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DC FieldValueLanguage
dc.contributorMulti-disciplinary Studiesen_US
dc.contributorDepartment of Managementen_US
dc.creatorWu, Yiu-fai-
dc.identifier.urihttps://theses.lib.polyu.edu.hk/handle/200/135-
dc.languageEnglishen_US
dc.publisherHong Kong Polytechnic University-
dc.rightsAll rights reserveden_US
dc.titleImproving the service efficiency in Hong Kong investor-owned electricity industry by applying data envelopment analysis and regression approachesen_US
dcterms.abstractThis dissertation applies data envelopment analysis (DEA) and regression analysis (RA) for evaluating and comparing the efficiency measures of two investor-owned power utilities in Hong Kong. Emphasis is placed on DEA process of screening the list of potential inputs and outputs factors and determining the most relevant ones. DEA results are derived under four different sets of assumptions. The efficiency scores for 4 models are ranged from 0.7 to 1.0 and the averages are over 90 percents. DEA results are also compared with efficiency measures produced by RA. The result shows that a consistent effect cannot be obtained. This dissertation also combines DEA with RA to evaluate the efficiency measures. Efficiency is first estimated using DEA with the choice of inputs and outputs being specific to utility operations. A multiple regression model is then employed in which the efficiency score obtained from the DEA computations is used as the dependent variable, and a number of utility operating characteristics are chosen as the independent variables. The regression results indicate that plant availability, load factor and proportion of distribution to non-domestic customers support the results are negative associated with the efficiency.en_US
dcterms.extentv, 202 leaves : ill. ; 30 cmen_US
dcterms.isPartOfPolyU Electronic Thesesen_US
dcterms.issued2000en_US
dcterms.educationalLevelAll Masteren_US
dcterms.educationalLevelM.Sc.en_US
dcterms.LCSHElectric utilities -- China -- Hong Kongen_US
dcterms.LCSHData envelopment analysisen_US
dcterms.LCSHRegression analysisen_US
dcterms.LCSHHong Kong Polytechnic University -- Dissertationsen_US
dcterms.accessRightsrestricted accessen_US

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Please use this identifier to cite or link to this item: https://theses.lib.polyu.edu.hk/handle/200/135