Sustainability of aviation fuel technologies depends on geography, energy, and feedstock choice: 50-year development scenarios

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Borrill, Ruoyang Yuan, Lenny S.C. Koh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8968110/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract This work presents a combined geographically-resolved prospective lifecycle modelling and multi-criteria decision analysis of five sustainable aviation fuel (SAF) production technologies using six waste-derived feedstocks across four leading producer nations (US, China, India, Brazil) from 2025 to 2075. 540 pathway-scenarios covering the 50-year evolution of each waste-to-fuel pathway are evaluated under three Shared Socioeconomic Pathways. Here we show that the sustainability of SAF production is highly sensitive to feedstock, conversion technology, geography, and background systems. Grid decarbonisation drives SAF global warming potential (GWP) reductions by 2075, yet electrification triggers significant non-carbon trade-offs. While gasification-Fischer-Tropsch achieves the lowest GWP, 2050 multi-criteria leaders are exclusively hydrothermal liquefaction pathways in the US, Brazil, and China, the latter overtaken in 2075 by sugarcane trash gasification-Fischer-Tropsch and US used cooking oil valorisation. The US mostly demonstrates the lowest environmental impacts and India the highest due to waste disposal practices and paradoxical ‘sustainability’ strategies that prioritise short-term coal use to secure future development targets. Physical sciences/Energy science and technology Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction In 2023, the aviation sector accounted for approximately 2.5% of total anthropogenic CO 2 emissions 1 . However, when accounting for non-CO 2 effects, the total effective radiative forcing is estimated to contribute at three times the rate of CO 2 alone 2 . Despite this, global demand for air travel continues to accelerate, with forecasts predicting a doubling of global air traffic by 2045 3 . Meanwhile, aviation remains a hard-to-abate sector due to the high energy density requirements of fuels and stringent safety regulations 4 . Sustainable aviation fuel (SAF) has emerged as the primary mid-term solution for meeting the aviation sector’s decarbonisation targets; notably, global warming potential (GWP) reductions compared to an 89 gCO 2eq /MJ baseline of 10% by CORSIA standards 5 , 40% in the UK 6 , 47.2% in the US 7 (for tax credits), and 63.0% in the EU 8 . SAF is a drop-in fuel, having similar properties to fossil jet fuel; this compatibility allows for immediate use in existing engines and refuelling infrastructure without requiring modification 9 . SAF comprises of an array of production pathways resulting from diverse feedstock and technology combinations 10 and can contribute toward circular economy objectives via production from waste-derived biomass, whilst minimising land-use change impacts. However, several prominent challenges persist. The scale of production required to meet net-zero targets is vast; current global SAF production accounts for less than 1% of total jet fuel demand 11 . For biofuels, supply-chain scalability is fundamentally constrained by feedstock availability 12,13 . Furthermore, uncertainties remain regarding the environmental impacts of different SAF production pathways across diverse geographic regions. Previous research highlights that the environmental performance of SAF is highly sensitive to the feedstock, conversion technology, and system boundaries 14-18 . Third-generation feedstocks often exhibit superior performance, with GWP values reported below 10 gCO 2eq /MJ 16,18,19 . Impacts are not globally uniform but vary according to regional electricity and hydrogen mixes, transport emissions, and feedstock production 19-22 , highlighting that production pathways must be tailored to local resources and capabilities. Additionally, the inclusion of the end-use phase significantly alters the benefits of SAF. When accounting for in-flight non-CO 2 emissions, the total GWP reduction may be less than 50% compared to fossil jet, underscoring the necessity for further SAF combustion and lifecycle analysis (LCA) research using well-to-wake system boundaries 12,19,23 . However, current decarbonisation frameworks neglect non-CO 2 combustion emissions 6-8,24 . Prospective LCAs are essential for understanding how SAF will perform as global systems transition toward net-zero. The Shared Socioeconomic Pathways (SSP) 25 are a set of five global scenarios describing divergent societal, economic, and technological futures that have been used in certain prospective LCA to adjust background inventory data such as electricity mixes and industrial efficiencies 12,13,19,26,27 . Modelling through 2100, the electricity sector is shown to have the largest GWP contribution for fossil jet with carbon capture and storage (CCS) and power-to-liquid fuels 27 . Similarly, forecasting to 2050 shows that common GWP drivers across bio-jet pathways come from electricity and hydrogen, resulting in the largest reductions across future decarbonisation scenarios 26 . By 2050, combined jet fuel supply from biofuels, power-to-liquid fuels, and hydrogen can achieve 89% CO 2 reductions compared to 2019 levels; however, non-CO 2 impacts are 10% higher due to limited reductions in contrail avoidance and water vapour emissions 12 . Meanwhile, rapid technological developments and earlier decarbonisation can considerably reduce emissions by 2050; although, in medium to low ambition scenarios, residual emissions after 2050 must be offset by CCS technologies 13 . However, achieving ambitious targets may only be possible with high blending ratios, reduced demand, improved fuel efficiency, and transitioning to low-carbon energy systems 27 . Despite technological advancements, research suggests that without non-CO 2 emission avoidance and rapid fossil fuel phase-out, the aviation sector maintains a 90% likelihood of surpassing the Paris Agreement temperature goals under current policies 28 . This work combines geographically-resolved prospective LCA, spatiotemporal scenario modelling based on three of the IPCC’s SSP development scenarios, and two multi-criteria decision analysis (MCDA) approaches to evaluate the sustainability of 540 SAF production pathway-scenarios comprising of five conversion technologies and six biowaste-derived feedstocks across the four largest feedstock-producing nations (US, China, India, Brazil 29,30 ) over a 50-year time horizon (2025-2075). All currently ASTM-certified SAF conversion technologies that can utilise bio-waste feedstocks are evaluated 10 ; namely, hydroprocessed esters and fatty acids (HEFA), hydrothermal liquefaction (HTL), gasification with Fischer-Tropsch synthesis (GFT), gasification with alcohol-to-jet conversion (GATJ), and fermentation with alcohol-to-jet conversion (FATJ), presented in Table 1. The selected feedstocks include the two globally most-produced bio-waste categories (Fig. 1): municipal solid waste (MSW) and agricultural residues – specifically, maize stover (MST), sugarcane trash (SCT), rice straw (RST), and wheat straw (WST) 29 – as well as used cooking oil (UCO) due to the high technology readiness level of its associated conversion process. The selected SSP scenarios with current national policy implementation (NPi) 25,31 are: SSP1-NPi ‘sustainability’ : a greener future characterised by rapid shifts towards energy efficiency and low-carbon technologies, SSP2-NPi ‘middle-of-the-road’ : a future where social, economic, and technological trends do not shift markedly from historical patterns, SSP5-NPi ‘fossil-fuelled-development’ : a future driven by competitive markets and fossil-fuelled industrial developments, with a slower transition to renewable energies 25 . Multi-criteria decision analysis (MCDA) is incorporated using both the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) 32 and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) 33 methods, offering a comparative overview of each pathway-scenario’s performance across all environmental impact categories. Key findings reveal that SAF environmental performance changes over time and is strongly coupled to regional background systems, conversion technology, and feedstock choices. GWP reductions are observed over time across all pathway-scenarios as the primary drivers (electricity and hydrogen) decarbonise. While GFT pathways consistently achieve the lowest GWP, they incur high acidification impacts from catalyst requirements; in contrast, HTL achieves the best aggregated performance across all impact categories, whereas MSW-FATJ is not currently environmentally viable by exceeding fossil jet benchmarks. Although ‘sustainability’ scenarios accelerate decarbonisation, they introduce critical trade-offs with toxicity impacts due to electrification-induced copper demand. Geographically, the US exhibits the lowest impacts in most scenarios due to its advanced background systems, while India shows the highest, driven by open dumping of waste and a short-term heavy reliance on fossil fuels to facilitate long-term development goals. Table 1. Evaluated SAF production pathways and modelling assumptions. Results Fig. 2, 3, and 4 show the life cycle impact assessment (LCIA) results over 50 years (2025-2075) of the evaluated SAF production pathways for the four largest feedstock-producing countries in the world (US, China, India, Brazil) under the SSP1-NPi, SSP2-NPi, and SSP5-NPi development scenarios, respectively. Four key environmental impact categories are displayed: global warming potential over a 1000-year time horizon (GWP) in Fig. 2a-4a, terrestrial ecotoxicity potential (TETP) in Fig. 2b-4b, freshwater eutrophication potential (FEP) in Fig. 2c-4c, and terrestrial acidification potential (TAP) in Fig. 2d-4d. Several universal trends emerge that apply across almost every evaluated feedstock, technology, and geography. These represent the fundamental shifts in the global energy system as it transitions towards net-zero over a 50-year time horizon. Table 2. Minimum and maximum global warming potential (GWP) results across all pathway-scenarios (gCO 2eq /MJ). HEFA HTL GFT GATJ FATJ Min Max Min Max Min Max Min Max Min Max UCO 3.6 38.5 2.2 22.3 MSW 1.9 41.4 1.6 12.0 3.4 51.5 97.0 180.4 MST 1.5 21.2 2.4 6.8 9.4 23.8 7.9 23.2 SCT 1.5 40.5 2.4 6.9 9.3 37.3 13.8 47.1 RST 1.8 40.9 3.2 16.8 9.6 40.0 22.4 84.6 WST 1.7 41.3 4.5 12.7 10.0 38.7 19.8 59.1 Table 2 summarises the minimum and maximum GWP results for each feedstock-technology combination across all 540 pathway-scenarios. The majority demonstrate compliance with sustainability standards, with 511, 501, 497, and 455 meeting the CORSIA, UK, US, and EU standards, respectively. Notably, nearly half of the pathway-scenarios (259/540) achieved a GWP reduction of at least 90% compared to the fossil jet benchmark 89 gCO 2eq /MJ 24 , underscoring the long-term decarbonisation potential of many SAF production routes. However, a critical set of 27 pathway-scenarios exceeded the fossil benchmark, concentrated exclusively in the MSW-FATJ pathway-scenarios, with the highest values located in India. Decarbonisation convergence: GWP shows a consistent decrease over time (0.6-93.2%) across all pathway-scenarios, primarily driven by the spatiotemporal decarbonisation of the background energy systems (Fig. 2a-4a). In 2025, impacts are coal-dominated, particularly in electricity production (mainly coal and natural gas) and hydrogen (coal gasification and steam methane reforming (SMR)). By 2075, these process emissions are largely eliminated as technologies align under global net-zero targets, and impacts become increasingly infrastructure-dominated, with transportation and upstream feedstock collection representing the remaining hard-to-abate carbon floor. Additionally, while countries start at very different baselines in 2025 (India and China generally showing larger impact than the US), they all approach similar values by 2075, suggesting that global grid parity in a net-zero future minimizes the geographic ‘penalty’ of production. The waste management penalty: Countries like India and Brazil generally show higher GWP (Fig. 2a-4a) and FEP (Fig. 2c-4c) for most pathway-scenarios, particularly in the short-term. This is largely attributed to open dumping of waste generated during the SAF production process. This demonstrates that the impact of SAF production in these regions is more sensitive to waste management practices than to the conversion process itself, contrasting with the more grid-driven impacts in the US and China. Conversely, this suggests that SAF produced in India or Brazil offer the greatest environmental ‘return on investment’ by diverting waste-derived feedstocks from high-impact open dumping to generate a useful product, leading to more significant avoided emissions. The carbon-toxicity trade-off: TETP shows a consistent increase over time (32.1-38.2% for SSP1 and SSP2) across most pathway-scenarios, demonstrating burden-shifting from GWP (Fig. 2b-4b). While transport brake wear emissions remain the key hotspot in TETP across all scenarios, the increase in TETP over time is attributed to copper smelting. As we move towards 2075, the electrification of industry and grid expansion necessary for large-scale decarbonisation leads to a significant requirement for metals. Consequently, the SSP5 scenario often demonstrates a lower TETP because it avoids the copper-intensive infrastructure of greener power systems (Fig. 4b). The mining transition: A universal shift is observed over time from energy-related mining to resource-related mining. In 2025, FEP (Fig. 2c-4c) and TAP (Fig. 2d-4d) are driven by coal mining and coal-fired power. By 2075, these same categories are driven primarily by copper mining, followed by platinum mining for catalysts. Generally, this creates a U-shaped curve or plateau for these impact categories, initially declining as coal is phased out but rising again as the demand for metals accelerates. Scenario sensitivity and trade-offs: Consequently, although the SSP1 scenarios generally yield the lowest GWP, they often result in the highest TETP and TAP due to the accelerated pace of electrification and infrastructure development (Fig. 2a,b,d). The SSP5 scenarios usually show the highest GWP and FEP due to sustained reliance on coal mining and high-carbon electricity but often show lower toxicity impacts due to slower grid expansion (Fig. 4a,c). Conversely, for certain cases, the US exhibits lower GWP in the SSP5 scenario than in SSP2, and sometimes even SSP1 (Fig. S1). This is driven by accelerated fossil fuel usage with CCS in the US SSP5 scenarios, effectively creating a ‘cleaner’ fossil-fuelled pathway. India’s short-term sacrifice: In India, the SSP1 scenarios in 2025 show higher GWP than the SSP2 scenarios (and often the SSP5 scenarios) due to heavy initial coal usage but anticipate the most drastic reductions by 2075 (Fig. S1). This demonstrates their policy of intensive short-term development for long-term sustainability and highlights the environmental sensitivity of SAF deployment to national development policies. Technological robustness: GFT consistently shows the least variation in environmental impacts across pathway-scenarios (Fig. S1). This is driven by less reliance on external hydrogen and background energy compared to the other pathways, leading to it being ‘shielded’ against the volatility of national grid and industrial changes. Therefore, GFT pathways may be a more stable choice for countries with slower decarbonisation strategies. Conversely, this simultaneously indicates less predicted improvement for this technology, showcasing a trade-off of process stability vs anticipated development. Used cooking oil Across all scenarios, UCO pathways achieve a 56.8-97.4% reduction in GWP compared to the fossil jet benchmark (Fig. 2a-4a; Table 2), agreeing with previous research 14,15,18 . For both HEFA and HTL, the US displays a lower GWP than China for most scenarios (by up to 58.0%). In 2025, this is primarily due to the method of electricity production; the US uses a mixture of natural gas and coal, whereas China relies heavily on coal power which carries a significantly higher climate impact. Additionally, the hydrogen source has a significant role as the US mainly uses SMR while China relies on coal gasification. By 2075, GWP values decline significantly (by 50.4-89.5% across all pathway-scenarios) due to electricity and hydrogen decarbonisation. Generally, HTL achieves a lower GWP than HEFA (by up to 44.0%) across most temporal and geographic scenarios due to its reduced reliance on the background electricity mix, with hydrogen production remaining the largest single contributor in 2025. In both the HEFA and HTL pathways, the US exhibits a higher TETP than China in 2050 (by up to 66.5%) due to more rapid grid expansion (Fig. 2b-4b). By 2075, however, this trend reverses as China’s TETP exceeds the US for both technologies (by up to 51.9%). Moreover, while HEFA shows a significant reduction in FEP over time (by up to 86.4%) following the phase-out of coal-intensive background processes, this reduction is less pronounced for HTL (up to 42.7%) due to its lower reliance on the electricity grid (Fig. 2c-4c). A similar temporal decline in TAP is uniquely evident for HEFA in China (up to 60.0%), reflecting the drastic transition from coal power to more sustainable sources (Fig. 2d-4d). Furthermore, the HEFA pathway demonstrates consistently higher sensitivity to the development scenarios, particularly between SSP2 and SSP5, due to its stronger coupling to the background processes (Fig. S1a). By contrast, HTL shows greater technological stability, maintaining a more consistent environmental profile between the development scenarios, except for the 2075 scenarios in China which exhibit the most variability. This is attributed to China’s greater capacity for change given its currently carbon-intensive grid, paired with the divergence of its projected development scenarios. Comparing against all 18 environmental impact categories, the temporal shifts in UCO-HEFA ranking reflect the evolving trade-offs between decarbonisation and material intensity. UCO-HEFA-US only enters the top 10 pathways in 2075 because its higher process energy demand is initially penalised by the carbon and sulphur intensity of the US grid (Fig. 5a-c). Only after grid decarbonisation can HEFA leverage its high conversion efficiency to secure a top-tier rank. Conversely, UCO-HTL-US maintains a more consistent top 10 rank due to its lower reliance on background electricity, shielding it from early-year grid emissions. Municipal solid waste Across all scenarios, the MSW pathways achieved GWP reductions of 42.1-98.1%, except for the FATJ pathway which led to an increase by 10.8-103%, shown in Table 2. Across the MSW pathways, GWP is characterised by a universal decline over time (by 4.7-92.1% across pathway-scenarios), although the magnitude and drivers vary by technology and region (Fig. 2a-4a). MSW-FATJ stands out as a significant outlier (Fig. S1b); its GWP results are considerably higher than all other pathways (by 994-5980% compared to GFT) and the fossil jet benchmark due to its low conversion efficiency which aligns with the results in Zhang et al. 42 , leading to its exclusion from Fig. 2-4. Among the remaining technologies, GFT exhibits the least variation across pathway-scenarios because of its reduced reliance on hydrogen and electricity, with its low GWP values aligning with Prussi et al. 15 . Geographically, India consistently shows higher GWP than the US or China (by up to 739% and 781% for GATJ, respectively). For GFT, this is driven by open dumping practices, while for GATJ and HTL, the 2025 spikes are additionally attributed to hydrogen production via coal gasification and electricity production from coal. For the US and China, early drivers of GWP arise from electricity production from coal and natural gas and hydrogen production from coal gasification and SMR. These reduce over time, leaving transport and the conversion process itself as the largest single contributors. By 2075, the US consistently achieves the lowest GWP, as significant contributions shift away from energy-intensive background processes. Meanwhile, non-carbon impacts reveal a shift from process-related emissions to material-intensity burdens. FEP is notably higher in India than the US or China (Fig. 2c-4c), driven by open dumping and coal mining (by up to 3170% and 1490% for HTL, respectively). Despite sharing common drivers across pathways, GATJ maintains the highest TETP due to its lower yield, increasing the specific impact of background processes compared to other conversion technologies (Fig. 2b-4b). For most scenarios, GFT shows the highest TAP due to its higher reliance on platinum (Fig. 2d-4d). The exceptions are the GATJ and HTL pathways in India in 2025, which exhibit higher values (by up to 87.1% and 291% compared to GFT, respectively) due to a heavier reliance on coal-fired electricity and hydrogen production. However, as these fossil sources are phased out, TAP converges across all locations by 2075; at this point, the impacts are dominated by copper smelting and, for HTL, platinum mining for catalysts. As expected, the results from the SSP1 scenarios are generally slightly lower or similar to SSP2, except for TETP which is higher in SSP1 (by 0.7-16.2% across scenario-pathways for GFT, GATJ, and FATJ) due to increased copper smelting for grid expansion (Fig. S1b). The exceptions highlight the growth dynamics in developing regions; for HTL and GATJ in India, the SSP1 scenario yields higher impacts across all environmental categories in 2025 (GWP is 31.