ارزیابی کارایی زیست‌محیطی شرکت‌های برق منطقه‌ای: مقایسه مدل‌های شعاعی و غیرشعاعی

نوع مقاله: مقاله پژوهشی

نویسندگان

1 استادیار اقتصاد دانشگاه خوارزمی

2 دانشجوی کارشناسی ارشد مهندسی سیستم‌های اقتصادی-اجتماعی دانشگاه خوارزمی

چکیده

در این مطالعه با بهره‌گیری از الگوی ناپارامتریک تحلیل پوششی داده‌ها (DEA) کارایی زیست‌محیطی 16 شرکت برق منطقه‌­ای کشور در بازه زمانی 1389-1393محاسبه شده است. بر خلاف مدل­‌های سابق کارایی که بیشتر بر مدل­‌های شعاعی تمرکز داشته­‌اند در این تحقیق ضمن معرفی چند مدل غیرشعاعی (شامل مازاد‌مبنا، تابع فاصله جهت­‌دار بر مبنای مازادها و بُرد تنظیم شده) و مقایسه روش آن­ها با مدل­‌های شعاعی (شامل CCR و تابع فاصله جهت­‌دار)، محاسبه کارایی شرکت­‌های برق منطقه‌­ای با این دو الگو انجام شده و نتایج مدل­‌های شعاعی و غیرشعاعی با هم مقایسه شد‌‌ه‌­اند. همچنین در این تحقیق علاوه بر این که ستاند‌ه‌­ها به دو گروه مطلوب (تولید برق) و نامطلوب (انتشار کربن دی‌اکسید) تقسیم شد‌ه‌­اند، نهاد‌ه‌­ها نیز به دو گروه انرژی و غیرانرژی تفکیک شده‌اند. نتایج نشان می‌دهد که در بین شرکت­‌های برق منطقه­‌ای شرکت­‌های برق منطقه­‌ای کرمان و خوزستان بالاترین کارایی و شرکت­‌های برق منطقه‌­ای فارس و سیستان و بلوچستان پایین ترین کارایی را دارند. همچنین نتایج آزمون­‌های آماری با استفاده از رتبه کارایی شرکت­‌ها نشانگر دو مفهوم اقتصادی است، اول این که تغییر مهمی در کارایی و عملکرد صنعت برق ایران بین سال­‌های 1389 تا 1393 اتفاق نیفتاده است؛ دوم این که بعد از آزادسازی قیمت حامل­‌های انرژی استراتژی­‌ها و رویکردهای متفاوتی توسط شرکت­‌های برق منطقه­‌ای اتخاذ شده است. 

کلیدواژه‌ها


عنوان مقاله [English]

Environmental Efficiency Assessment of Iranian Electric Power Companies: Comparison between Radial and Non-Radial Models

نویسندگان [English]

  • Siab Mamipour 1
  • Behnam Najafzadeh 2
1 Assistant Professor in Economics, Kharazmi University
2 MA Student of Socio-Economic Systems, Kharazmi University
چکیده [English]

This study uses DEA non-parametric approach (Data Envelopment Analysis) to measure the environmental efficiency of 16 Iranian electric power companies during the period (2010-2014). The proposed approach incorporates not only the output separation (desirable and undesirable) but also the input separation (energy and non-energy). This study discusses some non-radial models) slack based measure, Slack based measure based on directional distance function, Range adjusted measure) and compare them with other previous DEA radial models (CCR, Directional distance function) used for the performance evaluation of electric power companies. After the methodological comparison, this study applies the proposed approaches for measuring the environmental efficiency of Iranian fossil fuel power generation. The results show that Kerman and Khuzestan electric power companies belong to a high level of environmental efficiency and the worst performers are Sistan and Baluchestan and Fars companies in term of fossil fuel power generation. Finally, we can conduct a rank sum test based upon their ranking scores to obtain a statistical inference. We find two economic implications. One of the two implications is that no major change has occurred in the operational performance of Iranian electric power industry from 2010 to 2014. The other implication indicates that there are strategic differences in the operation of Iranian electric power firms after the liberalization.

