Behavioral Economics Applied to 

Energy Demand Analysis: A 

Foundation

October 2014

Independent Statistics & Analysis

www.eia.gov

U.S. Department of Energy

Washington, DC 20585
U.S. Energy Information Administration | Behavioral Economics Applied to Energy Demand Analysis i

This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and 

analytical agency within the U.S. Department of Energy. By law, EIA’s data, analyses, and forecasts are 

independent of approval by any other officer or employee of the United States Government. The views 

in this report therefore should not be construed as representing those of the U.S. Department of Energy 

or other Federal agencies.
October 2014

U.S. Energy Information Administration | Behavioral Economics Applied to Energy Demand Analysis 1

Behavioral Economics Applied to Energy Demand Analysis: A 

Foundation

Neoclassical economics has shaped our understanding of human behavior for several decades. While 

still an important starting point for economic studies, neoclassical frameworks have generally imposed 

strong assumptions, for example regarding utility maximization, information, and foresight, while 

treating consumer preferences as given or external to the framework. In real life, however, such strong 

assumptions tend to be less than fully valid. Behavioral economics refers to the study and formalizing of 

theories regarding deviations from traditionally-modeled economic decision-making in the behavior of 

individuals. The U.S. Energy Information Administration (EIA) has an interest in behavioral economics as 

one influence on energy demand.

Leidos Engineering, LLC (Leidos), previously known as Science Applications International Corporation 

(SAIC), conducted research on behavioral economics and energy demand, and reports the following in 

the contract report in Appendix A:

• “Research revealing that energy consumption can vary widely (by a factor of nearly three) 

among homes and households with nearly identical characteristics1,2

• Research revealing widespread and consistent disconnects between attitudes and behaviors 

regarding the importance of the impact of energy consumption on the environment and 

awareness regarding energy consumption or conservation behavior3

• A variety of papers and studies suggesting energy efficiency policies and program adjustments 

to address the implications of particular irrational behaviors and cognitive limitations, such as 

labeling schemes, framing of energy efficient choices as avoiding losses rather than making 

gains, replacing small value rebates with larger value lottery-based awards, among other tactics4

• Research suggesting that households that received reports regarding their consumption relative 

to neighbors were demonstrated to cut their usage by 2.5 percent, in a sustained manner.

• Research work suggesting that a large portion of subsidies for hybrid automobiles and solar 

panels go to free riders, who would have adopted the more energy efficient technology 

anyway.”

These above findings lend strong evidence to the need for the current National Energy Modeling System 

(NEMS) framework to continue keeping pace with either existing or developing best practices in energy 

economics with respect to consumer behavior.

There is substantial research interest within the government, academia, and trade organization 

communities in consumer behavior with respect to energy demand and efficiency, especially as program 

 1 Parker, Hoak, Meier, and Brown. “How Much Energy Are We Using? Potential of Residential Energy Demand Feedback 

Devices,” Proceedings of the 2006 Summer Study on Energy Efficiency in Buildings. American Council for an Energy Efficient 

Economy. Asilomar, CA. August 2006.

2 Socolow, R. H. “The Twin Rivers program on energy conservation in housing: Highlights and conclusions,” Saving Energy in the 

Home: Princeton’s Experiments at Twin Rivers. Cambridge, MA; Ballinger Publishing Company. 3 Logica Survey. (2007). Turning Concern into Action: Energy Efficiency and the European Consumer.

4 Allcott H. and Mullainathan S. “Behavioral Science and Energy Policy,” February 2010.
October 2014

U.S. Energy Information Administration | Behavioral Economics Applied to Energy Demand Analysis 2

funding targeting energy efficiency continues to increase. EIA hosted a technical workshop5 on 

behavioral economics and recently released a nationwide inventory providing detailed summaries of 

energy efficiency evaluation reports—commonly called evaluation, measurement, and verification

(EM&V) reports6—on electricity and natural gas programs. Energy efficiency program budgets have 

rapidly expanded, and in many states now approach supply-side capital investment in scale. Behavior is 

commonly considered a key aspect of energy efficiency programs.

7

A key finding of the contract report, reflecting expert input from the technical workshop as well as 

subsequent research, is that the implementation of the modeling structures in NEMS has an inherent 

tendency to relax key assumptions in the neoclassical framework. While this finding supports the 

current implementation of demand modeling in NEMS, experimentation with aggregate demand 

specifications remains warranted. Preliminary approaches are described in the report.

The contract report in Appendix A characterizes and defines behavioral economics with respect to 

energy economics and demand analysis, and helps to both inform the public and to provide the 

information and foundational concepts for potential enhancements in EIA’s statistical and modeling 

programs. When referencing the contract report in Appendix A, it should be cited as a report by Leidos 

Engineering, LLC prepared for the U.S. Energy Information Administration.

 5 U.S. Energy Information Administration, Technical Workshop on Behavior Economics Presentations, accessed September 25, 

2014.

6 U.S. Energy Information Administration, State Energy Efficiency Program Evaluation Inventory, accessed September 25, 2014. 7 For example the annual Behavior, Energy and Climate Change (BECC) conference co-hosted by Stanford University, American 

Council for an Energy-Efficiency Economy, and the University of California has documented an expanding set of related 

research. http://beccconference.org/archives/ accessed September 26, 2014.
October 2014

U.S. Energy Information Administration | Behavioral Economics Applied to Energy Demand Analysis 1

Appendix A
 

Final Analytic Report 

Behavioral Economics Applied to 

Energy Demand Analysis 

Energy Information Administration 

August 2014

Final Analytic Report 

Behavioral Economics Applied to 

Energy Demand Analysis 

Energy Information Administration 

August 2014

 
This report has been prepared for the use of the client for the specific purposes identified in the 

report. The conclusions, observations and recommendations contained herein attributed to 

Leidos constitute the opinions of Leidos. To the extent that statements, information and 

opinions provided by the client or others have been used in the preparation of this report, Leidos 

has relied upon the same to be accurate, and for which no assurances are intended and no 

representations or warranties are made. Leidos makes no certification and gives no 

assurances except as explicitly set forth in this report. 

 © 2014 Leidos, Inc. 

 All rights reserved. 
Analytic Report 4-2_20140822_FINAL.docx

Behavioral Economics Applied to Energy Demand 

Analysis 

Energy Information Administration 

Table of Contents 

Table of Contents 

List of Tables 

Section 1 OVERVIEW OF RESEARCH OBJECTIVES ..................................... 1-1

1.1 Understand Current State of Behavioral Economics Field ...................... 1-1

1.2 Prioritize Behavioral Factors that may Significantly Impact 

Demand .................................................................................................... 1-2

1.3 Enhance the Quality of EIA Products through Improved 

Representation of Consumer Behavior .................................................... 1-2

1.4 Maintain Relevancy and Consistency with Best Practices in 

Energy Economics ................................................................................... 1-2

1.5 Enhance Capabilities to Support the Residential and Commercial 

Demand Modules ..................................................................................... 1-3

1.6 Investigate and Experiment with Alternative Aggregate Demand 

Specifications ........................................................................................... 1-3

1.6.1 "Sandbox" Approach ................................................................... 1-3

1.7 Emphasize Leveraging Existing EIA Framework to Infuse 

Behavioral Factors in Lieu of Wholesale Replacement ........................... 1-3

Section 2 BEHAVIORAL ECONOMICS BACKGROUND 

INFORMATION ....................................................................................................... 2-1

2.1 Definition of Behavioral Economics ....................................................... 2-1

2.2 Initial Leidos Research ............................................................................ 2-2

2.3 Contravening Behavioral Economics Concepts ....................................... 2-3

Section 3 U.S. National Energy Modeling System .................................................. 3-1

3.1 NEMS Overview ...................................................................................... 3-1

3.2 Component Modules ................................................................................ 3-2

3.3 NEMS Energy Demand Representation .................................................. 3-5

3.3.1 Residential Demand Module (RDM) ........................................... 3-5

3.3.2 Commercial Demand Module (CDM) ......................................... 3-8

3.3.3 Transportation ............................................................................ 3-10

Section 4 EIA/Leidos TECHNICAL WORKSHOP .............................................. 4-1

4.1 Purpose ..................................................................................................... 4-1

4.2 Meeting Summary .................................................................................... 4-1
Table of Contents 

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4.3 Key Potential Behavioral Concepts ......................................................... 4-7

Section 5 LEIDOS TARGETED LITERATURE SEARCH ................................ 5-1

5.1 Overview of Selected Literature .............................................................. 5-2

5.2 Concepts and Methodologies in Behavioral Economics Literature ........ 5-1

5.3 Commonly-encountered Behavioral Issues ............................................. 5-1

Section 6 INITIAL FINDINGS ................................................................................ 6-1

6.1 Commonly Encountered Behavioral Variables in the Literature 

(priority list) ............................................................................................. 6-1

6.2 Alternative Aggregate Demand Specifications – “Math 

Workspace” .............................................................................................. 6-2

6.3 Tractability of Behavioral Economics within existing EIA 

framework – Summary of March 2014 Technical Workshop ................. 6-7

6.4 Suggested Next Research Steps ............................................................. 6-11

Section 7 BEHAVIORAL ECONOMICS BIBLIOGRAPHY .............................. 7-1

7.1 Bibliography Sources ............................................................................... 7-1
Table of Contents 

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List of Tables 

Table 3-1: NEMS Modular Component Design ........................................................ 3-1

Table 3-2: Example: Behavior Rule Proportions ....................................................... 3-9

Table 3-3: Time Preference Premiums .................................................................... 3-10

Table 5-1: Overview of Behavioral Theories ............................................................. 5-2

Table 5-2: Summary of Targeted Literature Review ................................................. 5-1

Table 6-1: Preliminary Rankings of Behavioral Factors ............................................ 6-2

Table 6-2: Potential Segmentation Axes .................................................................... 6-4

Table 7-1: Behavioral Economics Bibliography ........................................................ 7-2

Analytic Report 4-2_20140822_FINAL.docx

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Section 1 

OVERVIEW OF RESEARCH OBJECTIVES 

The Energy Information Administration (EIA) is the nation's premier source of energy 

information. By law, its data, analyses, and forecasts are independent of approval by 

any other officer or employee of the United States government. The EIA created the 

National Energy Modeling System (NEMS), a computer-based, energy-economy 

modeling system of the U.S., to project the production, imports, conversion, 

consumption, and prices of energy over a long-term (30-year) forecast horizon, subject 

to assumptions on macroeconomic and financial factors, world energy markets, 

resource availability and costs, behavioral and technological choice criteria, cost and 

performance characteristics of energy technologies, and demographics. NEMS is a 

modular system, of which four of the modules are designed to represent specific 

aspects of U.S. energy demand—residential buildings, commercial buildings, 

industrial facilities, and the transportation sector. NEMS is the tool used to make 

official government energy projections. It is by law not a regulatory tool; rather, it is 

used to provide public information and to evaluate policy options, including pending 

energy-related legislation via Congressional service report requests. Refer to 

Section 3 of this report for a detailed description of the current NEMS architecture. 

From an overarching perspective, the research, workshops, documentation, and 

findings detailed in this report are ultimately aimed at recommending (i) research 

agenda items to be carried out in the behavioral economics domain that can help EIA 

ascertain the extent to which best-in-class behavioral economics theory can inform 

NEMS, most notably suggestions for experimentation with alternative aggregate 

demand specifications, and (ii) an initial assessment of implications for energy 

efficiency (EE) programs and trends. Ultimate objectives include enhancing the 

quality of EIA products through improved consumer behavior and policy 

representation in NEMS and maintaining relevancy and consistency with developing 

best practices in energy economics. Enhanced capabilities, to the extent deployed 

within the existing EIA framework, will support the Residential Demand Module 

(RDM) and Commercial Demand Module (CDM), which are major components of 

NEMS that project energy consumption for marketed energy sources plus distributed 

solar and geothermal energy. 

In the development of this analytic report, the following major research objectives of 

the EIA, for which this report serves as an initial foundation upon which further study 

will be undertaken, were priorities that drove the nature of the activities undertaken by 

Leidos Engineering, LLC (Leidos) in partnership with the EIA. 

1.1 Understand Current State of Behavioral Economics 

Field 

The EIA desires to understand whether the behavioral economics field has developed 

a body of work from which an alternative model can be developed that is cogent 
Section 1 

1-2 Leidos, Inc. Analytic Report 4-2_20140822_FINAL.docx

enough to complement the existing EIA framework or from which significant 

adjustments to the existing EIA policy analysis model can be made. 

1.2 Prioritize Behavioral Factors that may Significantly 

Impact Demand 

The EIA desires to identify a range of behavioral factors that are likely to have a 

significant impact on energy demand and prioritize these factors in terms of their 

impact, the level of precision of estimates that can be developed of this impact, and the 

ease of their development and incorporation into the existing EIA framework. 

1.3 Enhance the Quality of EIA Products through 

Improved Representation of Consumer Behavior 

The EIA desires to determine whether the existing NEMS forecasting framework 

appropriately captures consumer behavior patterns that deviate from the traditional 

neoclassical economic paradigm (refer to Section 2 of this report for a comparison of 

neoclassical and behavioral schools of thought). To the extent that either the existing 

body of literature or a series of workshops can help to surface alternative 

configurations, and those experimental configurations can ultimately be infused into 

the existing EIA models, the downstream quality of the EIA products, which are relied 

upon by a vast body of external stakeholders, will be improved. Enhanced capabilities 

to capture the potential variation in projections related to prioritized behavioral factors 

that may currently be absent from the modeling architecture may also spur the 

generation of additional scenarios relative to the EIA reference case to capture a range 

of potential futures given varying assumptions about those same behavioral factors. 

1.4 Maintain Relevancy and Consistency with Best 

Practices in Energy Economics 

The EIA desires to solicit feedback from the academic community and industry 

experts to ensure that the current NEMS framework is keeping pace with either 

existing or burgeoning best practices in energy economics. To the extent that the 

existing behavioral economics literature does not contain sufficiently developed 

methods to quantify and analyze certain behavioral factors deemed to be important to 

residential and commercial demand, then such a finding is valuable in and of itself. 

Alternatively, to the extent that research and collaboration with stakeholders can help 

to crystallize alternative demand specifications, and/or it can be shown that the 

existing EIA framework contains embedded behavioral levers that capture consumer 

behavior appropriately, then extensions of the existing framework may be possible to 

fill gaps in lieu of replacement of, or significant revisions to, certain modules within 

NEMS. 
OVERVIEW OF RESEARCH OBJECTIVES 

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1.5 Enhance Capabilities to Support the Residential and 

Commercial Demand Modules 

The EIA desires to enhance the capabilities of the existing EIA forecasting 

architecture to support the RDM and CDM. It is as yet undetermined whether such 

capability enhancements will be in the form of direct intervention within those 

modules (refer to research objectives below). Refer to Section 3 of this report for 

further details on how the CDM and RDM are currently structured. 

1.6 Investigate and Experiment with Alternative 

Aggregate Demand Specifications 

The EIA desires to investigate whether alternative aggregate demand specifications 

exist within the current behavioral economics cannon. To the extent that the literature 

can directly inform new ideas that capture previously unconsidered behavioral factors, 

then the EIA intends to determine whether and if sufficient data exists to develop 

alternative demand models that can be used to test alternative projections as a 

benchmark and complement to the existing framework. In parallel with such 

investigations, Leidos and EIA intend to participate in further workshops to generate 

additional ideas based on prior experience, leveraging synergies within the working 

group resulting from varying backgrounds in demand forecasting wherever possible. 

1.6.1 "Sandbox" Approach 

The EIA’s intention is to collect a list of ideas associated with alternative demand 

specifications (the results of our investigations as summarized later in this report) and 

experiment with them in a “sandbox” environment. A “sandbox” environment implies 

that EIA staff will construct standalone modeling frameworks based on gathering of 

raw data, most likely extracted from a combination of existing NEMS raw data, 

secondary data to capture behavioral factors, and/or the parameterization of theoretical 

equations that capture behavioral factors. The advantage of sandbox implementation is 

that additional logic for this purpose will not have to be carefully integrated into the 

entire NEMS structure until a rigorous and thorough experimentation phase is 

completed that can uncover data limitations and can ensure that any alternative 

specifications are subjected to a fair amount of scrutiny for quality. 

1.7 Emphasize Leveraging Existing EIA Framework to 

Infuse Behavioral Factors in Lieu of Wholesale 

Replacement 

Ultimately, the EIA desires to infuse high-priority behavioral factors that may be 

missing from the existing NEMS architecture into the appropriate module(s) in lieu of 

wholesale replacement of the CDM and RDM modules. As summarized in Section 4 

of this report, the results of the first workshop on the topic and the entirety of 
Section 1 

1-4 Leidos, Inc. Analytic Report 4-2_20140822_FINAL.docx

additional follow-up feedback and interfacing conducted suggests that the existing 

EIA framework does indeed contain certain behavioral elements, and the NEMS CDM 

and RDM modules are not a strict deployment of a rigid neoclassical economic 

framework. Furthermore, as summarized in Section 5 of this report, the literature does 

not currently offer an overarching mathematical framework inclusive of behavioral 

economics theory that can viably displace the core elements of the CDM and RDM 

modules. Consequently, it is likely that the infusion of certain additional behavioral 

factors will be preferable to a wholesale replacement of the RDM and CDM. 
Analytic Report 4-2_20140822_FINAL.docx

Section 2 

BEHAVIORAL ECONOMICS BACKGROUND INFORMATION 

2.1 Definition of Behavioral Economics 

Behavioral Economics refers to the research of, and formalizing of theories regarding, 

deviations from rational economic decision-making in the behavior of individuals. 

These deviations result in market behavior that is counter to theoretical economic 

models and can cause outcomes to differ from expectations derived from these 

models. Behavioral Economics draws upon cognitive psychology and other fields to 

inform experimental and theoretical analyses aimed at understanding how individual 

market agents make decisions.1 This research has demonstrated consistent and 

widespread departures from rational choice theory and expected utility maximization 

behavior. 

Neoclassical economic theory rests on the assumption that individuals make decisions 

aimed at maximizing their individual utility based on complete information. Referred 

to as rational choice theory, the concept implies that individuals can be counted on to 

consistently behave in ways that are intended to benefit them. Many economic 

researchers that adhere to this theory suggest that individuals may sometimes deviate 

from rational behavior for various reasons but that, on a wider scale, such deviations 

are sufficiently minor and infrequent so as to not invalidate the usefulness of the 

theory in developing models. 

