The Analysis of Factors That Influence the Environmental Efficiency in Iranian Electric Industry: DEA Approach and Panel Data

Najafzadeh, Behnam; Mamipour, Siab

doi:10.29252/jemr.7.27.41

Volume 8, Issue 27 (3-2017) jemr 2017, 8(27): 41-83 | Back to browse issues page

‎ 10.29252/jemr.7.27.41

‎ 20.1001.1.22286454.1396.7.27.5.7

Mendeley

Zotero

RefWorks

Najafzadeh B, Mamipour S. The Analysis of Factors That Influence the Environmental Efficiency in Iranian Electric Industry: DEA Approach and Panel Data. jemr 2017; 8 (27) :41-83
URL: http://jemr.khu.ac.ir/article-1-1404-en.html

The Analysis of Factors That Influence the Environmental Efficiency in Iranian Electric Industry: DEA Approach and Panel Data

Behnam Najafzadeh¹

, Siab Mamipour ^*

1- Kharazmi University
2- Kharazmi University , mamipours@gmail.com

Abstract: (8374 Views)

In this paper to assess the environmental efficiency of electric power companies two-stage approach has been used Which means that the first step is to calculate the environmental efficiency score of electric power companies with Slack-Based Measure during the period (2004-2014). Then, the second step various factors effects have been evaluated on environmental efficiency by using Tobit and Ordinary Least Squares models. The result of first step show that environmental performance of the electricity industry has seen a reduction in performance during the period of 2004 to 2006, While environmental performance had a rising trend between 2007 to 2009 and then it has had a considerable reduction in the period 2010-2014 (after the liberalization of energy prices). Finally, in 2004, the average efficiency of the industry is reached to lowest level (0.65). The result of second step show that factors affecting efficiency namely Size and Liberation dummy variables have negative effects but the proportion of electricity produced by the thermal power plants, the proportion of gas used in the fuel, capacity utilization rate and electricity exports have positive effects. The results show that importing electricity doesn’t have any significance effect on the efficiency. In the end, the results of adding a new variable (variable log of per capita GDP) showed that except for the proportion of gas used in the fuel, the explanatory variables has robust coefficient.

Keywords: Environmental Efficiency, Electric Power Companies, Slack Based Measure, Ordinary Least Squares, Tobit Model.

Full-Text [PDF 2683 kb] (3443 Downloads)

Type of Study: Applicable | Subject: انرژی، منابع و محیط زیست
Received: 2016/01/21 | Accepted: 2016/12/31 | Published: 2017/05/17

References

1.  Amadeh, H., & Rezaei, A. (2011). Measuring environmental efficiency with non seperable-overall model of desirable and undesirable outputs in electrical energy production sector of Iranian electric companies. Energy Economics Review, 30, 125-154.

2.  Ang, B. J. (2009). CO2 emissions, research and technology transfer in China. Ecological Economics, 68(10), 2658–2665. [DOI:10.1016/j.ecolecon.2009.05.002]

4.  Banker, R. D., & Natarajan, R. (2008). Evaluating contextual variables affecting productivity using data envelopment analysis. Operations Research, 56, 48–58. [DOI:10.1287/opre.1070.0460]

6.  Barros, C. P., & Antunes, O. S. (2011). Performance assessment of portuguese wind farms. Energy Policy, 39(6), 3055-3063. [DOI:10.1016/j.enpol.2011.01.060]

8.  Barros, C. P., & Peypoch, N. (2007). The determinants of cost effciency of hydroelectric generating plants: a random frontier approach. Energy Policy, 35, 4463–4470. [DOI:10.1016/j.enpol.2007.03.019]

10.  Bi, G. B., Song, W., Zhou, P., & Liang, L. (2014). Does environmental regulation affect energy efficiency in China's thermal power generation? Empirical evidence from a slacks-based DEA model. Energy Policy, 66, 537-546. [DOI:10.1016/j.enpol.2013.10.056]

12.  Calvet, R., Conesa, D., Calvet, A., & Ausina, E. (2014). Energy efficiency in the European :::union:::: What can be learned from the joint application of directional distance functions and slacks-based measures?. Applied Energy, 132, 137–154. [DOI:10.1016/j.apenergy.2014.06.053]

14.  Cameron, A. C., & Trivedi, P. K. (2005). Micro econometrics: Methods and Applications. New York: Cambridge University Press.

