Comparative Risk Assessment of energy supply technologies: a Data Envelopment Analysis approach

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1 Energy 26 (2001) Comparative Risk Assessment of energy supply technologies: a Data Envelopment Analysis approach R. Ramanathan * Systems Analysis Laboratory, Helsinki University of Technology, P.O. Box 1100, HUT, Finland Received 11 une 1999 Abstract The diverse characteristics of eight different energy technologies are summarized in the form of relative efficiencies using Data Envelopment Analysis. Data from a previously published study was employed for the purpose. The analysis showed that, given the data and variables considered in the analysis, solar photovoltaic and nuclear are the relatively best efficient technologies, followed by natural gas and oil. The main problem of several renewable technologies appears to be the large land area that they require Elsevier Science Ltd. All rights reserved. 1. ntroduction Comparative Risk Assessment (CRA) is the balancing of the benefit cost risk estimates of all the alternatives for accomplishing the same end purpose [1]. Risk is generally defined as the potential exposure to a loss created by a hazard. A hazard is a situation (physical or societal) which, if encountered, could initiate a range of undesirable consequences. Risk assessment is the process of obtaining a quantitative estimate of a risk (probability and consequences). For ease of comparison, CRA approaches generally rely on converting the risks to a single quantitative measure, though there is opposition to such an approach [2]. CRA of different energy supply technologies has been an active area of research over more than three decades, and will continue to be so given the importance of energy-related environmental issues such as global warming. Several CRA studies of energy supply technologies are available [3 9]. The aim of this paper is not to present yet another CRA, but to explore the strengths of quantitative tools to obtain further insights in CRA. Specifically, Data Envelopment Analysis * Fax: address: r.ramanathan@hut.fi (R. Ramanathan) /01/$ - see front matter 2001 Elsevier Science Ltd. All rights reserved. P: S (00)00058-X

2 198 R. Ramanathan / Energy 26 (2001) (DEA) has been employed in this paper to synthesize the diverse characteristics of different energy systems, and to obtain further insights through sensitivity analysis. 2. DEA DEA is a methodology based upon an interesting application of linear programming. t has been successfully employed for assessing the relative performance of a set of firms, usually called decision-making units (DMU), which use a variety of identical inputs to produce a variety of identical outputs. The basic ideas behind DEA date back to Farrel [10], but the recent series of discussions started with the article by Charnes et al. [11]. We give very briefly the salient features of DEA. More detailed information can be obtained elsewhere [12 14]. Assume that there are n DMUs, and that the DMUs under consideration convert inputs to outputs. n particular, let the mth DMU produces outputs y jm using x im inputs. To measure the efficiency of this conversion process by a DMU, a fractional mathematical programming model, denoted as Eq. (1) below, is proposed. The objective function of the model maximizes the ratio of weighted outputs to weighted inputs for the DMU under consideration subject to the condition that the similar ratios for all DMUs be less than or equal to one. That is: max j 1 i 1 v jm y jm u im x im subject to 0 j 1,2,, v jm y jn j 1 i 1 u im x in 1; n 1,2,,N v jm, u im ; i 1,2,,; (1) where the subscript i stands for inputs, j stands for outputs and n stands for the DMUs. The variables v jm and u im are the weights to be determined by the above mathematical program. The term is an arbitrarily small positive number introduced to ensure that all of the known inputs and outputs have positive weight values. The mth DMU is the base DMU in the above model. The optimal value of the objective function of Model 1 is the DEA efficiency score assigned to the mth DMU. f the efficiency score is 1 (or 100%), the mth DMU satisfies the necessary condition to be DEA efficient; otherwise, it is DEA inefficient. Note that the inefficiency is relative to the performance of other DMUs under consideration. t is difficult to solve the above model because of its fractional objective function. However, if either the denominator or numerator of the ratio is forced to be unity, then the objective function will become linear, and a linear programming problem can be obtained. For example, by setting the denominator of the ratio equal to unity, one can obtain the following output maximization linear programming problem, denoted as Eq. (2) below. Note that by setting the numerator equal to unity, it is equally possible to produce input minimization linear programming problem. max j 1 v jm y jm subject to i 1 u im x im 1; j 1 v jm y jn i 1 u im x in 0; n 1,2,,N v jm,u im ; (2)

