# Greening aviation with sustainable aviation fuels :
## Insights from decarbonization scenarios

Paul Bardon, Olivier Massol, Arthur Thomas

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To cite this version:

Paul Bardon, Olivier Massol, Arthur Thomas. Greening aviation with sustainable aviation fuels: Insights from decarbonization scenarios. Journal of Environmental Management, 2025, 374, pp.123943. 10.1016/j.jenvman.2024.123943. hal-04874217

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Research article

# Greening aviation with sustainable aviation fuels: Insights from decarbonization scenarios

<figure><img src="image_3.png" /></figure>

Paul Bardon<sup>a,b,**</sup> <sup>D</sup>, Olivier Massol<sup>c,d,*</sup> <sup>D</sup>, Arthur Thomas<sup>e</sup>

## ARTICLE INFO

Dataset link: Future Aviation Scenarios: Projected GHG Emissions and Technology use (Original data)

**Keywords:**

Aviation

Environmental impacts

Pathways to net-zero emissions

Decarbonization

Sustainable aviation fuels

Scenarios

Meta-analysis

## ABSTRACT

Recent studies outline markedly different possible decarbonization pathways for civil aviation by 2050. This paper examines how the key assumptions retained in these scenarios (i.e., the posited deployment of sustainable aviation fuels [SAFs], the projected demand trajectory, and the availability of electric and hydrogen-fueled solutions) affect the sector's future emissions of greenhouse gas. Data for 67 recent scenarios from industry-related, academic, institutional, and think tanks/NGO sources are used to perform the analysis. The results shed light on the shared properties of these scenarios. First, we find a clear consensus on the negative impact of SAFs on residual GHG emissions by 2050, conditioning to a high level of SAF penetration. Second, these scenarios posit a smaller decarbonizing power of biomass-based SAF than that of e-fuel. Third, we find signs of authorship bias in some scenarios. This last finding, therefore, raises concerns about the direct use of these scenarios in policymaking.

## 1. Introduction

In 2022, civil aviation accounted for 2.4% of global energy-related carbon dioxide (CO₂) emissions, with the sector's greenhouse gas (GHG) emissions growing faster than those of rail, road, or shipping over the last decade (Shukla et al., 2022). The annual growth rates of aviation emissions increased from 2.2% between 1970 and 2012 to 5% between 2013 and 2018 (Lee et al., 2021). Excluding the COVID-19 episode that temporarily grounded the sector, air transportation demand has been rising worldwide, driven by the emergence of large developing economies, growth in international trade and migration, and the increasing interconnectedness of people and businesses. This trend is projected to continue through 2050 (IATA, 2023; ICAO, 2022), raising concerns about the sector's climate footprint as international climate goals imply reaching net-zero global carbon emissions by that time.

Decarbonizing air travel is particularly challenging because of the

sector's reliance on energy-dense liquid hydrocarbons that lack commercially competitive substitutes and the long-lived nature of existing aircraft. Incremental technological improvements in fuel efficiency, air traffic management, and aircraft operating procedures are unlikely to achieve the necessary emission reductions. Furthermore, the introduction of new, more fuel-efficient, aircraft is cost-ineffective as a CO₂-reduction policy when accounting for aircraft lifetime and fleet dynamics (Kito, 2021). Therefore, the industry is focusing on radical innovations in aircraft designs, and new powering solutions such as sustainable aviation fuels (SAFs), hydrogen, or electricity (Delbecq et al., 2021; ATAG, 2021; IATA, 2021; ICAO, 2022). However, these supply-side options have markedly different levels of technological maturity and potential (Bardon and Massol, 2025).

Among these options, SAFs – a generic category that includes biofuel (i.e., bio-jet fuel made from renewable biomass and waste resources) and electrofuels, also known as e-fuels (i.e., synthetic liquid

<footnote><sup>a</sup> IFP Énergies nouvelles, 1-4 avenue de Bois Préau, 92852, Rueil-Malmaison, France</footnote>

<footnote><sup>b</sup> Center for Energy Economics and Management, IFP School, 228-232 av. Napoléon Bonaparte, 92852, Rueil-Malmaison, France</footnote>

<footnote><sup>c</sup> Industrial Engineering Research Department, CentraleSupélec, Université Paris-Saclay, 9 ter rue Joliot-Curie, 92190, Gif-sur-Yvette, France</footnote>

<footnote><sup>d</sup> Department of Economics, City St George's, University of London, Northampton Square, London, EC1V 0HB, UK</footnote>

<footnote><sup>e</sup> Université Paris Dauphine - PSL, UMR, CNRS 8007, LEDa-CGEMP, 75016, Paris, France</footnote>

<footnote>* Corresponding author. Industrial Engineering Research Department, CentraleSupélec, Université Paris-Saclay, 9 ter rue Joliot-Curie, 92190, Gif-sur-Yvette, France.</footnote>

<footnote>** Corresponding author. Center for Energy Economics and Management, IFP School, 228-232 av. Napoléon Bonaparte, 92852, Rueil-Malmaison, France.
E-mail addresses: paul.bardon@sciencespo.fr (P. Bardon), olivier.massol@centralesupelec.fr (O. Massol).</footnote>
hydrocarbon fuels manufactured from hydrogen produced from low-
carbon electricity and CO2) – are recurrently heralded as the most
promising avenues for greening air travel.<fnref n="1" /> By design, SAFs have a lower
carbon footprint than fossil-based jet fuel and can serve as ‘drop-in’
replacements for kerosene in existing aircrafts. However, that wide-
spread substitution requires massive scaling up of SAF production,
which cannot be achieved in a timely manner without some form of
government intervention (Dodd and Yengin, 2021; Gössling and Lyle,
2021; Ng et al., 2021; Santos and Delina, 2021; Kallbekken and Victor,
2022). By nature, public policies and environmental regulations directly
impact the future of civil aviation as they can influence market growth
and accelerate or retard the emergence of new technologies. To
formulate effective policies, it is crucial to understand the sector’s
long-term trajectory and anticipate how it will respond to policy in-
terventions. The increasing intensity of public policy debates on aviation
is reflected in the growing number of studies outlining possible path-
ways to net-zero emissions aviation by mid-century. These studies are
issued by a variety of entities including industry sources (e.g., industry
bodies, aircraft manufacturers, airlines),<fnref n="2" /> international organizations,
governmental agencies, academic research groups (Delbecq et al., 2021;
Planès et al., 2021; Dray et al., 2022; Bergero et al., 2023; Gössling and
Humpe, 2023; Salgas et al., 2023), independent scholars (Fragkos,
2022), NGOs (Transport & Environment, 2022) and think tanks
(Supaero Decarbo and The Shift Project, 2021; Institut Montaigne, 2022;
Graver et al., 2022). These studies are systematically collected using a
structured approach to identify, screen, and select the studies for in-
clusion in the analysis (see Data section).

These studies typically explore alternative trajectories for deploying
aviation decarbonization technologies and evaluate the residual annual
emissions to be offset in 2050. Despite their similarities, these studies
convey significantly different messages. Depending on the assumptions
retained (i.e., demand forecasts, availability of SAF, electric and
hydrogen-powered solutions, efficiency gains, and emission calculation
methods), the projected 2050 GHG emissions range from 60 megatons of
CO2 equivalent (MtCO2eq) per annum (Dray et al., 2022) to 3,391
MtCO2eq (Bergero et al., 2023).

From a policymaking perspective, the wide range of projected
emissions – even for relatively similar scenarios – may be misperceived
as signs of radical uncertainty, which could have important policy im-
plications. Faced with a lack of scientific certainty regarding the sector’s
future needs in terms of energy, emission offsetting mechanisms, and
investment, a confused authority might be tempted to defer decisions
pending clarification. Against this background, understanding how as-
sumptions about SAF penetration affect the sector’s projected environ-
mental performance is essential. This paper thus reviews recent aviation
decarbonization scenarios involving SAF-powered aircraft to explore
how the diffusion of SAFs reduces the sector’s residual GHG emissions in
2050. To investigate it, we assemble a sample of recent scenarios and
examine their statistical properties by estimating log-linear regressions
that use residual emissions as the dependent variable and the volume of
SAFs as one of the explanatory variables.

By investigating the trajectories in these individual scenarios and
controlling for differences in assumptions, and methodologies, our sta-
tistical investigations indicate that the observed differences in the

projected residual emissions are largely explained by differences in as-
sumptions. It also offers three key findings. First, we find a clear
consensus on the negative impact of SAFs on residual GHG emissions by
2050, conditioning to a high level of SAF penetration results. Indeed, our
statistical analysis reveals the presence of a structural break: for low
penetration levels of SAFs, the insertion of SAFs is not very statistically
significant, whereas it becomes highly statistically significant above a
certain level. Second, the scenarios under scrutiny posit a smaller
decarbonizing power of biomass-based SAFs than that of e-fuels. Finally,
we find signs of authorship bias in some scenarios, which raises concerns
about their direct use in policymaking. Altogether, these findings pro-
vide additional transparency and information for policymakers.

In recent years, alternative jet fuels have become the subject of an
expanding literature that has explored several directions. An important
portion of this literature, authored by science and technology experts,
examines various conversion technologies alongside their costs (e.g.,
Watson et al., 2024), evaluates environmental performance (e.g., Kolosz
et al., 2020), and discusses the prospects for the mass implementation of
SAFs (e.g., Cabrera and de Sousa, 2022; Kramer et al., 2022) as well as
the factors constraining their future availability (e.g., Bardon and Mas-
sol, 2025). Another strand of the literature focuses specifically on
incentive policies. Keshavarzzadeh et al. (2022) show that a combina-
tion of quotas, carbon taxes, and supportive policies can foster the
adoption of alternative jet fuel. Ridjan Skov and Schneider (2022)
examine the legal framework, incentives, and barriers affecting e-fuel
pathways for the EU transport sector. Mayeres et al. (2023) evaluate the
costs of EU policies promoting SAFs and compare their effectiveness to
carbon offsetting trading schemes. Lastly, another direction delves into
industrial organization considerations and examines airlines’ attitudes
toward SAF adoption. This research examines the limitations hindering
the commercialization of biofuel-based SAF (Dodd et al., 2018), and
underscores the necessity of collective action by airlines, fuel com-
panies, and policymakers to unlock the adoption of SAFs (Dodd and
Yengin, 2021).

