Assumptions to the Annual 

Energy Outlook 2015

September 2015

Independent Statistics & Analysis

www.eia.gov

U.S. Department of Energy

Washington, DC 20585
U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 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.
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Table of Contents

Introduction ..................................................................................................................................................1

The National Energy Modeling System....................................................................................................1

Component modules...............................................................................................................................3

Macroeconomic Activity Module......................................................................................................3

International Energy Module ............................................................................................................4

Residential and Commercial Demand Modules................................................................................4

Industrial Demand Module ...............................................................................................................4

Transportation Demand Module ......................................................................................................5

Electricity Market Module.................................................................................................................6

Renewable Fuels Module..................................................................................................................6

Oil and Gas Supply Module ...............................................................................................................7

Natural Gas Transmission and Distribution Module.........................................................................7

Liquids Fuels Market Module............................................................................................................8

Coal Market Module .........................................................................................................................9

Annual Energy Outlook 2015 cases .......................................................................................................10

Carbon dioxide emissions......................................................................................................................13

Macroeconomic Activity Module................................................................................................................16

Key assumptions....................................................................................................................................16

International Energy Module......................................................................................................................18

Key assumptions....................................................................................................................................18

Residential Demand Module ......................................................................................................................23

Key assumptions....................................................................................................................................24

Legislation and regulations....................................................................................................................30

Commercial Demand Module.....................................................................................................................33

Key assumptions....................................................................................................................................33

Technology Choice Submodule..............................................................................................................36

Legislation and regulations....................................................................................................................43

Industrial Demand Module.........................................................................................................................47

Key assumptions - manufacturing .........................................................................................................48

Petrochemical feedstock requirement..................................................................................................58
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Buildings component.............................................................................................................................58

Boiler, steam, and cogeneration component........................................................................................60

Combined heat and power....................................................................................................................60

Key assumptions - non-manufacturing..................................................................................................65

Legislation and regulations....................................................................................................................68

Transportation Demand Module ................................................................................................................72

Key assumptions....................................................................................................................................72

Commercial light-duty fleet assumptions..............................................................................................78

Legislation and regulations....................................................................................................................90

Transportation alternative case.............................................................................................................92

Electricity Market Module ........................................................................................................................102

EMM regions........................................................................................................................................102

Model parameters and assumptions...................................................................................................103

New generating plant characteristics..................................................................................................104

Technological optimism and learning..................................................................................................106

Electricity pricing .................................................................................................................................114

Legislation and regulations..................................................................................................................116

Oil and Gas Supply Module.......................................................................................................................128

Key assumptions..................................................................................................................................128

Lower 48 onshore................................................................................................................................132

CO2 enhanced oil recovery..................................................................................................................137

Lower 48 offshore................................................................................................................................138

Alaska crude oil production.................................................................................................................141

Legislation and regulations..................................................................................................................143

Oil and gas supply alternative cases....................................................................................................144

Natural Gas Transmission and Distribution Module.................................................................................147

Key assumptions..................................................................................................................................148

Legislation and regulations..................................................................................................................155

Liquid Fuels Market Module .....................................................................................................................157

Key assumptions..................................................................................................................................158

Non-petroleum fossil fuel supply ........................................................................................................169

Combined heat and power (CHP)........................................................................................................170
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Short-term methodology.....................................................................................................................170

Legislation and regulation ...................................................................................................................170

Coal Market Module .................................................................................................................................173

Key assumptions..................................................................................................................................173

Legislation and regulations..................................................................................................................183

Renewable Fuels Module..........................................................................................................................188

Key assumptions..................................................................................................................................188

Capital costs.........................................................................................................................................189

Solar Submodule..................................................................................................................................190

Wind Energy Power Submodule..........................................................................................................191

Geothermal Electricity Submodule......................................................................................................193

Biomass Submodule.............................................................................................................................194

Landfill Gas (LFG) Submodule..............................................................................................................195

Conventional Hydroelectricity Submodule..........................................................................................196

Legislation and regulations..................................................................................................................196

State RPS programs .............................................................................................................................197

Appendix A: Handling of federal and selected state legislation and regulations in the AEO...................200
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Figures

Figure 1. National Energy Modeling System.................................................................................................3

Figure 2. World oil prices in three cases, 1995-2040..................................................................................19

Figure 3. OPEC total liquids production in the Reference case, 1995-2040...............................................20

Figure 4. Non-OPEC total liquids production in the Reference case, 1995-2040.......................................20

Figure 5. United States Census Divisions....................................................................................................23

Figure 6. Electricity Market Model Supply Regions..................................................................................102

Figure 7. States covered by the Clean Air Interstate Rule ........................................................................117

Figure 8. Oil and Gas Supply Model regions.............................................................................................128

Figure 9. Natural Gas Transmission and Distribution regions ..................................................................147

Figure 10. Liquid Fuels Market Module Regions.......................................................................................157

Figure 11. Coal Supply Regions.................................................................................................................176

Figure 12. Coal Demand Regions..............................................................................................................177
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Tables

Table 1. 1. Summary of AEO2014 cases......................................................................................................12

Table 1. 2. Carbon dioxide emission factors...............................................................................................14

Table 2.1. Growth in gross domestic product, nonfarm employment and productivity............................17

Table 3.1. Worldwide oil reserves as of January 1, 2013............................................................................21

Table 3.2. Average annual real gross domestic product rates, 2010-40 ....................................................21

Table 3.3. Average annual growth rates for total liquids demand in the Reference case, 2010-40 ..........22

Table 4.1. 2009 Households........................................................................................................................25

Table 4.2. Installed cost and efficiency ratings of selected equipment......................................................26

Table 4.3. Capital cost and performance parameters of selected residential distributed generation 

technologies................................................................................................................................................27

Table 4.4. Minimum and maximum life expectancies of equipment.........................................................28

Table 5.1. 2003 Total floorspace by Census Division and principal building activity .................................35

Table 5.2. Floorspace attrition parameters................................................................................................35

Table 5.3. Capital cost and performance parameters of selected commercial distributed generation 

technologies................................................................................................................................................37

Table 5.4. Assumed behavior rules for choosing space heating equipment in large office buildings........39

Table 5.5. Assumed distribution of risk-adjusted time preference premiums for space heating and 

lighting equipment in 2015.........................................................................................................................39

Table 5.6. Capital cost and efficiency ratings of selected commercial space heating equipment1

............41

Table 6.1. Industry categories and NAICS codes.........................................................................................47

Table 6.2. Retirement rates........................................................................................................................49

Table 6.3. Coefficients for technology possibility curve for manufacturing industries and all industrial 

scenarios.....................................................................................................................................................50

Table 6.4. Cost and performance parameters for industrial motor choice model.....................................57

Table 6.5. Non-combustion use of fossil fuels, selected years 1980 - 2013...............................................59

Table 6.6. 2010 Building component energy consumption........................................................................61

Table 6.7. 2010 Boiler fuel component and logit parameter .....................................................................63

Table 6.8. Cost characteristics of industrial CHP systems...........................................................................65

Table 6.9. Regional collaboration coefficients for CHP deployment..........................................................65

Table 6.10. 2010 UECs for nonmanufacturing............................................................................................66
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Tables

Table 7.1. Standard technology matrix for cars1 ........................................................................................73

Table 7.2. Standard technology matrix for light trucks

1

.............................................................................75

Table 7.3. Car and light truck degradation factors .....................................................................................77

Table 7.4. Vehicle miles traveled equation coefficients, by age and gender cohorts................................77

Table 7.5. Percent of fleet alternative fuel vehicles by fleet type by size class, 2005................................78

Table 7.6. Commercial fleet size class shares by fleet and vehicle type, 2005...........................................79

Table 7.7. Share of new vehicle purchases by fleet type and technology type, 2009................................79

Table 7.8. Vehicle technology category for technology matrix for freight trucks......................................81

Table 7.9. Standard technology matrix for freight trucks...........................................................................82

Table 7.10. Thirteen regions for the world model......................................................................................85

Table 7.11. 2013 Regional population, GDP, per capita GDP, domestic and international RPM and percapita RPM..................................................................................................................................................86

Table 7.12. 2013 Regional passenger and cargo aircraft supply ................................................................87

Table 7.13. Standard technology matrix for air travel...............................................................................90

Table 7.14. EPACT legislative mandates for AFV purchases by fleet type and year...................................92

Table 7.15. High technology matrix for cars...............................................................................................93

Table 7.16. High technology matrix for light trucks....................................................................................95

Table 7.17. High technology matrix for freight trucks................................................................................97

Table 7.18. High technology matrix for air travel .......................................................................................98

Table 8.1. Generating capacity types represented in the Electricity Market Module..............................103

Table 8.2. Cost and performance characteristics of new central station electricity generating 

technologies..............................................................................................................................................105

Table 8.3. Learning parameters for new generating technology components ........................................107

Table 8.4. Component cost weights for new technologies.......................................................................109

Table 8.5. Component capacity weights for new technologies................................................................109

Table 8.6. Coal plant retrofit costs............................................................................................................119

Table 8.7. Mercury emission modification factors...................................................................................119

Table 8.8. Planned SO2 scrubber additions by EMM region ....................................................................120

Table 8.9. Existing coal plant types in the NEMS Electricity Market Module...........................................122

Table 8.10. Heat rate improvement (HRI) potential and cost (capital, fixed O&M) by Plant Code and 

quartile as used for input to NEMS...........................................................................................................123

Table 9.1. Technically recoverable U.S. crude oil resources as of January 1, 2013..................................129

Table 9.2. Technically recoverable U.S. dry natural gas resources as of January 1, 2012........................130

Table 9.3. U.S. unproved technically recoverable tight/shale oil and gas resources by play (as of January 

1, 2013) .....................................................................................................................................................133

Table 9.4. U.S. unproved technically recoverable coalbed methane resources by play (as of January 1, 

2013).........................................................................................................................................................135

Table 9.5. Distribution of crude oil EURs in the Bakken ...........................................................................136
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Table 9.6. Maximum volume of CO2 available .........................................................................................137

Table 9.7. CO2 availability assumptions ...................................................................................................138

Table 9.8. Industrial CO2 capture and transportation costs by region.....................................................138

Table 9.9. Assumed size and initial production year of major announced deepwater discoveries.........139

Table 9.10. Offshore exploration and production technology levels.......................................................140

Table 10.1. Assumptions related to CNG and LNG fuel prices..................................................................150

Table 10.2. Primary assumptions for natural gas pipelines from Alaska and Mackenzie Delta into Alberta, 

Canada ......................................................................................................................................................152

Table 10.3. Exogenously specified Mexico natural gas consumption, production, and LNG imports......153

Table 10.4. Exogenously specified Canada natural gas consumption, production, and LNG exports.....154

Table 10.5. Charges related to LNG exports.............................................................................................155

Table 10.6. International natural gas volume drivers for world LNG Europe and Asia market price 

projections................................................................................................................................................155

Table 11.1. Petroleum product categories ...............................................................................................158

Table 11.2. Year-round gasoline specifications by Petroleum Administration for Defense 

District (PADD) ..........................................................................................................................................159

Table 11.3. Market share for gasoline types by Census Division..............................................................160

Table 11.4. Petroleum product end-use markups by sector and Census Division ...................................162

Table 11.5. State and local taxes on petroleum transportation fuels by Census Division........................163

Table 11.6. Federal taxes..........................................................................................................................163

Table 11.7. Crude oil specifications..........................................................................................................163

Table 11.8. Alternative fuel technology product type..............................................................................165

Table 11.9. Non-petroleum fuel technology characteristics1

...................................................................167

Table 12.1. Coal mining productivity by region ........................................................................................174

Table 12.2. Transportation rate multipliers..............................................................................................179

Table 12.3. World steam coal import demand by import region1

............................................................182

Table 12.4. World metallurgical coal import demand by import region1

.................................................183

Table 12.5. Production, heat content, sulfur, mercury and carbon dioxide emission factors by 

coal type and region .................................................................................................................................184

Table 13.1. Aggregate regional renewable portfolio standard requirements..........................................198
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Introduction

This report presents the major assumptions of the National Energy Modeling System (NEMS) used to 

generate the projections in the Annual Energy Outlook 2015 [1] (AEO2015), including general features of the 

model structure, assumptions concerning energy markets, and the key input data and parameters that are 

the most significant in formulating the model results. Detailed documentation of the modeling system is 

available in a series of documentation reports [2].

The National Energy Modeling System

Projections in AEO2015 are generated using NEMS [3], developed and maintained by the Office of Energy 

Analysis of the U.S. Energy Information Administration (EIA). In addition to its use in developing the Annual 

Energy Outlook (AEO) projections, NEMS is used to complete analytical studies for the U.S. Congress, the 

Executive Office of the President, other offices within the U.S. Department of Energy (DOE), and other 

federal agencies. NEMS is also used by nongovernmental groups, such as the Electric Power Research 

Institute, Duke University, and Georgia Institute of Technology. In addition, AEO projections are used by 

analysts and planners in other government agencies and nongovernmental organizations.

The projections in NEMS are developed with the use of a market-based approach, subject to regulations and 

standards. For each fuel and consuming sector, NEMS balances energy supply and demand, accounting for 

economic competition across the various energy fuels and sources. The time horizon of NEMS extends to 

2040. To represent regional differences in energy markets, the component modules of NEMS function at the 

regional level: the 9 Census divisions for the end-use demand modules; production regions specific to oil, 

natural gas, and coal supply and distribution; 22 regions and subregions of the North American Electric 

Reliability Corporation for electricity; and 9 refining regions within the 5 Petroleum Administration for 

Defense Districts (PADDs). Complete regional and detailed results are available on the EIA Analysis and 

Projections Home Page (www.eia.gov/analysis/). 

NEMS is organized and implemented as a modular system (Figure 1). The modules represent each of the fuel 

supply markets, conversion sectors, and end-use consumption sectors of the energy system. The modular 

design also permits the use of the methodology and level of detail most appropriate for each energy sector. 

NEMS executes each of the component modules to solve for prices of energy delivered to end users and the 

quantities consumed, by product, region, and sector. The delivered fuel prices encompass all activities 

necessary to produce, import, and transport fuels to end users. The information flows also include such 

areas as economic activity, domestic production, and international petroleum supply. NEMS calls each 

supply, conversion, and end-use demand module in sequence until the delivered prices of energy and the 

quantities demanded have converged within tolerance, thereby achieving an economic equilibrium of supply 

and demand in the consuming sectors. A solution is reached for each year from 2014 through 2040. Other 

variables, such as petroleum product imports, crude oil imports, and several macroeconomic indicators, also 

are evaluated for convergence.
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Each NEMS component represents the effects and costs of legislation and environmental regulations that 

affect that sector. NEMS accounts for all combustion-related carbon dioxide (CO2) emissions, as well as 

emissions of sulfur dioxide (SO2), nitrogen oxides (NOX), and mercury from the electricity generation sector.

The integrating module of NEMS controls the execution of each of the component modules. To facilitate 

modularity, the components do not pass information to each other directly but communicate through a 

central data storage location. This modular design provides the capability to execute modules individually, 

thus allowing decentralized development of the system and independent analysis and testing of individual 

modules that appropriately reflect each energy sector. 

The version of NEMS used for AEO2015 generally represents current legislation and environmental 

regulations, including recent government actions for which implementing regulations were available as of 

the end of October, 2014, as discussed in the Legislation and Regulations section of the AEO. The potential 

effects of proposed federal and state legislation, regulations, or standards—or of sections of legislation that 

have been enacted but require funds or implementing regulations that have not been provided or 

specified—are not reflected in NEMS. Because AEO2015 is a short version of the report, the scenarios are 

limited. EIA is undertaking a separate analysis of the proposed rules of the Environmental Protection 

Agency’s Clean Power Plan, which will be available after AEO2015 is published. A list of the specific federal 

and selected state legislation and regulations included in the AEO, including how they are incorporated, is 

provided in Appendix A.
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Figure 1. National Energy Modeling System

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 prices or expenditures for energy delivered to the consuming 

sectors and the quantities of end-use energy consumption. This section provides brief summaries of each of 

the modules.

Macroeconomic Activity Module

The Macroeconomic Activity Module (MAM) provides a set of macroeconomic drivers to the energy 

modules and receives energy-related indicators from the NEMS energy components as part of the 

macroeconomic feedback mechanism within NEMS. Key macroeconomic variables used in the energy 

modules include gross domestic product (GDP), disposable income, value of industrial shipments, new 

housing starts, sales of new light-duty vehicles, interest rates, and employment. Key energy indicators fed 

back to the MAM include aggregate energy prices and costs. The MAM uses the following models from IHS 

Global Insight: Macroeconomic Model of the U.S. Economy, National Industry Model, and National 

Employment Model. In addition, EIA has constructed a Regional Economic and Industry Model to project 

regional economic drivers, and a Commercial Floorspace Model to project 13 floorspace types in 9 Census 
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divisions. The accounting framework for industrial value of shipments uses the North American Industry 

Classification System (NAICS).

International Energy Module

The International Energy Module (IEM) uses assumptions of economic growth and expectations of future 

U.S. and world petroleum and other liquids production and consumption, by year, to project the interaction 

of U.S. and international petroleum and other liquids markets. This module provides a world crude-like 

liquids supply curve and generates a worldwide oil supply/demand balance for each year of the projection 

period. 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 petroleum and other liquids supply 

and demand, current investment trends in exploration and development, and long-term resource economics 

by country and territory. The oil production estimates include both petroleum and other liquids supply 

recovery technologies. The IEM also provides, for each year of the projection period, endogenous 

assumptions for petroleum products for import and export in the United States. The IEM, through 

interacting with the rest of NEMS, changes North Sea Brent and West Texas Intermediate prices in response 

to changes in expected production and consumption of crude-like liquids in the United States.

Residential and Commercial Demand Modules

The Residential Demand Module (RDM) projects energy consumption in the residential sector by Census 

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

availability of renewable sources of energy, and changes in the housing stock. The Commercial Demand 

Module (CDM) projects energy consumption in the commercial sector by Census division, building type, and 

category of end use, based on delivered prices of energy, availability of renewable sources of energy, and 

changes in commercial floorspace.

The RDM estimates the equipment stock for major end-use services, while the CDM estimates service 

demand met by major end-use equipment. Both incorporate assessments of advanced technologies, 

representations of renewable energy technologies, projections of distributed generation including 

commercial combined heat and power (CHP), and the effects of both building shell and appliance standards. 

The modules incorporate changes to heating and cooling degree days by Census division, based on a 30-year 

historical trend and state-level population projections. The RDM projects an increase in the average square 

footage of both new construction and existing structures, based on trends in new construction and 

remodeling, and commercial floorspace increases as a result of projected growth within the Macroeconomic 

Activity Module of NEMS.

Industrial Demand Module

The Industrial Demand Module (IDM) projects the consumption of energy for heat and power, as well as the 

consumption of feedstocks and raw materials in each of 21 industry groups, subject to the delivered prices 

of energy and macroeconomic estimates of employment and the value of shipments for each industry. As 

noted in the description of the MAM, the representation of industrial activity in NEMS is based on the 

NAICS. The industries are classified into three groups: energy-intensive manufacturing, non-energy-intensive 

manufacturing, and nonmanufacturing. Seven of eight energy-intensive manufacturing industries are 

modeled in the IDM, including energy-consuming components for boiler/steam/cogeneration, buildings, and 

process/assembly use of energy. Energy demand for petroleum and other liquids refining (the other energy-
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intensive manufacturing industry) is modeled in the Liquid Fuels Market Module (LFMM) as described 

below, but the projected consumption is reported under the industrial totals.

The only modeling change in AEO2015 was the introduction of a new Bayesian Dynamic Linear Model (DLM) 

for ethane and propane price projections in the industrial sector. To compute sectoral propane prices, 

historical differences between the base and sectoral prices for propane were applied to the DLM projections 

for propane. The resulting AEO2015 ethane and propane price projections exhibit a dominant natural gas 

price influence in the near term and a growing oil price influence in the long term.

One data change was made for AEO2015. Historical natural gas feedstock data in the industrial sector were 

updated and implemented through 2013, based on data from GlobalData. [4]

Transportation Demand Module

The Transportation Demand Module projects consumption of energy by mode and fuel type in the 

transportation sector, subject to delivered energy prices, macroeconomic variables such as GDP, and other 

factors such as technology adoption. Transportation modes include light-duty vehicles, heavy-duty vehicles, 

air, marine, and rail. Fuel types include motor gasoline, distillate, jet fuel, and alternate fuels such as ethanol 

(E85) and compressed and liquefied natural gas (CNG/LNG). The light-duty vehicle travel component uses 

fuel prices, personal income, and ten age and gender population groups to generate projections. The 

Transportation Demand Module considers legislation and regulations, such as the Energy Policy Act of 2005 

(EPACT2005), the Energy Improvement and Extension Act of 2008 (EIEA2008), and the American Recovery 

and Reinvestment Act of 2009 (ARRA2009), which contain tax credits for the purchase of alternatively fueled 

vehicles. Representations of light-duty-vehicle Corporate Average Fuel Economy (CAFE) and Greenhouse Gas 

(GHG) emissions standards, heavy-duty vehicle fuel consumption and GHG emissions standards, and biofuels 

consumption reflect requirements enacted by NHTSA and the EPA, as well as provisions in the Energy 

Independence and Security Act of 2007 (EISA2007).

The air transportation component of the Transportation Demand Module represents air travel in thirteen 

domestic and foreign markets (U.S., Canada, Central America, South America, Europe, Africa, Middle East, 

CIS, China, Northeast Asia, Southeast Asia, Southwest Asia, and Oceania) and includes the industry practice 

of parking aircraft in both domestic and international markets to reduce operating costs, as well as the 

movement of aging aircraft from passenger to cargo markets. For passenger travel and air freight shipments, 

the module represents regional fuel use and travel demand for three aircraft types: regional, narrow-body, 

and wide-body. An infrastructure constraint, which is also modeled, can potentially limit overall growth in 

passenger and freight air travel to levels commensurate with industry-projected infrastructure expansion 

and capacity growth.

The Transportation Demand Module projects energy consumption for freight trucks (heavy-duty vehicles 

including buses, vocational vehicles, and tractor trailers), freight and passenger rail, and international and 

domestic marine vessels by fuel and Census division. Freight trucks, freight rail, and marine are subject to 

macroeconomic variables such as the value and type of industrial shipments. Passenger rail projections are 

subject to personal income and fuel prices.
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Electricity Market Module

There are three primary submodules of the Electricity Market Module (EMM): capacity planning, fuel 

dispatching, and finance and pricing. The capacity expansion submodule uses the stock of existing 

generation capacity, the cost and performance of future generation capacity, expected fuel prices, expected 

financial parameters, expected electricity demand, and expected environmental regulations to project the 

optimal mix of new generation capacity that should be added in future years. The fuel dispatching 

submodule uses the existing stock of generation equipment types, their operation and maintenance costs 

and performance, fuel prices to the electricity sector, electricity demand, and all applicable environmental 

regulations to determine the least-cost way to meet that demand. The submodule also determines interregional trade and costs of electricity generation. The finance and pricing submodule uses capital costs, fuel 

and operating costs, macroeconomic parameters, environmental regulations, and load shapes to estimate 

retail electricity prices for each sector.

All final regulations, as of September 2014, issued by the EPA for compliance with the Clean Air Act 

Amendments of 1990 are explicitly represented in the capacity expansion and dispatch decisions. All 

financial incentives for power generation expansion and dispatch specifically identified in EPACT2005 have 

been implemented. Several states, primarily in the northeast, have enacted air emission regulations for CO2 

that affect the electricity generation sector, and those regulations are represented in AEO2015. The 

AEO2015 Reference case also imposes a limit on CO2 emissions for specific covered sectors, including the 

electric power sector, in California, as represented in California’s AB 32. The AEO2015 Reference case 

assumes implementation of the Clean Air Interstate Rule (CAIR), which has been replaced by the Cross-State 

Air Pollution Rule (CSAPR) following a recent D.C. Circuit Court of Appeals decision to lift a stay on CSAPR. 

CAIR incorporates a cap and trade program for annual emissions of SO2 and annual and seasonal emissions 

of NOx from fossil power plants. Although CAIR and CSAPR are broadly similar, future AEOs will incorporate 

CSAPR, absent further court action to stay its implementation. Reductions in mercury emissions from coaland oil-fired power plants also are reflected through the inclusion of the Mercury and Air Toxics Standards 

for power plants, finalized by the EPA on December 16, 2011.

Although currently there is no federal legislation in place that restricts GHG emissions, regulators and the 

investment community have continued to push energy companies to invest in technologies that are less 

GHG-intensive. The trend is captured in the AEO2015 Reference case through a 3-percentage-point increase 

in the cost of capital, when evaluating investments in new coal-fired power plants, new coal-to-liquids (CTL) 

plants without carbon capture and storage, and pollution control retrofits.

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 (dedicated biomass plants and co-firing in existing coal 

plants), geothermal, landfill gas, solar thermal electricity, solar photovoltaics, and both onshore and 

offshore wind energy. The RFM contains renewable resource supply estimates representing the regional 

opportunities for renewable energy development. Investment tax credits (ITCs) for renewable fuels are 

incorporated, as currently enacted, including a permanent 10% ITC for business investment in solar energy 

(thermal nonpower uses as well as power uses) and geothermal power (available only to those projects not 

accepting the production tax credit [PTC] for geothermal power). In addition, the module reflects the 
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increase in the ITC to 30% for solar energy systems installed before January 1, 2017. The extension of the 

credit to individual homeowners under EIEA2008 is reflected in the Residential and Commercial Demand 

Modules.

PTCs for wind, geothermal, landfill gas, and some types of hydroelectric and biomass-fueled plants also are 

represented, based on the laws in effect in December 2014. They provide a credit of up to 2.3 

cents/kilowatthour (kWh) for electricity produced in the first 10 years of plant operation. For AEO2015, new 

plants under construction before January 1, 2015, are eligible to receive the PTC. As part of ARRA2009, 

plants eligible for the PTC may instead elect to receive a 30% ITC or an equivalent direct grant. AEO2015 also 

accounts for new renewable energy capacity resulting from state renewable portfolio standards.

Oil and Gas Supply Module

The Oil and Gas Supply Module 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 all production techniques, including natural gas recovery from coalbeds and lowpermeability geologic formations. 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 natural gas production activities 

are modeled for 12 supply regions, including six onshore, three offshore, and in three Alaska regions.

The Onshore Lower 48 Oil and Gas Supply Submodule evaluates the economics of future exploration and 

development projects for crude oil and natural gas plays. Crude oil resources include structurally reservoired 

resources (i.e., conventional) as well as highly fractured continuous zones, such as the Austin Chalk and

Bakken shale formations. Production potential from advanced secondary recovery techniques (such as infill 

drilling, horizontal continuity, and horizontal profile) and enhanced oil recovery (such as CO2 flooding, 

steam flooding, polymer flooding, and profile modification) are explicitly represented. Natural gas resources 

include high-permeability carbonate and sandstone, tight gas, shale gas, and coalbed methane.

Domestic crude oil production volumes are used as inputs to the LFMM for conversion and blending into 

refined petroleum products. Supply curves for natural gas are used as inputs to the Natural Gas 

Transmission and Distribution Module (NGTDM) for determining natural gas wellhead prices and domestic 

production.

Natural Gas Transmission and Distribution Module

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 balances natural gas supply and demand, tracks the flows of natural gas, and 

determines the associated capacity expansion requirements in an aggregate pipeline network, connecting 

domestic and limited foreign supply sources with 12 lower 48 states regions.

The 12 lower 48 states regions align with the nine Census divisions, with three subdivided, and Alaska 

handled separately. The flow of natural gas is determined for both a peak and off-peak period in the year, 

assuming a historically based seasonal distribution of natural gas demand. Key components of pipeline and 
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 8

distributor tariffs are included in separate pricing algorithms. The primary outputs of the module are 

delivered natural gas prices by region and sector, supply prices, and realized domestic natural gas 

production. The module also projects natural gas pipeline imports and exports to Canada and Mexico, as 

well as LNG imports and exports.

Liquids Fuels Market Module

The LFMM projects prices of petroleum products, crude oil and product import activity, as well as domestic 

refinery operations, subject to demand for petroleum products, availability and price of imported 

petroleum, and domestic production of crude oil, HGL, and biofuels and nonpetroleum liquids—ethanol, 

biodiesel, biomass-to-liquids (BTL), coal-to-liquids (CTL), gas-to-liquids (GTL), and coal-and-biomass-toliquids (CBTL). Costs, performance, and first dates of commercial availability for the advanced liquid fuels 

technologies [9] are reviewed and updated annually.

The module represents refining activities in eight domestic U.S. regions, and a Maritime Canada/Caribbean 

refining region (created to represent short-haul international refineries that predominantly serve U.S. 

markets). In order to better represent policy, import/export patterns, and biofuels production, the eight U.S. 

regions were defined by subdividing three of the five U.S. PADDs. All nine refining regions are defined 

below:

PADD I – East Coast 

PADD II – Interior 

PADD II – Great Lakes 

PADD III – Gulf Coast 

PADD III – Interior 

PADD IV – Mountain 

PADD V – California 

PADD V – Other

Maritime Canada/Caribbean

The LFMM models the costs of automotive fuels, such as conventional and reformulated gasoline, and 

includes production of biofuels for blending in gasoline and diesel. Fuel ethanol and biodiesel are included in 

the LFMM because they are commonly blended into petroleum products. The module allows ethanol 

blending into gasoline at 10% by volume (E10), 15% by volume (E15) in states that lack explicit language 

capping ethanol volume or oxygen content, and high-volume blends (E85) for use in flex-fuel vehicles. The 

module also includes a 16% by volume biobutanol/gasoline blend. Crude oil and refinery product imports 

are represented by supply curves defined by the NEMS IEM. Products also can be imported from refining 

region nine (Maritime Canada/Caribbean). Refinery product exports are represented by demand curves, also 

provided by the IEM. Based on the ruling by the U.S. Department of Commerce, Bureau of Industry and 

Security (BIS) that allows for the export of processed lease condensate, the LFMM also allows export of 

condensate (defined in the model as API 50+ crude).
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 9

Capacity expansion of refinery process units and nonpetroleum liquid fuels production facilities is also 

modeled in the LFMM. The model uses current liquid fuels production capacity, the cost and performance of 

each production unit, expected fuel and feedstock costs, expected financial parameters, expected liquid 

fuels demand, and relevant environmental policies to project the optimal mix of new capacity that should be 

added in the future.

The LFMM includes representation of the renewable fuels standard (RFS) specified in EISA2007, which 

mandates the use of 36 billion ethanol-equivalent gallons of renewable fuel by 2022. Both domestic and 

imported biofuels count toward the RFS. Domestic ethanol production is modeled for three feedstock 

categories: corn, cellulosic plant materials, and advanced feedstock materials. Starch-based ethanol plants 

are numerous (more than 175 are now in operation, with a total maximum sustainable nameplate capacity 

of more than 13 billion gallons annually), and are based on a well-known technology that converts starch 

and sugar into ethanol. Ethanol from cellulosic sources is a relatively new technology with only a few 

commercial plants built and working towards full operation. Ethanol from advanced feedstocks—grains 

other than corn and which reduce GHG emissions by at least 50%—is another new technology modeled in 

the LFMM. The LFMM also has the capability to produce biobutanol from a retrofitted corn ethanol facility, 

if economically competitive.

Fuels produced by Fischer-Tropsch synthesis and through a pyrolysis process are also modeled in the LFMM, 

based on their economics compared with competing feedstocks and products. The five processes modeled 

are CTL, CBTL, GTL, BTL, and pyrolysis.

Two California-specific policies are also represented in the LFMM: the low carbon fuel standard (LCFS) and 

the AB 32 cap-and-trade program. The LCFS requires the carbon intensity (amount of GHG per unit of 

energy) of transportation fuels sold for use in California to decrease according to a schedule published by 

the California Air Resources Board. California’s AB 32 cap-and-trade program is established to help California 

achieve its goal of reducing CO2 emissions to 1990 levels by 2020. Working with other NEMS modules (IDM, 

EMM, and Emissions Policy Module), the LFMM provides emissions allowances and actual emissions of CO2 

from California refineries, and NEMS provides the mechanism (carbon price) to trade allowances such that 

the total CO2 emissions cap is met.

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 41 separate supply curves—differentiated by region, mine type, coal rank, and sulfur content. The coal 

supply curves respond to mining capacity, capacity utilization of mines, labor productivity, and factor input 

costs (mining equipment, mining labor, and fuel requirements).

Projections of U.S. coal distribution are determined by minimizing the cost of coal supplied, given coal 

demands by region and sector; environmental restrictions; and accounting for minemouth prices, 

transportation costs, and coal supply contracts. Over the projection horizon, coal transportation costs in the 

CMM vary in response to changes in the cost of rail investments.

The CMM produces projections of U.S. steam and metallurgical coal exports and imports in the context of 

world coal trade, determining the pattern of world coal trade flows that minimizes production and 
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 10

transportation costs while meeting a specified set of regional coal import demands, subject to constraints on 

export capacities and trade flows. The international coal market component of the module computes trade 

in two types of coal (steam and metallurgical) for 17 export regions and 20 import regions. U.S. coal 

production and distribution are computed for 14 supply regions and 16 demand regions.

Annual Energy Outlook 2015 cases

In preparing projections for AEO2015, EIA evaluated a wide range of trends and issues that could have major 

implications for U.S. energy markets between now and 2040. Besides the Reference case, AEO2015 presents 

detailed results for four alternative cases that differ from each other due to fundamental assumptions 

concerning the domestic economy and world oil market conditions. These alternative cases include the 

following:

Economic Growth:

• In the Reference case, population grows by 0.7%/year, nonfarm employment by 0.8%/year, and 

labor productivity by 2.0%/year from 2013 to 2040. Economic output as measured by real GDP 

increases by 2.4%/year from 2013 through 2040, and growth in real disposable income per capita 

averages 1.9%/year.

• The Low Economic Growth case assumes lower growth rates for population (0.6%/year) and labor 

productivity (1.5%/year), resulting in lower nonfarm employment (0.6%/year), higher prices and 

interest rates, and lower growth in industrial output. In the Low Economic Growth case, economic 

output as measured by real GDP increases by 1.8%/year from 2013 through 2040, and growth in real 

disposable income per capita averages 1.7%/year.

• The High Economic Growth case assumes higher growth rates for population (0.8%/year) and labor 

productivity (2.2%/year), resulting in higher nonfarm employment (1.0%/year). With higher 

productivity gains and employment growth, inflation and interest rates are lower than in the 

Reference case, and consequently economic output grows at a higher rate (2.9%/year) than in the 

Reference case (2.4%). Real disposable income per capita grows by 2.1%/year.

Oil Price Cases:

• The benchmark oil price is the price for Brent crude oil, which best reflects the marginal price paid 

by refineries for imported light, sweet crude oil used to produce petroleum products for consumers. 

EIA continues to report the WTI price and the Imported Refiner Acquisition Cost.

• The historical record shows substantial variability in oil prices, and there is arguably even more 

uncertainty about future prices in the long term. AEO2015 considers three oil price cases 

(Reference, Low Oil Price, and High Oil Price) to allow an assessment of alternative views on the 

future course of oil prices.

• The Low and High Oil Price cases reflect a wide range of potential price paths, resulting primarily 

from variation in demand for petroleum and other liquid fuels in non-OECD countries due to 

different levels of economic growth. The Low and High Oil Price cases also reflect different 

assumptions about decisions by members of OPEC regarding the preferred rate of oil production 

and about the future finding and development costs and accessibility of non-OPEC oil resources.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 11

• In the Reference case, real oil prices (in 2013 dollars) decline from $109/barrel in 2013 to $56/ 

barrel in 2015 and then rise to $141/barrel in 2040. The Reference case represents EIA’s current 

judgment regarding exploration and development costs and accessibility of oil resources. Compared 

with AEO2014, EIA sees increasing production from non-OPEC countries, particularly the United 

States. However, EIA also assumes that OPEC producers will choose to maintain their share of the 

market and will schedule investments in incremental production capacity so that OPEC oil 

production will represent between 36% and 41% of the world’s total petroleum and other liquids 

production over the projection period.

• In the Low Oil Price case, crude oil prices remain around $52/barrel (2013 dollars) from 2015 

through 2017, then begin to rise slowly to almost $66/b in 2026, and to $76/b in 2040. The low price 

results from lower costs of production and lower demand from China and the Middle East compared 

with the Reference case. Crude and lease condensate production from across OPEC rises during the 

second half of the projection period in this case. OPEC market share of petroleum production rises 

steadily from 40% in 2020 to 55% in 2040. In addition, in this case, bitumen production in Canada 

and renewable fuels from Brazil and the United States see decreases in costs, leading to increased 

production that keeps OPEC’s market share between 38% and 51% of the total liquids market. With 

the exceptions of China and the Middle East, which see reduced economic growth in this case, the 

lower prices generally lead to higher demand than projected in the Reference case.

• In the High Oil Price case, oil prices reach about $252/barrel (2013 dollars) in 2040. The high prices 

result primarily from higher costs of petroleum supply. Fewer structurally reservoired crude oil 

supplies are developed than in the Reference case, leading to increased development of more costly 

resources, including tight oil and bitumen. Higher prices also lead to significant increases in 

renewable liquid fuels and coal-to-liquid products as compared with the Reference case. In this case, 

OPEC’s share of world liquids production never exceeds the high of 41% that it reaches in 2012 and 

drops over the projection period, reaching as low as 30%. The higher supply costs depress demand 

globally through 2028, but stronger growth in non-OECD countries than is projected in the 

Reference case leads to higher demand than in the Reference case, starting in these countries in 

2028, and starting globally in 2035.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 12

Table 1.1. Summary of AEO2015 cases

Case name Description

Reference Real GDP grows at an average annual rate of 2.4% from 2013 to 2040. Crude oil price rises to 

about $140/barrel (Brent 2013 dollars) in 2040.

Low Economic Growth Real GDP grows at an average annual rate of 1.8% from 2013 to 2040. Other energy market 

assumptions are the same as in the Reference case.

High Economic Growth Real GDP grows at an average annual rate of 2.9% from 2013 to 2040. Other energy market 

assumptions are the same as in the Reference case.

Low Oil Price Low prices result from a combination of low demand for petroleum and other liquids in the 

non-Organization for Economic Cooperation and Development (non-OECD) nations and higher 

global supply. Lower demand is measured by lower economic growth compared with the 

Reference case. On the supply side, the Organization of the Petroleum Exporting Countries 

(OPEC) increases its liquids market share to 51%, and the costs of other liquids production 

technologies are lower than in the Reference case. Light, sweet crude oil prices remain 

around $52/b (2013 dollars) through 2017, then begin to rise slowly to almost $66/bbl in 

2026, and to $76/b in 2040.

High Oil Price High prices result from a combination of higher demand for liquid fuels in non-OECD nations 

and lower global supply. Higher demand is measured by higher economic growth compared 

with the Reference case. OPEC market share averages 32% throughout the projection. NonOPEC petroleum production expands more slowly in the short to middle term compared to 

the Reference case. Crude oil prices rise to $252/barrel (2013 dollars) in 2040.

Oil and Gas: High Oil and Gas 

Resource

Estimated ultimate recovery (EUR) per shale gas, tight gas, and tight oil well is 50% higher, 

well spacing is 50% closer (i.e., the number of wells left to be drilled is 100% higher) than in 

the Reference case. In addition, tight oil resources are added to reflect new plays or the 

expansion of known tight oil plays, and the EUR for tight and shale wells increases by 1%/year 

beyond the Reference case annual increase to reflect additional technology improvements. 

This case also includes kerogen development, as well as undiscovered resources in the 

offshore lower 48 states, Alaska, and shale gas and coalbed resources in Canada that are 50% 

higher than in the Reference case.
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Carbon dioxide emissions

CO2 emissions from energy use are dependent on the carbon content of the fossil fuel, the fraction of the 

fuel consumed in combustion, and the consumption of that fuel. The product of the carbon content at full 

combustion and the combustion fraction yields an adjusted CO2 factor for each fossil fuel. The emissions 

factors are expressed in millions of metric tons of carbon dioxide emitted per quadrillion Btu of energy use, 

or equivalently, in kilograms of CO2 per million Btu. The adjusted emissions factors are multiplied by the 

energy consumption of the fossil fuel to arrive at the CO2 emissions projections.

For fuel uses of energy, all of the carbon is assumed to be oxidized, so the combustion fraction is equal to 

1.0 (in keeping with international conventions). Previously, a small fraction of the carbon content of the fuel 

was assumed to remain unoxidized. The carbon in nonfuel use of energy, such as for asphalt and 

petrochemical feedstocks, is assumed to be sequestered in the product and not released to the atmosphere. 

For energy categories that are mixes of fuel and nonfuel uses, the combustion fractions are based on the 

proportion of fuel use. In calculating CO2 emissions for motor gasoline, the direct emissions from renewable 

blending stock (ethanol) is omitted. Similarly, direct emissions from biodiesel are omitted from reported 

CO2 emissions.

Any CO2 emitted by biogenic renewable sources, such as biomass and alcohols, is considered balanced by 

the CO2 sequestration that occurred in its creation. Therefore, following convention, net emissions of CO2

from biogenic renewable sources are assumed to be zero in reporting energy-related CO2 emissions; 

however, to illustrate the potential for these emissions in the absence of any offsetting sequestration, as 

might occur under related land use change, the CO2 emissions from biogenic fuel use are calculated and 

reported separately.

Table 1.2 presents the assumed CO2 coefficients at full combustion, the combustion fractions, and the 

adjusted CO2 emission factors used for AEO2015.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 14

Table 1.2. Carbon dioxide emission factors

million metric tons carbon dioxide equivalent per quadrillion Btu

Fuel Type

Carbon Dioxide 

Coefficient at Full 

Combustion

Combustion 

Fraction

Adjusted Emission 

Factor

Petroleum

 Propane

 Used as fuel 63.07 1.000 63.07

 Used as feedstock 61.07 0.200 12.61

 Ethane used as feedstock 59.58 0.200 11.92

 Butane used as feedstock 64.94 0.200 12.98

 Isobutane used as feedstock 65.08 0.200 13.02

 Natural gasoline used as feedstock 66.88 0.300 21.12

 Motor gasoline (net of ethanol) 71.26 1.000 71.26

 Jet fuel 70.88 1.000 70.88

 Distillate fuel (net of biodiesel) 73.15 1.000 73.15

 Residual fuel 78.80 1.000 78.80

 Asphalt and road oil 75.61 0.000 0.00

 Lubricants 74.21 0.500 37.11

 Petrochemical feedstocks 71.02 0.410 29.11

 Kerosene 72.31 1.000 72.31

 Petroleum coke 101.09 0.956 97.64

 Petroleum still gas 64.20 1.000 64.20

 Other industrial 74.54 1.000 74.54

Coal

 Residential and commercial 95.33 1.000 95.33

 Metallurgical 93.72 1.000 93.72

 Coke 117.81 1.000 117.81

 Industrial other 93.98 1.000 93.98

 Electric utility1 95.52 1.000 95.52

Natural gas

 Used as fuel 53.06 1.000 53.06

 Used as feedstock 53.06 0.437 23.21

Biogenic energy sources

 Biomass 93.81 1.000 93.81

 Biogenic waste 90.64 1.000 90.64

 Biofuels heats and coproducts 93.81 1.000 93.81

 Ethanol 68.42 1.000 68.42

 Biodiesel 72.73 1.000 72.73

 Liquids from biomass 73.15 1.000 73.15

 Green liquids 73.15 1.000 73.15 1

Emission factors for coal used for electric power generation within NEMS are specified by coal 

supply region and types of coal, so the average CO2 content for coal varies throughout the 

projection. The value of 95.52 shown here is representative of recent history.

Source: U.S. Energy Information Administration, Monthly Energy Review, November 2014, 

DOE/EIA-0035(2014/11), (Washington, DC, November 2014). 
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 15

Notes and sources

[1] U.S. Energy Information Administration, Annual Energy Outlook 2015 (AEO2015), DOE/EIA-0383(2015) 

(Washington, DC, April 2015).

[2] NEMS documentation reports are available on the EIA Homepage 

(http://www.eia.gov/reports/index.cfm#/KNEMS Documentation).

[3] U.S. Energy Information Administration, The National Energy Modeling System: An Overview 2009, 

DOE/EIA-0581(2009) (Washington, DC, October 2009), http://www.eia.gov/oiaf/aeo/overview.

[4] GlobalData (New York, NY, 2014) http://www.globaldata.com (subscription site).
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 16

Macroeconomic Activity Module

The Macroeconomic Activity Module (MAM) represents interactions between the U.S. economy and energy 

markets. How fast the economy grows, as measured by either growth in gross domestic product or 

industrial shipments, is a key determinant of growth in the demand for energy. Associated economic factors, 

such as interest rates and disposable income, strongly influence various elements of the supply and demand 

for energy. At the same time, reactions to energy markets by the aggregate economy, such as a slowdown in

economic growth resulting from increasing energy prices, are also reflected in this module. A detailed 

description of the MAM is provided in the EIA publication, Model Documentation Report: Macroeconomic 

Activity Module (MAM) of the National Energy Modeling System (Washington, DC, May 2014).

Key assumptions

The output of the U.S. economy, measured by GDP, is expected to increase by 2.4% per year between 2013

and 2040 in the Reference case. Two key factors help explain the growth in GDP: the growth rate of nonfarm 

employment and the rate of productivity change associated with employment. As Table 2.1 indicates, in the 

Reference case, real GDP grows by 2.6% per year from 2015-25 and 2.3% from 2025-40, the last fifteen 

years of the forecast. Both the high and low macroeconomic growth cases show roughly 0.5-percentagepoint differences in growth as compared with the Reference case. Non-farm employment shows higher 

growth from 2013-2015 and then returns to its long-run trend growth of 0.8%. In the High and Low Growth 

cases, nonfarm employment growth differs by 0.2 percentage points compared with the Reference case 

growth of 0.8% from 2013 to 2040, reaching 1.0% and 0.6% in the High Growth and Low Growth cases, 

respectively. In the Reference case, productivity (measured as output per hour in nonfarm businesses) 

grows by 2.0% from 2013 to 2040 showing similar growth to that experienced during the previous 30 years. 

Business fixed investment as a share of nominal GDP is expected to grow over the last 10 years of the 

projection. The resulting growth in the capital stock and the technology base of that capital stock helps to 

sustain productivity growth of 2.0% from 2013 to 2040.

The Census Bureau’s middle series population projection is used as a basis for population growth in 

AEO2014. Total population is expected to grow by 0.7% per year between 2013 and 2040, and the share of 

population over 65 is expected to increase over time. However, the share of the labor force in the 

population over 65 is also projected to increase in the projection period.

To achieve the Reference case’s long-run 2.4% economic growth, there is an anticipated steady growth in 

labor productivity. The improvement in labor productivity reflects the positive effects of a growing capital 

stock as well as technological change over time. Nonfarm labor productivity growth is expected to remain 

between 1.8 and 2.2% throughout of the projection period from 2015 through 2040.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 17

Table 2.1. Growth in gross domestic product, nonfarm employment and productivity

Assumptions 2013-2020 2020-2030 2030-2040 2013-2040

Real GDP (Billion Chain-weighted $2005)

 High Growth 3.2% 2.9% 2.7% 2.9%

 Reference 2.6% 2.4% 2.3% 2.4%

 Low Growth 1.8% 1.8% 2.0% 1.8%

Nonfarm Employment

 High Growth 1.6% 0.9% 0.6% 1.0%

 Reference 1.3% 0.6% 0.6% 0.8%

 Low Growth 1.0% 0.5% 0.5% 0.6%

Productivity

 High Growth 2.1% 2.3% 2.1% 2.2%

 Reference 1.8% 2.1% 1.9% 2.0%

 Low Growth 1.4% 1.7% 1.4% 1.5%

Source: U.S. Energy Information Administration, AEO2015 National Energy Modeling system runs: AEO2015.d021915A, 

LM2015.d021915A, and HM2015. D021915A.

To reflect uncertainty in the projection of U.S. economic growth, the AEO2015 uses High and Low Economic 

Growth cases to project the possible impacts of alternative economic growth assumptions on energy 

markets. The High Economic Growth case incorporates higher population, labor force and productivity 

growth rates than the Reference case. Due to the higher productivity gains, inflation and interest rates are 

lower than the Reference case. Investment, disposable income and industrial production are greater. 

Economic output is projected to increase by 2.9% per year between 2013 and 2040. The Low Economic 

Growth case assumes lower population, labor force, and productivity gains, with resulting higher prices and 

interest rates and lower industrial output growth. In the Low Economic Growth case, economic output is 

expected to increase by 1.8% per year over the projection horizon.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 18

International Energy Module

The National Energy Modeling System International Energy Module (IEM) simulates the interaction between 

U.S. and global petroleum markets. It uses assumptions of economic growth and expectations of future U.S. 

and world crude-like liquids production and consumption to estimate the effects of changes in U.S. liquid 

fuels markets on the international petroleum market. For each year of the forecast, the IEM computes Brent 

and WTI prices, provides a supply curve of world crude-like liquids, and generates a worldwide oil supplydemand balance with regional detail. The IEM also provides, for each year of the projection period, 

endogenous and exogenous assumptions for petroleum products for import and export in the United States.

Changes in the oil price (Brent) are computed in response to:

1. The difference between projected U.S. total crude-like liquids production and the expected U.S. total 

crude-like liquids production at the current oil price (estimated using the current oil price and the 

exogenous U.S. total crude-like liquids supply curve for each year).

and

2. The difference between projected U.S. total crude-like liquids consumption and the expected U.S. total 

crude-like liquids consumption at the current oil price (estimated using the current oil price and the 

exogenous U.S. total crude-like liquids demand curve).

Key assumptions

AEO2015 considers a number of factors related to the uncertainty of future oil prices, including changes in 

worldwide demand for petroleum products, OPEC investment and production decisions, non-OPEC 

petroleum liquid fuels supply, and supplies of other liquid fuels.

In the AEO2015 Reference case, the growth in U.S. crude oil production, combined with the fall in world oil 

prices, contributes to a decrease in the oil price to $56 (2013 dollars) per barrel in 2015. Oil prices rise 

steadily after 2015 in response to growth in demand from countries outside of the Organization for 

Economic Cooperation and Development (OECD) even if downward pressure from increases in U.S. oil 

production keeps the oil price below $80 per barrel through 2020. Growth in demand from non-OECD 

countries combined with a decline in U.S. crude oil production after 2020 will push the oil price to $141 per 

barrel in 2040. The AEO2015 Reference case also assumes that the OPEC market share of liquids production 

will decrease from 39% in 2014 to 37% in 2020 followed by an increase to 41% by 2040.

In the AEO2015 Low Oil Price case, the oil price drops to $52 per barrel in 2015 followed by a slow increase 

to $76 per barrel in 2040. This is in response to higher upstream investment by OPEC and lower non-OECD 

demand. In the AEO2015 Low Oil Price case, OPEC countries increase their liquids production to obtain an 

increase in market share from 38% in 2015 to 51% in 2040.

In the AEO2015 High Oil Price case, the oil price increases to $122 per barrel in 2015 and to above $252 per 

barrel in 2040. This is in response to significantly lower OPEC production and higher non-OECD demand, 

higher demand for petroleum products, and a more limited supply of other liquid fuels than in the Reference 

case. Also, U.S. production is significantly greater, resulting in lower net imports of crude oil. In the
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 19

AEO2015 High Oil Price case, OPEC countries’ share of world liquids production decreases to 30% by 2025 

followed by an increase to 33% by 2040.

OPEC oil production in the AEO2015 Reference case is assumed to increase throughout the 2015-2040 

projection period (Figure 3), at a rate that enables the organization to achieve a 41% market share of the 

world’s total petroleum and other liquids in 2040. OPEC is assumed to be an important source of additional 

production because its member nations hold a major portion of the world’s total reserves—around 1,200 

billion barrels, about 73% of the world’s estimated total, at the end of 2014. [5] 

Non-U.S., non-OPEC oil production projections in the AEO2015 are developed in two stages. Projections of 

liquids production before 2015 are based largely on a project-by-project assessment of major fields, 

including volumes and expected schedules, with consideration given to the decline rates of active projects, 

planned exploration and development activity, and country- specific geopolitical situations and fiscal 

regimes. Incremental production estimates from existing and new fields after 2015 are estimated based on 

country-specific consideration of economics and ultimate technically recoverable resource estimates. The 

non-OPEC production path for the AEO2015 Reference case is shown in Figure 4.

Figure 2. World oil prices in three cases, 1995-2040

million barrels per day
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 20

Figure 3. OPEC total liquids production in the Reference case, 1995-2040

million barrels per day

Figure 4. Non-OPEC total liquids production in the Reference case, 1995-2040

million barrels per day
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 21

The non-U.S. oil production projections in AEO2015 are limited by country-level assumptions regarding 

technically recoverable oil resources. Inputs to these resource estimates include the USGS World Petroleum 

Assessment of 2000 and oil reserves published in the Oil & Gas Journal by PennWell Publishing Company, a 

summary of which is shown in Table 3.1.

The AEO2015 Reference case growth rates for GDP for various regions in the world are shown in Table 3.2. 

The GDP growth rate assumptions for non-U.S. countries/regions are taken from Oxford Economic Model 

(October, 2012).

The values for growth in total liquids demand in the International Energy Module, which depend upon the 

oil price levels as well as GDP growth rates, are shown in Table 3.3 for the Reference case by region.

Table 3.1. Worldwide oil reserves as of January 1, 2014

million barrels

Region Proved Oil Reserves

Western Hemisphere 544.9

Western Europe 11.1

Asia-Pacific 46.0

Eastern Europe and Former Soviet Union (F.S.U.) 120.0

Middle East 798.6

Africa 126.7

Total World 1,647.4

Total OPEC 1,200.8

Source: Pennwell Corporation, Oil and Gas Journal, Vol 112. 12 (Dec. 1, 2014).

Table 3.2. Average annual real gross domestic product rates, 2010-40

2005 purchasing power parity weights and prices

Region Average Annual Percentage Change

OECD 2.0%

OECD Americas 2.4%

OECD Europe 2.0%

OECD Asia 1.4%

Non-OECD 4.6%

Non-OECD Europe and Eurasia 2.9%

Non-OECD Asia 5.3%

Middle East 3.9%

Africa 4.8%

Central and South America 3.2%

Total World 3.5%

Source: U.S. Energy Information Administration, Derived from Oxford Economic Model (February 2014).
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 22

Table 3.3. Average annual growth rates for total liquids demand in the Reference case, 2010-40

percent per year

Region Demand Growth

OECD -0.05%

OECD Americas 0.07%

OECD Europe -0.16%

OECD Asia 0.07%

Non-OECD 2.07%

Non-OECD Europe and Eurasia 0.51%

Non-OECD Asia 2.66%

Middle East 1.69%

Africa 2.04%

Central and South America 1.27%

Total World 1.09%

Source: U.S. Energy Information Administration, National Energy Modeling System run REF2015.d010515A.

Notes and sources

[5] PennWell Corporation, Oil and Gas Journal, Vol. 111.12 (December 1, 2014).
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 23

Residential Demand Module

The NEMS Residential Demand Module projects future residential sector energy requirements based on 

projections of the number of households and the stock, efficiency, and intensity of energy-consuming 

equipment. The Residential Demand Module projections begin with a base year estimate of the housing 

stock, the types and numbers of energy-consuming appliances servicing the stock, and the “unit energy 

consumption” (UEC) by appliance (in million Btu per household per year). The projection process adds new 

housing units to the stock, determines the equipment installed in new units, retires existing housing units, 

and retires and replaces appliances. The primary exogenous drivers for the module are housing starts by 

type (single-family, multifamily and mobile homes) and by Census division, and prices for each energy source 

for each of the nine Census divisions (see Figure 5).

The Residential Demand Module also requires projections of available equipment and their installed costs 

over the projection horizon. Over time, equipment efficiency tends to increase because of general 

technological advances and also because of federal and/or state efficiency standards. As energy prices and 

available equipment change over the projection horizon, the module includes projected changes to the type 

and efficiency of equipment purchased as well as projected changes in the usage intensity of the equipment 

stock.

Figure 5. United States Census Divisions

Source: U.S. Energy Information Administration, Office of Energy Analysis.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 24

The end-use equipment for which stocks are modeled include those major end uses that often span several 

fuels, such as space conditioning (heating and cooling) equipment, water heaters, refrigerators, freezers, 

dishwashers, clothes washers, cookstoves, clothes dryers, light bulbs, furnace fans, as well as several 

miscellaneous electric loads: televisions and related equipment (set-top boxes, home theater systems, DVD 

players, and video game consoles), computers and related equipment (desktops, laptops, monitors, 

networking equipment), rechargeable electronics, ceiling fans, coffee makers, dehumidifiers, microwaves, 

pool heaters and pumps, home security systems, and portable electric spas. In addition to the modeled end 

uses previously listed, the average energy consumption per household is projected for other electric and 

nonelectric uses. The fuels represented are distillate fuel oil, liquefied petroleum gas, natural gas, kerosene, 

electricity, wood, geothermal, and solar energy. The module’s output includes number of households, 

equipment stock, average equipment efficiencies, and energy consumed by service, fuel, and geographic 

location.

One of the implicit assumptions embodied in the residential sector Reference case projections is that, 

through 2040, there will be no radical changes in technology or consumer behavior. No new regulations of 

efficiency beyond those currently embodied in law or new government programs fostering efficiency 

improvements are assumed. Technologies which have not gained widespread acceptance today will 

generally not achieve significant penetration by 2040. Currently available technologies will evolve in both 

efficiency and cost. In general, future technologies at the same efficiency level will be less expensive, in real 

dollar terms, than those available today. When choosing new or replacement technologies, consumers will 

behave similarly to the way they now behave, and the intensity of end uses will change moderately in 

response to price changes. [6]

Key assumptions

Housing Stock Submodule

An important determinant of future energy consumption is the projected number of households. Base year 

estimates for 2009 are derived from the U.S. Energy Information Administration’s (EIA) Residential Energy 

Consumption Survey (RECS) (Table 4.1). The projection for occupied households is done separately for each 

Census division. It is based on the combination of the previous year’s surviving stock with projected housing 

starts provided by the NEMS Macroeconomic Activity Module. The Housing Stock Submodule assumes a 

constant survival rate (the percentage of households which are present in the current projection year, which 

were also present in the preceding year) for each type of housing unit: 99.7% for single-family units, 99.5% 

for multifamily units, and 96.6% for mobile home units.

Projected fuel consumption is dependent not only on the projected number of housing units, but also on the 

type and geographic distribution of the houses. The intensity of space heating energy use varies greatly 

across the various climate zones in the United States. Also, fuel prevalence varies across the country—oil 

(distillate) is more frequently used as a heating fuel in the New England and Middle Atlantic Census divisions 

than in the rest of the country, while natural gas dominates in the Midwest. An example of differences by 

housing type is the more prevalent use of liquefied petroleum gas in mobile homes relative to other housing 

types.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 25

Table 4.1. 2009 Households

Census Single-Family Units Multifamily Units Mobile Homes Total Units

New England 3,374,597 2,052,063 84,437 5,511,097

Middle Atlantic 9,287,267 5,536,739 435,344 15,259,350

East North Central 13,077,414 4,217,199 558,802 17,853,414

West North Central 6,153,386 1,406,903 503,817 8,064,106

South Atlantic 15,162,865 4,656,262 2,405,757 22,224,884

East South Central 5,480,023 945,846 658,471 7,084,340

West South Central 9,095,440 2,822,348 853,143 12,770,931

Mountain 5,983,945 1,258,517 662,813 7,905,276

Pacific 10,937,616 5,226,838 778,377 16,942,832

United States 78,552,553 28,122,715 6,940,961 113,616,230

Source: U.S. Energy Information Administration, 2009 Residential Energy Consumption Survey.

Technology Choice Submodule

The key inputs for the Technology Choice Submodule are fuel prices by Census division and characteristics of 

available equipment (installed cost, maintenance cost, efficiency, and equipment life). The Integrating 

Module of NEMS estimates fuel prices through an equilibrium simulation that balances supply and demand 

and passes the prices to the Residential Submodule.

Prices combined with equipment UEC (a function of efficiency) determine the operating costs of equipment. 

Equipment characteristics are exogenous to the model and are modified to reflect both federal standards 

and anticipated changes in the market place. Table 4.2 lists capital costs and efficiency for selected 

residential appliances for the years 2010 and 2020.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 26

Table 4.2. Installed cost and efficiency ratings of selected equipment

Equipment Type

Relative 

Performance1

2013 

Installed 

Cost (2013$)

2013 

Efficiency2

2020 

Efficiency2

Approximate 

Hurdle 

Rate

Electric Heat Pump (heating component) Minimum $3,150 7.7 8.2

Best $4,500 9.0 10.8 25%

Natural Gas Furnace Minimum $1,900 0.80 0.80 

Best $2,950 0.98 0.98 15%

Room Air Conditioner Minimum $385 9.8 10.8

Best $565 11.5 11.9 42%

Central Air Conditioner3

Minimum $2,100 13.0 13.0

Best $5,100 24.0 24.0 25%

Refrigerator4

Minimum $580 541 406

Best $930 349 349 10%

Electric Water Heater Minimum $615 0.90 0.95

Best $2,170 2.45 2.75 50%

Solar Water Heater N/A $7,840 N/A N/A 30% 1

Minimum performance refers to the lowest-efficiency equipment available. Best refers to the highest-efficiency equipment 

available.

2

Efficiency measurements vary by equipment type. Electric heat pumps are based on Heating Seasonal Performance Factor (HSPF); 

natural gas furnaces are based on Annual Fuel Utilization Efficiency (AFUE); central air conditioners are based on Seasonal Energy 

Efficiency Ratio (SEER); room air conditioners are based on Energy Efficiency Ratio (EER); refrigerators are based on kilowatt-hours 

per year; and water heaters are based on Energy Factor (delivered Btu divided by input Btu). 3

Values are for northern regions of United States.

4

Reflects a refrigerator with a top-mounted freezer with 20.6 cubic feet nominal volume.

Source: EIA - Technology Forecast Updates – Residential and Commercial Building Technologies – Reference Case, prepared for U.S. 

Energy Information Administration, Navigant Consulting, Inc., March 2014.

Table 4.3 provides the cost and performance parameters for representative distributed generation 

technologies. The model also incorporates endogenous “learning” for the residential distributed generation 

technologies, allowing for declining technology costs as shipments increase. For fuel cell and photovoltaic 

systems, learning parameter assumptions for the Reference case result in a 13% reduction in capital costs 

each time the installed capacity in buildings doubles (in the case of photovoltaics, utility-scale capacity is 

also included for learning). Capital costs for small wind, a relatively mature technology, only decline 3% with 

each doubling of shipments.

The Residential Demand Module projects equipment purchases based on a nested choice methodology. The 

first stage of the choice methodology determines the fuel and technology to be used. The equipment 

choices for cooling and water heating are linked to the space heating choice for new construction. 

Technology and fuel choice for replacement equipment uses a nested methodology similar to that for new 

construction, but includes (in addition to the capital and installation costs of the equipment) explicit costs 

for fuel or technology switching (e.g., costs for installing gas lines if switching from electricity or oil to gas, or 

costs for adding ductwork if switching from electric resistance heat to central heating types). Also, for 

replacements, there is no linking of fuel choice for water heating and cooking as is done for new 

construction. Technology switching across fuels upon replacement is allowed for space heating, air 

conditioning, water heating, cooking and clothes drying.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 27

Table 4.3. Capital cost and performance parameters of selected residential distributed generation 

technologies

Technology Type

Year of 

Introduction

Average Generating 

Capacity (kWDC)

Electrical 

Efficiency

Combined 

Efficiency 

(Elec. + 

Thermal)

Installed 

Capital Cost 

(2009 $ per 

kWDC)

1

Service 

Life 

(Years)

Solar Photovoltaic 2010 3.5 0.150 N/A $7,117 30

2015 4.0 0.175 N/A 3,903 30

2025 5.0 0.197 N/A 2,831 30

2035 5.0 0.200 N/A 2,674 30

Fuel Cell 2010 10 0.364 0.893 $14,837 20

2015 10 0.429 0.859 $14,837 20

2025 10 0.456 0.842 $14,837 20

2035 10 0.479 0.842 $14,837 20

Wind 2010 2 0.13 N/A $7,802 30

2015 3 0.13 N/A $6,983 30

2025 3 0.13 N/A $6,234 30

2035 4 0.13 N/A $5,903 30 1

The original source documents presented solar photovoltaic costs in 2008 dollars, fuel cell and wind costs in 2010 dollars.

Source: EIA analysis, as well as technology-specific reports: Solar photovoltaic: Photovoltaic (PV) Cost and Performance 

Characteristics for Residential and Commercial Applications (ICF International, 2010). Fuel cell: Commercial and Industrial CHP 

Technology Cost and Performance Data Analysis for EIA (SENTECH Incorporated, 2010). Wind: The Cost and Performance of 

Distributed Wind Turbines, 2010-35 (ICF International, 2010).

Once the fuel and technology choice for a particular end use is determined, the second stage of the choice 

methodology determines efficiency. In any given year, there are several available prototypes of varying 

efficiency (minimum standard, some intermediate levels, and highest efficiency). Efficiency choice is based 

on a functional form and coefficients which give greater or lesser importance to the installed capital cost 

(first cost) versus the operating cost. Generally, within a technology class, the higher the first cost, the lower 

the operating cost. For new construction, efficiency choices are made based on the costs of both the heating 

and cooling equipment and the building shell characteristics.

Once equipment efficiencies for a technology and fuel are determined, the installed efficiency for its entire 

stock is calculated.

Appliance Stock Submodule

The Appliance Stock Submodule is an accounting framework which tracks the quantity and average 

efficiency of equipment by end use, technology, and fuel. It separately tracks equipment requirements for 

new construction and existing housing units. For existing units, this module calculates the number of units 

which survive from previous years, allows certain end uses to further penetrate into the existing housing 

stock and calculates the total number of units required for replacement and further penetration. Air 

conditioning, dishwashing, and clothes drying are three major end uses not considered to be “fully 

penetrated.”
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 28

Once a piece of equipment enters into the stock, an accounting of its remaining life begins. The decay 

function is based on Weibull distribution shape parameters that approximate linear decay functions. The 

estimated maximum and minimum equipment lifetimes used to inform the Weibull shape parameters are 

shown in Table 4.4. Weibull shapes allow some retirement before the listed minimum lifetime, as well as 

allow some equipment to survive beyond its listed maximum lifetime. It is assumed that, when a house is 

retired from the stock, all of the equipment contained in that house retires as well; i.e., there is no secondhand market for this equipment.

Table 4.4. Minimum and maximum life expectancies of equipment

Equipment Minimum Life Maximum Life

Heat Pumps 7 21

Central Forced-Air Furnaces 10 25

Hydronic Space Heaters 20 30

Room Air Conditioners 8 16

Central Air Conditioners 7 21

Gas Water Heaters 4 14

Electric Water Heaters 5 22

Cooking Stoves 16 21

Clothes Dryers 11 20

Refrigerators 7 26

Freezers 11 31

Source: Lawrence Berkeley National Laboratory, Baseline Data for the Residential Sector and Development of a Residential 

Forecasting Database, May 1994, and analysis of RECS 2001 data.

Fuel Consumption Submodule

Energy consumption is calculated by multiplying the vintage equipment stocks by their respective UECs. The 

UECs include adjustments for the average efficiency of the stock vintages, short-term price elasticity of 

demand and “rebound” effects on usage (see discussion below), the size of new construction relative to the 

existing stock, people per household, shell efficiency and weather effects (space heating and cooling). The 

various levels of aggregated consumption (consumption by fuel, by service, etc.) are derived from these 

detailed equipment-specific calculations.

Equipment efficiency

The average energy consumption for most technology types is initially based on estimates derived primarily 

from RECS 2009. As the stock efficiency changes over the projection period, energy consumption decreases 

in inverse proportion to efficiency. Also, as efficiency increases, the efficiency rebound effect (discussed 

below) will offset some of the reductions in energy consumption by increased demand for the end-use 

service. For example, if the stock average for electric heat pumps is now 10% more efficient than in 2005, 

then all else constant (weather, real energy prices, shell efficiency, etc.), energy consumption per heat pump 

would average about 9% less.

Miscellaneous electric loads (MELs)

Unlike the technology choice submodule’s accounting framework, the energy consumption projection of 

several miscellaneous electric loads is characterized by assumed changes in per-unit consumption multiplied 
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 29

by assumed changes in the number of units. In this way, stock and UEC concepts are projected, but without 

the decision-making parameters or investment calculations of the technology choice submodule. The UECs 

of certain MELs may be further modified beyond their input assumption by factors such as income, square 

footage, and/or degree days, where relevant.

Adjusting for the size of housing units

Estimates for the size of each new home built in the projection period vary by type and region, and are 

determined by a projection based on historical data from the U.S. Bureau of the Census [7]. For existing 

structures, it is assumed that about 1% of households that existed in 2009 add about 600 square feet to the 

heated floor space in each year of the projection period [8]. The energy consumption for space heating, air 

conditioning, and lighting is assumed to increase with the square footage of the structure. This results in an 

increase in the average size of a housing unit from 1,644 to 1,855 square feet from 2009 through 2040.

Adjusting for weather and climate

Weather in any given year always includes short-term deviations from the expected longer-term average (or 

climate). Recognition of the effect of weather on space heating and air conditioning is necessary to avoid 

inadvertently projecting abnormal weather conditions into the future. The residential module adjusts space 

heating and air conditioning UECs by Census division using data on heating and cooling degree days (HDD 

and CDD). Short-term projections are informed by the National Oceanic and Atmospheric Administration’s 

(NOAA) 15-month outlook from their Climate Prediction Center [9], which often encompasses the first 

forecast year. Projections of degree days beyond that are informed by a 30-year linear trend of each state’s 

degree days, which are then population-weighted to the Census division level. In this way, the projection 

accounts for projected population migrations across the nation and continues any realized historical changes 

in degree days at the state level.

Short-term price effect and efficiency rebound

It is assumed that energy consumption for a given end-use service is affected by the marginal cost of 

providing that service. That is, all else equal, a change in the price of a fuel will have an opposite, but less 

than proportional, effect on fuel consumption. The current value for the short-term elasticity parameter for 

non-electric fuels is -0.15 [10]. This value implies that for a 1% increase in the price of a fuel, there will be a 

corresponding decrease in energy consumption of -0.15%. Changes in equipment efficiency affect the 

marginal cost of providing a service. For example, a 10% increase in efficiency will reduce the cost of 

providing the end-use service by 10%. Based on the short-term elasticity, the demand for the service will rise 

by 1.5%(-10% multiplied by -0.15). Only space heating, cooling, and lighting are assumed to be affected by 

both elasticities and the efficiency rebound effect. For electricity, the short-term elasticity parameter is set 

to -0.30 to account for successful deployment of smart grid projects funded under the American Recovery 

and Reinvestment Act of 2009.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 30

Shell efficiency

The shell integrity of the building envelope is an important determinant of the heating and cooling load for 

each type of household. In the NEMS Residential Demand Module, the shell integrity is represented by an 

index, which changes over time to reflect improvements in the building shell. The shell integrity index is 

dimensioned by vintage of house, type of house, fuel type, service (heating and cooling), and Census 

division. The age, type, location, and type of heating fuel are important factors in determining the level of 

shell integrity. Homes are classified by age as new (post-2009) or existing. Existing homes are represented by 

the most recent RECS survey and are assigned a shell index value based on the mix of homes that exist in the 

base year. The improvement over time in the shell integrity of these homes is a function of two factors: an 

assumed annual efficiency improvement and improvements made when real fuel prices increase. No pricerelated adjustment is made when fuel prices fall. For new construction, building shell efficiency is 

determined by the relative costs and energy bill savings for several levels of heating and cooling equipment, 

in conjunction with the building shell attributes. The packages represented in NEMS range from homes that 

meet the International Energy Conservation Code (IECC) [11] to homes that are built with the most efficient 

shell components. Shell efficiency in new homes increases over time when energy prices rise, or the cost of 

more-efficient equipment falls, all else equal.

