Decarbonising aviation does not imply successful climate change mitigation

Decarbonising aviation does not imply successful climate change mitigation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Decarbonising aviation does not imply successful climate change mitigation Thomas Arblaster, Nils Thonemann, Bernhard Steubing This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6146306/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Nov, 2025 Read the published version in Communications Earth & Environment → Version 1 posted You are reading this latest preprint version Abstract We explore under what conditions climate targets for commercial aviation in Europe can be met, following the recent European regulations for the increased use of alternative fuels and in the absence of effective offsetting. Our analysis considers the role of hydrogen in decarbonising the aviation system with an unprecedented completeness in environmental and socio-technical dimensions. Our assessment shows that, by 2050, the additional climate forcing resulting from aviation can be stabilised. However, the level at which this stabilisation occurs varies widely, depending on the trajectory of air traffic growth (4.4–12.4 mW/m 2 estimated by 2070), with all scenarios featuring some degree of overshoot. This variation is primarily driven by differences in near-term fuel demand, as technologies that promise to reduce dependence on fossil resources are still in development. Therefore, we recommend reassessing aviation climate targets, including stronger incentives for near-term reduction of fossil kerosene use and demand management. Earth and environmental sciences/Environmental social sciences/Climate-change mitigation Earth and environmental sciences/Environmental social sciences/Climate-change policy Earth and environmental sciences/Environmental social sciences/Energy and society/Energy policy Earth and environmental sciences/Environmental social sciences/Environmental impact Earth and environmental sciences/Environmental social sciences/Socioeconomic scenarios Figures Figure 1 Figure 2 Figure 3 Introduction The air transport sector has become a linchpin of the global economy, but it is also a large contributor to environmental degradation, including climate change 1 , 2 . The climate change mitigation required to limit global warming to well below 2°C, following the Paris Agreement 3 , is commonly interpreted as reducing GHG emissions to ‘net zero’ by 2050. In a net-zero scenario, any greenhouse gasses (GHGs) emitted through human activities must be ‘offset’, i.e., balanced by an equivalent reduction of GHGs. The International Air Transport Association (IATA) is among industry organisations making a voluntary net-zero commitment, with a considerable reliance on offsetting 4 . Furthermore, the International Civil Aviation Organization (ICAO) is demonstrating the practical implementation of a climate change mitigation framework through the Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA) 5 . This scheme aims to offset CO 2 emissions from international aviation in excess of a yearly limit, currently defined as 85% of such emissions in 2019. Coverage of greenhouse gases other than CO 2 and lifecycle phases beyond the combustion phase is limited 6 . CORSIA maintains eligibility criteria aligned with its (limited) scope 7 , yet the legitimacy of offsetting as an effective climate mitigation tool is challenged by the abundance of low-quality efforts where offset sales do not result in the promised emission reduction 8 – 10 . Broadly, the value of offsets in long-term impact reduction is debatable 11 . As such, the reliance on offsetting and the focus on CO 2 at the expense of other climate forcers limits the suitability of these targets to the Paris Agreement. In addition to offsetting, aviation industry roadmaps focus on measures centred around alternative aviation fuels (AAF) and energy efficiency 12 – 15 . Industry actors have set ambitious targets for these two measures in past decades but have not been met 16 . In contrast to these voluntary targets, the European Union formalised targets for the large-scale use of alternative aviation fuels in 2023 under the so-called ReFuelEU Aviation initivative 17 . Still, the development of AAF infrastructure faces logistical and technological challenges. Currently, most production methods are based on biomass feedstocks 18 , which have a limited potential supply 19 . Therefore, there is great interest in fuels that do not rely on biomass. Hydrogen (H 2 ) can be such a low-impact fuel if obtained from water electrolysis powered by renewable energy 20 . However, the relatively low volumetric energy density of hydrogen, even in liquid form, presents a challenge for aviation 21 , 22 . New technologies are needed to adapt aircraft accordingly, with the market entry of narrow-body hydrogen-powered aircraft expected for 2035 23 . Hydrogen can also be used to produce synthetic drop-in fuels by combining it with a carbon source. This source can also come from biomass 24 , industrial flue gas, resulting in the delayed emission of fossil carbon, or direct air capture (DAC) of atmospheric carbon dioxide (CO 2 ) 25 . To address environmental concerns of these emerging technologies, industrial ecologists use a forward-looking approach called prospective lifecycle assessment (pLCA) 26 – 28 . Using such methods, recent research has identified that, for the climate change impacts of synthetic fuels to be lower than those of fossil kerosene, the synthesis must be powered by a low-carbon energy system 29 – 31 . For fossil kerosene, the production phase plays a less influential role in its climate impact, but it should not be neglected: simplifications which leave out the production phase of fossil kerosene or the use phase of alternative fuels are common 31 , 32 but prevent a consistent comparison between fuels. While the above strategies provide some pathways for reducing aviation's climate impact, challenges arise in both the conception of these pathways—through reliance on offsetting and narrow technological solutions—and their assessment, lacking consistent coverage of the aviation system and its resulting climate impact. To address these limitations, a more holistic approach is needed. This study takes up this challenge by quantifying the climate impacts of future European aviation through a pLCA across various socio-technical scenarios. We focus on hydrogen-based AAF, deliberately excluding bio-based AAF and offsetting. Our analysis includes diverse projections for aircraft technology and air traffic volume. To address the limitations of current aviation climate targets, which primarily focus on CO 2 , we quantify all relevant climate forcers. The relatively short lifetime of climate forcers such as nitrogen oxides (NO x ) and persistent contrails stemming from aviation emissions present a challenge to conventional climate metrics, which rely on an arbitrary time horizon. As a result, the use of climate metrics for aviation is the subject of active scientific debate 33 , 34 . To circumvent the need for such a climate metric, we use a lightweight climate model and introduce two complementary criteria for successful climate change mitigation. First, we assess the ambition to achieve ‘net zero’ targets by 2050 by evaluating whether aviation’s climate impact increases from 2050 to 2070. Second, we use sectoral emission limits to evaluate a proxy warming budget for the coming decades. This assessment highlights technological limitations, policy gaps, and industry adoption barriers that need to be addressed for effective climate change mitigation. Results Climate impacts The climate impacts of future aviation scenarios are assessed through both CO 2 emissions and radiative forcing from 2024 to 2070. Initially, the CO 2 emissions from fossil-powered scenarios are comparable to those of AAF-powered scenarios (Fig. 1 a), primarily due to the fossil energy content in the AAF production process ( Supplementary Fig. 4 ). However, as the energy mix transitions to renewable sources, AAF-powered scenarios begin to show substantial benefits, leading to a reduction in CO 2 emissions and radiative forcing over time (Figs. 1 a and 1 b). The differences among the three scenarios stem from variations in key parameters—air traffic volumes, technological advancements in aircraft, and the evolution of the aviation fuel supply—resulting in distinct patterns of fuel demand and environmental impacts. Air traffic is modelled with three projections: a 70% reduction by 2035 due to demand management measures and low and high growth scenarios based on EUROCONTROL's forecasts 35 , 36 . Technological improvements in aircraft efficiency vary from a 'business as usual' scenario with limited progress to more optimistic scenarios that include significant advances, particularly with hydrogen-powered aircraft. The latest aircraft generation we include is introduced in 2050, which is why we do not forecast beyond 2070. The fuel supply follows the ReFuelEU Aviation initiative, aiming to reduce fossil kerosene to 30% of the fuel supply by 2050. We assume that additional measures will be taken to eliminate fossil kerosene by 2060, with a focus on hydrogen propulsion and e-fuels, relying on an increasingly renewable energy grid. Figure 2 provides an overview of the key parameters, parameter values, and the system boundary, while a further detailed description of the scenarios is given in the ‘Methods’ section. Total fuel demand Figure 3 illustrates how different parameter combinations affect fuel use and hydrogen demand. In the baseline scenarios—where AAF is not scaled up—total fuel demand initially diverges but then stabilizes around 2035 with the introduction of future aircraft (Fig. 3 a). This shows that demand plays a significant role in the short term, while the contrast between low-end and high-end technological development (Table 5 ) becomes more influential over time. The low-end scenario stabilizes fuel use with 0% demand growth, while the high-end scenario stabilizes fuel use with a year-on-year growth of 1.8%. The introduction of hydrogen aircraft in these scenarios leads to a reduction in e-fuel use, regardless of the overall AAF adoption level. Hydrogen aircraft typically have a higher energy demand per revenue passenger kilometre (RPK) (compare Fig. 3 b to Table 5 ). However, to produce 1 MJ of e-fuel, 1.61 MJ of hydrogen is consumed, whereas fuelling an aircraft with 1 MJ of liquid hydrogen requires only 1.02 MJ of hydrogen (see 'Methods'). As a result, the higher energy consumption of hydrogen aircraft is offset, leading to an overall reduction in hydrogen production if hydrogen aircraft are introduced (Fig. 3 c). Nonetheless, the hydrogen demand remains significant across all scenarios. By 2050, hydrogen demand in aviation could exceed 20% of European production in a ‘net-zero emissions’ pathway 20 in both low-demand and high-demand scenarios. CO 2 limit and air traffic volumes We combine the ICAO and IATA targets mentioned earlier into a single annual CO 2 limit (Fig. 1 a). This limit can be translated into a corresponding limit for radiative forcing from CO 2 at any point in the future (Fig. 1 b), taking into account the lifetime and radiative efficiency of CO 2 (see ‘Methods’). Figure 1 shows that both the low-growth and high-growth scenarios exceed this combined target. In contrast, AAF-powered degrowth scenarios remain close to the radiative forcing limit by staying below the annual CO 2 target for several decades. This helps offset the overshoot seen between 2045 and 2070. Additional representations of this limit for different scenarios can be found in Supplementary Fig. 5 . Climate neutrality and non-CO 2 effects In addition to CO 2 , aviation affects the climate through emissions such as methane (CH 4 ), nitrogen oxides (NO x ), hydrogen (H 2 ), and the formation of persistent contrails (see ‘Methods’). When considering these non-CO 2 effects, the overall radiative forcing is much higher (Fig. 1 b), mainly due to the inclusion of aviation-induced cloudiness ( Supplementary Fig. 6 ). Although non-CO 2 effects are still highly uncertain, the effect of persistent contrails is assumed to lessen with the introduction of AAF 37 : soot particles act as condensation nuclei in contrail formation, and as synthetic fuels undergo cleaner combustion, less soot is emitted (see ‘Methods’). As a result, AAF-powered scenarios show a sharp decrease in radiative forcing as the AAF share increases around 2045–2050 (compare Fig. 1 b to Fig. 3 c), reaching a relative minimum by 2060 (Fig. 1 b). In the degrowth scenario, radiative forcing stabilizes at this lower level, while in the high-growth scenario, it increases again as air traffic rises. Achieving ‘climate-neutral aviation’ by 2050 could be defined as a reduction in radiative forcing in subsequent years 31 , 38 , providing a broader definition that includes other climate forcers beyond just net-zero CO 2 emissions. All scenarios here meet this definition, highlighting a key shortcoming of metrics with a reference year in the future: they neglect the harmful effects of emissions occurring before the reference year. To better measure success, a budget-based metric (e.g., using the CO 2 limit) is needed. Discussion The analysis indicates that by 2060, net CO 2 emissions from a hydrogen-powered aviation sector are less than one-fifth of those from an equivalent fossil-fueled scenario (Fig. 3 a). This suggests that achieving a ‘climate-neutral’ aviation sector is feasible, with a (temporary) peak in radiative forcing expected before 2050 (Fig. 3 b). This conclusion is based on the assumption that alternative fuels reduce the impact of contrail formation, thereby compensating for the remaining CO 2 emissions. The challenge remains to prevent environmental catastrophe during this transition. This challenge is twofold. First, GHG emissions before 2050 must be limited. Emission targets set by ICAO and IATA are likely to be exceeded unless accompanied by short-term demand management (Fig. 3 b). Therefore, it is essential to reconsider what constitutes a desirable trajectory for aviation emissions leading up to 2050. The global carbon budget could be surpassed before 2050 39 . In such a scenario, reducing energy-intensive, non-essential activities might come too late. Kito et al. describe this trade-off as ‘intergenerational equity’ 40 : future generations’ ability to benefit from aviation could hinge on current generations choosing to fly less. From this perspective, demand management becomes a temporal redistribution of air travel. Although limiting growth may disadvantage some stakeholders in the short term, it could benefit everyone in the long term 41 , 42 . A second challenge for achieving sustainable, climate-neutral aviation is the rapid and fundamental transformation of societal energy systems. Our scenarios envision the