments Equipment Stock ( STOCK V ) National Annual Energy Consumption ( AEC ) Site-to-Source Conversion Factor ( src_conv )

11.3.2.1 Annual Energy Consumption per Unit

DOE presented the per-unit annual energy and water consumption as a function of product efficiency in both Chapter 6, Energy a nd Water Use Determination, and section 8.2.2.1 of Chapter 8, LCC and PBP Analysis. Becau se the per-unit annual energy and water consumption is directly depende nt on efficiency, DOE used the base case and standards case

11-5SWEFs presented above in section 11.2, in comb ination with the annual energy and water use data presented in Chapters 6 and 8, to estim ate the shipment-weighted average annual per-unit energy and water consumption under th e base case and standards cases.

Using the relationship between clothes wa sher MEF and WF and annual energy and water consumption described in Chapter 8, Tables 11.3.2 and 11.3.3 present the per-unit annual electrical and gas energy use, and water consum ption, based on the SWEFs corresponding to the base case and each TSL for top-loading and front-loading washers, respectively. Because establishments that use commercial clothes washers use either electric or gas water heaters and dryers, the per-unit annual energy use values in the table below are provided by fuel type. The energy and water use values shown in Tables 11.3.2 and 11.3.3 are weighted-average values reflecting the market share of washers in multi-family buildings (85 percent) and laundromats (15 percent), as provided by the Cons ortium for Energy Efficiency (CEE). 3 The water heating and drying energy consumption data reflect the pe rcentage of gas and electric water heaters and dryers in the combined multi-family and laundromat market. Based on data from the CEE, in the combined market, 17 and 83 percent of water heaters are electric and gas, respectively, while 34 and 66 percent of dryers are electric and gas, respectively. 3

As described in Chapter 10, section 10.3, DOE assumed that market share of washers in multi-family buildings and laundrom ats will remain constant at current levels. But because the assumed shares of laundromats and multi-family housing in shipments can have an effect on overall CCW shipments and, in turn, the NES and NPV results, DOE conducted a sensitivity analysis in which it used data from Alliance La undry System’s 2008 10-K report, coupled with a number of assumptions, to estimate the shares of laundromats and multi-family housing in shipments of CCWs in 2008. The analysis, whic h is described in appendix 11C, yields an estimate of a 66 percent share for multi-family housing and a 34 percent share for laundromats.

Table 11.3.2 Top-Loading Commercial Clothes Washers : Base Case and Standards Case Shipment-Weighted Average Per-Unit Annual Energy and Water Consumption in 2013

Base Case Trial Standard Level

1 2 3 4 5 SWEF (MEF) 1.32 1.42 1.42 1.60 1.60 1.60

SWEF (WF) 9.49 9.49 9.49 8.50 8.50 8.50

Annual Energy Use Electric (kWh/yr) 950 902 902 812 812 812 Gas (MMBtu/yr) 8.30 7.52 7.52 6.60 6.60 6.60 Annual Water Use (1000 gal/ yr) 36.9 36.9 36.9 33.0 32.2 33.0

11-6Table 11.3.3 Front-Loading Commercial Clothes Washers: Base Case and Standards Case Shipment-Weighted Average Per-Unit Annual Energy and Water Consumption in 2013

Base Case Trial Standard Level

1 2 3 4 5 SWEF (MEF) 2.04 2.04 2.05 2.05 2.20 2.35

SWEF (WF) 5.50 5.48 5.41 5.41 5.10 4.40

Annual Energy Use Electric (kWh/yr) 683 682 680 680 643 610 Gas (MMBtu/yr) 4.96 4.94 4.92 4.92 4.51 4.19 Annual Water Use (1000 gal/ yr) 21.4 21.3 21.0 21.3 19.8 17.1

As noted above in section 11.2, DOE assumed that forecasted efficiencies in the base case and standards cases remain frozen at the effi ciency levels in 2013. Therefore, because the per-unit annual energy and water consumption are a function of efficiency, DOE held the values shown above in Tables 11.3.2 and 11.3.3 constant over the forecast period (2013–2043).

As described in section 10.4 of Chapter 10, Shipments Analysis, DOE forecasted a slight drop in commercial clothes washer shipments due to purchase price increases caused by standards-related efficiency increases. DOE assumed that those establishments forgoing the purchase of a commercial clothes washer due to st andards would instead purchase a used clothes washer with an efficiency equal to the baseline level, i.e., 1.26 MEF/9.5 WF for top-loading washers and 1.72 MEF/8.0 WF for front-loading washers. In the NIA Model for commercial clothes washers, DOE accounted for the number of used washers purchased in order to quantify the energy and water impacts of those establishments that were estimated to forgo a new commercial clothes washer purchase.

11.3.2.2 Shipments

DOE forecasted shipments for the base case and all standards cases. Several factors, including total installed costs (purchase price pl us installation costs), operating cost, household income, and equipment lifetime, all impact forecasted shipments. Of the above factors, total installed cost was the primary driver in forecasting the impact of standards on shipments. As noted earlier, the increased total installed cost of more-efficient equipment causes some customers to forego equipment purchases. Consequently, shipments forecasted under the standards cases are lower than under the base case.

As described in Chapter 8, section 8.2.6 and Chapter 10, section 10.3, DOE estimated that 70 percent of the market is comprised of t op-loading washers and 30 percent is comprised of front-loading washers. DOE estimated that the market share of top-loading and front-loading washers would remain fixed at 70 percent and 30 percent, respectively, through the forecast period.

