Explanatory factors of CO2 per capita emission inequality in the European Union

EXPLANATORY FACTORS OF CO2 PER CAPITA EMISSION INEQUALITY IN THE EUROPEAN UNION Emilio Padilla, Juan Antonio Duro 11.07 De p artament d'Economia A p licada Facultat d'Economia i Emp Aquest document pertany al Departament d'Economia Aplicada. Data de publicació : Departament d'Economia Aplicada Edifici B Campus de Bellaterra 08193 Bellaterra Telèfon: (93) 581 1680 Fax:(93) 581 2292 E-mail: [email protected] http://www.ecap.uab.es Maig 2011 EXPLANATORY FACTORS OF CO2 PER CAPITA EMISSION INEQUALITY IN THE EUROPEAN UNION Emilio Padilla1* and Juan Antonio Duro2 1 Department of Applied Economics, Univ. Autónoma de Barcelona. 08193 Bellaterra, Spain. 2 Department of Economics, Univ. Rovira i Virgili. Av. de la Universitat, 1. 43204 Reus, Spain. Abstract The design of European mitigation policies requires a detailed examination of the factors explaining the unequal emissions in the different countries. This research analyzes the evolution of inequality in CO2 per capita emissions in the European Union (EU-27) in the 1990–2006 period and its explanatory factors. For this purpose, we decompose the Theil index of inequality into the contributions of the different Kaya factors. The decomposition is also applied to the inequality between and within groups of countries (North Europe, South Europe, and East Europe). The analysis shows an important reduction in inequality, to a large extent due to the smaller differences between groups and because of the lower contribution of the energy intensity factor. The importance of the GDP per capita factor increases and becomes the main explanatory factor. However, within the different groups of countries the carbonization index appears to be the most relevant factor in explaining inequalities. Key words: CO2 emissions, emission inequality, European Union, Kaya factors, Theil index. * Author to whom all correspondence should be addressed: email: [email protected], Phone: +34 935811276, Fax +34 935812292. 1 1. Introduction The European Union has been the political community that, to date, has assumed the greatest commitments to the fight against climate change on a worldwide level. In March 2007, the European Council adopted a mitigation commitment of 20% of 1990 greenhouse gases by 2020 (extendable to 30% if the other developed countries assumed a similar objective). It was also committed to improving energy efficiency by 20% and increasing the percentage of energy consumption from renewable sources to 20%. The European Union has also played a very active role, though without the expected success to date, in the search for post Kyoto international agreements involving all countries in the fight against climate change. However, the situations of the current member countries are very different—major differences in income, emissions per capita, energy provision structure, production structure and energy efficiency—and ambition with respect to objectives vary greatly among them. In spite of the disagreements, in April 2009 (decision n. 406/2009/CE of the European Parliament and the Council), the target of the different member states to reduce their emissions to fulfill the 2020 objectives was finally determined. The differences in emissions per capita between the different countries of the European Union are very relevant for establishing the different mitigation policy targets and these differences are due to factors that have evolved in different ways in different countries. Several studies have analyzed international differences in emissions per capita by applying synthetic indicators of inequality, such as the Gini, Theil or Atkinson indexes (Heil and Wodon, 1997, 2000; Millimet and Slottje, 2002; Hedenus and Azar, 2005; Padilla and Serrano, 2006; Duro and Padilla, 2006, 2008; Cantore and Padilla, 2010a, 2010b; Groot, 2010). These studies have focused on international inequalities on a worldwide level or across OECD countries. In the present paper we will analyze the inequality in