Researchers in the Behavioral Economics field have identified a host of behaviors that 

are counter to rational choice theory and can generally be classified under the 

umbrellas of cognitive bias and bounded rationality. Cognitive bias describes 

behavior that reveals inconsistencies in the evaluation of choices, such as higher 

implied discount rates on purchase decisions relative to savings decisions, violation of 

transitive principles (i.e., rational preference axioms), and greater aversion to losses 

than desire for gains. Bounded rationality describes decision-making based on 

imperfect information and includes behaviors such as procrastination, simplified 

decision-making heuristics, and disproportionate weight to readily observable factors, 

which result from a lack of readily available and complete information. Behavioral 

Economics research suggests that these deviations from neoclassical assumptions are 

sufficiently consistent to shed doubt on the usefulness of the neoclassical paradigm in 

modeling the decision-making of economic agents. 

The confluence of these behavioral “failures” and certain market failures is viewed by 

some energy industry researchers as explaining the difference between observed levels 

of energy efficiency and a socially optimal level of efficiency, referred to as an 

“energy efficiency gap.” This gap takes the form of an underinvestment in energy 

efficiency relative to the level that should be economically optimal and/or a slower 

 

1

 Gillingham, Newell, and Palmer. Energy Efficiency Economics and Policy. National Bureau of 

Economic Research Working Paper 15031. June 2009. 
Section 2 

2-2 Leidos, Inc. Analytic Report 4-2_20140822_FINAL.docx

than optimal rate of adoption of energy efficiency products. This phenomenon is also 

viewed as a rationale for policy intervention to alleviate or circumvent the impacts of 

these failures. 

2.2 Initial Leidos Research 

Prior to the Behavioral Economics workshop, which is summarized in detail in Section 

4 of this report, Leidos conducted preliminary research regarding Behavioral 

Economics and its application to the field of energy demand analysis in order to 

provide context to the prospective participants in the workshop and to motivate the 

incorporation of Behavioral Economics concepts and overall paradigm into NEMS. 

The following are key highlights of the documents that were surfaced by this effort. 

 Research revealing that energy consumption can vary widely (by a factor of 

nearly three) among homes and households with nearly identical 

characteristics2,3

 Research revealing widespread and consistent disconnects between attitudes 

and behaviors regarding the importance of the impact of energy consumption 

on the environment and awareness regarding energy consumption or 

conservation behavior4. 

 A variety of papers and studies suggesting energy efficiency policies and 

program adjustments to address the implications of particular irrational 

behaviors and cognitive limitations, such as labeling schemes, framing of 

energy efficient choices as avoiding losses rather than making gains, and 

replacing small value rebates with larger value lottery-based awards, among 

other tactics.5. 

 Research suggesting that households that received reports regarding their 

consumption relative to neighbors were demonstrated to cut their usage by 

2.5 percent, in a sustained manner. 

 Research work suggesting that a large portion of subsidies for hybrid 

automobiles and solar panels go to free riders, who would have adopted the 

more energy efficient technology anyway. 

 

2

 Parker, Hoak, Meier, and Brown. “How Much Energy Are We Using? Potential of Residential Energy 

Demand Feedback Devices,” Proceedings of the 2006 Summer Study on Energy Efficiency in 

Buildings. American Council for an Energy Efficient Economy. Asilomar, CA. August 2006. 

3

 Socolow, R. H. “The Twin Rivers program on energy conservation in housing: Highlights and 

conclusions,” Saving Energy in the Home: Princeton’s Experiments at Twin Rivers. Cambridge, MA; 

Ballinger Publishing Company. 

4

 Logica Survey. (2007). Turning Concern Into Action: Energy Efficiency and the European Consumer. 

5

 Allcott and Mullainathan (2009). “Behavioral Economics and Energy Policy”. 
BEHAVIORAL ECONOMICS BACKGROUND INFORMATION 

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2.3 Contravening Behavioral Economics Concepts 

The following are behaviors and concepts that have been identified within the 

behavioral economics field as sufficiently consistent and widespread to contravene the 

neoclassical paradigm and confound models developed on that basis. While these 

concepts are separately discussed and an attempt is made to classify them as either 

cognitive biases or symptomatic of bounded rationality, there is a large degree of 

overlap and reinforcement across the concepts and between these labels. 

 Inconsistent Temporal Framing – Consumers tend to have higher implied 

discount rates on purchase decisions relative to decisions regarding savings, 

placing lower value on future costs relative to an upfront purchase consistent 

with discount rates of 25% to over 100%. However, the irreversibility of many 

energy efficiency decisions is viewed as supporting some level of differential 

in implied time value of money. 

 Status Quo Bias – Consumers tend to dislike change and will more strongly 

weight current equipment and energy consumption and cost characteristics, 

regardless of information to the contrary. This behavior has been widely 

recognized in numerous programs that reflect an opt-out rather than an opt-in 

to increase participation. People also tend to become psychologically invested 

in existing equipment, regardless of the costs and benefits of replacement. 

 Loss Aversion – Consumers tend to have greater aversion to losses than desire 

for gains, all else equal. 

 Decision-making Heuristics – Consumers revert to simple rules of thumb and 

simplified math when faced with complex decisions. For example, consumers 

tend to choose an option perceived as a compromise or “middle of the road” 

choice. 

 Salience Effect – Consumers attach a disproportionate weight to readily 

observable factors, contributing for example to an overemphasis on the initial 

cost of energy efficient appliances.6

 Prosocial Behavior – Consumers tend to be readily influenced by what others 

are doing, regardless of costs and benefits, and care more about levels of 

performance and participation relative to others rather than absolute levels. 

 Permanent Income Hypothesis Paralysis – Consumers may be fully aware of 

the long term economic benefits of a decision to make a change and also be 

fully aware of their higher short-term costs resulting from not making a 

particular decision, making them rational economic agents from an analytical 

perspective. However, these same consumers are irrationally concerned with 

long term economic security (perception of permanent income) and their 

ability to service debt payments associated with the purchase of a highly 

efficient end-use, leading to a state of paralysis and inaction. 

 

6

 Gillingham, Newell, and Palmer. Energy Efficiency Economics and Policy. National Bureau of 

Economic Research Working Paper 15031. June 2009. 
Section 2 

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The EIA wishes to uncover additional contravening behavioral concepts from a more 

thorough literature review, ascertain how and if such concepts can lend themselves to 

alternative aggregate demand specifications, and then determine the extent to which 

available data can help mold experimental models as a comparison to EIA’s existing 

framework. Refer to Section 4 of this report for a detailed description of the outcomes 

of the EIA’s first workshop on this topic, as it is important to note that the existing 

modeling architecture was found to contain certain key behavioral levers, and is not a 

strictly neoclassical economic model. 
Analytic Report 4-2_20140822_FINAL.docx

Section 3 

U.S. National Energy Modeling System 

The National Energy Modeling System (NEMS) is a publicly-available, economywide, integrated energy model that includes 12 sub-modules covering energy supply, 

conversion, and demand. It is used by the U.S. Energy Information Administration 

(EIA) to annually provide 30-year energy market and infrastructure forecasts, referred 

to as the Annual Energy Outlook (AEO), and is the principal tool for the analysis of 

energy and greenhouse gas policies used by the U.S. government. 

The following sections provide an overview of the purpose and architecture of NEMS, 

a description of its component modules, and some details regarding the representation 

of energy demand in NEMS, with a focus on how consumer behavior is captured. 

3.1 NEMS Overview 

NEMS integrates every energy sector in the U.S. economy, including the gas, oil and 

power industries, the renewable energy sector, the transportation demand sector and 

the residential, commercial and industrial energy demand sectors. The model is 

capable of analyzing overall impacts on the US economy of alternative energy and 

environmental policies. 

Table 3-1: NEMS Modular Component Design

Oil and Gas

Supply Module

Natural Gas

Transmission

and Distribution

Module

Coal Market

Module

Renewables

Module

Supply

Industrial Demand 

Module

Residential

Demand Module

Transportation

Demand

Module

Demand 

Electricity

Market

Module

Liquid Fuels

Market

Module

Macroeconomic 

Activity Module

International 

Energy Activity 

Module

Integrating

Module

Commercial 

Demand Module

Conversion

The forecast horizon of NEMS is approximately 30 years (currently through 2040). 

Because of the diverse nature of energy supply, demand, and conversion in the United 

States, the model supports regional modeling and analysis in order to represent the 
Section 3 

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regional differences in energy markets, to provide policy impacts at the regional level, 

and to portray transportation flows. The regional detail of the end-use demand 

modules corresponds to the nine Census divisions. Other regional structures include 

production and consumption regions specific to oil, natural gas, and coal supply and 

distribution, the North American Electric Reliability Council (NERC) regions and subregions for electricity, and the Petroleum Administration for Defense Districts 

(PADDs) for refineries. 

For each fuel and consuming sector, NEMS balances the energy supply and demand, 

accounting for the economic competition between the various energy fuels and 

sources. NEMS is organized and implemented as a modular system (Table 3-1). The 

modules represent each of the fuel supply markets, conversion sectors, and end-use 

consumption sectors of the energy system. The model also includes a macroeconomic 

and an international module. The primary flows of information between each of these 

modules are the delivered prices of energy to the end user and the quantities consumed 

by product, region, and sector. The delivered prices of fuel encompass all the activities 

necessary to produce, import, and transport fuels to the end user. The information 

flows also include other data such as economic activity, domestic production, and 

international petroleum supply availability. 

NEMS solves by calling each supply, conversion, and end-use demand module in 

sequence until the delivered prices of energy and the quantities demanded have 

converged within tolerance, thus achieving an economic equilibrium of supply and 

demand in the consuming sectors. Solution is reached annually through the forecast 

horizon. Other variables are also evaluated for convergence such as petroleum product 

imports, crude oil imports, and several macroeconomic indicators. 

Each NEMS component also represents the impact and cost of Federal legislation and 

regulation that affect the sector and reports key emissions. NEMS generally reflects all 

current legislation and regulation that are defined sufficiently to be modeled. 

However, the potential impacts of pending or proposed legislation, regulations, and 

standards—or of sections of legislation that have been enacted but that require 

implementing regulations or appropriation of funds that are not provided or specified 

in the legislation itself—are not typically reflected in the model. 

3.2 Component Modules 

The component modules of NEMS represent the individual supply, demand, and 

conversion sectors of domestic energy markets and also include international and 

macroeconomic modules. In general, the modules interact through values representing 

the prices of energy delivered to the consuming sectors and the quantities of end-use 

energy consumption. Brief summaries of each of the modules are provided below. 

Macroeconomic Activity Module 

The Macroeconomic Activity Module (MAM) provides a set of macroeconomic 

drivers to the energy modules, along with a macroeconomic feedback mechanism that 

iteratively adjusts measures of economic output to comport with energy prices (which 

are, in turn, dependent on macroeconomic activity). Key macroeconomic variables 
U.S. National Energy Modeling System 

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used in the energy modules include gross domestic product (GDP), disposable income, 

value of industrial shipments, new housing starts, new light-duty vehicle sales, interest 

rates, and employment. Key energy indicators fed back to the MAM include 

aggregate energy prices and costs. 

International Module 

The International Module represents the response of world oil markets (supply and 

demand) to assumed world oil prices. The output of the module is a set of crude oil 

and product supply curves that are available to U.S. markets. The petroleum import 

supply curves are made available to U.S. markets through the Liquid Fuels Market 

Module of NEMS. The supply-curve calculations are based on historical market data 

and a world oil supply/demand balance, which is developed from reduced-form 

models of international liquids supply and demand, current investment trends in 

exploration and development, and long-term resource economics for 221 

countries/territories. The oil production estimates include both conventional and 

unconventional supply recovery technologies. 

Residential and Commercial Demand Modules 

The Residential Demand Module projects energy consumption in the residential sector 

by housing type and end use, based on delivered energy prices, the menu of equipment 

available, the availability of renewable sources of energy, and housing starts. The 

Commercial Demand Module projects energy consumption in the commercial sector 

by building type and non-building uses of energy and by category of end use, based on 

delivered prices of energy, availability of renewable sources of energy, and 

macroeconomic variables representing interest rates and commercial floorspace 

construction. 

Industrial Demand Module 

The Industrial Demand Module projects the consumption of energy for heat and power 

and for feedstocks and raw materials in each of 21 industries, subject to the delivered 

prices of energy and macroeconomic variables representing employment and the value 

of shipments for each industry. 

Transportation Demand Module 

The Transportation Demand Module projects consumption of fuels in the 

transportation sector, including petroleum products, electricity, methanol, ethanol, 

compressed natural gas, and hydrogen, by transportation mode, vehicle vintage, and 

size class, subject to delivered prices of energy fuels and macroeconomic variables 

representing disposable personal income, GDP, population, interest rates, and 

industrial shipments. 

Electricity Market Module 

The Electricity Market Module (EMM) represents generation, transmission, and 

pricing of electricity, subject to delivered prices for coal, petroleum products, natural 

gas, and biofuels; costs of generation by all generation plants, including capital costs 

and macroeconomic variables for costs of capital and domestic investment; 
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environmental emissions laws and regulations; and electricity load shapes and 

demand. There are three primary submodules—capacity planning, fuel dispatching, 

and finance and pricing. 

Renewable Fuels Module 

The Renewable Fuels Module (RFM) includes submodules representing renewable 

resource supply and technology input information for central-station, grid-connected 

electricity generation technologies, including conventional hydroelectricity, biomass 

(wood, energy crops, and biomass co-firing), geothermal, landfill gas, solar thermal 

electricity, solar photovoltaics (PV), and wind energy. The RFM contains renewable 

resource supply estimates representing the regional opportunities for renewable energy 

development. 

Oil and Gas Supply Module 

The Oil and Gas Supply Module (OGSM) represents domestic crude oil and natural 

gas supply within an integrated framework that captures the interrelationships among 

the various sources of supply—onshore, offshore, and Alaska, by both conventional 

and unconventional techniques, including natural gas recovery from coalbeds and lowpermeability formations of sandstone and shale. The framework analyzes cash flow 

and profitability to compute investment and drilling for each of the supply sources, 

based on the prices for crude oil and natural gas, the domestic recoverable resource 

base, and the state of technology. Oil and gas production functions are computed for 

12 supply regions, including 3 offshore and 3 Alaskan regions. The module also 

represents foreign sources of natural gas, including pipeline imports and exports to 

Canada and Mexico, and liquefied natural gas (LNG) imports and exports. 

Natural Gas Transmission and Distribution Module (NGTDM) 

The NGTDM represents the transmission, distribution, and pricing of natural gas, 

subject to end-use demand for natural gas and the availability of domestic natural gas 

and natural gas traded on the international market. The module tracks the flows of 

natural gas and determines the associated capacity expansion requirements in an 

aggregate pipeline network, connecting the domestic and foreign supply regions with 

12 U.S. demand regions. The flow of natural gas is determined for both a peak and 

off-peak period in the year. Key components of pipeline and distributor tariffs are 

included in separate pricing algorithms. The module also represents foreign sources of 

natural gas, including pipeline imports and exports to Canada and Mexico and LNG 

imports and exports. 

Liquid Fuels Market Module 

The Liquid Fuels Market Module (LFMM) projects petroleum product prices and 

sources of supply for meeting petroleum product demand. The sources of supply 

include crude oil (both domestic and imported), petroleum product imports, unfinished 

oil imports, other refinery inputs (including alcohols, ethers, esters, corn, biomass, and 

coal), natural gas plant liquids production, and refinery processing gain. In addition, 

the LFMM projects capacity expansion and fuel consumption at domestic refineries. 
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The recent adoption of a new LFMM in place of the Petroleum Market Module 

(PMM) used in earlier NEMS studies provides for more granular and integrated 

modeling of petroleum refineries and other types of current and potential future liquid 

fuels production technologies. This allows more direct analysis and modeling of the 

regional supply and demand effects involving crude oil and other feedstocks, current 

and future processes, and marketing to consumers. 

Coal Market Module 

The Coal Market Module (CMM) simulates mining, transportation, and pricing of 

coal, subject to end-use demand for coal differentiated by heat and sulfur content. U.S. 

coal production is represented in the CMM by 40 separate supply curves—

differentiated by region, mine type, coal rank, and sulfur content. The coal supply 

curves include a response to capacity utilization of mines, mining capacity, labor 

productivity, and factor input costs (mining equipment, mining labor, and fuel 

requirements), and other mine supply costs. Projections of U.S. coal distribution are 

determined by minimizing the cost of coal supplied, given coal demands by demand 

region and sector, environmental restrictions, and accounting for mine-mouth prices, 

transportation rates, and coal supply contracts. Over the forecast horizon, coal 

transportation rates in the CMM are projected to vary in response to changes in 

railroad investment and market share (for western coal only). 

3.3 NEMS Energy Demand Representation 

The subsections below provide an outline of the modeling of energy demand within 

the Residential, Commercial, and Transportation sectors. Within these demand sectors 

of NEMS, there are model elements that appear to be designed to capture the effects of 

consumer behavior, primarily within the calculations regarding technology choice to 

meet service demands. The discussion below is heavily weighted toward those model 

aspects. As a review of the Industrial Demand Model (IDM) did not identify specific 

elements that estimate the results of consumer choice, it is excluded from this section. 

3.3.1 Residential Demand Module (RDM) 

Residential energy demand is determined by existing and projected housing stock, by 

housing type, based on estimates of housing starts from the Macroeconomic Activity 

Module (MAM), and the retirement (demolishment) of older housing at a constant 

rate. Housing is expected to provide a variety of major end-use services, such as space 

heating and cooling, water heating, cooking, dishwashing, laundering, and 

refrigeration. Consumer behavior is reflected in the choices made in the technologies 

to provide these services, both in new construction and in the replacement of obsolete 

appliances. These major services currently represent approximately 80 percent of 

residential end-use energy consumption. Other, minor services provided by such 

items as televisions, PC’s, or other small appliances are addressed in less detail, using 

either exogenous modeling or projections of saturation rates and Unit Energy 

Consumption (UEC) estimates. 
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Major End-Use Services 

Within each of the major service categories, consumers select new and replacement 

appliances by equipment class (e.g., natural gas water heater), and by equipment type 

(e.g., among competing models of natural gas water heaters). The RDM calculates 

market shares based on consumer behavior as a function of capital and operating costs 

(i.e., life-cycle cost) and bias (described below). The consumer is allowed to choose 

among the various levels of cost and efficiency for a given class of equipment. The 

concept of price-induced technology change is also included in the formulation of 

equipment costs, which allows future technologies faster diffusion into the 

marketplace if fuel prices increase markedly and remain high over a multi-year period. 

