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Aviation CO2 emissions are growing along with traffic growth and expected technological efficiency improvements are not enough to reduce this continuous increase. International organizations are concerned and are implementing incentive rules in order to reduce them. IATA has stated a carbon neutral growth of emissions from 2020 onward within its roadmap. This paper aims to analyze suitable measures that could help to reach this target and focuses on their impact on the finances of 15 varied Spanish airlines. With these goals, an estimation model is designed in order to carry out a forecast of the 2017–2025 Spanish air market. This is comprised by three submodels: (i) the traffic model estimates the annual performance for Spanish carriers in each of their routes, (ii) the biofuel model is in charge of estimating the biofuel prices, emissions and regulations (with special attention to mandatory blending percentage), and (iii) the operating cost model estimates the carrier’s expenses structure. Data from several sources (regarding 2016 traffic statistics and forecasts of growth and fuel prices to name but two) is gathered and merged in order to feed the model. Aggregated results show that an average 3% per year increase of mandatory blending percentage should be applied for a 2020 CNG in the base scenario. Regarding the different biofuel feedstocks investigated, Camelina’s performance presents a good compromise in respect to price, emissions, and production issues. A further study on the airline’s cost structure shows that differences in the operating model (legacy, low cost, etc.) and route configurations can lead to big differences in terms of impact of biofuels introduction on the total airline costs. This could indicate that perhaps the design of distance-specific financial schemes would be desirable. In addition, high sensitivity to changes in common fuel price and traffic growth is observed.

Fatigue loads represent a critical element in several aeronautical applications and Wind Turbines (WTs) are not an exception. Their evolution over the machine service life usually determines its life span, hence their behavior knowledge can cause a relevant economic impact on the Cost of Energy (CoE). Nevertheless, their measurements are frequently difficult or expensive and estimations arising from other known signals can be carried out instead. This thesis aims to design a load estimation model using standard signal as inputs by means of statistical and machine learning procedures. It includes an input selection stage and the models layout arrangement and assessment, which are based on Artificial Neural Networks (ANNs). The whole process is described in the following lines. First of all, data from simulations is gathered and examined. Damage Equivalent Load (DEL) (fatigue indicator which acts as target) and stats (statistical parameters from standard signals that constitute possible model inputs) are computed and their cross relationships analyzed. Secondly, input dimensions are reduced due to their great volume. In this regard several input subsets formed by small quantities of stats are built using two different filters: Correlation (COR) and Principal Component Analysis (PCA). Once this done, each subset estimation power is compared along with a great variety of ANNs configurations. Eventually, an innovative input selection method is developed using a genetic optimization. The outcome of this process allows several observations. On one hand, estimation results seem promising as long as all of them fall below 4%. On the other hand, multilayered nets with a neurons number between 30 and 60 seem a suitable configuration for these purposes. Furthermore, the genetic optimization results also show a great performance without the necessity of the preliminary work carried out by filters (which in fact is bias ed by some a priori assumptions). To sum up, inside the wind power field -where lifespan is an important factor due to its influence on the CoE- this method may promote a more efficient usage of structural parts enhancing their service life.