Technical Report

NREL/TP-6A2-47807

July 2010

Benefits to the United States of 

Increasing Global Uptake of 

Clean Energy Technologies

David Kline
National Renewable Energy Laboratory

1617 Cole Boulevard, Golden, Colorado 80401-3393

303-275-3000 • www.nrel.gov

NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency and Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

Contract No. DE-AC36-08-GO28308

Technical Report

NREL/TP-6A2-47807

July 2010

Benefits to the United States of 

Increasing Global Uptake of 

Clean Energy Technologies

David Kline

Prepared under Task No. DOCC.1002
NOTICE

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iii

Acknowledgments

A number of people contributed to the development of this paper. Thanks to Rachel Gelman and 

Sarah Busche, National Renewable Energy Laboratory (NREL), for research assistance in data 

collection and literature review. Walter Short, David Hurlbut, and Toby Couture, NREL; Tom 

Lyon, University of Michigan; and Keith Maskus, University of Colorado, provided useful 

comments on earlier drafts. I owe a particularly large debt to Jaquelin Cochran at NREL for very 

thoughtful discussions of the approach and for careful proofing of the algebra. Any errors or 

omissions in this paper remain my own responsibility.
iv

Table of Contents

List of Figures ............................................................................................................................................ vi

List of Tables .............................................................................................................................................. vi

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

1.1 Summary of Results.............................................................................................................1

1.1.1 Background.................................................................................................................1

1.1.2 Overview of Results....................................................................................................1

1.1.3 Sources of Benefits.....................................................................................................2

1.1.4 Key Input Variables....................................................................................................2

1.2 Limitations of this Analysis.................................................................................................3

2 Details of the Methodology.................................................................................................................. 3

2.1 Preliminaries........................................................................................................................3

2.1.1 Overview.....................................................................................................................3

2.1.2 Naming Conventions for Variables ............................................................................4

2.1.3 Scenario Variables......................................................................................................6

2.1.4 Parameters...................................................................................................................7

2.1.5 Uncertainty..................................................................................................................7

2.2 Benefits Arising from Clean Energy Markets .....................................................................8

2.2.1 Impacts of International Initiatives on CET Markets .................................................8

2.2.2 Impacts on CET Producers .........................................................................................8

2.2.3 Impacts of Clean Energy Markets on U.S. Consumers ............................................10

2.2.4 Impact of Increased CET Sales on Balance of Payments.........................................11

2.3 Impacts on World Oil Markets ..........................................................................................11

2.3.1 Impacts of Reduced Oil Price on Domestic Producers.............................................11

2.3.2 Impacts of Lower Oil Prices on U.S. Oil Consumers...............................................13

2.3.3 Impact of Reduced Oil Imports on Balance of Trade ...............................................13

2.4 Economic Benefits of Improved Trade Balance................................................................14

2.4.1 Change in Import Quantity and Price as a Function of Change in the Exchange

Rate ....................................................................................................................................15

2.4.2 Change in Export Quantity and Price as a Function of Change in the Exchange

Rate ....................................................................................................................................17

2.4.3 Induced Change in Exchange Rate ...........................................................................19

2.4.4 Benefits of Exchange Rate Changes to Consumers..................................................19

2.4.5 Impacts of Exchange Rate Change on Producers.....................................................20

2.5 Input Assumptions.............................................................................................................20

2.5.1 Sources for Nominal Values.....................................................................................20

2.5.2 Values for 2020.........................................................................................................22

2.5.3 Values for 2050.........................................................................................................24
v

2.6 Estimating Probability Distributions for Output Variables ...............................................26

2.6.1 Monte Carlo Simulation............................................................................................26

2.6.2 Sensitivity Analysis ..................................................................................................26

2.7 Conclusions........................................................................................................................28

References................................................................................................................................................. 29
vi

List of Figures

Figure 1. Probability distribution of total benefits in 2020..............................................................1

Figure 2. Probability distribution of total benefits in 2050..............................................................2

Figure 3. Influence of oil price changes on total benefits in 2050...................................................3

Figure 4. Change in producer surplus in the market for U.S. clean energy exports........................8

Figure 5. Change in consumer surplus in the market for U.S. clean energy exports.....................10

Figure 6. Change in U.S. oil producer surplus...............................................................................11

Figure 7. Change in consumer surplus in the U.S. oil market .......................................................13

Figure 8. Effect of exchange rate change on supply and U.S. demand for imports ......................15

Figure 9. Effects of the change in the exchange rate on U.S. exports...........................................17

List of Tables

Table 1. 2020 Scenario Variables..................................................................................................22

Table 2. 2020 Economic Parameters .............................................................................................23

Table 3. 2050 Scenario Variables..................................................................................................24

Table 4. 2050 Economic Parameters .............................................................................................25

Table 5. Determinants of Total Benefits in 2020...........................................................................27

Table 6. Determinants of Total Benefits in 2050...........................................................................27
1 

1 Introduction

1.1 Summary of Results 

1.1.1 Background 

The paper “Opportunities for High Impact United States Government International Renewable 

Energy and Energy Efficiency Initiatives” (NREL 2008) describes an opportunity for the United 

States to take leadership in efforts to transform the global energy system toward clean energy 

technologies (CET). An accompanying analysis provides estimates of the economic benefits to 

the United States of such a global transformation on the order of several hundred billion dollars 

per year by 2050. The current paper describes the methods and assumptions used in developing 

those benefit estimates. It begins with a summary of the results of the analysis based on an 

updated and refined model completed since the publication of the report mentioned above1

. 

1.1.2 Overview of Results 

An effective international effort to speed the uptake of clean energy technology would have 

significant benefits to the United States. In 2020, estimated benefits are on the order of $40 

billion per year, and in 2050, benefits range around $260 billion per year. Because the results 

depend on a number of uncertain variables, the analysis explicitly incorporated uncertainty, 

resulting in estimates of the benefits and their key components. Figures 1 and 2 illustrate the 

range of total benefits estimated for 2020 and 2050, respectively. 