15.  Charnes, A., Cooper, W. W., & Rhodes, E. )1978(. Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.

16.  Chung, Y., & Fare, R., & Grosskopf, S. (1997). Productivity and undesirable outputs: a directional distance function approach. Environmental Management, 51, 229–240. [DOI:10.1006/jema.1997.0146]

18.  Cook, W. D., & Seiford, L. M. (2009). Data envelopment analysis (DEA)-thirty years on. European Journal Operation Research, 192(1), 1-17. [DOI:10.1016/j.ejor.2008.01.032]

20.  Du, L., He, Y., & Yan, J. (2013). The effects of electricity reforms on productivity and efficiency of China's fossil-fired power plants: An empirical analysis. Energy Economics, 40, 804-812. [DOI:10.1016/j.eneco.2013.09.024]

22.  Du, L., & Mao, J. (2015). Estimating the environmental efficiency and marginal CO2 abatement cost of coal-fired power plants in China. Energy Policy, 85, 347-356. [DOI:10.1016/j.enpol.2015.06.022]

24.  Du, L., Wei, C., & Cai, S. (2012). Economic development and carbon dioxide emissions in China: Provincial panel data analysis. China economic review, 23, 371-384. [DOI:10.1016/j.chieco.2012.02.004]

26.  Fallahi, M. A., Kazemi, M., & Seyedzadeh, M. (2012). Investigating the effective factors on Iranian electric companies with emphasis on information technology. Economics Science, 6(12), 85-106.

27.  Fare, R., & Grosskopf, S. (2004). Modeling undesirable factors in efficiency evaluation: Comment. European Journal of Operational Research, 157(1), 242–245. [DOI:10.1016/S0377-2217(03)00191-7]

29.  Greene, W. H. (2011). Econometric Analysis, Seventh ed. New York: Pearson

30.  Hailu, A., & Veeman, T. S. (2001). Non-parametric productivity analysis with undesirable outputs: An application to the Canadian pulp and paper industry. American Journal of Agricultural Economics, 83(3), 805–816. [DOI:10.1111/0002-9092.00181]

32.  Haynes, K. E., Ratick, S., & Cummings, S. J. (1997). Pollution prevention frontiers: a data envelopment simulation. In: Knaup GL, Kim TJ, editors. environmental program evaluation: a primer. Urbana, IL: University of Illinois Press.

33.  Hiebert, D. L. (2002). The determinants of the cost efficiency of electric generating plants: a stochastic frontier approach. Southern Economic Journal, 68(4), 935-946. [DOI:10.2307/1061501]

35.  Hoff, A. (2007). Interfaces with Other Disciplines Second stage DEA: Comparison of approaches for modelling the DEA score. European Journal of Operational Research, 181, 425-435. [DOI:10.1016/j.ejor.2006.05.019]

37.  Hongwu, W., Xiaoli, H., & Junhai, M. (2011). The analysis of the energy efficiency and its influence factors in TianJin. Energy Procedia, 5, 1671-1675. [DOI:10.1016/j.egypro.2011.03.285]

39.  Jalil, A., & Mahmud, S. F. (2009). Environment Kuznets curve for CO2 emissions: A cointegration analysis for China. Energy Policy, 37(12), 5167–5172. [DOI:10.1016/j.enpol.2009.07.044]

41.  Jaraitė, J., & Di Maria, C. (2012). Efficiency, productivity and environmental policy: A case study of power generation in the EU. Energy Economics. 34(5), 1557-1568. [DOI:10.1016/j.eneco.2011.11.017]

43.  June, F., & Collins-Dodd, C. (2004). Impact of export promotion programs on firm competencies, strategies and performance: The case of Canadian high-technology SMEs, International Marketing Review, 21(4/5), 474-495.

