ORNL/TM-2012/568

Estimating the Energy-Efficiency 

Potential in the Eastern Interconnection

January 2013 (Revised April 2013)

Prepared by 

Marilyn A. Brown1

Yu Wang1

1School of Public Policy, Georgia Institute of Technology
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iii

ORNL/TM-2012/568

Power and Energy Systems Group

ESTIMATING THE ENERGY-EFFICIENCY POTENTIAL IN THE EASTERN 

INTERCONNECTION 

Marilyn A. Brown

Yu Wang

School of Public Policy

Georgia Institute of Technology

Date Published: January 2013

Prepared by

OAK RIDGE NATIONAL LABORATORY

Oak Ridge, Tennessee 37831-6283

managed by

UT-BATTELLE, LLC

for the

U.S. DEPARTMENT OF ENERGY

under contract DE-AC05-00OR22725
iv

TABLE OF CONTENTS

Table of Contents........................................................................................................................... iv

List of Figures and Tables................................................................................................................v

Acknowledgments........................................................................................................................ viii

Executive Summary....................................................................................................................... ix

1 Introduction, Objectives, and Background ................................................................... 1

1.1 Objectives ................................................................................................................. 1

1.2 Review of Energy-Efficiency Potential .................................................................... 2

1.3 Barriers, Drivers, and Current Policies..................................................................... 6

2 Methodology ..................................................................................................................... 9

2.1 Overview of Approach.............................................................................................. 9

2.2 National Energy Modeling System........................................................................... 9

2.3 Energy Efficiency Policy Levers............................................................................ 10

2.4 Calculation of Levelized Cost of Electricity........................................................... 12

2.5 Method for Deriving State-Specific Estimates....................................................... 13

3 Energy-Efficiency Potential in Residential Buildings................................................. 15

3.1 Overview of Previous Literature on the Energy-Efficiency

Potential in Residential Buildings........................................................................... 16

3.2 Residential Efficiency Policy Options and Modeling Approach............................ 17

3.3 Modeling Results for Individual Residential Policies ........................................... 21

3.4 Integrated Residential Policy Bundle..................................................................... 27

3.5 Summary and Discussion....................................................................................... 31

4 Energy-Efficiency Potential in Commercial Buildings............................................... 33

4.1 Overview of Previous Literature on the Energy-Efficiency

Potential of Commercial Buildings......................................................................... 34

4.2 Commercial Efficiency Policy Options and Modeling Approach .......................... 35

4.3 Modeling Results for Individual Commercial Building Policies............................ 38

4.4 Integrated Commercial Policy Bundle.................................................................... 42

4.5 Conclusion and Discussion..................................................................................... 44

5 Energy Efficiency Potential in Industrial Sector ........................................................ 45

5.1 Overview of Previous Literature on the Energy-Efficiency

Potential of Industry................................................................................................ 47

5.2 Industrial Efficiency Policy Options and Modeling Approach............................... 49

5.3 Modeling Results for Industry Policies................................................................... 51

5.4 Integrated Industrial Policy Bundle ........................................................................ 58

5.5 Conclusions and Discussions.................................................................................. 61

6 Integrated Policy Case................................................................................................... 63

6.1 The Achievable Potential of Electricity Savings.................................................... 66

6.2 Policy Impacts on Electricity Rates and the Power Sector..................................... 70
v

6.3 Policy Impacts on Other Fuels and Carbon Emissions........................................... 73

6.4 Policy Impacts on Energy Intensity and GDP ........................................................ 75

6.5 Discussion and Conclusion..................................................................................... 77

7 References....................................................................................................................... 79

Appendices

A GT-NEMS Modeling of Residential Energy Efficiency Policies.................................... 85

B GT-NEMS Modeling of Commercial Energy Efficiency Policies.................................. 89

C GT-NEMS Modeling of Industrial Energy Efficiency Policies....................................... 96

D State Results..................................................................................................................... 97

LIST OF FIGURES AND TABLES

Figures

ES.1 Total Energy Savings Potential of an Integrated Policy Scenario in the Eastern 

Interconnection (in Quads) ...............................................................................................ix

ES.2 Protential Electricity Savings (all sectors) from the Integrated Policy Scenario in the 

Eastern Interconnection ..................................................................................................... x

ES.3 Electricity-Savings Potential by Sector Based on the Integrated Policy Scenario (in 

TWh) ..........................................................................................................................xi

ES.4 Supply Curve for Electricity Efficiency Policies in 2020 in the Eastern 

Interconnection ................................................................................................................xii

1.1 Interconnections of the National Electricity Reliability Council (top) and Regions and 

Divisions of the U.S. Census Bureau (bottom).................................................................. 2

3.1 Residential Energy Consumption in the Eastern Interconnection in 2009 ...................... 15

3.2 Reference Case Forecast of Residential Consumption in the Eastern Interconnection ... 16

3.3 Building Energy Code Retirement Years by Census Division ........................................ 19

3.4 Adoption Status of Residential Building Energy Codes as of April, 2012...................... 19

3.5 States with On-Bill Financing Programs ......................................................................... 20

3.6 Estimated Reduction in Electricity Demand by Individual Residential Policies............. 22

3.7 Electricity Savings by Census Division for Individual Policies...................................... 23

3.8 Residential Electricity Savings Potential in the Eastern Interconnection........................ 28

4.1 Commercial Energy Consumption in the Eastern Interconnection.................................. 33

4.2 Reference Case Forecast of Commercial Electricity Consumption in the Eastern 

Interconnection ................................................................................................................ 34

4.3 Mandated Disclosure and Benchmarking Efforts in the United States............................ 36

4.4 PACE-Enabled States ...................................................................................................... 37

4.5 Estimated Reductions in Electricity Demand by Individual Policies.............................. 38

4.6 Electricity Savings by Census Division for Individual Policies...................................... 39

4.7 Commercial Electricity Savings Potential in the Eastern Interconnection ...................... 42

5.1 Industrial Energy Consumption in the Eastern Interconnection...................................... 45

5.2 Primary Energy Consumption by Manufacturing Industries in 2006, Nationwide ......... 46

5.3 Gross Output by Manufacturing Industries in 2006 ........................................................ 46
vi

5.4 Reference Case Forecast of Industrial Electricity Consumption in the Eastern 

Interconnection ................................................................................................................ 47

5.5 Estimated Reductions in Electricity Demand by Individual Policies (Excluding CHP) . 51

5.6 CHP Generation and Reductions in Electricity Purchase ................................................ 52

5.7 Potential for Expanded CHP Generating Capacity in the Eastern Interconnection......... 53

5.8 Natural Gas Consumption by Industry Sector in the Eastern Interconnection ................ 53

5.9 Electricity Retail Price in the Industry Sector.................................................................. 54

5.10 Electricity Savings by Census Division from Motor Rebates, Motor Standard, and 

Advanced Manufacturing Initiative (West South Central Excluding Texas).................. 55

5.11 Electricity Savings by Census Division from CHP (West South Central Excluding 

Texas) ......................................................................................................................... 55

5.12 U.S. Industrial Consumption by Size of Firm.................................................................. 58

5.13 Industrial Electricity Savings Potential in the Eastern Interconnection........................... 59

5.14 Electricity Savings by Industry Subsectors...................................................................... 59

5.15 Increases in Electricity Generation from Industrial CHP ................................................ 60

5.16 Natural Gas Consumption in the Industry Sector ............................................................ 60

5.17 Electricity Retail Price in the Industry Sector.................................................................. 61

6.1 Supply Curve for Electricity Policies in 2020 ................................................................. 65

6.2 Electricity Savings from the Integrated Policy Case in the Eastern Interconnection ...... 67

6.3 Electricity-Savings Potential by Sector, Based on the Integrated Policy Case (in TWh) 67

6.4 Achievable Potential for Electricity Savings by State in 2020 ........................................ 69

6.5 Electricity Price Changes by Census Division................................................................. 71

6.6 Power Generations by Source in the U.S., in TWh (EE=Energy Efficiency) ................. 73

6.7 Total Energy Savings in the Eastern Interconnection (Quads)........................................ 74

6.8 CO2 Emissions in the Eastern Interconnection (Million Tonnes).................................... 74

6.9 Forecasted Changes in Energy Intensity by Economic Sectors....................................... 76

A.1 Share of New Houses Built in the Policy Cases.............................................................. 87

Tables

ES-1 Potential Electricity Savings by Sector Policy Bundles in the Eastern Interconnection ..xi

1.1 Assessments of Energy-Efficiency Potential..................................................................... 4

1.2 Market Failures and Barriers Inhibiting Energy Efficiency .............................................. 7

2.1 Selected Policies for Electric End-Use Efficiency........................................................... 11

3.1 GT-NEMS Lever for Residential Efficiency Policies ..................................................... 18

3.2 Natural Gas Savings from Individual Policies................................................................. 25

3.3 Total Energy Savings from Individual Policies, Residential Sector................................ 25

3.4 Cost Estimations from Appliance Incentives................................................................... 26

3.5 Cost Estimations from Building Codes............................................................................ 26

3.6 Cost Estimations from On-Bill Financing ....................................................................... 27

3.7 Cost Estimations from Market Priming ........................................................................... 27

3.8 Cost Estimations from Aggressive Appliance Policy...................................................... 27

3.9 Electricity Savings Potential by End-use in the US......................................................... 29

3.10 Technology Demand Shift from Residential Policies in the Eastern Interconnection..... 29

3.11 Demand share of Electric Heat Pumps for Cooling in the Eastern Interconnection........ 30

3.12 Potential savings of natural gas and total energy in the Eastern Interconnection............ 30

3.13 Potential Reduction in CO2 Emissions from Residential Policies................................... 30
vii

3.14 Comparison of Policy Impacts in the Eastern Interconnection........................................ 31

4.1 GT-NEMS Lever for Commercial Energy Efficiency Policies....................................... 35

4.2 Natural Gas Savings from Individual Policies................................................................. 40

4.3 Total Energy Savings from Individual Policies.............................................................. 40

4.4 Cost Estimates from Benchmarking ................................................................................ 41

4.5 Cost Estimates from Financing........................................................................................ 41

4.6 Cost Estimates from Building Codes............................................................................... 41

4.7 Electricity-Savings Potential by End-Use in the US........................................................ 43

4.8 Potential Savings of Natural Gas and Total Energy ........................................................ 43

4.9 Potential Reduction in CO2 Emissions from Commercial Policies................................. 43

5.1 GT-NEMS Lever for Industrial Efficiency Policies........................................................ 50

5.2 Natural Gas Savings from Advanced Manufacturing Initiative ...................................... 56

5.3 Cost Estimations from Motor Rebate .............................................................................. 56

5.4 Cost Estimations from Motor Standard ........................................................................... 57

5.5 Cost Estimations from CHP Incentive............................................................................. 57

5.6 Cost Estimations from Advanced Manufacturing Initiative ............................................ 58

5.7 Reductions in Carbon Emissions..................................................................................... 61

6.1 Savings Potential and Levelized Cost of Electric End-Use Efficiency, by Policy .......... 64

6.2 The Levelized Cost of Electricity Efficiency by Type of Policy (in cents/kWh)............ 66

6.3 Electricity-Savings Potential in 2020, by Census Division (as percentages of 

Reference case consumption in 2020) ............................................................................. 68

6.4 Electricity Rate Changes in 2020, Reference versus Integrated Policy Case .................. 70

6.5 Electricity Generation by Source in the U.S. (in TWh), Reference Forecast versus 

Integrated Policy Case ..................................................................................................... 72

6.6 Energy-Savings Potential of Natural Gas and Total Energy ........................................... 73

6.7 Reduction in CO2 Emissions............................................................................................ 75

6.8 GDP Impact ..................................................................................................................... 76

A.1 Most Efficient Home Appliances and Equipment ........................................................... 85

A.2 Building Energy Codes Profile for Residential Buildings............................................... 86

A.3 Residential Technologies Forced Early Retirement ........................................................ 88

B.1 Discount Rates Across Scenarios for Space Heating and Lighting in 2015 .................... 90

B.2 Commercial Building Shell Efficiency Improvement* ................................................... 91

B.3 Incentivized Technologies in Financing Policy Case ...................................................... 91

C.1 Electricity and Natural Gas Saving Estimations from IAC Reports................................ 96

D.1 Electricity Efficiency Potential By State (TWh) ............................................................. 97
viii

ACKNOWLEDGMENTS

Funding for this research was provided by Oak Ridge National Laboratory and the 

Department of Energy (DOE). This sponsorship is greatly appreciated. 

Bob Pauley of Eastern Interconnection States Planning Council (EISPC) contributed 

meaningfully to communications with EISPC members and the Navigant team regarding the 

completion of this report. Valuable comments on earlier drafts of the report were received 

from Youngson Baek and Stanton Hadley (Oak Ridge National Laboratory). Faculty and 

students in the School of Public Policy at Georgia Tech also provided feedback on this 

research, including Professors Paul Baer and Doug Noonan and PhD students Matt Cox, 

Gyungwon Kim, Alexander Smith, and Xiaojing Sun. Gyungwon Kim also provided 

valuable assistance with the graphics, especially the GIS analysis and maps.

The authors are grateful for the willingness of these individuals to engage in a dialogue about 

the potential to expand energy-efficiency resources. 

Any remaining errors are the sole responsibility of the authors.
ix

EXECUTIVE SUMMARY

Comprehensive and integrated resource planning considers the potential for increases in 

energy efficiency to reduce the requirements for new generation and transmission 

investments. This study supports such planning efforts by developing robust estimates of the 

economically achievable potential for improving the energy-efficiency of homes, commercial 

buildings, and industrial plants located in the Eastern Interconnection. The approach of this 

study involves identifying a series of energy-efficiency policies and examining their impacts 

and cost-effectiveness using Georgia Tech‟s version of the National Energy Modeling 

System (GT-NEMS). The project emphasizes the impacts on electricity consumption, the 

levelized cost of policy-driven electricity savings, and distributive effects at the state and 

regional levels. Thirty-six states and the District of Columbia are covered by this study, and 

the time frame extends to 2035.

Using GT-NEMS, we model twelve policy options designed to stimulate energy-efficiency 

investments and practices by providing better information, more effective regulations, and 

affordable financing. These policies are evaluated individually, combined into Policy 

Bundles and evaluated for each sector, and evaluated in an Integrated Policy scenario. While 

the policies are designed to address different market failures and barriers and to focus on 

distinct market segments and technologies, the level of savings driven by the policies 

diminishes as more policies are combined. We focus on the achievable energy-efficiency 

potential, which is the subset of the technically feasible and cost-effective potential that can 

be achieved through policy intervention.

Based on GT-NEMS estimates from the Integrated Policy scenario, the twelve policies could

significantly drive down energy consumption, reduce energy rates, and generate energy bill 

savings for end-users. Figure ES.1 illustrates the projected energy consumption of the region 

in the Reference case and Integrated Policy case. 

Figure ES.1 Total Energy Savings Potential of an Integrated Policy Scenario in the 

Eastern Interconnection (in Quads)

50

55

60

65

70

75

80

2010 2015 2020 2025 2030 2035

Reference Integrated Policy Case

5.2%

3.6%

2.6 quads of savings 

in 2020

4.0 quads of savings in 

2035

Energy Consumption (Quads)
x

In the Eastern Interconnection, electricity consumption is forecast to grow at an average rate 

of 0.77% per year, and to go up to 3,200 TWh in 2035 in the Reference case. With the twelve 

energy-efficiency policies, the growth of electricity consumption slows down to an average 

rate of 0.35% per year. The combination of twelve policies could save the region about 190 

TWh (6.6%) of electricity in 2020, and 332 TWh (10.3%) of electricity in 2035 (Figure ES.2).

Figure ES.2 Potential Electricity Savings (all sectors) from the Integrated Policy 

Scenario in the Eastern Interconnection

The energy-efficiency potential driven by the twelve policies comes largely from the 

residential sector, with additional savings from the commercial and industrial sectors (Figure 

ES.3). In the Integrated Policy case, the residential sector is estimated to save 13.2% of 

electricity in 2035, which is slightly lower than the estimated savings (15.4% in 2035) from 

the residential Policy Bundle. Similarly, the estimated savings potential from the commercial 

sector is 7.9% in 2035, lower than the estimation (8.4% in 2035) from the commercial sector 

Policy Bundle. Overlap in the electricity savings identified by the policies underlies these 

differences, and an overlap of 10-20% is suggested by these results. In contrast, the industrial 

sector potential in the Integrated Policy case is estimated to be comparable (10.4% in 2035) 

to the savings potential identified by the industrial Policy Bundle. The electricity from 

industrial CHP generation that is sold back to the grid is not shown in this figure. An 

additional 39 TWh are generated by CHP systems (28 TWh are consumed at the industrial 

plant and 11 TWh are sold back to the grid).

2,500

2,600

2,700

2,800

2,900

3,000

3,100

3,200

3,300

2010 2015 2020 2025 2030 2035

Delivered Electricity, All Sectors 

(TWh)

Reference Integrated Policy Case

10.3%

6.6%

190 TWh of 

savings in 2020

332 TWh of electricity 

savings in 2035
xi

Figure ES.3 Electricity-Savings Potential by Sector, 

Based on the Integrated Policy Scenario (in TWh)

Table ES.1 illustrates the detailed savings potential for electricity end-use by sector, based on 

the individual policy bundles.

Table ES-1. Potential Electricity Savings by Sector Policy Bundles in the Eastern 

Interconnection

Residential Sector Commercial Sector Industrial Sector

Potential 

Savings 

(TWh)

% 

Savings

Potential 

Savings 

(TWh)

% 

Savings

Potential 

Savings 

(TWh)

% 

Savings

2020 102 10.1% 60 3.9% 48 6.3%

2035 179 15.4% 153 8.4% 68 10.4%

According to GT-NEMS modeling supplemented by spreadsheet calculations, the electricity 

savings could be achieved at relatively low cost. Specifically, the levelized cost of electricity

savings estimates in 2009 $ per kWh range from 0.5-0.8 cents/kWh for Residential Building 

Codes and 0.6-0.7 cents/kWh for the Aggressive Appliance Policy to 6.7-8.0 cents/kWh for 

Appliance Incentives and 9.3-11.5 for Motor Rebates.

Together with the levelized cost estimations, the electricity-savings potentials produce a 

policy supply curve (Figure ES.3). This curve suggests that regulatory policies have 

relatively low levelized cost of electricity and financial policies have relatively high LCOEs, 

9.3%

12.4%

13.2%

13.2%

4.0%

5.9%

7.1%7.9%

6.9%8.2%9.0%

10.4%

0

50

100

150

200

250

2020202520302035 2020202520302035 2020202520302035

Residential Sector Commercial Sector Industrial Sector

Electricity Savings (TWh)

Rest of the 

Nation

Eastern 

Interconnectio

n
xii

with the CHP Incentive as an exception. Taking average electricity rates as benchmarks, all 

twelve policies except for the Motor Rebate are cost-effective, representing 98% of the 

savings potential. Information programs have particularly large energy-savings potential, 

most notably residential Market Priming and commercial Benchmarking.

Figure ES.3 Supply Curve for Electricity Efficiency Policies in 2020 

in the Eastern Interconnection

The electricity savings of energy-efficiency policies would be accompanied by other benefits, 

including natural gas savings and savings in other fuel types. These energy-efficiency 

policies are able to drive down electricity retail prices in most regions and produce savings

for consumers in their energy bills. For example, residential customers are estimated to save 

about $13 Billion in reduced energy costs in 2020; savings would also be experienced by 

commercial customers ($5 Billion) and industrial customers ($4 Billion), and energy bill 

savings for all sectors would expand in later years. The electric power sector is also affected 

by these policies, as a result of slower generation growth. Overall, these policies are 

estimated to reduce energy and carbon intensity without significantly impacting GDP growth.

The sizable reduction in energy consumption is associated with reductions in carbon dioxide 

emissions. The energy efficiency policies trim carbon emissions by 161 million tonnes of 
xiii

CO2 (4.0%) in 2020, increasing to 246 million tonnes of CO2 (5.6%) in 2035. The percentage 

numbers of carbon emission reductions are slightly higher than the percentage reductions in 

energy consumption, reflecting a shift to less-intensive energy sources. The energy efficiency 

policies are estimated to generate higher savings in electricity, which is more carbon 

intensive than natural gas in the EIPC region. 

In summary, this study shows that energy-efficiency policies would save the Eastern 

Interconnection 332 TWh of electricity in 2035. The primary energy savings would reach 4.0

Quads if all types of energy are included. The reduced demand for electricity leads to lower 

electricity rates, which result in significant energy bill savings for residential, commercial 

and industrial consumers. The policies would also reduce the carbon emissions of the region, 

with no significant impact on GDP.
xiv
1

1. INTRODUCTION, OBJECTIVES, AND BACKGROUND

The potential for improved electric end-use efficiency has invoked great interest over the past 

several decades because the cheapest megawatt hour of electricity is often the one that is not 

needed (Croucher, 2011). In addition, reducing electricity consumption through energy 

efficiency also helps reduce carbon dioxide emissions, improve air quality, and strengthen grid 

stability.

Comprehensive and integrated resource planning should consider the potential for increases in 

energy efficiency to reduce the requirements for new generation and transmission investments.

Electricity planners have many options at their disposal: supply-side options, such as central 

power plants, distributed generation, and energy storage; as well as demand-side options, such as 

demand response and energy efficiency. What combination of these resources can deliver the 

most reliable, affordable, and clean electricity? Improvements to the knowledge base and 

modeling capabilities surrounding energy-efficiency resources are critical to the integrity of 

transmission expansion planning and the optimization of state and regional energy policies.

1.1 Objectives

The principal objective of this study is to accurately estimate the achievable potential for 

improving the energy-efficiency of homes, commercial buildings, and industrial plants located in

the Eastern Interconnection. What are the potential impacts of energy-efficiency programs and 

policies in this region? A companion study led by Oak Ridge National Laboratory is examining 

the potential for greater demand response in this same region (Baek, et al., 2012). The results of 

both studies are intended to be used by the Eastern Interconnection States Planning Council 

(EISPC) and the Eastern Interconnection Planning Collaborative (EIPC) in their transmission 

planning. 

Secondary objectives include:

Reviewing existing assessments of energy-efficiency potential in the Eastern

Interconnection, and 

Refining methodologies to construct estimates of energy-efficiency potential in the 

Eastern Interconnection.

The region served by the EIPC consists of 36 states and the District of Columbia corresponding

closely to the coverage of seven census divisions: the East North Central, West North Central, 

East South Central, West South Central, Middle Atlantic, South Atlantic, and New England 

divisions. The Eastern Interconnection, including Washington D.C., was home to 211 million 

individuals in 2010, or 68.6% of the nation‟s population of 309 million in in that year.
2

Figure 1.1 Interconnections of the National Electricity Reliability Council (top) and

Regions and Divisions of the U.S. Census Bureau (bottom)

(Source: http://www.ercot.com/content/news/mediakit/maps/NERC_Interconnections_color.jpg)

)

1.2 Review of Energy-Efficiency Potential 

1.2.1 The Energy-Efficiency Gap

The term “energy-efficiency gap” refers to the unexploited economic potential for energy 

efficiency; in other words, it emphasizes the technically feasible energy-efficiency technologies 

and practices that are cost-effective but are not being deployed. The energy-efficiency gap has 
3

attracted wide attention among energy policy analysts, since society has forgone many costeffective investments in energy efficiency. This term was first "coined" by Hirst and Brown 

(1990) in a paper titled "Closing the Efficiency Gap: Barriers to the Efficient Use of Energy."