per capita emissions in the European Union—a political unit that is composed of 27 countries and whose mitigation objectives are jointly assumed—, as well as its different explanatory factors. As explanatory factors we will analyze the evolution of the well-known Kaya identity (Kaya 1989), which decomposes emissions per capita into the contribution of the energy intensity of carbon (or carbonisation index), the energy intensity of product and GDP per capita. A good knowledge of the factors behind the differences in emissions and their evolution in the different countries is essential guidance for better policy design. We present and apply a decomposition of a synthetic inequality index, the Theil index, which serves to show the 2 contribution to global inequality of the different explanatory factors on a European level. The methodology also enables analysis of the inequalities between groups and within different groups of countries in the European Union, which will serve to check whether the greater differences, and the contribution of the different factors, are centered on the differences between or within the groups of countries that share some common characteristics. Duro and Padilla (2006) analyzed the factors behind emissions per capita inequality on a worldwide level. There have been no similar analyses for the European Union. In any case, the analysis of inequality and its major causes complements the existing literature on the convergence in emissions per capita and the different trends in the European Union countries (see Jobert et al., 2010). In the next section we will analyze the emission data for the different countries of the European Union and will expose the methodology, which consists of a decomposition of the Theil index of inequality into the different Kaya factors and two interaction components. Section 3 presents the results. Section 4 gathers the main conclusions of the paper. 2. Data and methodology 2. 1. Data and Kaya factors For the present paper we have used data from the International Energy Agency (IEA, 2009a, 2009b, 2009c). According to these, CO2 emissions from fossil fuel combustion experienced a mild reduction over the 1990–2006 period (a 2% cutback). However, there is highly heterogeneous behavior among the different countries of the European Union, as well as important differences in the emissions per capita of the different countries. One of the factors that determine the differences in the level of emissions and their evolution is economic activity. However, there may be economic growth due to there being more affluent inhabitants, or simply due to a greater population consuming the same. Moreover, the different technologies employed in production might cause more or less pollution depending on the energy requirements or the type of energy employed. Multiple factors affect CO2 emissions, such as economic growth, demographic growth, technological change, resource endowment, institutional structures, modes of transport, lifestyles and international trade. A frequently used analytical tool to explore the main driving forces of pollution is the Kaya identity (Kaya, 1989). According to this, a country’s emissions can be decomposed into the product of four basic products (which, in turn, are determined by other factors): carbon intensity of energy 3 or carbonization index (defined as the carbon dioxide emitted per unit of energy consumed, i i E CO2), energy intensity (defined as the primary energy quantity consumed per unit of GDP, i i E GDP ), economic affluence (defined as GDP per capita, i i GDP P) and population. The first component shows the mix of fuels of a given country; the second is associated both to energy efficiency and to the sectoral structure of the economy and the transport model; and the third is a measure of economic income. i CO2= i i E CO2·i i E GDP ·i i GDP P· Pi (1) The identity might also be used to analyze per capita emissions: 2i i CO P= i i E CO2·i i E GDP ·i i GDP P (2) This approach can be used to decompose the main driving forces of CO2 emissions, which serves to make a first description of the important differences observed between countries 1. Table 1 shows the values of the different factors for the different European countries. 