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Energy Efficiency

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01.04.2015 | Original Article

The economic efficiency of energy-consuming equipment: a DEA approach

verfasst von: Helcio Blum

Erschienen in: Energy Efficiency | Ausgabe 2/2015

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Abstract

The market for an energy-consuming device offers a range of models that will meet consumers’ needs for an energy service with different levels of energy efficiency. A more efficient model is likely to have greater up-front costs, but the increased efficiency will eventually translate into energy cost savings over the device’s lifespan. Cost-effectiveness indicators (namely, net benefit and benefit-cost ratio) can be used to assess whether a more efficient model can be a better alternative for consumers. However, whereas these indicators express to what extent the additional benefits outweigh the additional costs, they do not indicate how efficiently each model allocates capital and energy to provide the energy service. They, therefore, lack the economic efficiency dimension of the problem. This paper introduces a data-oriented, non-parametric approach to evaluate such efficiency for a set of alternative models of an energy-consuming device. It relies on data envelopment analysis (DEA) to calculate relative efficiency coefficients. The coefficients establish an input efficient frontier for the energy service provided and indicate the models that provide the energy service at the least cost. DEA is further extended to calculate the highest cost-effectiveness achievable and indicate the most cost-effective alternatives. The approach proves useful to support consumers’ decision-making when shopping for energy-consuming equipment, to guide manufacturers when benchmarking the models they produce, and to inform energy efficiency policy-making and program designing.

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Examples of energy-consuming devices and the corresponding energy services they provide are air-conditioners for space cooling, furnaces for space heating, boilers for water heating, clothes washers for clothes washing, air compressors for air compressing, and pumps for water pumping.

More on applications of DEA to energy and environmental studies can be found in Zhou et al. (2008).

Capital here refers to total equipment-related costs. It includes purchase, shipment, and installation costs, as well as any non-energy-related operating costs. For the sake of comparability, these costs should not include any costs associated with features, utilities, or services that are not available in all models under assessment.

The reference model is usually one that is less energy-efficient and less costly than the model being assessed. Typically, it will be the least costly alternative in the market that provides the energy service at the highest energy efficiency and, consequently, with the lowest energy consumption.

See, for example, Short et al. (1995), Elliot et al. (1997), Wroblewski et al. (1997), Mahlia et al. (2002), Lee et al. (2003), EPA (2008), Nikolaidis et al. (2009), McNeil and Bojda (2012), and the Technical Support Documents the U.S. Department of Energy publishes as part of its energy efficiency standard rulemakings. (https://www1.eere.energy.gov/buildings/appliance_standards/standards_test_procedures.html)

See section “Estimating the economic efficiency of energy-consuming devices with DEA” for a discussion on weak efficiency.

This is illustrated in section “Illustrative case”.

More on DEA concepts and applications can be found in Cooper et al. (2011b). For literature reviews of DEA, see Gattoufi et al. (2004b) and Emrouznejad et al. (2008).

The term “virtual” refers to the fact that a benchmark production unit for a non-efficient one may not be an actual unit but rather a hypothetical one that incorporates characteristics from other efficient units in the frontier.

An input distance function characterizes a DMU by looking at a minimal proportional contraction of its inputs, given its outputs. An output distance function considers a maximal proportional expansion of the DMU’s outputs, given its inputs (definition adapted from Coelli et al. (2005)).

According to Sengupta (2003), cost-oriented efficiency models can be used when outputs are not easily quantified or not measurable at all. They, therefore, provide a more general framework than a production efficiency approach.

The reduced format of Eq. (3c) operates as a convexity constraint that drives the linear program to solve for solutions with non-decreasing returns to scale. However, since in this case all DMUs produce the same amount of energy service, they all operate at the same scale and the constraint has no effect on the DMU’s efficiency. For more on returns to scale in DEA see, for example, Golany and Yu (1997) or Cooper et al. (2011b).

“Other models” will typically refer to all other existing models of the energy-consuming device in the market. One can also consider extending the set of existing models with additional theoretical models that will work as benchmarks (or standards) for the models available in the market. This may be of particular interest to policy analysis. For more on the benefits and limitations from incorporating standards in DEA, see Golany and Roll (1994).

