This study breaks down carbon emissions into six effects within the current 28 European Union (EU) countries group, thereafter, they are divided into two different groups (the first 15 countries (EU-15) and the last 13 entering the EU (EU13)). Country-specific highlights are also examined. It analyses the evolution of the effects using a data span that runs from 1990 to 2014, to determine which of them had more impact on the intensity of emissions, while also breaking down the complete period into two distinct periods (before the Kyoto protocol (1990–2004) and after Kyoto (2005–2014)). In order to add more knowledge to the current literature, both the additive and multiplicative decomposition techniques were used to examine carbon dioxide (CO2) emissions and the selected six components: carbon intensity, fossil fuel consumption, energy intensity, oil imports intensity, oil dependence, and population effect. Results point to different adapting velocities for Kyoto targets and necessary compromises. The different velocities were translated into different positive and negative impacts in the change of behavior of CO2 emissions throughout Europe. A stress in the fluctuations in CO2 variations before and after Kyoto and between the two different groups of EU countries could be noticed. Moreover, energy intensity and per capita dependence of oil products were identified as the major responsible components for the total and negative changes of emissions in recent years. A decrease in total changes of emissions is observed due to the fossil fuel energy consumption effect and total petroleum products effects. It is possible to infer from here that increased renewable capacity is contributing in a positive way to eco-efficiency, and should therefore be accounted for in national policymakers’ decisions in the strongest way possible. Results also seem to indicate that per capita dependence of oil products has decreased, despite oil imports intensity constancy and increased renewable capacity, however, with clear heterogeneous effects, worthy of consideration when defining policies.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
The Europe 2020 strategy sets the following three objectives for climate and energy policy, to be reached by 2020: (1) reducing GHG emissions by at least 20% compared with 1990 levels, (2) increasing the share of renewable energy in final energy consumption to 20%, and (3) moving towards a 20% increase in energy efficiency. These targets are also known as the “20-20-20” targets. Additionally, the strategy points out that the EU is committed to taking a decision to move to a 30% reduction by 2020 compared to 1990 levels (European Commission, 2014).
The author’s results show that the efficiency improvement in CO2 emissions by European countries (EU 27 during the 2001–2008 period) has been enough to override the joint pressure of population (structural effect) and economic growth (activity effect) of CO2 emissions.
It is important to mention here that these factors are dependent and causally linked to each other given that carbon intensity increases when energy intensity and fossil fuel energy consumption increases. With increasing emissions, the higher the oil imports intensity is. When the oil imports intensity increases, this also means that the economic activity increases. In fact, economic activity could be simultaneously cause and consequence of oil imports intensity. Moreover, high net oil imports may also be dependent on per capita dependence of oil products and on population increases, which lead to higher levels of consumption. More relationship
sdetails among the factors considered here are provided in the data section. We will also study the interrelated effects here by using both additive and multiplicative decompositions to explain CO2 emissions.
In contrast to Obadi and Korcek (2015), we investigate the drivers of CO2 emissions and not of energy consumption.
The Protocol’s first commitment period started in 2008 and ended in 2012. A second commitment period was agreed on in 2012. It became known as the Doha Amendment to the protocol, in which 37 countries have binding targets, including EU-28.
Austria (1995), Belgium (1958), Bulgaria (2007), Croatia (2013), Cyprus (2004), Czech Republic (2004), Denmark (1973), Estonia (2004), Finland (1995), France (1958), Germany (1958), Greece (1981), Hungary (2004), Ireland (1973), Italy (1958), Latvia (2004), Lithuania (2004), Luxembourg (1958), Malta (2004), Netherlands (1958), Poland (2004), Portugal (1986), Romania (2007), Slovakia (2004), Slovenia (2004), Spain (1986), Sweden (1995), and the UK (1973). EU 15 refers to those whose entrance was prior to 1995, inclusively, whereas EU 13 refers to those whose entrance was after 1995.
The Kaya Identity is an equation for computing the total carbon dioxide (CO2) emissions caused by humans or a specific country. The formula allows to determine the total CO2 emissions by calculating the product of population, GDP per capita, energy use per unit of GDP, and carbon emissions per unit of energy consumed, in accordance to: carbon dioxide emissions = population × per capita GDP × energy intensity × carbon intensity.
In all the presented formulas when using subscript 0, we mean that when considering decompositions by periods the base years 1990 and 2005 were used, but when initially presenting changes through years the 0 subscript is replaced by the t-1 period with respect to t in order to provide changes from year to year.
Eco-efficiency is measured as the ratio between the value added of what has been produced (e.g., GDP) and the added environment impacts of the product or service (e.g., CO2 emissions). The term was first presented by the World Business Council for Sustainable Development in 1992 (WBCSD 2000).