Within the broad literature on SAF, this paper contributes to the
burgeoning, and much-needed, subgroup of works attempting to shed
light on the properties of recent aviation decarbonization scenarios. To
our knowledge, there are no statistical analyses investigating the in-
teractions between the sector's projected environmental performances
and the assumptions and methodologies underlying the construction of
these scenarios. Therefore, this quantitative study extends recent
research that qualitatively compares the scenarios to highlight differ-
ences in assumptions and methodologies. For example, Becken et al.
(2023) compare 12 decarbonization scenarios, with a specific focus on
SAF availability. The extensive review by Delbecq et al. (2023) considers
recent decarbonization scenarios produced by academic, institutional,
and industrial sources and evaluates their compatibility with the ob-
jectives of the Paris Agreement. Several studies also assess the reference
scenarios, usually ATAG's Waypoint 2050 in ATAG (2021) (e.g., Delbecq
et al., 2021; Conseil pour la recherche et l'aviation civile, 2021; Institut
Montaigne, 2022; Beevor and Keith, 2022). Muehlberger et al. (2024)
explore the socio-technical imaginaries of six climate-neutral aviation
roadmaps to identify partly conflicting visions of the sector's future. The
present analysis supplements these earlier studies by considering a sta-
tistical analysis involving a greater number of scenarios (i.e., a sample of
67 observations) issued by an enlarged group of sources in academia,
industry, think tanks, and institutions.

From a sectoral policy standpoint, our analysis is also related, though
less directly, to the broader literature on energy scenarios that either
investigates their quantitative properties (see e.g., Al Khourdajie et al.,
2024) or discusses their use in the design of energy and transportation
policies. Ideally, scenarios are intended to enlighten the decisions of
benevolent policymakers who are supposed to make rational decisions
that best serve the common good (Weber et al., 2021). According to that
perspective, scenarios are designed to explore a range of possible future
states of the world that reflect the key uncertainties driving the future.

<footnote n="1">¹ “Ways to make aviation fuel green,” *The Economist*, August 17, 2022.</footnote>

<footnote n="2">² Among these scenarios, the aviation sector has produced three roadmaps (see the most recent projections in ATAG, 2021; IATA, 2023; ICAO, 2022) that provide the baseline used in many decarbonization roadmaps presented by aircraft manufacturers (Airbus, 2021a) and airlines (e.g., Air France-KLM Group, 2022; Delta, 2022). However, these scenarios have been criticized for methodological biases (Beevor and Keith, 2022; Delbecq et al., 2021; Supaero Decarbo and The Shift Project, 2021) and/or the use of bold assumptions on energy availability or technological progress (Becken et al., 2023; Beevor and Keith, 2022; Ploetner et al., 2017).</footnote>
<table><caption>**Table 1**
The studies under scrutiny.</caption><thead><tr><th rowspan="2">Study</th><th rowspan="2">Authorship type</th><th rowspan="2">Number of scenarios</th><th rowspan="2">Emission scope</th><th rowspan="2">Comment</th><th colspan="2">Number of scenarios in the sample</th></tr><tr><th>67- obs.</th><th>52- obs.</th></tr></thead><tbody><tr><td>ATAG (2021)</td><td>Industry</td><td>5</td><td>Full life cycle, ILUC unspecified</td><td>Air Transport Action Group gathers representatives from industry associations</td><td>5</td><td>3</td></tr><tr><td>Bergero et al. (2023) <sup>(1)</sup></td><td>Academia</td><td>9</td><td>Tank-to-wake; and non-CO<sub>2</sub> emissions</td><td>3 scenarios positing zero volume of SAF are discarded</td><td>6</td><td>0</td></tr><tr><td>Conseil pour la recherche et l'aviation civile, 2021</td><td>Institution</td><td>4</td><td>Well-to-wake, without ILUC</td><td>An institution related to the French Administration</td><td>4</td><td>4</td></tr><tr><td>Danicourt et al. (2023) <sup>(2)</sup></td><td>Industry</td><td>2</td><td>Not specified</td><td>A study by a consultancy firm. No information on hydrogen or electricity consumption</td><td>0</td><td>0</td></tr><tr><td>Dray et al. (2022)</td><td>Academia</td><td>18</td><td>Well-to-wake, with ILUC and non-CO<sub>2</sub> effects</td><td></td><td>18</td><td>18</td></tr><tr><td>Fragkos (2022)</td><td>Academia</td><td>5</td><td>Not specified</td><td></td><td>5</td><td>5</td></tr><tr><td>Gössling and Humpe (2023)</td><td>Academia</td><td>4</td><td>Tank-to-wake and non-CO<sub>2</sub> emissions</td><td></td><td>4</td><td>4</td></tr><tr><td>Graver et al. (2022) (ICCT) <sup>(2)</sup></td><td>Think tank/ NGO</td><td>3</td><td>Well-to-wake, with ILUC</td><td>A study by the International Council on Clean Transportation</td><td>2</td><td>2</td></tr><tr><td>IATA, 2023; IATA, 2021</td><td>Industry</td><td>1</td><td>Well-to-wake, without ILUC</td><td>A study by the International Air Transport Association</td><td>1</td><td>0</td></tr><tr><td>ICAO (2022)</td><td>Institution</td><td>9</td><td>Well-to-wake, without ILUC</td><td>United Nations' International Civil Aviation Organization</td><td>9</td><td>9</td></tr><tr><td>IEA (2021)</td><td>Institution</td><td>1</td><td>Not specified</td><td>International Energy Agency</td><td>1</td><td>1</td></tr><tr><td>Malina et al. (2022)</td><td>Institution</td><td>6</td><td>Well-to-wake, with ILUC</td><td>A study by the World Bank</td><td>6</td><td>0</td></tr><tr><td>Mission Possible Partnership, 2022</td><td>Industry</td><td>2</td><td>Well-to-wake, with ILUC</td><td>Mission Possible Partnership (MPP)</td><td>2</td><td>2</td></tr><tr><td>Planès et al. (2021); Salgas et al. (2023)</td><td>Academia</td><td>1</td><td>Well-to-wake, without ILUC and with non-CO<sub>2</sub> effects</td><td>A study by the Institut Supérieur de l'Aéronautique et de l'Espace ISAE-SUPAERO</td><td>1</td><td>1</td></tr><tr><td>Supaero Decarbo & The Shift Project (2021)</td><td>Think tank/ NGO</td><td>3</td><td>Well-to-wake, without ILUC</td><td>A joint publication by an NGO and a think-tank</td><td>3</td><td>3</td></tr></tbody></table>

Note.

¹ Three scenarios in Bergero et al. (2023) presume the unavailability of SAFs and thus include zero volume of SAFs. We omit these three observations because that subsample is too small to gain valuable statistical insights on the case where no SAF is available.

² We discard two observations in Danicourt et al. (2023) and one in Graver et al. (2022) that do not explicitly report the posited consumption of energy in the form of hydrogen or electricity. Indeed, new technologies based on either hydrogen or increased electrification are currently being developed to lower the environmental footprint of air transportation. Their diffusion is also expected to attenuate the future consumption of SAFs (see Bardon and Massol (2025)), and controlling for that possible substitution is critical to evaluating SAF's impacts on future unabated emissions.

They are thus meant to give authorities the knowledge needed to weigh the uncertain consequences of choices. However, this idealistic view overlooks the possible strategic use of scenarios. By adequately selecting the assumptions, scenario developers can adopt a partisan concept of enlightenment and design scenarios that best support their views of the future to influence the framing of the decision problem faced by policymakers (Tversky and Kahneman, 1981; Oreskes and Conway, 2010; Weber et al., 2021). With these views in mind, the present analysis also aims to disentangle whether recent aviation roadmaps are affected by that partisan concept of enlightenment.

This paper is organized as follows. Section 2 presents the dataset used in this study. Section 3 outlines our empirical analysis and Section 4 discusses the implications. Finally, the last section offers a summary and some concluding remarks. For clarity, all empirical specifications are detailed in Appendix A.

## 2. The data

We assemble a sample of 67 recent aviation decarbonization scenarios from 15 studies publicly issued between March 2021 and June 2023. Table 1 presents the data sources. These studies are drawn from both the scientific and the gray literatures. Academic studies are collected from the Scopus database using the search query TITLE-ABSTRACT ("emissions" AND "aviation" AND ("pathways" OR "scenario" OR "net-zero")), applied to English-only or French-only journal articles from 2021 to 2023 in the following subject areas: energy, engineering, environmental science, social science, economics, econometrics and finance, and business, management, and accounting. The query

generated 189 results. Articles focused solely on technology without addressing emissions scenarios or energy inputs in the aviation sector are excluded. We also remove studies limited to specific regional areas, retaining only those with a common geographical scope that includes both international and domestic flights. For studies published by institutions, we consider those published during that period by international organizations (e.g., the World Bank, the International Energy Agency, UN ICAO, IATA) and industry bodies (e.g., IATA). To further enrich the dataset, Google queries were prompted in English and French in June 2023, which allowed us to identify scenarios issued by the International Council on Clean Transportation, the Mission Possible Partnership, the French Conseil pour la Recherche et l'Aviation Civile, and those issued by the Shift Project (an NGO).

Depending on the type of authorship, each source is assigned to a category (see Table 1): industry-related (i.e., studies from industry participants, consultancy firms, or industry bodies), academia, institutions, and think tanks/NGOs.<fnref n="3" /> Table 1 also indicates the scope of the emission accounting procedure used in each scenario.

Each of the 67 simulations under scrutiny reports the main variables of interest: (i) the annual volume of projected GHG emissions by 2050 requiring offsetting, (ii) the volume of SAF consumed by aviation that year, (iii) the projected size of the aviation market in 2050, and (iv) the

<footnote n="3">³ For that delineation, we assume that whenever a study is issued by an institution (respectively NGO or industry) or involves an author from an institution (respectively NGO or industry), it should be categorized as “institution” (respectively “think tanks/NGO” or “industry”).</footnote>
<table><caption>**Table 2**
Conversion factors.</caption><thead><tr><th></th><th>Value</th><th>Source</th></tr></thead><tbody><tr><td>Jet fuel density</td><td>0.81 kg.L<sup>-1</sup></td><td>DOE (2020)</td></tr><tr><td>Jet fuel specific energy</td><td>43 MJ.kg<sup>-1</sup></td><td>DOE (2020)</td></tr><tr><td>Hydrogen specific energy</td><td>120 MJ.kg<sup>-1</sup></td><td>DOE (2023)</td></tr><tr><td>Passenger mass (conversion from RTK and FTK to RPK equivalent)</td><td>100 kg/ passenger</td><td>ICAO (2009)</td></tr></tbody></table>

posited residual consumption of energy in the form of either hydrogen or electricity. Whenever needed, the raw data presented in unusual energy units (e.g., megatons or billion liters) are converted into exajoules (EJ) using Jet-A standard values. The conversion factors are shown in Table 2.