Legislation and regulations

American Recovery and Reinvestment Act of 2009 (ARRA2009) 

The ARRA2009 legislation passed in February 2009 provides energy-efficiency funding for federal agencies, 

State Energy Programs, and block grants, as well as a sizable increase in funding for weatherization. To 

account for the impact of this funding, it is assumed that the total funding is aimed at increasing the 

efficiency of the existing housing stock. The assumptions regarding the energy savings for heating and 

cooling are based on evaluations of the impact of weatherization programs over time. Further, it is assumed 

each house requires a $2,600 investment to achieve the heating and cooling energy savings cited in [12] and 

that the efficiency measures last approximately 20 years. Assumptions for funding amounts and timing were 

revised downward and further into the future based on analysis of the weatherization program by the 

Inspector General of the U.S. Department of Energy [13].

The ARRA2009 provisions remove the cap on the 30% tax credit for ground-source heat pumps, solar PV, 

solar thermal water heaters, and small wind turbines through 2016. Additionally, the cap for the tax credits 

for other energy-efficiency improvements, such as windows and efficient furnaces, was increased to $1,500 

through the end of 2010. Several tax credits were extended at reduced credit levels through the end of 

2011 as part of the Tax Relief, Unemployment Insurance Reauthorization, and Job Creation Act of 2010. 

These tax credits were further extended through the end of 2013 as part of the American Taxpayer Relief 

Act of 2012, but since those tax credits were not in existence during 2012 and thus were not part of 

consumers’ decision-making process, these tax credits were only modeled for 2013, not for 2012.

Successful deployment of smart grid projects based on ARRA2009 funding could stimulate more rapid 

investment in smart grid technologies, especially smart meters on buildings and homes, which would make 

consumers more responsive to electricity price changes. To represent this, the price elasticity of demand for 

residential electricity was increased for the services that have the ability to alter energy intensity (e.g., 

lighting).
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 31

Energy Improvement and Extension Act of 2008 (EIEA2008)

EIEA2008 extends and amends many of the tax credits that were made available to residential consumers in 

EPACT2005. The tax credits for energy-efficient equipment can now be claimed through 2016, while the 

$2,000 cap for solar technologies has been removed. Additionally, the tax credit for ground-source 

(geothermal) heat pumps was increased to $2,000. The production tax credits for dishwashers, clothes 

washers, and refrigerators were extended by one to two years, depending on the efficiency level and 

product. See the EPACT2005 section below for more details about product coverage.

Energy Independence and Security Act of 2007 (EISA2007)

EISA2007 contains several provisions that impact projections of residential energy use. Standards for general 

service incandescent light bulbs are phased in over 2012-2014, with a more restrictive standard specified in 

2020. It is estimated that these standards require 29% less watts per bulb in the first phase-in, increasing to 

67% in 2020. General service incandescent bulbs become substandard in the 2012-2014 period and during 

this time halogen bulbs serve as the incandescent option. These halogen bulbs then become substandard in 

the 2020 specification, reducing general service lighting options to compact fluorescent and light-emitting 

diode (LED) technologies.

Energy Policy Act of 2005 (EPACT2005)

The passage of EPACT2005 in August 2005 provides additional minimum efficiency standards for residential 

equipment and provides tax credits to producers and purchasers of energy-efficient equipment and builders 

of energy-efficient homes. The standards contained in EPACT2005 include: 190-watt maximum for torchiere 

lamps in 2006; dehumidifier standards for 2007 and 2012; and ceiling fan light kit standards in 2007. For 

manufactured homes that are 30% better than the latest code, a $1,000 tax credit can be claimed in 2006 

and 2007. Likewise, builders of homes that are 50% better than code can claim a $2,000 credit over the 

same period. The builder tax credits and production tax credits are assumed to be passed through to the 

consumer in the form of lower purchase cost. EPACT2005 includes production tax credits for energyefficient refrigerators, dishwashers, and clothes washers in 2006 and 2007, with dollar amounts varying by 

type of appliance and level of efficiency met, subject to annual caps. Consumers can claim a 10% tax credit 

in 2006 and 2007 for several types of appliances specified by EPACT2005, including: energy-efficient gas, 

propane, or oil furnaces or boilers, energy-efficient central air conditioners, air and ground source heat 

pumps, water heaters, and windows. Lastly, consumers can claim a 30% tax credit in 2006 and 2007 for 

purchases of solar PV, solar water heaters, and fuel cells, subject to a cap.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 32

Notes and sources

[6] The Model Documentation Report contains additional details concerning model structure and operation. 

Refer to Energy Information Administration, Model Documentation Report: Residential Sector Demand 

Module of the National Energy Modeling System, DOE/EIA-M067(2014) (August 2014). 

http://www.eia.gov/forecasts/aeo/nems/documentation/residential/pdf/m067(2014).pdf.

[7] U.S. Bureau of Census, Series C25 Data from various years of publications.

[8] U.S. Bureau of Census, Annual Housing Survey 2001 and Professional Remodeler, 2002 Home 

Remodeling Study. 

[9] National Oceanic and Atmospheric Administration, National Weather Service, Experimental Monthly 

Degree Day Forecast, http://www.cpc.ncep.noaa.gov/pacdir/DDdir/ddforecast.txt. Explanation of forecast 

available at http://www.cpc.ncep.noaa.gov/pacdir/DDdir/N1.html.

[10] See Dahl, Carol, A Survey of Energy Demand Elasticities in Support of the Development of the NEMS, 

October 1993.

[11] The IECC established guidelines for builders to meet specific targets concerning energy efficiency with 

respect to heating and cooling load.

[12] Oak Ridge National Laboratory, Estimating the National Effects of the U.S. Department of Energy’s 

Weatherization Assistance Program with State-Level Data: A Metaevaluation Using Studies from 1993 to 

2005, September 2005.

[13] U.S. Department of Energy, Office of Inspector General, Office of Audit Services, Special Report: 

Progress in Implementing the Department of Energy’s Weatherization Assistance Program under the 

American Recovery and Reinvestment Act, February 2010.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 33

Commercial Demand Module

The NEMS Commercial Demand Module (CDM) generates projections of commercial sector energy demand 

through 2040. The definition of the commercial sector is consistent with EIA’s State Energy Data System 

(SEDS). That is, the commercial sector includes business establishments that are not engaged in 

transportation, manufacturing, or other types of industrial activity (e.g., agriculture, mining, or 

construction). The bulk of commercial sector energy is consumed within buildings; however, street lights, 

pumps, bridges, and public services are also included if the establishment operating them is considered 

commercial.

Because most of commercial energy consumption occurs in buildings, the commercial module relies on the 

data from the EIA Commercial Buildings Energy Consumption Survey (CBECS) for characterizing the 

commercial sector activity mix as well as the equipment stock and fuels consumed to provide end-use 

services [14].

The CDM projects consumption by fuel [15] at the Census division level using prices from the NEMS energy 

supply modules, macroeconomic variables from the NEMS Macroeconomic Activity Module (MAM), and

external data sources for technology characterizations and other inputs. Energy demands are projected for 

10 end-use services [16] for 11 building categories [17] in each of the 9 Census divisions (see Figure 5). The 

model begins by developing projections of floorspace for the 99 building category and Census division 

combinations. Next, the ten end-use service demands required for the projected floorspace are developed. 

The electricity generation and water and space heating supplied by distributed generation (DG) and 

combined heat and power (CHP) technologies are projected. Technologies are then chosen to meet the 

projected service demands for the seven major end uses. Once technologies are chosen, the energy 

consumed by the equipment stock (both existing and purchased equipment) is developed to meet the 

projected end-use service demands [18]. Minor end uses are modeled in less detail. Annual energy 

consumption of select miscellaneous end-use loads (MELs) are derived by combining existing and projected 

equipment stock, energy consumption per device, and hours of use where applicable.

Key assumptions

The key assumptions made by the commercial module are presented in terms of the flow of the calculations 

described above. The sections below summarize the assumptions in each of the CDM Submodules: 

floorspace, service demand, distributed generation, technology choice, and end-use consumption. The 

submodules are executed sequentially in the order presented, and the outputs of each submodule become 

the inputs to subsequently executed submodules. As a result, key projection drivers for the floorspace 

submodule are also key drivers for the service demand submodule, and so on.

Floorspace Submodule

Floorspace is projected by starting with the previous year’s stock of floorspace and eliminating a portion to 

represent the age-related removal of buildings. Total floorspace is the sum of the surviving floorspace and

new additions to the stock derived from the MAM floorspace growth projection [19].
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 34

Existing floorspace and attrition

Existing floorspace is based on the estimated floorspace reported in the 2003 Commercial Buildings Energy 

Consumption Survey (Table 5.1). Over time, the 2003 stock is projected to decline as buildings are removed 

from service (floorspace attrition). Floorspace attrition is estimated by a logistic decay function, the shape of 

which is dependent upon the values of two parameters: average building lifetime and gamma. The average 

building lifetime refers to the median expected lifetime of a particular building type. The gamma parameter 

corresponds to the rate at which buildings retire near their median expected lifetime. The current values for 

the average building lifetime and gamma vary by building type as presented in Table 5.2 [20].

New construction additions to floorspace

The commercial module develops estimates of projected commercial floorspace additions by combining the 

surviving floorspace estimates with the total floorspace projection from MAM. A total NEMS floorspace 

projection is calculated by applying the MAM assumed floorspace growth rate within each Census division 

and MAM building type to the corresponding NEMS CDM building types based on the CBECS building type 

shares. The NEMS surviving floorspace from the previous year is then subtracted from the total NEMS 

floorspace projection for the current year to yield new floorspace additions [21].

Service demand Submodule

Once the building stock is projected, the CDM develops a projection of demand for energy-consuming 

services required for the projected floorspace. The module projects service demands for the following 

explicit end-use services: space heating, space cooling, ventilation, water heating, lighting, cooking, 

refrigeration, personal computer office equipment, and other office equipment [22]. The service demand 

intensity (SDI) is measured in thousand Btu of end-use service demand per square foot and differs across 

service, Census division, and building type. The SDIs are based on a hybrid engineering and statistical 

approach of CBECS consumption data [23]. Projected service demand is the product of square feet and SDI 

for all end uses across the eleven building categories with adjustments for changes in shell efficiency for 

space heating and cooling.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 35

Table 5.1. 2003 Total floorspace by Census division and principal building activity

millions of square feet

Assembly Education 

Food 

Sales 

Food 

Service 

Health 

Care Lodging

Large 

Office

Small 

Office

Merc/

Service Warehouse Other Total

New England 431 299 75 45 48 374 282 320 819 411 351 3,452

Middle 

Atlantic 1,243 1,384 163 127 310 797 1,523 1,065 1,641 1,112 1,177 10,543

East North 

Central 1,355 1,990 218 248 316 549 1,297 1,129 2,148 2,023 1,152 12,424

West North 

Central 772 552 102 206 123 595 219 704 1,045 994 369 5,580

South 

Atlantic 1,161 2,445 223 433 469 939 1,173 1,065 3,391 1,836 865 13,999

East South 

Central 546 341 67 99 134 368 195 371 985 390 223 3,719

West South 

Central 965 1,198 197 232 235 387 195 371 985 390 223 3,719

Mountain 411 640 64 32 94 438 230 535 1,087 506 168 4,207

Pacific 809 1,027 146 232 176 649 1,028 915 2,051 1,066 515 8,613

Total United 

States 7,693 9,874 1,255 1,654 1,905 5,096 6,861 6,605 15,242 10,078 5,395 71,658

Note: Totals may not equal sum of components due to independent rounding.

Source: U.S. Energy Information Administration, 2003 Commercial Buildings Energy Consumption Survey Public Use Data.

Table 5.2. Floorspace attrition parameters

Assembly Education 

Food 

Sales 

Food 

Service 

Health 

Care Lodging

Large 

Office

Small 

Office

Merc/

Service

Warehouse Other

Median Expected 

Lifetime (years) 55 62 55 50 55 53 65 58 50 58 60

Gamma 2.2 2.1 2.3 2.0 2.5 2.1 2.0 2.0 2.2 2.0 2.3

Source: U.S. Energy Information Administration, Commercial Buildings Energy Consumption Survey 2003, 1999, 1995, 1992, and 

1989 Public Use Data, 1986 Nonresidential Buildings Energy Consumption Survey, McGraw-Hill Construction Dodge Annual Startsnon-residential building starts, Northwest Energy Efficiency Alliance, Assessment of the Commercial Building Stock in the Pacific 

Northwest, KEMA-XENERGY, Inc., March 2004, and public information on demolitions.

Shell efficiency

The shell integrity of the building envelope is an important determinant of the heating and cooling loads for 

each type of building. In the NEMS Commercial Demand Module, the shell efficiency is represented by 

separate heating and cooling factors that change over time to reflect improvements in the building shell. 

The factors, dimensioned by building type and Census division, affect the space heating and cooling service 

demand intensities causing changes in fuel consumed for these services as the shell integrity improves. In 

the AEO2015 Reference case, building shells for new construction built in 2003 are up to 49% more efficient 

with respect to heating and up to 30% more efficient with respect to cooling relative to the average shell for 
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 36

existing buildings of the same type. Over the projection horizon, new building shells improve in efficiency by 

15% relative to their efficiency in 2003. For existing buildings, efficiency is assumed to increase by 6.9% over 

the 2003 stock average.

Distributed generation and combined heat and power

Program-driven installations of solar photovoltaic systems are based primarily on information from the 

Interstate Renewable Energy Council (IREC) annual report on U.S. solar market trends. Historical data from 

Form EIA-860, Annual Electric Generator Report, are used to derive electricity generation by Census division, 

building type, and fuel. A projection of distributed generation (DG) and combined heat and power (CHP) of 

electricity is developed based on the economic returns projected for DG and CHP technologies. The model 

uses a detailed cash-flow approach to estimate the internal rate of return for an investment. Penetration 

assumptions for distributed generation and CHP technologies are a function of the estimated internal rate of 

return relative to purchased electricity. Table 5.3 provides the cost and performance parameters for 

representative distributed generation and CHP technologies.

The model also incorporates endogenous learning for new DG and CHP technologies, allowing for declining 

technology costs as shipments increase. For fuel-cell and photovoltaic systems, parameter assumptions for 

the AEO2015 Reference case result in a 13% reduction in capital costs each time the installed capacity in the 

residential and commercial building sectors doubles (in the case of photovoltaics, utility-scale capacity is 

also included for learning). Doubling the installed capacity of microturbines results in a 10% reduction in 

capital costs and doubling the installed capacity of distributed wind systems results in a 3% reduction.

Technology Choice Submodule

The technology choice submodule develops projections of major end-use equipment to meet projected 

service demands using the three major fuels: electricity, natural gas, and distillate fuel. Capital purchase 

decisions are driven by assumptions concerning behavioral rule proportions and time preferences, described 

below, as well as projected fuel prices, average annual utilization of equipment (capacity factors), relative 

technology capital costs, and operating and maintenance (O&M) costs.

Decision types

In each projection year, equipment is potentially purchased for three decision types. Equipment must be 

purchased for newly added floorspace and to replace the portion of equipment in existing floorspace that is 

projected to wear out [24]. Equipment is also potentially purchased for retrofitting equipment that has 

become economically obsolete. The purchase of retrofit equipment occurs only if the annual operating costs 

of a current technology exceed the annualized capital and operating costs of a technology available as a 

retrofit candidate.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 37

Table 5.3. Capital cost and performance parameters of selected commercial distributed generation 

technologies

Technology Type

Year of 

Introduction

Average 

Generating 

Capacity 

(kWDC)

Electrical 

Efficiency

Combined 

Efficiency 

(Elec. + 

Thermal)

Installed 

Capital Cost 

(2009 $ per 

kWDC)

*

Service 

Life 

(Years)

Solar Photovoltaic 2013 32 0.15 N/A $4,073 30

2015 35 0.18 N/A $3,404 30

2025 40 0.20 N/A $2,320 30

2035 45 0.20 N/A $2,189 30

Fuel Cell 2013 300 0.46 0.65 $5,973 20

2015 300 0.49 0.66 $5,019 20

2025 300 0.51 0.69 $4,016 20

2035 300 0.54 0.73 $3,180 20

Natural Gas Engine 2013 334 0.30 0.84 $1,690 20

2015 334 0.31 0.85 $1,630 20

2025 334 0.30 0.87 $1,251 20

2035 334 0.30 0.91 $831 20

Oil-fired Engine 2013 300 0.34 0.74 $2,042 20

2015 300 0.34 0.74 $1,746 20

2025 300 0.35 0.80 $1,669 20

2035 300 0.36 0.78 $1,592 20

Natural Gas Turbine 2013 3510 0.25 0.77 $1,871 20

2015 3510 0.25 0.77 $1,858 20

2025 3510 0.25 0.80 $1,760 20

2035 3510 0.25 0.82 $1,645 20

Natural Gas Microturbine 2013 200 0.33 0.62 $2,224 20

2015 200 0.34 0.67 $2,098 20

2025 200 0.37 0.73 $1,467 20

2035 200 0.41 0.80 $836 20

Wind 2013 32 0.13 N/A $4,926 30

2015 35 0.13 N/A $4,715 30

2025 40 0.13 N/A $3,973 30

2035 50 0.13 N/A $3,627 30

*The original source documents presented solar photovoltaic costs in 2008 dollars and all other technologies in 2010 dollars. Costs 

for solar photovoltaic, fuel cell, microturbine, and wind technologies include learning effects.

Sources: U.S. Energy Information Administration, Commercial and Industrial CHP Technology Cost and Performance Data Analysis

for EIA SENTECH, Inc., and SAIC, Inc., June 2010; U.S. Energy Information Administration, Photovoltaic (PV) Cost and Performance 

Characteristics for Residential and Commercial Applications Final Report, ICF International, August 2010; and U.S. Energy 

Information Administration, The Cost and Performance of Distributed Wind Turbines, 2010-35 Final Report, ICF International, August 

2010.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 38

Behavioral rules

The commercial module allows the use of three alternate assumptions about equipment choice behavior. 

These assumptions constrain the equipment selections to three choice sets, which are progressively more 

restrictive. The choice sets vary by decision type and building type:

• Unrestricted Choice Behavior - This rule assumes that commercial consumers consider all types of 

equipment that meet a given service, across all fuels, when faced with a capital purchase decision.

• Same Fuel Behavior - This rule restricts the capital purchase decision to the set of technologies that 

consume the same fuel that currently meets the decision maker’s service demand.

• Same Technology Behavior - Under this rule, commercial consumers consider only the available 

models of the same technology and fuel that currently meet service demand when facing a capital 

stock decision.

Under any of the above three behavior rules, equipment that meets the service at the lowest annualized 

lifecycle cost is chosen. Table 5.4 illustrates the proportions of floorspace subject to the different behavior 

rules for space heating technology choices in large office buildings.

Time preferences

Commercial building owners’ time preferences regarding current versus future expenditures are assumed to 

be distributed among seven alternate time preference premiums. Adding the risk-adjusted time preference 

premiums to the 10-year Treasury note rate from the MAM results in implicit discount rates, also known as 

hurdle rates, applicable to the assumed proportions of commercial floorspace. The effect of the use of this 

distribution of discount rates is to prevent a single technology from dominating purchase decisions in the 

lifecycle cost comparisons. The distribution used for AEO2015 assigns some floorspace a very high discount 

or hurdle rate to simulate floorspace which will never retrofit existing equipment and which will only 

purchase equipment with the lowest capital cost. Discount rates for the remaining six segments of the 

distribution get progressively lower, simulating increased sensitivity to the fuel costs of the equipment that 

is purchased. The share of floorspace assigned to each rate in the distribution varies by end-use service. 

Table 5.5 illustrates the distribution of time preference premiums for space heating and lighting in 2015. The 

proportion of floorspace assumed for the 0.0 time preference premium represents an estimate of the 

federally-owned commercial floorspace that is subject to purchase decisions in a given year. The federal 

sector is expected to purchase energy-efficient equipment to meet the federal buildings performance 

standards of the Energy Policy Act of 2005 (EPAct2005) and the Energy Independence and Security Act of 

2007 (EISA2007) whenever cost-effective. For federal purchase decisions relating to energy conservation, 

cost-effectiveness is determined using a discount rate based on long-term Treasury bond rates, 

approximated in the commercial module by the 10-year Treasury note rate. For lighting, the proportion of 

floorspace assumed for the 0.0 time preference premium is increased to include all federal floorspace 

starting in 2009 to represent the EISA2007 provision that all federal buildings be equipped with energyefficient lighting fixtures and bulbs to the maximum extent feasible, including when replacing bulbs in 

existing fixtures.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 39

Table 5.4. Assumed behavior rules for choosing space heating equipment in large office buildings

percent

Unrestricted Same Fuel Same Technology Total

New Equipment Decision 21 30 49 100

Replacement Decision 7 31 62 100

Retrofit Decision 1 4 95 100

Source: U.S. Energy Information Administration, Model Documentation Report: Commercial Demand Module of the National

Energy Modeling System, DOE/EIA-M066(2014) (August 2014).

Table 5.5. Assumed distribution of risk-adjusted time preference premiums for space heating and lighting 

equipment in 2015

percent

Time Preference Premium Proportion of Floorspace-space Heating (2015) Proportion of Floorspace-Lighting (2015)

1000.0 26.5 26.4

100.0 22.6 22.5

45.0 19.6 19.3

25.0 19.2 19.3

15.0 10.5 8.5

6.5 1.3 1.3

0.0 0.3 2.7

-- 100.0 100.0

Source: U.S. Energy Information Administration, Model Documentation Report: Commercial Demand Module of the National Energy 

Modeling System, DOE/EIA-M066(2014) (August 2014).

The distribution of hurdle rates used in the commercial module is also affected by changes in fuel prices. If a 

fuel’s price rises relative to its price in the base year (2003), the nonfinancial portion of each hurdle rate in 

the distribution decreases to reflect an increase in the relative importance of fuel costs, expected in an 

environment of rising prices. Parameter assumptions for AEO2015 result in a 30% reduction in the 

nonfinancial portion of a hurdle rate if the fuel price doubles. If the risk-adjusted time preference premium 

input by the model user results in a hurdle rate below the assumed financial discount rate–15% for the 

commercial sector–with base year fuel prices (such as the 0.0 rate given in Table 5.5), no response to 

increasing fuel prices is assumed.

Technology characterization menu

The technology characterization menu organizes all relevant major end-use equipment data. Equipment is 

indexed by technology, vintage, fuel, end-use service provided, and Census division (or building type for 

ventilation, lighting, and refrigeration end uses). Initial market share, efficiency (coefficient of performance 

or efficacy in the case of lighting equipment), installed capital cost per unit of service demand satisfied, 

operating and maintenance cost per unit of service demand satisfied, average service life, year of initial 

availability, and last year available for purchase are also characterized. Equipment may only be selected to 

satisfy service demand if the year in which the decision is made falls within the window of availability. 
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 40

Equipment acquired prior to the lapse of its availability continues to be treated as part of the existing stock 

and is subject to replacement or retrofitting. This flexibility in limiting equipment availability allows the 

direct modeling of equipment efficiency standards. Table 5.6 provides a sample of the technology data for 

space heating in the New England Census division.

An option has been included to allow endogenous price-induced technological 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. The 

option was not exercised for the AEO2015 model runs.

End-Use Consumption Submodule

The end-use consumption submodule calculates the consumption of each of the three major fuels 

(electricity, natural gas, and distillate fuel oil) for the ten end-use services plus fuel consumption for CHP and 

district services. For the ten end-use services, energy consumption is calculated as the end-use service 

demand met by a particular type of equipment divided by its efficiency, summed over all existing equipment 

types. This calculation includes dimensions for Census division, building type, and fuel. Consumption of the 

five minor fuels (residual fuel oil, liquefied petroleum gas, motor gasoline, kerosene, and coal) is projected 

based on historical trends.

Equipment efficiency

The average energy consumption of a particular appliance is based initially on estimates derived from the 

2003 CBECS. As the stock efficiency changes over the model simulation, energy consumption decreases 

nearly as much as, but not quite proportionally to, the increase in efficiency. The difference is due to the 

calculation of efficiency using the harmonic average and also the efficiency rebound effect discussed below. 

For example, if on average, electric heat pumps are now 10% more efficient than in 2003, then all else 

constant (weather, real energy prices, shell efficiency, etc.), energy consumption per heat pump would now 

average about 9% less. The service demand and technology choice submodules together determine the 

average efficiency of the stocks used in adjusting the initial average energy consumption.

Adjusting for weather and climate

Recognition of the effect of weather on space heating and air conditioning is necessary to avoid projecting 

abnormal weather conditions into the future. In the CDM, proportionate adjustments are made to space 

heating and air conditioning demand by Census division. These adjustments are based on National Oceanic 

and Atmospheric Administration (NOAA) heating degree day (HDD) and cooling degree day (CDD) data. 

Short-term projections are informed by NOAA’s 15-month outlook from their Climate Prediction Center [25], 

which often encompasses the first forecast year. Projections of degree days beyond that are informed by a 

30-year linear trend of each state’s degree days, which are then population-weighted to the Census division 

level. In this way, the CDM accounts for projected population migrations across the nation and continues 

any realized historical changes in degree days at the state level. A 10% increase in HDD would increase space 

heating consumption by 10% over what it would have been, while a 10% increase in CDD would increase 

cooling consumption by about 12.5%.
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 41

Table 5.6. Capital cost and efficiency ratings of selected commercial space heating equipment1

Equipment Type Vintage Efficiency2

Capital Cost 

(2013$ per MBtu/

hour)3

Maintenance Cost 

(2013$ per MBtu/

hour)3

Service 

Life 

(Years)

Rooftop Air-Source Heat Pump 2003 installed base 3.10 $67.78 $1.47 15

2013 current standard/typical 3.30 $81.39 $1.47 15

2013 high 3.40 $102.78 $1.47 15

2020 typical 3.30 $81.39 $1.47 15

2020 high 3.40 $102.78 $1.47 15

Ground-Source Heat Pump 2003 installed base 3.40 $545.83 $3.13 25

2013 typical 3.60 $514.58 $3.13 25

2013 mid 3.70 $530.21 $3.13 25

2013 high 4.00 $571.88 $3.13 25

2020 typical 3.80 $514.88 $3.13 25

2020 high 4.20 $571.88 $3.13 25

2030 typical 4.00 $514.58 $3.13 25

2030 high 4.40 $571.88 $3.13 25

Electric Boiler 2003 installed base 0.94 $16.68 $0.26 15

2012 installed base 0.94 $21.13 $0.26 15

Electric Resistance Heater 2003 installed base 0.98 $21.76 $0.01 18

Natural Gas Heat Pump 2003 Installed base (residential type) 1.30 $218.33 $2..67 15

2013 typical (engine-driven rooftop) 1.40 $300.00 $4..92 30

2020 typical (engine-driven rooftop) 1.40 $300.00 $4.92 30

2030 typical (engine-driven rooftop) 1.40 $300.00 $4.92 30

Natural Gas Furnace 2003 installed base 0.71 $8.46 $1.13 15

2013 current standard/typical 0.78 $9.21 $1.03 15

2013 high 0.88 $11.78 $2.66 15

2020 typical 0.78 $10.95 $1.03 15

2020 high 0.88 $11.78 $2.66 15

2030 typical 0.78 $10.95 $1.03 15

2030 high 0.89 $11.78 $2.66 15

Natural Gas Boiler 2003 installed base 0.76 $29.36 $0.79 30

2013 current standard/typical 0.80 $31.64 $0.75 30

2013 mid-range 0.85 $33.97 $071 30

2013 high 0.98 $32.33 $0.61 30

2020 typical 0.82 $32.62 $0.73 30

2020 high 0.98 $32.33 $0.61 30

2030 typical 0.83 $33.06 $0.72 30
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 42

Table 5.6. Capital cost and efficiency ratings of selected commercial space heating equipment1 (cont.)

Equipment Type Vintage Efficiency2

Capital Cost 

(2013$ per MBtu/

hour)3

Maintenance Cost 

(2013$ per MBtu/

hour)3

Service 

Life 

(Years)

Distillate Oil Furnace 2003 installed base 0.76 $14.46 $1.05 15

2013 typical 0.80 $14.40 $1.01 15

2020 typical 0.80 $14.40 $1.01 15

Distillate Oil Boiler 2003 installed base 0.79 $17.83 $0.17 30

2013 current standard 0.82 $19.82 $0.17 30

2013 typical 0.83 $20.68 $0.17 30

2013 high 0.89 $30.90 $0.15 30

2020 typical 0.83 $20.68 $0.17 30

2020 high 0.89 $30.90 $0.15 30 1

Equipment listed is for the New England Census division, but is also representative of the technology data for the rest of the United 

States. See the source reference below for the complete set of technology data. 2

Efficiency metrics vary by equipment type. Electric rooftop air-source heat pumps, ground-source and natural gas heat pumps are 

rated for heating performance using coefficient of performance (COP); natural gas and distillate furnaces and boilers reflect thermal 

efficiency.

Source: U.S. Energy Information Administration, “EIA - Technology Forecast Updates - Residential and Commercial Building 

Technologies - Reference Case,” Navigant Consulting, Inc., March 2014.

Short-term price effect and efficiency rebound

It is assumed that energy consumption for a given end-use service is affected by the marginal cost of 

providing that service. That is, all else equal, a change in the price of a fuel will have an inverse, but less than 

proportional, effect on fuel consumption. The current value for the short-term price elasticity parameter is

-0.25 for all major end uses except refrigeration. A value of -0.10 is currently used for commercial 

refrigeration. A value of -0.05 is currently used for personal computers (PC) and non-PC office equipment 

and other minor uses of electricity. For example, for lighting, this value implies that for a 1.0% increase in 

the price of a fuel, there will be a corresponding decrease in energy consumption of 0.25%. Another way of 

affecting the marginal cost of providing a service is through equipment efficiency. As equipment efficiency 

changes over time, so will the marginal cost of providing the end-use service. For example, a 10% increase in 

efficiency will reduce the cost of providing the service by 10%. The short-term elasticity parameter for 

efficiency rebound effects is -0.15 for affected end uses; therefore, the demand for the service will rise by 

1.5% (-10% x -0.15). Currently, all services are affected by the short-term price effect and services affected 

by efficiency rebound are space heating and cooling, water heating, ventilation and lighting.
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 43

Legislation and regulations

American Recovery and Reinvestment Act of 2009 (ARRA2009)

The ARRA2009 legislation passed in February 2009 provides energy-efficiency funding for federal agencies, 

State Energy Programs, and block grants. To account for the impact of this funding, states are assumed to 

adopt and enforce the ASHRAE 90.1-2007 standard by 2018 for building shell measures, and all public 

buildings (federal, state, and local) are assumed to use the 10-year Treasury note rate for purchase decisions 

related to both new construction and replacement equipment while stimulus funding is available. A 

percentage of the State Energy Program and Conservation Block Grant funding is assumed to be used for 

solar photovoltaic and small wind turbine installations. Additional stimulus funding is applied to fuel cell 

installations.

The ARRA2009 provisions remove the cap on the 30% business investment tax credit (ITC) for wind turbines. 

The ITC is still available for systems installed through 2016. These credits are directly incorporated into the 

cash-flow approach for distributed generation systems.

Energy Improvement and Extension Act of 2008 (EIEA2008)

The EIEA2008 legislation passed in October 2008 extends the ITC provisions of the Energy Policy Act of 2005 

and expands the credit to include additional technologies. The ITCs of 30% for solar energy systems and fuel 

cells and 10% for microturbines are extended through 2016. The cap on the fuel cell credit has been 

increased from $500 to $1,500 per half kilowatt of capacity. The EIEA2008 provisions expand the ITC to 

include a 10% credit for CHP systems and ground-source heat pumps and a 30% credit for wind turbines 

with the wind credit capped at $4,000. The expanded credits are available for systems installed through 

2016. These credits are directly incorporated into the cash-flow approach for distributed generation 

systems, including CHP, and factored into the installed capital cost assumptions for solar hot water heaters 

and ground-source heat pumps.

Energy Independence and Security Act of 2007 (EISA2007) 

The EISA2007 legislation passed in December 2007 provides standards for specific explicitly modeled 

commercial equipment. The EISA2007 requires specific energy-efficiency measures in commercial walk-in 

coolers and walk-in freezers effective January 1, 2009, with an additional update effective in 2017. 

Incandescent and halogen lamps must meet standards for maximum allowable wattage based on lumen 

output starting in 2012 and metal halide lamp fixtures using lamps between 150 and 500 watts are required 

to have a minimum ballast efficiency ranging from 88% to 94%, depending on ballast type, effective January 

1, 2009. Additional requirements become effective in 2017.

The EISA2007 requirement for federal buildings to use energy-efficient lighting fixtures and bulbs to the 

maximum extent possible is represented by adjusting the proportion of the commercial sector assumed to 

use the 10-year Treasury note rate as an implicit discount or hurdle rate for lighting.

Energy Policy Act of 2005 (EPACT2005)

The passage of the EPACT2005 in August 2005 provides additional minimum efficiency standards for 

commercial equipment. Some of the standards for explicitly modeled equipment, effective January 1, 2010, 

include an increased Energy Efficiency Rating (EER) for small package air conditioning and heating 

equipment; daily electricity consumption limits by volume for commercial refrigerators, freezers, and 
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 44

refrigerator-freezers; and electricity consumption limits per 100 pounds of ice produced based on 

equipment type and capacity for automatic ice makers. The EPACT2005 adds standards for medium-base 

compact fluorescent lamps effective January 1, 2006, for ballasts for Energy Saver fluorescent lamps

effective in 2009 and 2010, and bans the manufacture or import of mercury vapor lamp ballasts effective 

January 1, 2008.

Several efficiency standards in the EPACT2005 pertain to equipment not explicitly represented in the NEMS 

Commercial Demand Module. For low-voltage dry-type transformers, effects of the standard are included in 

estimating the share of projected miscellaneous electricity use attributable to transformer losses. For 

illuminated exit signs, traffic signals, and commercial premise spray valves, assumed energy reductions are 

calculated based on per-unit savings relative to a baseline unit and the estimated share of installed units and 

sales that already meet the standard. Total projected reductions are phased in over time to account for 

stock turnover. Under the EPACT2005 standards, illuminated exit signs and traffic signal modules must meet 

ENERGY STAR program requirements as of January 1, 2006. The requirements limit input power demand to 

5 watts or less per face for exit signs. Nominal wattages for traffic signal modules are limited to 8 to 15 

watts, based on module type. Effective January 1, 2007, low-voltage dry-type distribution transformers are 

required to meet the National Electrical Manufacturers Association Class I Efficiency Levels with minimum 

efficiency levels ranging from 97% to 98.9% based on output. Commercial pre-rinse spray valves [26] must 

have a maximum flow rate of 1.6 gallons per minute, effective January 1, 2006, with energy reductions 

attributed to hot water use.

The EPACT2005 expands the business investment tax credit to 30% for solar property installed in 2006 and 

2007. ITCs of 30% for fuel cells and 10% for microturbine power plants are also available for property 

installed in 2006 and 2007. The EPACT2005 tax credit provisions were extended in December 2006 to cover 

equipment installed in 2008. These credits are directly incorporated into the cash-flow approach for 

distributed generation systems and factored into the installed capital cost assumptions for solar hot water 

heaters.

Energy Policy Act of 1992 (EPACT1992)

A key assumption incorporated in the technology selection process is that the equipment efficiency 

standards described in the EPACT1992 constrain minimum equipment efficiencies. The effects of standards 

are modeled by modifying the technology database to eliminate equipment that no longer meets minimum 

efficiency requirements. Some of the EPACT1992 standards implemented in the module include: gas and oilfired boilers—minimum combustion efficiency of 0.80 and 0.83, respectively, amended to minimum thermal 

efficiency of 0.80 and 0.81, respectively, in 2012; gas and oil-fired furnaces—minimum thermal efficiency of

0.80 and 0.81, respectively; electric water heaters—minimum energy factor of 0.85; and gas and oil water 

heaters—minimum thermal efficiency of 0.80 and 0.78, respectively. A fluorescent lamp ballast standard 

effective in 2005 mandates electronic ballasts with a minimum ballast efficacy factor of 1.17 for 4-foot, 2-

lamp ballasts and 0.63 for 8-foot, 2-lamp ballasts. Fluorescent lamps and incandescent reflector lamb bulbs 

must meet amended standard levels for minimum average lamp efficacy in 2012. Recent updates for 

commercial refrigeration equipment include maximum energy consumption standards for refrigerated 

vending machines and display cases based on volume.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 45

The 10% Business Investment Tax Credit for solar energy property included in EPACT1992 is directly 

incorporated into the cash-flow approach for projecting distributed generation by commercial photovoltaic 

systems. For solar water heaters, the tax credit is factored into the installed capital cost assumptions used in 

the technology choice submodule.

Energy efficiency programs

Several energy efficiency programs affect the commercial sector. These programs are designed to stimulate 

investment in more efficient building shells and equipment for heating, cooling, lighting, and miscellaneous

end-use loads (MELs). The commercial module includes several features that allow projected efficiency to 

increase in response to voluntary programs (e.g., the distribution of risk-adjusted time preference premiums 

and shell efficiency parameters). Retrofits of equipment for space heating, air conditioning and lighting are 

incorporated in the distribution of premiums given in Table 5.5. Also, the shell efficiency of new and existing 

buildings is assumed to increase from 2003 through 2040. Shells for new buildings increase in efficiency by 

15.0% over this period, while shells for existing buildings increase in efficiency by 6.9%.

Notes and sources

[14] U.S. Energy Information Administration, 2003 Commercial Buildings Energy Consumption Survey 

(CBECS) Public Use Files, 

http://www.eia.gov/consumption/commercial/data/2003/index.cfm?view=microdata

[15] The fuels accounted for by the commercial module are electricity, natural gas, distillate fuel oil, residual 

fuel oil, liquefied petroleum gas (LPG), coal, motor gasoline, and kerosene. Current commercial use of 

biomass (wood, municipal solid waste) is also included. In addition to these fuels, the use of solar energy is 

projected based on an exogenous estimate of existing solar photovoltaic system installations, projected 

installations due to state and local incentive programs, and the potential endogenous penetration of solar 

photovoltaic systems and solar thermal water heaters. The use of wind energy is projected based on an 

estimate of existing distributed wind turbines and the potential endogenous penetration of wind turbines in 

the commercial sector.

[16] The end-use services in the commercial module are heating, cooling, water heating, ventilation, 

cooking, lighting, refrigeration, PC and non-PC office equipment and a category denoted “miscellaneous 

end-use loads (MELs)” to account for all other minor end uses.

[17] The 11 building categories are assembly, education, food sales, food services, health care, lodging, large 

offices, small offices, mercantile/services, warehouse, and other.

[18] The detailed documentation of the commercial module contains additional details concerning model 

structure and operation. Refer to U.S. Energy Information Administration, Model Documentation Report: 

Commercial Demand Module of the National Energy Modeling System, DOE/EIA M066(2014) (August 2014).

[19] The commercial floorspace equations of the Macroeconomic Activity Model are estimated using the 

McGraw-Hill Construction Research & Analytics database of historical floorspace estimates. The McGraw-Hill 

Construction estimate for commercial floorspace in the United States is approximately 16% lower than the 
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 46

estimate obtained from the CBECS used for the Commercial module. See F.W. Dodge, Building Stock 

Database Methodology and 1991 Results, Construction Statistics and Forecasts, F.W. Dodge, McGraw-Hill.

[20] The commercial module performs attrition for nine vintages of floorspace developed using stock 

estimates from the previous five CBECS and historical floorspace additions data from McGraw-Hill 

Construction data.

[21] In the event that the computation of additions produces a negative value for a specific building type, it 

is assumed to be zero.

[22] “Other office equipment” includes copiers, fax machines, scanners, multi-function devices, data center 

servers, and other miscellaneous office equipment. A tenth category denoted “miscellaneous end-use loads 

(MELs)” includes equipment such as elevators, escalators, medical and other laboratory equipment, laundry, 

communications equipment, security equipment, transformers, and miscellaneous electrical appliances. 

Commercial energy consumed outside of buildings and for combined heat and power is also included in the 

“MELs” category.

[23] Based on 2003 CBECS end-use-level consumption data developed using the methodology described in 

Estimation of Energy End-Use Intensities, 

http://www.eia.gov/consumption/commercial/estimation-enduse-consumption.cfm . 

[24] The proportion of equipment retiring is inversely related to the equipment life.

[25] National Oceanic and Atmospheric Administration, National Weather Service, Experimental Monthly 

Degree Day Forecast, http://www.cpc.ncep.noaa.gov/pacdir/DDdir/ddforecast.txt. Explanation of forecast is 

available at http://www.cpc.ncep.noaa.gov/pacdir/DDdir/N1.html.

[26] Commercial pre-rinse spray valves are handheld devices used to remove food residue from dishes and 

flatware before cleaning.
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 47

Industrial Demand Module

The NEMS Industrial Demand Module (IDM) estimates energy consumption by energy source (fuels and 

feedstocks) for 15 manufacturing and 6 non-manufacturing industries. The manufacturing industries are 

subdivided further into the energy-intensive manufacturing industries and non-energy-intensive 

manufacturing industries (Table 6.1). The manufacturing industries are modeled through the use of a 

detailed process-flow or end-use accounting procedure. The non-manufacturing industries are modeled with 

less detail because processes are simpler and there is less available data. The petroleum refining industry is 

not included in the IDM, as it is simulated separately in the Liquid Fuels Market Module (LFMM) of NEMS. 

The IDM calculates energy consumption for the four Census Regions (see Figure 5) and disaggregates 

regional energy consumption to the nine Census Divisions based on fixed shares from the U.S. Energy 

Information Administration (EIA) State Energy Data System [27]. The model base year for the IDM was 

updated to 2010 for AEO2014.

Table 6.1. Industry categories and NAICS codes

Energy-Intensive Manufacturing Non-Energy-Intensive Manufacturing Non-Manufacturing

Food products (NAICS 311) Metal-based durables

Paper and allied 

products

(NAICS 322)

 Fabricated metal products (NAICS 332)

Agricultural crop 

production (NAICS 111)

Bulk chemicals Machinery (NAICS 333)

 Inorganic (NAICS 32512-

32518)

 Computer and electronic 

 products

(NAICS 334) Other agricultural 

production

(NAICS 112, 

113, 115)

 Organic (NAICS 32511, 

32519)

 Electrical equipment and

 appliances

(NAICS 335) Coal mining (NAICS 2121)

 Resins (NAICS 3252) Transportation equipment

(NAICS 336)

Oil and gas 

extraction (NAICS 211)

 Agricultural 

 Chemicals

(NAICS 3253) Other Metal and other 

non-metallic mining

(NAICS 2122-

2123)

Glass and glass 

products

(NAICS 3272, 

327993) Wood products (NAICS 321)

Cement and Lime (NAICS 32731, 

32741)

 Plastic and rubber 

 products

(NAICS 326) Construction (NAICS 23)

Iron and Steel (NAICS 3311-

3312)

 Balance of manufacturing (NAICS 31-33 

not already 

classified)

Aluminum (NAICS 3313)

NAICS = North American Industry Classification System (2007).

Source: Office of Management and Budget, North American Industry Classification system (NAICS) - United States (Springfield, VA,

National Technical Information Service).
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 48

The energy-intensive manufacturing industries, consisting of food products, paper and allied products, bulk 

chemicals, glass and glass products, cement and lime, iron and steel, and aluminum, are modeled in 

considerable detail. Each industry is modeled as three separate but interrelated components: the Process 

and Assembly (PA) Component, the Buildings (BLD) Component, and the Boiler, Steam, and Cogeneration 

(BSC) Component. The BSC Component satisfies the steam demand from the PA and BLD Components. In 

some industries, the PA Component produces byproducts that are consumed in the BSC Component. For the 

manufacturing industries, the PA Component is separated into the major production processes or end uses. 

Petroleum refining (NAICS 32411) is modeled in detail in the LFMM of NEMS, and the projected energy 

consumption is reported in the manufacturing total.

Projections of refining energy use, lease and plant fuel, and fuels consumed in cogeneration in the oil and 

gas extraction industry (NAICS 211) are exogenous to the IDM, but endogenous to the NEMS modeling 

system.

Key assumptions - manufacturing

The IDM primarily uses a bottom-up modeling approach. An energy accounting framework traces energy 

flows from fuels to the industry’s output. An important assumption in the development of this system is the 

use of 2010 baseline Unit Energy Consumption (UEC) estimates based on analysis and interpretations of the 

2010 Manufacturing Energy Consumption Survey (MECS), which is conducted by EIA on a four-year survey 

cycle [28]. The UECs represent the energy required to produce one unit of the industry’s output. A unit of 

output may be defined in terms of physical units (e.g., tons of steel) or in dollar value of shipments.

The IDM depicts the manufacturing industries, except for petroleum refining, with either a detailed process 

flow or end use approach. Generally, industries with homogeneous products use a process flow approach, 

and those with heterogeneous products use an end-use approach. Industries that use a process flow 

approach are paper, glass, cement and lime, iron and steel, and aluminum. Industries that use an end-use 

approach are food, bulk chemicals, the five metal-based durables industries, wood, plastic and rubber 

products, and balance of manufacturing. The dominant process technologies are characterized by a 

combination of unit energy consumption estimates and Technology Possibility Curves (TPC). With the 

exception of the cement and lime industries, the aluminum industry, and the glass industry, the TPC depicts 

the assumed average annual rate of change in energy intensity of either a process step or an energy end use 

(e.g., heating or cooling). The TPCs for new and existing plants vary by industry, vintage and process. These 

assumed rates were developed using professional engineering judgments regarding the energy 

characteristics, year of availability, and rate of market adoptions of new process technologies.

For the aluminum, glass, and combined cement and lime industries, energy projections are endogenously 

derived based on data obtained from the technology estimates (e.g., expenditures, energy coefficients, 

utility needs) in the Consolidated Impacts Modeling System (CIMS) database prepared by the Pacific 

Northwest National Laboratory, as calibrated using inputs from the U.S. Geological Survey (USGS) of the U.S. 

Department of the Interior, Portland Cement Association and MECS 2010 released by EIA [29,30,31].
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 49

Process/assembly component

The PA Component models each major manufacturing production step or end use for the manufacturing 

industries. The throughput production for each process step is computed, as well as the energy required to 

produce it. The unit energy consumption (UEC) is defined as the amount of energy to produce a unit of 

output; it measures the energy intensity of the process or end use.

The module distinguishes the UECs by three vintages of capital stock. The amount of energy consumption

reflects the assumption that new vintage stock will consist of state-of-the-art technologies that have 

different efficiencies from the existing capital stock. Consequently, the amount of energy required to 

produce a unit of output using new capital stock is often less than that required by the existing capital stock. 

The old vintage consists of capital existing in 2010 and surviving after adjusting for assumed retirements 

each year (Table 6.2). New production capacity is assumed to be added in a given projection year such that 

sufficient surviving and new capacity is available to meet the level of an industry’s output as determined in 

the NEMS Regional Macroeconomic Module. Middle vintage capital is that which is added after 2010 up 

through the year prior to the current projection year.

To simulate technological progress and adoption of more-efficient energy technologies, the UECs are 

adjusted each projection year based on the assumed TPC for each step. The TPCs are derived from 

assumptions about the relative energy intensity (REI) of productive capacity by vintage (new capacity 

relative to existing stock in a given year) or over time (new or surviving capacity in 2040 relative to the 2010 

stock). For example, state-of-the-art additions to steel hot-rolling capacity in 2010 are assumed to require 

only 80% as much energy as does the average existing plant, so the REI for new capacity in 2010 is 0.80 (see 

Table 6.3). Over time, the UECs for new capacity change, and the rate of change is given by the TPC. The 

UECs of the surviving 2010 capital stock are also assumed to change over time, but not as rapidly as for new 

capital stock because of retrofitting. For example, with hot rolling, the TPC for new facilities is -0.804% per 

year, while the TPC for existing facilities is -0.699% per year. Table 6.3 provides more examples, including 

alternative assumptions used to reflect an advanced, “high tech” case.

Table 6.2. Retirement rates

Industry

Retirement Rate

(percent) Industry

Retirement Rate 

(percent)

Iron and Steel

 Blast Furnace and Basic Steel Products 1.5 Food Products 1.7

 Electric Arc Furnace 1.5 Pulp and Paper 2.3

 Coke Oven 2.5 Metal-Based Durables 1.3

 Other Steel 2.9 Other Non-intensive Manufacturing 1.3

Bulk Chemicals 1.7

Note: Except for the Blast Furnace and Basic Steel Products Industry, the retirement rate is the same for each process step or enduse within an industry. 

Source: Energy Information Administration, Model Documentation Report: Industrial Sector Demand Module of the National Energy 

Modeling System, (Washington, DC, September 2013).
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 50

Table 6.3. Coefficients for technology possibility curve for manufacturing industries and all industrial 

scenarios

applies to all fuels unless specified

Existing Facilities New Facilities

Industry/Process Unit 

Reference 

REI20401

High Tech 

REI 20401

Reference 

TPC%2

High 

Tech 

TPC%2

REI 

20103

Reference 

REI20404

High 

Tech REI 

20404

Reference 

TPC%2

High 

Tech 

TPC%2

Food Products-Milling

 Process Heating-Electricity 0.900 0.987 -0.351 -0.045 0.900 0.800 0.875 -0.392 -0.094

 Process Heating-Steam 0.810 0.947 -0.701 -0.182 0.900 0.711 0.804 -0.784 -0.375

 Process Cooling-Electricity 0.900 0.983 -0.351 -0.057 0.900 0.800 0.873 -0.392 -0.100

 Process Cooling-Natural Gas 0.900 0.987 -0.351 -0.045 0.900 0.800 0.875 -0.392 -0.094

 Other-Electricity 0.900 0.993 -0.351 -0.024 0.900 0.800 0.875 -0.392 -0.093

 Other-Natural Gas 0.950 0.987 -0.171 -0.045 0.950 0.850 0.924 -0.370 -0.094

Food Products-Daily

 Process Heating-Electricity 0.980 0.987 -0.067 -0.045 0.970 0.950 0.943 -0.059 -0.094

 Process Heating-Steam 0.930 0.947 -0.242 -0.182 0.950 0.850 0.849 -0.370 -0.375

 Process Cooling-Electricity 0.900 0.983 -0.351 -0.057 0.900 0.800 0.873 -0.392 -0.100

 Process Cooling-Natural Gas 0.980 0.987 -0.067 0.045 0.970 0.950 0.943 -0.069 -0.094

 Other-Electricity 0.930 0.993 -0.242 0.024 0.960 0.850 0.934 -0.405 -0.093

 Other-Natural Gas 0.980 0.987 -0.067 -0.045 0.970 0.950 0.943 -0.069 -0.094

Food Products-Animal Processing

 Process Heating-Electricity 0.980 0.987 -0.067 -0.045 0.970 0.950 0.943 -0.069 -0.094

 Process Heating-Steam 0.950 0.947 -0.171 -0.182 0.950 0.900 0.849 -0.180 -0.375

 Process Cooling-Electricity 0.930 0.983 -0.242 -0.057 0.950 0.850 0.922 -0.370 -0.100

 Process Cooling-Natural Gas 0.980 0.987 -0.067 -0.045 0.970 0.950 0.943 -0.069 -0.094

 Other-Electricity 0.950 0.993 -0.171 -0.024 0.980 0.900 0.953 -0.283 -0.093

 Other-Natural Gas 0.980 0.987 -0.067 -0.045 0.970 0.950 0.943 -0.069 -0.094

Food Products Other

 Process Heating-Electricity 0.980 0.987 -0.067 0.045 0.970 0.950 0.943 -0.069 -0.094

 Process Heating-Steam 0.930 0.947 -0.242 -0.182 0.950 0.850 0.849 -0.370 -0.375

 Process Cooling-Electricity 0.930 0.983 -0.242 -0.057 0.950 0.850 0.922 -0.370 -0.100

 Process Cooling-Natural Gas 0.980 0.987 -0.067 -0.045 0.970 0.950 0.943 -0.069 -0.094

 Other-Electricity NA NA -0.171 -0.024 NA NA NA -0.215 -0.097

 Other-Natural Gas 0.980 0.987 -0.067 -0.045 0.970 0.950 0.943 -0.069 -0.094

Paper & Allied Products

 Wood Preparation 0.785 0.990 -0.802 -0.033 0.882 0.696 0.990 -0.790 -0.386

 Waste Pulping-Electricity 0.934 0.953 -0.228 -0.161 0.936 0.936 0.874 0.000 -0.228

 Waste Pulping-Steam 0.872 0.908 -0.456 -0.322 0.936 0.936 0.816 0.000 -0.456

 Mechanical Pulping-Electricity 0.794 1.006 -0.767 0.021 0.931 0.613 1.215 -1.380 0.893
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 51

Table 6.3. Coefficients for technology possibility curve for manufacturing industries and all industrial 

scenarios (cont.)

applies to all fuels unless specified

Existing Facilities New Facilities

Industry/Process Unit

Reference 

REI20401

High Tech 

REI 20401

Reference 

TPC%2

High 

Tech 

TPC%2

REI 

20103

Reference 

REI20404

High 

Tech REI 

20404

Reference 

TPC%2

High 

Tech 

TPC%2

Paper & Allied Products (cont.)

 Mechanical Pulping-Steam 0.629 1.013 -1.533 -0.043 0.931 0.402 1.583 -2.760 1.767

 Semi-Chemical-Electricity 0.949 0.983 -0.173 -0.025 0.971 0.929 0.956 -0.149 -0.052

 Semi-Chemical-Steam 0.901 0.985 -0.346 -0.051 0.971 0.888 0.941 -0.297 -0.105

 Kraft, Sulfite, Misc. Chemicals 0.856 0.928 -0.519 -0.249 0.914 0.807 0.786 -0.415 -0.502

 Kraft, Sulfite, Misc. Chemicals-

 Steam 0.731 0.861 -1.037 -0.498 0.914 0.712 0.675 -0.830 -1.004

 Bleaching-Electricity 0.773 0.927 -0.853 -0.252 0.878 0.674 0.913 -0.878 0.129

 Bleaching-Steam 0.596 0.859 -1.706 -0.504 0.878 0.516 0.949 -1.756 0.259

 Paper Making 0.864 0.830 -0.485 -0.621 0.855 0.851 0.584 -0.132 -1.376

 Paper Making-Steam 0.747 0.687 -0.969 -1.242 0.855 0.818 0.383 -0.264 -2.753

Iron and Steel

 Coke Oven-Electricity 0.932 0.879 -0.233 -0.429 0.902 0.868 0.652 -0.128 -1.216

Coke Oven-Steam 0.869 0.772 -0.467 -0.858 0.902 0.835 0.470 -0.257 -2.152

 BF/BPF-Electricity 0.993 0.950 -0.022 -0.172 0.987 0.987 0.882 NA -0.375

 BF/BOF-Steam 0.987 0.902 -0.045 -0.345 0.987 0.987 0.787 NA -0.751

 EAF-Electricity 0.912 0.901 -0.308 -0.346 0.990 0.825 0.775 -0.606 -0.813

 Hot Rolling7 0.810 0.902 -0.699 -0.344 0.800 0.628 0.596 -0.804 -0.978

 Hot Rolling-Steam7 0.656 0.813 -1.397 -0.687 0.800 0.492 0.442 -1.608 -1.956

Cold Rolling7 0.709 0.947 -1.397 -0.183 0.924 0.422 0.851 -2.580 -0.273

 Cold Rolling-Steam7 0.500 0.896 -2.281 -0.365 0.924 0.189 0.784 -5.160 -0.546

Bulk Chemicals

 Process Heating-Electricity 0.893 0.965 -0.376 -0.120 0.900 0.793 0.861 -0.420 -0.149

 Process Heating-Steam 0.798 0.866 -0.751 -0.478 0.900 0.699 0.752 -0.840 -0.597

 Process Cooling-Electricity 0.867 0.951 -0.476 -0.168 0.850 0.743 0.810 -0.446 -0.159

 Process Cooling-Natural Gas 0.893 0.965 -0.376 -0.120 0.900 0.793 0.861 -0.420 -0.149

 Electro-Chemicals 0.979 0.993 -0.072 -0.025 0.950 0.843 0.911 -0.396 -0.141

 Other-Electricity 0.908 0.967 -0.321 -0.113 0.915 0.803 0.873 -0.434 -0.155

 Other-Natural Gas 0.893 0.965 -0.376 -0.120 0.900 0.793 0.861 -0.420 -0.149

Metal-based Durables

Fabricated Metals

 Process Heating-Electricity 0.712 0.642 -1.427 -1.468 0.675 0.406 0.329 -1.679 -2.370

 Process Cooling-Electricity 0.650 0.576 -1.427 -1.820 0.636 0.371 0.299 -1.784 -2.493

 Process Cooling-Natural Gas 0.712 0.642 -1.127 -1.468 0.675 0.406 0.329 -1.679 -2.370
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 52

Table 6.3. Coefficients for technology possibility curve for manufacturing industries and all industrial 

scenarios (cont.)

applies to all fuels unless specified

Existing Facilities New Facilities

Industry/Process Unit

Reference 

REI20401

High Tech 

REI 20401

Reference 

TPC%2

High 

Tech 

TPC%2

REI 

20103

Reference 

REI20404

High 

Tech REI 

20404

Reference 

TPC%2

High 

Tech 

TPC%2

Metal-based Durables

Fabricated Metals (cont.)

Electro-Chemical Process 0.937 0.887 -0.216 -0.398 0.713 0.441 0.359 -1.586 -2.261

Other-Electricity 0.748 0.681 -0.962 -1.274 0.686 0.406 0.327 -1.737 -2.439

Machinery

Process Heating-Electricity 0.712 0.642 -1.427 -1.468 0.675 0.314 0.329 -2.519 -2.370

 Process Cooling-Electricity 0.650 0.576 -1.427 -1.820 0.638 0.283 0.299 -2.676 -2.493

 Process Cooling-Natural Gas 0.712 0.642 -1.127 -1.468 0.675 0.314 0.329 2.519 -2.370

 Electro-Chemical Process 0.937 0.887 -0.216 -0.398 0.713 0.346 0.359 2.379 -2.251

Other-Electricity 0.748 0.681 -0.962 -1.274 0.686 0.311 0.327 -2.606 -2.439

Computers and Electronics

 Process Heating-Electricity 0.798 0.744 -0.751 -0.979 0.720 0.559 0.504 -0.840 -1.185

 Process Cooling-Electricity 0.751 0.693 -0.952 -1.213 0.680 0.520 0.467 -0.892 -1.247

 Process Cooling-Natural Gas NA NA -0.751 -0.979 NA NA NA -0.840 -1.185

 Electro-Chemical Process 0.958 0.923 -0.144 -0.265 0.760 0.599 0.540 -0.793 -1.130

Other-Electricity 0.824 0.774 -0.641 -0.850 0.732 0.563 0.507 -0.869 -1.219

Electrical Equipment

 Process Heating-Electricity 0.798 0.744 -0.751 -0.979 0.720 0.559 0.504 -0.840 -1.185

 Process Heating-Steam NA NA -1.502 -3.914 NA NA NA -1.679 -4.740

 Process Cooling-Electricity 0.751 0.693 -0.952 -1.213 0.680 0.520 0.467 -0.892 -1.247

 Process Cooling-Natural Gas 0.798 0.744 -0.751 -0.979 0.720 0.559 0.504 -0.840 -1.185

 Electro-Chemical Process 0.958 0.923 -0.144 -0.265 0.760 0.599 0.540 -0.793 -1.130

 Other-Electricity 0.824 0.774 -0.641 -0.850 0.732 0.563 0.507 -0.869 -1.219

Transportation Equipment

 Process Heating-Electricity 0.854 0.814 -0.526 -0.685 0.765 0.625 0.575 -0.672 -0.948

 Process Heating-Steam 0.728 0.435 -1.052 -2.740 0.765 0.510 0.240 -1.343 -3.792

 Process Cooling-Electricity 0.818 0.774 -0.666 -0.849 0.723 0.583 0.535 -0.714 -0.997

 Process Cooling-Natural Gas 0.854 0.814 -0.526 -0.685 0.765 0.625 0.575 -0.672 -0.948

 Electro-Chemical Process 0.970 0.946 -0.101 -0.186 0.808 0.667 0.615 -0.635 -0.904

 Other-Electricity 0.874 0.836 -0.449 -0.595 0.778 0.631 0.580 -0.695 -0.975
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 53

Table 6.3. Coefficients for technology possibility curve for manufacturing industries and all industrial 

scenarios (cont.)

applies to all fuels unless specified

Existing Facilities New Facilities

Industry/Process Unit

Reference 

REI20401

High Tech 

REI 20401

Reference 

TPC%2

High 

Tech 

TPC%2

REI 

20103

Reference 

REI20404

High 

Tech REI 

20404

Reference 

TPC%2

High 

Tech 

TPC%2

Other-Non-Intensive Manufacturing

Wood Products

 Process Heating-Electricity 0.712 0.747 -0.751 -0.968 0.675 0.524 0.308 -1.679 -2.358

 Process Heating-Steam 0.505 0.306 -1.502 -3.861 0.675 0.406 0.032 -3.358 -9.432

 Process Cooling-Electricity 0.650 0.696 -0.952 -1.203 0.638 0.487 0.280 -1.784 -2.481

 Process Cooling-Natural Gas 0.712 0.747 -0.751 -0.968 0.675 0.524 0.308 -1.679 -2.558

 Electro-Chemical Process 0.937 0.979 -0.144 -0.070 0.713 0.561 0.600 -1.586 -0.342

 Other-Electricity 0.748 0.915 -0.641 -0.295 0.686 0.528 0.310 -1.737 -2.388

Plastic Products

 Process Heating-Electricity 0.798 0.747 -0.751 -0.968 0.675 0.524 0.473 -0.840 -1.179

 Process Heating-Steam 0.635 0.306 -1.052 -3.871 0.675 0.406 0.158 -1.679 -4.716

 Process Cooling-Electricity 0.751 0.696 -0.952 -1.203 0.638 0.487 0.438 -0.892 -1.241

 Process Cooling-Natural Gas 0.798 0.747 -0.751 -0.968 0.675 0.524 0.473 -0.840 -1.179

 Electro-Chemical Process 0.958 0.969 -0.144 -0.070 0.713 0.561 0.677 -0.793 -0.171

 Other-Electricity 0.824 0.915 -0.641 -0.295 0.686 0.528 0.479 -0.869 --1.194

Balance of Manufacturing

 Process Heating-Electricity 0.844 0.804 -0.563 -0.726 0.675 0.551 0.508 -0.672 -0.943

 Process Heating-Steam 0.712 0.627 -1.127 -1.546 0.675 0.450 0.292 -1.343 -2.753

 Process Cooling-Electricity 0.825 0.762 -0.714 -0.902 0.638 0.514 0.473 -0.714 -0.992

 Process Cooling-Natural Gas 0.844 0.804 -0.563 -0.726 0.675 0.551 0.508 -0.672 -0.943

 Electro-Chemical Process 0.968 0.984 -0.108 -0.053 0.713 0.589 0.684 -0.634 -0.137

 Other Natural Gas 0.844 0.826 -0.563 -0.636 0.675 0.551 0.517 -0.672 -0.883

1

REI 2040 Existing Facilities = Ratio of 2040 energy intensity to average 2010 energy intensity for existing facilities. 2

TPC = annual rate of change between 2010 and 2040.

3

REI 2010 New Facilities = For new facilities, the ratio of state-of-the-art energy intensity to average 2010 energy intensity for 

existing facilities.

4

REI 2040 New Facilities = Ratio of 2040 energy intensity for a new state-of-the-art facility to the average 2010 intensity for existing 

facilities.

5

REIs and TPCs apply to virgin and recycled materials. 6

No new plants are likely to be built with these technologies. 7

Net shape casting is projected to reduce the energy requirements for hot and cold rolling rather than for the continuous casting 

step. NA = Not applicable.

BF = Blast furnace.

BOF = Basic oxygen furnace. EAF = Electric arc furnace.

Source: SAIC, IDM Base Year Update with MECS 2010 Data, unpublished data prepared for the Industrial Team, Office of Energy 

Consumption and Efficiency Analysis, Energy Information Administration, Washington, DC, July 2013.
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 54

For the new submodules cement and lime, aluminum, and glass baseline capacity (as of year 2008 or 2009) 

is assumed to retire at a linear rate over a fixed period of time (20 years). Incremental, or added, capacity is 

assumed to retire according to a logistic survival function. In addition, for the cement industry, retirement of 

existing wet process kiln technology is assumed to be permanent; only dry process kilns can be added to 

replace retired wet kilns or to satisfy needed additional capacity.

The concepts of REI and TPCs are a means of embodying assumptions regarding new technology adoption in 

the manufacturing industry and the associated change in energy consumption of capital without 

characterizing individual technologies in detail. The approach reflects the assumption that industrial plants 

will change energy consumption as owners replace old equipment with new, sometimes more-efficient 

equipment, add new capacity, add new products, or upgrade their energy management practices. The 

reasons for the increased efficiency are not likely to be directly attributable to technology choice decisions, 

changing energy prices, or other factors readily subject to modeling. Instead, the module uses the REI and 

TPC concepts to characterize intensity trends for bundles of technologies available for major process steps 

or end use.

There are two exceptions to the general approach in the PA component. The first is for electric motor 

technology choice implemented for nine industries to simulate their electric machine drive energy end use. 

Machine drive electricity consumption in the bulk chemicals industry, the food industry, the five metalbased durables industries, wood, plastics and rubber products, and balance of manufacturing is calculated 

by a motor stock model. The beginning stock of motors is modified over the projection horizon as motors 

are added to accommodate growth in shipments for each sector, as motors are retired and replaced, and as 

failed motors are rewound. When an old motor fails, an economic choice is made on whether to repair or 

replace the motor. When a new motor is added, either to accommodate growth or as a replacement, the 

motor must meet the minimum efficiency standard and a premium efficiency motor is also available. Table 

6.4 provides the beginning stock efficiency for seven motor size groups in each of the three industry groups, 

as well as efficiencies for EPACT minimum and premium motors [32]. As the motor stock changes over the 

projection horizon, the overall efficiency of the motor population changes as well.

The second exception in the PA component is the Cement and Lime, Aluminum, and glass submodules. The 

methodology is described below.

The addition of the cement and lime submodule, the aluminum submodule, and the glass submodule are 

among several enhancements of the energy-intensive industries within the IDM. Instead of the aggregate 

energy intensity evolving according to TPCs for both new and vintage equipment for the process flows, the 

new submodules utilize detailed technology choice for each process flows. The new modules calculate 

surviving capacity based on retirement and needed capacity based on shipments and surviving capacity. 

Existing capital equipment retires linearly and new capital equipment retires according to a non-linear “S” 

curve over the useful life of the equipment. The exact shape of the “S” curve can be obtained by 

parameters adjusted by the user. New capital equipment information (capital and operating costs, energy 

use, and emissions) were obtained from the Consolidated Impacts Modeling System (CIMS) database. Each 

step of the process flow allows for multiple technology choices whose fuel type and efficiency are known at 

the national level, as regional fuel breakouts are fixed using available EIA data.
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Combined cement and lime industry

For the cement process flow, each step (raw material grinding, kiln – both rotation and burner, finished 

grinding) allows for multiple technology choices whose fuel type and efficiency are known at the national 

level, as regional fuel breakouts are fixed using available EIA data.

Cement has both dry and wet mill processes. Some technologies are available to both processes, while 

others are available to only one process. The technology choices within each group are:

1. Raw materials grinding: ball mill, roller mill

2. Kilns (rotators): rotary long with preheat, precalcining, and computer control (dry process only), rotary 

preheat with high-efficiency cooler (dry only), rotary preheat, precalcine with efficient cooler (dry 

process only), rotary wet standard with waste heat recovery boiler and cogeneration (wet process only)

3. Kilns (burners): standard fired by natural gas, efficient fired by natural gas, standard fired by oil, efficient 

fired by oil, standard fired by coal, standard fired by petroleum coke, standard fired by hazardous waste, 

standard fired by residue-derived fuel

4. Finished grinders: standard ball mill, finishing ball mill with high-efficiency separator, standard roller 

mill, finishing roller mill with high-efficiency separator

The technology slate in each of these process steps evolves over time and depends on the relative cost of 

equipment, cost of fuel, and fuel efficiency.

The base year technology slate is determined from the latest CIMS database and calibrated for the year 

2008 with dry and wet mill capacity cement fuel use data from the Portland Cement Association, the USGS, 

and the 2010 MECS. All new cement capacity, both for replacement and increased production, is assumed to 

be dry cement capacity. Existing wet capacity is assumed to retire at a linear rate over 20 years with no 

replacement. Imported clinker, additives, and fly-ash are assumed to make constant percentage 

contributions to the finished product and thus “displace” a certain amount of domestic clinker production, 

and therefore energy use.

Lime shipments are estimated using a fixed percentage of nonmetallic minerals shipments (NAICS 327). 

Lime shipments, plus cement shipments, are presented together as the consolidated cement and lime 

output. Energy consumption and technology evolution in the lime industry are driven by the same methods 

implemented for cement, with different, industry-specific equipment choices.

Aluminum industry

For the aluminum process flow, each step (alumina production, anode production, and electrolysis for 

primary aluminum production, and melting for secondary production), allows for multiple technology 

choices whose fuel type and efficiency are known, as well as other operating characteristics. Technology 

shares are known at the national level, with regional fuel breakouts based on fixed allocations using 

available EIA data.
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The aluminum industry has both primary and secondary production processes, which vary greatly in their 

energy demands. As such, the extents of these processes are based on the aluminum industry’s projected 

production and its historical share of production processes attenuated by relevant regional energy prices. 

Therefore, the fraction of total throughput from each aluminum production process varies over the model 

projections. However, it is assumed based on expert judgment that no new primary aluminum plants will be 

built in the United States before 2040, although capacity expansion of existing primary smelters may occur.

Some technologies are available to both processes, while others are available to only one process. The 

technology choices within each production processing group are:

1. Primary smelting (Hall-Heroult electrolysis cell) is represented as smelting in four pre-bake anode 

technologies that denote standard and retrofitted choices and one inert anode wetted cathode choice.

2. Anode production, used in primary production only, is represented by three natural gas-fired furnaces 

under various configurations in forming and baking pre-bake anodes and the formation of Söderberg 

anodes. Note that anodes are a major requirement for the Hall-Heroult process.

3. Alumina production (Bayer Process) is used in primary production only and selects between existing 

natural gas facilities and those with retrofits.

4. Secondary production selects between two natural gas-fired melters – i.e., a standard and a melter with 

high efficiency

The technology slate in each of these process steps evolves over time and depends on the relative cost of 

equipment, cost of fuel, and fuel efficiency. The base year technology slate is determined from the latest 

CIMS database and calibrated for the base year 2010 MECS and the USGS. All new capacities for aluminum 

production, both for replacement and increased production needs, are now assumed to be either preexisting primary production or new secondary production, based on historical trend data and projected 

energy prices. Similar to the energy-intensive technology of the cement industry, the lifespan of existing and 

new production capacity is assumed to be 20 and 30 years, respectively. In addition, production that has 

been idled is allowed to re-enter production before new equipment is built.

Glass industry

For the glass process flow, each step of the three glass product processes modeled in the IDM (flat glass, 

pressed and blown glass, glass containers) allows for multiple technology choices whose fuel type and 

efficiency are known, as well as other operating characteristics.