44.  Kok, F. S., & Coelli, T. J. (2012). An analysis of factors that influence the technical efficiency of malaysian thermal power plants. Energy Economics, 34(3), 677-685. [DOI:10.1016/j.eneco.2011.09.005]

46.  Koopmans, T. C. (1951). An analysis of production as an efficient combination of activities. In Koopmans, T. C. (Ed.), Activity Analysis of production and allocation. New York: Wiley.

47.  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(5-6), 1018-1031. [DOI:10.1016/j.mcm.2012.09.007]

49.  Li, X. G., Yang, J., Liu, X. J. (2013). Analysis of Beijing's environmental efficiency and related factors using a DEA model that considers undesirable outputs. Mathematical and Computer Modelling, 58(5-6), 956-960. [DOI:10.1016/j.mcm.2012.10.016]

51.  Lin, B., & Jiang, Z. (2011). Estimates of energy subsidies in China and impact of energy subsidy reform. Energy Economics, 33, 273–283. [DOI:10.1016/j.eneco.2010.07.005]

53.  Liu, J. S., Lu, L. Y. Y., Lu, W. M., & Lin, B. J. Y. (2013). Data envelopment analysis 1978-2010: a citation-based literature survey. Omega, 41(1), 3-15. [DOI:10.1016/j.omega.2010.12.006]

55.  Maddala, G. S. (1984). Limited–dependent and qualitative variables in econometrics. England: Cambridge University Press.

56.  McDonald, J. (2009). Using Least Squares and Tobit in Second Stage DEA Efficiency Analyses, European Journal of Operational Research, 197, 792–798.

57.  Motafakker Azad, M. A., Pourebadollahan Covich, M., Fallahi, F., Ranj Pour, R., & Sojoodi , S. (2014). Measuring the Technical Efficiency of Iranian Thermal Power Plants and Analysis of its Determinants: Application of Stochastic Nonparametric Data Envelopment Method.Economic research, 49(1), 93-113.

58.  Nahra, T. A., Mendez, D., & Alexander, J. A. (2009). Employing super-efficiency analysis as an alternative to DEA: An application in outpatient substance abuse treatment. European Journal of Operational Research, 196, 1097–1106. [DOI:10.1016/j.ejor.2008.04.022]

60.  Olatubi, W. O., & Dismukes, D. E. (2000). Data envelopment analysis of the levels and determinants of coal-fired electric power generation performance. Utilities Policy, 9, 47-59. [DOI:10.1016/S0957-1787(01)00004-2]

62.  Rajabi, M., Oloomi baigi, M., Javidi, M. H., Mousavi, H., & Gholami, G. (2005). The Strategy for Development of electricity imports based on Iranian electricity market rules: Case Study electricity imports from Turkmenistan. 20TH international power system conference.

63.  Rezaei, A. (2013). Efficiency and Productivity Analysis in Iranian Electricity Distribution Companies: Slack Based Model (SBM) Approach. Economic Modeling Research, 4(13), 119-146.

64.  Sahoo, B. K., Luptacik, M., & Mahlberg, B. (2011). Alternative measures of environmental technology structure in DEA: An application. European Journal of Operational Research, 215, 750-762. [DOI:10.1016/j.ejor.2011.07.017]

66.  Sarica, K., & Or, I. (2007). Efficiency assessment of turkish power plants using data envelopment analysis. Energy Policy, 32, 1484-1499. [DOI:10.1016/j.energy.2006.10.016]

68.  Scheel, H. (2001). Undesirable outputs in efficiency evaluation. European Journal of Operational Research, 132, 400–410. [DOI:10.1016/S0377-2217(00)00160-0]

70.  Seiford, L., & Zhu, J. (2002). Modeling undesirable factors in efficiency evaluation. European Journal of Operational Research, 142(1), 16–20. [DOI:10.1016/S0377-2217(01)00293-4]

72.  Simar, L., & Wilson, P. (2002). Non-parametric tests of returns to scale. European Journal of Operation Research, 139(1), 115-132. [DOI:10.1016/S0377-2217(01)00167-9]

74.  Sokhanvar, M., Sadeghi, H., Asari, A., Yavari, K., & Mehregan, N. (2012). Structural Analysis and Efficiency Trend of Electricity Distribution Companies in Iran by Using Window Data Envelopment Analysis. Journal of Economic Growth and Development Research, 1(4), 145-182.