development of a hydrogen economy powered almost entirely by renewable energy. If hydrogen is produced through electrolysis powered by fossil fuels or via steam methane reforming, even with carbon capture and storage, the reduction in environmental impact is limited ( Supplementary Fig. 7 ). Moreover, these systems have broader environmental effects beyond radiative forcing, such as resource depletion and air, soil, and water pollution ( Supplementary Fig. 8 ). Acknowledging this reality, the aviation sector faces limits to the amount of low-impact hydrogen it can responsibly claim. This study does not quantify these limits; however, European policy outlines ambitions for future hydrogen production and supply. According to these ambitions, a hydrogen-based aviation sector would consume a substantial portion (16–43%) of Europe’s domestic hydrogen production by 2050 (Fig. 2 ). Substituting some e-fuels with bio-based fuels in the presented scenarios would decrease the hydrogen consumption, but likely increase the climate impact 25 , 30 , 43 . It is important to recognize that the aviation sector does not commit itself to meeting the evaluated climate targets solely by reducing its own emissions. Instead, CORSIA operates as an offsetting scheme, where the aviation sector either prevents emissions in other sectors or facilitates negative emissions. Relying heavily on such measures reflects a limited vision of the future and may undermine alternative approaches 11 , 44 . Even if effective offsetting is achievable, can it be justified for European aviation to utilize this mechanism ahead of regions less prepared for the large-scale deployment of AAF? While this approach might technically comply with CORSIA, it raises similar concerns about intergenerational equity and broader environmental pressures beyond climate change. Even within our analysis, climate targets were formulated based on the historical distribution of environmental burdens. This should not be mistaken for a fair or just distribution 45 and highlights the normative issues at play in international aviation policy 46 . These issues cannot be resolved without addressing the ethical questions and value judgments they entail. This lies beyond the scope of this study but offers valuable insights for future research. Our results support the adoption of measures to limit or reduce air travel. These restrictions should effectively mitigate climate change while fostering an equitable society on a liveable planet. However, unintended incentives that reinforce current traffic patterns or encourage additional long-distance flights could undermine these goals. The short-term social cost of demand management must be weighed against the social costs of climate change. Ensuring an equitable distribution of flight activity within and between regions for both present and future generations will require innovative policy solutions. Our findings highlight the urgent need for such solutions. Methods We model the period 2024–2070 using a time resolution of one year. The sectoral model has a general workflow which starts by quantifying the air transport volume at each time step, depending on the traffic projection used. This feeds into a connected stock-and-flow model, creating persistent fleets of aircraft and fuel infrastructure to meet the required air transport and fuel supply. Time-explicit lifecycle inventories are generated and aggregated for the matching stocks and flows, creating timeseries of emissions. These time series are used to estimate radiative forcing with the LWE method. Previous work Pathways for climate change mitigation in the aviation sector have been analysed in previous work, with several recent articles considering the cumulative effect of aviation-related emissions over time 31 , 40 , 47 – 50 . These articles provide insightful perspectives on the future of aviation’s impact on climate change, although most neglect or highly simplify the impacts of fuel production, fuel use, or both. A notable exception to these shortcomings is the recent work of Sacchi et al. 31 . Our assessment includes hydrogen aircraft and its evaluation of climate targets. Aviation climate targets Achieving so-called ‘climate-neutral’ or ‘net-zero’ aviation by 2050 are recurring concepts without a universal definition. Industry roadmaps are typically defined in terms of CO 2 emitted during flight, with the only coverage of other climate impacts being AAF fuel production 12 , 13 . This approach neglects other activities supporting flight, as well as non-CO 2 effects such as contrail formation. We consider the 2050 net-zero goal using what Sacchi et al. term ‘warming neutrality’, which ‘requires that the [radiative] forcing is stabilized at the 2050 level’ 31 , considering all climate forcers associated with the aviation system. This definition is conceptually similar to the ‘Bronze’ standard for climate neutrality, defined by Brazzola et al. 38 . One measure indicating whether or not commercial aviation has achieved its climate targets is whether the radiative forcing resulting from the sector increased past 2050. We evaluate this by comparing radiative forcing in 2050 and 2070, the end of our temporal scope. In addition to the trend in warming beyond 2050, which the above definition considers, there is the question of what magnitude of warming the aviation sector should remain within. This is an emerging subject of discussion 51 and cannot be fully operationalised within the scope of this study. As a proxy, we follow the reasoning of Kito et al. 40 , who created a CO 2 budget based on the emission limits for CO 2 of the International Civil Aviation Organization (ICAO) and the International Air Transport Association (IATA). The limits are based on the Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA). In short, this scheme, which currently extends to 2035, sets a yearly limit to emissions from international aviation, covering CO 2 and a few other greenhouse gases 6 . The yearly limit is quantified as 85% of covered emissions in 2019 5 . IATA has set the goal that, after 2035, this limit will be linearly reduced such that it reaches zero in 2050 4 (Fig. 3 a). Using our model, we estimate 150 Mton CO 2 emissions for 2019. We use this value to scale the yearly emission limit, rather than the 147 Mton CO 2 reported by the European Union Aviation Safety Agency 52 . This yearly emission limit creates an equivalent warming limit (Fig. 3 b). Since this limit is specific to CO 2 emissions, it does not apply to other climate forcers. System boundary As described above, sectoral targets often do not adopt a consistent lifecycle perspective. Nevertheless, we attempt to amend this, using a so-called well-to-wing perspective for fossil fuel and AAF, which spans the cradle-to-grave construction, operation, and decommissioning of fuel production infrastructure in addition to fuel use ( Supplementary Fig. 1 ). Additionally, the lifecycles of the aircraft structures are included, as their share in environmental impacts has been speculated to increase with the use of AAF 53 . Due to a lack of prospective data, airports and the construction of new fuel distribution infrastructure are excluded. These are known to make a small contribution to the climate change impact of the sector 54 . When considering the environment more broadly, their share becomes more prominent 53 , 54 , but this falls outside the scope of the present work. Despite their prominent role in sectoral narratives, offsetting and negative carbon technologies are excluded from our analysis. As discussed in our introduction, we make this choice based on the inefficacy of past offsetting measures. Sacchi et al. 31 include a detailed description of carbon capture and storage across various scenarios, illustrating that this shifts the climate burden to ‘excessive pressure on economic and natural resources’. Although our inventories allow the quantification of additional environmental impacts ( Supplementary Fig. 8 ), we focus on climate change and do not quantify economic indicators but further discuss these limitations (see ‘Discussion’). Impact mitigation measures relating to passenger occupancy rate and technological improvement of AAF infrastructure were assessed, but excluded from the main results due to their limited influence ( Supplementary Fig. 9 ). Several other measures were not quantified here, as prior research indicated that these would have a similarly limited influence. Such measures include increased aircraft replacement rates, improved air traffic management, and the introduction of electric aircraft. However, this does not mean that these measures are unimportant. European aviation and air transport demand International agreements generally define the aviation emissions allocated to a particular region based on the flights departing from that region. This approach is used here as well. In the context of this research, ‘Europe’ is defined as a selection of countries closely aligned in their approach to reducing GHG emissions, being the European Union (EU), the European Free Trade Association (EFTA), which includes Iceland, Norway, Switzerland, and Liechtenstein, and additionally, the United Kingdom. This geographical definition is adopted from the Destination 2050 report 12 . The volume of air transport is estimated by processing flight data 55 from 2019 and projecting this into the future. To evaluate commercial aviation, the scope is limited to the most common types of flight, being passenger transport on scheduled flights. It can, therefore, be described in terms of revenue passenger-kilometres (RPK). As we approximate the sector as generic narrow-body and wide-body aircraft, the RPK for these generic aircraft is determined based on the movements of the most commonly used narrow-body and wide-body aircraft 56 . For both aircraft types, a number of flight segments are created (Table 2 ), which are used to generate aircraft fleets (see ‘Fleet dynamics’). Flights are divided into distance segments, each given a representative flight distance, used when modelling the segment’s flights (see ‘Aircraft performance and aircraft product systems’). Furthermore, separate segments are identified for intra-Europe or extra-Europe flights, using our definition of ‘Europe’, as this factor is considered in the introduction of hydrogen aircraft, shown in the last column of Table 2 (see ‘Fleet dynamics’). A generic seat occupancy of 80% is assumed. This value is relatively optimistic 54 but does not influence the comparative assessment between scenarios. Seat occupancy was found to have a relatively limited impact on the overall results when compared to other variables ( Supplementary Fig. 9 ). Table 1 Characteristics and initial conditions of air traffic segments. Aircraft type Destination Distance segment [km] Reference flight [km] Air traffic in 2019 [RPK] H 2 aircraft potential [-] Narrow-body Intra-Europe (0, 1000] 500 1.71e11 1 Narrow-body Intra-Europe (1000, 2000] 1500 3.58e11 1 Narrow-body Intra-Europe (2000, 3000] 2500 1.24e11 1 Narrow-body Intra-Europe > 3000 3500 2.34e10 1 Narrow-body Extra-Europe (0, 1000] 500 4.39e9 0 Narrow-body Extra-Europe (1000, 2000] 1500 3.95e10 0 Narrow-body Extra-Europe (2000, 3000] 2500 7.47e10 0 Narrow-body Extra-Europe > 3000 3500 6.58e10 0 Wide-body Intra-Europe (0, 4000] 2000 9.85e9 1 Wide-body Extra-Europe (0, 4000] 2000 1.49e10 0.5 Wide-body Extra-Europe (4000, 6000] 5000 1.21e11 0.5 Wide-body Extra-Europe (6000, 8000] 7000 1.80e11 0.5 Wide-body Extra-Europe > 8000 9000 2.27e11 0.5 Future demand following the EUROCONTROL pathways (‘low growth’, ‘base growth’, and ‘high growth’) is determined by combining EUROCONTROL’s seven-year 35 and 2050 forecasts 36 . The 2050 forecast is extrapolated further to reach 2070. Our ‘degrowth’ pathway strongly deviates from any industry forecast. This pathway is based on a panel consultation of the Dutch population on achieving national climate targets 57 . Several of the options most widely chosen by participants involve the suppression of commercial aviation, aligning with public support in France and the United Kingdom to disincentivise air travel 58 , 59 . The authors report this as two measures: a ban on flights to destinations within 600 km and a reduction in the number of flights by 30%. We, therefore, opt for a linear reduction in yearly RPK from 2024 to 2034, after which it stagnates at 70% of the 2019 value. No distinction is made among distance segments when applying these growth trajectories. Fleet dynamics There are several ways to represent the technological development of aircraft as a function of time. We use a dynamic stock-and-flow model representing long-lived capital goods, covering both aircraft and key AAF production infrastructure ( Supplementary Fig. 2 and Supplementary Fig. 3 ). Fleets are formed by combining stocks that perform the same function, e.g., representing different generations of narrow-body aircraft. After defining an initial condition for each fleet, its composition changes at each time interval: units which pass their maximum age leave the fleet, after which enough units enter the fleet so that the fleet can meet its required output (see Table 2 ). To this end, the seating capacity, yearly distance flown, fuel type, and operational lifetime are key aircraft characteristics (Table 3 ), while plants in the AAF supply chain are described by their yearly production capacity and operational lifetime (Table 4 ). Generally, an older aircraft is replaced before reaching its design lifetime, in the range of 25–30 years, provided that the airline operating it can afford the new model 40 , 54 , 60 . Several economic factors feed into this, which are beyond our scope. Instead, we use a constant maximum age of 22 years for all aircraft and set up an initial fleet with a mean age of around 11 years 47 . The fuel type of aircraft factors into their fleet dynamics based on the compatibility of hydrogen aircraft with the flight segments. The value used here (Table 2 ) indicates what share of aircraft introduced to satisfy the segment’s demand is hydrogen aircraft, provided a suitable hydrogen aircraft exists for the specified time and scenario ( Supplementary Fig. 3 ). Following the Destination 2050 report 12 , we assume that early hydrogen aircraft exclusively service intra-Europe routes. A potential of 1 implies that all intra-Europe flights are eventually serviced by hydrogen aircraft. However, this does not require all European airports to immediately accommodate hydrogen aircraft, as the new aircraft introduced yearly only represent a minority of the total fleet. Furthermore, to reflect that there could be hydrogen-compatible airports outside of Europe, the extra-Europe wide-body segments are given a hydrogen potential of 0.5. Table 2 Characteristics of aircraft used in determining composition and performance of aircraft fleets, as well as operating empty weight (OEW). NB: narrow-body, WB: wide-body. Aircraft Introduction [year] Seating capacity [-] Yearly operations [km/year] OEW [kg] Pre-2000 NB 1988 180 2.26e6 4.26e4 Pre-2020 NB 2016 189 2.26e6 4.26e4 2035 NB 2035 189 2.26e6 3.96e4 2035 NB (H 2 ) 2035 189 2.26e6 4.36e4 2050 NB 2050 189 2.26e6 3.71e4 2050 NB (H 2 ) 2050 189 2.26e6 4.09e4 Pre-2000 WB 1995 360 4.07e6 1.61e5 Pre-2020 WB 2015 350 4.07e6 1.42e5 2035 WB 2035 350 4.07e6 1.33e5 2050 WB 2050 350 4.07e6 1.26e5 2050 WB (H 2 ) 2050 350 4.07e6 1.38e5 Table 3 Characteristics of industrial plants used in determining the composition of plant fleets. PEM: proton exchange membrane electrolysis, DAC: direct air capture. Plant type Unit of output Yearly output capacity Maximum age [year] PEM electrolysis plant MJ H 2 1.78e7 20 DAC plant kg CO 2 1.00e8 20 Fischer-Tropsch plant MJ e-fuel 2.35e11 30 Hydrogen liquefaction plant MJ H 2 9.90e7 20 Fuel supply and fuel product system We base the introduction of AAF on the ReFuelEU Aviation rules. These describe the minimum volume of compliant AAF that must be used in a given period. This is generalised to a set share per year (Fig. 1 c). To evaluate this share, ReFuelEU Aviation specifies that hydrogen should be considered based on energy content 17 . We extend this to e-fuel, assuming a lower heating value (LHV) for each fuel: 43 MJ/kg for fossil kerosene, 45 MJ/kg for e-fuel, and 120 MJ/kg for hydrogen. The logical order applied when quantifying the fuel supply starts from the reference flow, with the air traffic demands being used to construct the aircraft fleets (see ‘Fleet dynamics’). For each time interval, the activity of the aircraft fleet requires a certain volume of hydrocarbon fuels (here, fossil kerosene and e-fuel) and liquid hydrogen. In scenarios that comply with a certain minimum volume of AAF, all liquid hydrogen contributes to this minimum, with the remainder achieved through e-fuel as part of the total hydrocarbon demand. Having determined the volume of each fuel required per time interval, the fuel production chains are quantified. For fossil kerosene, this is done by connecting a relevant background process (see ‘Prospective background lifecycle inventories’). For the AAF, fleets of key production plants are created. The efficiency of production plants based on emerging technologies is defined as a function of the plant’s construction year. As estimated performances vary, this is considered as sensitivity ( Supplementary Fig. 9 ). Hydrogen is assumed to be produced through water electrolysis using proton exchange membrane (PEM) electrolysers. The performance of the electrolysers, including future improvements, is based on literature 27 , 61 , 62 ( Supplementary Table 1 ). The oxygen molecules (O 2 ) obtained from electrolysis are not considered a co-product but are left out of the inventory. Hydrogen distribution is based on Sacchi et al. 31 , requiring 3.2 kWh/kg electricity for compression. For use on hydrogen aircraft, hydrogen is liquefied after distribution 63 . Liquefaction is represented by its operational energy demand alone ( Supplementary Table 1 ). Transportation and boil-off losses are each estimated at 1%, resulting in hydrogen emission to the air. The sorbent-based direct air capture (DAC) system is based on the inventories of Terlouw et al. 64 . We estimate its performance and learning rates by comparing and combining several sources 65 – 67 ( Supplementary Table 2 ). Fischer-Tropsch plants are also described by combining several sources 68 – 70 . Several products are created in such plants. To isolate impacts related to the production of e-fuel, physical allocation centred around lower heating value (LHV) is applied, in line with the literature. The production process is simplified to flows of CO 2 , H 2 , and electricity ( Supplementary Table 3 ). Due to a lack of data, cooling water and wastewater are cut off. The fuel tank-to-wing phase (i.e., combustion) distinguishes between the three fuels while accounting for the aircraft using the fuel and aspects of the flight itself. The flight is split up into the landing and take-off cycle (LTO), where emissions are relatively low to the ground, and climb/cruise/descent (CCD), where emissions are higher up. Fuel use for LTO is assumed to be consistent across flights, while CCD scales with the flight distance. Inventories for hydrocarbon fuels are based on the EMEP/EEA air pollutant emission inventory guidebook 71 . Metal impurities released when combusting fossil kerosene, taken from the ecoinvent 3.9.1 database 72 , are also included in these inventories but do not affect climate change. E-fuel is assumed not to have these metal impurities nor sulphur impurities, meaning that no sulphur oxides (SO x ) are formed. Inventories for hydrogen use assume a gas turbine 73 , although we make no further distinction with a fuel cell-driven electric powertrain. Aircraft performance and aircraft product systems The present fleet is represented using the fuel use, emissions, and seating capacity of common aircraft, used as proxies. These are the Airbus A320 and A320neo narrow-body aircraft and, the Boeing 777 − 300 and Airbus A350-900 wide-body aircraft (Table 2 and Table 4 ). This choice of reference aircraft is adopted from Grewe et al. 47 . The newer of these aircraft (meaning the A320neo and A350-900) are also used as reference for future generations (Table 5 ). The business-as-usual scenario is based on the historical trends in performance observed by Cox et al. 54 . Grewe et al. 47 provide expert estimates for the improvements that could be achieved in conventional aircraft over the course of the coming decades, which forms our optimistic scenario. Speculatively, larger improvements are possible when introducing new aircraft concepts, represented in the breakthrough scenario, which uses values obtained from Cox et al. 54 . For hydrogen aircraft, estimates reported by ICAO are used 74 . These express three scenarios for the fuel use of hydrogen aircraft relative to their contemporary conventional aircraft while accounting for payload capacity. The resulting range aligns with values reported elsewhere 63 , 75 , 76 . To streamline the scenario space, hydrogen and hydrocarbon aircraft performances are combined into a single variable. Table 4 Fuel use [MJ] of the reference aircraft used for the landing and take-off (LTO) phase and the climb/cruise/descent (CCD) phase of reference distance flights. NB: narrow-body, WB: wide-body. Flight phase Pre-2000 NB (Airbus A320) Pre-2020 NB (Airbus A320neo) LTO 3.23e4 2.60e4 CCD, 500 km 7.19e4 6.40e4 CCD, 1500 km 1.90e5 1.64e5 CCD, 2500 km 3.09e5 2.66e5 CCD, 3500 km 4.30e5 3.70e5 Flight phase Pre-2000 WB (Boeing 777 − 300) Pre-2020 WB (Airbus A350-900) LTO 1.02e5 8.42e4 CCD, 2000 km 7.11e5 5.84e5 CCD, 5000 km 1.77e6 1.44e6 CCD, 7000 km 2.46e6 2.01e6 CCD, 9000 km 3.32e7 2.59e6 Table 5 Fuel use of aircraft generations for each of the three pathways for aircraft technology considered. Values are relative to the current generation and expressed based on the lower heating value of the fuel used. The current generation of narrow-body (NB) and wide-body (WB) aircraft only consists of aircraft powered by hydrocarbons, but introducing hydrogen-powered aircraft in future generations is considered. Generation and introduction year Pre-2020 generation Generation 2035 Generation 2050 Fuel type Hydrocarbon Hydrocarbon Hydrogen Hydrocarbon Hydrogen Aircraft type NB & WB NB WB NB NB WB NB WB Business as usual 1.00 0.87 0.87 1.04 0.78 0.78 0.94 1.09 Optimistic 1.00 0.78 0.82 0.90 0.62 0.66 0.71 0.66 Breakthrough 1.00 0.70 0.70 0.67 0.50 0.50 0.48 0.45 Cradle-to-gate and end-of-life processes are included for each aircraft entering and leaving the fleet. We adapt aircraft material composition (Table 6 ) and the energy demands of these processes from Cox et al. 54 . Industry estimates 77 – 79 are used to quantify manufacturing waste through so-called buy-to-fly rations: 8:1 for aluminium alloy 77 , 1.5:1 for composites 78 , and 2.2:1 for other materials 79 . Since hydrogen aircraft are likely to have a higher operating empty weight (OEW) than contemporary hydrocarbon aircraft, their mass is increased by 10% 21 , but no change to their relative material composition is considered. For the sake of simplicity, aircraft cradle-to-gate and end-of-life processes, including OEW, are considered independent from aircraft performance. Table 6 Material composition of reference aircraft, as a share of operating empty weight. All aircraft are assumed to have the same composition within a given generation. CFRP: carbon fibre-reinforced composite. Material Pre-2000 generation Pre-2020 generation 2035 generation 2050 generation Aluminium alloy 0.68 0.48 0.29 0.15 CFRP 0.14 0.33 0.51 0.65 Steel 0.12 0.11 0.1 0.09 Titanium 0.04 0.05 0.06 0.06 Miscellaneous 0.02 0.03 0.04 0.05 Scenario construction The reduction of the scenario space is performed following principles of compatibility and distinctiveness, as demonstrated by, e.g. Delpierre et al. 61 and Langkau et al. 80 . First, we consider the compatibility of the technology and demand scenario parameters. Transportation efficiency improvement is considered to stimulate demand, limiting its effectiveness as a climate change mitigation measure 81 . However, experts disagree on how introducing novel aircraft and fuel technologies interacts with measures to limit air traffic growth. Some argue that, with projected growth, the energy demand of aviation will be incompatible with the available energy resources 31 . Others argue that the higher cost associated with AAF will somewhat slow growth but that further limitations would delay the sector’s sustainability transition by diverting financial resources 12 , 13 . At least in some European countries, there appears to be public support to increase barriers to flight 57 – 59 , although political uptake is limited 82 , 83 . Based on these perspectives, we decide to positively correlate air traffic demand with the development of aircraft technology, leading to three technology-demand pairs (Fig. 1 a). This correlation can be described narratively as follows: ‘ In a high-growth scenario, the fuel efficiency imperative of the aviation sector enabled it to maintain its substantial growth, which in turn provided space for revolutionary aircraft technologies to mature. In combination with broad public support, this enabled the construction of AAF infrastructure at rates thought impossible. Conversely, in a degrowth scenario, the aviation sector lacked financial resources, which reinforced the slow development of AAF infrastructure and halted technological breakthroughs. Limited access to clean technology, in turn, limited the air traffic that could be achieved within stringent environmental regulations, further restricting available resources.’ Nine scenarios are selected by combining the three sets of demand-technology pairs with three distinct fuel mix scenario values (Fig. 1 a). These fuel mixes consist of (1) a baseline, where the share of AAF is not increased, (2) the implementation of an extended version of ReFuelEU Aviation (Fig. 1 c), but with no commercial introduction of hydrogen aircraft, meaning that all AAF is modelled as e-fuel, and (3) the extended version of ReFuelEU Aviation, also featuring commercial hydrogen aircraft in future generations (Table 1 ), meaning that the share of AAF is met through a combination of liquid hydrogen and e-fuel. Prospective background lifecycle inventories The inventories created through the methods described above use economic activities not modelled within this work, but directly adopted from another source. Through these activities, the service system connects itself to the background. The background databases used here are each generated using the Python library premise 27 . This library enables the transformation of an ecoinvent database to align with the regions and scenarios of an integrated assessment model, adding several additional activities in the process. The ‘SSP2-PkBudg1150’ pathway of the REMIND model 84 is used to generate background databases for the narrative scenarios. In this pathway, the global mean surface temperature increase by 2100 is around 1.7°C, thereby achieving the goal of the Paris Agreement. Databases are generated for five-year time intervals. Selected flows which connect to the foreground are then exported and linearly interpolated to align with the one-year time interval. The ‘EUR’ region of REMIND is assumed to be representative of the geographic region considered. As a sensitivity analysis, a prospective hydrogen market generated for this pathway by Wei et al. 20 is used instead of the assumption that all hydrogen is created through electrolysis ( Supplementary Fig. 7 ). Impact assessment Aviation has several environmental effects, noise, air quality degradation, and climate change being among the most prevalent. Focussing on climate change, a topic of interest is the non-CO 2 effects of aviation. The most prevalent of these are caused by nitrogen oxides (NO x ) and by condensation of water into contrails, resulting in aviation-induced cloudiness (AIC) 2 . The short lifespan of these climate forcers raises conceptual challenges to consider their warming impacts holistically 33 , 34 . In response to observing this same challenge across CO 2 and methane (CH 4 ), Allen et al. 85 developed the linear-warming-equivalent (LWE) metric, transforming a time series of emissions into the radiative forcing expected from these emissions. This is a computationally light alternative to contemporary dynamic impact models 1 , 47 . Quantifying climate impact in this way does not rely on an arbitrary time horizon in comparing climate forcers, at the cost of obfuscating climate forcing beyond the temporal scope at hand. Sacchi et al. 31 adapted the LWE metric further to aviation. We change their code to include all distances flown and all NO x emissions from flight in the calculations. Furthermore, the influence of NO x on climate change (through ozone and methane) is assumed to have a duration of 0.267 years 86 , changed from 11.8 years, to represent its short-term warming impacts better, although this representation of atmospheric chemistry remains highly simplified. The principle used by Sacchi et al. 31 to consider the effect of fuel composition on ice crystal formation 37 is adopted. This is estimated using the hydrogen mass fraction of the fuel, which is taken to be 13.73% for fossil kerosene and 15.29% for e-fuel. Therefore, using e-fuel instead of fossil kerosene would reduce AIC impacts by 65.95%. For hydrogen-powered aircraft, we furthermore adopt the assumption of Kossarev et al. 21 that e-fuel and hydrogen result in similar AIC impacts. However, Kossarev et al. estimate this reduction to be no more than 40%, indicating that our estimates for AIC of AAF are relatively optimistic. Still, AIC maintains a large share of impacts across scenarios ( Supplementary Fig. 4 ). It must be stressed that the AIC impact of future fuels is a subject of ongoing research, with additional uncertainty added by the possibility of contrail avoidance strategies, not are not covered in this work. Declarations Acknowledgements T.P.S.A. and N.T. are supported by the SSbD4CheM project. SSbD4CheM receives funding from the European Union’s Horizon Europe research and innovation programme under grant agreement number 101138475. We are grateful for the advice of Ir. Isabel Nieuwenhuijse and Dr. Stefan Grebe. We thank Ir. Liam Megill for his comments on the manuscript. Data availability Recreating the full workflow requires access to the ecoinvent 3.9.1 database (licence required) and the EUROCONTROL Aviation Data Repository for Research (access restricted). To facilitate reproduction, the Zenodo repository for this study ( https://doi.org/10.5281/zenodo.14222579 ) 87 includes intermediate data. All graphed data is also provided in this repository. Code availability The Zenodo repository for this study ( https://doi.org/10.5281/zenodo.14222579 ) 87 provides all scripts used in generating the results. Supplementary information Supplementary information file (‘Supplementary information.docx’). References Grobler, C. et al. Marginal climate and air quality costs of aviation emissions. Environ. Res. Lett. 14, (2019). Lee, D. S. et al. The contribution of global aviation to anthropogenic climate forcing for 2000 to 2018. Atmos. Environ. 