DECOUPLING IN THE EU AS AN INSTRUMENT TOWARDS SUSTAINABLE DEVELOPMENT Magdaléna Drastichová 1 1 Vysoká škola báňská - Technická univerzita Ostrava, Ekonomická fakulta, Sokolská třída 33, 701 21 Ostrava 1 Email:magdalena.drastichova@vsb.cz Abstract: Sustainable development is a global challenge that requires a progressive transformation of economies, specifically substantial changes in production processes and lifestyles. Already in 1997 sustainable development became a fundamental objective of the EU and in 2001 the EU leaders launched the EU Sustainable Development Strategy. The process of decoupling is inevitable to draw closer and achieve the sustainable development path. Therefore, decoupling is monitored in the EU with the decoupling indicators. The aim of the paper is to detect the level of de/coupling of the Domestic Material Consumption from the economic production and standard of living in the EU and its countries. The Resource Productivity indicator, which is one of the decoupling indicators included in the EU Sustainable Development Indicators set, and its partial indicators, are used for this investigation. The results are not straightforward. They often indicate the impacts of the short term economic shocks on the simultaneous development of the monitored economic and environmental variables. The EU is still far from the Sustainable Development path and the appropriate measures need to be adopted. Keywords: Sustainable Development, Decoupling, Domestic Material Consumption, Resource Productivity, European Union. JEL classification: Q51, Q56. 1. Introduction Sustainable development (SD) is a global challenge that requires a progressive transformation of economies (Hediger, 2004), specifically substantial changes in production processes and lifestyles (FEEM, 2011). According to the WCED (1987), SD is development that meets the needs of the present without compromising the ability of future generations to meet their own needs. Although this term is still vague there is an emerging political consensus on the desirability of SD (Daly, 1996). Since the 1992 United Nations (UN) Conference on Environment and Development in Rio de Janeiro, the European Union (EU) has played a leading role in supporting the idea of balanced and sustainable development. The SD would be of little interest if the current patterns of economic development were judged to be sustainable. However, this is not the case (OECD, 2001). The aim of the paper is to detect the level of de/coupling of the Domestic Material Consumption from the economic production and standard of living in the EU and its countries. The paper has the following structure. The second section is focused on the theoretical and practical aspects of the SD, the approaches of the EU to the SD and on the decoupling characteristic. The third one describes the data and the methodology. The fourth section contains the results of the analysis and the discussion. The last section concludes. 2. Theoretical and practical aspects of the Sustainable Development SD is a concept which emerged in the context of a growing awareness of an imminent environmental crisis. The basic aspects related to the emergence of the SD concept at the international level followed by the approach of the EU to SD and the decoupling specification are the topics of this section. 2.1 Sustainable Development Concept The conceptual foundation for the current use of the SD term was consolidated in the early 1970s. In the 1980s the new paradigm of SD was popularized and became more widely used. In this time the World Commission on Environment and Development (WCED), informally the Brundtland Commission, submitted the report, entitled Our common future, to the United Nations (UN) in 1987. -47-
In this Report, the SD was defined as development that meets the needs of the present without compromising the ability of future generations to meet their own needs (WCED, 1987). Although already in 1992 around 70 definitions of the SD existed (Holmberg and Sandbrook, 1992), the definition form the Brundtland Report is still the most frequently quoted definition. The Report acknowledged the tension between economic growth and environmental protection. It concluded that economic growth was essential, particularly in the developing world, but that there should be a switch to SD, which would be environmentally friendly (Du Pisani, 2015). 2.2 The European Union s approach to the Sustainable Development Already in 1997 SD became a fundamental objective of the EU when it was included in the Treaty of Amsterdam Consolidated Version of the Treaty Establishing the European Community in Article 2 as an overarching objective of the EU policies. The Article 6 is then crucial in this field because it requires that Environmental protection requirements must be integrated into the definition and implementation of the Community policies and activities (Article 3) in particular with a view to promoting sustainable development. (EU, 1997) At the Gothenburg Summit of the European Council in June 2001, EU leaders launched the first EU Sustainable Development Strategy (EU SDS) based on the Commission Communication of 15 th May 2001 (Commission of the European Communities, 2001) which limited itself to the internal aspects of SD. With the subsequent Commission Communication of 13 th February 2002 (Commission of the European Communities, 2002) the external dimension was added to the EU SDS at the European Council in Barcelona in March 2002 (cited from Drastichová, 2014). The 2001 strategy is composed of two main parts. The first proposed objectives and policy measures to tackle the key unsustainable trends while the second part, arguably more ambitious, called for a new approach to policy-making that ensures the EU's economic, social and environmental policies mutually reinforce each other. The strategy set overall objectives and concrete actions for seven key priority challenges for the period until 2010, many of which were environmental (see Tab. 1) (European Commission, 2015). However, the main themes / topics are similar in the recent time according to the renewed SDS and are reflected in the Sustainable Development Indicators (SDIs). Tab. 1. Priority challenges of the EU SDS Climate change and clean energy Conservation and management of natural resources Sustainable transport Public Health Sustainable consumption and production Social inclusion, demography and migration Global poverty and sustainable development challenges Source: European Commission (2015) Despite the achievements in implementing the EU SDS, unsustainable trends have persisted, especially the climate change, the ageing of societies in developed countries and a widening gap between the developed and the developing countries and many others. A renewed strategy for an enlarged EU was adopted by Heads of State and Governments at the European Council of 15 th 16 th June 2006. Finally, it must be pointed out that the SD will not be achieved by policies only. It must be accepted and adopted by the overall society as a principle guiding the choices of the citizens, businesses as well as the crucial political and economic decisions (European Commission, 2015). 