Many other studies have used similar definitions, such as the International Energy Agency (2007) 

and Jaffe and Stavins (1994). The energy-efficiency gap exists in many sectors, including

households, small businesses, corporations, and governments (Dietz, 2010).

It is difficult to quantify the exact magnitude of the energy-efficiency gap. One approach to 

characterizing its size is through modeling. This typically involves enumerating on a technologyby-technology basis the difference between current practice and best practice, where best 

practice is defined as the utilization of the most cost-effective energy-efficient technologies. 

Keeping in mind the natural rate of equipment turnover through consumer purchases, one can 

then estimate the size of the gap that exists and that can be reduced by policy efforts. 

1.2.2 Defining the Potential for Energy Efficiency

We define the “achievable potential” for energy efficiency as that portion of the energyefficiency gap that can be narrowed by the implementation of policies and programs. This 

terminology has been used by other analysts, in conjunction with the terms “technical potential”

and “economic potential” (Rufo and Coito, 2002; NYSERDA, 2003). Specifically:

Technical potential: Assumes that all technically feasible energy-efficiency measures are 

adopted. These are also referred to as engineering estimates because they do not consider costs. 

In some cases, the technical potential assumes that the technology available changes over time 

via innovation. 

Economic potential: Generally a subset of technical potential that must pass a cost test. For 

example, the net present value of total benefits minus costs must be positive, and the rate of 

return on the investment must meet a particular threshold. In a utility planning context, this 

means that the levelized cost of the electricity saved must be less than the forecast cost of 

electricity generation that it would offset. 

Achievable potential: Generally a subset of economic potential that considers what portion of 

the cost-effective efficiency measures could reasonably be achieved by policy efforts; estimates 

generally consider a policy incentive of less than 30% of the energy-efficiency investment’s

incremental costs. 

Among the three types of potential, we focus on estimating the achievable potential for improved 

energy efficiency in the EIPC region. 

1.2.3 A Review of Estimates of Energy-Efficiency Potential

A large body of literature has focused on the economic potential for energy-efficiency measures, 

as summarized in Table 1.1. These assessments of energy-efficiency potential are derived from 

theory, simulation, and real-world practices, and they have been conducted at various geographic 

scales, covering different time frames. 
4

Table 1.1. Assessments of Energy-Efficiency Potential 

Publication Application Potential Assessment Cost Estimate Policy Relevance

Area End-use sector Fuel Type Estimation

Brown et al (2001) U.S.

Residential

Total 

energy Achievable

9-20% by 2020

Total net saving: 62-

108 Billion 1997$ in 

2020

Estimation based on 

moderate and advanced 

policy scenarios examining 

about 50 policy options

Commercial 9-18% by 2020

Industrial 8-17% by 2020

Tonn & Peretz 

(2007) U.S.

Residential and 

Industry

Total 

energy Achievable

20-30% over a 20-

year period

B/C ratio greater than 

3:1

Potential achieved by 

standard energy efficiency 

programs

Scott, et al (2008) U.S.

Residential and 

Commercial

Total 

energy Achievable 27% by 2030

Impacts of the 2005 Building 

Technology program 

Granade et al. 

(2009) U.S.

Building and 

Industry

Total 

energy Economic 23% by 2020

Average annualized 

cost: $4.4/MMBtu (

Brown, et al. 

(2010)

U.S. 

southeast 

states

Residential, 

Commercial and 

Industry

Total 

energy Achievable

9-12% in 2020;

13-18% in 2035

Levelized cost of 

electricity: 0.9-15 

cent/kWh

Explores 8 policy options 

promoting efficiency

Kneifel (2010)

16 cities in 

the U.S. Commercial

Total 

energy Economic

20-30% for new 

buildings 

Saygin, et al 

(2011) U.S.

Chemical and 

petrochemical 

industry 

Total 

energy Economic

24% with top-down 

approach; 10.9% with 

bottom-up approach 

Sadineni,et al 

(2011) U.S. Residential

Total 

energy Economic 42.5%

Laitner, et al. 

(2012) U.S. All Sectors

Total 

energy Economic 42-59% by 2050

Technology advances and 

policy improvements 

modeled

McKane and 

Hasanbeigi (2011) Global

Industrial motor 

systems 

Total 

energy

Technical 27-57% 

Economic 14-49% 

Fleiter, et al (2012) Germany Pulp and paper 

industry

Total 

energy Economic 21% by 2035

Electricity Economic 16% by 2035
5

Scenarios for a Clean Energy Future (Brown, et al., 2001) and a study by McKinsey and 

Company (Granade, et al., 2009) are national in scope and focus on the 2020 time frame. Brown 

et al. (2001) used this technology-based accounting approach and concluded that removing 

obstacles to energy efficiency through policy interventions initiated in the year 2000 could have 

reduced the forecasted U.S. energy consumption in 2010 by 10%. The McKinsey and Company

study estimates the NPV-positive potential for energy efficiency savings in non-transportation 

uses of energy. It finds that energy efficiency programs can save the nation 9.1 quadrillion Btu 

(23%) in end-use energy and 18.4 quadrillion Btu in primary energy by 2020. The potential in 

the commercial sector is 29% of end-use energy and 30% of primary energy, which sums up to 

$290 billion in total energy savings from 2009-2020.

An achievable potential study conducted by the Electric Power Research Institute (EPRI) 

forecasts the potential energy saving that would be achieved by energy-efficiency programs in 

2030 to be 398-566 billion kWh (8-11%), with estimated levelized cost between $0.022/kWh 

and 0.032/kWh (EPRI, 2009).

The American Council for an Energy-Efficient Economy (ACEEE) completed a meta-review of

energy efficiency potential assessments in the U.S. (Nadel et al., 2004). This review concluded

that, across the U.S., the median technical potential is 33%, the median economic potential is 

20%, and the median achievable potential is 24%. The overall median achievable potential is an 

annual energy savings of 1.2%, with similar savings from each end-use sector. The number of 

years estimated and the type of potential estimated vary widely from study to study, making the 

median numbers relatively unreliable estimations. The ACEEE study also summarizes the 

electricity savings actually achieved by utilities in some of the leading states based on historical 

data. The leading utilities were estimated to achieve annual electricity savings of 0.5-2.0%.

A recent ACEEE study investigates the long-term efficiency potential associated with 

technology advances and policy improvements (Laitner, et al, 2012). By comparing their policy 

scenarios with EIA‟s projection – the Annual Energy Outlook 2010 – the overall potential of 

energy efficiency is estimated to be 42-59% by 2050. 

Many studies produce potential estimates at the state level. One of these, by Tonn and Peretz 

(2007), estimated that standard residential and industrial energy-efficiency programs have 

energy-efficiency potentials of 20-30% over a 20-year period. The programs studied in that

review are generally cost-effective, with benefit-to-cost ratios exceeding 3:1. Neuhoff, et al. 

(2012) conclude that the most aggressive Energy Efficiency Resource Standards (requiring 

utilities or program administrators to reach specific goals for energy savings) target energyefficiency savings of about 2% per year, or electricity savings of about 20% in total between 

2010 and 2020. Policy instruments, such as subsidies, income taxes, and carbon taxes can make 

efficiency investments more profitable (Amstalden, et al, 2007), and energy labeling can 

improve efficiency in household energy use (Feng, 2010). Recent modeling assessments of 
6

energy-efficiency potential have documented a significant achievable potential in the South 

(Brown et al., 2010), in Appalachia (Brown et al., 2009), and in industry (Brown, et al., 2011). 

1.3 Barriers, Drivers, and Current Policies 

Numerous obstacles – including market failures and barriers – contribute to the energy efficiency 

gap (Table 1.2). Market failures occur when there are flaws in the way markets operate. The 

traditional literature on market failures tends to examine circumstances in which market prices 

fail to provide sufficient information or incentives to achieve Pareto optimality within the 

context of a perfectly competitive market. Pareto optimality exists when gains from trade are 

exhausted and when the situation prevails that no individual‟s well-being can be improved 

without a worsening of some other individual‟s well-being.

Market failures are conditions of a market that violate one or more neoclassical economic 

assumptions that define a competitive market, such as rational behavior, costless transactions, 

and perfect information. Traditionally, market failures were attributed to (1) misplaced 

incentives; (2) distortionary fiscal and regulatory policies; (3) unpriced costs such as air pollution;

(4) unpriced goods such as education, training, and technological advances; and (5) monopoly 

power. A more recent literature focuses on information-based market failures including a general 

lack of information, information asymmetries, and price signaling. This literature draws upon 

advances in behavioral economics that focus on incentives provided by distributions of 

information that are less than perfect, but which are arguably commonplace (Jaffe and Stavins, 

1994) (Table 1.2). 

“Market barriers” refer to other obstacles that contribute to the slow diffusion and adoption of 

energy-efficient innovations (Jaffe and Stavins, 1994, Hirst and Brown, 1990, Levine et al., 1995, 

and U.S. Department of Energy). To the extent that it is in society‟s best interest to use its energy 

more efficiently, improve grid reliability, and reduce emissions from fossil fuel combustion, it is 

important to understand the full range of obstacles to energy-efficient technologies. These 

barriers include: (1) the low priority of energy issues among consumers, (2) capital market 

imperfections, (3) incomplete markets for energy-efficient features and products, and (4) 

infrastructure longevity.
7

Table 1.2 Market Failures and Barriers Inhibiting Energy Efficiency

Market Failures: Flaws in the way markets 

operate

Market Barriers: Other factors inhibiting 

rapid adoption

Misplaced incentives including the 

“principal-agent” problem

Low priority of energy issues resulting 

from the fact that energy is often a 

small part of the cost of owning and

operating a building or factory

Distortionary fiscal and regulatory policies

including the preferential tax 

treatment of capital versus operating 

costs, fuel subsidies, and the coupling 

of electricity sales to profits

Capital market barriers due in part to the 

lower interest rates available to 

energy suppliers relative to energy 

consumers

Unpriced costs such as air and water 

pollution and greenhouse gas 

emissions

Incomplete markets for energy efficiency, 

compounded by the complexity of 

design, construction, and operation of 

buildings making it difficult to 

characterize their energy efficiency

Unpriced goods such as education, training, 

and technological advances 

Infrastructure longevity contributing to the 

“lock-in” of incumbent technologies

Monopoly power hindering competition

Insufficient, inaccurate and asymmetric 

information leading to suboptimal 

decision-making

Apart from barriers, estimations of the achievable potential of energy efficiency and the design 

of effective policies must also consider economic and social/institutional drivers of energy 

efficiency. The business case for energy efficiency varies across market sectors, types of 

households, firm size, and region of the country, and it reflects a variety of motivations for using 

energy more wisely. Nevertheless, common motivations emerge from the literature, as 

summarized in the report on “Strategies for the Commercialization and Deployment of 

Greenhouse Gas Intensity-Reducing Technologies and Practices” (CCCSTI, 2009):

Volatile and rising energy prices – “The sustained pain” of rising oil, coal, natural gas, 

and electricity prices is motivating a renewed interest in energy efficiency

Environmental concerns and regulations – Energy-efficiency investments can tap into 

potentially lucrative revenue streams from NOx and SO2 offsets in non-attainment zones, 

RES/EERS compliance requirements, and tradable carbon allowances 

Demand charges and demand response incentives – The ability to cut peak electric 

load motivates many utilities to incentivize demand-side management. 
8

Collateral benefits – Increased productivity, improved product quality, reduced labor 

costs, and enhanced reliability can result from energy-efficiency investments. In addition, 

improved stability of financial performance should lead to lower cost of capital, in terms 

of both access and cost of financing.

International competition – When the cost of energy inputs makes a firm unable to 

compete, energy-efficiency improvements are often sought out. Energy cost reductions 

can also be used to offset other rising costs, such as labor.

Corporate sustainability – Energy efficiency as a climate change mitigation strategy 

offers a way to boost shareholder/investor confidence, profit from future legislation, and 

access new markets.

Consumer and shareholder activism, good corporate governance, and reputation 

management – these have proven to be a strong motivators for energy-efficiency and

other investments in sustainability.

Insurance access and costs, legal compliance, and concerns regarding fiduciary duty 

– All represent additional potential drivers. 

Most of these drivers were highlighted in the study of Real Prospects for Energy Efficiency in 

the United States (National Academies, 2009).

Numerous policy levers are available to address traditional market failures and barriers and to 

leverage drivers for energy efficiency (Geller, 2002; Brown and Sovacool, 2011). One succinct 

typology of policies identifies three ways of exploiting the achievable potential for energy 

efficiency: (1) financial assistance, including subsidies, bulk procurements, and loan guarantees;

(2) regulatory requirements, such as codes, standards, and cap and trade programs; and (3) 

information programs including labeling, education, R&D support, and workforce training 

(Brown et al., 2011). The costs and benefits of different policy approaches will vary by market 

sector and technology opportunity, as reflected in subsequent chapters.
9

2. METHODOLOGY

2.1 Overview of Approach

A portfolio of twelve energy-efficiency policies is modeled with the Georgia Institute of 

Technology‟s version of National Energy Modeling Systems (GT-NEMS) to estimate the longterm achievable potential in the Eastern Interconnection. Supplemental spreadsheet analysis is 

used to estimate the levelized cost of electricity (LCOE) that could potentially be saved, based on 

GT-NEMS output for each of the financial, regulatory and information policies. Similarly, 

estimates of carbon dioxide emissions and reductions in fuel consumption for all end-use sectors 

can also be extracted from GT-NEMS output. Based on this information, the benefit of reduced 

carbon emissions and avoided criteria air pollutions will be evaluated for climate change and 

environmental impacts in a benefit-cost analysis.

2.2 National Energy Modeling System

GT-NEMS is the principal modeling tool used in this study, supplemented by spreadsheet 

calculations. Specifically, we employ the version of NEMS that generated EIA‟s Annual Energy 

Outlook 2011 (EIA, 2011a), which forecasts energy supply and demand for the nation out to 

2035. NEMS models U.S. energy markets and is the principal modeling tool used to forecast 

future U.S. energy supply and demand. Twelve modules represent supply (oil and gas, coal, and 

renewable fuels), demand (residential, commercial, industrial, and transportation sectors), energy 

conversion (electricity and petroleum markets), and macroeconomic and international energy 

market factors. A thirteenth “integrating” module ensures that a general market equilibrium is 

achieved among the other modules. Beginning with current resource supply and price data and 

making assumptions about future use patterns and technological development, NEMS carries 

through the market interactions represented by the thirteen modules and solves for the price and 

quantity of each energy type that balances supply and demand in each sector and region 

represented (EIA, 2009). Outputs are intended as forecasts of general trends rather than precise 

statements of what will happen in the future. As such, NEMS is highly suited to projecting how 

alternative assumptions about resource availability, consumer demand, and policy 

implementation may impact energy markets over time.

The GT-NEMS “Reference case” projections are based on federal, state, and local laws and 

regulations in effect at the time of the analysis. The baseline projections developed by the EIA

via NEMS are published annually in the Annual Energy Outlook, which is regarded as a reliable 

reference in the field of energy and climate policy. We have used GT-NEMS to perform scenario 

analysis under a consistent modeling framework in order to compare policy options to the 

Reference case projections. 

GT-NEMS also provides estimates of the carbon intensity of electricity generation based on 

generation resources over time. The benefit of reduced CO2 emissions are estimated by 

subtracting the emissions in the Reference case from the policy scenario and then multiplying by 

the “social cost of carbon” (SCC). The SCC is an estimate of the monetized damages caused by a 

metric ton of CO2 emitted in a given year. The social cost of carbon used in this analysis is the 
10

central value of the U.S. Government Interagency Working Group on the Social Cost of Carbon 

(EPA, 2010), growing from $23/metric ton in 2011 to $47/metric ton in 2050 (all values are in 

2008-$ and account for global avoided damages). 

Present-value calculations for the levelized cost of electricity use a 3% discount rate, consistent 

with Office of Management and Budget guidelines (OMB, 2002; 2010). The OMB guidelines 

recommend the use of 3% discount rates when evaluating regulatory proposals from a social 

perspective. This is less than the 10% value used in some other energy-efficiency studies such as 

McKinsey and Company‟s analysis (Granade, et al., 2009), which focus on investment decisionmaking from a private market perspective.

2.3 Energy Efficiency Policy Levers

A suite of twelve policies was selected to estimate the achievable potential for energy efficiency: 

four regulatory policies, five financial policies, and three information policies (Table 2.1). For 

residential buildings, five policies are designed to reduce the up-front cost of end-use

technologies and energy-efficient building envelopes. For commercial buildings, three policies 

are designed to expand investments in energy-efficiency improvements. In the industrial sector, 

the policies target motor systems and other efficiency improvements in various industrial 

processes, as well as combined heat and power (“CHP”) systems to make use of waste heat. 

In the policy scenario, financial incentives are offered to energy-efficient technologies in the 

end-use sectors. For residential buildings, 25 energy-efficient home appliances and equipment 

were selected from the NEMS technology profile. Financial incentives (either a subsidy or zerointerest loan) were offered to cut down capital costs of these selected technologies. Similarly, 

110 commercial building technologies were selected and offered flexible financing options. For 

industries, energy-efficient technologies include combined heat and power (CHP) systems which 

utilize waste heat, and electric motor systems which satisfy the Energy Independence and 

Security Act of 2007 minimum efficiency requirement. Industrial energy efficiency policies 

provide rebates and subsidies for CHP systems and premium motors.

Regulatory policies impose standards and mandates to enhance efficiency improvements. 

Building energy codes were modeled to represent shell efficiency improvements for residential 

and commercial buildings. For residential buildings, compliance with new building codes was

assumed implicitly when less stringent codes were forced to gradually retire. In the commercial 

sector, the whole building stock is assumed to progress in shell efficiency gradually to reach the 

efficiency level equivalent to the most recent code, ASHRAE 90.1-2010, with 100% compliance 

in 2035. Appliance standards were applied to remove inefficient residential technologies from 

the market. Motor standard were established to increase efficiency standard for electric motors 

used in industries.

In addition, a broad set of information instruments was explored in the policy scenario. For 

homes, the Market Priming policy is a combination of several information options, including 

mandated disclosure of home energy consumption or performance at the point of sale or lease of 

a residential unit, home rating, green labeling, and other technical assistance features such as 

home energy audits and assistance with green leases, etc. For commercial buildings, the 

benchmarking policy requires utilities to submit whole building energy consumption data to a 
11

uniform database accessible by building owners. Studies suggest that providing information can 

reduce discount rates used in investment decisions from 3% to 22% (Coller and Williams, 1999; 

Goett, 1983). Thus, adjusting discount rate was the NEMS lever used for modeling Market 

Priming and Benchmarking. For industries, Advanced Manufacturing Initiative is the 

information-based policy that provides information about efficiency opportunities for plant 

utility upgrades. The impact of this information is based on the potential efficiency 

improvements from the Industrial Assessment Centers (IAC) database. 

Table 2.1. Selected Policies for Electric End-Use Efficiency

Sector Policy Type Policy Scenario Description

Residential Financial Appliance 

Incentives

Providing a 30% subsidy to cut down capital 

costs for the most efficient technologies

Financial On-Bill 

Financing

Offering zero-interest loans for the most 

efficient technologies

Regulatory Building Codes Adding four new building codes to improve

shell and equipment efficiency

Regulatory Aggressive 

Appliance Policy

Accelerate market penetration for energy 

efficiency technologies by eliminating the least 

efficient ones from the market

Information Market Priming Reducing high discount rates (10-50%) to 7% 

for private investment in efficient technologies

Commercial Financial Financing Offering flexible financing options to lower the 

up-front costs of highly energy-efficient 

equipment

Regulatory Building Codes Requiring higher building shell efficiency and 

more stringent standards on space heating and 

cooling equipment

Information Benchmarking Requiring utilities to submit whole building 

energy consumption data to a uniform database

accessible by building owners

Industrial Financial Motor Rebate Providing a 30% subsidy for premium motors 

that satisfy the minimum efficiency 

requirement of EISA 2007 

Regulatory Motor Standard New motor standard in 2017 requiring 25% 

efficiency improvement and higher system 

savings 

Financial CHP Incentives Offering a 30% investment tax credit for 

industrial CHP systems for 10 years 

Information Advanced 

Manufacturing 

Initiative 

Promoting plant utility upgrades by identifying

efficiency opportunities with cost assessments 

and estimations of potential energy savings. 
12

All twelve selected policies were modeled in GT-NEMS with energy consumption forecasts up 

to 2035. The specific levers used to model each of these policies is described in subsequent 

chapters and appendices. 

2.4 Calculation of Levelized Cost of Electricity

The LCOE of each policy was calculated to estimate the cost of achieving the electricity-savings 

potentials in individual policy scenarios. The calculation of LCOE is based on the total resource 

cost test, where costs include the incremental private investment in energy-efficient equipment 

and improvements, program costs for incentives and loans, and program administrative costs.

We estimate the magnitude of technology investment costs differently for the three end-use 

sectors. In the residential sector, costs are an output of the GT-NEMS model, and are a function 

of the purchase costs assumed for a range of technologies in different equipment classes. In the 

commercial sector, the investment costs are estimated separately for new purchases, 

replacements, and retrofits for approximately 350 technologies uniquely defined by technology 

type, fuel use, purchase price, energy efficiency, and time frame of availability in the 

marketplace. Total costs are calculated using a spreadsheet analysis developed by Georgia 

Tech‟s Climate and Energy Policy Laboratory (CEPL). In each case, the calculation is based on 

GT-NEMS estimates of service demand for energy (SD), costs per unit of SD, and capacity 

factors (see Appendix B). In industry, costs for CHP investments are based on the installed costs 

per KW of capacity for eight different types of CHP systems. These costs per kW of installed 

capacity are specified in GT-NEMS. Other costs are based on multipliers derived from audit 

information produced by DOE‟s Industrial Assessment Centers and the “Save Energy Now”

program as developed by CEPL and described in Brown, et al. (2011).

The LCOE is the weighted average cost, calculated by dividing the present value of total costs by 

total electricity savings, following the methodology described by the Electric Power Research 

Institute (EPRI, 2009). In addition to electricity benefits, natural gas savings are also generated 

for energy users impacted by energy efficiency policies. We singled out the part of the cost 

needed to achieve electricity savings by proportionate total cost to the value of electricity versus 

natural gas savings through 2035. Present-value calculations for the levelized cost of electricity 

use a 3% discount rate from a social perspective and 7% discount rate for the private-sector 

assessment. This is consistent with Office of Management and Budget guidelines (OMB, 2002; 

2010), which recommend the use of 3% and 7% discount rates when evaluating regulatory 

proposals. Our use of a 7% discount rate for evaluating the private perspective is less than the 10% 

value used in some other energy-efficiency studies such as McKinsey and Company‟s analysis 

(Granade, et al., 2009). Since the social appropriateness of policies is being examined, a 

sensitivity was conducted where all costs were discounted at 3% for LCOE calculation.

Other main assumptions in the LCOE calculation include: 

Avoided transmission and distribution losses are included as part of electricity savings. A 

multiplier of 1.07 was applied to electricity savings to account for the benefit of avoided 

electricity related losses.

Program administrative costs are estimated to be $0.13/MMBtu energy saved, unless 

specified otherwise (see Brown, et al., 2009, for details on these estimates).
13

We assume the twelve policies start from 2012 and end at 2035. Any costs stimulated 

from the policies occur through 2035. 