SAF is a generic name given to low-emission fuels produced from biomass or synthetic materials (aka, e-fuel), which can be used directly in existing aircrafts without major technical changes (i.e., ‘drop-in’ fuels). Out of the 67 scenarios under scrutiny, 52 scenarios report dis-aggregated data on the quantities of biofuels and e-fuels consumed by civil aviation, and thus on the projected share of e-fuel in SAFs. In this paper, we therefore present estimates using the full 67-observation sample and this 52-observation subsample. Among the 52 scenarios in this subsample, 14 observations are such that the pool of SAFs only contains biofuel and no e-fuel, and three observations presume the opposite (zero biofuel and a 100% share of e-fuels in the pool of SAFs). In the remaining 35 observations, e-fuel proportions range from 9.3% to 74.8% of the volume of SAFs consumed by civil aviation.

Table 3 provides the mean, standard deviation, minimum and maximum values, and a brief description of the real-valued variables used in this study for the primary and 52-observation subsample. We observe that the 67-observation sample can be partitioned into two categories, as 30 scenarios posit the unavailability of new hydrogen- and battery-based technologies (and thus assume zero consumption of energy in these forms), whereas in the other scenarios, that annual consumption is positive and ranges from 0.28 to 13.85 exajoules (EJ). In the sequel, we introduce a dedicated binary indicator signaling the complete omission of these forms of energy. We also define a real-valued variable that takes the value 0 whenever that dummy variable equals one and the logged value of the positive annual energy consumption in the form of hydrogen and electricity otherwise.

In addition, Table 4 presents a group of dummy variables conveying further information on the scenarios. These binary indicators aim to correct the substantial differences prevailing in the assumptions and treatments conducted in the studies under scrutiny. The list includes a binary indicator signaling whether freight transportation is included or not; a variable indicating whether non-CO₂ effects are accounted for in the projected GHG emissions; a variable indicating whether the emission accounting procedure includes indirect land-use change (ILUC); and a variable indicating a scenario with zero posited consumption of hydrogen or electricity (i.e., a zero value for the variable H₂_Elec).<fnref n="4" /> In addition, for each of the four types (i.e., academia, industry, institution, think-tank & NGO), we generate a specific dummy variable that takes the value one for each scenario emanating from a source in that type and zero otherwise.

Fig. 1 provides a scatter plot of the two main variables under scrutiny: the projected emissions and the consumption of SAFs by 2050. From a visual inspection of Fig. 1, no clear relation emerges between these two variables.

Table 5 is a correlation matrix for all the variables in the two samples with the correlations for the 67-observation sample below the diagonal and those for the 52-observation subsample above the diagonal. From these correlation factors, three main remarks emerge. First, as one can expect, a negative correlation is observed between future emission levels and the volume of SAFs. Second, positive and significant correlation values are observed among variables that exhibit direct interactions (e.g., freight and the projected market size, future emissions and the inclusion of non-CO₂ effects). Lastly, we observe that studies in academia exhibit a positive correlation with the dummy variable signaling the

<table><caption>**Table 3**
Sample statistics for the real-valued variables.</caption><thead><tr><th>Variable</th><th>Observations</th><th>Description</th><th>Mean</th><th>Minimum</th><th>Maximum</th><th>Standard Deviation</th></tr></thead><tbody><tr><td>Emissions</td><td>67</td><td rowspan="2">GHG emissions by 2050 (million tCO<sub>2eq</sub>)</td><td>555.335</td><td>59.800</td><td>1,948.000</td><td>436.623</td></tr><tr><td></td><td>52</td><td>470.163</td><td>59.800</td><td>1,800.000</td><td>378.808</td></tr><tr><td>Log(Emissions)</td><td>67</td><td rowspan="2">SAF consumption (EJ)</td><td>5.964</td><td>4.091</td><td>7.575</td><td>0.910</td></tr><tr><td></td><td>52</td><td>5.798</td><td>4.091</td><td>7.496</td><td>0.897</td></tr><tr><td>SAF</td><td>67</td><td rowspan="2"></td><td>12.966</td><td>0.265</td><td>27.200</td><td>6.516</td></tr><tr><td></td><td>52</td><td>13.949</td><td>0.265</td><td>27.200</td><td>6.466</td></tr><tr><td>Log(SAF)</td><td>67</td><td rowspan="2"></td><td>2.376</td><td>-1.328</td><td>3.303</td><td>0.751</td></tr><tr><td></td><td>52</td><td>2.477</td><td>-1.328</td><td>3.303</td><td>0.715</td></tr><tr><td>Market_size</td><td>67</td><td rowspan="2">2050 market size (in RPKeq)</td><td>23.114</td><td>4.852</td><td>33.490</td><td>6.127</td></tr><tr><td></td><td>52</td><td>23.561</td><td>4.852</td><td>33.490</td><td>6.284</td></tr><tr><td>Log(Market_size)</td><td>67</td><td rowspan="2"></td><td>3.097</td><td>1.579</td><td>3.511</td><td>0.321</td></tr><tr><td></td><td>52</td><td>3.114</td><td>1.579</td><td>3.511</td><td>0.337</td></tr><tr><td>H<sub>2</sub>_Elec</td><td>67</td><td rowspan="2">Hydrogen and power consumption (EJ)</td><td>2.586</td><td>0.000</td><td>13.854</td><td>4.362</td></tr><tr><td></td><td>52</td><td>2.687</td><td>0.000</td><td>13.854</td><td>4.208</td></tr><tr><td>Log(H<sub>2</sub>_Elec) <sup>a)</sup></td><td>67</td><td rowspan="2"></td><td>0.552</td><td>-1.273</td><td>2.629</td><td>1.010</td></tr><tr><td></td><td>52</td><td>0.553</td><td>-1.273</td><td>2.629</td><td>1.040</td></tr><tr><td>E-Fuel ratio</td><td>52</td><td>The proportion of e-fuel in SAF</td><td>0.391</td><td>0.000</td><td>1.000</td><td>0.297</td></tr></tbody></table>

Note: Some scenarios do not report the projected market size in RPKeq but display the posited compound annual growth rates (CAGR) used in the projection. For these occasions, we use the posited CAGR figure with the 2019 market size, namely, 10.77 trillion RPK when freight is included, and 8.68 trillion RPK otherwise.

a That real-valued variable takes the value 0 whenever the dummy variable signaling a scenario without hydrogen and power consumption equals one, and the logged value of the positive annual consumption of energy in the form of hydrogen and electricity otherwise.

<footnote n="4">⁴ We also considered introducing dummy variables to control for differences in the emission accounting approaches retained in the manuscript (e.g., Well-to-wake, Tank-to-wake). However, as the estimates of these dummy variables were not statistically significantly different from zero, we omitted them.</footnote>
inclusion of non-CO₂ effects. In contrast, negative correlations are observed when the scenario emanates from industry or institutional sources. This finding calls for further investigations to identify whether authorship bias exists in the data.

## 3. Empirical analysis

We are primarily interested in using these scenarios to empirically examine the relationship between the sector's projected annual GHG emissions in 2050, and the volume of alternative fuels (SAFs) consumed by aviation that year. In Subsection 3.1, we first outline the microeconomic considerations supporting the specification of a simple linear regression, and then present the estimates obtained with that specification. As these results reveal the need for a revised specification, Subsection 3.2 then explores the presence of nonlinearities in the observed relationship and details an extended regression model to serve as a reference model. Finally, this specification is used to test for biases affecting either authorship or decarbonizing technologies (see Subsections 3.3 and 3.4). For clarity, all empirical specifications are summarized in Appendix A.

### 3.1. Theoretical considerations and preliminary analysis

#### 3.1.1. Model specification

As a point of departure, we consider a simple production function of the Cobb-Douglas type that gives the sector's output in 2050 (labeled Market_size and measured in revenue passenger kilometer (RPK)) as a function of two input variables representing the quantity of fossil-based jet-fuel, and SAF the quantity of SAFs. More specifically, we retain the associated sector's input requirement function that determines the quantity of fossil-based jet-fuel – and thus the corresponding carbon emissions labeled Emissions – needed to transport a given output Market_size given the quantity SAF. After a log transformation, one can

<table><caption>**Table 4**
Presentation of the dummy variables.</caption><thead><tr><th>Indicator</th><th>Number of observations</th><th>Description</th><th>Comment</th></tr></thead><tbody><tr><td rowspan="2">Freight</td><td>67</td><td rowspan="2">D = 1 if freight is included</td><td>(D = 1; 27 observations)</td></tr><tr><td>52</td><td>(D = 1; 21 observations)</td></tr><tr><td rowspan="2">Non-CO<sub>2</sub> effects</td><td>67</td><td rowspan="2">D = 1 if non-CO<sub>2</sub> effects are included in GHG emissions</td><td>(D = 1; 19 observations)</td></tr><tr><td>52</td><td>(D = 1; 13 observations)</td></tr><tr><td rowspan="2">ILUC</td><td>67</td><td rowspan="2">D = 1 if Indirect Land Use Change (ILUC) is included</td><td>(D = 1; 28 observations)</td></tr><tr><td>52</td><td>(D = 1; 22 observations)</td></tr><tr><td rowspan="2">No_H<sub>2</sub>_Elec</td><td>67</td><td rowspan="2">D = 1 if zero consumption of hydrogen and power</td><td>(D = 1; 30 observations)</td></tr><tr><td>52</td><td>(D = 1; 25 observations)</td></tr><tr><td rowspan="2">Academia</td><td>67</td><td rowspan="2">D = 1 if authored by a scholar</td><td>(D = 1; 34 observations)</td></tr><tr><td>52</td><td>(D = 1; 28 observations)</td></tr><tr><td rowspan="2">Industry</td><td>67</td><td rowspan="2">D = 1 if authored by an industry source</td><td>(D = 1; 8 observations)</td></tr><tr><td>52</td><td>(D = 1; 5 observations)</td></tr><tr><td rowspan="2">Institution</td><td>67</td><td rowspan="2">D = 1 if authored by an institutional source</td><td>(D = 1; 20 observations)</td></tr><tr><td>52</td><td>(D = 1; 14 observations)</td></tr><tr><td rowspan="2">Think tank/ NGO</td><td>67</td><td rowspan="2">D = 1 if authored by a think tank or an NGO</td><td>(D = 1; 5 observations)</td></tr><tr><td>52</td><td>(D = 1; 5 observations)</td></tr></tbody></table>

propose a log-linear specification linking these three variables.

