The Economics of Sustainable Aviation Fuels: Market Trends and Policy Challenges in Selected EU Countries
Abstract
1. Introduction
1.1. Context and Problem Definition
1.2. Research Questions and Objectives
1.3. Contribution and Structure of the Paper
2. Literature Review
2.1. Sustainable Aviation Fuels: Definition, Benefits and Technological Context
2.2. Policy and Regulatory Frameworks in the EU and International Context
3. Materials and Method
3.1. Sampling and Data Collection
3.2. Measurement Model
- Yt—The time series value at a specific time (for example, biofuel infrastructure capacity),
- 1, 2,…, p—autoregressive coefficients (AR component),
- et—error or balance at the time,
- θ1, θ2,…—moving average coefficients (MA component),
- p ir q—model lag degrees indicating how many previous values and errors should be included in the model.
- Yt—a vector of all the values of the variables in period t, that is [Y1,t,Y2,t,…,Yp,t],
- C—constant vector (each variable can be assigned a constant),
- A1,A2,…,Ak—autoregression coefficients, which define how previous periods Yt − 1,Yt − 2,…,Yt − kY affects existing values Yt. Each A is an element of a matrix representing the interdependence of different variables, k—the number of lags (for example, k = 1 would mean that only first-degree values are used),
- et—an error vector containing residuals, i.e., inaccuracies that cannot be explained by past data.
- Y—biofuel infrastructure capacity,
- X1,X2,…,Xn—independent variables (number of flights, number of passengers, GDP),
- β0—constant,
- β1, β2,…, βn—regression coefficients showing the extent to which each variable influences the dependent variable,
- e\epsilon—error.
3.3. Research Structure and Organization
- Introduction—Definition of the research problem, formulation of research objectives, and justification of the study’s relevance in the context of the EU aviation sector.
- Literature Review—Critical overview of existing scientific works, policy documents, and statistical reports. This section provides the contextual foundation by summarizing prior findings and outlining existing regulatory and economic challenges associated with sustainable aviation fuels (SAFs).
- Materials and Methods—Description of data sources, econometric techniques (ARIMA, VAR, regression analysis), and analytical framework applied. This section also specifies the criteria for selecting indicators and the procedures used for data harmonization.
- Results—Presentation of the econometric outputs, including forecasts of SAF development trajectories and the statistical relationship between national GDP and SAF deployment.
- Discussion—Comparative interpretation of the findings in light of previous studies and regulatory frameworks. This section emphasizes the implications of policy design, institutional coherence, and economic feasibility for SAF market development.
- Conclusions—Synthesis of the study’s key insights. The section addresses research questions, highlights the theoretical and practical contributions, and formulates recommendations for policymakers, with directions for future research.
4. Results
4.1. Descriptive Part
4.2. ARIMA Model Results
4.3. VAR Model Results
4.4. Regression Analysis
5. Discussion
6. Conclusions
7. Limitations and Future Research