Figure 1. Probability distribution of total benefits in 2020 

 

1

 The results shown here show slightly smaller benefits than those presented in NREL et al. (2008), but the policy 

implications remain the same. 
2 

Figure 2. Probability distribution of total benefits in 2050 

1.1.3 Sources of Benefits 

As described in more detail in Section 2, the analysis considers three sources of benefits to the 

U.S. economy of enhanced global clean energy use. In order of importance, the sources are 

reduced oil prices that result from reduced global oil demand, increased U.S. exports of CET, 

and improved terms of trade that result from those two impacts. 

The oil market impacts tend to dominate the total benefits, contributing about 80% of the total in 

2020 and 66% in 2050. Benefits of increased clean energy exports come to about 20% of the 

total in 2020 and 26% of the total in 2050. Terms of trade benefits are near zero in 2020 but 

grow to about 7% of the total in 2050. 

1.1.4 Key Input Variables 

The components described above are determined by a number of input variables. In this model, 

the most important determinant of total benefits is the change in world oil price, and the next 

most important determinant is the cost of U.S. oil imports, which is a scenario variable. In 2050, 

U.S. oil producer revenue and some of the economic parameters that influence the value of the 

U.S. dollar are also important. Figure 3 illustrates the strong influence of the oil price change on 

total benefits in 2050. 
3 

Figure 3. Influence of oil price changes on total benefits in 2050 

Section 2.5 includes further discussion of the sensitivities of the results to key input variables. 

1.2 Limitations of this Analysis 

There are two key limitations to this analysis. The first is the wide uncertainty surrounding the 

input variables. Those parameters include projections of market sizes and other macroeconomic 

variables and behavioral parameters such as demand and supply elasticities. This relatively 

simple model relies on a fairly large number of assumptions, as discussed below under “Scenario 

Variables” and “Parameters.” 

The second limitation is that this analysis is conducted in a partial equilibrium framework. It 

does include estimates for the impacts of enhanced global clean energy technology use on 

domestic oil producers, which are likely to be the most important adverse effects. However, a 

number of other sectors are not included; perhaps most importantly, intermediate energy goods 

such as power plant manufacturers and their supply chains were excluded. A useful extension of 

this work would be to recast it in a computable general equilibrium setting where all economic 

sectors could be examined. 

2 Details of the Methodology 

2.1 Preliminaries 

2.1.1 Overview 

The net benefit model used to estimate benefits considers three sources of economic impacts to 

U.S. producers and consumers: reduced global oil demand, expanded global clean energy 

markets, and the improved balance of payments that flow from the first two impacts. In this 
4 

simplified representation, CETs2

 are modeled as one aggregate good with a single price. 

Similarly, “oil” is treated as a single good. 

Economic benefits are measured as changes in consumer and producer surplus between a 

postulated business-as-usual scenario and the successful CET initiative scenario. Those 

scenarios are described in Section 2.5. 

2.1.2 Naming Conventions for Variables 

This paper uses the following naming conventions for variables: 

2.1.2.1 Quantity and prices in U.S. goods markets 

P and Q are price and quantity variables, respectively. P and Q refer to market clearing values. 

Subscripts indicate which goods the prices and quantities refer to. 

Subscript L: Oil. PL represents the price of oil and similarly for other variables. 

A distinction is made between U.S. and world quantities of oil3. 

Subscript LW: World oil. QLW represents world oil quantity 

Subscript LU: U.S. oil. QLU represents U.S. oil quantity. 

SLU denotes quantity of oil supplied by U.S. producers. 

2.1.2.2 Imports and Exports 

The discussion uses the following import and export variables: 

QXT: Quantity of total U.S. exports in physical units. 

QIT: Quantity of total U.S. imports in physical units. 

QXR: Quantity of U.S. clean energy exports in physical units. (R is a mnemonic for “renewable 

energy and energy efficiency.”) 

QIL: Quantity of U.S. oil imports in physical units. 

Prices of imports and exports are sometimes expressed in dollars and sometimes in terms of a 

basket of foreign currencies denoted by a second subscript $ or F. For example, the price of U.S. 

imports in foreign currency and dollars is written as PIF and PI$, respectively. 

Exchange rates give the conversion rate between prices in dollars and foreign currency. In most 

cases, the exchange used is E, the value of the U.S. dollar expressed in terms of a basket of 

foreign currencies (F). In price-quantity graphs, the price axis will be labeled “E (F/$),” where 

“F/$” indicates the units mentioned above. So we can write, for example, PIF = EPI$. 

 

2

 The model is not intended to include nuclear energy technologies as part of “clean energy,” and the input 

assumptions were derived by looking at statistics that do not include nuclear. 

3 L is used as a subscript for oil rather than O, which could be mistaken for a zero implying an initial value. 
5 

In some cases, the exposition will be clearer if the exchange rate is expressed as the value of the 

basket of foreign currencies in U.S. dollars, G =1/E. That axis will be labeled “G ($/F).” 

The presentation also relies on the corresponding dollar values of import and export quantities: 

XT: Value of total U.S. exports, considered as a single aggregate good. 

IT: Value of total U.S. imports, considered as a single aggregate good. 

IL: Value of U.S. oil imports. 

XR: Value of U.S. CET exports. 

X and I are in units of dollar values, so that, for example, XT = PX$QXT

2.1.2.3 Producer and Consumer Surplus 

CS: Consumer surplus in the market indicated by the subscript. CSL represents consumer surplus 

in the market for oil products and similarly for other subscripts. 

PS: Producer surplus in the market indicated by the subscript.

2.1.2.4 Elasticities 

ε: Price elasticity of demand for the good. For example, εL represents the price elasticity of U.S. 

demand for oil. 

β: Price elasticity of supply for the good indicated by the subscript. 

2.1.2.5 Difference Operators 

This analysis derives estimates of the change in relevant metrics (e.g., CS and PS) between a 

baseline (ex ante) value and the value in a case including the program or initiative under 

consideration (the “ex post” value). 

Baseline values for variables are indicated by a “hat.” For example, PL

ˆ is the baseline world oil 

price. 

The operator Δ(·) gives the change in the variable between the ex post and ex ante values. For 

example ΔPL is the difference between ex post and ex ante world oil price. 

