Energy Security Impacts of Renewable 

Fuel Use Under RFS2 -Methodology

Paul N. Leiby

Oak Ridge National Laboratory

Version 1. January 19, 2010

Summary

Increasing usage of renewable fuels helps to reduce U.S. petroleum imports and 

consumption. These reductions can reduce both financial and strategic risks associated with a 

potential disruption in supply or a spike in cost of oil. They also can reduce the sustained cost of 

oil use and imports over the longer term. These reductions in risks and cost improve U.S. energy 

security. This document describes a methodology for estimating the energy security benefits of 

reduced U.S. oil imports and consumption which explicitly includes renewable fuels. Based upon 

this updated approach, we estimate the monetary value of the energy security benefits of the 

RFS2 rule, which requires increased usage of renewable fuels in the U.S. 

Contents:

1. Background on U.S. Energy Security

2. Methodology Used to Estimate U.S. Energy Security Benefits

3. Effect of Oil Use on Long-Run Oil Price, U.S. Import Costs

3.1 Oil Price Effect of Expanded Renewable Fuels Use

3.2 Biofuel Price Effect of Expanded Renewable Fuels Use

4. Impacts of Oil Displacement and Renewable Fuel/FFV Introduction on Demand 

Elasticity

5. Representing Biofuel and Oil Supply Shocks

5.1 Accounting for Renewable Fuel Supply Shocks and Cost Volatility

5.2. Accounting for Oil Price Shocks

6. Macroeconomic Dislocation Costs Associated with Oil and Renewable Fuel Price 

Variability

7. Probabilistic Risk Analysis Approach

8. Results: Monopsony Benefit and Avoided Macroeconomic Costs

1. Background on U.S. Energy Security

U.S. energy security is broadly defined as protecting the U.S. economy against 

circumstances that threaten significant short- and long-term increases in energy costs.

Most discussion of U.S. energy security revolves around the topic of the economic costs of U.S. 

dependence on oil imports, although energy security is also a function of the stability of overall 

fuel supply and the flexibility of demand. An important part of the problem stems from U.S. 

reliance on imported oil, and the global oil market is strongly influenced by potentially 

unfriendly and unstable sources. In addition, oil exporters have the ability to raise the price of 

oil by exerting monopoly power through the formation of a cartel, the Organization of Petroleum 

Exporting Countries (OPEC). Finally, these factors contribute to the vulnerability of the U.S. 
2

economy to episodic oil supply shocks and price spikes. In 2008, U.S. imports of crude oil were 

roughly $391 billion (2007$, see Figure 1).

Figure 1. U.S. Expenditures on Crude Oil

U.S. Expenditures on Crude Oil

0

100

200

300

400

500

600

700

1970 1975 1980 1985 1990 1995 2000 2005

Year

(2007$ Billions)

Domestic

Imported

Source: Annual Energy Reviews and AEO 2009.

By requiring the wider use of renewable fuels, the RFS2 not only reduces oil imports, but 

promotes diversification of transportation fuels in the U.S., reduces oil consumption, and 

improves the stability of overall liquid fuels supply, thereby helping to improve U.S. energy 

security. In prior work, an ―oil import premium‖ approach was utilized to identify those energysecurity related impacts which are expected to change in response to an incremental change in 

the level of U.S. oil imports, but which are not reflected in the market price of oil. To 

understand the energy security implications of reducing U.S. oil imports, we applied an updated 

approach for estimating the oil import premium to the Renewable Fuel Standard (RFS) Rule of 

2007.A This work updated and applied the method used by Leiby, Jones, Curlee and Lee 

(1997).B The updated 2007 report was included as part of the record in the final RFS1 

rulemaking, and revisions were made based on external comment and peer review.C

 

ALeiby, Paul N. “Estimating the Energy Security Benefits of Reduced U.S. Oil Imports: Final Report”, ORNL/TM2007/028, Oak Ridge National Laboratory, March 6, 2008.

B Leiby, Paul N., Donald W. Jones, T. Randall Curlee, and Russell Lee, Oil Imports: An Assessment of Benefits and 

Costs, ORNL-6851, Oak Ridge National Laboratory, November, 1997. This paper was cited and its results used in 

DOT/NHTSA’s rules establishing CAFE standards for 2008 through 2011 model year light trucks. See 
3

For this analysis, the ―oil import premium‖ methodology was extended to more explicitly 

consider the energy security implications of the expansion of renewable fuel use under the RFS2 

rule. This involved accounting for the impact of changed levels of renewable fuels demand and 

supply on U.S. oil demand and imports, and estimating implications for oil and renewable fuels 

costs, including the costs of possible oil and renewable fuels supply shocks.

2. Methodology Used to Estimate U.S. Energy Security Benefits

Significant factors that drive energy security costs have been changing over the last 

decade, including: projected world oil prices, current and anticipated levels of OPEC production, 

U.S. oil import levels, the estimated responsiveness of regional oil supplies and demands to 

price, and the likelihood of oil supply disruptions. For this analysis, oil prices and reference 

supply and demand energy balances from the EIA's AEO 2009 Reference Case were used. In 

many instances, the recent market trends and projections suggest reasons for greater concern 

about oil security costs, compared to the prior decade: higher oil prices; growing U.S. import 

levels; and a larger value-share of oil in GDP. To the extent that the U.S. economy has become 

more resilient, and less sensitive to oil shocks, or that improved macroeconomic policies have 

reduced the impact of oil shocks, there may be influences countervailing to the oil market trends. 

This possibility is considered in the security estimates, but recent macroeconomic disturbances 

indicate that greater future macroeconomic stability cannot be assured. The degree to which 

sharply higher oil prices contributed to, or exacerbated, the recent global recession has not yet 

been resolved.D 

This study extends the oil import premium work by using the Oil Security Metrics Model 

(OSMM)E,Fto estimate the U.S. energy security benefits from increased availability and use of 

 

DOT/NHTSA, Final Regulatory Impacts Analysis: Corporate Average Fuel Economy and CAFE Reform MY 

2008-2011, March 2006.

C Updating the ORNL methodology to incorporate the comments from the Peer Reviewers, based on AEO2007, 

ORNL estimated that the total energy security benefits associated with a reduction of imported oil is $12.38/barrel, 

with a range of $6.88 - $18.52/barrel of imported oil reduced ($2006). When the same methods and assumptions are 

applied to the AOE2009 Reference outlook, comparable estimates for 2025 are $19.21/barrel, with a range of $10.8 

to $29.6.

