ارزیابی کارایی انرژی استانی با درنظرگرفتن هزینه اجتماعی گازهای گلخانه‌ای: رویکرد فرامرزی سه‌سطحی در ایران

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری، گروه توسعه و برنامه ریزی اقتصادی، دانشکده مدیریت و اقتصاد، دانشگاه تربیت مدرس، تهران، ایران

2 دانشیار، گروه توسعه و برنامه‌ریزی اقتصادی، دانشکده مدیریت و اقتصاد، دانشگاه تربیت مدرس، تهران، ایران

3 دانشیار، گروه اقتصاد، دانشکده مدیریت و اقتصاد، دانشگاه تربیت مدرس، تهران، ایران

چکیده

این پژوهش با هدف سنجش کارایی انرژی و تفکیک منابع ناکارایی در ۳۱ استان ایران طی سال ۱۴۰۰، چارچوب فرامرزی سه‌سطحی مبتنی بر مدل SBM–VRS را به‌کار می‌گیرد. در مدل، موجودی سرمایه، نیروی کار و مصرف انرژی بر حسب بشکه معادل نفت خام به‌عنوان نهاده، تولید ناخالص استانی به‌عنوان ستاده مطلوب و هزینه اجتماعی انتشار دی‌اکسید کربن، متان و اکسید نیتروژن به‌عنوان ستاده نامطلوب وارد شده‌اند. کارایی استان‌ها در سه سطح مرز اقلیم-بخش اقتصادی، مرز بخش اقتصادی و فرامرزی ملی محاسبه و ناکارایی کل به مؤلفه‌های مدیریتی، اقلیمی-منطقه‌ای و فناورانه-ساختاری تفکیک شد. نتایج نشان می‌دهد میانگین کارایی انرژی با گسترش مجموعه مرجع، از 0.۹۲۶ در سطح اقلیم-بخش اقتصادی به 0.۷۴۲ در سطح بخش اقتصادی و 0.۴۶ در سطح فرامرزی ملی کاهش می‌یابد. تنها هفت استان ایلام، بوشهر، تهران، خراسان جنوبی، خراسان شمالی، خوزستان و کهکیلویه‌وبویراحمد در هر سه سطح کارا هستند. ظرفیت فنی صرفه‌جویی انرژی در سطح کشور در سال 1400 حدود ۸۸5.۵ میلیون بشکه معادل نفت خام برآورد شد که ۱۳ درصد آن به ناکارایی مدیریتی، ۴6.۳ درصد به مؤلفه اقلیمی-منطقه‌ای و ۴0.۷ درصد به مؤلفه فناورانه-ساختاری مربوط است. بنابراین، بخش عمده ظرفیت بهبود کارایی به فاصله میان مرزهای منطقه‌ای و فناورانه مربوط می‌شود و سیاست‌گذاری یکسان برای همه استان‌ها مناسب نیست. ارتقای کارایی انرژی مستلزم ترکیبی از بهبود مدیریت مصرف، توسعه زیرساخت‌های متناسب با شرایط منطقه‌ای و نوسازی فناوری و ساختار تولید است.

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Assessing Provincial Energy Efficiency Considering the Social Cost of Greenhouse Gases: A Three-Level Cross-Frontier Approach in Iran

نویسندگان [English]

  • Seyed Hamid reza Zakeri 1
  • Sajjad Faraji Dizaji 2
  • Abbas Assari Arani 3
  • Hoseein Sadeghi Saghdel 3
1 PhD student , Department of Economic Development and Planning, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran
2 Associate Professor, Department of Economic Development and Planning, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran
3 Associate Professor, Department of Economics, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran
چکیده [English]