کلیدواژه‌ها [English]

  • Environmental efficiency
  • Power generation companies
  • Radial and Non-Radial models
  1. آماده، حمید، و رضایی، علی (1390). اندازه‌گیری کارایی زیست‌محیطی با استفاده از مدل کارایی سراسری ستاده مطلوب و نامطلوب تفکیک‌ناپذیری سراسری در بخش تولید انرژی الکتریکی شرکت‌های برق منطقه‌ای. مطالعاتاقتصادانرژی، 8(30)، 154-125.
  2. شهیکی‌تاش، محمدنبی، خواجه‌حسنی، مصطفی، و جعفری، سعید (1394). محاسبه کارایی زیست‌محیطی در صنایع انرژی‌بر ایران با استفاده از رویکرد تابع فاصله جهت‌دار. نظریه‌های کاربردی اقتصاد، 2(1)، 120-99.
  3. ناصرزاده، سمیه (1389). ارزیابی زیست‌کارایی نیروگاه‌های حرارتی کشور با استفاده از روش تحلیل پوششی داده‌ها DEA. پایان‌نامه کارشناسی ارشد، دانشگاه علامه طباطبایی. 
  1. 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.
  2. 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.
  3. Bian, Y. (2008). Efficiency evaluation with undesirable factors based on dea. Proceedings of 4th International Conference on Wireless Communications, Networking and Mobile Computing.
  4. 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.
  5. Chambers, R. G., Chung, Y., & Fare, R. )1996(. Benefit and distance functions. Journal of Economic Theory, 70, 407-419.
  6. Charnes, A., Cooper, W., & Rhodes, E.) 1978(. Measuring the efficiency of DMU. European Journal of Operation Research, 2(6), 429-444.
  7. Charnes, A., Cooper, W. W., Golany, B., Seiford, L. M., & Stutz, J. (1985). Foundations of data envelopment analysis for Pareto-Koopmans efficient empirical production functions. Journal of Econometrics, 30, 91-107.
  8. Chung, Y. H., Fare, R., & Grosskopf, S. (1997). Productivity and undesirable outputs:a directional distance function approach. Journal of Environmental Management, 51, 229–240.
  9. Cooper, W. W., Park, K. S., & Pastor, J. T. (1999). RAM: A range adjusted measure of inefficiency for use with additive models, and relations to other models and measures in DEA. Journal of Productivity Analysis,11(1), 5-42.
  10. Cooper, W. W., Seiford, L. M., & Tone, K. (2007). Data envelopment analysis: A comprehensive text with model, applications, references and DEA-solver software (2nd Ed.). Springer US.
  11. Du, L., Hanley, A., & Zhang, N. (2016). Environmental technical efficiency, technology gap and shadow price of coal-fuelled power plants in China: A parametric meta-frontier analysis. Resource and Energy Economics, 43, 14-32.
  12. Ewertowska, A., Martin, A. G., Gosalbez, G. G., Gavalda, J., & Jimenez, L. (2016). Assessment of the environmental efficiency of the electricity mix of the top European economies via data envelopment analysis. Journal of Cleaner Production, 116, 13-22.
  13. Fare, R., & Grosskopf, S. (2010). Directional distance functions and slacks-based measures of efficiency. European Journal of Operation Research, 200(1), 320–322.
  14. Färe, R., Grosskopf, S., Lovell, C. A. K., & Pasurka, C. (1989). Multilateral productivity comparisons when some outputs are undesirable: A non-parametric approach. The Review of Economics and Statistics,71, 90-98.
  15. Fare, R., & Lovell, C. A. K. (2005). Measuring the technical efficiency of production. Journal of Economic Theory, 19(1), 150–162.
  16. Fukuyama, H., & Weber, W. L. (2009). A directional slacks-based measure of technical inefficiency. Socio-Economic Planning Science, 43(4), 274–287.
  17. Fukuyama, H., & Weber, W. L. (2010). A slacks-based inefficiency measure for a two-stage system with bad outputs. Omega, 38, 398-409.
  18. Golany, B., & Roll, Y. (1989). An application procedure for DEA. Omega, 17, 237-250.
