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Abstract
Achieving the aerospace industry target of net-zero emissions by 2050 requires rapidly scaling up sustainable aviation fuel (SAF) production. Leveraging existing infrastructure, proven technologies like Alcohol-to-Jet (ATJ), and low carbon intensity (CI) feedstocks (e.g., switchgrass, miscanthus) can support this transition and help achieve near-term emissions reduction targets. This study evaluates the implications of lignocellulosic ethanol biorefinery siting and integration with petroleum refineries to produce SAF across 1,000 randomly sampled candidate locations in the U.S. rainfed region. To better understand logistics of material transport and handoffs, we integrated models of biomass harvest, transport, ethanol and ATJ production in a stochastic framework to simulate performance under uncertainty and characterized SAF minimum selling price (MSP) and carbon intensity (CI), considering site-specific parameters (e.g., feedstock production, transportation, taxes, and incentives). Results indicate trade-offs between MSP and CI across locations, with median MSP ranging from 7.2 to 11.1 USD·gal-1 and CI from -4 to 31 gCO2e·MJ-1. While the estimated SAF decarbonization cost is high (500 USD·tonCO2e-1), integrating ATJ with low CI feedstocks remains promising. This analysis highlights the importance of location-specific approaches to identifying feasible sites for investment in SAF production, considering the end-to-end supply chain and providing insight into potential cost and sustainability trade-offs.
Supplementary materials
Title
Supporting Information
Description
Assumptions for feedstock CI calculation based on SOC sequestration results from DayCent Simulation; Feedstock yield, GHG emissions and breakeven price at farmgate maps; Breakdown of transportation costs by U.S. region and average state transportation prices maps for US; Assumptions used to calculate transportation GHG emissions and average state values map; Variation of average tortuosity factors for farm to ethanol refinery transportation by state and crop; Map of 1,000 candidate locations used for each crop; State-specific parameters used for ethanol biorefinery and SAF refinery simulations; Detailed TEA and LCA assumptions; Schematic figure to explain (i) how the simulation is executed and (ii) how data is transferred and integrated across the modeling components; Results of disaggregated cost and CI contributors for all Pareto front locations for SAF derived from switchgrass and miscanthus; Results for SAF derived from miscanthus; Breakdown of costs for ethanol conversion when feedstock price is zero.
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