We thank the anonymous referee for advising this explanatory statement inclusion.
Negative values of CO2 variations (Var CO2) mean a decline or reduction of CO2 emissions. Positive values of CO2 variations mean an increase or growth of CO2 emissions.
See footnote 6.
However, the focus of our current analysis was in identifying the relative contribution of the oil imports intensity, eco-efficiency and energy intensity levels, leading us to consider solely the present six factors.
Albrecht J, Francois D, Schoors K (2002) A Shapley decomposition of carbon emissions without residuals. Energy Policy 30(9):727–736
Alcántara VE, Padilla ER (2005) Análisis de las emisiones de CO2 y sus factores explicativos en las diferentes áreas del mundo. Revista de Economía Crítica, Asociación de Economía Crítica 4:17–37
Ang BW (1995) Decomposition methodology in industrial energy demand analysis. Energy 20(11):1081–1095
Ang BW (2004) Decomposition analysis for policymaking in energy: which is the preferred method? Energy Policy 32(9):1131–1139. doi:10.1016/S0301-4215(03)00076-4
Ang BW (2005) The LMDI approach to decomposition analysis: a practical guide. Energy Policy 33:867–871
Ang BW, Choi KH (1997) Decomposition of aggregate energy and gas emission intensities for industry: a refined divisia index method. Energy J 18(3):59–73
Ang BW, Lee SY (1994) Decomposition of industrial energy consumption: some methodological and application issues. Energy Econ 16(2):83–92
Ang BW, Liu FL (2001) A new energy decomposition method: perfect in decomposition and consistent in aggregation. Energy Policy 26:537–548
Ang BW, Pandiyan G (1997) Decomposition of energy induced CO2 emissions in manufacturing. Energy Econ 19(3):363–374
Ang BW, Xu XY (2013) Tracking industrial energy efficiency trends using index decomposition analysis. Energy Econ 40:1014–1021
Ang BW, Zhang F (1999) Inter-regional comparisons of energy-related CO2 emissions using the decomposition technique. Energy 24(4):297–305
Ang BW, Zhang F (2000) A survey of index decomposition analysis in energy and environmental studies. Energy 25(12):1149–1176
Ang BW, Zhang FQ, Choi KH (1998) Factorizing changes in energy and environmental indicators through decomposition. Energy J 23(6):489–495
Ang BW, Liu F, Chew E (2003) Perfect decomposition techniques in energy and environmental analysis. Energy Policy 31:1561–1566
Bhattacharyya SC, Matsumura W (2010) Changes in the GHG emission intensity in EU-15: lessons from a decomposition analysis. Energy 35:3315–3322
Boyd GA, McDonald JF, Ross M, Hanson DA (1987) Separating the changing composition of US manufacturing production from energy efficiency improvements: a Divisia index approach. Energy J 8(2):77–96
Brizga J, Feng KS, Hubacek K (2013) Drivers of CO2 emissions in the former Soviet Union: a country level IPAT analysis from 1990 to 2010. Energy 59:743–753
Choi KH, Ang BW (2012) Attribution of changes in Divisia real energy intensity index e an extension to index decomposition analysis. Energy Econ 34:171–176
Chung HS, Rhee HC (2001) A residual free decomposition of the sources of carbon dioxide emissions: a case of the Korean industries. Energy 26(1):15–30
Cicea C, Marinescu C, Popa I, Dobrin C (2014) Environmental efficiency of investments in renewable energy: comparative analysis at macroeconomic level. Renew Sust Energ Rev 30:555–564
Commission of the European Communities (2008) Communication from the Commission to the European Parliament, the Council, the European Economics and Social Committee and the Committee of the Regions.
Diakoulaki D, Mandaraka M (2007) Decomposition analysis for assessing the progress in decoupling industrial growth from CO2 emissions in the EU manufacturing sector. Energy Econ 29(4):636–664. doi:10.1016/j.eneco.2007.01.005
ECOFYS (2014) Increasing the EU’s energy independence Available from http://www.ecofys.com/files/files/ecofys-ocn-2014-increasingthe-eu-s-energy-independence.pdf
ESCAP (2009) Eco-efficiency indicators: measuring resource-use efficiency and the impact of economic activities on the environment https://sustainabledevelopment.un.org/content/documents/785eco.pdf
European Commission (2014) European Commission: taking stock of the Europe 2020 strategy for smart, sustainable and inclusive growth, COM(2014) 130 final, Brussels http://ec.europa.eu/eurostat/statistics-explained/index.php/Europe_2020_indicators_-_climate_change_and_energy
European Environment Agency (2016) EEA national emissions reported to the UNFCCC and to the EU Greenhouse Gas Monitoring Mechanism.
