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Composite energy intensity index estimation in Iran: an exploration of index decomposition analysis

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PL
Oszacowanie zagregowanego wskaźnika energochłonności w Iranie: badanie na temat analizy rozkładu wskaźników
Języki publikacji
EN
Abstrakty
EN
The role of energy as a key factor in enhancing sustainable development, energy security, and economic competitiveness is a reason that has made energy efficiency trends tracking essential and is why policymakers and energy planners have focused on energy intensity and its following issues. Also, the inadequate operation of the traditional energy intensity index and the overestimation of its results turned this index into a weak one. Hence, it is necessary to employ a new index that can be decomposed and is capable of considering both monetary and physical activity indicators to offer a more accurate view of the energy intensity variation. This paper develops a Composite Energy Intensity Index by combining monetary and physical activity indicators by applying the multiplicative Logarithmic Mean Divisia Index (LMDI) in 2001–2011 to decompose the factors affecting energy intensity change and seeks to fill the gap between the EGR and CEI indices. The results of the survey demonstrate more economy-wide energy consumption reduction while using the composite energy intensity index as compared to the traditional energy intensity index; also, the results show the relatively important role of the overall structure effect. From Sectoral perspective results, both energy to GDP index (EGR) and composite energy intensity index (CEI) have shown passenger transport as the most energy-consuming sector. The passenger transport sector reveals an urgent need for implementing appropriate policies to reduce the high energy consumption of the sector.
PL
Energia jest kluczowym czynnikiem w procesie wzmacniania zrównoważonego rozwoju, bezpieczeństwa energetycznego i konkurencyjności gospodarczej i z tego powodu śledzenie trendów w zakresie efektywności energetycznej jest niezbędne. Dlatego też decydenci i planiści zajmujący się problemami energii poświęcają dużo uwagi energochłonności i związanym z nią kwestiom. Ale tradycyjny wskaźnik energochłonności nie stanowi właściwej miary i często prowadzi do przeszacowania wyników, co powoduje, że wskaźnik ten stał się mało przydatny. W związku z tym konieczne jest zastosowanie nowego wskaźnika, który można rozłożyć i który jest w stanie uwzględnić zarówno wskaźniki pieniężne, jak i wskaźniki aktywności fizycznej, aby zapewnić dokładniejszy obraz zmian energochłonności. W niniejszym artykule opracowano zagregowany wskaźnik energochłonności, który łączy wskaźniki pieniężne i wskaźniki aktywności fizycznej, stosując multiplikatywny logarytmiczny średni indeks Divisia (Logarytmic Mean Divisia Index – LMDI) w latach 2001–2011 w celu dekompozycji czynników wpływających na zmianę energochłonności i stara się wypełnić lukę między wskaźnikiem udziału energii w PKB (EGR) a złożonym wskaźnikiem energochłonności (CEI). Wyniki badania wskazują na większą redukcję zużycia energii w całej gospodarce przy zastosowaniu zagregowanego wskaźnika energochłonności w porównaniu z tradycyjnym wskaźnikiem energochłonności. Wyniki pokazują również relatywnie ważną rolę ogólnego efektu struktury. Z perspektywy sektorowej, zarówno wskaźnik energii do PKB (EGR), jak i złożony wskaźnik energochłonności (CEI) wykazały, że transport pasażerski jest sektorem najbardziej energochłonnym. Sektor transportu pasażerskiego ujawnia pilną potrzebę wdrożenia odpowiedniej polityki w celu zmniejszenia wysokiego zużycia energii w tym obszarze.
Rocznik
Strony
5--28
Opis fizyczny
Bibliogr. 46 poz., tab., wykr.
Twórcy
  • Department of Energy, Agriculture and Environmental Economics, Faculty of Economics, Allameh Tabataba’i University, Iran
autor
  • Department of Energy, Agriculture and Environmental Economics, Faculty of Economics, Allameh Tabataba’i University, Iran
Bibliografia
  • Ang, B.W. 2004. Decomposition analysis for policymaking in energy: Which is the preferred method? Energy Policy 32(9), pp. 1131–1139, DOI: 10.1016/S0301-4215(03)00076-4.
  • Ang, B.W. 2006. Monitoring changes in economy-wide energy efficiency: From energy – GDP ratio to composite efficiency index. Energy Policy 34(5), pp. 574–582, DOI: 10.1016/j.enpol.2005.11.011.
  • Ang, B.W. 2015. LMDI decomposition approach: A guide for implementation. Energy Policy 86, pp. 233– –238, DOI: 10.1016/j.enpol.2015.07.007.
  • Ang, B.W. and Goh, T. 2018. Bridging the gap between energy-to-GDP ratio and composite energy inten sity index. Energy Policy 119(April), pp. 105–112, DOI: 10.1016/j.enpol.2018.04.038.
  • Ang, B.W. and Xu, X.Y. 2013. Tracking industrial energy efficiency trends using index decomposition analysis. Energy Economics 40, pp. 1014–1021, DOI: 10.1016/j.eneco.2013.05.014.