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Komarova, V.; Čižo, E.; Balodis, J.; Kokarēviča, A.; Ruža, O.; Kudiņš, J. Development of transport infrastructure and its impact on territorial production. Entrep. Sustain. Issues 2023, 10, 338–356. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Raman, R.; Gunasekar, S.; Dávid, L.D.; Rahmat, A.F.; Nedungadi, P. Aligning sustainable aviation fuel research with sustainable development goals: Trends and thematic analysis. Energy Rep. 2024, 12, 2642–2652. [Google Scholar] [CrossRef] [Scilit]
- Kirmizi, M.; Aygun, H.; Turan, O. Energetic and exergetic metrics of a cargo aircraft turboprop propulsion system by using regression method for dynamic flight. Energy 2024, 296, 131153. [Google Scholar] [CrossRef] [Scilit]
- Champeecharoensuk, A.; Dhakal, S.; Chollacoop, N.; Phdungsilp, A. Greenhouse gas emissions trends and drivers insights from the domestic aviation in Thailand. Heliyon 2024, 10, e24206. [Google Scholar] [CrossRef] [Scilit]
- Lau, J.I.C.; Wang, Y.S.; Ang, T.; Seo, J.C.F.; Khadaroo, S.N.B.A.; Chew, J.J.; Lup, A.N.K.; Sunarso, J. Emerging technologies, policies and challenges toward implementing sustainable aviation fuel (SAF). Biomass Bioenergy 2024, 186, 107277. [Google Scholar] [CrossRef] [Scilit]
- Bag, S.; Routray, S.; Rahman, M.S.; Shrivastav, S.K. Investigate the effect of green hydrogen supply chain integration on supply chain resilience: Organization information processing theory perspective. Int. J. Prod. Econ. 2025, 284, 109613. [Google Scholar] [CrossRef] [Scilit]
- Okunevičiūtė Neverauskienė, L.; Dirma, V.; Tvaronavičienė, M.; Danilevičienė, I. Assessing the Role of Renewable Energy in the Sustainable Economic Growth of the European Union. Energies 2025, 18, 760. [Google Scholar] [CrossRef] [Scilit]
- Sabauri, L.; Kvatashidze, N. Sustainability reporting issues. Entrep. Sustain. Issues 2023, 11, 282–289. [Google Scholar] [CrossRef] [Scilit]
- Thummala, V.; Hiremath, R.B. Green aviation in India: Airline’s implementation for achieving sustainability. Clean. Responsible Consum. 2022, 7, 100082. [Google Scholar] [CrossRef] [Scilit]
- Okunevičiūtė Neverauskienė, L.; Tvaronavičienė, M.; Linkevičius, D. Energy Efficiency, CO2 Emission Reduction, and Real Estate Investment in Northern Europe: Trends and Impact on Sustainability. Buildings 2025, 15, 1195. [Google Scholar] [CrossRef] [Scilit]
- Wan, K.; Fan, Y.; Liu, B.-Y. Energy supply resilience under low-carbon transition: Long-term multi-national assessment. Energy 2025, 326, 136311. [Google Scholar] [CrossRef] [Scilit]
- Chen, H.; Yan, H.; Gong, K.; Geng, H.; Yuan, X.C. Assessing the business interruption costs from power outages in China. Energy Econ. 2022, 105, 105757. [Google Scholar] [CrossRef] [Scilit]
- Han, S.; Bin Kamaruddin, B.H.; Shi, X.; Zhu, J. Achieving energy resilience: Studying renewable and fossil fuel energy generation drivers and COPE-28 pathways of China. Energy Strategy Rev. 2025, 58, 101669. [Google Scholar] [CrossRef] [Scilit]
- Shafiei, K.; Seifi, A.; Hagh, M.T. A novel multi-objective optimization approach for resilience enhancement considering integrated energy systems with renewable energy, energy storage, energy sharing, and demand-side management. J. Energy Storage 2025, 115, 115966. [Google Scholar] [CrossRef] [Scilit]
- Richie, H. What Share of Global CO2 Emissions come from Aviation?—Our World in Data 2024. Available online: https://ourworldindata.org/global-aviation-emissions (accessed on 1 April 2025).