 
6 

More frequently, the changes are expressed in proportional terms, denoted by the difference 

operator δ(·). For example, δPL is the proportional change in world oil price, or 

L

L

L P

P P ˆΔ δ =

2.1.3 Scenario Variables 

As described in more detail in Section 2.5, the independent variables for our model are derived 

from scenarios that describe a global initiative to significantly reduce greenhouse gas (GHG) 

emissions by the year 2050. Accordingly, these variables are often referred to collectively as 

“scenario variables.” The nominal values of the scenario variables are chosen as representatives 

of the recently published estimates of CET penetrations needed to support 50%-80% reductions 

in GHG emissions by 2050. The scenario variables are the following: 

δQL Proportional change in world oil consumption. 

δPR Proportional change in the world price of the aggregate good CET. 

δPL Proportional change in world oil price4. 

QP LULˆ ˆ Baseline U.S. oil import bill5

. 

LU

IL

Q

Q

ˆ

ˆ

 Ratio of U.S. oil imports to U.S. oil consumption (“dependence ratio”)6. 

δXR Proportional change in dollar value of U.S. CET exports. 

QP RURˆ ˆ Baseline U.S. expenditures on CET. 

QP XRRˆ ˆ Baseline value of U.S. CET exports. 

XI TT + ˆˆ Baseline dollar value of U.S. total trade. 

TT

TT

XI

XI D ˆˆ

ˆˆ

+

− = Trade balance as a fraction of total trade. 

 

4 δPL is a function of δQL as described in Eq. [5]. It is included as a separate scenario variable because it is modeled 

as random, as described in Section 2.5. See note to Table 1. 

5

 For U.S. oil imports, CET expenditures, and CET exports, prices and quantities need not be forecast separately 

because the model only requires their product. Results are expressed in terms of those products and the proportional 

changes in prices and quantities. That approach is particularly important for the aggregate good CET because it 

avoids the problem of constructing a price index scheme. 

6

 In this model, neither the physical quantity of oil consumed in the U.S. nor the price of oil are used explicitly. The 

model relies only on the value of U.S. oil imports, which is the product of the two. In order to estimate the impact 

of oil price changes on imports, the model also uses an exogenous value for the dependence ratio defined here. 
7 

FI Value of U.S. imports as a fraction of global trade in the import good. 

FX Value of U.S. exports as a fraction of global exports. 

2.1.4 Parameters 

In addition to the scenario variables, the model uses economic parameters that describe producer 

and consumer behavior for the relevant markets. Parameters include price elasticities of supply 

and demand. 

ε represents price elasticity of demand, qualified by the following subscripts: 

RU ε Price elasticity of U.S. demand for CETs.

LU ε Price elasticity of U.S. demand for oil. 

I ε Price elasticity of U.S. demand for imports. 

X ε Price elasticity of demand overseas for U.S. exports. 

β represents price elasticity of supply, qualified by the following subscripts: 

β LU Price elasticity of U.S. oil supply. 

β LW Price elasticity of world oil supply. 

β R Price elasticity of U.S. CET supply. 

β X Price elasticity of total U.S. exports supply. 

β I Price elasticity of total U.S. imports supply.

2.1.5 Uncertainty 

The scenario variables and parameters can only be very imperfectly estimated. One source of 

uncertainty comes from forecasts of variables such as U.S. expenditures on oil imports. Other 

uncertain inputs are associated with behavioral parameters, e.g., the elasticity of demand for U.S. 

oil imports. To account for these uncertainties, the analysis defines probability distributions for 

both scenario variables and parameters. Those distributions are then used to estimate probability 

distributions for the outputs using Monte Carlo simulation. The discussion that follows first 

describes the deterministic model and then describes the input parameters and their probability 

distributions in Section 2.5. 
8 

2.2 Benefits Arising from Clean Energy Markets 

2.2.1 Impacts of International Initiatives on CET Markets 

U.S.-led CET efforts are expected to have two impacts that will benefit the U.S. economy. First 

and probably most importantly, those efforts will increase the global demand for CETs by 

addressing market barriers and providing technical assistance in the design of CET policies and 

programs. U.S. programs will also include trade-related efforts that will enable U.S. producers 

to gain market share. Increased CET exports from the United States are one of the two primary 

drivers of U.S. economic benefits. 

The second impact of CET initiatives in this market will be on CET prices. The direction of this 

effect is equivocal. Increased demand will increase market clearing prices if the supply curve 

does not change. However, since U.S. CET industries are expected to be involved in the overall 

international efforts, their supply capability is expected to increase in response to their increased 

export opportunities. 

2.2.2 Impacts on CET Producers 

Figure 4. Change in producer surplus in the market for U.S. clean energy exports 

We use the change in producer surplus as the measure of impacts on U.S. CET producers. 

Figure 4 illustrates the ex ante and ex post producer surplus. Next, we describe the difference 

between ex post and ex ante producer surplus. 

CET export revenue ܺ෠

ோ ൌ ܲ෠ோܳ෠௑ோ and the proportional changes δPR and δXR are scenario 

variables. We would expect dramatically larger world demand to increase overseas demand for 

U.S. clean energy exports, increasing QXR . The sign of δPR depends on how U.S. industry 

responds to the increased demand. For small increases in QR, it may be reasonable to assume 

that U.S. industry expands enough to keep PR approximately constant. If the program under 

consideration includes a large component of cooperative research and development, PR could 
9 

decrease. If demand increases faster than the industry expands, prices could increase. As 

described in Section 2.5, the analysis considers a range of values for δPR. 

The change in producer surplus is given by the difference between Areas A and B in Figure 4. 