D

See e.g. Hamilton, J. D. 2009, Causes and Consequences of the Oil Shock of 2007-08 Brookings Papers on 

Economic Activity, 2009, or the congressional testimony of Yergin, D. "The Long Aftershock: Oil: Oil and Energy 

Security After the Price Collapse" Testimony to U.S. Congress, Hearings, Joint Economic Committee, Oil and the 

Economy: The Impact of Rising Global Demand on the U.S. Recovery. May 20, 2009.

E Greene D.L. and P.N. Leiby, 2006. The Oil Security Metrics Model: A Tool for Evaluating the Prospective Oil 

Security Benefits of DOE's Energy Efficiency and Renewable Energy R&D Programs, ORNL/TM-2006/505, Oak 

Ridge National Laboratory (ORNL), 2006.

F The OSMM methods are consistent with the recommended methodologies of the National Resource Council’s 

(NRC’s) (2005) Committee on Prospective Benefits of DOE’s Energy Efficiency and Fossil Energy R&D Programs. 

The OSMM defines and implements a method that makes use of the NRC’s typology of prospective benefits and 

methodological framework, satisfies the NRC’s criteria for prospective benefits evaluation, and permits 

measurement of prospective energy security benefits for policies and technologies related to oil. It has been used to 

estimate the prospective oil security benefits of Department of Energy’s Energy Efficiency and Renewable Energy 

R&D programs, and is also applicable to other strategies and policies aimed at changing the level and composition 

of U.S. petroleum demand. To evaluate the RFS2, the OSMM was modified to include supplies and demand of 

biofuels as well as petroleum. 
4

renewable transportation fuels. OSMM is a dynamic model of oil supply and demand for the 

U.S. and other world regions that accounts for principal cost components associated with oil 

security and import dependence: oil import (wealth transfer) costs, potential GDP costs, and the 

macroeconomic dislocation costs due to disruptions. Uncertainty about technology, oil market 

outcomes and disruption impacts is reflected by probabilistic simulation through the year 2030. 

An extended description of the OSMM and documentation of its approach is found in Greene 

and Leiby (2006). This document focuses on the features and changes that were made to 

incorporate renewable fuels and the RFS2 policy. 

For Renewable Fuels analysis, the OSMM is augmented to account for the use of 

renewable fuels as motor fuel, either in lower concentration blends (e.g. E10 – E20) or in E85 by 

FFVs. Renewable fuel can displace both gasoline and diesel fuel use. The OSMM took as 

inputs the renewable fuel volumes that are required under Energy Independence and Security Act 

of 2007 (EISA) as well at the renewable fuel costs estimated by EPA (Chapter 4.4 of the RIA). 

In addition, it assumed EPA’s projections of flexible fueled vehicles and use of E85. In 

conducting this analysis, ORNL considered the full economic cost of importing petroleum into 

the U.S. In keeping with the oil import premium analysis used for the RFS2 Proposal, the full 

economic cost of importing petroleum into the U.S. is defined for this analysis to include two 

components in addition to the purchase price of petroleum itself. These are: (1) the higher costs 

for oil imports resulting from the effect of U.S. import demand on the world oil price and OPEC 

market power (i.e., the "import demand" or "monopsony" costs); and (2) the risk of reductions in 

U.S. economic output and disruption of the U.S. economy caused by sudden disruptions in the 

supply of imported oil to the U.S. (i.e., macroeconomic disruption/adjustment costs). 

Analogously, this analysis of the RFS2 rule also considers the economic costs of importing

renewable fuel to meet the RFS2 requirements, and the estimated disruption/adjustment costs to 

the economy of biofuel price volatility due to biofuel supply disruptions (e.g. droughts, floods

etc.).

This energy security analysis extends the oil import premium analysis by considering 

risk-shifting that might occur as the U.S. reduces its dependency on petroleum by increasing its 

use of renewable fuels. The analysis accounts for the energy security implications associated 

with renewable fuels, such as possible supply disruptions of ethanol made from corn or ethanol 

derived from cellulosic feedstocks such as switchgrass. The use of OSMM broadens our energy 

security analysis to incorporate estimates of overall motor fuel supply and demand flexibility and 

reliability, and the impacts of possible agricultural sector market disruptions. For example, the 

use of renewable fuels can modestly alter short and long run demand elasticities (i.e., flexibility) 

in the motor fuel market, with implications for robustness of the fuel system in the face of 

diverse supply shocks.

As in the oil import premium analysis for the RFS2 proposal, U.S. military costs are 

excluded from the analysis performed by ORNL because their attribution to particular missions 

or activities is difficult. Most military forces serve a broad range of security and foreign policy 

objectives. Attempts to attribute some share of U.S. military costs to oil imports are further 

challenged by the need to estimate how those costs might vary with incremental variations in 

U.S. oil consumption and imports. Similarly, the Strategic Petroleum Reserve (SPR) size and 

policy is assumed unchanged by the RFS2 rule.
5

3. Effect of Oil Use on Long-Run Oil Price and U.S. Import Costs

The first component of the full economic costs of oil use in the U.S. follows from the 

effect of U.S. import demand on the world oil price over the long-run. Because the U.S. is a 

sufficiently large purchaser of foreign oil supplies, its purchases can affect the world oil price. 

This monopsony power means that increases in U.S. petroleum demand can cause the world 

price of crude oil to rise, and conversely, that reduced U.S. petroleum demand can reduce the 

world price of crude oil. Thus, one benefit of decreasing U.S. oil purchases, due to the increased 

availability and use of other transportation fuels, is the potential decrease in the crude oil price 

paid for all crude oil purchased.

The demand or monopsony effect can be readily illustrated with an example. If the U.S. 

imports 10 million barrels per day at a world oil price of $50 per barrel, its total daily bill for oil 

imports is $500 million. If a decrease in U.S. imports to 9 million barrels per day causes the 

world oil price to drop to $49 per barrel, the daily U.S. oil import bill drops to $441 million (9 

million barrels times $49 per barrel). While the world oil price only declines $1, the resulting 

decrease in oil purchase payments of $59 million per day ($500 million minus $441 million) is 

equivalent to an incremental benefit of $59 per barrel of oil imports reduced, or $10 more than 

the newly-decreased world price of $49 per barrel. This additional $10 per barrel ―import cost 

premium‖ or ―monopsony‖ benefit represents the incremental external benefit to U.S. society as 

a whole for avoided import costs beyond the price paid for oil purchases. This additional benefit 

arises only to the extent that the reduction in U.S. oil imports affects the world oil price.