11-7As detailed in Chapter 10, section 10.3, DOE based its Shipments Model on the assumption that the relative market shares of the two product appli cations, laundromats and common-area laundry fac ilities in multi-family housing, are constant over time. DOE did not have sufficient data to forecast whether the ma rket shares of laundromats and multi-family housing will vary over time. Therefore, DOE assume d that market shares will remain constant at current levels. DOE relied on a report from CEE to estimate that multi-family buildings and laundromats comprise 85 percent and 15 percen t of the commercial clothes washer market, respectively. As noted above in section 11.3.2. 1, because the assumed shares of laundromats and multi-family housing in shipments can have an e ffect on overall CCW shipments and, in turn, the NES and NPV results, DOE conduc ted a sensitivity analysis to estimate the shares of laundromats and multi-family housing in shipment s of CCWs in 2008. The analysis, which is described in appendix 11C, yields an estimate of a 66 percent share for multi-family housing and a 34 percent share for laundromats.

An extensive description of the me thodology for conducting and generating the shipments forecasts for each of the considered products can be found in Chapter 10, Shipments Analysis.

11.3.2.3 Equipment Stock

The equipment stock in a given year is th e number of products shipped from earlier years that survive in the given year. The NIA Models keep track of the number of units shipped each year. DOE assumes that the produc ts have an increasing probability of retiring as they age. The probability of survival as a function of years-since-purchase is the survival function. See Chapter 10, Shipments Analysis, for further details on the survival functions that DOE used in its analysis.

11.3.2.4 National Annual Energy Consumption

The national annual energy consumption is the product of the annual energy consumption per unit and the number of units of each vintage. This approach accounts for differences in unit energy consumption from year to year. The equation for determining the annual energy consumption was shown above in secti on 11.3.1 and is repeated below.

AEC = 

## ∑ STOCK V × UEC V

In determining national annual energy cons umption, DOE initially calculated the annual energy consumption at the site, and then applied a conversion factor to ca lculate primary energy consumption (see section 11.3.2.5).

11.3.2.5 Energy Site-to-Source Conversion Factors

In determining national annual energy cons umption, DOE initially calculated the annual energy consumption at the site (e.g., for electricity, the energy in kWh consumed at the

11-8household or establishment). It then calculate d primary (source) energy consumption from site energy consumption by applying a conversion fa ctor to account for losses associated with the generation, transmission, and dist ribution of electricity and gas. The site-to-source conversion factor is the multiplicative factor used for conve rting site energy consumption into primary or source energy consumption, expressed in quads . DOE used annual site-to-source conversion factors based on the version of the National En ergy Modeling System (NEMS) that corresponds to the DOE-Energy Information Administration’s (EIA) Annual Energy Outlook 2008 ( AEO2008 ). 4 The factors used are marginal values, which represent the response of the system to an incremental decrease in consumption. Natural gas losses include pipeline leakage, pumping energy, and transportation fuel. The AEO2008 forecasts losses of about seven percent for the natural gas used on site, with only slight variation from year to year. For electricity, the conversion factors vary over time, due to projected changes in generation sources (i.e., the power plant types projected to provide electricity to th e country). DOE assumed that conversion factors remain constant at 2030 values throughout the rema inder of the forecast. Table 11.3.4 shows the site-to-source conversion factors from 2005 to the end of the forecast period (2043).

Table 11.3.4 Site-to-Source Conversion Factors for Electricity and Natural Gas

Year Electricity Btu/kWh Natural Gas Btu/Btu Year Electricity Btu/kWh Natural Gas Btu/Btu
2005 11500 1.085 2019 7917 1.087
2006 11500 1.085 2020 7917 1.091
2007 11200 1.085 2021 7900 1.094
2008 10250 1.082 2022 7557 1.094
2009 10000 1.084 2023 6765 1.094
2010 10000 1.086 2024 6327 1.095
2011 10000 1.086 2025 6327 1.095
2012 10000 1.086 2026 6327 1.095
2013 10000 1.087 2027 6327 1.096
2014 9500 1.086 2028 6327 1.096
2015 7917 1.086 2029 6327 1.096
2016 7917 1.087 2030 7917 1.087
2017 7917 1.086 2031–2043 7917 1.091
2018 7917 1.086

Source: NEMS, 2008.

11.4 NET PRESENT VALUE

11.4.1 NPV Definition

The NPV is the value in the present of a time series of costs and savings. The NPV is described by the equation:

11-9_ NPV = PVS PVC

where:

PVS = Present value of operating cost savings (including energy, water, repair, and maintenance costs), and PVC = Present value of increased total in stalled costs (including equipment and installation).

DOE determined the PVS and PVC according to the following expressions:

PVS = 

## ∑ OCS y × DF y

PVC = 

## ∑ TIC y × DF y

DOE calculated the total annual operating co st savings by multiplying the number or stock of the given product (by vintage) by its pe r-unit operating cost savings (also by vintage). DOE calculated the total annual in stalled cost increases by multiplying the number or stock of the given product (by vintage) by its per-unit total installed cost increase (also by vintage). The calculation of the annual operating costs savings and total annual installed cost increases is represented with the following equations:

OCS y = 

## ∑ STOCK V × UOCS V

TIC y = 

## ∑ STOCK V × UTIC V

For the above expressions, DOE defi ned the various quantities as follows:

OCS = Total annual operating cost savings each year summed over vintages of the product stock, STOCK V , TIC = Total annual installed cost increases each year summed over vintages of the product stock, STOCK V , DF = Discount factor in each year, STOCK V

= Stock of product (millions of units) of vintage V surviving in the year for which DOE calculated annual energy c onsumption (vintages range from five to approximately 15.5 years, depending on the retirement function of the washer based on its pr oduct application), UOCS V = Annual operating cost savings per unit, UTIC V = Annual total installed cost increase per unit, V

= Year in which the product was purchased as a new unit, and

y = Year in the forecast (i.e., 2013 to 2043).

