### Energy Security Impacts of Renewable

Fuel Use Under RFS2

- Methodology

Paul N. Leiby Oak Ridge National Laboratory Version 1. January 1 9 , 2010

Summary

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

. These red uctions 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 reduction s i n risks and cost impr ove 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

. B ased upon this updated approach, we estimate the monetary v alue of the energy security benefits of the

RFS2 rule, which require s increased usage of renewable fuel s 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. I mport 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 Biofu el and Oil Supply Shocks

5.1 Accounting for Renewable Fuel Supply Shock s 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 in creases 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 d emand

. An important part of the problem stems from U.S.

reli ance 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 ex erting 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 sho cks and price spikes. In 2008 , U.S. im ports of crude oil were roughly $ 391 billion ( 2007$, see Figure 1 ).

Figure 1. U.S. Expenditures on Crude Oil

Source: Annual Energy Review s and AEO 2009

.

By requiring the wider use of renewable fuels, t he RFS 2 not only reduces oil imports, but

promot es diversification of transportation fuels in the U.S

. , reduces oil consumption, and improves the stability of overall liquid fuels supply, thereby help ing to improve U.S. energy security. In prior work, an ―oil import premium‖ approach wa s utilized to id entify those energy

security 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 o il

. To understand the energy security implications of redu cing 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 repor t was included as part of the record in the final RFS1 rulemaking, and revisions were made based on external comment and peer review. C

A Leiby, Paul N. “Estimating the Energy Security Benefits of Reduced U.S. Oil Imports: Final Report”, ORNL/TM

2007/028, Oak Ridge National Laboratory, March 6, 2008.

B Leiby, Paul N., Donald W. Jones, T. Randa ll 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 mode l year light trucks. See3

For this analysis, the ―oil import premium‖ methodology was extended to more explicitly consider the energy security imp lications 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

co sts, 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, oi l prices and refere nce 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 price s; 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 ma y 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 hi gher 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 u sing the Oil Security Metrics Model (OSMM) E

, F to 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 R eform MY

2008

- 2011, March 2006.

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

ORNL es timated that the total energy security benefits associated with a reduction of imported oil is $ 12.3 8 /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 Y ergin, 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 metho ds 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 t he 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 tra nsportation 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 imp ort (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 exte nded 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 F uels 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

con ducting this analysis, ORNL considered t he full economic cost of importing petroleum into

the U.S. In keeping with the oil import premium analysis used for the RFS2 Proposal , t he 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 deman d" 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 c osts ). Analogously, thi s analysis of the RFS2 rule also consider s 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. drough ts, 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 incorpor ate estimates of overall motor fuel supply and demand flexibility and reliability, and the impacts of possible agricultural sector market disruptions . For example, t he

use of renewable fuels can mode stly alter short and long run demand elasticit ies ( 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 RFS 2 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

chal lenged 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 RFS 2 rule

.5

3

. Effect of Oil Use on Lon g

- Run Oil Price and U.S. I mport 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. B ecause the U.S. is a sufficiently large purchaser of for eign 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 pri ce 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 effe ct 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 da y ($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‖ b enefit represents t he 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 ren ewable 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 pro

jected 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 Howev er, 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 1 1) 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 balanc e 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 cos ts, 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 r educe 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) part ial 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 P t . D it and S it are the resulting short

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

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 year

by

- year to conform to the AEO2009 reference projections, given exogenously sp ecified 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 Secti on 4

.

In the O SMM 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, sacrif icing 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 be havior 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 exp anded 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 D Uct under policy case c is : Equation 2

Here D i ct and S i ct are regional demands and supplies from Non

- OPE C regions, S O ct is OPEC supply, and P ct is the market price. The sum over regions i is meant to exclude OPEC region O. The price coefficients b Di t and b Si t reflect the price elasticities of demand and supply. This market

balance i s solved recursively fo r 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

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 impa ct 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. T he 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 Incentiv es for Faster Output Growth," The Energy Journal, 25(2):75

- 96

, "What Oil Export Levels Should We Expect From OPEC?" Th e Energy Journal, 28(2):151

- 173, 2007.8

Premium analysis, and many other studies of the monopsony effect ( b etween $0.60/bbl and $1.20/bbl per MMBD reduction in imports)

.

