APPENDIX K. ESTIMATION OF UTILITY AND ENVIRONMENTAL
 

RESULTS FROM NEMS-BT OUTPUT 

TABLE OF CONTENTS 

K.1 Allocation of Input Electricity Savings by Census Division and End Use..................... K-1 

K.2 Choice of Impact Multipliers.......................................................................................... K-2 

K.3 Regression Smoothing of NEMS-BT Outputs................................................................ K-4 

K.4 Estimation of Interpolating Functions from Savings Multipliers ................................... K-5 

K.5 Environmental Impacts ................................................................................................... K-7
 

LIST OF TABLES 

Table K.1.1 Shares of National Savings by Census Division Used in NEMS-BT.................... K-2 

Table K.2.1 Comparison of Refrigeration Electricity Savings with Different 

Multipliers with Total Generation ...................................................................... K-3 

Table K.2.2 Final Implied Multipliers by Trial Standard Level – All 

Refrigeration Equipment..................................................................................... K-4 

LIST OF FIGURES 

Figure K.3.1 Example of Regression Smoothing Fits for Fossil-Fired and Total 

Installed Capacity (4X case) ............................................................................... K-5 

Figure K.4.1 Response (Interpolating) Functions for Selected Generation Outputs 

for 2025............................................................................................................... K-6 

Figure K.5.1 Response (Interpolating) Functions for Carbon Dioxide for Years 

2015, 2020, 2025 and 2030................................................................................. K-8 

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 K-ii 

EERE-2006-BT-STD-0126 COMMENT 52.2 
 

 

APPENDIX K. ESTIMATION OF UTILITY AND ENVIRONMENTAL 

RESULTS FROM NEMS-BT OUTPUT
 

The effects of proposed energy conservation standards for commercial refrigeration 

equipment was analyzed using a variant of National Energy Modeling System (NEMS) model, 

called NEMS-BT.1a Because the magnitude of national energy savings as a result of the 

proposed standards is very small relative to total electric generation, the U.S. Department of 

Energy (DOE) used an estimation method involving regression smoothing and interpolation. 

This appendix describes the major elements of this method. 

To run a simulation in NEMS-BT model, the refrigeration load in the commercial 

building module is reduced annually according to the electricity savings estimated by the 

National Energy Savings (NES) spreadsheet model (see Chapter 10 of the TSD) for each trial 

standard level (TSL). The electricity savings increase over time as various types of commercial 

refrigeration equipment are replaced with more efficient equipment meeting the proposed 

conservation standards. 

The magnitude of the electricity decrement that would be required for NEMS-BT to 

produce stable results out of the range of numerical noise is greater than the highest TSL under 

consideration. Therefore, to estimate results for the TSLs evaluated here, a series of NEMS-BT 

simulations is performed using higher values for the input electricity savings. These simulations 

establish the relationship between the NEMS-BT output variables (electricity generation by fuel, 

installed electricity generation capacity, and emissions) and the electricity savings inputs. For 

each output variable, a regression model is estimated to represent an average response across the 

series of simulations. Impacts are obtained using predicted values from the regression models 

corresponding to the (smaller) levels of savings corresponding to TSLs. 

K.1 Allocation of Input Electricity Savings by Census Division and End Use 

Although the utility and environmental impacts are national in scope, the NEMS-BT 

model operates at a census division level. An allocation of savings was made on the basis of 

estimated sales distributions to various categories of buildings (supermarkets, convenience and 

specialty stores, gas stations with convenience stores, and multi-line retail stores). The shares 

used to distribute the savings across census divisions are shown in Table K.1.1. The shares are 

held constant for the forecast period through 2030. 

a

 DOE’s EIA approves of the name NEMS to describe only an official version of the model without any 

modification of the code or data. Because the analysis of commercial refrigeration energy conservation standards 

entails some minor code modifications and the model is run under policy scenarios that are variations of DOE/EIA 

assumptions, the name NEMS-BT refers to the model as used here. (The Building Technologies [BT] program is 

one of the major program offices within DOE’s Office of Energy Efficiency and Renewable Energy. The Building 

Technologies program is responsible for promulgating energy efficiency standards for the buildings sector mandated 

under the 2005 Energy Policy Act and earlier federal legislation.) 