An inefficient DMU can be referred to different points in the efficiency frontier depending on the preferences of the decision-maker. See Lins et al. (2004) for a summary of approaches to refer inefficient DMUs to the efficiency frontier and for a multi-objective linear programming model that allows an inefficient DMU to be projected onto any point of the efficiency frontier.

Equation 4c, similarly to Eq. (3c), is presented in its reduced form and does not explicit the outputs from the DMUs. Further, in a standard slack-based program, this expression would include an output slack. However, because in this application all DMUs provide the same amount of energy service as output, all output slacks are zero.

If an inefficient model has only one or no fully efficient peers, the reference capital and energy for the inefficient model to become efficient should be the capital and energy calculated from Eqs. (5a) and (5b).

This is illustrated in “Illustrative case” section.

The second term of the right side of Eq. (7) is the cost-efficiency score of the model. For more on the use of DEA to calculate allocative- and cost-efficiency scores, see Cooper et al. (2011a) or Coelli et al. (2005).

Productivity is measured by the ratio of outputs to inputs. Since all models provide the same energy service, the ones that provide the energy service with the least costs clearly present the highest productivity. In addition, it can be demonstrated that these models are also the ones with the greatest difference between energy cost savings and additional capital when compared to the most efficient least expensive model.

Example of other works that have extended DEA to calculate cost-effectiveness indicators with DMUs’ inputs and outputs different than the cost and benefit components of cost-effectiveness indicators are Kuosmanen and Kortelainen (2007), where a “DEA-type framework” is used to calculate a competitive advantage coefficient based on net-benefits and Kortelainen and Kuosmanen (2007), where an eco-efficiency measure—also based on net-benefits—is calculated for consumer durables.

Transformation developed upon Bisschop (2012), who relied on Charnes and Cooper (1962) to setup a step-by-step approach for the transformation process.

The benefit-cost ratio is calculated relative to model m ₀ = (k ₀, g ₀), and therefore, m ₀ is not part of this program. Consequently, k* > k ₀, and there is no risk in letting u* ≥ 0 as u* will never be zero.

See "Appendix: Original data" for the original version of the data used in the illustrative case.

All non-energy-related operating costs—namely, maintenance and repair costs—are assumed to be zero for the eight models.

A line linking “m5” to “m5′” in Fig. 2 would extend through the origin. Note in this figure the intersection of the axes is at point (400, 51).

This is a proxy to the average electricity rate projected by the U.S. Department of Energy (DOE 2014) to be paid by commercial consumers in the USA over the next 10 years ($0.101 per kWh).

The linear program in Box 2 was used to maximize the benefit-cost ratio.

Using a different reference model to calculate the cost-effectiveness indicators would lead to different results. Table 6 presents cost-effectiveness indicators calculated relative to models “m1,” “m5,” and “m8.” Interesting to note, due to the definition of net-benefit and benefit-cost ratio, whereas the former preserves the ranking across models, the latter does not.

Baker, R. C., & Talluri, S. (1997). A closer look at the use of data envelopment analysis for technology selection. Computers & Industrial Engineering, 32(1), 101–108.CrossRef

Baxter, L. W., et al. (1986). An efficiency analysis of household energy use. Energy Economics, 8(2), 62–73.CrossRef

Bisschop, J. (2012). AIMMS—optimization modeling. Bellevue: Paragon Decision Technology B.V.

Blum, H., Atkinson, B., & Lekov, A. B. (2013). A methodological framework for comparative assessments of equipment energy policy measures. Energy Efficiency, 6(1), 65–90.CrossRef

Boyd, G. A., & Pang, J. X. (2000). Estimating the linkage between energy efficiency and productivity. Energy Policy, 28(5), 289–296.CrossRef

Boyd, G., et al. (1993). Energy intensity improvements in steel minimills. Contemporary Policy Issues, 11(3), 88–100.CrossRef

Charnes, A., & Cooper, W. W. (1962). Programming with linear fractional functionals. Naval Research Logistics Quarterly, 9(3–4), 181–186.CrossRefMATHMathSciNet

Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision-making units. European Journal of Operational Research, 2(6), 429–444.CrossRefMATHMathSciNet

Charnes, A., et al. (1985). Foundation of data envelopment analysis for Pareto-Koopmans efficient empirical production functions. Journal of Econometrics, 30(1–2), 91–107.CrossRefMATHMathSciNet

Chauhan, N. S., Mohapatra, P. K. J., & Pandey, K. P. (2006). Improving energy productivity in paddy production through benchmarking—an application of data envelopment analysis. Energy Conversion and Management, 47(9–10), 1063–1085.CrossRef

Chen, T. Y., & Yu, O. S. (1997). Performance evaluation of selected US utility commercial lighting demand-side management programs. Energy Engineering: Journal of the Association of Energy Engineering, 94(4), 50–66.

