Abstract
Data envelopment analysis (DEA) and inverted data envelopment analysis (inverted-DEA) are used so that the desirable and undesirable outputs of decision-making units (DMUs) exist simultaneously. We developed a new approach based on the concept of utilizing both DEA and inverted-DEA to enhance the discrimination power of DMUs with undesirable outputs. DMUs are ranked by the Z-score method and classified based on the efficiency scores of DEA and inverted-DEA. Then, the characteristics of the DMUs are analyzed based on the classification result. This paper performs an efficiency evaluation of 21 industrial parks in China in 2017 using this new approach. The overall evaluation results indicate that the proposed new approach increases the discrimination ability in this empirical study.
Highlights
Data envelopment analysis (DEA) was proposed by three operational research experts, A
People often have more than one reference perspective in assessing decisionmaking units (DMUs). e standard DEA models have employed the best-practice DMUs to construct the efficient frontier and have not fully taken advantage of the information implied in the data, especially for DMUs with undesirable outputs
In DEA, the maximum ratio of outputs is assumed to be efficiency, which is calculated from the optimistic perspective for each DMU. e efficiency for DMU0, which is analyzed as an object, is evaluated based on the efficiency values of the other DMUs. e following basic DEA model evaluates the efficiency of DMU0 with s dimensional input vectors and m dimensional output vectors: max uTy0 vTx0 (1)
Summary
Data envelopment analysis (DEA) was proposed by three operational research experts, A. People often have more than one reference perspective in assessing DMUs. e standard DEA models have employed the best-practice DMUs to construct the efficient frontier and have not fully taken advantage of the information implied in the data, especially for DMUs with undesirable outputs. E DEA method is not affected by the input and output dimensions of the problem and can comprehensively evaluate the data of different indicators. If one treats the undesirable outputs as inputs, the method is simple and easy to implement, the resulting DEA model does not reflect the actual production process. Erefore, the use of an inverted-DEA model to address undesirable outputs can reflect the actual production process and is more straightforward and reasonable than other methods.
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