Distinctive data envelopment analysis model for evaluating global environment performance

Evaluations of world environmental activities comprise an important research area when obtaining a better understanding of global efforts. However, some environmental criteria might include imprecise data. Environmental criteria can be classified according to four categories: discretionary, non-discretionary, desirable, and undesirable factors. The data envelopment analysis (DEA) technique has been applied widely to assess environmental performance. Classical DEA models evaluate performance of decision making units (DMUs) individually. However, the classical DEA models have some weaknesses. First, they focus on individual DMUs, where they freely assign weights to DMUs to obtain the best efficiency scores. Second, classical DEA models do not aggregate the performance of all DMUs to obtain an overall performance score. Finally, the calculations employed by classical DEA models are very long. To overcome these weaknesses, we propose DEA models for evaluating the individual and overall environmental performance of countries. The proposed models consider discretionary, non-discretionary, desirable, and undesirable factors simultaneously. Countries (DMUs) are ranked using a minimax regret-based approach (MRA). We provide a numerical example that illustrates the application of the proposed models.