IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v58y2016icp1108-1113.html
   My bibliography  Save this article

Kaya identity for analysis of the main drivers of GHG emissions and feasibility to implement EU “20–20–20” targets in the Baltic States

Author

Listed:
  • Štreimikienė, Dalia
  • Balezentis, Tomas

Abstract

The Kaya identity relates the greenhouse gas (GHG) emissions to energy use, economic growth and population growth. Therefore, it can be applied to decompose GHG emissions with respect to the latter terms. European Union (EU) has established the main targets of sustainable energy development in Energy and Climate package 20–20–20. EU member states conformed to achieve in 2020 GHG emission reduction by 20% comparing with year 1990 level, to achieve 20% of renewable energy sources (RES) in final energy consumption and to reduce energy intensity by 20% compared to year 2005 level. As all of these targets are interrelated and have to be achieved together, it is very important to track the changes of GHG emissions in EU member states alongside the changes in energy intensity and increase in the share of renewables in final energy consumption. The Kaya identity and decomposition analysis allow the linking of changes in GHG emissions to changes in energy intensity and carbon intensity of energy which directly reflects the increase of the share of renewables in final energy consumption. The impact of GDP growth on GHG emissions is also assessed by Kaya identity. The paper applies a novel approach in tracking progress achieved in implementing 20–20–20 targets by employing Kaya identity and Shapley value for decomposition analysis. The conducted analysis showed that policies to increase energy efficiency are the most important drivers of GHG emission reduction and achieving EU 20–20–20 targets in Baltic States. Though the share of RES has increased significantly in Baltic States its impact on GHG emissions reduction per capita in Baltic States was insignificant therefore more emphasis on energy efficiency policies is necessary as they are the most effective measures for achieving GHG emission reduction targets.