To control for the possible presence of bias and the effects of other assumptions, we supplement it with a constant, two variables representing the assumptions regarding the diffusion of hydrogen and power technologies (i.e., a dummy variable indicating the omission of these technologies and the logged quantity of energy consumed in the form of hydrogen and electricity), and three dummy variables indicating the inclusion of non-CO₂ effects and freight, and the account for ILUC respectively.

The resulting reference specification labeled Model I is thus:

$$ \text{Log}(\text{Emissions}_i) = c + a.\text{Log}(\text{SAF}_i) + b.\text{Log}(\text{Market\_Size}_i) + d.\text{No\_H2\_Elec}_i + e.\text{Log}(\text{H2\_Elec}_i) + f.\text{Non\_CO2}_i + g.\text{Freight}_i + h.\text{ILUC}_i + \varepsilon_i $$

where *i* indexes scenarios, *a*, *b*, *c*, *d*, *e*, *f*, *g*, and *h* are coefficients to be estimated.

The coefficient *a* (respectively, *b* and *e*) measures the marginal impact that the volume of SAFs (respectively, the size of the market and the quantity of energy consumed in the form of hydrogen and power) has on the remaining emissions. As usual with log-linear specification, it suggests that a 1% rise in SAFs (respectively, market size and hydrogen and power consumption) is met by *a%* (respectively, *b%* and *e%*) change in remaining emissions. We expect the coefficients *a* and *e* to be negative as the deployment of SAFs and hydrogen and power consumption are posited to lower the sector's future emissions. We also expect the coefficient *b* to be positive as a larger market size should go hand in hand with larger remaining emissions. Regarding the dummy variables, we expect the estimated coefficient *f* to be positive, as the volume of projected emissions is expected to be larger whenever the environmental impacts of the non-CO₂ effects are accounted for. Similarly, we expect the estimated coefficient *h* to be positive whenever it is statistically significant, as the volume of projected emissions is expected to be larger whenever ILUC is accounted for. The error term $\varepsilon_i$ is posited independently distributed and is drawn from a normal distribution with mean zero. If that assumption is verified, then OLS will yield an unbiased estimator of the coefficients of interest.

#### 3.1.2. OLS estimates

Table 6 reports the estimation results obtained with our simple model. The first column presents estimates using the 67-observation sample and the second column contains estimates for the 52-observation subsample.

The desirable properties of the OLS estimates depend on the strong assumptions made about the error structure. From the Jarque-Bera test results, we cannot reject the normality assumption. However, there are signs of heteroskedasticity in the residuals, which can be explained by possible interdependencies in the construction of the scenarios. To correct for that, we report White's heteroskedasticity consistent standard errors.

Overall, we observe similar findings for the two samples. In both cases, the OLS estimates are consistent with our hypotheses. We observe negative and very significant coefficients for log (SAF) and positive and very significant estimates for the market size coefficients. The estimated coefficients for the quantity of hydrogen and electricity are negative, as expected. The estimated coefficients for the dummy variable indicating the inclusion of non-CO₂ effects are positive and statistically significant. From the estimated values, the emissions of the scenarios that account for these additional environmental impacts are $exp(1.671) = 5.317$ and $exp(1.597) = 4.938$ times larger than the ones observed in scenarios excluding these emissions.

That said, one can wonder about the possible presence of unmodeled nonlinearities. In both cases, the Ramsey RESET test for model mis-specification based on the squared fitted values of logged emissions is firmly rejected. This finding calls for further investigations.
<figure><img src="image_4.png" /><figcaption>Fig. 1. – Projected emissions of greenhouse gases in 2050 and corresponding SAF quantities per simulation.</figcaption></figure>

## 3.2. Testing for nonlinear interactions between SAFs and emissions

### 3.2.1. Specification of an extended model

To gain further insight on the nature of the relation between SAFs and the volume of projected emissions by 2050, we explore the presence of possibly nonlinear interactions and consider an extended specification that includes an additional regressor: the squared value of log (SAF). With the 67-observation sample, the estimation results<fnref n="5" /> indicate the presence of a negative – and statistically significant at the 1% level) – coefficient of that squared variable (the estimate is -0.277 with a standard deviation of 0.046 and a p-value of 0.000). This finding suggests that the marginal impact of a large quantity of SAFs on projected emissions is larger than that obtained when smaller amounts of SAFs are used.

To investigate that assumption further, we test for the presence of structural change in the relation between the volume of SAFs and projected emissions. We thus rank the sample values of SAFs in ascending order. After trimming the 15 lowest and 15 highest values, we consider each of the remaining SAF values as a potential threshold delineating two regimes by defining a dummy variable $D \ge Th$ that takes the value one whenever the observed SAF value is not less than that threshold value. For each of these possible threshold values, we specify and estimate an extended version of the preceding specification that includes an additional regressor: the product of that dummy and the logged value of the SAF (see Model II in Appendix A). We select the one yielding the highest log-likelihood score among these extended models. In both samples, that procedure yields the same threshold value which corresponds to a quantity of SAFs equal to 10.664 EJ.

### 3.2.2. Estimation results

Table 7 reports the estimation results obtained with that threshold

value. In both cases, the Wald test firmly rejects the null hypothesis of no break thereby confirming the presence of a structural change.

The goodness of fit of the estimated models is rather high, which suggests that the posited specification adequately captures the common effects that the explanatory variables have on the projected emissions. This finding offers a sharp contrast to the perception of diverging pathways as it incidentally reveals that the scenarios at hand share a common structure.

Compared with the previous model, including a structural break does not substantially modify our findings regarding the marginal impacts of the posited market size and the inclusion of non-CO₂ emissions. In contrast, including a break substantially changes the nature of the relationship between the volume of SAFs and emissions. For low volumes of SAF, the magnitude of the marginal impact of SAFs is negative but has a substantially smaller magnitude than the one observed when larger volumes of SAF are included. In the latter case, the marginal impact observed with the 67-observation (respectively, 52-observation) sample is 3.2 times (respectively, 2.4 times) larger than that obtained when the volume of SAFs is lower than the threshold. For the 67-observation sample, we also find that the effects of SAFs are not statistically significant in the low volume regime.

Overall, these estimates provide valuable insights into the relative performance of the different decarbonization options. From these estimates, a 1% reduction in the demand for air transportation yields a more significant decrease in projected emissions by 2050 (i.e., -2.03% in the 67-observation sample and -1.7% in the subsample) than a 1% increase in either SAF (i.e., -0.48% and -0.51%, respectively, whenever the total volume of SAFs exceeds the 10.664 EJ threshold) or hydrogen or electricity (i.e., -0.19% and -0.25%, respectively).

As the results of the diagnostic tests show no signs of misspecification, we proceed using Model II as the preferred specification in subsequent analyses.

<footnote n="5">⁵ The complete estimation results are available upon request from the authors.</footnote>
<table><caption>Table 5
Correlation matrix.</caption><thead><tr><th colspan="2">67-observation sample</th><th colspan="2">52-observation sample</th></tr><tr><th>Log (Emissions)</th><th>Log (Market_size)</th><th>Log (SAF)</th><th>Log (H<sub>2</sub>Elec)</th><th>No<sub>H<sub>2</sub>Elec</th><th>Non-CO<sub>2</sub></th><th>Freight</th><th>ILUC</th><th>Academia</th><th>Industry</th><th>Institution</th><th>Think_Tank_NGO</th><th>E-Fuel ratio</th></tr></thead><tbody><tr><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Log (Emissions)</td><td></td><td>0.07</td><td>-0.35**</td><td>-0.24*</td><td>0.36***</td><td>0.50***</td><td>-0.08</td><td>0.36**</td><td>0.34**</td><td>0.31**</td><td>0.16</td><td>0.10</td><td>-0.26*</td></tr><tr><td>Log(Market_size)</td><td>0.12</td><td>-</td><td>0.24*</td><td>-0.08</td><td>-0.04</td><td>-0.04</td><td>0.16</td><td>0.10</td><td>0.11</td><td>0.13</td><td>0.10</td><td>0.06</td><td>0.06</td></tr><tr><td>Log(SAF)</td><td>-0.33**</td><td>0.32***</td><td>-</td><td>-</td><td>-0.52***</td><td>-</td><td>0.09</td><td>0.22</td><td>0.23</td><td>0.19</td><td>0.23</td><td>0.18</td><td>-0.16</td></tr><tr><td>Log(H<sub>2</sub>Elec) <sup>(a)</sup></td><td>-0.17</td><td>0.21*</td><td>0.04</td><td>-0.50***</td><td>-0.02</td><td>0.10</td><td>0.23</td><td>0.34**</td><td>0.31**</td><td>0.31**</td><td>0.19</td><td>0.19</td><td>-0.06</td></tr><tr><td>No<sub>H<sub>2</sub>Elec</sub></td><td>0.29**</td><td>-0.02</td><td>0.02</td><td>-0.02</td><td>0.12</td><td>0.06</td><td>0.50***</td><td>-</td><td>0.54***</td><td>0.21*</td><td>0.07</td><td>0.07</td><td>0.07</td></tr><tr><td>Non-CO<sub>2</sub></td><td>0.39***</td><td>-0.21*</td><td>0.05</td><td>0.24*</td><td>0.24*</td><td>0.12</td><td>0.24*</td><td>0.24*</td><td>0.11</td><td>0.11</td><td>0.23**</td><td>-0.30**</td><td>-0.27**</td></tr><tr><td>Freight</td><td>-0.08</td><td>0.30**</td><td>0.24*</td><td>0.28**</td><td>0.28**</td><td>0.22*</td><td>0.22*</td><td>0.03</td><td>-0.09</td><td>-0.03</td><td>-0.05</td><td>-0.06</td><td>-0.06</td></tr><tr><td>ILUC</td><td>-0.12</td><td>0.11</td><td>0.03</td><td>-0.03</td><td>-0.03</td><td>-0.03</td><td>-0.03</td><td>-0.03</td><td>-0.09</td><td>-0.03</td><td>-0.05</td><td>-0.06</td><td>-0.06</td></tr><tr><td>Academia</td><td>0.04</td><td>0.04</td><td>0.01</td><td>0.08</td><td>0.08</td><td>0.08</td><td>0.08</td><td>0.08</td><td>0.08</td><td>0.08</td><td>0.08</td><td>0.08</td><td>0.08</td></tr><tr><td>Industry</td><td>-0.06</td><td>-</td><td>0.09</td><td>-0.09</td><td>0.31**</td><td>-</td><td>0.14</td><td>-0.18</td><td>-</td><td>0.14</td><td>-0.18</td><td>-</td><td>-</td></tr><tr><td>Institution</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Think_Tank_NGO</td><td>-</td><td>-</td><td>0.09</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td>E-Fuel ratio</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr></tbody></table>

Note: Figures below the diagonal are for the 67-observation sample; those above the diagonal are for the 52-observation subsample. Asterisks indicate significance at 0.1\*, 0.05\*\* and 0.01\*\*\* levels, respectively.