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EERE-2006-BT-STD-0126 COMMENT 52.2 
 

 

 

 

 

 

Table K.1.1 Shares of National Savings by Census Division Used in NEMS-BT 

Census Division Share (%) 

 New England 

 Middle Atlantic 

 East North Central 

West North Central 

 South Atlantic 

 East South Central 

West South Central 

 Mountain 

Pacific 

5.9%

13.8%

14.7% 

6.6%

19.4%

5.6% 

10.8%

6.7%

16.6% 

K.2 Choice of Impact Multipliers 

To gain some understanding of the how NEMS-BT would respond to various magnitudes 

of savings, a “reference” time series of savings was initially selected from a preliminary version 

of the NES spreadsheet model. This reference series was selected as the savings that would 

result from the most stringent TSL (“max technologically feasible) for all refrigeration 

equipment considered in this rulemaking (TSL 5). The estimated savings from this particular 

equipment type in 2030 was 6.0 terrawatt-hours (TWh) or 20.5 Trillion Btu (in site energy 

terms). NEMS-BT was run using the projected time series of savings pertaining to this case as 

well as for higher multipliers (designated here with a suffix X), including 1X, 2X, 4X, 6X, and 

8X. (Thus, for example, the 2030 refrigeration savings imposed upon the commercial module in 

NEMS-BT in the 4X case was 41.0 TWh.) 

It was judged, on the basis of graphical output and comparison of trends among the 

output variables, that the numerical noise from the model was overwhelming the impact of the 

savings decrement for the smaller multiplier factors less than 2X.b

 Table K.2.1 compares the 

savings decrement applied to the model under various multipliers and how the magnitude of the 

savings compares with total electricity generation in EIA’s Annual Energy Outlook (AEO2007) 

reference case.2c 

b

 The numerical noise stems, in part, from the fact that a precise balancing between the demand and supply sides of 

NEMS is not always achieved, given the iterative nature of the solution process. c

 The reader should note that the values of the multipliers used here are solely dependent on the initial (and arbitrary) 

choice of a particular refrigeration product and a specific trial standard level (TSL) for that product. The NEMS-BT 

simulations were performed prior to the final selection of TSLs and so the 1X multiplier does not correspond to any 

specific TSL for a product category in previous sections of the report. However, it was judged that the savings 

decrements (in TWh) tested in NEMS-BT, shown in lower portion of Table K.2.1, were in the relevant range of 

magnitudes to yield stable and robust predictions by the model and so additional runs using different multipliers 

were deemed unnecessary. For use in the predicting the impacts from the set of final TSLs, the savings decrement 

from any TSL was converted into an appropriate multiplier by scaling in proportion to the values shown in the first 

row of Table K.2.1. 

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EERE-2006-BT-STD-0126 COMMENT 52.2 
 

 

 

 

 

Table K.2.1 Comparison of Refrigeration Electricity Savings with Different Multipliers 

with Total Generation 

2015 2020 2025 2030 

1X Savings (TWh) 

% of total generation 

1.44 

0.03% 

3.64 

0.08% 

5.66 

0.11% 

5.99 

0.11% 

2X Savings (TWh) 

% of total generation 

2.89 

0.06% 

7.29 

0.15% 

11.31 

0.23% 

11.99 

0.23% 

4X Savings (TWh) 

% of total generation 

5.77 

0.13% 

14.57 

0.31% 

22.63 

0.46% 

23.98 

0.46% 

6X Savings (TWh) 

% of total generation 

8.66 

0.19% 

21.86 

0.46% 

33.94 

0.68% 

35.97 

0.69% 

8X Savings (TWh) 

% of total generation 

11.54 

0.26% 

29.14 

0.62% 

45.26 

0.91% 

47.95 

0.92% 

Based on this testing, a decision was made to use the multipliers in the range of 2X 

through 6X. While these multipliers are greater than 1, they still do not yield large perturbations 

to the base case simulation. For example, as Table K.1.1 indicates, even at 6X in the year 2025, 

the total savings decrement applied to the model represents only about 0.7 percent of total 

electricity generation. 