Electricity savings are then modeled to degrade at a rate of 5% after 2035, such that 

benefits from the policy have ended by 2055.

A spreadsheet tool was developed to estimate the LCOE for each of the twelve energy-efficiency 

policy options. 

2.5 Method for Deriving State-Specific Estimates 

A “proportioning” methodology is used to produce state specific electricity-demand forecasts for 

the 39 individual states that comprise the EIPC region. The forecasts are derived from the Annual 

Energy Outlook 2011 (EIA, 2011) reference projections. The methodology is based on the 

approach used by Hadley (2003) in a study of the energy efficiency and renewable energy 

potential in North Carolina.

EIA’s Annual Energy Outlook 2011 provides energy consumption and production projections for 

the nation, its four census regions, and its nine census divisions. To create state estimates, we 

combine the electricity demand values from EIA’s Annual Energy Outlook 2011 for all states in 

each census division and calculate the share of the total that each state constitutes. The process is 

described in greater detail through the bulleted list below. 

The previous studies that have developed measurement and verification protocols to estimate the 

energy consumption of individual states have shown that the energy use per capita of each state 

in each sector has been relatively constant over the last decade (Brown, et al., 2010). This

previous work supports our assumption that the energy use by state generally increases 

proportionally to population growth. The methodology used here involves four steps: 

First, we calculate the average electricity consumption from 2005 to 2009 with the 

historical energy consumption by sector and by state from the EIA‟s State Energy Data 

System (EIA, SEDS, 2011).

.

Then, we derived the share of each state to the total division for residential, commercial, 

and industrial electricity usages, respectively.

We assume that population distribution will continue with current trend and the state 

share of electricity consumption can be used to proportionate consumption to the state 

levels.

Finally, using the state specific percentages, we allocated out the regional projections of 

the Annual Energy Outlook 2011 (EIA, 2011) to each state.

A more precise “proportioning” methodology would reflect the fact that states with strong 

energy-efficiency policies would not increase their energy consumption in lock-step with 

population growth. While there is an emerging literature that rates the effectiveness of state-level 

energy-efficiency policies (Foster, et al., 2012), the link to future energy and population growth 

rates has not been established.
14
15

3. ENERGY-EFFICIENCY POTENTIAL IN RESIDENTIAL BUILDINGS

In 2010, the residential sector in the Eastern Interconnection consumed 1,069 TWh of electricity, 

which represents 74.0% of national residential electricity consumption. The region is home to 

68.6% of the U.S. population, indicating a higher than national average residential electricity

consumption per capita. In fact, the per capita residential electricity consumption in the Eastern 

Interconnection was about 5,070 kWh in 2009 (EIA, SEDS, 2010). In 2009, the households in 

the region consumed 990 TWh of electricity, slightly less than in 2008 due to the country‟s 

economic downturn. The 990 TWh of electricity consumption accounts for 68% of the total 

residential energy consumed in the region in 2009 (15,600 TBtu) (Fig 3.1): delivered electricity 

consumption accounts for 22% and electric system energy losses make up another 46%.

Figure 3.1 Residential Energy Consumption in the Eastern Interconnection in 2009

(Source: EIA, SEDS, 2010)

Residential electricity demand is forecast to be lower than the 2010 level over the next 10 years, 

but by 2035, EIA forecasts that residential electricity demand will grow to 1,166 TWh in the 

region, 9% higher than in 2010 (Figure 3.2).
16

Figure 3.2 Reference Case Forecast of Residential Electricity Consumption in the Eastern 

Interconnection (EIA, 2011)

This chapter examines the achievable potential for increased residential energy efficiency from 

five policies. These policies target electricity consumption from all segments of residential 

buildings, including the building shell, HVAC equipment, and household appliances for both 

existing and new buildings. This chapter explores the electricity savings from these policies and 

examines the regional patterns by comparing the results across Census divisions. The other 

benefits from the energy-efficiency policies, such as natural gas savings, are also investigated. 

The last section of this chapter examines the impacts of implementing all five residential policies 

simultaneously.

3.1 Overview of Previous Literature on the Energy-Efficiency Potential in Residential 

Buildings

Numerous assessments of residential energy-efficiency potential in the U.S. have been conducted, 

at various geographic scales and covering different time frames. Some of these have estimated 

the impact and cost-effectiveness of implementing new or stronger residential energy policies. 

A recent study of state and local programs in Arkansas estimates the total energy efficiency 

potential in the state to be 15% of the electricity demand, 14% of the natural gas demand, and 20% 

of the peak demand by 2025. The study focuses on the cost-effective resources in residential 

buildings in 2025, which is projected to produce 31 trillion Btu of energy savings (37% of 

Reference case electricity consumption). The projected potential can be achieved by efficiency 

improvements in the building shell, HVAC equipment and other appliances in existing buildings 

with estimated levelized cost of $5.76/MMBtu saved (Neubauer et al, 2011). A different study, 

however, examines the energy efficiency in residential and non-profit buildings in Cincinnati 

(Mackres, et al, 2011) and finds that the potential energy savings associated with current 

efficiency programs is 2.8% of electricity demand and 6.9% of natural gas demand in 2030. Its

lower estimates reflect the focus on existing energy-efficiency programs and not the potential of 

new policies, which is the focus of our study.

For residential and commercial buildings, Laitner, et al. (2012) considers technology 

advancement as well as policy updates (e.g., energy code, appliance and equipment standards)

and estimated efficiency potential to be 49-61% in 2050. More specifically, the electric load 

reduction in 2050 is 73-87% from building codes and 38-47% from appliance standards. The 
17

savings from building codes are quite large compared with the Arkansas study, which models 

only 1.9-2.2% of energy savings from building codes. This difference reflects the different

modeling assumptions and time frames used in these two studies. 

Numerous studies have focused specifically on energy-efficiency measures in the building sector. 

A recent study on energy efficiency in residential buildings estimates the cost-effective potential 

to be 42.5%, with encouraging benefit/cost ratios (Sadineni, France, & Boehm, 2011). In a 

different study, DOE estimated the impacts of its energy efficiency programs and found that the 

2005 Building Technology program could save 27% of the energy consumed in residential and 

commercial buildings by 2030. These savings were evaluated to have the potential of increasing 

employment and reducing the need for capital stock in the energy sector (Scott, et al, 2008). 

These studies present a wide range of energy-efficiency potentials due to differences in forecast 

methods, and state- and local-level estimates tend to generate projections slightly smaller than 

national-level studies. The difference calls for the attentions to the choice of forecast approaches 

and the definition of potential (technical, economic, or achievable potential).

3.2 Residential Efficiency Policy Options and Modeling Approach

Energy consumption in residential buildings comes from space heating and cooling, water 

heating, lighting and other end-uses, which are closely related to the efficiency of building shell, 

HVAC equipment and household appliances. Many state-, local- and utility-level programs and 

policies are designed to improve energy efficiency in residential buildings. To assess the 

electricity-savings potential from all building components, a policy bundle of five efficiency 

policies was designed, including: Appliance Incentives, National Building Codes, On-Bill 

Financing, an Aggressive Appliance policy, and Market Priming. Two of these policies are 

regulatory (energy codes and the aggressive appliance policy), two involve financial assistance 

(appliance incentives and on-bill financing), and one focuses on providing building performance 

information (market priming). Table 3.1 summarizes each of these policy scenarios, along with 

the GT-NEMS levers used to model it. Further details are provided in Appendix A.

The Appliance Incentives policy offers a 30% subsidy to reduce the capital costs of the most 

efficient technologies in residential buildings based on the technology inventory in GT-NEMS, 

where the best type of technology in each of the equipment classes is selected (but only when 

there is more than one type specified in GT-NEMS). A list of the 25 incentivized technologies 

can be found in Table A.1. They cover space heating, space cooling, clothes washing, 

dishwashing, water heating, cooking, clothes drying, refrigeration, and freezing. Capital costs of 

these technologies (from the rtekty input file in GT-NEMS) were reduced by 30% in this policy 

scenario.

The Aggressive Appliance Policy uses appliance standards to eliminate the least efficient 

appliances from the marketplace. Appliance standards impose minimum efficiency requirements 

on equipment manufacturers. With the standards, certain appliances and equipment being 

manufactured must meet the minimum efficiency requirements before entering the market. EPA 

2005 and EISA 2007 are both examples of federal appliance standards. These two federal rules 

cover a broad set of home appliances and building equipment, as well as motor systems for the 

industrial sector. Besides the federal rules, 11 states have adopted appliance/equipment standards 

with efficiency requirements for state selected efficiency technologies (DSIRE, 2012). 
18

Table 3.1 GT-NEMS Lever for Residential Efficiency Policies

Policy type Policy Policy Scenario Description GT-NEMS Lever 

Financial Appliance 

Incentives 

30% subsidy to reduce the capital costs 

for the most efficient equipment

Equipment cost in 

rtekty.txt 

Regulatory Aggressive 

Appliance Policy 

Eliminating the least efficient equipment Equipment available 

years in rtekty.txt 

Regulatory National Building 

Codes 

Additional three rounds of improvement 

of building codes 

Building shell profile: 

rtektyc.txt

Financial On-bill Financing Offering low interest loans for high 

efficient appliances through on-bill 

financing options 

Source code + rtekty.txt 

Information Market Priming 7% hurdle rate for the most efficient 

equipment 

Equipment choice 

parameters in rtekty.txt

Appliance/equipment standards tackle the first cost sensitivity barriers by preventing consumers 

from buying the least-cost, least-efficient models. The observed savings from adopted federal or 

state appliance and equipment efficiency standards have been shown to be significant (Geller, 

1997; Meier, 1997). The appliance standard programs are cost-effective in promoting energy 

efficiency in many case studies (Geller, 1997; Vine, du Pont, & Waide, 2001). A recent study of

high-efficient appliances from the U.S. Department of Energy's Appliances and Commercial 

Equipment Standards Program estimates the potential of cost-effective improvement ranging 

typically from 15% to 20% (McNeil & Bojda, 2012). 

Building Energy Codes generally impose efficiency requirements on building shell, HVAC and 

lighting equipment for new buildings. For residential buildings, states tend to adopt the 

International Energy Conservation Code (IECC), a prototypical codes developed and updated 

periodically by the International Code Council. Most of the states have adopted the IECC 2003 

codes (or equivalent) or more stringent codes, with only 9 of the states having no statewide code 

for residential buildings (Figure 3.4) (DOE, 2012). Maryland is the first state to adopt the most 

recent 2012 codes for residential buildings (DOE, 2012).

This policy adds four more efficient codes to the suite of five less efficient codes available in the 

Reference case. The policy case also forces early retirement of the less stringent codes, as 

described in Appendix A. This policy recognizes the regional differences in code adoption based 

on historic patterns(Figure 3.4).
19

Figure 3.3 Adoption Status of Residential Building Energy Codes as of April, 2012 

(Source: DOE, 2012)

Figure 3.4 Building Energy Code Retirement Years by Census Division
20

Building energy codes have been shown to generate large energy savings in various regions 

under diverse climates conditions (Chirarattananon, et al, 2010; Fayaz & Kari, 2009). Studies on 

building code programs in the U.S. also exhibit evidence of cost-effectiveness in promoting 

energy efficiency: generating significant savings in energy expenditure with relatively low costs 

(Brown, 1993; Nelson, 2012). However, compared to other energy-efficiency programs, 

participation rates and voluntary compliance of building codes programs are relatively low, 

making it necessary to run assistance tools and supporting programs to increase compliance 

(Brown, 1993; Reichard & Papamichael, 2005).

On-bill Financing refers to a financial product that is serviced by, or in partnership with, a 

utility company for energy efficiency improvements in buildings, and repaid by homeowners on 

their monthly utility bills (Bell, Nadel, and Hayes, 2011). While these programs require the 

participation of a local utility (investor-owned or municipal), they also often benefit from support 

of local governments in terms of legal authority and the initial financing. They provide a stream 

of revenue to the utility or financing institution and, as part of the utility bill, are not burdensome 

for the customers where long-term savings outweigh upfront costs. On-bill financing 

opportunities through utilities are also expanding, as shown in Figure 3.5 and, despite a variety 

of implementation barriers, energy savings have expanded with minimal defaults (Bell, Nadel, 

and Hayes, 2011).

Figure 3.5 States with On-Bill Financing Programs

Source: Bell, Nadel, and Hayes, 2011

The policy modeled here offers zero-interest loans to upgrade to the most efficient residential

appliances and equipment. The technologies eligible for zero-interest loans are the same 

technologies that are eligible for appliance subsidies as listed in Table A.1. 
21

Market Priming programs offer information and education to reduce consumers‟ perception of 

the uncertainties and investment risks associated with new and innovative energy-efficient 

technologies. This policy also targets the set of technologies shown in Table A.1, but with a 

different GT-NEMS lever. In this scenario, market assistance programs offer consumers 

information, technical support, and educational demonstration to help them gain better 

understanding of the energy-efficient technologies, and therefore lower their perception of risks 

which is manifested in a lower private discount rate for the technologies. We modeled this policy 

by changing the hurdle rates of these technologies to 7%.

The achievable potentials of energy efficiency from these policies were modeled in GT-NEMS, 

which forecasts the future energy consumption out to 2035. The results of these policies were 

then compared to the Reference case forecast from AEO 2011, and the difference between the 

Reference case and policy scenario forecasts is the efficiency potential in residential buildings. 

The achievable potentials were modeled using the GT-NEMS High Tech scenario. 

The GT-NEMS modeling levers for these five policies were purposely chosen to be relatively 

independent in terms of their policy impacts (Table 3.1). The technology profile from the 

Reference case was used to identify the equipment with the highest and lowest efficiencies of the 

same types. The most efficient equipment was offered either a 30% subsidy as in the Appliance 

Incentives case, or a zero-interest loan for 10 years as in the On-Bill Financing case. The least 

efficient technologies were eliminated in the Aggressive Appliance Standards case. Building 

Energy Codes policy would add four new codes to the building shell profile to reflect the 

anticipation of future updates to building energy code, and also forces the less stringent codes to 

retire earlier.

3.3 Modeling Results for Individual Residential Policies

The energy consumption forecast of the each policy case was compared with the Reference case

forecast, and the difference represents the achievable energy-efficiency potential. Figure 3.6

shows the estimated potential of each policy. The percentage number is the reduction in 

residential electricity demand as a percentage of the reference case forecast in each year.

In 2035, the electricity savings from these five policies in the Eastern Interconnection ranges 

from 19.0 – 119 TWh (1.6% - 10.2% of reference case consumption), while the total electricity 

savings potential in the nation ranges from 33.4-164 TWh (2.1% - 10.2% of reference case 

consumption). 

By lowering the hurdle rate for efficiency investment through the Market Priming policy, the 

residential sector is able to generate the highest level of electricity savings, amounting to 119 

TWh in 2035 in the Eastern Interconnection (Fig 3.6). An Aggressive Appliance Policy is also 

able to produce a significant amount of electricity savings, about 42.3 TWh in 2035 in the 

Eastern Interconnection. Comparing to the Market Priming and Aggressive Appliance Policy, the 

other three residential policies are estimated to have smaller but still meaningful efficiency 

potentials for residential electricity savings, ranging from 19.0 - 25.6 TWh in 2035 in the region. 
22

Figure 3.6 Estimated Reductions in Electricity Demand by Individual Residential Policies

(Percentages of Residential Buildings Electricity Consumption)

All five policies, except for the National Building Energy Codes, generate electricity savings in 

the same proportion in the Eastern Interconnection as in the whole country (Figure 3.6). The 

percentage of electricity savings from National Building Codes in the Eastern Interconnection is 

only half (or less) of the national percentage saving. In the rest of the country, National Building 

Codes are able to generate electricity savings of 3.6% in 2020, 5.9% in 2025, and 7.2% in 2035. 

The reason that National Building Codes benefit the Eastern Interconnection less than the other 

regions is partly due to the differences in technology profiles applied to building shell and 

HVAC systems in different areas. It also reflects different paces of code retirement: the Pacific 

region moves rapidly to the four new codes and the East South Central region is assumed to be 

the slowest to adopt new codes (Figure 3.5). GT-NEMS assumes a five-year lag in adoption 

between these two regions. 

In the Eastern Interconnection, the cumulative electricity savings (2012 – 2035) generated from 

Appliance Incentives, Building Codes, On-bill Financing, Aggressive Appliance Standards, and 

Market Priming policy cases are 382 TWh, 257 TWh, 366 TWh, 531 TWh, and 2,407 TWh, 

respectively. In the meanwhile, the Reference case forecasts that electricity demand in the 

residential sector will increase from 986 TWh in 2011 to 1,166 TWh in 2035. The cumulative 

growth in electricity consumption can be offset by the five policies by 24%, 16%, 23%, 33%, 

and 149%, respectively. Since these policies deal with separate segments of energy consumption 

in residential buildings, it is plausible to assume that the demand growth of electricity usage can 

be completely offset by the combination of residential policies.
23

3.3.1 Regional Differences

In the individual policy scenarios, each policy is assumed to be applied on the national level with 

regional deviations in implementation details, for example, the adoption years of Building 

Energy Codes. Local situations, such as the current state policies and current consumption levels, 

vary from state to state. Thus, the same policy would likely to generate different efficiency 

impacts in different regions. Figure 3.7 illustrates the regional differences in electricity savings 

from each residential policy, as estimated by GT-NEMS. To show the electricity savings in the 

Eastern Interconnection, Texas was excluded from the West South Central division, using the 

proportioning method described in Chapter 2.

Figure 3.7 Electricity Savings by Census Division for Individual Policies

(*West South Central Excluding Texas)
24

Appliance Incentives, On-bill Financing, and Market Priming share similar regional patterns. For 

each of these policies, the South Atlantic division, which is the largest area with the most 

population, possesses the largest electricity saving potential. The projected savings in the South 

Atlantic region as a percentage of regional electricity consumption is also higher than the 

percentage in other regions. 

The regional distribution of electricity savings from the Aggressive Appliance Policy is more 

geographically dispersed. While the South Atlantic region has the highest energy savings, the 

Middle Atlantic and North Central regions also have large savings potential both in absolute 

terms and as a percentage of regional electricity consumption. This more equally distributed 

pattern may reflect the fact that the energy consumption of appliances is less weather-dependent 

than the energy consumption of HVAC equipment.

The National Building Energy Codes also shows an electricity savings potential that is 

geographically distributed quite differently from the other residential policies. In 2035, this 

policy generates 7.9 TWh of electricity savings in the South Atlantic region, and 6.9 TWh of 

electricity savings in the West South Central region (excluding Texas). The West South Central 

region is estimated to reduce 8.2% of its electricity consumption under the national building code 

policy, which is a much higher percentage than any other region. The West South Central region

receives much higher benefit than other regions due to its unique technology profile. In singlefamily buildings, the heating and cooling equipment in the WSC region after code 

implementation generally exhibits relatively high cooling efficiency. For example, in houses 

using heat pumps for air conditioning and heating, the WSC has the highest cooling efficiency at 

the lowest cost. 

3.3.2 Spillover Benefits

All selected policies aim to promote efficiency in residential buildings, including the efficient 

use of electricity and other fuel types. These policies are able to generate significant electricity 

savings as well as natural gas and fuel oil savings. Table 3.2 illustrates the projected reduction in 

natural gas consumption that is estimated to result from each individual policy. 
25

Table 3.2 Natural Gas Savings from Individual Policies

Reference 

Forecast

(TBtu) 

Natural gas Savings (TBtu)

Appliance 

Incentives

Building 

Codes

On-bill 

Financing

Aggressive 

Appliance Policy

Market 

Priming

2020

EI 4,683 94 39 98 80 230

US 4,973 127 183 183 138 236

2025

EI 3,612 120 64 134 144 241

US 4,960 162 245 223 238 353

2030

EI 3,587 144 90 165 201 249

US 4,953 196 305 302 327 421

2035

EI 3,530 159 110 186 244 245

US 4,902 218 357 313 397 361

Unlike the policy-specific estimates of electricity savings potential where Market Priming 

dominated, Aggressive Appliance Policy is the residential policy with the largest natural gas 

saving potential in the Eastern Interconnection as well as in the whole nation. The other four 

policies are also able to generate significant natural gas savings. Building Energy Codes offers 

the smallest natural gas savings in the Eastern Interconnection, but large savings in other areas of 

the nation. The observation of natural gas savings for Building Codes is consistent with its 

regional distribution of electricity savings.

The policies are also able to generate savings in fuels other than electricity and natural gas, such 

as fuel oil, diesel, and propane. Table 3.3 shows the potential savings in total energy, which is 

the projected reduction in total residential energy consumption. These savings were calculated 

without including the avoided electricity related losses. For the saving potential in total energy 

consumption, the difference between Market Priming and other residential policies is not as big 

as the difference in electricity saving potentials. Market Priming stands out differently from 

other policies because it imposes a big push for energy savings from the beginning of the 

program, while energy savings in other policy cases grow steadily with program implementation 

time.

Table 3.3 Total Energy Savings from Individual Policies, Residential Sector 

(TBtu, avoided electricity related losses not included)

Appliance 

Incentives

Building 

Codes

On-bill 

Financing

Aggressive 

Appliance 

Policy

Market 

Priming

2020 210 260 260 210 830

2025 280 350 350 360 910

2030 330 430 420 490 930

2035 370 510 480 600 940
26

3.3.3 Cost Estimation

The Appliance Incentives policy would incur two types of costs: the, private investment which is 

the expenditure spent by residential consumers to purchase equipment, and the public costs that 

include the cost of subsidizing the most efficient technologies, and program administrative costs. 

The total cost of the policy is the sum of both the private and public costs, and it is estimated to 

be $2.9 billion in 2035. The levelized cost of energy of this policy is estimated to be $0.13/MM 

Btu energy saved and the levlized cost of electricity (LCOE) in this policy case is estimated at 

6.7-8.0 cent/kWh. Details of cost estimations associated with Appliance Incentives are shown in 

Table 3.4.

Table 3.4 Cost Estimations from Appliance Incentives

Cost (Billion $2009) a2020 2025 2030 2035

Private cost -1.37 -1.01 -0.73 -0.53

Subsidy Cost 4.51 4.25 3.83 3.42

Administration Cost 0.01 0.01 0.01 0.01

Total 3.15 3.25 3.11 2.90

LCOE (cent/kWh) 6.7 b-8.0

a. Private cost was discounted at 7%, and public cost was discounted at 3%.

b. Levelized cost calculated in a sensitivity when all costs were discounted at 3%.

For Building Energy Codes, the private investment is the incremental cost on equipment 

expenditure plus the installation cost for improvements to the building shell. By promoting 

investments in more thermally efficient envelopes, HVAC equipment can be down-sized, 

resulting in lower equipment expenditures relative to the Reference case. There is no public cost 

except for the program administrative costs. This policy assumes cost associated with building 

code enforcement would be represented by the budget of each state hiring their building code 

officials and inspectors. The administrative costs are based on each state adding one 

administrative office run at $150,000 per annum budget and one code official at $75,000 salary 

per annum. It also includes two additional building code inspectors for the verification of every 

100 million square feet in the state at $75,000 per year (Brown, et al., 2009). The LCOE in this 

policy case is estimated to be 0.5-0.8 cent/kWh. Details of cost estimations associated with 

Building Codes are shown in Table 3.5.