244, 117834 (2021). UNFCCC. The Paris Agreement . (2015). IATA. Net zero carbon 2050 resolution. (2022). ICAO. 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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-6146306","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":432651728,"identity":"29e50092-22b6-44ca-8df3-7af17d472dc9","order_by":0,"name":"Thomas Arblaster","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0005-4968-740X","institution":"Leiden University","correspondingAuthor":true,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Arblaster","suffix":""},{"id":432651729,"identity":"fff74091-4393-4c4d-adb2-d145c349be79","order_by":1,"name":"Nils Thonemann","email":"","orcid":"","institution":"Leiden University","correspondingAuthor":false,"prefix":"","firstName":"Nils","middleName":"","lastName":"Thonemann","suffix":""},{"id":432651730,"identity":"d3efef90-e2ad-4b92-b68d-8c62c7804947","order_by":2,"name":"Bernhard Steubing","email":"","orcid":"https://orcid.org/0000-0002-1307-6376","institution":"Leiden University","correspondingAuthor":false,"prefix":"","firstName":"Bernhard","middleName":"","lastName":"Steubing","suffix":""}],"badges":[],"createdAt":"2025-03-03 12:40:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6146306/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6146306/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s43247-025-02935-5","type":"published","date":"2025-11-27T05:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79612637,"identity":"c58cc982-b043-41c5-8b04-ad15fcee733f","added_by":"auto","created_at":"2025-03-31 18:07:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":171143,"visible":true,"origin":"","legend":"\u003cp\u003eResults for the European commercial aviation from 2024-2070 under the nine selected scenarios. Panel (a) shows the emission of CO\u003csub\u003e2\u003c/sub\u003e only, alongside a representation of the CO\u003csub\u003e2\u003c/sub\u003e limit described by ICAO and IATA. The saturated lines in panel (b) result from the calculation of additional radiative forcing from these emissions. The desaturated lines in panel (b) indicate the radiative forcing of aviation when climate forcers besides CO\u003csub\u003e2\u003c/sub\u003e are considered, notably including persistent contrails which result from aircraft (AC) emissions.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6146306/v1/280a48092c91f727d166febd.png"},{"id":79612453,"identity":"2a7e8239-a9d2-441b-9dba-119c79fbf149","added_by":"auto","created_at":"2025-03-31 17:59:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":141965,"visible":true,"origin":"","legend":"\u003cp\u003eScenarios representing the future of commercial air transport in Europe. Panel (a) illustrates the system boundary, comprising the lifecycle of the aircraft fleet and the lifecycle of the fuels. Scenario parameter values are shown with the corresponding colours and line styles used to distinguish scenarios in later figures. The dashed arrows connecting the upper and lower boxes indicate how key scenario parameters influence the system, e.g., the fuel supply adapts to the demands of the aircraft fleet, but also to the prescribed share of alternative aviation fuels (AAF) (see also \u003cstrong\u003eSupplementary Figures\u003c/strong\u003e \u003cstrong\u003e1-3\u003c/strong\u003e). Values are shown for the volume of air traffic in revenue passenger kilometres (RPK) per year (b) and the share of AAF in the aviation fuel supply per year (c).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6146306/v1/7b93bafe5dd5c691af9dc5b2.png"},{"id":79612451,"identity":"d7d163a9-d5ce-4933-afbb-b032a496b89a","added_by":"auto","created_at":"2025-03-31 17:59:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":176845,"visible":true,"origin":"","legend":"\u003cp\u003eYearly demand for fuels by European commercial aviation from 2024-2070 under the nine selected scenarios, which vary the air traffic volume, level of aircraft (AC) technological improvements, and the evolution of the fuel mix. Panels (a) and (b) show fuel used during flight, while panel (c) shows the total hydrogen production required in these scenarios, accounting for e-fuel synthesis and distribution losses. Note that the scenarios where the share of alternative aviation fuels (AAF) is not increased (i.e., those represented with a dashed line) remain close to zero in panels (b) and (c).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6146306/v1/2eb1a11be632288e9f6d08a8.png"},{"id":96975625,"identity":"980d3540-6e23-4cb8-b840-38154025ea49","added_by":"auto","created_at":"2025-11-28 08:28:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1607039,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6146306/v1/bf0d4e61-bad9-4bd3-9748-986374168294.pdf"},{"id":79612458,"identity":"735bca4b-1ddd-4cbb-9d6e-8462b7161afb","added_by":"auto","created_at":"2025-03-31 17:59:03","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2356927,"visible":true,"origin":"","legend":"Supplementary information","description":"","filename":"Supplementaryinformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-6146306/v1/08a3a565617035fde25662b8.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Decarbonising aviation does not imply successful climate change mitigation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe air transport sector has become a linchpin of the global economy, but it is also a large contributor to environmental degradation, including climate change\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The climate change mitigation required to limit global warming to well below 2\u0026deg;C, following the Paris Agreement\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, is commonly interpreted as reducing GHG emissions to \u0026lsquo;net zero\u0026rsquo; by 2050. In a net-zero scenario, any greenhouse gasses (GHGs) emitted through human activities must be \u0026lsquo;offset\u0026rsquo;, i.e., balanced by an equivalent reduction of GHGs. The International Air Transport Association (IATA) is among industry organisations making a voluntary net-zero commitment, with a considerable reliance on offsetting\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Furthermore, the International Civil Aviation Organization (ICAO) is demonstrating the practical implementation of a climate change mitigation framework through the Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA)\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. This scheme aims to offset CO\u003csub\u003e2\u003c/sub\u003e emissions from international aviation in excess of a yearly limit, currently defined as 85% of such emissions in 2019. Coverage of greenhouse gases other than CO\u003csub\u003e2\u003c/sub\u003e and lifecycle phases beyond the combustion phase is limited\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. CORSIA maintains eligibility criteria aligned with its (limited) scope\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, yet the legitimacy of offsetting as an effective climate mitigation tool is challenged by the abundance of low-quality efforts where offset sales do not result in the promised emission reduction\u003csup\u003e\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Broadly, the value of offsets in long-term impact reduction is debatable\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. As such, the reliance on offsetting and the focus on CO\u003csub\u003e2\u003c/sub\u003e at the expense of other climate forcers limits the suitability of these targets to the Paris Agreement.\u003c/p\u003e \u003cp\u003eIn addition to offsetting, aviation industry roadmaps focus on measures centred around alternative aviation fuels (AAF) and energy efficiency\u003csup\u003e\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Industry actors have set ambitious targets for these two measures in past decades but have not been met\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. In contrast to these voluntary targets, the European Union formalised targets for the large-scale use of alternative aviation fuels in 2023 under the so-called ReFuelEU Aviation initivative\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Still, the development of AAF infrastructure faces logistical and technological challenges. Currently, most production methods are based on biomass feedstocks\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, which have a limited potential supply\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Therefore, there is great interest in fuels that do not rely on biomass. Hydrogen (H\u003csub\u003e2\u003c/sub\u003e) can be such a low-impact fuel if obtained from water electrolysis powered by renewable energy\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. However, the relatively low volumetric energy density of hydrogen, even in liquid form, presents a challenge for aviation\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. New technologies are needed to adapt aircraft accordingly, with the market entry of narrow-body hydrogen-powered aircraft expected for 2035\u003csup\u003e23\u003c/sup\u003e. Hydrogen can also be used to produce synthetic drop-in fuels by combining it with a carbon source. This source can also come from biomass\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, industrial flue gas, resulting in the delayed emission of fossil carbon, or direct air capture (DAC) of atmospheric carbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e)\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. To address environmental concerns of these emerging technologies, industrial ecologists use a forward-looking approach called prospective lifecycle assessment (pLCA)\u003csup\u003e\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Using such methods, recent research has identified that, for the climate change impacts of synthetic fuels to be lower than those of fossil kerosene, the synthesis must be powered by a low-carbon energy system\u003csup\u003e\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. For fossil kerosene, the production phase plays a less influential role in its climate impact, but it should not be neglected: simplifications which leave out the production phase of fossil kerosene or the use phase of alternative fuels are common\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e but prevent a consistent comparison between fuels.\u003c/p\u003e \u003cp\u003eWhile the above strategies provide some pathways for reducing aviation's climate impact, challenges arise in both the conception of these pathways\u0026mdash;through reliance on offsetting and narrow technological solutions\u0026mdash;and their assessment, lacking consistent coverage of the aviation system and its resulting climate impact. To address these limitations, a more holistic approach is needed. This study takes up this challenge by quantifying the climate impacts of future European aviation through a pLCA across various socio-technical scenarios. We focus on hydrogen-based AAF, deliberately excluding bio-based AAF and offsetting. Our analysis includes diverse projections for aircraft technology and air traffic volume. To address the limitations of current aviation climate targets, which primarily focus on CO\u003csub\u003e2\u003c/sub\u003e, we quantify all relevant climate forcers. The relatively short lifetime of climate forcers such as nitrogen oxides (NO\u003csub\u003ex\u003c/sub\u003e) and persistent contrails stemming from aviation emissions present a challenge to conventional climate metrics, which rely on an arbitrary time horizon. As a result, the use of climate metrics for aviation is the subject of active scientific debate\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. To circumvent the need for such a climate metric, we use a lightweight climate model and introduce two complementary criteria for successful climate change mitigation. First, we assess the ambition to achieve \u0026lsquo;net zero\u0026rsquo; targets by 2050 by evaluating whether aviation\u0026rsquo;s climate impact increases from 2050 to 2070. Second, we use sectoral emission limits to evaluate a proxy warming budget for the coming decades. This assessment highlights technological limitations, policy gaps, and industry adoption barriers that need to be addressed for effective climate change mitigation.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eClimate impacts\u003c/p\u003e \u003cp\u003eThe climate impacts of future aviation scenarios are assessed through both CO\u003csub\u003e2\u003c/sub\u003e emissions and radiative forcing from 2024 to 2070. Initially, the CO\u003csub\u003e2\u003c/sub\u003e emissions from fossil-powered scenarios are comparable to those of AAF-powered scenarios (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea), primarily due to the fossil energy content in the AAF production process (\u003cb\u003eSupplementary Fig.