Coelli, T. J., Prasada Rao D. S., O'Donnell C. J., et al. (2005). An introduction to efficiency and productivity analysis. New York: Springer Science + Business Media.

Cook, W. D., & Zhu, J. (2005). Modeling performance measurement: applications and implementation issues in DEA. New York, NY, USA: Springer Science + Business Media.

Cooper, W.W., Seiford, L.M., Zhu, J. (2011a). Data envelopment analysis: history, models, and interpretations. In W.W. Cooper, L.M. Seiford, J. Zhu (Eds.), Handbook on data envelopment analysis (International series in operations research & management science, Vol. 164). New York: Springer.

Cooper, W. W., Seiford, L. M., & Zhu, J. (2011b). Handbook on data envelopment analysis (International series in operations research & management science, Vol. 164). New York: Springer.

DOE (2014). Electricity supply, disposition, prices, and emissions. In Annual energy outlook, interactive table viewer. U.S. Energy Information Administration, U.S. Department of Energy, Washington, DC, USA. http://www.eia.gov/oiaf/aeo/tablebrowser/#release=AEO2014&subject=3-AEO2014&table=8-AEO2014&region=0-0&cases=ref2014-d102413a.

Doyle, J. R., & Green, R. H. (1991). Comparing products using data envelopment analysis. Omega International Journal of Management Science, 19(6), 631–638.CrossRef

Doyle, J. R., & Green, R. H. (1994). Strategic choice and data envelopment analysis: comparing computers across many attributes. Journal of Information Technology, 9(1), 61–69.CrossRef

Elliot, R.N., Laitner, S., Pye, M. (1997). Considerations in the estimation of costs and benefits of industrial energy efficiency projects. Energy conversion engineering conference 1997. IECEC-97 proceedings of the 32 ^nd intersociety energy conversion engineering congress vol 3: 2143–2147. Honolulu, HI, USA.

Emrouznejad, A., Parker, B. R., & Tavares, G. (2008). Evaluation of research in efficiency and productivity: a survey and analysis of the first 30 years of scholarly literature in DEA. Journal of Socio-Economic Planning Sciences, 42(3), 151–157.CrossRef

EPA (2008). Understanding cost-effectiveness of energy efficiency programs: best practices, technical methods, and emerging issues for policy-makers. National Action Plan for Energy Efficiency. Energy and Environmental Economics, Inc. and Regulatory Assistance Project.

Färe, R., & Primont, D. (1995). Multi-output production and duality: theory and applications. Norwell: Kluwer Academic Publishers.CrossRef

Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society, 120(III), 253–281.CrossRef

Fernandez-Castro, A. S., & Smith, P. C. (2002). Lancaster’s characteristics approach revisited: product selection using non-parametric methods. Managerial and Decision Economics, 23(2), 83–91.CrossRef

Gattoufi, S., et al. (2004a). Epistemology of data envelopment analysis and comparison with other fields of OR/MS for relevance to applications. Journal of Socio-Economic Planning Sciences, 38(2–3), 123–140.CrossRef

Gattoufi, S., Oral, M., & Reisman, A. (2004b). Data envelopment analysis literature: a bibliography update (1951–2001). Journal of Socio-Economic Planning Sciences, 38(2–3), 159–229.

Golany, B., & Roll, Y. (1994). Incorporating standards via DEA. In A. Charnes, W. W. Cooper, A. Y. Lewin, & L. M. Seiford (Eds.), Data envelopment analysis: theory, methodology, and application. New York: Springer.