A similar rationale can be applied to estimate the monopsony disbenefits of the increased 

use of renewable fuels from the RFS2 rule. In the same way, but working in the opposite 

direction of the oil market, increased use of renewable fuels in the U.S. is expected to increase 

demand for domestic and imported renewable fuels, and to modestly increase the price of 

renewable fuels. This results in higher total costs of U.S. renewable fuel imports. While the total 

cost of renewable fuel imports under a policy like RFS2 rule will include the cost of the 

additional imports, the monopsony cost portion is the added amount paid for the imports that 

would have occurred without the RFS2 rule. Thus, to look at the total monopsony impacts of the 

RFS2 rule, two separate impacts need to be assessed. First, U.S. oil import reductions result in 

paying lower prices for all barrels of U.S. imported oil, providing monopsony benefits. Second, 

increased use of renewable fuel results in higher prices of U.S. imported renewable fuels, 

yielding monopsony disbenefits for renewable fuel. The total monopsony benefit is the combined 

sum of these separate market impacts.

This analysis of the import cost and monopsony effect is based on the net import levels of 

petroleum as projected by the EIA, and is not sensitive to the mix of crude and product imports. 

It is possible that in the future, while the U.S. will import most of its crude oil and some 

petroleum products, it may be a net exporter of others, e.g. diesel fuel.G However, oil security 

 

G While at the time of the implementation of this rule, the U.S. exports some diesel fuel, it is not clear that this 

situation will long persist. Under EIA AEO2009, the U.S. is a net importer of refined products (as well as crude) 

throughout the forecast horizon. In particular, over the RFS2 horizon (2010-2022) diesel fuel consumption is 

expected to grow at 1.4 per cent per year (Table 11) while gasoline demand will decline at 1.0 per cent per year. 
6

concerns stem from the total consumption and net import of all petroleum fuels, whose prices are 

all directly dependent on the volatile (and non-competitive) world crude oil market. For this 

analysis, the key issue is not the trade balance for particular petroleum products, but the net level 

of U.S. consumption and import of all petroleum, both crude and products. Reducing domestic 

gasoline or diesel fuel with renewable fuels use can reduce net imports of all petroleum, and net 

import costs, even if the U.S. remains a net exporter of some petroleum products. Consider the 

case of U.S. diesel fuel. Replacing U.S. diesel fuel consumption with renewable biodiesel, whose 

root supply volatility is largely independent of that of petroleum, can reduce the volatility of 

productive inputs to the macroeconomy, regardless of the trade balance in diesel fuel.

3.1. Oil Price Effect of Expanded Renewable Fuels Use

In the OSMM, world oil supplies and demand are represented for three major regions: the 

U.S., OPEC, and Rest of World. Supplies and demands are dynamically specified over the time 

horizon of 2010 to 2030 using partial adjustment framework that allows a distinction between 

short-run and long-run elasticities. Eq. 1 shows the (Koyck Lag) partial adjustment framework 

for demand and supply, with t indicating time period, i indicating region, and denoting the 

adjustment rate each year toward the long-run supply or demand level at price Pt. Dit and Sit are 

the resulting short-run (actual achieved) levels of supply and demand.

1

1

( ) (1 )

( ) (1 )

it S i Sit Sit t S i it

it Di Dit Dit t Di it

S a b P S

D a b P D

 Equation 1

This formulation means that the effects of policies, and shocks, take time to work through 

the market system. Parameters for the linear supply and demand curves are benchmarked yearby-year to conform to the AEO2009 reference projections, given exogenously specified 

adjustment rates and reference supply and demand elasticities. Reference supply and demand 

elasticities are those that apply without the RFS2 policy change, and are treated as uncertain 

parameters. During the simulation of the impacts of the RFS2, the elasticity of U.S. oil demand 

is adjusted over time to account for the reduction in oil demand, and for the (limited) substitution 

possibilities between oil-based fuels and renewable fuels in the motor fuel sector. This is 

described further in Section 4.

In the OSMM framework, OPEC-behavior, and the resulting price outcome, is treated as 

a fundamental uncertainty, with a wide range of possible responses represented by three 

behavioral patterns. Two bounding behaviors with opposite implications for price and quantity 

changes are: OPEC preserves its output (quantity is unchanged so price must fall to rebalance 

markets); and OPEC preserves price (OPEC adjusts its output to completely accommodate any 

reduction in U.S. oil demand due to biofuels, sacrificing market share for price). These two 

behaviors imply, respectively, short-term price elasticities of OPEC supply that are zero and 

unbounded. Alternatively, a candidate rule for OPEC strategic response behavior, adapted from 

 

Thus, under this outlook, crude refinery runs needed to meet gasoline supply will decline, while U.S. demand for the 

distillate/diesel cuts will grow.
7

a lead article on what behavior maximizes OPEC’s long run net revenue in a robust way,H would 

have OPEC responding to preserve its share of the worldwide oil market. In the simulations, 

each of these three cases for OPEC behavioral response is taken as equally probable.

The expanded production and use of renewable fuels both changes the fraction of fuel 

supplied that is alternative fuel, and leads to a reduction in the domestic use of oil-based fuels by 

the vehicle sector. Given the dynamic supply and demand formulations in Eq. 1, the market 

balancing condition in the oil market for a sequence of U.S. oil demand reductions DUct under 

policy case c is:

Equation 2

i

DU Uct DU Uct S i Sit Di Dit ct

i

Oct S i Sit S i ict Di Dit Di ict

D D b b P

S a S a D

(1 ) 0

(1 ) (1 )

1

1 1

Here Dict and Sict are regional demands and supplies from Non-OPEC regions, SOct is OPEC 

supply, and Pct is the market price. The sum over regions i is meant to exclude OPEC region O. 

The price coefficients bDit and bSit reflect the price elasticities of demand and supply. This market 

balance is solved recursively for either world oil price or OPEC supply, if the other is given, as is 

the case for the ―OPEC Maintains Supply‖ and ―OPEC Maintains Price‖ behavioral assumptions 

respectively. For the third case of OPEC adopting the ―Maintain Share‖ strategy, OPEC’s share 

under policy case c matches its share in the reference case r:

Equation 3

i

ixt

Ort

Ort

Oct

Ort

i

Ort irt

Ort

i

Oct ict

Oct

S S

S S

S

S S

S

1

This condition can be applied as a restriction to Eq. 2, which can then be solved (recursively 

through time) for both OPEC supply and world oil price.

