The Impact of Targeted Subsidies plan on Combination of Energy Consumption in Iran: The Case of Non-Metal Mineral Manufacturing Industries


1 Department of Economics, Business School, Yazd University

2 Department of Economics and Administrative Sciences, Ferdowsi University of Mashhad


Although the implementation of targeted energy subsidies plan has been considered by policymakers since the First 5-year Economic, Social and Cultural Development Plan in Iran, it is only partially implemented in 2010. This issue has great importance for industry sector that has benefited from subsidized energy prices. This research attempts to investigate the possible impact of implementing Subsidy Reform Plan on manufacture of non-metal mineral products industry by using Artificial Neural Network approach. This investigation focuses on new entered firms into this industry as one of the most energy intensive industries. The results indicate that by replacing subsidized prices with electricity real price, the consumption of this energy carrier will generally decline by 16 percent. Despite this, the large entrance of new firms has raised the energy consumption in some years. Therefore, the results show that targeted subsidies plan does not have a large impact on energy consumption in non-metal mineral manufacturing industries. Furthermore, we find out that the impact of this policy depends on combination of firms in each industry.


Article Title [Persian]

تأثیر هدفمندی یارانه‌ها بر ترکیب مصرفی انرژی در ایران:مطالعه موردی صنعت کانی‌های غیر فلزی

Authors [Persian]

  • محمد علی فیض‌پور 1
  • محمد خیاط سرکار 2
1 عضو هیات علمی دانشکده اقتصاد، مدیریت و حسابداری دانشگاه یزد
2 کارشناس ارشد اقتصاد انرژی دانشگاه فردوسی مشهد
Abstract [Persian]

هدفمندی یارانه‌های انرژی اگرچه از دیرباز و از برنامه نخست پنج ساله توسعه اقتصادی، اجتماعی و فرهنگی ایران همواره مد نظر سیاست‌گذاران اقتصادی بوده‌ اما تنها در سال‌های اخیر این امر تا حدودی تحقق یافته ‌است. این موضوع بویژه برای بخش صنعت که از مزایای دسترسی به انرژی ارزان قیمت بهره برده است از اهمیتی دوچندان برخوردار است. این مقاله می‌کوشد تا با استفاده از شبکه عصبی مصنوعی تاثیر احتمالی اجرای قانون هدفمندی یارانه‌ها را بر صنعت تولید کانی‌های غیر فلزیاز بعد تغییر ترکیب مصرف انرژی مورد بررسی قرار دهد. این بررسی نیز بر بنگاه‌های جدیدالورود این صنعت به عنوان یکی از انرژی‌برترین صنایع تمرکز یافته ‌است. نتایج این پژوهش نشان می‌دهد که با جایگزینی قیمت واقعی برق به جای قیمت‌های یارانه‌ای، مصرف این حامل انرژی در مجموع به میزان 16 درصد کاهش خواهد یافت. با این وجود این کاهش، با توجه به بنگاه‌های جدیدالورود در هر سال از دامنه پراکندگی بسیاری برخوردار بوده و حتی در سال‌هایی با افزایش مصرف مواجه بوده است. در نتیجه، هدفمندی یارانه‌ها نه تنها تاثیر چندانی بر مصرف حامل‌های انرژی در بخش صنایع تولیدی نخواهد داشت بلکه تأثیر آن به شدت به ترکیب بنگاه‌های وارد شده در هر صنعت بستگی دارد.

Keywords [Persian]

  • هدفمند سازی یارانه‌ها
  • قیمت حامل‌های انرژی
  • بنگاههای جدیدالورود
  • شبکه عصبی مصنوعی ایران
Abasian, E., & Asadbeigi, Z. (2011). The relationship between targeted energy subsidies with social welfare through economic growth. Journal of social welfare, 44, 143-173.

Advari, M. (2009). Report of comparing per capita employment of cooperative and private projects from the funds of the early productive companies. Journal of the Production and Service Cooperatives of Ministry of Cooperatives.