Final Implied Multipliers by Trial Standard Level for All Refrigeration Equipment 

Subsequent to the production of the NEMS simulations described in the previous section, 

some revisions were made to the final set of energy savings estimates for the final rule. In 

addition, for purposes of reporting utility and environmental impacts for refrigeration, all 15 

types of refrigeration equipment were aggregated. To adjust for these changes, the difference 

between the final savings estimate (by trial standard level) and the savings from the initial 

“reference” time series was represented as a scale factor. (For example, the energy savings for 

the final version of the energy standard was 5.28 TWh at TSL-5, as compared to the actual value 

used as the 1X case in NEMS-BT of 5.99 TWh. Thus, the scale factor for TSL-5 is 5.28/5.99 = 

0.881). The scale factor essentially adjusts the savings from all refrigeration equipment for a 

specific TSL to be consistent with the reference series and, thus, be consistent with the 

interpolation procedure described above. 

The first column in Table K.2.2 displays the scale factors for each TSL.d

 The remaining 

columns display the implied multipliers from the three simulations performed with NEMS-BT 

(using their designations of 2X, 4X, and 6X). For example, after combining all the refrigeration 

equipment, the actual final multipliers used for TSL-4 were 2.8, 5.7, and 8.5. Because the 

savings for TSL-1 through TSL-3 are smaller than those for TSL-4, the implied multipliers are 

larger than those for TSL-4 (and vice versa for TSL-5). 

d

 The scale factors can also be viewed as interpolating values relative to the set of NEMS-BT impact runs. Thus, for 

example, if an impact upon a particular variable from the “2X” case in the original set of simulations was 

interpolated to be a value of, say, 0.10, then the impact for TSL-4 for all refrigeration equipment would be 1⁄2 x 

0.703 x 0.10 = 0.035. 

 K-3 

EERE-2006-BT-STD-0126 COMMENT 52.2 
 

 

 

 

 

 

 

 

Table K.2.2 Final Implied Multipliers by Trial Standard Level – All Refrigeration 

Equipment 

Scale 

Factor 

Implied Multipliers 

NEMSBT 

("2X") 

NEMSBT 

("4X") 

NEMSBT 

("6X") 

All Refrigeration Equipment 

 TSL – 1 0.114 

 TSL – 2 0.438 

 TSL – 3 0.678 

 TSL – 4 0.703 

 TSL – 5 0.881 

17.6 35.1 

4.6 9.1 

3.0 5.9 

2.8 5.7 

2.3 4.5 

52.7 

13.7 

8.9 

8.5 

6.8 

K.3 Regression Smoothing of NEMS-BT Outputs 

Even at the selected multiplier levels shown in Table K.1.1, the simulations with 

NEMS-BT produces considerable year-to-year variation that appears to be caused by numerical 

noise from the model solution. This variation appears to affect the outputs related to specific 

generation fuel and technologies more so than total generation or total installed capacity. For the 

utility impact assessment, it is more important to identify distinct trends in the utility sector 

adjustment process rather than year-to-year variation that may be spurious results from the model 

solution process. 

To reduce the potential effect of numerical noise from NEMS-BT simulation, a 

smoothing regression was estimated for each of the model output variables. The output variables 

are measured as the differences between the (AEO) reference case and the energy efficiency 

standards case. The formulation of the smoothing regression was a simple quadratic function of 

time (in years) with the following form: 

Y(T) = a1 x T + a2 x T2

 + u Eq. K.1 

where 

Y = Output variable for time period T (difference from reference case) 

T = Time in years, T = 0 in 2012, T = 1 in 2013, etc. 

a1, a2 = estimated regression coefficients 

u = random disturbance term. 

The regression specification is such that the fitted (predicted) value of the variable is 

forced through the origin in the year prior to the implementation of the standard (2012). By 

forcing the impact to be zero in 2012, this procedure generates estimated impacts that typically 

could be expected to change in a systematic fashion beginning in 2013. 

Figure K.3.1 illustrates this procedure for the differences in total installed capacity and 

the three types of fossil fuel capacity. The particular case shown is for commercial refrigeration 

equipment at the “4X” level of savings (or 5.7X relative TSL-4 for all commercial refrigeration 

equipment—see Table K.2.2). Clearly, NEMS-BT indicates a very smooth path of differences in 

total generation capacity as shown in the top left panel of the figure. (In this case, the regression 

smoothing would not be necessary.) 