Figure 2

. Dynamic Est imates 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, parti cularly 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, b iofuel consumption quantities and prices (apart from deviations due to shocks) are taken from exogenous scenarios specified for consistency wit h other analysis done for the Regulatory Impact Analysis of the RFS2 rule (see Figures 3 and 4)

.9

Figure 3 : Total Biofuels Use and Change from Reference to RFS2 Case10

Figure 4. The Plant gate price for corn

- ethanol is s omewhat 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 woul d have been imported

under the R eference case to estimate the incremental costs of biofuels attributable to higher demand (biofuel monopson y 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 F uel/FFV Introduction on Demand Elasticity

U.S. ener gy 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 f uel, 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 valu e 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 fue l 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 longer11

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 f

ollowing equation : Equation 4

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 sh are 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 s hare 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 ( K FFV / K LDV ).

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, Commerci al 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

The ap proach modeled here is co nsistent 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 su bstantial fraction of

renewable fuel is blended in diesel blends) then this substitution term c ould 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 a ccounted 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 degr ee of E85 station availability I , 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

i mpact 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 negligib le. 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 a fter 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 elasti city.

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 Shock s and Cost Volatility

One feature of the OSMM is its explicit treatmen t 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 a re 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 Januar y, 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 sub ject 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 Mo nthly 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%

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

T he macroeconomic cost of renewable fuel disruptions, like oil disruptions, i s 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 i n 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 ma croeconomic/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 t he 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 var iety of other factors. J

Th e 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 Development K , as presented in Table 2 below. These estimates of feedstock yield volatility are then used to estimate variations in feedstock cost L and 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

eac h 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 f ro m 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 a n Ethanol Use

Mandate? ‖ Energy Policy , Elsevier, 2009, 37, Pages 745

- 749.

M B iofuel 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. D ev.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 19 60

- 2008 1997

- 2008

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

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

Le iby/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 3 a ) . Finally,

b ase d on renewable fuel production cost economics characterized by Tao & Aden 2009, N yield volatilities were converted to measures of volatility of renewable fuel production cost at the plant gate (see Table 3 b ). For each renewable fuel

, Tao & Aden estimate fuel unit production cost as a

linear function of feedstock cost s: Equation 6

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

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 perc ent, with ethanol derived from sugar cane estimated to have the lowest volatility, and ethanol derived from corn, the highest volati lity. As one would expect, biofuel cost volatility increases with the volatility of feedstock supply, but decreases in case s 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 es timated 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

3 a. Biofuel

Feedstock Corn Soybeans Wheat Sugar Cane

Elasticity of

Feedstock Price w ith

respect t o Yield*

- 1.2

- 1.2

- 1.2

- 1.2

3 b. Bio fuel 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 Sho cks

S tochastic o il supply shocks lead to price excursions superimposed on reference oil prices . S ingle 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), t he 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 an nual 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 f uture 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 oi l 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

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

6. Macroeconomic Dislocation Costs Associated with Oil and Renewable F uel 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 a nd 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 o n 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 facto rs 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. Thes e 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. I n 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 cost s 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 macroe conomic 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 P oMct and

renewable fuel price P oMct , which is based on a lagged partial adjustment process, essentially yielding a weighted average of past prices with geometric ally declining weights.

Equation 8

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. Further more, 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 o bserved 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 E conometrics

, 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 t he 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 pri ces 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 effect s, 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 expenditu re 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 f urther refinement, the economic impacts of price shocks are spread over the year of t he 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

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

a djustment c ost U elasticity as that used for GDP adjustment costs with respect to oil prices, but it

is rescaled in accordance wi th 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

t he 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 volati lity 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 expenditur e share in GDP. In the AEO 2009 r eference c ase, 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 202 2 . 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 reflect s changing fuel share, the

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

referenc e (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 decreas e 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 agric ultural 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 adju stment 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, l eads 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 ―adj usted 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 s till 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

li ttle 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 t o 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 av ailable 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 initial20

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 ela sticities.

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 2 022.

The Reference Case renewable fuel s 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, princ ipally 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 quantit ies 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 fuel s imports than U.S. oil imports, the monopsony disbenefit per

barrel of increa sed renewable fuel s use is much smaller

, only $0.02/barrel, compared to the oil monopsony benefit

, $7.88/barrel. Thus, including the impact of expanded renewable fuel s use

on renewable fuel s imports and price yields a slightly lower estimate of the total monoposony benefit , or incremental import cos t 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 fuel s 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 d isplayed 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

Avoid ed Macroeconomic Dislocation Benefits $6.56