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# SAFs and the 2050 Aviation Challenge: Between Myth and Reality

Paul Bardon, Arthur Thomas, Olivier Massol

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## To cite this version:

Paul Bardon, Arthur Thomas, Olivier Massol. SAFs and the 2050 Aviation Challenge: Between Myth and Reality. Workshop of the MINDSET Clean H2 Project, Technical University of Munich Center for Energy Markets, Oct 2025, Munich, Germany. hal-05308083

HAL Id: hal-05308083

https://hal.science/hal-05308083v1

Submitted on 10 Oct 2025

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Distributed under a Creative Commons Attribution 4.0 International License
SAFs and the 2050 Aviation Challenge:
Between Myth and Reality

Paul BARDON

Arthur THOMAS

Olivier MASSOL
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# Motivation

1. Aviation: **2.4% of global CO₂ emissions** in 2022

- The sector's emissions grow faster than rail, road, or shipping (Shukla et al., 2022)

2. A source of concern: the **rapidly growing demand** for air transportation

- a strong post-COVID19 demand recovery

- & a growing trend by 2050

* pulled by the emergence of LDCs, international trade, migration...

3. A notoriously **hard-to-decarbonize sector**

- a need for energy-dense liquid hydrocarbons with no competitive substitutes

- the long-lived nature of the fleet of existing aircrafts

<figure><img src="image_5.png" /></figure>
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# The industry's bet on technology

A combination of options with different potential & maturity

* Incremental improvements

- e.g., in fuel-efficiency, air traffic management, aircraft operating procedures

* Sustainable Aviation Fuels (SAFs),

aka 'Drop in' fuels with low CO₂ footprint

- Biofuels

- E-fuels

* Radical innovation in aircraft designs

- innovative powering solutions

* H₂?

* electric-powered planes?
<figure><img src="image_7.png" /></figure>

Air Transport Action Group, 2021

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<figure><img src="image_9.png" /></figure>

International Civil Aviation Organization, 2022

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**A recent proliferation of divergent decarbonization pathways**

Supaero Decarbo and
The Shift Project, 2021
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# Overview of the two studies

<table><thead><tr><td>Paper</td><td>Focus</td><td>Method</td></tr></thead><tbody><tr><td>Bardon & Massol (RESR, 2025)</td><td>Myths & realities of SAF roadmaps</td><td>An exhaustive review of the elements hindering drop-in and near drop-in SAF deployment</td></tr><tr><td>Bardon, Thomas & Massol (JEM, 2025)</td><td>Quantitative scenario analysis</td><td>A statistical analysis</td></tr></tbody></table>
<figure><img src="image_12.png" /></figure>

# RSER (2025)

<table><thead><tr><td>Paper</td><td>Focus</td><td>Method</td></tr></thead><tbody><tr><td>Bardon & Massol (RESR, 2025)</td><td>Myths & realities of SAF roadmaps</td><td>An exhaustive review of the elements hindering drop-in and near drop-in SAF deployment</td></tr></tbody></table>

## Four objectives:

1. Review the determinants of SAF availability to identify possible bottlenecks for deployment
   - resources, energy inputs, technological readiness

2. Evaluate cost implications

3. Analyze current deployment dynamics
   - discuss the credibility of SAF development prospects,

4. Examine policy frameworks and complementary measures.
<figure><img src="image_13.png" /></figure>

# RSER (2025): The Myths and Realities

## Four common myths:

1. SAF can fully replace fossil jet fuel by 2050.

2. All SAF are equally sustainable.

3. Carbon prices alone will make SAF competitive.

4. Scaling SAF is mostly a matter of investment.

## Reality:

- **Feedstock limitations**

- Bio-based SAF (UCO, waste lipids) are near-term but limited in scale (<5% of aviation fuel needs).

- **Slow technological learning**

- E-fuels (PtL) are scalable but extremely capital- and electricity-intensive.

