Using Data Envelopment Analysis in Measuring Eco-Efficiency of
9.3 Eco-Efficiency of Power Plants
In this section, we illustrate how we used our approach to evaluate the eco-efficiency and the emission reduction program of 24 power plants in a Euro-pean country. The desirable output is electricity generation, with a minimum of 576,000 MW and a maximum of 2,160,000 MW. The total costs are considered as an input (min. US$ 1,345,448, max. US$ 13,014,761). The undesirable outputs or the pollutants are dust, NOx, and SO2. By the emission reduction program, the power plants reduced the emission quantities considerably. The emission levels are available before and after emission reduction. Before reduction, the emission quan-tities of dust (in tons/year) ranged between 574 and 14,097; after reduction they ranged between 175 and 1,418. The corresponding ranges for NOx, in tons/year, were: before reduction [1,926; 5,509] and after reduction [963; 2,754]. For SO2, in tons/year, levels were, before [1,401; 24,459], and after [1,401; 12,230].
Solving Models I [Equation (9.1)] and II [Equation (9.2)], we obtained the mea-sures of the technical and ecological efficiency, respectively. Those meamea-sures pro-vide the first indicators for the performance of power plants from the eco-efficiency point of view. The results are given in Table 9.2. The column denoted by “Tech-nical efficiency" shows the results of Model I using total costs as an input and the electricity generation as an output. It is a very simple CCR model with only one efficient unit: namely, power plant 1, which is a small one with the lowest out-put level and the lowest total costs. Column 3 [“Ecological efficiency (before)”], presents the ecological efficiency before the emission reduction program. The re-sults are obtained by solving Model II with electricity generation as the desirable output and with dust, NOx, and SO2 as pollutants or undesirable outputs. The ecologically efficient power plants are 1, 2, 4, 8, 13, and 14. The fourth column stands for the ecological efficiency after emission reduction. Only power plant 1 is technically and ecologically efficient – before emission reduction.
To get an indicator of the eco-efficiency, we took technical and ecological effi-ciency as output variables for the new DEA model with input equal to 1. In this way, the efficiency is decomposed into technical and ecological efficiency. The eco-efficiency frontier before and after emission reduction is illustrated in Figure 9.1 and Figure 9.2. These figures show that, before emission reduction, only power plant 1 is eco-efficient, and, afterwards, only power plants 1 and 2 are eco-efficient.
The units 2, 4, 8, 13, and 14 (before emission reduction) and the units 4, 5, 8, 13, and 14 (after emission reduction) are only weakly efficient, because they are techni-cally inefficient. Because all eco-inefficient units lie outside the eco-efficiency cone
Measuring Power Plant Eco-Efficiency with DEA 179 Table 9.2. Technical and ecological efficiency analysis (CCR model).
Ecological Ecological
in both cases, the indicator of eco-efficiency is simply the better value of efficiency scores obtained from Models I and II. Thus the eco-efficient scores are the same as ecological efficiency scores in Table 9.2, except that Unit 1 is also eco-efficient after emission reduction.
To show the importance of the technical (ecological) efficiency in determining the eco-efficiency of units 1 and 2, we computed the ratio of weighted technical (ecological) efficiency to the virtual output (the weighted sum of technical and eco-logical efficiency). This is a useful indication of the importance of technical (eco-logical) efficiency in determining the eco-efficiency. In both power plants, the tech-nical efficiency was given an importance of approximately 60% and the ecological efficiency of approximately 40% in determining the eco-efficiency. The strength of both units lies more in the area of technical efficiency, while the eco-inefficient units have a weakness primarily in the technical inefficiency. From the correspond-ing slack variables of the new DEA model, the potential eco-efficiency
improve-180 Pekka Korhonen and Mikulas Luptacik
T e c h n i c a l e f f i c i e n c y
0 2 0 4 0 6 0 8 0 1 0 0
0
2 0 4 0 6 0 8 0 1 0 0
Ecological efficiency
U n i t 1 4 U n i t 1 3 U n i t 8 U n i t 4 U n i t 2 U n i t 1
Figure 9.1. Eco-efficiency frontier before emission reduction.
T e c h n i c a l e f f i c i e n c y
0 2 0 4 0 6 0 8 0 1 0 0
0
2 0 4 0 6 0 8 0 1 0 0
Ecological efficiency
U n i t 1 4 U n i t 1 3 U n i t 8 U n i t s 5 , 4 U n i t 2
U n i t 1
Figure 9.2. Eco-efficiency frontier after emission reduction.
ment with respect to technical and ecological efficiency, respectively, can be seen.
It is obvious that the eco-efficiency after the reduction program is on average higher than before.