Table 3.5 Cost Estimations from Building Codes

Cost (Billion $2009) a2020 2025 2030 2035

Equipment Expenditure -0.02 -0.02 -0.01 -0.01

Shell Installation Cost 0.33 0.28 0.39 0.25

Administration Cost 0.02 0.02 0.02 0.02

Total 0.33 0.29 0.40 0.26

LCOE (cent/kWh) 0.5-0.8b

a. Private cost was discounted at 7%, and public cost was discounted at 3%.

b. Levelized cost calculated in a sensitivity when all costs were discounted at 3%.

In the On-bill Financing policy, private investment is the expenditure for purchasing equipment. 

Loan cost is the initial seed money put into the program for zero-interest loans. Program 
27

administrative cost is estimated as $0.13/MMBtu energy saved. The LCOE in this policy case is 

estimated to be 6.6-7.4 cent/kWh. Details of cost estimations associated with On-bill Financing 

are shown in Table 3.6.

Table 3.6 Cost Estimations from On-Bill Financing

Cost (Billion $2009) a2020 2025 2030 2035

Private cost 0.95 0.64 0.40 0.25

Loan Cost 1.48 0.02 -0.09 0.01

Administrative Cost 0.01 0.01 0.01 0.01

Total 2.44 0.67 0.32 0.27

LCOE (cent/kWh) 6.6-7.4 b

a. Private cost was discounted at 7%, and public cost was discounted at 3%.

b. Levelized cost calculated in a sensitivity when all costs were discounted at 3%.

The Market Priming policy lowers the hurdle rate to 7% for the most efficient equipment types. 

Private investment is the expenditure for purchasing equipment. There is no public cost except 

for program administrative cost, estimated as $0.13/MM Btu energy saved. The LCOE in this 

policy case is estimated to be 2.7-3.6 cent/kWh. Details of cost estimations associated with 

Market Priming are shown in Table 3.7.

Table 3.7 Cost Estimations from Market Priming

Cost (Billion $2009) a2020 2025 2030 2035

Private cost 6.91 3.76 2.90 1.44

Administration Cost 0.03 0.03 0.02 0.02

Total 6.94 3.79 2.92 1.46

LCOE (cent/kWh) 2.7-3.6 b

a. Private cost was discounted at 7%, and public cost was discounted at 3%.

b. Levelized cost calculated in a sensitivity when all costs were discounted at 3%.

Similar to the Market Priming policy, the cost estimation for Aggressive Appliance Policy has 

private cost from the expenditure for purchasing equipment, and public cost from program 

administrative costs estimated as $0.13/MM Btu energy saved. The LCOE in this policy case is 

estimated to be 0.6-0.7 cent/kWh. Details of cost estimations associated with Aggressive 

Appliance Policy are shown in Table 3.8.

Table 3.8 Cost Estimations from Aggressive Appliance Policy

Cost (Billion $2009) a

2020 2025 2030 2035

Private cost 0.25 0.16 0.11 0.08

Administration Cost 0.01 0.01 0.01 0.01

Total 0.26 0.18 0.13 0.09

LCOE (cent/kWh) 0.6-0.7 b

a. Private cost was discounted at 7%, and public cost was discounted at 3%.

b. Levelized cost calculated in a sensitivity when all costs were discounted at 3%.

3.4 Integrated Residential Policy Bundle
28

The projected savings change a little when the residential policies were integrated into one policy 

bundle. Figure 3.8 shows the efficiency potential estimated by the residential policy bundle. In 

2035, the savings potential from the integrated policy scenario reaches 179 TWh (15.4% 

reduction from the reference consumption) in the Eastern Interconnection. 

Figure 3.8 Residential Electricity Savings Potential in the Eastern Interconnection

With all five energy efficiency policies, homes and residential buildings is estimated to use 987 

TWh of electricity in 2035 , which is below the 2010 consumption level. The cumulative savings 

in electricity is 2,886 TWh from 2011 to 2035, which is 1.8 times the cumulative growth in 

electricity consumption in the Reference case over the same time period. 

For the whole nation, the potential electricity savings from residential policies is 260 TWh (16.1% 

of Reference case consumption) in 2035. The cumulative savings residential electricity demand 

is 4,205 TWh from 2011 to 2035. In the last year of our projection, the total energy consumption 

per household decreases from 155 million Btu in the reference case to 135 million Btu in our 

integrated policy scenario.

3.4.1 Electricity Savings Potential by End Use

Most of the potential electricity savings in residential buildings comes from space cooling (46%) 

and water heating (36%). Space heating has the third highest electricity savings potential, 

accounting for 9% of electricity savings. Table 3.9 illustrates the potential electricity savings 

from residential policies by end-use, which shows that refrigeration, dishwashers and other enduses are able to generate small amount of electricity savings (2-4%) in the nation.

800

850

900

950

1,000

1,050

1,100

1,150

1,200

2010 2015 2020 2025 2030 2035

Electricity Consumption (TWh)

AEO 2011

Residential

Policies

15.4%

179 TWh of 

electricity 

savings in 2035

102 TWh of 

electricity 

savings in 

2020
29

Table 3.9 Electricity Savings Potential by End-use in the US

End-use

Annual Savings (TWh) Cumulative Savings

2020 2025 2030 2035 TWh %

Space Heating 13.0 17.7 21.8 25.2 384.2 9%

Space Cooling 69.0 92.5 108.4 119.3 1,923.2 46%

Water Heating 55.0 77.2 88.5 90.8 1,531.2 36%

Refrigeration 3.0 3.3 3.3 3.2 71.5 2%

Dishwashers 5.8 9.2 11.3 12.0 176.8 4%

Other uses 3.2 5.4 7.1 9.2 118.9 3%

Total 149.0 205.3 240.4 259.7 4205.8 ---

The large electricity savings from space cooling and water heating is not driven by technology 

demand shift between equipment classes, but rather from the shift of different equipment types 

with different efficiencies within the same equipment class. Table 3.10 shows the technology 

demand shift of equipment classes in the Eastern Interconnection. There is no significant shift 

between equipment classes since technology demand stays almost the same in the Reference case 

and the residential policy bundle. 

Table 3.10 Technology Demand Shift from Residential Policies in the Eastern 

Interconnection

Enduse Equipment class

Reference Case Policy Bundle

2015 2025 2035 2015 2025 2035

Space 

cooling

Central A/C 47% 50% 52% 47% 48% 51%

Electric Heat Pump 11% 12% 13% 11% 13% 13%

Geothermal Heat Pump 1% 1% 2% 1% 1% 2%

Natural Gas Heat Pump 0% 0% 0% 0% 0% 0%

Room A/C 41% 37% 33% 41% 37% 34%

Water 

heating

Distillate Fuel Oil Water Heater 3% 2% 2% 4% 3% 3%

Electric Water Heater 47% 49% 50% 47% 49% 49%

LPG Water Heater 2% 1% 1% 2% 2% 2%

Natural Gas Water Heater 47% 47% 47% 47% 45% 46%

Solar Water Heater 0% 0% 0% 0% 0% 0%

However, technology shifts dramatically among equipment types within the same equipment 

class. Table 3.11 illustrates the demand shift between four types of electric heat pumps in the 

Eastern Interconnection. Type 1 and 2 has relatively low efficiencies, while type 4 represents 

high efficient electric heat pumps. Most of the purchases (about 90%) go to type 1 and 2 in the 

reference case since they have relatively low costs. However, in the integrated policy case, we 

offer subsidies, no-interest loans, and market priming programs for the most efficient electric 

heat pumps, type 4. The result is that, over 95% of the purchase goes to type 4 instead of type 

1&2 in the policy bundle scenario. It is clear that the efficient equipment types dominant the 

market, offering great electricity savings in residential buildings.
30

Table 3.11 Demand share of Electric Heat Pumps for Cooling in the Eastern 

Interconnection

Efficiency

Reference Case Policy Bundle

2015 2025 2035 2015 2025 2035

Type 1 2.26-2.40 67% 70% 69% 0% 5% 2%

Type 2 2.40-2.58 22% 20% 20% 1% 0% 0%

Type 3 2.75-2.80 9% 8% 9% 1% 0% 0%

Type 4 3.11-3.19 2% 2% 2% 98% 95% 97%

3.4.2 Potential for Natural Gas Saving and Carbon Dioxide Emission Reductions

In addition to reducing the demand for electricity, the residential policies are also able to 

generate significant savings in natural gas and other fuel types. With the policy bundle, the 

natural gas savings reach 621 TBtu in the Eastern Interconnection, while savings in total energy 

consumption reach 1,291 TBtu in the region (Table 3.12). The savings in the Eastern 

Interconnection represents 63% of national savings.

Table 3.12 Potential savings of natural gas and total energy in the Eastern Interconnection

Natural gas savings (TBtu) Total Energy Savings (TBtu)

2020 2025 2030 2035 2020 2025 2030 2035

EI 390 515 587 621 761 1,039 1,204 1,291

US 668 870 995 1,069 1,220 1,630 1,880 2,040

Carbon emissions from the consumption of electricity and other fuels will be reduced since the 

demand of primary energy drops in the integrated policy scenario. Cumulatively, the reduction in 

carbon emission from electricity consumption is 1,136 million tonnes in the Eastern 

Interconnection from 2012 to 2035, while total reduction in emission from primary energy is 

1,727 million tonnes in the region (Table 3.13). This represents about 66% of total emission 

reductions in the nation.

Table 3.13 Potential Reduction in CO2 Emissions from Residential Policies

Carbon Emission Reduction (Million Tonnes)

From electricity From primary energy

2020 2025 2030 2035 2020 2025 2030 2035

EI 39 57 67 72 60 86 100 107

US 56 80 93 100 93 127 148 159

 Note: Primary energy includes all fuel types.

3.4.3 Additive Effects of the Residential Policy Bundle

Based on the integrated policy scenario, each of the five residential policies was taken out of the 

integrated policy scenario one-at-a-time to examine their contribution to the estimate of 
31

efficiency potential, assuming that the policy environment already has implemented the other 

residential policies. It is plausible that, when only one policy is implemented, the estimate of 

potential savings will be greater than when it is analyzed using this “one-off” approach. This is 

because there are overlapped focuses among the policies, and when they interact with each other, 

the synergetic effect would result in a smaller energy savings than just simple addition of savings 

from individual policies. If this is true, then the electricity savings potential estimated by the 

integrated GT-NEMS analysis would be lower than the sum of the savings from the individual 

policies. For example, because the estimated savings are significantly higher in the policy bundle, 

the rebound effect will likely be higher as well, since energy efficiency policies tend to lower 

electricity prices, which prompt a rebound in overall consumption. In addition, the potential 

represented by the various policies may be duplicative. This duplication is a function of overlap 

in the end uses and market segments that the policies target.

Table 3.14 shows the impact of each policy when they are taken out of the integration one-at-atime, compared with the impact when only one of the policies is implemented.

Table 3.14 Comparison of Policy Impacts in the Eastern Interconnection

Policy

Electricity savings from 

individual policies (TWh)

Reduction in electricity savings 

from policy bundle (TWh)

2020 2025 2030 2035 2020 2025 2030 2035

Appliance Incentives 12.7 19.0 23.0 25.6 6.8 11.5 14.3 16.2

Building Codes 7.9 11.8 15.7 19.0 3.0 3.7 4.5 5.2

On-bill Financing 13.5 17.6 20.6 22.4 3.9 2.7 1.8 1.5

Aggressive Appliance Policy 16.0 25.6 34.3 40.3 6.2 13.1 20.3 27.3

Market Priming 99.8 113.9 117.0 118.8 62.0 81.2 87.0 83.4

Note: the reduction in electricity savings from integration by each policy is the decrease of savings by 

turning one policy off, compared with the full integration scenario. 

It is clear that the significance of individual policy diminishes when integrated into the integrated 

residential policy scenario. For example, Appliance Incentives alone can save the region 25.6 

TWh of electricity in 2035. But, the all-but-Appliance Incentives scenario only indicates 16.2 

TWh savings in 2035. The amount of potential savings from each of the five policies when they 

are taken out from integration is 30-93% lower than the potential savings from each individual 

policy scenarios. The potential of On-bill Financing would diminish the most, while Market 

Priming is successful in retaining 70% of the potential from its individual policy scenario.

3.5 Summary and Discussion 

The five polices selected in this chapter target the potential to improve the efficiency of energy

use in residential buildings. A combination of the five policies can generate significant savings,

more than offsetting the growth in electricity demand between 2012 and 2035. Each of these 

policies is associated with relatively low levelized costs, ranging from 0.011/kWh for Market 

Priming, to $0.137/kWh for On-bill Financing. Three of the policies have LCOEs that are lower 

than the retail electricity price for the residential sector. 

In addition to the electricity savings, the policies also produce spillover benefits of large savings 

of natural gas and other fuels. The savings are not distributed in the Eastern Interconnection
32

proportionally to area or population. But rather, local situations, such as current state policies and 

energy consumption, influence the local savings potential.

Our estimate of electricity efficiency potential is in the middle of the range of potential 

assessments from a broad set of studies summarized in our review of the previous literature. It 

should also be acknowledged that our estimation is the achievable potential driven by possible 

future energy policies, making it smaller than assessments of technical or economic potential.

Potential estimation in this study is based on GT-NEMS forecast of future energy consumption 

in the U.S. Our estimation is limited by the GT-NEMS characterization of the U.S. energy 

market, especially the residential building sector. In fact, GT-NEMS modeling of residential 

buildings is rather constrained by a limited number of technology choices, GT-NEMS 

characterization of market penetration rates for home appliances and equipment, and other model 

limitations. The decision-making process in GT-NEMS mimics rational choices based on 

benefit-cost analysis, but it has limited modeling power to reflect behavioral constraints and suboptimal rationality.

Although estimation is limited by the chosen modeling tool, electricity savings estimated in this 

study provides a reliable and illustrative assessment of the achievable potential, with some

analysis of regional impacts, impacts on other fuels and carbon emissions. Findings in this study 

are able to assist policy-makers with detailed information about policy options available for 

promoting electricity end-use efficiency in the Eastern Interconnection.
33

4. ENERGY-EFFICIENCY POTENTIAL IN COMMERCIAL BUILDINGS

In 2010, the commercial sector in the Eastern Interconnection consumed 945 TWh, which was 

71.1% of national electricity consumption in commercial buildings (compared with 68.6% of the 

national population). The 2009 consumption level was slightly lower, at 923 TWh, due to the 

country‟s economic downturn. Delivered electricity consumption (at 25%) combined with 

electric system energy losses (at 51%) make electricity responsible for 76% of the total energy 

consumption attributed to the commercial buildings sector in the Eastern Interconnection in 2009 

(12,900 TBtu) (Fig 4.1). 

Figure 4.1 Commercial Energy Consumption in the Eastern Interconnection

(Source: EIA, SEDS, 2010)

According to the forecast of the Annual Energy Outlook 2011 by EIA (2011), commercial 

electricity consumption in the Eastern Interconnection will grow steadily at the rate of about 1.3% 

annually. In 2035, EIA forecasts the commercial electricity demand is 1,337 TWh in the region, 

41% higher than the 2010 level (Figure 4.2).
34

Figure 4.2 Reference Case Forecast of Commercial Electricity Consumption in the Eastern 

Interconnection (Source: EIA, AEO 2011)

This chapter examines the achievable potential of energy efficiency from selected policies

addressing both existing commercial buildings and new construction. The chapter explores the 

electricity savings from these policies and examines the regional distribution of the estimated 

savings potential. The other benefits from the energy-efficiency policies, such as natural gas 

savings, are also investigated. The last section of this chapter tests the effects and potentials of 

integrating all commercial policies together.

4.1 Overview of Previous Literature on the Energy-Efficiency Potential of Commercial 

Buildings

Evaluations of energy efficiency potential have been done by studies across multiple scales and 

time frames. McKinsey (Granade, et al., 2009) estimates that the NPV-positive potential for 

efficiency savings in the commercial sector is 29% in end-use energy, which sums up to $290 

billion of total energy savings from 2009-2020. A state-level study of state and local programs in 

Arkansas estimated the total energy-efficiency potential to be 15% of electricity demand, 14% of 

natural gas demand, and 20% of peak demand by 2025. The study focused on cost-effective 

resources, forecasted to produce 4,700 BBtu (9%) of electricity savings and 12,800 (8%) of

natural gas savings by 2025 in the commercial sector. The projected potential can be achieved at 

a relatively low cost where the estimated levelized cost of electricity is only $0.02/kWh 

(Neubauer et al, 2011). Another state-level study, however, examines the energy efficiency in 

residential and non-profit buildings in Cincinnati (Mackres, et al, 2011). This study finds that the 

potential energy savings associated with current efficiency programs is 2.8% of electricity 

demand and 6.9% of natural gas demand in 2030.

Many researchers also study energy-efficiency measures applied to the building sector. A 

simulation of 12 prototypical buildings in 16 cities provides the basis for assessing the energy 

efficiency potential in commercial buildings, with cost-effective estimates of 20-30% on average 

for new buildings and up to over 40% for some building types and locations (Kneifel, 2010). 

These studies present a wide range of energy-efficiency potentials due to differences in time

frames and forecasting methods. State-level estimates tend to generate projections slightly 
35

smaller than national-level studies. The difference calls for paying attention to the choice of 

forecasting approaches and potential-type for evaluation (technical, economic, or achievable).

4.2 Commercial Efficiency Policy Options and Modeling Approach

Energy consumption in commercial buildings comes from space heating and cooling, water 

heating, lighting and other end-uses, which are closely related to the efficiency of building shell, 

HVAC equipment and other appliances. Many state, local, and utility-level programs and 

policies are designed to improve energy efficiency in commercial buildings. To assess the 

electricity saving potential from all building components, a policy bundle of three efficiency 

policies was designed, including: Benchmarking (an information policy), Flexible Innovative 

Financing (a financing policy), and National Building Codes (a regulatory policy). Table 4.1 

summarizes each of these policy scenarios, along with the GT-NEMS levers used to model it. 

Further details are provided in Appendix B.

Table 4.1 GT-NEMS Levers for Commercial Energy Efficiency Policies

Policy type Policy Scenario description GT-NEMS

Lever

Information Benchmarking Lower the time preference premium for 

commercial technologies.

Kprem.txt

Financial Flexible 

Innovative 

Financing

A 30% subsidy was applied to the capital costs of 

the winning technologies under Carbon Tax

Ktek.xml

Regulatory National 

Building 

Codes

Revised factor of efficiency improvement in 

2035 for new buildings and major renovations

KSHEFF.txt

Ktek.xml

The Benchmarking policy focuses on giving building owners in the country access to baseline 

information on their building‟s energy consumption, as described in Cox, Brown, and Sun (2012). 

This could be accomplished by requiring utilities to submit energy data in a standard format to a 

widely used database, such as Portfolio Manager, which currently maintains information on 

hundreds of thousands of buildings in the U.S., submitted by building owners, managers, and 

utilities. Using existing software packages, combining the meter data, tenants within a building

could provide a “virtual building meter,” allowing for building-wide analysis. The data would 

then be available to the building owner and the utility and maintained by the Environmental 

Protection Agency (EPA).

Benchmarking helps building owners and tenants focus awareness on poor-performing buildings 

and subsystems, and enables high-performing buildings to participate in various certification 

programs that can lead to higher occupancy rates, rents, and property values. It requires an 

expenditure of time and effort, but this can be minimized by using Portfolio Manager as the 

benchmarking tool. In fact, every one of the existing programs, including the international effort 

between the U.S. and Canada, uses Portfolio Manager (Figure 4.3). The Institute for Market 

Transformation summarized the recent experiences of nine current U.S. programs (Burr, Keicher, 
36

and Leipziger, 2011). As a result of program reviews and in-depth stakeholder discussions, a 

series of best practices were recommended for outreach and education, benchmarking, 

compliance, data quality, energy consumption data, and disclosure. For benchmarking, the main 

recommendation is to follow EPA guidelines surrounding the use of Portfolio Manager.

Figure 4.3 Mandated Disclosure and Benchmarking Efforts in the United States

Source: www.IMT.org

Benchmarking has the potential to reduce or eliminate information asymmetries in the 

marketplace and to reduce the discount rates used by consumers in the sector. Thus, we model 

this policy by lowering the time preference premium for energy-efficient commercial 

technologies, as described in Appendix B.

Flexible Innovative Financing is a federal policy that would support state and local initiatives 

via the financially sustainable mechanisms of loan loss reserves, property assessed clean energy 

(PACE) tax liens, revolving loans, performance contracts, and on-bill programs, as described in 

Deitchman, Brown, and Wang (2012). The lack of attractive financing options remains one of 

the most significant barriers to deep energy efficiency upgrades in private commercial buildings 

(Prindle, 2010; Johnson Controls/IFMA, 2010; Kammen, 2009). Recognizing this barrier to 

energy efficiency investments, many states passed legislation enabling the creation of PACE 

districts between 2008 and 2010, as shown in Figure 4.4. 

San Francisco

Seattle

Portland

Santa Fe

Denver

Austin, TX

Montgomery Co., MD

Arlington, VA

Washington, DC

KANSAS

SOUTH DAKOTA

TENNESSEE

ILLINOIS

MAINE

NEW YORK

MINNESOTA VERMONT

MASSACHUSETTS

CONNECTICUT

OHIO

CALIFORNIA

OREGON

WASHINGTON

NEW MEXICO

COLORADO

HAWAII

ALASKA

MICHIGAN

MARYLAND

New York City

Philadelphia, PA

San Jose

Chicago

U.S. Building Rating and Disclosure Policies

Minneapolis, MN

Boston, MA

Existing Commercial Rating & Disclosure Public Buildings Only

Commercial Policy Under Consideration Existing Residential Disclosure
37

Figure 4.4. PACE-Enabled States

Source: DSIRE (2012)

A fundamental assumption of this analysis is that flexible innovative financing could be modeled 

as a subsidy on the first costs of energy-efficient technologies as firms consider the net present 

value of an investment. Reducing interest rates, entering into ESPCs, or financing through the 

other mechanisms have the long-term impact of making upfront investments more economically 

attractive. We use the results of Brown, Cox, and Sun (2012) to identify the subset of 350 

technologies characterized in the GT-NEMS commercial module that would be modeled with a 

30% subsidy. Specifically, we subsidize the 107 technologies that attract greater energy service 

demand following the introduction of a Carbon Tax. This set of 107 technologies draws on the 

suite of technologies from EIA‟s “High Tech” side case that assumes higher efficiencies for 

equipment, and earlier availability of some advanced equipment. Use of the high-technology 

equipment menu in GT-NEMS is predicated on the presumption that creating flexible innovative 

financing would facilitate research, development, and the demand for better-than-baseline 

technologies. These technologies are listed in Table B.3.

The National Building Codes policy for commercial buildings would set higher building shell 

efficiency requirements and more stringent energy efficiency standards for space heating and 

cooling technologies in both new construction and existing buildings that undergo major 

modifications, It is modeled after the aggressive building codes policy described in Sun, Brown, 

Jackson, and Cox (2012), but it does not include requirements for improved lighting and water 

heating. To maximize code adoption and compliance, DOE would facilitate the establishment of 

building code compliance assistance programs at the state and local level to help developers and 

builders analyze and comprehend the code, train code officials and inspectors to oversee the code 

compliance, provide information and training opportunities to parties involved in the building 

industry, and engage utilities to promote the adoption and compliance of the codes. The policy 

would also encourage establishing a building code liability structure under which relevant parties 

including developers, design companies, builders, building owners, and others have the 
38

responsibility to ensure their project is in compliance with the building code. Code officials and 

inspectors at the state and local level would be able to exercise their authority established by law 

to enforce the codes and hold the relevant parties accountable in case of non-compliance.