Given the dynamic specification of supply and demand in OSMM, which is more realistic 

than a static market analysis, the market impacts of the RFS vary over time as renewable fuel use 

expands and the market adjusts. The initial impact on price of a given reduction in oil use is 

larger because short-run elasticities are low, and adjustment is slow. These dynamic effects are 

less pronounced under the RFS2 than they might have been, however, since the incremental 

expansion of renewable fuels is gradual. The net effect of RFS2 on oil imports and oil price 

changes over time, and varies with particular values of the simulation parameters. Simulations 

show that the impact of a net U.S. oil import reduction on world oil price declines over time (see 

Figure 2), and trends toward a long-run range that is consistent with the prior Oil Import 

 

H Gately, Dermot 2004. "OPEC's Incentives for Faster Output Growth," The Energy Journal, 25(2):75-96, "What 

Oil Export Levels Should We Expect From OPEC?" The Energy Journal, 28(2):151-173, 2007.
8

Premium analysis, and many other studies of the monopsony effect (between $0.60/bbl and 

$1.20/bbl per MMBD reduction in imports).

Change in Import Price per MMBD change in U.S. Imports with 

Confidence Intervals

$0.00

$1.00

$2.00

$3.00

$4.00

$5.00

$6.00

201020112012201320142015201620172018201920202021202220232024202520262027202820292030

($/BBL) per MMBD

Mean + 2 Std. Deviations

Mean

Mean - 2 Std. Deviations

Figure 2. Dynamic Estimates of the Effect of U.S. Import on World Oil Prices ($/BBL/MMBD), from OSMM. 

This plot shows the mean price effect and a confidence interval (plus and minus 2 standard deviations).

Given the limited elasticities of regional supplies and demand, particularly in the short run, and 

the comparatively small percentage change in world oil price, the U.S. oil supply response is 

limited, and much of the oil demand reduction due to biofuels is translated to a reduction in 

imports.

3.2. Biofuel Price Effect of Expanded Renewable Fuels Use

Just as the reduction in oil consumption and imports is expected to diminish the cost of 

oil imports, the increase in renewable fuel demand is expected to increase the price of renewable 

fuels and the cost of importing those renewable fuels. In both the Reference and RFS2 Control 

Case, biofuel consumption quantities and prices (apart from deviations due to shocks) are taken 

from exogenous scenarios specified for consistency with other analysis done for the Regulatory 

Impact Analysis of the RFS2 rule (see Figures 3 and 4).
9

Total Biofuels Use Under Reference and Alternative Case

0.000

0.500

1.000

1.500

2.000

2.500

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 MMBD (physical barrels)

Reference MMBD

RFS2 Control Case MMBD

Difference MMBD

Figure 3: Total Biofuels Use and Change from Reference to RFS2 Case
10

Plant-gate Ethanol Price Under Reference and Alternative Case

$1.30

$1.35

$1.40

$1.45

$1.50

$1.55

$1.60

$1.65

2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022

Plant-gate Ethanol Price ($/gal)

$0.00

$0.01

$0.02

$0.03

$0.04

$0.05

$0.06

$0.07

$0.08

$0.09

$0.10

Price Change ($/gal)

Reference MMBD

RFS2 Control Case 0

Difference Units

Figure 4. The Plant gate price for corn-ethanol is somewhat higher under RFS2

The base price of ethanol imports is assumed to track the cost of corn ethanol. The ethanol price 

increase under the RFS2 Case is applied to the ethanol imports that would have been imported 

under the Reference case to estimate the incremental costs of biofuels attributable to higher 

demand (biofuel monopsony effect). No biodiesel imports are projected under the Reference 

case, so there are no monopsony costs associated with induced changes in biofuels price.

4. Impacts of Oil Displacement and Renewable Fuel/FFV Introduction on Demand 

Elasticity

U.S. energy security depends on the elasticities of demand and supply because those 

elasticities determine both the ability to respond to price shocks in the short run, and the ability 

to reduce consumption or net imports in the long run, if prices are persistently high. The 

expanded use renewable fuels is anticipated to alter the demand elasticity for motor fuel, and 

hence oil, in multiple ways, some offsetting. The two main mechanisms are:

As alternative fuels become a larger fraction of motor-fuel use, the value share of 

gasoline and diesel diminishes, and motor-fuel demand becomes slightly less sensitive to 

petroleum fuel prices. Thus the overall elasticity of U.S. oil demand diminishes, in 

accordance with the share of total oil demand that is in the motor fuel sector and the 

relative elasticities of motor-fuel and non-motor-fuel oil demand.

Given the introduction of alternative fuels in blends and E85, there are opportunities for 

substitution between alternative fuel and gasoline (or diesel), both in the short and longer 
11

run. This can increase the price elasticity of demand for gasoline/diesel, as well as the 

elasticity of overall motor-fuel demand.

The combined effect of these influences on the price elasticity of gasoline demand can be 

summarized with the following equation:

Equation 4

LDVs

FFVs

AF LDV r t FuelpriceLDV gasoliner t

grt art

grt

g Pg t MotorfuelPmotorfuel K

K

P

E E

E

, , , ,0 , , , ,

The price elasticity of demand for gasoline is the sum of the elasticity of final motor-fuel 

demand with respect to gasoline price plus the elasticity of gasoline demand share with respect to 

price. The first term on the right shows how the overall elasticity of motor-fuel demand with 

respect to gasoline price is derived from the elasticity of motor-fuel demand with respect to 

average motor-fuel price, and declines as the share of gasoline in motor fuel expenditures

declines. The second term is based on standard discrete choice theory for fuels, where the 

elasticity of choice share for gasoline grows with the share of alternative fuel use AF,LDV, 

gasoline price, and the fraction of FFVs in the vehicle fleet (KFFV/ KLDV).

The elasticity of U.S. oil demand with respect to oil price is then a weighted combination 

of all sectoral elasticities (non-Vehicle, Light Duty Vehicle demand, Commercial Light Truck 

demand, and Heavy Duty Truck demand). The weights depend on the share of oil use used by 

each sector, and the fraction of product price that is attributable to crude oil:

Equation 5

diesel t

oil t

g Pg HDT t HDT oil t

gasot

oil t

g Pg CLT t CLT oil t

gasot

oil t

Doil Poil t oil Poil NonVehiclet g Pg LDV t LDV oil t

P

P

P

P

P

P

,

,

, , , , ,

,

,

, , , , ,

,

,

, , , , , , , , , ,

The approach modeled here is consistent with the case where renewable fuel blends are at 

constrained by the blend wall, so substitution is limited to FFVs. If the blend wall were not 

constraining (if either the ethanol blend limit is raised sufficiently or a substantial fraction of 

renewable fuel is blended in diesel blends) then this substitution term could be much larger. 

Over the longer run, the share of FFVs could also increase in an endogenous response to 

sustained higher oil prices, although this is not accounted here. The number of FFVs is taken as 

an exogenous input from EPA scenarios. The RFS2 scenario encompasses a strong growth in 

both FFVs (to 44.5 million by 2022) and stations offering E85 (to 24 thousand by 2022, or 15% 

station share). This degree of E85 station availabilityI, and the implied E85 use per FFV (well 

below 100% of fuel use), suggest that there is scope for fuel share shifts and substitution.

