The Major goal of this manuscript is to evaluate the long run relationship of economic growth, energy consumption, CO2 emissions and air transportation within the context of EKC hypothesis. In this sense, ADF and PP unit root tests, FMOLS, DOLS, CCR and ARDL tests are performed in order to determine the coefficient of effects of independent variables on dependent variable CO2 emissions. The periods from 1970 to 2020 for variables are derived from World Banks and Ourworldindata official website as annual data. According to FMOLS, DOLS, CCR tests there is a long-term stable linkage between CO2 emissions and energy consumption from 1970 to 2020 for all NAFTA countries including USA, Canada, and Mexico which is demonstrated empirically. It can be interpreted that increased consumption of the non-renewable energy or fossil will increase the amount of carbon dioxide emissions. For this reason, all three countries need to benefit from more environmentally friendly renewable energy sources.

By means of its technological infrastructure, the civil aviation sector has the capacity to generate a huge amount of data. Thanks to big data technology, the civil aviation industry will be able to refresh business processes using this enormous data reserve. For instance, using various prediction algorithms or deep learning methods based on big data technology, it will be easier to reduce flight risks, predict changes in the market, reduce costs, plan flight operations and maintain and repair activities. During the COVID-19 pandemic, which affected the whole world, almost all airline companies faced an unprecedented financial crisis due to travel restrictions. In virtue of the dynamic data analysis that can be obtained through big data technology, it is possible to plan the most appropriate flight operations by taking into account the changing pandemic conditions in order to meet the global and country-based demands of airlines. During the analysis, sample implementations and method concepts describe the use of big data technologies in civil aviation and the possibilities it can bring to the companies in the field. This research should increase knowledge of the field and lead to further science studies.

The major goal of this research paper is to determine the long-run linkage among variables and the impact of civil aviation, energy productivity (efficiency), economic growth (GDP), on ecological footprint through conducting the multivariate regression method, Phillips‑Ouliaris and Engle‑Granger, Jarque – Bera Normality, and Cusum tests from 1970 to 2020. According to results of multivariate regression method, civil aviation, energy efficiency, and economic growth affect the ecological footprint from 1970 to 2020 in France which is coincide with Phillips‑Ouliaris and Engle‑Granger tests. On the contrary, there is no effect of independent variables on dependent variable (ecological footprint) in Finland which is consistent with Phillips‑Ouliaris and Engle‑Granger tests. In this respect, The long-run relation of the model is verified by the cointegration test of Engle-Granger and Phillips-Ouliaris for France. However, there is no long-run co-integration among variables for Finland from 1970-2020. To sum up, empirical results of France is verified EKC hypothesis. However, EKC hypothesis is not verified for Finland.

Runway excursion continues to cause serious deaths and economic losses in aviation. 179 people died in the accidents occurring between 2010 and 2019. The direct cost of accidents in 2019 is estimated at over $4 billion. A new method known as Global Reporting Format (GRF) is introduced after studies carried out by the International Civil Aviation Organization (ICAO) to prevent aircraft accidents caused by runway excursion and to minimize risks associated with it. GRF is considered an important milestone to flight safety by ICAO. In this study, GRF is explained with all its components, and benefits and transition to the application process are discussed. As a result of the study, it is assessed that GRF is a positive contribution to flight safety in the process of take-off or landing by matching information about runway surface conditions obtained to the flight crews especially in adverse conditions.

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