% and 11.2% higher than for SSP2, respectively) due to temporarily higher impact from coal-based electricity and hydrogen, but demonstrates the most drastic reductions over time (by 92.1% and 42.3%, respectively), reaching parity with SSP2 by 2075. Conversely, SSP5 in India shows the smallest variations over time, where sustained coal usage prevents significant GWP and FEP reductions by 2075. When compared against all 18 impact categories, MSW-HTL-US stands out as a high-performing pathway, consistently ranking in the top 10 across every scenario and always ranked in the top 5 by at least one MCDA method (Fig. 5a-c). This is primarily driven by the relatively high efficiency of the HTL conversion process and the progressively decarbonised energy infrastructure in the US. By contrast, while MSW-HTL-CN is highly competitive in the short-term, ranking in the top 10 for all 2025 scenarios, it fails to maintain this position by 2075. This decline suggests that while HTL is initially superior to regional alternatives in China, other feedstock-technology combinations eventually surpass it in the long-term rankings across all impact categories. Conversely, the MSW-FATJ pathway is consistently identified as the worst performing option in all regions under all scenarios (Fig. 6a-c). The technology’s low conversion yield results in high material input requirements per MJ SAF produced, creating a significant environmental burden across all categories. This is primarily driven by the enzyme input; agricultural residues are assumed to produce enough enzymes internally to sustain the fermentation process, whereas MSW requires external input due to its more complex and less predictable chemical composition. Additionally, MSW fermentation requires two orders of magnitude more caustic for pH control and wastewater treatment compared to the agricultural residue pathways due the feedstock’s more acidic and variable composition. This fundamental inefficiency remains the primary driver of its low ranking as it cannot be offset by the decarbonisation of background systems. Agricultural residues For all agricultural feedstocks, GFT emerges as the most stable technology, consistently delivering some of the lowest GWP across scenarios due to its minimal reliance on external hydrogen and electricity (Fig. 2a-4a); this agrees with the results in Prussi et al. 15 and other lignocellulosic GFT pathways in the literature 19,26 . The GWP of the GFT pathway is dominated by waste disposal practices and diesel burned in agricultural machinery, also consistent with Prussi et al. 15 , which is dependent on the regional crop yield and is not significantly affected across scenarios. By contrast, FATJ and GATJ generally exhibit the highest GWP due to their lower conversion efficiencies, higher process-energy demands, and wastewater treatment requirements, reflecting the previous research 15,26 . Across all agricultural feedstocks, the initial GWP for GATJ, FATJ, and HTL is primarily driven by hydrogen production; specifically, SMR in the US and Brazil, a combination of SMR and coal gasification in China, and coal gasification in India. Both ATJ pathways also reveal significant GWP impacts from coal-fired electricity production, as well as wastewater treatment for GATJ. Over time, this impact decreases, leaving wastewater treatment and resource production (methyl chloride, silicon, and quicklime) as the major contributors. Geographically, Brazil and India exhibit the highest GWP results for all feedstocks, primarily driven by open dumping practices and lower average crop yields, which necessitate higher consumption of diesel in agricultural machinery per unit of SAF produced. While GWP decreases over time for all pathways and scenarios (0.6-93.2% across pathway-scenarios), the end-state drivers in 2075 differ. For GFT, contributions from electricity become negligible, but a secondary impact emerges from hydrogen production via biomass gasification as a replacement for fossil inputs. For GATJ and HTL, these pathways see the most dramatic reductions (by 61.5% and 93.2%, respectively). By 2075, the impacts of hydrogen and electricity production largely diminish, leaving transport or the conversion process itself as the primary remaining contributor. While for FATJ, even in 2075, this pathway remains burdened by the production of chemical intermediates like methyl chloride, which prevents it from achieving comparable results to GFT or HTL. The response to development scenarios reveals a notable regional anomaly in India and Brazil (Fig. S1c-f). The short-term development penalty is seen for FATJ, GATJ, and HTL as their GWP is higher in SSP1 than SSP2 during the 2025-2050 period (by up to 5.2%). This is caused by the transition to more sustainable hydrogen production technologies, like SMR with CCS in the US or biomass gasification in Brazil, that have not yet reached full systemic efficiency. Some stagnation is seen in SSP5 in China as this scenario results in slower reductions in GWP by 2075 compared to other locations. This is due to a sustained and heavy reliance on hydrogen production from SMR without the decarbonisation benefits seen in SSP1 and SSP2. The exception is seen for RST which shows the fewest significant differences between development scenarios, largely because the dominant impact in India (open dumping) and agricultural yields are treated as constants. While TETP values are similar across countries by 2075, GATJ and FATJ remain the technologies with the highest toxicity results due to their lower yields (Fig. 2b-4b). FEP results reveal a major divide based on technology and regional waste management (Fig. 2c-4c). For GFT, FATJ, and HTL, FEP is primarily driven by copper and coal mining; in these cases, India and Brazil show higher results due to the added impact of open dumping, particularly for SCT and RST. However, GATJ is the outlier across all feedstocks, exhibiting the highest FEP by a significant margin (e.g., for SSP1-2025, RST-GATJ-IN is 22.3-1090% higher than the other RST-IN pathways) due to its higher wastewater treatment requirements. TAP remains relatively constant across locations and years but highlights a specific regional anomaly (Fig. 2d-4d). In India, TAP is significantly higher in 2025 (by up to 93.4%) across all feedstocks for FATJ, GATJ, and HTL due to a heavy reliance on coal-fired electricity. This impact reduces drastically by 2050 (by up to 92.1%) as coal is phased out. By 2075, TAP converges for all locations and is dominated by copper smelting, mine blasting, and platinum mining. Overall, the results reveal several trade-offs between conversion technologies that remain consistent across all agricultural residues. GFT provides the lowest GWP, TETP, and FEP in most scenarios and shows the least variation across locations and time (Fig. 2-4). However, it consistently results in the highest TAP due to its high consumption of platinum for catalysts. Nevertheless, its consistency allows it to gain competitiveness over time when compared against all impact categories. By 2075, SCT-GFT reaches the top 10 in India, China, and Brazil, proving its long-term viability (Fig. 5a-c). HTL offers moderate GWP and TETP and generally provides the lowest TAP across all scenarios (Fig. 2-4), although it shows larger variations in performance across years and locations compared to GFT. MST-HTL-US and WST-HTL-US consistently rank in the top 10 pathways across all scenarios and are always in the top 5 pathways for at least one MCDA method (Fig. 5a-c). Similarly, SCT-HTL-BR and MST-HTL-BR maintain top 10 positions in almost all cases, only falling in SSP1-2025. However, a notable temporal decline is seen for HTL in China, where SCT-HTL, RST-HTL, and MST-HTL all rank in the top 10 in 2025, but are all outranked by 2075 as other regional pathways surpass them. Compared to the other technologies, GATJ performs moderately in GWP, TETP, and TAP but is penalised by having the highest FEP due to wastewater generation (Fig. 2-4). FATJ often results in the highest TETP and GWP across scenarios, largely due to low conversion efficiency and the environmental burden of producing chemical intermediates like methyl chloride. Consequently, FATJ frequently appears in the bottom 10 pathways (Fig. 6a-c). RST-FATJ-IN and WST-FATJ-IN consistently rank in bottom 4 th and 5 th respectively, while SCT-FATJ-IN and RST-FATJ-CN remain in the bottom 10 across all scenarios. Even in the US, WST-FATJ is a persistent low-ranking performer, with the only exception in the SSP1-2025 scenario. Cross-feedstock technology comparison Among the evaluated pathways, HTL is the only robust technology capable of utilising the full range of feedstocks, maintaining remarkably consistent trends and impact values regardless of the biomass source. By contrast, FATJ performance is highly sensitive to the feedstock used (Fig. S2e). Overall, MST yields the lowest environmental impacts across all 18 impact categories in all locations (Fig. 5a-c), while RST generally leads to the highest impacts out of the agricultural residues as its lower heating value (LHV) results in lower conversion yield. Certain MSW pathways stand out as major outliers; for SSP2-2025, the largest difference in GWP (6180%) is seen between the MSW-FATJ-IN and MSW-GFT-US pathways, while the largest difference in TETP (7850%) is seen between the MSW-FATJ-CN and MST-HTL-US pathways. The GFT and GATJ pathways reveal distinct trade-offs between agricultural residues and MSW (Fig. S2c-d). SCT-GFT demonstrates the lowest GWP and TETP across most locations and scenarios. The performance of MSW-GFT is highly regional; while it is a high-carbon-impact pathway in India due to open dumping of waste generated during the conversion process, it becomes a low-carbon-impact leader in the US and China, even outperforming several agricultural residues. RST-GFT consistently yields the highest GWP in India, although it performs competitively in China. Meanwhile, all agricultural residues demonstrate nearly identical GWP and TETP values and trends under the GATJ pathway. However, MSW deviates significantly from this pattern; while it carries a higher TETP and TAP than agricultural residues, it results in significantly lower FEP (e.g., MSW-GATJ-IN is 71.9% lower than RST-GATJ-IN in SSP1-2050), suggesting a different balance of wastewater and chemical burdens. Discussion This prospective geographically-resolved LCA of SAF production pathways reveals that, while all pathways generally achieve a downward trend in GWP by 2075, the non-carbon trade-offs are significant. This reflects the findings in Lai et al. 19 and D’Ascenzo et al. 18 who also noted burden-shifting from GWP to toxicity impacts due to grid decarbonisation. Consistent with findings reported in the literature 20,26,27 , primary GWP contributions and variations across pathway-scenarios arise from the regional background energy systems, as well as the hydrogen sector 21,26 . Regionally, the short-term development penalty in India and Brazil under the SSP1 scenario highlights that aggressive sustainability transitions may initially spike environmental impacts. GFT emerges as the most stable technology across all feedstocks, regions, and scenarios, agreeing with Quiroz et al. 26 , due to its reduced reliance on external energy and hydrogen. However, it is consistently penalised by higher TAP results stemming from platinum catalyst requirements. HTL provides a high-performing alternative, particularly in the US, where it consistently ranks in the top tier of pathways against all impact categories. Conversely, the ATJ pathways frequently occupy the bottom of the environmental rankings due to lower yields and burdens from wastewater treatment, aligning with literature 15,26 . Based on the LCIA and MCDA results, the following recommendations are proposed: Prioritise HTL and GFT for immediate scaling: Given their low GWP and consistent top 10 rankings against all impact categories, HTL (particularly in the US and Brazil) and GFT should be the focus of near-term investment. Diversify mitigation strategies: Although China’s HTL pathways converge on minimal GWP by 2075, they do not maintain the lead across all impact categories, suggesting that China’s development policies should evolve beyond only carbon emissions to cover a broader range of environmental burdens for future HTL scaling. Invest in yield improvements for ATJ: To move FATJ and GATJ out of the bottom-tier rankings, research must focus on increasing conversion efficiency and reducing the chemical and water intensity of these pathways to mitigate their higher burdens. Regional feedstock optimisation: The US, China, and Brazil should all leverage their agricultural residue resources to minimise environmental impacts, while MSW utilisation is preferred in the US and China, and UCO preferred in the US. India must address open dumping practices, as the baseline waste management significantly hinders the carbon mitigation potential of all pathways. Decouple electricity and hydrogen from fossil sources: In regions like India, the transition to renewable electricity and green hydrogen is highly important in reducing the early-year GWP spike for more energy-intensive pathways like HTL and ATJ. Material circularity: As energy decarbonisation shifts environmental burdens from climate to material toxicity, policy frameworks must incorporate the recycling and recovery of these materials to mitigate rising TETP and TAP trends. The limitations of this work include data availability constraints necessitating several simplifying assumptions, including the use of catalyst proxies, uniform spatiotemporal feedstock compositions, linear LHV-based yield scaling, and standardised operating conditions across pathways (unless otherwise stated), which may not fully capture the complex process dynamics and feedstock-specific optimisations for each pathway. Similarly, the use of disparate baseline data publication years (2009-2024) may introduce inconsistencies in industrial efficiency modelling. Achieving more accurate analyses requires additional granular process data on the operating conditions, resource inputs, product yields, emissions, and wastes for each established and emerging SAF production pathway. Moreover, treating agricultural residues as zero-burden wastes neglects land-use and agricultural impacts 43,44 that could be reallocated if future regulatory frameworks reclassify these residues as valuable by-products. Consequently, future research should employ consequential LCA to evaluate bio-waste-derived SAF production against current practices and alternative waste disposal or valorisation methods. Finally, to accurately evaluate the full lifecycle impacts of SAF, well-to-wake system boundaries must be adopted, including the supply chain and end-use combustion emissions accounting for the variability between different SAF types. Methods LCA Goal and Scope The aim of this geographically-resolved prospective LCA is to identify environmental hotspots, perform comparative scenario modelling, and establish performance benchmarks for a range of feedstocks, conversion technologies, and geographic locations outlined in Table 1 across three of the IPCC’s prospective development scenarios. Recognising that net-zero targets vary by region (2050 for the US and Brazil, 2060 for China, and 2070 for India), the temporal horizon for this study is extended to 2075, providing a comprehensive forecast of environmental performance as national energy grids undergo long-term transitions over a 50-year horizon. To ensure regional relevance, SAF production is only modelled in one of the selected countries if that country ranked among the top 3 (or top 6 for UCO) global producers of that feedstock in the past three years 29,30,45 . The only exception is for WST; while Russia was the third-largest producer in 2023, the US (fourth largest) is substituted as a representative location due to its higher availability of location-specific data. This LCA is conducted in accordance with ISO 14040 and 15055 standards 46,47 . Well-to-pump system boundaries are adopted, encompassing the following stages: feedstock collection and transportation to the SAF plant, feedstock pretreatment and conversion, hydrotreatment and upgrading, transportation of the finished product to the end-user. In alignment with standard waste-to-energy LCA practices, the life cycle of the feedstock is assumed to begin at the point of waste generation. Consequently, upstream impacts related to the production of the waste (e.g. crop cultivation or consumer goods manufacturing) are excluded to avoid double-counting, as these impacts are attributed to the primary products. Furthermore, it is assumed that the bio-wastes are ‘genuine wastes’ that are not being diverted from existing uses. The end-use phase is also excluded from the scope as non-CO 2 effects such as NO x and contrail formation are still subject to ongoing scientific study and vary between different SAF compositions 23,48 . The SAF production plant construction and background infrastructure are also excluded. The functional unit is 1 MJ SAF delivered to the end-user, calculated with energy-based allocation based on the LHV of the end products. Life Cycle Inventory (LCI) The LCI is constructed by coupling foreground data derived from peer-reviewed literature and technical reports with background data from the ecoinvent database. Location-dependent variables include: agricultural yields, energy mixes, hydrogen production mixes, landfill practices (for secondary waste generated during the conversion process), and catalysts and material inputs. To ensure an accurate comparison across all evaluated pathways, several consistent assumptions are applied. Transport: A standard transport distance of 50 km is assumed for both the upstream delivery of feedstocks to the SAF plant and the downstream distribution of SAF to the end-user. For agricultural residues and MSW, transportation is modelled using a 16-32 tonne truck. For UCO, collection is modelled using a light commercial vehicle to reflect the more fragmented and urban nature of its supply chain. Average road transport emission factors, shown in Table 1, are localised for each country in accordance with national legislation and estimated vehicle lifetimes, including assumptions of EPA Tier 4 in the US 49 , China IV in China 50 , Bharat Stage III in India 51 , and Proconve P-7 in Brazil 52 which are mapped onto the EURO standards for evaluation. Feedstock properties: For UCO and agricultural residues, the chemical composition of each feedstock is assumed to be uniform across all geographic locations in order to isolate the impact of the conversion technologies. For UCO, this standardisation is necessary due to the lack of international data on waste oil compositions. Meanwhile, a literature review revealed a high level of intra-regional variability for each agricultural residue due to differences in local weather conditions and farming practices 53 . By using average composition values for each feedstock, the model ensures a consistent baseline that prevents feedstock fluctuations from obscuring regional technological performance. Conversely, a