Area A is the producer surplus when market clearing values are given by PR

ˆ and QXR

ˆ . By 

assumption, U.S. supply of CET is represented by a constant elasticity supply curve, which is 

given by 

R

R

XR XR P

p QpQ

β















 = ˆ ˆ )(

The running variable p represents clean energy price. Area A is given by 















 = R R

R

P

R

XR dpp P

Q A ˆ

ˆ 0

ˆ β

β

 which on carrying out the integration and simplifying gives 

1

ˆ ˆ

+ =

R

QP XRR A

β 

Similarly, Area B is given by 

( )( )

1

ˆ ˆ

+

Δ+Δ+ =

R

R XRR QQPP XR B

β

The difference between the two is then 

1

ˆ ˆ

+

ΔΔ+Δ+Δ =Δ

R

XRR XRR XRR

R

QPQPQP PS

β [1] 

Equation [1] expresses ΔPSR in terms of scenario variables and parameters and is used in the 

benefits calculations. Note that the numerator is simply the change in the value of U.S. CET 

exports, Δ(PRQR). 
10 

2.2.3 Impacts of Clean Energy Markets on U.S. Consumers 

Figure 5. Change in consumer surplus in the market for U.S. clean energy exports 

Since we are trying to isolate the impacts of U.S. leadership internationally, we do not include 

any impacts of that program on the domestic demand for CET. Accordingly, the estimated 

impact on U.S. CET consumers depends only on the change in price and on baseline CET 

expenditures. The consumer surplus impact is illustrated in Figure 5 and can be found by 

integrating under the U.S. CET demand curve QRU (p). 

 Δ+

=Δ R

R

P

PP

CSRU RU dppQ ˆ

ˆ )( [2] 

The assumed constant elasticity demand curve can be written 

RU

R

RU RU P

p QpQ

ε















 = ˆ ˆ )(

which substituted into Eq. [2] gives 

 Δ+















=Δ R

RR

RU

RU

P

PP

R

RU

RU dpp P

Q CS ˆ

ˆ ˆ

ˆ ε

ε 

Carrying out the integration gives 

















+

Δ+−















 =Δ

+ +

1

) ˆ ( ˆ

ˆ

ˆ 1

1

RU

R RR

R

RU

RU

RU

RU

RU

PPP

P

Q CS

ε

ε ε

ε

 
11 

which can be simplified to 





 





+

+− =Δ

+

1

)1(1 ˆ ˆ 1

RU

R

RURRU

RU P QPCS

ε

δ ε

 [3] 

2.2.4 Impact of Increased CET Sales on Balance of Payments 

The balance of trade impact of the increase in CET exports can also be visualized in Figure 4. 

That impact is the difference between ex post and ex ante CET export revenue. Since both XT

ˆ

and δXR are scenario variables, we can express the change in the value of exports as 

ΔXR= XT

ˆ · δXR [4] 

2.3 Impacts on World Oil Markets 

Global cooperation on CET technologies will include fuel substitution and efficiency 

technologies that will reduce global oil demand. Reduced demand will tend to reduce world oil 

prices relative to baseline values. U.S. oil consumers will benefit. Although U.S. oil producers 

will see lower revenues, those losses will be more than offset by gains to consumers7

. 

2.3.1 Impacts of Reduced Oil Price on Domestic Producers 

Figure 6. Change in U.S. oil producer surplus 

Figure 6 illustrates the effect of reduced oil prices on domestic oil producers. To quantify those 

impacts, we first find the change in world oil price that results from the given change in world oil 

 

7

 The impact of changes in oil demand on oil price is modeled simply using the elasticity of supply for world oil. 

The nominal value for world supply elasticity is +2 with a large uncertainty range as described in Section 2.5. The 

nominal value is judged to be high in order to give a conservative estimate of the impact on world oil prices. (In 

contrast, U.S. supply is assumed to be relatively inelastic with a supply elasticity in the range of 0.3.) In general, the 

benefits to oil consumers will be larger than costs to domestic producers as long as oil is imported. 
12 

demand δQLW. Assuming that the world supply curve has constant elasticity βLW, the inverse 

supply curve can be written8

LW

LW

L L

Q

q PqP /1 β ) ˆ ( ˆ )( = 

Setting LW Δ+= QQq LW ˆ gives 

( ) LW

LW

L LW

LW

LW LW

LLL QP Q

QQ PPP β

β

δ /1

/1

1 ˆ ˆ

ˆ ˆˆ  +=



 



 Δ+ =Δ+

Subtracting PL

ˆ from both sides and dividing through by PLˆ yields 

( ) 1 1 /1 += − LW

L QP LW

β δδ [5] 

The impact of reduced oil prices on producer surplus can be calculated by integrating under the 

U.S. oil supply curve, 

 Δ+

=Δ

PP

P RU LU

R

R

PS dppS ˆ

ˆ )(

The supply curve is assumed to have constant elasticity βLU so that 

LU

L

LU LU P

p SpS

β















 = ˆ ˆ )(

Carrying out the integration and simplifying gives 

( ) 



 





+

+ − =Δ

+

1

1 1 ˆ ˆ 1

LU

L

LLL

LU P QPPS β

δ β

 [6] 

Baseline U.S. oil producer revenue, ˆ

LQP ˆ , can be found from the baseline import bill and the 

dependence ratio, which are both scenario variables. 

 

8

 The complexities of the world oil market make the concept of an “oil supply curve” a questionable concept. As 

explained in Section 2.5, treating the elasticity of supply as a random variable is our way of accounting for the fact 

that the supply-price relationship is known only imperfectly. 
13 

2.3.2 Impacts of Lower Oil Prices on U.S. Oil Consumers 

Figure 7. Change in consumer surplus in the U.S. oil market 

The change in consumer surplus arising from lower oil prices is shown as Area A in Figure 7, 

which can be written in terms of scenario variables as 

( ) 



 





+

+− =Δ

+

1

11 ˆ ˆ 1

LU

L

LU LULU

LU P QPCS

ε

δ ε

 [7] 

The derivation is exactly analogous to the one for Eq. [3]. 

2.3.3 Impact of Reduced Oil Imports on Balance of Trade 

Reduced oil imports improve the balance of trade. The change can be written in terms of the 

initial oil import bill and the proportional changes in oil price and oil imports.

To find the change in oil imports, first write U.S. imports as the difference between domestic 

consumption and domestic supply: 

IL LU LU −= SQQ

This implies 

IL LU LU Δ−Δ=Δ SQQ

 
14 

From the respective demand and supply curves for U.S. oil, we can write 

[( ) 1 1] ˆ −+=Δ LU

LU LU PQQ L

ε δ and 

[( ) 1 1] ˆ −+=Δ LU

LU LU PSS L

β δ

Subtracting the two previous expressions and dividing through by ܳ෠ூ௅ gives 

[ ] ( ) [ ] ( ) 1 1 ˆ

ˆ 1 1 ˆˆ −+−−+= LU LU

L

IL

LU

L

IL

LU

IL P

Q

S P

Q

Q Q ε β δ δ δ

Recall that ܳ෠ூ௅/ܳ෠௅௎ is a scenario variable, and note that ܵመ

መܫ/௎௅

௅௎ can be derived from it using 

ܳ෠௅௎ ൌ ܵመ

௅௎ ൅ ܳ෠ூ௅. Thus, the previous expression gives δQIL in terms of scenario variables. 