This study aims to measure energy efficiency and decompose the sources of inefficiency across 31 provinces of Iran during 202 by employing a three-level cross-frontier framework based on the SBM–VRS model. In the model, capital stock, labor, and energy consumption measured in barrels of oil equivalent are included as inputs; gross provincial product is specified as the desirable output; and the social costs of emissions of carbon dioxide, methane, and nitrogen oxides are incorporated as undesirable outputs. Provincial efficiency is calculated at three levels: the climate–economic-sector frontier, the economic-sector frontier, and the national cross-frontier, and overall inefficiency is decomposed into managerial, climatic-regional, and technological-structural components. The results show that average energy efficiency decreases as the reference set expands, from 0.926 at the climate–economic-sector level to 0.742 at the economic-sector level and 0.46 at the national cross-frontier level. Only seven provinces—Ilam, Bushehr, Tehran, South Khorasan, North Khorasan, Khuzestan, and Kohgiluyeh and Boyer-Ahmad—are efficient at all three levels. The national technical energy-saving potential in 2021 is estimated at approximately 885.5 million barrels of oil equivalent, of which 13 percent is attributable to managerial inefficiency, 46.3 percent to the climatic-regional component, and 40.7 percent to the technological-structural component. Therefore, the majority of the potential for improving efficiency is associated with the gap between regional and technological frontiers, and a uniform policy approach for all provinces is not appropriate. Improving energy efficiency requires a combination of better consumption management, development of infrastructure suited to regional conditions, and modernization of production technology and structure.

کلیدواژه‌ها [English]

  • Total-Factor Energy Efficiency
  • SBM–VRS
  • Super-SBM
  • Three-Level SBM-Based Cross-Frontier Model
  • Social Cost of Greenhouse Gas Emissions