2.3 Decoupling definition and aspects The decoupling concept refers to breaking the link between two variables, often referred to as the driving force, mainly economic growth expressed in terms of GDP, and the environmental pressures, -48-
such as the use of natural resources (materials, energy, land etc.), the generation of waste, and the emission of pollutants to air or water and many others. Thus, decoupling indicates breaking the link between environmental bads and economic goods (OECD, 2002). It points out the relative growth rates of a direct pressure on the environment and of an economically relevant variable to which it is causally linked. The purpose of decoupling indicators is to monitor the interdependence between these two different spheres. Accordingly, these indicators usually measure the decoupling of the environmental pressure from the economic growth over a given period (OECD, 2003). Decoupling occurs when the growth rate of the economic driving force exceeds the growth rate of the environmental pressure over a given period (OECD, 2002). Decoupling can be either absolute or relative. Absolute decoupling implies that the environmental variable is stable or decreasing while the economic one is growing. Decoupling is relative when the environmental variable is growing, but at a lower rate than the economic variable (OECD, 2002). It is obvious from the definition of decoupling that this process is inevitable to draw closer and achieve the SD path. Therefore, decoupling is also applied in the monitoring of the SD in the EU using the decoupling indicators (cited from Drastichová, 2014). The concrete data and methodology are described in the next section. 3. Materials and Methods In this section the Data and Methodology applied in this Paper are described. 3.1 Data Measuring progress towards the agreed goals is an integral part of the EU SDS (Eurostat, 2015d). The 2006 renewed EU SDS calls for the European Commission to monitor the progress of the EU against the challenges identified in this strategy and specifically to draw up a comprehensive set of Sustainable Development Indicators (SDIs). In order to address these requirements, Eurostat has developed a set of SDIs, with the help of a group of national experts. The first set of indicators was adopted by the Commission in 2005 and then it was updated in 2007 in order to adjust to the renewed EU SDS. The development of the SDIs set is still ongoing (Adelle and Pallemaerts, 2009). Monitoring reports using the SDIs are published by Eurostat every two years (Eurostat, 2015d). The SDIs are organized in a theme-oriented framework, i.e. structured in a hierarchical theme framework, and presented in ten themes (Adelle and Pallemaerts, 2009). Of more than 100 indicators, eleven have been identified as headline indicators. They give an overall picture of whether the EU has achieved progress towards SD in terms of the objectives and targets defined in the strategy. For a more complete picture it is necessary to examine the development of all indicators within a theme. These themes are: Socio-economic development, Sustainable consumption and production, Social inclusion, Demographic changes, Public health, Climate change and energy, Sustainable transport, Natural resources, Global partnership, Good governance (Eurostat, 2015c). It can be seen that four of them exactly reflect the challenges indicated in Tab. 1. and the other reflect the parts of the other challenges. To sum up ten main themes reflect the key challenges of the strategy (see Tab. 1.) and they are further divided into subthemes reflecting the operational objectives and actions of the SDS. 3.1.1 Methodology of Resource Productivity and Domestic Material Consumption calculation Resource productivity (RP) is one of the headlines indicators, concretely in the Sustainable consumption and production theme, which are designed to monitor the extent of decoupling between economic growth and environmental pressures. The RP is measured as the ratio between gross domestic product (GDP) and domestic material consumption (DMC). Due to its construction the RP growth rate is exactly equal to the difference between the GDP and DMC growth rates. -49-
The DMC indicator is based on the Economy-wide Material Flow Accounts. It measures the total amount of materials directly used by an economy and is defined as the annual quantity of raw materials extracted from the domestic territory of the focal economy, plus all physical imports minus all physical exports. The term consumption, as used in DMC, denotes apparent consumption and not final consumption. DMC does not include upstream flows related to imports and exports of raw materials and products originating outside of the focal economy. The DMC indicator is one of the explanatory indicators of the Sustainable consumption and production theme and thus it is related to actions described in the SDS which are useful for analysing progress towards the SDS's objectives (Eurostat, 2015b, c, d, f). For the calculation of RP, Eurostat uses GDP either in EUR in chain-linked volumes unit to the reference year 2010 at 2010 exchange rates or in Purchasing Power Standard unit (PPS). Consequently, the indicator is expressed: i) in euro per kg, for comparing the changes in one country over time; ii) iii) in PPS per kg, for comparing different countries in one specific year; It is also calculated as an index on year 2005, for comparing countries in different years. (Eurostat, 2015b, c) There are also the other decoupling indicators in the EU SDIs set. The indicator of Energy intensity of the economy is the ratio between the gross inland consumption of energy and the GDP for a given calendar year. The energy consumption of transport relative to GDP is defined as the ratio between the energy consumption of transport and GDP (chain-linked volumes, at 2000 exchange rates). The former is the explanatory indicator of the Socio-economic development theme and the latter is the headline indicator of the Sustainable transport theme. There are also the other SDIs, especially in the Climate change and energy theme, which have the features of the decoupling indicators (See more on Eurostat, 2015c, d). 