Unlike the commercial end-use technologies that have detailed characteristics profiles, GTNEMS does not include a building shell technology profile. Instead, the shell efficiency 

improvement over time is indexed by a set of efficiency factors that represent the overall shell 

efficiency improvement of the U.S. commercial building stock in 2035 over the 2003 level. 

Table B.2 summarizes the building shell efficiency assumptions of the reference case with the 

National Building Code policy case examined here. 

4.3 Modeling Results for Individual Commercial Building Policies

The energy consumption forecasts of the policy cases were compared with the reference case

forecast, and the differences represent the achievable energy efficiency potential. Figure 4.5

shows the estimated potential of each policy. The percentage number is the reduction in 

electricity demand as a percentage of the reference case forecast in each year. 

In 2035, the electricity savings from these four policies in the Eastern Interconnection is 3.4 -

76.8 TWh (0.3% - 5.8% of reference case consumption), while the total electricity savings in the 

nation is 5.0-107.0 TWh (0.3% - 5.7% of Reference case consumption). 

The Benchmarking policy generates the highest level of electricity savings among all four 

commercial policies. Flexible Innovative Financing also generates significant savings. The 

deployment of commercial CHP systems, on the other hand, indicates less potential for energy 

saving, due partly to the slow penetration of CHP facilities in commercial buildings.

Figure 4.5 Estimated Reductions in Electricity Demand by Individual Policies

(Percentages of Commercial Buildings Electricity Consumption)

2.9%

4.4%

5.4%5.8%

1.5%2.1%

3.5%

4.6%

0.7%1.4%2.0%

2.6%

0

20

40

60

80

100

120

2020202520302035 2020202520302035 2020202520302035

Benchmarking Financing Building Codes

Reduction in Electricity 

Consumption (TWh)

Rest of the 

Nation

Eastern 

Interconnection 
39

4.3.1 Regional Differences

Within the Eastern Interconnection, the policies generate different impacts in different census 

divisions. It is notable that Benchmarking and Flexible and Innovative Financing have a large 

portion of savings in South Atlantic, East North Central and Middle Atlantic regions. But it is 

different for Commercial CHP in the pattern of benefit distribution among regions. The largest 

proportion of electricity savings goes to the Middle Atlantic and East North Central regions 

(Figure 4.6).

Figure 4.6 Electricity Savings by Census Division for Individual Policies

(*West South Central Excluding Texas)

4.3.2 Spillover Benefits

All selected policies aim to promote efficiency in commercial buildings, including the efficient 

use of electricity and other fuel types. Table 4.2 illustrates the reduction in natural gas 

consumption that is estimated to result from each individual policy. 

Different from the policy-specific estimates of electricity-savings potential where Benchmarking 

dominated, National Building Codes is the commercial policy with the largest natural gas 

savings potentials in the Eastern Interconnection and in the whole nation. The financing policy is 

similar to the benchmarking policy in its natural gas savings in 2035, but its cumulative savings 

are greater because its impact is more significant in earlier years. 

West 

South 

Central*
40

Table 4.2 Natural Gas Savings from Individual Policies

Reference Demand 

Forecast (TBtu)

Natural Gas Savings (TBtu)

Benchmarking Financing Building Codes

2020 EI 2,708 60 106 125

US 3,594 75 134 156

2025 EI 2,755 82 112 149

US 3,661 104 145 189

2030 EI 2,838 95 105 165

US 3,775 120 139 208

2035 EI 2,946 116 103 183

US 3,919 146 135 230

The policies are also able to generate savings in fuels other than electricity and natural gas, such 

as fuel oil, diesel, and propane. Table 4.3 shows the potential savings in total energy, which is 

the projected reduction in total commercial energy consumption. These savings were calculated 

without including the avoided electricity related losses. In terms of total energy savings, 

Benchmarking has the greatest potential. Benchmarking also exerts a bigger push for energy 

efficiency from the beginning of the program, while for National Building Codes, energy savings 

start slowly but grow steadily over time.

Table 4.3 Total Energy Savings from Individual Policies

Reference Demand 

Forecast (TBtu)

Total Energy Savings (TBtu)

Benchmarking Financing Building Codes

2020 EI 14,372 447 354 280

US 20,300 610 450 360

2025 EI 15,220 604 401 395

US 21,460 880 540 520

2030 EI 16,020 720 515 433

US 22,720 1,060 740 600

2035 EI 16,903 857 687 537

US 24,000 1,220 960 730

4.3.3 Cost Estimation

The Benchmarking policy lowers the hurdle rate used by consumers in their equipmentpurchasing decisions. Private investment is the expenditure for purchasing equipment. There is 

no public cost except for program administrative costs, estimated as $0.13/MMBtu energy saved. 

The LCOE in this policy case is estimated to be 0.7-1.2 cent/kWh. Details of the cost estimations 

associated with Benchmarking are shown in Table 4.4.
41

Table 4.4 Cost Estimations from Benchmarking

Cost (Billion $2009) a2020 2025 2030 2035

Private Cost 0.93 0.85 0.85 0.82

Compliance Cost -0.001 -0.001 -0.001 -0.001

Total 0.92 0.85 0.84 0.82

LCOE (cent/kWh) 0.7-1.2 b

a. Private cost was discounted at 7%, and public cost was discounted at 3%.

b. Levelized cost calculated in a sensitivity when all costs were discounted at 3%.

In the Financing case, private investment is the expenditure for purchasing equipment. Total cost 

was estimated to be the sum of private cost, the cost of subsidizing the most efficient 

technologies, and program administrative costs, estimated as $0.13/MMBtu energy saved. The 

LCOE in this policy case is estimated to be 6.4-6.6 cent/kWh. Details of cost estimations 

associated with Appliance Incentives are shown in Table 4.5.

Table 4.5 Cost Estimations from Financing

Cost (Billion $2009) a2020 2025 2030 2035

Private Cost 0.71 0.58 0.49 0.44

Subsidy Cost 9.17 8.07 9.07 8.64

Administration Cost 0.06 0.06 0.07 0.06

Total 9.94 8.71 9.64 9.14

LCOE (cent/kWh) 6.4-6.6 b

a. Private cost was discounted at 7%, and public cost was discounted at 3%.

b. Levelized cost calculated in a sensitivity when all costs were discounted at 3%.

For Building Codes, private investment is the incremental cost of equipment and building 

envelope expenditures to meet new building codes. There is no public cost except for program 

administration. This policy assumes costs associated with building code enforcement carried out

by state building code officials and inspectors. The administrative costs are based on each state‟s 

adding one administrative office, operated for $150,000 per annum, and one code official at 

$75,000 salary per annum. It also includes two additional building code inspectors for the 

verification of every 100 million square feet in the state at $75,000 per year (Brown, et al., 2009).

The LCOE in this policy case is estimated to be 3.5-4.6 cent/kWh. Details of cost estimations 

associated with Building Codes are shown in Table 4.6.

Table 4.6 Cost Estimations from Building Codes

Cost (Billion $2009) a2020 2025 2030 2035

Private Cost 1.08 -1.91 0.39 0.25

Shell Improvement Cost 0.07 -0.15 0.05 0.03

Administration Cost 0.04 -0.10 0.04 0.03

Total 1.19 -2.16 0.48 0.31

LCOE (cent/kWh) 3.5-4.6 b

a. Private cost was discounted at 7%, and public cost was discounted at 3%.

b. Levelized cost calculated in a sensitivity when all costs were discounted at 3%.
42

4.4 Integrated Commercial Policy Bundle

The projected savings change when the commercial policies are evaluated as one policy bundle. 

Figure 4.7 shows the efficiency potential resulting from the integrated commercial policy case. 

The savings potential from the integrated policy scenario is 40 TWh in 2020, expanding to 153 

TWh (8.4% of reference consumption) in 2035 in the Eastern Interconnection. 

Figure 4.7 Commercial Electricity Savings Potential in the Eastern Interconnection

4.4.1 Electricity Savings Potential By End Use

Electricity consumption by space cooling, space heating, and ventilation would be reduced 

significantly by the Commercial Policies bundle (Table 4.7). However, electricity consumption 

by “other” end-uses services has increased in the Commercial Policies case; these represent an 

array of miscellaneous “plug loads” that are not targeted by any of our commercial building 

energy policies. 

800

900

1,000

1,100

1,200

1,300

1,400

2010 2015 2020 2025 2030 2035

Electricity consumption (TWh)

AEO 2011

Commercial Policies

8.4%

3.7%

40 TWh of 

savings in 2020

153 TWh of 

savings in 2035
43

Table 4.7 Electricity-Savings Potential by End-Use in the US

End Use

2010 2020 2035

Reference Reference Policy

Case

Annual 

Savings

Percent 

Savings Reference Policy Case Annual Savings

Percent 

Savings

Space Heating 51 50 43 7 15% 52 42 10 18%

Space Cooling 170 160 137 23 14% 180 141 39 22%

Water Heating 27 28 27 1 4% 28 25 3 10%

Ventilation 150 176 102 74 42% 209 107 102 49%

Lighting 300 321 311 10 3% 365 303 62 17%

Refrigeration 115 105 100 5 5% 114 105 9 8%

Personal 

Computer 62 56 56 0 0% 62 62 0 0%

Other Office 

Equipment 77 108 108 0 0% 137 137 0 0%

Other Uses 391 518 578 -60 -11% 730 802 -72 -10%

Total 1343 1522 1462 60 3.9% 1877 1724 153 8.2%

4.4.2 Potential for Natural Gas Savings and Carbon Dioxide Emission Reductions

The commercial policies are also able to generate significant savings in natural gas and other fuel 

types. In the integrated policy scenario, the natural gas savings reach 368 TBtu in the Eastern 

Interconnection, while savings in total energy consumption reach 1,930 TBtu in the region 

(Table 4.8). 

Table 4.8 Potential Savings of Natural Gas and Total Energy

Natural gas savings (TBtu) Total Energy Savings (TBtu)

2020 2025 2030 2035 2020 2025 2030 2035

EI 187 222 253 288 681 939 1,167 1,375

US 236 283 324 368 900 1,300 1,640 1,930

In addition, carbon emissions from the consumption of electricity and other fuels would be 

reduced since the demand for primary energy drops in the Integrated Policy scenario (Table 4.9). 

Table 4.9 Potential Reduction in CO2 Emissions from Commercial Policies

Carbon Emission Reduction (Million Tonnes)

From electricity From primary energy

2020 2025 2030 2035 2020 2025 2030 2035

EI 31 44 55 64 42 57 70 81

US 40 56 83 83 54 72 89 103

4.5 Conclusion and Discussion
44

The three polices examined in this chapter target the electricity efficiency potential in 

commercial buildings. A combination of the policies can generate significant savings, more than 

offsetting the growth in electricity demand between 2012 and 2035. Each of these policies is 

associated with relatively low levelized costs, ranging from $0.017/kWh for Benchmarking, to 

$0.104/kWh for Flexible Innovative Financing. All three policies have LCOEs that are lower 

than the retail electricity price for the commercial buildings sector. 

These electricity savings are not distributed in the Eastern Interconnection proportionally to area 

or population. Rather, local situations, such as state policies and energy consumption, influence 

the local savings potential.

Our estimate of electricity efficiency potential is in the middle of the range of potential 

assessments from a broad set of studies summarized in our review of the previous literature. It 

should also be acknowledged that our estimate is the achievable potential driven by selected 

policies, making it different from assessments of technical or economic potential.

In addition to saving electricity, the policies also produce spillover benefits of large savings of 

natural gas and other fuels.

The potential estimation in this study is based on GT-NEMS forecast of future energy 

consumption in the U.S. Our estimation is limited by the GT-NEMS characterization of the U.S. 

energy market and the commercial building sector. In fact, GT-NEMS‟s modeling of commercial

buildings is enhanced by a sizeable number of technology choices. The decision-making process 

in GT-NEMS mimics rational choices based on benefit-cost analysis, but it has limited modeling 

power to reflect behavioral constraints and sub-optimal rationality.
45

5. ENERGY EFFICIENCY POTENTIAL IN INDUSTRIAL SECTOR

In 2010, the industrial sector in the Eastern Interconnection consumed 700 TWh of electricity, 

which was 72.1% of national industrial electricity consumption (compared with 68.6% of the 

national population). The 2009 consumption level was higher, indicating the continued impacts 

of the country‟s economic downturn on U.S. manufacturing. Delivered electricity consumption 

(at 12% of total industrial energy use in the region) combined with electric system energy losses 

(at 26%) make electricity responsible for 38% of the total energy consumption attributed to the 

industry in the Eastern Interconnection in 2009 (18,300 TBtu) (Fig 5.1). 

Figure 5.1 Industrial Energy Consumption in the Eastern Interconnection

(Source: EIA, SEDS, 2010)

The U.S. industrial sector, which encompasses diverse manufacturing and non-manufacturing 

activities, has energy requirements for all kinds of fuel types. As the most diversified economic 

sector, the industrial sector includes manufacturing, agriculture, mining, and construction. 

Manufacturing industries dominate industrial energy demand. Petroleum and coal products (at 

27%) and chemicals (at 23%) are the two most energy-consuming manufacturing industries in 

terms of primary energy consumption (Figure 5.2), but these same two industries account for 

lower proportions of manufacturing‟s total value of production (at 11% and 13%, respectively) 

(Figure 5.3).
46

Figure 5.2 Primary Energy Consumption by Manufacturing Industries in 2006, Nationwide

(Source: MECS, 2006)

Figure 5.3 Gross Output by Manufacturing Industries in 2006

(Source: DOC, Gross Domestic Product by Industry 1998-2007, 2009)

Food

6%

Wood Products

2%

Paper

11% Plastics and 

Rubber Products

3%

Petroleum and 

Coal Products

27%

Fabricated Metal 

Products

3%

Chemicals

23%

Nonmetallic 

Mineral 

Products

5%

Primary Metals

10%

Transportation 

Equipment

3%

Other 

Manufacturing

7%

Food Manufacturing

11%

Wood Product 

Manufacturing

2%

Paper 

Manufacturing

3% Plastics and Rubber 

Products 

Manufacturing

4%

Petroleum and Coal 

Products 

Manufacturing

11%

Fabricated Metal 

Product 

Manufacturing

6%

Chemical 

Manufacturing

13% Nonmetallic 

Mineral Product 

Manufacturing

3%

Primary Metal 

Manufacturing

5%

Transportation 

Equipment 

Manufacturing

14%

Other 

Manufacturing

28%
47

According to the forecast of future electricity demand in the industrial sector provided by the 

Annual Energy Outlook 2011, industry reaches a peak in 2021, rising to about 771 TWh in the 

Eastern Interconnection. The industrial electricity consumption decreases steadily after 2021 

(Figure 5.4). The U.S. as a whole has similar trends as the Eastern Interconnection. The declining 

demand in the later years of projection is due to the increasing energy efficiency, when 

electricity requirement for industrial activities drops from 443 Btu/dollar in 2021 to 352 

Btu/dollar in 2035 (EIA, 2011).

Figure 5.4 Reference Case Forecast of Industrial Electricity Consumption in the Eastern 

Interconnection (Source: EIA, 2011)

This chapter examines the achievable potential of energy efficiency from selected policies that 

promote technology advancement and improved energy management practices in industry. The 

chapter explores the electricity savings from these policies and examines how the savings are 

distributed among regions. The other benefits from the energy-efficiency policies, such as natural 

gas savings, are also investigated. The last section of this chapter explores the effects and 

potentials of concurrently implementing the entire industrial policy bundle.

5.1 Overview of Previous Literature on the Energy-Efficiency Potential of Industry

A long-term efficiency potential assessment done by a recent ACEEE study indicates a 33% 

electricity-savings potential in the industrial sector in 2050 (Laitner, et al, 2012). This potential 

can be reached by a set of efficiency improvements and technology advancement in the industrial 

sector, including recycled feedstock and materials substitution, transformative processes, and 

smart manufacturing, and supply chain integration. 

The McKinsey report estimates the potential of electricity savings in the industrial sector to be 

about 190 TWh (18% of baseline demand) in 2020 provided by a combination of energy 

efficiency programs (Granade, et al., 2009). The industries have great opportunities in various 

energy-efficiency initiatives and CHP deployment, which are able to generate cost-effective 
48

energy savings. This study found that 33% of the potential comes from cross-cutting energy 

support systems (such as steam systems, motors, and buildings), and the rest resides in the 

process and assembly steps of industry subsector. It is also found that 61% of the potential can 

be realized in energy-intensive industries. 

Brown et al. (2011) evaluated seven industrial policies at the national scale. The three policies 

with the greatest energy savings potential were Output-Based Emissions Standards (3.4% 

savings in 2020 rising to 9.0% savings in 2035), the Superior Energy Performance program (2.2% 

savings in 2020 rising to 7.7% savings in 2035), and a 30% Investment Tax Credit for CHP (2.3% 

savings in 2020 rising to 5.6% savings in 2035). These savings are not additive because the 

policies overlap in their targeted markets, but each of these policies when examined individually 

was highly cost-effective.

A state-level study done in Arkansas estimates the total electric efficiency potential in the 

industrial sector to be 2,880 GWh (16% of baseline consumption) in 2025 at a levelized cost of 

about $0.02/kWh (Neubauer et al, 2011). However, the study only estimates the electric 

efficiency potential for cross-cutting measures, excluding the efficiency opportunities of processspecific measures. As a result, the percentage potential from the study is lower than the other two 

nation-level assessments mentioned above.

A meta-review done by Nadel et al (2004) reviews eleven energy-efficiency potential studies and 

summarizes them by type of potential (technical, economic, and achievable) and by sector 

(residential, commercial and industrial). It concludes that the median achievable potential for 

electricity savings in the industrial sector is about 14%. 

The America‟s Energy Future study by the National Academy of Sciences (2009) examined the 

potential for increasing efficiency, and estimated the potential to generate 14-20% of energy 

savings by 2020 using financially attractive technologies. The study also forecasted that 

industrial CHP could cost-effectively save approximately 4.4 to 6.8 quadrillion Btu of energy by 

2020 (National Academy of Sciences, 2009, Table 4.4). 

Studies on specific industry subsectors have shown wide ranges of savings potentials. A study of 

the pulp and paper industry (Fleiter, et al, 2012) estimated the savings due to 17 process 

technologies for improving energy efficiency up to 2035. The economic potential assessed is 16% 

for electricity and 21% for all fuels. An assessment based on Best Practice Technology in the 

chemical and petrochemical industry estimate the potential energy savings for the U.S. to be 

about 24% with top-down approach and about 10.9% with bottom-up approach (Saygin, et al, 

2011). The energy efficiency for industrial motor systems is estimated for both cost-effective 

and technical potential in McKane and Hasanbeigi (2011)‟s study. The assessment is 14-49% as 

the economic potential, and 27-57% as the technical potential. 

These assessments are relatively aggressive comparing with an earlier work, which estimates the 

achievable potential for industry to be 7-17% by 2020 from different policy scenarios (Worrell & 

Price, 2001). A review of assessment studies on the U.S. industrial sector shows a wide range of 

estimates of energy efficiency potential within and across industries: 3-18% savings for chemical 

industry, 5-23% for petroleum refining industry, and 6-37% for pulp and paper industry in 2020 

(Brown, Cox, & Cortes, 2010).
49

5.2 Industrial Efficiency Policy Options and Modeling Approach

Energy consumption in the industrial sector comes from all kinds of process-specific 

technologies and cross-cutting systems. A policy bundle of four efficiency policies were 

designed to tackle the efficiency potential of industrial processes, motors, and buildings. The 

industrial policy bundle includes Motor Standards, Motor Rebate, Advanced Manufacturing 

Initiative, and CHP Incentives. 

Approximately two-thirds of industrial electricity consumption is used to operate motors. As a 

result, two of our four industrial policies are designed to improve the efficiency of industrial 

motor systems. Over the life of a motor 96% of the expenditure is for electricity, with initial 

purchase and equipment maintenance accounting for a small fraction of the cost (DOE/ITP, 

2008). Motor replacement is a long-term endeavor, with an estimated time frame of 15 to 20 

years for a 90% turnover in the market (Xnergy Inc., 2002). 

The Motor Rebate is a financial policy for promoting the deployment of premium motors that 

satisfy the efficiency requirement of the Energy Independence and Security Act (EISA) of 2007. 

EISA upgraded standards on all new motors sold in the U.S. from requirements laid out in the 

Energy Policy Act of 1992 (EPACT-92) to National Electrical Manufacturers Association 

(NEMA) Premium®certified levels, but does not offer incentives to encourage firms to replace 

functional motors that predate EPACT-92 standards with more efficient models. In fact, an 

unintended consequence of the new standards policy could be that industrial firms choose to 

repair instead of replace older, inefficient motors rather than pay the additional costs of new 

motors under the new regulations (Elliot, 2007). 

This policy would provide industrial firms with rebates for purchases of certified high-efficiency 

motors of 25 to 500 horsepower. It is similar to the Motor Efficiency Rebate Program authorized 

in Section 245 of the American Clean Energy and Security (ACES) Act, as passed in the House 

of Representatives, which would operate for five years. It is modeled here as a 30% subsidy to 

reduce the capital costs of premium motors and to facilitate the removal of the most inefficient 

motors.

A Motor Standard is another policy targeting motor systems in industries, which assumes a 

new motor standard comes into performance 10 years after EISA 2007, requiring efficiency 

improvement for premium motors. Also, for systems that use motors, this new motor standard 

would require higher efficiency, which allows energy usage of these systems to be reduced by 

25%. This policy should encourage the development and production of a next generation of 

premium electric motors. 

The Advanced Manufacturing Initiative is a combined program of R&D and technology 

demonstration, which aims at identifying the most promising opportunities associated with new 

technologies. This policy can be applied to various industrial processes and sectors, including 

advanced robotics, nano-electronics, materials by design, and biomanufacturing. An assessment 

for plant utility upgrades for efficiency potential was derived from the Industrial Assessment 

Centers (IAC) database (see Table C.1 for details). Potential energy savings (both electricity and 

natural gas savings) from this dataset was modeled in GT-NEMS to forecast the influence of this 

policy on industrial electricity demand.
50

The CHP Incentive tries to promote the deployment of CHP systems by offering a 30% 

Investment Tax Credit (ITC) for 10 years across all type of systems (unlike the current expiring 

ITC that applies only to systems up to 50 MW in capacity). This policy aims at decreasing the 

up-front capital costs of CHP equipment, which is more electric efficient by using waste heat to 

generate on-site electricity. If the electricity generated by CHP systems exceeds the demand from 

equipment owners, the excess electricity is allowed to be sold back to the grid, which generates 

an extra stream of revenue for equipment owners. With a large potential as a distributed 

generation resource, CHP is identified as one of the most important clean resources in the most 

recent proposal of the Clean Energy Standard Act of 2012 (Bingaman Bill, 2012).

Table 5.1 GT-NEMS Levers for Indusial Efficiency Policies

Policy type Policy Scenario Description GT-NEMS

Lever 

Financial Motor rebate A 30% subsidy for premium motors 

which satisfies the minimum efficiency 

requirement of EISA 2007 

Indmotor.xml 

Regulatory Motor 

standard 

New motor standard at 2017 which has 

efficiency improvement for premium 

motor and system savings increase by 

25%. Systems include fans, pumps, and 

compressed air.

