A baseline mark-up is applied over the full production cost to arrive at the manufacturer price

at which the firm can recover all costs, both production and non-production, and earn a profit. In general, the manufacturer mark-up should ensure that the manufacturer price of the product is high enough (1) to recover the full cost of the product and (2) to yield a satisfactory profit. Such a price is called a normal price.a A baseline manufacturer mark-up of 1.35 over full production cost is calculated from the cost information in Table 6.9.
6.4.3 Manufacturer Price and Mark-ups for Different Efficiency Levels 6.4.3.1 Pre-ANOPR Analysis For the ANOPR analysis the Department used an average manufacturer mark-up of 1.35as an upper bound in conducting the LCC analysis. The Department used a mark-up of 1.00 as a lower bound. This lower bound represents pass-through of incremental full product costs, includingdepreciation of new capital expenditures. There is, however, no recuperation of incremental nonproduction costs (which include R&D and marketing expenses). In order to characterize the uncertainty in manufacturer mark-ups, the Department used a triangular distribution characterized by a maximum manufacturer mark-up of 1.35, a minimum manufacturer mark-up of 1.00, and a most likely mark-up of 1.18 (the average). Adding a fixed mark-up to retail price (of 1.40) and then to consumer price (mark-up of1.052), as discussed in Section 6.3, the total mark-up from manufacturing cost to consumer price was bound by the following values: minimum value of 1.00 x 1.40 x 1.052 1.473 maximum value of 1.35 x 1.40 x 1.052 = 1.990 The Department characterized the total mark-up using a triangular distribution with minimum, maximum, and most likely values of 1.473, 1.990, and 1.732, respectively. 6.4.3.2 NOPR Analysis NOPR analyses used a consistent set of assumptions for prices across all analyses sections (manufacturer impact, national benefits, and consumer impacts). Manufacturer and retail price are

aSome people question whether full costs are widely used as a basis for pricing. In a survey of large industrial companies, 85%reported that they do use full cost pricing. (Source: V. Govindarajan and Robert N. Anthony, "How Firms Use Cost Data in Pricing Decisions," Management Accounting, July 1993, pp. 30-36.) 6-12related by a fixed mark-up of 1.40, as discussed in Section 6.3 of this document, and a sales tax

mark-up of1.052 is applied to arrive at the consumer price. For the detailed economic analyses conducted for the NOPR, manufacturer mark-ups were determined by conducting financial analysis. The Government Regulatory Impact Model (GRIM) was used to carry out the cash flow analysis. The results of this analysis are presented in Chapter 11 of this TSD. The GRIM is a standard annual cash flow analysis which uses price, quantity, and cost information to assess the impact of regulatory conditions on manufacturer income and cash flow. The model calculates the actual cash flows, by year, and then determines the present value of those cash flows, both without regulations and with regulations.

6-13REFERENCES

1. U.S. Department of Labor - Bureau of Labor Statistics, Consumer Expenditure Surveys. Washington, DC. 2. Statistical Survey and Report. Dealerscope, 1998. June: p. 22 3. Elrick & Lavidge, EL CAP Consumer Appliance Purchases Database, 1996. Atlanta, GA. 4. U.S. Department of Commerce-Bureau of the Census, Current Industrial Report for Major Household Appliances (IvL436F), 1997. Published 25 August, 1998, revised 23 September, 1998. <http ://www.census.gov/industry/ma3 6f97.txt> 5. Association of Home Appliance Manufacturers, 1997 Major Appliance Industry Fact Book. 1998: Chicago, IL. Arthur D. Little mc,Financial Impact of DOE Top Loading Horizontal Axis Standards on US. Washing Machine Manufacturers, 1991, Report to Association of Home Appliance Manufacturers Horizontal Axis Task Force. Cambridge, MA.

6-14CHAPTER 7. LIFE-CYCLE COSTSANDPAYBACK PERIOD

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..................... TABLE OF CONTENTS 7.1 7-1 INTRODUCTION 7.1.1 General Approach for LCC and Distribution PBP 7-1 7.2 LIFE-CYCLE COST (LCC) 7-2 7.2.1 Definition 7-2 7.2.2 LCC Inputs 7-2 7.2.2.1 Purchase Expense 7-3 7.2.2.2 Lifetime 7-4 7.2.2.3 Cycles Per Year 7-5 7.2.2.4 Energy Per Cycle 7-6 7.2.2.5 Electricity Marginal Prices 7-7 7.2.2.6 Gas Marginal Prices 7-11 7.2.2.7 Oil Prices 7-14 7.2.2.8 LPG (propane) Prices 7-15 7.2.2.9 WaterPerCycle 7-16 7.2.2.10 Water and Wastewater Removal Prices 7-17 7.2.2.11 Discount Rate 7-21 7.2.2.12 Fuel Use of Water Heater and Clothes Dryer 7-24 7.2.2.13 Year to Start Date 7-25 7.2.2.14 Base Case Design 7-25 7.2.2.15 Standard Case Design 7-25 7.2.3 LCC Results 7-25 7.3 DISTRIBUTION PAYBACK PERIOD 7-34 7.3.1 Metric 7-34 7.3.2 Inputs 7-34 7.3.3 Results 7-35 7.4 REBUTTABLE PBP 7-39 7.4.1 Metric 7-39 7.4.2 Inputs 7-40 7.4.3 Results 7-41 7.5 USER INSTRUCTIONS FOR SPREADSHEET 7-42 7.5.1 What does the Life-Cycle Cost (LCC) Spreadsheet Do? 7-42 7.5.2 What are the Worksheets in the Workbook7 7-42 7.5.3 How Does the User Operate the Spreadsheet7 7-43 7.5.4 What Is the LCC (Sample Calculation) Sheet Used For9 7-44 7.5.5 How Does the User Run the Crystal Ball Simulation? (LCC Distribution Sheet) 7-44 7.5.6 What Kind of Output Does Crystal Ball Generate9 7-45

7-iLIST OF TABLES

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......... Table 7.1 Quantities in LCC Spreadsheet 7-3 Table 7.2 Cycles per Year for Persons per Household 7-6 Table 7.3 Summary of Nominal Interest Rates 7-22 Table 7.4 Distribution of Discount Rates 7-23 Table 7.5 Shares in Households of Water Heaters and Dryers by Fuel Type 7-24 Table 7.6 Summary of LCC Results for the Reference Case 7-30 Table 7.7 Summary of Life-cycle Cost Results - AEO High Growth & High Water