Golany, B., & Tamir, E. (1995). Evaluating efficiency-effectiveness-equality trade-offs: a data envelopment analysis approach. Management Science, 41(7), 1172–1184.CrossRefMATH

Golany, B., & Yu, G. (1997). Estimating returns to scale in DEA. European Journal of Operational Research, 103(1), 28–37.CrossRefMATH

Hu, J. L., & Kao, C. H. (2007). Efficient energy-saving targets for APEC economies. Energy Policy, 35(1), 373–382.CrossRef

Hu, J. L., & Wang, S. C. (2006). Total-factor energy efficiency of regions in China. Energy Policy, 34(17), 3206–3217.CrossRef

Khouja, M. (1995). The use of data envelopment analysis for technology selection. Computers & Industrial Engineering, 28(1), 123–132.CrossRef

Kortelainen, M., & Kuosmanen, T. (2007). Eco-efficiency analysis of consumer durables using absolute shadow prices. Journal of Productivity Analysis, 28(1–2), 57–69.CrossRef

Kuosmanen, T., & Kortelainen, M. (2007). Valuing environmental factors in cost-benefit analysis using data envelopment analysis. Ecological Economics, 62(1), 56–65.CrossRef

Lee, W.-S. (2008). Benchmarking the energy efficiency of government buildings with data envelopment analysis. Energy and Buildings, 40(5), 891–895.CrossRef

Lee, W. L., Yik, F. W. H., & Jones, P. (2003). A strategy for prioritising interactive measures for enhancing energy efficiency of air-conditioned buildings. Energy, 28(8), 877–893.CrossRef

Lins, M. P. E., Angulo-Meza, L., & Silva, A. C. M. (2004). A multi-objective approach to determine alternative targets in data envelopment analysis. The Journal of the Operational Research Society, 55(10), 1090–1101.CrossRefMATH

Mahlia, T. M. I., Masjuki, H. H., & Choudhury, I. A. (2002). Theory of energy efficiency standards and labels. Energy Conversion and Management, 43(6), 743–761.CrossRef

McNeil, M. A., & Bojda, N. (2012). Cost-effectiveness of high-efficiency appliances in the U.S. residential sector: a case study. Energy Policy, 45, 33–42.CrossRef

Nikolaidis, Y., Pilavachi, P. A., & Chletsis, A. (2009). Economic evaluation of energy saving measures in a common type of Greek building. Applied Energy, 86(12), 2550–2559.CrossRef

Odeck, J., & Hjalmarsson, L. (1996). The performance of trucks—an evaluation using data envelopment analysis. Transportation Planning and Technology, 20(1), 49–66.CrossRef

Onut, S., & Soner, S. (2006). Energy efficiency assessment for the Antalya Region hotels in Turkey. Energy and Buildings, 38(8), 964–971.CrossRef

Sengupta, J. K. (2003). New efficiency theory—with applications of data envelopment analysis. Berlin, Germany: Springer.MATH

Short, W., Packey, D. J., & Holt, T. (1995). A manual for the economic evaluation of energy efficiency and renewable energy technologies (Report NREL/TP-462-5173). Golden: National Renewable Energy Laboratory.CrossRef

Staat, M. R., Bauer, H. H., & Hammerschmidt, M. (2002). Structuring product markets: an approach based on customer value. Marketing Educators’ Conference Proceedings, 13(1), 205–212.

Womer, N. K., et al. (2006). Benefit-cost analysis using data envelopment analysis. Annals of Operations Research, 145(1), 229–250.CrossRefMATH

Wroblewski, R.G., et al. (1997). An analysis of motor system optimization options—a question of diminishing return on investment. Proceedings from the Nineteenth Industrial Energy Technology Conference ESL-IE-97-04-17: 2143-2147. Houston, TX, USA.

Zhou, P., & Ang, B. W. (2008). Linear programming models for measuring economy-wide energy efficiency performance. Energy Policy, 36(8), 2911–2916.CrossRef

Zhou, P., Ang, B. W., & Poh, K. L. (2008). A survey of data envelopment analysis in energy and environmental studies. European Journal of Operational Research, 189(1), 1–18.CrossRefMATHMathSciNet

Titel: The economic efficiency of energy-consuming equipment: a DEA approach
verfasst von: Helcio Blum
Publikationsdatum: 01.04.2015
Verlag: Springer Netherlands
Erschienen in: Energy Efficiency / Ausgabe 2/2015
Print ISSN: 1570-646X
Elektronische ISSN: 1570-6478
DOI: https://doi.org/10.1007/s12053-014-9283-5

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