Knowing δQIL, we can write the balance change in payments induced by reduced oil imports as 

( ) ILL =Δ≡Δ LILLLU ++ δδδδ QPQPQPIQP ILLIL ˆ ˆ )( [8] 

2.4 Economic Benefits of Improved Trade Balance 

The impact of the improved trade balance operates through the change in exchange rates. The 

fundamental assumption used here is that exchange rates move so as to maintain the ex ante 

value of the trade balance. That is, the balance of payments change induced by the exchange rate 

adjustment will just offset the balance of trade changes in the oil and CET markets given in Eqs. 

[4] and [8]. 

In Section 2.4.1 we solve for the change in total imports as a function of changes in the exchange 

rate. Section 2.4.2 repeats the process for exports. Section 2.4.3 uses those results to solve for 

the exchange rate that meets the balance of trade condition given in the paragraph above. The 

trade balance is shown as zero in Figures 8 and 9, but the model treats the trade balance as an 

uncertain variable with a wide range (see Section 2.5). 
15 

2.4.1 Change in Import Quantity and Price as a Function of Change in the 

Exchange Rate 

Figure 8. Effect of exchange rate change on supply and U.S. demand for imports 

Figure 8 shows the supply of imports to the United States as a function of PIF and the demand as 

a function of PI$. A proportional change in the indirect exchange rate of δE functions like a 

subsidy on imports, lowering PI$ and raising PIF. 

Assuming that the price of the basket of imported goods will be the same regardless of where 

they are purchased (“purchasing power parity,” or PPP), we can write 

















Δ+

Δ+ =Δ+

$ $ ˆ

ˆ ˆ

I I

IF IF

T T

PP

PP EE

Multiplying by EP

P

IF

I

ˆ

1

ˆ

ˆ $ = yields 

1 $

1 1

I

IF

T P

P E

δ

δ δ

+

+

=+ [9] 

 

Since the changes in prices and the exchange rates are small,9

 we can use a linear approximation 

based on a constant elasticity demand curve to approximate the change in U.S. import quantity as 

δPQ IIIT $ δ ≈ ε [10] 

U.S. consumption of imports and the total world supply of the import good QIW are related by

 

9

 Numerical experiments confirm that price and exchange rates change by at most a few percent. 
16 

QFQ IWIIT

ˆˆ = 

To move from the ex ante to the ex post state shown in Figure 8 requires that 

IT Δ=Δ QQ IW 

which implies that

δ δQFQ IWIIT

−1 = [11]

The linear approximation to the world supply curve for the import good (priced in foreign 

currency) gives 

δ IW = βδPQ IF [12] 

Equations [10]-[12] can be combined to eliminate the quantity variables and solve for PIF to give 

$

1 δεβδ PFP IIIIIF

− =

 [13] 

This equation can be combined with Eq. [9] to give 

$

$

1

1

1

1

I

IIII

T P

PF

E

δ

δεβ δ

+

+

=+

−

 

which leads to10 

$

1

$$ 1 1 δδδδ TIIT δεβ PFEPPE IIII

− +=+++ 

Dropping the second order term and solving for δPI$ gives 

III

IT

I F

E P

βε

βδ δ − $ =

 [14]

 

 

10 In multiplying by the numerator, we can be confident it is nonzero. It could only become zero if PI$ has gone to 

zero, which can be ruled out. 
17 

which can be combined with Eq. [10] to give 

III

II

IT F

E Q βεβεδ δ − =

 [15] 

Using the linear approximation 

δ $ ITI δ I $ +≈ δQPQP I )( 

we can add Eqs. [14] and [15] to write the change in imports, ΔIT ≡ Δ(PI$QI), as 

III

II

TT F

EII

βε

εβ δ −+ ≈Δ )1( ˆ

 [16] 

Equation [16] gives the change in the value of imports as a function of changes in the exchange 

rate. Equation [14] gives the change in price as a function of the change in exchange rates. 

2.4.2 Change in Export Quantity and Price as a Function of Change in the 

Exchange Rate 

The derivation of the change in exports proceeds similarly to that for imports, as illustrated in 

Figure 9. 

Figure 9. Effects of the change in the exchange rate on U.S. exports 

As illustrated in Figure 9, the change in exchange rates functions like a tax on exports. The 

change in the exchange rate induces both a decrease in the overseas price PF and an increase in 

the dollar price P$. 
18 

As in Section 2.4.1, PPP implies 

 

$ 1

1 1

X

XF

T P

P E

δ

δ δ

+

+

=+ [17] 

The linear approximation to the supply condition for U.S .exports is 

δ XT ≈ β δPQ XX $ [18] 

and the demand curve gives 

QXW δPXFX δ ≈ ε

Similarly to Eqs. [11] and [13] for imports, we have 

δδ QFQ XWIXT

−1 = [19]

and 

$

1

δεδ PFP XXXXF

− = [20] 

Substituting from Eq. [20] into Eq. [17], dropping second order terms and solving for δPX$ gives 

( ) $

XXX

X

X T F

EP

εβ

ε δδ − ≈

 

[21]

which together with Eq. [18] gives

( ) XXX

XX

XT T F EQ εβ

ε β δδ − ≈

Using the linear approximation 

δ $ XTX δ XI $ +≈ δQPQP XT )( 

we can then write 

XXX

XX

TT F

EXX

εβ

βε δ −+ ≈Δ )1( ˆ

 [22] 
19 

2.4.3 Induced Change in Exchange Rate 

For compactness, rewrite [16] as 

EIaI TT δ ≈Δ ˆ

and [22] as 

T ≈Δ ˆTδEXbX

with a and b having the appropriate values as given in Eqs. [14] and [17]. The equilibrium 

assumption is that the balance of payments change given by the difference between those two 

expressions just offset the balance of payments changes in the CET and oil markets, i.e., 

T T R LU δ ( ˆˆ ) Δ−Δ=− IXXbIaE

which immediately gives the required change in exchange rate as 

T T

R LU

XbIa

IX E − ˆˆ

Δ−Δ δ =

 [23] 

Note that in the model, XT

ˆ and TIˆ are computed from total trade and the trade deficit, which are 

scenario variables. The constants a and b are functions of other scenario variables. 