3.1.2 Description of the methodology for calculation of the economic variable The real GDP, concretely the GDP at market prices, chain linked volumes (2010) in million euro is used as an economic variable representing the overall product of the economy. The real GDP, chain linked volumes (2010) in euro per capita is as an economic variable representing the standard of living of the economy. Volume figures show the development of aggregates excluding inflation. Chain-linked level series are obtained by successively applying previous year's price's growth rates to the current price figure of a specific reference year e.g. 2010. Chain-linking involves the loss of additivity for all years except the reference year and the directly following year, because these are the only periods expressed in prices of the reference year. For other years, chain-linked components of GDP will not sum to chain-linked GDP, and chain-linked Member States' GDP will not sum to chain-linked EU GDP (Eurostat, 2014, 2015e). It is important to note that there are no GDP figures which enable comparisons in two dimensions, both time and geographic area. To compare countries over time it would be necessary to have GDP in chain-linked volume PPS to a reference year. This type of GDP does not exist, therefore these types of comparisons are difficult to make (Eurostat, 2015b). Thus, for the analysis carried out in this paper, the GDP in chain linked volumes is used because of the focus on the relation between the economic product and the standard of living on the one side and the resource usage on the other side in each country over time. 3.2 Methods Firstly, the overall percentage changes of the indicators are used to detect the level of the decoupling in the EU plus Norway. Secondly, individual country trajectories are investigated by measuring the -50-
coefficient of linear or log-log de/coupling given by the linear least-squares regression. The countries included in the sample are the EU-28 plus Norway, which is one of the countries of the European Economic Area (EEA). The time coverage is 2003 2013 where data are available for all the indicators and countries except for Lithuania for which data were available since 2005. Although only the developed countries of the EU and EEA are included in the sample, these countries also face different economic conditions and resource base that affect the structures of sectors in their economies. Thus they face a unique set of circumstances. To assess the coupling between the material use and the economic product / standard of living (measured as the real GDP / per capita) of the EU in period 2003 2013 on the basis of the average data and then of the EU and each country individually over time period 2003 2013 the applied formula is as follows: (1) where DMC is Domestic Material Consumption of the country and GDP (p.c). are the GDP/ per capita of country i at time t (see the detailed methodology of their calculations in the subsections 3.1.1 and 3.1.2). Symbol ln represents the natural logarithm. The economic variable is used in the volumes as well as per capita, i.e. both indicators, GDP and GDP per capita are applied. 1 The DMC is only used in the absolute volumes not only because of the lack of data but for the purpose of this analysis it is not inevitable. Moreover, the effort was to apply two version of the Equation 1: the first one uses the differences and the second one the levels (absolute values). The coupling coefficient b is of particular significance. It is the product / income elasticity, or slope 2, of material consumption represented by DMC of a country. It measures the magnitude of the coupling between its economy and resource use. It quantifies the growth in material use for a given growth in the GDP/per capita. If, material use per capita will grow exactly proportionally to product (income). The product / income elasticities (slopes) between 0 and 1 thus correspond to relative decoupling: material use grows with the economy, but not as fast as GDP growth. Absolute decoupling occurs when the income elasticity is below 0 and thus the physical dependency of the economy actually declines with economic growth (Steinberger et al., 2013). The same can be applied the the relations between the differences of the variables. The b coefficient higher than 1 indicates that the coupling occurs, i.e. provided the change of GDP/per capita by 1 percentage point (p. p.), the DMC changes by more than 1 p. p. Decoupling requires the b coefficient lower than 1. The assumptions of linear regression such as (1) statistical independence of the errors, (2) homoscedasticity (constant variance) of the errors, and (3) normality of the error distribution are proved with the suitable tests such as Breusch-Godfrey Serial Correlation LM Test for the first assumption, Breusch-Pagan-Godfrey, Harvey, Glejser and White test for the second one and the Jarque-Bera test for the third one. 4. Results and Discussion The several alternatives of the Equation 1 are used to investigate the de/coupling between the DMC and the real GDP / per capita and the Results followed by the Discussion are presented in this section. 4.1 Results At the beginning the rate of the coupling is examined for the whole EU-28 and Norway using the linear regression and the Method of Least Squares. Two alternatives of the Eq. 1 are used, i.e. firstly the real GDP and secondly the real GDP per capita are used as the explanatory variable. In this case 1 Further in the text the expression GDP/per capita is used which embodies both alternatives. When only the one alternative is described, the variable is indicated in the abbreviation for a concrete country or model. 2 It depends on the unit in which the variables are expressed. -51-
