US alternative jet fuel deployment scenario analyses identifying key drivers and geospatial patterns for the first billion gallons

• Published in: Biofuels, bioproducts and biorefining Vol. 13; no. 3
• Main Authors: Lewis, Kristin C., Newes, Emily K., Peterson, Steven O., Pearlson, Matthew N., Lawless, Emily A., Brandt, Kristin, Camenzind, Dane, Wolcott, Michael P., English, Burton C., Latta, Gregory S., Malwitz, Andrew, Hileman, James I., Brown, Nathan L., Haq, Zia
• Format: Journal Article
• Language: English
• Published: United States Wiley 07.12.2018
• Subjects: alternative jet fuel; aviation; biofuel; ENERGY PLANNING, POLICY, AND ECONOMY; geospatial optimization; sustainable aviation fuel; systems dynamics modeling
• ISSN: 1932-104X; 1932-1031

The aviation sector's commitments to carbon-neutral growth in international aviation starting in 2020, and the desire to improve supply surety, price stability, and the environmental performance of aviation fuels, have led to broad interest in sustainable alternative jet fuels. Here, we use the system-dynamics-based biomass scenario model (BSM), focused on alternative jet fuel production capacity evolution, and the geospatially explicit Freight and Fuel Transportation Optimization Tool (FTOT), focused on optimal feedstock and fuel flows over the transportation system, to explore the incentive effects on alternative jet fuel production capacity trajectories and potential geospatial patterns of production and delivery in the USA. Scenarios presented here focus on readily available waste feedstocks (waste fats, oils and greases, municipal solid waste, and crop and forestry residues) and conversion technologies included in the ASTM D7566 synthesized aviation turbine fuels specification. The BSM modeling of possible deployment trajectories from 2015 to 2045 suggests that up to 8 billion gallons may be available by 2045 depending on the policies and incentives implemented. Both approaches suggest that 200 million to 1 billion gallons per year of alternative jet fuel production are possible in 2030 given multiple incentives and a favorable investment climate, and that capital costs and technology maturation rates 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.