1 One problem is that these factors might not be independent from each other (e.g., there might be a positive correlation between greater affluence, greater capital level and the development of certain technologies that reduce energy intensity). This question is reflected in the inequality decomposition methodology developed below, where the corresponding interrelation components are included. 4 Table 1. Decomposition of CO2 emissions per capita in Kaya factors, year 2006 Kaya factors Emissions per capita Carbonization index Energy intensity GDP per capita CO2/P CO2/EP EP/GDP GDP/P Austria 8.80 2.13 132.10 31.29 Belgium 11.12 1.92 194.26 29.79 Denmark 10.14 2.64 123.08 31.26 Finland 12.68 1.79 236.87 29.99 France 5.97 1.38 160.87 26.82 Germany 10.00 2.36 154.59 27.37 Ireland 10.57 2.91 102.97 35.32 Luxembourg 23.79 2.37 158.26 63.36 Netherlands 10.91 2.23 156.58 31.31 Sweden 5.32 0.94 176.62 31.99 United Kingdom 8.86 2.32 132.18 28.89 North 8.78 2.01 152.68 28.53 Cyprus 9.14 2.69 162.28 20.94 Greece 8.43 3.02 120.14 23.23 Italy 7.61 2.43 119.98 26.08 Malta 6.10 2.86 123.94 17.22 Portugal 5.32 2.22 138.07 17.41 Spain 7.43 2.27 138.22 23.73 South 7.43 2.41 127.58 24.21 Bulgaria 6.18 2.30 307.08 8.76 Czech Republic 11.78 2.63 234.14 19.15 Estonia 11.30 3.10 229.48 15.90 Hungary 5.60 2.04 171.73 15.96 Latvia 3.51 1.74 147.04 13.70 Lithuania 4.02 1.60 179.34 14.01 Poland 8.02 3.13 195.89 13.08 Romania 4.39 2.36 213.69 8.70 Slovak Republic 6.95 2.00 232.25 14.92 Slovenia 7.71 2.13 174.00 20.76 East 7.00 2.59 207.05 13.05 EU-27 8.07 2.19 152.35 24.23 Variation coefficient x 100 45.69 22.38 27.34 45.83 Source: Prepared by the authors using IEA data (IEA, 2009a, 2009b, 2009c). 5 Note: per capita emissions in metric tons; carbonization index in tons of CO2 per ton of oil equivalent; energy intensity in tons of oil equivalent per million of PPP-adjusted 2000 US dollars; GDP per capita in thousands of PPP-adjusted 2000 US dollars. The variation coefficient is considered for the 27 countries and is computed without weighting. Table 1 shows major differences between the European Union countries, both in their emissions per capita and in the different factors determining these emissions. GDP per capita is one of the most relevant factors explaining these differences, the variation coefficient of this factor being the most relevant. However, variability is also very important in the other factors, so we find high income countries, such as France or Sweden, with emissions per capita well below the global mean and even below the average for the countries from the east and south of Europe. The variation coefficient is mildly greater for the energy intensity than for the carbonization index (27.34 and 22.38 respectively). The different energy intensities, which are especially large between East Europe and the other groups of countries, show both the different efficiencies in the use of energy as well as the different production structures. The differences in the carbonization index show the important disparities in the energy mix in the different European countries: while in some countries the share of fossil fuels is high, including coal, in others, the presence of renewable and nuclear power leads to lower indexes. The table shows the (unweighted) variation coefficient for each of the different factors. However, this does not report precisely on the importance of each factor, and their interaction, on the global inequalities and their evolution. Moreover, it seems interesting to explore the behavior of the factorial components for various groups of countries. In order to explore these issues, the next subsection develops a decomposition methodology of inequality that makes it possible to explore the weight of each factor in it. 2.2. Synthetic decomposition of inequality into explanatory factors: Methodology Although there are many measures of inequality, the Theil index (1967) has many desirable properties. Bourguignon (1979) showed that this measure is the only population weighted inequality index that can be broken down into groups of observations, is differentiable, symmetric, invariant with scale and satisfies the Pigou-Dalton criterion. In order to compute the inequality in CO2 per capita emissions among countries, this measure might be written as: 6 Tc,p pi i ln c ci            (3)  where c are the CO2 per capita emissions of country i, pi is the share of population of country i of the total European population and i c is the average European emissions per capita. The lower limit is zero, and the upper limit depends on the sample. A value close to 1 indicates high inequality levels2. In order to investigate the sources of CO2 per capita emission inequalities in the European Union, we start from the Kaya identity defined in equation (2). To simplify notation, we denote the three factors of the identity (carbonization index, energy intensity of GDP, and GDP per capita) as a, b and y, respectively, for each country: ciai*bi*yi (4) We then measure the contribution of each individual Kaya factor to the global inequality index. To do this, we define three hypothetical vectors allowing, for each factor, only the values of one of the factors to diverge from the mean. We obtain the following result3: ciaai*b * y ciba *bi*y (5) ciya *b *yi where a , b and y are the European averages. The degree of inequality of the individual factors is then computed using the Theil index: Tapi i ln c a ci a             2 Theil (1967) also offered an alternative inequality index, which might be obtained by interchanging the positions of c and c in the logarithm and substituting the population weighting scheme by a CO2 weighting. However, the population weighted index—expression (1)—seems a better measure because: i) if CO2 dispersion is analysed, the different observations should be weighted according to population; ii) there are various problems associated to the interpretation of results when the alternative index is decomposed by groups (see Shorroks, 1980). i 3 This decomposition technique was developed by Duro (2003) for the analysis of income spatial inequality. 7 Table 4. Decomposition of within-group inequality. Details by groups. Tc,p   A T B T Y T Wi 1990 N orth Europe 0.0357 0.0285 (79.9%) 0.0058 (16.2%) 0.0014 (3.9%) 52.9% South Europe 0.0160 0.0075 (47.2%) -0.0017 (-10.4%) 0.0101 (63.2%) 24.7% E ast Europe 0.0364 0.0113 (31.0%) 0.0035 (9.6%) 0.0216 (59.4%) 22.4% 1995 N orth Europe 0.0290 0.0249 (85.9%) 0.0047 (16.4%) -0.0006 (-2.2%) 53.4% South Europe 0.0079 0.0039 (49.1%) -0.0033 (-41.5%) 0.0073 (92.4%) 24.6% E ast Europe 0.0472 0.0228 (48.2%) 0.0012 (2.5%) 0.0233 (49.3%) 21.9% 2000 N orth Europe 0.0239 0.0208 (87.2%) 0.0041 (17.2%) -0.0010 (-4.4%) 53.8% South Europe 0.0030 0.0019 (65.0%) -0.0017 (-58.2%) 0.0028 (93.2%) 24.7% E ast Europe 0.0701 0.0272 (38.8%) 0.0051 (7.3%) 0.0377 (53.9) 21.5% 2006 N orth Europe 0.0281 0.0251 (89.4%) 0.0043 (15.4%) -0.0013 (-4.8%) 53.8% South Europe 0.0053 0.0025 (47.0%) -0.0002 (-3.8%) 0.0030 (56.7%) 25.5% E ast Europe 0.0532 0.0190 (35.8%) 0.0058 (10.9) 0.0283 (53.3%) 20.7% Source: Prepared by the authors using IEA data (IEA, 2009a, 2009b, 2009c). The data show a different level of inequality within the different groups of countries considered. East Europe is the group with the greatest level of internal inequality, it being somewhat lower in North Europe, and much lower in the case of South Europe, whose contribution to the within-group component of European inequality is of low significance. The evolution of the inequality and its components are also quite different. The evolution of the inequality within the North Europe group shows a major reduction during the first ten years of the period and an increase at the end. In this case, the disparity in emissions per capita is mainly explained by the different carbonization indexes. The relative importance