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EERE-2006-BT-STD-0126 COMMENT 52.2 
 

 

 

 

 

For plants using specific fuels or technologies, the differences in particular years from a 

smooth trend line are more substantial. The deviations from the fitted trend line are most 

pronounced for combustion turbines. In this case, the model produces a significant negative 

deviation from trend in the years between 2020 and 2027.e 

Nevertheless, over the entire forecast period, the long-term trends in the capacity of all of 

these types of plants are apparent from the graphs. The approach in this analysis is to abstract 

these short-term deviations from what appear to be long-term trends. 

Total Generation 

-2.5 

-2 

-1.5 

-1 

-0.5 

0 

0.5 

2010 2015 2020 2025 2030 

Difference (GW)

NEMS-BT 

Fitted 

Fossil Steam 

-2.5 

-2 

-1.5 

-1 

-0.5 

0 

0.5 

2010 2015 2020 2025 2030 

Difference (GW)

NEMS-BT 

Fitted 

Combined Cycle 

-0.6 

-0.5 

-0.4 

-0.3 

-0.2 

-0.1 

0 

0.1 

0.2 

2010 2015 2020 2025 2030 

Difference (GW)

NEMS-BT 

Fitted 

Combustion Turbine 

-0.8 

-0.6 

-0.4 

-0.2 

0 

0.2 

0.4 

2010 2015 2020 2025 2030 

Difference (GW)

NEMS-BT 

Fitted 

Figure K.3.1 Example of Regression Smoothing Fits for Fossil-Fired and Total Installed 

Capacity (4X case) 

K.4 Estimation of Interpolating Functions from Savings Multipliers 

The development of utility and environmental impacts focused on specific years from the 

NEMS-BT simulations: 2015, 2020, 2025, and 2030. For each output variable (e.g., coal-fired 

electricity generation), the predicted values from the trend regression were extracted for each 

savings multiplier level (2X, 4X, and 6X). A subsequent linear regression was performed using 

e

 In this particular case, NEMS-BT appears to show some anomalous behavior in the last several years of the 

simulation period, especially for combined cycle and combustion turbines. The regression smoothing approach 

provides a means of generating more plausible predictions in these instances. In some cases, the last year or two of 

the projection was not used in regression fitting procedure. 

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EERE-2006-BT-STD-0126 COMMENT 52.2 
these values for the dependent variable and the values of the saving multiplier as the independent 

or explanatory variable. The y-intercept of this regression is forced through the origin. This 

restriction ensures that the predicted value of a NEMS-BT output variable is zero when the 

savings multiplier is zero. Predicted values for non-zero multipliers are calculated by taking the 

estimated regression coefficient (slope) times the value of the savings multiplier. 

Figure K.4.1 shows several examples of this estimation approach. The magnitude of the 

energy savings multiplier is plotted on the x-axis against the total reduction in generation 

capacity for the year 2025. The results shown are based on the three simulations selected for 

commercial refrigeration equipment (2X, 4X, and 6X as described above). The fitted 2025 values 

from the smoothing regression of the NEMS-BT output are shown in the large solid (black) 

diamonds. In the case of total generation capacity, the linear regression line is closely aligned to 

the three points corresponding to the 2X, 4X, and 6X savings multipliers. 

Total Generation 

-35 

-30 

-25 

-20 

-15 

-10 

-5 

0 

0 1 2 3 4 5 6 7 8 9 10 

Savings Multiplier 

Difference (TWh) 

Coal 

-25 

-20 

-15 

-10 

-5 

0 

0 10 

Saving Multiplier 

Difference(TWh) 

Natural Gas 

-10 

-8 

-6 

-4 

-2 

0 

0 1 2 3 4 5 6 7 8 9 10 

Savings Multiplier 

Difference (TWh) 

Renewables 

-5 

-4 

-3 

-2 

-1 

0 

0 1 2 3 4 5 6 7 8 9 10 

Savings Multiplier 

Difference (TWh) 

Figure K.4.1 Response (Interpolating) Functions for Selected Generation Outputs for 

2025 

Within this range of savings, the graph demonstrates the impact on total capacity 

predicted by the model is proportional to the magnitude of the refrigeration electricity savings. 