Figure 5 depicts the impact of RFS2 policy on gasoline demand elasticity, and the smaller 

impact on U.S. oil demand elasticity. Numerically, it turns out that the impact of the first 

mechanism alone (the effect of declining gasoline share making LDV motor fuel demand less 

sensitive to gasoline price) on overall oil demand elasticity is negligible. This appears to be 

because the reduction in the elasticity of gasoline demand is accompanied by a reduction in the 

 

I Most studies conclude that the consumer disutility of limited fuel availability drops sharply after 10% station share.
12

share of oil use attributable to the LDV sector, so the gasoline elasticity reduction has less weight 

in the aggregate oil demand elasticity.

Introducing Alt Fuels Changes Demand Elasticity

-0.600

-0.500

-0.400

-0.300

-0.200

-0.100

0.000

2005 2010 2015 2020 2025 2030

Ref LDV Gasoline Demand

Elasticity

Ref Oil Price Elasticity

Policy LDV Gasoline Demand

Elasticity

Policy Oil Price Elasicity

Figure 5: Elasticity of Demand for Gasoline, and Oil, Grow Gradually Under RFS2

5. Representing Biofuel and Oil Supply Shocks

5.1. Accounting for Renewable Fuel Supply Shocks and Cost Volatility

One feature of the OSMM is its explicit treatment of renewable fuel volatility. Renewable 

fuel supply and hence, the price of renewable fuel, is also subject to disturbances, and the 

resulting production cost volatility is anticipated to impose some costs on the economy. While 

E85 and gasoline prices are often strongly correlated at the level of individual retail stations, this 

correlation reflects primarily the process of market substitution, and the expected phenomenon 

that ordinarily end-use prices for close substitutes will equilibrate and track one another. Further 

upstream (i.e., closer to the terminal and production plant gate), this price correlation diminishes. 

The historical prices of agricultural products that are likely to be used as feedstocks for 

renewable fuel are somewhat volatile, but almost completely uncorrelated with oil prices. For 

example, consider monthly price changes between crude oil and key agricultural crops that 

would be used as feedstocks for biofuel—sugar, wheat, corn and softwood lumber—during the 

time period from January, 1990 to December, 2008 (Table 1 below). In the case considered here, 

wheat prices are used as a surrogate for switchgrass prices, since both crops are grasses, likely to 

be grown in similar agricultural areas in the U.S. (i.e., the Great Plains) and subject to similar 

weather patterns. These agricultural commodities have relatively low correlations with crude oil; 

3 percent, 5 percent, -1 percent and 1 percent, for sugar, wheat, corn and softwood lumber, 

respectively. 
13

Table 1. Cross Correlations of Monthly Price Changes of Crude Oil

and Selected Key Biofuel Feedstocks, January, 1990 to December, 2008

Crude Oil Sugar Wheat Corn Softwood

Crude Oil 100% 3% 5% -1% 1%

Sugar 100% 20% 10% 2%

Wheat 100% 46% 23%

Corn 100% -10%

Softwood 100%

Source: Leiby 2009, based on IMF/IFS database, Commodity Prices & Indices, Monthly, 1970 to December 2008.

The macroeconomic cost of renewable fuel disruptions, like oil disruptions, is a function 

of the fundamental supply cost volatility at the feedstock level for each respective fuel type. 

While renewable fuel and gasoline prices are expected to be correlated in those vehicle motor 

fuel end-use markets where substitution occurs, renewable fuels are an imperfect substitute for 

oil. The price increases in E85 induced by oil price shocks reflect the possibilities of 

substitution, a beneficial response to an oil supply shock, rather a shortage in renewable fuel. 

For this reason we measure macroeconomic costs associated with renewable fuel price changes 

not from the variability in renewable fuel demand and price that stems from substitution and 

equilibration with petroleum fuels, but from the independent shifts in biomass supply and 

renewable fuel production costs.

From the standpoint of quantifying the macroeconomic/disruption of the RFS2 rule, two 

factors are important. First, is an estimate of the variability in the supply of renewable fuel. The 

second factor is changes in renewable fuel production costs that stems from fundamental supply 

volatility at the feedstock level. This analysis represents renewable fuel supply volatility and risk 

based on historical variations in annual crop yields. Crop yields vary substantially from year to 

year based on growing conditions, including droughts, floods, and a variety of other factors.J 

The supply risk of variable feedstock production volumes is assumed to be independent of oil 

prices, consistent with basic principles, and observed historical commodity prices. 

Data on biofuel feedstock yield volatilities were obtained from the Iowa State University 

Center for Agricultural and Rural DevelopmentK, as presented in Table 2 below. These estimates 

of feedstock yield volatility are then used to estimate variations in feedstock costLand implied 

variations in renewable fuel production costs at the plant gate.M 

 

J

In applying historical yield and feedstock price variations to projected outcomes, it was recognized that two 

offsetting factors may cause future biofuel feedstock supply risk to differ from the past. These factors may offset 

each other: future drought risk may increase with climate change; yet some crops are also becoming increasingly 

drought resistant.

K

Personal communication from Bruce Babcock, CARD, Nov. 12, 2009.

L Data on yield volatility were obtained from Bruce Babcock of CARD, Iowa State. Variations in yield were 

converted to estimated variations in feedstock cost based on elasticities from Thompson, W., Meyer, S. & Westhoff, 

P. ―How Does Petroleum Price and Corn Yield Volatility Affect Ethanol Markets With and Without an Ethanol Use 

Mandate?‖ Energy Policy, Elsevier, 2009, 37, Pages 745-749. 

M

 Biofuel production cost economics used in this analysis are based upon estimates from Tao L. and A. Aden 2009, 

"The Economics of Current and Future Biofuels," In Vitro Cell. Dev.Biol. - Plant, 45:199-217.
14

Table 2. Selected Key Biofuel 

Feedstocks Annual Yield Volatility

Biofuel 

Feedstock

Corn Soybeans Wheat Sugar Cane

Historical Yield 

Volatility

8.69% 6.81% 13.07% 2.37%

Based on Years 1960-2008 1960-2008 1960-2008 1997-2008

Source: Volatility is measured as the standard deviation of annual percentage deviation from historical trend yields. 

Yield data and volatility are from Bruce Babcock, CARD, Iowa State, November, 2009. OSSM Supporting Data, 

Leiby/ORNL 2009. 

