Measuring sustainable performance of OECD countries considering CO2 emissions: A new optimistic-pessimistic distance friction Minimization Model

Abstract

In today's competitive business environment, evaluating the performance of decision-making units (DMUs) such as countries and institutions is paramount. Data Envelopment Analysis (DEA) is widely used for this purpose. One prevalent model, the Distance Friction Minimization (DFM) method, is effective in devising improvement strategies for low-efficiency DMUs. However, it has limitations as it only assesses the distance of DMUs to the efficient frontier, neglecting the inefficient frontier and providing an overly optimistic assessment. Hence, there is a growing need for methods that consider both frontiers to overcome this issue.

In this study, we introduce an enhanced DFM model that integrates both optimistic and pessimistic distance analyses. The research methodology is as follows: IDMU-based CCR and ADMU-based CCR models are designed and implemented to calculate the optimistic and pessimistic efficiency of DMUs, respectively. Then, additive models based on virtual IDMU and ADMU units are designed and implemented. Subsequently, DMUs in both approaches are categorized, and DMUs of the third category of each approach are entered into the respective DFM model. After calculating the distance of each DMU from both efficient and inefficient frontiers, the relative closeness (RC) index is employed to aggregate the distances of DMUs from the efficient and inefficient frontiers. Finally, the DMUs are ranked based on the RC index. To demonstrate the practicality of the model, we evaluate the sustainable performance of OECD countries concerning CO2 emissions. Our findings illustrate that the model can measure DMUs' distances to both efficient and inefficient frontiers, providing policymakers dealing with Sustainable Development Goals (SDGs) a more nuanced understanding of the situation.

In summary, the DFM model proposed in this study bridges the gap by considering optimistic and pessimistic perspectives, offering a more comprehensive view of DMU performance.