\u0026nbsp;4\u003c/b\u003e). However, as the energy mix transitions to renewable sources, AAF-powered scenarios begin to show substantial benefits, leading to a reduction in CO\u003csub\u003e2\u003c/sub\u003e emissions and radiative forcing over time (Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe differences among the three scenarios stem from variations in key parameters\u0026mdash;air traffic volumes, technological advancements in aircraft, and the evolution of the aviation fuel supply\u0026mdash;resulting in distinct patterns of fuel demand and environmental impacts. Air traffic is modelled with three projections: a 70% reduction by 2035 due to demand management measures and low and high growth scenarios based on EUROCONTROL's forecasts\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Technological improvements in aircraft efficiency vary from a 'business as usual' scenario with limited progress to more optimistic scenarios that include significant advances, particularly with hydrogen-powered aircraft. The latest aircraft generation we include is introduced in 2050, which is why we do not forecast beyond 2070. The fuel supply follows the ReFuelEU Aviation initiative, aiming to reduce fossil kerosene to 30% of the fuel supply by 2050. We assume that additional measures will be taken to eliminate fossil kerosene by 2060, with a focus on hydrogen propulsion and e-fuels, relying on an increasingly renewable energy grid. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides an overview of the key parameters, parameter values, and the system boundary, while a further detailed description of the scenarios is given in the \u0026lsquo;Methods\u0026rsquo; section.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTotal fuel demand\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e illustrates how different parameter combinations affect fuel use and hydrogen demand. In the baseline scenarios\u0026mdash;where AAF is not scaled up\u0026mdash;total fuel demand initially diverges but then stabilizes around 2035 with the introduction of future aircraft (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). This shows that demand plays a significant role in the short term, while the contrast between low-end and high-end technological development (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) becomes more influential over time. The low-end scenario stabilizes fuel use with 0% demand growth, while the high-end scenario stabilizes fuel use with a year-on-year growth of 1.8%.\u003c/p\u003e \u003cp\u003eThe introduction of hydrogen aircraft in these scenarios leads to a reduction in e-fuel use, regardless of the overall AAF adoption level. Hydrogen aircraft typically have a higher energy demand per revenue passenger kilometre (RPK) (compare Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb to Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). However, to produce 1 MJ of e-fuel, 1.61 MJ of hydrogen is consumed, whereas fuelling an aircraft with 1 MJ of liquid hydrogen requires only 1.02 MJ of hydrogen (see 'Methods'). As a result, the higher energy consumption of hydrogen aircraft is offset, leading to an overall reduction in hydrogen production if hydrogen aircraft are introduced (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Nonetheless, the hydrogen demand remains significant across all scenarios. By 2050, hydrogen demand in aviation could exceed 20% of European production in a \u0026lsquo;net-zero emissions\u0026rsquo; pathway\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e in both low-demand and high-demand scenarios.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCO\u003csub\u003e2\u003c/sub\u003e limit and air traffic volumes\u003c/p\u003e \u003cp\u003eWe combine the ICAO and IATA targets mentioned earlier into a single annual CO\u003csub\u003e2\u003c/sub\u003e limit (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). This limit can be translated into a corresponding limit for radiative forcing from CO\u003csub\u003e2\u003c/sub\u003e at any point in the future (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb), taking into account the lifetime and radiative efficiency of CO\u003csub\u003e2\u003c/sub\u003e (see \u0026lsquo;Methods\u0026rsquo;). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows that both the low-growth and high-growth scenarios exceed this combined target. In contrast, AAF-powered degrowth scenarios remain close to the radiative forcing limit by staying below the annual CO\u003csub\u003e2\u003c/sub\u003e target for several decades. This helps offset the overshoot seen between 2045 and 2070. Additional representations of this limit for different scenarios can be found in \u003cb\u003eSupplementary Fig.\u0026nbsp;5\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eClimate neutrality and non-CO\u003csub\u003e2\u003c/sub\u003e effects\u003c/p\u003e \u003cp\u003eIn addition to CO\u003csub\u003e2\u003c/sub\u003e, aviation affects the climate through emissions such as methane (CH\u003csub\u003e4\u003c/sub\u003e), nitrogen oxides (NO\u003csub\u003ex\u003c/sub\u003e), hydrogen (H\u003csub\u003e2\u003c/sub\u003e), and the formation of persistent contrails (see \u0026lsquo;Methods\u0026rsquo;). When considering these non-CO\u003csub\u003e2\u003c/sub\u003e effects, the overall radiative forcing is much higher (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb), mainly due to the inclusion of aviation-induced cloudiness (\u003cb\u003eSupplementary Fig.\u0026nbsp;6\u003c/b\u003e). Although non-CO\u003csub\u003e2\u003c/sub\u003e effects are still highly uncertain, the effect of persistent contrails is assumed to lessen with the introduction of AAF\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e: soot particles act as condensation nuclei in contrail formation, and as synthetic fuels undergo cleaner combustion, less soot is emitted (see \u0026lsquo;Methods\u0026rsquo;). As a result, AAF-powered scenarios show a sharp decrease in radiative forcing as the AAF share increases around 2045\u0026ndash;2050 (compare Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb to Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec), reaching a relative minimum by 2060 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). In the degrowth scenario, radiative forcing stabilizes at this lower level, while in the high-growth scenario, it increases again as air traffic rises. Achieving \u0026lsquo;climate-neutral aviation\u0026rsquo; by 2050 could be defined as a reduction in radiative forcing in subsequent years\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, providing a broader definition that includes other climate forcers beyond just net-zero CO\u003csub\u003e2\u003c/sub\u003e emissions. All scenarios here meet this definition, highlighting a key shortcoming of metrics with a reference year in the future: they neglect the harmful effects of emissions occurring before the reference year. To better measure success, a budget-based metric (e.g., using the CO\u003csub\u003e2\u003c/sub\u003e limit) is needed.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe analysis indicates that by 2060, net CO\u003csub\u003e2\u003c/sub\u003e emissions from a hydrogen-powered aviation sector are less than one-fifth of those from an equivalent fossil-fueled scenario (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). This suggests that achieving a \u0026lsquo;climate-neutral\u0026rsquo; aviation sector is feasible, with a (temporary) peak in radiative forcing expected before 2050 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). This conclusion is based on the assumption that alternative fuels reduce the impact of contrail formation, thereby compensating for the remaining CO\u003csub\u003e2\u003c/sub\u003e emissions.\u003c/p\u003e \u003cp\u003eThe challenge remains to prevent environmental catastrophe during this transition. This challenge is twofold. First, GHG emissions before 2050 must be limited. Emission targets set by ICAO and IATA are likely to be exceeded unless accompanied by short-term demand management (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Therefore, it is essential to reconsider what constitutes a desirable trajectory for aviation emissions leading up to 2050. The global carbon budget could be surpassed before 2050\u003csup\u003e39\u003c/sup\u003e. In such a scenario, reducing energy-intensive, non-essential activities might come too late. Kito et al. describe this trade-off as \u0026lsquo;intergenerational equity\u0026rsquo;\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e: future generations\u0026rsquo; ability to benefit from aviation could hinge on current generations choosing to fly less. From this perspective, demand management becomes a temporal redistribution of air travel. Although limiting growth may disadvantage some stakeholders in the short term, it could benefit everyone in the long term\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eA second challenge for achieving sustainable, climate-neutral aviation is the rapid and fundamental transformation of societal energy systems. Our scenarios envision the development of a hydrogen economy powered almost entirely by renewable energy. If hydrogen is produced through electrolysis powered by fossil fuels or via steam methane reforming, even with carbon capture and storage, the reduction in environmental impact is limited (\u003cb\u003eSupplementary Fig.\u0026nbsp;7\u003c/b\u003e). Moreover, these systems have broader environmental effects beyond radiative forcing, such as resource depletion and air, soil, and water pollution (\u003cb\u003eSupplementary Fig.\u0026nbsp;8\u003c/b\u003e). Acknowledging this reality, the aviation sector faces limits to the amount of low-impact hydrogen it can responsibly claim. This study does not quantify these limits; however, European policy outlines ambitions for future hydrogen production and supply. According to these ambitions, a hydrogen-based aviation sector would consume a substantial portion (16\u0026ndash;43%) of Europe\u0026rsquo;s domestic hydrogen production by 2050 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Substituting some e-fuels with bio-based fuels in the presented scenarios would decrease the hydrogen consumption, but likely increase the climate impact\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIt is important to recognize that the aviation sector does not commit itself to meeting the evaluated climate targets solely by reducing its own emissions. Instead, CORSIA operates as an offsetting scheme, where the aviation sector either prevents emissions in other sectors or facilitates negative emissions. Relying heavily on such measures reflects a limited vision of the future and may undermine alternative approaches\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Even if effective offsetting is achievable, can it be justified for European aviation to utilize this mechanism ahead of regions less prepared for the large-scale deployment of AAF? While this approach might technically comply with CORSIA, it raises similar concerns about intergenerational equity and broader environmental pressures beyond climate change. Even within our analysis, climate targets were formulated based on the historical distribution of environmental burdens. This should not be mistaken for a fair or just distribution\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e and highlights the normative issues at play in international aviation policy\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. These issues cannot be resolved without addressing the ethical questions and value judgments they entail. This lies beyond the scope of this study but offers valuable insights for future research.\u003c/p\u003e \u003cp\u003eOur results support the adoption of measures to limit or reduce air travel. These restrictions should effectively mitigate climate change while fostering an equitable society on a liveable planet. However, unintended incentives that reinforce current traffic patterns or encourage additional long-distance flights could undermine these goals. The short-term social cost of demand management must be weighed against the social costs of climate change. Ensuring an equitable distribution of flight activity within and between regions for both present and future generations will require innovative policy solutions. Our findings highlight the urgent need for such solutions.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe model the period 2024\u0026ndash;2070 using a time resolution of one year. The sectoral model has a general workflow which starts by quantifying the air transport volume at each time step, depending on the traffic projection used. This feeds into a connected stock-and-flow model, creating persistent fleets of aircraft and fuel infrastructure to meet the required air transport and fuel supply. Time-explicit lifecycle inventories are generated and aggregated for the matching stocks and flows, creating timeseries of emissions. These time series are used to estimate radiative forcing with the LWE method.\u003c/p\u003e \u003cp\u003ePrevious work\u003c/p\u003e \u003cp\u003ePathways for climate change mitigation in the aviation sector have been analysed in previous work, with several recent articles considering the cumulative effect of aviation-related emissions over time\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan additionalcitationids=\"CR48 CR49\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. These articles provide insightful perspectives on the future of aviation\u0026rsquo;s impact on climate change, although most neglect or highly simplify the impacts of fuel production, fuel use, or both. A notable exception to these shortcomings is the recent work of Sacchi et al.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Our assessment includes hydrogen aircraft and its evaluation of climate targets.\u003c/p\u003e \u003cp\u003eAviation climate targets\u003c/p\u003e \u003cp\u003eAchieving so-called \u0026lsquo;climate-neutral\u0026rsquo; or \u0026lsquo;net-zero\u0026rsquo; aviation by 2050 are recurring concepts without a universal definition. Industry roadmaps are typically defined in terms of CO\u003csub\u003e2\u003c/sub\u003e emitted during flight, with the only coverage of other climate impacts being AAF fuel production\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. This approach neglects other activities supporting flight, as well as non-CO\u003csub\u003e2\u003c/sub\u003e effects such as contrail formation. We consider the 2050 net-zero goal using what Sacchi et al. term \u0026lsquo;warming neutrality\u0026rsquo;, which \u0026lsquo;requires that the [radiative] forcing is stabilized at the 2050 level\u0026rsquo;\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, considering all climate forcers associated with the aviation system. This definition is conceptually similar to the \u0026lsquo;Bronze\u0026rsquo; standard for climate neutrality, defined by Brazzola et al.\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. One measure indicating whether or not commercial aviation has achieved its climate targets is whether the radiative forcing resulting from the sector increased past 2050. We evaluate this by comparing radiative forcing in 2050 and 2070, the end of our temporal scope.