OCS Study

BOEM 2015-054

Consumer Surplus and Energy Substitutes for 

OCS Oil and Gas Production: The 2015 Revised 

Market Simulation Model (MarketSim) 

Model Description

US Department of the Interior

Bureau of Ocean Energy Management

Headquarters

December 2015
[This page intentionally left blank.]
OCS Study

BOEM 2015-054

Consumer Surplus and Energy Substitutes for 

OCS Oil and Gas Production: The 2015 Revised 

Market Simulation Model (MarketSim) 

Model Description

Prepared under BOEM Order No.

M14PD00057

by

Industrial Economics, Incorporated

2067 Massachusetts Avenue

Cambridge, MA 02140

under subcontract to

SC&A, Incorporated

1608 Spring Hill Road, Suite 400

Vienna, VA 22182

December 2015
DISCLAIMER

Study concept, oversight, and funding were provided by the US Department of the Interior, Bureau of 

Ocean Energy Management, Environmental Studies Program, Washington, DC, under Contract Number 

GS-10F-0093K, order number M14PD00057. This report has been technically reviewed by BOEM and it 

has been approved for publication. The views and conclusions contained in this document are those of the 

authors and should not be interpreted as representing the opinions or policies of the US Government, nor 

does mention of trade names or commercial products constitute endorsement or recommendation for use.

REPORT AVAILABILITY

To download a PDF file of this Environmental Studies Program report, go to the US Department of the 

Interior, Bureau of Ocean Energy Management, Environmental Studies Program Information System

website and search on OCS Study BOEM 2015-054.

CITATION

Industrial Economics, Inc. 2015. Consumer Surplus and Energy Substitutes for OCS Oil and Gas 

Production: The 2015 Revised Market Simulation Model (MarketSim). U.S. Department of the

Interior, Bureau of Ocean Energy Management. OCS Study BOEM 2015-054.
i

Table of Contents

Background................................................................................................................................................... 1

Model Description ........................................................................................................................................ 1

Baseline Supply and Demand Projections.................................................................................................... 2

Model Framework......................................................................................................................................... 2

Oil Market..................................................................................................................................................... 3

U.S. Oil Demand ......................................................................................................................................3

Non-U.S. Oil Demand ..............................................................................................................................3

U.S. Oil Supply.........................................................................................................................................4

Non-U.S. Oil Supply ................................................................................................................................4

Oil Imports Delivered Via Tanker............................................................................................................4

U.S. Crude Oil Exports.............................................................................................................................5

U.S. Exports of Refined Petroleum Products ...........................................................................................6

Natural Gas Market....................................................................................................................................... 6

U.S. Natural Gas Demand ........................................................................................................................6

Demand for U.S. Natural Gas Exports .....................................................................................................7

U.S. Natural Gas Supply...........................................................................................................................7

Coal Market .................................................................................................................................................. 7

U.S. Coal Demand....................................................................................................................................7

Demand for U.S. Coal Exports.................................................................................................................8

U.S. Coal Supply ......................................................................................................................................8

Electricity Market ......................................................................................................................................... 8

U.S. Electricity Demand...........................................................................................................................9

U.S. Electricity Supply .............................................................................................................................9

Demand for Fossil Fuel Energy to Produce Electricity ..........................................................................10

Model Calibration ....................................................................................................................................... 10

Equilibrium................................................................................................................................................. 10

Adjustment Rates and Elasticities............................................................................................................... 11

Derivation of Default Adjustment Rates ................................................................................................11

Selection of Default Elasticity Values....................................................................................................11

Demand Elasticities............................................................................................................................16

Supply Elasticities..............................................................................................................................18

Consumer Surplus in MarketSim................................................................................................................ 21

Primary versus Secondary Markets........................................................................................................21

Effects of Persisting Quantity on Consumer Surplus.............................................................................24

Exclusion of Domestic Producer Surplus Losses...................................................................................26

Oil.......................................................................................................................................................26

Natural Gas.........................................................................................................................................26

Electricity ...........................................................................................................................................27

Coal ....................................................................................................................................................27

References................................................................................................................................................... 28
1

Background

The Bureau of Ocean Energy Management (BOEM) is charged with assisting the U.S. Secretary of the 

Interior in carrying out the mandates of the Outer Continental Shelf (OCS) Lands Act, which calls for 

expedited exploration and development of the OCS to, among other goals, “reduce dependence on foreign 

sources and maintain a favorable balance of payments in world trade.” The OCS Lands Act also requires 

that BOEM prepare forward-looking five-year schedules of proposed OCS lease sales that define as 

specifically as possible the size, timing, and location of the OCS area(s) to be offered for lease. 

As part of the development of these “Five-Year Programs,” BOEM completes an analysis of the energy 

market’s response to production anticipated to emerge from leases issued under these programs. This 

document comprises a detailed description of the methodology used by BOEM to measure the energy 

market response to new production on leases issued in different planning areas under a specific program. 

The analytical tool which BOEM employs internally to estimate this market response is called the Market 

Simulation Model (MarketSim). As might be expected, key inputs to the model are the anticipated 

amounts of new annual OCS oil and gas production. The magnitudes of these production estimates are 

based on assessments of the non-leased, economically recoverable oil and gas resources in each planning 

area, and on historical trends in leasing and production. 

The timing of these production estimates emerges from stipulated Exploration and Development (E&D) 

scenarios in each planning area. An E&D scenario defines the incremental level of OCS exploration, 

development and production activity anticipated to occur within Planning Areas expected to be made 

available for leasing in the BOEM 5-Year OCS Oil and Gas Leasing Program. Elements of an E&D 

scenario include the number of exploration wells drilled, the number of platforms installed, the number of 

development wells drilled, miles of new pipeline constructed, anticipated aggregate oil and gas 

production, and the number of platforms removed. A crucial output of the model calculations based on 

the E&D scenario production is the net change in consumer surplus, which is an important part of the 

measure of net social benefits from adoption of the Five-Year Program. 

A companion document (Forecasting Environmental and Social Externalities Associated with OCS Oil 

and Gas Development: The 2015 Revised Offshore Environmental Cost Model (OECM)) describes the 

calculations and supporting data for another model BOEM uses to estimate the net environmental and 

social costs attributable to the program proposal, net of the environmental and social costs attributable to 

the No Action Alternative, that is, from energy sources that would substitute for OCS production in the 

absence of the Five-Year Program.1 These outputs of this model’s calculations represent another 

important part of the measure of net social benefits from adoption of the Five-year Program. 

Model Description

What follows is the general framework for MarketSim’s economics-based model representation of U.S. 

energy markets. The model simulates end-use domestic consumption of oil, natural gas, coal and 

electricity in four sectors (residential, commercial, industrial and transportation); primary energy 

production; and the transformation of primary energy into electricity. The model mostly represents U.S. 

energy markets, but it also captures interaction with world energy markets as appropriate. As in the 

previous version, the current MarketSim takes current measures of energy production, consumption, and 

prices assuming no new OCS leasing as a baseline to which a given scenario of OCS production is added. 

 

1

See Industrial Economics et al. (2015).
2

Accounting for substitution between different sources of energy, the model calculates equilibrating prices 

for oil, natural gas, coal, and electricity based upon the user-specified increase in OCS production of oil 

and gas. 

As a point of departure for scenario analyses, MarketSim is calibrated to reproduce a specified baseline 

projection, such as the reference case in the Energy Information Administration’s (EIA) Annual Energy 

Outlook (AEO) or other output produced by the EIA’s National Energy Modeling System (NEMS), for 

the baseline projection. The user-specified offshore production scenario then is added to the production 

side of the market equilibration, and the model adjusts prices until all markets converge on a new 

equilibrium. 

Baseline Supply and Demand Projections

The baseline supply and demand projections in MarketSim were obtained from a customized model run 

of EIA’s NEMS model.2 The standard NEMS runs conducted for EIA’s AEO series assume the issuance 

of new leases for OCS oil and natural gas production. Given that the purpose of MarketSim is to assess 

the market impacts of new leases relative to a scenario without new leasing, these new leases should not 

be included in the MarketSim baseline. Thus, the customized NEMS runs developed for use in 

MarketSim deviated from the reference case in the AEO by removing new offshore leasing on the OCS 

off the lower 48 states from the model’s calculations.3 The results of this NEMS run constitute the 

baseline data incorporated into the model. 

While the customized NEMS run includes no new leasing on the OCS off the lower 48, EIA was unable 

to develop a NEMS scenario that restricted new leasing on the Alaska OCS. Given the structure of

NEMS’ oil and gas module for Alaska, it was not possible to limit offshore Alaskan production in the 

model. The baseline data in MarketSim therefore reflect some new leasing activity in the Alaska OCS 

Region. MarketSim makes an adjustment for leasing activity on existing Alaska leases based on 

BOEM’s estimate of the offshore oil and gas production likely to take place on these leases. 

Model Framework 

MarketSim’s approach to developing an energy model for policy evaluation is to represent the observed

conditions prevailing at any moment in the market as observable short-run conditions that are the result of 

a market equilibrating process and the partial adjustment toward long-run demand and supply conditions. 