<table><caption>Table 6
Estimation results for the simple specification Model I.</caption><thead><tr><th rowspan="2"></th><th colspan="2">67 sample</th><th colspan="2">52 sample</th></tr><tr><th>Log (Emissions)</th><th>***</th><th>Log (Emissions)</th><th>***</th></tr></thead><tbody><tr><td>Log(SAF)</td><td>-0.562<br>(0.160)</td><td>***</td><td>-0.563<br>(0.186)</td><td>***</td></tr><tr><td>Log(Market_size)</td><td>1.795<br>(0.261)</td><td>***</td><td>1.480<br>(0.205)</td><td>***</td></tr><tr><td>No<sub>H<sub>2</sub>Elec</sub></td><td>0.277<br>(0.162)</td><td>*</td><td>0.216<br>(0.175)</td><td>**</td></tr><tr><td>Log(H<sub>2</sub>Elec)</td><td>-0.145<br>(0.078)</td><td>*</td><td>-0.188<br>(0.082)</td><td>**</td></tr><tr><td>Non_CO<sub>2</sub></td><td>1.671<br>(0.224)</td><td>***</td><td>1.597<br>(0.191)</td><td>***</td></tr><tr><td>Freight</td><td>-1.206<br>(0.289)</td><td>***</td><td>-0.381<br>(0.265)</td><td>***</td></tr><tr><td>ILUC</td><td>0.306<br>(0.225)</td><td>***</td><td>-0.381<br>(0.254)</td><td>***</td></tr><tr><td>C</td><td>1.581<br>(0.670)</td><td>**</td><td>2.501<br>(0.453)</td><td>***</td></tr><tr><td>Observations</td><td>67</td><td></td><td>52</td><td></td></tr><tr><td>Adjusted R-squared</td><td>0.617</td><td></td><td>0.688</td><td></td></tr><tr><td>S.E. of regression</td><td>0.563</td><td></td><td>0.501</td><td></td></tr><tr><td>Log-likelihood</td><td>-52.326</td><td></td><td>-33.488</td><td></td></tr><tr><td>J-B (p-value)</td><td>0.286</td><td>(0.867)</td><td>0.298</td><td>(0.862)</td></tr><tr><td>RESET(2) (p-value)</td><td>7.637</td><td>(0.008)</td><td>14.302</td><td>(0.000)</td></tr></tbody></table>

Note: OLS estimates. White heteroskedasticity consistent standard errors of co-efficient estimates are shown in parentheses. Asterisks indicate significance at 0.1\*, 0.05\*\* and 0.01\*\*\* levels, respectively. J-B is the Jarque-Bera test for the null hypothesis of a normal distribution. RESET(2) is the Ramsey Regression Equation Specification Error Test for model misspecification based on the square of fitted values.

<table><caption>Table 7
Estimation results for the model with structural break (Model II).</caption><thead><tr><th rowspan="2"></th><th colspan="2">67 sample</th><th colspan="2">52 sample</th></tr><tr><th>Log (Emissions)</th><th>***</th><th>Log (Emissions)</th><th>***</th></tr></thead><tbody><tr><td>Log(SAF)</td><td>-0.147<br>(0.096)</td><td></td><td>-0.211<br>(0.100)</td><td>**</td></tr><tr><td>Log(SAF)×D ≥ Th</td><td>-0.329<br>(0.058)</td><td>***</td><td>-0.300<br>(0.069)</td><td>***</td></tr><tr><td>Log(Market_size)</td><td>2.028<br>(0.199)</td><td>***</td><td>1.700<br>(0.157)</td><td>***</td></tr><tr><td>No<sub>H<sub>2</sub>Elec</sub></td><td>0.296<br>(0.136)</td><td>**</td><td>0.248<br>(0.147)</td><td>*</td></tr><tr><td>Log(H<sub>2</sub>Elec)</td><td>-0.194<br>(0.076)</td><td>**</td><td>-0.252<br>(0.070)</td><td>***</td></tr><tr><td>Non_CO<sub>2</sub></td><td>1.845<br>(0.191)</td><td>***</td><td>1.737<br>(0.178)</td><td>***</td></tr><tr><td>Freight</td><td>-1.305<br>(0.222)</td><td>***</td><td>-0.755<br>(0.208)</td><td>***</td></tr><tr><td>ILUC</td><td>0.371<br>(0.177)</td><td>**</td><td>-0.009<br>(0.199)</td><td>***</td></tr><tr><td>C</td><td>0.428<br>(0.648)</td><td></td><td>1.502<br>(0.497)</td><td>***</td></tr><tr><td>Threshold value for SAF (in EJ)</td><td>10.664</td><td></td><td>10.664</td><td></td></tr><tr><td>Wald test (H<sub>0</sub>: no structural change) (p-value)</td><td>31.936</td><td>(0.000)</td><td>18.719</td><td>(0.000)</td></tr><tr><td>Observations</td><td>67</td><td></td><td>52</td><td></td></tr><tr><td>Adjusted R-squared</td><td>0.726</td><td></td><td>0.772</td><td></td></tr><tr><td>S.E. of regression</td><td>0.476</td><td></td><td>0.428</td><td></td></tr><tr><td>Log likelihood</td><td>-40.541</td><td></td><td>-24.771</td><td></td></tr><tr><td>J-B (p-value)</td><td>0.337</td><td>(0.844)</td><td>0.543</td><td>(0.763)</td></tr><tr><td>RESET(2) (p-value)</td><td>1.059</td><td>(0.308)</td><td>1.308</td><td>(0.259)</td></tr></tbody></table>

Note: OLS estimates. White heteroskedasticity consistent standard errors of co-efficient estimates are shown in parentheses. Asterisks indicate significance at 0.1\*, 0.05\*\* and 0.01\*\*\* levels, respectively. J-B is the Jarque-Bera test for the null hypothesis of a normal distribution. RESET(2) is the Ramsey Regression Equation Specification Error Test for model misspecification based on the square of fitted values.
## 3.3. Testing for the presence of authorship bias

### 3.3.1. Model specification

As the scenarios emanate from different sources, one can wonder about the possible presence of authorship bias affecting the relationship between the projected volume of CO₂ emissions and the volume of SAFs. To investigate that, we consider two extended specifications. First, we supplement the specification above with three authorship dummy variables to test whether the authorship type affects the projected emission level (see Model III-A in Appendix A). Second, we also explore whether the authorship type affects the relation between the posited volume of SAFs and the projected future emissions. To explore that, we also consider another extended specification that includes three authorship dummy variables and three interaction terms defined as the product of these dummy variables with the logged volume of SAFs (see Model III-B in Appendix A). The latter coefficient is aimed at assessing whether the authorship under scrutiny changes the marginal impact of the volume of SAFs on these emissions.

### 3.3.2. Estimation results

We report the estimates of these specifications in Table 8 and the associated Table 9 reports the results of a series of Wald tests conducted with the estimated models.

By nature, Model III-A enables a *ceteris paribus* comparison of the projected emission levels between the different types of authorships.

<caption>Table 9</caption>

Wald tests of the authorship impacts.

<table><thead><tr><th rowspan="2"></th><th colspan="2">67 sample</th><th colspan="2">52 sample</th></tr><tr><th>Test statistics</th><th>(p-value)</th><th>Test statistics</th><th>(p-value)</th></tr></thead><tbody><tr><td colspan="5"><strong>Model III-A</strong></td></tr><tr><td>H₀: no joint authorship bias<br>(i.e., (I) = 0, (III) = 0, (V) = 0)</td><td>1.455</td><td>(0.693)</td><td>2.886</td><td>(0.409)</td></tr><tr><td colspan="5"><strong>Model III-B</strong></td></tr><tr><td>H₀: no authorship bias in<br>industry scenarios<br>(i.e., (I) = 0, (II) = 0)</td><td>13.435***</td><td>(0.001)</td><td>3.031</td><td>(0.220)</td></tr><tr><td>H₀: no authorship bias in<br>institutional scenarios<br>(i.e., (III) = 0, (IV) = 0)</td><td>2.289</td><td>(0.318)</td><td>0.187</td><td>(0.911)</td></tr><tr><td>H₀: no authorship bias in<br>scenarios produced by think<br>tanks and NGOs<br>(i.e., (V) = 0, (VI) = 0)</td><td>0.630</td><td>(0.730)</td><td>0.286</td><td>(0.867)</td></tr></tbody></table>

Note: See Table 8 for definitions of the restricted coefficients (I), (II), (III), (IV), (V) and (VI). Asterisks indicate rejection of the null hypothesis at 0.1\*, 0.05\**, and 0.01\*** significance levels, respectively.

From the estimation results obtained with both the large and the smaller samples, we observe that the coefficients of the three authorship dummy variables are not statistically significant. The Wald test results confirm this finding, as the joint exclusion of these dummy variables is never

<caption>Table 8</caption>

Estimation results to test for the presence of authorship bias (Models III-A and III-B).