3 R. Ramanathan / Energy 26 (2001) i 1,2,,; j 1,2,, A complete DEA exercise involves solution of N such models, each for a base DMU (m=1, 2,, N), yielding N different set of weights (v jm, u im ). n each model, the constraints are the same while the ratio to be maximized is changed. DEA literature uses more advanced concepts such as the dual of the above program (Model 2), and incorporation of returns to scale. They are discussed elsewhere [12]. There are also many extensions to the basic models described here (e.g. [13]). Charnes et al. [14] have provided detailed accounts of the important developments in the history of DEA. Thus, DEA has the ability to give a single index of performance, usually called the efficiency score, synthesizing diverse characteristics of different DMUs. Because of this ability, DEA has received numerous applications over the past two decades. Applications include the education sector [15,16], banks [17], health sector [18], environmental performance evaluation [19] and transport [20,21]. Further details of other applications are available in the bibliography compiled at the University of Warwick [22]. n this paper, DEA has been applied to study CRA of energy systems. The DEA efficiency score of the energy systems gives a summary measure of their risk level. 3. CRA of energy systems As mentioned earlier, a number of studies are available in the literature for CRA. For further analysis in this paper, we take up the study by Nathwani et al. [9]. The choice is guided by the strong quantitative approach adopted in the study, and presentation of the comparative numerical data for eight important energy technologies. According to the authors of the study, for discussion of world energy policy, the risks associated with various ways of converting energy must be expressed in forms that are comparable with each other. This has been done in their study by reducing all risks and safety benefits to the common measures of loss or gain of life expectancy (LLE or GLE). The study has considered LLE or GLE which would result if 20% of the total energy supply of a population in the highincome category (with Canada as a typical representative) of the world population were obtained from the technology considered. n addition, the expected land use and carbon dioxide emissions resulting from the use of technologies have also been estimated. Their estimates of the risk and benefit parameters and other variables associated with different energy supply technologies are given in Table 1. For example, according to Nathwani et al. [9], if solar photovoltaic (PV) technology is used to supply 20% of the total energy supply to the high-income category of the world population, it will result in a level of risk equivalent to LLE of 1 day, require 630 km 2 of land, and release 600 tonnes of CO 2 per GW per year. Note that though solar PV does not result in net carbon emission while in operation, it does result in carbon emission in a life-cycle point of view as the production of the materials needed for solar PV power plant will result in CO 2 emissions. One can include several more factors for CRA, such as local pollution impacts of energy technologies. However, we could not consider more factors in this paper, as consistent data for their estimation is not available.

4 200 R. Ramanathan / Energy 26 (2001) Table 1 Comparison of impacts of different energy supply technologies (source: Ref. [9]) a Technology supplying 20% of Land use in km 2 CO 2 emissions in tonnes LLE (days) GLE (days) total demand (LAND) per GW per year (CO 2 ) Solar PV Biomass Windmills Hydroelectric Oil Natural gas Coal Nuclear a Notes: please note that these values are based on several assumptions. These assumptions are explained in the source study. For example, GLE is obtained by the authors of the source study as the estimated fractional contribution of the energy component to the GLE observed in advanced ndustrial societies. 4. Application of DEA to the results of CRA The analysis in this paper has been carried using the software package from the University of Warwick (Windows version 1.03). As mentioned earlier, we use data from the study by Nathwani et al. [9] for the analysis. Of the four variables used in that study to assess the positive and negative impacts associated with the eight energy supply technologies, LAND is the input, while LLE, GLE and CO 2 are the outputs. Accordingly, our DEA analysis considers them as input and outputs respectively. Further, as LLE and CO 2 are undesirable outputs, we associate a negative sign for them. Wherever a range is specified in the original study, we have considered their arithmetic average. First, a straightforward application of DEA to the data given in Table 1 is attempted. The relative efficiency scores are given in Table 2. t is important to note that the efficiency ratings shown are only relative, relative to the best of the technologies considered. The most efficient of all the technologies under consideration is assigned an efficiency score of 100%, while the ratings of others represent their ranking relative to the best technology. Table 2 Efficiency scores of energy supply technologies Technology Score (%) Solar PV Biomass 2.46 Windmills 6.36 Hydroelectric 1.94 Oil Natural gas Coal Nuclear