These long-run conditions are not directly observable, but can be inferred from observed market 

conditions and the underlying parameters of the model. The result is a model that is characterized by 

partial adjustment toward a long-run equilibrium in each time period.

To create such a model, it is necessary to provide a set of assumed long-run elasticities and partial 

adjustment parameters. These are developed by reviewing the appropriate economic research, using 

technology assessments and by making comparisons across existing runs of NEMS to infer elasticities 

 

2 This NEMS projection forecasts production and consumption through the year 2040. This forecast was 

extrapolated through 2084.

3

See supporting documentation accompanying the delivery of prior NEMS output, “Alternate Scenarios of Energy 

Markets under Various Offshore Crude Oil and Natural Gas Resource Assumptions,” attachment of letter from 

Howard Gruenspecht, Acting EIA Administrator, to Walter D. Cruickshank, Acting Director. Mineral Management 

Service, 4 June 2009. The data incorporated into MarketSim are from the “Constrained Supply” scenario described 

in this document.
3

(see below). The supply and demand equations in the sections that follow show how MarketSim applies 

these partial adjustment parameters and long-run supply and demand elasticities.

Oil Market

MarketSim represents the world oil market with sector detail for the United States and single supply and 

demand equations for non-U.S. production and consumption. Oil use for electricity generation is 

represented in the section on electricity below. The equations that follow specify MarketSim’s estimation 

of U.S. oil demand, non-U.S. oil demand, U.S. oil supply, non-U.S. oil supply, oil imports delivered to 

the U.S. by tanker, U.S. crude oil exports, and U.S. exports of refined petroleum products.

U.S. Oil Demand

for each U.S. end-use sector i; and j = g (gas), c (coal), and e (electricity) where:

QDoi,trepresents the quantity of oil demanded in sector i at time t, 

Aoi,t is a constant calibrated to baseline data, 

Po,t is the price of oil at time t, 



oi is the long-run price elasticity of oil demand in sector i, 

Pj,t is the price of energy source j at time t, 



oji is the long-run elasticity of demand for oil with respect to the price of energy source j in 

sector i, and 



Doi is the rate at which demand for oil in sector i adjusts.4

The four U.S. end use sectors i are residential, commercial, industrial, and transportation.

Non-U.S. Oil Demand

where 

QDox,t represents the quantity of non-U.S. oil demand at time t, 

Aox,t is a constant calibrated to baseline data,

 ox is the long-run price elasticity of non-U.S. oil demand, and 



Dox is the rate at which non-U.S. oil demand adjusts. 

Non-U.S. oil demand is strictly a function of the oil price, and no other prices, domestic or foreign.

 

4 Note that this deviates from standard notation used in the empirical literature on demand and supply estimation by 

using gammas to represent adjustment rather than persistence.

, , , , , 1

(1 )       Doi Doi t

j

QDoi t Aoi t Po t Pj t Q oi oji 

 

, , , , 1

(1 )      QDox t Aox t Po t Dox QDox t

ox 


4

U.S. Oil Supply

for each domestic source u = lower 48 onshore, lower 48 offshore, Alaska, biofuels, other, rest of world; 

where 

QSou,t represents the quantity of oil supplied from U.S. source u at time t, 

Bou,t is a constant calibrated to baseline data, 

 ou is the long-run elasticity of oil supply from source u, and 



Sou is the rate at which U.S. oil supply u adjusts. 

Consistent with the EIA classification, the term “oil” includes all liquid fuels that are close substitutes for 

petroleum products (e.g., biofuels). 

Non-U.S. Oil Supply

where 

QSoy,t represents the quantity of non-U.S. oil supplied at time t, 

Boy,t is a constant calibrated to baseline data, 

 oy is the long-run elasticity of non-U.S. oil supply, and 



Soy is the rate at which non-U.S. oil supply adjusts. 

Non-U.S. oil supply is estimated in MarketSim’s equilibrating equations as a separate value that 

represents tanker imports and pipeline imports combined, consistent with AEO reporting.

Oil Imports Delivered Via Tanker

MarketSim uses the equations outlined above to find changes in oil market consumption, production, and 

prices under a given E&D scenario. These equilibrating equations do not distinguish between pipeline 

and tanker imports of oil. To assess the environmental impacts of the No Action Alternative, however, 

the OECM requires estimates of the change in imports delivered via tanker.5 MarketSim therefore uses a 

post-processing approach to estimate the change in oil tanker imports. Under this approach, MarketSim 

assumes that all pipeline imports of oil are produced in Canada. Based on this assumption, MarketSim 

estimates the change in tanker imports as the difference between total imports (estimated as U.S. 

consumption less U.S. production) and imports from Canada. The model’s calculation for imports from 

Canada is similar to the non-U.S. oil supply formula except with its own parameter, elasticity, and 

adjustment rate.

where 

QSoc,t represents the quantity of Canadian pipeline oil imports supplied at time t, 

Boc,t is a constant, 

 

5 The No Action Alternative is defined as the scenario in which BOEM holds no OCS oil and gas lease sales during 

the 5-year period covered by the program or, in other words, in which the No Sale Option is selected for each 

program area. 

, , , , 1

(1 )      QSou t Bou t Po t Sou QSou t

ou 



, , , , 1

(1 )      QSoy t Boy t Po t Soy QSoy t

oy 



, , , , 1

(1 )      QSoc t Boc t Po t Soc QSoc t

oc 


5

 oc

is the long-run elasticity of Canadian pipeline oil imports, and 



Soc

is the rate at which Canadian pipeline oil import supply adjusts.

U.S. Crude Oil Exports

As described above, MarketSim models oil as a global market with supply and demand specified 

separately for the U.S. and the rest of the world. This structure allows MarketSim to estimate changes in 

U.S. net imports of oil, but does not allow for estimation of gross U.S. crude oil exports. To capture 

changes in gross exports, MarketSim applies a post-processing approach outside the model’s equilibration 

process that is informed by recent developments in global markets for crude oil and differences between 

the U.S. refining sector and refineries in other countries. 

As described in Brown et al. (2014), the U.S. currently has an excess of light crude that is considered 

more valuable on the global market than it is domestically. This excess reflects the combined effects of 

the U.S. ban on crude oil exports (with the exception of crude oil exports to Canada) and significant 

increases in the production of (light) crude in North Dakota and other areas. Because most U.S. refineries 

are more oriented toward handling a heavier mix of crudes than their foreign counterparts, global refinery 

operations are rendered less efficient by forcing the lighter crude oil produced in the United States to be 

processed domestically, as this action reduces the amount of heavy crude that U.S. refineries are able to 

handle. Global refinery operations would become less costly by allowing the lighter crude oil to be 

exported and heavier crude oil produced abroad to be imported into the U.S. 

Considering the importance of these dynamics in the market for crude oil, the specification of non-U.S. 

demand for U.S. crude oil exports in MarketSim is informed by Brown et al.’s (2014) assessment of the 

impacts associated with lifting the U.S. export ban. Brown et al. determined that lifting the ban on U.S. 

oil exports would reduce the global cost of producing refined products by 0.5 percent by allowing a 

reallocation of crude oil across refineries worldwide. Essentially, this improvement in global refinery 

efficiency means that the effective price of crude oil would be seen by end users as 0.5 percent lower than

the market price of crude oil.

To estimate changes in U.S. crude oil exports in the context of the export ban and to account for potential 

changes in this policy that might affect the crude oil exports associated with the Five Year Program, 

MarketSim specifies gross U.S. exports of crude as follows.

 (  ( 

))

 

 ( ) 

where 

QDoxcus,t is the quantity of U.S. crude oil demanded by other countries (e.g., U.S. exports) at time t,

Aoxcus,t is a constant,

fxct represents the percentage reduction in non-U.S. refining costs associated with non-U.S. refiners using 

U.S. crude,



oxcus is the long-run price elasticity of non-U.S. demand for U.S. oil, and





Doxcus is the rate at which non-U.S. demand for U.S. oil adjusts

As a default, MarketSim sets fxct equal to 0 to reflect the current ban on U.S. crude oil exports.
6

U.S. Exports of Refined Petroleum Products

MarketSim’s specification of oil markets focuses on domestic and non-U.S. sources of crude oil that may 

be refined into gasoline, diesel, and other petroleum products. Because these products are derived from 

crude oil, they are not included as separate categories in MarketSim’s equilibration calculations. As a 

post-processing calculation to MarketSim’s specification of the new market equilibrium, however, the 

model estimates changes in U.S. exports of refined petroleum products. MarketSim’s equation for nonU.S. demand for U.S. crude oil exports is similar to that for other categories of demand.

where

QDoxrus,t represents the quantity of refined product exported from the United States at time t,

Aoxrus,t is a constant,



oxrus is the long-run price elasticity of non-U.S. demand for U.S. refined products, and 





Doxrus is the rate at which non-U.S. demand for U.S. refined products adjusts. 

MarketSim applies the above equation after the model converges on a new market equilibrium and 

estimates new world oil prices, by year (Po,t).

Natural Gas Market

MarketSim represents the U.S. natural gas market with exports and imports. This stands in contrast to the 

oil market, which MarketSim simulates as a global market. Natural gas use for electricity generation is 

represented in the section on electricity below. The equations that follow specify MarketSim’s estimation 

of U.S. natural gas demand, demand for U.S. natural gas exports, and U.S. natural gas supply.

