Developing criteria for evaluating the sustainability of emerging energy

Transcription

1 Iowa State University From the SelectedWorks of Kurt A. Rosentrater April, 2011 Developing criteria for evaluating the sustainability of emerging energy Kurt A. Rosentrater, United States Department of Agriculture Elif Kongar, University of Bridgeport Available at:

2 Developing Criteria for Evaluating the Sustainability of Emerging Energy Technologies Kurt A. Rosentrater 1 Elif Kongar 2 USDA, NC Agricultural Research Lab., USA University of Bridgeport, USA Abstract Over the last decade, the interest in and production of biofuels has grown rapidly. Renewable transportation fuels can be produced from a variety of substrates, using various processing strategies. Feedstock preference and factory scale are geographic dependent. However, it is important to understand the implications of deploying these types of systems on a large scale, both throughout the U.S. as well as globally. To assess the sustainability of various biofuel options, it is crucial to evaluate their performance according to a number of attributes. This has been done using Life Cycle Assessment (LCA). Even so, comparisons among biofuel options are not easily accomplished. The objective of this study was to examine the efficiency of various biofuel options by using criteria which are important to LCA, namely, net energy balance and net carbon dioxide emission, and then compare them using Data Envelopment Analysis (DEA). Biofuel options examined included soy biodiesel, conventional corn ethanol, cellulosic ethanol (switchgrass, poplar, corn stover), and algae biofuel. This comparative approach is illustrated with a numerical example, which found that, using these criteria, soy biodiesel 1 Kurt A. Rosentrater, Ph.D., USDA, ARS, North Central Agricultural Research Laboratory, 2923 Medary Ave., Brookings, SD, 57006, USA, Phone: (605) ; Fax: (605) ; krosentr@ngirl.ars.usda.gov 2 Elif Kongar, Ph.D., Departments of Mechanical Engineering and Technology Management, University of Bridgeport, 221 University Avenue, School of Engineering, 141 Technology Building, Bridgeport, CT 06604, USA, Phone: (203) , Fax: (203) , kongar@bridgeport.edu 1

3 was most efficient. This study is a first step toward a more comprehensive assessment of biofuels, which will need to include additional criteria beyond those considered here. Each of these biofuels will need to be investigated for each criterion, not just from a production perspective, (i.e., quantity required and cost per unit), but also in terms of intangible criteria as well. Keywords: Alternative Energy Sources, Biofuels, CO 2 Emission, Data Envelopment Analysis, Greenhouse Gases, Life Cycle Assessment. INTRODUCTION Over the last decade, the interest in use and production of renewable energy technologies has grown rapidly to include biofuels as an economically and environmentally benign option. Renewable transportation fuels can be produced from a variety of substrates, including agricultural residues, corn stover, grasses, legumes, algae, food processing wastes, and other biological materials. Feedstock preference and factory scale are geographic dependent. For example, in the U.S. corn grain is primarily used, but in Brazil sugarcane is predominant. There have been many questions over the years regarding the sustainability of corn-based ethanol; these have been asked by the scientific community, policymakers, as well as the public itself. Many of these questions have focused on the production of the corn, manufacturing efficiencies, resource and energy inputs versus outputs during fuel 2

4 manufacturing (i.e., the net energy balance and the life cycle of ethanol), process economics, performance of ethanol in vehicles vis-à-vis gasoline, water consumption, land use change, greenhouse gas emissions, and the use of corn for fuel instead of food. To address these questions, many studies have been conducted to examine the overall costs and benefits of this biofuel and to assess its sustainability. Some overviews of these studies have been published as well [1-4]. Modeling and simulation studies, including Life Cycle Assessment (LCA), of the sustainability of biofuels have not yet been completely definitive. Results are dependent upon initial assumptions and system boundaries, as well as the specific steps which are included in the models. Moreover, there is no unanimity in the scientific community concerning adverse environmental impacts of energy extraction, conversion, transportation, and end use, making incorporation of environmental parameters disputable [5]. However, it is important to try to understand the implications of deploying these types of systems on a large scale, both throughout the U.S. as well as globally. Beyond LCA, other approaches to assess sustainability of systems do exist. One of the most relevant research studies has been published by Ulutas [6]; in this research, the author aimed at applying an analytic network process (ANP) model to evaluate alternative energy sources for Turkey. Along similar lines, Junnila [7] performed a scenario analysis for a life-cycle assessment of a service sector company; the analysis involved six case companies to test the influence of 32 alternative scenarios on the 3