Escalation Rate 7-31 Table 7.8 Summary of Life-Cycle Cost Results - AEO Low Growth & Low Water

Escalation Rate 7-32 Table 7.9 Summary of Payback Period Results-AEO Reference 7-36 Table 7.10 Summary of Payback Period Results-AEO High Growth & Water

Escalation Rate 7-37 Table 7.11 Summary of Payback Period Results -AEO Low Growth & Water

Escalation Rate 7-38 Table 7.12 Rebuttable Presumption Payback in Years 7-41

LIST OF FIGURES Figure 7.1 Percent of Clothes Washers Surviving since Year of Original Purchase 7-5 Figure 7.2 Weighting of the Number of Washer Cycles per Year 7-6 Figure 7.3 Distribution of 1997 Marginal Residential Electricity Prices (In 1997$) 7-9 Figure 7.4 Alternative Electricity Price Trends 7-10 Figure 7.5 Distribution of 1997 Marginal Residential Gas Prices (In 1997$) 7-12 Figure 7.6 Alternative Natural Gas Price Trends 7-13 Figure 7.7 Alternative Oil Price Trends 7-15 Figure 7.8 Alternative LPG Price Trends 7-16 Figure 7.9 Water use Per Cycle vs. MEF 7-17 Figure 7.10 1998 Urban Water & Wastewater Price Distribution, in 1997 Dollars 7-19 Figure 7.11 1998 Urban Water Price Distribution, in 1997 Dollars 7-20 Figure 7.12 Water and Wastewater Price Trends 7-21 Figure 7.13 Distribution of Consumer Discount Rates 7-23 Figure 7.14 MEF = 1.089, Reference Case. Frequency Chart of LCC Differences 7-27 Figure 7.15 MEF = 1.089, Reference Case. Cumulative Chart of LCC Differences 7-28 Figure 7.16 Baseline Life-Cycle Cost Distribution 7-29 Figure 7.17 Consumers Having LCC Savings for Reference,

High and Low Growth Scenarios 7-33 Figure 7.18 MEF = 1.089, Reference Case-Frequency Chart of Payback Periods 7-35 Figure 7.19 Payback Periods for Reference, High and Low Growth Scenarios 7-39

7-uCHAPTER 7. LIFE-CYCLE COSTS ANDPAYBACK PERIOD

7.1 INTRODUCTION This chapter describes the method for analyzing the economic impacts of possible standards on individual consumers. The effect of standards on individual consumers includes a change in operating expense (usually decreased) and a change in purchase price (usually increased). This chapter describes three metrics used in the consumer analysis, to determine the effect of standards on individual consumers: Life-cycle cost (LCC) captures the tradeoff between purchase price and operating expenses for appliances. Payback period (PBP) measures the amount of time it takes consumers to recover the assumed higher purchase expense of more energy-efficient equipment through lower operating costs. Rebuttable Payback Period (RPP) is a special case of PBP. Where LCC and PBP are estimated over a range of inputs reflecting actual conditions, Rebuttable Payback Period is based on laboratory conditions, specifically, DOE test procedure inputs. These are discussed in sections 7.2, 7.3 and 7.4 of this chapter, respectively. Inputs and results are presented for each metric. Key quantities, current assumptions and calculations are detailed for each metric. The calculations discussed here are performed on a series of Microsoft ExcelTM spreadsheets which are accessible over the Internet.
7.1.1 General Approach for LCC and Distribution PBP In recognition that each household is unique, variability is analyzed in order to account for many of the differences among them by performing the calculations detailed here for a large sample of individual households. The results are expressed as the number of households having impacts of particular magnitudes. The LCC and PBP model was developed using Microsoft ExcelTM in Windows 95, combined with Crystal Ba11TM (a commercially available add-in, available at http://www.decisioneering.com). The analysis explicitly specifies both the uncertainty and variability in the model's inputs using probability distributions. A Monte Carlo simulation then takes thousands of random samples from each probability distribution within the model to calculate the outputs. Each input probability distribution is sampled in a way that reproduces the distributions' shape. The distribution of the results therefore reflects the probability of the values that could occur given the range of input values. This technique helps provide an insight into the likelihood of various possible results. The results are typically displayed as distributions of the impact examined as compared to the baseline. The following charts were created: i) a cumulative probability distribution showing 7-1the percentage of American households which would have a net saving by owning a more energy

efficient appliance or ii) a frequency chart that depicts variation of the difference in life-cycle cost for each efficiency level considered. 7.2 LIFE-CYCLE COST (LCC) 7.2.1 Definition Life-cycle cost is the total consumer expense over the life of an appliance, including purchase expense and operating expenses (including energy expenditures). Future operating expenses are discounted to the time of purchase, and summed over the lifetime of the appliance. Life-cycle cost is defined by the following equation:


# ot LCC=P+ (1)

(1+r)t where: P = Purchase expense

### ($) = Sum over the lifetime, from year 1 to year N, where N = lifetime of appliance (years) 0 = Annual operating expense ($)

[Note:for clothes washers 0 = (cycles per year) * [(energy per cycle) * (energy rate) + (water per cycle) * (water rate)}] r = Discount rate (real) t = Years after purchase of appliance

7.2.2 LCC InputsThis section provides information about the quantities and assumptions used to calculate lifecycle cost for clothes washers. For each quantity, the discussion includes: • definition • approach • current assumption Inputs for the LCC analysis are shown in Table 7.1 below.

7-2Table 7.1 Quantities in LCC Spreadsheet

Purchase expense($) Lifetime (years) Cycles per year Energy per cycle (kWh/cycle) Electricity rate (cents/kWh) Gas rate ($IMMBtu) Oil price ($IMMBtu) LPG price ($IMMBtu) Water per cycle (gallons) Water rate ($/thousand gallons) Discount rate Water Heater! Dryer Share Year to Start Date Base Case Design Standard Case Design
7.2.2.1 Purchase Expense Definition. Consumer expense for purchasing an appliance. Approach. Consumer purchase expense includes the retail price of the appliance plus sales tax. The baseline purchase expense is the retail price plus sales tax for a clothes washer at the baseline efficiency level. The baseline price is a shipment weighted average (SWA) price of washers currently being sold. Efficiency and price are not necessarily related for clothes washers; price is more a function of features and brand name. Example of Baseline Purchase Expense (all values in this description are rounded to the nearest dollar): Baseline retail price = $400 Sales tax (U.S. average) = 5.2% Therefore, baseline purchase expense = $421 The purchase expense for each efficiency level is calculated as the sum of baseline purchase expense plus the incremental purchase expense for the efficiency level. The incremental purchase expense is the product of incremental manufacturing cost, manufacturer variable markup, and retail variable markup, plus the sales tax. The Association of Home Appliance Manufacturers (AHAM) provided incremental manufacturing costs for 9 efficiency levels.