A logistic function is used to estimate the market shares of competing technologies 

within each major service category. The function assigns market shares for competing 

technologies based on the relative weights of capital/installed (first cost) and 

discounted operating (annual fuel) costs. A time dependent log-linear function 

calculates the installed capital cost of equipment in new construction. If fuel prices 

increase markedly and remain high over a multi-year period, more efficient appliances 

may be available earlier in the projection period than would have been the case 

otherwise. 

For new construction, market shares of building shell options are also determined 

using a similar logistic calculation. The shell options are linked to heating and cooling 

equipment, as building codes can be met using more efficient equipment in addition to 

structural options (like windows and insulation levels). The linked, minimum 

efficiencies for heating and cooling equipment in new construction can be increased, 

but not decreased, based on the logistic calculation. 

Space heaters, air conditioners (heat pumps and central air conditioners), water 

heaters, ranges, and clothes dryers may be replaced with competing technologies in 

single-family homes. It is assumed that 20 percent of the replacement market in 

single-family homes is eligible to switch fuels in any projection year, but multifamily 

and mobile homes are not considered capable of fuel-switching. The technology 

choice for fuel-switching decisions is based on a log-linear function, which is flexible 

to allow the user to specify parameters, such as weighted retail equipment cost, 

technology switching cost, and bias. Replacements in multifamily and mobile homes 

are constrained to the same technology. 

As mentioned above, in addition to economic factors in the technology choice 

functions, consumer preference, or “bias” parameters are used to calibrate the model’s 

estimated market share of a given technology to historical shipment data. These 

factors may be interpreted as indicators of the aggregate consumer’s predilection for 

or against a particular fuel or technology. These behavioral parameters are static and 

are obtained from exogenous data input files. These parameters are incorporated into 

the model elements that capture the following consumer behaviors: 

 Choice of heating system for new residential construction, by building type 

and census division 
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 Consumer receptiveness to fuel-switching when choosing replacement 

technologies 

 Consumer preference for a specific technology within an appliance class in the 

efficiency choice model 

Minor End-Use Services 

 Personal computers: The RDM uses an exogenous spreadsheet model, 

assuming certain market penetration rates for the different technologies over the 

forecast period, including desktops vs. laptops, LCD vs. CRT monitors, etc. Outputs 

from the model include the penetration rate (PCs/housing unit) and a usage trend, 

influenced by a short-term price elasticity function. 

 Televisions: As with personal computers, the RDM employs an exogenous 

model for TVs, set-top boxes, and video game consoles that assumes certain market 

penetration rates for the different technologies over the forecast period, including 

plasma vs. LCD vs. CRT, high definition vs. standard definition, cable vs. satellite, 

etc. Outputs from the spreadsheet model, the penetration rate (devices/housing unit) 

and UEC trend, are used to estimate total television energy consumption. 

 Other Electric Appliances: The remaining electricity consumption is 

captured in a catch-all category that includes miscellaneous electrical uses such as 

small kitchen appliances, small consumer electronics, and small motor devices that are 

used in homes but do not fall into any of the other categories of equipment that have 

their own module components. The component computes the UEC on a per-housingunit basis, by housing type and Census division. Based on historical data, a growth 

rate is estimated and applied to the UEC to project future energy consumption. 

 Other Non-Electric Appliances: The RDM treats this as another catch-all 

category, where total consumption is based on housing stock, unit energy 

consumption, and a short-term price elasticity function. 

Distributed Generation 

Three technologies are considered in the RDM for residential generation: Solar PV, 

Wind, and Fuel Cells. Distributed generation penetration is based on a cash flow 

simulation model. For each year in a NEMS run, a complete 30-year cash flow 

analysis is done for each of the three distributed generation technologies. Simulations 

are carried out for single family homes. System characteristics, financial variables, 

solar insolation and program-driven systems (e.g., the California solar program) are 

supplied to the submodule via an exogenous input file. 

Technology penetration rates for distributed generating technologies installed in new 

construction are determined by how quickly an investment in a technology is 

estimated to recoup its flow of costs. This penetration rate is allowed to be as high as 

75% for distributed technologies if the investment “pays back” in less than one year, 

30% if the investment pays back in one year, and correspondingly less for longer 

paybacks. The penetration function is assumed to follow a logistic functional form. 

For retrofitting distributed generation into existing construction, penetration is capped 
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by assumption at the lesser of 0.5% and the penetration rate into new construction 

divided by 40. The cap is in effect if penetration into new construction exceeds 20%. 

3.3.2 Commercial Demand Module (CDM) 

Commercial energy demand is determined by existing and projected commercial 

floorspace, by commercial building type, based on floorspace growth rates provided to 

the CDM by the Macroeconomic Activity Module, and the retirement of floorspace 

based on a survival algorithm. The resulting total commercial floorspace, by census 

division and building type, is used to calculate demand for the major services of space 

heating, space cooling, water heating, ventilation, cooking, refrigeration, and lighting. 

These service demands provide input to the Technology Choice subroutine, and 

subsequently contribute to the development of end-use consumption projections. 

Technology Choice 

Given the level of energy services demanded, the CDM projects the class and model 

of equipment selected to satisfy the demand. The model is designed to choose among a 

discrete set of technologies exogenously characterized by commercial availability, 

capital cost, operating and maintenance (O&M) cost, removal/disposal cost, 

efficiency, and equipment life. The menu of equipment depends on technological 

innovation, market development and policy intervention. The CDM allows for 

endogenous price-induced technology change in the determination of equipment costs 

and availability for the menu of equipment. This concept allows future technologies 

faster diffusion into the marketplace if fuel prices increase markedly for a sustained 

period of time. 

Commercial consumers purchase energy-using equipment to meet three types of 

demand, referred to as “Decision Types”: 

• New: Service demand in newly-constructed buildings; 

• Replacement: Service demand formerly met by equipment that is at the end of its 

useful life and must be replaced; 

• Retrofit: Service demand formerly met by equipment with a remaining useful life 

that is nevertheless subject to retirement on economic grounds. 

Because evidence suggests that traditional cost-minimizing models do not adequately 

account for the full range of economic factors that influence consumer behavior, the 

CDM is designed to allow the use of several possible assumptions. These assumptions, 

referred to as “Behavior Rules”, are summarized as follows: 

• Least Cost (LC): Choose the equipment that minimizes the total expected cost over 

the life of the equipment; 

• Same Fuel (SF): Buy equipment that uses the same fuel as existing or retiring 

equipment, but minimizes life-cycle costs under that constraint; 

• Same Technology (ST): Buy (or keep) the same technology as the existing or 

retiring equipment, but choose between models with different efficiency levels 

based upon minimum life-cycle costs. 
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These behavior rules are designed, based on empirical research, to represent the range 

of economic factors that influence the consumer's decision. The consumers who 

minimize life-cycle cost are the most sensitive to energy price changes; thus, the pricesensitivity of the model depends in part on the share of consumers using each behavior 

rule. The proportion of consumers in each behavior rule segment varies by building 

type, the end-use service under consideration, and decision type, for the three decision 

types of new construction, replacement, or retrofit. The following table provides a 

sample of the Space Heating behavior rule proportions under each of the “decision 

types” for several commercial buildings. 

Table 3-2: Example: Behavior Rule Proportions 

Space Heating

New Replacement Retrofit

LC SF ST LC SF ST LC SF ST

  

Assembly 0.38 0.43 0.19 0.09 0.35 0.56 0.01 0.04 0.95

Education 0.40 0.45 0.15 0.13 0.42 0.45 0.02 0.07 0.91

Food sales 0.26 0.35 0.39 0.04 0.26 0.70 0.00 0.03 0.97

Food service 0.26 0.35 0.39 0.04 0.24 0.72 0.00 0.03 0.97

  

In addition to the behavior rules, the CDM further segments commercial agents into 

seven distinct risk-adjusted time preference premium categories. This type of 

segmentation incorporates the notion that all agents do not consider the same set of 

parameters in the optimization within the commercial sector. Some participants may 

display specific behavior due to existing biases regarding certain equipment types or 

fuels. In addition, the distribution of risk-adjusted time preference premiums 

represents a variety of commercial agents' attitudes about the desirability of current 

versus future expenditures with regard to capital, O&M, and fuel costs. 

The value of this interest rate premium influences the annualized installed capital cost 

through an annuity payment financial factor based on the 10-year Treasury note rate, 

the risk-adjusted time preference premium, and expected physical equipment lifetime. 

The sum of the 10-year Treasury note rate and the consumer risk-adjusted time 

preference premium is referred to as the implicit discount rate, i.e., the interest rate 

required to reflect actual purchases. The implicit discount rate is also known as a 

“hurdle rate” to emphasize the consideration of all factors, both financial and 

nonfinancial, that affect an equipment purchase decision. The combination of these 

factors results in the height of the “hurdle” for the purchase decision. 

An example of the time preference premiums for space heating in 2009 is shown in the 

following table. 
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Table 3-3: Time Preference Premiums 

Consumer Segment Time Preference Premium

26.7% 1000%

22.6% 100%

19.6% 45%

19.0% 25%

10.5% 15%

1.3% 6.5%

0.3% 0%

The assumed distribution of consumer risk-adjusted time preference premiums is 

generally assumed constant over the projection period. However, the CDM allows 

variation in the distribution on an annual basis to accommodate targeted policies that 

may affect decision-making for specific time periods, such as Recovery Act spending, 

and for simulation of policy scenarios targeting consumers’ implicit discount rates. 

The model results are sensitive to the distribution of the risk-adjusted time preference 

premiums. If the distribution is denser at the high premiums, the annualized cost of 

capital for all new equipment will rise. Higher annualized capital cost implies that 

fewer buildings will be retrofitted and that equipment that has a higher installed capital 

cost is less likely to be chosen over a technology with a lower initial cost and higher 

operating and fuel costs. Typically, those technology and vintage combinations with 

high installed capital costs are high-efficiency pieces of equipment, so that the indirect 

effect of this scenario is that fuel consumption is likely to be higher. 

The interaction of technology characteristics, behavior rules, decision types, and 

hurdle rates determines the market share of equipment used to satisfy commercial 

service demand. 

3.3.3 Transportation 

The TDM is designed to generate projections of transportation energy demand at the 

national and Census Division level, endogenously incorporating the effects of 

technological innovation, macroeconomic feedback, infrastructure constraints, and 

vehicle choice in making the projections. The largest component of the TDM, and the 

one most sensitive to consumer behavior, is the Light Duty Vehicle (LDV) 

Submodule. This submodule tracks the purchase and retirement of cars and light 

trucks, projects fuel efficiency, and estimates the consumption of transportation fuels 

based on projections of travel demand. The LDV Submodule consists of several 

components, including the following that model consumer choice and travel behavior: 

 Manufacturer Technology Choice Component (MTCC) 

 Consumer Vehicle Choice Component (CVCC) 

 Vehicle-Miles Traveled Component (VMTC)
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Manufacturer Technology Choice Component (MTCC) 

The MTCC component produces estimates of new light-duty vehicle fuel economy. 

Fuel economy is a significant aspect of the TDM because automotive fuel demand is 

directly affected by the efficiency with which that fuel is used. The fuel economy of 

new vehicles is impacted by changes in four factors: 

 Technology penetration 

 Level of acceleration performance achieved 

 Mix of vehicle size classes and vehicle technology types (e.g., hybrid and 

diesel) sold 

 Vehicle fuel economy, safety, and emission standards 

The demand for increased acceleration performance for each market class is estimated 

based on an econometric equation relating fuel prices and personal disposable income 

to demand for performance or horsepower, by market class. These relationships are 

used to project the change in horsepower, which is then used to project the change in 

fuel economy through an engineering relationship that links performance and fuel 

economy. 

The change in the mix of market classes sold is projected as a function of fuel price, 

vehicle price, and personal disposable income. The sales mix by market class is used 

to calculate new fuel economy. 

Each available technology is subjected to a cost-effectiveness test that balances the 

cost of the technology against the potential fuel savings and the value of any increase 

in performance provided by the technology. The cost-effectiveness test is used to 

generate an economic market share for the technology. 

Consumer Vehicle Choice Component (CVCC) 

The objective of the CVCC is to estimate the market penetration of conventional and 

alternative-fuel vehicles during the period 1995-2040. To project technology market 

shares, the component uses estimates of the following variables and vehicle attributes: 

new car fuel economy (obtained from the MTCC), vehicle price, vehicle range, fuel 

availability, battery replacement cost, performance (measured by the horsepower-toweight), home refueling capability, maintenance costs, luggage space, make and 

model diversity or availability, and fuel price estimates generated by NEMS. 

The CVCC uses attribute-based discrete choice techniques and logit-type choice 

functions, which represent a demand function for vehicle sales in the United States. 

The demand function uses projections of the changes in vehicle and fuel attributes for 

the considered technologies to estimate the market share penetration for the various 

technologies. 

The component projects market shares for 14 alternative-fuel technologies as well as 

for conventional gasoline and diesel technologies. There are three stages or levels to 

the “tree” structure of the CVCC-logit model. In the first stage, the shares of vehicle 

sales are determined for five aggregate vehicle groups: conventional, hybrid, dedicated 

alternative fuel, fuel cell, and electric. The second stage of the logit model subdivides 
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each of the five groups to estimate sales shares for the specific vehicle types within 

each group. The third level of the CVCC estimates the proportion of the travel in 

which flex or bi-fuel vehicles are using the alternative or gasoline fuel. 

Several vehicle attributes are weighted and evaluated in the logit function. The 

following vehicle and fuel attributes are considered: vehicle price, fuel cost or cost of 

driving per mile (fuel price divided by fuel efficiency), vehicle range, fuel availability, 

battery replacement cost, performance (measured by the horsepower-to-weight ratio), 

home refueling capability, maintenance costs, luggage space, and make and model 

diversity or availability. All of the coefficients associated with the various vehicle 

attributes are static, with the exception of a constant term, which varies by year. This 

constant term is initially used to calibrate market shares of new vehicle purchases but 

is not necessarily constant across the forecast period. This may be varied based on the 

modelers’ judgment about consumer behavior that is not captured by an econometric 

response to vehicle attributes and may be used to guide the projected mix of vehicle 

sales to meet targeted goals or the modelers’ expectations. 

Vehicle-Miles Traveled Component (VMTC) 

The VMTC uses NEMS estimates of fuel price and personal income, along with 

population projections, to generate a projection of the demand for personal travel, 

expressed in vehicle-miles traveled per driver. This is subsequently combined with 

projections of car fleet efficiency to estimate fuel consumption. The primary concern 

in projecting VMT per licensed driver in the mid- to long-term is to address those 

effects that alter historical growth trends. The factors affecting future VMT trends in 

the model are the fuel cost of driving, disposable personal income, the unemployment 

rate, and past VMT trends. VMT per licensed driver is estimated using a log-linear 

econometric equation. 
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Section 4 

EIA/Leidos TECHNICAL WORKSHOP 

4.1 Purpose 

The EIA conducted a technical workshop “Meeting of Experts” on July 17, 2013 in 

Washington, D.C. to assess methodological developments in the field of behavioral 

economics as applied to energy demand analysis and energy efficiency programs. The 

meeting was jointly planned and facilitated by EIA and Leidos (then SAIC) staff. This 

meeting was intended to support the EIA goals of updating its analytic assumptions 

and methods associated with the modeling of changing energy markets, potentially 

improving consumer behavior and policy representation in NEMS, and maintaining 

relevancy and consistency with developing best practices in energy economics. This 

section provides a synopsis of the key discussion points, comments, and suggestions 

for further examination of behavioral factors that were surfaced during the meeting. 

4.2 Meeting Summary 

The following discussion provides a summary of the meeting, including introductory 

remarks by EIA and Leidos staff, an outline of major discussion topics, and 

summarized example participant remarks. A more complete summary has also been 

made available on the EIA website. 

EIA staff provided a series of introductory remarks to provide context and get the 

conversation started, including goals of the meeting, background on behavioral 

economics, and EIA’s anticipated path forward. The stated goals of the meeting 

included the following: 

 Formulation and capture of insights on the application of behavioral economics 

to energy demand analysis 

 Starting point to a longer, broader analytical effort (i.e., as opposed to 

immediately surfacing the “silver bullet”) 

 Momentum towards either a working group of interested parties and/or further 

direct investigation by EIA 

The longer term goals outlined by the EIA included discovering whether it was the 

case that the neoclassical paradigm was significantly challenged by behavioral 

economics principles and research and if so, finding or developing a new aggregate 

demand paradigm that reflected these principles and research. 

The process to developing alternative modeling constructs outlined by the EIA 

revolved around experimentation within a “sandbox” environment (i.e., divorced from 

the actual inner workings of the NEMS model) in order to stress test and scrutinize the 

implications of any technical adjustments in a parallel path. It was made clear that 
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adjustments within NEMS itself would not be made without a careful, extended, and 

deliberate review and experimentation process, possibly including the testing of more 

detailed representations of demographics and regional variability. 

The discussion next turned to a description of the NEMS architecture. The following 

are key highlights of the discussion: 

 NEMS was described as a modular equilibrium model that reaches agreement 

iteratively. Accordingly, changes to a given model assumption may have 

significant system-wide ramifications. 

 The NEMS RDM and CDM contain weight parameters and behavior rules, 

respectively, which drive technological adoption of energy efficient or new 

end-uses. These rules are paired with hurdle rates to drive longer term 

projections. 

 One potential outcome of the process is to have either the weight parameters or 

the hurdle rates be in some way informed by behavioral rules or factors that 

incorporate societal trends/issues beyond the traditional cost-benefit/payback 

approach. 