مراجع

  1. Andersen, P., & Petersen, N. C. (1993). A procedure for ranking efficient units in data envelopment analysis. Management science, 39(10), 1261-1264.‏
  2. Aydin, S., & Şenel, T. (2025). Evaluating energy efficiency in Turkish electric distribution using network DEA and GA models. Scientific Reports, 15(1), 21790.‏
  3. Azami, S., & Azhand, H. (2025). Total factor ecological energy efficiency and total factor carbon efficiency of Iranian provinces: An application of data envelopment analysis and Tobit model. Applied Theories of Economics, 12(3), 49–80 (In Persian).
  4. Azami, S., Pour-Karimi, L., & Sadri, S. (2018). Total factor CO₂ emission performance in Iranian manufacturing industries: Meta-frontier non-radial Malmquist index approach. Journal of Research in Economic Modeling, 8(31), 131–163 (In Persian).
  5. Balali, H., Farzanegan, M. R., Zamani, O., & Baniasadi, M. (2023). Air pollution and economic sanctions in Iran (No. 03-2023). MAGKS Joint Discussion Paper Series in Economics.‏
  6. 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.‏
  7. Battese, G. E., Rao, D. S. P., & O'Donnell, C. J. (2004). A metafrontier production function for estimation of technical efficiencies and technology gaps for firms operating under different technologies. Journal of productivity analysis, 21(1), 91-103.‏
  8. (2022). BP statistical review of world energy 2022 (71st ed.). bp p.l.c.
  9. Central Bank of the Islamic Republic of Iran. (n.d.). Net capital stock by type of asset at constant 1395 prices [Data set]. Tehran, Iran (In Persian).
  10. Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European journal of operational research, 2(6), 429-444.‏
  11. Chen, Y., Wang, M., Feng, C., Zhou, H., & Wang, K. (2021). Total factor energy efficiency in Chinese manufacturing industry under industry and regional heterogeneities. Resources, Conservation and Recycling, 168, 105255.‏
  12. Cook, W. D., Tone, K., & Zhu, J. (2014). Data envelopment analysis: Prior to choosing a model. Omega, 44, 1-4.‏
  13. Crippa, M., Guizzardi, D., Pagani, F., Banja, M., Muntean, M., Schaaf, E., ... & Pekar, F. (2025). GHG emissions of all world countries—2025 report. Publications Office of the European Union.
  14. Dyson, R. G., Allen, R., Camanho, A. S., Podinovski, V. V., Sarrico, C. S., & Shale, E. A. (2001). Pitfalls and protocols in DEA. European Journal of operational research, 132(2), 245-259.‏
  15. Eftekhary, M., Gholami, P., Safari, S., & Shojaee, M. (2012). Ranking normalization methods for improving the accuracy of SVM algorithm by DEA method. Modern applied science, 6(10), 26–36.‏
  16. Ervural, B. C., Ervural, B., & Zaim, S. (2016). Energy efficiency evaluation of provinces in Turkey using data envelopment analysis. Procedia-Social and Behavioral Sciences, 235, 139-148.‏
  17. Feng, C., Wang, M., Zhang, Y., & Liu, G. C. (2018). Decomposition of energy efficiency and energy-saving potential in China: A three-hierarchy meta-frontier approach. Journal of Cleaner Production, 176, 1054-1064.‏
  18. Hosseini, K., & Stefaniec, A. (2019). Efficiency assessment of Iran's petroleum refining industry in the presence of unprofitable output: A dynamic two-stage slacks-based measure. Energy, 189, 116112.‏
  19. Hosseinzadeh-Bandbafha, H., Safarzadeh, D., Ahmadi, E., & Nabavi-Pelesaraei, A. (2018). Optimization of energy consumption of dairy farms using data envelopment analysis–A case study: Qazvin city of Iran. Journal of the Saudi Society of Agricultural Sciences, 17(3), 217-228.‏
  20. Huang, X., & Zhao, R. (2024). Regional differences and improvement potential of carbon emission efficiency in China’s power industry: insights from meta-frontier DEA model considering integer constraints. Frontiers in Energy Research, 12, 1339553.‏
  21. Intergovernmental Panel on Climate Change. (2006). 2006 IPCC guidelines for national greenhouse gas inventories (Vol. 2: Energy; Vol. 3: Industrial Processes and Product Use). Institute for Global Environmental Strategies.
  22. Intergovernmental Panel on Climate Change. (2019). 2019 refinement to the 2006 IPCC guidelines for national greenhouse gas inventories (E. Calvo Buendia, K. Tanabe, A. Kranjc, J. Baasansuren, M. Fukuda, S. Ngarize, A. Osako, Y. Pyrozhenko, P. Shermanau, & S. Federici, Eds.). Geneva, Switzerland: Author.
  23. Iqbal, M., Ma, J., Mushtaq, Z., Ahmad, N., Yousaf, M. Z., Tarawneh, B., ... & Zaitsev, I. (2025). Energy efficiency evaluation of construction projects using data envelopment analysis and Tobit regression. Scientific Reports, 15(1), 11444.‏
  24. Ministry of Energy of the Islamic Republic of Iran. (2022). Energy balance sheet of Iran, 1400 [2021/22]. Tehran, Iran (In Persian).
  25. Mirzaei, M., & Bekri, M. (2017). Energy consumption and CO2 emissions in Iran, 2025. Environmental research, 154, 345-351.‏