11-10DOE determined the PVC for each year, from the effective date of the standard to the year 2043. It determined the PVS for each year, from the effective date of the standard to the year when units purchased in 2043 retire. DOE calculated costs and sa vings as the difference between a standards case and a base case (i.e., without new standards).

DOE calculated a discount factor from the discount rate and the number of years between the “present” (i.e., year to which the sum is being discounted) and the year in which the costs and savings occur. The NPV is the sum ove r time of the discounted net savings.

11.4.2 NPV Inputs

Table 11.4.1 summarizes the i nputs to the NPV calculation.

Table 11.4.1 Net Present Value Inputs Total Installed Cost per Unit Annual Operating Cost Savings per Unit

Total Annual Installed Cost Increases Total Annual Operating Costs

Discount Factor

Present Value of Costs Present Value of Savings

The increase in the total annual installed cost is equal to the annual change in the per-unit total installed cost (difference between base case and standards case) multiplied by the shipments forecasted in the standards case. As with the calculation of the NES, DOE assumed that any drop in shipments caused by standards would result in the purchase of used machines.

The total annual operating cost savings are equal to the change in the annual operating costs (difference between base case and standards case) per unit multiplied by the shipments forecasted in the standards case. The annual ope rating cost includes energy, water, repair, and maintenance costs.

11.4.2.1 Total Installed Cost per Unit

DOE first presented the per-unit total installe d cost as a function of product efficiency in section 8.2.1.5 of Chapter 8, LCC and PBP Analys is. Because the per-unit total annual installed cost is directly dependent on efficiency, DOE used the base case and standards case SWEFs presented above in section 11.2, in combination with the tota l installed costs presented in Chapter 8, to estimate the shipment-weighted av erage annual per-unit to tal installed cost under the base case and standards cases.

11-11The total installed cost includes both the equipment price and the installation cost. DOE based average equipment prices on average manufacturer costs multiplied by average overall markup values. Using the relationship between commercial clot hes washer MEF and WF and total installed cost presented in Chapter 8, Tables 11.4.2 and 11.4.3 show the per-unit total installed cost based on the SWEF s corresponding to the base case and each TSL for top-loading and front-loading washers, respectively.

Table 11.4.2 Top-Loading Commercial Clothes Washers : Base Case and Standards Case Shipment-Weighted Average Per-Unit Total Installed Costs in 2013

Base Case Trial Standard Level

1 2 3 4 5 SWEF (MEF) 1.32 1.42 1.42 1.60 1.60 1.60

SWEF (WF) 9.49 9.49 9.49 8.50 8.50 8.50 Total Installed Cost (2008$) $805 $885 $885 $975 $975 $975

Table 11.4.3 Front-Loading Commercial Clothes Washers: Base Case and Standards Case Shipment-Weighted Average Per-Un it Total Installed Costs in 2013

Base Case Trial Standard Level

1 2 3 4 5 SWEF (MEF) 2.04 2.04 2.05 2.05 2.20 2.35

SWEF (WF) 5.50 5.48 5.41 5.41 5.10 4.40 Total Installed Cost (2008$) $1,398 $1,398 $1,399 $1,399 $1,429 $1,472

As noted above in section 11.2, DOE assumed that forecasted efficiencies in the base case and TSLs remain frozen at the efficiency levels in 2013. Therefore, because the per-unit total installed costs are a function of efficiency , DOE held the values shown above in Tables 11.4.2 and 11.4.3 constant over the forecast period (2013–2043).

11.4.2.2 Annual Operating Cost Savings per Unit

The per-unit annual operating cost includes the energy, water, repair, and maintenance

costs.

Annual Energy and Water Costs DOE determined the per-unit annual energy and water cost savings by taking the per-unit annual energy (and water) cons umption savings developed for each product and multiplying it by the appropriate energy (and water) price.

For all of the considered products, estimat es of the per-unit annual energy and water consumption for the base case and each standards cas e were presented earlier in section 11.3.2.1. As described previously, DOE forecasted the per-unit annual energy (and water) consumption for the base case and each standards case for all products by freezing the consumption at levels estimated for the year 2013.

11-12Energy and water prices are described in se ction 8.2.2.2 of Chapter 8. DOE forecasted energy prices based on EIA’s AEO2009

. 5 DOE forecasted water prices based on trends in the national water price index as provided by the Bureau of Labor Statistics. 6 The energy and water price trends are described in s ection 8.2.2.3 of Chapter 8.

Annual Repair and Maintenance Costs As described in section 8.2.2.4 of Chap ter 8, DOE estimated increased repair and maintenance costs for commercial clothes washers. DOE estimated that repair costs would increase as a function of equipment efficiency. Using the relationship between clothes washer MEF and WF and repair describe d in Chapter 8, Tables 11.4.4 and 11.4.5 present the per-unit annualized repair cost based on the SWEFs corres ponding to the base case and each TSL for toploading and front-loading washers, respectively. The repair costs shown in Tables 11.4.4 and 11.4.5 are weighted-average values reflecting the market share of washers in multi-family buildings (85 percent) and laundromats (15 percen t), as provided by the Consortium for Energy Efficiency (CEE).

