Factors Affecting Energy Consumption in the Agricultural Sector of Iran: The Application of ARDL-FUZZY
الموضوعات :
مریم ضیاء آبادی
1
(استادیار دانشکده گردشگری، اقتصاد منابع طبیعی و محیط زیست، مجتمع آموزش عالی بم، بم، ایران)
محمدرضا زارع مهرجردی
2
(دانشیار اقتصاد منابع طبیعی و محیط زیست، دانشگاه شهید باهنر کرمان، کرمان، ایران)
الکلمات المفتاحية: Iran, agricultural sector, energy consumption, ARDL-FUZZY,
ملخص المقالة :
Given the unlimited needs of mankind and the limited resources available, human beings have always been thinking about how to use the available resources and facilities optimally. Energy plays an important role in economic activities and it is of great importance in agriculture. Over the past four decades, energy consumption in the agricultural sector has increased tremendously. In Iran, energy used to be provided with subsidies to various economic sectors like agriculture in order to support the production. In this study, the ARDL- FUZZY method is used to study the effect of various factors on energy consumption in Iran's agricultural sector. The data on energy consumption by the agricultural sector, the share of the agricultural sector in the economy, the ratio of capital to labor, energy intensity, and energy prices were collected for the period 1974-2015. The results indicate that the share of the agricultural sector has a positive and significant effect on energy consumption over the studied period. The capital/labor ratio has a positive effect on energy consumption. Energy intensity in the studied period eventually has an irregular trend and has a positive effect on energy consumption in this sector. Energy prices (fossil fuels and electricity) have a negative effect (a low level of significance) on energy consumption. Therefore, it is suggested to give more consideration to energy consumption and its underlying factors in policymaking due to the importance of energy and the problem of pollution.
Abbasi, E. (2015). Forecasting of energy consumption in agricultural sector of Iran. Journal of Economic Sciences, 9 (32), 81-102.
Ahamed, J.U., Saidur, R., Masjuki, H.H., Mekhilef, S., Ali, M.B., & Furqon, M.H. (2011). An application of energy and exergy analysis in agricultural sector of Malaysia. Energy Policy, 39, 7922–7929.
Amadeh, H. (2014). Energy demand analysis in Iran's agricultural sector. Iranian Energy Economics, 2(8), 21-43.
Armen, S. A., & Zare, R. (2009). Energy consumption in different sections and its relationship with economic growth in Iran: analyzing causality based on Toda Yamamoto approach. Journal of Energy Economy Studies, 6 (20), 67-92.
Azadeh, A., Khakestani, M., & Saberi, M. (2009). A flexible fuzzy regression algorithm for forecasting oil consumption estimation. Energy Policy, 37, 5567-5579.
Bandaranaike, R. D., & Munasighe, M. (1983). The Demand for Electricity Service and the Quality of Supply. Energy Journal, 4(2), 49-71.
Barati Malayeri, A., & Hoori Jaffari, H. (2008). Estimation of energy consumption in final consuming sectors. Journal of Energy Economics Surveys, 1(1), 56-97.
Central Bank of the Islamic Republic of Iran. (2017). Statistics, Economic time series database. https://www.cbi.ir.
Cetin, T., & Yuksel, F. (2014). Empirical investigation on energy dependence-consumption nexus: Evidence from Turkish natural gas market. Applied Energy, 133(15), 243-251.
Chung, W. (2012). Using the fuzzy linear regression method to benchmark the energy efficiency of commercial buildings. Applied Eenergy, 95, 45-49.
Farahi, G., Khodashenas, S. R., & Alizade, A. (2011). Estimating the sediment of catchment areas of Northern Khorasan using fuzzy regression model. Iran Watershed Science and Engineering, 5 (15), 11-24.
Farajian, L., Moghaddasi, R., & Hosseini, S. (2018). Agricultural energy demand modeling in Iran: Approaching to a more sustainable situation. Energy Reports, 4, 260–265.
Fei, R., & Lin, B. (2017). Estimates of energy demand and energy-saving potential in China's agricultural sector. Energy, 135(15), 865-875.
Ghanbari, A., Galvani, A., & Javad Nejad, F. (2012). Studying the relationship between energy consumption and urbanization in Iran using ARDL. Journal of Energy Economy Studies, 9 (35), 101-119.
Gholizdeh, M. H., &Vahidpour, G. (2011). Predicting stock price using fuzzy regression method. Journal of Economic Sciences, 6 (12), 127-107.
Heidari, E. (2005). Forecast of energy demand in Iranian economy based on the analysis method. Economics Research, 69, 27-69.
