Date: 2/1/10

To: EPA-HQ-OAR-2005-0161

Subject: Petroleum Indirect Impacts Analysis

I. Overview

Per the statutory requirements of Section 201 of the Energy Independence and Security 

Act (EISA), EPA was required to establish a baseline based on the “average lifecycle 

greenhouse gas emissions, as determined by the Administrator, after notice and 

opportunity for comment, for gasoline or diesel (whichever is being replaced by the 

renewable fuel) sold or distributed as transportation fuel in 2005.”1 This baseline is used 

to determine whether certain renewable fuel pathways are able to meet the GHG 

reduction thresholds also mandated in Section 201 of EISA.

In the preamble to the proposed Renewable Fuel Standard (RFS2) regulation, we 

acknowledged that it was possible that renewable fuels may actually displace fuels which 

on the margin had higher lifecycle GHG emissions than the average petroleum fuel 

baseline. Furthermore, we took comment “on whether-strictly for purposes of assessing 

the benefits of the rule (and not for purposes of determining whether certain renewable 

fuel pathways meet the GHG reduction thresholds set forth in EISA), we should assess 

benefits based on a marginal displacement approach and, if so, what assumptions we should 

use for the marginal displacements.” We also received numerous comments that urged us 

to assess the indirect impacts of petroleum use.

 

For this final RFS2 rulemaking, we carried out an analysis of the indirect impacts of 

petroleum, employing the Department of Energy’s (DOE) Energy Technology 

Perspectives (ETP) model. The ETP model is a MARKAL-based model that was 

developed by the International Energy Agency and was modified and updated by DOE.2 3

It is a partial equilibrium model that incorporates a representation of the physical energy 

system and represents the flow of energy carriers through the physical infrastructure from 
the resource base through the various energy conversion technologies to the end user. 

The MARKAL modeling framework allows for a full “well-to-wheels” comparison.4 5 6

The main objective of our study was to understand the sources of marginal crude oil that 

would be displaced by renewable fuels in 2022. This would give us insight into whether 

the lifecycle GHG emissions for marginal crude in 2022 would differ significantly from 

the 2005 average petroleum baseline established by this rule.

II. Model Background

The ETP model consists of fifteen world regions. These are broken out into: the United 

States; Canada; Mexico; IEA Europe; Japan; South Korea; Australia/New Zealand; 

Central and South America; Eastern Europe; Former Soviet Union; Middle East; China;

India; Other Developing Asia; and Africa. All major energy sources are covered, 

including coal, oil, natural gas, nuclear power, and renewable energy. Energy conversion 

processes include power generation and refinery operations and other non-conventional 

processes such as coal-to-liquids and biomass-to-liquids. Demand is represented through 

industrial, commercial, residential, and transportation sectors. The model runs through 

2050 in five year increments. For this study, ETP has been benchmarked and updated 

using the Energy Information Administration’s (EIA) Annual Energy Outlook (AEO).7

III. Scenario Description

The purpose of this study was to isolate the impact of petroleum use on total energy 

sector greenhouse gas emissions. Thus, we created a “demand boost case”, which was a 

domestic gasoline demand increase of one million barrels per day1. The boost was 

phased in beginning in 2012 with annual 200,000 barrel per day increments over a five

 

1 The ETP model operates in energy units on a lower heating value basis. The energy equivalent of 1 

million barrels/day was obtained by using the lower heating value energy content of conventional motor 

gasoline as reported in EIA’s Annual Energy Review 2008 (approximately 4.90 MMBtu/barrel). The 1 

million barrels/day converts on an annual basis to about 1.78 quads of gasoline.
year period. The increase in demand could be met by both foreign and domestic refined 

products. 

IV. Assumptions and Limitations

There are a number of economic assumptions in the ETP model. For this scenario, the 

ETP projected demand-driven perturbations from the AEO oil price path. Thus, 

alternative oil price paths could affect the market share and energy consumption results

anticipated by the model. 

In addition, the global oil market is not modeled as fully competitive. It is assumed that 

the Middle East members of OPEC have significant market power and act as a dominant 

firm. In this representation, the Middle East OPEC countries set world oil prices, and 

other countries and regions produce up until the point where their marginal cost of 

production equals the world oil price. 

In ETP, the oil price consists of five different components: finding costs; production 

costs; transportation costs; oil company profits; and government revenues. Government 

revenues in the Middle East are the main lever used to set oil prices. A high oil price case 

is thus one where Middle East producers restrict supply, which pushes prices higher and 

allows them to extract a higher economic rent on each barrel of oil produced. In ETP, this 

“rent seeking” is the variable used to calibrate to a given oil price path. For the purposes 

of this study, the Middle East rent seeking is set to the level that is implicit in the AEO 

2009 oil price path, given the production cost of Middle East producers. This level of rent 

seeking is held constant between scenarios.

The ETP model is driven by exogenously specified demands for energy services (e.g. 

light-duty vehicle miles traveled, ton-miles of rail freight, etc.). The U.S. petroleum 

demand base case was calibrated to the AEO 2009 projections by adjusting these service 

demands to the levels forecasted by the EIA. Demands were derived from AEO trends 

for the model years after the AEO forecast period ends.
Overall oil resources are based on IEA (2008) ETP data for conventional and 

unconventional resources. Unconventional resources include ultra-deepwater crude, 

Canadian oil sands, Venezuelan extra-heavy crude, and coal-to-liquids. Canadian oil 

sands production rates were endogenously determined based on the price of other 

competing resources. Within ETP, data are limited for other unconventional resources; 

however, the completed scenario suggests that other unconventional production would 

not play a large role in meeting the boosted demand.