7-3For example, the incremental manufacturing cost (shipment-weighted average) for a35%

energy reduction from baseline is $128, with a customized distribution provided by AHAM. The mean manufacturer variable markup is 1.27 (range of 1.26 to 1.28, uniform distribution), retail variable markup is 1.40, and sales tax adds 5.2% to the retail price. The incremental purchase expense is: $128 x 1.27 x 1.40 x 1.052 = $239. The purchase expense for the unit having a 35% energy reduction is the baseline purchase expense plus the incremental purchase expense = $421 + 239 = $660. A distribution of prices is used for the LCC analysis based on a distribution of incremental manufacturing costs and a distribution of markups. Single values are shown in this discussion for illustration purposes only. Note that some of the incremental costs and markups are treated as distributions while others are treated as fixed numbers that represent the averages. The incremental manufacturing cost is treated as a distribution that is weighted according to the current manufacturer market shares. The manufacturer markup on incremental cost increases is also a distribution that ranges between a minimum and maximum markup and has constant probability in this range. The range of manufacturer markups is determined from the manufacturer impact analysis. In contrast to the manufacturer markups, the retail and tax markups are fixed at their average values. Assumptions. AHAM data is used for manufacturing costs. Data compiled by Arthur D. Little (ADL) is used for markups and sales tax. In addition, we assume in the current calculations that there is no price-based shift in consumer equipment choices. 7.2.2.2Lifetime Definition. The period of time the clothes washer will provide service. Approach. The lifetime is estimated by comparing historical shipments to percent of households owning the appliance.1 Assumption. For clothes washers, lifetimes range from 12 to 16 years, with an average of 14.1 years. Figure 7.1 shows the percent of washers surviving since year of original purchase.

7-4100% 90% 80% .E 70% 60% 50% 40% i 30% 20% 10% 0% 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Years since original purchase Figure 7.1 Percent of Clothes Washers Surviving since Year of Original Purchase 7.2.2.3 Cycles Per Year Definition. Loads of laundry washed per household per year. Approach. The DOE test procedure assumes 392 cycles per year. In actuality, the number of loads of laundry washed per household per year depends upon the number of persons in the household and on other factors. A survey of U.S. households indicates a relationship between family size and cycles per year.2 Using the sample of households having both a clothes washer and a clothes dryer from the Energy Information Administration's 1993 Residential Energy Consumption Survey (RECS),3 we assigned a cycles per year value to each household according to the persons per household. The weighted average result is 379 cycles per year for the U.S. mix of households. This value agrees closely with (is 4% lower than) the DOE test procedure. To the extent that households use different fuels for water heaters and clothes dryers, the cycles per year also differ among those households. Assumption. To simulate a sample of households, each household is assigned a given number of cycles per year based upon Table 7.2 (the values are then scaled so the average agrees with the test procedure assumption). Figure 7.2 shows the percent of households (i.e., the weighting of households) for a given number of washer cycles per year.

7-5Table7.2 Cycles ner Year for Persons ner Household

Persons per household Cycles per year Cycles per year (scaled)

1 200 207 2 310 321 3-4 454 469 5+ 624 645

Number of Washer Cycles per Year
40% 35.1% 36.5%
30% 

## 1'

___ 20% 17.1%

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.

# ]

10%

) L.1 I 0%

-

207 321 469 645

Washer Cycles Figure 7.2 Weighting of the Number of Washer Cycles per Year 7.2.2.4 Energy Per Cycle Definition. Energy consumed per cycle. One cycle corresponds to one load of laundry. Approach. In accordance with the DOE test procedure (for modified energy factor, MEF), energy consumption per cycle includes electricity used by the clothes washer, and energy used by the water heater to provide hot water, and energy used by the clothes dryer to dry the clothing. Assumptions. Each of these components of energy consumption per cycle is obtained from data submitted by AHAM, for both the baseline and the various efficiency levels (see TSD Engineering Analysis). The following equations show the relationship between energy use per cycle and MEF.

7-6Energy per cycle = motor energy + energy to heat water+ energy to dry clothes (2)
capacity (cu.

## ft.) MEF = (3)

energy per cycle

7.2.2.5 Electricity Marginal Prices Definition. The value to a residential customer of saving electricity, expressed as cents per kilowatt-hour. Issues. All households with clothes washers use electricity for the washer, and some households use electricity for water heating and clothes drying as well. Showing the change the customer will see in his/her electricity bill due to increased energy efficiency is complex because: 1) electricity rate varies from house to house, depending upon the rate schedule charged by the utility and the consumption pattern of the particular customer; and 2) future rates cannot be known with certainty, especially since the market is currently restructuring and a growing number of consumers are able to choose among competing utilities. Approach. The variations in electricity rate are handled by separating the problem into three parts: a) variability in energy rate among households; b) value of savings; and c) future trends. Each of these parts is discussed and current assumptions are described. A significant change from previous analyses is the use of marginal electric prices. Marginal prices refer to what the electric price is for the last incremental kWh used. This is further discussed in the sub-heading on the value of savings. a) Variability. The Energy Information Administration's 1993 RECS gathered information on electricity bills which is used to represent the variability from house to house3. An advantage of using a survey containing information on specific households is the ability to directly capture correlations among quantities. Such correlations are important for electric and gas prices, for example, or among household characteristics (e.g., fuel of the water heater and clothes dryer, persons per household) that affect energy consumption. Figure 7.3 shows the distribution of marginal electricity prices. Assumption regarding variability: All those households with complete information from RECS which have both a clothes washer and a clothes dryer are used. Energy prices are reported in the survey results for each household. These households are assigned weights to represent the U.S. Marginal residential electricity prices derived from RECS in Figure 7.3 were adjusted to 1997 prices in 1997 dollars by multiplying the marginal prices derived from RECS by the ratio of average national residential electricity price in 1997 and the average national residential electricity price in 1993 In other words, the conversion to change 1993 RECS household prices to 1997 prices is the ratio of 1997 national average residential prices
7-7by 1993 prices, with both prices in current years. This takes into account both inflation and