Table 11.4.4 Top-Loading Commercial Clothes Washers : Base Case and Standards Case Shipment-Weighted Average Per-Unit Annualized Repair Costs in 2013

Base Case Trial Standard Level

1 2 3 4 5 SWEF (MEF) 1.32 1.42 1.42 1.60 1.60 1.60

SWEF (WF) 9.49 9.49 9.49 8.50 8.50 8.50 Annualized Repair Cost (2008$) $30 $34 $34 $38 $38 $38

Table 11.4.5 Front-Loading Commercial Clothes Washers: Base Case and Standards Case Shipment-Weighted Average Per-Unit Annualized Repair Costs in 2013

Base Case Trial Standard Level

1 2 3 4 5 SWEF (MEF) 2.04 2.04 2.05 2.05 2.20 2.35

SWEF (WF) 5.50 5.48 5.41 5.41 5.10 4.40 Annualized Repair Cost (2008$) $58 $58 $58 $58 $60 $62

As noted above in section 11.2, DOE assumed that forecasted efficiencies in the base case and TSLs remain frozen at the efficiency levels in 2013. Therefore, because the per-unit total installed costs are a function of efficiency , DOE held the values shown above in Tables 11.4.4 and 11.4.5 constant over the forecast period (2013–2043).

11.4.2.3 Total Annual Installed Cost Increases

The total annual installed cost increase for an y given standards case is the product of the total installed cost increase per unit due to the standard and the number of units of each vintage. This approach accounts for differences in total inst alled cost from year to year. The equation for

11-13determining the total annual installed cost increase for a given standards case was shown in section 11.4.1 and is repeated below.

TIC = 

## ∑ STOCK V × UTIC V

As noted earlier for commercial clothes washers, DOE accounted for the total installed cost of those establishments that were forced into purchasing used clothes washers due to standards. Therefore, the total annual installed cost increase takes into account the total installed cost of the used washers that were purchased in lieu of the purchase of new washers.

11.4.2.4 Total Annual Operating Cost Savings

The total annual operating cost savings for an y given standards case is the product of the annual operating cost savings pe r unit due to the standard and the number of units of each vintage. This approach account s for differences in annual operating cost savings from year to year. The equation for determining the total annual operating cost savings for a given standards case was shown above and is repeated below.

OCS = 

## ∑ STOCK V × UOCS V

As noted earlier, DOE accounted for the ener gy and water use of those establishments that were forced into purchasing used washers due to standards. Therefore, the total annual operating cost savings take into account the energy and water costs of the used washers that were purchased in lieu of the pu rchase of new washers.

11.4.2.5 Discount Factors

DOE multiplies monetary values in future years by a discount factor to determine the present value. The discount factor (DF) is described by the equation:

1 DF =

_ ( y yp ) ( 1+ r )

where:

r = discount rate,

y = year of the monetary value, and

y P = year in which the present value is being determined.

DOE estimated national impacts with both a three-percent and a seven-percent real discount rate as the average real rate of return on private investment in the U.S. economy. It used these discount rates in accordance with th e Office of Management and Budget (OMB)’s guidance to Federal agencies on the development of regulatory analysis (OMB Circular A-4,

11-14September 17, 2003), and section E., “Identifying a nd Measuring Benefits and Costs,” therein. DOE defines the present year as 2009.

11.4.2.6 Present Value of Costs

The present value of increased installed costs is the annual installed cost increase in each year (i.e., the difference between the standards case and base case), discounted to the present, and summed for the time period over which DOE is considering the installation of equipment (i.e., from the effective date of standards, 2013, to the year 2043).

The increase in total installed cost refers to both equipment cost and installation cost associated with the higher energy efficiency of equipment purchased in the standards case compared to the base case. DOE calculated annua l increases in installed costs as the difference in total installed cost for new equipment purchas ed each year, multiplied by the shipments in the standards case.

11.4.2.7 Present Value of Savings

The present value of operating cost savings is the annual operating cost savings (i.e., the difference between the base case and standards cas e) discounted to the present, and summed over the period from the effective date, 2013, to the time when the last unit installed in 2043 is retired from service.

Savings are decreases in operating costs (inc luding electricity, repair, and maintenance costs) associated with the higher energy efficiency of equipment purchased in the standards case compared to the base case. Total annual ope rating cost savings are the savings per unit multiplied by the number of units of each vintage surviving in a particular year. Equipment consumes energy over its entire lifetime, and for units purchased in 2043, the consumption includes energy consumed until the unit is retired from service.

11.5 NES AND NPV RESULTS

The NIA Model provides estimates of th e NES and NPV due to various candidate standards levels. The inputs to the NIA Model have been discussed earlier in sections 11.3.2 (NES Inputs) and 11.4.2 (NPV Inputs). DOE generated the NES and NPV results using a Microsoft Excel ® spreadsheet, which is accessible on the Internet ( http://www.eere.energy.gov/buildings/ appliance_standards/). Details and instructions for using the spreadsheet are provided in Appendix 11A.

11.5.1 NES and NPV Input Summary

Table 11.5.1 summarizes the inputs to the NIA Model. For each input a brief description of the data source is given.

