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 fromthe 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 day 1 . 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 OECDEurope Central and South America Mexico

Country of crude originPercentoftotalcrude Marginal Crude Mix

Average Crude Mix

VI. Discussion of Results
Using estimates of well-to-tank GHG emissions for crudes produced in various countries 8 , 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 bytaking 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 CO 2 e/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 theEconomic 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.