U.S. Natural Gas Demand

for each U.S. end-use sector i; and j = o (oil), c (coal), and e (electricity) where 

QDgi,t represents the quantity of natural gas demanded in sector i at time t, 

Agi,t is a constant calibrated to baseline data, 

Pg,t is the price of natural gas at time t, 



gi is the long-run price elasticity of natural gas demand in sector i, 

Pj,t is the price of energy source j at time t, 



gji is the long-run elasticity of demand for natural gas with respect to the price of energy source 

j in sector i, and 



Dgi is the rate at which demand for natural gas in sector i adjusts. 

The U.S. natural gas demand sectors represented in MarketSim include the residential, commercial, 

industrial, and transportation sectors.

, , , , , 1

(1 )       Dgi Dgi t

j

QDgi t Agi t Pg t Pj t Q

g i gji 

 

, , , , 1

(1 )      QDoxrust Aoxrust Po t Doxrus QDoxrust

oxrus 


7

Demand for U.S. Natural Gas Exports

where 

QDgx,t represents the quantity of U.S. natural gas exports at time t, 

Agx,t is a constant calibrated to baseline data, 

 gx is the long-run price elasticity of export demand for U.S. natural gas, and 



Dgx is the rate at which export demand for natural gas adjusts. 

U.S. natural gas exports are dependent only upon the domestic price of natural gas and no other prices, 

domestic or international.

U.S. Natural Gas Supply

for each domestic and imported source, u where 

QSgu,t represents the quantity of natural gas supplied to the U.S. market from domestic or imported 

source u at time t, 

Bgu,t is a constant calibrated to baseline data, 

 gu is the long-run elasticity of natural gas supply to the U.S. market from source u, and 



Sgu is the rate at which natural gas from source u adjusts. 

Natural gas production categories included in MarketSim are (1) lower 48 conventional, (2) lower 48 

unconventional, (3) Alaska, (4) offshore, (5) other, (6) pipeline imports, and (7) tanker imports. 

Coal Market

MarketSim represents the U.S. coal market with exports and imports. Coal use for electricity generation 

is represented in the section on electricity below. The equations that follow present the model’s 

estimation of U.S. coal demand, demand for U.S. coal exports, and U.S. coal supply.

U.S. Coal Demand

for each U.S. end-use sector i; and j = g (gas), o (oil), and e (electricity) where 

QDci,t represents the quantity of coal demanded in sector i at time t, 

Aci,t is a constant calibrated to baseline data, 

Pc,t is the price of coal at time t, 



ci is the long-run price elasticity of coal demand in sector i,

Pj,t is the price of energy source j at time t,



cji is the long-run elasticity of demand for coal with respect to the price of energy source j in 

sector i, and 



Dci is the rate at which demand for coal in sector i adjusts. 

, , , 1

(1 )      QDgx t Agx t Pg Dgx QDgx t

gx 



, , , , 1

(1 )      QSgu t Bgu t Pg t Sgu QSgu t

gu 



, , , , , 1

(1 )       Dci Dci t

j

QDci t Aci t Pc t Pj t Q

cji ci 

 
8

Other than the electricity sector, whose coal demand is modeled separately, MarketSim’s domestic 

demand sectors for coal include industrial and other.

Demand for U.S. Coal Exports

where 

QDcx,t represents the quantity of U.S. coal exports at time t,

Acx,t is a constant calibrated to baseline data, 

 cx is the long-run price elasticity of export demand for U.S. coal, and 



Dcx is the rate at which export demand for coal adjusts. 

Exports are dependent only upon the domestic price of coal. No other energy prices, domestic or 

international, affect exports of coal.

U.S. Coal Supply

for each domestic and imported source, u where 

QScu,t represents the quantity of coal supplied to the U.S. market from domestic or imported 

source u at time t,

Bcu,t is a constant calibrated to baseline data, 

 cu is the long-run elasticity of coal supply to the U.S. market from source u, and 



Scu is the rate at which coal from source u adjusts.

Electricity Market

MarketSim represents the U.S. electricity market and models U.S. exports and imports of electricity as net 

imports. The electricity sector in MarketSim also provides additional demand for oil, natural gas and 

coal. The equations below present MarketSim’s approach for estimating U.S. electricity demand, U.S. 

electricity supply, and demand for fossil fuels for electricity production.

, , , 1

(1 )      QDcx t Acx t Pc Dcx QDcx t

cx 



, , , , 1

(1 )      QScu t Bcu t Pc t Scu QScu t

cu 


9

U.S. Electricity Demand

 

for each U.S. end-use sector i; and j = g (gas), c (coal), and o (oil) where 

QDei,t represents the quantity of electricity demanded in sector i at time t, 

Aei,t is a constant calibrated to baseline data, 

Pe,t is the price of electricity at time t, 



ei is the long-run price elasticity of electricity demand in sector i, 

Pjis the price of energy source j, 



eji is the long-run elasticity of demand for electricity with respect to the price of energy source j

in sector i, and 



Dei is the rate at which demand for electricity in sector i adjusts. 

The U.S. demand sectors for electricity in MarketSim include (1) residential, (2) commercial, (3) 

industrial, (4) transport, and (5) other.

U.S. Electricity Supply

MarketSim uses separate approaches for the estimation of electricity derived from fossil fuels and 

electricity derived from other sources. While the quantity of electricity generated from fossil fuels is 

dependent on fossil fuel prices, changes in these prices do not factor into the generation of electricity from 

non-fossil energy sources. To account for this difference in the economics of electricity generation for 

different types of power producers, MarketSim specifies electricity supply as follows:

for j = oil, natural gas and coal

for l = nuclear, hydro, wind, solar, other electric, net imports

where 

QSej,t represents the quantity of electricity supplied from fossil fuel energy source j at time t, 

QSel,t represents the quantity of electricity supplied from source l at time t,

Cj,t and Cl,t are constants calibrated to baseline data, 

Pe,t is the price of electricity at time t, 

Pj,t is the price of fossil fuel energy source j at time t,



ej is the long-run elasticity of electricity supply from fuel j, 



el is the long-run elasticity of electricity supply from source l, 



Sej is the rate at which electric power from fossil energy j adjusts, and 



Sel is the rate at which electric power from source l adjusts.

, , , , , 1

(1 )       Dei Dei t

j

QDei t Aei t Pe t Pj t Q

eji ei 

 

, , , , 1

(1 )      QSel t Cl t Pe t Sel QSel t

el 



, , , , , 1

( / ) (1 )      QSej t C j t Pe t Pj t Sej QSej t

ej 


10

Demand for Fossil Fuel Energy to Produce Electricity

for j = oil, natural gas and coal

where QDje,t represents the quantity of energy source j used to produce electricity at time t and Kj,t is a 

constant.

Model Calibration

For a given set of elasticities, adjustment parameters, market quantities, and prices in the baseline 

projection, MarketSim uses the series of supply and demand equations outlined above to calculate the 

parameters A, B, C, and K in these equations. These parameters, having been calculated on the baseline 

projection equilibrium state, calibrate the model formulas directly to the market conditions observed in 

the baseline projection data. MarketSim then uses these parameters as constants in the simulation supply 

and demand formulas that equilibrate all four fuel markets under a given E&D scenario. 

The model automatically updates the calibration parameters to match new baseline projection data

immediately when entered into the baseline projection worksheet tables.

Equilibrium

The equilibration calculation of MarketSim selects Po,t, Pg,t, Pc,t and Pe,t for each period t such that the 

quantity of oil, natural gas, coal, and electricity demanded equals the quantity supplied in each period t:

 World Oil Market

 U.S. Natural Gas Market (with exports and imports)

 U.S. Coal Market (with exports and imports)

U.S. Electricity Market (with net imports)

To initiate the equilibration process for a given E&D scenario, MarketSim first adds the incremental 

increase in OCS production to the oil and gas supply terms in the above equilibrating equations. Because 

supply has changed, markets are not in equilibrium under the original baseline prices. Using Excel’s 

solver function, MarketSim then uses reduced gradient methods to iterate through several combinations of 

the four fuel prices until it can bring all four fuel markets’ supply and demand into equilibrium. During 

this process, all simulated supply and demand values are calculated using the same elasticity, adjustment, 

and parameter values used to represent the baseline. When zero disparity between supply and demand 

across all four fuel markets is achieved, MarketSim saves the market-clearing prices and proceeds to the 

next year to perform the same equilibration.

   

u

Dgx t Sgu t

i

QDge,t QDgi,t Q

,

Q

,     

j l

Sej t Sel t

i

QDei,t Q

,

Q

,

    

u

Dcx t Scu t

i

QDce,t QDci,t Q

,

Q

,

     

u

Soy t Sju t

i

QDoe,t QDox,t QDoi,t Q

,

Q

,

QDje,t K j,t QSej,t 
11

Adjustment Rates and Elasticities 

All elasticities and adjustment rates in MarketSim have default values that are obtained from the 

literature, inferred from NEMS output, or provided by energy economist Dr. Stephen Brown of the 

University of Nevada, Las Vegas (UNLV). In addition, all values can be edited easily by the user to 

incorporate the user’s best judgment for any given elasticity value or adjustment rate. Further, all default 

values can be automatically restored after editing to return the values to their original settings. The 

sections below document the derivation of the default adjustment rates and elasticities included in 

MarketSim.