the real change in prices. b) Value of Savings. The prices shown in Figure 7.3 are marginal prices empirically derived from RECS. The RECS data on residential energy use includes information on the monthly billing and energy use for the surveyed households. The electricity use from month-to-month has a natural variability that allows one to calculate the effective marginal price of electricity. We therefore calculated seasonal marginal electricity prices by measuring the slope of a linear least squares fit of the relationship between energy use and cost. The data was divided into a summer and non-summer season. Summer was defined in the analysis as June 1 to September 30, so if the midpoint of the billing period fell within that date range the bill was defined as a summer bill. As it turned out the summer/winter distinction is not important for clothes washers, since the average summer clothes washer use is equal to the average use for the rest of the year. For some surveyed households the billing data was not available, or the data was not of sufficient quality for the marginal energy rate analysis. Households for which marginal energy prices could not be calculated were eliminated, resulting in a reduction of approximately 10% of the households used from RECS. The households that were excluded from the marginal rate analysis tended to be those households where the utility bill was included in the rent. For details on the methods and results, see the full report on the marginal price research effort, Marginal Energy Prices Report, available on DOE's webs ite.5

7-8c) Future Trend. Estimating future electricity rates is now more difficult than ever. In some

states, the electricity supply industry is undergoing restructuring. Previously, each household was assigned to a particular utility company, and the rates offered by that utility obtained from surveys. In the future, with restructuring, households will be able to purchase electricity from a large set of suppliers. A projected trend in national average prices is applied to each household's marginal energy prices. The price trend is applied by assuming that the household's energy prices vary in proportion to the projected national average energy price. In the spreadsheets, the user can select from the following scenarios: 1) Constant energy prices at 1997 values 2) Energy Information Administration Annual Energy Outlook 1 999,(AE099)6 High Economic Growth 3) Energy Information Administration Annual Energy Outlook 1999, Reference Case 4) Energy Information Administration Annual Energy Outlook 1999, Low Economic Growth 5) Gas Research Institute 1998 Baseline Projection7 7-9 Figure 7.3 Distribution of 1997 Marginal Residential Electricity Prices (In 199Th)9

0. •0.

t

# :

___________________ 0 o-"AEO 1999 High Growth 6
-.-AEO 1999 Reference -oGRI1998 AEO 1999 Low Growth

5 I

I I I I I I 1995 2000 2005 2010 2015 2020 2025 2030 Figure7.4 Alternative Electricity Price Trends Figure 7.4 shows the trends for the last four of those projections.a The values in later years (i.e. after 2015 for GRI and after 2020 for all others) are extrapolated from their earlier years. Extrapolation is needed because the sources used do not forecast beyond 2020 (or 2015 in the case of the GRI forecast). With the GRI forecast, prices are simply left constant at 2015 levels. For the AE099 projections, electricity prices are kept constant at 2020 levels because it is assumed that the transition to a restructured utility industry will be completed. Assumption regarding future trend: This analysis assumes the trend from AE099 Reference Case. In addition, a sensitivity analysis was performed at AEO high and low economic growth assumptions..

aAlthough for purposes of the LCC analysis, fuel price data is not needed beyond 2020, additional data is presented here up to the year 2030. National impacts discussed elsewhere are determined to the year 2030.

7-107.2.2.6 Gas Marginal Prices

Definition. The value to a residential customer of saving gas, expressed as dollars per million Btu. Issues. Some households use gas for their water heater or clothes diyer. Showing the change the customer will see in his/her gas bill due to increased energy efficiency is complex because: 1) gas rate varies from house to house, depending upon the rate schedule charged by the utility and the consumption pattern of the particular customer; and 2) future rates cannot be known with certainty. Approach. The variations in gas rate are handled by separating the problem into three parts: a) variability in energy price among households; b) value of savings; and c) future trends. Each of these parts is discussed and current assumptions described. a) Variability. The RECS survey gathered information on gas bills which is used to represent the variability from house to house. An advantage of using a survey containing information on specific households is the ability to directly capture correlations among quantities. Such correlations are important for electric and gas rates, for example, or among household characteristics (e.g., fuel of the water heater and clothes dryer, persons per household) that affect energy consumption. Assumption regarding variability: All those households from the RECS survey which have both a clothes washer and a clothes dryer are used. For each household, relevant energy prices are reported. These households are assigned weights to represent the U.S. Figure 7.5 shows residential marginal gas prices ranging from approximately 2 to 17 $/million Btu (MMBtu). b) Value of Savings. The prices shown in Figure7.5are marginal prices empirically derived from RECS. The RECS data on residential energy use includes information on the monthly billing and energy use for the surveyed households. We used an empirical approach to calculate marginal energy rates. The RECS data on residential energy use includes information on the monthly billing and energy use for the surveyed households. The energy use from month-to-month has a natural variability that allows one to calculate the effective marginal price of energy. We therefore calculated marginal energy prices by measuring the slope of a linear least squares fit of the relationship between energy use and cost. Unlike for the electricity marginal price determination, gas marginal prices were not separated into summer and non-summer divisions. For some surveyed households the billing data was not available, or the data was not of sufficient quality for the marginal energy rate analysis. Households for which marginal energy prices could not be calculated were eliminated, resulting in a reduction of approximately 10% of the households used from RECS. The households that were excluded from the marginal rate analysis tended to be those households where the utility bill was included in the rent. 7-11Fordetails on the methods and results of the marginal energy price calculations, see the full

report on marginal prices.5

6.0%

5.0%

4.0% a) N a) E 0 3.0% 

# I 

# Ih

1.0%
dH .fl

# Ik1hk Ji .L 5 16 17 18 19 0 1 0.0% 6 dollar/MMBtu Figure7.5 Distribution of 1997 Marginal Residential Gas Prices (In 1997$)
c) Future Trend. A projected trend in national average prices is applied to each household's energy prices, after accounting for "value of savings" (described above). In the spreadsheets, the user can select from the following scenarios: 1) Constant energy prices at 1997 values 2) Energy Information Administration Annual Energy Outlook 1999, High Economic Growth 3) Energy Information Administration Annual Energy Outlook 1999, Reference Case 4) Energy Information Administration Annual Energy Outlook 1999, Low Economic Growth 5) Gas Research Institute 1998 Baseline Projection.