For flat glass (NAICS 327211) the process steps include batch preparation, furnace, form & finish, and 

tempering. For pressed and blown glass (NAICS 327212), the process steps include preparation, furnace, 

form & finish, and fire polish. For glass containers (NAICS 327213), the process steps include preparation, 

furnaces, and form & finish. For fiberglass (“mineral wool” – NAICS 327993), the process steps include 

preparation, furnaces, and form & finish. The final category (“glass from glass products” – NAICS 327215) 

was not modeled as a process flow with technology choice but instead endowed with fuel-specific UECs 

which evolved over time via TPC.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 57

Table 6.4. Cost and performance parameters for industrial motor choice model

Industrial Sector 2002 Stock Premium Premium Cost

Horsepower Range Efficiency (%) Efficiency (%) (2002$)

Food

1-5 hp 81.3 89.2 601

6 - 20 hp 87.1 92.5 1,338

21 - 50 hp 90.1 93.8 2,585

51 - 100 hp 92.7 95.3 6,290

101 - 200 hp 93.5 95.2 11,430

201 - 500 hp 93.8 95.4 29,991

> 500 hp 93.0 96.2 36,176

Bulk Chemicals

1-5 hp 82.0 89.4 601

6 - 20 hp 87.4 92.6 1,338

21 - 50 hp 90.4 93.9 2,585

51 - 100 hp 92.4 95.4 6,290

101 - 200 hp 93.5 95.3 11,430

201 - 500 hp 93.3 95.5 29,991

> 500 hp 93.2 96.2 36,176

Metal-Based Durablesa

1-5 hp 81.9 89.2 601

6-20 hp 87.0 92.5 1,338

21-50 hp 89.9 93.9 2,585

51-100 hp 92.0 95.3 6,290

101-200 hp 93.5 95.2 11,430

201-500 hp 93.7 95.4 29,991

>500 hp 93.0 96.2 36,176

Balance of Manufacturingb

1-5 hp 83.0 89.2 601

6-20 hp 88.3 92.5 1,338

21-50 hp 90.3 93.9 2,585

51-100 hp 92.7 95.3 6,290

101-200 hp 94.3 95.2 11,430

201-500 hp 94.2 95.4 29,991

>500 hp 92.9 96.2 36,176 a

The metal-based durables group includes five sectors that are modeled separately: Fabricated Metals; Machinery; Computers and 

Electronics; Electrical Equipment; and Transportation Equipment.

b

The balance of manufacturing group includes three sectors that are modeled separately: Wood Products; Plastic and Rubber 

Products; and All Other Manufacturing.

Source: U.S. Energy Information Administration, Model Documentation Report, Industrial Sector Demand Module of the National 

Energy Modeling System (Washington, DC, September 2013).

Note: The efficiencies listed in this table are operating efficiencies based on average part-loads. Because the average part-load is 

not the same for all industries, the listed efficiencies for the different motor sizes vary across industries.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 58

Below is a summary list of technologies used in the glass sub-module. Not all of the technologies below are 

available to all processes.

1. The preparation step (collection, grinding, and mixing of raw materials including cullet) uses either a 

standard set of grinders/motors or an advanced set that is computer-controlled.

2. The furnaces, which melt the glass, are air-fueled or oxy-fueled burners which employ natural gas. 

Electric boosting furnace technology is also available. Direct electric (or Joule) heating is available for 

fiberglass production.

3. The form & finish process is done for all glass products and the technologies can be selected from highpressure gas-fired computer-controlled or basic technology.

4. There is no known technology choice for the tempering step (flat glass) or the polish (blown glass). 

Placeholders for more-efficient future technology choices were implemented, but their introduction into 

these processes was rather limited.

As with the other sub-modules, the technology slate in each of these process steps evolves over time and 

depends on the relative cost of equipment, cost of fuel, and fuel efficiency. Oxy-fueled burners were added 

as a retrofit to the burner technologies, and their additive impact is determined by the relative price of 

natural gas vs. electricity.

Petrochemical feedstock requirement

This subroutine estimates feedstock requirements for the major petrochemical intermediates such as 

ethylene, propylene, and butadiene. The primary feedstocks used to produce these chemicals are natural 

gas liquids (NGL) (ethane, propane, butane) and petrochemical (oil-based) feedstocks (gas oil, naphtha) [33]. 

Biomass is a potential raw material source, but it is assumed that there will be no biomass-based capacity 

over the projection period because of economic barriers. The type of feedstock not only determines the 

source of feedstock but also the energy for heat and power requirements to produce the chemicals.

To determine the relative amounts of feedstock (NGL or oil-based) baseline intensities, feedstock 

consumption intensities are derived from the 2010 MECS. Feedstock consumption of both types grows or 

declines with organic chemicals shipment value. It should be noted that there is no change in the feedstock 

intensity over time, i.e., all feedstock TPCs are assumed to be zero. Unlike most other processes represented 

in manufacturing PA components, chemical yields are governed by basic chemical stoichiometry which 

allows for specific yields under set conditions of pressure and temperature. For the projected LPG feedstock 

quantities, a further subdivision is made into refinery-produced propylene and ethane. All ethane produced 

by the NEMS Oil & Gas Supply Module is absorbed by the chemical model. The remaining balance of LPG 

feedstock requirement is a mixture of pentanes plus, butane, and propane. The historical (baseline) 

feedstock consumption values for 2013 were obtained from Table 6.5 which displays EIA’s assessment of 

historical annual feedstock consumption for chemicals.

Buildings component

The total buildings energy demand by industry for each region is a function of regional industrial 

employment and output. Building energy consumption was estimated for building lighting, HVAC (heating, 

ventilation, and air conditioning), facility support, and on-site transportation. Space heating was further 

divided to estimate the amount provided by direct combustion of fossil fuels and that provided by steam 
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 59

(Table 6.5). Energy consumption in the BLD Component for an industry is estimated based on regional 

employment and output growth for that industry using the 2010 MECS as a basis.

Table 6.5. Non-combustion use of fossil fuels, selected years 1980-2013

quadrillion Btu

Year

Hydrocarbon Gas

Liquids1

Petrochemical 

Feedstocks Other2 Natural Gas3

2001 1.78 1.16 0.27 0.70

2002 1.88 1.21 0.28 0.68

2003 1.83 1.31 0.28 0.61

2004 1.92 1.53 0.28 0.54

2005 1.78 1.41 0.29 0.48

2006 1.85 1.42 0.30 0.40

2007 1.86 1.31 0.27 0.43

2008 1.70 1.12 0.28 0.47

2009 1.85 0.90 0.35 0.48

2010 1.99 0.94 0.37 0.51

2011 2.12 0.88 0.40 0.55

2012 2.16 0.74 0.37 0.58

2013p 2.22 0.74 0.39 0.59

1

Includes natural gasoline -- hydrocarbon gas liquids were previously called natural gas liquids and 

natural gasoline is pentanes plus.

2

Distillate fuel oil, residual fuel oil, waxes, still gas not burned as a refinery fuel and miscellaneous 

products.

3

U.S. Energy Information Administration (EIA) has altered the methodology for the natural gas

estimates. The estimates are based on data for methanol and ammonia production that are used to 

move the MECS values for nonfuel uses of natural gas in non-MECS years.

P=Preliminary.

Notes: Estimates of consumption for non-combustion use shown in this table are included in total

energy consumption (see Table 1.3). See Note 2, "Non-Combustion Use of Fossil Fuels," at end of

section. Because of changes in methodology, data series may be revised annually. Estimates of noncombustion use in this table are considered industrial uses with the exception of approximately half 

of the lubricants which are considered transportation use. Totals may not equal sum of components 

due to independent rounding. Web Pages: See 

http://www.eia.gov/totalenergy/data/annual/#summary for all data beginning in 1980.

For related information, see http://www.eia.gov/environment/.

Sources: Petroleum Products: 1980—EIA, Energy Data Reports, Petroleum Statement, Annual and

Sales of Liquefied Petroleum Gases and Ethane in 1980. 1981 forward—EIA, Petroleum Supply 

Annual, annual reports, and unpublished data. Natural Gas: 1980—Bureau of the Census, 1980 

Survey of Manufactures, Hydrocarbon, Coal, and Coke Materials Consumed. 1981 forward—U.S. 

Department of Commerce. Coal: 1980 forward—EIA estimates based on the methodology underlying 

the nonfuel emissions calculations in EIA’s Emissions of Greenhouse Gases in the United States 2008. 

Percent of Total Energy Consumption: Derived by dividing total nonfuel by total energy consumption 

on Table 1.3.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 60

Boiler, steam, and cogeneration component

The steam demand and byproducts from the PA and BLD Components are passed to the BSC Component, 

which applies a heat rate and a fuel share equation (Table 6.6) to the boiler steam requirements to compute 

the required energy consumption.

The boiler fuel shares apply only to the fuels that are used in boilers for steam-only applications. Fuel use for 

the portion of the steam demand associated with combined heat and power (CHP) is described in the next 

section. Some fuel switching for the remainder of the boiler fuel use is assumed and is calculated with a 

logit-sharing equation where fuel shares are a function of fuel prices. The equation is calibrated to 2010 so 

that the 2010 fuel shares are produced for the relative prices that prevailed in 2010.

The byproduct fuels, production of which is estimated in the PA Component, are assumed to be consumed 

without regard to price, independent of purchased fuels. The boiler fuel share equations and calculations 

are based on the 2010 MECS and information from the Council of Industrial Boiler Owners. [34]

Combined heat and power

CHP plants, which are designed to produce both electricity and useful heat, have been used in the industrial 

sector for many years. The CHP estimates in the module are based on the assumption that the historical 

relationship between industrial steam demand and CHP will continue in the future, and that the rate of 

additional CHP penetration will depend on the economics of retrofitting CHP plants to replace steam 

generated from existing non-CHP boilers. The technical potential for CHP is primarily based on supplying 

thermal requirements (i.e., matching thermal loads). Capacity additions are then determined by the 

interaction of CHP investment payback periods (with the time value of money included) derived using 

operating hours reported in EIA’s published statistics, market penetration rates for investments with those 

payback periods, and regional deployment for these systems as characterized by the “collaboration 

coefficients” in Table 6.9. Assumed installed costs for the CHP systems are given in Table 6.8.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 61

Table 6.6. 2010 Building component energy consumption

trillion Btu

Building Use and Energy Source

Industry Region

Lighting 

Electricity 

Consumption

HVAC 

Electricity 

Consumption

HVAC Natural 

Gas 

Consumption

HVAC Steam 

Consumption

Facility Support 

Total 

Consumption

Onsite 

Transportation 

Total 

Consumption

Food Products 1 2.1 2.1 3.3 2.1 1.7 0.9

2 9.7 9.7 14.8 4.9 7.4 0.9

3 6.8 6.8 8.7 5.5 4.4 1.6

4 3.5 3.5 7.4 4.7 3.8 3.0

Paper & Allied Products 1 1.2 1.3 2.8 0.0 0.3 0.9

2 3.7 4.0 3.3 0.0 0.3 0.9

3 6.8 7.4 7.1 0.0 0.7 2.0

4 3.2 3.4 2.2 0.0 0.3 0.9

Bulk Chemicals 1 0.8 1.0 3.7 0.0 2.8 5.2

2 2.9 3.5 5.8 0.0 3.9 5.6

3 7.7 9.3 15.0 0.0 9.0 9.7

4 0.9 1.0 3.7 0.0 2.8 5.0

Glass & Glass Products 1 0.4 0.5 3.8 0.0 3.2 3.4

2 0.7 0.9 4.1 0.0 3.3 3.4

3 0.9 1.2 4.5 0.0 3.4 3.5

4 0.3 0.4 3.4 0.0 3.1 3.4

Cement 1 0.1 0.1 0.6 0.0 0.6 1.1

2 0.3 0.3 0.6 0.0 0.6 1.1

3 0.4 0.4 0.7 0.0 0.7 1.1

4 0.2 0.2 0.5 0.0 0.5 0.6

Iron & Steel 1 0.8 0.8 1.9 0.0 0.7 0.6

2 2.7 2.7 8.7 0.0 1.9 2.4

3 3.1 3.1 3.6 0.0 1.0 1.7

4 0.4 0.4 1.0 0.0 0.6 0.6

Aluminum 1 0.2 0.2 0.5 0.0 0.2 0.2

2 0.8 0.8 1.0 0.0 0.3 0.3

3 0.8 8.8 2.6 0.0 0.7 0.8

4 0.3 0.3 0.4 0.0 0.1 0.2

Metal-Based Durables 1 1.8 1.5 5.1 2.9 0.6 1.4

 Fabricated Metal Products 2 6.6 5.6 16.3 9.1 1.2 1.5

3 5.2 4.4 8.8 5.0 0.8 1.7

4 1.4 1.2 2.6 1.5 0.2 0.3

 Machinery 1 1.6 2.3 4.2 0.7 0.2 0.2

2 4.8 6.8 20.7 3.6 0.9 0.9

3 3.1 4.3 8.6 1.5 0.5 0.7

4 0.6 0.8 0.5 0.1 0.1

0.2
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 62

Table 6.6. 2010 Boiler fuel component energy consumption (cont.)

trillion Btu

Building Use and Energy Source

Industry Region

Lighting 

Electricity 

Consumption

HVAC 

Electricity 

Consumption

HVAC Natural 

Gas 

Consumption

HVAC Steam 

Consumption

Facility Support 

Total 

Consumption

Onsite 

Transportation 

Total 

Consumption

 Computers & Electronic 

 Products 1 2.2 5.6 4.2 2.5 0.9 0.8

2 2.0 4.0 4.4 2.7 0.9 0.8

3 4.2 10.5 4.4 2.7 0.9 0.8

4 4.1 10.2 9.4 5.7 1.2 0.8

 Transportation Equipment 1 1.6 2.0 4.8 0.4 0.6 0.2

2 10.5 13.1 23.1 2.1 2.0 1.2

3 6.1 7.6 10.1 0.9 1.1 0.8

4 2.5 3.1 3.9 0.4 0.3 0.2

 Electrical Equipment 1 0.7 1.0 1.7 1.3 0.5 0.5

2 1.1 1.6 2.6 2.1 0.4 0.4

3 2.1 3.1 4.0 3.1 0.6 0.4

4 0.2 0.3 0.1 0.1 0.1 0.4

Other Non-Intensive 

 Manufacturing 

 Wood Products 1 0.2 0.2 0.8 2.5 0.5 0.4

2 0.6 0.5 1.6 4.9 0.7 1.7

3 2.4 1.8 2.7 8.4 0.7 2.1

4 0.8 0.6 1.3 4.0 0.3 4.2

 Plastic Products 1 0.8 0.9 1.8 0.0 0.2 0.3

2 4.5 5.6 7.7 0.0 0.5 0.7

3 5.5 6.8 10.3 0.0 0.7 0.8

4 2.5 3.0 2.1 0.0 0.2 0.2

 Balance of Manufacturing 1 5.5 9.1 13.4 0.0 0.0 1.2

2 10.5 17.4 20.6 0.0 0.0 2.1

3 15.7 26.0 28.1 0.0 0.0 3.4

4 4.5 7.5 9.5 0.0 0.0 0.8

HVAC = Heating, Ventilation, Air Conditioning.

Source: SAIC, IDM Base Year Update with MECS 2010 Data, unpublished data prepared for the Industrial Team, Office of Energy 

Consumption and Efficiency Analysis, Energy Information Administration, Washington, DC, July 2013.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 63

Table 6.7. 2010 Boiler fuel component and logit parameter

trillion Btu

Region Alpha Natural Gas Coal Oil Renewables

Food Products 1 -2.0 33 1 3 1

2 -2.0 147 131 3 31

3 -2.0 85 14 6 31

4 -2.0 74 18 3 8

Paper & Allied Products 1 -2.0 44 44 8 122

2 -2.0 55 55 9 97

3 -2.0 113 81 45 898

4 -2.0 35 16 2 108

Bulk Chemicals 1 -2.0 17 0 55 0

2 -2.0 164 43 17 0

3 -2.0 705 18 56 0

4 -2.0 21 44 6 0

Glass & Glass Products 1 -2.0 1 0 2 1

2 -2.0 1 0 2 1

3 -2.0 1 0 2 1

4 -2.0 0 0 2 1

Cement 1 -2.0 0 0 0 1

2 -2.0 0 0 0 5

3 -2.0 0 0 0 3

4 -2.0 0 0 0 1

Iron & Steel 1 -2.0 4 6 16 0

2 -2.0 24 0 64 0

3 -2.0 9 0 14 0

4 -2.0 2 0 0 0

Aluminum 1 -2.0 1 0 0 0

2 -2.0 3 0 0 1

3 -2.0 8 0 1 1

4 -2.0 1 0 0 0

Metal-Based Durables

 Fabricated Metal Products 1 -2.0 4 0 0 0

2 -2.0 12 0 0 0

3 -2.0 6 0 0 0

4 -2.0 2 0 0 0

 Machinery 1 -2.0 1 0 0 1

2 -2.0 4 0 1 1

3 -2.0 2 0 0 0

4 -2.0 0 0 0 0
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 64

Table 6.7. 2010 Boiler fuel component and logit parameter (cont.)

trillion Btu

Region Alpha Natural Gas Coal Oil Renewables

Computer & Electronic

 Products 1 -2.0 3 0 1 0

2 -2.0 3 0 1 0

3 -2.0 3 0 1 0

4 -2.0 7 0 1 0

 Electrical Equipment 1 -2.0 1 0 1 0

2 -2.0 2 0 0 0

3 -2.0 3 0 0 0

4 -2.0 0 0 0 0

 Transportation Equipment 1 -2.0 3 8 2 1

2 -2.0 17 -5 1 3

3 -2.0 7 1 2 1

4 -2.0 3 0 0 0

Other Non-Intensive 

 Manufacturing 

 Wood Products 1 -2.0 0 0 1 79

2 -2.0 1 0 2 31

3 -2.0 4 0 2 188

4 -2.0 2 0 2 54

 Plastic Products 1 -2.0 3 2 1 0

2 -2.0 16 0 0 0

3 -2.0 21 0 1 0

4 -2.0 4 0 0 0

 Balance of Manufacturing 1 -2.0 35 -10 3 1

2 -2.0 54 29 37 5

3 -2.0 74 42 118 10

4 -2.0 25 7 0 1

Alpha: User-specified.

Source: U.S. Energy Information Administration, Model Documentation Report, Industrial Sector Demand Module of 

the National Energy Modeling System, (Washington, DC 2014).
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 65

Table 6.8. Cost characteristics of industrial CHP systems

Installed Cost

Size Kilowatts 2005$ per KWh)1

System (kW) Reference 2010 Reference: 040 High Tech 2040

Engine 1,000 1,440 576 535

2,000 1,260 396 354

Gas turbine 3,510 1,719 1,496 1,450

5,670 1,152 1,023 1,006

14,990 982 869 869

25,000 987 860 860

40,000 875 830 830

Combined cycle 100,000 723 684 668 1

Costs are given in 2005 dollars in original source document.

Source: U.S. Energy Information Administration, Model Documentation Report, Industrial Sector Demand Module 

of the National Energy Modeling System (Washington, DC, September 2013).

Table 6.9. Regional collaboration coefficients for CHP deployment

Census Region Collaboration Coefficient

Northeast 1.46

Midwest 1.34

South 0.33

West 1.06

Source: Calculated from American Council for an Energy-Efficient Economy, “Challenges Facing 

Combined Heat and Power Today: A State-by-State Assessment," September 2011,

www.aceee.org/research-report/ie111 and Energy Information Administration, Office of Energy 

Analysis.

Key assumptions - non-manufacturing

The non-manufacturing sector consists of three industries: agriculture, mining and construction. These 

industries all use electricity, natural gas, diesel fuel, and gasoline. The mining industry also uses coal, natural 

gas liquids (NGL), and residual fuel oil, and the construction industry also uses other petroleum in the form 

of asphalt and road oil. Except for oil and gas extraction, almost all of the energy use in the nonmanufacturing sector takes place in the process and assembly step. Oil and gas extraction uses a significant 

amount of residual fuel oil in the BSC component. Table 6.10 shows the baseline unit energy consumption 

values for the non-manufacturing subsectors in 2010.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 66

Table 6.10. 2010 UECs for non-manufacturing

Unit Energy Consumption (UEC) in thousand Btu/2005$)

Industry/Process UEC 2010

Agriculture

 Crop Production

 Buildings 1.82

 Vehicles 2.21

 Irrigation 0.70

 Other Agricultural Production

 Buildings 0.64

 Vehicles 1.01

 Irrigation 0.27

Construction 1.65

Mining

 Coal 6.97

 Oil & Gas Extraction 1.74

 Metal/Non-metal 8.97

Sources: Calculations based on EIA’s State Energy Data System (SEDS), EIA’s Fuel Oil and Kerosene 

Survey (FOKS), USDA’s Agriculture Research Management Survey (ARMS), and Department of

Commerce’s Economic Census, Construction and Mining Series. See Note and sources page notes 

[27], [35], [36], [38], [37] for complete citations.

Unlike the manufacturing sector, the non-manufacturing sector does not have a single source of data for 

energy consumption estimates. Instead, UECs for the non-manufacturing sector are derived from various 

sources of data collected by a number of government agencies. Furthermore, unlike the majority of 

manufacturing industries displayed in Table 6.3, the TPCs for non-manufacturing are not “fixed” in that they 

are dynamic and evolve over time. This evolution depends on output from other modules such as building 

and transportation equipment efficiencies which are employed in the agriculture and construction models 

as well as oil and gas well and coal mine productivity which is employed in the mining model.

Non-manufacturing data was revised using EIA and Census Bureau sources to provide more realistic 

projections of diesel and gasoline for off-road vehicle use, allocate natural gas and hydrocarbon gas liquids 

(HGL) use, and electricity. Sources used are EIA’s Fuel Oil and Kerosene Sales (FOKS) [35] for distillate 

consumption, Agricultural Resource Management Survey (ARMS) [36] and the Census Bureau’s Census of 

Mining [37] and Census of Construction. [38] Combining these sources, there is now more consumption of 

distillate and less consumption of motor gasoline. Also, the use of hydrocarbon gas liquids (HGL) is now 

accounted for in the agriculture and especially the construction industries. Nonmanufacturing consumption 

is no longer dictated solely by the SEDS–MECS difference as it has been in previous years.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 67

Agriculture

U.S. agriculture consists of three major sub-sectors:

• crop production, which is dependent primarily on regional environments and crops demanded;

• animal production, which is largely dependent on food demands and feed accessibility;

• all remaining agricultural activities, which are primarily composed of forestry and logging.

These sub-industries have historically been tightly coupled due to competing use of land area. For example, 

crops produced for animal feed cannot be consumed by humans; forests provide the feedstock of the paper 

and wood industries but in turn do not allow the growth of crops or limit or prevent grazing of animals.

However, energy consumption in these industries is tied to specialized equipment, which often determines 

the fuel requirement with little flexibility. Within each of these sub-industries the key energy-using 

equipment can be broken into three major categories: off-road vehicles, buildings, and other equipment, 

which is primarily irrigation equipment for crop production. Agriculture TPCs are calculated within the NEMS 

model by further subdividing consumption in each sub-industry based on the equipment in which the energy 

is used: off-road vehicles, which are trucks, tractors, and other specialty vehicles; buildings, which require 

lighting and temperature control; and other equipment, which covers a variety of both common (e.g., 

pumps) and specialty (e.g. cotton gins) equipment used in all the various types of agricultural production. 

Thus, changes in efficiencies in heavy and medium-duty vehicles from the Transportation Demand Module 

and changes in lighting and heating efficiencies from the Commercial Demand Module are used to 

dynamically modify the agriculture TPCs.

Baseline energy consumption data for the two agriculture sectors (crops and other agriculture) are based on 

data from the Census of Agriculture and a special tabulation from the National Agricultural Statistics Service 

(USDA-NASS). Expenditures for four energy sources are collected from crop farms and livestock farms as 

part of the Agricultural Resource Management Survey (ARMS). These data are converted from dollar 

expenditures to energy quantities using fuel prices from NASS and EIA.

Mining

The mining sector comprises of three sectors: coal mining, metal and nonmetal mining, and oil and gas 

extraction. Energy use is based on what equipment is used at the mine and onsite vehicles used. All mines 

use extraction equipment and lighting, but only coal and metal and nonmetal mines use grinding and 

ventilation. As with the agriculture module described above, TPCs are influenced by efficiency changes in 

buildings and transportation equipment.

Coal mining production is obtained from the Coal Market Module (CMM). Currently, it is assumed that 70%

of the coal is mined at the surface and the rest is mined underground. As these shares evolve, however, so 

does the energy consumed, since surface mines use less energy overall than underground mining. 

Moreover, the energy consumed for coal mining depends on coal mine productivity, which is also obtained 

from the CMM. Diesel fuel and electricity are the predominant fuels used in coal mining. Electricity used for 

coal grinding is calculated using the raw grinding process step from the cement sub-module. In metal and 

non-metal mining, energy use is similar to coal mining. Output used for metal and non-metal mining is 

derived from the Macroeconomic Activity Module’s (MAM) variable for “other” mining which also provides 

the shares of each.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 68

For oil and natural gas extraction, production is derived from the OGSM. Energy use depends upon the fuel 

extracted as well as whether the well is conventional or unconventional (e.g., extraction from tight and shale 

formations), percentage of dry wells, and well depth.

Construction

Construction uses diesel fuel, gasoline, electricity and natural gas as energy sources. Construction also uses 

asphalt and road oil as a nonfuel energy source. Asphalt and road oil use is tied to state and local 

government real investment in highways and streets, and this investment is derived from the MAM. TPCs for 

diesel and gasoline fuels are directly tied to the Transportation Demand Module’s heavy-and medium-duty 

vehicle efficiency projections. For non-vehicular construction equipment, TPCs are a weighted average of 

vehicular TPCs and highway investment.

Legislation and regulations

Energy Improvement and Extension Act of 2008

Under EIEA2008 Title I, “Energy Production Incentives,” Section 103 provides an Investment Tax Credit (ITC) 

for qualifying Combined Heat and Power (CHP) systems placed in service before January 1, 2017. Systems 

with up to 15 megawatts of electrical capacity qualify for an ITC up to 10% of the installed cost. For systems 

between 15 and 50 megawatts, the percentage tax credit declines linearly with the capacity, from 10% to 

3%. To qualify, systems must exceed 60% fuel efficiency, with a minimum of 20% each for useful thermal 

and electrical energy produced. The provision was modeled in AEO2015 by adjusting the assumed capital 

cost of industrial CHP systems to reflect the applicable credit.

The Energy Independence and Security Act of 2007 (EISA2007)

Under EISA2007, the motor efficiency standards established under the Energy Policy Act of 1992 

(EPACT1992) are superseded for purchases made after 2011. Section 313 of EISA2007 increases or creates

minimum efficiency standards for newly manufactured and imported general purpose electric motors. The 

efficiency standards are raised for general purpose, integral-horsepower induction motors with the 

exception of fire pump motors. Minimum standards were created for seven types of poly-phase, integralhorsepower induction motors and NEMA design “B” motors (201-500 horsepower) that were not previously 

covered by EPACT standards. In 2014, efficiency standards were revised. For motors manufactured after 

June 1, 2016, efficiency standards for current regulated motor types [39] were expanded to include 201-500 

horsepower motors. Also, special and definite purpose motors of from 1-500 horsepower and NEMA design 

“A” motors from 201-500 horsepower became subject to efficiency standards. The industrial module’s 

motor efficiency assumptions reflect the EISA2007 efficiency standards for new motors added after 2011 but 

do not include the 2014 efficiency standards. Current motor efficiency assumptions meet or exceed the

2014 efficiency standards for all horsepower ratings except 201-500 hp. For 201-500 hp motors, the 

difference between IDM assumptions and the new standards is slight.

Energy Policy Act of 1992 (EPACT1992)

EPACT1992 contains several implications for the industrial module. These implications concern efficiency 

standards for boilers, furnaces, and electric motors. The industrial module uses heat rates of at least 1.25 

(80% efficiency) and 1.22 (82% efficiency) for gas and oil burners, respectively. These efficiencies meet the 

EPACT1992 standards. EPACT1992 mandates minimum efficiencies for all motors up to 200 horsepower 
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 69

purchased after 1998. The choices offered in the motor efficiency assumptions are all at least as efficient as 

the EPACT minimums.

Clean Air Act Amendments of 1990 (CAAA1990)

The CAAA1990 contains numerous provisions that affect industrial facilities. Three major categories of such 

provisions are as follows: process emissions, emissions related to hazardous or toxic substances, and SO2 

emissions. Process emissions requirements were specified for numerous industries and/or activities (40 CFR 

60). Similarly, 40 CFR 63 requires limitations on almost 200 specific hazardous or toxic substances. These 

specific requirements are not explicitly represented in the NEMS industrial model because they are not 

directly related to energy consumption projections.

Section 406 of the CAAA1990 requires the U.S. Environmental Protection Agency (EPA) to regulate industrial 

SO2 emissions at such time that total industrial SO2 emissions exceed 5.6 million tons per year (42 USC 

7651). Since industrial coal use, the main source of SO2 emissions, has been declining, EPA does not 

anticipate that specific industrial SO2 regulations will be required (Environmental Protection Agency, 

National Air Pollutant Emission Trends: 1990-1998, EPA-454/R-00-002, March 2000, Chapter 4). Further, 

since industrial coal use is not projected to increase, the industrial cap is not expected be a factor in 

industrial energy consumption projections. (Emissions due to coal-to-liquids CHP plants are included with 

the electric power sector because they are subject to the separate emission limits of large electricity 

generating plants.)

Maximum Achievable Control Technology for Industrial Boilers (Boiler MACT) 

Section 112 of the Clean Air Act (CAA) requires the regulation of air toxics through implementation of the 

National Standards for Hazardous Air Pollutants (NESHAP) for industrial, commercial, and institutional 

boilers. The final regulations, known as Boiler MACT, are modeled in AEO2015. Pollutants covered by Boiler 

MACT include the hazardous air pollutants (HAP), hydrogen chloride (HCI), mercury (HG), dioxin/furan, 

carbon monoxide (CO), and particulate matter (PM). Generally, industries comply with the Boiler MACT 

regulations by including regular maintenance and tune-ups for smaller facilities and emission limits and 

performance tests for larger facilities. Boiler MACT is modeled as an upgrade cost in the Macroeconomic 

Activity Module (MAM). These upgrade costs are classified as “nonproductive costs” which are not 

associated with efficiency improvements. The effect of these costs in the MAM is a reduction in shipments 

coming into the IDM.

California Assembly Bill 32: Emissions cap-and-trade as part of the Global Warming Solutions 

Act of 2006 (AB32)

AB32 established a comprehensive, multi-year program to reduce Greenhouse Gas (GHG) emissions in 

California, including a cap-and-trade program. In addition to the cap-and-trade program, AB32 also 

authorizes the low carbon fuel standard (LCFS); energy efficiency goals and programs in transportation, 

buildings; and industry; combined heat and power goals; and renewable portfolio standards.
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 70

For AEO2015, the cap-and-trade provisions were modeled for industrial facilities, refineries, and fuel 

providers. GHG emissions include both non-CO2 and specific non-CO2 GHG emissions. The allowance price, 

representing the incremental cost of complying with AB32 cap-and-trade, is modeled in the NEMS Electricity 

Market Module via a region-specific emissions constraint. This allowance price, when added to market fuel 

prices, results in higher effective fuel prices in the demand sectors. Limited banking and borrowing, as well 

as a price containment reserve and offsets, have been modeled in NEMS. AB32 is not modeled explicitly in 

the Industrial Demand Module, but enters the module implicitly through higher effective fuel prices and 

macroeconomic effects of higher prices, all of which affect energy demand and emissions. In June 2014, 

AB32 regulations were clarified and revised [40], but these revisions were not added to the IDM because 

they did not materially affect model calculation or results.
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 71

Notes and sources

[27] U.S. Energy Information Administration, State Energy Data System, based on energy consumption by 

state through 2011, as downloaded in August, 2013, from www.eia.gov/state/seds/.

[28] U. S. Energy Information Administration, Manufacturing Energy Consumption Survey 2010, 

http://www.eia.gov/consumption/manufacturing/index.cfm.

[29] Roop, Joseph M., “The Industrial Sector in CIMS-US,” Pacific Northwest National Laboratory, 28th 

Industrial Energy Technology Conference, May, 2006.

[30] Portland Cement Association, U.S. and Canadian Portland Cement Industry Plant Information Summary, 

cement data was made available under a non-disclosure agreement, http://www.cement.org .

[31] U.S. Department of the Interior, U.S. Geological Survey, Minerals Yearbook, cement data was made 

available under a non-disclosure agreement, 

http://minerals.usgs.gov/minerals/pubs/commodity/cement/myb1-2012-cemen.pdf.

[32] In NEMS, hydrocarbon gas liquids (HGL), which comprise natural gas liquids (NGL) and olefins, are

reported as Liquefied Petroleum Gas (LPG).

[33] U.S. Department of Energy (2007). Motor Master+ 4.0 software database; available at updated link 

http://energy.gov/eere/amo/articles/motormaster.

[34] Personal correspondence with the Council of Industrial Boiler Owners.

[35] U.S. Energy Information Administration, Fuel Oil and Kerosene Survey (FOKS), 

http://www.eia.gov/dnav/pet/pet_cons_821usea_dcu_nus_a.htm.

[36] Agriculture Research Management Survey (ARMS), United States Dept. of Agriculture, Economic 

Research Service, http://ers.usda.gov/data-products/arms-farm-financial-and-crop-productionpractices.aspx.

[37] 2007 Economic Census, Construction Summary Series, 

http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ECN_2007_US_23SG01

&prodType=table.

[38] 2007 Economic Census, Mining Industry Series, 

http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=ECN_2007_US_21I1&pr

odType=table.

[39] Federal Register 79 FR 103 pp. 30934-31014, Washington, DC: May 29, 2014. Available at 

http://www.gpo.gov/fdsys/pkg/FR-2014-05-29/pdf/2014-11201.pdf

[40] California Air Resources Board “California Code of Regulations, Title 17, Division 3, Chapter 1, 

Subchapter 10, Article 5 §95800 - §96022” Sacramento, CA: June 14, 2014. Available at 

http://www.arb.ca.gov/cc/capandtrade/capandtrade/unofficial_c&t_012015.pdf
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 72

Transportation Demand Module

The NEMS Transportation Demand Module estimates transportation energy consumption across the nine 

Census Divisions (see Figure 5) and over ten fuel types. Each fuel type is modeled according to fuel-specific 

and associated technology attributes applicable by transportation mode. Total transportation energy 

consumption is the sum of energy use in eight transport modes: light-duty vehicles (cars and light trucks), 

commercial light trucks (8,501-10,000 pounds gross vehicle weight), freight trucks (>10,000 pounds gross 

vehicle weight), buses, freight and passenger aircraft, freight and passenger rail, maritime freight shipping, 

and miscellaneous transport such as recreational boating. Light-duty vehicle fuel consumption is further 

subdivided into personal usage and commercial fleet consumption.

Key assumptions

Light-duty vehicle assumptions

The light-duty vehicle Manufacturers Technology Choice Component (MTCC) includes 86 advanced 

technology input assumptions specific to cars and light trucks (Tables 7.1 and 7.2) that include incremental 

fuel economy improvement, incremental cost, incremental weight change, first year of introduction, and 

fractional horsepower change.

The vehicle Regional Sales Component holds the share of vehicle sales by manufacturers constant within a 

vehicle size class at 2010 levels based on National Highway Traffic Safety Administration (NHTSA) data [41]. 

Environmental Protection Agency (EPA) size class sales shares are projected as a function of income per 

capita, fuel prices, and average predicted vehicle prices based on endogenous calculations within the MTCC 

[42].

The MTCC utilizes 86 technologies for each size class and manufacturer based on the cost-effectiveness of 

each technology and an initial year of availability. A discounted stream of fuel savings is calculated for each 

technology that is compared with the marginal cost to determine cost-effectiveness and market 

penetration. The fuel economy calculations assume the following:

• The financial parameters used to determine technology economic effectiveness are evaluated based 

on the need to improve fuel economy to meet CAFE standards versus consumer willingness to pay 

for fuel economy improvement beyond those minimum requirements.

• Fuel economy standards for light-duty vehicles reflect current law through model year 2025, 

according to NHTSA model year 2011 final rulemaking, joint EPA and NHTSA rulemaking for 2012 

through 2016, and joint EPA and NHTSA rulemaking for 2017 through 2025. CAFE standards enacted 

for model years 2022 through 2025 will undergo a midterm evaluation by NHTSA and could be 

subject to change. For model years 2026 through 2040, fuel economy standards are held constant at 

model year 2025 levels with fuel economy improvements still possible based on continued 

improvements in economic effectiveness.

• Expected future fuel prices are calculated based on an extrapolation of the growth rate between a 

five-year moving average of fuel price 3 years and 4 years prior to the present year. This assumption 

is founded upon an assumed lead time of 3 to 4 years to significantly modify the vehicles offered by 

a manufacturer.
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 73

Table 7.1. Standard technology matrix for cars1

Absolute Per Unit

Fuel Incremen- Incremen- IncremenEfficiency Incremental tal Cost tal Weight tal Weight Introduc- Horsepower

Change % Cost 2000$ ($/UnitWt.) (Lbs.) (Lbs./UnitWt.) tion Year Change %

Unit Body Construction 4.0 99.91 0.00 0 -6 1980 0

Mass Reduction I 1.0 0.00 0.06 0 -1.5 2005 0

Mass Reduction II 2.6 0.00 0.14 0 -3.5 2009 0

Mass Reduction III 5.4 0.00 0.42 0 -10 2011 0

Mass Reduction IV 8.4 0.00 0.62 0 -15 2099 0

Mass Reduction V 11.6 0.00 0.72 0 -20 2099 0

Aerodynamics I 2.4 48.17 0.00 0 0.5 2000 0

Aerodynamics II 4.9 203.29 0.00 0 1 2011 0

6 Speed Manual 2.2 255.59 0.00 20 0 1995 0

Aggressive Shift Logic I 2.5 32.44 0.00 0 0 1999 0

Aggressive Shift Logic II 6.7 27.18 0.00 0 0 2017 0

Early Torque Converter Lockup 0.5 29.49 0.00 0 0 2002 0

High Efficiency Gearbox 1.6 200.63 0.00 0 0 2017 0

5 Speed Automatic 1.4 103.91 0.00 20 0 1995 0

6 Speed Automatic 2.2 270.05 0.00 30 0 2003 0

7 Speed Automatic 5.1 401.04 0.00 40 0 2009 0

8 Speed Automatic 8.0 532.83 0.00 50 0 2010 0

Dual Clutch Automated Manual 5.5 56.75 0.00 -10 0 2004 0

CVT 8.4 250.98 0.00 -25 0 1998 0

Low Friction Lubricants 0.7 3.20 0.00 0 0 2003 0

Engine Friction Reduction I-4 cyl 2.0 47.16 0.00 0 0 2000 1.25

Engine Friction Reduction I-6 cyl 2.6 71.14 0.00 0 0 2000 1.25

Engine Friction Reduction I-8 cyl 2.8 94.32 0.00 0 0 2000 1.25

Engine Friction Reduction II-4 cyl 3.6 100.71 0.00 0 0 2017 2.25

Engine Friction Reduction II-6 cyl 4.7 147.87 0.00 0 0 2017 2.25

Engine Friction Reduction II-8 cyl 5.1 195.03 0.00 0 0 2017 2.25

Cylinder Deactivation-6 cyl 6.5 187.06 0.00 10 0 2004 0

Cylinder Deactivation-8 cyl 6.9 209.97 0.00 10 0 2004 0

VVT I-OHV Intake Cam Phasing-6 cyl 2.6 43.90 0.00 20 0 2051 1.25

VVT I-OHV Intake Cam Phasing-8 cyl 2.7 43.90 0.00 30 0 2051 1.25

VVT I-OHC Intake Cam Phasing-4 cyl 2.1 43.90 0.00 10 0 1993 1.25

VVT I-OHC Intake Cam Phasing-6 cyl 2.6 88.76 0.00 20 0 1993 1.25

VVT I-OHC Intake Cam Phasing-8 cyl 2.7 88.76 0.00 30 0 1993 1.25

VVT II-OHV Coupled Cam Phasing-6 cyl 5.4 43.90 0.00 20 0 2009 1.25

VVT II-OHV Coupled Cam Phasing-8 cyl 5.8 43.90 0.00 30 0 2009 1.25

VVT II-OHC Coupled Cam Phasing-4 cyl 4.3 43.90 0.00 10 0 2009 1.25

VVT II-OHC Coupled Cam Phasing-6 cyl 5.4 88.76 0.00 20 0 2009 1.25

VVT II-OHC Coupled Cam Phasing-8 cyl 5.8 88.76 0.00 30 0 2009 1.25

VVT III-OHV Dual Cam Phasing-6 cyl 5.4 99.26 0.00 25 0 2051 1.56

VVT III-OHV Dual Cam Phasing-8 cyl 5.8 99.26 0.00 37.5 0 2051 1.56

VVT III-OHC Dual Cam Phasing-4 cyl 4.3 90.67 0.00 12.5 0 2009 1.56

VVT III-OHC Dual Cam Phasing-6 cyl 5.4 195.65 0.00 25 0 2009 1.56

VVT III-OHC Dual Cam Phasing-8 cyl 5.8 195.65 0.00 37.5 0 2009 1.56

VVL I-OHV Discrete-6 cyl 5.5 225.24 0.00 40 0 2000 2.5

VVL I-OHV Discrete-8 cyl 5.9 322.59 0.00 50 0 2000 2.5

VVL I-OHC Discrete-4 cyl 4.3 155.57 0.00 25 0 2000 2.5

VVL I-OHC Discrete-6 cyl 5.5 225.24 0.00 40 0 2000 2.5

VVL I-OHC Discrete-8 cyl 5.9 322.59 0.00 50 0 2000 2.5

VVL II-OHV Continuous-6 cyl 7.0 1150.07 0.00 40 0 2011 2.5
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 74

Table 7.1. Standard technology matrix for cars1 (cont.)

Absolute Per Unit

Fuel Incremen- Incremen- IncremenEfficiency Incremental tal Cost tal Weight tal Weight Introduc- Horsepower

Change % Cost 2000$ ($/UnitWt.) (Lbs.) (Lbs./UnitWt.) tion Year Change %

VVL II-OHV Continuous-8 cyl 7.5 1256.96 0.00 50 0 2011 2.5

VVL II-OHC Continuous-4 cyl 5.4 232.88 0.00 25 0 2011 2.5

VVL II-OHC Continuous-6 cyl 7.0 427.58 0.00 40 0 2011 2.5

VVL II-OHC Continuous-8 cyl 7.5 466.71 0.00 50 0 2011 2.5

Stoichiometric GDI-4 cyl 1.5 264.37 0.00 20 0 2006 2.5

Stoichiometric GDI-6 cyl 1.5 397.99 0.00 30 0 2006 2.5

Stoichiometric GDI-8 cyl 1.5 478.16 0.00 40 0 2006 2.5

OHV to DOHC TBDS-I4 21.6 1383.90 0.00 -100 0 2009 3.75

OHV to DOHC TBDS I-V6 20.2 2096.84 0.00 -100 0 2009 3.75

SOHC to DOHC TBDS I-I4 21.6 827.47 0.00 -100 0 2009 3.75

SOHC to DOHC TBDS I-V6 20.2 1605.80 0.00 -100 0 2009 3.75

DOHC TBDS I-I3 17.5 915.28 0.00 -100 0 2009 3.75

DOHC TBDS I-I4 21.6 747.30 0.00 -100 0 2009 3.75

DOHC TBDS I-V6 20.2 1530.88 0.00 -100 0 2009 3.75

OHV to DOHC TBDS II-I4 26.3 1586.36 0.00 -100 0 2012 3.75

OHV to DOHC TBDS II-V6 24.5 2445.33 0.00 -100 0 2012 3.75

SOHC to DOHC TBDS II-I4 26.3 1046.15 0.00 -100 0 2012 3.75

SOHC to DOHC TBDS II-V6 24.5 1968.59 0.00 -100 0 2012 3.75

DOHC TBDS II-I3 21.2 1130.47 0.00 -100 0 2012 3.75

DOHC TBDS II-I4 26.3 968.31 0.00 -100 0 2012 3.75

DOHC TBDS II-V6 24.5 1895.85 0.00 -100 0 2012 3.75

OHV to DOHC TBDS III-I4 (from V6) 32.6 2031.83 0.00 -100 0 2017 3.75

OHV to DOHC TBDS III-I4 (from V8) 30.7 1601.81 0.00 -200 0 2017 3.75

SOHC to DOHC TBDS III-I4 (from V6) 32.6 1565.84 0.00 -100 0 2017 3.75

SOHC to DOHC TBDS III-I4 (from V8) 30.7 1380.40 0.00 -200 0 2017 3.75

DOHC TBDS III-I3 (from I4) 27.1 1634.58 0.00 -100 0 2017 3.75

DOHC TBDS III-I4 (from V6) 32.6 1498.70 0.00 -100 0 2017 3.75

DOHC TBDS III-I4 (from V8) 30.7 1302.07 0.00 -200 0 2017 3.75

Electric Power Steering 1.3 107.15 0.00 0 0 2004 0

Improved Accessories I 0.7 87.49 0.00 0 0 2005 0

12V Micro Hybrid w/EPS and IACC 7.0 640.24 0.00 45 0 2005 0

Improved Accessories II 2.5 128.69 0.00 0 0 2012 0

Mild Hybrid w/EPS and IACC II 11.0 2902.00 0.00 80 0 2012 -2.5

Tires I 2.0 5.60 0.00 -12 0 2005 0

Tires II 4.0 58.35 0.00 -15 0 2017 0

Low Drag Brakes 0.8 59.15 0.00 0 0 2000 0

Secondary Axle Disconnect 1.3 96.34 0.00 0 -1 2012 0

1

Fractional changes refer to the percentage change from the base technology.

Sources: U.S. Energy Information Administration, Energy and Environment Analysis, Documentation of Technology included in the 

NEMS Fuel Economy Model for Passenger Cars and Light Trucks (September 2002).

National Research Council, Effectiveness and Impact of Corporate Average Fuel Economy (CAFE) Standards (Copyright 2002).

National Highway Traffic Safety Administration, Corporate Average Fuel Economy for MY 2011-2015 Passenger Cars and Light Trucks 

(April 2008).

U.S. Environmental Protection Agency, Interim Report: New Powertrain Technologies and Their Projected Costs (October 2005).

Environmental Protection Agency and Department of Transportation National Highway Traffic Safety Administration, “2017 and Later 

Model Year Light-Duty Vehicle Greenhouse Gas Emissions and Corporate Average Fuel Economy Standards; Final Rule,” Federal 

Register Vol. 77, No. 199, October 15, 2012. 40 CFR Parts 85, 86, 600, 49 CFR Parts 523, 531, 533, et al. and 600.
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 75

Table 7.2. Standard technology matrix for light trucks1

Absolute

Per Unit 

IncremenFuel Incremen- Incremen- tal Weight

Efficiency Incremental tal Cost tal Weight (Lbs./ Introduc- Horsepower

Change % Cost 2000$ ($/UnitWt.) (Lbs.) UnitWt.) tion Year Change %

Unit Body Construction 4.0 100.00 0.00 0 -6 1980 0

Mass Reduction I 1.0 0.00 0.06 0 -1.5 2005 0

Mass Reduction II 2.6 0.00 0.14 0 -7.5 2009 0

Mass Reduction III 5.4 0.00 0.42 0 -10 2011 0

Mass Reduction IV 8.4 0.00 0.62 0 -15 2016 0

Mass Reduction V 11.6 0.00 0.72 0 -20 2020 0

Aerodynamics I 2.4 48.17 0.00 0 0.5 2000 0

Aerodynamics II 4.9 203.29 0.00 0 1 2011 0

6 Speed Manual 2.0 255.59 0.00 20 0 1995 0

Aggressive Shift Logic I 2.3 32.44 0.00 0 0 1999 0

Aggressive Shift Logic II 6.3 27.18 0.00 0 0 2017 0

Early Torque Converter Lockup 0.5 29.49 0.00 0 0 2002 0

High Efficiency Gearbox 1.6 200.63 0.00 0 0 2017 0

5 Speed Automatic 1.3 103.91 0.00 20 0 1995 0

6 Speed Automatic 2.0 270.05 0.00 30 0 2003 0

7 Speed Automatic 5.0 401.04 0.00 40 0 2009 0

8 Speed Automatic 8.0 532.83 0.00 50 0 2014 0

Dual Clutch Automated Manual 4.9 182.24 0.00 -10 0 2004 0

CVT 7.8 250.98 0.00 -25 0 1998 0

Low Friction Lubricants 0.7 3.20 0.00 0 0 2003 0

Engine Friction Reduction I-4 cyl 2.0 47.16 0.00 0 0 2000 1.25

Engine Friction Reduction I-6 cyl 2.6 71.14 0.00 0 0 2000 1.25

Engine Friction Reduction I-8 cyl 2.5 94.32 0.00 0 0 2000 1.25

Engine Friction Reduction II-4 cyl 3.6 100.71 0.00 0 0 2017 2.25

Engine Friction Reduction II-6 cyl 4.7 147.87 0.00 0 0 2017 2.25

Engine Friction Reduction II-8 cyl 4.4 195.03 0.00 0 0 2017 2.25

Cylinder Deactivation-6 cyl 6.4 187.06 0.00 10 0 2004 0

Cylinder Deactivation-8 cyl 6.0 209.97 0.00 10 0 2004 0

VVT I-OHV Intake Cam Phasing-6 cyl 2.6 43.90 0.00 20 0 2051 1.25

VVT I-OHV Intake Cam Phasing-8 cyl 2.5 43.90 0.00 30 0 2051 1.25

VVT I-OHC Intake Cam Phasing-4 cyl 2.1 43.90 0.00 10 0 1993 1.25

VVT I-OHC Intake Cam Phasing-6 cyl 2.6 88.76 0.00 20 0 1993 1.25

VVT I-OHC Intake Cam Phasing-8 cyl 2.5 88.76 0.00 30 0 1993 1.25

VVT II-OHV Coupled Cam Phasing-6 cyl 5.4 43.90 0.00 20 0 2009 1.25

VVT II-OHV Coupled Cam Phasing-8 cyl 5.1 43.90 0.00 30 0 2009 1.25

VVT II-OHC Coupled Cam Phasing-4 cyl 4.3 43.90 0.00 10 0 2009 1.25

VVT II-OHC Coupled Cam Phasing-6 cyl 5.4 88.76 0.00 20 0 2009 1.25

VVT II-OHC Coupled Cam Phasing-8 cyl 5.1 88.76 0.00 30 0 2009 1.25

VVT III-OHV Dual Cam Phasing-6 cyl 5.4 99.26 0.00 25 0 2051 1.56

VVT III-OHV Dual Cam Phasing-8 cyl 5.1 99.26 0.00 37.5 0 2051 1.56

VVT III-OHC Dual Cam Phasing-4 cyl 4.3 90.67 0.00 12.5 0 2009 1.56

VVT III-OHC Dual Cam Phasing-6 cyl 5.4 195.65 0.00 25 0 2009 1.56

VVT III-OHC Dual Cam Phasing-8 cyl 5.1 195.65 0.00 37.5 0 2009 1.56

VVL I-OHV Discrete-6 cyl 5.5 225.24 0.00 40 0 2000 2.5

VVL I-OHV Discrete-8 cyl 5.2 322.59 0.00 50 0 2000 2.5

VVL I-OHC Discrete-4 cyl 4.2 155.57 0.00 25 0 2000 2.5

VVL I-OHC Discrete-6 cyl 5.5 225.24 0.00 40 0 2000 2.5

VVL I-OHC Discrete-8 cyl 5.2 322.59 0.00 50 0 2000 2.5

VVL II-OHV Continuous-6 cyl 7.0 1150.07 0.00 40 0 2011 2.5

VVL II-OHV Continuous-8 cyl 6.5 1256.96 0.00 50 0 2011 2.5

VVL II-OHC Continuous-4 cyl 5.3 232.88 0.00 25 0 2011 2.5

VVL II-OHC Continuous-6 cyl 7.0 427.58 0.00 40 0 2011 2.5

VVL II-OHC Continuous-8 cyl 6.5 466.71 0.00 50 0 2011 2.5

Stoichiometric GDI-4 cyl 1.5 264.37 0.00 20 0 2006 2.5
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 76

Table 7.2. Standard technology matrix for light trucks1 (cont.)

Absolute

Per Unit 

IncremenFuel Incremen- Incremen- tal Weight

Efficiency Incremental tal Cost tal Weight (Lbs./ Introduc- Horsepower

Change % Cost 2000$ ($/UnitWt.) (Lbs.) UnitWt.) tion Year Change %

Stoichiometric GDI-6 cyl 1.5 397.99 0.00 30 0 2006 2.5

Stoichiometric GDI-8 cyl 1.5 478.16 0.00 40 0 2006 2.5

OHV to DOHC TBDS-I4 21.6 1383.90 0.00 -100 0 2009 3.75

OHV to DOHC TBDS I-V6 20.2 2096.84 0.00 -100 0 2009 3.75

SOHC to DOHC TBDS I-I4 21.6 827.47 0.00 -100 0 2009 3.75

SOHC to DOHC TBDS I-V6 20.2 1605.80 0.00 -100 0 2009 3.75

DOHC TBDS I-I3 17.5 915.28 0.00 -100 0 2009 3.75

DOHC TBDS I-I4 21.6 747.30 0.00 -100 0 2009 3.75

DOHC TBDS I-V6 20.2 1530.88 0.00 -100 0 2009 3.75

OHV to DOHC TBDS II-I4 26.3 1586.36 0.00 -100 0 2012 3.75

OHV to DOHC TBDS II-V6 24.5 2445.33 0.00 -100 0 2012 3.75

SOHC to DOHC TBDS II-I4 26.3 1046.15 0.00 -100 0 2012 3.75

SOHC to DOHC TBDS II-V6 24.5 1968.59 0.00 -100 0 2012 3.75

DOHC TBDS II-I3 21.2 1130.47 0.00 -100 0 2012 3.75

DOHC TBDS II-I4 26.3 968.31 0.00 -100 0 2012 3.75

DOHC TBDS II-V6 24.5 1895.85 0.00 -100 0 2012 3.75

OHV to DOHC TBDS III-I4 (from V6) 32.6 2031.83 0.00 -100 0 2017 3.75

OHV to DOHC TBDS III-I4 (from V8) 30.7 1601.81 0.00 -200 0 2017 3.75

SOHC to DOHC TBDS III-I4 (from V6) 32.6 1565.84 0.00 -100 0 2017 3.75

SOHC to DOHC TBDS III-I4 (from V8) 30.7 1380.40 0.00 -200 0 2017 3.75

DOHC TBDS III-I3 (from I4) 27.1 1634.58 0.00 -100 0 2017 3.75

DOHC TBDS III-I4 (from V6) 32.6 1498.70 0.00 -100 0 2017 3.75

DOHC TBDS III-I4 (from V8) 30.7 1302.07 0.00 -200 0 2017 3.75

Electric Power Steering 1.0 107.15 0.00 0 0 2004 0

Improved Accessories I 0.7 87.49 0.00 0 0 2005 0

12V Micro Hybrid w/EPS and IACC 6.7 697.79 0.00 45 0 2005 0

Improved Accessories II 2.4 128.69 0.00 0 0 2012 0

Mild Hybrid w/EPS and IACC II 10.6 2902.00 0.00 80 0 2012 -2.5

Tires I 2.0 5.60 0.00 -12 0 2005 0

Tires II 4.0 58.35 0.00 -15 0 2017 0

Low Drag Brakes 0.8 59.15 0.00 0 0 2000 0

Secondary Axle Disconnect 1.4 96.34 0.00 0 -1 2012 0 1

Fractional changes refer to the percentage change from the base technology.

Sources: U.S. Energy Information Administration, Energy and Environment Analysis, Documentation of Technology included in 

the NEMS Fuel Economy Model for Passenger Cars and Light Trucks (September 2002).

National Research Council, Effectiveness and Impact of Corporate Average Fuel Economy (CAFE) Standards (Copyright 2002).

National Highway Traffic Safety Administration, Corporate Average Fuel Economy for MY 2011-2015 Passenger Cars and Light 

Trucks (April 2008).

U.S. Environmental Protection Agency, Interim Report: New Powertrain Technologies and Their Projected Costs (October 2005).

Environmental Protection Agency and Department of Transportation National Highway Traffic Safety Administration, “2017 and 

Later Model Year Light-Duty Vehicle Greenhouse Gas Emissions and Corporate Average Fuel Economy Standards; Final Rule,” 

Federal Register Vol. 77, No. 199, October 15, 2012. 40 CFR Parts 85, 86, 600, 49 CFR Parts 523, 531, 533, et al. and 600.

Degradation factors are used to convert new vehicle tested fuel economy values to “on-road” fuel economy 

values (Table 7.3). The degradation factors represent adjustments made to tested fuel economy values to 

account for the difference between fuel economy performance realized in the CAFE test procedure and fuel 

economy realized under normal driving conditions.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 77

Table 7.3. Car and light truck degradation factors

2005 2010 2015 2020 2030 2040

Cars 79.8 81.7 81.7 81.7 81.7 81.7

Light Trucks 80.6 80.0 80.0 80.0 80.0 80.0

Source: U.S. Energy Information Administration, Transportation Demand Module of the 

National Energy Modeling System, Model Documentation 2014, DOE/EIA-M070(2014),

(Washington, DC, 2012).

The light-duty Vehicle Miles Traveled (VMT) Component uses fuel prices, personal income, and population 

to generate projections of demand for personal travel (VMT). Population distribution assumptions are taken 

from the U.S. Census Bureau and are divided into 13 age categories, by both male and female. Licensing 

rates by these 13 age categories are also used, taken from the U.S. Department of Transportation’s Federal 

Highway Administration (FHWA). Licensing rates are then projected for each age category using the 

population estimates from the U.S. Census Bureau. These licensing rate projections are then aggregated into 

5 age categories, and applied to the historical VMT per licensed driver taken from FHWA, in order to project 

the VMT per licensed driver, using the below VMT coefficients (Table 7.4).

Table 7.4. Vehicle miles traveled equation coefficients, by age and gender cohorts

15-19 20-34 35-54 55-64 65+

BETACOST

 Male -0.0665 -0.065 -0.0593 -0.0642 -0.0368

 Female -0.0365 -0.0481 -0.0395 0 -0.0396

ALPHA

 Male 0.4677 0.8184 0.5136 0.1558 0.6229

 Female 1.5347 0.4812 0.1582 0.5222 -1.2401

BETAVMT

 Male 0.8591 0.8314 0.6793 0.774 0.8655

 Female 0.8123 0.8771 0.8997 0.9888 0.8937

BETAINC

 Male 0.0034 -0.0145 0.0516 0.0603 -0.0401

 Female -0.1244 -0.0146 0.0007 -0.0453 0.1187

BETAVPLD

 Male 0.0961 0.1752 0.2736 0.1966 0.3619

 Female 0.4336 0.2171 0.2983 0.0621 0.4023

BETAEMP

 Male 0.1742 0.1232 0.0265 0.0705 -0.3023

 Female -0.2581 -0.1151 -0.2775 0.019 -0.4545

Source: U.S. Energy Information Administration, Transportation Demand Module of the National Energy 

Modeling System, Model Documentation 2014, DOE/EIA-M070(2014), (Washington, DC, 2012).
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Commercial light-duty fleet assumptions

The Transportation Demand Module separates commercial light-duty fleets into three types: business, 

government, and utility. Based on these classifications, commercial light-duty fleet vehicles vary in survival 

rates and duration of in-fleet use before sale for use as personal vehicles. The average length of time 

passenger cars are kept before being sold for personal use is 3 years for business use, 6 years for 

government use, and 5 years for utility use. Of total passenger car sales to fleets in 2009, 75.1% are used in 

business fleets, 9.6% in government fleets, and 15.3% in utility fleets. Of total light truck sales to fleets in 

2009, 47.3% are used in business fleets, 15.1% in government fleets, and 37.6% in utility fleets [43]. Both the 

automobile and light truck shares by fleet type are held constant from 2009 through 2040. In 2009, 18.2% of 

all automobiles sold and 16.9% of all light trucks sold were for fleet use. The share of total automobile and 

light truck sales slowly declines over the forecast period based on historic trends.

Alternative-fuel shares of fleet vehicle sales by fleet type are held constant at 2005 levels (Table 7.5). Size 

class sales shares of vehicles are also held constant at 2005 levels (Table 7.6) [44]. Individual sales shares of 

new vehicles purchased by technology type are assumed to remain relatively constant for utility, 

government, and business fleets using the previous 5-year average (Table 7.7) [45].

Annual vehicle miles traveled (VMT) per vehicle by fleet type stays constant over the projection period 

based on the Oak Ridge National Laboratory fleet data.

Fleet fuel economy for both conventional and alternative-fuel vehicles is assumed to be the same as the 

personal new vehicle fuel economy and is subdivided into six EPA size classes for cars and light trucks.

Table 7.5. Percent of fleet alternative fuel vehicles by fleet type by size class, 2005

Mini Subcompact Compact Midsize Large 2-Seater

Car

 Business 0.0 10.5 10.7 42.7 36.1 0.0

 Government 0.0 2.8 40.0 2.8 54.4 0.0

 Utility 0.0 7.9 34.7 12.3 45.1 0.0

Sm Pickup Lg Pickup Sm Van Lg Van Sm Utility Lg Utility

Light Truck

 Business 7.9 35.1 7.9 26.8 5.5 16.8

 Government 6.7 50.8 28.4 4.6 1.6 7.8

 Utility 8.2 52.1 6.0 32.7 0.3 0.7

Source: U.S. Energy Information Administration, “Archive--Alternative Transportation Fuels (ATF) 

and Alternative Fueled Vehicles (AFV),” http://www.eia.gov/renewable/afv/archive/
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 79

Table 7.6. Commercial fleet size class shares by fleet and vehicle type, 2005

percentage

Mini Subcompact Compact Midsize Large 2-Seater

Car

 Business 3.1 23.4 26.6 36.2 9.9 0.8

 Government 0.2 4.6 20.6 28.6 46.0 0.0

 Utility 1.5 12.5 10.0 59.2 16.4 0.4

Sm Pickup Lg Pickup Sm Van Lg Van Sm Utility Lg Utility

Light Truck

 Business 2.5 8.4 23.3 8.1 14.2 43.6

 Government 6.7 43.6 10.4 17.1 3.8 18.4

 Utility 7.3 38.7 11.8 18.9 7.2 16.1

Source: Oak Ridge National Laboratory, “Fleet Characteristics and Data Issues,” Stacy Davis and Lorena 

Truett, final report prepared for the Department of Energy, Energy Information Administration, Office 

of Energy Analysis (Oak Ridge, TN, January 2003).

Table 7.7. Share of new vehicle purchases by fleet type and technology type, 2009

percentage

Technology Business Government Utility

Cars

 Gasoline 99.10 72.78 95.52

 Ethanol Flex 0.46 26.20 2.11

 Electric 0.00 0.02 0.07

 CNG/LNG Bi-Fuel 0.14 0.56 1.08

 LPG Bi-Fuel 0.16 0.11 0.40

 CNG/LNG 0.08 0.33 0.63

 LPG 0.08 0.01 0.19

Light Trucks

 Gasoline 71.71 59.46 98.22

 Ethanol Flex 16.29 35.09 0.49

 Electric 0.04 0.07 0.05

 CNG/LNG Bi-Fuel 1.28 2.29 0.51

 LPG Bi-Fuel 7.93 2.55 0.31

 CNG/LNG 1.54 0.49 0.24

 LPG 1.22 0.05 0.18

Source: U.S. Energy Information Administration, Archive - Alternative Transportation Fuels (ATF) and 

Alternative Fueled Vehicles (AFV), http://www.eia.gov/renewable/afv/archive/index.cfm .
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 80

The light commercial truck component

The Light Commercial Truck Component of the NEMS Transportation Demand Module represents light 

trucks that have an 8,501 to 10,000 pound gross vehicle weight rating (Class 2B vehicles). These vehicles are 

assumed to be used primarily for commercial purposes. The component implements a twenty-year stock 

model that estimates vehicle stocks, travel, fuel economy, and energy use by vintage. Historic vehicle sales 

and stock data, which constitute the baseline from which the projection is made, are taken from an Oak 

Ridge National Laboratory study [46]. The distribution of vehicles by vintage, and vehicle scrappage rates are 

derived from R.L. Polk & Co. registration data [47],[48]. Vehicle travel by vintage was constructed using 

vintage distribution curves and estimates of average annual travel by vehicle [49],[50].

The growth in light commercial truck VMT is a function of industrial output for agriculture, mining, 

construction, total manufacturing, utilities, and personal travel. The overall growth in VMT reflects a 

weighted average based on the distribution of total light commercial truck VMT by sector. Projected fuel 

efficiencies are assumed to increase at the same annual growth rate as conventional gasoline light-duty 

trucks (≤8,500 pounds gross vehicle weight).

Consumer vehicle choice assumptions

The Consumer Vehicle Choice Component (CVCC) utilizes a nested multinomial logit (NMNL) model that 

predicts sales shares based on relevant vehicle and fuel attributes. The nesting structure first predicts the 

probability of fuel choice for multi-fuel vehicles within a technology set. The second-level nesting predicts 

penetration among similar technologies within a technology set (e.g., gasoline versus diesel hybrids). The 

third-level choice determines market share among the different technology sets [51]. The technology sets 

include:

• Conventional fuel capable (gasoline, diesel, bi-fuel compressed natural gas (CNG) and liquefied 

natural gas (LNG), bi-fuel liquefied petroleum gas (LPG), and flex-fuel)

• Hybrid (gasoline and diesel)

• Plug-in hybrid (10-mile all-electric range and 40-mile all-electric range)

• Dedicated alternative fuel (CNG, LNG, and LPG)

• Fuel cell (gasoline, methanol, and hydrogen)

• Electric battery powered (100-mile range and 200-mile range) [52]

The vehicle attributes considered in the choice algorithm include: vehicle price, maintenance cost, battery 

replacement cost, range, multi-fuel capability, home refueling capability, fuel economy, acceleration and 

luggage space. With the exceptions of maintenance cost, battery replacement cost, and luggage space, 

vehicle attributes are determined endogenously [53]. Battery costs for plug-in hybrid electric and all-electric 

vehicles are based on a production-based function over several technology phase periods. The fuel 

attributes used in market share estimation include availability and price. Vehicle attributes vary by six EPA 

size classes for cars and light trucks and fuel availability varies by Census division. The NMNL model 

coefficients were developed to reflect purchase decisions for size classes, cars and light trucks separately.

Where applicable, CVCC fuel-efficient technology attributes are calculated relative to conventional gasoline 

miles per gallon. It is assumed that many fuel efficiency improvements in conventional vehicles will be 

transferred to alternative-fuel vehicles. Specific individual alternative-fuel technological improvements are 
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 81

also dependent upon the CVCC technology type, cost, research and development, and availability over time. 

Make and model availability estimates are assumed according to a logistic curve based on the initial 

technology introduction date and current offerings. Coefficients summarizing consumer valuation of vehicle 

attributes were derived from assumed economic valuation compared with vehicle price elasticities. Initial 

CVCC vehicle sales shares are calibrated to data from R.L. Polk & Co., fleet data from Bobit Publishing 

Company, and sales data from Wards Auto [54]. A fuel-switching algorithm based on the relative fuel prices 

for alternative fuels compared with gasoline is used to determine the percentage of total fuel consumption 

represented by alternative fuels in bi-fuel and flex-fuel alcohol vehicles.

Freight truck assumptions

The Freight Truck Component estimates vehicle stocks, travel, fuel efficiency, and energy use for three size 

classes of trucks: light-medium (Class 3), heavy-medium (Classes 4-6), and heavy (Classes 7-8). The three size 

classes are further broken down into 13 subclasses for fuel economy classification purposes (Table 7.8). 

These subclasses include two breakouts for light-medium size class, including pickup/van and vocational, 

one breakout for heavy-medium, including vocational, and ten breakouts for heavy. The ten subclasses 

parse the heavy size class into class 7 or class 8, day cab or sleeper cab, and low, mid or high roof. Within the 

size classes, the stock model structure is designed to cover 34 vehicle vintages and to estimate energy use 

by four fuel types: diesel, gasoline, LPG, and natural gas (CNG and LNG). Fuel consumption estimates are 

reported regionally (by Census Division) according to the distillate fuel shares from the State Energy Data 

System [55]. The technology input data are specific to the different types of trucks and include the year of 

introduction, incremental fuel efficiency improvement, and capital cost (Table 7.9).

Table 7.8. Vehicle technology category for technology matrix for freight trucks

Vehicle category Class Type Roof1

1 3 Pickup and Van -

2 3 Vocational -

3 4-6 Vocational -

4 7-8 Vocational -

5 7 Tractor - day cab low

6 7 Tractor - day cab mid

7 7 Tractor - day cab high

8 8 Tractor - day cab low

9 8 Tractor - day cab mid

10 8 Tractor - day cab high

11 8 Tractor - sleeper cab low

12 8 Tractor - sleeper cab mid

13 8 Tractor - sleeper cab high 1

Applies to Class 7 and 8 day and sleeper cabs only.
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 82

Table 7.9. Standard technology matrix for freight trucks

Incremental

Fuel

Economy

Vehicle Introduction Capital Costs Improvement

Technology Type Category Year (2009$) (%)

Aerodynamics I: streamlined bumper, grill, windshield, roof 1 2010 58 1.5

Aerodynamics I: conventional features; general aerodynamic shape, removal of

classic non-aerodynamic features 5,8,11 1995 1000 4.1

Aerodynamics I 7,10,13 1995 1000 4.6

Aerodynamics II: SmartWay features; streamlined shape, bumper grill, hood, 5,8 2004 1126 1.5

mirrors, side fuel tank and roof fairings, side gap extenders

Aerodynamics II 7,10 2004 1126 3.1

Aerodynamics II 11 2004 1155 4.2

Aerodynamics II 13 2004 1506 4.2

Aerodynamics III: underbody airflow, down exhaust, lowered ride height 7 2014 2303 4.2

Aerodynamics III 10 2014 2489 5.0

Aerodynamics III 13 2014 2675 5.8

Aerodynamics IV: skirts, boat tails, nose cone, vortex stabilizer, pneumatic

blowing 5-13 1995 5500 13.0

Tires I: low rolling resistance 1 2010 7 1.5

Tires I 2,3 2010 162 2.6

Tires I 4, 8-13 2010 194 2.0

Tires I 5-7 2010 130 2.0

Tires II: super singles 5-13 2000 150 5.3

Tires III: single wide tires on trailer 5-13 2000 800 3.1

Weight Reduction I 1 2010 127 1.6

Weight Reduction I: aluminum dual tires or super singles 5-13 2010 650 1.0

Weight Reduction II: weight reduction 15% 3-13 2018 6200 3.0

Weight Reduction III: weight reduction 20% 3-13 2022 11000 3.5

Accessories I: Electric/electrohydraulic improvements; electric power steering 

or electrohydraulic power steering

1 2010 115 1.5

Accessories II: Improved accessories; electrified water, oil, fuel injection, power

steering pump, air compressor 1 2010 93 1.5

Accessories III: Auxiliary Power Unit 11-13 2000 5400 5.8

Transmission I: 8-speed Automatic from 6-speed automatic 1 2000 280 1.7

Transmission II: 6-Manual from 4-speed automatic 1 1995 150 1.0

Transmission III: Automated Manual Transmission 2-13 2000 5000 3.5

Diesel Engine I: after treatment improvements 1 2010 119 4.0

Diesel Engine I 2 2010 117 2.6

Diesel Engine II: low-friction lubricants 1-13 2005 4 0.5

Diesel Engine III: variable valve actuation 2 2010 0 1.0

Diesel Engine III 3-13 2005 300 1.0

Diesel Engine IV: engine friction reduction, improved bearings to allow lowerviscosity oil1-2 2010 116 1.0

Diesel Engine IV 3-13 2010 250 1.0

Diesel Engine V: improved turbo efficiency 2-13 2010 18 1.5

Diesel Engine VI: improved water, oil, fuel pump; pistons; valve train friction

reduction

2 2010 213 1.3

Diesel Engine VI 3, 5-8 2010 186 1.3

Diesel Engine VI: improved water, oil, and fuel pump; pistons 4, 9-13 2010 150 1.3

Diesel Engine VII: improved cylinder head, fuel rail and injector, EGR cooler 2 2010 42 4.7

Diesel Engine VII 3-13 2010 31 4.7

Diesel Engine VIII: turbo mechanical compounding 5-13 2017 1000 3.9
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 83

Table 7.9. Standard technology matrix for freight trucks (cont.)

