Abstract
To estimate the efficiency scores of companies, various methods have been developed during the past two decades. These methods are generally classified as parametric and non-parametric methods. In the parametric methods, a cost of production function is estimated, whereas in the non-parametric methods, it is not necessary to estimate the cost or production function. Stochastic Frontier Analysis (SFA) and Data Envelopment Analysis (DEA) are the major parametric and non-parametric models respectively. In this chapter a Multiple Criteria Data Envelopment Analysis (MCDEA) model is developed and applied to the selection of a renewable project based on the concept of efficiency.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision-making units. Eur J Oper Res 2:429–444
Hu JL, Kao CH (2007) Efficient energy-savings targets for APEC economies. Energ Policy 35:373–382
Hu JL, Wang SC, Yeh FY (2006) Total-factor water efficiency of regions in China. Res Policy 31:217–230
Li XB, Reeves GR (1999) A multiple criteria approach to data envelopment analysis. Theory and Methodology. Eur J Oper Res 115:507–517
San Crist\(\acute{\rm{o}}\)bal JR (2011) A multi-criteria data envelopment analysis model to evaluate the efficiency of the renewable energy technologies. Renew Energ 36(10):2742–2746
Cote DO (1989) Firm efficiency and ownership structure-the case of U.S. electric utilities using panel data. Ann Public Coop Econ 60(4):431–450
Hjalmarsson L, Veiderpass A (1992) Efficiency and ownershipin Swedish electricity retail distribution. J Prod Anal 3(1/2):7–23
Milliotis PA (1992) Data envelopment analysis applied to electricity distribution districts. J Oper Res Soc 43:549–555
Golany B, Roll Y, Ryback D (1994) Measuring efficiency of power plants in Israel by data envelopment analysis. IEEE T Eng Manage 41(3):291–301
Jha DK, Shrestha R (2006) Measuring efficiency of hydropower plants in Nepal using data envelopment analysis. IEEE T Power Syst 21(4):1502–1511
Vaninsky A (2006) Efficiency of electric power generation in the United States: analysis and forecast based on data envelopment analysis. Energ Econ 28(3):326–338
Azadeh A, Ghaderi SF, Maghsoudi A (2008) Location optimization of solar plants by an integrated hierarchical DEA PCA approach. Energ Policy 36:3393–4004
Sadjadi SJ, Omrani H (2008) Data envelopment analysis with uncertain data: a application for Iranian electricity distribution companies. Energ Policy 36:4247–4254
Jayanthi S, Witt EC, Singh V (2009) Evaluation of potential innovations: a DEA-based application to U.S. photovoltaic industry. IEEE T Eng Manage 56(3):478 August
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2012 Springer-Verlag London Limited
About this chapter
Cite this chapter
Mateo, J.R.S.C. (2012). A Multi-Criteria Data Envelopment Analysis. In: Multi Criteria Analysis in the Renewable Energy Industry. Green Energy and Technology. Springer, London. https://doi.org/10.1007/978-1-4471-2346-0_9
Download citation
DOI: https://doi.org/10.1007/978-1-4471-2346-0_9
Published:
Publisher Name: Springer, London
Print ISBN: 978-1-4471-2345-3
Online ISBN: 978-1-4471-2346-0
eBook Packages: EngineeringEngineering (R0)