literature review of MSW compositions revealed significant variations between nations. Hence, the biogenic proportion of MSW is adjusted per location to reflect the varying regional waste streams 54-56 . Carbon accounting: This work follows the principle of biogenic carbon neutrality. All CO 2 emitted at the refinery directly from the feedstock during the conversion of biomass to fuel is set to zero, under the assumption that these emissions are sequestered from the atmosphere during the biomass growth. This ensures that the environmental evaluation focuses on the fossil-based burdens associated with the processing, energy inputs, and auxiliary chemical requirements of each pathway. Agricultural residue collection: To quantify the energy intensity of the recovery of each agricultural residue, the available biomass is modelled using a five-year average yield (2018-2022) of the primary crop in each associated country 29 . Residue yield is then estimated by applying residue-to-product mass ratios derived from established literature 57-62 . Field-side collection is assumed to involve the same process for all residues: swathing, baling, carting, and transport to on-site storage 63 . The diesel consumption of these farm machinery operations is calculated based on the land area required to meet the SAF plant’s feedstock demand, specific to each feedstock and location. Agricultural residue pretreatment: For certain pathways (GFT, GATJ, and FATJ), each agricultural residue undergoes mechanical and thermal pretreatment to ensure feedstock uniformity. This stage uses an integrated system consisting of a crusher, drying machine, and an induced draft fan to reduce the biomass to a 5 wt% moisture content 64 . The quantity of water vapour emitted during this stage is calculated as a function of each feedstock’s initial moisture content. It is assumed that the thermal energy required for the drying process is generated internally since all evaluated pathways produce excess heat. MSW pretreatment: After transportation of MSW to the SAF plant, a series of mechanical separation stages are employed to isolate the biogenic fraction from the recyclables and non-organic residue. This process uses an integrated system comprising of a shredder, trommel, air classifier, magnetic separator, and eddy current separator 65 . It is assumed that 100% of the non-biogenic fraction is removed. The downstream processing of recyclables and non-biogenic residues are excluded from the system boundary to avoid double-counting of environmental burdens in the recycling and waste management sectors. For certain pathways (GFT and GATJ), the recovered biogenic portion undergoes further pretreatment to meet the reactor requirements; this involves drying the material from 40% to 10% moisture content, followed by pelletisation 66 . Feedstock-to-fuel conversion is modelled according to the pathways outlined in Table 1. For detailed information on the processes, assumptions, calculations, and data sources, see the Supplementary Methods section. For UCO only, the HEFA process is modelled using data from Mannion et al. 34 . This process includes filtration and dehydration of the collected UCO to remove residue and water, followed by hydrotreatment using hydrogen. Propane is removed and purified, while the remaining mixture undergoes deoxygenation with a Ni-Mo catalyst to produce paraffins. The liquid and gaseous products are separated, after which excess hydrogen in the gas stream is separated and recycled. The liquid phase undergoes hydrocracking using hydrogen and a catalyst, targeting the jet fuel range. Finally, fractional distillation of the mixture produces SAF, diesel, LPG, and naphtha. HTL is modelled using data primarily from Feng et al. 35 . This pathway is characterised by its ability to process wet biomass, reducing pretreatment requirements. Water is added to the feedstock before it is pressurised and heated to subcritical conditions, producing biocrude, an aqueous phase, off-gas, and hydrochar. The biocrude is hydrotreated using hydrogen and a CoMo/alumina catalyst, producing fuel gas (for energy generation), naphtha, jet, diesel, and heavy oil. Hydrogen is recovered from the fuel gas and is recycled. The heavy oil fraction is hydrocracked using hydrogen and a CoMo catalyst to produce naphtha, jet, and diesel. The hydrochar and aqueous phase undergo nutrient recovery, ultimately producing ammonium sulphate fertiliser and fuel gas for energy generation. MSW gasification, producing either syngas or alcohol, is modelled using data from Jones et al. 36 . The feed is gasified with steam in a fluidised bed gasifier. Char and ash are separated and combusted for energy recovery, while the syngas is reformed using an iron chelate catalyst to increase CO and H 2 yield. The syngas is scrubbed to remove residual contaminants, a carbon bed removes mercury, and liquid-phase oxidation removes sulphur. The syngas, high-temperature steam, and recycled CO 2 are sent to a steam reformer to adjust the H 2 /CO ratio via the water-gas-shift reaction. An amine unit removes excess CO 2 before the syngas is fed into a fixed-bed reactor for mixed alcohol synthesis using a K/Co/MoS catalyst. The product is cooled before the gas phase is recycled back to the steam reformer and the liquid phase is fractionated into ethanol and higher alcohols. Agricultural residue gasification, producing either syngas or alcohol, is modelled using data from Pati et al. 38 . Following gasification, ash is separated, and the syngas is cleaned, removing NH 3 via a water wash and H 2 S via an activated carbon bed. For the GFT pathways, the syngas is cooled to 150°C, while for the GATJ pathways, it is cooled to 37°C for fermentation. This occurs in a bioreactor where acetogenic bacteria convert the syngas into ethanol via the Wood-Ljungdahl pathway. The resulting liquid and gaseous phases are separated before being distilled to extract the ethanol which is finally dehydrated using a molecular sieve. FT synthesis is modelled using data from Ahire et al. 37 . The syngas feed undergoes further purification using diethanolamine to strip any remaining H2S and CO2. A water-gas-shift reaction is performed using a Cu-ZnO-Al 2 O 3 catalyst, followed by low-temperature FT synthesis using a Co-based catalyst. The gaseous and liquid products are separated and the aqueous phase removed. The liquid products are hydrocracked using hydrogen and a 0.5Pt/Y(100)35A catalyst, before the products are distilled into flue gases (for energy generation), jet, and diesel. MSW fermentation is modelled using data from Meng et al. 40 . This pathway can process wet biomass, avoiding the need for further feedstock pretreatment. The biogenic MSW is first autoclaved to break it down into cellulose-rich fibres; these are milled and added to water for enzymatic hydrolysis using Novozymes Cellic CTec2. A sugar solution is generated which is filtered and conditioned with nutrients and additives before being inoculated with the microorganism Clostridium acetobutylicum ATCC 824. Nitrogen gas stripping is used to remove the liquid and gaseous fermented products. Hydrogen is recovered from the gas stream, while the liquids are distilled, yielding acetone, ethanol, and butanol. An integrated wastewater treatment facility reduces the utility demand as well as filtering residual solids and producing biogas via digestion, both for energy recovery. Agricultural residue fermentation is modelled using data from Edwards et al. 41 , following similar enzymatic hydrolysis, fermentation, and energy recovery processes to the MSW pathway. ATJ conversion is modelled using data from Romero-Izquierdo et al. 39 . First, the ethanol is dehydrated using a La-P-HZSM-5 catalyst, followed by ethylene conversion using saturated steam. The ethylene undergoes oligomerisation using Ni-La/ACB and HZSM5 catalysts to produce longer-chain olefins. These are then hydrogenated using hydrogen and a ATHZ5-Cs catalyst, producing paraffins. Finally, the products are distilled to separate the light gases, naphtha, jet, and diesel. Life Cycle Impact Assessment (LCIA) The LCIA quantifies the environmental burdens associated with the defined pathways, performed using the Brightway2 Python package 67 . An attributional LCA approach was employed to evaluate the absolute environmental impacts of each pathway. The environmental impacts are characterised using the ReCiPe 2016 (v1.03) midpoint (H) method. The midpoint approach is chosen to minimise uncertainties and value-based assumptions often associated with endpoint modelling. The Hierarchist (H) perspective is adopted as it represents the scientific consensus on environmental mechanisms and time horizons. The results are benchmarked against the fossil jet fuel baseline value of 89 gCO 2eq /MJ 24 . Prospective Scenario Modelling To account for the evolving nature of global energy and industrial systems 68 , this study employs prospective scenario modelling spanning a 50-year time horizon (2025-2075) under three distinct development scenarios. The REMIND SSP-NPi framework 31 is used to construct the development scenarios, established from Integrated Assessment Models (IAMs), specifically SSP1-NPi ‘sustainability’, SSP2-NPi ‘middle-of-the-road’, and SSP5 ‘fossil-fuelled-development’ 25 . The ‘NPi’ designation signifies that these scenarios assume countries implement only their currently stated national policies, without the introduction of more ambitious climate commitments 31 . To transform to a dynamic LCI, the Premise Python scenario-integration tool 69 is used to modify the base LCI database (ecoinvent v3.9.1, cut-off) to align with the REMIND SSP-NPi projections. This transformation has several key areas of modification: Energy system transitions: future electricity mixes and energy carriers are updated to match the scenario-specific narrative for the target year. Technology and process evolution: the database is altered to reflect projected efficiency changes and the uptake of emerging industrial technologies. Market and supply-chain shifts: changes in demographics, policy constraints, and technology costs drive shifts in the market shares for materials and supply mixes. Emission trajectories: greenhouse gases and pollutant emission factors are adjusted to reflect the climate policies for each scenario. By integrating these prospective databases with Brightway2, this work captures the background changes in the global economy that ultimately dictate the environmental viability of these SAF production pathways over the next 50 years. In total, 540 pathway-scenarios were evaluated in this work. Multi-Criteria Decision Analysis (MCDA) To synthesise the results across all 18 ReCiPe impact categories, MCDA is conducted using Python. This approach allows for a holistic evaluation of all sixty SAF production pathways over the 18 environmental criteria for each of the nine scenarios, building on methods seen in previous studies 70 and moving beyond the single-indicator representation in Fig 2-4. Two distinct MCDA methods are employed to ensure the robustness of the rankings and to identify potential methodological biases. The TOPSIS method is used to represent the goal-based MCDA category. This method ranks alternatives based on their geometric distance from the ‘positive ideal solution’ (the best value in each category) and the ‘negative ideal solution’ (the worst value) 32 . PROMETHEE II is used to represent the outranking MCDA method category, performing pairwise comparisons between pathways to determine the extent to which one alternative is preferred over another 33 . By utilising both a distance-based and an outranking method, this work identifies pathways that consistently perform well regardless of the mathematical logic applied. The raw LCIA results are subjected to normalisation to ensure each criterion contributes proportionally to the final score. For the TOPSIS method, vector normalisation is applied, transforming the performance matrix into a dimensionless scale by dividing each score by the square root of the sum of squares for that category. For PROMETHEE, a type III V-shape preference function is applied after normalisation to define the intensity of preference between two pathways based on their relative performance. The direction of preference for all criteria is set to minimise their values as lower impact values represent reduced environmental burdens. An equal weighting distribution is assigned to all 18 preference criteria in order to give an overview of the environmental performance of each pathway across all impact categories. This allows for the identification of hidden trade-offs as some pathways excel in one impact category but perform poorly in another. To ensure the reliability of the top-tier and bottom-tier classifications, the results are filtered to highlight pathways that appeared in the top or bottom 10 ranking for both MCDA methods simultaneously. This intersection-based approach minimises the impact of method-specific outliers and provides a more conservative, robust assessment of the SAF production rankings. Declarations Data availability The datasets generated during and/or analysed during the current study are available from the corresponding authors on reasonable request. Acknowledgements The authors would like to acknowledge UKRI EPSRC (Grant Ref: EP/W524360/1), UKRI EPSRC IGNITE and CS sustainable manufacturing hubs, UKRI EPSRC CHIMES IKC, Leverhulme Centre for Climate Change Mitigation, South Yorkshire Sustainability Centre, FLAME (Grant Ref: EP/Y020839/1), and Supergen (Grant Ref: EP/Y016300/1) for the support of this research. Author contributions E.B. and L.K. conceived the study. E.B. conducted data collection through literature review, developed the Python scripts, conducted the LCA and MCDA analyses, plotted figures, and wrote the first draft of the manuscript. L.K. provided advice on the methods and modelling. E.B. and L.K. discussed the implications of the results and finalised the manuscript. L.K. and R.Y. reviewed the manuscript and provided supervision. Competing interests The authors declare no competing interests. References International Energy Agency (IEA). Aviation , (2025). Lee, D. S. et al. The contribution of global aviation to anthropogenic climate forcing for 2000 to 2018. Atmospheric Environment 244 (2021). https://doi.org/10.1016/j.atmosenv.2020.117834 Airports Council International (ACI). ACI World releases global airport traffic forecasts as long-term demand growth continues to reshape aviation , (2026). American Society for Testing and Materials (ASTM). Standard Specification for Aviation Turbine Fuels. Report No. D1655-22, (American Society for Testing and Materials, 2023). International Civil Aviation Organization (ICAO). CORSIA Sustainability Criteria for CORSIA Eligible Fuels. (International Civil Aviation Organization, 2025). Department for Transport. The SAF Mandate: an essential guide , (2024). Congress Research Service (CRS). Inflation Reduction Act of 2022 (IRA): Provisions Related to Climate Change. Report No. R47262, (Congress Research Service, 2023). European Civil Aviation Conference (ECAC). ECAC Guidance on Sustainable Aviation Fuels (SAF). (European Civil Aviation Conference, 2023). European Union Aviation Safety Agency (EASA). What are Sustainable Aviation Fuels? , (2026). International Civil Aviation Organization (ICAO). SAF Conversion processes , (2023). International Air Transport Association (IATA). Disappointingly Slow Growth in SAF Production , (2024). Dray, L. et al. Cost and emissions pathways towards net-zero climate impacts in aviation. Nature Climate Change 12 , 956–962 (2022). https://doi.org/10.1038/s41558-022-01485-4 Speizer, S. et al. Integrated assessment modeling of a zero-emissions global transportation sector. Nature Communications 15 (2024). https://doi.org/10.1038/s41467-024-48424-9 Chen, P. H., Lee, U., Liu, X., Cai, H. & Wang, M. Life-cycle analysis of sustainable aviation fuel production through catalytic hydrothermolysis. Biofuels Bioproducts and Biorefining 18 , 42–54 (2024). https://doi.org/10.1002/bbb.2574 Prussi, M. et al. CORSIA: The first internationally adopted approach to calculate life-cycle GHG emissions for aviation fuels. Renewable and Sustainable Energy Reviews 150 (2021). https://doi.org/10.1016/j.rser.2021.111398 Khalifa, R., Bicer, Y., Al-Ansari, T. & Abu-Rayash, A. A lifecycle assessment of waste-based sustainable aviation fuels blends for advancing the circular aviation economy. Energy Conversion and Management: X 27 (2025). https://doi.org/10.1016/j.ecmx.2025.101141 Borrill, E., Koh, S. C. L. & Yuan, R. Review of technological developments and LCA applications on biobased SAF conversion processes. Frontiers in Fuels 2 (2024). https://doi.org/10.3389/ffuel.2024.1397962 D’Ascenzo, F., Vinci, G., Savastano, M., Amici, A. & Ruggeri, M. Comparative Life Cycle Assessment of Sustainable Aviation Fuel Production from Different Biomasses. Sustainability 16 (2024). https://doi.org/10.3390/su16166875 Lai, Y. Y., Karakaya, E. & Björklund, A. Employing a Socio-Technical System Approach in Prospective Life Cycle Assessment: A Case of Large-Scale Swedish Sustainable Aviation Fuels. Frontiers in Sustainability 3 (2022). https://doi.org/10.3389/frsus.2022.912676 Greene, J. M., Quiroz, D., Limb, B. J. & Quinn, J. C. Geographically-Resolved Techno-Economic and Life Cycle Assessment Comparing Microalgae-Based Renewable Diesel and Sustainable Aviation Fuel in the United States. Environmental Science and Technology 59 , 3472–3483 (2025). https://doi.org/10.1021/acs.est.4c06742 Romero, M. A., Mayorga, M. A., Gómez, Z. E., Correa-Mahecha, F. & Suarez, L. A. Life Cycle Assessment (LCA) of the Production of Sustainable Aviation Fuels (SAF) in Colombia. Chemical Engineering Transactions 117 , 343–348 (2025). https://doi.org/10.3303/CET25117058 Jing, L. et al. Understanding variability in petroleum jet fuel life cycle greenhouse gas emissions to inform aviation decarbonization. Nature Communications 13 (2022). https://doi.org/10.1038/s41467-022-35392-1 Boerboom, L., Rao, A. G., Grewe, V. & Yin, F. A comprehensive well-to-wake climate impact assessment of sustainable aviation fuel. Scientific Reports 15 (2025). https://doi.org/10.1038/s41598-025-13445-x International Civil Aviation Organization (ICAO). CORSIA Methodology for Calculating Actual Life Cycle Emissions Values. (International Civil Aviation Organization, 2025). O’Neill, B. C. et al. A new scenario framework for climate change research: the concept of shared socioeconomic pathways. Climatic Change 122 , 387–400 (2014). https://doi.org/10.1007/s10584-013-0905-2 Quiroz, D., Greene, J. M., Limb, B. J. & Quinn, J. C. Prospective Life Cycle Assessment of Sustainable Aviation Fuel Systems. Environmental Science and Technology 59 , 19269–19282 (2025). https://doi.org/10.1021/acs.est.5c09113 Sacchi, R. et al. How to make climate-neutral aviation fly. Nature Communications 14 (2023). https://doi.org/10.1038/s41467-023-39749-y Grewe, V. et al. Evaluating the climate impact of aviation emission scenarios towards the Paris agreement including COVID-19 effects. Nature Communications 12 (2021). https://doi.org/10.1038/s41467-021-24091-y Ritchie, H., Rosado, P. & Roser, M. Agricultural Production , (2023). Hoornweg, D. & Bhada-Tata, P. What a Waste : A Global Review of Solid Waste Management. Report No. 15, (The World Bank, 2012). Soergel, B. et al. A sustainable development pathway for climate action within the UN 2030 Agenda. Nature Climate Change 11 , 656–664 (2021). https://doi.org/10.1038/s41558-021-01098-3 Madanchian, M. & Taherdoost, H. A comprehensive guide to the TOPSIS method for multi-criteria decision making. Sustainable Social Development 1 (2023). https://doi.org/10.54517/ssd.v1i1.2220 Abdullah, L., Chan, W. & Afshari, A. Application of PROMETHEE method for green supplier selection: a comparative result based on preference functions. Journal of Industrial Engineering International 15 , 271–285 (2019). https://doi.org/10.1007/s40092-018-0289-z Mannion, L. A. et al. A physics constrained methodology for the life cycle assessment of sustainable aviation fuel production. Biomass and Bioenergy 185 (2024). https://doi.org/10.1016/j.biombioe.2024.107169 Feng, J., Li, Y., Strathmann, T. J. & Guest, J. S. Characterizing the Opportunity Space for Sustainable Hydrothermal Valorization of Wet Organic Wastes. Environmental Science and Technology 58 , 2528–2541 (2024). https://doi.org/10.1021/acs.est.3c07394 