Investigating the energy efficiencies of OECD countries via a slack-based undesirable output model

Author/s Fazil Gokgoz, Serap Pelin Turkoglu
Publishing Year 2018 Issue 2017/3 Language Italian
Pages 22 P. 73-94 File size 249 KB
DOI 10.3280/EFE2017-003005
DOI is like a bar code for intellectual property: to have more infomation click here

Below, you can see the article first page

If you want to buy this article in PDF format, you can do it, following the instructions to buy download credits

Article preview

FrancoAngeli is member of Publishers International Linking Association, Inc (PILA), a not-for-profit association which run the CrossRef service enabling links to and from online scholarly content.

Energy efficiency has a growing importance both for developed and developing countries within OECD. This study investigates the energy efficiency levels for OECD countries for the 2010-2014 period via a slack-based, undesirable output model, a popular data envelopment analysis (DEA) approach. Efficiency analyses have been carried out with two sub-groups of OECD countries. Within the framework of the DEA analyses, the primary energy consumption, total number of employees and gross capital formation as inputs and CO2 emission and gross domestic product (GDP) as outputs have been selected. In further, a binary logistic regression method has also been applied so as to analyse the factors affecting the energy efficiencies of OECD countries. The effect levels of these factors have been determined by logistic regression analysis. Empirical analyses have shown that minimizing an undesirable output while maximizing desirable outputs for the energy has a crucial role for OECD countries in reducing environmental pollution and increase the competition capacity. Besides, logistic regression results have shown significant results for decision and policy makers in the energy sector. The results of this study show that OECD countries can achieve a good level of energy efficiency if they improve their economic activities by improving their environmental performance.

Keywords: A slack-based undesirable output model, binary logistic regression analysis, energy efficiency, data envelopment analysis, OECD.

