Future Aviation Biofuel Analysis 

Using The Biomass Scenario 

Model

Emily Newes 

National Renewable Energy Laboratory

CAAFI Biennial General Meeting

December 5, 2018

NREL/PR-6A20-72865
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Overview

Today’s talk will:

• Provide a brief overview of the Biomass 

Scenario Model (BSM) 

• Summarize findings from two articles that 

use the BSM to explore potential future 

aviation biofuels scenarios.

– Newes, E., J. Han, and S. Peterson. “Potential Avenues 

for Significant Biofuels Penetration in the U.S. Aviation 

Market.” Golden, CO: National Renewable Energy 

Laboratory, 2017. 

http://www.nrel.gov/docs/fy17osti/67482.pdf.

– Lewis, K., E. Newes, S. Peterson, M. Pearlson, E. Lawless, 

K. Brandt, D. Camenzind, et al. “U.S. Alternative Jet Fuel 

Deployment Scenario Analyses Identifying Key Drivers 

and Geospatial Patterns for the First Billion Gallons.” 

Biofuels, Bioproducts and Biorefining, Accepted 2018. 

https://doi.org/10.1002/bbb.1951.
Overview of the BSM
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The BSM models the bioeconomy

Source: 2017 MYPP

Enabling Infrastructures

q Fuels: Fueling station investment 

decisions

q Power: Electrical system

q Products: Distribution coverage

SUPPLY CHAIN

Feedstock 

Production

Feedstock 

Logistics Conversion Distribution End Use

Feedstock Logistics Module

q 2 logistics systems

q Cost breakdowns

q Transportation distance

q Land eligibility

Distribution Logistics Module

q Distribution node focus

q Differential cost structure, based on 

infrastructure (storage and intra/

inter-region transport costs)

Consumer Choice (long-term)

q Fuels: vehicle choice

q Power: retail market structures

q Chemicals: rules / standards for 

labeling 

DYNAMIC MODELS OF SUPPLY INFRASTRUCTURE, 

PHYSICAL CONSTRAINTS, MARKETS, AND DECISION MAKING

POLICIES, INCENTIVES, EXTERNALITIES

Conversion Module

q 15 conversion platforms

q 3 development stages

q 5 learning attributes

q Cascading learning curves

q Project economics

q Industry growth and investment 

dynamics

q Fuels, products, chemicals

Feedstock Supply Module

q 9 feedstock types

q 10 geographic regions

q Farmer decision logic

q Land allocation dynamics

q New agriculture practices

q Endogenous markets and prices

q Pellet supply

q Demand for feedstock from 

different end uses

Consumer Choice (short-term)

q Biofuel, biopower, and bioproduct 

labeling / preferences

q Relative price/choice dynamics
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System relationships drive progress across the 

bioeconomy

The BSM allows scenario exploration to support decision making 

highlighting interactions across systems, with nonlinearity, 

constant change, historical dependence, and evolving markets.

Simplistic representation of basic feedback between supply chain sectors

Conversion of 

feedstock

Distribution of 

finished product

Demand for 

feedstock

Price of finished 

product
Potential Future 

Scenarios for Aviation 

Biofuel
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Analysis Questions

• Is it possible to displace 30% of the jet fuel market (6 billion 

gallons) with biofuels by 2030? 2040?

• What are the characteristics of scenarios in which the aviation 

biofuel system, as modeled with current policy and prices, 

reaches 1 billion annual gallons of production in the near 

term?

• What would you have to assume to reach 1 billion annual 

gallons of aviation biofuel at an earlier date?
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Wide Range of Jet Fuel Production Trajectories

For these studies, we simulated jet fuel production 

in the BSM with 288 runs. Construction capacity 

and feedstock supply are the main barriers in the 

initial 5-7 years.

Aviation Biofuel Production (gal/yr)

Time

Source = Lewis et al. (Accepted 2018)
Potential Avenues for Significant Biofuels 

Penetration in the U.S. Aviation Market

Newes, E., Jeongwoo H., and S. Peterson. “Potential 

Avenues for Significant Biofuels Penetration in 

the U.S. Aviation Market.” Golden, CO: National 

Renewable Energy Laboratory, 2017. 

http://www.nrel.gov/docs/fy17osti/67482.pdf.
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Analysis suggests 6 billion gallons of aviation biofuel by 2030 is 

possible with aggressive assumptions

Increasing Direct Investment

Increasing Incentives

Source = Newes et al. (2017)

Assumptions:

• U.S. jet fuel production is maximized and not dynamically determined.

• Biorefinery construction is limited to 25 plants/year, due to labor and 

materials constraints.

• CO2 tax is represented as an advantage, based on relative carbon intensity 

versus petroleum.

• The $1/gal production incentive is only applied to jet fuel and sunsets in 

2030.

• DMT = R&D investment in pre-commercial techs.