Jones, S. B., Zhu, Y. & Valkenburg, C. Municipal Solid Waste (MSW) to Liquid Fuels Synthesis, Volume 2: A Techno-economic Evaluation of the Production of Mixed Alcohols. Report No. PNNL-18482, (Pacific Northwest National Laboratory, 2009). Ahire, J. P. et al. Techno-economic and environmental impacts assessments of sustainable aviation fuel production from forest residues. Sustainable Energy & Fuels 8 , 4602–4616 (2024). https://doi.org/10.1039/d4se00749b Pati, S., De, S. & Chowdhury, R. Exploring the hybrid route of bio-ethanol production via biomass co-gasification and syngas fermentation from wheat straw and sugarcane bagasse: Model development and multi-objective optimization. Journal of Cleaner Production 395 (2023). https://doi.org/10.1016/j.jclepro.2023.136441 Romero-Izquierdo, A. G., Gomez-Castro, F. I., Gutierrez-Antonio, C., Hernandez, S. & Errico, M. Intensification of the alcohol-to-jet process to produce renewable aviation fuel. Chemical Engineering and Processing - Process Intensification 160 (2021). https://doi.org/10.1016/j.cep.2020.108270 Meng, F. et al. Process simulation and life cycle assessment of converting autoclaved municipal solid waste into butanol and ethanol as transport fuels. Waste Management 89 , 177–189 (2019). https://doi.org/10.1016/j.wasman.2019.04.003 Edwards, R. et al. Definition of input data to assess GHG default emissions from biofuels in EU legislation. (JCR, 2017). Zhang, L., Oladejo, J., Dawodu, A., Yang, L. & Xiao, Y. Sustainable jet fuel from municipal solid waste–Investigation of carbon negativity and affordability claims. Resources, Conservation & Recycling 210 (2024). https://doi.org/10.1016/j.resconrec.2024.107819 Beerling, D. J. et al. Transforming US agriculture for carbon removal with enhanced weathering. Nature 638 , 425–434 (2025). https://doi.org/10.1038/s41586-024-08429-2 Eufrasio, R. M. et al. Environmental and health impacts of atmospheric CO2 removal by enhanced rock weathering depend on nations’ energy mix. Communications Earth and Environment 3 (2022). https://doi.org/10.1038/s43247-022-00436-3 GlobalData. UCO Supply Outlook. (2023). International Organization for Standardization (ISO). ISO 14040:2006 Environmental management — Life cycle assessment — Principles and framework. (International Organization for Standardization, 2006). International Organization for Standardization (ISO). ISO 14044:2006 Environmental management — Life cycle assessment — Requirements and guidelines. (International Organization for Standardization, 2006). Voigt, C. et al. Cleaner burning aviation fuels can reduce contrail cloudiness. Communications Earth and Environment 2 (2021). https://doi.org/10.1038/s43247-021-00174-y International Council on Clean Transportation (ICCT) & DieselNet. US: Heavy-Duty: Emissions , (2018). International Council on Clean Transportation (ICCT) & DieselNet. China: Heavy-Duty: Emissions , (2018). International Council on Clean Transportation (ICCT) & DieselNet. India: Heavy-Duty: Emissions , (2018). International Council on Clean Transportation (ICCT) & DieselNet. Brazil: Heavy-Duty: Emissions , (2018). Gummert, M., Hung, N. V., Chivenge, P. & Douthwaite, B. (eds Martin Gummert, Nguyen Van Hung, Pauline Chivenge, & Boru Douthwaite) (Springer Open, 2020). Ding, Y. et al. A review of China’s municipal solid waste (MSW) and comparison with international regions: Management and technologies in treatment and resource utilization. Journal of Cleaner Production 293 (2021). https://doi.org/10.1016/j.jclepro.2021.126144 R-Cities, Ocean Conservancy (OC), The Circulate Initiative (TCI), Brihanmumbai Municipal Corporation & Centre for Environment Education. Mumbai, India City Waste Management Profile. (2024). CalRecycle. 2021 Disposal Facility-Based Waste Characterization Data Tables. (California Department of Resources Recycling and Recovery, 2022). Agriculture and Horticulture Development Board (AHDB). The main components of yield in wheat , (2025). Solangi, S. H., Jakhrani, A. Q., Mukwana, K. C., Jatoi, A. R. & Luhur, M. R. Investigation of Quantity, Quality and Energy Content of Indigenous Sugarcane Trash in Naoshehro Feroze District, Sindh. Engineering, Technology and Applied Science Research 8 , 3609–3613 (2018). https://doi.org/10.48084/etasr.2411 Franco, H. C. J. et al. Assessment of sugarcane trash for agronomic and energy purposes in Brazil. Scientia Agricola 70 , 305–312 (2013). https://doi.org/10.1590/S0103-90162013000500004 Mensah, M. B., Jumpah, H., Boadi, N. O. & Awudza, J. A. M. Assessment of quantities and composition of corn stover in Ghana and their conversion into bioethanol. Scientific African 12 (2021). https://doi.org/10.1016/j.sciaf.2021.e00731 Summers, M. D. et al. Biomass production and allocation in rice with implications for straw harvesting and utilization. Biomass and Bioenergy 24 , 163–173 (2003). https://doi.org/10.1016/S0961-9534(02)00132-0 Home-Grown Cereals Authority (HGCA). The wheat growth guide. (2008). Glithero, N. J., Ramsden, S. J. & Wilson, P. Farm systems assessment of bioenergy feedstock production: Integrating bio-economic models and life cycle analysis approaches. Agricultural Systems 109 , 53–64 (2012). https://doi.org/10.1016/j.agsy.2012.02.005 Han, D., Yang, X., Li, R. & Wu, Y. Environmental impact comparison of typical and resource-efficient biomass fast pyrolysis systems based on LCA and Aspen Plus simulation. Journal of Cleaner Production 231 , 254–267 (2019). https://doi.org/10.1016/j.jclepro.2019.05.094 Lee, U. et al. Life cycle analysis of gasification and Fischer-Tropsch conversion of municipal solid waste for transportation fuel production. Journal of Cleaner Production 382 (2023). https://doi.org/10.1016/j.jclepro.2022.135114 Wang, H., Wang, L. & Shahbazi, A. Life cycle assessment of fast pyrolysis of municipal solid waste in North Carolina of USA. Journal of Cleaner Production 87 , 511–519 (2015). https://doi.org/10.1016/j.jclepro.2014.09.011 Mutel, C. Brightway: An open source framework for Life Cycle Assessment. Journal of Open Source Software 12 (2017). https://doi.org/10.21105%2Fjoss.00236 Koroma, M. S. & Alwosheel, A. Aligning vehicle electrification with power sector transitions: life cycle insights across diverse grids. npj Sustainable Mobility and Transport 3 (2026). https://doi.org/10.1038/s44333-025-00076-y Hedderich, M. A., Fischer, J., Klakow, D. & Vreeken, J. in International Conference on Machine Learning (ICML),. Shamoushaki, M. & Koh, S. C. L. Solar cells combined with geothermal or wind power systems reduces climate and environmental impact. Communications Earth and Environment 5 (2024). https://doi.org/10.1038/s43247-024-01739-3 Table Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files SAFLCAPaper1FINALsupplementaryinformation.docx Table1.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 30 Mar, 2026 Reviews received at journal 24 Mar, 2026 Reviews received at journal 22 Mar, 2026 Reviewers agreed at journal 12 Mar, 2026 Reviewers agreed at journal 27 Feb, 2026 Reviewers invited by journal 27 Feb, 2026 Editor assigned by journal 27 Feb, 2026 Submission checks completed at journal 26 Feb, 2026 First submitted to journal 25 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8968110","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":610167714,"identity":"fcc29290-9661-4226-a2b2-fde759405304","order_by":0,"name":"Eleanor L. Borrill","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIie3RsWrDMBCA4QsH8nKyVhkX5xVUAsFT+io2foGOLYSgYHCmds6TdOogY8jk0jVrMHQuGDqVUCUeslgkYwf9CC/iQ3cYwOf7jwV0+sq7ENEgQCIYoJbnK3IQHAixgGWWzKIKJjcRICZInYm+RgTyunt+T4kh9f3jk5JhsF7vYbkA1ZpREpVhcd9+2cGQv8XbVklGdZnCrgD1oUeJamgeaTMQ5NXvism8ksAMqM/xwR4uhLqeH+0r04MlRzdReCEQc22JnFT2GOdgsrG7DITNY9qddsnLNH8tKHKsLzYv9UGbVSJE0/W0VHK6aer9988iCdtsfLLxMveP9Pl8Pt8N/QGkc0uGJwrbZgAAAABJRU5ErkJggg==","orcid":"","institution":"University of Sheffield","correspondingAuthor":true,"prefix":"","firstName":"Eleanor","middleName":"L.","lastName":"Borrill","suffix":""},{"id":610167715,"identity":"e87fd615-6ae7-46db-9a86-66c7f2377f92","order_by":1,"name":"Ruoyang Yuan","email":"","orcid":"","institution":"University of Sheffield","correspondingAuthor":false,"prefix":"","firstName":"Ruoyang","middleName":"","lastName":"Yuan","suffix":""},{"id":610167716,"identity":"3c1fd7fc-876f-4b0f-9bec-9b28d4bb211d","order_by":2,"name":"Lenny S.C. Koh","email":"","orcid":"","institution":"University of Sheffield","correspondingAuthor":false,"prefix":"","firstName":"Lenny","middleName":"S.C.","lastName":"Koh","suffix":""}],"badges":[],"createdAt":"2026-02-25 13:08:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8968110/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8968110/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105388981,"identity":"b08eff3f-1621-43ad-89b4-84b82e5d5fd5","added_by":"auto","created_at":"2026-03-25 12:57:44","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":216776,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWell-to-pump life cycle flow diagram for SAF production from bio-waste feedstocks.\u003c/strong\u003e The dashed box indicates processes included in the LCA system boundaries used in this study. The dotted line represents other arbitrary processes.\u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8968110/v1/61f0bc04d4fe2d76627a3f96.jpg"},{"id":105388853,"identity":"dcbc9435-315a-45bf-a937-b3f9e3fbd292","added_by":"auto","created_at":"2026-03-25 12:57:37","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1008669,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProspective environmental impacts of waste-derived SAF production pathways under the SSP1-NPi ‘sustainable’ global development scenario over a 50-year time horizon.\u003c/strong\u003e Rows show impact categories: a) global warming potential (GWP), b) terrestrial ecotoxicity potential (TETP), c) freshwater eutrophication potential (FEP), and d) terrestrial acidification potential (TAP). Columns represent regions: United States, China, India, and Brazil. Colour indicates feedstock type, and marker shape indicates SAF conversion technology.\u003c/p\u003e","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8968110/v1/a226fba11cabc86a699f0c21.jpg"},{"id":105388978,"identity":"22f1c9ad-d240-4b99-bf04-3067bd9ba376","added_by":"auto","created_at":"2026-03-25 12:57:43","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1004111,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProspective environmental impacts of waste-derived SAF production pathways under the SSP2-NPi ‘middle-of-the-road’ global development scenario over a 50-year time horizon.\u003c/strong\u003e Rows show impact categories: a) global warming potential (GWP), b) terrestrial ecotoxicity potential (TETP), c) freshwater eutrophication potential (FEP), and d) terrestrial acidification potential (TAP). Columns represent regions: United States, China, India, and Brazil. Colour indicates feedstock type, and marker shape indicates SAF conversion technology.\u003c/p\u003e","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8968110/v1/be3fb2d4ed078fd6b882bde3.jpg"},{"id":105565410,"identity":"4fa395a9-f7e5-4e36-abff-b67d79a2baf6","added_by":"auto","created_at":"2026-03-27 12:53:09","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1007203,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProspective environmental impacts of waste-derived SAF production pathways under the SSP5-NPi ‘fossil-fuelled-development’ global development scenario over a 50-year time horizon.\u003c/strong\u003e Rows show impact categories: a) global warming potential (GWP), b) terrestrial ecotoxicity potential (TETP), c) freshwater eutrophication potential (FEP), and d) terrestrial acidification potential (TAP). Columns represent regions: United States, China, India, and Brazil. Colour indicates feedstock type, and marker shape indicates SAF conversion technology.\u003c/p\u003e","description":"","filename":"Fig.4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8968110/v1/e5eb173a338d816fc80f12fe.jpg"},{"id":105388866,"identity":"6f703ea7-840a-41af-bd37-ee26abbb2129","added_by":"auto","created_at":"2026-03-25 12:57:40","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":604393,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTop 10 most preferred SAF production pathways over a 50-year time horizon, ranked using the TOPSIS and PROMETHEE MCDA methods.\u003c/strong\u003eRankings are based on performance across all 18 ReCiPe mid-point environmental impact categories. Rows correspond to global decarbonisation pathways: a) SSP1-NPi ‘sustainability’, b) SSP2-NPi ‘middle-of-the-road’, and c) SSP5-NPi ‘fossil-fuelled development’. Columns represent evaluated years (2025, 2050, and 2075). Colour indicates feedstock type, and marker shape indicates SAF conversion technology. Locations are labelled directly on the figure adjacent to the corresponding markers for the United States (US), China (CN), India (IN), and Brazil (BR).\u003c/p\u003e","description":"","filename":"Fig.5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8968110/v1/625ac9822f5756f26dcafdda.jpg"},{"id":105388980,"identity":"e58bdb9d-a27d-43f9-aaaf-f1f51783ec7a","added_by":"auto","created_at":"2026-03-25 12:57:44","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":566090,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBottom 10 least preferred SAF production pathways over a 50-year time horizon, ranked using the TOPSIS and PROMETHEE MCDA methods.\u003c/strong\u003eRankings are based on performance across all 18 ReCiPe mid-point environmental impact categories. Rows correspond to global decarbonisation pathways: a) SSP1-NPi ‘sustainability’, b) SSP2-NPi ‘middle-of-the-road’, and c) SSP5-NPi ‘fossil-fuelled development’. Columns represent evaluated years (2025, 2050, and 2075). Colour indicates feedstock type, and marker shape indicates SAF conversion technology. Locations are labelled directly on the figure adjacent to the corresponding markers for the United States (US), China (CN), India (IN), and Brazil (BR).\u003c/p\u003e","description":"","filename":"Fig.6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8968110/v1/5a1f61d31c69f86a68ee7e75.jpg"},{"id":105569758,"identity":"215aa4f3-142c-4654-b4a3-f46c82656ce7","added_by":"auto","created_at":"2026-03-27 13:13:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5831453,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8968110/v1/4208b65e-1c28-48f6-82fc-3b3e134d4711.pdf"},{"id":105388968,"identity":"b7d63a6f-60a2-456e-b4a3-780602ce31e4","added_by":"auto","created_at":"2026-03-25 12:57:43","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2754208,"visible":true,"origin":"","legend":"","description":"","filename":"SAFLCAPaper1FINALsupplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-8968110/v1/ef9eb21f097c0f5c2f607659.docx"},{"id":105388844,"identity":"4878ea3e-014c-43c2-9713-609641977bc3","added_by":"auto","created_at":"2026-03-25 12:57:36","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":24136,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8968110/v1/9021bc23a9f2391a732290a2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sustainability of aviation fuel technologies depends on geography, energy, and feedstock choice: 50-year development scenarios","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn 2023, the aviation sector accounted for approximately 2.5% of total anthropogenic CO\u003csub\u003e2\u003c/sub\u003e emissions\u003csup\u003e1\u003c/sup\u003e. However, when accounting for non-CO\u003csub\u003e2\u003c/sub\u003e effects, the total effective radiative forcing is estimated to contribute at three times the rate of CO\u003csub\u003e2\u003c/sub\u003e alone\u003csup\u003e2\u003c/sup\u003e. Despite this, global demand for air travel continues to accelerate, with forecasts predicting a doubling of global air traffic by 2045\u003csup\u003e3\u003c/sup\u003e. Meanwhile, aviation remains a hard-to-abate sector due to the high energy density requirements of fuels and stringent safety regulations\u003csup\u003e4\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSustainable aviation fuel (SAF) has emerged as the primary mid-term solution for meeting the aviation sector\u0026rsquo;s decarbonisation targets; notably, global warming potential (GWP) reductions compared to an 89 gCO\u003csub\u003e2eq\u003c/sub\u003e/MJ baseline of 10% by CORSIA standards\u003csup\u003e5\u003c/sup\u003e, 40% in the UK\u003csup\u003e6\u003c/sup\u003e, 47.2% in the US\u003csup\u003e7\u003c/sup\u003e (for tax credits), and 63.0% in the EU\u003csup\u003e8\u003c/sup\u003e. SAF is a drop-in fuel, having similar properties to fossil jet fuel; this compatibility allows for immediate use in existing engines and refuelling infrastructure without requiring modification\u003csup\u003e9\u003c/sup\u003e. SAF comprises of an array of production pathways resulting from diverse feedstock and technology combinations\u003csup\u003e10\u003c/sup\u003e and can contribute toward circular economy objectives via production from waste-derived biomass, whilst minimising land-use change impacts. However, several prominent challenges persist. The scale of production required to meet net-zero targets is vast; current global SAF production accounts for less than 1% of total jet fuel demand\u003csup\u003e11\u003c/sup\u003e. For biofuels, supply-chain scalability is fundamentally constrained by feedstock availability\u003csup\u003e12,13\u003c/sup\u003e. Furthermore, uncertainties remain regarding the environmental impacts of different SAF production pathways across diverse geographic regions.\u003c/p\u003e\n\u003cp\u003ePrevious research highlights that the environmental performance of SAF is highly sensitive to the feedstock, conversion technology, and system boundaries\u003csup\u003e14-18\u003c/sup\u003e. Third-generation feedstocks often exhibit superior performance, with GWP values reported below 10 gCO\u003csub\u003e2eq\u003c/sub\u003e/MJ\u003csup\u003e16,18,19\u003c/sup\u003e. Impacts are not globally uniform but vary according to regional electricity and hydrogen mixes, transport emissions, and feedstock production\u003csup\u003e19-22\u003c/sup\u003e, highlighting that production pathways must be tailored to local resources and capabilities. Additionally, the inclusion of the end-use phase significantly alters the benefits of SAF. When accounting for in-flight non-CO\u003csub\u003e2\u003c/sub\u003e emissions, the total GWP reduction may be less than 50% compared to fossil jet, underscoring the necessity for further SAF combustion and lifecycle analysis (LCA) research using well-to-wake system boundaries\u003csup\u003e12,19,23\u003c/sup\u003e. However, current decarbonisation frameworks neglect non-CO\u003csub\u003e2\u003c/sub\u003e combustion emissions\u003csup\u003e6-8,24\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eProspective LCAs are essential for understanding how SAF will perform as global systems transition toward net-zero. The Shared Socioeconomic Pathways (SSP)\u003csup\u003e25\u003c/sup\u003e are a set of five global scenarios describing divergent societal, economic, and technological futures that have been used in certain prospective LCA to adjust background inventory data such as electricity mixes and industrial efficiencies\u003csup\u003e12,13,19,26,27\u003c/sup\u003e. Modelling through 2100, the electricity sector is shown to have the largest GWP contribution for fossil jet with carbon capture and storage (CCS) and power-to-liquid fuels\u003csup\u003e27\u003c/sup\u003e. Similarly, forecasting to 2050 shows that common GWP drivers across bio-jet pathways come from electricity and hydrogen, resulting in the largest reductions across future decarbonisation scenarios\u003csup\u003e26\u003c/sup\u003e. By 2050, combined jet fuel supply from biofuels, power-to-liquid fuels, and hydrogen can achieve 89% CO\u003csub\u003e2\u003c/sub\u003e reductions compared to 2019 levels; however, non-CO\u003csub\u003e2\u003c/sub\u003e impacts are 10% higher due to limited reductions in contrail avoidance and water vapour emissions\u003csup\u003e12\u003c/sup\u003e. Meanwhile, rapid technological developments and earlier decarbonisation can considerably reduce emissions by 2050; although, in medium to low ambition scenarios, residual emissions after 2050 must be offset by CCS technologies\u003csup\u003e13\u003c/sup\u003e. However, achieving ambitious targets may only be possible with high blending ratios, reduced demand, improved fuel efficiency, and transitioning to low-carbon energy systems\u003csup\u003e27\u003c/sup\u003e. Despite technological advancements, research suggests that without non-CO\u003csub\u003e2\u003c/sub\u003e emission avoidance and rapid fossil fuel phase-out, the aviation sector maintains a 90% likelihood of surpassing the Paris Agreement temperature goals under current policies\u003csup\u003e28\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis work combines geographically-resolved prospective LCA, \u0026nbsp;spatiotemporal scenario modelling based on three of the IPCC\u0026rsquo;s SSP development scenarios, and two multi-criteria decision analysis (MCDA) approaches to evaluate the sustainability of 540 SAF production pathway-scenarios comprising of five conversion technologies and six biowaste-derived feedstocks across the four largest feedstock-producing nations (US, China, India, Brazil\u003csup\u003e29,30\u003c/sup\u003e) over a 50-year time horizon (2025-2075). All currently ASTM-certified SAF conversion technologies that can utilise bio-waste feedstocks are evaluated\u003csup\u003e10\u003c/sup\u003e; namely, \u0026nbsp;hydroprocessed esters and fatty acids (HEFA), hydrothermal liquefaction (HTL), gasification with Fischer-Tropsch synthesis (GFT), gasification with alcohol-to-jet conversion (GATJ), and fermentation with alcohol-to-jet conversion (FATJ), presented in Table 1. The selected feedstocks include the two globally most-produced bio-waste categories (Fig. 1): municipal solid waste (MSW) and agricultural residues \u0026ndash; specifically, maize stover (MST), sugarcane trash (SCT), rice straw (RST), and wheat straw (WST)\u003csup\u003e29\u003c/sup\u003e \u0026ndash; as well as used cooking oil (UCO) due to the high technology readiness level of its associated conversion process. The selected SSP scenarios with current national policy implementation (NPi)\u003csup\u003e25,31\u003c/sup\u003e are: SSP1-NPi \u003cem\u003e\u0026lsquo;sustainability\u0026rsquo;\u003c/em\u003e: a greener future characterised by rapid shifts towards energy efficiency and low-carbon technologies, SSP2-NPi \u003cem\u003e\u0026lsquo;middle-of-the-road\u0026rsquo;\u003c/em\u003e: a future where social, economic, and technological trends do not shift markedly from historical patterns, SSP5-NPi \u003cem\u003e\u0026lsquo;fossil-fuelled-development\u0026rsquo;\u003c/em\u003e: a future driven by competitive markets and fossil-fuelled industrial developments, with a slower transition to renewable energies\u003csup\u003e25\u003c/sup\u003e. Multi-criteria decision analysis (MCDA) is incorporated using both the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)\u003csup\u003e32\u003c/sup\u003e and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE)\u003csup\u003e33\u003c/sup\u003e methods, offering a comparative overview of each pathway-scenario\u0026rsquo;s performance across all environmental impact categories.