75.  Song, M., Song, Y., An, Q., & Yu, H. (2013). Review of environmental efficiency and its influencing factors in China: 1998–2009. Renewable and Sustainable Energy Reviews, 20, 8-14. [DOI:10.1016/j.rser.2012.11.075]

77.  Song, M., Zhang, L., An, Q., Wang, Z., & Li, Z. H. (2013). Statistical analysis and combination forecasting of environmental efficiency and its influential factors since China entered the WTO: 2002-2010-2012. Journal of Cleaner Production, 42, 42-51. [DOI:10.1016/j.jclepro.2012.11.010]

79.  Sueyoshi, T., Goto, M., & Omi, Y. (2010). Corporate governance and firm performance: Evidence from Japanese manufacturing industries after the lost decade. European Journal of Operational Research, 203(3), 724-736. [DOI:10.1016/j.ejor.2009.09.021]

81.  Taskin, F., & Zaim, O. (2001). The role of international trade on environmental efficiency: a DEA approach. Economic Modelling, 18(1), 1-17. [DOI:10.1016/S0264-9993(00)00025-0]

83.  Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130, 498–509. [DOI:10.1016/S0377-2217(99)00407-5]

85.  Tone, K. (2004). Dealing with undesirable outputs in DEA: a slacks-based measure (SBM) approach. Toronto: Presentation at NAPW III.

86.  Tulkens, H., & Vanden, E. P.) 1995(. Non-parametric efficiency, progress and regress measures for panel data: methodological aspects. European Journal of Operation Research, 80(3), 474–499.

87.  Wei, C., Loschel, A., & Liu, B. (2013). An empirical analysis of the CO2 shadow price in Chinese thermal power enterprises. Energy Economics, 40, 22-31. [DOI:10.1016/j.eneco.2013.05.018]

89.  Wooldridge, J. M. (2002). Econometric Analysis of Cross Section and Panel Data. Cambridge, MA: The MIT Press.

90.  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]

92.  Xu, Y. (2011). Improvements in the operation of SO2 scrubbers in China's coal power plants. Environmental Science & Technology, 45, 380–385. [DOI:10.1021/es1025678] [PMID]

94.  Zhang, Z. (2000). Decoupling China's carbon emissions increase from economic growth: An economic analysis and policy implications. World Development, 28(4), 739–752. [DOI:10.1016/S0305-750X(99)00154-0]

96.  Zhang, N., Kong, F., Choi, Y., & Zhou, P. (2014). The effect of size-control policy on unified energy and carbon efficiency for Chinese fossil fuel power plants. Energy Policy, 70, 193-200. [DOI:10.1016/j.enpol.2014.03.031]

98.  Zhao, X., Yin, H., & Zhao, Y. (2015). Impact of environmental regulations on the efficiency and CO2 emissions of power plants in China. Applied Energy, 149, 238-247. [DOI:10.1016/j.apenergy.2015.03.112]

100.  Zhou, P., Ang, B. W. (2006). Slacks-based efficiency measure for modeling environmental performance. Ecological Economics, 60, 111–118. [DOI:10.1016/j.ecolecon.2005.12.001]

102.  Zhou, P., Ang, B. W., & Poh, K. L. (2008). Measuring environmental performance under different environmental DEA technologies. Energy Economics, 30, 1–14. [DOI:10.1016/j.eneco.2006.05.001]

104.  Zhou, Y., Xing, X., Fang, K., Liang, D., & Xu, CH. (2013). Environmental efficiency analysis of power industry in China based on an entropy SBM model. Energy Policy, 57, 68-75. [DOI:10.1016/j.enpol.2012.09.060]

106.  Zhu, J. (2003). Quantitative models for performance evaluation and benchmarking. Norwell, MA: Kluwer Academic Publishers. [DOI:10.1007/978-1-4757-4246-6]