5 R. Ramanathan / Energy 26 (2001) Note that nuclear and solar PV are rated to be the most efficient technologies in the sense that they produce the maximum aggregate outputs (formed by GLE and negative values of LLE and CO 2 ) with the minimum LAND inputs. These are followed by Natural gas and oil, which are rated as only half as efficient compared to the best technologies. The main problems of nuclear technologies occur because of radioactivity. The original study has considered the radioactivity problems in their calculation of LLE. However, we have found that nuclear energy is rated to be 100% relative efficient even if its LLE is increased. This means that in spite of radioactivity problems, nuclear technologies are rated well for the technologies and variables considered in this study. When the LLE value of Nuclear is changed from 0.8 to 1.0, the only change observed was that the efficiency score of Natural Gas increased from 50 to 100%. Some results of sensitivity analysis are presented in Table 3. The table highlights the very important problems with renewable technologies. They require very large land area, which is reducing their efficiency score. Table 3 shows that hydroelectric power generation technology becomes 100% relatively efficient if its land requirements reduce to about 148 km 2. This means that smaller scale hydro plants may be preferable. Hydroelectric technology can become relatively most efficient if the risks associated with the technology improves so that its LLE decreases by about 65%. Technologies based on Natural gas can achieve the best relative efficiency if the land used by them is reduced by 50% or their carbon emissions reduce by 99%. The table also highlights the trade-off among land requirements, LLE and CO 2 emissions. For example, for coal technologies, a reduction by 71% of land requirements is equivalent to a reduction by 90% of LLE or a reduction by 99% of CO 2 emissions, as all of them will result in cent percentage relative efficiency. Further sensitivity analysis is attempted to obtain more insights on the comparative risk analysis. The original data pertain to the beginning of the 1990s. Obviously, the threat of global warming is considered more real and acute at present than in the beginning of this decade. Hence, it is informative to consider the sensitivity of the efficiency ratings for variations in CO 2 emissions. To assess this, CO 2 emission is excluded from the criterion set and efficiency scores were obtained without this variable. The results showed that, hydroelectric systems, which had higher CO 2 emissions compared to biomass and windmills, has improved its ranking moving ahead of them. We have witnessed a general reduction in preference for nuclear technologies by many developed countries in the past two decades. Hence, we excluded this option from the set of technologies. The resulting efficiency ratings showed that natural gas and oil technologies are Table 3 Targets and reduction (in %) needed for inefficient technologies to reach 100% relative efficiency Technology LAND Reduction LLE Reduction CO 2 Reduction Biomass Windmills Hydroelectric Oil Natural gas Coal

6 202 R. Ramanathan / Energy 26 (2001) rated best technologies along with solar PV. Similarly, when solar PV is excluded, windmills are rated as the best relatively efficient technology along with nuclear. 5. Summary and conclusion CRA involves identifying the risks and benefits associated with different options available for meeting a given purpose. Normally, different options will have diverse characteristics in terms of risks and other benefits. t will be useful if the divergent characteristics can be combined objectively to a single measure. This paper has proposed the use of DEA for synthesizing the diverse characteristics of energy supply technologies into a single objective efficiency score. t has been found that, of the eight technologies considered, nuclear and solar PV technologies were rated to be the most relatively efficient. Further insights can be gained using the sensitivity analysis. For example, nuclear technologies retained their high efficiency even if the radioactivity risks are considered more seriously resulting in an increase in the LLE. t has been found that the main issue associated with large-scale deployment of renewable technologies is the large land area needed by them. References [1] Starr C, Chris W. The strategic defense initiative and nuclear proliferation from a risk analysis perspective. n: Shubik M, editor. Risk, organizations, and society. Boston: Kluwer Academic, 1991: [2] Hansson SO. Dimensions of risk. Risk Analysis 1989;9(1): [3] Rasmussen NC. The application of probabilistic risk assessment techniques to energy technologies. Annual Review of Energy 1981;6: [4] ansiti E, Niehaus F. mpact of energy production on atmospheric concentration of greenhouse gases. AEA Bulletin 1989;2: [5] Cohen AV. Comparative risks of electricity generating systems. ournal of Society of Radiological Protection 1983;3:9 14. [6] World Energy Council. Environmental effects arising from electrical supply and utilisation and the resulting costs to the utility. n: World Energy Conference Report. London: World Energy Council, [7] Fritzsche AF. The health risks of energy production. Risk Analysis 1989;9(4): [8] Chadwick M.., et al. Comparative environmental and health effects of different energy systems for electricity generation. Key ssues Paper No. 3, SM-323/3. Senior Expert Symposium on Electricity and the Environment, Helsinki. Vienna: nternational Atomic Energy Agency, 1991: [9] Nathwani S, Siddall E, Lind NC. Energy for 300 years: Benefits and risks. Ontario, Canada: nstitute for Risk Research, University of Waterloo, [10] Farrel M. The measurement of productive efficiency. ournal of Royal Statistical Society (A) 1957;120: [11] Charnes A, Cooper WW, Rhodes E. Measuring efficiency of decision making units. European ournal of Operational Research 1978;2: [12] Ganley A, Cubbin S. Public sector efficiency measurement: Applications of data envelopment analysis. Amsterdam: Elsevier, [13] Banker RD, Charnes A, Cooper WW. Some models for estimating technical and scale efficiencies in data envelopment analysis. Management Science 1984;30(9): [14] Charnes A, Cooper WW, Lewin AY, Seiford LM. ntroduction. n: Charnes A, Cooper WW, Lewin AY, Seiford LM, editors. Data envelopment analysis: theory, methodology and applications. Boston: Kluwer Academic, 1994:3 21.

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