- Gössling, S.; Humpe, A. The global scale, distribution and growth of aviation: Implications for climate change. Glob. Environ. Change 2020, 65, 102194. [Google Scholar] [CrossRef] [Scilit]
- Lv, X.; Zhao, C.; Yan, N.; Ma, X.; Feng, S.; Shuai, L. Sustainable aviation fuel (SAF) from lignin: Pathways, catalysts, and challenges. Bioresour. Technol. 2025, 419, 132039. [Google Scholar] [CrossRef] [Scilit]
- Yu, L.; Huang, M.M.; Jiang, S.; Wang, C.; Wu, M. Unmanned aircraft path planning for construction safety inspections. Autom. Constr. 2023, 154, 105005. [Google Scholar] [CrossRef] [Scilit]
- Xu, Y.; Zhang, Y.; Deng, X.; Lee, S.Y.; Wang, K.; Li, L. Bibliometric analysis and literature review on sustainable aviation fuel (SAF): Economic and management perspective. Transp. Policy 2025, 162, 296–312. [Google Scholar] [CrossRef] [Scilit]
- Zhang, R.; Cao, J.; Wang, W.; Tan, E.; Zhu, R.; Chen, W.; Zhang, Y. Research on design strategies and sensing applications of energy storage system based on renewable methanol fuel. Results Eng. 2023, 20, 101439. [Google Scholar] [CrossRef] [Scilit]
- Li, Y.; Guan, B.; Guo, J.; Chen, Y.; Ma, Z.; Zhuang, Z.; Zhu, C.; Dang, H.; Chen, L.; Shu, K.; et al. Renewable synthetic fuels: Research progress and development trends. J. Clean. Prod. 2024, 450, 141849. [Google Scholar] [CrossRef] [Scilit]
- Cui, Q.; Jia, Z.; Li, Y. Path analysis of using hydrogen energy to reduce greenhouse gas emissions in global aviation. Cell Rep. Sustain. 2024, 1, 100133. [Google Scholar] [CrossRef] [Scilit]
- Cui, Q.; Chen, B. Cost-benefit analysis of using sustainable aviation fuels in South America. J. Clean. Prod. 2024, 435, 140556. [Google Scholar] [CrossRef] [Scilit]
- Aksoy, H.; Domene, M.G.; Loganathan, P.; Blakey, S.; Zea, E.; Vinuesa, R.; Otero, E. Case study on SAF emissions from air travel considering emissions modeling impact. Transp. Res. Interdiscip. Perspect. 2025, 29, 101341. [Google Scholar] [CrossRef] [Scilit]
- Wang, B.; Ting, Z.J.; Zhao, M. Sustainable aviation fuels: Key opportunities and challenges in lowering carbon emissions for aviation industry. Carbon Capture Sci. Technol. 2024, 13, 100263. [Google Scholar] [CrossRef] [Scilit]
- Bardon, P.; Massol, O.; Thomas, A. Greening aviation with sustainable aviation fuels: Insights from decarbonization scenarios. J. Environ. Manag. 2025, 374, 123943. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Wei, M.; Jiang, Z.; Pandey, P.; Liu, M.; Li, R.; O’Neill, Z.; Dong, B.; Hamdy, M. Energy resilience in the built environment: A comprehensive review of concepts, metrics, and strategies. Renew. Sustain. Energy Rev. 2025, 210, 115258. [Google Scholar] [CrossRef] [Scilit]
- Zubi, G.; Kuhn, M.; Makridis, S.; Coutinho, S.; Dorasamy, S. Aviation sector decarbonization within the hydrogen economy—A UAE case study. Energy Policy 2025, 198, 114520. [Google Scholar] [CrossRef] [Scilit]
- European Commission. Reducing Emissions from Aviation—European Commission 2025. Available online: https://climate.ec.europa.eu/eu-action/transport/reducing-emissions-aviation_en (accessed on 1 April 2025).
- Ovaere, M.; Proost, S. Cost-effective reduction of fossil energy use in the European transport sector: An assessment of the Fit for 55 Package. Energy Policy 2022, 168, 113085. [Google Scholar] [CrossRef] [Scilit]
- Gnadt, A.R.; Speth, R.L.; Sabnis, J.S.; Barrett, S.R.H. Technical and environmental assessment of all-electric 180-passenger commercial aircraft. Prog. Aerosp. Sci. 2019, 105, 1–30. [Google Scholar] [CrossRef] [Scilit]
- Sgouridis, S.; Kimmich, C.; Solé, J.; Černý, M.; Ehlers, M.H.; Kerschner, C. Visions before models: The ethos of energy modeling in an era of transition. Energy Res. Soc. Sci. 2022, 88, 102497. [Google Scholar] [CrossRef] [Scilit]
- EASA SAF Market|EASA. 2025. Available online: https://www.easa.europa.eu/en/domains/environment/eaer/sustainable-aviation-fuels/saf-market#current-and-future-saf-production-capacity (accessed on 15 April 2025).