European Union (2001) Directive 2001/77/EC of the European parliament and of the council of 27 September 2001 on the promotion of electricity produced from renewable energy sources in the internal electricity market.
European Union (2003) Directive 2003/30/EC of the European parliament and of the council of 8 may 2003 on the promotion of the use of biofuels or other renewable fuels for transport.
European Union (2009) Decision no 406/2009/EC of the European parliament and of the council of 23 April 2009 on the effort of member states to reduce their greenhouse gas emissions to meet the community’s greenhouse gas emission reduction commitments up to 2020.
Eurostat (2015) Energy production and imports Retrieved from: http://ec.europa.eu/eurostat/statistics-explained/index.php/Energy_production_and_imports
Eurostat (2016) Final consumption of solid fuels, gas and petroleum products. Eurostat (online data code: nrg_100a).
Filippini M, Hunt LC (2011) Energy demand and energy efficiency in the OECD countries: a stochastic demand frontier approach. Energy J 32:59–80
González FP, Landajo M, Presno MJ (2013) The Divisia real energy intensity indices evolution and attribution of percent changes in 20 European countries from 1995 to 2010. Energy 58(1):340–349
González PF, Landajo M, Presno MJ (2014a) The driving forces behind changes in CO2 emission levels in EU-27. Differences between member states. Environ Science & Policy 38:11–16. doi:10.1016/j.envsci.2013.10.007
González PF, Landajo M, Presno MJ (2014b) Tracking European Union CO2 emissions through LDMI (logarithmic mean divisia índex) decomposition. The activity Revaluation approach. Energy 73:741–750. doi:10.1016/j.enpol.2005.02.005
Greening LA, Davis WB, Schipper L, Khrushch M (1997) Comparison of six decomposition methods: application to aggregate energy intensity for manufacturing in 10 OECD countries. Energy Econ 19(3):375–390
Greening LA, Davis W, Schipper L (1998) Decomposition of aggregate carbon intensity for the manufacturing sector: comparison of declining trends from 10 OECD countries for the period 1971–1991. Energy Econ 13(3):43–65
Hatzigeorgiou E, Polatidis H, Haralambopoulos D (2010) Energy CO2 emissions for 1990–2020: a decomposition analysis for EU-25 and Greece. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 32(20):908–1917. doi:10.1080/15567030902937101
Howarth R, Schipper L, Duerr P, Strøm S (1991) Manufacturing energy use in eight OECD countries: decomposing the impacts of changes in output, industry structure and energy intensity. Energy Econ 13(2):135–142
Hwang IC (2008) Ecological modernization and climate change policy—driving forces for the changes in CO2 emissions of ecological modernization countries. ECO 12:53–184
IEA (2012a) International Energy Agency, IEA statistics electricity information 2012, Paris
IEA (2012b) International Energy Agency, World energy outlook 2012, Paris
IEA (2013) International Energy Agency, Energy statistics 2013, Paris
IEA (2015) International Energy Agency. CO2 emissions from fuel combustion highlights. IEA statistics. https://www.iea.org/publications/freepublications/publication/CO2EmissionsFromFuelCombustionHighlights2015.pdf
IEA (2016) OECD crude supply, OECD product supply and consumption, OECD imports, OECD exports, OECD conversion factors. July 2016 updated data.
International Monetary Fund (2015) IMF statistics. International Financial Statistics (IFS).
Jeong K, Kim S (2013) LMDI decomposition analysis of greenhouse gas emissions in the Korean manufacturing sector. Energy Policy 62:1245–1253
Kim S, Kim H (2011) LMDI decomposition analysis for energy consumption of Korea’s manufacturing industry. Korea Energy Econ Rev 10:51–78
Lee SK, Ahn Y (2007) Strategy for the National Energy saving and energy efficiency improvements—industry efficiency assessment. Korea Energy Econ Inst:07–09
Lee K, Oh W (2006) Analysis of CO2 emissions in APEC countries: a time-series and a cross-sectional decomposition using the log mean Divisia method. Energy Policy 34(17):2779–2787
Lee S, Kim J, Lee J, Lee S, Jeon E-C (2013) A study on the evaluations of emission factors and uncertainty ranges for methane and nitrous oxide from combined-cycle power plant in Korea. Environ Sci Pollut Res 20:461–468. doi:10.1007/s11356-012-1144-1
Liaskas K, Mavrotas G, Mandaraka M, Diakoulaki DU (2000) Decomposition of industrial CO2 emissions: the case of European Union. Energy Econ 22(4):383–394
Lin B, Moubarak M (2013) Decomposition analysis: change of carbon dioxide emissions in the Chinese textile industry. Renew Sust Energy R 26:389–396
Liu XQ, Ang BW, Ong HL (1992) The application of the Divisia index to the decomposition of changes in industrial energy consumption. Energy J 13(4):161–177
Liu LC, Ying F, Gang W, Wei YM (2007) Using LMDI method to analyze the change of China's industrial CO2 emissions from final fuel use: an empirical analysis. Energy Policy 35(11):5892–5900
Marrero GA, Ramos-Real FJ (2013) Activity sectors and energy intensity decomposition analysis and policy implications for European countries (1991–2005). Energies 6(5):2521–2540
Mennicken L, Janz A, Roth S (2016) The German R&D program for CO2 utilization—innovations for a green economy. Environ Sci Pollut Res 23:11386–11392. doi:10.1007/s11356-016-6641-1
Moutinho V, Madaleno M, Silva P M (2015) Which factors drive CO2 emissions in EU-15? Decomposition and innovative accounting. Energy Eff 1-27:First online: 04 December 2015 (forthcoming). DOI 10.1007/s12053-015-9411-x.