11-15Table 11.5.1 NES and NPV Inputs Input Data Description Shipments Annual shipments from Shipments Model. (See Chapter 10.)
Effective Date of Standard 2013.
Base Case Forecasted Efficiencies SWEF determined in the year 2009. SWEF held constant over forecast period of 2009–2042. (See Section 11.2.)
Standards Case Efficiencies Roll-up scenario assumed for determining SWEF in the year 2013 for each standards case and for each of the considered products. SWEF held constant over forecast period of 2013–2043. (See Section 11.2.) Annual Energy Consumption per Unit Annual weighted-average values are a function of SWEF. (See Section 11.3.2.1.) Total Installed Cost per Unit Annual weighted-average values are a function of SWEF. (See Section 11.4.2.1.) Energy and Water Cost per Unit Annual weighted-average values are a function of the annual energy consumption per unit and energy (and water) prices. (See Section 8.2.2.3 of Chapter 8 for energy and water prices.) Repair Cost and Maintenance Cost per Unit Annual weighted-average values are a function of SWEF. (See Section 8.2.2.4 of Chapter 8 for repair and maintenance costs.) Escalation of Energy and Water Prices Energy Prices: EIA AEO2009 forecasts (to 2030) and extrapolation to 2043. (See Section 8.2.2.3 of Chapter 8.) Water Prices: Linear extrapolation off historical trend in national water price index. (See Section 8.2.2.3 of Chapter 8.) Energy Site-to-Source Conversion Conversion varies yearly and is generated by DOE/EIA’s NEMS* program (a time series conversion factor; includes electric generation, transmission, and distribution losses).
Discount Rate 3 percent and 7 percent real.

Present Year Future expenses are discounted to year 2009.

* Chapter 14 on the Utility Impact Analysis and the Environmental Assessment Report provide more detail on NEMS.

11.5.2 NES Results

The following sections provide NES results and national water savings (NWS) results for the TSLs analyzed for commercial clothes washers. NES results are cumulative to 2043 and are shown as primary energy savings. NWS results are expressed in billions of gallons. DOE based the inputs to the NIA Model on weighted-average va lues, yielding results that are discrete point values, rather than a distribution of values as in the LCC and PBP Analysis.

Table 11.5.2 shows the NES and NWS results for the TSLs analyzed for commercial clothes washers. The results are based on the estimate that multi-family buildings and laundromats comprise 85 percent and 15 percen t of the commercial clothes washer market, respectively. As noted earlier in section 11.3.2.1, because the assumed shares of laundromats and multi-family housing in shipments can have an e ffect on overall CCW shipments and, in turn, the NES and NPV results, DOE conduc ted a sensitivity analysis to estimate the shares of laundromats and multi-family housing in shipment s of CCWs in 2008. The analysis, which is described in appendix 11C, yields an estimate of a 66 percent share for multi-family housing and

11-16a 34 percent share for laundromats. The impact of the sensitivity on NES and NWS is nine percent and approximately 10 percent, respectively.

Table 11.5.2 Commercial Clothes Washers: Cumulative NES and NWS Results Top-Loading Washers Front-Loading Washers
NWS NWS NES billion NES billion CSL TSL MEF/WF quads gallons TSL MEF/WF quads gallons

1 1, 2 1.42/9.50 0.04 0 1 1.80/7.50 0.00 1
2 3, 4, 5 1.60/8.50 0.10 135 2, 3 2.00/5.50 0.00 6
3 4 2.20/5/10 0.01 26
4 5 2.35/4.40 0.02 72

Table 11.5.3 shows the magnitude of the NE S and NWS if the savings are discounted at rates of seven percent and three percent.

Table 11.5.3 Commercial Clothes Washers: Cumulative NES and NWS Results Discounted at Seven Percent and Three Percent

CSL Top-Loading Washers Front-Loading Washers

TSL MEF/WF 7% Disc Rate 3% Disc Rate

TSL MEF/WF 7% Disc Rate 3% Disc Rate

NES quads NWS billion gallons NES quads NWS billion gallons NES quads NWS billion gallons NES quads NWS billion gallons

1 1, 2 1.42/9.50 0.01 0 0.02 0 1 1.80/7.50 0.00 0 0.00 1

2 3, 4, 5 1.60/8.50 0.03 37 0.05 75 2, 3 2.00/5.50 0.00 2 0.00 3

3 4 2.20/5/10 0.00 7 0.01 14

4 5 2.35/4.40 0.01 19 0.01 39

11.5.3 Annual Costs and Savings

To illustrate the basic inputs to the NPV calculations, Figure 11.5.1 presents the nondiscounted annual installed cost increases and annual operating cost savings at the national level for TSL 3 for top-loading washers. The figure also shows the net savings, which is the difference between the savings and costs for e ach year. The annual equipment cost is the increase in the total installed cost for produ cts purchased each year over the period 2013–2043. The annual operating cost savings is the savings in operating costs for products operating in each year. The NPV is the difference between th e cumulative annual discounted savings and the cumulative annual discounted costs. DOE coul d create figures like the one shown below for each of the standard cases.