of this component increased from 79.9% to 89.4%, as its contribution decreased less 14 than global inequality. It is these countries’ share of population that determines the preponderance of the carbonization factor in the results in Table 3. South Europe shows a very similar evolution of inequality to North Europe (a reduction between 1990 and 2000 and an increase afterwards). The contribution of the component associated to the GDP per capita factor might be highlighted, although the carbonisation index factor is also very important. The contribution of energy intensity is negative and highly variable over the period. Finally, the evolution is very different for the East Europe group. In this case, inequality increases considerably between 1990 and 2000, experiencing a reduction over the last 6 years of the period. In this case, the GDP per capita factor explains the main differences, although the carbonization index is also significant. Of the three groups of countries considered, this one presents the greatest internal disparities and is the only one in which these increase, especially between 1990 and 2000. 4. Conclusions The discussion within the European Union of the targets to achieve in the mitigation of greenhouse gases and the distribution of mitigation efforts between countries is a controversial issue that requires the maximum knowledge of the factors that influence the different member countries’ emissions as well as the changes in inequality levels at communitarian level. The greater inequality, the more likely the difficulty to share objectives, especially if the different factors explaining this inequality are not taken into account in correct policy design. In the present paper we have applied a decomposition of a synthetic indicator of inequality, the Theil index, which makes it possible to analyze the factors behind inequalities in CO2 emissions per capita at communitarian level. The virtue of this decomposition is that it can be used to obtain the contribution of different factors—Kaya factors—to the global inequality and its trajectory. Moreover, it has the advantage of also being applicable to the analysis of inequality between and within the groups of countries considered—North Europe, South Europe, and East Europe—thanks to the fact that the Theil index enables a perfect decomposition of the betweenand within-group components of this inequality. 15 The results indicate an important reduction in the inequality of CO2 emissions per capita between European countries. Lower divergences would presumably tend to facilitate the rapprochement of positions on how to mitigate the problem at communitarian level. The reduction is explained to a large extent by the lower contribution of energy intensity, which was the most important factor at the beginning of the period but has a negative contribution at the end, now being much less relevant than the other factors. As for the betweenand within-group components, the reduction in inequality is mostly explained by the reduction in inequality between the groups of countries considered. Nowadays, the major factor explaining European inequalities in CO2 per capita is the important inequality that still exists in GDP per capita. Therefore, different affluence levels tend to group the interests of the different countries and groups of countries in the discussions on efforts distribution. The carbonization index has also maintained a relevant role in the explanation of inequalities. This is explained by the persistence of important differences in the energy mix, with some countries having an important share of coal (Poland and Czech Republic) and others having a relevant share of nuclear and renewable power (France and Sweden). However, the important differences in energy intensities do not make a positive contribution to total inequality. That is to say, the differences in energy efficiency and/or production structures that lead to a different level of energy consumption per product unit, do not contribute to global inequalities, as