Quarterly Publication

Document Type : Original Article

Authors

1 Assistant Professor, Department of Energy Economics and Management, Tehran Faculty of Petroleum, Petroleum University of Technology, Tehran, Iran

2 M.A. Student in Oil & Gas Economics, Energy Economics & Management Department, Petroleum Faculty of Tehran, Petroleum University of Technology, Tehran, Iran

Abstract

This study aimed to evaluate the effects of environmental policies including price and non-price policies on natural gas demand in Iranian industrial sector. For this purpose, considering the dynamic nature of our panel data, we adopted Generalized Method of Moments (GMM) method to estimate natural gas consumption for 22 Iranian industries from 2005 to 2015. The results illustrated that the annual average of natural gas consumption has been rising, reaching five times higher than the consumption of other fossil fuels. Among the industries, other non-metallic minerals industry with 8 percent of the total production and more than 25 percent natural gas consumption have been regarded as the most natural gas consumer industry. The results of our GMM model showed that non-price environmental policies are more effective than the price policies on natural gas consumption. Overall, in non-price policies, energy intensity seems more important comparing to CO2 emission reduction. We recommend that governmental energy policies should focus more on energy intensity improvement in Iranian industries through technological enhancement and fuel energy saving regulations.

Keywords

Main Subjects

Abounori, A. G., S.H. (2011). Estimation of Supply and Demand for Natural Gas in Iran and Forecast for 1404. Economic Modeling, 2(12), 117-136.

Aras, N. (2008). Forecasting Residential Consumption of Natural Gas Using Genetic Algorithms. Energy Explor. Exploit, 26(4), 241–266.

Aydin, G. (2014). Production Modeling in the Oil and Natural Gas Industry: An Application of Trend Analysis. Pet. Sci. Technol, 32(5), 555–564.

Azadeh, A., Zarrin, M., Rahdar Beik, H., & Aliheidari Bioki, T. (2015). A neuro-fuzzy algorithm for improved gas consumption forecasting with economic, environmental and IT/IS indicators. J. Pet. Sci. Eng, 133, 716–739.

Balestra, P., Nerlove, M. (1966). Pooling cross section and time series data in the estimation of a dynamic model: The demand for natural gas. Econometrica,, 34(3), 585-612.

Bianco, V., Scarpa, F., & Tagliafico, L. (2014). Scenario analysis of non-residential natural gas consumption in Italy. Applied Energy, 113, 392-403.

BP. (2019). BP Statistical Review.

Energy, I. M. o. (2016). Iran Energy Balance.

Hansen, L. P. (2008). Generalized Method of Moments Estimation. The New Palgrave Dictionary of Economics.

Hubbert, M. K. Energy from fossil fuels Science 109, 103-109.

Huntington, H. G. (2007). Industrial natural gas consumption in the United States: An empirical model for evaluating future trends. Energy Economics, 29(4), 743-759.

Kaboudan, M. A. L., L. (2004). Forecasting quarterly us demand for natural gas. Inf. Technol. Econ. Manag, 2(1), 4.

Keshavarz haddad, G.H., & Mirbagheri Jam, M. (2007). The Examine of Natural Gas Demand Function (Residential and Commercial) of Iran. Iranian research magazine, 9(32), 137-160.

Khan, M. A. (2015). Modeling and forecasting the demand for natural gas Renewable and Sustainable Energy Reviews, 49, 1145-1159.

Li, J., Dong, X., Shangguan, J., & Hook, M. (2011). Forecasting growth of China’s natural gas consumption. Energy, 36, 1380-1385.

Mirzaei, M. B., M. (2017). Energy consumption and CO2 emissions in Iran, 202. Environ. Res, 154, 345-351.

Özmen, A., Yılmaz, Y., & Weber, G. (2018). Natural gas consumption forecast with MARS and CMARS models for residential users. Energy Economics, 70, 357-381.

Roodman, D. (2009). A Note on the Theme of Too Many Instruments. Oxf. Bull. Econ. Stat, 71(1), 135-158.

Sargan, J. (1958). The Estimation of Economic Relationships Using Instrumental Variables. Econometrica,, 26, 393-415.

Seyyed Javadin, R., Shahhosseini, M., & HosseiniPour, V. (2011). Forecasting the consumption of natural gas in the horizon of fifth economic, social and political development plan of the country and critique of related policies. Business Management Magazine, 3(7), 109-126.

Soldo, B. (2002). Forecasting natural gas consumption. Appl. Energy, 92, 26-37.

Tej K. Gautam, K. P. P. (2018). The demand for natural gas in the Northeastern United States. Energy, 158, 890-898.

Tonkovic, M., Zekic-Susac, Z., & Somolanji, M. (2009). Predicting natural gas consumption by neural networks. Strojarski fakultet u Slavonskom Brodu, Elektrotehnički fakultet u Osijeku, 3.

Xiong, P., Dang, Y., Yao, T., & Wang, Z. (2014). Optimal modeling and forecasting of the energy consumption and production in China. Energy, 77, 623–634.

Xu, B. Lin., B. (2016). Assessing CO2 emissions in China’s iron and steel industry: A dynamic vector auto regression model. Appl. Energy, 161(375-386).

Zeng, B. (2017). Forecasting the relation of supply and demand of natural gas in China during 2015-2020 using a novel grey model. Journal of Intelligent & Fuzzy Systems (32), 141-155.

Zeng, B. L., C. (2016). Forecasting the natural gas demand in China using a self-adapting intelligent grey mode. Energy, 112, 810-825.