## 4 Discussion  
For the first time, we compare a broad set of different operational measures to mitigate the climate impact of aviation. By doing so, we confirm the climate mitigation potentials of in-flight operational measures of up to 25% depending on the implementation scope and associated with different levels of cost increases for the operators. On-ground measures are found to provide even larger mitigation potentials of up to 85%, but are associated with significantly lower absolute climate mitigation potentials due to the lower overall on-ground emission quantities. While the investigated on-ground OIs are based on existing technologies, in-flight OIs are at a lower degree of maturity. In this context, reliability and applicability of the achieved results need to be interpreted with care on the basis of the applied assumptions, hypotheses, and boundary conditions. Nevertheless, the developed and validated method to extrapolate case study results to a broader scale, e.g., regarding the considered flight plan, investigated airports, or geographical restrictions, enables a quantitative comparison of different operational mitigation measures. The method can also be applied to other operational improvements (e.g., continuous climb/descent operations, night-time contrail avoidance, or single-engine taxiing) as long as case study results and boundary conditions are determined. The accuracy of the selected approach is strongly influenced by the uncertainties in the assessment process. In general, estimating the in-flight climate effects of non-CO\({}_{2}\) emissions in aviation underlies large uncertainties [3]. Also, the assessment accuracy of CO\({}_{2}\)-Eq for the scaling approach is determined by the assessment method. The applied aCCFs for trajectory-related OIs are currently considered prototypical and have been validated for a limited geo-temporal scope only [68]. The route-specific assessment of CO\({}_{2}\)-Eq introduced by [PERSON] et al. [66] shows a relatively high mean square error (0.19) in comparison to a more detailed assessment. Moreover, these regression formulas have so far only been derived for a characteristic wide-body aircraft, namely A330, and do not provide reliable estimates for short-range missions. Furthermore, a quantitative extrapolation from individually investigated days per case study to longer time periods, e.g., aggregated results for one year, is currently not possible due to the high dependency of non-CO\({}_{2}\) effects from atmospheric boundary conditions. Hence, further research will be required in this context. Validation of the in-flight scaling method (Appendix B) has shown a significant impact of the size of the reference flight plan on the accuracy of the scaling results. Therefore, we expect a higher level of confidence for the generalized results from LOSL, NETW and ISOC sub-studies whereas the assessment of the CLIM OI with only four reference flights is expected to be limited in its significance. For ground-related OIs, the scaling procedures aim to generalize information that is typically highly airport-specific. This study is based on the limited information publicly available and validated using the Malpensa airport data, hence depending on the specific characteristics of this airport. Further analysis is needed to validate the methods on a broad selection of airports. The direct quantitative comparability between the different categories of the OIs is limited due to different modeling assumptions: While trajectory-related OIs have been assessed for particular days of the year only, network- and ground-related OIs have been assessed for one year. In this context, the different assessment approaches to the climate impact also need to be considered and are expected to influence the comparability of results. For instance, the analysis of trajectory-related OIs has incorporated specific meteorological conditions during the flight, while network-related OIs have been assessed with a climatological approach applying annual mean values [67; 77].