Abstract
The achievement of the 32.5% energy efficiency target set for 2030 in the Energy Efficiency Directive 2018/2002 could determine the success of the EU Member States’ actions and policy measures to improve energy efficiency. However, the way the target was set presents several limitations, and the target is based on a hypothetical percentage of future primary energy use rather than absolute energy savings. Thus, the objectives of this study are to provide new insight into (i) the levels of energy efficiency improvements achieved by the EU over the period 1995–2015 by employing a decomposition analysis approach—Logarithm Mean Divisia Index—and using disaggregated final energy consumption data, (ii) the progress of the EU towards the energy efficiency target set for 2030, and (iii) the energy security and climate benefits associated with energy efficiency improvements. The results show that from 1995 to 2015, efficiency allowed the EU to save approximately 235 Mtoe of final energy. Additionally, energy efficiency improvements reduced the EU’s dependence on energy imports at the average rate of 1% per year, saved 811 MtCO2, and contributed to achieving 52.5% of the energy efficiency target set for 2030.
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Notes
Motorcycles and small appliances are excluded from the analysis due to the lack of data regarding the passenger kilometre for motorcycles and the stock of small appliances.
The decomposition is perfect and there is no residual at the aggregate (single-step procedure) and subcategory (step-by-step procedure) levels.
‘Gross inland energy consumption’ (GIC) is the total energy demand in a country or region. GIC represents the quantity of energy necessary to satisfy the inland consumption of the geographical entity under consideration. GIC covers consumption by the energy sector (primary energy), the final energy consumption by end users, distribution and transformation losses, and the energy consumed for purposes other than producing useful energy.
According to Reuter et al. (2019), from 2000 to 2015, the intensity effect contributed to a reduction of 210 Mtoe in the EU final energy, counteracting the increase in final energy due to activity effects (125 Mtoe).
In May 2018, the European Commission presented a legislative proposal setting the first-ever CO2 emission standards for heavy-duty vehicles in the EU. The proposal establishes an indicative reduction target of 15% in 2025 and at least of 30% in 2030 compared with 2019 average CO2 emission levels (European Commission 2018).
From 1995 to 2015, the contribution of the services valued added to the economy increased by 3.8%.
These calculations are based on the methodology used by the EC (European Commission 2016b) to determine the contribution of each sector to the final energy consumption reduction (compared with the historical 2005 final energy consumption levels) in different scenarios.
In 2015, 29.4% of the EU imports of natural gas, 27.7% of the EU imports of crude oil, and 25.8% of the EU imports of solid fuels were obtained from Russia, whereas 25.9% of the EU imports of natural gas and 11.4% of the EU imports of crude oil were obtained from Norway (Eurostat 2018b, c, d). The amount of these imported sources combined is 360.3 Mtoe.
The ‘Greenhouse gases’ (GHGs) include the following: CO2 (carbon dioxide), N2O (nitrous oxide) in CO2 equivalent, CH4 (methane) in CO2 equivalent, HFCs (hydrofluorocarbons) in CO2 equivalent, PFCs (perfluorocarbons) in CO2 equivalent, SF6 (sulfur hexafluoride) in CO2 equivalent, and NF3 (nitrogen trifluoride) in CO2 equivalent.
‘CO2e’ or ‘carbon dioxide equivalent’ is a term used to describe different greenhouse gases in a common unit. For any quantity and type of greenhouse gas, CO2e is the amount of CO2 that could have the equivalent global warming impact. This term allows “bundles” of greenhouse gases to be expressed as a single number and different bundles of GHGs to be easily compared (Brander and Davis 2012).
References
Achour, H., & Belloumi, M. (2016). Decomposing the influencing factors of energy consumption in Tunisian transportation sector using the LMDI method. Transport Policy, 52, 64–71.
Ang, B. W. (2004). Decomposition analysis for policymaking in energy: which is the preferred method? Energy Policy, 32(9), 1131–1139.
Ang, B. W. (2005). The LMDI approach to decomposition analysis: a practical guide. Energy Policy, 33(7), 867–871.
Ang, B. W. (2015). LMDI decomposition approach: a guide for implementation. Energy Policy, 86, 233–238.
Ang, B. W., & Wang, H. (2015). Index decomposition analysis with multidimensional and multilevel energy data. Energy Economics, 51, 67–76.
Ang, B. W., & Xu, X. Y. (2013). Tracking industrial energy efficiency trends using index decomposition analysis. Energy Economics, 40, 1014–1021.