Variations in yield were converted to estimated variations in feedstock cost based on 

elasticities from Thompson 2009, taken as uniform across feedstocks (see Table 3a). Finally, 

based on renewable fuel production cost economics characterized by Tao & Aden 2009,Nyield 

volatilities were converted to measures of volatility of renewable fuel production cost at the plant 

gate (see Table 3b). For each renewable fuel, Tao & Aden estimate fuel unit production cost as a 

linear function of feedstock costs:

Equation 6

total f non feedstock f bfCfeedstock f

c c

, , ,

For this cost function, if non-feedstock costs are fixed and feedstock cost varies randomly around 

a reference value then the relative variation of total biofuel cost is a linear function of the relative 

variation of feedstock cost, where the slope feedstock,f is the reference cost share for feedstock: 

Equation 7

feedstock f feedstock f feedstock f feedstock f ref

total f ref

total f

C C

c

c

, , , , ,

, ,

,

(1 ) /

From this expression we observe that the volatility of fuel production cost will equal the 

volatility of feedstock cost times the reference feedstock cost share. 

Resulting estimates of renewable fuel cost volatility, based upon standard deviations from 

historical trends in biofuel feedstock yields, vary from 2 percent to 8 percent, with ethanol 

derived from sugar cane estimated to have the lowest volatility, and ethanol derived from corn, 

the highest volatility. As one would expect, biofuel cost volatility increases with the volatility of 

feedstock supply, but decreases in cases where feedstock comprises a lower percentage of total 

production cost (as is the case with cellulosic ethanol). By way of comparison, the historical 

volatility of world prices over the last twenty-five years is 28 percent, considerably higher than 

the estimated volatility of renewable fuels.O

 

N Tao L. and A. Aden 2009. "The economics of current and future biofuels," In Vitro Cell. Dev.Biol. - Plant, 

45:199-217

O Oil price volatility is calculated as the standard deviation of annual percentage price changes in the real price of 

imported crude oil, 1984-2009, U.S. EIA data. This result is robust over the choice of the starting year for the 

volatility calculation: 2000-2009: 30.0 percent; 29.2 percent; 1990-2009: 26.4 percent; 1980-2009: 27.2 percent; and 

1970-2009: 29.5 percent.
15

Table 3. Parameters for Economics and Volatility of Biofuels Production Cost

3a. Biofuel 

Feedstock

Corn Soybeans Wheat Sugar Cane

Elasticity of 

Feedstock Price with 

respect to Yield* -1.2 -1.2 -1.2 -1.2

3b. Biofuel Corn Ethanol Soy Biodiesel

Switchgrass 

Ethanol

Sugar Cane 

Ethanol

Feedstock 

Cost/Total Cost** 79.1% 86.7% 34.5% 72.1%

Estimated Volatility 

of Biofuel Cost 8.2% 7.1% 5.4% 2.0%

* Based on W. Thompson et al. 2009, Energy Policy, 37, 745-749.

** Based on Tao L. and A. Aden 2009. "The economics of current and future biofuels," In Vitro Cell. 

Dev.Biol. - Plant, 45:199-217

In applying historical yield and feedstock price variations to projected outcomes, it was 

recognized that two offsetting factors may cause future bio-feedstock supply risk to differ from 

the past. These factors may offset each other: future drought risk may increase with climate 

change; yet some crops are also becoming increasingly drought resistant.

5.2. Accounting for Oil Price Shocks

Stochastic oil supply shocks lead to price excursions superimposed on reference oil 

prices. Single or multiple supply shocks can, in some scenarios, have long-lasting price effects. 

World oil supply disruptions are expressed as percentage losses in supply quantity, and market 

clearing prices are determined based on the markets ability to respond over the course of the 

disruption. As described in Greene and Leiby (2006, pp. 45-50), the frequency, magnitude, and 

duration of oil shocks is established with reference to the historical record, and the expert 

assessment reported by Beccue and Huntington (2005)P. Monte Carlo simulations produce an 

average of 2.7 price shocks over a 25 year period, with a standard deviation of 1.1. The resultant 

modeled annual volatility of future oil prices is on the order of 12%, which is greater than that of 

renewable fuels, but lower than the historical oil price volatility of 28% for the last 25 years. 

Figure 5 shows one of many thousands of stochastically simulated future oil price scenarios. 

While the magnitude and frequency of shocks matters for total costs to the U.S., the estimated 

incremental benefits of expanded renewable fuels use is based on the extent to which it can 

slightly moderate the magnitude of the oil price excursions, along with shifting the overall price 

trajectory. This impact depends also on OPEC behavior, in disrupted periods as in non-disrupted 

periods.

 

P Beccue, P.C. and H.G. Huntington. 2005. ―An Assessment of Oil Market Disruption Risks,‖ Final Report EMF SR 

8, Energy Modeling Forum, Stanford University, Palo Alto, California, October 3, 2005.
16

World Oil Price: 2009 AEO Reference Case & Supply 

Shock Simulation

$0

$20

$40

$60

$80

$100

$120

$140

1970 1980 1990 2000 2010 2020 2030 2007 $/BBL

Reference case - Stimulus2009

Shocked Ref Case (No Policy)

World Oil Price to Maintain OPEC Prod

Estimated benefits of 

policy (EISA use of 

biofuels) are based 

on (small) difference 

of these two outcome 

paths through 2030.

World Oil Price: 2009 AEO Reference Case & Supply 

Shock Simulation

$0

$20

$40

$60

$80

$100

$120

$140

1970 1980 1990 2000 2010 2020 2030 2007 $/BBL

Reference case - Stimulus2009

Shocked Ref Case (No Policy)

World Oil Price to Maintain OPEC Prod

Estimated benefits of 

policy (EISA use of 

biofuels) are based 

on (small) difference 

of these two outcome 

paths through 2030.

Figure 6: Example of price disturbances superimposed on AEO2009 reference oil price path.

6. Macroeconomic Dislocation Costs Associated with Oil and Renewable Fuel Price 

Variability

Fluctuations in oil and renewable fuel prices are estimated to cause macroeconomic 

losses due to dislocations and adjustment costs. Macroeconomic losses during price shocks 

reflect both aggregate output losses and so called ―allocative‖ losses. The former are a reduction 

in the level of output that the U.S. economy can produce fully using its available resources; and 

the latter stem from temporary dislocation and underutilization of available resources due to the 

shock, such as labor unemployment and idle plant capacity. The aggregate output effect, a 

reduction in ―potential‖ economic output, will last so long as the price is elevated. It depends on 

the extent and duration of any disruption in the world supply of oil, since these factors determine 

the magnitude of the resulting increase in prices for petroleum products, as well as whether and 

how rapidly these prices return to their pre-disruption levels.

