Abstract
With the implementation of new round electricity system reform in China, the provincial electricity grid enterprises (EGEs) of China should focus on improving their operational efficiency to adapt to the increasingly fierce market competition and satisfy the requirements of the electricity industry reform. Therefore, it is essential to conduct operational efficiency evaluation on provincial EGEs. While considering the influences of exterior environmental variables on the operational efficiency of provincial EGEs, a three-stage data envelopment analysis (DEA) methodology is first utilized to accurately assess the real operational efficiency of provincial EGEs excluding the exterior environmental values and statistical noise. The three-stage DEA model takes the amount of employees, the fixed assets investment, the 110 kV and below distribution line length, and the 110 kV and below transformer capacity as input variables and the electricity sales amount, the amount of consumers, and the line loss rate as output variables. The regression results of the stochastic frontier analysis model indicate that the operational efficiencies of provincial EGEs are significantly affected by exterior environmental variables. Results of the three-stage DEA model imply that the exterior environmental values and statistical noise result in the overestimation of operational efficiency of provincial EGEs, and the exclusion of exterior environmental values and statistical noise has provincial-EGE-specific influences. Furthermore, 26 provincial EGEs are divided into four categories to better understand the differences of operational efficiencies before and after the exclusion of exterior environmental values and statistical noise.
Highlights
As one of the most significant energy sources of the fundamental industry of the national economy, the electricity industry plays a critical role in the economic progress
To satisfy the requirements of the electricity industry reform, provincial electricity grid enterprises (EGEs) of China should focus on improving their operational efficiency to adapt to the increasingly fierce market competition
To fill the research gap, this investigation for the first time utilizes the three-stage data envelopment analysis (DEA) model integrating the traditional input-oriented BC2 model and stochastic frontier analysis (SFA) methodology to exclude the influences of statistical noise and exotic environmental factors on operational efficiency quantification to evaluate and rank the real operational efficiency of provincial EGEs in China so as to discover the development pathway of EGEs under the new electricity industry reform
Summary
As one of the most significant energy sources of the fundamental industry of the national economy, the electricity industry plays a critical role in the economic progress. The established model failed to eliminate the impacts of statistical noise on efficiency calculation To resolve this issue, Fried et al [17] established the three-stage DEA method integrating the stochastic frontier analysis (SFA) model with the non-parametric DEA model to identify and exclude the influences of statistical noise and exotic environmental factors on the efficiency evaluation aiming at reflecting the real efficiency of each DMU. To fill the research gap, this investigation for the first time utilizes the three-stage DEA model integrating the traditional input-oriented BC2 model and SFA methodology to exclude the influences of statistical noise and exotic environmental factors on operational efficiency quantification to evaluate and rank the real operational efficiency of provincial EGEs in China so as to discover the development pathway of EGEs under the new electricity industry reform. AAtt tthhee fifirrsstt ssttaaggee,, tthhee ooppeerraattiioonnaall eefffificciieenncciieess ooff pprroovviinncciiaall EEGGEEss uunnddeerr tthhee iimmppaaccttss ooff eexxoottiicc eennvviirroonnmmeennttaall ffaaccttoorrss aanndd ssttaattiissttiiccaall nnooiissee aarree ccaallccuullaatteedd bbyy tthhee iinnppuutt--oorriieenntteedd BBCC22 mmooddeel,l,wwhhicihcharaerneanmaemdeads caosmcopmrephreenhseivnesiovpeeroapteiornatailoenfafilcieefnficciieesnicnietshiisnrethseisarrcehs.eAartcthh.e sAectotnhde ssetacgoen,dthsetasglea,ckthceomslapcoknecnotmopfoenaecnht inofpuetacvhariinapbluet ivs adreiacbolme pisosdeedcoinmtopothserede icnattoegthorreiees,cianteclguodriinesg, ienncvluirdoinnmg eenntvailrovnalmueesn,tmalavnaalugeesr,iaml ianneafgfiecrieianlciyn,eaffincdiesntactyi,satincadlsntoatiisset,icaanldntohiseea,dajnudsttehdeiandpjuutstveadriianbpluest vvaarluiaebsleosf veaacluhepsroofveinaccihalpEroGvEinacriealoEbGtaEinaerde tohbrtoauingehdeltihmroinuagthineglitmheiniamtipnagcttsheofimstpataicsttiscoalf nstoaitsiestaicnadl nenovisieroannmd eenntvailrovnamlueenst.alAvtaluthees. tAhtirtdhestthaigrde,sttahgee,otpheeroaptieornaatilonefafliceifefinccieienscieosf opfrporvoivnicnicailalEEGGEEss aarree rreeeevvaalluuaatteedd wwhhiillee eemmppllooyyiinngg tthhee iinnppuutt--oorriieenntteedd BBCC22 mmooddeell,, wwhhiicchh iiss tthhee ssaammee aass tthhee mmooddeell tthhaatt wwaass uusseedd aatt tthhee fifirrsstt ssttaaggee bbuutt iinnppuuttttiinngg tthhee aaddjjuusstteedd iinnppuutt vvaarriiaabblleess vvaalluueess tthhaatt wweerree oobbttaaiinneedd ffrroomm tthhee sseeccoonndd ssttaaggee ttoo ccaallccuullaattee tthhee rreeaall ooppeerraattiioonnaall eefffificciieenncciieessooffpprroovviinncciiaallEEGGEEss
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