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Slack-based efficiency assessment of electrical distribution regions in Ghana

Treść / Zawartość
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Warianty tytułu
PL
Oparta na luzie ocena wydajności regionów dystrybucji energii elektrycznej w Ghanie
Języki publikacji
EN
Abstrakty
EN
This paper used the Slack-based efficiency data envelopment analysis model (DEA) to assess the efficiency of electrical distribution regions (EDRs) in Ghana, using Electricity Company of Ghana as a case study, an analysis that had not been previously conducted on the ECG. Results showed that the efficiency dipped drastically in 2013, but improved from 2014 to 2016, stagnating in 2017 and dropping further in 2018. The consistency of the estimations was ensured by establishing the production frontier's form, variable returns to scale.
PL
W tym artykule wykorzystano oparty na Slack model analizy danych dotyczących wydajności (DEA) do oceny wydajności regionów dystrybucji energii elektrycznej (EDR) w Ghanie, wykorzystując Electricity Company of Ghana jako studium przypadku, analizę, która nie została wcześniej przeprowadzona na EKG Wyniki pokazały, że wydajność drastycznie spadła w 2013 r., ale poprawiła się od 2014 do 2016 r., stagnacja w 2017 r. i dalszy spadek w 2018 r. Spójność szacunków została zapewniona poprzez ustalenie postaci granicy produkcji, zmiennych korzyści skali.
Rocznik
Strony
94--99
Opis fizyczny
Bibliogr. 45 poz., rys., tab.
Twórcy
  • University of Johannesburg, Johannesburg, South Africa
autor
  • University of Johannesburg, Johannesburg, South Africa
  • Lead City University, Ibadan, Nigeria
  • University of Johannesburg, Johannesburg, South Africa
Bibliografia
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  • [13] Çelen, A., Efficiency and productivity (TFP) of the Turkish electricity distribution companies: An application of two-stage (DEA&Tobit) analysis. Energy Policy, 2013. 63: p. 300-310.
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  • [17] Fang, H.-H., et al., A slacks-based measure of super-efficiency in data envelopment analysis: An alternative approach. Omega, 2013. 41(4): p. 731-734.
  • [18] Çelen, A. and N. Yalçın, Performance assessment of Turkish electricity distribution utilities: An application of combined FAHP/TOPSIS/DEA methodology to incorporate quality of service. Utilities Policy, 2012. 23(0): p. 59-71.
  • [19] Nwulu, N., A novel method for electricity price determination in deregulated markets. Przeglad Elektrotechniczny, 2020. 1: p. 144-148.
  • [20] Galán, J.E. and M.G. Pollitt, Inefficiency persistence and heterogeneity in Colombian electricity utilities. Energy Economics, 2014. 46(0): p. 31-44.
  • [21] Ramos-Real, F.J., et al., The evolution and main determinants of productivity in Brazilian electricity distribution 1998–2005: An empirical analysis. Energy Economics, 2009. 31(2): p. 298-305.
  • [22] Hemapala, K., K. Perera, and O. Gnana Swathika. Performance Evaluation of Power Distribution Sector of Srilanka based on Data Envelopment Analysis. in Proceedings of International Conference on Sustainable Computing in Science, Technology and Management (SUSCOM), Amity University Rajasthan, Jaipur-India. 2019.
  • [23] Bobde, S.M. and M. Tanaka, Efficiency evaluation of electricity distribution utilities in India: A two-stage DEA with bootstrap estimation. Journal of the Operational Research Society, 2018. 69(9): p. 1423-1434.
  • [24] Martina, S., R. Hakvoort, and V. Ajodhia, Benchmarking as a management and regulatory instrument for Caribbean electric utilities. International Journal of Energy Sector Management, 2008. 2(1): p. 75-89.
  • [25] Pérez-Reyes, R. and B. Tovar, Peruvian Electrical Distribution Firms’ Efficiency Revisited: A Two-Stage Data Envelopment Analysis. Sustainability, 2021. 13(18): p. 10066.
  • [26] Gómez-Calvet, R., et al., Energy efficiency in the European Union: What can be learned from the joint application of directional distance functions and slacks-based measures? Applied Energy, 2014. 132(0): p. 137-154.
  • [27] Chen, L.C., W.M. Lu, and C. Yang, Does knowledge management matter? Assessing the performance of electricity distribution districts based on slacks-based data envelopment analysis. J Oper Res Soc, 2009. 60(11): p. 1583-1593.
  • [28] Tavassoli, M., G.R. Faramarzi, and R. Farzipoor Saen, Ranking electricity distribution units using slacks-based measure, strong complementary slackness condition, and discriminant analysis. International Journal of Electrical Power & Energy Systems, 2015. 64(0): p. 1214-1220.
  • [29] Santos, S.P., C.A.F. Amado, and J.R. Rosado, Formative evaluation of electricity distribution utilities using data envelopment analysis. J Oper Res Soc, 2011. 62(7): p. 12981319.
  • [30] Simar, L. and P.W. Wilson, Non-parametric tests of returns to scale. European Journal of Operational Research, 2002. 139(1): p. 115-132.
  • [31] Kuosmanen, T., A. Saastamoinen, and T. Sipiläinen, What is the best practice for benchmark regulation of electricity distribution? Comparison of DEA, SFA and StoNED methods. Energy Policy, 2013. 61(0): p. 740-750.
  • [32] Pereira de Souza, M.V., et al., An application of data envelopment analysis to evaluate the efficiency level of the operational cost of Brazilian electricity distribution utilities. Socio-Economic Planning Sciences, 2014. 48(3): p. 169-174.
  • [33] Yang, H. and M. Pollitt, Incorporating both undesirable outputs and uncontrollable variables into DEA: The performance of Chinese coal-fired power plants. European Journal of Operational Research, 2009. 197(3): p. 1095-1105.
  • [34] Tone, K., A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 2001. 130(3): p. 498-509.
  • [35] Lo, S.-F. and W.-M. Lu, An integrated performance evaluation of financial holding companies in Taiwan. European Journal of Operational Research, 2009. 198(1): p. 341-350.
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  • [37] Charnes, A., W.W. Cooper, and E. Rhodes, Measuring the Efficiency of Decision-Making Units. European Journal of Operations Research, 1978. 2: p. 429-444.
  • [38] Färe, R. and S. Grosskopf, A Nonparametric Cost Approach to Scale Efficiency. The Scandinavian Journal of Economics, 1985. 87(4): p. 594-604.
  • [39] Banker, R.D., Hypothesis tests using data envelopment analysis. Journal of Productivity Analysis, 1996. 7(2): p. 139159.
  • [40] Simar, L. and P.W. Wilson, Inference by the m out of n bootstrap in nonparametric frontier models. Journal of Productivity Analysis, 2011. 36(1): p. 33-53.
  • [41] Simar, L. and P.W. Wilson, Hypothesis testing in nonparametric models of production using multiple sample splits. Journal of Productivity Analysis, 2020. 53(3): p. 287-303.
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  • [43] Isser, Overview: Global Economic Developments and Ghana’s Economic Performance. 2014. p. 1-22.
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  • [45] Energy Commission, 2020 ENERGY (SUPPLY AND DEMAND) OUTLOOK FOR GHANA. 2020, Ghana Energy Commission: Accra, Ghana.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d28a0bb7-0707-4f0e-b71e-da22fe584ea2
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