Incremental

Fuel

Economy

Vehicle Introduction Capital Costs Improvement

Technology Type Category Year (2009$) (%)

Diesel Engine IX: low temperature EGR, improved turbochargers 1 2010 184 5.0

Diesel Engine X: sequential downsizing/turbocharging 5-13 2010 1200 2.5

Diesel Engine XI: waste heat recovery, Organic Rankine Cycle (bottoming 

cycle)

3-13 2019 10000 8.0

Diesel Engine XII: electric turbo compounding 4-13 2020 8000 7.6

Gasoline Engine I: low friction lubricants 1-13 2010 4 0.5

Gasoline Engine II: coupled cam phasing 2-4 2010 46 2.6

Gasoline Engine III: engine friction reduction; low tension piston rings, 

roller cam followers, piston skirt design, improved crankshaft design, and

bearings; coating 1-2 2010 116 2.0

Gasoline III 3-4 2010 95 2.0

Gasoline Engine IV: stoichiometric gasoline direct injection V8 1 2006 481 1.5

Gasoline Engine IV 2 2010 481 1.5

Gasoline Engine IV 3-4 2014 450 1.5

Gasoline Engine V: turbocharging and downsizing SGDI V8 to V6 1-4 2006 1743 2.1

Gasoline Engine VI: lean burn GDI 1-4 2020 450 1.5

Gasoline Engine VII: HCCI 1-4 2035 685 12.0

Hybrid System I: 42V engine off at idle 1-2 2005 1500 7.0

Hybrid System I 3-4 2005 1500 4.5

Hybrid System II: dual mode hybrid 1-2 2008 12000 25.0

Hybrid System II: electric, ePTO, or hydraulic 3-4 2009 26667 30.0

Hybrid System II: 4 kWh battery, 50 kW motor generator 5-13 2012 26000 5.5

Sources: Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Medium- and Heavy-Duty Engines and Vehicles, U.S. 

Environmental Protection Agency and U.S. Department of Transportation, Final Rules, Federal Register, Vol. 76, No. 179 (September 

2011).

Final Rulemaking to Establish Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Medium- and Heavy-Duty 

Engines and Vehicles, Regulatory Impact Analysis, U.S. Environmental Protection Agency and U.S. Department of Transportation, 

(August 2011).

Reducing Heavy-Duty Long Haul Combination Truck Fuel Consumption and CO2 Emissions, Final Report, TIAX, LLC. (October 2009).

Update of Technology Information for Forecasting Heavy-Duty On-Road Vehicle Fuel Economy, Final Report, ICF International, 

Prepared for the U.S. Energy Information Administration (August 2010). Technologies and Approaches to Reducing the Fuel 

Consumption of Medium- and Heavy-Duty Vehicles, National Research Council of the National Academy of Sciences (2010).

The Freight Truck Component uses projections of industrial output to estimate growth in freight truck travel. 

Regional heavy-duty freight truck vehicle travel is determined using a ton-mile per dollar of industrial output 

measure that is converted to freight vehicle miles traveled using shares developed from the Freight Analysis 

Framework (FAF) [56] with GIS-based regionalization between origin/destination points [57]. Freight truck 

ton-miles, by Census division and industrial commodity, and historical truck vehicle miles traveled are 

developed using Department of Transportation and Federal Highway Administration data [58],[59].

Fuel economy of new freight trucks is dependent on the market penetration of advanced technology 

components [60]. For the advanced technology components, market penetration is determined as a 

function of technology type, cost-effectiveness, and introduction year. Cost-effectiveness is calculated as a 

function of fuel price, vehicle travel, fuel economy improvement, and incremental capital cost.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 84

Heavy truck freight travel is estimated by class size and fuel type based on matching projected freight travel 

demand (measured by industrial output) to the travel supplied by the current fleet. Travel by vintage and 

size class is then adjusted so that total travel meets total demand.

Initial heavy vehicle travel, by vintage and size class, is derived by EIA using Vehicle Inventory and Use 

Survey (VIUS) data [61]. Initial freight truck stocks by vintage are obtained from R. L. Polk & Co. and are 

distributed by fuel type using VIUS data. Vehicle scrappage rates are also estimated by EIA using R. L. Polk & 

Co. data.

Freight rail assumptions

The Rail Freight Component uses the industrial output by NAICS code measured in real 2009 dollars and a 

ton-mile per dollar output measure to project rail ton-miles by Census division and commodity developed 

from the FAF [62]. Coal production from the NEMS Coal Market Module is used to adjust coal-based rail 

travel. Freight rail historical ton-miles are developed from U.S. Department of Transportation data [63]. 

Historic freight rail efficiencies are based on historical data taken from the U.S. Department of

Transportation [64]. The distribution of rail fuel consumption by fuel type is based on the cost-effectiveness 

of LNG as compared with diesel considering fuel costs and incremental locomotive costs [65]. 

Domestic and international shipping assumptions

Similar to the previous component, the domestic freight shipping within the Waterborne Freight Component

uses the industrial output by NAICS code measured in real 2005 dollars and a ton-mile per dollar output

measure to project domestic marine ton-miles by Census division and industrial commodity to develop 

domestic marine travel [66], [67].

Domestic shipping efficiencies are taken from the Transportation Energy Data Book [68]. The energy 

consumption in the international shipping within the Waterborne Freight Component is a function of the 

total level of imports and exports. The distribution of domestic and international shipping fuel consumption 

by fuel type is based on historical data through 2013 and allows for LNG as a marine fuel starting in 2013 

based on fuel economics [69]. Historic regional domestic shipping fuel share estimates are distributed 

according to regional shares in the State Energy Data System (SEDS) [70].

Air Travel Submodual

The Air Travel Submodule is a 13-region world demand and supply model (Table 7.10). For each region, 

demand is computed for domestic travel (both takeoff and landing occur in the same region) and 

international travel (either takeoff or landing is in the region but not both). Once the demand for aircraft is 

determined, the Aircraft Fleet Efficiency Component moves aircraft between regions to satisfy the projected 

demand for air travel.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 85

Table 7.10. Thirteen regions for the world model

Region Number Region Major Countries in Region

1 United States United States

2 Canada Canada

3 Central America Mexico

4 South America Brazil

5 Europe France, Germany

6 Africa S. Africa

7 Middle East Egypt

8 CIS Russia

9 China China

10 Northeast Asia Japan, Korea

11 Southeast Asia Vietnam

12 Southwest Asia India

13 Oceania Australia, New Zealand

Source: Jet Information Services, 2013 World Jet Inventory, data tables (2013).

Air travel demand assumptions

The Air Travel Demand Component calculates the domestic and international per capita revenue passenger 

miles (RPM-PC) for each region. Domestic and international revenue passenger miles are based on the 

historical data in Table 7.11 [71], per capita income for the United States, per capita GDP for the non-U.S. 

regions, and ticket prices. The revenue ton miles of air freight for the United States are based on 

merchandise exports, gross domestic product, and fuel cost. For the non-U.S. regions, revenue ton-miles are 

based on GDP growth in the region [72].

Airport capacity constraints based on the Federal Aviation Administration (FAA) Airport Capacity Benchmark 

Report 2004 are incorporated into the Air Travel Demand Component using airport capacity measures [73]. 

Airport capacity is defined by the maximum number of flights per hour airports can routinely handle, the 

amount of time airports operate at optimal capacity, and passenger load factors. Capacity expansion is 

expected to be delayed due to the economic environment and fuel costs.

Aircraft stock/efficiency assumptions

The Aircraft Fleet Efficiency Component consists of a world regional stock model of wide body, narrow body, 

and regional jets by vintage. Total aircraft supply for a given year is based on the initial supply of aircraft for 

model year 2009, new passenger aircraft sales, and the survival rate by vintage (Table 7.12) [74]. New 

passenger aircraft sales are a function of revenue passenger miles and gross domestic product.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 86

Table 7.11. 2013 Regional population, GDP, per capita GDP, domestic and international RPM and per

capita RPM

Population

Region (million) GDP (2005$) GDP_PC

United States 317.0 11,651 36,752.78

Canada 35.3 1,274 36,115.40

Central America 166.0 2,181 13,136.93

South America 443.2 5,033 11,355.94

Europe 649.7 16,041 24,689.83

Africa 1076.3 4,374 4,063.54

Middle East 220.9 2,562 11,597.03

Russia 252.5 3,752 14,856.79

China 1,463.9 12,124 8,282.08

Northeast Asia 176.1 5,508 31,277.72

Southeast Asia 778.6 4,350 5,587.67

Southwest Asia 1,502.7 5,580 3,713.48

Oceania 29.6 866 29,300.60

Region RPM (billion) RPM_PC (thousand)

Domestic

 United States 602.5 1,900.7

 Canada 31.4 890.6

 Central America 23.2 139.6

 South America 93.6 211.2

 Europe 453.4 697.8

 Africa 34.1 31.7

 Middle East 54.8 248.1

 Russia 75.1 297.5

 China 292.6 199.9

 Northeast Asia 66.0 374.6

 Southeast Asia 105.8 135.9

 Southwest Asia 43.2 28.8

 Oceania 62.9 2,126.4

International

 United States 263.6 832.1

 Canada 62.5 1,770.6

 Central America 79.0 476.0

 South America 66.7 150.5
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 87

Table 7.11. 2013 Regional population, GDP, per capita GDP, domestic and international RPM and per 

capita RPM (cont.)

Region

RPM

(billion)

RPM PC

(thousand)

 Europe 411.6 633.5

 Africa 65.9 61.2

 Middle East 154.0 696.9

 Russia 100.3 397.0

 China 115.8 79.1

 Northeast Asia 131.6 747.4

 Southwest Asia 150.9 193.8

 Southwest Asia 70.0 46.6

 Oceania 54.5 1,845.0

Source: Global Insight 2005 chain-weighted dollars, Boeing Current Market Outlook 2013.

Table 7.12. 2013 Regional passenger and cargo aircraft supply

Age of Aircraft (years)

Aircraft Type New 1-10 11-20 21-30 >30 Total

Passenger

Narrow Body

 United States 154 1019 1588 886 42 3756

 Canada 6 127 94 49 4 297

 Central America 21 190 60 64 19 374

 South America 49 329 159 109 53 778

 Europe 139 1705 890 307 9 3059

 Africa 11 149 150 136 36 559

 Middle East 30 319 119 85 11 600

 Russia 33 265 333 242 44 1081

 China 235 1250 274 36 0 1795

 Northeast Asia 49 205 106 10 2 374

 Southeast Asia 135 533 167 123 10 1007

 Southwest Asia 40 254 44 31 0 394

 Oceania 21 197 51 10 0 279

Wide Body

 United States 20 88 323 171 3 618

 Canada 4 23 28 25 0 81

 Central America 3 9 9 5 0 28

 South America 9 60 42 6 2 120
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 88

Table 7.12. 2013 Regional passenger and cargo aircraft supply (cont.)

Age of Aircraft (years)

Aircraft Type New 1-10 11-20 21-30 >30 Total

 Europe 38 327 366 89 0 831

 Africa 9 60 39 34 6 154

 Middle East 46 310 157 72 0 608

 Russia 9 41 89 35 0 178

 China 57 195 109 4 0 365

 Northeast Asia 23 177 137 20 0 357

 Southeast Asia 50 227 158 31 0 473

 Southwest Asia 7 53 36 14 0 114

 Oceania 8 65 33 25 0 131

Regional Jets

 United States 49 1222 1024 177 3 2476

 Canada 14 152 96 146 16 433

 Central America 13 80 68 57 0 218

 South America 38 190 98 63 3 392

 Europe 38 635 531 249 0 1453

 Africa 7 152 145 98 3 415

 Middle East 1 98 76 38 0 216

 Russia 18 98 122 98 1 352

 China 15 137 46 1 0 199

 Northeast Asia 7 59 26 3 0 95

 Southeast Asia 35 121 103 67 2 338

 Southwest Asia 3 52 32 0 0 88

 Oceania 10 108 132 107 0 357

Cargo

Narrow Body

 United States 0 3 68 108 109 288

 Canada 0 0 5 2 26 33

 Central America 0 1 3 6 9 19

 South America 0 0 2 6 46 54

 Europe 0 0 18 87 5 110

 Africa 0 0 4 16 45 65

 Middle East 0 0 1 6 9 16

 Russia 1 8 6 3 4 22

 China 0 2 36 23 0 61

 Northeast Asia 0 0 0 1 0 1

 Southeast Asia 0 0 1 13 19 33
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 89

Table 7.12. 2013 Regional passenger and cargo aircraft supply (cont.)

Age of Aircraft (years)

Aircraft Type New 1-10 11-20 21-30 >30 Total

 Southwest Asia 0 0 1 5 6 12

 Oceania 0 0 0 11 3 14

Wide Body

 United States 15 101 194 200 89 599

 Canada 0 0 0 3 4 7

 Central America 0 2 1 3 3 9

 South America 3 8 7 4 5 27

 Europe 10 48 54 40 19 171

 Africa 0 2 2 1 2 7

 Middle East 12 17 11 18 13 71

 Russia 2 7 8 7 4 28

 China 8 47 20 15 2 92

 Northeast Asia 6 25 17 10 0 58

 Southeast Asia 0 22 29 5 0 56

 Southwest Asia 0 0 0 2 3 5

 Oceania 0 1 0 0 0 1

Regional Jets

 United States 0 0 5 37 0 42

 Canada 0 0 2 8 0 10

 Central America 0 0 4 2 0 6

 South America 0 0 0 3 0 3

 Europe 0 0 18 93 0 111

 Africa 0 0 3 6 1 10

 Middle East 0 0 0 2 0 2

 Russia 0 0 0 2 0 2

 China 0 0 0 0 0 0

 Northeast Asia 0 0 0 0 0 0

 Southeast Asia 0 0 0 5 0 5

 Southwest Asia 0 0 3 0 0 3

 Oceania 0 0 0 8 0 8

Survival Curve (fraction) New 5 10 20 40

Narrow Body 1.000 0.9998 0.9994 0.9970 0.8000

Wide Body 1.000 0.9983 0.9961 0.9870 0.7900

Regional Jets 1.000 0.9971 0.9950 0.9830 0.7800

Source: Jet Information Services, 2013 World Jet Inventory (2013).
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 90

Wide and narrow body planes over 25 years of age are placed as cargo jets according to a cargo percentage 

varying from 50% of 25-year-old planes to 100% of those aircraft 30 years and older. The available seatmiles per plane, which measure the carrying capacity of the airplanes by aircraft type, increase gradually 

over time. Domestic and international travel are combined into a single regional demand for seat-miles and 

passed to the Aircraft Fleet Efficiency Component, which adjusts the initial aircraft stock to meet that 

demand. For each region, starting with the United States, the initial stock is adjusted by moving aircraft 

between regions.

Technological availability, economic viability, and efficiency characteristics of new aircraft are assumed to 

grow at a fixed rate. Fuel-efficiency of new aircraft acquisitions represents an improvement over the stock 

efficiency of surviving airplanes. Generic sets of new technologies (Table 7.13) are introduced in different 

years and with a set of improved efficiencies over the base year (2007). Regional shares of all types of 

aircraft fuel use are assumed to be constant and are consistent with the SEDS estimate of regional jet fuel 

shares.

Table 7.13. Standard technology matrix for air travel

Technology Introduction Year Fractional Efficiency Improvement Jet Fuel Trigger Price (1987$/gallon)

Technology #1 2008 0.03 1.34

Technology #2 2014 0.05 1.34

Technology #3 2020 0.09 1.34

Technology #4 2025 0.13 1.34

Technology #5 2018 0.17 1.34

Technology #6 2018 0.00 1.34

Source: Jet Information Services, 2013 World Jet Inventory, data tables (2013).

Legislation and regulations

Light-Duty Vehicle Combined Corporate Average Fuel Economy (CAFE) Standards

The AEO2015 Reference case includes the attribute-based CAFE standards for LDVs for Model Year (MY) 

2011, the joint attribute-based CAFE and vehicle GHG emissions standards for MY 2012 through MY 2016 

and for MY 2017 through 2025. CAFE standards are then held constant in subsequent model years, although 

the fuel economy of new LDVs continues to rise modestly over time.

Heavy-Duty Vehicle Combined Corporate Average Fuel Economy Standards

On September 15, 2011, EPA and NHTSA jointly announced a final rule, called the HD National Program [75], 

which for the first time establishes greenhouse gas (GHG) emissions and fuel consumption standards for onroad heavy-duty trucks and their engines. The AEO2015 Reference case incorporates the new standards for 

heavy-duty vehicles (HDVs) with gross vehicle weight rating (GVWR) above 8,500 pounds (Classes 2b 

through 8). The HD National Program standards begin for MY 2014 vehicles and engines and are fully phased 

in by MY 2018. AEO2015 models standard compliance among 13 HDV regulatory classifications that 

represent the discrete vehicle categories set forth in the rule.

Energy Independence and Security Act of 2007 (EISA2007)

A fuel economy credit trading program is established based on EISA2007. Currently, CAFE credits earned by 

manufacturers can be banked for up to 3 years and can only be applied to the fleet (car or light truck) from 

which the credit was earned. Starting in model year 2011, the credit trading program allows manufacturers 

whose automobiles exceed the minimum fuel economy standards to earn credits that can be sold to other 
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 91

manufacturers whose automobiles fail to achieve the prescribed standards. The credit trading program is 

designed to ensure that the total oil savings associated with manufacturers that exceed the prescribed 

standards are preserved when credits are sold to manufacturers that fail to achieve the prescribed 

standards.

While the credit trading program began in 2011, EISA2007 allows manufacturers to apply credits earned to 

any of the three model years prior to the model year the credits are earned, and to any of the five model 

years after the credits are earned. The transfer of credits within a manufacturer’s fleet is limited to specific 

maximums. For model years 2011 through 2013, the maximum transfer is 1.0 mpg; for model years 2014 

through 2017, the maximum transfer is 1.5 mpg; and for model years 2018 and later, the maximum credit 

transfer is 2.0 mpg. NEMS currently allows for sensitivity analysis of CAFE credit banking by manufacturer 

fleet, but does not model the trading of credits across manufacturers. AEO2015 does not consider trading 

of credits since this would require significant modifications to NEMS and detailed technology cost and 

efficiency data by manufacturer, which are not readily available.

The CAFE credits specified under the Alternative Motor Fuels Act (AMFA) through 2019 are extended. Prior 

to passage of this Act, the CAFE credits under AMFA were scheduled to expire after model year 2010. 

Currently, 1.2 mpg is the maximum CAFE credit that can be earned from selling alternative fueled vehicles. 

EISA2007 extends the 1.2 mpg credit maximum through 2014 and reduces the maximum by 0.2 mpg for 

each following year until it is phased out by model year 2020. NEMS does model CAFE credits earned from 

alternative fuel vehicle sales.

American Recovery and Reinvestment Act of 2009 and Energy Improvement and Extension Act of 

2008

ARRA Title I, Section 1141, modified the EIEA2008 Title II, Section 205, tax credit for the purchase of new, 

qualified plug-in electric drive motor vehicles. According to the legislation, a qualified plug-in electric drive 

motor vehicle must draw propulsion from a traction battery with at least 4 kWh of capacity and be propelled 

to a significant extent by an electric motor which draws electricity from a battery that is capable of being 

recharged from an external source of electricity.

The tax credit for the purchase of a plug-in electric vehicle is $2,500, plus, starting at a battery capacity of 5 

kWh, an additional $417 per kWh battery credit up to a maximum of $7,500 per vehicle. The tax credit 

eligibility and phase-out are specific to an individual vehicle manufacturer. The credits are phased out once 

a manufacturer’s cumulative sales of qualified vehicles reach 200,000. The phaseout period begins two 

calendar quarters after the first date in which a manufacturer’s sales reach the cumulative sales maximum 

after December 31, 2009. The credit is reduced to 50% of the total value for the first two calendar quarters 

of the phase-out period and then to 25% for the third and fourth calendar quarters before being phased out 

entirely thereafter. The credit applies to vehicles with a gross vehicle weight rating of less than 14,000 

pounds.

ARRA also allows a tax credit of 10% against the cost of a qualified electric vehicle with a battery capacity of 

at least 4 kWh subject to the same phase-out rules as above. The tax credits for qualified plug-in electric 

drive motor vehicles and electric vehicles are included in AEO2015.
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 92

Energy Policy Act of 1992 (EPACT1992) 

Fleet alternative-fuel vehicle sales necessary to meet the EPACT regulations are derived based on the 

mandates as they currently stand and the Commercial Fleet Vehicle Component calculations. Total projected 

AFV sales are divided into fleets by government, business, and fuel providers (Table 7.14).

Table 7.14. EPACT legislative mandates for AFV purchases by fleet type and year

Year Federal State Fuel Providers Electric Utilities

2005 75 75 70 90

Source: U.S. Energy Information Administration, Energy Efficiency and Renewable Energy 

(Washington, DC, 2005), 

http://www1.eere.energy.gov/vehiclesandfuels/epact/statutes_regulations.html.

Because the commercial fleet model operates on three fleet type representations (business, government, 

and utility), the federal and state mandates are weighted by fleet vehicle stocks to create a composite 

mandate for both. The same combining methodology is used to create a composite mandate for electric 

utilities and fuel providers based on fleet vehicle stocks [76].

Low-Emission Vehicle Program (LEVP)

The LEVP was originally passed into legislation in 1990 in the State of California. It began as the 

implementation of a voluntary opt-in pilot program under the purview of Clean Air Act Amendments of 1990 

(CAAA1990), which included a provision that other states could opt in to the California program to achieve 

lower emissions levels than would otherwise be achieved through CAAA1990. Fourteen states have elected 

to adopt the California LEVP.

The LEVP is an emissions-based policy, setting sales mandates for six categories of low-emission vehicles: 

low-emission vehicles (LEVs), ultra-low-emission vehicles (ULEVs), super-ultra-low-emission vehicles 

(SULEVs), partial zero-emission vehicles (PZEVs), advanced technology partial zero-emission vehicles (ATPZEVs), and zero-emission vehicles (ZEVs). The LEVP requires that in 2005, 10% of a manufacturer’s sales are 

ZEVs or equivalent ZEV-earned credits, increasing to 11% in 2009, 12% in 2012, 14% in 2015, and 16% in 

2018 where it remains constant thereafter. In August 2004, California Air Resources Board (CARB) enacted 

further amendments to the LEVP that place a greater emphasis on emissions reductions from PZEVs and ATPZEVs and requires that manufacturers produce a minimum number of fuel cell and electric vehicles. In 

addition, manufacturers are allowed to adopt alternative compliance requirements for ZEV sales that are 

based on cumulative fuel cell vehicle sales targets for vehicles sold in all states participating in California’s 

LEVP. Under the alternative compliance requirements, ZEV credits can also be earned by selling battery 

electric vehicles. Currently, all manufacturers have opted to adhere to the alternative compliance 

requirements. The mandate still includes phase-in multipliers for pure ZEVs and allows 20% of the sales 

requirement to be met with AT-PZEVs and 60% of the requirement to be met with PZEVs. AT-PZEVs and 

PZEVs are allowed 0.2 credits per vehicle. EIA assumes that credit allowances for PZEVs will be met with 

conventional vehicle technology, hybrid vehicles will be sold to meet the AT-PZEV allowances, and hydrogen 

fuel cell vehicles will be sold to meet the pure ZEV requirements under the alternative compliance path.

Transportation alternative case

Integrated High Technology case

In the Integrated High Technology case for cars and light trucks, the conventional fuel-saving technology 
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 93

characteristics are based on NHTSA and EPA values [77]. Tables 7.15 and 7.16 summarize the high 

technology matrices for cars and light trucks. Table 7.17 reflects the high technology case assumptions for 

freight trucks. These reflect optimistic values, with respect to efficiency improvement and capital cost, for 

advanced technologies [78-81]. For the Air Travel Submodule, the Integrated High Technology case reflects 

earlier introduction years for the new aircraft technologies and a greater penetration share (Table 7.18).

Table 7.15. High technology matrix for cars

Per Unit

Absolute Incremental

Fuel Incremental Incremental Weight

Efficiency Incremental Cost ($/ Weight (Lbs./ Introduction Horsepower

Change % Cost 2000$ UnitWt.) (Lbs.) UnitWt.) Year Change %

Unit Body Construction 4.4 89.92 0.00 0 -6 1980 0

Mass Reduction I 1.1 0.00 0.06 0 -1.5 2005 0

Mass Reduction II 2.9 0.00 0.13 0 -3.5 2009 0

Mass Reduction III 5.9 0.00 0.37 0 -10 2011 0

Mass Reduction IV 9.2 0.00 0.56 0 -15 2099 0

Mass Reduction V 12.8 0.00 0.65 0 -20 2099 0

Aerodynamics I 2.6 43.35 0.00 0 0.5 2000 0

Aerodynamics II 5.4 182.96 0.00 0 1 2011 0

6 Speed Manual 2.4 230.03 0.00 20 0 1995 0

Aggressive Shift Logic I 2.8 29.20 0.00 0 0 1999 0

Aggressive Shift Logic II 7.4 24.46 0.00 0 0 2017 0

Early Torque Converter Lockup 0.6 26.54 0.00 0 0 2002 0

High Efficiency Gearbox 1.8 180.56 0.00 0 0 2017 0

5 Speed Automatic 1.5 93.52 0.00 20 0 1995 0

6 Speed Automatic 2.4 243.05 0.00 30 0 2003 0

7 Speed Automatic 5.6 360.93 0.00 40 0 2009 0

8 Speed Automatic 8.8 479.55 0.00 50 0 2010 0

Dual Clutch Automated Manual 6.1 51.08 0.00 -10 0 2004 0

CVT 9.2 225.88 0.00 -25 0 1998 0

Low Friction Lubricants 0.8 2.88 0.00 0 0 2003 0

Engine Friction Reduction I-4 cyl 2.2 42.44 0.00 0 0 2000 1.25

Engine Friction Reduction I-6 cyl 2.9 64.02 0.00 0 0 2000 1.25

Engine Friction Reduction I-8 cyl 3.1 84.89 0.00 0 0 2000 1.25

Engine Friction Reduction II-4 cyl 4.0 90.64 0.00 0 0 2017 2.25

Engine Friction Reduction II-6 cyl 5.2 133.08 0.00 0 0 2017 2.25

Engine Friction Reduction II-8 cyl 5.6 175.53 0.00 0 0 2017 2.25

Cylinder Deactivation-6 cyl 7.2 168.36 0.00 10 0 2004 0

Cylinder Deactivation-8 cyl 7.6 188.97 0.00 10 0 2004 0

VVT I-OHV Intake Cam Phasing-6 cyl 2.9 39.51 0.00 20 0 2051 1.25

VVT I-OHV Intake Cam Phasing-8 cyl 3.0 39.51 0.00 30 0 2051 1.25

VVT I-OHC Intake Cam Phasing-4 cyl 2.3 39.51 0.00 10 0 1993 1.25

VVT I-OHC Intake Cam Phasing-6 cyl 2.9 79.88 0.00 20 0 1993 1.25

VVT I-OHC Intake Cam Phasing-8 cyl 3.0 79.88 0.00 30 0 1993 1.25

VVT II-OHV Coupled Cam Phasing-6 cyl 5.9 39.51 0.00 20 0 2009 1.25

VVT II-OHV Coupled Cam Phasing-8 cyl 6.4 39.51 0.00 30 0 2009 1.25

VVT II-OHC Coupled Cam Phasing-4 cyl 4.7 39.51 0.00 10 0 2009 1.25

VVT II-OHC Coupled Cam Phasing-6 cyl 5.9 79.88 0.00 20 0 2009 1.25

VVT II-OHC Coupled Cam Phasing-8 cyl 6.4 79.88 0.00 30 0 2009 1.25

VVT III-OHV Dual Cam Phasing-6 cyl 5.9 89.33 0.00 25 0 2051 1.56

VVT III-OHV Dual Cam Phasing-8 cyl 6.4 89.33 0.00 37.5 0 2051 1.56

VVT III-OHC Dual Cam Phasing-4 cyl 4.7 81.60 0.00 12.5 0 2009 1.56

VVT III-OHC Dual Cam Phasing-6 cyl 5.9 176.09 0.00 25 0 2009 1.56

VVT III-OHC Dual Cam Phasing-8 cyl 6.4 176.09 0.00 37.5 0 2009 1.56

VVL I-OHV Discrete-6 cyl 6.1 202.72 0.00 40 0 2000 2.5
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 94

Table 7.15. High technology matrix for cars (cont.)

Per Unit

Absolute Incremental

Fuel Incremental Incremental Weight

Efficiency Incremental Cost ($/ Weight (Lbs./ Introduction Horsepower

Change % Cost 2000$ UnitWt.) (Lbs.) UnitWt.) Year Change %

VVL I-OHV Discrete-8 cyl 6.5 290.33 0.00 50 0 2000 2.5

VVL I-OHC Discrete-4 cyl 4.7 140.01 0.00 25 0 2000 2.5

VVL I-OHC Discrete-6 cyl 6.1 202.72 0.00 40 0 2000 2.5

VVL I-OHC Discrete-8 cyl 6.5 290.33 0.00 50 0 2000 2.5

VVL II-OHV Continuous-6 cyl 7.7 1035.06 0.00 40 0 2011 2.5

VVL II-OHV Continuous-8 cyl 8.3 1131.26 0.00 50 0 2011 2.5

VVL II-OHC Continuous-4 cyl 5.9 209.59 0.00 25 0 2011 2.5

VVL II-OHC Continuous-6 cyl 7.7 384.82 0.00 40 0 2011 2.5

VVL II-OHC Continuous-8 cyl 8.3 420.04 0.00 50 0 2011 2.5

Stoichiometric GDI-4 cyl 1.7 237.93 0.00 20 0 2006 2.5

Stoichiometric GDI-6 cyl 1.7 358.19 0.00 30 0 2006 2.5

Stoichiometric GDI-8 cyl 1.7 430.34 0.00 40 0 2006 2.5

OHV to DOHC TBDS-I4 23.8 1245.51 0.00 -100 0 2009 3.75

OHV to DOHC TBDS I-V6 22.2 1887.16 0.00 -100 0 2009 3.75

SOHC to DOHC TBDS I-I4 23.8 744.73 0.00 -100 0 2009 3.75

SOHC to DOHC TBDS I-V6 22.2 1445.22 0.00 -100 0 2009 3.75

DOHC TBDS I-I3 19.3 823.75 0.00 -100 0 2009 3.75

DOHC TBDS I-I4 23.8 672.57 0.00 -100 0 2009 3.75

DOHC TBDS I-V6 22.2 1377.79 0.00 -100 0 2009 3.75

OHV to DOHC TBDS II-I4 28.9 1427.73 0.00 -100 0 2012 3.75

OHV to DOHC TBDS II-V6 27.0 2200.80 0.00 -100 0 2012 3.75

SOHC to DOHC TBDS II-I4 28.9 941.53 0.00 -100 0 2012 3.75

SOHC to DOHC TBDS II-V6 27.0 1771.73 0.00 -100 0 2012 3.75

DOHC TBDS II-I3 23.3 1017.42 0.00 -100 0 2012 3.75

DOHC TBDS II-I4 28.9 871.48 0.00 -100 0 2012 3.75

DOHC TBDS II-V6 27.0 1706.27 0.00 -100 0 2012 3.75

OHV to DOHC TBDS III-I4 (from V6) 35.9 1828.65 0.00 -100 0 2017 3.75

OHV to DOHC TBDS III-I4 (from V8) 33.8 1441.63 0.00 -200 0 2017 3.75

SOHC to DOHC TBDS III-I4 (from V6) 35.9 1409.25 0.00 -100 0 2017 3.75

SOHC to DOHC TBDS III-I4 (from V8) 33.8 1242.36 0.00 -200 0 2017 3.75

DOHC TBDS III-I3 (from I4) 29.8 1471.12 0.00 -100 0 2017 3.75

DOHC TBDS III-I4 (from V6) 35.9 1348.83 0.00 -100 0 2017 3.75

DOHC TBDS III-I4 (from V8) 33.8 1171.86 0.00 -200 0 2017 3.75

Electric Power Steering 1.4 96.44 0.00 0 0 2004 0

Improved Accessories I 0.8 78.74 0.00 0 0 2005 0

12V Micro Hybrid w/EPS and IACC 7.7 576.22 0.00 45 0 2005 0

Improved Accessories II 2.8 115.82 0.00 0 0 2012 0

Mild Hybrid w/EPS and IACC II 12.1 2611.80 0.00 80 0 2012 -2.5

Tires I 2.2 5.04 0.00 -12 0 2005 0

Tires II 4.4 52.51 0.00 -15 0 2017 0

Low Drag Brakes 0.9 53.23 0.00 0 0 2000 0

Secondary Axle Disconnect 1.4 86.70 0.00 0 -1 2012 0

Sources: Energy and Environmental Analysis, Documentation of Technology included in the NEMS Fuel Economy Model for Passenger 

Cars and Light Trucks (September 2002). National Research Council, Effectiveness and Impact of Corporate Average Fuel Economy (CAFÉ) 

Standards (Copyright 2002).

National Highway Traffic Safety Administration, Corporate Average Fuel Economy for MY 2011-2015 Passenger Cars and Light Trucks 

(April 2008).

U.S. Environmental Protection Agency, Interim Report: New Powertrain Technologies and Their Projected Costs (October 2005).

Environmental Protection Agency and Department of Transportation National Highway Traffic Safety Administration, “2017 and Later 

Model Year Light-Duty Vehicle Greenhouse Gas Emissions and Corporate Average Fuel Economy Standards; Final Rule,” Federal Register 

Vol. 77, No. 199, October 15, 2012. 40 CFR Parts 85, 86, 600, 49 CFR Parts 523, 531, 533, et al. and 600.
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 95

Table 7.16. High technology matrix for light trucks

Per Unit

Absolute Incremental

Fuel Incremental Incremental Weight

Efficiency Incremental Cost ($/ Weight (Lbs./ Introduction Horsepower

Change % Cost 2000$ UnitWt.) (Lbs.) UnitWt.) Year Change %

Unit Body Construction 4.4 90.00 0.00 0 -6 1980 0

Mass Reduction I 1.1 0.00 0.06 0 -1.5 2005 0

Mass Reduction II 2.9 0.00 0.13 0 -7.5 2009 0

Mass Reduction III 5.9 0.00 0.37 0 -10 2011 0

Mass Reduction IV 9.2 0.00 0.56 0 -15 2016 0

Mass Reduction V 12.8 0.00 0.65 0 -20 2020 0

Aerodynamics I 2.6 43.35 0.00 0 0.5 2000 0

Aerodynamics II 5.4 182.96 0.00 0 1 2011 0

6 Speed Manual 2.2 230.03 0.00 20 0 1995 0

Aggressive Shift Logic I 2.5 29.20 0.00 0 0 1999 0

Aggressive Shift Logic II 6.9 24.46 0.00 0 0 2017 0

Early Torque Converter Lockup 0.6 26.54 0.00 0 0 2002 0

High Efficiency Gearbox 1.8 180.56 0.00 0 0 2017 0

5 Speed Automatic 1.5 93.52 0.00 20 0 1995 0

6 Speed Automatic 2.2 243.05 0.00 30 0 2003 0

7 Speed Automatic 5.5 360.93 0.00 40 0 2009 0

8 Speed Automatic 8.8 479.55 0.00 50 0 2014 0

Dual Clutch Automated Manual 5.4 164.02 0.00 -10 0 2004 0

CVT 8.6 225.88 0.00 -25 0 1998 0

Low Friction Lubricants 0.8 2.88 0.00 0 0 2003 0

Engine Friction Reduction I-4 cyl 2.2 42.44 0.00 0 0 2000 1.25

Engine Friction Reduction I-6 cyl 2.9 64.02 0.00 0 0 2000 1.25

Engine Friction Reduction I-8 cyl 2.8 84.89 0.00 0 0 2000 1.25

Engine Friction Reduction II-4 cyl 4.0 90.64 0.00 0 0 2017 2.25

Engine Friction Reduction II-6 cyl 5.2 133.08 0.00 0 0 2017 2.25

Engine Friction Reduction II-8 cyl 4.8 175.53 0.00 0 0 2017 2.25

Cylinder Deactivation-6 cyl 7.0 168.36 0.00 10 0 2004 0

Cylinder Deactivation-8 cyl 6.6 188.97 0.00 10 0 2004 0

VVT I-OHV Intake Cam Phasing-6 cyl 2.9 39.51 0.00 20 0 2051 1.25

VVT I-OHV Intake Cam Phasing-8 cyl 2.8 39.51 0.00 30 0 2051 1.25

VVT I-OHC Intake Cam Phasing-4 cyl 2.3 39.51 0.00 10 0 1993 1.25

VVT I-OHC Intake Cam Phasing-6 cyl 2.9 79.88 0.00 20 0 1993 1.25

VVT I-OHC Intake Cam Phasing-8 cyl 2.8 79.88 0.00 30 0 1993 1.25

VVT II-OHV Coupled Cam Phasing-6 cyl 5.9 39.51 0.00 20 0 2009 1.25

VVT II-OHV Coupled Cam Phasing-8 cyl 5.6 39.51 0.00 30 0 2009 1.25

VVT II-OHC Coupled Cam Phasing-4 cyl 4.7 39.51 0.00 10 0 2009 1.25

VVT II-OHC Coupled Cam Phasing-6 cyl 5.9 79.88 0.00 20 0 2009 1.25

VVT II-OHC Coupled Cam Phasing-8 cyl 5.6 79.88 0.00 30 0 2009 1.25

VVT III-OHV Dual Cam Phasing-6 cyl 5.9 89.33 0.00 25 0 2051 1.56

VVT III-OHV Dual Cam Phasing-8 cyl 5.6 89.33 0.00 37.5 0 2051 1.56

VVT III-OHC Dual Cam Phasing-4 cyl 4.7 81.60 0.00 12.5 0 2009 1.56

VVT III-OHC Dual Cam Phasing-6 cyl 5.9 176.09 0.00 25 0 2009 1.56

VVT III-OHC Dual Cam Phasing-8 cyl 5.6 176.09 0.00 37.5 0 2009 1.56

VVL I-OHV Discrete-6 cyl 6.1 202.72 0.00 40 0 2000 2.5

VVL I-OHV Discrete-8 cyl 5.7 290.33 0.00 50 0 2000 2.5

VVL I-OHC Discrete-4 cyl 4.6 140.01 0.00 25 0 2000 2.5

VVL I-OHC Discrete-6 cyl 6.1 202.72 0.00 40 0 2000 2.5

VVL I-OHC Discrete-8 cyl 5.7 290.33 0.00 50 0 2000 2.5

VVL II-OHV Continuous-6 cyl 7.7 1035.06 0.00 40 0 2011 2.5

VVL II-OHV Continuous-8 cyl 7.2 1131.26 0.00 50 0 2011 2.5

VVL II-OHC Continuous-4 cyl 5.8 209.59 0.00 25 0 2011 2.5
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 96

Table 7.16. High technology matrix for light trucks (cont.)

Per Unit

Absolute Incremental

Fuel Incremental Incremental Weight

Efficiency Incremental Cost ($/ Weight (Lbs./ Introduction Horsepower

Change % Cost 2000$ UnitWt.) (Lbs.) UnitWt.) Year Change %

VVL II-OHC Continuous-6 cyl 7.7 384.82 0.00 40 0 2011 2.5

VVL II-OHC Continuous-8 cyl 7.2 420.04 0.00 50 0 2011 2.5

Stoichiometric GDI-4 cyl 1.7 237.93 0.00 20 0 2006 2.5

Stoichiometric GDI-6 cyl 1.7 358.19 0.00 30 0 2006 2.5

Stoichiometric GDI-8 cyl 1.7 430.34 0.00 40 0 2006 2.5

OHV to DOHC TBDS-I4 23.8 1245.51 0.00 -100 0 2009 3.75

OHV to DOHC TBDS I-V6 22.2 1887.16 0.00 -100 0 2009 3.75

SOHC to DOHC TBDS I-I4 23.8 744.73 0.00 -100 0 2009 3.75

SOHC to DOHC TBDS I-V6 22.2 1445.22 0.00 -100 0 2009 3.75

DOHC TBDS I-I3 19.3 823.75 0.00 -100 0 2009 3.75

DOHC TBDS I-I4 23.8 672.57 0.00 -100 0 2009 3.75

DOHC TBDS I-V6 22.2 1377.79 0.00 -100 0 2009 3.75

OHV to DOHC TBDS II-I4 28.9 1427.73 0.00 -100 0 2012 3.75

OHV to DOHC TBDS II-V6 27.0 2200.80 0.00 -100 0 2012 3.75

SOHC to DOHC TBDS II-I4 28.9 941.53 0.00 -100 0 2012 3.75

SOHC to DOHC TBDS II-V6 27.0 1771.73 0.00 -100 0 2012 3.75

DOHC TBDS II-I3 23.3 1017.42 0.00 -100 0 2012 3.75

DOHC TBDS II-I4 28.9 871.48 0.00 -100 0 2012 3.75

DOHC TBDS II-V6 27.0 1706.27 0.00 -100 0 2012 3.75

OHV to DOHC TBDS III-I4 (from V6) 35.9 1828.65 0.00 -100 0 2017 3.75

OHV to DOHC TBDS III-I4 (from V8) 33.8 1441.63 0.00 -200 0 2017 3.75

SOHC to DOHC TBDS III-I4 (from V6) 35.9 1409.25 0.00 -100 0 2017 3.75

SOHC to DOHC TBDS III-I4 (from V8) 33.8 1242.36 0.00 -200 0 2017 3.75

DOHC TBDS III-I3 (from I4) 29.8 1471.12 0.00 -100 0 2017 3.75

DOHC TBDS III-I4 (from V6) 35.9 1348.83 0.00 -100 0 2017 3.75

DOHC TBDS III-I4 (from V8) 33.8 1171.86 0.00 -200 0 2017 3.75

Electric Power Steering 1.1 96.44 0.00 0 0 2004 0

Improved Accessories I 0.8 78.74 0.00 0 0 2005 0

12V Micro Hybrid w/EPS and IACC 7.4 628.01 0.00 45 0 2005 0

Improved Accessories II 2.6 115.82 0.00 0 0 2012 0

Mild Hybrid w/EPS and IACC II 11.7 2611.80 0.00 80 0 2012 -2.5

Tires I 2.2 5.04 0.00 -12 0 2005 0

Tires II 4.4 52.51 0.00 -15 0 2017 0

Low Drag Brakes 0.9 53.23 0.00 0 0 2000 0

Secondary Axle Disconnect 1.5 86.70 0.00 0 -1 2012 0

Sources: Energy and Environmental Analysis, Documentation of Technology included in the NEMS Fuel Economy Model for Passenger 

Cars and Light Trucks (September 2002).

National Research Council, Effectiveness and Impact of Corporate Average Fuel Economy (CAFE) Standards (Copyright 2002).

National Highway Traffic Safety Administration, Corporate Average Fuel Economy for MY 2011-2015 Passenger Cars and Light Trucks 

(April 2008).

U.S. Environmental Protection Agency, Interim Report: New Powertrain Technologies and Their Projected Costs (October 2005).

Environmental Protection Agency and Department of Transportation National Highway Traffic Safety Administration, “2017 and Later 

Model Year Light-Duty vehicle Greenhouse Gas Emissions and Corporate Average Fuel Economy Standards; Final Rule,” Federal

Register Vol. 77, No. 199, October 15, 2012. 40 CFR Parts 85, 86, 600, 49 CFR Parts 523, 531, 533, et al. and 600.
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 97

Table 7.17. High technology matrix for freight trucks

Incremental Fuel

Economy

Vehicle Capital Costs Improvement

Technology Type Category (2009$) (%)

Aerodynamics I: streamlined bumper, grill, windshield, roof 1 53 2.0

Aerodynamics I: conventional features; general aerodynamic shape, removal of 

classic non-aerodynamic features 5,8,11 900 4.5

Aerodynamics I 7,10,13 900 5.1

Aerodynamics II: SmartWay features; streamlined shape, bumper grill, hood, mirrors, 

side fuel tank and roof fairings, side gap extenders 5,8 997 2.0

Aerodynamics II 7,10 997 4.0

Aerodynamics II 11 1040 5.0

Aerodynamics II 13 1355 5.0

Aerodynamics III: underbody airflow, down exhaust, lowered ride height 7 1552 5.0

Aerodynamics III 13 1803 7.0

Aerodynamics IV: skirts, boat tails, nose cone, vortex stabilizer, pneumatic blowing 5-13 4950 14.0

Tires I: low rolling resistance 1 6 2.0

Tires I 2,3 110 3.0

Tires I 4 131 2.2

Tires I 5-7 114 2.2

Tires I 8-13 172 2.2

Tires II: super singles 5-13 140 6.2

Tires III: single wide tires on trailer 5-13 720 3.4

Weight Reduction I 1 116 1.8

Weight Reduction I: aluminum dual tires or super singles 5-13 580 1.1

Weight Reduction II: weight reduction 15% 3-13 5580 3.3

Weight Reduction III: weight reduction 20% 3-13 9900 3.9

Accessories I: Electric/electrohydraulic improvements; electric power steering or 

electrohydraulic power steering 1 105 2.0

Accessories II: Improved accessories; electrified water, oil, fuel injection, power 

steering pump, air compressor 1 85 2.0

Accessories III: Auxiliary Power Unit 11-13 4834 6.4

Transmission I: 8-speed Automatic from 6-speed automatic 1 248 1.9

Transmission II: 6-Manual from 4-speed automatic 1 135 1.1

Transmission III: Automated Manual Transmission 2-13 4500 3.9

Diesel Engine I: aftertreatment improvements 1 109 5.0

Diesel Engine I 2 109 4.0

Diesel Engine II: low friction lubricants 1-13 3 1.0

Diesel Engine III: variable valve actuation 2 0 1.1

Diesel Engine III 3-13 270 1.1

Diesel Engine IV: engine friction reduction, low tension piston rings, roller cam 

followers, piston skirt design, improved crankshaft design and bearings; coating 1-2 111 2.0

Diesel Engine IV: engine friction reduction, improved bearings to allow lower 

viscosity oil 3-13 225 2.0

Diesel Engine V: improved turbo efficiency 2-13 15 2.0

Diesel Engine VI: improved water, oil, fuel pump; pistons; valve train friction 

reduction 2 192 2.0

Diesel Engine VI 3, 5-7 167 2.0

Diesel Engine VI: improved water, oil, fuel pump; pistons 4, 8-13 135 2.0

Diesel Engine VII: improved cylinder head, fuel rail and injector, EGR cooler 2 36 7.0

Diesel Engine VII 3-13 26 7.0

Diesel Engine VIII: turbo mechanical compounding 5-13 900 5.0

Diesel Engine IX: low temperature EGR, improved turbochargers 1 166 6.0

Diesel Engine X: sequential downsizing/turbocharging 5-13 1080 2.8

Diesel Engine XI: waste heat recovery, Organic Ranking Cycle (bottoming cycle) 3-13 9000 8.8

Diesel Engine XII: electric turbo compounding 4-13 7200 10.0

Gasoline Engine I: low friction lubricants 1-13 3 0.6
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 98

Table 7.17. High technology matrix for freight trucks (cont.)

Incremental Fuel

Economy

Vehicle Capital Costs Improvement

Technology Type Category (2009$) (%)

Gasoline Engine II: combined cam phasing 2-4 43 4.0

Gasoline Engine III: engine friction reduction; low tension piston rings, roller cam 

followers, piston skirt design, improved crankshaft design and bearings, costing 1 111 3.0

Gasoline III 2 104 3.0

Gasoline III 3-4 86 3.0

Gasoline Engine IV: stoichiometric gasoline direct injection V8 1-2 425 2.0

Gasoline Engine IV 3-4 430 2.0

Gasoline Engine V: turbocharging and downsizing SGDI V8 to V6 1-4 1569 2.2

Gasoline Engine VI: lean burn GDI 1-4 675 14.0

Gasoline Engine VII: HCCI 1-4 617 14.0

Hybrid System I: 42V engine off at idle 1-2 1350 7.7

Hybrid System I 3-4 1350 5.0

Hybrid System II: dual mode hybrid 1-2 10800 27.5

Hybrid System II: electric, ePTO, or hydraulic 3-4 24000 33.0

Hybrid System II: 4 kWh battery, 50 kW motor generator 5-13 24000 6.0

Sources: Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Medium- and Heavy-Duty Engines and Vehicles, 

U.S. Environmental Protection Agency and U.S. Department of Transportation, Final Rules, Federal Register, Vol. 76, No. 179

(September 2011).

Final Rulemaking to Establish Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Medium- and Heavy-Duty 

Engines and Vehicles, Regulatory Impact Analysis, U.S. Environmental Protection Agency and U.S. Department of Transportation

(August 2011).

Reducing Heavy-Duty Long Haul Combination Truck Fuel Consumption and CO2 Emissions, Final Report, TIAX, LLC. (October 2009).

Update of Technology Information for Forecasting Heavy-Duty On-Road Vehicle Fuel Economy, Final Report, ICF International, 

Prepared for the U.S. Energy Information Administration (August 2010).

Technologies and Approaches to Reducing the Fuel Consumption of Medium- and Heavy-Duty Vehicles, National Research Council 

of the National Academy of Sciences (2010).

Table 7.18. High technology matrix for air travel

Technology Introduction Year Fractional Efficiency Improvement Jet Fuel Trigger Price (1987$ per gallon)

Technology #1 2008 0.03 1.34

Technology #2 2014 0.05 1.34

Technology #3 2020 0.09 1.34

Technology #4 2025 0.13 1.34

Technology #5 2018 0.22 1.34

Technology #6 2018 0.10 1.34

Technology #7 2025 0.04 1.00

Technology #8 2020 0.05 1.34

Sources: Jet Information Services, 2013 World Jet Inventory, data tables (2013).

Energy Information Administration, Transportation Demand Module of the National Energy Modeling System, Model 

Documentation 2014, DOE/EIA-M070(2014) (Washington, DC, 2014).
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 99

Notes and sources

[41] U.S. Department of Transportation, National Highway Traffic Safety Administration, “Summary of Fuel 

Economy Performance” (Washington, DC, December 2009).

[42] Goldberg, Pinelopi Koujianou, “Product Differentiation and Oligopoly In International Markets: The Case 

of The U.S. Automobile Industry,” Econometrica, Vol. 63, No.4 (July 1995), 891-951.

[43] Oak Ridge National Laboratory, “Fleet Characteristics and Data Issues,” Stacy Davis and Lorena Truett, 

final report prepared for the Department of Energy, Energy Information Administration, Office of Energy 

Analysis (Oak Ridge, TN, January 2003).

[44] Bobit Publishing Company, The Fleet Fact Book, various issues (Redondo Beach, California).

[45] Bobit Publishing Company, Note 44.

[46] Davis, Stacy C., Lorena F. Truett, “Investigation of Class 2B Trucks (Vehicles of 8,500 to 10,000 LBS 

GVWR),” Oak Ridge National Laboratory, ORNL/TM-2002/49, March 2002. 

[47] Davis, Stacy C., Lorena F. Truett, op.cit., Note 46.

[48] Greenspan, Alan, and Darrel Cohen, “Motor Vehicle Stocks, Scrappage, and Sales,” Federal Reserve 

Board (Washington, DC, October 30, 1996).

[49] Oak Ridge National Laboratory, Transportation Energy Data Book: 31st Edition (Oak Ridge, TN, 2012). 

[50] Davis, Stacy C., Lorena F. Truett, op.cit., Note 46.

[51] Greene, David L. and S.M. Chin, “Alternative Fuels and Vehicles (AFV) Model Changes,” Center for 

Transportation Analysis, Oak Ridge National Laboratory, (Oak Ridge TN, November 14, 2000).

[52] U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, prepared by 

Interlaboratory Working Group, Scenarios of U.S. Carbon Reductions: Potential Impacts of Energy 

Technologies by 2010 and Beyond (Washington, DC, 1998).

[53] Energy and Environmental Analysis, Inc., Updates to the Fuel Economy Model (FEM) and Advanced 

Technology Vehicle (ATV) Module of the National Energy Modeling System (NEMS) Transportation Model, 

Prepared for the Energy Information Administration (EIA), (Arlington, VA, October 23, 2000).

[54] WardsAuto, “U.S. Vehicle Sales by Vehicle Type and Source, 1931-2013, 

http://wardsauto.com/keydata/historical/UsaSa01summary.

[55] U.S. Energy Information Administration, State Energy Data System 2009.

[56] U.S. Department of Transportation, “Freight Analysis Framework” (2007). 
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 100

Notes and sources (cont.)

[57] IHS Global, Inc., “NEMS Freight Transportation Module Improvement Study,” final report prepared for 

the Department of Energy, Energy Information Administration, Office of Energy Analysis (Lexington, MA, 

June 2014), http://www.eia.gov/analysis/studies/transportation/.

[58] U.S. Department of Transportation, National Transportation Statistics (1995-2009).

[59] U.S. Department of Transportation, Federal Highway Administration, Highway Statistics (1995-2011).

[60] Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Medium- and Heavy-Duty 

Engines and Vehicles, U.S. Environmental Protection Agency and U.S. Department of Transportation, Final 

Rules, Federal Register, Vol. 76, No. 179 (September 2011).

[61] U.S. Department of Commerce, Bureau of the Census, “Vehicle Inventory and Use Survey,” ECO2TV 

(Washington, DC, December 2009).

[62] U.S. Department of Transportation, op. cit. Note 56.

[63] U.S. Department of Transportation, Surface Transportation Board, Annual Reports (R-1) (1995-2011). 

[64] U.S. Department of Transportation, op. cit. Note 62.

[65] U.S. Department of Transportation, Federal Railroad Administration, “1989 Carload Waybill Statistics; 

Territorial Distribution, Traffic and Revenue by Commodity Classes” (September 1991 and prior issues). 

[66] U.S. Army Corps of Engineers, Waterborne Commerce of the United States, (Waterborne Statistics 

Center: New Orleans, LA, 1993).

[67] U.S. Department of Transportation, op. cit., Note 56. 

[68] Oak Ridge National Lab, op. cit., Note 49.

[69] U.S. Army Corps of Engineers, op. cit., Note 66.

[70] U.S. Energy Information Administration, op. cit., Note 55.

[71] U.S. Department of Transportation, Research and Innovative Technology Administration, Bureau of 

Transportation Statistics, Air Carrier Summary Data (Form 41, Schedules T-1 and T-2), (2011) 

http://www.transtats.bts.gov/DataIndex.asp; link: Air Carrier Summary Data (Form 41 and 298C Summary 

Data).

[72] U.S. Department of Transportation, Research and Innovative Technology Administration, Bureau of 

Transportation Statistics, op. cit., Note 67.

[73] U.S. Department of Transportation, Federal Aviation Administration, Airport Capacity Benchmark 

Report, 2004 (2004). 
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 101

Notes and sources (cont.)

[74] Jet Information Services Inc., World Jet Inventory: Year-End 2009 (December 2009).

[75] Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for Medium- and Heavy-Duty 

Engines and Vehicles, U.S. Environmental Protection Agency and U.S. Department of Transportation, Final 

Rules, Federal Register, Vol. 76, No. 179 (September 2011).

[76] Energy Information Administration, op. cit., Note 55.

[77] Energy and Environmental Analysis, Inc., “Documentation of Technologies Included in the NEMS Fuel 

Economy Model for Passenger Cars and Light Trucks,” Prepared for the U.S. Energy Information 

Administration (Arlington, VA, September 30, 2002).

[78] Final Rulemaking to Establish Greenhouse Gas Emissions Standards and Fuel Efficiency Standards for 

Medium- and Heavy-Duty Engines and Vehicles, Regulatory Impact Analysis, U.S. Environmental Protection 

Agency and U.S. Department of Transportation (August 2011).

[79] Reducing Heavy-Duty Long Haul Combination Truck Fuel Consumption and CO2 Emissions, Final Report, 

TIAX, LLC (October 2009).

[80] Update of Technology Information for Forecasting Heavy-Duty On-Road Vehicle Fuel Economy, Final 

Report, ICF International, Prepared for the U.S. Energy Information Administration (August 2010).

[81] Technologies and Approaches to Reducing the Fuel Consumption of Medium- and Heavy-Duty Vehicles, 

National Research Council of the National Academy of Sciences (2010).
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 102

Electricity Market Module

The NEMS Electricity Market Module (EMM) represents the capacity planning, dispatching, and pricing of 

electricity. It is composed of four submodules: electricity load and demand, electricity capacity planning, 

electricity fuel dispatching, and electricity finance and pricing. It includes nonutility capacity and generation, 

and electricity transmission and trade. A detailed description of the EMM is provided in the EIA publication, 

Electricity Market Module of the National Energy Modeling System 2014, DOE/EIA-M068(2014).

Based on fuel prices and electricity demands provided by the other modules of NEMS, the EMM determines 

the most economical way to supply electricity, within environmental and operational constraints. There are 

assumptions about the operations of the electricity sector and the costs of various options in each of the 

EMM Submodules. This section describes the model parameters and assumptions used in the EMM. It 

includes a discussion of legislation and regulations that are incorporated in the EMM, as well as information

about the climate change action plan.

EMM regions

The supply regions used in the EMM were developed for the Annual Energy Outlook 2011, and correspond 

to the North American Electric Reliability Corporation (NERC) regions in place at that time, divided into 

subregions, as shown in Figure 6.

Figure 6. Electricity Market Model Supply Regions
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 103

Model parameters and assumptions

Generating capacity types

The capacity types represented in the EMM are shown in Table 8.1.

Table 8.1. Generating capacity types represented in the Electricity Market Module

Capacity Type

Existing coal steam plants1

High Sulfur Pulverized Coal with Wet Flue Gas Desulfurization

Advanced Coal - Integrated Coal Gasification Combined Cycle (IGCC)

IGCC with Carbon Sequestration

Oil/Gas Steam - Oil/Gas Steam Turbine

Combined Cycle - Conventional Gas/Oil Combined Cycle Combustion Turbine

Advanced Combined Cycle - Advanced Gas/Oil Combined Cycle Combustion Turbine

Advanced Combined Cycle with carbon sequestration

Combustion Turbine - Conventional Combustion Turbine

Advanced Combustion Turbine - Steam Injected Gas Turbine

Molten Carbonate Fuel Cell

Conventional Nuclear

Advanced Nuclear - Advanced Light Water Reactor

Generic Distributed Generation - Baseload

Generic Distributed Generation - Peak

Conventional Hydropower - Hydraulic Turbine

Pumped Storage - Hydraulic Turbine Reversible

Geothermal

Municipal Solid Waste

Biomass - Fluidized Bed

Solar Thermal - Central Tower

Solar Photovoltaic - Fixed Tilt

Wind

Wind Offshore

1

The EMM represents 32 different types of existing coal steam plants, based on the different possible 

configuration of NOx, particulate and SO2 emission control devices, as well as future options for controlling 

mercury and carbon. (See Table 8.9.)

Source: U.S. Energy Information Administration.
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 104

New generating plant characteristics

The cost and performance characteristics of new generating technologies are inputs to the electricity 

capacity planning submodule (Table 8.2). These characteristics are used in combination with fuel prices from 

the NEMS fuel supply modules and foresight on fuel prices to compare options when new capacity is 

needed. Heat rates for new fossil-fueled technologies are assumed to decline linearly through 2025.

For AEO2013, EIA commissioned an external consultant to update current cost estimates for utility-scale 

electric generating plants [83]. This report used a consistent methodology, similar to the one used to 

develop the estimates for AEO2011 and AEO2012, but accounted for more recent data and experience. 

Because the costs from the report were assumed to be consistent with plants that would be ordered in 

2012, for AEO2015 the initial costs were adjusted to account for learning from capacity built during 2012 

and 2013. Additionally, EIA reviewed capital cost assumptions for solar PV and wind plants based on an 

assessment of costs reported in trade press and data compiled in Lawrence Berkeley National Laboratory 

publications [84]. Wind costs were updated based on clear trends in the data, while EIA’s review of PV costs 

indicated that no revision was warranted, beyond the learning adjustment for recent builds. A cost 

adjustment factor, based on the producer price index for metals and metal products, allows the overnight 

costs to fall in the future if this index drops, or rise further if it increases.

The overnight costs shown in Table 8.2 represent the estimated cost of building a plant in a typical region of 

the country. Differences in plant costs due to regional distinctions are calculated by applying regional 

multipliers. Regional multipliers by technology are also based on regional cost estimates developed by the 

consultant. The regional variations account for multiple factors, such as differences in terrain, weather, 

population, and labor wages. For power plants that use combustion turbine technologies, regional 

adjustments are also made to reflect the impacts of ambient conditions (temperature, humidity and 

pressure) on output. The base overnight cost is multiplied by a project contingency factor and a 

technological optimism factor (described later in this chapter), resulting in the total construction cost for the 

first-of-a-kind unit used for the capacity choice decision.
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 105

Table 8.2. Cost and performance characteristics of new central station electricity generating technologies

Contingency Factors 

Base nth-ofOvernight Project Techno- Total Variable Fixed Heatrate6 a-kind

Lead Cost in Contin- logical Overnight O&M5 O&M in 2014 Heatrate

Online Size time 2014 gency Optimism Cost in 20144 (2013 $/ (2013 $/ (Btu/ (Btu/

Technology Year1 (MW) (years) (2013 $/kW) Factor

2 Factor3 (2013 $/kW) mWh) kW/yr.) kWh) kWh

Scrubbed Coal 

New 2018 1300 4 2,726 1.07 1.00 2,917 4.47 31.16 8,800 8,740 

Integrated 

Coal- Gasifification Comb

Cycle (IGCC) 2018 1200 4 3,483 1.07 1.00 3,727 7.22 51.37 8,700 7,450 

IGCC with 

Carbon

sequestion 2018 520 4 5,891 1.07 1.03 6,492 8.44 72.80 10,700 8,307 

Conv Gas/Oil 

Comb Cycle 2017 620 3 869 1.05 1.00 912 3.60 13.16 7,050 6,800 

Adv Gas/Oil 

Comb Cycle

(CC) 2017 400 3 942 1.08 1.00 1,017 3.27 15.36 6,430 6,333 

Adv CC with 

carbon

sequestration 2017 340 3 1,845 1.08 1.04 2,072 6.78 31.77 7,525 7,493 

Conv Comb 

Turbine8 2016 85 2 922 1.05 1.00 968 15.44 7.34 10,783 10,450 

Adv Comb 

Turbine 2016 210 2 639 1.05 1.00 671 10.37 7.04 9,750 8,550 

Fuel Cells 2017 10 3 6,042 1.05 1.10 6,978 42.97 0.00 9,500 6,960 

Adv Nuclear 2022 2234 6 4,646 1.10 1.05 5,366 2.14 93.23 10,479 10,479 

Distributed

Generation -

Base 2017 2 3 1,407 1.05 1.00 1,477 7.75 17.44 9,015 8,900 

Distributed

Generation - 

Peak 2016 1 2 1,689 1.05 1.00 1,774 7.75 17.44 10,015 9,880 

Biomass 2018 50 4 3,399 1.07 1.01 3,659 5.26 105.58 13,500 13,500 

Geothermal7,9 2018 50 4 2,331 1.05 1.00 2,448 0.00 112.85 9,516 9,516 

Muncipal 

Solid Waste 2017 50 3 7,730 1.07 1.00 8,271 8.74 392.60 14,878 18,000 

Conventional

Hydropower9 2018 500 4 2,410 1.10 1.00 2,651 5.76 15.15 9,516 9,516 
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 106

Table 8.2. Cost and performance characteristics of new central station electricity generating technologies

(cont.)

Contingency Factors 

Base nth-ofOvernight Project Techno- Total Variable Fixed Heatrate6 a-kind

Lead Cost in Contin- logical Overnight O&M5 O&M in 2014 Heatrate

Online Size time 2014 gency Optimism Cost in 20144 (2013 $/ (2013 $/ (Btu/ (Btu/

Technology Year1 (MW) (years) (2013 $/kW) Factor2 Factor3 (2013 $/kW) mWh) kW/yr.) kWh) kWh

Wind 2017 100 3 1,850 1.07 1.00 1,980 0.00 39.53 9,516 9,516 

Wind Offshore 2018 400 4 4,476 1.10 1.25 6,154 0.00 73.96 9,516 9,516 

Solar Thermal7 2017 100 3 3,787 1.07 1.00 4,052 0.00 67.23 9,516 9,516 

Photovoltaic7,10 2016 150 2 3,123 1.05 1.00 3,279 0.00 24.68 9,516 9,516 

1

Online year represents the first year that a new unit could be completed, given an order date of 2014. 

2

A contingency allowance is defined by the American Association of Cost Engineers as the “specific provision for unforeseeable elements of 

costs within a defined project scope; particularly important where previous experience has shown that unforeseeable events which will 

increase costs are likely to occur.” 3

The technological optimism factor is applied to the first four units of a new, unproven design; it reflects the demonstrated tendency to 

underestimate actual costs for a first-of-a-kind unit. 4

Overnight capital cost including contingency factors, excluding regional multipliers and learning effects. Interest charges are also excluded. 

These represent costs of new projects initiated in 2014.

5

O&M = Operations and maintenance.

6

For hydropower, wind, solar and geothermal technologies, the heat rate shown represents the average heat rate for conventional thermal 

generation as of 2013. This is used for purposes of calculating primary energy consumption displaced for these resources, and does not 

imply an estimate of their actual energy conversion efficiency. 7

Capital costs are shown before investment tax credits are applied.

8

Combustion turbine units can be built by the model prior to 2016 if necessary to meet a given region’s reserve margin. 9

Because geothermal and hydropower cost and performance characteristics are specific for each site, the table entries represent the cost of 

the least expensive plant that could be built in the Northwest Power Pool region, where most of the proposed sites are located. 10Costs and capacities are expressed in terms of net AC power available to the grid for the installed capacity.

Sources: For the AEO2015 cycle, EIA continues to use the previously developed cost estimates for utility-scale electric generating plants, 

updated by external consultants for AEO2013. This report can be found at http://www.eia.gov/forecasts/capitalcost/. The costs were

assumed to be consistent with plants that would be ordered in 2012, and learning from capacity built in 2012 and 2013 has been applied in

the initial costs above. Wind capital costs were updated for AEO2015 using recent reports from trade press and reports from Lawrence 

Berkeley National Laboratory. Site-specific costs for geothermal were provided by the National Renewable Energy Laboratory, "Updated 

U.S. Geothermal Supply Curve,” February 2010.

Technological optimism and learning

Overnight costs for each technology are calculated as a function of regional construction parameters, 

project contingency, and technological optimism and learning factors.

The technological optimism factor represents the demonstrated tendency to underestimate actual costs for 

a first-of-a-kind, unproven technology. As experience is gained (after building four units) the technological 

optimism factor is gradually reduced to 1.0.
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 107

The learning function in NEMS is determined at a component level. Each new technology is broken into its 

major components, and each component is identified as revolutionary, evolutionary or mature. Different

learning rates are assumed for each component, based on the level of experience with the design 

component (Table 8.3). Where technologies use similar components, these components learn at the same 

rate as these units are built. For example, it is assumed that the underlying turbine generator for a 

combustion turbine, combined cycle and integrated coal-gasification combined cycle unit is basically the 

same. Therefore construction of any of these technologies would contribute to learning reductions for the 

turbine component.

The learning function, OC, has the nonlinear form:

 OC(C) = a*C-b

,

where C is the cumulative capacity for the technology component.

Table 8.3. Learning parameters for new generating technology components

Period 1 Period 2 Period 3

Learning 

Rate

Learning 

Rate

Learning 

Rate Period 1 Period 2 Minimum Total

Technology Component (LR1) (LR2) (LR3) Doublings Doublings Learning by 2035

Pulverized Coal - - 1% - - 5%

Combustion Turbine - conventional - - 1% - - 5%

Combustion Turbine - advanced - 10% 1% - 5 10%

HRSG1 - - 1% - - 5%

Gasifier - 10% 1% - 5 10%

Carbon Capture/Sequestration 20% 10% 1% 3 5 20%

Balance of Plant - IGCC - - 1% - - 5%

Balance of Plant - Turbine - - 1% - - 5%

Balance of Plant - Combined Cycle - - 1% - - 5%

Fuel Cell 20% 10% 1% 3 5 20%

Advanced Nuclear 5% 3% 1% 3 5 10%

Fuel prep - Biomass - 10% 1% - 5 10%

Distributed Generation - Base - 5% 1% - 5 10%

Distributed Generation - Peak - 5% 1% - 5 10%

Geothermal - 8% 1% - 5 10%

Municipal Solid Waste - - 1% - - 5%

Hydropower - - 1% - - 5%

Wind - - 1% - - 5%

Wind Offshore 20% 10% 1% 3 5 20%

Solar Thermal 20% 10% 1% 3 5 10%

Solar PV - Module - 10% 1% - 5 10%

Balance of Plant - Solar PV - 10% 1% - 5 10% 1

HRSG = Heat Recovery Steam Generator

Note: Please see the text for a description of the methodology for learning in the Electricity Market Module.

Source: U.S. Energy Information Administration, Office of Electricity, Coal, Nuclear and Renewables Analysis.
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The progress ratio (pr) is defined by speed of learning (i.e., how much costs decline for every doubling of 

capacity). The reduction in capital cost for every doubling of cumulative capacity (LR) is an exogenous 

parameter input for each component (Table 8.3). The progress ratio and LR are related by:

 pr = 2-b = (1 - LR)

The parameter “b” is calculated from the second equality above (b =-(ln(1-LR)/ln(2))). The parameter “a” is 

computed from initial conditions, i.e.,

 a =OC(C0)/Co –b

where C0 is the initial cumulative capacity. Once the rates of learning (LR) and the cumulative capacity (C0) 

are known for each interval, the parameters (a and b) can be computed. Three learning steps were 

developed to reflect different stages of learning as a new design is introduced into the market. New designs 

with a significant amount of untested technology will see high rates of learning initially, while more 

conventional designs will not have as much learning potential. Costs of all design components are adjusted 

to reflect a minimal amount of learning, even if new capacity additions are not projected. This represents 

cost reductions due to future international development or increased research and development.

Once the learning rates by component are calculated, a weighted average learning factor is calculated for 

each technology. The weights are based on the share of the initial cost estimate that is attributable to each 

component (Table 8.4). For technologies that do not share components, this weighted average learning rate 

is calculated exogenously, and input as a single component.

These technologies may still have a mix of revolutionary components and more mature components, but it 

is not necessary to include this detail in the model unless capacity from multiple technologies would 

contribute to the component learning. In the case of the solar PV technology, it is assumed that the module 

component accounts for 50% of the cost, and that the balance of system components accounts for the 

remaining 50%. Because the amount of end-use PV capacity (existing and projected) is significant relative to 

total solar PV capacity, and because the technology of the module component is common across the enduse and electric power sectors, the calculation of the learning factor for the PV module component also 

takes into account capacity built in the residential and commercial sectors.

Table 8.5 shows the capacity credit toward component learning for the various technologies. It was assumed 

that for all combined-cycle technologies, the turbine unit contributed two-thirds of the capacity, and the 

steam unit one-third. Therefore, building one gigawatt of gas combined cycle would contribute 0.67 

gigawatts (GW) toward turbine learning, and 0.33 GW toward steam learning. Components that do not 

contribute to the capacity of the plant, such as the balance of plant category, receive 100% capacity credit 

for any capacity built with that component. For example, when calculating capacity for the “Balance of plant 

- CC” component, all combined cycle capacity would be counted 100%, both conventional and advanced.
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Table 8.4. Component cost weights for new technologies

Technology

Pulverized 

Coal

Combustion 

Turbineconventional

Combustion 

Turbine -

advanced HRSG Gasifier

Carbon 

Capture/

Sequestion

Balance 

of PlantIGCC

Balance 

of PlantTurbine

Balance 

of PlantCombined 

Cycle

Fuel 

Prep 

Biomass

Integrated

Coal-Gasification

Comb Cycle (IGCC) 0% 0% 15% 20% 41% 0% 24% 0% 0% 0%

IGCC with carbon

sequestration 0% 0% 10% 15% 30% 30% 15% 0% 0% 0%

Conv Gas/Oil 

Comb

Cycle 0% 30% 0% 40% 0% 0% 0% 0% 30% 0%

Adv Gas/Oil Comb

Cycle (CC) 0% 0% 30% 40% 0% 0% 0% 0% 30% 0%

Adv CC with 

carbon

sequestration 0% 0% 20% 25% 0% 40% 0% 0% 15% 0%

Conv Comb 

Turbine 0% 50% 0% 0% 0% 0% 0% 50% 0% 0%

Adv Comb Turbine 0% 0% 50% 0% 0% 0% 0% 50% 0% 0%

Biomass 50% 0% 0% 0% 0% 0% 0% 0% 0% 50%

Note: All unlisted technologies have a 100% weight with the corresponding component. Components are not broken out for all 

technologies unless there is overlap with other technologies.

