Application of the Combined Index, Decomposing-Decoupling of Energy Consumption in the Agricultural and Industrial Sectors of Iran

Document Type : Research Article

Author

• S. Naghavi

Department of Agricultural Economics, Faculty of Agriculture, University of Jiroft, Jiroft, Iran

https://doi.org/10.22067/jead.2022.74585.1111

Abstract

Introduction
 In today's world, energy plays a prominent role in various economic and political fields. A role that has plagued many countries with a monopoly economy and its effects, and has left others with costly problems caused by rising energy prices, and in general, even international relations and disputes has made an impact. Accordingly, communities are looking for ways to reduce energy consumption without harming their economic growth. Due to the shortage energy resources and its role in economic growth, its optimal use is an important goal in the economic development of each country. Therefore, it is necessary to know the factors affecting changes in energy consumption, which requires the decomposing of energy consumption into different factors affecting changes in energy consumption. In the present study, the factors affecting energy consumption are analyzed using statistics and information of industry and agriculture sectors during the period 1397-1387 to three factors: structural effect, activity effect and energy intensity effect. The relationship between energy consumption and GDP growth in these two sectors has been studied to determine which factor has the greatest role in energy consumption changes in industry and agriculture and to suggest appropriate policy solutions.
Materials and Methods
 Decomposing Analysis is one of the most widely used approaches in analyzes related to energy consumption and CO2 emissions. It is concluded that one is structural analysis (SDA) and the other is index analysis (IDA). These two analysis techniques are different and each has advantages and disadvantages. The SDA technique is more complex and requires a lot of data. On the other hand, the data-output table is prepared every few years and annual data are not available. Instead, more information and findings are obtained using the SDA technique. The IDA technique is simple and does not require much data. It can be used with big data and does not need the data of any particular department or product. For this reason, the SDA technique has been used more. This study is used the Tapio decoupling index to explain the decoupling status. Decoupling index is the best technique to describe the dependence of economic growth on energy consumption and it is used to discover the relationship between energy consumption and economic growth. In the present study, using the Logarithmic Mean Division (LMDI) index, to analyze the factors affecting energy consumption in industry and agriculture and then using the decoupling index to examine the relationship between energy consumption and GDP growth in these two sections are discussed.
Results
The results of the decomposing  of energy consumption from GDP in the industrial sector showed that in most of the studies on the effect of energy intensity in agricultural sector, the effect of production had the greatest role in the decompose of energy consumption from GDP.
Conclusion
This study examined the decoupling of energy consumption from GDP of the industrial and agricultural sectors and the factors affecting energy consumption for each sector in Iran. The results of decomposition of energy consumption from GDP in the industrial sector showed that in most of the studied years, the effect of energy intensity and in the agricultural sector, the production effect had the greatest role in decomposing energy consumption from GDP. In 2010 and 2016, the production effect had a negative effect on energy consumption and increased energy consumption. In 1394, the index of decoupling of energy consumption from the growth of the agricultural sector was the best and most ideal possible, strong decoupling, and this indicates that the growth rate of the agricultural sector was higher than the growth rate of energy consumption. This year, the effect of energy intensity has had a positive effect on reducing energy consumption. The composite index has not experienced much fluctuation. Different modes of decoupling index and factors affecting energy consumption in industry and agriculture can be used as evidence for countries efforts to strongly decoupling and save energy in industry and agriculture. Advances in technology and innovation can reduce the growth dependence of sectors on energy consumption. Also, attention should be paid to optimizing the economic structure to create economic stability and reach the ideal state of the decoupling index.