\u003c/p\u003e \u003cp\u003eIn addition to the \u003cem\u003etrend\u003c/em\u003e in warming beyond 2050, which the above definition considers, there is the question of what \u003cem\u003emagnitude\u003c/em\u003e of warming the aviation sector should remain within. This is an emerging subject of discussion\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e and cannot be fully operationalised within the scope of this study. As a proxy, we follow the reasoning of Kito et al.\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e, who created a CO\u003csub\u003e2\u003c/sub\u003e budget based on the emission limits for CO\u003csub\u003e2\u003c/sub\u003e of the International Civil Aviation Organization (ICAO) and the International Air Transport Association (IATA). The limits are based on the Carbon Offsetting and Reduction Scheme for International Aviation (CORSIA). In short, this scheme, which currently extends to 2035, sets a yearly limit to emissions from international aviation, covering CO\u003csub\u003e2\u003c/sub\u003e and a few other greenhouse gases\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. The yearly limit is quantified as 85% of covered emissions in 2019\u003csup\u003e5\u003c/sup\u003e. IATA has set the goal that, after 2035, this limit will be linearly reduced such that it reaches zero in 2050\u003csup\u003e4\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Using our model, we estimate 150 Mton CO\u003csub\u003e2\u003c/sub\u003e emissions for 2019. We use this value to scale the yearly emission limit, rather than the 147 Mton CO\u003csub\u003e2\u003c/sub\u003e reported by the European Union Aviation Safety Agency\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. This yearly emission limit creates an equivalent warming limit (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Since this limit is specific to CO\u003csub\u003e2\u003c/sub\u003e emissions, it does not apply to other climate forcers.\u003c/p\u003e \u003cp\u003eSystem boundary\u003c/p\u003e \u003cp\u003eAs described above, sectoral targets often do not adopt a consistent lifecycle perspective. Nevertheless, we attempt to amend this, using a so-called well-to-wing perspective for fossil fuel and AAF, which spans the cradle-to-grave construction, operation, and decommissioning of fuel production infrastructure in addition to fuel use (\u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e). Additionally, the lifecycles of the aircraft structures are included, as their share in environmental impacts has been speculated to increase with the use of AAF\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Due to a lack of prospective data, airports and the construction of new fuel distribution infrastructure are excluded. These are known to make a small contribution to the climate change impact of the sector\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. When considering the environment more broadly, their share becomes more prominent\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e, but this falls outside the scope of the present work.\u003c/p\u003e \u003cp\u003eDespite their prominent role in sectoral narratives, offsetting and negative carbon technologies are excluded from our analysis. As discussed in our introduction, we make this choice based on the inefficacy of past offsetting measures. Sacchi et al.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e include a detailed description of carbon capture and storage across various scenarios, illustrating that this shifts the climate burden to \u0026lsquo;excessive pressure on economic and natural resources\u0026rsquo;. Although our inventories allow the quantification of additional environmental impacts (\u003cb\u003eSupplementary Fig.\u0026nbsp;8\u003c/b\u003e), we focus on climate change and do not quantify economic indicators but further discuss these limitations (see \u0026lsquo;Discussion\u0026rsquo;).\u003c/p\u003e \u003cp\u003eImpact mitigation measures relating to passenger occupancy rate and technological improvement of AAF infrastructure were assessed, but excluded from the main results due to their limited influence (\u003cb\u003eSupplementary Fig.\u0026nbsp;9\u003c/b\u003e). Several other measures were not quantified here, as prior research indicated that these would have a similarly limited influence. Such measures include increased aircraft replacement rates, improved air traffic management, and the introduction of electric aircraft. However, this does not mean that these measures are unimportant.\u003c/p\u003e \u003cp\u003eEuropean aviation and air transport demand\u003c/p\u003e \u003cp\u003eInternational agreements generally define the aviation emissions allocated to a particular region based on the flights departing from that region. This approach is used here as well. In the context of this research, \u0026lsquo;Europe\u0026rsquo; is defined as a selection of countries closely aligned in their approach to reducing GHG emissions, being the European Union (EU), the European Free Trade Association (EFTA), which includes Iceland, Norway, Switzerland, and Liechtenstein, and additionally, the United Kingdom. This geographical definition is adopted from the Destination 2050 report\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe volume of air transport is estimated by processing flight data\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e from 2019 and projecting this into the future. To evaluate commercial aviation, the scope is limited to the most common types of flight, being passenger transport on scheduled flights. It can, therefore, be described in terms of revenue passenger-kilometres (RPK). As we approximate the sector as generic narrow-body and wide-body aircraft, the RPK for these generic aircraft is determined based on the movements of the most commonly used narrow-body and wide-body aircraft\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. For both aircraft types, a number of flight segments are created (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), which are used to generate aircraft fleets (see \u0026lsquo;Fleet dynamics\u0026rsquo;). Flights are divided into distance segments, each given a representative flight distance, used when modelling the segment\u0026rsquo;s flights (see \u0026lsquo;Aircraft performance and aircraft product systems\u0026rsquo;). Furthermore, separate segments are identified for intra-Europe or extra-Europe flights, using our definition of \u0026lsquo;Europe\u0026rsquo;, as this factor is considered in the introduction of hydrogen aircraft, shown in the last column of Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (see \u0026lsquo;Fleet dynamics\u0026rsquo;). A generic seat occupancy of 80% is assumed. This value is relatively optimistic\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e but does not influence the comparative assessment between scenarios. Seat occupancy was found to have a relatively limited impact on the overall results when compared to other variables (\u003cb\u003eSupplementary Fig.\u0026nbsp;9\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics and initial conditions of air traffic segments.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAircraft type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDestination\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDistance segment [km]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference flight [km]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAir traffic in 2019 [RPK]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH\u003csub\u003e2\u003c/sub\u003e aircraft potential [-]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNarrow-body\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntra-Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0, 1000]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.71e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNarrow-body\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntra-Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1000, 2000]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.58e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNarrow-body\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntra-Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2000, 3000]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.24e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNarrow-body\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntra-Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.34e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNarrow-body\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtra-Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0, 1000]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.39e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNarrow-body\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtra-Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1000, 2000]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.95e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNarrow-body\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtra-Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(2000, 3000]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.47e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNarrow-body\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtra-Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.58e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWide-body\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntra-Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0, 4000]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.85e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWide-body\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtra-Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0, 4000]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.49e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWide-body\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtra-Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(4000, 6000]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.21e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWide-body\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtra-Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(6000, 8000]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.80e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWide-body\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExtra-Europe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;8000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.27e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFuture demand following the EUROCONTROL pathways (\u0026lsquo;low growth\u0026rsquo;, \u0026lsquo;base growth\u0026rsquo;, and \u0026lsquo;high growth\u0026rsquo;) is determined by combining EUROCONTROL\u0026rsquo;s seven-year\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e and 2050 forecasts\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. The 2050 forecast is extrapolated further to reach 2070. Our \u0026lsquo;degrowth\u0026rsquo; pathway strongly deviates from any industry forecast. This pathway is based on a panel consultation of the Dutch population on achieving national climate targets\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Several of the options most widely chosen by participants involve the suppression of commercial aviation, aligning with public support in France and the United Kingdom to disincentivise air travel\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e,\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. The authors report this as two measures: a ban on flights to destinations within 600 km and a reduction in the number of flights by 30%. We, therefore, opt for a linear reduction in yearly RPK from 2024 to 2034, after which it stagnates at 70% of the 2019 value. No distinction is made among distance segments when applying these growth trajectories.\u003c/p\u003e \u003cp\u003eFleet dynamics\u003c/p\u003e \u003cp\u003eThere are several ways to represent the technological development of aircraft as a function of time. We use a dynamic stock-and-flow model representing long-lived capital goods, covering both aircraft and key AAF production infrastructure (\u003cb\u003eSupplementary Fig.\u0026nbsp;2\u003c/b\u003e and \u003cb\u003eSupplementary Fig.\u0026nbsp;3\u003c/b\u003e). Fleets are formed by combining stocks that perform the same function, e.g., representing different generations of narrow-body aircraft. After defining an initial condition for each fleet, its composition changes at each time interval: units which pass their maximum age leave the fleet, after which enough units enter the fleet so that the fleet can meet its required output (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). To this end, the seating capacity, yearly distance flown, fuel type, and operational lifetime are key aircraft characteristics (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), while plants in the AAF supply chain are described by their yearly production capacity and operational lifetime (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Generally, an older aircraft is replaced before reaching its design lifetime, in the range of 25\u0026ndash;30 years, provided that the airline operating it can afford the new model\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e,\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Several economic factors feed into this, which are beyond our scope. Instead, we use a constant maximum age of 22 years for all aircraft and set up an initial fleet with a mean age of around 11 years\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe fuel type of aircraft factors into their fleet dynamics based on the compatibility of hydrogen aircraft with the flight segments. The value used here (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) indicates what share of aircraft introduced to satisfy the segment\u0026rsquo;s demand is hydrogen aircraft, provided a suitable hydrogen aircraft exists for the specified time and scenario (\u003cb\u003eSupplementary Fig.\u0026nbsp;3\u003c/b\u003e). Following the Destination 2050 report\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, we assume that early hydrogen aircraft exclusively service intra-Europe routes. A potential of 1 implies that all intra-Europe flights are eventually serviced by hydrogen aircraft. However, this does not require all European airports to immediately accommodate hydrogen aircraft, as the new aircraft introduced yearly only represent a minority of the total fleet. Furthermore, to reflect that there could be hydrogen-compatible airports outside of Europe, the extra-Europe wide-body segments are given a hydrogen potential of 0.5.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of aircraft used in determining composition and performance of aircraft fleets, as well as operating empty weight (OEW). NB: narrow-body, WB: wide-body.