- **Fragmented policy frameworks**

- **Persistently high costs**

- Cost of abatement: >400 €/tCO₂ for most pathways in 2030 → falling but still high by 2050.
<figure><img src="image_14.png" /></figure>

# JEM (2025): Insights from scenarios

<table><thead><tr><td>Paper</td><td>Focus</td><td>Method</td></tr></thead><tbody><tr><td>Bardon & Massol (RESR, 2025)</td><td>Myths & realities of SAF roadmaps</td><td>An exhaustive review of the elements hindering drop-in and near drop-in SAF deployment</td></tr><tr><td>Bardon, Massol & Thomas (JEM, 2025)</td><td>Quantitative scenario analysis</td><td>Statistical analysis</td></tr></tbody></table>
<figure><img src="image_15.png" /></figure>

## A recent proliferation of divergent scenarios...

**To what extent do the main assumptions retained in these scenarios affect the projected GHG emissions by 2050?**

- the posited deployment of SAFs (and the type of SAF)

- the projected demand

- and the availability of electricity and H₂-fueled planes
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67 aviation decarbonization scenarios from 15 studies

Publication date: [ March 2021 ; June 2023 ]

Key variables:

* Annual GHG emissions in 2050

* Volume of SAF in 2050

* Projected market size in 2050

* H₂ and electricity consumption in 2050

* Inclusion of non-CO₂ effects (binary)

* Inclusion of freight (binary)

* Inclusion of ILUC effects (binary)

<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">Comment</th><th colspan="2">Number of scenarios in the</th></tr><tr><th>67-observation sample</th><th>52-observation sample</th></tr></thead><tbody><tr><td>ATAG, 2021</td><td>Industry</td><td>5</td><td>Air Transport Action Group is an association of actors from all of the air transport sector value chain</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>3 scenarios posing 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>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>That study by a consultancy firm provides no information on the consumption of energy in the form of hydrogen or electricity</td><td>0</td><td>0</td></tr><tr><td>Dray et al., 2022</td><td>Academia</td><td>18</td><td></td><td>18</td><td>18</td></tr><tr><td>Fragkos, 2022</td><td>Academia</td><td>5</td><td></td><td>5</td><td>5</td></tr><tr><td>Gössling and Humpe, 2023</td><td>Academia</td><td>4</td><td></td><td>4</td><td>4</td></tr><tr><td>Graver et al., 2022 <sup>(3)</sup></td><td>Think tank/NGO</td><td>3</td><td>A study by the International Council on Clean Transportation (ICCT), a non-profit think tank</td><td>2</td><td>2</td></tr><tr><td>IATA, 2023 and IATA, 2021</td><td>Industry</td><td>1</td><td>The International Air Transport Association represents the airlines</td><td>1</td><td>0</td></tr><tr><td>ICAO, 2022</td><td>Institution</td><td>9</td><td>A study by the United Nations' International Civil Aviation Organization (ICAO)</td><td>9</td><td>9</td></tr><tr><td>International Energy Agency, 2021</td><td>Institution</td><td>1</td><td>International Energy Agency (IEA)</td><td>1</td><td>1</td></tr><tr><td>Malina et al., 2022</td><td>Institution</td><td>6</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>Mission Possible Partnership (MPP)</td><td>2</td><td>2</td></tr><tr><td>Planés et al., 2021 and Salgas et al., 2023</td><td>Academia</td><td>1</td><td>Collection of studies by ISAE-SUPAERO</td><td>1</td><td>1</td></tr><tr><td>Supaero Decarbo and The Shift Project, 2021</td><td>Think tank/NGO</td><td>3</td><td>A joint publication by an NGO and a think-tank</td><td>3</td><td>3</td></tr></tbody></table>