7-120) 0) 5 AEO 1999 High Growth

-°--AEO 1999 Reference GRI 1998 • AEO 1999 Low Growth

I 1 I U I P I 1111 11 I I iiIipupIp ip I 1995 2000 2005 2010 2015 2020 2025 2030 Figure 7.6 Alternative Natural Gas Price Trends Figure7.6shows the projections. The values in later years (i.e. after2015for GRIand after 2020 for all others) are extrapolated from their relative sources Extrapolation is necessary because the sources used do not forecast beyond 2020 (or 2015 in the case of the GRI forecast). To arrive at values for these later years forecast prices are simply left constant 2015 levels for GRI. For AE099 projections, extrapolated values are the same as used by the Federal Energy Management Program (FEMP).8 Welihead natural gas prices after 2020 were extrapolated to 2030 using the average annual growth rate from 1997 to 2020. The regional, end-use margins (end-use minus welihead prices) in 2020 were added to the welihead natural gas price from 2021 to 2030 to arrive at regional, end-use prices. Assumption regarding future trend. This analysis assumes the trend from the AE099 Reference Case. In addition, sensitivities analysis is performed at AEO high and low growth projections.

7-137.2.2.7 Oil Prices

Definition. Residential price of distillate. Approach. a) Variability and value of Savings. Variability in prices is taken from the RECS sample and adjusted to 1997 prices. No attempt is made to define a consumer marginal energy rate. b) Future Trend. Figure 7.7 shows the oil price time trend. The values in later years (i.e. after 2015 for GRI and after 2020 for all others) are extrapolated from their relative sources. Extrapolation is necessary because the sources used do not forecast beyond 2020 (or 2015 in the case of the GRI forecast). For GRI projections beyond 2015 values are simply left constant at 2015 prices. Since the AE099 version of NEMS forecasts only to the year 2020, we had to use a method for extrapolating price data to 2030. The method that we adopted uses the ETA approach to forecast fuel prices for FEMP. Assumptions. Variability in price from 1993 RECS sample of households owning washers and dryers. The future trend is taken from the AEO 1999 Reference Case.

7-149

..-o._AEO 1999 High Growth

6

-.-AEO 1999 Reference oGR11998 --AEO 1999 LowGrowth 5 111111 I I I I I I I I I I 1995 2000 2005 2010 2015 2020 2025 2030 Figure 7.7 Alternative Oil Price Trends

7.2.2.8 LPG (propane) Prices Definition. Residential price of LPG. Approach. a) Variability and Value of Savings. Variability in prices is taken from the RECS sample and adjusted to 1997 prices. No attempt is made to define a consumer marginal energy rate. b) Future Trend. Figure 7.8 shows the LPG price tiime trend. LPG prices beyond 2020 are the same as those forecasted by ETA to forecast fuel prices for FEMP. GRI projections for LPG were not available so the prices for the AE099 Reference Case were used with the exception of keeping the price constant after 2015 to be consistent with the way GRI projections were dealt with for other fuels.

7-1515

14

0• .0•• 0-••• - .-o. .0

- 10

- -0- AEO 1999 High Growth 9

-.-AEO 1999 Reference -°-GRI 1998 8 AEO 1999 Low Growth 7 I 1995 2000 2005 2010 2015 2020 2025 2030 Figure 7.8 Alternative LPG Price Trends
7.2.2.9 Water per Cycle Definition. Water consumed by clothes washer in one cycle. Approach. Data as submitted by AHAM. Current Assumption. Use numerical values for baseline and each efficiency level, as provided by AHAM. Figure 7.9 shows the water use per cycle vs. modified energy factor (MEF).a

a Data for the figure were provided by clothes washer manufacturers and then aggregated for this analysis; see TSD Engineering Analysis for further details. 7-1645 39 39 39 39 38 40- 

# ,-' ,,_

C.j iD

- 30- 27 23 23 21 21 20- 15- 10- 5- 0-- I 0.82 0.86 0.91 0.96 1.02 1.09 1.26 1.36 1.49 1.63



# _____ 

# iii.ii

MEF Figure 7.9 Water use Per Cycle vs. MEF 7.2.2.10 Water and Wastewater Removal Prices Definition. Residential customer price for water and wastewater (sewer) (expressed in $1thousand gallons), excluding fixed charges. Issues. The typical single family home uses 22.7% of its water on clothes washers.'° Price and price escalation rates for water and wastewater need to be determined; unlike fuel prices, RECS does not provide information on water prices. The price relevant to the LCC is the marginal price, the incremental cost of water for each gallon saved. Background & Overview. In the preliminary TSD analysis, we based water and wastewater prices on urban data only. Stakeholder comments at the November 1998 DOE clothes washer workshop suggested that rural water and rural sewage rates should also be considered in the analysis. A water utility consultant, Raftelis Environmental Consulting Group, disaggregated water & wastewater utilities into three utility size categories and reported that the largest utilities as a group had lower water and wastewater charges than the smallest utilities." Assuming that, on average, rural households are served by smaller utilities, we used urban water and/or sewer rates as an approximation of connected water and sewer rates in rural areas; i.e., we assumed rural water and 7-17wastewater removal prices to be at least as high as those charged by urban utilities. For households

not having a wastewater utility, only the urban water price was used. For households having individual wells and septic systems, only the cost to pump water was used. Approach. There are two objectives of the water and wastewater price study: • distribution of current prices consumers pay to purchase water and dispose of wastewater, and • projection of water and wastewater prices into the future. Current Price Distribution Data. The most extensive current data containing both water and wastewater pricing was compiled by Raftelis.'2 The 1998 Raftelis survey data was used for the distribution of water and wastewater prices. A water usage of 10 ccfaper month was assumed for the analysis. To be consistent with other data in the LCC and NES spreadsheets, prices were converted to 1997 dollars. The database included 115 service areas (cities) representing a population of approximately56million. Because water prices can vary widely even within a small geographic area, average national values were used rather than separating values by region. Marginal price. The marginal price was estimated by subtracting the fixed prices or minimum charges from the monthly wastewater charge and from the total charges for a water usage of 10 ccf per month. The marginal prices for water and wastewater were added together and the units were converted to dollars per 1000 gallons. A weighted marginal price was ascertained from the product of the marginal price and the population weighting for each utility. For households not having a utility wastewater removal service, only the water price was used. In addition, water cost was estimated for households having individual well pumps and a septic tank. See Appendix F for additional detail. The distribution of marginal water and wastewater prices and water-only prices are shown in Figures 7.10 and 7.11, respectively.

accf = hundreds of cubic feet (cu.ft. x 7.48 = U.s. gallons) 7-187-19

Figure 7.10 1998 Urban Water & Wastewater Price Distribution, in 1997 DollarsPrice Escalation Data. The escalation of water and wastewater disposal costs, i.e., the rate

at which water and wastewater prices are changing, was determined through an examination of trends in historical prices. Future trends for average, high, and low prices were estimated. Figure 7.12 summarizes these trends. See Appendix F for a detailed discussion of the development of these price data and their use in the LCCspreadsheet.