- Gupta, S.; Kumar, R.; Kumar, A. Green hydrogen in India: Prioritization of its potential and viable renewable source. Int. J. Hydrog. Energy 2024, 50, 226–238. [Google Scholar] [CrossRef] [Scilit]
- Watson, M.J.; Machado, P.G.; da Silva, A.V.; Saltar, Y.; Ribeiro, C.O.; Nascimento, C.A.O.; Dowling, A.W. Sustainable aviation fuel technologies, costs, emissions, policies, and markets: A critical review. J. Clean. Prod. 2024, 449, 141472. [Google Scholar] [CrossRef] [Scilit]
- IATA IATA—Sustainable Aviation Fuel (SAF). 2025. Available online: https://www.iata.org/en/programs/sustainability/sustainable-aviation-fuels/ (accessed on 8 April 2025).
- Okuneviciute Neverauskiene, L.; Klepone, D. Empirical evidence on the startup growth in the Baltic Region high tech land-scape. Transform. Bus. Econ. 2024, 23, 1164–1191. [Google Scholar]
- Nagralel, P. Global Sustainable Aviation Fuel Market Overview. 2025. Available online: https://www.marketresearchfuture.com/reports/sustainable-aviation-fuel-market-11965?utm_term=&utm_campaign=&utm_source=adwords&utm_medium=ppc&hsa_acc=2893753364&hsa_cam=20817078453&hsa_grp=156383738099&hsa_ad=683078494771&hsa_src=g&hsa_tgt=dsa-21894277868 (accessed on 14 April 2025).
- Bazienė, K.; Gargasas, J. Sustainable innovative technology solutions for the energy sector. Entrep. Sustain. Issues 2023, 11, 215–226. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- Igielski, M. Project management in the renewable energy sources industry in Poland—Identification of conditions and barriers. Entrep. Sustain. Issues 2023, 10, 135–151. [Google Scholar] [CrossRef] [Scilit]
- Zhong, W.; Zhai, D.; Xu, W.; Gong, W.; Yan, C.; Zhang, Y.; Qi, L. Accurate and efficient daily carbon emission forecasting based on improved ARIMA. Appl. Energy 2024, 376, 124232. [Google Scholar] [CrossRef] [Scilit]
- Al-lami, A.; Török, Á. Regional forecasting of driving forces of CO2 emissions of transportation in Central Europe: An ARIMA-based approach. Energy Rep. 2025, 13, 1215–1224. [Google Scholar] [CrossRef] [Scilit]
- İnan, T.T. Post-Covid air transport recovery: A multidimensional analysis of European airports under eurocontrol. Case Stud. Transp. Policy 2025, 20, 101444. [Google Scholar] [CrossRef] [Scilit]
- Boonen, T.J.; Chen, Y. VAR Model with Sparse Group LASSO for Multi-population Mortality Forecasting. Int. J. Forecast. 2025, 42, 259–280. [Google Scholar] [CrossRef] [Scilit]
- Akyuz, E.; Tezer, T. Techno-economic feasibility and regression analysis of green hydrogen production from solar and wind energy in Türkiye. Int. J. Hydrog. Energy 2025, 142, 1184–1195. [Google Scholar] [CrossRef] [Scilit]
- Singh, R.; Umrao, R.K.; Ahmad, M.; Ansari, M.K.; Sharma, L.K.; Singh, T.N. Prediction of geomechanical parameters using soft computing and multiple regression approach. Measurement 2017, 99, 108–119. [Google Scholar] [CrossRef] [Scilit]
- Pan American Finance. Global Sustainable Aviation Fuel Report. 2025. Available online: https://panamericanfinance.com/insights/energy-transition/global-saf-report-2024/regional-markets-overview-saf24/france-saf24/ (accessed on 15 April 2025).