<table><thead><tr><th rowspan="3"></th><th colspan="2">67 sample</th><th colspan="4">52 sample</th></tr><tr><th colspan="2">Model III-A</th><th colspan="2">Model III-B</th><th colspan="2">Model III-A</th></tr><tr><th>Log (Emissions)</th><th></th><th>Log (Emissions)</th><th></th><th>Log (Emissions)</th><th></th></tr></thead><tbody><tr><td>Log(SAF)</td><td>-0.162<br>(0.119)</td><td></td><td>-0.060<br>(0.103)</td><td></td><td>-0.216<br>(0.118)</td><td>*</td><td>-0.191<br>(0.128)</td></tr><tr><td>Log(SAF)×D ≥ Th</td><td>-0.319<br>(0.061)</td><td>***</td><td>-0.286<br>(0.081)</td><td>***</td><td>-0.276<br>(0.065)</td><td>***</td><td>-0.299<br>(0.091)</td></tr><tr><td>Log(Market_size)</td><td>2.010<br>(0.208)</td><td>***</td><td>1.971<br>(0.194)</td><td>***</td><td>1.659<br>(0.170)</td><td>***</td><td>1.705<br>(0.182)</td></tr><tr><td>No_H₂_Elec</td><td>0.293<br>(0.135)</td><td>**</td><td>0.211<br>(0.137)</td><td>***</td><td>0.218<br>(0.146)</td><td></td><td>0.244<br>(0.161)</td></tr><tr><td>Log(H₂_Elec)</td><td>-0.188<br>(0.079)</td><td>**</td><td>-0.185<br>(0.077)</td><td>**</td><td>-0.262<br>(0.074)</td><td>***</td><td>-0.258<br>(0.083)</td></tr><tr><td>Non_CO₂</td><td>1.825<br>(0.214)</td><td>***</td><td>1.799<br>(0.218)</td><td>***</td><td>1.719<br>(0.218)</td><td>***</td><td>1.733<br>(0.229)</td></tr><tr><td>Freight</td><td>-1.297<br>(0.254)</td><td>***</td><td>-1.419<br>(0.243)</td><td>***</td><td>-0.932<br>(0.377)</td><td>**</td><td>-0.938<br>(0.398)</td></tr><tr><td>ILUC</td><td>0.362<br>(0.175)</td><td>**</td><td>0.295<br>(0.181)</td><td></td><td>0.158<br>(0.314)</td><td></td><td>0.097<br>(0.331)</td></tr><tr><td>C</td><td>0.515<br>(0.861)</td><td></td><td>0.511<br>(0.639)</td><td></td><td>1.634<br>(0.544)</td><td>***</td><td>1.499<br>(0.587)</td></tr><tr><td>Industry (I)</td><td>-0.161<br>(0.228)</td><td></td><td>0.725<br>(0.211)</td><td>***</td><td>-0.313<br>(0.294)</td><td></td><td>2.950<br>(2.759)</td></tr><tr><td>Log(SAF)×Industry (II)</td><td>-</td><td></td><td>-0.434<br>(0.129)</td><td>***</td><td>-</td><td></td><td>-1.180<br>(0.970)</td></tr><tr><td>Institution (III)</td><td>0.048<br>(0.193)</td><td></td><td>0.858<br>(0.596)</td><td></td><td>0.020<br>(0.236)</td><td></td><td>-0.252<br>(0.621)</td></tr><tr><td>Log(SAF)×Institution (IV)</td><td>-</td><td></td><td>-0.379<br>(0.251)</td><td></td><td>-</td><td></td><td>0.090<br>(0.271)</td></tr><tr><td>Think_Tank_NGO (V)</td><td>-0.051<br>(0.345)</td><td></td><td>-2.049<br>(2.622)</td><td></td><td>0.118<br>(0.350)</td><td></td><td>-1.179<br>(2.675)</td></tr><tr><td>Log(SAF)×Think_Tank_NGO (VI)</td><td>-</td><td></td><td>0.918<br>(1.222)</td><td></td><td>-</td><td></td><td>0.587<br>(1.226)</td></tr><tr><td>Observations</td><td>67</td><td></td><td>67</td><td></td><td>52</td><td></td><td>52</td></tr><tr><td>Adjusted R-squared</td><td>0.717</td><td></td><td>0.729</td><td></td><td>0.767</td><td></td><td>0.755</td></tr><tr><td>S.E. of regression</td><td>0.484</td><td></td><td>0.474</td><td></td><td>0.433</td><td></td><td>0.444</td></tr><tr><td>Log likelihood</td><td>-39.893</td><td></td><td>-36.546</td><td></td><td>-23.496</td><td></td><td>-22.727</td></tr><tr><td>J-B (p-value)</td><td>0.186</td><td>(0.911)</td><td>0.374</td><td>(0.829)</td><td>0.988</td><td>(0.610)</td><td>0.743</td><td>(0.690)</td></tr><tr><td>RESET(2) (p-value)</td><td>1.648</td><td>(0.205)</td><td>1.497</td><td>(0.227)</td><td>0.603</td><td>(0.442)</td><td>0.516</td><td>(0.447)</td></tr></tbody></table>

Note: OLS estimates. White heteroskedasticity consistent standard errors of coefficient estimates are shown in parentheses. Asterisks indicate significance at 0.1\*, 0.05\**, and 0.01\*** levels, respectively. J-B is the Jarque-Bera test for the null hypothesis of a normal distribution. RESET(2) is the Ramsey Regression Equation Specification Error Test for model misspecification based on the square of fitted values.
rejected at the 10% level. This finding indicates that the type of authorship has no significant impact on the projected emission levels.

The estimation results obtained for Model III-B with the large 67-observation sample reveal the presence of authorship bias in the marginal effects of SAF. We observe that the industry coefficients are statistically significant at the 1% significance level. The Wald test results in Table 9 corroborate this finding as the joint exclusion of the two industry coefficients at the 10% level, and the null hypothesis of a joint exclusion of both the industry and institution coefficients is firmly rejected at the 5% level.

In Table 8, the estimate of the interaction coefficient for industry (i. e., that for the product of the logged volume of SAFs and the authorship dummy) is negative and significant (i.e., -0.434). Compared with the benchmark scenarios in academic studies, the marginal decarbonizing impact of SAFs is thus posited to have a greater magnitude in scenarios produced by industry sources. The observed industry's inclination to produce scenarios emphasizing a larger environmental impact of SAFs on the reduction of projected emissions is consistent with the rich energy literature documenting a partisan use of scenarios to influence policy-making (see Weber et al., 2021). For industry participants whose future profitability can be impacted by new environmental policies, rent-seeking behavior makes it rational to generate scenarios emphasizing the environmental benefits of SAFs, as these scenarios can affect the political environment and lower the authorities' propensity to decide adverse measures (e.g., restrictive regulations aimed at lowering the demand for air transportation). We also observe that the estimated interaction coefficient for institutions has a magnitude similar to that of industry but, because of a larger standard deviation, is not statistically significant.

In contrast, the 52-observation subsample seems little affected by authorship bias. Using Model III-B, the estimated coefficients (I) and (II) are not statistically significant, and all the Wald test results confirm that they can be safely omitted. Indeed, as a confirmation, one can compare that model with the restricted specification in Table 7 and conduct a log-likelihood ratio test of the joint omission of these six coefficients. Yet, the test statistic is 4.088 with a p-value of 0.665 and fails to reject the validity of the restricted model. Hence, we proceed with assuming the absence of authorship effect in the 52-observation subsample and only consider that smaller sample in the sequel.

## 3.4. Testing for the presence of technological bias

### 3.4.1. Model specification

We now concentrate on the technology-rich 52-observation subsample and examine whether the type of SAF used in civil aviation has an impact on the projected volumes of GHG emissions by 2050. To investigate that, we consider an extension of our preferred specification (i.e., the one in Section 3.2) and supplement it with an additional explanatory variable capturing the interactions between the volume of SAFs and the proportion of e-fuel. This extended model is labeled Model IV (see Appendix A).

### 3.4.2. Estimation results

From the estimation results presented in Table 10, we observe that the proportion of e-fuel in the pool of SAFs has an impact on the projected emissions by 2050. In the case of a pure biofuel-SAF (i.e., a zero value for the e-fuel ratio), a 1% augmentation of the total volume of biofuel has no significant impact unless the 10.664 EJ threshold is attained. Above that level, a 1% rise in SAFs translates into a 0.29% reduction in projected emissions. Should a 100%-e-fuel-based SAF be used instead the reduction would be twice as large and would reach 0.58%. This finding is surprising and raises concerns about a possible technological bias toward e-fuels with greater environmental virtues. It incidentally reveals an implicit assumption that is little detailed in the narratives that describe the scenarios: it presupposes that the manufacturing of these synthetic fuels uses only green electricity

<table><caption>**Table 10**
Estimation results for the model with the e-fuel ratio (Model IV).</caption><thead><tr><th></th><th>52 sample<br>Log (Emissions)</th><th></th></tr></thead><tbody><tr><td>Log(SAF)</td><td>-0.094<br>(0.070)</td><td></td></tr><tr><td>Log(SAF)×D ≥ Th</td><td>-0.288<br>(0.073)</td><td>***</td></tr><tr><td>Log(Market_size)</td><td>1.613<br>(0.118)</td><td>***</td></tr><tr><td>No_H₂_Elec</td><td>0.218<br>(0.125)</td><td>*</td></tr><tr><td>Log(H₂_Elec)</td><td>-0.301<br>(0.066)</td><td>***</td></tr><tr><td>Non_CO₂</td><td>1.734<br>(0.132)</td><td>***</td></tr><tr><td>Freight</td><td>-0.850<br>(0.278)</td><td>***</td></tr><tr><td>ILUC</td><td>-0.018<br>(0.281)</td><td></td></tr><tr><td>C</td><td>1.838<br>(0.373)</td><td>***</td></tr><tr><td>Log (SAF)×Efuel_ratio</td><td>-0.294<br>(0.074)</td><td>***</td></tr><tr><td>Observations</td><td>52</td><td></td></tr><tr><td>Adjusted R-squared</td><td>0.830</td><td></td></tr><tr><td>S.E. of regression</td><td>0.370</td><td></td></tr><tr><td>Log likelihood</td><td>-16.554</td><td></td></tr><tr><td>J-B (p-value)</td><td>0.647</td><td>(0.724)</td></tr><tr><td>RESET(2) (p-value)</td><td>3.533</td><td>(0.067)</td></tr></tbody></table>

Note: OLS estimates. White heteroskedasticity consistent standard errors of coefficient estimates are shown in parentheses. Asterisks indicate significance at 0.1*, 0.05** and 0.01*** levels, respectively. J-B is the Jarque-Bera test for the null hypothesis of a normal distribution. RESET(2) is the Ramsey Regression Equation Specification Error Test for model misspecification based on the square of fitted values.

sources. Arguably, some correction is required if that manufacturing mobilizes a positive percentage of polluting thermal generation. However, the documentation accompanying the scenarios does not allow this correction to be calibrated.