HRSG = Heat Recovery Steam Generator.

Source: Market-Based Advanced Coal Power Systems, May 1999, DOE/FE-0400.

Table 8.5. Component capacity weights for new technologies

Combustion Combus- Carbon

Balance 

of

Turbine- tion Capture/ Balance Balance Plant- Fuel

Pulverized conven- Turbine- Seques- of Plant- of Plant- Combined Prep

Technology Coal tional advanced HRSG Gasifier tration IGCC Turbine Cycle Biomass

Integrated CoalGasification Comb

Cycle (IGCC) 0% 0% 67% 33% 100% 0% 100% 0% 0% 0%

IGCC with Carbon 

sequestration 0% 0% 67% 33% 100% 100% 100% 0% 0% 0%

Conv Gas/Oil Comb 

Cycle 0% 67% 0% 33% 0% 0% 0% 0% 100% 0%

Adv Gas/Oil Comb 

Cycle (CC) 0% 0% 67% 33% 0% 0% 0% 0% 100% 0%

Adv CC with carbon 

sequestration 0% 0% 67% 33% 0% 100% 0% 0% 100% 0%

Conv Comb Turbine 0% 100% 0% 0% 0% 0% 0% 100% 0% 0%

Adv Comb Turbine 0% 0% 100% 0% 0% 0% 0% 100% 0% 0%

Biomass 50% 0% 0% 0% 0% 0% 0% 0% 0% 100%

HRSG = Heat Recovery Steam Generator.

Source: U.S. Energy Information Administration, Office of Electricity, Coal, Nuclear and Renewables Analysis.
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Distributed generation

Distributed generation is modeled in the end-use sectors (as described in the appropriate chapters) as well 

as in the EMM. This section describes the representation of distributed generation in the EMM only. Two 

generic distributed technologies are modeled. The first technology represents peaking capacity (capacity 

that has relatively high operating costs and is operated when demand levels are at their highest). The 

second generic technology for distributed generation represents base load capacity (capacity that is 

operated on a continuous basis under a variety of demand levels). See Table 8.2 for costs and performance 

assumptions. It is assumed that these plants reduce the costs of transmission upgrades that would 

otherwise be needed.

Demand storage

The EMM includes the option to build a new demand storage technology to simulate load shifting, through 

programs such as smart meters. This is modeled as a new technology build, but with operating 

characteristics similar to pumped storage. The technology is able to decrease the load in peak slices, but 

must generate to replace that demand in other time slices. There is an input factor that identifies the 

amount of replacement generation needed, where a factor of less than 1.0 can be used to represent peak 

shaving rather than purely shifting the load to other time periods. This plant type is limited to operating only 

in the peak load slices, and it is assumed that this capacity is limited to 3.5% of peak demand on average in 

2040, with limits varying from 2.2% to 6.8% of peak across the regions.

Coal-to-gas conversion

For the AEO2015, the EMM includes the representation of conversion of existing coal plants to burn natural 

gas. In recent years, a number of companies have announced plans to retrofit their coal plants to operate as 

single cycle steam plants, to reduce emissions from the plant or to take advantage of low natural gas prices 

[85]. AEO2015 includes explicit representation of conversions of 4.1 GW by changing the plant type and fuel 

source for specific units, based on announced plans. Additionally, the EMM was revised to include the 

option to convert additional coal plants to gas-fired steam plants if economic.

The modeling structure for coal-to-gas conversions was based on EPA’s modeling for the Base Case v5.13

[86]. For this modeling, coal-to-gas conversion refers to the modification of an existing boiler to allow it to 

fire natural gas. It does not refer to the addition of a gas turbine, the replacement of a coal boiler with a new 

natural gas combined cycle plant, or to the gasification of coal for use in a combustion turbine. There are 

two components of cost for the retrofit option – boiler modification costs and the cost of extending natural 

gas lateral pipeline spurs from the boiler to a natural gas main pipeline. 

Allowing natural gas firing in a coal boiler typically involves installation of new gas burners as well as 

modifications to the boiler and possibly environmental equipment. EPA’s estimates were developed by 

engineering staff and discussions with industry engineers, and were designed to be applicable across the 

existing coal fleet. In the EMM, costs were estimated for eligible coal plants identified by EPA, which 

excluded units under 25 MW as well as units with fluidized bed combustion or stoker boilers. There is no 

capacity penalty for conversion to gas, but there is a 5% heat rate penalty to reflect reduced efficiency due 

to lower stack temperature and the corresponding higher moisture loss when gas is combusted instead of 

coal. Fixed O&M costs are assumed to be reduced by 33% for the converted plant due to reduced needs for
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operators, maintenance materials and maintenance staff. Variable O&M costs are reduced by 25% due to 

reduced waste disposal and other costs. The incremental capital cost is described by the following functions:

For pulverized-coal-fired boilers:

Cst per kW = 267 * (75 / CAP)0.35

For cyclone boilers:

Cst per kW = 374 * (75 / CAP)0.35

Where CAP is the capacity of the unit in megawatts and the calculated cost is in 2011 dollars per kW.

EIA used EPA’s assumptions regarding natural gas pipeline requirements, which were based on a detailed 

assessment for every coal boiler in the United States, to determine gas volumes needed, distance to the 

closest pipeline, and size of the lateral pipeline required to get unit-specific costs. The resulting cost per kW 

of boiler capacity varies widely, from $1/kW to $2,908/kW, with an average cost of $185/kW.

Representation of electricity demand

The annual electricity demand projections from the NEMS demand modules are converted into load 

duration curves for each of the EMM regions (based on North American Electric Reliability Corporation 

regions and subregions) using historical hourly load data. The load duration curve in the EMM is made up of 

nine time slices. First, the load data is split into three seasons: winter (December through March), summer 

(June through September), and fall/spring). Within each season the load data is sorted from high to low, 

and three load segments are created: a peak segment representing the top 1% of the load, and then two offpeak segments representing the next 49% and 50%, respectively. The seasons were defined to account for 

seasonal variation in supply availability.

Reserve margins (the percentage of capacity in excess of peak demand required to adequately maintain 

reliability during unforeseeable outages) are established for each region by its governing body–public utility 

commission, NERC region or Independent System Operators (ISOs)/Regional Transmission Operators (RTOs). 

The reserve margin values from the AEO2015 Reference case are set based on these regional Reference 

Margins reported to NERC, and range from 14% to 17% [87].

Operating reserves

In addition to the planning reserve margin requirement, system operators typically require a specific level of 

operating reserves–generators available within a short period of time to meet demand in case a generator 

goes down or there is another disruption to supply. These reserves can be provided through plants that are 

already operating but not at full capacity (spinning reserves) as well as through capacity not currently 

operating but that can be brought online quickly (non-spinning reserves). This is particularly important as 

more intermittent generators are added to the grid, because technologies like wind and solar have uncertain 

availability that can be difficult to predict. Since AEO2014, the capacity and dispatch submodules of the 

EMM have been updated to include explicit constraints requiring spinning reserves in each load slice. The 

amount of spinning reserves required is computed as a percentage of the load height of the slice plus a 

percentage of the distance between the load of the slice and the seasonal peak. An additional requirement 

is calculated that is a percentage of the intermittent capacity available in that time period to reflect the 
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greater uncertainty associated with the availability of intermittent resources. All technologies except for 

storage, intermittents and distributed generation can be used to meet spinning reserves. Different operating 

modes are developed for each technology type to allow the model to choose between operating a plant to 

maximize generation versus contributing to spinning reserves, or a combination of both. Minimum levels of 

generation are required if a plant is contributing to spinning reserves, and vary by plant type, with plant 

types typically associated with baseload operation having higher minimums than those that can operate 

more flexibly to meet intermediate or peak demand.

Fossil fuel-fired and nuclear steam plant retirement

Fossil fired steam plant retirements and nuclear retirements are calculated endogenously within the model. 

Generating units are assumed to retire when it is no longer economical to continue running them. Each year, 

the model determines whether the market price of electricity is sufficient to support the continued 

operation of existing plant generators. A generating unit is assumed to retire if the expected revenues from 

the generator are not sufficient to cover the annual going-forward costs and if the overall cost of producing 

electricity can be lowered by building new replacement capacity. The going-forward costs include fuel, 

operations and maintenance costs and annual capital additions, which are unit-specific and based on 

historical data. The average capital additions for existing plants are $8 per kilowatt (kW) for oil and gas 

steam plants, $17 per kW for coal plants, and $22 per kW for nuclear plants (in 2013 dollars). These costs 

are added to the estimated costs at existing plants regardless of their age. Beyond 30 years of age an 

additional $7 per kW capital charge for fossil plants and $33 per kW charge for nuclear plants is included in 

the retirement decision to reflect further investment to address the impacts of aging. Age-related cost 

increases are attributed to capital expenditures for major repairs or retrofits, decreases in plant 

performance, and/or increases in maintenance costs to mitigate the effects of aging.

EIA assumes all retirements reported as planned during the next ten years on the Form EIA-860 will occur. 

Additionally, the AEO2015 nuclear projection assumes a decrease of 2.0 GW by 2020 to reflect existing 

nuclear units that appear at risk of early closure due to a combination of high operating costs and low 

electricity prices.

Biomass co-firing

Coal-fired power plants are assumed to co-fire with biomass fuel if it is economical. Co-firing requires a 

capital investment for boiler modifications and fuel handling. This expenditure is assumed to be $285 per 

kW of biomass capacity. A coal-fired unit modified to allow co-firing can generate up to 15% of the total 

output using biomass fuel, assuming sufficient residue supplies are available.

Nuclear uprates

The AEO2015 nuclear power projection assumes capacity increases at existing units. Nuclear plant operators 

can increase the rated capacity at plants through power uprates, which are license amendments that must 

be approved by the U.S. Nuclear Regulatory Commission (NRC). Uprates can vary from small (less than 2%) 

increases in capacity, which require very little capital investment or plant modification, to extended uprates 

of 15-20%, requiring significant modifications. Recently, several companies have canceled previously 

planned extended uprates due to lower demand projections and low electricity prices [88]. AEO2015 

assumes that only those uprates reported to EIA as planned modifications on the Form EIA-860 will take 

place in the Reference case, representing 0.2 GW of additional capacity. 
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Interregional electricity trade

Both firm and economy electricity transactions among utilities in different regions are represented within 

the EMM. In general, firm power transactions involve the trading of capacity and energy to help another 

region satisfy its reserve margin requirement, while economy transactions involve energy transactions 

motivated by the marginal generation costs of different regions. The flow of power from region to region is 

constrained by the existing and planned capacity limits as reported in the NERC and Western Electricity 

Coordinating Council Summer and Winter Assessment of Reliability of Bulk Electricity Supply in North 

America, as well as information obtained from the Open Access Same-Time Information System (OASIS). 

Known firm power contracts are compiled from NERC’s Electricity Supply and Demand Database as well as 

information provided in the 2013 Summer and Winter Assessments and individual ISO reports. They are 

locked in for the term of the contract. Contracts that are scheduled to expire by 2018 are assumed not to be 

renewed. Because there is no information available about expiration dates for contracts that go beyond 

2018, they are assumed to be phased out linearly over 10 years. The EMM includes an option to add 

interregional transmission capacity. In some cases it may be more economical to build generating capacity in 

a neighboring region, but additional costs to expand the transmission grid will be incurred as well. Explicitly 

expanding the interregional transmission capacity may also make the transmission line available for 

additional economy trade.

Economy transactions are determined in the dispatching submodule by comparing the marginal generating 

costs of adjacent regions in each time slice. If one region has less-expensive generating resources available 

in a given time period (adjusting for transmission losses and transmission capacity limits) than another 

region, the regions are assumed to exchange power.

International electricity trade

Two components of international firm power trade are represented in the EMM—existing and planned 

transactions, and unplanned transactions. Data on existing and planned transactions are compiled from 

NERC’s Electricity Supply and Demand Database and Canada’s National Energy Board. Unplanned firm power 

trade is represented by competing Canadian supply with U.S. domestic supply options. Canadian supply is 

represented via supply curves using cost data from the Department of Energy report, “Northern Lights: The 

Economic and Practical Potential of Imported Power from Canada” (DOE/PE-0079). International economy 

trade is determined endogenously based on surplus energy expected to be available from Canada by region 

in each time slice. Canadian surplus energy was determined using Canadian electricity supply and demand 

projections from the MAPLE-C model developed for Natural Resources Canada.
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Electricity pricing

Electricity pricing is projected for 22 electricity market regions in AEO2015 for fully competitive, partially 

competitive and fully regulated supply regions. The price of electricity to the consumer comprises the price 

of generation, transmission, and distribution, including applicable taxes. 

Transmission and distribution are considered to remain regulated in the AEO; that is, the price of 

transmission and distribution is based on the average cost to build, operate and maintain these systems 

using a cost of service regulation model. The price of electricity in the regulated regions consists of the 

average cost of generation, transmission, and distribution for each customer class. 

In the competitive regions, the energy component of price is based on marginal cost, which is defined as the 

cost of the last (or most expensive) unit dispatched. The competitive generation price includes the marginal 

cost (fuel and variable operations and maintenance costs), taxes, and a capacity payment. The capacity 

payment is calculated as a combination of levelized costs for combustion turbines and the marginal value of 

capacity calculated within the EMM. The capacity payment is calculated for all competitive regions and 

should be viewed as a proxy for additional capital recovery that must be procured from customers rather 

than the representation of a specific market. The capacity payment also includes the costs associated with 

meeting the spinning reserves requirement discussed earlier. The total cost for both reserve margin and 

spinning reserve requirements in a given region is calculated within the EMM, and allocated to the sectors 

based on their contribution to overall peak demand. 

The price of electricity in the regions with a competitive generation market consists of the competitive cost 

of generation summed with the average costs of transmission and distribution. The price for mixed regions 

reflects a load-weighted average of the competitive price and the regulated price, based on the percent of 

electricity load in the region subject to deregulation. In competitively supplied regions, a transition period is 

assumed to occur (usually over a 10-year period) from the effective date of restructuring, with a gradual 

shift to marginal cost pricing.

The Reference case assumes a transition to full competitive pricing in the three New York regions and in the 

ReliabilityFirst Corporation/East region, and a 97% transition to competitive pricing in New England 

(Vermont being the only fully-regulated state in that region). Eight regions fully regulate their electricity 

supply, including the Florida Reliability Coordinating Council, four of the SERC Reliability Corporation 

subregions–Delta (SRDA), Southeastern (SRSE), Central (SRCE) and Virginia-Carolina (SRVC), the Southwest 

Power Pool Regional Entities (SPNO and SPSO), and the Western Electricity Coordinating Council/Rockies 

(RMPA). The Texas Reliability Entity, which in the past was considered fully competitive by 2010, is now only 

88% competitive, since many cooperatives have declined to become competitive or allow competitive 

energy to be sold to their customers. California returned to almost fully regulated pricing in 2002, after 

beginning a transition to competition in 1998, with only 10% competitive supply sold currently in the 

Western Electricity Coordinating Council (WECC)/California region. All other regions reflect a mix of both 

competitive and regulated prices.

There have been ongoing changes to pricing structures for ratepayers in competitive states since the 

inception of retail competition. The AEO has incorporated these changes as they have been incorporated 

into utility tariffs. These have included transition period rate reductions and freezes instituted by various
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states, and surcharges in California relating to the 2000-2001 energy crisis in the state. Since price freezes 

have ended, many costs related to the transition to competition are now explicitly added to the distribution 

portion and sometimes the transmission portion of the customer bill, regardless of whether or not the 

customer bought generation service from a competitive or regulated supplier. There have also been 

unexpected costs relating to unforeseen events that have been included in the calculation of electricity 

prices. For instance, as a result of volatile fuel markets, state regulators have sometimes had a hard time 

enticing retail suppliers to offer competitive supply to residential and smaller commercial and industrial 

customers. Subsequent state legislation has led to generation service supplied by regulator or utility-run 

auction or competitive bid for the market energy price plus an administration fee.

For AEO2015, typical charges that all customers must pay on the distribution portion of their bill (depending 

on where they reside) include transition charges (including persistent stranded costs), public benefits 

charges (usually for efficiency and renewable energy programs), administrative costs of energy 

procurement, and nuclear decommissioning costs. Costs added to the transmission portion of the bill 

include the Federally Mandated Congestion Charges (FMCC), a bill pass-through associated with the Federal 

Energy Regulatory Commission passage of Standard Market Design (SMD) to enhance reliability of the 

transmission grid and control congestion. Additional costs not included in historical data sets have been 

added in adjustment factors to the transmission and distribution capital, operations and maintenance costs, 

which impact the cost of both competitive and regulated electricity supply. Since most of these costs, such 

as transition costs, are temporary in nature, they are gradually phased out throughout the projection.

Fuel price expectations

Capacity planning decisions in the EMM are based on a life cycle cost analysis over a 30-year period. This 

requires foresight assumptions for fuel prices. Expected prices for coal, natural gas and oil are derived using 

rational expectations, or ‘perfect foresight.’ In this approach, expectations for future years are defined by 

the realized solution values for these years in a prior run. The expectations for the world oil price and 

natural gas wellhead price are set using the resulting prices from a prior run. The markups to the delivered 

fuel prices are calculated based on the markups from the previous year within a NEMS run. Coal prices are 

determined using the same coal supply curves developed in the Coal Market Module. The supply curves 

produce prices at different levels of coal production, as a function of labor productivity, and costs and 

utilization of mines. Expectations for each supply curve are developed in the EMM based on the actual 

demand changes from the prior run throughout the projection horizon, resulting in updated mining 

utilization and different supply curves.

The perfect foresight approach generates an internally consistent scenario for which the formation of 

expectations is consistent with the projections realized in the model. The NEMS model involves iterative 

cycling of runs until the expected values and realized values for variables converge between cycles.

Nuclear fuel prices

Nuclear fuel prices are calculated through an offline analysis which determines the delivered price to 

generators in mills per kilowatthour. To produce reactor-grade uranium, the uranium (U308) must first be 

mined, and then sent through a conversion process to prepare for enrichment. The enrichment process 

takes the fuel to a given purity of U235, typically 3-5% for commercial reactors in the United States. Finally, 

the fabrication process prepares the enriched uranium for use in a specific type of reactor core. The price of
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each of the processes is determined, and the prices are summed to get the final price of the delivered fuel. 

The one mill per kilowatthour charge that is assessed on nuclear generation to go to DOE’s Nuclear Waste 

Fund is also included in the final nuclear price. The analysis uses forecasts from Energy Resources 

International for the underlying uranium prices.

Legislation and regulations

Clean Air Act Amendments of 1990 (CAAA1990) and Clean Air Interstate Rule (CAIR)

The Clean Air Interstate Rule (CAIR) was promulgated in 2005 to regulate SO2 and nitrogen oxide NOx

emissions, but has been challenged in court several times. The Cross-State Air Pollution Rule (CSAPR) was 

released by EPA in July 2011 and was intended to replace CAIR, but it was vacated by the U.S. Court of 

Appeals for the District of Columbia Circuit, and CAIR was reinstated. However, in late 2014, the stay was 

lifted and CSAPR is now being implemented on a new schedule. However, for AEO2015 the EMM still 

assumes implementation of CAIR. Future AEOs will incorporate changes necessary to represent CSAPR.

CAIR covers all fossil-fueled power plants greater than 25 megawatts in 27 states and the District of 

Columbia. There are annual emissions caps for SO2 and NOx and different states fall under each cap, as 

shown in the map in Figure 7, although all are in the eastern half of the United States. The caps for SO2 and 

NOx were set to allow states to achieve their National Ambient Air Quality Standards (NAAQS) for 

particulate matter (impacted by SO2 levels) and ground level ozone (impacted by NOx). Allowances can be 

traded among all participants in the CAAA1990 Title IV program, not just those in CAIR states; however 

allowances are traded at a discount in non-CAIR states. AEO2015 represents emissions trading in both the 

CAIR and non-CAIR regions, as specified by the regulation, and includes banking of allowances consistent 

with CAIR’s provisions.

As specified in CAAA1990, EPA developed a two-phase nitrogen oxide (NOx) program, with the first set of 

standards for existing coal plants applied in 1996 while the second set was implemented in 2000. Dry 

bottom wall-fired and tangential-fired boilers, the most common boiler types, are referred to as Group 1 

Boilers, and were required to make significant reductions beginning in 1996 and further reductions in 2000. 

Relative to their uncontrolled emission rates, which range roughly between 0.6 and 1.0 pounds per million 

Btu, they are required to make reductions between 25% and 50% to meet the Phase I limits and further 

reductions to meet the Phase II limits. EPA did not impose limits on existing oil and gas plants, but some 

states have instituted additional NOx regulations. All new fossil units are required to meet current 

standards. In pounds per million Btu, these limits are 0.11 for conventional coal, 0.02 for advanced coal, 0.02 

for combined cycle, and 0.08 for combustion turbines. These NOx limits are incorporated in EMM.
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Figure 7. States covered by the Clean Air Interstate Rule

Table 8.6 shows the average capital costs for environmental control equipment utilized by NEMS for existing 

coal plants as retrofit options in order to remove sulfur dioxide (SO2), nitrogen oxides (NOX), mercury 

and/or hydrogen chloride (HCl). In the EMM, plant-specific costs are calculated based on the size of the unit 

and other operating characteristics. The table reflects the capacity-weighted averages of all plants falling 

into each size category. FGD units are assumed to remove 95% of the SO2, while SCR units are assumed to 

remove 90% of the NOX. The EMM also includes an option to install a dry sorbent injection (DSI) system, 

which is assumed to remove 70% of the SO2. However, the DSI option is only available under the mercury 

and air toxics rule discussed in the next section, as its primary benefit is for reducing hydrogen chloride 

(HCl).

Mercury regulation

The Mercury and Air Toxics Standards (MATS) were finalized in December 2011 to fulfill EPA’s requirement 

to regulate mercury emissions from power plants. MATS also regulate other hazardous air pollutants (HAPS) 

such as hydrogen chloride (HCl) and fine particulate matter (PM2.5). MATS applies to coal- and oil-fired 

power plants with a nameplate capacity greater than 25 megawatts. The standards are scheduled to take 

effect in 2015, but allow for a one-year waiver to comply, and require that all qualifying units achieve the 

maximum achievable control technology (MACT) for each of the three covered pollutants. For AEO2015, EIA 

assumes that all coal-fired generating units with a capacity greater than 25 megawatts will comply with the 
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rule beginning in 2016, due to the large number of plants requesting the one-year extension. All power 

plants are required to reduce their mercury emissions to 90% below their uncontrolled emissions levels.

Because the EMM does not explicitly model HCl or PM2.5, specific control technologies are assumed to be 

used to achieve compliance. In order to meet the HCl requirement, units must have either flue gas 

desulfurization (FGD) scrubbers or dry sorbent injection (DSI) systems in order to continue operating. A full

fabric filter (FF) is also required to meet the PM2.5 limits and to improve the effectiveness of the DSI 

technology. When plants alter their configuration by adding equipment such as an SCR to remove NOx or an 

SO2 scrubber, removal of mercury is often a resulting cobenefit. The EMM considers all combinations of 

controls and may choose to add NOx or SO2 controls purely to lower mercury if it is economic to do so. 

Plants can also add activated carbon injection systems specifically designed to remove mercury. Activated 

carbon can be injected in front of existing particulate control devices or a supplemental fabric filter can be 

added with activated carbon injection capability.

The equipment to inject activated carbon in front of an existing particulate control device is assumed to cost 

approximately $6 (2013 dollars) per kilowatt of capacity [89]. The costs of a supplemental fabric filter with 

activated carbon injection (often referred as a COPAC unit) are calculated by unit, with average costs shown 

in Table 8.6. The amount of activated carbon required to meet a given percentage removal target is given by 

the following equations [90].

For a unit with a cold side electrostatic precipitator (CSE), using subbituminous coal, and simple activated 

carbon injection:

• Hg Removal (%) = 65 - (65.286 / (ACI + 1.026))

For a unit with a CSE, using bituminous coal, and simple activated carbon injection:

• Hg Removal (%) = 100 - (469.379 / (ACI + 7.169))

For a unit with a CSE, and a supplemental fabric filter with activated carbon injection:

• Hg Removal (%) = 100 - (28.049 / (ACI + 0.428))

For a unit with a hot side electrostatic precipitator (HSE) or other particulate control, and a supplemental 

fabric filter with activated carbon injection:

• Hg Removal (%) = 100 - (43.068 / (ACI + 0.421))

ACI = activated carbon injection rate in pounds per million actual cubic feet of flue gas
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Table 8.6. Coal plant retrofit costs

2013 dollars per kW

FF Capital

Coal Plant Size (MW) FGD Capital Costs SCR Capital Costs DSI Capital Costs Costs

<100 887 405 184 256

100 - 299 632 266 93 191

300 - 499 500 215 49 161

500 - 699 444 200 37 147

>=700 398 183 31 135

Source: Documentation for EPA Base Case v4.10 using the Integrated Planning Model, August 2010, EPA Contract EPW-08-018, Appendices to Chapter 5.

Power plant mercury emissions assumptions

The EMM represents 35 coal plant configurations and assigns a mercury emissions modification factor (EMF) 

to each configuration. Each configuration represents different combinations of boiler types, particulate 

control devices, sulfur dioxide (SO2) control devices, nitrogen oxide (NOx) control devices, and mercury 

control devices. An EMF represents the amount of mercury that was in the fuel that remains after passing 

through all the plant’s systems. For example, an EMF of 0.60 means that 40% of the mercury that was in the 

fuel is removed by various parts of the plant. Table 8.7 provides the assumed EMFs for existing coal plant 

configurations without mercury-specific controls.

Table 8.7. Mercury emission modification factors

Configuration EIA EMFs EPA EMFs

SO2 Control 

Particulate 

Control

NOx

Control Bit Coal Sub Coal Lignite Coal Bit Coal Sub Coal Lignite Coal

None BH -- 0.11 0.27 0.27 0.11 0.26 1.00

Wet BH None 0.05 0.27 0.27 0.03 0.27 1.00

Wet BH SCR 0.10 0.27 0.27 0.10 0.15 0.56

Dry BH -- 0.05 0.75 0.75 0.50 0.75 1.00

None CSE -- 0.64 0.97 0.97 0.64 0.97 1.00

Wet CSE None 0.34 0.73 0.73 0.34 0.84 0.56

Wet CSE SCR 0.10 0.73 0.73 0.10 0.34 0.56

Dry CSE -- 0.64 0.65 0.65 0.64 0.65 1.00

None HSE/Oth -- 0.90 0.94 0.94 0.90 0.94 1.00

Wet HSE/Oth None 0.58 0.80 0.80 0.58 0.80 1.00

Wet HSE/Oth SCR 0.42 0.76 0.76 0.10 0.75 1.00

Dry HSE/Oth -- 0.60 0.85 0.85 0.60 0.85 1.00

Notes: SO2 Controls - Wet = Wet Scrubber and Dry = Dry Scrubber, Particulate Controls, BH - fabric filter/baghouse. CSE = cold 

side electrostatic precipitator, HSE = hot side electrostatic precipitator, NOx Controls, SCR = selective catalytic reduction.

— = not applicable, Bit = bituminous coal, Sub = subbituminous coal. The NOx control system is not assumed to enhance 

mercury removal unless a wet scrubber is present, so it is left blank in such configurations. Sources: EPA, EMFs.

www.epa.gov/clearskies/technical.html . EIA EMFs not from EPA: Lignite EMFs, Mercury Control Technologies for Coal-Fired

Power Plants, presented by the Office of Fossil Energy on July 8, 2003. Bituminous coal mercury removal for a

Wet/HSE/Oth/SCR configured plant, Table EMF1, Analysis of Mercury Control Cost and Performance, Office of Fossil Energy & 

National Energy Technology, U.S. Department of Energy, January 2003, Washington, DC.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 120

Planned SO2 Scrubber and NOx control equipment additions

EIA assumes that all planned retrofits, as reported on the Form EIA-860, will occur as currently scheduled. 

For AEO2015, this includes 13.1 GW of planned SO2 scrubbers (Table 8.8) and 7.7 GW of planned selective 

catalytic reduction (SCR).

Carbon capture and sequestration retrofits

Although a federal greenhouse gas program is not assumed in the AEO2015 Reference case, the EMM 

includes the option of retrofitting existing coal plants for carbon capture and sequestration (CCS). This 

option is important when considering alternate scenarios that do constrain carbon emissions. The modeling 

structure for CCS retrofits within the EMM was developed by the National Energy Technology

Laboratory[91] and uses a generic model of retrofit costs as a function of basic plant characteristics (such as 

heat rate). The costs have been adjusted to be consistent with costs of new CCS technologies. The CCS 

retrofits are assumed to remove 90% of the carbon input. The addition of the CCS equipment results in a 

capacity derate of around 30% and reduced efficiency of 43% at the existing coal plant. The costs depend on 

the size and efficiency of the plant, with the capital costs averaging $1,628 per kilowatt, and ranging from 

$1,192 to $2,254 per kilowatt. It was assumed that only plants greater than 500 megawatts and with heat 

rates below 12,000 Btu per kilowatthour would be considered for CCS retrofits.

Table 8.8. Planned SO2 scrubber additions by EMM region

Regions gigawatts

Texas Reliability Entity 0.0

Florida Reliability Coordinating Council 0.0

Midwest Reliability Council - East 1.5

Midwest Reliability Council - West 1.5

Northeast Power Coordinating Council/New England 0.0

Northeast Power Coordinating Council/NYC-Westchester 0.0

Northeast Power Coordinating Council/Long Island 0.0

Northeast Power Coordinating Council/Upstate 0.0

ReliabilityFirst Corporation/East 0.0

ReliabilityFirst Corporation/Michigan 2.8

ReliabilityFirst Corporation/West 2.5

SERC Reliability Corporation/Delta 0.0

SERC Reliability Corporation/Gateway 1.2

SERC Reliability Corporation/Southeastern 2.7

SERC Reliability Corporation/Central 0.0

SERC Reliability Corporation/Virginia-Carolina 0.0

Southwest Power Pool/North 0.9

Southwest Power Pool/South 0.0

Western Electricity Coordinating Council/Southwest 0.0

Western Electricity Coordinating Council/California 0.0

Western Electricity Coordinating Council/Northwest Power Pool Area 0.0

Western Electricity Coordinating Council/Rockies 0.0

Total 13.1

Source: U.S. Energy Information Administration, Form EIA-860, “Annual Electric Generator Report.”
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 121

Heat rate improvement retrofits

The EMM was updated for AEO2015 to include the capability to evaluate the potential for making heat rate 

improvements at existing coal-fired generators. A generator with a lower heat rate can generate the same 

quantity of electricity while consuming less fuel, and therefore reducing corresponding emissions of SO2, 

NOx, mercury and CO2. Improving heat rates at power plants can lower fuel costs and help achieve 

compliance with environmental regulations. Heat rate improvement is a planning activity as it considers the 

tradeoff between the investment expenditures and the savings in fuel and/or environmental compliance 

costs. The amount of potential increase in efficiency can vary depending on the type of equipment installed 

at a unit, as well as the beginning configuration of the plant. The EMM represents 32 configurations of 

existing coal-fired plants based on different combinations of particulate, sulfur dioxide (SO2), nitrogen oxide 

(NOx), mercury, and carbon emission controls (Table 8.9). These categories form the basis for evaluating the 

potential for heat rate improvements.

EIA entered into a contract with Leidos, Inc. to develop a methodology to evaluate the potential for heat 

rate improvement at existing coal-fired generating plants [92]. Leidos performed a statistical analysis of the 

heat rate characteristics of coal-fired generating units modeled by EIA in the EMM. Specifically, Leidos 

developed a predictive model for coal-fired electric generating unit heat rates as a function of various unit 

characteristics. Leidos employed statistical modeling techniques to create the predictive models.

For the EMM plant types, the coal-fired generating units were categorized according to quartiles, based on 

observed versus predicted heat rates. Units in the first quartile (Q1), which perform better than predicted, 

were generally associated with the least potential for heat rate improvement. Units in the fourth quartile 

(Q4), representing the least-efficient units relative to predicted values, were generally associated with the 

highest potential for heat rate improvement. Leidos developed a matrix of heat rate improvement options 

and associated costs, based on a literature review and the application of engineering judgment.

Little or no coal-fired capacity exists for the EMM plant types with mercury and carbon control 

configurations; therefore, estimates were not developed for those plant types. These plant types were 

ultimately assigned the characteristics of the plants with the same combinations of particulate, SO2, and 

NOx controls. Plant types with relatively few observations were combined with other plant types having 

similar improvement profiles. As a result, nine unique plant type combinations were developed for the 

purposes of the quartile analysis and for each of these combinations Leidos created a minimum and a 

maximum potential for heat rate improvement along with the associated costs to achieve those improved 

efficiencies. 

Leidos used the minimum and maximum characteristics as a basis for developing estimates of mid-range 

cost and heat rate improvement potential. The mid-range estimates were used as the default values for 

AEO2015 (Table 8.10).
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 122

Table 8.9. Existing pulverized coal plant types in the NEMS Electricity Market Module

Plant 

Type

Particulate SO2 NOx Mercury Carbon

Controls Controls Controls Controls Controls

B1 BH None Any None None

B2 BH None Any None CCS

B3 BH Wet None None None

B4 BH Wet None None CCS

B5 BH Wet SCR None None

B6 BH Wet SCR None CCS

B7 BH Dry Any None None

B8 BH Dry Any None CCS

C1 CSE None Any None None

C2 CSE None Any FF None

C3 CSE None Any FF CCS

C4 CSE Wet None None None

C5 CSE Wet None FF None

C6 CSE Wet None FF CCS

C7 CSE Wet SCR None None

C8 CSE Wet SCR FF None

C9 CSE Wet SCR FF CCS

CX CSE Dry Any None None

CY CSE Dry Any FF None

CZ CSE Dry SCR FF CCS

H1 HSE/Oth None Any None None

H2 HSE/Oth None Any FF None

H3 HSE/Oth None Any FF CCS

H4 HSE/Oth Wet None None None

H5 HSE/Oth Wet None FF None

H6 HSE/Oth Wet None FF CCS

H7 HSE/Oth Wet SCR None None

H8 HSE/Oth Wet SCR FF None

H9 HSE/Oth Wet SCR FF CCS

HA HSE/Oth Dry Any None None

HB HSE/Oth Dry Any FF None

HC HSE/Oth Dry Any FF CCS

Notes: Particulate Controls, BH – baghouse, CSE = cold side electrostatic 

precipitator, HSE/Oth = hot side electrostatic precipitator/other/none;

SO2 Controls - wet = wet scrubber, Dry = dry scrubber;

NOx Controls, SCR = selective catalytic reduction;

Mercury Controls - FF = fabric filter;

Carbon Controls - CCS = carbon capture and storage
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 123

Table 8.10. Heat rate improvement (HRI) potential and cost (capital, fixed O&M) by plant type and 

quartile as used for input to NEMS

Plant type and 

quartile 

combination

Count of Total 

Units

Percentage HRI 

Potential

Capital Cost 

(million 2014 $/MW)

Average Fixed O&M Cost 

(2014 $/MW-yr)

B1-Q1 32 (s) 0.01 200

B1-Q2 15 0.8% 0.10 2,000

B1-Q3 18 4% 0.20 4,000

B1-Q4 20 6% 0.90 20,000

B3-Q1 13 (s) 0.01 300

B3-Q2 24 0.7% 0.05 1,000

B3-Q3 16 6% 0.20 3,000

B3-Q4 15 9% 0.60 10,000

B5C7-Q1 16 (s) (s) 80

B5C7-Q2 42 0.8% 0.03 700

B5C7H7-Q3 84 7% 0.10 2,000

B5C7H7-Q4 59 10% 0.20 4,000

B7-Q1 27 (s) (s) 70

B7-Q2 25 0.8% 0.04 800

B7-Q3Q4 30 7% 0.30 5,000

C1H1-Q1 148 (s) 0.01 200

C1H1-Q2 117 0.8% 0.10 2,000

C1H1-Q3 72 4% 0.40 8,000

C1H1-Q4 110 7% 1.00 30,000

C4-Q1 15 (s) (s) 80

C4-Q2 27 0.8% 0.04 900

C4-Q3 32 6% 0.20 2,000

C4-Q4 39 10% 0.30 5,000

CX-Q1Q2Q3Q4 15 7% 0.20 4,000

H4-Q1Q2Q3 13 3% 0.20 3,000

IG-Q1 3 (s) (s) 60

TOTAL SET 1027 4% 0.30 6,000

(s) = less than 0.05% for HRI potential or less than 0.005 million $/MW for capital cost.

Source: U.S. Energy Information Administration/Leidos Corporation.
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 124

State air emissions regulation

AEO2015 continues to model the Northeast Regional Greenhouse Gas Initiative (RGGI), which applies to 

fossil-fuel powered plants over 25 megawatts in the northeastern United States. The state of New Jersey 

withdrew from the program at the end of 2011, leaving nine states in the accord. The rule caps CO2 

emissions from covered electricity generating facilities and requires that they account for each ton of CO2

emitted with an allowance purchased at auction. Because the baseline and projected emissions were 

calculated before the economic recession that began in 2008, the actual emissions in the first years of the 

program have been less than the cap, leading to excess allowances and allowance prices at the floor price. 

As a result, in February 2013 program officials announced a tightening of the cap starting in 2014. Beginning 

with AEO2014, the EMM applies these revised targets, which reflect a cap that is 45% of the original target 

for 2014.

The California Assembly Bill 32 (AB 32), the Global Warming Solutions Act of 2006, authorized the California 

Air Resources Board (CARB) to set California’s GHG reduction goals for 2020 and establish a comprehensive, 

multi-year program to reduce GHG emissions in California. As one of the major initiatives for AB 32, CARB 

designed a cap-and-trade program that started on January 1, 2012, with the enforceable compliance 

obligations beginning in 2013 for the electric power sector and industrial facilities. Fuel providers must 

comply starting in 2015. The AB 32 cap-and-trade provisions are incorporated in NEMS through an emission 

constraint in the EMM that also accounts for the emissions determined by the other sectors. Within the 

power sector, emissions from plants owned by California utilities but located out of state as well as 

emissions from electricity imports into California count toward the emission cap, and estimates of these 

emissions are included in the EMM constraint. An allowance price is calculated and added to fuel prices for 

the affected sectors. Limited banking and borrowing of allowances as well as an allowance reserve and 

offsets have been modeled, as specified in the Bill, providing some compliance flexibility and cost 

containment.

Energy Policy Acts of 1992 (EPACT1992) and 2005 (EPACT2005)

The provisions of EPACT1992 include revised licensing procedures for nuclear plants and the creation of 

exempt wholesale generators (EWGs). EPACT1992 also implemented a permanent 10% investment tax 

credit for geothermal and solar facilities, and introduced a production tax credit for eligible renewable 

technologies (subsequently extended and expanded). EPACT2005 provides a 20% investment tax credit for 

Integrated Coal-Gasification Combined Cycle capacity and a 15% investment tax credit for other advanced 

coal technologies. These credits are limited to 3 GW in both cases. These credits have been fully allocated 

and are not assumed to be available for new, unplanned capacity built within the EMM. EPACT2005 also 

contains a production tax credit (PTC) of 1.8 cents (nominal) per kWh for new nuclear capacity beginning 

operation by 2020. This PTC is specified for the first 8 years of operation, is limited to $125 million annually, 

and is limited to 6 GW of new capacity. However, this credit may be shared to additional units if more than 6 

GW are under construction by January 1, 2014. EPACT2005 extended the PTC for qualifying renewable 

facilities by 2 years, or through December 31, 2007. It also repealed the Public Utility Holding Company Act 

(PUHCA).
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 125

Energy Improvement and Extension Act 2008 (EIEA2008)

EIEA2008 extended the investment tax credit of 30% through 2016 for solar and fuel cell facilities. After 

2016, the tax credit for solar facilities reverts back to the 10% level set by EPACT1992.

American Recovery and Reinvestment Act (ARRA)

Updated tax credits for renewables

ARRA allows companies to choose an investment tax credit (ITC) of 30% in lieu of the PTC. For some 

technologies, such as wind, the full PTC would appear to be more valuable than the 30% ITC; however, the 

difference can be small. Qualitative factors, such as the lack of partners with sufficient tax liability, may 

cause companies to favor the ITC grant option. AEO2015 generally assumes that renewable electricity 

projects will claim the more favorable tax credit or grant option available to them.

Loan guarantees for renewables

ARRA provided $6 billion to pay the cost of guarantees for loans authorized by the Energy Policy Act of 2005. 

While most renewable projects which started construction prior to September 30, 2011 are potentially 

eligible for these loan guarantees, the application and approval of guarantees for specific projects is a highly 

discretionary process, and has thus far been limited. While AEO2015 includes projects that have received 

loan guarantees under this authority, it does not assume automatic award of the loans to potentially eligible 

technologies.

Support for CCS

ARRA provided $3.4 billion for additional research and development on fossil energy technologies. A 

portion of this funding is expected to be used to fund projects under the Clean Coal Power Initiative 

program, focusing on projects that capture and sequester greenhouse gases. To reflect the impact of this 

provision, AEO2014 Reference case assumes that an additional 1 GW of coal capacity with CCS will be 

stimulated by 2018.

Smart grid expenditures

ARRA provides $4.5 billion for smart grid demonstration projects. While somewhat difficult to define, smart 

grid technologies generally include a wide array of measurement, communications, and control equipment 

employed throughout the transmission and distribution system that will enable real-time monitoring of the 

production, flow, and use of power from the generator to the consumer. Among other things, these smart 

grid technologies are expected to enable more-efficient use of the transmission and distribution grid, lower 

line losses, facilitate greater use of renewables, and provide information to utilities and their customers that 

will lead to greater investment in energy efficiency and reduced peak load demands. The funds provided will 

not fund a widespread implementation of smart grid technologies, but could stimulate more rapid 

investment than would otherwise occur.

Several changes were made throughout NEMS to represent the impacts of the smart grid funding provided 

in ARRA. In the electricity module, it was assumed that line losses would fall slightly, peak loads would fall as 

customers shifted their usage patterns, and customers would be more responsive to pricing signals. 

Historically, line losses, expressed as the percentage of electricity lost, have been falling for many years as 

utilities make investments to replace aging or failing equipment.
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 126

Smart grid technologies also have the potential to reduce peak demand through the increased deployment 

of demand response programs. In AEO2014, it is assumed that the Federal expenditures on smart grid 

technologies will stimulate efforts that reduce peak demand from what they otherwise would be, with the 

amount of total peak load reduction growing from 2.2% initially to 3.5% by 2040, although the shifts vary by 

region. Load is shifted to offpeak hours, so net energy consumed remains largely constant.

Smart grid technologies also have the potential to reduce peak demand through the increased deployment 

of demand response programs. In AEO2014, it is assumed that the Federal expenditures on smart grid 

technologies will stimulate efforts that reduce peak demand from what they otherwise would be, with the 

amount of total peak load reduction growing from 2.2% initially to 3.5% by 2040, although the shifts vary by 

region. Load is shifted to offpeak hours, so net energy consumed remains largely constant.

American Taxpayer Relief Act of 2012 (ATRA)

Passed in January 2013, ATRA redefined the criteria for all qualifying projects to be ‘under construction’ 

rather than placed in service by the policy expiration date, effectively extending the credit for the length of 

the typical construction period.

Tax Increase Prevention Act of 2014 (TIPA)

Passed in December 2014, TIPA retroactively extended the expiration date of the production tax credit for 

all eligible renewable fuels by one year to January 1, 2015. TIPA was passed after the timeframe EIA typically 

uses for inclusion of "current laws." AEO2015 was not explicitly updated to reflect it, however any plants 

affected by the change would already be reported as planned capacity additions.

FERC Orders 888 and 889

The Federal Energy Regulatory Commission (FERC) issued two related rules (Orders 888 and 889) designed 

to bring low-cost power to consumers through competition, ensure continued reliability in the industry, and 

provide for open and equitable transmission services by owners of these facilities.

Specifically, Order 888 requires open access to the transmission grid currently owned and operated by 

utilities. The transmission owners must file nondiscriminatory tariffs that offer other suppliers the same 

services that the owners provide for themselves. Order 888 also allows these utilities to recover stranded 

costs (investments in generating assets that are unrecoverable due to consumers selecting another 

supplier). Order 889 requires utilities to implement standards of conduct and an Open Access Same-Time 

Information System (OASIS) through which utilities and non-utilities can receive information regarding the 

transmission system. As a result, utilities have functionally or physically unbundled their marketing functions 

from their transmission functions.

These orders are represented in EMM by assuming that all generators in a given region are able to satisfy 

load requirements anywhere within the region. Similarly, it is assumed that transactions between regions 

will occur if the cost differentials between them make such transactions economical.
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 127

Notes and sources

[83] Review of Power Plant Cost and Performance Assumptions for NEMS, Science Applications International 

Corporation, April 2013, http://www.eia.gov/forecasts/capitalcost/.

[84] R. Wiser and M. Bolinger, 2013 Wind Technologies Market Report, DOE/GO-102014-4459 (Washington, 

DC: August 2014), http://emp.lbl.gov/sites/all/files/2013_Wind_Technologies_Market_Report_Final3.pdf

and M. Bolinger and S. Weaver, Utility-Scale Solar 2013 (Washington, DC: September 2014), 

http://emp.lbl.gov/sites/all/files/LBNL_Utility-Scale_ Solar_2013_report.pdf. 

[85] Numerous press announcements regarding coal to gas conversions, for example, “NRG closing 

Romeoville coal plant, converting Joliet plant to natural gas,” August 7, 2014, 

http://thetimesweekly.com/news/2014/aug/07/nrg-closing-romeoville-coal-plant-converting-jolie/. 

[86] Documentation for EPA Base Case v.5.13 using the Integrated Planning Model, November 2013, 

http://www.epa.gov/airmarkets/documents/ipm/Documentation.pdf

[87] “Exelon Cancels Power Uprates for Lasalle, Limerick,” June 12, 2013, http://www.platts.com/latestnews/electric-power/washington/exelon-cancels-power-uprates-for-lasalle-limerick-21152061.

[88] North American Electric Reliability Corporation, 2013 Summer Reliability Assessment (Atlanta, GA: May 

2013), http://www.nerc.com/pa/RAPA/ra/Reliability%20Assessments%20DL/2013SRA_Final.pdf.

[89] These costs were developed using the National Energy Technology Laboratory Mercury Control 

Performance and Cost Model, 1998.

[90] U.S. Department of Energy, Analysis of Mercury Control Cost and Performance, Office of Fossil Energy & 

National Energy Technology Laboratory, January 2003.

[91] Retrofitting Coal-Fired Power Plants for Carbon Dioxide Capture and Sequestration - Exploratory Testing 

of NEMS for Integrated Assessments, DOE/NETL-2008/1309, P.A. Geisbrecht, January 18, 2009.

[92] Analysis of Heat Rate Improvement Potential at Coal-Fired Power Plants, May 2015, Leidos, Inc.

http://www.eia.gov/analysis/studies/powerplants/heatrate/pdf/heatrate.pdf
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 128

Oil and Gas Supply Module

The NEMS Oil and Gas Supply Module (OGSM) constitutes a comprehensive framework with which to 

analyze crude oil and natural gas exploration and development on a regional basis (Figure 8). The OGSM is 

organized into 4 submodules: Onshore Lower 48 Oil and Gas Supply Submodule, Offshore Oil and Gas Supply 

Submodule, Oil Shale Supply Submodule [93], and Alaska Oil and Gas Supply Submodule. A detailed 

description of the OGSM is provided in the EIA publication, Oil and Gas Supply Module of the National 

Energy Modeling System: Model Documentation 2014, DOE/EIA-M063 (2014), (Washington, DC, 2014). The 

OGSM provides crude oil and natural gas short-term supply parameters to both the Natural Gas 

Transmission and Distribution Module and the Petroleum Market Module. The OGSM simulates the activity 

of numerous firms that produce oil and natural gas from domestic fields throughout the United States.

Figure 8. Oil and Gas Supply Model regions

OGSM encompasses domestic crude oil and natural gas supply by several recovery techniques and sources. 

Crude oil recovery includes improved oil recovery processes such as water flooding, infill drilling, and 

horizontal drilling, as well as enhanced oil recovery processes such as CO2 flooding, steam flooding, and 

polymer flooding. Recovery from highly fractured, continuous zones (e.g. Austin chalk and Bakken shale 

formations) is also included. Natural gas supply includes resources from low- permeability tight sand 

formations, shale formations, coalbed methane, and other sources.

Key assumptions

Domestic oil and natural gas technically recoverable resources

The outlook for domestic crude oil production is highly dependent upon the production profile of individual 

wells over time, the cost of drilling and operating those wells, and the revenues generated by those wells. 
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 129

Every year EIA re-estimates initial production (IP) rates and production decline curves, which determine 

estimated ultimate recovery (EUR) per well and total technically recoverable resources (TRR) [94].

A common measure of the long-term viability of U.S. domestic crude oil and natural gas as an energy source 

is the remaining technically recoverable resource, consisting of proved reserves [95] and unproved 

resources [96]. Estimates of TRR are highly uncertain, particularly in emerging plays where few wells have 

been drilled. Early estimates tend to vary and shift significantly over time as new geological information is 

gained through additional drilling, as long-term productivity is clarified for existing wells, and as the 

productivity of new wells increases with technology improvements and better management practices. TRR 

estimates used by EIA for each AEO are based on the latest available well production data and on 

information from other federal and state governmental agencies, industry, and academia. Published 

estimates in Tables 9.1 and 9.2 reflect the removal of intervening reserve additions and production between 

the date of the latest available assessment and January 1, 2012.

The resources presented in the tables in this chapter are the starting values for the model. Technology 

improvements in the model add to the unproved TTR, which can be converted to reserves and finally 

production. The tables in this chapter do not include these increases in TRR.

Table 9.1. Technically recoverable U.S. crude oil resources as of January 1, 2013

billion barrels

Total Technically

Proved 

Reserves Unproved Resources

Recoverable 

Resources

Lower 48 Onshore 24.5 144.8 169.3

 East 0.3 2.6 2.9

 Gulf Coast 5.3 29.1 34.4

 Midcontinent 2.1 13.1 15.2

 Southwest 7.4 48.1 55.4

 Rocky Mountain 6.7 42.0 48.7

 West Coast 2.8 10.0 12.7

Lower 48 Offshore 5.6 47.5 53.1

 Gulf (currently available) 5.0 35.5 40.5

 Eastern/Central Gulf (unavailable until 2022) 0.0 3.7 3.7

 Pacific 0.5 5.9 6.4

 Atlantic 0.0 2.5 2.5

Alaska (Onshore and Offshore) 3.4 34.0 37.4

Total U.S. 33.4 226.4 259.8

Note: Crude oil resources include lease condensates but do not include natural gas plant liquids or kerogen (oil 

shale). Resources in areas where drilling is officially prohibited are not included in this table. The estimate of 7.3 

billion barrels of crude oil resources in the Northern Atlantic, Northern and Central Pacific, and within a 50-mile 

buffer off the Mid and Southern Atlantic Outer Continental Shelf (OCS) is also excluded from the technically 

recoverable volumes because leasing is not expected in these areas by 2040.

Source: Onshore and State Offshore - U.S. Energy Information Administration; Alaska - U.S. Geological Survey 

(USGS); Federal (Outer Continental Shelf) Offshore - Bureau of Ocean Energy Management (formerly the Minerals 

Management Service); Proved Reserves - U.S. Energy Information Administration. Table values reflect removal of 

intervening reserve additions between the date the latest available assessment and January 1, 2013.
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 130

Table 9.2. Technically recoverable U.S. dry natural gas resources as of January 1, 2012

trillion cubic feet

Proved Unproved Total Technically

Reserves Resources Recoverable Resources

Lower 48 Onshore 287.3 1,392.0 1,679.3

 Tight Gas 65.3 353.7 418.9

 East 0.5 38.0 38.5

 Gulf Coast 13.4 159.1 172.5

 Midcontinent 6.9 10.8 17.7

 Southwest 7.2 33.3 40.5

 Rocky Mountain 37.2 112.5 149.7

 West Coast 0.0 0.0 0.0

 Shale Gas & Tight Oil 141.2 595.7 736.9

 East 44.6 254.2 298.8

 Gulf Coast 37.6 154.2 191.8

 Midcontinent 23.4 45.6 69.0

 Southwest 32.0 74.7 106.7

 Rocky Mountain 3.6 51.7 55.4

 West Coast 0.0 15.2 15.2

 Coalbed Methane 13.6 119.6 133.2

 East 1.7 4.1 5.8

 Gulf Coast 1.1 2.2 3.3

 Midcontinent 0.6 38.3 38.9

 Southwest 0.4 5.8 6.1

 Rocky Mountain 9.7 59.0 68.7

 West Coast 0.0 10.3 10.3

 Other 67.4 323.0 390.4

 East 11.5 29.6 41.1

 Gulf Coast 7.7 116.4 124.1

 Midcontinent 17.4 35.1 52.5

 Southwest 7.8 70.5 78.3

 Rocky Mountain 21.1 52.6 73.7

 West Coast 1.9 18.8 20.7

Lower 48 Offshore 11.2 305.3 316.5

 Gulf (currently available) 10.5 250.9 261.4

 Eastern/Central Gulf (unavailable until 2022) 0.0 21.5 21.5

 Pacific 0.7 9.3 10.1

 Atlantic 0.0 23.6 23.6

Alaska (Onshore and Offshore) 9.7 271.1 280.7

Total U.S. 308.2 1,968.4 2,276.5

Note: Resources in other areas where drilling is officially prohibited are not included. The estimate of 32.9 trillion cubic 

feet of natural gas resources in the Northern Atlantic, Northern and Central Pacific, and within a 50-mile buffer off the 

Mid and Southern Atlantic OCS is also excluded from the technically recoverable volumes because leasing is not 

expected in these areas by 2040. 

Source: Onshore and State Offshore - U.S. Energy Information Administration; Alaska - U.S. Geological Survey (USGS); 

Federal (Outer Continental Shelf) Offshore - Bureau of Ocean Energy Management (formerly the Minerals Management 

Service); Proved Reserves - U.S. Energy Information Administration. Table values reflect removal of intervening reserve 

additions between the date the latest available assessment and January 1, 2013.
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 131

The remaining unproved TRR for a continuous-type shale gas or tight oil area is the product of (1) area with 

potential, (2) well spacing (wells per square mile), and (3) EUR per well. The play-level unproved technically 

recoverable resource assumptions for tight oil, shale gas, tight gas, and coalbed methane are summarized in 

Tables 9.3-9.4. The model uses a distribution of EUR per well in each play and often in sub-play areas. Table 

9.5 provides an example of the distribution of EUR per well for each of the Bakken areas. The Bakken is 

subdivided into five areas: Central Basin, Eastern Transitional, Elm Coulee-Billings Nose, Nesson-Little Knife, 

and Northwest Transitional [97]. Because of the significant variation in well productivity within an area, the 

wells in each Bakken area are further delineated by county. This level of detail is provided for select plays in 

Appendix 2.C of the AEO2014 Documentation for the OGSM. The USGS periodically publishes tight and shale 

resource assessments that are used as a guide for selection of key parameters in the calculation of the TRR 

used in the AEO. The USGS seeks to assess the recoverability of shale gas and tight oil based on the wells 

drilled and technologies deployed at the time of the assessment. AEO2015 introduces a contour map based 

approach for incorporating geology parameters into the calculation of resources; recognizing that geology 

can vary significantly within counties. This new approach was only applied to the Marcellus play.

The AEO TRRs incorporate current drilling, completion, and recovery techniques, requiring adjustments to 

some of the assumptions used by the USGS to generate their TRR estimates, as well as the inclusion of shale 

gas and tight oil resources not yet assessed by the USGS. If well production data are available, EIA analyzes 

the decline curve of producing wells to calculate the expected ̶ EUR per well from future drilling.
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 132

The underlying resource for the Reference case is uncertain, particularly as exploration and development of 

tight oil continues to move into areas with little to no production history. Many wells drilled in tight or shale 

formations using the latest technologies have less than two years of production history so the impact of 

recent technological advancement on the estimate of future recovery cannot be fully ascertained. 

Uncertainty also extends to areal extent of formations and the number of layers that could be drilled within 

formations. An alternative resource cases are discussed at the end of this chapter.

Lower 48 onshore

The Onshore Lower 48 Oil and Gas Supply Submodule (OLOGSS) is a play-level model used to analyze crude 

oil and natural gas supply from onshore lower 48 sources. The methodology includes a comprehensive 

assessment method for determining the relative economics of various prospects based on financial 

considerations, the nature of the resource, and the available technologies. The general methodology relies 

on a detailed economic analysis of potential projects in known fields, enhanced oil recovery projects, and 

undiscovered resources. The projects which are economically viable are developed subject to the availability 

of resource development constraints which simulate the existing and expected infrastructure of the oil and 

gas industries. For crude oil projects, advanced secondary or improved oil recovery techniques (e.g. infill 

drilling and horizontal drilling) and enhanced oil recovery (e.g. CO2 flooding, steam flooding, and polymer 

flooding) processes are explicitly represented. For natural gas projects, the OLOGSS represents supply from 

shale formations, tight sands formations, coalbed methane, and other sources.

The OLOGSS evaluates the economics of future crude oil and natural gas exploration and development from 

the perspective of an operator making an investment decision. An important aspect of the economic 

calculation concerns the tax treatment. Tax provisions vary with the type of producer (major, large 

independent, or small independent). For AEO2015, the economics of potential projects reflect the tax 

treatment provided by current laws for large independent producers. Relevant tax provisions are assumed 

unchanged over the life of the investment. Costs are assumed constant over the investment life but vary 

across region, fuel, and process type. Operating losses incurred in the initial investment period are carried 

forward and used against revenues generated by the project in later years.
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 133

Table 9.3. U.S. unproved technically recoverable tight/shale oil and gas resources by play (as of 

January 1, 2013)

Average EUR

Technically Recoverable 

Resources

Area with 

Potentiall

1

Average 

Spacing 

(wells/

Crude Oil2

(MMbls/

Natural 

Gas

Crude 

Oil

Natural 

Gas NGPL

Region/Basin Play (mi2) mi2) well) (Bcf/well) (Bbls) (Tcf) (Bbls)

East

 Appalachian Clinton-Medina 26,907 8.0 0.002 0.058 0.4 12.4 0.0

 Appalachian Devonian 51,842 7.5 0.000 0.061 0.0 23.7 0.4

 Appalachian Marcellus Foldbelt 869 4.3 0.000 0.323 0.0 1.2 0.0

 Appalachian Marcellus Interior 17,200 4.4 0.003 1.897 0.3 143.8 5.5

 Appalachian Marcellus Western 2,688 5.5 0.000 0.255 0.0 3.7 0.2

 Appalachian Tuscarora 765 8.0 0.000 0.724 0.0 4.4 0.0

 Appalachian Utica-Gas Zone Core 11,398 4.3 0.001 0.726 0.0 35.3 3.1

 Appalachian Utica-Gas Zone Extension 15,062 4.3 0.003 0.277 0.2 17.8 2.6

 Appalachian Utica-Oil Zone Core 2,300 2.6 0.052 0.176 0.3 1.0 0.0

 Appalachian Utica-Oil-Zone Extension 3,861 2.6 0.039 0.047 0.4 0.5 0.0

 Illinois New Albany 3,059 8.0 0.000 1.188 0.0 29.1 2.3

 Michigan Antrim Shale 13,178 8.0 0.000 0.120 0.0 12.7 1.0

 Michigan Berea Sand 7,451 8.0 0.000 0.110 0.0 6.6 0.1

Gulf Coast

 Black Warrior Floyd-Neal/Conasauga 1,402 2.0 0.000 1.520 0.0 4.3 0.0

 TX-LA-MS Salt Cotton Valley 8,773 12.0 0.008 1.323 0.8 139.3 2.7

 TX-LA-MS Salt Haynesville-Bossier-LA 1,883 6.0 0.002 4.279 0.0 48.3 0.0

 TX-LA-MS Salt Haynesville-Bossier-TX 1,521 6.0 0.001 2.735 0.0 25.0 0.0

 Western Gulf Austin Chalk-Giddings 2,572 8.0 0.059 0.255 1.2 5.2 0.7

 Western Gulf Austin Chalk-Outlying 9,866 7.1 0.061 0.075 4.3 5.2 0.3

 Western Gulf Buda 8,640 4.0 0.059 0.109 2.0 3.8 0.1

 Western Gulf Eagle Ford-Dry Zone 2,085 6.0 0.090 1.473 1.1 18.4 0.0

 Western Gulf Eagle Ford-Oil Zone 4,967 6.4 0.123 0.227 3.9 7.2 0.1

 Western Gulf Eagle Ford-Wet Zone 3,509 6.0 0.250 1.416 5.3 29.8 0.6

 Western Gulf Olmos 5,463 4.0 0.008 1.162 0.2 25.4 0.0

 Western Gulf Pearsall 1,196 6.0 0.001 0.676 0.0 4.9 0.0

 Western Gulf Tuscaloosa 7,448 4.0 0.107 0.021 3.2 0.6 0.0

 Western Gulf Vicksburg 329 8.0 0.026 0.980 0.1 2.6 0.1

 Western Gulf Wilcox Lobo 1,142 8.0 0.000 1.403 0.0 12.8 0.3

 Western Gulf Woodbine 1,222 4.0 0.120 0.061 0.6 0.3 0.0

Midcontinent

 Anadarko Cana Woodford-Dry Zone 737 4.0 0.023 1.995 0.1 5.9 0.0

 Anadarko Cana Woodford-Oil Zone 460 6.0 0.041 0.961 0.1 2.7 0.1

 Anadarko Cana Woodford-Wet Zone 1,068 4.0 0.016 1.310 0.1 5.6 0.5

 Anadarko Cleveland 639 4.1 0.048 0.384 0.1 1.0 0.0

 Anadarko Granite Wash 3,788 4.0 0.040 0.579 0.6 8.8 0.5

 Anadarko Red Fork 513 4.0 0.019 0.459 0.0 0.9 0.1

 Arkoma Carney 797 4.0 0.000 0.973 0.0 3.1 0.0

 Arkoma Fayetteville-Central 2,065 8.0 0.000 0.973 0.0 16.1 0.0

 Arkoma Fayetteville-West 773 8.0 0.000 0.700 0.0 4.3 0.0

 Arkoma Woodford-Arkoma 591 8.0 0.003 1.342 0.0 6.3 0.6

 Black Warrior Chattanooga 205 8.0 0.000 0.968 0.0 1.6 0.0

Southwest

 Fort Worth Barnett-Core 363 8.0 0.000 1.621 0.0 4.7 0.2

 Fort Worth Barnett-North 1,611 8.2 0.008 0.467 0.1 6.2 0.2

 Fort Worth Barnett-South 5,368 8.0 0.003 0.154 0.1 6.6 0.3
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 134

Table 9.3. U.S. unproved technically recoverable tight/shale oil and gas resources by play (as of 

January 1, 2013) (cont.)

Average EUR

Technically Recoverable 

Resources

Area with 

Potentiall

1

Average 

Spacing 

(wells/

Crude Oil2

(MMbls/

Natural 

Gas

Crude 

Oil

Natural 

Gas NGPL

Region/Basin Play (mi2) mi2) well) (Bcf/well) (Bbls) (Tcf) (Bbls)

 Permian Abo 2,540 4.0 0.067 0.149 0.7 1.5 0.1

 Permian Avalon/BoneSpring 5,444 4.5 0.118 0.367 2.9 9.0 0.5

 Permian Barnett-Woodford 2,618 4.0 0.000 1.181 0.0 12.4 1.7

 Permian Canyon 6,482 8.1 0.016 0.209 0.9 10.9 0.0

 Permian Spraberry 12,209 6.6 0.131 0.152 10.6 12.3 1.2

 Permian Wolfcamp 17,560 4.1 0.085 0.347 6.1 24.9 2.0

Rocky Mountain

 Denver Muddy 3,999 16.0 0.001 0.180 0.1 11.5 0.0

 Denver Niobrara 6,985 5.0 0.013 0.078 0.4 2.7 0.1

 Greater Green River Hilliard-Baxter-Mancos 4,477 8.0 0.000 0.293 0.0 10.5 0.5

 Greater Green River Tight Oil Plays 1,452 5.8 0.105 0.014 0.9 0.1 0.0

 Montana Thrust Belt Tight Oil Plays 2,630 2.3 0.100 0.068 0.6 0.4 0.0

 North Central Montana Bowdoin-Greenhorn 461 4.0 0.000 0.078 0.0 0.1 0.0

 Paradox Fractured Interbed 1,171 1.6 0.534 0.427 1.0 0.8 0.0

 Powder River Tight Oil Plays 19,464 3.0 0.035 0.040 2.1 2.4 0.1

 San Juan Dakota 1,819 8.0 0.002 0.258 0.0 3.8 0.0

 San Juan Lewis 1,481 3.0 0.000 2.200 0.0 9.8 0.0

 San Juan Mesaverde 1,036 12.0 0.002 0.548 0.0 6.8 0.0

 San Juan Pictured Cliffs 101 4.0 0.000 0.262 0.0 0.1 0.0

 Southwestern Wyoming Fort Union-Fox Hills 1,888 8.0 0.010 0.792 0.1 12.0 1.0

 Southwestern Wyoming Frontier 2,827 8.0 0.017 0.312 0.4 7.1 0.0

 Southwestern Wyoming Lance 2,297 8.0 0.020 1.152 0.4 21.2 3.8

 Southwestern Wyoming Lewis 3,885 8.0 0.006 0.312 0.2 9.7 0.2

 Southwestern Wyoming Tight Oil Plays 1,730 5.8 0.108 0.014 1.1 0.1 0.0

 Uinta-Piceance Iles-Mesaverde 3,997 8.0 0.001 0.372 0.0 11.9 0.0

 Uinta-Piceance Mancos 1,553 8.0 0.000 0.752 0.0 9.3 0.0

 Uinta-Piceance Tight Oil Plays 91 16.0 0.047 0.105 0.1 0.2 0.0

 Uinta-Piceance Wasatch-Mesaverde 2,161 8.0 0.030 0.568 0.5 9.8 0.0

 Uinta-Piceance Williams Fork 1,619 10.0 0.004 0.692 0.1 11.2 0.0

 Williston Bakken Central 4,516 2.9 0.202 0.153 2.6 2.0 0.2

 Williston Bakken Eastern 3,240 2.7 0.243 0.093 2.1 0.8 0.1

 Williston Bakken Elm Coulee 1,912 2.0 0.139 0.098 0.5 0.4 0.0

 Williston Bakken Nesson 3,476 3.2 0.260 0.162 2.9 1.8 0.2

 Williston Bakken Northwest 2,857 2.0 0.077 0.020 0.4 0.1 0.0

 Williston Bakken Three Forks 28,016 4.0 0.127 0.062 14.2 7.0 0.7

 Williston Gammon 3,836 2.0 0.000 0.433 0.0 3.3 0.0

 Williston Judith River-Eagle 1,449 4.0 0.000 0.154 0.0 0.9 0.0

 Wind River Fort Union-Lance 709 8.0 0.018 1.131 0.1 6.4 0.3

West Coast

 Columbia Basin Central 943 8.0 0.000 1.620 0.0 12.2 0.0

 San Joaquin/Los Angeles Monterey/Santos 3,141 6.4 0.028 0.151 0.6 3.0 0.0

Total Tight/Shale 78.2 949.3 35.8

EUR = estimated ultimate recovery; NGPL=Natural Gas Plant Liquids

1

Area of play that is expected to have unproved technically recoverable resources remaining. 2

Includes lease condensates.

Source: U.S. Energy Information Administration, Office of Energy Analysis.
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 135

Table 9.4. U.S. unproved technically recoverable coalbed methane resources by play (as of January 1, 

2013)

Average EUR

Technically Recoverable 

Resources

Region/Basin Play

Area with 

Potential1 

(mi2)

Average 

Spacing 

(wells/mi2)

Crude Oil2

(MMbls/

well)

Natural 

Gas 

(Bcf/well)

Crude 

Oil 

(Bbls)

Natural 

Gas 

(Tcf)

NGPL 

(Bbls)

1-East

 Appalachian Central Basin 1,302 8 0.000 0.176 0.0 1.8 0.0

 Appalachian North Appalachian Basin - High 361 12 0.000 0.125 0.0 0.5 0.0

 Appalachian North Appalachian Basin – Mod/Low 493 12 0.000 0.080 0.0 0.5 0.0

 Illinois Central Basin 1,277 8 0.000 0.120 0.0 1.2 0.0

2-Gulf Coast

Black Warrior Extention Area 148 8 0.000 0.080 0.0 0.1 0.0

Black Warrior Main Area 694 12 0.000 0.206 0.0 1.7 0.0

Cahaba Cahaba Coal Field 264 8 0.000 0.179 0.0 0.4 0.0

3-Midcontinent

Forest City Central Basin 23,110 8 0.022 0.172 4.0 31.8 0.0

Midcontinent Arkoma 2,718 8 0.000 0.216 0.0 4.7 0.0

Midcontinent Cherokee 3,436 8 0.000 0.065 0.0 1.8 0.0

4-Southwest 

 Raton Southern 1,925 8 0.000 0.375 0.0 5.8 0.0

5-Rocky Mountain 

Greater Green River Deep 1,620 4 0.000 0.600 0.0 3.9 0.0

Greater Green River Shallow 644 8 0.000 0.204 0.0 1.1 0.0

Piceance Deep 1,534 4 0.000 0.600 0.0 3.7 0.0

Piceance Divide Creek 135 8 0.000 0.179 0.0 0.2 0.0

Piceance Shallow 1,865 4 0.000 0.299 0.0 2.2 0.0

Piceance White River Dome 201 8 0.000 0.410 0.0 0.7 0.0

Powder River Big George/Lower Fort Union 1,570 16 0.000 0.260 0.0 6.5 0.0

Powder River Wasatch 206 8 0.000 0.056 0.0 0.1 0.0

Powder River Wyodak/Upper Fort Union 6,162 20 0.000 0.136 0.0 16.8 0.0

Raton Northern 343 8 0.000 0.350 0.0 1.0 0.0

Raton Purgatoire River 174 8 0.000 0.311 0.0 0.4 0.0

San Juan Fairway NM 169 4 0.000 1.142 0.0 0.8 0.0

San Juan North Basin 1,353 4 0.000 0.280 0.0 1.5 0.0

San Juan North Basin CO 1,673 4 0.000 1.515 0.0 10.1 0.0

San Juan South Basin 1,030 4 0.000 0.199 0.0 0.8 0.0

San Juan South Menefee NM 373 5 0.000 0.095 0.0 0.2 0.0

Uinta Ferron 227 8 0.000 0.776 0.0 1.4 0.0

Uinta Sego 341 4 0.000 0.306 0.0 0.4 0.0

Wind River Mesaverde 418 2 0.000 2.051 0.0 1.7 0.0

Wyoming Thrust Belt All Plays 5,200 2 0.000 0.462 0.0 5.5 0.0

6-West Coast

Western Washington Bellingham 441 2 0.000 2.391 0.0 2.1 0.0

Western Washington Southern Puget Lowlands 1,102 2 0.000 0.687 0.0 1.5 0.0

Western Washington Western Cascade Mountains 2,152 2 0.000 1.559 0.0 6.7 0.0

 Total Coalbed Methane 4.0 119.5 0.0 0.0

EUR = estimated ultimate recovery; NGPL = Natural Gas Plant Liquids. 1

Area of play that is expected to have unproved technically recoverable resources remaining.

2

Includes lease condensates.

