Wind energy resources are typically classed into 7 or 8 power classes \cite{Lopez_2012}. The classes are in direct correlation with wind speed, power density (W/m2) and capacity factor (CF). The relationship between the three is not linear, but an empirical conversion expression is known \cite{nrel2019}. The resource class is linearly correlated to the per unit area power return. As a result, the relationship between the resource class and the EROEI is a scalar multiplier. The ranges of wind EROEI vary across the literature 5-18  \cite{King_2018}, 10-30+ \cite{Davidsson_2012}, 25 \cite{Kubiszewski_2010}. Wind EROEI depends on the wind resource class or alternatively, the mean wind speed at a certain project location. Unfortunately, wind speed data are very location specific, and therefore require high resolution geographic data to be utilized. For example, the wind profile between a hill's crest and the area in the hill's aerodynamic shadow would be very different and averages are not accurate in representing the actual potential of the hill area. A recent study that attempted to estimate the global wind potential for different EROEI studies may have been influenced by this large-scale aggregation in estimating a lower wind EROEI \cite{Dupont_2018}.