Note: <sup>(1)</sup> Three scenarios in Bergero et al. (2023) presume the unavailability of SAF and thus include zero volume of SAF. We omit these three observations because that subsample is too small to gain valuable statistical insights on the case where no SAF is available. <sup>(2)</sup> We discard two observations in Danicourt et al. (2023) and one in Graver et al. (2022) that do not explicitly report the posted 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 arremate the future consumption of SAF (see Bardon and Massol, 2023), and controlling for that possible substitution is critical to evaluating SAF's impacts on future unabated emissions.
<figure><img src="image_17.png" /><figcaption>Figure: Projected emissions of GHG in 2050 and corresponding SAF quantities</figcaption></figure>

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# Methodology

The usual production function approach...

$$Q = F(JetFuel, SAF, H_2 ... )$$

... is associated with an imput requirement function

$$JetFuel = G(Q, SAF, H_2 ... )$$

Assuming a Cobb Douglas function, it yields this log-linear specification:

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

## Results (1): simple regression model

### Finding #1
=> Signs of unmodelled nonlinearities

67 sample

Log (Emissions)

Log(SAF) -0.562 ***
(0.160)

Log(Market_size) 1.795 ***
(0.261)

No_H₂_Elec 0.277 *
(0.162)

Log(H₂_Elec) -0.145 *
(0.078)

Non_CO₂ 1.671 ***
(0.224)

Freight -1.206 ***
(0.289)

ILUC 0.306
(0.225)

C 1.581 **
(0.670)

Observations 67
Adjusted R-squared 0.617
S.E. of regression 0.563
Log-likelihood -52.326
J-B (p-value) 0.286 (0.867)
RESET(2) (p-value) 7.637 (0.008)
<figure><img src="image_21.png" /></figure>

# Results: Nonlinearity?

## Finding #2

Presence of a marked structural break in the relation between SAF & emissions

## Tentative explanation

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 SAF).

<table><thead><tr><th></th><th>67 sample</th><th>Log (Emissions)</th></tr></thead><tbody><tr><td>Log(SAF)</td><td>-0.147<br>(0.096)</td><td></td></tr><tr><td>Log(SAF)×D ≥ Th</td><td>-0.329<br>(0.058)</td><td>***</td></tr><tr><td>Log(Market_size)</td><td>2.028<br>(0.199)</td><td>***</td></tr><tr><td>No_H₂_Elec</td><td>0.296<br>(0.136)</td><td>**</td></tr><tr><td>Log(H₂_Elec)</td><td>-0.194<br>(0.076)</td><td>**</td></tr><tr><td>Non_CO₂</td><td>1.845<br>(0.191)</td><td>***</td></tr><tr><td>Freight</td><td>-1.305<br>(0.222)</td><td>***</td></tr><tr><td>ILUC</td><td>0.371<br>(0.177)</td><td>**</td></tr><tr><td>C</td><td>0.428<br>(0.648)</td><td></td></tr><tr><td>Threshold value for SAF (in EJ)</td><td>10.664</td><td></td></tr><tr><td>Wald test (H₀: no structural change)<br>(p-value)</td><td>31.936</td><td>(0.000)</td></tr><tr><td>Observations</td><td>67</td><td></td></tr><tr><td>Adjusted R-squared</td><td>0.726</td><td></td></tr><tr><td>S.E. of regression</td><td>0.476</td><td></td></tr><tr><td>Log likelihood</td><td>-40.541</td><td></td></tr><tr><td>J-B (p-value)</td><td>0.337</td><td>(0.844)</td></tr><tr><td>RESET(2) (p-value)</td><td>1.059</td><td>(0.308)</td></tr></tbody></table>
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# Results
## The type of SAF

### Finding #3 (puzzling)

The type of SAF used has an impact on residual emissions

<table><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>
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# Results: accounting for authorship bias

## Method

* Dummy variables to flag scenarios authored by industry, institution and think tanks/NGOs

We use academic studies as a reference and supplement the regression model with:

- 3 authorship_dummies

- 3 (authorship_dummies x log (SAF))

## Results

confirm the presence of authorship bias in the decarbonization power of SAF!

