IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/79237.html
   My bibliography  Save this paper

Energy Intensity of GDP: A Nonlinear Estimation of Determinants in Iran

Author

Listed:
  • Heidari, Hassan
  • Babaei Balderlou, Saharnaz
  • Ebrahimi Torki, Mahyar

Abstract

Energy intensity is a measure of the energy efficiency of a nation’s economy. Many factors influence a country’s energy intensity. In this paper, however, we note the effective factors of energy intensity and decompose it by applying Logistic Smooth Transition Regression (LSTR) in Iran during the period 1980- 2013. The main factors are the ratio of the added value of services to GDP (explaining both linear and nonlinear part of the energy intensity), the percentage of internet users, income per capita and Human Development Index (explaining nonlinear part of the energy intensity). The results indicated that the lifestyle and structural changes had a significant and considerable effect on decreasing energy intensity and that the ratio of services value-added to GDP as a transition variable caused an asymmetric behavior of energy intensity affected from explanatory variables. The most effective factor on energy intensity was Human Development Index

Suggested Citation

  • Heidari, Hassan & Babaei Balderlou, Saharnaz & Ebrahimi Torki, Mahyar, 2016. "Energy Intensity of GDP: A Nonlinear Estimation of Determinants in Iran," MPRA Paper 79237, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:79237
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/79237/1/khu-eco-v1n2p1-en.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. K. S. Chan & H. Tong, 1986. "On Estimating Thresholds In Autoregressive Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 7(3), pages 179-190, May.
    2. Wu, Yanrui, 2012. "Energy intensity and its determinants in China's regional economies," Energy Policy, Elsevier, vol. 41(C), pages 703-711.
    3. Maringer Dietmar G. & Meyer Mark, 2008. "Smooth Transition Autoregressive Models -- New Approaches to the Model Selection Problem," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 12(1), pages 1-21, March.
    4. Shahiduzzaman, Md. & Alam, Khorshed, 2013. "Changes in energy efficiency in Australia: A decomposition of aggregate energy intensity using logarithmic mean Divisia approach," Energy Policy, Elsevier, vol. 56(C), pages 341-351.
    5. Baksi, Soham & Green, Chris, 2007. "Calculating economy-wide energy intensity decline rate: The role of sectoral output and energy shares," Energy Policy, Elsevier, vol. 35(12), pages 6457-6466, December.
    6. Santosh Kumar SAHU & K NARAYANAN, 2010. "Decomposition Of Industrial Energy Consumption In Indian Manufacturing The Energy Intensity Approach," Journal of Advanced Research in Management, ASERS Publishing, vol. 1(1), pages 22-38.
    7. Sue Wing, Ian, 2008. "Explaining the declining energy intensity of the U.S. economy," Resource and Energy Economics, Elsevier, vol. 30(1), pages 21-49, January.
    8. Li, Ke & Lin, Boqiang, 2014. "The nonlinear impacts of industrial structure on China's energy intensity," Energy, Elsevier, vol. 69(C), pages 258-265.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hong, Junjie & Shi, Fangyuan & Zheng, Yuhan, 2023. "Does network infrastructure construction reduce energy intensity? Based on the “Broadband China” strategy," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    2. Ajayi, V. & Reiner, D., 2018. "European Industrial Energy Intensity: The Role of Innovation 1995-2009," Cambridge Working Papers in Economics 1835, Faculty of Economics, University of Cambridge.
    3. Parker, Steven & Liddle, Brantley, 2016. "Energy efficiency in the manufacturing sector of the OECD: Analysis of price elasticities," Energy Economics, Elsevier, vol. 58(C), pages 38-45.
    4. Pan, Xiongfeng & Uddin, Md. Kamal & Saima, Umme & Jiao, Zhiming & Han, Cuicui, 2019. "How do industrialization and trade openness influence energy intensity? Evidence from a path model in case of Bangladesh," Energy Policy, Elsevier, vol. 133(C).
    5. Löschel, Andreas & Pothen, Frank & Schymura, Michael, 2015. "Peeling the onion: Analyzing aggregate, national and sectoral energy intensity in the European Union," Energy Economics, Elsevier, vol. 52(S1), pages 63-75.
    6. Adom, Philip Kofi, 2015. "Business cycle and economic-wide energy intensity: The implications for energy conservation policy in Algeria," Energy, Elsevier, vol. 88(C), pages 334-350.
    7. Kadilli, Anjeza & Krishnakumar, Jaya, 2022. "Smooth Transition Simultaneous Equation Models," Journal of Economic Dynamics and Control, Elsevier, vol. 145(C).
    8. P. Fernández-González & M. Landajo & M.J. Presno, 2013. "Factors Influencing Changes In Aggregate Energy Consumption. An European Cross-Country Analysis," Regional and Sectoral Economic Studies, Euro-American Association of Economic Development, vol. 13(2), pages 18-30.
    9. Jin, Taeyoung, 2022. "Impact of heat and electricity consumption on energy intensity: A panel data analysis," Energy, Elsevier, vol. 239(PA).
    10. Li, Ke & Lin, Boqiang, 2015. "The improvement gap in energy intensity: Analysis of China's thirty provincial regions using the improved DEA (data envelopment analysis) model," Energy, Elsevier, vol. 84(C), pages 589-599.
    11. Shahiduzzaman, Md & Layton, Allan, 2017. "Decomposition analysis for assessing the United States 2025 emissions target: How big is the challenge?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 372-383.
    12. Dargahi, Hassan & Khameneh, Kazem Biabany, 2019. "Energy intensity determinants in an energy-exporting developing economy: Case of Iran," Energy, Elsevier, vol. 168(C), pages 1031-1044.
    13. Jain, Princy & Goswami, Binoy, 2021. "Energy efficiency in South Asia: Trends and determinants," Energy, Elsevier, vol. 221(C).
    14. Guo, Jinyu & Ma, Jinji & Li, Zhengqiang & Hong, Jin, 2022. "Building a top-down method based on machine learning for evaluating energy intensity at a fine scale," Energy, Elsevier, vol. 255(C).
    15. Voigt, Sebastian & De Cian, Enrica & Schymura, Michael & Verdolini, Elena, 2014. "Energy intensity developments in 40 major economies: Structural change or technology improvement?," Energy Economics, Elsevier, vol. 41(C), pages 47-62.
    16. Huang, Junbing & Hao, Yu & Lei, Hongyan, 2018. "Indigenous versus foreign innovation and energy intensity in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1721-1729.
    17. Akbar Ullah & Karim Khan & Munazza Akhtar, 2014. "Energy Intensity: A Decomposition Exercise for Pakistan," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 53(4), pages 531-549.
    18. Huang, Junbing & Du, Dan & Tao, Qizhi, 2017. "An analysis of technological factors and energy intensity in China," Energy Policy, Elsevier, vol. 109(C), pages 1-9.
    19. Jimenez, Raul & Mercado, Jorge, 2014. "Energy intensity: A decomposition and counterfactual exercise for Latin American countries," Energy Economics, Elsevier, vol. 42(C), pages 161-171.
    20. Fernández González, P. & Landajo, M. & Presno, M.J., 2014. "Multilevel LMDI decomposition of changes in aggregate energy consumption. A cross country analysis in the EU-27," Energy Policy, Elsevier, vol. 68(C), pages 576-584.

    More about this item

    Keywords

    Energy Intensity; Energy Efficiency; LSTR Model; Iran;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:79237. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.