Derivation of Default Adjustment Rates 

As described above, MarketSim uses a series of adjustment rates to capture the transition from short-run 

to long-run market effects. These adjustment rates account for the portion of demand or supply that is 

allowed to change per time period. In the case of this model, the time period is one year. No data on the 

adjustment rates for specific energy sources were readily available. In the absence of such data, 

MarketSim assumes that the adjustment rate is related to the retirement of energy producing and 

consuming capital (i.e., equipment that produces energy or consumes energy), as indicated by its lifespan. 

Based on lifespan values obtained from the literature, adjustment rates are calculated as follows:

or

where 



Dji is the rate at which the quantity demanded adjusts in each U.S. end-use sector i for each fuel j

and LDji is the lifespan of the main consumption capital in each submarket. Similarly, 



Sju is the rate at 

which the quantity supplied adjusts from each production source u for each fuel j and LSju is the lifespan 

of the main production capital equipment in each submarket. 

Tables 1 and 2 present the adjustment rates included in MarketSim as well as the lifespan values 

supporting each adjustment rate. 

For U.S. exports of crude oil and refined petroleum products, MarketSim uses adjustment rates developed 

from a slightly different approach than outlined above. For these categories, default adjustment rates 

were calculated by estimating the ratio of the short run demand elasticity implied by AEO 2014 to the 

long run demand elasticity derived from AEO 2014 data. Detailed information on the estimation of these 

elasticity values is presented below in the discussion of default elasticity values. If these elasticity values 

are updated in future versions of the model, the same approach may be applied to develop updated 

adjustment rates.

Selection of Default Elasticity Values 

To the extent possible, MarketSim relies upon demand and supply elasticities obtained from peerreviewed studies in the empirical economics literature. Using peer-reviewed values is central to ensuring 

that MarketSim’s simulation of energy markets reflects the best information available on the demand and 

supply responses that result from changes in energy prices. As suggested above, in the few cases where 

peer-reviewed values are not available, elasticity estimates were derived from NEMS outputs or from 

expert input provided by Dr. Stephen Brown of UNLV.

Dji

Dji L

1

 

Sju

Sju L

1

 
12

Table 1. Adjustment Rates for Energy Demand

SECTOR

ADJUSTMENT 

RATE SUPPORTING LIFESPAN INFORMATION

Oil

Residential 0.10

Adjustment rate based on low end of residential oil boiler lifespan range (10 years) presented in U.S. Department 

of Energy (DOE), Final Rule: Furnaces and Boilers Technical Support Document, September 2007. Low end of 

the lifespan range used to allow for the possibility of early boiler replacement in response to changes in energy 

prices.

Commercial 0.05

Value reflects 20-year service life of oil-fired commercial boilers obtained from U.S. DOE/EIA, "The National 

Energy Modeling System: An Overview The Commercial Demand Module," October 2009, available at 

http://www.eia.gov/oiaf/aeo/overview/commercial.html#equip.

Industrial 0.04

Adjustment rate reflects the low end of the 25 to 40 year life of industrial oil-fired boilers as reported in

International Energy Agency (IEA), "Technology Brief 101: Industrial Combustion Boilers," May 2010, available 

at http://www.iea-etsap.org/web/E-TechDS/PDF/I01-ind_boilers-GS-AD-gct1.pdf. The low end of the lifespan 

range was chosen to allow for maximum energy substitution.

Transport 0.11

Value reflects median age of automobiles in operation in the U.S. (9.2 years), as reported in Bureau of 

Transportation Statistics, National Transportation Statistics 2008. Median age used rather than lifespan to allow 

for greater demand response to price changes within MarketSim. The adjustment rate implied by the median age 

of automobiles in operation (0.11) is similar to the 5 percent reduction in transport sector petroleum consumption 

between 2007 and 2008, as reported in U.S. DOE, Annual Energy Review 2011, October 2011. The reduction 

between 2007 and 2008 was the largest reported in DOE's time series extending back to 1949.

Rest of World 0.10 Assumed value.

Non-U.S. Demand 

for U.S. Refined 

Product Exports

0.25

Derived by dividing the short run demand elasticity estimated from the AEO 2014 by the long run elasticity from 

the AEO 2014.

Non-U.S. Demand 

for U.S. Crude Oil 

Exports

0.25

Assumed to be same as value for non-U.S. demand for U.S. refined product exports.

Natural Gas

Residential 0.10

Adjustment rate based on low end of residential gas boiler lifespan range (10 years) presented in U.S. DOE, Final 

Rule: Furnaces and Boilers Technical Support Document, September 2007. Low end of the lifespan range used to 

allow for the possibility of early boiler replacement in response to changes in energy prices.

Commercial 0.05

Value reflects 20-year service life of gas-fired commercial boilers obtained from U.S. DOE/EIA, "The National 

Energy Modeling System: An Overview The Commercial Demand Module," October 2009, available at 

http://www.eia.gov/oiaf/aeo/overview/commercial.html#equip.
13

Table 1. Adjustment Rates for Energy Demand

SECTOR

ADJUSTMENT 

RATE SUPPORTING LIFESPAN INFORMATION

Industrial 0.04

Adjustment rate reflects the low end of the 25 to 40 year life of industrial gas-fired boilers as reported in IEA, 

"Technology Brief 101: Industrial Combustion Boilers," May 2010, available at http://www.iea-etsap.org/web/ETechDS/PDF/I01-ind_boilers-GS-AD-gct1.pdf. The low end of the lifespan range was chosen to allow for 

maximum energy substitution.

Transport 0.08

Value based on 12-year lifespan for gas powered buses obtained from U.S. Department of Transportation, Federal 

Transit Administration, "Transit Bus Life Cycle Cost and Year 2007 Emissions Estimation," July 2, 2007, 

available at http://www.trb.org/Main/Blurbs/159061.aspx.

Exports 0.04 Assumed same as industrial value.

Electricity

Residential 0.10 Based on expert input of Dr. Stephen Brown, UNLV, July 2011.

Commercial 0.10 Based on expert input of Dr. Stephen Brown, UNLV, July 2011.

Industrial 0.20 Based on expert input of Dr. Stephen Brown, UNLV, July 2011.

Transport 0.10 Assumed value.

Other 0.10 Assumed value.

Coal

Industrial 0.04

Adjustment rate reflects the low end of the 25 to 40 year life of industrial coal-fired boilers as reported in IEA, 

"Technology Brief 101: Industrial Combustion Boilers," May 2010, available at http://www.iea-etsap.org/web/ETechDS/PDF/I01-ind_boilers-GS-AD-gct1.pdf. The low end of the lifespan range was chosen to allow for 

maximum energy substitution.

Exports 0.04 Assumed same as industrial value.

Other 0.04 Assumed same as industrial value.
14

Table 2. Adjustment Rates for Energy Supply

SECTOR

ADJUSTMENT 

RATE SUPPORTING LIFESPAN INFORMATION

Oil

Lower 48 Onshore 0.15 Based on expert input of Dr. Stephen Brown, UNLV, July 2011.

Lower 48 Offshore 0.15 Based on expert input of Dr. Stephen Brown, UNLV, July 2011.

Alaska 0.15 Based on expert input of Dr. Stephen Brown, UNLV, July 2011.

Other 0.15 Based on expert input of Dr. Stephen Brown, UNLV, July 2011.

Biodiesel 0.15 Based on expert input of Dr. Stephen Brown, UNLV, July 2011.

Rest of World 0.15 Based on expert input of Dr. Stephen Brown, UNLV, July 2011.

Pipeline Imports 0.15 Assumed to be same as other oil categories.

Natural Gas

Lower 48 

Conventional 0.15

Adjustment rate based on lifespan of 5 to 10 years for conventional gas production as reported by Encana in U.S. 

Department of Interior, National Park Service, Potential Development of the Natural Gas Resources in the 

Marcellus Shale New York, Pennsylvania, West Virginia, and Ohio, December 2008.

Lower 48 

Unconventional 0.30 Based on expert input of Dr. Stephen Brown, UNLV, July 2011.

Alaska 0.15 Assumed to be same as Lower 48 Conventional.

Offshore 0.15 Assumed to be same as Lower 48 Conventional.

Other 0.15 Assumed to be same as Lower 48 Conventional.

Imports - Pipeline 0.15 Assumed to be same as Lower 48 Conventional.

Imports – Liquefied 

Natural Gas (LNG) 0.15 Assumed to be same as Lower 48 Conventional.

Electricity

Oil 0.03 Adjustment rate based on an assumed 30-year lifespan for oil-fired electricity generation units, consistent with 

the values below for natural gas and coal units.

Natural Gas 0.03

Adjustment rate reflects 30-year gas-fired power plant life, as reported in U.S. DOE, National Energy 

Technology Laboratory (NETL), "Cost and Performance Baseline for Fossil Energy Plants: Volume 1: 

Bituminous Coal and Natural Gas to Electricity Final Report," August 2007.