  • Appropriate taxes and incentives do affect purchases of new cars 2018. European Environment Agency.
  • Asadi et al. 2015 – Asadi, Z., Abbasi, E. and Salahi, J. 2015. Factors Affecting Energy Intensity of Iranian Industrial. Islamic Azad University-Central Tehran Branch.
  • Bhattacharyya, C. 2018. Energy Economics: Concepts, Issues, Markets and Governance. Translated by Faridzad A. Leicester: Springer.
  • Cahill, C.J. and Brian, P.O. 2012. Combining physical and economic output data to analyse energy and CO2 emissions trends in industry. Energy Policy 49, pp. 422–429, DOI: 10.1016/j.enpol.2012.06.041.
  • Chontanawat et al. 2019 – Chontanawat, J., Wiboonchutikula, P. and Buddhivanich, A. 2019. An LMDI decomposition analysis of carbon emissions in the Thai manufacturing sector. Energy Reports 6, pp. 705–710, DOI: 10.1016/j.egyr.2019.09.053.
  • Dargahi, H. and Biabany Khameneh, K. 2016. Determinant of price and income factors, and efficiency in Iran’s energy intensity. Journal of Economic Research 2(51), pp. 384–355.
  • Dehghan Shabani, Z. and Shahnazi, R. 2018. Energy consumption, carbon dioxide emissions, informa tion and communications technology, and gross domestic product in Iranian economic sectors: A panel causality analysis. Energy 169, pp. 1064–1078, DOI: 10.1016/j.energy.2018.11.062.
  • Dorisavibehmanshir et al. 2016 – Dorisavibehmanshir, R., Najimeydani, A., Khodaparastmashahdi, M. and Salehnia, N. 2016. Estimating The Factors Affecting Energy Demand in Sea Transport Using Econometrics methods (Case study: General Administration of Ports and Khorramshahr maritime). The Journal of Transportation Research 13(4), pp. 1–16. economic time series data (n.d.). Central Bank of the Islamic Republic of Iran.
  • Energy balance-sheet (n.d.). The Iran’s Ministry of Power.
  • Energy balance-sheet (2002–2012) (n.d.). The Iran’s Ministry of Power.
  • Farajzadeh, Z. 2015. Energy Intensity inIranian Economy: Determining components and factors. Journal of Iranian Energy Economics 15(4), pp. 43–86.
  • Faridzad, A. 2012. Investigating the capability and validity of using input-output table tables during the structural changes in Iranian economy. Islamic Parliament Research Center of The Islamic Republic of IRAN, pp. 1–28. [Online] https://rc.majlis.ir/fa/report/show/828482 [Accessed: 2020-12-05].
  • Faridzad, A. 2015. Energy Intensity Decomposition Analysis in Iranian energy Intensive Industries by Logarithmic Mean Divisia Index with focus on Period-wise and Chaind- linked Analysis. Journal of Iranian Energy Economics 15(4), pp. 87–117.
  • Fotros, J. and Baraty, M. 2010. Decomposition of CO2 Emissions of Iranian Transport Sector in Sub-sec tors and Component Fuels An Application of Decomposition Analysis. Scientific Research Quarterly Applied Economics in Iran 6(2), pp. 83–64.
  • Fotros et al. 2014 – Fotros, M., Sahraie, R. and Yavari, M. 2014. Estimating the Energy Demnd Func tion in Iran’s road transport (1978–2013). Quarterly Journal of the Macro and Strategic Policies 2(7), pp. 23–42.
  • Geravand et al. 2013 – Geravand, S., Mehregan, N., Sadeghi, H. and Malekshahi, M. 2013. Evalu ation of energy efficiency in the petroleum industry in Iran. The Journal of Economic Policy 10(5), pp. 57–74.
  • Homaie Morad et al. 2016 – Homaie Morad, T., Khorsandi, M. and Ghasemi, A. 2016. Analyzing the Factors Affecting Energy Intensity in Iran with Emphasis on the Role of Implementing the Policy of Making Targeted Subsidies. Allameh Tabataba’i University.
  • Hosseini, K. and Stefaniec, A. 2019. Efficiency assessment of Iran’s petroleum refining industry in the presence of unprofitable output: A dynamic two-stage slacks-based measure. Energy 189, pp. 1–12.
  • Kafaie, S.M.A. and Nejadaghaeianvash, P. 2017. Identifying the factors affecting energy efficiency in the Iranian economy. Quarterly Energy Economics Review 52(13), pp. 1–34.
  • KhaliliAraghi et al. 2012 – KhaliliAraghi, M., Sharzeie, G. and Barkhordari, S. 2012. Decomposi tion Analysis of Carbon di oxide emssion. Journal of Ecology 61(38), pp. 93–104.
  • Lotfi et al. 2018 – Lotfi, S., Faridzad, A. and Ali Asghar, S. 2018. Decomposition of Energy Intensity of Iranian Economic Sectors: Index Decomposition Analysis Approach Combine with Production-Theo retical Decomposition Analysis. Journal of Energy Planning and Policy Research 85(26), pp. 151–187.