11-17-0.025 0.000 0.025 0.050 0.075 0.100

2013 2016 2019 2022 2025 2028 2031 2034 2037 2040 2043 2046 2049 2052 2055 2058

YearBillion of 2008 Dollars Equipmetnt Cost Operating Cost Savings Net Savings

Figure 11.5.1 Non-Discounted Annual Installed Cost Increases and Annual Operating Cost Savings for Top-Load ing Clothes Washers, TSL 3

11.5.4 NPV Results

The following sections provide NPV results fo r the candidate standards levels considered for the product classes of commerc ial clothes washers. Results are cumulative and are shown as the discounted value of these savings in dollar te rms. DOE based the inputs to the NIA Model on weighted-average values, yielding results th at are discrete point values, rather than a distribution of values as in the LCC and PBP Analysis.

The present value of increased total insta lled costs is the total annual installed cost increase (i.e., the difference between the standards case and base case), discounted to the present, and summed over the time period in which DOE evalua tes the impact of standards (i.e., from the effective date of standards, 2013, to the year 2043).

Savings are decreases in operating costs (including energy and water) associated with the higher energy efficiency of equipment purchased in the standards case compared to the base case. Total operating cost savings are the savings per unit multiplied by the number of units of each vintage (i.e., the year of manufacture) surviving in a particular year. Equipment consumes energy and must be maintained over its entire lif etime. For units purchased up through 2043, the operating cost includes energy and water consumed until the last unit is retired from service.

11-18NPV results based on discount rates of seve n percent and three percent are provided for the standard levels considered for commercial clothes washers. Table 11.5.4 shows the NPV results for the candidate standard levels. Under the base case (i.e., without standards), the cumulative national equipment a nd operating costs of commercia l clothes washers based on a seven-percent discount ra te equal $2.0 billion and $9.7 billion, respectively. Based on a threepercent discount rate, the cumu lative national equipment and ope rating costs equal $3.6 billion and $19.2 billion, respectively. Detailed resu lts showing the breakdown of the NPV into national equipment costs a nd national operating costs are provided in Appendix 11B.

The results in Table 11.5.4 are based on the estimate that multi-family buildings and laundromats comprise 85 percent and 15 percen t of the commercial clothes washer market, respectively. As noted earlier in section 11.3.2.1, because the assumed shares of laundromats and multi-family housing in shipments can have an e ffect on overall CCW shipments and, in turn, the NES and NPV results, DOE conduc ted a sensitivity analysis to estimate the shares of laundromats and multi-family housing in shipment s of CCWs in 2008. The analysis, which is described in appendix 11C, yields an estimate of a 66 percent share for multi-family housing and a 34 percent share for laundromats. For TSLs 3, 4, and 5, the impact of the sensitivity on NPV is approximately 14 percent based on a seven-percen t discount rate and ap proximately 11 percent based on a three-percent discount rate.

Table 11.5.4 Commercial Clothes Washers: Cumula tive NPV Results based on SevenPercent and Three-Percent Discount Rates Top-Loading Washers Front-Loading Washers
NPV @

7% Billion NPV @ 3% Billion NPV @ 7% Billion NPV @ 3% Billion CSL TSL MEF/WF 2008$ 2008$ TSL MEF/WF 2008$ 2008$

1 1, 2 1.42/9.50 0.01 0.07 1 1.80/7.50 0.004 0.01
2 3, 4, 5 1.60/8.50 0.34 0.86 2, 3 2.00/5.50 0.01 0.03
3 4 2.20/5/10 0.07 0.17
4 5 2.35/4.40 0.17 0.39

11.6 IMPACT OF STANDARDS ON NATURAL GAS AND ELECTRICITY
PRICES AND ASSOCIATED CONSUMER BENEFITS

11.6.1 Impact on Natural Gas Prices

DOE conducted an analysis on the potential impact on natural gas prices resulting from the promulgation of new standards on commercial clothes washers at maximum technologically feasible (i.e., “Max Tech”) standard levels. “Max Tech” standard levels correspond to TSL 5 for commercial clothes washers. The analysis used a version of NEMS called NEM-BT, modified to account for energy savings associated with the pr oposed standards. As described in Chapter 14 on the utility impact analysis, using NEMS-BT fo r such an analysis typically involves using

11-19higher-decrement runs to isolate the impacts of the standards from numerical noise. DOE derived the price effect results associated with the energy savings resulting from the Max Tech standards using a regressed inte rpolation toward the origin.

Figure 11.6.1 shows the change in total natural gas consumption for the year 2030 (on the x-axis) resulting from Max Tech standard levels and the corresponding change in U.S. average wellhead natural gas price (on the y-axis). The points in Figure 11.6.1 that are labeled with “100x” and “125x” represent the results for a series of higher decrement runs assuming various multiples of the savings resulting from the Max T ech scenarios. Each scenario has multiple points that represent the results using the A nnual Energy Outlook (AEO ) 2008 Reference Case. 7 The interpolated results for the Max Tech standard level is shown in the upper right hand corner of Figure 11.6.1. For the Max Tech standard level, NEMS predicts that the 2030 average wellhead natural gas price will remain unchanged for commercial clothes washer standards. As shown in Table 11.6.1, relative to AEO 2008 Re ference Case values, the forecasted wellhead price decline in 2030 is zero percent. Othe r outputs from NEMS show that the predicted reduction in the average-customer natura l gas price in 2030 is also unchanged. a

Because the results shown in Table 11.6.1 s how that commercial clothes washer energy efficiency standards at the Max Tech level have no impact on average well head prices, efficiency standards for commercial clothes washers, even at the Max Tech level, will have no effect on natural gas consumer prices.

a The main customer sectors are electric power, industry, residential and commercial.

