Assessing power stations performance using a DEA-bootstrap approach

Tsolas, IE

dc.contributor.author	Tsolas, IE	en
dc.date.accessioned	2014-03-01T01:59:36Z
dc.date.available	2014-03-01T01:59:36Z
dc.date.issued	2010	en
dc.identifier.issn	17506220	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/29007
dc.subject	Computer bootstrapping	en
dc.subject	Data analysis	en
dc.subject	Electric power stations	en
dc.subject	Fossil fuels	en
dc.subject	Greece	en
dc.subject.other	Aggregate performance	en
dc.subject.other	Bias correction	en
dc.subject.other	Bootstrap approach	en
dc.subject.other	Bootstrapping model	en
dc.subject.other	Computer bootstrapping	en
dc.subject.other	Confidence interval	en
dc.subject.other	Data analysis	en
dc.subject.other	DEA models	en
dc.subject.other	Design/methodology/approach	en
dc.subject.other	Electric power stations	en
dc.subject.other	Electricity production	en
dc.subject.other	Energy economics	en
dc.subject.other	Environmental performance	en
dc.subject.other	Fired power station	en
dc.subject.other	Greece	en
dc.subject.other	Initial point	en
dc.subject.other	Performance metrics	en
dc.subject.other	Point estimate	en
dc.subject.other	Power station	en
dc.subject.other	Statistical properties	en
dc.subject.other	Statistical significance	en
dc.subject.other	Aggregates	en
dc.subject.other	Benchmarking	en
dc.subject.other	Data envelopment analysis	en
dc.subject.other	Data reduction	en
dc.subject.other	Economics	en
dc.subject.other	Environmental management	en
dc.subject.other	Fossil fuels	en
dc.subject.other	Lignite	en
dc.subject.other	Power plants	en
dc.subject.other	Statistical methods	en
dc.subject.other	Uncertainty analysis	en
dc.title	Assessing power stations performance using a DEA-bootstrap approach	en
heal.type	journalArticle	en
heal.identifier.primary	10.1108/17506221011073833	en
heal.identifier.secondary	http://dx.doi.org/10.1108/17506221011073833	en
heal.publicationDate	2010	en
heal.abstract	Purpose: The purpose of this paper is to assess the performance of Greek fossil fuel-fired power stations employing a data envelopment analysis (DEA) model combined with bootstrapping. Design/methodology/approach: DEA is used to derive aggregate performance indicators using data on inputs and desirable and undesirable outputs for a sample of fossil fuel-fired power stations. The statistical significance of the derived aggregate performance indicators is assessed via the bootstrapping approach. Findings: The results suggest that the power stations in the sample are considerably more inefficient than revealed by the initial point estimates of inefficiency. Moreover, the non-lignite-fired stations of the sample are on an average more efficient than the lignite-fired stations. Research limitations/implications: DEA represents a useful framework for exploring the current state to derive aggregate performance indicators of power stations, and moreover, the statistical properties of these metrics can be assessed via the bootstrapping approach. Practical implications: The bootstrapping approach in DEA shows its superiority over DEA models that do not address the uncertainty surrounding point estimates. The DEA bootstrapping model used in this study to model environmental performance in the power station electricity production setting provides bias correction and confidence intervals for the point estimates and it is therefore more preferable. Originality/value: The derivation of aggregate performance indicators of Greek fossil fuel-fired power stations is an important addition to the existing literature on energy economics. The paper is also innovated in providing the statistical properties of the derived performance metrics. © Emerald Group Publishing Limited.	en
heal.journalName	International Journal of Energy Sector Management	en
dc.identifier.doi	10.1108/17506221011073833	en
dc.identifier.volume	4	en
dc.identifier.issue	3	en
dc.identifier.spage	337	en
dc.identifier.epage	355	en