Currently there exist a wide variety of models that can be used to assess the fuel consumption of a single flight, from conventional models based on physics and flight performance to more innovative ones based on avant-garde techniques such as artificial intelligence. However, the quality of the fuel consumption estimated by these models usually relies strongly on the quality of data available. As consumed fuel is impacted by a wide variety of features, such as aircraft type, engine family, meteorological conditions, flight path, etc, the more information available, the more accurate the estimations will be. However, having access to such granulated data is not always trivial and, moreover, the computational cost that could be derived from assembling data coming from different agents in the aviation field (airports, airlines, manufacturers, meteorological stations), plus the processing of the data and afterwards the computation of a refined fuel consumption model will be very high. The work presented here has been developed within the framework of the project E.R.A. (Environmentally Responsible Aviation) funded by Red.es, and it presents an extensive analysis on how consumed fuel and carbon dioxide emissions estimations could be made with limited access to information. Moreover, the aim is to be able to prove that for aggregated metrics, that being a set of flights and not a single flight, the consumed fuel can be easily estimated thus helping accounting for the carbon dioxide emissions that are produced at a global level.