Data Envelopment Analysis (DEA) is a nonparametric, linear programming-based method used to evaluate alternative units that produce similar outputs using similar inputs. Alternatives are called Decision Making Units (DMUs) in DEA. While some DEA methods proposed to evaluate DMUs can only rank efficient or inefficient DMUs, others aiming for full ranking cannot consider some types of DMUs and distributions in the data set. In this study, a new DEA method for full ranking, ASES (Area of Super Efficiency Score Graph), is proposed. ASES evaluates all DMUs by their super efficiency scores as other DMUs are deleted from the data set iteratively. Due to super efficiency scores, all types of efficient DMUs can be differentiated. Moreover, ASES produces consistent and objective results in cases of clustering and outliers in the data set. For the application of ASES, 18 European countries are evaluated with respect to OECD (The Organisation for Economic Co-operation and Development) statistics related to environmental awareness and development of environmental technologies. Four main factors related to reduction in environmental harm due to technology are considered as DEA outputs. Four different benchmark DEA models are also applied to the same data. Results show that ASES eliminates the disadvantages of other methods and provides full ranking.