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAircraft\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntroduction [year]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSeating capacity [-]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYearly operations [km/year]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOEW [kg]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-2000 NB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.26e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.26e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-2020 NB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.26e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.26e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2035 NB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.26e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.96e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2035 NB (H\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.26e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.36e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2050 NB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.26e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.71e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2050 NB (H\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.26e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.09e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-2000 WB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.07e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.61e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre-2020 WB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.07e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.42e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2035 WB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.07e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.33e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2050 WB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.07e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.26e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2050 WB (H\u003csub\u003e2\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.07e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.38e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of industrial plants used in determining the composition of plant fleets. PEM: proton exchange membrane electrolysis, DAC: direct air capture.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlant type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnit of output\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYearly output capacity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaximum age [year]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePEM electrolysis plant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMJ H\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.78e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDAC plant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ekg CO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFischer-Tropsch plant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMJ e-fuel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.35e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHydrogen liquefaction plant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMJ H\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.90e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFuel supply and fuel product system\u003c/p\u003e \u003cp\u003eWe base the introduction of AAF on the ReFuelEU Aviation rules. These describe the minimum volume of compliant AAF that must be used in a given period. This is generalised to a set share per year (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). To evaluate this share, ReFuelEU Aviation specifies that hydrogen should be considered based on energy content\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. We extend this to e-fuel, assuming a lower heating value (LHV) for each fuel: 43 MJ/kg for fossil kerosene, 45 MJ/kg for e-fuel, and 120 MJ/kg for hydrogen.\u003c/p\u003e \u003cp\u003eThe logical order applied when quantifying the fuel supply starts from the reference flow, with the air traffic demands being used to construct the aircraft fleets (see \u0026lsquo;Fleet dynamics\u0026rsquo;). For each time interval, the activity of the aircraft fleet requires a certain volume of hydrocarbon fuels (here, fossil kerosene and e-fuel) and liquid hydrogen. In scenarios that comply with a certain minimum volume of AAF, all liquid hydrogen contributes to this minimum, with the remainder achieved through e-fuel as part of the total hydrocarbon demand.\u003c/p\u003e \u003cp\u003eHaving determined the volume of each fuel required per time interval, the fuel production chains are quantified. For fossil kerosene, this is done by connecting a relevant background process (see \u0026lsquo;Prospective background lifecycle inventories\u0026rsquo;). For the AAF, fleets of key production plants are created. The efficiency of production plants based on emerging technologies is defined as a function of the plant\u0026rsquo;s construction year. As estimated performances vary, this is considered as sensitivity (\u003cb\u003eSupplementary Fig.\u0026nbsp;9\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eHydrogen is assumed to be produced through water electrolysis using proton exchange membrane (PEM) electrolysers. The performance of the electrolysers, including future improvements, is based on literature\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e,\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e). The oxygen molecules (O\u003csub\u003e2\u003c/sub\u003e) obtained from electrolysis are not considered a co-product but are left out of the inventory. Hydrogen distribution is based on Sacchi et al.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, requiring 3.2 kWh/kg electricity for compression. For use on hydrogen aircraft, hydrogen is liquefied after distribution\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. Liquefaction is represented by its operational energy demand alone (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e). Transportation and boil-off losses are each estimated at 1%, resulting in hydrogen emission to the air.\u003c/p\u003e \u003cp\u003eThe sorbent-based direct air capture (DAC) system is based on the inventories of Terlouw et al.\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. We estimate its performance and learning rates by comparing and combining several sources\u003csup\u003e\u003cspan additionalcitationids=\"CR66\" citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e (\u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e). Fischer-Tropsch plants are also described by combining several sources\u003csup\u003e\u003cspan additionalcitationids=\"CR69\" citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e. Several products are created in such plants. To isolate impacts related to the production of e-fuel, physical allocation centred around lower heating value (LHV) is applied, in line with the literature. The production process is simplified to flows of CO\u003csub\u003e2\u003c/sub\u003e, H\u003csub\u003e2\u003c/sub\u003e, and electricity (\u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e). Due to a lack of data, cooling water and wastewater are cut off.\u003c/p\u003e \u003cp\u003eThe fuel tank-to-wing phase (i.e., combustion) distinguishes between the three fuels while accounting for the aircraft using the fuel and aspects of the flight itself. The flight is split up into the landing and take-off cycle (LTO), where emissions are relatively low to the ground, and climb/cruise/descent (CCD), where emissions are higher up. Fuel use for LTO is assumed to be consistent across flights, while CCD scales with the flight distance. Inventories for hydrocarbon fuels are based on the EMEP/EEA air pollutant emission inventory guidebook\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. Metal impurities released when combusting fossil kerosene, taken from the ecoinvent 3.9.1 database\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e, are also included in these inventories but do not affect climate change. E-fuel is assumed not to have these metal impurities nor sulphur impurities, meaning that no sulphur oxides (SO\u003csub\u003ex\u003c/sub\u003e) are formed. Inventories for hydrogen use assume a gas turbine\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e, although we make no further distinction with a fuel cell-driven electric powertrain.\u003c/p\u003e \u003cp\u003eAircraft performance and aircraft product systems\u003c/p\u003e \u003cp\u003eThe present fleet is represented using the fuel use, emissions, and seating capacity of common aircraft, used as proxies. These are the Airbus A320 and A320neo narrow-body aircraft and, the Boeing 777\u0026thinsp;\u0026minus;\u0026thinsp;300 and Airbus A350-900 wide-body aircraft (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This choice of reference aircraft is adopted from Grewe et al.\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. The newer of these aircraft (meaning the A320neo and A350-900) are also used as reference for future generations (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The business-as-usual scenario is based on the historical trends in performance observed by Cox et al.\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Grewe et al.\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e provide expert estimates for the improvements that could be achieved in conventional aircraft over the course of the coming decades, which forms our optimistic scenario. Speculatively, larger improvements are possible when introducing new aircraft concepts, represented in the breakthrough scenario, which uses values obtained from Cox et al.\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFor hydrogen aircraft, estimates reported by ICAO are used\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e. These express three scenarios for the fuel use of hydrogen aircraft relative to their contemporary conventional aircraft while accounting for payload capacity. The resulting range aligns with values reported elsewhere\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e,\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e,\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. To streamline the scenario space, hydrogen and hydrocarbon aircraft performances are combined into a single variable.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFuel use [MJ] of the reference aircraft used for the landing and take-off (LTO) phase and the climb/cruise/descent (CCD) phase of reference distance flights. NB: narrow-body, WB: wide-body.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFlight phase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-2000 NB (Airbus A320)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePre-2020 NB (Airbus A320neo)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLTO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.23e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.60e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCD, 500 km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.19e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.40e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCD, 1500 km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.90e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.64e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCD, 2500 km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.09e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.66e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCD, 3500 km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.30e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.70e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFlight phase\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePre-2000 WB (Boeing 777\u0026thinsp;\u0026minus;\u0026thinsp;300)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003ePre-2020 WB (Airbus A350-900)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLTO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.42e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCD, 2000 km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.11e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.84e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCD, 5000 km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.77e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.44e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCD, 7000 km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.46e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.01e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCCD, 9000 km\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.32e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.59e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFuel use of aircraft generations for each of the three pathways for aircraft technology considered. Values are relative to the current generation and expressed based on the lower heating value of the fuel used. The current generation of narrow-body (NB) and wide-body (WB) aircraft only consists of aircraft powered by hydrocarbons, but introducing hydrogen-powered aircraft in future generations is considered.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneration and introduction year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-2020 generation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eGeneration 2035\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eGeneration 2050\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFuel type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHydrocarbon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eHydrocarbon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHydrogen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eHydrocarbon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eHydrogen\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAircraft type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNB \u0026amp; WB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eWB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBusiness as usual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOptimistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreakthrough\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCradle-to-gate and end-of-life processes are included for each aircraft entering and leaving the fleet. We adapt aircraft material composition (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) and the energy demands of these processes from Cox et al.