Coal 0.03

Adjustment rate reflects 30-year coal-fired power plant life, as reported in U.S. DOE, NETL, "Cost and 

Performance Baseline for Fossil Energy Plants: Volume 1: Bituminous Coal and Natural Gas to Electricity Final 

Report," August 2007.
15

Table 2. Adjustment Rates for Energy Supply

SECTOR

ADJUSTMENT 

RATE SUPPORTING LIFESPAN INFORMATION

Nuclear 0.02 Based on 60-year nuclear power plant life, as reported in U.S. DOE EIA, Annual Energy Outlook 2010, May 

2010.

Hydro 0.01 Value reflects assumed 75-year lifespan of hydro-electric facilities, based on the 50 to 100 year range presented 

in US Geologic Survey, "Advantages of Hydroelectric Power Production and Usage," 2010.

Wind

(Offshore) 0.05 Value assumes 20-year lifespan for wind power units, based on American Wind Energy Association, "Wind 

Turbine Operations & Maintenance," available at http://awea.org/learnabout/publications/upload/O-M-PPR_1-

Wind pager-3.pdf.

(Onshore) 0.05

Solar 0.04

Adjustment rate reflects an effective 25-year lifespan for solar systems. This reflects the 30-year operational life 

of crystalline modules, adjusted for the approximate 20 percent output degradation over a module's lifetime. 

Lifespan and output degradation estimates from Windy Dankoff and Joe Schwartz, "Ask the Experts: PV 

Longevity & Degradation," Home Power Magazine, April/May 2007, available at 

http://homepower.com/article/?file=HP118_pg12_AskTheExperts_1.

Other Electric 0.031 Adjustment rate is the average of the values for electricity produced from oil, natural gas, coal, nuclear energy, 

hydro, solar, and wind.

Imports 0.026

Adjustment rate is the average of the values for electricity produced from oil, natural gas, coal, nuclear energy, 

and hydro. Solar and wind were not included in the calculation under the assumption that little solar or wind 

energy is imported into the United States.

Coal

Domestic 0.10 Based on expert input of Dr. Stephen Brown, UNLV, July 2011.

Imports 0.10 Based on expert input of Dr. Stephen Brown, UNLV, July 2011.
16

Demand Elasticities

To capture the complex interactions between different segments of U.S. energy markets, MarketSim 

requires own-price and cross-price demand elasticities for every energy source included in the model. For 

each major energy consuming sector (e.g., the residential sector), BOEM strove to use own-price and 

cross-price demand elasticities from the same empirical study to ensure that a sector’s simulated 

responses to energy price changes were based on price sensitivities derived from the same methods and 

data. The selection of demand elasticities also considered the quality of the estimates produced by each 

study. BOEM’s assessment of quality for individual elasticity estimates considered, among other factors, 

(1) whether they are statistically significant, (2) methods by which they were derived, and (3) the richness 

of the data supporting each estimate (e.g, whether they are based on a multi-year panel or reflect energy 

market data for a single year). 

Based on these criteria, MarketSim relies heavily on own-price and cross-price demand elasticities from 

Serletis et al. (2010) for the residential and commercial sectors and Jones (2014) for the industrial sector. 

Serletis et al. (2010) investigate inter-fuel substitution possibilities for energy demand across four fuels 

(i.e., oil, gas, electricity, and coal) using EIA data for the 1960-2007 period. Based on these data, Serletis 

et al. estimated own-price and cross-price elasticities for the commercial, residential, and industrial 

sectors, using a flexible translog functional form. Across most sectors, Serletis et al. produced 

statistically significant elasticity values of the expected sign.

Jones (2014) focuses on inter-fuel substitution in the industrial sector, using EIA data for the 1960-2011 

period for the same fuels included in Serletis et al. (2010) plus biomass. Jones specifies a dynamic linear 

logit model to estimate own-price and cross-price elasticities, and within this framework, estimates both 

short-run and long-run elasticities. In addition, to assess the role of biomass in industrial sector interfuel 

substitution, Jones develops two sets of models, one including the four fuels traditionally included in 

industrial sector energy models (i.e., natural gas, oil, coal, and electricity) and another that includes these 

energy sources plus biomass. Jones finds that the addition of biomass reduces both the own price and 

cross-price elasticities of demand for the four traditionally modeled fuels. The effect is most significant 

for those values associated with electricity. In both models, the four traditional fuels are found to be 

substitutes with each other with the exception of electricity and oil; the cross-price elasticities for these 

energy sources are not statistically significant.

Table 3 presents the default own-price and cross-price demand elasticities included in MarketSim for the 

residential, commercial, industrial, and transport sectors. The table also shows the default elasticity 

values for miscellaneous demand sectors included in MarketSim (e.g., natural gas demand in U.S. export 

markets). As indicated in the table, MarketSim uses results from Serletis et al. (2010) as defaults for the 

commercial and residential sectors, except for the elasticity of demand for natural gas with respect to the 

price of oil and the elasticity of demand for oil with respect to the price of natural gas. The estimates for 

these cross-price elasticities in Serletis et al. were of the unexpected sign (negative) and were not 

statistically significant. Therefore, in lieu of Serletis et al., MarketSim uses results from Newell and Pizer 

(2008) for these values, for both the commercial and residential sectors. Newell and Pizer (2008) 

estimate these cross-price relationships for the commercial sector only. While MarketSim would ideally 

use default values specific to the residential sector, alternative values for these cross-price elasticities 

were not readily available for the residential sector. Given the similarities between the commercial and 

residential sectors, MarketSim uses these two cross-price demand elasticities from Newell and Pizer 

(2008) as a reasonable approximation of the corresponding residential sector values.
17

Table 3. MarketSim Default Demand Elasticities

ELASTICITY 

WITH RESPECT 

TO CHANGE IN 

OIL PRICE

ELASTICITY 

WITH RESPECT 

TO CHANGE IN 

GAS PRICE

ELASTICITY WITH 

RESPECT TO 

CHANGE IN 

ELECTRICITY 

PRICE

ELASTICITY 

WITH RESEPCT 

TO CHANGE IN 

COAL PRICE

Commercial Sector1

Oil -0.939 0.2 1.08 -

Natural Gas 0.07 -0.296 0.419 -

Electric 0.092 0.041 -0.134 -

Coal - - - -

Residential Sector1

Oil -1.002 0.2 1.151 -

Natural Gas 0.07 -0.313 0.507 -

Electric 0.214 0.072 -0.287 -

Coal - - - -

Industrial Sector2

Oil -0.264 0.249 0.01 0.090

Natural Gas 0.172 -0.468 0.178 0.050

Electric 0.009 0.118 -0.125 0.061

Coal 0.440 0.351 0.652 -1.468

Miscellaneous Demand Categories3

Oil – Transport Sector3-0.300 - - -

Oil - Rest of World4-0.45 - - -

Crude Oil – U.S. Export 

Markets5 -0.162

Refined Petroleum –

U.S. Export Markets5 -0.162

Natural Gas - U.S. 

Export Markets4 - -0.89 - -

Electricity – “Other”6- - -0.18 -

Coal – Other7- - - -1.468

Coal – U.S. Export 

Markets8 - - - -1.00

Notes:

1. Commercial and residential sector values are from Serletis et al. (2010), except for the cross-price elasticity for 

gas in response to oil prices and the cross-price elasticity of oil in response to gas prices. For these latter two 

values, MarketSim uses demand elasticities from Newell and Pizer (2008). 

2. For the industrial sector, MarketSim uses demand elasticities from Jones (2014), except for the cross-price 

elasticity of electricity in response to oil prices and the cross-price elasticity of oil in response to electricity 

prices. For these values, MarketSim uses demand elasticities from Serletis et al. (2010).

3. Dahl (2012)

4. Dahl (2010)

5. Derived from AEO (2014)

6. Assumed to be average of own-price elasticity values for industrial, commercial, and residential sectors.

7. Industrial sector value from Jones (2014).

8. Assumed to be -1.00.
18

For the industrial sector, MarketSim relies almost exclusively on demand elasticities from Jones (2014) as 

defaults. Although Serletis et al. (2010) estimate elasticity values for the industrial sector, the values in 

Jones (2014) are based on fuel consumption data that exclude fuel use for purposes other than energy 

(e.g., petroleum products used as lubricants). As described above, Jones (2014) estimates long-run 

demand elasticities with two specifications, one including biomass as a substitute and another excluding 

biomass. Based on the statistical significance of the elasticities with biomass included, MarketSim uses 

the elasticities from the specification that includes biomass. The two exceptions to this are the cross-price 

elasticity of demand for oil with respect to the price of electricity and the cross-price elasticity of 

electricity in response to oil prices, as Jones’ estimates for these values are not statistically significant. 

For these values, MarketSim uses estimates from Serletis et al. (2010).

Table 3 also shows MarketSim’s default own-price demand elasticities for the transport sector and various 

miscellaneous demand categories. For these categories, MarketSim relies upon elasticity values from 

multiple sources. For oil demand in the transportation sector, MarketSim uses a U.S.-specific elasticity 

value obtained from Dahl’s (2012) review of price elasticities estimated for more than 100 countries. 