- Pan American Finance. Germany SAF24|Pan American Finance. 2025. Available online: https://panamericanfinance.com/insights/energy-transition/global-saf-report-2024/regional-markets-overview-saf24/germany-saf24/ (accessed on 15 April 2025).
- International Trade Administration. Germany Sustainable Aviation Industry. 2024. Available online: https://www.trade.gov/market-intelligence/germany-sustainable-aviation-industry (accessed on 15 April 2025).
- Pan American Finance. Denmark SAF24|Pan American Finance. 2025. Available online: https://panamericanfinance.com/insights/energy-transition/global-saf-report-2024/regional-markets-overview-saf24/denmark-saf24/ (accessed on 15 April 2025).
- Pan American Finance. Spain SAF24|Pan American Finance. 2025. Available online: https://panamericanfinance.com/insights/energy-transition/global-saf-report-2024/regional-markets-overview-saf24/spain-saf24/ (accessed on 15 April 2025).
- Italian Civil Aviation Authority. A Roadmap for Sustainable Aviation Fuels in Italy. Enac Path for the Definition of SAF Policy; ENAC: Roma, Italy, 2024. [Google Scholar]
- Pan American Finance. Italy SAF24|Pan American Finance. 2025. Available online: https://panamericanfinance.com/insights/energy-transition/global-saf-report-2024/regional-markets-overview-saf24/italy-saf24/ (accessed on 16 April 2025).
- Raman, R.; Sreenivasan, A.; Kulkarni, N.V.; Suresh, M.; Nedungadi, P. Analyzing the contributions of biofuels, biomass, and bioenergy to sustainable development goals. iScience 2025, 28, 112157. [Google Scholar] [CrossRef] [Scilit] [PubMed]
- McCausland, R. ReFuelEU Aviation Handbook; IATA: Montreal, QC, Canada, 2024. [Google Scholar]
- Dobruszkes, F.; Decroly, J.M.; Suau-Sanchez, P. The monthly rhythms of aviation: A global analysis of passenger air service seasonality. Transp. Res. Interdiscip. Perspect. 2022, 14, 100582. [Google Scholar] [CrossRef] [Scilit]
- Eurostat. [avia_paoc] Air Passenger Transport by Type of Schedule, Transport Coverage and Country. 2025. Available online: https://ec.europa.eu/eurostat/databrowser/view/avia_paoc__custom_16310774/default/table?lang=en (accessed on 18 April 2025).
- Eurostat. [avia_tf_acc] Aircraft Traffic Data by Reporting Country. 2025. Available online: https://ec.europa.eu/eurostat/databrowser/view/avia_tf_acc__custom_16297747/default/table?lang=en (accessed on 18 April 2025).
- Midilli, A.; Aluç, A.E.; Şengüler, F.; Köse, A. Sustainable aviation fuel production. Ref. Modul. Earth Syst. Environ. Sci. 2025, 6, 660–689. [Google Scholar] [CrossRef] [Scilit]
- Wei, M.; Xiong, Y.; Sun, B. Spatial effects of urban economic activities on airports’ passenger throughputs: A case study of thirteen cities and nine airports in the Beijing-Tianjin-Hebei region, China. J. Air Transp. Manag. 2025, 125, 102765. [Google Scholar] [CrossRef] [Scilit]
- World Bank Group. GDP (Current US$)—Spain, France, Italy, Denmark, Germany|Data. 2025.