Baseline:

$0.50/RIN; tax credit extension and 80% loan guarantee for first billion gallons 

of total production; AEO 2015 Reference Case
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Analysis suggests 6 billion gallons of aviation biofuel by 2030 is 

possible with aggressive assumptions

Increasing Direct Investment

Increasing Incentives

Source = Newes et al. (2017)

[d]

[2]

[3]

[1]
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Analysis suggests 6 billion gallons of aviation biofuel by 2030 is 

possible with aggressive assumptions

Increasing Direct Investment

Increasing Incentives

Source = Newes et al. (2017)

[d]

[2]

[3]

[1]

Incentives targeted toward aviation biofuel 

production, such as financial incentives (e.g., 

producer tax credit, CO2 tax) can spur growth 

without other incentive types, as in panel [d].
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Analysis suggests 6 billion gallons of aviation biofuel by 2030 is 

possible with aggressive assumptions

Increasing Direct Investment

Increasing Incentives

Source = Newes et al. (2017)

[d]

[2]

[3]

[1]

Production incentives + R&D 

investment in pre-commercial 

technologies can accelerate industrial 

growth relative to production 

incentives alone. 

Pre-commercial R&D investments plus loan: Row [2] vs. Row [1]

Pre-commercial R&D investments plus offtake agreements: Row [3] vs. Row [1]
U.S. Alternative Jet Fuel Deployment Scenario 

Analyses Identifying Key Drivers and Geospatial 

Patterns for the First Billion Gallons

Lewis, K., E. Newes, S. Peterson, M. Pearlson, E. 

Lawless, K. Brandt, D. Camenzind, et al. “U.S. 

Alternative Jet Fuel Deployment Scenario 

Analyses Identifying Key Drivers and Geospatial 

Patterns for the First Billion Gallons.” Biofuels, 

Bioproducts and Biorefining, Accepted 2018. 

https://doi.org/10.1002/bbb.1951.
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Necessary Assumptions for One Billion Gallons by ____ Year

With 2016 conditions continuing, 1 billion gallons 

of renewable jet fuel are reached by 2042

This figure shows possible scenarios that would accelerate reaching 1 billion gallons of renewable jet 

fuel production by 5, 10, 15, or 20 years.

Source = Lewis et al. (Accepted 2018)
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Future Alternative Jet Fuel Deployment

FTOT Scenario Year

BSM Results

• Analysis focused on two questions:

1) How much alternative jet fuel (AJF) can be produced and how 

soon?

2) What is the likely geospatial distribution of feedstock and fuel 

production and AJF delivery? 

• Freight and Fuel Transportation Optimization Tool (FTOT) results for 

certain scenarios are well within BSM results.
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Key Takeaways

Newes et al. (2017)

• Construction/build out capabilities and development of the feedstock market are key 

bottlenecks in the initial years.

• Displacement of jet fuel by 30% with biofuels by 2030 is possible, but several factors 

related to policy design—in the absence of high oil prices or policy uncertainty— contribute to the timing and magnitude of aviation biofuels production: 

– Incentives targeted toward jet fuel production, such as financial incentives (e.g., a 

producer tax credit or a CO2 tax) could be sufficient to reach six billion gallons. 

– R&D investment in pre-commercial technologies is needed to reduce the cost of 

production through learning-by-doing. 

– Reduction of investment risk through loan guarantees and offtake agreements may 

allow production to ramp up more quickly through accelerating industry learning. 

Lewis et al. (Accepted 2018)

• Both BSM and FTOT suggest that 

– 200 million to 1 billion gallons per year of alternative jet fuel production are 

possible by 2030 given multiple incentives and a favorable investment climate.

– However, different capital costs and technology maturation rates in the two models 

will affect deployment of different fuel production technologies and therefore the 

feedstocks needed. 

– Further collaboration on these modeling approaches would reduce methodological 

blind spots while providing insights into future industry trajectories.

For a full list of BSM publications go to https://www.zotero.org/groups/bsm_publications/items
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Acknowledgements

Funding and analysis support:

Zia Haq and Alicia Lindauer (U.S. Department of Energy, Bioenergy Technologies Office) 

Co-authors:

“Potential Avenues for Significant Biofuels Penetration in the U.S. Aviation Market”

• Jeongwoo Han (formerly Argonne National Laboratory)

• Steve Peterson (Lexidyne LLC and Dartmouth College)

“U.S. Alternative Jet Fuel Deployment Scenario Analyses Identifying Key Drivers and Geospatial 

Patterns for the First Billion Gallons”

• Kristin Lewis, Steve Peterson Matthew Pearlson, Emily Lawless, and Andrew Malwitz (Volpe)

• Kristin Brandt, Dane Camenzind, Michael Wolcott (Washington State University)

• James Hileman, Nathan Brown (U.S. Federal Aviation Administration)

• Zia Haq (Bioenergy Technologies Office)

BSM Team:

Brian Bush, Daniel Inman, Amy Schwab, Dana Stright, Steve Peterson, Laura Vimmerstedt
Thank you!

Emily.Newes@nrel.gov

This work was authored by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy 

(DOE) under Contract No. DE-AC36-08GO28308. Funding provided by U.S. Department of Energy Office of Energy Efficiency and Renewable Energy 

Bioenergy Technologies Office. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S.

Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up,

irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes.