## Appendix B Validation of In-Flight Scaling Approach  
A validation of the above-described scaling approach (Section 2.3.1) and analysis of the resulting goodness-of-fit is performed based on the detailed assessment of the ISOC study. Further details on the study can be found in Section A.2. Based on a broader sample of long-range flights with determined climate and non-climate KPIs, we assess ATR100 for different randomly generated flight plans of different reference sample sizes (n\({}_{ref}\)). Results can then be related to the known ATR. Furthermore, the applicability of the approach to assess mitigation potentials from implementing the OI on a restricted flight sample (e.g., limited by geographic or fleet restrictions) is demonstrated based on the individual study results. Figure A3 illustrates the deviation in estimated ATR100 from the originally calculated value for 100 randomly generated flight plans (n\({}_{it}\) = 100) with a sample size of 500 flights (n\({}_{ref}\) = 500), where an aggregated scaling is performed on different available parameters. We find that the combination of ASK and CO\({}_{2}\)-Eq scaling reduces deviations to values between \(\pm\)4% compared to approaches based on alternative parameters. We observe that the estimation can be improved by applying matching schemes instead of aggregated scaling. Different matching approaches can incorporate effects from flight distance, aircraft type, and geographic location. For instance, a cluster-based matching approach (CLU1) selects a sample of comparable reference flights for every target flight based on the connected origin and destination airports, which have previously been assigned to 15 global clusters. Different individual matching approaches (IND1 and IND2) identify the best matching flight from the reference sample for every target flight based on aircraft type, flight distance, and average flight latitude with different weighting factors. Figure A4 left shows larger deviations for the randomly generated flight plans (n\({}_{it}\) = 100, n\({}_{ref}\) = 300) for the aggregated approach between +4.3% and \(-\)4.2%, while this can be reduced to values between +1.3% and \(-\)1.5% (excluding outliers) with a flight matching. The sample size of the reference flight plan has a significant influence on the accuracy of results and the related goodness-of-fit. Figure A4 right illustrates deviations in scaled and originally estimated ATR100 showing improvements with increasing n\({}_{ref}\). Hence, the explanatory power of the above-described method also varies for different sample sizes andis limited for a very small sample of flights. Also, composition of the reference flight plan in terms of covered missions and aircraft types influences the accuracy of the scaling approach. The above-described method is also applicable to transfer mitigation potentials obtained for a limited scope to the mitigation potentials of a broader scale. For instance, implementation of an OI for North Atlantic flights leading to a certain mitigation potential would be reduced if considering all long-range flights to or from European destinations as a reference. For instance, an individual study of ISOC for flights crossing the North Atlantic shows a mitigation potential in terms of ATR100 of 35.6%. If this is related to the total European long-range traffic, implementation of ISO on North Atlantic flights results in a mitigation potential of 14.0%. Applying the previously described scaling method results in estimations of the impact between 13.9% and 15.3% dependent of the matching applied. We find that easy scaling approaches, e.g., considering flight plan size, flight distance, ASK, or fuel consumption, tend to overestimate the mitigation potential (estimates between 15.1% and 16.7%). By contrast, a flight plan matching improves the estimation quality to a deviation of less than 0.1% in this example. * [PERSON] et al.