Heidari, H., & Saiedpour, L. (2012). Econometric dynamic analysis from the relationship between GDP, energy consumption and non-oil exports in Iran. Journal of Energy Economy Studies, 9 (33), 53-83.
Jahangard, A., Golshani, V., Abdolah Milani, M., & Ghafarzadeh, H. R. (2017). Energy sector analysis in Iran 2001- 2011 (A comparative static analysis with approach SDA). Journal of Applied Economics, 7(20), 1-10.
Khashei, M., & Bijari, M. (2010). Applying the hybrid model of artificial nervous networks using fuzzy regression model with the aim of predicting the gold price. Journal of Industrial Engineering, 44 (1), 39-47.
Kohansal, M. R., & Rafie, H. (2012). Examining the consequences of increased spending of urban and rural households on energy consumption in agriculture and production of environmental pollutants. Journal of Agricultural Development and Economy, 26 (4), 287-297.
Kordoni, F., Jamialahmadi, M., & Bakhshi, M.R. (2018). Econometric analysis of energy consumption pattern in Iranian crop (Case Study: Wheat, Barley, Rice and Maize). Agricultural Economics Research, 10(1), 133-148.
Mehrabi Boshrabadi, H., & Naghavi, S. (2011). Estimation of energy demand function in agricultural sector of Iran. Agricultural Economics Research, 3(2), 147-162.
Ministry of Energy of Iran (2015). Energy balance sheets. Office of Macroeconomic Planning of Electricity and Energy of Iran. http://pep.moe.gov.ir
Moradi, M., & Habibzade, S. J. (2012). Application of fuzzy regression in explaining the relationship between characteristics of board of directors and performance of companies listed in Tehran Stock Market. Journal of Accounting Progress of Shiraz University, 2(63), 119-149.
Mousavi, S. N., Mokhtari, Z., & Farajzade, Z. (2010). Predicting consumption of energy carriers in Iran agricultural sector using ARCH ARIMA models. Journal of Energy Economy Studies, 27 (7), 181-195.
Mousavi, S.N. (2015). The determinants of energy intensity and carbon emissions in agricultural sector. Agricultural Economics Research, 7(2), 197-214.
Pourkazemi, M.H., Naderi, R. & Hatami, M. (2016). Iran's natural gas pricing in the industrial sector using a Ramzy model and fuzzy regression method. Journal of Engineering and Energy Management, 6(1), 52-69.
Sadatinejad, S. J., Hassan Shahi, R., Shayan Nejad, M., & Abdollahi, K. (2011). Evaluating the efficiency of the fuzzy regression model in reconstructing missing data of annual rainfall in Karoon catchment area. Environmental Science, 8(3), 109-116.
Sadeghi, S. K., Senobar, N., Behboudi, D., & Dehghan, A. (2012). The relationship between energy and production in Iran industrial sector. Journal of Economic Modeling, 6 (1), 91-110.
Schulte, I., & Heindl, P. (2017). Price and income elasticities of residential energy demand in Germany. Energy Policy, 102, 512-528.
Shayan Nejad, M., Sadati Nejad, S. J., & Fahmi, H. (2007). Determining potential evapotranspiration using the fuzzy regression. Iran Water Resources Research, 3(3), 9-19.
Tanaka, H. (1987). Fuzzy data analysis by possibility linear models. Fuzzy Sets and Systems, 24(3), 363-375.
United Nations Conference on Trade and Development (2011). Annual Report of United Nations Conference on Trade and Development, UNCTAD. http//data.un.org /en/iso/ir.html.
World Bank (2016). World Development Indicators WDI. Retrieved from: http://www.worldbank.org/data/onlinedatabases/onlinedatabases.html.
Yan, Q., Yin, J., Balezentis, T., Makuteniene, D., & Streimikiene, D. (2017). Energy-related GHG emission in agriculture of the European countries: An application of the Generalized Divisia Index. Journal of Cleaner Production. 164, 686-694.
Yan-Qun, H., Lai-Kow, Ch., & Ming –Le, W. (2007). Balancing productivity and consumer satisfaction for profitability: Statistical and fuzzy regression analysis. European Journal of Operational Research, 176, 252-263.
Zare Mehrjerdi, M. R., Ziaabadi, M., & Irani Kermani, F. (2012). Investigating the factors affecting Energy Consumption in Iran agricultural sector using parametric and nonparametric methods. Iranian Journal of Economic Studies, 1(1) 111- 125.
Ziaabadi, M., Alavi, S., Zare Mehrjerdi, M. R., & Irani Kermani,F. (2013). Forecaste of energy consumption in agricultural sector of Iran using neural network. Interdisciplinary Journal, 4(9), 1042-1053.