Source: U.S. Energy Information Administration, Office of Energy Analysis
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 136

Table 9.5. Distribution of crude oil EURs in the Bakken

Play Name State County

Number of 

potential wells

EUR 

(Mbbls/well)

Bakken Central Basin MT Daniels 112 117

Bakken Central Basin MT McCone 313 117

Bakken Central Basin MT Richland 646 138

Bakken Central Basin MT Roosevelt 2933 169

Bakken Central Basin MT Sheridan 439 44

Bakken Central Basin ND Divide 21 241

Bakken Central Basin ND Dunn 160 266

Bakken Central Basin ND McKenzie 4,652 231

Bakken Central Basin ND Williams 3,769 227

Bakken Eastern Transitional ND Burke 1,382 114

Bakken Eastern Transitional ND Divide 632 116

Bakken Eastern Transitional ND Dunn 2,108 212

Bakken Eastern Transitional ND Hettinger 4 169

Bakken Eastern Transitional ND McLean 1046 214

Bakken Eastern Transitional ND Mercer 135 9

Bakken Eastern Transitional ND Mountrail 3,082 374

Bakken Eastern Transitional ND Stark 194 169

Bakken Eastern Transitional ND Ward 57 145

Bakken Elm Coulee-Billings Nose MT McCone 67 163

Bakken Elm Coulee-Billings Nose MT Richland 1,623 163

Bakken Elm Coulee-Billings Nose ND Billings 799 79

Bakken Elm Coulee-Billings Nose ND Golden Valley 130 96

Bakken Elm Coulee-Billings Nose ND McKenzie 1,204 151

Bakken Nesson-Little Knife ND Billings 573 80

Bakken Nesson-Little Knife ND Burke 312 188

Bakken Nesson-Little Knife ND Divide 596 133

Bakken Nesson-Little Knife ND Dunn 3,227 284

Bakken Nesson-Little Knife ND Hettinger 110 223

Bakken Nesson-Little Knife ND McKenzie 2011 344

Bakken Nesson-Little Knife ND Mountrail 1060 323

Bakken Nesson-Little Knife ND Slope 172 223

Bakken Nesson-Little Knife ND Stark 1,066 183

Bakken Nesson-Little Knife ND Williams 1997 249

Bakken Northwest Transitional MT Daniels 1,550 82

Bakken Northwest Transitional MT McCone 97 82

Bakken Northwest Transitional MT Roosevelt 787 82

Bakken Northwest Transitional MT Sheridan 1,710 62

Bakken Northwest Transitional MT Valley 603 1

Bakken Northwest Transitional ND Divide 616 160

Bakken Northwest Transitional ND Williams 350 106

Source: U.S. Energy Information Administration, Office of Energy Analysis.
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 137

CO2 enhanced oil recovery

For CO2 miscible flooding, the OLOGSS incorporates both industrial and natural sources of CO2. The 

industrial sources of CO2 are:

• Hydrogen plants

• Ammonia plants

• Ethanol plants

• Cement plants

• Refineries (hydrogen)

• Fossil fuel power plants

• Natural gas processing

• Coal/biomass to liquids (CBTL)

The volume and cost of CO2 available from fossil fuel power plants and CBTL, are determined in the 

Electricity Market Module and the Liquid Fuels Market Module, respectively. Technology and market 

constraints prevent the total volumes of CO2 from the other industrial sources (Table 9.6) from 

becoming immediately available. The development of the CO2 market is divided into two periods: 1) 

development phase and 2) market acceptance phase. During the development phase, the required 

capture equipment is developed, pipelines and compressors are constructed, and no CO2 is available. 

During the market acceptance phase, the capture technology is being widely implemented and volumes 

of CO2 first become available. The number of years in each development period is shown in Table 9.7. 

CO2 is available from planned Carbon Sequestration and Storage (CSS) power plants funded by 

American Recovery and Reinvestment Act of 2009 (ARRA) starting in 2016.

Table 9.6. Maximum volume of CO2 available

Billion cubic feet

OGSM Region Natural Hydrogen Ammonia Ethanol Cement

Refineries 

(hydrogen)

Natural Gas 

Processing

East 0 3 0 52 94 17 23

Gulf Coast 292 0 78 0 86 114 114

Midcontinent 16 0 0 175 48 1 0

Southwest 657 0 0 68 74 0 0

Rocky Mountains 80 0 3 23 35 78 18

West Coast 0 0 0 4 48 93 40

Source: U.S. Energy Information Administration, Office of Energy Analysis.
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 138

Table 9.7. CO2 availability assumptions

Source Type Development Phase (years)

Market Acceptance Phase 

(years) 

Ultimate Market 

Acceptance

Natural 1 10 100%

Hydrogen 4 10 100%

Ammonia 2 10 100%

Ethanol 4 10 100%

Cement 7 10 100%

Refineries (hydrogen) 4 10 100%

Natural Gas Processing 2 10 100%

Source: U.S. Energy Information Administration, Office of Energy Analysis.

The cost of CO2 from natural sources is a function of the oil price. For industrial sources of CO2, the cost 

to the producer includes the cost to capture, compress to pipeline pressure, and transport to the project 

site via pipeline within the region (Table 9.8). Inter- regional transportation costs add $0.40 per Mcf for 

every region crossed.

Table 9.8. Industrial CO2 capture and transportation costs by region

$/Mcf

OGSM Region Hydrogen Ammonia Ethanol Cement

Refineries 

(hydrogen)

Natural Gas 

Processing

East $2.44 $2.10 $2.23 $4.29 $2.44 $1.92 

Gulf Coast $1.94 $2.10 $2.23 $4.29 $1.94 $1.92 

Midcontinent $2.07 $2.10 $2.23 $4.29 $2.07 $1.92 

Southwest $2.02 $2.10 $2.23 $4.29 $2.02 $1.92 

Rocky Mountains $2.03 $2.10 $2.23 $4.29 $2.03 $1.92 

West Coast $2.01 $2.10 $2.23 $4.29 $2.01 $1.92 

Source: U.S. Energy Information Administration. Office of Energy Analysis.

Lower 48 offshore

Most of the Lower 48 offshore oil and gas production comes from the deepwater of the Gulf of Mexico 

(GOM). Production from currently producing fields and industry-announced discoveries largely 

determines the short-term oil and natural gas production projection.

For currently producing fields, a 20% exponential decline is assumed for production except for natural 

gas production from fields in shallow water, which uses a 30% exponential decline. Fields that began 

production after 2008 are assumed to remain at their peak production level for 2 years before declining.

The assumed field size and year of initial production of the major announced deepwater discoveries that 

were not brought into production by 2012 are shown in Table 9.9. A field that is announced as an oil 

field is assumed to be 100% oil and a field that is announced as a gas field is assumed to be 100% gas. If 

a field is expected to produce both oil and gas, 70% is assumed to be oil and 30% is assumed to be gas.
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 139

Production is assumed to:

• ramp up to a peak level in 2 to 4 years depending on the size of the field,

• remain at the peak level until the ratio of cumulative production to initial resource reaches 20% 

for oil and 30% for natural gas, and

• then decline at an exponential rate of 20-30%.

The discovery of new fields (based on BOEM’S field size distribution) is assumed to follow historical 

patterns. Production from these fields is assumed to follow the same profile as the announced 

discoveries (as described in the previous paragraph). Advances in technology for the various activities 

associated with crude oil and natural gas exploration, development, and production can have a 

profound impact on the costs associated with these activities. The specific technology levers and values 

for the offshore are presented in Table 9.10.

Leasing is assumed to be available in 2018 in the Mid-and South Atlantic, in 2023 in the South Pacific, 

and after 2035 in the North Atlantic, Florida straits, Pacific Northwest, and North and Central California.

Table 9.9. Assumed size and initial production year of major announced deepwater discoveries

Field/Project Name Block

Water 

Depth (feet)

Year of 

Discovery

Field Size 

Class

Field Size 

(MMBoe)

Start Year of 

Production

Silvertip AC815 9,280 2004 12 90 2015

Gotcha AC865 7,844 2006 13 176 2015

Telemark Phase 2 AT063 4,450 2010 12 90 2015

Dalmation North DC004 5,831 2010 12 90 2014

Dalmatian DC048 5,917 2008 12 90 2014

Vicksburg DC353 7,457 2009 14 357 2019

Raptor DC535 8,200 2013 12 90 2018

Cardamom GB427 2,720 2010 13 176 2014

Bushwood GB463 2,700 2009 13 176 2019

Danny II GB506 2,800 2012 12 90 2022

Winter GB605 3,400 2009 11 44 2019

North Platte GB959 4,400 2012 13 176 2022

Samurai GC432 3,400 2009 12 90 2017

Stampede-Pony GC468 3,497 2006 14 357 2018

Stampede-Knotty Head GC512 3,557 2005 14 357 2018

Parmer GC823 3,821 2012 12 90 2022

Heidelberg GC903 5,271 2009 13 176 2017

Gila KC093 4,900 2013 11 44 2017

Tiber KC102 4,132 2009 15 693 2019

Kaskida KC292 5,860 2006 15 693 2015

Moccasin KC736 6,759 2011 13 176 2018

Buckskin KC872 6,920 2009 13 176 2018

Lucius KC875 6,822 2009 12 90 2016

Hadrian North KC919 7,000 2010 14 357 2020

Hadrian South KC964 7,586 2009 13 176 2019

Diamond LL370 9,975 2008 11 44 2018

Cheyenne East LL400 9,200 2010 9 12 2020

Amethyst MC026 1,200 2014 10 23 2019

Cardona South MC029 2,185 2014 8 6 2014

Mandy MC199 2,478 2010 13 176 2020
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 140

Table 9.9. Assumed size and initial production year of major announced deepwater discoveries (cont.)

Field/Project Name Block

Water Depth 

(feet)

Year of 

Discovery

Field Size 

Class

Field Size 

(MMBoe)

Start Year of 

Production

Marmalard MC300 6,148 2012 12 90 2016

Appomattox MC392 7,217 2009 15 693 2017

Son Of Bluto 2 MC431 6,461 2012 12 90 2016

Santiago MC519 6,526 2011 12 90 2021

Rydberg MC525 7,500 2014 14 357 2019

Isabela MC562 6,535 2007 11 44 2017

Santa Cruz MC563 6,515 2009 12 90 2015

Big Bend MC698 7,273 2012 12 90 2016

Troubadour MC699 7,273 2013 12 90 2018

Tubular Bells MC725 4,334 2003 12 90 2014

Deimos South MC762 3,122 2010 12 90 2014

Kodiak MC771 4,986 2008 13 176 2018

Dantzler MC782 6,565 2013 12 90 2016

West Boreas MC792 3,112 2004 12 90 2014

Gunflint MC948 6,095 2008 12 90 2016

Vito MC984 4,038 2009 13 176 2020

Phobos SE039 8,500 2013 13 176 2018

Tomcat WC176 1,200 2014 12 90 2019

Big Foot WR029 5,235 2005 12 90 2015

Shenandoah WR052 5,874 2009 13 176 2017

Yucatan North WR095 5,860 2013 12 90 2017

Coronado WR098 6,127 2013 14 357 2017

Stones WR508 9,556 2005 12 90 2018

Julia WR627 7,087 2007 12 90 2016

St. Malo WR678 7,000 2003 14 357 2014

Jack WR759 6,963 2004 14 357 2014

Hal WR848 7,657 2008 11 44 2019

Source: U.S. Energy Information Administration, Office of Energy Analysis.

Table 9.10. Offshore exploration and production technology levels

Technology Level

Total Improvement over 

30 years (%)

Exploration success rates 30

Delay to commence first exploration and between 15

Exploration & development drilling costs 30

Operating cost 30

Time to construct production facility 15

Production facility construction costs 30

Initial constant production rate 15

Decline rate 0

Source: U.S. Energy Information Administration, Office of Energy Analysis.
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Alaska crude oil production

Projected Alaska oil production includes both existing producing fields and undiscovered fields that are 

expected to exist, based upon the region’s geology. The existing fields category includes the expansion 

fields around the Prudhoe Bay and Alpine Fields for which companies have already announced 

development schedules. Projected North Slope oil production also includes the initiation of oil 

production in the Point Thomson Field in 2016. Alaska crude oil production from the undiscovered fields 

is determined by the estimates of available resources in undeveloped areas and the net present value of 

the cash flow calculated for these undiscovered fields based on the expected capital and operating 

costs, and on the projected prices.

The discovery of new Alaskan oil fields is determined by the number of new wildcat exploration wells 

drilled each year and by the average wildcat success rate. The North Slope and South-Central wildcat 

well success rates are based on the success rates reported to the Alaska Oil and Gas Conservation 

Commission for the period of 1977 through 2008.

New wildcat exploration drilling rates are determined differently for the North Slope and South-Central 

Alaska. North Slope wildcat well drilling rates were found to be reasonably well correlated with 

prevailing West Texas Intermediate crude oil prices. Consequently, an ordinary least squares statistical 

regression was employed to develop an equation that specifies North Slope wildcat exploration well 

drilling rates as a function of prevailing West Texas Intermediate crude oil prices. In contrast, SouthCentral wildcat well drilling rates were found to be uncorrelated to crude oil prices or any other 

criterion. However, South-Central wildcat well drilling rates on average equaled just over three wells per 

year during the 1977 through 2008 period, so three South-Central wildcat exploration wells are assumed 

to be drilled every year in the future.

On the North Slope, the proportion of wildcat exploration wells drilled onshore relative to those drilled 

offshore is assumed to change over time. Initially, only a small proportion of all the North Slope wildcat 

exploration wells are drilled offshore. However, over time, the offshore proportion increases linearly, so 

that after 20 years, 50% of the North Slope wildcat wells are drilled onshore and 50% are drilled 

offshore. The 50/50 onshore/offshore wildcat well apportionment remains constant through the 

remainder of the projection in recognition of the fact that offshore North Slope wells and fields are 

considerably more expensive to drill and develop, thereby providing an incentive to continue drilling 

onshore wildcat wells even though the expected onshore field size is considerably smaller than the oil 

fields expected to be discovered offshore.

The size of the new oil fields discovered by wildcat exploration drilling is based on the expected field 

sizes of the undiscovered Alaska oil resource base, as determined by the U.S. Geological Survey for the 

onshore and state offshore regions of Alaska, and by the Bureau of Ocean Energy Management (BOEM) 

(formerly known as the U.S. Minerals Management Service) for the federal offshore regions of Alaska. It 

is assumed that the largest undiscovered oil fields will be found and developed first and in preference to 

the small and midsize undiscovered fields. As the exploration and discovery process proceeds and as the 

largest oil fields are discovered and developed, the discovery and development process proceeds to find 

and develop the next largest set of oil fields. This large to small discovery and development process is 

predicated on the fact that developing new infrastructure in Alaska, particularly on the North Slope, is 
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an expensive undertaking and that the largest fields enjoy economies of scale, which make them more 

profitable and less risky to develop than the smaller fields.

Oil and gas exploration and production currently are not permitted in the Arctic National Wildlife 

Refuge. The projections for Alaska oil and gas production assume that this prohibition remains in effect 

throughout the projection period.

Three uncertainties are associated with the Alaska oil projections:

• whether the heavy oil deposits located on the North Slope, which exceed 20 billion barrels of 

oil-in-place, will be producible in the foreseeable future at recovery rates exceeding a few 

percent.

• the oil production potential of the North Slope shale formations is unknown at this time.

• the North Slope offshore oil resource potential, especially in the Chukchi Sea, is untested.

In June 2011, Alyeska Pipeline Service Company released a report regarding potential operational 

problems that might occur as Trans-Alaska Pipeline System (TAPS) throughput declines from the current 

production levels. [98] Although the onset of TAPS low flow problems could begin at around 550,000 

barrels per day (bbl/d), absent any mitigation, the severity of the TAPS operational problems is 

expected to increase significantly as throughput declines. As the types and severity of problems 

multiply, the investment required to mitigate those problems is expected to increase significantly. 

Because of the many and diverse operational problems expected to occur below 350,000 bbl/d of 

throughput, considerable investment might be required to keep the pipeline operational below this 

threshold. Thus, North Slope fields are assumed to be shut down, plugged, and abandoned when the 

following two conditions are simultaneously satisfied: 1) TAPS throughput would have to be at or below 

350,000 bbl/d and two) total North Slope oil production revenues would have to be at or below $5.0 

billion per year.
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Legislation and regulations

The Outer Continental Shelf Deep Water Royalty Act (Public Law 104-58) gave the Secretary of the 

Interior the authority to suspend royalty requirements on new production from qualifying leases and 

required that royalty payments be waived automatically on new leases sold in the five years following its 

November 28, 1995 enactment. The volume of production on which no royalties were due for the five 

years was assumed to be 17.5 million barrels of oil equivalent (BOE) in water depths of 200 to 400 

meters, 52.5 million BOE in water depths of 400 to 800 meters, and 87.5 million BOE in water depths 

greater than 800 meters. In any year during which the arithmetic average of the closing prices on the 

New York Mercantile Exchange for light sweet crude oil exceeded $28 per barrel or for natural gas 

exceeded $3.50 per million Btu, any production of crude oil or natural gas was subject to royalties at the 

lease-stipulated royalty rate. Although automatic relief expired on November 28, 2000, the act provided 

the Minerals Management Service (MMS) the authority to include royalty suspensions as a feature of 

leases sold in the future. In September 2000, the MMS issued a set of proposed rules and regulations 

that provide a framework for continuing deep water royalty relief on a lease-by-lease basis. In the model 

it is assumed that relief will be granted at roughly the same levels as provided during the first five years 

of the Act.

Section 345 of the Energy Policy Act of 2005 provides royalty relief for oil and gas production in water 

depths greater than 400 meters in the Gulf of Mexico from any oil or gas lease sale occurring within five 

years after enactment. The minimum volumes of production with suspended royalty payments are:

1. 5,000,000 BOE for each lease in water depths of 400 to 800 meters; 

2. 9,000,000 BOE for each lease in water depths of 800 to 1,600 meters;

3. 12,000,000 BOE for each lease in water depths of 1,600 to 2,000 meters; and

4. 16,000,000 BOE for each lease in water depths greater than 2,000 meters.

The water depth categories specified in Section 345 were adjusted to be consistent with the depth 

categories in the Offshore Oil and Gas Supply Submodule. The suspension volumes are 5,000,000 BOE 

for leases in water depths of 400 to 800 meters; 9,000,000 BOE for leases in water depths of 800 to 

1,600 meters; 12,000,000 BOE for leases in water depths of 1,600 to 2,400 meters; and 16,000,000 for 

leases in water depths greater than 2,400 meters. Examination of the resources available at 2,000 to 

2,400 meters showed that the differences between the depths used in the model and those specified in 

the bill would not materially affect the model result.

The MMS published its final rule on the “Oil and Gas and Sulphur Operations in the Outer Continental 

Shelf Relief or Reduction in Royalty Rates Deep Gas Provisions” on January 26, 2004, effective March 1, 

2004. The rule grants royalty relief for natural gas production from wells drilled to 15,000 feet or deeper 

on leases issued before January 1, 2001, in the shallow waters (less than 200 meters) of the Gulf of 

Mexico. Production of gas from the completed deep well must begin before five years after the effective 

date of the final rule. The minimum volume of production with suspended royalty payments is 15 billion 

cubic feet for wells drilled to at least 15,000 feet and 25 billion cubic feet for wells drilled to more than 

18,000 feet. In addition, unsuccessful wells drilled to a depth of at least 18,000 feet would receive a 

royalty credit for 5 billion cubic feet of natural gas. The ruling also grants royalty suspension for volumes 

of not less than 35 billion cubic feet from ultra-deep wells on leases issued before January 1, 2001.
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Section 354 of the Energy Policy Act of 2005 established a competitive program to provide grants for 

cost-shared projects to enhance oil and natural gas recovery through CO2 injection, while at the same 

time sequestering CO2 produced from the combustion of fossil fuels in power plants and large industrial 

processes.

From 1982 through 2008, Congress did not appropriate funds needed by the MMS to conduct leasing 

activities on portions of the federal Outer Continental Shelf (OCS) and thus effectively prohibited 

leasing. Further, a separate Executive ban in effect since 1990 prohibited leasing through 2012 on the 

OCS, with the exception of the Western Gulf of Mexico and portions of the Central and Eastern Gulf of 

Mexico. When combined, these actions prohibited drilling in most offshore regions, including areas 

along the Atlantic and Pacific coasts, the eastern Gulf of Mexico, and portions of the central Gulf of 

Mexico. In 2006, the Gulf of Mexico Energy Security Act imposed yet a third ban on drilling through 2022 

on tracts in the Eastern Gulf of Mexico that are within 125 miles of Florida, east of a dividing line known 

as the Military Mission Line, and in the Central Gulf of Mexico within 100 miles of Florida.

On July 14, 2008, President Bush lifted the Executive ban and urged Congress to remove the 

Congressional ban. On September 30, 2008, Congress allowed the Congressional ban to expire. Although 

the ban through 2022 on areas in the Eastern and Central Gulf of Mexico remains in place, the lifting of 

the Executive and Congressional bans removed regulatory obstacles to development of the Atlantic and 

Pacific OCS.

Oil and gas supply alternative cases

Tight Oil and Shale Gas Resource case

Estimates of technically recoverable tight/shale crude oil and natural gas resources are particularly 

uncertain and change over time as new information is gained through drilling, production, and 

technology experimentation. Over the last decade, as more tight/shale formations have gone into 

production, the estimate of technically recoverable tight oil and shale gas resources has increased. 

However, these increases in technically recoverable resources embody many assumptions that might 

not prove to betrue over the long term and over the entire tight/shale formation. For example, these 

resource estimates assume that crude oil and natural gas production rates achieved in a limited portion 

of the formation are representative of the entire formation, even though neighboring well production 

rates can vary by as much as a factor of three within the same play. Moreover, the tight/ shale 

formation can vary significantly across the petroleum basin with respect to depth, thickness, porosity, 

carbon content, pore pressure, clay content, thermal maturity, and water content. Additionally, 

technological improvements and innovations may allow development of crude oil and natural gas 

resources that have not been identified yet, and thus are not included in the Reference case.

High Oil and Gas Resource case

The High Oil and Gas Resource case was developed using assumptions that result in higher estimates of 

technically recoverable crude oil and natural gas resources than those in the Reference case. This case 
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allows for an examination of the potential impacts of higher domestic supply on energy demand, 

imports, and prices, but it does not represent an upper bound for future domestic oil and natural gas 

supply. The assumptions includes:

• 50% higher EUR per tight oil, tight gas, and shale gas well than in the reference case;

• 50% closer acre well spacing for tight and shale formations (minimum of 40 acres per well) than 

in the Reference case as well as additional unidentified tight oil resources to reflect the 

possibility that additional layers or new areas of low-permeability zones are identified and 

developed;

• diminishing returns on the EUR once drilling levels in a county exceed the number of potential 

wells assumed in the Reference case to reflect the increased probability that wells begin to 

interfere with one another at greater drilling density;

• long term technology improvement trends beyond what is assumed in the Reference case, 

represented as a 1% annual increase in the estimated ultimate recovery for tight oil, tight gas, 

and shale gas wells;

• kerogen development reaching 135,000 bbl/d by 2025; and

• 50% higher technically recoverable undiscovered resources in Alaska and the offshore Lower 48 

States than in the Reference case. A few offshore Alaska fields are assumed to be discovered 

and thus developed earlier than in the Reference case.

The total unproved technically recoverable resources are 453 billion barrels of crude oil and 3,267

trillion cubic feet in the High Oil and Gas Resource case compared to 226 billion barrels of crude oil and 

1,968 trillion cubic feet of dry natural gas in the Reference case. Proved reserves of oil and natural gas 

are the same in both cases; 33 billion barrels of crude oil and 308 trillion cubic feet of dry natural gas.
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Notes and sources

[93] The current development of tight oil plays has shifted industry focus and investment away from the 

development of U.S. oil shale (kerogen) resources. Considerable technological development is required 

prior to the large-scale in-situ production of oil shale being economically feasible. Consequently, the Oil 

Shale Supply Submodule assumes that large-scale in-situ oil shale production is not commercially 

feasible in the Reference case prior to 2040.

[94] Technically recoverable resources are resources in accumulations producible using current recovery 

technology but without reference to economic profitability.

[95] Proved reserves are the estimated quantities that analysis of geological and engineering data 

demonstrates with reasonable certainty to be recoverable in future years from known reservoirs under 

existing economic and operating conditions.

[96] Unproved resources include resources that have been confirmed by exploratory drilling and 

undiscovered resources, which are located outside oil and gas fields in which the presence of resources 

has been confirmed by exploratory drilling; they include resources from undiscovered pools within 

confirmed fields when they occur as unrelated accumulations controlled by distinctly separate structural 

features or stratigraphic conditions.

[97] The Bakken areas are consistent with the USGS Bakken formation assessment units shown in Figure 

1 of Fact Sheet 2013-3013, Assessment of Undiscovered Oil Resources in the Bakken and Three Forks 

Formations, Williston Basin Province, Montana, North Dakota, and South Dakota, 2013 at 

http://pubs.usgs.gov/fs/2013/3013/fs2013-3013.pdf.

[98] Alyeska Pipeline Service Company, Low Flow Impact Study, Final Report, June 15, 2011, Anchorage, 

Alaska, at 

www.alyeskapipe.com/Inthenews/LowFlow/LoFIS_Summary_Report_P6%2027_FullReport.pdf.
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Natural Gas Transmission and Distribution Module

The NEMS Natural Gas Transmission and Distribution Module (NGTDM) derives domestic natural gas 

production, wellhead and border prices, end-use prices, and flows of natural gas through a regional 

interstate representative pipeline network, for both a peak (December through March) and off-peak 

period during each projection year. These are derived by solving for the market equilibrium across the 

three main components of the natural gas market: the supply component, the demand component, and 

the transmission and distribution network that links them. Natural gas flow patterns are a function of 

the pattern in the previous year, coupled with the relative prices of the supply options available to bring 

gas to market centers within each of the NGTDM regions (Figure 9). The major assumptions used within 

the NGTDM are grouped into four general categories. They relate to (1) structural components of the 

model, (2) capacity expansion and pricing of transmission and distribution services, (3) Arctic pipelines, 

and (4) imports and exports. A complete listing of NGTDM assumptions and in-depth methodology 

descriptions are presented in Model Documentation: Natural Gas Transmission and Distribution Module 

of the National Energy Modeling System, Model Documentation 2014, DOE/EIA-M062 (2014) 

(Washington, DC, 2014).

Figure 9. Natural Gas Transmission and Distribution regions
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Key assumptions

Structural components

The primary and secondary region-to-region flows represented in the model are shown in Figure 9. 

Primary flows are determined, along with nonassociated gas production levels, as the model equilibrates 

supply and demand. Associated-dissolved gas production is determined in the Oil and Gas Supply 

Module (OGSM). Secondary flows are established before the equilibration process and are generally set 

exogenously. In the Northeast, where secondary flows are expected to grow significantly, secondary 

flows are endogenously set based on price differentials between sending and receiving regions. 

Liquefied natural gas (LNG) imports and domestically-produced natural gas exports are also not directly 

part of the equilibration process, but are set at the beginning of each NEMS iteration in response to the 

price from the previous iteration and projected future prices, respectively. LNG re-exports are set 

exogenously to the model. Flows and production levels are determined for each season, linked by 

seasonal storage. When required, annual quantities (e.g., consumption levels) are split into peak and 

off-peak values based on historical averages. When multiple regions are contained in a Census Division, 

regional end-use consumption levels are approximated using historical average shares. Pipeline and 

storage capacity are added as warranted by the relative volumes and prices. Regional pipeline fuel and 

lease and plant fuel consumption are established by applying an historically based factor to the flow of 

gas through a region and the production in a region, respectively. Prices within the network, including 

at the borders and the wellhead, are largely determined during the equilibration process. Delivered 

prices for each sector are set by adding an endogenously estimated markup (generally a distributor 

tariff) to either the regional representative city gate price or the regional market hub price. Supply 

curves and electric generator gas consumption are provided by other NEMS modules for subregions of 

the NGTDM regions, reflective of how their internal regions overlap with the NGTDM regions.

Capacity expansion and pricing of transmission and distribution

For the first two projection years, announced pipeline and storage capacity expansions (that are 

deemed highly likely to occur) are used to establish limits on flows and seasonal storage in the model. 

Subsequently, pipeline and storage capacity is added when increases in consumption, coupled with an 

anticipated price increase, warrant such additions (i.e., flow is allowed to exceed current capacity if the 

demand still exists given an assumed increased tariff). Once it is determined that an expansion will 

occur, the associated capital costs are applied in the revenue requirement calculations in future years. 

Capital costs are assumed based on average costs of recent comparable expansions for compressors, 

looping, and new pipeline.

It is assumed that pipeline and local distribution companies build and subscribe to a portfolio of 

interstate pipeline and storage capacity to serve a region-specific colder-than-normal winter demand 

level, currently set at 30% above the daily average. Maximum pipeline capacity utilization in the peak 

period is set at 99%. In the off-peak period, the maximum is assumed to vary between 75% and 99% of 

the design capacity. The overall level and profile of consumption, as well as the availability and price of 

supplies, generally cause realized pipeline utilization levels to be lower than the maximum.
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Pricing of services

Transport rates between regions are set for the purposes of determining natural gas flows through the 

representative pipeline network based on historical observed differentials between regional spot prices. 

Ultimately regional city gate prices reflect the addition of reservation charges along each of the 

connecting routes and within a region. Per-unit pipeline reservation charges are initially based on a 

regulated cost-of-service calculation and an assumed flow rate, and are dynamically adjusted based on 

the realized utilization rate. Reservation rates for interstate pipeline services (both between NGTDM 

regions and within a region) are calculated assuming that the costs of new pipeline capacity will be 

rolled into the existing rate base.

For the industrial and electric generator sectors delivered natural gas prices are based on regional prices

which do not directly include any pipeline reservation fees (i.e., spot prices), with an added markup 

based on averaged or econometrically estimated historical values. For the residential and commercial 

customers delivered natural gas prices are based on regional city gate prices with an added 

econometrically estimated distributor markup. The distributor tariffs are projected using 

econometrically estimated equations, primarily in response to changes in consumption levels. 

Historically based differentials are used to establish separate prices for energy-intensive and nonenergy-intensive industrial customers. Prices are originally set on a seasonal basis and are averaged 

with quantity-weights to derived annual prices.

The natural gas vehicle sector is segregated into compressed natural gas (CNG) and liquefied natural gas 

(LNG) at private refueling stations (fleets) and at public retail stations. The distributor markup for natural 

gas delivered via pipeline to a CNG station is based off historical data for the sector. A retail markup and 

motor fuel (excise) taxes are added to set the final retail price. The excise taxes applied and the value 

and assumptions behind the retail markups assumed are shown in Table 10.1. The price for delivered 

dry natural gas to a liquefaction plant is approximated by using the price to electric generators. The 

price for LNG is therefore set to the price to electric generators, plus the assumed price to liquefy and 

transport the LNG to a station, the retail price markup at the station, and the excise taxes. The values 

for these components and the primary assumptions behind them are shown in Table 10.1. The table 

shows the national average state excise tax, while in the model these taxes vary by region.
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Table 10.1. Assumptions related to CNG and LNG fuel prices

CNG CNG LNG LNG

Year fleet retail fleet retail

Retail markup after dry gas pipeline delivery, with no excise tax (2010$/dge) 0.80 0.93 1.39 1.58

 Capacity (dge/day) 1600 1100 4000 4000

 Usage (percent of capacity) 80 60 80 60

 Capital cost (million 2010$) 80 0.5 1.0 1.0

 Capital recovery (years) 5 10 5 10

 Weighted average cost of capital (rate) 0.10 0.15 0.10 0.15

 Operating cost (2010$/dge) 0.34 0.51 0.41 0.59

 Charge for liquefying and delivering LNG (2010$/dge) -- -- 0.75 0.75

Federal excise tax (nominal$/dge) 0.21 0.21 0.42 0.42

State excise tax (nominal$/dge) 0.15 0.15 0.24 0.24

Fuel loss for liquefying and delivering LNG (percent of input volumes) -- -- 10 10

Fuel loss at station (percent of input volumes) 0.5 0.5 1.0 2.0

Dge=diesel gallon equivalent.

Source: U.S. Energy Information Administration, Office of Petroleum, Natural Gas, and Biofuels Analysis, U.S. Tax Code and 

State Tax Codes.

Prices for natural gas to fuel ships are set at the same rate as for vehicles less state motor fuel taxes. For 

trains, both federal and state motor fuels taxes are not included in the price. In addition, the retail 

markup above the cost of dry gas for LNG for rail was assumed at $0.90 (2010$) per diesel gallon 

equivalent (dge) (compared to $1.39/dge for fleet vehicles as shown in Table 10.1), with the assumption 

that liquefaction would occur at the refueling point and cost $0.53/dge (compared to $0.75/dge for 

vehicles), operating costs would be $0.21/dge (compared to $0.41/dge for fleet vehicles), and capital 

cost recovery for additional equipment beyond the liquefiers would be $0.16/dge (compared to 

$0.23/dge for fleet vehicles, not shown in table).

Pipelines from arctic areas into Alberta

The outlook for natural gas production from the North Slope of Alaska is affected strongly by its extreme 

distance from the necessary infrastructure to transport it to major commercial markets. At present 

there are three basic options for commercializing the natural gas that has been produced in association 

with the Prudhoe Bay oil fields and reinjected in the oil wells to maintain pressure, and is therefore 

relatively low-cost to recover: build a pipeline to the Lower 48 states via Alberta, produce and transport 

the natural gas as liquid fuel in a gas-to-liquid (GTL) plant, or pipe the natural gas to a seaport, liquefy 

and ship it overseas. The GTL option was not considered for AEO2015. Which, if any, of the other two 

options that is determined within the model to be economically viable first is assumed to have primary 

access to the proved low-cost reserves on the North Slope and preclude the economic viability of the 

other option. The assumptions associated with the LNG option are provided in a later section. The 

primary assumptions associated with estimating the cost of North Slope Alaskan gas to Alberta, as well 

as for Mackenzie Delta gas from Canada’s Northwest Territories to Alberta, are shown in Table 10.2. A 

calculation is performed to estimate a regulated, levelized tariff for each pipeline. Additional items are 
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added to account for the wellhead price, treatment costs, pipeline fuel costs, and a risk premium to 

reflect the potential impact on the market price if the pipeline comes online.

To assess the market value of Alaskan and Mackenzie Valley gas against the Lower 48 market, a price 

differential of $0.77 (2013 dollars per Mcf) is assumed between the price in Alberta and the average 

Lower 48 wellhead price. The resulting cost of Alaska gas, relative to the Lower 48 wellhead price, is 

approximately $8.32 (2013 dollars per Mcf), with some variation across the projection due to changes in 

gross domestic product. Construction of an Alaska-to-Alberta pipeline is projected to commence if the 

assumed total costs for Alaska gas in the Lower 48 states exceed the average Lower 48 gas price in each 

of the previous two years, on average over the previous five years (with greater weight applied to more 

recent years), and as expected to average over the next three years. An adjustment is made if prices 

were declining over the previous five years. Once the assumed four-year construction period is 

complete, expansion can occur if the price exceeds the initial trigger price by $7.09 (2013 dollars per 

Mcf). Supplies to fill an expanded pipeline are assumed to require new gas wells. When the Alaska-toAlberta pipeline is built in the model, additional pipeline capacity is added to bring the gas across the 

border into the United States. For accounting purposes, the model assumes that all of the Alaska gas will 

be consumed in the United States and that sufficient economical supplies are available at the North 

Slope to fill the pipeline over the depreciation period.

Natural gas production from the Mackenzie Delta is assumed to be sufficient to fill a pipeline over the 

projection period should one be built connecting the area to markets in the south in the United States 

and Canada. The basic methodology used to represent the decision to build a Mackenzie pipeline is 

similar to the process used for an Alaska-to-Lower 48 pipeline, using the primary assumed parameters 

listed in Table 10.2.

Supplemental natural gas

The projection for supplemental gas supply is identified for three separate categories: pipeline quality 

synthetic natural gas (SNG) from coal or coal-to-gas (CTG), SNG from liquids, and other supplemental 

supplies (propane-air, coke oven gas, refinery gas, biomass air, air injected for Btu stabilization, and 

manufactured gas commingled and distributed with natural gas). The third category, other supplemental 

supplies, are held at a constant level of 8.8 billion cubic feet per year throughout the projection because 

this level is consistent with historical data and it is not believed to change significantly in the context of a 

Reference case. SNG from liquid hydrocarbons in Hawaii is assumed to continue over the projection at 

the average historical level of 2.6 billion cubic feet per year. SNG production from coal at the currently 

operating Great Plains Coal Gasification Plant is also assumed to continue through the projection period 

at an average historical level of 52.2 billion cubic feet per year. It is assumed that additional CTG 

facilities will be built if and when natural gas prices are high enough to make them economic. One CTG 

facility is assumed capable of processing 6,040 tons of bituminous coal per day, with a production 

capacity of 0.1 billion cubic feet per day of synthetic fuel and approximately 100 megawatts of capacity 

for electricity cogeneration sold to the grid. A CTG facility of this size is assumed to cost nearly $1 billion 

in initial capital investment (2013 dollars). CTG facilities are assumed to be built near existing coal mines. 

All NGTDM regions are considered potential locations for CTG facilities except for New England. 

Synthetic gas products from CTG facilities are assumed to be competitive when natural gas prices rise 
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above the cost of CTG production (adjusted for credits from the sale of cogenerated electricity). It is 

assumed that CTG facilities will not be built before 2015.

Table 10.2. Primary assumptions for natural gas pipelines from Alaska and Mackenzie Delta into 

Alberta, Canada

Alaska to Alberta Mackenzie Delta to Alberta

Initial flow into Alberta 3.8 billion cubic feet per day 1.1 billion cubic feet per day

Expansion potential 22% 58%

Initial capitalization $38.0 billion (2013 dollars) $11.4 billion (2013 dollars)

Cost of debt (premium over 10-year treasury note yield) 0.75% 0.00%

Cost of equity (premium over 10-year treasury note yield) 6.50% 7.50%

Debt fraction 70% 60%

Depreciation period 20 years 20 years

Minimum wellhead price (including treatment and fuel costs) $3.76 (2013 dollars per Mcf) $4.46 (2013 dollars per Mcf)

Expected price reduction $1.07 (2013 dollars per Mcf) $0.06 (2013 dollars per Mcf)

Construction period 4 years 4 years

Planning period 5 years 2 years

Earliest start year 2021 2018

Source: U.S. Energy Information Administration, Office of Petroleum, Natural Gas, and Biofuels Analysis. Alaska pipeline cost data are 

based on Federal Energy Regulatory Commission, Docket PF09-11-001, “Open Season Plan Documents Submitted in

Connection with Request for Commission Approval of Detailed Plan for Conducting an Open Season,” submitted by TransCanada

Alaska Company LLC on January 29, 2010, Volume III of III, Appendix C, Exhibit J - Recourse Rate Output, various pages. Note that 

the capital cost figure is the arithmetic average of the two $30.7 and $40.4 billion 2009 dollars capital cost estimates that

include the mainline gas pipeline and the gas treatment plant, but which exclude the gas field line from Point Thomson to the

gas treatment plant. National Energy Board of Canada, “Mackenzie Gas Project - Hearing Order GH-1-2004, Supplemental 

Information - Project Update 2007,” dated May 15, 2007; National Energy Board of Canada, “Mackenzie Gas Project - Project Cost 

Estimate and Schedule Update,” dated March 12, 2007; Canada Revenue Agency, “T2 Corporation Income Tax Guide 2006,” T4012(E) 

Rev. 07. National Energy Board of Canada, “Application for Approval of the Development Plan for Taglu Field - Project Description,” 

submitted by Imperial Oil Resources Ltd., TDPA-P1, August 2004; National Energy Board of Canada, “Application for Approval of the 

Development Plan for Niglintgak Field - Project Description,” submitted by Shell Canada Ltd., NDPA-P1, August 2004; and National 

Energy Board of Canada, “Application for Approval of the Development Plan for Parsons Lake Field - Project Description.

Note: These assumptions (particularly the earliest start year) were not updated for AEO2015 since these projects were not 

economically viable under any of the scenarios included.”

Natural gas imports and exports

U.S. natural gas trade with Mexico is determined endogenously based on various assumptions about the 

natural gas market in Mexico. Natural gas consumption levels in Mexico are set exogenously based on 

projections from the International Energy Outlook 2013 and are provided in Table 10.3, along with 

initially assumed Mexico natural gas production and LNG import levels targeted for markets in Mexico. 

Adjustments to production are made endogenously within the model to reflect a response to price 

fluctuations within the market and reflect laws concerning foreign investment at the time of the 

projection. Domestic production is assumed to be supplemented by LNG from receiving terminals 

constructed on both the east and west coasts of Mexico. Maximum LNG import volumes targeted for 

markets in Mexico are set exogenously and will be realized if endogenously determined LNG imports 

into North America are sufficient. The difference between production plus LNG imports and 
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consumption in Mexico in any year is assumed to be either imported from, or exported to, the United 

States.

Table 10.3. Exogenously specified Mexico natural gas consumption, production,

and LNG imports

billion cubic feet per year

Consumption Initial Dry Production Initial LNG Imports

2015 2,785 1,591 318

2020 3,228 1,475 633

2025 3,801 1,735 798

2030 4,576 2,111 813

2035 5,586 2,825 678

2040 6,981 3,781 393

Source: U.S. Energy Information Administration, Office of Petroleum, Natural Gas, and Biofuels Analysis, 

based on U.S. Energy Information Administration, International Energy Outlook 2013 DOE/EIA0484(2013).

Note: Excludes any LNG imported to Mexico for export to the United States.

Similarly to Mexico, Canada is modeled through a combination of exogenously and endogenously 

specified components. Western Canadian production, U.S. import flows from Canada, and U.S. export 

flows to Canada are determined endogenously within the model. Canadian natural gas production in 

Eastern Canada, consumption, and LNG exports are set exogenously in the model and are shown in 

Table 10.4. Production from conventional and tight formations in the Western Canadian Sedimentary 

Basin (WCSB) is calculated endogenously to the model using annual supply curves based on beginningof-year proved reserves and an estimated production-to-reserve ratio. Reserve additions are set equal 

to the product of successful natural gas wells and a finding rate (both based on an econometric 

estimation). The initial coalbed methane, shale gas, and conventional WCSB economically recoverable 

unproved resource base estimates assumed in the model are 45 trillion cubic feet, 90 trillion cubic feet, 

and 127 trillion cubic feet, respectively, all as of 2011.[99] Potential production from tight formations 

was approximated by increasing the conventional resource level by 2.35% annually. Production from 

coalbed and shale sources is established based on an assumed production path which varies in response 

to the level of remaining resources and the solution price in the previous projection year. LNG imports 

to Canada are set in conjunction with the LNG import volumes for the Lower 48 states.
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Table 10.4. Exogenously specified Canada natural gas consumption, production, and LNG 

exports 

billion cubic feet per year

Year Consumption Production Eastern Canada LNG Exports

2015 3,196 159 0

2020 3,711 67 255

2025 4,157 29 771

2030 4,475 12 1,022

2035 4,762 0 1,228

2040 5,062 0 1,603

Source: Consumption – Based on U.S. Energy Information Administration, International Energy Outlook 2013, 

DOE/EIA-0484(2013); Production - Energy Information Administration, Office of Petroleum, Natural Gas, and 

Biofuels Analysis. LNG exports – U.S. Energy Information Administration, International Energy Outlook 2013, 

DOE/EIA-0848(2013).

LNG imports to the United States and Canada are determined endogenously within the model using 

Atlantic/Pacific and peak/off-peak supply curves derived from model results generated by EIA’s 

International Natural Gas Model (INGM). Prices from the previous model iteration are used to establish 

the total level of U.S./Canada LNG imports in the peak and off-peak period and in the Atlantic and Pacific 

regions. First, assumed LNG imports which are consumed in Mexico are subtracted (presuming the 

volumes are sufficient). Then, the remaining levels are allocated to the model regions based on last 

year’s import levels, the available regasification capacity, and the relative prices. Regasification capacity 

is limited to facilities currently in existence and those already under construction, which is fully sufficient 

to accommodate import levels projected by the model.

LNG exports of domestically produced natural gas from the Lower 48 states and Alaska are set 

endogenously in the model. The model assesses the relative economics of a generic 200 billion cubic 

feet train/project in operation over the next 20 years in each viable coastal region by comparing a 

model-generated estimate of the expected market price in Europe and Asia over the next 20 years 

against the expected price of natural gas in each coastal region plus assumed charges for liquefaction, 

shipping, and regasification (shown in Table 10.5). A present value of the differential is set using a 

discount rate of 10%. The model limits the annual liquefaction capacity builds to three trains a year. 

Once it is determined that a train is economically viable, the size and location of the train is set based on 

a list of trains that have been approved and it is phased in over a two-year period. If the list is 

exhausted, a generic 200 billion cubic feet per year train is added in the region showing the greatest 

positive economic potential. Once a facility is built, it is assumed to operate at its design capacity 

throughout the projection period unless the competing price in Asia or Europe falls below the delivered 

price of U.S. LNG in the region, excluding assumed reservation charges (i.e., “sunk” costs) for 

liquefaction. 
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Table 10.5. Charges related to LNG exports

2010 dollars per million Btu

South Atlantic West South Central Washington/Oregon Alaska

Liquefaction & Pipe Fee 3.30 3.00 4.10 7.00

Shipping to Europe 0.98 1.28 3.86 3.65

Shipping to Asia 2.63 2.55 1.15 0.90

Regasification 0.10 0.10 0.10 0.10

Fuel charge (percent)* 15 15 15 15

*Percent increase in market price of natural gas charged by liquefaction facility to cover fuel-related expenses, largely 

fuel used in the liquefaction process.

Other constraining assumptions are considered, such as earliest start year and maximum export 

volumes. Any existing facilities or ones under construction are set exogenously to the model, which for 

AEO2015 include the four trains under construction at Sabine Pass (starting in mid-2015 and reaching 

800 billion cubic feet per year in 2018). The projected market prices of LNG in Europe (National 

Balancing Point) and Asia (Japan) are based on the assumed values shown in Table 10.6, projected Brent 

oil prices, and the level of North American LNG exports. Annual U.S. exports of liquefied natural gas 

(LNG) to Japan via Alaska’s existing Kenai facility are assumed to cease in 2015. LNG re-exports are 

assumed to stay at 3 billion cubic feet per year throughout the projection period.

Table 10.6. International natural gas volume drivers for world LNG Europe and Asia market price 

projections

billion cubic feet

Consumption Consumption Consumption Consumption Production

Flexible LNG* OECD Europe Japan S. Korea China China

2015 4,362 19,714 4,318 1,532 5,615 3,806

2020 5,821 20,378 4,583 1,657 7,752 4,242

2025 7,273 20,774 4,937 1,858 10,270 5,165

2030 8,577 22,052 5,143 1,968 13,041 6,702

2035 10,097 23,183 5,233 2,291 15,634 8,500

2040 11,452 24,478 5,242 2,502 17,498 10,119

*Flexible LNG is a baseline projection of the volumes of LNG sold in the spot market or effectively available for sale 

at flexible destinations.

Source: U.S. Energy Information Administration, International Energy Outlook 2013, DOE/EIA-0484(2013) and U.S. 

Energy Information Administration, Office of Petroleum, Natural Gas, and Biofuels Analysis.

Legislation and regulations

The methodology for setting reservation fees for transportation services is initially based on a regulated 

rate calculation, but is ultimately consistent with FERC’s alternative ratemaking and capacity release 

position in that it allows some flexibility in the rates pipelines ultimately charge. The methodology is 

market-based in that rates for transportation services will respond positively to increased demand for 

services while rates will decline should the demand for services decline.
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Section 116 of the Military Construction Appropriations and Emergency Hurricane Supplemental 

Appropriations Act, 2005 (Public Law 108-324) gives the Secretary of Energy the authority to issue 

Federal loan guarantees for an Alaska natural gas transportation project, including the Canadian portion, 

that would carry natural gas from northern Alaska, through the Canadian border south of 68 degrees 

north latitude, into Canada, and to the Lower 48 states. This authority would expire 2 years after the 

final certificate of public convenience and necessity is issued. In aggregate, the loan guarantee would 

not exceed: (1) 80% of total capital costs (including interest during construction); (2) $18 billion (indexed 

for inflation at the time of enactment); or (3) a term of 30 years. The Act also promotes streamlined 

permitting and environmental review, an expedited court review process, and protection of rights-ofway for the pipeline. The assumed costs of borrowing money for the pipeline were reduced to reflect 

the decreased risk as a result of the loan guarantee.

Section 706 of the American Jobs Creation Act of 2004 (Public Law 108-357) provided a 7-year cost-ofinvestment recovery period for the Alaska natural gas pipeline, as opposed to the previously allowed 15-

year recovery period, for tax purposes. The provision is effective for property placed in service after 

2013 (or treated as such) and is assumed to have minimal impact on the decision to build the pipeline.

Section 707 of the American Jobs Creation Act extended the 15-percent tax credit previously applied to 

costs related to enhanced oil recovery to construction costs for a gas treatment plant that supplies 

natural gas to a 2 trillion Btu per day pipeline, lies in Northern Alaska, and produces carbon dioxide for 

injection into hydrocarbon-bearing geological formations. A gas treatment plant on the North Slope that 

feeds gas into an Alaska pipeline to Canada is expected to satisfy this requirement. The provision is 

effective for costs incurred after 2004. The impact of this tax credit is assumed to be factored into the 

cost estimates filed by the participating companies.

Section 312 of the Energy Policy Act of 2005 authorizes the Federal Energy Regulatory Commission 

(FERC) to allow natural gas storage facilities to charge market-based rates if it was believed that they 

would not exert market power. Storage rates are allowed to vary in the model from regulation-based 

rates, depending on market conditions.

Notes and sources

[99] Coalbed, shale gas, and tight sands unproved resource based on the National Energy Board of 

Canada’s “Canada’s Energy Future: Energy supply and demand projections to 2035,” November 2011.
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Liquid Fuels Market Module

The NEMS Liquid Fuels Market Module (LFMM) projects petroleum product prices and sources of liquid 

fuels supply for meeting petroleum product demand. The sources of liquid fuels supply include 

petroleum-based fuels, such as crude oil (both domestic and imported), petroleum product imports, and 

unfinished oil imports. It also includes non-petroleum-based inputs, including alcohols, ethers, esters, 

corn, biomass, natural gas, and coal. In addition, liquid fuels supply includes natural gas plant liquids 

production, and refinery processing gain. The LFMM also projects capacity expansion and fuel 

consumption at domestic refineries.

Figure 10. Liquid Fuels Market Module Regions

The LFMM contains a linear programming (LP) representation of U.S. petroleum refining activities, 

biofuels production activities, and other non-petroleum liquid fuels production activity in eight U.S. 

regions. It also represents refining activity in the non-U.S. Maritime Canada/Caribbean refining region, 

which predominantly serves U.S. markets. In order to better represent policy, import/export patterns, 

and biofuels production, the eight U.S. regions are defined by subdividing three of the five Petroleum 

Administration for Defense Districts (PADDs) (Figure 10). The LP model also represents supply curves for 

crude imports, petroleum product imports and exports, biodiesel imports, and advanced ethanol 

imports from Brazil. The nine LFMM regions and import/export curves are connected in the LP via crude 

and product transport links. In order to interact with other NEMS modules with different regional 
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representations, certain LFMM inputs and outputs are converted from sub-PADD regions to other 

regional structures and vice versa. For example, the LP model converts end-use product prices from the 

LFMM regions (excluding Maritime Canada/Caribbean region) into prices for the nine U.S. Census 

Divisions (shown in Figure 5) using the assumptions and methods described below.

Key assumptions

Product types and specifications

The LFMM models refinery production of the products shown in Table 11.1.

The LFMM assumes no change in the state and federal specifications for the products listed below. The 

costs of producing different formulations of gasoline and diesel fuel required under current regulations 

are determined within the LP representation of refineries.

Table 11.1. Petroleum product categories

Product Category Specific Products

Motor Gasoline Conventional, Reformulated (including CARB gasoline)

Jet Fuel Kerosene-type

Distillates Kerosene, Heating Oil, Low-Sulfur, Ultra-Low-Sulfur and CARB Diesel

Residual Fuels Low-Sulfur, High-Sulfur

Liquefied Petroleum Gases Ethane, Propane, Propylene, normal- and iso-Butane

Petrochemical Feedstock Petrochemical Naphtha, Petrochemical Gas Oil, Aromatics

Others Lubricating Products and Waxes, Asphalt/Road Oil, Still Gas

Petroleum Coke, Special Naphthas, Aviation Gasoline

Source: U.S. Energy Information Administration, Office of Energy Analysis.

Motor gasoline specifications and market shares

The LFMM models the production and distribution of two different types of gasoline: conventional and 

reformulated. The following specifications are included in the LFMM to differentiate between 

conventional and reformulated gasoline blends (Table 11.2): Reid vapor pressure (RVP), benzene 

content, aromatic content, sulfur content, olefins content, and the percent evaporated at 200 and 300 

degrees Fahrenheit (E200 and E300). The LFMM incorporates the EPA Tier 3 program requirement that 

the sulfur content of delivered gasoline be no greater than 10 parts per million (PPM) by January 1, 

2017. [100]
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Table 11.2. Year-round gasoline specifications by Petroleum Administration for Defense District 

(PADD)

Reid Vapor

Aromatics 

Volume

Benzene 

Volume Sulfur1

Olefin 

Volume Percent Percent

Pressure Percent Percent PPM Percent Evaporated Evaporated

PADD/Type (Max PSI) (Max) (Max) (Max) (Max) at 200o (Min) at 300o (Min)

Conventional

PADD I 10.11 24.23 0.62 22.4/5.0 10.8 45.9 81.7

PADD II 10.11 24.23 0.62 22.4/5.0 10.8 45.9 81.7

PADD III 10.11 24.23 0.62 22.4/5.0 10.8 45.9 81.7

PADD IV 10.11 24.23 0.62 22.4/5.0 10.8 45.9 81.7

PADD V 10.11 24.23 0.62 22.4/5.0 10.8 45.9 81.7

Reformulated

PADD I 8.8 21.0 0.62 23.8/5.0 10.36 54.0 81.7

PADD II 8.8 21.0 0.62 23.8/5.0 10.36 54.0 81.7

PADD III 8.8 21.0 0.62 23.8/5.0 10.36 54.0 81.7

PADD IV 8.8 21.0 0.62 23.8/5.0 10.36 54.0 81.7

PADD V

Nonattainment 8.8 21.0 0.62 23.8/5.0 10.36 54.0 81.7

CARB (attainment) 7.7 23.12 0.58 10/5.0 6.29 42.9 86.3 1

Values reflect sulfur levels “prior to / after” January 1, 2017, to meet EPA final ruling: “EPA Sets Tier 3 Motor Vehicle Emission 

and Fuel Standards,” http://www.epa.gov/otaq/documents/tier3/420f14009.pdf.

Max = maximum, Min. = minimum, PADD = Petroleum Administration for Defense District. PPM = parts per million by weight, PSI 

= pounds per square inch.

Benzene volume percent changed to 0.62 for all regions and types in 2011 to meet the MSAT2 ruling.

Source: U.S. Energy Information Administration, Office of Energy Analysis.

Reformulated gasoline must meet the Complex Model II compliance standards, which cannot exceed 

average 1990 levels of toxic and nitrogen oxide emissions [101]. Reformulated gasoline has been 

required in many areas in the United States since January 1995. In 1998, EPA began certifying 

reformulated gasoline using the “Complex Model,” which allows refiners to specify reformulated 

gasoline based on emissions reductions from their companies’ respective 1990 baselines or EPA’s 1990 

baseline. The LFMM reflects “Phase 2” reformulated gasoline requirements which began in 2000. The 

LFMM uses a set of specifications that meet the “Complex Model” requirements, but it does not 

attempt to determine the optimal specifications that meet the “Complex Model.”

Cellulosic biomass feedstock supplies and costs are provided by the NEMS Renewable Fuels Model. 

Initial capital costs for biomass cellulosic ethanol were obtained from a research project reviewing cost 

estimates from multiple sources [102]. Operating costs and credits for excess electricity generated at 

biomass ethanol plants were obtained from a survey of literature [103].
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Corn supply prices are estimated from the USDA baseline projections to 2019 [104]. Operating costs of 

corn ethanol plants are obtained from USDA survey of ethanol plant costs [105]. Energy requirements 

are obtained from a study of carbon dioxide emissions associated with ethanol production [106].

AEO2015 assumes a minimum 10% blend of ethanol in domestically consumed motor gasoline. Federal 

reformulated gasoline (RFG) and conventional gasoline can be blended with up to 15% ethanol (E15) in 

light-duty vehicles of model year 2001 and later. Reformulated and conventional gasoline can also be 

blended with 16% biobutanol. Actual levels will depend on the ethanol and biobutanol blending value 

and relative cost-competitiveness with other gasoline blending components. In addition, current state 

regulation, along with marketplace constraints, limit the full penetration of E15 in the projection. The 

Energy Independence and Security Act of 2007 (EISA2007) defines a requirements schedule for having 

renewable fuels blended into transportation fuels by 2022.

RVP limitations are in effect during summer months, which are defined differently by consuming region. 

In addition, different RVP specifications apply within each PADD. The LFMM assumes that these 

variations in RVP are captured in the annual average specifications, which are based on summertime 

RVP limits, wintertime estimates, and seasonal weights.

Within the LFMM, total gasoline demand is disaggregated into demand for conventional and 

reformulated gasoline by applying assumptions about the annual market shares for each type. In 

AEO2015 the annual market shares for each region reflect actual 2010 market shares and are held 

constant throughout the projection (see Table 11.3 for AEO2015 market share assumptions).

Table 11.3. Market share for gasoline types by Census Division

Gasoline Type

New 

England

Middle 

Atlantic

East 

North 

Central

West 

North 

Central

South 

Atlantic

East 

South 

Central

West 

South 

Central Mountain Pacific

Conventional Gasoline 18 41 81 88 81 95 72 86 25

Reformulated Gasoline 82 59 19 12 19 5 28 14 75

Note: Data derived from EIA-782C, “Monthly Report of Prime Supplier Sales of Petroleum Products Sold for Local Consumption,” 

January-December 2010.

Note: As of January 2007, Oxygenated Gasoline is included within Conventional Gasoline.

Source: U.S. Energy Information Administration, Office of Energy Analysis.

Diesel fuel specifications and market shares

In order to account for ultra-low-sulfur diesel (ULSD, or highway diesel) regulations related to the Clean 

Air Act Amendments of 1990 (CAAA90), ULSD is differentiated from other distillates. In NEMS, the 

California portion of the Pacific Region (Census Division 9) is required to meet California Air Resources 

Board (CARB) standards. Both Federal and CARB standards currently limit sulfur to 15 parts per million 

(ppm). 
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Demand for highway-grade diesel in LFMM is assumed to equal total transportation distillate demand. 

Over the past few years, highway-grade diesel supplies have nearly matched total transportation 

distillate sales, although some highway-grade diesel has gone to non-transportation uses such as 

construction and agriculture.

AEO2015 incorporates the “nonroad, locomotive, and marine” (NRLM) diesel regulation finalized in May 

2004 for large refiners and importers. The final NRLM rule established a new ULSD limit of 15 ppm for 

nonroad diesel by mid-2010. For locomotive and marine diesel, the rule established an ULSD limit of 15 

ppm in mid-2012.

End-use product prices

End-use petroleum product prices are based on marginal costs of production, plus production-related 

fixed costs, plus distribution costs and taxes. The marginal costs of production are determined within 

the LP and represent variable costs of production, including additional costs for meeting reformulated 

fuels provisions of CAAA90. Environmental costs associated with controlling pollution at refineries are 

implicitly assumed in the annual update of the refinery investment costs for the processing units.

The costs of distributing and marketing petroleum products are represented by adding product-specific 

distribution costs to the marginal refinery production costs (product wholesale prices). The distribution 

costs are derived from a set of base distribution markups (Table 11.4).

State and federal taxes are also added to transportation fuels to determine final end-use prices (Tables 

11.5 and 11.6). Tax trend analysis indicates that state taxes increase at the rate of inflation; therefore, 

state taxes are held constant in real terms throughout the projection. This assumption is extended to 

local taxes, which are assumed to average 1% of motor gasoline prices [107]. Federal taxes are assumed 

to remain at current levels in accordance with the overall AEO2015 assumption of current laws and 

regulations. Federal taxes are not held constant in real terms, but are deflated as follows:

Federal Tax product, year = Current Federal Tax product /GDP Deflator year

Crude oil quality

In the LFMM, the quality of crude oil is characterized by average gravity and sulfur levels. Both domestic 

and imported crude oil are divided into eleven categories as defined by the ranges of gravity and sulfur 

shown in Table 11.7.

A “composite” crude oil with the appropriate yields and qualities is developed for each category by 

averaging the characteristics of specific crude oil streams in the category. Each category includes both 

domestic and foreign crude oil, which are both used to determine category characteristics. For each 

category’s domestic crude oil volumes, estimates of total regional production are made first. Each 

region’s production is then divided among each of the eleven categories based on that region’s 

distribution of average API gravity and sulfur content. For AEO2015, processed API 50+ crude is assumed 

to be processed condensate and, under the current ruling by the US Bureau of Industry and Security 

(BIS), is allowed to be exported [108]. For imported crude oil, a separate supply curve is provided (by the 

IEM) for each category.
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Table 11.4. Petroleum product end-use markups by sector and Census Division

2013 dollars per gallon

Census Division

East West East West

New Middle North North South South South

Sector/Product England Atlantic Central Central Atlantic Central Central Mountain Pacific

Residential Sector

Distillate Fuel Oil 0.59 0.69 0.52 0.52 0.58 0.62 0.64 0.49 0.62

Kerosene 0.19 0.77 0.63 0.64 0.68 0.59 0.66 0.79 0.00

Liquefied Petroleum Gases 1.16 1.23 0.63 0.42 1.15 0.99 0.98 0.82 1.07

Commercial Sector

Distillate Fuel Oil 0.47 0.42 0.59 0.54 0.52 0.54 0.54 0.49 0.61

Gasoline 0.16 0.15 0.15 0.13 0.13 0.16 0.14 0.18 0.19

Kerosene 0.18 0.78 0.63 0.64 0.75 0.69 0.55 1.05 0.00

Liquefied Petroleum Gases 0.42 0.66 0.42 0.42 0.55 0.52 0.57 0.47 0.36

Low-Sulfur Residual Fuel Oil1 0.15 0.06 -0.15 -0.13 0.12 -0.13 0.01 0.00 0.00

Utility Sector

Distillate Fuel Oil 0.20 0.20 0.35 0.32 0.27 0.35 0.27 0.22 0.25

Residual Fuel Oil1 -0.38 -0.11 0.78 0.65 -0.07 -0.35 -0.60 0.00 0.00

Transportation Sector

Distillate Fuel Oil 0.56 0.45 0.51 0.50 0.45 0.51 0.51 0.47 0.62

E852 0.15 0.14 0.12 0.11 0.12 0.13 0.10 0.16 0.15

Gasoline 0.18 0.18 0.15 0.14 0.14 0.15 0.12 0.20 0.19

High-Sulfur Residual Fuel Oil1 0.12 -0.01 0.09 0.06 0.17 0.25 0.18 0.47 0.28

Jet Fuel 0.03 -0.03 0.01 0.01 0.00 0.05 0.03 0.01 -0.03

Liquefied Petroleum Gases 0.37 0.71 0.91 0.91 0.68 0.95 0.99 0.82 0.79

Industrial Sector

Asphalt and Road Oil 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Distillate Fuel Oil 0.54 0.56 0.56 0.59 0.49 0.54 0.51 0.46 0.39

Gasoline 0.18 0.17 0.15 0.14 0.14 0.16 0.15 0.19 0.19

Kerosene 0.00 0.09 0.00 0.03 0.03 0.02 0.01 0.41 0.00

Liquefied Petroleum Gases 0.76 0.97 0.63 0.63 0.63 0.54 0.21 0.52 0.70

Low-Sulfur Residual Fuel Oil1 0.47 0.43 0.00 0.09 0.24 0.24 0.29 0.13 0.00 1

Negative values indicate that average end-use sales prices were less than wholesale prices. This often occurs with residual fuel 

which is produced as a byproduct when crude oil is refined to make higher-value products like gasoline and heating oil. 2

E85 refers to a blend of 85% ethanol (renewable) and 15 % motor gasoline (non-renewable). To address cold starting issues, the 

percentage of ethanol varies seasonally. The annual average ethanol content of 74% is used.

Note: Data from markups based on Form EIA-782A, Refiners’/Gas Plant Operators’ Monthly Petroleum Product Sales Report; EIA, 

Form EIA-782B, Resellers’/Retailers’ Monthly Petroleum Report Product Sales Report; Form FERC-423, Monthly Report of Cost and 

Quality of Fuels for Electric Plants prior to 2008; Form EIA-923, Power Plant Operations Report starting in 2008; EIA Form EIA-759 

Monthly Power Plant Report; EIA, State Energy Data Report 2010, Consumption (June 2012); EIA, State Energy Data 2010: Prices and 

Expenditures (June 2012).

Sources: U.S. Energy Information Administration (EIA), Office of Energy Analysis.
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Table 11.5. State and local taxes on petroleum transportation fuels by Census Division

2013 dollars per gallon

Census Division

Year/Product

New 

England

Middle 

Atlantic

East 

North 

Central

West 

North 

Central

South 

Atlantic

East 

south 

Central

West 

South 

Central Mountain Pacific

Gasoline1 0.32 0.28 0.27 0.26 0.23 0.24 0.24 0.25 0.25

Diesel 0.29 0.33 0.23 0.23 0.22 0.19 0.19 0.23 0.32

Liquefied Petroleum Gases 0.14 0.14 0.19 0.21 0.20 0.19 0.15 0.16 0.06

E852 0.22 0.23 0.18 0.17 0.15 0.16 0.15 0.17 0.27

Jet Fuel 0.07 0.06 0.00 0.04 0.08 0.08 0.03 0.05 0.03 1

Tax also applies to gasoline consumed in the commercial and industrial sectors. 2

E85 refers to a blend of 85% ethanol (renewable) and 15% motor gasoline (non-renewable). To address cold starting issues, the 

percentage of ethanol varies seasonally. The annual average ethanol content of 74% is used.

Source: “Compilation of United States Fuel Taxes, Inspection, Fees and Environmental Taxes and Fees,” Defense Energy Support

Center, Editions 2011-09, May 18, 2011).

Table 11.6. Federal taxes

nominal dollars per gallon

Product Tax

Gasoline 0.184

Diesel 0.244

Jet Fuel 0.043

E851 0.20 1

74% ethanol and 26% gasoline.

Note: IRS Internal Revenue Bulletin 2006-43 available on the web at 

www.irs.gov/pub/irs-irbs/irb06-43.pdf.

Sources: Omnibus Budget Reconciliation Act of 1993 (H.R. 2264); Tax 

Payer Relief Act of 1997 (PL 105-34), Clean Fuels Report (Washington, 

DC, April 1998) and Energy Policy Act of 2005 (PL 109-58).

Table 11.7. Crude oil specifications

Crude oil categories a.k.a. Sulfur (%) Gravity (degrees API)

API 50+ Light Sweet <0.5 API>=50

API 40-50 Light Sweet <0.5 40<=API<50

API 35-40 sweet Light Sweet <0.5 35<=API<40

API 35+ sour Light Sour >=0.5 API>=35

API 27-35 Med-sour Medium Sour <1.1 27<=API<35

API 27-35 sour Medium Sour >=1.1 27<=API<35

API<27 sweet Heavy Sweet <1.1 API<27

API<27 sour Heavy Sour >=1.1 API<27

California 1.1-2.6 API<27

Syncrude <0.5 API>=35

DilBit/SynBit >1.1 API<27

Note: Sources include U.S. Energy Information Administration, “U.S. Crude Oil Production Forecast- Analysis of Crude Types,” 

Dilbit/Synbit definition = Bitumen diluted with lighter petroleum products or synthetic crude

May 29, 2014, (http://www.eia.gov/analysis/petroleum/crudetypes/

Sources: U.S. Energy Information Administration.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 164

Capacity expansion

The LFMM allows for capacity expansion of all processing unit types. This includes distillation units like 

the atmospheric distillation unit (ADU), vacuum distillation unit (VDU), and condensate splitters, as well 

as secondary processing units like the hydrotreating, coking, fluid catalytic cracking, hydrocracking, and 

alkylation units. Capacity expansion occurs by processing unit, starting from regional capacities 

established using historical data.

Expansion occurs in LFMM when the value received from the sale of products that could be produced 

with new processing capacity exceeds the investment and operating costs of adding this new capacity, 

plus the cost of purchasing additional feedstock. The investment costs assume a financing ratio of 60% 

equity and 40% debt, with a hurdle rate and an after-tax return on investment ranging from 6% for 

building new refinery processing units to over 13% for higher-risk projects like the construction of a coalto-liquids plant. 

The LFMM models capacity expansion using a three-period planning approach similar to that used in the 

NEMS Electricity Market Module (EMM). The first two periods contain a single planning year (current 

year and next year, respectively), and the third period represents a net present value of the next 19 

years in the projection. The second and third planning periods are used to establish an economic plan 

for capacity expansion for the next NEMS model year. In period 2, product demands and legislative 

requirements must be met. Period 3 acts like a leverage in the capacity expansion decision for period 2, 

and is controlled by the discount rate assumptions. Larger discount rates increase the net present value 

(NPV) of revenue and expenditures in earlier periods and decrease the NPV of revenue and expenditure 

in later periods. The LFMM uses multiple discount rates for the NPV calculation to represent various 

categories of risk. For AEO2015, the LFMM uses an 18% discount rate. 

Capacity expansion is also modeled for production of corn and cellulosic ethanol, biobutanol, biomass 

pyrolysis oil, biodiesel, renewable diesel, coal-to-liquids, gas-to-liquids, and biomass-to-liquids. All 

process unit capacity that is expected to begin operating in the future is added to existing capacities in 

their respective start year. The retirement and replacement of existing refining capacity is not explicitly 

represented in the LFMM.

For the High Oil and Gas Resource case with restricted crude exports (current regulations), refiners 

would need to expand processing capacity in response to the higher oil production level and export 

restriction. The cost of expanding crude oil refining capacity is used to determine the discount at which 

domestic crude oil is sold relative to an otherwise free market environment. This discount provides the 

refinery margin needed to motivate refiners to expand processing capacity and assume any associated 

market or policy risk that could impact an expansion project’s long-term profitability.

The amortized capital cost is the revenue per barrel needed to pay the total cost of a processing capacity 

expansion over a determined time period for paying back the initial investment, plus interest. The time 

period and interest rate are adjusted to account for the level of risk associated with a given project, with 

a shorter payback period and higher interest rates for riskier projects. A 100,000-bbl/d hydroskimmer 

built with a 10-year investment payback period at 18% annual interest is estimated by EIA to have an

amortized capital cost of $6.00/bbl (2013 dollars). [109][110] This reduction is phased in over time, 

reaching the $6.00 when the first crude processing capacity is built.
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Capacity utilization of a process unit is the ratio of the actual throughput for a unit to the total capacity 

for that unit. The throughput for an atmospheric distillation unit (ADU) typically is a blend of crude oils, 

but historically has included unfinished oil imports at some refineries. Therefore, historical ADU capacity 

utilization at these refineries includes both crude oil and unfinished oil imports. Since the LFMM only 

processes unfinished oil imports in secondary units, downstream from the ADU, an assumption was 

made to include a historical percentage of the unfinished oils imported to the refinery as part of the 

throughput when calculating the ADU capacity utilization reported in AEO2015.

Non-petroleum fuel technology characteristics

The LFMM explicitly models a number of liquid fuels technologies that do not require petroleum 

feedstock. These technologies produce both fuel-grade products for blending with traditional petroleum 

products, and alternative feedstock for the traditional petroleum refinery (Table 11.8).