11-20CLW Max Std

100xCLW

125xCLW

-0.7 -0.6 -0.5 -0.4 -0.3 -0.2 -0.1 Change in Total Natural Gas Consumption (Quads) 0.02Change in Wellhead Natural Gas Price (2006$/MBtu) 0.00

-0.02

-0.04

-0.06

-0.08

-0.10
0.0

Figure 11.6.1 Wellhead Natural Gas Price Ch ange in 2030 Resulting from Reduced Natural Gas Consumption Associated with Max Tech Standard Level

Table 11.6.1 Impact of Max Tech Standard Levels on Natural Gas Consumption and Average Wellhead Price in 2030
No-Standard Case Max Tech Scenario Result

Nat Gas Consumption Nat Gas Price Nat Gas Consumption Change in Consumption Nat Gas Price Change in Price

Quads 2006$/
MMBtu Quads Quads % 2006$/
MMBtu 2006$/
MMBtu % Commercial Clothes Washer Max Tech 21.4 6.63 21.4 0.00 0.0 6.63 0.00 0.0

The inverse price elasticity seen in NEMS to estimate the effect of consumption on price varies over the forecast period due to the dynami cs of natural gas supply and demand in NEMS

11-21(Figure 11.6.2). b The average value over the forecast period is 0.02 for commercial clothes washer standards.

-0.50 -0.40 -0.30 -0.20 -0.10 0.00 0.10 0.20 0.30Inverse Price Elasticity2015201620172018201920202021202220232024202520262027202820292030

Year
Figure 11.6.2 Inverse Natural Gas Price Elasticity from NEMS Analysis for Commercial Clothes Washer Max Tech Standard Level

The average inverse price elasticity (0.02) reported above is genera lly consistent with other studies that used various versions of NEMS to examine the impacts to natural gas prices resulting from reductions in natural gas demand. In a review of a number of such studies, Wiser and Bollinger 8 examined analyses that reported th e reduction in nationa l natural gas demand from certain energy policies and the resulting decline in the wellhead gas price. Examining the average values of the inverse price elasticity ove r the forecast period in each study, they found the average values to range from 0.7 to 4.7. It is important to note that the average inverse price elasticities in 13 of 19 analyses were between 0.8 and 2.0, and the average value from DOE's current analysis falls below this range.

Although NEMS provides a detailed charac terization of natural gas supply, it is understandable that NEMS may not accurately repres ent the long-run supply of natural gas of the country since all aspects of the future are not certain. However, other widely used complex energy demand/supply models produce results that are similar to NEMS results, and NEMS is recognized as a reliable analytical tool. Table 11.6.2 compares the results of modeling studies of the response of natural gas price to change in ga s consumption. These results are summarized in a 2003 study by Stanford's Energy Modeling Forum. 9 While the magnitude of the price response

b Since the demand reduction in the early years after the standard takes effect is small, the model results for those years may be less accurate than in later years.

11-22(as summarized by the inverse price elasticity) varies considerably among the models reviewed, in general, they confirm that a sizable price impact is associated with changes in natural gas consumption. c It is worth noting that the NEMS long -run (2020) response is smaller than that of most of the other models.

Table 11.6.2 Implicit Natural Gas Inverse Price Elasticity from Seven Energy Models

2005 2010 2015 2020 Average
NEMS 1.8 2.2 0.53 0.11 1.2
POEMS 2.4 1.8 2.5 1.8 2.1
CRA 3.5 2.5 1.1 0.9 2.0
NANGAS 5.4 7.0 7.6 5.1 6.3
E2020 1.5 1.0 1.0 0.7 1.1

MARKAL N/A 2.0 N/A 2.1 2.1

NARG 8.7 12.4 5.6 2.4 7.3 NEMS (National Energy Modeling System, 2002); POEMS (Policy Office Electricity Modeling System), CRA (Charles River Associates), NANGAS (North American Natural Gas Analysis System), E2020 (Energy 2020), MARKAL (MARKet ALlocation), NARG (North American Regional Gas model)
Source: Based on EMF, 2003

In addition to the models listed in Table 11.6.2, there exist studies conducted by the National Petroleum Council 10 and the National Commission on Energy Policy 11 that are based on the EEA model. The EEA model shows higher inverse price elasticities (between 4.0 and 16.8), leading to larger natural gas price reductions fo r a given demand decrease, than those predicted by NEMS and most of the other national energy models.

11.6.2 Impact on Electricity Prices

DOE also conducted an analysis on the poten tial impact on electricity prices resulting from the promulgation of new standards on commercial clothes washers at maximum technologically feasible (i.e., “Max Tech”) standard levels. “Max Tech” standard levels correspond to TSL 5 for commercial clothes washers. The analysis used a version of NEMS called NEMS-BT, modified to account for ener gy savings associated with the proposed standards. Again, using NEMS-BT for such an analysis typically involves using higherdecrement runs to isolate the impacts of the standards from numerical noise. DOE derived the price effect results associated with the energy savings resulting from the Max Tech standards using a regressed interpolation toward the origin.

Figure 11.6.3 shows the change in total el ectric power consumption for the year 2030 (on the x-axis) resulting from Max Tech standard le vels and the corresponding change in U.S.

c The EMF scenarios actually modeled the impact of increased gas demand on price. Assuming a smooth supply curve over the long term, however, the elasticities implied by an increase in demand should be essentially equivalent to those implied by a decrease in demand. Two of the models (NANGAS and NARG) report somewhat anomalous inverse elasticities for undetermined reasons.