5 environmental impact of the median company. Furthermore, Geldermann and Rentz [8] described a multicriteria analysis (MCA) of environmentally relevant installations based on a case study of the surface coating sector. In addition, Ramanathan and Ganesh [9] developed an integrated model for the household sector of Madras, India, using goal programming (GP) and the Analytic Hierarchy Process (AHP) for energy resource allocation. Regarding alternative energy byproducts, Rosentrater and Kongar [10] performed a techno-economic simulation study to compare various fuel ethanol byproduct pelleting processes; the authors indicated that value-added processing is cost effective for a variety of scenarios for biofuel manufacturing residues. Data Envelopment Analysis (DEA) is a well proven methodology allowing the introduction of multiple inputs and multiple outputs and obtains an efficiency score of each DMU with the conventional output/input ratio analysis [11-15]. The method has been extensively used in the literature to evaluate environmental sustainability. Sarkis and his colleagues utilized DEA to evaluate technical and ecological efficiencies of various industries and programs [16-20]. Chien and Lu [21] used the DEA method to estimate the technical efficiency for 45 economies in the years 2001 and Lozano and Gutiérrez [22] also proposed a DEA based approach to model the relationships among population, GDP, energy consumption and CO 2 emissions. Mukherjee [23] utilized DEA to analyze the energy efficiency for the aggregate manufacturing sector as well as for the six highest energy consuming sub-sectors for the period , and to propose various alternative models. Criswell and Thompson [24] used DEA 4

6 methodology to compare the technical efficiency of large-scale power systems needed to meet the growing energy needs of society. Methods other than DEA have also been utilized to study material life cycles and ecoefficiency issues. Moyer and Gupta [25] studied minimization of life cycle scrap via a comprehensive survey of previous work on environmentally conscious manufacturing practices. Further, Isaacs and Gupta [26] analyzed the effects of substitution of highgrade plastics for steel using GP techniques. Boon et al. [27] also applied GP to examine the economic impact of aluminum-intensive vehicles on US automotive recycling. Creating a model of the automobile-recycling infrastructure, Boon et al. [28] also used the GP technique to assess material streams and process profitabilities for several different clean vehicles. In this study, we combine LCA and DEA approaches to assess alternative energy technologies, specifically biofuels, and compare them using various sustainability criteria. This approach is illustrated with a numerical example, based on literature data for various biofuel options, providing a relative comparison for some of the technologies that were analyzed in a recent study [29]. MATERIALS & METHODS DEA allows the introduction of multiple inputs and multiple outputs and obtains an efficiency score for each decision making unit (DMU) with the conventional 5

7 output/input ratio analysis [11]. DEA algorithms can be categorized using two criteria, namely, the orientation and the optimality scale criteria. The orientation criterion categorizes DEA algorithms into two depending on whether the definition of efficiency used in the algorithm is input- or output-oriented. Input-oriented DEA models are suitable for the least input for the same amount of output problems, whereas outputoriented DEA models target the most output for the same amount of input. The optimality scale criterion categorizes DEA models into four based on the returns to scale. If production increases, efficiency may increase, remain constant, or decrease, thus, demonstrating Increasing Returns to Scale (IRS), Constant Returns to Scale (CRS), or Decreasing Returns to Scale (DRS), respectively. Variable Returns to Scale (VRS) refers to a case where both an increase and a decrease in returns to scale are observed at alternative levels of output. First introduced by Banker et al. [30] as an extension of the CRS DEA model, the VRS model assumes that not all DMUs operate on an optimal scale. In this study we propose a basic input-oriented Constant Returns to Scale (CRS) model, assuming constant returns to scale for all of the inputs and outputs. DEA Methodology DEA defines basic efficiency as the ratio of the weighted sum of outputs to the weighted sum of inputs, the relative efficiency score of a test DMU. This non-linear problem can be converted into a linear program as follows: max s k= 1 v k y kp ( 1 ) 6

8 s. t. m j= 1 s k= 1 v u k j x jp y ki = 1 m u j x j= 1 ji 0 DMUs i v k, u j 0 k, j. m In Equation (1), the u = 1 constraint sets an upper bound of 1 for the relative j= 1 j x jp efficiency score, and, k = 1 to s, j = 1 to m, i = 1 to n, y ki = amount of output k produced by DMU i, x ji = amount of input j produced by DMU i, v k = weight given to output k, u j = weight given to input j. The CCR model given in Equation (1) must be run n times for n DMUs to obtain the technical efficiency (TE) of each DMU. The model is characterized by constant returns to scale (CRS). Please see [11-15] for further information on DEA methodology and the details of the CRS DEA model. 7

9 DEA System Definition for the Life Cycle of Biofuels Figure 1 shows the DEA system definition for the proposed model, which describes the biofuel life cycle using two output variables, i.e., CO 2 offset (y 1 ), and net energy produced (y 2 ). The only input variable is the type of biofuel utilized (x 1 ). DEA system y 1 x 1 Energy Conversion y 2 Inputs: x 1 = 1 unit (L) Output: y 1 = (Net Energy (MJ/L) y 2 = CO 2 Offset (%) Figure 1. DEA system with input and outputs for the biofuel life cycle. The DEA model was run by DEA-Solver-PRO v.5.0 by SAITECH, designed on the basis of the work by Cooper et al [15]. Table 1 depicts the data utilized in the DEA model, which was based on information found in the literature. Table 1. Data used for the DEA model to compare various biofuels. DMU y 1 [Citation] y 2 [Citation] No Biofuel Type Feedstock CO 2 Offset (%) Net Energy (MJ/L) 1 Biodiesel Soybean Oil 95 [31] [32] 2 Ethanol (conventional) Corn 30.5 [31] 5.88 [33] 3 Ethanol (cellulosic) Switchgrass 68.6 [31] 23 [1] 4 Ethanol (cellulosic) Hybrid Poplar 61.9 [31] 21 [3] 5 Ethanol (cellulosic) Corn Residue 74 [31] 19.7 [3] 6 Algae biofuel Algae 85 [34] 5.86 [35] 8

