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The online version of this article (https://doi.org/10.1007/s12053-018-9627-7) contains supplementary material, which is available to authorized users.
This article has not been reviewed by its institutional funders. The assumptions, views and opinions expressed in the article are solely those of the authors, and do not reflect or represent any official view, opinion, position or policy of Lawrence Berkeley National Laboratory or any agency of the U.S. government.
Dishwashers are a ubiquitous appliance in households in the USA. They combine capital, energy, and water to provide a relevant household service, namely dishwashing. The economic efficiency of dishwashers has been previously assessed using data envelopment analysis (DEA). The approach addresses the technical efficiency of dishwashers based on possible trade-offs between capital and energy. It further draws from the technical efficiency scores an efficient frontier for dishwashing based on these two input factors. We argue that water could also be a relevant input factor to that frontier, especially from the perspective of consumer choice. We develop a DEA model that includes water as an additional input and test if adding water to the analysis contributes to the efficiency frontier. We find that water does have some effect on the frontier, as the DEA model that includes water as an input factor leads to a richer set of efficient possibilities for dishwashing, where energy and water are traded off. We rely on our method and findings to propose two approaches to inform dishwasher consumer choice. One is extending an energy label to include dishwasher water consumption, as a means to inform consumers on their possible trade-offs between energy and water consumption at different levels of appliance price and quality. The other one is disclosing the DEA efficiency scores we estimate as an indicator of the overall economic efficiency of each dishwasher model.
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ESM 1 (CSV 9 kb)12053_2018_9627_MOESM1_ESM.csv
AHAM. (2014). Energy Efficiency and Consumption Trends 1990–2014. Washington, DC: Association of Home Appliance Manufacturers.
AHAM. (2015). Total unit shipments of dishwashers in the U.S. from 2005 to 2016. In Statista.com. [ http://www.statista.com/statistics/220166/unit-shipments-of-dishwashers/].
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
Banker, R. D., & Natarajan, R. (2011). Chapter 11: Statistical tests based on DEA efficiency scores. In W. W. Cooper et al. (Eds.), Handbook on data envelopment analysis, International Series in Operations Research & Management Science 164. New York: Springer.
Bayraktar, E., et al. (2012). Measuring the efficiency of customer satisfaction and loyalty for mobile phone brands with DEA. Expert Systems with Applications, 39(1), 99–106. CrossRef
Bi, G., Song, W., & Wu, J. (2014). A clustering method for evaluating the environmental performance based on slacks-based measure. Computers & Industrial Engineering, 72, 169–177. CrossRef
Blum, H. (2015). The economic efficiency of energy-consuming equipment: a DEA approach. Energy Efficiency, 8(2), 281–298. CrossRef
Blum, H., & Okwelum, E. (2016). Estimating returns to scale and scale efficiency for energy consuming appliances. Berkeley: Lawrence Berkeley National Laboratory.
Castro, M. F., & Guccio, C. (2014). Searching for the source of technical inefficiency in Italian judicial districts: an empirical investigation. European Journal of Law and Economics, 38(3), 369–391. CrossRef
Charnes, A., Cooper, W. W., & Thrall, R. M. (1991). A structure for classifying and characterizing efficiency and inefficiency in data envelopment analysis. Journal of Productivity Analysis, 2(3), 197–237. CrossRef
Chini, C. M., et al. (2016). Quantifying energy and water savings in the U.S. residential sector. Environmental Science & Technology, 50(17), 9003–9012. CrossRef
Davis, L. W. (2008). Durable goods and residential demand for energy and water: evidence from a field trial. RAND Journal of Economics, 39(2), 530–546. CrossRef
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
Dubin, J. A. (1985). Consumer durable choice and the demand for electricity, Contributions to Economic Analysis 155. Amsterdam: North-Holland Publishing Company.
Emrouznejad, A., & Yang, G. (2017). A survey and analysis of the first 40 years of scholarly literature in DEA: 1978-2016. Socio-Economic Planning Sciences. https://doi.org/10.1016/j.seps.2017.01.008.
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
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., Oral, M., & Reisman, A. (2004). Data envelopment analysis literature: a bibliography update (1951–2001). Journal of Socio-Economic Planning Sciences, 38(2–3), 159–229.
Hong, H. K., et al. (1999). Evaluating the efficiency of system integration projects using data envelopment analysis (DEA) and machine learning. Expert Systems with Applications, 16(3), 283–296. CrossRef
Kahrl, F., & Roland-Holst, D. (2008). China’s water-energy nexus. Water Policy, 10(S1), 51–65. CrossRef
Khouja, M. (1995). The use of data envelopment analysis for technology selection. Computers & Industrial Engineering, 28(1), 123–132. CrossRef
Kumar, S., & Gulati, R. (2008). An examination of technical, pure technical, and scale efficiencies in Indian public sector banks using data envelopment analysis. Eurasian Journal of Business and Economics, 1(2), 33–69.