11-23average electricity price (on the y-axis). The points in Figure 11.6.3 that are labeled with “100x”, “125x”, or “150x” represent the results for a series of higher decrement runs assuming various multiples of the savings resulting from the Max Tech scenarios. Decrement runs for commercial clothes washers are labeled as “CLW”. Each scenario has multiple points that represent the results using the Annual Energy Outlook (AEO) 2008 Reference Case. 4 The interpolated results for the Max Tech standard level is shown in the upper right hand corner of Figure 11.6.3. For the Max Tech standard levels, NEMS predicts that the 2030 average electricity price will remain unchanged for commercial clothes washer standards. As shown in Table 11.6.3, relative to AEO 2008 Reference Case values, the forecast ed electricity price decline in 2030 is zero percent. Other outputs from NEMS show that the predicted reduction in the average-customer electricity price in 2030 is also unchanged. d

Because the results shown in Table 11.6.3 s how that commercial clothes washer energy efficiency standards at the Max Tech level have no impact on average electricity prices, efficiency standards for commercial clothes washers, even at the Max Tech level, will have no effect on the average electricity price to end users.

125xCLW 100xCLW

150xCLW 0.08

0.07

0.06

0.05

0.04

0.03

0.02

0.01

0.00$/MBtu)

CLW Max Std -5.0 -4.0 -3.0 -2.0 -1.0 0.0 Change in Total Electricity Consumption (Quads)Change in Electricity Price (2006-

Figure 11.6.3 Electricity Price Change in 2030 Resulting fr om Reduced Electricity Consumption Associated with Max Tech Standard Level

d The main customer sectors are electric power, industry, residential and commercial.

11-24Table 11.6.3 Impact of Max Tech Standard Levels on Electricity Consumption and Price in 2030
No-Standard Case Max Tech Scenario Result Electricity Consumption Electricity Price Electricity Consumption Change in Consumption Electricity Price Change in Price

Quads 2006$/
MMBtu Quads Quads % 2006$/
MMBtu 2006$/
MMBtu % Commercial Clothes Washer Max Tech 49.21 25.93 49.19 -0.02 -0.02% 25.93 0.00 0.0

The inverse price elasticity seen in NEMS is used to estimate the effect of consumption on price varies over the forecast period due to the dynamics of energy supply and demand in NEMS (Figure 11.6.4). e The average value over the forecast period is 0.30 for commercial clothes washer standards.

-0.30 -0.20 -0.10 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.802015201620172018201920202021202220232024202520262027202820292030

YearInverse Price Elasticity

Figure 11.6.4 Inverse Electricity Price Elasticity from NEMS Analysis for Commercial Clothes Washer Max Tech Standard Levels

e Since the demand reduction in the early years after the standard takes effect is small, the model results for those years may be less accurate than in later years.

11-25REFERENCES

1 U.S. Department of Energy-Office of Ener gy Efficiency and Renewable Energy, Energy Conservation Standards for Certain Consumer Products (Dishwashers, Dehumidifiers, Microwave Ovens, and Electric and Gas Kitchen Ranges and Ovens) and for Certain Commercial and Industrial E quipment (Commercial Clothe s Washers); Final Rule. Federal Register , 2009. 74(66): pp. 16040-16096.

2 U.S. Department of Energy-Office of Energy Efficiency and Renewable Energy. Technical Support Document: Energy Efficiency Program for Consumer Products and Commercial and Industrial Equipment: Residential Dishwa shers, Dehumidifiers, Cooking Products, and Commercial Clothes Washers , March, 2009. Washington, DC.

< http://www1.eere.energy.gov/buildings/appliance_standards/residential/cooking_products_final_rul e_tsd.html >

3 Consortium for Energy Efficiency. Commercial Family-Sized Washers: An Initiative Description of the Consortium for Energy Efficiency , 1998. Boston, MA.

4 U.S. Department of Energy-E nergy Information Administration. Annual Energy Outlook 2008 with Projections to 2030 , February, 2008. Washington, DC. DOE/EIA-0383(2008).

5 U.S. Department of Energy-E nergy Information Administration. An Updated Annual Energy Outlook 2009 Reference Case Reflecting Provisions of the American Recovery and Reinvestment Act and Recent Changes in the Economic Outlook , April, 2009. Washington, DC. SR/OIAF/2009-03.

< http://www.eia.doe.gov/oiaf/servicer pt/stimulus/pdf/sroiaf(2009)03.pdf >

6 U.S. Department of Labor-Bureau of Labor Statistics. Consumer Price Indexes, Item: Water and sewerage maintenance, Series Id: CUUR0000 SEHG01, U.S. city average (not seasonally adjusted), 2008. Washington, DC. < http://www.bls.gov/cpi/home.htm#data >

7 U.S. Department of Energy - Energy Information Administration, Annual Energy Outlook 2008 (Revised Early Release) with Projections to 2030 . 2008: Washington, DC.

< http://www.eia.doe.gov/oiaf/aeo / >

8 Ryan Wiser, Mark Bolinger, and Matt St. Clair, Easing the Natural Gas Crisis: Reducing Natural Gas Prices through Increased Deploy ment of Renewable Energy and Energy Efficiency . 2005, Lawrence Berkeley National Laboratory.

9 Natural Gas, Fuel Diversity and North American Energy markets , Energy Modeling Forum, Stanford University.

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