\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Industry estimates\u003csup\u003e\u003cspan additionalcitationids=\"CR78\" citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e are used to quantify manufacturing waste through so-called buy-to-fly rations: 8:1 for aluminium alloy\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e, 1.5:1 for composites\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e, and 2.2:1 for other materials\u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e. Since hydrogen aircraft are likely to have a higher operating empty weight (OEW) than contemporary hydrocarbon aircraft, their mass is increased by 10%\u003csup\u003e21\u003c/sup\u003e, but no change to their relative material composition is considered. For the sake of simplicity, aircraft cradle-to-gate and end-of-life processes, including OEW, are considered independent from aircraft performance.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMaterial composition of reference aircraft, as a share of operating empty weight. All aircraft are assumed to have the same composition within a given generation. CFRP: carbon fibre-reinforced composite.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaterial\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-2000 generation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePre-2020 generation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2035 generation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2050 generation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAluminium alloy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCFRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSteel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTitanium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiscellaneous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eScenario construction\u003c/p\u003e \u003cp\u003eThe reduction of the scenario space is performed following principles of compatibility and distinctiveness, as demonstrated by, e.g. Delpierre et al.\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e and Langkau et al.\u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e. First, we consider the compatibility of the technology and demand scenario parameters. Transportation efficiency improvement is considered to stimulate demand, limiting its effectiveness as a climate change mitigation measure\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. However, experts disagree on how introducing novel aircraft and fuel technologies interacts with measures to limit air traffic growth. Some argue that, with projected growth, the energy demand of aviation will be incompatible with the available energy resources\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Others argue that the higher cost associated with AAF will somewhat slow growth but that further limitations would delay the sector\u0026rsquo;s sustainability transition by diverting financial resources\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. At least in some European countries, there appears to be public support to increase barriers to flight\u003csup\u003e\u003cspan additionalcitationids=\"CR58\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e, although political uptake is limited\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e,\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e. Based on these perspectives, we decide to positively correlate air traffic demand with the development of aircraft technology, leading to three technology-demand pairs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). This correlation can be described narratively as follows: \u0026lsquo;\u003cem\u003eIn a high-growth scenario, the fuel efficiency imperative of the aviation sector enabled it to maintain its substantial growth, which in turn provided space for revolutionary aircraft technologies to mature. In combination with broad public support, this enabled the construction of AAF infrastructure at rates thought impossible. Conversely, in a degrowth scenario, the aviation sector lacked financial resources, which reinforced the slow development of AAF infrastructure and halted technological breakthroughs. Limited access to clean technology, in turn, limited the air traffic that could be achieved within stringent environmental regulations, further restricting available resources.\u0026rsquo;\u003c/em\u003e\u003c/p\u003e \u003cp\u003eNine scenarios are selected by combining the three sets of demand-technology pairs with three distinct fuel mix scenario values (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). These fuel mixes consist of (1) a baseline, where the share of AAF is not increased, (2) the implementation of an extended version of ReFuelEU Aviation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec), but with no commercial introduction of hydrogen aircraft, meaning that all AAF is modelled as e-fuel, and (3) the extended version of ReFuelEU Aviation, also featuring commercial hydrogen aircraft in future generations (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), meaning that the share of AAF is met through a combination of liquid hydrogen and e-fuel.\u003c/p\u003e \u003cp\u003eProspective background lifecycle inventories\u003c/p\u003e \u003cp\u003eThe inventories created through the methods described above use economic activities not modelled within this work, but directly adopted from another source. Through these activities, the service system connects itself to the background. The background databases used here are each generated using the Python library premise\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. This library enables the transformation of an ecoinvent database to align with the regions and scenarios of an integrated assessment model, adding several additional activities in the process.\u003c/p\u003e \u003cp\u003eThe \u0026lsquo;SSP2-PkBudg1150\u0026rsquo; pathway of the REMIND model\u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e is used to generate background databases for the narrative scenarios. In this pathway, the global mean surface temperature increase by 2100 is around 1.7\u0026deg;C, thereby achieving the goal of the Paris Agreement. Databases are generated for five-year time intervals. Selected flows which connect to the foreground are then exported and linearly interpolated to align with the one-year time interval. The \u0026lsquo;EUR\u0026rsquo; region of REMIND is assumed to be representative of the geographic region considered. As a sensitivity analysis, a prospective hydrogen market generated for this pathway by Wei et al.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e is used instead of the assumption that all hydrogen is created through electrolysis (\u003cb\u003eSupplementary Fig.\u0026nbsp;7\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eImpact assessment\u003c/p\u003e \u003cp\u003eAviation has several environmental effects, noise, air quality degradation, and climate change being among the most prevalent. Focussing on climate change, a topic of interest is the non-CO\u003csub\u003e2\u003c/sub\u003e effects of aviation. The most prevalent of these are caused by nitrogen oxides (NO\u003csub\u003ex\u003c/sub\u003e) and by condensation of water into contrails, resulting in aviation-induced cloudiness (AIC)\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The short lifespan of these climate forcers raises conceptual challenges to consider their warming impacts holistically\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. In response to observing this same challenge across CO\u003csub\u003e2\u003c/sub\u003e and methane (CH\u003csub\u003e4\u003c/sub\u003e), Allen et al.\u003csup\u003e\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e developed the linear-warming-equivalent (LWE) metric, transforming a time series of emissions into the radiative forcing expected from these emissions. This is a computationally light alternative to contemporary dynamic impact models\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Quantifying climate impact in this way does not rely on an arbitrary time horizon in comparing climate forcers, at the cost of obfuscating climate forcing beyond the temporal scope at hand.\u003c/p\u003e \u003cp\u003eSacchi et al.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e adapted the LWE metric further to aviation. We change their code to include all distances flown and all NO\u003csub\u003ex\u003c/sub\u003e emissions from flight in the calculations. Furthermore, the influence of NO\u003csub\u003ex\u003c/sub\u003e on climate change (through ozone and methane) is assumed to have a duration of 0.267 years\u003csup\u003e\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e\u003c/sup\u003e, changed from 11.8 years, to represent its short-term warming impacts better, although this representation of atmospheric chemistry remains highly simplified. The principle used by Sacchi et al.\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e to consider the effect of fuel composition on ice crystal formation\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e is adopted. This is estimated using the hydrogen mass fraction of the fuel, which is taken to be 13.73% for fossil kerosene and 15.29% for e-fuel. Therefore, using e-fuel instead of fossil kerosene would reduce AIC impacts by 65.95%. For hydrogen-powered aircraft, we furthermore adopt the assumption of Kossarev et al.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e that e-fuel and hydrogen result in similar AIC impacts. However, Kossarev et al. estimate this reduction to be no more than 40%, indicating that our estimates for AIC of AAF are relatively optimistic. Still, AIC maintains a large share of impacts across scenarios (\u003cb\u003eSupplementary Fig.\u0026nbsp;4\u003c/b\u003e). It must be stressed that the AIC impact of future fuels is a subject of ongoing research, with additional uncertainty added by the possibility of contrail avoidance strategies, not are not covered in this work.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eT.P.S.A. and N.T. are supported by the SSbD4CheM project. SSbD4CheM receives funding from the European Union\u0026rsquo;s Horizon Europe research and innovation programme under grant agreement number 101138475. We are grateful for the advice of Ir. Isabel Nieuwenhuijse and Dr. Stefan Grebe. We thank Ir. Liam Megill for his comments on the manuscript.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eRecreating the full workflow requires access to the ecoinvent 3.9.1 database (licence required) and the EUROCONTROL Aviation Data Repository for Research (access restricted). To facilitate reproduction, the Zenodo repository for this study (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5281/zenodo.14222579\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.14222579\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e87\u003c/sup\u003e includes intermediate data. All graphed data is also provided in this repository.\u003c/p\u003e \u003cp\u003eCode availability\u003c/p\u003e \u003cp\u003eThe Zenodo repository for this study (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5281/zenodo.14222579\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.14222579\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e87\u003c/sup\u003e provides all scripts used in generating the results.\u003c/p\u003e \u003cp\u003eSupplementary information\u003c/p\u003e \u003cp\u003eSupplementary information file (\u0026lsquo;Supplementary information.docx\u0026rsquo;).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGrobler, C. \u003cem\u003eet al.\u003c/em\u003e Marginal climate and air quality costs of aviation emissions. \u003cem\u003eEnviron. Res. 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S. aviation_climate_targets. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5281/zenodo.14222579\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.14222579\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6146306/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6146306/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWe explore under what conditions climate targets for commercial aviation in Europe can be met, following the recent European regulations for the increased use of alternative fuels and in the absence of effective offsetting. Our analysis considers the role of hydrogen in decarbonising the aviation system with an unprecedented completeness in environmental and socio-technical dimensions. Our assessment shows that, by 2050, the additional climate forcing resulting from aviation can be stabilised. However, the level at which this stabilisation occurs varies widely, depending on the trajectory of air traffic growth (4.4\u0026ndash;12.4 mW/m\u003csup\u003e2\u003c/sup\u003e estimated by 2070), with all scenarios featuring some degree of overshoot. This variation is primarily driven by differences in near-term fuel demand, as technologies that promise to reduce dependence on fossil resources are still in development. Therefore, we recommend reassessing aviation climate targets, including stronger incentives for near-term reduction of fossil kerosene use and demand management.\u003c/p\u003e","manuscriptTitle":"Decarbonising aviation does not imply successful climate change mitigation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-31 17:58:58","doi":"10.21203/rs.3.rs-6146306/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-earth-and-environment","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsenv","sideBox":"Learn more about [Communications Earth and Environment](https://www.nature.com/commsenv/)","snPcode":"","submissionUrl":"","title":"Communications Earth \u0026 Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"115156bd-dbe9-4e97-9500-86cff6f51985","owner":[],"postedDate":"March 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":46077528,"name":"Earth and environmental sciences/Environmental social sciences/Climate-change mitigation"},{"id":46077529,"name":"Earth and environmental sciences/Environmental social sciences/Climate-change policy"},{"id":46077530,"name":"Earth and environmental sciences/Environmental social sciences/Energy and society/Energy policy"},{"id":46077531,"name":"Earth and environmental sciences/Environmental social sciences/Environmental impact"},{"id":46077532,"name":"Earth and environmental sciences/Environmental social sciences/Socioeconomic scenarios"}],"tags":[],"updatedAt":"2025-11-28T08:28:31+00:00","versionOfRecord":{"articleIdentity":"rs-6146306","link":"https://doi.org/10.1038/s43247-025-02935-5","journal":{"identity":"communications-earth-and-environment","isVorOnly":false,"title":"Communications Earth \u0026 Environment"},"publishedOn":"2025-11-27 05:00:00","publishedOnDateReadable":"November 27th, 2025"},"versionCreatedAt":"2025-03-31 17:58:58","video":"","vorDoi":"10.1038/s43247-025-02935-5","vorDoiUrl":"https://doi.org/10.1038/s43247-025-02935-5","workflowStages":[]},"version":"v1","identity":"rs-6146306","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6146306","identity":"rs-6146306","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}