Keywords

• Agriculture
• Decoupling Index
• Energy consumption
• Industry
• LMDI Index

20.1001.1.20084722.1401.36.3.4.5

Main Subjects

• Agricultural Economics

References

1. Abounoori, A.A., & Nikban, A. (2009). A research on factors, affecting Intensity of energy use, based on divisia Model 1(7): 77-92.
2. 2. Amadeh, H.,Ghazi M.,& Abbasifar, Z. (2009). Causality relation between energy consumption and economic growth and employment in Iranian economy. Journal Economic Research (TAHGHIGHAT-E-EGHTESADI). 44(86): 1-38. https://doi.org/1001.1.00398969.1388.44.1.1.5.
3. Amadeh, H., & Hashemi, M. (2020). Using combined approach of logarithmic mean divisia Index and decoupling Index for Decomposition of Energy Consumption in Agricultural Sector of Iran. Iranian Energy Economics 9(33): 19-39. https://doi.org/20.1001.1.24235954.1398.9.33.2.0.
4. Ang, B.W., & Zhang, F.Q. (2006). A survey of index decomposition analysis in energy and environmental studies. Energy 25: 1145-1176. https://doi.org/10.1016/S0360-5442(00)00039-6.
5. Bagherzadeh, A. (2019). Investigating the trend of energy consumption in the agricultural sector during 5 development programs in the country. Tehran: Ministry of Jihad Agriculture, Planning Research Institute, Agricultural Economics and Rural Development.
6. Barati Malaieri, A., Hoori Jaafari, H. (2008). Investigating the energy consumption in marginal consumer sectors. Energy Economics Issues 1: 56-97.
7. Chen, X., Shuai, Ch., Zhang, Y., & Wu, Y. (2020). Decomposition of energy consumption and its decoupling with economic growth in the global agricultural industry. Environmental Impact Assessment Review 81: 1-10.8. Energy Balance, Ministry of Energy, 1395-1397. https://doi.org/10.1016/j.eiar.2019.106364.
8. 8. Engo, J. (2018). Decomposing the decoupling of CO₂emissions from economic growth in Cameroon. Environmental Science and Pollution Reseach 25: 35451–35463. https://doi.org/10.1007/s11356-018-3511-z.
9. Faridzad, A. (2015). Energy Intensity decomposition analysis in Iranian Eenegy-intensive industries using the Logarithmic Mean Divisia Index with Emphasis on the Chain-Linked and Period-Wise Approach. Iranian Energy Economics 15: 87-117. https://doi.org/10.22054/jiee.2016.1881.
10. Fotros, M.H.,& Barati, J. (2011). Decomposition of Iranian energy consumption-related Co₂ emissions in economic Sectors, an index decomposition Analysis.  Energy Economics Review 8(28): 49-73.
11. Hashemi, M., & Amadeh, H. (2020). Analysis of energy consumption in sectors of industry and transportation (Integrated Approach LMDI methods and indicators. Quarterly Journal of New Economy and Trade 14(7): 125-148.
12. Khalili Araghi, A.,Sharzehi, G.A.,& Barkhordari, S. (2012). A decomposition analysis of Co₂ emissions related energy consumption In Iran.  Journal of Environmental Studies 38(61): 93-103. https://doi.org/10.22059/JES.2012.29015.
13. Long, L., & Wang, X. (2017). A study on the relationship among ecologaical loss growth and welfare level in the process of urbanization in China: based on Tapio decoupling analysis and Granger Causality test. Inquiry Into Economics 3: 98–106.
14. Luo, Y., Long, X., Wu, C., & Zhang, J. (2017). Decoupling CO₂ emissions from economic growth in agricultural sector across 30 Chinese provinces from 1997 to 2014. Journal of Cleaner Production 159: 220–228. https://doi.org/10.1016/j.jclepro.2017.05.076.
15. Mobini Dehkordi, A., & Houri Jafari, H. (2017). Analytical review on energy consumption pattern policies and plans effect on the energy intensity of Iran. Journal of Science and Technology Policy Letters (JSTPL) 7(20): 45-60. https://doi.org/20.1001.1.24767220.1396.07.3.4.8.
16. OECD (Organization for Economic Co-operation and Development). Indicators to measure decoupling of environmental pressure from economic growth. Sustainable Development. SG/SD (2002), 1/Final. Website: http://www. olis.oecd.org/olis/2002doc.nsf/LinkTo/sg-sd (2002)1-final accessed on August 28, 2010.
17. Pourebadollahan Covich, M.,  Panahi, H., Homonlo, SH., & Salehi Aba, K. (2016). Decomposing Influencing Factors of Energy Consumption Changes in the Iranian Industrial Subsectors: The lomparison of laspeyres and divisia Methods. Quarterly Journal of Applied Theories of Economics 2(4): 49-70.
18. Ren, Sh., Yin, H., & Chen, X. (2014). Using LMDI the decoupling of darbon dioxide Emissions by Industry, Environmental Development 9: 61–75. https://doi.org/10.1016/j.envdev.2013.11.003.
19. Naghavi, S., Ebrahimi-Khusfi, Z., & Mirzaei, A. (2021). Decoupling pollution-agricultural growth and predicting climate change impacts on decoupling index using Bayesian network in different climatic regions. Environmental Science Poluution Researches. https://doi.org/10.1007/s11356-021-16662-9.
20. Sadeghi, Z.,Jalaee Seyed, A.,& Nikravesh, M. (2019). Investigating energy consumption performance in Iranian provinces: index decomposition analysis. Regional Development (Knowledge and Development) 25(16): 57-86. https://doi.org/10.22067/erd.v25i16.68848.
21. Tapio Petri. (2005). Towards a Theory of Decoupling: Degrees of Decoupling in the EU and the Case of Road in Finland Between 1970 and 2001, Transport Policy 12: 137–151.
22. Tavana Najar, A., & Feizi, M. (2015). Investigating the factors affecting the changes in the consumption of petroleum products in the domestic sector of Iran. 10^th International Confernce of Energy.
23. Wang, W., Liu R., Zhang, M., & Li, H. (2013). Decomposing the decoupling of energy- Related CO₂ Emissions and Economic Growth in Jiangsu Province, Energy for Sustainable Development 17(1): 62-71. https://doi.org/10.1016/j.esd.2012.11.007.
24. Wang, Q., & Wang, S. (2019). A comparison of decomposition the decoupling carbon emissions from economic growth in transport sector of selected provinces in eastern, central and western China. Journal of Cleaner Production 229: 570-581. https://doi.org/10.1016/j.jclepro.2019.04.375.
25. Zhang, M., Song, Y., Su, B., & Suni, X. (2015). Decomposition the decoupling indicator detween the economic growth and energy consumption in China. Energy Efficiency 8: 1231-1239. https://doi.org/10.1007/s12053-015-9348-0.
26. Zhang, Z.X. (2000). Decoupling China’s carbon emissions increase from economic growth: an economic analysis and policy implications. World Development 28: 739–75. https://doi.org/10.1016/S0305-750X(99)00154-0.
27. Zhou, Y., & Bai, Y. (2016). Decoupling economic growth from energy consumption: an analysis of Beijing, China, Journal of Residuals Science & Technology 13(7).
28. Zhou, M., & Hu, B. (2020). Decoupling of carbon emissions from agricultural land utilization from economic growth in China. Agricultural Economics-Czech 66(11): 510-518. https://doi.org/10.17221/290/2020-AGRICECON.