# 4. Discussion: implications and future research scope

## 4.1. Deciphering the structural break in the relationship between SAFs and residual emissions

The relationship between SAF penetration and residual aviation emissions needing offset is more complex than anticipated, as our findings reveal the presence of a structural break. This 10.664 EJ threshold, below which SAFs are less effective for decarbonization, can be interpreted as an indication that scenarios presuming a smaller penetration of SAFs could be affected by a less committed attitude concerning the decarbonization ambitions. Other studies suggest the opposite, namely that biofuel overuse limits their decarbonization benefits (Staples et al., 2017). Yet, an examination of the documentation supporting the scenarios has allowed us to identify the reasons supporting that joint assumption. We tentatively hypothesize that, below a certain penetration level, the market size is small, which possibly goes hand in hand with a slow renewal of the aircraft fleet that negatively affects the diffusion of newer technologies such as SAFs (Conseil pour la recherche et l'aviation civile, 2021). Nonetheless, our empirical investigations have not documented the presence of unmodeled nonlinearities in the market size coefficient and, to the best of our knowledge, this effect has never been empirically observed in the
academic literature. This point indeed calls for further investigations.<fnref n="6" />

Another puzzling phenomenon is the technological bias favoring e-fuels. Manufacturing e-fuels is presumed to be more costly and mobilizes less mature technologies than biofuel processing. The consensus describes e-fuel production as a response to scarcity issues and resource limitations affecting biomass processing (Christensen and Petrenko, 2017; World Economic Forum, 2020; ATAG, 2021; Dray et al., 2022; Institut Montaigne, 2022). Arguably, below the 10 EJ threshold of SAFs, e-fuels will not have significantly emerged by 2050, whereas, if resource constraints restrict the availability of bio-jet fuel, e-fuels may become the primary solution to meet growing SAF demand. Only four scenarios out of the 52 sample project biofuel quantities above 15 EJ, which can be posited as a reasonable order of magnitude for the biofuel limit, in line with the findings from other studies (Staples et al., 2018; Bardon and Massol, 2025) and the demands of the industry (Fleming et al., 2022). The four outliers are from the same study (Dray et al., 2022) and emphasize a maximum biofuel capability of 21.7 EJ, which makes it unable to meet all of the energy demand in 2050. The exact value remains debatable since it is dependent on several hypotheses including feedstock quantities and prices, and allocation to the aviation sector (IRENA, 2016; Staples et al., 2018). Nevertheless, the fact that biofuels are a limited resource which will likely be insufficient to reach net zero is relatively consensual (Staples et al., 2018; World Economic Forum, 2020; ATAG, 2021; Dray et al., 2022; Institut Montaigne, 2022; Becken et al., 2023). Because of the small size of our sample containing detailed information on SAF types, it has not been possible to statistically investigate the combined interactions between the threshold affecting the penetration of SAFs and the e-fuel ratio. Should an enlarged sample become available, exploring these interactions further could represent a valuable research avenue.

Overall, further investigations could also examine the validity of industry narratives stressing the complementarity between market growth and decarbonization. A crucial element here is the technological bias on e-fuels. While many studies agree that e-fuels are almost carbon neutral in theory (El Takriti et al., 2017; Christensen and Petrenko, 2017), others point out that their environmental performance is critically dependent upon the availability of a decarbonized electricity mix, as e-fuels could be worse than fossil fuels if a carbon-intensive electricity mix is used (Schmidt et al., 2018; Delbecq et al., 2021; Barke et al., 2022; Transport & Environment, 2022).

## 4.2. Demand management: an omitted discussion, possible framing biases, and some methodological issues

An emerging literature examines reducing travel consumption to support the transition to a low-carbon pathway and investigates consumption behaviors that elicit the willingness to engage in voluntary travel reductions (O'Garra and Fouquet, 2022).

However, thus far, the sector's discussion on the decarbonization of civil aviation has mainly focused on supply-side responses. Yet, our results reveal the environmental performance of demand management. Specifically, a 1% moderation in the projected market size still corresponds to a net increase in air travel demand (compared with contemporary demand levels) and results in emission reductions that are three to four times (respectively, 5 to 10 times) larger than that obtained with a 1% increase in the volume of SAFs (respectively, in the use of electric or hydrogen-powered planes). This finding is consistent with the views presented in Gössling and Lyle (2021) and Valdés et al. (2021).

Unsurprisingly, the industry scenarios in our sample feature a narrower range of demand projections compared to other sources. Though the mean projection is similar (i.e., 21.2 for industry scenarios vs 23.4 for other sources), the coefficient of variation is markedly smaller (i.e., 8.5% vs. 27.6%). The most pessimistic demand projection used in industry studies posits a constant average annual growth rate of +2.5%. In contrast, some academic scenarios consider smaller growth hypotheses (e.g., as low as 0.4% per annum in one of the scenarios in Bergero et al., 2023). These differences can be interpreted as possible signs of framing bias in the selection of the demand hypotheses. That topic indeed calls for further investigations. Yet, due to the small size of our sample, we have been unable to investigate this statistically. At least two arguments stress the need to refine the demand projections retained in these scenarios. First, Sub and Ryerson (2019) underline an optimism bias in air travel demand forecasts and stress an inability to predict severe market contractions. Second, one wonders whether – and if yes, to what extent – behavioral changes (e.g., the flygskäm designating the passenger reluctance to fly due to excess environmental impact) should be accounted for in demand projections.

The preparation of the present statistical study also prompted us to examine the methodology used to generate the scenarios in our sample. With the exception of Dray et al. (2022), Fragkos (2022), Gössling and Humpe (2023), Salgas et al. (2023), IEA (2021), and Graver et al. (2022), demand is typically treated as an exogenous parameter, assumed purely inelastic to price. Neglecting the passengers' reaction to rising airfares has important implications. For example, suppose the price of a SAF remains higher than that of conventional jet fuel; only a handful of the reviewed scenarios account for the reductions in demand caused by including price-induced negative demand feedbacks, which lower the compound annual growth rate hypotheses by about 0.5% (Dray et al., 2022; Fragkos, 2022; Graver et al., 2022; Gössling and Humpe, 2023; Salgas et al., 2023). Consequently, the models used to generate the projections struggle to address the effects of policies targeting the demand-side, such as short-haul flight restrictions or carbon prices, which are included in only two studies within the analyzed (IEA, 2021; Gössling and Humpe, 2023). An enhanced, price-elastic representation of demand may also be essential for adequately accounting for the effects of climate change, as price variations are also predicted to increase with climate change. Rising temperatures and increased extreme weather frequencies affect infrastructure and aircraft performances, leading to higher costs reflected in airfares (Ryley et al., 2020). These considerations call for the development of a new generation of models that incorporate an enriched and price-elastic representation of the demand side.

## 4.3. Other possible framing biases: authorship, emission measures, and technology

Our findings reveal signs of authorship bias affecting the marginal decarbonization impact of SAFs. The results in Table 8 indicate that industry scenarios are more inclined than those issued by other sources to consider the environmental benefits of SAF penetration. This bias can be explained by rent-seeking behavior (i.e., the “partisan conception of enlightenment” in Weber et al., (2021)). While our results do not show bias in the subgroup of scenarios issued by institutions, a more disaggregated approach may be needed to detect potential vulnerabilities to regulatory capture. One may hypothesize that this risk could be more pronounced for institutions closely interacting with the aviation sector (e.g., the United Nations’ ICAO) than for those with a broader mandate (e.g., the International Energy Agency or the World Bank). Although we attempted further statistical investigation in that direction, our small sample size led to inconclusive results. Should an enlarged sample become available, future research could statistically explore whether the observed industry bias permeates certain institutional scenarios, potentially signaling the presence of regulatory capture.

Another possible framing bias is the treatment of non-CO₂ effects.

<footnote n="6">⁶ Following a remark raised by a reviewer, the presence of a non-linear relationship could show heterogeneity in the emission factors of the SAF fuels. While the supporting literature for the scenarios does not explicitly specify the exact value of SAF decarbonization rates, this explanation remains pertinent, especially in the context of the technological bias favoring e-fuels, which are more present in scenarios above 10 EJ of SAF.</footnote>
Academic studies commonly include them, whereas industrial scenarios systematically omit them. Arguably, the industry voluntarily chooses to frame decarbonization primarily as a CO₂ issue, whereas 66% of the sector's effective radiative forcing comes from non-CO₂ effects (Shukla et al., 2022), which calls for further measures, notably offsetting (Bergero et al., 2023). By correcting for that important difference, our analysis provides important insights into the magnitude of that bias. Our estimates show that non-CO₂ effects will increase residual CO₂ emissions five to six-fold in 2050. A CO₂-focused strategy could lead the sector to make inefficient choices, such as evenly distributing SAFs among all aircraft by blending them with fossil fuel: this would reduce the carbon footprint of all flights but with minor impacts on non-CO₂ effects, such as contrail cirrus persistence, which are more effectively reduced with only a specific portion of airplanes flying 100% SAF (DeWitt et al., 2008; Leipold et al., 2021).

Two technological assumptions also critically impact the trajectory of future emissions by 2050 and merit discussion. First, we examine the deployment assumptions retained for hydrogen and electric-powered aircraft, technologies with low maturity levels. By construction, our regression results explicitly account for the role of these two decarbonization technologies. Yet, marked differences exist in our sample regarding their posited penetration. Supaero Decarbo & The Shift Project (2021) and Dray et al. (2022) contain scenarios with optimistic hypotheses on liquid hydrogen deployment, exceeding 50% of final energy demand. The former study posits that all new short and medium-haul aircraft will be hydrogen-powered by 2035. While this aligns with Airbus's roadmap (Airbus, 2021b), Boeing shows less commitment to hydrogen aircraft development (Vignon, 2022)<fnref n="7" /> raising questions about the realism of hypotheses exceeding 50%. Many scenarios assume a 0% (or near zero) liquid hydrogen hypothesis. Additionally, electricity also includes outliers projecting up to 1.2 EJ of electricity usage for fully electric flight (Fragkos, 2022). This figure is three times larger than the hypotheses retained in the other optimistic studies. It is worth noting that most scenarios in our sample assume that fully electric aviation will remain negligible by 2050. The spread of these assumptions stresses the importance of adequately correcting for the diffusion of these technologies when examining future aviation decarbonization pathways.