Naims H (2016) Economics of carbon dioxide capture and utilization—a supply and demand perspective. Environ Sci Pollut Res. doi:10.1007/s11356-016-6810-2
Obadi SM, Korček M (2015) Investigation of driving forces of energy consumption in European Union 28 countries. Int J Energy Econ Policy 5(2):–432
Obadi SM, Othmanová S, Abdová M (2013) What are the causes of high crude oil price? Causality investigation. Int J Energy Econ Policy 3:80–92
OECD (2016) Energy balances of non-OECD countries and energy balances of OECD countries.
Oh I, Wehrmeyer W, Mulugetta Y (2010) Decomposition analysis and mitigation strategies of CO2 emissions from energy consumption in South Korea. Energy Policy 38(1):364–377
Reitler W, Rudolph M, Schaefer M (1987) Analysis of the factors influencing energy consumption in industry: a revised method. Energy Econ 9:145–148
Robaina-Alves M, Moutinho V, Macedo P (2015) A new frontier approach to model the eco-efficiency in European countries. J Clean Prod 103(15):562–573
Schipper L, Murtishaw S, Khrushch M, Ting M, Karbuz S, Unander F (2001) Carbon emissions from manufacturing energy use in 13 IEA countries: long-term trends through 1995. Energy Policy 29(9):667–688
Shrestha RM, Anandarajah G, Liyanage MH (2009) Factors affecting CO2 emission from the power sector of selected countries in Asia and the Pacific. Energy Policy 37(6):2375–2384
Sun JW (1998) Changes in energy consumption and energy intensity: a complete decomposition model. Energy Econ 20(19):85–100
Torvanger A (1991) Manufacturing sector carbon dioxide emissions in nine OECD countries, 1973–87: a Divisia index decomposition to changes in fuel mix, emission coefficients, industry structure, energy intensities and international structure. Energy Econ 13(3):168–186
Unander F, Karbuz S, Schipper L, Khrushch M, Ting M (1999) Manufacturing energy use in OECD countries: decomposition of long-term trends. Energy Policy 27(13):769–778
United Nations (1998) Kyoto Protocol to the United Nations Framework Convention on Climate Change.
WBCSD (2000). Eco-efficiency: creating more value with less impact. World Business Council for Sustainable Development. ISBN 2–940240–17–5.
World Bank (2014) World development indicators. Washington (DC).
Zhang J, Zhang Y, Yang Z, Fath BD, Li S (2013) Estimation of energy-related carbon emissions in Beijing and factor decomposition analysis. Ecol Modelling 252:258–265
Zhao M, Tan LR, Zhang WG, Ji MH, Liu Y, Yu LZ (2010) Decomposing the influencing factors of industrial carbon emissions in shanghai using the LMDI method. Energy 35(6):2505–2510
This work was financially supported by the Research Unit on Governance, Competitiveness and Public Policy (project POCI-01-0145-FEDER-006939), funded by FEDER through COMPETE2020 - Programa Operacional Competitividade e Internacionalização (POCI) – and by national funds through FCT - Fundação para a Ciência e a Tecnologia. We thank the anonymous reviewers and the editor for their careful reading of our manuscript and their many insightful comments and suggestions. The authors are solely responsible for any shortcomings, errors or misconceptions that remain.
Responsible editor: Philippe Garrigues
About this article
Cite this article
Madaleno, M., Moutinho, V. A new LDMI decomposition approach to explain emission development in the EU: individual and set contribution. Environ Sci Pollut Res 24, 10234–10257 (2017). https://doi.org/10.1007/s11356-017-8547-y
- Decomposition analysis
- Emissions intensity
- European countries
- Petroleum per capita dependence
- Individual and set contributions
- Components of carbon dioxide