- Cai, Y.; Zhang, Y.; Zhang, A. Oil price shocks and airlines stock return and volatility—A GFEVD analysis. Econ. Transp. 2025, 41, 100396. [Google Scholar] [CrossRef] [Scilit]
- Liang, Y.Y.; Shahabuddin, M.; Ahmed, S.F.; Tan, J.X.; Ali, S.M. Optimizing sustainable aviation fuel supply chains: Challenges, mitigation strategies and modeling advances. Fuel 2025, 402, 135972. [Google Scholar] [CrossRef] [Scilit]
- Churikanova, O.; Pilova, D.; Giordano, B.; Piccinetti, L.; Amoruso, M. Circular economy and smart specialisation business strategies: The Dnipropetrovsk region case. Insights Into Reg. Dev. 2025, 7, 109–130. [Google Scholar] [CrossRef] [Scilit]











| 1. Identification of the problem and definition of the context. |
| Objective: to define the research problem and indicate the need to assess the economic and political aspects of SAF in selected EU countries. |
| 2. Literature review. |
| Objective: to review existing research on SAF, regulatory frameworks, and economic flexibility. |
| 3. Data collection and preparation. |
| Objective: to collect secondary data. |
| 4. Econometric modeling. |
| Objective: to apply ARIMA and VAR models for forecasting SAF infrastructure trajectories and regression analysis for determining economic relationships. |
| 5. Results interpretation and discussion. |
| Objective: Interpret model outcomes in the context of EU policy frameworks and economic literature. |
| 6. Conclusions and policy recommendations. |
| Objective: to summarize conclusions, identify limitations, and suggest directions for future research. |
| Metric | Value | p-Value | Interpretation |
|---|---|---|---|
| Multiple R | 0.969 | – | Very strong positive correlation between the independent and dependent variables |
| R2 | 0.939 (93.99%) | – | Model explains 93.99% of the variation in biofuel infrastructure capacity |
| Adjusted R2 | 0.904 | – | Adjusted for number of predictors—still shows very good explanatory power |
| Standard Error | 162.95 MW | – | Typical deviation of observed values from the model’s predictions |
| F-statistic | 26.06 | – | Model is significant (F > 1 and low p for overall fit) |
| Significance F (p) | – | 0.0018 | Since p < 0.05, the overall model fit is statistically significant |
| Intercept (constant) | 1638.34 MW | 0.607 | Not statistically significant (p > 0.05); intercept could be omitted in a refined model |
| Predictor | Coefficient | p-Value | Interpretation |
|---|---|---|---|
| Passenger Numbers | 5.80589 × 10−6 MW | 0.135 | Each additional passenger adds ~0.0000058 MW; not statistically significant (p > 0.05) |
| Flight Counts | –0.001149 MW | 0.071 | Each additional flight reduces capacity by ~0.001149 MW; marginal effect, not fully significant |
| GDP (trillion units) | 1341.24 MW | 0.011 | Each extra trillion in GDP adds ~1341 MW; statistically significant (p < 0.05), strongest effect |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
Share and Cite
Okunevičiūtė Neverauskienė, L.; Sikorskaitė-Narkun, E.; Tvaronavičienė, M. The Economics of Sustainable Aviation Fuels: Market Trends and Policy Challenges in Selected EU Countries. Sustainability 2026, 18, 127. https://doi.org/10.3390/su18010127
Okunevičiūtė Neverauskienė L, Sikorskaitė-Narkun E, Tvaronavičienė M. The Economics of Sustainable Aviation Fuels: Market Trends and Policy Challenges in Selected EU Countries. Sustainability. 2026; 18(1):127. https://doi.org/10.3390/su18010127
Chicago/Turabian StyleOkunevičiūtė Neverauskienė, Laima, Eglė Sikorskaitė-Narkun, and Manuela Tvaronavičienė. 2026. "The Economics of Sustainable Aviation Fuels: Market Trends and Policy Challenges in Selected EU Countries" Sustainability 18, no. 1: 127. https://doi.org/10.3390/su18010127
APA StyleOkunevičiūtė Neverauskienė, L., Sikorskaitė-Narkun, E., & Tvaronavičienė, M. (2026). The Economics of Sustainable Aviation Fuels: Market Trends and Policy Challenges in Selected EU Countries. Sustainability, 18(1), 127. https://doi.org/10.3390/su18010127