 Hurdle rates are used to differentiate consumers, and are informed by surveys 

of commercial managers and estimates of consumer preferences for future 

energy savings. 

 The RDM bias parameters were previously based on shipment data by 

efficiency level and a goal seek exercise; more recently, data is harder to find, 

and reliance upon alternative sources has been necessary. There may be 

potential to inform the bias parameters with recent studies in the behavioral 

economics realm.

 Both the RDM and the CDM have a group of fuel price elasticity parameters 

that capture an allowance for the “rebound effect”, which postulates that as the 

efficiency of a given end-use increases, so does service demand (albeit the 

impact is usually small). In addition, there is a parameter in the model that 

pushes more efficient technologies based on price, but that parameter is not 

currently active in the EIA reference case. Activating or performing further 

review on behavioral elements of price response is one possible avenue for 

further study.

 There was discussion around whether EE programs impact the NEMS model 

and how. While no specific EE programs are explicitly captured, and there is 

no policy lever in the model, such issues are captured to the extent they color 

the underlying technologies being modeled. The comment was made that, as 

historical demand side management (DSM) has impacted appliance stock and 

consumption, the “momentum” of existing programs was implicitly captured. 
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What followed was a far ranging discussion of behavioral concepts and energy 

efficiency policy issues. The following are key highlights of the discussion: 

 The existing modeling framework is pragmatically convenient, in the sense 

that the modeling structure works and is based on data that can be collected 

within a reasonably constrained amount of time/resource dedication. But is 

pragmatic convenience coupled with a theoretical framework that is potentially 

unsound ultimately problematic? 

 It was suggested that the challenges with the current framework may best be 

handled at the macroeconomic level and then transferred to the micro level, i.e. 

the NEMS end use consumption modules. While EIA has such debates 

internally rather frequently, the general consensus is that coverage of the 

macroeconomic drivers is good and well-captured. When thinking about how 

to deal with variables that may be impacting the future in a new way, the issue 

is predominantly a “micro” one, as it pertains to either an individual or a firm’s 

behavior, and whether such agents actually maximize utility, have transitive 

preferences, etc., and if they don’t, to what extent such deviation is grounds for 

a completely new framework and/or modifications to the existing framework. 

 There was discussion as to the priority that should be placed on forecasting the 

future as opposed to understanding strategies that will cause agents to change 

their behavior. It was noted that strategies to encourage or engage agents to 

adopt new technologies may be less important/relevant if they are not expected 

to occur in the future (i.e., if we don’t expect certain strategies, then they 

become irrelevant from a forecasting/modeling perspective). Embedding 

behavioral economics into the equation may be more a function of additional 

constraints within the existing framework, as opposed to trying to encourage 

agents with particular strategies, which is an external policy issue rather than 

an issue affecting EIA’s tangible forecasting needs.

 No consumers have total information and seldom make rational economic 

decisions. As a result of a long tenure of EE program evaluation, it is fairly 

clear that various levels of information and financial stimulus can get people to 

invest in or procure particular end use measures such as technologies with 

“good certainty.” What is less clear is how the information flow and content 

impacts behavior – information flow can be thought of as the various types of 

information exchange mechanisms (e.g., types of media, word of mouth, etc.). 

 Energy demand and energy efficiency analysts do not have a good handle on 

behavioral change as it relates to EE/DSM programs, and that behavior is a 

function of three overarching issues: 
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o Psychological issues such as predisposition to efficient appliances, 

location on the adoption curve (early adopter, laggard), etc. 

o Market issues such as direct incentives, program information, 

education, etc. 

o Cultural issues such as day-to-day interaction with others, word of 

mouth, and other “extra” trends within a given cohort 

 It was noted that people are different, and behavioral economics has 

demonstrated this somewhat obvious notion. However, given the right amount 

of information, issues such as payback and Internal Rate of Return (IRR) are 

valid substitutes for elusive and subjective issues or alternative variables that 

may not currently be measured. It is possible that some of these 

behavioral/non-economic issues are “in the noise”.

 It was noted that the heterogeneity of the human condition is not in question, 

and this is not as much due to behavioral economics as it is common sense. 

However, when devising a model or an equation(s), you must, by definition, 

homogenize. Furthermore, there will always be some amount of omitted 

variable bias. Consequently, the combative characterization of neoclassical and 

behavioral economics in the literature must be replaced with a more 

complementary approach that attempts to infuse what data does exist on 

behavioral issues into the existing framework, which, as an abstraction of 

reality, for all practical intents and purposes, “works”. Additional caveats were 

made as follows: 

o Limited interval surveys, anecdotal evidence, and isolated studies have 

limited tractability over an annual modeling cycle, since you cannot 

easily aggregate or synthesize disparate data elements. Such work is 

nonetheless extremely valuable, and EIA hopes to provide feedback 

about which specific end uses, consumer segments, etc., would be 

helpful for future research. 

o EIA already performs a significant number of “what-if” cases that 

should not be ignored, despite the reference case tending to be the main 

focus of stakeholders. 

o There is a cost-benefit issue intrinsic to this pursuit itself, in that the 

EIA and other agencies have limited resources, and the incremental 

improvement to forecast accuracy resulting from such efforts must be 

weighed against the cost, much like the “rational agent” paradigm 

being stress tested; therefore, in the long run, behavioral factors worthy 

of further study should be catalogued and prioritized. 
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 With regard to how one might consider adjusting the current model, the 

following observations were made: 

o The RDM allocates shares of different equipment types based on a 

weighted formulation of capital cost and operating cost. This is not 

strictly neoclassical economics, since the model is not simply 

computing an NPV based on a discount rate. The weights are chosen in 

part based on behavioral issues. 

o With regard to the microeconomic issues and notions of aggregate 

demand, the elasticity parameters embedded in the model, if 

statistically estimated, are informed by the behavior that has occurred 

historically. 

o Furthermore, the hurdle rate structure is a potential avenue to 

incorporate behaviorally based risk premiums into the analysis, 

although what the mechanics of that would look like will take work. 

 It was noted that the current model imposes constraints on human/economic 

behavior, and the model works pretty well. Assuming that forecasting is 

important, the issue is whether any of the structural assumptions or constraints 

misses something important. One participant suggested that the model may 

work well only in describing the current situation but not in forecasting, as it 

can be calibrated to current data but miss the long-term. 

 One opinion was that the behavioral economics discipline is impactful in terms 

of policy design, as it can help shape how incentives and programs associated 

with purchase of durable goods are designed due to insights regarding 

subjective rates of time preference. However, at the aggregated level at which 

EIA is tasked with making long term projections, where more macro-level 

variables such as income tend to swamp heterogenic nuances, we end up with 

models that rationalize behavior well. EIA may utilize an essentially 

neoclassical model that has been roughly modified to handle behavioral 

concepts. 

 A participant suggested that, as NEMS is at essence a policy evaluation tool, it 

should address (or incorporate) behavioral issues. 

 If changes are long and protracted, then they will bear themselves out in the 

historical data over time, and the current model and forecasts may be adequate. 

However, if changes are rapid, then EIA may be ill-equipped to do much other 

than make analyst judgments and calibrations. 

 In distinguishing between (i) throwing the existing framework away and 

building something new or (ii) adding more detail and structure to the existing 
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framework, the working group was in general agreement that the latter 

approach appears more reasonable. Specifically, it was noted that behavioral 

economics can help most immediately by relaxing constraints or adding 

constraints (or both). 

 One participant suggested examining the technology diffusion models used in 

the DOE Solar Program for other factors that could be used in the NEMS 

building demand modules. 

The meeting transitioned into the concluding phase. The following summary of the 

overarching meeting themes was discussed: 

 While behavioral economics in the academic literature can help to modify and 

tweak the traditional neoclassical framework, there is no fully fleshed out 

alternative model in the behavioral economics literature that is cogent enough 

to supplant the existing EIA framework, which is a combination of rational 

agent model elements and certain behavioral elements; 

 While there may be some behavioral variables omitted from the structure, there 

appear to be avenues within the model for updating or creating additional 

parameter values; if key variables can be extracted from the literature and 

appended, that is a good thing; 

 If what is absent is nascent or slow moving in terms of its impact and things 

change gradually, then the underlying historical trends are also self-informed 

and self-updating, and the modeling structure implicitly captures these 

movements through the estimation/specification process; 

 There may be certain policies that are anticipated to have a significant short-tomedium term impact on demand for energy efficient products and that are 

worthy of further analysis and review; and 

 Further examination of adjustments must carefully balance the cost of 

gathering additional data with the perceived impact on the modeling structure 

from a forecasting perspective, as resources at EIA are not unlimited. 

A memo communicating a detailed summary of the meeting was sent to all meeting 

invitees to obtain any additional feedback. A dossier regarding a current list of 

research papers on behavioral economics in energy demand analysis was provided to 

the group of experts (including those who did not attend) for them to suggest 

additional sources of data and information. However, Leidos received no response 

from this communication. 
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4.3 Key Potential Behavioral Concepts 

While the meeting participants were generally supportive of the ideas that (i) the 

existing NEMS framework captured many behavioral issues fairly well and (ii) the 

state of behavioral economic research is unlikely to provide for a completely 

alternative model to the existing EIA framework, several key concepts and ideas 

around “levers” within the existing NEMS architecture to better capture behavioral 

issues came out of the meeting discussion and are outlined below. 

 Weight parameters or hurdle rates could incorporate behavioral rules or factors 

that capture societal trends and other issues beyond the traditional costbenefit/payback approach. 

 The bias parameters in the RDM, which it seemed were not currently active in 

NEMS, might be infused with assumptions from recent studies in the 

behavioral economics realm. 

 Infusing behavior into NEMS may take the form of embedding trends in some 

simple way to capture cultural issues and trends regarding particular cohorts or 

market segments (e.g., demographics, level of sophistication/predisposition to 

new technology). 

 In addition to the hurdle rate structure and bias parameters that are 

incorporated into NEMS (although the latter may be inactive), there is also a 

time element to consumers’ decision-making, that may need to be incorporated 

and may in fact mislead analysts regarding the strength of a relationship (i.e., 

impact may be stronger but lag the event significantly). 

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Section 5 

LEIDOS TARGETED LITERATURE SEARCH 

The landscape of literature on behavioral economics is vast and at times disparate in 

nature. Given the constraints of review time and the vast volume of content, Leidos’ 

literature review was focused on a three-phase approach that was designed to provide 

a targeted review of critical papers that could serve as a catalyst for alternative 

approaches to modeling aggregate demand. 

The first phase involved collecting as many raw pieces of literature as possible. The 

data sources for the literature consisted of (i) pieces generated in advance of the first 

technical workshop by the Leidos team, (ii) pieces surfaced as part of the review of the 

attendees list for the workshop in terms of literature they may have been involved 

with, (iii) pieces circulated and/or distributed by the EIA and relayed to Leidos during 

and after the initial workshop, and (iv) a second pass by Leidos staff at uncovering 

existing research. 

The second phase involved a high-level review of abstracts, conclusions, and key 

features of each paper in the queue. This was done in tandem with development of the 

bibliography that constitutes Section 7 of this report. Papers that were deemed to have 

the most potential to serve as catalysts for further development of potential alternative 

demand approaches were marked for more in-depth review in the third phase. The 

papers reviewed herein belong to the sub-group of filtered pieces reviewed in the third 

phase of the effort. 

Papers encountered in the literature can broadly be categorized as one of the following 

three paper types: 

 Papers that focus on theoretical constructs associated with optimization and 

objective functions and alternative approaches to such formulae that deviate 

from the traditional paradigm, but that generally are purely theoretical and not 

applied with either empirical data or experimental design; 

 Papers that involve some sort of empirical exercise to demonstrate certain key 

theses of the given paper, and 

 Papers that are focused on examination of how behavioral economics can be 

applied in a marketing or policy context in terms of making certain energy 

efficiency programs or conservation measures more effective. 

The overview presented in this section attempts to organize these papers in a sequence 

that relays a variety of possibilities based on the universe of different approaches and 

perspectives taken. The section that follows provides a summary and overview of key 

behavioral factors encountered in the literature, the data sources (if any) encountered, 

and the possible nexus of certain key concepts with the existing NEMS architecture 

(including notes on where certain elements may already be in NEMS that are of note 

in the papers reviewed). 
Section 5 

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5.1 Overview of Selected Literature 

Many papers have been developed that catalogue the general theories of behavior. 

Martiskainen (2007) provides a concise treatise on these various theories of behavior 

as based on extensive review of prior psychological models. The table below provides 

a high-level overview of these schools of thought, which can be thought of as 

theoretical precursors to examining which group of variables can be causally linked to 

changes in energy demand, and the extent to which those variables are tangible and 

can be potentially added to an aggregate demand modeling paradigm. The third 

column in the table attempts to provide insights on whether the core theory itself lends 

any suggestions on variables that can be collected to measure the specific behavioral 

phenomenon in question. 

Table 5-1: Overview of Behavioral Theories 

Behavioral 

Theory/Model 

Key Concept(s) Tangible Variables for 

Aggregate Demand Modeling 

Rational Choice 

Theory 

End-users weigh the 

expected costs and benefits 

of different actions and 

choose accordingly. 

Traditional neoclassical utility 

maximizing paradigm; direct costs, 

discount rates, payback periods, 

etc. 

Theory of 

Reasoned Action 

People expect certain values 

from the outcomes of their 

behavior. 

Limited; theory is focused on 

beliefs about evaluations of 

outcomes, people’s subjective 

norms, and the attitudes and 

intentions that result from such 

beliefs. 

Theory of 

Planned Behavior 

Reasoned Action + concept 

of perceived behavioral 

control, which is defined as 

the individual’s belief 

regarding the difficulty or 

discomfort of a given 

behavior 

Limited; theory has not been 

applied to the measurement of 

actual behavior, but rather to the 

theoretical linkage between 

attitude, intention, and perceived 

control over one’s behavior. 

Ecological Value 

Theory 

Egoism and self-interest are 

values that tend to be less 

correlated to proenvironmental behavior as 

compared to pro-social 

values. 

Direct components of the theory 

may have limited value in terms of 

aggregate demand modeling; 

however, if households who are in 

higher socio-economic groups tend 

toward pro-environmentalism, and 

in turn have the highest level of 

domestic energy consumption, then 

there may be a way to characterize 

groups of socio-economic 

attainment as having some 
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Behavioral 

Theory/Model 

Key Concept(s) Tangible Variables for 

Aggregate Demand Modeling 

influence on future demand. 

Value Belief 

Norm Theory 

There are predictors of prosocial behaviors that can be 

compartmentalized into 

several categories of causal 

variables: attitudinal factors, 

contextual forces, personal 

capabilities, and 

habit/routine 

Highest potential appears to reside 

within the contextual forces 

(monetary costs and benefits, 

support policies), and certain 

personal capabilities that can more 

easily be measured. Examples: 

energy price, owner/renter 

occupancy flags, literacy, income, 

and social status; NEMS model 

already captures certain elements 

of such factors 

Symbolic 

Interactionism 

and Symbolic 

Self-Completion 

Theories 

People purchase certain 

goods to construct their 

identity and to portray a 

certain image 

N/A; highly nuanced personal 

influences related to identity 

cannot easily be measured without 

prohibitively costly survey work 

Attitude Behavior 

Context Model 

Behavior is a function of 

attitudinal variables and 

contextual factors. 

Cross-referenced with Value Belief 

Norm Theory above, does not 

provide additional potential 

variables, save for the notion that if 

the general contextual framework 

around energy conservation and 

energy efficiency could somehow 

be characterized as a variable, and 

then coupled with survey data on 

attitudes, that could help determine 

what proportion of people would 

engage in a certain behavior 

through understanding the ease of 

the action coupled with the 

attitudes involved. 

Theory of 

Interpersonal 

Behavior 

Intentions and habits must 

be considered as drivers of 

behavior in addition to other 

contextual factors. 

Model is a complex interaction of 

rational, social, normative, and 

emotional factors. Less utilized in 

empirical work due to its 

complexity; certain habits can be 

very difficult to measure. 

Persuasion theory Behavior is a function of the 

credibility, persuasiveness, 

and message content 

associated with a given 

persuasive effort. Behavior 

can be impacted by 

Persuasion and advertising impacts 

are generally captured in certain 

diffusion constructs (e.g. Bass 

Diffusion) or alternatives; 

however, the intersection of such 

concepts with aggregate demand 
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Behavioral 

Theory/Model 

Key Concept(s) Tangible Variables for 

Aggregate Demand Modeling 

messaging that is 

sufficiently persuasive. 

modeling is less well defined. 

Social Learning 

Theory 

There are learning effects 

associated with personal 

experience and observing 

the behavior of others 

within a close social circle 

Limited; learning impacts are 

difficult to measure without 

prohibitively costly and time 

consuming survey work 

Arising from the tabularization above and from the investigations alluded to in 

Martiskainen are several factors that may be worthy of further consideration as it 

relates to aggregate demand modeling. These factors are (i) measures of feedback and 

the associated dollars expended on providing detailed feedback to end-users, (ii) 

measures of the amounts expended on rewards, incentives, or competitions associated 

with inciting behaviorally induced change, and (iii) certain causal variables, derived 

from the Value Behavior Norm theory and related theories, that have potential to be 

measured or which are already measured and included in the NEMS framework, such 

as energy price, owner/renter occupancy flags, literacy, income, and social status. 

A compelling list of behavioral economics principles that can inform energy policy 

was prepared by Houde and Todd in 2011. This list captures such principles and 

places them into the following categories: 

 Framing principles, which focus on how information is transferred, or framed, 

relative to how consumers perceive reality. Examples include loss aversion, 

default/status quo effects, and sunk costs. 

 Choice architecture and heuristics, which relates to how choices are organized. 

Examples include choice overload and the compromise effect. The authors 

suggest that certain choice architectures can be designed to “nudge” consumers 

into a particular decisional direction. 

 Pro-social behavior, which refers to how agents respond to societal cues 

regarding a particular decision or perspective; examples include acting for 

status/self-image improvement or visibility, inequality/punishment factors, and 

social norms (or achievement and well-being relative to others). 

 Time Inconsistency/Commitment Mechanisms, which relates to individuals 

tending to procrastinate and put off costly changes. Examples include a distinct 

preference towards avoiding near term costs even in the face of longer-term 

savings (which may go beyond any reasonable discount rate) and habit 

formation. 