In addition to the aggregate contraction, there are ―allocative‖ or ―adjustment‖ costs 

associated with dislocated energy markets. Because supply disruptions and resulting price 

increases occur suddenly, empirical evidence shows they impose additional costs on businesses 

and households which must seek to adjust their use of petroleum and other productive factors 

more rapidly than if the same price increase had occurred gradually. Opportunities for short run 

adjustments of energy use and other productive factors of the economy are limited and costly. 

Dislocational effects include the unemployment of workers and other resources during the time 

needed for their intersectoral or interregional reallocation, and pauses in capital investment due 

to uncertainty. These adjustments temporarily reduce the level of economic output that can be 

achieved even below the ―potential‖ output level that would ultimately be reached once the 

economy’s adaptation to higher petroleum prices was complete. The additional costs imposed on 

businesses and households for making these adjustments reflect their limited ability to adjust 

prices, output levels, and their use of energy, labor and other inputs quickly and smoothly in 

response to rapid changes in prices for petroleum products.
17

In the prior ―oil import premium‖ analysis undertaken for the RFS2 proposal, oil price 

shocks were estimated to have macroeconomic losses based on a single fixed elasticity of Gross 

Domestic Product (GDP) with respect to oil price. In this study with the OSMM, it is recognized 

that the dislocation portion of disruption costs depends not only on the magnitude of the price 

change, but on the changing importance of both oil and renewable fuels in the economy. The 

approach also recognizes that disruption costs depend on the degree to which the price 

movement is novel and disturbing.Q Thus, when a shock causes fuel prices to jump up to new 

levels and stay up, initially the dislocation is larger, and over time the economy adjusts to higher 

prices and the macroeconomic dislocation dissipates. Alternatively, if the oil price rises sharply, 

falls, and then rises shortly thereafter to similar levels, this second increase will have diminished 

effect since the price is not outside the realm of recent experience. To account for these 

accommodation effects, OSMM tracks the evolution of an ―adjusted‖ oil price PoMct and 

renewable fuel price PoMct, which is based on a lagged partial adjustment process, essentially 

yielding a weighted average of past prices with geometrically declining weights. 

Equation 8

, , 1

(1 ) PowMct adj MCPoct adj MC PoMct

This weighted-lag adjusted price is constructed to represent an average price level that the 

economy has already had time to adjust to.R It is deviations from this level that are dislocational 

and most costly. 

For both oil price and renewable fuel price fluctuations, the macroeconomic dislocation 

cost is calculated by applying a GDP loss elasticity to the ratio of the current price to the adjusted 

price. Furthermore, the applied GDP elasticity varies with the value share of expenditure on the 

fuel, both for oil and renewable fuels, in the economy.S,T For example, for macroeconomic 

adjustment losses with respect to oil price movements:

 

Q This attention to the degree to which the observed price is unusual or ―novel‖ follows the work of Lee et al. (Lee, 

K.; Ni, S. & Ratti, R. ―Oil shocks and the Macroeconomy: The Role of Price Variability‖ Energy Journal, 1995, 16, 

39-56, and Hamilton’s NOPI formulation (e.g. Hamilton, J. ―What is an Oil Shock?‖, Journal of Econometrics, 

Elsevier, 2003, 113, 363-398). Several works of Brown, Huntington, and Gately, for example, also consider the role 

of an ―adjusted price‖ in the determination of supply and demand responses.

R The adjusted price is the geometric distributed (Koyck) lag average of all prior prices, to represent that the 

economy only partially adjusts to changing prices each year. The annual adjustment rate of this price, which 

corresponds to the assumed annual accommodation of new prices by the macroeconomy, is taken as 33%. This is 

consistent with empirical evidence that the dislocational impact of energy price shocks extends more than one year, 

but is mostly complete after three years.

S Huntington (2005, ―The Economic Consequences of Higher Crude Oil Prices,‖ Final Report EMF SR 9, Energy 

Modeling Forum, Stanford University, October, 3, p. 43) notes ―Economic theory suggests strongly that, in the 

absence of major threshold effects, the direct response of the GDP and price levels to oil price changes should be 

proportional to oil’s value share in total output.‖ In the OSMM, for both oil and biofuels, the adjustment cost 

elasticity varies from year to year according to the expenditure share in GDP. For biofuels it is given by the 1983 

reference value for oil times the ratio of current biofuel expenditure share to 1983 oil expenditure share.

T As a further refinement, the economic impacts of price shocks are spread over the year of the shock and the 

subsequent year, consistent with the observed delayed effects of historical shocks. This does not have a substantial 

impact on the total costs of shocks, or estimated benefits of biofuels, so is left out of the simplified expression 

above.
18

Equation 9

, ,1983

,

/

1 /

YApt Ypshockref oct o r

LMacAdjoct Poct PoMct YUt

YApt

The estimated GDP losses from renewable fuel price fluctuations are based on the same GDP 

adjustment costUelasticity as that used for GDP adjustment costs with respect to oil prices, but it

is rescaled in accordance with the ratio of renewable fuel expenditures to total oil expenditures.

The introduction of renewable fuels affects macroeconomic disruption costs from oil by 

reducing the oil-intensity of the economy, and by changing oil price movements slightly (since 

the addition of renewable fuels slightly alters the elasticity of demand for motor fuels). In 

addition, the introduction of renewable fuels affects the macroeconomic disruption costs by 

adding separate disruption costs associated with the independent volatility of biofuels supply. 

The magnitude of GDP dislocation losses for a given oil price change is calculated based on a 

summary parameter, ―GDP elasticity.‖ That elasticity is adjusted from historical (i.e., early 

1980’s) levels based on the oil expenditure share in GDP. In the AEO 2009 reference case, from 

2010 to 2022, oil expenditures as a cost share of the U.S. economy vary from 2.5 percent in 2010 

to peak at 4.7 percent in 2017, while declining thereafter to 3.7 percent by 2022. By way of 

comparison, in 1983, the oil cost share was 4.6 percent (for which the assumed 1983 

macroeconomic adjustment elasticity is -0.041). Under the RFS2 rule, renewable fuel cost shares 

in the economy are quite small, growing from under 0.1 percent to about 0.2 percent, an order of 

magnitude lower than the oil cost shares. 

Based on this approach of applying a GDP elasticity that reflects changing fuel share, the 

central value for total GDP elasticity with respect to oil price that was applied to oil shocks in the 

reference (no policy case) ranged between -0.022 (in 2010) to a peak of -0.039 in 2017, and then 

declined thereafter to -0.033 by 2030.

The reduction in GDP adjustment losses due to oil shocks is a result of both the reduction 

in oil price and the slight decrease in oil share (about 0.2%). As mentioned above, the estimated 

volatility of crude oil price is greater than renewable fuel production costs under the RFS2 rule. 