only the overall changes of the variables in 2003 2013 are examined. It means it is investigated if the relations between the DMC and GDP/per capita percentage changes in the EU exist. The relations between the absolute levels and differences of the variables are subsequently examined by means of the models of the individual countries. For the whole EU the results are indicated in Eq. 2 and Eq. 3. However, Norway had to be left out of the models because the data for this county represent the outliers and both models, with GDP and GDP per capita as explanatory variables, are statistically insignificant. When GDP per capita is used as the explanatory variable the signs of relative decoupling are indicated by the Eq. 3 and when GDP is used the slight coupling still persists (Eq. 2): (2). (3). In these models (Eq. 2 and Eq. 3) one assumption the linear regression could be violated. This is the assumption of homoscedasticity of the errors according to the Breusch-Pagan-Godfrey test in both, Glejser and White test in the model with the GDP per capita. It was not proved by all four tests and overall the more reliable model seems to be that with the GDP as an explanatory variable and the slight coupling between GDP and DMC can be assumed. The relations between the explanatory and explained variables from Eq. 2 are indicated in Fig. 1 and those from Eq. 2 in Fig. 2 to better examine the trends in the individual countries. Regarding the GDP as an explanatory economic variable we can see in Fig. 1 that three countries, concretely Norway, Estonia and Romania, showed no decoupling, i.e. DMC increased more significantly than GDP. Norway is not included in Fig. 1 because of the outliers, it means the DMC indicator increased by 316.007% while the real GDP only by 17.829%. This indicates the significant dependence on the material resources and no decoupling between the indicators. Even if not so high, the differences between the DMC and GDP change were also significant in Romania (DMC: 74.89%; GDP: 44.615%) and Estonia (DMC: 76.341%; GDP: 42.161%). Seven countries showed the relative decoupling whereas Malta and Sweden are on the threshold, i.e. the negative differences between the positive DMC and GDP changes are only marginal. On the other hand Slovakia drew close to the absolute decoupling with the DMC increase only by 1.776% and that of GDP by 56.912%. The remaining 19 EU countries achieved the decrease of the DMC indicator. However, three of them, concretely Greece, Portugal and Italy, showed the drop in GDP as well. Since the drop of the DMC was higher than that of GDP and it led to the increase in the RP. This is associated with the significant impacts of the economic crisis on the economic growth and cannot be assessed as a positive phenomenon in terms of decoupling. Fig. 1. Overall change of the real GDP and DMC in the EU and its countries, 2003 2013 Source: Data from Eurostat (2015a, f), own elaboration -52-
Overall, the best results were achieved by the countries such as Cyprus, Ireland, Slovenia, the Czech Republic (CR), the United Kingdom (the UK) and Hungary, all with the relatively high decrease of the DMC and high increase of the GDP. Overall, the highest extent of the absolute decoupling, i.e. highest difference between the negative DMC change and positive GDP change was achieved by Spain, where the GDP increase was relatively lower, however, the DMC decrease was the highest among the countries. Regarding the negative difference between the DMC and GDP change, Spain was followed by Slovakia and Cyprus. Thus even with the relative decoupling, Slovakia must be added to the group with the best results because of slight positive change in DMC but the highest increase of GDP among the EU countries. The CR and Luxembourg (to a lesser extent) must be emphasised as the countries with the relatively high GDP growth being simultaneously able to achieve the absolute decoupling. The other important conclusion can be divided from Fig. 1. This is the fact that the countries with the high growth rates of GDP are predominantly able to achieve the relative decoupling only, such as Slovakia, Poland, Latvia and Bulgaria, whereas some do not achieve decoupling at all, such as Estonia and Romania. To sum up, ten countries achieved positive DMC growth, three of them higher DMC than GDP growth (no decoupling), nineteen countries the DMC decrease, however, three of them showed decrease in both variables, i.e. the DMC as well as GDP. The relations indicated in Eq. 3 are rather similar and they are depicted in Fig. 2. Norway could not be displayed again. The change of the real GDP per capita in Norway was 5.38% whereas that of DMC 316.007%, which indicates even higher difference between the environmental and economic variable percentage change, and thus the strongest coupling in the sample of the countries. No coupling is typical of Romania and Estonia as well whereas for both countries the GDP per capita change was higher than that of GDP, both countries showed the significant increase in the standard of living and thus the extent of coupling is little lower than in the previous case (Fig.1.). Two other countries showed no decoupling, i.e. Malta and Sweden, when the real GDP per capita is used as an explanatory variable. We can see again that all the countries with the high GDP per capita growth showed either no or relative decoupling with the exception of the CR. Even if with little lower increase of GDP per capita than that of GDP, this country stands out in the group of countries with the absolute decoupling as well. Two other countries with ones of the best results in the previous model with the GDP (Fig.1.), concretely Cyprus and Spain, dropped to the group in the bottom left quadrant in which the decrease of both variables occurs. Fig. 2. Overall change of the real GDP per capita and DMC in the EU and its countries, 2003 2013 Source: Data from Eurostat (2015a, e, f), own elaboration As indicated in Fig. 2. the best results were achieved by Latvia and Slovakia with the highest GDP per capita growth rates and the relative decoupling and the CR, Hungary and Slovenia with relatively high GDP per capita growth rates especially in the CR and the significant drop of the DMC in two others. To sum up ten countries achieved positive DMC growth in total, five of them the higher DMC than the GDP per capita growth (no decoupling), nineteen countries the opposite, however, five of the latter showed the decrease in both variables. -53-