Ang, B. W., Huang, H. C., & Mu, A. R. (2009). Properties and linkages of some index decomposition analysis methods. Energy Policy, 37(11), 4624–4632.
Brander, M., & Davis, G. (2012). Greenhouse gases, CO2, CO2e, and carbon: what do all these terms mean. Econometrica, White Papers.
Braungardt, S., Eichhammer, W., Elsland, R., Fleiter, T., Klobasa, M., Krail, M., Pfluger, B., Reuter, M., Schlomann, B., Sensfuss, F., & Tariq, S. (2014). Study evaluating the current energy efficiency policy framework in the EU and providing orientation on policy options for realising the cost-effective energy-efficiency/saving potential until 2020 and beyond. Report for the European Commission, Directorate-General for Energy. https://ec.europa.eu/energy/sites/ener/files/documents/2014_report_2020-2030_eu_policy_framework.pdf.
Carmona, M. J. C., & Collado, R. R. (2016). LMDI decomposition analysis of energy consumption in Andalusia (Spain) during 2003–2012: the energy efficiency policy implications. Energy Efficiency, 9(3), 807–823.
Delgado, O., & Gonzalez, F. (2018). CO2 emissions and fuel consumption standards for heavy-duty vehicles in the European Union. https://www.theicct.org/sites/default/files/publications/Efficiency_standards_HDVs_EU_Briefing_051618.pdf.
E3MLab, & IIASA. (2016). Technical report on member state results of the EUCO policy scenarios. https://ec.europa.eu/energy/sites/ener/files/documents/20170125_-_technical_report_on_euco_scenarios_primes_corrected.pdf.
Economidou, M. (2017). Assessing the progress towards the EU energy efficiency targets using index decomposition analysis, EUR 28710 EN, Publications Office of the European Union, Luxembourg. http://publications.jrc.ec.europa.eu/repository/bitstream/JRC106782/jrc_decomposition_report_final(1).pdf.
European Commission. (2011). A roadmap for moving to a competitive low carbon economy in 2050. http://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:52011DC0112&from=EN.
European Commission. (2016a). Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee, the Committee of the Regions and the European Investment Bank. Clean Energy for All Europeans. COM(2016) 860 final. https://eur-lex.europa.eu/resource.html?uri=cellar:fa6ea15b-b7b0-11e6-9e3c-01aa75ed71a1.0001.02/DOC_1&format=PDF.
European Commission. (2016b). Commission staff working document impact assessment. Accompanying the document. Proposal for a Directive of the European Parliament and of the Council amending Directive 2012/27/EU on Energy Efficiency. SWD(2016) 405 final. https://eur-lex.europa.eu/resource.html?uri=cellar:56466305-b7f6-11e6-9e3c-01aa75ed71a1.0001.02/DOC_1&format=PDF.
European Commission. (2016c). Proposal for a Directive of the European Parliament and of the Council amending Directive 2012/27/EU on energy efficiency. COM(2016) 761 final http://eur-lex.europa.eu/resource.html?uri=cellar:efad95f3-b7f5-11e6-9e3c-01aa75ed71a1.0009.02/DOC_1&format=PDF.
European Commission. (2018). Proposal for a regulation of the European parliament and of the council setting CO2 emission performance standards for new heavy-duty vehicles. COM(2018) 284 final. https://eur-lex.europa.eu/resource.html?uri=cellar:f38df734-59da-11e8-ab41-01aa75ed71a1.0001.02/DOC_1&format=PDF.
Eurostat. (2018a). Energy dependence. Retrieved January 9th from http://ec.europa.eu/eurostat/tgm/table.do?tab=table&init=1&language=en&pcode=t2020_rd320&plugin=1.
Eurostat. (2018b). Imports - gas - annual data. Retrieved January 9th from http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nrg_124a&lang=en.
Eurostat. (2018c). Imports - oil - annual data. Retrieved January 9th from http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nrg_123a&lang=en.
Eurostat. (2018d). Imports - solid fuels - annual data. Retrieved January 9th from http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nrg_122a&lang=en.
Fowlie, M., Greenstone, M., & Wolfram, C. (2018). Do energy efficiency investments deliver? Evidence from the weatherization assistance program. The Quarterly Journal of Economics, 133(3), 1597–1644.
Gillingham, K., Keyes, A., & Palmer, K. (2018). Advances in evaluating energy efficiency policies and programs. Annual Review of Resource Economics, 10(0), 511–532.
Goh, T., & Ang, B. W. (2018). Tracking economy-wide energy efficiency using LMDI: approach and practices. Energy Efficiency, 1–19.