7-20 Figure 7.11 1998 Urban Water Price Distribution, in 1997 Dollars$16

$14

$12
High

Average $6

$4 Low $2

$01995 2000 2005 2010 2015 2020 2025 2030 Figure 7.12 Water and Wastewater Price Trends

7.2.2.11 Discount Rate Definition. The rate at which future expenditures are discounted to establish their present value. Approach. A distribution of discount rates was derived to represent the variability in financing methods consumers use in purchasing appliances. The resulting distribution of discount rates is used to calculate a distribution of life-cycle costs for clothes washers. Table 7.3 summarizes the interest rate assumptions discussed below.

7-21Table 7.3 Summary of Nominal Interest Rates

Financing Percent of Clothes Washer Range of Real Interest Rates Minimum Maximum Mean New home (after 20% 1.60% 3.76% 2.68% Cash 32% 0.00% 3.00% 1.50% Credit Card 28% 6.00% 15.00% 10.5% Retailer Loan 20% 6.00% 15.00% 10.50% Consumer Purchase and Financing Methods. Consumers purchase appliances in new homes and as retail purchases. The retail purchases are paid by cash, credit cards, or retailer loans. A stakeholder comment indicated that for white goods about 40% of retail purchases are paid in cash, 35% use credit cards, and 25% use retailer loans 13 The same comment indicates 25% of appliance purchases are for new homes. (For clothes washers, DOE estimates that purchases for new homes are now about 20%.) The method of purchase used by consumers is assumed to be indicative of the source of the funds and the type of financing used by these consumers. Interest Rates. DOE estimated a range of interest rates that may reasonably be expected to apply in the future to different types of consumer savings or financing. These are estimates, based upon historical data and judgment about how the future may differ from the historical period. For new housing, DOE estimated nominal mortgage rates (5-8%), derived the after-tax rate assuming a tax of 28%, then subtracted the inflation rate (assume 2%). The result is a range of real mortgage rates of 1.60-3.76%. (Example: 5% * (100%-28%) - 2% = 1.6%) For cash, the minimum rate is 0%. This rate applies to purchasers making cash purchases without withdrawing from savings accounts. The maximum is taken to be the opportunity cost represented by the interest that could have been earned in a savings account. The historical nominal maximum savings rate ranged from4.5-5.5% from 1970 to 1986 (real rates of -8.27 to +3.58%). A real rate of 3% as indicative of the maximum. The interest rates for retailer loans and credit cards are assumed to have the same range. The minimum credit card rate is taken as 6% real. Introductory rates on some credit cards today are 5.9% nominal, but after the introductory period (often 6 months), the rate becomes much higher. Maximum rates are over 20% nominal. On the other hand, most retail purchasers will not make payments for a clothes washer over the entire lifetime (12-16 years) of the appliance. If the purchase is paid for initially by a credit card, but the consumer pays off the balance in less than the life of the washer, then the effective interest rate is lower than the nominal credit card rate. The current assumption is a range of6-15%real. Assumption. The real interest rate associated with financing an appliance purchase or with the savings from which the necessary funds were drawn are good indicators of the additional costs incurred by consumers who pay a higher first cost, but enjoy future savings. For this reason, it is
7-22assumed that these rates are appropriate discount rates for use in this analysis. Table 7.4 summarizes

the ranges of discount rates used in the LCC distribution analysis, derived from the analysis of financing methods described above. Figure 7.13 shows the distribution of real discount rates, ranging from 0 to 15%, with a mean of 6%. The weights noted on the y-axis indicate percent of purchases. Table 7.4 uses the data in Table 7.3 and accounts for overlap between the new home and cash interest rates. Table 7.4 Distribution of Discount Rates Minimum Value Maximum Value Probability 0.00% 1.60% 17% 1.60% 3.00% 28% 3.00% 3.76% 7% 3.76% 6.00% 0% 6.00% 15.00% 48%



## Consumer Real Discount Rate

20% 16% 12% 8% 4%

0% I I I I 

### 0% 2% 4% 6% 8% 10% 12% 14% 16% Discount Rate Figure 7.13 Distribution of Consumer Discount Rates
7-23DOE recognizes that other factors might be considered in the estimation of real consumer

discount rates, such as the actual impacts of appliance purchases on consumer savings, indebtedness or consumption, and expressed or imputed consumer preferences. While such data, if it were to become available, might provide a stronger analytical basis for DOE's choice of discount rates, it is considered unlikely that such data would have a significant effect on the range of values considered in the current analysis. 7.2.2.12 Fuel Use of Water Heater and Clothes Dryer Definition. Thefuel used to heat water in the water heater and the fuel used to provide heat in the clothes dryer. Approach. When using the Crystal Ba11TM distribution option on the spreadsheet, the distribution of fuel used is determined by the sampling of the RECS households. The fuel type used for the water heater and the clothes dryer are provided in the RECS database. Only those houses having both a clothes washer and dryer are included in the sample. Households using either electricity, natural gas, fuel oil or LPG are sampled. The DOE test procedure on which reported energy use is based accounts for the fact that not all clothes washed in a clothes washer are dried in a clothes dryer. The DOE test procedure assumes an electric water heater and an electric dryer. The DOE test procedure is used for the Rebuttable Payback calculation. Assumption. The distribution of fuel use as provided by RECS is shown in Table 7.5. Currently only the households in the RECS data base using these fuels are sampled using the Monte Carlo method.