An alternative approach to analyzing eco-efficiency is to use models A, B, and C. Table 9.3 shows the results of model B before and after emission reduction, both
Measuring Power Plant Eco-Efficiency with DEA 181 Table 9.3. Eco-efficiency scores using the combined model B.
Before emission After emission
Units reduction reduction
1 1.00 1.00
2 1.00 1.00
3 0.99 0.99
4 1.00 1.00
5 1.00 1.00
6 0.97 0.95
7 0.98 0.99
8 1.00 1.00
9 0.94 0.94
10 0.91 0.91
11 0.96 0.96
12 0.86 0.92
13 1.00 1.00
14 1.00 1.00
15 0.73 0.83
16 0.73 0.83
17 0.72 0.82
18 0.75 0.85
19 0.78 0.88
20 0.77 0.88
21 0.77 0.87
22 0.78 0.89
23 0.77 0.88
24 0.76 0.86
under the assumption of constant returns to scale. We will discuss in more detail the results of model B after emission reduction. A similar analysis can be done for the models A and C (before and after emission reduction) and for variable returns to scale.
The input variables in model B are total costs; the investment for emission re-duction; and the emission of dust, NOx, and SO2– all after the reduction program.
The only output variable is electricity generation.
Comparing the results of model B (Table 9.3) with the eco-efficiency obtained as a composition of technical and ecological efficiency (Table 9.2 and Figure 9.2) the tendency of the same results can be observed. Because DEA models yield the best possible results for every decision making unit, the eco-efficiency defined by model B cannot be lower than the eco-efficiency in Figure 9.1. For instance, the weakly eco-efficient power plants 4, 5, 8, and 13 from Figure 9.1 (after) are eco-efficient according to model B. Units 1 and 2 are efficient in both cases. But
182 Pekka Korhonen and Mikulas Luptacik model B provides a deeper insight into the causes of eco-inefficiency and shows the potential improvement to particular inputs and outputs. Nevertheless, decomposi-tion of eco-efficiency into technical and ecological efficiency can be useful.
Computing the ratio of weighted inputs to the weighted sum of inputs we obtain an indication for the importance of particular inputs. For example, in units 2 and 5, an importance of 82% was given to abatement investment in determining their eco-efficiency. The investment in emission reduction was highly efficient. The strengths of unit 1 lie in its abatement investment (48%) and in the lower level of dust emission (47%). The abatement activity was oriented to reduction of NOx only.
An interesting result is seen in power plant 8. The most important factor for the eco-efficiency of this unit is the low level of SO2 emission (the lowest level of all power plants). This input was given an importance ranking of 99% in determining eco-efficiency.
The most important factors for power plant 13 are the abatement investment and the relatively low level of NOx emission in comparison to the high level of output. Power plant 13 is the plant with the highest electricity generation. Power plant 14 is only weakly eco-efficient because of input inefficiency. The potential improvements lie in reducing abatement investment by 52% and in reducing total costs by 24%. Similar results can be found for the inefficient power plants 15–24.
They have a weakness in technical efficiency and should primarily reduce their inputs.
9.4 Concluding Remarks
In this chapter we presented two approaches which can be used for the estimation of eco-efficiency. In the first approach, we measured the eco-efficiency in two steps: We estimated the technical efficiency and the so-called ecological efficiency separately. Then we took the results of both models as the output variables for the new DEA model (with the inputs equal to 1), which provides the indicator for eco-efficiency.
In the second approach, we formulated the different variants of DEA models, which simultaneously take into account the inputs, the pollutants or undesirable outputs, and the desirable outputs. It was shown that the efficient units are efficient, no matter which model variant is used. However, the efficiency scores may differ.
When one compares these two approaches, both tend to lead to the same results.
However, the second approach provides a deeper insight into the causes of the eco-inefficiency and shows where potential improvements lie with respect to the particular inputs and outputs. The first approach yields the decomposition of eco-efficiency into technical eco-efficiency and ecological eco-efficiency.
Measuring Power Plant Eco-Efficiency with DEA 183 As a topic for further research, we intend to introduce environmental standards into our models. In this way, we will be able to evaluate the impact of environmen-tal policy on measures of efficiency, and to make multilateral productivity compar-isons across the firms or particular industries in different countries.
Acknowledgment
This research was supported, in part, by grants from the Foundation of the Helsinki School of Economics and the Academy of Finland.
All rights reserved. This chapter may not be reproduced in whole or in part without the authors’ permission.
The topic of the chapter was one of the activities of the Decision Analysis and Support (DAS) Project at IIASA and research was started while the first author worked as the project leader of the DAS Project.
Note
[1] Because the results concerning u and U are also valid for
for simplicity, we often refer to u and U, although we are factually interested in results concerning
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