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Ethanol

Ethanol is a renewable fuel. Made from biomass (i.e., a variety of plant materials), ethanol is used in 98% of U.S. gasoline. Typically, gasoline comprises E10 (10% ethanol, 90% blendstock for oxygenate blending (BOB)(DOE, 2019). For additional background, see the Alternative Fuels Data Center's Ethanol Fuel Basics webpage.

Detailed information about ethanol is presented next. 

Emissions estimates use the Argonne National Laboratory's Research & Development Greenhouse gases, Regulated Emissions, and Energy use in Technologies (R&D GREET) model (Wang et al., 2023). The underlying source for a value in the table can be seen by placing your mouse cursor over that value.

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Note: These results are highly context dependent and may not represent the optimal values for each fuel pathway. We recommend caution—and review of other sources—before making comparisons between the cases reported in the table above.

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Key Assumptions

The data and estimates presented here are based on the following key assumptions:

  • Fuel Price: The fuel price (e.g., Lowest Cost, Lowest Emissions) is associated with a single year. Because we do not provide a time-series trajectory, here we show fuel price at a frozen level for all years so we can offer a range of fuel price values. In the levelized cost of driving (LCOD) and emissions charts, this approach clearly distinguishes effects of fuels from those of vehicle technologies because fuels remain constant, whereas vehicle technologies change over time.
  • Plant Gate Blendstock Fuel Prices: The plant gate blendstock fuel prices shown here are meant to reflect minimum fuel selling prices (and do not include distribution costs or taxes).
  • Ethanol from Corn Grain Pathway Cost: The starch ethanol cost is based on the original process design (McAloon et al., 2000), with updated costs. The plant characteristic are updated to be consistent with current design cases. 
  • Ethanol from Lignocellulosic Biomass via Biochemical Pathway Cost: The biochemical ethanol cost is based on the process design of (Humbird et al., 2011), with dollar year updated, and is consistent with current design cases. Biochemical ethanol plant characteristics are based on the process design of (Humbird et al., 2011), with updated costs.
  • Ethanol from Lignocellulosic Biomass via Thermochemical Pathway Cost: The thermochemical ethanol cost is based on analysis from Dutta et al. (Dutta et al., 2011), with the dollar year updated, and is consistent with current design cases. Thermochemical ethanol plant characteristics are based on the process design of (Dutta et al., 2011), with updated costs.
  • Ethanol from Corn Grain Emissions: The emissions intensities for starch ethanol are based on (1) the default values from the R&D GREET model (Wang et al., 2023) and (2) (Lee et al., 2021). These estimates assume the following mix of plant types and energy use as an industry average: 89% dry milling (92% natural gas, 8% coal) and 11% wet milling plant (72.5% natural gas, 27.5% coal). Note the emissions intensity of starch ethanol might vary from the industry average based on plant types and production process assumptions; for example, the well-to-wheels CO2e emissions of corn starch ethanol may vary between 51,100 g/mmBtu and 117,000 g/mmBtu (California Air Resources Board, 2020)(US EPA, 2016)(Argonne National Laboratory, 2018).
  • Ethanol from Lignocellulosic Biomass via Biochemical Pathway Emissions: The emissions for biochemical ethanol are based on the R&D GREET model (Wang et al., 2023) and assumptions from (Wang et al., 2012).
  • Ethanol from Lignocellulosic Biomass via Thermochemical Pathway Emissions: Emissions intensities for cellulosic thermochemical ethanol are based on the R&D GREET model (Wang et al., 2023); the indirect gasification pathway from Dunn et al. (Dunn et al., 2013), which assumes a southern pine feedstock and does not include land use change emissions; and (Dunn et al., 2018).
  • Biogenic Carbon: The biogenic carbon in a biofuel such as ethanol is considered carbon neutral in the R&D GREET model because the biogenic carbon is assumed to be sourced from the atmosphere during biomass growth. According to the R&D GREET model convention, the biogenic carbon credit is allocated to the well-to-tank phase of the biofuel life cycle, which often results in a negative well-to-tank CO2e emissions value after considering greenhouse gas emissions associated with all upstream activities (e.g., farming, land use change, feedstock transportation, and biomass conversion to biofuel).

The data downloads include additional details of assumptions and calculations for each metric.