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKey findings reveal that SAF environmental performance changes over time and is strongly coupled to regional background systems, conversion technology, and feedstock choices. GWP reductions are observed over time across all pathway-scenarios as the primary drivers (electricity and hydrogen) decarbonise. While GFT pathways consistently achieve the lowest GWP, they incur high acidification impacts from catalyst requirements; in contrast, HTL achieves the best aggregated performance across all impact categories, whereas MSW-FATJ is not currently environmentally viable by exceeding fossil jet benchmarks. Although \u0026lsquo;sustainability\u0026rsquo; scenarios accelerate decarbonisation, they introduce critical trade-offs with toxicity impacts due to electrification-induced copper demand. Geographically, the US exhibits the lowest impacts in most scenarios due to its advanced background systems, while India shows the highest, driven by open dumping of waste and a short-term heavy reliance on fossil fuels to facilitate long-term development goals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e \u003cstrong\u003eEvaluated SAF production pathways and modelling assumptions.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eFig. 2, 3, and 4 show the life cycle impact assessment (LCIA) results over 50 years (2025-2075) of the evaluated SAF production pathways for the four largest feedstock-producing countries in the world (US, China, India, Brazil) under the SSP1-NPi, SSP2-NPi, and SSP5-NPi development scenarios, respectively. Four key environmental impact categories are displayed: global warming potential over a 1000-year time horizon (GWP) in Fig. 2a-4a, terrestrial ecotoxicity potential (TETP) in Fig. 2b-4b, freshwater eutrophication potential (FEP) in Fig. 2c-4c, and terrestrial acidification potential (TAP) in Fig. 2d-4d. Several universal trends emerge that apply across almost every evaluated feedstock, technology, and geography. These represent the fundamental shifts in the global energy system as it transitions towards net-zero over a 50-year time horizon.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e \u003cstrong\u003eMinimum and maximum global warming potential (GWP) results across all pathway-scenarios (gCO\u003csub\u003e2eq\u003c/sub\u003e/MJ).\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eHEFA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eHTL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eGFT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eGATJ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eFATJ\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eMax\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUCO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMSW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"5\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e41.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e51.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e97.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e180.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e21.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e23.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e40.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e37.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e47.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e40.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e16.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e40.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e22.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e84.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eWST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e41.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e38.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e19.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e59.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable 2 summarises the minimum and maximum GWP results for each feedstock-technology combination across all 540 pathway-scenarios. The majority demonstrate compliance with sustainability standards, with 511, 501, 497, and 455 meeting the CORSIA, UK, US, and EU standards, respectively. Notably, nearly half of the pathway-scenarios (259/540) achieved a GWP reduction of at least 90% compared to the fossil jet benchmark 89 gCO\u003csub\u003e2eq\u003c/sub\u003e/MJ\u003csup\u003e24\u003c/sup\u003e, underscoring the long-term decarbonisation potential of many SAF production routes. However, a critical set of 27 pathway-scenarios exceeded the fossil benchmark, concentrated exclusively in the MSW-FATJ pathway-scenarios, with the highest values located in India.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDecarbonisation convergence:\u0026nbsp;\u003c/strong\u003eGWP shows a consistent decrease over time (0.6-93.2%) across all pathway-scenarios, primarily driven by the spatiotemporal decarbonisation of the background energy systems (Fig. 2a-4a). In 2025, impacts are coal-dominated, particularly in electricity production (mainly coal and natural gas) and hydrogen (coal gasification and steam methane reforming (SMR)). By 2075, these process emissions are largely eliminated as technologies align under global net-zero targets, and impacts become increasingly infrastructure-dominated, with transportation and upstream feedstock collection representing the remaining hard-to-abate carbon floor. Additionally, while countries start at very different baselines in 2025 (India and China generally showing larger impact than the US), they all approach similar values by 2075, suggesting that global grid parity in a net-zero future minimizes the geographic \u0026lsquo;penalty\u0026rsquo; of production.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe waste management penalty:\u0026nbsp;\u003c/strong\u003eCountries like India and Brazil generally show higher GWP (Fig. 2a-4a) and FEP (Fig. 2c-4c) for most pathway-scenarios, particularly in the short-term. This is largely attributed to open dumping of waste generated during the SAF production process. This demonstrates that the impact of SAF production in these regions is more sensitive to waste management practices than to the conversion process itself, contrasting with the more grid-driven impacts in the US and China. Conversely, this suggests that SAF produced in India or Brazil offer the greatest environmental \u0026lsquo;return on investment\u0026rsquo; by diverting waste-derived feedstocks from high-impact open dumping to generate a useful product, leading to more significant avoided emissions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe carbon-toxicity trade-off:\u003c/strong\u003e TETP shows a consistent increase over time (32.1-38.2% for SSP1 and SSP2) across most pathway-scenarios, demonstrating burden-shifting from GWP (Fig. 2b-4b). While transport brake wear emissions remain the key hotspot in TETP across all scenarios, the increase in TETP over time is attributed to copper smelting. As we move towards 2075, the electrification of industry and grid expansion necessary for large-scale decarbonisation leads to a significant requirement for metals. Consequently, the SSP5 scenario often demonstrates a lower TETP because it avoids the copper-intensive infrastructure of greener power systems (Fig. 4b).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe mining transition:\u003c/strong\u003e A universal shift is observed over time from energy-related mining to resource-related mining. In 2025, FEP (Fig. 2c-4c) and TAP (Fig. 2d-4d) are driven by coal mining and coal-fired power. By 2075, these same categories are driven primarily by copper mining, followed by platinum mining for catalysts. Generally, this creates a U-shaped curve or plateau for these impact categories, initially declining as coal is phased out but rising again as the demand for metals accelerates.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScenario sensitivity and trade-offs:\u003c/strong\u003e Consequently, although the SSP1 scenarios generally yield the lowest GWP, they often result in the highest TETP and TAP due to the accelerated pace of electrification and infrastructure development (Fig. 2a,b,d). The SSP5 scenarios usually show the highest GWP and FEP due to sustained reliance on coal mining and high-carbon electricity but often show lower toxicity impacts due to slower grid expansion (Fig. 4a,c). Conversely, for certain cases, the US exhibits lower GWP in the SSP5 scenario than in SSP2, and sometimes even SSP1 (Fig. S1). This is driven by accelerated fossil fuel usage with CCS in the US SSP5 scenarios, effectively creating a \u0026lsquo;cleaner\u0026rsquo; fossil-fuelled pathway.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIndia\u0026rsquo;s short-term sacrifice:\u003c/strong\u003e In India, the SSP1 scenarios in 2025 show higher GWP than the SSP2 scenarios (and often the SSP5 scenarios) due to heavy initial coal usage but anticipate the most drastic reductions by 2075 (Fig. S1). This demonstrates their policy of intensive short-term development for long-term sustainability and highlights the environmental sensitivity of SAF deployment to national development policies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTechnological robustness:\u003c/strong\u003e GFT consistently shows the least variation in environmental impacts across pathway-scenarios (Fig. S1). This is driven by less reliance on external hydrogen and background energy compared to the other pathways, leading to it being \u0026lsquo;shielded\u0026rsquo; against the volatility of national grid and industrial changes. Therefore, GFT pathways may be a more stable choice for countries with slower decarbonisation strategies. Conversely, this simultaneously indicates less predicted improvement for this technology, showcasing a trade-off of process stability vs anticipated development.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUsed cooking oil\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcross all scenarios, UCO pathways achieve a 56.8-97.4% reduction in GWP compared to the fossil jet benchmark (Fig. 2a-4a; Table 2), agreeing with previous research\u003csup\u003e14,15,18\u003c/sup\u003e. For both HEFA and HTL, the US displays a lower GWP than China for most scenarios (by up to 58.0%). In 2025, this is primarily due to the method of electricity production; the US uses a mixture of natural gas and coal, whereas China relies heavily on coal power which carries a significantly higher climate impact. Additionally, the hydrogen source has a significant role as the US mainly uses SMR while China relies on coal gasification. By 2075, GWP values decline significantly (by 50.4-89.5% across all pathway-scenarios) due to electricity and hydrogen decarbonisation. Generally, HTL achieves a lower GWP than HEFA (by up to 44.0%) across most temporal and geographic scenarios due to its reduced reliance on the background electricity mix, with hydrogen production remaining the largest single contributor in 2025.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn both the HEFA and HTL pathways, the US exhibits a higher TETP than China in 2050 (by up to 66.5%) due to more rapid grid expansion (Fig. 2b-4b). By 2075, however, this trend reverses as China\u0026rsquo;s TETP exceeds the US for both technologies (by up to 51.9%). Moreover, while HEFA shows a significant reduction in FEP over time (by up to 86.4%) following the phase-out of coal-intensive background processes, this reduction is less pronounced for HTL (up to 42.7%) due to its lower reliance on the electricity grid (Fig. 2c-4c). A similar temporal decline in TAP is uniquely evident for HEFA in China (up to 60.0%), reflecting the drastic transition from coal power to more sustainable sources (Fig. 2d-4d).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurthermore, the HEFA pathway demonstrates consistently higher sensitivity to the development scenarios, particularly between SSP2 and SSP5, due to its stronger coupling to the background processes (Fig. S1a). By contrast, HTL shows greater technological stability, maintaining a more consistent environmental profile between the development scenarios, except for the 2075 scenarios in China which exhibit the most variability. This is attributed to China\u0026rsquo;s greater capacity for change given its currently carbon-intensive grid, paired with the divergence of its projected development scenarios.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eComparing against all 18 environmental impact categories, the temporal shifts in UCO-HEFA ranking reflect the evolving trade-offs between decarbonisation and material intensity. UCO-HEFA-US only enters the top 10 pathways in 2075 because its higher process energy demand is initially penalised by the carbon and sulphur intensity of the US grid (Fig. 5a-c). Only after grid decarbonisation can HEFA leverage its high conversion efficiency to secure a top-tier rank. Conversely, UCO-HTL-US maintains a more consistent top 10 rank due to its lower reliance on background electricity, shielding it from early-year grid emissions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMunicipal solid waste\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcross all scenarios, the MSW pathways achieved GWP reductions of 42.1-98.1%, except for the FATJ pathway which led to an increase by 10.8-103%, shown in Table 2. Across the MSW pathways, GWP is characterised by a universal decline over time (by 4.7-92.1% across pathway-scenarios), although the magnitude and drivers vary by technology and region (Fig. 2a-4a). MSW-FATJ stands out as a significant outlier (Fig. S1b); its GWP results are considerably higher than all other pathways (by 994-5980% compared to GFT) and the fossil jet benchmark due to its low conversion efficiency which aligns with the results in Zhang et al.\u003csup\u003e42\u003c/sup\u003e, leading to its exclusion from Fig. 2-4. Among the remaining technologies, GFT exhibits the least variation across pathway-scenarios because of its reduced reliance on hydrogen and electricity, with its low GWP values aligning with Prussi et al.\u003csup\u003e15\u003c/sup\u003e. Geographically, India consistently shows higher GWP than the US or China (by up to 739% and 781% for GATJ, respectively). For GFT, this is driven by open dumping practices, while for GATJ and HTL, the 2025 spikes are additionally attributed to hydrogen production via coal gasification and electricity production from coal. For the US and China, early drivers of GWP arise from electricity production from coal and natural gas and hydrogen production from coal gasification and SMR. These reduce over time, leaving transport and the conversion process itself as the largest single contributors. By 2075, the US consistently achieves the lowest GWP, as significant contributions shift away from energy-intensive background processes.\u003c/p\u003e\n\u003cp\u003eMeanwhile, non-carbon impacts reveal a shift from process-related emissions to material-intensity burdens. FEP is notably higher in India than the US or China (Fig. 2c-4c), driven by open dumping and coal mining (by up to 3170% and 1490% for HTL, respectively). Despite sharing common drivers across pathways, GATJ maintains the highest TETP due to its lower yield, increasing the specific impact of background processes compared to other conversion technologies (Fig. 2b-4b). For most scenarios, GFT shows the highest TAP due to its higher reliance on platinum (Fig. 2d-4d). The exceptions are the GATJ and HTL pathways in India in 2025, which exhibit higher values (by up to 87.1% and 291% compared to GFT, respectively) due to a heavier reliance on coal-fired electricity and hydrogen production. However, as these fossil sources are phased out, TAP converges across all locations by 2075; at this point, the impacts are dominated by copper smelting and, for HTL, platinum mining for catalysts.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAs expected, the results from the SSP1 scenarios are generally slightly lower or similar to SSP2, except for TETP which is higher in SSP1 (by 0.7-16.2% across scenario-pathways for GFT, GATJ, and FATJ) due to increased copper smelting for grid expansion (Fig. S1b). The exceptions highlight the growth dynamics in developing regions; for HTL and GATJ in India, the SSP1 scenario yields higher impacts across all environmental categories in 2025 (GWP is 31.% and 11.2% higher than for SSP2, respectively) due to temporarily higher impact from coal-based electricity and hydrogen, but demonstrates the most drastic reductions over time (by 92.1% and 42.3%, respectively), reaching parity with SSP2 by 2075. Conversely, SSP5 in India shows the smallest variations over time, where sustained coal usage prevents significant GWP and FEP reductions by 2075.