V. Description of Results

In 2022, ETP predicted a 0.73 million barrels of oil equivalent (MMboe) per day increase 

in conventional oil production and a 0.04 MMboe per day increase in oil sands/bitumen 

production. The increase in petroleum production came mainly from the Middle East 

(0.46 MMboe per day, 59%), with no other region accounting for more than 8% of the

total increase in production. Oil sands/bitumen, where production was split between 

Canada and Central/South America, accounted for less than 5% of the total increase in 

production. The ETP results show a considerably different production distribution for the 

2022 marginal crude mix versus the 2005 average crude mix used for the final rule.
Comparison of 2022 marginal crude mix from ETP run to 2005 crude mix used for 

RFS2 petroleum baseline calculation

0%

10%

20%

30%

40%

50%

60%

Middle East Africa Former

Soviet Union

United

States

Canadian oil

sands/

Venezuelan

bitumen

OECDEuropeCentral and

South

America

Mexico

Country of crude origin

P

erc

ent of total crud

e

Marginal Crude Mix

Average Crude Mix

VI. Discussion of Results

Using estimates of well-to-tank GHG emissions for crudes produced in various

countries8, we calculated the well-to-tank GHG emissions for the marginal crude mix. 

As ETP did not have data for specific countries, emissions for regions were estimated by 

taking the straight average emissions rate for countries in that region. For example, wellto-tank values for Africa were estimated by taking the average of the values for Nigeria, 

Angola, and Algeria. However, there were some regions, such as the “Former Soviet 

Union”, where well-to-tank estimates were not available. In these cases, the value for 

“Other Imports” was used as a default value. 
Well-to-tank GHG emissions for domestic diesel production by crude source

Country of Origin Well-to-Tank Values

(kg CO2e/MMBtu LHV)

Canada Oil Sands 34.0

Venezuela Bitumen 30.8

Nigeria 29.7

Mexico 24.1

Angola 23.0

Kuwait 19.6

Iraq 18.7

Venezuela Conventional 18.6

Canada Conventional 18.0

Ecuador 17.8

Saudi Arabia 17.4

Other Imports 17.3

U.S. 13.5

Algeria 12.4

These calculations yielded a well-to-tank value for the marginal crude mix of 19.0

kg/MMBtu. This value was slightly higher than the well-to-tank value used for the 2005 

average petroleum baseline in the final rule. When combined with the tailpipe emissions, 

this yielded a well-to-wheels value of 98.0 kg/MMBtu, about 0.6% larger than the 

average petroleum baseline value.2

One area we examined in more detail was the use of the “Other Imports” value as a 

default. Overall, the default value was used for 18% of the marginal crude mix. To test 

the sensitivity of this value in the overall calculations, we considered a range of default 

values for conventional crude oil, from 12.4-29.7 kg/MMBtu. Utilizing this range of

default values, we find that, for the marginal crude mix, this yielded well-to-tank values

 

2 The marginal value is higher than the average because low-GHG domestic crudes are replaced by Middle 

East crudes, and there is a slightly higher amount of oil sands.
from 18.1-21.2 kg/MMBtu and well-to-wheels values from 97.1-100.2 kg/MMBtu. The 

marginal well-to-wheels values ranged from being 0.3% less to 2.9% greater than the 

average 2005 petroleum baseline value used in the final rule depending on the default 

value used.

VII. Conclusions

We created a scenario with increased domestic demand for gasoline and used DOE’s ETP 

model to analyze how the increased demand would be met in 2022. The model indicated 

that crude oil production would increase, with close to 60% of the increase in production 

occurring in the Middle East and less than 5% of the increase in production coming from 

oil sands/bitumen. Overall, well-to-wheels emissions for the marginal crude oil were 

virtually the same as the average well-to-wheels emissions calculated for the 2005 

petroleum baseline.

 

1

 Clean Air Act Section 211(o)(1)

2 IEA, 2006. Energy Technology Perspectives 2006. Organisation for Economic Cooperation and Development (OECD)/IEA, Paris, France. 

3 IEA, 2008. Energy Technology Perspectives 2008. OECD/IEA, Paris, France.

4 Fishbone, L., Abilock, H., 1981. MARKAL, a linear-programming model for energy 

systems analysis: Technical description of the BNL version. Energy Research, 5, 353-

375.

5 Hamilton, L.D., Goldstein, G.A., Lee, J., et al., 1992. MARKAL-MACRO: An 

overview. Report No. BNL-48377. Brookhaven National Laboratory, Upton, NY. 

6 Loulou, R., Goldstein, G., Noble, K., 2004. Documentation for the MARKAL Family 

of Models. Available at <http://www.etsap.org> Accessed February 2009.

7 EIA, 2009. An Updated Annual Energy Outlook 2009 Reference Case Reflecting 

Provisions of the American Recovery and Reinvestment Act and Recent Changes in the 
 

Economic Outlook. Report #: SR-OIAF/2009-03. Available at: 

<http://www.eia.doe.gov/oiaf/servicerpt/stimulus/index.html> Accessed December 2009.

8 Department of Energy: National Energy Technology Laboratory. 2009. NETL: 

Petroleum-Based Fuels Life Cycle Greenhouse Gas Analysis - 2005 Baseline Model. 

Available at <www.netl.doe.gov/energy-analyses> Accessed December 2009.