To see additional information, place your mouse cursor over a value in the table.  

Definitions

For detailed definitions, see:

CO2e

NOx

SOx

PM

Fuel price

Scenarios

Well-to-tank emissions

Well-to-wheels emissions

References

The following references are specific to this page; for all references in this ATB, see References.

Argonne National Laboratory. GREET Model: The Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation Model. Argonne, IL (United States): Argonne National Laboratory, 2018. https://greet.es.anl.gov/.

California Air Resources Board. “LCFS Pathway Certified Carbon Intensities.” California Air Resources Board, April 27, 2020. https://ww2.arb.ca.gov/resources/documents/lcfs-pathway-certified-carbon-intensities.

DOE. “Alternative Fuels Data Center,” 2019. https://afdc.energy.gov/.

Dunn, Jennifer, Michael Johnson, Zhichao Wang, Michael Wang, Kara Cafferty, Jake Jacobson, Erin Searcy, et al. “Supply Chain Sustainability Analysis of Three Biofuel Pathways: Biochemical Conversion of Corn Stover to Ethanol Indirect Gasification of Southern Pine to Ethanol Pyrolysis of Hybrid Poplar to Hydrocarbon Fuels.” Argonne, IL (United States): Argonne National Laboratory, November 2013. https://publications.anl.gov/anlpubs/2014/07/78878.pdf.

Dunn, Jennifer B., Mary Biddy, Susanne Jones, Hao Cai, Pahola Thathiana Benavides, Jennifer Markham, Ling Tao, et al. “Environmental, Economic, and Scalability Considerations and Trends of Selected Fuel Economy-Enhancing Biomass-Derived Blendstocks.” ACS Sustainable Chemistry & Engineering 6, no. 1 (January 2, 2018): 561–69. https://doi.org/10.1021/acssuschemeng.7b02871.

Dutta, A., M. Talmadge, J. Hensley, M. Worley, D. Dudgeon, D. Barton, P. Groendijk, et al. “Process Design and Economics for Conversion of Lignocellulosic Biomass to Ethanol: Thermochemical Pathway by Indirect Gasification and Mixed Alcohol Synthesis.” Golden, CO (United States): National Renewable Energy Laboratory, May 1, 2011. https://doi.org/10.2172/1015885.

Humbird, D, R Davis, L Tao, C Kinchin, D Hsu, A Aden, P Schoen, et al. “Process Design and Economics for Biochemical Conversion of Lignocellulosic Biomass to Ethanol: Dilute-Acid Pretreatment and Enzymatic Hydrolysis of Corn Stover,” March 1, 2011. https://doi.org/10.2172/1013269.

Lee, Uisung, Hoyoung Kwon, May Wu, and Michael Wang. “Retrospective Analysis of the U.S. Corn Ethanol Industry for 2005–2019: Implications for Greenhouse Gas Emission Reductions.” Biofuels, Bioproducts, and Biorefining 15, no. 5 (2021): 1318–31. https://doi.org/10.1002/bbb.2225.

McAloon, Andrew, Frank Taylor, and Winnie Yee. “Determining the Cost of Producing Ethanol from Corn Starch and Lignocellulosic Feedstocks,” 2000.

US EPA. “Lifecycle Greenhouse Gas Results.” Data and Tools. US EPA, January 11, 2016. https://www.epa.gov/fuels-registration-reporting-and-compliance-help/lifecycle-greenhouse-gas-results.

Wang, Michael, Jeongwoo Han, Jennifer B Dunn, Hao Cai, and Amgad Elgowainy. “Well-to-Wheels Energy Use and Greenhouse Gas Emissions of Ethanol from Corn, Sugarcane and Cellulosic Biomass for US Use.” Environmental Research Letters 7, no. 4 (December 2012): 045905. https://doi.org/10.1088/1748-9326/7/4/045905.

Wang, Michael, Amgad Elgowainy, Uisung Lee, Kwang Hoon Baek, Sweta Balchandani, Pahola Thathiana Benavides, Andrew Burnham, et al. “Summary of Expansions and Updates in R&D GREET® 2023.” Argonne National Lab. (ANL), Argonne, IL (United States), December 1, 2023. https://doi.org/10.2172/2278803.

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