Source code 

R&D and 

Demonstration 

Program

Advanced 

Manufacturing 

Initiative 

Assessments for Plant Utility Upgrades

for efficiency opportunities. Energy 

savings potential are based on audit 

information in the Industrial Assessment 

Centers (IAC) dataset.

itech.txt 

Financial CHP Incentive A 30% ITC for industrial CHP for 10 

years 

indcogen.xml 

The GT-NEMS modeling levers for these four policies were purposely chosen to generate 

separate policy impacts (Table 5.1). The Industrial CHP Incentive is modeled in the cogeneration profile of GT-NEMS, where we add a 30% ITC for 10 years for CHP starting in 2015. 

The modeling of Motor Rebate was done by changing the deal rebate variable for premium 

motors in the motor profile. Within the same profile, the Motor Standard policy was modeled by 

modifying two other variables, the efficiency for premium motors by system size, and system 

savings. These modifications were combined with source code changes to reflect the correct 

history. To model the Advanced Manufacturing Initiative, the technology profile from the 

Reference case was modified to change the Unit Energy Consumption (UEC) for industry 

subsector in different census divisions to reflect the potential energy savings based on the IAC 

assessment.

5.3 Modeling Results for Industry Policies
51

The energy consumption forecasts of the policy cases were compared with the reference case

forecast, and the differences represent the achievable energy-efficiency potential. Figure 5.5

shows the estimated potential from Motor Rebates, Motor Standards, and the Advanced 

Manufacturing Initiative. The percentage numbers are the reduction in electricity demand as a 

percentage of the Reference case forecast in each year. 

In 2035, the reduction in electricity consumption from these three policies in the Eastern 

Interconnection ranges from 1.62 TWh (0.2% of reference case consumption) for the Motor 

rebate policy to 16.4 TWh (2.3%) for the Advanced Manufacturing Initiative. For the nation as a 

whole, the electricity savings range from 2.3-21.7 TWh (0.3 - 3.1% of Reference case 

consumption). 

The Motor Rebate policy is the only policy that does not have an expanding level of energy 

savings over the 20-year planning horizon. Its decline in energy-savings potential is a function of 

the 5-year duration of the rebate, which has limited impact in later years. 

Figure 5.5 Estimated Reductions in Electricity Demand by Individual Policies (Excluding 

CHP) (Percentages of Industrial Electricity Consumption Savings)

The Reference case forecasts a doubling of the capacity of industrial CHP systems in the Eastern 

Interconnection from 2012 to 2035. In the Industry CHP scenario, incentives are provided to 

CHP systems that utilize waste heat to generate electricity for on-site uses and to sell the excess 

power back to the grid. In addition to the reduction in electricity demand, the contribution of new 

generation by industrial CHP is significant. Based on our projection, the extended ITC will 

incentivize 33.8 TWh more generation in 2020, and 39.7 TWh more generation in 2035 in the 

Eastern Interconnection (Figure 5.6). This represents a 10 to 15% increase in electricity 

generating capacity of industrial CHP systems above and beyond the doubling that is forecast to 

occur without any additional policy intervention (Figure 5.7).
52

Figure 5.6 CHP Generation and Reduction in Electricity Purchase

(Percentages of Industrial Electricity Consumption Growth and Reduction in Electricity Purchase)

Of the electricity generated by industrial CHP systems in 2020, about 78.1% is consumed for 

industry‟s own use, with the excessive part being sold back to the grid. The percentage of CHP 

generation sold back to the grid grows to 27.4% in 2035 and represents a significant new revenue 

stream for manufacturing facilities. The reduction in electricity purchase is the net reduction 

from the CHP policy compared to the Reference case. 

3.4%

3.9%4.0%4.1%

1.0%

1.5%1.5%1.5%

3.0%

3.9%4.0%4.1%

0

5

10

15

20

25

30

35

40

45

202020252030203520202025203020352020202520302035

Generation for Own

Use

Sales back to the Grid Reduction in

Electricity

Purchase

Electricity Generation (TWh)

Rest of the 

Nation

Eastern 

Interconnecti

on
53

Figure 5.7 Potential for Expanded CHP Generating Capacity in the Eastern 

Interconnection

The increase in generation is facilitated by the increased industrial CHP capacity (Figure 5.7). 

Due to the ITC incentives, the deployment of industrial CHP systems keeps increasing at a 

steady rate. While more CHP systems are installed in the policy scenario, the consumption of 

natural gas also increases (Figure 5.8). The usage of natural gas by industrial sector increases by 

474 TBtu (22.8%) in 2035.

Figure 5.8 Natural Gas Consumption by Industry Sector in the Eastern Interconnection

The majority of electricity is consumed by industry for its own use, with the rest of generated 

electricity sold back to the grid. In the CHP policy scenario, one-fourth to one-fifth of the on-site 

generated electricity is sold back to the grid between 2020 and 2035. These shifts are able to 

motivate a small decrease in electricity retail price in the industrial sector (Figure 5.9). The price 

reduction ranges from 0.5% to 1% across the 20-year period.
54

Figure 5.9 Electricity Retail Price in the Industry Sector

5.3.1 Regional Differences

The distribution of electricity savings across seven census divisions in the industrial sector is 

distinct from the pattern of energy-efficiency potential in the residential and commercial sectors. 

Motor Rebates, Motor Standard, and Advanced Manufacturing Initiative have similar regional 

distributions. In absolute savings, the East North Central region (ENC includes five states: 

Indiana, Illinois, Michigan, Ohio, and Wisconsin) enjoys the largest portion of benefits from 

these three policies (Figure 5.10). These five states produce a total Gross State Product (GSP) of 

about 14.0% of the nation‟s GDP (DOC, 2010). The ENC region is particularly dominant in its 

potential to reduce electricity consumption via the Advanced Manufacturing Initiative.

The distribution of potential from the CHP Incentive is quite different from the other three 

industrial policies. The electricity generation and reduction in consumption would mainly occur 

in the South Atlantic (SA), the ENC and the East South Central (ESC) regions (Figure 5.11). SA 

and ESC could have 7.5% of the electricity consumed for manufacturing be generated by CHP 

facilities in 2035. SA is the largest census division including nine states and generating 18.5% of 

the nation‟s GDP. The five states in ENC and the four states in ESC produce 14.0% and 4.7% of 

the nation‟s GDP (DOC, 2010). 
55

Figure 5.10 Electricity Savings by Census Division from Motor Rebates, Motor Standard, 

and Advanced Manufacturing Initiative (*West South Central Excluding Texas)

Figure 5.11 Electricity Savings by Census Division from CHP

(*West South Central Excluding Texas)

0.4%

0.4%

0.2%

0.2%

0.2% 0.2%

0.2%

0

0.2

0.4

0.6

0.8

1

1.2

1.4

Reduction in Electricity Demand (TWh)

Motor Rebate

2020

2025

2030

2035

1.55

1.5%

1.1%

1.0%

1.0%

1.0%

0.8%

0

0.5

1

1.5

2

2.5

3

Reduction in Electricity Demand (TWh)

Motor Standard

2020

2025

2030

2035

2.5%

2.4%

2.8%

2.6% 1.9%

1.9%

1.6%

0

1

2

3

4

5

6

7

New

England

Middle

Atlantic

East

North

Central

West

North

Central

South

Atlantic

East

South

Central

West

South

Central*

Reduction in Electricity Demand (TWh)

Advanced Manufacturing 

Initiative

2020

2025

2030

2035

4.3%

4.0%

3.0%

2.6%

5.4%

5.4%

4.9%

0

1

2

3

4

5

6

7

8

New

England

Middle

Atlantic

East

North

Central

West

North

Central

South

Atlantic

East

South

Central

West

South

Central*

Reduction in Electricity Demand (TWh)

CHP

2020

2025

2030

2035

6.0%

5.6%

4.1%

3.6%

7.5%

7.5%

6.9%

0

2

4

6

8

10

12

New

England

Middle

Atlantic

East

North

Central

West

North

Central

South

Atlantic

East

South

Central

West

South

Central*

Electricity Generation by CHP (TWh)

CHP

2020

2025

2030

2035
56

5.3.2 Spillover Benefits

The policies aim to promote energy efficiency in industry, and each of them improves the 

efficient use of electricity. Their impact on the use of other fuels is highly variable. 

CHP systems utilize waste heat to generate electricity; increasingly these systems are powered 

by natural gas. As a result, the CHP Incentive policy would produce electricity savings, but 

natural gas consumption in the industrial sector would increase relative to the reference case. 

On the other hand, the two policies promoting premium motors would have minimal influence 

on natural gas usage since the only fuel used by motors is electricity. In contrast, the Advanced 

Manufacturing Initiative is able to generate savings in both electricity and natural gas. Table 5.2 

shows the significant natural gas savings from this policy.

Table 5.2 Natural Gas Savings from Advanced Manufacturing Initiative

Reference 

Forecast for 

Eastern 

Interconnection

(TBtu) 

Natural gas savings 

(TBtu) 

Eastern 

Interconnection

U.S.

2020 9,690 18 20 

2025 9,550 31 40 

2030 9,550 43 50 

2035 9,510 56 60 

Total 654 731

Note: the total number is the cumulative savings from 2012-2035.

5.3.3 Cost Estimation

The levelized cost of electricity saved from each industrial energy efficiency policy was 

calculated to evaluate the cost impacts of these policies. In the Motor Rebate scenario, failed 

motors can be rewound with relatively low cost or replaced by EISA 2007 premium motors with 

considerably high costs. A 30% subsidy was applied to premium efficiency motors to lower the 

replacement costs. Private investment was calculated based on the cost of rewound motors and 

replaced motors, while subsidy cost was estimated based on motor replacement. Program 

Administration Cost is estimated as 2.5% of subsidy cost. The LCOE in this policy case is 

estimated to be 9.3-11.5 cent/kWh. Details of cost estimations associated with Motor Rebate are 

shown in Table 5.3.

Table 5.3 Cost Estimations from Motor Rebate

Cost (Billion $2009) a2020 2025 2030 2035

Private Cost 0.45 0.34 0.25 0.19

Subsidy Cost 0.38 0.35 0.31 0.29

Administration Cost 0.01 0.01 0.01 0.01

Total 0.85 0.69 0.57 0.48

LCOE (cent/kWh) 9.3-11.5 b
57

a. Private cost was discounted at 7%, and public cost was discounted at 3%.

b. Levelized cost calculated in a sensitivity when all costs were discounted at 3%.

Similarly, private costs in the Motor Standard case were estimated based on the cost of 

rewinding and replacing failed motors. However, public cost is only the program administrative 

cost estimated as $0.13/MMBtu energy saved. The LCOE in this policy case is estimated to be $-

2.4-3.9cent/kWh. Details of cost estimations in Table 5.4 suggest that Motor Standard is able to 

save industries in equipment expenditure. In the policy case, the portion of failed motors got 

rewound with relatively low cost are much higher than the portion of rewound motors in the 

reference case. The new motor standard restrains industries in favoring replacing failed motors 

with premium motors. By going with the less costly option of rewinding failed motors, the 

industries are able to save money in equipment expenditure, thus, leading to a negative LCOE 

for this policy. 

Table 5.4 Cost Estimations from Motor Standard

Cost (Billion $2009) a2020 2025 2030 2035

Private Cost 0.408 0.224 0.182 0.204

Administration Cost 0.001 0.002 0.002 0.002

Total 0.410 0.226 0.184 0.207

LCOE (cent/kWh) 2.4-3.9 b

a. Private cost was discounted at 7%, and public cost was discounted at 3%.

b. Levelized cost calculated in a sensitivity when all costs were discounted at 3%.

In the CHP Incentive case, private cost is the investment in CHP systems in the private sector. 

Subsidy cost was estimated based on the amount of incremental cost in CHP investments, while 

program administrative cost was estimated as 2% of subsidy cost. The LCOE in this policy case 

is estimated to be 1.5-2.3 cent/kWh. Details of cost estimations associated with CHP Incentive 

are shown in Table 5.5.

Table 5.5 Cost Estimations from CHP Incentive

Cost (Billion $2009) a2020 2025 2030 2035

Increased Natural Gas Expenditure 1.55 1.15 0.62 0.54

CHP system 0.25 -0.02 -0.01 0.00

Subsidy cost 0.56 0.00 0.00 0.00

Administration cost 0.01 0.00 0.00 0.00

Total 2.37 1.13 0.62 0.54

LCOE (cent/kWh) 1.5-2.3 b

a. Private cost was discounted at 7%, and public cost was discounted at 3%.

b. Levelized cost calculated in a sensitivity when all costs were discounted at 3%.

The Advanced Manufacturing Initiative is a combination of R&D and demonstration programs, 

which aims at identifying the most promising opportunities associated with new technologies 
58

that can be applied to various industrial processes and sectors. This policy is able to stimulate 

volunteer upgrades in plants and firms. 

Figure 5.12 U.S. Industrial Consumption by Size of Firm

(Source: Brown, et al., 2011)

To calculate private investment, this study follows the division of industrial plants by Brown et 

al (2011), grouping firms into small, medium and large firms (Figure 5.12). It is assumed that the 

private investment is $14/MMBtu energy saved for large firms and $12.6/MMBtu energy saved 

for small and medium firms (Brown et al., 2011). Public cost includes only the program 

administrative cost, estimated as $0.13/MMBtu energy saved. The LCOE in this policy case is 

estimated to be 3.0-4.8 cent/kWh, with investment cost declining from $1.36 Billion in 2020 to 

$0.97 Billion in 2035 (in 2009 dollar, Table 5.6).

Table 5.6 Cost Estimations from Advanced Manufacturing Initiative

Cost (2009$Billion) a2020 2025 2030 2035

Private Cost 1.34 1.26 0.89 0.94

Public Cost 0.02 0.03 0.03 0.03

Total 1.36 1.29 0.93 0.97

LCOE (cent/kWh) 3.0-4.8 b

a. Private cost was discounted at 7%, and public cost was discounted at 3%.

b. Levelized cost calculated in a sensitivity when all costs were discounted at 3%.

5.4 Integrated Industrial Policy Bundle

By evaluating all four industrial energy-efficiency policies as one policy bundle, the electricity 

consumption by the Eastern Interconnection drops by about 68.3 TWh (9.7%) in 2035 compared 

to the Reference case consumption (Figure 5.13). In the integrated policy bundle, the projected 

level of consumption in 2035 is 9.4% lower than the current level (700 TWh in 2010). From 
59

2012 to 2035, the total reduction in electricity demand is 1,195 TWh in the Eastern 

Interconnection. In the integrated policy scenario, the electricity consumption in the industrial 

sector decreases at an annual rate of 0.5%, which is much higher than the annual decrease rate of 

0.05% in the reference case. 

68 TWh of

electricity 

savings in 

2035

Figure 5.13 Industrial Electricity Savings Potential in the Eastern Interconnection

The majority of electricity savings come from the bulk chemical, food, and paper industries. 

Mining and iron and steel industries also have the potential to generate significant amount of 

savings (Figure 5.14). According to ICF International (2010, Table 5) and Brown, Cox and Baer 

(2012), these same industries have greater opportunities for utilizing waste heat to generate 

electricity by CHP facilities than other industries.

Figure 5.14 Electricity Savings by Industry Subsectors

0

2

4

6

8

10

12

14

16

Electricity Savings in US (TWh)

2020

2025

2030

2035
60

Due to the incorporation of CHP incentives into the policy bundle, the electricity generation by 

industrial CHP also increases. By comparing the generation in the integrated policy case (Figure 

5.15) with the stand-alone CHP policy case (Figure 5.6), the amount of increase and the trend 

over time are almost the same. This result suggests that the CHP incentives do not interact with 

other industrial policies in terms of generation. This interpretation is plausible since the other 

three policies do not address distributed generation issues in their policy design. 

Figure 5.15 Increases in Electricity Generation from Industrial CHP

The increase in CHP capacity precipitates an increase in natural gas consumption in the 

industrial sector. However, the amount of increase is offset by the significant potential for natural 

gas savings from the Advanced Manufacturing Initiative. In the year 2035, the natural gas 

consumption in the integrated policy case increases by only 154 TBtu (2.8%), which is 

significantly less than in the stand-alone CHP policy case (Figure 5.16).

Figure 5.16 Natural Gas Consumption in the Industry Sector
61

Similar to the stand-alone CHP incentives case, about one-fifth of the electricity generation (by 

CHP and other distributed generation technologies) is sold back to the grid each year. Grid sales 

are able to drive the electricity retail prices down beginning in 2015 (Figure 5.17). However, this 

price decrease is no bigger than 0.12 cent/kWh through 2035.

Figure 5.17 Electricity Retail Price in the Industry Sector 

The level of carbon dioxide emissions also decreases in the integrated policy case (Table 5.7). 

U.S. carbon emissions are reduced by 54 million tonnes in 2035 due to the decrease in electricity 

consumption. However, the slight increase in natural gas consumption makes the emissions 

reduction from total primary energy consumption slightly lower than the emission reduction

from electricity, which is 43 million tonnes in 2035.

Table 5.7 Reductions in Carbon Emissions

Carbon Emission Reduction (Million Tonnes)

From electricity From primary energy

2020 2025 2030 2035 2020 2025 2030 2035

EI 37 41 41 44 30 30 29 34

US 45 50 54 54 31 36 37 43

5.5 Conclusions and Discussions

This chapter investigates the energy efficiency potential driven by four industrial policies, 

including Motor Rebates, Motor Standard, CHP Incentive, and Advanced Manufacturing 

Initiative. Together these four policies are estimated to generate significant electricity savings 

potential in the Eastern Interconnection. Each of these policies has levelized costs that are lower 

than the retail electricity price for industry. CHP Incentive is the industrial policy with the largest 

electricity-savings potential. This policy also drives up natural gas consumption while lowering 
62

electricity prices. Other benefits of energy-efficiency policies include climate change impacts; 

carbon emissions are projected to be reduced significantly in the integrated policy case. 

This study focuses on the achievable potential driven by energy efficiency policies. The 

estimation of achievable potential in this study is slightly lower than the estimates summarized in 

our review of the previous literature. However, it is in the range of potential savings assessments 

from real-world practices and programs. Although limited by the modeling tool, this chapter is 

able to offer reliable assessment of electric end-use efficiency potential and cost estimation of 

industrial energy-efficiency policies.
63

6. INTEGRATED POLICY CASE

The previous three chapters examined a suite of twelve energy-efficiency policies, as a means of 

quantifying the potential for electricity efficiency improvements that are both cost-effective and 

achievable. The results are summarized in Table 6.1. The estimated electricity savings from 

individual policies sum up to reach 241 TWh in 2020, which is higher than the estimation from 

the Integrated Policy case (Figure1). This indicates that part of the policy impacts cancels out 

when all energy efficiency policies are implemented together. Although modeling levers were 

chosen purposely to avoid overlap, some of the policies target the same set of technologies. It is 

quite possible that their ability to promote energy efficiency diminishes when overlapping 

policies co-exist. A related impact is the rebound effect, where energy usage increases when 

consumers save more in the Integrated Policy case, because of electricity rate reductions.

Market Priming is the energy efficiency policy with most significant savings potential and 

relatively low levelized cost. Information-based instruments, such as the mandated disclosure of 

home energy performance with home ratings, green labeling and leasing, home energy audits, 

etc., are able to promote inclusion of energy efficiency when selling or renting. Efficiency 

improvements from these policies can generate noticeable home equity premiums (Nevin & 

Watson, 1998). Because of the potential policy impacts on efficiency improvements and equity 

value, the Federal Office of Management and Budget (OMB) suggests that policies or measures 

explicitly designed to alleviate asymmetric information should be given preference over other 

measures, as a general rule-of-thumb (OMB Circular A-4).

To explore the combined effects of these twelve policies, all twelve energy-efficiency options 

(five from the residential sector, three from the commercial sector, and four from the industrial 

sector) are modeled in the Integrated Policy scenario. This scenario was prepared to determine 

whether applying multiple policies at once would enhance or reduce the achievable energysavings potential. On the one hand, the integrated energy-savings potential could be less than the 

sum of the individual policy savings potentials because the policies target overlapping 

technologies, barriers, and energy consumers. If the rebound effect is strong, the reduction of 

electricity rates resulting from reduced energy consumption could cause consumers to take back 

some of their potential savings by using their bill savings to buy more energy services. On the 

other hand, synergistic policy combinations could produce greater energy-savings potential. For 

example, by providing better energy benchmarking data, consumers might be more responsive to 

an opportunity to secure low-cost financing to invest in more energy-efficient equipment. The 

results from the Integrated Policy scenario can help us understand the dynamics among the 

selected policies and their interactive effects on the energy-efficiency potential of the Eastern

Interconnection.
64

Table 6.1 Savings Potential and Levelized Cost of Electric End-Use Efficiency, by Policy

Sector Policy Type Policy Electricity 

Efficiency 

Potential 

(TWh) in 

2020

Electricity 

Efficiency 

Potential 

(TWh) in 

2035

Levelized Cost 

of Electric EndUse Efficiency

(cent/kWh)

Residential Financial Appliance 

Incentives 13 26 6.7-8.0

Financial On-Bill 

Financing 13 22 6.6-7.4

Regulatory Building 

Codes 8 19 0.5-0.8

Regulatory Aggressive 

Appliance 

Policy

16 40 0.6-0.7

Information Market 

Priming 100 119 2.7-3.6

Commercial Financial Financing 31 77 6.4-6.6

Regulatory Building 

Codes 8 34 3.5-4.6

Information Benchmarking 16 61 0.7-1.2

Industrial Financial Motor Rebate 4 2 9.3 – 11.5

Regulatory Motor 

Standard 3 8 2.4-3.9

Information Advanced 

Manufacturing 

Initiative 

6 16 3.0-4.8

Financial CHP 

Incentives 23 29 1.5- 2.3

Although the target technologies, barriers, and energy consumers may be common to two or 

more policies, the modeling of policy integration in GT-NEMS is straightforward since the GTNEMS levers for each individual policy have no overlap. The supply-side modules, the 

economics and emission modules, and all three end-use sector modules were used together to 

incorporate feedback loops between multiple segments of the economy to examine policy 

impacts. By using the IHS Global Dynamics general equilibrium model, the GT-NEMS analysis 

optimizes energy prices and quantities across energy fuels and across sectors of end-use demand.
65

Figure 6.1 Supply Curve for Electricity Efficiency Policies in 2020

A careful reconciliation of the potential estimations from individual policies and the Integrated 

Policy scenario reveals the dynamics among energy-efficiency policies. Together with the 

levelized cost estimations, the reconciled electricity-savings potentials produce a policy supply 

curve. Supply curves for energy-efficient equipment have been evaluated since the early 1980‟s 

(Meier, Wright, and Rosenfeld, 1983; Brown, Levine, Romm, Rosenfeld, and Koomey, 1998), 

culminating with the well-known study by McKinsey & Co. (2009). Supply curves for energyefficiency policies are a recent extension of this approach.

Figure 6.1 indicates that regulatory policies have relatively low levelized costs of electricity and 

financial policies have relatively high LCOEs. In fact, the weighted average LCOE estimations 

suggests that financing policies have higher levelized cost than regulatory and information 

policies when considering cumulative electricity savings (Table 6.2). Similarly, a previous 

analysis of energy efficiency in the U.S. South concluded that the two least cost-effective 

policies involved financial subsidies (Brown, et al., 2010). The CHP Incentive, as an exception, 

offers subsidies for industrial CHP systems while having very low levelized cost because of its 

significant electricity-savings by utilizing waste heat.

Currently, the national average electricity price for rate payers is approximately 9.0 cent/kWh. 