\u003c/p\u003e\n\u003cp\u003eWhen compared against all 18 impact categories, MSW-HTL-US stands out as a high-performing pathway, consistently ranking in the top 10 across every scenario and always ranked in the top 5 by at least one MCDA method (Fig. 5a-c). This is primarily driven by the relatively high efficiency of the HTL conversion process and the progressively decarbonised energy infrastructure in the US. By contrast, while MSW-HTL-CN is highly competitive in the short-term, ranking in the top 10 for all 2025 scenarios, it fails to maintain this position by 2075. This decline suggests that while HTL is initially superior to regional alternatives in China, other feedstock-technology combinations eventually surpass it in the long-term rankings across all impact categories. Conversely, the MSW-FATJ pathway is consistently identified as the worst performing option in all regions under all scenarios (Fig. 6a-c). The technology\u0026rsquo;s low conversion yield results in high material input requirements per MJ SAF produced, creating a significant environmental burden across all categories. This is primarily driven by the enzyme input; agricultural residues are assumed to produce enough enzymes internally to sustain the fermentation process, whereas MSW requires external input due to its more complex and less predictable chemical composition. Additionally, MSW fermentation requires two orders of magnitude more caustic for pH control and wastewater treatment compared to the agricultural residue pathways due the feedstock\u0026rsquo;s more acidic and variable composition. This fundamental inefficiency remains the primary driver of its low ranking as it cannot be offset by the decarbonisation of background systems.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAgricultural residues\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor all agricultural feedstocks, GFT emerges as the most stable technology, consistently delivering some of the lowest GWP across scenarios due to its minimal reliance on external hydrogen and electricity (Fig. 2a-4a); this agrees with the results in Prussi et al.\u003csup\u003e15\u003c/sup\u003e and other lignocellulosic GFT pathways in the literature\u003csup\u003e19,26\u003c/sup\u003e. The GWP of the GFT pathway is dominated by waste disposal practices and diesel burned in agricultural machinery, also consistent with Prussi et al.\u003csup\u003e15\u003c/sup\u003e, which is dependent on the regional crop yield and is not significantly affected across scenarios.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBy contrast, FATJ and GATJ generally exhibit the highest GWP due to their lower conversion efficiencies, higher process-energy demands, and wastewater treatment requirements, reflecting the previous research\u003csup\u003e15,26\u003c/sup\u003e. Across all agricultural feedstocks, the initial GWP for GATJ, FATJ, and HTL is primarily driven by hydrogen production; specifically, SMR in the US and Brazil, a combination of SMR and coal gasification in China, and coal gasification in India. Both ATJ pathways also reveal significant GWP impacts from coal-fired electricity production, as well as wastewater treatment for GATJ. Over time, this impact decreases, leaving wastewater treatment and resource production (methyl chloride, silicon, and quicklime) as the major contributors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGeographically, Brazil and India exhibit the highest GWP results for all feedstocks, primarily driven by open dumping practices and lower average crop yields, which necessitate higher consumption of diesel in agricultural machinery per unit of SAF produced.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile GWP decreases over time for all pathways and scenarios (0.6-93.2% across pathway-scenarios), the end-state drivers in 2075 differ. For GFT, contributions from electricity become negligible, but a secondary impact emerges from hydrogen production via biomass gasification as a replacement for fossil inputs. For GATJ and HTL, these pathways see the most dramatic reductions (by 61.5% and 93.2%, respectively). By 2075, the impacts of hydrogen and electricity production largely diminish, leaving transport or the conversion process itself as the primary remaining contributor. While for FATJ, even in 2075, this pathway remains burdened by the production of chemical intermediates like methyl chloride, which prevents it from achieving comparable results to GFT or HTL.\u003c/p\u003e\n\u003cp\u003eThe response to development scenarios reveals a notable regional anomaly in India and Brazil (Fig. S1c-f). The short-term development penalty is seen for FATJ, GATJ, and HTL as their GWP is higher in SSP1 than SSP2 during the 2025-2050 period (by up to 5.2%). This is caused by the transition to more sustainable hydrogen production technologies, like SMR with CCS in the US or biomass gasification in Brazil, that have not yet reached full systemic efficiency. Some stagnation is seen in SSP5 in China as this scenario results in slower reductions in GWP by 2075 compared to other locations. This is due to a sustained and heavy reliance on hydrogen production from SMR without the decarbonisation benefits seen in SSP1 and SSP2. The exception is seen for RST which shows the fewest significant differences between development scenarios, largely because the dominant impact in India (open dumping) and agricultural yields are treated as constants.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile TETP values are similar across countries by 2075, GATJ and FATJ remain the technologies with the highest toxicity results due to their lower yields (Fig. 2b-4b).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFEP results reveal a major divide based on technology and regional waste management (Fig. 2c-4c). For GFT, FATJ, and HTL, FEP is primarily driven by copper and coal mining; in these cases, India and Brazil show higher results due to the added impact of open dumping, particularly for SCT and RST. However, GATJ is the outlier across all feedstocks, exhibiting the highest FEP by a significant margin (e.g., for SSP1-2025, RST-GATJ-IN is 22.3-1090% higher than the other RST-IN pathways) due to its higher wastewater treatment requirements.\u003c/p\u003e\n\u003cp\u003eTAP remains relatively constant across locations and years but highlights a specific regional anomaly (Fig. 2d-4d). In India, TAP is significantly higher in 2025 (by up to 93.4%) across all feedstocks for FATJ, GATJ, and HTL due to a heavy reliance on coal-fired electricity. This impact reduces drastically by 2050 (by up to 92.1%) as coal is phased out. By 2075, TAP converges for all locations and is dominated by copper smelting, mine blasting, and platinum mining.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOverall, the results reveal several trade-offs between conversion technologies that remain consistent across all agricultural residues. GFT provides the lowest GWP, TETP, and FEP in most scenarios and shows the least variation across locations and time (Fig. 2-4). However, it consistently results in the highest TAP due to its high consumption of platinum for catalysts. Nevertheless, its consistency allows it to gain competitiveness over time when compared against all impact categories. By 2075, SCT-GFT reaches the top 10 in India, China, and Brazil, proving its long-term viability (Fig. 5a-c).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHTL offers moderate GWP and TETP and generally provides the lowest TAP across all scenarios (Fig. 2-4), although it shows larger variations in performance across years and locations compared to GFT. MST-HTL-US and WST-HTL-US consistently rank in the top 10 pathways across all scenarios and are always in the top 5 pathways for at least one MCDA method (Fig. 5a-c). Similarly, SCT-HTL-BR and MST-HTL-BR maintain top 10 positions in almost all cases, only falling in SSP1-2025. However, a notable temporal decline is seen for HTL in China, where SCT-HTL, RST-HTL, and MST-HTL all rank in the top 10 in 2025, but are all outranked by 2075 as other regional pathways surpass them.\u003c/p\u003e\n\u003cp\u003eCompared to the other technologies, GATJ performs moderately in GWP, TETP, and TAP but is penalised by having the highest FEP due to wastewater generation (Fig. 2-4). FATJ often results in the highest TETP and GWP across scenarios, largely due to low conversion efficiency and the environmental burden of producing chemical intermediates like methyl chloride. Consequently, FATJ frequently appears in the bottom 10 pathways (Fig. 6a-c). RST-FATJ-IN and WST-FATJ-IN consistently rank in bottom 4\u003csup\u003eth\u003c/sup\u003e and 5\u003csup\u003eth\u003c/sup\u003e respectively, while SCT-FATJ-IN and RST-FATJ-CN remain in the bottom 10 across all scenarios. Even in the US, WST-FATJ is a persistent low-ranking performer, with the only exception in the SSP1-2025 scenario.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCross-feedstock technology comparison\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the evaluated pathways, HTL is the only robust technology capable of utilising the full range of feedstocks, maintaining remarkably consistent trends and impact values regardless of the biomass source. By contrast, FATJ performance is highly sensitive to the feedstock used (Fig. S2e). Overall, MST yields the lowest environmental impacts across all 18 impact categories in all locations (Fig. 5a-c), while RST generally leads to the highest impacts out of the agricultural residues as its lower heating value (LHV) results in lower conversion yield. Certain MSW pathways stand out as major outliers; for SSP2-2025, the largest difference in GWP (6180%) is seen between the MSW-FATJ-IN and MSW-GFT-US pathways, while the largest difference in TETP (7850%) is seen between the MSW-FATJ-CN and MST-HTL-US pathways.\u003c/p\u003e\n\u003cp\u003eThe GFT and GATJ pathways reveal distinct trade-offs between agricultural residues and MSW (Fig. S2c-d). SCT-GFT demonstrates the lowest GWP and TETP across most locations and scenarios. The performance of MSW-GFT is highly regional; while it is a high-carbon-impact pathway in India due to open dumping of waste generated during the conversion process, it becomes a low-carbon-impact leader in the US and China, even outperforming several agricultural residues. RST-GFT consistently yields the highest GWP in India, although it performs competitively in China. Meanwhile, all agricultural residues demonstrate nearly identical GWP and TETP values and trends under the GATJ pathway. However, MSW deviates significantly from this pattern; while it carries a higher TETP and TAP than agricultural residues, it results in significantly lower FEP (e.g., MSW-GATJ-IN is 71.9% lower than RST-GATJ-IN in SSP1-2050), suggesting a different balance of wastewater and chemical burdens.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis prospective geographically-resolved LCA of SAF production pathways reveals that, while all pathways generally achieve a downward trend in GWP by 2075, the non-carbon trade-offs are significant. This reflects the findings in Lai et al.\u003csup\u003e19\u003c/sup\u003e and D\u0026rsquo;Ascenzo et al.\u003csup\u003e18\u003c/sup\u003e who also noted burden-shifting from GWP to toxicity impacts due to grid decarbonisation. Consistent with findings reported in the literature\u003csup\u003e20,26,27\u003c/sup\u003e, primary GWP contributions and variations across pathway-scenarios arise from the regional background energy systems, as well as the hydrogen sector\u003csup\u003e21,26\u003c/sup\u003e. Regionally, the short-term development penalty in India and Brazil under the SSP1 scenario highlights that aggressive sustainability transitions may initially spike environmental impacts.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGFT emerges as the most stable technology across all feedstocks, regions, and scenarios, agreeing with Quiroz et al.\u003csup\u003e26\u003c/sup\u003e, due to its reduced reliance on external energy and hydrogen. However, it is consistently penalised by higher TAP results stemming from platinum catalyst requirements. HTL provides a high-performing alternative, particularly in the US, where it consistently ranks in the top tier of pathways against all impact categories. Conversely, the ATJ pathways frequently occupy the bottom of the environmental rankings due to lower yields and burdens from wastewater treatment, aligning with literature\u003csup\u003e15,26\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eBased on the LCIA and MCDA results, the following recommendations are proposed:\u0026nbsp;\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003ePrioritise HTL and GFT for immediate scaling:\u003c/strong\u003e Given their low GWP and consistent top 10 rankings against all impact categories, HTL (particularly in the US and Brazil) and GFT should be the focus of near-term investment.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDiversify mitigation strategies:\u0026nbsp;\u003c/strong\u003eAlthough China\u0026rsquo;s HTL pathways converge on minimal GWP by 2075, they do not maintain the lead across all impact categories, suggesting that China\u0026rsquo;s development policies should evolve beyond only carbon emissions to cover a broader range of environmental burdens for future HTL scaling.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eInvest in yield improvements for ATJ:\u003c/strong\u003e To move FATJ and GATJ out of the bottom-tier rankings, research must focus on increasing conversion efficiency and reducing the chemical and water intensity of these pathways to mitigate their higher burdens.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eRegional feedstock optimisation:\u0026nbsp;\u003c/strong\u003eThe US, China, and Brazil should all leverage their agricultural residue resources to minimise environmental impacts, while MSW utilisation is preferred in the US and China, and UCO preferred in the US. India must address open dumping practices, as the baseline waste management significantly hinders the carbon mitigation potential of all pathways.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDecouple electricity and hydrogen from fossil sources:\u003c/strong\u003e In regions like India, the transition to renewable electricity and green hydrogen is highly important in reducing the early-year GWP spike for more energy-intensive pathways like HTL and ATJ.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eMaterial circularity:\u0026nbsp;\u003c/strong\u003eAs energy decarbonisation shifts environmental burdens from climate to material toxicity, policy frameworks must incorporate the recycling and recovery of these materials to mitigate rising TETP and TAP trends.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe limitations of this work include data availability constraints necessitating several simplifying assumptions, including the use of catalyst proxies, uniform spatiotemporal feedstock compositions, linear LHV-based yield scaling, and standardised operating conditions across pathways (unless otherwise stated), which may not fully capture the complex process dynamics and feedstock-specific optimisations for each pathway. Similarly, the use of disparate baseline data publication years (2009-2024) may introduce inconsistencies in industrial efficiency modelling. Achieving more accurate analyses requires additional granular process data on the operating conditions, resource inputs, product yields, emissions, and wastes for each established and emerging SAF production pathway.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMoreover, treating agricultural residues as zero-burden wastes neglects land-use and agricultural impacts\u003csup\u003e43,44\u003c/sup\u003e that could be reallocated if future regulatory frameworks reclassify these residues as valuable by-products. Consequently, future research should employ consequential LCA to evaluate bio-waste-derived SAF production against current practices and alternative waste disposal or valorisation methods. Finally, to accurately evaluate the full lifecycle impacts of SAF, well-to-wake system boundaries must be adopted, including the supply chain and end-use combustion emissions accounting for the variability between different SAF types.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eLCA Goal and Scope\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe aim of this geographically-resolved prospective LCA is to identify environmental hotspots, perform comparative scenario modelling, and establish performance benchmarks for a range of feedstocks, conversion technologies, and geographic locations outlined in Table 1 across three of the IPCC\u0026rsquo;s prospective development scenarios. Recognising that net-zero targets vary by region (2050 for the US and Brazil, 2060 for China, and 2070 for India), the temporal horizon for this study is extended to 2075, providing a comprehensive forecast of environmental performance as national energy grids undergo long-term transitions over a 50-year horizon.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eTo ensure regional relevance, SAF production is only modelled in one of the selected countries if that country ranked among the top 3 (or top 6 for UCO) global producers of that feedstock in the past three years\u003csup\u003e29,30,45\u003c/sup\u003e. The only exception is for WST; while Russia was the third-largest producer in 2023, the US (fourth largest) is substituted as a representative location due to its higher availability of location-specific data.\u003c/p\u003e\n\u003cp\u003eThis LCA is conducted in accordance with ISO 14040 and 15055 standards\u003csup\u003e46,47\u003c/sup\u003e. Well-to-pump system boundaries are adopted, encompassing the following stages: feedstock collection and transportation to the SAF plant, feedstock pretreatment and conversion, hydrotreatment and upgrading, transportation of the finished product to the end-user. In alignment with standard waste-to-energy LCA practices, the life cycle of the feedstock is assumed to begin at the point of waste generation. Consequently, upstream impacts related to the production of the waste (e.g. crop cultivation or consumer goods manufacturing) are excluded to avoid double-counting, as these impacts are attributed to the primary products. Furthermore, it is assumed that the bio-wastes are \u0026lsquo;genuine wastes\u0026rsquo; that are not being diverted from existing uses. The end-use phase is also excluded from the scope as non-CO\u003csub\u003e2\u003c/sub\u003e effects such as NO\u003csub\u003ex\u003c/sub\u003e and contrail formation are still subject to ongoing scientific study and vary between different SAF compositions\u003csup\u003e23,48\u003c/sup\u003e. The SAF production plant construction and background infrastructure are also excluded. The functional unit is 1 MJ SAF delivered to the end-user, calculated with energy-based allocation based on the LHV of the end products.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLife Cycle Inventory (LCI)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe LCI is constructed by coupling foreground data derived from peer-reviewed literature and technical reports with background data from the ecoinvent database. Location-dependent variables include: agricultural yields, energy mixes, hydrogen production mixes, landfill practices (for secondary waste generated during the conversion process), and catalysts and material inputs. To ensure an accurate comparison across all evaluated pathways, several consistent assumptions are applied.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTransport:\u003c/strong\u003e A standard transport distance of 50 km is assumed for both the upstream delivery of feedstocks to the SAF plant and the downstream distribution of SAF to the end-user. For agricultural residues and MSW, transportation is modelled using a 16-32 tonne truck. For UCO, collection is modelled using a light commercial vehicle to reflect the more fragmented and urban nature of its supply chain. Average road transport emission factors, shown in Table 1, are localised for each country in accordance with national legislation and estimated vehicle lifetimes, including assumptions of EPA Tier 4 in the US\u003csup\u003e49\u003c/sup\u003e, China IV in China\u003csup\u003e50\u003c/sup\u003e, Bharat Stage III in India\u003csup\u003e51\u003c/sup\u003e, and Proconve P-7 in Brazil\u003csup\u003e52\u003c/sup\u003e which are mapped onto the EURO standards for evaluation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFeedstock properties:\u003c/strong\u003e For UCO and agricultural residues, the chemical composition of each feedstock is assumed to be uniform across all geographic locations in order to isolate the impact of the conversion technologies. For UCO, this standardisation is necessary due to the lack of international data on waste oil compositions. Meanwhile, a literature review revealed a high level of intra-regional variability for each agricultural residue due to differences in local weather conditions and farming practices\u003csup\u003e53\u003c/sup\u003e. By using average composition values for each feedstock, the model ensures a consistent baseline that prevents feedstock fluctuations from obscuring regional technological performance. Conversely, a literature review of MSW compositions revealed significant variations between nations. Hence, the biogenic proportion of MSW is adjusted per location to reflect the varying regional waste streams\u003csup\u003e54-56\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCarbon accounting:\u003c/strong\u003e This work follows the principle of biogenic carbon neutrality. All CO\u003csub\u003e2\u003c/sub\u003e emitted at the refinery directly from the feedstock during the conversion of biomass to fuel is set to zero, under the assumption that these emissions are sequestered from the atmosphere during the biomass growth. This ensures that the environmental evaluation focuses on the fossil-based burdens associated with the processing, energy inputs, and auxiliary chemical requirements of each pathway.