Second, it is important to stress the role of a hidden and yet important parameter: the posited gains in energy efficiency. That parameter is seldom mentioned in the documentation supporting the scenarios under scrutiny, which explains why that parameter is not included in our empirical specification. Nonetheless, we suspect the scenarios at hand posit efficiency gains of between 1% and 2% per year, in line with the historical trend of about 1.5% per year (Planes et al., 2021). Still, some argue that these gains may stall at under 1.3% per year, falling short of ICAO aspirational goals (Peeters et al., 2016; Cumpsty et al., 2019; Kallbekken and Victor, 2022). Others note that, historically, many gains were achieved on load factor increases – included in fuel burn per revenue ton-kilometer (RTK) – and not so much on fuel burn per available ton-kilometer (ATK) (Beevor and Keith, 2022). Additionally, these gains may be offset by rebound effects (Evans and Schäfer, 2013; Miyoshi and Fukui, 2018), or, as mentioned in the previous section, by the efficiency losses due to climate change (Coffel and Horton, 2015; Coffel et al., 2017; Zhou et al., 2018; ICAO, 2020; Ryley et al., 2020). Overall, that discussion calls for more transparency regarding the posited efficiency gains in aviation roadmaps.

## 5. Conclusion and policy implications

Greening aviation is a critical challenge for policymakers seeking to

mitigate GHG emissions in the transportation sector. Against this background, aviation decarbonization scenarios are key for evaluating various options, which can combine supply-side elements with demand-side management. Yet, these scenarios often show marked discrepancies in projected residual GHG emissions, which casts doubts on the robustness of decarbonization claims. To understand the root causes of these discrepancies, this paper has assembled a sample of decarbonization scenarios to statistically investigate the relationship between the sector's projected annual emissions by 2050, the penetration of decarbonization technologies like SAFs, and anticipated market growth.

Our findings unveil a degree of consensus regarding the influence of these latter variables on the trajectory of residual emissions requiring offset. As expected, the deployment of SAFs is posited to significantly reduce emissions by 2050 whereas an expansion in the demand for air transportation significantly increases them. That said, our estimation results also raise important questions regarding the projected environmental impacts of SAFs. Firstly, we detect the presence of a structural break in the relationship between the envisioned deployment of SAFs and projected emissions: for low penetration levels of SAFs, that insertion does not significantly impact emissions, whereas its marginal impact becomes highly statistically significant above a certain penetration level. Secondly, all types of SAFs are not equally treated in the construction of these scenarios. Indeed, our findings indicate that the marginal decarbonization impact of biomass-based SAFs is lower (in absolute terms) than that of e-fuel. Thirdly, our results stress the potential of demand management as a decarbonization option. Lastly, our results unveil signs of authorship bias in some scenarios.

Overall, our research emphasizes the need for more transparent and robust decarbonization pathways in the aviation sector. It also stresses the importance of critically evaluating the assumptions retained in the construction of these pathways. Policymakers should thus approach these scenarios with caution, keeping in mind the potential biases that may influence their decisions.

Looking ahead, several unsolved challenges emerge as valuable future research avenues to better inform policymaking in civil aviation. Firstly, current scenarios often neglect demand-side options, despite their environmental potential. Given the simplistic representation of the demand side used in generating these scenarios, developing models that incorporate a price-elastic demand function could yield valuable insights. Secondly, given the detected presence of framing biases, further investigations could explore in more detail whether scenarios created by institutions are subject to regulatory capture. Finally, if further information on funding sources becomes available, exploring the relationship between authorship biases and funding sources could offer deeper insights into the influences shaping these scenarios. In that vein, it could also be opportune to explore whether – and if yes how – the finding obtained in previous scenarios affect the construction of subsequent scenarios and whether funding considerations affect those dynamics. Due to the global consensus surrounding scenario development, some results may be built upon or influenced by prior scenarios, creating a dependency between them, especially given the timing of their publication.

### CRediT authorship contribution statement

**Paul Bardon:** Writing – review & editing, Visualization, Validation, Investigation, Formal analysis, Data curation, Conceptualization. **Olivier Massol:** Writing – review & editing, Writing – original draft, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Conceptualization. **Arthur Thomas:** Writing – review & editing, Validation, Methodology.

### Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

<footnote n="7">⁷ See also Raymond, C. (Boeing's chief sustainability officer): 'We can't count on hydrogen-powered commercial flights before 2050'. *Fortune*, January 26, 2023.</footnote>
Paul Bardon reports financial support was provided by Tuck Foundation. The other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgments

We are indebted to the Editor, two anonymous referees, Guido Amato, Anna Creti, Jean-Pierre Deflandre, Adrien Nicolle, Yannick Perez, and participants at the 10th Conference on Mobility Issues (Paris, 2023), and at the 4th AREA Workshop "Econometric Methods and Environmental Challenges" (Paris, 2024) for valuable comments and

suggestions. Paul Bardon gratefully acknowledges the support of the Chair “Carbon Management and Negative CO2Technologies: Towards a low carbon future (CarMa)” funded by the Tuck Foundation. Olivier Massol gratefully acknowledges funding from the Franco-German research project MInDSET\_Clean\_H₂ jointly funded by the Agence Nationale de la Recherche (ANR grant number ANR-24-ENER-0003) on behalf of the French Ministry of Higher Education and Research (MESR), and the German Federal Ministry of Education and Research (BMBF). This study also benefited from the research environment of the Climate Economics Chair. However, the views expressed here, along with any remaining errors, are solely the responsibility of the authors.

## Appendix A. Model specifications, a summary

Here, i indexes scenarios, and a, b, c, d, e, f, g, h, g, j, k, l, m, n, p, q and r are coefficients to be estimated. We let $ε_i$ denote the error term.

### Model I:

$$ \text{Log}(\text{Emissions}_i) = c + a.\text{Log}(\text{SAF}_i) + b.\text{Log}(\text{Market\_Size}_i) + d.\text{No\_H2\_Elec}_i + e.\text{Log}(\text{H2\_Elec}_i) + f.\text{Non\_CO2}_i + g.\text{Freight}_i + h.\text{ILUC}_i + \varepsilon_i $$

The interpretation of the coefficients is detailed in Section 3.1.

### Model II (preferred specification):

$$ \text{Log}(\text{Emissions}_i) = c + a.\text{Log}(\text{SAF}_i) + j.\text{Log}(\text{SAF}_i) \times D_{\geq \text{Th}_i} + b.\text{Log}(\text{Market\_Size}_i) + d.\text{No\_H2\_Elec}_i + e.\text{Log}(\text{H2\_Elec}_i) + f.\text{Non\_CO2}_i + g.\text{Freight}_i + h.\text{ILUC}_i + \varepsilon_i $$

Compared to Model I, that specification includes the term in bold type where $D_{\geq \text{Th}_i}$ is the dummy variable defined in Section 3.2. If the volume of SAF is not less than the threshold value, the marginal impact that the volume of SAFs has on the remaining emissions is $(a + j)$. Below that threshold, that marginal impact is a.

### Model III-A:

$$ \text{Log}(\text{Emissions}_i) = c + a.\text{Log}(\text{SAF}_i) + j.\text{Log}(\text{SAF}_i) \times D_{\geq \text{Th}_i} + b.\text{Log}(\text{Market\_Size}_i) + d.\text{No\_H2\_Elec}_i + e.\text{Log}(\text{H2\_Elec}_i) + f.\text{Non\_CO2}_i + g.\text{Freight}_i + h.\text{ILUC}_i + k.\text{Industry}_i + l.\text{Institution}_i + m.\text{Think\_Tank\_NGO}_i + \varepsilon_i $$

Compared to Model II, Model III-A accounts for possible authorship bias (see the terms in bold type). The coefficient k (respectively, l and m) measures the bias observed in the projected emissions whenever the scenario emanates from a source in industry (respectively, in an Institution or a Think Tank/NGO).

### Model III-B:

$$ \text{Log}(\text{Emissions}_i) = c + a.\text{Log}(\text{SAF}_i) + j.\text{Log}(\text{SAF}_i) \times D_{\geq \text{Th}_i} + b.\text{Log}(\text{Market\_Size}_i) + d.\text{No\_H2\_Elec}_i + e.\text{Log}(\text{H2\_Elec}_i) + f.\text{Non\_CO2}_i + g.\text{Freight}_i + h.\text{ILUC}_i + k.\text{Industry}_i + n.\text{Log}(\text{SAF}_i) \times \text{Industry}_i + l.\text{Institution}_i + p.\text{Log}(\text{SAF}_i) \times \text{Institution}_i + m.\text{Think\_Tank\_NGO}_i + q.\text{Log}(\text{SAF}_i) \times \text{Think\_Tank\_NGO}_i + \varepsilon_i $$

Model III-B is an extended version of Model IIIc-A (see the terms in bold type) where the coefficients n, p, and q capture the interactions between authorship dummies and the projected use of SAF.

### Model IV:

$$ \text{Log}(\text{Emissions}_i) = c + a.\text{Log}(\text{SAF}_i) + j.\text{Log}(\text{SAF}_i) \times D_{\geq \text{Th}_i} + b.\text{Log}(\text{Market\_Size}_i) + d.\text{No\_H2\_Elec}_i + e.\text{Log}(\text{H2\_Elec}_i) + f.\text{Non\_CO2}_i + g.\text{Freight}_i + h.\text{ILUC}_i + r.\text{Log}(\text{SAF}_i) \times \text{Efuel\_ratio}_i + \varepsilon_i $$

Model IV is an extended version of Model II that accounts for possible interactions between the volume of SAF and the posited proportion of E-fuel in the scenario (see the term in bold type).

## Data availability

I have shared the link to the data repository at the Attach File step.

Future Aviation Scenarios: Projected GHG Emissions and Technology use (Original data) (Mendeley Data)
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