DEA 모형을 이용한 온실가스･에너지 목표관리제 지정기업의 효율성 평가

Many nations in the world have been preparing and fulfilling the programs and strategies designedto deal with the climate change. The Korea government also suggested a paradigm of new nationaldevelopment, that is, “Low-Carbon Green Growth based on Green Management” and enacted“Fundamental Law on Low-Carbon Green Growth (Jan., 2010)” to support legally. Based on the amount of green house gas emissions and energy consumption, the governmentselected some enterprises that need to manage and set the target on not only green house gas emissionsbut also energy consumption, and controlled whether they meet their reducing targets or not. Theseenterprises are called TMEs (Target Management Enterprises). In this study, we analyze eco-efficiencyof TMEs from 2008 to 2011. Eco-efficiency is the indicator of environmental efficiency to improveenvironment with the improvement of the productivity of natural resources. In order to evaluate and analyze eco-efficiency, DEA (Data Envelopment Analysis) model is used.The process of this study is composed of 6 stages. The detailed explanation may be described asfollows: 1)We newly define the eco-efficiency of TMEs. The product or service value of eco-efficiencyis sales and/or economic value added, and the environmental influence of eco-efficiency is greenhouse gas emissions and/or energy consumption respectively. 2)We select proper input variablesfor evaluating eco-efficiency of TMEs. The input variables used for this research are green housegas emissions and energy consumption. 3)For selecting the output variables, the correlation analysisis performed. The alternative output variables used in correlation analysis are sales, net profit duringthe term, economic value added and operating profit. Because the sales and economic value addedhave a low correlation, we use these two for output variables. 4)We conduct a DEA analysis toevaluate eco-efficiency of TMEs. We apply the input-oriented BCC model because the one of outputvariables (economic value added) includes the negative values. 5)By the experimental results, theDMUs are classified into two groups, that is efficient and inefficient groups. The number of efficientDMUs for each year is 8 (2008), 5 (2009), 7 (2010), and 5 (2011). We also perform the scale efficiency analysis and the reference set analysis to figure out what causes the result of inefficient DMUsand to provide the desirable value for the efficiency score at which each inefficient DMU can beconverted into an efficient DMU. 6)We investigate the dynamic change of eco-efficiency using DEAWindow model. Window analysis assesses the efficiency of a DMU over time by treating it asa different unit in each time period. This method allows for tracking the performance of a DMU. The eco-efficiency of 5 TMEs increase over time. More statistical analysis are needed for explainingthe detailed results of TMEs. There are various theoretical and working-level consequences in this study. First, though the actualmeasurement of eco-efficiency remains ambiguous, we propose the new and common definition ofeco-efficiency for TMEs. Second, the coverage capability of input-oriented BCC model is extendedbecause of dealing with negative output values. Third, this approach may motivate many researchersto develop the quantitative analysis methodologies for green management. Finally, this study willalso suggest useful information on making and deciding the policies for climate change andsustainable management.