Jel codes: C44, C61, C67, Q43

  1. Akbulut R., Rençber Ö.F. (2015). Evaluation of efficiencies based on financial performance with data envelopment and logistic regression analysis in the cement businesses. Journal of Alanya Faculty of Business, 7(3): 123-135.
  2. Apergis N., Aye G.C., Barros C.P., Gupta R., Wanke P. (2015). Energy efficiency of selected OECD countries: A slacks based model with undesirable outputs. Energy Economics, 51: 45-53. DOI: 10.1016/J.ENECO.2015.05.022
  3. Arazmuradov A. (2011). Energy consumption and carbon dioxide environmental efficiency for former Soviet Union economies. evidence from DEA window analysis. MPRA, Paper No. 36903.
  4. Banker R.D., Charnes A., Cooper W.W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9): 1078-1092. DOI: 10.1287/MNSC.30.9.1078
  5. BP (2015). BP Statistical Review of World Energy June 2015 -- (Accessed Date: 23.01.2016).
  6. Camioto F.D.C., Rebelatto D.A.D.N., Rocha R.T. (2015). Energy efficiency analysis of BRICS countries: A study using data envelopment analysis. Gestão & Produção, 1-12. DOI: 10.1590/0104-530X1567-13
  7. Charnes A., Cooper W.W., Rhodes E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6): 429-444. DOI: 10.1016/0377-2217(79)90229-7
  8. Chien T., Hu J.L. (2007). Renewable energy and macroeconomic efficiency of OECD and non-OECD economies. Energy Policy, 35(7): 3606-3615. DOI: 10.1016/J.ENPOL
  9. 2006.12.033. Cooper W.W., Seiford L.M., Tone K. (2007). Data Envelopment Analysis A Comprehensive Text with Models, Applications, References and DEA-Solver Software. Springer, New York.
  10. Cooper W.W., Seiford L.M., Zhu J. (2011). Handbook on Data Envelopment Analysis. Springer, New York.
  11. Çokluk Ö., Şekercioğlu G., Büyüköztürk Ş. (2016). Sosyal Bilimler için Çok Değişkenli İstatistik SPSS ve LISREL Uygulamaları “Multivariate Statistics SPSS and LISREL Applications for Social Sciences”. Pegem Academy, Ankara.
  12. De Castro Camioto F., Moralles H.F., Mariano E.B., do Nascimento Rebelatto D.A. (2016). Energy efficiency analysis of G7 and BRICS considering total-factor structure. Journal of Cleaner Production, 122: 67-77. DOI: 10.1016/J.JCLEPRO.2016.02.061
  13. Diaz-Balteiro L., Herruzo A. C., Martinez M., González-Pachón J. (2006). An analysis of productive efficiency and innovation activity using DEA: An application to Spain’s woodbased industry. Forest Policy and Economics, 8(7): 762-773.
  14. European Environment Agency (2015). Approximated EU GHG Inventory: Proxy GHG Emission Estimates for 2014. EEA Technical Report, No. 15: 1-139.
  15. Eurostat, Primary Energy Consumption -- (Accessed date: 23.02.2016).
  16. Eurostat Newsrelease (2015). Early Estimates of CO2 Emissions from Energy Use in 2014. CO2 Emissions in The EU Estimated to Have Decreased by 5% Compared with 2013, No. 105: 1-2.
  17. Farrell M.J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society. Series A (General), 120(3): 253-290. DOI: 10.2307/2343100
  18. Gökgöz F., Erkul E. (2014). Energy efficiency analysis for the European countries. Economy & Business Journal, 8(1): 124-140.
  19. IEA (2015). Energy Balances of OECD Countries. IEA Publications, France.
  20. IEA&OECD/IEA (2015) -- (Accessed date: 24.02.2016).
  21. Jia Y.P., Liu R.Z. (2012). Study of the energy and environmental efficiency of the Chinese economy based on a DEA model. Procedia Environmental Sciences, 13: 2256-2263. DOI: 10.1016/J.PROENV.2012.01.214
  22. Kumar S. (2006). Environmentally sensitive productivity growth: a global analysis using Malmquist-Luenberger index. Ecological Economics, 56(2): 280-293. DOI: 10.1016/J.ECOLECON.2005.02.004
  23. Li H., Fang K., Yang W., Wang D., Hong X. (2013). Regional environmental efficiency evaluation in China: Analysis based on the Super-SBM model with undesirable outputs. Mathematical and Computer Modelling, 58(5): 1018-1031. DOI: 10.1016/J.MCM.2012.09.007
  24. Liou J.L., Chiu C.R., Huang F.M., Liu W.Y. (2015). Analyzing the relationship between CO2 emission and economic efficiency by a relaxed two-stage DEA model. Aerosol Air Qual. Res, 15: 694-701. DOI: 10.4209/AAQR.2014.04.0074
  25. Lobo M.S.C., Ozcan Y.A., Lins M.P.E., Silva A.C.M., Fiszman R. (2014). Teaching hospitals in Brazil: Findings on determinants for efficiency. International Journal of Healthcare Management, 7(1): 60-68. DOI: 10.1179/2047971913Y.0000000055