Table 11.8. Alternative fuel technology product type

Technology Product Type

Biochemical

Corn Ethanol Fuel Grade

Advanced Grain Ethanol Fuel Grade

Cellulosic Ethanol Fuel Grade

Biobutanol Fuel Grade

Thermochemical Catalytic

Methyl Ester Biodiesel Fuel Grade

Non-Ester Renewable Diesel Fuel Grade

Pyrolysis Fuel Grade

Thermochemical Fischer-Tropsch

Gas-to-Liquids (GTL) Fuel Grade/Refinery Feed

Coal-to-Liquids (CTL) Fuel Grade/Refinery Feed

Biomass-to-Liquids (BTL) Fuel Grade/Refinery Feed

Source: U.S. Energy Information Administration, Office of Energy Analysis.

Estimates of capital and operating costs corresponding to specified nameplate capacities for these 

technologies are shown in Table 11.9. The cost data are defined assuming a 2020 base year, and are 

deflated to 2013 dollars using the GDP deflator in NEMS.

Overnight Capital Cost is defined as the anticipated cost for a standard size commercial-scale plant. 

Since some components of technologies have not yet been proven at a commercial scale, a technology 

optimism factor is applied to the assumed first- of-a-kind capital cost, a multiplier that increases the 

first-of-a-kind plant cost (e.g., 1.25). The multiplier is an estimate of the underestimated construction 

errors (redos) and underestimated costs in building the first full-scale commercial plant. As experience 

is gained (after building the first 4 units), the technological optimism factor is gradually reduced to 1.0.
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The learning function has the nonlinear form: 

 OC(C) = a*C-b,

where C is the cumulative capacity (or number of standard-sized units) for each technology component 

and OC represents the overnight capital cost expected with cumulative capacity C of the technology.

The learning function in NEMS is determined at a component level. Each new technology is broken into 

its major components, and each component is identified as revolutionary, evolutionary or mature. 

Different learning rates are assumed for each component, based on the level of construction experience 

with the component. In the case of the LFMM, the second and third phases of a technology will have 

only evolutionary/revolutionary (fast) and mature (slower) learning components, depending on the mix 

(percent) of new and mature processes that compose a particular technology.

The progress ratio (pr) is related by the speed of learning or learning rate (LR) (e.g., how much costs 

decline for every doubling of capacity). The reduction in capital cost for every doubling of cumulative 

capacity (LR) is an exogenous input parameter for each component. The progress ratio and LR are 

related by:

 pr = 2-b = (1 - LR).

The parameter “b” is calculated from the second half of the equality above:

 b =-(ln(1-LR)/ln(2)).

The parameter “a” is computed from initial overnight cost and capacity conditions of the nonlinear 

learning curve. a = OC (C0)/Co-b

Note that Co is the cumulative capacity or number of units built as of the beginning of the current time 

period/year.

As a new technology matures, the capital cost is expected to decline, reflecting the principle of “learn by 

doing” and manufacturing experience. This principle is implemented in the LFMM similar to the 

methodology used in the EMM. The learning occurs in three phases. The first phase is represented by 

the linear phase out of optimism (and some revolutionary learning) over the first four plants (such that 

the optimism factor for the fifth and later plant is 1.0). The non-linear learning function shown above is 

used for the second (up to 32 plants built) and third (beyond 32 plants) phases.

Each technology was assessed to determine the mix of technological maturity of each component 

(revolutionary/evolutionary or mature). This was used to define what percent (m) of the cost would 

decline slowly (slow for mature) versus quickly (fast for evolutionary/revolutionary) due to learning. 

Next, for each learning category (fast and slow), a rate of learning (f) is assumed (i.e., a percent 

reduction in overnight capital cost for every doubling of cumulative capacity).

The overall learning factor is the weighted combination of the fast and slow learning factors (OC), 

weighted by the percentage that each component represents of the technology. Model parameters are 

shown in Table 11.10.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 167

Table 11.9. Non-petroleum fuel technology characteristics1

Nameplate

Base 

Overnight3 Contingency Factors4, 5 Overnight Thermal

AEO2015 2020

Basis (2013$)

Online

Year

Capacity2

b/sd

Capital 

$/b/sd Project Optimism

Capital6

$/b/sd

Efficiency 

Energy %

Biochemical

Corn Ethanol - 6,800 $18,500 3% 0% $19,100 49%

Advanced Grain Ethanol 2014 3,400 $44,000 3% 0% $45,300 49%

Cellulosic Ethanol 2013 4,400 $114,700 10% 20% $152,700 28%

Biobutanol (retrofit of corn ethanol 

plant) 2015 6,500 $9,500 10% 20% $12,500 62%

Thermochemical Catalytic

Methyl Ester Biodiesel 

(FAME) - 1,200 $20,000 3% 0% $20,600 21%

Non-Ester Renewable 

Diesel (NERD) - 2,100 $28,400 10% 0% $31,200 21%

Pyrolysis 2013 5,200 $235,500 10% 20% $310,900 60%

Thermochemical Fischer-Tropsch

Gas-to-Liquids (GTL)7 2020 48,000 $127,900 10% 10% $154,800 55%

Coal-to-Liquids (CTL) 2025 48,000 $152,200 10% 15% $192,500 49%

Biomass-to-Liquids (BTL) 2020 6,000 $265,900 10% 20% $351,000 38% 1

This table is based on the AEO2015 Reference case projections for year 2020.

2

For all processes except corn ethanol and FAME biodiesel, annual capacity refers to the capacity of one plant as defined in the Liquid Fuels 

Market Module of NEMS. For corn ethanol and FAME biodiesel, annual capacity is the most common plant size as of 2013. Capacity amounts 

provided on an output basis.

3

Overnight cost is defined as the cost of a project with no interest incurred, or the lump sum cost of a project as if it were completed 

overnight. It excludes additional costs from regional adjustments and project delays due to learning effects.

4

Contingency is defined by the American Association of Cost Engineers as a “specific provision for unforeseeable elements in costs within a 

defined project scope; particularly important where previous experience has shown that unforeseeable events which will increase costs are 

likely to occur.”

5

The technology optimism factor is applied to the first four units of an unproven design, reflecting a demonstrated tendency to underestimate 

costs for a first-of-a-kind unit. 6

Total overnight capital cost is the base overnight capital cost, plus the cost of contingency factors.

7

While these costs are for a Gulf Coast facility, the costs in other regions, particularly Alaska, are expected to be much higher.

b/sd = barrels per stream day.

$/b/sd = dollars per barrel per stream day

Note: Values from this table come from analysis of reports and discussions with various sources from industry, government, and the 

Department of Energy Fuel Offices and National Laboratories. They are meant to represent the cost and performance of typical plants under 

normal operating conditions for each technology. 

Key sources reviewed are listed in “Notes and Sources” at the end of the chapter.

Source: U.S. Energy Information Administration.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 168

Table 11.10. Non-petroleum fuel technology learning parameters

Phase 1 Phase 2 Phase 3

1st of a Kind 5th of a Kind 32nd of a Kind

Technology Type Cumulative Plants (k) Optimism Fast1 Slow1 Fast1 Slow1

Optimism Factor and

Revolutionary Learning 1.25 1.0 1.0 1.0 1.0

Cellulosic Ethanol Learning Type Fraction (m) -- 33% 67% 33% 67%

Learning Rate (f) -- 0.25 0.10 0.10 0.05

Optimism Factor and

Revolutionary Learning 1.25 1.0 1.0 1.0 1.0

Pyrolysis Learning Type Fraction (m) -- 33% 67% 33% 67%

Learning Rate (f) -- 0.25 1.0.10 0.10 0.05

Optimism Factor and

Revolutionary Learning 1.25 1.0 1.0 1.0 1.0

Biomass-to-Liquids (BTL) Learning Type Fraction (m) -- 15% 85% 15% 85%

Learning Rate (f) -- 0.10 0.01 0.10 0.01

Optimism Factor and

Revolutionary Learning 1.25 1.0 1.0 1.0 1.0

Coal-to-Liquids (CTL) Learning Type Fraction (m) -- 15% 85% 15% 85%

Learning Rate (f) -- 0.10 0.01 0.10 0.01

Optimism Factor and

Revolutionary Learning 1.25 1.0 1.0 1.0 1.0

Gas-to-Liquids (GTL) Learning Type Fraction (m) -- 10% 90% 10% 90%

Learning Rate (f) -- 0.10 0.01 0.10 0.01 1

Fast = evolutionary/revolutionary learning; slow = mature learning.

Source: U.S. Energy Information Administration, Office of Energy Analysis.

Biofuels supply

Supply functions for corn, non-corn grain, and cellulosic biomass feedstocks are provided on an annual 

basis through 2040 for the production of ethanol (blended into transportation fuel). Supply functions for 

soy oil, other seed-based oils, and grease are provided on an annual basis through 2040 for the 

production of biodiesel and renewable diesel.

• Corn feedstock supplies and costs are provided exogenously to NEMS. Feedstock costs reflect 

credits for co-products (livestock feed, corn oil, etc.). Feedstock supplies and costs reflect the 

competition between corn and its co-products and alternative crops, such as soybeans and their 

co-products.
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• Biodiesel and renewable diesel feedstock supplies and costs are provided exogenously to NEMS.

• Cellulosic (biomass) feedstock supply and costs are provided by the Renewable Fuels Module in 

NEMS. 

• To model the Renewable Fuels Standard in EISA2007, several assumptions were required.

o The penetration of cellulosic ethanol into the market is limited before 2020 to several 

planned projects with aggregate nameplate capacity of approximately 180 million 

gallons per year. Planned capacity through 2019 for Pyrolysis and Biomass-to- Liquids 

processes is approximately 110 million gallons per year.

o Methyl ester biodiesel production contributes 1.5 credits towards the advanced 

mandate.

o Renewable diesel fuel and cellulosic diesel fuel, including that from Pyrolysis oil, and 

Fischer-Tropsch diesel contribute 1.7 credits toward the cellulosic mandate.

o Cellulosic drop-in gasoline contributes 1.54 credits toward the cellulosic mandate.

o Imported Brazilian sugarcane ethanol counts towards the advanced renewable 

mandate.

o Separate biofuel waivers can be activated for each of the four RFS fuel categories.

o Biodiesel and BTL diesel are assumed to be compatible with diesel engines without 

significant infrastructure modification (either vehicles or delivery infrastructure).

o Ethanol is assumed to be consumed as E10, E15 or E85, with no intermediate blends. 

The cost of placing E85 pumps at the most economic stations is spread over diesel and 

gasoline.

o To accommodate the ethanol requirements in particular, transportation modes are 

expanded or upgraded for E10, E15 and E85, and it is assumed that most ethanol 

originates from the Midwest, with nominal transportation costs of a few cents per 

gallon.

o For E85 dispensing stations, it is assumed the average cost of a retrofit and new station 

is about $154,000 per station (2013 dollars). Interregional transportation is assumed to 

be by rail, ship, barge, and truck, and the associated costs are included in the LFMM.

o Potential RFS target reductions by EPA are provided exogenously to NEMS.

Non-petroleum fossil fuel supply

Gas-to-liquids (GTL) facilities convert natural gas into distillates, and are assumed to be built if the prices 

for lower-sulfur distillates reach a high enough level to make them economic. The earliest start date for 

a GTL facility is set at 2020.

It is also assumed that coal-to-liquids (CTL) facilities will be built when low-sulfur distillate prices are high 

enough to make them economic. A 48,000-barrel-per-day CTL facility is assumed to cost over $7 billion 

in initial capital investment (2013 dollars). These facilities could be built near existing refineries. For the 

East Coast, potential CTL facilities could be built near the Delaware River basin; for the Central region, 

near the Illinois River basin or near Billings, Montana; and for the West Coast, in the vicinity of Puget 

Sound in Washington State. It is further assumed that CTL facilities can only be built after 2025.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 170

Combined heat and power (CHP)

Electricity consumption at the refinery and other liquid fuels production facilities is a function of the 

throughput of each unit. Sources of electricity consist of refinery power generation, utility purchases, 

and CHP from other liquid fuels producers (including cellulosic/advanced ethanol, coal and biomass to 

liquids). Power generators and CHP plants are modeled in the LFMM linear program as separate units, 

and are allowed to compete along with purchased electricity. Operating characteristics for these 

electricity producers are based on historical parameters and available data. Sales to the grid or own-use 

decisions are made on an economic basis within the LP solution. The price for electricity sales to the grid 

is set to the marginal energy price for baseload generation (provided by the EMM).

Short-term methodology

Petroleum balance and price information for 2014 and 2015 are projected at the U.S. level in the ShortTerm Energy Outlook, (STEO). The LFMM adopts the STEO results for 2014 and 2015, using regional 

estimates derived from the national STEO projections.

Legislation and regulation

The Tax Payer Relief Act of 1997 reduced excise taxes on liquefied petroleum gases and methanol 

produced from natural gas. The reductions set taxes on these products equal to the Federal gasoline tax 

on a Btu basis.

Title II of CAAA90 established regulations for oxygenated and reformulated gasoline and on-highway 

diesel fuel. These are explicitly modeled in the LFMM. Reformulated gasoline represented in the LFMM 

meets the requirements of Phase 2 of the Complex Model, except in the Pacific region where it meets 

CARB 3 specifications.

AEO2015 reflects “Tier 3 Vehicle Emissions and Fuel Standards” which states that the average annual 

sulfur content of federal gasoline will not contain more than 10 ppm by January 1, 2017. For projection 

years prior to 2017, AEO2015 reflects the “Tier 2” Motor Vehicle Emissions Standards and Gasoline 

Sulfur Control Requirements which requires that the average annual sulfur content of all gasoline used 

in the United States be 30 ppm.

AEO2015 reflects Heavy-Duty Engine and Vehicle Standards and Highway Diesel Fuel Sulfur Control 

Requirements. All highway diesel is required to contain no more than 15 ppm sulfur at the pump.

AEO2015 reflects nonroad locomotive and marine (NRLM) diesel requirements that nonroad diesel 

supplies contain no more than 15 ppm sulfur. For locomotive and marine diesel, the action establishes a 

NRLM limit of 15 ppm in mid-2012.

AEO2015 represents major provisions in the Energy Policy Act of 2005 (EPACT05) concerning the 

petroleum industry, including removal of the oxygenate requirement in RFG.

AEO2015 includes provisions outlined in the Energy Independence and Security Act of 2007 (EISA2007) 

concerning the petroleum industry, including a Renewable Fuels Standard (RFS) increasing total U.S. 

consumption of renewable fuels. In order to account for the possibility that RFS targets might be 

unattainable at reasonable cost, LFMM includes a provision for purchase of waivers. The price of a 

cellulosic waiver is specified in EISA2007. The non-cellulosic LFMM RFS waivers function as maximum 
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 171

allowed RIN prices. LFMM also assumes that EPA will reduce RFS targets as allowed by the EISA2007 

statute.

AEO2015 includes the EPA Mobil Source Air Toxics (MSAT 2) rule which includes the requirement that all 

gasoline products (including reformulated and conventional gasoline) produced at a refinery during a 

calendar year will need to contain no more than 0.62 percent benzene by volume. This does not include 

gasoline produced or sold in California, which is already covered by the current California Phase 3 

Reformulated Gasoline Program.

AEO2015 includes California’s Low Carbon Fuel Standard which aims to reduce the Carbon Intensity (CI) 

of gasoline and diesel fuels in that state by about 10% respectively from 2012 through 2020.

AEO2015 incorporates the cap-and-trade program within the California Assembly Bill (AB 32), the Global 

Warming Solutions Act of 2006. The program started January 1, 2012, with enforceable compliance 

obligations beginning in 2013. Petroleum refineries are given allowances (calculated in the LFMM) in the 

cap-and-trade system based on the volumetric output of aviation gasoline, motor gasoline, kerosenetype jet fuel, distillate fuel oil, renewable liquid fuels and asphalt. Suppliers of Reformulated Blend Stock 

for Oxygenate Blending (RBOB) and Distillate Fuel Oil #1 and #2 are required to comply starting in 2015 

if the emissions from full combustion of these products are greater than or equal to 25,000 metric tons 

CO2 equivalent (MTCO2e) in any year 2011-2014.

AEO2015 includes mandates passed in 2010 by Connecticut, Maine, New York, and New Jersey that aim 

to lower the sulfur content of all heating oil to ultra-low-sulfur diesel over different time schedules. It 

also includes transition to a 2% biodiesel content in the case of Maine and Connecticut.

Due to the uncertainty surrounding compliance options, AEO2015 did not include any explicit modeling 

treatment of the International Maritime Organization’s “MARPOL Annex 6” rule covering cleaner marine 

fuels and ocean ship engine emissions.

The AEO2015 Reference Case does not extend the $1.00-per-gallon biodiesel excise tax credit or the 

$1.01-per-gallon cellulosic biofuels production tax credit over the projection.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 172

Notes and sources

[100] U.S. Environmental Protection Agency (EPA), “EPA Sets Tier 3 Vehicle Emission and Fuel 

Standards,” http://www.epa.gov/otaq/documents/tier3/420f14009.pdf. 

[101] Federal Register, U.S. Environmental Protection Agency, 40 CFR Part 80, Regulation of Fuels and 

Fuel Additives: Standards for Reformulated and Conventional Gasoline, Rules and Regulations, p. 7800, 

(Washington, DC, February 1994). 

[102] Marano, John, “Alternative Fuels Technology Profile: Cellulosic Ethanol”, March 2008.

[103] Ibid.

[104] U.S. Department of Agriculture, “USDA Agricultural Baseline Projections to 2019,” February 2009, 

www.ers.usda.gov/publications/oce-usda-agricultural-projections/oce-2010-1.aspx. 

[105] Shapouri, Hosein and Gallagher, Paul. USDA’s 2002 Ethanol Cost-of-Production Survey, July 2005. 

[106] U.S. Department of Agriculture. 2008 Energy Balance for the Corn-Ethanol Industry, June 2010. 

[107] American Petroleum Institute, How Much We Pay for Gasoline: 1996 Annual Review, May 1997.

[108] U.S. Department of Commerce, Bureau of Industry and Security (BIS) allows, “under 754.2(a), lease 

condensate that has been processed through a crude oil distillate tower” to be classified as product, 

with no export restrictions. See FAQ#3 under the heading “FAQs – Crude Oil and Petroleum Products 

December 30, 2014,” https://www.bis.doc.gov/index.php/policy-guidance/deemed-exports/deemedexports-faqs

[109] U.S. Energy Information Administration, “Technical options for processing additional light tight oil 

volumes within the United States,” April 6, 2015, 

http://www.eia.gov/analysis/studies/petroleum/lto/pdf/lightightoil.pdf

[110] After AEO2015 was released, an adjustment was made that changed the $6.00/bbl (2013 dollars) 

to $6.90/bbl (2013 dollars) to reflect policy and market risks.
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Coal Market Module

The NEMS Coal Market Module (CMM) provides projections of U.S. coal production, consumption, 

exports, imports, distribution, and prices. The CMM comprises three functional areas: coal production, 

coal distribution, and coal exports. A detailed description of the CMM is provided in the EIA publication, 

Coal Market Module of the National Energy Modeling System 2014, DOE/EIA-M060 (2014) (Washington, 

DC, 2014).

Key assumptions

Coal production

The coal production submodule of the CMM generates a different set of supply curves for the CMM for 

each year of the projection. Combinations of 14 supply regions, nine coal types (unique groupings of 

thermal grade and sulfur content), and two mine types (underground and surface), result in 41 separate 

supply curves. Supply curves are constructed using an econometric formulation that relates the 

minemouth prices of coal for the supply regions and coal types to a set of independent variables. The 

independent variables include: capacity utilization of mines, mining capacity, labor productivity, the user 

cost of capital of mining equipment, the cost of factor inputs (labor and fuel), and other mine supply 

costs.

The key assumptions underlying the coal production modeling are:

• As capacity utilization increases, higher minemouth prices for a given supply curve are 

projected. The opportunity to add capacity is allowed within the modeling framework if capacity 

utilization rises to a pre-determined level, typically in the 80% range. Likewise, if capacity 

utilization falls, mining capacity may be retired. The amount of capacity that can be added or

retired in a given year depends on the level of capacity utilization, the supply region, and the 

mining process (underground or surface). The volume of capacity expansion permitted in a 

projection year is based upon historical patterns of capacity additions.

• Between 1980 and 2000, U.S. coal mining productivity increased at an average rate of 6.6% per 

year, from 1.93 to 6.99 short tons per miner per hour. The major factors underlying these gains 

were interfuel price competition, structural change in the industry, and technological 

improvements in coal mining [111]. Between 2000 and 2013, however, growth in overall U.S. 

coal mining productivity has been negative, declining at a rate of 1.8% per year to 5.54 short 

tons per miner-hour in 2013. By region, productivity in all but one (Alaska/Washington) of the 

coal producing basins represented in the CMM has declined from its productivity level in 2000. 

In the Central Appalachian coal basin, which has been mined extensively, productivity declined

by 50% between 2000 and 2013, corresponding to an average decline of 5.2% per year. While 

productivity declines have been more moderate at the highly productive mines in Wyoming’s 

Powder River Basin, coal mining productivity in this region still fell by 30% between 2000 and 

2013 corresponding to an average rate of decline of 2.7% per year. Of the top coal producing 

regions showing declines, the Eastern Interior has shown the best overall performance, with 

coal mining productivity declining by only 2.7% between 2000 and 2013, or 0.2% per year. The 

Eastern Interior region, which has a substantial amount of thick, underground-minable coal 
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reserves, is currently experiencing a resurgence in coal mining activity, with several coal 

companies either opening or in the process of opening new, highly productive longwall mines.

• Over the projection period, labor productivity is expected to decline in most coal supply regions, 

reflecting the trend of the previous decade. Higher stripping ratios and the added labor needed 

to maintain more extensive underground mines offset productivity gains achieved from 

improved equipment, automation, and technology. Productivity in some areas of the eastern 

coalfields is projected to decline as operations move from mature coalfields to marginal reserve 

areas. Regulatory restrictions on surface mines and fragmentation of underground reserves limit 

the benefits that can be achieved by Appalachian producers from economies of scale.

• In the CMM, different rates of productivity improvement are assumed for each of the 41 coal 

supply curves used to represent U.S. coal supply. These estimates are based on recent historical 

data and expectations regarding the penetration and impact of new coal mining technologies. 

Data on labor productivity are provided on a quarterly and annual basis by individual coal mines 

and preparation plants on the U.S. Department of Labor, Mine Safety and Health 

Administration’s Form 7000-2, “Quarterly Mine Employment and Coal Production Report,” and 

the U.S. Energy Information Administration’s Form EIA-7A, “Annual Survey of Coal Production 

and Preparation”. In the Reference case, overall U.S. coal mining labor productivity declines at 

rate of 0.9% per year between 2013 and 2040. Reference case projections of coal mining 

productivity by region are provided in Table 12.1.

Table 12.1. Coal mining productivity by region

short tons per miner hour

Supply Region 2013 2020 2025 2030 2035 2040

Average Annual 

Growth 13-40

Northern Appalachia 3.19 2.99 2.78 2.59 2.44 2.32 -1.2%

Central Appalachia 2.07 1.70 1.40 1.18 1.05 0.94 -2.9%

Southern Appalachia 1.90 1.57 1.43 1.31 1.23 1.15 -1.8%

Eastern Interior 4.59 5.01 5.11 5.25 5.37 5.49 0.7%

Western Interior 2.17 1.87 1.76 1.66 1.61 1.57 -1.2%

Gulf Lignite 6.85 6.22 5.92 5.63 5.43 5.24 -1.0%

Dakota Lignite 11.36 11.44 10.88 10.35 9.99 9.64 -0.6%

Western Montana 16.74 15.10 14.65 14.16 13.55 12.93 -1.0%

Wyoming, Northern Powder River Basin 28.41 26.08 24.43 22.88 21.98 21.12 -1.1%

Wyoming, Southern Powder River Basin 33.88 31.10 29.13 27.29 26.21 25.18 -1.1%

Western Wyoming 6.93 6.21 5.94 5.68 5.61 5.45 -0.9%

Rocky Mountain 5.57 4.64 4.28 3.94 3.79 3.66 -1.5%

Arizona/New Mexico 7.98 7.95 7.83 7.20 7.04 6.83 -0.6%

Alaska/Washington 5.70 5.87 5.99 6.11 6.17 6.23 0.3%

U.S. Average 5.54 5.50 5.21 4.79 4.55 4.40 -0.9%

Source: U.S. Energy Information Administration, AEO2015 National Energy Modeling System run REF2015.D021915A.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 175

• In the AEO2015 Reference case, the wage rate for U.S. coal miners increases by 0.9% per year 

and mine equipment costs are assumed to remain constant in 2013 dollars (i.e., increase at the 

general rate of inflation) over the projection period.

Coal distribution

The coal distribution submodule of the CMM determines the least-cost (minemouth price plus 

transportation cost) supplies of coal by supply region for a given set of coal demands in each demand 

sector using a linear programming algorithm. Production and distribution are computed for 14 supply 

(Figure 11) and 16 demand regions (Figure 12) for 49 demand subsectors.

The projected levels of coal-to-liquids, industrial steam, coking, and commercial/institutional coal 

demand are provided by the liquid fuel market, industrial, and commercial demand modules, 

respectively. Electricity coal demands are projected by the Electricity Market Module (EMM). Coal 

imports and coal exports are projected by the CMM based on non-U.S. supply availability, endogenously 

determined U.S. import demand, and exogenously determined world coal import demands (non-U.S.).

The key assumptions underlying the coal distribution modeling are as follows:

• Base-year (2013) transportation costs are estimates of average transportation costs for each 

origin-destination pair without differentiation by transportation mode (rail, truck, barge, and 

conveyor). These costs are computed as the difference between the average delivered price for 

a demand region (by sector and for export) and the average minemouth price for a supply curve. 

Delivered price data are from Form EIA-3, “Quarterly Coal Consumption and Quality Report, 

Manufacturing and Transformation/Processing Coal Plants and Commercial and Institutional 

Coal Users”, Form EIA-5, Quarterly Coal Consumption and Quality Report, Coke Plants”, Form 

EIA-923, “Power Plant Operations Report”, and the U.S. Bureau of the Census, “Monthly Report 

EM-545”. Minemouth price data are from Form EIA-7A, “Coal Production and Preparation 

Report”.

For the electricity sector only, a two-tier transportation rate structure is used for those regions which, in 

response to rising demands or changes in demands, may expand their market share beyond historical 

levels. The first-tier rate is representative of the historical average transportation rate. The second-tier 

transportation rate is used to capture the higher cost of expanded shipping distances in large demand 

regions. The second tier is also used to capture costs associated with the use of subbituminous coal at 

units that were not originally designed for its use. This cost is estimated at $0.10 per million Btu (2000 

dollars) [112].
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 176

Figure 11. Coal Supply Regions
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 177

Figure 12. Coal Demand Regions
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 178

• Coal transportation costs, both first- and second-tier rates, are modified over time by two 

regional (east and west) transportation indices. The indices, calculated econometrically, are 

measures of the change in average transportation rates for coal shipments on a tonnage basis, 

which occurs between successive years for coal shipments. An east index is used for coal 

originating from coal supply regions located east of the Mississippi River, while a west index is 

used for coal originating from coal supply regions located west of the Mississippi River. The east 

index is a function of railroad productivity, the user cost of capital for railroad equipment, and 

national average diesel fuel price. The user cost of capital for railroad equipment is calculated 

from the producer price index (PPI) for railroad equipment, and accounts for the opportunity 

cost of money used to purchase equipment, depreciation occurring as a result of use of the 

equipment (assumed at 10%), less any capital gain associated with the worth of the equipment. 

In calculating the user cost of capital, three percentage points are added to the cost of 

borrowing in order to account for the possibility that a national level program to regulate 

greenhouse gas emissions may be implemented in the future. The west index is a function of 

railroad productivity, investment, and the western share of national coal consumption. The 

indices are universally applied to all domestic coal transportation movements within the CMM. 

In the AEO2015 Reference case, both eastern and western coal transportation rates are 

projected to remain near their 2013 levels in 2013 dollars.

• For the projection period, the explanatory variables are assumed to have varying impacts on the 

calculation of the multipliers. For the west, investment is the analogous variable to the user cost 

of capital of railroad equipment. The investment value and the PPI for rail equipment, which is 

used to derive the user cost of capital, increase with an increase in national ton-miles (total tons 

of coal shipped multiplied by the average distance). Increases in investment (west) or the user 

cost of capital for rail road equipment (east) cause projected transportation rates to increase

while a relative decline in these values cause rates to fall. For both the east and the west, any 

related financial savings due to productivity improvements are assumed to be retained by the 

railroads and are not passed on to shippers in the form of lower transportation rates. For that 

reason, transportation productivity is held flat for the projection period for both regions. For 

the projection period, diesel fuel is removed from the equation for the east in order to avoid 

double-counting the influence of diesel fuel costs with the impact of the fuel surcharge program. 

The transportation rate indices for six AEO2015 cases are shown in Table 12.2.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 179

Table 12.2. Transportation rate multipliers

constant dollar index, 2013=1.000

Scenario Region: 2013 2020 2025 2030 2035 2040

Reference East 1.0000 0.9596 0.9502 0.9437 0.9448 0.9482

West 1.0000 1.0306 1.0602 1.0559 1.0453 1.0381

Low Oil Price East 1.0000 0.9567 0.9522 0.9421 0.9419 0.9433

West 1.0000 1.0303 1.0630 1.0625 1.0517 1.0491

High Oil Price East 1.0000 0.9692 0.9527 0.9488 0.9507 0.9510

West 1.0000 1.0187 1.0440 1.0533 1.0515 1.0633

Low Economic Growth East 1.0000 0.9611 0.9558 0.9499 0.9535 0.9570

West 1.0000 1.0203 1.0485 1.0450 1.0347 1.0278

High Economic Growth East 1.0000 0.9551 0.9487 0.9414 0.9397 0.9427

West 1.0000 1.0358 1.0678 1.0620 1.0516 1.0478

High Resource East 1.0000 0.9577 0.9650 0.9500 0.9529 0.9542

West 1.0000 0.9956 1.0171 1.0110 1.0023 0.9960

Source: Projections: U.S. Energy Information Administration, National Energy Modeling System runs 

REF2015.D021915A, LOWPRICE.D021915A, HIGHPRICE. D021915A, LOWMACRO.D021915A, 

HIGHMACRO.D021915A, and HIGHRESOURCE.D021915B. Based on methodology described in Coal Market 

Module of the National Energy Modeling System 2014, DOE/EIA-M060(2014) (Washington, DC, 2014).

• Major coal rail carriers have implemented fuel surcharge programs in which higher 

transportation fuel costs have been passed on to shippers. While the programs vary in their 

design, the Surface Transportation Board (STB), the regulatory body with limited authority to 

oversee rate disputes, recommended that the railroads agree to develop some consistencies 

among their disparate programs and likewise recommended closely linking the charges to actual 

fuel use. The STB cited the use of a mileage-based program as one means to more closely 

estimate actual fuel expenses.

• For AEO2015, representation of a fuel surcharge program is included in the coal transportation 

costs. For the west, the methodology is based on BNSF Railway Company’s mileage-based 

program. The surcharge becomes effective when the projected nominal distillate price to the 

transportation sector exceeds $1.25 per gallon. For every $0.06 per gallon increase above $1.25, 

a $0.01 per carload mile is charged. For the east, the methodology is based on CSX 

Transportation’s mileage-based program. The surcharge becomes effective when the projected 

nominal distillate price to the transportation sector exceeds $2.00 per gallon. For every $0.04 

per gallon increase above $2.00, a $0.01 per carload mile is charged. The number of tons per 

carload and the number of miles vary with each supply and demand region combination and are 

a pre-determined model input. The final calculated surcharge (in constant dollars per ton) is 

added to the escalator-adjusted transportation rate. For every projection year, it is assumed 

that 100% of all coal shipments are subject to the surcharge program.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 180

• Coal contracts in the CMM represent a minimum quantity of a specific electricity coal demand 

that must be met by a unique coal supply source prior to consideration of any alternative 

sources of supply. Base-year (2013) coal contracts between coal producers and electricity 

generators are estimated on the basis of receipts data reported by generators on the Form EIA923, “Power Plant Operations Report”. Coal contracts are specified by CMM supply region, coal 

type, demand region, and whether or not a unit has flue gas desulfurization equipment. Coal 

contract quantities are reduced over time on the basis of contract duration data from 

information reported on the Form EIA-923, “Power Plant Operations Report”, historical patterns 

of coal use, and information obtained from various coal and electric power industry publications 

and reports.

• Coal-to-liquids (CTL) facilities are assumed to be economic when low-sulfur distillate prices 

reach high enough levels. These plants are assumed to be co-production facilities with 

generation capacity of 832 megawatts (MW) (295 MW for the grid and 537 MW to support the 

conversion process) and the capability of producing 48,000 barrels of liquid fuels per day. The 

technology assumed is similar to an integrated gasification combined cycle, first converting the 

coal feedstock to gas, and then subsequently converting the syngas to liquid hydrocarbons using 

the Fisher-Tropsch process. Of the total amount of coal consumed at each plant, 40% of the 

energy input is retained in the product with the remaining energy used for conversion and for 

the production of power sold to the grid. For AEO2015, coal-biomass-to-liquids (CBTL) are not 

modeled. CTL facilities produce paraffinic naptha used in plastics production and blendable 

naptha used in motor gasoline (together about 28% of the total by volume) and distillate fuel oil 

(about 72%).

Coal imports and exports

Coal imports and exports are modeled as part of the CMM’s linear program that provides annual 

projections of U.S. steam and metallurgical coal exports, in the context of world coal trade. The CMM 

projects steam and metallurgical coal trade flows from 17 coal-exporting regions of the world to 20 

import regions for two coal types (steam and metallurgical). It includes five U.S. export regions and four 

U.S. import regions. The linear program determines the pattern of world coal trade flows that minimizes 

the production and transportation costs of meeting U.S. import demand and a pre-specified set of 

regional coal import demands. It does this subject to constraints on export capacity and trade flows.

The key assumptions underlying coal export modeling are:

• Coal buyers (importing regions) tend to spread their purchases among several suppliers in order 

to reduce the impact of potential supply disruptions, even though this may add to their 

purchase costs. Similarly, producers choose not to rely on any one buyer and instead endeavor 

to diversify their sales.

• Coking coal is treated as homogeneous. The model does not address quality parameters that 

define coking coals. The values of these quality parameters are defined within small ranges and 

affect world coking coal flows very little.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 181

The data inputs for coal trade modeling are:

• World steam and metallurgical coal import demands for the AEO2015 cases (Tables 12.3 and 

12.4). U.S. coal exports are determined, in part, by these estimates of world coal import 

demand. The assumed demands for AEO2015 are the same as in AEO2014.

• Step-function coal export supply curves for all non-U.S. supply regions. The curves provide 

estimates of export prices per metric ton, inclusive of minemouth and inland freight costs, as 

well as the capacities for each of the supply steps.

• Ocean transportation rates (in dollars per metric ton) for feasible coal shipments between 

international supply regions and international demand regions. The rates take into account 

typical vessel sizes and route distances in thousands of nautical miles between supply and 

demand regions.

Coal quality

Each year the values of base year coal production-̶heat, sulfur, and mercury content-̶and carbon 

dioxide emission factors for each coal source in CMM are calibrated to survey data. Surveys used for this 

purpose are the Form EIA-923, a survey of the origin, cost, and quality of fossil fuels delivered to

generating facilities, the Form EIA-3, which records the origin, cost, and quality of coal delivered to U.S. 

manufacturers, transformation and processing plants, and commercial and institutional users, and the 

Form EIA-5, which records the origin, cost, and quality of coal delivered to domestic coke plants. 

Estimates of coal quality for the export sector are based on coal quality data collected on EIA surveys for 

domestic shipments. Mercury content data for coal by supply region and coal type, in units of pounds of 

mercury per trillion Btu, shown in Table 12.5, were derived from shipment-level data reported by 

electricity generators to the U.S. Environmental Protection Agency in its 1999 Information Collection 

Request. Carbon dioxide emission factors for each coal type, based on data published by the U.S. 

Environmental Protection Agency, are shown in Table 12.5 in units of pounds of carbon dioxide emitted 

per million Btu [113].
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 182

Table 12.3. World steam coal import demand by import region1

million metric tons of coal equivalent

2012 2020 2025 2030 2035 2040

The Americas 30.8 29.3 28.7 29.4 32.2 32.9

 United States2 6.4 0.8 0.4 0.0 0.8 0.0

 Canada 3.5 2.0 1.2 0.7 0.7 0.7

 Mexico 6.0 6.6 6.5 7.1 7.5 7.5

 South America 14.9 19.9 20.6 21.6 23.2 24.7

Europe 165.9 188.5 185.8 181.7 176.7 172.6

 Scandinavia 8.2 6.6 5.9 5.0 4.6 4.1

 U.K./Ireland 36.0 29.2 26.4 24.6 22.8 20.1

 Germany/Austria/Poland 39.3 38.8 37.8 36.8 35.8 34.8

 Other NW Europe 15.4 21.6 20.5 19.5 18.5 17.5

 Iberia 17.5 17.3 16.9 16.9 15.7 14.0

 Italy 14.3 22.8 21.9 20.1 18.2 16.4

 Med/E Europe 35.2 52.2 56.4 58.8 61.1 65.7

Asia 561.0 626.6 684.5 739.1 795.9 849.0

Japan 94.2 94.3 93.5 92.2 90.6 88.0

East Asia 123.9 129.0 134.4 136.5 144.5 149.9

China/Hong Kong 189.0 212.8 236.3 258.1 272.2 286.2

ASEAN 44.1 60.0 79.3 94.9 113.0 131.2

Indian Sub 109.8 130.5 141.0 157.4 175.6 193.7

TOTAL 757.7 844.4 899.0 950.2 1,004.8 1,054.5

1

Import Regions: United States: East Coast, Gulf Coast, Northern Interior, Non-Contiguous; Canada: 

Eastern, Interior; South America: Argentina, Brazil, Chile, Puerto Rico; Scandinavia: Denmark, Finland, 

Norway, Sweden; Other NW Europe: Belgium, France, Luxembourg, Netherlands; Iberia: Portugal, Spain; 

Med/E. Europe: Algeria, Bulgaria, Croatia, Egypt, Greece, Israel, Malta, Morocco, Romania, Tunisia, 

Turkey; East Asia: North Korea, South Korea, Taiwan; ASEAN: Malaysia, Philippines, Thailand, Vietnam; 

Indian Sub: Bangladesh, India, Iran, Pakistan, Sri Lanka. 2

Excludes imports to Puerto Rico and the U.S. Virgin Islands.

Notes: One “metric ton of coal equivalent” equals 27.78 million Btu. Totals may not equal sum of 

components due to independent rounding.
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 183

Table 12.4. World metallurgical coal import demand by import region1

million metric tons of coal equivalent

2012 2020 2025 2030 2035 2040

The Americas 21.6 31.0 34.0 37.0 41.0 47.1

 United States2 1.0 1.0 1.0 1.0 1.0 1.0

 Canada 4.5 3.9 3.9 3.7 3.6 3.5

 Mexico 1.0 1.5 1.5 1.5 1.5 3.1

 South America 15.1 24.6 27.7 30.8 34.9 39.5

Europe 57.1 62.1 60.9 59.9 59.8 59.8

 Scandinavia 2.9 2.7 2.7 2.7 2.7 2.7

 U.K./Ireland 5.6 6.0 6.0 6.0 6.0 6.0

 Germany/Austria/Poland 10.4 13.2 12.2 11.2 11.2 11.2

 Other NW Europe 13.1 14.7 14.5 14.4 14.2 14.1

 Iberia 2.2 3.9 3.8 3.8 3.6 3.5

 Italy 7.1 7.4 7.3 7.2 7.3 7.3

 Med/E Europe 15.8 14.2 14.4 14.6 14.8 15.0

Asia 193.9 240.3 256.7 266.3 274.0 280.0

 Japan 70.5 79.0 77.2 74.8 71.9 67.1

 East Asia 36.0 41.2 43.3 45.5 47.1 48.8

 China/Hong Kong 52.0 60.9 63.0 65.0 67.2 69.5

 ASEAN3 0.0 0.0 0.0 0.0 0.0 0.0

 Indian Sub 35.4 59.2 73.2 81.0 87.8 94.6

TOTAL 272.6 333.4 351.6 363.2 374.8 386.9 1

Import Regions: United States: East Coast, Gulf Coast, Northern Interior, Non-Contiguous; Canada: 

Eastern, Interior; South America: Argentina, Brazil, Chile, Puerto Rico; Scandinavia: Denmark, Finland, 

Norway, Sweden; Other NW Europe: Belgium, France, Luxembourg, Netherlands; Iberia: Portugal, Spain; 

Med/E. Europe: Algeria, Bulgaria, Croatia, Egypt, Greece, Israel, Malta, Morocco, Romania, Tunisia, 

Turkey; East Asia: North Korea, South Korea, Taiwan; ASEAN: Malaysia, Philippines, Thailand, Vietnam; 

Indian Sub: Bangladesh, India, Iran, Pakistan, Sri Lanka. 2

Excludes imports to Puerto Rico and the U.S. Virgin Islands.

3

Malaysia, Philippines, Thailand, and Vietnam are not expected to import significant amounts of 

metallurgical coal in the projection.

Notes: One “metric ton of coal equivalent” equals 27.78 million Btu. Totals may not equal sum of 

components due to independent rounding.

Legislation and regulations

The AEO2015 is based on current laws and regulations in effect before September 30, 2014. The 

Mercury Air Toxics Standard MATS), finalized in December 2011, is included in the AEO2015 Reference 

case, as is the Clean Air Interstate Rule (CAIR). MATS sets emissions limits for mercury, other heavy 

metals, and acid gases from coal and oil power plants that are 25 MW or greater. Since generators are 

expected to request one-year extensions for compliance, MATS is assumed to be fully in place by 2016 

rather than 2015 as stated in the regulation.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 184

CAIR is a cap-and-trade program that regulates sulfur dioxide and nitrous oxide (NOX) emissions from 

fossil-fueled power plants with a nameplate capacity greater than 25 MW in 27 states and the District of 

Columbia. Initial implementation of CAIR for NOX occurred in 2009 and for SO2 in 2010, with both caps 

subject to further tightening in 2015. The AEO2015 includes trading and banking of allowances 

consistent with CAIR’s provisions. States covered by CAIR can trade allowances amongst themselves or 

with non-CAIR states participating in the Clean Air Act Amendment Title IV program. Non-CAIR state 

allowances are considered less valuable than CAIR state allowances and are traded at a discounted rate.

Table 12.5. Production, heat content, sulfur, mercury and carbon dioxide emission factors by coal type 

and region

Coal Supply 

Region States

Coal Rank and 

Sulfur Level Mine Type

2013 

Production 

(million 

short tons)

Heat Content 

(million Btu 

per short 

ton)

Sulfur 

Content 

(pounds per 

million Btu)

Mercury 

Content 

(pounds per 

trillion Btu)

CO2 

(pounds 

per million 

Btu)

Northern PA, OH, MD, Metallurgical Underground 16.8 28.71 0.89 N/A 204.7

Appalachia WV (North) Mid-Sulfur

Bituminous All 31.5 25.29 1.41 11.17 204.7

High-Sulfur

Bituminous All 76.4 24.70 2.72 11.67 204.7

Waste Coal 

(Gob and 

Gum) Surface 10.2 12.43 3.60 63.90 204.7

Central KY (East), WV Metallurgical Underground 55.5 28.71 0.65 N/A 206.4

Appalachia (South), VA, TN Low-Sulfur

(North) Bituminous All 6.9 25.02 0.55 5.61 206.4

Mid-Sulfur

Bituminous All 65.8 24.47 0.93 7.58 206.4

Southern AL, TN (South) Metallurgical Underground 12.7 28.71 0.48 N/A 204.7

Appalachia Low-Sulfur

Bituminous All 0.2 24.90 0.51 3.87 204.7

Mid-Sulfur

Bituminous All 5.7 24.52 1.29 10.15 204.7

East Interior

IL, IN, KY(West), MS

Mid-Sulfur

Bituminous All 6.7 22.68 1.29 5.60 203.1

High-Sulfur

Bituminous All 125.8 22.61 2.69 6.35 203.1

Mid-Sulfur 

Lignite Surface 3.3 10.49 0.88 14.11 216.5

West Interior IA, MO, KS, AR, High-Sulfur

OK, TX (Bit) Bituminous Surface 1.6 22.05 1.35 21.55 202.8

Gulf Lignite TX (Lig), LA Mid-Sulfur 

Lignite Surface 35.4 13.55 1.28 14.11 212.6

High-Sulfur 

Lignite Surface 10.3 11.98 2.67 15.28 212.6

Dakota 

Lignite

ND, MT (Lig) Mid-Sulfur 

Lignite Surface 28.0 13.23 1.24 8.38 219.3

Western MT (Bit & Sub) Low-Sulfur

Montana Bituminous Underground 8.7 20.33 0.38 5.06 215.5

Low-Sulfur

Subbituminous Surface 20.5 18.51 0.38 5.06 215.5

Mid-Sulfur

Subbituminous Surface 12.7 17.04 0.79 5.47 215.5

Wyoming,

Northern PRB

WY (Northern Powder 

River Basin

Low-Sulfur

Subbituminous Surface 129.6 16.85 0.37 7.08 214.3

Mid-Sulfur 

Subbituminous Surface 1.7 16.18 0.67 7.55 214.3

Wyoming,

Southern PRB

WY (Southern Powder 

River Basin)

Low-Sulfur

Subbituminous Surface 243.0 17.66 0.28 5.22 214.3
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 185

Table 12.5. Production, heat content, sulfur, mercury and carbon dioxide emission factors by coal type 

and region (cont.)

Coal Supply Region States

Coal Rank and 

Sulfur Level Mine Type

2013 

Production 

(million 

short tons)

Heat 

Content 

(million 

Btu per 

short 

ton)

Sulfur 

Content 

(pounds 

per 

million 

Btu)

Mercury 

Content 

(pounds 

per 

trillion 

Btu)

CO2 

(pounds 

per 

million 

Btu)

Western Wyoming WY (Other basins, Low-Sulfur

excluding Powder Subbituminous Underground 4.4 18.76 0.67 2.19 214.3

River Basin) Low-Sulfur

Subbituminous Surface 4.3 19.10 0.47 4.06 214.3

Mid-Sulfur

Subbituminous Surface 4.8 19.77 0.79 4.35 214.3

Rocky Mountain CO, UT Metallurgical Surface 0.7 28.71 0.48 N/A 209.6

Low-Sulfur

Bituminous Underground 34.9 22.55 0.50 3.82 209.6

Low-Sulfur

Subbituminous Surface 5.6 20.07 0.54 2.04 212.8

Southwest AZ, NM Low-Sulfur

Bituminous Surface 7.9 21.53 0.56 4.66 207.1

Mid-Sulfur

Subbituminous Surface 15.7 18.13 0.96 7.18 209.2

Mid-Sulfur

Bituminous Underground 6.0 18.45 0.88 7.18 207.1

Northwest WA, AK Low-Sulfur

Subbituminous Surface 1.6 15.27 0.26 6.99 216.1

N/A = not available.

Source: U.S. Energy Information Administration, Form EIA-3, “Quarterly Coal Consumption and Quality Report, Manufacturing and 

Transformation/Processing Coal Plants and Commercial and Institutional Coal Users”; Form EIA-5, “Quarterly Coal Consumption and 

Quality Report, Coke Plants”; Form EIA-7A, “Coal Production and Preparation Report”, and Form EIA-923, “Power Plant Operations 

Report”. U.S. Department of Commerce, Bureau of the Census, “Monthly Report EM-545.” U.S. Environmental Protection Agency, 

Emission Standards Division, Information Collection Request for Electric Utility Steam Generating Unit, Mercury Emissions Information 

Collection Effort (Research Triangle Park, NC, 1999). U.S. Environmental Protection Agency, Inventory of U.S. Greenhouse Gas Emissions 

and Sinks: 1990-2008, ANNEX 2 Methodology and Data for Estimating CO2 Emissions from Fossil Fuel Combustion, EPA 430-R-10-006 

(Washington, DC, April 2010), Table A-37, http://www.epa.gov/climatechange/ghgemissions/usinventoryreport/archive.html.

The Energy Improvement and Extension Act of 2008 (EIEA) and Title IV, under Energy and Water 

Development, of the American Recovery and Revitalization Act of 2009 (ARRA), together, are assumed 

to result in the development of about 1 gigawatt of coal- fired generating capacity with carbon capture 

and sequestration by the end of 2018.

EIEA was passed in October 2008 as part of the Emergency Economic Stabilization Act of 2008. Subtitle B 

provides investment tax credits for various projects sequestering CO2. Subtitle B of EIEA, which extends 

the payment of current coal excise taxes for the Black Lung Disability Trust Fund program of $1.10 per 

ton on underground-mined coal and $0.55 per ton on surface-mined coal from 2013 to 2018, is also 

represented in the AEO2015. Prior to the enactment of EIEA, contribution rates for the Black Lung 

Disability Trust Fund were to be reduced in 2014 to $0.50 per ton on underground-mined coal and to 

$0.25 per ton on surface- mined coal. Lignite production is not subject to the Black Lung Disability Trust 

Fund program’s coal excise taxes.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 186

Title IV under ARRA provides $3.4 billion for additional research and development on fossil energy 

technologies. This includes $800 million to fund projects under the Clean Coal Power Initiative (CCPI) 

program, focusing on projects that capture and sequester greenhouse gases or use captured carbon 

dioxide for enhanced oil recovery (EOR). The Hydrogen Energy California (HECA) project in Kern County, 

California and the Texas Clean Energy Project (TCEP) in Penwell, Texas include efforts to use captured 

carbon dioxide for EOR.

Title XVII of the Energy Policy Act of 2005 (EPACT2005) authorized loan guarantees for projects that 

avoid, reduce, or sequester greenhouse gasses. EPACT05 also provided a 20% investment tax credit for 

Integrated Coal-Gasification Combined Cycle (IGCC) capacity and a 15% investment tax credit for other 

advanced coal technologies. EIEA allocated an additional $1.25 billion in investment tax credits for IGCC 

and other advanced, coal-based generation technologies. For the AEO2015, all of the EPACT 2005 and 

EIEA investment tax credits are assumed to have been fully allocated and, therefore, not available for 

new, unplanned capacity builds in the NEMS Electricity Market Module.

Beginning in 2008, electricity generating units of 25 megawatts or greater were required to hold an 

allowance for each ton of CO2 emitted in nine Northeastern States as part of the Regional Greenhouse 

Gas Initiative (RGGI). The States currently participating in RGGI include Connecticut, Maine, Maryland, 

Massachusetts, Rhode Island, Vermont, New York, New Hampshire, and Delaware. RGGI is modeled in 

AEO2015 as an emissions reduction program for the Central Atlantic region. In the AEO2015, the impact 

on coal use is generally small, as the CO2 allowance price remains relatively low in the projections.

The AEO2015 includes a representation of California Assembly Bill 32 (AB32), the California Global 

Warming Solutions Act of 2006. The bill authorized the California Air Resources Board (CARB) to set 

California’s overall GHG emissions reduction goal to its 1990 level by 2020 and establish a 

comprehensive, multi-year program to reduce GHG emissions in California, including a cap-and-trade 

program. The cap-and-trade program features an enforceable cap on GHG emissions that will decline 

over time. In the AEO2015, an allowance price, representing the incremental cost of complying with 

AB32 cap-and-trade, is modeled in the NEMS Electricity Market Module via a region-specific emissions 

constraint. This allowance price increases the effective delivered price of coal, reducing its ability to 

compete with other generating sources such as natural gas which emits less CO2 per unit of electricity 

produced.

In accordance with California Senate Bill 1368 (SB 1368), which established a greenhouse gas emission 

performance standard for electricity generation, the AEO2015 prohibits builds of new coal-fired 

generating capacity without carbon capture and storage (CCS) for satisfying electricity demand in 

California. SB 1368 limits the generating emissions rate for all power plants that California utilities build, 

invest in, or sign a long-term contract with to be no more than 1,100 pounds of CO2 per megawatthour, 

which is the approximate emissions rate for a new natural gas combined-cycle power plant [114]. For 

AEO2015, the methodology to represent SB 1368 was further refined by modeling the expiration of 

contracts for imported coal-fired generation from the Four Corners, Navajo, Reid Gardner, San Juan, and 

Boardman plants and the retirement of the Intermountain plant in 2025.
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U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 187

Notes and sources

[111] Flynn, Edward J., “Impact of Technological Change and Productivity on The Coal Market,” U.S. 

Energy Information Administration (Washington, DC, October 2000), 

http://www.eia.gov/oiaf/analysispaper/pdf/coal.pdf ; and U.S. Energy Information Administration, The 

U.S. Coal Industry, 1970-1990: Two Decades of Change, DOE/EIA-0559 (Washington, DC, November 

1992).

[112] The estimated cost of switching to subbituminous coal, $0.10 per million Btu (2000 dollars), was 

derived by Energy Ventures Analysis, Inc. and was recommended for use in the CMM as part of an 

Independent Expert Review of the Annual Energy Outlook 2002’s Powder River Basin production and 

transportation rates. Barbaro, Ralph and Schwartz,Seth, Review of the Annual Energy Outlook 2002 

Reference Case Forecast for PRB Coal, prepared for the Energy Information Administration (Arlington, 

VA: Energy Ventures Analysis, Inc., August 2002).

[113] U.S. Environmental Protection Agency, Inventory of U.S. Greenhouse Gas Emissions and Sinks: 

1990-2008, Annex 2 Methodology and Data for Estimating CO2 Emissions from Fossil Fuel Combustion, 

EPA 430-R-10-006 (Washington, DC, April 2010), Table A-37, 

http://www.epa.gov/climatechange/ghgemissions/usinventoryreport/archive.html.

[114] California Energy Commission, SB 1368 Emission Performance Standards, 

http://www.energy.ca.gov/emission_standards/index.html.
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Renewable Fuels Module

The NEMS Renewable Fuels Module (RFM) provides natural resources supply and technology input 

information for projections of new central-station U.S. electricity generating capacity using renewable 

energy resources. The RFM has six submodules representing various renewable energy sources: 

biomass, geothermal, conventional hydroelectricity, landfill gas, solar thermal, solar photovoltaic, and 

wind [115].

Some renewables, such as landfill gas (LFG) from municipal solid waste (MSW) and other biomass 

materials, are fuels in the conventional sense of the word, while others, such as water, wind, and solar 

radiation, are energy sources that do not involve the production or consumption of a fuel. Commercial 

market penetration of renewable technologies varies widely.

The submodules of the RFM interact primarily with the Electricity Market Module (EMM). Because of 

the high level of integration with the EMM, the final outputs (levels of consumption and market 

penetration over time) for renewable energy technologies are largely dependent upon the EMM. 

Because some types of biomass fuel can be used for either electricity generation or for the production of 

liquid fuels, such as ethanol, there is also some interaction with the Liquid Fuels Market Module 

(LFMM), which contains additional representation of some biomass feedstocks that are used primarily 

for liquid fuels production.

Projections for residential and commercial grid-connected photovoltaic systems are developed in the 

end-use demand modules and not in the RFM; see the Distributed Generation and Combined Heat and 

Power description in the “Commercial Demand Module” and “Residential Demand Module” sections of 

the report. Descriptions for biomass energy production in industrial settings, such as the pulp and paper 

industries, can be found in the “Industrial Demand Module” section of the report.

Key assumptions

Nonelectric renewable energy uses

In addition to projections for renewable energy used in central station electricity generation, AEO2015 

contains projections of nonelectric renewable energy consumption for industrial and residential wood 

heating, solar residential and commercial hot water heating, biofuels blending in transportation fuels, 

and residential and commercial geothermal (ground-source) heat pumps. Assumptions for these 

projections are found in the Residential Demand, Commercial Demand, Industrial Demand, and Liquid 

Fuels Market Module (LFMM) sections of this report. Additional minor renewable energy applications 

occurring outside energy markets, such as direct solar thermal industrial applications or direct lighting, 

off-grid electricity generation, and heat from geothermal resources used directly (e.g., district heating 

and greenhouses) are not included in the projections.

Electric power generation

The RFM considers only grid-connected central station electricity generation systems. The RFM 

submodules that interact with the EMM are the central station grid-connected biomass, geothermal, 

conventional hydroelectricity, landfill gas, solar (thermal and photovoltaic), and wind submodules, 

which provide specific data or estimates that characterize the respective resource. A set of technology
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cost and performance values is provided directly to the EMM and is central to the build and dispatch 

decisions of the EMM. The technology cost and performance values are summarized in Table 8.2 in the 

chapter discussing the EMM.

Capital costs

Capital costs for renewable technologies are affected by several factors. Capital costs for technology to

exploit some resources, especially geothermal, hydroelectric, and wind power resources, are assumed 

to be dependent on the quality, accessibility, and/or other site-specific factors in the areas with 

exploitable resources. These factors can include additional costs associated with reduced resource 

quality; need to build or upgrade transmission capacity from remote resource areas to load centers; or 

local impediments to permitting, equipment transport, and construction in good resource areas due to 

siting issues, inadequate infrastructure, or rough terrain.

Short-term cost adjustment factors increase technology capital costs as a result of a rapid U.S. buildup in 

a single year, reflecting limitations on the infrastructure (for example, limits on manufacturing, resource 

assessment, and construction expertise) to accommodate unexpected demand growth. These factors, 

which are applied to all new electric generation capacity, are a function of past production rates and are 

further described in “The Electricity Market Module of the National Energy Modeling System: Model 

Documentation 2014” report, available at 

www.eia.gov/forecasts/aeo/nems/documentation/electricity/pdf/m068(2014).pdf. 

Also assumed to affect all new capacity types are costs associated with construction commodities. 

Through much of the period from 2000 to 2008, the installed cost for most new plants was observed to 

increase. Although several factors contributed to this cost escalation, some of which may be more or 

less important to specific types of new capacity, much of the overall cost increase was correlated with 

increases in the cost of construction materials, such as bulk metals, specialty metals, and concrete. 

Capital costs are specifically linked to the projections for the metals producer price index found in the 

Macroeconomic Module of NEMS. Independent of the other two factors, capital costs for all electric 

generation technologies, including renewable technologies, are assumed to decline as a function of 

growth in installed capacity for each technology.

For a description of NEMS algorithms lowering generating technologies’ capital costs as more units enter 

service (learning), see “Technological optimism and learning” in the EMM chapter of this report. A 

detailed description of the RFM is provided in the EIA publication, Renewable Fuels Module of the 

National Energy Modeling System, Model Documentation 2014, DOE/EIA-M069(2014) Washington, DC, 

2014. To access please see 

www.eia.gov/forecasts/aeo/nems/documentation/renewable/pdf/m069(2014).pdf. 
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Solar Submodule

Background

The Solar Submodule currently includes both solar thermal (also referred to as “concentrating solar 

power” or CSP) and photovoltaic (PV) technologies. The representative solar thermal technology 

assumed for cost estimation is a 100-megawatt central-receiver tower without integrated energy 

storage, while the representative solar PV technology is a 150-megawatt array of flat plate PV modules 

using single-axis tracking. PV is assumed available in all EMM regions, while CSP is available only in the 

Western regions with the arid atmospheric conditions that result in the most cost-effective capture of 

direct sunlight. Cost estimates for both technologies are based on a report by SAIC entitled “EOP III Task 

1606, Subtask 3 – Review of Power Plant Cost and Performance Assumptions for NEMS: Technology 

Documentation Report,” published in 2013, 

http://www.eia.gov/forecasts/capitalcost/pdf/updated_capcost.pdf. Technology-specific performance 

characteristics are obtained from information provided by the National Renewable Energy Laboratory 

(NREL).

Assumptions

• NEMS represents the Energy Policy Act of 1992 (EPACT92) permanent 10% investment tax credit 

(ITC) for solar electric power generation by tax-paying entities. In addition, the current 30% ITC, 

scheduled to expire at the end of 2016, is also represented to qualifying new capacity 

installations.

• Existing capacity and planned capacity additions are based on EIA survey data from the Form 

EIA-860, “Annual Electric Generator Report” and Form EIA-860M, “Monthly Update to the 

Annual Electric Generator Report.” Planned capacity additions under construction or having all 

regulatory approvals and having an expected completion date prior to the expiration of the 30% 

ITC were included in the model’s planned capacity additions, according to respondents’ planned 

completion dates..

• Capacity factors for solar technologies are assumed to vary by time of day and season of the 

year, such that nine separate capacity factors are provided for each modeled region, three for 

time of day and for each of three broad seasonal groups (summer, winter, and spring/fall). 

Regional capacity factors vary from national averages based on climate and latitude.

• Solar resources are well in excess of conceivable demand for new capacity; energy supplies are 

considered unlimited within regions (at specified daily, seasonal, and regional capacity factors). 

Therefore, solar resources are not estimated in NEMS. In the regions where CSP technology is 

not modeled, the level of direct, normal insolation (the kind needed for that technology) is 

assumed to be insufficient to make that technology commercially viable through the projection 

horizon.
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Wind Energy Power Submodule

Background

Because of limits to windy land areas, wind is considered a finite resource, so the submodule calculates 

maximum available capacity by NEMS EMM (Electricity Market Module) regions. The minimum 

economically viable average wind speed is about 15 mph at a hub-height of 80 meters (m), and wind 

speeds are categorized by annual average wind speed based on a classification system originally from 

the Pacific Northwest Laboratory (see http://rredc.nrel.gov/wind/pubs/atlas/tables/1-1T.html). The 

RFM tracks wind capacity by resource quality and costs within a region, and moves to the next best wind 

resource when one category is exhausted. Wind resource data on the amount and quality of wind per 

EMM region come from the National Renewable Energy Laboratory [116]. T The technological 

performance, cost, and other wind data used in NEMS are based on a report by SAIC entitled “EOP III 

Task 1606, Subtask 3 – Review of Power Plant Cost and Performance Assumptions for NEMS: Technology

Documentation Report”, published in 2013. To access, please see 

http://www.eia.gov/forecasts/capitalcost/pdf/updated_capcost.pdf. Cost estimates for AEO2015 were 

updated based on the report by the Department of Energy entitled “2013 Wind Technologies Market 

Report” [117]. To access, please see 

http://emp.lbl.gov/sites/all/files/2013_Wind_Technologies_Market_Report_Final3.pdf. Maximum wind 

capacity, capacity factors, and incentives are provided to the EMM for capacity planning and dispatch 

decisions. These form the basis on which the EMM decides how much power generation capacity is 

available from wind energy. The fossil-fuel heat rate equivalents for wind are used for energy 

consumption calculation purposes only.

Assumptions

• Only grid-connected (utility and nonutility) generation is included. Projections for distributed 

wind generation are included in the commercial and residential modules.

• In the wind energy submodule, wind supply costs are affected by three modeling measures 

addressing: (1) average wind speed, (2) distance from existing transmission lines, and (3) 

resource degradation, transmission network upgrade costs, and market factors.

• Available wind resource is reduced by excluding all windy lands not suited for the installation of 

wind turbines because of: excessive terrain slope (greater than 20%); reservation of land for 

non-intrusive uses (such as national parks, wildlife refuges, etc.); inherent incompatibility with 

existing land uses (such as urban areas, areas surrounding airports and water bodies, including 

offshore locations); and insufficient contiguous windy land to support a viable wind plant (less 

than 5 square kilometers of windy land in a 100-square-kilometer area). Half of the wind 

resource located on military reservations, U.S. Forest Service land, state forested land, and all 

non-ridge-crest forest areas is excluded from the available resource base to account for the 

uncertain ability to site projects at such locations. These assumptions are detailed in Appendix 

3-E of the “The Renewable Fuels Module of the National Energy Modeling System: Model 

Documentation”, DOE/EIA-MO69(2014), Washington, DC, 2014. To access please see 

http://www.eia.gov/forecasts/aeo/nems/documentation/renewable/pdf/m069(2014).pdf
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• Capital costs for wind technologies are assumed to increase in response to: (1) declining 

natural resource quality as the best sites are utilized, such as terrain slope, terrain roughness, 

terrain accessibility, wind turbulence, wind variability, or other natural resource factors , (2) 

increasing costs of upgrading existing local and network distribution and transmission lines to 

accommodate growing quantities of remote wind power, and (3) market conditions, such as the 

increasing costs of alternative land uses, including aesthetic or environmental reasons. Capital 

costs are left unchanged for some initial share, then increased by 10%, 25%, 50%, and finally 

100%, to represent the aggregation of these factors.

• Proportions of total wind resources in each category vary by EMM region. For all EMM regions 

combined, about 1% of windy land (107 GW of 11,600 GW in total resource) is available with no 

cost increase, 3.4% (390 GW) is available with a 10% cost increase, 2% (240 GW) is available with 

a 25% cost increase, and over 90% is available with a 50% or 100% cost increase.

• Depending on the EMM region, the cost of competing fuels, and other factors, wind plants can 

be built to meet system capacity requirements or as a “fuel saver” to displace generation from 

existing capacity. For wind to penetrate as a fuel saver, its total capital and fixed operations and 

maintenance costs minus applicable subsidies must be less than the variable operating costs, 

including fuel, of the existing (non-wind) capacity. When competing in the new capacity market, 

wind is assigned a capacity credit that declines based on its estimated contribution to regional 

reliability requirements.

• Because of downwind turbulence and other aerodynamic effects, the model assumes an 

average spacing between turbine rows of 5 rotor diameters and a lateral spacing between 

turbines of 10 rotor diameters. This spacing requirement determines the amount of power that 

can be generated from wind resources, about 6.5 megawatts per square kilometer of windy 

land, and is factored into requests for generating capacity by the EMM.

• Capacity factors for each wind class are calculated as a function of overall wind market growth. 

The capacity factors are assumed to be limited to about 50% for a typical Class 6 site. As better 

wind resources are depleted, capacity factors are assumed to go down, corresponding with the 

use of less-desirable sites. By 2040, the typical wind plant build will have a somewhat lower 

capacity factor than those found in the best wind resource areas. Capacity factors in the 

Reference case increase to about 40% in the best wind class resulting from taller towers, more 

reliable equipment, and advanced technologies, although, as noted, these may not represent 

the best available sites because of other site-specific factors.

• AEO2015 allows plants under construction by 2015 to claim the federal Production Tax Credit 

(PTC), a 2.3-cent-per-kilowatthour tax incentive for wind that was initially set to expire for wind 

only on December 31, 2014. Wind plants are assumed to depreciate capital expenses using the 

Modified Accelerated Cost Recovery System with a 5-year tax life and 5-year double declining 

balance depreciation.
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Offshore wind resources are represented as a separate technology from onshore wind resources. 

Offshore resources are modeled with a similar model structure as onshore wind. However, because of 

the unique challenges of offshore construction and the somewhat different resource quality, the 

assumptions with regard to capital cost, learning-by-doing cost reductions, and variation of resource 

exploitation costs and performance differ significantly from onshore wind.

• Like onshore resources, offshore resources are assumed to have an upwardly sloping supply 

curve, in part influenced by the same factors that determine the onshore supply curve (such as 

distance to load centers, environmental or aesthetic concerns, variable terrain/seabed) but also 

explicitly by water depth.

• Because of the more difficult maintenance challenges offshore, performance for a given annual 

average wind power density level is assumed to be somewhat decreased by reduced turbine 

availability. Offsetting this, however, is the availability of resource areas with higher overall 

power density than is assumed available onshore. Capacity factors for offshore are limited to be 

about 53% for a Class 7 site.

• Cost reductions in the offshore technology result in part from learning reductions in onshore 

wind technology as well as from cost reductions unique to offshore installations, such as 

foundation design and construction techniques. Because offshore technology is significantly less 

mature than onshore wind technology, offshore-specific technology learning occurs at a 

somewhat faster rate than on-shore technology. A technological optimism factor (see EMM 

documentation: 

www.eia.gov/forecasts/aeo/nems/documentation/electricity/pdf/m068(2014).pdf) is included 

for offshore wind to account for the substantial cost of establishing the unique construction 

infrastructure required for this technology.

Geothermal Electricity Submodule

Background

Beginning in AEO2011, all geothermal supply curve data come from the National Renewable Energy 

Laboratory’s updated U.S. geothermal supply curve assessment. The report, released in February 2010, 

assigns cost estimates to the U.S. Geological Survey’s (USGS) 2008 geothermal resource assessment. 

Some data from the 2006 report, “The Future of Geothermal Energy,” prepared for Idaho National 

Laboratory by the Massachusetts Institute of Technology, were also incorporated into the NREL report; 

however, this would be more relevant to deep, dry, and unknown geothermal resources, which EIA did 

not include in its supply curve. NREL took the USGS data and used the Geothermal Electricity Technology 

Evaluation Model (GETEM), a techno-economic systems analysis tool, to estimate the costs [118]. Only 

resources with temperatures above 110 degrees Celsius were considered. There are approximately 125 

of these known, hydrothermal resources which EIA used in its supply curve. Each of these sites also has 

what NREL classified as “near-field enhanced geothermal energy system potential” which are in areas 

around the identified site that lack the permeability of fluids that are present in the hydrothermal 

potential. Therefore, there are 250 total points on the supply curve since each of the 125 hydrothermal 

sites has corresponding enhanced geothermal system (EGS) potential.
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In the past, EIA cost estimates were broken down into cost-specific components. Unfortunately, this 

level of detail was not available in the NREL data. A site-specific capital cost and fixed operations and 

maintenance cost were provided. Two types of technology, flash and binary cycle, are also included with 

capacity factors ranging from 90% to 95%. While the source of the data was changed beginning in 

AEO2011, the site-by-site matrix input that acts as the supply curve has been retained.

Assumptions

• Existing and identified planned capacity data are obtained directly by the EMM from Form EIA860 and Form EIA-860M.

• The permanent investment tax credit of 10% available in all projection years, based on EPACT92, 

applies to all geothermal capital costs, except through December 2014 when the 2.3-cent year 

kilowatthour production tax credit is available to this technology and is assumed chosen instead. 

Projects that began construction and are beyond the exploratory drilling phase by that date are 

eligible for this production tax credit.

• Plants are not assumed to retire unless their retirement is reported to EIA. The Geysers units are 

not assumed to retire but instead are assigned the 35% capacity factors reported to EIA 

reflecting their reduced performance in recent years.

Biomass Submodule

Background

Biomass consumed for electricity generation is modeled in two parts in NEMS. Capacity in the wood 

products and paper industries, the so-called captive capacity, is included in the Industrial Demand 

Module as cogeneration. Generation in the electricity sector is represented in the EMM. Fuel costs are 

calculated in NEMS and passed to EMM, while capital and operating costs and performance 

characteristics are assumed as shown in Table 8.2, available at 

http://www.eia.gov/forecasts/aeo/assumptions/pdf/electricity.pdf. Fuel costs are provided in sets of 

regional supply schedules. Projections for ethanol production are produced by the LFMM, with the 

quantities of biomass consumed for ethanol decremented from, and prices obtained from, the EMM 

regional supply schedules.

Assumptions

• Existing and planned capacity data are obtained from Form EIA-860 and Form EIA-860M.

• The conversion technology represented is an 80-MW dedicated combustion plant. The cost 

estimates for this technology are based on a report by SAIC entitled “EOP III Task 1606, Subtask 

3 – Review of Power Plant Cost and Performance Assumptions for NEMS: Technology 

Documentation Report,” published in 2013, 

http://www.eia.gov/forecasts/capitalcost/pdf/updated_capcost.pdf.

• Biomass co-firing can occur up to a maximum of 15% of fuel used in coal-fired generating plants.

Fuel supply schedules are a composite of four fuel sources: forestry materials, wood residues from 

federal forests and from non-federal forests, agricultural residues and energy crops. Feedstock potential 

from agricultural residues and dedicated energy crops are calculated from a version of the Policy
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Analysis (POLYSYS) agricultural model that uses the same oil price information as the rest of NEMS. 