the countries with greater energy intensity tend to be those with lower GDP per capita levels. Of course, one cannot conclude from this that there is no need to make efforts to reduce inequalities in energy intensities that are due to an inefficient use of energy, although the present work does not make it possible to differentiate which part is due to this and which is due to a different specialization in more energy intensive sectors. The greater energy intensity in lower income countries could reduce the difficulties that income inequality imposes on the possibility of reaching agreements, especially when these are due to lower efficiency. The major reduction in inequality between groups is to a large extent the result of the reduction in the contribution of the energy intensity component between groups (mainly in the first years of the period). At the end of the period, the differences between the groups are mainly explained by the component associated to the GDP per capita factor and to a lesser extent to the carbonization index. The differences between the groups of countries according to GDP per capita would mainly explain the differences in emissions per capita levels, the differences in carbonization indexes that respond to different energy source mixes in the primary energy used in the different 16 groups also being relevant, with a greater relative importance of coal in East Europe, and of nuclear and renewable power in North Europe. However, at the end of the period the differences are concentrated within the groups of countries considered, the carbonization index being the most relevant within-group component of inequality. Countries classified according to similar geographic and socio-economic characteristics have very different compositions of energy sources (energy mix)—which is very clear in the group of higher income countries, North Europe. It might then be expected that, within the groups of countries considered, the different interests when negotiating mitigation policies may be based on this different importance of the use of more polluting fossil fuels, the energy intensity factor being of lower—although still significant—importance. The present research complements the information provided by the data with synthetic indicators that reveal changes in the contribution of different factors to inequality. Discussions within the European Union on the ambition of mitigation objectives will continue in the future and it is essential to analyze the roots of the divergence through disaggregated analysis of the situation in each country as well as with aggregated indicators such as that proposed, which show the main factors behind the magnitude and evolution of the observed European disparities. The ability to reach agreements on the distribution of the burden in order to achieve the common objectives will depend on the proposals being seen as fair and taking the differences in the European Union adequately into account. A continuous trend in the reduction of income inequality in the future would facilitate a common position. With respect to the other factors, a reduction in energy intensity inequalities would be desirable, with convergence towards the situation in the most energy-efficient countries, although this has its limits as these inequalities might be due to different sectoral specializations. Finally, one measure of the success of common climate policies in the long run could be a reduction in the contribution of the carbonization index to inequality accompanied by a general downward trend in the level of the carbonization index in Europe. Ultimately, only a shift towards a decarbonized economy will lead to long-term sustainable use of energy. Acknowledgements The authors acknowledge support from projects ECO2010-18158 and ECO2009-10003 (Ministerio de Ciencia e Innovación), 2009SGR-600 and XREPP (DGR). 