  26. Lu C.C., Chiu Y.H., Shyu M.K., Lee J.H. (2013). Measuring CO2 emission efficiency in OECD countries: application of the hybrid efficiency model. Economic Modelling, 32: 130-135. DOI: 10.1016/J.ECONMOD.2013.01.047
  27. OECD (2015). Environment at a Glance 2015: OECD Indicators. OECD Publishing, France. OECD/IEA (2016). Recent Trends in The OECD: Energy and CO2 Emissions. IEA Publications, France.
  28. Pan H., Zhang H., Zhang X. (2013). China’s provincial industrial energy efficiency and its determinants. Mathematical and Computer Modelling, 58(5): 1032-1039. DOI: 10.1016/J.MCM.2012.09.006
  29. Ramezani-Tarkhorani S., Khodabakhshi M., Mehrabian S., Nuri-Bahmani F. (2014). Ranking decision-making units using common weights in DEA. Applied Mathematical Modelling, 38(15-16): 3890-3896.
  30. Rashidi K., Saen R.F. (2015). Measuring eco-efficiency based on green indicators and potentials in energy saving and undesirable output abatement. Energy Economics, 50: 18-26. DOI: 10.1016/J.ENECO.2015.04.018
  31. Rashidi K., Shabani A., Saen R.F. (2015). Using data envelopment analysis for estimating energy saving and undesirable output abatement: a case study in the Organization for Economic Co-Operation and Development (OECD) countries. Journal of Cleaner Production, 105: 241-252. DOI: 10.1016/J.JCLEPRO.2014.07.083
  32. Simsek N. (2014). Energy efficiency with undesirable output at the economy-wide level: cross country comparison in OECD sample. American Journal of Energy Research, 2(1): 9-17. DOI: 10.12691/AJER-2-1-2
  33. The European Commission, Energy Statistics by Country -- (Accessed Date: 24.02.2016).
  34. The World Bank, World Development Indicators -- (Accessed date: 23.02.2016).
  35. The World Bank, World Development Indicators GDP Per Capita (Constant 2005 US$) -- (Accessed date: 17.04.2016).
  36. Wang K., Wei Y.M., Zhang X. (2013). Energy and emissions efficiency patterns of Chinese regions: a multi-directional efficiency analysis. Applied Energy, 104: 105-116. DOI: 10.1016/J.APENERGY.2012.11.039
  37. Wang Z., He W., Chen K. (2016). The integrated efficiency of economic development and CO2 emissions among Asia Pacific Economic Cooperation members. Journal of Cleaner Production, 131: 765-772. DOI: 10.1016/J.JCLEPRO.2016.04.097
  38. Wei C., Ni J., Shen M. (2009). Empirical analysis of provincial energy efficiency in China. China & World Economy, 17(5): 88-103. DOI: 10.1111/J.1749-124X.2009.01168.X
  39. Woo C., Chung Y., Chun D., Seo H., Hong S. (2015). The static and dynamic environmental efficiency of renewable energy: A Malmquist index analysis of OECD countries. Renewable and Sustainable Energy Reviews, 47: 367-376. DOI: 10.1016/J.RSER.2015.03.070
  40. Wu A.H., Cao Y.Y. and Liu B. (2014). Energy efficiency evaluation for regions in China: an application of DEA and Malmquist indices. Energy Efficiency, 7(3): 429-439. DOI: 10.1007/S12053-013-9232-8
  41. Xie B.C., Shang L.F., Yang S.B., Yi B.W. (2014). Dynamic environmental efficiency evaluation of electric power industries: Evidence from OECD (Organization for Economic Cooperation and Development) and BRIC (Brazil, Russia, India and China) countries. Energy, 74, 147-157. DOI: 10.1016/J.ENERGY.2014.04.109
  42. Zhou P., Ang B.W. (2008). Linear programming models for measuring economy-wide energy efficiency performance. Energy Policy, 36(8): 2911-2916. DOI: 10.1016/J.ENPOL.2008.03.041
  43. Zhou P., Ang B.W., Han J.Y. (2010). Total factor carbon emission performance: a Malmquist index analysis. Energy Economics, 32(1): 194-201. DOI: 10.1016/J.ENECO.2009.10.003
  44. Zhou P., Poh K.L., Ang B.W. (2007). A non-radial DEA approach to measuring environmental performance. European Journal of Operational Research, 178(1): 1-9. DOI: 10.1016/J.EJOR.2006.04.038

  • Technical Efficiency and Productivity Change in the European Union with Undesirable Output Considered Djula Borozan, in Energies /2021 pp.4937
    DOI: 10.3390/en14164937
  • Role of climate fund raising under fiscal balance on climate change mitigation: an analysis from Pareto optimality Jie Ding, Ying Wang, Siqi Wang, Muhammad Mohsin, in Environmental Science and Pollution Research /2022 pp.19047
    DOI: 10.1007/s11356-022-22620-w

Fazil Gokgoz, Serap Pelin Turkoglu, Investigating the energy efficiencies of OECD countries via a slack-based undesirable output model in "ECONOMICS AND POLICY OF ENERGY AND THE ENVIRONMENT" 3/2017, pp 73-94, DOI: 10.3280/EFE2017-003005