 Incentive structures, which focus on the nature of incentives, both monetary 

and non-monetary, and the impact such structures have on choices. Examples 

include in-kind gifts, tournaments/competitions, and lotteries (which provide a 

small chance for a very large reward). 
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Stern (2000) defines environmentally significant behavior as consisting of (i) 

environmental activism, (ii), non-activist public sphere behaviors, (iii) private sphere 

environmentalism, and (iv) other behaviors. While Stern does not posit a general 

causal theory regarding which variables can be used to determine the extent and nature 

of environmentally significant behavior, there is evidence to suggest that personal 

capabilities, such as literacy, social status, financial resources, and behavior-specific 

knowledge and skills can be a determinant for the amount of private sphere 

environmentalism that can be expected at the agent level. Private sector 

environmentalism, which includes consumer purchase behaviors, maintenance of 

household equipment, and changes in equipment use, including curtailment, can have 

the potential to have the most significant impact on long-term energy usage, as 

contrasted to more extrinsic elements of environmentally significant behavior, such as 

petitioning or joining activist groups. This suggests that directly measureable financial 

factors may well drive a good portion of the environmentally significant behaviors, but 

perhaps in a manner counter to historical expectations (i.e., certain income ranges or 

levels of educational attainment may well be negatively correlated with consumption, 

all else equal, due to the impact these factors may have on values). Interestingly, Stern 

finds that environmental activism was significantly associated (negatively) with age 

and income. 

A team of researchers from Portland State University, the California Energy 

Commission, and Pacific Gas and Electric (Lutzenhiser, 2010) reviewed the state of 

residential energy consumption models to identify behavioral issues and other factors 

that lead to inaccurate modeling outcomes and conservative policy approaches with 

respect to energy efficiency. The discussion captures viewpoints and issues from the 

technology, economics, psychology, and social studies perspectives to collect 

examples of departures in assumptions or flaws in the typical energy modeling 

paradigm. The paper closes with thoughts on rationalizing the typical modeling 

approaches, particularly whether aggregate models miss underlying variability in 

behavior, resulting in suboptimal energy policy. 

Diamond and others from the Lawrence Berkeley Nuclear Laboratory and the 

University of California Energy Institute argue, in a recent paper, for changing the 

energy efficiency focus to look at absolute levels of energy consumption (i.e., an 

extensive variable) rather than the relative energy efficiency for a given level of 

energy services (i.e., an intensive variable). Their arguments include that doing so 

simplifies and clarifies the policy prescriptions and more effectively serves the goal of 

sustainability. The paper includes discussions regarding the “rebound effect” (i.e., an 

increase in energy services demand driven from an effective drop in the cost of energy 

services due to improved energy efficiency), labeling related to energy efficiency that 

is inconsistent with the ultimate goals of EE policy, particularly given changing 

consumer and business behavior, and concluding remarks on policy prescriptions. 

RLW Analytics, assisted by Skumatz Economic Research Associates, Inc., prepared a 

lengthy report for the California Public Utilities Commission on the efficacy of the 

2004 and 2005 California Statewide Energy Star® New Homes Program (ESH 

Program). The ESH Program provides financial incentives, education, and marketing 

assistance to California builders who construct new residences that exceed the state’s 
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mandatory minimum EE standards. The evaluation relied primarily upon engineeringbased estimates, calibrated to modeled usage data for affected end uses, of the impact 

of EE measures, adjusted by a net-to-gross (NTG) factor. The NTG factor was based 

on surveys to determine free ridership, behavioral changes to the EE measures (e.g., 

rebound effect), and market effects (e.g., spillover due to awareness of program-driven 

EE measures by non-participants), although only the free ridership issue was reflected 

in the ultimate NTG used in the savings estimates. The report conclusions include a 

summary of participating homes and energy savings estimates, compared to savings 

anticipated by the utilities administering the program. 

Gillingham and others (2009) present a framework for conceptualizing energy 

efficiency decisions as a tradeoff between energy and capital for the provision of 

energy services. They utilize this framework for clarifying a range of market and 

behavior failures as divergences from economically efficient outcomes. The paper 

provides a discussion of, and classification regime for these failures, and offers 

example energy policy measures to alleviate the resulting gap in energy efficiency 

investment relative to economically optimal levels. 

In a 2009 paper, Gowdy calls rational choice theory untenable and offers that 

behavioral failures, rather than reflecting anomalies or unusual departures from the 

rational actor model, reflect the complexity of human decision-making. The paper 

anticipates that behavioral economics will result in a unified model of decisionmaking to inform, for example, sustainability policy. Examples of behavioral issues 

driving sustainability mores are analyzed with policy implications in mind. 

The International Energy Agency published a report in 2007 providing a detailed 

discussion regarding a widespread market barrier to optimal energy efficiency 

investment—the principle-agent problem. The book provides a detailed explanation 

of the principle-agent problem and presents analyses of the magnitude of energy 

demand for a range of affected appliances via case studies in several different 

countries. 

Researchers from the University of California Center for Energy and Environmental 

Economics (2012) analyzed the extent to which dwelling and demographic 

characteristics determined residential energy consumption in the Netherlands. The 

analysis reflected a cross sectional econometric framework involving observations 

from 300,000 dwellings over January 2008 to December 2009. Dwelling 

characteristics reflected in the analysis included dwelling type (single family, duplex, 

etc.), age, number of rooms, thermal and quality characteristics, and location. 

Demographic characteristics included family size, age, marital status, ethnicity, and 

income. Data regarding appliances in the dwellings were not researched. Their 

analysis concluded that natural gas consumption tended to be more a function of 

dwelling characteristics than demographic variables, while electricity consumption 

tended to be more strongly a function of income and family composition. 

Sanstad and Howarth (1994) argue that even “substantive rationality” is an inaccurate 

description of consumer decision-making and present a case for policy intervention to 

reduce the impacts of market and behavioral failures on energy efficiency. However, 

they also recognize that the theoretical framework for incorporating behavior into a 
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unified energy demand modeling approach is not sufficiently developed (or was not at 

the time). The authors suggest the phasing out of heuristic and constant energy 

services assumptions in favor of more realistic behavioral assumptions and the need to 

cut across the disciplines of economics, psychology, sociology, and other fields to 

addressing complex policy questions. 

Allcott (2009) notes that consumers may be “inattentive”, and myopic with respect to 

energy price expectations for the future, which can in large part explain the gap 

between the predictions of the rational agent framework, and could provide an 

economic justification for a standards-based approach that moves consumers into 

more efficient goods. The presentation recommends further laboratory 

experimentation to target customer attentiveness. 

Dahl (1993) provides an extensive amount of documentation regarding econometric 

modeling frameworks that can be used to project energy demand and elasticity. These 

models are generally based on relating energy to measures of income, own-price and 

cross price elasticity, weather variables, lag structures of the dependent variable, 

appliance stock, and various other mathematical variations of traditional utility 

forecasting models. Of note in this piece is the relatively limited emphasis on the key 

behavioral variables noted in Martiskainen, which is in part due to the tractability of 

the behavioral concepts from a variable construction and quantitative perspective. 

Gabaix (2011) presents a sparsity-based model of bounded rationality. Within this 

framework, the agent in question is represented much like an economic modeler, in the 

sense that a given decision is made with a few key variables that have non-zero 

decisional weights, thereby making the model “sparse”, and consequently, the loss 

associated with making a more complex decision with far more variables is minimized 

by the decision maker using a parsimonious framework. As examples, Gabaix cites the 

fact that decision makers may be anchored on certain default values, such as discount 

rates, which only in certain instances adjust towards the truth. Furthermore, Gabaix 

cites evidence that sales matter more for lower priced goods than higher priced goods, 

and that regardless of the consequences, people generally spend around one hour on 

retirement planning. This suggests that the minimization problem is predicated upon 

minimizing the expected loss from an imperfect model. Gabaix cites further work that 

will explore bounded rationality in a dynamic programming context. However, the 

current framework is entirely theoretical and does not present any empirical work or 

experimental economics design. 

Iacoviello and Pavan (2008) present three different models of housing transactional 

costs. In the first model, transactional costs are set to zero, rendering housing fully 

liquid. In the second model, transactional costs are free if the net housing investment 

does not exceed 3% of the initial period value. Finally, in a “lumpy” housing model, 

every housing transaction incurs a 3% transaction cost proportional to the value of the 

initial stock. The paper shows that during “lumpy” housing market conditions, 

personal savings rates are higher and interest rates are lower. Furthermore, they 

conclude that patience is a key driver for why a certain portion of the population never 

accumulates any wealth beyond their initial housing down payment. The paper’s three 

separate modeling constructs all demonstrate a pro-cyclical relationship between 

housing investment and the economy, the fact that housing investment is a leading 
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indicator of GDP, and that housing investment is more volatile than business 

investment. From the perspective of aggregate demand modeling, a key variable of 

note is the personal savings rate. As uncertainty regarding the housing market and 

future wealth shocks increases, the personal savings rate should, in theory, increase, as 

end users hedge against said future wealth shocks. In the commercial utility 

forecasting space, the use of the personal savings rate can be an effective explanatory 

variable in terms of explaining more recent softness in residential loads7

. 

Moss (2008) reviews the main approaches to market segmentation, and notes that 

behavioral economics principles can be used to map messaging and collateral to 

carefully segmented groups within a given utility. He goes on to note that such 

segments are not necessarily commensurate with the classical utility rate 

classifications or retail classes (e.g. residential, commercial, or industrial). 

Segmentation may indirectly lend itself to certain quantitative approaches that attempt 

to segment demand. However, given that reports of demand are typically provided 

commensurate with retail classes, certain sub-segment nuances related to consumption 

patterns may not be tractable for modeling purposes, and would best be served as a 

basis for marketing effective energy efficiency programs. 

 

7

 Based on energy demand forecasting work prosecuted by Leidos for a variety of electric utilities 

across the United States. 
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5.2 Concepts and Methodologies in Behavioral Economics Literature The table below summarizes the targeted literature review, capturing the authorship details, nature of the pub

primary BE theme(s) and factors that are discussed therein, whether the paper reflected a modeling effort utilizinwhether the study had implications that were useful for aggregate energy demand modeling (and whether such 

meaning that the concept(s) were immediately portable to a model like NEMS rather than being associated simp

issues or requiring some additional modeling infrastructure). 

Table 5-2: Summary of Targeted Literature Review 

Title Author(s) Year Nature of Paper

Behavioral Economics Theme

and Factors

Sticky Points in Modeling

Household Energy

Consumption

Portland StateUniversity, CEC,

and PG&E

2010

Discussion regarding the state ofenergy demand analysis and

modeling with a focus on

representation of behavior from

various discipline perspectives

Lack of behavior representatio

in energy demand models;

considers rebound, high implie

discount factors, and social

norms; perspective is primarilyEE program design

Don’t Supersize Me!

Toward a Policy of

Consumption‐Based

Energy Efficiency

LBNL and Univ. of

CA Energy

Institute

2006

Discussion regarding transitioning EE

goals from intensive (e.g., energy useper $ of GDP) to “extensive” (simply

energy use or carbon output) and

drivers of increasing energy

consumption

Failures in current EE policy;

Discusses rebound, various

market failures, and alternativemeasures of energy efficiency;perspective is EE policy
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Title Author(s) Year Nature of Paper

Behavioral Economics Theme

and Factors

Evaluation, Measurement,

and Verification of the2004 & 2004 CaliforniaStatewide ENERGY STAR

New Homes Program

RLW Analytics and

SERA 2007

Discussion regarding factors affecting

EE program measurement and

evaluation; captures multiplemodeling and estimation algorithms

Measuring savings from EE

programs; Considers and defin

free ridership, rebound and

spillover in EE program savings

(i.e., net‐to‐gross); perspectiveis EE program design,

quantification, and economics

Energy Efficiency

Economics and Policy

Gillingham,Newell, andPalmer (NBER)

2009

Discussion regarding market and

behavior failures that lead to an EE

“gap”, providing a rationale for EE

policy/programs; addressesconsiderable breadth of EE taxonomy

Market and behavior failures

provide a rationale for EE

programs to achieve

economically efficient EE;

Rebound, high implied discoun

rates, bounded rationality are

discussed; perspective is EE

policy

Behavioral Economics and

Climate Change Policy

Gowdy, John

(Rensselaer Poly

Institute)

2008

Discussion regarding rational choice

theory shortcomings and a suggested

unified model

Factors arising from consumer

irrational behavior should

directly inform policy and

associated modeling paramete

(e.g., discount rates)
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Title Author(s) Year Nature of Paper

Behavioral Economics Theme

and Factors

Mind the Gap IEA 2007

Report on estimation of impact of

market barriers to socially optimal

energy efficiency, particularly the

principle‐agent (PA) problem;

Extension of agency theory to energy

efficiency issues; Estimates of

amount of energy use subject to PA

issues

Market barriers that can be

circumvented are a significant

contributor to the EE gap; PA

issues, lack of information,

externalities, and poor capitalaccess are discussed in detail

across numerous case studies

Behavioral Economics and

Energy Policy Hunt Allcott 2009

Presentation on the perception of

consumers as it relates to “mis‐

optimization” around gasoline prices

and fuel efficient vehicles; suggests

that laboratory experiments may be a

good way to decipher whether

consumers truly undervalue future

energy costs

Consumers may be inattentive

to changes in gasoline price

expectations; myopia and

inattention could explain the

“Energy Efficiency Gap”; suggefield experiments as a key

potential approach to

falsification of rational agent

theories

Toward a Coherent Theory

of EnvironmentallySignificant Behavior

Paul Stern 2000

Review of existing theories on causal

factors and key driving variables that

determine the nature and extent of

environmentally significant behavior

in an agent

Literacy, social status, and

financial resources are posited

to drive private‐sphere

environmentalism, whichincludes consumer purchase

behaviors and maintenance ofequipment, which directlyimpacts energy usage
Section 5 

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Title Author(s) Year Nature of Paper

Behavioral Economics Theme

and Factors

List of Behavioral

Economics Principles that

can inform Energy Policy

Sebastien Houde

& Annika Todd 2011

Compartmentalizes a series of key

factors that can inform energy policy

into five key areas, namely framing,

choice architecture, pro‐social

behavior, time inconsistency, and

incentives

Nature of EE incentives

(program expenditures tracked

as inputs to NEMs) may be a

potential variable for

experimentation; discount rate

are also mentioned, which are

already a component of theNEMS framework

Affecting ConsumerBehavior on Energy

Demand

Mari Martiskainen 2007

An in‐depth review of the current

state of behavioral theories, and a

discussion of the best methods to link

behavioral change to a particular set

of tactics. Based on the UK Energy

Efficiency Commitment.

Feedback on energy use, invarious forms, as well as the

nature of financial rewards and

incentives, community based

campaigns and contests, and

other direct behavioral

interventions at the utility leve

for which data could be

collected, may be useful in

aggregate demand modeling.

A Survey of Energy

Demand Elasticities inSupport of theDevelopment of NEMS

Carol Dahl 1993

Summarizes and specifies a vastnumber of modeling constructs

aimed at estimating elasticity for

various consumables and reports

such elasticity values

Detailed description of existing

econometric specificationsassociated with determination

of elasticity; treatment of

commonly noted behavioral

factors is limited
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Title Author(s) Year Nature of Paper

Behavioral Economics Theme

and Factors

Residential Energy Useand Conservation:Economics and

Demographics

Brounen & Kok(Netherlands),Quigley (UCBerkeley)

2011

Report on a study of the impact of

physical housing and demographic

characteristics on energy

consumption in the Netherlands

Demographic characteristicsmatter more for electricity

consumption than dwellingcharacteristics, while the

reverse appears to be true for

gas consumption

Consumer Rationality and

Energy Efficiency

Sanstad (LBNL)

and Howarth (UC

Santa Cruz)

1994

Paper examines methodological

disparities between rational choice

theory and alternative approaches,

noting some shortcomings of BE

research

Substantial departures from

rationality that are evident in

consumer behavior result in

energy market inefficiency and

justify EE programs; bounded

rationality is the primary

identified factor

A Sparsity‐Based Model of

Bounded Rationality Xavier Gabaix 2011

An end‐user simplifies the world by

assigning zero weights to a host of

decision variables, such that the

minimization of loss from a more

simplified model for decision making

is the focus of most decisions.

Rationality is thereby “bounded” by

the parsimonious nature of how anagent removes variables from reality

People pay attention to large

deviations from the average an

a limited set of intertemporal

variables, such as their short‐

term income; Paper is highly

theoretical and generally not

empirically‐based
Section 5 

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Title Author(s) Year Nature of Paper

Behavioral Economics Theme

and Factors

An Equilibrium Model of

Lumpy Housing

Investment

Iacoviello andPavan 2008

“Lumpy” housing does matter in

terms of aggregate consumption and

investment. The authors use three

modeling frameworks to

demonstrate that lumpiness in

housing adjustment increases the

volatility of aggregate consumption,

and that savings rates are higher

during periods of “lumpy” housing.

The personal savings rate is

higher during periods of“lumpy” housing. The persona

savings rate has promise as a

basis for explaining recent

softness in residential sector

energy demand.

Market Segmentation and

Energy Efficiency Program

Design

Steven J. Moss 2008

Market segmentation as applied to a

whole host of other consumer goods

can be a powerful tool for utilities

that are looking to market Energy

Efficiency programs more effectively.

Market segments must be

differentiated from classical utility

notions of retail classes (e.g.

residential, commercial, industrial,etc.).

Behavioral factors can be used

to better design messaging andmarketing collateral based on

attitudes, tastes, and

preferences. Detailedinformation on how utilities ar

segmenting customers would b

divergent and disparate and

may not translate well for

aggregate demand modeling.
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5.3 Commonly-encountered Behavioral Issues 

In light of the above targeted literature review and Leidos’ internal discussions 

regarding the themes and factors encountered within the literature that may serve as 

catalysts for further experimentation, the following variables are noted as being frontof-mind throughout the pieces reviewed (note: there are clearly a far more vast 

universe of behavioral factors and nuances that were mentioned in the literature than 

what is contained in the targeted list below): 

 Bounded rationality – Consumers do not always make optimal economic 

decisions reflective of rational behavior. Previously identified behaviors, such 

as simplified decision rules (e.g., default preference, compromise effect), 

salience effect (i.e., over-weighting of readily observable factors), status quo 

preference (e.g., proclivity to existing technologies) and sunk cost effect (i.e., 

preference for currently installed equipment), tend to limit the impacts of shifts 

in the relative economics of competing fuels. 