17 References Alcántara V., Duro J.A., (2004), Inequality of energy intensities across OECD countries, Energy Policy, 32, 1257–1260. Alcántara V., Padilla E., (2005), Analysis of CO2 emissions and its explanatory factors in the different regions of the world, (in Spanish), Revista de Economía Crítica, 4, 17–37. Ang B.W., (1999), Is the energy intensity a less useful indicator than the carbon factor in the study of climate change?, Energy Policy, 27, 943–946. Bourguignon F., (1979), Decomposable income inequality measures, Econometrica, 47, 901–920. Cantore N., Padilla E., (2010a), Equality and CO2 emissions distribution in climate change integrated assessment modeling, Energy, 35, 298–313. Cantore N., Padilla E., (2010b), Emissions distribution in post – Kyoto international negotiations: a policy perspective, In: Nanotechnology and Microelectronics: Global Diffusion, Economics and Policy, Ekekwe N. (Ed.), IGI Global, Hershey, USA, 243–260. Cowell F., (1995), Measuring Inequality, Prentice Hall, London. Duro J.A., Alcántara V., Padilla E., (2010), International inequality in energy intensity levels and the role of production composition and energy efficiency: an analysis of OECD countries, Ecological Economics, 69, 2468–474. Duro J.A., Padilla E., (2006), International inequalities in per capita CO2 emissions: a decomposition methodology by Kaya factors, Energy Economics, 28, 170–187. Greening L.A., Davis W.B., Schipper L., Khrushch M., (1997), Comparison of six decomposition methods: application to aggregate energy intensity for manufacturing in 10 OECD countries, Energy Economicsk, 19, 375–390. Groot L., (2010), Carbon Lorenz curves, Resource and Energy Economics, 32, 45–64. Hedenus F., Azar C., (2005), Estimates of trends in global income and resource inequalities, Ecological Economics, 55, 351–364. Heil M.T., Wodon Q.T., (1997), Inequality in CO2 emissions between poor and rich countries, Journal of Environment and Development, 6, 426–452. International Energy Agency (IEA), (2009a), CO2 Emissions from Fuel Combustion, IEA, OECD, Paris. International Energy Agency (IEA), (2009b), Energy Balances of OECD Countries, IEA, OECD, Paris. International Energy Agency (IEA), (2009c), Energy Balances of non OECD Countries, IEA, OECD, Paris. 18 Jobert T., Karanfil F., Tykhonenko A., (2010), Convergence of per capita carbon dioxide emissions in the EU: Legend or reality?, Energy Economics, 21, 1364–1373. Kaya Y., (1989), Impact of Carbon Dioxide Emission Control on GNP Growth: Interpretation of Proposed Scenarios, paper presented to the Energy and Industry Subgroup, Response Strategies Working Group, Intergovernmental Panel on Climate Change, Paris, France. Millimet D.L., Slottje D., (2002), An Environmental Paglin-Gini, Applied Economics Letters, 9, 271–74. Padilla E., Serrano A., (2006), Inequality in CO2 emissions across countries and its relationship with income inequality: a distributive approach, Energy Policy, 34, 1762–1772. Shorrocks A., (1980), The class of additively decomposable inequality measures, Econometrica, 48, 613–625. Sun J.W., (2002), The decrease in the difference of energy intensities between OECD countries from 1971 to 1998, Energy Policy, 30, 631–635. Shorrocks A.F., (1980), The Class of Additively Decomposable Inequality Measures, Econometrica, 48, 613–25. Theil H, (1967), Economics and Information Theory, North Holland, Amsterdam. 19 Annex I. Decomposition of inequality into Kaya factors and interaction terms Table 5. Decomposition of European inequality in CO2 emissions per capita into the contributions of Kaya factors and interaction terms Tc,p   T a T b T y Interacta,by Interactb,y 1990 0.0467 0.0256 (54.8%) 0.0773 (165.5%) 0.0699 (149.7%) -0.0171 (-36.7%) -0.1089 (-233.3%) 1991 0.0430 0.0249 (57.9%) 0.0757 (176.2%) 0.0836 (194.6%) -0.0228 (-53.1%) -0.1184 (-275.6%) 1992 0.0384 0.0237 (61.8%) 0.0738 (192.3%) 0.0896 (233.5%) -0.0244 (-63.5%) -0.1244 (-324.5%) 1993 0.0424 0.0261 (61.7%) 0.0718 (169.3%) 0.0870 (205.4%) -0.0236 (-55.8%) -0.1189 (-280.6%) 1994 0.0421 0.0258 (61.3%) 0.0634 (150.4%) 0.0856 (203.2%) -0.0258 (-61.3%) -0.1069 (-253.7%) 1995 0.0368 0.0259 (70.3%) 0.0585 (158.9%) 0.0810 (220.0%) -0.0253 (-68.6%) -0.1033 (-280.7%) 