González, P. F. (2015). Exploring energy efficiency in several European countries. An attribution analysis of the Divisia structural change index. Applied Energy, 137, 364–374.
González, P. F., 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, 68, 576–584.
Hammond, G. P., & Norman, J. B. (2012). Decomposition analysis of energy-related carbon emissions from UK manufacturing. Energy, 41(1), 220–227.
IEA. (2015). Energy efficiency market report 2015. Paris: OECD/IEA https://www.iea.org/publications/freepublications/publication/MediumTermEnergyefficiencyMarketReport2015.pdf.
IEA. (2016a). Energy efficiency market report 2016. Paris: OECD/IEA https://www.iea.org/eemr16/files/medium-term-energy-efficiency-2016_WEB.PDF.
IEA. (2016b). Energy efficiency indicators highlights 2016. Paris: OECD/IEA https://www.iea.org/publications/freepublications/publication/EnergyEfficiencyIndicatorsHighlights_2016.pdf.
IEA. (2017). Energy efficiency 2017. Paris: OECD/IEA http://www.iea.org/publications/freepublications/publication/Energy_Efficiency_2017.pdf.
Kim, S. (2017). LMDI decomposition analysis of energy consumption in the Korean manufacturing sector. Sustainability, 9(2), 202.
Liu, N., Ma, Z., & Kang, J. (2015). Changes in carbon intensity in China’s industrial sector: decomposition and attribution analysis. Energy Policy, 87, 28–38.
Ma, C., & Stern, D. I. (2008). China’s changing energy intensity trend: a decomposition analysis. Energy Economics, 30(3), 1037–1053.
Mairet, N., & Decellas, F. (2009). Determinants of energy demand in the French service sector: a decomposition analysis. Energy Policy, 37(7), 2734–2744.
Marrero, G. A., & Ramos-Real, F. J. (2013). Activity sectors and energy intensity: decomposition analysis and policy implications for European countries (1991–2005). Energies, 6(5), 2521–2540.
Nie, H., & Kemp, R. (2013). Why did energy intensity fluctuate during 2000–2009?: a combination of index decomposition analysis and structural decomposition analysis. Energy for Sustainable Development, 17(5), 482–488.
Obadi, S. M., & Korček, M. (2015). Investigation of driving forces of energy consumption in EU 28 countries. International Journal of Energy Economics and Policy, 5(2).
Odyssee database. (2017). Database on the energy consumption drivers by end-use, energy efficiency and CO2 related indicators. Retrieved September 7th from http://www.odyssee-indicators.org/database/database.php (subscription-based).
PRIMES MODEL. (2013-2014). Detailed model description. E3MLab/ICCS at National Technical University of Athens, NTUA, Zografou Campus Athens, Greece, 155p. https://ec.europa.eu/clima/sites/clima/files/strategies/analysis/models/docs/primes_model_2013-2014_en.pdf.
Reuter, M., Patel, M. K., & Eichhammer, W. (2017). Applying ex-post index decomposition analysis to primary energy consumption for evaluating progress towards European energy efficiency targets. Energy Efficiency, 10(6), 1381–1400.
Reuter, M., Patel, M. K., & Eichhammer, W. (2019). Applying ex post index decomposition analysis to final energy consumption for evaluating European energy efficiency policies and targets. Energy Efficiency, 1–29.
Rosenow, J., & Fawcett, T. (2016). The Member States’ plans and achievements towards the implementation of Article 7 of the Energy Efficiency Directive. http://eng.janrosenow.com/uploads/4/7/1/2/4712328/s_2014_2019_plmrep_committees_itre_dv_2016_03-16_study_plans_achievements_en-1_1_.pdf.
Shahiduzzaman, M., & Alam, K. (2013). Changes in energy efficiency in Australia: a decomposition of aggregate energy intensity using logarithmic mean Divisia approach. Energy Policy, 56, 341–351.
Sheinbaum, C., Ozawa, L., & Castillo, D. (2010). Using logarithmic mean Divisia index to analyze changes in energy use and carbon dioxide emissions in Mexico’s iron and steel industry. Energy Economics, 32(6), 1337–1344.
Sorrell, S. (2007). The rebound effect: an assessment of the evidence for economy-wide energy savings from improved energy efficiency. http://ukerc.rl.ac.uk/UCAT/PUBLICATIONS/The_Rebound_Effect_An_Assessment_of_the_Evidence_for_Economy-wide_Energy_Savings_from_Improved_Energy_Efficiency.pdf.