 Procrastination – Consumers tend to take an inordinate amount of time 

between making decisions and acting on them. Accordingly, the lag between 

economic justification of action and the resulting action can be very long, 

dampening the effect of changes in relative economics of competing fuels or 

technologies. 

 Principal-agent situations – Some proportion of residential and commercial 

structures are subject to rental arrangements for which the tenant has no 

control over major end uses but is responsible for the operating costs. Studies 

estimate the amount of energy use by appliance type subject to this situation at 

25 percent to nearly 70 percent. This leads to suboptimal economic outcomes 

in the form of purchases of inefficient appliances whose cheaper capital costs 

are more than offset by more expensive operating costs. The differential in 

ability to make improvements to the home or business that would help reduce 

usage is one of the main points surrounding the potential for occupancy status 

to have an impact on pro-social behavior and concordantly consumption. 

 Demographic issues – Households with young or old householders tend to 

consume less energy, all else equal. A related issue is that, while household 

size is a significant determinant of energy consumption, particularly electrical 

energy, young children tend to be correlated with energy consumption. 

 Personal savings behavior – While income is a commonly-accepted 

determinant of energy consumption (perhaps to a point), changes in the 

propensity to spend v. save have some impact on energy consumption. Recent 

empirical work at the individual utility level suggests that the personal savings 

rate can be a functional explanatory variable; furthermore certain literature on 

the “lumpiness” of the housing market (e.g. high transactional costs associated 

with moving) appears to suggest that the personal savings rate is much higher 

during periods of more uncertainty in the housing market. End-user concerns 
Section 5 

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regarding longer term income prospects and other conservative behaviors that 

may not be strictly “rational” relative to the costs and benefits of a given 

choice bundle of goods may be indirectly measured by how and if saving is 

occurring across end-users. This appears likely to be a factor in the post-2009 

recession weakness of residential energy demand. 

 Adoption of social norms – Consumers tend to be influenced by the actions, 

or even perceived actions, of others. This includes a range of energy efficiency 

behaviors and energy-related capital spending decisions, like the purchase of 

energy efficient appliances. This may be a factor in the long lag between 

changes in energy prices and influences on energy consumption behavior. 

 Environmentalism behavior – While income tends to be positively related to 

energy consumption, some studies have shown that higher incomes are also 

related to improved energy efficiency, perhaps due to higher education 

attainment (literacy) or greater leisure. 

 Segmentation of Income Levels – The literature suggests theories predicated 

upon the positive relationship between increasing levels of income and a 

greater desire to engage in environmentally significant and/or pro-social 

behavior. This suggests that the traditional elastic relationship between income 

levels and ever-increasing amounts of consumption may reach an inflection 

point, after which pro-social behavior creates a more inelastic income-to-usage 

relationship. Similar to the bullet above, segmenting end-users based on 

income level may be a tractable approach to generating more realistic trends in 

usage or even in terms of determining alternative discount rates.

 Monies Expended on Feedback on Energy Usage, Competition, and 

Advertising of Behaviorally Based Programs – The literature suggests that 

one of the main uses of behavioral economics is as a tool to prepare marketing 

collateral to encourage pro-social behavior or uptake of certain energy 

efficiency programs. Specifically, feedback on one’s usage, the introduction of 

competitions and associated campaigns, and advertising of other programs that 

are rooted in behavioral economics theory can have a significant impact on 

impacting end-user choices. Consequently, some financial measure of such 

expenditures may help explain deviations in aggregate demand and the speed 

at which certain demand-altering constructs diffuse into the general populace. 

 Large Deviations from Average or “Steady-State” commodity prices – The 

sparsity-based model suggests that consumers assign zero values to a large 

number of potential choice vector parameters, thereby paying attention to a 

smaller, more manageable and parsimonious set of factors when making 

decisions. Large deviations from average level energy prices, both own-price 

and cross-price, could potentially spark a certain amount of attention in the 

non-zero variable set for a consumer base that has been shown in the literature 

to be at least partially “inattentive” to energy prices given their relative 

standing as compared to other key expenses. Whereas decreases in energy 
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prices may not be afforded specific attention, large deviations on the high-side 

may be given undue attention relative to their magnitude. 

Analytic Report 4-2_20140822_FINAL.docx

Section 6 

INITIAL FINDINGS 

6.1 Commonly Encountered Behavioral Variables in the 

Literature (priority list) 

Leveraging the variable list summarized in Section 5, Leidos has created a threepronged rubric for assigning preliminary priority to each behavioral issue. The prongs 

of this approach are as follows: 

1. Tractability. Tractability is herein defined as a preliminary view regarding the 

availability of actual data that is subject to an allowable amount of 

measurement error and that may be directly applicable to sandbox 

experimentation without prohibitively expensive or time-consuming primary 

research. While the literature has certainly pointed out a slew of interesting 

theoretical principles (e.g. buying something to craft one’s identity), not all of 

these factors are tractable for aggregate demand modeling. Additionally, to the 

extent that the NEMS model already contains some structure that captures a 

behavioral concept that could be tweaked or adjusted, then said concept has 

relatively high tractability, as there is no need to create any new infrastructure 

around that notion. 

2. Impact. Impact is herein defined as a preliminary view on the likelihood that a 

given behavioral factor will have a significant impact on either the existing 

modeling framework on within the confines of an alternative modeling 

framework. This view on impact is generally based on the literature review, 

Leidos’ existing and prior forecasting work in the utility markets, and our 

preliminary brainstorming regarding what is likely to work and not work as the 

experimental phase unfolds. 

3. Consensus. Consensus is herein defined as the level to which the existing 

literature that has been reviewed is of common opinion regarding the 

directionality or significance of a given behavioral factor. To the extent that 

such consensus does not exist, additional difficulties related to crossreferencing any sandbox findings with theoretical expectations may be present. 

The table below attempts to assign a preliminary ranking for each behavioral factor 

based on the three prongs above, as a precursor to additional research and 

experimentation. 
Section 6 

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Table 6-1: Preliminary Rankings of Behavioral Factors 

Factor/Concept Tractability Impact Consensus 

Bounded 

Rationality 

High (existing 

infrastructure in 

NEMS) 

Medium (already 

captured to some 

degree) 

High 

Procrastination 

High (lag structures 

on existing 

concepts) 

Medium High 

Principle-Agent 

Situations 

Medium 

(renter/owner 

tracked) 

Medium High 

Demographic 

Issues High Medium/Low Med 

Personal Savings 

Behavior 

Medium (forecast 

availability?) Med 

Low (can have 

countervailing 

effects) 

Social Norms Low Low Medium 

Environmentalism 

Behavior Low Low Medium 

Segmentation of 

Income 

Medium (segment 

definitions are a 

challenge; 

forecasts?) 

Medium/Low Medium 

Monies expended 

on EE Programs 

Medium/Low 

(forecast?) Medium/Low Medium/Low 

Large price 

deviations High Medium Medium 

6.2 Alternative Aggregate Demand Specifications – “Math 

Workspace” 

The extraction of potentially viable aggregate demand specifications within the 

existing literature has not revealed a “silver bullet” framework that holistically defines 

an alternative specification for aggregate demand that is fully reflective of behavioral 

variables. Compounding this challenge further is the reality that while many 

behavioral variables have been postulated to have a significant impact on consumption 

and end-user decision making, a smaller subset of such variables is readily 

measurable. Examining certain behavioral nuances is possible with detailed survey 

work and better interaction with existing modules that provide certain data to NEMS 
INITIAL FINDINGS 

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(e.g. macroeconomic module)8, but, in general, the most promising alternatives are 

predicated upon re-simulating (with different assumptions) or altering certain sub 

components of the NEMS modules. 

The above challenges notwithstanding, Leidos’ review of the literature uncovered the 

following top-level specifications that warrant further study. These mathematical 

concepts are the result of the literature review that Leidos conducted and a subsequent 

open brainstorming session by Leidos staff. These concepts are intended to spark 

further critical thinking and research regarding exactly how to translate these ideas 

into a mathematical structure suitable for experimentation within a portion of the 

NEMS architecture or offline side analyses. It is anticipated that this “Math 

Workspace” will evolve into a separate working file to house alternative specifications 

for demand that originate from EIA staff or other sources and that Leidos will 

participate in ongoing discussions regarding whether and how to experiment with 

certain ideas (if at all) in an effort to codify how the process will move forward and 

determine Leidos support activities in the next project phase. 

1. Sparsity-based model of Bounded Rationality – individuals minimize the 

economic loss due to an imperfect but parsimonious choice domain filled with 

a subset of all possible choice variables, much like an economic modeler. The 

decision model is “sparse”, in the sense that it contains a limited number of 

non-zero parameters. 

a. min ଵ

ଶ ∑ ሺ݉௜ െ ݑ௜ሻଶ ൅ ݇ ∑ |݉௜|

ఈ ௜ ௜

In terms of interpretation of this framework for aggregate demand, one way to 

think about this theory would be to resolve that aggregate demand is a function 

of a sparse set of variables, and that only large perturbations in prices or other 

deviations from moving averages will be sufficiently attention-worthy to spark 

behavioral change. 

b. ܦ௧ ൌ ݂ሺܺ௧ሻ ൅ ݃ሺܺ௧ െ ܺ௧ି௡	ሻ ൅ 	ߝ, where the second function 

represents deltas in a given variable(s) that may be significant 

determinants of demand over a longer lag period (or may not, 

depending on whether they are deemed important by the consumer). 

2. Segmentation of demand. A significant amount of feedback was garnered 

regarding the need to perhaps further segment demand based on some key 

 

8

 Refer to the subsection below for ideas raised at the March 2014 Follow-up workshop in this regard. 
Section 6 

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characteristics. If there is a vector of x elements, we can represent 

segmentation in a simplified way. Let those primary segmentation elements be, 

for example, i for urban versus rural populations (perhaps with discrete states 

ranging from 1 to m), and j for various income segments (with discrete states 

ranging from 1 to n). Then, one way to represent aggregate demand would be 

as the sum of individual demand equations associated with each of the 

segments that comprise the entire system. For example: 

௝,௜ܺ1௝,௜1ߚ	 ൅ ௝,௜ߙ ∑	 ൌ ሻݐሺܦ .a

௠,௡

௝,௜ߝ ൅ ௝,௜ܺ݊௝,௜݊ߚ ⋯ ൅ ௝,௜ܺ2௝,௜2ߚ ൅ ୀଵ௝,௜

Where: 

i,j represent states of the segmentation terms 

X1 – Xn represent the same demand covariates but estimated with different 

parameters based on the nature of the segmentation 

Table 6-2 below presents some possible segmentation axes and some high-level notes 

regarding the potential value of segmentation along a given path. 

Table 6-2: Potential Segmentation Axes 

Segmentation Axis Potential Value 

Income Price elasticity (or lack thereof); % of energy consumption 

cost contribution to budgets may drive behavior 

Urban vs. Rural Space intensity (e.g. larger versus smaller homes); 

behavioral norms/practices may be driven from locational 

diversity 

Age Cohort Indirect splicing to address renters/principal agent problem; 

age cohorts may indirectly align with attitudinal inclinations 

(e.g. conservation) 

Age of Home Driver of baseline consumption levels; correlation to age of 

specific end-uses 

More granular regional Enhance existing regional architecture with more specific 
INITIAL FINDINGS 

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Segmentation Axis Potential Value 

attributes (e.g. 

weather) 

variables and/or create regional cohorts based on intensity of 

winter/summer weather and associated impacts on 

consumption 

For certain consumers, the equation parameters might vary dramatically relative to the 

average, and, in fact, behavioral factors that we cannot measure might well matter to a 

large degree. Parameter estimates may be negative or counterintuitive, perhaps based 

on the breakdown in positive income elasticity to usage at ever-increasing tiers of 

income. Segmentation might represent a good compromise between modeling every 

individual consumer with their own equation and having a singular neoclassical 

prototype consumer. The granularity around that segmentation and the nature of the 

segmentation variables themselves, as well as parameter determination, should be 

discussed in more depth. 

3. Lazy User Theory. This theory is predicated on the notion that when 

examining the potential possibilities of new “solutions” to serve an existing 

need, the choice is in part predicated upon a vector of variables that determines 

the level of “effort” involved in choosing otherwise fully viable alternatives. 

The theory postulates that the end-user will select the option that minimizes the 

amount of effort involved in uptake. This theory could have some material 

consequences for aggregate demand, as, in addition to traditionally specified 

covariates (extending from idea 1b above), demand may be a function that 

looks like the following: 

a. ܦ௧ ൌ ݂ሺܺ௧ሻ ൅ ݃ሺܺ௧ െ ܺ௧ି௡	ሻ ൅ ࡱሺࡺሻ ൅ 	ߝ

Where N comprises the effort vector variables, which could include the 

following: 

 Age of the end-user 

 Locational issues 

 Monetary cost 

 Time involved in making the transition 
Section 6 

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 Mental effort involved in making the transition (or educational 

attainment as a potential proxy) 

While some of these variables may not be directly measureable, others could 

be used to enhance either the segmentation of certain cohorts when examining 

demand for various “new technologies” in the RDM, or perhaps as key driving 

variables previously unconsidered. For example, the demand for electric 

vehicles could directly be tied to data on regional/locational age of the 

population. The same could be true for new appliances that, while significantly 

more efficient, carry learning curve issues related to the “smart” elements of 

their product design. 

4. Generalized Bass Diffusion. Based on the Leidos team review of the NEMS 

model as detailed in Section 3, the Generalized Bass Model is not currently 

used in any of the demand models of NEMS. This framework could be adapted 

for use in forecasting consumer response to not only new technology, but also 

response to policy initiatives intended to modify consumer behavior. 

One translation that has been deployed in recent load forecasting work by 

Leidos to capture distributed solar penetration is the Discrete Bass Model, 

which can be expressed in time series format using the following equation: 

a. ܵሺݐሻ ൌ 	ߙ ൅ ߚܻሺݐെ1ሻ ൅ ߚܻଶሺݐ െ 1ሻ ൅ ߝ

Given an initial or recent period data set on uptake, a traditional linear 

regression of uptake as a function of the cumulative sales up to the prior time 

period and the square of cumulative sales up to the prior time period can be 

estimated. While there are translations required to get from the estimated 

parameters to the projected diffusion, there may be a way to leverage this 

framework (or extension of the model) to project uptake of new technology 

that may fall into the “other category”, given reliable initial period or multiperiod data. 

Refer to the Suggested Next Research Steps below for a summary of a suggested Math 

Workspace path to be pursued by the EIA team and supported by Leidos. 
INITIAL FINDINGS 

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Additionally, another set of unique ideas was uncovered as a function of the March 

2014 Follow-on Technical Workshop hosted by EIA and attended by Leidos. These 

ideas are also summarized below in bulleted format. 

6.3 Tractability of Behavioral Economics within existing 

EIA framework – Summary of March 2014 Technical 

Workshop 

Below is a bullet-list summary of the conceptual ideas regarding NEMS energy 

demand experimentation that were originated during a technical workshop held at the 

EIA in March 2014. Certain ideas flowed from the conversation as the workshop 

progressed, while other ideas extended from the initial technical workshop conducted 

in 2013. The concepts, in tandem with the Math Workspace ideas discussed in the 

prior section, form the basis for the universe of potential next steps to be undertaken to 

explore this topic further. 

 Segmentation of appliances based on a more granular approach to features 

within RDM and CDM may help to capture some of the hedonic influences 

associated with consumer choice, much like what is captured in the 

transportation module (e.g., horsepower). Examples of hedonic appliance 

features include changeable color panels and stainless steel finish for 

refrigerators, quiet mode and delayed start functionality for dishwashers, and 

advanced temperature and cycle settings for clothes washers. As the 

segmentation becomes more granular (or perhaps even regional), hedonic 

issues become a more significant driver of choices. 

 More detailed modeling of the “minor” end uses (e.g., TVs), as opposed to the 

current trending or other simplified forecasting approaches, was mentioned as 

a potential NEMS improvement. Are there behavioral factors at play, for 

example, with regard to the increasing numbers, and average screen size, of 

TVs (or is that merely a function of the declining marginal cost of producing 

larger TVs due to the advent of new technology, and if so, can the existing 

model capture cost in some more rigorous fashion)? Is there a more rigorous 

forecasting framework that might better capture trends regarding TVs, rather 

than some simple trending functions? More broadly, more detailed modeling of 

some of the “minor” end-uses, which may take up a larger and larger share of 
Section 6 

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overall consumption, regardless of whether or not that modeling is focused on 

behavioral issues, may be in order. 

 There was a concept mentioned related to avoiding “technology lock-in” in the 

commercial sector, wherein certain commercial customers would turn around 

end-use appliances before the end of their useful life. Perhaps we could 

explore CBECS data to determine “deviations” from the general rules 

regarding turnover and create a new “rapid turnaround” rule/grouping. 

 The commercial model has 7 types of hurdle rates, which resembles a weighted 

average decision associated with making a change for a particular end-use. 

There may be room to experiment with these existing hurdle rates to capture 

more granularity – this is an idea that was also mentioned in the 2013 

workshop. 

 Price lags may need to be further explored and expanded within the CDM 

framework (or perhaps a variable that measures deviations in price over n

periods to capture that variable becoming a non-zero choice parameter in the 

agent’s “sparse” decision-making approach). 

 The notion from the 2013 workshop related to the fact that NEMS may not 

need to engage in larger or more granular segmentation was raised, as the 

segmentation that is already done via hurdle rates “gets you a long way there”. 

The idea that people still seek “max for min” is there, but it may be sloppy, and 

there may not be a better decision framework for forecasting purposes. 

 Ecological modeling, such as that used for traffic forecasting, was brought up, 

but was noted as a potentially impractical alternative. If deviations from 

preference order, on the average, are small and not systemic, then they may not 

matter. 

