Dealing with Interval DEA Based on Error Propagation and Entropy: A Case Study of Energy Efficiency of Regions in China Considering Environmental Factors

Abstract

AbstractThe conventional data envelopment analysis (DEA) measures the relative efficiency of decision making units (DMUs) consuming multiple inputs to produce multiple outputs under the assumption that all the data are exact. In the real world, however, it is possible to obtain interval data rather than exact data because of various limitations, such as statistical errors and incomplete information, et al. To overcome those limitations, researchers have proposed kinds of approaches dealing with interval DEA, which either use traditional DEA models by transforming interval data into exact data or get an efficiency interval by using the bound of interval data. In contrast to the traditional approaches above, the paper deals with interval DEA by combining traditional DEA models with error propagation and entropy, uses idea of the modified cross efficiency to get the ultimate cross efficiency of DMUs in the form of error distribution and ranks DMUs using the calculated ultimate cross efficiency by directional distance index. At last we illustrate the feasibility and effectiveness of the proposed method by applying it to measure energy efficiency of regions in China considering environmental factors.