This value represents the average of the elasticity values identified in the empirical literature. For nonU.S. oil demand and U.S. natural gas exports, MarketSim uses estimates from Dahl’s prior (2010) review 

of the elasticity literature as defaults. For non-U.S. demand of U.S. crude oil and refined product exports, 

elasticity values were derived from the AEO 2014, using the formula below: 

Where

t

is the inferred elasticity in year t, QA,t and QB,trepresent the quantities demanded in year t for 

cases A and B respectively (the AEO high growth and reference cases, respectively), and PA,t and PB,t are 

the prices at time t for cases A and B. For the purposes of estimating long-run elasticity values, the year 

2040 was used as time t.

6

Two categories for which appropriate demand elasticity values were not identified in the literature are 

miscellaneous coal demand and demand for U.S. coal exports. MarketSim uses the same industrial sector 

value obtained from Jones (2014) for the former and assumes a value of -1.00 for the latter.

Supply Elasticities

MarketSim includes default supply elasticities, summarized in Table 4, for every production category 

modeled for a given fuel (e.g, onshore oil production in the lower 48 states). Consistent with the demand 

elasticities summarized above, several of MarketSim’s supply elasticities were obtained from the 

economic literature. In particular, many of these values are from Brown (1998), which includes 

representative long-run supply elasticities obtained from the literature for petroleum-based oil and coal. 

 

6 To estimate short run demand elasticities, year t is assumed to be the year 2015. As described in the previous 

section, the adjustment rates for U.S. crude oil exports and U.S. refined product exports are calculated based on 

short run and long run demand elasticities. 



































B t

A t

B t

A t

t

P

P

Q

Q

,

,

,

,

ln

ln


19

Brown (1998) includes a single supply elasticity for each of these fuels but does not report values specific 

to individual regions (i.e., Alaska versus the lower 48 states) or production methods. Supply elasticities 

that reflect these geographic and technological differences were not readily available from other studies in 

the empirical literature. In the absence of such data, MarketSim uses the supply elasticies from Brown 

(1998) for each domestic production source of petroleum-based oil and coal.

Table 4. MarketSim Default Supply Elasticities

FUEL SOURCE

SUPPLY 

ELASTICITY

Oil

Lower 48 Onshore10.51

Lower 48 Offshore10.51

Alaska10.51

Other10.51

Biodiesel20.24

Rest of World70.40

Canadian Pipeline Imports61.00

Natural Gas

Lower 48 Conventional40.29

Lower 48 Unconventional51.60

Alaska40.29

Offshore40.29

Other10.51

Pipeline Imports30.34

LNG Tanker Imports61.00

Electricity

Oil30.80

Natural Gas61.00

Coal81.41

Nuclear32.06

Other Electric61.00

Hydro31.10

Wind Onshore61.00

Wind Offshore61.00

Solar31.24

Imports80.87

Coal Domestic1

1.86

Imports61.00

Notes:

1. Brown (1998).

2. Luchansky and Monks (2009).

3. Derived from AEO 2015.

4. Medlock (2012).

5. Medlock (2012) and EMF (2013).

6. Assumed value.

7. Brown et. al (2014).

8. Derived from AEO 2014.

For most sources of domestic gas production, MarketSim uses supply elasticities obtained from Medlock 

(2012). Medlock (2012) develops a basic international trade framework to assess the consequences of 

allowing U.S. LNG exports. As an initial step in developing this analysis, Medlock considers the shape 

of U.S. natural gas supply curves, noting that the U.S. supply elasticity has grown substantially larger 
20

since the emergence of shale gas. Using data from a separate study,

7 Medlock estimates that the elasticity 

of supply has risen by a factor of five from 0.29 pre-2000 to 1.52 in 2000 and thereafter. Assuming that 

the pre-2000 estimate is indicative of the elasticity for conventional production methods (i.e., before 

shale-based production became so prevalent), MarketSim uses the elasticity value of 0.29 for the lower 48 

conventional, Alaska, and offshore production categories. For unconventional gas production, 

MarketSim applies a supply elasticity of 1.6, which is based on the value of 1.52 reported in Medlock 

(2012) and the range of values reported in Energy Modeling Forum (EMF) Report 26 (2013). The EMF 

report presents multiple elasticity values, by year, inferred by 12 separate energy models. For other gas 

production, MaretkSim applies the supply elasticity reported in Brown (1998).

Luchansky and Monks (2009) serves as the source for MarketSim’s default supply elasticity for domestic 

biodiesel. This paper uses monthly data for 1997 through 2006 to estimate the market supply and demand 

for ethanol at the national level. Applying these data to four specifications of supply, Luchansky and 

Monks (2009) estimate supply elasticities ranging from 0.224 to 0.258. MarktSim uses the midpoint of 

this range (0.24) as the default supply elasticity for biodiesel.

Where appropriate economic research does not exist or could not be obtained for a specific supply 

elasticity value, projections from the constrained runs of the AEO 2015 low-world price, high-world 

price, and reference cases were used to infer these values.8,9 Elasticity estimates may be inferred from the 

AEO projection for a given year by comparing the differences in energy prices between two scenarios 

with the differences in energy quantities. For a given energy source and fuel, an annual inferred elasticity 

value was calculated three times: (1) based on the low growth case vs. the high growth case, (2) the low 

growth case vs. the reference case, and (3) the reference case vs. the high growth case, for all AEO 2015

projection years from 2015 through 2040. The formula for this annual inferred elasticity is as follows.

Where

t

is the inferred elasticity in year t, QA,t and QB,trepresent the quantities supplied in year t for 

cases A and B respectively (each case is compared with both of the other cases), PA,t and PB,t are the prices 

at time t for cases A and B, and 



is the rate at which supply adjusts. The resulting series of inferred 

elasticities are averaged, excluding extreme outlier results derived from the AEO data.10

For a limited number of producing sectors, elasticity values were unavailable from the literature and the 

data reported in AEO 2015 and AEO 2014 either yielded elasticity values that appeared unrealistically 

 

7

See Medlock III et al. (2011). 

8

In some cases, the supply elasticities were derived from AEO 2014 rather than AEO 2015 when results from the 

2015 data resulted in unrealistic elasticity values.

9

See Tables A-4 and A-5 in the appendix for the AEO 2015 table numbers, line numbers, and line names for the 

data used to derive select supply elasticities.

10 More specifically, elasticities were estimated based on differentials between the low-growth case and reference 

case, the reference case and the high-growth case, and the low-growth case and the high-growth case. They then 

were averaged across these three variants and across years.























  

 





 B t A t B t B t A t A t

t P

P

Q Q

Q Q

,

,

, , 1

, , 1

ln

(1 )

(1 )

ln






21

high or were insufficient to support estimation of a supply elasticity. In such cases, MarketSim uses a 

default supply elasticity of 1.0.

Consumer Surplus in MarketSim

To assess changes in the welfare of U.S. consumers

11 under a given E&D scenario, MarketSim estimates 

the change in consumer surplus for each of the end-use energy markets included in the model (e.g., 

residential sector gas, industrial sector oil, etc.). For a given energy source, these changes in consumer 

surplus reflect changes in both price and quantity relative to baseline conditions. Under the model 

structure outlined above, price and quantity may change due to shifts in supply functions driven by the 

E&D scenario itself or from shifts in demand functions associated with cross-price effects. In addition, 

changes in quantity and price for a given year (relative to the baseline) reflect the assumption in 

MarketSim that the amount of energy consumed and produced in a given year depends partially on the 

quantity consumed and produced in the prior year.

MarketSim’s estimation of the change in consumer surplus focuses on welfare changes associated with 

the consumption of energy within the United States. Although the model accounts for international trade 

in oil, natural gas, electricity, and coal, it distinguishes between U.S. and non-U.S. consumers of each 

energy source. This is consistent with the structure of the baseline energy demand projections from EIA 

that serve as the foundation of MarketSim. These projections reflect U.S. consumption within the 

residential, commercial, industrial, and transportation sectors. MarketSim’s assessment of changes in 

consumer surplus is limited to these specific demand sectors. None of the model’s consumer surplus 

calculations consider changes in consumption in non-U.S. markets. 

Primary versus Secondary Markets

With four types of energy included in the model (oil, natural gas, electricity, and coal), MarketSim’s 

calculation of the market equilibrium associated with new OCS oil and gas production accounts for 

spillover effects to other segments of U.S. energy markets. For example, increased OCS oil production 

would likely reduce oil prices and lead to a reduction in coal demand due to cross-price effects. Changes 

in this and other indirectly affected markets may also have feedback effects on oil and natural gas 

markets. Estimating changes in consumer surplus associated with a given E&D scenario therefore 

requires careful consideration of surplus changes across multiple markets. 

To estimate changes in consumer surplus within the model’s multi-market structure, MarketSim draws on 

the approach outlined in Boardman et al. (1996).12 Recognizing that government interventions in one 

market (i.e., the primary market) may have spillover effects on other markets (i.e., secondary markets), 

Boardman et al. (1996) present a systematic approach for appropriately estimating welfare changes in 

general equilibrium. Putting the Boardman et al. approach in the context of OCS oil and gas production, 

BOEM’s Five-Year Program leads to an outward shift in the supply function within one or more primary 

markets such as oil and/or natural gas. This shift leads to a price reduction in the primary market, as 

 

11 MarketSim was designed to estimate changes in consumer surplus for U.S. consumers only. The model results do 

not include changes in consumer surplus for foreign consumers. 