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAgricultural residue collection:\u003c/strong\u003e To quantify the energy intensity of the recovery of each agricultural residue, the available biomass is modelled using a five-year average yield (2018-2022) of the primary crop in each associated country\u003csup\u003e29\u003c/sup\u003e. Residue yield is then estimated by applying residue-to-product mass ratios derived from established literature\u003csup\u003e57-62\u003c/sup\u003e. Field-side collection is assumed to involve the same process for all residues: swathing, baling, carting, and transport to on-site storage\u003csup\u003e63\u003c/sup\u003e. The diesel consumption of these farm machinery operations is calculated based on the land area required to meet the SAF plant\u0026rsquo;s feedstock demand, specific to each feedstock and location.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAgricultural residue pretreatment:\u0026nbsp;\u003c/strong\u003eFor certain pathways (GFT, GATJ, and FATJ), each agricultural residue undergoes mechanical and thermal pretreatment to ensure feedstock uniformity. This stage uses an integrated system consisting of a crusher, drying machine, and an induced draft fan to reduce the biomass to a 5 wt% moisture content\u003csup\u003e64\u003c/sup\u003e. The quantity of water vapour emitted during this stage is calculated as a function of each feedstock\u0026rsquo;s initial moisture content. It is assumed that the thermal energy required for the drying process is generated internally since all evaluated pathways produce excess heat.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMSW pretreatment:\u003c/strong\u003e After transportation of MSW to the SAF plant, a series of mechanical separation stages are employed to isolate the biogenic fraction from the recyclables and non-organic residue. This process uses an integrated system comprising of a shredder, trommel, air classifier, magnetic separator, and eddy current separator\u003csup\u003e65\u003c/sup\u003e. It is assumed that 100% of the non-biogenic fraction is removed. The downstream processing of recyclables and non-biogenic residues are excluded from the system boundary to avoid double-counting of environmental burdens in the recycling and waste management sectors. For certain pathways (GFT and GATJ), the recovered biogenic portion undergoes further pretreatment to meet the reactor requirements; this involves drying the material from 40% to 10% moisture content, followed by pelletisation\u003csup\u003e66\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFeedstock-to-fuel conversion is modelled according to the pathways outlined in Table 1. For detailed information on the processes, assumptions, calculations, and data sources, see the Supplementary Methods section.\u003c/p\u003e\n\u003cp\u003eFor UCO only, the HEFA process is modelled using data from Mannion et al.\u003csup\u003e34\u003c/sup\u003e. This process includes filtration and dehydration of the collected UCO to remove residue and water, followed by hydrotreatment using hydrogen. Propane is removed and purified, while the remaining mixture undergoes deoxygenation with a Ni-Mo catalyst to produce paraffins. The liquid and gaseous products are separated, after which excess hydrogen in the gas stream is separated and recycled. The liquid phase undergoes hydrocracking using hydrogen and a catalyst, targeting the jet fuel range. Finally, fractional distillation of the mixture produces SAF, diesel, LPG, and naphtha.\u003c/p\u003e\n\u003cp\u003eHTL is modelled using data primarily from Feng et al.\u003csup\u003e35\u003c/sup\u003e. This pathway is characterised by its ability to process wet biomass, reducing pretreatment requirements. Water is added to the feedstock before it is pressurised and heated to subcritical conditions, producing biocrude, an aqueous phase, off-gas, and hydrochar. The biocrude is hydrotreated using hydrogen and a CoMo/alumina catalyst, producing fuel gas (for energy generation), naphtha, jet, diesel, and heavy oil. Hydrogen is recovered from the fuel gas and is recycled. The heavy oil fraction is hydrocracked using hydrogen and a CoMo catalyst to produce naphtha, jet, and diesel. The hydrochar and aqueous phase undergo nutrient recovery, ultimately producing ammonium sulphate fertiliser and fuel gas for energy generation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMSW gasification, producing either syngas or alcohol, is modelled using data from Jones et al.\u003csup\u003e36\u003c/sup\u003e. The feed is gasified with steam in a fluidised bed gasifier. Char and ash are separated and combusted for energy recovery, while the syngas is reformed using an iron chelate catalyst to increase CO and H\u003csub\u003e2\u003c/sub\u003e yield. The syngas is scrubbed to remove residual contaminants, a carbon bed removes mercury, and liquid-phase oxidation removes sulphur. The syngas, high-temperature steam, and recycled CO\u003csub\u003e2\u003c/sub\u003e are sent to a steam reformer to adjust the H\u003csub\u003e2\u003c/sub\u003e/CO ratio via the water-gas-shift reaction. An amine unit removes excess CO\u003csub\u003e2\u003c/sub\u003e before the syngas is fed into a fixed-bed reactor for mixed alcohol synthesis using a K/Co/MoS catalyst. The product is cooled before the gas phase is recycled back to the steam reformer and the liquid phase is fractionated into ethanol and higher alcohols.\u003c/p\u003e\n\u003cp\u003eAgricultural residue gasification, producing either syngas or alcohol, is modelled using data from Pati et al.\u003csup\u003e38\u003c/sup\u003e. Following gasification, ash is separated, and the syngas is cleaned, removing NH\u003csub\u003e3\u003c/sub\u003e via a water wash and H\u003csub\u003e2\u003c/sub\u003eS via an activated carbon bed. For the GFT pathways, the syngas is cooled to 150\u0026deg;C, while for the GATJ pathways, it is cooled to 37\u0026deg;C for fermentation. This occurs in a bioreactor where acetogenic bacteria convert the syngas into ethanol via the Wood-Ljungdahl pathway. The resulting liquid and gaseous phases are separated before being distilled to extract the ethanol which is finally dehydrated using a molecular sieve.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFT synthesis is modelled using data from Ahire et al.\u003csup\u003e37\u003c/sup\u003e. The syngas feed undergoes further purification using diethanolamine to strip any remaining H2S and CO2. A water-gas-shift reaction is performed using a Cu-ZnO-Al\u003csub\u003e2\u003c/sub\u003eO\u003csub\u003e3\u003c/sub\u003e catalyst, followed by low-temperature FT synthesis using a Co-based catalyst. The gaseous and liquid products are separated and the aqueous phase removed. The liquid products are hydrocracked using hydrogen and a 0.5Pt/Y(100)35A catalyst, before the products are distilled into flue gases (for energy generation), jet, and diesel.\u003c/p\u003e\n\u003cp\u003eMSW fermentation is modelled using data from Meng et al.\u003csup\u003e40\u003c/sup\u003e. This pathway can process wet biomass, avoiding the need for further feedstock pretreatment. The biogenic MSW is first autoclaved to break it down into cellulose-rich fibres; these are milled and added to water for enzymatic hydrolysis using Novozymes Cellic CTec2. A sugar solution is generated which is filtered and conditioned with nutrients and additives before being inoculated with the microorganism \u003cem\u003eClostridium acetobutylicum\u003c/em\u003e ATCC 824. Nitrogen gas stripping is used to remove the liquid and gaseous fermented products. Hydrogen is recovered from the gas stream, while the liquids are distilled, yielding acetone, ethanol, and butanol. An integrated wastewater treatment facility reduces the utility demand as well as filtering residual solids and producing biogas via digestion, both for energy recovery. Agricultural residue fermentation is modelled using data from Edwards et al.\u003csup\u003e41\u003c/sup\u003e, following similar enzymatic hydrolysis, fermentation, and energy recovery processes to the MSW pathway.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eATJ conversion is modelled using data from Romero-Izquierdo et al.\u003csup\u003e39\u003c/sup\u003e. First, the ethanol is dehydrated using a La-P-HZSM-5 catalyst, followed by ethylene conversion using saturated steam. The ethylene undergoes oligomerisation using Ni-La/ACB and HZSM5 catalysts to produce longer-chain olefins. These are then hydrogenated using hydrogen and a ATHZ5-Cs catalyst, producing paraffins. Finally, the products are distilled to separate the light gases, naphtha, jet, and diesel.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLife Cycle Impact Assessment (LCIA)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe LCIA quantifies the environmental burdens associated with the defined pathways, performed using the Brightway2 Python package\u003csup\u003e67\u003c/sup\u003e. An attributional LCA approach was employed to evaluate the absolute environmental impacts of each pathway. The environmental impacts are characterised using the ReCiPe 2016 (v1.03) midpoint (H) method. The midpoint approach is chosen to minimise uncertainties and value-based assumptions often associated with endpoint modelling. The Hierarchist (H) perspective is adopted as it represents the scientific consensus on environmental mechanisms and time horizons. The results are benchmarked against the fossil jet fuel baseline value of 89 gCO\u003csub\u003e2eq\u003c/sub\u003e/MJ\u003csup\u003e24\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProspective Scenario Modelling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo account for the evolving nature of global energy and industrial systems\u003csup\u003e68\u003c/sup\u003e, this study employs prospective scenario modelling spanning a 50-year time horizon (2025-2075) under three distinct development scenarios. The REMIND SSP-NPi framework\u003csup\u003e31\u003c/sup\u003e is used to construct the development scenarios, established from Integrated Assessment Models (IAMs), specifically SSP1-NPi \u0026lsquo;sustainability\u0026rsquo;, SSP2-NPi \u0026lsquo;middle-of-the-road\u0026rsquo;, and SSP5 \u0026lsquo;fossil-fuelled-development\u0026rsquo;\u003csup\u003e25\u003c/sup\u003e. The \u0026lsquo;NPi\u0026rsquo; designation signifies that these scenarios assume countries implement only their currently stated national policies, without the introduction of more ambitious climate commitments\u003csup\u003e31\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo transform to a dynamic LCI, the Premise Python scenario-integration tool\u003csup\u003e69\u003c/sup\u003e is used to modify the base LCI database (ecoinvent v3.9.1, cut-off) to align with the REMIND SSP-NPi projections. This transformation has several key areas of modification:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eEnergy system transitions: future electricity mixes and energy carriers are updated to match the scenario-specific narrative for the target year.\u003c/li\u003e\n \u003cli\u003eTechnology and process evolution: the database is altered to reflect projected efficiency changes and the uptake of emerging industrial technologies.\u003c/li\u003e\n \u003cli\u003eMarket and supply-chain shifts: changes in demographics, policy constraints, and technology costs drive shifts in the market shares for materials and supply mixes.\u003c/li\u003e\n \u003cli\u003eEmission trajectories: greenhouse gases and pollutant emission factors are adjusted to reflect the climate policies for each scenario.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eBy integrating these prospective databases with Brightway2, this work captures the background changes in the global economy that ultimately dictate the environmental viability of these SAF production pathways over the next 50 years. In total, 540 pathway-scenarios were evaluated in this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMulti-Criteria Decision Analysis (MCDA)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo synthesise the results across all 18 ReCiPe impact categories, MCDA is conducted using Python. This approach allows for a holistic evaluation of all sixty SAF production pathways over the 18 environmental criteria for each of the nine scenarios, building on methods seen in previous studies\u003csup\u003e70\u003c/sup\u003e and moving beyond the single-indicator representation in Fig 2-4.\u003c/p\u003e\n\u003cp\u003eTwo distinct MCDA methods are employed to ensure the robustness of the rankings and to identify potential methodological biases. The TOPSIS method is used to represent the goal-based MCDA category. This method ranks alternatives based on their geometric distance from the \u0026lsquo;positive ideal solution\u0026rsquo; (the best value in each category) and the \u0026lsquo;negative ideal solution\u0026rsquo; (the worst value)\u003csup\u003e32\u003c/sup\u003e. PROMETHEE II is used to represent the outranking MCDA method category, performing pairwise comparisons between pathways to determine the extent to which one alternative is preferred over another\u003csup\u003e33\u003c/sup\u003e. By utilising both a distance-based and an outranking method, this work identifies pathways that consistently perform well regardless of the mathematical logic applied.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe raw LCIA results are subjected to normalisation to ensure each criterion contributes proportionally to the final score. For the TOPSIS method, vector normalisation is applied, transforming the performance matrix into a dimensionless scale by dividing each score by the square root of the sum of squares for that category. For PROMETHEE, a type III V-shape preference function is applied after normalisation to define the intensity of preference between two pathways based on their relative performance. The direction of preference for all criteria is set to minimise their values as lower impact values represent reduced environmental burdens.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAn equal weighting distribution is assigned to all 18 preference criteria in order to give an overview of the environmental performance of each pathway across all impact categories. This allows for the identification of hidden trade-offs as some pathways excel in one impact category but perform poorly in another.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo ensure the reliability of the top-tier and bottom-tier classifications, the results are filtered to highlight pathways that appeared in the top or bottom 10 ranking for both MCDA methods simultaneously. This intersection-based approach minimises the impact of method-specific outliers and provides a more conservative, robust assessment of the SAF production rankings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding authors on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to acknowledge UKRI EPSRC (Grant Ref: EP/W524360/1), UKRI EPSRC IGNITE and CS sustainable manufacturing hubs, UKRI EPSRC CHIMES IKC, Leverhulme Centre for Climate Change Mitigation, South Yorkshire Sustainability Centre, FLAME (Grant Ref: EP/Y020839/1), and Supergen (Grant Ref: EP/Y016300/1) for the support of this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eE.B. and L.K. conceived the study. E.B. conducted data collection through literature review, developed the Python scripts, conducted the LCA and MCDA analyses, plotted figures, and wrote the first draft of the manuscript. L.K. provided advice on the methods and modelling. E.B. and L.K. discussed the implications of the results and finalised the manuscript. L.K. and R.Y. reviewed the manuscript and provided supervision.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eInternational Energy Agency (IEA). \u003cem\u003eAviation\u003c/em\u003e, \u0026lt;https://www.iea.org/energy-system/transport/aviation\u0026gt; (2025).\u003c/li\u003e\n\u003cli\u003eLee, D. S.\u003cem\u003e et al.\u003c/em\u003e The contribution of global aviation to anthropogenic climate forcing for 2000 to 2018. \u003cem\u003eAtmospheric Environment\u003c/em\u003e \u003cstrong\u003e244\u003c/strong\u003e (2021). https://doi.org/10.1016/j.atmosenv.2020.117834\u003c/li\u003e\n\u003cli\u003eAirports Council International (ACI). \u003cem\u003eACI World releases global airport traffic forecasts as long-term demand growth continues to reshape aviation\u003c/em\u003e, \u0026lt;https://aci.aero/2026/01/28/aci-world-releases-global-airport-traffic-forecasts-as-long-term-demand-growth-continues-to-reshape-aviation/\u0026gt; (2026).\u003c/li\u003e\n\u003cli\u003eAmerican Society for Testing and Materials (ASTM). Standard Specification for Aviation Turbine Fuels. Report No. D1655-22, (American Society for Testing and Materials, 2023).\u003c/li\u003e\n\u003cli\u003eInternational Civil Aviation Organization (ICAO). CORSIA Sustainability Criteria for CORSIA Eligible Fuels. (International Civil Aviation Organization, 2025).\u003c/li\u003e\n\u003cli\u003eDepartment for Transport. \u003cem\u003eThe SAF Mandate: an essential guide\u003c/em\u003e, \u0026lt;https://www.gov.uk/government/publications/about-the-saf-mandate/the-saf-mandate-an-essential-guide\u0026gt; (2024).\u003c/li\u003e\n\u003cli\u003eCongress Research Service (CRS). Inflation Reduction Act of 2022 (IRA): Provisions Related to Climate Change. Report No. R47262, (Congress Research Service, 2023).\u003c/li\u003e\n\u003cli\u003eEuropean Civil Aviation Conference (ECAC). ECAC Guidance on Sustainable Aviation Fuels (SAF). (European Civil Aviation Conference, 2023).\u003c/li\u003e\n\u003cli\u003eEuropean Union Aviation Safety Agency (EASA). \u003cem\u003eWhat are Sustainable Aviation Fuels?\u003c/em\u003e, \u0026lt;https://www.easa.europa.eu/en/domains/environment/eaer/sustainable-aviation-fuels/what-are-sustainable-aviation-fuels\u0026gt; (2026).\u003c/li\u003e\n\u003cli\u003eInternational Civil Aviation Organization (ICAO). \u003cem\u003eSAF Conversion processes\u003c/em\u003e, \u0026lt;https://www.icao.int/SAF/saf-conversion-processes\u0026gt; (2023).\u003c/li\u003e\n\u003cli\u003eInternational Air Transport Association (IATA). \u003cem\u003eDisappointingly Slow Growth in SAF Production\u003c/em\u003e, \u0026lt;https://www.iata.org/en/pressroom/2024-releases/2024-12-10-03/\u0026gt; (2024).\u003c/li\u003e\n\u003cli\u003eDray, L.\u003cem\u003e et al.\u003c/em\u003e Cost and emissions pathways towards net-zero climate impacts in aviation. \u003cem\u003eNature Climate Change\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e, 956\u0026ndash;962 (2022). https://doi.org/10.1038/s41558-022-01485-4\u003c/li\u003e\n\u003cli\u003eSpeizer, S.\u003cem\u003e et al.\u003c/em\u003e Integrated assessment modeling of a zero-emissions global transportation sector. \u003cem\u003eNature Communications\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e (2024). https://doi.org/10.1038/s41467-024-48424-9\u003c/li\u003e\n\u003cli\u003eChen, P. H., Lee, U., Liu, X., Cai, H. \u0026amp; Wang, M. Life-cycle analysis of sustainable aviation fuel production through catalytic hydrothermolysis. \u003cem\u003eBiofuels Bioproducts and Biorefining\u003c/em\u003e \u003cstrong\u003e18\u003c/strong\u003e, 42\u0026ndash;54 (2024). https://doi.org/10.1002/bbb.2574\u003c/li\u003e\n\u003cli\u003ePrussi, M.\u003cem\u003e et al.