Data Envelopment Analysis (DEA) has been long employed as a popular methodology to evaluate the performance of various production activities with multiple inputs and outputs. However, an important issue is that the production process in the real world inevitably generates undesirable outputs (like wastes and pollutants) along with desirable outputs. Therefore, the undesirable outputs should be ...

Different from traditional efficiency research and previous studies considering undesirable outputs, this paper proposes models which distinguish weak and strong disposability features among various undesirable outputs based on the technical nature of the undesirable outputs. The paper illustrates the approach using a research sample covering 582 base-load Chinese coal-fired power plants in 200...

the calculation of rts amounts to measuring a relationship between inputs and outputs in a production structure. there are many methods to measure rts in the primal space or the dual space. one of the main approaches is using the multiplier on the convexity constraint. but returns to scale measurements in dea models are affected by the presence of regulatory constraints. these additional constr...

recently, lotfi et al. [10] established an equivalence model between the output-oriented dual ccr model and multiple objective linear programming (molp) and used an interactive method for finding target unit on efficiency frontier such that the decision maker preferences can be taken into account. however their method is not applicable for situations in which some outputs are undesirable and ne...

Data envelopment analysis (DEA) is an effective tool to measure the relative efficiency of decision-making units (DMUs) with multiple inputs and outputs. This study classifies the outputs into desirable and undesirable outputs, and then presents two DEA models, radial and non-radial models, to measure the environment efficiency for DMUs with undesirable outputs. Finally, two proposed DEA models...