Forestry residues are calculated from inventories conducted by the U.S. Forest Service and Oak Ridge 

National Laboratory. The forestry materials component is made up of logging residues, rough rotten 

salvageable dead wood, and excess small pole trees [119]. The wood residue component consists of 

primary mill residues, silvicultural trimmings, and urban wood such as pallets, construction waste, and 

demolition debris that are not otherwise used [120]. Agricultural residues are wheat straw, corn stover, 

and a number of other major agricultural crops [121]. Energy crop data are for hybrid poplar, willow, 

and switchgrass grown on existing cropland. POLYSYS assumes that the additional cropland needed for 

energy crops will displace existing pasturelands. The maximum amount of resources from forestry is 

fixed based on “U.S. Billion-Ton Update: Biomass Supply for a Bioenergy and Bioproducts Industry” 

prepared by Oak Ridge National Laboratory [122]. Urban wood waste is determined dynamically based 

on activity in the industry sectors that produce usable biomass feedstocks. Agricultural resource 

(agricultural residues and energy crops) supply is determined dynamically, and supplies available within 

the model at any point in time may not reflect the maximum potential for that region. In 2040, the 

estimated supplies of the feedstock categories are as follows: agricultural residues and energy crops are 

estimated at 5,559 trillion Btu; wood residues are estimated at 1,215 trillion Btu; forestry materials 

(from public and private lands) are estimated at 1,915 trillion Btu. For 2040, supplies of 797.1 trillion Btu 

from all sectors could be available given prevailing demand in the AEO2015 Reference case.

Landfill Gas (LFG) Submodule

Background

Landfill-gas-to-electricity capacity competes with other technologies using supply curves that are based 

on the amount of “high,” “low,” and “very low” methane-producing landfills located in each EMM 

region. An average cost of electricity for each type of landfill is calculated using gas collection system 

and electricity generator costs and characteristics developed by EPA’s “Energy Project Landfill Gas 

Utilization Software” (E-PLUS) [123].

Assumptions

• Gross domestic product (GDP) and population are used as the drivers in an econometric 

equation that establishes the supply of landfill gas.

• Recycling is assumed to account for 50% of the waste stream in 2010 (consistent with EPA’s 

recycling goals).

• The waste stream is characterized into three categories: readily, moderately, and slowly 

decomposable material.

• Emission parameters are the same as those used in calculating historical methane emissions in 

EIA’s “Emissions of Greenhouse Gases in the United States 2003” [124].

• The ratio of “high,” “low,” and “very low” methane production sites to total methane 

production is calculated from data obtained for 156 operating landfills contained in the 

Governmental Advisory Associates Inc., METH2000 database [125].

• Cost of electricity for each site was calculated by assuming each site to be a 100-acre by 50-foot 

deep landfill and by applying methane emission factors for “high,” “low,” and “very low” 

methane-emitting wastes.
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Conventional Hydroelectricity Submodule

The conventional hydroelectricity submodule represents U.S. potential for new conventional 

hydroelectric capacity of 1 megawatt or greater from new dams, existing dams without hydroelectricity, 

and from adding capacity at existing hydroelectric dams. Summary hydroelectric potential is derived 

from reported lists of potential new sites assembled from Federal Energy Regulatory Commission (FERC) 

license applications and other survey information, plus estimates of capital and other costs prepared by 

the Idaho National Engineering and Environmental Laboratory (INEEL) [126]. Annual performance 

estimates (capacity factors) were taken from the generally lower but site-specific FERC estimates rather 

than from the general estimates prepared by INEEL, and only sites with estimated costs of 10 cents per 

kilowatthour or lower are included in the supply. Pumped storage hydroelectric, considered a 

nonrenewable storage medium for fossil and nuclear power, is not included in the supply; moreover, the 

supply does not consider offshore or in-stream hydroelectric, efficiency or operational improvements 

without capital additions, or additional potential from refurbishing existing hydroelectric capacity.

In the hydroelectricity submodule, sites are first arrayed by NEMS region from least to highest cost per 

kilowatthour. For any year’s capacity decisions, only those hydroelectric sites whose estimated levelized 

costs per kilowatthour are equal to or less than an EMM-determined avoided cost (the least cost of 

other technology choices determined in the previous decision cycle) are submitted. Next, the array of 

below-avoided-cost sites is parceled into three increasing cost groups, with each group characterized by 

the average capacity-weighted cost and performance of its component sites. Finally, the EMM receives 

from the conventional hydroelectricity submodule the three increasing-cost quantities of potential 

capacity for each region, providing the number of megawatts potential along with their capacityweighted average overnight capital cost, operations and maintenance cost, and average capacity factor. 

After choosing from the supply, the EMM informs the hydroelectricity submodule, which decrements 

available regional potential in preparation for the next capacity decision cycle.

Legislation and regulations

Renewable electricity tax credits

The RFM includes the investment and energy production tax credits codified in EPACT92 as amended. 

The investment tax credit established by EPACT92 provides a credit to federal income tax liability worth 

10% of initial investment cost for a solar, geothermal, or qualifying biomass facility. This credit was 

raised to 30% through 2016 for some solar projects and extended to residential projects. This change is 

reflected in the RFM, Commercial Demand Module, and Residential Demand Module. The production 

tax credit, as established by EPACT92, applied to wind and certain biomass facilities. As amended, it 

provides a 2.3-cent tax credit for every kilowatthour of electricity produced for the first 10 years of 

operation for a wind facility, under construction by December 31, 2014. The value of the credit, 

originally 1.5 cents, is adjusted annually for inflation. With the various amendments, the production tax 

credit is available for electricity produced from qualifying geothermal, animal waste, certain small-scale 

hydroelectric, landfill gas, municipal solid waste, and additional biomass resources. Wind, poultry litter, 

geothermal, and closed loop [127] biomass resources receive a 2.3-cent tax credit per Kwh for the first 

10 years of facility operations. All other renewable resources receive a 1.1-cent (that is, one-half the 

value of the credit for other resources) tax credit for the first 10 years of facility operations. EIA assumes 
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that biomass facilities obtaining the PTC will use open-loop fuels, as closed- loop fuels are assumed to be 

unavailable and/or too expensive for widespread use during the period that the tax credit is available. 

The investment and production tax credits are exclusive of one another, and thus may not both be 

claimed for the same geothermal facility (which is eligible to receive either).

The American Taxpayer Relief Act passed in January 2012 extended the wind PTC expiration date until 

December 31, 2014. In addition, it modified the requirement for all technologies that plants be 

operational by the expiration date to allow any plant under construction by the expiration date to 

qualify. This update has been included in the AEO2015 Reference case.

State RPS programs

EIA represents various state-level policies generally referred to as Renewable Portfolio Standards (RPS). 

These policies vary significantly among states, but typically require the addition of renewable generation 

to meet a specified share of state-wide generation. Any non-discretionary limitations on meeting the 

generation or capacity target are modeled to the extent possible. However, because of the complexity 

of the various requirements, the regional target aggregation, and the nature of some of the limitations, 

the measurement of compliance is assumed to be approximate.

Regional renewable generation targets were estimated using the renewable generation targets in each 

state within the region. In many cases, regional boundaries intersect state boundaries; in these cases 

state requirements were divided among relevant regions based on sales. Using state-level RPS 

compliance schedules and preliminary estimates of projected sales growth, EIA estimated the amount of 

renewable generation required in each state within a region. Required generation in each state was 

then summed to the regional level for each year, and a regional renewable generation share of total 

sales was determined, as shown in Table 13.1.

Only targets with established enforcement provisions or established state funding mechanisms were 

included in the calculation; non-enforceable goals were not included. Compliance enforcement 

provisions vary significantly among states, and most states have established procedures for waiving 

compliance through the use of alternative compliance payments, penalty payments, discretionary 

regulatory waivers, or retail price impact limits. Because of the variety of mechanisms, even within a 

given electricity market region, these limits are not modeled.
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Table 13.1. Aggregate regional renewable portfolio standard requirements

percentage share of total values

Region1 2020 2030 2040

Texas Reliability Entity 4.4% 4.4% 4.4%

Midwest Reliability Council Organization - East 10.0% 10.0% 10.0%

Midwest Reliability Council Organization - West 10.3% 11.4% 11.4%

Northeast Power Coordinating Council – New England 14.3% 14.6% 14.6%

Northeast Power Coordinating Council - NYC/Westchester 24.6% 24.6% 24.6%

Northeast Power Coordinating Council - Long Island 24.6% 24.6% 24.6%

Northeast Power Coordinating Council - Upstate NY 24.4% 24.5% 24.5%

ReliabilityFirst Corporation - East 13.6% 14.8% 14.8%

ReliabilityFirst Corporation - Michigan 10.0% 10.0% 10.0%

ReliabilityFirst Corporation - West 7.1% 9.3% 9.3%

SERC Reliability Corporation - Delta 0.6% 0.6% 0.6%

SERC Reliability Corporation - Gateway 11.2% 15.8% 15.8%

SERC Reliability Corporation - Central 0.0% 0.1% 0.1%

SERC Reliability Corporation - Virginia/Carolina 5.0% 5.5% 5.5%

Southwest Power Pool Regional Entity- North 11.9% 13.2% 13.2%

Southwest Power Pool Regional Entity- South 2.1% 2.2% 2.2%

Western Electricity Coordinating Council - Southwest 9.4% 11.1% 11.1%

Western Electricity Coordinating Council - California 33.0% 33.0% 33.0%

Western Electricity Coordinating Council – Northwest Power 

Pool Area 10.1% 11.0% 11.0%

Western Electricity Coordinating Council - Rockies 23.3% 23.3% 23.3% 1

See chapter on the Electricity Market Module for a map of the electricity market module supply 

regions. Regions not shown do not have renewable portfolio standard requirements.
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 199

Notes and sources

[115] For a comprehensive description of each submodule, see U.S. Energy Information Administration, 

Office of Integrated Analysis and Forecasting, Model Documentation, Renewable Fuels Module of the 

National Energy Modeling System, DOE/EIA-M069(2014), (Washington, DC, August 2014), 

http://www.eia.gov/forecasts/aeo/nems/documentation/renewable/pdf/m069(2014).pdf. 

[116] Revising the Long Term Multipliers in NEMS: Quantifying the Incremental Transmission Costs Due 

to Wind Power, Report to EIA from Princeton Energy Resources International, LLC. May 2007.

[117] U.S. Department of Energy. “2013 Wind Technologies Market Report”. (Washington, DC, August 

2014). http://emp.lbl.gov/sites/all/files/2013_Wind_Technologies_Market_Report_Final3.pdf. 

[118] The one exception applies to the Salton Sea resource area. For that site, EIA used cost estimates 

provided by R.W. Beck, Inc. rather than NREL.

[119] U.S. Department of Energy. “U.S. Billion-Ton Update: Biomass Supply for a Bioenergy and 

Bioproducts Industry”, August 2011.

[120] Ibid.

[121] De la Torre Ugarte, D. “Biomass and bioenergy applications of the POLYSYS modeling framework” 

Biomass and Bioenergy Vol. 18 (April 2000), pp 291-308. 

[122] U.S. Department of Energy. “U.S. Billion-Ton Update: Biomass Supply for a Bioenergy and 

Bioproducts Industry”, August 2011.

[123] U.S. Environmental Protection Agency, Atmospheric Pollution Prevention Division, Energy Project 

Landfill Gas Utilization Software (E-PLUS) Version 1.0, EPA-430-B-97-006 (Washington, DC, January 

1997).

[124] U.S. Energy Information Administration, “Emissions of Greenhouse Gases in the United States 

2003,” DOE/EIA- 0573(2003) (Washington, DC, December 2004), 

www.eia.gov/oiaf/1605/archive/gg04rpt/index.html. 

[125] Governmental Advisory Associates, Inc., METH2000 Database, Westport, CT, January 25, 2000.

[126] Hall, Douglas G., Richard T. Hunt, Kelly S. Reeves, and Greg R. Carroll, Idaho National Engineering 

and Environmental Laboratory, “Estimation of Economic Parameters of U.S. Hydropower Resources” 

INEEL/EXT-03-00662, (Idaho Falls, Idaho, June 2003), 

http://www1.eere.energy.gov/water/pdfs/doewater-00662.pdf

[127] Closed-loop biomass are crops produced explicitly for energy production. Open-loop biomass are 

generally wastes or residues that are a byproduct of some other process, such as crops grown for food, 

forestry, landscaping, or wood milling.
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 200

Appendix A: Handling of federal and selected state legislation and 

regulations in the AEO

Residential sector

Legislation Brief description AEO handling Basis

A. National Appliance 

Energy Conservation

Act of 1987 (NAECA87)

Requires Secretary of Energy

to set minimum efficiency

standards for various

appliance categories with 

periodic updates

Include categories

represented in the AEO

residential sector forecast

Public Law 100-12

a. Room air conditioners Sets standards for room air

conditioners in 2014

Require new purchases of

room air conditioners to 

meet the standard

Federal Register Notice

of Final Rulemaking

b. Central air

 conditioners

 and heat pumps

Sets standards for central air

conditioners in 2015

Require new purchases of

other air conditioners to 

meet the standard

Federal Register Notice

of Final Rulemaking

c. Water heaters Sets standards for water

heaters in 2015

Require new purchases of

water heaters to meet the

standard

Federal Register Notice

of Final Rulemaking

d. Refrigerators and 

 freezers

Sets standards for water

heaters in 2014

Require new purchases of

refrigerators/freezers to 

meet the standard

Federal Register Notice

of Final Rulemaking

e. Dishwashers Sets standards for 

dishwasher in 2010

Require new purchases of

dishwashers to meet the

standard

Federal Register Notice

of Final Rulemaking

f. Fluorescent lamp 

 ballasts

Sets standards for 

fluorescent lamp ballasts in 

2005

Require new purchases of

fluorescent lamp ballasts to

meet the standard

Federal Register Notice

of Final Rulemaking

g. Clothes washers Sets standards for clothes

washers in 2011

Require new purchases of

clothes washers to meet the

standard

Federal Register Notice

of Final Rulemaking

h. Furnaces Sets standards for furnaces 

in 2013

Require new purchases of

furnaces to meet the 

standard

Federal Register Notice

of Final Rulemaking

i. Clothes dryers Sets standards for clothes

dryers in 2015

Require new purchases of

clothes dryers to meet the

standard

Federal Register Notice

of Final Rulemaking

j. Boilers Sets standards for boilers in

2012

Require new purchases of

boilers to meet the standard

Federal Register Notice

of Final Rulemaking

B. Energy Policy Act of 1992 (EPACT92) Public Law 102-486

a. Building codes For the IECC 2006, specifies

whole house efficiency

minimums

Assume that all states adopt

the IECC 2006 code by 2017

Trend of states’

adoption of codes,

allowing for lead times

for enforcement and

builder compliance
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 201

Legislation Brief description AEO handling Basis

b. Various lighting 

types

Sets standards for various 

lighting types in 2012

Require new purchases of 

various lighting types to 

meet the standards

Federal Register Notice of 

Final Rulemaking

C. Energy Policy Act of 2005 (EPACT05) Public Law 109-58

a. Torchiere lamp 

 standard

Sets standard for torchiere

lamps in 2006

Require new purchases of

torchiere bulbs to meet the

standard

Federal Register Notice

of Final Rulemaking

b. Compact fluorescent 

 lamp standard

Sets standard for 

fluorescent

lamps in 2006

Require new purchases of

compact fluorescent bulbs 

to meet the standard

Federal Register Notice

of Final Rulemaking

c. Ceiling fan light kit

 standard

Sets standard for ceiling

fans and ceiling fan light

kits in 2007

Reduce lighting electricity

consumption by 

appropriate

amount

Number of ceiling fan

shipments and estimated

kWh savings per unit

determine overall savings

d. Dehumidifier 

 standard

Sets standard for

dehumidifiers in 2012

Reduce dehumidifier

electricity consumption by

appropriate amount

Number of dehumidifier

shipments and estimated

kWh savings per unit

determine overall savings

e. Energy-efficient 

 equipment tax credit

Provides tax credits to 

purchasers of certain

energy-efficient equipment 

in 2006 and 2007

Reduce cost of applicable

equipment by specified

amount

Federal Register Notice of 

Final Rulemaking

f. New home tax credit Provides $1,000 or

$2,000 tax credit to builders 

if they construct homes that 

are 30% or 50%, 

respectively, more efficient 

than code in 2006 and 2007

Reduce shell package cost

for these homes by 

specified amount

Cost reductions to

consumers are assumed to 

be 100% of the builder’s tax 

credit

g. Energy-efficient 

 appliance tax credit

Provides tax credits to 

producers of energyefficient refrigerators, 

dishwashers, and clothes 

washers for each unit they

produce that meets certain 

efficiency specifications

Assume that the cost 

savings are passed on to 

the consumer, reducing the 

price of the appliance by 

the specified amount

Cost reductions to 

consumers are assumed to 

be 100% of the producer’s 

tax credit

D. Energy Independence and Security Act of 2007 (EISA 07) Public Law 110-140

a. General service 

 incandescent lamp

 standard

Require less wattage for 

bulbs in 2012–2014 and

2020

Reduce wattage for new 

bulbs by 28% in 2013 and 

67% in 2020

Federal Register Notice of

Final Rulemaking

b. External power 

 supply standard

Sets standards for external 

power supplies in 2008

Reduce external power 

supply electricity 

consumption by 

appropriate amount

Number of shipments and 

estimated kWh savings per

unit determine overall 

savings

c. Manufactured

 housing code

Require manufactured 

homes to meet latest IECC in 

2011

Require that all 

manufactured homes 

shipped after 2011 meet 

the IECC 2006

Federal Register Notice of

Final Rulemaking
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 202

Legislation Brief description AEO handling Basis

E. Energy Improvement and Extension Act of 08 (EIEA08) Public Law 110-343

a. Energy-efficient

 equipment tax credit

Purchasers of certain energyefficient equipment can claim 

tax credits through 2016

Reduce the cost of applicable 

equipment by specified 

amount

Federal Register Notice of

Final Rulemaking

b. Energy-efficient 

 appliance tax credit

Producers of energy-efficient 

refrigerators, clothes washers, 

and dishwashers receive tax 

credits for each unit they 

produce that meets certain 

efficiency specifications, 

subject to an annual cap

Assume that the cost savings 

are passed on to the 

consumer, reducing the price 

of the appliance by the 

specified amount

Cost reductions to consumer 

are assumed to be 100% of 

the producer’s tax credit

F. American Recovery and Reinvestment Act of 2009 (ARRA09) Public Law 111-5

a. Energy-efficient

 equipment tax credit

Increases cap of energyefficient equipment specified 

under Section E(a) above to 

$1,500; removes cap for solar 

PV, wind, and geothermal heat 

pumps

Reduce the cost of applicable 

equipment by specified 

amount

Federal Register Notice of

Final Rulemaking

b. Weatherization and

 State Energy Programs

Increases funding for 

weatherization and other 

programs to improve the 

energy efficiency of existing 

housing stock

Apply annual funding amount 

to existing housing retrofits; 

base savings for heating and 

cooling on $2,600 per-home 

investment as specified in 

weatherization program

evaluation

Federal Register Notice of

Final Rulemaking

G. Tax Relief, Unemployment Insurance Reauthorization, and Job Creation Act of 2010 Public Law 111-312

a. Energy-efficient

 equipment tax credit

Extends tax credits for some 

energy-efficient equipment, 

generally to EISA07 amounts

Reduce the cost of applicable 

equipment by specified 

amount
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 203

Commercial sector

Legislation Brief description AEO handling Basis

A. National Appliance 

Energy Conservation Act 

of 1987 (NAECA87)

Requires Secretary of 

Energy to set minimum 

efficiency standards for

various appliance 

categories with periodic 

updates

Include categories 

represented in the AEO 

commercial sector forecast

Public Law 100-12

a. Room air conditioners Change room air conditioner 

efficiency, including metric, 

from 9.8 Energy Efficiency 

Ratio (EER) to 10.9 Combined 

Energy Efficiency Ratio (CEER) 

in 2014

Federal Register Notice of

Final Rulemaking

b. Other residential-size

 air conditioners (<5.4

 tons)

Set central air conditioning 

and heat pump efficiency to

10 SEER before 2006, 13 SEER 

in 2006, and 14 SEER in 2015

Federal Register Notice of

Final Rulemaking

c. Fluorescent lamp

 ballasts

Set standard of 0.90 power 

factor and minimum efficacy 

factor for F40 and F96 lamps 

based on lamp size and 

wattage, increasing to higher 

efficacy factor in 2005 that 

limits purchases to electronic 

ballasts

Federal Register Notice of

Final Rulemaking

B. Energy Policy Act of 1992 (EPACT92) Public Law 102-486

a. Building codes Incorporate into commercial 

building shell assumptions; 

represent efficiency of new

shell relative to existing shell 

in shell efficiency indices;

assumes shell efficiency 

improves 6.9% and 15.0% by 

2040 for existing buildings and 

new construction, respectively

Based on Science Applications 

International Corporation 

commercial shell indices for 2003 

developed for EIA in 2008 and 2011

b. Window labeling Helps consumers 

determine which 

windows are more 

energy efficient

Incorporate into commercial 

building shell assumptions; 

represent efficiency of new 

shell relative to existing shell 

in shell efficiency indices; 

assume shell efficiency 

improves 6.9% and 15.0% by 

2040 for existing buildings and

new construction, respectively

Based on Science Applications 

International Corporation 

commercial shell indices for 2003 

developed for EIA in 2008 and 2011

c. Commercial furnaces

 and boilers

Set gas-fired furnace and 

boiler thermal efficiency to 

80%; set oil furnace thermal 

efficiency to 81%; set oil boiler 

thermal efficiency to 83%

Federal Register Notice of Final 

Rulemaking
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 204

Legislation Brief description AEO handling Basis

d. Commercial air

 conditioners and heat 

 pumps

Set air-source air 

conditioners and heat 

pumps less than 135,000

Btu to 8.9 EER and 

greater than 135,000 Btu 

to 8.5 EER in 2001

Federal Register Notice of Final 

Rulemaking

e. Commercial water 

 heaters

Set gas and oil thermal 

efficiency to 78%,

increasing to 80% 

thermal efficiency for 

gas units in 2003

Federal Register Notice of Final 

Rulemaking

f. Lamps Set incandescent efficacy 

to 16.9 lumens per watt

and fluorescent efficacy 

to 75 and 80 lumens per 

watt for 4- and 8-foot 

lamps, respectively

g. Electric motors Specifies minimum 

efficiency levels for a 

variety of motor types 

and sizes

Model end-use services 

at the equipment level 

(motors contained in 

new equipment must 

meet the standards)

Federal Register Notice of Final 

Rulemaking

h. Federal energy management Requires federal 

agencies to reduce 

energy consumption

20% by 2000 relative to 

1985

Use the 10-year Treasury 

note rate for federal 

share of the commercial 

sector as a discount rate 

in equipment purchase 

decisions

Superseded by Executive

Order 13123 , EPACT05, and EISA07

i. Business investment energy

 credit

Provides a permanent

10% investment tax 

credit for solar property

Incorporate tax credit

into cash flow for solar 

generation systems;

reduced investment cost

for solar water heaters

by 10%

Federal Register Notice of Final 

Rulemaking

C. Executive Order 13123: Greening 

 the Government Through Efficient 

 Energy Management

Requires federal 

agencies to reduce 

energy consumption

30% by 2005 and 35% by 

2010 relative to 1985 

through cost-effective 

life-cycle energy 

measures

Use the 10-year Treasury 

note rate for federal 

share of the commercial 

sector as a discount rate 

in equipment purchase 

decisions

Superseded by EPACT05 and EISA07
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 205

Legislation Brief description AEO handling Basis

D. Energy Policy Act of 2005 (EPACT05) Public Law 109-58

a. Commercial package air 

 conditioners and heat pumps

Sets minimum efficiency 

levels in 2010

Set air-cooled air 

conditioners/ heat pumps 

less than 135,000 Btu to

11.2/ 11.0 EER and heating 

COP of 3.3 and greater than 

135,000 Btu to 11.0/ 10.6 

EER and heating COP of 3.2

Federal Register Notice of Final 

Rulemaking

b. Commercial refrigerators, 

 freezers, and automatic 

 icemakers

Sets minimum efficiency 

levels in 2010 and 2017 

(refrigerators and 

freezers)

Remove refrigerator and 

freezer systems that do not 

meet standard from 

technology choice.

Federal Register Notice of Final 

Rulemaking

c. Lamp ballasts Bans manufacture or 

import of mercury vapor 

lamp ballasts in 2008; 

sets minimum efficacy 

level for T12 energy 

saver ballasts in 2009

and 2010 based on 

application

Remove mercury vapor 

lighting system from 

technology choice menu in 

2008; set minimum efficacy 

of T12 ballasts at specified 

standard levels

Federal Register Notice of Final 

Rulemaking

d. Compact fluorescent lamps Sets standard for 

medium base lamps to

ENERGY STAR 

specifications in 2006

Set efficacy level of compact 

fluorescent lamps at 

required level.

Federal Register Notice of Final 

Rulemaking

e. Illuminated exit signs and traffic

 signals

Sets standards to

ENERGY STAR 

specifications in 2006

Reduce miscellaneous 

electricity consumption by 

appropriate amount

Number of shipments, share of 

shipments that currently meet 

standard, and estimated kWh 

savings per unit determine 

overall savings

f. Distribution transformers Sets standard as National 

Electrical Manufacturers 

Association Class I 

Efficiency levels in 2007, 

with an update effective 

in 2016

Include effects of standard

in estimating the share of 

miscellaneous electricity 

consumption attributable to 

transformer losses

Federal Register Notice of Final 

Rulemaking

g. Pre-rinse spray valves Sets maximum flow rate 

to 1.6 gallons per minute 

in 2006

Reduce energy use for water 

heating by appropriate 

amount

Number of shipments, share of 

shipments that currently meet 

standard, and estimated kWh 

savings per unit determine 

overall savings

h. Federal energy management Requires federal 

agencies to reduce 

energy consumption

20% by 2015 relative to

2003 through costeffective life-cycle 

energy measures

Use the 10-year Treasury 

note rate as a discount rate

for federal share of the 

commercial sector as a

discount rate in equipment 

purchase decisions as 

opposed to adding risk 

premiums to the 10-year 

Treasury note rate to 

develop discount rates for 

other commercial decisions

Superseded by EISA07
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 206

Legislation Brief description AEO handling Basis

i. Business investment tax credit 

 for fuel cells and microturbines

Provides a 30% 

investment tax credit for 

fuel cells and a 10%

investment tax credit for 

microturbines installed 

in 2006 through 2016

Incorporate tax credit into

cash flow for fuel cells and 

microturbines

Extended through 2008 by 

Public Law 109-432 and

through 2016 by EIEA08

j. Business solar investment tax 

 credit

Provides a 30% 

investment tax credit for 

solar property installed 

in 2006 through 2016

Incorporate tax credit into

cash flow for solar generation 

systems; reduce investment 

cost for solar water heaters

by 30%

Extended through 2008 by 

Public Law 109-432 and 

through 2016 by EIEA08

E. Energy Independence and Security Act of 2007 (EISA07)

a. Commercial walk-in coolers and 

walk-in freezers

Requires use of specific

energy efficiency 

measures in equipment 

manufactured in or after 

2009, with an update 

effective in 2017

Remove walk-in refrigerator 

and freezer systems that do 

not meet standard from 

technology choice

Federal Register Notice of Final 

Rulemaking

b. Incandescent and halogen

 lamps

Sets maximum allowable

wattage based on lumen

output starting in 2012

Remove incandescent and

halogen general service 

lighting systems that do not 

meet standard from 

technology choice menu in 

2012

Federal Register Notice of Final 

Rulemaking

c. Metal halide lamp ballasts Sets minimum efficiency 

levels for metal halide 

lamp ballasts starting in 

2009, with an update 

effective in 2017

Remove metal halide lighting

systems that do not meet

standard from technology 

choice menu; set minimum 

system efficiency to include

specified standard levels for 

ballasts based on type

Federal Register Notice of Final 

Rulemaking

d. Federal use of energy-efficient

 lighting

Requires use of energyefficient lighting fixtures 

and bulbs in federal 

buildings to the 

maximum extent 

possible starting in 2009

Increase proportion of sector

using 10-year Treasury note 

rate for lighting purchase 

decisions to represent all 

existing and new federal 

floorspace in 2009

Federal Register Notice of Final 

Rulemaking

e. Federal energy management Requires federal 

agencies to reduce 

energy consumption per 

square foot 30% by 2015 

relative to 2003 through 

cost-effective life-cycle 

energy measures

Uses the 10-year Treasury 

note rate for federal share of 

the commercial sector as a

discount rate in equipment 

purchase decisions as 

opposed to adding risk 

premiums to the 10-year 

Treasury note rate to develop 

discount rates for other 

commercial decisions

Federal Register Notice of Final 

Rulemaking
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 207

Legislation Brief description AEO handling Basis

F. Energy Improvement and Extension Act of 2008 (EIEA08) Public Law 110-343

a. Business solar investment tax

 credit

Extends the EPACT05 

30% investment tax 

credit for solar property 

through 2016

Incorporate tax credit into

cash flow for solar generation 

systems; reduce investment 

cost for solar water heaters

by 30%

Federal Register Notice of Final 

Rulemaking

b. Business investment tax credit

 for fuel cells and microturbines

Extends the EPACT05 

30% investment tax 

credit for fuel cells and 

10% investment tax 

credit for micro-turbines 

through 2016

Incorporate tax credit into

cash flow for fuel cells and 

microturbines

Federal Register Notice of Final 

Rulemaking

c. Business investment tax credit 

 for CHP systems

Provides a 10%

investment tax credit for 

CHP systems installed in 

2009 through 2016

Incorporate tax credit into

cash flow for CHP systems

Federal Register Notice of Final 

Rulemaking

d. Business investment tax credit 

 for small wind turbines

Provides a 30%

investment tax credit for 

wind turbines installed in 

2009 through 2016

Incorporate tax credit into

cash flow for wind turbines

Federal Register Notice of Final 

Rulemaking

e. Business investment tax credit 

 for geothermal heat pumps

Provides a 10% 

investment tax credit for 

geothermal heat pump 

systems installed in 2009 

through 2016

Reduce investment cost for 

geothermal heat pump 

systems by 10%

Federal Register Notice of Final 

Rulemaking

G. American Recovery and Reinvestment Act of 2009 (ARRA09) Public Law 111-5

a. Business investment tax credit 

 for small wind turbines

Removes the cap on the 

EIEA08 30% investment 

tax credit for wind 

turbines through 2016

Incorporate tax credit into

cash flow for wind turbines

Federal Register Notice of Final 

Rulemaking

b. Stimulus funding to

 federal agencies

Provides funding for 

efficiency improvement 

in federal buildings and 

facilities

Increase the proportion of 

sector using the 10-year 

Treasury note rate for 

purchase decisions to include 

all existing and new federal 

floorspace in years stimulus 

funding is available to 

account for new, 

replacement, and retrofit 

projects; assume some 

funding is used for solar PV, 

small wind turbine, and fuel 

cell installations

Federal Register Notice of Final 

Rulemaking
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 208

Legislation Brief description AEO handling Basis

c. State Energy Program funding 

 and energy efficiency and 

 conservation block grants

Provides grants for state 

and local governments for 

energy efficiency and 

renewable energy purposes

(State Energy Program 

funding conditioned on 

enactment of new building 

codes)

Increase the proportion of 

sector using the 10-year 

Treasury note rate for 

purchase decisions to 

include all public buildings in 

years stimulus funding is 

available; increase new 

building shell efficiency to 

10% better than 2003 by 

2018 for improved building 

codes; assume some funding 

is used for solar PV and 

small wind turbine

installations

Federal Register Notice of 

Final Rulemaking

d. Funding for smart grid projects Provides funding for smart 

grid demonstration 

projects

Assume smart grid 

technologies cause 

consumers to become more 

responsive to electricity 

price changes by increasing 

the price elasticity of 

demand for certain end uses

Federal Register Notice of 

Final Rulemaking
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 209

Industrial sector

Legislation Brief description AEO handling Basis

A. Energy Policy Act of 1992 (EPACT92)

a. Motor efficiency 

 standards

Specifies minimum 

efficiency levels for a 

variety of motor

types and sizes.

Not modeled because 

participation is voluntary; 

actual reductions will depend 

on future, unknown 

commitments.

EPACT1992, Section 342 (42 USC 

6313).

b. Boiler efficiency 

 standards

Specifies minimum 

combustion efficiency 

for package boilers

larger than 300,000 

Btu/hr. Natural Gas 

boilers: 80 percent. Oil 

boilers: 83 percent.

All package boilers are 

assumed to meet the 

efficiency standards.

While the standards do not 

apply to field-erected boilers, 

which are typically used in 

steam-intensive industries, we 

assume they meet the 

standard in the AEO.

Standard specified in

EPACT92. 10 CFR 431.

B. Clean Air Act Amendments (CAAA90)

a. Process emissions Numerous process 

emissions requirements for specified

industries and/or 

activities.

Not modeled because they 

are not directly related to 

energy projections.

CAAA90, 40 CFR 60.

b. Emissions related to 

 hazardous/toxic 

 substances

Numerous emissions 

requirements relative to 

hazardous and/or toxic 

substances.

Not modeled because they 

are not directly related to 

energy projections.

CAAA90, 40 CFR 60.

c. Industrial SO2 

 emissions

Sets annual limit for 

industrial SO2 emissions 

at 5.6 million tons. If 

limit is reached, specific 

regulations could be 

implemented.

Industrial SO2 emissions are 

not projected to reach the 

limit (Source: EPA, National 

Air Pollutant Emissions 

Trends:1990-1998, EPA454/R-00-002, March

2000, p. 4-3.)

CAAA90, Section 406 (42 USC 7651)

d. Industrial boiler 

 hazardous air 

 pollutants

Requires industrial 

boilers and process 

heaters to conduct 

periodic tune-ups or 

meet emissions limits on 

HAPs to comply with the 

Maximum Achievable 

Control Technology 

(MACT) Floor. 

Regulations finalized 

December 2012.

Costs of compliance that are 

not offset by efficiency gains 

(non-recoverable costs) 

modeled as an additional 

capital cost in the 

Macroeconomic Activity 

Module (MAM) based on 

proposed regulations as of 

September 2012.

U.S. Environmental Protection 

Agency, National Emissions 

Standards for Hazardous Air 

Pollutants for Industrial, 

Commercial, and Institutional 

Boilers, Major Source (40 CFR

63, Subpart DDDDD) and

Area Source (40 CFR 63

Part JJJJJJ)

e. Emissions from 

 stationary diesel 

 engines

Requires engine 

manufacturers to meet 

the same emission 

standards as nonroad 

diesel engines. Fully 

effective in 2011.

New stationary engines meet 

the standards.

40 CFR Parts 60, 85, 89,

94, 1039, 1065, and 1068.
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 210

Legislation Brief description AEO handling Basis

C. Energy Policy Act of 2005 (EPACT05)

a. Physical energy

 intensity

Voluntary commitments 

to reduce physical 

energy intensity by 2.5 

percent annually for 

2007-2016.

Not modeled because 

participation is voluntary; 

actual reductions will depend 

on future, unknown 

commitments.

EPACT2005, Section 106 (42 USC 

15811)

b. Mineral components 

 of cement of concrete

Increase in mineral 

component of federally 

procured cement or 

concrete.

Not modeled. EPACT2005, Section 108 (42 USC 

6966).

c. Tax credits for coke

 oven

Provides a tax credit of 

$3.00 per barrel oil 

equivalent, limited to 

4000 barrels per day 

average. Applies to most 

producers of coal coke 

or coke gas.

Not modeled because no

impact on U.S. coke plant 

activity is anticipated.

EPACT2005, Section 1321 (29 USC 

29).

D. The Energy Independence and Security Act of 2007 (EISA2007)

a. Motor efficiency 

 standards

Supersedes EPACT1992 

Efficiency Standards no 

later than 2011.

Motor purchases must meet 

the EPAct1992 standards 

through 2010; afterwards 

purchases must meet the 

EISA2007 standards.

EISA2007.

E. The Energy Improvement and Extension Act of 2008 (EISA2008)

e. Combined heat and 

 power tax incentive

Provides an investment 

tax credit for up to 15 

megawatts of capacity in 

combined heat and 

power systems of 50 

megawatts or less 

through 2016.

Costs of systems adjusted to 

reflect the credit.

EIEA2008, Title I, Sec. 103
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 211

Transportation sector

Legislation Brief description AEO handling Basis

A. Energy Policy Act of 1992

(EPACT92)

Increases the number of 

alternative fuel vehicles and 

alternative fuel use in federal, 

state, and fuel-provided fleets

Assumes federal, state and

fuel-provided fleets meet 

the mandated sales 

requirements.

Energy Policy Act of 1992,

Public Law 102-486-Oct. 24,

1992.

B. Low Emission Vehicle

Program (LEVP)

The Clean Air Act provides 

California the authority to set 

vehicle criteria emission 

standards that exceed federal

standards. A part of that program

mandates the sale of zeroemission vehicles by 

manufacturers. Other

nonattainment states are given 

the option of opting into the 

federal or California emission 

standards.

Incorporates the LEVP 

program as amended on 

August 4, 2005. Assumes 

California, Connecticut,

Maine, Massachusetts, New

Jersey, New York, Rhode

Island, Vermont, Oregon, 

and Washington adopt the 

LEVP program as amended 

and that the proposed sales 

requirements for hybrid,

electric, and fuel cell 

vehicles are met.

Section 177 of the Clean Air

Act, 42 U.S.C. sec. 7507 (1976) 

and CARB, California Exhaust 

Emissions Standards and Test 

Procedures for

Passenger Cars, Light-Duty

Trucks, and Medium-Duty

Vehicles, August 4, 2005.

C. Corporate Average Fuel

Economy (CAFE) 

Standard for Light Duty 

Vehicles

Requires manufacturers to 

produce vehicles that meet a 

minimum federal average fuel 

economy standard, promulgated jointly for model years

2012-2016 and 2017-2025 with 

an average greenhouse emissions 

standard; cars and light trucks are 

regulated separately.

CAFE standards are 

increased for model years 

2011 through 2016 to meet 

the final CAFE rulemakings 

for model year 2011 and 

2012 to 2016, respectively.

CAFE standards are 

increased for model years 

2017 to 2025 to meet final 

CAFE joint rulemakings for

model year 2017 to 2021

and to meet augural CAFE 

standards for model year 

2022 to 2025, which will 

undergo a midterm evaluation to finalize. CAFE 

standards are held constant 

through the end of the 

projection.

Energy Policy Conservation Act of 

1975; Title 49 USC, Chapter 329;

Energy Independence and

Security Act of 2007, Title 1, 

Section 102; Average Fuel 

Economy Standards Passenger 

Cars and Light Trucks Model Year 

2011; Federal Register, Vol. 74, 

No. 59, March 2009; Light-Duty

Vehicle Greenhouse Gas Emission 

Standards and Corporate Average 

Fuel Economy Standards, Final

Rule, Federal Register, Vol. 75, 

No. 88, May 2010; 2017 and Later 

Model Year Light-Duty Vehicle 

Greenhouse Gas Emissions and 

Corporate Average Fuel Economy

Standards Federal Register, Vol. 

77, No. 199, October 2012.

D. Electric, Hybrid, and

Alternative Fuel Vehicle 

Tax Incentives

Federal tax incentives are 

provided to encourage the 

purchase of electric, hybrid 

and/or alternative fuel vehicles.

For example, tax incentives for 

hybrid vehicles in the form of a 

$2,000 income tax deduction.

Incorporates the federal tax

incentives for hybrid and

electric vehicles.

IRS Technical Publication 535;

Business Expenses.

E. Plug-in Hybrid Vehicle 

Tax Credit

EIEA2008 grants a tax credit of 

$2,500 for PHEVs with at least 

4kWh of battery capacity, with 

larger batteries earning an 

additional $417 per kWh up to a 

maximum of $7,500 for light-duty

PHEVs. The credit will apply until 

250,000 eligible PHEVs are sold or 

until 2015, whichever comes first.

Incorporates the federal tax

credits for PHEVs.

Energy Improvement and

Extension Act of 2008, H.R.6049.
September 2015

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Legislation Brief description AEO handling Basis

F. State Electric, Hybrid, 

and Alternative Fuel 

Vehicle Tax and Other 

Incentives

Approximately 20 states 

provide tax and other 

incentives to encourage

the purchase of electric, hybrid 

and/or alternative fuel 

vehicles. The tax incentives are 

in the form of income

reductions, tax credits, and 

exemptions. Other incentives 

include use of HOV lanes and 

exemptions from emissions

inspections and licensing fees. 

The incentives offered and the 

mix varies by state. For 

example, Georgia offers a tax

credit of $5,000 for electric 

vehicles and Oklahoma offers a 

tax credit of $1,500 for hybrid 

and alternative fuel vehicles.

Does not incorporate 

state tax and other 

incentives for hybrid, 

electric, and other

alternative fuel 

vehicles.

State laws in Arizona, Arkansas,

California, Colorado, Delaware,

Florida, Georgia, Iowa, Kansas,

Louisiana, Maine, Maryland,

Michigan, New Hampshire, New

York, Oklahoma, Pennsylvania,

Utah, Virginia, and Washington

G. HD National Program;

Greenhouse Gas 

Emissions and Fuel 

Consumption Standards 

for Heavy-Duty Vehicles

Requires on-road heavy-duty 

vehicle manufacturers to 

produce vehicles that meet a 

minimum federal average

greenhouse gas emission 

standard, issued by the EPA, 

for model years 2014-2018. 

NHTSA established voluntary 

fuel consumption standards for 

MY 2014-2015, and mandatory 

fuel consumption standards for 

MY 2016 and beyond for 

onroad heavy-duty trucks and 

their engines; vocational and 

combination engines are

regulated separately.

HD National program

standards begin for MY 

2014 as set by the GHG 

emissions portion of 

the rule with the

assumption that the 

vehicles comply with 

the voluntary portion of 

the rule for fuel

consumption. The 

model allows for both 

the engine and chassis 

technologies to meet 

the standards to 

finalize. CAFE standards 

are held constant 

through the end of the 

projection.

Section 202 of the Clean Air

Act; Title 49 USC, Chapter 32902[k]; 

Energy Independence and Security Act

of 2007, Title 1, Section 102;

Federal Register, Vol. 76, No. 179,

September 2011.

H. The International 

Convention for the 

Prevention of Pollution

from Ships (MARPOL) 

Annex VI

Sets limits on sulfur oxides and 

oxides of nitrogen emissions 

from ship exhausts and 

prohibits deliberate emissions 

of ozone depleting substances. 

First entered into force on May 

19, 2005. New requirements 

added on January 1, 2015, 

mandating a maximum of 0.1% 

sulfur fuel or scrubber use, 

from a previous 1% limit.

MARPOL Annex VI fuel

sulfur mandates 

reflected in domestic 

and international 

shipping fuel shares

starting in 2015.

MARPOL 73/78, (33 U.S.C 1901(a) (4) 

& (5), 1902(a)(1)&(5), and 1907 (a), as 

amended by the Maritime Pollution 

Prevention Act of 2008 (MPPA), Pub.L. 

110-280, 122 Stat 2611).
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 213

Electric power generation

Legislation Brief description AEO handling Basis

A. Clean Air Act 

Amendments of 1990 

(CAAA90)

Established a national limit on 

electricity generator emissions 

of sulfur dioxide to be achieved 

through a cap-and-trade

program.

Sulfur dioxide cap-and-trade 

program is explicitly modeled, 

choosing the optimal mix of 

options for meeting the national 

emissions cap.

Clean Air Act Amendments 

of 1990, Title IV, Sections 

401 through 406, Sulfur 

Dioxide Reduction Program, 

42 U.S.C.7651a through 

7651e.

Requires the EPA to establish 

National Ambient Air Quality 

Standards (NAAQS). In 1997, 

EPA is currently determining 

which areas of the country are 

not in compliance with the 

new standards. Area 

designations were made in 

December 2004. States 

submitted their compliance 

plans, and have until 2009-

2014 to bring all areas into 

compliance.

These standards are not 

explicitly represented, but the 

Clean Air Interstate Rule is 

incorporated (described below) 

and was developed to help 

states meet their NAAQS.

Clean Air Act Amendment 

of 1990, Title I, Sections 108 

and 109, National Ambient 

Air Quality Standards for 

Ozone, 40 CFR Part 50, 

Federal Register, Vol 68, No 

3, January 8, 2003. 

National Ambient Air 

Quality Standards for 

Particulate Matter, 40 CFR 

Part 50, Federal Register, 

Vol 62, No. 138, July 18, 

1997.

B. Clean Air Interstate Rule 

(CAIR)

CAIR imposes a two-phased 

limit on emissions of sulfur 

dioxide and/or nitrogen oxide 

from electric generators in 27 

states and the District of 

Columbia.

Cap-and-trade programs for SO2 

and NOX are modeled explicitly, 

allowing the model to choose 

the best method for meeting 

the emission caps.

Federal Register, Vol. 70, 

No. 91 (May 12, 2005), 40 

CFR Parts 51, 72, 73, 74, 77, 

78 and 96.

C. Mercury and Air Toxics

Standards (MATS)

MATS sets standards to reduce 

air pollution from coal-and oilfired power plants greater than 

25 megawatts. The rule 

requires plants achieve the 

maximum achievable control

technology for mercury, 

hydrogen chloride (HCl) and 

fine particulate matter (PM 

2.5).

The EMM assumes that all coalfired generating plants above 25 

megawatts will comply

beginning in 2016. Plants are

assumed to reduce mercury

emissions by 90 percent relative

to uncontrolled levels. Because

the EMM does not model HCl or

PM 2.5 explicitly, to meet those

requirements, coal plants are

required to install either an FGD

or a dry sorbent injection 

system including a full fabric 

filter.

U. S. Environmental 

Protection

Agency, “Mercury and Air 

Toxics Standards,” website 

epa.gov/mats.

D. Energy Policy Act of 1992

(EPACT92)

Created a class of generators 

referred to as exempt 

wholesale generators (EWGs), 

exempt from PUHCA as long as 

they sell wholesale power.

Represents the development

of Exempt Wholesale

Generators (EWGs) or what

are now referred to as

independent power producers

(IPPs) in all regions.

Energy Policy Act of 1992, 

Title VII, Electricity, Subtitle 

A, Exempt Wholesale 

Generators.
September 2015

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Legislation Brief description

AEO 

handling Basis

E. The Public Utility 

Holding Company Act 

of 1935 (PUHCA)

PUHCA is a federal statue which was

enacted to legislate against abusive

practices in the utility industry. The

act grants power to the U.S. Securities

and Exchange Commission (SEC)

to oversee and outlaw large holding

companies which might otherwise

control the provision of electrical

service to large regions of the country.

It gives the SEC power to approve or

deny mergers and acquisitions and, if

necessary, force utility companies to

dispose of assets or change business

practices if the company’s structure of

activities are not deemed to be in the

public interest.

It is assumed that holding

companies act competitively

and do not use their regulated

power businesses to crosssubsidize their unregulated

businesses.

Public Utility Holding 

Company Act of 1936.

E. FERC Orders 888 and 

889

FERC has issued two related rules: Orders 

888 and 889, designed to bring low-cost 

power to consumers through competition, 

ensure continued reliability in the 

industry, and provide for open and 

equitable transmission services by owners 

of these facilities. Specifically, Order 888 

requires open access to the transmission 

grid currently owned and operated by 

utilities. The transmission owners must file 

nondiscriminatory tariffs that offer other 

suppliers the same services that the 

owners provide for themselves. Order 888 

also allows these utilities to recover 

stranded costs (investments in generating 

assets that are unrecoverable due to 

consumers selecting another supplier). 

Order 889 requires utilities to implement

standards of conduct and an Open Access 

Same-time Information System (OASIS) 

through which utilities and non-utilities 

can receive information regarding the 

transmission system. Consequently, 

utilities are expected to functionally or 

physically unbundle their marketing 

functions from their

transmission functions.

These orders are represented in

the forecast by assuming that all

generators in a given region are

able to satisfy load requirements

anywhere within the region.

Similarly, it is assumed that

transactions between regions will 

occur if the cost differentials

between them make it economic

to do so.

Promoting Wholesale 

Competition Through 

Open Access, NonDiscriminatory 

Transmission

Services by Public 

Utilities; Public

Utilities and 

Transmitting Utilities,

ORDER NO. 888 Issued 

April 24, 1996), 18 CFR 

Parts 35 and 385, 

Docket Nos. RM95-8-

000 and RM94-7-001. 

Open Access SameTime Information 

System (formerly RealTime Information

Networks) and 

Standards of Conduct, 

ORDER NO. 889, (Issued 

April 24, 1996), 18 CFR

Part 37, Docket No. 

RM95-9-000.
September 2015

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Legislation Brief description AEO handling Basis

G. New Source Review 

(NSR)

On August 28, 2003, the EPA issued

a final rule defining certain power

plant and industrial facility activities

as routine maintenance, repair, and

replacement, which are not subject to

new source review (NSR). As stated

by EPA, these changes provide a

category of equipment replacement

activities that are not subject to major

NSR requirements under the routine

maintenance, repair, and replacement

(RMRR) exclusion.[1] Essentially this

means that power plants and industrial

facilities engaging in RMRR activities

will not have to get preconstruction

approval from the state or EPA and

will not have to install best available

emissions control technologies that might 

be required if NSR were triggered.

It is assumed that coal plants will

be able to increase their output

as electricity demand increases.

Their maximum capacity factor is 

set at 75 percent. No increases in 

the capacity of existing plants is 

assumed. If further analysis

shows that capacity uprates may

result from the NSR rule, they will 

be incorporated in future AEOs. 

However, at this time, the NSR 

rule is being contested in the 

courts.

EPA, 40 CFR Parts 51 

and 52, Deterioration 

(PSD) and NonReplacement Provision

of the Vol. 68, No. 207, 

page 61248, Prevention 

of Significant

Attainment New Source 

Review (NSR): 

Equipment Routine

Maintenance, Repair 

and Replacement 

Exclusion; Final Rule, 

Federal Register,

October 27, 2003.

H. State Renewable 

Portfolio Standards 

(RPS) Laws, 

Mandates, and Goals

Several states have enacted laws

requiring that a certain percentage

of their generation come from

qualifying renewable sources.

The AEO reference case

represents the Renewable

Portfolio Standard (RPS) or

substantively similar laws from

States with established 

enforcement provisions for their 

targets. As described in the

Renewable Fuels Module chapter 

of this document, mandatory 

targets from the various states 

are aggregated at the regional 

level, and achievement of 

nondiscretionary compliance

criteria is evaluated for each 

region.

The states with RPS or 

other mandates 

providing quantified

projections are detailed 

in the Legislation and 

Regulations section of 

AEO2014.

I. Regional and State 

Air Emissions 

Regulations

The Northeast Regional Greenhouse Gas

Initiative (RGGI) applies to fossil-fueled

power plants over 25 megawatts in the

northeastern United States. 

New Jersey withdrew in 2011, leaving 9 

states in the program. The rule caps CO2 

emissions and requires they account for 

CO2 emitted with allowances purchased at 

auction. In February 2013, program 

officials announced a tightening of the cap

beginning in 2014.

The impact of RGGI is included in 

the EMM, making adjustments

when needed to estimate the

emissions caps at the regional

level used in NEMS. AEO2015 

incorporates the revised target

beginning in 2014.

Regional Greenhouse

Gas Initiative Model 

rule, www.rggi.org.
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 216

Legislation Brief description AEO handling 

The California Assembly Bill 32 (AB32)

sets GHG reduction goals for 2020 for

California. A cap-and-trade program was

designed to enforce the caps. The capand-trade program applies to multiple

economic sectors including electric power

plants, large industrial facilities, suppliers

of transportation fuel, and suppliers of

natural gas. Emissions resulting from

electricity generated outside California but

consumed in the state are also subject to

the cap.

The EMM models the cap-andtrade program explicitly for CO2

for California through an 

emission constraint that 

accounts for emissions from the 

other sectors. Limited banking

and borrowing of allowances as 

well as an allowance reserve 

and offsets are incorporated as 

specified in the Bill.

California Code of 

Regulations,

Subchapter 10 Climate 

Change, Article 5, 

Sections 95800 to 

96023, Title 17, 

“California Cap on 

Greenhouse Gas 

Emissions and MarketBased Compliance 

Mechanisms,”

(Sacramento, CA: July 

2011).

J. Energy Policy Act of 

2005

Extended and substantially expanded and 

modified the Production Tax Credit, 

originally created by EPACT1992.

EPACT2005 also adds a PTC for 

up to 6,000 megawatts of new 

nuclear capacity and a $1.3 

billion investment tax credit

for new or repowered coal-fired

power projects. The tax credits

for renewables, nuclear and coal

projects are explicitly modeled 

as specified in the law and 

subsequent amendments. 

Because the tax credits for new 

coal projects have been fully 

allocated, the EMM does not 

assume future coal capacity will

receive any tax credits.

Energy Policy Act of 

2005, Sections 1301, 

1306, and 1307.

K. American Recovery 

and Reinvestment

Act of 2009

Extends the Production Tax Credit (PTC)

to wind facilities constructed by December

31, 2012 and to other eligible renewable

facilities constructed by December 31,

2013. Allows PTC-eligible facilities

to claim a 30-percent investment tax

credit (ITC) instead of the PTC. Projects

starting construction by the end of 2010

(subsequently extended to the end of

2011) may elect to take a cash grant equal

to the value of the 30-percent ITC instead

of either tax credit.

The extensions of the PTC

and 30-percent ITC are 

represented in the AEO

reference case as specified

in the law (since revised). The 

AEO does not distinguish 

between the effects of the 30-

percent ITC and the equivalent 

cash grant, and the cash grant is 

not specifically modeled.

American Recovery

and Reinvestment

Act of 2009, Division B, 

Title I, Sec. 1101, 1102, 

and 1603.

ARRA provided $6 billion to pay the cost of 

guarantees for loans authorized by the 

Energy Policy Act of 2005. The purpose of 

these loan guarantees is to stimulate the

deployment of conventional renewable

and transmission technologies and

innovative biofuels technologies.

However, to qualify, eligible projects

must be under construction by

September 30, 2011.

AEO2015 includes projects that

have received loan guarantees

under this authority, but does

not assume automatic award of

the loans to potentially eligible

technologies.

American Recovery 

and Reinvestment Act 

of 2009, Title IV, 

“Energy and Water

Development”, Section 

406.
September 2015

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Legislation Brief description AEO handling Basis

ARRA provides $4.5 billion for smart

grid demonstration projects. These

generally include a wide array of

measurement, communications, and

control equipment employed throughout

the transmission and distribution

system that will enable real-time

monitoring of the production, flow,

and use of power from generator to

consumer.

In the electricity module, it was

assumed that line losses would

fall slightly, peak loads would

fall as customers shifted their

usage patterns, and customers

would be more responsive to

pricing signals

American Recovery 

and Reinvestment Act 

of 2009, Title IV, 

“Energy and Water

Development”, Section 

405.

ARRA provides $800 million to fund

projects under the Clean Coal Power

Initiative program focusing on capture

and sequestration of greenhouse gases.

It was assumed that one 

gigawatt of new coal with 

sequestration capacity would 

come online by 2018.

American Recovery 

and Reinvestment Act 

of 2009, Title IV, 

“Energy and Water

Development”

L. American Taxpayer 

Relief Act of 2012 

(ATRA)

ATRA was passed on January 1,

2013 and included several provisions

extending tax credits to the energy

sector. Most significantly, it extended

the PTC for wind by one year, and

redefined the criteria for all qualifying

projects to be based on “under

construction” by December 31, 2013

rather than in service by that same date,

thus extending the credit for length of

the typical construction period.

AEO2015 explicitly models the

revised dates for these tax 

credits.

American Taxpayer 

Relief Act of 2012, P.L. 

112-240, Sections 401

through 412.

M. Tax Increase 

Prevention Act of 

2014 (TIPA)

TIPA was passed in December 2014 and 

retroactively extended the expiration date 

of the production tax credit for all eligible 

renewable fuels by one year to January 1, 

2015.

TIPA was passed after the 

timeframe EIA typically uses for 

inclusion of “current laws”. The 

AEO2015 was not explicitly 

updated to reflect it, however 

any plants affected by the 

change would already be 

reported as planned capacity 

additions.

Tax Increase 

Prevention Act of 

2014, P.L. 113-295, 

Sections 151-161.
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 218

Oil and gas supply

Legislation Brief description AEO handling Basis

A. The Outer Continental 

Shelf Deep Water Royalty 

Relief Act (DWRRA)

Mandates that all tracts offered 

by November 22, 2000, in deep 

water in certain areas of the Gulf 

of Mexico must be offered under 

the new bidding system 

permitted by the DWRRA. The 

Secretary of the Interior must 

offer such tracts with a specific 

minimum royalty suspension 

volume based on water depth.

Incorporates royalty rates 

based on water depth.

43 U.S.C. SS 1331-1356 (2002).

B. Energy Policy and

Conservation Act

Amendments of 2000

Required the USGS to inventory 

oil and gas resources beneath 

federal lands.

To date, the Rocky 

Mountain oil and gas 

resource inventory has

been completed by the 

USGS. The results of this 

inventory have been 

incorporated in the

technically recoverable oil 

and gas resource volumes 

used for the Rocky 

Mountain region.

Scientific Inventory of Onshore

Federal Lands: Oil and Gas

Resources and Reserves and

the Extent and Nature of

Restrictions or Impediments

to their Development: The

Paradox/San Juan, Uinta/

Piceance, Greater Green River,

and Powder River Basins and 

the Montana Thrust Belt. 

Prepared by the Departments 

of Interior, Agriculture and 

Energy, January 2003.

C. Section 29 Tax Credit for

Nonconventional Fuels

The Alternative Fuel Production 

Credit (Section 29 of the IRC) 

applies to qualified 

nonconventional fuels from wells 

drilled or facilities placed in

service between January 1, 1980 

and December 31, 1992. Gas 

production from qualifying wells 

could receive a $3 (1979 constant 

dollars) per barrel of oil equivalent credit on volumes produced

through December 31, 2002. The

qualified fuels are: oil produced 

from shale and tar sands; gas 

from geopressurized brine, 

Devonian shale, coal seams, tight 

formations, and biomass; liquid, 

gaseous, or solid synthetic fuels 

produced from coal; fuel from 

qualified processed formations or

biomass; and steam from 

agricultural products.

The Section 29 Tax Credit

expired on December 31, 

2002, and it is not 

considered in new

production decisions. 

However, the effect of 

these credits is implicitly 

included in the

parameters that are 

derived from historical 

data reflecting such

credits.

Alternative Fuel Production

Credit (Section 29 of the

Internal Revenue Code), 

initially established in the 

Windfall Profit Tax of 1980.

D. Energy Policy Act of 2005 Established a program to provide

grants to enhance oil and gas

recovery through CO2 injection

Additional oil resources 

were added to account for 

increased use of CO2-

enhanced oil recovery.

Title III, Section 354 of the

Energy Policy Act of 2005.
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 219

Natural gas transmission and distribution

Legislation Brief description AEO handling Basis

A. Alaska Natural Gas

Pipeline Act, Sections

101-116 of the Military

Construction Hurricane

Supplemental

Appropriations Act, 2005.

Disallows approval for a natural gas

pipeline to enter Canada via Alaska 

north of 68 degrees latitude. Also,

provides federal guarantees for 

loans and other debt obligations 

assigned to infrastructure in the 

United States or Canada related to 

any natural gas pipeline system that 

carries Alaska natural gas to the 

border between Alaska and Canada 

south of 68 degrees north latitude. 

This authority would expire 2 years 

after the final certificate of public

convenience and necessity is issued. 

The guarantee will not exceed 1) 80 

percent of the total capital costs 

(including interest during 

construction), 2) $18 billion 

(indexed for inflation at the time of

enactment), or 3) a term of 30 

years.

Assumes the pipeline 

construction cost estimate 

for the “southern” Alaska 

pipeline route in projecting

when an Alaska gas pipeline 

would be profitable to build. 

With more recent increases 

in cost estimates, well 

beyond $18 billion, the loan

guarantee is assumed to 

have a minimal impact on 

the build decision.

P.L. 108-324.

B. American Jobs Creation

Act of 2004, Sections

706 and 707.

Provides a 7-year cost-ofinvestment recovery period for the 

Alaska natural gas pipeline, as 

opposed to the currently allowed 

15-year recovery period, for tax 

purposes. The provision would be

effective for property placed in 

service after 2013, or treated as 

such. Effectively extends the 15-

percent tax credit currently applied 

to costs related to enhanced oil 

recovery to construction costs for a 

gas treatment plant on the North 

Slope that would feed gas into an

Alaska pipeline to Canada.

The change in the recovery

period is assumed to have a

minimal impact on the 

decision to build the 

pipeline. The assumed 

treatment costs are based 

on company estimates

made after these tax 

provisions were enacted.

P.L. 108-357.

C. Pipeline Safety, 

Regulatory Certainty, and 

Job Creation Act of 2011 

and other previous laws 

and regulations on 

pipeline safety.

Provides for enhanced safety, 

reliability and environmental 

protection in the transportation of 

energy products by pipeline.

Costs associated with 

previously imposed pipeline 

safety laws are assumed to 

already be reflected in 

historical capital and 

operating

 

 

 

 

 

 

 

P.L. 112-90, 125 Stat. 1904
September 2015

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Legislation Brief description AEO handling Basis

D. Energy Policy Act of 

2005.

Allowed natural gas storage

facilities to charge market-based

rates if it was believed they would 

not exert market power.

Storage rates are allowed to 

vary from regulation-based 

rates depending on market 

conditions.

Title III, Section 312 of the 

Energy Policy Act of 2005.

E. Federal Motor Fuels 

Excise Taxes for 

Compressed Natural Gas 

and Liquefied Natural 

Gas in Vehicles.

Taxes are levied on each gallon or

gasoline-gallon equivalent of 

natural gas.

Federal motor fuels excise 

taxes on natural gas fuel for 

vehicles are included in 

retail prices and are 

assumed to be extended

indefinitely at current 

nominal rates.

26 USC 4041.

F. State Motor Fuels Taxes 

for Compressed Natural 

Gas and Liquefied 

Natural Gas in Vehicles

Taxes are levied on each gallon, 

gasoline-gallon equivalent, or 

diesel-gallon equivalent of natural 

gas.

State motor fuels excise 

taxes on natural gas fuel for 

vehicles are included in 

retail prices and are 

assumed to be extended

indefinitely at current 

nominal rates.

Determined by review of 

existing state laws.
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 221

Liquid fuels market

Legislation Brief description AEO handling Basics

A Ultra-Low-Sulfur 

Diesel (ULSD) 

regulations under 

the Clean Air Act 

Amendment of 1990

Since mid-2012, all diesel for 

domestic use (highway, nonroad, locomotive, marine) 

may contain at most 15 ppm 

sulfur.

Reflected in diesel 

specifications.

40 CFR Parts 69, 80, 86, 89, 94, 1039, 

1048, 1065, and 1068.

B. Mobile Source Air 

Toxics (MSAT) 

Controls Under the 

Clean Air Act 

Amendment of 1990

Establishes a list of 21 

substances emitted from 

motor vehicles and known to 

cause serious human health 

effects, particularly benzene, 

formaldehyde, 1.3 butadiene, 

acetaldehyde, diesel exhaust 

organic gases, and diesel 

particulate matter. 

Establishes anti-backsliding 

and anti- dumping rules for 

gasoline.

Modeled by updating 

gasoline specifications to 

most current EPA 

gasoline survey data 

(2005) representing antibacksliding 

requirements.

40 CFR Parts 60 and 86.

C. Low-Sulfur Gasoline 

Regulations Under 

the Clean Air Act 

Amendment of 1990

Gasoline must contain an 

average of 30 ppm sulfur or 

less by 2006. Small refiners 

may be permitted to delay 

compliance until 2008.

Reflected in gasoline 

specifications.

40 CFR Parts 80, 85 and 86.

D. Tier 3 Vehicle 

Emission and Fuel 

Standards Program

Gasoline must contain an 

average of 10 ppm sulfur or 

less by January 1, 2017. Small 

refiners may be permitted a 

3 year delay.

Reflected in gasoline 

specifications beginning 

in 2017.

40 CFR Parts 79, 80, 85, et. al., final rule: 

http://www.gpo.gov/fdsys/pkg/FR-2014-

04-28/pdf/2014-06954.pdf

E. MTBE Bans in 25 

states

25 states ban the use of 

MTBE in gasoline by 2007.

Ethanol assumed to be 

the oxygenate of choice 

for all motor gasoline 

blends.

State laws in Arizona, California, 

Colorado, Connecticut, Illinois, Indiana, 

Iowa, Kansas, Kentucky, Maine, Michigan, 

Minnesota, Missouri, Montana, 

Nebraska, New Hampshire, New Jersey, 

New York, North Carolina, Ohio, Rhode 

Island, South Dakota, Vermont, 

Washington, and Wisconsin.

F. Regional Clean Fuel 

Formations

States with air quality 

problems can specify 

alternative gasoline or diesel 

formulations with EPA’s 

permission. California has 

long had authority to set its 

own fuel standards.

Reflected in PADD-level 

gasoline and diesel 

specifications.

State implementation plans required by 

the Clean Air Act Amendments of 1990, 

as approved by EPA.

G. Federal Motor Fuels 

Excise Taxes

Taxes are levied on each 

gallon of transportation fuels 

to fund infrastructure and 

general revenue. These taxes 

are set to expire at various 

times in the future but are 

expected to be renewed, as 

they have been in the past.

Gasoline, diesel, and 

ethanol blend tax rates 

are included in end-use 

prices and are assumed 

to be extended 

indefinitely at current 

nominal rates.

26 USC 4041 Extended by American Jobs 

Creation Act of 2004
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Legislation Brief description AEO handling Basics

H. State Motor Fuel Taxes Taxes are levied on each gallon of 

transportation fuels. The assumption 

that state taxes will increase at the 

rate of inflation supports an implied 

need for additional highway 

revenues as driving increases.

Gasoline and diesel rates are 

included in end-use prices and 

are assumed to be extended 

indefinitely in real terms (to 

keep pace with inflation).

Determined by review of 

existing state laws 

performed semi- annually 

by EIA’s Office of Energy 

Statistics.

I. Diesel Excise Taxes Phases out the 4.3 cents excise tax 

on railroads between 2005 and 

2007.

Modeled by phasing out. American Jobs Creation 

Act of 2004, Section 241.

J. Energy Policy Act of 2005 (EPACT05)

a. Ethanol/biodiesel tax 

credit

Petroleum product blenders may 

claim tax credits for blending ethanol 

into gasoline and for blending 

biodiesel into diesel fuel or heating 

oil. The credits may be claimed 

against the federal motor fuels 

excise tax or the income tax. Most 

recent tax credits are $1.01 per 

gallon of cellulosic ethanol, and 

$1.00 per gallon of biodiesel. Both 

tax credits expire after 2014.

The tax credits are applied 

against the production costs of 

the products into which they 

are blended. Ethanol is used 

in gasoline and E85. Biodiesel 

is assumed to be blended into 

highway diesel, and nonroad 

diesel or heating oil.

26 USC 40, and 26 USC 

6426. Tax credits 

extended through 

December 31, 2014 by 

Public Law 113-195) .

b. Renewable Fuels 

Standard (RFS)

This section has largely been 

redefined by EISA07 (see below); 

however, EPA rulemaking completed 

for this law was assumed to contain 

guiding principles of the rules and 

administration of EISA07.

Energy Policy Act of 2005, 

Section 1501.

c. Elimination of oxygen 

content requirement in 

reformulated gasoline

Removes the 2% oxygen 

requirement for reformulated 

gasoline (RFG) nationwide.

Oxygenate waiver already an 

option of the model. MTBE 

was phased out in 2006 

resulting from the petroleum 

industry’s decision to 

discontinue use.

Energy Policy Act of 2005, 

Section 1504.

d. Coal gasification 

provisions

Investment tax credit program for 

qualifying advanced clean coal 

projects including Coal-to- Liquids 

Projects.

Two CTL units are available to 

build with lower capital costs 

reflecting the provision’s 

funding.

Energy Policy Act of 2005, 

Section 1307.

K. Energy Independence and Security Act of 2007 (EISA07)

a. Renewable Fuels 

Standard (RFS)

Requires the use of 36 billion gallons 

of ethanol per year by 2022, with 

corn ethanol limited to 15 billion 

gallons. Any other biofuel may be 

used to fulfill the balance of the 

mandate, but the balance must 

include 16 billion gallons per year of 

cellulosic biofuel by 2022 and 1 

billion gallons per year of biodiesel 

by 2012

The RFS is included in 

AEO2014, however it is 

assumed that the schedule for 

cellulosic biiofuel is adjusted 

downward consistent with 

waiver provisions contained in 

the law.

40 CFR Part 80, Subpart M
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L. State Heating Oil 

Mandates

A number of Northeastern 

states passed legislation 

that reduces the maximum 

sulfur content of heating oil 

to between 15 and 50 ppm 

in different phases through 

2016.

All state regulations 

included as legislated in 

AEO2014. 2013 EIA 

heating oil consumption 

data is used to calculate 

respective state Census 

Division shares for new 

consumption of low 

sulfur diesel as heating 

oil.

Vermont Energy Act of 2011, Maine State 

Legislature HP1160, NJ State Department 

of Environmental Protection, Amendment 

N.J.A.C. 7:27-9.2, New York State Senate 

Bill 51145C.

M California Low 

Carbon Fuel 

Standard (LCFS)

California passed legislation 

which is designed to reduce 

the Carbon Intensity (CI) of 

motor gasoline and diesel 

fuels sold in California by 10 

percent between 2012 and 

2020 through the increased 

sale of alternative “lowcarbon” fuels.

The LCFS is included in 

AEO2014 as legislated 

for gasoline and diesel 

fuel sold in California, 

and for other regulated 

fuels.

California Air Resources Board, “Final 

Regulation Order: Subarticle 7. Low Carbon 

Fuel Standard.”

N California Assembly 

Bill 32 (AB32)

The California Assembly Bill 

32 (AB32), the Global 

Warming Solutions Act of 

2006, authorized the 

California Air Resources 

Board (CARB) to set GHG 

reduction goals for 2020 for 

California. A cap-and-trade 

program was designed to 

enforce the caps. The capand-trade program applies 

to multiple economic 

sectors including electric 

power plants, large industrial facilities, suppliers of 

natural gas. Emissions 

resulting from electricity 

generated outside California 

but consumed in the State

are also subject to the cap.

The AB32 cap-and-trade 

was more fully 

implemented in 

AEO2013, adding 

industrial facilities, 

refineries, fuel providers, 

and non-CO2 GHG 

emissions to the 

representation already 

in the electrical power 

sector of NEMS. Also, 

limited banking and 

borrowing, as well as an 

allowance reserve and 

offset purchases, were 

modeled, providing 

some compliance 

flexibility and cost 

containment. 

California Code of Regulations, Subchapter 

10 Climate Change, Article 5, Sections 

95800 to 96023, Title 17, “California Cap 

on Greenhouse Gas Emissions and MarketBased Compliance Mechanisms,” 

(Sacramento, CA: July 2011)

O EPA ETS Waiver EPA approved two waivers 

for the use of ethanol motor 

gasoline blends of up to 15 

percent in vehicles 2001 and 

newer.

These two waivers were 

included and modeled in 

AEO2013 (and remain in 

AEO2015) based on 

forecasted vehicle fleets 

and potential 

infrastructure and 

liability setbacks.

EPA–HQ–OAR–2009–0211; FRL–9215–5, 

EPA–HQ– OAR–2009–0211; FRL–9258–6.

P US Department of

commerce, Bureau 

of Industry and 

Security (BIS): 

clarification on the 

export of lease 

condensate

Under 754.2(a), “lease 

condensate that has been 

processed through a crude 

oil distillate tower is not 

crude oil but a petroleum 

product” which have few 

export restrictions.

Processed API 50+ crude 

is assumed to be 

processed condensate, 

and is allowed to be 

exported

See FAQ#3 under the heading “FAQs –

Crude Oil and Petroleum Products 

December 30, 2014,” 

https://www.bis.doc.gov/index.php/policyguidance/deemed-exports/deemedexports-faqs

Source: U.S. Energy Information Administration, Office of Energy Analysis.
September 2015

U.S. Energy Information Administration | Assumptions to the Annual Energy Outlook 2015 224