2.4.4 Benefits of Exchange Rate Changes to Consumers 

The derivation of the change in consumer surplus in the market for U.S. imports proceeds 

analogously to the derivation of Eq. [3]. We can write 

 Δ+

=Δ $

$$

ˆ

ˆ )( I

II

P

PP

CSI IT dppQ

Representing the U.S. demand for imports with a constant elasticity demand function, we can 

write 

LU

I

IT IT P

p QpQ

ε















 =

$ ˆ ˆ ( )

Following the steps in the derivation in Section 2.2.3 leads to the analogous result 

( )

















+

+−

=Δ

+

1

11 ˆ 1

$

I

I

TI

I P

ICS

ε

δ ε

 [24] 

δPI$ is found from δE using Eq. [14]. 
20 

2.4.5 Impacts of Exchange Rate Change on Producers 

Similarly, the change in producer surplus can be found by carrying out the integration 

 Δ+

=Δ $$

$

ˆ

ˆ )( XX

X

PP

P

PS X XT dppQ

Representing exports with a constant elasticity demand function, we can write 

( ) X

X

p

P

Q pQ

X

XT

XT

β

β 















=

$ ˆ

ˆ )(

Substituting into the expression for ΔPSX, carrying out the integration, and simplifying gives 

( )

















+

+ − =Δ

+

1

1 1 ˆ 1

$

X

X

TX

X P XPS

β

δ β

 [25] 

where δPX$ is found from δE using Eq. [21]. 

2.5 Input Assumptions 

This section describes the way that input values were characterized in this analysis. Section 

2.5.1 describes the sources for nominal parameter values for scenario variables and parameters. 

Sections 2.5.2 and 2.5.3 describe the probability distributions used to represent the uncertainty in 

the input variable estimates. Section 2.6 describes the methods used to calculate probability 

distributions for the dependent variables in the model. 

2.5.1 Sources for Nominal Values 

Baseline assumptions for the parameters and scenario variables used in this analysis are based on 

a number of published projections. The approach was to use baseline values in line with 

published projections and use wide uncertainty ranges as described in Sections 2.5.2 and 2.5.3. 

Projections of the size of the clean energy markets are based on the “Blue Map” scenario in 

Energy Technology Perspectives (IEA 2008). This scenario will be referred to here as “Blue 

Map”. U.S. clean energy businesses are assumed to capture 10% of the increase in CET 

expenditures implied by the Blue Map scenario. Increased U.S. imports of CET are assumed to 

offset half of those increased exports so that net U.S. exports amount to 5% of the increased CET 

market. 

Oil market projections also make use of the IEA (2008) work, setting baseline assumptions 

figures at a 25% reduction in global oil use in 2020 and 60% reduction in 2050. Oil price 

reductions are taken to be half the percentage reduction in consumption which is intended as a 
21 

conservative estimate of a highly uncertain market response to those demand reductions11. 

Baseline projections of U.S. oil consumption and U.S. oil producer revenue are extrapolated 

from the Energy Information Administration’s (EIA) Annual Energy Outlook (EIA 2008b). 

Other scenario variables include baseline U.S. exports and imports. Baseline assumptions are 

based first on a projection of U.S. GDP, which assumes 2.4% annual growth from 2006 based on 

EIA projections to 2030. Total trade (exports plus imports) is assumed to be 30% of U.S. GDP 

based on recent trends for that ratio according to data from the United States International Trade 

Commission (USITC). 

Baseline renewable energy export figures were also developed from USITC (2005) data, which 

provided an estimate for that year. Energy efficiency markets are difficult to estimate because of 

their diversity and the ambiguity in their definition. Energy efficiency exports were assumed to 

be of the same order of magnitude as renewable energy exports. U.S. consumer investments in 

CET were estimated as 1% of U.S. energy expenditures in 2020 and 2% in 2050. 

In addition to these scenario variables, the analysis relies on economic parameters such as 

elasticities. Very little empirical data were found on which to base estimates for those values. 

Those values must be treated as very uncertain. Likewise, the projections of market conditions 

10 and 40 years in the future are also quite uncertain. 

Accordingly, we have used wide ranges for both the scenario variables and the input parameters. 

The remainder of Section 2.5 describes the nominal values and ranges for the input parameters. 

Section 2.6 describes how those ranges were used to develop ranges for the benefit estimates. 

In the tables that follow, the following notation is used for probability density functions: 

T(a,b,c) Triangular distribution with lower value a, maximum likelihood at b, and upper 

value c. a ≤ b ≤ c 

N(m,s) Normal distribution with mean m and standard deviation

 

11 The response of oil prices to exogenous changes in oil demand has been a subject of vigorous debate since the first 

oil shock of 1973, with a number of competing models of the behavior of the OPEC cartel and other players. 

Estimating the price response to be half the consumption change corresponds to an assumption that OPEC will 

respond aggressively by reducing output dramatically in response to reduced prices. This estimate was chosen in 

order to estimate a price reduction that would be on the low end of reasonable projections. 

s
22 

2.5.2 Values for 2020 

Table 1. 2020 Scenario Variables 

Parameter Name Units Nominal 

Value 

Probability Distribution 

So

Proportional change in world oil consumption 

δQL Dimensionless -15% T(-22.5%, -15%, 

-7.5%) 