For the other generation types shown in Figure K.4.1, the responses deviate from strict 

proportionality. For example, for coal, the response at the 4X is proportionately less than either 

the 2X or 4X levels. The approach taken here essentially produces a weighted average of the 

responses across these various savings multipliers. 

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EERE-2006-BT-STD-0126 COMMENT 52.2 
The predicted value of a NEMS-BT output variable for a given standards level is 

interpolated along the regression line estimated for that variable. The computation of savings 

from a particular standards case first takes into account the scale factors shown in the first 

column in Table K.2.1. Thus, for example, the scale factor associated with Trial Standard Level 

4 is 0.703. For total electricity generation in 2025, the estimated response coefficient is -5.36 

(e.g., the slope of regression line in the top left panel Figure K.4.1). Thus, the estimated impact 

on total generation in 2025 from this specific trial standard level would be 0.703 x -5.36 = -3.77 

TWh. 

K.5 Environmental Impacts 

The general approach for estimating the impacts on the utility sector was also used to 

develop an environmental assessment in terms of changes in emissions. Annual values of the 

emissions for NOx, mercury, and carbon dioxide were extracted from the relevant tables 

produced by NEMS. 

Similar to examples shown in Figure K.3.1, the projections of emissions from 2012 

through 2030 were smoothed using the quadratic function in time discussed above. Response 

functions were then estimated at 5-year intervals between 2015 and 2030. 

Figure K.5.1 shows the estimated response (or interpolating) functions for carbon dioxide 

emissions. There is very little disparity in the magnitudes of carbon dioxide emissions across the 

three multipliers selected. However, the 4X case shows a somewhat smaller response than the 

2X and 6X cases. However, the regression-based average takes all three cases into account, 

leading to strong indication of a reduction in carbon dioxide emissions. 

The scale of the y-axis is held constant for each of the plots and so the clockwise rotation 

of the slopes across the years 2015 through 2030 reflects the increasing reduction in emissions 

over selected years. There is little change in the slopes between 2025 and 2030, reflecting some 

slowdown in the rate of increase of savings. By the middle part of the next decade, most 

refrigeration equipment will have already been replaced by more efficient equipment mandated 

by the standards. Moreover, increases in coal generation toward the end of the forecast period 

have the effect of retarding further reductions in carbon dioxide emissions. 

 K-7 

EERE-2006-BT-STD-0126 COMMENT 52.2 
Carbon Dioxide Emissions (2015) 

-30 

-25 

-20 

-15 

-10 

-5 

0 

0 1 2 3 4 5 6 7 8 

Savings Multiplier 

Difference (MMT-CE) 

Carbon Dioxide Emissions (2020) 

-30 

-25 

-20 

-15 

-10 

-5 

0 

0 2 4 6 8 

Savings Multiplier 

Difference (MMT-CE) 

Carbon Dioxide Emissions (2025) 

-30 

-25 

-20 

-15 

-10 

-5 

0 

0 1 2 3 4 5 6 7 8 

Savings Multiplier 

Difference (MMT-CE) 

Carbon Dioxide Emissions (2030) 

-30 

-25 

-20 

-15 

-10 

-5 

0 

0 2 4 6 8 

Savings Multiplier 

Difference (MMT-CE) 

Figure K.5.1 Response (Interpolating) Functions for Carbon Dioxide for Years 2015, 

2020, 2025 and 2030. 

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EERE-2006-BT-STD-0126 COMMENT 52.2 
REFERENCES 

1. U.S. Department of Energy. Energy Information Administration. National Energy Modeling 

System: An Overview 2003. (DOE/EIA-0581(2003), March 2003. 

http://tonto.eia.doe.gov/reports/reports_kindD.asp?type=model%20documentation. 

2. U.S. Department of Energy/Energy Information Administration (DOE/EIA), Annual Energy 

Outlook 2008, June 2008. Washington, D.C. DOE/EIA-0383(2008). 

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