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Since 1999, every report released by the International Panel on Climate Change has advocated a decrease in the greenhouse gas emissions associated with aviation in order to preserve the current climate. This study used a two variable differential equations model with a non-linear control term to address several aspects of the emissions stabilization issue. By optimizing the control term parameter, several management alternatives can be obtained based on the properties of the phase plane of the model solutions, as identified by a stability analysis. The system can be stabilised around an equilibrium point that maintains the present number of passengers, or maintains the emissions level or is nearest to its present state. Each of these options entails different issues of growth or reduction in the number of passengers and/or the emissions rate, directly obtained from the model results. The last option seems especially novel and promising, since only short-distance flight passengers are severely reduced, while long-distance and international passengers are allowed to growth, and their associated emissions are reduced to below 50 percent of their actual value. Moreover, in a scenario of slow growth in air traffic, these rates could improve, with fewer reductions in the number of short-distance passengers., Competing Interests: Declarations. Competing interest: The authors declare no competing interest. Ethical compliance: This study involved no experiments or data obtained from experiments with human participants, animals or vegetables or their tissues or samples., (© 2025. The Author(s).)

The wind energy requirements for high temporal and spatial observations, unavailable in many countries, are met by reanalysed wind data, which are increasingly used in wind power assessments. However, these data also include unrealistic features, making their validation compulsory. The validation analysis of ERA5 and MERRA-2 wind data, two of the most successful wind reanalyses, with observations near Morocco's coast, highlighted the existence of bias in both datasets, and other shortcomings. The study was conducted at eight selected sites in the period 2011-2020 using linear and probability density function based statistical tests. The local ERA5-BA and MERRA-2 BA velocity fields were created using Bias Adjustment (BA) Techniques. Only part of the observed and modelled datasets (training period) were used to identify the transfer function. The BA fields were verified against observations during the remaining period (verification period). They outperformed their respective non-adjusted fields and also the WFDE5 (the global bias-corrected ERA5 wind field) in the wind potential assessments, represented by capacity factor and low-wind day. At every location, the relative errors in the latter's estimates using ERA5-BA were lowered to less than 10 %, and the same was true for MERRA-2-BA. Thus, an observational dataset of reduced length can help overcome some reanalysed wind data limitations for wind power estimations. Additionally, this methodology can be applied to the correction of each reanalysis update., Competing Interests: Declarations Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

In this study, we introduce a sensitivity analysis of modelled CO2 aviation emissions to changes in the model parameters, which is intended as a contribution to the understanding of the atmospheric composition stabilization issue. The two variable dynamic model incorporates the effects of the technological innovations on the emissions rate, the environmental feedback, and a non-linear control term on the passengers rate. The model parameters, estimated from different air traffic sources, are subject to considerable uncertainty. The stability analysis of Monte Carlo simulations revealed that, for certain values of the non-linear term parameter and depending on the type of flight, the passengers number at some equilibrium points exceeded its initial value, while the emissions level was below the initial corresponding one. The results of two global sensitivity analyses indicated that the influence of the non-linear term prevailed on the passengers number rate, followed distantly by the environmental feedback. For the emissions rate, the non-linear term contribution dominated, with the technological term influence placing second.