 The topic of segmentation of various populations was re-raised. Based on the 

totality of feedback on segmentation, the following concepts or areas of focus 

for segmentation were raised: 
INITIAL FINDINGS 

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o If hurdle rates represent different segments and those segments make 

different decisions, then should the segments themselves be static? 

How would changes in sub-segments be “forecasted”? 

o Limitations were mentioned relative to the end-use consumption survey 

in terms of further segmentation. However, the RECS data does 

provide single-family, multi-family, and mobile home housing types. 

o A nested logit model was mentioned that assigns hedonic values to 

attributes of a car (e.g. horsepower) that may be in some way replicable 

within the RDM and CDM. 

o It was noted that Leidos may want to review RECS to provide feedback 

on potential segmentation variables. 

o Stratification of the modules by income tier was again mentioned, with 

the challenge being the forecast of the income distribution. 

o Varying technology choice by building type was mentioned. 

o Other segmentation axes were mentioned as follows: urban vs. rural, 

weather/climate zones, age cohorts (which may be duplicative of stageof-life issues), and household composition. 

o Additional data sources related to segmentation: 

 Neilsen database 

 BLS time-use survey 

 Add/refine questions within RECS – a new model for RECS 

where the output could become more customizable – if new 

data is collected, how will it be forecasted? 

 University efforts (University of CA?) – can potentially buy 

masked data for $1,000 a question – may be a cost effective 

way to gather more data. 

 The behavioral literature is generally “case study based”. There is a need for 

“aggregation and synthesis”, and interacting with the behavioral economics 
Section 6 

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community to perform case studies on “major” end uses, such as HVAC and 

lighting, may be a good short-term strategy. 

 With burgeoning programs to fund or help fund capital costs for energy 

efficiency improvements (e.g., Property-Assessed Clean Energy), there may be 

an opportunity to capture the impact of the “capital availability” being taken 

out of the decision framework as an up-front expense (in lieu of a loan that is 

paid back over a broader period as part of a property’s tax liability) as a subsegment. 

 There may be ways to influence the RECS data gathering process to provide 

additional data that would allow for better segmentation. For example, the 

RECs data does not capture SEER ratings or age of HVAC units. 

 Within the later brainstorming-focused sessions, the following concepts were 

outlined in an open brainstorming context: 

o Further disaggregation/segmentation of a regional nature or within the 

commercial/industrial sectors, or perhaps based on income tiers. 

o Should we focus more on modeling the consumer versus modeling a 

given product? As an adder, can we in some way model the 

manufacturers or gain intelligence about their intentions, as is done in 

the auto/transportation modeling? 

o If we were to change the product/technology slate offered, how would 

that impact the results of RDM/CDM, particularly if certain items are 

removed from the menu as options (e.g. CFLs)? Furthermore, could 

we experiment with changing the product menu and the hurdle rates at 

the exact same time? 

o An elasticity study could be done by feeding a whole range of energy 

prices to the RDM and CDM modules without too much disruption of 

the underlying process. 

o What if we “turned the standards off” in the model and re-ran the 

modules? 
INITIAL FINDINGS 

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o Modeling of behavior in the “minor” end uses was mentioned again, in 

the context of modeling regional skew or stage-of-life characteristics as 

it pertains to choices for the smaller end uses. 

o The idea of extending the complexity of the home size model was 

offered up, as preferences for home size may be a function of certain 

factors that may not be currently captured. This could be projected 

based on the other macroeconomic data that is available but not 

currently utilized, which in part could indirectly capture behavioral 

trends. There are a lot of variables within the macroeconomic module 

that are not currently used within NEMS – there may be opportunities 

to get more information from the macroeconomic module (e.g. 

population model?) 

o Using lag structures and finding a way to better represent amortized 

capital cost were also brought up as possible avenues for further 

experimentation. 

o EIA staff offered up the option of simulating retrofitting behavior by 

cutting down appliance lifetimes and examining how the model 

behaves – this would be a much easier way to experiment than to add 

segmentation to the model. 

6.4 Suggested Next Research Steps 

Based on the totality of research, workshops, and internal brainstorming sessions 

conducted in support of this report, as well as via interaction with the EIA team 

involved in initiating this investigation, the following is a top-level summary of 

suggested next research steps that should be undertaken in parallel paths to continue to 

advance this topic. 

1. Continue to engage technical workshop invitees and participants on a 

recurring basis. The invitees and technical individuals who participated in one 

or both of the workshops should be re-engaged on a recurring basis. It is very 

likely that the core research issues being pursued by this group of practitioners 

and academics is going to evolve over time. New ideas and developments, as 

well as the longer term “sandbox” experimentation by the EIA should be resurfaced within the group, and that may spark additional ideas or areas of 
Section 6 

6-12 Leidos, Inc. Analytic Report 4-2_20140822_FINAL.docx

investigation. The working group can be thought of as a virtual research 

evaluation panel. While the extent of participation in a longer-term effort is 

likely to be uncertain, the ease of communication through an email list will 

hopefully offset attrition. 

2. Work to develop and extend/enhance Math Workspace. The Math 

Workspace currently contains several open-brainstorm ideas from Leidos as a 

function of the literature we have reviewed. This workspace should be 

enhanced to capture other EIA ideas and can serve as the starting point for 

determining exactly how to deploy these potential alternative specifications 

within the confines of RDM or CDM. It can also serve as a platform to 

eliminate ideas due to lack of data/measurability concerns, cost prohibitive 

deployment issues, or a theoretical basis for exclusion. Note that Section 6 also 

contains a tabularized summary of variables commonly encountered in the 

literature, and that this list and the mathematical ideas are not mutually 

inclusive.

3. Leverage ideas from the March 2014 workshop to further filter ideas for 

actual experimentation. EIA staff time and resources are limited. 

Furthermore, EIA has certain on-going critical reporting and analysis 

responsibilities. Consequently, not all of the ideas surfaced for further 

experimentation are feasible to pursue. Some further effort is needed to 

generate a punch list of the most immediately tractable ideas, leaving others as 

possibilities only.

4. Conduct follow-on analytical research on data sources/options for 

variables that fall into the “gray area” in terms of measurability. It may be 

valuable to further investigate survey data, alternative data banks, or other 

experimental structures that could reveal tractable data for variables that 

appear compelling but for which no data currently exists.

5. Execute the 2-3 most promising experiments from #3 with readily 

available data. This amount of experimentation would follow logically from 

item #3 above, in that a carefully bounded number of experiments could be 

conducted, most likely in parallel. 

6. Generate exhibits to characterize tangible forecast differences resulting 

from experiments from #5 and share with workshop invitees. The entire 

realm of experimentation with behavioral concepts is predicated upon the 

notion that deviations from a classical utility maximization paradigm (which 

we have established is neither a boundary nor an appropriate definition of 

NEMS as it is currently designed), if captured and parameterized 

appropriately, can be used to generate tangible differences in the forecasts that 

the EIA produces. The experimentation performed in the next phase should 

attempt to falsify as many of the commonly held perceptions regarding 

behavioral factors as possible. To the extent variables can be translated to data 
INITIAL FINDINGS 

Analytic Report 4-2_20140822_FINAL.docx Leidos, Inc. 6-13

and represent some statement regarding behavioral factors, then an experiment 

should be able to be designed that can falsify the postulate or theory being 

purported. Said differently, scientific statements must be falsifiable. To the 

extent EIA can set up these various “sandbox” experiments and quantify 

tangible differences (if any), this can serve to better define the realm of ideas 

emanating from behavioral economics that meet this criterion and can help 

eliminate generalizations regarding potential deviations that are untestable or 

intractable due to lack of data/measurability challenges. 

Analytic Report 4-2_20140822_FINAL.docx

Section 7 

BEHAVIORAL ECONOMICS BIBLIOGRAPHY 

7.1 Bibliography Sources 

The following table represents the sum total of the literature pursued as part of this 

analytic report. The vintage, author(s), title/description, and a link to the source paper 

(whenever available) are provided. 

The literature reviewed can be categorized as being sourced from the following four 

core areas: 

1. Literature collected by Leidos in advance of the initial technical workshop, 

which was either independently sourced or based on the prior work of the 

technical workshop invitees. 

2. Literature provided directly by the EIA for consideration by Leidos. 

3. Literature extracted by Leidos during a more extensive follow-up search after 

the first workshop. 

4. Literature provided as follow-up by the expert panel that participated in the 

initial workshop, as well as additional literature provided by invitees 

subsequent to the re-submittal of the full draft bibliography in March 2014. 

The bibliography was forwarded in complete form to the original broad list of 

workshop invitees in March 2014. The purpose of the re-submittal of the extent of all 

material gathered was to (i) understand whether there were any critical or seminal 

piece(s) of literature germane to the issue that were not captured and (ii) to solicit 

further focused feedback on possible alternative aggregate demand specifications that 

either spring-boarded from a particular piece, or were concepts being considered by 

the expert working group. 

While feedback was somewhat limited, the feedback that was received indicated that 

this represented a comprehensive bibliography. While it is not fully possible to be 

“exhaustive” in generating this bibliography as a function of the tangential relevance 

of certain pieces of literature to the core topic, the table below represents a dossier that 

is assumed to capture the preponderance of critical work done on behavioral 

economics, particularly relevant to energy consumption. As noted in Section 6, 

Leidos’ proposed Math Workspace approach to developing further ideas, as well as 

the suggestion to continually reengage this working group for more insights is 

intended to further advance the baseline content in the table below. 
Section 7 

7-2 Leidos, Inc. Analytic Report 4-2_20140822_FINAL.docx 8/25/14

Table 7-1: Behavioral Economics Bibliography 

Year Author(s) Title Source Location 

Leidos Research Items 

2010 Portland State 

University, 

California 

Energy 

Commission, 

Pacific Gas 

and Electric 

Sticky Points in Modeling 

Household Energy 

Consumption 

http://aceee.org/files/proceedings/2010/data/paper

s/2144.pdf

2007 IEA Mind the Gap http://www.iea.org/publications/freepublications/

publication/mind_the_gap.pdf

2006 Harris, 

Jeffrey, 

Richard C. 

Diamond, 

Maithili Iyer, 

Christopher 

Payne, and 

Carl 

Blumstein 

Don’t Supersize Me! Toward a 

Policy of Consumption-Based 

Energy Efficiency 

http://epb.lbl.gov/RCDiamond/docs/sufficiencyaceee-06.pdf

2007 RLW 

Analytics, 

Inc. and 

SERA 

Evaluation, Measurement, and 

Verification of the 2004 & 

2004 California Statewide 

ENERGY STAR New Homes 

Program 

http://www.sdge.com/sites/default/files/regulaory/

SDGE%20ABesa%20Chap%20II%20APP%20DVol2%20and%20E%20FINAL.pdf

1993 Dahl, Carol A Survey of Energy Demand 

Elasticities in Support of the 

Development of the NEMS 

http://mpra.ub.unimuenchen.de/13962/1/MPRA_paper_13962.pdf

2005 Reiss, Peter 

and Matthew 

White 

Household Energy Demand, 

Revisited 

http://www.nber.org/papers/w8687.pdf?new_win

dow=1

2008 Kristöm, 

Bengt 

Residential Energy Demand. http://www.jstor.org/discover/10.2307/3097026?u

id=3739560&uid=2&uid=4&uid=3739256&sid=

21102821250463

1993 Lutzenhiser, 

Loren and 

Bruce Hackett 

Social Stratification and 

Environmental Degradation: 

Understanding Household 

CO2 Production 

http://www.jstor.org/discover/10.2307/3097026?u

id=3739560&uid=2&uid=4&uid=3739256&sid=

21102821250463
BEHAVIORAL ECONOMICS BIBLIOGRAPHY 

File: 000000/99-99999-99999-9999 Leidos, Inc. 7-3 

Year Author(s) Title Source Location 

2008 Gowdy, John 

M. 

Behavioral Economics and 

Climate Change Policy 

http://evolution.binghamton.edu/evos/wpcontent/uploads/2010/01/Gowdy-et-al2008_economics-and-climate-change.pdf

2008 Thaler, 

Richard and 

Cass Sunstein 

Nudge (Book for purchase) http://www.amazon.com/Nudge-ImprovingDecisions-Health-Happiness/dp/014311526X

2009 Gillingham, 

Kenneth, 

Richard G. 

Newell, and 

Karen Palmer 

Energy Efficiency Economics 

and Policy 

http://www.rff.org/documents/rff-dp-09-13.pdf 

2007 Martiskainen, 

Mari 

Affecting Consumer Behavior 

on Energy Demand 

https://www.sussex.ac.uk/webteam/gateway/file.p

hp?name=seg-consumer-behaviour-finalreport.pdf&site=264

2000 Stern, Paul. C Toward a Coherent Theory of 

Environmentally Significant 

Behavior 

http://www.worldresourcesforum.org/files/file/Ste

rn%20-

%20Toward%20a%20Coherent%20Theory%20of

%20Environmentally%20Significant%20Behavio

r.pdf

1985 J. Stanley 

Black, Paul C. 

Stern, Julie 

Elworth 

Personal and Contextual 

Influences on Household 

Energy Adaptations 

http://psycnet.apa.org/index.cfm?fa=buy.optionT

oBuy&id=1985-16122-001

2007 Schultz, P. 

Wesley, 

Jessica Nolan, 

Robert B. 

Cialdini, 

Noah J. 

Goldstein, 

and Vladas 

Griskevicius 

The Constructive, Destructive, 

and Reconstructive Power of 

Social Norms 

http://piee.stanford.edu/cgibin/docs/behavior/becc/2008/presentations/17-

1C-01-

The_Constructive_Destructive_and_Reconstructi

ve_Power_of_Social_Norms.pdf

1993 Lutzenhiser, 

Loren 

Social and Behavioral Aspects 

of Energy Use 

http://www.annualreviews.org/doi/abs/10.1146/an

nurev.eg.18.110193.001335?journalCode=energy

.2
Section 7 

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Year Author(s) Title Source Location 

2002 O'Neill, 

Brian, and 

Belinda Chen 

Demographic Determinants of 

Household Energy Use in the 

United States (article for 

purchase) 

http://www.jstor.org/discover/10.2307/3115268?u

id=3739560&uid=2&uid=4&uid=3739256&sid=

21102821318593

1997 Eiji 

Yamasaki, 

Norio 

Tminaga 

Evolution of an aging society 

and effect on residential 

energy demand 

http://www.sciencedirect.com/science/article/pii/

S0301421597000402

2012 Dirk Brounen, 

Nils Kok, 

John Quigley 

Residential Energy Use and 

Conservation: Economics and 

Demographics 

http://www.uce3.berkeley.edu/WP_036.pdf

2000 Lutzenhiser, 

Loren and 

Marcia Hill 

Gossard 

Lifestyle, Status and Energy 

Consumption 

http://www.eceee.org/library/conference_proceed

ings/ACEEE_buildings/2000/Panel_8/p8_17/pape

r

2007 Aune, 

Margrethe 

Energy Comes Home http://www.solutionconcerto.org/IMG/pdf/Energy_comes_home.pdf

2007 CarlssonKanyama, 

Annika and 

Anna-Lisa 

Lindén 

Energy Efficiency in 

Residences: Challenges for 

Women and Men in the North 

http://www.sciencedirect.com/science/article/pii/

S0301421506002746

2002 Sanne, 

Christer 

Willing Consumers–or 

Locked-in? Policies for a 

Sustainable Consumption 

http://www.ce.cmu.edu/~hsm/sust2008/readings/

Sane_Willing.pdf

2003 Shove, 

Elizabeth 

Comfort, Cleanliness, and 

Convenience: The Social 

Organization of Normality 

http://www.amazon.com/Comfort-CleanlinessConvenience-OrganizationTechnologies/dp/1859736300

1998 Cooper, Gail Air-Conditioning America. 

Engineers and the Controlled 

Environment 1900-1960 

http://www.amazon.com/Air-ConditioningAmerica-Controlled-EnvironmentTechnology/dp/0801871131

2011 Houde, 

Sebastien; 

Todd, Annika 

(LBNL, 

Precourt 

Center at 

Stanford) 

List of Behavioral Economics 

Principles that can Inform 

Energy Policy 

http://www.annikatodd.com/List_of_Behavioral_

Economics_for_Energy_Programs.pdf
BEHAVIORAL ECONOMICS BIBLIOGRAPHY 

File: 000000/99-99999-99999-9999 Leidos, Inc. 7-5 

Year Author(s) Title Source Location 

2007 Jeff Rubin 

and Benjamin 

Tal 

Does Energy Efficiency Save 

Energy? 

http://research.cibcwm.com/economic_public/do

wnload/snov07.pdf

2009 Loren 

Lutzenhiser, 

Laura 

Cesafsky, 

Heather 

Chappells, 

and many 

more 

Behavioral Assumptions 

Underlying California 

Residential Sector Energy 

Efficiency Programs 

http://www1.eere.energy.gov/buildings/betterbuil

dings/neighborhoods/pdfs/ba_ee_res_wp.pdf

2011 Harry D. 

Saunders 

Historical Evidence for Energy 

Consumption Rebound in 30 

US Sectors and a Toolkit for 

Rebound Analysts 

http://thebreakthrough.org/blog/Historical%20Evi

dence%20Article%2011-11-10.pdf

1986 Paul Stern Blind Spots in Policy Analysis: 

What Economics Doesn’t Say 

about Energy Use 

http://ideas.repec.org/a/wly/jpamgt/v5y1986i2p20

0-227.html#abstract

2009 Hunt Allcott Behavioral Economics and 

Energy Policy 

http://www.stanford.edu/group/peec/cgibin/docs/events/2009/becc/presentations/5F%20A

lcott.pdf

2012 Joe Bull Loads of green washing—can 

behavioral economics increase 

willingness-to-pay for efficient 

washing machines in the UK? 

http://www.sciencedirect.com/science/article/pii/

S0301421512005769

2011 Opinion 

Dynamics 

Massachusetts Cross-Cutting 

Behavioral Program 

Evaluation 

http://www.maeeac.org/Docs/8.1_EMV%20Page/2011/2011%20

Residential%20Studies/MACC%20Behavioral%2

0Report%20Volume%202%20Final.pdf 

2012 Wu, May Sacramento Municipal Utility 

District - Impact and 

Persistence Evaluation Report 

http://www1.integralanalytics.com/files/document

s/relateddocuments/FinalSMUDHERSEval2012v4.pdf 

Experts Meeting Participants Research Items 

2010 Todd, Annika Behavioral economics is the 

New Green (presentation) 

http://www.annikatodd.com/Annika_Todd_Behav

ioral_Economics_is_the_New_Green.pdf 
Section 7 

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Year Author(s) Title Source Location 

