## 3 Results and Discussion
### Energy Consumption Results  
According to the data shown in Table 1 (energy consumption for each simulated propulsion and phase per one flight), energy consumption in ATPL integrated training resulting from propulsion was calculated. As an example, results for scenario A are shown in Table 8.  
Based on data shown in Table 2, energy consumption (EC) and fuel consumption (FC) in each scenario were calculated. Using obtained values and the price for 1 kWh of electricity and 1 kg of fuel in Poland, the price of pilot training was calculated. The results are shown in Table 9. In Poland, 1 kWh of electricity costs on average PLN 1.2 and 1 kg of aviation fuel (AVGAS 100 LL) costs PLN 8.19. Values were converted to EUR (EUR exchange rate on 8 July 2024 was PLN 4.29). Figure 5: Emission of (**a**) CO\({}_{2}\), (**b**) NO\({}_{\times}\), (**c**) CO, and their (**d**) reduction due to scenarios. Figure 6: Emission of CO\({}_{2}\) during flight simulation training. By different color marked countries mentioned in the figure analysis. According to data from Table 9, we prepared the plots shown in Figure 7. Referring to cost of training and energy consumption in scenario D (current situation), it can be seen that by implementing scenario C, the cost of ATPL integrated training could be about 23% smaller, and in scenario B as much as 76% smaller. Energy consumption is much higher in scenario A, B, and C than in current situations (scenario D). On the other hand, at this level of energy consumption during flight simulation training, the cost and emissions are significantly smaller than compare to current solutions. Using LCOE (Levelized Cost of Electricity) average value in EU, we prepared the characteristics shown at Figure 8. LCOE is a measure enabling reliable economic comparison of different sources of electricity. The average cost of different energy sources in EU are as follows: wind EUR 0.23, photovoltaics EUR 0.19, carbon EUR 0.31, and gas EUR 0.34 [27]. As can be seen, there are insignificant differences between prices of electric energy production from the analyzed sources; as such, the cost of training in all scenarios is very similar for each energy source. However, the smallest cost at this moment will be for flight simulator training with energy provided from photovoltaic sources. This solution will also lead to much lower greenhouse gases emissions. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Phase**} & \multicolumn{3}{c}{**Energy Consumptions [kWh]**} \\ \cline{2-4}  & **PE** & **2 PE** & **VLJ** \\ \hline Take-off & 20.176 & 0.927 & 0.160 \\ \hline Climb out & 86.19 & 13.047 & 1.331 \\ \hline Approach & 57.902 & 15.657 & 1.598 \\ \hline Cruise & 21.23 & 37.065 & 6.391 \\ \hline Taxi & 35.148 & 56.663 & 11.183 \\ \hline Total & 220.646 & 123.359 & 20.663 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Energy consumption in ATPL integrated training. Figure 7: Change of ATPL integrated training (**a**) cost; (**b**) energy consumption depending on scenario. Air traffic operational efficiency has a crucial impact on energy consumption and exhaust emission reduction. The lack of models and tools makes it difficult to numerically calculate and evaluate the effect of new aviation technologies on green operations [28]. In this article, we decided to analyze the energy consumption, CO\({}_{2}\), CO, and NOx emissions in different scenarios of using flight simulation devices during ATPL integrated training. From these analyses, it was possible to demonstrate the ecological benefits resulting from the use of flight simulator devices, which is also proved in different literature sources [4; 5; 29]. This topic is significant due to European Union green policy and the lack of this type of calculation and modelling in air transport research, especially in general aviation [1], which mostly concern the pilot training area.