The European Parliament and the Council of the European Union. (2018). Directive (EU) 2018/2002 of the European Parliament and of the Council of 11 December 2018 amending Directive 2012/27/EU on energy efficiency. https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32018L2002&from=EN.
The World Bank, World Development Indicators. (2017a). Gross value added at factor cost (GVA) (constant 2010 US$) [Data file]. Retrieved September 7th from https://data.worldbank.org/indicator/NY.GDP.FCST.KD.
The World Bank, World Development Indicators. (2017b). Services, etc., value added (constant 2010 US$) [Data file]. Retrieved September 7th from https://data.worldbank.org/indicator/NV.SRV.TOTL.KD.
The World Bank, World Development Indicators. (2017c). Agriculture, value added (constant 2010 US$) [Data file]. Retrieved September 7th from https://data.worldbank.org/indicator/NV.AGR.TOTL.KD.
The World Bank, World Development Indicators. (2017d). Industry, value added (constant 2010 US$) [Data file]. Retrieved September 7th from https://data.worldbank.org/indicator/NV.IND.TOTL.KD.
Trotta, G., Spangenberg, J., & Lorek, S. (2018). Energy efficiency in the residential sector: identification of promising policy instruments and private initiatives among selected European countries. Energy Efficiency, 11(8), 2111–2135.
Valentová, M., Karásek, J., & Knápek, J. (2018). Ex post evaluation of energy efficiency programs: case study of Czech Green Investment Scheme. Wiley Interdisciplinary Reviews: Energy and Environment, e323.
Wang, M., & Feng, C. (2017). Decomposing the change in energy consumption in China’s nonferrous metal industry: an empirical analysis based on the LMDI method. Renewable and Sustainable Energy Reviews.
Wang, W., Liu, X., Zhang, M., & Song, X. (2014). Using a new generalized LMDI (logarithmic mean Divisia index) method to analyze China’s energy consumption. Energy, 67, 617–622.
Wu, Y. (2012). Energy intensity and its determinants in China’s regional economies. Energy Policy, 41, 703–711.
Xu, X. Y., & Ang, B. W. (2014). Multilevel index decomposition analysis: approaches and application. Energy Economics, 44, 375–382.
Xu, J. H., Fleiter, T., Eichhammer, W., & Fan, Y. (2012). Energy consumption and CO2 emissions in China’s cement industry: a perspective from LMDI decomposition analysis. Energy Policy, 50, 821–832.
Xu, S. C., He, Z. X., & Long, R. Y. (2014a). Factors that influence carbon emissions due to energy consumption in China: decomposition analysis using LMDI. Applied Energy, 127, 182–193.
Xu, J. H., Fan, Y., & Yu, S. M. (2014b). Energy conservation and CO2 emission reduction in China’s 11th five-year plan: a performance evaluation. Energy Economics, 46, 348–359.
Xu, S. C., He, Z. X., Long, R. Y., Chen, H., Han, H. M., & Zhang, W. W. (2016a). Comparative analysis of the regional contributions to carbon emissions in China. Journal of Cleaner Production, 127, 406–417.
Xu, S. C., He, Z. X., Long, R. Y., & Chen, H. (2016b). Factors that influence carbon emissions due to energy consumption based on different stages and sectors in China. Journal of Cleaner Production, 115, 139–148.
Xu, S. C., Han, H. M., Zhang, W. W., Zhang, Q. Q., Long, R. Y., Chen, H., & He, Z. X. (2017). Analysis of regional contributions to the national carbon intensity in China in different five-year plan periods. Journal of Cleaner Production, 145, 209–220.
Zhao, X., Li, N., & Ma, C. (2012). Residential energy consumption in urban China: a decomposition analysis. Energy Policy, 41, 644–653.
Acknowledgements
The author would like to thank the participants of the ‘Energizing Futures—Sustainable Development and Energy in Transition Conference’ (June 13–14, 2018) in Tampere, Sylvia Lorek, and the six anonymous reviewers for their valuable comments on the earlier version of this paper.
Funding
This work was partially financed by the Jenny and Antti Wihuri Foundation (Jenny ja Antti Wihurin rahasto) under project grant Trotta/00180402, the European Commission under project grant EUFORIE/H2020-EE-2014-2015-RIA/649342, and the Academy of Finland under project grant Kalmi/2700041011.
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Trotta, G. Assessing energy efficiency improvements, energy dependence, and CO2 emissions in the European Union using a decomposition method. Energy Efficiency 12, 1873–1890 (2019). https://doi.org/10.1007/s12053-019-09818-7
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DOI: https://doi.org/10.1007/s12053-019-09818-7