Table7.5 Shares in Households of Water Heaters and Dryers by Fuel Type Water Heater Dryer Percent of Households Electric Electric 40.3% Electric Gas 0.5% Gas Electric 33.9% Gas Gas 18.5% Oil Electric 3.7% Oil Gas 0.2% LPG Electric 2.9% Source: EIA, RECS 1993
7-247.2.2.13 Year to Start Date

Definition. This is the year at which a new standard is expected to become effective. Approach. The LCC is calculated for all households as if they each purchase a new washer in the year the standard takes affect. The cost of appliances are based on this year, however, all dollar values are expressed in 1997 dollars. Annual energy prices are included for the life of the washer.Current Assumption. The new energy efficiency standard for clothes washers is assumed to take effect in the year 2004. 7.2.2.14 Base Case Design Definition. This is the cost and efficiency of the starting point to which different improvement levels of washers are compared. Approach. AIIAM supplied cost and efficiency data for a baseline and nine higher efficiency levels. The user can select any level against which to compare higher efficiency levels. Assumption. The default assumption for the base case design is the baseline design option. The baseline MEF is defined as the efficiency level that would just meet the current minimum efficiency requirements, with some assumptions made from converting from EF (Energy Factor) to MEF (Modified Energy Factor). The energy usage values supplied for the baseline case or any other MEF level are based on a clothes washer the manufacturers would build if that level became the new minimum efficiency level. In some cases, the manufacturer would build a more efficient washer than required. See Chapter 4, Engineering for more detail. 7.2.2.15 Standard Case Design Definition. The improved efficiency level for comparison with the base case design. Approach. The spreadsheet user selects the level for the analysis. Assumption. Analysis is done for all levels for which data were provided. 7.2.3 LCC Results This section presents results for life-cycle cost (LCC) for the efficiency improvement levels specified in the Engineering Analysis. Results presented here are based on the inputs described in section 7.2.2. Since the value of most inputs are uncertain and must be represented by a distribution of values rather than a discrete value, the results are presented here as a distribution of values. Where distributions are shown, the mean value of the distribution is also presented. 7-25LCC results are presented as differences in the LCC relative to the baseline clothes washer

design. As mentioned previously, the LCC differences are depicted as a distribution of values. The primary results are presented in two types of charts: 1) a frequency chartshowing the distribution ofLCC differences with its corresponding probability of occurrence and 2) a cumulative chart showing the cumulative distribution of LCC differences along with the corresponding probability of occurrence. In each chart, the mean LCC difference is provided along with the percent of the population for which the LCC will decrease. Complete results are presented in Appendices F. 1 and F.2. In the explanation below, the two charts depicting the case for a 1.089 MEF level (a 25% reduction in energy use) are used (Figure 7.14 and 7.15). In either chart (frequency or cumulative), the mean change (reduction of $21 1 in the examples here) is shown in a text box next to a vertical line at that value on the x-axis. The phrase "Certainty is 87.35% from -Infinity to $0" means that 87% of households will have reduced LCC with the increased efficiency level compared to the baseline efficiency level. In the figure caption "Reference case" refers to the scenario of using the AE099 reference economic growth assumption. Figure 7.14 is an example ofafrequency chart. The y-axes show the number of households ("Frequency" at right y-axis) and percent of all households ("Probability" at left y-axis). In this example, 10,000 households were examined ("10,000 trials") and all results are displayed ("0 outliers"). The x-axis is the difference in LCC between a baseline efficiency level and a higher efficiency level (in this example, MEF is 1.089, a 25%energy reduction). The x-axis begins with negative values on the left, which indicate that standards for those households provide savings (reduced LCC). Reduced LCC occurs when reduced operating expenses-energy and water-more than compensate for increased purchase expense. In Figure 7.14, going from the baseline efficiency level to the 1.089 MEF level provides households with an average LCC reduction of $211, and range from reductions of $1,686 (at the left) to increases of $140 (at the right) depending upon the household. (The minimum and maximum values cannot be read with precision for the graph, but rather, the program provides them in a statistical summary.)

7-26

### 10,000 Trials Forecast: LCC Difference Frequency Chart 203 Outliers .026

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Certainty is 87.35% from -Infinity to $0$ Figure 7.14 MIEF = 1.089, Reference Case. Frequency Chart of LCC Differences The vertical axis in Figure 7.15 is the cumulative probability (left axis) or frequency (right axis) that the LCC difference will be less than the value on the horizontal axis. Starting at the left, there is a 0% probability that a household will have a reduction in LCC larger than $1,686 in absolute value. Toward the middle, there is a50%probability that a household will have a reduction in LCC larger than about $166. At the right, there is a 100% probability that a household will have either a decrease in LCC or an increase in LCC of less than $140•

a See Table 7.6 for intermediate values. 7-27Forecast: LCC Difference 10,000 Trials Cumulative Chart 203 Outliers 1.000

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Certainty is 87.35% from -Infinity to $0 $ Figure 7.15 MEF = 1.089, Reference Case. Cumulative Chart of LCC Differences

7-28Figure 7.16 shows the LCC distribution for the baseline case. This is the distribution of lifecycle costs that all standard level life-cycle costs are compared to.



### 10,000 Trials Forecast: Baseline-LCC Frequency Chart 222 Outliers 

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# 11ii $500 $1,250 $2,000 $2,750 $3,500 Dollars Figure 7.16 Baseline Life-Cycle Cost Distribution
Appendix G of the TSD contains thefrequency andcumulative chartsfor all the efficiency levels and for high and low sensitivities. These charts provide more complete information than summary statistics, but a summary of the change in LCC from the baseline by percentile groupings (i.e., of the distribution of results) is given below in Table 7.6.

7-29Table 7.6 Summary of LCC Results for the Reference Case

Trial Change in LCC from Baseline1 Percent Std. MEF Shown by Percentiles of the Distribution of Results2 with Level (values in $) LCC Less than 0 10 25 50 75 90 100 Mean Baseline 0.860 (92) (34) (25) (16) (11) (7) (1) (19) 100 0.908 (267) (78) (54) (35) (22) (15) 19 (42) 99 0.961 (458) (131) (88) (54) (33) (18) 60 (66) 96 1 1.021 (619) (149) (93) (52) (22) 19 136 (61) 84 2 1.089 (1,686) (491) (311) (166) (59) 13 140 (211) 87 4 1.257 (2,341) (663) (406) (194) (33) 111 616 (242) 79 5 1.362 (2,462) (670) (414) (192) (35) 115 662 (243) 80

1.485 (2,323) (609) (356) (132) 44 205 721 (178) 69 6 1.634 (2,198) (611) (357) (126) 52 203 656 (176) 69 1.04 M1EF (880) (235) (150) (81) (34) 0 126 (103) 90 in 2004 1.26 MEF (2,487) (701) (427) (208) (38) 102 598 (260) 81 in2007 The baseline LCC, based on shipment weighted averages of the most likely costs, is $1,633 2 For sample size of 10,000 trials. Energy price trends are from AE099. Operating costs include water.