Taking the average price as a benchmark, all twelve policies except for Motor Rebate are costeffective (i.e., having LCOEs lower than the average electricity price), representing 98% of the 
66

savings. All financial policies except for the CHP Incentive have levelized costs higher than the 

national average electricity price. Benchmarking the policies using sector-specific retail rates, 

Motor Rebate is the only energy-efficiency policy that is not cost-effective.

Table 6.2 The Levelized Cost of Electricity Efficiency by Type of Policy (in cents/kWh)

-

EI US EI US

Information Policies 2.1 2.1 2.9 2.9

Regulations 2.8 3.4 3.5 4.1

Financing Policies 4.6 4.8 4.7 5.0

Information Policies 3.0 3.3 3.6 3.9

*3% discount rate for public and private costs.

**7% discount rate for private costs and 3% discount rate for public costs.

Our estimation of the weighted average LCOE of 3.0-3.6 cent/kWh is in the middle range of cost 

estimates from previous studies (all cost estimates are reported in 2009$). An ex ante estimation 

by the Electric Power Research Institute (EPRI) found the levelized cost of energy efficiency 

programs to be $0.022-0.032/kWh (EPRI, 2009). Another study developed an average estimation 

of the cost of saved energy to be $0.026/kWh based on utility and state evaluations and reports 

for electricity programs in 14 states (Friedrich et al, 2009). Other ex post estimations have

reported higher levelized costs for energy efficiency. For example, Arimura, et al. (2011) 

estimate that utility-operated demand-side management programs between 1992 and 2006 saved 

electricity at a program cost averaging $0.05/kWh using a 5% discount rate, with a 90% 

confidence interval ranging from $0.03 to $0.98/kWh. Auffhammer, Blumstein and Fowlie 

(2008) use utility panel data to construct weighted average cost estimates for demand-side 

management programs. Their findings suggest low cost-effectiveness, ranging from $0.053 to 

$0.151/kWh.

Cost estimations of energy efficiency depend on accurate assessments of energy savings, which 

can be problematic because of free ridership (Gellings et al., 2006). Alcott and Greenstone (2009) 

question these ex ante estimates of cost-effectiveness by noting that programs typically reduce 

electricity demand by only 1-2%, which does not suggest a large energy-efficiency gap.

Alternatively, it could be that the energy-efficiency programs were simply underfunded and 

unable to completely address market failures.
67

6.1 The Achievable Potential of Electricity Savings

In the Eastern Interconnection, the electricity consumption is forecasted to grow at an average 

rate of 0.77% per year and to rise to 3,200 TWh in 2035 in the reference case. With the twelve 

energy efficiency policies, the growth of electricity consumption slows down to an average rate 

of 0.35% per year. The combination of twelve policies could save the region about 190 TWh 

(6.6%) of electricity in 2020, and 332 TWh (10.3%) of electricity in 2035 (Figure 6.2).

The energy-efficiency potential driven by the bundle of twelve policies largely comes from the 

residential sector, with significant savings from the commercial and industrial sectors as well 

(Figure 6.3). In the Integrated Policy scenario, the residential sector is estimated to save 13.2% 

of electricity in 2035, which is slightly lower than the estimated savings (15.4% in 2035) from 

the residential Policy Bundle. Similarly, the estimated savings potential from the commercial 

sector is 7.9% in 2035 under the Integrated Policy case, lower than the estimation (8.4% in 2035) 

for the commercial sector Policy Bundle. In contrast, the industrial sector potential in the 

Integrated Policy case is estimated to be comparable (10.4% in 2035) to the savings potential of 

the industrial Policy Bundle. The electricity from industrial CHP generation that is sold back to 

the grid is not shown in Figure 6.3. An additional 39 TWh are generated by CHP systems (28 

TWh are consumed at the industrial plant and 11 TWh are sold back to the grid).

Figure 6.2 Electricity Savings from the Integrated Policy Case in the Eastern 

Interconnection

2,500

2,600

2,700

2,800

2,900

3,000

3,100

3,200

3,300

2010 2015 2020 2025 2030 2035

Delivered Electricity, All Sectors (TWh)

Reference Integrated Policy Case

10.3%

6.6%

190 TWh of 

savings in 2020

332 TWh of electricity 

savings in 2035
68

Figure 6.3 Electricity-Savings Potential by Sector, 

Based on the Integrated Policy Case (in TWh)

Table 6.3 illustrates the achievable potential for electricity savings as a percentage of Reference 

case consumption in 2020. The energy-efficiency policies are estimated to generate the highest 

savings potentials (as a percentage-of-consumption) in the residential and industrial sectors of 

the South (i.e., the South Atlantic, East South Central and West South Central regions). For the 

commercial sector, the same three Census divisions have the highest percent savings estimates, 

but the rank order is different: with the West South Central; having the highest percent, and the 

East South Central division having the third highest estimate of percent savings potential. 

Table 6.3 Electricity-Savings Potential in 2020, by Census Division

(as percentages of Reference case consumption in 2020)

Census Division

Residential

Sector

Commercial

Sector

Industrial

Sector

Eastern

Interconnection

New England 7.98% 2.88% 5.40%

Middle Atlantic 7.71% 3.27% 5.29%

East North Central 7.81% 4.13% 6.60%

West North 

Central 6.40% 3.33% 6.02%

South Atlantic 11.19% 4.54% 8.13%

East South Central 8.52% 3.63% 7.98%

West South 

Central 13.39% 4.55% 6.88%

Eastern Interconnection 9.3% 4.0% 6.9% 6.6%
69

The electricity-savings potential was proportioned to the state-level in accordance with the share 

of energy consumption each state held within its own census division. Figure 6.4 illustrates the 

achievable potential for electricity savings by state in 2020. A large portion of the total savings is 

estimated to be available in the South Atlantic region, especially Florida. In that same year, the

highest savings potentials in the residential and commercial sectors reside in three states: Florida, 

North Carolina, and Virginia. The highest savings potentials in the industrial sector reside in in 

Ohio, Kentucky, Indiana, and Illinois (see Table D.1 for details). 

In contrast, our twelve policies suggest much smaller savings potentials in some of the states of 

the West North Central and New England regions. Especially low across all three sectors in 2020 

are Vermont, Rhode Island, and New Hampshire. The reason for the disproportionate 

distribution of electricity savings among states is partially due to differences among state 

economic structures and partially due to the differences in each state‟s share of the energy 

market.

Figure 6.4 Achievable Potential for Electricity Savings by State in 2020
70

6.2 Policy Impacts on Electricity Rates and the Power Sector

Generally, the energy-efficiency policies are projected to reduce electricity retail rates in all three 

end-use sectors (Table 6.3). For retail prices, New England and Middle Atlantic exhibit higher 

rates than the other census divisions in the Eastern Interconnection, and large rate decreases 

occur in these two divisions. New England has the biggest decrease in commercial and industrial 

electricity rates. The biggest decline in residential retail rates happens in the West North Central 

division. The East South Central division, on the other hand, experiences essentially no 

reductions in either commercial or industrial electricity rates.

Table 6.4 Electricity Rate Changes in 2020, Reference versus Integrated Policy Case

Census Division Scenario

Residential Retail 

Rate (cent/kWh) 

Commercial Retail 

Rate (cent/kWh)

Industrial Retail 

Rate (cent/kWh)

New England Reference 17.7 12.0 7.8

Integrated Policy Case 17.6 11.3 7.2

Change -0.1 -0.7 -0.6

Middle Atlantic Reference 14.6 11.2 6.3

Integrated Policy Case 14.4 11.0 6.0

Change -0.2 -0.2 -0.3

West North Central Reference 8.5 7.1 5.3

Integrated Policy Case 7.9 7.0 5.2

Change -0.6 -0.1 -0.1

East North Central Reference 10.0 8.4 5.9

Integrated Policy Case 9.6 8.3 5.8

Change -0.4 -0.1 -0.1

West South Central Reference 9.7 7.2 5.4

Integrated Policy Case 9.2 7.0 5.2

Change -0.5 -0.2 -0.2

South Atlantic Reference 10.8 9.0 6.5

Integrated Policy Case 10.5 9.1 6.5

Change -0.3 0.1 0

East South Central Reference 8.3 7.9 5.2

Integrated Policy Case 7.9 7.9 5.2

Change -0.4 0 0

Although the absolute value of the rate decrease never exceeds 0.7 cents/kWh, it is estimated to 

result in large energy expenditure savings for customers. With the energy-efficiency policies, 

residential customers are estimated to save about $13 billion in 2020. Similarly, commercial 

customers will save $5 billion and industrial customers will save $4 billion in 2020.

Interestingly, the regions with the largest decreases in electricity rates do not correspond to the 

regions with the highest electricity savings potentials (Tables 6.1 and 6.2). In 2020, New 

England is estimated to have the biggest decreases in electricity rates but only moderate 

electricity-savings potential. 
71

Figure 6.5 Electricity Price Changes by Census Division
72

On the other hand, the South Atlantic is the only census division with rate increases in 2020 and 

it has only a moderate rate decreases in 2035, but it is estimated to have the most electricity 

savings in the Eastern Interconnection. This suggests that electricity savings dynamics, including

the rebound effect, may play an important role in this rate change – consumers tend to demand

more electricity services when rates are low.

Similar to the South Atlantic states, the Mountain states experience higher commercial electricity 

rates on average in 2020 under the Integrated Policy case. But rates drop across the board in the 

Integrated Policy case in comparison with the Reference case after 2025. As shown in Figure 6.5, 

the electricity retail rate decrease is universal in 2035. 

A principal driver of the electricity rate decreases is the decline in consumption from improved 

end-use efficiencies. The electricity market can be treated as a partially competitive market. 

Consistent with economic theory, energy-efficiency policies drive down demand, which results 

in a new equilibrium with lower prices as a result of competition. 

In the Integrated Policy case, low consumption levels and low electricity retail rates are 

estimated to influence the power sector. Table 6.4 suggests that fewer power plants (7.2% fewer 

in 2020 and 11.4% fewer in 2035) are being built as a result of the energy-efficiency policies in 

the Integrated Policy case. Natural gas power plants experience the greatest generation losses 

relative to the reference case as a result of the twelve policies (9.1% less generation in 2020, and 

25.2% less generation in 2035). In the Integrated Policy case, electricity generated from 

renewable sources does not decrease as much as generation from other sources in 2020. By 2035, 

however renewables are reduced proportionately more than coal or nuclear (9.6% versus 8.2% 

and 6.1%), but natural gas generation is offset most dramatically – by more than 200 TWh 

(25.2%), when compared with the reference case. If natural gas hydrofracking continues to 

produce low-cost gas in the U.S., coal, nuclear and renewables might be further reduced while 

combined cycle natural gas plants would likely retain more of their market share.

Table 6.5 Electricity Generation by Source in the U.S. (in TWh) 

Reference Forecast versus Integrated Policy Case

Fuel Type

2010 2020 2035

Reference

Forecast

Reference

Forecast

Integrated 

Policy 

Case

%

Change

Reference

Forecast

Integrated 

Policy Case

%

Change

Coal 1812 1879 1738 -7.5% 2082 1912 -8.2%

Petroleum 39.45 39.02 36.84 -5.6% 41.32 39.41 -4.6%

Natural Gas 779.1 696.4 632.7 -9.1% 914.1 683.6 -25.2%

Nuclear 802.9 877.3 823.6 -6.1% 874.4 821.3 -6.1%

Renewables 371 519.3 494.6 -4.8% 567.3 512.9 -9.6%

Total 3804 4013 3726 -7.2% 4483 3972 -11.4%

EnergyEfficiency 

Potential

287 491
73

Figure 6.6 also illustrates the policy impacts on the power sector. In the Reference case, the share 

of power generated from natural gas grows from 17% in 2020 to 20% in 2035. But this growth is 

estimated to be largely eliminated by energy-efficiency policies in the Integrated Policy case. 

Although the share of electricity from coal goes up to 48% in the Integrated Policy case, 

electricity generation from coal grows only slightly. This suggests that most of the new coal 

power plants with relatively lower carbon technologies will not be built in the policy case. 

Figure 6.6 Power Generation by Source in the U.S., in TWh (EE=Energy Efficiency)

6.3 Policy Impacts on Other Fuels and Carbon Emissions

Although the twelve energy-efficiency policies aim to shrink electricity consumption, most have 

spillover benefits that may also cause significant savings in natural gas and other energy sources 

(Table 6.5). In 2020, the Eastern Interconnection could save 0.5 quadrillion Btus of natural gas 

with energy-efficiency policies. The savings potential in natural gas could grow to 1.5 quads in 

2035, which is about 38% of the total energy-savings potential. 

Table 6.6 Energy-Savings Potential of Natural Gas and Total Energy

Energy Savings (Quads)

Natural Gas Total Energy

2020 2025 2030 2035 2020 2025 2030 2035

EI 0.5 0.9 1.3 1.5 2.6 3.3 3.6 4.0

U.S. 0.9 1.6 2.1 2.3 3.5 4.7 5.3 5.6

Meanwhile, energy-efficiency policies exhibit significant potential in total energy savings in the 

Eastern Interconnection, as well as the other regions of the nation. Table 6.4 suggests that the 

nation could benefit from these policies, resulting in significant energy savings every year.

Figure 6.7 illustrates the forecast of total energy consumption in the Eastern Interconnection in 

the reference case and the Integrated Policy case. In 2020, energy efficiency policies are 
74

estimated to save 2.6 quads (3.6%) of energy, which is four times the energy savings in 

electricity for that year. In 2035, the potential of savings in total energy will grow to 4.0 quads 

(5.2%), which is 3.5 times the energy savings in electricity for that year.

Figure 6.7 Total Energy Savings in the Eastern Interconnection (Quads)

These sizable reductions in energy consumption are associated with reductions in carbon dioxide 

emissions. Figure 6.8 suggests that the energy-efficiency policies could reduce carbon emissions 

by 161 million tonnes of CO2 (4.0%) in 2020, while the potential for emission reductions 

increases to 246 million tonnes of CO2 (5.6%) in 2035. 

Figure 6.8 CO2 Emissions in the Eastern Interconnection (Million Tonnes)

The percent of carbon emission reductions is slightly higher than the percent of reductions in 

energy consumption, which suggests that the energy savings are larger in more carbon-intensive 

50

55

60

65

70

75

80

2010 2015 2020 2025 2030 2035

Reference Integrated Policy Case

5.2%

3.6%

2.6 quads of savings 

in 2020

4.0 quads of savings in 

2035

3,600

3,800

4,000

4,200

4,400

4,600

2010 2015 2020 2025 2030 2035

Reference Integrated Policy Case

5.6%

4.0%

161 Million tonnes 

of reduction in 2020

246 Million tonnes of 

CO2reduction in 2035

Energy Consumption (Quads)
75

energy sources (Table 6.6). The energy efficiency policies are clearly estimated to generate 

larger savings in electricity, which is more carbon-intensive than natural gas. 

Table 6.7 Reduction in CO2 Emissions

Reduction in CO2 Emissions 

(Million Tonnes)

2020 2025 2030 2035

Eastern

Interconnection 161 211 221 246

United States 211 270 291 317

Energy savings throughout the nation would produce carbon emission reductions in a manner 

similar to that observed in the Eastern Interconnection. Table 6.5 suggests that the other regions 

of the country would also experience significant reduction in carbon emissions produced by 

energy-efficiency policies.

6.4 Policy Impacts on Energy Intensity and GDP

The impact of energy-efficiency policies on different sectors of the economy can be compared 

through energy intensity metrics. Residential building energy intensity is measured by primary

energy per household, while commercial building energy intensity is measured by primary 

energy use per square footage.

Two intensity measures were constructed for the industrial sector since electricity represents a 

relatively small part of industrial energy use. The usual industrial energy intensity is measured 

by primary energy per dollar of shipment, and the industrial electricity intensity is constructed 

similarly as electricity per dollar of shipment. The energy intensity of the whole economy is

represented by primary energy use per GDP.

Figure 6.9 suggests that energy efficiency policies and programs would reduce the energy 

intensity in residential and commercial buildings more than the energy intensity of the industrial 

sector or the economy as a whole. For example, in 2020, energy use per household decreases by 

10.6%, and energy use per square footage of commercial building decreases by 4.4%, while 

energy use per GDP declines by only 3.3%.
76

Figure 6.9 Forecasted Changes in Energy Intensity by Economic Sectors

To the contrary, industrial energy intensity is much less influenced by the twelve energyefficiency policies than energy intensity of the nation. Figure 6.8 illustrates that the energy use 

per dollar of shipment decreases by only 1.2% in 2020, which is barely half of the intensity 

decline of the nation. However, our policies target only the electricity savings potentials in the 

industrial sector, while electricity represents merely one third of industrial energy consumption. 

The industrial electricity intensity, measured by electricity use per dollar of shipment, is 

estimated to decrease by 7.1% in 2020, suggesting that our policies could be quite influential in 

transforming industrial energy systems.

Table 6.7 suggests that our energy-efficiency policies have a negligible negative impact on 

GDP. The national GDP is estimated by NEMS to grow $15 Billion (0.08%) less in the policy 

case in 2020, which is equivalent to only 7 hours of delay in GDP growth. In 2035, the GDP is 

estimated to drop by $47 Billion (0.17%), which is equivalent to about 25 hours of delay in GDP 

growth.

Table 6.8 GDP Impact

Scenario GDP (Billion 2009$) 2020 2025 2030 2035

Reference GDP 19,168 22,021 25,000 28,260

Integrated Policy Case

GDP 19,152 22,009 24,974 28,213

Change -0.08% -0.05% -0.10% -0.17%

Delay (hour) 7 7 15 25

*Numbers are percentage change relative to the Reference case 

** “Delay” in GDP growth is defined as the number of days in a year required to make up the 

difference between GDP in the Reference case versus GDP in the Integrated Policy scenario.
77

The higher equipment investments prompted by the twelve policies would divert the capital that 

could have been invested in other economic activities. Results from GT-NEMS suggest that this 

reallocation of capital resources would affect the national GDP, albeit to a small extent. In 

addition, the policies would reduce energy consumption and production, which also has GDP 

consequences. As an energy-economic model, GT-NEMS is capable of modeling the 

macroeconomic impact of any energy policy by incorporating Global Insight‟s model of the U.S. 

economy in its Macroeconomic Activity Module (MAM). Both energy demand and supply sides 

interact with MAM through a Cobb-Douglas production function to calculate the national GDP. 

However, the IHS Global Insights model assumes the U.S. economy has a 0.07 energy elasticity, 

which means that a 1% decrease in energy supply decreases potential GDP by 0.07% (EIA, 

2012), but unlike input-output models such as IMPLAN, the reduction in energy expenditures is 

not recycled back into the economy to reflect re-spending of the energy savings. As a result, 

NEMS tends to produce estimates of decreased GDP when energy-efficiency investments 

increase (Laitner, 2013).

6.5 Discussion and Conclusion

With energy-efficiency policies, we estimate that the Eastern Interconnection could costeffectively achieve significant savings in electricity consumption by 2035. Driven by policy, this 

achievable potential for greater electric end-use efficiency is relatively low compared with some 

prior assessments of the technical and economic potential. Our review of the literature, however, 

indicates that this estimated potential for the Eastern Interconnection is comparable to many 

estimates of the achievable potential for increased electric end-use efficiency at various scales of 

analysis, ranging from the metropolitan to the national. 

The electricity savings benefit of energy-efficiency policies is accompanied by other benefits, 

including natural gas savings, savings in other fuel types, and reduced carbon emissions. In 

addition, the twelve energy-efficiency policies are able to drive electricity retail prices down in 

many regions and produce large energy bill savings for consumers. The electric power sector is 

also affected by these policies, in that generation growth is slowed in the Integrated Policy case, 

reducing the need for capital-intensive new generation. Overall, these policies are able to 

decrease the energy and carbon intensity of the Eastern Interconnection with no significant 

impact on GDP growth.

In sum, this study offers a reliable assessment of achievable potential and an in-depth analysis of 

the impacts of energy-efficiency policies in the Eastern Interconnection.
78
79

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Energy, 36(9), 5779–5790. doi:10.1016/j.energy.2011.05.019

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DOE building technology energy efficiency programs on U.S. employment, income, and 

investment. Energy Economics, 30(5), 2283–2301. Retrieved from 

http://www.sciencedirect.com/science/article/pii/S0140988307001119
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Part of the Climate Solution by Enforcing Aggressive Commercial Building Codes,” 

Georgia Institute of Technology, School of Public Policy Working Paper #71

(http://www.spp.gatech.edu/aboutus/workingpapers).

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3665–3674. doi:10.1016/j.enpol.2007.01.009

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Motor and Drive System Performance.Washington, DC: DOE.

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Assessment. Burlington, MA: Xnergy. 
86

Appendix A. GT-NEMS Modeling of Residential Energy Efficiency Policies

To assess the electricity savings potential, energy consumption forecast by GT-NEMS was 

modeled with five residential energy efficiency policies, including Appliance Incentives, 

Aggressive Appliance Policy, Building Energy Codes, On-Bill Financing and Market Priming. 

GT-NEMS modeling of each of the five policies took distinct approaches in the purpose of 

separating policy impacts by avoiding modeling overlaps. 

Appliance Incentives offer 30% subsidy to reduce the capital cost for the most efficient 

technologies in residential buildings based on the technology inventory of GT-NEMS. A list of 

25 selected technologies from the major end-uses eligible for incentives can be found in Table 

A.1. Capital costs of these technologies (from the rtekty input file) were reduced by 30% in the 

Appliance Incentives policy scenario.

Table A.1 Most Efficient Home Appliances and Equipment

End-Use Equipment Type

Average Cost 

($2007)

Average 

Efficiency

Available 

Years

Space Heating

Fuel Oil Furnace 3 4,983 0.95 2009-2023

Fuel Oil Radiator 3 4,513 0.95 2012-2022

Electric Heat Pump 4 3,567 3.14 2014-2021

Geothermal Heat Pump 2 6,414 5 2010-2018

Kerosene Furnace 3 4,983 0.95 2009-2023

LPG Furnace 5 2,470 0.96 2009-2019

Natural Gas Furnace 5 2,470 0.96 2009-2020

Natural Gas Radiator 3 4,513 0.95 2012-2022

Space Cooling

Central Air Conditioner 4 5,290 6.504 2011-2019

Electric Heat Pump 4 3,567 5.325 2014-2021

Geothermal Heat Pump 2 5,749 30 2011-2021

Room Air Conditioner 3 900 3.52 2012-2026

Clothes Washing Clothes Washer 3 958 0.114 2008-2022

Dishwashing Dishwasher 3 1,181 1.1 2010-2020

Water Heating

Fuel Oil Water Heater 3 2,400 0.68 2012-2026

Electric Water Heater 5 1,430 2.4 2009-2023

LPG Water Heater 4 852 0.746 2014-2022

Natural Gas Water Heater 4 852 0.746 2014-2023

Cooking

Electric Stove 2 400 601 2006-2050

LPG Stove 2 500 0.42 2006-2050

Natural Gas Stove 2 500 0.42 2006-2050

Clothes Drying Electric Clothes Dryer 2 500 3.74 2009-2023

Natural Gas Clothes Dryer 2 515 0.931 2007-2028

Refrigeration Refrigerator 4 1,107 399 2009-2023

Freezing Freezer 3 626 290 2010-2032

Note: The cost, efficiency and available years for each equipment type vary by region. The efficiency for different 

equipment types are measured by different metrics. 
87

In the Building Energy Codes policy case, four new codes were added to the residential 

building codes profile (in the rtektyc input file) to force shell efficiency improvements. These 

codes were modeled with relatively high heating and cooling shell efficiency, and relatively high 

shell installation costs, trying to mimic the periodic code updates. 