  • Moghaddasi, R. and Anoushe pour, A. 2016. Energy consumption and total factor productivity growth in Iranian agriculture. Energy Reports Journal 2, pp. 218–220.
  • Moshiri, S. 2020. Consumer responses to gasoline price and non-price policies. Energy Policy 137 (Sep tember), 111078, DOI: 10.1016/j.enpol.2019.111078.
  • Norman, J.B. 2017. Measuring improvements in industrial energy efficiency: A decomposition analysis applied to the UK. Energy 137, pp. 1144–1151, DOI: 10.1016/j.energy.2017.04.163.
  • Ossowska, L.J. and Janiszewska, D.A. 2020. Toward sustainable energy consumption in the European Union. Polityka Energetyczna – Energy Policy Journal 23(1), pp. 37–48.
  • Pourabadellahankavich et al. 2015 – Pourabadellahankavich, M., Panahi, H., Shahryar, S. and SalehiAbar, K. 2015. Decomposition of Factor Affecting Energy Consumption Changes in Irans’ In dustrial Sub-Sectors. Quarterly Journal If Applied Theories of Economics 4(2), pp. 49–70.
  • Pourkazemi, M. and Heydari, K. 2002. Using Data Envelopment Analysis (DEA) in Evaluating the Effi ciency of Thermal Power Plants. Journal of Management Research in Iran 6(1), pp. 35–54.
  • Ranjbari et al. 2018 – Ranjbari, F., Heydari, E. and Parsa, H. 2018. The study of factors affecting changes in carbon dioxide emissions in selected economic sectors over the years (1996–2014 with the LMDI Index Decomposition Analysis aapproach). National Conference on Management, Economics and Re sistance Economics. [Online] https://www.tpbin.com/article/69678 [Accessed: 2020-12-15].
  • Report on the Iran’s economic condition, gross domestic product and cost 2007. Central Bank of the Isla mic Republic of Iran, chapter 2, pp. 1–16. [Online] https://www.cbi.ir/simplelist/6746.aspx [Accessed: 2020-12-15].
  • Sadraei Javaheri, A. and Ostadzad, A.H. 2014. Estimating Efficiency of Thermal and Hydroelectric Po wer Plants in Iranian Provinces. Iranian Journal of Economic Studies 3(2), pp. 19–42, DOI: 10.22099/ IJES.2014.3668.
  • Sadeghi et al. 2016 – Sadeghi, N., Zabihi, Z. and MostaeliParsa, M. 2016. Concept of iranian economic sectors 3. Estimation of energy consumption and CO2 emmision in economic-sectors. Islamic Parlia ment Research Center Of The Islamic Republic Of IRAN, pp. 1–67.
  • Seifipour, R. and Afroozamini, F. 2012. Effect of Increase in Energy Price on The Rail Demand and Its share from ground transport. Journal of Transportation Engineering 2(4), pp. 315–324.
  • Statistical Summary of Iran Electricity Industry (2001–2011) (n.d.). Electric Power Industry Statics, Ta vanir.
  • Szép, T.S. 2013. Eight Methods for Decomposing the Aggregate Energy Intensity of the Economic Structu re. Club of Economics in Miskolc’ 9(1), pp. 77–84.
  • The Statistics of the Ministry of Roads and Urban Development 2011. [Online] http://ifco.ir/images/97/ hamlonaghl/TransportationBook90.pdf [Accessed: 2021-01-15].
  • Time series of Iran’s national account (2001–2011). (n.d.). Central Bank of the Islamic Republic of Iran.
  • Trotta, G. 2019. Assessing energy efficiency improvements and related energy security and climate benefits in Finland: An ex post multi-sectoral decomposition analysis. Energy Economics 86, DOI: /10.1016/j.eneco.2019.104640
  • Valizadeh et al. 2017 – Valizadeh, A., Sadeghi, N. and Akhavan, B. 2017. 2001–2006 Input-Output ta ble with constant price (data base and calculation method). Islamic Parliament Research Center Of The Islamic Republic Of IRAN, pp. 1–31. [Online] https://rc.majlis.ir/fa/report/show/1049215 [Accessed: 2021-01-20].
  • Wang et al. 2017 – Wang, H., Ang, B.W. and Su, B. 2017. Assessing drivers of economy-wide ener gy use and emissions: IDA versus SDA. Energy Policy 107(April), pp. 585–599, DOI: 10.1016/j. enpol.2017.05.034.
  • Zhang et al. 2019 – Zhang, X., Su, B., Yang, J. and Cong, J. 2019. Index decomposition and attribution analysis of aggregate energy intensity in Shanxi Province (2000–2015). Journal of Cleaner Production 238.
  • Zhou et al. 2019 – Zhou, X., Zhou, D., Wang, Q. and Su, B. 2019. Who shapes China’s carbon in tensity and how? A demand-side decomposition analysis. Energy Economics 85, DOI: 10.1016/j.ene co.2019.104600.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-23b9e96e-bc11-4c9f-ac43-3fb5784d56ea
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