As aviation's contribution to anthropogenic climate change is increasing, the sector aims at reducing its climate effect in accordance with international agreements. The strong and variable non-CO2 effects are complex, making reliable climate effect quantification a necessary first step. To support this, we develop the easy-to-use first-order climate effect estimator for single flights FlightClim v1.0. The tool estimates the flight-specific climate effect with a simplified calculation model, without requiring detailed information on exact routing, amount of fuel burn, or weather conditions. For this purpose, we first analyze a global flight dataset containing detailed trajectories, associated flight emissions, and climate responses. Similar flights are grouped into clusters, and regression formulas are derived to estimate the Average Temperature Response over 100 years (ATR100) for CO2 and non-CO2 effects. To prevent abrupt changes at cluster boundaries, we apply linear smoothing as postprocessing. Second, we compare a Multiple and a Symbolic Regression approach, where choice of method depends on the specific application as they differ in effort and complexity. The two approaches offer similar estimation quality, which shows that the errors are based on the database, the regression parameters as well as the regression error metric and the physical processes rather than on too easy regression models. Both methods are designed for climate footprint assessments due to their simplicity though not suitable for policy measures. Emission trading or monitoring and reporting systems instead require detailed weather and route data to incentivize operational non-CO2 mitigation. Compared to previous studies, our approach relies on a globally representative and considerably larger dataset covering more aircraft types, including most commercial airliners. In addition it improves precision through smoothed clustering and a dedicated parameterization of aircraft size influence on the contrail effects. The resulting climate effect functions are embedded into the Excel-based tool FlightClim v1.0, which implements the formulas of the Multiple Regression approach due to slight qualitative advantages. Requiring only aircraft size and origin-destination airports as input, FlightClim estimates climate effect for CO2, H2O, NOx emissions and contrail-induced cloudiness. It includes per seat allocation and supports different climate metrics.

Aviation emissions are responsible for climate impacts through both carbon dioxide emissions and other emissions, in particular, of nitrogen oxides, water vapour, particulates, and contrail formation. In December 2022, the European Commission, Parliament and Council agreed to revise the European Union Emission Trading System for aviation. As such, from January 1, 2025, aircraft operators must monitor non-carbon dioxide climate effects, but suitable metrics for climate impact, handling of uncertainties and practical implementation are still under discussion or at least heavily debated. In this perspective, we propose a procedure for how to include non-carbon dioxide aviation effects into political frameworks. The main goal must be to create incentives for climate change mitigation for the aviation industry. Uncertainties in atmospheric processes need to be appropriately incorporated to minimise risk, and pilot projects are required to test implementation capabilities. Analysing risk, employing consistent monitoring, and determining economic effects will provide scientific grounds for including non-carbon dioxide effects in the European Union Emission Trading System. For the inclusion of aviation non-carbon dioxide climate effects in the European Union Emission Trading System, uncertainties in atmospheric processes can be addressed through careful calculation and risk assessment.

To comply with defined ambitious climate goals, technical and operational improvements are required to reduce the climate impact of aviation. Non-CO2 emissions contribute to a majority of aviation’s total effect and a reduction of the associated climate impact is often associated with an increase in fuel consumption and CO2 effects. These trade-offs typically result in higher operating costs, which create a lack of economic incentive to pursue such measures, ultimately slowing implementation. Therefore, this study investigates implications from a market-based policy scheme designed to support the implementation of climate mitigation strategies by internalizing non-CO2 effects. An extension of the EU ETS to account for non-CO2 effects in terms of CO2 equivalents is modeled and resulting climate mitigation potentials and operating cost changes are analyzed. The results demonstrate the suitability of an extension of the existing accounting to non-CO2 effects as this reduces cost in relation to the reference without measure implementation. Furthermore, benefits of operational climate mitigation measures are demonstrated in comparison to the use of sustainable fuels. Technical efficiency improvements can further help to increase mitigation potentials and reduce operating cost, while reduced accounting shares can help to limit cost and ticket price increase.

This study examines the airline network effects of possible non-CO2 climate effect pricing mechanisms under the European Union Emissions Trading System (EU ETS) on air transport demand and ticket pricing. It is focusing on three regulatory scopes: full coverage of all flights including those in and out of the EEA (European Economic Area), a reduced scope limited to intra-European routes and a departure-allocation scope covering outbound flights only. Ticket price adjustments are analyzed using pre-calculated trajectory optimizations that account for varying weather conditions, particularly regarding the formation of contrails. Contrail-induced cloudiness significantly enhances non-CO2 climate effects increasing costs to different extents depending on the geographical scope of the regulation. Based on these cost increases, demand changes are assessed through passenger preference models that incorporate intra-market dynamics, capturing the competitive and behavioral responses of passengers across different market segments. This analysis provides valuable insights into the interplay between environmental policy and market behavior. It particularly includes mitigation strategies for non-CO2 impacts, airline pricing strategies, and passenger behavior, offering guidance for the design of effective and equitable climate mitigation measures in aviation by quantifying the demand and network effect of these measures.

The climate impact of aviation resulting from both CO_2 emissions and non-CO_2 effects is gaining attention from aviation stakeholders seeking to identify potential mitigation options. There is a need to consolidate the use of assessment methods and climate metrics, which are required to convert aviation non-CO_2 effects into CO_2 -equivalent emissions. We provide an overview of the operational, technological and scenario-based climate impact assessment methods that have been applied in literature as well as considerations and requirements for the choice of climate metric. We propose a four-layer technology climate impact assessment methodology, which includes: (1) the technology parameters, such as entry into service and temporal and spatial network use; (2) the calculation of three-dimensional aircraft trajectories and emission inventories; (3) the calculation of radiative forcing and induced temperature change time series; and (4) the overall climate impact, measured using a climate metric. We recommend two climate metrics that best fulfill the requirements, the Average Temperature Response (ATR100) and the Efficacy-weighted Global Warming Potential (EGWP100), both over 100 years. Additionally, we discuss further steps, such as the understanding of the most sensitive parameters in this approach, how uncertainties can be included to provide robust estimates, aspects of verification and update possibilities for new findings in research.

Abstract. As aviation's contribution to anthropogenic climate change is increasing, the sector aims at reducing its climate effect in accordance with international agreements. The strong and variable non-CO2 effects are complex, making reliable climate effect quantification a necessary first step. To support this, we develop the easy-to-use first-order climate effect estimator for single flights FlightClim v1.0. The tool estimates the flight-specific climate effect with a simplified calculation model, without requiring detailed information on exact routing, amount of fuel burn, or weather conditions. For this purpose, we first analyze a global flight dataset containing detailed trajectories, associated flight emissions, and climate responses. Similar flights are grouped into clusters, and regression formulas are derived to estimate the Average Temperature Response over 100 years (ATR100) for CO2 and non-CO2 effects. To prevent abrupt changes at cluster boundaries, we apply linear smoothing as postprocessing. Second, we compare a Multiple and a Symbolic Regression approach, where choice of method depends on the specific application as they differ in effort and complexity. The two approaches offer similar estimation quality, which shows that the errors are based on the database, the regression parameters as well as the regression error metric and the physical processes rather than on too easy regression models. Both methods are designed for climate footprint assessments due to their simplicity though not suitable for policy measures. Emission trading or monitoring and reporting systems instead require detailed weather and route data to incentivize operational non-CO2 mitigation. Compared to previous studies, our approach relies on a globally representative and considerably larger dataset covering more aircraft types, including most commercial airliners. In addition it improves precision through smoothed clustering and a dedicated parameterization of aircraft size influence on the contrail effects. The resulting climate effect functions are embedded into the Excel-based tool FlightClim v1.0, which implements the formulas of the Multiple Regression approach due to slight qualitative advantages. Requiring only aircraft size and origin-destination airports as input, FlightClim estimates climate effect for CO2, H2O, NOx emissions and contrail-induced cloudiness. It includes per seat allocation and supports different climate metrics.

Abstract. Sustainable aviation fuels (SAFs) reduce CO2 life-cycle emissions and the climate effect of contrail-induced cirrus cloudiness (CiC). In contrast to the CO2 emissions of air traffic, the CiC climate effect varies strongly from flight to flight. Hence, SAF allocation to specific flights can maximise climate mitigation efforts for limited amounts of SAF, especially as long as SAF supply is meagre. In this study, we assess the climate-optimal SAF distribution for the flights departing from Copenhagen Airport (CPH) and determine the potential climate benefit achievable through this measure. The study particularly targets year 2030, when the 6 % SAF mandate by ReFuelEU Aviation is expected to allow large climate benefits through targeted SAF use and time still remains for infrastructural adaptions. For this, we use the AirClim model with a refined SAF parameterisation that considers non-volatile particulate matter (nvPM) reduction dependency on SAF blending ratio. We are allocating SAF to those flights that have the largest CiC climate effect to fuel use ratio in a climatological sense. In our scenario simulations, the additional climate benefit through targeted SAF use is quantified and the tradeoff between larger SAF blending ratios for less flights as a consequence of the limited SAF amount is analysed. Targeted use of the 6 % SAF can enhance the role of CiC regarding climate benefit of SAF from 20 % (uniform use) to almost 50 % (targeted use) when the optimal SAF blending ratio is chosen. In our results for CPH in 2030, this leads to a total additional reduction of 60 kt CO2e. However, the results show large uncertainties and strongly vary with choice of climate metric as well as with focus on long-term or short-term climate goals and depend on the specific flight plan. In addition, we quantify how much of the additional climate benefit would be sacrificed if a semi-optimal flight selection was chosen that potentially could reduce logistical cost by decreasing the number of SAF allocated flight routes. The results of this study set ground for cost-benefit analyses that take into account all airport operations associated with targeted SAF use at Copenhagen Airport.

To date, only CO2 is addressed in the EU aviation Emission Trading System (EU-ETS), which implies that the major part of aviation climate effects, the non-CO2 effects, is not included. An agreement on a revision of the CO2 EU-ETS by the EU trilogue from 2022 now includes monitoring, reporting and verification (MRV) of non-CO2 aviation climate effects starting in 2025. However, the detailed steps towards an inclusion of non-CO2 effects are controversially debated for i) the calculation of aviation CO2 equivalents (CO2e), regarding suitable models as well as the choice of a climate metric and its time horizon, ii) the complexity of the entire system including data requirements, availability and streams generating administrative burden for various parties and iii) the large uncertainties in non-CO2 climate effects and their associated risks. Here, we discuss, analyse and put forward these points aiming at an inclusion of aviation non-CO2 effects into a political framework, as results of a project with the German Environmental Agency and current activities at EU-commision level as part of a EU-tender. In this presentation, we lay out a plan for an MRV system including tasks for monitoring and reporting by aircraft operators as well as verifying by competent autorities. Our work aims at supporting the process towards an EU-wide MRV system and its way to operationalisation. This includes analysis of suitable climate metrics and climate models for CO2e calculations of non-CO2 emissions by aviation. Moreover, the data needed to apply the models for CO2e computation of individual flights is defined. For this, a minimum set of data and a possible extended list of data for both a climatological and a weather-based approach is determined. The more complex solutions can be used to on the one hand increase accuracy of the results and on the other hand allow more incentives for aircraft operators to mitigate climate effects. However, increases in administrative burden have to be considered to maintain acceptance by all parties. Advice on default values for data gaps is provided too, which is needed in case of individually or generally missing data for example due to technical issues or confidentiality. Lastly, uncertainties in the context of non-CO2 aviation effects and their associated risks for the MRV are discussed. The governing overarching goal in this undertaking must always remain the contribution to reaching the Paris Agreement targets through climate change mitigation incentives for the aviation industry.

The implementation of Urban Air Mobility represents a complex challenge in aviation due to the high degree of innovation required across various domains to realize it. From the use of advanced aircraft powered by novel technologies, the management of the air space to enable high density operations, to the operation of vertidromes serving as a start and end point of the flights, Urban Air Mobility paradigm necessitates significant innovation in many aspects of civil aviation as we know it today. In order to understand and assess the many facets of this new paradigm, a Collaborative Agent-Based Simulation is developed to holistically evaluate the System of Systems through the modeling of the stakeholders and their interactions as per the envisioned Concept of Operations. To this end, models of vertidrome air-side operations, unmanned/manned air space management, demand estimation and passenger mode choice, vehicle operator cost and revenues, vehicle design, and fleet management are brought together into a System of Systems Simulation of Urban Air Mobility. Through collaboration, higher fidelity models of each domain can be integrated into a single environment achieving fidelity levels not easily achievable otherwise. Furthermore, the integration enables the capture of cross-domain effects and allows domain-specific studies to be evaluated at a holistic level. This work demonstrates the Collaborative Simulation and the process of building it through the integration of several geographically distributed tools into an Agent-Based Simulation without the need for sharing code.

While different vehicle configurations enter the AAM market, airlines declare different ticket fares for their operations. This research investigates the operating cost of an airline and the economic viability with the announced fare per km rates. For this purpose, three use cases in the metropolitan area of Hamburg showcase representative applications of an AAM system, whereby a flight trajectory model calculates a flight time in each case. The direct operating cost are investigated for each use case individually and are sub-classified in five categories: fee, crew, maintenance, fuel and capital costs. Here, each use case has its own cost characteristics, in which different cost elements dominate. Additionally, a sensitivity analysis shows the effect of a variation of the flight cycles and load factor, that influences the costs as well as the airline business itself. Based on the occurring cost, a profit margin per available seat kilometer lead to a necessary fare per km, that an airline has to charge.

No AccessEngineering NotesNote on the Non-CO2 Mitigation Potential of Hybrid-Electric Aircraft Using “Eco-Switch”Malte Niklaß, Benjamin Lührs and Majed SwaidMalte NiklaßDLR, German Aerospace Center, 21079 Hamburg, Germany, Benjamin LührsDLR, German Aerospace Center, 21079 Hamburg, Germany and Majed SwaidDLR, German Aerospace Center, 21079 Hamburg, GermanyPublished Online:12 Sep 2022https://doi.org/10.2514/1.C036826SectionsRead Now ToolsAdd to favoritesDownload citationTrack citations ShareShare onFacebookTwitterLinked InRedditEmail About References [1] Global Market Forecast. Cities, Airports & Aircraft 2019–2038, Airbus, Blagnac, France, 2019, Chap. 1. Google Scholar[2] Technology Roadmap of the International Air Transport Association, Vol. 4, International Air Transport Assoc., Montreal, 2013, Fig. 1–2. Google Scholar[3] Kharina A. and Rutherford D., Fuel Efficiency Trends for New Commercial Jet Aircraft: 1960 to 2014, International Council on Clean Transportation, Washington, D.C., 2015, Fig. ES-1. Google Scholar[4] Lee D. S., Pitari G., Grewe V., Gierens K., Penner J. E., Petzold A., Prather M. J., Schumann U., Bais A., Berntsen T., Iachetti D., Lim L. L. and Sausen R., “Transport Impacts on Atmosphere and Climate: Aviation,” Atmospheric Environment, Vol. 44, No. 37, 2010, pp. 4678–4734. https://doi.org/10.1016/j.atmosenv.2009.06.005 CrossrefGoogle Scholar[5] Lee D. S., Fahey D. W., Skowron A., Allen M. R., Burkhardt U., Chen Q., Doherty S. J., Freeman S., Forster P. M., Fuglestvedt J., Gettelman A., De León R. R., Lim L. L., Lund M. T., Millar R. J., Owen B., Penner J. E., Pitari G., Prather M. J., Sausen R. and Wilcox L. J., “The Contribution of Global Aviation to Anthropogenic Climate Forcing for 2000 to 2018,” Atmospheric Environment, Vol. 244, Jan. 2021, Paper 117834. https://doi.org/10.1016/j.atmosenv.2020.117834 CrossrefGoogle Scholar[6] Grewe V., Dahlmann K., Flink J., Frömming C., Ghosh R., Gierens K., Heller R., Hendricks J., Jöckel P., Kaufmann S., Kölker K., Linke F., Luchkova T., Lührs B., van Manen J., Matthes S., Minikin A., Niklaß M., Plohr M., Righi M., Rosanka S., Schmitt A., Schumann U., Terekhov I., Unterstrasser S., Vazquez-Navarro M., Voigt C., Wicke K., Yamashita H., Zahn A. and Ziereis H., “Mitigating the Climate Impact from Aviation: Achievements and Results of the DLR WeCare Project,” Aerospace, Vol. 44, No. 3, 2017, pp. 1–50. https://doi.org/10.3390/aerospace4030034 Google Scholar[7] Irvine E. A., Hoskins B. J. and Shine K. P., “The Dependence of Contrail Formation on the Weather Pattern and Altitude in the North Atlantic,” Geophysical Research Letters, Vol. 39, No. 12, 1998, pp. 1–17. https://doi.org/10.1029/2012GL051909 Google Scholar[8] Frömming C., Grewe V., Brinkop S., Jöckel P., Haslerud A., Rosanka S., van Manen J. and Matthes S., “Influence of Weather Situation on Non-CO2 Aviation Climate Effects: the REACT4C Climate Change Functions,” Atmospheric Chemistry and Physics, Vol. 21, No. 11, 2021, pp. 9151–9172. https://doi.org/10.5194/acp-21-9151-2021 CrossrefGoogle Scholar[9] Rosanka S., Frömming C. and Grewe V., “The Impact of Weather Patterns and Related Transport Processes on Aviation’s Contribution to Ozone and Methane Concentrations from NOx Emissions,” Atmospheric Chemistry and Physics, Vol. 20, No. 20, 2020, pp. 12,347–12,361. https://doi.org/10.5194/acp-20-12347-2020 CrossrefGoogle Scholar[10] Spichtinger P., Gierens K., Leiterer U. and Dier H., “Ice Supersaturation in the Tropopause Region over Lindenberg, Germany,” Meteorologische Zeitschrift, Vol. 12, No. 3, 2003, pp. 143–156. https://doi.org/10.1127/0941-2948/2003/0012-0143 CrossrefGoogle Scholar[11] Jensen E. J., Toon O. B., Tabazadeh A., Sachse G. W., Anderson B. E., Chan K. R., Twohy C. W., Gandrud B., Aulenbach S. M., Heymsfield A., Hallett J. and Garry B., “Ice Nucleation Processes in Upper Tropospheric Wave-Clouds During SUCCESS,” Geophysical Research Letters, Vol. 25, No. 9, May 1998, pp. 1363–1366. https://doi.org/10.1029/98GL00299 CrossrefGoogle Scholar[12] Ovarlez J., van Velthoven P., Sachse G., Vay S., Schalger H. and Overlez H., “Comparison of Water Vapor Measurements from POLINAT 2 with ECMWF Analyses in High Humidity Conditions,” Journal of Geophysical Research, Vol. 105, No. D3, Feb. 2005, pp. 3737–3744. 10.1029/1999 JD900954 CrossrefGoogle Scholar[13] Vay S. A., Anderson B. E., Jensen E. J., Sachse G. W., Ovarlez J., Gregory G. L., Nolf S. R., Podolske J. R., Slate T. A. and Sorensen C. E., “Tropospheric Water Vapor Measurements over the North Atlantic During the Subsonic Assessment Ozone and Nitrogen Oxide Experiment (SONEX),” Journal of Geophysical Research, Vol. 105, No. D3, Feb. 2005, pp. 3745–3755. 10.1029/1999 JD901019 CrossrefGoogle Scholar[14] Mannstein H., Spichtinger P. and Gierens K., “A Note on How to Avoid Contrail Cirrus,” Transport Research Part D, Vol. 10, No. 5, Sept. 2005, pp. 421–426. https://doi.org/10.1016/j.trd.2005.04.012 CrossrefGoogle Scholar[15] Lührs B., Niklaß M., Frömming C., Grewe V. and Gollnick V., “Cost-Benefit Assessment of 2D and 3D Climate and Weather Optimized Trajectories,” AIAA Aviation Technology, Integration, and Operations Conference, AIAA Paper 2016-3758, 2016. https://doi.org/10.2514/6.2016-3758 LinkGoogle Scholar[16] Lührs B., Niklaß M., Frömming C., Grewe V. and Gollnick V., “Cost-Benefit Assessment of Climate and Weather Optimized Trajectories for Different North Atlantic Weather Patterns,” ICAS, Vol. 31, June 2018, pp. 1–10. Google Scholar[17] Lührs B., Linke F., Matthes S., Grewe V. and Yin F., “Climate Impact Mitigation Potential of European Air Traffic in a Weather Situation with Strong Contrail Formation,” Aerospace, Vol. 8, No. 50, 2021, pp. 1–14. https://doi.org/10.3390/aerospace8020050 Google Scholar[18] Niklaß M., Lührs B., Dahlmann K., Frömming C., Grewe V. and Gollnick V., “Cost-Benefit Assessment of Climate-Restricted Airspaces as an Interim Climate Mitigation Option,” Journal of Air Transportation, Vol. 25, No. 2, 2017, pp. 27–38. https://doi.org/10.2514/1.D0045 LinkGoogle Scholar[19] Niklaß M., Lührs B., Grewe V., Dahlmann K., Luchkova T., Linke F. and Gollnick V., “Potential to Reduce the Climate Impact of Aviation by Climate Restricted Airspaces,” Transport Policy, Vol. 83, Nov. 2019, pp. 102–110. https://doi.org/10.1016/j.tranpol.2016.12.010 CrossrefGoogle Scholar[20] Niklaß M., Grewe V., Gollnick V. and Dahlmann K., “Concept of Climate-Charged Airspaces: a Potential Policy Instrument for Internalizing Aviation’s Climate Impact of Non-CO2 Effects,” Climate Policy, Vol. 21, No. 8, 2021, pp. 1–20. https://doi.org/10.1080/14693062.2021.1950602 CrossrefGoogle Scholar[21] Matthes S., Grewe V., Dahlmann K., Frömming C., Irvine E., Lim L., Linke F., Lührs B., Owen B., Shine K., Stromatas S., Yamashita H. and Yin F., “A Concept for Multi-Criteria Environmental Assessment of Aircraft Trajectories,” Aerospace, Vol. 4, No. 42, 2017, pp. 1–25. https://doi.org/10.3390/aerospace4030042 Google Scholar[22] Matthes S., Lührs B., Dahlmann K., Grewe V., Linke F., Yin F., Klingman E. and Shine K., “A Concept for Multi-Criteria Environmental Assessment of Aircraft Trajectories,” Aerospace, Vol. 7, No. 126, 2020, pp. 1–25. https://doi.org/10.3390/aerospacexx010005 Google Scholar[23] Grewe V., Champougny T., Matthes S., Frömming C., Brinkop V., Søvde O., Irvine E. A. and Halscheidt L., “Reduction of the Air Traffic’s Contribution to Climate Change: A REACT4C Case Study,” Atmospheric Environment, Vol. 94, Sept. 2014, pp. 616–625. https://doi.org/10.1016/j.atmosenv.2014.05.059 CrossrefGoogle Scholar[24] Grewe V., Frömming C., Matthes S., Brinkop V., Ponater M., Dietmüller S., Jöckel P., Tsati E. and Dahlmann K., “Aircraft Routing with Minimal Climate Impact: The REACT4C Cost Function Modelling Approach,” Geoscientific Model Development Discussions, Vol. 7, No. 1, 2014, pp. 175–201. https://doi.org/10.5194/gmd-7-175-2014 CrossrefGoogle Scholar[25] Yamashita H., Yin F., Grewe V., Jöckel P., Sigrun M., Kern B., Dahlmann K. and Frömming C., “Newly Developed Aircraft Routing Options for Air Traffic Simulation in the Chemistry-Climate Model EMAC 2.53: AirTraf 2.0,” Geoscientific Model Development, Vol. 13, No. 10, 2020, pp. 4869–4890. https://doi.org/10.5194/gmd-13-4869-2020 CrossrefGoogle Scholar[26] Teoh R., Schumann U., Majumdar A. and Stettler M. E. J., “Mitigating the Climate Forcing of Aircraft Contrails by Small-Scale Diversions and Technology Adoption,” Environmental Science Technology, Vol. 54, No. 5, 2020, pp. 2941–2950. https://doi.org/10.1021/acs.est.9b05608 CrossrefGoogle Scholar[27] Pornet C., Gologan C., Vratny P. C., Seitz A., Schmitz O., Isikveren A. T. and Hornung M., “Methodology for Sizing and Performance Assessment of Hybrid Energy Aircraft,” Journal of Aircraft, Vol. 52, No. 1, 2015, pp. 341–352. https://doi.org/10.2514/1.C032716 LinkGoogle Scholar[28] Atanasov G. and Silberhorn D., “Hybrid Aircraft for Improved Off-Design Performance and Reduced Emissions,” AIAA Scitech 2020 Forum, AIAA Paper 2020-0753, 2020. https://doi.org/10.2514/6.2020-0753 LinkGoogle Scholar[29] Wroblewski G. E. and Ansell P. J., “Mission Analysis and Emissions for Conventional and Hybrid-Electric Commercial Transport Aircraft,” Journal of Aircraft, Vol. 56, No. 3, 2019, pp. 1200–1213. https://doi.org/10.2514/1.C035070 LinkGoogle Scholar[30] de Vries R., Brown M. and Vos R., “Preliminary Sizing Method for Hybrid-Electric Distributed-Propulsion Aircraft,” Journal of Aircraft, Vol. 56, No. 6, 2019, pp. 2172–2188. https://doi.org/10.2514/1.C035388 LinkGoogle Scholar[31] Finger D. F., Braun C. and Bill C., “Comparative Assessment of Parallel-Hybrid-Electric Propulsion Systems for Four Different Aircraft,” Journal of Aircraft, Vol. 57, No. 5, 2020, pp. 843–853. https://doi.org/10.2514/1.C035897 LinkGoogle Scholar[32] Isikveren A. T., “Method of Quadrant-Based Algorithmic Nomographs for Hybrid/Electric Aircraft Predesign,” Journal of Aircraft, Vol. 55, No. 1, Jan. 2018, pp. 396–405. https://doi.org/10.2514/1.C034355 LinkGoogle Scholar[33] Nuic A. and Mouillet V., “User Manual for the Base of Aircraft Data (BADA) Family 4,” ECC Technical/Scientific Rept. 12/11/22-58, Eurocontrol, Brussels, Belgium, 2012. Google Scholar[34] Jelinek F., Carlier S. and Smith J., “Advanced Emission Model (AEM3) v1.5—Validation Report,” EEC Rept. EEC/SEE/2004/004, Eurocontrol, Brussels, Belgium, 2004. Google Scholar[35] Dubois D. and Paynter G., “ ‘Fuel Flow Method 2’ for Estimating Aircraft Emissions,” Society of Automotive Engineers (SAE), SAE TP 2006-01-1987, 2006. Google Scholar[36] van Manen J. and Grewe V., “Algorithmic Climate Change Functions for the Use in Eco-Efficient Flight Planning,” Transportation Research Part D: Transport and Environment, Vol. 67, Feb. 2019, pp. 388–405. https://doi.org/10.1016/j.trd.2018.12.016 CrossrefGoogle Scholar[37] Niklaß M., Linke F., Dahlmann K., Grewe V., Matthes S., Plohr M., Maertens S., Wozny F. and Scheelhaase J., “Decision Parameters of an MRV Scheme for Integrating Non-CO2 Aviation Effects into EU ETS,” Climate Change, 2022. Google Scholar[38] Yin F., Grewe V., Gierens K., Linke F., Lau A., Niklaß M., Potthast R., Beckmann B., Keckhut P., Lagrave P.-Y., Celike A., Raper D., Roetger T., Matthes S. and Blakey S., “Research Towards Weather Induced Uncertainties for Contrail Persistence and Mitigation Strategies for Contrail Impact,” International Conference on Transport, Atmosphere and Climate, Vol. 5, June 2022. Google Scholar[39] Fuglestvedt J. S., Shine K. P., Berntsen T., Cook J., Lee D. S., Stenke A., Skeie R. B., Velders G. J. M. and Waitz I. A., “Transport Impacts on Atmosphere and Climate: Metrics,” Atmospheric Environment, Vol. 44, No. 37, 2010, pp. 4648–4677. https://doi.org/10.1016/j.atmosenv.2009.04.044 CrossrefGoogle Scholar[40] Grewe V. and Dahlmann K., “How Ambiguous Are Climate Metrics? And Are We Prepared to Assess and Compare the Climate Impact of New Air Traffic Technologies?” Atmospheric Environment, Vol. 106, 2015, pp. 373–374. CrossrefGoogle Scholar Previous article Next article FiguresReferencesRelatedDetails What's Popular Volume 60, Number 1January 2023 CrossmarkInformationCopyright © 2022 by the authors. Published by the American Institute of Aeronautics and Astronautics, Inc., with permission. All requests for copying and permission to reprint should be submitted to CCC at www.copyright.com; employ the eISSN 1533-3868 to initiate your request. See also AIAA Rights and Permissions www.aiaa.org/randp. PDF Received10 January 2022Accepted18 July 2022Published online12 September 2022

In this study, we present an approach that allows to design efficient UAM fleets and corresponding vertiport networks for specific demand patterns. Therefore, we apply a trajectory-based simulation model that controls the circulation of vehicles in a UAM network. The model allocates vehicles from an unlimited fleet pool to the requested missions, such that the boundary conditions of the optimization are fulfilled. Applying a combination of graph-based optimization and solving integer linear programming problems, a ride matching algorithm is implemented that minimizes empty relocation flights in the network and reduces the fleet size. The results comprise the quantification of a fleet pool, defined by fleet size, fleet mix, and starting positions in the vertiport network. We analyze a set of 20 vertiports in the City of Hamburg, Germany, regarding the local peak loads of parking positions that are needed for battery charging and waiting periods of unoccupied vehicles. The results show that the reduction of battery charging time has a significant impact on fleet size, which affects the minimum ground infrastructure requirements as well. Finally, the fleet analysis shows that average load factors of 45% are feasible at fleet sizes with varying occupancy rates of up to 80%.

Abstract. As aviation's contribution to anthropogenic climate change is increasing, industry aims at reducing the aviation climate effect. However, the large contribution of non-CO2 effects to the total climate effect of aviation and their large variability for each individual flight inhibit finding appropriate guidance. Here, we present a method for the simplified calculation of CO2 equivalent emissions, expressed using the physical climate metrics ATR100 or AGWP100, from CO2 and non-CO2 effects for a given flight, exclusively based on the aircraft seat category as well as the origin and destination airports. The simplified calculation method estimates non-CO2 climate effects of air traffic as precisely as possible, without detailed information on the actual flight route, actual fuel burn, and current weather situation. For this purpose, we evaluate a global data set containing detailed flight trajectories, flight emissions, and climate responses, and derive a set of regression formulas for climate effects, which we call climate effect functions, as well as regression formulas for fuel consumption and NOx emissions. Compared to previous studies, this method is available for a larger number of aircraft types, including most commercial airliners with seat capacities starting from 101 passengers, and delivers more specific results through a clustering approach. The climate effects calculated using the climate effect functions derived in this study exhibit a mean absolute relative error of 15.0 % and a root mean square error of 1.24 nK with respect to results from the climate response model AirClim. The climate effect functions are designed for climate footprint assessments, but would not create an incentive in an emission trading system, for which detailed information on the current weather as well as the actual flight route and profile would be required.

<p>Aviation as an important transport sector contributes to anthropogenic climate change via CO<sub>2</sub> effects and non-CO<sub>2</sub> effects. Non-CO<sub>2</sub> effects include e.g., effects from NO<sub>x</sub> emissions, H<sub>2</sub>O emissions and the formation of contrails. Mitigation options include optimization of aircraft operations, e.g., re-routing, and optimization of the aircraft design, while this work focuses on the second option via providing a model for aircraft design optimization. Furthermore, we take CO<sub>2</sub> and non-CO<sub>2</sub> effects into account.<br>Previous research (e.g. Grewe et al., 2014) investigated the optimization of aircraft operations with the use of climate cost functions. With these functions, the climate impact per unit non-CO<sub>2</sub> emission/flown distance is described depending on the type of emission, the emission location and corresponding time. An equivalent model for aircraft design purposes is currently missing. It has to cover a suitable route network with emission locations and altitudes to be able to optimize regarding the climate impact of CO<sub>2</sub> and non-CO<sub>2</sub> effects. Within the EU Clean Sky 2 project GLOWOPT, this concept is applied for aircraft design features, presented as climate functions for aircraft design (CFAD).<br>Here, we present the development routine for the CFAD. As input, emission inventories based on a long-range aircraft (A350 as baseline in this study) are used. The emission inventories cover a set of climb angles and final cruise altitudes to combine both the aircraft design parameter and geographical information of emissions. The climate impact is calculated with the climate-chemistry response model AirClim (Grewe and Stenke, 2008; Dahlmann et al., 2016) to create a response surface. The climate metric Average Temperature Response with a time horizon of 100 years is used as a measure for the climate impact. The created response surface, the CFAD, can be integrated in the aircraft design process to optimize the aircraft design. The CFAD are to be verified with additional emission inventories to evaluate the accuracy.</p><p><br>Grewe, V., Frömming, C., Matthes, S., Brinkop, S., Ponater, M., Dietmüller, S., Jöckel, P., Garny, H., Tsati, E., Dahlmann, K., Søvde, O. A., Fuglestvedt, J., Berntsen, T. K., Shine, K. P., Irvine, E. A., Champougny, T., and Hullah, P.: Aircraft routing with minimal climate impact: the REACT4C climate cost function modelling approach (V1.0), Geoscientific Model Development, 7, 175–201, https://doi.org/10.5194/gmd-7-175-2014, 2014.</p><p><br>Grewe, V. and Stenke, A.: AirClim: an efficient tool for climate evaluation of aircraft technology, Atmospheric Chemistry and Physics, 8, 4621–4639, https://doi.org/10.5194/acp-8-4621-2008, 2008.</p><p><br>Dahlmann, K., Grewe, V., Frömming, C., and Burkhardt, U.: Can we reliably assess climate mitigation options for air traffic scenarios despite large uncertainties in atmospheric processes?, Transportation Research Part D, 46, 40-55, https://doi.org/10.1016/j.trd.2016.03.006, 2016.</p>

Efficiency, safety, feasibility, sustainability and affordability are among the key characteristics of future urban mobility. The project “HorizonUAM – Urban Air Mobility Research at the German Aerospace Center (DLR)” provides first answers to this vision by pooling existing competencies of individual institutes within DLR. HorizonUAM combines research about urban air mobility (UAM) vehicles, the corresponding infrastructure, the operation of UAM services, as well as public acceptance and market development of future urban air transportation. Competencies and current research topics including propulsion technologies, flight system technologies, communication and navigation go along in conjunction with the findings of modern flight guidance and airport technology techniques. The project analyses possible UAM market scenarios up to the year 2050 and assesses economic aspects such as the degree of vehicle utilization or cost-benefit potential via an overall system model. Furthermore, the system design for future air taxis is carried out on the basis of vehicle family concepts, onboard systems, aspects of safety and security as well as the certification of autonomy functions. The analysis of flight guidance concepts and the sequencing of air taxis at vertidromes is another central part of the project. Selected concepts for flight guidance, communication and navigation technology will also be demonstrated with drones in a scaled urban scenario. This paper gives an overview of the topics covered in the HorizonUAM project, running from mid-2020 to mid-2023, as well as an early progress

Approximately 50-75% of aviation's climate impact is caused by non-CO2 effects, like the production of ozone and the formation of contrail cirrus clouds, which can be effectively prevented by re-routing flights around highly climate-sensitive areas. Here, we discuss options how to incentivize re-routing approaches and apply multicriteria trajectory optimizations to demonstrate the feasibility of the concept of climate-charged airspaces (CCAs). We show that although climate-optimized rerouting results in slightly longer flight times, increased fuel consumption and higher operating costs, it is more climate-friendly compared to a cost-optimized routing. In accordance to other studies, we find that the averaged temperature response over 100 years (ATR100) of a single flight can be reduced by up to 40%. However, if mitigation efforts are associated with a direct increase in costs, there is a need for climate policies. To address the lack of incentivizing airlines to internalize their climate costs, this study focuses on the CCA concept, which imposes a climate charge on airlines when operating in highly climate-sensitive areas. If CCAs are (partly) bypassed, both climate impact and operating costs of a flight can be reduced: a more climate-friendly routing becomes economically attractive. For an exemplary North-Atlantic network, CCAs create a financial incentive for climate mitigation, achieving on average more than 90% of the climate impact reduction potential of climate-optimized trajectories (theoretical maximum, benchmark). Key policy insights. Existing climate policies for aviation do not address non-CO2 effects, which are very sensitive to the location and the timing of the emission.. By imposing a temporary climate charge for airlines that operate in highly climatesensitive regions, the trade-off between economic viability and environmental compatibility could be resolved: Climate impact mitigation of non-CO2 effects coincides with cutting costs.. To ensure easy planning and verification, climate charges are calculated analogously to en-route and terminal charges. For climate mitigation it is therefore neither necessary to monitor emissions (CO2, NOx, etc.) nor to integrate complex non-CO2 effects into flight planning procedures of airlines.. Its implementation is feasible and effective.