This is understandable for two reasons: historical crude prices are more volatile than agricultural 

commodity prices that are projected to be the biofuel feedstocks; and biofuel production costs are 

less volatile than feedstock costs. The combination of these effects—lower costs shares of oil 

because the introduction of higher levels of biofuels and less volatility in biofuel costs compared 

to oil—leads to the offsetting gains and losses in terms of macroeconomic dislocation costs, with 

the avoided losses from crude shocks being greater than the added losses from biofuel shocks. 

This approach has implications for estimated GDP losses due to shock and price 

fluctuations. If the use of renewable fuel reduces the expenditure share of oil in the U.S. 

economy, then the sensitivity of the economy to oil shocks is also reduced, and estimated GDP 

adjustment losses from oil shocks are lower. These effects are all relatively modest, since the 

 

U

Following the pattern for oil, GDP adjustment costs from biofuel price fluctuations are based on applying the 

adjustment cost elasticity to the ratio of the current year price to the adjusted biofuel price. 
19

changes in the renewable fuel cost share are fairly modest, but the overall importance of oil in 

the economy, and the greater estimated volatility of oil prices, leads to a significant gain per 

barrel of oil use avoided. The applicable GDP elasticity for renewable fuels price shocks is 

much smaller than that for oil, in proportion to the expenditure share of renewable fuels. It does, 

of course, grow over time with increased use renewable fuels.

Other implications of this approach relate to the time-varying sensitivity of GDP to 

shocks and price fluctuations:

- If a supply shock and price increase was recently preceded by other such shocks, then the 

constructed ―adjusted price‖ is higher (indicating the economy has adjusted somewhat to 

higher prices), and the macroeconomic adjustment losses will be lower;

- In a period of lower oil prices, such as observed in the early 2000s, the applicable GDP 

loss elasticity for GDP adjustment costs is estimated to be markedly lower that for certain 

prior periods, such as the early 1980s. However, if oil expenditures grow more rapidly 

than GDP, the sensitivity of the economy to oil shocks increases; 

Although retail renewable fuel prices will move closely with gasoline prices, and to a 

lesser extent the biofuel plant-gate market prices and even feedstock prices may be correlated 

with oil prices, there is still a macroeconomic benefit from replacing oil with renewable fuel. 

This is true for four reasons. First, renewable fuels and oil are imperfect substitutes, even in the 

specific end use markets for retail gasoline and E85. Second, the nature of the oil price increase 

and renewable fuel price increases are quite distinct. When the price of oil increases from an oil 

supply shock, the resulting increase in renewable fuels has a very different origin–it is due to 

substitution toward renewable fuels, and thus reflects a demand response rather than a supply 

shock. While price change is commonly used as the summary measure of the shock, the 

underlying quantity changes cannot be forgotten as the root cause of the economic loss. Unlike 

oil, the quantity of renewable fuel supplied during an oil shock will increase, or at least remain 

little changed. Third, the short-run increase in the price of renewable fuel is no different from 

the induced price increase in all other substitutes for oil: energy or non-energy. Adding a new, 

independent supply source like renewable fuel is equivalent to adding any other alternative fuel, 

or even to adding a conservation alternative. The price of all of these will rise to some extent 

with oil prices, but this substitution effect is part of the solution to the disruption rather than part 

of the problem. Through substitution, renewable fuels can also dampen the oil price shock, a 

beneficial effect that is only partially represented in this analysis. Fourth, to the extent that 

renewable fuel prices rise with little change in production, the payments will be largely to 

domestic producers, as windfall gains rather than losses.

7. Probabilistic Risk Analysis Approach

Clearly there is an unavoidable degree of uncertainty about the magnitude of marginal 

economic costs from the use of oil, and the incremental benefits from expanded renewable fuel 

use. We sought to reflect this with probabilistic risk analysis over key input factors, guided by 

the available literature and the judgment of oil market experts. Key uncertainties in the 

probabilistic risk analysis were:

The reference oil price path and supply, demand, and import quantities; (However, for 

this analysis only the AEO2009 Midcase (Stimulus Version) was used, since the initial 
20

AEO Low, Mid and High cases were made obsolete by supplemental EIA analysis after 

the stimulus act (ARRA 2009))

Oil market disruption events, and biofuel feedstock supply fluctuations;

GDP loss elasticity for macroeconomic dislocation losses;

Short-run U.S. and ROW Oil supply elasticities; and

Short-run U.S. and ROW Oil demand elasticities.

8. Results: Monopsony Benefit and Avoided Macroeconomic Costs

Table 4 shows the RFS2 Reference Case levels of U.S. oil and renewable fuel imports as 

well as the average change in oil prices and renewable fuel prices due to the RFS2 rule in 2022. 

The Reference Case renewable fuels imports are relatively modest compared to oil (roughly 

0.015 billions of barrels of renewable fuel are imported versus 3.283 billion barrels of oil). 

Projected U.S. renewable fuel imports in 2022 are ethanol, principally made from sugar cane

harvested in Brazil. In 2022, the estimated change in ethanol price due to RFS2 rule is 

$0.61/barrel, and the estimated reduction in the world oil price is $1.05/barrel The monopsony 

effect is the change in costs of the quantities of fuel imported without the RFS2 rule (i.e., the 

Reference Case fuel volumes). Since the change in renewable fuel price applies to a much 

smaller quantity of renewable fuels imports than U.S. oil imports, the monopsony disbenefit per 

barrel of increased renewable fuels use is much smaller, only $0.02/barrel, compared to the oil 

monopsony benefit, $7.88/barrel. Thus, including the impact of expanded renewable fuels use 

on renewable fuels imports and price yields a slightly lower estimate of the total monoposony

benefit, or incremental import cost reduction

Table 4. Determinates of Monopsony Benefits of the RFS2 Rule 

Fuel Reference Case 

Import Quantity

(billions of barrels 

in 2022)

Change in 

Price ($ per 

barrel in 2022)

Monopsony 

Benefit

($ billion in 

2022)

Monopsony Benefit

($/barrel renewable

fuel in 2022)

Renewable fuels 0.015 0.61 -0.009 -0.02

Oil 3.282 -1.06 3.476 7.88

Total 7.86

Estimates of the avoided macroeconomic dislocation benefits, in dollars per barrel of renewable 

fuel, are displayed in Table 5. below.

Table 5. Avoided Macroeconomic Dislocations Costs for the RFS2 Rule

($ per barrel of renewable fuel)

Reduction in Dislocation Cost from Oil Shocks $7.08

Reduction in Dislocation Cost from Renewable fuel Shocks -$0.52

Avoided Macroeconomic Dislocation Benefits $6.56