12 This approach is highlighted in Boardman et al. (1996), Gramlich (1998), Mohring (1993), Thurman (1991), and 

Thurman and Wohlgenant (1989). 
22

shown by the change from PP0 to PP1 in Figure 1. Due to cross-price effects, this reduction in price in the 

primary market causes the demand function for substitutes to shift inward, as shown in Figure 2, reducing 

the quantity of substitutes demanded from QS0 to QS1. As explained in Boardman et al. (1996), this 

reduction in quantity demanded for substitutes does not lead to a change in consumer surplus that is not 

already reflected in the primary market surplus change (described below), because the location of the 

demand curve within the primary market reflects the existence of substitutes. Due to the budget 

constraint faced by consumers, willingness to pay at a given quantity along the primary market demand 

function reflects the incremental utility derived from consuming more in the primary market, net of the 

utility lost from reducing consumption in the secondary market. Thus, changes in consumer welfare

associated with changes in quantity in the primary market reflect not only the quantity changes in the 

primary market but also the corresponding quantity changes in secondary markets. Put differently, the 

demand function in the primary market is located further to the left than it would be in the absence of 

substitutes. Without substitutes, the quantity demanded by consumers in the primary market would be 

higher at each price point.

The shift in demand in the secondary market also leads to a reduction in price within that market, from 

PS0 to PS1 in Figure 2. As described in Boardman et al. (1996), this reduction in price leads to an increase 

in consumer surplus represented by area PS0dePS1 in Figure 2. This surplus change is not reflected in the 

primary market. Within MarketSim, this area is estimated as two components. For the rectangle PS0dfPS1,

this portion of consumer surplus is simply ΔP × Q*. To calculate the area of def, MarketSim calculates 

the definite integral of DS1 over the range [Q*, QS1] and subtracts the area of the rectangle Q*feQS1. 

Figure 1. Primary Market Consumer Surplus Change Price ($)

Quantity

PP0

PP1

QP0

DP1 DP0

D*

QP1

a

b

c
23

Boardman et al. (1996) suggest a different approach for estimating consumer surplus changes within the 

primary market. Returning to the context of BOEM’s Five Year Program, the program itself causes a

shift in supply, which, as described above, causes a reduction in price for substitutes (see Figure 2). As 

the price of substitutes decreases, demand within the primary market declines, as represented by the 

inward shift in demand in Figure 1. Equilibrium in the primary market therefore changes from point a in 

the baseline to point b following implementation of the 5-year program. Boardman et al. (1996) suggest 

that the associated change in consumer surplus should be estimated along the equilibrium demand curve 

represented by the line D* connecting points a and b in Figure 1. Unlike DP0 and DP1, which hold the 

prices of all other goods constant, the equilibrium demand curve shows demand once prices in other 

markets have fully adjusted to the change in the primary market. Using the equilibrium demand curve, 

the change in the primary market’s consumer surplus includes two components. First, the price effect on 

the baseline quantity is represented by rectangle PP0acPP1, calculated as ΔP × QP0. Second, the additional 

consumer surplus associated with the increase in quantity is calculated as triangle abc, calculated as 

0.5(ΔQ × ΔP). In total, the change in consumer surplus for this primary market is the trapezoid PP0abPP1.

Figure 2. Secondary Market Consumer Surplus Change (reduced quantity and price) 

QS0 Price ($)

Quantity

PS1

PS0

QS1

DS1 DS0

Q*

d

e

S

f
24

To estimate the changes in consumer surplus associated with BOEM’s Five Year Program, MarketSim 

applies the approach from Boardman et al. (1996) outlined in Figures 1 and 2. One complicating factor in 

the application of this approach is that oil and natural gas may be both primary and secondary markets. 

That is, OCS production of oil may affect natural gas markets and OCS natural gas production may affect 

oil markets. Similarly, because electricity may be produced with OCS natural gas and, to a much lesser 

extent OCS oil, the electricity market may be both a primary and secondary market.13 A key distinction 

between primary and secondary markets in Boardman et al. (1996), however, is that primary markets see 

an increase in the equilibrium quantity demanded while secondary markets experience a reduction in 

quantity.14 For the purposes of estimating the change in consumer surplus, MarketSim therefore treats oil, 

natural gas, or electricity as primary markets if the quantity demanded under the E&D scenario increases 

relative to the baseline. For example, if the equilibrium quantity of oil in the 2020 transportation market 

is higher in the E&D scenario than the baseline EIA NEMS projection quantity, the 2020 oil 

transportation market will be treated as a primary market and its change in consumer surplus will be 

calculated based on the approach shown in Figure 1. Conversely, if the quantity of oil, natural gas, or 

electricity demanded decreases from the baseline to the E&D scenario, MarketSim calculates the 

consumer surplus change based on the secondary market approach.15 

This rule does not apply to coal which always is treated as a secondary market in MarketSim. Because 

E&D oil and natural gas production effects on coal markets are only indirect, coal is never considered a 

primary market for the purposes of MarketSim’s consumer surplus change calculations. 

Effects of Persisting Quantity on Consumer Surplus

MarketSim’s supply and demand equations include lagged or persisting quantities such that the minimum 

quantity of fuel demanded or supplied in any given year is a certain percentage of the quantity demanded 

or supplied, respectively, during the previous year. In cases of large quantities added or removed from 

the E&D scenario production schedule year-on-year, the lagged structure of the model may result in 

short-term swings in price in certain markets as the model responds to these changes in OCS production. 

This sometimes leads to counterintuitive results within the model. For example, in some years,

particularly those following a sharp reduction in production associated with the OCS program, the 

quantity demanded may decline relative to the baseline while price increases. As shown above in Figure

2, however, reductions in demand in secondary markets typically are accompanied by reductions in price 

instead of an increase—the inward shift in demand reduces both quantity and price.

To estimate the consumer surplus change in secondary markets under these conditions of declining 

quantity with increasing price, we follow the approach shown in Figure 3. Similar to the situation 

depicted in Figure 2 where price declines, the approach shown in Figure 3 limits estimation of the change 

in consumer surplus to effects associated with the change in price projected by MarketSim for the portion 

 

13 To avoid double counting consumer surplus changes associated with oil and natural gas used for electricity 

production, MarketSim’s estimation of the consumer surplus changes for oil and natural gas does not include oil and 

gas used for electricity generation. Changes in consumer surplus associated with oil and natural gas used for this 

purpose are reflected in the model’s consumer surplus calculations for electricity consumers.

14 The opposite would be true for policies that reduce supply.

15 MarketSim may treat a given market as a primary market one year and as a secondary market in other years. For 

any given year, MarketSim determines primary/secondary market status based on the change in quantity demanded 

relative to the baseline.
25

of the quantity demanded that remains unchanged relative to the baseline, as represented by rectangle 

PS1ghPS0 in Figure 3. Surplus changes associated with the reduction in quantity are reflected in the 

estimated surplus change in primary markets.16

The lagged quantities in MarketSim’s demand equations also may lead to situations where consumer 

surplus decreases following sharp reductions in E&D production after the E&D peak. For example, as 

E&D production peaks, the quantity of energy demanded increases in response to lower energy prices. 

After the E&D production peak, however, the subsequent reduction in energy consumption for any given 

year is limited by the lagged quantities in MarketSim’s demand equations and cannot drop below a certain 

threshold defined by (1-γ)Qt-1. To meet this demand following the sharp reductions in E&D production, 

supply must increase from other sources, but an increase in price is necessary to achieve such an increase. 

Thus, price and the quantity demanded may both increase relative to the baseline, leading to a reduction 

in consumer surplus. These reactions to sudden increases and decreases can be minimized by smoothing 

E&D production schedules over time, or by setting all of the adjustment rates (γ values) to 1 in the model.

Figure 3: Secondary Market Consumer Surplus Change (reduced quantity, increase in 

price)

 

16 In primary markets with increasing quantity, increased prices imply a consumer surplus loss. MarketSim 

estimates such losses using the general approach outlined in Figure 1, but estimates a reduction in consumer surplus 

for the baseline quantity (QP0) and over the increase in quantity (QP1-QP0). Price ($)

Quantity

PS1

PS0

g

h

QS1

D1 D0

QS0
26

Exclusion of Domestic Producer Surplus Losses

Following the approach described above, MarketSim estimates the full change in consumer surplus 

associated with a given E&D scenario. This includes transfers in surplus from (or to) energy producers

resulting from changes in energy prices. For example, if the price of energy declines as shown above in 

Figure 1, the portion of the consumer surplus impact represented by rectangle PP0acPP1 in Figure 1 is a 

transfer of surplus from producers to consumers. To the extent that consumer surplus gains such as those 

represented by rectangle PP0acPP1 are a transfer from U.S. producers, they do not represent a welfare gain 

for the U.S., as the gain to U.S. consumers is offset by the loss to U.S. producers. In contrast, transfers 

from non-U.S. producers to U.S. consumers do represent an increase in U.S. welfare.