The individual countries are investigated in the next subsection. The Equation 1 is applied to both the differences and the absolute levels of the variables. Moreover, both explanatory variables, the real GDP and GDP per capita are used in the models. The results are indicated in Tab. 2 for the differences and in Tab. 3 for the absolute levels. Whereas for the EU countries and Norway 11 observations for differences (2003 2013) and 12 observations for the level (2002 2013) are used, for Lithuania only 8 observations (2006 2013) and 9 observations (2005 2013) respectively are used because of lack of data for the real GDP/per capita in 2002 2004. The models without intercept were only used in those cases, when the intercept was statistically insignificant and it was concluded that that kind of model will lead to the better results (or not their significant worsening), it means the b coefficient remains statistically significant, the R 2 sufficiently high and the model meets the assumptions. The coefficients of the linear regression (a, b) are indicated in the fields for the particular countries together with the coefficient of determination in the abbreviations. In the following text the results which require some comments are described more in detail. Firstly, the results for the differences of the variables are described. For Belgium the results are ambiguous for GDP as explanatory variable. After omitting the intercept (p-value = 0.055) the b coefficient will significantly decrease, i.e. from 2.931 to 1.77. Thus both results are indicated in Tab. 2. On the other hand the models still indicate high positive coupling and the former results are closer to the results of the model with the real GDP per capita as an explanatory variable. It was difficult to decide on the model construction in the case of Croatia, Lithuania, and Romania, where in both models the a coefficients are statistically insignificant. The omission of this coefficient leads in both alternatives of the models to slight decrease of the b and R 2 coefficient. Because the R 2 coefficients were still relatively high and the assumptions are met the omission of the a coefficient was favoured. The same was typical of the models of Portugal and Italy (the model with the GDP per capita). In the case of Italy the b coefficient slightly increased (from 1.744) and the R 2 decreased (from 0.378), however, in the model with the GDP the R 2 will drop significantly (to 0.044). In this latter case, however, the a coefficient can still be regarded as statistically significant (p-value = 0.052). In the case of Portugal the b coefficients slightly increased. In the case of Latvia the both models without intercept would lead to the drop of b coefficient to 1.031 (GDP) and to 0.929 (GDP per capita) but they could not be used, because the homoscedasticity assumptions would be violated. In the case of the Austrian economy, the model with GDP as an explanatory variable, the intercept is on the threshold of statistical significance. After omitting the intercept the b coefficient becomes lower in the models with both explanatory variables, which indicates the relative decoupling. However, in both models it also becomes statistically insignificant. Thus the first alternatives are indicated in Tab. 2 and the results are inconclusive. In the model of Finland with the GDP and Poland with the GDP per capita, the model with the intercept was used even if it was insignificant (p-value = 0.082; 0.086 respectively) because of the violation of the homoscedasticity assumption according to White test for Finland and the no autocorrelation assumption for Poland in the models without the intercept. For the economy of Malta the overall model and both coefficients, i.e. a and b are statistically insignificant. Moreover, the statistical insignificance of b coefficient becomes even more obvious if the a coefficient is omitted and its absolute number would decrease. If the results of Malta were proven as statistically significant, these would indicate the most significant decoupling among the EU countries. It means the highest level of decoupling between the DMC and the real GDP per capita differences. However, Malta is the economy with the extraordinary conditions, a small island without heavy industry, almost extraordinary oriented on tourism which also significantly affects the resource usage. Thus in the majority of the economic analyses this economy will show the extraordinary results. On the other hand, another small island country Cyprus showed the highest positive coupling -54-
among the EU countries when the differences of the variables are used. The b coefficient is higher than 5 in both models indicating that the GDP/per capita growth rates changes lead to higher changes of the DMC by more than 5 p. p. (Tab. 2). However, the problematic aspect of the model with GDP is that the assumption of the normality of the error distribution can be violated. The previous cross section analysis (Fig. 1. and Fig. 2.) showed that Luxembourg and Slovakia (GDP per capita) could achieve decoupling. However, the analysis of the differences of the variables does not lead to the convincing results. Some, although not very precise, guide can be provided only by the magnitude of the b coefficients. The model of Luxembourg s and Slovakia s coupling as well as their coefficients are statistically insignificant and the R 2 coefficients are low. Luxembourg s economy is also one of the EU economies with the extraordinary characteristics and the reliable conclusions cannot be derived. Regarding Hungary and Norway the characteristic feature would be the strong positive coupling for the growth rates, however, the models are similarly problematic as those of the previous three economies. Regardless the statistical significance of these models, these results confirm the significant coupling which was indicated with the previous cross-section analysis. Tab. 2. Coupling coefficients of the real GDP, real GDP per capita and the DMC differences C. GDP: GDPp.c.: C GDP: GDPp.c. C. GDP EU -0.033-0.027 FR -0.039 -; 2.446 AT -0.026 2.076 (0.793) 2.164 (0.812) 2.846 (0.705) (0.517) 1.306 (0.414) BE -0.039; 2.931 - HR -; 1.971 -;1.953 PL -0.126 (0.576) (0.497) 2.469 (0.473) (0.503) 3.863 (0.494) -; 1.77 (0.347) BG -0.052 2.079 (0.681) -0.076 2.31 (0.794) CZ -0.038 1.139 (0.492) -0.037 1.198 (0.561) DK -; 2.873 (0.604) 2.949 (0.664) DE -; 0.718 (0.398) EE -; 0.957 (0.279) -; 0.701 (0.38) -; 0.908 (0.271) IT -0.038 1.771 (0.387) CY -0.115 5.254 (0.651) LT -0.027 1.171 (0.538) LV -; 1.892 (0.703) LU -0.007 0.31* (0.018) -55- -; 2.136 (0.201) -; 5.32 (0.548) -0.046 1.227 (0.539) -;1.674 (0,649) -;0,435* (0.198) PT GDPp.c.: -0.02 1.244 (0.363) -0.104; 3.347 (0.434) -; 2.88 (0.433) -; 3.003 (0.36) RO -; 2.085 (0.714) SL -0.06 2.001 (0.6) SK -0.044 1.115*(0.213) -;1.848 (0.669) -0.054 1.872 (0.575) -0.039 0.993* (0.179) FI -0.019 1.647 (0.823) -;1.565 ( 0.804) IE -0.074 -;-2.456 HU -0.058-0.068 SE -; 1.496 (0.456) -;1,626 (0.483) 2.312 (0.557) (0.38) 2.749* (0.297) 3.059* (0.339) GR 1.552 -;1.628 MT 0.095 0.063 UK -0.043-0.032 (0.392) (0.393) -3.47*(0.163) -2.732*(0.115) 1.519 (0.734) 1.458 (0.726) ES -0.101-0.062 NL -0.029-0.023; NO 0.059 0.108; 3.595 (0.857) 4.529 (0.838) 1.769 (0.606) 1.742 (0.684) 4.747*(0.081) 4.497* (0.09) Source: Data from Eurostat (2015a, e, f), own elaboration in EViews Programme Note: The R 2 and adj. R 2 coefficients are in abbreviations. The statistical insignificant results are indicated in italic. Symbol * means that the overall model is statistically insignificant. Providing that the intercept is statistically insignificant and it is omitted, the model without intercept is used and a dash ( - ) instead of the a coefficient is indicated in the field. If the model meets all the assumptions the results are in bold letters, if some assumptions are violated the results are in ordinary letters. Notes to the violation of the assumptions of linear regression: Non-normality of error terms: Cyprus (GDP), Greece, Luxembourg and Norway; Heteroscedasticity: Spain (GDP per capita): the three tests except White test; Autocorrelation: France (GDP). Other two countries, Germany and Estonia, with the b coefficients below 1 in Tab. 2 showed completely different trends according to Fig. 1 and Fig. 2. Estonia is the country with the high economic growth and even higher growth of DMC. Thus the growth rates of DMC should be higher, however, the b coefficients for the differences are close to one. It is affected by the inclusion of the
period of the economic crisis again, when both variables drop in some years, or by other short term features of the development. In 2009 the GDP/per capita decreased more significantly than DMC and thus the RP decreased, which is the opposite trend to the development in many EU countries in the period of significant impacts of the economic crisis (in 2008 the opposite development occurred in Estonia). There are also the periods of DMC decrease together with the economic growth (2004 and 2005) and in other years the results were varying. In some of them the DMC and in the others GDP/per capita grew faster. The analysis showed only the average trends, however, the development in this economy is considerably variable. On the other hand, Germany showed the absolute decoupling of DMC from GDP/per capita by the simultaneous relatively slow economic growth in the analysis of the overall changes (Fig. 1 and Fig. 2) and b coefficients around 0.7 from the analysis of the differences. In Germany the real GDP/per capita dropped more significantly in 2009 similarly to Estonia and thus the RP decreased too. However, in more years of the monitored period Germany was able to maintain the economic growth, even though often very slight, with the simultaneous decrease of the DMC variable. This is also obvious in Fig. 1 and Fig. 2. Moreover, Slovakia achieved the b coefficients below one in the model with the GDP per capita as an explanatory variable. Even if statistical insignificant, Slovakia is close to absolute decoupling according to the overall change of the variables (see Fig. 1 and Fig. 2). In more years of the monitored period this country was able to increase the economic product, both GDP and GDP per capita, and decrease the DMC. On the contrary to German economy this took place even by the high rates of the economic growth. When GDP/per capita decreased, which happened only in 2009, the DMC decreased much more significantly and thus this did not affect the results in the similar way as in Estonia and Germany. Despite the fact that there are two years, 2004 and 2008, with the more significant growth of the DMC than that of GDP/ per capita, these years were likely affected by the actual development. The overall trend of the development of the investigated economic and environmental variables indicates some signs of the structural changes which can lead to decoupling. Overall, the latter three countries have the potential to achieve decoupling in the future if, on average, the positive growth of economic variables will exceed that of the DMC. The conclusions are not clear and it is questionable, how the future economic shocks can influence the development of the variables. To sum up some results are not quite conclusive, because some models and coefficients are insignificant or the assumptions of the models are not met. However, some trends can be indicated. The positive b coefficients and moreover those higher than 1 prevail in the EU countries which indicates the positive coupling between the GDP/per capita and the DMC differences. This is also the case of the overall EU (first field in the first line of Tab. 2). Overall these results indicate that the absolute values of GDP/per capita changes by 1 p. p. lead to those of the DMC by more than 1 p. p. in the same direction. Or in other words, the changes of the growth rates of the economic variable accelerate the DMC changes. As it was indicated, these trends occur in both directions. Thus if the period of economic recession is included in the monitored period, which is the case of this analysis, it often happens that the DMC drop exceeds that of the GDP/per capita. Accordingly, the cross-section analysis (Eq. 2, Fig. 2 and Fig. 3.) more likely shows the signs of decoupling when such periods are included because as a result of such trends the DMC can grow slower and its absolute volumes can be lower. Thus it is not in conflict, when countries with significant coupling of differences of variables (Tab. 2.) showed decoupling in Fig. 1 and Fig. 2. The only negative b coefficients for Malta and the lowest positive ones for Luxembourg are, together with the overall models, statistically insignificant. These economies are the small EU economies with the extraordinary characteristics. Next, the relations between the DMC and the real GDP/per capita levels are examined. The results for the overall EU are first compared to those of the analysis of the differences (first lines in Tab. 2. -56-