### Wald tests of the authorship impacts

<table><thead><tr><th></th><th colspan="2" style="text-align: center;">67 sample</th></tr><tr><th></th><th style="text-align: center;">Test statistics</th><th style="text-align: center;">(p-value)</th></tr></thead><tbody><tr><td>H₀: no joint authorship bias<br>(i.e., (I)=0, (III)=0, (V)=0)</td><td style="text-align: right;">1.455</td><td style="text-align: right;">(0.693)</td></tr><tr><td>H₀: no authorship bias in industry<br>scenarios<br>(i.e., (I)=0, (II)=0)</td><td style="text-align: right;"><strong>13.435***</strong></td><td style="text-align: right;"><strong>(0.001)</strong></td></tr><tr><td>H₀: no authorship bias in institutional<br>scenarios<br>(i.e., (III)=0, (IV)=0)</td><td style="text-align: right;">2.289</td><td style="text-align: right;">(0.318)</td></tr><tr><td>H₀: no authorship bias in scenarios<br>produced by think tanks and NGOs<br>(i.e., (V)=0, (VI)=0)</td><td style="text-align: right;">0.630</td><td style="text-align: right;">(0.730)</td></tr></tbody></table>
<figure><img src="image_24.png" /></figure>

<table><thead><tr><th></th><th colspan="2">Log (Emissions)</th></tr></thead><tbody><tr><td>Log(SAF)</td><td>-0.162<br>(0.119)</td><td></td></tr><tr><td>Log(SAF)×D ≥ Th</td><td>-0.319<br>(0.061)</td><td>***</td></tr><tr><td>Log(Market_size)</td><td>2.010<br>(0.208)</td><td>***</td></tr><tr><td>No_H₂_Elec</td><td>0.293<br>(0.135)</td><td></td></tr><tr><td>Log(H₂_Elec)</td><td>-0.188<br>(0.079)</td><td>**</td></tr><tr><td>Non_CO₂</td><td>1.825<br>(0.214)</td><td>***</td></tr><tr><td>Freight</td><td>-1.297<br>(0.254)</td><td>***</td></tr><tr><td>ILUC</td><td>0.362<br>(0.175)</td><td>**</td></tr><tr><td>C</td><td>0.515<br>(0.861)</td><td></td></tr><tr><td>Industry (I)</td><td>-0.161<br>(0.228)</td><td></td></tr><tr><td>Log(SAF)×Industry (II)</td><td>-</td><td></td></tr><tr><td>Institution (III)</td><td>0.048<br>(0.193)</td><td></td></tr><tr><td>Log(SAF)×Institution (IV)</td><td>-</td><td></td></tr><tr><td>Think_Tank_NGO (V)</td><td>-0.051<br>(0.345)</td><td></td></tr><tr><td>Log(SAF)×Think_Tank_NGO (VI)</td><td>-</td><td></td></tr><tr><td>Observations</td><td>67</td><td></td></tr><tr><td>Adjusted R-squared</td><td>0.717</td><td></td></tr><tr><td>S.E. of regression</td><td>0.484</td><td></td></tr><tr><td>Log likelihood</td><td>-39.893</td><td></td></tr><tr><td>J-B (p-value)</td><td>0.186</td><td>(0.911)</td></tr><tr><td>RESET(2) (p-value)</td><td>1.648</td><td>(0.205)</td></tr></tbody></table>

Demand management is little considered in aviation decarbonization pathways

Yet, it is:

* 4 times more effective than SAF

* 8 to 15 times more effective than H₂ or electricity

A framing issue?
<figure><img src="image_25.png" /></figure>

Discussion

Other framing biases

* Non-CO₂ effects

* H₂ and electricity use

* Projected efficiency gains?
  - The omitted assumption in most pathways
<figure><img src="image_26.png" /></figure>

Conclusions

1. A consensus: the decarbonization impact of SAF
Yes, but only above a certain threshold !

2. A posited stronger decarbonizing power for e-fuels
Remaining question: Why?

3. Presence of marked authorship biases
And signs of other biases