IEA 

Proportional change in world oil price 

δPR Dimensionless -7.8% Function of change 

in world oil consumption 

See 

Baseline U.S. oil import bill QP LULˆ ˆ $billion per 

year 

125 T(100, 125, 150) EIA 

Ratio of U.S. oil imports to U.S. oil consumption (“dependence ratio”) LU

IL

Q

Q

ˆ

ˆ Dimensionless 75% T(60%, 70%, 90%) EIA 

Proportional change in U.S. clean technology exports 

δXR Dimensionless 32% N(32%, 16%) IEA 

Base U.S. expenditures on CET QP XRRˆ ˆ $billion per 

year 

16 T(8, 16, 24) US

Baseline value of U.S. CET Exports QP XRR

ˆ ˆ $billion per 

year 

100 T(50, 100, 150) Ro

US

Dollar value of total U.S. trade (imports + exports) 

XI TT +

ˆˆ $billion per 

year 

4750 T(3325, 4750, 6175) GD

(20tra

Trade deficit as a fraction of total trade 

TT

TT

XI

XI D ˆˆ

ˆˆ

+

−

= Dimensionless 

-10% N(10%, 10%) As

impval

Value of U.S. imports as a fraction of global imports 

FI Dimensionless 13% T(10%, 13%, 16%) WT

Value of U.S. imports as a fraction of global imports 

FX Dimensionless 10% T(8%, 10%, 12%) WT

12 δPL is calculated as a random function of its baseline value as follows. First, a baseline value is calculated from the value for δQ

randomness is introduced by making δPL a normally distributed random variable with mean value given by the results of Eq. [5] and 

equal to half the mean. 
23 

Table 2. 2020 Economic Parameters 

Parameter Name Units Nominal 

Value 

Probability Distribution 

Price elasticity of U.S. demand for CETs RU ε Dimensionless -.6 T(-01, -0.6, -0.2) Price elasticity of U.S. demand for oil LU ε Dimensionless -.5 T(-0.6, -0.5, -0.4) Price elasticity of U.S. demand for total imports I ε Dimensionless -.99 T(-1.5, -0.99, -0.5Price elasticity of demand overseas for U.S. exports 

X ε Dimensionless -.8 T(-.1.2, -0.8, -0.4) 

Price elasticity of U.S. supply of oil β lu Dimensionless 0.3 T(0.15, 0.3, 0.45) Price elasticity of world oil supply β lw Dimensionless 2 T(1, 2, 3) Price elasticity of U.S. CET supply β R Dimensionless 0.8 T(0.5, 0.8, 1.05) Price elasticity of supply of total U.S. exports β X Dimensionless 0.8 T(0.5, 0.8, 1.05) Price elasticity of supply of imports to the United States

β I Dimensionless 0.8 T(0.5, 0.8, 1.05) 

13 This high value for the supply elasticity of world oil was chosen in order to produce a conservative estimate of the potential world 

response of world oil prices to exogenous changes in demand is a complex phenomenon that has been the subject of a large literaturuse a conservative elasticity to generate the mean of the constant-elasticity representation of Eq. [5] and used a wide range around thin note Section 2.5. 
24 

Values for 2050 

Table 3. 2050 Scenario Variables 

Parameter Name Units Nominal 

Value 

Probability Distribution 

Proportional change in world oil consumption 

δQL Dimensionless -37.5% T(18.8%, 37.5%, 

56.3%) 

Proportional change in world oil price 

δPR Dimensionless -20.9% Function of change 

world oil consumpti

Baseline U.S. oil import bill QP LULˆ ˆ $billion per 

year 

850 T(425, 850, 1275) 

Ratio of U.S. oil imports to U.S. oil consumption (“dependence ratio”) LU

IL

Q

Q

ˆ

ˆ Dimensionless 0.8 T(0.7, 0.8, 0.85) 

Proportional change in U.S. CET exports 

δXR Dimensionless 80% T(20%, 80%, 140%

Base U.S. expenditures on CET QP XRRˆ ˆ $billion per 

year 

65 T(33, 65, 98) 

Baseline value of U.S. CET exports 

QP XRR

ˆ ˆ $billion per 

year 

150 T(75, 150, 225) 

Dollar value of total U.S. trade (imports + exports) 

XI TT +

ˆˆ $billion per 

year 

9760 T(6830, 9760, 1270

Trade deficit as a fraction of total trade 

TT

TT

XI

XI D ˆˆ

ˆˆ

+

−

= Dimensionless -10% N(-10%, 10%) 

Value of U.S. imports as a fraction of global imports 

FI Dimensionless 10% T(7%, 10%, 13%) 

Value of U.S. imports as a fraction of global imports 

FX Dimensionless 8% T(5.6%, 8%, 10.4%
25 

Table 4. 2050 Economic Parameters 

Parameter Name Units Nominal 

Value 

Probability Distribution 

Price elasticity of U.S. demand for CETs

RU ε Dimensionless -0.6 T(-0.99, -0.6, -0.2) 

Price elasticity of U.S. demand for oil

LU ε Dimensionless -0.5 T(-0.75, -0.5, -0.25) 

Price elasticity of U.S. demand for total imports 

I ε Dimensionless -0.99 T(-1.5, -1, -0.5) 

Price elasticity of demand overseas for U.S. exports 

X ε Dimensionless -0.8 T(-1.2, -0.8, -0.4) 

Price elasticity of U.S. supply of oil

β lu Dimensionless 0.3 T(0.15, 0.3, 0.45) 

Price elasticity of world oil supply β lw Dimensionless 2 T(1, 2, 3) Price elasticity of U.S. CET supply β R Dimensionless 0.8 T(0.5, 0.8, 1.05) Price elasticity of supply of total U.S. exports

β X Dimensionless 0.8 T(0.5, 0.8, 1.05) 

Price elasticity of supply of imports to the United States

β I Dimensionless 0.8 T(0.5, 0.8, 1.05) 
26 

2.6 Estimating Probability Distributions for Output Variables 

2.6.1 Monte Carlo Simulation 

In developing the uncertainty estimates for the benefits estimates, the uncertainty ranges 

described in Tables1-4 are used as probability distributions. The functions in those tables 

express estimates of the likelihood that the input parameters lie in a given range. 

Given those estimates, the distributions of the output values were calculated by Monte 

Carlo simulation. The model described in Sections 2.1-2.5 was implemented in an Excel 

spreadsheet and the Monte Carlo simulation was implemented using the Excel add-in 

@Risk 5.5. The resulting distributions for total benefits were shown in Figures 1 and 2. 