2009 Sendhil 

Mullainathan, 

Eldar Shafir 

Savings Policy and DecisionMaking in Low-Income 

Households 

http://povertyaction.org/sites/default/files/Savings%20Policy%

20and%20Decision-making.pdf 

2005 Eldar Shafir A behavioral perspective on 

consumer protection 

http://povertyaction.org/sites/default/files/ConsProtect%20-

%20cclj302.pdf 

2012 Hunt Allcott, 

Michael 

Greenstone 

Is there an energy Efficiency 

Gap? 

http://dspace.mit.edu/bitstream/handle/1721.1/71

853/Greenstone12-03.pdf?sequence=1 

2009 Hunt Allcott Rethinking Real Time 

Electricity Pricing 

http://web.mit.edu/ceepr/www/publications/worki

ngpapers/2009-015.pdf 

2010 Hunt Allcott Social Norms and Energy 

Conservation 

http://opower.com/uploads/library/file/1/hunt_allc

ott__june_2010_-

_social_norms_and_energy_conservation.pdf 

2011 Hunt Allcott Consumers’ Perceptions and 

Misperceptions of Energy 

Costs 

https://files.nyu.edu/ha32/public/research/Allcott

%202011%20AERPP%20-

%20Consumers'%20Perceptions%20and%20Mis

perceptions%20of%20Energy%20Costs.pdf 

2010 Hunt Allcott, 

Sendhil 

Mullainathan 

Behavior and Energy Policy https://files.nyu.edu/ha32/public/research/Allcott

%20and%20Mullainathan%202010%20-

%20Behavior%20and%20Energy%20Policy.pdf 

2013 Matthew 

Harding, Alic 

Hsiaw 

Goal Setting and Energy 

Conservation 

http://www.stanford.edu/~mch/resources/Harding

_Goals.pdf 

2011 Kenneth 

Gillingham, 

Matthew 

Harding, 

David Rapson 

Split Incentives in Residential 

Energy Consumption 

http://www.stanford.edu/~mch/resources/Harding

_SplitIncentives.pdf 

2012 Matthew 

Harding, 

Alice Hsiaw 

Goal Setting and Energy 

Efficiency 

http://www.aeaweb.org/aea/2013conference/progr

am/retrieve.php?pdfid=214 
BEHAVIORAL ECONOMICS BIBLIOGRAPHY 

File: 000000/99-99999-99999-9999 Leidos, Inc. 7-7 

Year Author(s) Title Source Location 

2011 Emir 

Kamenica, 

Sendhil 

Mullainathan, 

Richard 

Thaler 

Behavioral Economics and 

Consumer Regulation 

http://faculty.chicagobooth.edu/richard.thaler/rese

arch/pdf/Helping%20Consumers%20Know%20T

hemselves.pdf 

2013 James Sallee Rational inattention and 

Energy Efficiency 

(for purchase) 

http://www.nber.org/papers/w19545 

2010 Lucas Davis Evaluating the Slow Adoption 

of Energy Efficient 

Investments: Are Renters Less 

Likely to have Energy 

Efficient Appliances 

http://www.nber.org/papers/w16114.pdf?new_wi

ndow=1 

2008 Maximilian 

Auffhammer, 

Carl 

Blumstein, 

Meredith 

Fowlie 

Demand-Side Management 

and Energy Efficiency 

Revisited 

http://nature.berkeley.edu/~fowlie/demandsidema

nagementrevisited.pdf 

2012 Meredith 

Fowlie, 

Catherine 

Wolfram, 

Michael 

Greenstone 

(presentation) Reconcilable 

differences? Examining the 

gap between engineering 

projections and ex post 

realized gains from energy 

efficiency investments 

http://eetd.lbl.gov/sites/all/files/lbnl_2012-

m_fowlie.pdf 

2013 Nicolas Ryan Is There an Energy-Efficiency 

Gap? Measuring Returns to 

Efficiency with a Field 

Experiment in India (study 

results upcoming) 

http://www.hks.harvard.edu/centers/mrcbg/progra

ms/sustsci/activities/programinitiatives/india/overview/is-there-an-energyefficiency-gap-measuring-returns-to-efficiencywith-a-field-experiment-in-india 

EIA Research Items 

2009 Matteo 

Iacoviello, 

Marina Pavan 

Housing and Debt Over the 

Life Cycle and Over the 

Business Cycle 

http://www.federalreserve.gov/pubs/ifdp/2011/10

32/ifdp1032.pdf 
Section 7 

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Year Author(s) Title Source Location 

2011 Boris Jaggi, 

Kay 

Axhausen 

Modeling Long Term 

Investment Decisions in 

Housing and Transportation 

http://www.strc.ch/conferences/2011/Jaeggi.pdf 

2003 Yuxin Chen 

Sha Yang 

Estimating Disaggregate 

Model Using Aggregate Data 

via Augmentation of 

Individual Choice 

http://www.researchgate.net/publication/2289344

14_Estimating_disaggregate_models_using_aggr

egate_data_through_augmentation_of_individual

_choice/file/50463521b1765e4a12.pdf 

2012 Dirk Brounen, 

Nils Kok, 

John Quigley 

Residential Energy Use and 

Conservation: Economics and 

Demographics 

http://uce3.ucsb.edu/WP_036.pdf 

2010 Aviv Nevo Empirical Models of 

Consumer Behavior 

http://faculty.wcas.northwestern.edu/~ane686/res

earch/ARE2011.pdf 

1998 Rama Cont, 

Jean-Philipe 

Bouchaud 

Herd Behavior and Aggregate 

Fluctuations in Financial 

Markets 

http://arxiv.org/pdf/cond-mat/9712318v2.pdf 

1994 John Hill, 

D’Ann 

Petersen 

Demographics and the LongTerm Outlook for Housing 

Investment 

http://www.dallasfed.org/assets/documents/

research/er/1994/er9401b.pdf

2008 Matteo 

Iacoviello, 

Marina Pavan 

An Equilibrium Model of 

Lumpy Housing Investment 

https://www2.bc.edu/~iacoviel/research_files/RP

E.pdf 

2002 Masanao 

Aoki 

An Equilibrium Model of 

Lumpy Housing Investment: 

Stochastic Views of Interacting 

Agents 

N/A 

2008 Steven Moss, 

M. Cubed 

Market Segmentation and 

Energy Efficiency Program 

Design 

http://ucciee.org/downloads/MarketSegmentationWhitePa

perSummary.pdf 

2011 Xavier 

Gabaix 

A Sparsity-Based Model of 

Bounded Rationality 

http://pages.stern.nyu.edu/~xgabaix/papers/sparse

brconsumer.pdf 
BEHAVIORAL ECONOMICS BIBLIOGRAPHY 

File: 000000/99-99999-99999-9999 Leidos, Inc. 7-9 

Year Author(s) Title Source Location 

Supplemental Research Items 

2013 Susan MazurStommen, 

Kate Farley 

ACEEE Field Guide to UtilityRun Behavior Programs http://www.iseif.org/wpcontent/uploads/2014/01/ACEEE-Report.pdf 

2012 Birgit Gotz, 

Alfred Vos, 

Markus Blesl, 

Ulrich Fahl 

Main Challenges for Modeling 

Policy Instruments in Energy 

System Models: First Results 

[presentation] 

http://www.ieaetsap.org/web/Workshop/CapeTown_June2012/E

TSAPJune2012_BG.pdf 

N/A Pantelis 

Capros, 

Leonidas 

Mantzos, E. 

Lakis 

Vouyoukas 

Technology Evolution and 

Energy Modelling: Overview 

of Research and Findings 

http://www.e3mlab.eu/e3mlab/papers/AMSTERD

AM.pdf 

Resources Received Prior to EIA Meeting 

N/A Bonneville 

Power 

Administratio

n 

Behavior Change Homepage: 

Behavior Based Energy 

Efficiency Programs 

http://www.bpa.gov/energy/n/behavior.cfm 

2012 Eugene 

Rosolie 

Innovative Behavior Based 

Energy Efficiency Pilot 

(presentation) 

http://www.bpa.gov/energy/n/Utilities_Sharing_E

E/Utility_Summit/Workshop2012/Can_We_Chan

ge_Behavior_Innovation_In_The%20_Region.pd

f 

2010 Hunt Allcott, 

Sendhil 

Mullainathan 

Behavioral Science and Energy 

Policy 

https://files.nyu.edu/ha32/public/research/Allcott

%20and%20Mullainathan%202010%20-

%20Behavioral%20Science%20and%20Energy%

20Policy.pdf 

2007 Charlie 

Wilson, Hadi 

Dowlatabadi 

Models of Decision Making 

and Residential Energy Use 

Requires subscription: 

http://www.annualreviews.org/doi/pdf/10.1146/an

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Section 7 

7-10 Leidos, Inc. Analytic Report 4-2_20140822_FINAL.docx 8/25/14

Year Author(s) Title Source Location 

2003 Colin 

Camerer, 

George 

Loewenstein, 

Matthew 

Rabin 

Advances in Behavioral 

Economics 

Book: http://press.princeton.edu/titles/7607.html 

2009 Jon Froehlich Promoting Energy Efficient 

Behaviors in the Home 

through Feedback: The Role of 

Human-Computer Interaction 

ftp://trout.cs.washington.edu/tr/2009/02/UWCSE-09-02-01.PDF 

2006 Keith Dennis The Compatibility of 

Economic Theory and 

Proactive Energy Efficiency 

Policy 

Requires purchase: 

http://www.sciencedirect.com/science/article/pii/

S1040619006000893 

2006 Chris Bataille, 

Mark Jaccard, 

John Nyboer 

and Nic 

Rivers 

Towards General Equilibrium 

in a Technology-Rich Model 

with Empirically Estimated 

Behavioral Parameters 

http://www.emrg.sfu.ca/media/publications/2006/

EJ%20Hybrid%20Special%20Issue%20Paper%2

0Bataille%20et%20al%20FINAL.pdf 

1982 Robin 

Winkler, 

Richard 

Winett 

Behavioral interventions in 

resource conservation: A 

systems approach based on 

behavioral economics 

Requires purchase: 

http://psycnet.apa.org/journals/amp/37/4/421/ 

2003 Diane 

DiClemente 

Applied behavioral economics 

and consumer choice 

Requires purchase: 

http://www.sciencedirect.com/science/article/pii/

S0167487003000035 

2007 Lorna 

Greening, 

Gale Boyd, 

Joseph Roop 

Modeling of industrial energy 

consumption: An introduction 

and context 

Requires purchase: 

http://www.sciencedirect.com/science/article/pii/

S0140988307000448 

2004 Adam Jaffe, 

Richard 

Newell, 

Robert 

Stavins 

Economics of Energy 

Efficiency 

Link is dead: 

http://resume.marcbrands.com/classfolder/45-

859/https@blackboard.andrew.cmu.edu/courses/1

/s04-

45859/content/_185112_1/economics_of_energy_

efficiency.pdf 
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Year Author(s) Title Source Location 

 Alan Sanstad, 

Richard 

Howarth 

Consumer Rationality and 

Energy Efficiency 

http://enduse.lbl.gov/Info/ACEEE-Efficiency.pdf 

2004 Ernst Worrell, 

Stephan 

Ramesohl, 

Gale Boyd 

Advances in Energy 

Forecasting Models Based on 

Engineering Economics 

Requires purchase: 

http://www.annualreviews.org/doi/abs/10.1146/an

nurev.energy.29.062403.102042 

2010 Robert 

Weber, 

Robyn Dawes 

Behavioral Economics Google books: 

http://books.google.com/books?hl=en&lr=&id=2

ZAV5fCs1NcC&oi=fnd&pg=PA90&dq=behavio

ral+economics+and+energy+consumption&ots=p

35cLvgs8h&sig=ASblErL0NeldBVLcniQq9KQi

OzU#v=onepage&q&f=false 

2009 Josef 

Kaenzig, Rolf 

Wustenhagen 

The Effect of Life Cycle Cost 

Information on Consumer 

Investment Decisions 

Regarding Eco-Innovation 

Requires subscription: 

http://onlinelibrary.wiley.com/doi/10.1111/j.1530

-9290.2009.00195.x/full 

2012 Hunt Alcott, 

Todd Rogers 

The Short-Run and Long-Run 

Effects of Behavioral 

Interventions: Experimental 

Evidence from Energy 

Conservation 

Google Docs: 

https://docs.google.com/viewer?a=v&pid=sites&s

rcid=ZGVmYXVsdGRvbWFpbnxyb2dlcnNiZW

hhdmlvcmFsc2NpZW5jZXxneDo2N2MwMGFl

YjM4NDA1ZGRi 

2005 Wokje 

Abrahamse, 

Linda Steg, 

Charles Vlek, 

Talib 

Rothengatter 

A Review of Intervention 

Studies Aimed at Household 

Energy Conservation 

Requires purchase: 

http://www.sciencedirect.com/science/article/pii/

S027249440500054X 

2007 Wokje 

Abrahamse, 

Linda Steg, 

Charles Vlek, 

Talib 

Rothengatter 

The effect of tailored 

information, goal setting, and 

tailored feedback on household 

energy use, energy-related 

behaviors, and behavioral 

antecedents. 

Requires purchase: 

http://www.sciencedirect.com/science/article/pii/

S0272494407000540 
Section 7 

7-12 Leidos, Inc. Analytic Report 4-2_20140822_FINAL.docx 8/25/14

Year Author(s) Title Source Location 

2011 Toshi 

Arimura, 

Shanjun Li, 

Richard 

Newell, 

Karen Palmer 

Cost-Effectiveness of 

Electricity Energy Efficiency 

Programs 

http://www.nber.org/papers/w17556 

1994 Adam Jaffe, 

Robert 

Stavins 

The Energy Paradox and the 

Diffusion of Conservation 

Technology 

http://www.hks.harvard.edu/fs/rstavins/Papers/Th

eEnergyParadox.REE1994.pdf 

2005 Adam Jaffe, 

Richard 

Newell, 

Robert 

Stavins 

A Tale of Two Market 

Failures: Technology and 

Environmental Policy 

Requires purchase: 

http://www.sciencedirect.com/science/article/pii/

S0921800905000303 

2008 Jason 

Shogren, 

Laura Taylor 

On Behavioral-Environmental 

Economics 

http://reep.oxfordjournals.org/content/2/1/26.abstr

act 

2000 Richard 

Howarth, 

Brent 

Haddad, 

Bruce Paton 

The economics of energy 

efficiency: insights from 

voluntary participation 

programs 

Requires purchase: 

http://www.sciencedirect.com/science/article/pii/

S0301421500000264 

2010 Bonneville 

Power 

Administratio

n 

Residential Sector Research 

Findings for Behavior Based 

Energy Efficiency 

http://www.bpa.gov/energy/n/pdf/Behavior_Chan

ge_Report_Dec_2010_July_5.pdf 

2012 Scott 

Dimetrosky 

(Apex 

Analytics) 

Are Savings from Behavioral 

Programs Ready for TRM 

Prime Time? 

http://beccconference.org/wpcontent/uploads/2012/11/BECC-TRMPresentation_111112-Dimetrosky.pdf

2012 Philip 

Mosenthal 

A New Regulatory Framework 

to Support Behavioral 

Programs 

http://ilsagfiles.org/SAG_files/Meeting_Materials

/2012/November%2027,%202012%20Meeting/A

_New_Regulatory_Framework_to_Support_Beha

vioral_Programs_Optimal_Energy_Phil_Mosenth

al.pdf 
BEHAVIORAL ECONOMICS BIBLIOGRAPHY 

File: 000000/99-99999-99999-9999 Leidos, Inc. 7-13 

Year Author(s) Title Source Location 

2012 Mitchell 

Rosenberg 

Evaluating Feedback 

Programs: Results to Date, 

Challenges for the Next Wave 

http://beccconference.org/wpcontent/uploads/2012/11/Feedback-ReportsBECC-2012.pdf 

2012 Michael Li, 

Anika Todd 

Evaluation, Measurement, and 

Verification (EM&V) of 

Residential Behavior-Based 

Energy Efficiency Programs: 

Issues and Recommendations 

http://behavioranalytics.lbl.gov/reports/behaviorbased-emv-slides.pdf 

2013 Edward Vine The California Behavioral Gap 

Analysis 

http://www.aceee.org/files/pdf/conferences/mt/20

13/EdVine_D1.pdf 

2012 Ahmad 

Faruqui, 

Sanem 

Sergici, Neil 

Lessem 

Consistency of Results in 

Dynamic Pricing Experiments 

http://beccconference.org/wpcontent/uploads/2012/12/Consistency-of-resultsin-dynamic-pricing-experiments-11-21-12.pdf

2007 Charles, 

Wilson 

Non-Economic Models of 

Behavior & Decision Making 

Dead link: 

http://www.mendeley.com/research/noneconomic

-models-behavior-decision-making-noneconomicapproaches/ 

Resources Received Subsequent to March 2014 Bibliography Distribution 

2012 Hunt Allcott 

and Todd 

Rogers 

The Short-Run and Long-Run 

Effects of Behavioral 

Interventions: Experimental 

Evidence from Energy 

Conservation 

http://www.nber.org/papers/w18492 

2013 Hunt Allcott 

and Dmitry 

Taubinski 

The Lightbulb Paradox: 

Evidence from Two 

Randomized Experiments 

http://www.nber.org/papers/w19713.pdf 

2014 Hunt Allcott, 

Sendhil 

Mullainathan, 

and Dmitry 

Taubinski 

Energy Policy with 

Externalities and Internalities 

http://www.hks.harvard.edu/m-rcbg/heep/eeworkshop_131024-25/supp/3.ce%20Allcott/AMT%20NBER%20WP%20-

%20Energy%20Policy%20with%20Externalities

%20and%20Internalities.pdf 

2007 Collan, 

Mikael 

Lazy User Theory of Solution 

Selection 

http://ideas.repec.org/p/pra/mprapa/4330.html