and Tab. 3.) It can be concluded that the statistically significant relationship can be found between growth rates of the DMC and GDP/per capita while the DMC growth rates surpass those of the economic variables (GDP/per capita). On the other hand no statistically significant relationship can be detected between the DMC and real GDP/per capita levels. A large number of these models in the EU countries and the overall EU are statistically insignificant as a whole or at least one of their coefficients (see Tab. 3). They often do not meet the assumptions of linear regression. Moreover, the coefficients of determination are often low. This is especially the case of the models of the overall EU, Austria, the CR, Germany, France, Hungary, Ireland (GDP), Luxembourg (GDP per capita), Malta, Netherlands (GDP), Slovenia, Spain (GDP) and the UK (GDP per capita) which is a large number of countries. The models without intercept are used more rarely because they were distorted and led to very biased results. These models are used in such cases, in which intercepts are insignificant and the models without them did not lead to the worsening of the assumptions. After omitting these coefficients the b coefficients and the coefficients of determination would change only very slightly. For Belgium in both types of the model (GDP) some problems with the possible violation of the assumptions appeared (see Tab. 3 and the notes below Tab 3). In the model of Spain with the GDP per capita the intercept was on the threshold of the statistical significance (p-value = 0.0599). However, the model without intercept was not used because of the autocorrelation problem in this model and the significant decrease of the R 2 coefficient compared to the model with the intercept. On the other hand, the b coefficient would drop to 1.336. C. GDP: EU BE 18.474-0.161*(0.01) -;0.939 (0.251) BG 6.073 0.545 (0.51) CZ 13.446-0.114* (0.023) DK -11.83 1.903 (0.352) DE 13.133 0.065*(0.016) Tab. 3. Coupling coefficients of the real GDP, real GDP per capita and the DMC levels GDPp.c.: C GDP: GDPp.c. C. GDP 14.141 FR 18.281 11.085 AT 14.644 0.168* -0.321* (0.051) 0.247*(0.009) -0.198* (0.059) (0.006) -5.362 1.668 (0.545) 8.205 0.423 (0.429) 12.716-0.065* (0.006) -20.068 2.976 0.732) 13.61 0.046* (0.009) HR -; 1.013 (0.221) -; 1.173 (0.162) IT -47.007 4.234 (0.468) CY -; 1.01(0.225) -57- -28.651 4.127 (0.868) -30.763 4.054 (0.681) LT 5.193 0.549 (0.432) 7.251 0.369*(0.299) LV 1.035 (0.515) 1.164 (0.105) PL -;1.05 (0.789) PT -34.722 3.885 (0.7) RO -6.551 1.661 (0.95) SL 9.473 0.095*(0.001) SK 8.419 0.248*(0.148) GDPp.c.: 14.257-0.202*(0.035) 5.652 0.848 (0.846) -35.185 4.873 (0.763) -; 1.486 (0.926) 6.68 0.388* (0.021) 8.827 0.249*(0.147) EE -; 1.115 -; 1.08 (0.763) LU 12.403 10.234 FI 6.081 3.755 (0.787) -0.297* (0.128) -0.085* (0.002) 0.5*(0.237) 0.803 (0.447) IE 11.769-36.196 HU 18.196 20.703 SE -;0.954 (0.73) -;1.154 (0.697) 0.022* (0) 4.575 (0.601) -0.562* (0.016) -0.977* (0.055) GR -6.734 -;1.222 MT 7.326 7.376 UK 30.697 15.756 1.53 (0.823) (0.78) 0.103*(0.004) 0.088* (0.002) -1.199* (0.299) -0.226* (0.004) ES 22.317-49.08 NL 7.993 5.879 NO -117.889-155.066-0.641* (0.014) 6.218 (0.422) 0.31* (0.064) 0.593* (0.188) 10.21 (0.891) 14.994 (0.482) Source: Data from Eurostat (2015a, e, f), own elaboration in EViews Programme Note: The R 2 and adj. R 2 coefficients are in abbreviations. The statistical insignificant results are indicated in italic. Symbol * means that the overall model is statistically insignificant. Providing that the intercept is statistically insignificant and it is omitted, the model without intercept is used and a dash ( - ) instead of the a coefficient is indicated in the field. If the model meets all the assumptions the results are in bold letters, if some assumptions are violated the results are in ordinary letters. Notes to the violation of the assumptions of linear regression: Non-normality of error terms: Netherlands (GDP per capita), Poland (GDP per capita). Heteroscedasticity: Finland (GDP): Harvey test; Belgium (GDP, including the model without the intercept), Ireland (GDP), Greece (GDP), Poland (GDP per capita): Harvey and Glejser test; the EU, Ireland (GDP per capita), Luxembourg (GDP per capita), Spain (GDP per capita), France (GDP per capita), the UK (GDP): all
four tests. Autocorrelation: the EU, Croatia, Hungary, Ireland, Poland (GDP per capita), Norway (GDP per capita), Spain, Slovenia, the UK, the CR (GDP per capita), Denmark (GDP). The results of Cyprus are curious. The high level of the b coefficient in the model with the GDP per capita corresponds with the previous results where the growth rates of the variables were used, however, when the GDP volume are used the coefficient shoved much lower level and even decreased when the model without intercept was used. However, we can see the differences in the development of the variables and the coupling in the first part of the analysis (see Fig. 1 and Fig. 2.). While Cyprus showed the absolute decoupling between the GDP and DMC, its GDP per capita dropped in the observed period and this country can be found in the left bottom quadrant in Fig. 2. For Ireland it is difficult to assess the development because the model with the GDP is quite unreliable, i.e. statistically insignificant and with the R 2 equal to zero and in the model with GDP per capita more assumption are violated (see Tab. 3 and the notes below Tab 3) The final conclusions about the decoupling from the analysis of the individual countries can be made according to the magnitude of the b coefficients from two types of the models, each with two explanatory variables, and their statistical significance and compliance with the assumptions of the linear regression. It needs to be taken into account that many results are statistically insignificant or do not meet the assumptions of linear regression. However