Future Aviation Biofuel Analysis Using the Biomass Scenario Model

Research output: NRELPresentation


This presentation summarizes the findings from two articles that use the Biomass Scenario Model (BSM) to explore potential future aviation biofuels scenarios. Newes et al. (2017) concluded that 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. First, 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. Second, R&D investment in pre-commercial technologies is needed to reduce the cost of production through learning-by-doing. Third, 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. (2018) showed that both BSM and the Freight and Fuel Transportation Optimization Tool 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.
Original languageAmerican English
Number of pages19
StatePublished - 2018

Publication series

NamePresented at the CAAFI Biennial General Meeting, 4-6 December 2018, Washington, D.C.

NREL Publication Number

  • NREL/PR-6A20-72865


  • alternative jet fuel
  • aviation
  • biofuel
  • biomass scenario model
  • system dynamics modeling


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