\u003c/em\u003e CORSIA: The first internationally adopted approach to calculate life-cycle GHG emissions for aviation fuels. \u003cem\u003eRenewable and Sustainable Energy Reviews\u003c/em\u003e \u003cstrong\u003e150\u003c/strong\u003e (2021). https://doi.org/10.1016/j.rser.2021.111398 \u003c/li\u003e\n\u003cli\u003eKhalifa, R., Bicer, Y., Al-Ansari, T. \u0026amp; Abu-Rayash, A. A lifecycle assessment of waste-based sustainable aviation fuels blends for advancing the circular aviation economy. \u003cem\u003eEnergy Conversion and Management: X\u003c/em\u003e \u003cstrong\u003e27\u003c/strong\u003e (2025). https://doi.org/10.1016/j.ecmx.2025.101141\u003c/li\u003e\n\u003cli\u003eBorrill, E., Koh, S. C. L. \u0026amp; Yuan, R. Review of technological developments and LCA applications on biobased SAF conversion processes. \u003cem\u003eFrontiers in Fuels\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e (2024). https://doi.org/10.3389/ffuel.2024.1397962\u003c/li\u003e\n\u003cli\u003eD\u0026rsquo;Ascenzo, F., Vinci, G., Savastano, M., Amici, A. \u0026amp; Ruggeri, M. Comparative Life Cycle Assessment of Sustainable Aviation Fuel Production from Different Biomasses. \u003cem\u003eSustainability\u003c/em\u003e \u003cstrong\u003e16\u003c/strong\u003e (2024). https://doi.org/10.3390/su16166875\u003c/li\u003e\n\u003cli\u003eLai, Y. Y., Karakaya, E. \u0026amp; Bj\u0026ouml;rklund, A. Employing a Socio-Technical System Approach in Prospective Life Cycle Assessment: A Case of Large-Scale Swedish Sustainable Aviation Fuels. \u003cem\u003eFrontiers in Sustainability\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e (2022). https://doi.org/10.3389/frsus.2022.912676\u003c/li\u003e\n\u003cli\u003eGreene, J. M., Quiroz, D., Limb, B. J. \u0026amp; Quinn, J. C. Geographically-Resolved Techno-Economic and Life Cycle Assessment Comparing Microalgae-Based Renewable Diesel and Sustainable Aviation Fuel in the United States. \u003cem\u003eEnvironmental Science and Technology\u003c/em\u003e \u003cstrong\u003e59\u003c/strong\u003e, 3472\u0026ndash;3483 (2025). https://doi.org/10.1021/acs.est.4c06742\u003c/li\u003e\n\u003cli\u003eRomero, M. A., Mayorga, M. A., G\u0026oacute;mez, Z. E., Correa-Mahecha, F. \u0026amp; Suarez, L. A. Life Cycle Assessment (LCA) of the Production of Sustainable Aviation Fuels (SAF) in Colombia. \u003cem\u003eChemical Engineering Transactions\u003c/em\u003e \u003cstrong\u003e117\u003c/strong\u003e, 343\u0026ndash;348 (2025). https://doi.org/10.3303/CET25117058\u003c/li\u003e\n\u003cli\u003eJing, L.\u003cem\u003e et al.\u003c/em\u003e Understanding variability in petroleum jet fuel life cycle greenhouse gas emissions to inform aviation decarbonization. \u003cem\u003eNature Communications\u003c/em\u003e \u003cstrong\u003e13\u003c/strong\u003e (2022). https://doi.org/10.1038/s41467-022-35392-1\u003c/li\u003e\n\u003cli\u003eBoerboom, L., Rao, A. G., Grewe, V. \u0026amp; Yin, F. A comprehensive well-to-wake climate impact assessment of sustainable aviation fuel. \u003cem\u003eScientific Reports\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e (2025). https://doi.org/10.1038/s41598-025-13445-x\u003c/li\u003e\n\u003cli\u003eInternational Civil Aviation Organization (ICAO). CORSIA Methodology for Calculating Actual Life Cycle Emissions Values. (International Civil Aviation Organization, 2025).\u003c/li\u003e\n\u003cli\u003eO\u0026rsquo;Neill, B. C.\u003cem\u003e et al.\u003c/em\u003e A new scenario framework for climate change research: the concept of shared socioeconomic pathways. \u003cem\u003eClimatic Change\u003c/em\u003e \u003cstrong\u003e122\u003c/strong\u003e, 387\u0026ndash;400 (2014). https://doi.org/10.1007/s10584-013-0905-2\u003c/li\u003e\n\u003cli\u003eQuiroz, D., Greene, J. M., Limb, B. J. \u0026amp; Quinn, J. C. Prospective Life Cycle Assessment of Sustainable Aviation Fuel Systems. \u003cem\u003eEnvironmental Science and Technology\u003c/em\u003e \u003cstrong\u003e59\u003c/strong\u003e, 19269\u0026ndash;19282 (2025). https://doi.org/10.1021/acs.est.5c09113\u003c/li\u003e\n\u003cli\u003eSacchi, R.\u003cem\u003e et al.\u003c/em\u003e How to make climate-neutral aviation fly. \u003cem\u003eNature Communications\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e (2023). https://doi.org/10.1038/s41467-023-39749-y\u003c/li\u003e\n\u003cli\u003eGrewe, V.\u003cem\u003e et al.\u003c/em\u003e Evaluating the climate impact of aviation emission scenarios towards the Paris agreement including COVID-19 effects. \u003cem\u003eNature Communications\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e (2021). https://doi.org/10.1038/s41467-021-24091-y\u003c/li\u003e\n\u003cli\u003eRitchie, H., Rosado, P. \u0026amp; Roser, M. \u003cem\u003eAgricultural Production\u003c/em\u003e, \u0026lt;https://ourworldindata.org/agricultural-production\u0026gt; (2023).\u003c/li\u003e\n\u003cli\u003eHoornweg, D. \u0026amp; Bhada-Tata, P. What a Waste : A Global Review of Solid Waste Management. Report No. 15, (The World Bank, 2012).\u003c/li\u003e\n\u003cli\u003eSoergel, B.\u003cem\u003e et al.\u003c/em\u003e A sustainable development pathway for climate action within the UN 2030 Agenda. \u003cem\u003eNature Climate Change\u003c/em\u003e \u003cstrong\u003e11\u003c/strong\u003e, 656\u0026ndash;664 (2021). https://doi.org/10.1038/s41558-021-01098-3\u003c/li\u003e\n\u003cli\u003eMadanchian, M. \u0026amp; Taherdoost, H. A comprehensive guide to the TOPSIS method for multi-criteria decision making. \u003cem\u003eSustainable Social Development\u003c/em\u003e \u003cstrong\u003e1\u003c/strong\u003e (2023). https://doi.org/10.54517/ssd.v1i1.2220 \u003c/li\u003e\n\u003cli\u003eAbdullah, L., Chan, W. \u0026amp; Afshari, A. Application of PROMETHEE method for green supplier selection: a comparative result based on preference functions. \u003cem\u003eJournal of Industrial Engineering International\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 271\u0026ndash;285 (2019). https://doi.org/10.1007/s40092-018-0289-z\u003c/li\u003e\n\u003cli\u003eMannion, L. A.\u003cem\u003e et al.\u003c/em\u003e A physics constrained methodology for the life cycle assessment of sustainable aviation fuel production. \u003cem\u003eBiomass and Bioenergy\u003c/em\u003e \u003cstrong\u003e185\u003c/strong\u003e (2024). https://doi.org/10.1016/j.biombioe.2024.107169\u003c/li\u003e\n\u003cli\u003eFeng, J., Li, Y., Strathmann, T. J. \u0026amp; Guest, J. S. Characterizing the Opportunity Space for Sustainable Hydrothermal Valorization of Wet Organic Wastes. \u003cem\u003eEnvironmental Science and Technology\u003c/em\u003e \u003cstrong\u003e58\u003c/strong\u003e, 2528\u0026ndash;2541 (2024). https://doi.org/10.1021/acs.est.3c07394\u003c/li\u003e\n\u003cli\u003eJones, S. B., Zhu, Y. \u0026amp; Valkenburg, C. Municipal Solid Waste (MSW) to Liquid Fuels Synthesis, Volume 2: A Techno-economic Evaluation of the Production of Mixed Alcohols. Report No. PNNL-18482, (Pacific Northwest National Laboratory, 2009).\u003c/li\u003e\n\u003cli\u003eAhire, J. P.\u003cem\u003e et al.\u003c/em\u003e Techno-economic and environmental impacts assessments of sustainable aviation fuel production from forest residues. \u003cem\u003eSustainable Energy \u0026amp; Fuels\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 4602\u0026ndash;4616 (2024). https://doi.org/10.1039/d4se00749b\u003c/li\u003e\n\u003cli\u003ePati, S., De, S. \u0026amp; Chowdhury, R. Exploring the hybrid route of bio-ethanol production via biomass co-gasification and syngas fermentation from wheat straw and sugarcane bagasse: Model development and multi-objective optimization. \u003cem\u003eJournal of Cleaner Production\u003c/em\u003e \u003cstrong\u003e395\u003c/strong\u003e (2023). https://doi.org/10.1016/j.jclepro.2023.136441\u003c/li\u003e\n\u003cli\u003eRomero-Izquierdo, A. G., Gomez-Castro, F. I., Gutierrez-Antonio, C., Hernandez, S. \u0026amp; Errico, M. Intensification of the alcohol-to-jet process to produce renewable aviation fuel. \u003cem\u003eChemical Engineering and Processing - Process Intensification\u003c/em\u003e \u003cstrong\u003e160\u003c/strong\u003e (2021). https://doi.org/10.1016/j.cep.2020.108270\u003c/li\u003e\n\u003cli\u003eMeng, F.\u003cem\u003e et al.\u003c/em\u003e Process simulation and life cycle assessment of converting autoclaved municipal solid waste into butanol and ethanol as transport fuels. \u003cem\u003eWaste Management\u003c/em\u003e \u003cstrong\u003e89\u003c/strong\u003e, 177\u0026ndash;189 (2019). https://doi.org/10.1016/j.wasman.2019.04.003\u003c/li\u003e\n\u003cli\u003eEdwards, R.\u003cem\u003e et al.\u003c/em\u003e Definition of input data to assess GHG default emissions from biofuels in EU legislation. (JCR, 2017).\u003c/li\u003e\n\u003cli\u003eZhang, L., Oladejo, J., Dawodu, A., Yang, L. \u0026amp; Xiao, Y. Sustainable jet fuel from municipal solid waste\u0026ndash;Investigation of carbon negativity and affordability claims. \u003cem\u003eResources, Conservation \u0026amp; Recycling\u003c/em\u003e \u003cstrong\u003e210\u003c/strong\u003e (2024). https://doi.org/10.1016/j.resconrec.2024.107819\u003c/li\u003e\n\u003cli\u003eBeerling, D. J.\u003cem\u003e et al.\u003c/em\u003e Transforming US agriculture for carbon removal with enhanced weathering. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e638\u003c/strong\u003e, 425\u0026ndash;434 (2025). https://doi.org/10.1038/s41586-024-08429-2\u003c/li\u003e\n\u003cli\u003eEufrasio, R. M.\u003cem\u003e et al.\u003c/em\u003e Environmental and health impacts of atmospheric CO2 removal by enhanced rock weathering depend on nations\u0026rsquo; energy mix. \u003cem\u003eCommunications Earth and Environment\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e (2022). https://doi.org/10.1038/s43247-022-00436-3\u003c/li\u003e\n\u003cli\u003eGlobalData. UCO Supply Outlook. (2023).\u003c/li\u003e\n\u003cli\u003eInternational Organization for Standardization (ISO). ISO 14040:2006 Environmental management \u0026mdash; Life cycle assessment \u0026mdash; Principles and framework. (International Organization for Standardization, 2006).\u003c/li\u003e\n\u003cli\u003eInternational Organization for Standardization (ISO). ISO 14044:2006 Environmental management \u0026mdash; Life cycle assessment \u0026mdash; Requirements and guidelines. (International Organization for Standardization, 2006).\u003c/li\u003e\n\u003cli\u003eVoigt, C.\u003cem\u003e et al.\u003c/em\u003e Cleaner burning aviation fuels can reduce contrail cloudiness. \u003cem\u003eCommunications Earth and Environment\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e (2021). https://doi.org/10.1038/s43247-021-00174-y\u003c/li\u003e\n\u003cli\u003eInternational Council on Clean Transportation (ICCT) \u0026amp; DieselNet. \u003cem\u003eUS: Heavy-Duty: Emissions\u003c/em\u003e, \u0026lt;https://www.transportpolicy.net/standard/us-heavy-duty-emissions/\u0026gt; (2018).\u003c/li\u003e\n\u003cli\u003eInternational Council on Clean Transportation (ICCT) \u0026amp; DieselNet. \u003cem\u003eChina: Heavy-Duty: Emissions\u003c/em\u003e, \u0026lt;https://www.transportpolicy.net/standard/china-heavy-duty-emissions/\u0026gt; (2018).\u003c/li\u003e\n\u003cli\u003eInternational Council on Clean Transportation (ICCT) \u0026amp; DieselNet. \u003cem\u003eIndia: Heavy-Duty: Emissions\u003c/em\u003e, \u0026lt;https://www.transportpolicy.net/standard/india-heavy-duty-emissions/\u0026gt; (2018).\u003c/li\u003e\n\u003cli\u003eInternational Council on Clean Transportation (ICCT) \u0026amp; DieselNet. \u003cem\u003eBrazil: Heavy-Duty: Emissions\u003c/em\u003e, \u0026lt;https://www.transportpolicy.net/standard/brazil-heavy-duty-emissions/\u0026gt; (2018).\u003c/li\u003e\n\u003cli\u003eGummert, M., Hung, N. V., Chivenge, P. \u0026amp; Douthwaite, B. (eds Martin Gummert, Nguyen Van Hung, Pauline Chivenge, \u0026amp; Boru Douthwaite) (Springer Open, 2020).\u003c/li\u003e\n\u003cli\u003eDing, Y.\u003cem\u003e et al.\u003c/em\u003e A review of China\u0026rsquo;s municipal solid waste (MSW) and comparison with international regions: Management and technologies in treatment and resource utilization. \u003cem\u003eJournal of Cleaner Production\u003c/em\u003e \u003cstrong\u003e293\u003c/strong\u003e (2021). https://doi.org/10.1016/j.jclepro.2021.126144\u003c/li\u003e\n\u003cli\u003eR-Cities, Ocean Conservancy (OC), The Circulate Initiative (TCI), Brihanmumbai Municipal Corporation \u0026amp; Centre for Environment Education. Mumbai, India City Waste Management Profile. (2024).\u003c/li\u003e\n\u003cli\u003eCalRecycle. 2021 Disposal Facility-Based Waste Characterization Data Tables. (California Department of Resources Recycling and Recovery, 2022).\u003c/li\u003e\n\u003cli\u003eAgriculture and Horticulture Development Board (AHDB). \u003cem\u003eThe main components of yield in wheat\u003c/em\u003e, \u0026lt;https://ahdb.org.uk/knowledge-library/the-main-components-of-yield-in-wheat\u0026gt; (2025).\u003c/li\u003e\n\u003cli\u003eSolangi, S. H., Jakhrani, A. Q., Mukwana, K. C., Jatoi, A. R. \u0026amp; Luhur, M. R. Investigation of Quantity, Quality and Energy Content of Indigenous Sugarcane Trash in Naoshehro Feroze District, Sindh. \u003cem\u003eEngineering, Technology and Applied Science Research\u003c/em\u003e \u003cstrong\u003e8\u003c/strong\u003e, 3609\u0026ndash;3613 (2018). https://doi.org/10.48084/etasr.2411\u003c/li\u003e\n\u003cli\u003eFranco, H. C. J.\u003cem\u003e et al.\u003c/em\u003e Assessment of sugarcane trash for agronomic and energy purposes in Brazil. \u003cem\u003eScientia Agricola\u003c/em\u003e \u003cstrong\u003e70\u003c/strong\u003e, 305\u0026ndash;312 (2013). https://doi.org/10.1590/S0103-90162013000500004\u003c/li\u003e\n\u003cli\u003eMensah, M. B., Jumpah, H., Boadi, N. O. \u0026amp; Awudza, J. A. M. Assessment of quantities and composition of corn stover in Ghana and their conversion into bioethanol. \u003cem\u003eScientific African\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e (2021). https://doi.org/10.1016/j.sciaf.2021.e00731\u003c/li\u003e\n\u003cli\u003eSummers, M. D.\u003cem\u003e et al.\u003c/em\u003e Biomass production and allocation in rice with implications for straw harvesting and utilization. \u003cem\u003eBiomass and Bioenergy\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, 163\u0026ndash;173 (2003). https://doi.org/10.1016/S0961-9534(02)00132-0\u003c/li\u003e\n\u003cli\u003eHome-Grown Cereals Authority (HGCA). The wheat growth guide. (2008).\u003c/li\u003e\n\u003cli\u003eGlithero, N. J., Ramsden, S. J. \u0026amp; Wilson, P. Farm systems assessment of bioenergy feedstock production: Integrating bio-economic models and life cycle analysis approaches. \u003cem\u003eAgricultural Systems\u003c/em\u003e \u003cstrong\u003e109\u003c/strong\u003e, 53\u0026ndash;64 (2012). https://doi.org/10.1016/j.agsy.2012.02.005\u003c/li\u003e\n\u003cli\u003eHan, D., Yang, X., Li, R. \u0026amp; Wu, Y. Environmental impact comparison of typical and resource-efficient biomass fast pyrolysis systems based on LCA and Aspen Plus simulation. \u003cem\u003eJournal of Cleaner Production\u003c/em\u003e \u003cstrong\u003e231\u003c/strong\u003e, 254\u0026ndash;267 (2019). https://doi.org/10.1016/j.jclepro.2019.05.094\u003c/li\u003e\n\u003cli\u003eLee, U.\u003cem\u003e et al.\u003c/em\u003e Life cycle analysis of gasification and Fischer-Tropsch conversion of municipal solid waste for transportation fuel production. \u003cem\u003eJournal of Cleaner Production\u003c/em\u003e \u003cstrong\u003e382\u003c/strong\u003e (2023). https://doi.org/10.1016/j.jclepro.2022.135114\u003c/li\u003e\n\u003cli\u003eWang, H., Wang, L. \u0026amp; Shahbazi, A. Life cycle assessment of fast pyrolysis of municipal solid waste in North Carolina of USA. \u003cem\u003eJournal of Cleaner Production\u003c/em\u003e \u003cstrong\u003e87\u003c/strong\u003e, 511\u0026ndash;519 (2015). https://doi.org/10.1016/j.jclepro.2014.09.011\u003c/li\u003e\n\u003cli\u003eMutel, C. Brightway: An open source framework for Life Cycle Assessment. \u003cem\u003eJournal of Open Source Software\u003c/em\u003e \u003cstrong\u003e12\u003c/strong\u003e (2017). https://doi.org/10.21105%2Fjoss.00236\u003c/li\u003e\n\u003cli\u003eKoroma, M. S. \u0026amp; Alwosheel, A. Aligning vehicle electrification with power sector transitions: life cycle insights across diverse grids. \u003cem\u003enpj Sustainable Mobility and Transport\u003c/em\u003e \u003cstrong\u003e3\u003c/strong\u003e (2026). https://doi.org/10.1038/s44333-025-00076-y\u003c/li\u003e\n\u003cli\u003eHedderich, M. A., Fischer, J., Klakow, D. \u0026amp; Vreeken, J. in \u003cem\u003eInternational Conference on Machine Learning (ICML),.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eShamoushaki, M. \u0026amp; Koh, S. C. L. Solar cells combined with geothermal or wind power systems reduces climate and environmental impact. \u003cem\u003eCommunications Earth and Environment\u003c/em\u003e \u003cstrong\u003e5\u003c/strong\u003e (2024). https://doi.org/10.1038/s43247-024-01739-3\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-sustainable-mobility-and-transport","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Sustainable Mobility and Transport](https://www.nature.com/npjsustainmobil)","snPcode":"44333","submissionUrl":"https://submission.springernature.com/new-submission/44333/3","title":"npj Sustainable Mobility and Transport","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8968110/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8968110/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis work presents a combined geographically-resolved prospective lifecycle modelling and multi-criteria decision analysis of five sustainable aviation fuel (SAF) production technologies using six waste-derived feedstocks across four leading producer nations (US, China, India, Brazil) from 2025 to 2075. 540 pathway-scenarios covering the 50-year evolution of each waste-to-fuel pathway are evaluated under three Shared Socioeconomic Pathways. Here we show that the sustainability of SAF production is highly sensitive to feedstock, conversion technology, geography, and background systems. Grid decarbonisation drives SAF global warming potential (GWP) reductions by 2075, yet electrification triggers significant non-carbon trade-offs. While gasification-Fischer-Tropsch achieves the lowest GWP, 2050 multi-criteria leaders are exclusively hydrothermal liquefaction pathways in the US, Brazil, and China, the latter overtaken in 2075 by sugarcane trash gasification-Fischer-Tropsch and US used cooking oil valorisation. The US mostly demonstrates the lowest environmental impacts and India the highest due to waste disposal practices and paradoxical \u0026lsquo;sustainability\u0026rsquo; strategies that prioritise short-term coal use to secure future development targets.\u003c/p\u003e","manuscriptTitle":"Sustainability of aviation fuel technologies depends on geography, energy, and feedstock choice: 50-year development scenarios","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-25 12:56:39","doi":"10.21203/rs.3.rs-8968110/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-30T09:27:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-24T12:39:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-22T15:00:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"304332454638815260528321361018181002007","date":"2026-03-12T17:24:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"1819637349428760981270467691614322996","date":"2026-02-27T10:11:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-27T08:21:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-27T08:16:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-27T04:32:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Sustainable Mobility and Transport","date":"2026-02-25T12:52:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-sustainable-mobility-and-transport","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Sustainable Mobility and Transport](https://www.nature.com/npjsustainmobil)","snPcode":"44333","submissionUrl":"https://submission.springernature.com/new-submission/44333/3","title":"npj Sustainable Mobility and Transport","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ea4a5fb9-047b-43be-809d-85bca5014eb6","owner":[],"postedDate":"March 25th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":64923047,"name":"Physical sciences/Energy science and technology"},{"id":64923048,"name":"Earth and environmental sciences/Environmental sciences"},{"id":64923049,"name":"Earth and environmental sciences/Environmental social sciences"}],"tags":[],"updatedAt":"2026-04-17T13:08:30+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-25 12:56:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8968110","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8968110","identity":"rs-8968110","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}