  26. Mohammadi, M., Fattahi, S., & Sohaili, K. (2021). Examination of relationship between energy consumption and Iran’s provinces gross product using wavelet analysis with panel data. Iranian Energy Economics, 10(40), 125–159 (In Persian).
  27. Mozayani, A. H. (2019). Estimating provincial capital stock in Iran economy. Quarterly Journal of Fiscal and Economic Policies, 7(26), 7-29.‏
  28. Nazemi, A., Karimi, F., Mamipoor, S., & Feshari, M. (2019). Energy efficiency in Iran provinces: DEA approach. Quarterly Journal of Energy Policy and Planning Research, 5(1), 103–142. (In Persian).
  29. O’Donnell, C. J., Rao, D. S. P, & Battese, G. E. (2008). Metafrontier frameworks for the study of firm-level efficiencies and technology ratios. Empirical economics, 34(2), 231-255.‏
  30. Pakkar, M. S. (2015). Using DEA and AHP for Multiplicative Aggregation of Hierarchical Indicators. American Journal of Operations Research, 5(05), 327–336.‏
  31. Seiford, L. M., & Zhu, J. (2002). Modeling undesirable factors in efficiency evaluation. European journal of operational research, 142(1), 16-20.‏
  32. Seyef, A. M., & Hamidi Razi, D. (2017). Identification of factors that influence Iran’s provincial energy intensity index: A spatial dynamic panel data approach. Quarterly Energy Economics Review, 13(53), 61–103 (In Persian).
  33. Shafie-Pour, M., & Ardestani, M. (2007). Environmental damage costs in Iran by the energy sector. Energy Policy, 35(9), 4413-4423.‏
  34. Shahraki, A. S., Caloiero, T., & Bazrafshan, O. (2023). Influence of climatic factors on yields of pistachio, mango, and bananas in Iran. Sustainability, 15(11), 8962.‏
  35. Statistical Center of Iran. (2022a). Results of the labour force survey, 1400 [2021/22]. Tehran, Iran (In Persian).
  36. Statistical Center of Iran. (2022b). Statistical yearbook of Iran, 1400 [2021/22]. Tehran, Iran (In Persian).
  37. Statistical Center of Iran. (2023). Provincial accounts: Gross value added of economic activities by province at constant 1400 prices [Data set]. Tehran, Iran (In Persian).
  38. Tian, Z., Hu, G., & Mu, X. (2024). Urban green total factor energy efficiency and its decomposition under a multidimensional heterogeneity framework: a case study of the Yellow River Basin, China. Environmental Science and Pollution Research, 31(36), 48999-49025.‏
  39. Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European journal of operational research, 130(3), 498-509.‏
  40. Tone, K. (2002). A slacks-based measure of super-efficiency in data envelopment analysis. European Journal of Operational Research, 143(1), 32–41.
  41. Tone, K. (2004). Dealing with undesirable outputs in DEA: A slacks-based measure (SBM) approach. Paper presented at the North American Productivity Workshop III, Toronto, Canada.
  42. S. Environmental Protection Agency. (2023). Report on the social cost of greenhouse gases: Estimates incorporating recent scientific advances. Washington, DC: National Center for Environmental Economics.
  43. Valadkhani, A., & Moradi-Motlagh, A. (2025). Examining inefficiency in countries with high energy consumption: A benchmarking approach. Energy Economics, 145, 108487.‏
  44. Wang, J., Xu, R., & Ye, S. (2025). Spatiotemporal evolution characteristics and influencing factors of China’s interprovincial industrial green efficiency based on the super-efficiency SBM model. Frontiers in Environmental Science, 13, 1625233.‏
  45. Wang, K., Wang, Y., & Zhang, J. (2025). Regional differences and convergence of land use efficiency towards carbon neutrality in China’s Yangtze river economic belt. Frontiers in Environmental Science, 13, 1577863.‏
  46. Wang, X., Lu, Y., Chen, C., Yi, X., & Cui, H. (2024). Total-factor energy efficiency of ten major global energy-consuming countries. Journal of Environmental Sciences, 137, 41-52.‏
  47. Ye, Y., Xu, Z., & Chen, W. (2023). The heterogeneous effect of financial technology on green total factor productivity in China. Journal of Innovation & Knowledge, 8(3), 100390.
  48. Yu, J., Zhou, K., & Yang, S. (2019). Regional heterogeneity of China's energy efficiency in “new normal”: A meta-frontier Super-SBM analysis. Energy Policy, 134, 110941.‏
  49. Zhang, N., & Choi, Y. (2013). Environmental energy efficiency of China's regional economies: A non-oriented slacks-based measure analysis. The social science journal, 50(2), 225-234.‏
  50. Zhao, P. J., Zeng, L. E., Lu, H. Y., Zhou, Y., Hu, H. Y., & Wei, X. Y. (2020). Green economic efficiency and its influencing factors in China from 2008 to 2017: Based on the super-SBM model with undesirable outputs and spatial Dubin model. Science of the Total Environment, 741, 140026.‏
نظریه های کاربردی اقتصاد
دوره 13، شماره 3 - شماره پیاپی 50
شماره پاییز 1405
شهریور 1405
صفحه 81-112

فایل ها

هم رسانی

ارجاع به این مقاله

آمار