1996 0.0410 0.0261 (63.7%) 0.0592 (144.3%) 0.0779 (190.0%) -0.0248 (-60.5%) -0.0974 (-237.5%) 1997 0.0372 0.0250 (67.2%) 0.0544 (146.4%) 0.0781 (210.2%) -0.0254 (-68.4%) -0.0949 (-255.4%) 1998 0.0351 0.0234 (66.6%) 0.0455 (129.7%) 0.0783 (223.2%) -0.0281 (-80.2%) -0.0839 (-239.2%) 1999 0.0357 0.0240 (67.3%) 0.0375 (105.3%) 0.0786 (220.4%) -0.0301 (-84.3%) -0.0744 (-208.7%) 2000 0.0366 0.0253 (69.1%) 0.0348 (95.1%) 0.0774 (211.4%) -0.0305 (-83.3%) -0.0704 (-192.3%) 2001 0.0355 0.0258 (72.7%) 0.0346 (97.5%) 0.0732 (206.3%) -0.0315 (-88.7%) -0.0667 (-187.8%) 2002 0.0338 0.0261 (77.3%) 0.0322 (95.4%) 0.0685 (203.0%) -0.0316 (-93.5%) -0.0615 (-182.2%) 2003 0.0342 0.0259 (75.6%) 0.0299 (87.2%) 0.0635 (185.4%) -0.0298 (-87.0%) -0.0552 (-161.2%) 2004 0.0339 0.0272 (80.0%) 0.0251 (74.1%) 0.0583 (171.8%) -0.0318 (-93.8%) -0.0448 (-132.1%) 2005 0.0299 0.0268 (89.6%) 0.0222 (74.0%) 0.0544 (181.6%) -0.0326 (-108.8%) -0.0408 (-136.4%) 2006 0.0322 0.0269 (83.4%) 0.0223 (69.1%) 0.0501 (155.7%) -0.0284 (-88%) -0.0387 (-120.2%) Source: Prepared by the authors using IEA data (IEA, 2009a, 2009b, 2009c). 20 Table 6. Decomposition of inequality into Kaya factors and interaction terms for groups of European countries (North Europe, South Europe and East Europe) Tc,p   T a T b T y Interacta,by Interactb,y 1990 Between 0.0157 (33.6%) 0.0054 (34.4%) 0.0653 (416.2%) 0.0569 (362.2%) -0.0156 (-99.6%) -0.0962 (-613.2%) Within 0.0310 (66.4%) 0.0203 (65.4%) 0.0084 (27.1%) 0.0130 (42.1%) -0.0016 (-5.1%) -0.0091 (-29.5%) 1995 Between 0.0090 (24.5%) 0.0061 (67.3%) 0.0487 (539.3%) 0.0690 (763.8%) -0.0234 (-259.5%) -0.0913 (-1011.0%) Within 0.0278 (75.5%) 0.0205 (73.8%) 0.0074 (26.8%) 0.0120 (43.3%) -0.0025 (-9.1%) -0.0097 (-34.8%) 2000 Between 0.0080 (21.8%) 0.0072 (90.0%) 0.0241 (301.8%) 0.0635 (794.5%) -0.0284 (-354.7%) -0.0585 (-731.6%) Within 0.0286 (78.2%) 0.0191 (66.6%) 0.0086 (29.9%) 0.0139 (48.6%) -0.0031 (-10.9%) -0.0098 (-34.3%) 2006 Between 0.0048 (14.8%) 0.0059 (123.0%) 0.0140 (293.9%) 0.0401 (840.2%) -0.0218 (-456.1%) -0.0335 (-701.0%) Within 0.0274 (85.2%) 0.0217 (79.1%) 0.0076 (27.8%) 0.0100 (36.6%) -0.0073 (-26.5%) -0.0046 (-16.9%) Source: Prepared by the authors using IEA data (IEA, 2009a, 2009b, 2009c). Note: first column shows (within brackets) the percentages with respect to global inequality, other columns show the percentages with respect to the betweenand within-group components. 21 Table 7. Decomposition of within-groups inequality into Kaya factors and interaction terms. Details by groups Tc,p   T a T b T y Interacta,by Interactb,y wi 1990 North Europe 0.0357 0.0303 (84.9%) 0.0060 (16.9%) 0.0016 (4.5%) -0.0036 (-10.0%) 0.0013 (3.6%) 52.9% South Europe 0.0160 0.0058 (36.2%) 0.0035 (21.9%) 0.0153 (95.5%) 0.0035 (22.1%) -0.0121 (-75.7%) 24.7% East Europe 0.0364 0.0125 (34.5%) 0.0193 (53.2%) 0.0375 (103.0%) -0.0025 (-7.0%) -0.0304 (-83.7%) 22.4% 1995 North Europe 0.0290 0.0286 (98.9%) 0.0069 (24.0%) 0.0016 (5.4%) -0.0076 (-26.1%) -0.0006 (-2.2%) 53.4% South Europe 0.0079 0.0050 (62.6%) 0.0042 (53.2%) 0.0149 (187.1%) -0.0021 (-27.0%) -0.0140 (-175.9%) 24.6% East Europe 0.0472 0.0182 (38.5%) 0.0123 (26.0%) 0.0344 (72.8%) 0.0092 (19.6%) -0.0268 (-56.8%) 21.9% 2000 North Europe 0.0239 0.0255 (106.7%) 0.0068 (28.5%) 0.0016 (6.9%) -0.0093 (-39.1%) -0.0007 (-3.0%) 53.8% South Europe 0.0030 0.0040 (135.2%) 0.0049 (166.5%) 0.0094 (317.9%) -0.0042 (-140.5%) -0.0112 (-379.1%) 24.7% East Europe 0.0701 0.0204 (29.1%) 0.0172 (24.5%) 0.0498 (71.1%) 0.0136 (19.4%) -0.0309 (-44.1%) 21.5% 2006 North Europe 0.0281 0.0317 (113.0%) 0.0083 (29.7%) 0.0027 (9.5%) -0.0132 (-47.1%) -0.0014 (-5.0%) 53.8% South Europe 0.0053 0.0035 (66.0%) 0.0026 (49.9%) 0.0058 (110.4%) -0.0020 (-38.0%) -0.0046 (-88.4%) 25.5% East Europe 0.0532 0.0182 (34.2%) 0.0119 (22.4%) 0.0344 (64.7%) 0.0017 (3.2%) -0.0130 (-24.5%) 20.7% Source: Prepared by the authors using IEA data (IEA, 2009a, 2009b, 2009c). Note: Within brackets the percentage with respect to within-group inequality of each group. 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