Arman, A., & Taghizadeh, S. (2013). Assessment of effective factors on energy intensity in Iran's industrial manufacturing. Journal of Iranian energy Economics, 8, 1-20.

Bazazan, F., Musavi, M., & Gheshmi, F. (20015). The impact of electricity subsidies targeting on households demand. Journal of Iranian Energy Economics, 14, 1-32.

Boghzian, A., Nasrabadi, E. (2006). Forecast of consumption of petroleum products Compare econometric equations systems and neural networks. Quarterly Journal of Energy Economics Studies, 10, 47-67.

Chadwick, R., & Grimes, D. (2012). An Artificial Neural Network approach to multispectral rainfall estimation over Africa. International Journal of Hydrometeorology, 13, 913-931.

Decoster, J. (2001). Transforming and Restructuring Data. University of Alabama, Department of Psychology, 7-8.

Enery balance sheet of iran. (2005). Ministry of energy.

Hope, E., & Singh, B. (1995). Energy price increases in developing countries: Case study of Colombia, Ghana, Indonesia, Malaysia, Turkey, and Zimbabwe. The World Bank, Policy Research Department, Working Paper 1442.

Iran statistical yearbook. (2013). Statistical center of iran.

Naji, A., Sotodeh, S. (2014). Impact of elimination of energy subsidies on industry cost structure in iran. Quarterly Journal of Economic Modeling, 28, 45-62.

Jalali, A., Pakravan, M. R., & Gilanpour, O. (2010). Forecasting export of agricultural products: an application of regression models and Artificial Neural Network. Journal of Agricultural Economics and Development, 72, 24-48.

Mahmoodzadeh, M., Sadeghi, S., & Heidari, F. (2012). The impact of eliminating electricity subsidy on energy intensity. Quarterly Journal of Planning and Budget, 4, 113-127.

Manzoor, D., Shahmoradi, A., & Haghighi, I. (2010). Evaluating the effects of eliminating energy apparent and hidden subsidies in Iran using a computable general equilibrium model based on adjusted micro data matrix. Quarterly Journal of Energy Economics Studies, 26, 21-54.

Mirsoltani, M., & Akhavan, T. (2013). Forecasting energy price and consumption for Iranian industrial sectors using ANN and ANFIS. Iranian Journal of Economic Studies, 2(1), 73-102.

Monajemi, A., Abzari, M., & Raiyati, A. (2009). Forecasting stock price in stock exchange using neural network. Quarterly Journal of Quantitative Economy, 6(3), 1-26.

Naji, A., & Niakrani, S. (2014). Impact of elimination of energy subsidies on industry cost structure in Iran. Quarterly Journal of Economic Modeling, 28, 45-62.

Nauck, D., Klawonn, F. & Kruse, R. (1997). Foundation of neuro-fuzzy systems. New York: John Wiley & Sons Co.

Sadeghi, H., Lavasani, SH., Baghjari, K., & Baghjari, M. (2010). The impact of reforming energy carrier prices on economic macro variables using the structural autoregressive model. Quarterly Journal of Economic Modeling Researches, 1, 49-77.

Sadeghi, H., Zolfaghari, M., & Elhaminejad, M. (2011). Comparison of the performance of neural networks and ARIMA model in modeling and short-term forecast of the price of OPEC crude oil basket. Quarterly Journal of Energy Economics Studies, 28, 25-47.

Sharifi, A., Sadeghi, M., & Ghasemi, A. (2008). Assessment of inflationary impacts of eliminating energy carrier subsidies in Iran. Journal of Economic Research Letter, 8(4), 91-120.

Shi, X., & Polenske, k. (2005). Energy prices and energy intensity in china: a structural decomposition analysis and econometrics study. Massachusetts Institute of Technology, Center for Energy and Environmental policy research.

Tashkini, A., Oryani, B., & Sabouri, M. (2009). Payment system of energy subsidies. Journal of Investigation of Economic Policies and Issues, 101, 143-162.

Uri, N.D., & Boyd, R. (1997). An evaluation of the economic effects of higher energy prices in Mexico. Journal of Energy Policy, 25, 205-215.