10 RESULTS & DISCUSSION By solving the CRS DEA model, the technical efficiency (TE) score of each biofuel type (DMU) was obtained (Table 2). Table 2. Technical efficiency and rank of each biofuel type. DMU TE DMU No. Biofuel Type Feedstock Score Rank 1 Biodiesel Soybean Oil Ethanol (conventional) Corn Ethanol (cellulosic) Switchgrass Ethanol (cellulosic) Hybrid Poplar Ethanol (cellulosic) Corn Residue Algae biofuel Algae Overall Average [TE] = The average technical efficiency was calculated to be (Figure 1). Conventional ethanol (i.e., using corn as a feedstock) was the only biofuel that was below the average efficiency. This was expected, since the CO 2 offset of conventional corn ethanol was significantly lower compared to the remaining biofuels considered in the model. The low value of net energy obtained from algae was compensated by the significant reduction in CO 2 emissions it provides, making algae a relatively efficient option (89.4%). Among all biofuel types, soybean biodiesel was calculated to be the most efficient (100%) option. This was due to its relatively high CO 2 offset and high net energy gain. 9

11 Efficiency Score , 1 Average TE = , , , , , DMU Figure 2. Efficiency score for each biofuel option (DMU). 0 In this study, a numerical case study has been presented as a first step towards a systematic interpretation of various biofuel options. Using this approach will eventually allow us to evaluate the overall efficiency of biofuels within a geographic area, for a variety of substrates and technologies, while emphasizing the relative importance of the different stages of the life cycle for each option. Another important extension of this study will be to include other criteria (beyond CO 2 offset and net energy). Some examples of these additional criteria are provided in Table 3. By adding such information, the role of LCA can be extended to reach out to decision makers in industry and government, as well as members of the general public, who may be able to relate to a physical system, but may have different criteria for evaluating the pros and cons of a 10

12 biofuel. Such additions will therefore enhance the practical relevance of LCA and provide guidance as the industry moves forward. Table 3. Additional criteria for evaluating the sustainability of biofuel options. Financial Economic Technological Transportation cost New job creation Compatibility with current technology Resale revenue Governmental incentives Peripheral equipment requirements Processing cost Governmental subsidy New technology requirements Peripheral equipment cost Tax deduction Technology upgrade rate New technology Patent/copyright/trademark Impact on local economy implementation cost requirements New technology equipment Contribution to the national cost grid Skill/training requirements Maintenance/repair cost Alternative energy utilization Compatibility with national/international standards Societal Processing related Environmental Prioritization of agricultural by-products and residues Gross energy required Shortened crop rotation Impact on local food supply Gross energy produced Reduction in green house has emission Manufacturing safety Cogeneration Noise/light pollution Impact on rural society Quality of fuel Gaseous/solid/liquid waste generation Contribution to national Ease in efficiency independence improvement Losses in biodiversity Self-sustainability Net energy balance Raw material consumption Impact on local society Density of maize cultivation Water recycling/discharge rates Our proposed approach to combining LCA and DEA has the following advantages: - An explicit system definition in LCA terms makes the cycles and the indicators transparent. This is an important prerequisite for any comparison between energy 11

13 technologies, and, as a consequence, for determining the most relevant potentials for increasing the utilization efficiency of alternative energy sources. - DEA can be applied in such a way that the Decision Making Units (DMUs) correspond to specific processes in the LCA. This enables us to define an efficiency indicator for each process in an LCA system. - In contrast to conventional indicators, DEA is much more flexible, allowing for example, multi-dimensional efficiency definitions, which can simultaneously minimize or maximize multiple variables. - Furthermore, criteria in DEA are not limited to information about physical flows alone, but can include any other variable relevant to the performance of a decision making unit, such as cost, health, or pollution. DEA models do not require any a priori weights for either the input or the output variables of interest. However, the results are sensitive to the choice of dataset, and their reliability increases with sample size and, needless to say, accuracy of the data. Unfortunately, data quality and availability for new biofuels technologies are in general very low, and our quantitative results therefore need to be interpreted with caution. Indeed CO 2 offsets and net energy for each of these biofuels are somewhat disputable, as many estimates have been published over the years. Lack of data also affected our model development, only allowing simplified system definitions. Therefore, our proposed model falls somewhat short in providing a better understanding of biofuels. The model would certainly benefit from a logistics system 12

14 infrastructure that includes additional criteria such as scale of production, production and transportation costs, geographical information, etc. But it is a first step toward developing a new approach to examining the efficiency of various biofuels. And nevertheless, this paper has demonstrated the usefulness of combining LCA and DEA approaches for an evaluation of biofuels. 13

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