  19. Hollander, M., & Wolfe, D. A. (1999). Nonparametric statistical methods. John Wiley & Sons, Inc, New York.
  20. Iqbal Ali, A., & Seiford, L. M. (1990). Translation invariance in data envelopment analysis. Operations Research Letters, 9, 403-405.
  21. 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.
  22. Lovell, C. A. K., & Pastor, J. T. (1995). Units invariant and translation invariant DEA models. Operations Research Letters, 18(3), 147-151.
  23. Ramli, N. A., & Munisamy, S. (2013).  Modeling undesirable factors in efficiency measurement using data envelopment analysis: A review. Journal of Sustainability Science and Management, 8 (1), 126-135.
  24. Scheel, H. (2001). Undesirable outputs in efficiency valuations. European Journal of Operational Research, 132, 400-410.
  25. Seiford, L. M., & Zhu, J. (2002). Modeling undesirable factors in efficiency evaluation. European Journal of Operational Research, 142, 16-20.
  26. S zen, A., Alp, I., & zdemir, A. (2010). Assessment of operational and environmental performance of the thermal power plants in Turkey by using data envelopment analysis. Energy policy, 38, 6194-6203.
  27. Sueyoshi, T., & Goto, M. (2010b). Should the US clean air act include C  emission control?: Examination by data envelopment analysis. Energy Policy, 38, 5902–5911.
  28. Sueyoshi, T., & Goto, M. (2011a). DEA approach for unified efficiency measurement: Assessment of Japanese fossil fuel power generation. Energy Economics, 33, 195–208.
  29. Sueyoshi, T., & Goto, M. (2012b). DEA radial measurement for environmental assessment and planning: desirable procedures to evaluate fossil fuel power plants. Energy Policy, 41, 422–432.
  30. Sueyoshi, T., & Goto, M. (2012g). DEA radial and non-radial models for unified efficiency under natural and managerial disposability: theoretical extension by strong complementary slackness conditions. Energy Economics, 34, 700–713.
  31. Sueyoshi, T., & Goto, M. (2012j). DEA environmental assessment of coal fired power plants:methodological comparison between radial and non-radial models. Energy Economics, 34, 1854–1863.
  32. Sueyoshi, T., & Wang, D. (2014). Radial and non-radial approaches for environmental assessment by data envelopment analysis: Corporate sustainability and effective investment for technology innovation. Energy Economics,45, 537–551.
  33. Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130, 498-509.
  34. Tyteca, D. (1997). Linear programming models for the measurement of environmental performance of firms-concepts and empirical results. Productivity Analysis, 8, 183-197.
  35. Yang, H., & Pollitt, M. (2007). Distinguishing weak and strong disposability among undesirable outputs in DEA: The example of the environmental efficiency of chinese coal-fired power plants. http://dx.doi.org/10.17863/ CAM. 5094.
  36. Yang, H., & Pollitt, M. (2010). The necessity of distinguishing weak and strong disposability among undesirable outputs in DEA: Environmental performance of Chinese coal-fired power plants. Energy Policy, 38(8), 4440-4444.
  37. Zhou, P., Ang, B. W., & Poh, K. L. (2008). Slacks-based efficiency measures for modeling environmental performance. Ecological Economics, 60, 111-118.
  38. Zhou, P., Ang, B. W., & Wang, H. (2012). Energy and C  emission performance in electricity generation: A non-radial directional distance function approach. European Journal of Operational Research,221, 625-635.
  39. Zhou, Y., Xinpeng, X., Kuangnan, F., Dapeng, L., & Chunlin, X. (2013). Environmental efficiency analysis of power industry in China based on an entropy SBM model. Energy Policy, 57, 68-75.