a view can be taken on the basis of the results of more models. In Fig. 3 the b coefficients from the models with the absolute levels and the differences of the variables, with the GDP and GDP per capita as explanatory variables, are summarized. Providing that both models, i.e. with and without intercept, were indicated for some countries, the b coefficients from the models with the better characteristics are indicated in Fig. 3. The b coefficients in Fig. 3 are ordered according to those from the GDP levels analysis. We can see that Norway dominates in terms of positive coupling in levels, both for GDP and GDP per capita as an explanatory variable and Cyprus in terms of the differences. The negative coefficients indicating absolute decoupling resulted from the analysis very rarely, moreover, they are often statistically insignificant. Regarding the models with the absolute levels of the variables the negative values were typical of the UK, Spain, Hungary, France, Luxembourg, Austria, the overall EU-28 and the CR in the models with the GDP as the explanatory variable (see Tab. 3). All these countries and the EU as a whole as well showed the absolute decoupling when the overall changes of the DMC and GDP variables are taken into account (see Fig. 1). When GDP per capita was used as an explanatory variable the negative coefficients were only typical of Hungary, the UK, Austria, Luxembourg and the CR (see Tab. 3). All these countries also achieved the absolute decoupling when the overall changes of the DMC and GDP per capita variables are taken into account (see Fig. 2). Regarding the models with the differences of the variables the negative coefficients were only typical of Malta in the models with the GDP as well as GDP per capita as the explanatory variables (see Tab. 2). This is the country with very slight relative decoupling from the analysis overall changes of the DMC and GDP variables (see Fig. 1) and no decoupling when GDP per capita is used as the explanatory variable (see Fig. 2). No of these models of absolute levels and differences is statistically significant simultaneously meeting the assumptions of linear regression and thus in no country in the sample the negative relationship between the DMC and GDP/per capita both for the absolute values and the differences can be reliably proved. Although, the b coefficients are in compliance with the results of the cross section analysis in the individual countries the decoupling cannot be significantly confirmed in any of the countries. Fig. 3. Results for b coefficients from four analyses of individual countries -58-
14 12 10 8 6 4 2 0-2 -4 GDP GDP p c dgdp dgdp p c Source: Data from Eurostat (2015a, c, d), own elaboration Regarding the relative decoupling, i.e. the b coefficient in the (0, 1) interval there are many countries especially as regards the absolute levels of the variables (see Tab. 3). The reliable models are those of Belgium, Latvia and Sweden (GDP), Finland (GDP per capita) and Bulgaria (both models). Moreover, the model of Finland has to be assessed cautiously as well because the intercept is statistically insignificant. Thus for these countries, and most reliably for Bulgaria, we can confirm that positive relationship exist between the DMC and GDP and / or GDP per capita, however the relative decoupling occurs. The material use changes at a slower rate than GDP and / or GDP per capita. The results are in compliance with the analysis of the overall changes of the variables again. In the analysis of the overall changes, the majority of these countries showed the relative decoupling, Finland even the absolute decoupling, of the DMC and the economic variable for which the relative decoupling was indicated in the analysis of the levels of variables. The positive coupling of the levels of the variables can be reliably proved in the Northern economies such as Denmark and Sweden (GDP per capita) and Norway (GDP), Estonia and Lithuania (both models), Southern economies such as Greece and Spain (GDP per capita) and Italy, Portugal, Cyprus and Romania (both models) and moreover in Belgium and Poland. Whereas in many of them the b coefficient is near 1, some Southern economies achieved the coefficients higher than 3 and Norway even higher than 10. The compliance with the results of the analysis of the overall changes is more or less obvious. Norway, Estonia and Romania showed strong coupling for both explanatory variables. Sweden showed no decoupling only for the GDP per capita as an explanatory variable which is in compliance of these results as well. For Lithuania and Poland the relative decoupling and Belgium the absolute decupling was indicated in the analysis of the overall changes. The Southern countries such as Greece, Italy, Portugal, Spain and Cyprus showed the drop of the GDP per capita together with the DMC in the overall period whereas GDP increased in Spain and Cyprus and this analysis showed absolute decoupling. The results for the latter two countries are also clearly indicated in this analysis where the b coefficients are very high when GDP per capita is used as an explanatory variable. On the other hand the b coefficient is negative for Spain and around 1 for Cyprus when GDP as an explanatory variable is used (see Tab. 3). Although in the case of Spain this model is insignificant and quite unreliable, some trends can be derived again. Since Spain and Cyprus showed the different kinds of coupling in the analysis of the overall changes the significant differences between the b coefficients are also obvious between the models with the GDP and GDP per capita levels as the explanatory variables. Portugal and Italy achieved the worse results at all. Concretely, ones of the highest b coefficients were achieved in the analysis of the levels of the variables together with the positive and relatively high coupling b coefficients for the differences and overall decrease of the DMC and GDP/per capita in the monitored period. The analysis of the differences predominantly indicates the strong coupling (see Tab. 2), i.e. the higher percentage changes of the material consumption than those of the GDP/per capita in the individual countries. The only coefficients in the (0, 1) interval were typical of Luxembourg, Germany and Estonia (for both explanatory variables) and also in Slovakia when GDP per capita as -59-