2.6.2 Sensitivity Analysis 

The Monte Carlo simulation enables sensitivity analysis that identifies which inputs have 

the most impact in determining the value of each output variable of interest. Figure 3 

illustrates the sensitivity of the total benefits in 2050 to the change in world oil prices. 

An analysis of the most important determinants of total benefits for 2020 is shown in 

Table 5. 

Table 5 shows the normalized regression coefficients for the output variable representing 

total benefits in 202014. The two strongest determinants of the overall benefits are the 

change in world oil prices and baseline U.S. oil import cost. Even though the benefits 

derived from the CET market are a relatively small fraction of the total, the growth in 

clean energy exports also has a significant impact. The trade surplus, which influences 

the terms of trade benefit, also has a measurable influence. All the remaining variables 

are estimated to have normalized impacts of less than 10% of one standard deviation in 

the total benefits. For reference, the standard deviation of total benefits in 2020 is about 

$24 billion per year. 

 

14 The normalized coefficients in Tables 5 and 6 give the regression estimate of the impact on the output 

variable, measured in standard deviations, induced by a one-standard deviation change in the input variable. 
27 

Table 5. Determinants of Total Benefits in 2020 

Rank Name 

Regression 

Coefficient

Correlation 

Coefficient 

1 Fractional change in oil price -0.859 -0.836 

2 Oil import bill 0.374 0.371 

3 Growth in CET exports 0.213 0.229 

4 Fractional trade surplus -0.152 -0.178 

5 Change in CET price 0.044 0.066 

6 Demand elasticity of imports -0.041 -0.017 

7 U.S. oil producer revenue -0.040 -0.014 

8 Supply elasticity of CET exports -0.020 -0.05 

Table 6 presents the corresponding analysis of total benefits in 2050. 

Table 6. Determinants of Total Benefits in 2050 

Rank Name 

Regression 

Coefficient

Correlation 

Coefficient 

1 Fractional change in oil price -0.855 -0.887 

2 Oil import bill 0.291 0.308 

3 Fractional trade surplus -0.186 -0.218 

4 U.S. oil producer revenue 0.120 0.151 

5 Change in CET price 0.054 0.074 

6 Demand elasticity of imports -0.054 -0.055 

7 Growth in CET exports 0.048 0.041 

The two most important determinants of total benefits in 2050 are the same as for those in 

2020: change in oil price and baseline oil import bill. Next in importance are the balance 

of trade, baseline revenue for U.S. CET producers, change in CET price, and elasticity of 

demand for imports. Other input variables have less than 5% impact in terms of standard 

deviations of total benefits. (The standard deviation of total benefits in 2050 is $150 

billion per year.) 

2
28 

2.7 Conclusions 

The framework described above can be used to estimate the economic benefits to the 

United States of coordinated global action to increase the uptake of CETs worldwide. 

Together with a Monte Carlo simulation engine, the framework can be used to develop 

plausible ranges for benefits, taking into account the large uncertainty in the driving 

variables and economic parameters. The resulting estimates illustrate that larger global 

clean energy markets offer significant opportunities to the U.S. economy. 

This analysis also leaves several important questions unanswered. Some of the technical 

ones have been mentioned in Section 2.2. More importantly, this analysis does not 

address the determinants of whether and how U.S. businesses can capture a significant 

share of growing global clean energy markets. Further work in that area would refine our 

understanding of the plausible range of future U.S. clean energy exports; the estimate 

provided here could be considered conservative. A better understanding of the dynamics 

of those markets will also inform U.S. strategy for maintaining and enhancing its 

competitiveness in this increasingly important global sector. 
29

References

Dahl, C. (June, 2008). Personal E-mail. Colorado School of Mines, Golden, CO. 

Energy Information Administration (EIA). (2008a). “International Energy Outlook 

2008.” U.S. Energy Information Administration. http://www.eia.doe.gov/oiaf/ieo/

EIA. (2008b). “Annual Energy Outlook 2008.” U.S. Energy Information Administration. 

. 

Accessed June 2008. 

http://www.eia.doe.gov/oiaf/aeo/. Accessed June 2008.

International Energy Agency (IEA). (2008). Energy Technology Perspectives. Presented at a 

2008 G8 Summit in Hokkaido/Toyako (Japan). July 2008. 

National Renewable Energy Laboratory (NREL), and World Resources Institute, in 

cooperation with the Center for Strategic and International Studies. (2008). 

Strengthening U.S. Leadership of International Clean Energy Cooperation: Proceedings 

of Stakeholder Consultations. NREL/TP-6A0-44261. Golden, CO: National Renewable

Energy Laboratory.

http://www.iea.org/Textbase/techno/etp/ETP_2008.pdf. Accessed June 2010.

U.S. International Trade Commission (USITC). (October 2005). Renewable Energy 

Services: An Examination of U.S. and Foreign Markets.” Investigation No. 332-462, 

USITC Publication 3805, USITC, Washington D.C. 

World Trade Organization (WTO). 2008. “Statistics Database: United States.”U.S.

http://stat.wto.org/CountryProfile/WSDBCountryPFView.aspx?Language=E&Country=

US. Accessed March 27, 2010.
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Benefits to the United States of Increasing Global Uptake of Clean 

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14. ABSTRACT (Maximum 200 Words)

A previous report describes an opportunity for the United States to take leadership in efforts to transform the global 

energy system toward clean energy technologies (CET). An accompanying analysis to that report provides estimates 

of the economic benefits to the United States of such a global transformation on the order of several hundred billion 

dollars per year by 2050. This report describes the methods and assumptions used in developing those benefit 

estimates. It begins with a summary of the results of the analysis based on an updated and refined model completed 

since the publication of the previous report. The framework described can be used to estimate the economic benefits 

to the U.S. of coordinated global action to increase the uptake of CETs worldwide. Together with a Monte Carlo 

simulation engine, the framework can be used to develop plausible ranges for benefits, taking into account the large 

uncertainty in the driving variables and economic parameters. The resulting estimates illustrate that larger global 

clean energy markets offer significant opportunities to the United States economy. 

15. SUBJECT TERMS

energy policy; clean energy technology; CET; greenhouse gas; GHG; oil; Monte Carlo simulation; global; 

international; market

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