Suggested Citation

  • Štreimikienė, Dalia & Balezentis, Tomas, 2016. "Kaya identity for analysis of the main drivers of GHG emissions and feasibility to implement EU “20–20–20” targets in the Baltic States," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1108-1113.
    • Handle: RePEc:eee:rensus:v:58:y:2016:i:c:p:1108-1113
    DOI: 10.1016/j.rser.2015.12.311
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032115016949
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2015.12.311?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ang, B. W., 2004. "Decomposition analysis for policymaking in energy:: which is the preferred method?," Energy Policy, Elsevier, vol. 32(9), pages 1131-1139, June.
    2. Albrecht, Johan & Francois, Delphine & Schoors, Koen, 2002. "A Shapley decomposition of carbon emissions without residuals," Energy Policy, Elsevier, vol. 30(9), pages 727-736, July.
    3. Ang, B. W. & Liu, F. L. & Chew, E. P., 2003. "Perfect decomposition techniques in energy and environmental analysis," Energy Policy, Elsevier, vol. 31(14), pages 1561-1566, November.
    4. Sun, J. W., 1998. "Changes in energy consumption and energy intensity: A complete decomposition model," Energy Economics, Elsevier, vol. 20(1), pages 85-100, February.
    5. Baležentis, Alvydas & Baležentis, Tomas & Streimikiene, Dalia, 2011. "The energy intensity in Lithuania during 1995–2009: A LMDI approach," Energy Policy, Elsevier, vol. 39(11), pages 7322-7334.
    6. Hoekstra, Rutger & van den Bergh, Jeroen C. J. M., 2003. "Comparing structural decomposition analysis and index," Energy Economics, Elsevier, vol. 25(1), pages 39-64, January.
    7. Raslavičius, Laurencas & Narbutas, Laimonas & Šlančiauskas, Anupras & Džiugys, Algis & Bazaras, Žilvinas, 2012. "The districts of Lithuania with low heat demand density: A chance for the integration of straw biomass," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 3259-3269.
    8. Xu, X.Y. & Ang, B.W., 2013. "Index decomposition analysis applied to CO2 emission studies," Ecological Economics, Elsevier, vol. 93(C), pages 313-329.
    9. Brizga, Janis & Feng, Kuishuang & Hubacek, Klaus, 2014. "Drivers of greenhouse gas emissions in the Baltic States: A structural decomposition analysis," Ecological Economics, Elsevier, vol. 98(C), pages 22-28.
    10. Brizga, Janis & Feng, Kuishuang & Hubacek, Klaus, 2013. "Drivers of CO2 emissions in the former Soviet Union: A country level IPAT analysis from 1990 to 2010," Energy, Elsevier, vol. 59(C), pages 743-753.
    11. Ang, B.W. & Huang, H.C. & Mu, A.R., 2009. "Properties and linkages of some index decomposition analysis methods," Energy Policy, Elsevier, vol. 37(11), pages 4624-4632, November.
    12. Robaina Alves, Margarita & Moutinho, Victor, 2013. "Decomposition analysis and Innovative Accounting Approach for energy-related CO2 (carbon dioxide) emissions intensity over 1996–2009 in Portugal," Energy, Elsevier, vol. 57(C), pages 775-787.
    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. Li, Tianxiang & Baležentis, Tomas & Makutėnienė, Daiva & Streimikiene, Dalia & Kriščiukaitienė, Irena, 2016. "Energy-related CO2 emission in European Union agriculture: Driving forces and possibilities for reduction," Applied Energy, Elsevier, vol. 180(C), pages 682-694.
    2. Zhiqian Yu & Dalia Streimikiene & Tomas Balezentis & Rimantas Dapkus, 2017. "Final Energy Consumption Trends and Drivers in Czech Republic and Latvia," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 19(46), pages 866-866, August.
    3. Banie Naser Outchiri, 2020. "Contributing to better energy and environmental analyses: how accurate are decomposition analysis results?," Cahiers de recherche 20-11, Departement d'économique de l'École de gestion à l'Université de Sherbrooke.
    4. Lan, Jun & Malik, Arunima & Lenzen, Manfred & McBain, Darian & Kanemoto, Keiichiro, 2016. "A structural decomposition analysis of global energy footprints," Applied Energy, Elsevier, vol. 163(C), pages 436-451.
    5. Su, Bin & Ang, B.W., 2012. "Structural decomposition analysis applied to energy and emissions: Some methodological developments," Energy Economics, Elsevier, vol. 34(1), pages 177-188.
    6. Shigemi Kagawa & Yuriko Goto & Sangwon Suh & Keisuke Nansai & Yuki Kudoh, 2012. "Accounting for Changes in Automobile Gasoline Consumption in Japan: 2000–2007," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 1(1), pages 1-27, December.
    7. Lin, Boqiang & Ouyang, Xiaoling, 2014. "Analysis of energy-related CO2 (carbon dioxide) emissions and reduction potential in the Chinese non-metallic mineral products industry," Energy, Elsevier, vol. 68(C), pages 688-697.
    8. Fernández González, P. & Presno, M.J. & Landajo, M., 2015. "Regional and sectoral attribution to percentage changes in the European Divisia carbonization index," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1437-1452.
    9. Wang, H. & Ang, B.W. & Su, Bin, 2017. "Assessing drivers of economy-wide energy use and emissions: IDA versus SDA," Energy Policy, Elsevier, vol. 107(C), pages 585-599.
    10. Lu, Qinli & Yang, Hong & Huang, Xianjin & Chuai, Xiaowei & Wu, Changyan, 2015. "Multi-sectoral decomposition in decoupling industrial growth from carbon emissions in the developed Jiangsu Province, China," Energy, Elsevier, vol. 82(C), pages 414-425.
    11. Román-Collado, Rocío & Cansino, José M. & Botia, Camilo, 2018. "How far is Colombia from decoupling? Two-level decomposition analysis of energy consumption changes," Energy, Elsevier, vol. 148(C), pages 687-700.
    12. Cahill, Caiman J. & Ó Gallachóir, Brian P., 2012. "Combining physical and economic output data to analyse energy and CO2 emissions trends in industry," Energy Policy, Elsevier, vol. 49(C), pages 422-429.
    13. Wang, Qunwei & Wang, Yizhong & Zhou, P. & Wei, Hongye, 2017. "Whole process decomposition of energy-related SO2 in Jiangsu Province, China," Applied Energy, Elsevier, vol. 194(C), pages 679-687.
    14. Liobikienė, Genovaitė & Butkus, Mindaugas & Bernatonienė, Jurga, 2016. "Drivers of greenhouse gas emissions in the Baltic states: decomposition analysis related to the implementation of Europe 2020 strategy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 309-317.
    15. Wood, Richard & Lenzen, Manfred, 2006. "Zero-value problems of the logarithmic mean divisia index decomposition method," Energy Policy, Elsevier, vol. 34(12), pages 1326-1331, August.
    16. Carlino, Laurent & Coppens, François & González, Javier & Ortega, Manuel & Pérez-Duarte, Sébastien & Rubbrecht, Ilse & Vennix, Saskia, 2017. "Decomposition techniques for financial ratios of European non-financial listed groups," Statistics Paper Series 21, European Central Bank.
    17. Ang, B.W., 2015. "LMDI decomposition approach: A guide for implementation," Energy Policy, Elsevier, vol. 86(C), pages 233-238.
    18. Lenzen, Manfred, 2006. "Decomposition analysis and the mean-rate-of-change index," Applied Energy, Elsevier, vol. 83(3), pages 185-198, March.
    19. Nishijima, Daisuke, 2017. "The role of technology, product lifetime, and energy efficiency in climate mitigation: A case study of air conditioners in Japan," Energy Policy, Elsevier, vol. 104(C), pages 340-347.
    20. Fernández, Esteban & Fernández, Paula, 2008. "An extension to Sun's decomposition methodology: The Path Based approach," Energy Economics, Elsevier, vol. 30(3), pages 1020-1036, May.

    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:eee:rensus:v:58:y:2016:i:c:p:1108-1113. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    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.