In the Reference case, new residential buildings are built in compliance to five different levels of 

codes: no code, IECC 2006 code, Energy Star code, forty-percent above IECC 2006 code, and 

the most efficient code: PATH code. In this study, the Building Code scenario were set up based 

on EIA‟s Expanded Standards and Codes side case, where three new codes were added to the 

code profile: 'IECC 2006+', 'IECC 2006++', and 'IECC 2006+++' to reflect code improvements. 

We managed to add one more code, the „New Code‟ to push further shell efficiency 

improvement. Table A.2 shows the details about the residential building codes in our policy case.

Table A.2 Building Energy Codes Profile for Residential Buildings

Building Codes

Average Shell

Installation Cost

Average Heating Shell 

Efficiency Factor

Average Cooling Shell 

Efficiency Factor

'No IECC' 7 1.21 1.15

'IECC 2006' 5,251 0.81 1.06

'Energy Star' 5,508 0.79 1.03

'FORTY%' 6,797 0.68 0.97

'PATH' 7,868 0.51 0.93

'IECC 2006+' 5,580 0.69 0.90

'IECC 2006++' 6,018 0.65 0.85

'IECC 2006+++' 6,128 0.61 0.80

'NEW CODE' 7,392 0.56 0.85

 Note: The cost and efficiency factors for each building shell type vary by region. 

The policy case also forces early retirement of less stringent codes. For example, Energy Star, 

Forty and IECC 2006+ retire in the same year, which is five years later than IECC 2006‟s 

retirement; IECC 2006++ retires at 2023 for all regions; IECC 2006+++ retires at 2028 for all 

regions; New Code and PATH, which are the two most efficiency codes, are available for all 

years for all regions.

New houses built in compliance to new codes are able to save energy significantly for home 

owners due to the high shell efficiency with better insulation and building design. They 

eventually gain market share, although new codes have higher shell installation costs than 

existing codes (Figure A.1). 
88

Figure A.1 Share of New Houses Built in the Policy Case

The On-bill Financing program offers zero-interest loans to the most efficient house appliances 

and equipment. The technologies eligible for no interest loans are the same technologies that are 

eligible for appliance subsidies as listed in Table A.1. In GT-NEMS modeling, two new 

parameters for residential technologies were added to the rtekty input file: CAPDIST assigns 

interest rate (or discount rate for non-eligible technologies), and CAPHOR assigns payback time 

(or time horizon for non-eligible technologies) for residential technologies. 

To realize the input file changes, cost calculation equations in the residential module source code 

were also modified. In the reference case, the life-cycle costs for residential technologies are 

calculated as following:

With interest rate option, we changed the life-cost equation to:

When interest rate equals to 0%, we have,

When interest rate is greater than 0%, we have,

Where, LFCYCLE is the lifecycle costs for appliances; 

CAPITAL: the capital costs for appliances;

OPCOST: the operational costs for appliances;

DIST: the discount rate for the operational cost during the life time of the appliances

HORIZON: the appliance life time

ANNUALPAY: the annual payment for on-bill financing equipment
89

CAPHOR: the payback time

CAPDIST: the interest rate

In the rtekty input file, the selected technologies with high efficiencies were assigned with 0% 

interest rate and 10-years payback time, indicating that the life-cycle costs for these technologies 

were calculated with the revised equation. Other technologies were assigned with the default 

setting, and their life-cycle costs were calculated with the original equation. 

The Market Priming policy also target at the same set of technologies as shown in Table A.1, 

but with different GT-NEMS lever. In this policy scenario, market assistance programs offer 

consumers with information, technical support, and educational demonstration to lower the 

private discount rate for the most efficient technologies. GT-NEMS modeling of this policy 

changed the hurdle rates of these technologies to 7% by modifying the beta 2 parameter for the 

logit model of technology choice in the rtehty input file.

The Aggressive Appliance Policy forces rapid market turnover for residential technologies by 

retiring the least efficient technologies from the market place. Assuming aggressive appliance 

standards, the policy forces some of the technologies to retire from the market at 2012. In GTNEMS, the selected technologies were made either unavailable after 2012, or assigned a hurdle 

rate equals to 100% in the rtehty input file. A list of house appliances and equipment forced 

retired in this policy scenario is shown in Table A.3.

Table A.3 Residential Technologies Forced Early Retirement

End-Use Equipment Type Average Efficiency Available Years

Space Heating

Fuel Oil Furnace 1 0.82 2010 - 2032

Fuel Oil Radiator 1 0.825 2010 - 2031

Electric Heat Pump 1 2.35 2014 - 2028

Kerosene Furnace 1 0.82 2010 - 2032

LPG Furnace 1 0.818 2010 - 2032

Natural Gas Furnace 1 0.818 2010 - 2032

Natural Gas Radiator 1 0.815 2010 - 2031

Space Cooling

Central Air Conditioner1 3.899 2009 - 2039

Electric Heat Pump 1 4.003 2014 - 2028

Room Air Conditioner 1 3.103 2013 - 2027

Clothes Washing Clothes Washer 1 0.160 2012 - 2022

Dish Washing Dishwasher 1 0.587 2010 - 2024

Water Heating

Fuel Oil Water Heater 1 0.585 2011 - 2032

Electric Water Heater 1 0.925 2011 - 2032

LPG Water Heater 1 0.59 2006 - 2050

Natural Gas Water Heater 1 0.605 2011 - 2032

Refrigeration Refrigerator 1 428. 7 2013 - 2027

Freezing Freezer 1 347.5 2010 - 2032

Note: The efficiency and available years for each equipment type vary by region. The efficiency for different 

equipment types are measured by different metrics.
90

Appendix B. GT-NEMS Modeling of Commercial Energy Efficiency Policies

We estimate the magnitude of technology investment costs in the commercial sector separately 

for new purchases, replacements, and retrofits. In each case, the calculation is based on GTNEMS estimates of service demand (SD) for energy.

 New Purchases 

 SDnew x (Cost/8760) x 1/CF = Investment Cost

 SDnew is a KSDOUT output, as are SDreplacement and SDsurviving

 CF is the equipment-specific capacity factor

 Replacements

 SDreplacement x (Cost/8760) x 1/CF = Investment Cost

 Retrofits

 SDsurviving x (Cost/8760) x 1/CF x 0.022/(SDsurviving/SDtotal)

 Where SDtotal = SDnew + SDreplacement + SDsurviving and 0.022 is the average 

amount of commercial floorspace undergoing a retrofit

 This proportions the surviving service demand to the commercial sector 

retrofit average

In the Benchmarking policy case, GT-NEMS uses a combination of discount rates and the rate 

for U.S. government ten-year Treasury notes to calculate consumer hurdle rates used in making 

equipment-purchasing decisions. While the macroeconomic module of GT-NEMS determines 

the rate for ten-year Treasury notes endogenously, the discount rates are inputs to the model. 

Modifying these inputs is the primary means of estimating the impact of benchmarking for the 

commercial sector in this analysis. This is done in two steps: first, by updating the discount rates 

to reflect a broader selection of the literature; and second, by adjusting the updated discount rates 

to account for the effects of a national benchmarking policy.

To illustrate, Table B.1 presents the 2015 hurdle rates used in GT-NEMS across scenarios for 

two major end-uses in the commercial sector, space heating and lighting (these values represent 

the sum of the Treasury bill rates and the discount rates).
91

Table B.1: Discount Rates Across Scenarios for Space Heating and Lighting in 2015

% of Population Discount Rate*

Reference Benchmarking Reference Benchmarking

Space Heating

27 14.2 1005.75 40.4

23 14.3 105.75 19.6

19 14.3 50.75 15.4

18.6 14.3 30.75 12.4

10.7 14.3 20.75 9.8

1.5 14.3 12.25 7.4

0.2 14.3 5.75 4.8

Lighting

27 14.2 1005.75 57.3

23 14.3 105.75 40.8

18.6 14.3 50.75 36.5

18.6 14.3 30.75 33

8.8 14.3 20.75 30.4

1.5 14.3 12.25 26.9

2.5 14.3 5.75 21.7

*Discount rates presented include the projected Treasury bill rate for 2015. Bold numbers 

represent the median estimate for the specific scenario.

Source: Cox, Brown, and Sun, 2012.

The National Building Code is modeled, in part, by assuming a more rapid rate of commercial 

shell efficiency improvement, as shown in Table B.2. 
92

Table B.2. Commercial Building Shell Efficiency Improvement*

New Construction Existing Buildings

EIA Reference case 14% 6%

EIA High Tech Case 17.4% 7.5%

Building Code Scenario 30% 19%

* Improvement of 2035 efficiency over 2003 efficiency 

In the Commercial Financing policy case, a 30% subsidy was provided to 107 technologies, 

based on a prior analysis of the impact of implementing a carbon tax (Brown, Cox, and Sun, 

2012). The subsidized technologies are listed in Table B.3.

Table B.3 Incentivized Technologies in Financing Policy Case

End-use

Fuel 

type Technology

Average 

efficiency

Average 

cost

First 

available 

year

Last 

available 

year

Space 

Heating

Electricity

comm_GSHP-heat 2011 

high 4.90 150.00 2011 2052

comm_GSHP-heat 2011 

high 10% ITC w MACRS 4.90 108.00 2011 2016

comm_GSHP-heat 2011 typ 3.50 120.00 2011 2052

comm_GSHP-heat 2011 typ 

10% ITC w MACRS 3.50 87.00 2011 2016

comm_GSHP-heat 2020-30 

typical 4.00 120.00 2020 2052

rooftop_ASHP-heat 2007 

high 3.40 96.67 2003 2052

rooftop_ASHP-heat 2030 

high 3.80 96.67 2030 2052

Natural 

Gas

gas_boiler 2011 high 0.95 37.08 2011 2052

gas_furnace 2011 high 0.94 9.76 2011 2052

res_type_gasHP-heat 2020 

typical 1.50 150.00 2020 2052

res_type_gasHP-heat 2030 

typical 1.50 141.67 2030 2052

Space 

Cooling Electricity

centrifugal_chiller 2007 

high 7.30 43.33 2003 2052

centrifugal_chiller 2007 mid 

range 6.90 40.83 2003 2052

centrifugal_chiller 2010 

typical 6.40 36.67 2010 2052

centrifugal_chiller 2020 

typical 7.00 36.67 2020 2052

centrifugal_chiller ASHRAE 6.10 35.42 2003 2052
93

90.1-2004

comm_GSHP-cool 2011 

high 8.15 150.00 2011 2052

comm_GSHP-cool 2011 

high 10% ITC w MACRS 8.15 108.00 2011 2016

comm_GSHP-cool 2011 typ 4.10 120.00 2011 2052

comm_GSHP-cool 2011 typ

10% ITC w MACRS 4.10 87.00 2011 2016

comm_GSHP-cool 2020-30 

typical 4.10 120.00 2020 2052

reciprocating_chiller 2007 

high 3.52 47.08 2003 2052

reciprocating_chiller 2020 

high 3.63 42.08 2020 2052

reciprocating_chiller 2020 

typical 3.20 38.75 2020 2052

reciprocating_chiller 2030 

high 3.78 42.08 2030 2052

res_type_central_AC 2003 

installed base 2.84 47.84 2003 2003

res_type_central_AC 2030 

typical 4.40 80.95 2030 2052

res_type_central_AC 

NAECA standard-pre-2006 2.93 49.13 2003 2005

rooftop_AC 2003 installed 

base 2.70 58.33 2003 2003

rooftop_AC 2007 typical 2.96 65.56 2003 2009

rooftop_AC 2010 high 3.52 80.56 2011 2052

rooftop_AC 2011 typical 3.28 66.67 2011 2052

rooftop_AC 2030 high 3.81 80.56 2030 2040

rooftop_ASHP-cool 2030 

high 3.81 96.67 2030 2040

screw_chiller 2020 high 3.63 42.08 2020 2052

screw_chiller 2030 high 3.91 42.08 2030 2052

scroll_chiller 2007 typical 2.93 36.25 2003 2052

wall-window_room_AC 

2011 typical 3.05 33.81 2011 2052

wall-window_room_AC 

2020 typical 3.22 33.81 2020 2052

Water 

Heating

Natural 

Gas

gas_water_heater 2020 

high 0.95 26.40 2020 2052

Electricity

HP water heater 2011 

typical 2.30 225.00 2011 2052

HP water heater 2020 

typical 2.30 210.71 2020 2052

Solar water heater 2010 typ 2.50 249.12 2010 2052
94

south

Solar water heater 2011 typ 

30 pct ITC south 2.50 193.76 2011 2016

Solar water heater 2020 typ 

south 2.50 205.16 2020 2052

Solar water heater 2030 typ 

south 2.50 175.85 2030 2052

Ventilation Electricity

CAV_Vent 2008 high 1.10 8833.35 2004 2050

CAV_Vent 2020 typical 0.63 8326.82 2020 2050

CAV_Vent 2030 typical 0.73 8326.82 2030 2050

VAV_Vent 2008 high 1.63 8790.87 2004 2050

VAV_Vent 2020 typical 0.73 8398.71 2020 2050

Cooking

Electricity

Range, Electric-induction, 4 

burner, oven, 11 0.80 46.57 2000 2052

Natural 

Gas

Range, Gas, 4 powered 

burners, convect. oven, 11 0.60 38.92 1995 2052

Lighting Electricity

72W Inc (Halogena Type 

HIR) 12.21 79.19 2008 2050

F28T5 71.50 31.98 2003 2050

F32T8 Super 65.20 21.71 2003 2050

F96T8 High 95.10 10.96 2003 2050

F96T8HO LB 76.90 18.81 2003 2050

LED 2011-2019 Typical for 

high tech 86.80 196.79 2011 2019

LED 2020-2029 Typical 181.00 134.18 2020 2050

Refrigeration Electricity

Bevrg_Mchndsr 2008 high 1.87 1674.84 2004 2050

Bevrg_Mchndsr 2008 low 0.88 1102.94 2004 2009

Bevrg_Mchndsr 2011 

typical 1.34 1348.04 2011 2050

Bevrg_Mchndsr 2020 

typical 1.43 1348.04 2020 2050

Bevrg_Mchndsr 2030 

typical 1.54 1348.04 2030 2050

Bevrg_Mchndsr installed 

base 0.79 1266.34 2003 2009

Ice_machine 2010 EPACT 

standard 0.50 1142.41 2010 2050

Ice_machine 2011-2020 

typical 0.53 1186.34 2011 2050

Reach-in_fzr 2008 high 2.26 1270.06 2004 2050

Reach-in_fzr 2020 typical 1.66 1180.93 2020 2050

Reach-in_fzr 2030 typical 1.77 1180.93 2030 2050

Reach-in_fzr installed base 1.23 1136.37 2003 2009

Reach-in_refrig 2008/2010 

high 5.13 898.69 2004 2050

Reach-in_refrig 2011 typical 3.42 866.01 2011 2050
95

Reach-in_refrig 2020 typical 3.67 866.01 2020 2050

Reach-in_refrig 2030 typical 3.85 866.01 2030 2050

Reach-in_refrig installed 

base 2.03 931.37 2003 2009

Supermkt_compressor_rack 

2011 high 3.06 130.72 2011 2050

Supermkt_compressor_rack 

2011 typical 2.75 116.71 2011 2050

Supermkt_compressor_rack 

2020 high 3.06 130.72 2020 2050

Supermkt_compressor_rack 

2020 typical 2.81 116.71 2020 2050

Supermkt_compressor_rack 

2030 high 3.06 130.72 2030 2050

Supermkt_compressor_rack 

2030 typical 2.87 116.71 2030 2050

Supermkt_condenser 2008 

high 27.84 32.25 2004 2050

Supermkt_condenser 2020 

typical 22.27 25.80 2020 2050

Supermkt_condenser 

installed base 17.82 29.02 2003 2050

Supermkt_display_case

2008 high-2012 standard 3.02 436.28 2004 2050

Supermkt_display_case 

2011 typical 2.57 303.92 2011 2011

Supermkt_display_case 

2020 high 3.42 436.28 2020 2050

Supermkt_display_case 

installed base 2.45 303.92 2003 2011

Vend_Machine 2008 low 0.53 2201.69 2004 2012

Vend_Machine 2008-10 

high-2013 standard 1.06 2621.85 2004 2050

Vend_Machine 2008-10 

typical 0.75 2341.74 2004 2012

Vend_Machine 2011 high 1.17 2621.85 2011 2050

Vend_Machine 2011 typical 0.84 2341.74 2011 2012

Vend_Machine 2020 high 1.24 2621.85 2020 2050

Vend_Machine 2030 high 1.32 2621.85 2030 2050

Walk-In_fzr 2008 high 1.21 2148.16 2004 2050

Walk-In_fzr 2009 EISA stnd-

2010 typical 1.83 2068.59 2009 2050

Walk-In_fzr 2020 typical 1.86 2068.59 2020 2050

Walk-In_fzr 2030 typical 1.89 2068.59 2030 2050

Walk-In_fzr installed base 0.81 1650.90 2003 2008

Walk-In_refrig 2008 high 6.73 725.11 2004 2050
96

Walk-In_refrig 2009 EISA 

stnd-2010 typical 6.24 710.16 2009 2050

Walk-In_refrig 2020 typical 6.54 710.16 2020 2050

Walk-In_refrig 2030 typical 6.94 710.16 2030 2050

Walk-In_refrig installed 

base 2.73 490.52 2003 2008
97

Appendix C. GT-NEMS Modeling of Industrial Energy Efficiency Policies

The Motor Rebate policy offers a 30% subsidy for premium motor. GT-NEMS modeling of this 

policy was to increase the dealer rebate parameter (in the indmotor.xml input file) from 30% to 

60%. The Motor Standard policy assumes efficiency improvement in 2017 that systems using 

motor have additional 25% savings in energy. In order not to change history, the modification 

was made effective from 2017 in the industrial source code, ind.f. 

In the CHP Incentive scenario, subsidies were applied to industrial CHP systems to promote 

efficient usage of waste heat in various industrial processes. A 10-year subsidy increasing from 

15% in 2012 to 30% was applied to the total installed cost parameter in the indcogen.xml input 

file. To reflect the benefit share between customers and CHP retailers, a 15% subsidy was 

applied for the first three years, rising by 5% every year from 2015 and staying at 30% from 

2017 to 2021. GT-NEMS represents CHP as a combination of eight technology systems, 

including two internal combustion CHP systems (ranging from 1 to 3 MW), five gas turbine 

CHP systems (3 to 40 MW) and one combined cycle system (with two 40 MW gas turbines and a 

20 MW steam turbine). Investment costs are provided for each of these systems.

The Advanced Manufacturing Initiative policy mimics the voluntary plant upgrades by the 

private sector. It took the estimated electricity and natural gas savings from efficiency 

improvements reported from 2010 to 2012 in the Industrial Assessment Center (IAC) database 

(Table C.1). The percentage savings were applied to change the TPC parameter in the itech.txt 

input file.

Table C.1 Electricity and Natural Gas Saving Estimations from IAC Reports

Industry

Electricity savings Natural Gas

Northeast Midwest South West Northeast Midwest South West

311 Food 47.58% 47.98% 37.48% 47.08% 0.58% 25.75% 2.00% 0.00%

322 Paper 29.20% 31.24% 15.06% 11.51% 5.19% 15.98% 11.15% 0.00%

325 Chemicals 62.73% 13.04% 51.98% 35.74% -32.64% 25.06% -3.76% -98.63%

327 Non Metals 9.82% 20.23% 46.18% 37.26% 5.12% 29.45% 0.00%

331 Iron and Steel 15.14% 57.13% 28.02% 5.03% 13.75% 18.44% 3.44% 2.61%

332 Fabricated Metals 29.27% 46.89% 42.70% 28.74% -49.03% 16.55% 11.15% N/A

333 Machinery 19.61% 54.08% 45.88% 40.35% 29.22% 53.70% 27.33% N/A

334 Computers and 

Electronics 79.76% 58.39% 15.75% 31.06% 28.54% 22.85% 7.08% N/A

336 Transportation 

Equipment 17.42% 40.24% 56.61% 9.93% 16.85% 4.79% N/A N/A

335 Electrical 8.88% 12.20% 24.35% 36.22% 21.71% 21.81% 3.55% N/A

321 Wood 22.94% 38.47% 34.16% 76.18% 23.14% 3.86% 55.09%

326 Plastics Others 44.69% 26.43% 27.64% 24.39% 15.24% 480.64% 16.78% 10.36%

313 Textile 5.25% 24.03% 4.61% 8.81%

314 Textile product 89.25% 10.83% 13.15% 13.20% -11.25% 95.10% 23.81% 29.45%

324 Petroleum and 

Coal 13.98% 16.05% 6.64% 12.65% 74.27% 16.57% 20.72% 3.57%
98

Appendix D. State Results

Table D.1. Electricity Efficiency Potential By State (TWh)

Residential Commercial Industrial

2020 2035 2020 2035 2020 2035

AL 2.85 4.16 0.92 2.39 2.88 3.92

AR 2.51 3.84 0.67 1.48 1.21 1.62

CT 1.04 1.79 0.44 1.23 0.18 0.22

DC 0.22 0.37 0.51 1.27 0.03 0.04

DE 0.50 0.85 0.23 0.57 0.26 0.35

FL 13.25 22.52 4.89 12.23 1.58 2.17

GA 6.07 10.32 2.42 6.04 2.75 3.76

IA 0.91 1.44 0.66 1.50 1.09 1.57

IL 3.21 5.41 2.68 6.30 3.37 4.76

IN 2.28 3.85 1.24 2.90 3.50 4.94

KS 0.90 1.42 0.53 1.21 0.80 1.16

KY 2.40 3.51 0.80 2.07 3.69 5.03

LA 4.21 6.44 1.29 2.88 1.92 2.57

MA 1.58 2.71 0.59 1.65 0.80 1.00

MD 3.17 5.38 1.38 3.46 0.49 0.68

ME 0.34 0.59 0.13 0.38 0.14 0.17

MI 2.42 4.08 1.99 4.67 2.23 3.14

MN 1.45 2.30 0.76 1.73 1.57 2.25

MO 2.26 3.58 1.08 2.46 1.20 1.72

MS 1.62 2.37 0.54 1.40 1.31 1.78

NC 6.17 10.48 2.43 6.07 2.35 3.21

ND 0.26 0.42 0.15 0.33 0.29 0.42

NE 0.63 1.00 0.33 0.76 0.76 1.09

NH 0.34 0.59 0.15 0.41 0.09 0.11

NJ 2.11 4.00 1.43 3.60 0.54 0.67

NY 3.60 6.83 2.82 7.06 0.87 1.10

OH 3.65 6.16 2.39 5.61 4.02 5.68

OK 3.15 4.81 1.06 2.35 1.06 1.43

PA 3.84 7.29 1.70 4.26 2.84 3.56

RI 0.24 0.41 0.12 0.34 0.05 0.06

SC 3.30 5.61 1.13 2.83 2.38 3.25

SD 0.27 0.43 0.14 0.32 0.18 0.26

TN 3.68 5.38 1.20 3.13 2.67 3.64

VA 5.01 8.51 2.42 6.06 1.56 2.14

VT 0.17 0.29 0.07 0.18 0.07 0.08

WI 1.54 2.60 1.11 2.61 1.82 2.57

WV 1.29 2.20 0.41 1.03 1.03 1.41