Each row in Table 7.6 corresponds to the impacts on a population of households from a possible standard level. For example, for a MEF of 1.089, a25%reduction in energy, the sample household with the largest reduction in LCC saves $1686 compared to the baseline. This result is in the "0" percentile columm For the same MEF of 1.089, the sample household with the largest increase in LCC, $140, is represented in the "100" percentile column. In between, 10% of sample households lower LCC by at least $491, 25% of sample households lower LCC by at least $311, 75% of sample households lower LCC by at least $59, and 90% of households have either reduced LCC or increases in LCC no greater than $13. The mean impact is an LCC reduced by $211 (in column "Mean"). The right-most column shows the percent of sample households for which the LCC is lower under a standards scenario. Just prior to the publication of this analysis both RECS97 and AEO2000 data became available. We have updated the analysis for Trial Standard Level 3 using RECS97 and AEO2000 and have included it in Appendix R. As shown in Appendix R, there is no significant change in the results.

7-30In addition to the AEO reference case scenario shown above, with expected economic growth

and expected water price escalations, two sensitivity scenarios were run. Table 7.7 shows the LCC for the scenario of AEO high economic growth and high water and wastewater escalation rate. Table 7.8 shows the case for the assumption of AEO low economic growth and low water and wastewater price escalation.
Table 7.7 Summary of Life-cycle Cost Results

-AEO High Growth & High Water Escalation

Rate Trial MEF Change in LCC from Baseline Percent Std. Shown by Percentiles of the Distribution of Results with Level (values in$) LCC Less than Baseline 0 10 25 50 75 90 100 Mean 0.860 (113) (40) (29) (19) (12) (8) (1) (22) 100 0.908 (262) (86) (60) (39) (25) (16) 17 (46) 99 0.961 (477) (141) (94) (59) (35) (20) 59 (71) 96 1 1.021 (529) (165) (102) (57) (25) 16 133 (68) 85 2 1.089 (2,005) (610) (393) (218) (89) (6) 144 (271) 91 4 1.257 (3,070) (846) (529) (255) (67) 75 645 (332) 83 5 1.362 (3,690) (847) (529) (264) (72) 79 628 (335) 83 1.485 (2,993) (772) (461) (192) 2 175 667 (258) 75 6 1.634 (3,010) (784) (465) (189) 2 162 653 (265) 75 1.04MEF (791) (273) (179) (100) (45) (7) 117 (124) 92 in 2004 3 1.26MEF (3,428) (961) (601) (296) (86) 58 631 (385) 85 in 2007

7-31Table 7.8 Summary of Life-Cycle Cost Results - AEOLow Growth & Low Water Escalation Rate Trial Change in LCC from Baseline Percent Std. MEF Shown by Percentiles of the Distribution of Results with Level (values in$) LCC Less than 0 10 25 50 75 90 100 Mean Baseline 0.860 (75) (29) (21) (14) (10) (7) (1) (16) 100 0.908 (249) (71) (49) (32) (21) (13) 19 (38) 99 0.961 (421) (121) (81) (51) (30) (16) 61 (61) 96 1 1.021 (483) (135) (86) (48) (18) 22 135 (54) 83 2 1.089 (1,316) (388) (245) (126) (34) 32 168 (158) 83 4 1.257 (2,022) (522) (312) (138) (2) 141 644 (169) 75 5 1.362 (2,156) (518) (317) (130) 8 156 636 (163) 73 1.485 (1,686) (482) (266) (77) 89 244 643 (106) 62 6 1.634 (1,894) (487) (273) (71) 85 241 679 (107) 61 1.04MEF (764) (203) (126) (67) (24) 11 126 (84) 87 in 2004 3 1.26 IVIEF (2,012) (515) (307) (133) 2 138 632 (165) 74 in 2007

7-32Figure 7.17 below summarizes the life-cycle cost results for the reference case and both high

and low economic growth sensitivities.

70 60



### _ _

° I 40

_ ______ _______________ __ .-ø-- Reference 30 -.- AEO-high 20 -a- AEO-Iow

________________________________________________________________ 10

0 I P I P I p 5 10 15 20 1.04 25 35 1.26 40 45 50 MEFin MEFIn 2004 2007 Standard Level Figure 7.17 Consumers Having LCC Savings for Reference, High and Low Growth

Scenarios

7-337.3 DISTRIBUTION PAYBACK PERIOD

7.3.1 Metric The payback period (PBP) measures the amount of time it takes the consumer to recover the assumed higher purchase expense of more energy-efficient equipment through lower operating costs. Numerically, the PBP is the ratio of the increase in purchase expense (i.e., from a less efficient design to a more efficient design) to the decrease in annual operating expenditures. This type of calculation is known as a "simple" payback period, because is does not take into account changes in operating expense over time or the time value of money, that is, the calculation is done at an effective discount rate of 0%. PBP is found by solving the equation

PAY=- A01 (4)

forPAY,whereAP= difference in purchase expense between the more efficient and the less efficient design options, andA01 = difference in annual operating expenses. PBPs are expressed in years. PBPs greater than the life of the product mean that the increased purchase expense is not recovered in reduced operating expenses.
7.3.2 Inputs The data inputs to PBP are the purchase expense for each design option and the annual (first year) operating expenditures for each design option. The inputs to the operating costs are the annual energy savings, the energy price, the change in annual water consumption, and the water price. The Distribution PBP uses the same inputs as the LCC analysis described in section 7.2 except for a few exceptions described below. Since this is a "simple" payback the fuel rates used are only for the year the standard takes effect, assumed here to be the year 2004. The price of electricity, gas and oil are those projected for that year. Similarly for water rates, a single value for the price of water and wastewater disposal is used. Discount rates are not used for the payback calculation.

7-347.3.3 Results Figure 7.18 is an example of a chart showing the distribution of payback periods for a MEF of 1.089, a25%reduction in energy consumption level. Note that the mean value of the payback period distribution(5.0years) is provided.



### 10,000 Trials Forecast: Payback (yrs) Frequency Chart 166 Outliers .026 

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_______________________________________ 0.0 4.4 8.8 13.1 17.5 Figure 7.18 MEF = 1.089, Reference Case-Frequency Chart of Payback Periods
Appendix H contains Distribution PBP charts for all the efficiency levels. Again, these charts provide more complete information than summary statistics, but a summary of payback values by percentile groupings (i.e., of the distribution of results) is given below in Table 7.9. Each chart is the result of 10,000 Monte Carlo runs or, in other words, 10,000 samples from each of the distribution inputs. Tables 7.10 and 7.11 summarize payback period values for AE099 high and low growth scenarios, respectively.

7-35