Milgrom, P., & Roberts, J. (1986). Price and advertising signals of product quality. Journal of Political Economy, 94(4), 796–821. CrossRef
Nataraja, N. R., & Johnson, A. L. (2011). Guidelines for using variable selection techniques in data envelopment analysis. European Journal of Operational Research, 215(3), 662–669. CrossRef
Pan, X., Ratchford, B. T., & Shankar, V. (2004). Price dispersion on the internet: a review and directions for future research. Journal of Interactive Marketing, 18(4), 116–135. CrossRef
Papke, L. E., & Wooldridge, J. M. (1996). Econometric methods for fractional response variables with an application to 401(k) plan participation rates. Journal of Applied Econometrics, 11(6), 619–632. CrossRef
Park, D. H., Lee, J., & Han, J. (2007). The effect of online consumer reviews on consumer purchasing intention: the moderating role of involvement. International Journal of Electronic Commerce, 11(4), 125–148. CrossRef
Podinovski, V. V., & Thanassoulis, E. (2007). Improving discrimination in data envelopment analysis: some practical suggestions. Journal of Productivity Analysis, 28(1–2), 117–126. CrossRef
Qin, Z., & Song, I. (2014). Joint variable selection for data envelopment analysis via group sparsity. Social Science Research Network (SSRN). https://doi.org/10.2139/ssrn.2406690.
Ramalho, E. A., Ramalho, J. J. S., & Henriques, P. D. (2010). Fractional regression models for second stage DEA efficiency analyses. Journal of Productivity Analysis, 34(3), 239–255. CrossRef
Richter, C. P. (2010). Automatic dishwashers: efficient machines or less efficient consumer habits? International Journal of Consumer Studies, 34(2), 228–234. CrossRef
Richter, C. P. (2011). Usage of dishwashers: observation of consumer habits in the domestic environment. International Journal of Consumer Studies, 35(2), 180–186. CrossRef
Ruddell, D. M., & Dixon, P. G. (2014). The energy-water nexus: are there tradeoffs between residential energy and water consumption in arid cities? International Journal of Biometeorology, 58(7), 1421–1431. CrossRef
Ruggiero, J. (2005). Impact assessment of input omission on DEA. International Journal of Information Technology and Decision Making, 4(3), 359–368. CrossRef
Sexton, T. R., Silkman, R. H., & Hogan, A. J. (1986). Data envelopment analysis: critique and extensions. In R. H. Silkman (Ed.), Measuring efficiency: an assessment of data envelopment analysis, New Directions for Program Evaluation 32. San Francisco: Jossey-Bass.
Siebens, J. (2013). Extended measures of well-being: living conditions in the United States: 2011, Household Economic Studies (pp. 70–136). Washington, DC: United States Census Bureau.
Simar, L., & Wilson, P. W. (2008). Statistical inference in nonparametric frontier models: recent developments and perspectives. In H. O. Fried et al. (Eds.), The measurement of productive efficiency and productivity growth. New York: Oxford University Press.
Simar, L., & Wilson, P. W. (2011). Two-stage DEA: caveat emptor. Journal of Productivity Analysis, 36, 205–218. 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.
Thanassoulis, E. (1996). A data envelopment analysis approach to clustering operating units for resource allocation purposes. Omega, International Journal of Management Science, 24(4), 463–476. CrossRef
US Census Bureau. (2010a). Manufacturers’ shipments, inventories & orders [historical data]. Washington, DC, USA: United States Census Bureau http://www.census.gov/manufacturing/m3/historical_data/index.html.
US Census Bureau. (2010b). Shipment value of household dishwashing machines in the U.S. from 2008 to 2010. In Statista.com. http://www.statista.com/statistics/221241/shipment-value-of-dishwashers-since-2008/.
US DOE. (2012). Energy conservation program: test procedures for residential dishwashers, dehumidifiers, and conventional cooking products. 10 CFR Parts 429 and 430 [Docket No. EERE–2010–BT–TP–0039], RIN 1904–AC01. Federal Register 77 (211): 65,942–65,997. http://www.regulations.gov/#!documentDetail;D=EERE-2010-BT-TP-0039-0040.
US DOE. (2015). U. S. Department of Energy’s compliance certification database. Washington, DC: U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy http://www.regulations.doe.gov/certification-data/.
US DOE. (2016a). Technical support document: energy efficiency program for consumer products and commercial and industrial equipment—residential dishwashers. Washington, DC: U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy https://www.regulations.gov/document?D=EERE-2014-BT-STD-0021-0029.
US DOE. (2016b). How is electricity used in U.S. homes? Washington, DC: U.S. Department of Energy, Energy Information Administration https://www.eia.gov/tools/faqs/faq.cfm?id=96&t=3.
- Estimating an economic-efficient frontier for dishwasher consumer choice
- Springer Netherlands
- Energy Efficiency
Print ISSN: 1570-646X
Elektronische ISSN: 1570-6478
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