To enable model users to estimate changes in U.S. consumer surplus net of transfers from U.S. producers, 

MarketSim generates an alternative set of consumer surplus estimates that exclude welfare transfers from 

(or to) U.S. energy producers. To generate these estimates, MarketSim multiplies the portion of the 

consumer surplus impact that represents a transfer from (or to) producers by the fraction of demand met 

by non-U.S. sources. In situations where a given energy source is treated as a primary market, the 

transfer portion of the consumer surplus impact is rectangle PP0acPP1 in Figure 1. When MarketSim 

treats an energy source as a secondary market, the entire change in consumer surplus represents a transfer.

Following this approach, an important step in evaluating consumer surplus net of transfers from domestic 

producers is estimating the fraction of demand met by non-U.S. sources. Because the specification of 

supply and demand differs somewhat across oil, gas, electricity, and coal markets in MarketSim (e.g., oil 

is modeled as a global market, whereas natural gas is modeled for the U.S. market with imports and 

exports), our approach for estimating the fraction of U.S. demand met by non-U.S. sources varies by 

energy source, as detailed below.

Oil

MarketSim models the world oil market but distinguishes between supply and demand in the U.S. and in 

other countries. Based on this information, we estimate the fraction of U.S. oil demand met by non-U.S. 

sources as follows:

 

( ) ( )

( )

Where Lf = fraction of U.S. oil demand met by non-U.S. sources of supply,

DL,W = global oil demand,

DL,ROW = non-U.S. oil demand,

SL,W = global oil supply, and

SL,ROW = non-U.S. oil supply.

The numerator of this formula represents oil consumed in the U.S. but produced in other countries, 

estimated as the difference between U.S. demand and U.S. production. The denominator represents U.S. 

oil demand.

Natural Gas

For natural gas, MarketSim simulates the U.S. market (rather than the global market) but estimates U.S. 

imports and exports of natural gas. Based on this specification of the market, we estimate the percentage 

of U.S. natural gas demand met by non-U.S. sources as follows:
27

 

 

( )

Where Gf = fraction of U.S. natural gas demand met by non-U.S. sources of supply,

SG,I = U.S. natural gas imports,

DG,T = Total U.S. natural gas demand, including demand for U.S. natural gas exports, and

DG,X = Demand for U.S. natural gas exports.

Electricity

MarketSim models the U.S. electricity market at the national level and includes net imports in its 

specification of electricity supply. We therefore estimate the share of U.S. demand satisfied by non-U.S. 

generation as follows:

 

 

 

Where Ef = fraction of U.S. electricity demand met by non-U.S. sources of supply,

SE,NI = U.S. net imports of electricity, and

DE,T = total U.S. demand for electricity (including net imports).17

Coal

Similar to its treatment of natural gas markets, MarketSim simulates coal as a national market and 

estimates imports and exports separately (rather than estimating net imports). Based on this model 

structure, we estimate the percentage of U.S. coal demand met by non-U.S. producers as follows:

 

 

( )

Where Cf = fraction of U.S. coal demand met by non-U.S. sources of supply,

SC,I = U.S. coal imports,

DC,T = Total U.S. coal demand, including demand for U.S. exports, and

DC,X = Demand for U.S. coal exports.

 

17 Given the structure of MarketSim, the total U.S. demand for electricity is equal to the U.S. electricity supply 

(including net imports). Thus, the formula for Ef could be re-written with the U.S. supply of electricity in the 

denominator.
28

References

American Wind Energy Association. "Wind Turbine Operations & Maintenance," available at 

http://awea.org/learnabout/publications/upload/O-M-PPR_1-pager-3.pdf.

Boardman, Anthony E., David H. Greenberg, Aidan R. Vining, and David Weimer. 1996. CostBenefit Analysis Concepts and Practice, Prentice Hall, Upper Saddle River, New Jersey.

Brown, Stephen P. A. 1998. “Global Warming Policy: Some Economic Implications,” Federal 

Reserve Bank of Dallas Economic Review, Fourth Quarter 1998.

Brown, Stephen P. A., Charles Mason, Alan Krupnick and Jan Mares. 2014. “Crude Behavior: 

How Lifting the Export Ban Reduces Gas Prices in the United States," Issue Brief 14-03, 

Resources for the Future, March. <http://www.rff.org/RFF/Documents/RFF-IB-14-03-

REV.pdf>

Bureau of Transportation Statistics. 2008. National Transportation Statistics 2008. 

Dahl, Carol. 2010. “Review and Critique of Elasticities Used in the World Energy Projections 

Plus Model,” for EIA/DOE, Office of Integrated Forecasting and Analysis, Washington 

D.C.

Dahl, Carol A. 2012. “Measuring global gasoline and diesel price and income elasticities,” 

Energy Policy, Vol. 41: 2-13.

Dankoff, Windy and Joe Schwartz, 2007. "Ask the Experts: PV Longevity & Degradation," 

Home Power Magazine, April/May 2007, available at 

http://homepower.com/article/?file=HP118_pg12_AskTheExperts_1.

Energy Modeling Forum. 2013. “Changing the Game?: Emissions and Market Implications of 

New Natural Gas Supplies,” Stanford University EMF Report 26, Vol. 1.

Gramlich, Edward M. 1998. A Guide to Benefit-Cost Analysis, Second Edition, Waveland Press, 

Inc., Prospect Heights, Illinois.

Industrial Economics, Inc.; Applied Sciences Associates, Inc.; Northern Economics; Dr. 

Nicholas Z. Muller; and SC&A, Inc. 2015. Forecasting Environmental and Social 

Externalities Associated with Outer Continental Shelf (OCS) Oil and Gas Development -

Volume 1: The 2015 Revised Offshore Environmental Cost Model (OECM). U.S. 

Department of the Interior, Bureau of Ocean Energy Management. OCS Study BOEM 2015-

052. 

International Energy Agency (IEA). 2010. "Technology Brief 101: Industrial Combustion 

Boilers", available at http://www.iea-etsap.org/web/E-TechDS/PDF/I01-ind_boilers-GSAD-gct1.pdf. 

Jones, Clifton T. 2014. “The role of biomass in US industrial interfuel substitution,” Energy 

Policy, Vol. 69, Feb. 2014, pp 122-126.

Luchansky, Matthew S. and James Monks. 2009. “Supply and demand elasticities in the U.S. 

ethanol fuel market,” Energy Economics, Vol. 31: 403-410.

Medlock III. Kenneth B. 2012. “U.S. LNG Exports: Truth and Consequence,” James A. Baker 

III Institute for Public Policy, Rice University.
29

Medlock III , Kenneth B., Amy Meyers Jaffe, and Peter R. Hartley. 2011. “Shale Gas and U.S. 

National Security”, Energy Forum, James A. Baker III Institute for Public Policy, Rice 

University.

Mohring, Herbert. 1993. “Maximizing, Measuring, and Not Double Counting TransportationImprovement Benefits: A Primer on Closed- and Open-Economy Cost-Benefit Analysis,” 

Transportation Research Part B. Vol. 27B, No. 6: 413-424.

Newell Richard G. and William A. Pizer. 2008. “Carbon mitigation costs for the commercial 

building sector: Discrete-continuous choice analysis of multifuel energy demand,” 

Resource and Energy Economics, Vol. 30: 527-539.

Personal communication with Stephen P. A. Brown, University of Nevada at Las Vegas, July 

2011.

Serletis, Apostolos, Govinda. R. Timilsina, and Olexandr Vasetsky. 2010. "Interfuel 

substitution in the United States," Energy Economics, Vol. 32: 737-745 

Thurman, Walter N. 1991. “Applied General Equilibrium Welfare Analysis,” American Journal 

of Agricultural Economics. Vol. 73, No. 5.

Thurman, Walter N. and Michael K. Wohlgenant. 1989. “Consistent Estimation of General 

Equilibrium Welfare Effects,” American Journal of Agricultural Economics. Vol. 71, No. 

4.

U.S. Department of Energy. 2011. Annual Energy Review 2011.

U.S. Department of Energy (DOE). 2007. Final Rule: Furnaces and Boilers Technical Support 

Document. 

U.S. Department of Energy, Energy Information Administration. 2009. "The National Energy 

Modeling System: An Overview The Commercial Demand Module," available at 

http://www.eia.gov/oiaf/aeo/overview/commercial.html#equip.

U.S. Department of Energy, Energy Information Administration. 2010. Annual Energy Outlook 

2010.

U.S. Department of Energy, Energy Information Administration. 2014. Annual Energy Review 

2014.

U.S. Department of Energy, Energy Information Administration. 2015. Annual Energy Review 

2015.

U.S. Department of Energy, National Energy Technology Laboratory (NETL). 2007. "Cost and 

Performance Baseline for Fossil Energy Plants: Volume 1: Bituminous Coal and Natural 

Gas to Electricity Final Report".

U.S. Department of Interior, National Park Service. 2008. Potential Development of the Natural 

Gas Resources in the Marcellus Shale New York, Pennsylvania, West Virginia, and Ohio.

U.S. Department of Transportation, Federal Transit Administration. 2007. "Transit Bus Life 

Cycle Cost and Year 2007 Emissions Estimation," available at 

http://www.trb.org/Main/Blurbs/159061.aspx.

US Geologic Survey. 2010. "Advantages of Hydroelectric Power Production and Usage".